Amino acid metabolism-related genes as potential biomarkers and the role of MATN3 in stomach adenocarcinoma: A bioinformatics, mendelian randomization and experimental validation study.

BACKGROUND: Stomach adenocarcinoma (STAD) is a major contributor to cancer-related mortality worldwide. Alterations in amino acid metabolism, which is integral to protein synthesis, have been observed across various tumor types. However, the prognostic significance of amino acid metabolism-related genes in STAD remains underexplored. METHODS: Transcriptomic gene expression and clinical data for STAD patients were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Amino acid metabolism-related gene sets were sourced from the Gene Set Enrichment Analysis (GSEA) database. A prognostic model was built using LASSO Cox regression based on the TCGA cohort and validated with GEO datasets (GSE84433, GSE84437, GSE84426). Kaplan-Meier analysis compared overall survival (OS) between high- and low-risk groups, and ROC curves assessed model accuracy. A nomogram predicted 1-, 3-, and 5-year survival. Copy number variations (CNVs) in model genes were visualized using data from the Xena platform, and mutation profiles were analyzed with "maftools" to create a waterfall plot. KEGG and GO enrichment analyses were performed to explore biological mechanisms. Immune infiltration and related functions were evaluated via ssGSEA, and Spearman correlation analyzed associations between risk scores and immune components. The TIDE database predicted immunotherapy efficacy, while FDA-approved drug sensitivity was assessed through CellMiner database. The role of MATN3 in STAD was further examined in vitro and in vivo, including amino acid-targeted metabolomic sequencing to assess its impact on metabolism. Finally, Mendelian randomization (MR) analysis evaluated the causal relationship between the model genes and gastric cancer. RESULTS: In this study, we developed a prognostic risk model for STAD based on three amino acid metabolism-related genes (SERPINE1, NRP1, MATN3) using LASSO regression analysis. CNV amplification was common in SERPINE1 and NRP1, while CNV deletion frequently occurred in MATN3. STAD patients were classified into high- and low-risk groups based on the median risk score, with the high-risk group showing worse prognosis. A nomogram incorporating the risk score and clinical factors was created to estimate 1-, 3-, and 5-year survival rates. Distinct mutation profiles were observed between risk groups, with KEGG pathway analysis showing immune-related pathways enriched in the high-risk group. High-risk scores were significantly associated with the C6 (TGF-ß dominant) subtype, while low-risk scores correlated with the C4 (lymphocyte-depleted) subtype. Higher risk scores also indicated increased immune infiltration, enhanced immune functions, lower tumor purity, and poorer immunotherapy response. Model genes were linked to anticancer drug sensitivity. Manipulating MATN3 expression showed that it promoted STAD cell proliferation and migration in vitro and tumor growth in vivo. Metabolomic sequencing revealed that MATN3 knockdown elevated levels of 30 amino acid metabolites, including alpha-aminobutyric acid, glycine, and aspartic acid, while reducing (S)-ß-Aminoisobutyric acid and argininosuccinic acid. MR analysis found a significant causal effect of NRP1 on gastric cancer, but no causal relationship for MATN3 or SERPINE1. CONCLUSION: In conclusion, the amino acid metabolism-related prognostic model shows promise as a valuable biomarker for predicting the clinical prognosis, selecting immunotherapy and drug treatment for STAD patients. Furthermore, our study has shed light on the potential value of the MATN3 as a promising strategy for combating the progression of STAD.

Melatonin receptor 1A variants as genetic cause of idiopathic osteoporosis.

Idiopathic osteoporosis (IOP) is a rare form of early-onset osteoporosis diagnosed in patients with no known metabolic or hormonal cause of bone loss and unknown pathogenesis. Patients with IOP commonly report both childhood fractures and family history of osteoporosis, raising the possibility of genetic etiologies of IOP. Whole-exome sequencing analyses of different IOP cohorts identified multiple variants in melatonin receptor 1A (MTNR1A) with a potential pathogenic outcome. A rare MTNR1A variant (rs374152717) was found in members of an Ashkenazi Jewish family with IOP, and an MTNR1A variant (rs28383653) was found in a nonrelated female IOP cohort (4%). Both variants occur at a substantially higher frequency in Ashkenazi Jewish individuals than in the general population. We investigated consequences of the heterozygous (rs374152717) variant [MTNR1Ac.184+1G>T (MTNR1Ac.184+1G>T)] on bone physiology. A mouse model of the human rs374152717 variant reproduced the low bone mass (BM) phenotype of young-adult patients with IOP. Low BM occurred because of induction of senescence in mutant osteoblasts followed by compromised differentiation and function. In human cells, introduction of rs374152717 led to translation of a nonfunctional protein and subsequent dysregulation of melatonin signaling. These studies provide evidence that MTNR1A mutations entail a genetic etiology of IOP and establish the rs374152717 variant as a loss-of-function allele that impairs bone turnover by inducing senescence in osteoblasts. The higher prevalence of the MTNR1A variants identified in IOP cohorts versus the general population indicates a greater risk of IOP in those carrying these variants, especially Ashkenazi Jewish individuals bearing the rs374152717 variant.

