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SCIGA provides a complete and quick analysis for 10X single-cell V(D)J sequencing datasets. It can help researchers to interpret B-cell immunoglobulin repertoires with paired heavy and light chain.

SCIGA provides a complete and quick analysis for 10X single-cell V(D)J sequencing datasets. It can help researchers to interpret B-cell immunoglobulin repertoires with paired heavy and light chain.Machine learning brings the hope of finding new biomarkers extracted from cohorts with rich biomedical measurements. A good biomarker is one that gives reliable detection of the corresponding condition. However, biomarkers are often extracted from a cohort that differs from the target population. Such a mismatch, known as a dataset shift, can undermine the application of the biomarker to new individuals. Dataset shifts are frequent in biomedical research, e.g.,  because of recruitment biases. When a dataset shift occurs, standard machine-learning techniques do not suffice to extract and validate biomarkers. This article provides an overview of when and how dataset shifts break machine-learning-extracted biomarkers, as well as detection and correction strategies.Deep neural networks are frequently employed to predict survival conditional on omics-type biomarkers, e.g., by employing the partial likelihood of Cox proportional hazards model as loss function. Due to the generally limited number of observations in clinical studies, combining different data sets has been proposed to improve learning of network parameters. However, if baseline hazards differ between the studies, the assumptions of Cox proportional hazards model are violated. Based on high dimensional transcriptome profiles from different tumor entities, we demonstrate how using a stratified partial likelihood as loss function allows for accounting for the different baseline hazards in a deep learning framework. Additionally, we compare the partial likelihood with the ranking loss, which is frequently employed as loss function in machine learning approaches due to its seemingly simplicity. Using RNA-seq data from the Cancer Genome Atlas (TCGA) we show that use of stratified loss functions leads to an overall better discriminatory power and lower prediction error compared to their non-stratified counterparts. We investigate which genes are identified to have the greatest marginal impact on prediction of survival when using different loss functions. We find that while similar genes are identified, in particular known prognostic genes receive higher importance from stratified loss functions. Taken together, pooling data from different sources for improved parameter learning of deep neural networks benefits largely from employing stratified loss functions that consider potentially varying baseline hazards. For easy application, we provide PyTorch code for stratified loss functions and an explanatory Jupyter notebook in a GitHub repository.Some studies reported that genomic RNA of SARS-CoV-2 can absorb a few host miRNAs that regulate immune-related genes and then deprive their function. In this perspective, we conjecture that the absorption of the SARS-CoV-2 genome to host miRNAs is not a coincidence, which may be an indispensable approach leading to viral survival and development in host. In our study, we collected five datasets of miRNAs that were predicted to interact with the genome of SARS-CoV-2. The targets of these miRNAs in the five groups were consistently enriched immune-related pathways and virus-infectious diseases. Interestingly, the five datasets shared no one miRNA but their targets shared 168 genes. The signaling pathway enrichment of 168 shared targets implied an unbalanced immune response that the most of interleukin signaling pathways and none of the interferon signaling pathways were significantly different. Protein-protein interaction (PPI) network using the shared targets showed that PPI pairs, including IL6-IL6R, were related to the process of SARS-CoV-2 infection and pathogenesis. In addition, we found that SARS-CoV-2 absorption to host miRNA could benefit two popular mutant strains for more infectivity and pathogenicity. Conclusively, our results suggest that genomic RNA absorption to host miRNAs may be a vital approach by which SARS-CoV-2 disturbs the host immune system and infects host cells.Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism, which play an important role in a variety of biological activities. Viruses can encode circRNA, and viral circRNAs have been found in multiple single-stranded and double-stranded viruses. However, the characteristics and functions of viral circRNAs remain unknown. Sequence alignment showed that viral circRNAs are less conserved than circRNAs in animal, indicating that the viral circRNAs may evolve rapidly. Through the analysis of the sequence characteristics of viral circRNAs and circRNAs in animal, it was found that viral circRNAs and animals circRNAs are similar in nucleic acid composition, but have obvious differences in secondary structure and autocorrelation characteristics. Based on these characteristics of viral circRNAs, machine learning algorithms were employed to construct a prediction model to identify viral circRNA. Additionally, analysis of the interaction between viral circRNA and miRNAs showed that viral circRNA is expected to interact with 518 human miRNAs, and preliminary analysis of the role of viral circRNA. And it has been also found that viral circRNAs may be involved in many KEGG pathways related to nervous system and cancer. We curated an online server, and the data and code are available http//server.malab.cn/viral-CircRNA/.Acinetobacter baumannii is an insidious emerging nosocomial pathogen that has developed resistance to all available antimicrobials, including the last resort antibiotic, colistin. Colistin resistance often occurs due to mutations in the PmrAB two component regulatory system. To better understand the regulatory mechanisms contributing to colistin resistance, we have biochemically characterized the A. baumannii PmrA response regulator. Initial DNA-binding analysis shows that A. baumannii PmrA bound to the Klebsiella pneumoniae PmrA box motif. This prompted analysis of the putative A. baumannii PmrAB regulon which indicated that the A. baumannii PmrA consensus box is 5'- HTTAAD N5 HTTAAD. MEK inhibitor Additionally, we provide the first structural information for the A. baumannii PmrA N-terminal domain through X-ray crystallography, and we present a full-length model using molecular modeling. From these studies, we were able to infer the effects of two critical PmrA mutations, PmrAI13M and PmrAP102R, both of which confer increased colistin resistance. Based on these data, we suggest structural and dynamic reasons for how these mutations can affect PmrA function and hence encourage resistive traits. Understanding these mechanisms will aid in the development of new targeted antimicrobial therapies.

