Hasil untuk "Diseases of the genitourinary system. Urology"

Chen Wang, Dongfeng Chen, Yonghui Yin et al.

Diabetic kidney disease (DKD) is a common chronic microvascular complication of diabetes and the leading cause of end-stage renal disease. Its pathogenesis is closely linked to cellular senescence. Studies have shown that cellular senescence drives DKD progression via multiple mechanisms, including tubulointerstitial fibrosis, glomerulosclerosis, oxidative stress, and aberrant AMP-activated protein kinase (AMPK)/mammalian target of rapamycin (mTOR) signaling. However, there remains a lack of specific therapeutic targets or effective drugs for DKD treatment. Traditional Chinese medicine (TCM), characterized by its multidimensional, multi-target, significant efficacy, and fewer adverse effects, has shown unique advantages in DKD prevention and treatment. This article systematically examines the role of cellular senescence in DKD pathogenesis and, guided by TCM theory, explores how TCM may regulate cellular senescence to mitigate DKD, offering novel perspectives for enhancing clinical management of this condition.

Jie Song, Mengqiao He, Shumin Ren et al.

Many rare genetic diseases exhibit recognizable facial phenotypes, which are often used as diagnostic clues. However, current facial phenotype diagnostic models, which are trained on image datasets, have high accuracy but often suffer from an inability to explain their predictions, which reduces physicians' confidence in the model output.In this paper, we constructed a dataset, called FGDD, which was collected from 509 publications and contains 1147 data records, in which each data record represents a patient group and contains patient information, variation information, and facial phenotype information. To verify the availability of the dataset, we evaluated the performance of commonly used classification algorithms on the dataset and analyzed the explainability from global and local perspectives. FGDD aims to support the training of disease diagnostic models, provide explainable results, and increase physicians' confidence with solid evidence. It also allows us to explore the complex relationship between genes, diseases, and facial phenotypes, to gain a deeper understanding of the pathogenesis and clinical manifestations of rare genetic diseases.

Omar Faruq Shikdar, Fahad Ahammed, B. M. Shahria Alam et al.

Tea is among the most widely consumed drinks globally. Tea production is a key industry for many countries. One of the main challenges in tea harvesting is tea leaf diseases. If the spread of tea leaf diseases is not stopped in time, it can lead to massive economic losses for farmers. Therefore, it is crucial to identify tea leaf diseases as soon as possible. Manually identifying tea leaf disease is an ineffective and time-consuming method, without any guarantee of success. Automating this process will improve both the efficiency and the success rate of identifying tea leaf diseases. The purpose of this study is to create an automated system that can classify different kinds of tea leaf diseases, allowing farmers to take action to minimize the damage. A novel dataset was developed specifically for this study. The dataset contains 5278 images across seven classes. The dataset was pre-processed prior to training the model. We deployed three pretrained models: DenseNet, Inception, and EfficientNet. EfficientNet was used only in the ensemble model. We utilized two different attention modules to improve model performance. The ensemble model achieved the highest accuracy of 85.68%. Explainable AI was introduced for better model interpretability.

Camilo Gonzalez, Edna Zambrano, Dario Londoño et al.

Zhe Yu, Yuan-Zhi Xie, Xiao-Lan Huang et al.

Platelet-rich plasma (PRP) shows promise as a regenerative modality for mild-to-moderate erectile dysfunction (ED). However, its efficacy in treating severe ED remains unknown. Blood samples from 8-week-old male rats were used to prepare PRP through a two-step centrifugation procedure, followed by chitosan activation and freeze thaw cycle. A hyperhomocysteinemia (HHcy)-related ED model was established using a methionine-enriched diet, and an apomorphine (APO) test was conducted during the 4th week. APO-negative rats were divided into two groups and were injected with PRP or saline every 2 weeks. Erectile function and histological analyses of the corpus cavernosum were performed during the 16th week. The results revealed that erectile function was significantly impaired in rats with HHcy-related ED compared to that in age-matched rats but was improved by repeated PRP injections. Immunofluorescence staining revealed a reduction in reactive oxygen species and additional benefits on the recovery of structures within the corpus cavernosum in rats that received PRP treatment compared to those in the saline-injected control group. Therefore, PRP could enhance functional and structural recovery in a severe HHcy-related ED model. A notable strength of the present study lies in the use of a repeated intracavernous injection method, mirroring protocols used in human studies, which offers more reliable results for translating the findings to humans.

Mateusz Daniol, Daria Hemmerling, Jakub Sikora et al.

