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To explore a robust tool for advancing digital breeding practices through an artificial intelligence-driven phenotype prediction expert system, we undertook a thorough analysis of 11 non-linear regression models. Our investigation specifically emphasized the significance of Support Vector Regression (SVR) and SHapley Additive exPlanations (SHAP) in predicting soybean branching. By using branching data (phenotype) of 1918 soybean accessions and 42 k SNP (Single Nucleotide Polymorphism) polymorphic data (genotype), this study systematically compared 11 non-linear regression AI models, including four deep learning models (DBN (deep belief network) regression, ANN (artificial neural network) regression, Autoencoders regression, and MLP (multilayer perceptron) regression) and seven machine learning models (e.g., SVR (support vector regression), XGBoost (eXtreme Gradient Boosting) regression, Random Forest regression, LightGBM regression, GPs (Gaussian processes) regression, Decision Tree regression, and Polynomial regression). After being evaluated by four valuation metrics: R 2 (R-squared), MAE (Mean Absolute Error), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error), it was found that the SVR, Polynomial Regression, DBN, and Autoencoder outperformed other models and could obtain a better prediction accuracy when they were used for phenotype prediction. In the assessment of deep learning approaches, we exemplified the SVR model, conducting analyses on feature importance and gene ontology (GO) enrichment to provide comprehensive support. After comprehensively comparing four feature importance algorithms, no notable distinction was observed in the feature importance ranking scores across the four algorithms, namely Variable Ranking, Permutation, SHAP, and Correlation Matrix, but the SHAP value could provide rich information on genes with negative contributions, and SHAP importance was chosen for feature selection. The results of this study offer valuable insights into AI-mediated plant breeding, addressing challenges faced by traditional breeding programs. The method developed has broad applicability in phenotype prediction, minor QTL (quantitative trait loci) mining, and plant smart-breeding systems, contributing significantly to the advancement of AI-based breeding practices and transitioning from experience-based to data-based breeding.

The retrieval of hit/lead compounds with novel scaffolds during early drug development is an important but challenging task. Various generative models have been proposed to create drug-like molecules. However, the capacity of these generative models to design wet-lab-validated and target-specific molecules with novel scaffolds has hardly been verified. We herein propose a generative deep learning (GDL) model, a distribution-learning conditional recurrent neural network (cRNN), to generate tailor-made virtual compound libraries for given biological targets. The GDL model is then applied to RIPK1. Virtual screening against the generated tailor-made compound library and subsequent bioactivity evaluation lead to the discovery of a potent and selective RIPK1 inhibitor with a previously unreported scaffold, RI-962. This compound displays potent in vitro activity in protecting cells from necroptosis, and good in vivo efficacy in two inflammatory models. Collectively, the findings prove the capacity of our GDL model in generating hit/lead compounds with unreported scaffolds, highlighting a great potential of deep learning in drug discovery. Retrieval of a new starting active compound with a novel scaffold during early drug development is an important but challenging task. Here, the authors propose a generative deep learning model and by applying this model they discover a potent and highly selective RIPK1 inhibitor with a previously unreported scaffold.

Genome-wide non-invasive prenatal testing (GW-NIPT) for prenatal screening has been widely implemented. However, the related clinical data is still insufficient. Here, we evaluated the clinical performance of GW-NIPT as a first-tier screening test for detecting fetal aneuploidy and copy number variation (CNV). The study included 59,877 pregnant women who underwent GW-NIPT at Shenzhen Baoan Women's and Children's Hospital, China, from November 2017 to May 2021. NIPT was performed on the BGISEQ-500 platform. Fetal karyotype analysis, chromosomal microarray analysis (CMA) and fluorescence in situ hybridization were used for invasive diagnostic procedures, and postnatal outcomes were collected. Among 59,877 pregnant women who underwent GW-NIPT, 59,771 were successfully tested. Of these, 499 (0.83%) were identified with 504 high-risk fetal chromosomal abnormalities, including 5 cases each carrying two distinct abnormalities. Follow-up analysis demonstrated that GW-NIPT sensitivity exceeded 97% for fetal aneuploidies and was 63.6% for CNV (≥5 Mb). The positive predictive values for T21, T18, T13, sex chromosome aneuploidy, rare autosomal aneuploidy, and CNV (≥5 Mb) were calculated as 83.1%, 25.8%, 10.3%, 51.9%, 2.0%, and 33.9%, respectively. For confirmed fetal mosaicism, the detection rate of NIPT was 70.6%, which was consistent with that of CMA (70.6%). GW-NIPT has high sensitivity in screening fetal aneuploidy and moderate clinical utility in detecting CNV and fetal mosaicism, demonstrating that GW-NIPT holds significant application value in current and future prenatal screening procedures.

