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Protein hotspot residues are key sites that mediate protein-protein interactions. Accurate identification of these residues is essential for understanding the mechanism from protein to function and for designing drug targets. Current research has mostly focused on using machine learning methods to predict hot spots from known interface residues, which artificially extract the corresponding features of amino acid residues from sequence, structure, evolution, energy, and other information to train and test machine learning models. The process is cumbersome, time-consuming and laborious to some extent. This paper proposes a novel idea that develops a pre-trained protein sequence embedding model combined with a one-dimensional convolutional neural network, called Embed-1dCNN, to predict protein hotspot residues. In order to obtain large data samples, this work integrates and extracts data from the datasets of ASEdb, BID, SKEMPI and dbMPIKT to generate a new dataset, and adopts the SMOTE algorithm to expand positive samples to form the training set. The experimental results show that the method achieves an F1 score of 0.82 on the test set. Compared with other hot spot prediction methods, our model achieved better prediction performance.

The complexity of estimating multivariate GARCH models increases significantly with the increase in the number of asset series. To address this issue, we propose a general regularization framework for high-dimensional GARCH models with BEKK representations, and obtain a penalized quasi-maximum likelihood (PQML) estimator. Under some regularity conditions, we establish some theoretical properties, such as the sparsity and the consistency, of the PQML estimator for the BEKK representations. We then carry out simulation studies to show the performance of the proposed inference framework and the procedure for selecting tuning parameters. In addition, we apply the proposed framework to analyze volatility spillover and portfolio optimization problems, using daily prices of 18 U.S. stocks from January 2016 to January 2018, and show that the proposed framework outperforms some benchmark models.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited cardiac disorder characterized by sodium channel dysfunction. However, the clinical management of ARVC remains challenging. Identifying novel compounds for the treatment of ARVC is crucial for advancing drug development. In this study, we aim to identify novel compounds for treating ARVC. Machine learning (ML) models were constructed using proteins analyzed from the scRNA-seq data of ARVC rats and their corresponding protein-protein interaction (PPI) network to predict binding affinity (BA). To validate these predictions, a series of experiments in cardiac organoids were conducted, including Western blotting, ELISA, MEA, and Masson staining to assess the effects of these compounds. We first discovered and identified SCN5A as the most significantly affected sodium channel protein in ARVC. ML models predicted that Kaempferol binds to SCN5A with high affinity. experiments further confirmed that Kaempferol exerted therapeutic effects in ARVC. This study presents a novel approach for identifying potential compounds to treat ARVC. By integrating ML modeling with organoid validation, our platform provides valuable support in addressing the public health challenges posed by ARVC, with broad application prospects. Kaempferol shows promise as a lead compound for ARVC treatment.

Xin-Li-Fang (XLF), a representative Chinese patent medicine, was derived from years of clinical experience by academician Chen Keji, and is widely used to treat chronic heart failure (CHF). However, there remains a lack of high-quality evidence to support clinical decision-making. Therefore, we designed a randomized controlled trial (RCT) to evaluate the efficacy and safety of XLF for CHF. This multicenter, double-blinded RCT will be conducted in China. 300 eligible participants will be randomly assigned to either an XLF group or a control group at a 1:1 ratio. Participants in the XLF group will receive XLF granules plus routine care, while those in the control group will receive placebo granules plus routine care. The study period is 26 weeks, including a 2-week run-in period, a 12-week treatment period, and a 12-week follow-up. The primary outcome is the proportion of patients whose serum NT-proBNP decreased by more than 30%. The secondary outcomes include quality of life, the NYHA classification evaluation, 6-min walking test, TCM symptom evaluations, echocardiography parameters, and clinical events (including hospitalization for worsening heart failure, all-cause death, and other major cardiovascular events). The results of the study are expected to provide evidence of high methodological and reporting quality on the efficacy and safety of XLF for CHF. Chinese Clinical Trial Registration Center (www.chictr.org.cn). The trial was registered on 13 April 2022 (ChiCTR2200058649).

