Hasil untuk "Diseases of the genitourinary system. Urology"

Jorge Panach-Navarrete, Lorena Valls-González, Marcos Antonio Lloret-Durà et al.

In cases of failure after pyeloplasty, redo pyeloplasty remains the most definitive procedure, although it demands high technical expertise. In this context, we present a new multimodal surgical technique that combines laser endopyelotomy, dilation with a paclitaxel–dextran drug-coated balloon, and placement of a self-expandable ureteral stent for 3 months. Through 4 cases of failed pyeloplasty, we show resolution of ureteropelvic junction strictures after undergoing this multimodal procedure. The results of this series demonstrate a new treatment option in the challenging setting of pyeloplasty failure.

Mariell Rivedal, Ole Petter Nordbø, Yngvar Lunde Haaskjold et al.

Abstract Background IgA nephropathy (IgAN) exhibits an unpredictable trajectory, creating difficulties in prognostication, monitoring, treatment, and research planning. This study provides a comprehensive depiction of the progression of kidney function throughout the disease course, from diagnosis to a span of 36 years post-diagnosis. Methods We utilized a cohort of 400 Norwegian IgAN patients, from diagnosis to the occurrence of death, initiation of kidney replacement therapy (KRT), or the latest follow-up. Recorded proteinuria (n = 2676) and creatinine (n = 8738) measurements were retrieved. Patients were divided into subgroups based on their specific estimated glomerular filtration rate (eGFR) slopes. Results Median follow-up was 16 years. During this period, 34% of patients either died or initiated KRT. Among patients who reached endpoint, the median duration from diagnosis to the initiation of KRT or death was 8 years. Notably, 34% of the cohort exhibited a stable disease course, characterized by an eGFR decline of less than 20% between two consecutive measurements. Differences in subsequent disease trajectories among two subgroups with similar eGFR levels at diagnosis could not be accounted for by variations in treatment strategies. Among patients with proteinuria < 1 g/24 h in less than half of the measurements, KRT was five times more prevalent compared to those with more than half of the measurements recording proteinuria < 1 g/24 h (p-value = 0.001). Conclusions While a significant proportion of IgAN patients reach kidney failure within their lifetimes, outcomes vary widely. Clinical data at diagnosis offer limited insights into long-term risks. Enhanced risk stratification necessitates data collection at multiple time points.

Catarina Canastra, Cátia Pesquita

Discovery gene-disease links is important in biology and medicine areas, enabling disease identification and drug repurposing. Machine learning approaches accelerate this process by leveraging biological knowledge represented in ontologies and the structure of knowledge graphs. Still, many existing works overlook ontologies explicitly representing diseases, missing causal and semantic relationships between them. The gene-disease association problem naturally frames itself as a link prediction task, where embedding algorithms directly predict associations by exploring the structure and properties of the knowledge graph. Some works frame it as a node-pair classification task, combining embedding algorithms with traditional machine learning algorithms. This strategy aligns with the logic of a machine learning pipeline. However, the use of negative examples and the lack of validated gene-disease associations to train embedding models may constrain its effectiveness. This work introduces a novel framework for comparing the performance of link prediction versus node-pair classification tasks, analyses the performance of state of the art gene-disease association approaches, and compares the different order-based formalizations of gene-disease association prediction. It also evaluates the impact of the semantic richness through a disease-specific ontology and additional links between ontologies. The framework involves five steps: data splitting, knowledge graph integration, embedding, modeling and prediction, and method evaluation. Results show that enriching the semantic representation of diseases slightly improves performance, while additional links generate a greater impact. Link prediction methods better explore the semantic richness encoded in knowledge graphs. Although node-pair classification methods identify all true positives, link prediction methods outperform overall.

