Nevin Manimala

Ren Fail. 2026 Dec;48(1):2687235. doi: 10.1080/0886022X.2026.2687235. Epub 2026 Jun 18.

ABSTRACT

Acute kidney injury (AKI) is a common and severe complication following renal transplantation, largely driven by ischemia-reperfusion injury (IRI). However, reliable biomarkers for post-transplant AKI remain unavailable. In this study, we analyzed transcriptomic datasets from multiple cohorts to identify robust gene signatures associated with transplant-related AKI. Weighted gene co-expression network and differential expression analyses in the discovery cohort revealed 222 candidate genes altered during early allograft IRI, which were subsequently refined in an integrated training cohort using least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) to a four-gene signature comprising SOCS3, MYC, TGIF1, and LETM2. This signature showed excellent discriminative performance in the training cohort (10-fold cross-validation AUC = 0.969) and was validated in independent external datasets, including a large cohort (AUC = 0.942). Decision curve analyses indicated potential clinical utility for early AKI identification across a broad threshold-probability range, with favorable performance compared with several established biomarkers such as neutrophil gelatinase-associated lipocalin. Single-cell transcriptomics revealed cell type-specific expression of the four genes across renal compartments. Moreover, their protein levels were elevated at 24 h after IRI in mouse kidneys, and displayed high expression in human AKI biopsies. Additionally, serum protein levels of SOCS3 and LETM2 were elevated in patients with cardiac surgery-associated AKI, whereas TGIF1 and MYC did not reach statistical significance. Collectively, this study identified a four-gene signature with potential utility as an early diagnostic biomarker panel for post-transplant AKI. Further large-scale clinical trials are needed to validate its diagnostic efficacy.

PMID:42315974 | DOI:10.1080/0886022X.2026.2687235

Ren Fail. 2026 Dec;48(1):2687920. doi: 10.1080/0886022X.2026.2687920. Epub 2026 Jun 18.

ABSTRACT

Although multiple risk prediction models have been developed to identify malnutrition in dialysis patients, their quality and performance remain unclear, limiting their practicality in current clinical practice and future research. Therefore, we conducted a systematic review and meta-analysis to evaluate these models. Searches were conducted in PubMed, Embase, Web of Science, The Cochrane Library, CINAHL, SinoMed, CNKI, Wanfang, and VIP Database from inception to January 26, 2026. Two investigators independently screened the literature, extracted data, and assessed quality using the Prediction model Risk of Bias Assessment Tool (PROBAST). Meta-analyses of the prevalence of malnutrition, common predictors and model performance were performed using Stata 18.0 and R 4.5.1. A total of 12 eligible studies conducted in China were included, and the pooled prevalence of malnutrition in dialysis patients was 41%. Meta-analysis identified age, serum calcium, Kt/V, triglycerides, sex, vitamin D, NT-proBNP, and comorbid diabetes as statistically significant predictors. The pooled effect of the nine internal validated models was 0.83, indicating good discriminatory performance. However, all included models were rated at high risk of bias, primarily due to inappropriate data sources and poor reporting of the analysis. The current analysis reveals a high prevalence of malnutrition among dialysis patients. Eight significant predictors were identified, guiding future selection for constructing predictive models of malnutrition risk in this population. Although existing models demonstrate adequate discriminatory performance, their methodological limitations constrain clinical applicability. Future studies should prioritize the development of standardized, externally validated models to enable early identification and intervention, thereby improving outcomes in this vulnerable group.

PMID:42315969 | DOI:10.1080/0886022X.2026.2687920

J Am Med Inform Assoc. 2026 Jun 19:ocag091. doi: 10.1093/jamia/ocag091. Online ahead of print.

ABSTRACT

OBJECTIVES: To leverage the evaluation of the environmental impact of the Direct AP-HP/Lorah e-referral service offered in Paris (France) hospitals to create recommendations for evaluating the impact of digital health services.

MATERIALS AND METHODS: We review the tools and methods currently available to measure the carbon footprint and electricity consumption of digital services in the context of Life Cycle Assessment (LCA) for a comprehensive evaluation. We use the recent deployment of a telemedicine communication service at a major French hospital as a case study to understand the practical implications of conducting an impact study. Three-deployment scenarii are considered: current usage, double usage and maximum capacity.