Explainable machine learning framework to predict the risk of work-related neck and shoulder musculoskeletal disorders among healthcare professionals.

Objective: This study aims to develop risk prediction models for neck and shoulder musculoskeletal disorders among healthcare professionals. Methods: A stratified sampling method was employed to select employees from medical institutions in Nanning City, yielding 617 samples. The Boruta algorithm was used for feature selection, and various models, including Tree-Based Models, Single Hidden-Layer Neural Network Models (MLP), Elastic Net Models (ENet), and Support Vector Machines (SVM), were applied to predict the selected variables, utilizing SHAP algorithms for individual-level local explanations. Results: The SVM model excels in both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) and exhibits more stable performance when generalizing to unseen data. The Random Forest model exhibited relatively high overall performance on the training set. The MLP model emerges as the most consistent and accurate in predicting shoulder musculoskeletal disorders, while the SVM model shows strong fitting capabilities during the training phase, with occupational factors identified as the main contributors to WMSDs. Conclusion: This study successfully constructs work-related musculoskeletal disorder risk prediction models for healthcare professionals, enabling a quantitative analysis of the impact of occupational factors. This advancement is beneficial for future economical and convenient work-related musculoskeletal disorder screening in healthcare professions.

Blockchain-based proxy re-encryption access control method for biological risk privacy protection of agricultural products.

In today's globalized agricultural system, information leakage of agricultural biological risk factors can lead to business risks and public panic, jeopardizing corporate reputation. To solve the above problems, this study constructs a blockchain network for agricultural product biological risk traceability based on agricultural product biological risk factor data to achieve traceability of biological risk traceability data of agricultural product supply chain to meet the sustainability challenges. To guarantee the secure and flexible sharing of agricultural product biological risk privacy information and limit the scope of privacy information dissemination, the blockchain-based proxy re-encryption access control method (BBPR-AC) is designed. Aiming at the problems of proxy re-encryption technology, such as the third-party agent being prone to evil, the authorization judgment being cumbersome, and the authorization process not automated, we design the proxy re-encryption access control mechanism based on the traceability of agricultural products' biological risk factors. Designing an attribute-based access control (ABAC) mechanism based on the traceability blockchain for agricultural products involves defining the attributes of each link in the agricultural supply chain, formulating policies, and evaluating and executing these policies, deployed in the blockchain system in the form of smart contracts. This approach achieves decentralization of authorization and automation of authority judgment. By analyzing the data characteristics within the agricultural product supply chain to avoid the malicious behavior of third-party agents, the decentralized blockchain system acts as a trusted third-party agent, and the proxy re-encryption is combined with symmetric encryption to improve the encryption efficiency. This ensures a efficient encryption process, making the system safe, transparent, and efficient. Finally, a prototype blockchain system for traceability of agricultural biological risk factors is built based on Hyperledger Fabric to verify this research method's reliability, security, and efficiency. The experimental results show that this research scheme's initial encryption, re-encryption, and decryption sessions exhibit lower computational overheads than traditional encryption methods. When the number of policies and the number of requests in the access control session is 100, the policy query latency is less than 400 ms, the request-response latency is slightly more than 360ms, and the data uploading throughput is 48.7 tx/s. The data query throughput is 81.8 tx/s, the system performance consumption is low and can meet the biological risk privacy protection needs of the agricultural supply chain. The BBPR-AC method proposed in this study provides ideas for achieving refined traceability management in the agricultural supply chain and promoting digital transformation in the agricultural industry.

Mining Candidate Genes and Identifying Risk Factors for Leg Disease in Broilers: A Mendelian Randomization Study.