Bolin Liu, Alexander Fulton, Hector Zenil

Understanding disease relationships through blood biomarkers offers a pathway toward data driven taxonomy and precision medicine. We constructed a digital blood twin from 103 disease signatures comprising longitudinal hematological and biochemical analytes. Profiles were standardized into a unified disease analyte matrix, and pairwise Pearson correlations were computed to assess similarity. Hierarchical clustering revealed robust grouping of hematopoietic disorders, while metabolic, endocrine, and respiratory diseases were more heterogeneous, reflecting weaker cohesion. To evaluate structure, the tree was cut at a stringent threshold, yielding 16 groups. Enrichment of the largest heterogeneous cluster (Cluster 9) showed convergence on cytokine-signaling pathways, indicating shared immunological and inflammatory mechanisms across clinical boundaries. Dimensionality reduction with PCA and UMAP corroborated these results, consistently separating hematological diseases. Random Forest feature selection identified neutrophils, mean corpuscular volume, red blood cell count, and platelets as the most discriminative analytes, reinforcing hematopoietic markers as key drivers. Collectively, these findings show that blood-derived digital signatures can recover clinically meaningful clusters while revealing mechanistic overlaps across categories. The coherence of hematological diseases contrasts with the dispersion of systemic and metabolic disorders, underscoring both the promise and limits of blood-based classification. This framework highlights the potential of integrating routine laboratory data with computational methods to refine disease ontology, map comorbidities, and advance precision diagnostics.

Lemei Hu, Changqing Zheng, Ying Kong et al.

Background Arteriovenous fistula (AVF) is currently the preferred vascular access for hemodialysis patients. However, the low maturation rate of AVF severely affects its use in patients. A more comprehensive understanding and study of the mechanisms of AVF maturation is urgently needed.Methods and results In this study, we downloaded the publicly available datasets (GSE119296 and GSE220796) from the Gene Expression Omnibus (GEO) and merged them for subsequent analysis. We screened 84 differentially expressed genes (DEGs) and performed the functional enrichment analysis. Next, we integrated the results obtained from the degree algorithm provided by the Cytohubba plug-in, Molecular complex detection (MCODE) plug-in, weighted gene correlation network analysis (WGCNA), and Least absolute shrinkage and selection operator (LASSO) logistic regression. This integration allowed us to identify CTSG as a hub gene associated with AVF maturation. Through the literature search and Pearson’s correlation analysis, the genes matrix metalloproteinase 2 (MMP2) and MMP9 were identified as potential downstream effectors of CTSG. We then collected three immature clinical AVF vein samples and three mature samples and validated the expression of CTSG using immunohistochemistry (IHC) and double-immunofluorescence staining. The IHC results demonstrated a significant decrease in CTSG expression levels in the immature AVF vein samples compared to the mature samples. The results of double-immunofluorescence staining revealed that CTSG was expressed in both the intima and media of AVF veins. Moreover, the expression of CTSG in vascular smooth muscle cells (VSMCs) was significantly higher in the mature samples compared to the immature samples. The results of Masson’s trichrome and collagen I IHC staining demonstrated a higher extent of collagen deposition in the media of immature AVF veins compared to the mature. By constructing an in vitro CTSG overexpression model in VSMCs, we found that CTSG upregulated the expression of MMP2 and MMP9 while downregulating the expression of collagen I and collagen III. Furthermore, CTSG was found to inhibit VSMC migration.Conclusions CTSG may promote AVF maturation by stimulating the secretion of MMP2 and MMP9 from VSMCs and reducing the extent of medial fibrosis in AVF veins by inhibiting the secretion of collagen I and collagen III.

Alice L Kennard, Suzanne Rainsford, Kelly L Hamilton et al.

Abstract Background Understanding the patient perspective of frailty is critical to offering holistic patient-centred care. Rehabilitation strategies for patients with advanced chronic kidney disease (CKD) and frailty are limited in their ability to overcome patient-perceived barriers to participation, resulting in high rates of drop-out and non-adherence. The aim of this study was to explore patient perspectives and preferences regarding experiences with rehabilitation to inform a CKD/Frailty rehabilitation model. Methods This qualitative study involved two focus groups, six individual semi-structured interviews and three caregiver semi-structured interviews with lived experience of advanced kidney disease and frailty. Interviews were recorded, transcribed, and coded for meaningful concepts and analysed using inductive thematic analysis using constant comparative method of data analysis employing Social Cognitive Theory. Results Six major themes emerged including accommodating frailty is an act of resilience, exercise is endorsed for rehabilitation but existing programs have failed to meet end-users’ needs. Rehabilitation goals were framed around return to normative behaviours and rehabilitation should have a social dimension, offering understanding for “people like us”. Participants reported on barriers and disruptors to frailty rehabilitation in the CKD context. Participants valued peer-to-peer education, the camaraderie of socialisation and the benefit of feedback for maintaining motivation. Patients undertaking dialysis described the commodity of time and the burden of unresolved symptoms as barriers to participation. Participants reported difficulty envisioning strategies for frailty rehabilitation, maintaining a focus on the immediate and avoidance of future uncertainty. Conclusions Frailty rehabilitation efforts in CKD should leverage shared experiences, address comorbidity and symptom burden and focus on goals with normative value.

