Kavya Rajan,1 Arjun Krishen Jutley,2 Michael W Holliday Jr,3,4 Sankar D Navaneethan3– 5 1Innovation Academy High School, Alpharetta, GA, USA; 2Tomball Memorial High School, Houston, TX, USA; 3Selzman Institute for Kidney Health, Section of Nephrology, Baylor College of Medicine, Houston, TX, USA; 4Renal Section, Medical Care Line, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA; 5Institute of Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USACorrespondence: Sankar D Navaneethan, Baylor College of Medicine, Department of Medicine, Renal Section, 1 Baylor Plaza, Suite 100.37D, Houston, TX, 77030, USA, Email sankar.navaneethan@bcm.eduAbstract: Chronic kidney disease (CKD) incidence continues to rise along with obesity and diabetes, driving substantial medical, psychosocial, and economic burdens for patients. Beyond glycemic control and the recommended therapies of ACEI/ARB, SGLT2 inhibitors and non-steroidal mineralocorticoid receptor antagonists, glucagon-like peptide-1 receptor agonists (GLP-1RAs) have emerged as a cornerstone therapy to benefit mortality, heart and kidney outcomes. The following review will discuss recent advances to our understanding of the kidney benefits of GLP1 agonism in high-risk populations, including patients with type 2 diabetes mellitus, obesity, those with established cardiovascular disease. Renal signals from cardiovascular outcomes trials disclosed less albuminuria and slower estimated glomerular filtration rate (eGFR) decline with GLP1RA therapy, often additive to sodium–glucose cotransporter-2 inhibition. Dedicated kidney studies now show semaglutide slows CKD progression and lowers mortality in diabetics with CKD, underscoring the relevance of new guidelines that recommend GLP1RA therapy for specific populations. Future priorities should include trials of GLP-1RA in non-diabetic patients with CKD, as well as further evaluation of dual or triple agonists (GLP-1/GIP/glucagon) and clarification of oral GLP1RA efficacy. Overall, GLP-1–based therapies represent a transformative strategy to improve weight, cardiovascular health, and kidney outcomes in diabetic CKD patients.Keywords: cardiorenal outcomes, chronic kidney disease, diabetes, obesity, nephroprotection
Md. Ehsanul Haque, Md. Saymon Hosen Polash, Rakib Hasan Ovi
et al.
Grapes are among the most economically and culturally significant fruits on a global scale, and table grapes and wine are produced in significant quantities in Europe and Asia. The production and quality of grapes are significantly impacted by grape diseases such as Bacterial Rot, Downy Mildew, and Powdery Mildew. Consequently, the sustainable management of a vineyard necessitates the early and precise identification of these diseases. Current automated methods, particularly those that are based on the YOLO framework, are often computationally costly and lack interpretability that makes them unsuitable for real-world scenarios. This study proposes grape leaf disease classification using Optimized DenseNet 121. Domain-specific preprocessing and extensive connectivity reveal disease-relevant characteristics, including veins, edges, and lesions. An extensive comparison with baseline CNN models, including ResNet18, VGG16, AlexNet, and SqueezeNet, demonstrates that the proposed model exhibits superior performance. It achieves an accuracy of 99.27%, an F1 score of 99.28%, a specificity of 99.71%, and a Kappa of 98.86%, with an inference time of 9 seconds. The cross-validation findings show a mean accuracy of 99.12%, indicating strength and generalizability across all classes. We also employ Grad-CAM to highlight disease-related regions to guarantee the model is highlighting physiologically relevant aspects and increase transparency and confidence. Model optimization reduces processing requirements for real-time deployment, while transfer learning ensures consistency on smaller and unbalanced samples. An effective architecture, domain-specific preprocessing, and interpretable outputs make the proposed framework scalable, precise, and computationally inexpensive for detecting grape leaf diseases.