Carotid atherosclerosis is a chronic inflammatory disease, which is a major cause of ischemic stroke. The purpose of this study was to analyze the relationship between carotid atherosclerosis and novel inflammatory markers, including platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), platelet to neutrophil ratio (PNR), neutrophil to lymphocyte platelet ratio (NLPR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI), in order to find the best inflammatory predictor of carotid atherosclerosis. We included 10015 patients who underwent routine physical examinations at the physical examination center of our hospital from January 2016 to December 2019, among whom 1910 were diagnosed with carotid atherosclerosis. The relationship between novel inflammatory markers and carotid atherosclerosis was analyzed by logistic regression, and the effectiveness of each factor in predicting carotid atherosclerosis was evaluated by receiver operating characteristic (ROC) curve and area under the curve (AUC). The level of PLR, LMR and PNR in the carotid atherosclerosis group were lower than those in the non-carotid atherosclerosis group, while NLR, NLPR, SII, SIRI and AISI in the carotid atherosclerosis group were significantly higher than those in the non-carotid atherosclerosis group. Logistic regression analysis showed that PLR, NLR, LMR, PNR, NLPR, SII, SIRI, AISI were all correlated with carotid atherosclerosis. The AUC value of NLPR was the highest, which was 0.67, the cut-off value was 0.78, the sensitivity was 65.8%, and the specificity was 57.3%. The prevalence rate of carotid atherosclerosis was 12.4% below the cut-off, 26.6% higher than the cut-off, and the prevalence rate increased by 114.5%. New inflammatory markers were significantly correlated with carotid atherosclerosis, among which NLPR was the optimum inflammatory marker to predict the risk of carotid atherosclerosis.

Background Accumulating studies have suggested metabolic syndrome (MetS) contributed to colorectal cancer (CRC) development. However, advanced CRC might decrease the detection proportion of MetS due to chronic malnutrition, we included patients with early-stage CRC to examine the associations among MetS, onset age, and different tumorigenesis pathways of CRC. Methods We conducted a retrospective study that included 638 patients with early-stage CRC from January 2014 to December 2018. Patient information was collected from the medical record system and further refined during the follow-up. Stratified analyses of the associations between MetS and different stratification factors were determined by the Cochran‒Mantel‒Haenszel test. Results There were 16 (13.3%) and 111 (21.4%) cases suffering from MetS in the early-onset and late-onset CRC groups, respectively. MetS coexisted in early-stage CRC patients ≥ 50 years of age more frequently than patients < 50 years of age (OR 1.77; 95% CI 1.01 to 3.12), but not for women patients (OR 0.84; 95% CI 0.79 to 0.90). MetS patients were associated with a higher risk of advanced serrated lesions than that of conventional adenomas (OR 1.585; 95% CI 1.02 to 2.45), especially in patients ≥ 50 years (OR 1.78; 95% CI 1.11 to 2.85). Conclusions Metabolic dysregulation might partly contribute to the incidence of colorectal serrated lesions. Prevention of MetS should be highly appreciated in the early diagnosis and early treatment of the colorectal cancer system, especially in patients ≥ 50 years.

