scDOT: enhancing single-cell RNA-Seq data annotation and uncovering novel cell types through multi-reference integration.

The proliferation of single-cell RNA-seq data has greatly enhanced our ability to comprehend the intricate nature of diverse tissues. However, accurately annotating cell types in such data, especially when handling multiple reference datasets and identifying novel cell types, remains a significant challenge. To address these issues, we introduce Single Cell annotation based on Distance metric learning and Optimal Transport (scDOT), an innovative cell-type annotation method adept at integrating multiple reference datasets and uncovering previously unseen cell types. scDOT introduces two key innovations. First, by incorporating distance metric learning and optimal transport, it presents a novel optimization framework. This framework effectively learns the predictive power of each reference dataset for new query data and simultaneously establishes a probabilistic mapping between cells in the query data and reference-defined cell types. Secondly, scDOT develops an interpretable scoring system based on the acquired probabilistic mapping, enabling the precise identification of previously unseen cell types within the data. To rigorously assess scDOT's capabilities, we systematically evaluate its performance using two diverse collections of benchmark datasets encompassing various tissues, sequencing technologies and diverse cell types. Our experimental results consistently affirm the superior performance of scDOT in cell-type annotation and the identification of previously unseen cell types. These advancements provide researchers with a potent tool for precise cell-type annotation, ultimately enriching our understanding of complex biological tissues.

ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler.

Numerous investigations increasingly indicate the significance of microRNA (miRNA) in human diseases. Hence, unearthing associations between miRNA and diseases can contribute to precise diagnosis and efficacious remediation of medical conditions. The detection of miRNA-disease linkages via computational techniques utilizing biological information has emerged as a cost-effective and highly efficient approach. Here, we introduced a computational framework named ReHoGCNES, designed for prospective miRNA-disease association prediction (ReHoGCNES-MDA). This method constructs homogenous graph convolutional network with regular graph structure (ReHoGCN) encompassing disease similarity network, miRNA similarity network and known MDA network and then was tested on four experimental tasks. A random edge sampler strategy was utilized to expedite processes and diminish training complexity. Experimental results demonstrate that the proposed ReHoGCNES-MDA method outperforms both homogenous graph convolutional network and heterogeneous graph convolutional network with non-regular graph structure in all four tasks, which implicitly reveals steadily degree distribution of a graph does play an important role in enhancement of model performance. Besides, ReHoGCNES-MDA is superior to several machine learning algorithms and state-of-the-art methods on the MDA prediction. Furthermore, three case studies were conducted to further demonstrate the predictive ability of ReHoGCNES. Consequently, 93.3% (breast neoplasms), 90% (prostate neoplasms) and 93.3% (prostate neoplasms) of the top 30 forecasted miRNAs were validated by public databases. Hence, ReHoGCNES-MDA might serve as a dependable and beneficial model for predicting possible MDAs.

Subtype-DCC: decoupled contrastive clustering method for cancer subtype identification based on multi-omics data.

Due to the high heterogeneity and complexity of cancers, patients with different cancer subtypes often have distinct groups of genomic and clinical characteristics. Therefore, the discovery and identification of cancer subtypes are crucial to cancer diagnosis, prognosis and treatment. Recent technological advances have accelerated the increasing availability of multi-omics data for cancer subtyping. To take advantage of the complementary information from multi-omics data, it is necessary to develop computational models that can represent and integrate different layers of data into a single framework. Here, we propose a decoupled contrastive clustering method (Subtype-DCC) based on multi-omics data integration for clustering to identify cancer subtypes. The idea of contrastive learning is introduced into deep clustering based on deep neural networks to learn clustering-friendly representations. Experimental results demonstrate the superior performance of the proposed Subtype-DCC model in identifying cancer subtypes over the currently available state-of-the-art clustering methods. The strength of Subtype-DCC is also supported by the survival and clinical analysis.

SAM-DTA: a sequence-agnostic model for drug-target binding affinity prediction.

