A human iPSC-array-based GWAS identifies a virus susceptibility locus in the NDUFA4 gene and functional variants.

Population-based studies to identify disease-associated risk alleles typically require samples from a large number of individuals. Here, we report a human-induced pluripotent stem cell (hiPSC)-based screening strategy to link human genetics with viral infectivity. A genome-wide association study (GWAS) identified a cluster of single-nucleotide polymorphisms (SNPs) in a cis-regulatory region of the NDUFA4 gene, which was associated with susceptibility to Zika virus (ZIKV) infection. Loss of NDUFA4 led to decreased sensitivity to ZIKV, dengue virus, and SARS-CoV-2 infection. Isogenic hiPSC lines carrying non-risk alleles of SNPs or deletion of the cis-regulatory region lower sensitivity to viral infection. Mechanistic studies indicated that loss/reduction of NDUFA4 causes mitochondrial stress, which leads to the leakage of mtDNA and thereby upregulation of type I interferon signaling. This study provides proof-of-principle for the application of iPSC arrays in GWAS and identifies NDUFA4 as a previously unknown susceptibility locus for viral infection.

Machine Learning Identification of Self-reported COVID-19 Symptoms from Tweets in Canada

Surveillance of open-source media, such as social media, has become an essential complement to traditional surveillance data for quickly detecting changes in the occurrence of diseases in time and space. We present our method for classifying Tweets into narratives about COVID-19 symptoms to produce a dataset for downstream surveillance applications. A dataset of 10,405 tweets has been manually classified as relevant or not to self-reported symptoms of COVID-19. Five machine learning classification algorithms, with different tokenization methods, were trained on the dataset and tested. The Support vector machine (SVM) algorithm, with a term frequency-inverse document frequency (TF-IDF) 3-4 n-grams on character as the tokenization method, was the classification algorithm with the highest F1-score of 0.70. However, the training dataset showed an imbalanced classification problem. To reduce the bias of the imbalance classes, the crowdsourcing website Mechanical Turk was used to add 133 relevant tweets. This addition improved the F1-score from 0.70 to 0.77. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

How L’Oréal adopted new technologies to scale personalisation, adapt to new customer demands and evolve into the top beauty tech company

Across the globe, the COVID-19 pandemic has had a massive — and likely long-lasting — impact on commercial and consumer trends. For companies specialising in consumer packaged goods and traditionally reliant on physical stores for the majority of their sales, success is no longer about staying relevant, but rather on preparing for a world where significant portions of their business will emanate from e-commerce. With brick-and-mortar sales declining and consumers becoming more tech-savvy, this paper argues that beauty companies (both large and small) must look to advanced ways for customers to discover, consider and purchase beauty products. By way of illustration, the paper describes how L’Oréal embarked on a programme of digital transformation that would prepare it to outlast the current context and continue to advance in spite of a challenging market. © 2022 Henry Stewart.

SARS-CoV-2 Genomic Surveillance Reveals Little Spread between a Large University Campus and the Surrounding Community

Background. Understanding SARS-CoV-2 transmission dynamics is critical for controlling and preventing outbreaks. The genomic epidemiology of SARS-CoV-2 on college campuses has not been comprehensively studied, and the extent to which campus-associated outbreaks lead to transmission in nearby communities is unclear. We used high-density genomic surveillance to track SARS-CoV-2 transmission across the University of Michigan-Ann Arbor campus and Washtenaw County during the Fall 2020 semester. Methods. We retrieved all available residual diagnostic specimens from the Michigan Medicine Clinical Microbiology Laboratory and University Health Service that were positive for SARS-CoV-2 from August 16th - November 25th, 2020 (n = 2245). We extracted viral RNA, amplified the SARS-CoV-2 genome by multiplex RT-PCR, and sequenced these amplicons on an Illumina MiSeq. We applied maximum likelihood phylogenetic analysis to whole genome sequences to define and characterize transmission lineages. Results. We assembled complete viral genomes from 1659 individual infections, representing roughly 25% of confirmed cases in Washtenaw County across the fall semester. Of these cases, 468 were University of Michigan students. Phylogenetic analysis revealed 203 genetically distinct introductions of SARS-CoV-2 into the student population, most of which were singletons (n = 171) or small clusters of 2 - 8 students. We identified two large SARS-CoV-2 transmission lineages (115 and 73 students, respectively), including individuals from multiple on-campus residences. Viral descendants of these student outbreaks were rare, constituting less than 4% of cases in the community. Conclusion. We identified many SARS-CoV-2 transmission introductions into the University of Michigan campus in Fall 2020. While there was widespread transmission among students, there is little evidence that these outbreaks significantly contributed to the rise in COVID-19 cases that Washtenaw County experienced in November 2020.

Hematology oncology COllaborativeVIDeoconferencing (COVID) Learning Initiative: A multi-institutional trainee alliance to rapidly disseminateemerging SARS-CoV2 and cancer best practices

Introduction: The SARS-CoV2 pandemic impacted numerous aspects of medical practice, including continuingmedical education. In-person and single-institution educational formats could not address the challenges of socialdistancing, heterogeneous regional experiences, and continuously emerging data. The vulnerability of cancerpatients to SARS-CoV2 added further urgency to overcoming these barriers. To fulfill these unmet educational andpatient care needs, we established a novel cross-institutional trainee-driven, on-line collaborative for the purpose ofgenerating multidisciplinary seminars on emerging best practices for the acute management of patients with SARS-CoV2. Methods: The COVID Learning Initiative is currently managed by clinical trainees and faculty from 13 institutionsacross 10 states. Weekly Zoom conferences were led by a rotating team consisting of 2-3 fellows overseen by 4-5expert faculty from throughout the country. Format consisted of two 15-minute instructional segments presented bytrainees, followed by a concluding 30-minute faculty Q&A panel moderated by a trainee. Attendees completedbaseline demographics, SARS-CoV2 experience surveys, and pre/post conference knowledge questions.Conferences were recorded and archived to enhance access and dissemination of knowledge. Results: Within 6 weeks and beginning just 2 weeks after inception we produced five 1-hour-longmultidisciplinary video conferences covering emerging antiviral therapies, coagulopathy, pulmonary complications, provider resilience, and ethics of resource scarcity. On average, there were 100 participants per seminar. Post-conference questioning consistently demonstrated acquisition of knowledge across topics and disciplines. Attendeesalso improved in their self-assessed comfort managing multidisciplinary aspects of SARS-CoV2. Overall, presentingcollaborations involved 11 fellows and 28 faculty representing 6 medical specialties and 17 institutions. Severalcollaborations persisted, resulting in further dissemination of knowledge with tangible outcomes such as generationof peer-reviewed manuscripts. Conclusions: The COVID Learning Initiative demonstrated a novel continuing medical education platform capableof rapidly disseminating knowledge at a national scale, while realizing new opportunities for remote traineementoring and skills development. With initial feasibility and continued interest among participating institutions, COVID Learning Initiative plans to evolve to Fellows ACHIEVE: Alliance for Collaborative Hematology OncologyInter-Institutional Education through Videoconferencing to conduct an extended multi-institutional educational serieson adapting cancer management and training program best practices.