Hasil untuk "Diseases of the genitourinary system. Urology"

Garazi Retegui, María Dolores Ugarte, Jaione Etxeberria et al.

To date, we have seen the emergence of a large literature on multivariate disease mapping. That is, incidence of (or mortality from) multiple diseases is recorded at the scale of areal units where incidence (mortality) across the diseases is expected to manifest dependence. The modeling involves a hierarchical structure: a Poisson model for disease counts (conditioning on the rates) at the first stage, and a specification of a function of the rates using spatial random effects at the second stage. These random effects are specified as a prior and introduce spatial smoothing to the rate (or risk) estimates. What we see in the literature is the amount of smoothing induced under a given prior across areal units compared with the observed/empirical risks. Our contribution here extends previous research on smoothing in univariate areal data models. Specifically, for three different choices of multivariate prior, we investigate both within prior smoothing according to hyperparameters and across prior smoothing. Its benefit to the user is to illuminate the expected nature of departure from perfect fit associated with these priors since model performance is not a question of goodness of fit. We propose both theoretical and empirical metrics for our investigation and illustrate with both simulated and real data.

I. Znotina, K. Rubene, V. Paradnieks et al.

Genevieve Baddoo, Adriana Ene, Zubia Merchant et al.

Continued efforts to isolate and sequence bacteria of the urinary tract has increased representation of these species in publicly available databases. This in turn has improved taxonomic classifications of the urinary microbiome (urobiome). Short-read sequencing targeting a variable region(s) of the 16S rRNA gene sequence has been fundamental in characterizing the urobiomes of males and females with and without lower urinary tract symptoms, as well as cancers of the urinary tract. Here, we have compiled a data set of full-length or near-full-length 16S rRNA gene sequences for the urobiome. To generate this data set, we first plated 203 isolates from the bladder on differential media and sequenced their full-length 16S rRNA gene sequence. We combined this data set with publicly available genomes from primarily the female urinary tract. The final data set includes 399 sequences representative of 160 different species from 73 genera. We assessed the ability of publicly available databases to correctly predict these sequences based on the V1-V3, V4, and V4-V6 variable regions. As expected, species designations based upon these variable regions is often not possible or incorrect. We also detected incorrect genus-level classifications. This data set can be used to supplement existing databases, by increasing urobiome species variation, and thus improve future studies characterizing urobiomes.

Osorio‐Aragón I. Yolanda, Milián‐Solís Ximena, Berumen‐Glinz Cristina et al.

Abstract We present the case of a 26‐year‐old female with chronic urticaria (CU) with systemic symptoms in whom comprehensive laboratory and imaging studies led to the diagnosis of idiopathic multicentric Castleman disease. Treatment with rituximab induced resolution of CU. The association between these two entities is rare, and common autoimmune and autoinflammatory pathways may be involved in their production. Our case also emphasized the association of CU with an underlying systemic disease.

Katharina Beyer, Ailbhe Lawlor, Sebastiaan Remmers et al.

Background and objective: The ability of health care professionals to communicate with patients compassionately and effectively is crucial for shared decision-making, but little research has investigated patient-clinician communication. As part of PIONEER—an international Big Data Consortium led by the European Association of Urology to answer key questions for men with prostate cancer (PCa), funded through the IMI2 Joint Undertaking under grant agreement 777492— we investigated communication between men diagnosed with PCa and the health care professional(s) treating them across Europe. Methods: We used the European Organisation for Research and Treatment of Cancer Quality-of-Life Questionnaire-Communication 26, which was shared via the PIONEER and patient organisations on March 11, 2022. We sought men who spoke French, Italian, Spanish, German, Dutch, or English who were diagnosed with PCa and were undergoing or had already received treatment for their PCa. Results and limitations: A total of 372 men reported that they communicated with their clinician during either the diagnostic or the treatment period. Overall, the majority of participants reported positive experiences. However, important opportunities to enhance communication were identified, particularly with regard to correcting misunderstandings, understanding the patient’s preferred approach to information presentation, addressing challenging questions, supporting the patient’s comprehension of information, attending to the patient’s emotional needs, and assessing what information had already been given to patients about their disease and treatment, and how much of it was understood. Conclusions and clinical implications: These results help us to identify gaps and barriers to shared treatment decision making. This knowledge will help devise measures to improve patient-health care professional communication in the PCa setting. Patient summary: As part of the PIONEER initiative, we investigated the communication between men diagnosed with prostate cancer and their health care professionals across Europe. A total of 372 men from six different countries participated in the study. Most participants reported positive experiences, but areas where communication could be improved were identified. These included addressing misunderstandings, tailoring the presentation of information to the patient’s preferences, handling difficult questions, supporting emotional needs, and assessing the patient’s understanding of their diagnosis and treatment.

