Asthma is traditionally regarded as a chronic airway disease, and recent literature proves its heterogeneity, based on distinctive clusters or phenotypes of asthma. In defining such asthma clusters, the nature of comorbidity among patients with asthma is poorly understood, by assuming no causal relationship between asthma and other comorbid conditions, including both communicable and noncommunicable diseases. However, emerging evidence suggests that the status of asthma significantly affects the increased susceptibility of the patient to both communicable and noncommunicable diseases. Specifically, the impact of asthma on susceptibility to noncommunicable diseases such as chronic systemic inflammatory diseases (e.g., rheumatoid arthritis), may provide an important insight into asthma as a disease with systemic inflammatory features, a conceptual understanding between asthma and asthma-related comorbidity, and the potential implications on the therapeutic and preventive interventions for patients with asthma. This review discusses the currently under-recognized clinical and immunological phenotypes of asthma; specifically, a higher risk of developing a systemic inflammatory disease such as rheumatoid arthritis and their implications, on the conceptual understanding and management of asthma. Our discussion is divided into three parts: literature summary on the relationship between asthma and the risk of rheumatoid arthritis; potential mechanisms underlying the association; and implications on asthma management and research.
Arthritis, Rheumatoid* ; Asthma* ; Comorbidity ; Epidemiology ; Genetic Heterogeneity ; Humans ; Phenotype* ; Population Characteristics*

BACKGROUND/AIMS: Despite the potent suppression of the hepatitis B virus with modern antiviral agents, only a minority of HBeAg-positive patients achieve hepatitis B e antigen seroconversion. We aimed to explore the potential efficacy of combination therapy consisting of pegylated interferon (p-IFN) and an oral antiviral agent in patients with HBeAg-positive chronic hepatitis B. METHODS: The treatment protocol consisted of p-IFN-α-2a at 180 μg/wk for 48 weeks, with either entecavir or tenofovir added 8 weeks after the initiation of p-IFN and continued for at least 6 months after HBe seroconversion was achieved. RESULTS: To date, 10 patients have been treated under the protocol (eight adults, mean age 36±8 years; two adolescents, aged 12 and 16 years). All eight adult patients experienced loss of HBeAg at a mean of 72.3±66.9 weeks, including six patients who also developed anti-HBe and one patient who had HBs seroconversion. Although both adolescents remain on therapy, one adolescent had HBs seroconversion without HBe seroconversion. A total of nine of our 10 patients experienced a favorable serological transition. CONCLUSIONS: The combination of p-IFN and a modern oral antiviral agent may be more effective than monotherapy with either class of agent in the treatment of HBeAg-positive chronic hepatitis B patients.
Adolescent ; Adult ; Antiviral Agents ; Clinical Protocols ; Hepatitis B ; Hepatitis B e Antigens ; Hepatitis B virus ; Hepatitis B, Chronic* ; Hepatitis, Chronic* ; Humans ; Interferons ; Seroconversion ; Tenofovir

The development of total biliary casts is very unusual, and especially in patients who have not undergone liver transplantation. There are only a few reports of total biliary casts in non-liver transplantation patients who have antiphospholipid antibody syndrome, B-cell non-Hodgkin's lymphoma, cholecystectomy or allogenic hematopoietic stem cell transplantation. Here we present the case of a previously well 77-year-old man who developed a total biliary casts without any risk factors and there was no obvious liver insult. The casts were managed endoscopically.
Aged ; Antiphospholipid Syndrome ; B-Lymphocytes ; Cholangiopancreatography, Endoscopic Retrograde ; Cholecystectomy ; Hematopoietic Stem Cell Transplantation ; Humans ; Liver ; Liver Transplantation ; Lymphoma, Non-Hodgkin ; Risk Factors ; Superior Mesenteric Artery Syndrome ; Transplants

Background: and Purpose Accurate classification of ischemic stroke subtype is important for effective secondary prevention of stroke. We used diffusion-weighted image (DWI) and atrial fibrillation (AF) data to train a deep learning algorithm to classify stroke subtype. Methods: Model development was done in 2,988 patients with ischemic stroke from three centers by using U-net for infarct segmentation and EfficientNetV2 for subtype classification. Experienced neurologists (n=5) determined subtypes for external test datasets, while establishing a consensus for clinical trial datasets. Automatically segmented infarcts were fed into the model (DWI-only algorithm). Subsequently, another model was trained, with AF included as a categorical variable (DWI+AF algorithm). These models were tested: (1) internally against the opinion of the labeling experts, (2) against fresh external DWI data, and (3) against clinical trial dataset. Results: In the training-and-validation datasets, the mean (±standard deviation) age was 68.0±12.5 (61.1% male). In internal testing, compared with the experts, the DWI-only and the DWI+AF algorithms respectively achieved moderate (65.3%) and near-strong (79.1%) agreement. In external testing, both algorithms again showed good agreements (59.3%–60.7% and 73.7%–74.0%, respectively). In the clinical trial dataset, compared with the expert consensus, percentage agreements and Cohen’s kappa were respectively 58.1% and 0.34 for the DWI-only vs. 72.9% and 0.57 for the DWI+AF algorithms. The corresponding values between experts were comparable (76.0% and 0.61) to the DWI+AF algorithm. Conclusion Our model trained on a large dataset of DWI (both with or without AF information) was able to classify ischemic stroke subtypes comparable to a consensus of stroke experts.