BACKGROUND The main aim of specific immunotherapy is sustained effect due to changes in the immune system that can be demonstrated only in long-term trials. OBJECTIVE To investigate sustained efficacy and disease modification in a 5-year double-blind, placebo-controlled trial, including 2 years of blinded follow-up after completion of a 3-year period of treatment, with the SQ-standardized grass allergy immunotherapy tablet, Grazax (Phleum pratense 75,000 SQ-T/2,800 BAU,(∗) ALK, Denmark) or placebo. METHODS A randomized, double-blind, placebo-controlled, multinational, phase III trial included adults with a history of moderate-to-severe grass pollen-induced allergic rhinoconjunctivitis, with or without asthma, inadequately controlled by symptomatic medications. Two hundred thirty-eight participants completed the trial. End points included rhinoconjunctivitis symptom and medication scores, combined scores, asthma symptom and medication scores, quality of life, days with severe symptoms, immunologic end points, and safety parameters. RESULTS The mean rhinoconjunctivitis daily symptom score was reduced by 25% to 36% (P ≤ .004) in the grass allergy immunotherapy tablet group compared with the placebo group over the 5 grass pollen seasons covered by the trial. The rhinoconjunctivitis DMS was reduced by 20% to 45% (P ≤ .022 for seasons 1-4; P = .114 for season 5), and the weighted rhinoconjunctivitis combined score was reduced by 27% to 41% (P ≤ .003) in favor of active treatment. The percentage of days with severe symptoms during the peak grass pollen exposure was in all seasons lower in the active group than in the placebo group, with relative differences of 49% to 63% (P ≤ .0001). Efficacy was supported by long-lasting significant effects on the allergen-specific antibody response. No safety issues were identified. CONCLUSION The results confirm disease modification by SQ-standardized grass allergy immunotherapy tablet in addition to effective symptomatic treatment of allergic rhinoconjunctivitis.
Shaheer Ahmad Khan, Muhammad Usamah Shahid, Muddassar Farooq
Chronic diseases are long-lasting conditions that require lifelong medical attention. Using big EMR data, we have developed early disease risk prediction models for five common chronic diseases: diabetes, hypertension, CKD, COPD, and chronic ischemic heart disease. In this study, we present a novel approach for disease risk models by integrating survival analysis with classification techniques. Traditional models for predicting the risk of chronic diseases predominantly focus on either survival analysis or classification independently. In this paper, we show survival analysis methods can be re-engineered to enable them to do classification efficiently and effectively, thereby making them a comprehensive tool for developing disease risk surveillance models. The results of our experiments on real-world big EMR data show that the performance of survival models in terms of accuracy, F1 score, and AUROC is comparable to or better than that of prior state-of-the-art models like LightGBM and XGBoost. Lastly, the proposed survival models use a novel methodology to generate explanations, which have been clinically validated by a panel of three expert physicians.
Andrew W. L. Rogers, Renée M. Tsolis, Andreas J. Bäumler
A balanced gut microbiota contributes to health, but the mechanisms maintaining homeostasis remain elusive. Microbiota assembly during infancy is governed by competition between species and by environmental factors, termed habitat filters, that determine the range of successful traits within the microbial community.
