Binding of zebrafish lipovitellin and L1­ORF2 increases the accessibility of L1­ORF2 via interference with histone wrapping.

Long interspersed nuclear element­1 (L1) is highly expressed in the early embryos of humans, rodents and fish. To investigate the molecular mechanisms underlying high expression of L1 during early embryonic development, a C1­open reading frame (ORF)2 vector was constructed in which ORF2 of human L1 (L1­ORF2) was inserted into a pEGFP­C1 plasmid. C1­ORF2 vector was injected into early zebrafish embryos (EZEs) to observe expression of EGFP reporter protein by fluorescence microscopy. RNA­seq and RT­qPCR were used to detect the effects of lipovitellin (LV)  on gene expression in EZEs. The binding ability of LV to L1­ORF2 DNA was detected by electrophoretic mobility­shift assay (EMSA). The chromatin recombinant DNase I digestion and ATAC­seq assay were used to evaluate the accessibility of plasmid DNA. C1­ORF2 vector induced high expression of enhanced green fluorescent protein (EGFP) reporter gene after it had been injected into 0 h post­fertilization (hpf) zebrafish embryos, although histone octamer inhibited expression of EGFP in C1­ORF2. SDS­PAGE was used to show that LV was the predominant protein binding ORF2 DNA in 0 hpf zebrafish embryo lysate (ZEL). Both ZEL and purified LV from ZEL attenuated the inhibitory effects induced by histone. LV bound histone to interfere with the binding of histone to ORF2 DNA. Both in vitro chromatin reconstitution experiments and assay for transposase­accessible chromatin with sequencing with HeLa cells were utilized to demonstrate that the interference induced by LV resulted in increased accessibility of C1­ORF2. Transcription experiments in vitro verified that LV could enhance the mRNA levels of zebrafish early embryo expression genes grainyhead­like transcription factor 3 (GRHL3), SRY­box transcription factor 19a (SOX19A) and nanor (NNR) and also of the EGFP gene. LV was found to increase the expression levels of the zebrafish early embryo expression genes in liver tissue after LV had been injected into the abdominal cavity of adult male zebrafish. Taken together, the findings of the present study demonstrated that LV activates the expression of EGFP induced by ORF2 in EZEs by enhancing the accessibility of ORF2 DNA.

Multi-View Integrative Approach For Imputing Short-Chain Fatty Acids and Identifying Key factors predicting Blood SCFA.

Short-chain fatty acids (SCFAs) are the main metabolites produced by bacterial fermentation of dietary fiber within gastrointestinal tract. SCFAs produced by gut microbiotas (GMs) are absorbed by host, reach bloodstream, and are distributed to different organs, thus influencing host physiology. However, due to the limited budget or the poor sensitivity of instruments, most studies on GMs have incomplete blood SCFA data, limiting our understanding of the metabolic processes within the host. To address this gap, we developed an innovative multi-task multi-view integrative approach (M2AE, Multi-task Multi-View Attentive Encoders), to impute blood SCFA levels using gut metagenomic sequencing (MGS) data, while taking into account the intricate interplay among the gut microbiome, dietary features, and host characteristics, as well as the nuanced nature of SCFA dynamics within the body. Here, each view represents a distinct type of data input (i.e., gut microbiome compositions, dietary features, or host characteristics). Our method jointly explores both view-specific representations and cross-view correlations for effective predictions of SCFAs. We applied M2AE to two in-house datasets, which both include MGS and blood SCFAs profiles, host characteristics, and dietary features from 964 subjects and 171 subjects, respectively. Results from both of two datasets demonstrated that M2AE outperforms traditional regression-based and neural-network based approaches in imputing blood SCFAs. Furthermore, a series of gut bacterial species (e.g., Bacteroides thetaiotaomicron and Clostridium asparagiforme), host characteristics (e.g., race, gender), as well as dietary features (e.g., intake of fruits, pickles) were shown to contribute greatly to imputation of blood SCFAs. These findings demonstrated that GMs, dietary features and host characteristics might contribute to the complex biological processes involved in blood SCFA productions. These might pave the way for a deeper and more nuanced comprehension of how these factors impact human health.