Yani Wang, Lihua Liao, Qian Guo et al.

Background Studies have shown that in hypertensive patients, chronic kidney disease (CKD) is associated with a poor prognosis. Inflammation is a highly important factor in the progression of CKD. Detecting systemic inflammation and intervening promptly in patients with hypertension may help reduce the risk of CKD. The systemic inflammatory response index (SIRI) is a tool used to measure the systemic inflammatory response, but its relationship with CKD in patients with hypertension remains uncertain.Methods We utilized data from the National Health and Nutrition Examination Survey (NHANES), which was conducted between 1999 and 2018. The analysis included a total of 20,243 participants, categorized into three groups based on SIRI tertiles. Logistic regression analysis and restricted cubic spline analysis were used to examine the relationship between the SIRI and CKD.Results In patients with hypertension, there was a notable relationship between the SIRI and the odds of developing CKD. After full adjustment, there was a 31% greater likelihood of developing CKD associated with each incremental increase of 1 unit in the SIRI (OR: 1.31, 95% CI: 1.24–1.39, p < 0.001). The groups with greater SIRI values exhibited greater odds of developing CKD than did the T1 group (T2: OR: 1.20, 95% CI: 1.04–1.38, p = 0.015; T3: OR: 1.69, 95% CI: 1.47–1.94, p < 0.001).Conclusion A high SIRI is associated with an increased risk of CKD in hypertensive patients. The greater the SIRI is, the greater the risk of CKD in hypertensive patients.

Luiza Lober, Kirstin O. Roster, Francisco A. Rodrigues

Supervised machine learning models and public surveillance data has been employed for infectious disease forecasting in many settings. These models leverage various data sources capturing drivers of disease spread, such as climate conditions or human behavior. However, few models have incorporated the organizational structure of different geographic locations for forecasting. Traveling waves of seasonal outbreaks have been reported for dengue, influenza, and other infectious diseases, and many of the drivers of infectious disease dynamics may be shared across different cities, either due to their geographic or socioeconomic proximity. In this study, we developed a machine learning model to predict case counts of four infectious diseases across Brazilian cities one week ahead by incorporating information from related cities. We compared selecting related cities using both geographic distance and GDP per capita. Incorporating information from geographically proximate cities improved predictive performance for two of the four diseases, specifically COVID-19 and Zika. We also discuss the impact on forecasts in the presence of anomalous contagion patterns and the limitations of the proposed methodology.

Suma K, Deepali Koppad, Preethi Kumar et al.

In recent years, advancements in deep learning techniques have considerably enhanced the efficiency and accuracy of medical diagnostics. In this work, a novel approach using multi-task learning (MTL) for the simultaneous classification of lung sounds and lung diseases is proposed. Our proposed model leverages MTL with four different deep learning models such as 2D CNN, ResNet50, MobileNet and Densenet to extract relevant features from the lung sound recordings. The ICBHI 2017 Respiratory Sound Database was employed in the current study. The MTL for MobileNet model performed better than the other models considered, with an accuracy of74\% for lung sound analysis and 91\% for lung diseases classification. Results of the experimentation demonstrate the efficacy of our approach in classifying both lung sounds and lung diseases concurrently. In this study,using the demographic data of the patients from the database, risk level computation for Chronic Obstructive Pulmonary Disease is also carried out. For this computation, three machine learning algorithms namely Logistic Regression, SVM and Random Forest classifierswere employed. Among these ML algorithms, the Random Forest classifier had the highest accuracy of 92\%.This work helps in considerably reducing the physician's burden of not just diagnosing the pathology but also effectively communicating to the patient about the possible causes or outcomes.

