Predicting prognostic factors in kidney transplantation using a machine learning approach to enhance outcome predictions: a retrospective cohort study.

BACKGROUND: Accurate forecasting of clinical outcomes after kidney transplantation is essential for improving patient care and increasing the success rates of transplants. Our study employs advanced machine learning (ML) algorithms to identify crucial prognostic indicators for kidney transplantation. By analyzing complex datasets with ML models, we aim to enhance prediction accuracy and provide valuable insights to support clinical decision-making. MATERIALS AND METHODS: Analyzing data from 4077 KT patients (June 1990 - May 2015) at a single center, this research included 27 features encompassing recipient/donor traits and peri-transplant data. The dataset was divided into training (80%) and testing (20%) sets. Four ML models-eXtreme Gradient Boosting (XGBoost), Feedforward Neural Network, Logistic Regression, and Support Vector Machine-were trained on carefully selected features to predict the success of graft survival. Performance was assessed by precision, sensitivity, F1 score, Area Under the Receiver Operating Characteristic (AUROC), and Area Under the Precision-Recall Curve. RESULTS: XGBoost emerged as the best model, with an AUROC of 0.828, identifying key survival predictors like T-cell flow crossmatch positivity, creatinine levels two years post-transplant and human leukocyte antigen mismatch. The study also examined the prognostic importance of histological features identified by the Banff criteria for renal biopsy, emphasizing the significance of intimal arteritis, interstitial inflammation, and chronic glomerulopathy. CONCLUSION: The study developed ML models that pinpoint clinical factors crucial for KT graft survival, aiding clinicians in making informed post-transplant care decisions. Incorporating these findings with the Banff classification could improve renal pathology diagnosis and treatment, offering a data-driven approach to prioritizing pathology scores.

Development and validation of risk prediction model for post-donation renal function in living kidney donors.

This study aimed to create and validate a predictive model for renal function following live kidney donation, using pre-donation factors. Accurately predicting remaining renal function post live kidney donation is currently insufficient, necessitating an effective assessment tool. A multicenter retrospective study of 2318 live kidney donors from two independent centers (May 2007-December 2019) was conducted. The primary endpoint was the reduction in eGFR to below 60 mL/min/m2 6 months post-donation. The primary endpoint was achieved in 14.4% of the training cohort and 25.8% of the validation cohort. Sex, age, BMI, hypertension, preoperative eGFR, and remnant kidney proportion (RKP) measured by computerized tomography (CT) volumetry were found significant in the univariable analysis. These variables informed a scoring system based on multivariable analysis: sex (male: 1, female: 0), age at operation (< 30: 0, 30-39: 1, 40-59: 2, ≥ 60: 3), preoperative eGFR (≥ 100: 0, 90-99: 2, 80-89: 4, < 80: 5), and RKP (≥ 52%: 0, < 52%: 1). The total score ranged from 0 to 10. The model showed good discrimination for the primary endpoint in both cohorts. The prediction model provides a useful tool for estimating post-donation renal dysfunction risk, factoring in the side of the donated kidney. It offers potential enhancement to pre-donation evaluations.

Diagnosis and classification of kidney transplant rejection using machine learning-assisted surface-enhanced Raman spectroscopy using a single drop of serum.

The quest to reduce kidney transplant rejection has emphasized the urgent requirement for the development of non-invasive, precise diagnostic technologies. These technologies aim to detect antibody-mediated rejection (ABMR) and T-cell-mediated rejection (TCMR), which are asymptomatic and pose a risk of potential kidney damage. The protocols for managing rejection caused by ABMR and TCMR differ, and diagnosis has traditionally relied on invasive biopsy procedures. Therefore, a convergence system using a nano-sensing chip, Raman spectroscopy, and AI technology was introduced to facilitate diagnosis using serum samples obtained from patients with no major abnormality, ABMR, and TCMR after kidney transplantation. Tissue biopsy and Banff score analysis were performed across the groups for validation, and 5 µL of serum obtained at the same time was added onto the Au-ZnO nanorod-based Surface-Enhanced Raman Scattering sensing chip to obtain Raman spectroscopy signals. The accuracy of machine learning algorithms for principal component-linear discriminant analysis and principal component-partial least squares discriminant analysis was 93.53% and 98.82%, respectively. The collagen (an indicative of kidney injury), creatinine, and amino acid-derived signals (markers of kidney function) contributed to this accuracy; however, the high accuracy was primarily due to the ability of the system to analyze a broad spectrum of various biomarkers.