RESULTS: The bulk of the carbon footprint of the Direct AP-HP/Lorah service is due to servers vs network and user terminals in all scenarios considered. Computing hardware production impacts was instrumental in the overall impact assessment, as embodied impact represent 45% of Carbon footprint and the most of Metallic resource depletion. Recommendations for further studies notably include adequate anticipation of service usage and data collection.

DISCUSSION: The environmental impact of the new telemedicine service could be assessed in sufficient level of details to provide decision makers with an adequate comparison of the service with alternative email communication.

CONCLUSION: The recommendations derived from this use case should facilitate adequate impact data collection for future studies.

PMID:42315963 | DOI:10.1093/jamia/ocag091

Sci Rep. 2026 Jun 18. doi: 10.1038/s41598-026-58784-5. Online ahead of print.

ABSTRACT

Rockfalls are a destructive geo-hazard in Romania’s steep environments that pose significant material damage and loss of life, even when involving small rock volumes, owing to their high mobility and unpredictability. This study presents the first national-scale assessment of rockfall potential occurrence in Romania, applying a multi-criteria statistical and geospatial analysis based on the Weight of Evidence method. By integrating 1,743 historical rockfall sites and 19 environmental variables as driving factors, we mapped and statistically assessed the potential rockfall distribution of their occurrence across the Carpathian and Subcarpathian regions. Findings reveal that over 18,000 km² of Romanian territory is suitable for rockfall assessment, while approximately 1,500 km² face high rockfall occurrence potential. The Southern Carpathians emerged as the most vulnerable area, and all over the Carpathians and Subcarpathians statistical analysis highlighted that topographical and environmental factors are the primary drivers of rockfall distribution. Our model achieved 94% predictive accuracy, confirming the reliability of the resulting potential rockfall occurrence map, as well as its utility as a decision-support tool. These findings provide a robust scientific reference for national and local authorities to mitigate rockfall geohazards, manage rockfall risks and improve infrastructure resilience in Romania.

PMID:42315949 | DOI:10.1038/s41598-026-58784-5

Sci Rep. 2026 Jun 18. doi: 10.1038/s41598-026-57852-0. Online ahead of print.

ABSTRACT

Abnormal uterotubal junction (UTJ) structure is implicated in the pathogenesis of endometriosis and infertility; however, this relationship remains poorly characterized. We quantitatively characterized the orientation of collagen fiber bundles in the UTJ using optical coherence tomography (OCT). UTJ tissue from nine individuals undergoing hysterosalpingectomy were collected and longitudinally opened to expose the lumen. Volumetric OCT images were acquired from proximal (uterine), middle, and distal (isthmic) UTJ segments. Images were preprocessed to enhance contrast and continuity of fiber bundles. Local fiber bundle orientation was extracted from Sobel-filtered intensity gradients. Orientation values were binned corresponding to the expected alignment of collagen fiber bundle groups in UTJ smooth muscle: longitudinal (0°-30°), oblique (30°-60°), and circumferential (60°-90°). Proportions were plotted as a function of depth, and depth-dependent trends in alignment were compared across UTJ segments using mixed-effects models. Measurements revealed a prominent inner longitudinal muscle layer which emerged proximally and thinned distally, and an outer muscle layer composed of oblique uterine fiber bundles proximally and a mixed oblique-circumferential contribution distally. These findings provide the first quantitative, depth-resolved analysis of UTJ collagen architecture and establish an analytical framework which can support future studies investigating how UTJ structure relates to function and disease.

PMID:42315947 | DOI:10.1038/s41598-026-57852-0

Sci Rep. 2026 Jun 18. doi: 10.1038/s41598-026-56941-4. Online ahead of print.

