Leaders in Pharmaceutical Business Intelligence Group, LLC, Doing Business As LPBI Group, Newton, MA

Archive for the ‘Computational Biology/Systems and Bioinformatics’ Category

On its way for an IPO: mRNA platform Immune-Oncology, Moderna, is recruiting 100 new Life Scientists in Cambridge, MA

Posted in BioTechnology - Venture Creation, BioTechnology - Venture Creation, Venture Capital, Computational Biology/Systems and Bioinformatics, Diagnostic Immunology, Drug Delivery Platform Technology, Drug Development Process, Drug Discovery Chemistry, Genetics & Innovations in Treatment, Genome Biology, Genomic Testing: Methodology for Diagnosis, Human Immune System in Health and in Disease, Immuno-Oncology & Genomics, Immunodiagnostics, Immunotherapy, Infectious Disease Immunodiagnostics, mRNA platform in Drug DIscovery, RNA Biology, RNA Biology, Cancer and Therapeutics, tagged mRNA-based therapy, vascular endothelial growth factor-A (VEGF-A) on September 7, 2016| Leave a Comment »

On its way for an IPO: mRNA platform, Moderna, Immune Oncology is recruiting 100 new Life Scientists in Cambridge, MA

Curator: Aviva Lev-Ari, PhD, RN

 

Deals:

Moderna has now raised $1.9 billion from investors like AstraZeneca – 9% stack [AstraZeneca’s Pascal Soriot helped get that all started with a whopping $240 million upfront in its 2013 deal, which was tied to $180 million in milestones.], with another $230 million on the table from grants. In addition to the financing announcement this morning, Moderna is also unveiling a pact to develop a new Zika vaccine, with BARDA putting up $8 million to get the program started while offering an option on $117 million more to get through a successful development program.

Novel Strategy in Biotech:

in biotech. Instead of grabbing one or two new drugs and setting out to gather proof-of-concept data to help establish its scientific credibility, the company has harvested a huge windfall of cash and built a large organization before even entering the clinic. And it did that without turning to an IPO.

Pipeline include:

• The deal with AstraZeneca covers new drugs for cardiovascular, metabolic and renal diseases as well as cancer.
• partners filed a European application to start a Phase I study of AZD8601, an investigational mRNA-based therapy that encodes for vascular endothelial growth factor-A (VEGF-A)
• Moderna CEO spelled out plans to get the first 6 new drugs in the clinic by the end of 2016.
• The first human study was arranged for the infectious disease drug mRNA 1440, which began an early stage study in 2015.
• Moderna built up a range of big preclinical partnerships.
• CEO Bancel says the number of drugs in development has swelled to 11, with the first set of data slated to be released in 2017.
• Moderna also plans to add about 10 drugs to the clinic by next summer,

 

SOURCES

UPDATED: Booming Moderna is raising $600M while ramping up manufacturing and clinical studies

by john carroll – August 29, 2016

http://endpts.com/booming-moderna-is-adding-600m-while-ramping-up-manufacturing-and-clinical-studies/ 

$1.9B in: Moderna blueprints $100M facility, plans to double the pipeline after a $474M megaround

by john carroll – September 7, 2016

http://endpts.com/moderna-blueprints-100m-facility-plans-to-double-the-pipeline-after-a-474m-megaround/?utm_source=Sailthru&utm_medium=email&utm_campaign=Issue:%202016-09-07%20BioPharma%20Dive%20%5Bissue:7155%5D&utm_term=BioPharma%20Dive

 

Moderna Therapeutics Deal with Merck: Are Personalized Vaccines here?

Curator & Reporter: Stephen J. Williams, PhD – August 11, 2016

https://pharmaceuticalintelligence.com/2016/08/11/moderna-therapeutics-deal-with-merck-are-personalized-vaccines-here/

 

at #JPM16 – Moderna Therapeutics turns away an extra $200 million: with AstraZeneca (collaboration) & with Merck ($100 million investment)

Reporter: Aviva Lev-Ari, PhD, RN – January 13, 2016

https://pharmaceuticalintelligence.com/2016/01/13/at-jpm16-moderna-therapeutics-turns-away-an-extra-200-million-with-astrazeneca-collaboration-with-merck-100-million-investment/

Share this:

• X
• LinkedIn
• Facebook
• Print
• Email

Like this:

Like Loading…

Read Full Post »

A New Computational Method illuminates the Heterogeneity and Evolutionary Histories of cells within a Tumor

Posted in Cancer Informatics, Cell Biology, Computational Biology/Systems and Bioinformatics, Gene Regulation and Evolution, Genome Biology, Next Generation Sequencing (NGS), Statistical Methods for Research Evaluation, tagged and phylogenies by simultaneously modeling single-nucleotide variants, clonal fractions, clonal genotypes, multi-state perfect phylogeny mixture deconvolution problem, phylogenetic trees, single-nucleotide variants, tumor genome sequencing data, variant allele frequency estimates on August 11, 2016| Leave a Comment »

A New Computational Method illuminates the Heterogeneity and Evolutionary Histories of cells within a Tumor

Reporter: Aviva Lev-Ari, PhD, RN

 

Start Quote

Numerous computational approaches aimed at inferring tumor phylogenies from single or multi-region bulk sequencing data have recently been proposed. Most of these methods utilize the variant allele fraction or cancer cell fraction for somatic single-nucleotide variants restricted to diploid regions to infer a two-state perfect phylogeny, assuming an infinite-site model such that each site can mutate only once and persists. In practice, convergent evolution could result in the acquisition of the same mutation more than once, thereby violating this assumption. Similarly, mutations could be lost due to loss of heterozygosity. Indeed, both single-nucleotide variants and copy number alterations arise during tumor evolution, and both the variant allele fraction and cancer cell fraction depend on the copy number state whose inference reciprocally relies on the relative ordering of these alterations such that joint analysis can help resolve their ancestral relationship (Figure 1). To tackle this outstanding problem, El-Kebir et al. (2016) formulated the multi-state perfect phylogeny mixture deconvolution problem to infer clonal genotypes, clonal fractions, and phylogenies by simultaneously modeling single-nucleotide variants and copy number alterations from multi-region sequencing of individual tumors. Based on this framework, they present SPRUCE (Somatic Phylogeny Reconstruction Using Combinatorial Enumeration), an algorithm designed for this task. This new approach uses the concept of a ‘‘character’’ to represent the status of a variant in the genome.

Commonly, binary characters have been used to represent single-nucleotide variants— that is, the variant is present or absent. In contrast, El-Kebir et al. use multi-state characters to represent copy number alterations, which may be present in zero, one, two, or more copies in the genome.

SPRUCE outperforms existing methods on simulated data, yielding higher recall rates under a variety of scenarios. Moreover, it is more robust to noise in variant allele frequency estimates, which is a significant feature of tumor genome sequencing data. Importantly, El-Kebir and colleagues demonstrate that there is often an ensemble of phylogenetic trees consistent with the underlying data. This uncertainty calls for caution in deriving definitive conclusions about the evolutionary process from a single solution.”

End Quote

 

From Original Paper

Inferring Tumor Phylogenies from Multi-region Sequencing

Zheng Hu1,2 and Christina Curtis1,2,*

1Departments of Medicine and Genetics

2Stanford Cancer Institute

Stanford University School of Medicine, Stanford, CA 94305, USA

*Correspondence: cncurtis@stanford.edu

http://dx.doi.org/10.1016/j.cels.2016.07.007

Share this:

• X
• LinkedIn
• Facebook
• Print
• Email

Like this:

Like Loading…

Read Full Post »

DISCUSSION – Genomics-driven personalized medicine for Pancreatic Cancer

Posted in Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Computational Biology/Systems and Bioinformatics, Exosomes, Genome Biology, Mutagenesis, Personalized and Precision Medicine & Genomic Research, tagged Akt, CHK or BCL2 inhibitors as agents, coupled with MTOR, docetaxel, EGFR, gemcitabine and MEK inhibition, germline BRCA deficiency is the basis for ongoing poly(ADP-ribose) polymerase (PARP) inhibitor, KRAS has been proposed to modify therapeutic dependency to EZH2 inhibitors, MEK and docetaxel inhibition, MEK and MTOR inhibitors, MEK inhibition, or MTOR (mammalian target of rapamycin) signaling., or tyrosine kinase inhibitors, Rare BRAF V600E mutations are identified in PDAC, Resistance to MEK inhibitors, Tyrosine Kinase on August 10, 2016| Leave a Comment »

DISCUSSION – Genomics-driven personalized medicine for Pancreatic Cancer

Reporter: Aviva Lev-Ari, PhD, RN

[bold face added, ALA]

Integrated Patient-Derived Models Delineate Individualized Therapeutic Vulnerabilities of Pancreatic Cancer –>>> Personalized Tumor Models Could Help Identify Combination Therapies for Hard-to-Treat Cancers

 

Original article

Pancreatic Cancer – Genomics-driven personalized medicine

 

PDAC has a particularly poor prognosis, and even with new targeted therapies and chemotherapy, the survival is poor. Here, we show that patient-derived models can be developed and used to investigate therapeutic sensitivities determined by genetic features of the disease and to identify empirical therapeutic vulnerabilities. These data reveal several key points that are of prime relevance to pancreatic cancer and tumor biology in general.

The Challenges of Using Genetic Analysis to Inform Treatment in PDAC

Precision oncology is dependent on the existence of known vulnerabilities encoded by high-potency genetic events and drugs capable of exploiting these vulnerabilities. At present, the repertoire of actionable genetic events in PDAC is limited.

• Rare BRAF V600E mutations are identified in PDAC and could represent the basis for targeted inhibition, as our group and others have previously published (Collisson et al., 2012; Witkiewicz et al., 2015).

Similarly,

• germline BRCA deficiency is the basis for ongoing poly(ADP-ribose) polymerase (PARP) inhibitor clinical trials (Lowery et al., 2011).

As shown here, out of 28 cases, only one genetic event was identified that yielded sensitivity to a therapeutic strategy. In this case, existence of the matched model allowed us to confirm the biological relevance of the

• STAG2 mutation by showing sensitivity of the model to a DNA cross-linking agent.

Therefore, annotated patient-derived models provide a substrate upon which to functionally dissect the significance of novel and potentially actionable genetic events that occur within a tumor.