Drug-target binding affinity prediction is a fundamental task for drug discovery and has been studied for decades. Most methods follow the canonical paradigm that processes the inputs of the protein (target) and the ligand (drug) separately and then combines them together. In this study we demonstrate, surprisingly, that a model is able to achieve even superior performance without access to any protein-sequence-related information. Instead, a protein is characterized completely by the ligands that it interacts. Specifically, we treat different proteins separately, which are jointly trained in a multi-head manner, so as to learn a robust and universal representation of ligands that is generalizable across proteins. Empirical evidences show that the novel paradigm outperforms its competitive sequence-based counterpart, with the Mean Squared Error (MSE) of 0.4261 versus 0.7612 and the R-Square of 0.7984 versus 0.6570 compared with DeepAffinity. We also investigate the transfer learning scenario where unseen proteins are encountered after the initial training, and the cross-dataset evaluation for prospective studies. The results reveals the robustness of the proposed model in generalizing to unseen proteins as well as in predicting future data. Source codes and data are available at https://github.com/huzqatpku/SAM-DTA.

The FLIP-FIGNL1 complex regulates the dissociation of RAD51/DMC1 in homologous recombination and replication fork restart.

Recruitment of RAD51 and/or DMC1 recombinases to single-strand DNA is indispensable for homology search and strand invasion in homologous recombination (HR) and for protection of nascent DNA strands at stalled replication forks. Thereafter RAD51/DMC1 dissociate, actively or passively, from these joint molecules upon DNA repair or releasing from replication stress. However, the mechanism that regulates RAD51/DMC1 dissociation and its physiological importance remain elusive. Here, we show that a FLIP-FIGNL1 complex regulates RAD51 and DMC1 dissociation to promote meiotic recombination and replication fork restart in mammals. Mice lacking FLIP are embryonic lethal, while germline-specific deletion of FLIP leads to infertility in both males and females. FLIP-null meiocytes are arrested at a zygotene-like stage with massive RAD51 and DMC1 foci, which frequently co-localize with SHOC1 and TEX11. Furthermore, FLIP interacts with FIGNL1. Depletion of FLIP or FIGNL1 in cell lines destabilizes each other and impairs RAD51 dissociation. Thus, the active dissociation of RAD51/DMC1 by the FLIP-FIGNL1 complex is a crucial step required for HR and replication fork restart, and represents a conserved mechanism in somatic cells and germ cells.

scHiCStackL: a stacking ensemble learning-based method for single-cell Hi-C classification using cell embedding.

Single-cell Hi-C data are a common data source for studying the differences in the three-dimensional structure of cell chromosomes. The development of single-cell Hi-C technology makes it possible to obtain batches of single-cell Hi-C data. How to quickly and effectively discriminate cell types has become one hot research field. However, the existing computational methods to predict cell types based on Hi-C data are found to be low in accuracy. Therefore, we propose a high accuracy cell classification algorithm, called scHiCStackL, based on single-cell Hi-C data. In our work, we first improve the existing data preprocessing method for single-cell Hi-C data, which allows the generated cell embedding better to represent cells. Then, we construct a two-layer stacking ensemble model for classifying cells. Experimental results show that the cell embedding generated by our data preprocessing method increases by 0.23, 1.22, 1.46 and 1.61$\%$ comparing with the cell embedding generated by the previously published method scHiCluster, in terms of the Acc, MCC, F1 and Precision confidence intervals, respectively, on the task of classifying human cells in the ML1 and ML3 datasets. When using the two-layer stacking ensemble framework with the cell embedding, scHiCStackL improves by 13.33, 19, 19.27 and 14.5 over the scHiCluster, in terms of the Acc, ARI, NMI and F1 confidence intervals, respectively. In summary, scHiCStackL achieves superior performance in predicting cell types using the single-cell Hi-C data. The webserver and source code of scHiCStackL are freely available at http://hww.sdu.edu.cn:8002/scHiCStackL/ and https://github.com/HaoWuLab-Bioinformatics/scHiCStackL, respectively.

T4SEfinder: a bioinformatics tool for genome-scale prediction of bacterial type IV secreted effectors using pre-trained protein language model.

