Objective: To analyze the risk factors for poor prognosis in children with steroid-resistant nephrotic syndrome (SRNS) and to construct and validate a prognostic model. Methods: A retrospective cohort study was conducted. Clinical data of 456 children with SRNS who were initially diagnosed and hospitalized at the Children's Hospital of Chongqing Medical University from January 2009 to December 2024 was collected, including general information, laboratory and pathological indicators, gene types, treatment, and prognosis. Follow-up was conducted for more than 12 months. The endpoint event was defined as a decrease in the estimated glomerular filtration rate (eGFR) of more than 30% compared to the baseline for three consecutive times (with an interval of at least one month between each measurement). The patients were divided into the event group and the non-event group based on whether the endpoint event occurred. Independent sample t-test, Mann-Whitney U test, test χ2, or Fisher's exact probability test were used for comparison between groups. Multivariate Logistic regression was used to rank the importance of variables to screen for characteristic variables. The top 6 ranked characteristic variables were used to construct four machine learning models using Python: extreme gradient boosting, random forest, support vector machine, and logistic regression. The area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, and F1 score were used to evaluate the discrimination and performance of the models. Calibration curves and clinical decision curves were drawn to assess the prediction accuracy and clinical net benefit of the models. The Shapley Additive Explanations (SHAP) method was applied to analyze the contribution of features. Results: Among the 456 children, 306 were male and 150 were female. The age of onset was 4.1 (2.3, 8.0) years, and the follow-up time was 2.2 (1.0, 4.0) years. There were 195 cases (42.7%) in the event group and 261 cases (57.3%) in the non-event group. The time of occurrence of the endpoint event in the event group was 1.0 (0.5, 2.5) years. Univariate analysis showed that there were statistically significant differences between the two groups in initial serum creatinine, initial eGFR, eGFR change rate after 3 months of treatment, gender, calcineurin inhibitor (CNI) treatment response, gene variation, white blood cell count, absolute monocyte count, blood urea nitrogen, and urine red blood cells (all P<0.05). The top 6 characteristic variables, including the eGFR change rate after 3 months of treatment, genetic variation, baseline eGFR, CNI treatment responsiveness, gender and baseline serum creatinine (the AUC decrease percentages of 20.1%, 9.4%, 1.6%, 1.0%, 0.3% and 0.1% respectively after permutation test). Among the four machine learning models, the random forest model performed the best (test set AUC was 0.77 (95%CI 0.73-0.81), accuracy 73.9%, specificity 78.5%, sensitivity 71.2%, and F1 score 68.9%). The Hosmer-Lemeshow test showed that the predicted risk and actual risk of the random forest model were in good agreement (χ2=7.72, P=0.461); the clinical decision curve showed that the random forest model performed best and had the highest net benefit within a certain threshold range (0-0.5). Through SHAP, it was determined that the eGFR change rate after 3 months of treatment, initial eGFR, gene variation, and CNI treatment response were important predictors of poor prognosis in SRNS (average SHAP absolute values were 0.18, 0.08, 0.07 and 0.02, respectively). Conclusions: A random forest model was constructed based on machine learning and explained using the SHAP method. The eGFR change rate after 3 months of treatment, initial eGFR, gene variation, and CNI treatment response are important factors affecting prognosis.

Peri-arterial neural tissue surrounding the renal artery contributes to sympathetic overactivity in hypertension. During adrenal surgery, surgical peri-arterial neural dissection may interrupt these fibers and exert a renal denervation-like effect. Whether this maneuver improves postoperative blood pressure control remains unclear. We retrospectively reviewed 127 hypertensive patients who underwent adrenal surgery between January 2022 and March 2025. According to intraoperative findings, patients were classified into a surgical peri-arterial neural dissection group or a nondissection group. After 1:2 manual matching by age, sex, and hypertension duration, 54 patients were included. Postoperative hypertension remission and changes in antihypertensive medication use, quantified by the defined daily dose, were assessed. Multivariable logistic regression was used to explore factors associated with postoperative nonremission. Renal and adrenal function markers were compared between groups. The surgical peri-arterial neural dissection group had a higher remission rate than the nondissection group (50.0% versus 16.1%; P=0.008). Surgical peri-arterial neural dissection was independently associated with lower odds of postoperative nonremission (odds ratio, 0.150 [95% CI, 0.030-0.744]; P=0.020), supporting its potential role in improving postoperative blood pressure control. Patients in the dissection group showed greater reductions in antihypertensive medication use (median change in defined daily dose, -1.0 versus 0.0; P=0.006). Creatinine, blood urea nitrogen, cortisol, and adrenocorticotropic hormone levels were similar between groups. Surgical peri-arterial neural dissection performed during adrenal surgery was associated with improved postoperative blood pressure control and reduced medication burden without evidence of impaired renal or adrenal function.