ABSTRACT

Eye Tracking (ET) can help improve understanding of visual attention in computer-supported interactive environments. In attention tasks, distinguishing between relevant target objects and distractors is crucial for effective performance, yet the underlying gaze patterns that drive successful task completion remain incompletely understood. Traditional gaze analyses provide limited insight into the temporal dynamics of attention allocation and the relationship between gaze behavior and task performance. When applied to complex visual search scenarios, current gaze analysis methods face several limitations, including the isolation of measurements in dynamic environments, visual stability, search efficiency, and the task-solving processes involved. This paper proposes an analysis tool, VisiTrail, that considers time-series eye-tracking data on task performance and gaze measures; temporal pattern analysis that reveals how attention evolves throughout task performance; object-click sequence tracking that directly links visual attention to user actions; and performance metrics that quantify both accuracy and efficiency of right actions. The proposed analysis is applied to data collected from the Mushroom Hunter serious game, a multilevel visual search task in which participants identify target mushrooms among distractors of increasing complexity across three difficulty levels. This tool focuses on two scenarios: subject-specific analysis and general analysis across all subjects from which a project collected data from Romania and Portugal. Subject-specific analysis uses only one participant’s data and yields two types of results: a first-level overall analysis that provides detailed insights and a multilevel analysis that compares performance across all three levels, where Level 1 presents the easiest task with few distractors, Level 2 increases difficulty by adding more similar distractors, and Level 3 is the hardest with many closely resembling distractors and more complex layouts. The generalized analysis reveals that gaze stability (Fixation %) was broadly consistent across both cohorts. Romanian participants exhibited faster median reaction times than Portuguese participants; however, given the small sample size (N=7 per cohort), the presence of data-quality anomalies in three Portuguese sessions, and the absence of inferential statistical testing, this difference should be regarded as preliminary and hypothesis-generating rather than conclusive. By using standardized parameters, the results reduce accidental interactions and hardware noise, providing a reliable methodological basis for preliminary analysis and highlighting the need for larger sample sizes to establish statistical validity.

PMID:42315946 | DOI:10.1038/s41598-026-56941-4

Sci Rep. 2026 Jun 22. doi: 10.1038/s41598-026-56504-7. Online ahead of print.

ABSTRACT

Pharmaceutical pollutant, such as amoxicillin was selected because it is one of the most widely consumed antibiotics worldwide and is frequently detected in hospital and municipal wastewater. Its high excretion rate in active form and persistence in aquatic environments make it an important target contaminant for water treatment studies. To address this issue, we developed a dendrimer-functionalized graphene quantum dot-mesoporous silica hybrid (GQDs@mSiO₂@Dend.G₃) as an effective and reusable adsorbent. AMX is an ideal model contaminant for studying adsorption mechanisms and evaluating the performance of this advanced hybrid material. Mechanistic interpretation suggests that hydrogen bonding, π-π, and electrostatic interactions collectively contribute to antibiotic binding at the hybrid interface. The nanoadsorbent was characterized using XRD, TGA, FTIR, BET-BJH, FE-SEM, EDX, and zeta potential measurements. Response surface methodology with a central composite design was used to optimize and validate the removal efficiency, yielding statistically significant results. The drug removal efficiency of 96% was achieved under the following optimal conditions: pH 6, temperature of 35 °C, and contact time of 45 min. The kinetics followed the pseudo-second-order model (R² = 0.9979) and the equilibrium fit the Langmuir isotherm (R² = 0.9657-0.9675) confirming monolayer physisorption onto uniform active sites. Thermodynamics showed that the process was spontaneous and endothermic (ΔH° = 8562.589 J mol^-1, ΔS° = 155.530 J mol^-1 K^-1, and ΔG° = -37.808 to -40.919 kJ mol^-1). The nanoadsorbent showed consistently high removal efficiency in real water samples. It retained over 70% of its original performance after multiple regeneration cycles. This indicates that GQDs@mSiO₂@Dend.G₃ is a promising and reusable solution for reducing pharmaceutical pollution in environment samples.

PMID:42315942 | DOI:10.1038/s41598-026-56504-7

Sci Rep. 2026 Jun 18. doi: 10.1038/s41598-026-58516-9. Online ahead of print.

