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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

When a baby is born through its mother’s birth canal, it is bathed in a soup of microbes. Those born by caesarean section (C-section) miss out on this bacterial baptism. The differences in microbe exposure at birth and later health could be caused by other factors, such as whether a mother takes antibiotics during her surgery, and whether a baby is breastfed or has a genetic predisposition to obesity. So, the researchers are sharply split on whether or not this missing of bacterial exposure increases the risk of chronic health problems such as obesity and asthma.

 

Researchers found that babies delivered surgically harboured different collections of bacteria than did those born vaginally. C-section babies, which comprise more than 30% of births in the United States, are also more prone to obesity and immune diseases such as diabetes. Experiments show that mice born by C-section are more prone to obesity and have impaired immune systems. There are fewer factors that could account for these differences in the rodents, which can be studied in controlled conditions, than in people.

 

A wave of clinical trials now under way could help to settle the question — and feed into the debate over whether seeding babies born by C-section with their mother’s vaginal bacteria is beneficial or potentially harmful. Several groups of researchers will be swabbing hundreds of C-section babies with their mother’s microbes, while comparing them to a control group. Each team plans to monitor its study participants over several years in the hope of learning more about how the collection of microbes in their bodies might influence weight, allergy risk and other factors.

 

But some scientists say that the trials could expose C-section babies to infection, or encourage mothers to try do-it-yourself swabbing, without much evidence that there is a benefit or risk. Moreover, there is no evidence that differing exposure to vaginal microbes at birth can help explain variation in people’s health over time. Presently the whole concept is in very much a state of uncertainty.

 

Researchers in near future will compare swabbed C-section babies with a placebo group and with infants delivered vaginally. They confirmed that their protocols will not increase the risk of infection for C-section babies. Scientists will also rigorously screen mothers participating in these trials for microbes such as HIV and group B streptococcus — a common vaginal bacterium that causes respiratory problems in newborns.

 

References:

 

https://www.nature.com/articles/d41586-019-02348-3?utm_source=Nature+Briefing

 

https://www.ncbi.nlm.nih.gov/pubmed/31431742

 

https://www.ncbi.nlm.nih.gov/pubmed/20566857

 

https://www.ncbi.nlm.nih.gov/pubmed/25452656

 

https://www.ncbi.nlm.nih.gov/pubmed/22939691

 

https://www.ncbi.nlm.nih.gov/pubmed/24030708

 

https://pharmaceuticalintelligence.com/2017/02/22/babys-microbiome-changing-due-to-caesarean-birth-and-formula-feeding/

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Post TAVR: Management of conduction disturbances and number of valve recapture and/or repositioning attempts – Optimize self-expanding transcatheter aortic valve replacement (TAVR) positioning reduced the need for permanent pacemaker (PPM) implants down the road

Reporter: Aviva Lev-Ari, PhD, RN

  • The PPM rate dropped from 9.7% to 3.0% (P=0.035), according to a team led by Hasan Jilaihawi, MD, of NYU Langone Health in New York City.
  • the PARTNER 3 and CoreValve Low Risk trials in patients at low surgical risk showed PPM implant rates of 17.4% with the Evolut line, 6.6% with the balloon-expandable Sapien 3, and 4.1%-6.1% with surgery.

 

  • “The His bundle passes through the membranous septum, a few millimeters beneath the non-coronary/right coronary cusps. It is therefore not surprising that a deeper valve implantation increases the likelihood of mechanical damage of the His bundle leading to a transient or persistent conduction disturbance,” according to Rodés-Cabau.

To capture factors that contributed to need for PPM implantation, Jilaihawi and colleagues performed a detailed restrospective analysis on 248 consecutive Evolut recipients at Langone treated with the standard TAVR approach — aiming for 3-4 mm implant depth (in relation to the non-coronary cusp) and recapturing and repositioning when the device landed considerably lower. Patients with prior PPM implantation were excluded. Devices used were Medtronic’s Evolut R, Evolut Pro, and Evolut 34XL.

This analysis revealed that use of the large Evolut 34XL (OR 4.96, 95% CI 1.68-14.63) and implant depth exceeding membranous septum length (OR 8.04, 95% CI 2.58-25.04) were independent predictors of later PPM implantation.

From there, operators came up with the MIDAS technique and applied it prospectively to another 100 consecutive patients.

