Takafumi Yagisawa, Tomokazu Shimizu, Ayane Tachiki
et al.
Introduction The management of urinary tract stones, particularly de novo kidney allograft stones, presents unique challenges for kidney transplant recipients because of their prevalence and specific clinical considerations. Here, we describe a case in which percutaneous nephrolithotomy was successfully used to fragment a large kidney allograft stone ≥20 mm in size. Case presentation A 57‐year‐old woman who underwent ureteroureterostomy post simultaneous pancreas–kidney transplantation presented with gross hematuria after 15 years. Computed tomography revealed a 23‐mm stone in the transplanted kidney. Initial attempts at endoscopic combined intrarenal surgery were changed to percutaneous nephrolithotomy because of poor ureter mobility and tortuosity. Stone fragmentation was achieved using pneumatic and ultrasonic lithotripsy. A second procedure using Swiss LithoClast® Trilogy enabled complete stone clearance and ureteral stent placement. Conclusion By understanding the peculiarities of the percutaneous approach, we demonstrated the safe and effective use of a pneumatic and ultrasonic lithotripter for kidney allograft stone fragmentation.
Maryam Javed, Aruna Sanghera, Azhar Ali Khan
et al.
IntroductionEarly kidney transplant failure has significant negative impact for individuals and healthcare systems. Contemporary data investigating early allograft failure are lacking. We undertook a retrospective observational cohort study of adult patients who underwent kidney transplantation at a single European centre.MethodsWe determined causes of allograft failure between 1 and 5 years after transplant and explored clinical variables present at 1 year that predicted allograft loss.Results591 patients (median age 50 years, 64.1% male, and 44% white) were included; 531 (89.8%) had graft survival and 60 (10.2%) had graft loss between 1- and 5-years. Rejection was the primary cause of graft failure in 24 (40%) cases and 54% had undetectable tacrolimus levels prior to failure event. Female sex, serum creatinine at 1 year, the occurrence of rejection, and undetectable tacrolimus levels were associated with increased odds of graft loss. In subsequent analysis of 787 patients alive with a functioning graft at 1 year, recipient age, serum creatinine, proteinuria, any rejection episode, and tacrolimus intrapatient variability (IPV) at 1 yearwere associated with an increased hazard of graft loss.DiscussionHence, graft losses were predominantly alloimmune mediated, often associated with non-adherence, and were predicted by tacrolimus IPV at 1 year.
Abstract Objective Patients with bladder cancer (BCa) have a poor prognosis and are prone to metastasis. Deficiency of 1,25-dihydroxyvitamin D3 (VD) is associated with increased incidence and decreased survival in various tumors. Herein, we aimed to examine the effect of VD combined with cisplatin (DDP) on the proliferation and apoptosis of BCa cells and elucidate the underlying mechanism. Methods T24 and 5637 BCa cell lines were treated with different concentrations of DDP and VD to assess the effects of various doses of DDP and VD on BCa cytotoxicity and determine the appropriate combination dose. T24 cells were treated with DDP and VD to assess the effects of the drug combination on cell proliferation, apoptosis, cycling, Warburg effect, and DDP sensitivity. In addition, cells were treated with DDP, VD, pyruvic acid sodium (PAS), or SC79 to determine the effect of VD on the sensitivity of BCa cells to DDP mediated by inhibiting the Warburg effect through AKT/mTOR signaling. Results VD and DDP inhibited BCa cell proliferation; promoted apoptosis; downregulated the protein expression of GLUT1, LDHA, HK2, c-Myc, MRP1, and P-gp; and upregulated the expression of p-mTOR protein. VD combined with DDP reversed the effects of PAS on cells and promoted apoptosis by inhibiting the cellular Warburg effect. In addition, VD combined with DDP activated the AKT/mTOR pathway and reversed the effects of SC79 on cell proliferation and the Warburg effect. Conclusion VD could promote apoptosis and enhance DDP sensitivity in BCa cells by inhibiting the Warburg effect via the AKT/mTOR pathway.