Johannes Cansius Prihadi, Antoninus Hengky, Stevan Kristian Lionardi et al.

Abstract Introduction Bladder leiomyomas (LM) are uncommon, non-cancerous growths that originate from the smooth muscle cells of the bladder and constitute 0.5% of bladder tumor cases. This review aims to compile existing data and present a summary of bladder leiomyomas’ characteristics, management, and related outcomes. Method We conducted systematic review of studies that investigated bladder leiomyoma. Case studies or series describing individuals with bladder leiomyoma who underwent surgery and the outcomes were included. Four databases were used in our literature search, which was carried out until January 2024: PubMed, Proquest, EBSCOHost, and Google Scholar. We utilized MeSH terms such as “leiomyoma,” “urinary bladder,” and looked for synonyms of “bladder leiomyoma” in free text. Results A total of 99 studies with 119 patients were included. Most reported bladder leiomyoma cases were female, accounting for 79.0% of all cases. While symptomatic patients primarily presented with lower urinary tract symptoms (LUTS) (59.7%), hematuria (24.4%), acute urine retention (11.8%), and about 16.8% of cases were incidentally detected. Storage-related symptoms were the most common LUTS (37.0%). Different diagnostic techniques were used, frequently combining CT (Computed Tomography), MRI (Magnetic Resonance Imaging), USG (Ultrasonography), and/or cystoscopy. Bladder leiomyomas were commonly found on the left lateral wall (26.9%) and the bladder neck (17.6%). In more than half of the cases (52.1%) the treatment techniques used were transurethral resection (TURBt/TUR). Fifteen out of 119 cases (12.6%) had recurrence or remain symptomatic. Symptomatic symptoms at first presentation and extended location are frequently found among recurrent or symptomatic cases after first management. Conclusion Management of bladder leiomyoma should focus on the relief of symptoms and recurrence and be personalized based on the tumor characteristics, patient symptoms, and surgeon’s expertise. Further investigation is necessary to fully understand the best course of treatment and long-term results for bladder leiomyomas. In particular, prospective trials with bigger participant pools and meticulously controlled factors should be the main emphasis of this research.

Di Wang, Yidan Hu, Eng Sing Lee et al.

Early detection of chronic diseases is beneficial to healthcare by providing a golden opportunity for timely interventions. Although numerous prior studies have successfully used machine learning (ML) models for disease diagnoses, they highly rely on medical data, which are scarce for most patients in the early stage of the chronic diseases. In this paper, we aim to diagnose hyperglycemia (diabetes), hyperlipidemia, and hypertension (collectively known as 3H) using own collected behavioral data, thus, enable the early detection of 3H without using medical data collected in clinical settings. Specifically, we collected daily behavioral data from 629 participants over a 3-month study period, and trained various ML models after data preprocessing. Experimental results show that only using the participants' uploaded behavioral data, we can achieve accurate 3H diagnoses: 80.2\%, 71.3\%, and 81.2\% for diabetes, hyperlipidemia, and hypertension, respectively. Furthermore, we conduct Shapley analysis on the trained models to identify the most influential features for each type of diseases. The identified influential features are consistent with those reported in the literature.

Khandoker Nosiba Arifin, Sayma Akter Rupa, Md Musfique Anwar et al.