Discovering long noncoding RNA (lncRNA)-disease associations is a fundamental and critical part in understanding disease etiology and pathogenesis. However, only a few lncRNA-disease associations have been identified because of the time-consuming and expensive biological experiments. As a result, an efficient computational method is of great importance and urgently needed for identifying potential lncRNA-disease associations. With the ability of exploiting node features and relationships in network, graph-based learning models have been commonly utilized by these biomolecular association predictions. However, the capability of these methods in comprehensively fusing node features, heterogeneous topological structures and semantic information is distant from optimal or even satisfactory. Moreover, there are still limitations in modeling complex associations between lncRNAs and diseases.

In this paper, we develop a novel heterogeneous graph attention network framework based on meta-paths for predicting lncRimental results demonstrate the effectiveness of our proposed framework.Alzheimer's disease (AD) is a global health concern owing to its complexity, which often poses a great challenge to the development of therapeutic approaches. No single theory has yet accounted for the various risk factors leading to the pathological and clinical manifestations of dementia-type AD. Therefore, treatment options targeting various molecules involved in the pathogenesis of the disease have been unsuccessful. However, the exploration of various immunotherapeutic avenues revitalizes hope after decades of disappointment. The hallmark of a good immunotherapeutic candidate is not only to remove amyloid plaques but also to slow cognitive decline. In line with this, both active and passive immunotherapy have shown success and limitations. Recent approval of aducanumab for the treatment of AD demonstrates how close passive immunotherapy is to being successful. However, several major bottlenecks still need to be resolved. This review outlines recent successes and challenges in the pursuit of an AD vaccine.

To evaluate the comparative efficacy, safety, tolerability, and effectiveness of atypical antipsychotics (AAPs) for the treatment of dementia related psychosis (DRP) in older adults.

In this systematic literature review (SLR), we qualitatively synthesized evidence on the comparative efficacy (based on neuropsychiatric inventory), tolerability (weight gain), and safety (cerebrovascular adverse events [CVAE], cardiovascular events, mortality, somnolence, extrapyramidal symptoms [EPS]) of AAPs used to treat DRP. We also assessed effectiveness based on all-cause discontinuations and discontinuations due to lack of efficacy or adverse events (AE). Published articles from through March 2021 from PubMed, EMBASE, PsycINFO, and Cochrane databases evaluated. We included double-blind, active-comparator/placebo-controlled randomized trials, open-label trials, and observational studies.

This qualitative synthesis included 51 eligible studies with sample size of 13,334 and mean age of 79.36 years. Risperidone, olanzaer mortality. These results underscore the need for new treatments with a favorable benefit-risk profile for treating DRP.The 2020 COVID-19 pandemic has disrupted Alzheimer's disease (AD) clinical studies worldwide. Digital technologies may help minimize disruptions by enabling remote assessment of subtle cognitive and functional changes over the course of the disease. The EU/US Clinical Trials in Alzheimer's Disease (CTAD) Task Force met virtually in November 2020 to explore the opportunities and challenges associated with the use of digital technologies in AD clinical research. While recognizing the potential of digital tools to accelerate clinical trials, improve the engagement of diverse populations, capture clinically meaningful data, and lower costs, questions remain regarding the stability, validity, generalizability, and reproducibility of digital data. Substantial concerns also exist regarding regulatory acceptance and privacy. Nonetheless, the Task Force supported further exploration of digital technologies through collaboration and data sharing, noting the need for standardization of digital readouts. They also concluded that while it may be premature to employ remote assessments for trials of novel experimental medications, remote studies of non-invasive, multi-domain approaches may be feasible at this time.