Another challenge of genomics-driven personalized medicine is

• assessing the effect of specific molecular aberrations on therapeutic response in the context of complex genetic changes present in individual tumors.

• KRAS has been proposed to modify therapeutic dependency to EZH2 inhibitors (Kim et al., 2015), and in the models tested, responses to this class of drugs were not uniformly present in cases harboring mutations in chromatin-remodeling genes.

This finding suggests that, although tumors acquire genetic alterations in specific genes, the implicated pathway may not be functionally inactive or therapeutically actionable. Therefore, annotated patient derived models provide a unique test bed for interrogating specific therapeutic dependencies in a genetically tractable system.

Empirical Definition of Therapeutic Sensitivities and Clinical Relevance

Cell lines offer the advantage of the ability to conduct high throughput approaches to interrogate many therapeutic agents. A large number of failed clinical trials have demonstrated the difficulty in treating PDAC. Based on the data herein, the paucity of clinical success is, most probably, due to the diverse therapeutic sensitivity of individual PDAC cases, suggesting that, with an unselected patient population, it will be veritably impossible to demonstrate clinical benefit. Additionally,

•  very few models exhibited an exceptional response to single agents across the breadth of a library encompassing 305 agents.
• We could identify only one tumor that was particularly sensitive to MEK inhibition and another model that was sensitive to
• EGFR and
• tyrosine kinase inhibitors.

In contrast to the limited activity of single agents, combination screens yielded responses at low-dose concentrations in the majority of models. Specific combinations were effective across several models, indicating that, by potentially screening more models, therapeutic sensitivity clades of PDAC will emerge. In the pharmacological screens performed in this study,

• MEK inhibition, coupled with MTOR, docetaxel, or tyrosine kinase inhibitors, was effective in _30% of models tested.
• Resistance to MEK inhibitors occurs through several mechanisms, including
• Upregulation of oncogenic bypass signaling pathways such as AKT, tyrosine kinase, or MTOR (mammalian target of rapamycin) signaling.

In the clinic, the MEK and MTOR inhibitors (e.g., NCT02583542) are being tested. An intriguing finding from the drug screen was

• sensitivity of a subset of models to combined MEK and docetaxel inhibition. This combination has been observed to synergistically enhance apoptosis and inhibit tumor growth in human xenograft tumor models (Balko et al., 2012; McDaid et al., 2005) and is currently being tested in a phase III study in patients with KRAS-mutated, advanced non-small-cell lung adenocarcinoma (Ja¨ nne et al., 2016).

Interestingly, in the models tested herein, there was limited sensitivity imparted through

•  the combination of gemcitabine and MEK inhibition.

This potentially explains why the combination of MEK inhibitor and gemcitabine tested in the clinic did not show improved efficacy over gemcitabine alone (Infante et al., 2014).

Another promising strategy that emerged from this study involves using

• CHK or BCL2 inhibitors as agents that drive enhanced sensitivity to chemotherapy.

Together, the data suggest that the majority of PDAC tumors have intrinsic therapeutic sensitivities, but the challenge is to prospectively identify effective treatment.

Patient-Derived Model-Based Approach to Precision Medicine

This study supports a path for guiding patient treatment based on the integration of genetic and empirically determined sensitivities of the patient’s tumor (Figure S7). In reference to defined genetic susceptibilities, the models provide a means to interrogate the voracity of specific drug targets. Parallel unbiased screening enables the discovery of sensitivities that could be exploited in the clinic. The model-guided treatment must be optimized, allowing for the generation of data in a time frame compatible with clinical decision making and appropriate validation.

In the present study, the majority of models were developed, cell lines were drug screened, and select hits were validated in PDX models within a 10- to 12-month window (Figure S7). This chronology would allow time to inform frontline therapy for recurrent disease for most patients who were surgically resected and treated with a standard of care where the median time to recurrence is approximately 14 months (Saif, 2013).

Although most models were generated from surgically resected specimens, two of the models (EMC3226 and EMC62) were established from primary tumor biopsies, indicating that this approach could be used with only a limited amount of tumor tissue available.

In the context of inoperable pancreatic cancer, application of data from a cell-line screen without in vivo validation in PDX would permit the generation of sensitivity data in the time frame compatible with treatment.

[We] acknowledge that model-guided treatment is also not without significant logistical hurdles, including the availability of drugs for patient treatment, clinically relevant time frames, patient-performance status, toxicity of combination regiments, and quality metrics related to model development and therapeutic response evaluation.

Additionally, it will be very important to monitor ex vivo genetic and phenotypic divergence with passage and try to understand the features of tumor heterogeneity that could undermine the efficacy of using models to direct treatment. As shown here, drug sensitivities remained stable with passage in cell culture and, importantly, were confirmed in PDX models, suggesting that the dominant genetic drivers and related therapeutic sensitivities are conserved.

In spite of these challenges, progressively more effort is going into the development of patient-derived models for guidance of disease treatment (Aparicio et al., 2015; Boj et al., 2015; Crystal et al., 2014; van de Wetering et al., 2015).

Several ongoing trials use PDX models to direct a limited repertoire of agents (e.g., NCT02312245, NCT02720796, and ERCAVATAR2015). Given the experience here, PDAC cell lines would provide the opportunity to rapidly interrogate a larger portfolio of combinations that could be used to guide patient care and provide a novel approach to precision medicine.

Validation of this approach would require the establishment of challenging multi-arm or N-of-1 clinical trials. However, considering the dire outcome for PDAC patients and the long-lasting difficulty in developing effective treatments, this non-canonical approach might be particularly impactful in pancreatic cancer.

SOURCE

Witkiewicz et al., 2016, Cell Reports 16, 1–15

August 16, 2016 ª 2016 The Author(s).

http://dx.doi.org/10.1016/j.celrep.2016.07.023

Agnieszka K. WitkiewiczPress enter key for correspondence information

Press enter key to Email the author

Uthra Balaji

Cody Eslinger

Elizabeth McMillan

William Conway

Bruce Posner

Gordon B. Mills

Eileen M. O’Reilly

Erik S. KnudsenPress enter key for correspondence information

Press enter key to Email the author

Publication stage: In Press Corrected Proof

Open Access

DOI: http://dx.doi.org/10.1016/j.celrep.2016.07.023

Resource Integrated Patient-Derived Models Delineate Individualized Therapeutic Vulnerabilities of Pancreatic Cancer

Correspondence

awitki@email.arizona.edu (A.K.W.),

eknudsen@email.arizona.edu (E.S.K.)

Accession Numbers: GSE84023

Other related articles on this topic published in this Open Access Online Scientific Journal include the following:

Pancreatic Cancer: Articles of Note @PharmaceuticalIntelligence.com

Curator: Aviva Lev-Ari, PhD, RN

Pancreatic Cancer: Articles of Note @PharmaceuticalIntelligence.com

Share this:

• X
• LinkedIn
• Facebook
• Print
• Email

Like this:

Like Loading…

Read Full Post »

Crowdsourcing Genetic Data Yields Discovery of DNA loci associated with Major Depressive Disorder (MDD) in European Descendants

Posted in Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Computational Biology/Systems and Bioinformatics, Exosomes, Gene Regulation, Gene Regulation and Evolution, Genetics & Innovations in Treatment, Genome Biology, Genomic Endocrinology, Genomic Testing: Methodology for Diagnosis, Innovations in Neurophysiology & Neuropsychology, Neurological Diseases, Next Generation Sequencing (NGS), Personalized and Precision Medicine & Genomic Research, tagged behavioral phenotypes, biological mechanisms of depression, diagnoses of depression, European ancestry, genome-wide association studies (GWAS), histone methylation, loci associated with the risk of developing major depressive disorder (MDD), neurobiology of depression, neurodevelopment, neurogenesis and synaptic modification, Tissue and gene enrichment assays, weighted genetic risk score (GRS) on August 9, 2016| Leave a Comment »

Crowdsourcing Genetic Data Yields Discovery of DNA loci associated with Major Depressive Disorder (MDD) in European Descendants

 

Reporter: Kelly Perlman, Life Sciences Student and Research Assistant, McGill University

 

UPDATED on 11/24/2019

Can AI help diagnose depression? It’s a long shot

At the moment, machine intelligence is just as subjective as human intelligence

Alejandra Canales

https://www.salon.com/2019/11/23/can-ai-help-diagnose-depression-its-a-long-shot_partner/amp?__twitter_impression=true

Researchers from Pfizer Global Research and Development, 23andMe, and the Massachusetts General Hospital have published a study in Nature Genetics, pinpointing 15 genetic loci associated with the risk of developing major depressive disorder (MDD) in individuals of European ancestry. Evidence from previous research suggests that MDD is heritable, but the details of the specific gene correlates are unclear. The identification of loci where single nucleotide polymorphisms (SNPs) related to MDD exist could provide better insight into the neurobiology of depression, and therefore better treatment options.

23andMe, a private biotechnology company situated in California, offers a DNA sequencing service in which consumers send in a saliva swab for testing, and later receive a report listing the findings of the analysis related to ancestry, physical and behavioral traits, along with risk of inheriting certain diseases. The participants of this study had agreed to provide the results of their genetic testing for scientific research.

The results of 75,607 participants with self-reported diagnoses of depression were compared to the results of 231,747 participants reporting having never experienced depression. This data was combined with the results of previously published MDD genome-wide association studies (GWAS). To test the whether these results could be replicated, another set of results from 23andMe was analyzed, in which there were 45,773 MDD subjects, and 106,354 controls.

After the joint analysis, 17 SNPs were identified at 15 different loci. Tissue and gene enrichment assays showed that the genes that were over-expressed in the CNS were related to functions including neurodevelopment, histone methylation, neurogenesis and synaptic modification.

The team then created a weighted genetic risk score (GRS) in which they compared the 17 SNPs with factors including medication use, comorbid diseases and behavioral phenotypes, all of which were correlated with the GRS. Of note, the GRS was very highly correlated with age of onset of MDD.

The crowdsourcing of genetic data proves to be an efficient and powerful tool for large-scale MDD studies. Pooling large subject databases together is essential in order to account for the heterogeneous nature of the disease. Despite not being able to precisely assess each subject’s disease phenotype, scientists can make more rapid headway by collaborating with biotechnology companies in the quest to better understand the biological mechanisms of depression. Ron Perlis, M.D., M.Sc., of the Massachusetts General Hospital and co-author of this paper explained that “finding genes associated with depression should help make clear that this is a brain disease, which we hope will decrease the stigma still associated with these kinds of illnesses”.

