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Background The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD).Methods After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC). Furthermore, the interpretation of the chosen model was facilitated through the utilization of SHapley Additive exPlanation (SHAP) values and the Local Interpretable Model-Agnostic Explanations (LIME) algorithm.Results This study collected data and enrolled 5041 patients on CHF combined with CKD from 2008 to 2019, utilizing the Medical Information Mart for Intensive Care Unit. After selection, 22 of the 47 variables collected post-intensive care unit admission were identified as mortality-associated and subsequently utilized in the development of ML models. Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. Notably, the SHAP values highlighted the sequential organ failure assessment score, age, simplified acute physiology score II, and urine output as the four most influential variables in the XGBoost model. In addition, the LIME algorithm explains the individualized predictions.Conclusions In conclusion, our study accomplished the successful development and validation of ML models for predicting in-hospital mortality in critically ill patients with CHF combined with CKD. Notably, the XGBoost model emerged as the most efficacious among all the ML models employed.

Abstract Background Sodium-glucose cotransporter 2 (SGLT2) inhibition is recognized for its evident renoprotective benefits in diabetic renal disease. Recent data suggest that SGLT2 inhibition also slows down kidney disease progression and reduces the risk of acute kidney injury, regardless of whether the patient has diabetes or not, but the mechanism behind these observed effects remains elusive. The objective of this study is to utilize a mendelian randomization (MR) methodology to comprehensively examine the influence of metabolites in circulation regarding the impact of SGLT2 inhibition on kidney function. Methods We used a MR study to obtain associations between genetic proxies for SGLT2 inhibition and kidney function. We retrieved the most recent and comprehensive summary statistics from genome-wide association studies (GWAS) that have been previously published and involved kidney function parameters such as estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (UACR), and albuminuria. Additionally, we included blood metabolite data from 249 biomarkers in the UK Biobank for a more comprehensive analysis. We performed MR analyses to explore the causal relationships between SGLT2 inhibition and kidney function and two-step MR to discover potential mediating metabolites. Results The study found that a decrease in HbA1c levels by one standard deviation, which is genetically expected to result in SGLT2 inhibition, was linked to a decreased likelihood of developing type 2 diabetes mellitus (T2DM) (odds ratio [OR] = 0.55 [95% CI 0.35, 0.85], P = 0.007). Meanwhile, SGLT2 inhibition also protects eGFR (β = 0.05 [95% CI 0.03, 0.08], P = 2.45 × 10− 5) and decreased UACR (-0.18 [95% CI -0.33, -0.02], P = 0.025) and albuminuria (-1.07 [95% CI -1.58, -0.57], P = 3.60 × 10− 5). Furthermore, the study found that of the 249 metabolites present in the blood, only one metabolite, specifically the concentration of small high-density lipoprotein (HDL) particles, was significantly correlated with both SGLT2 inhibition and kidney function. This metabolite was found to play a crucial role in mediating the improvement of renal function through the use of SGLT2 inhibition (β = 0.01 [95% CI 0.005, 0.018], P = 0.001), with a mediated proportion of 13.33% (95% CI [5.71%, 26.67%], P = 0.020). Conclusions The findings of this investigation provide evidence in favor of a genetically anticipated biological linkage between the inhibition of SGLT2, the presence of circulating metabolites, and renal function. The findings demonstrate that the protective effect of SGLT2 inhibition on renal function is mostly mediated by HDL particle concentrations in circulating metabolites. These results offer significant theoretical support for both the preservation of renal function and a better comprehension of the mechanisms underlying SGLT2 inhibition.

Tert-butylhydroquinone (TBHQ) is a phenolic substance that is commonly employed to prevent food oxidation. Excessive or improper utilization of this antioxidant can not only impact food quality but may also pose potential risks to human health. In this study, an ultrasensitive, stable, and easily operable ratiometric electrochemical sensor was successfully fabricated by combining the tubular (3,4-ethylenedioxythiophene) (T-PEDOT) with single-wall carbon nanohorns (SWCNHs) for the detection of TBHQ antioxidants in food. The SWCNHs/T-PEDOT nanocomposite fabricated through ultrasound-assisted and template approaches was employed as the modified substrate for the electrode interface. The synergistic effect of SWCNHs and T-PEDOT, which possess excellent electrical conductivity and catalytic properties, enabled the modified electrode to showcase remarkable electrocatalytic performance towards TBHQ, with the redox signal of methylene blue serving as an internal reference. Under optimized conditions, the SWCNHs/T-PEDOT-modified electrode demonstrated good linearity within the TBHQ concentration range of 0.01–200.0 μg mL−1, featuring a low limit of detection (LOD) of 0.005 μg mL−1. The proposed ratiometric electrochemical sensor displayed favorable reproducibility, stability, and anti-interference capacity, thereby offering a promising strategy for monitoring the levels of TBHQ in oil-rich food products.

