Yasar Caliskan, Virginie Royal, Stéphan Troyanov
et al.
Key Points Complement activation plays a critical role in FSGS pathogenesis and progression.The urine soluble C5b9 protein ratio is emerging as a significant biomarker for FSGS progression.Targeting complement pathways and monitoring urinary soluble C5b9 levels may improve risk stratification and therapeutic approaches in FSGS. Background The interplay between complement activation and the immune response in FSGS warrants further investigation. We investigated the association of glomerular C3 and IgM immunostaining with FSGS disease activity, complement system activation, chronicity on kidney biopsy, and initial and follow-up clinical data in the Cure Glomerulonephropathy Network FSGS cohort. Methods Data for patients with FSGS with available pathology assessment from the Cure Glomerulonephropathy Network cohort were reviewed. We tested associations between glomerular immunoglobulins and C3 staining intensity by immunofluorescence with the Columbia classification, the urinary membrane attack complex (soluble C5b9 [sC5b9]) level, proteinuria, and time to a composite outcome, defined by ESKD or a 40% decline in eGFR. Urinary sC5b9 levels, expressed as ratios to creatinine (sC5b9-to-creatinine ratio) and to protein (urine sC5b9-to-creatinine ratio-to-protein ratio [C5b9uPR]), were also examined. Results The study cohort comprised 175 patients with FSGS, including 63 (36%) incident subjects enrolled within 6 months of pathology review. Glomerular IgM, C3, and IgG deposits were found in 88 (50%), 48 (27.4%), and 27 (15.4%) patients, respectively. C3 deposition was correlated with global sclerosis (r=0.27, P < 0.001), tubular microcystic changes (r=0.19, P < 0.01), interstitial fibrosis (IF) tubular atrophy (r=0.17, P = 0.03), interstitial inflammation (r=0.17, P = 0.03), and tip lesion (r=-0.16, P = 0.04). In incident patients, C5b9uPR correlated with total segmental sclerosis (r=0.35, P < 0.01), IF (r=0.33, P = 0.01), IF tubular atrophy (r=0.35, P < 0.01), and interstitial inflammation (r=0.29, P = 0.03). Only C5b9uPR (hazard ratio, 1.64 [95% confidence interval, 1.03 to 2.60; P = 0.03]) and age at enrollment (hazard ratio, 1.01 [95% confidence interval, 1.00 to 1.03; P = 0.02]) were significantly associated with the composite outcome in the adjusted Cox survival models. Conclusions C5b9uPR is emerging as a significant biomarker for FSGS progression, reflecting the complex interplay between complement activation, inflammation, and kidney injury. The evidence suggests that elevated C5b9uPR levels are associated with poor kidney outcomes and may serve as a valuable tool in the noninvasive assessment of kidney fibrosis and disease progression.