How technical and situational cues affect impulse buying behavior in social commerce? Evidence from bored consumers.

Introduction: With the rise of social media and web technologies, users are increasingly spending time on browsing and purchasing on social commerce, particularly during idle moments of casual scrolling. Social commerce applications with sophisticated social features and security measures may tend to attract a significant number of highly engaged users. The purpose of this study is to find out whether customers will be interested in the content posted on the applications and generate impulse consumption when they are bored. Methods: Drawing on stimulus-organism-response framework, this paper explores how technical cues and situational cues affect impulse buying behavior in social commerce applications and the mediating impact of consumer-perceived values. Data were gathered from 395 respondents who frequently utilize and have shopping experience on social commerce applications. The PLS-SEM and fsQCA were applied to formulate and test the proposed hypotheses. Results: The results of PLS-SEM reveal technical cues (ease of use, visual appeal and security) and situational cues (passing time and serendipity) positively influenced impulse buying. The results of fsQCA offer six solutions of different combinations of constructs which can lead to high impulse buying. Discussion: These findings may extend existing research on impulse buying behavior and consumer psychology, offering valuable insights for marketers. They also point towards strategies for more effectively encouraging impulse purchase in digital retail environments, particularly among consumers who are browsing out of boredom.

Integrative Multi-Omics Approach for Improving Causal Gene Identification.

Transcriptome-wide association studies (TWAS) have been widely used to identify thousands of likely causal genes for diseases and complex traits using predicted expression models. However, most existing TWAS methods rely on gene expression alone and overlook other regulatory mechanisms of gene expression, including DNA methylation and splicing, that contribute to the genetic basis of these complex traits and diseases. Here we introduce a multi-omics method that integrates gene expression, DNA methylation, and splicing data to improve the identification of associated genes with our traits of interest. Through simulations and by analyzing genome-wide association study (GWAS) summary statistics for 24 complex traits, we show that our integrated method, which leverages these complementary omics biomarkers, achieves higher statistical power, and improves the accuracy of likely causal gene identification in blood tissues over individual omics methods. Finally, we apply our integrated model to a lung cancer GWAS data set, demonstrating the integrated models improved identification of prioritized genes for lung cancer risk.

Integrating Multi-Omics Data to Uncover Prostate Tissue DNA Methylation Biomarkers and Target Genes for Prostate Cancer Risk.

Previous studies have indicated that specific CpG sites may be linked to the risk of prostate cancer (PCa) by regulating the expression of PCa target genes. However, most existing studies aim to identify DNA methylation (DNAm) biomarkers through blood tissue genetic instruments, which impedes the identification of relevant biomarkers in prostate tissue. To identify PCa risk-associated CpG sites in prostate tissue, we established genetic prediction models of DNAm levels using data from normal prostate samples in the GTEx (N = 108) and assessed associations between genetically predicted DNAm in prostate and PCa risk by studying 122,188 cases and 604,640 controls. We observed significant associations for 3879 CpG sites, including 926 at novel genomic loci. Among them, DNAm levels of 80 CpG sites located at novel loci are significantly associated with expression levels of 45 neighboring genes in normal prostate tissue. Of these genes, 11 further exhibit significant associations with PCa risk for their predicted expression levels in prostate tissue. Intriguingly, a total of 31 CpG sites demonstrate consistent association patterns across the methylation-gene expression-PCa risk pathway. Our findings suggest that specific CpG sites may be related to PCa risk by modulating the expression of nearby target genes.

Identification of proteins associated with type 2 diabetes risk in diverse racial and ethnic populations.

