GMFGRN: a matrix factorization and graph neural network approach for gene regulatory network inference.

The recent advances of single-cell RNA sequencing (scRNA-seq) have enabled reliable profiling of gene expression at the single-cell level, providing opportunities for accurate inference of gene regulatory networks (GRNs) on scRNA-seq data. Most methods for inferring GRNs suffer from the inability to eliminate transitive interactions or necessitate expensive computational resources. To address these, we present a novel method, termed GMFGRN, for accurate graph neural network (GNN)-based GRN inference from scRNA-seq data. GMFGRN employs GNN for matrix factorization and learns representative embeddings for genes. For transcription factor-gene pairs, it utilizes the learned embeddings to determine whether they interact with each other. The extensive suite of benchmarking experiments encompassing eight static scRNA-seq datasets alongside several state-of-the-art methods demonstrated mean improvements of 1.9 and 2.5% over the runner-up in area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). In addition, across four time-series datasets, maximum enhancements of 2.4 and 1.3% in AUROC and AUPRC were observed in comparison to the runner-up. Moreover, GMFGRN requires significantly less training time and memory consumption, with time and memory consumed <10% compared to the second-best method. These findings underscore the substantial potential of GMFGRN in the inference of GRNs. It is publicly available at https://github.com/Lishuoyy/GMFGRN.

TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level.

Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis pipelines. However, manual cell-type annotation heavily relies on the quality of scRNA-seq data and marker genes, and therefore can be laborious and time-consuming. Furthermore, the heterogeneity of scRNA-seq datasets poses another challenge for accurate cell-type annotation, such as the batch effect induced by different scRNA-seq protocols and samples. To overcome these limitations, here we propose a novel pipeline, termed TripletCell, for cross-species, cross-protocol and cross-sample cell-type annotation. We developed a cell embedding and dimension-reduction module for the feature extraction (FE) in TripletCell, namely TripletCell-FE, to leverage the deep metric learning-based algorithm for the relationships between the reference gene expression matrix and the query cells. Our experimental studies on 21 datasets (covering nine scRNA-seq protocols, two species and three tissues) demonstrate that TripletCell outperformed state-of-the-art approaches for cell-type annotation. More importantly, regardless of protocols or species, TripletCell can deliver outstanding and robust performance in annotating different types of cells. TripletCell is freely available at https://github.com/liuyan3056/TripletCell. We believe that TripletCell is a reliable computational tool for accurately annotating various cell types using scRNA-seq data and will be instrumental in assisting the generation of novel biological hypotheses in cell biology.

Effect of Stroke Etiology on Endovascular Treatment for Acute Basilar-Artery Occlusion: A Post Hoc Analysis of the ATTENTION Randomized Trial.

BACKGROUND: Stroke etiology could influence the outcomes in patients with basilar-artery occlusion (BAO). This study aimed to evaluate the differences in efficacy and safety of best medical treatment (BMT) plus endovascular treatment (EVT) versus BMT alone in acute BAO across different stroke etiologies. METHODS: The study was a post hoc analysis of the ATTENTION trial (Trial of Endovascular Treatment of Acute Basilar-Artery Occlusion), which was a multicenter, randomized trial at 36 centers in China from February 2021 to September 2022. Patients with acute BAO were classified into 3 groups according to stroke etiology (large-artery atherosclerosis [LAA], cardioembolism, and undetermined cause/other determined cause [UC/ODC]). The primary outcome was a favorable outcome (modified Rankin Scale score of 0-3) at 90 days. Safety outcomes included symptomatic intracranial hemorrhage and 90-day mortality. RESULTS: A total of 340 patients with BAO were included, 150 (44.1%) had LAA, 72 (21.2%) had cardioembolism, and 118 (34.7%) had UC/ODC. For patients treated with BMT plus EVT and BMT alone, respectively, the rate of favorable outcome at 90 days was 49.1% and 23.8% in the LAA group (odds ratio, 3.08 [95% CI, 1.38-6.89]); 52.2% and 30.8% in the cardioembolism group (odds ratio, 2.45 [95% CI, 0.89-6.77]); and 37.5% and 17.4% in the UC/ODC group (odds ratio, 2.85 [95% CI, 1.16-7.01]), with P=0.89 for the stroke etiology×treatment interaction. The rate of symptomatic intracranial hemorrhage in EVT-treated patients with LAA, cardioembolism, and UC/ODC was 8.3%, 2.2%, and 3.2%, respectively, and none of the BMT-treated patients. Lower 90-day mortality was observed in patients with EVT compared with BMT alone across 3 etiology groups. CONCLUSIONS: Among patients with acute BAO, EVT compared with BMT alone might be associated with favorable outcomes and lower 90-day mortality, regardless of cardioembolism, LAA, or UC/ODC etiologies. The influence of stroke etiology on the benefit of EVT should be explored by further trials. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04751708.

MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network.

Accurate identification of transcription factor binding sites is of great significance in understanding gene expression, biological development and drug design. Although a variety of methods based on deep-learning models and large-scale data have been developed to predict transcription factor binding sites in DNA sequences, there is room for further improvement in prediction performance. In addition, effective interpretation of deep-learning models is greatly desirable. Here we present MAResNet, a new deep-learning method, for predicting transcription factor binding sites on 690 ChIP-seq datasets. More specifically, MAResNet combines the bottom-up and top-down attention mechanisms and a state-of-the-art feed-forward network (ResNet), which is constructed by stacking attention modules that generate attention-aware features. In particular, the multi-scale attention mechanism is utilized at the first stage to extract rich and representative sequence features. We further discuss the attention-aware features learned from different attention modules in accordance with the changes as the layers go deeper. The features learned by MAResNet are also visualized through the TMAP tool to illustrate that the method can extract the unique characteristics of transcription factor binding sites. The performance of MAResNet is extensively tested on 690 test subsets with an average AUC of 0.927, which is higher than that of the current state-of-the-art methods. Overall, this study provides a new and useful framework for the prediction of transcription factor binding sites by combining the funnel attention modules with the residual network.

A Head-to-Head Comparison of EQ-HWB and EQ-5D-5L in Patients, Carers, and General Public in China.

OBJECTIVES: This study aimed to understand the psychometric properties of EQ Health and Wellbeing (EQ-HWB) and to examine its relationship with EQ-5D-5L in a sample covering patients, carers, and general public. METHODS: A cross-sectional study was conducted in Guizhou Province, China. The acceptability, convergent validity (using Spearman correlation coefficients), internal structure (using exploratory factor analysis), and known-group validity of EQ-HWB, EQ-HWB-Short (EQ-HWB-S), and EQ-5D-5L were reported and compared. RESULTS: A total of 323 participants completed the survey, including 106 patients, 101 carers, and 116 individuals from the general public. Approximately 7.4% of participants had at least 1 missing response. In the EQ-HWB and EQ-5D-5L items related to activities, there were more level 1 responses. The correlations between EQ-HWB and EQ-5D-5L items ranged from low to high, confirming the convergent validity of similar aspects between the 2 instruments. Notably, EQ-HWB measures 2 additional factors compared with EQ-5D-5L or EQ-HWB-S, both of which share 3 common factors. When the patient group was included, EQ-5D-5L had the largest effect size, but it failed to differentiate between the groups of general public and carers. Both EQ-HWB and EQ-HWB-S demonstrated better known-group validity results when carers were included. CONCLUSIONS: EQ-HWB measures a broader quality of life construct that goes beyond health measured by EQ-5D-5L. By encompassing a broader scope, the impact of healthcare interventions may become diluted, given that other factors can influence well-being outcomes as significantly as health conditions do.

SAResNet: self-attention residual network for predicting DNA-protein binding.