Parkinson's disease ranks as the second most prevalent neurodegenerative disorder globally. This research aims to develop a system leveraging Mixed Reality capabilities for tracking and assessing eye movements. In this paper, we present a medical scenario and outline the development of an application designed to capture eye-tracking signals through Mixed Reality technology for the evaluation of neurodegenerative diseases. Additionally, we introduce a pipeline for extracting clinically relevant features from eye-gaze analysis, describing the capabilities of the proposed system from a medical perspective. The study involved a cohort of healthy control individuals and patients suffering from Parkinson's disease, showcasing the feasibility and potential of the proposed technology for non-intrusive monitoring of eye movement patterns for the diagnosis of neurodegenerative diseases. Clinical relevance - Developing a non-invasive biomarker for Parkinson's disease is urgently needed to accurately detect the disease's onset. This would allow for the timely introduction of neuroprotective treatment at the earliest stage and enable the continuous monitoring of intervention outcomes. The ability to detect subtle changes in eye movements allows for early diagnosis, offering a critical window for intervention before more pronounced symptoms emerge. Eye tracking provides objective and quantifiable biomarkers, ensuring reliable assessments of disease progression and cognitive function. The eye gaze analysis using Mixed Reality glasses is wireless, facilitating convenient assessments in both home and hospital settings. The approach offers the advantage of utilizing hardware that requires no additional specialized attachments, enabling examinations through personal eyewear.

Qinkai Yu, Mingyu Jin, Dong Shu et al.

Recent advancements in artificial intelligence (AI), especially large language models (LLMs), have significantly advanced healthcare applications and demonstrated potentials in intelligent medical treatment. However, there are conspicuous challenges such as vast data volumes and inconsistent symptom characterization standards, preventing full integration of healthcare AI systems with individual patients' needs. To promote professional and personalized healthcare, we propose an innovative framework, Heath-LLM, which combines large-scale feature extraction and medical knowledge trade-off scoring. Compared to traditional health management applications, our system has three main advantages: (1) It integrates health reports and medical knowledge into a large model to ask relevant questions to large language model for disease prediction; (2) It leverages a retrieval augmented generation (RAG) mechanism to enhance feature extraction; (3) It incorporates a semi-automated feature updating framework that can merge and delete features to improve accuracy of disease prediction. We experiment on a large number of health reports to assess the effectiveness of Health-LLM system. The results indicate that the proposed system surpasses the existing ones and has the potential to significantly advance disease prediction and personalized health management.

Yuka Ko, Ryo Fukuda, Yuta Nishikawa et al.

This paper describes NAIST's submission to the simultaneous track of the IWSLT 2024 Evaluation Campaign: English-to-{German, Japanese, Chinese} speech-to-text translation and English-to-Japanese speech-to-speech translation. We develop a multilingual end-to-end speech-to-text translation model combining two pre-trained language models, HuBERT and mBART. We trained this model with two decoding policies, Local Agreement (LA) and AlignAtt. The submitted models employ the LA policy because it outperformed the AlignAtt policy in previous models. Our speech-to-speech translation method is a cascade of the above speech-to-text model and an incremental text-to-speech (TTS) module that incorporates a phoneme estimation model, a parallel acoustic model, and a parallel WaveGAN vocoder. We improved our incremental TTS by applying the Transformer architecture with the AlignAtt policy for the estimation model. The results show that our upgraded TTS module contributed to improving the system performance.

Allen Yang, Edward Yang

According to PBS, nearly one-third of Americans lack access to primary care services, and another forty percent delay going to avoid medical costs. As a result, many diseases are left undiagnosed and untreated, even if the disease shows many physical symptoms on the skin. With the rise of AI, self-diagnosis and improved disease recognition have become more promising than ever; in spite of that, existing methods suffer from a lack of large-scale patient databases and outdated methods of study, resulting in studies being limited to only a few diseases or modalities. This study incorporates readily available and easily accessible patient information via image and text for skin disease classification on a new dataset of 26 skin disease types that includes both skin disease images (37K) and associated patient narratives. Using this dataset, baselines for various image models were established that outperform existing methods. Initially, the Resnet-50 model was only able to achieve an accuracy of 70% but, after various optimization techniques, the accuracy was improved to 80%. In addition, this study proposes a novel fine-tuning strategy for sequence classification Large Language Models (LLMs), Chain of Options, which breaks down a complex reasoning task into intermediate steps at training time instead of inference. With Chain of Options and preliminary disease recommendations from the image model, this method achieves state of the art accuracy 91% in diagnosing patient skin disease given just an image of the afflicted area as well as a patient description of the symptoms (such as itchiness or dizziness). Through this research, an earlier diagnosis of skin diseases can occur, and clinicians can work with deep learning models to give a more accurate diagnosis, improving quality of life and saving lives.