Nitrogen (N) is an essential macronutrient for plant growth and productivity. Leguminous plants establish symbiotic relationships with nitrogen-fixing rhizobial bacteria to use atmospheric dinitrogen gas to meet high N demand under low-N conditions. Nodule formation and N fixation are energy-consuming processes and are inhibited by nitrate present in the environment. Previous studies in model leguminous plants characterized NIN-LIKE PROTEIN (NLP) proteins that mediate nitrate control of root nodule symbiosis, but the mechanism by which nitrate regulates soybean root nodules via NLP remains unclear. In the soybean genome we found four homologs of AtNLP7, named GmNLP7a–GmNLP7d. We showed that the expression of GmNLP7s is responsive to nitrate but not to rhizobial infection and localized GmNLP7a to the nucleus. Downregulation of GmNLP7s increased nodule number, and overexpression of GmNLP7a (GmNLP7aOE) reduced nodule number regardless of nitrate availability, suggesting a negative role for GmNLP7s in nodulation. Nitrogenase activity in the GmNLP7aOE line was comparable to that of the wild type, indicating that GmNLP7a does not affect mature nodule activity. Overexpression of GmNLP7a downregulated the expression of GmNIN1a and GmENOD40-1. GmNLP7a interacted with GmNIN1a via the PB1 domain. Our results reveal a new regulator of GmNLP7 in nodulation and a molecular mechanism by which nitrate affects nodule number in soybean.

Introduction: Observational studies have shown that obesity is a risk factor for various autoimmune diseases. However, the causal relationship between obesity and autoimmune diseases is unclear. Mendelian randomization (MR) was used to investigate the causal effects of obesity on 15 autoimmune diseases. Methods: MR analysis employed instrumental variables, specifically single-nucleotide polymorphisms associated with obesity measures such as body mass index (BMI), waist circumference, hip circumference, and waist-to-hip ratio. The study utilized UK Biobank and FinnGen data to estimate the causal relationship between obesity and autoimmune diseases. Results: Genetically predicted BMI was associated with risk for five autoimmune diseases. The odds ratio per 1-SD increase in genetically predicted BMI, the OR was 1.28 (95% CI, 1.18–1.09; p < 0.001) for asthma, 1.37 (95% CI, 1.24–1.51; p < 0.001) for hypothyroidism, 1.52 (95% CI, 1.27–1.83; p < 0.001) for psoriasis, 1.22 (95% CI, 1.06–1.40; p = 0.005) for rheumatoid arthritis, and 1.55 (95% CI, 1.32–1.83; p < 0.001) for type 1 diabetes. However, after adjusting for genetic susceptibility to drinking and smoking, the correlation between BMI and rheumatoid arthritis was not statistically significant. Genetically predicted waist circumference, hip circumference, and waist and hip circumference were associated with 6, 6, and 1 autoimmune disease, respectively. Conclusion: This study suggests that obesity may be associated with an increased risk of several autoimmune diseases, such as asthma, hypothyroidism, psoriasis, rheumatoid arthritis, and type 1 diabetes.