ABSTRACT

The impact of network depth on the performance of deep residual networks (DRNs) for short-term load forecasting (STLF) remains insufficiently understood. This study conducts a systematic empirical investigation of network depth within DRN-based frameworks, with a particular focus on its relationship with forecasting performance, model behavior, and dataset characteristics. Two representative models, namely the original DRN and the Principal Component Analysis-Deep Residual Network (PCA-DRN), are evaluated under a wide range of depth configurations using two real-world datasets with different load and meteorological characteristics: ISO-NE and MyPJ. The results suggest that the influence of network depth on forecasting performance is inherently nonlinear and highly dataset-dependent. For the ISO-NE dataset, medium-depth configurations achieve favorable performance by effectively modeling more pronounced seasonal variability and long-term temporal patterns. In contrast, relatively shallow configurations generally exhibit improved performance on the MyPJ dataset under the current experimental setting, suggesting that deeper architectures may introduce additional model complexity without consistent performance gains. Furthermore, within the MyPJ dataset containing multiple meteorological variables, the incorporation of PCA improves feature representation, enhances model robustness, and is associated with reduced sensitivity to depth variations under the current experimental setting. Comparative evaluations against mainstream deep learning (DL) models indicate that DRN-based frameworks achieve competitive forecasting performance with relatively efficient structural designs. In addition, bootstrap-based statistical analysis suggests that the observed performance differences remain distinguishable under sample-level variability within the current experimental setting. These findings suggest that appropriate network depth selection should be considered together with dataset characteristics and feature representation, providing practical insights for the design of efficient and robust STLF models.

PMID:42315935 | DOI:10.1038/s41598-026-58516-9

Heredity (Edinb). 2026 Jun 18. doi: 10.1038/s41437-026-00855-4. Online ahead of print.

ABSTRACT

Seaweed farming, as a part of aquaculture, offers a sustainable alternative to modern agricultural practices; however, genetic enhancement and breeding programs for most species are underdeveloped. We aimed to advance seaweed domestication by focusing on giant kelp (Macrocystis pyrifera), the fastest-growing haplodiplontic brown alga, which has significant ecological and commercial importance. We analyzed phenotypic data from two offshore experimental farms conducted in 2019 and 2020, which involved hundreds of outplanted genetically diverse sporophytes. We found that outplanting season and farm design had significant effects on giant kelp biomass. Broad-sense heritability estimates showed moderate (0.27-0.50) genetic contributions to two phenotypes, carbon content and total biomass. Genome-wide association studies for these phenotypes resulted in three statistically significant SNPs, located near or within genes involved in carbohydrate metabolism and cytoskeletal functions. In addition, we applied genomic selection models that integrated sporophyte phenotypes and parental gametophyte genotypes. These models utilized reduced sets of GWAS-ranked SNPs obtained by a procedure based on linkage disequilibrium estimations. Model testing yielded cross-validation accuracy values of up to 0.84 and predictive accuracy values of up to 0.40, demonstrating the potential of marker-assisted breeding for phenotype improvement. Our results provide foundational genomic resources and tools for domesticating and breeding M. pyrifera, forming a basis for developing giant kelp varieties with desirable traits.

PMID:42315921 | DOI:10.1038/s41437-026-00855-4

Sci Rep. 2026 Jun 19. doi: 10.1038/s41598-026-57916-1. Online ahead of print.

ABSTRACT

Older adults experience a high risk of falls, often due to impaired stepping responses that require rapid and efficient weight transfer. This pilot study examined whether age group and principal component analysis (PCA)-derived profiles of muscle morphology, muscle quality, and strength were associated with weight-transfer performance during voluntary forward and backward stepping. Twenty-three younger and older adults underwent ultrasound imaging of the tensor fasciae latae (TFL), vastus lateralis (VL), and biceps femoris (BF), assessments of knee extensor, knee flexor, and hip abductor strength, and completion of a Choice Reactive Stepping Test (CRST). PCA was applied to nine muscle-related variables to summarize interrelated measures of muscle size, echo intensity, and strength into neuromuscular profiles. The first four principal components (PCs) explained approximately 80.8% of the total variance. PC1 reflected a favorable muscle quality and strength profile, characterized by lower echo intensity and greater hip abduction and knee flexion strength. PC2 suggested a dissociation between TFL thickness and force-generating capacity, whereas PC3 and PC4 reflected muscle-specific structural and strength heterogeneity. PCA-derived PC scores and age group were then entered into a multivariate multiple regression (MMR) model, with weight-transfer onset (WTO) and weight-transfer duration (WTD) during forward and backward stepping as response variables. The MMR model did not provide statistical evidence that age group or any PC was associated with the combined WTO and WTD outcomes. These findings suggest that, in this sample, resting muscle morphology, muscle quality, and maximal strength did not explain weight-transfer performance when modeled as integrated neuromuscular profiles. Weight-transfer performance during stepping may depend on dynamic neuromuscular factors, such as rapid force generation, activation timing, coordination, and task-specific balance strategies, which should be examined in larger studies.

PMID:42315916 | DOI:10.1038/s41598-026-57916-1