Bacterial type IV secretion systems (T4SSs) are versatile and membrane-spanning apparatuses, which mediate both genetic exchange and delivery of effector proteins to target eukaryotic cells. The secreted effectors (T4SEs) can affect gene expression and signal transduction of the host cells. As such, they often function as virulence factors and play an important role in bacterial pathogenesis. Nowadays, T4SE prediction tools have utilized various machine learning algorithms, but the accuracy and speed of these tools remain to be improved. In this study, we apply a sequence embedding strategy from a pre-trained language model of protein sequences (TAPE) to the classification task of T4SEs. The training dataset is mainly derived from our updated type IV secretion system database SecReT4 with newly experimentally verified T4SEs. An online web server termed T4SEfinder is developed using TAPE and a multi-layer perceptron (MLP) for T4SE prediction after a comprehensive performance comparison with several candidate models, which achieves a slightly higher level of accuracy than the existing prediction tools. It only takes about 3 minutes to make a classification for 5000 protein sequences by T4SEfinder so that the computational speed is qualified for whole genome-scale T4SEs detection in pathogenic bacteria. T4SEfinder might contribute to meet the increasing demands of re-annotating secretion systems and effector proteins in sequenced bacterial genomes. T4SEfinder is freely accessible at https://tool2-mml.sjtu.edu.cn/T4SEfinder_TAPE/.

MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism.

One of the main problems with the joint use of multiple drugs is that it may cause adverse drug interactions and side effects that damage the body. Therefore, it is important to predict potential drug interactions. However, most of the available prediction methods can only predict whether two drugs interact or not, whereas few methods can predict interaction events between two drugs. Accurately predicting interaction events of two drugs is more useful for researchers to study the mechanism of the interaction of two drugs. In the present study, we propose a novel method, MDF-SA-DDI, which predicts drug-drug interaction (DDI) events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism. MDF-SA-DDI is mainly composed of two parts: multi-source drug fusion and multi-source feature fusion. First, we combine two drugs in four different ways and input the combined drug feature representation into four different drug fusion networks (Siamese network, convolutional neural network and two auto-encoders) to obtain the latent feature vectors of the drug pairs, in which the two auto-encoders have the same structure, and their main difference is the number of neurons in the input layer of the two auto-encoders. Then, we use transformer blocks that include self-attention mechanism to perform latent feature fusion. We conducted experiments on three different tasks with two datasets. On the small dataset, the area under the precision-recall-curve (AUPR) and F1 scores of our method on task 1 reached 0.9737 and 0.8878, respectively, which were better than the state-of-the-art method. On the large dataset, the AUPR and F1 scores of our method on task 1 reached 0.9773 and 0.9117, respectively. In task 2 and task 3 of two datasets, our method also achieved the same or better performance as the state-of-the-art method. More importantly, the case studies on five DDI events are conducted and achieved satisfactory performance. The source codes and data are available at https://github.com/ShenggengLin/MDF-SA-DDI.

Cell-type annotation with accurate unseen cell-type identification using multiple references.

The recent advances in single-cell RNA sequencing (scRNA-seq) techniques have stimulated efforts to identify and characterize the cellular composition of complex tissues. With the advent of various sequencing techniques, automated cell-type annotation using a well-annotated scRNA-seq reference becomes popular. But it relies on the diversity of cell types in the reference, which may not capture all the cell types present in the query data of interest. There are generally unseen cell types in the query data of interest because most data atlases are obtained for different purposes and techniques. Identifying previously unseen cell types is essential for improving annotation accuracy and uncovering novel biological discoveries. To address this challenge, we propose mtANN (multiple-reference-based scRNA-seq data annotation), a new method to automatically annotate query data while accurately identifying unseen cell types with the aid of multiple references. Key innovations of mtANN include the integration of deep learning and ensemble learning to improve prediction accuracy, and the introduction of a new metric that considers three complementary aspects to distinguish between unseen cell types and shared cell types. Additionally, we provide a data-driven method to adaptively select a threshold for identifying previously unseen cell types. We demonstrate the advantages of mtANN over state-of-the-art methods for unseen cell-type identification and cell-type annotation on two benchmark dataset collections, as well as its predictive power on a collection of COVID-19 datasets. The source code and tutorial are available at https://github.com/Zhangxf-ccnu/mtANN.