When using transformer insulation oil as a liquid dielectric, the oil is easily polluted by the solid particles generated in the operation of the transformer, and these metallic impurity particles have a significant impact on the insulation performance inside the power transformer. The force of the metal particles suspended in the flow insulation oil is multidimensional, which will lead to a change in the movement characteristics of the metal particles. Based on this, this study explored the motion rules of suspended metallic impurity particles in mobile insulating oil in different electric field environments and the influencing factors. A multiphysical field model of the solid–liquid two-phase flow of single-particle metallic impurity particles in mobile insulating oil was constructed using the dynamic analysis method, and the particles’ motion characteristics in the oil in different electric field environments were simulated. The motion characteristics of metallic impurity particles under conditions of different particle sizes, oil flow velocities, and insulation oil qualities and influencing factors were analyzed to provide theoretical support for the detection of impurity particles in transformer insulation oil and enable accurate estimations of the location of equipment faults. Our results show that there are obvious differences in the trajectory of metallic impurity particles under different electric field distributions. The particles will move towards the region of high field intensity under an electric field, and the metallic impurity particles will not collide with the electrode under an AC field. When the electric field intensity and particle size increase, the trajectory of the metallic impurity particles between electrodes becomes denser, and the number of collisions between particles and electrodes and the motion speed both increase. Under the condition of a higher oil flow velocity, the number of collisions between metal particles and electrodes is reduced, which reduces the possibility of particle agglomeration. When the temperature of the insulation oil changes and the quality deteriorates, its dynamic viscosity changes. With a decrease in the dynamic viscosity of the insulation oil, the movement of the metallic impurity particles between the electrodes becomes denser, the collision times between the particles and electrodes increase, and the maximum motion speed of the particles increases.

Background This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD).Methods This study collected data on CKD patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Six ML approaches were used to build the model. Accuracy and area under the curve (AUC) were used to choose the best model. In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values.Results There were 8527 CKD patients eligible for participation; the median age was 75.1 (interquartile range: 65.0–83.5) years, and 61.7% (5259/8527) were male. We developed six ML models with clinical variables as input factors. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.860. According to the SHAP values, the sequential organ failure assessment score, urine output, respiratory rate, and simplified acute physiology score II were the four most influential variables in the XGBoost model.Conclusions In conclusion, we successfully developed and validated ML models for predicting mortality in critically ill patients with CKD. Among all ML models, the XGBoost model is the most effective ML model that can help clinicians accurately manage and implement early interventions, which may reduce mortality in critically ill CKD patients with a high risk of death.

Abstract This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in patients with sepsis-associated acute kidney injury (SA-AKI). This study collected data on SA-AKI patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. After employing Lasso regression for feature selection, six ML approaches were used to build the model. The optimal model was chosen based on precision and area under curve (AUC). In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values and Local Interpretable Model-Agnostic Explanations (LIME) algorithms. There were 8129 sepsis patients eligible for participation; the median age was 68.7 (interquartile range: 57.2–79.6) years, and 57.9% (4708/8129) were male. After selection, 24 of the 44 clinical characteristics gathered after intensive care unit admission remained linked with prognosis and were utilized developing ML models. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.794. According to the SHAP values, the sequential organ failure assessment score, respiration, simplified acute physiology score II, and age were the four most influential variables in the XGBoost model. Individualized forecasts were clarified using the LIME algorithm. We built and verified ML models that excel in early mortality risk prediction in SA-AKI and the XGBoost model performed best.

Abstract Background Taurine performs multiple physiological functions, and the maintenance of taurine level for most mammals relies on active uptake from diet and endogenous taurine synthesis through its synthesis enzymes, including cysteine dioxygenase (CDO). In addition, uterus tissue and uterus fluid are rich in taurine, and taurine synthesis is regulated by estrogen (E2) and progesterone (P4), the key hormones priming embryo-uterine crosstalk during embryo implantation, but the functional interactions and mechanisms among which are largely unknown. The present study was thus proposed to identify the effects of CDO and taurine on embryo implantation and related mechanisms by using Cdo knockout (KO) and ovariectomy (OVX) mouse models. Results The uterine CDO expression was assayed from the first day of plugging (d 1) to d 8 and the results showed that CDO expression level increased from d 1 to d 4, followed by a significant decline on d 5 and persisted to d 8, which was highly correlated with serum and uterine taurine levels, and serum P4 concentration. Next, Cdo KO mouse was established by CRISPER/Cas9. It was showed that Cdo deletion sharply decreased the taurine levels both in serum and uterus tissue, causing implantation defects and severe subfertility. However, the implantation defects in Cdo KO mice were partly rescued by the taurine supplementation. In addition, Cdo deletion led to a sharp decrease in the expressions of P4 receptor (PR) and its responsive genes Ihh, Hoxa10 and Hand2. Although the expression of uterine estrogen receptor (ERα) had no significant change, the levels of ERα induced genes (Muc1, Ltf) during the implantation window were upregulated after Cdo deletion. These accompanied by the suppression of stroma cell proliferation. Meanwhile, E2 inhibited CDO expression through ERα and P4 upregulated CDO expression through PR. Conclusion The present study firstly demonstrates that taurine and CDO play prominent roles in uterine receptivity and embryo implantation by involving in E2-ERα and P4-PR signaling. These are crucial for our understanding the mechanism of embryo implantation, and infer that taurine is a potential agent for improving reproductive efficiency of livestock industry and reproductive medicine.

