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Abstract Sulfur-containing units are fundamental components widely found in bioactive compounds, prompting notable efforts toward developing synthetic methodologies for incorporating sulfur functionality into organic precursors. The synthesis of sulfinate esters and sulfinamides has garnered significant interest owing to their immense potential for applications, especially in drug development. However, most existing synthetic protocols suffer from some limitations. To address these challenges, we herein present a practical and efficient approach for the reductive sulfinylation of diverse nucleophiles with sulfonylpyridinium salts (SulPy) through the nucleophilic chain substitution, namely SNC reaction, which involves S(VI) to S(IV) nucleophilic chain isomerization process. These versatile sulfinylation reagents can be readily accessed from diverse commercially available resourses. The late-stage modification of complex molecules and the ability to rapidly synthesize numerous sulfinyl bioisosteres of various drugs highlights the utility of this protocol.

Abstract Background Teat number is one of the most important indicators to evaluate the lactation performance of sows, and increasing the teat number has become an important method to improve the economic efficiency of farms. Therefore, it is particularly important to deeply analyze the genetic mechanism of teat number traits in pigs. In this study, we detected Single Nucleotide Ploymorphism (SNP), Insertion-Deletion (InDel) and Structural variant (SV) by high-coverage whole-genome resequencing data, and selected teat number at birth and functional teat number as two types of teat number traits for genome-wide association study (GWAS) to reveal candidate genes associated with pig teat number traits. Results In this study, we used whole genome resequencing data from 560 Yorkshire sows to detect SNPs, InDels and SVs, and performed GWAS for the traits of born teat number and functional teat number, and detected a total of 85 significant variants and screened 214 candidate genes, including HEG1, XYLT1, SULF1, MUC13, VRTN, RAP1A and NPVF. Among them, HEG1 and XYLT1 were the new candidate genes in this study. The co-screening and population validation of multiple traits suggested that HEG1 may have a critical effect on the born teat number. Conclusion Our study shows that more candidate genes associated with pig teat number traits can be identified by GWAS with different variant types. Through large population validation, we found that HEG1 may be a new key candidate gene affecting pig teat number traits. In conclusion, the results of this study provide new information for exploring the genetic mechanisms affecting pig teat number traits and genetic improvement of pigs.

Spiking Neural Network (SNN) simulation is very important for studying brain function and validating the hypotheses for neuroscience, and it can also be used in artificial intelligence. Recently, GPU-based simulators have been developed to support the real-time simulation of SNN. However, these simulators’ simulating performance and scale are severely limited, due to the random memory access pattern and the global communication between devices. Therefore, we propose an efficient distributed heterogeneous SNN simulator based on the Sunway accelerators (including SW26010 and SW26010pro), named SWsnn, which supports accurate simulation with small time step (1/16 ms), randomly delay sizes for synapses, and larger scale network computing. Compared with existing GPUs, the Local Dynamic Memory (LDM) (similar to cache) in Sunway is much bigger (4 MB or 16 MB in each core group). To improve the simulation performance, we redesign the network data storage structure and the synaptic plasticity flow to make most random accesses occur in LDM. SWsnn hides Message Passing Interface (MPI)-related operations to reduce communication costs by separating SNN general workflow. Besides, SWsnn relies on parallel Compute Processing Elements (CPEs) rather than serial Manage Processing Element (MPE) to control the communicating buffers, using Register-Level Communication (RLC) and Direct Memory Access (DMA). In addition, SWsnn is further optimized using vectorization and DMA hiding techniques. Experimental results show that SWsnn runs 1.4−2.2 times faster than state-of-the-art GPU-based SNN simulator GPU-enhanced Neuronal Networks (GeNN), and supports much larger scale real-time simulation.