Besides bringing down the PPM implant rate to 3.0%, there were no more cases of valve embolization, dislocation, or need for a second valve.

The standard and MIDAS groups shared similar membranous septum lengths but diverged in average actual device depth, such that the standard group tended to have Evolut devices positioned deeper (3.3 mm vs 2.3 mm, P<0.001).

SOURCE

https://www.medpagetoday.com/cardiology/pci/81849

 


CRISPR cuts turn gels into biological watchdogs

Reporter: Irina Robu, PhD

Genome editing if of significant interest in the prevention and treatment of human diseases including single-gene disorders such as cystic fibrosis, hemophilia and sickle cell disease. It also shows great promise for the prevention and treatment of diseases such as cancer, heart disease, mental illness and human immunodeficiency virus infection. However, ethical concerns arise when genome editing, using technologies such as CRISPR-Cas9 is used to alter human genomes.

James Collins, bioengineer at MIT and his team worked with water-filled polymers that are held together by strands of DNA, known as DNA hydrogels. To alter the properties of these materials, these scientists turned to a form of CRISPR that uses a DNA-snipping enzyme called Cas12a, which can be programed to recognize a specific DNA sequence. The enzyme then cuts its target DNA strand, then severs single strands of DNA nearby. This property lets scientists to build a series of CRISPR-controlled hydrogels encapsulating a target DNA sequence and single strands of DNA, which break up after Cas12a identifies the target sequence in a stimulus. The break-up of the single DNA strands activates the hydrogels to change shape or completely dissolve, releasing a payload.

According to Collins and his team, the programmed hydrogels will release enzymes, small molecules and human cells as part of a smart therapy in response to stimuli. However, in order to make it a smart therapeutic, the researchers in collaboration with Dan Luo, bioengineer at Cornell University placed the CRISPR- controlled hydrogels into electric circuits. The circuit is switched off in response to the detection of the genetic material of harmful pathogens such as Ebola virus and methicillin-resistant Staphylococcus aureus. The team used these hydrogels to develop a prototype diagnostic tool that sends a wireless signal to identify Ebola in lab samples.

Yet, it is evident that these CRISPR-controlled hydrogels show great potential for the prevention and treatment of diseases.

SOURCE

https://www.nature.com/articles/d41586-019-02542-3?utm_source=Nature+Briefing

 

 

 


Showcase: How Deep Learning could help radiologists spend their time more efficiently

Reporter and Curator: Dror Nir, PhD

 

The debate on the function AI could or should realize in modern radiology is buoyant presenting wide spectrum of positive expectations and also fears.

The article: A Deep Learning Model to Triage Screening Mammograms: A Simulation Study that was published this month shows the best, and very much feasible, utility for AI in radiology at the present time. It would be of great benefit for radiologists and patients if such applications will be incorporated (with all safety precautions taken) into routine practice as soon as possible.

In a simulation study, a deep learning model to triage mammograms as cancer free improves workflow efficiency and significantly improves specificity while maintaining a noninferior sensitivity.

Background

Recent deep learning (DL) approaches have shown promise in improving sensitivity but have not addressed limitations in radiologist specificity or efficiency.

Purpose

To develop a DL model to triage a portion of mammograms as cancer free, improving performance and workflow efficiency.

Materials and Methods

In this retrospective study, 223 109 consecutive screening mammograms performed in 66 661 women from January 2009 to December 2016 were collected with cancer outcomes obtained through linkage to a regional tumor registry. This cohort was split by patient into 212 272, 25 999, and 26 540 mammograms from 56 831, 7021, and 7176 patients for training, validation, and testing, respectively. A DL model was developed to triage mammograms as cancer free and evaluated on the test set. A DL-triage workflow was simulated in which radiologists skipped mammograms triaged as cancer free (interpreting them as negative for cancer) and read mammograms not triaged as cancer free by using the original interpreting radiologists’ assessments. Sensitivities, specificities, and percentage of mammograms read were calculated, with and without the DL-triage–simulated workflow. Statistics were computed across 5000 bootstrap samples to assess confidence intervals (CIs). Specificities were compared by using a two-tailed t test (P < .05) and sensitivities were compared by using a one-sided t test with a noninferiority margin of 5% (P < .05).