Knowledge of the specificity of DNA-protein binding is crucial for understanding the mechanisms of gene expression, regulation and gene therapy. In recent years, deep-learning-based methods for predicting DNA-protein binding from sequence data have achieved significant success. Nevertheless, the current state-of-the-art computational methods have some drawbacks associated with the use of limited datasets with insufficient experimental data. To address this, we propose a novel transfer learning-based method, termed SAResNet, which combines the self-attention mechanism and residual network structure. More specifically, the attention-driven module captures the position information of the sequence, while the residual network structure guarantees that the high-level features of the binding site can be extracted. Meanwhile, the pre-training strategy used by SAResNet improves the learning ability of the network and accelerates the convergence speed of the network during transfer learning. The performance of SAResNet is extensively tested on 690 datasets from the ChIP-seq experiments with an average AUC of 92.0%, which is 4.4% higher than that of the best state-of-the-art method currently available. When tested on smaller datasets, the predictive performance is more clearly improved. Overall, we demonstrate that the superior performance of DNA-protein binding prediction on DNA sequences can be achieved by combining the attention mechanism and residual structure, and a novel pipeline is accordingly developed. The proposed methodology is generally applicable and can be used to address any other sequence classification problems.

Improving protein fold recognition using triplet network and ensemble deep learning.

Protein fold recognition is a critical step toward protein structure and function prediction, aiming at providing the most likely fold type of the query protein. In recent years, the development of deep learning (DL) technique has led to massive advances in this important field, and accordingly, the sensitivity of protein fold recognition has been dramatically improved. Most DL-based methods take an intermediate bottleneck layer as the feature representation of proteins with new fold types. However, this strategy is indirect, inefficient and conditional on the hypothesis that the bottleneck layer's representation is assumed as a good representation of proteins with new fold types. To address the above problem, in this work, we develop a new computational framework by combining triplet network and ensemble DL. We first train a DL-based model, termed FoldNet, which employs triplet loss to train the deep convolutional network. FoldNet directly optimizes the protein fold embedding itself, making the proteins with the same fold types be closer to each other than those with different fold types in the new protein embedding space. Subsequently, using the trained FoldNet, we implement a new residue-residue contact-assisted predictor, termed FoldTR, which improves protein fold recognition. Furthermore, we propose a new ensemble DL method, termed FSD_XGBoost, which combines protein fold embedding with the other two discriminative fold-specific features extracted by two DL-based methods SSAfold and DeepFR. The Top 1 sensitivity of FSD_XGBoost increases to 74.8% at the fold level, which is ~9% higher than that of the state-of-the-art method. Together, the results suggest that fold-specific features extracted by different DL methods complement with each other, and their combination can further improve fold recognition at the fold level. The implemented web server of FoldTR and benchmark datasets are publicly available at http://csbio.njust.edu.cn/bioinf/foldtr/.

Learning Cell Annotation under Multiple Reference Datasets by Multisource Domain Adaptation.

Accurate and efficient cell type annotation is essential for single-cell sequence analysis. Currently, cell type annotation using well-annotated reference datasets with powerful models has become increasingly popular. However, with the increasing amount of single-cell data, there is an urgent need to develop a novel annotation method that can integrate multiple reference datasets to improve cell type annotation performance. Since the unwanted batch effects between individual reference datasets, integrating multiple reference datasets is still an open challenge. To address this, we proposed scMDR and scMultiR, respectively, using multisource domain adaptation to learn cell type-specific information from multiple reference datasets and query cells. Based on the learned cell type-specific information, scMDR and scMultiR provide the most likely cell types for the query cells. Benchmark experiments demonstrated their state-of-the-art effectiveness for integrative single-cell assignment with multiple reference datasets.

Structural Validation and Measurement Invariance Testing of the Chinese Version of the eHealth Literacy Scale Among Undergraduates: Cross-Sectional Study.