Abstract Studies in SIV-infected macaques show that the virus reservoir is particularly refractory to conventional suppressive antiretroviral therapy (ART). We posit that optimized ART regimens designed to have robust penetration in tissue reservoirs and long-lasting antiviral activity may be advantageous for HIV or SIV remission. Here we treat macaques infected with RT-SHIV with oral emtricitabine/tenofovir alafenamide and long-acting cabotegravir/rilpivirine without (n = 4) or with (n = 4) the immune activator vesatolimod after the initial onset of viremia. We document full suppression in all animals during treatment (4-12 months) and no virus rebound after treatment discontinuation (1.5-2 years of follow up) despite CD8 + T cell depletion. We show efficient multidrug penetration in virus reservoirs and persisting rilpivirine in plasma for 2 years after the last dose. Our results document a type of virus remission that is achieved through early treatment initiation and provision of ultra long-lasting antiviral activity that persists after treatment cessation.

Abstract Background The use of mesenchymal stem cells (MSCs) is recognized as a promising strategy for the treatment of androgenetic alopecia (AGA). However, the underlying mechanism remains to be explored. Here, we evaluated the therapeutic effects and potential mechanisms of the use of human umbilical cord mesenchymal stem cells (hUCMSCs) in dihydrotestosterone (DHT)-induced AGA models in vivo and in vitro. Methods Intradermal transplantation of hUCMSCs was performed in AGA model mice and therapeutic effects were evaluated using histological and immunofluorescence staining. Transwell assays were used for co-culture of hUCMSCs and dermal papilla cells (DPCs), and communication was assessed using RT-qPCR, immunofluorescence, and apoptosis analysis. Interactions between DPCs and hair follicle stem cells (HFSCs) were investigated using RT-qPCR, EdU assays, and cell cycle analysis. Results Treatment of AGA mice with hUCMSCs promoted hair growth, HFs density, skin thickness, and anagen phase activation, while inhibiting DPCs apoptosis, and promoting HFSCs proliferation. In vitro, hUCMSCs activated Wnt/β-catenin signaling in DPCs via Wntless (Wls), while stimulating growth factor secretion and HFSCs proliferation. Blocking β-catenin degradation with MSAB increased DPCs apoptosis, reduced growth factor secretion, and retarded HFSCs proliferation. Conclusion hUCMSCs promoted hair regeneration in AGA model mice. This was found to be dependent on reducing DPCs apoptosis, thereby relieving the inhibitory effects of DPCs on the growth of HFSCs. The activation of the Wnt/β-catenin signaling pathway was shown to play a crucial role in the promotion of hair growth by hUCMSCs in AGA mice.

Abstract Background Gliomas are the most common malignant brain tumors characterized by angiogenesis and invasive growth. A detailed understanding of its molecular characteristics could provide potential therapeutic targets. In the present study, we sought to explore the key gene CXCL8 in methionine metabolism in gliomas and its potential role in angiogenesis. Methods U251 glioma cells were divided into control and methionine-restriction tolerant (constructed with 1/4 of the standard level of methionine in the culture medium) groups for transcriptome and metabolome analysis. To confirm the functions and mechanism of CXCL8 in glioma, heat map, volcano map, Go enrichment, gene set enrichment analysis (GSEA), protein–protein interaction network analysis, RT-PCR, western blotting assays, chicken embryo chorioallantoic membrane (CAM) test, chicken embryo yolk sac membrane (YSM) test and transplantation tumor nude mice model were performed. The TCGA database, CGGA database and clinical tissue samples were used to analyze CXCL8’s significance on prognosis for patients with glioma. Results CXCL8 expression was significantly up-regulated in methionine-restricted tolerance cells, it also activated vascular system development and triggered angiogenesis. CXCL8 expression is negatively correlated with survival prognosis in gliomas. Conclusions Glioma cells promote angiogenesis in methionine-restricted environments through the activation of CXCL8, compensating for nutrient deprivation, and possibly contributing to the failure of antiangiogenic therapy.

Introduction Material and methods Results Conclusions

Oil shale, the most important unconventional oil and gas reservoir resource, is characterized by large geological reserves, difficult development technology, and great development potential. Although it cannot be exploited in a large area due to cost issues, with the development and utilization of conventional oil and gas reservoir resources, it is the main direction of future oil exploitation. Based on the classification of in situ conversion technologies of oil shale electric heating into in situ conversion process technology, ElectrofracTM technology, geothermal fuel cell heating technology, high-voltage power frequency electric heating technology, and other electric heating technology, this paper summarizes the research progress on existing electric heating technologies to provide a reference for the engineering research and development of oil shale electric heating in situ mining technology.

