Core-shell nanofibers based on microalgae proteins/alginate complexes for enhancing survivability of probiotics.

In this study, a novel one-step coaxial electrospinning process is employed to fabricate shell-core structure fibers choosing Chlorella pyrenoidosa proteins (CP) as the core material. These nanofibers, serving as the wall material for probiotic encapsulation, aimed to enhance the stability and antioxidant activity of probiotics in food processing, storage, and gastrointestinal environments under sensitive conditions. Morphological analysis was used to explore the beads-on-a-string morphology and core-shell structure of the electrospun fibers. Probiotics were successfully encapsulated within the fibers (7.97 log CFU/g), exhibiting a well-oriented structure along the distributed fibers. Compared to free probiotics and uniaxial fibers loaded with probiotics, encapsulation within microalgae proteins/alginate core-shell structure nanofibers significantly enhanced the probiotic cells' tolerance to simulated gastrointestinal conditions (p < 0.05). Thermal analysis indicated that microalgae proteins/alginate core-shell structure nanofibers displayed superior thermal stability compared to uniaxial fibers. The introduction of CP resulted in a 50 % increase in the antioxidant capacity of probiotics-loaded microalgae proteins/alginate nanofibers compared to uniaxial alginate nanofibers, with minimal loss of viability (0.8 log CFU/g) after 28 days of storage at 4 °C. In summary, this dual-layer carrier holds immense potential in probiotic encapsulation and enhancing their resistance to harsh conditions.

Predicting the next sentence (not word) in large language models: What model-brain alignment tells us about discourse comprehension.

Current large language models (LLMs) rely on word prediction as their backbone pretraining task. Although word prediction is an important mechanism underlying language processing, human language comprehension occurs at multiple levels, involving the integration of words and sentences to achieve a full understanding of discourse. This study models language comprehension by using the next sentence prediction (NSP) task to investigate mechanisms of discourse-level comprehension. We show that NSP pretraining enhanced a model's alignment with brain data especially in the right hemisphere and in the multiple demand network, highlighting the contributions of nonclassical language regions to high-level language understanding. Our results also suggest that NSP can enable the model to better capture human comprehension performance and to better encode contextual information. Our study demonstrates that the inclusion of diverse learning objectives in a model leads to more human-like representations, and investigating the neurocognitive plausibility of pretraining tasks in LLMs can shed light on outstanding questions in language neuroscience.

High Performance As-Cast Organic Solar Cells Enabled by a Refined Double-Fibril Network Morphology and Improved Dielectric Constant of Active Layer.

High performance organic solar cells (OSCs) are usually realized by using post-treatment and/or additive, which can induce the formation of metastable morphology, leading to unfavorable device stability. In terms of the industrial production, the development of high efficiency as-cast OSCs is crucially important, but it remains a great challenge to obtain appropriate active layer morphology and high power conversion efficiency (PCE). Here, efficient as-cast OSCs are constructed via introducing a new polymer acceptor PY-TPT with a high dielectric constant into the D18:L8-BO blend to form a double-fibril network morphology. Besides, the incorporation of PY-TPT enables an enhanced dielectric constant and lower exciton binding energy of active layer. Therefore, efficient exciton dissociation and charge transport are realized in D18:L8-BO:PY-TPT-based device, affording a record-high PCE of 18.60% and excellent photostability in absence of post-treatment. Moreover, green solvent-processed devices, thick-film (300 nm) devices, and module (16.60 cm2) are fabricated, which show PCEs of 17.45%, 17.54%, and 13.84%, respectively. This work brings new insight into the construction of efficient as-cast devices, pushing forward the practical application of OSCs.

Hollow UiO-66-NH2 Encapsulated Pd Catalysts for Highly Selective Hydrogenation of Furfural to Furfuryl Alcohol.