Thế, of Ramadan, fasting et al.

Abstract The importance of mindfulness in promoting mental health and well-being has been increasingly recognised in recent years. As a result, mindfulness-based interventions have been introduced to improve various aspects of life, including quality of life and social support. The aim of this study was to examine the effectiveness of a seven-week mindfulness-based workshop programme in improving quality of life and social support among participants in the intervention compared to a control group. A total of 257 participants (65+) were recruited and assigned to either the intervention group, which participated in the seven-week mindfulness-based workshop programme, or the control group, which received no intervention. The workshop programme combined two evidence-based programmes: The Chronic Disease Self-Management Programme (CDSMP) and the Mindfulness-based Living Programme. Participants completed two questionnaires (EQ-5D-5L and OSSS-3) to assess quality of life and social support before and after the intervention. Data were analysed using appropriate statistical tests to compare pre- and post-intervention outcomes between groups. The intervention group showed significant improvement in quality of life (p<.001) and social support scores (p = 0.002) after the seven-week mindfulness-based workshop programme. The control group, on the other hand, showed no significant changes in these measures. The significant improvement indicates the effectiveness of the mindfulness-based workshop programme. The results of this study show the positive effects of a seven-week mindfulness-based workshop programme on the quality of life and social support of older people. The results suggest that mindfulness-based interventions can be an effective tool for improving mental health and well-being by promoting quality of life and strengthening social support networks. Key messages • The seven-week mindfulness-based workshop programme improves the quality of life and social support of older people, which can have an impact on overall mental health and well-being in general. • The results of this study can ensure a sustainable impact on achieving better health outcomes and saving resources in the health care system through reduced and more effective use of services.

Jihen Amara, Birgitta König-Ries, Sheeba Samuel

Plant diseases remain a considerable threat to food security and agricultural sustainability. Rapid and early identification of these diseases has become a significant concern motivating several studies to rely on the increasing global digitalization and the recent advances in computer vision based on deep learning. In fact, plant disease classification based on deep convolutional neural networks has shown impressive performance. However, these methods have yet to be adopted globally due to concerns regarding their robustness, transparency, and the lack of explainability compared with their human experts counterparts. Methods such as saliency-based approaches associating the network output to perturbations of the input pixels have been proposed to give insights into these algorithms. Still, they are not easily comprehensible and not intuitive for human users and are threatened by bias. In this work, we deploy a method called Testing with Concept Activation Vectors (TCAV) that shifts the focus from pixels to user-defined concepts. To the best of our knowledge, our paper is the first to employ this method in the field of plant disease classification. Important concepts such as color, texture and disease related concepts were analyzed. The results suggest that concept-based explanation methods can significantly benefit automated plant disease identification.

Génesis Luigi-Bravo, Roopan Kaur Gill

Zhen Song, Zhiyu Zhang, Zhang Chen et al.

Giant hydronephrosis is uncommon, and malignant hydronephrosis of unknown origin is even rarer. We report a case of a 43-year-old male patient with giant hydronephrosis without painless carnivorous hematuria. Preoperative imaging and urinary search for exfoliated cells revealed no evidence of renal pelvic cancer. The patient underwent simple nephrectomy and pathology confirmed the diagnosis of multifocal high-grade papillary uroepithelial carcinoma of the renal pelvis.

Patrick Saba, Lauren Shepard, Vivek Nithipalan et al.