AIMS/HYPOTHESIS: Several studies have reported associations between specific proteins and type 2 diabetes risk in European populations. To better understand the role played by proteins in type 2 diabetes aetiology across diverse populations, we conducted a large proteome-wide association study using genetic instruments across four racial and ethnic groups: African; Asian; Hispanic/Latino; and European. METHODS: Genome and plasma proteome data from the Multi-Ethnic Study of Atherosclerosis (MESA) study involving 182 African, 69 Asian, 284 Hispanic/Latino and 409 European individuals residing in the USA were used to establish protein prediction models by using potentially associated cis- and trans-SNPs. The models were applied to genome-wide association study summary statistics of 250,127 type 2 diabetes cases and 1,222,941 controls from different racial and ethnic populations. RESULTS: We identified three, 44 and one protein associated with type 2 diabetes risk in Asian, European and Hispanic/Latino populations, respectively. Meta-analysis identified 40 proteins associated with type 2 diabetes risk across the populations, including well-established as well as novel proteins not yet implicated in type 2 diabetes development. CONCLUSIONS/INTERPRETATION: Our study improves our understanding of the aetiology of type 2 diabetes in diverse populations. DATA AVAILABILITY: The summary statistics of multi-ethnic type 2 diabetes GWAS of MVP, DIAMANTE, Biobank Japan and other studies are available from The database of Genotypes and Phenotypes (dbGaP) under accession number phs001672.v3.p1. MESA genetic, proteome and covariate data can be accessed through dbGaP under phs000209.v13.p3. All code is available on GitHub ( https://github.com/Arthur1021/MESA-1K-PWAS ).

UBR5 mediates colorectal cancer chemoresistance by attenuating ferroptosis via Lys 11 ubiquitin-dependent stabilization of Smad3-SLC7A11 signaling.

Chemoresistance remains a principal culprit for the treatment failure in colorectal cancer (CRC), especially for patients with recurrent or metastatic disease. Deciphering the molecular basis of chemoresistance may lead to novel therapeutic strategies for this fatal disease. Here, UBR5, an E3 ubiquitin ligase frequently overexpressed in human CRC, is demonstrated to mediate chemoresistance principally by inhibiting ferroptosis. Paradoxically, UBR5 shields oxaliplatin-activated Smad3 from proteasome-dependent degradation via Lys 11-linked polyubiquitination. This novel chemical modification of Smad3 facilitates the transcriptional repression of ATF3, induction of SLC7A11 and inhibition of ferroptosis, contributing to chemoresistance. Consequently, targeting UBR5 in combination with a ferroptosis inducer synergistically sensitizes CRC to oxaliplatin-induced cell death and control of tumor growth. This study reveals, for the first time, a major clinically relevant chemoresistance mechanism in CRC mediated by UBR5 in sustaining TGFß-Smad3 signaling and tuning ferroptosis, unveiling its potential as a viable therapeutic target for chemosensitization.

An atlas of genetic effects on the monocyte methylome across European and African populations.

Elucidating the genetic architecture of DNA methylation (DNAm) is crucial for decoding the etiology of complex diseases. However, current epigenomic studies often suffer from incomplete coverage of methylation sites and the use of tissues containing heterogeneous cell populations. To address these challenges, we present a comprehensive human methylome atlas based on deep whole-genome bisulfite sequencing (WGBS) and whole-genome sequencing (WGS) of purified monocytes from 298 European Americans (EA) and 160 African Americans (AA) in the Louisiana Osteoporosis Study. Our atlas enables the analysis of over 25 million DNAm sites. We identified 1,383,250 and 1,721,167 methylation quantitative trait loci (meQTLs) in cis -regions for EA and AA populations, respectively, with 880,108 sites shared between ancestries. While cis -meQTLs exhibited population-specific patterns, primarily due to differences in minor allele frequencies, shared cis -meQTLs showed high concordance across ancestries. Notably, cis -heritability estimates revealed significantly higher mean values in the AA population (0.09) compared to the EA population (0.04). Furthermore, we developed population-specific DNAm imputation models using Elastic Net, enabling methylome-wide association studies (MWAS) for 1,976,046 and 2,657,581 methylation sites in EA and AA, respectively. The performance of our MWAS models was validated through a systematic multi-ancestry analysis of 41 complex traits from the Million Veteran Program. Our findings bridge the gap between genomics and the monocyte methylome, uncovering novel methylation-phenotype associations and their transferability across diverse ancestries. The identified meQTLs, MWAS models, and data resources are freely available at www.gcbhub.org and https://osf.io/gct57/ .

Variability in performance of genetic-enhanced DXA-BMD prediction models across diverse ethnic and geographic populations: A risk prediction study.

BACKGROUND: Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations. METHODS AND FINDINGS: We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5×10-6 and 5×10-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors. CONCLUSIONS: In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5×10-6 or 5×10-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups.