The selective hydrogenation of furfural (FFA) to furfuryl alcohol (FA) is regarded as attractive transformation to achieve the sustainable synthesis of value-added chemicals from biomass resources. However, the conventional supported catalysts are significantly restricted by their narrow pore size, ununiform dispersion and easy leaching or aggregation of catalytic sites. Herein, we designed hollow UiO-66-NH2 as the support to encapsulate Pd nanoparticles (Pd@H-UiO-66-NH2) to achieve the highly active and selective conversion of FFA to FA. Benefiting from the void-confinement effect and substrate enrichment of hollow structure, as well as the surface wrinkles, the as-prepared catalyst Pd@H-UiO-66-NH2 exhibited 96.8 % conversion of FFA with satisfactory selectivity reaching up to 92.4 % at 80 °C, 0.5 MPa H2 in isopropanol solvent within 6 h. More importantly, as-prepared Pd@H-UiO-66-NH2 catalyst exhibited excellent long-term stability, as well as good universality toward a series of hydrogenation of unsaturated hydrocarbons.

The effects of visual art therapy on adults with depressive symptoms: A systematic review and meta-analysis.

Depression constitutes a pervasive global mental health concern and stands as a principal determinant of elevated suicide rates worldwide. Recent empirical investigations have showcased the significant potential of visual art therapy (VAT) in ameliorating symptoms among individuals with depression. Nevertheless, specific studies have yielded findings marked by inconclusiveness, underscoring the imperative need for further research to comprehensively establish its efficacy. This study is a systematic review and meta-analysis of extant research, to ascertain the efficacy and effect size of VAT as an intervention for adults with depressive symptoms. A comprehensive search was conducted across 10 databases. The search encompassed articles published from the inception of these databases up until October 18, 2023. Two researchers screened the literature in accordance with inclusion and exclusion criteria and performed a thorough quality assessment. The original data and the data obtained from the literature were extracted for further analysis. The statistical analysis of the data was performed using Stata 17.0 software. fifteen studies were included, encompassing a total of 932 participants. The outcomes of meta-analysis unveiled a statistically significant effect of VAT in diminishing depressive symptoms among adults (SMD = -0.73; 95% CI, -1.07 to -0.39; p < 0.001; 15 randomised controlled trials (RCTs); low-quality evidence). The subgroup analysis indicated that VAT exhibited heightened effectiveness among adults below 65 years of age, with interventions lasting ≤12 weeks demonstrating superior efficacy. Additionally, sensitivity analysis underscored the robustness and reliability of the findings. VAT appears to alleviate depressive symptoms among adults. Existing research indicates that the effectiveness of VAT is influenced by factors, such as intervention population characteristics and intervention duration. However, to comprehensively probe the efficacy of VAT, future studies should strive for larger sample sizes, multicentre collaborations, and long-term follow-ups.

Generation and characterization of monoclonal antibodies against pathologically phosphorylated TDP-43.

Inclusions containing TAR DNA binding protein 43 (TDP-43) are a pathological hallmark of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). One of the disease-specific features of TDP-43 inclusions is the aberrant phosphorylation of TDP-43 at serines 409/410 (pS409/410). Here, we developed rabbit monoclonal antibodies (mAbs) that specifically detect pS409/410-TDP-43 in multiple model systems and FTD/ALS patient samples. Specifically, we identified three mAbs (26H10, 2E9 and 23A1) from spleen B cell clones that exhibit high specificity and sensitivity to pS409/410-TDP-43 peptides in an ELISA assay. Biochemical analyses revealed that pS409/410 of recombinant TDP-43 and of exogenous 25 kDa TDP-43 C-terminal fragments in cultured HEK293T cells are detected by all three mAbs. Moreover, the mAbs detect pS409/410-positive TDP-43 inclusions in the brains of FTD/ALS patients and mouse models of TDP-43 proteinopathy by immunohistochemistry. Our findings indicate that these mAbs are a valuable resource for investigating TDP-43 pathology both in vitro and in vivo.

Mucoadhesive liposomal delivery system synergizing anti-inflammation and anti-oxidation for enhanced treatment against dry eye disease.

