Hasil untuk "Immunologic diseases. Allergy"

Kejin Lu, Jianfa Bai, Qingqiu Li et al.

Accurate classification of lung diseases from chest CT scans plays an important role in computer-aided diagnosis systems. However, medical imaging datasets often suffer from severe class imbalance, which may significantly degrade the performance of deep learning models, especially for minority disease categories. To address this issue, we propose a gender-aware two-stage lung disease classification framework. The proposed approach explicitly incorporates gender information into the disease recognition pipeline. In the first stage, a gender classifier is trained to predict the patient's gender from CT scans. In the second stage, the input CT image is routed to a corresponding gender-specific disease classifier to perform final disease prediction. This design enables the model to better capture gender-related imaging characteristics and alleviate the influence of imbalanced data distribution. Experimental results demonstrate that the proposed method improves the recognition performance for minority disease categories, particularly squamous cell carcinoma, while maintaining competitive performance on other classes.

Xi Rong, Xupeng Sun, Li Wang et al.

Background and aimsTreating refractory acetylcholine receptor-positive generalized myasthenia gravis (AChR+ gMG) remains challenging, especially for patients requiring long-term immunosuppressive therapy. Current treatments often lack specificity and pose significant long-term risks, underscoring the need for alternatives. Telitacicept, a novel dual inhibitor of B lymphocyte stimulator (BLyS) and proliferation-inducing ligand (APRIL), offers a promising targeted therapeutic approach. This study aimed to evaluate the efficacy and safety of telitacicept in the treatment of refractory AChR+ generalized myasthenia gravis.MethodsThis retrospective study included 42 patients with refractory AChR+ gMG who received telitacicept. The primary outcomes assessed were changes from baseline in Quantitative Myasthenia Gravis (QMG) scores, analyzed using mixed-effects models. Secondary outcomes comprised cumulative response rates, reductions in concomitant immunosuppressive medications, and safety events.ResultsA total of 42 refractory MG patients with MGFA class II–IV were enrolled. Significant improvements were observed in the QMG total score (least-squares [LS] mean change at month 5: -2.24, 95% CI -3.34 to -1.15, p<0.001), with sustained benefits across ocular, limb, and bulbar areas. Cumulative response rates reached 69.9% for MGFA-PIS and 73.8% for QMG improvement (≥3-point reduction) by 6 months. Notable decreases in prednisone (LS mean -10.17 mg/day, p<0.001) and immunosuppressant use were also seen. The therapy demonstrated a promising safety profile.ConclusionsTelitacicept demonstrated significant efficacy in refractory AChR+ gMG and may reduce dependence on traditional immunosuppressants. These real-world findings support its use as a valuable treatment choice for this challenging patient group.

Rafi Hassan Chowdhury, Sabbir Ahmed

Ensuring food safety is critical due to its profound impact on public health, economic stability, and global supply chains. Cultivation of Mango, a major agricultural product in several South Asian countries, faces high financial losses due to different diseases, affecting various aspects of the entire supply chain. While deep learning-based methods have been explored for mango leaf disease classification, there remains a gap in designing solutions that are computationally efficient and compatible with low-end devices. In this work, we propose a lightweight Vision Transformer-based pipeline with a self-attention mechanism to classify mango leaf diseases, achieving state-of-the-art performance with minimal computational overhead. Our approach leverages global attention to capture intricate patterns among disease types and incorporates runtime augmentation for enhanced performance. Evaluation on the MangoLeafBD dataset demonstrates a 99.43% accuracy, outperforming existing methods in terms of model size, parameter count, and FLOPs count.

Fei Yuhuan, Wang Gengchen, Liu Fenghao et al.

Shrimp diseases are one of the primary causes of economic losses in shrimp aquaculture. To prevent disease transmission and enhance intelligent detection efficiency in shrimp farming, this paper proposes a lightweight network architecture based on YOLOv8n. First, by designing the RLDD detection head and C2f-EMCM module, the model reduces computational complexity while maintaining detection accuracy, improving computational efficiency. Subsequently, an improved SegNext_Attention self-attention mechanism is introduced to further enhance the model's feature extraction capability, enabling more precise identification of disease characteristics. Extensive experiments, including ablation studies and comparative evaluations, are conducted on a self-constructed shrimp disease dataset, with generalization tests extended to the URPC2020 dataset. Results demonstrate that the proposed model achieves a 32.3% reduction in parameters compared to the original YOLOv8n, with a mAP@0.5 of 92.7% (3% improvement over YOLOv8n). Additionally, the model outperforms other lightweight YOLO-series models in mAP@0.5, parameter count, and model size. Generalization experiments on the URPC2020 dataset further validate the model's robustness, showing a 4.1% increase in mAP@0.5 compared to YOLOv8n. The proposed method achieves an optimal balance between accuracy and efficiency, providing reliable technical support for intelligent disease detection in shrimp aquaculture.