Abstract Objective This study investigates the association between A Body Shape Index (ABSI) and risk of benign prostatic hyperplasia (BPH) in middle-aged and older men. Methods Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS), including 3,223 men aged ≥ 45 years. ABSI was calculated from waist circumference, BMI, and height. Logistic regression and restricted cubic spline models were used to assess the association and potential nonlinear relationship between ABSI and incident BPH. Results During 4 years of follow-up, 260 new BPH cases were identified. A nonlinear association was observed: ABSI was significantly associated with increased BPH risk within a moderate range (7.420–8.916), but not at lower or higher ABSI levels (OR = 1.532, 95% CI: 1.015–2.314, p = 0.042). Conclusion ABSI was associated with BPH risk in a nonlinear pattern. These findings suggest that ABSI may serve as a potential risk indicator, although further research is needed. Limitations include reliance on self-reported BPH diagnosis and lack of biomarker data.
Salman Soltani, Nooshin Tafazoli, Maryam Emadzadeh
et al.
Abstract. Objective. This study aimed to perform an evaluation of changes in spermogram parameters after follicle-stimulating hormone (FSH) therapy in infertile males having oligo-asthenospermia at different levels of DNA fragmentation index (DFI).
Materials and methods. Infertile men with oligo-asthenospermia, no underlying urogenital disease (such as varicocele), and medically fertile partners were enrolled over 1 year. Semen parameters, FSH, luteinizing hormone, and testosterone levels were determined; also, a Sperm DNA Fragmentation Assay Kit (Hamun Teb Pishro, Tehran, Iran) was used for determining sperm DFI at baseline. Participants were categorized into 3 groups based on DFI: DFI <15% (group 1), DFI of 15%–30% (group 2), and DFI >30% (group 3). All participants received subcutaneous recombinant FSH (150 mg every other day) for 6 months. Sperm specimens were tested 6 months after intervention (a single sperm control test).
Results. Sixty males whose average age was 28.4 years were enrolled. Only group 3 (poor fertility) exhibited a significant rise in sperm concentration (p = 0.001) and motility (p < 0.05) after FSH treatment. Group 1 (DFI <15%) and group 2 (DFI of 15%–30%) showed increased mean sperm concentration and motility postintervention, although these alterations were not significantly different. Follicle-stimulating hormone levels increased significantly in all 3 groups after FSH administration. Serum luteinizing hormone and testosterone levels were not significantly increased in any of the groups.
Conclusions. Follicle-stimulating hormone treatment improves sperm concentration and motility in men with oligo-asthenospermia, with significant improvements observed in men with DFI >30%. DNA fragmentation index can be a predictive indicator of response to FSH treatment in such patients.
Background IgA nephropathy (IgAN) presents a challenging spectrum of outcomes, often complicated by intrarenal arterial/arteriolar lesions (IALs) in affected individuals. Despite their clinical relevance, existing criteria for classifying and assessing the severity of these lesions remain undefined. This study aimed to establish semi-quantitative assessment criteria for grading IALs and to evaluate their prognostic significance in patients with IgAN.Method We conducted a retrospective cohort study of 417 cases of primary IgAN in which IALs were meticulously scored in individual biopsies. Kaplan–Meier survival analysis was employed to compare the time to the renal composite endpoint between different IALs severity groups. The association between the severity of IALs and clinical outcomes was further evaluated using multivariate Cox regression models to control for potential confounders.Results Among the 417 patients studied, 230 (55.2%) exhibited IALs. Kaplan-Meier curve analysis showed a higher cumulative incidence of the composite endpoint in patients with IALs (p < 0.001). In a compelling multivariate analysis, we identified IALs and its subclassifications, including moderate to severe intimal fibrosis and hyalinosis, as strong independent risk factors for poor prognosis (IALs: HR = 2.15, p = 0.009; moderate to severe hyalinosis: HR = 3.58, p = 0.001; moderate to severe intimal fibrosis: HR = 3.56, p = 0.001).Conclusion Our findings underscore the prognostic significance of IALs in IgAN, particularly moderate to severe intimal fibrosis and hyalinosis and highlight the urgent need for novel therapeutic strategies specifically designed to mitigate the impact of IALs in high-risk IgAN patients.