Dry eye disease (DED) is a common and frequent ocular surface disease worldwide, which can cause severe ocular surface discomfort and blurred vision. Inflammation and reactive oxygen species (ROS) play decisive roles in the development of DED. However, existing treatments usually focus on anti-inflammation while ignore the role of ROS in DED. Ever worse, the clinical preparations are easily cleared by nasolacrimal ducts, resulting in poor therapeutic effect. To circumvent these obstacles, here we designed a phenylboronic acid (PBA) modified liposome co-loading immunosuppressant cyclosporin A (CsA) and antioxidant crocin (Cro). The CsA/Cro PBA Lip achieved mucoadhesion through the formation of covalent bonds between PBA and the sialic acid residues on mucin, and consequently improved the retention of drugs on the ocular surface. By inhibiting ROS production and blocking NF-κB inflammatory pathway, CsA/Cro PBA Lip successfully promoted the healing of damaged corneal epithelium, eventually achieving the goal of relieving DED. CsA/Cro PBA Lip is proven a simple yet effective dual-drug delivery system, exhibiting superior antioxidant and anti-inflammatory effects both in vitro and in vivo. This approach holds great potential in the clinical treatment of DED and other related mucosal inflammations.

Debunking Mental Health Misperceptions in Short-Form Social Media Videos: An Experimental Test of Scientific Credibility Cues.

In recent years, short-form social media videos have emerged as an important source of health-related advice. In this study, we investigate whether experts or ordinary users in such videos are more effective in debunking the common misperception that talking about suicide should be avoided. We also explore a new trend on TikTok and other platforms, in which users attempt to back up their arguments by displaying scientific articles in the background of their videos. To test the effect of source type (expert vs. ordinary user) and scientific references (present or absent), we conducted a 2 × 2 between-subject plus control group experiment (n = 956). In each condition, participants were shown a TikTok video that was approximately 30 seconds long. Our findings show that in all four treatment groups, participants reduced their misperceptions on the topic. The expert was rated as being more authoritative on the topic compared to the ordinary user. However, the expert was also rated as being less credible compared to the ordinary user. The inclusion of a scientific reference did not make a difference. Thus, both experts and ordinary users may be similarly persuasive in a short-form video environment.

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review.

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.

AgeAnnoMO: a knowledgebase of multi-omics annotation for animal aging.

Aging entails gradual functional decline influenced by interconnected factors. Multiple hallmarks proposed as common and conserved underlying denominators of aging on the molecular, cellular and systemic levels across multiple species. Thus, understanding the function of aging hallmarks and their relationships across species can facilitate the translation of anti-aging drug development from model organisms to humans. Here, we built AgeAnnoMO (https://relab.xidian.edu.cn/AgeAnnoMO/#/), a knowledgebase of multi-omics annotation for animal aging. AgeAnnoMO encompasses an extensive collection of 136 datasets from eight modalities, encompassing 8596 samples from 50 representative species, making it a comprehensive resource for aging and longevity research. AgeAnnoMO characterizes multiple aging regulators across species via multi-omics data, comprehensively annotating aging-related genes, proteins, metabolites, mitochondrial genes, microbiotas and age-specific TCR and BCR sequences tied to aging hallmarks for these species and tissues. AgeAnnoMO not only facilitates a deeper and more generalizable understanding of aging mechanisms, but also provides potential insights of the specificity across tissues and species in aging process, which is important to develop the effective anti-aging interventions for diverse populations. We anticipate that AgeAnnoMO will provide a valuable resource for comprehending and integrating the conserved driving hallmarks in aging biology and identifying the targetable biomarkers for aging research.

StemDriver: a knowledgebase of gene functions for hematopoietic stem cell fate determination.

StemDriver is a comprehensive knowledgebase dedicated to the functional annotation of genes participating in the determination of hematopoietic stem cell fate, available at http://biomedbdc.wchscu.cn/StemDriver/. By utilizing single-cell RNA sequencing data, StemDriver has successfully assembled a comprehensive lineage map of hematopoiesis, capturing the entire continuum from the initial formation of hematopoietic stem cells to the fully developed mature cells. Extensive exploration and characterization were conducted on gene expression features corresponding to each lineage commitment. At the current version, StemDriver integrates data from 42 studies, encompassing a diverse range of 14 tissue types spanning from the embryonic phase to adulthood. In order to ensure uniformity and reliability, all data undergo a standardized pipeline, which includes quality data pre-processing, cell type annotation, differential gene expression analysis, identification of gene categories correlated with differentiation, analysis of highly variable genes along pseudo-time, and exploration of gene expression regulatory networks. In total, StemDriver assessed the function of 23 839 genes for human samples and 29 533 genes for mouse samples. Simultaneously, StemDriver also provided users with reference datasets and models for cell annotation. We believe that StemDriver will offer valuable assistance to research focused on cellular development and hematopoiesis.