Bioaccumulation and Male Reproductive Toxicity of the New Brominated Flame Retardant Tetrabromobisphenol A-Bis(2,3-dibromo-2-methylpropyl ether) in Comparison with Hexabromocyclododecane.

Tetrabromobisphenol A-bis(2,3-dibromo-2-methylpropyl ether) (TBBPA-DBMPE) has come into use as an alternative to hexabromocyclododecane (HBCD), but it is unclear whether TBBPA-DBMPE has less hazard than HBCD. Here, we compared the bioaccumulation and male reproductive toxicity between TBBPA-DBMPE and HBCD in mice following long-term oral exposure after birth. We found that the concentrations of TBBPA-DBMPE in livers significantly increased with time, exhibiting a bioaccumulation potency not substantially different from HBCD. Lactational exposure to 1000 µg/kg/d TBBPA-DBMPE as well as 50 µg/kg/d HBCD inhibited testis development in suckling pups, and extended exposure up to adulthood resulted in significant molecular and cellular alterations in testes, with slighter effects of 50 µg/kg/d TBBPA-DBMPE. When exposure was extended to 8 month age, severe reproductive impairments including reduced sperm count, increased abnormal sperm, and subfertility occurred in all treated animals, although 50 µg/kg/d TBBPA-DBMPE exerted lower effects than 50 µg/kg/d HBCD. Altogether, all data led us to conclude that TBBPA-DBMPE exerted weaker male reproductive toxicity than HBCD at the same doses but exhibited bioaccumulation potential roughly equivalent to HBCD. Our study fills the data gap regarding the bioaccumulation and toxicity of TBBPA-DBMPE and raises concerns about its use as an alternative to HBCD.

Nitrogen and Sulfur co-doped Carbon dots as an "on-off-on" Fluorescent Sensor for the Detection of Hg2+ and Ampicillin.

Herein, a fluorescent "on-off-on" nanosensor based on N,S-CDs was developed for highly precise and sensitive recognition of Hg2+ and ampicillin (AMP). Nitrogen and sulfur co-doped carbon dots with blue fluorescence were synthesized by one-pot hydrothermal method using ammonium citrate and DL-methionine as precursors. N,S-CDs exhibited a surface abundant in -OH, -COOH, and -NH2 groups, aiding in creating non-fluorescent ground state complexes when combined with Hg2+, leading to the suppression of N,S-CDs' fluorescence. Subsequent to additional AMP application, the mixed system's fluorescence was restored. Based on this N,S-CDs sensing system, the thresholds for detection for AMP and Hg2+ were discovered to be 0.121 µM and 0.493 µM, respectively. Furthermore, this methodology proved effective in identifying AMP in real samples of tap and lake water, yielding satisfactory results. Consequently, in the area of bioanalysis in intricate environmental sample work, the sensing system showed tremendous promise.

Non-enzymatic Detection of Uric Acid in Serum and Urine by Fluorescent and Visual Dual-Mode Sensor Based on 3-aminophenylboric Acid Functionalized Carbon Dots.

Herein, we developed a sophisticated dual-mode sensor that utilized 3-aminophenylboric acid functionalized carbon dots (APBA-CDs) to accurately detect uric acid (UA). Our innovative process involved synthesizing APBA-CDs that emitted at 369 nm using a one-step hydrothermal method with 3-aminophenylboric acid and L-glutamine as precursors, ethanol and deionized water as solvents. Once UA was introduced to the APBA-CDs, the fluorescence of the system became visibly quenched. The results of Zeta potential, Fourier transformed infrared (FTIR) spectra, fluorescence lifetime, and other characteristics were analyzed to determine that the reaction mechanism was static quenching. This meant that after UA was mixed with APBA-CDs, it combined with the boric acid function on the surface to form complexes, resulting in a decrease in fluorescence intensity and a blue shift in the absorption peak at about 295 nm in the Ultraviolet-visible (UV-vis) absorption spectra. We were pleased to report that we have successfully used the dual-reading platform to accurately detect UA in serum and human urine. It provided a superior quantitative and visual analysis of UA without the involvement of enzymes. We firmly believe that our innovative dual-mode sensor has immense potential in the fields of biosensing and health monitoring.

