Exploring the Role of Platelets in Virus-Induced Inflammatory Demyelinating Disease and Myocarditis.

Theiler's murine encephalomyelitis virus (TMEV) infection has been used as a mouse model for two virus-induced organ-specific immune-mediated diseases. TMEV-induced demyelinating disease (TMEV-IDD) in the central nervous system (CNS) is a chronic inflammatory disease with viral persistence and an animal model of multiple sclerosis (MS) in humans. TMEV infection can also cause acute myocarditis with viral replication and immune cell infiltration in the heart, leading to cardiac fibrosis. Since platelets have been reported to modulate immune responses, we aimed to determine the role of platelets in TMEV infection. In transcriptome analyses of platelets, distinct sets of immune-related genes, including major histocompatibility complex (MHC) class I, were up- or downregulated in TMEV-infected mice at different time points. We depleted platelets from TMEV-infected mice by injecting them with platelet-specific antibodies. The platelet-depleted mice had significantly fewer viral antigen-positive cells in the CNS. Platelet depletion reduced the severities of TMEV-IDD and myocarditis, although the pathology scores did not reach statistical significance. Immunologically, the platelet-depleted mice had an increase in interferon (IFN)-γ production with a higher anti-TMEV IgG2a/IgG1 ratio. Thus, platelets may play roles in TMEV infection, such as gene expression, viral clearance, and anti-viral antibody isotype responses.

Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning.

PURPOSE: To develop deep learning models using thoracoscopic images to identify visceral pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if these models can be applied clinically. METHODS: Two deep learning models, one based on a convolutional neural network (CNN) and the other based on a vision transformer (ViT), were applied and trained via 463 images (VPI negative: 269 images, VPI positive: 194 images) captured from surgical videos of 81 patients. Model performances were validated via an independent test dataset containing 46 images (VPI negative: 28 images, VPI positive: 18 images) from 46 test patients. RESULTS: The areas under the receiver operating characteristic curves of the CNN-based and ViT-based models were 0.77 and 0.84 (p = 0.304), respectively. The accuracy, sensitivity, specificity, and positive and negative predictive values were 73.91, 83.33, 67.86, 62.50, and 86.36% for the CNN-based model and 78.26, 77.78, 78.57, 70.00, and 84.62% for the ViT-based model, respectively. These models' diagnostic abilities were comparable to those of board-certified thoracic surgeons and tended to be superior to those of non-board-certified thoracic surgeons. CONCLUSION: The deep learning model systems can be utilized in clinical applications via data expansion.

Anti-Glycolipid Antibody Examination in Five EAE Models and Theiler's Virus Model of Multiple Sclerosis: Detection of Anti-GM1, GM3, GM4, and Sulfatide Antibodies in Relapsing-Remitting EAE.

Anti-glycolipid antibodies have been reported to play pathogenic roles in peripheral inflammatory neuropathies, such as Guillain-Barré syndrome. On the other hand, the role in multiple sclerosis (MS), inflammatory demyelinating disease in the central nervous system (CNS), is largely unknown, although the presence of anti-glycolipid antibodies was reported to differ among MS patients with relapsing-remitting (RR), primary progressive (PP), and secondary progressive (SP) disease courses. We investigated whether the induction of anti-glycolipid antibodies could differ among experimental MS models with distinct clinical courses, depending on induction methods. Using three mouse strains, SJL/J, C57BL/6, and A.SW mice, we induced five distinct experimental autoimmune encephalomyelitis (EAE) models with myelin oligodendrocyte glycoprotein (MOG)35-55, MOG92-106, or myelin proteolipid protein (PLP)139-151, with or without an additional adjuvant curdlan injection. We also induced a viral model of MS, using Theiler's murine encephalomyelitis virus (TMEV). Each MS model had an RR, SP, PP, hyperacute, or chronic clinical course. Using the sera from the MS models, we quantified antibodies against 11 glycolipids: GM1, GM2, GM3, GM4, GD3, galactocerebroside, GD1a, GD1b, GT1b, GQ1b, and sulfatide. Among the MS models, we detected significant increases in four anti-glycolipid antibodies, GM1, GM3, GM4, and sulfatide, in PLP139-151-induced EAE with an RR disease course. We also tested cellular immune responses to the glycolipids and found CD1d-independent lymphoproliferative responses only to sulfatide with decreased interleukin (IL)-10 production. Although these results implied that anti-glycolipid antibodies might play a role in remissions or relapses in RR-EAE, their functional roles need to be determined by mechanistic experiments, such as injections of monoclonal anti-glycolipid antibodies.