Results

The test set included 7176 women (mean age, 57.8 years ± 10.9 [standard deviation]). When reading all mammograms, radiologists obtained a sensitivity and specificity of 90.6% (173 of 191; 95% CI: 86.6%, 94.7%) and 93.5% (24 625 of 26 349; 95% CI: 93.3%, 93.9%). In the DL-simulated workflow, the radiologists obtained a sensitivity and specificity of 90.1% (172 of 191; 95% CI: 86.0%, 94.3%) and 94.2% (24 814 of 26 349; 95% CI: 94.0%, 94.6%) while reading 80.7% (21 420 of 26 540) of the mammograms. The simulated workflow improved specificity (P = .002) and obtained a noninferior sensitivity with a margin of 5% (P < .001).

Conclusion

This deep learning model has the potential to reduce radiologist workload and significantly improve specificity without harming sensitivity.


Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line

Reporter: Stephen J. Williams, PhD

In a Genome Research report by Marie Nattestad et al. [1], the SK-BR-3 breast cancer cell line was sequenced using a long read single molecule sequencing protocol in order to develop one of the most detailed maps of structural variations in a cancer genome to date.  The authors detected over 20,000 variants with this new sequencing modality, whereas most of these variants would have been missed by short read sequencing.  In addition, a complex sequence of nested duplications and translocations occurred surrounding the ERBB2 (HER2) while full-length transcriptomic analysis revealed novel gene fusions within the nested genomic variants.  The authors suggest that combining this long-read genome and transcriptome sequencing results in a more comprehensive coverage of tumor gene variants and “sheds new light on the complex mechanisms involved in cancer genome evolution.”

Genomic instability is a hallmark of cancer [2], which lead to numerous genetic variations such as:

  • Copy number variations
  • Chromosomal alterations
  • Gene fusions
  • Deletions
  • Gene duplications
  • Insertions
  • Translocations

Efforts such as the Cancer Genome Atlas [3], and the International Genome Consortium (2010) use short-read sequencing technology to detect and analyze thousands of commonly occurring mutations however short-read technology has a high false positive and negative rate for detecting less common genetic structural variations {as high as 50% [4]}. In addition, short reads cannot detect variations in close proximity to each other or on the same molecule, therefore underestimating the variation number.

Methods:  The authors used a long-read sequencing technology from Pacific Biosciences (SMRT) to analyze the mutational and structural variation in the SK-BR-3 breast cancer cell line.  A split read and within-read mapping approach was used to detect variants of different types and sizes.  In general, long-reads have better alignment qualities than short reads, resulting in higher quality mapping. Transcriptomic analysis was performed using Iso-Seq.

Results: Using the SMRT long-read sequencing technology from Pacific Biosciences, the authors were able to obtain 71.9% sequencing coverage with average read length of 9.8 kb for the SK-BR-3 genome.

A few notes:

  1. Most amplified regions (33.6 copies) around the locus spanning the ERBB2 oncogene and around MYC locus (38 copies), EGFR locus (7 copies) and BCAS1 (16.8 copies)
  2. The locus 8q24.12 had the most amplifications (this locus contains the SNTB1 gene) at 69.2 copies
  3. Long-read sequencing showed more insertions than deletions and suggests an underestimate of the lengths of low complexity regions in the human reference genome
  4. Found 1,493 long read variants, 603 of which were between different chromosomes
  5. Using Iso-Seq in conjunction with the long-read platform, they detected 1,692,379 isoforms (93%) mapping to the reference genome and 53 putative gene fusions (39 of which they found genomic evidence)

A table modified from the paper on the gene fusions is given below:

Table 1. Gene fusions with RNA evidence from Iso-Seq and DNA evidence from SMRT DNA sequencing where the genomic path is found using SplitThreader from Sniffles variant calls. Note link in table is  GeneCard for each gene.