DRMref: comprehensive reference map of drug resistance mechanisms in human cancer.

Drug resistance poses a significant challenge in cancer treatment. Despite the initial effectiveness of therapies such as chemotherapy, targeted therapy and immunotherapy, many patients eventually develop resistance. To gain deep insights into the underlying mechanisms, single-cell profiling has been performed to interrogate drug resistance at cell level. Herein, we have built the DRMref database (https://ccsm.uth.edu/DRMref/) to provide comprehensive characterization of drug resistance using single-cell data from drug treatment settings. The current version of DRMref includes 42 single-cell datasets from 30 studies, covering 382 samples, 13 major cancer types, 26 cancer subtypes, 35 treatment regimens and 42 drugs. All datasets in DRMref are browsable and searchable, with detailed annotations provided. Meanwhile, DRMref includes analyses of cellular composition, intratumoral heterogeneity, epithelial-mesenchymal transition, cell-cell interaction and differentially expressed genes in resistant cells. Notably, DRMref investigates the drug resistance mechanisms (e.g. Aberration of Drug's Therapeutic Target, Drug Inactivation by Structure Modification, etc.) in resistant cells. Additional enrichment analysis of hallmark/KEGG (Kyoto Encyclopedia of Genes and Genomes)/GO (Gene Ontology) pathways, as well as the identification of microRNA, motif and transcription factors involved in resistant cells, is provided in DRMref for user's exploration. Overall, DRMref serves as a unique single-cell-based resource for studying drug resistance, drug combination therapy and discovering novel drug targets.

The role of BATF2 deficiency in immune microenvironment rearrangement in cervical cancer - New biomarker benefiting from combination of radiotherapy and immunotherapy.

Despite the significant progress in immunotherapy for certain cancers, including cervical cancer, most patients remain unresponsive or derive limited benefits from combined radiotherapy and chemotherapy. The factors underlying treatment resistance are unknown and there are few reliable predictive biomarkers. BATF2 is a member of the basic leucine zipper transcription factor family and is involved in immune response and immune cell development. However, the role of BATF2 in the immune microenvironment of patients with cervical cancer after radiotherapy remains unclear. In this study, immunohistochemistry and multicolour immunofluorescence analyses of patient tumor samples were used to assess BATF2 expression. We found that cervical cancer patients with high BATF2 expression had higher infiltration levels of CD4+ T cells, CD8+ T cells, and macrophages within the tumor than those with low expression levels. Furthermore, BATF2 expression was positively correlated with the prognosis of patients after concurrent chemoradiotherapy. A wild-type mouse model with BATF2-knockdown U14 cell-derived subcutaneous tumors and a Batf2-/- mouse model with wild-type U14 cell-derived subcutaneous tumors were used to assess CD8+ T cell infiltration and function. As expected, the knockdown of BATF2 in the U14 cell line substantially promoted tumor growth, which was mediated by a reduction in CD8+ T cell infiltration and antitumor function in vivo. Additionally, the Batf2-/- mouse model demonstrated that host BATF2 is also involved in controlling tumor growth. Furthermore, the combination of radiotherapy and anti-PD-1 therapy showed synergistic antitumour effects. These findings collectively suggest that BATF2 may serve as a potent positive regulator of the tumor immune microenvironment of cervical cancer after radiotherapy, and has the potential to be a prognostic biomarker to guide the application of a combination of radiotherapy and immunotherapy.

Research on blasting mechanism and blasting effect of aqueous media in open pit coal mines.

Surface coal mining procedures include piercing-blasting-mining and loading-transportation-discharging, blasting link exists due to the poor blasting effect leads to low loading efficiency, blasting dust caused by environmental pollution and other problems. In this paper, from the mechanical characteristics of the water medium, we analyze in detail the transferring effect, transducing effect and bubble pulsation phenomenon of the water medium in the blasting process. The results show that when the blasting medium is water medium, the maximum principal stress is 1.53 times that of air medium; the peak energy transfer can be up to 2.73 times that of air medium. With the help of TrueGrid/LS-DYNA finite element analysis software to simulate the dynamic process of blasting, the study of the maximum principal stresses around the hole, the top of the slope, the foot of the slope on the maximum principal stress changes, the results show that the maximum principal stresses around the hole, the top of the slope, the foot of the slope unit with the increase in the water content is gradually increasing trend. Finally, combined with the actual mine production conditions for blasting field test, water-mediated blasting dust reduction rate of 75%, the use of AHP-fuzzy comprehensive evaluation method of two groups of traditional dry hole blasting and three groups of water-mediated blasting comprehensive evaluation, the results show that the water-mediated blasting scores are higher than the traditional dry hole blasting, proving that the water-mediated blasting has a certain prospect of engineering applications.