MATT-DDI: Predicting multi-type drug-drug interactions via heterogeneous attention mechanisms.

The joint use of multiple drugs can result in adverse drug-drug interactions (DDIs) and side effects that harm the body. Accurate identification of DDIs is crucial for avoiding accidental drug side effects and understanding potential mechanisms underlying DDIs. Several computational methods have been proposed for multi-type DDI prediction, but most rely on the similarity profiles of drugs as the drug feature vectors, which may result in information leakage and overoptimistic performance when predicting interactions between new drugs. To address this issue, we propose a novel method, MATT-DDI, for predicting multi-type DDIs based on the original feature vectors of drugs and multiple attention mechanisms. MATT-DDI consists of three main modules: the top k most similar drug pair selection module, heterogeneous attention mechanism module and multi­type DDI prediction module. Firstly, based on the feature vector of the input drug pair (IDP), k drug pairs that are most similar to the input drug pair from the training dataset are selected according to cosine similarity between drug pairs. Then, the vectors of k selected drug pairs are averaged to obtain a new drug pair (NDP). Next, IDP and NDP are fed into heterogeneous attention modules, including scaled dot product attention and bilinear attention, to extract latent feature vectors. Finally, these latent feature vectors are taken as input of the classification module to predict DDI types. We evaluated MATT-DDI on three different tasks. The experimental results show that MATT-DDI provides better or comparable performance compared to several state-of-the-art methods, and its feasibility is supported by case studies. MATT-DDI is a robust model for predicting multi-type DDIs with excellent performance and no information leakage.

Lipid alterations play a role in the integration of PD-1/PD-L1 inhibitors and anlotinib for the treatment of advanced non-small-cell lung cancer.

BACKGROUND: Studies have shown that integrating anlotinib with programmed death 1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors enhances survival rates among progressive non-small-cell lung cancer (NSCLC) patients lacking driver mutations. However, not all individuals experience clinical benefits from this therapy. As a result, it is critical to investigate the factors that contribute to the inconsistent response of patients. Recent investigations have emphasized the importance of lipid metabolic reprogramming in the development and progression of NSCLC. METHODS: The objective of this investigation was to examine the correlation between lipid variations and observed treatment outcomes in advanced NSCLC patients who were administered PD-1/PD-L1 inhibitors alongside anlotinib. A cohort composed of 30 individuals diagnosed with advanced NSCLC without any driver mutations was divided into three distinct groups based on the clinical response to the combination treatment, namely, a group exhibiting partial responses, a group manifesting progressive disease, and a group demonstrating stable disease. The lipid composition of patients in these groups was assessed both before and after treatment. RESULTS: Significant differences in lipid composition among the three groups were observed. Further analysis revealed 19 differential lipids, including 2 phosphatidylglycerols and 17 phosphoinositides. CONCLUSION: This preliminary study aimed to explore the specific impact of anlotinib in combination with PD-1/PD-L1 inhibitors on lipid metabolism in patients with advanced NSCLC. By investigating the effects of using both anlotinib and PD-1/PD-L1 inhibitors, this study enhances our understanding of lipid metabolism in lung cancer treatment. The findings from this research provide valuable insights into potential therapeutic approaches and the identification of new therapeutic biomarkers.

Association between screen time and physical activity on mental health among preschoolers: a cross-sectional study from Southwest China.

