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Homogenization of pigment is the key to coloring a plastic product evenly. In this article, the tensile properties of recovered carbon black merge with low molecular weight lubricants and other compounding ingredients in the form of pigment masterbatch (PM) added in a recycled low-density polyethylene (rLDPE) resin were evaluated. The prepared masterbatch with the varying amount and types of processing aids (A and B) was first compounded using the heated two-roll mill. Subsequently, the manually mixed masterbatch in rLDPE was put through an injection molding machine for the shaping process to produce an rLDPE pigment masterbatch composite (PMC). The tensile test was performed on the samples to evaluate the mechanical properties of the PMC. Meanwhile, the melt flow index test was executed to justify the composite flow characteristics. Fourier-transform infrared spectroscopy analysis and scanning electron microscopy were also carried out to analyze the PM and PMC chemical properties and their constructed surface morphology. Besides, X-ray diffraction analysis was performed to determine the changes in degree of crystallinity before and after the water absorption test. The addition of PM in rLDPE has slightly increased the rLDPE matrix tensile properties. While, the usage of more processing aid B in the PMC has turned out to secure better tensile properties compared to the addition of higher amount of processing aid A in the PMC. Interestingly, the tensile properties of all composites after the water absorption test were enhanced, suggesting that a stronger bond was formed during the immersion period.

This paper discusses the factors affecting the behaviours for coping with fake news among young people. The data were collected from a survey conducted in late 2019, which sampled 2112 secondary school students from 21 partnering schools. This study aims to understand the opinions and behaviours of teenagers towards disinformation when fake news was prevalent during the anti-extradition bill protests in Hong Kong. It finds that awareness of the problem alone had limited influence in facilitating coping strategies. Civic awareness and interaction with social media were useful predictors of internal and external coping behaviours, respectively. Confidence about one's ability to detect fake news was a crucial factor, yet a concern for the value of truth stood out as the strongest predictor of fake news coping behaviours.

Abstract Introduction The benefit and risk of aerobic exercise among older people harboring advanced cerebral small vessel disease (CSVD) upon cognition, mood, and motor functions are unknown. Methods This rater‐blind randomized trial examined effects of a 24‐week aerobic exercise training (60 min/session, twice/week) upon clinical (cognition, mood, motor functions) and hemodynamic (pulse pressure [PP], blood pressure [BP], pulsatility index) measures in older people harboring moderate to severe CSVD, as evidenced by confluent white matter hyperintensity and/or ≥2 lacunes on magnetic resonance imaging. We further investigated interactions between treatment conditions and hemodynamics measures. Results Fifty‐three and 54 subjects were randomized into the active and control group, respectively. There was no between‐group difference in any of the clinical outcomes. The active group had a greater between‐group reduction in systolic BP and PP than the control group. Within‐group comparison showed that global cognition of the active group remained similar at end of the study compared to baseline, whereas it declined significantly in the control group. We observed “diverging” interaction effects in that greater reduction in systolic BP/PP was associated with greater improvement in memory functions and global cognition but worsening in processing speed in the active group. Side effects were comparable between the two groups. Discussion Future study should investigate the mechanisms of the diverging impacts of aerobic exercise upon different cognitive domains so that the benefit–risk ratio of aerobic exercise in older people harboring more advanced CSVD can be better defined.

Abstract The ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus, represents one of the most significant global health crises in recent history. Despite extensive research into the immune mechanisms and therapeutic options for COVID-19, there remains a paucity of studies focusing on plasma cells. In this study, we utilized the DESeq2 package to identify differentially expressed genes (DEGs) between COVID-19 patients and controls using datasets GSE157103 and GSE152641. We employed the xCell algorithm to perform immune infiltration analyses, revealing notably elevated levels of plasma cells in COVID-19 patients compared to healthy individuals. Subsequently, we applied the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm to identify COVID-19 related plasma cell module genes. Further, positive cluster biomarker genes for plasma cells were extracted from single-cell RNA sequencing data (GSE171524), leading to the identification of 122 shared genes implicated in critical biological processes such as cell cycle regulation and viral infection pathways. We constructed a robust protein-protein interaction (PPI) network comprising 89 genes using Cytoscape, and identified 20 hub genes through cytoHubba. These genes were validated in external datasets (GSE152418 and GSE179627). Additionally, we identified three potential small molecules (GSK-1070916, BRD-K89997465, and idarubicin) that target key hub genes in the network, suggesting a novel therapeutic approach. These compounds were characterized by their ability to down-regulate AURKB, KIF11, and TOP2A effectively, as evidenced by their low free binding energies determined through computational analyses using cMAP and AutoDock. This study marks the first comprehensive exploration of plasma cells’ role in COVID-19, offering new insights and potential therapeutic targets. It underscores the importance of a systematic approach to understanding and treating COVID-19, expanding the current body of knowledge and providing a foundation for future research.