SplitThreader path

 

# Genes Distance
(bp)
Number
of variants
Chromosomes
in path
Previously observed in references
1 KLHDC2 SNTB1 9837 3 14|17|8 Asmann et al. (2011) as only a 2-hop fusion
2 CYTH1 EIF3H 8654 2 17|8 Edgren et al. (2011); Kim and Salzberg
(2011); RNA only, not observed as 2-hop
3 CPNE1 PREX1 1777 2 20 Found and validated as 2-hop by Chen et al. 2013
4 GSDMB TATDN1 0 1 17|8 Edgren et al. (2011); Kim and Salzberg
(2011); Chen et al. (2013); validated by
Edgren et al. (2011)
5 LINC00536 PVT1 0 1 8 No
6 MTBP SAMD12 0 1 8 Validated by Edgren et al. (2011)
7 LRRFIP2 SUMF1 0 1 3 Edgren et al. (2011); Kim and Salzberg
(2011); Chen et al. (2013); validated by
Edgren et al. (2011)
8 FBXL7 TRIO 0 1 5 No
9 ATAD5 TLK2 0 1 17 No
10 DHX35 ITCH 0 1 20 Validated by Edgren et al. (2011)
11 LMCD1-AS1 MECOM 0 1 3 No
12 PHF20 RP4-723E3.1 0 1 20 No
13 RAD51B SEMA6D 0 1 14|15 No
14 STAU1 TOX2 0 1 20 No
15 TBC1D31 ZNF704 0 1 8 Edgren et al. (2011); Kim and Salzberg
(2011); Chen et al. (2013); validated by
Edgren et al. (2011); Chen et al. (2013)

 

SplitThreader found two different paths for the RAD51B-SEMA6D gene fusion and for the LINC00536-PVT1 gene fusion. Number of Iso-Seq reads refers to full-length HQ-filtered reads. Alignments of SMRT DNA sequence reads supporting each of these gene fusions are shown in Supplemental Note S2.

 

 References

 

  1. Nattestad M, Goodwin S, Ng K, Baslan T, Sedlazeck FJ, Rescheneder P, Garvin T, Fang H, Gurtowski J, Hutton E et al: Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line. Genome research 2018, 28(8):1126-1135.
  2. Hanahan D, Weinberg RA: The hallmarks of cancer. Cell 2000, 100(1):57-70.
  3. Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael JF, Wyczalkowski MA et al: Mutational landscape and significance across 12 major cancer types. Nature 2013, 502(7471):333-339.
  4. Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, Zhang Y, Ye K, Jun G, Fritz MH et al: An integrated map of structural variation in 2,504 human genomes. Nature 2015, 526(7571):75-81.

 

Other articles on Cancer Genome Sequencing in this Open Access Journal Include:

 

International Cancer Genome Consortium Website has 71 Committed Cancer Genome Projects Ongoing

Loss of Gene Islands May Promote a Cancer Genome’s Evolution: A new Hypothesis on Oncogenesis

Identifying Aggressive Breast Cancers by Interpreting the Mathematical Patterns in the Cancer Genome

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

 


Inputs in consideration for Pricing LPBI Groups IP Three Classes of Assets

Curator: Aviva Lev-Ari, PhD, RN

 

  • About LPBI Group: Entrepreneurial Venture

 

Executive Summary

https://pharmaceuticalintelligence.com/2019-vista/executive-summary/

 

Founder’s Vision

https://pharmaceuticalintelligence.com/vision/

 

  • Leadership by example

 

Reflections on a Four-phase Career: Aviva Lev-Ari, PhD, RN, March 2018

https://pharmaceuticalintelligence.com/2018/03/06/reflections-on-a-four-phase-career-aviva-lev-ari-phd-rn-march-2018/

 

Pioneering implementations of analytics to business decision making: contributions to domain knowledge conceptualization, research design, methodology development, data modeling and statistical data analysis: Aviva Lev-Ari, UCB, PhD’83; HUJI, MA’76

https://pharmaceuticalintelligence.com/2018/05/28/pioneering-implementations-of-analytics-to-business-decision-making-contributions-to-domain-knowledge-conceptualization-research-design-methodology-development-data-modeling-and-statistical-data-a/

 

Thriving at the Survival Calls during Careers in the Digital Age – An AGE like no Other, also known as, DIGITAL

https://pharmaceuticalintelligence.com/2018/06/11/thriving-at-the-survival-calls-during-careers-in-the-digital-age-an-age-like-no-other-also-known-as-digital/

 

 

  • Intangible Assets of Firm’s Reputation in the Digital Age

 

The Digital Age Gave Rise to New Definitions – New Benchmarks were born on the World Wide Web for the Intangible Asset of Firm’s Reputation: Pay a Premium for buying e-Reputation

https://pharmaceuticalintelligence.com/2019/07/31/the-digital-age-gave-rise-to-new-definitions-new-benchmarks-were-born-on-the-world-wide-web-for-the-intangible-asset-of-firms-reputation-pay-a-premium-for-buying-e-reputation/

