Hasil untuk "Diseases of the genitourinary system. Urology"

Marjan Huizing, Anirban Ganguli, Jonathan Bolaños et al.

Introduction: Terminal sialic acid (SA) residues on glycoconjugates are essential for maintaining the glomerular filtration barrier’s charge selectivity and podocyte ultrastructure. SA depletion affects key podocyte glycoproteins, contributing to podocytopathy and proteinuria. Glomerular hyposialylation is commonly seen in experimental podocytopathies and human renal biopsies. In nephrotic mouse models, oral administration of the metabolic SA precursor, N-acetylmannosamine (ManNAc) restored sialylation and reduced proteinuria, suggesting therapeutic potential. Methods: In this single-center, single-arm, ascending dose phase 1 trial, we evaluated safety and pharmacokinetics (PKs) of oral ManNAc in primary podocytopathies (ClinicalTrials.gov: NCT02639260). Eligible participants had urine protein-to-creatinine ratio (UPCR) > 1 g/g and estimated glomerular filtration rates (eGFR) > 15 ml/min per 1.73 m2. Six subjects received a single 3g ManNAc dose followed by 5 days of 1.5 g twice-daily (BID) dosing. One subject received a single 6 g dose. Results: All enrolled participants had primary podocytopathy, with eGFR of 25 to 89 ml/min per 1.73 m2 and UPCR of 1.1 to 9.21 g/g. ManNAc was well-tolerated without serious adverse events (AEs). Maximum plasma ManNAc concentration was reached within 2 to 4 hours postdose, with dose-dependent increases in plasma SA. Subjects with eGFR < 45 ml/min per 1.73 m2 showed elevated maximum plasma ManNAc concentration and area under curve for both ManNAc and SA, reflecting reduced renal clearance. Proteinuria reduction of 12% to 52% (regression-adjusted mean 9.69%, P < 0.0001) was observed in subjects receiving ManNAc BID, correlating with glomerular hyposialylation in pre-study renal biopsies. Conclusion: Oral ManNAc demonstrated short-term safety and increased plasma SA levels in podocytopathy subjects. Early efficacy signals suggest that proteinuria reduction may correlate with glomerular hyposialylation, identifying a potential treatment biomarker. A phase 2 trial (NCT06664814) is underway to assess long-term outcomes.

Ademilson Leite

Dongxu Zhang, Bowen Xia, Xin Zhang et al.

Abstract Background and objective The effectiveness of immunosuppressive and corticosteroid treatments for Immunoglobulin A (IgA) nephropathy (IgAN) remains thoroughly evaluated. We undertook a meta-analysis to investigate the efficacy and safety of low-dose corticosteroids plus leflunomide for progressive IgA nephropathy. Methods Eligible studies were obtained from PubMed, Embase, and Cochrane Library databases. We also searched the references of the included studies. Our protocol followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) checklist. Eligibility criteria were defined using a PICOS framework. Results Our study included three articles presenting 342 patient cases. Findings revealed that low-dose corticosteroids combined with the leflunomide group were effective in relieving urine protein excretion (UPE) [mean difference (MD) = -0.35, 95% confidence interval (CI): -0.41 to -0.30, P < 0.00001] compared with the full-dose corticosteroids group. Regarding serum creatinine (SCr), estimated glomerular filtration rate (eGFR), complete remission rate, and overall response rate, there was no difference between the groups (p > 0.05). Regarding safety, low-dose corticosteroids combined with leflunomide significantly reduced the risk of serious adverse events [odds ratio (OR): 0.11, 95% CI: 0.01 to 0.91, P = 0.04]. Besides, no significant differences were observed between the two groups in the incidence of respiratory infection, abnormal liver function, diarrhea, herpes zoster, alopecia, pruritus, insomnia, pneumonia, diabetes, and urinary tract infection (P > 0.05). Conclusions Low-dose corticosteroids combined with leflunomide are a safe and effective treatment for progressive IgA nephropathy. Trial registration The PROSPERO registration number is CRD42022361883.