Introduction and objective: Transrectal ultrasound (TRUS) biopsy teaches the basics of ultrasound guided techniques while also providing a basis for more advanced prostate oncology diagnosis methods. TRUS can cause discomfort to patients and requires skills in three-dimensional orientation and interpretation of findings. We sought to design a high-fidelity simulation model for resident education which can be used to further TRUS training techniques. Furthermore, Mixed reality (MR) allows the fusion of two video streams allowing real time overlay of a remote instructors’ hands onto the trainee's view. We further aim to evaluate remote MR training compared to in person (IP) training using a validated TRUS-Bx hydrogel simulation model. Methods: Validation was completed in 3 phases: Phase 1, Delphi methodology to gain consensus from an expert panel of endourologists. Consensus (>80% agreement) over 3 rounds defined 81 essential elements. Phase 2, Prototype development: these essential items were incorporated into prototypes fabricated using a combination of hydrogel molding and 3D printing. Phase 3: Validation comparing 6 experts and 6 novices performance from 4 centers using the consensus based objective and subjective metrics. Following this,14 participants with <5 case experience were randomized into MR and IP arms to reviewed educational videos of relevant anatomy and TRUS-BX steps prior to completing a pre-test, 3 training sessions, and post-test. In pre- and post-test participants independently measured the prostate, administered anesthetic and completed 14 biopsies on a validated hydrogel model with each biopsy area colored separately. Accuracy was defined as percentage of each core with the correct color corresponding to biopsy area. In training sessions faculty guided trainees through the procedure steps on a non-colored model either remotely using a MR platform or in person. MR set up included transmitting ultrasound view and audio via Zoom and a tablet displaying the merged surgical field with proctor hands. The remote faculty annotated the ultrasound view and guided trainees with their hands using the merged surgical view. Post-training surveys evaluated trainee perceptions and proctor assessment. Results: When asked how the model replicates the relevant human anatomy for the procedure, experts and novices rated the model 3.75/5 and 4.5/5 respectively. Additionally, both rated the model >4 when asked if the overall simulated tissue accurately resembles the appearance of live human tissue. Furthermore, both groups rated the model highly (≥4) for the procedural realism. When asked about teaching using the model, experts and novices rated the model highly (≥4) agreeing that the model is useful for improving technical skills, teaching the procedure, and assessing the user's ability to perform the procedure. Experts took significantly less attempts and time per biopsy region, less time per attempt, and reported significantly lower difficulty than novices (2.4 v 3.7, p = 0.001; 59.8 v 123.9, p < 0.001; 23.3 v 31.3, p = 0.001; 3.0 v 4.8, p = 0.001, respectively). However, both groups best core accuracy in each region was similar for all attempts (88% v 92%, p = 0.31). In our comparative study, participants reported equal confidence in knowledge (MR: 80.6/100 vs IP: 87.8/100, p = 0.49), ability to perform simulated TRUS (89.8 vs 90, p == 0.97) and live TRUS (66.8 vs 71, p == 0.73) on completion. Pre-test core percentage was similar (MR: 17.9% vs IP: 26.4%, p == 0.44). Both groups experienced significant increase in post-test scores (75.9 and 62.3% for MR and IP group respectively).MR groups increase was x1.5 times greater (MR: +58.0%, p < 0.01, IP: +35.9%, p < 0.01) despite trainee perceptions that remote training may hinder their ability to learn. Faculty rated the trainee skills from 1 (below expectations) to 3 (exceeds expectations) at TRUS manipulation, measurement, anesthetic, and biopsy. The MR group averaged 0.5, 0.1, 0.8 and 0.5 higher respectively. Conclusions: This TRUS biopsy model incorporates essential components for inclusion in resident training curriculum as a teaching and assessment tool by providing instantaneous feedback and procedural metrics while displaying high anatomical and procedural realism ratings. Ultimately this model can be utilized for virtual learning, utilizing its portable and non-biohazardous properties in combination with merged reality software that has been proven to be equivalent learning to in-person simulation training. This technology has the potential for cross-institutional training. Further studies will seek to increase our sample size and obtain external validation.

Sun Tae Ahn, Ji Sung Shim, Woong Jin Bae et al.

Purpose: We aimed to evaluate the efficacy and safety of penile girth enhancement (PGE) using hyaluronic acid (HA) filler with different physical properties from previous studies. Additionally, we evaluated the clinical impact on ejaculation after PGE. Materials and Methods: This was a prospective, patient/evaluator-blinded, randomized, active-controlled, multicenter trial. Patients recruited between December 2017 and March 2018 were randomly assigned to the HA filler or control group (polylactic acid [PLA] filler). Penile girth, satisfaction level, Premature Ejaculation Profile (PEP), and self-estimated intravaginal ejaculation latency time (IELT) were assessed at baseline and at 24 weeks post-injection. Results: Sixty-four subjects (32 in each group) completed the trial. The mean increase in girth was 22.74±12.60 mm and 20.23±8.73 mm in the HA and control groups, respectively. Satisfaction level regarding penile appearance and sexual life significantly increased in both groups. There was no statistically significant difference between the groups in terms of increase in penile girth or change in satisfaction level. Both groups showed significant improvements in PEP index scores. Selfestimated IELT also significantly increased in the HA group (from 5.36±3.51 to 7.86±4.73 minutes, p=0.0001) and control group (from 5.23±3.55 to 6.43±4.22 minutes, p=0.021). No serious adverse events (AEs) were reported. Conclusions: PGE with HA and PLA fillers resulted in significant enhancement of girth without serious AEs with no significant differences. Furthermore, PGE using filler improved clinical symptoms related to ejaculation.