Liangyu Xu, Zichen Wei, Rongfang Wu et al.

BackgroundOvalbumin (OVA) and house dust mite (HDM) are widely used allergenic proteins in murine models of allergic asthma. In our previous studies, shrimp tropomyosin (ST) was shown to induce type I hypersensitivity, including asthma-like responses. Here, we compared airway eosinophilic inflammation models induced by OVA, HDM, or ST using a protocol of three intraperitoneal (i.p.) sensitizations followed by a single intratracheal (i.t.) allergen challenge.MethodsC57BL/6J mice were sensitized via three i.p. injections of OVA, HDM, or ST mixed with Al(OH)3, followed by a single i.t. challenge with the respective allergen. Lung transcriptomic analysis, plasma IgE levels, bronchoalveolar lavage (BAL) fluid cell counts, cytokine and chemokine mRNA levels, and histopathological assessments were performed to evaluate airway inflammation.ResultsA single i.t. challenge with ST or HDM significantly increased the lung-to-body weight ratio, eosinophil infiltration, and mucus hypersecretion, accompanied by elevated mRNA levels of Th2 cytokines (Il-4, Il-5, Il-13) and increased the total cell count and eosinophil count in the BAL fluid. In contrast, OVA induced only mild eosinophilic inflammation, suggesting that repeated exposures may be required to elicit a robust allergic response. RNA sequencing and qRT-PCR further identified key chemokines associated with eosinophil recruitment (Ccl-11, Ccl-24), Th2 polarization (Ccl-17), and neutrophil activation (Cxcl-1).ConclusionA single i.t. challenge of ST, similar to HDM, exhibits a potent ability to induce eosinophilic inflammation and Th2-type immune responses in a murine model of allergic asthma, surpassing the effects of OVA.

Dessie Tarekegn, Samuel Derbie Habtegiorgis, Animut Takele Telayneh et al.

Conclusion: This study examines the prevalence and key factors influencing early index case HIV testing among adult patients in Debre Markos town. By identifying critical determinants such as gender, residence, and disclosure status, it provides valuable insights into how early testing can be enhanced to reduce transmission and improve health outcomes.

Gheorghe Traian Braileanu

Xenotransplantation of pig hearts may help address the current human shortage of human donors once rejection is controlled. One innovative approach to combat rejection in humans is the use of regulatory cell (RC) therapy. The term RC refers to all cell populations that share immunosuppressive functions. The use of RC, including mesenchymal stem cells (MSC) and CD4+CD125lowCD25highFoxp3+ T cells (Treg), may potentially reduce or eliminate the need for chronic general immunosuppression (IS). This approach is hypothesized to act by augmenting suppressive immune mechanisms that maintain tolerance by prevailing over the immune effector mechanisms responsible for rejection. Increasing RC numbers through adoptive cell transfer (ACT) and enhancing their functions via chimeric antigen receptor (CAR) technology are two promising strategies for RC therapy applications. During the various steps of rejection, monitoring specific biomarkers can guide the use of the corresponding RC subpopulation, preferably available off-the-shelf, either alone or in combination, administered once or multiple times. In the future, exosomes or RC-derived active molecules (or their antagonists) may supplement or replace whole-cell therapy. With further research, RC therapy, which has not yet been used in clinics to induce functional tolerance to pig heart xenotransplants in humans, has the potential to become a routine, personalized treatment.

Paul Pereira, Hervé Minoux, Aleksandra M. Walczak et al.

Since the approval of the first antibody drug in 1986, a total of 162 antibodies have been approved for a wide range of therapeutic areas, including cancer, autoimmune, infectious, or cardiovascular diseases. Despite advances in biotechnology that accelerated the development of antibody drugs, the drug discovery process for this modality remains lengthy and costly, requiring multiple rounds of optimizations before a drug candidate can progress to preclinical and clinical trials. This multi-optimization problem involves increasing the affinity of the antibody to the target antigen while refining additional biophysical properties that are essential to drug development such as solubility, thermostability or aggregation propensity. Additionally, antibodies that resemble natural human antibodies are particularly desirable, as they are likely to offer improved profiles in terms of safety, efficacy, and reduced immunogenicity, further supporting their therapeutic potential. In this article, we explore the use of energy-based generative models to optimize a candidate monoclonal antibody. We identify tradeoffs when optimizing for multiple properties, focusing on solubility, humanness and affinity and use the generative model we develop to generate candidate antibodies that lie on optimal Pareto fronts with respect to these properties.