Ferroptosis is a new type of programmed cell death characterized by iron-dependent lipid peroxidation. Ferroptosis inhibition is thought as a promising therapeutic strategy for a variety of diseases. Currently, a majority of known ferroptosis inhibitors belong to either antioxidants or iron-chelators. Here we report a new ferroptosis inhibitor, termed YL-939, which is neither an antioxidant nor an iron-chelator. Chemical proteomics revealed the biological target of YL-939 to be prohibitin 2 (PHB2). Mechanistically, YL-939 binding to PHB2 promotes the expression of the iron storage protein ferritin, hence reduces the iron content, thereby decreasing the susceptibility to ferroptosis. We further showed that YL-939 could substantially ameliorate liver damage in a ferroptosis-related acute liver injury model by targeting the PHB2/ferritin/iron axis. Overall, we identified a non-classical ferroptosis inhibitor and revealed a new regulation mechanism of ferroptosis. These findings may present an attractive intervention strategy for ferroptosis-related diseases. Ferroptosis is a promising therapeutic target for a variety of diseases, but a majority of known ferroptosis inhibitors belong to either antioxidants or iron chelators. Here, the authors discover a new non-classical small molecule inhibitor that is a PHB2 binder and show it ameliorates liver damage in an acute liver injury model.

Background Particulate matter (PM) refers to solid or liquid particles suspended in the atmosphere. These particles can be inhaled during normal respiration, leading to various respiratory diseases including upper respiratory tract infections (URTIs). We comprehensively evaluated the ambient particulate matter pollution-related disease burden. Methods Due to the particularity of Global Burden of Disease 2021 study (GBD 2021), this study only included data on URTIs attributed to PM, and the age limit was under 5 years old. We first assessed the global and subtype-specific mortality, disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs) in 2021, along with their age-standardized rates. Additionally, linear regression models were employed to analyze temporal trends in disease burden. We will calculate the corresponding estimated annual percentage change (EAPC) based on the changes in the number of deaths, DALYs and the age-standardized data of both from 1990 to 2021. Cluster analysis was used to examine regional variations in disease burden across Global Burden of Disease study regions. Finally, ARIMA and exponential smoothing (ES) models were applied to forecast disease burden over the next 25 years. Results For indoor PM pollution, in 2021, there were 93.98 deaths per 100,000 population and 9,195.39 DALYs globally in children under 5 years of age. Females exhibited higher risks than males, and regions with a low sociodemographic index (SDI) faced elevated risks. Significant disparities in disease burden were observed across GBD regions and nations. Compared to 1990, mortality and DALYs per 100,000 population declined by 52.28% and 51.38%, respectively. ARIMA projections suggest continued declines in the absolute number of mortality and DALYs for both sexes by 2050, though age-standardized rates may increase for males while decreasing for females. For outdoor PM pollution, 2021 recorded 22.58 deaths per 100,000 population and 2,511.13 DALYs globallyin children under 5 years of age. Females and low-middle SDI regions were at higher risk. Mortality and DALYs per 100,000 population decreased by 55.10% and 48.37% compared to 1990. ARIMA forecasts indicate further reductions in mortality, DALYs, and age-standardized rates for both sexes by 2050. Conclusion Particulate pollutants, particularly indoor PM, pose a significant global public health threat. Tailored strategies based on national conditions are urgently needed to mitigate their impact.

