Harold Wilson‐Morkeh, Lior Seluk, Philipp Bosch
et al.
ABSTRACT Eosinophilic granulomatosis with polyangiitis (EGPA) is a rare and potentially life‐threatening systemic, inflammatory disease with multi‐organ manifestations, variable presentation and complex pathology. Multiple interconnected immunological pathways are implicated in EGPA pathology, including a type‐2 immune response driving predominantly eosinophilic inflammation, B‐cell mediated autoimmunity, neutrophil activation, and the generation of pathogenic anti‐neutrophil cytoplasmic antibodies, all of which can contribute to tissue/organ damage. High‐dose glucocorticoids are the mainstay treatment for EGPA, but over the past two decades the development of biologic treatments targeting interleukin (IL)‐5, eosinophils and B‐cells has revitalized the treatment landscape. Mepolizumab, a humanized monoclonal antibody that specifically targets IL‐5, and benralizumab, which targets the IL‐5 receptor (IL‐5Rα), are both approved for the treatment of patients with non‐severe relapsing or refractory EGPA. In Phase III trials, these biologics have demonstrated favorable safety profiles and efficacy, with treatment leading to remission induction, remission maintenance, and oral glucocorticoid sparing benefits. However, as understanding of the full complexity of EGPA pathogenesis improves, new treatment targets are emerging. Consequently, understanding key pathogenic mechanisms at the patient level, enabling a more tailored treatment approach, is an important goal for future research.
Samanta Ghosh, Jannatul Adan Mahi, Shayan Abrar
et al.
Tea is a valuable asset for the economy of Bangladesh. So, tea cultivation plays an important role to boost the economy. These valuable plants are vulnerable to various kinds of leaf infections which may cause less production and low quality. It is not so easy to detect these diseases manually. It may take time and there could be some errors in the detection.Therefore, the purpose of the study is to develop an automated deep learning model for tea leaf disease classification based on the teaLeafBD dataset so that anyone can detect the diseases more easily and efficiently. There are 5,278 high-resolution images in this dataset. The images are classified into seven categories. Six of them represents various diseases and the rest one represents healthy leaves. The proposed pipeline contains data preprocessing, data splitting, adversarial training, augmentation, model training, evaluation, and comprehension made possible with Explainable AI strategies. DenseNet201 and EfficientNetB3 were employed to perform the classification task. To prepare the model more robustly, we applied adversarial training so it can operate effectively even with noisy or disturbed inputs. In addition, Grad-CAM visualization was executed to analyze the model's predictions by identifying the most influential regions of each image. Our experimental outcomes revealed that EfficientNetB3 achieved the highest classification accuracy of 93%, while DenseNet201 reached 91%. The outcomes prove that the effectiveness of the proposed approach can accurately detect tea leaf diseases and provide a practical solution for advanced agricultural management.
Mohammad Tahmid Noor, Shayan Abrar, Jannatul Adan Mahi
et al.
Early and accurate classification of retinal diseases is critical to counter vision loss and for guiding clinical management of retinal diseases. In this study, we proposed a deep learning method for retinal disease classification utilizing optical coherence tomography (OCT) images from the Retinal OCT Image Classification - C8 dataset (comprising 24,000 labeled images spanning eight conditions). Images were resized to 224x224 px and tested on convolutional neural network (CNN) architectures: Xception and InceptionV3. Data augmentation techniques (CutMix, MixUp) were employed to enhance model generalization. Additionally, we applied GradCAM and LIME for interpretability evaluation. We implemented this in a real-world scenario via our web application named RetinaVision. This study found that Xception was the most accurate network (95.25%), followed closely by InceptionV3 (94.82%). These results suggest that deep learning methods allow effective OCT retinal disease classification and highlight the importance of implementing accuracy and interpretability for clinical applications.
BackgroundKrüppel-like factor 5 (KLF5) is involved in various aspects of tumor development, metastasis, and drug resistance through their regulation of transcription and translation, yet its functions in a comprehensive cancer framework are still unclear.MethodsOur research involved a detailed pan-cancer analysis using multi-omics data sourced from various public databases. We investigated the clinical characteristics, prognostic significance, mutations, and methylation patterns of KLF5 across various cancer types.ResultsWe discovered that KLF5 is implicated in tumor progression and are prognostic markers across pan-cancer. KLF5 is significantly linked to various malignant pathways across different types of cancer. Additionally, KLF5 has associations with several immune-related features. Ultimately, experiments were carried out to investigate whether KLF5 could serve as a promising indicator for glioma and bladder cancer.ConclusionKLF5 may be utilized as a diagnostic tool for cancer, a predictor of its progression, and a guide for treatment., with particular promise as a therapeutic target for glioma and bladder cancer.
