Hasil untuk "Diseases of the genitourinary system. Urology"

Sarah K. Nelson-Taylor, Jonathan Troost, Courtney Giannini et al.

Rationale & Objective: Idiopathic nephrotic syndrome (INS) is viewed as a podocyte-specific disease. Recent reports indicate endothelial involvement, but its significance is unclear. Here, we investigated the relationship between the glomerular expression of selected genes relevant to endothelial health and clinical markers of disease severity. Study Design: A cross-sectional study. Setting & Participants: Patients with INS (n = 70 minimal change disease and n = 83 focal segmental glomerulosclerosis) from the Nephrotic Syndrome Study Network cohort study and 53 control participants. Validation studies, including animal and cell culture experiments, were performed. Exposure: Gene expression analysis from micro-dissected human glomeruli. The study is focused on 10 genes highly relevant for endothelial homeostasis and barrier integrity (nitric oxide synthase 3 [NOS3], endothelial cell adhesion molecule, and endothelial cell specific molecule 1 [ESM1]), endothelial glycocalyx remodeling (HPSE, HYAL1, MMP2, MMP9, and ADAMTS1), and endothelial activation (ICAM1 and CAV1). Outcomes: Kidney function, ultrastructural changes in podocytes and glomerular endothelium, interstitial fibrosis and tubular atrophy. Analytical Approach: One-way ANOVA and Tukey’s multiple comparisons test, Pearson Correlation and Cohen’s d statistics. Results: Transcriptomic analysis revealed that all genes of interest were highly expressed in glomeruli from INS patients compared with controls, except for ESM1 and MMP9, which were decreased. Expression of endothelial-specific genes correlated with those of glycocalyx injury and cell activation. HPSE, ADAMTS1, ICAM1, and CAV1 expression was inversely associated with kidney function, whereas ADAMTS1 showed a positive association with proteinuria. NOS3, HPSE, and ADAMTS1 were associated with podocyte foot process effacement, and ICAM1 with podocyte detachment. HPSE and MMP2 were associated with ultrastructural endothelial injury, whereas HPSE, MMP2, ICAM1, and CAV1 were associated with interstitial fibrosis and tubular atrophy. Several genes (ESM1, HPSE, HYAL1, MMP2, and ICAM1) were also dysregulated in experimental INS and validated in cultured glomerular endothelial cells (NOS3 and heparanase) following exposure to INS sera. Limitations: Observational study, selection bias, unmeasured confounders. Conclusions: INS involves dysregulation of genes relevant for endothelial health. Plain-Language Summary: Idiopathic Nephrotic Syndrome (INS) is a common condition associated with massive proteinuria and substantial morbidity. Most attention has focused on injury to the glomerular podocyte in driving the disease, but there is growing evidence that endothelial cells may also be involved. Here we analyzed mRNA levels of selected targets from micro-dissected human glomeruli, combined with validation cell culture studies. We identified an altered expression pattern of genes specific for endothelium or biologically relevant for endothelial health in INS. Some mRNA alterations correlated with clinical or histological features of disease severity. Thus, dysregulation of the vascular lining of glomerular capillaries (endothelial cells) occurs in INS and suggests that INS involves injury to other cells besides the podocyte.

Chinedu Udeze, Ugochi Onu, Onyinye Nwikwu et al.

Ola Bratt, Salma Tunå Butt, Charlotte Carlsson et al.

Objective: This study aimed to describe the regional, population-based, organised prostate cancer testing (OPT) programmes that are being introduced throughout Sweden: motives, structure, target population, diagnostic algorithm, quality control, outcomes, research, and future perspectives. Results: In 2018, the Swedish National Board of Health and Welfare renewed their recommendation against screening for prostate cancer. Despite this, regional OPT was considered motivated to (1) improve cost-effectiveness compared with unorganised testing, (2) improve equity by giving every man in the target population a chance to make an informed choice, and (3) gain diagnostic and organisational knowledge. The OPT programmes are provided as a regional public healthcare service. They are coordinated by a national working group. The final target population is all men aged 50–74 years. Regional OPT offices use a national administrative system to organise all steps from sending invitation letters to prostate biopsy according to a strict diagnostic algorithm. General practice is involved for blood draw only or not at all. Data are registered in a national register (SweOPT); an annual report is published with the regions’ performance on key indicators. At the end of 2024, 16 of the 21 Swedish regions had started OPT and invited 256,000 men with an average cumulative participation rate of 43%. A consortium co-ordinates OPT-related research. A general experience is that communication and organisational matters have been more challenging than medical decisions. Conclusions: The Swedish population-based OPT programmes provide organisational experiences, diagnostic outcomes, and research results of value for future national prostate cancer screening programmes.