BACKGROUND: The eHealth Literacy Scale (eHEALS) was introduced in China in 2013 as one of the most important electronic health literacy measurement instruments. After a decade of development in China, it has received widespread attention, although its theoretical underpinnings have been challenged, thus demanding more robust research evidence of factorial validity and multigroup measurement properties. OBJECTIVE: This study aimed to evaluate the Chinese version of the eHEALS in terms of its measurement properties. METHODS: A cross-sectional survey was conducted in a university setting in China. Item statistics were checked for response distributions and floor and ceiling effects. Internal consistency reliability was confirmed with Cronbach α, split-half reliability, Cronbach α if an item was deleted, and item-total correlation. A total of 5 representative eHEALS factor structures were examined and contrasted using confirmatory factor analysis. The study used the item-level content validity index (I-CVI) and the average of the I-CVI scores of all items on the scale to assess the content validity of the dominance model. Furthermore, the validated dominance model was subsequently used to evaluate the relevance and representation of elements in the instrument and to assess measurement invariance across genders. RESULTS: A total of 972 respondents were identified, with a Cronbach α of .92, split-half reliability of 0.88, and item-total score correlation coefficients ranging from 0.715 to 0.781. Cronbach α if an item was deleted showed that all items should be retained. Acceptable content validity was supported by I-CVIs ≥0.80. The confirmatory factor analysis confirmed that the 3-factor model was acceptable. The measurement model met all relevant fit indices: average variance extracted from 0.663 to 0.680, composite reliability from 0.810 to 0.857, chi-square divided by the df of 4.768, root mean square error of approximation of 0.062, standardized root mean squared residual of 0.020, comparative fit index (CFI) of 0.987, and Tucker-Lewis index of 0.979. In addition, the scale demonstrated error variance invariance (Δnormed fit index=-0.016, Δincremental fit index=-0.012, ΔTucker-Lewis index=0.005, Δcomparative fit index=-0.012, Δrelative fit index=0.005, and Δroot mean square error of approximation=0.005). CONCLUSIONS: A 3-factor model of the Chinese version of the eHEALS fits best, and our findings provide evidence for the strict measurement invariance of the instrument regarding gender.

[Meta-analysis of Simotang Oral Liquid in treatment of functional dyspepsia in adults].

This study was conducted to evaluate the efficacy and safety of Simotang Oral Liquid in the treatment of functional dyspepsia in adults. "Simotang Oral Liquid" "Simotang" "Si Mo Tang" "Si Mo Tang Oral Liquid" were used for retrieval of the relevant papers from CNKI, Wanfang, VIP, SinoMed, PubMed, Cochrane Library, Springer Link, and Web of Science from database inception to June 2021. Randomized controlled trial(RCT) of Simotang Oral Liquid in the treatment of functional dyspepsia in adults was screened out for Meta-analysis which was conducted in RevMan 5.3. A total of 16 RCTs were included. Meta-analysis showed that compared with the control group, Simotang Oral Liquid increased the total response rate and lowered the traditional Chinese medicine syndrome scores, serum cholecystokinin(CCK), serum nitric oxide(NO), and incidence of adverse reactions. However, the serum substance P(SP) had no statistical difference between the two groups. Simotang Oral Liquid is effective and safe in the treatment of functional dyspepsia in adults. However, this study has evidence and limitations, so the conclusions need to be further verified by large sample and multicenter clinical studies.

Improving the prediction of DNA-protein binding by integrating multi-scale dense convolutional network with fault-tolerant coding.

Accurate prediction of DNA-protein binding (DPB) is of great biological significance for studying the regulatory mechanism of gene expression. In recent years, with the rapid development of deep learning techniques, advanced deep neural networks have been introduced into the field and shown to significantly improve the prediction performance of DPB. However, these methods are primarily based on the DNA sequences measured by the ChIP-seq technology, failing to consider the possible partial variations of the motif sequences and errors of the sequencing technology itself. To address this, we propose a novel computational method, termed MSDenseNet, which combines a new fault-tolerant coding (FTC) scheme with the dense connectional deep neural networks. Three important factors can be attributed to the success of MSDenseNet: First, MSDenseNet utilizes a powerful feature representation approach, which transforms the raw DNA sequence into fusion coding using the fault-tolerant feature sequence; Second, in terms of network structure, MSDenseNet uses a multi-scale convolution within the dense layer and the multi-scale convolution preceding the dense block. This is shown to be able to significantly improve the network performance and accelerate the network convergence speed, and third, building upon the advanced deep neural network, MSDenseNet is capable of effectively mining the hidden complex relationship between the internal attributes of fusion sequence features to enhance the prediction of DPB. Benchmarking experiments on 690 ChIP-seq datasets show that MSDenseNet achieves an average AUC of 0.933 and outperforms the state-of-the-art method. The source code of MSDenseNet is available at https://github.com/csbio-njust-edu/msdensenet. The results show that MSDenseNet can effectively predict DPB. We anticipate that MSDenseNet will be exploited as a powerful tool to facilitate a more exhaustive understanding of DNA-binding proteins and help toward their functional characterization.