Cell-type-specific alternative polyadenylation promotes oncogenic gene expression in non-small cell lung cancer progression.

Disrupted alternative polyadenylation (APA) is frequently involved in tumorigenesis and cancer progression by regulating the gene expression of oncogenes and tumor suppressors. However, limited knowledge of tumor-type- and cell-type-specific APA events may lead to novel APA events and their functions being overlooked. Here, we compared APA events across different cell types in non-small cell lung cancer (NSCLC) and normal tissues and identified functionally related APA events in NSCLC. We found several cell-specific 3'-UTR alterations that regulate gene expression changes showed prognostic value in NSCLC. We further investigated the function of APA-mediated 3'-UTR shortening through loss of microRNA (miRNA)-binding sites, and we identified and experimentally validated several oncogene-miRNA-tumor suppressor axes. According to our analyses, we found SPARC as an APA-regulated oncogene in cancer-associated fibroblasts in NSCLC. Knockdown of SPARC attenuates lung cancer cell invasion and metastasis. Moreover, we found high SPARC expression associated with resistance to several drugs except cisplatin. NSCLC patients with high SPARC expression could benefit more compared to low-SPARC-expression patients with cisplatin treatment. Overall, our comprehensive analysis of cell-specific APA events shed light on the regulatory mechanism of cell-specific oncogenes and provided opportunities for combination of APA-regulated therapeutic target and cell-specific therapy development.

Scientific discovery in the age of artificial intelligence.

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.

Predicting transcriptional outcomes of novel multigene perturbations with GEARS.

Understanding cellular responses to genetic perturbation is central to numerous biomedical applications, from identifying genetic interactions involved in cancer to developing methods for regenerative medicine. However, the combinatorial explosion in the number of possible multigene perturbations severely limits experimental interrogation. Here, we present graph-enhanced gene activation and repression simulator (GEARS), a method that integrates deep learning with a knowledge graph of gene-gene relationships to predict transcriptional responses to both single and multigene perturbations using single-cell RNA-sequencing data from perturbational screens. GEARS is able to predict outcomes of perturbing combinations consisting of genes that were never experimentally perturbed. GEARS exhibited 40% higher precision than existing approaches in predicting four distinct genetic interaction subtypes in a combinatorial perturbation screen and identified the strongest interactions twice as well as prior approaches. Overall, GEARS can predict phenotypically distinct effects of multigene perturbations and thus guide the design of perturbational experiments.

Effects of non-pharmacological interventions on depressive symptoms and risk of major depressive disorder in adults with subthreshold depression: A systematic review and meta-analysis.

Subthreshold depression (StD) is a condition that significantly reduces the quality of life and increases the risk of developing major depressive disorder (MDD). In order to investigate the effectiveness of non-pharmacological interventions (NPIs) in preventing the onset of MDD and improving depressive symptoms in adults with StD (AStDs), we conducted a systematic search of nine databases and included a total of 15 studies. Standardized mean differences (SMDs) were calculated using random effects models. RoB2 tool and GRADEpro software were used to assess the methodological quality and evidence. Funnel plots, Egger's, and Begg's tests were used to analyze publication bias. Sensitivity, subgroup and meta-regression analyses were performed to explore potential sources of heterogeneity. The results showed that NPIs had a significant effect in preventing the onset of MDD and improving depressive symptoms. Subgroup analysis revealed that NPIs were particularly effective in general adult populations, during short-term follow-up (FU) periods, among pregnant women, and in universal prevention programs. The results were found to be robust and credible, as they were less sensitive to changes in the analysis method. Timely detection and treatment of StD is feasible and important, as it can effectively delay or prevent the onset of MDD.