BACKGROUND: Screen time and physical activity behaviors undergo development during early childhood and impact mental health. However, there is limited knowledge regarding the associations between physical activity, screen time, and mental health problems (MHP) in preschoolers. This study examines these associations using a large sample size and brief measures. METHODS: A multistage cluster stratified sampling method was used to conduct an observational cross-sectional study of 19,015 Chinese preschoolers in 2020. Information on physical activity, and screen time was collected by a self-administered questionnaire; MHP was assessed by the parent-reported Strengths and Difficulties Questionnaire (SDQ). Logistic regression models were used to obtain the odds ratios (ORs) and 95% confidence intervals (95% CIs) of preschoolers' MHP associated with screen time, total physical activities, moderate to vigorous physical activity (MVPA), and outdoor physical activities. RESULTS: A total of 19,015 participants from the 19,548 recruited population were included in the analyses (missing rate: 2.73%), 52.60% were boys. 64.01%, 57.96%, 35.98%, and 82.64% of preschoolers were reported to meet total physical activities, MVPA, and outdoor activities with screen time recommendations level. The results of multivariable-adjusted ORs (95% CIs) of preschoolers' MHP for comparisons of different levels of screen time (< 2 h/day, 2-4 h/day,≥4 h/day) show that screen time positively associated with MHP after adjusting for confounders (P < 0.05), but the association was not significant among girls with screen time ≥ 4 h/day. In addition, increased engagement in physical activity was reversely linked to MHP (P < 0.05). A stronger association between MHP and MVPA was observed in boys, however, this association was weakened when the total time spent engaging in MVPA exceeded two hours per day (P < 0.05). CONCLUSION: Less physical activity and more screen time positively relate to MHP, but the relationship differs by type of physical activity, total time, and gender. These findings provide novel insights and evidence supporting for guidelines on physical activity, screen time, and improvement of mental health for preschoolers.

Bioactivities and Action Mechanisms of Ellipticine Derivatives Reported Prior to 2023.

Currently, natural products are one of the priceless options for finding novel chemical pharmaceutical entities. Ellipticine is a naturally occurring alkaloid isolated from the leaves of Ochrosia elliptica Labill. Ellipticine and its derivatives are characterized by multiple biological activities. The purpose of this review was to provide a critical and systematic assessment of ellipticine and its derivatives as bioactive molecules over the last 60 years. Publications focused mainly on the total synthesis of alkaloids of this type without any evaluation of bioactivity have been excluded. We have reviewed papers dealing with the synthesis, bioactivity evaluation and mechanism of action of ellipticine and its derivatives. It was found that ellipticine and its derivatives showed cytotoxicity, antimicrobial ability, and anti-inflammatory activity, among which cytotoxicity toward cancer cell lines was the most investigated aspect. The inhibition of DNA topoisomerase II was the most relevant mechanism for cytotoxicity. The PI3K/AKT pathway, p53 pathway, and MAPK pathway were also closely related to the antiproliferative ability of these compounds. In addition, the structure-activity relationship was deduced, and future prospects were outlined. We are confident that these findings will lay a scientific foundation for ellipticine-based drug development, especially for anticancer agents.

Genome-Wide Analysis of the Oat (Avena sativa) HSP90 Gene Family Reveals Its Identification, Evolution, and Response to Abiotic Stress.

Oats (Avena sativa) are an important cereal crop and cool-season forage worldwide. Heat shock protein 90 (HSP90) is a protein ubiquitously expressed in response to heat stress in almost all plants. To date, the HSP90 gene family has not been comprehensively reported in oats. Herein, we have identified twenty HSP90 genes in oats and elucidated their evolutionary pathways and responses to five abiotic stresses. The gene structure and motif analyses demonstrated consistency across the phylogenetic tree branches, and the groups exhibited relative structural conservation. Additionally, we identified ten pairs of segmentally duplicated genes in oats. Interspecies synteny analysis and orthologous gene identification indicated that oats share a significant number of orthologous genes with their ancestral species; this implies that the expansion of the oat HSP90 gene family may have occurred through oat polyploidization and large fragment duplication. The analysis of cis-acting elements revealed their influential role in the expression pattern of HSP90 genes under abiotic stresses. Analysis of oat gene expression under high-temperature, salt, cadmium (Cd), polyethylene glycol (PEG), and abscisic acid (ABA) stresses demonstrated that most AsHSP90 genes were significantly up-regulated by heat stress, particularly AsHSP90-7, AsHSP90-8, and AsHSP90-9. This study offers new insights into the amplification and evolutionary processes of the AsHSP90 protein, as well as its potential role in response to abiotic stresses. Furthermore, it lays the groundwork for understanding oat adaptation to abiotic stress, contributing to research and applications in plant breeding.

