Hasil untuk "Diseases of the genitourinary system. Urology"

Jing Ma, Ruiyan Liu, Xin Feng et al.

Abstract Background Chronic kidney disease (CKD) is a leading cause of morbidity and mortality worldwide, yet existing risk models have limited ability to identify individuals at high long-term risk. Whether integrating circulating biochemical and metabolomic biomarkers can improve CKD prediction and risk stratification remains unclear. Methods We included 233,589 UK Biobank participants without CKD at baseline. Biomarkers were screened using multiple feature selection strategies. Predictive performance and effect sizes were evaluated using Cox proportional hazards models. CatBoost and SHAP were applied to identify key predictors, derive interpretable binary thresholds, and construct a simplified biomarker risk score (BRS). Relative and absolute CKD risks were assessed across tertiles of the BRS. Model discrimination and calibration were evaluated in an England development cohort and a geographically independent validation cohort from Scotland and Wales. Results A combined biochemical–metabolomic signature (BioMet) showed good discrimination for incident CKD and CKD-related mortality and consistently outperformed conventional risk models in both cohorts. Key risk-elevating biomarkers included cystatin C, HbA1c, CRP, and urea, whereas higher eGFR, M-VLDL-CE, histidine, and IGF-1 were inversely associated with CKD risk. A SHAP-derived Top10 BRS (Top10BRS) effectively stratified individuals into distinct risk groups. Compared with the lowest tertile, participants in the highest tertile had a substantially higher risk of incident CKD (HR: 3.73) and CKD-related mortality (HR: 10.40). Discrimination improved after adding Top10BRS to conventional models, while calibration and prediction error remained stable. Similar patterns were observed in the validation cohort. Conclusion Integrating biochemical and metabolomic biomarkers with conventional clinical predictors improves long-term prediction and risk stratification for CKD. An interpretable SHAP-derived BRS enables robust identification of individuals at elevated risk and may support earlier risk assessment and personalized prevention strategies for CKD.

Himanshu Verma, Sourabh Sharma, Pallavi Prasad et al.

Asli Metin Mahmutoglu, Ashok Agarwal, Bahadir Sahin et al.

Purpose: This study investigated 1) the frequency of quotation errors in multi-authored medical manuscripts in andrology, 2) analyzed common types of quotation errors and the methods used to rectify them, and 3) evaluated their impact on manuscript accuracy, credibility, and research conclusions. Materials and Methods: Twelve manuscripts written by the Global Andrology Forum (GAF) members between 2023 and 2024 were randomly selected for this study. The manuscripts and “Quotation Verification Sheets” were analyzed by senior GAF researchers to detect the number and types of quotation errors. The error rate was calculated by the total number of quotation errors and total number of all cited references in each manuscript. The impact on manuscript sections was assessed using a 0–4 grading scale. The Spearman correlation test was used to assess the correlation between scalar variables, and the Mann–Whitney U test was utilized to compare scalar variables between two groups. Results: The median value of quotation errors was 10.3%. Factual inaccuracy was the most common type of error, and was observed in all twelve manuscripts at various rates. The number of errors was significantly associated with the number of references (ρ=0.706; p=0.010) and in-text citations (ρ=0.636; p=0.026). Factual inaccuracy (ρ=0.588; p=0.044) and factual interpretation (ρ=0.861; p=0.013) were also correlated with the total number of quotation errors. However, no significant associations were found between quotation errors and author numbers or their qualifications. The quotation errors adversely impacted the manuscript discussion, followed by the overall message. Conclusions: Quotation errors are common in multi-authored medical manuscripts in andrology-related scientific articles. Journal editorial offices should incorporate quotation verification into the review process. Limiting references and in-text citations to only strictly necessary ones may help improve quotation accuracy. The quotation verification model proposed by GAF offers a practical and structured approach for detecting and correcting quotation errors.

Ryota Mori, Satoru Taguchi, Masahiro Yamamoto et al.