Paridhi Mundra, Manik Sharma, Yashwardhan Chaudhuri et al.

As respiratory illnesses become more common, it is crucial to quickly and accurately detect them to improve patient care. There is a need for improved diagnostic methods for immediate medical assessments for optimal patient outcomes. This paper introduces VoxMed, a UI-assisted one-step classifier that uses digital stethoscope recordings to diagnose respiratory diseases. It employs an Audio Spectrogram Transformer(AST) for feature extraction and a 1-D CNN-based architecture to classify respiratory diseases, offering professionals information regarding their patients respiratory health in seconds. We use the ICBHI dataset, which includes stethoscope recordings collected from patients in Greece and Portugal, to classify respiratory diseases. GitHub repository: https://github.com/Sample-User131001/VoxMed

Jie Chen, Lei Nie, Shiowjen Lee et al.

Developing drugs for rare diseases presents unique challenges from a statistical perspective. These challenges may include slowly progressive diseases with unmet medical needs, poorly understood natural history, small population size, diversified phenotypes and geneotypes within a disorder, and lack of appropriate surrogate endpoints to measure clinical benefits. The Real-World Evidence (RWE) Scientific Working Group of the American Statistical Association Biopharmaceutical Section has assembled a research team to assess the landscape including challenges and possible strategies to address these challenges and the role of real-world data (RWD) and RWE in rare disease drug development. This paper first reviews the current regulations by regulatory agencies worldwide and then discusses in more details the challenges from a statistical perspective in the design, conduct, and analysis of rare disease clinical trials. After outlining an overall development pathway for rare disease drugs, corresponding strategies to address the aforementioned challenges are presented. Other considerations are also discussed for generating relevant evidence for regulatory decision-making on drugs for rare diseases. The accompanying paper discusses how RWD and RWE can be used to improve the efficiency of rare disease drug development.

Yuan Yin, Jennifer A. Flegg, Mark B. Flegg

In the infectious disease literature, significant effort has been devoted to studying dynamics at a single scale. For example, compartmental models describing population-level dynamics are often formulated using differential equations. In cases where small numbers or noise play a crucial role, these differential equations are replaced with memoryless Markovian models, where discrete individuals can be members of a compartment and transition stochastically. Classic stochastic simulation algorithms, such as the next reaction method, can be employed to solve these Markovian models exactly. The intricate coupling between models at different scales underscores the importance of multiscale modelling in infectious diseases. However, several computational challenges arise when the multiscale model becomes non-Markovian. In this paper, we address these challenges by developing a novel exact stochastic simulation algorithm. We apply it to a showcase multiscale system where all individuals share the same deterministic within-host model while the population-level dynamics are governed by a stochastic formulation. We demonstrate that as long as the within-host information is harvested at a reasonable resolution, the novel algorithm will always be accurate. Furthermore, our implementation is still efficient even at finer resolutions. Beyond infectious disease modelling, the algorithm is widely applicable to other multiscale systems, providing a versatile, accurate, and computationally efficient framework.

Jesse Phitidis, Alison Q. O'Neil, William N. Whiteley et al.

Cerebrovascular diseases (CVD) can lead to stroke and dementia. Stroke is the second leading cause of death world wide and dementia incidence is increasing by the year. There are several markers of CVD that are visible on brain imaging, including: white matter hyperintensities (WMH), acute and chronic ischaemic stroke lesions (ISL), lacunes, enlarged perivascular spaces (PVS), acute and chronic haemorrhagic lesions, and cerebral microbleeds (CMB). Brain atrophy also occurs in CVD. These markers are important for patient management and intervention, since they indicate elevated risk of future stroke and dementia. We systematically reviewed automated systems designed to support radiologists reporting on these CVD imaging findings. We considered commercially available software and research publications which identify at least two CVD markers. In total, we included 29 commercial products and 13 research publications. Two distinct types of commercial support system were available: those which identify acute stroke lesions (haemorrhagic and ischaemic) from computed tomography (CT) scans, mainly for the purpose of patient triage; and those which measure WMH and atrophy regionally and longitudinally. In research, WMH and ISL were the markers most frequently analysed together, from magnetic resonance imaging (MRI) scans; lacunes and PVS were each targeted only twice and CMB only once. For stroke, commercially available systems largely support the emergency setting, whilst research systems consider also follow-up and routine scans. The systems to quantify WMH and atrophy are focused on neurodegenerative disease support, where these CVD markers are also of significance. There are currently no openly validated systems, commercially, or in research, performing a comprehensive joint analysis of all CVD markers (WMH, ISL, lacunes, PVS, haemorrhagic lesions, CMB, and atrophy).