Lemons and oranges, both are the most economically significant citrus fruits globally. The production of lemons and oranges is severely affected due to diseases in its growth stages. Fruit quality has degraded due to the presence of flaws. Thus, it is necessary to diagnose the disease accurately so that we can avoid major loss of lemons and oranges. To improve citrus farming, we proposed a disease classification approach for lemons and oranges. This approach would enable early disease detection and intervention, reduce yield losses, and optimize resource allocation. For the initial modeling of disease classification, the research uses innovative deep learning architectures such as VGG16, VGG19 and ResNet50. In addition, for achieving better accuracy, the basic machine learning algorithms used for classification problems include Random Forest, Naive Bayes, K-Nearest Neighbors (KNN) and Logistic Regression. The lemon and orange fruits diseases are classified more accurately (95.0% for lemon and 99.69% for orange) by the model. The model's base features were extracted from the ResNet50 pre-trained model and the diseases are classified by the Logistic Regression which beats the performance given by VGG16 and VGG19 for other classifiers. Experimental outcomes show that the proposed model also outperforms existing models in which most of them classified the diseases using the Softmax classifier without using any individual classifiers.

Avneet Kaur, Rebecca Tyson, Iain Moyles

We analyze a disease transmission model that allows individuals to acquire fear and change their behaviour to reduce transmission. Fear is acquired through contact with infected individuals and through the influence of fearful individuals. We analyze the model in two limits: First, an Established Disease Limit (EDL), where the spread of the disease is much faster than the spread of fear, and second, a Novel Disease Limit (NDL), where the spread of the disease is comparable to that of fear. For the EDL, we show that the relative rate of fear acquisition to disease transmission controls the size of the fearful population at the end of a disease outbreak, and that the fear-induced contact reduction behaviour has very little impact on disease burden. Conversely, we show that in the NDL, disease burden can be controlled by fear-induced behaviour depending on the rate of fear loss. Specifically, fear-induced behaviour introduces a contact parameter $p$, which if too large prevents the contact reduction from effectively managing the epidemic. We analytically identify a critical prophylactic behaviour parameter $p=p_c$ where this happens leading to a discontinuity in epidemic prevalence. We show that this change in disease burden introduces delayed epidemic waves.

Christo Morison, Małgorzata Fic, Thomas Marcou et al.

Cooperation arises in nature at every scale, from within cells to entire ecosystems. In the framework of evolutionary game theory, public goods games (PGGs) are used to analyse scenarios where individuals can cooperate or defect, and can predict when and how these behaviours emerge. However, too few examples motivate the transferal of knowledge from one application of PGGs to another. Here, we focus on PGGs arising in disease modelling of cancer evolution and the spread of infectious diseases. We use these two systems as case studies for the development of the theory and applications of PGGs, which we succinctly review and compare. We also posit that applications of evolutionary game theory to decision-making in cancer, such as interactions between a clinician and a tumour, can learn from the PGGs studied in epidemiology, where cooperative behaviours such as quarantine and vaccination compliance have been more thoroughly investigated. Furthermore, instances of cellular-level cooperation observed in cancers point to a corresponding area of potential interest for modellers of other diseases, be they viral, bacterial or otherwise. We aim to demonstrate the breadth of applicability of PGGs in disease modelling while providing a starting point for those interested in quantifying cooperation arising in healthcare.

Yusuke Matsunaga, Yuta Suzuki, Shohei Yamamoto et al.

Abstract Background Clarification of the factors associated with decreased physical activity is crucial for effective disease management in patients undergoing hemodialysis. Although evidence suggests that physical activity may be associated with physical function or depressive symptoms, limited studies have demonstrated these factors to be independently associated with the former in patients undergoing hemodialysis. This study aimed to examine whether physical function and depressive symptoms were independently or interactively associated with daily physical activity in patients undergoing hemodialysis. Methods This cross-sectional study analyzed 157 clinically stable outpatients (median age: 68 years) receiving hemodialysis. Physical activity (steps/day) was measured using an accelerometer for four consecutive non-dialysis days. Physical function was evaluated by calculating usual walking speed along a 10-m walkway. The 10-item version of the Center for Epidemiologic Studies for Depression Scale was used to assess depressive symptoms. Results The hierarchical multiple regression model (Model 2), constructed by adding physical function to Model 1 (clinical characteristics), showed a significant increase in coefficient of determination (R 2), compared to Model 1 (ΔR 2 = 0.15, P < 0.01). There was no significant increase in R 2 between Models 2 and 3, where depressive symptoms were added to Model 2. The interaction term of physical function with depressive symptoms in Model 4 indicated an increase in R 2 (ΔR 2 = 0.01, P = 0.03), compared with Model 3. The simple slope analysis demonstrated that the difference in physical activity between patients with or without depressive symptoms at higher levels of physical function was greater than that at its lower levels. Conclusions Physical function was a strong and independent factor associated with physical activity, but no independent relationship between depressive symptoms and the physical activity was observed in patients undergoing hemodialysis. In contrast, physical function and depressive symptoms were interactively associated with daily physical activity in patients undergoing hemodialysis who were capable of walking independently. Our findings suggested that a two-step screening, primarily based on physical function and then depressive symptoms, can be useful to identify factors associated with physical inactivity in hemodialysis patients.