α- and ß-Ag4P2S7: Two Semiconductors with Promising Photocatalytic Hydrogen Production Based on a Density Functional Theory Study.

Herein, a new chiral compound with short Ag-Ag distances, namely, ß-Ag4P2S7 (P3121), has been discovered by a solid-state method. Density functional theory (DFT) calculations show that both α and ß phases exhibit suitable band gaps, low reduction potentials, and large visible-light absorption coefficients, as well as excellent band edges for carrier separation, suggesting their promising application in photocatalytic hydrogen production.

Physiological and transcriptional changes provide insights into the effect of root waterlogging on the aboveground part of Pterocarya stenoptera.

Pterocarya stenoptera is a tree species that occurs along rivers and has high tolerance to waterlogging. Identification of waterlogging response genes in the aboveground part of P. stenoptera will increase understanding of tolerance mechanisms under root waterlogging conditions. In this study, we employed four physiological indicators and comparative transcriptome sequencing to investigate the waterlogging tolerance mechanism in P. stenoptera. The physiological results showed that the aboveground part of P. stenoptera was not obviously affected by waterlogging. P. stenoptera enhanced waterlogging tolerance by increasing the synthesis of alpha-Linolenic acids and flavonoids and activating the jasmonic acid, ethylene, and auxin signaling pathways. Our results confirmed our hypothesis that P. stenoptera, a species that is widely distributed along rivers, has evolved a range of mechanisms in response to waterlogging. Our research will provide new insights for understanding the tolerance mechanism of species to waterlogging.

Effect of Intravenous Tirofiban vs Placebo Before Endovascular Thrombectomy on Functional Outcomes in Large Vessel Occlusion Stroke: The RESCUE BT Randomized Clinical Trial.

Importance: Tirofiban is a highly selective nonpeptide antagonist of glycoprotein IIb/IIIa receptor, which reversibly inhibits platelet aggregation. It remains uncertain whether intravenous tirofiban is effective to improve functional outcomes for patients with large vessel occlusion ischemic stroke undergoing endovascular thrombectomy. Objective: To assess the efficacy and adverse events of intravenous tirofiban before endovascular thrombectomy for acute ischemic stroke secondary to large vessel occlusion. Design, Setting, and Participants: This investigator-initiated, randomized, double-blind, placebo-controlled trial was implemented at 55 hospitals in China, enrolling 948 patients with stroke and proximal intracranial large vessel occlusion presenting within 24 hours of time last known well. Recruitment took place between October 10, 2018, and October 31, 2021, with final follow-up on January 15, 2022. Interventions: Participants received intravenous tirofiban (n = 463) or placebo (n = 485) prior to endovascular thrombectomy. Main Outcomes and Measures: The primary outcome was disability level at 90 days as measured by overall distribution of the modified Rankin Scale scores from 0 (no symptoms) to 6 (death). The primary safety outcome was the incidence of symptomatic intracranial hemorrhage within 48 hours. Results: Among 948 patients randomized (mean age, 67 years; 391 [41.2%] women), 948 (100%) completed the trial. The median (IQR) 90-day modified Rankin Scale score in the tirofiban group vs placebo group was 3 (1-4) vs 3 (1-4). The adjusted common odds ratio for a lower level of disability with tirofiban vs placebo was 1.08 (95% CI, 0.86-1.36). Incidence of symptomatic intracranial hemorrhage was 9.7% in the tirofiban group vs 6.4% in the placebo group (difference, 3.3% [95% CI, -0.2% to 6.8%]). Conclusions and Relevance: Among patients with large vessel occlusion acute ischemic stroke undergoing endovascular thrombectomy, treatment with intravenous tirofiban, compared with placebo, before endovascular therapy resulted in no significant difference in disability severity at 90 days. The findings do not support use of intravenous tirofiban before endovascular thrombectomy for acute ischemic stroke. Trial Registration: Chinese Clinical Trial Registry Identifier: ChiCTR-IOR-17014167.

