Prevalence of Contact Allergens in Natural Skin Care Products From US Commercial Retailers.
View details for DOI 10.1001/jamadermatol.2022.3180
View details for PubMedID 36103164
Fatty Acid Synthesis Knockdown Promotes Biofilm Wrinkling and Inhibits Sporulation in Bacillus subtilis.
Many bacterial species typically live in complex three-dimensional biofilms, yet much remains unknown about differences in essential processes between nonbiofilm and biofilm lifestyles. Here, we created a CRISPR interference (CRISPRi) library of knockdown strains covering all known essential genes in the biofilm-forming Bacillus subtilis strain NCIB 3610 and investigated growth, biofilm colony wrinkling, and sporulation phenotypes of the knockdown library. First, we showed that gene essentiality is largely conserved between liquid and surface growth and between two media. Second, we quantified biofilm colony wrinkling using a custom image analysis algorithm and found that fatty acid synthesis and DNA gyrase knockdown strains exhibited increased wrinkling independent of biofilm matrix gene expression. Third, we designed a high-throughput screen to quantify sporulation efficiency after essential gene knockdown; we found that partial knockdowns of essential genes remained competent for sporulation in a sporulation-inducing medium, but knockdown of essential genes involved in fatty acid synthesis exhibited reduced sporulation efficiency in LB, a medium with generally lower levels of sporulation. We conclude that a subset of essential genes are particularly important for biofilm structure and sporulation/germination and suggest a previously unappreciated and multifaceted role for fatty acid synthesis in bacterial lifestyles and developmental processes. IMPORTANCE For many bacteria, life typically involves growth in dense, three-dimensional communities called biofilms that contain cells with differentiated roles held together by extracellular matrix. To examine how essential gene function varies between vegetative growth and the developmental states of biofilm formation and sporulation, we created and screened a comprehensive library of strains using CRISPRi to knockdown expression of each essential gene in the biofilm-capable Bacillus subtilis strain 3610. High-throughput assays and computational algorithms identified a subset of essential genes involved in biofilm wrinkling and sporulation and indicated that fatty acid synthesis plays important and multifaceted roles in bacterial development.
View details for DOI 10.1128/mbio.01388-22
View details for PubMedID 36069446
New-onset pemphigus vegetans and pemphigus foliaceus following SARS-CoV-2 vaccination: a case series.
JAAD case reports
View details for DOI 10.1016/j.jdcr.2022.07.002
View details for PubMedID 35845348
Explaining Deep Learning Models for Low Vision Prognosis
ASSOC RESEARCH VISION OPHTHALMOLOGY INC. 2022
View details for Web of Science ID 000844437005157
Looking for low vision: Predicting visual prognosis by fusing structured and free-text data from electronic health records.
International journal of medical informatics
1800; 159: 104678
INTRODUCTION: Low vision rehabilitation improves quality-of-life for visually impaired patients, but referral rates fall short of national guidelines. Automatically identifying, from electronic health records (EHR), patients with poor visual prognosis could allow targeted referrals to low vision services. The purpose of this study was to build and evaluate deep learning models that integrate EHR data that is both structured and free-text to predict visual prognosis.METHODS: We identified 5547 patients with low vision (defined as best documented visual acuity (VA)less than20/40) on≥1 encounter from EHR from 2009 to 2018, with≥1year of follow-up from the earliest date of low vision, who did not improve togreater than20/40 over 1year. Ophthalmology notes on or prior to the index date were extracted. Structured data available from the EHR included demographics, billing and procedure codes, medications, and exam findings including VA, intraocular pressure, corneal thickness, and refraction. To predict whether low vision patients would still have low vision a year later, we developed and compared deep learning models that used structured inputs and free-text progress notes. We compared three different representations of progress notes, including 1) using previously developed ophthalmology domain-specific word embeddings, and representing medical concepts from notes as 2) named entities represented by one-hot vectors and 3) named entities represented as embeddings. Standard performance metrics including area under the receiver operating curve (AUROC) and F1 score were evaluated on a held-out test set.RESULTS: Among the 5547 low vision patients in our cohort, 40.7% (N=2258) never improved to better than 20/40 over one year of follow-up. Our single-modality deep learning model based on structured inputs was able to predict low vision prognosis with AUROC of 80% and F1 score of 70%. Deep learning models utilizing named entity recognition achieved an AUROC of 79% and F1 score of 63%. Deep learning models further augmented with free-text inputs using domain-specific word embeddings, were able to achieve AUROC of 82% and F1 score of 69%, outperforming all single- and multiple-modality models representing text with biomedical concepts extracted through named entity recognition pipelines.DISCUSSION: Free text progress notes within the EHR provide valuable information relevant to predicting patients' visual prognosis. We observed that representing free-text using domain-specific word embeddings led to better performance than representing free-text using extracted named entities. The incorporation of domain-specific embeddings improved the performance over structured models, suggesting that domain-specific text representations may be especially important to the performance of predictive models in highly subspecialized fields such as ophthalmology.
View details for DOI 10.1016/j.ijmedinf.2021.104678
View details for PubMedID 34999410
Types of information that patients with lung cancer with targetable driver mutations and their caregivers learn from online forums: Results of a qualitative study
LIPPINCOTT WILLIAMS & WILKINS. 2021
View details for DOI 10.1200/JCO.2020.39.28_suppl.161
View details for Web of Science ID 000707130200160
Three-dimensional biofilm colony growth supports a mutualism involving matrix and nutrient sharing.
Life in a three-dimensional biofilm is typical for many bacteria, yet little is known about how strains interact in this context. Here, we created essential-gene CRISPRi knockdown libraries in biofilm-forming Bacillus subtilis and measured competitive fitness during colony co-culture with wild type. Partial knockdown of some translation-related genes reduced growth rates and led to out-competition. Media composition led some knockdowns to compete differentially as biofilm versus non-biofilm colonies. Cells depleted for the alanine racemase AlrA died in monoculture but survived in a biofilm-colony co-culture via nutrient sharing. Rescue was enhanced in biofilm-colony co-culture with a matrix-deficient parent, due to a mutualism involving nutrient and matrix sharing. We identified several examples of mutualism involving matrix sharing that occurred in three-dimensional biofilm colonies but not when cultured in two dimensions. Thus, growth in a three-dimensional colony can promote genetic diversity through sharing of secreted factors and may drive evolution of mutualistic behavior.
View details for DOI 10.7554/eLife.64145
View details for PubMedID 33594973