BackgroundCopy number aberrations (CNAs), involving the amplification or deletion of DNA segments, are prevalent in cancer and hold great promise as an alternative approach for predicting responses to immune checkpoint inhibitors (ICIs), complementing established biomarkers such as tumor mutation burden (TMB).MethodsWe investigated global and local CNAs in predicting patient survival outcomes, while examining the relationship between CNA status and tumor immune scores determined using gene expression data. Real-world lung cancer genomic data from the Oncology Research Information Exchange Network (ORIEN) Avatar project, a network of 18 cancer centers utilizing a common protocol (Total Cancer Care protocol; NCT03977402) to which patients provided written informed consent were utilized. We analyzed copy number profiles from a cohort of 1250 lung cancer patients, including 112 patients who underwent ICI treatments. Global CNA signatures included total copy number burden (TCB) and homologous recombination deficiency (HRD) score. Matched gene expression data were analyzed for immune states using ESTIMATE, CIBERSORTx and EcoTyper. Predictive signatures associated with survival outcomes were identified using Kaplan-Meier analysis, Cox regression, and the R package Xsurv.ResultsWithin the entire cohort, a higher TCB showed a significant association with smoking history (P<0.001) and metastatic status (P<0.001). Additionally, it was also significantly associated with poorer overall survival (P<0.001). The top prognostic CNA genes were identified in 1q21.1 (gain) and 22q11.23 (loss). In the ICI-treated cohort, higher HRD score, rather than TCB, were significantly associated with unfavorable survival (P=0.03). According to the univariable Cox model, the two most significant prognostic CNA signatures were FAM231C (1p36.13 gain) and OR4F5 (1p36.33 loss). Utilizing survival boosting (XSurv), the top prognostic aberrant genes were OR11H12 (14q11.2), SLC2A14 (12p13.31), POM121(7q11.23) and NBPF1(1p36.13). The final Xsurv-based predictive model achieved a C-index of approximately 0.8, indicating a strong predictive capability using local CNV information. Immune phenotyping analysis on matched gene expression data revealed that higher TCB or HRD score is significantly associated with the lower overall immune infiltration score as determined by ESTIMATE and suppressed immune states defined by Ecotyper.ConclusionsCollectively, our analysis underscores the potential of utilizing CNA signatures to optimize cancer prognosis and immunotherapeutic outcomes, as demonstrated in the context of lung cancer. Our data indicate that, while both TCB and HRD are closely linked with tumor immune status, only HRD demonstrated predictive value in determining ICI survival outcomes. Further analysis is needed to evaluate the clinical utility of these CNA signatures in other cancer types.AcknowledgementsWe are grateful to the participating patients and their family members as well as all research staff supporting the conduct of the Total Cancer Care protocol. Trial Registration: NCT03977402. This project is partly supported by ORIEN NOVA Team Science Award (PI: Ahmad Tarhini, MD, PhD), Moffitt Biostatistics and Bioinformatics Shared Resource, and National Institute of Health grant R01DE030493 (PI: Xuefeng Wang, PhD). Trial RegistrationTrial RegistrationNCT03977402Ethics ApprovalFor this study, ORIEN members utilized a standard protocol, Total Cancer Care (TCC®; NCT03977402), to which patients provided an IRB-approved written informed consent at their participating institutions.

A facultative anaerobic bacterium, designated as A25T, was isolated from a mangrove sediment sample collected in Shenzhen, China. Cells of strain A25T were found to be Gram-staining negative, rod-shaped, flagella-harboring, and oxidase- and catalase-positive. The isolate was able to grow at 4–40 °C (optimum 28 °C) and pH 5.0–9.0 (optimum pH 6.0), and in 0–10% NaCl concentration (w/v) (optimum 1%). Strain A25T was capable of reducing Fe(III) citrate under anaerobic conditions. The major fatty acids of this strain was C16:1ω7c/C16:1ω6c (summed feature 3), C17:1ω8c and iso-C15:0. Results of phylogenetic analyses based on 16S rRNA gene sequences indicated that strain A25T is affiliated with the genus Shewanella, showing the highest similarity to Shewanella seohaensis S7-3T (98.4% similarity). The average nucleotide identity and digital DNA-DNA hybridization values between the genomes of strain A25T and its closely related strains were ≤ 79.0% and ≤ 22.8%, respectively. Based on its phenotypic, phylogenetic properties and physiological and biochemical characteristics, strain A25T (= JCM 34900T = GDMCC 1.2731T) was designated as the type strain of a novel species of the genus Shewanella, for which the name Shewanella shenzhenensis sp. nov. was proposed.