ABSTRACT Introduction Drug‐induced thrombocytopenia (DITP) can be caused by many kinds of drugs. Its treatment generally involves discontinuation of the responsible drug. Case Presentation A 70‐year‐old man received a subcutaneous injection of long‐acting (24‐week) leuprorelin depot as androgen deprivation therapy for prostate cancer. Four days after the injection, he presented with gingival bleeding and his platelet count was remarkably decreased (< 1000/μL). There was no sign of malignancy but the presence of megakaryocytes on bone‐marrow examinations. Considering immune and/or DITP, he started immunoglobulin and steroid therapy while stopping all suspected medications. However, even a month later, his platelet count did not recover with the need for frequent platelet transfusions. Therefore, he eventually underwent surgical resection of the leuprorelin injection site. After the surgery, his platelet count drastically recovered and platelet transfusion became unnecessary. Conclusion We report a case of leuprorelin‐induced thrombocytopenia that was successfully treated with surgical resection of the injection site.

Abduz Zami, Shadman Sobhan, Rounaq Hossain et al.

Image segmentation plays a vital role in the medical field by isolating organs or regions of interest from surrounding areas. Traditionally, segmentation models are trained on a specific organ or a disease, limiting their ability to handle other organs and diseases. At present, few advanced models can perform multi-organ or multi-disease segmentation, offering greater flexibility. Also, recently, prompt-based image segmentation has gained attention as a more flexible approach. It allows models to segment areas based on user-provided prompts. Despite these advances, there has been no dedicated work on prompt-based interactive multi-organ and multi-disease segmentation, especially for Chest X-rays. This work presents two main contributions: first, generating doodle prompts by medical experts of a collection of datasets from multiple sources with 23 classes, including 6 organs and 17 diseases, specifically designed for prompt-based Chest X-ray segmentation. Second, we introduce Prompt2SegCXR, a lightweight model for accurately segmenting multiple organs and diseases from Chest X-rays. The model incorporates multi-stage feature fusion, enabling it to combine features from various network layers for better spatial and semantic understanding, enhancing segmentation accuracy. Compared to existing pre-trained models for prompt-based image segmentation, our model scores well, providing a reliable solution for segmenting Chest X-rays based on user prompts.

Maxim Rubchinsky, Ella Rabinovich, Adi Shraibman et al.

We present a novel approach to automating the identification of risk factors for diseases from medical literature, leveraging pre-trained models in the bio-medical domain, while tuning them for the specific task. Faced with the challenges of the diverse and unstructured nature of medical articles, our study introduces a multi-step system to first identify relevant articles, then classify them based on the presence of risk factor discussions and, finally, extract specific risk factor information for a disease through a question-answering model. Our contributions include the development of a comprehensive pipeline for the automated extraction of risk factors and the compilation of several datasets, which can serve as valuable resources for further research in this area. These datasets encompass a wide range of diseases, as well as their associated risk factors, meticulously identified and validated through a fine-grained evaluation scheme. We conducted both automatic and thorough manual evaluation, demonstrating encouraging results. We also highlight the importance of improving models and expanding dataset comprehensiveness to keep pace with the rapidly evolving field of medical research.

Mingle Zhou, Rui Xing, Delong Han et al.

UAVs emerge as the optimal carriers for visual weed iden?tification and integrated pest and disease management in crops. How?ever, the absence of specialized datasets impedes the advancement of model development in this domain. To address this, we have developed the Pests and Diseases Tree dataset (PDT dataset). PDT dataset repre?sents the first high-precision UAV-based dataset for targeted detection of tree pests and diseases, which is collected in real-world operational environments and aims to fill the gap in available datasets for this field. Moreover, by aggregating public datasets and network data, we further introduced the Common Weed and Crop dataset (CWC dataset) to ad?dress the challenge of inadequate classification capabilities of test models within datasets for this field. Finally, we propose the YOLO-Dense Pest (YOLO-DP) model for high-precision object detection of weed, pest, and disease crop images. We re-evaluate the state-of-the-art detection models with our proposed PDT dataset and CWC dataset, showing the completeness of the dataset and the effectiveness of the YOLO-DP. The proposed PDT dataset, CWC dataset, and YOLO-DP model are pre?sented at https://github.com/RuiXing123/PDT_CWC_YOLO-DP.

