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Monosomy 7 and del(7q) are among the most common cytogenetic abnormalities in myeloid malignancies, yet their underlying pathogenesis remains unclear. Using an array-based CRISPR screen and orthogonal machine learning approach, we identify potential chromosome 7 tumor suppressor genes (TSGs). We selected candidate TSGs via datamining of genome-scale studies, individually CRISPR-edited 108 candidates, and measured the subsequent impact on the proliferation and erythroid differentiation of primary, human CD34+ hematopoietic stem and progenitor cells (HSPCs). An unexpected 39% of genes increased proliferation when edited and were significantly enriched in commonly deleted regions. The only two genes that both increased proliferation and decreased erythroid differentiation when edited were the CUX1 transcription factor and ACHE, encoding acetylcholinesterase, both located in the 7q22.1 commonly deleted region. We demonstrate a novel role for ACHE in regulating erythropoiesis through acetylcholine receptor signaling. The defects stemming from loss of ACHE were corrected by a muscarinic receptor inhibitor, implicating muscarinic antagonists as potential treatments for −7/del(7q)-associated anemia. While chromosome-level deletions were historically thought to harbor a single TSG, the significant enrichment of TSGs within commonly deleted regions suggests a contiguous gene syndrome, wherein combinatorial loss of multiple neighboring genes drives disease.

Managing chronic diseases requires ongoing monitoring of disease activity and therapeutic responses to optimize treatment plans. With the growing availability of disease-modifying therapies, it is crucial to investigate comparative effectiveness and long-term outcomes beyond those available from randomized clinical trials. We introduce a comprehensive pipeline for generating reproducible and generalizable real-world evidence on disease outcomes by leveraging electronic health record data. The pipeline first generates scalable disease outcomes by linking electronic health record data with registry data containing a small sample of labeled outcomes. It then applies causal analysis using these scalable outcomes to evaluate therapies for chronic diseases. The implementation of the pipeline is illustrated in a case study based on multiple sclerosis. Our approach addresses challenges in real-world evidence generation for disease activity of chronic conditions, specifically the lack of direct observations on key outcomes and biases arising from imperfect or incomplete data. We present advanced machine learning techniques such as semisupervised and ensemble methods to impute missing outcome data, further incorporating steps for calibrated causal analyses and bias correction.

Based on preclinical studies showing synergism with simultaneous inhibition of the estrogen receptor (ER), CDK4/6 and PI3K pathways and based on window of opportunity studies showing that metformin suppresses PI3K/mTOR signaling in endometrial cancer (EC), we conduct a non-randomized phase 2 study of letrozole/abemaciclib/metformin in ER positive endometrioid EC (NCT03675893). Primary objectives include objective response rate (ORR) and rate of progression-free survival (PFS) at 6 months (PFS6) while secondary objectives include PFS, overall survival, duration of response and toxicity. Twenty-five patients initiate protocol therapy [letrozole 2.5 mg orally (PO) once a day (qd), abemaciclib 150 mg PO twice a day (bid) and metformin 500 mg PO qd]. ORR is 32% (3 complete and 5 partial responses, 95% CI 14.9%-53.5%), Kaplan Meier estimate of PFS6 is 69.8% (95% CI 46.9%-84.3%) and median PFS is 19.4 months (95% CI 5.7 months–not estimable). No patients discontinue therapy because of toxicity. There are no objective responses among TP53 mutated ECs and among NSMP (no specific molecular profile) tumors with RB1 or CCNE1 alterations; CTNNB1 mutations correlate with clinical benefit. Pharmacokinetic analyses demonstrate that administration of letrozole and abemaciclib with metformin result in a more than 3-fold increase in metformin exposure. Combined hormonal therapy and CDK4/6 inhibition face resistance challenges in endometrial cancer. Here, the authors present a phase 2, one-arm clinical trial, where metformin is combined with letrozole (hormonal therapy) and abemaciclib (a CDK4/6 inhibitor) reporting safety and efficacy in patients with endometrial cancer.