N-glycosylation, a critical quality attribute of monoclonal antibodies, plays a pivotal role in regulating pharmacokinetics and pharmacodynamics through high-mannose (Man5) glycoform modulation. While our previous work demonstrated that N-acetyl-D-mannosamine (ManNAc) supplementation effectively reduces Man5 levels without compromising antibody yield or other critical quality attributes, the mechanistic basis remained unclear. This study systematically investigates ManNAc’s regulatory mechanism through a multi-parametric analysis. Cellular uptake studies revealed a 3-day latency period preceding Man5 reduction post-ManNAc administration. Subsequent transcriptional profiling showed no significant alterations in Man5-associated enzyme expression (Mgat1, Mgat2, Man2a1, SLC35A3), while metabolomic analysis demonstrated marked elevation of intracellular ManNAc, uridine-diphosphate-N-acetylglucosamine (UDP-GlcNAc), and cytidine-5’-monophospho-N-acetylneuraminic acid (CMP-Neu5Ac) levels. Mechanistic studies revealed two critical findings: (1) Chinese hamster ovary cells exhibit minimal endogenous N-acetyl-D-glucosamine-2-epimerase expression, and (2) CMP-Neu5Ac exerts potent inhibition on glucosamine (UDP-N-acetyl)-2-epimerase/N-acetylmannosamine kinase (GNE) activity in vitro, despite ManNAc’s lack of transcriptional regulation on GNE. We propose a metabolic flux redirection model, where ManNAc-derived CMP-Neu5Ac accumulation inhibits GNE activity, thereby shunting UDP-GlcNAc from sialic acid biosynthesis toward N-glycosylation pathways to reduce Man5 levels. This work not only identifies UDP-GlcNAc substrate limitation as a key constraint in antibody glycosylation but also establishes exogenous monosaccharide supplementation as a novel metabolic engineering strategy for Man5 optimization. These findings provide critical mechanistic insights for precision glycoengineering of therapeutic antibodies.
Food allergy (FA) and hereditary alpha-tryptasemia (HαT) are both relatively common conditions, but potential associations between these diagnoses have not been well-studied. Prior studies have suggested that acute rises in tryptase following food allergy reactions may not be as significant as reactions triggered by venom or drug allergy, but preliminary evidence suggests that the presence of α-tryptase and HαT is a risk factor for more severe reactions to foods. This mini review summarizes the epidemiology and diagnostic considerations of FA for patients with co-morbid HαT, potential effect of α-tryptase on food allergy reaction severity, and implications of tryptase genotyping in the management of FA. Additional research is needed to further investigate the relationship between FA and HαT.
Lyna-Nour Hamidi, Jack Christopher Drda, Meriem Belhocine
et al.
Mevalonate kinase deficiency (MKD) is an inherited autoinflammatory syndrome resulting from impaired isoprenoid biosynthesis due to biallelic mevalonate kinase (MVK) mutations. This metabolic defect leads to dysregulated innate immunity, particularly excessive interleukin-1β release. While typically presenting in childhood with periodic fevers, expanding evidence links MKD to heterogeneous adult phenotypes with immune-mediated end-organ damage. We report an adult male presenting with leg pain and finger cyanosis followed by acute ischemic stroke, macular rash, and lymphadenopathies. He exhibited classical markers of innate immune activation, including persistent elevation of C-reactive protein. Genetic testing identified compound heterozygosity for the known MVK pathogenic variant c.1129G>A (V377I) and a novel missense variant, c.1049A>C (Q350P). Structural modeling of Q350P revealed disruption of the GHMP kinase domain, predicted to destabilize mevalonate kinase conformation and impair its function. The measurement of mevalonate kinase activity in lymphocytes was at 55% (normal >60%). Interleukin-1β blockade with canakinumab was initiated, and the blood markers of inflammation normalized, further supporting a central role for innate immune dysregulation. This case highlights a novel MVK missense variant (Q350P) with subnormal mevalonate kinase activity. The patient’s compound heterozygous state with partially preserved mevalonate kinase activity may explain the attenuated systemic features and the delayed clinical onset. Remarkably, ischemic stroke was part of the initial presentation, suggesting that mevalonate kinase deficiency can manifest primarily through thrombo-inflammatory complications in adulthood, even in the absence of recurrent febrile episodes. This expands the phenotypic spectrum of MKD and underscores the need to consider adult-onset autoinflammatory syndromes in the differential diagnosis of cryptogenic ischemic strokes with markers of systemic inflammation. It also supports the utility of cytokine-targeted therapies in such contexts.