Clinical investigations have highlighted disruptions in bone metabolic processes and abnormal fluctuations in serum indicator levels during the onset of leg disease (LD) in broilers. However, the presence of a genetic causal relationship for this association remains undetermined. Therefore, the aim of this study is to discern the risk factors underlying LD development using 1235 sequenced white-feathered broilers. We employed Mendelian randomization (MR) analysis to assess the associations of bone strength (BS), bone mineral density (BMD), tibial bone weight (TBW), tibial bone length (TBL), tibial bone diameter (TBD), bone ash (BA), ash calcium (Ash Ca), ash phosphorus (Ash P), serum calcium (Ca), serum phosphorus (P), serum alkaline phosphatase (ALP), and serum osteoprotegerin (OPG) with the incidence of LD. Compelling evidence underscores a causal link between the risk of developing LD and decreased BMD (odds ratio (OR) = 0.998; 95% CI: 0.983, 0.993; P < 0.001) and narrower TBD (OR = 0.985, 95% CI: 0.975, 0.994, P = 0.002). Additionally, serum OPG concentrations (OR: 0.995, 95% CI: 0.992, 0.999, P = 0.008) were associated with BMD (OR = 0.0078, 95% CI = 0.0043 to 0.0140, P < 0.001), indicating a robust genetic relationship between ALP concentrations (OR: 0.988, 95% CI: 0.984, 0.993, P < 0.001) and TBD (OR = 0.0046, 95% CI = 0.0026, 0.0083, P < 0.001). Moreover, elevated serum Ca (OR: 0.564, 95% CI: 0.487, 0.655, P < 0.001) and P (OR: 0.614, 95% CI: 0.539, 0.699, P < 0.001) levels were associated with a narrower TBD. Elevated serum levels of Ca, P, ALP, and OPG contribute to disturbances in bone metabolism, while decreased BMD and narrower TBD are associated with a greater risk of developing LD in broilers. This discovery elucidates the metabolic risk factors for LD in broilers and could provide information on LDs, such as osteoporosis, in humans.

Defective autophagy of pericytes enhances radiation-induced senescence promoting radiation brain injury.

BACKGROUND: Radiation-induced brain injury (RBI) represents a major challenge for cancer patients undergoing cranial radiotherapy. However, the molecular mechanisms and therapeutic strategies of RBI remain inconclusive. With the continuous exploration of the mechanisms of RBI, an increasing number of studies have implicated cerebrovascular dysfunction as a key factor in RBI-related cognitive impairment. As pericytes are a component of the neurovascular unit, there is still a lack of understanding in current research about the specific role and function of pericytes in RBI. METHODS: We constructed a mouse model of RBI-associated cognitive dysfunction in vivo and an in vitro radiation-induced pericyte model to explore the effects of senescent pericytes on the blood-brain barrier and normal CNS cells, even glioma cells. To further clarify the effects of pericyte autophagy on senescence, molecular mechanisms were explored at the animal and cellular levels. Finally, we validated the clearance of pericyte senescence by using senolytic drug and all-trans retinoic acid to investigate the role of radiation-induced pericyte senescence. RESULTS: Our findings indicated that radiation-induced pericyte senescence plays a key role in blood-brain barrier dysfunction, leading to RBI and subsequent cognitive decline. Strikingly, pericyte senescence also contributes to the growth and invasion of glioma cells. We further demonstrate that defective autophagy in pericytes is a vital regulatory mechanism for pericyte senescence. Moreover, autophagy activated by rapamycin can reverse pericyte senescence. Notably, the elimination of senescent cells by senolytic drugs significantly mitigated radiation-induced cognitive dysfunction. DISSCUSSION: Our results demonstrated that pericyte senescence may be a promising therapeutic target for RBI and glioma progression.

Toxicity evolution of triclosan during environmental transformation and human metabolism: Misgivings in the post-pandemic era.

In the context of pandemic viruses and pathogenic bacteria, triclosan (TCS), as a typical antibacterial agent, is widely used around the world. However, the health risks from TCS increase with exposure, and it is widespread in environmental and human samples. Notably, environmental transformation and human metabolism could induce potentially undesirable risks to humans, rather than simple decontamination or detoxification. This review summarizes the environmental and human exposure to TCS covering from 2004 to 2023. Particularly, health impacts from the environmental and metabolic transformation of TCS are emphasized. Environmental transformations aimed at decontamination are recognized to form carcinogenic products such as dioxins, and ultraviolet light and excessive active chlorine can promote the formation of these dioxin congeners, potentially threatening environmental and human health. Although TCS can be rapidly metabolized for detoxification, these processes can induce the formation of lipophilic ether metabolic analogs via cytochrome P450 catalysis, causing possible adverse cross-talk reactions in human metabolic disorders. Accordingly, TCS may be more harmful in environmental transformation and human metabolism. In particular, TCS can stimulate the transmission of antibiotic resistance even at trace levels, threatening public health. Considering these accruing epidemiological and toxicological studies indicating the multiple adverse health outcomes of TCS, we call on environmental toxicologists to pay more attention to the toxicity evolution of TCS during environmental transformation and human metabolism.