Imaging and genomic data offer distinct and rich features, and their integration can unveil new insights into the complex landscape of diseases. In this study, we present a novel approach utilizing radiogenomic data including structural MRI images and gene expression data, for Alzheimer's disease detection. Our framework introduces a novel heterogeneous bipartite graph representation learning featuring two distinct node types: genes and images. The network can effectively classify Alzheimer's disease (AD) into three distinct stages:AD, Mild Cognitive Impairment (MCI), and Cognitive Normal (CN) classes, utilizing a small dataset. Additionally, it identified which genes play a significant role in each of these classification groups. We evaluate the performance of our approach using metrics including classification accuracy, recall, precision, and F1 score. The proposed technique holds potential for extending to radiogenomic-based classification to other diseases.
The computational complexity of large language model (LLM) inference significantly constrains their deployment efficiency on edge devices. In contrast, small language models offer faster decoding and lower resource consumption but often suffer from degraded response quality and heightened susceptibility to hallucinations. To address this trade-off, collaborative decoding, in which a large model assists in generating critical tokens, has emerged as a promising solution. This paradigm leverages the strengths of both model types by enabling high-quality inference through selective intervention of the large model, while maintaining the speed and efficiency of the smaller model. In this work, we present a novel collaborative decoding inference system that allows small models to perform on-device inference while selectively consulting a cloud-based large model for critical token generation. Remarkably, the system achieves a 60% performance gain on CommonsenseQA using only a 0.5B model on an M1 MacBook, with under 7% of tokens generation uploaded to the large model in the cloud.
Demographic attributes are universally present in electronic health records. They are the most widespread information across populations and diseases, and serve as vital predictors in clinical risk stratification and treatment decisions. Despite their significance, these attributes are often treated as auxiliaries in model design, with limited attention being paid to learning their representations. This study explored the development of a General Demographic Pre-trained (GDP) model as a foundational model tailored to demographic attributes, focusing on age and gender. The model is pre-trained and evaluated using datasets with diverse diseases and populations compositions from different geographic regions. The composition of GDP architecture was explored through examining combinations of ordering approaches and encoding methods to transform tabular demographic inputs into effective latent embeddings. Results demonstrate the feasibility of GDP to generalize across task, diseases, and populations. In detailed composition, the sequential ordering substantially improves model performance in discrimination, calibration, and the corresponding information gain at each decision tree split, particularly in diseases where age and gender contribute significantly to risk stratification. Even in datasets where demographic attributes hold relatively low predictive value, GDP enhances the representational importance, increasing their influence in downstream gradient boosting models. The findings suggest that foundation models for tabular demographic attributes offer a promising direction for improving predictive performance in healthcare applications.
When particles interact via two-body short-range central potential wells, binding can occur for some critical values of the coupling constants. Using the envelope theory, upper bounds for critical coupling constants are computed for quantum nonrelativistic systems containing identical particles and systems containing identical particles plus a different one.
Abstract Background Interstitial Pneumonitis (IP) is one of the pulmonary complications associated with mammalian Target of Rapamycin-Inhibitors (mTOR-Is). Sirolimus and everolimus belong to mTOR-Is. According to studies, IP is caused by both. Case presentation This is a case report in a kidney transplant recipient. We want to present a case of IP after 50 months of sirolimus consumption. Sirolimus was discontinued, and cyclosporine was started. Thirty-seven months later, everolimus was prescribed as an alternative to cyclosporine due to the recurrence of skin Squamous Cell Carcinoma (SCC). Fortunately, no respiratory manifestations were seen after more than 8 years of everolimus consumption. Conclusions In conclusion, in cases with sirolimus-induced IP, discontinuation of sirolimus and replacement with everolimus are recommended after resolving clinical symptoms and pulmonary lesions.
Irene Martin Capon, Eduardo Gutierrez, Eduardo Gutierrez
et al.