Arginine-linked solid basil polyphenol self-assembly stabilizes Pickering nanoemulsions: In vitro oxidative stress relief and early evidence of in vivo nephroprotection.

Morin hydrate mitigates renal ischemia-reperfusion injury in association with modulation of TXNIP/NLRP3 inflammasome signaling and FOXO1/PGC-1α-mediated mitochondrial regulation.

Nesfatin-1 attenuates renal interstitial fibrosis, oxidative stress, and inflammation through dual suppression of TGF-β1/Smad and NF-κB/GPR12 signaling.

Biotransformation-derived metabolites from Astragalus membranaceus and Cordyceps militaris alleviate hyperuricemia via multi-target regulation.

Interaction of Kidney Function and Dapagliflozin in Patients With Acute Heart Failure: A Pre-Specified Analysis of DICTATE-AHF.

Prognostic assessment in critically ill cancer patients is challenging due to the suboptimal performance of traditional severity scores. We developed and validated machine learning models to provide an objective triage and risk-stratification tool at admission, identifying patients who require high-intensity organ support. We conducted a retrospective cohort study including 997 critically ill cancer patients admitted to the ICU. Forty-six demographic, oncologic, physiological, laboratory, and therapeutic variables collected at ICU admission were used to train and validate ML models. Eight algorithms were evaluated using stratified cross-validation with feature selection and hyperparameter optimization. Model performance was assessed using discrimination, calibration, and classification metrics. Model interpretability was explored using Shapley additive explanations (SHAP). CatBoost achieved the best performance for ICU mortality prediction (AUROC 0.94), showing excellent discrimination and calibration, and outperforming other ML models. Prediction of 30-day survival was less accurate (best AUROC 0.74), reflecting the influence of post-ICU factors not captured at admission. Key predictors of ICU mortality included severity of organ dysfunction, therapeutic objectives, vasopressor and methylene blue use, SAPS III score, lactate, platelet count, and blood urea nitrogen. For 30-day survival, baseline physiological status, admission type, SAPS III, lactate, creatinine, age, and body mass index were most relevant. SHAP analysis demonstrated that acute physiology and organ dysfunction, rather than cancer diagnosis alone, primarily drove short-term outcomes. ML-based models, particularly CatBoost, outperformed traditional tools in predicting ICU mortality. Within this oncologic cohort, short-term outcomes were driven primarily by acute physiological derangements rather than specific cancer characteristics. These results support using ML-based risk stratification as an objective triage tool at admission. External validation is warranted to confirm generalizability before clinical integration.

Evaluation of the relationship between signal time in blood cultures and clinical findings in brucellosis cases.

Glyoxylic acid-induced calcium oxalate crystal deposition drives nephrotoxicity via SPHK1-mediated ferroptosis: Insights from untargeted lipidomics.