Hiroyuki Kawahara, Satoshi Hara, Noriko Iwaki et al.

TAFRO syndrome is a systemic inflammatory disorder of unknown etiology, and its diagnosis requires the exclusion of autoimmune diseases. A 42-year-old Japanese woman presented with TAFRO syndrome-like manifestations, but had undiagnosed limited-cutaneous systemic sclerosis preventing a definitive diagnosis of TAFRO syndrome. However, her clinical course and pathological findings, including renal glomerular microangiopathy, were consistent with TAFRO syndrome. We performed a systematic review of the literature to evaluate how autoimmunity affects the clinical characteristics of TAFRO syndrome/idiopathic multicentric Castleman disease (iMCD)-TAFRO. We reviewed 95 reported cases of TAFRO syndrome/iMCD-TAFRO and found that at least 41 (43.6%) had various autoantibodies. In particular, the positive rates of anti-nuclear antibody, anti-SS-A antibody, anti-SS-B antibody, PA-IgG, and direct Coombs test were high. Furthermore, we identified 14 cases of autoimmune diseases with TAFRO syndrome-like manifestations. We compared the clinical characteristics of these 14 with those of the autoantibody-positive and -negative cases among the 95 cases of TAFRO syndrome/iMCD-TAFRO. Apart from sex ratio, we found no significant difference in clinical presentation, treatment, or outcome among the groups. In conclusion, TAFRO syndrome/iMCD-TAFRO often accompanies autoantibodies and shares many clinical characteristics with other autoimmune diseases. Clinicians should be aware that some autoimmune diseases mimic TAFRO syndrome/iMCD-TAFRO.

Shyam Dongre, Ritesh Chandra, Sonali Agarwal

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

Yizhen Xu, Scott Zeger, Zheyu Wang

The preclinical stage of many neurodegenerative diseases can span decades before symptoms become apparent. Understanding the sequence of preclinical biomarker changes provides a critical opportunity for early diagnosis and effective intervention prior to significant loss of patients' brain functions. The main challenge to early detection lies in the absence of direct observation of the disease state and the considerable variability in both biomarkers and disease dynamics among individuals. Recent research hypothesized the existence of subgroups with distinct biomarker patterns due to co-morbidities and degrees of brain resilience. Our ability to early diagnose and intervene during the preclinical stage of neurodegenerative diseases will be enhanced by further insights into heterogeneity in the biomarker-disease relationship. In this paper, we focus on Alzheimer's disease (AD) and attempt to identify the systematic patterns within the heterogeneous AD biomarker-disease cascade. Specifically, we quantify the disease progression using a dynamic latent variable whose mixture distribution represents patient subgroups. Model estimation uses Hamiltonian Monte Carlo with the number of clusters determined by the Bayesian Information Criterion (BIC). We report simulation studies that investigate the performance of the proposed model in finite sample settings that are similar to our motivating application. We apply the proposed model to the BIOCARD data, a longitudinal study that was conducted over two decades among individuals who were initially cognitively normal. Our application yields evidence consistent with the hypothetical model of biomarker dynamics presented in Jack et al. (2013). In addition, our analysis identified two subgroups with distinct disease-onset patterns. Finally, we develop a dynamic prediction approach to improve the precision of prognoses.

Chaitya Shah, Kashish Gandhi, Javal Shah et al.

Many uncontrollable disease outbreaks of the past exposed several vulnerabilities in the healthcare systems worldwide. While advancements in technology assisted in the rapid creation of the vaccinations, there needs to be a pressing focus on the prevention and prediction of such massive outbreaks. Early detection and intervention of an outbreak can drastically reduce its impact on public health while also making the healthcare system more resilient. The complexity of disease transmission dynamics, influence of various directly and indirectly related factors and limitations of traditional approaches are the main bottlenecks in taking preventive actions. Specifically, this paper implements deep learning algorithms and LLM's to predict the severity of infectious disease outbreaks. Utilizing the historic data of several diseases that have spread in India and the climatic data spanning the past decade, the insights from our research aim to assist in creating a robust predictive system for any outbreaks in the future.