Background Regulatory B (Breg) cells represent one of the B cell subsets that infiltrate solid tumors and exhibit distinct phenotypes in different tumor microenvironments. However, the phenotype, function and clinical relevance of Breg cells in human hepatocellular carcinoma (HCC) are presently unknown. Methods Flow cytometry analyses were performed to determine the levels, phenotypes and functions of TIM-1 + Breg cells in samples from 51 patients with HCC. Kaplan-Meier plots for overall survival and disease-free survival were generated using the log-rank test. TIM-1 + Breg cells and CD8 + T cells were isolated, stimulated and/or cultured in vitro for functional assays. Exosomes and B cells were isolated and cultured in vitro for TIM-1 + Breg cell expansion assays. Results Patients with HCC showed a significantly higher TIM-1 + Breg cell infiltration in their tumor tissue compared with the paired peritumoral tissue. The infiltrating TIM-1 + Breg cells showed a CD5 high CD24 − CD27 −/+ CD38 +/high phenotype, expressed high levels of the immunosuppressive cytokine IL-10 and exhibited strong suppressive activity against CD8 + T cells. B cells activated by tumor-derived exosomes strongly expressed TIM-1 protein and were equipped with suppressive activity against CD8 + T cells similar to TIM-1 + Breg cells isolated from HCC tumor tissue. Moreover, the accumulation of TIM-1 + Breg cells in tumors was associated with advanced disease stage, predicted early recurrence in HCC and reduced HCC patient survival. Exosome-derived HMGB1 activated B cells and promoted TIM-1 + Breg cell expansion via the Toll like receptor (TLR) 2/4 and mitogen-activated protein kinase (MAPK) signaling pathways. Conclusions Our results illuminate a novel mechanism of TIM-1 + Breg cell-mediated immune escape in HCC and provide functional evidence for the use of these novel exosomal HMGB1-TLR2/4-MAPK pathways to prevent and to treat this immune tolerance feature of HCC.

Linear ubiquitination is a critical regulator of inflammatory signaling pathways. However, linearly ubiquitinated substrates and the biological significance of linear ubiquitination is incompletely understood. Here, we show that STAT1 has linear ubiquitination at Lys511 and Lys652 residues in intact cells, which inhibits STAT1 binding to the type-I interferon receptor IFNAR2, thereby restricting STAT1 activation and resulting in type-I interferon signaling homeostasis. Linear ubiquitination of STAT1 is removed rapidly by OTULIN upon type-I interferon stimulation, which facilitates activation of interferon-STAT1 signaling. Furthermore, viruses induce HOIP expression through the NF-κB pathway, which in turn increases linear ubiquitination of STAT1 and thereby inhibits interferon antiviral response. Consequently, HOIL-1L heterozygous mice have active STAT1 signaling and enhanced responses to type-I interferons. These findings demonstrate a linear ubiquitination-mediated switch between homeostasis and activation of type-I interferon signaling, and suggest potential strategies for clinical antiviral therapy. LUBAC is involved in adding linear ubiquitin chains to important immune signaling proteins. Here the authors show that this mechanism is effective in inhibiting STAT1-mediated interferon signaling, and that the deubiquitinase OTULIN can remove these linear ubiquitins from STAT1 to reactivate this antiviral signaling pathway.

Wilms tumor (WT) is a common renal malignancy in pediatric patients. Interleukin (receptors) (IL(R)s) play significant roles in tumor biology, however, their specific involvement in WT remains inadequately understood. We employed univariate Cox regression analysis to screen for certain IL(R) genes associated with prognosis and then analyzed their expression patterns. A prognostic model was constructed based on five selected IL(R)s using the LASSO Cox regression algorithm. To further elucidate the relationship between the prognostic model and the immune microenvironment, we conducted immune-related analyses. Additionally, we performed experiments to verify the roles of IL20RA and IL19 in WT. Finally, CNV, methylation and pan-cancer analysis were performed for IL19 and IL20RA . Our analysis ultimately identified five genes associated with prognosis: IL20RA, IL19, IL24, IL11 and IL17RD . The prognostic model incorporating these five genes demonstrated robust predictive power in both training and validation cohorts. Notably, IL19 and IL20RA were found to promote epithelial-mesenchymal transition (EMT) through the STAT3/SNAIL pathway, thereby contributing to tumor progression. Furthermore, significant differences in immune function and checkpoint expression were observed between the two groups. The high-risk group exhibiting a lower TIDE score, which suggests a potentially better response to immunotherapy. This study introduces a novel IL(R)-based prognostic signature for WT, highlighting IL20RA as a potential therapeutic target. These findings offer valuable insights for future studies on WT.