Identifying targeted cell-free DNA methylation regions in head and neck cancer via paired methylome analysis.

Despite the critical need, progress in developing cell-free DNA (cfDNA) liquid biopsy biomarkers for the diagnosis and risk stratification of head and neck squamous cell carcinoma (HNSC) has been limited. In this study, we present a comprehensive paired-sample differential methylation region (psDMR) analysis in HNSC, aimed at identifying reliable and HNSC-specific regions for cfDNA biomarker discovery. Traditional DMR analyses often overlook paired-sample information and fail to account for the heterogeneity within HNSC tissues.

CGPA: multi-context insights from the cancer gene prognosis atlas.

Cancer transcriptomic data are used extensively to interrogate the prognostic value of targeted genes, yet basic scientists and clinicians have predominantly relied on univariable survival analysis for this purpose. This method often fails to capture the full prognostic potential and contextual relevance of the genes under study, inadvertently omitting a group of genes we term univariable missed-opportunity prognostic (UMOP) genes. Recognizing the complexity of revealing multifaceted prognostic implications, especially when extending the analysis to include various covariates and thresholds, we present the Cancer Gene Prognosis Atlas (CGPA).

GPS-Net: discovering prognostic pathway modules based on network regularized kernel learning.

The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing gene expression in tissue samples and other molecular profiles, remains largely on single-gene-based or global-gene-search approaches. Gene-centric approaches, while foundational, fail to capture the higher-order dependencies that reflect the activities of co-regulated processes, pathway alterations, and regulatory networks, all of which are crucial in determining the patient outcomes in complex diseases like cancer. Here, we introduce GPS-Net, a computational framework that fills the gap in efficiently identifying prognostic modules by incorporating the holistic pathway structures and the network of gene interactions.

SmartImpute: A Targeted Imputation Framework for Single-cell Transcriptome Data.

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity and tissue transcriptomic complexity. However, the high frequency of dropout events in scRNA-seq data complicates downstream analyses such as cell type identification and trajectory inference. Existing imputation methods address the dropout problem but face limitations such as high computational cost and risk of over-imputation.

ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional Networks.

Protein thermodynamic stability is essential to clarify the relationships among structure, function, and interaction. Therefore, developing a faster and more accurate method to predict the impact of the mutations on protein stability is helpful for protein design and understanding the phenotypic variation. Recent studies have shown that protein embedding will be particularly powerful at modeling sequence information with context dependence, such as subcellular localization, variant effect, and secondary structure prediction.

Bidirectional extracellular electron transfer pathways of Geobacter sulfurreducens biofilms: Molecular insights into extracellular polymeric substances.

The basis for bioelectrochemical technology is the capability of electroactive bacteria (EAB) to perform bidirectional extracellular electron transfer (EET) with electrodes, i.e. outward- and inward-EET.

The survival strategy of direct interspecies electron transfer-capable coculture under electron donor-limited environments.

Direct interspecies electron transfer (DIET) has been considered as an effective mechanism for interspecies electron exchange in microbial syntrophy. Understanding DIET-capable syntrophic associations under energy-limited environments is important because these conditions more closely approximate those found in natural subsurface environments than in the batch cultures in the laboratory. This study, investigated the metabolic dynamics and electron transfer mechanisms in DIET-capable syntrophic coculture of Geobacter metallireducens and Geobacter sulfurreducens under electron donor-limited condition.

Characterization of two novel Fe(III)-reducing and electrogenic bacteria, sp. nov. and sp. nov., isolated from mangrove sediment.

Three novel strains in the genus , designated A3A, C31 and C32, were isolated from mangrove sediment samples. They were facultative anaerobic, Gram-stain-negative, rod-shaped, flagellum-harbouring, oxidase- and catalase-positive, electrogenic and capable of using Fe(III) as an electron acceptor during anaerobic growth. Results of phylogenetic analysis based on 16S rRNA gene and genomic sequences revealed that the strains should be assigned to the genus .

Prediction of hot spots towards drug discovery by protein sequence embedding with 1D convolutional neural network.

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.

Prediction-oriented prognostic biomarker discovery with survival machine learning methods.