We have recently demonstrated that partial inhibition of the cluster of differentiation 14 (CD14) innate immunity co-receptor pathway improves the long-term performance of intracortical microelectrodes better than complete inhibition. We hypothesized that partial activation of the CD14 pathway was critical to a neuroprotective response to the injury associated with initial and sustained device implantation. Therefore, here we investigated the role of two innate immunity receptors that closely interact with CD14 in inflammatory activation. We implanted silicon planar non-recording neural probes into knockout mice lacking Toll-like receptor 2 (Tlr2−/−), knockout mice lacking Toll-like receptor 4 (Tlr4−/−), and wildtype (WT) control mice, and evaluated endpoint histology at 2 and 16 weeks after implantation. Tlr4−/− mice exhibited significantly lower BBB permeability at acute and chronic time points, but also demonstrated significantly lower neuronal survival at the chronic time point. Inhibition of the Toll-like receptor 2 (TLR2) pathway had no significant effect compared to control animals. Additionally, when investigating the maturation of the neuroinflammatory response from 2 to 16 weeks, transgenic knockout mice exhibited similar histological trends to WT controls, except that knockout mice did not exhibit changes in microglia and macrophage activation over time. Together, our results indicate that complete genetic removal of Toll-like receptor 4 (TLR4) was detrimental to the integration of intracortical neural probes, while inhibition of TLR2 had no impact within the tests performed in this study. Therefore, approaches focusing on incomplete or acute inhibition of TLR4 may still improve intracortical microelectrode integration and long term recording performance.

Abstract Proteins and nucleic-acids are essential components of living organisms that interact in critical cellular processes. Accurate prediction of nucleic acid-binding residues in proteins can contribute to a better understanding of protein function. However, the discrepancy between protein sequence information and obtained structural and functional data renders most current computational models ineffective. Therefore, it is vital to design computational models based on protein sequence information to identify nucleic acid binding sites in proteins. Here, we implement an ensemble deep learning model-based nucleic-acid-binding residues on proteins identification method, called SOFB, which characterizes protein sequences by learning the semantics of biological dynamics contexts, and then develop an ensemble deep learning-based sequence network to learn feature representation and classification by explicitly modeling dynamic semantic information. Among them, the language learning model, which is constructed from natural language to biological language, captures the underlying relationships of protein sequences, and the ensemble deep learning-based sequence network consisting of different convolutional layers together with Bi-LSTM refines various features for optimal performance. Meanwhile, to address the imbalanced issue, we adopt ensemble learning to train multiple models and then incorporate them. Our experimental results on several DNA/RNA nucleic-acid-binding residue datasets demonstrate that our proposed model outperforms other state-of-the-art methods. In addition, we conduct an interpretability analysis of the identified nucleic acid binding residue sequences based on the attention weights of the language learning model, revealing novel insights into the dynamic semantic information that supports the identified nucleic acid binding residues. SOFB is available at https://github.com/Encryptional/SOFB and https://figshare.com/articles/online_resource/SOFB_figshare_rar/25499452 .

Abstract Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (Mpro) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity.