The cingulate cortex is highly vulnerable in end-stage renal disease (ESRD) patients, but whether ESRD disrupts its functional gradient and the links to clinical phenotypes and underlying molecular mechanisms remain unclear. We prospectively enrolled clinical and resting-state functional MRI data from 125 participants (77 ESRD patients, 48 healthy controls) to explore cingulate gradient alterations. Associations between cingulate gradients and canonical functional networks, clinical phenotypes, and meta-analytic behavioral domains were analyzed. A gene expression decoding analysis based on the Allen Human Brain Atlas was performed to advance understanding of how molecular mechanisms relate to hierarchical changes in ESRD. Across global, network, and regional scales, patients with ESRD showed significant cingulate gradient dysfunction, with these anomalies exhibiting associations across multiple functional domains. Notably, serum urea and hemoglobin levels were correlated with cingulate gradient dysfunction. Spatially, these alterations correlated with genes enriched in neurodegenerative processes. Excitatory and inhibitory neurons' specific transcriptional changes account for most of the observed correlation with ESRD-specific cingulate gradient alterations. Our findings highlight ESRD-related cingulate gradient dysfunction and its links to clinical phenotypes and gene expression profiles, providing critical insights into the neurodegenerative underpinnings of cerebral dysfunction in ESRD.

Drug-induced nephrotoxicity (DIN) is a frequent and serious complication associated with antibacterial, antiviral, anticancer, antifungal and several other commonly used medications, such as painkillers and antacids. A literature survey was conducted on DIN, its biomarkers, phytotherapy and mechanisms in PubMed, Scopus and Web of Science from 2000 to 2025. It distinguished between preclinical studies and available clinical evidence. The drugs can damage renal cells through diverse mechanisms, resulting in both acute and chronic kidney injury. The consequent dysregulation of key biomarkers, such as serum creatinine, urea and kidney injury molecule-1, hinders early detection and complicates clinical management. A clearer understanding of these biomarkers is critical for improving diagnostic precision and developing targeted therapeutic strategies. Current treatment approaches for DIN are largely limited to discontinuing the offending drug, dose reduction or supportive care, all of which provide only partial benefit. This underscores the need for safer and more effective interventions. Phytochemicals and other natural compounds are increasingly recognised for showing promising nephroprotective effects in preclinical studies owing to their broad pharmacological activities and relatively low toxicity. The review summarises the role of bioactive agents in modulating multiple pathways, including apoptosis, oxidative stress and inflammation, while also enhancing endogenous antioxidant defences, promoting cellular repair and preserving renal function. Advancing knowledge of the molecular mechanisms underlying phytochemical-mediated renoprotection and their roles in biomarker-mediated therapeutics may facilitate the development of innovative therapeutic and preventive approaches. Such strategies hold the potential to move beyond symptomatic management, offering improved outcomes and better quality of life for patients at risk of DIN.

Angus-based heifers (F0; n = 72; 14 to 15 mo; initial body weight [BW] = 380.4 ± 50.56 kg) were ranked by BW, bred via artificial insemination (AI) with female-sexed semen, and assigned to receive a basal diet (CON; n = 36) or the basal diet plus a vitamin and mineral supplement (VTM; n = 36) with the total mixed ration. Treatments were applied from breeding through calving, after which cow-calf pairs (n = 14 CON; n = 17 VTM) received a common diet. A subset of F1 heifers (n = 7 CON; n = 9 VTM) were bred via AI with female-sexed semen and evaluated from breeding through d 250 of gestation when pregnant heifers were slaughtered. Nutrient balance and energy metabolism were measured each trimester via apparent total tract digestibility and indirect calorimetry. Blood samples were collected each trimester and at harvest for hormone/metabolite analysis. Data were analyzed using the MIXED procedure of SAS with repeated measures where appropriate, with treatment, time, and interaction included as fixed effects and animal as the experimental unit. In F1 heifers, digestibility of dry matter, organic matter, neutral detergent fiber, acid detergent fiber, and N was not influenced by treatment (P ≥ 0.21) but decreased (P ≤ 0.03) as gestation advanced. Fecal energy (FE) losses, heat production (HP) as a percentage of gross energy intake, and retained energy (RE) were not affected by treatment (P ≥ 0.52); however, FE and HP increased (P ≤ 0.03) while RE decreased (P = 0.01) with advancing gestation. Circulating insulin concentration was greater (P < 0.01) in VTM heifers, whereas glucose decreased (P < 0.01) and non-esterified fatty acids and blood urea nitrogen increased (P ≤ 0.01) as gestation progressed. Body weight was greater (P < 0.01) in VTM heifers and at slaughter, VTM heifers tended (P ≤ 0.10) to have heavier carcasses and greater ribeye area than CON. The F1 CON heifers had heavier spleens relative to BW (P = 0.04) whereas other organs did not differ (P ≥ 0.17). In F2 fetuses, CON tended to have heavier reproductive tracts and spleens (P ≤ 0.10) with no other differences in organ weights (P ≥ 0.13), whereas VTM fetuses had greater (P = 0.01) blood glucose concentration. These results indicate that prenatal micronutrient supplementation programs growth and metabolic function across generations, with minimal effects on organ mass. Advancing gestation reduced efficiency of energy and nitrogen use, reflecting nutrient partitioning shifts supporting fetal growth.