Benchmarking DNA Foundation Models for Genomic Sequence Classification.

The rapid advancement of DNA foundation language models has revolutionized the field of genomics, enabling the decoding of complex patterns and regulatory mechanisms within DNA sequences. However, the current evaluation of these models often relies on fine-tuning and limited datasets, which introduces biases and limits the assessment of their true potential. Here, we present a benchmarking study of three recent DNA foundation language models, including DNABERT-2, Nucleotide Transformer version-2 (NT-v2), and HyenaDNA, focusing on the quality of their zero-shot embeddings across a diverse range of genomic tasks and species through analyses of 57 real datasets. We found that DNABERT-2 exhibits the most consistent performance across human genome-related tasks, while NT-v2 excels in epigenetic modification detection. HyenaDNA stands out for its exceptional runtime scalability and ability to handle long input sequences. Importantly, we demonstrate that using mean token embedding consistently improves the performance of all three models compared to the default setting of sentence-level summary token embedding, with average AUC improvements ranging from 4.3% to 9.7% for different DNA foundation models. Furthermore, the performance differences between these models are significantly reduced when using mean token embedding. Our findings provide a framework for selecting and optimizing DNA language models, guiding researchers in applying these tools effectively in genomic studies.

Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data.

BACKGROUND: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. METHOD: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. RESULTS: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites. CONCLUSION: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations.

Although DNA methylation (DNAm) has been implicated in the pathogenesis of numerous complex diseases, from cancer to cardiovascular disease to autoimmune disease, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified. However, current MWAS models are primarily trained using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). Through the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study on high cholesterol, pinpointing 146 putatively causal CpG sites.

Increasing Cyetpyrafen Spray Volume and Ozone Spray Improves the Control Effects against Two-Spotted Spider Mite (Tetranychus urticae) in Strawberries.

The two-spotted spider mite (Tetranychus urticae) is a constant threat to greenhouse strawberry production. The application of synthetic acaricides is the main method of controlling T. urticae. However, resistance development to traditional acaricides reduces their efficacy and eventually leads to control failure. It is important for strawberry growers to look for new acaricides and application technologies that can limit the harmfulness of T. urticae in environmentally friendly ways. In the current study, laboratory toxicity tests and field trials were performed to screen high-efficiency acaricides, and then application technologies were improved to enhance the management of T. urticae. In the laboratory toxicity tests, the results showed that the LC50 (median lethal concentration) value of cyetpyrafen, cyenopyrafen, cyflumetofen, bifenazate, abamectin, azocyclotin, pyridaben, spirodiclofen, and etoxazole against adult T. urticae was 0.226, 0.240, 0.415, 3.583, 5.531, 25.58, 39.69, 140.3, and 267.7 mg/L, respectively. In addition, the LC50 value of the nine acaricides against eggs of T. urticae was 0.082, 0.097, 0.931, 18.56, 25.52, 45.61, 36.32, 1.954, and 0.040 mg/L, respectively. The field trial results showed that the best control effect was obtained in cyetpyrafen at 300 mL/ha treatment. Cyetpyrafen was chosen for further application technology tests. In the spray volume tests, the results showed that increasing the spray volume from 900 to 1050 L/ha significantly improved the control of T. urticae. In addition, the results from the spray instrument tests demonstrated that the control effects on T. urticae in the ozone spray treatments were significantly higher than those of the conventional and electrostatic sprays 1 and 3 days after treatment (DAT). Therefore, this study suggested that cyetpyrafen effectively controlled T. urticae both in the laboratory tests and in the field trials. Increasing the spray volume and application of ozone spray significantly improved T. urticae management.

The gut microbiota-aromatic hydrocarbon receptor (AhR) axis mediates the anticolitic effect of polyphenol-rich extracts from Sanghuangporus.