In the engineering of oil shale, in situ extraction technology using electric heating involves heating the oil shale reservoir with an electric heater at high temperatures to convert the solid kerogen in oil shale into liquid hydrocarbons. These liquid hydrocarbons are then extracted from underground using traditional oil and gas drilling and production techniques. This paper discusses the pyrolysis mechanism, pore evolution, mineral transformation, and classification of electric heating technology for oil shale. It also summarizes research progress aimed at improving in situ extraction technology for oil shale, providing valuable insights for further research and development in this field.

Accurately diagnosing Alzheimer’s disease is essential for improving elderly health. Meanwhile, accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer’s disease. However, most of the existing methods perform Alzheimer’s disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two tasks. To address this challenging problem, we propose a novel multi-task learning method, which uses feature interaction to explore the relationship between Alzheimer’s disease diagnosis and mini-mental state examination score prediction. In our proposed method, features from each task branch are firstly decoupled into candidate and non-candidate parts for interaction. Then, we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches, which can promote the learning of each task. We validate the effectiveness of our proposed method on multiple datasets. In Alzheimer’s disease neuroimaging initiative 1 dataset, the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86% and 2.5, respectively. Experimental results show that our proposed method outperforms most state-of-the-art methods. Our proposed method enables accurate Alzheimer’s disease diagnosis and mini-mental state examination score prediction. Therefore, it can be used as a reference for the clinical diagnosis of Alzheimer’s disease, and can also help doctors and patients track disease progression in a timely manner.

Autism Spectrum Disorder (ASD) is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients, making early screening and intervention of ASD critical. With the development of the machine learning and neuroimaging technology, extensive research has been conducted on machine classification of ASD based on structural Magnetic Resonance Imaging (s-MRI). However, most studies involve with datasets where participants’ age are above 5 and lack interpretability. In this paper, we propose a machine learning method for ASD classification in children with age range from 0.92 to 4.83 years, based on s-MRI features extracted using Contrastive Variational AutoEncoder (CVAE). 78 s-MRIs, collected from Shenzhen Children’s Hospital, are used for training CVAE, which consists of both ASD-specific feature channel and common-shared feature channel. The ASD participants represented by ASD-specific features can be easily discriminated from Typical Control (TC) participants represented by the common-shared features. In case of degraded predictive accuracy when data size is extremely small, a transfer learning strategy is proposed here as a potential solution. Finally, we conduct neuroanatomical interpretation based on the correlation between s-MRI features extracted from CVAE and surface area of different cortical regions, which discloses potential biomarkers that could help target treatments of ASD in the future.

Abstract Single-cell RNA sequencing (scRNA-seq) is a key technology for investigating cell development and analysing cell diversity across various diseases. However, the high dimensionality and extreme sparsity of scRNA-seq data pose great challenges for accurate cell type annotation. To address this, we developed a new cell-type annotation model called scGAA (general gated axial-attention model for accurate cell-type annotation of scRNA-seq). Based on the transformer framework, the model decomposes the traditional self-attention mechanism into horizontal and vertical attention, considerably improving computational efficiency. This axial attention mechanism can process high-dimensional data more efficiently while maintaining reasonable model complexity. Additionally, the gated unit was integrated into the model to enhance the capture of relationships between genes, which is crucial for achieving an accurate cell type annotation. The results revealed that our improved transformer model is a promising tool for practical applications. This theoretical innovation increased the model performance and provided new insights into analytical tools for scRNA-seq data.