Transcriptomic responses to drought stress among natural populations provide insights into local adaptation of weeping forsythia.

BACKGROUND: Understanding the genetic mechanisms of local adaptation is an important emerging topic in molecular ecology and evolutionary biology. RESULTS: Here, we identify the physiological changes and differential expression of genes among different weeping forsythia populations under drought stress in common garden experiments. Physiological results showed that HBWZ might have higher drought tolerance among four populations. RNA-seq results showed that significant differential expression in the genes responding to the synthesis of flavonoids, aromatic substances, aromatic amino acids, oxidation-reduction process, and transmembrane transport occured among four populations. By further reanalysis of results of previous studies, sequence differentiation was found in the genes related to the synthesis of aromatic substances among different weeping forsythia populations. CONCLUSIONS: Overall, our study supports the hypothesis that the dual differentiation in gene efficiency and expression increases among populations in response to heterogeneous environments and is an important evolutionary process of local adaptation. Here, we proposed a new working model of local adaptation of weeping forsythia populations under different intensities of drought stress, which provides new insights for understanding the genetic mechanisms of local adaptation for non-model species.

Cigarette smoke extract modulates Pseudomonas aeruginosa bacterial load via USP25/HDAC11 axis in lung epithelial cells.

Cigarette smoking increases susceptibility for microbial infection in respiratory system. However, the underlying molecular mechanism(s) is not fully elucidated. Here we report that cigarette smoking extract (CSE) increases bacterial load in lung epithelial cells via downregulation of the ubiquitin-specific protease 25 (USP25)/histone deacetylase 11 (HDAC11) axis. CSE treatment decreases HDAC11 at protein level in lung epithelial cells without significant changes of its transcription. Concomitantly, CSE treatment accelerates a ubiquitin-specific protease USP25 ubiquitination and degradation. Coimmunoprecipitation studies showed that USP25 associated with HDAC11. USP25 catalyzes deubiquitination of HDAC11, which regulates HDAC11 protein stability. CSE-mediated degradation of USP25 thereafter reduces HDAC11 at the protein level. Interestingly, CSE-downregulated USP25/HDAC11 axis increases the bacterial load of Pseudomonas aeruginosa in lung epithelial cells. These findings suggest that CSE-downregulated USP25 and HDAC11 may contribute to high susceptibility of bacterial infection in the cigarette smoking population.

ZCCHC13-mediated induction of human liver cancer is associated with the modulation of DNA methylation and the AKT/ERK signaling pathway.

BACKGROUND: Previous studies have shown that zinc-finger CCHC-type containing 13 (ZCCHC13) is located in an imprinted gene cluster in the X-inactivation centre, but few published studies have provided evidence of its expression in cancers. The CCHC-type zinc finger motif has numerous biological activities (such as DNA binding and RNA binding) and mediates protein-protein interactions. In an effort to examine the clinical utility of ZCCHC13 in oncology, we investigated the expression of the ZCCHC13 mRNA and protein in hepatocellular carcinoma (HCC). METHODS: The expression of the ZCCHC13 mRNA and protein was evaluated using real-time reverse transcriptase-PCR, Western blotting and immunochemistry. DNA methylation was measured by methylation-specific PCR and bisulfite sequencing. The role of ZCCHC13 methylation was further evaluated using the demethylating agent, 5-aza-2'-deoxycytidine. The presence of anti-ZCCHC13 antibodies was determined by an ELISA. RESULTS: ZCCHC13 expression was frequently upregulated in human liver cancer cells and tissues. Compared with heathy individuals, sera from patients with HCC displayed a significant response to the recombinant ZCCHC13 protein. The overexpression of ZCCHC13 in HCC was attributed to DNA hypomethylation in the promoter region. Moreover, overexpression of ZCCHC13 in liver cancer cells promoted cell cycle progression by facilitating the G1-S transition, which was related to aberrant activation of the ATK/ERK/c-MYC/CDK pathway. CONCLUSIONS: Based on our findings, ZCCHC13 functions an oncogene for HCC, and DNA hypomethylation is a driving factor in carcinogenesis.