ABSTRACT Upon activation, B cells undergo either the germinal center (GC) or extrafollicular (EF) response. While GC are known to generate high‐affinity memory B cells and long‐lived plasma cells, the role of the EF response is less well understood. Initially, it was thought to be limited to that of a source of fast but lower‐quality antibodies until the GC can form. However, recent evidence strongly supports the EF response as an important component of the humoral response to infection. EF responses are now also recognized as a source of pathogenic B cells in autoimmune diseases. The EF response itself is dynamic and regulated by pathways that are only recently being uncovered. We have identified that the cytokine IL‐12 acts as a molecular switch, enhancing the EF response and suppressing GC through multiple mechanisms. These include direct effects on both B cells themselves and the coordinated differentiation of helper CD4 T cells. Here, we explore this pathway in relation to other recent advancements in our understanding of the EF response's role and highlight areas for future research. A better understanding of how the EF response forms and is regulated is essential for advancing treatments for many disease states.
Rachel Scheck, Mark Melzer, Gregory Gladkov
et al.
Abstract Precise and scalable quantification of the genetically intact HIV reservoir is critical for advancing curative strategies. However, current HIV reservoir assays such as the intact proviral DNA assay (IPDA) are limited by quantification failures or misclassification of defective proviral genomes due to HIV sequence heterogeneity. Q4ddPCR is a modular, droplet digital PCR assay that simultaneously targets four conserved regions in the HIV genome to improve specificity, reduce quantification gaps, and provide multi-layered readouts. We benchmarked Q4ddPCR against 3,650 near full-length proviral sequences from 13 virally suppressed people with HIV (PWH) generated by Q4PCR using the same primer/probe sets. Q4ddPCR enabled intact reservoir quantification in 95% of samples from three independent cohorts and closely matched sequence-confirmed Q4PCR reservoir measurements. In addition, multi-probe readouts revealed clonal intact reservoir dynamics that are not detectable by IPDA. In longitudinal samples from 42 participants over the first 4.5 years on antiretroviral therapy (ART), Q4ddPCR reported lower proviral frequencies and a steeper decline in intact proviral DNA compared to IPDA. Collectively, our findings confirm key predictions from mathematical modeling, demonstrating that multi-target assays provide greater specificity and more accurately capture the dynamics of the intact HIV reservoir.
Scott E. Call, MD, Lisa Goto, MD, Gwynne Latimer, MD
et al.
Background: Social drivers of health have been implicated as playing a major role in determining pediatric asthma outcomes. However, the impact of self-reported, family-level unmet social needs on asthma outcomes in critically ill pediatric patients is unknown. Objective: Our aim was to determine whether the presence of unmet social needs at the time of intensive care unit (ICU) admission are associated with ICU-related and postadmission outcomes. Methods: This was a 12-month (February 2022-January 2023) prospective cohort study at a single, urban pediatric health care system. Families of patients admitted to the pediatric ICU for asthma were screened for unmet social needs in multiple domains. Regression analyses were performed to correlate unmet needs with the following clinical outcomes: duration of bilevel positive airway pressure use; lengths of ICU and hospital stay; and rates of 6-month outpatient follow-up, ED visitation, and hospital readmission. Results: Of 164 screened families, 57% reported at least 1 unmet social need. Unmet needs were significantly associated with longer hospitalizations (ie, a 3% increase per year of age (odds ratio =1.03 [95% CI = 1.00-1.07]) and a higher likelihood of returning for emergency care (adds ratio =2.6 [95% CI = 1.1-6.2]), even after accounting for race, insurance payer, and medical comorbidities. Additionally, patients provided with resources reported fewer needs when rescreened at outpatient follow-up (median = –1 need [P = .001]). Conclusion: Families of critically ill pediatric patients with asthma reported a high rate of unmet social needs. Furthermore, those with needs were vulnerable to longer stays and repeat asthma exacerbations requiring emergency care. Identification of these families presents an opportunity to target a high-risk population with durable medical and social interventions.
Mahesh S. Nagargoje, Eneko Lazpita, Jesús Garicano-Mena
et al.