 

For @AVIVA1950, Founder, LPBI Group @pharma_BI: Twitter Analytics [Engagement Rate, Link Clicks, Retweets, Likes, Replies] & Tweet Highlights [Tweets, Impressions, Profile Visits, Mentions, New Followers]

https://pharmaceuticalintelligence.com/2019/08/06/twitter-analytics-engagement-rate-link-clicks-retweets-likes-replies-tweet-highlights-tweets-impressions-profile-visits-mentions-new-followers-for-aviva1950-founder-lpbi-group-ph/

 

  • Components from IP-V-Cal – Tool for IP Valuation of Intangibles

 

Dr. Williams’ 7 Parameters
1 – Ongoing (currently 7 years article generation on LPBI) 85 3
2 – Co-editor on LPBI ebooks on Amazon and generation of ebook covers 65 1
3 – Daily Engagement on Twitter @pharma_BI, @StephenJWillia2 70 3
4 – Development of Class Curriculum utilizing LPBI site & curation methodology & model for Medical Educational 85 4
5 – Co-development methodology of Scientific Conference Coverage in social media using LPBI platform 70 5
6 – Ongoing and increasing readership on online Journal 85 3
7 – High visibility and reputation of online access Journal and offerings by LPBI 80 6

 

Dr. D. Nir’s 5 Parameters
1 – Originality of content 90 1
2 – Number of market-niche related tangible advice; i.e. action that readers can/should take 90 1 100 4
3 – Team commitment; the level of legally-binding agreements 75 5 100 5
4 – Show cases (testimonials) of specific business consultancy (doesn’t matter if paid or not) 80 2 80 3
5 – Tangible business-benefit attested by followers of the Open Access Online Scientific Journal and by Page downloads from each ebooks [96,000 across all volumes in 4/2019], eBook borrowing and e-Book sales 50 4 80 2

 

Dr. Irina Robu 5 Parameters
1- Ongoing (currently 7 years article generation on LPBI) 1 1 100 89 2
2 – Brought strong writing/editing skills to LPBI 2 2 95 80 1
3 – Regular engagement on Twitter @pharma_BI, @irirobu 3 3 70 60 4
4 – Editor on 3-D Bioprinting LPBI ebook on Amazon and participating writer on other LPBI ebooks 2 2 90 90 3
5-Team Commitment; Information organizer and IT 5 5 85 60 5

 

Gail’s 5 Parameters
1 – Expertise and dedication of highly experienced group of consultants who offer synthesis, analysis and interpretation of complex medical and scientific areas 90 1 40 1
2 – Expertise and dedication of highly experienced group of consultants who offer synthesis, analysis and interpretation of complex medical and scientific areas 60 4 20 2
3 -Brought strong strategy and writing/editing skills to LPBI 60 2 20 3
4 -Wrote original patient-focused articles for Voices of Patients e-book + continued article publishing 80 3 10 4
5 -Explored new and different communications avenues, i.e, recent Bioprinting audio podcast brings 40,000 new listeners 70 5 10 5

 

Amnon’s Parameters
Who cares (the audience)? 70 4 100 The scientists should answer it
Why do the audience care? 80 3 100
How does the audience use the service/product? 90 1 100
What is the real experience of using the LPBI products and services (testimonials) 80 2 100

 

Aviva’s Parameters
1 – Low barriers to entry 10 95
2 – Zero labor cost 8 100 1
3 – Virtual no overhead 7 100 1
4 – Run rate Hosting website $200/yr 95 5
5 – LinkedIn Corp Account $1000/yr 95 5
6 – Leadership demonstrated CAPACITY for new domain entry 100 3
7 – Team ability to swarm to new domains 100 2
8 – Leadership maintains Fidelity of Scientists Core 100 1
9 – Founder’s ability to multitask 5 people in One person 1 100 1
10A – Daily Engagement in Twitter @pharma_Bi # Followers = 519 RatioTweets to Likes: 25,000/3,086 2 95 5
10B – @AVIVA1950 # Followers = 439 RatioTweets to Likes: 11,000/5,615 2 100 5
11 – Daily engagement in LinkedIn 7,000 1st contacts 3 90 4
12 – Daily engagement in Facebook 4 50 5
13 – In-Person at 60 confrences brinking back contacts and Ideas 1 100 1
14 – 60 conferences yielded Corpus of eProceedings – IP Asset Class III [10 by Dr. Williams N= 70] 2 100 1
15 – Founder as Curator of New content – Journal is LIVE 1 100 3
16 – Founder as Book Editor in multiple domains: Series A, B, D, E 4 98 3
17 – Founder as Editor-in-Chief: 16 Titles, content acquisition, eTOCs Designer 2 150 2
18. – Founder as Relations builder with multiple Ecosystems: Israel, US, Europe, Japan 3 120 1