3-ketoacyl-CoA synthase 19 contributes to the biosynthesis of seed lipids and cuticular wax in Arabidopsis and abiotic stress tolerance.

Very-long-chain fatty acids (VLCFAs) are essential precursors for plant membrane lipids, cuticular waxes, suberin, and storage oils. Integral to the fatty acid elongase (FAE) complex, 3-ketoacyl-CoA synthases (KCSs) function as crucial enzymes in the VLCFA pathway, determining the chain length of VLCFA. This study explores the in-planta role of the KCS19 gene. KCS19 is predominantly expressed in leaves and stem epidermis, sepals, styles, early silique walls, beaks, pedicels, and mature embryos. Localized in the endoplasmic reticulum, KCS19 interacts with other FAE proteins. kcs19 knockout mutants displayed reduced total wax and wax crystals, particularly alkanes, while KCS19 overexpression increased these components and wax crystals. Moreover, the cuticle permeability was higher for the kcs19 mutants compared to the wild type, rendering them more susceptible to drought and salt stress, whereas KCS19 overexpression enhanced drought and salt tolerance. Disrupting KCS19 increased C18 species and decreased C20 and longer species in seed fatty acids, indicating its role in elongating C18 to C20 VLCFAs, potentially up to C24 for seed storage lipids. Collectively, KCS19-mediated VLCFA synthesis is required for cuticular wax biosynthesis and seed storage lipids, impacting plant responses to abiotic stress.

The catalytic asymmetric polyene cyclization of homofarnesol to ambrox.

Polyene cyclizations are among the most complex and challenging transformations in biology. In a single reaction step, multiple carbon-carbon bonds, ring systems and stereogenic centres are constituted from simple, acyclic precursors1-3. Simultaneously achieving this kind of precise control over product distribution and stereochemistry poses a formidable task for chemists. In particular, the polyene cyclization of (3E,7E)-homofarnesol to the valuable naturally occurring ambergris odorant (-)-ambrox is recognized as a longstanding challenge in chemical synthesis1,4-7. Here we report a diastereoselective and enantioselective synthesis of (-)-ambrox and the sesquiterpene lactone natural product (+)-sclareolide by a catalytic asymmetric polyene cyclization by using a highly Brønsted-acidic and confined imidodiphosphorimidate catalyst in the presence of fluorinated alcohols. Several experiments, including deuterium-labelling studies, suggest that the reaction predominantly proceeds through a concerted pathway in line with the Stork-Eschenmoser hypothesis8-10. Mechanistic studies show the importance of the enzyme-like microenvironment of the imidodiphosphorimidate catalyst for attaining exceptionally high selectivities, previously thought to be achievable only in enzyme-catalysed polyene cyclizations.

Genome-wide association analysis identify candidate genes for feed efficiency and growth traits in Wenchang chickens.

BACKGROUND: Wenchang chickens are one of the most popular local chicken breeds in the Chinese chicken industry. However, the low feed efficiency is the main shortcoming of this breed. Therefore, there is a need to find a more precise breeding method to improve the feed efficiency of Wenchang chickens. In this study, we explored important candidate genes and variants for feed efficiency and growth traits through genome-wide association study (GWAS) analysis. RESULTS: Estimates of genomic heritability for growth and feed efficiency traits, including residual feed intake (RFI) of 0.05, average daily food intake (ADFI) of 0.21, average daily weight gain (ADG) of 0.24, body weight (BW) at 87, 95, 104, 113 days of age (BW87, BW95, BW104 and BW113) ranged from 0.30 to 0.44. Important candidate genes related to feed efficiency and growth traits were identified, such as PLCE1, LAP3, MED28, QDPR, LDB2 and SEL1L3 genes. CONCLUSION: The results identified important candidate genes for feed efficiency and growth traits in Wenchang chickens and provide a theoretical basis for the development of new molecular breeding technology.