The heart is the central part of the cardiovascular network. Its role is to pump blood to various body organs. Many cardiovascular diseases occur due to an abnormal functioning of the heart. A diseased heart leads to severe complications and in some cases death of an individual. The medical community believes that early diagnosis and treatment of heart diseases can be controlled by referring to numerical simulations of image-based heart models. Computational Fluid Dynamics (CFD) is a commonly used tool for patient-specific simulations in the cardiac flows, and it can be equipped to allow a better understanding of flow patterns. In this paper, we review the progress of CFD tools to understand the flow patterns in healthy and dilated cardiomyopathic (DCM) left ventricles (LV). The formation of an asymmetric vortex in a healthy LV shows an efficient way of blood transport. The vortex pattern changes before any change in the geometry of LV is noticeable. This flow change can be used as a marker of DCM progression. We can conclude that understanding vortex dynamics in LV using various vortex indexes coupled with data-driven approaches can be used as an early diagnosis tool and improvement in DCM treatment.
Elliot M. Miller, Tat Chung D. Chan, Carlos Montes-Matamoros
et al.
Many neurodegenerative diseases (NDs) are characterized by the slow spatial spread of toxic protein species in the brain. The toxic proteins can induce neuronal stress, triggering the Unfolded Protein Response (UPR), which slows or stops protein translation and can indirectly reduce the toxic load. However, the UPR may also trigger processes leading to apoptotic cell death and the UPR is implicated in the progression of several NDs. In this paper, we develop a novel mathematical model to describe the spatiotemporal dynamics of the UPR mechanism for prion diseases. Our model is centered around a single neuron, with representative proteins P (healthy) and S (toxic) interacting with heterodimer dynamics (S interacts with P to form two S's). The model takes the form of a coupled system of nonlinear reaction-diffusion equations with a delayed, nonlinear flux for P (delay from the UPR). Through the delay, we find parameter regimes that exhibit oscillations in the P- and S-protein levels. We find that oscillations are more pronounced when the S-clearance rate and S-diffusivity are small in comparison to the P-clearance rate and P-diffusivity, respectively. The oscillations become more pronounced as delays in initiating the UPR increase. We also consider quasi-realistic clinical parameters to understand how possible drug therapies can alter the course of a prion disease. We find that decreasing the production of P, decreasing the recruitment rate, increasing the diffusivity of S, increasing the UPR S-threshold, and increasing the S clearance rate appear to be the most powerful modifications to reduce the mean UPR intensity and potentially moderate the disease progression.
Normative modeling has emerged as a pivotal approach for characterizing heterogeneity and individual variance in neurodegenerative diseases, notably Alzheimer's disease(AD). One of the challenges of cortical normative modeling is the anatomical structure mismatch due to folding pattern variability. Traditionally, registration is applied to address this issue and recently many studies have utilized deep generative models to generate anatomically align samples for analyzing disease progression; however, these models are predominantly applied to volume-based data, which often falls short in capturing intricate morphological changes on the brain cortex. As an alternative, surface-based analysis has been proven to be more sensitive in disease modeling such as AD, yet, like volume-based data, it also suffers from the mismatch problem. To address these limitations, we proposed a novel generative normative modeling framework by transferring the conditional diffusion generative model to the spherical non-Euclidean domain. Additionally, this approach generates normal feature map distributions by explicitly conditioning on individual anatomical segmentation to ensure better geometrical alignment which helps to reduce anatomical variance between subjects in analysis. We find that our model can generate samples that are better anatomically aligned than registered reference data and through ablation study and normative assessment experiments, the samples are able to better measure individual differences from the normal distribution and increase sensitivity in differentiating cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) patients.
Shahran Rahman Alve, Muhammad Zawad Mahmud, Samiha Islam
et al.
Artificial intelligence and deep learning are increasingly applied in the clinical domain, particularly for early and accurate disease detection using medical imaging and sound. Due to limited trained personnel, there is a growing demand for automated tools to support clinicians in managing rising patient loads. Respiratory diseases such as cancer and diabetes remain major global health concerns requiring timely diagnosis and intervention. Auscultation of lung sounds, combined with chest X-rays, is an established diagnostic method for respiratory illness. This study presents a Deep Convolutional Neural Network (CNN)-based approach for the analysis of respiratory sound data to detect Chronic Obstructive Pulmonary Disease (COPD). Acoustic features extracted with the Librosa library, including Mel-Frequency Cepstral Coefficients (MFCCs), Mel-Spectrogram, Chroma, Chroma (Constant Q), and Chroma CENS, were used in training. The system also classifies disease severity as mild, moderate, or severe. Evaluation on the ICBHI database achieved 96% accuracy using 10-fold cross-validation and 90% accuracy without cross-validation. The proposed network outperforms existing methods, demonstrating potential as a practical tool for clinical deployment.