Bushra F. Alsaqer, Alaa F. Alsaqer, Amna Asif

The use of Electronic Health Records (EHRs) has increased dramatically in the past 15 years, as, it is considered an important source of managing data od patients. The EHRs are primary sources of disease diagnosis and demographic data of patients worldwide. Therefore, the data can be utilized for secondary tasks such as research. This paper aims to make such data usable for research activities such as monitoring disease statistics for a specific population. As a result, the researchers can detect the disease causes for the behavior and lifestyle of the target group. One of the limitations of EHRs systems is that the data is not available in the standard format but in various forms. Therefore, it is required to first convert the names of the diseases and demographics data into one standardized form to make it usable for research activities. There is a large amount of EHRs available, and solving the standardizing issues requires some optimized techniques. We used a first-hand EHR dataset extracted from EHR systems. Our application uploads the dataset from the EHRs and converts it to the ICD-10 coding system to solve the standardization problem. So, we first apply the steps of pre-processing, annotation, and transforming the data to convert it into the standard form. The data pre-processing is applied to normalize demographic formats. In the annotation step, a machine learning model is used to recognize the diseases from the text. Furthermore, the transforming step converts the disease name to the ICD-10 coding format. The model was evaluated manually by comparing its performance in terms of disease recognition with an available dictionary-based system (MetaMap). The accuracy of the proposed machine learning model is 81%, that outperformed MetaMap accuracy of 67%. This paper contributed to system modelling for EHR data extraction to support research activities.

Dingkun Liu, Hongjie Zhou, Yilu Qu et al.

The abundance of intestinal flora is closely related to human diseases, but diseases are not caused by a single gut microbe. Instead, they result from the complex interplay of numerous microbial entities. This intricate and implicit connection among gut microbes poses a significant challenge for disease prediction using abundance information from OTU data. Recently, several methods have shown potential in predicting corresponding diseases. However, these methods fail to learn the inner association among gut microbes from different hosts, leading to unsatisfactory performance. In this paper, we present a novel architecture, Unsupervised Multi-graph Merge Adversarial Network (UMMAN). UMMAN can obtain the embeddings of nodes in the Multi-Graph in an unsupervised scenario, so that it helps learn the multiplex association. Our method is the first to combine Graph Neural Network with the task of intestinal flora disease prediction. We employ complex relation-types to construct the Original-Graph and disrupt the relationships among nodes to generate corresponding Shuffled-Graph. We introduce the Node Feature Global Integration (NFGI) module to represent the global features of the graph. Furthermore, we design a joint loss comprising adversarial loss and hybrid attention loss to ensure that the real graph embedding aligns closely with the Original-Graph and diverges from the Shuffled-Graph. Comprehensive experiments on five classical OTU gut microbiome datasets demonstrate the effectiveness and stability of our method. (We will release our code soon.)

María Teresa García-Ordás, Martín Bayón-Gutiérrez, Carmen Benavides et al.

Cardiovascular diseases state as one of the greatest risks of death for the general population. Late detection in heart diseases highly conditions the chances of survival for patients. Age, sex, cholesterol level, sugar level, heart rate, among other factors, are known to have an influence on life-threatening heart problems, but, due to the high amount of variables, it is often difficult for an expert to evaluate each patient taking this information into account. In this manuscript, the authors propose using deep learning methods, combined with feature augmentation techniques for evaluating whether patients are at risk of suffering cardiovascular disease. The results of the proposed methods outperform other state of the art methods by 4.4%, leading to a precision of a 90%, which presents a significant improvement, even more so when it comes to an affliction that affects a large population.

Abigail Cheever, Chloe C. Kang, Kim L. O’Neill et al.

Chimeric antigen receptor (CAR) T cell therapy has become an important treatment for hematological cancers, and its success has spurred research into CAR T cell therapies for other diseases, including solid tumor cancers and autoimmune diseases. Notably, the development of CAR-based treatments for autoimmune diseases has shown great progress recently. Clinical trials for anti-CD19 and anti-BCMA CAR T cells in treating severe B cell-mediated autoimmune diseases, like systemic lupus erythematosus (SLE), have shown lasting remission thus far. CAR T cells targeting autoreactive T cells are beginning clinical trials for treating T cell mediated autoimmune diseases. Chimeric autoantigen receptor (CAAR) T cells specifically target and eliminate only autoreactive B cells, and they have shown promise in treating mucosal pemphigus vulgaris and MuSK myasthenia gravis. Regulatory CAR T cells have also been developed, which show potential in altering autoimmune affected areas by creating a protective barrier as well as helping decrease inflammation. These new treatments are only the beginning of potential CAR T cell applications in treating autoimmune disease. Novel CAR technologies have been developed that increase the safety, potency, specificity, and efficacy of CAR T cell therapy. Applying these novel modifications to autoimmune CARs has the potential to enhance the efficacy and applicability of CAR therapies to autoimmune disease. This review will detail several recently developed CAR technologies and discuss how their application to autoimmune disease will improve this emerging field. These include logic-gated CARs, soluble protein-secreting CARs, and modular CARs that enable CAR T cell therapies to be more specific, reach a wider span of target cells, be safer for patients, and give a more potent cytotoxic response. Applying these novel CAR technologies to the treatment of autoimmune diseases has the potential to revolutionize this growing application of CAR T cell therapies.