Cadmium (Cd), a widespread and serious environmental pollutant, has recently garnered increasing scientific scrutiny due to its profound adverse effects. Although the evidence for Cd-induced reproductive toxicity is well established, it remains elusive on the intricate dose-response relationship and underlying molecular mechanisms, especially for transgenerational toxicity in animals. Here, we employed fruit fly (Drosophila melanogaster) as a model organism to examine the reproductive performance across five generations by parental exposure to varying concentrations of Cd (5, 50, and 500 μM). Firstly, our observations on the number of eggs laid, pupae formed, and adult flies emerged on the directly exposed generation (F0) confirmed a dose-dependent decline in fecundity. Transcriptome analysis revealed that, Cd-induced oxidative stress and ion transport disruption in the F0 generation could underlie synaptic dysfunction and impaired follicle cell development, impacting reproductive behavior and oocyte fertility. Employing dose-response analysis, Wnt signaling pathway and mTOR signaling pathway were identified as early molecular responses to Cd-induced toxicity. Secondly, sustained detrimental effects were observed for at least two to three generations after Cd removal. At the epigenetic level, Cd could perturb fecundity across generations by modulating Dnmt2 expression, a pivotal regulator of methylation processes. Moreover, despite phenotypic recovery in F4, persistent molecular changes indicate enduring toxicity, highlighting the need for vigilance against environmental Cd contamination and its long-term effects. Collectively, our findings enhance the understanding of Cd-induced reproductive toxicity and its transgenerational effects, and highlight the need to further improve the assessment of the multigenerational consequences of environmental Cd contamination.

BackgroundDiagnosing bipolar disorder poses a challenge in clinical practice and demands a substantial time investment. With the growing utilization of artificial intelligence in mental health, researchers are endeavoring to create AI-based diagnostic models. In this context, some researchers have sought to develop machine learning models for bipolar disorder diagnosis. Nevertheless, the accuracy of these diagnoses remains a subject of controversy. Consequently, we conducted this systematic review to comprehensively assess the diagnostic value of machine learning in the context of bipolar disorder.MethodsWe searched PubMed, Embase, Cochrane, and Web of Science, with the search ending on April 1, 2023. QUADAS-2 was applied to assess the quality of the literature included. In addition, we employed a bivariate mixed-effects model for the meta-analysis.Results18 studies were included, covering 3152 participants, including 1858 cases of bipolar disorder. 28 machine learning models were encompassed. Sensitivity and specificity in discriminating between bipolar disorder and normal individuals were 0.88 (9.5% CI: 0.74~0.95) and 0.89 (95% CI: 0.73~0.96) respectively, and the SROC curve was 0.94(95% CI: 0.92~0.96). The sensitivity and specificity for distinguishing between bipolar disorder and depression were 0.84 (95%CI: 0.80~0.87) and 0.82 (95%CI: 0.75~0.88) respectively. The SROC curve was 0.89 (95%CI: 0.86~0.91).ConclusionsMachine learning methods can be employed for discriminating and diagnosing bipolar disorder. However, in current research, they are predominantly utilized for binary classification tasks, limiting their progress in clinical practice. Therefore, in future studies, we anticipate the development of more multi-class classification tasks to enhance the clinical applicability of these methods.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023427290, identifier CRD42023427290.

Introduction: EGFR tyrosine kinase inhibitor (TKI) is the standard adjuvant treatment for patients with stages IB to IIIA EGFR-mutated NSCLC. Nevertheless, adapting this approach to include a molecular residual disease (MRD)-guided de-escalation strategy warrants further investigation. Methods: From January 2019 to December 2022, 71 patients with stages I to III NSCLC and EGFR (exon 19 deletion or L858R) mutations were enrolled in this observational study. A total of 375 blood samples were analyzed using the MRD_Navigator assay. Among them, 27 patients suspended EGFR TKI treatment based on undetectable MRD and were thus included in the adaptive, de-escalation group. Results: Overall, the sensitivity of longitudinal MRD was 86.2%. Only four patients (11.8%) recurred with longitudinal undetectable MRD, indicating a negative predictive value of 88.2%. Of the patients who had detectable MRD after surgery, nine subsequently received EGFR TKI treatment, with only one (11.1%) achieving persistent circulating tumor DNA clearance post–EGFR TKI. Furthermore, 22 patients with stages IB to III disease who had previously suspended their TKI treatment based on undetectable MRD were included in the adaptive group, with an average duration of TKI 3.9 (range: 0–35.0) months. The 2-year disease-free survival rate of these 22 patients was 80.2%, and the median was not reached. Five patients (n = 5 of 22, 22.7%) had disease recurrence during the period of drug cessation but were stable under EGFR TKI treatment until the latest follow-up. Two patients remained in complete remission. Conclusions: Our initial findings underscore the potential of an adaptive, de-escalation approach to adjuvant EGFR TKIs based on circulating tumor DNA-MRD monitoring.