Current trends and perspectives on salty and salt taste-enhancing peptides: A focus on preparation, evaluation and perception mechanisms of salt taste.

Long-term excessive intake of sodium negatively impacts human health. Effective strategies to reduce sodium content in foods include the use of salty and salt taste-enhancing peptides, which can reduce sodium intake without compromising the flavor or salt taste. Salty and salt taste-enhancing peptides naturally exist in various foods and predominantly manifest as short-chain peptides consisting of < 10 amino acids. These peptides are primarily produced through chemical or enzymatic hydrolysis methods, purified, and identified using ultrafiltration + gel filtration chromatography + liquid chromatography-tandem mass spectrometry. This study reviews the latest developments in these purification and identification technologies, and discusses methods to evaluate their effectiveness in saltiness perception. Additionally, the study explores four biological channels potentially involved in saltiness perception (epithelial sodium channel, transient receptor potential vanilloid 1, calcium-sensing receptor (CaSR), and transmembrane channel-like 4 (TMC4)), with the latter three primarily functioning under high sodium levels. Among the channels, salty taste-enhancing peptides, such as γ-glutamyl peptides, may co-activate the CaSR channel with calcium ions to participate in saltiness perception. Salty taste-enhancing peptides with negatively charged amino acid side chains or terminal groups may replace chloride ions and activate the TMC4 channel, contributing to saltiness perception. Finally, the study discusses the feasibility of using these peptides from the perspectives of food material constraints, processing adaptability, multifunctional application, and cross-modal interaction while emphasizing the importance of utilizing computational technology. This review provides a reference for advancing the development and application of salty and salt-enhancing peptides as sodium substitutes in low-sodium food formulations.

A nomogram for predicting 28-day mortality in elderly patients with acute kidney injury receiving continuous renal replacement therapy: a secondary analysis based on a retrospective cohort study.

BACKGROUND: Acute kidney injury (AKI) is a common and serious condition, particularly among elderly patients. It is associated with high morbidity and mortality rates, further compounded by the need for continuous renal replacement therapy in severe cases. To improve clinical decision-making and patient management, there is a need for accurate prediction models that can identify patients at a high risk of mortality. METHODS: Data were extracted from the Dryad Digital Repository. Multivariate analysis was performed using least absolute shrinkage and selection operator (LASSO) logistic regression analysis to identify independent risk factors and construct a predictive nomogram for mortality within 28 days after continuous renal replacement therapy in elderly patients with acute kidney injury. The discrimination of the model was evaluated in the validation cohort using the area under the receiver operating characteristic curve (AUC), and calibration was evaluated using a calibration curve. The clinical utility of the model was assessed using decision curve analysis (DCA). RESULTS: A total of 606 participants were enrolled and randomly divided into two groups: a training cohort (n = 424) and a validation cohort (n = 182) in a 7:3 proportion. A risk prediction model was developed to identify independent predictors of 28-day mortality in elderly patients with AKI. The predictors included age, systolic blood pressure, creatinine, albumin, phosphorus, age-adjusted Charlson Comorbidity Index (CCI), Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and sequential organ failure assessment (SOFA) score. These predictors were incorporated into a logistic model and presented in a user-friendly nomogram. In the validation cohort, the model demonstrated good predictive performance with an AUC of 0.799. The calibration curve showed that the model was well calibrated. Additionally, DCA revealed significant net benefits of the nomogram for clinical application. CONCLUSION: The development of a nomogram for predicting 28-day mortality in elderly patients with AKI receiving continuous renal replacement therapy has the potential to improve prognostic accuracy and assist in clinical decision-making.

Baseline neurological deficit and argatroban plus alteplase in acute ischemic stroke: A post hoc analysis of ARAIS trial.