Neil A. Mabbott, Koji Hase, Koji Hase et al.

Zhihan Yang, Zhiming Cheng, Tengjin Weng et al.

Retinal disease is one of the primary causes of visual impairment, and early diagnosis is essential for preventing further deterioration. Nowadays, many works have explored Transformers for diagnosing diseases due to their strong visual representation capabilities. However, retinal diseases exhibit milder forms and often present with overlapping signs, which pose great difficulties for accurate multi-class classification. Therefore, we propose a new framework named Multi-Scale Patch Message Passing Swin Transformer for multi-class retinal disease classification. Specifically, we design a Patch Message Passing (PMP) module based on the Message Passing mechanism to establish global interaction for pathological semantic features and to exploit the subtle differences further between different diseases. Moreover, considering the various scale of pathological features we integrate multiple PMP modules for different patch sizes. For evaluation, we have constructed a new dataset, named OPTOS dataset, consisting of 1,033 high-resolution fundus images photographed by Optos camera and conducted comprehensive experiments to validate the efficacy of our proposed method. And the results on both the public dataset and our dataset demonstrate that our method achieves remarkable performance compared to state-of-the-art methods.

Isabelle Williame, Marina George, Hiral Anil Shah et al.

Varicella is a highly contagious disease caused by the varicella zoster virus (VZV). While the disease is usually mild, severe complications can occur requiring costly hospitalization. A thorough understanding of the healthcare resource use (HCRU) and costs of varicella is needed to inform health-economic models of preventive strategies. A systematic literature review was carried out to retrieve relevant publications between 1999 and 2021, reporting HCRU and cost outcomes for varicella and its complications. Data were extracted and stratified according to pre-specified age groups and complication categories. Costs were re-based to a $US2020 footing using both purchasing power parity and the medical component of consumer price indexes. Data were summarized descriptively due to high heterogeneity in study design and outcome reporting. Forty-four publications fulfilled the inclusion and exclusion criteria of which 28 were conducted in Europe, 6 in Middle East and Asia, 5 in South America, 3 in North America, and 2 in multiple regions. Primary healthcare visits accounted for 30% to 85% of total direct costs. Hospitalization costs varied between $1,308 and $38,268 per episode depending on country, complication type, and length of stay, contributing between 2% and 60% to total direct costs. Indirect costs, mostly driven by workdays lost, accounted for approximately two-thirds of total costs due to varicella. The management of varicella and related complications can lead to substantial HCRU and costs for patients and the healthcare system. Additional research is needed to further characterize the varicella-associated economic burden and its broader impact from a societal standpoint.

Shasha Li, Shasha Li, Youwei Guo et al.

BackgroundGlioma, the most prevalent malignant intracranial tumor, poses a significant threat to patients due to its high morbidity and mortality rates, but its prognostic indicators remain inaccurate. Although TRAF-associated NF-kB activator (TANK) interacts and cross-regulates with cytokines and microenvironmental immune cells, it is unclear whether TANK plays a role in the immunologically heterogeneous gliomas.MethodsTANK mRNA expression patterns in public databases were analyzed, and qPCR and IHC were performed in an in-house cohort to confirm the clinical significance of TANK. Then, we systematically evaluated the relationship between TANK expression and immune characteristics in the glioma microenvironment. Additionally, we evaluated the ability of TANK to predict treatment response in glioma. TANK-associated risk scores were developed by LASSO-Cox regression and machine learning, and their prognostic ability was tested.ResultsTANK was specifically overexpressed in glioma and enriched in the malignant phenotype, and its overexpression was related to poor prognosis. The presence of a tumor microenvironment that is immunosuppressive was evident by the negative correlations between TANK expression and immunomodulators, steps in the cancer immunity cycle, and immune checkpoints. Notably, treatment for cancer may be more effective when immunotherapy is combined with anti-TANK therapy. Prognosis could be accurately predicted by the TANK-related risk score.ConclusionsHigh expression of TANK is associated with the malignant phenotype of glioma, as it shapes an immunosuppressive tumor microenvironment. Additionally, TANK can be used as a predictive biomarker for responses to various treatments and prognosis.