Structural alterations in butyrylated and recrystallized high-amylose maize starch and their effect on gut microbiota during in vitro fermentation.

In this study, type-3 resistant starch (RS) with enhanced thermal stability and excellent short-chain fatty acid (SCFA) production was obtained through the butyrylation and subsequent recrystallization at 4 °C of high-amylose maize starch (HAMS). We comprehensively examined and contrasted the structural attributes and in vitro human fecal fermentation behavior of butyrylated RS (BRS) with varying degrees of substitution. Fourier-transform infrared analysis validated the successful integration of carbonyl groups into the starch matrix.

Isobavachalcone: A redox antifungal agent impairs the mitochondria protein of Cryptococcus neoformans.

Isobavachalcone (IBC) is a natural small molecule with various biological activities; however, its inhibitory effects on Cryptococcus neoformans remain unclear. In our study, IBC showed a good antifungal effect. Through in vitro experiments, its minimum inhibitory concentration was 0.

Multiple aortic root pseudoaneurysms with dissecting aneurysm of the interventricular septum diagnosed by multimodal imaging.

Aortic root pseudoaneurysm is a devastating complication post aortic valve replacement with a high mortality rate. And dissecting aneurysm into the interventricular septum is a rare variant of aortic root pseudoaneurysm, which is scarcely reported. Multimodal imaging is of great value in its diagnosis and differential diagnosis.

Exploring Factors Affecting Acceptance of Fecal Microbiota Transplantation for Patients with Recurrent Urinary Tract Infections: a Descriptive Qualitative Study.

Purpose: Patients with recurrent urinary tract infections face complex management challenges. Fecal microbiota transplantation is a superior treatment for chronic infectious diseases, but limited patient knowledge affects treatment decisions. This study aims to identify factors associated with hesitancy towards fecal microbiota transplantation among patients with recurrent urinary tract infections, to help physicians and nurses in providing accurate and useful information to patients.

A novel subtype based on driver methylation-transcription in lung adenocarcinoma.

Aims: To identify driver methylation genes and a novel subtype of lung adenocarcinoma (LUAD) by multi-omics and elucidate its molecular features and clinical significance.

Methods: We collected LUAD patients from public databases, and identified driver methylation genes (DMGs) by MethSig and MethylMix algrothms. And novel driver methylation multi-omics subtypes were identified by similarity network fusion (SNF).

A bidirectional interpretable compound-protein interaction prediction framework based on cross attention.

The identification of compound-protein interactions (CPIs) plays a vital role in drug discovery. However, the huge cost and labor-intensive nature in vitro and vivo experiments make it urgent for researchers to develop novel CPI prediction methods. Despite emerging deep learning methods have achieved promising performance in CPI prediction, they also face ongoing challenges: (i) providing bidirectional interpretability from both the chemical and biological perspective for the prediction results; (ii) comprehensively evaluating model generalization performance; (iii) demonstrating the practical applicability of these models.

Impact of coexisting type 2 diabetes mellitus on the urinary microbiota of kidney stone patients.

Objectives: Type 2 diabetes mellitus (T2DM) commonly complicates kidney stone disease (KSD). Our objective is to investigate the variations in the urinary microbiota between individuals with KSD alone and those with KSD plus T2DM. This exploration could have implications for disease diagnosis and treatment strategies.

A case of minimally invasive small-incision off-pump ligation of a coronary artery fistula monitored by transesophageal echocardiography.

This case demonstrated intraoperative real-time transesophageal echocardiographic monitoring in minimally invasive small-incision Off-pump ligation of a coronary artery fistula,demonstrating the importance of esophageal echocardiography in surgical monitoring.

Self-Supervised Spatiotemporal Clustering of Vehicle Emissions With Graph Convolutional Network.

Spatiotemporal clustering of vehicle emissions, which reveals the evolution pattern of air pollution from road traffic, is a challenging representation learning task due to the lack of supervision. Some recent work building upon graph convolutional network (GCN) models the intrinsic spatiotemporal correlations among the nodes in road networks as graph representations for clustering. However, these existing methods ignore the interactions between spatial and temporal variations in vehicle emissions, resulting in incomplete descriptions and inaccurate detection of the evolution pattern of air pollution.