Oocyte-dependent and cumulus cell-derived transforming growth factor-β promotes natriuretic peptide type C expression in mouse ampullary epithelial cells, which promotes sperm migration into the ampulla for fertilization.

Cytokinin is essential for regulation of nodulation. Here, the authors identified a B-type response regulator GmRR11d that governs a transcriptional program associated with nodulation and cytokinin activation essential for systemic regulation of nodulation.

Abstract Germ cell division and differentiation require intimate contact and interaction with the surrounding somatic cells. Luteinizing hormone (LH) triggers epidermal growth factor (EGF)-like growth factors to promote oocyte maturation and developmental competence by activating EGF receptor (EGFR) in somatic cells. Here, we showed that LH-EGFR signaling-activated sphingosine kinases (SphK) in somatic cells. The activation of EGFR by EGF increased S1P and calcium levels in cumulus-oocyte complexes (COCs), and decreased the binding affinity of natriuretic peptide receptor 2 (NPR2) for natriuretic peptide type C (NPPC) to release the cGMP-mediated meiotic arrest. These functions of EGF were blocked by the SphK inhibitor SKI-II, which could be reversed by the addition of S1P. S1P also activated the Akt/mTOR cascade reaction in oocytes and promoted targeting protein for Xklp2 (TPX2) accumulation and oocyte developmental competence. Specifically depleting Sphk1/2 in somatic cells reduced S1P levels and impaired oocyte meiotic maturation and developmental competence, resulting in complete female infertility. Collectively, SphK-produced S1P in somatic cells serves as a functional transmitter of LH-EGFR signaling from somatic cells to oocytes: acting on somatic cells to induce oocyte meiotic maturation, and acting on oocytes to improve oocyte developmental competence.

Background: This study aimed to develop a nomogram for predicting gram-negative bacterial (GNB) infections in patients with peritoneal dialysis-associated peritonitis (PDAP) to identify patients at high risk for GNB infections. Methods: In this investigation, hospitalization information was gathered retrospectively for patients with PDAP from January 2016 to December 2021. The concatenation of potential biomarkers obtained by univariate logistic regression, LASSO analysis, and RF algorithms into multivariate logistic regression was used to identify confounding factors related to GNB infections, which were then integrated into the nomogram. The concordance index (C-Index) was utilized to assess the precision of the model's predictions. The area under the curve (AUC) and decision curve analysis (DCA) was used to assess the predictive performance and clinical utility of the nomogram. Results: The final study population included 217 patients with PDAP, and 37 (17.1%) patients had gram-negative bacteria due to dialysate effluent culture. After multivariate logistic regression, age, procalcitonin, and hemoglobin were predictive factors of GNB infections. The C-index and bootstrap-corrected index of the nomogram for estimating GNB infections in patients were 0.821 and 0.814, respectively. The calibration plots showed good agreement between the predictions of the nomogram and the actual observation of GNB infections. The AUC of the receiver operating characteristic curve was 0.821, 95% CI: 0.747–0.896, which indicates that the model has good predictive accuracy. In addition, the DCA curve showed that the nomogram had a high clinical value in the range of 1%–94%, which further demonstrated that the nomogram could accurately predict GNB infection in patients with PDAP. Conclusions: We have created a new nomogram for predicting GNB infections in patients with PDAP. The nomogram model may improve the identification of GNB infections in patients with PDAP and contribute to timely intervention to improve patient prognosis.