Summary: Current studies in early cancer detection based on liquid biopsy data often rely on off-the-shelf models and face challenges with heterogeneous data, as well as manually designed data preprocessing pipelines with different parameter settings. To address those challenges, we present AutoCancer, an automated, multimodal, and interpretable transformer-based framework. This framework integrates feature selection, neural architecture search, and hyperparameter optimization into a unified optimization problem with Bayesian optimization. Comprehensive experiments demonstrate that AutoCancer achieves accurate performance in specific cancer types and pan-cancer analysis, outperforming existing methods across three cohorts. We further demonstrated the interpretability of AutoCancer by identifying key gene mutations associated with non-small cell lung cancer to pinpoint crucial factors at different stages and subtypes. The robustness of AutoCancer, coupled with its strong interpretability, underscores its potential for clinical applications in early cancer detection.

Abstract The microbiome in a specific human organ has been well-studied, but few reports have investigated the multi-organ microbiome as a whole. Here, we aim to analyse the intra-individual inter-organ and intra-organ microbiome in deceased humans. We collected 1608 samples from 53 sites of 7 surface organs (oral cavity, esophagus, stomach, small intestine, appendix, large intestine and skin; n = 33 subjects) and performed microbiome profiling, including 16S full-length sequencing. Microbial diversity varied dramatically among organs, and core microbial species co-existed in different intra-individual organs. We deciphered microbial changes across distinct intra-organ sites, and identified signature microbes, their functional traits, and interactions specific to each site. We revealed significant microbial heterogeneity between paired mucosa-lumen samples of stomach, small intestine, and large intestine. Finally, we established the landscape of inter-organ relationships of microbes along the digestive tract. Therefore, we generate a catalogue of bacterial composition, diversity, interaction, functional traits, and bacterial translocation in human at inter-organ and intra-organ levels.

Cancer is a leading cause of death worldwide. Around one-third of the total global cancer incidence and mortality are related to gastrointestinal (GI) cancers. Over the past few years, rapid developments have been made in patient-derived organoid (PDO) models for gastrointestinal cancers. By closely mimicking the molecular properties of their parent tumors in vitro, PDOs have emerged as powerful tools in personalized medicine and drug discovery. Here, we review the current literature on the application of PDOs of common gastrointestinal cancers in the optimization of drug treatment strategies in the clinic and their rising importance in pre-clinical drug development. We discuss the advantages and limitations of gastrointestinal cancer PDOs and outline the microfluidics-based strategies that improve the throughput of PDO models in order to extract the maximal benefits in the personalized medicine and drug discovery process.

Abstract Single‐cell RNA sequencing (scRNA‐seq) is a robust method for studying gene expression at the single‐cell level, but accurately quantifying genetic material is often hindered by limited mRNA capture, resulting in many missing expression values. Existing imputation methods rely on strict data assumptions, limiting their broader application, and lack reliable supervision, leading to biased signal recovery. To address these challenges, authors developed Bis, a distribution‐agnostic deep learning model for accurately recovering missing sing‐cell gene expression from multiple platforms. Bis is an optimal transport‐based autoencoder model that can capture the intricate distribution of scRNA‐seq data while addressing the characteristic sparsity by regularizing the cellular embedding space. Additionally, they propose a module using bulk RNA‐seq data to guide reconstruction and ensure expression consistency. Experimental results show Bis outperforms other models across simulated and real datasets, showcasing superiority in various downstream analyses including batch effect removal, clustering, differential expression analysis, and trajectory inference. Moreover, Bis successfully restores gene expression levels in rare cell subsets in a tumor‐matched peripheral blood dataset, revealing developmental characteristics of cytokine‐induced natural killer cells within a head and neck squamous cell carcinoma microenvironment.

Summary: Gene regulatory networks (GRNs) involve complex and multi-layer regulatory interactions between regulators and their target genes. Precise knowledge of GRNs is important in understanding cellular processes and molecular functions. Recent breakthroughs in single-cell sequencing technology made it possible to infer GRNs at single-cell level. Existing methods, however, are limited by expensive computations, and sometimes simplistic assumptions. To overcome these obstacles, we propose scGREAT, a framework to infer GRN using gene embeddings and transformer from single-cell transcriptomics. scGREAT starts by constructing gene expression and gene biotext dictionaries from scRNA-seq data and gene text information. The representation of TF gene pairs is learned through optimizing embedding space by transformer-based engine. Results illustrated scGREAT outperformed other contemporary methods on benchmarks. Besides, gene representations from scGREAT provide valuable gene regulation insights, and external validation on spatial transcriptomics illuminated the mechanism behind scGREAT annotation. Moreover, scGREAT identified several TF target regulations corroborated in studies.