For @AVIVA1950, Founder, LPBI Group @pharma_BI: Twitter Analytics [Engagement Rate, Link Clicks, Retweets, Likes, Replies] & Tweet Highlights [Tweets, Impressions, Profile Visits, Mentions, New Followers]

Data collection from Twitter Analytics Pages for above Twitter Account: Aviva Lev-Ari, PhD, RN

https://analytics.twitter.com/user/AVIVA1950/tweets

Definitions

An engagement rate between 0.09% and 0.33% is considered to be high, where an influencer would expect 0.9 – 3.3 reactions for every 1000 followers onTwitter. Finally, an engagement rate between 0.33% and 1% is considered to be very high, with expected reactions to be between 3.3 – 10 for every 1000 Twitter followers.

What is a Good Engagement Rate on Twitter? – Scrunch

https://www.scrunch.com/blog/what-is-a-good-engagement-rate-on-twitter

How to calculate engagement rate on Twitter?

The Tweet Engagement Rate Formula.
The Tweet Engagement Rate takes into account the Replies and Retweets of the Tweet to the total number of Followers to date. Then it´s multiplied by 100 as well to provide you with the percentage of your Fan base that´s interacting with your Tweet.

Formulas Revealed: The Facebook and Twitter Engagement Rate …

6 engagement rate calculation methods

These are the most common formulas you’ll need to calculate engagement rates on social media.

Total engagements typically represents a tally of likes, favourites, reactions, comments, shares, views, retweets, and sometimes include clicks, depending on which platform you’re using.

1. Engagement rate by reach (ERR)

This formula is the most common way to calculate engagement with content.

ERR measures the percentage of people who chose to interact with your content after seeing it.

Use the first formula for a single post, and the second one to calculate the average rate across multiple posts.

  • ERR = total engagements per post / reach per post * 100

To determine the average, add up the all the ERRs from the posts you want to average, and divide by number of posts:

  • Average ERR = Total ERR / Total posts

In other words: Post 1 (3.4%) + Post 2 (3.5%) / 2 = 3.45%

Pros: Reach can be a more accurate measurement than follower count, since not all your followers will see all your content. And non-followers may have been exposed to your posts through shares, hashtags, and other means.

Cons: Reach can fluctuate for a variety of reasons, making it a different variable to control. A very low reach can lead to a disproportionately high engagement rate, and vice versa, so be sure to keep this in mind.

2. Engagement rate by posts (ER post)

Technically, this formula measures engagements by followers on a specific post. In other words, it’s similar to ERR, except instead of reach it tells you the rate at which followers engage with your content.

Most social media influencers calculate their average engagement rate this way.

  • ER post = Total engagements on a post / Total followers *100

To calculate the average, add up all the ER posts you want to average, and divide by number of posts:

  • Average ER by post = Total ER by post / Total posts

Example: Post 1 (4.0%) + Post 2 (3.0%) / 2 = 3.5%

Pros: While ERR is a better way to gauge interactions based on how many people have seen your post, this formula replaces reach with followers, which is generally a more stable metric.

In other words, if your reach fluctuates often, use this method for a more accurate measure of post-by-post engagement.

Cons: As mentioned, while this may be a more unwavering way to track engagements on posts, it doesn’t necessarily provide the full picture since it doesn’t account for viral reach. And, as your follower count goes up, your rate of engagement could drop off a little.

Make sure to view this stat alongside follower growth analytics.

Bonus: Get a free social media report template to easily and effectively present your social media performance to key stakeholders.

Get the free template now!

3. Engagement rate by impressions (ER impressions)

Another base audience metric you could choose to measure engagements by is impressions. While reach measures how many people see your content, impressions tracks how often that content appears on a screen.