 

Details on specific significant genes:

http://www.genecards.org/cgi-bin/carddisp.pl?gene=OLFM4

http://www.genecards.org/cgi-bin/carddisp.pl?gene=TMEM161B

http://www.genecards.org/cgi-bin/carddisp.pl?gene=MEF2C

http://www.genecards.org/cgi-bin/carddisp.pl?gene=MEIS2

http://www.genecards.org/cgi-bin/carddisp.pl?gene=TMCO5A

http://www.genecards.org/cgi-bin/carddisp.pl?gene=NEGR1

 

SOURCES

Hyde, C. L., Nagle, M. W., Tian, C., Chen, X., Paciga, S. A., Wendland, J. R., . . . Winslow, A. R. (2016). Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nature Genetics Nat Genet. doi:10.1038/ng.3623

Major Depressive Disorder Loci Discovered in Large GWAS Enabled by 23andMe Participants’ Data. (2016, August 01). Retrieved August 09, 2016, from https://www.genomeweb.com/microarrays-multiplexing/major-depressive-disorder-loci-discovered-large-gwas-enabled-23andme

 

Share this:

• X
• LinkedIn
• Facebook
• Print
• Email

Like this:

Like Loading…

Read Full Post »

Through Data Science and Cloud Technology: Stanford Medicine and Google will transform Patient Care and Medical Research

Posted in Accountable Care Organizations, Computational Biology/Systems and Bioinformatics, Genome Biology, Genomic Testing: Methodology for Diagnosis, HealthCare IT, Healthcare Reform, Personalized and Precision Medicine & Genomic Research on August 9, 2016| Leave a Comment »

 Through Data Science: Stanford Medicine and Google will transform Patient Care and Medical Research

Reporter: Aviva Lev-Ari, PhD, RN

 

Stanford Medicine integrates research, medical education and health care at its three institutions –Stanford University School of Medicine, Stanford Health Care (formerly Stanford Hospital & Clinics), and Lucile Packard Children’s Hospital Stanford. For more information, please visit the Office of Communication & Public Affairs site at http://mednews.stanford.edu.

 

Stanford’s forthcoming Clinical Genomics Service, which puts genomic sequencing into the hands of clinicians to help diagnose disease, will be built using Google Genomics, a service that applies the same technologies that power Google Search and Maps to securely store, process, explore and share genomic data sets.

The Clinical Genomics Service will enable physicians at Stanford Health Care and Stanford Children’s Health to order genome sequencing for patients who have distinctive or unusual symptoms that might be caused by a wayward gene. The genomic data would then go to the Google Cloud Platform to join masses of aggregated and anonymous data from other Stanford patients. “As the new service launches,” said Euan Ashley, MRCP, DPhil, a Stanford associate professor of medicine and of genetics, “we’ll be doing hundreds and then thousands of genome sequences.”

The Clinical Genomics Service aims to make genetic testing a normal part of health care for patients. “Genetic testing is built into the whole system,” said Ashley. A physician who thinks a genome-sequencing test could help a patient can simply request sequencing along with other blood tests, he said. “The DNA gets sequenced and a large amount of data comes back,” he said. At that point, Stanford can use Google Cloud to analyze the data to decide which gene variants might be responsible for the patient’s health condition. Then a data curation team will work with the physician to narrow the possibilities, he said.

“This collaboration will enable Stanford to discover new ways to advance medicine to the benefit of Stanford patients and families,” said Ed Kopetsky, chief information officer at Lucile Packard Children’s Hospital Stanford and Stanford Children’s Health. “Together, Stanford Medicine and Google are making a major contribution and commitment in curing diseases that afflict children not just in our community, but throughout the world. It’s an extraordinary investment, and we’re proud to play such a large role in transforming patient care and research.”

Read more at the SOURCE

 

Stanford Medicine, Google team up to harness power of data science for health care

By JENNIE DUSHECK

Jennie Dusheck is a science writer for the medical school’s Office of Communication & Public Affairs. Email her at dusheck@stanford.edu.

SOURCE

http://med.stanford.edu/news/all-news/2016/08/stanford-medicine-google-team-up-to-harness-power-of-data-science.html

Share this:

• X
• LinkedIn
• Facebook
• Print
• Email

Like this:

Like Loading…

Read Full Post »

CancerBase.org – The Global HUB for Diagnoses, Genomes, Pathology Images: A Real-time Diagnosis and Therapy Mapping Service for Cancer Patients – Anonymized Medical Records accessible to

Posted in CANCER BIOLOGY & Innovations in Cancer Therapy, Computational Biology/Systems and Bioinformatics, Genetics & Innovations in Treatment, Genetics & Pharmaceutical, Genome Biology, Imaging-based Cancer Patient Management on July 28, 2016| Leave a Comment »

CancerBase.org – The Global HUB for Diagnoses, Genomes, Pathology Images: A Real-time Diagnosis and Therapy Mapping Service for Cancer Patients – Anonymized Medical Records accessible to anyone on Earth

Reporter: Aviva Lev-Ari, PhD, RN

 

UPDATED on 10/29/2019

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Yu Fu1, Alexander W Jung1, Ramon Viñas Torne1, Santiago Gonzalez1,2, Harald Vöhringer1, Mercedes Jimenez-Linan3, Luiza Moore3,4, and Moritz Gerstung#1,5 # to whom correspondence should be addressed 1) European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK. 2) Current affiliation: Institute for Research in Biomedicine (IRB Barcelona), Parc Científic de Barcelona, Barcelona, Spain. 3) Department of Pathology, Addenbrooke’s Hospital, Cambridge, UK. 4) Wellcome Sanger Institute, Hinxton, UK 5) European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.

Correspondence:

Dr Moritz Gerstung European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Hinxton, CB10 1SA UK. Tel: +44 (0) 1223 494636 E-mail: moritz.gerstung@ebi.ac.uk

Abstract

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Here we use deep transfer learning to quantify histopathological patterns across 17,396 H&E stained histopathology image slides from 28 cancer types and correlate these with underlying genomic and transcriptomic data. Pan-cancer computational histopathology (PC-CHiP) classifies the tissue origin across organ sites and provides highly accurate, spatially resolved tumor and normal distinction within a given slide. The learned computational histopathological features correlate with a large range of recurrent genetic aberrations, including whole genome duplications (WGDs), arm-level copy number gains and losses, focal amplifications and deletions as well as driver gene mutations within a range of cancer types. WGDs can be predicted in 25/27 cancer types (mean AUC=0.79) including those that were not part of model training. Similarly, we observe associations with 25% of mRNA transcript levels, which enables to learn and localise histopathological patterns of molecularly defined cell types on each slide. Lastly, we find that computational histopathology provides prognostic information augmenting histopathological subtyping and grading in the majority of cancers assessed, which pinpoints prognostically relevant areas such as necrosis or infiltrating lymphocytes on each tumour section. Taken together, these findings highlight the large potential of PC-CHiP to discover new molecular and prognostic associations, which can augment diagnostic workflows and lay out a rationale for integrating molecular and histopathological data.

https://www.biorxiv.org/content/10.1101/813543v1

 

Key points

● Pan-cancer computational histopathology analysis with deep learning extracts histopathological patterns and accurately discriminates 28 cancer and 14 normal tissue types

● Computational histopathology predicts whole genome duplications, focal amplifications and deletions, as well as driver gene mutations

● Wide-spread correlations with gene expression indicative of immune infiltration and proliferation

● Prognostic information augments conventional grading and histopathology subtyping in the majority of cancers

 

Discussion

Here we presented PC-CHiP, a pan-cancer transfer learning approach to extract computational histopathological features across 42 cancer and normal tissue types and their genomic, molecular and prognostic associations. Histopathological features, originally derived to classify different tissues, contained rich histologic and morphological signals predictive of a range of genomic and transcriptomic changes as well as survival. This shows that computer vision not only has the capacity to highly accurately reproduce predefined tissue labels, but also that this quantifies diverse histological patterns, which are predictive of a broad range of genomic and molecular traits, which were not part of the original training task. As the predictions are exclusively based on standard H&E-stained tissue sections, our analysis highlights the high potential of computational histopathology to digitally augment existing histopathological workflows. The strongest genomic associations were found for whole genome duplications, which can in part be explained by nuclear enlargement and increased nuclear intensities, but seemingly also stems from tumour grade and other histomorphological patterns contained in the high-dimensional computational histopathological features. Further, we observed associations with a range of chromosomal gains and losses, focal deletions and amplifications as well as driver gene mutations across a number of cancer types. These data demonstrate that genomic alterations change the morphology of cancer cells, as in the case of WGD, but possibly also that certain aberrations preferentially occur in distinct cell types, reflected by the tumor histology. Whatever is the cause or consequence in this equation, these associations lay out a route towards genomically defined histopathology subtypes, which will enhance and refine conventional assessment. Further, a broad range of transcriptomic correlations was observed reflecting both immune cell infiltration and cell proliferation that leads to higher tumor densities. These examples illustrated the remarkable property that machine learning does not only establish novel molecular associations from pre-computed histopathological feature sets but also allows the localisation of these traits within a larger image. While this exemplifies the power of a large scale data analysis to detect and localise recurrent patterns, it is probably not superior to spatially annotated training data. Yet such data can, by definition, only be generated for associations which are known beforehand. This appears straightforward, albeit laborious, for existing histopathology classifications, but more challenging for molecular readouts. Yet novel spatial transcriptomic44,45 and sequencing technologies46 bring within reach spatially matched molecular and histopathological data, which would serve as a gold standard in combining imaging and molecular patterns. Across cancer types, computational histopathological features showed a good level of prognostic relevance, substantially improving prognostic accuracy over conventional grading and histopathological subtyping in the majority of cancers. It is this very remarkable that such predictive It is made available under a CC-BY-NC 4.0 International license. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. bioRxiv preprint first posted online Oct. 25, 2019; doi: http://dx.doi.org/10.1101/813543. The copyright holder for this preprint signals can be learned in a fully automated fashion. Still, at least at the current resolution, the improvement over a full molecular and clinical workup was relatively small. This might be a consequence of the far-ranging relations between histopathology and molecular phenotypes described here, implying that histopathology is a reflection of the underlying molecular alterations rather than an independent trait. Yet it probably also highlights the challenges of unambiguously quantifying histopathological signals in – and combining signals from – individual areas, which requires very large training datasets for each tumour entity. From a methodological point of view, the prediction of molecular traits can clearly be improved. In this analysis, we adopted – for the reason of simplicity and to avoid overfitting – a transfer learning approach in which an existing deep convolutional neural network, developed for classification of everyday objects, was fine tuned to predict cancer and normal tissue types. The implicit imaging feature representation was then used to predict molecular traits and outcomes. Instead of employing this two-step procedure, which risks missing patterns irrelevant for the initial classification task, one might directly employ either training on the molecular trait of interest, or ideally multi-objective learning. Further improvement may also be related to the choice of the CNN architecture. Everyday images have no defined scale due to a variable z-dimension; therefore, the algorithms need to be able to detect the same object at different sizes. This clearly is not the case for histopathology slides, in which one pixel corresponds to a defined physical size at a given magnification. Therefore, possibly less complex CNN architectures may be sufficient for quantitative histopathology analyses, and also show better generalisation. Here, in our proof-of-concept analysis, we observed a considerable dependence of the feature representation on known and possibly unknown properties of our training data, including the image compression algorithm and its parameters. Some of these issues could be overcome by amending and retraining the network to isolate the effect of confounding factors and additional data augmentation. Still, given the flexibility of deep learning algorithms and the associated risk of overfitting, one should generally be cautious about the generalisation properties and critically assess whether a new image is appropriately represented. Looking forward, our analyses revealed the enormous potential of using computer vision alongside molecular profiling. While the eye of a trained human may still constitute the gold standard for recognising clinically relevant histopathological patterns, computers have the capacity to augment this process by sifting through millions of images to retrieve similar patterns and establish associations with known and novel traits. As our analysis showed this helps to detect histopathology patterns associated with a range of genomic alterations, transcriptional signatures and prognosis – and highlight areas indicative of these traits on each given slide. It is therefore not too difficult to foresee how this may be utilised in a computationally augmented histopathology workflow enabling more precise and faster diagnosis and prognosis. Further, the ability to quantify a rich set of histopathology patterns lays out a path to define integrated histopathology and molecular cancer subtypes, as recently demonstrated for colorectal cancers47 .