Objective To explore the nuanced relationship between sleep patterns, chronotype, quality and the influence of health and lifestyle factors on cognitive performance.Design, setting, participants This cross-sectional analysis used ordinary least squares regression within the UK Biobank database, assessing 26 820 participants aged 53–86 years, categorised into two cohorts: Cohort 1 (10 067 participants, 56% female; completed all four cognitive tests of Fluid Intelligence/reasoning, Pairs Matching, Reaction Time and Prospective Memory) and Cohort 2 (16 753 participants, 56% female; completed only two cognitive assessments of Pairs Matching and Reaction Time).Exposures Participant’s self-reported sleep duration, chronotype and quality. Cognitive function was assessed through standardised computerised tests. The analysis was adjusted for demographic and comorbidity covariates.Main outcomes and measures Cognitive performance scores were evaluated against sleep parameters and health and lifestyle factors including sex, age, vascular and cardiac conditions, diabetes, alcohol intake, smoking habits and body mass index.Results The regression highlighted a positive association between normal sleep duration (7–9 hours) and cognitive scores in Cohort 1 (β=0.0567, 95% CI 0.0284 to 0.0851), while extended sleep duration negatively impacted scores across both cohorts (Cohort 1: β=−0.188, 95% CI −0.2938 to −0.0822; Cohort 2: β=−0.2619, 95% CI −0.3755 to −0.1482). Chronotype distinctions, particularly intermediate and evening types, were linked to superior cognitive function. Gender, age, angina, high blood pressure, diabetes, alcohol intake and smoking emerged as significant cognitive influencers.Conclusions and relevance The study delineates a multifaceted and nuanced relationship between sleep variables, health and lifestyle factors in determining cognitive outcomes. These findings highlight the vital role of sleep quality on cognitive health.

Introduction: Hybrid training mode comprising in-person and teleconferencing sessions is effective and sustainable, yet no standardized principles guide its development for older people. This study aimed to develop a set of principles for hybrid-mode psychoeducation for older people from the experiences of middle-aged and older people in two folds: (1) examining the effects of hybrid-mode community psychoeducation and (2) identifying features that could enhance participants' experience. Methods: We delivered 12-hour Older Person Mental Health First Aid and 3-hour late-life depression training to adults aged 50 and older in in-person and hybrid modes. Hybrid group participants received technology-related support, including in-advance training and on-site support. All participants completed assessments on depression literacy, depression stigma, meaning in life, social support, depressive symptoms, and anxiety pre-and post-intervention and evaluated the program in open-ended questions. Results: A total of 471 in-person and 346 hybrid group participants completed the psychoeducation and post-assessment (80.4 % female, mean age = 64.73 years, SD = 7.29). Linear mixed models revealed improvements in depression literacy, depression stigma, meaning in life, social support, and anxiety (B = −1.43 to 0.13, all p

Sustaining physical activity may require incorporating activity into everyday routines. Yet, many such routines are executed habitually, so people may not recognise physical activity opportunities. ‘Script Elicitation’—a novel intervention method whereby participants detail the content and structure of their routines and are supported to plan modifications to those routines—has not yet been applied to physical activity. This mixed-methods study assessed the acceptability of Script Elicitation for increasing physical activity among office workers. Eleven UK office workers completed the one-to-one Script Elicitation procedure, describing their typical before-, during-, or after-work routines and receiving guidance on incorporating activity into those routines. One week later, they rated the acceptability of the method and completed a semi-structured interview. Physical activity was self-reported at baseline and at the one-week follow-up. Acceptability was descriptively assessed on two quantitative criteria (no clear decrease in physical activity; above-midpoint acceptability scores) and qualitatively explored via Thematic Analysis. The acceptability criteria were met, and participants reported raised awareness of routines and physical activity increases. Script Elicitation appears potentially suitable for promoting activity within everyday routines. If effectiveness is shown in a more rigorous trial, future work will need to develop script-based methods for efficient delivery at scale as a public health intervention.