Asthma has become one of the most serious chronic respiratory diseases threatening people's lives worldwide. The pathogenesis of asthma is complex and driven by numerous cells and their interactions, which contribute to its genetic and phenotypic heterogeneity. The clinical characteristic is insufficient for the precision of patient classification and therapies; thus, a combination of the functional or pathophysiological mechanism and clinical phenotype proposes a new concept called “asthma endophenotype” representing various patient subtypes defined by distinct pathophysiological mechanisms. High-throughput omics approaches including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiome enable us to investigate the pathogenetic heterogeneity of diverse endophenotypes and the underlying mechanisms from different angles. In this review, we provide a comprehensive overview of the roles of diverse cell types in the pathophysiology and heterogeneity of asthma and present a current perspective on their contribution into the bidirectional interaction between airway inflammation and airway remodeling. We next discussed how integrated analysis of multi-omics data via machine learning can systematically characterize the molecular and biological profiles of genetic heterogeneity of asthma phenotype. The current application of multi-omics approaches on patient stratification and therapies will be described. Integrating multi-omics and clinical data will provide more insights into the key pathogenic mechanism in asthma heterogeneity and reshape the strategies for asthma management and treatment.
Wim H. M. Vroemen, Shakira S. Agata, Joyce J. B. C. van Beers
et al.
Background: Therapeutic drug monitoring of biological Tumor Necrosis Factor (TNF)-alpha inhibitors is of critical importance. In this study, the performance of practically advantageous chemiluminescent immunoassays of Theradiag, assessing Infliximab and Adalimumab serum concentrations and anti-drug antibodies (ADA) against these biologics, were compared to the Enzyme-Linked Immuno-Sorbent Assays (ELISAs) from Sanquin Diagnostics. Methods: Leftover serum samples (<i>n</i> = 80 for each parameter) from patients treated with Infliximab or Adalimumab were collected. Correlation and agreement analyses for serum concentration and ADAs, respectively, were performed. Both Theradiag ADA assays, an assay targeting both free and bound ADAs and an assay targeting solely free ADAs, were investigated and compared to the Sanquin Diagnostics ADA assay, targeting both free and bound ADAs. Results: Strong positive correlations were observed between the biologic concentration assessment of Infliximab (Spearman’s Rho = 0.91) and Adalimumab (Spearman’s Rho = 0.94). However, there appeared to be significant bias in the Theradiag assay when compared to Sanquin (Infliximab median (Confidence Interval (CI)) = 2.1 (1.7–2.6) µg/mL; Adalimumab median (CI) = 0.8 (0.5–0.9) µg/mL). Agreement analyses showed moderate to good agreement for the Theradiag and Sanquin Diagnostics ADA assays, when detecting both free and bound ADAs, for Infliximab (Cohen’s <i>k</i> = 0.717) and Adalimumab (Cohen’s <i>k</i> = 0.802). In contrast, the Theradiag ADA assay detecting solely free ADAs had zero to poor agreement for Infliximab (Cohen’s <i>k</i> = 0.458) and Adalimumab (Cohen’s <i>k</i> = 0.119), respectively. Conclusions: This study demonstrated strong correlations and good agreement between the Theradiag and Sanquin Diagnostics assays measuring Infliximab and Adalimumab serum concentrations and ADAs, both free and bound, against these biologics. Discordance analyses showed significantly decreased drug concentrations in the solely free assays, indicating that the combined detection of free and bound ADAs better aligns with drug levels.
Luiza Lober, Kirstin O. Roster, Francisco A. Rodrigues
Supervised machine learning models and public surveillance data has been employed for infectious disease forecasting in many settings. These models leverage various data sources capturing drivers of disease spread, such as climate conditions or human behavior. However, few models have incorporated the organizational structure of different geographic locations for forecasting. Traveling waves of seasonal outbreaks have been reported for dengue, influenza, and other infectious diseases, and many of the drivers of infectious disease dynamics may be shared across different cities, either due to their geographic or socioeconomic proximity. In this study, we developed a machine learning model to predict case counts of four infectious diseases across Brazilian cities one week ahead by incorporating information from related cities. We compared selecting related cities using both geographic distance and GDP per capita. Incorporating information from geographically proximate cities improved predictive performance for two of the four diseases, specifically COVID-19 and Zika. We also discuss the impact on forecasts in the presence of anomalous contagion patterns and the limitations of the proposed methodology.