Effect of white jade snail secretion on antioxidant capacity and intestinal microbial diversity in mouse model of acute gastric ulcer.

BACKGROUND: In the present work, acute gastric ulcer models were constructed by administering hydrochloric acid/ethanol. The mice ingested white jade snail secretion (WJSS) through gastric infusion. Ulcer areas in gastric tissue were recorded, and malondialdehyde (MDA) and superoxide dismutase (SOD) were also measured. Notably, high-throughput 16S rDNA analysis of intestinal flora and determination of amino acid composition in feces were performed to understand the effect of WJSS on model mice. RESULTS: Compared with the control group, the ulcer area in the WJSS low-, medium- and high-concentration groups declined by 28.02%, 39.57% and 77.85%, respectively. MDA content decreased by 24.71%, 49.58% and 64.25%, and SOD relative enzyme activity fell by 28.19%, 43.37% and 9.60%, respectively. The amounts of amino acids in the low-, medium- and high-concentration groups were slightly lower, and probiotic bacteria such as Bacteroidetes and Lactobacillales increased in different-concentration WJSS groups. Adding WJSS contributes to the establishment of beneficial intestinal flora and the absorption of amino acids. CONCLUSION: Our results showed that WJSS has a beneficial effect on inhibiting hydrochloric acid-ethanolic gastric ulcers, suggesting that WJSS has excellent potential as a novel anti-ulcer agent. Combined with ulcer area, MDA content, SOD content, gut probiotics and other indicators, a high concentration of WJSS had the best protective effect on acute gastric ulcer. © 2023 Society of Chemical Industry.

Sculpting Mesoscopic Helical Chirality into Covalent Organic Framework Nanotubes from Entirely Achiral Building Blocks.

The diversification of chirality in covalent organic frameworks (COFs) holds immense promise for expanding their properties and functionality. Herein, we introduce an innovative approach for imparting helical chirality to COFs and fabricating a family of chiral COF nanotubes with mesoscopic helicity from entirely achiral building blocks for the first time. We present an effective 2,3-diaminopyridine-mediated supramolecular templating method, which facilitates the prefabrication of helical imine-linked polymer nanotubes using unprecedented achiral symmetric monomers. Through meticulous optimization of crystallization conditions, these helical polymer nanotubes are adeptly converted into imine-linked COF nanotubes boasting impressive surface areas, while well preserving their helical morphology and chiroptical properties. Furthermore, these helical imine-linked polymers or COFs could be subtly transformed into corresponding more stable and functional helical ß-ketoenamine-linked and hydrazone-linked COF nanotubes with transferred circular dichroism via monomer exchange. Notably, despite the involvement of covalent bonding breakage and reorganization, these exchange processes overcome thermodynamic disadvantages, allowing mesoscopic helical chirality to be perfectly preserved. This research highlights the potential of mesoscopic helicity in conferring COFs with favourable chiral properties, providing novel insights into the development of multifunctional COFs in the field of chiral materials chemistry.

Exploring microproteins from various model organisms using the mip-mining database.

Microproteins, prevalent across all kingdoms of life, play a crucial role in cell physiology and human health. Although global gene transcription is widely explored and abundantly available, our understanding of microprotein functions using transcriptome data is still limited. To mitigate this problem, we present a database, Mip-mining ( https://weilab.sjtu.edu.cn/mipmining/ ), underpinned by high-quality RNA-sequencing data exclusively aimed at analyzing microprotein functions. The Mip-mining hosts 336 sets of high-quality transcriptome data from 8626 samples and nine representative living organisms, including microorganisms, plants, animals, and humans, in our Mip-mining database. Our database specifically provides a focus on a range of diseases and environmental stress conditions, taking into account chemical, physical, biological, and diseases-related stresses. Comparatively, our platform enables customized analysis by inputting desired data sets with self-determined cutoff values. The practicality of Mip-mining is demonstrated by identifying essential microproteins in different species and revealing the importance of ATP15 in the acetic acid stress tolerance of budding yeast. We believe that Mip-mining will facilitate a greater understanding and application of microproteins in biotechnology. Moreover, it will be beneficial for designing therapeutic strategies under various biological conditions.