With styrene and acrylonitrile in ABS plastic toys as examples, this paper introduces to the development of a systematic strategy for studying the chemical migration risk in toys. The approach, included the detection method, establishment of migration model, model verification, and the practical application of the model in risk assessment. First, simple and sensitive methods for detecting analyte residues and migration were developed by headspace GC-MS. Then, the migration models were established based on the migration data from 5 min to 168 h and verified using 11 ABS samples. The results showed that the predicted values of the models and the experimental values had a good fit (RMSE=0.10–8.72 %). Subsequently, the migration of analytes in 94 ABS toys was predicted with these models at specific migration times. The daily average exposure level to styrene and acrylonitrile were estimated for children (3 months to 3 years). At last, the migration models reasonably predicted that the cancer risk of styrene and acrylonitrile in ABS toys were 1.6 × 10−8–1.4 × 10−6 and 3.1 × 10−8–1.6 × 10−6, respectively. This research contributes to promote toy safety and child health by enriching migration models and risk assessments.

This paper reports the non-targeted analysis of unknown volatile chemicals in medical masks through headspace gas chromatography-Orbitrap high-resolution mass spectrometry. In view of the difficulties that may be encountered in the qualitative analysis of unknown substances, several typical cases and the corresponding reliable solutions are given from the perspective of comprehensive score and retention index, chemical ionization identification molecular formula, fragment ion detail comparison for distinguishing isomers, and identification of alkanes. With this method, 69 volatile substances were identified in 60 masks. The identified substances were divided into nine categories. Alkanes, esters, benzenes, and alcohols were the top four groups of substances identified in masks and accounted for 34.8%, 15.9%, 10.1%, and 7.2% of the total substances, respectively. In addition, ketones, ethers, phenolics, amides, and other substances were identified. Ethanol, 1,4-dichlorobenzene, toluene, m-xylene, dimethyl glutarate, and N,N-dimethylacetamide had high detection rates. The identified substances were further filtered and screened according to their detection rate, toxicity, and response intensity. Finally, 12 high-risk volatile chemicals in medical masks were listed. This study could serve as a reference for identifying unknown substances and a guide for monitoring volatile chemicals in masks and promoting chemical safety improvements in products.

Background The prevalence of overweight and obesity among children and adolescents is continuously rising. As countries have developed several school-based nutrition policies to control overweight and obesity, it is important to examine the effectiveness of nutrition policies. Objective To evaluate the effect of the centralized distribution of food material system on overweight and obesity rates among children and adolescents aged 6-15 years. Methods In April 2023, a total of 104 749 BMI data from sixteen schools aged 6 to 15 years (8 schools were included in the unified food distribution system and recorded as the unified distribution group, and 8 schools were not included in the unified food distribution and supply system and recorded as the non-unified distribution group) in Wuxi were retrospectively collected between 2017 and 2021. The data of physical examination was collected from Student Health Monitoring System in Jiangsu Province. The overweight and obesity epidemiologic trends among children and adolescents were analyzed from 2017 to 2021 in Wuxi, and the changes in BMI-Z scores and trends in overweight and obesity rates among children and adolescents were compared before and after the implementation of the centralized distribution of food material system by the Joinpoint regression and grey GM (1, 1) model. Results The prevalence of overweight and obesity among in children and adolescents aged 6-15 years was increasing from 2017 to 2021. The results of the Joinpoint regression analysis indicated that the prevalence of overweight (APC 1.8% vs 4.6%) and obesity (APC 6.9% vs 13.3%) among students included in the system exhibited a slower rate of increase than among those not included. The disparity in the increase of BMI-Z scores among students included in the system and those not included changed from -0.055 (95%CI=-0.108--0.002) to -0.195 (95%CI=-0.246--0.145) before and after the implementation of the system. The results of grey GM (1, 1) model showed that the incidence rate of overweight among children and adolescents in Wuxi in 2022, 2023 and 2024 was 22.02%, 22.39% and 22.77% respectively, with the incidence rate of overweight being 17.02%, 18.50% and 20.12% respectively. Conclusion The increasing trend in the prevalence of overweight and obesity, as well as the increase in BMI-Z scores among children and adolescents, has slowed down after the implementation of the centralized distribution of food material system. The system may be an effective method of improving the nutritional status of children and adolescents. Therefore, it is recommended that the centralized distribution of food material system still needs to be implemented and the monitoring area needs to be expanded.