Accurate disease detection is of paramount importance for effective medical treatment and patient care. However, the process of disease detection is often associated with extensive medical testing and considerable costs, making it impractical to perform all possible medical tests on a patient to diagnose or predict hundreds or thousands of diseases. In this work, we propose Collaborative Learning for Disease Detection (CLDD), a novel graph-based deep learning model that formulates disease detection as a collaborative learning task by exploiting associations among diseases and similarities among patients adaptively. CLDD integrates patient-disease interactions and demographic features from electronic health records to detect hundreds or thousands of diseases for every patient, with little to no reliance on the corresponding medical tests. Extensive experiments on a processed version of the MIMIC-IV dataset comprising 61,191 patients and 2,000 diseases demonstrate that CLDD consistently outperforms representative baselines across multiple metrics, achieving a 6.33\% improvement in recall and 7.63\% improvement in precision. Furthermore, case studies on individual patients illustrate that CLDD can successfully recover masked diseases within its top-ranked predictions, demonstrating both interpretability and reliability in disease prediction. By reducing diagnostic costs and improving accessibility, CLDD holds promise for large-scale disease screening and social health security.
Bosubabu Sambana, Hillary Sunday Nnadi, Mohd Anas Wajid
et al.
Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to mitigating their effects and preventing widespread damage, as outbreaks can severely impact the productivity and quality of crops. With advancements in technology, there are increasing opportunities for automating the monitoring and detection of disease outbreaks in plants. This study proposed a system designed to identify and monitor plant diseases using a transfer learning approach. Specifically, the study utilizes YOLOv7 and YOLOv8, two state-ofthe-art models in the field of object detection. By fine-tuning these models on a dataset of plant leaf images, the system is able to accurately detect the presence of Bacteria, Fungi and Viral diseases such as Powdery Mildew, Angular Leaf Spot, Early blight and Tomato mosaic virus. The model's performance was evaluated using several metrics, including mean Average Precision (mAP), F1-score, Precision, and Recall, yielding values of 91.05, 89.40, 91.22, and 87.66, respectively. The result demonstrates the superior effectiveness and efficiency of YOLOv8 compared to other object detection methods, highlighting its potential for use in modern agricultural practices. The approach provides a scalable, automated solution for early any plant disease detection, contributing to enhanced crop yield, reduced reliance on manual monitoring, and supporting sustainable agricultural practices.
Heterozygous mutations in KMT2B are associated with an early-onset, progressive, and often complex dystonia (DYT28). Key characteristics of typical disease include focal motor features at disease presentation, evolving through a caudocranial pattern into generalized dystonia, with prominent oromandibular, laryngeal, and cervical involvement. Although KMT2B-related disease is emerging as one of the most common causes of early-onset genetic dystonia, much remains to be understood about the full spectrum of the disease. We describe a cohort of 53 patients with KMT2B mutations, with detailed delineation of their clinical phenotype and molecular genetic features. We report new disease presentations, including atypical patterns of dystonia evolution and a subgroup of patients with a non-dystonic neurodevelopmental phenotype. In addition to the previously reported systemic features, our study has identified co-morbidities, including the risk of status dystonicus, intrauterine growth retardation, and endocrinopathies. Analysis of this study cohort (n = 53) in tandem with published cases (n = 80) revealed that patients with chromosomal deletions and protein-truncating variants had a significantly higher burden of systemic disease (with earlier onset of dystonia) than those with missense variants. Eighteen individuals had detailed longitudinal data available after insertion of deep brain stimulation for medically refractory dystonia. Median age at deep brain stimulation was 11.5 years (range: 4.5 to 37.0 years). Follow-up after deep brain stimulation ranged from 0.25 to 22 years. Significant improvement of motor function and disability (as assessed by the Burke-Fahn-Marsden Dystonia Rating Scales, BFMDRS-M and BFMDRS-D) was evident at 6 months, 1 year, and last follow-up (motor, P = 0.001, P = 0.004, and P = 0.012; disability, P = 0.009, P = 0.002, and P = 0.012).
Nathalia A. Loureiro, Camilo R. Neto, Jack Sutton
et al.
Inter-city interactions are critical for the transmission of infectious diseases, yet their effects on the scaling of disease cases remain largely underexplored. Here, we use the commuting network as a proxy for inter-city interactions, integrating it with a general scaling framework to describe the incidence of seven infectious diseases across Brazilian cities as a function of population size and the number of commuters. Our models significantly outperform traditional urban scaling approaches, revealing that the relationship between disease cases and a combination of population and commuters varies across diseases and is influenced by both factors. Although most cities exhibit a less-than-proportional increase in disease cases with changes in population and commuters, more-than-proportional responses are also observed across all diseases. Notably, in some small and isolated cities, proportional rises in population and commuters correlate with a reduction in disease cases. These findings suggest that such towns may experience improved health outcomes and socioeconomic conditions as they grow and become more connected. However, as growth and connectivity continue, these gains diminish, eventually giving way to challenges typical of larger urban areas - such as socioeconomic inequality and overcrowding - that facilitate the spread of infectious diseases. Our study underscores the interconnected roles of population size and commuter dynamics in disease incidence while highlighting that changes in population size exert a greater influence on disease cases than variations in the number of commuters.