S.I.D.D.H.A.R.T.H.A. SATAPATHY, A.R.U.N.A. ACHARYA

Ghazaleh Babanejaddehaki, Aijun An, Manos Papagelis

Infectious diseases occur when pathogens from other individuals or animals infect a person, resulting in harm to both individuals and society as a whole. The outbreak of such diseases can pose a significant threat to human health. However, early detection and tracking of these outbreaks have the potential to reduce the mortality impact. To address these threats, public health authorities have endeavored to establish comprehensive mechanisms for collecting disease data. Many countries have implemented infectious disease surveillance systems, with the detection of epidemics being a primary objective. The clinical healthcare system, local/state health agencies, federal agencies, academic/professional groups, and collaborating governmental entities all play pivotal roles within this system. Moreover, nowadays, search engines and social media platforms can serve as valuable tools for monitoring disease trends. The Internet and social media have become significant platforms where users share information about their preferences and relationships. This real-time information can be harnessed to gauge the influence of ideas and societal opinions, making it highly useful across various domains and research areas, such as marketing campaigns, financial predictions, and public health, among others. This article provides a review of the existing standard methods developed by researchers for detecting outbreaks using time series data. These methods leverage various data sources, including conventional data sources and social media data or Internet data sources. The review particularly concentrates on works published within the timeframe of 2015 to 2022.

Md Aziz Hosen Foysal, Foyez Ahmed, Md Zahurul Haque

Plant diseases significantly impact agricultural productivity, resulting in economic losses and food insecurity. Prompt and accurate detection is crucial for the efficient management and mitigation of plant diseases. This study investigates advanced techniques in plant disease detection, emphasizing the integration of image processing, machine learning, deep learning methods, and mobile technologies. High-resolution images of plant leaves were captured and analyzed using convolutional neural networks (CNNs) to detect symptoms of various diseases, such as blight, mildew, and rust. This study explores 14 classes of plants and diagnoses 26 unique plant diseases. We focus on common diseases affecting various crops. The model was trained on a diverse dataset encompassing multiple crops and disease types, achieving 98.14% accuracy in disease diagnosis. Finally integrated this model into mobile apps for real-time disease diagnosis.

Shyam Dongre, Ritesh Chandra, Sonali Agarwal

In modern healthcare, addressing the complexities of accurate disease prediction and personalized recommendations is both crucial and challenging. This research introduces MLtoGAI, which integrates Semantic Web technology with Machine Learning (ML) to enhance disease prediction and offer user-friendly explanations through ChatGPT. The system comprises three key components: a reusable disease ontology that incorporates detailed knowledge about various diseases, a diagnostic classification model that uses patient symptoms to detect specific diseases accurately, and the integration of Semantic Web Rule Language (SWRL) with ontology and ChatGPT to generate clear, personalized health advice. This approach significantly improves prediction accuracy and ensures results that are easy to understand, addressing the complexity of diseases and diverse symptoms. The MLtoGAI system demonstrates substantial advancements in accuracy and user satisfaction, contributing to developing more intelligent and accessible healthcare solutions. This innovative approach combines the strengths of ML algorithms with the ability to provide transparent, human-understandable explanations through ChatGPT, achieving significant improvements in prediction accuracy and user comprehension. By leveraging semantic technology and explainable AI, the system enhances the accuracy of disease prediction and ensures that the recommendations are relevant and easily understood by individual patients. Our research highlights the potential of integrating advanced technologies to overcome existing challenges in medical diagnostics, paving the way for future developments in intelligent healthcare systems. Additionally, the system is validated using 200 synthetic patient data records, ensuring robust performance and reliability.