Melatonin targets MoIcl1 and works synergistically with fungicide isoprothiolane in rice blast control.

Melatonina natural harmless molecule-displays versatile roles in human health and crop disease control such as for rice blast. Rice blast, caused by the filamentous fungus Magnaporthe oryzae, is one devastating disease of rice. Application of fungicides is one of the major measures in the control of various crop diseases.

Can antibiotics for enteritis or for urinary tract infection disrupt the urinary microbiota in rats?

Background: To establish antibiotic preregimes and administration routes for studies on urinary microbiota.

Methods And Materials: Antibiotics for enteritis (Abx-enteritis) and UTIs (Abx-UTI) were administered via gavage and/or urinary catheterisation (UC) for 1 and/or 2 weeks. The effects of these Abx on the urinary microbiota of rats were examined via 16S rRNA sequencing and urine culture, including anaerobic and aerobic culture.

Outcomes and prognosis of non-small cell lung cancer patients who underwent curable surgery: a protocol for a real-world, retrospective, population-based and nationwide Chinese National Lung Cancer Cohort (CNLCC) study.

Introduction: Surgery is one of the main approaches for the comprehensive treatment of early and locally advanced non-small cell lung cancer (NSCLC). This study conducts a nationwide multicentre study to explore factors that could influence the outcomes of patients with I-IIIA NSCLC who underwent curable surgery in real-world scenarios.

Methods And Analysis: All patients diagnosed with NSCLC between January 2013 and December 2020 will be identified from 30 large public medical services centres in mainland China.

Therapeutic effect of demethylated hydroxylated phillygenin derivative on infection.

Modifying and transforming natural antibacterial products is a novel idea for developing new efficacious compounds. Phillygenin has an inhibitory effect on . The aim of the present study was to prepare a phillygenin derivative (PHI-Der) through demethylation and hydroxylation.

Biologically Interpretable Deep Learning To Predict Response to Immunotherapy In Advanced Melanoma Using Mutations and Copy Number Variations.

Only 30-40% of advanced melanoma patients respond effectively to immunotherapy in clinical practice, so it is necessary to accurately identify the response of patients to immunotherapy pre-clinically. Here, we develop KP-NET, a deep learning model that is sparse on KEGG pathways, and combine it with transfer- learning to accurately predict the response of advanced melanomas to immunotherapy using KEGG pathway-level information enriched from gene mutation and copy number variation data. The KP-NET demonstrates best performance with AUROC of 0.

E2EFP-MIL: End-to-end and high-generalizability weakly supervised deep convolutional network for lung cancer classification from whole slide image.

Efficient and accurate distinction of histopathological subtype of lung cancer is quite critical for the individualized treatment. So far, artificial intelligence techniques have been developed, whose performance yet remained debatable on more heterogenous data, hindering their clinical deployment. Here, we propose an end-to-end, well-generalized and data-efficient weakly supervised deep learning-based method.

HiRAND: A novel GCN semi-supervised deep learning-based framework for classification and feature selection in drug research and development.

The prediction of response to drugs before initiating therapy based on transcriptome data is a major challenge. However, identifying effective drug response label data costs time and resources. Methods available often predict poorly and fail to identify robust biomarkers due to the curse of dimensionality: high dimensionality and low sample size.

Preparation and Synergistic Effect of Biomimetic Poly(lactic acid)/Graphene Oxide Composite Scaffolds Loaded with Dual Drugs.

To promote the bone repair ability of drug-loaded scaffolds, poly(lactic acid) (PLA)/graphene oxide (GO)/Salvianolic acid B (Sal-B)/aspirin (ASA) dual drug-loaded biomimetic composite scaffolds were prepared. The results showed that the addition of these two drugs delayed the gel formation of the composite system, but a biomimetic nanofiber structure could still be obtained by extending the gel time. The addition of Sal-B increased the hydrophilicity of the scaffold, while an increase in ASA reduced the porosity.