Background. Metabolic-associated steatotic liver disease (MASLD) is one of the most common chronic liver disorders linked to systemic metabolic and inflammatory dysfunctions. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can aggravate the course of MASLD through immune and inflammatory mechanisms, emphasizing the need for a personalized diagnostic approach. The purpose was to determine the correlations between clinical and biochemical parameters, immune response, hemostatic profile, and metabolic alterations in patients with MASLD and immune response to SARS-CoV-2, in order to develop personalized diagnostic criteria. Materials and methods. A total of 105 patients with MASLD and 20 healthy controls were examined. Patients were stratified based on the presence of IgG antibodies to SARS-CoV-2. The comprehensive study included evaluation of lipid and carbohydrate metabolism, inflammatory markers (interleukin (IL) 6, IL-10, tumor necrosis factor α), hemostatic parameters, and insulin resistance index. Statistical analysis was performed using nonparametric Mann-Whitney and Kruskal-Wallis tests, and Spearman’s correlation (p < 0.05). Results. In patients with MASLD and immune response to SARS-CoV-2, moderate correlations were found between triglycerides (TG) and hepatic steatosis (r = 0.317; p < 0.01), very-low-density lipoprotein levels (r = 0.227; p < 0.05), and the atherogenic coefficient (r = 0.242; p < 0.05). The insulin resistance index (HOMA-IR) correlated positively with TG (r = 0.625; p < 0.01), while IL-6 correlated with the atherogenic coefficient (r = 0.25; p < 0.05). A negative correlation was found between mean platelet volume and nitric oxide metabolites (r = –0.332; p < 0.01). Elevated mean corpuscular hemoglobin (> 31) was observed in 55.2 % of patients and was associated with decreased NOx levels (p = 0.002) and reduced heptadecanoic acid concentrations (p = 0.005). A decrease in thrombin time (< 18.4 s) was detected in 40 % of patients and was accompanied by statistically significant changes in monounsaturated fatty acid levels (p = 0.021). Overall, post-COVID-19 patients demonstrated a 2.4-fold increase in TG, a 1.9-fold elevation in very-low-density lipoproteins, and a 1.5-fold reduction in high-density lipoproteins compared to controls (p < 0.01). Conclusions. The study confirms strong interrelations between metabolic, inflammatory, and hemostatic shifts in MASLD patients following SARS-CoV-2 infection. A personalized approach allows increasing the accuracy of assessing clinical and diagnostic indicators and predicting complications.
Diseases of the digestive system. Gastroenterology
The training of deep learning models relies on a large amount of labeled data. However, the high cost of medical labeling seriously hinders the development of deep learning in the medical field. Our study proposes a general disease diagnosis approach based on Zero-Shot Learning. The Siamese neural network is used to find similar diseases for the target diseases, and the U-Net segmentation model is used to accurately segment the key lesions of the disease. Finally, based on the ResNet-Agglomerative clustering algorithm, a clustering model is trained on a large number of sample data of similar diseases to obtain a approximate diagnosis of the target disease. Zero-Shot Learning of the target disease is then successfully achieved. To evaluate the validity of the model, we validated our method on a dataset of ophthalmic diseases in CFP modality. The external dataset was used to test its performance, and the accuracy=0.8395, precision=0.8094, recall=0.8463, F1 Score=0.8274, AUC=0.9226, which exceeded the indexes of most Few-Shot Learning and One-Shot Learning models. It proves that our method has great potential and reference value in the medical field, where annotation data is usually scarce and expensive to obtain.
With the rising global burden of chronic diseases and the multimodal and heterogeneous clinical data (medical imaging, free-text recordings, wearable sensor streams, etc.), there is an urgent need for a unified multimodal AI framework that can proactively predict individual health risks. We propose VL-RiskFormer, a hierarchical stacked visual-language multimodal Transformer with a large language model (LLM) inference head embedded in its top layer. The system builds on the dual-stream architecture of existing visual-linguistic models (e.g., PaLM-E, LLaVA) with four key innovations: (i) pre-training with cross-modal comparison and fine-grained alignment of radiological images, fundus maps, and wearable device photos with corresponding clinical narratives using momentum update encoders and debiased InfoNCE losses; (ii) a time fusion block that integrates irregular visit sequences into the causal Transformer decoder through adaptive time interval position coding; (iii) a disease ontology map adapter that injects ICD-10 codes into visual and textual channels in layers and infers comorbid patterns with the help of a graph attention mechanism. On the MIMIC-IV longitudinal cohort, VL-RiskFormer achieved an average AUROC of 0.90 with an expected calibration error of 2.7 percent.
Lawrence Kwape, Shiraaz Gabriel, Ahmad Abdelsalem
et al.
Conclusion: SSM and SSPS have the highest diagnostic accuracy for predicting the presence of EVs in patients with compensated cirrhosis. LSPS, LS3PS, and PSR come second at 94%. We recommend SSM and SSPS in institutions with transient elastography equipped with the software necessary to measure splenic stiffness. We introduce and propose LS3PS as a novel composite score for predicting the presence of EVs in patients with compensated cirrhosis. Large-sample-size studies are needed to validate these prediction scores and to allow direct comparison with Baveno VII. These prediction tools can help clinicians avoid unnecessary endoscopic procedures in patients with compensated cirrhosis, especially in developing countries with limited resources such as South Africa.