Lastly, our analyses provide It is made available under a CC-BY-NC 4.0 International license. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

bioRxiv preprint first posted online Oct. 25, 2019; doi: http://dx.doi.org/10.1101/813543.

The copyright holder for this preprint proof-of-concept for these principles and we expect them to be greatly refined in the future based on larger training corpora and further algorithmic refinements.

https://www.biorxiv.org/content/biorxiv/early/2019/10/25/813543.full.pdf

 

July 27, 2016

By Andrew Myers

Illustration by Tricia Seibold and iStock/liuzishan

During his 2016 State of the Union address, President Barack Obama called on Vice President Joe Biden – who had months earlier lost his son Beau to brain cancer – to head a “moonshot” to significantly accelerate research into the disease. The president said he wanted to harness the spirit of American innovation that took us from zero to landing a man on the moon in a decade to similarly find new ways to prevent, diagnose and treat cancer.

One of those intrigued by that call to action was Stanford’s Jan Liphardt, an associate professor of bioengineering who specializes in biophysics, the tumor microenvironment and data analysis. Stanford Engineering talked to Liphardt about how he came to be involved with the moonshot and his approach to using data and the voice of patients to better understand cancer and how it can be treated, and how sharing information can better inform the course of cancer research.

How did you get involved in the National Cancer Moonshot?

In March, after the president’s charge, the vice president challenged scientists, doctors, industry and patients to give their best ideas to the moonshot. The White House also reached out to a few outsiders, myself included. The White House instructions were unusual: “Do something big and different. There is no money and you have 87 days. Go.”

I like a challenge, and this was a chance to serve, even in the face of administrative hurdles. So I looked for advice, teammates and support. Russ Altman, a colleague at Stanford, suggested it was time to give patients a way to volunteer their own health data in order to help find cures. I collaborated with Peter Kuhn, a professor of medicine and engineering at the University of Southern California, who’s known for carefully listening to cancer patients, advocates and their supporters. In short order we had links with advocates like AnneMarie Ciccarella, Sonja Durham, Lori Marx-Rubiner, Jack Whelan and Jack Park. That’s how we got to CancerBase.org.

What’s the idea the team came up with?

We thought for about a week: What would matter to the patients that Stanford and other research institutions serve? What would scale? Well, we’re not going to run a clinical trial, go near protected health information, invent a new drug or write a research proposal. There’s no time for that. Whatever it was, it had to be useful, scalable, legal and different. That pointed to data, the web, patients and decisions.

One thing jumped out: Right now, there’s significant friction in medical data sharing. People all over the world can already effortlessly share other kinds of information – pictures, movies, ideas, stories, tweets. Increasingly, they are using the same tools to share personal medical information. It’s remarkable what cancer patients already share: diagnoses, genomes, pathology images. But that information is not yet widely used to understand where they are with their diseases.

Ideally, everyone, including scientists and doctors, would have as much information as possible at their fingertips. Many patients think when they give data for research, magically scientists all over the world can dig into this information, find patterns and help. The practical reality is that it’s nearly impossible for any one scientist to access the amounts of data they would like.

So that’s the simple idea: A global map and give patients the tools they need to share their data – if they want to. They can donate information for the greater good. In return, we make a simple promise: When you post data, we’ll anonymize them and make them available to anyone on Earth in one second. We plan to display this information like real-time traffic data. HIPAA doesn’t apply to this direct data-sharing. The patients can give us whatever information they want, and they can tell us what they want us to do with it. We’re a conduit. Their data belong to them, not to us.

How does it work?

Today we ask just five basic questions. Over time we will add more. You join, give some information, and we’ll put you on a global map. Right now, some of the things we don’t know about cancer are incredibly simple: Where is everyone on Earth with cancer? How old are they? What is their diagnosis? Did their cancers metastasize? Global, instantaneous data sharing is the story.

In a second phase, we are going to see if we can plot all the information just like Waze does for traffic. Our role is to synthesize the information and plot it in ways that ordinary people can understand. Think of it this way – patients want to be able to chart their treatment path. Who went straight, who went left? People just getting on the highway are curious about what people did who came before them, and what happened to those people. Did they arrive at the destination easily and promptly? We’re a real-time diagnosis and therapy mapping service for cancer.

You say that giving patients a way to share their health data is important to help finding cures. Why?

Let me give you a specific example. At Stanford, I’m part of a team of cancer biologists and clinicians funded by the Stanford Cancer Institute to think about the next generation of screening for breast cancer in the U.S. Every year, the U.S. uses mammography to screen more than 40 million women for breast cancer. In this project, it quickly became clear that there is currently no central, easy-to-access repository of mammograms for research use.

That’s a major lost opportunity – our nation spends billions on screening, but we don’t store, share and analyze this information in a scalable and simple manner. In the traditional approach, our team would spend several hundred thousand dollars, and about three years, to assemble perhaps 1,000 mammograms. We would then use this tiny dataset to try to find something interesting, but since the dataset is so small, we would be blind to rare features of breast cancer and its predictors. It clearly makes a lot more sense to compare and explore 100 million images.

This sounds completely impossible until you realize that Instagram users upload 58 million images every day. Once you start to think about supposedly intractable research problems from a web or social networking perspective, new possibilities open. Imagine, for example, if there were a simple way for every single woman on Earth to upload and share her de-identified mammogram? Or more generally, imagine a world in which patients have the tools to globally share de-identified health data, if they want to. That’s exactly the idea behind CancerBase – let’s just give people those tools and see what happens.

How much data and how many people are needed to make this viable?

We think we are going to need several tens-of-thousands of members. There are approximately 50 million people on Earth with a cancer diagnosed in the last five years, and 200 million more people have an immediate family member with cancer. Almost 2 billion people are active on Twitter and Facebook – a quarter of the world’s population. If just a few percent of those people sign up, we could do something no one on Earth has done before.

Are there hopes to create a “developer community,” people who find ways to use your data that you didn’t even think about or have the time to work on?

Definitely. As much as we think we can predict what these data are useful for, we don’t really know. By making the anonymized data available to everyone within one second, they might start to do things that we never dreamed of. The more eyes look at these data, the better off everyone will be. The dream is to have cancer-relevant medical data flow unimpeded around the world in seconds, so that everyone, wherever they are, can see and use this information.

SOURCE

https://engineering.stanford.edu/news/how-data-can-help-us-understand-cancer-and-its-treatment

Share this:

• X
• LinkedIn
• Facebook
• Print
• Email

Like this:

Like Loading…

Read Full Post »

A New Potential Target for Pancreatic Cancer Treatment: Rapid Screening Technique finds Gene Defending Tumors from DNA Damage @MD Anderson Cancer Center

Posted in CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Screening, Chemical Genetics, Computational Biology/Systems and Bioinformatics, tagged COMPASS complex regulating chromatin function, WD repeat-containing protein 5 (WDR5), WDR5 critical for cancer cell proliferation, WDR5 to be upregulated in prostate and bladder cancers on July 28, 2016| Leave a Comment »

A New Potential Target for Pancreatic Cancer Treatment: Rapid Screening Technique finds Gene Defending Tumors from DNA Damage @M. D. Anderson Cancer Center

Reporter: Aviva Lev-Ari, PhD, RN

Novel gene-hunting method implicates new culprit in pancreatic cancer

Researchers develop rapid screening technique; find gene defends tumors from DNA damage

Date:

June 23, 2016

Source:

University of Texas M. D. Anderson Cancer Center

Summary:

Using an innovative approach to identify a cancer’s genetic vulnerabilities by more swiftly analyzing human tumors transplanted into mice, researchers have identified a new potential target for pancreatic cancer treatment.

WDR5 emerges as robust “hit”

WD repeat-containing protein 5 (WDR5), a core part of the COMPASS complex regulating chromatin function, was implicated in multiple screens. Recent research by others had shown WDR5 to be upregulated in prostate and bladder cancers and critical for cancer cell proliferation.

The team confirmed WDR5 was highly expressed in pancreatic cancer compared to normal pancreas tissue and then conducted a series of experiments which showed knocking down the gene impaired cell proliferation and tumor growth and greatly increased survival in mice.