Felix Wagner, Wentian Xu, Pramit Saha et al.

Segmentation models for brain lesions in MRI are typically developed for a specific disease and trained on data with a predefined set of MRI modalities. Such models cannot segment the disease using data with a different set of MRI modalities, nor can they segment other types of diseases. Moreover, this training paradigm prevents a model from using the advantages of learning from heterogeneous databases that may contain scans and segmentation labels for different brain pathologies and diverse sets of MRI modalities. Additionally, the confidentiality of patient data often prevents central data aggregation, necessitating a decentralized approach. Is it feasible to use Federated Learning (FL) to train a single model on client databases that contain scans and labels of different brain pathologies and diverse sets of MRI modalities? We demonstrate promising results by combining appropriate, simple, and practical modifications to the model and training strategy: Designing a model with input channels that cover the whole set of modalities available across clients, training with random modality drop, and exploring the effects of feature normalization methods. Evaluation on 7 brain MRI databases with 5 different diseases shows that this FL framework can train a single model achieving very promising results in segmenting all disease types seen during training. Importantly, it can segment these diseases in new databases that contain sets of modalities different from those in training clients. These results demonstrate, for the first time, the feasibility and effectiveness of using FL to train a single 3D segmentation model on decentralised data with diverse brain diseases and MRI modalities, a necessary step towards leveraging heterogeneous real-world databases. Code: https://github.com/FelixWag/FedUniBrain

Beyazit Bestami Yuksel, Ayse Yilmazer Metin

This study introduces the development of a state of the art, real time ECG monitoring and analysis system, incorporating cutting edge medical technology and innovative data security measures. Our system performs three distinct functions thaat real time ECG monitoring and disease detection, encrypted storage and synchronized visualization, and statistical analysis on encrypted data. At its core, the system uses a three lead ECG preamplifier connected through a serial port to capture, display, and record real time ECG data. These signals are securely stored in the cloud using robust encryption methods. Authorized medical personnel can access and decrypt this data on their computers, with AES encryption ensuring synchronized real time data tracking and visualization. Furthermore, the system performs statistical operations on the ECG data stored in the cloud without decrypting it, using Fully Homomorphic Encryption (FHE). This enables privacy preserving data analysis while ensuring the security and confidentiality of patient information. By integrating these independent functions, our system significantly enhances the security and efficiency of health monitoring. It supports critical tasks such as disease detection, patient monitoring, and preliminary intervention, all while upholding stringent data privacy standards. We provided detailed discussions on the system's architecture, hardware configuration, software implementation, and clinical performance. The results highlight the potential of this system to improve patient care through secure and efficient ECG monitoring and analysis. This work represents a significant leap forward in medical technology. By incorporating FHE into both data transmission and storage processes, we ensure continuous encryption of data throughout its lifecycle while enabling real time disease diagnosis.

Claire S. Baptiste, Esther Adegbulugbe, Divya Shankaranarayanan et al.