Medical images and radiology reports are essential for physicians to diagnose medical conditions. However, the vast diversity and cross-source heterogeneity inherent in these data have posed significant challenges to the generalizability of current data-mining methods for clinical decision-making. Recently, multimodal large language models (MLLMs), especially Gemini-Vision-series (Gemini) and GPT-4-series (GPT-4) models, have revolutionized numerous domains, significantly impacting the medical field. In this study, we conducted a detailed evaluation of the performance of the Gemini series models (including Gemini-1.0-Pro-Vision, Gemini-1.5-Pro, and Gemini-1.5-Flash) and GPT series models (including GPT-4o, GPT-4-Turbo, and GPT-3.5-Turbo) across 14 medical datasets, covering 5 medical imaging categories (dermatology, radiology, dentistry, ophthalmology, and endoscopy) and 3 radiology report datasets. The investigated tasks encompass disease classification, lesion segmentation, anatomical localization, disease diagnosis, report generation, and lesion detection. Moreover, we also validated the performance of the Claude-3-Opus, Yi-Large, Yi-Large-Turbo, and LLaMA 3 models to gain a comprehensive understanding of the MLLM models in the medical field. Our experimental results demonstrated that Gemini-series models excelled in report generation and lesion detection but faces challenges in disease classification and anatomical localization. Conversely, GPT-series models exhibited proficiency in lesion segmentation and anatomical localization but encountered difficulties in disease diagnosis and lesion detection. Additionally, both the Gemini series and GPT series contain models that have demonstrated commendable generation efficiency. While both models hold promise in reducing physician workload, alleviating pressure on limited healthcare resources, and fostering collaboration between clinical practitioners and artificial intelligence technologies, substantial enhancements and comprehensive validations remain imperative before clinical deployment.

With the rapidly development of biotechnology, it is now possible to obtain single-cell multi-omics data in the same cell. However, how to integrate and analyze these single-cell multi-omics data remains a great challenge. Herein, we introduce an interpretable multitask framework (scMoMtF) for comprehensively analyzing single-cell multi-omics data. The scMoMtF can simultaneously solve multiple key tasks of single-cell multi-omics data including dimension reduction, cell classification and data simulation. The experimental results shows that scMoMtF outperforms current state-of-the-art algorithms on these tasks. In addition, scMoMtF has interpretability which allowing researchers to gain a reliable understanding of potential biological features and mechanisms in single-cell multi-omics data.

Porcine hemagglutinating encephalomyelitis virus (PHEV), porcine pseudorabies virus (PRV), and classical swine fever virus (CSFV) are currently prevalent worldwide and cause similar neurological symptoms in infected pigs. It is very important to establish a detection method that can rapidly and accurately detect and differentiate these three viruses. Targeting the PHEV N gene, PRV gB gene, and CSFV 5′ untranslated region (5′UTR), three pairs of specific primers and probes were designed, and a triplex crystal digital reverse transcription-PCR (cdRT-PCR) was developed to detect PHEV, PRV, and CSFV. The results indicated that this assay had high sensitivity, and the limitation of detection (LODs) for PHEV, PRV, and CSFV were 4.812, 4.047, and 5.243 copies/reaction, respectively, which was about 50 times higher than that of multiplex real-time quantitative RT-PCR (RT-qPCR). This assay showed good specificity, without cross-reaction with other important swine pathogens, i.e., FMDV, PRRSV, PEDV, SIV, TGEV, PoRV, and PCV2. This assay had high repeatability, with intra-assay coefficients of variation (CVs) of 0.73–1.87%, and inter-assay CVs of 0.57–2.95%. The developed assay was used to test 1,367 clinical tissue samples from Guangxi province in China, and the positive rates of PHEV, PRV, and CSFV were 3.44% (47/1,367), 1.24% (17/1,367), and 1.90% (26/1,367), respectively, with a coincidence rate of 98.98% and a Kappa value of 0.94 to the reference multiplex RT-qPCR. The established triplex cdRT-PCR was a highly rapid, sensitive, and accurate assay to detect and differentiate PHEV, PRV, and CSFV.