Ali Saadat, Jacques Fellay

Genetic diseases can be classified according to their modes of inheritance and their underlying molecular mechanisms. Autosomal dominant disorders often result from DNA variants that cause loss-of-function, gain-of-function, or dominant-negative effects, while autosomal recessive diseases are primarily linked to loss-of-function variants. In this study, we introduce a graph-of-graphs approach that leverages protein-protein interaction networks and high-resolution protein structures to predict the mode of inheritance of diseases caused by variants in autosomal genes, and to classify dominant-associated proteins based on their functional effect. Our approach integrates graph neural networks, structural interactomics and topological network features to provide proteome-wide predictions, thus offering a scalable method for understanding genetic disease mechanisms.

Rajnandini Dasgupta, Chandan J Das, Amit Gupta

Renal replacement lipomatosis (RRL) is a rare, benign entity characterized by marked fat proliferation within the renal sinus and perinephric space. We present images of a patient with RRL.

Alyssa M. Vanderbeek, Anna A. Vidovszky, Jessica L. Ross et al.

A crucial task for a randomized controlled trial (RCT) is to specify a statistical method that can yield an efficient estimator and powerful test for the treatment effect. A novel and effective strategy to obtain efficient and powerful treatment effect inferences is to incorporate predictions from generative artificial intelligence (AI) algorithms into covariate adjustment for the regression analysis of a RCT. Training a generative AI algorithm on historical control data enables one to construct a digital twin generator (DTG) for RCT participants, which utilizes a participant's baseline covariates to generate a probability distribution for their potential control outcome. Summaries of the probability distribution from the DTG are highly predictive of the trial outcome, and adjusting for these features via regression can thus improve the quality of treatment effect inferences, while satisfying regulatory guidelines on statistical analyses, for a RCT. However, a critical assumption in this strategy is homoskedasticity, or constant variance of the outcome conditional on the covariates. In the case of heteroskedasticity, existing covariate adjustment methods yield inefficient estimators and underpowered tests. We propose to address heteroskedasticity via a weighted prognostic covariate adjustment methodology (Weighted PROCOVA) that adjusts for both the mean and variance of the regression model using information obtained from the DTG. We prove that our method yields unbiased treatment effect estimators, and demonstrate via comprehensive simulation studies and case studies from Alzheimer's disease that it can reduce the variance of the treatment effect estimator, maintain the Type I error rate, and increase the power of the test for the treatment effect from 80% to 85%~90% when the variances from the DTG can explain 5%~10% of the variation in the RCT participants' outcomes.

Ritesh Chandra, Sadhana Tiwari, Sonali Agarwal et al.

Vector-borne diseases (VBDs) are a kind of infection caused through the transmission of vectors generated by the bites of infected parasites, bacteria, and viruses, such as ticks, mosquitoes, triatomine bugs, blackflies, and sandflies. If these diseases are not properly treated within a reasonable time frame, the mortality rate may rise. In this work, we propose a set of ontologies that will help in the diagnosis and treatment of vector-borne diseases. For developing VBD's ontology, electronic health records taken from the Indian Health Records website, text data generated from Indian government medical mobile applications, and doctors' prescribed handwritten notes of patients are used as input. This data is then converted into correct text using Optical Character Recognition (OCR) and a spelling checker after pre-processing. Natural Language Processing (NLP) is applied for entity extraction from text data for making Resource Description Framework (RDF) medical data with the help of the Patient Clinical Data (PCD) ontology. Afterwards, Basic Formal Ontology (BFO), National Vector Borne Disease Control Program (NVBDCP) guidelines, and RDF medical data are used to develop ontologies for VBDs, and Semantic Web Rule Language (SWRL) rules are applied for diagnosis and treatment. The developed ontology helps in the construction of decision support systems (DSS) for the NVBDCP to control these diseases.

Utkarsh Yashwant Tambe, A. Shobanadevi, A. Shanthini et al.

In this study, a Convolutional Neural Network (CNN) is used to classify potato leaf illnesses using Deep Learning. The suggested approach entails preprocessing the leaf image data, training a CNN model on that data, and assessing the model's success on a test set. The experimental findings show that the CNN model, with an overall accuracy of 99.1%, is highly accurate in identifying two kinds of potato leaf diseases, including Early Blight, Late Blight, and Healthy. The suggested method may offer a trustworthy and effective remedy for identifying potato diseases, which is essential for maintaining food security and minimizing financial losses in agriculture. The model can accurately recognize the various disease types even when there are severe infections present. This work highlights the potential of deep learning methods for categorizing potato diseases, which can help with effective and automated disease management in potato farming.