Abstract Background  End-stage renal disease patients on hemodialysis (ESRD) patients are at high risk for contracting COVID-19. In this propensity matched cohort study, we examined the prevalence of COVID-19 in emergency room (ER) patients and examined whether clinical outcomes varied by ESRD status. Methods Patients who visited George Washington University Hospital ER from April 2020 to April 2021 were reviewed for COVID-19 and ESRD status. Among COVID-positive ER patients, the propensity for ESRD was calculated using a logistic regression model to create a propensity-matched sample of ESRD vs non-ESRD COVID-19 patients. A multivariable model examined whether ESRD was an independent predictor of death and other outcomes in COVID-19 patients. Results Among the 27,106 ER patients, 2689 of whom were COVID-positive (9.9%). The odds of testing positive for COVID-19 were 0.97 ([95% CI: 0.78–1.20], p = 0.76) in ESRD vs non-ESRD patients after adjusting for age, sex, and race. There were 2414 COVID-positive individuals with non-missing data, of which 98 were ESRD patients. In this COVID-positive sample, ESRD patients experienced a higher incidence of stroke, sepsis, and pneumonia than non-ESRD individuals. Significant independent predictors of death included age, race, sex, insurance status, and diabetes mellitus. Those with no insurance had odds of death that was 212% higher than those with private insurance (3.124 [1.695–5.759], p < 0.001). ESRD status was not an independent predictor of death (1.215 [0.623–2.370], p = 0.57). After propensity-matching in the COVID-positive patients, there were 95 ESRD patients matched with 283 non-ESRD individuals. In this sample, insurance status continued to be an independent predictor of mortality, while ESRD status was not. ESRD patients were more likely to have lactic acidosis (36% vs 15%) and length of hospital stay ≥ 7 days (48% vs 31%), but no increase in odds for any studied adverse outcomes. Conclusions In ER patients, ESRD status was not associated with higher odds for testing positive for COVID-19. Among ER patients who were COVID positive, ESRD was not associated with mortality. However, insurance status had a strong and independent association with death among ER patients with COVID-19.

Akram Amani, Bentolhoda Rashidi

Introduction: Prostate cancer is a type of disease in which malignant cells originate from prostate tissue and multiply irregularly and increasingly, leading to an increase in volume in each of the cellular components of the prostate gland.  Methods: The search for studies published in Google Scholar, PubMed, ProQuest, Science Direct, and Elsevier sites was performed with the keywords prostate, radiopharmaceutical, cancer, and Lutetium. Results: The synthesis of articles related to prostate cancer and lutetium drug showed that the average prevalence of this complication and follow-up of its treatment in Iranian articles with the Beck tool is 52.13%, and in foreign articles, equal to 78.21%.  Conclusions: With the help of remarkable advances in bioinformatics & molecular methods, much information has been obtained that will help in the early recognition of cancer, and timely screening for some cancers helps in its early diagnosis. In general, today, scientists have concluded that they can get the best result by early diagnosis and complete removal of cancer cells before the spread of cancer. Prostate cancer is the most common type of cancer in men over the age of 50. Currently, researchers are unable to determine the exact cause of prostate cancer. DNA changes can be hereditary or caused by certain lifestyles that people have chosen. Lutetium-177 Prostate-specific membrane antigen  (PSMA), also called a prostate membrane-specific antigen, is becoming a popular treatment for men with advanced prostate cancer with metastatic (spread) or refractory tumors. This treatment method has successfully reduced the size of tumors (cancerous masses) in many patients. Lutetium-177 PSMA treatment (abbreviated as Lutetium) is a suitable treatment option for men undergoing radical prostatectomy or primary radiation therapy but still have disease recurrence and metastasis symptoms.

Alhaam Alariyibi, Mohamed El-Jarai, Abdelsalam Maatuk

The healthcare industry generates enormous amounts of complex clinical data that make the prediction of disease detection a complicated process. In medical informatics, making effective and efficient decisions is very important. Data Mining (DM) techniques are mainly used to identify and extract hidden patterns and interesting knowledge to diagnose and predict diseases in medical datasets. Nowadays, heart disease is considered one of the most important problems in the healthcare field. Therefore, early diagnosis leads to a reduction in deaths. DM techniques have proven highly effective for predicting and diagnosing heart diseases. This work utilizes the classification algorithms with a medical dataset of heart disease; namely, J48, Random Forest, and Naïve Bayes to discover the accuracy of their performance. We also examine the impact of the feature selection method. A comparative and analysis study was performed to determine the best technique using Waikato Environment for Knowledge Analysis (Weka) software, version 3.8.6. The performance of the utilized algorithms was evaluated using standard metrics such as accuracy, sensitivity and specificity. The importance of using classification techniques for heart disease diagnosis has been highlighted. We also reduced the number of attributes in the dataset, which showed a significant improvement in prediction accuracy. The results indicate that the best algorithm for predicting heart disease was Random Forest with an accuracy of 99.24%.

Sachit Anand, Hüseyin Özbey