BackgroundPsoriatic arthritis (PSA) is an inflammatory joint disease associated with psoriasis (PSO) that can be easily missed. Existing PSA screening tools ignore objective serologic indicators. The aim of this study was to develop a disease screening model and the Psoriatic Arthritis Inflammation Index (PSAII) based on serologic data to enhance the efficiency of PSA screening.MethodA total of 719 PSO and PSA patients from the National Health and Nutrition Examination Survey (NHANES) (as training set and test set) and 135 PSO and PSA patients who were seen at The First Affiliated Hospital of Zhejiang Chinese Medical University (as external validation set) were selected, 31 indicators for these patients were collected as potential input features for the model. Least Absolute Shrinkage and Selection Operator (LASSO) was used to identify PSA-related features. Five models of logistic regression (LR), random forest, k-nearest neighbor, gradient augmentation and neural network were developed in the training set using quintuple cross validation. And we developed PSAII based on the results of LASSO regression and weights of logistic model parameters. All performance metrics are derived on the test set and the external validation set.ResultsFive variables were selected to build models, including age, lymphocyte percentage, neutrophil count, eosinophilic count, and C-reactive protein. In all established models, the LR model performed the best, with an Area Under Curve (AUC) of 0.87 (95% confidence interval (CI): 0.83-0.90) on the test set; on the external validation set the AUC was 0.82 (95%CI: 0.74-0.90). The PSAII formula was PSAII = percentage of lymphocytes × C-reactive protein/(neutrophil count × eosinophilic count × 10). The AUC of PSAII in the test is 0.93 (95%CI: 0.88-0.97), and the cutoff value is 18. The AUC of the external validation set is 0.81 (95%CI: 0.72-0.89).ConclusionsThis study developed and validated five models to assist screening for PSA by analyzing serum data from NHANES and Chinese populations. The LR model demonstrated the best performance. We created PSAII for PSA screening. However, the high false positive rate of PSAII makes it necessary to combine it with other PSA screening tools when applied.

Acute-on-chronic liver failure (ACLF) and decompensated cirrhosis (DC) are life-threatening syndromes that can develop at the end-stage of chronic hepatitis B virus (HBV) infection. Both ACLF and DC are complicated by hepatic and extrahepatic pathogeneses. To better understand the compartment-specific metabolic modulations related to their pathogenesis, HBV-DC, HBV-ACLF patients, and controls (30 each) were analyzed by metabolomics using portal (Port), hepatic vein (Hep), and peripheral (Peri) serum. Compartment ratios of metabolites (RatioHep/Port, RatioPeri/Hep, and RatioPort/Peri) were calculated. The liver tissues (10 per group) were analyzed using transcriptomics and metabolomics. An additional 75 patients with ACLF, 20 with DC, and 20 with liver cirrhosis (LC) were used to confirm oxlipid dysregulation. Both multi-omics datasets suggest suppressed energy, amino acid, and pyrimidine metabolism in the ACLF/DC liver. The serum metabolomic variations were contributed primarily by disease rather than sampling compartments, as both HBV-ACLF and HBV-DC patients demonstrated abnormal profiles of amino acids and peptides, indoles, purines, steroids, and benzimidazoles. In ACLF/DC patients, impaired hepatic metabolism resulted in a highly correlated hepatic and portal vein serum metabolome and release of inflammatory lipids and heme metabolites from the liver. HBV-ACLF showed higher RatioPeri/Hep of extrahepatic inflammatory oxlipids, while HBV-DC patients showed higher RatioPort/Peri of gut microbial metabolites. An inflammatory oxlipid outburst was confirmed in the early stages of HBV-ACLF. The inflammatory effects of the selected oxlipids were confirmed in monocytes. These findings support a synergy between liver-specific mechanisms and systemic inflammation in ACLF/DC development, and that pro-inflammatory oxlipids are metabolic signatures of early HBV-ACLF.