Abstract Background Cancer‐associated fibroblasts (CAFs), integral to the tumour microenvironment, are pivotal in cancer progression, exhibiting either pro‐tumourigenic or anti‐tumourigenic functions. Their inherent phenotypic and functional diversity allows for the subdivision of CAFs into various subpopulations. While several classification systems have been suggested for different cancer types, a unified molecular classification of CAFs on a single‐cell pan‐cancer scale has yet to be established. Methods We employed a comprehensive single‐cell transcriptomic atlas encompassing 12 solid tumour types. Our objective was to establish a novel molecular classification and to elucidate the evolutionary trajectories of CAFs. We investigated the functional profiles of each CAF subtype using Single‐Cell Regulatory Network Inference and Clustering and single‐cell gene set enrichment analysis. The clinical relevance of these subtypes was assessed through survival curve analysis. Concurrently, we employed multiplex immunofluorescence staining on tumour tissues to determine the dynamic changes of CAF subtypes across different tumour stages. Additionally, we identified the small molecule procyanidin C1 (PCC1) as a target for matrix‐producing CAF (matCAF) using molecular docking techniques and further validated these findings through in vitro and in vivo experiments. Results In our investigation of solid tumours, we identified four molecular clusters of CAFs: progenitor CAF (proCAF), inflammatory CAF (iCAF), myofibroblastic CAF (myCAF) and matCAF, each characterised by distinct molecular traits. This classification was consistently applicable across all nine studied solid tumour types. These CAF subtypes displayed unique evolutionary pathways, functional roles and clinical relevance in various solid tumours. Notably, the matCAF subtype was associated with poorer prognoses in several cancer types. The targeting of matCAF using the identified small molecule, PCC1, demonstrated promising antitumour activity. Conclusions Collectively, the various subtypes of CAFs, particularly matCAF, are crucial in the initiation and progression of cancer. Focusing therapeutic strategies on targeting matCAF in solid tumours holds significant potential for cancer treatment.

ABSTRACT An effective vaccine for combination antiretroviral therapy (cART)-free virologic control of HIV-1 by enhancing antigen-specific effector-memory CD8+ T lymphocytes could be useful in controlling the AIDS pandemic. Previously, we demonstrated that a programmed death-1 (PD-1)-based DNA vaccine, pRhPD1-p27, was effective in inducing Gag-p27-specific effector-memory CD8+ T cells for viremia suppression in rhesus macaques infected with pathogenic SHIVSF162P3CN for 2 years. In this follow-up study, we report on two pRhPD1-p27-vaccinated and SHIVSF162P3CN-infected macaques that have remained alive to date with undetectable viremia levels and low proviral loads over 6 years in the absence of cART, achieving a state of sustained virologic control. Polyfunctional effector-memory Gag-p27-specific T cells were maintained in these macaques. Moreover, several T cell epitopes found at 6 years post-viral challenge were identical to those induced during the vaccination phase, indicating sustained vaccine-induced memory T cell responses. The viral challenge resulted in de novo Nef-specific T cell responses, which were also maintained. These Gag-p27- and Nef-specific T cell responses were stronger than those in some SHIVSF162P3CN-infected macaques that showed viremia control after experimental therapy using a tandem bispecific neutralizing antibody. Our findings indicate that the PD-1-based DNA vaccine strategy holds promise as a clinical immunotherapy for long-term HIV-1 suppression. IMPORTANCE Efficient strategies for HIV-1 cART-free virologic control are critical for ending the AIDS pandemic. The essential role of effector-memory CD8+ T cells in controlling viremia and eliminating virus-infected cells has made them a promising target for vaccine development. It has been previously reported that PD-1-based DNA vaccination was effective in inducing polyfunctional effector-memory CD8+ T cells for AIDS virus control for 2 years in rhesus monkeys. This follow-up study extends the findings and shows that a viremia-free period of over 6 years was detected in two monkeys immunized with PD-1-based DNA vaccine against pathogenic SHIVSF162P3CN infection in the absence of antiretroviral therapy. Long-term vaccine-induced memory T cell responses were detected. Our results warrant the clinical trials of PD-1-based DNA vaccines for achieving HIV-1 cART-free virologic control used either alone or in combination with other biomedical interventions.