  • ER impressions = Total engagements on a post / Total impressions *100
  • Average ER impressions = Total ER impressions / Total posts

Pros: This formula can be useful if you’re running paid content and need to evaluate effectiveness based on impressions.

Cons: An engagement rate calculated with impressions as the base is bound to be lower than ERR and ER post equations. Like reach, impression figures can also be inconsistent. It may be a good idea to use this method in conjunction with reach.

Read more about the difference between reach and impressions.

4. Daily engagement rate (Daily ER)

While engagement rate by reach measures engagement against maximum exposure, it’s still good to have a sense of how often your followers are engaging with your account on a daily basis.

  • Daily ER = Total engagements in a day / Total followers *100
  • Average Daily ER = Total engagements for X days / (X days *followers) *100

Pros: This formula is a good way to gauge how often your followers interact with your account on a daily basis, rather than how they interact with a specific post. As a result, it takes engagements on new and old posts into equation.

This formula can also be tailored for specific use cases. For instance, if your brand only wants to measure daily comments, you can adjust “total engagements” accordingly.

Cons: There’s a fair amount of room for error with this method. For instance, the formula doesn’t account for the fact that the same follower may engage 10 times in a day, versus 10 followers engaging once.

Daily engagements can also vary for a number of reasons, including how many posts you share. For that reason it may be worthwhile to plot daily engagement versus number of posts.

5. Engagement rate by views (ER views)

If video is a primary vertical for your brand, you’ll likely want to know how many people choose to engage with your videos after watching them.

  • ER view = Total engagements on video post / Total video views *100
  • Average ER view = Total ER view / Total posts

Pros: If one of your video’s objectives is to generate engagement, this can be a good way to track it.

Cons: View tallies often include repeat views from a single user (non-unique views). While that viewer may watch the video multiple times, they may not necessarily engage multiple times.

6. Factored Engagement Rate

In rare cases some marketers use a “factored engagement rate.” As the name suggests, factored engagement rates add more or less weight to certain factors in the equation.

For example, a marketer may wish to place a higher value on comments versus likes, weighting each comment as two versus one. The subsequent equation would look something like this:

  • Comment-weighted ER = (Total comments x 2) + all other engagements / Reach per post *100

Obviously, this technique inflates the resulting engagement rate and can be misleading, especially since the use of factored engagement rates is not widespread. For this reason, Hootsuite does not recommend its use.

SOURCE

https://blog.hootsuite.com/calculate-engagement-rate/

How To Measure An Influencer’s Engagement Rate (A Scientific …

 

 

July 2019

Impressions, Engagement Rate, Link Clicks, Retweets, Likes, Replies

 

2019

Average Impressions per day by Month

Monthly

Average

Engagement Rate

&

Last day of the month

Monthly

Average

Link Clicks

& Last day of the month

& average link clicks per day

Monthly

Average Retweets

& Last day of the month

& average Retweets per day

Monthly

Average

Likes

& Last day of the month

& average Likes per day

Monthly

Average Replies

& Last day of the month

& average Replies per day

January 31

N = 809

2.4% / 2.2% 33 / 3 / 1 80 / 4 / 3 129 / 5 / 4

2 /1 /0

February 28

N = 825

2.1% / 11.7% 22 / 0 / 1 24 / 3 / 1 41 / 1 / 1

1 / 0 / 0

March 31

N = 759

2.5% / 0.5% 18 / 1/ 1 37 / 1/ 1 63 / 1 / 2 1 / 0 / 0

April 30

N = 2.1K

1.0% / 1.8% 21 / 1/ 1 130 / 3 / 4 201 / 3 / 7

0 / 0/ 0

May 31

N = 2.3K

1.3% / 1.2% 51 / 3 / 2 117 / 2 / 4 142 / 2 / 5

3 / 0 / 0

June 30

N = 1.9K

1.2% / 0.1% 33 / 0 / 1 124 / 1 / 4 161 / 0 / 5

1 / 0 / 0

July 31

N = 824

0.9% / 1.1% 54 / 4/ 2 42 / 6 / 1 40 / 3 / 1

3 / 0 / 0

August

N =

October

N =

November

N =

December

N =

Tweets, Impressions, Profile Visits, Mentions, New Followers

2019

Tweets Impressions Profile visits Mentions New Followers

January

25.1K 21

February

1 23.1K 62

21

March 155 23.5K 533 153

10

April

257 64.2K 690 235 14

May

281 70.4K 691 238

28

June

206

58.4K 581 170

23

July

83 25.5K 321 62

15

August

September

October

November

December

TWEET HIGHLIGHTS
TWEET HIGHLIGHTS

Top Tweet earned 2,280 impressions

as and are in unidirectional symbiosis Food affect Microbiome in turn it affects of individuals over theirs
 5  4