Abstract Background Glucagon-like peptide-1 receptor (GLP1R) agonists have been shown to reduce major cardiovascular events in diabetic patients, but their role in heart failure (HF) remains controversial. Recent evidence implies their potential benefits on cardiometabolism such as lipid metabolism, which may contribute to lowering the risk of HF. Consequently, we designed a Mendelian randomization (MR) study to investigate the causal relationships of circulating lipids mediating GLP1R agonists in HF. Methods The available cis-eQTLs for GLP1R target gene were selected as instrumental variables (IVs) of GLP1R agonism. Positive control analyses of type 2 diabetes mellitus (T2DM) and body mass index (BMI) were conducted to validate the enrolled IVs. Two-sample MR was performed to evaluate the associations between GLP1R agonism and HF as well as left ventricular ejection fraction (LVEF). Summary data for HF and LVEF were obtained from two genome-wide association studies (GWASs), which included 977,323 and 40,000 individuals of European ancestry, respectively. The primary method employed was the random-effects inverse variance weighted, with several other methods used for sensitivity analyses, including MR-Egger, MR PRESSO, and weighted median. Additionally, multivariable MR and mediation MR were applied to identify potentially causal lipid as mediator. Results A total of 18 independent IVs were included. The positive control analyses showed that GLP1R agonism significantly reduced the risk of T2DM (OR = 0.79, 95% CI = 0.75–0.85, p

Abstract Seed size/weight plays an important role in determining crop yield, yet only few genes controlling seed size have been characterized in soybean. Here, we perform a genome-wide association study and identify a major quantitative trait locus (QTL), named GmSW17 (Seed Width 17), on chromosome 17 that determine soybean seed width/weight in natural population. GmSW17 encodes a ubiquitin-specific protease, an ortholog to UBP22, belonging to the ubiquitin-specific protease (USPs/UBPs) family. Further functional investigations reveal that GmSW17 interacts with GmSGF11 and GmENY2 to form a deubiquitinase (DUB) module, which influences H2Bub levels and negatively regulates the expression of GmDP-E2F-1, thereby inhibiting the G1-to-S transition. Population analysis demonstrates that GmSW17 undergo artificial selection during soybean domestication but has not been fixed in modern breeding. In summary, our study identifies a predominant gene related to soybean seed weight, providing potential advantages for high-yield breeding in soybean.

Abstract Alkylamine structures represent one of the most functional and widely used in organic synthesis and drug design. However, the general methods for the functionalization of the shielded and deshielded alkyl radicals remain elusive. Here, we report a general deoxygenative amination protocol using alcohol-derived carbazates and nitrobenzene under electrochemical conditions. A range of primary, secondary, and tertiary alkylamines are obtained. This practical procedure can be scaled up through electrochemical continuous flow technique.

This study explores the potential of Artificial Intelligence (AI) in early screening and prognosis of Dry Eye Disease (DED), aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners. Despite the promising opportunities, challenges such as diverse diagnostic evidence, complex etiology, and interdisciplinary knowledge integration impede the interpretability, reliability, and applicability of AI-based DED detection methods. The research conducts a comprehensive review of datasets, diagnostic evidence, and standards, as well as advanced algorithms in AI-based DED detection over the past five years. The DED diagnostic methods are categorized into three groups based on their relationship with AI techniques: (1) those with ground truth and/or comparable standards, (2) potential AI-based methods with significant advantages, and (3) supplementary methods for AI-based DED detection. The study proposes suggested DED detection standards, the combination of multiple diagnostic evidence, and future research directions to guide further investigations. Ultimately, the research contributes to the advancement of ophthalmic disease detection by providing insights into knowledge foundations, advanced methods, challenges, and potential future perspectives, emphasizing the significant role of AI in both academic and practical aspects of ophthalmology.