Jinzhi Shen, Ke Ma

In response to the COVID-19 pandemic, the integration of interpretable machine learning techniques has garnered significant attention, offering transparent and understandable insights crucial for informed clinical decision making. This literature review delves into the applications of interpretable machine learning in predicting the prognosis of respiratory diseases, particularly focusing on COVID-19 and its implications for future research and clinical practice. We reviewed various machine learning models that are not only capable of incorporating existing clinical domain knowledge but also have the learning capability to explore new information from the data. These models and experiences not only aid in managing the current crisis but also hold promise for addressing future disease outbreaks. By harnessing interpretable machine learning, healthcare systems can enhance their preparedness and response capabilities, thereby improving patient outcomes and mitigating the impact of respiratory diseases in the years to come.

Worapat Attawettayanon, Yosuke Yasuda, JJ H. Zhang et al.

Background: Partial nephrectomy (PN) is preferred for a renal mass in a solitary kidney (RMSK), although tumors with high complexity can be challenging. Objective: To evaluate the evolution of RMSK management with a focus on achievement of PN. Design, setting, and participants: Patients with nonmetastatic RMSK (n = 499) were retrospectively reviewed; 133 had high tumor complexity, including 80 in the pre-tyrosine kinase inhibitor (TKI) era (1999–2008) and 53 in the TKI era (2009–2022). After 2009, 23/53 patients received neoadjuvant TKI and 30/53 had immediate-surgery. Outcome measurements and statistical analysis: Functional outcomes, adverse events and complications, dialysis-free survival, and recurrence-free survival (RFS) were the measures evaluated. Mann-Whitney and χ2 tests were used to compare cohorts, and the log-rank test was applied for survival analyses. Results and limitations: Overall, the median RENAL score was 10 and the median tumor diameter was 5.2 cm. Demographic characteristics, tumor diameter, and RENAL scores were similar between the pre-TKI-era and TKI-era groups. In the TKI era, 23/53 patients (43%) with clear-cell histology were selected for neoadjuvant TKI. These 23 patients had a greater median tumor diameter (7.1 vs 4.4 cm; p = 0.02) and RENAL score (11 vs 10; p = 0.07). After TKI treatment, the median tumor diameter decreased to 5.6 cm and the RENAL score to 9, and tumor volume was reduced by 59% (all p < 0.05). PN was accomplished in 21/23 (91%) the TKI-treated cases and in 27/30 (90%) of the immediate-surgery cases (2009–2022). PN was only accomplished in 52/80 (65%) of the patients from the pre-TKI era (p < 0.01). The 5-yr dialysis-free survival rate was 59% in the pre-TKI-era group and 91% in the TKI-era group. The 5-yr RFS rate was lower in the TKI-era group (59% vs 74%; p = 0.21), which was mostly related to more aggressive tumor biology, as reflected by a predominance of systemic rather than local recurrences. Conclusions: Management of RMSK with high tumor complexity is challenging. Selective use of TKI therapy was associated with greater use of PN, although a randomized study is needed. RFS mostly reflected aggressive tumor biology rather than failure of local management. Patient summary: For complex kidney tumors in patients with a single kidney, management is challenging. Use of drugs called tyrosine kinase inhibitors before surgery was associated with reductions in tumor size and greater ability to achieve partial kidney removal for cancer control. Most recurrences were metastatic, which reflects aggressive tumor biology rather than failure of surgery.

Auvick Chandra Bhowmik, Md. Taimur Ahad, Yousuf Rayhan Emon

Jamun leaf diseases pose a significant threat to agricultural productivity, negatively impacting both yield and quality in the jamun industry. The advent of machine learning has opened up new avenues for tackling these diseases effectively. Early detection and diagnosis are essential for successful crop management. While no automated systems have yet been developed specifically for jamun leaf disease detection, various automated systems have been implemented for similar types of disease detection using image processing techniques. This paper presents a comprehensive review of machine learning methodologies employed for diagnosing plant leaf diseases through image classification, which can be adapted for jamun leaf disease detection. It meticulously assesses the strengths and limitations of various Vision Transformer models, including Transfer learning model and vision transformer (TLMViT), SLViT, SE-ViT, IterationViT, Tiny-LeViT, IEM-ViT, GreenViT, and PMViT. Additionally, the paper reviews models such as Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet)-50V2, EfficientNet, Ensemble model, Convolutional Neural Network (CNN), and Locally Reversible Transformer. These machine-learning models have been evaluated on various datasets, demonstrating their real-world applicability. This review not only sheds light on current advancements in the field but also provides valuable insights for future research directions in machine learning-based jamun leaf disease detection and classification.