Bowen Liu, Malwane M. A. Ananda

It was observed that the number of cases and deaths for infectious diseases were associated with heavy-tailed power law distributions such as the Pareto distribution. While Pareto distribution was widely used to model the cases and deaths of infectious diseases, a major limitation of Pareto distribution is that it can only fit a given data set beyond a certain threshold. Thus, it can only model part of the data set. Thus, we proposed some novel discrete composite distributions with Pareto tails to fit the real infectious disease data. To provide necessary statistical inference for the tail behavior of the data, we developed a hypothesis testing procedure to test the tail index parameter. COVID-19 reported cases in Singapore and monkeypox reported cases in France were analyzed to evaluate the performance of the newly created distributions. The results from the analysis suggested that the discrete composite distributions could demonstrate competitive performance compared to the commonly used discrete distributions. Furthermore, the analysis of the tail index parameter can provide great insights into preventing and controlling infectious diseases.

Ting Yin, Yilong Chen, Lei Tang et al.

Abstract Background Chronic kidney disease (CKD) affects 8 to 16% of the world’s population and is one of the top ten important drivers of increasing disease burden. Apart from genetic predisposition, lifestyle factors greatly contribute to the incidence and progression of CKD. The current bibliometric analysis aims to characterize the current focus and emerging trends of the research about the impact of modifiable lifestyle factors on CKD. Methods We searched articles addressing the impact of modifiable lifestyle factors on the incidence and/or progression of CKD, published between 2011 and 2020, from the Science Citation Index Expanded (SCIE) database. An adjusted citation index, which considered both the original citation count and publication year, was derived for the selection of most-cited publications. Publishing trends, co-authorship network, keywords, and research hotspots were analyzed and visualized. Results Among the top 100 most influential articles, 32 were narrative reviews, 16 systematic reviews and/or meta-analysis, 44 clinical research, and 8 basic research. The United States occupied a dominant position in the perspective of article numbers and international partnerships, followed by European countries. The modifiable factors that drew the most and constant attention over the decade were diet or nutrition management reported in 63 papers, followed by obesity or body mass index (n = 27), and physical activity or exercises (n = 8). Alcohol consumption, fish oil, chain fatty-acids, and water-soluble vitamins were emerging hotspots identified in the recent most cited publications. Conclusions Based on the bibliometric analysis of the most influential articles, our study provides a comprehensive description of publishing trends and research focus over a decade in the field of lifestyle factors’ impact on CKD. Diet, obesity, and physical activity were factors receiving the most attention in this topic.

Vikram Singh, Vikram Singh

Neoplasms (NPs) and neurological diseases and disorders (NDDs) are amongst the major classes of diseases underlying deaths of a disproportionate number of people worldwide. To determine if there exist some distinctive features in the local wiring patterns of protein interactions emerging at the onset of a disease belonging to either of these two classes, we examined 112 and 175 protein interaction networks belonging to NPs and NDDs, respectively. Orbit usage profiles (OUPs) for each of these networks were enumerated by investigating the networks' local topology. 56 non-redundant OUPs (nrOUPs) were derived and used as network features for classification between these two disease classes. Four machine learning classifiers, namely, k-nearest neighbour (KNN), support vector machine (SVM), deep neural network (DNN), random forest (RF) were trained on these data. DNN obtained the greatest average AUPRC (0.988) among these classifiers. DNNs developed on node2vec and the proposed nrOUPs embeddings were compared using 5-fold cross validation on the basis of average values of the six of performance measures, viz., AUPRC, Accuracy, Sensitivity, Specificity, Precision and MCC. It was found that nrOUPs based classifier performed better in all of these six performance measures.