BackgroundThe treatment of minimal change disease (MCD) consists of a high dose of steroids for several months, implying significant drug toxicity. Nevertheless, relapses of steroid-sensitive MCD usually respond to lower doses of steroids.MethodsThe objective of this study was to analyze whether a low dose of steroids (LDS) is effective for the treatment of MCD relapses. Since 2018, new relapses of steroid-sensitive adult patients with MCD in three Spanish centers have been treated with LDS. The cumulative dose of steroids, the time to remission, and the relapse-free time were compared between relapses treated with LDS and previous relapses of the same patients treated with a standard dose of steroids (SDS).ResultsA total of 51 relapses in 31 patients were treated with LDS and compared with 48 historical relapses of the same patients treated with SDS. The mean doses of prednisone adjusted by weight for the initial treatment were 0.45 mg/kg (0.40–0.51 mg/kg) in the relapses treated with LDS and 0.88 mg/kg (0.81–1.00 mg/kg) in those treated with SDS. The mean cumulative doses of prednisone in LDS- and SDS-treated relapses were 1,191 mg (801–1,890 mg) and 3,700 mg (2,755–5,800 mg), respectively. The duration of treatment was 63 days (42–117 days) in the LDS group and was 140 days (65–195 days) in the SDS group. All patients achieved complete remission within 1 month after steroid therapy in both groups. The times to remission of the LDS and SDS groups were 19.10 ± 12.80 and 18.93 ± 12.98 days, respectively (p = 0.95).ConclusionAmong the steroid-sensitive patients with MCD, relapse therapy with LDS (0.5 mg/kg) appears effective and allows minimization of the steroid cumulative dose.
Поширення в Європі з 2022 року інфекції, викликаної Group A β-haemolytic Streptococci (GAS), актуалізувало питання діагностики та лікування гострого постстрептококового гломерулонефриту (APSGN), що залишається найчастішою причиною імунокомплексної патології нирок у дітей. Специфіка етапу розвитку України, повязана з активними бойовими діями, не визначеність епідеміологічної ситуації та відсутність чинних національних протоколів ускладнюють процес прийняття клінічного рішення на локальному рівні. Мета роботи - висвітлення особливостей діагностики, перебігу та супроводу APSGN у дітей для покращення його наслідків в умовах воєнного стану в країні. За аналізом актуальних міжнародних настанов, рекомендацій та протоколів референтних центрів представлено основні характеристики GAS, лабораторні докази перенесеної інфекції та особливості їх інтерпретації, варіанти перебігу APSGN. Узагальнено досвід по терапевтичному супроводу цієї когорти з акцентом на обмежене застосування антибактеріальних засобів. Підкреслено відмінності української практики, які сформовані минулими регламентами і досі присутні в локальних центрах країни. Наведено типову послідовність відновлення після захворювання та ознаки, що потребують ревізії діагнозу і зміни тактики ведення. З позицій набутого досвіду представлено і переоцінено супровід декількох клінічних випадків.
The advances in computer vision made possible by deep learning technology are increasingly being used in precision agriculture to automate the detection and classification of plant diseases. Symptoms of plant diseases are often seen on their leaves. The leaf images in existing datasets have been collected either under controlled conditions or in the field. The majority of previous studies have focused on identifying leaf diseases using images captured in controlled laboratory settings, often achieving high performance. However, methods aimed at detecting and classifying leaf diseases in field images have generally exhibited lower performance. The objective of this study is to evaluate the impact of a data augmentation approach that involves removing complex backgrounds from leaf images on the classification performance of apple leaf diseases in images captured under real world conditions. To achieve this objective, the lightweight pre-trained MobileNetV2 deep learning model was fine-tuned and subsequently used to evaluate the impact of expanding the training dataset with background-removed images on classification performance. Experimental results show that this augmentation strategy enhances classification accuracy. Specifically, using the Adam optimizer, the proposed method achieved a classification accuracy of 98.71% on the Plant Pathology database, representing an approximately 3% improvement and outperforming state-of-the-art methods. This demonstrates the effectiveness of background removal as a data augmentation technique for improving the robustness of disease classification models in real-world conditions.