Albara Ah Ramli, Xin Liu, Erik K. Henricson

We introduce Walk4Me, a telehealth community mobility assessment system designed to facilitate early diagnosis, severity, and progression identification. Our system achieves this by 1) enabling early diagnosis, 2) identifying early indicators of clinical severity, and 3) quantifying and tracking the progression of the disease across the ambulatory phase of the disease. To accomplish this, we employ an Artificial Intelligence (AI)-based detection of gait characteristics in patients and typically developing peers. Our system remotely and in real-time collects data from device sensors (e.g., acceleration from a mobile device, etc.) using our novel Walk4Me API. Our web application extracts temporal/spatial gait characteristics and raw data signal characteristics and then employs traditional machine learning and deep learning techniques to identify patterns that can 1) identify patients with gait disturbances associated with disease, 2) describe the degree of mobility limitation, and 3) identify characteristics that change over time with disease progression. We have identified several machine learning techniques that differentiate between patients and typically-developing subjects with 100% accuracy across the age range studied, and we have also identified corresponding temporal/spatial gait characteristics associated with each group. Our work demonstrates the potential of utilizing the latest advances in mobile device and machine learning technology to measure clinical outcomes regardless of the point of care, inform early clinical diagnosis and treatment decision-making, and monitor disease progression.

Liangming Pan, Xinyuan Lu, Min-Yen Kan et al.

Fact-checking real-world claims often requires complex, multi-step reasoning due to the absence of direct evidence to support or refute them. However, existing fact-checking systems often lack transparency in their decision-making, making it challenging for users to comprehend their reasoning process. To address this, we propose the Question-guided Multi-hop Fact-Checking (QACHECK) system, which guides the model's reasoning process by asking a series of questions critical for verifying a claim. QACHECK has five key modules: a claim verifier, a question generator, a question-answering module, a QA validator, and a reasoner. Users can input a claim into QACHECK, which then predicts its veracity and provides a comprehensive report detailing its reasoning process, guided by a sequence of (question, answer) pairs. QACHECK also provides the source of evidence supporting each question, fostering a transparent, explainable, and user-friendly fact-checking process. A recorded video of QACHECK is at https://www.youtube.com/watch?v=ju8kxSldM64

Shenxiao Mei, Chenglong Ma, Feihong Shen et al.

Detecting dental diseases through panoramic X-rays images is a standard procedure for dentists. Normally, a dentist need to identify diseases and find the infected teeth. While numerous machine learning models adopting this two-step procedure have been developed, there has not been an end-to-end model that can identify teeth and their associated diseases at the same time. To fill the gap, we develop YOLOrtho, a unified framework for teeth enumeration and dental disease detection. We develop our model on Dentex Challenge 2023 data, which consists of three distinct types of annotated data. The first part is labeled with quadrant, and the second part is labeled with quadrant and enumeration and the third part is labeled with quadrant, enumeration and disease. To further improve detection, we make use of Tufts Dental public dataset. To fully utilize the data and learn both teeth detection and disease identification simultaneously, we formulate diseases as attributes attached to their corresponding teeth. Due to the nature of position relation in teeth enumeration, We replace convolution layer with CoordConv in our model to provide more position information for the model. We also adjust the model architecture and insert one more upsampling layer in FPN in favor of large object detection. Finally, we propose a post-process strategy for teeth layout that corrects teeth enumeration based on linear sum assignment. Results from experiments show that our model exceeds large Diffusion-based model.