Varghese Kurian, Navid Ghadipasha, Michelle Gee et al.

Chronic Obstructive Pulmonary Disease (COPD) is a progressive lung disease characterized by airflow limitation. This study develops a systems engineering framework for representing important mechanistic details of COPD in a model of the cardio-respiratory system. In this model, we present the cardio-respiratory system as an integrated biological control system responsible for regulating breathing. Four engineering control system components are considered: sensor, controller, actuator, and the process itself. Knowledge of human anatomy and physiology is used to develop appropriate mechanistic mathematical models for each component. Following a systematic analysis of the computational model, we identify three physiological parameters associated with reproducing clinical manifestations of COPD - changes in the forced expiratory volume (FEV), lung volumes, and pulmonary hypertension. We quantify the changes in these parameters (airway resistance, lung elastance, and pulmonary resistance) as the ones that result in a systemic response that is diagnostic of COPD. A multivariate analysis reveals that the changes in airway resistance have a broad impact on the human cardio-respiratory system, and that the pulmonary circuit is stressed beyond normal under hypoxic environments in most COPD patients.

Yunlong Liang, Fandong Meng, Jinan Xu et al.

This paper introduces the joint submission of the Beijing Jiaotong University and WeChat AI to the WMT'22 chat translation task for English-German. Based on the Transformer, we apply several effective variants. In our experiments, we utilize the pre-training-then-fine-tuning paradigm. In the first pre-training stage, we employ data filtering and synthetic data generation (i.e., back-translation, forward-translation, and knowledge distillation). In the second fine-tuning stage, we investigate speaker-aware in-domain data generation, speaker adaptation, prompt-based context modeling, target denoising fine-tuning, and boosted self-COMET-based model ensemble. Our systems achieve 0.810 and 0.946 COMET scores. The COMET scores of English-German and German-English are the highest among all submissions.

Nikita R. Bhatt, Kenneth MacKenzie, Taimur T. Shah et al.

Abstract Objectives To assess the feasibility of conducting a randomised controlled trial (RCT) to assess whether avoiding ureteric drainage is superior to performing ureteric drainage after Uncomplicated Ureteroscopy and/or Flexible Ureterorenoscopy (URS/FURS) treatment of a urinary tract stone in improving patient reported outcome measures (PROMs) and 30‐day unplanned readmission rates. A secondary objective was to understand current practice of urologists regarding ureteric drainage after uncomplicated URS/FURS (UU). Material and methods We undertook an online survey of urologists, circulated amongst members of international urological societies and through social media platforms. Uncomplicated URS/FURS was defined as completion of URS/FURS treatment for a urinary tract stone, with the absence of: ureteral trauma, residual fragments requiring further lithotripsy procedures, significant bleeding, perforation, prior urinary tract infection or pregnancy. The ureteric drainage options considered included an indwelling stent, stent on a string or a ureteric catheter. The primary outcome was to determine the proportion of urologists willing to take part in a RCT, randomising patients after UU to a “no ureteric drainage” arm or ureteric drainage arm. Secondary outcomes included determining in their current practice, the proportion of clinicians performing routine ureteric drainage after UU, the reasons for performing ureteric drainage following UU and their preferred optimal duration for ureteric drainage if it is used. The study was reported according to the Checklist for Reporting Results of Internet E‐Surveys (CHERRIES). Results Total of 468 respondents from 45 countries took part in the survey, of whom 303 completed the entire survey (65%). The majority agreed that they would be willing to randomise patients (244/303, 81%) in the proposed RCT. Perceived lack of equipoise to randomise was the most common reason for not being willing to participate (59/303, 19%). 92% (308/335) reported that they use ureteric drainage after UU. This was most often due to wanting to prevent possible complications from post‐operative ureteric oedema (77%) or to aid passage of small fragments (43%). Complexity of the case (i.e. impacted stone 90%) and length of the procedure (46%) were the most important intraoperative factors influencing the decision to use ureteric drainage post procedure. If required, the median stated ideal duration of ureteric drainage was 5 days (IQR: 3–7 days) after UU. If having UU personally, 30% would want no stent postoperatively and over half would prefer a stent on a string. Conclusion We have highlighted wide variation in practice regarding ureteric drainage after UU. Our results support the feasibility of an RCT evaluating if no ureteric drainage is superior to ureteric drainage in improving PROMs and 30‐day unplanned readmission rates following UU.

