Hereditary gingival fibromatosis (HGF) is the most common genetic form of gingival fibromatosis which is featured as a localized or generalized overgrowth of gingivae. Currently two genes (SOS1 and REST), as well as four loci (2p22.1, 2p23.3-p22.3, 5q13-q22, and 11p15), have been identified as associated with HGF in a dominant inheritance pattern. Here we report thirteen individuals with autosomal-dominant HGF from a four-generation Chinese family. Whole-exome sequencing followed by further genetic co-segregation analysis was performed for the family members across three generations. A novel heterozygous missense mutation (c.2812G>A) in zinc finger protein 862 gene (ZNF862) was identified, and it is absent among the population as per the Genome Aggregation Database. The functional study supports a biological role of ZNF862 for increasing the profibrotic factors particularly COL1A1 synthesis and hence resulting in HGF. Here for the first time we identify the physiological role of ZNF862 for the association with the HGF.

Chlorophyll biosynthesis plays a vital role in chloroplast development and photosynthesis in plants. In this study, we identified an orthologue of the rice gene TDR (Oryza sativa L., Tapetum Degeneration Retardation) in wheat (Triticum aestivum L.) called TaTDR-Like (TaTDRL) by sequence comparison. TaTDRL encodes a putative 557 amino acid protein with a basic helix-loop-helix (bHLH) conserved domain at the C-terminal (295-344 aa). The TaTDRL protein localised to the nucleus and displayed transcriptional activation activity in a yeast hybrid system. TaTDRL was expressed in the leaf tissue and expression was induced by dark treatment. Here, we revealed the potential function of TaTDRL gene in wheat by utilizing transgenic Arabidopsis plants TaTDRL overexpressing (TaTDRL-OE) and TaTDRL-EAR (EAR-motif, a repression domain of only 12 amino acids). Compared with wild-type plants (WT), both TaTDRL-OE and TaTDRL-EAR were characterized by a deficiency of chlorophyll. Moreover, the expression level of the chlorophyll-related gene AtPORC (NADPH:protochlorophyllide oxidoreductase C) in TaTDRL-OE and TaTDRL-EAR was lower than that of WT. We found that TaTDRL physically interacts with wheat Phytochrome Interacting Factor 1 (PIF1) and Arabadopsis PIF1, suggesting that TaTDRL regulates light signaling during dark or light treatment. In summary, TaTDRL may respond to dark or light treatment and negatively regulate chlorophyll biosynthesis by interacting with AtPIF1 in transgenic Arabidopsis.

Background Low birthweight (LBW) is a leading cause of neonatal mortality in the United States and a major causative factor of adverse health effects in newborns. Identifying high-risk patients early in prenatal care is crucial to preventing adverse outcomes. Previous studies have proposed various machine learning (ML) models for LBW prediction task, but they were limited by small and imbalanced data sets. Some authors attempted to address this through different data rebalancing methods. However, most of their reported performances did not reflect the models’ actual performance in real-life scenarios. To date, few studies have successfully benchmarked the performance of ML models in maternal health; thus, it is critical to establish benchmarks to advance ML use to subsequently improve birth outcomes. Objective This study aimed to establish several key benchmarking ML models to predict LBW and systematically apply different rebalancing optimization methods to a large-scale and extremely imbalanced all-payer hospital record data set that connects mother and baby data at a state level in the United States. We also performed feature importance analysis to identify the most contributing features in the LBW classification task, which can aid in targeted intervention. Methods Our large data set consisted of 266,687 birth records across 6 years, and 8.63% (n=23,019) of records were labeled as LBW. To set up benchmarking ML models to predict LBW, we applied 7 classic ML models (ie, logistic regression, naive Bayes, random forest, extreme gradient boosting, adaptive boosting, multilayer perceptron, and sequential artificial neural network) while using 4 different data rebalancing methods: random undersampling, random oversampling, synthetic minority oversampling technique, and weight rebalancing. Owing to ethical considerations, in addition to ML evaluation metrics, we primarily used recall to evaluate model performance, indicating the number of correctly predicted LBW cases out of all actual LBW cases, as false negative health care outcomes could be fatal. We further analyzed feature importance to explore the degree to which each feature contributed to ML model prediction among our best-performing models. Results We found that extreme gradient boosting achieved the highest recall score—0.70—using the weight rebalancing method. Our results showed that various data rebalancing methods improved the prediction performance of the LBW group substantially. From the feature importance analysis, maternal race, age, payment source, sum of predelivery emergency department and inpatient hospitalizations, predelivery disease profile, and different social vulnerability index components were important risk factors associated with LBW. Conclusions Our findings establish useful ML benchmarks to improve birth outcomes in the maternal health domain. They are informative to identify the minority class (ie, LBW) based on an extremely imbalanced data set, which may guide the development of personalized LBW early prevention, clinical interventions, and statewide maternal and infant health policy changes.

Background Vaping or e-cigarette use has become dramatically more popular in the United States in recent years. e-Cigarette and vaping use–associated lung injury (EVALI) cases caused an increase in hospitalizations and deaths in 2019, and many instances were later linked to unregulated products. Previous literature has leveraged social media data for surveillance of health topics. Individuals are willing to share mental health experiences and other personal stories on social media platforms where they feel a sense of community, reduced stigma, and empowerment. Objective This study aimed to compare vaping-related content on 2 popular social media platforms (ie, Twitter and Reddit) to explore the context surrounding vaping during the 2019 EVALI outbreak and to support the feasibility of using data from both social platforms to develop in-depth and intelligent vaping detection models on social media. Methods Data were extracted from both Twitter (316,620 tweets) and Reddit (17,320 posts) from July 2019 to September 2019 at the peak of the EVALI crisis. High-throughput computational analyses (sentiment analysis and topic analysis) were conducted. In addition, in-depth manual content analyses were performed and compared with computational analyses of content on both platforms (577 tweets and 613 posts). Results Vaping-related posts and unique users on Twitter and Reddit increased from July 2019 to September 2019, with the average post per user increasing from 1.68 to 1.81 on Twitter and 1.19 to 1.21 on Reddit. Computational analyses found the number of positive sentiment posts to be higher on Reddit (P<.001, 95% CI 0.4305-0.4475) and the number of negative posts to be higher on Twitter (P<.001, 95% CI –0.4289 to −0.4111). These results were consistent with the clinical content analyses results indicating that negative sentiment posts were higher on Twitter (273/577, 47.3%) than Reddit (184/613, 30%). Furthermore, topics prevalent on both platforms by keywords and based on manual post reviews included mentions of youth, marketing or regulation, marijuana, and interest in quitting. Conclusions Post content and trending topics overlapped on both Twitter and Reddit during the EVALI period in 2019. However, crucial differences in user type and content keywords were also found, including more frequent mentions of health-related keywords on Twitter and more negative health outcomes from vaping mentioned on both Reddit and Twitter. Use of both computational and clinical content analyses is critical to not only identify signals of public health trends among vaping-related social media content but also to provide context for vaping risks and behaviors. By leveraging the strengths of both Twitter and Reddit as publicly available data sources, this research may provide technical and clinical insights to inform automatic detection of social media users who are vaping and may benefit from digital intervention and proactive outreach strategies on these platforms.

In Chinese patients who were administered a single dose of rocuronium, the genetic variants ABCB1 rs12720464, and rs1055302 contribute to the individual< variability of time course of action.