Koichiro Takayama, Kazuyuki Numakura, Ryoma Igarashi et al.

Introduction Complete remission of cerebral metastasis is a rare consequence of tyrosine kinase inhibitor monotherapy in patients with metastatic renal cell carcinoma. Case presentation A 68‐year‐old woman, who presented with dyspnea, was diagnosed with left renal cell carcinoma with multiple brain and pleural metastases. Although nivolumab and ipilimumab combination treatment was initiated, it was discontinued because of an immune‐related adverse event. Two months after treatment cessation, brain metastases progressed regardless of shrinkage of primary renal tumor and pleural metastases. Therefore, axitinib was started as a second‐line treatment, which resulted in the complete disappearance of the brain metastases along with the stable disease of the other tumor lesions. Conclusion This is the first report of complete remission of brain metastases in renal cell carcinoma treated by axitinib.

Kaori Kohatsu, Tomo Suzuki, Madoka Takimoto et al.

Abstract Background Cytotoxic T lymphocyte antigen-4 (CTLA-4) is an essential inhibitory regulator of immune activation. CTLA-4 haploinsufficiency is known to be associated with dysregulation of FOXP3+ regulatory T cells, hyperactivation of effector T cells, and lymphocytic infiltration of multiple organs. However, there have only been a few reports of renal involvement with CTLA-4. Herein, we present a case of acute granulomatous tubulointerstitial nephritis (TIN) in a patient with CTLA-4 haploinsufficiency. Case presentation A 44-year-old man presented with a 3-week history of fever and malaise, and subsequently developed acute kidney injury (AKI) a few days after treatment with levofloxacin (LVFX). A kidney biopsy and immunohistochemical staining revealed granulomatous TIN with dominantly infiltrating CD4+ T cells. General symptoms and renal impairment showed improvement after discontinuation of LVFX and initiation of oral steroids. However, they worsened following steroid tapering. Further, a colon biopsy analysis showed similar findings to the renal tissue analysis. We suspected that granulomatous TIN was possibly associated with CTLA-4 haploinsufficiency. Therefore, the patient was transferred to another hospital for further treatment of CTLA-4 haploinsufficiency using immunosuppressive agents. Conclusions There have been few reports regarding renal involvement of CTLA-4 haploinsufficiency. In the present case, granulomatous TIN could have arisen due to instability of immune regulatory functions, such as CTLA-4 haploinsufficiency, and treatment with LVFX could have triggered immunologic activation and severe inflammation as well as renal dysfunction.

Maja Schlereth, Daniel Stromer, Katharina Breininger et al.

Neuromuscular diseases (NMDs) cause a significant burden for both healthcare systems and society. They can lead to severe progressive muscle weakness, muscle degeneration, contracture, deformity and progressive disability. The NMDs evaluated in this study often manifest in early childhood. As subtypes of disease, e.g. Duchenne Muscular Dystropy (DMD) and Spinal Muscular Atrophy (SMA), are difficult to differentiate at the beginning and worsen quickly, fast and reliable differential diagnosis is crucial. Photoacoustic and ultrasound imaging has shown great potential to visualize and quantify the extent of different diseases. The addition of automatic classification of such image data could further improve standard diagnostic procedures. We compare deep learning-based 2-class and 3-class classifiers based on VGG16 for differentiating healthy from diseased muscular tissue. This work shows promising results with high accuracies above 0.86 for the 3-class problem and can be used as a proof of concept for future approaches for earlier diagnosis and therapeutic monitoring of NMDs.

Piotr Nyczka, Johannes Falk, Marc-Thorsten Hütt

In a highly simplified view, a disease can be seen as the phenotype emerging from the interplay of genetic predisposition and fluctuating environmental stimuli. We formalize this situation in a minimal model, where a network (representing cellular regulation) serves as an interface between an input layer (representing environment) and an output layer (representing functional phenotype). Genetic predisposition for a disease is represented as a loss of function of some network nodes. Reduced, but non-zero, output indicates disease. The simplicity of this genetic disease model and its deep relationship to percolation theory allows us to understand the interplay between disease, network topology and the location and clusters of affected network nodes. We find that our model generates two different characteristics of diseases, which can be interpreted as chronic and acute diseases. In its stylized form, our model provides a new view on the relationship between genetic mutations and the type and severity of a disease.