Ajith Balineni, Varun kumar Bandi, Naga sai sri harsha Narilla

John E. Speich, Annapoorani Narayanan, Mrudula Bandaru et al.

Objective: Overactive bladder (OAB) is characterized by urinary urgency. To better characterize the relationship between bladder sensation and urgency, a tablet-based “Sensation Meter” was developed to enable construction of bladder sensation-capacity curves. The objective of this study was to correlate sensation-capacity curve shapes during urodynamics (UDS) with urgency severity and bother. Methods: Individuals with moderate-to-severe urgency presenting for clinically indicated UDS were prospectively enrolled after urgency characterization using the International Consultation on Incontinence OAB questionnaire (ICIq-OAB). Throughout UDS filling, participants recorded sensation of bladder fullness (0–100 %) using the Sensation Meter. Sensation-capacity curves were constructed and area-under-the-curve (AUC) analysis was implemented to differentiate between three curve-shape patterns defined as r, l, and j-shape. Curve shapes were correlated categorically to urgency severity and bother, UDS capacity, and the presence/absence of DO. Findings: The study included 69 participants (52F, 17M). The distribution of sensation-capacity curve shapes was 7 (10 %) r-shape, 43 (62 %) l-shape, and 19 (28 %) j-shape. A j-shaped curve was significantly associated with severe urgency and with high urgency bother, but not with bladder capacity or DO. Conclusions: The key finding was the association between a j-shaped sensation-capacity curve, demonstrating a rapid acceleration of sensation near the end of filling, with both urgency symptom severity and bother. Curve shapes were not associated with bladder capacity or DO, suggesting that a j-shaped curve may potentially identify a separate driver of urinary urgency. Additional studies are needed to determine whether sensation-capacity curves can be used to identify novel OAB/urgency phenotypes or guide therapy.

Yan Su, Qiulin Wu, Weizhen Li et al.

The training of deep learning models relies on a large amount of labeled data. However, the high cost of medical labeling seriously hinders the development of deep learning in the medical field. Our study proposes a general disease diagnosis approach based on Zero-Shot Learning. The Siamese neural network is used to find similar diseases for the target diseases, and the U-Net segmentation model is used to accurately segment the key lesions of the disease. Finally, based on the ResNet-Agglomerative clustering algorithm, a clustering model is trained on a large number of sample data of similar diseases to obtain a approximate diagnosis of the target disease. Zero-Shot Learning of the target disease is then successfully achieved. To evaluate the validity of the model, we validated our method on a dataset of ophthalmic diseases in CFP modality. The external dataset was used to test its performance, and the accuracy=0.8395, precision=0.8094, recall=0.8463, F1 Score=0.8274, AUC=0.9226, which exceeded the indexes of most Few-Shot Learning and One-Shot Learning models. It proves that our method has great potential and reference value in the medical field, where annotation data is usually scarce and expensive to obtain.

Dingxin Lu, Shurui Wu, Xinyi Huang

With the rising global burden of chronic diseases and the multimodal and heterogeneous clinical data (medical imaging, free-text recordings, wearable sensor streams, etc.), there is an urgent need for a unified multimodal AI framework that can proactively predict individual health risks. We propose VL-RiskFormer, a hierarchical stacked visual-language multimodal Transformer with a large language model (LLM) inference head embedded in its top layer. The system builds on the dual-stream architecture of existing visual-linguistic models (e.g., PaLM-E, LLaVA) with four key innovations: (i) pre-training with cross-modal comparison and fine-grained alignment of radiological images, fundus maps, and wearable device photos with corresponding clinical narratives using momentum update encoders and debiased InfoNCE losses; (ii) a time fusion block that integrates irregular visit sequences into the causal Transformer decoder through adaptive time interval position coding; (iii) a disease ontology map adapter that injects ICD-10 codes into visual and textual channels in layers and infers comorbid patterns with the help of a graph attention mechanism. On the MIMIC-IV longitudinal cohort, VL-RiskFormer achieved an average AUROC of 0.90 with an expected calibration error of 2.7 percent.