Inflammatory bowel disease (IBD) is a significant global health concern. The gut microbiota plays an essential role in the onset and development of IBD. Sanghuangporus (SH), a traditional Chinese medicinal mushroom, has excellent anti-inflammatory effects and is effective at modulating the gut microbiota. Despite these attributes, the specific anticolitic effects of SH and the mechanisms through which the gut microbiota mediates its benefits remain unclear. Herein, we demonstrated that polyphenol-rich extract from SH effectively alleviated the pathological symptoms of dextran sodium sulfate (DSS)-induced colitis in mice by modulating the gut microbiota. Treatment with SH distinctly enriched Alistipes, especially Alistipes onderdonkii, and its metabolite 5-hydroxyindole-3-acetic acid (5HIAA). Oral gavage of live A. onderdonkii or 5HIAA potently mitigated DSS-induced colitis in mice. Moreover, both 5HIAA and SH significantly activated the aromatic hydrocarbon receptor (AhR), and the administration of an AhR antagonist abrogated their protective effects against colitis. These results underscore the potent efficacy of SH in diminishing DSS-induced colitis through the promotion of A. onderdonkii and 5HIAA, ultimately activating AhR signaling. This study unveils potential avenues for developing therapeutic strategies for colitis based on the interplay between SH and the gut microbiota.

From ancient wisdom to modern science: Gut microbiota sheds light on property theory of traditional Chinese medicine.

The property theory of traditional Chinese medicine (TCM) has been practiced for thousands of years, playing a pivotal role in the clinical application of TCM. While advancements in energy metabolism, chemical composition analysis, machine learning, ion current modeling, and supercritical fluid technology have provided valuable insight into how aspects of TCM property theory may be measured, these studies only capture specific aspects of TCM property theory in isolation, overlooking the holistic perspective inherent in TCM. To systematically investigate the modern interpretation of the TCM property theory from multidimensional perspectives, we consulted the Chinese Pharmacopoeia (2020 edition) to compile a list of Chinese materia medica (CMM). Then, using the Latin names of each CMM and gut microbiota as keywords, we searched the PubMed database for relevant research on gut microbiota and CMM. The regulatory patterns of different herbs on gut microbiota were then summarized from the perspectives of the four natures, the five flavors and the meridian tropism. In terms of the four natures, we found that warm-natured medicines promoted the colonization of specific beneficial bacteria, while cold-natured medicines boosted populations of some beneficial bacteria while suppressing pathogenic bacteria. Analysis of the five flavors revealed that sweet-flavored and bitter-flavored CMMs positively influenced beneficial bacteria while inhibiting harmful bacteria. CMMs with different meridian tropism exhibited complex modulative patterns on gut microbiota, with Jueyin (Liver) and Taiyin (Lung) meridian CMMs generally exerting a stronger effect. The gut microbiota may be a biological indicator for characterizing the TCM property theory, which not only enhances our understanding of classic TCM theory but also contributes to its scientific advancement and application in healthcare. Please cite this article as: Yang YN, Zhan JG, Cao Y, Wu CM. From ancient wisdom to modern science: Gut microbiota sheds light on property theory of traditional Chinese medicine. J Integr Med 2024; 22(4): 413-445.

Cholinergic macrophages promote the resolution of peritoneal inflammation.

The non-neural cholinergic system plays a critical role in regulating immune equilibrium and tissue homeostasis. While the expression of choline acetyltransferase (ChAT), the enzyme catalyzing acetylcholine biosynthesis, has been well documented in lymphocytes, its role in the myeloid compartment is less understood. Here, we identify a significant population of macrophages (Mϕs) expressing ChAT and synthesizing acetylcholine in the resolution phase of acute peritonitis. Using Chat-GFP reporter mice, we observed marked upregulation of ChAT in monocyte-derived small peritoneal Mϕs (SmPMs) in response to Toll-like receptor agonists and bacterial infections. These SmPMs, phenotypically and transcriptionally distinct from tissue-resident large peritoneal macrophages, up-regulated ChAT expression through a MyD88-dependent pathway involving MAPK signaling. Notably, this process was attenuated by the TRIF-dependent TLR signaling pathway, and our tests with a range of neurotransmitters and cytokines failed to induce a similar response. Functionally, Chat deficiency in Mϕs led to significantly decreased peritoneal acetylcholine levels, reduced efferocytosis of apoptotic neutrophils, and a delayed resolution of peritonitis, which were reversible with exogenous ACh supplementation. Intriguingly, despite B lymphocytes being a notable ChAT-expressing population within the peritoneal cavity, Chat deletion in B cells did not significantly alter the resolution process. Collectively, these findings underscore the crucial role of Mϕ-derived acetylcholine in the resolution of inflammation and highlight the importance of the non-neuronal cholinergic system in immune regulation.