Sanjay B. Kulkarni, Marco Bandini, Amey Patil et al.

The inspection of the urethra in patients with documented or suspected urethral stricture should be carried out with small caliber ureteroscope of 6/7.5Ch. Different from flexible cystoscope (16Ch) or resectoscope (26Ch), small caliber ureteroscope allows a comprehensive evaluation of the stricture, including its length and the status of the mucosa in its proximity, without injuring or overstretching the urethra. With a small caliber ureteroscope it is also possible to cross the stricture, allowing the evaluation of the proximal urethra, the external urethral sphincter, and the bladder. A 6/7.5Ch ureteroscope also allows estimation of the real caliber of the stricture, providing a useful landmark for further treatment decisions.

Barry E. Perlman, D.O., Evelyn Minis, M.D., Patricia Greenberg, M.S. et al.

Objective: To investigate whether there is a difference in live-birth gender rates in blastocyst-stage frozen-thawed embryo transfers (FETs) compared with those in cleavage-stage FETs. Design: Retrospective cohort study. Setting: Academic medical center. Patient(s): All women with recorded live births who underwent FET at either the blastocyst or cleavage stage, reported to the Society for Assisted Reproductive Technology during 2004–2013. Intervention(s): None. Main Outcome Measure(s): The primary outcome was live-birth gender rates. Demographic criteria were also collected. The chi-square analyses were used for bivariate associations, and multiple logistic regression models were used for adjusted associations, with all two-sided P<.05 considered statistically significant. Result(s): A statistically significant increase was noted in the number of live male births after blastocyst-stage FET compared with that after cleavage-stage FET (51.9% vs. 50.5%). After controlling for potential confounders including age (odds ratio [OR], 1.06; 95% confidence interval [CI], 1.03, 1.08), body mass index (OR, 1.08; 95% CI, 1.04, 1.12), and male factor infertility (OR, 1.06; 95% CI, 1.03, 1.08), the increase in male live births after blastocyst-stage FET remained statistically significant. Conclusion(s): In patients undergoing FETs, blastocyst-stage transfers are associated with higher male gender live-birth rates compared with cleavage-stage transfers.

Emanuela Marchese, Margherita Ruoppolo, Alessandra Perna et al.

Bardet–Biedl syndrome (BBS) is a rare pleiotropic inherited disorder known as a ciliopathy. Kidney disease is a cardinal clinical feature; however, it is one of the less investigated traits. This study is a comprehensive analysis of the literature aiming to collect available information providing mechanistic insights into the pathogenesis of kidney disease by analyzing clinical and basic science studies focused on this issue. The analysis revealed that the syndrome is either clinically and genetically heterogenous, with 24 genes discovered to date, but with 3 genes (BBS1, BBS2, and BBS10) accounting for almost 50% of diagnoses; genotype–phenotype correlation studies showed that patients with BBS1 mutations have a less severe renal phenotype than the other 2 most common loci; in addition, truncating rather than missense mutations are more likely to cause kidney disease. However, significant intrafamilial clinical variability has been described, with no clear explanation to date. In mice kidneys, Bbs genes have relative low expression levels, in contrast with other common affected organs, like the retina; surprisingly, Bbs1 is the only locus with basal overexpression in the kidney. In vitro studies indicate that signalling pathways involved in embryonic kidney development and repair are affected in the context of BBS depletion; in mice, kidney disease does not have a full penetrance; when present, it resembles human phenotype and shows an age-dependent progression. Data on the exact contribution of local versus systemic consequences of Bbs dysfunction are scanty and further investigations are required to get firm conclusions.