Physical contact or proximity is often a necessary condition for the spread of infectious diseases. Common destinations, typically referred to as hubs or points of interest, are arguably the most effective spots for the type of disease spread via airborne transmission. In this work, we model the locations of individuals (agents) and common destinations (hubs) by random spatial point processes in $\mathbb{R}^d$ and focus on disease propagation through agents visiting common hubs. The probability of an agent visiting a hub depends on their distance through a connection function $f$. The system is represented by a random bipartite geometric (RBG) graph. We study the degrees and percolation of the RBG graph for general connection functions. We show that the critical density of hubs for percolation is dictated by the support of the connection function $f$, which reveals the critical role of long-distance travel (or its restrictions) in disease spreading.
Mizuki Izawa, Toshikazu Takeda, Tadatsugu Anno
et al.
Abstract Background Laparoscopic adrenalectomy is widely performed for a number of hormone-producing tumors and postoperative management depends on the hormones produced. In the present study, we conducted a retrospective analysis to clarify the risk factors for postoperative complications, particularly postoperative fever after laparoscopic adrenalectomy. Methods We analyzed 406 patients who underwent laparoscopic adrenalectomy at our hospital between 2003 and 2019. Postoperative fever was defined as a fever of 38 °C or higher within 72 h after surgery. We investigated the risk factors for postoperative fever after laparoscopic adrenalectomy. Results There were 188 males (46%) and 218 females (54%) with a median age of 52 years. Among these patients, tumor pathologies included 188 primary aldosteronism (46%), 75 Cushing syndrome (18%), and 80 pheochromocytoma (20%). Postoperative fever developed in 124 of all patients (31%), 30% of those with primary aldosteronism, 53% of those with pheochromocytoma, and 8% of those with Cushing syndrome. A multivariate logistic regression analysis identified pheochromocytoma and non-Cushing syndrome as independent predictors of postoperative fever. Postoperative fever was observed in 42 out of 80 cases of pheochromocytoma (53%), which was significantly higher than in cases of non-pheochromocytoma (82/326, 25%, p < 0.01). In contrast, postoperative fever developed in 6 out of 75 cases of Cushing syndrome (8%), which was significantly lower than in cases of non-Cushing syndrome (118/331, 35.6%, p < 0.01). Conclusion Since postoperative fever after laparoscopic adrenalectomy is markedly affected by the hormone produced by pheochromocytoma and Cushing syndrome, it is important to carefully consider the need for treatment.
Generalist Large Language Models (LLMs), such as GPT-4, have shown considerable promise in various domains, including medical diagnosis. Rare diseases, affecting approximately 300 million people worldwide, often have unsatisfactory clinical diagnosis rates primarily due to a lack of experienced physicians and the complexity of differentiating among many rare diseases. In this context, recent news such as "ChatGPT correctly diagnosed a 4-year-old's rare disease after 17 doctors failed" underscore LLMs' potential, yet underexplored, role in clinically diagnosing rare diseases. To bridge this research gap, we introduce RareBench, a pioneering benchmark designed to systematically evaluate the capabilities of LLMs on 4 critical dimensions within the realm of rare diseases. Meanwhile, we have compiled the largest open-source dataset on rare disease patients, establishing a benchmark for future studies in this domain. To facilitate differential diagnosis of rare diseases, we develop a dynamic few-shot prompt methodology, leveraging a comprehensive rare disease knowledge graph synthesized from multiple knowledge bases, significantly enhancing LLMs' diagnostic performance. Moreover, we present an exhaustive comparative study of GPT-4's diagnostic capabilities against those of specialist physicians. Our experimental findings underscore the promising potential of integrating LLMs into the clinical diagnostic process for rare diseases. This paves the way for exciting possibilities in future advancements in this field.
In this work, we describe our submissions for the Voice Privacy Challenge 2024. Rather than proposing a novel speech anonymization system, we enhance the provided baselines to meet all required conditions and improve evaluated metrics. Specifically, we implement emotion embedding and experiment with WavLM and ECAPA2 speaker embedders for the B3 baseline. Additionally, we compare different speaker and prosody anonymization techniques. Furthermore, we introduce Mean Reversion F0 for B5, which helps to enhance privacy without a loss in utility. Finally, we explore disentanglement models, namely $β$-VAE and NaturalSpeech3 FACodec.