Probiotic Potential of Bacillus Subtilis Strain I3: Antagonistic Activity Against Chalkbrood Pathogen and Pesticide Degradation for Enhancing Honeybee Health.

To explore the potential of probiotic candidates beneficial for honeybee health through the modulation of the gut microbiome, bee gut microbes were isolated from bumblebee (Bombus terrestris) and honeybee (Apis mellifera) using diverse media and cultural conditions. A total of 77 bee gut bacteria, classified under the phyla Proteobacteria, Firmicutes, and Actinobacteria, were identified. The antagonistic activity of the isolates against Ascosphaera apis, a fungal pathogen responsible for chalkbrood disease in honeybee larvae, was investigated. The highest growth inhibition percentage against A. apis was demonstrated by Bacillus subtilis strain I3 among the bacterial strains. The presence of antimicrobial peptide genes in the I3 strain was detected using PCR amplification of gene fragments encoding surfactin and fengycin utilizing specific primers. The export of antimicrobial peptides by the I3 strain into growth medium was verified using liquid chromatography coupled with mass spectroscopy. Furthermore, the strain's capabilities for degrading pesticides, used for controlling varroa mites, and its spent growth medium antioxidant activity were substantiated. The survival rate of honeybees infected with (A) apis was investigated after feeding larvae with only medium (fructose + glucose + yeast extract + royal jelly), (B) subtilis I3 strain, A. apis with medium and I3 strain + A. apis with medium. Honeybees receiving the I3 strain + A. apis exhibited a 50% reduction in mortality rate due to I3 strain supplementation under experimental conditions, compared to the control group. In silico molecular docking revealed that fengycin hydrolase from I3 strain effectively interacted with tau-fluvalinate, suggesting its potential in bee health and environmental protection. Further studies are needed to confirm the effects of the I3 strain in different populations of honey bees across several regions to account for genetic and environmental variations.

Immunoprotective Effect of Liver Allograft on Patients with Combined Liver and Kidney Transplantation.

BACKGROUND Simultaneous liver-kidney transplantation (SLKT) and kidney transplantation (KT) after liver transplantation (LT) provide potential treatment options for patients with end-stage liver and kidney disease. There is increasing attention being given to liver-kidney transplantation (LTKT), particularly regarding the immune-protective effects of the liver graft. This retrospective, single-center, observational study aimed to evaluate the clinical outcomes of KT in LTKT patients - either SLKT or KT after LT (KALT) - compared to KT alone (KTA). MATERIAL AND METHODS We included patients who underwent KT between January 2005 and December 2020, comprising a total of 4312 patients divided into KTA (n=4268) and LTKT (n=44) groups. The LTKT group included 11 SLKT and 33 KALT patients. To balance the difference in sample sizes between the 2 groups, we performed 3: 1 propensity score matching (PSM). RESULTS There was no significant difference in graft survival between the groups. However, the LTKT group exhibited significantly superior rejection-free survival compared to the KTA group (P.

Strain modulation in crumpled Si nanomembranes: Light detection beyond the Si absorption limit.

Although Si is extensively used in micro-nano electronics, its inherent optical absorption cutoff at 1100-nm limits its photonic and optoelectronic applications in visible to partly near infrared (NIR) spectral range. Recently, strain engineering has emerged as a promising approach for extending device functionality via tuning the material properties, including change in optical bandgap. In this study, the reduction in bandgap with applied strain was used for extending the absorption limit of crystalline Si up to 1310 nm beyond its intrinsic bandgap, which was achieved by creating the crumpled structures in Si nanomembranes (NMs). The concept was used to develop a prototype NIR image sensor by organizing metal-semiconductor-metal-configured crumpled Si NM photosensing pixels in 6 × 6 array. The geometry-controlled, self-sustained strain induction in Si NMs provided an exclusive photon management with shortening of optical bandgap and enhanced photoresponse beyond the conventional Si absorption limit.