Background Depression rates surge during adolescence. Early identification of youth at increased risk for depression is crucial for timely intervention and, ideally, prevention. This study aims to improve the prediction of future depressive symptoms in adolescents by using a multimodal approach that integrates relevant clinical, demographic, behavioral, and neural characteristics. Methods 103 adolescents (ages 12–18; 72.8% female) underwent a baseline assessment including self-report questionnaires, ecological momentary assessment, a clinical interview, and behavioral and neural measures of reward responsiveness. We used nested cross-validation to compare machine learning approaches as well as conventional linear regression in predicting depressive symptoms (Center for Epidemiological Studies Depression Scale [CES-D] and the Mood and Feelings Questionnaire [MFQ]) at a 3-month follow-up. Results For the prediction of CES-D depression scores, the best performing model was a multivariable linear regression using as predictors five principal component scores from a principal component analysis of baseline variables (RMSE = 6.501, R 2  = 0.688). For the MFQ, the best performing model was a univariable linear regression with baseline MFQ scores as the sole predictor (RMSE = 8.054, R 2  = 0.671). A factor analysis revealed that items assessing melancholic features were most predictive of future depressive symptoms. Conclusion More complex machine learning approaches did not outperform regression in predicting future depressive symptoms. The integration of relevant multimodal predictors reveals which adolescent characteristics (e.g., melancholic features and physical anxiety) have a larger contribution to predicting short-term future depressive symptoms. Future studies are needed with larger sample sizes and longer follow-up periods to provide a more comprehensive test of such models. Clinical trial number Not applicable.

Recent advances in single-cell epigenomic techniques have created a growing demand for scATAC-seq analysis. One key analysis task is to determine cell type identity based on the epigenetic data. We introduce scATAnno, a python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates the reference atlases from publicly available datasets enabling accurate cell type annotation by integrating query data with reference atlases, without the use of scRNA-seq data. To enhance annotation accuracy, we have incorporated KNN-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compare and benchmark scATAnno against 7 other published approaches for cell annotation and show superior performance in multiple data sets and metrics. We showcase the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cell (PBMC), Triple Negative Breast Cancer (TNBC), and basal cell carcinoma (BCC), and demonstrate that scATAnno accurately annotates cell types across conditions. Overall, scATAnno is a useful tool for scATAC-seq reference building and cell type annotation in scATAC-seq data and can aid in the interpretation of new scATAC-seq datasets in complex biological systems.

Abstract ChIP-seq and ATAC-seq have become essential technologies used as effective methods of measuring protein-DNA interactions and chromatin accessibility. However, there is a need for a scalable and reproducible pipeline that incorporates correct normalization between samples, adjustment of copy number variations, and integration of new downstream analysis tools. Here we present CoBRA, a modularized computational workflow which quantifies ChIP and ATAC-seq peak regions and performs unsupervised and supervised analysis. CoBRA provides a comprehensive state-of-the-art ChIP and ATAC-seq analysis pipeline that is usable by scientists with limited computational experience. This enables researchers to gain rapid insight into protein-DNA interactions and chromatin accessibility through sample clustering, differential peak calling, motif enrichment, comparison of sites to a reference DB and pathway analysis. Code availability: https://bitbucket.org/cfce/cobra Competing Interest Statement The authors have declared no competing interest.