The effect of social activities on the alienation and family resilience of Chinese caregivers for children with autism: a latent class analysis.

Introduction: Caregivers of children with autism spectrum disorder (ASD) in China often experience alienation due to societal stigma. While this alienation detrimentally impacts their mental well-being, family resilience serves as a protective factor. Previous research has predominantly examined the social support derived from social activities but has neglected to delve into the specific patterns of these activities. The primary objective of this study was twofold: firstly, to gain insights into the various social activities engaged in by caregivers of children with autism in China, and secondly, to ascertain the influence of these social activities on alienation and family resilience. Methods: Between June and August 2023, a cross-sectional survey was carried out across multiple cities in Jilin Province, aiming to gather data from a total of 205 Chinese caregivers of children with autism. Data collection was conducted through the utilization of a structured questionnaire. The assessment of social activity involved the completion of 12 questionnaires, while alienation was evaluated using the Generalized Alienation Scale (GSAS), and family resilience was gauged through the Chinese version of the Family Resilience Scale (FaRE). The classification of social activities was conducted through latent class analysis (LCA), while the impact of these social activities on alienation and family resilience was examined using linear regression analysis. Results: The findings revealed that social activities can be categorized into five types (Low, Self-Recreation, Communication, Web Surfing, High). Communication social activities were found to reduce family resilience(ß=.332, p<0.01), while high social activities were associated with reduced alienation(ß=-.349, p<0.05) and increased family resilience(ß=.417, p<0.01). Conclusion: Supporting these particular types of social activities has the potential to reduce alienation and bolster family resilience among caregivers for children with autism in China.

A novel machine learning prediction model for metastasis in breast cancer.

BACKGROUND: Breast cancer (BC) metastasis is the common cause of high mortality. Conventional prognostic criteria cannot accurately predict the BC metastasis risk. The machine learning technologies can overcome the disadvantage of conventional models. AIM: We developed a model to predict BC metastasis using the random survival forest (RSF) method. METHODS: Based on demographic data and routine clinical data, we used RSF-recursive feature elimination to identify the predictive variables and developed a model to predict metastasis using RSF method. The area under the receiver operating characteristic curve (AUROC) and Kaplan-Meier survival (KM) analyses were plotted to validate the predictive effect when C-index was plotted to assess the discrimination and Brier scores was plotted to assess the calibration of the predictive model. RESULTS: We developed a metastasis prediction model comprising three variables (pathological stage, aspartate aminotransferase, and neutrophil count) selected by RSF-recursive feature elimination. The model was reliable and stable when assessed by the AUROC (0.932 in training set and 0.905 in validation set) and KM survival analyses (p < .0001). The C-indexes (0.959) and Brier score (0.097) also validated the good predictive ability of this model. CONCLUSIONS: This model relies on routine data and examination indicators in real-time clinical practice and exhibits an accurate prediction performance without increasing the cost for patients. Using this model, clinicians can facilitate risk communication and provide precise and efficient individualized therapy to patients with breast cancer.

The cross-talk between B cells and macrophages.

B cells and macrophages are significant immune cells that maintain the immune balance of the body. B cells are involved in humoral immunity, producing immune effects mainly by secreting antibodies. Macrophages participate in non-specific and specific immune responses. To gain a further understanding of macrophages and B cells, researchers have not only paid attention to the unidirectional influence between B cells and macrophages, but also have focused on the cross-talk between them, and the effect of this cross talk on diseases. Therefore, this review summarizes the influence of macrophages on B cells, the ways and mechanisms by which B cells affect macrophages, and their cross-talk, leading to a more comprehensive understanding of the mechanism of the interaction between macrophages and B cells.