BACKGROUND: The ARAIS trial didn't demonstrate argatroban significantly improve functional outcome at 90 days in acute ischemic stroke. We conducted post hoc analysis of ARAIS to investigate whether baseline neurological deficit was associated with outcomes. METHODS: Patients without endovascular therapy who met screening criteria as protocol and completed argatroban treatment were enrolled and classified into two subgroups according to NIHSS score at admission. Primary outcome was excellent functional outcome at 90 days, defined as mRS score of 0 to 1. Early neurological deterioration (END), defined as an increase of ≥4 in the NIHSS score from baseline within 48 hours, was investigated as secondary outcome. Compared with alteplase alone, we investigated treatment effect of argatroban plus alteplase on outcomes in subgroups and interaction with subgroups. RESULTS: A total of 675 patients from full analysis set were included: 390 were assigned into NIHSS score <10 subgroup and 285 into NIHSS score ≥10 subgroup. For primary outcome, there was similar treatment effect between argatroban plus alteplase and alteplase alone in NIHSS score ≥10 subgroup (adjusted RD, 5.8%; 95% CI, -6.0% to 17.5%; P = 0.33) and in NIHSS score <10 subgroup (adjusted RD, -1.4%; 95% CI, -9.9% to 7.1%; P = 0.75), and no significant interaction (P = 0.43). Occurrence of early neurological deterioration within 48 hours were significantly lower in NIHSS score ≥10 subgroup, compared with NIHSS score <10 subgroup (P = 0.006). CONCLUSION: Among patients with NIHSS score ≥10, argatroban plus alteplase could safely reduce END within 48 hours.

Measurement invariance of the Coparenting Relationship Scale (CRS) across 10 countries.

The purpose of this study was to assess the factor structure and the measurement invariance of the Coparenting Relationship Scale (CRS) across 10 countries based on the seven-factor coparenting model (i.e., Coparenting Agreement, Coparenting Closeness, Exposure to Conflict, Coparenting Support, Endorsement of Partner's Parenting; Division of Labor) proposed by Feinberg (2003). The results of research on coparenting from numerous countries have documented its foundational importance for parent mental health, family relationship quality, child development, and psychopathology. Yet, a cross-country perspective is still lacking. Such a perspective can provide insight into which dimensions of coparenting are universally recognized and which are especially prone to variation. A unique multinational data set, comprised of 15 individual studies collected across 10 countries (Belgium, Brazil, China, Israel, Italy, Japan, Portugal, Switzerland, Turkey, USA) in nine languages was established (N = 9,292; 51.1% mothers). Measurement invariance analyses were conducted. A six-factor structure (original seven factors minus Division of Labor) of the measure was consistent across the different contexts and measurement invariance was achieved at the configural level. There was no support for metric or scalar invariance. These findings provide a basis for the CRS to be used across countries and should inspire future quantitative and qualitative research in cross-country coparenting research to understand what aspects are universal and what aspects of coparenting are linked to specific material, relational, or ideational conditions that underlie high-quality coparenting. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Nomograms for Predicting Risk and Survival of Esophageal Cancer Lung Metastases: a SEER Analysis.

Background: The overall survival rate is notably low for esophageal cancer patients with lung metastases (LM), presenting significant challenges in their treatment. Methods: Through the Surveillance, Epidemiology, and End Results (SEER) program, individuals diagnosed with esophageal cancer between 2010 and 2015 were enrolled. Based on whether esophageal cancer metastasized to the lungs, we used propensity score matching (PSM) to balance correlated variables. Propensity score matching was a critical step in our study that helped to minimize the impact of possible confounders on the study results. We balanced variables related to lung metastases using the PSM method to ensure more accurate comparisons between the study and control groups. Specifically, we performed PSM in the following steps. First, we performed a univariate logistic regression analysis to screen for variables associated with lung metastasis. For each patient, we calculated their propensity scores using a logistic regression model, taking into account several factors, including gender, T-stage, N-stage, surgical history, radiotherapy history, chemotherapy history, and bone/brain/liver metastases. We used a 1:1 matching ratio based on the propensity score to ensure more balanced baseline characteristics between the study and control groups after matching. After matching, we validated the balance of baseline characteristics to ensure that the effect of confounders was minimized. We used logistic regression to identify risk variables for LM, while Cox regression was used to find independent prognostic factors. We then created nomograms and assessed their accuracy using the calibration curve, receiver operating curves (ROC), and C index. Results: In the post-PSM cohort, individuals diagnosed with LM experienced a median overall survival (OS) of 5.0 months (95% confidence interval [CI] 4.3-5.7), which was significantly lower than those without LM (P<0.001). LM has been associated to sex, T stage, N stage, surgery, radiation, chemotherapy, and bone/brain/liver metastases. LM survival was affected by radiation, chemotherapy, and bone/liver metastases. The nomograms' predictive power was proved using the ROC curve, C-index, and validation curve. Conclusion: Patients with LM have a worse chance of surviving esophageal cancer. The nomograms can effectively predict the risk and prognosis of lung metastases from esophageal cancer.

Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features.

BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains challenging due to clinical subjectivity. To address this issue, we explore magnetic resonance imaging (MRI) as a tool to enhance SZ diagnosis and provide objective references and biomarkers. Using deep learning with graph convolution, we represent MRI data as graphs, aligning with brain structure, and improving feature extraction, and classification. Integration of multiple modalities is expected to enhance classification. STUDY DESIGN: Our study enrolled 683 SZ patients and 606 healthy controls from 7 hospitals, collecting structural MRI and functional MRI data. Both data types were represented as graphs, processed by 2 graph attention networks, and fused for classification. Grad-CAM with graph convolution ensured interpretability, and partial least squares analyzed gene expression in brain regions. STUDY RESULTS: Our method excelled in the classification task, achieving 83.32% accuracy, 83.41% sensitivity, and 83.20% specificity in 10-fold cross-validation, surpassing traditional methods. And our multimodal approach outperformed unimodal methods. Grad-CAM identified potential brain biomarkers consistent with gene analysis and prior research. CONCLUSIONS: Our study demonstrates the effectiveness of deep learning with graph attention networks, surpassing previous SZ diagnostic methods. Multimodal MRI's superiority over unimodal MRI confirms our initial hypothesis. Identifying potential brain biomarkers alongside gene biomarkers holds promise for advancing objective SZ diagnosis and research in SZ.

Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology.

The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.

Tanshinone IIA destabilizes SLC7A11 by regulating PIAS4-mediated SUMOylation of SLC7A11 through KDM1A, and promotes ferroptosis in breast cancer.

INTRODUCTION: Breast cancer (BC) is the most common malignancy in women with unfavorite prognosis. OBJECTIVES: Tanshinone IIA (Tan IIA) inhibits BC progression, however, the underlying mechanism remains largely undefined. METHODS: The cytotoxicity of Tan IIA was assessed by CCK-8 and LDH assays. Ferroptosis was monitored by the level of MDA, Fe2+, lipid ROS and GSH. IHC and western blot were employed to detect the localization and expression of SLC7A11, PIAS4, KDM1A and other key molecules. The SUMOylation of SLC7A11 was detected by Ni-beads pull-down assay and Co-IP. Luciferase and ChIP assays were employed to detect the direct association between KDM1A and PIAS4 promoter. The proliferative and metastatic properties of BC cells were assessed by colony formation, CCK-8 and Transwell assays, respectively. The in vitro findings were verified in xenograft and lung metastasis models. RESULTS: Tan IIA promoted ferroptosis by suppressing SLC7A11 in BC cells. Silencing of PIAS4 or KDM1A inhibited cell growth and metastasis in BC. Mechanistically, PIAS4 facilitated the SUMOylation of SLC7A11 via direct binding to SLC7A11, and KDM1A acted as a transcriptional activator of PIAS4. Functional studies further revealed that Tan IIA decreased KDM1A expression, thus suppressing PIAS4 expression transcriptionally. The inhibition of PIAS4-dependent SUMOylation of SLC7A11 further induced ferroptosis, thereby inhibiting proliferation and metastasis in BC. CONCLUSION: Tan IIA promoted ferroptosis and inhibited tumor growth and metastasis via suppressing KDM1A/PIAS4/SLC7A11 axis.

Sustainable efficiency in cities in China-An interaction model among water, energy, and industry.

With the inclusion of "Building Inclusive, safe, Resilient and Sustainable Cities and human Settlements" (SDG11) in the United Nations Sustainable Development Goals (SDGS), the movement to promote sustainable development from an urban perspective is growing globally. Many studies examine urban sustainability efficiency from multiple dimensions, but scant attention targets the interaction among various dimensions. This research combines the water-energy-industry subsystem to evaluate the sustainable development performance of 29 provinces in China from 2018 to 2020. The results show that 1) a water system plays an important role in promoting a city's overall sustainable performance. 2) Urban sustainable efficiency has the characteristics of low value aggregation and high value dispersion in space. 3) Regional and sub-system sustainability efficiencies exhibit clear heterogeneity. 4) Rainfall improves the sustainable efficiency of cities, mainly through water systems. 5) The coupling between water and industrial subsystems is better than that between energy and industrial subsystems, and the coupling between the central region subsystem is the best. This paper offers a new perspective for understanding the current state of sustainability in China's provinces and provides more specific suggestions for improving regional sustainability efficiency in the future.