A. ALONSO BETHENCOURT, M.J. Reguera Carmona, C. Rodríguez Álvarez et al.

Junjun Zhang, Ruwen Zhou, Yiming Mi et al.

Background: IgA nephropathy (IgAN) is characterized by the mesangial deposition of pathogenic IgA. We previously detected the deposition of pathogenic secretory IgA (SIgA) in the mesangium of about one-third of IgAN patients. Tubulointerstitial injury has an important role in the development of IgAN. However, the relationship between SIgA and tubulointerstitial damage is currently unclear. In this work, the role of the mesangial-tubular crosstalk was explored in the tubulointerstitial damage in SIgA-induced IgAN. Methods: SIgA deposition in renal tissues of IgAN patients was detected by immunofluorescence. Flow cytometry was used to assess the binding of SIgA to human renal mesangial cells (HRMC) and human proximal tubule epithelial (HK-2) cells. HK-2 was co-cultured with HRMC added with SIgA isolated from patients or normal volunteers. Protein synthesis and gene expressions of TNF-α, TGF-β1, and MCP-1 were determined by ELISA and PCR, respectively. The expressions of the above cytokines in renal tissues of patients and normal controls were detected by immunohistochemistry. Results: Twenty-nine of 96 patients had SIgA deposition in the mesangium, but SIgA was rarely detected in the tubulointerstitium. The binding rate of SIgA to HK-2 (2.79%) was significantly lower than that of HRMC (81.6%) (p &#x3c; 0.001). The expressions of TNF-α, TGF-β1, and MCP-1 in HRMC were significantly higher than in SIgA-stimulated HK-2 (p &#x3c; 0.05), and their expressions were significantly higher in the SIgA-stimulated co-culture group compared with SIgA-stimulated HRMC (p &#x3c; 0.05). The expressions of the above cytokines were mainly detected in tubulointerstitium of IgAN patients with positive and negative SIgA deposition, without significant difference between the 2 groups, but to a significantly higher level than that in normal controls, and their expressions positively correlated with tubulointerstitial injury. Conclusion: Inflammatory factors released from the mesangium after SIgA deposition might mediate tubulointerstitial damage via mesangial-tubular crosstalk in IgAN.

Keita Saito, Shigehiko Uchino, Tomoko Fujii et al.

Abstract Background Acute kidney injury (AKI) is a major comorbidity in critically ill patients. Low-dose atrial natriuretic peptide (ANP) has been shown to effectively prevent acute kidney injury (AKI), especially in cardiovascular surgery patients. However, its treatment effects for AKI in critically ill patients are unclear. Methods This single-center, retrospective, observational study included patients with AKI diagnosed within 7 days after intensive care unit (ICU) admission during the period January 2010 to December 2017. We conducted a propensity-matched analysis to estimate the treatment effect of low-dose carperitide (a recombinant human ANP) on the clinical outcomes. The primary outcome was a composite of death, renal replacement therapy dependence, or no recovery from AKI (defined as an increase of the serum creatinine level to ≥200% of baseline) at hospital discharge. Results During the study period, 4479 adult patients were admitted to the ICU. We identified 1374 eligible patients with AKI diagnosed within 7 days after ICU admission. Among these patients, 346 (25.2%) were treated with low-dose carperitide, with an average dose of 0.019 μg kg− 1 min− 1. The primary outcome occurred more often in the treatment group than in the control group (29.7% versus 23.4%, respectively; p = 0.022). After propensity score matching, characteristics of 314 patients from each group were well- balanced. Significant difference of the primary outcome, as seen with the full cohort, was no longer obtained; no benefit of carperitide was detected in the matched cohort (29.0% versus 25.2%; p = 0.281). Conclusions Low-dose ANP showed no treatment effect in general critically ill patients who developed AKI.