Xaliproden improves diabetic kidney disease through JNK-mediated renal tubular protection.

Heightened and persistent inflammation during the periparturient period impedes health and performance in early lactation. Identifying mitigation strategies capable of attenuating inflammatory cascades without interfering with the inflammation necessary to support normal physiological processes is of significant interest. Dietary supplementation of Scutellaria baicalensis extract (SBE; Dairy Relieve, Elanco Animal Health, Greenfield, IN) has been shown to improve milk performance, an effect hypothesized to be mediated by its anti-inflammatory and antioxidant properties. Thus, study objectives were to evaluate the effect of SBE supplementation on performance and inflammation in primiparous and multiparous cows and to determine the optimal feeding duration. Holstein cows (n = 404; 80 primiparous and 324 multiparous) were used in a randomized complete block design and randomly assigned within block (parity and expected calving date) to 1 of 4 dietary treatments: (1) control diet (CON), (2) 10 g/d of SBE from 255 ± 3 d carried calf (DCC) to 21 DIM (SBE42), (3) 10 g/d SBE from 255 ± 3 DCC to 90 DIM (SBE111), and (4) 10 g/d SBE from 1 to 90 DIM (SBE90). Cows were housed in a freestall barn and received the same base ration. Dietary treatments were administered individually by restraining cows in headlocks for a minimum of 10 min/d during which the top-dress pellet (113.5 g/d) containing 10 g of SBE was administered. Cows were milked 3 times daily for the first 6 wk of lactation and twice daily for the remainder of the data collection period (i.e., 40 wk). Milk yield was recorded from wk 1 to 40, and milk samples for composition analysis were obtained every 2 wk from wk 1 to 15 relative to calving. Blood samples were obtained via coccygeal venipuncture at 7, 21, and 90 DIM from a random subset of cows (24 cows/treatment). Data were analyzed using mixed effects repeated measures ANOVA. A preplanned contrast was used to estimate differences in CON versus cows supplemented with SBE to 90 DIM (SBE111 + SBE90). Over the 40-wk lactation, milk yield increased 1.5 kg/d in SBE111 relative to CON cows. Milk yield was similar in SBE42 and CON cows. Feeding SBE to 90 DIM (SBE111 and SBE90) tended to increase energy-corrected milk yield and milk fat and protein yields, whereas it tended to decrease MUN over the first 15 weeks of lactation relative to CON. Regardless of prepartum feeding, SBE supplementation to 90 DIM tended to increase circulating sirtuin-1 concentrations. Relative to CON, circulating α-1-acid glycoprotein (AGP) increased in SBE-fed cows at 7 DIM, whereas concentrations decreased at 21 DIM. In summary, the optimal feeding window of SBE for improving postpartum performance for cows enrolled in the current study was from 255 DCC to 90 DIM. Changes in circulating sirtuin-1 and AGP concentrations with SBE supplementation support the hypothesis that improved performance with SBE supplementation is partially mediated by the anti-inflammatory properties of SBE.

Split Feeding for semi-heavy laying hens from 105 to 120 weeks of age: Performance, egg quality, biochemical parameters, reproductive tract morphometry and bone quality.