Kazuki Nakamura, Eiichiro Uchino, Noriaki Sato et al.

Early disease detection and prevention methods based on effective interventions are gaining attention. Machine learning technology has enabled precise disease prediction by capturing individual differences in multivariate data. Progress in precision medicine has revealed that substantial heterogeneity exists in health data at the individual level and that complex health factors are involved in the development of chronic diseases. However, it remains a challenge to identify individual physiological state changes in cross-disease onset processes because of the complex relationships among multiple biomarkers. Here, we present the health-disease phase diagram (HDPD), which represents a personal health state by visualizing the boundary values of multiple biomarkers that fluctuate early in the disease progression process. In HDPDs, future onset predictions are represented by perturbing multiple biomarker values while accounting for dependencies among variables. We constructed HDPDs for 11 non-communicable diseases (NCDs) from a longitudinal health checkup cohort of 3,238 individuals, comprising 3,215 measurement items and genetic data. Improvement of biomarker values to the non-onset region in HDPD significantly prevented future disease onset in 7 out of 11 NCDs. Our results demonstrate that HDPDs can represent individual physiological states in the onset process and be used as intervention goals for disease prevention.

Leonardo Rossi, Marco Valenti, Sara Elisabetta Legler et al.

The Instance Segmentation task, an extension of the well-known Object Detection task, is of great help in many areas, such as precision agriculture: being able to automatically identify plant organs and the possible diseases associated with them, allows to effectively scale and automate crop monitoring and its diseases control. To address the problem related to early disease detection and diagnosis on vines plants, a new dataset has been created with the goal of advancing the state-of-the-art of diseases recognition via instance segmentation approaches. This was achieved by gathering images of leaves and clusters of grapes affected by diseases in their natural context. The dataset contains photos of 10 object types which include leaves and grapes with and without symptoms of the eight more common grape diseases, with a total of 17,706 labeled instances in 1,092 images. Multiple statistical measures are proposed in order to offer a complete view on the characteristics of the dataset. Preliminary results for the object detection and instance segmentation tasks reached by the models Mask R-CNN and R^3-CNN are provided as baseline, demonstrating that the procedure is able to reach promising results about the objective of automatic diseases' symptoms recognition.

Petchiammal A, Briskline Kiruba S, D. Murugan

Agriculture is the mainstay of human society because it is an essential need for every organism. Paddy cultivation is very significant so far as humans are concerned, largely in the Asian continent, and it is one of the staple foods. However, plant diseases in agriculture lead to depletion in productivity. Plant diseases are generally caused by pests, insects, and pathogens that decrease productivity to a large scale if not controlled within a particular time. Eventually, one cannot see an increase in paddy yield. Accurate and timely identification of plant diseases can help farmers mitigate losses due to pests and diseases. Recently, deep learning techniques have been used to identify paddy diseases and overcome these problems. This paper implements a convolutional neural network (CNN) based on a model and tests a public dataset consisting of 636 infrared image samples with five paddy disease classes and one healthy class. The proposed model proficiently identified and classified paddy diseases of five different types and achieved an accuracy of 88.28%

G. HUNDEMER, R. Talarico, M. Sood

Antonio Carotenuto, Teresa Costabile, Marcello Moccia et al.

Purpose The aim of this study was to evaluate the prevalence of bowel/bladder dysfunction in multiple sclerosis (MS) and its associations with cognitive impairment. Methods We prospectively enrolled 150 MS patients. Patients were administered the Symbol Digit Modality Test (SDMT), the Neurogenic Bowel Dysfunction Score (NBDS), and the Actionable Bladder Symptom Screening Tool (ABSST). The associations between bowel/bladder dysfunction and cognitive function were assessed through hierarchical regression models using the SDMT and clinicodemographic features as independent variables and NBDS and ABSST scores as dependent variables. Results The prevalence of bowel/bladder deficits was 44.7%, with 26 patients (17.3%) suffering from bowel deficits and 60 patients (40%) from bladder deficits. The total NBDS and ABSST scores were correlated with the SDMT (β=-0.10, P<0.001 and β=-0.03, P=0.04, respectively) after correction for demographic features and physical disability. Conclusions Bowel/bladder disorders are common in MS and are associated with both physical and cognitive disability burdens. As SDMT is embedded into routine clinical assessments, a lower score may warrant investigating bowel/bladder dysfunction due to the strong interplay of these factors.