Preparation and sustained-release properties of poly(lactic acid)/graphene oxide porous biomimetic composite scaffolds loaded with salvianolic acid B.

Biomimetic scaffolds loaded with drugs can improve the osteogenesis and neovascularisation of scaffolds. A series of PLA/GO/Sal-B drug-loaded scaffolds was prepared by thermally induced phase separation. The addition of Sal-B increased the diameter of the fibres, but the scaffold showed a porous nanofibrous structure after drug release.

A novel subtype to predict prognosis and treatment response with DNA driver methylation-transcription in ovarian cancer.

To identify a novel subtype with DNA driver methylation-transcriptomic multiomics and predict prognosis and therapy response in serous ovarian cancer (SOC). SOC cohorts with both mRNA and methylation were collected, and DNA driver methylation (DNAme) was identified with the MithSig method. A novel prognostic subtype was developed by integrating the information on DNAme and prognosis-regulated DNAme-associated mRNA by similarity network fusion.

Characterization of the Immune Cell Infiltration Landscape Uncovers Prognostic and Immunogenic Characteristics in Lung Adenocarcinoma.

The immune cell infiltration in TME has been reported to be associated with prognosis and immunotherapy efficiency of lung cancers. However, to date, the immune infiltrative landscape of lung adenocarcinoma (LUAD) has not been elucidated yet. Therefore, this study aimed to identify a new transcriptomic-based TME classification and develop a risk scoring system to predict the clinical outcomes of patients with LUAD.

Improving ovarian cancer treatment decision using a novel risk predictive tool.

Background: As a major component of the tumor tissue, the tumor microenvironment (TME) has been proven to associate with tumor progression and immunotherapy. Ovarian cancer accounts for the highest mortality rate among gynecologic malignancies. Its clinical treatment decision is highly correlated with the prognosis, underscoring the need to evaluate the prognosis and choose the proper clinical treatment through TME information.

Compound Event-Triggered Distributed MPC for Coupled Nonlinear Systems.

This article investigates the event-triggered distributed model predictive control (DMPC) for perturbed coupled nonlinear systems subject to state and control input constraints. A novel compound event-triggered DMPC strategy, including a compound triggering condition and a new constraint tightening approach, is developed. In this event-triggered strategy, two stability-related conditions are checked in a parallel manner, which relaxes the requirement of the decrease of the Lyapunov function.

Cross-talk of pyroptosis and tumor immune landscape in lung adenocarcinoma.

Background: Pyroptosis has been reported to exhibit a crucial effect on tumorigenesis, development and immune regulation in cancers. However, the potential roles of pyroptosis in the tumor immune landscape remain elusive in lung adenocarcinoma (LUAD) patients.

Methods: In this study, we curated 48 pyroptosis related genes (PRGs), used an unsupervised clustering method to determine pyroptosis patterns and comprehensively evaluated the pyroptosis patterns and tumor immune landscape of 500 LUAD patients.

The Discovery of New Drug-Target Interactions for Breast Cancer Treatment.

Drug-target interaction (DTIs) prediction plays a vital role in probing new targets for breast cancer research. Considering the multifaceted challenges associated with experimental methods identifying DTIs, the in silico prediction of such interactions merits exploration. In this study, we develop a feature-based method to infer unknown DTIs, called PsePDC-DTIs, which fuses information regarding protein sequences extracted by pseudo-position specific scoring matrix (PsePSSM), detrended cross-correlation analysis coefficient (DCCA coefficient), and an FP2 format molecular fingerprint descriptor of drug compounds.

Identification and validation of cellular senescence patterns to predict clinical outcomes and immunotherapeutic responses in lung adenocarcinoma.

Background: Aging and senescence can alter immune cell fitness and influence the efficacy of lung cancer treatments, especially immunotherapy. However, the correlations between cellular senescence and tumor microenvironment are still not clearly clarified and the value of cellular senescence-related genes in evaluating the immune infiltration and clinical outcomes of lung adenocarcinoma (LUAD) need further investigated.

Methods: We identified three cellular senescence clusters by NMF algorithm and correlated the cellular senescence clusters with the immune landscape in LUAD patients.