Subsequent experiments showed WDR5 works in concert with Myc to protect pancreatic cancer from DNA damage. There is no known method for targeting either WDR5 or Myc separately, Carugo said, but the team thinks there might be ways to block their interaction.

While the team targeted epigenetic regulators, Carugo noted the technique can be used with other shRNA libraries aimed at different classes of genes.

This technology is being widely adopted by MD Anderson’s moon shot teams to identify genetic vulnerabilities and cancer targets specific to various disease subtypes.

---

Story Source:

The above post is reprinted from materials provided by University of Texas M. D. Anderson Cancer Center. Note: Materials may be edited for content and length.

SOURCE

University of Texas M. D. Anderson Cancer Center. “Novel gene-hunting method implicates new culprit in pancreatic cancer: Researchers develop rapid screening technique; find gene defends tumors from DNA damage.” ScienceDaily. ScienceDaily, 23 June 2016. www.sciencedaily.com/releases/2016/06/160623115741.htm.

---

Alessandro Carugo et al. In Vivo Functional Platform Targeting Patient-Derived Xenografts Identifies WDR5-Myc Association as a Critical Determinant of Pancreatic Cancer. Cell Reports, June 2016 DOI:10.1016/j.celrep.2016.05.063

Cell Rep. 2016 Jun 28;16(1):133-47. doi: 10.1016/j.celrep.2016.05.063. Epub 2016 Jun 16.

In Vivo Functional Platform Targeting Patient-Derived Xenografts Identifies WDR5-Myc Association as a Critical Determinant of Pancreatic Cancer.

Carugo A¹, Genovese G², Seth S³, Nezi L², Rose JL², Bossi D⁴, Cicalese A⁴, Shah PK³, Viale A², Pettazzoni PF², Akdemir KC⁵, Bristow CA³,Robinson FS², Tepper J², Sanchez N², Gupta S⁶, Estecio MR⁷, Giuliani V³, Dellino GI⁸, Riva L⁹, Yao W², Di Francesco ME¹⁰, Green T², D’Alesio C⁴, Corti D², Kang Y¹¹, Jones P¹⁰, Wang H¹², Fleming JB¹¹, Maitra A⁶, Pelicci PG⁸, Chin L¹⁰, DePinho RA¹³, Lanfrancone L¹⁴, Heffernan TP¹⁵,Draetta GF¹⁶.

Author information

• ¹Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Molecular and Cellular Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Experimental Oncology, European Institute of Oncology, Milan 20139, Italy. Electronic address: acarugo@mdanderson.org.
• ²Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Molecular and Cellular Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ³Institute for Applied Cancer Science, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ⁴Department of Experimental Oncology, European Institute of Oncology, Milan 20139, Italy.
• ⁵Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ⁶Sheikh Ahmed Bin Zayed Al Nahyan Center for Pancreatic Cancer Research, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ⁷Department of Epigenetics and Molecular Carcinogenesis, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ⁸Department of Experimental Oncology, European Institute of Oncology, Milan 20139, Italy; Department of Oncology and Hemato-oncology, University of Milan, Milan 20139, Italy.
• ⁹Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milan 20139, Italy.
• ¹⁰Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Institute for Applied Cancer Science, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ¹¹Department of Surgical Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ¹²Department of Pathology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ¹³Department of Cancer Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ¹⁴Department of Experimental Oncology, European Institute of Oncology, Milan 20139, Italy. Electronic address: luisa.lanfrancone@ieo.eu.
• ¹⁵C-4 Therapeutics, Cambridge, MA 02142, USA. Electronic address: theffernan@c4therapeutics.com.
• ¹⁶Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Institute for Applied Cancer Science, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Molecular and Cellular Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA. Electronic address: gdraetta@mdanderson.org.

Abstract

Current treatment regimens for pancreatic ductal adenocarcinoma (PDAC) yield poor 5-year survival, emphasizing the critical need to identify druggable targets essential for PDAC maintenance. We developed an unbiased and in vivo target discovery approach to identify molecular vulnerabilities in low-passage and patient-derived PDAC xenografts or genetically engineered mouse model-derived allografts. Focusing on epigenetic regulators, we identified WDR5, a core member of the COMPASS histone H3 Lys4 (H3K4) MLL (1-4) methyltransferase complex, as a top tumor maintenance hit required across multiple human and mouse tumors. Mechanistically, WDR5 functions to sustain proper execution of DNA replication in PDAC cells, as previously suggested by replication stress studies involving MLL1, and c-Myc, also found to interact with WDR5. We indeed demonstrate that interaction with c-Myc is critical for this function. By showing that ATR inhibition mimicked the effects of WDR5 suppression, these data provide rationale to test ATR and WDR5 inhibitors for activity in this disease.

Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

PMID:

27320920

DOI:

10.1016/j.celrep.2016.05.063

SOURCE

http://www.ncbi.nlm.nih.gov/pubmed/27320920

Share this:

• X
• LinkedIn
• Facebook
• Print
• Email

Like this:

Like Loading…

Read Full Post »

« Newer Posts - Older Posts »

• Follow Blog via Email

  Enter your email address to follow this blog and receive notifications of new posts by email.

  Email Address

  Follow

  Join 2,038 other subscribers

• Recent Posts

  • Novartis and Bristol Myers Squibb halt CAR-T trial for autoimmune diseases over patient deaths: a potential issue with cell-based manufacturing September 15, 2026
  • FDA Contemplates Changes in Trial Design, Drug Development, and Use of AI in Clinical Trials September 15, 2026
  • FDA Moves Forward With Its Guidance Framework for Rare Disease Trials: 2026 August 3, 2026
  • Artificial Intelligence TRANSFORMS Medicine, Biotech and Healthcare, How? August 3, 2026
  • In Memoriam: Nobel Laureate J. Michael Bishop for discovery of Retroviral Oncogenes July 31, 2026
  • Companies Actively Engaging Artificial Intellegence for Drug Design: 2026 July 31, 2026
  • AI-Native Drug Discovery Landscape 2026 – How LPBI Group Differentiates in the New Era of Foundation Models July 19, 2026
  • In Memoriam: In Remembrance of Cancer Researchers who passed in 2026 July 13, 2026
  • Urgent Request for Action on Proposed Rule Affecting Federal Funding Grant Review July 7, 2026
  • 2026 Tang Prize in Biopharmaceutical Science Awarded to Three Pioneers of Cellular Immunotherapy Revolutionizing Treatments for Blood Cancers and Solid Tumors July 5, 2026

• Archives

  Archives Select Month September 2026  (2) August 2026  (2) July 2026  (6) June 2026  (4) May 2026  (1) January 2026  (10) December 2025  (8) November 2025  (8) October 2025  (12) September 2025  (2) July 2025  (4) June 2025  (8) May 2025  (4) April 2025  (2) March 2025  (1) February 2025  (2) January 2025  (3) December 2024  (1) November 2024  (5) October 2024  (7) September 2024  (7) August 2024  (1) June 2024  (1) May 2024  (2) April 2024  (2) January 2024  (1) November 2023  (1) October 2023  (3) September 2023  (3) August 2023  (2) July 2023  (2) June 2023  (7) May 2023  (4) April 2023  (2) March 2023  (5) February 2023  (2) January 2023  (3) December 2022  (2) October 2022  (6) September 2022  (2) August 2022  (1) July 2022  (3) June 2022  (3) May 2022  (8) April 2022  (10) March 2022  (7) February 2022  (7) January 2022  (3) December 2021  (3) November 2021  (4) October 2021  (11) September 2021  (5) August 2021  (8) July 2021  (21) June 2021  (8) May 2021  (10) April 2021  (8) March 2021  (15) February 2021  (17) January 2021  (20) December 2020  (10) November 2020  (12) October 2020  (26) September 2020  (17) August 2020  (22) July 2020  (27) June 2020  (33) May 2020  (26) April 2020  (42) March 2020  (22) February 2020  (11) January 2020  (7) December 2019  (20) November 2019  (13) October 2019  (11) September 2019  (3) August 2019  (8) July 2019  (25) June 2019  (47) May 2019  (40) April 2019  (27) March 2019  (29) February 2019  (17) January 2019  (50) December 2018  (14) November 2018  (17) October 2018  (18) September 2018  (17) August 2018  (8) July 2018  (20) June 2018  (22) May 2018  (17) April 2018  (12) March 2018  (20) February 2018  (26) January 2018  (16) December 2017  (6) November 2017  (20) October 2017  (13) September 2017  (16) August 2017  (16) July 2017  (19) June 2017  (20) May 2017  (32) April 2017  (21) March 2017  (10) February 2017  (24) January 2017  (21) December 2016  (43) November 2016  (8) October 2016  (40) September 2016  (67) August 2016  (59) July 2016  (75) June 2016  (37) May 2016  (95) April 2016  (152) March 2016  (175) February 2016  (196) January 2016  (139) December 2015  (90) November 2015  (287) October 2015  (237) September 2015  (115) August 2015  (96) July 2015  (63) June 2015  (44) May 2015  (53) April 2015  (76) March 2015  (95) February 2015  (83) January 2015  (75) December 2014  (75) November 2014  (88) October 2014  (135) September 2014  (117) August 2014  (88) July 2014  (88) June 2014  (109) May 2014  (60) April 2014  (85) March 2014  (58) February 2014  (72) January 2014  (107) December 2013  (104) November 2013  (136) October 2013  (62) September 2013  (32) August 2013  (46) July 2013  (74) June 2013  (110) May 2013  (112) April 2013  (86) March 2013  (92) February 2013  (63) January 2013  (63) December 2012  (46) November 2012  (71) October 2012  (84) September 2012  (82) August 2012  (122) July 2012  (59) June 2012  (34) May 2012  (46) April 2012  (2)