Mohammad Ashraf Bhat, Shahid Sulayman, Manzoor Ahmad Parry et al.

Renal biopsy is performed for various reasons depending on the clinical manifestations presented. Although percutaneous kidney biopsy is a safe procedure, major or minor complications could occur. Our study aimed to assess the indications for percutaneous renal biopsy and complications associated with the procedure. This was a prospective observational study conducted in the Department of Nephrology, Sher-i-Kashmir Institute of Medical Sciences, Srinagar, India. Patients who underwent percutaneous ultrasound-guided renal biopsy at the institute between October 2017 and June 2019 were enrolled in the study. Data regarding indications for performing a percutaneous renal biopsy and incidence of minor and major post-biopsy complications were collected. A total of 229 patients who underwent ultrasound-guided percutaneous renal biopsy were enrolled in the study. The most common indications for ultrasound-guided percutaneous renal biopsy were nephrotic syndrome (33.3%), subnephrotic proteinuria with azotemia (14%), and unexplained azotemia with proteinuria and hematuria (13.5%). Post-biopsy complications were observed in 89 (37.55%) patients. Minor complications developed in 83 (36.22%) patients and major complications in six (2.62%) patients. Among patients with major complications, two (0.87%) patients underwent invasive procedures (embolization and cystoscopic removal of bladder clot) and four patients developed hemodynamic instability. No procedure-related mortality was reported in the study. The most common indications for renal biopsy were nephrotic syndrome, subnephrotic proteinuria with azotemia, unexplained azotemia with proteinuria, and hematuria. The incidence of major complications was low.

Enrico Checcucci, Giovanni Enrico Cacciamani, Daniele Amparore et al.

Nikolay V. Sturov , Sergey V. Popov , Vladimir A. Zhukov et al.

Junbao Shi, Yue Wang, Song Wang et al.

Abstract Objective Maintaining a low-protein diet (LPD) is important for patients with chronic kidney disease (CKD) to delay renal degradation and alleviate clinical symptoms. For most patients with CKD, it is difficult to maintain the necessary low level of dietary protein intake (DPI). To improve the current dietary management of CKD, we conducted an intervention study by administering low-protein staple foods (LPSF). Design and methods We conducted a prospective case-crossover study among 25 patients with stage 3–4 CKD. During the initial 12 weeks of the study, we instructed the patients regarding a standard LPD according to the recommendations of a renal dietitian. In the second stage of the study, we requested the patients taking low-protein rice or low-protein flour (250 g/d) as an LPSF diet instead of regular staple food daily, and followed these patients up for 12 weeks. We compared the DPI, dietary energy intake (DEI), normalized protein equivalent of total nitrogen appearance (nPNA), serum creatinine levels, and nutritional index between baseline and the end of the study. Results We found no change in dietary variables among the patients during the first 12 weeks of the LPD. After subjecting them to an LPSF diet, the corresponding variables showed a pronounced change. The patients’ DPI decreased from 0.88 ± 0.20 to 0.68 ± 0.14 g/kg/d (P < 0.01) and the nPNA value decreased from 0.99 ± 0.18 to 0.87 ± 0.19 g/kg/d (P < 0.01). The high biological value protein intake proportion increased from 42% (baseline) to 57% (P < 0.01) during the 24 weeks. No variation was found in the measured DEI (28.0 ± 5.8 vs 28.6 ± 5.4 kcal/kg/d), nutrition assessment, or renal function and serum creatinine levels. Conclusion Our prospective case-crossover study demonstrated that an LPSF diet can help patients with stage 3–4 CKD reduce DPI and nPNA values, improve the proportion of highly bioavailable proteins, ensure adequate calorie intake, and avoid malnutrition. An LPSF diet is an effective and simple therapy for patients with stage 3–4 CKD.

Subhrangshu Guhathakurta, Min Kyung Song, Sambuddha Basu et al.