Rifqi Alfinnur Charisma, Faisal Dharma Adhinata

Potato plants are plants that are beneficial to humans. Like other plants in general, potato plants also have diseases; if this disease is not treated immediately, there will be a significant decrease in food production. Therefore, it is necessary to detect diseases quickly and precisely so that disease control can be carried out effectively and efficiently. Classification of potato leaf disease can be done directly. Still, the symptoms cannot always explain the type of disease that attacks potato leaves because there are many types of diseases with symptoms that look the same. Humans also have deficiencies in determining the results of identification of potato leaf disease, so sometimes the results of identification between individuals can be different. Therefore, the use of Deep Learning for the classification process of potato leaf disease is expected to shorten the time and have a high classification accuracy. This study uses a deep learning method with the DenseNet201 architecture. The choice to use the DenseNet201 algorithm in this study is because the model can identify important features of potato leaves and recognize early signs of emerging diseases. This study aimed to evaluate the effectiveness of the transfer learning method with the DenseNet201 architecture in increasing the classification accuracy of potato leaf disease compared to traditional classification methods. This study uses two types of scenarios, namely, comparing the number of dropouts and comparing the three optimizers. This test produces the best model using dropout 0.1 and Adam optimizer with an accuracy of 99.5% for training, 95.2% for validation, and 96% for the confusion matrix. In this study, using data testing, as many as 40 images were tested into the model that has been built. The test results on this model resulted in a new accuracy for classifying potato leaf disease, namely 92.5%.

Mohammed Shahait, Noor Buchholz

None.

J.I. Martínez Salamanca, G. Maiolino, E. Compérat et al.

Fangzhi Wang, Hua Liao, Richard S. J. Tol

We investigate optimal carbon abatement in a dynamic general equilibrium climate-economy model with endogenous structural change. By differentiating the production of investment from consumption, we show that social cost of carbon can be conceived as a reduction in physical capital. In addition, we distinguish two final sectors in terms of productivity growth and climate vulnerability. We theoretically show that heterogeneous climate vulnerability results in a climate-induced version of Baumol's cost disease. Further, if climate-vulnerable sectors have high (low) productivity growth, climate impact can either ameliorate (aggravate) the Baumol's cost disease, call for less (more) stringent climate policy. We conclude that carbon abatement should not only factor in unpriced climate capital, but also be tailored to Baumol's cost and climate diseases.

M. Marvá, E. Venturino, M. C. Vera

This work presents a model combining the simplest communicable and non-communicable disease models. The latter is, by far, the leading cause of sickness and death in the World, and introduces basal heterogeneity in populations where communicable diseases evolve. The model can be interpreted as a risk-structured model, another way of accounting for population heterogeneity. Our results show that considering the non-communicable disease (in the end, heterogeneous populations) allows the communicable disease to become endemic even if the basic reproduction number is less than $1$. This feature is known as subcritical bifurcation. Furthermore, ignoring the non-communicable disease dynamics results in overestimating the reproduction number and, thus, giving wrong information about the actual number of infected individuals. We calculate sensitivity indices and derive interesting epidemic-control information.

Yujie Gao, Malay Banerjee, Ton Viet Ta

This paper introduces an innovative model for infectious diseases in predator-prey populations. We not only prove the existence of global non-negative solutions but also establish essential criteria for the system's decline and sustainability. Furthermore, we demonstrate the presence of a Borel invariant measure, adding a new dimension to our understanding of the system. To illustrate the practical implications of our findings, we present numerical results. With our model's comprehensive approach, we aim to provide valuable insights into the dynamics of infectious diseases and their impact on predator-prey populations.

Muhammad Shoaib Farooq, Kiran Amjad

Most people around the globe are dying due to heart disease. The main reason behind the rapid increase in the death rate due to heart disease is that there is no infrastructure developed for the healthcare department that can provide a secure way of data storage and transmission. Due to redundancy in the patient data, it is difficult for cardiac Professionals to predict the disease early on. This rapid increase in the death rate due to heart disease can be controlled by monitoring and eliminating some of the key attributes in the early stages such as blood pressure, cholesterol level, body weight, and addiction to smoking. Patient data can be monitored by cardiac Professionals (Cp) by using the advanced framework in the healthcare departments. Blockchain is the world's most reliable provider. The use of advanced systems in the healthcare departments providing new ways of dealing with diseases has been developed as well. In this article Machine Learning (ML) algorithm known as a sine-cosine weighted k-nearest neighbor (SCA-WKNN) is used for predicting the Hearth disease with the maximum accuracy among the existing approaches. Blockchain technology has been used in the research to secure the data throughout the session and can give more accurate results using this technology. The performance of the system can be improved by using this algorithm and the dataset proposed has been improved by using different resources as well.