Jérémie Lespinasse, Carole Dufouil, Cécile Proust-Lima
et al.
Background. Alzheimer's disease and related dementia (ADRD) are characterized by multiple and progressive anatomo clinical changes. Yet, modeling changes over disease course from cohort data is challenging as the usual timescales are inappropriate and time-to-clinical diagnosis is available on small subsamples of participants with short follow-up durations prior to diagnosis. One solution to circumvent this challenge is to define the disease time as a latent variable. Methods: We developed a multivariate mixed model approach that realigns individual trajectories into the latent disease time to describe disease progression. Our methodology exploits the clinical diagnosis information as a partially observed and approximate reference to guide the estimation of the latent disease time. The model estimation was carried out in the Bayesian Framework using Stan. We applied the methodology to 2186 participants of the MEMENTO study with 5-year follow-up. Repeated measures of 12 ADRD markers stemmed from cerebrospinal fluid (CSF), brain imaging and cognitive tests were analyzed. Result: The estimated latent disease time spanned over twenty years before the clinical diagnosis. Considering the profile of a woman aged 70 with a high level of education and APOE4 carrier (the main genetic risk factor for ADRD), CSF markers of tau proteins accumulation preceded markers of brain atrophy by 5 years and cognitive decline by 10 years. We observed that individual characteristics could substantially modify the sequence and timing of these changes. Conclusion: Our disease progression model does not only realign trajectories into the most homogeneous way. It accounts for the inherent residual inter-individual variability in dementia progression to describe the long-term changes according to the years preceding clinical diagnosis, and to provide clinically meaningful information on the sequence of events.
Early and accurate detection systems for ear diseases, powered by deep learning, are essential for preventing hearing impairment and improving population health. However, the limited diversity of existing otoendoscopy datasets and the poor balance between diagnostic accuracy, computational efficiency, and model size have hindered the translation of artificial intelligence (AI) algorithms into healthcare applications. In this study, we constructed a large-scale, multi-center otoendoscopy dataset covering eight common ear diseases and healthy cases. Building upon this resource, we developed Best-EarNet, an ultrafast and lightweight deep learning architecture integrating a novel Local-Global Spatial Feature Fusion Module with a multi-scale supervision strategy, enabling real-time and accurate classification of ear conditions. Leveraging transfer learning, Best-EarNet, with a model size of only 2.94 MB, achieved diagnostic accuracies of 95.23% on an internal test set (22,581 images) and 92.14% on an external test set (1,652 images), while requiring only 0.0125 seconds (80 frames per second) to process a single image on a standard CPU. Further subgroup analysis by gender and age showed consistently excellent performance of Best-EarNet across all demographic groups. To enhance clinical interpretability and user trust, we incorporated Grad-CAM-based visualization, highlighting the specific abnormal ear regions contributing to AI predictions. Most importantly, we developed Ear-Keeper, a cross-platform intelligent diagnosis system built upon Best-EarNet, deployable on smartphones, tablets, and personal computers. Ear-Keeper enables public users and healthcare providers to perform comprehensive real-time video-based ear canal screening, supporting early detection and timely intervention of ear diseases.
Alfred is the first system for programmatic weak supervision (PWS) that creates training data for machine learning by prompting. In contrast to typical PWS systems where weak supervision sources are programs coded by experts, Alfred enables users to encode their subject matter expertise via natural language prompts for language and vision-language models. Alfred provides a simple Python interface for the key steps of this emerging paradigm, with a high-throughput backend for large-scale data labeling. Users can quickly create, evaluate, and refine their prompt-based weak supervision sources; map the results to weak labels; and resolve their disagreements with a label model. Alfred enables a seamless local development experience backed by models served from self-managed computing clusters. It automatically optimizes the execution of prompts with optimized batching mechanisms. We find that this optimization improves query throughput by 2.9x versus a naive approach. We present two example use cases demonstrating Alfred on YouTube comment spam detection and pet breeds classification. Alfred is open source, available at https://github.com/BatsResearch/alfred.