In recent years, advancements in deep learning techniques have considerably enhanced the efficiency and accuracy of medical diagnostics. In this work, a novel approach using multi-task learning (MTL) for the simultaneous classification of lung sounds and lung diseases is proposed. Our proposed model leverages MTL with four different deep learning models such as 2D CNN, ResNet50, MobileNet and Densenet to extract relevant features from the lung sound recordings. The ICBHI 2017 Respiratory Sound Database was employed in the current study. The MTL for MobileNet model performed better than the other models considered, with an accuracy of74\% for lung sound analysis and 91\% for lung diseases classification. Results of the experimentation demonstrate the efficacy of our approach in classifying both lung sounds and lung diseases concurrently. In this study,using the demographic data of the patients from the database, risk level computation for Chronic Obstructive Pulmonary Disease is also carried out. For this computation, three machine learning algorithms namely Logistic Regression, SVM and Random Forest classifierswere employed. Among these ML algorithms, the Random Forest classifier had the highest accuracy of 92\%.This work helps in considerably reducing the physician's burden of not just diagnosing the pathology but also effectively communicating to the patient about the possible causes or outcomes.
Paridhi Mundra, Manik Sharma, Yashwardhan Chaudhuri
et al.
As respiratory illnesses become more common, it is crucial to quickly and accurately detect them to improve patient care. There is a need for improved diagnostic methods for immediate medical assessments for optimal patient outcomes. This paper introduces VoxMed, a UI-assisted one-step classifier that uses digital stethoscope recordings to diagnose respiratory diseases. It employs an Audio Spectrogram Transformer(AST) for feature extraction and a 1-D CNN-based architecture to classify respiratory diseases, offering professionals information regarding their patients respiratory health in seconds. We use the ICBHI dataset, which includes stethoscope recordings collected from patients in Greece and Portugal, to classify respiratory diseases. GitHub repository: https://github.com/Sample-User131001/VoxMed
Developing drugs for rare diseases presents unique challenges from a statistical perspective. These challenges may include slowly progressive diseases with unmet medical needs, poorly understood natural history, small population size, diversified phenotypes and geneotypes within a disorder, and lack of appropriate surrogate endpoints to measure clinical benefits. The Real-World Evidence (RWE) Scientific Working Group of the American Statistical Association Biopharmaceutical Section has assembled a research team to assess the landscape including challenges and possible strategies to address these challenges and the role of real-world data (RWD) and RWE in rare disease drug development. This paper first reviews the current regulations by regulatory agencies worldwide and then discusses in more details the challenges from a statistical perspective in the design, conduct, and analysis of rare disease clinical trials. After outlining an overall development pathway for rare disease drugs, corresponding strategies to address the aforementioned challenges are presented. Other considerations are also discussed for generating relevant evidence for regulatory decision-making on drugs for rare diseases. The accompanying paper discusses how RWD and RWE can be used to improve the efficiency of rare disease drug development.
Segmentation models for brain lesions in MRI are typically developed for a specific disease and trained on data with a predefined set of MRI modalities. Such models cannot segment the disease using data with a different set of MRI modalities, nor can they segment other types of diseases. Moreover, this training paradigm prevents a model from using the advantages of learning from heterogeneous databases that may contain scans and segmentation labels for different brain pathologies and diverse sets of MRI modalities. Additionally, the confidentiality of patient data often prevents central data aggregation, necessitating a decentralized approach. Is it feasible to use Federated Learning (FL) to train a single model on client databases that contain scans and labels of different brain pathologies and diverse sets of MRI modalities? We demonstrate promising results by combining appropriate, simple, and practical modifications to the model and training strategy: Designing a model with input channels that cover the whole set of modalities available across clients, training with random modality drop, and exploring the effects of feature normalization methods. Evaluation on 7 brain MRI databases with 5 different diseases shows that this FL framework can train a single model achieving very promising results in segmenting all disease types seen during training. Importantly, it can segment these diseases in new databases that contain sets of modalities different from those in training clients. These results demonstrate, for the first time, the feasibility and effectiveness of using FL to train a single 3D segmentation model on decentralised data with diverse brain diseases and MRI modalities, a necessary step towards leveraging heterogeneous real-world databases. Code: https://github.com/FelixWag/FedUniBrain
Pierluigi Colli, Gabriela Marinoschi, Elisabetta Rocca
et al.