The complexity of estimating multivariate GARCH models increases significantly with the increase in the number of asset series. To address this issue, we propose a general regularization framework for high-dimensional GARCH models with BEKK representations, and obtain a penalized quasi-maximum likelihood (PQML) estimator. Under some regularity conditions, we establish some theoretical properties, such as the sparsity and the consistency, of the PQML estimator for the BEKK representations. We then carry out simulation studies to show the performance of the proposed inference framework and the procedure for selecting tuning parameters. In addition, we apply the proposed framework to analyze volatility spillover and portfolio optimization problems, using daily prices of 18 U.S. stocks from January 2016 to January 2018, and show that the proposed framework outperforms some benchmark models.

Protein hot spot residues are functional sites in protein–protein interactions. Biological experimental methods are traditionally used to identify hot spot residues, which is laborious and time-consuming. Thus a variety of computational methods were widely used in recent years. Despite the success of computational methods in hot spot identification, most of them are impractical in reality because they can recognize hot spot residues only from known protein–protein interface residues. Therefore, identifying hot spots from whole protein sequence is a meaningful and interesting issue. However, it will bring extreme imbalance between positive and negative samples. Hot spot residues only account for about 1–2% of whole protein sequences. To address the issue, this paper proposes a two-step ensemble model for identifying hot spot residues from extremely unbalanced data set. The model is composed of 134 classifiers constructed by base KNN and SVM. Compared to the previous methods, our model yields good performance with an F1 score of 0.593 on the BID test set. Furthermore, to validate the robustness of our model, it was tested on other three independent test sets and also achieved good predictions. More importantly, the performance of our model tested on unbalanced data set is comparable with other methods tested on balanced hot spot data set.

Protein hotspot residues are key sites that mediate protein-protein interactions. Accurate identification of these residues is essential for understanding the mechanism from protein to function and for designing drug targets. Current research has mostly focused on using machine learning methods to predict hot spots from known interface residues, which artificially extract the corresponding features of amino acid residues from sequence, structure, evolution, energy, and other information to train and test machine learning models. The process is cumbersome, time-consuming and laborious to some extent. This paper proposes a novel idea that develops a pre-trained protein sequence embedding model combined with a one-dimensional convolutional neural network, called Embed-1dCNN, to predict protein hotspot residues. In order to obtain large data samples, this work integrates and extracts data from the datasets of ASEdb, BID, SKEMPI and dbMPIKT to generate a new dataset, and adopts the SMOTE algorithm to expand positive samples to form the training set. The experimental results show that the method achieves an F1 score of 0.82 on the test set. Compared with other hot spot prediction methods, our model achieved better prediction performance.

Purpose of reviewThis review sought to define the emerging roles of urinary tumor DNA (utDNA) for diagnosis, monitoring, and treatment of bladder cancer. Building from early landmark studies the focus is on recent studies, highlighting how utDNA could aid personalized care.Recent findingsRecent research underscores the potential for utDNA to be the premiere biomarker in bladder cancer due to the constant interface between urine and tumor. Many studies find utDNA to be more informative than other biomarkers in bladder cancer, especially in early stages of disease. Points of emphasis include superior sensitivity over traditional urine cytology, broad genomic and epigenetic insights, and the potential for non-invasive, real-time analysis of tumor biology.SummaryutDNA shows promise for improving all phases of bladder cancer care, paving the way for personalized treatment strategies. Building from current research, future comprehensive clinical trials will validate utDNA's clinical utility, potentially revolutionizing bladder cancer management.