AIM: To investigate ocular surface disorders and tear function changes in patients with acne vulgaris and explore the potential relationship between acne vulgaris and dry eye. METHODS: This cross-sectional study included right eyes of 53 patients with acne vulgaris and 54 healthy controls. The participants completed the Ocular Surface Disease Index (OSDI) questionnaire. The following ocular surface-related parameters were measured: tear meniscus height (TMH), noninvasive tear breakup time (NIBUT), Schirmer I test (SIT), lipid layer thickness (LLT) score of the tear film, meibum score, meibomian gland orifice obstruction score, the ratio of meibomian gland loss, conjunctival hyperemia score, and corneal fluorescein staining (CFS) score. RESULTS: The stability of the tear film decreased in acne vulgaris patients. In the acne group, the TMH and NIBUT were lower, whereas the OSDI, meibum score, meibomian gland orifice obstruction score, ratio of meibomian gland loss, and conjunctival hyperemia score were higher compared with controls (P0.05). In two dry eye groups, the TMH, NIBUT, and LLT score were lower in the acne with dry eye (acne-DE) group, and the meibum score, meibomian gland orifice obstruction score, ratio of meibomian gland loss and conjunctival hyperemia score in the acne-DE group were higher (P0.05). CONCLUSION: Patients with moderate-to-severe acne vulgaris are more likely to experience dry eye than those without acne vulgaris. Reduced tear film stability and meibomian gland structure dysfunction are more pronounced in patients with moderate-to-severe acne and dry eye.

Abstract Introduction Postherpetic neuralgia (PHN), a complication of herpes zoster, significantly impacts the quality of life of affected patients. Research indicates that early intervention for pain can reduce the occurrence or severity of PHN. This study aims to develop a predictive model and scoring table to identify patients at risk of developing PHN following acute herpetic neuralgia, facilitating informed clinical decision-making. Methods We conducted a retrospective review of 524 hospitalized patients with herpes zoster at The First Affiliated Hospital of Zhejiang Chinese Medical University from December 2020 to December 2023 and classified them according to whether they had PHN, collecting a comprehensive set of 30 patient characteristics and disease-related indicators, 5 comorbidity indicators, 2 disease score values, and 10 serological indicators. Relevant features associated with PHN were identified using the least absolute shrinkage and selection operator (LASSO). Then, the patients were divided into a training set and a test set in a 4:1 ratio, with comparability tested using univariate analysis. Six models were established in the training set using machine learning methods: support vector machines, logistic regression, random forest, k-nearest neighbor, gradient boosting, and neural network. The performance of these models was evaluated in the test set, and a nomogram based on logistic regression was used to create a PHN prediction score table. Results Eight non-zero characteristic variables selected from the LASSO regression results were included in the model, including age [area under the curve (AUC) = 0.812, p

Abstract Recently, environmental temperature has been shown to regulate bone homeostasis. However, the mechanisms by which cold exposure affects bone mass remain unclear. In our present study, we observed that exposure to cold temperature (CT) decreased bone mass and quality in mice. Furthermore, a transplant of exosomes derived from the plasma of mice exposed to cold temperature (CT-EXO) can also impair the osteogenic differentiation of BMSCs and decrease bone mass by inhibiting autophagic activity. Rapamycin, a potent inducer of autophagy, can reverse cold exposure or CT-EXO-induced bone loss. Microarray sequencing revealed that cold exposure increases the miR-25-3p level in CT-EXO. Mechanistic studies showed that miR-25-3p can inhibit the osteogenic differentiation and autophagic activity of BMSCs. It is shown that inhibition of exosomes release or downregulation of miR-25-3p level can suppress CT-induced bone loss. This study identifies that CT-EXO mediates CT-induced osteoporotic effects through miR-25-3p by inhibiting autophagy via targeting SATB2, presenting a novel mechanism underlying the effect of cold temperature on bone mass.