Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis.

Background: Machine learning (ML) models can yield faster and more accurate medical diagnoses; however, developing ML models is limited by a lack of high-quality labeled training data. Crowdsourced labeling is a potential solution but can be constrained by concerns about label quality.

Objective: This study aims to examine whether a gamified crowdsourcing platform with continuous performance assessment, user feedback, and performance-based incentives could produce expert-quality labels on medical imaging data.

Accuracy of Parent-Reported Allergies and Medications in Pediatric Dentistry.

To measure the accuracy of parent-reported allergies and medication usage by comparing parental reports during dental con- sultations to medical reports from their child's primary care physician. A retrospective chart review was performed for 862 eligible patients 17 years and younger seen in the Department of Pediatric Dentistry at Franciscan Children's, Boston, Mass., USA, and who were required to obtain medical clearance prior to initiating dental treatment with sedation or general anesthesia.

Artificial Intelligence to Determine Fetal Sex.

Objective:  This proof-of-concept study assessed how confidently an artificial intelligence (AI) model can determine the sex of a fetus from an ultrasound image.

Study Design:  Analysis was performed using 19,212 ultrasound image slices from a high-volume fetal sex determination practice. This dataset was split into a training set (11,769) and test set (7,443).

Comparison of pulmonary congestion severity using artificial intelligence-assisted scoring versus clinical experts: A secondary analysis of BLUSHED-AHF.

Aim: Acute decompensated heart failure (ADHF) is the leading cause of cardiovascular hospitalizations in the United States. Detecting B-lines through lung ultrasound (LUS) can enhance clinicians' prognostic and diagnostic capabilities. Artificial intelligence/machine learning (AI/ML)-based automated guidance systems may allow novice users to apply LUS to clinical care.

Accuracy of parent-reported health history in a dental setting.

Background: Obtaining thorough documentation of a patient's medical history is important for dental care professionals, as oral health is connected intricately to systemic health. The purpose of this study was to assess the accuracy of parent-reported health history for pediatric patients in a dental setting.

Methods: A retrospective chart review was conducted on 863 patients 17 years and younger.

Programmable CRISPR-Cas Repression, Activation, and Computation with Sequence-Independent Targets and Triggers.

The programmability of CRISPR-derived Cas9 as a sequence-specific DNA-targeting protein has made it a powerful tool for genomic manipulation in biological research and translational applications. Cas9 activity can be programmably engineered to respond to nucleic acids, but these efforts have focused primarily on single-input control of Cas9, and until recently, they were limited by sequence dependence between parts of the guide RNA and the sequence to be detected. Here, we not only design and present DNA- and RNA-sensing conditional guide RNA (cgRNA) that have no such sequence constraints, but also demonstrate a complete set of logical computations using these designs on DNA and RNA sequence inputs, including AND, OR, NAND, and NOR.

Using ALoFT to determine the impact of putative loss-of-function variants in protein-coding genes.

Variants predicted to result in the loss of function of human genes have attracted interest because of their clinical impact and surprising prevalence in healthy individuals. Here, we present ALoFT (annotation of loss-of-function transcripts), a method to annotate and predict the disease-causing potential of loss-of-function variants. Using data from Mendelian disease-gene discovery projects, we show that ALoFT can distinguish between loss-of-function variants that are deleterious as heterozygotes and those causing disease only in the homozygous state.

A proteome-scale map of the human interactome network.

Just as reference genome sequences revolutionized human genetics, reference maps of interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ?14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ?30% larger than what is available from small-scale studies published in the literature in the last few decades.

A systematic survey of loss-of-function variants in human protein-coding genes.

Genome-sequencing studies indicate that all humans carry many genetic variants predicted to cause loss of function (LoF) of protein-coding genes, suggesting unexpected redundancy in the human genome. Here we apply stringent filters to 2951 putative LoF variants obtained from 185 human genomes to determine their true prevalence and properties. We estimate that human genomes typically contain ~100 genuine LoF variants with ~20 genes completely inactivated.

Enhanced lung tumor development in tobacco smoke-exposed p53 transgenic and Kras2 heterozygous deficient mice.

A/J mice bearing either a mutation in the p53 gene or a Kras2 heterozygous deficiency were investigated for their susceptibility to tobacco smoke-induced lung tumorigenesis. Transgenic mice and their wild-type littermates were exposed to mainstream tobacco smoke (MS) for 5 mo, followed by 4 mo of recovery in filtered air. In sham (filtered air) groups, p53 transgenic mice did not exhibit a higher tumor multiplicity but did exhibit larger tumors, with tumor load increased 3.