Diseases of the digestive system. Gastroenterology
Nasibe Golestani, Najmeh Shamspour, Jalal Azmandian
et al.
Abstract Introduction HBV infection is a significant concern in dialysis patients, influenced by various factors. This study aims to investigate the impact of serum uric acid levels on the immunogenicity of hepatitis Bvaccination in dialysis patients. Method A cross-sectional study was conducted, involving 125 hemodialysis patients. Prior to dialysis, assessments were made for uric acid, vitamin D, HBsAg, andHBsAb. Patients were divided into two groups based on uric acid levels: high level (≥ 6.5 mg/dl) and low level (< 6.5 mg/dl). Each group received three doses of a high-dose hepatitis B vaccine (40 mcg) at 0, 1, and 6 months. After 8 weeks of the 3rd dose of the vaccine, the anti-hepatitis B antibody titer (HBsAb) was measured and recorded. Data were analyzed using SPSS version 22. Results Among patients with high uric acid, 30 (26.8%) had low HBsAb and 82 (73.2%) had high HBsAb (> 10). In patients with low uric acid, 1 (7.7%) had low HBsAb and 12 (92.3%) had high HBsAb (> 10). There was no statistically significant difference inHBsAb between the two groups. The immune response of HBsAb and uric acid did not show significance based on demographic variables and laboratory results. Conclusion This study found no correlation between uric acid levels and the immunogenicity of hepatitis B vaccination in hemodialysis patients. However, it is important to note that the group with low serum uric acid was very small compared to the other group and this may have influenced these results. Further studies with larger patient populations are needed to provide more conclusive evidence in this area.
Surgery, Diseases of the digestive system. Gastroenterology
Potato plants are plants that are beneficial to humans. Like other plants in general, potato plants also have diseases; if this disease is not treated immediately, there will be a significant decrease in food production. Therefore, it is necessary to detect diseases quickly and precisely so that disease control can be carried out effectively and efficiently. Classification of potato leaf disease can be done directly. Still, the symptoms cannot always explain the type of disease that attacks potato leaves because there are many types of diseases with symptoms that look the same. Humans also have deficiencies in determining the results of identification of potato leaf disease, so sometimes the results of identification between individuals can be different. Therefore, the use of Deep Learning for the classification process of potato leaf disease is expected to shorten the time and have a high classification accuracy. This study uses a deep learning method with the DenseNet201 architecture. The choice to use the DenseNet201 algorithm in this study is because the model can identify important features of potato leaves and recognize early signs of emerging diseases. This study aimed to evaluate the effectiveness of the transfer learning method with the DenseNet201 architecture in increasing the classification accuracy of potato leaf disease compared to traditional classification methods. This study uses two types of scenarios, namely, comparing the number of dropouts and comparing the three optimizers. This test produces the best model using dropout 0.1 and Adam optimizer with an accuracy of 99.5% for training, 95.2% for validation, and 96% for the confusion matrix. In this study, using data testing, as many as 40 images were tested into the model that has been built. The test results on this model resulted in a new accuracy for classifying potato leaf disease, namely 92.5%.
In this paper, we study a well known two-step anaerobic digestion model in a configuration of two chemostats in series. This model is an eight-dimensional system of ordinary differential equations. Since the reaction system has a cascade structure, we show that the eight-order model can be reduced to a four-dimensional one. Using general growth rates, we provide an in-depth mathematical analysis of the asymptotic behavior of the system. First, we determine all the steady states of the model where there can be more than fifteen equilibria with a non-monotonic growth rate. Then, the necessary and sufficient conditions of existence and local stability of all steady states are established according to the operating parameters: the dilution rate, the input concentrations of the two nutrients, and the distribution of the total process volume considered. The operating diagrams are then analyzed theoretically to describe the asymptotic behavior of the process according to the four control parameters. There can be seventy regions with rich behavior where the system may exhibit bistability or tristability with the coexistence of both microbial species in the two bioreactors.