Partial differential equation (PDE) models for infectious disease have received renewed interest in recent years. Most models of this type extend classical compartmental formulations with additional terms accounting for spatial dynamics, with Fickian diffusion being the most common such term. However, while diffusion may be appropriate for modeling vector-borne diseases, or diseases among plants or wildlife, the spatial propagation of airborne diseases in human populations is heavily dependent on human contact and mobility patterns, which are not necessarily well-described by diffusion. By including an additional chemotaxis-inspired term, in which the infection is propagated along the positive gradient of the susceptible population (from regions of low- to high-density of susceptibles), one may provide a more suitable description of these dynamics. This article introduces and analyzes a mathematical model of infectious disease incorporating a modified chemotaxis-type term. The model is analyzed mathematically and the well-posedness of the resulting PDE system is demonstrated. A series of numerical simulations are provided, demonstrating the ability of the model to naturally capture important phenomena not easily observed in standard diffusion models, including propagation over long spatial distances over short time scales and the emergence of localized infection hotspots
Adrian Gheorghiu, Iulian-Marius Tăiatu, Dumitru-Clementin Cercel
et al.
The detection and classification of diseases in Robusta coffee leaves are essential to ensure that plants are healthy and the crop yield is kept high. However, this job requires extensive botanical knowledge and much wasted time. Therefore, this task and others similar to it have been extensively researched subjects in image classification. Regarding leaf disease classification, most approaches have used the more popular PlantVillage dataset while completely disregarding other datasets, like the Robusta Coffee Leaf (RoCoLe) dataset. As the RoCoLe dataset is imbalanced and does not have many samples, fine-tuning of pre-trained models and multiple augmentation techniques need to be used. The current paper uses the RoCoLe dataset and approaches based on deep learning for classifying coffee leaf diseases from images, incorporating the pix2pix model for segmentation and cycle-generative adversarial network (CycleGAN) for augmentation. Our study demonstrates the effectiveness of Transformer-based models, online augmentations, and CycleGAN augmentation in improving leaf disease classification. While synthetic data has limitations, it complements real data, enhancing model performance. These findings contribute to developing robust techniques for plant disease detection and classification.
The global public health landscape is perpetually challenged by the looming threat of infectious diseases. Central to addressing this concern is the imperative to prevent and manage disease transmission during pandemics, particularly in unique settings. This study addresses the transmission dynamics of infectious diseases within conference venues, presenting a computational model designed to simulate transmission processes within a condensed timeframe (one day), beginning with sporadic cases. Our model intricately captures the activities of individual attendees within the conference venue, encompassing meetings, rest intervals, and meal breaks. While meetings entail proximity seating, rest and lunch periods allow attendees to interact with diverse individuals. Moreover, the restroom environment poses an additional avenue for potential infection transmission. Employing an individual-based model, we meticulously replicated the transmission dynamics of infectious diseases, with a specific emphasis on close-contact interactions between infected and susceptible individuals. Through comprehensive analysis of model simulations, we elucidated the intricacies of disease transmission dynamics within conference settings and assessed the efficacy of control strategies to curb disease dissemination. Ultimately, our study proffers a numerical framework for assessing the risk of infectious disease transmission during short-duration conferences, furnishing conference organizers with valuable insights to inform the implementation of targeted prevention and control measures.