Won Suk Choi, Mary Patricia Nowalk, Krissy Moehling Geffel
et al.
Cigarette smoking confers additional risk from influenza. This study assessed the effect of smoking on humoral immune response to influenza vaccine. Adults ≥50 y of age were enrolled during the 2011–2016 influenza vaccination seasons in an observational prospective study. Non-fasting whole blood samples for hemagglutination inhibition (HAI) assays were obtained from participants at pre- and 28 d post-clinically administered, trivalent influenza vaccination. Among 273 participants, 133 subjects self-reported as never smokers, 87 as ex-smokers, and 53 as current smokers. Postvaccination geometric mean HAI titers were significantly higher among smokers for A/H1N1 (p = .031) and A/H3N2 (p = .001). Relative to never smokers, smoking was independently related to seroconversion to A/H1N1, A/H3N2 and B. The adjusted odd ratios (ORs) were 5.2 [95% confidence interval (CI), 2.3, 11.5] for seroconversion to A/H1N1, 5.4 (95% CI, 2.4, 12.1) for A/H3N2, and 2.7 (95% CI, 1.3, 5.7) for B. Smoking was also independently related to seroprotection to A/H1N1, A/H3N2 and B. The ORs were 3.6 (95% CI, 1.6, 8.08) for seroprotection to A/H1N1 in smokers, 2.7 (95% CI, 1.14, 6.5) for A/H3N2, and 2.5 (95% CI, 1.1, 5.7) for B. Although the mechanism is unclear, smokers showed a better immune response to influenza vaccination than never smokers and ex-smokers. The results can be used to emphasize the value of influenza vaccination for smokers.
Vikram S. Pothuri, Graham D. Hogg, Leah Conant
et al.
Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy that is refractory to immune checkpoint inhibitor therapy. However, intratumoral T-cell infiltration correlates with improved overall survival (OS). Herein, we characterized the diversity and antigen specificity of the PDAC T-cell receptor (TCR) repertoire to identify novel immune-relevant biomarkers. Demographic, clinical, and TCR-beta sequencing data were collated from 353 patients across three cohorts that underwent surgical resection for PDAC. TCR diversity was calculated using Shannon Wiener index, Inverse Simpson index, and “True entropy.” Patients were clustered by shared repertoire specificity. TCRs predictive of OS were identified and their associated transcriptional states were characterized by single-cell RNAseq. In multivariate Cox regression models controlling for relevant covariates, high intratumoral TCR diversity predicted OS across multiple cohorts. Conversely, in peripheral blood, high abundance of T-cells, but not high diversity, predicted OS. Clustering patients based on TCR specificity revealed a subset of TCRs that predicts OS. Interestingly, these TCR sequences were more likely to encode CD8+ effector memory and CD4+ T-regulatory (Tregs) T-cells, all with the capacity to recognize beta islet-derived autoantigens. As opposed to T-cell abundance, intratumoral TCR diversity was predictive of OS in multiple PDAC cohorts, and a subset of TCRs enriched in high-diversity patients independently correlated with OS. These findings emphasize the importance of evaluating peripheral and intratumoral TCR repertoires as distinct and relevant biomarkers in PDAC.
Immunologic diseases. Allergy, Neoplasms. Tumors. Oncology. Including cancer and carcinogens
The advancement of computer-aided detection systems had a significant impact on clinical analysis and decision-making on human disease. Lung cancer requires more attention among the numerous diseases being examined because it affects both men and women, increasing the mortality rate. LeNet, a deep learning model, is used in this study to detect lung tumors. The studies were run on a publicly available dataset made up of CT image data (IQ-OTH/NCCD). Convolutional neural networks (CNNs) were employed in the experiment for feature extraction and classification. The proposed system was evaluated on Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases datasets the success percentage was calculated as 99.51%, sensitivity (93%) and specificity (95%), and better results were obtained compared to the existing methods. Development and validation of algorithms such as ours are important initial steps in the development of software suites that could be adopted in routine pathological practices and potentially help reduce the burden on pathologists.