Prognostic and predictive value of examined lymph node count in stage III colorectal cancer: a population based study.

BACKGROUND: The role of tumor-draining lymph nodes in the progression of malignant tumors, including stage III colorectal cancer (CRC), is critical. However, the prognostic and predictive value of the number of examined lymph nodes (ELNs) are not fully understood. METHODS: This population-based study retrospectively analyzed data from 106,843 patients with stage III CRC who underwent surgical treatment and registered in three databases from 2004 to 2021. The Surveillance, Epidemiology, and End Results (SEER) cohort was divided using into training and test cohorts at a ratio of 3:2. We employed restricted cubic spline (RCS) curves to explore nonlinear relationships between overall survival (OS) and ELNs counts and performed Cox regression to evaluate hazard ratios across different ELNs count subtypes. Additional validation cohorts were utilized from the First Affiliated Hospital, Sun Yat-sen University and The Cancer Genome Atlas (TCGA) under the same criteria. Outcomes measured included OS, cancer-specific survival (CSS), and progression-free survival (PFS). Molecular analyses involved differential gene expression using the "limma" package and immune profiling through CIBERSORT. Tissue microarray slides and multiplex immunofluorescence (MIF) were used to assess protein expression and immune cell infiltration. RESULTS: Patients with higher ELNs counts (≥ 17) demonstrated significantly better long-term survival outcomes across all cohorts. Enhanced OS, CSS, and PFS were notably evident in the LN-ELN group compared to those with fewer ELNs. Cox regression models underscored the prognostic value of higher ELNs counts across different patient subgroups by age, sex, tumor differentiation, and TNM stages. Subtype analysis based on ELNs count revealed a marked survival benefit in patients treated with adjuvant chemotherapy in the medium and large ELNs counts (≥ 12), whereas those with fewer ELNs showed negligible benefits. RNA sequencing and MIF indicated elevated immune activation in the LN-ELN group, characterized by increased CD3+, CD4+, and CD8 + T cells within the tumor microenvironment. CONCLUSIONS: The number of ELNs independently predicts survival and the immunological landscape at the tumor site in stage III CRC, underscoring its dual prognostic and predictive value.

[Clinical Features and Prognostic of Patients with Primary Central Nervous System Lymphoma].

OBJECTIVE: To explore the clinical features and prognosis of patients with primary central nervous system lymphoma（PCNSL）. METHODS: A retrospective analysis was performed on the relationship between clinical features, treatment regimen and prognosis in 46 newly diagnosed patients with primary central nervous system lymphoma who were diagnosed and treated in The Second Hospital of Lanzhou University from January 2015 to September 2022. Fisher's exact probability method was used to analyze the differences in clinical data of different subgroups. Kaplan-Meier survival curve was used to analyze the overall survival rate and progression-free survival rate of patients with different treatments, and the factors influencing survival were analyzed. RESULTS: Among 46 patients with PCNSL, which pathological type were diffuse large B-cell lymphoma（DLBCL）. There were 26(56.5%) cases of male and 20(43.5%) of female, with a median age of 54(17-71) years. In Hans subtypes, 14 cases (30.4%) of GCB subtype, 32 cases (69.6%) of non-GCB subtype. 32 cases (69.6%) of Ki-67≥80%. Among 36 patients who completed at least 2 cycles of treatment with follow-up data, the efficacy evaluation was as follows: overall response rate(ORR) was 63.9%, complete response(CR) rate was 47.2%, 17 cases of CR, 6 cases of PR. The 1-year progression-free survival rate and 1-year overall survival rate was 73.6% and 84.9%, respectively. The 2-year progression-free survival rate and 2-year overall survival rate was 52.2% and 68.9%, respectively. The ORR and CR rate of 17 patients treated with RMT regimen was 76.5% and 52.9% (9 cases CR and 4 cases PR), respectively. Univariate analysis of 3 groups of patients treated with RMT regimen, RM-BTKi regimen, and RM-TT regimen as first-line treament showed that deep brain infiltration was associated with adverse PFS(P =0.032), and treatment regimen was associated with adverse OS in PCNSL patients（P =0.025）. CONCLUSION: Different treatment modalities were independent prognosis predictors for OS, the deep brain infiltration of PCNSL is a poor predictive factor for PFS. Patients with relapse/refractory (R/R) PCNSL have a longer overall survival time because to the novel medication BTKi. They have strong toleration and therapeutic potential as a first-line therapy for high-risk patients.