• Categories

  Categories Select Category 3D Printing for Medical Application  (240)    3D Plotting Scaffolds  (44)    BioInks  (34)       Biopolymer Blend Open Porous  (18)       Dental Applications  (10)    BioPrinting in Regenerative Medicine  (77)       Cell Level  (16)       MicroEngineering Cell-Tissue & Systems  (31)       Tissue Engineering  (37)    Cardiovascular and Vascular Systems  (30)       Artificial Vascular Structures  (9)       Cardiovascular Tissue  (8)    Drug Development using MultiOrgan Chip  (12)    Drug Development/Formulation using 3D Printing  (10)    MEMS  (16)       Bio-MEMS  (12)    Organ-on-a-Chip  (11)    Programmable Sensors (Carbon Nano Tubes)  (5) 3rd Party IP: Drug DIscovery  (1) 4D Printing and Meta Materials  (10) Academic Publishing  (223)    Key Opinion Leaders in eScientific Publishing – Interviews with  (8) Advanced Drug Manufacturing Technology  (97)    Antimalarial Preparation  (8)    Autologous Cell Therapy  (14)    Automated Cell Processing  (13) Allergy and Infectious Diseases  (12) Alzheimer’s Disease  (163)    Etiology  (77)    Medical Device Therapies for Altzheimer’s Disease  (15)    Pharmacotherapy and Cell Activity  (51) Anticancer Resistance  (31) Art Exhibits on the Human Condition  (1) Art Inspires Science  (4) Article Type  (2)    Authored  (1)    Curated  (2)    Scientific report  (1) Artificial Heart  (2) Artificial Intelligence – General  (240)    An executive’s guide to AI  (11)    Artificial Intelligence – Breakthroughs in Theories and Technologies  (119)    Artificial Intelligence Applications in Health Care  (131)       Artificial Intelligence in CANCER  (42)       Artificial Intelligence in Health Care – Tools & Innovations  (77)    Artificial Intelligence in Medicine – Application for Diagnosis  (72)       Artificial intelligence applications for cardiology  (33)          AI-assisted Cardiac MRI  (10)       Artificial Intelligence in Psychiatry  (7)       Deep Learning in Pathology  (19)    Artificial Intelligence in Medicine – Applications in Therapeutics  (71)    ChatGPT, GPT-4  (7)       ChatGPT at LPBI Group  (3)       ChatGPT in Academic Education  (3)    Machine Learning  (73)       Deep Learning  (26)       Natural Language Processing (NLP)  (45)    Machine learning in predicting type 2 diabetes  (6) Auditory and vision  (13) Autism Spectrum Disorders  (10) Autoimmune Inflammatory DIseases  (25)    Crohn’s disease  (5)    Tolerance-inducing Autoimmune Disease Therapeutics  (7)    Ulcerative Colitis  (3) Autophagosome  (4) Bacterial Resistance  (25) Behavior  (26) Behavioral Genetics  (21) Big Data  (88)    Intelligent Information Systems  (51) Bio Instrumentation in Experimental Life Sciences Research  (368)    Analytical Instruments Industry  (5)    Microfuidics  (8) Bio-Ethics  (16) BioBanking  (36) Biodegradable Drug-eluting Material  (8) Bioengineering & reverse engineering design  (43) BioIT: BioInformatics  (147) BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics  (135)    Advanced Computing Platform  (32)       Blockchain Transactions System  (6)          Platform for Content Monetization embedded Content Promotion  (1)    Pancreatic adenocarcinoma classifier  (4)    Simulation Modeling in NGS  (5) Biological Engineering  (43) Biological Networks  (193) Biological Networks, Gene Regulation and Evolution  (479)    Virology  (10) Biomarkers & Medical Diagnostics  (572)    VOC – Volatile Organic Compounds as BioMarkers  (2) Biomedical Measurement Science  (173) BioSimilars  (112) BioTechnology – Venture Creation  (133)    Foundations for supporting Science and Education  (14)    Philanthropy to Academic Institution  (3) BioTechnology – Venture Creation, Venture Capital  (110)    Seeking Talent  (3) BiVAD  (1) Blindness  (6) Bone Disease and Musculoskeletal Disease  (82) Brain Basis of Emotion  (5) Business Career Consideration  (1) Ca2+ triggered activation  (61) Calcium  (21) Calcium Signaling  (71) Calmodulin Kinase and Contraction  (36) Cancer – General  (186) Cancer and Current Therapeutics  (412)    interventional oncology  (49)       Breast Cancer – impalpable breast lesions  (20)       Prostate Cancer: Monitoring vs Treatment  (9) CANCER BIOLOGY & Innovations in Cancer Therapy  (1,127)    Anaerobic Glycolysis  (51)    Cachexia  (18)    Cancer Genomics  (85)       Circulating Tumor Cells (CTC)  (27)          Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood  (17)             mRNA  (6)          MagSifter chip  (1)       KRAS Mutation  (9)       Li-fraumeni syndrome.  (4)       TP53 – Germline mutations  (9)    cancer metabolism  (28)    Funding Opportunities for Cancer Research  (13)    Genomic Expression  (150)    Glioblastoma  (7)    Hexokinase  (14)    Loss of function gene  (18)    Metabolic Immuno-Oncology  (21)    Metastasis Process  (15)    Methylation  (33)    Microbiome and Responses to Cancer Therapy  (10)    Monoclonal Immunotherapy  (29)    mtDNA  (13)    Oxidative phosphorylation  (56)    Pancreatic cancer  (10)    Pyruvate Kinase  (27)    The NCI Formulary  (1)    tumor microenvironment  (16)    Warburg effect  (50) Cancer Informatics  (104) Cancer Prevention: Research & Programs  (132) Cancer Screening  (108) Cancer Vaccines: Targeting Cancer Genes for Immunotherapy  (30)    Engineering Enhanced Cancer Vaccines  (3) cancer-general  (7) Cardiac and Cardiovascular Surgical Procedures  (328)    Aortic Valve: TAVR, TAVI vs Open Heart Surgery  (91)       Prosthesis-Patient Mismatch (PPM) in TAVI vs SAVR  (2)       TAVI vs Open Heart Surgery  (6)       Transcatheter Aortic Valve Replacement via the Transcarotid Access  (3)       Transcaval TAVR Procedure  (3)       Valve-in-Valve Procedure  (5)    Atrial Fibrilation (a-Fib), Cardiac Pacing and Arrhythmias  (18)    BackBeat’s Cardiac Neuromodulation Therapy (CNT) bioelectronic therapy  (1)    CABG  (47)    Cancer Surgery of the Heart  (10)    Cardiovascular Fluid Management: algorithms  (1)    Heart Transplant  (16)    Heart-Lung Transplant  (11)    Implantation  (3)    Left Main Coronary Artery Disease (LMCAD)  (3)    Mechanical Assist Devices: LVAD, RVAD, BiVAD, Artificial Heart  (22)    Mitral Valve: Repair and Replacement  (64)       TMVR – Transcatheter Mitral Valve Repair  (5)    PCI  (108)       Bioresorbable Vascular Scaffold (BVS)  (4)       Invasive FFR for PCI  (2)       Multivessel PCI FFR Guidance  (1)       Robotic-assisted percutaneous coronary intervention  (2)       Stent Retriever in endovascular thrombectomy  (3)    Peripheral Arterial Disease & Peripheral Vascular Surgery  (65)       Abdominal Aorta  (20)       Carotid Artery  (15)       Limb ischemia  (4)       Thoracic Aorta  (16)    Pulmonary Valve Replacement and Repair  (3)    Renal Denervation  (20)    Replacement  (1)    Robotically assisted Cardiothoracic Surgery  (2)    Tricuspid Valve Repair  (7)    Vena Caval Filters: Device for Prevention of Pulmonary Embolism and Thrombosis  (2) Cardiovascular Pharmacogenomics  (171) Cargo  (3) Cell Biology  (323) Cell Biology, Signaling & Cell Circuits  (673)    Apoptosis  (17)    Autophagy-Modulating Proteins  (6)    “Antibody–enzyme conjugates”  (6)    Cell Processing System in Cell Therapy Process Development  (28)    Endoplasmic reticulum  (3)    Enzymatic mechanism underlying the synthesis of adenosine triphosphate (ATP)  (3)    Ubiquitin  (11) Cerebrovascular and Neurodegenerative Diseases  (123)    Stroke  (9) Chemical Biology and its relations to Metabolic Disease  (464)    #BIGAxisSummit  (1)    Gut Microbiome and Obesity  (8) Chemical Genetics  (350) Child and Adolescent Psychiatry  (13) Childhood cancer  (17) Childhood malnutrition  (3) children  (4) Circulating Progenitor Cells  (27)    Bone marrow derived cells  (17)    Endothelial cells  (7)    Umbilical cord cells  (6) Clinical & Translational  (239) Clinical Diagnostics  (220)    Mass automation of plasma proteins  (13) Clinical Genomics  (144) Coagulation Therapy and Internal Bleeding  (83) Cognition  (47) Commercialization  (20) Components and IRB related issues  (4) Computational Biology/Systems and Bioinformatics  (471)    Computational Histopathology  (3)    Meta-analysis of transcriptome data  (10) Conference Coverage with Social Media  (494)    MassBio  (3) coronavirus  (18) COVID-19  (20) CRISPR/Cas9 & Gene Editing  (192)    CRISPR alternative for editing genes without cutting  (14)       CAST – Alternative to CRISPR/Cas9  (3)       Transposon-encoded CRISPR–Cas systems direct RNA-guided DNA integration  (4)    CRISPR applied to Human Germ Line  (13)       Gene Editing Impact on Longevity  (6) CT  (20) Cytokines  (21) Cytoskeleton  (85) Developmental biology  (107) Diabetes Mellitus  (146)    Artificial Pancreas for Type 2 Diabetes  (4)    Artificial Pancreas for Type1 Diabetes  (8)    Gestational diabetes  (5) Diagnostic Immunology  (61) Diagnostics and Lab Tests  (128)    Liquid Biopsy: Circulating Tumor Cells in Urine and Blood  (9) Digital HealthCare – biotech & internet joint ventures  (47) Disease Biology  (334) Disease Biology, Small Molecules in Development of Therapeutic Drugs  (488) DNA repair  (73)    Apoptosis  (13)    Autophagy  (13)    Cell death pathways  (19)    Proteolysis  (8)    Proteosome  (8) Drug Delivery Platform Technology  (159)    Drug Carrier Design  (21)       Medication Remote Compliance Monitoring  (3)    Exosomes: Natural Carriers for siRNA Delivery  (9) Drug Toxicity  (74) Ecosystems & Industrial Concentration in the Medical Device Sector  (172)    Cardiac & Vascular Repair Tools Subsegment  (128)    Exec Compensation in the Cardiac & Vascular Repair Tools Subsegment  (14)    Massachusetts Niche Suppliers and National Leaders  (20) Electronic Health Record  (10) Embryology  (24) Empathy  (6) Endocrine Diseases  (60) Enzyme Induction  (46)    ion-transporting enzyme  (2)    K + -ATPase.  (1)    Na +  (2) Epigenetics and Environmental Factors  (35) Ethics and Leadership  (10) Executive Compensation – Big Pharma  (3) Fat soluble vitamins  (4) FDA  (73) FDA Regulatory Affairs  (360)    FDA, CE Mark & Global Regulatory Affairs: process management and strategic planning – GCP, GLP, ISO 14155  (52)    ISO 10993 for Product Registration: FDA & CE Mark for Development of Medical Devices and Diagnostics  (30) Fetal Ultrasound  (2) Fiction and medicine  (5) Frontiers in Cardiology and Cardiovascular Disorders  (929)    Acute Myocardial Infarction  (63)       Cardiogenic Shock  (5)    Anemia in CVD Patients  (6)    Cardio-Oncology  (4)    Cardiomyopathy  (57)    Cardiovascular Research  (187)       Myocardial metabolism, Myocardial ischemia, myocardial perfusion, Myocardial adenine nucleotide metabolism  (64)       Pre-Clinical Animal Model Development  (23)    Chronic Thromboembolic Pulmonary Hypertension (CTEPH) and Pulmonary Arterial Hypertension (PAH)  (20)    Cost of Inpatient Stay for Cardiovascular Diagnosis for Admission  (1)    Electrophysiology  (111)       Ablation Procedure vs Drug Therapy  (2)       Arrhythmia Detection with Machine Learning Algorithms  (7)       Cardiotoxic Risks of Immune Checkpoint Inhibitors  (1)       Genomic Analysis of EKG Electrophysiology Genomics  (3)       implantable cardioverter defibrillator (ICD) and External Defibrillation  (1)       implantable pacemaker  (2)       Rare heart rhythm disorders and Long QT syndrome (LQTS)  (7)    Epigenetics and Cardiovascular Risks  (57)    Heart Failure (HF)  (51)       Acute coronary syndrome  (7)       congestive heart failure  (19)       Hypertensive Disorders of Pregnancy  (1)    Medical Devices R&D and Inventions  (212)       Assist Devices: LV  (3)       RV  (1)       Stents & Tools  (90)       Valves & Tools  (67)    Origins of Cardiovascular Disease  (422)       Atherogenic Processes & Pathology  (180)       Congenital Heart Disease  (34)          Genetic Mutations in Congenital Heart Disease  (4)       inflammation independent of lipid levels  (13)       Systemic Inflammatory Diseases as CVD Risk  (12)    Pharmacotherapy of Cardiovascular Disease  (356)       HTN  (176)       HTN in Youth  (8)       Resident-cell-based  (43)       Subarachnoid Hemorrhage (SAH)  (1)       Transthyretin amyloid cardiomyopathy (ATTR-CM)  (1)    Spontaneous Coronary Artery Dissection (SCAD)  (6)    Stroke  (15)       endovascular thrombectomy  (6)    Vascular Diseases  (21)       mesenteric ischemia  (3) Future Pandemics Inform-graphics  (1) Gastroenterology  (38) Gene Regulation  (233) Gene Regulation and Evolution  (137) Genetics & Innovations in Treatment  (176) Genetics & Pharmaceutical  (204) Genome Biology  (967)    Exosomes  (25)    Gene Therapy & Gene Editing Development  (42)    mRNA Therapeutics  (20)    Mutagenesis  (27)    Single Cell Genomics  (25)    Single-cell sequencing  (20)    Variation in human protein-coding regions  (35) Genomic Endocrinology  (42) Genomic Testing: Methodology for Diagnosis  (416) Genomics Pharmacy  (40) Global Market of Medical Devices Technology  (3) Global Medical Publishers by Country  (3) Global Partnering & Biotech Investment  (48) GLP  (4) Glycobiology: Biopharmaceutical Production  (16) Glycobiology: Biopharmaceutical Production, Pharmacodynamics and Pharmacokinetics  (28) Health Care System by Country  (58)    AI Models in Healthcare  (12)    Centers for Medicare & Medicaid Services  (8)    Health in Israel  (2)    India  (1)    United States  (27)       Hospital-based Medical Innovations  (10)       Medical Recertification and Maintenance of Certification (MOC)  (1) Health Economics and Outcomes Research  (384)    Women Health  (18)       Heart and Brain Disease in Women  (3) Health Law & Patient Safety  (165)    and Bioethics  (14)    Biotechnology  (12)    Health Law Policy  (13) HealthCare IT  (139) Healthcare Reform  (118)    Accountable Care Organizations  (26)    Affordable Care Act  (29)       Repair  (1)       Repeal ACA  (1)       Replace  (1)    Federal Budget Appropriations  (27)    Healthcare costs and reimbursement  (59)    Indigent Nutrition  (14)    Medicare and Medicaid  (20)    Prescription Drugs Costs  (21)    Skilled Nursing Facilities  (6)    Technology Capital Expenses  (15)    Uninsured and Underinsured  (20) Hematology  (100)    Acute lymphocytic leukemia  (18)    Acute myelocytic leukemia  (21)    Hematopoiesis  (48)    Lymphoma  (21) History and physical exam  (6) Human aging  (27) Human Immune System in Health and in Disease  (246) Human Sensation and Cellular Transduction: Physiology and Therapeutics  (21) Image Processing/Computing  (41) Imaging-based Cancer Patient Management  (116) Immuno-Oncology & Genomics  (133)    mRNA platform in Drug DIscovery  (6) Immunodiagnostics  (141)    Cancer-Immune Interactions  (31)    CNS-Immune Interface  (5)    Neuronal Circuits  (4) Immunology  (123)    Adaptive Immune Response to Biomaterials and Tissue Repair  (13)    Antibody Responses Predict Antigen Exposure  (17)    Cytokine Receptor Structure  (8)    Human Antibody Response  (18)    Human Circulating Antibody Repertoire  (11)    Human Naive B Cell Repertoire  (8)    MHC Repertoires for Antigen Prediction  (8)    Protection Against Autoimmune Disease  (11)    Synthetic Immunology: Hacking Immune Cells  (15) Immunotherapy  (137)    CAR-T  (16)    combination immunotherapies.  (15)    Efficacy of Anti-Tumor T Cells  (15)    Engineering Better T Cells  (11)    Immune Engineering  (18)    Immune Modulatory  (16)    Integrin-targeted Combination Immunotherapy  (6)    Modulating Macrophages in Cancer Immunotherapy  (17)    NK Cell-Based Cancer Immunotherapy  (20)    Synergistic Innate and Adaptive Immunotherapy  (14) Income Disparity  (2) Infectious Disease & New Antibiotic Targets  (162)    Antibiotic resistance  (7)    Viral diseases  (37)       Myocarditis  (1) Infectious Disease Immunodiagnostics  (58)    Virology – Vector-borne DIsease  (17) Inflammasome  (29)    Anti-tumor necrosis factor drugs (TNF inhibitors)  (3) Innovation in Immunology Diagnostics  (111)    Universal Immune Cell Therapies (uICT)  (11) Innovations  (186)    R&D Expenditure  (8) Innovations in Neurophysiology & Neuropsychology  (145)    Age and Life Expectancy  (3)    autism spectrum disorder  (1)    Circadian Rhythm and Sleep  (6)    hNPCs  (1)    Nervous System Function  (2)    Relapsing Multiple Sclerosis  (1) Intellectual Property  (85)    Disputes and Settlements  (9)    IP Valuation Models – Pricing Intangible Assets  (7)    Patent Law in Biotech  (12) Intellectual Property, Innovations, Commercialization, Investment in technological breakthrough  (121) International Global Work in Pharmaceutical  (191) Interventional Oncology: Radiofrequency Ablation, Transarterial Chemoembolization, Microwave Ablation and Irreversible Electroporation (IRE)  (12) Interviews with Scientific Leaders  (319)    Albany Medical Center Prize in Medicine and Biomedical Research  (3)    Annual Breakthrough Prize  (4)    Annual Lewis S. Rosenstiel Award  (2)    Awards in Cardiology and Cardiovascular Medicine  (6)    CEOs of Biotech Companies  (3)    Gerald D Aurbach Award for Outstanding Translational Research  (2)    Interviews with Key Opinion Leaders (KOLs)  (11)    James Prize in Science and Technology Integration  (2)    Jessie Stevenson Kovalenko Medal – Medical Sciences  (2)    Lemelson-MIT Prize  (2)    Life Sciences Breakthrough Prize  (6)    Mosteller Statistician of the Year Award  (2)    Mourning the Loss of a Scientific Leader  (6)    National Academy of Sciences AWARDS  (4)    Nobel Prize Winners  (30)    Noble Prize (Not Nobel Prize)  (2)    Paul Marks Prize for Cancer Research  (2)    Russ Prize recognizes bioengineering achievements worldwide  (2)    The Dan David Prize  (6)    Warren Alpert Foundation Prize Recipients  (4)    Wolf Prize  (2)    Wolf Prize in Medicine  (2)    women in science  (5) Investment in Technological Breakthrough  (60) Ionic Transporters Na+  (25) IP Development by LPBI Group Team  (14) IP Development by LPBI Group Team & Other Organizations  (8) ISO 14155  (3) Joint Venture – SBH & M3DP: SOP and Arrangements  (1) Justice & Law  (1) K  (15) Lab-on-a-chip  (2) Landscape  (1) Lasers and photonics  (18)    Midrange IR spectroscopy  (1) Law and Medicine Conflicts  (15) Leadership, Power, Social Interlocking Connections  (13) Lipid metabolism  (109)    Fatty acids  (38)    Lipids  (38)    PCSK9 Inhibitor Therapy  (19) Lipidomics  (11) Liposome  (4) Liver & Digestive Diseases Research  (122) LPBI Group, e-Scientific Media, DFP, R&D-M3DP, R&D-Drug Discovery, US Patents: SOPs and Team Management  (143)    Award Nominations  (5)    BioMed e-Books e-Series by LPBI Group  (9)    Feedback from Investors: LPBI Portfolio of Five Businesses  (2)    LPBI Group Founder – Aviva Lev-Ari  (27)    PhD  (1)    RN  (1) LPBI Management  (23) Massachusetts Academy of Sciences (MAS)    (2) Materials Science & Engineering  (37)    Organic BioElectronics  (2) Math  (13)    Great Discoveries  (7) Mechanical Assist Devices: LVAD  (4) Medical and Population Genetics  (193) Medical Devices R&D Investment  (242)    21st Century Cures Act  (2)    CE Mark & Global Regulatory Affairs: process management and strategic planning – GCP  (14)    Rhythm management device hardware: dual-chamber pacemakers  (1)       Systolic HTN  (1) Medical Imaging Technology  (59) Medical Imaging Technology, Image Processing/Computing, MRI, CT, Nuclear Medicine, Ultra Sound  (164)    Circumferential-Intravascular-Radioluminescence-Photoacoustic-Imaging (CIRPI)  (3)    Echocardiography  (3)    MRI  (18)    Noninvasive Diagnostic Fractional Flow Reserve (FFR) CT  (6)    Ultra Sound  (10) Melatonin & Circadian Regulation  (9) memory loss  (1) Metabolism  (239)    Biochemical pathways  (144)       Enzymes and isoenzymes  (73)          Dehydrogenase  (16)          Kinase  (28)          Phosphatase  (9)          Phosphorylase  (22)          tyrosine decarboxylases  (3)    Inosine nucleotides  (9)    Leloir pathway  (6)    microbiome  (7)    Pentose monophosphate shunt  (14)    Pyridine nucleotides  (28)       Pyridine nucleotide transhydrogenase  (7) Metabolomics  (321) Methods  (37) Mg++  (10) Microbiologial genetics  (36) Microbiology  (68)    Virology  (14) Micronutrients  (16) Microvesicle  (7) Microwave Ablation and Irreversible Electroporation (IRE)  (1) Mobile Healthcare  (5) Molecular Genetics & Pharmaceutical  (377)    Organoids  (3) Monoclonal antibody therapy  (14) Mutant Gene Expression  (42) Myocardial adenine nucleotide metabolism  (6) Myocardial Ischemia  (26) Myocardial Metabolism  (36) Na-K transport  (40) Na-K-ATPase  (27) Nanotechnology for Drug Delivery  (99) Nephrology  (46) Nephrology & Regenerative medicine  (9) Neurodegenerative Diseases  (89)    hNPCs  (3)    MS  (5) Neurohumoral Transmission  (68)    Ca2+ triggered activation  (21)    Calmodulin  (13)    Parkinson’s disease dyskinesia levodopa  (3)    PKC  (13)    Synaptic vesicle  (20) Neurological Diseases  (121) Neuroscience  (126) Next Generation Sequencing (NGS)  (27)    Nanopore sequencing  (1) NIH Common Fund  (1) Nitric Oxide in Health and Disease  (136) Nuclear Medicine  (11) Nutrigenomics  (68) Nutrition  (195)    Nutritional Supplements: Atherogenesis, lipid metabolism  (50) Nutrition and Phytochemistry  (62)    Minerals in Medicine  (4)    Plant extract  (22) Nutrition Disorders  (31) Nutritional Supplements: Atherogenesis  (31) Obesity  (19) Oligonucleotide Therapeutics & Delivery  (7) Oncolytic virus & OncoViro-Therapy  (23)    Synthetic Virology  (1) Organ-on-a-Chip & 3D Printing in Life Sciences  (7) Pain: Etiology, Genetics & Innovations in Treatment  (24) Parasitology  (5)    Malaria  (4)    Tropical diseases  (1) Patents  (35) Patient Experience  (66)    Art therapy  (1)    Hospital reputation  (7)    Humor  (1)    Music therapy  (2)    Pain Alleviation  (7)    Patient Outlook  (28)    Reputation of Interventionist  (4)    Support Staff  (13)    Supportive therapies  (6)       laughfter  (1)    Surgical Procedure  (9) Patient Experience: Personal Memories of Invasive Medical Intervantion  (30) Patient’s Voice: Personal Experience with Invasive Medical Procedures  (18) Perioperative Statins at Noncardiac Surgery  (2) Personal Health Applications: Tech Innovations serves HealhCare  (43) Personalized and Precision Medicine & Genomic Research  (740)    Longevity  (3)    New Drug Approval  (7)    Patient-centered Medicine  (39)       cell-based therapy  (9)       stem cell biology and patient-specific  (7)    Personalized Medicine Coalition  (10)    Precision Cancer Medicine  (33) Phagophore  (2) Pharmaceutical Analytics  (262)    Pharmacologic toxicities  (63) Pharmaceutical Discovery  (112)    Rapid automation of plasma protein pools  (18) Pharmaceutical Drug Discovery  (207)    Drug Development Process  (62)       Drug Discovery Chemistry  (33)    drug repurposing  (11) Pharmaceutical Industry Competitive Intelligence  (347)    Pharmacovigilance  (8)    Value-based Drug Pricing  (8) Pharmaceutical R&D Informatics  (45) Pharmaceutical R&D Investment  (301) Pharmacodynamics and Pharmacokinetics  (24) Pharmacogenomics  (161) Photography Collection  (1) Physics  (12) Placenta  (7) Population genetics  (27) Population Health Management  (205)    Evolution of Biology Through Culture  (14)    U.S. Employment-to-Population Ratio  (8) Population Health Management, Genetics & Pharmaceutical  (643)    COVID-19  (157)       Biomarkers: Inflammation  (37)       Cancer Researchers Fighting COVID-19  (14)       COVID-19 effects on Human Heart  (15)       COVID-19: Pandemic Surgery Guidance  (19)       Economic Impact of Coronavirus Pandemic  (41)       number of asymptomatic infections  (27)       Treatment Protocols for COVID-19  (62)    SARS-CoV-2  (141)       Coronavirus Gene Expression  (66)          SARS-CoV-2 circulating variants   (14)          SARS-CoV-2 Viral Variants  (19)       Glycosylation and its Role in SARS-CoV-2 Viral Pathogenesis  (2)       Immune-Mediation (independent immunopathology: lung and reticuloendothelial system)  (30)       Mechanisms of infection by SARS-CoV-2  (21)       SAR-Cov-2 a vasculotropic (blood vessels) RNA Virus  (35)          SARS-COV2 Hijacking the Complement and Coagulation Systems  (19)       Seasonality & Environmental Factors in Resurgence  (26)       Serology tests for coronavirus antibodies  (44)       T-cell response to SARS-CoV-2 infection  (14)       Vaccinology  (51)          Mechanism of Thrombosis with AZ & J&J COVID-19 Vaccines  (5)       Virtual Drug Molecule Screening targeting SAR-CoV-2 proteins  (8)    Virus Infective Acute Respiratory Syndrome: SARS-CoV  (92) Population Health Management, Nutrition and Phytochemistry  (162) Preimplantation Genetic Diagnosis and Reproductive Genomics  (13) Protein-energy malnutrition  (12) Proteomics  (367)    Amino acids  (54)    Proteins  (138) Pulmonary diseases  (16)    Lung and pulmonology  (15) Pulmonary pathology  (8) Radio Frequency (RF)-based Surgical Solutions  (2) REAL TIME Conference Coverage Twitter’s Hashtags and Handles per Presentation/session  (50) Regenerative Biology and Medicine  (92) Regulated Clinical Trials: Design, Methods, Components and IRB related issues  (265) Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics  (113) Reproductive Biology & Bio Instrumentation  (78) RNA Biology  (81)    RNA interference (RNAi) therapeutic  (4) RNA Biology, Cancer and Therapeutics  (64) RVAD  (1) SBIR, SBA, NIH, NSF  (3) Schizophrenia  (16) Scientific & Biotech Conferences: Press Coverage  (182) Scientific Publishing  (609)    Curation  (585)       Curation methodology  (40)       De novo synthesis  (16)       Dialectic  (17)       Discovery process  (72)       Evolutionary cognition  (52)       Experimental validation  (91)       Explanatory  (91)       Historical relevance  (71)       Technology Advance Assessment of  (69)       Theoretic convergence  (27)    Open Access Journals  (37) Scientist: Career considerations  (198)    Big Data & Analytics  (17)    Data Science  (13)    Women in Life Sciences  (12) Scoop.it  (19) Sensors & Analytics  (18) Sepsis  (2) Severe Autism  (3) Signaling  (102)    Phosphorylation  (43)    S-nitrosylation  (13)    Ubiquitinylation  (34) Signaling & Cell Circuits  (130) single cell sequencing  (3) Small Molecules in Development of Therapeutic Drugs  (71) Social Development  (15) Specialized 3D BioPrinters (Cornea  (1) Statistical Methods for Research Evaluation  (137) Stem Cells for Regenerative Medicine  (177)    Cardiac muscle regeneration  (19)    Competition in regenerative stem cell science  (18)    Human neural progenitor cells  (8)    Skeletal muscle regeneration  (9) STORM  (2) Stress Disorders  (19) Substance Abuse  (4) Systemic Inflammatory Response Related Disorders  (161)    Inflammatory Pathophysiology  (9) Technology Transfer: Biotech and Pharmaceutical  (354) therapeutics  (3) Tissue Engineering and Regenerative Medicine  (22) Tissue Microenvironment  (5) Transarterial Chemoembolization  (2) Transcriptomics  (47) Transformative Technologies in Healthcare  (23) Translational Science  (230)    Best evidence  (52)    Bias measurement tools  (2)    Evidence-based decision-making  (29)    Inferential analysis  (25)    Intra-rater error  (1)    Quality Assurance  (8)    Random Error  (2)    Systematic Error (Bias)  (6)    Total error  (2)    Translational Effectiveness  (49)    Translational Research  (92) Trends in Global Economy  (6) Uncategorized  (1,019) Unfolded Protein Response (UPR)  (6) US and Global Economy: Trends and Findings  (9) Venture Capital  (47)    Income Geographic Distribution  (3)    Institutional Capital Raised by Female Founders  (6) virology  (10) Vision  (7) Voices of Patients and Healthcare Providers  (22) Water Transporters  (8) Wearable Tech + Digital Health  (5) Anemia  (22) Neutropenia  (8) Neutrophilia  (9) Platelet count disorder  (9) Folate and B12  (7) Iron deficiency  (5) Myelodysplasia  (13) Myelofibrosis  (10)

• Meta

  • Log in
  • Entries feed
  • Comments feed
  • WordPress.org

• • 2012pharmaceutical
  • sjwilliamspa