Purpose Deregulation of SNCA encoding α-synuclein (α-SYN) has been associated with both the familial and sporadic forms of Parkinson disease (PD). Epigenetic regulation plays a crucial role in PD. The intron1 of SNCA harbors a large unmethylated CpG island. Ten-eleven translocation methylcytosine dioxygenase 1 (TET1), a CpG island binding protein, can repress gene expression by occupying hypomethylated CpG-rich promoters, and therefore SNCA could be a target for TET1. We investigated whether TET1 binds to SNCA-intron1 and regulates gene expression. Methods The dopaminergic neuronal cell line, ReNcell VM, was used. Reverse transcription-polymerase chain reaction (RT-PCR), real time-quantitative PCR, Western blot, dot-blot, and Chromatin immunoprecipitation were conducted. The substantia nigra tissues of postmortem PD samples were used to confirm the level of TET1 expression. Results In the human dopaminergic cell line, ReNcell VM, overexpression of the DNA-binding domain of TET1 (TET1-CXXC) led to significant repression of α-SYN. On the contrary, knocking down of TET1 led to significantly higher expression of α-SYN. However, overexpression of the DNA-hydroxymethylating catalytic domain of TET1 failed to change the expression of α-SYN. Altogether, we showed that TET1 is a repressor for SNCA, and a CXXC domain of TET1 is the primary mediator for this repressive action independent of its hydroxymethylation activity. TET1 levels in PD patients are significantly lower than that in the controls. Conclusions We identified that TET1 acts as a repressor for SNCA by binding the intron1 regions of the gene. As a high level of α-SYN is strongly implicated in the pathogenesis of PD, discovering a repressor for the gene encoding α-SYN is highly important for developing novel therapeutic strategies for the disease.

Philippe E. Spiess, Andrea Necchi

Lee A. Richter, Harry Lee, Arlene Nishimwe et al.

Wei Shen Tan, Jeffrey J. Leow, Maya Marchese et al.

Background: Radical cystectomy (RC) is associated with high morbidity. Objective: To evaluate healthcare and surgical factors associated with high-quality RC surgery. Design, setting, and participants: Patients within the prospective British Association of Urological Surgeons (BAUS) registry between 2014 and 2017 were included in this study. Outcome measurements and statistical analysis: High-quality surgery was defined using pathological (absence of positive surgical margins and a minimum of a level I lymph node dissection template with a minimum yield of ten or more lymph nodes), recovery (length of stay ≤10 d), and technical (intraoperative blood loss <500 ml for open and <300 ml for minimally invasive RC) variables. A multilevel hierarchical mixed-effect logistic regression model was utilised to determine the factors associated with the receipt of high-quality surgery and index admission mortality. Results and limitations: A total of 4654 patients with a median age of 70.0 yr underwent RC by 152 surgeons at 78 UK hospitals. The median surgeon and hospital operating volumes were 23.0 and 47.0 cases, respectively. A total of 914 patients (19.6%) received high-quality surgery. The minimum annual surgeon volume and hospital volume of ≥20 RCs/surgeon/yr and ≥68 RCs/hospital/yr, respectively, were the thresholds determined to achieve better rates of high-quality RC. The mixed-effect logistic regression model found that recent surgery (odds ratio [OR]: 1.22, 95% confidence interval [CI]: 1.11–1.34, p < 0.001), laparoscopic/robotic RC (OR: 1.85, 95% CI: 1.45–2.37, p < 0.001), and higher annual surgeon operating volume (23.1–33.0 cases [OR: 1.54, 95% CI: 1.16–2.05, p = 0.003]; ≥33.1 cases [OR: 1.64, 95% CI: 1.18–2.29, p = 0.003]) were independently associated with high-quality surgery. High-quality surgery was an independent predictor of lower index admission mortality (OR: 0.38, 95% CI: 0.16–0.87, p = 0.021). Conclusions: We report that annual surgeon operating volume and use of minimally invasive RC were predictors of high-quality surgery. Patients receiving high-quality surgery were independently associated with lower index admission mortality. Our results support the role of centralisation of complex oncology and implementation of a quality assurance programme to improve the delivery of care. Patient summary: In this registry study of patients treated with surgical excision of the urinary bladder for bladder cancer, we report that patients treated by a surgeon with a higher annual operative volume and a minimally invasive approach were associated with the receipt of high-quality surgery. Patients treated with high-quality surgery were more likely to be discharged alive following surgery.