Abstract: Background The incidental detection of renal masses has been steadily rising. As a significant proportion of renal masses that are surgically treated are benign or indolent in nature, there is a clear need for better presurgical characterization of renal masses to minimize unnecessary harm. Ultrasound is a widely available and relatively inexpensive real-time imaging technique, and novel ultrasound-based applications can potentially aid in the non-invasive characterization of renal masses. Evidence acquisition: We performed a narrative review on novel ultrasound-based techniques that can aid in the non-invasive characterization of renal masses. Evidence synthesis: Contrast-enhanced ultrasound (CEUS) adds significant diagnostic value, particularly for cystic renal masses, by improving the characterization of fine septations and small nodules, with a sensitivity and specificity comparable to magnetic resonance imaging (MRI). Additionally, the performance of CEUS for the classification of benign versus malignant renal masses is comparable to that of computed tomography (CT) and MRI, although the imaging features of different tumor subtypes overlap significantly. Ultrasound molecular imaging with targeted contrast agents is being investigated in preclinical research as an addition to CEUS. Elastography for the assessment of tissue stiffness and micro-Doppler imaging for the improved detection of intratumoral blood flow without the need for contrast are both being investigated for the characterization of renal masses, though few studies have been conducted and validation is lacking. Conclusions: Several novel ultrasound-based techniques have been investigated for the non-invasive characterization of renal masses. CEUS has several advantages over traditional grayscale ultrasound, including the improved characterization of cystic renal masses and the potential to differentiate benign from malignant renal masses to some extent. Ultrasound molecular imaging offers promise for serial disease monitoring and the longitudinal assessment of treatment response, though this remains in the preclinical stages of development. While elastography and emerging micro-Doppler techniques have shown some encouraging applications, they are currently not ready for widespread clinical use.
This study aimed to estimate the cost and factors associated with severe hospitalized patients due to influenza in unvaccinated and vaccinated cases. The study had a cross-sectional design and included three influenza seasons in 16 sentinel hospitals in Catalonia, Spain. Data were collected from a surveillance system of influenza and other acute respiratory infections. Generalized linear models (GLM) were used to analyze mean costs stratified by comorbidities and pregnancy. Multivariate logistic models were used to analyze bacterial coinfection, multi-organ failure, acute respiratory distress syndrome, death and ICU admission by season and by vaccination status. Costs of ICU, hospitalization and total mean costs were analyzed using GLM, by season and by vaccination status. All models were adjusted for age and sex. A total of 2742 hospitalized cases were included in the analyses. Cases were mostly aged ≥ 60 years (70.17%), with recommended vaccination (86.14%) and unvaccinated (68.05%). The ICU admission level was statistically significant higher in unvaccinated compared to vaccinated cases. Costs of cases with more than or equal to two comorbidities (Diff = EUR − 1881.32), diabetes (Diff = EUR − 1953.21), chronic kidney disease (Diff = EUR − 2260.88), chronic cardiovascular disease (Diff = EUR − 1964.86), chronic liver disease (Diff = EUR − 3595.60), hospitalization (EUR 9419.42 vs. EUR 9055.45), and total mean costs (EUR 11,540.04 vs. 10,221.34) were statistically significant higher in unvaccinated compared to vaccinated patients. The influenza vaccine reduces the costs of hospitalization. There is a need to focus strategies in recommended vaccination groups.
Paulo Ricardo Gessolo Lins, Roberto Camargo Narciso, Leonardo Rolim Ferraz
et al.
Abstract Background Acute kidney injury is a common complication in solid organ transplants, notably liver transplantation. The MELD is a score validated to predict mortality of cirrhotic patients, which is also used for organ allocation, however the influence of this allocation criteria on AKI incidence and mortality after liver transplantation is still uncertain. Methods This is a retrospective single center study of a cohort of patients submitted to liver transplant in a tertiary Brazilian hospital: Jan/2002 to Dec/2013, divided in two groups, before and after MELD implementation (pre-MELD and post MELD). We evaluate the differences in AKI based on KDIGO stages and mortality rates between the two groups. Results Eight hundred seventy-four patients were included, 408 in pre-MELD and 466 in the post MELD era. The proportion of patients that developed AKI was lower in the post MELD era (p 0.04), although renal replacement therapy requirement was more frequent in this group (p < 0.01). Overall mortality rate at 28, 90 and 365 days was respectively 7%, 11% and 15%. The 1-year mortality rate was lower in the post MELD era (20% vs. 11%, p < 0.01). AKI incidence was 50% lower in the post MELD era even when adjusted for clinically relevant covariates (p < 0.01). Conclusion Liver transplants performed in the post MELD era had a lower incidence of AKI, although there were more cases requiring dialysis. 1-year mortality was lower in the post MELD era, suggesting that patient care was improved during this period.