Frederik T. Larsen, Daniel Hansen, Mike K. Terkelsen
et al.
Background & Aims: Histological assessment of liver biopsies is the gold standard for diagnosis of non-alcoholic steatohepatitis (NASH), the progressive form of non-alcoholic fatty liver disease (NAFLD), despite its well-established limitations. Therefore, non-invasive biomarkers that can offer an integrated view of the liver are needed to improve diagnosis and reduce sampling bias. Hepatic stellate cells (HSCs) are central in the development of hepatic fibrosis, a hallmark of NASH. Secreted HSC-specific proteins may, therefore, reflect disease state in the NASH liver and serve as non-invasive diagnostic biomarkers. Methods: We performed RNA-sequencing on liver biopsies from a histologically characterised cohort of obese patients (n = 30, BMI >35 kg/m2) to identify and evaluate HSC-specific genes encoding secreted proteins. Bioinformatics was used to identify potential biomarkers and their expression at single-cell resolution. We validated our findings using single-molecule fluorescence in situ hybridisation (smFISH) and ELISA to detect mRNA in liver tissue and protein levels in plasma, respectively. Results: Hepatic expression of SPARC-related modular calcium-binding protein 2 (SMOC2) was increased in NASH compared to no-NAFLD (p.adj <0.001). Single-cell RNA-sequencing data indicated that SMOC2 was primarily expressed by HSCs, which was validated using smFISH. Finally, plasma SMOC2 was elevated in NASH compared to no-NAFLD (p <0.001), with a predictive accuracy of AUROC 0.88. Conclusions: Increased SMOC2 in plasma appears to reflect HSC activation, a key cellular event associated with NASH progression, and may serve as a non-invasive biomarker of NASH. Impact and implications: Non-alcoholic fatty liver disease (NAFLD) and its progressive form, non-alcoholic steatohepatitis (NASH), are the most common forms of chronic liver diseases. Currently, liver biopsies are the gold standard for diagnosing NAFLD. Blood-based biomarkers to complement liver biopsies for diagnosis of NAFLD are required. We found that activated hepatic stellate cells, a cell type central to NAFLD pathogenesis, upregulate expression of the secreted protein SPARC-related modular calcium-binding protein 2 (SMOC2). SMOC2 was elevated in blood samples from patients with NASH and may hold promise as a blood-based biomarker for the diagnosis of NAFLD.
Diseases of the digestive system. Gastroenterology
Jhon Edison Prieto Ortíz, Carlos Bernardo Sánchez Luque, Rolando José Ortega Quiróz
Los pacientes con hígado graso son casi siempre asintomáticos, las aminotransferasas usualmente están elevadas dos a cinco veces el valor normal y son una causa importante de consulta inicial. Todas las imágenes pueden evidenciar el hígado graso y la biopsia hepática sigue siendo la prueba de oro para su diagnóstico. En cualquier paciente las pruebas no invasivas son una excelente alternativa a la biopsia para determinar el grado de fibrosis hepática y establecer en qué etapa de la fibrogénesis se encuentra. La pérdida de peso y el ejercicio son los pilares fundamentales del tratamiento indicado para todos los pacientes con sobrepeso u obesidad; se recomienda una pérdida de peso entre 5% y 10% del peso corporal y una dieta con restricción calórica de 500-1000 kcal/día, baja en grasas saturadas y rica en productos de la dieta mediterránea como fruta, pescado, verduras, frutos secos, aceite de oliva, entre otros. Hay otros tratamientos como las medidas farmacológicas y los procedimientos endoscópicos y quirúrgicos.