Objective To investigate the impact of COVID-19 infection on maternal and neonatal outcomes and immunity in pregnant women in China. Methods 283 pregnant women with COVID-19 were included in the prospective observational cohort study and divided into five groups based on infection stage. Antibody levels were measured in plasma, umbilical cord blood, and breast milk, and combined with clinical data and 6-month follow-up results. We measured SARS-CoV-2 antibody levels using a chemiluminescence immunoassay and analyzed the data with the Kruskal-Wallis test, χ2 test, or Fisher’s exact test. Results No significant differences were found in age, BMI, weight change during pregnancy, or the incidence of gestational hypertension, gestational diabetes, gestational hypothyroidism, intrahepatic cholestasis, transaminitis, preterm birth, small for gestational age, neonatal NICU transfers, developmental delays, and hearing damage among the five groups. The incidence of COVID-19 in infants from mothers infected at different stages of pregnancy was significantly lower than in the uninfected group ( P  < 0.05). Maternal and umbilical cord blood showed significantly higher IgG levels in the infected group compared to the uninfected group at different stages of pregnancy ( P  < 0.05). The median transplacental antibody transfer ratio across all infection groups was 1.15 (0.98–1.30), with no significant differences between them. The reinfection group had significantly higher IgA levels during pregnancy compared to other groups ( P  < 0.05). Conclusion No adverse outcomes were observed in mothers or infants at any stage of maternal SARS-CoV-2 infection. Antibodies in umbilical cord blood and breast milk may offer passive immunity to newborns for 1–3 months. Reinfection during pregnancy may extend this immunity without raising the risk of adverse outcomes.

Two-dimensional semiconductors could be used as a channel material in low-power transistors, but the deposition of high-quality, ultrathin high- κ dielectrics on such materials has proved challenging. In particular, atomic layer deposition typically leads to non-uniform nucleation and island formation, creating a porous dielectric layer that suffers from current leakage, particularly when the equivalent oxide thickness is small. Here, we report the atomic layer deposition of high- κ gate dielectrics on two-dimensional semiconductors using a monolayer molecular crystal as a seeding layer. The approach can be used to grow dielectrics with an equivalent oxide thickness of 1 nm on graphene, molybdenum disulfide (MoS 2 ) and tungsten diselenide (WSe 2 ). Compared with dielectrics created using established methods, our dielectrics exhibit a reduced roughness, density of interface states and leakage current, as well as an improved breakdown field. With the technique, we fabricate graphene radio-frequency transistors that operate at 60 GHz, and MoS 2 and WSe 2 complementary metal–oxide–semiconductor transistors with a supply voltage of 0.8 V and subthreshold swing down to 60 mV dec −1 . We also create MoS 2 transistors with a channel length of 20 nm, which exhibit an on/off ratio of over 10 7 . Using a monolayer molecular crystal as a seeding layer, hafnium oxide dielectrics with an equivalent oxide thickness of only 1 nm can be deposited on graphene, molybdenum disulfide and tungsten diselenide.

Two-dimensional (2D) semiconductors are widely recognized as attractive channel materials for low-power electronics. However, an unresolved challenge is the integration of high-quality, ultrathin high-\\k{appa} dielectrics that fully meet the roadmap requirements for low-power applications. With a dangling-bond free surface, the deposition of dielectrics by atomic layer deposition (ALD) on 2D materials is usually characterized with non-uniform nucleation and island formation, producing a highly porous dielectric layer with serious leakage particularly at the small equivalent oxide thickness (EOT) limit. Here, we report the robust ALD of highly uniform high-\\k{appa} dielectric on 2D semiconductors by using ~0.3 nm-thick exclusively monolayer molecular crystal as seeding layer. Ultrathin dielectrics down to 1 nm EOT is realized on graphene, MoS2 and WSe2, with considerably reduced roughness, density of interface states, leakage current and improved breakdown field compared to prior methods. Taking advantage of the reduced EOT, we demonstrate graphene RF transistors operating at 60 GHz, as well as MoS2 and WSe2 complementary metal-oxide-semiconductor (CMOS) transistors with Vdd =0.8 V and ideal subthreshold swing (SS) of 60 mV/dec, 20 nm-channel-length MoS2 transistors with on/off ratio over 10^7. These studies highlight that our dielectric integration method is generally applicable for different 2D materials, and compatible with top-down fabrication process on large-area chemical vapor deposited films.