Md. Hamjajul Ashmafee, Tasnim Ahmed, Sabbir Ahmed et al.

Correct identification and categorization of plant diseases are crucial for ensuring the safety of the global food supply and the overall financial success of stakeholders. In this regard, a wide range of solutions has been made available by introducing deep learning-based classification systems for different staple crops. Despite being one of the most important commercial crops in many parts of the globe, research proposing a smart solution for automatically classifying apple leaf diseases remains relatively unexplored. This study presents a technique for identifying apple leaf diseases based on transfer learning. The system extracts features using a pretrained EfficientNetV2S architecture and passes to a classifier block for effective prediction. The class imbalance issues are tackled by utilizing runtime data augmentation. The effect of various hyperparameters, such as input resolution, learning rate, number of epochs, etc., has been investigated carefully. The competence of the proposed pipeline has been evaluated on the apple leaf disease subset from the publicly available `PlantVillage' dataset, where it achieved an accuracy of 99.21%, outperforming the existing works.

Wanting Su, Dongwei Liu, Feng Tan et al.

There is rising evidence of the health benefit associated with specific dietary interventions. Current food-disease databases focus on associations and treatment relationships but haven't provided a reasonable assessment of the strength of the relationship, and lack of attention on food nutrition. There is an unmet need for a large database that can guide dietary therapy. We fill the gap with NutriFD, a scoring network based on associations and therapeutic relationships between foods and diseases. NutriFD integrates 9 databases including foods, nutrients, diseases, genes, miRNAs, compounds, disease ontology and their relationships. To our best knowledge, this database is the only one that can score the associations and therapeutic relationships of everyday foods and diseases by weighting inference scores of food compounds to diseases. In addition, NutriFD demonstrates the predictive nature of nutrients on the therapeutic relationships between foods and diseases through machine learning models, laying the foundation for a mechanistic understanding of food therapy.

Brynjar Sævarsson, Hjörtur Jóhannsson, Spyros Chatzivasileiadis

This paper introduces the first quantum computing framework for Stochastic Quantum Power Flow (SQPF) analysis in power systems. The proposed method leverages quantum states to encode power flow distributions, enabling the use of Quantum Monte Carlo (QMC) sampling to efficiently assess the probability of line overloads. Our approach significantly reduces the required sample size compared to traditional Monte Carlo methods, making it particularly suited for risk assessments in scenarios involving high uncertainty, such as renewable energy integration. We validate the method on two test systems, demonstrating the computational advantage of quantum algorithms in reducing sample complexity while maintaining accuracy. This work represents a foundational step toward scalable quantum power flow analysis, with potential applications in future power system operations and planning. The results show promising computational speedups, underscoring the potential of quantum computing in addressing the increasing uncertainty in modern power grids.

Takeshi Kishida

Chang Seong Kim, Soo Jin Na Choi, Sung-Sun Kim et al.

Abstract Background Although anastomosing hemangiomas are very rare and benign vascular neoplasms, these tumors are more common among patients with end-stage kidney disease. Incidental finding of these tumors in the kidney or adrenal gland has been reported. Herein, we describe a case in which an anastomosing hemangioma was misdiagnosed as a renal cell carcinoma before kidney transplant. Case presentation A 35-year-old woman with lupus nephritis was admitted to our emergency department for suspected uremic symptoms of nausea and general weakness. She had received hemodialysis due to end-stage kidney disease, and a living-donor kidney transplantation from her father was planned. On pre-operative contrast-enhanced computed tomography and magnetic resonance imaging, a 1.7 cm renal cell carcinoma was observed in the right kidney. On staining after radical nephrectomy, irregularly shaped vascular spaces of various sizes were observed, with these spaces having an anastomosing pattern. As the findings of the anastomosing hemangioma are similar to those of a renal cell carcinoma on imaging, histology examination was necessary to confirm the diagnosis of anastomosing hemangioma and to prevent delay in listing for kidney transplantation. Good kidney function was achieved after transplantation, with no tumor recurrence. Conclusion Our case underlines the importance for prompt surgical resection of an enhancing renal mass to confirm diagnosis in patients scheduled for kidney transplantation to avoid any delay.

G. Bozzini, M. Maltagliati, U. Besana et al.