[Whole Exome Sequencing Reveals Gene Mutation Characteristics of Primary Central Nervous System Lymphoma].

OBJECTIVE: To investigate gene mutation characteristics of primary central nervous system lymphoma (PCNSL) through whole exome sequencing (WES) to 18 patients with PCNSL. METHODS: Tumor tissues from 18 patients with diffuse large B-cell lymphoma who were diagnosed with PCNSL in Department of Hematology, Lanzhou University Second Hospital from September 2018 to December 2020 and had normal immune function, no history of HIV or immunosuppressant therapy were collected. High-throughput-based WES was performed on the tumor tissues, with an average sequencing depth of >100×. After data processing and bioinformatics analysis of sequencing results, the mutation maps and mutation characteristics of 18 PCNSL patients were obtained. RESULTS: Obvious somatic mutations were detected in all 18 patients. The median number of somatic mutations was 321. Missense mutations were most prominent (accounting for about 90%), and the mutation type was dominated by C>T (50.2%), reflecting the age-related mutation pattern. Among the top 15 frequently mutated genes, PSD3, DUSP5, MAGEB16, TELO2, FMO2, TRMT13, AOC1, PIGZ, SVEP1, IP6K3, and TIAM1 were the driver genes. The enrichment results of driver gene pathways showed that RTK-RAS, Wnt, NOTCH, Hippo and Cell-Cycle pathways were significantly enriched. The tumor mutation burden was between 3.558 48/Mb and 8.780 89/Mb, and the average was 4.953 32/Mb, which was significantly higher than other cancer research cohorts in the TCGA database. CONCLUSIONS: PCNSL occurs somatic missense mutations frequently, mainly point mutations, and the mutation type is mainly C>T. The driver genes are mainly involved in RTK-RAS, Wnt, NOTCH and Hippo pathways, indicating that the above pathways may be related to the pathogenesis of PCNSL. PCNSL has a significantly high tumor mutation burden, which may explain the efficacy of PD-1 inhibitors in PCNSL.

Dion-Jacobson-Phase 2D Sn-Based Perovskite Comprising a High Dipole Moment of π-Conjugated Short-Chain Organic Spacers for High-Performance Solar Cell Applications.

The stability issue of Sn-based perovskite solar cells (PSCs) is expected to be resolved by involving a two-dimensional (2D) layered structure. However, Sn-based 2D PSCs, especially Dion-Jacobson (DJ)-phase ones with potentially good stability, have rarely been reported. Herein, superior DJ-phase Sn 2D perovskites with 3-aminobenzylamine (3ABA2+) or 4-aminobenzylamine (4ABA2+) π-conjugated short-chain ligands are reported to fabricate efficient 2D lead-free PSCs. Notably, the high dipole moment of the 3ABAI2 organic spacer is approved to possess faster charge transfer for forming (3ABA)FA4Sn5I16 2D perovskite with an extremely low exciton binding energy (only 84 meV). In combination with a diacetate partial substitution and methylamine iodide/bromide (MAI/MABr) post-treatment strategy to delay crystallization and improve compactness and coverage of the perovskite film, a record power conversion efficiency (PCE) of 6.81% and stability of 840 h (less than 5% degradation in a N2 atmosphere for unencapsulated devices) are acquired in eventual (3ABA)FA4Sn5I16 2D PSCs, which are among the highest PCE and the longest stability of Sn-based 2D PSCs reported to date. Our work provides a prospective molecule design and film preparation strategy of 2D Sn perovskites toward nontoxic high-performance tin-based PSCs, which pushes the almost stagnant research forward.