Diseases of the digestive system. Gastroenterology
We investigate optimal carbon abatement in a dynamic general equilibrium climate-economy model with endogenous structural change. By differentiating the production of investment from consumption, we show that social cost of carbon can be conceived as a reduction in physical capital. In addition, we distinguish two final sectors in terms of productivity growth and climate vulnerability. We theoretically show that heterogeneous climate vulnerability results in a climate-induced version of Baumol's cost disease. Further, if climate-vulnerable sectors have high (low) productivity growth, climate impact can either ameliorate (aggravate) the Baumol's cost disease, call for less (more) stringent climate policy. We conclude that carbon abatement should not only factor in unpriced climate capital, but also be tailored to Baumol's cost and climate diseases.
This work presents a model combining the simplest communicable and non-communicable disease models. The latter is, by far, the leading cause of sickness and death in the World, and introduces basal heterogeneity in populations where communicable diseases evolve. The model can be interpreted as a risk-structured model, another way of accounting for population heterogeneity. Our results show that considering the non-communicable disease (in the end, heterogeneous populations) allows the communicable disease to become endemic even if the basic reproduction number is less than $1$. This feature is known as subcritical bifurcation. Furthermore, ignoring the non-communicable disease dynamics results in overestimating the reproduction number and, thus, giving wrong information about the actual number of infected individuals. We calculate sensitivity indices and derive interesting epidemic-control information.
This paper introduces an innovative model for infectious diseases in predator-prey populations. We not only prove the existence of global non-negative solutions but also establish essential criteria for the system's decline and sustainability. Furthermore, we demonstrate the presence of a Borel invariant measure, adding a new dimension to our understanding of the system. To illustrate the practical implications of our findings, we present numerical results. With our model's comprehensive approach, we aim to provide valuable insights into the dynamics of infectious diseases and their impact on predator-prey populations.
Most people around the globe are dying due to heart disease. The main reason behind the rapid increase in the death rate due to heart disease is that there is no infrastructure developed for the healthcare department that can provide a secure way of data storage and transmission. Due to redundancy in the patient data, it is difficult for cardiac Professionals to predict the disease early on. This rapid increase in the death rate due to heart disease can be controlled by monitoring and eliminating some of the key attributes in the early stages such as blood pressure, cholesterol level, body weight, and addiction to smoking. Patient data can be monitored by cardiac Professionals (Cp) by using the advanced framework in the healthcare departments. Blockchain is the world's most reliable provider. The use of advanced systems in the healthcare departments providing new ways of dealing with diseases has been developed as well. In this article Machine Learning (ML) algorithm known as a sine-cosine weighted k-nearest neighbor (SCA-WKNN) is used for predicting the Hearth disease with the maximum accuracy among the existing approaches. Blockchain technology has been used in the research to secure the data throughout the session and can give more accurate results using this technology. The performance of the system can be improved by using this algorithm and the dataset proposed has been improved by using different resources as well.
Md. Hamjajul Ashmafee, Tasnim Ahmed, Sabbir Ahmed
et al.
Correct identification and categorization of plant diseases are crucial for ensuring the safety of the global food supply and the overall financial success of stakeholders. In this regard, a wide range of solutions has been made available by introducing deep learning-based classification systems for different staple crops. Despite being one of the most important commercial crops in many parts of the globe, research proposing a smart solution for automatically classifying apple leaf diseases remains relatively unexplored. This study presents a technique for identifying apple leaf diseases based on transfer learning. The system extracts features using a pretrained EfficientNetV2S architecture and passes to a classifier block for effective prediction. The class imbalance issues are tackled by utilizing runtime data augmentation. The effect of various hyperparameters, such as input resolution, learning rate, number of epochs, etc., has been investigated carefully. The competence of the proposed pipeline has been evaluated on the apple leaf disease subset from the publicly available `PlantVillage' dataset, where it achieved an accuracy of 99.21%, outperforming the existing works.