The emergence or adaptation of pathogens may lead to epidemics, highlighting the need for a thorough understanding of pathogen evolution. The tradeoff hypothesis suggests that virulence evolves to reach an optimal transmission intensity relative to the mortality caused by the disease. This study introduces a mathematical model that incorporates key factors such as recovery times and mortality rates, focusing on the diminishing effects of parasite growth on transmission, with a focus on vector-borne diseases. The analysis reveals conditions under which heightened virulence occurs in hosts, indicating that these factors can support vector-host transmission of a pathogen, even if the host-only component is insufficient for sustainable transmission. This insight helps explain the significant presence of pathogens with high fatality rates, such as those in vector-borne diseases. The findings underscore an elevated risk for future outbreaks involving such diseases. Enhanced surveillance of mortality rates and techniques to monitor pathogen evolution are vital to effectively control future epidemics. This study provides essential insights for epidemic preparedness and highlights the need for ongoing research into pathogen evolution.
Christof Naumzik, Alice Kongsted, Werner Vach
et al.
Clinical data informs the personalization of health care with a potential for more effective disease management. In practice, this is achieved by subgrouping, whereby clusters with similar patient characteristics are identified and then receive customized treatment plans with the goal of targeting subgroup-specific disease dynamics. In this paper, we propose a novel mixture hidden Markov model for subgrouping patient trajectories from chronic diseases. Our model is probabilistic and carefully designed to capture different trajectory phases of chronic diseases (i.e., "severe", "moderate", and "mild") through tailored latent states. We demonstrate our subgrouping framework based on a longitudinal study across 847 patients with non-specific low back pain. Here, our subgrouping framework identifies 8 subgroups. Further, we show that our subgrouping framework outperforms common baselines in terms of cluster validity indices. Finally, we discuss the applicability of the model to other chronic and long-lasting diseases.
N6-methyladenosine (m6A) is a ubiquitous RNA modification in mammals. This modification is “written” by methyltransferases and then “read” by m6A-binding proteins, followed by a series of regulation, such as alternative splicing, translation, RNA stability, and RNA translocation. At last, the modification is “erased” by demethylases. m6A modification is essential for normal physiological processes in mammals and is also a very important epigenetic modification in the development of cancer. In recent years, cancer-related m6A regulation has been widely studied, and various mechanisms of m6A regulation in cancer have also been recognized. In this review, we summarize the changes of m6A modification in prostate cancer and discuss the effect of m6A regulation on prostate cancer progression, aiming to profile the potential relevance between m6A regulation and prostate cancer development. Intensive studies on m6A regulation in prostate cancer may uncover the potential role of m6A methylation in the cancer diagnosis and cancer therapy.
Objective By analyzing the clinical history, laboratory test indexes, and intraoperative ultrasound imaging data of patients receiving ultrasound-guided percutaneous transluminal angioplasty (UG-PTA) for the first time, the application value of UG-PTA in the treatment of peripheral stenosis of autogenous arteriovenous fistula (AVF) and the related factors affecting postoperative patency were investigated.Methods A total of 381 patients with dysfunction of radio-cephalic AVF were treated with UG-PTA from June 2017 to September 2019. According to the inclusion and exclusion criteria, 199 patients were included in this study. Baseline characteristics of patients, including demographic, clinical, and laboratory data, were collected. Kaplan–Meier’s survival curve was used to demonstrate the cumulative primary patency rate of UG-PTA. Univariate and multivariate Cox regression analysis was performed on clinical, anatomic, biochemical, and medication variables to identify the predictors of postintervention primary patency.Results The early technical success rate of UG-PTA was 98.4% (375/381). One hundred and ninety-nine patients, with an average age of 52.9 years, were analyzed, 97 of whom were males (48.7%). The median follow-up duration was 21 months. No major complication was observed. Postintervention primary patency rates were 87.7%, 75.8%, and 60.0% at 6, 12, and 24 months, respectively. A previously failed AVF (HR, 1.935, 95% CI 1.071–3.494; p = .029) and an increased level of parathyroid hormone (HR per 100 pg/mL increase, 1.105; 95% CI 1.014–1.203; p = .004) were identified as independent negative predictors of primary patency of UG-PTA.Conclusions UG-PTA is a safe and effective method for the treatment of peripheral stenosis of AVF. Previously failed AVF and elevated parathyroid hormone levels are associated with lower primary patency rate.