Identifying novel and reliable prognostic biomarkers for predicting patient survival outcomes is essential for deciding personalized treatment strategies for diseases such as cancer. Numerous feature selection techniques have been proposed to address the high-dimensional problem in constructing prediction models. Not only does feature selection lower the data dimension, but it also improves the prediction accuracy of the resulted models by mitigating overfitting.

Echoes in a "tree hole": A qualitative study on the service needs and responses at an antidrug helpline in China.

Introduction: China's antidrug measures have been slowly shifting from police-intervention and punitive approaches to supportive services. However, the system is still highly stigmatizing. Helpline services emerged to engage drug users, families, and friends and provide needed support as they seek rehabilitation.

Statistical and Machine Learning Methods for Discovering Prognostic Biomarkers for Survival Outcomes.

Discovering molecular biomarkers for predicting patient survival outcomes is an essential step toward improving prognosis and therapeutic decision-making in the treatment of severe diseases such as cancer. Due to the high-dimensionality nature of omics datasets, statistical methods such as the least absolute shrinkage and selection operator (Lasso) have been widely applied for cancer biomarker discovery. Due to their scalability and demonstrated prediction performance, machine learning methods such as XGBoost and neural network models have also been gaining popularity in the community recently.

Xin-Li-Fang efficacy and safety for patients with chronic heart failure: A study protocol for a randomized, double-blind, and placebo-controlled trial.

Introduction: 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.

sp. nov., a novel species isolated from mangrove sediment.

Three bacterial strains, designated as AS18, AS27 and AS39, were obtained from mangrove sediment sampled in Futian district, Shenzhen, PR China. Cells of these strains were Gram-negative rods with no flagella. They were able to grow at 10-42 °C (optimum, 37 °C), at pH 5-9 (optimum, pH 6) and in 1-11 % (w/v) NaCl (optimum, 2 %).

A subnetwork-based framework for prioritizing and evaluating prognostic gene modules from cancer transcriptome data.

Cancer prognosis prediction is critical to the clinical decision-making process. Currently, the high availability of transcriptome datasets allows us to extract the gene modules with promising prognostic values. However, the biomarker identification is greatly challenged by tumor and patient heterogeneity.

LOX-1 Regulation in Anti-atherosclerosis of Active Compounds of Herbal Medicine: Current Knowledge and the New Insight.

Lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1) have recently been identified to be closely related to the occurrence and development of atherosclerosis (AS). A growing body of evidence has suggested Chinese medicine takes unique advantages in preventing and treating AS. In this review, the related research progress of AS and LOX-1 has been summarized.

Shewanella shenzhenensis sp. nov., a novel Fe(III)-reducing bacterium with abundant possible cytochrome genes, isolated from mangrove sediment.

A facultative anaerobic bacterium, designated as A25, was isolated from a mangrove sediment sample collected in Shenzhen, China. Cells of strain A25 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.

A two-step ensemble learning for predicting protein hot spot residues from whole protein sequence.

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.

Efficient gradient boosting for prognostic biomarker discovery.

Motivation: A gradient boosting decision tree (GBDT) is a powerful ensemble machine-learning method that has the potential to accelerate biomarker discovery from high-dimensional molecular data. Recent algorithmic advances, such as extreme gradient boosting (XGB) and light gradient boosting (LGB), have rendered the GBDT training more efficient, scalable and accurate. However, these modern techniques have not yet been widely adopted in discovering biomarkers for censored survival outcomes, which are key clinical outcomes or endpoints in cancer studies.

gen. nov., sp. nov., a Fe(III)-reducing bacterium from petroleum-contaminated soil and reclassification of as comb. nov.

A strictly anaerobic bacterium, strain PLL0, was isolated from petroleum-contaminated soil sampled in Gansu Province, PR China. Cells were rods, non-motile and Gram-stain-positive. The strain grew at 25-37 °C (optimum, 30 °C) in the presence of 0-3 % (w/v) NaCl (optimum, 2 %).

Clinicopathological and genomic features of breast mucinous carcinoma.

Introduction: Mucinous carcinoma (MC) of the breast is a special histological type of breast cancer. Clinicopathological characteristics and genomic features of MC is not fully understood.

Materials And Methods: 186,497 primary breast cancer patients from SEER database diagnosed with invasive ductal carcinoma (IDC) or MC were included.