Top Follower followed by 289K people

@tveitdal FOLLOWS YOU

Tweeting Climate Change news. Climate lecturer: science, policy, solutions. Director Klima 2020, former UN Director. For contact use svein@klima2020.no

Top mention earned 21 engagements

eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PMET MIT Kresge Auditorium, Cambridge, MA via
 1  1
JUN 2019 SUMMARY

Tweets

206

Tweet impressions

58.4K

Profile visits

581

Mentions

170

New followers

23
TWEET HIGHLIGHTS

Top Tweet earned 3,800 impressions

create application for robots, we picked pruned by hand in high school as Summer job.
 6  5

Top Follower followed by 202K people

@Alex_Verbeek FOLLOWS YOU

Public Speaker | Moderator | Diplomat | Photographer | Yale World Fellow | Climate Change | Wildlife | Environment | Art | Energy-Water-Food | Sustainability 🌱

Top mention earned 36 engagements

 6  10

Top media Tweet earned 1,319 impressions

Dr. Maus ⁩ monitoring data from clinical trial is very important development of new targets multiple drugs multiple mechanism multiple specificities more modification to one cell contamination results
 3  3
MAY 2019 SUMMARY

Tweets

281

Tweet impressions

70.4K

Profile visits

691

Mentions

238

New followers

28
TWEET HIGHLIGHTS

Top Tweet earned 2,758 impressions

The premise of applied to 30 babies in 1st week of life new to intervention to cause desirable # molecular changes by search immunology search
 7  12

Top media Tweet earned 1,652 impressions

Amazing Disruptive Dozen Technologies ⁩ ⁦ Third Annette Kim streamlining Diagnosis Second Thomas McCoy prediction of Suicide risk FIRST Alexandra Golby Neurosurgery Imaging AI based system real time dynamic data intraop
 1  1
APR 2019 SUMMARY

Tweets

257

Tweet impressions

64.2K

Profile visits

690

Mentions

235

New followers

14
TWEET HIGHLIGHTS

Top Follower followed by 450K people

@CMichaelGibson FOLLOWS YOU

Non-Profit Founder/Leader | ❤️ Doc | Artist | Scientist | Educator | Med News Anchor https://t.co/LDrNxgwhA4 | RT ≠ endorse | Disclaimer here: https://t.co/2jtJQZQU0H

Top mention earned 8 engagements

impact of , how to maintain Boston as Biotech HUB? with ⁦⁩ attract great talent ⁦⁦⁩ culture is asset on the balance sheet PARKING
 1  2

Top media Tweet earned 440 impressions

MAR 2019 SUMMARY

Tweets

155

Tweet impressions

23.5K

Profile visits

533

Mentions

153

New followers

10
TWEET HIGHLIGHTS

Top Tweet earned 795 impressions

Top Follower followed by 252K people

@ArtistsandMusic FOLLOWS YOU

Music Lovers Network . ♫♫ Connecting  talented  with , A&Rs and Fans.   

Top mention earned 6 engagements

Top media Tweet earned 55 impressions

My week on Twitter 🎉: 34 Mentions, 10.6K Mention Reach, 18 Likes, 7 Retweets, 6.7K Retweet Reach. See yours with
 1
FEB 2019 SUMMARY

Tweets

1

Tweet impressions

23.1K

Profile visits

62

New followers

21
TWEET HIGHLIGHTS

Top Follower followed by 450K people

@CMichaelGibson FOLLOWS YOU

Non-Profit Founder/Leader | ❤️ Doc | Artist | Scientist | Educator | Med News Anchor https://t.co/LDrNxgwhA4 | RT ≠ endorse | Disclaimer here: https://t.co/2jtJQZQU0H

JAN 2019 SUMMARY

Tweet impressions

25.1K

New followers

21