The objective of this study was to evaluate the performance of multiple machine learning algorithms to provide evidence supporting early intervention for high-risk patients with oral and maxillofacial space infections (OMSI), thereby reducing complication rates and improving overall clinical outcomes. A retrospective cohort study was performed to analyse clinical data from 432 medical records, with a focus on key variables related to disease severity and treatment outcomes. The data included age, gender, height, weight, BMI, blood test results, such as the CRP, white blood cell count, neutrophil count, lymphocyte count, monocyte count, albumin, potassium, sodium, chlorine, calcium, uric acid, blood urea nitrogen, PCT, fibrinogen, blood glucose, vital signs, such as body temperature, heart rate, systolic blood pressure, diastolic blood pressure, respiration, days before hospitalization, the extent of mouth opening, the space of infected spaces, and the source of infection. We summarized the predictive performance of three models-Logistic regression, Random Forest, and XGBoost-across three key clinical outcomes. For predicting hospital stay duration, Logistic regression performed best. For predicting ICU admission, XGBoost exhibited the strongest performance. For predicting surgical intervention, Logistic regression achieved the optimal overall performance, while XGBoost and Random Forest demonstrated the highest specificity. In summary, we have successfully developed, validated, and compared predictive models for key clinical outcomes in patients with OMSI. They further hold promise for ultimately optimizing the diagnostic and therapeutic workflow and improving outcomes for patients with this common yet potentially life-threatening condition. Not applicable. Not applicable.

Epidemiological studies suggest that environmental noise is associated with kidney dysfunction in humans; however, the underlying characteristics, such as frequency, intensity, and duration, remain unclear. Effects of exposure to whole environmental noise generated by electric devices (all frequencies), low-frequency noise (LFN; ≤100 Hz), and non-LFN (>100 Hz) for 12-h/day for 5 consecutive days on kidney function were investigated in mice. Exposure to whole environmental noise at ≤ 88 dBA elevated serum creatinine and blood urea nitrogen (BUN). This effect was reproduced by LFN exposure alone (≤70 dBA) but not by non-LFN exposure (≤87 dBA). Biochemically, LFN increased renal expression of endothelin-1 and endothelial nitric oxide synthase, biomarkers of vascular injury. Histologically, LFN increased glomerular and mesangial areas and thickened glomerular basement membranes. Pharmacological inhibition of endothelin signaling reduced serum creatinine and BUN levels and mitigated the LFN-induced glomerular damage. Our findings provide the first direct evidence that the low-frequency component of environmental noise, well below the murine audible range, induces glomerular injury via endothelin signaling and results in kidney dysfunction in mice. Considering prior human cross-sectional studies, these murine findings warrant further investigation to determine their translational relevance in humans.

Theranekron Prevents Endotoxin-Induced Acute Kidney Injury by Modulating Inflammatory and Mitochondrial Apoptotic Pathways.

Delayed identification of acute kidney injury (AKI) limits timely intervention, as diagnosis often depends on serum creatinine elevation after renal damage has occurred. This study aims to develop a machine learning model capable of identifying early AKI risk at the time of hospital admission using routinely collected biochemical and physiological parameters and their prediagnostic trends. Clinical, biochemical, and physiological parameters were retrospectively extracted from the hospital database and discharge summaries of patients. For each patient's lab values, rate-of-change (RoC) values were calculated to capture dynamic trends preceding AKI onset. XGBoost models were trained using admission features and in combination with RoC values. Model performance was evaluated using accuracy, precision, recall, F1 score, and area under the curve (AUC), while Shapley Additive Explanations (SHAP) provided interpretable insights into feature importance. The XGBoost model achieved robust performance (AUC = 0.83; recall = 0.75) in predicting AKI risk using data available at admission. Incorporating RoC features further improved discrimination (AUC = 0.90), indicating that subtle biochemical trends enhance early detection. Key predictors included values at admission and RoC values of blood urea nitrogen, calcium, albumin-globulin ratio, lactate, alanine aminotransferase, and pulse/min, reflecting early renal, metabolic, and hemodynamic alterations preceding AKI onset. Machine learning models leveraging admission and temporal features can predict AKI risk with high accuracy using routinely collected clinical data. Future integration into electronic health records could enable real-time risk stratification and timely clinical intervention to improve patient outcomes.