Adjuvant Injections Altered the Ileal and Fecal Microbiota Differently with Changes in Immunoglobulin Isotypes and Antimycobacterial Antibody Responses.

Alterations in the gut microbiota, "dysbiosis," have been reported in autoimmune diseases, including multiple sclerosis (MS), and their animal models. Although the animal models were induced by injections of autoantigens with adjuvants, including complete Freund's adjuvant (CFA) and pertussis toxin (PT), the effects of adjuvant injections on the microbiota are largely unknown. We aimed to clarify whether adjuvant injections could affect the microbiota in the ileum and feces. Using 16S rRNA sequencing, we found decreased alpha diversities of the gut microbiota in mice injected with CFA and PT, compared with naïve mice. Overall, microbial profiles visualized by principal component analysis demonstrated dysbiosis in feces, but not in the ileum, of adjuvant-injected mice, where the genera Lachnospiraceae NK4A136 group and Alistipes contributed to dysbiosis. When we compared the relative abundances of individual bacteria, we found changes in 16 bacterial genera in feces and seven genera in the ileum of adjuvant-injected mice, in which increased serum levels of antibody against mycobacteria (a component of CFA) and total IgG2c were correlated with the genus Facklamia. On the other hand, increased IgG1 and IgA concentrations were correlated with the genus Atopostipes. Therefore, adjuvant injections alone could alter the overall microbial profiles (i.e., microbiota) and individual bacterial abundances with altered antibody responses; dysbiosis in animal models could be partly due to adjuvant injections.

Bacterial and fungal isolation from face masks under the COVID-19 pandemic.

The COVID-19 pandemic has led people to wear face masks daily in public. Although the effectiveness of face masks against viral transmission has been extensively studied, there have been few reports on potential hygiene issues due to bacteria and fungi attached to the face masks. We aimed to (1) quantify and identify the bacteria and fungi attaching to the masks, and (2) investigate whether the mask-attached microbes could be associated with the types and usage of the masks and individual lifestyles. We surveyed 109 volunteers on their mask usage and lifestyles, and cultured bacteria and fungi from either the face-side or outer-side of their masks. The bacterial colony numbers were greater on the face-side than the outer-side; the fungal colony numbers were fewer on the face-side than the outer-side. A longer mask usage significantly increased the fungal colony numbers but not the bacterial colony numbers. Although most identified microbes were non-pathogenic in humans; Staphylococcus epidermidis, Staphylococcus aureus, and Cladosporium, we found several pathogenic microbes; Bacillus cereus, Staphylococcus saprophyticus, Aspergillus, and Microsporum. We also found no associations of mask-attached microbes with the transportation methods or gargling. We propose that immunocompromised people should avoid repeated use of masks to prevent microbial infection.

TG/HDL-C ratio as a predictor of stroke in the population with healthy BMI: The Jichi Medical School Cohort Study.

BACKGROUND AND AIMS: The triglycerides-to-high-density lipoprotein cholesterol ratio (TG/HDL-C) is a predictor of metabolic syndrome and cardiovascular disease onset. However, the relationship between TG/HDL-C and stroke has not been established. This study examined whether TG/HDL-C helps in predicting stroke onset; this was compared between the whole population and healthy body mass index (BMI) population. METHODS AND RESULTS: The Jichi Medical School Cohort Study is a prospective cohort study involving baseline data collected in 12 Japanese districts between April 1992 and July 1995. We used data from 11,699 participants; participants with a healthy BMI (20.0-24.9 kg/m2) were grouped into sex-specific TG/HDL-C quartiles. Using the first quartile groups as references, the hazard ratios (HRs) and 95% confidence intervals (CIs) of the Cox proportional hazards model were calculated. During the mean 10.8 years of follow-up, 419 new stroke events were recorded. The multivariable-adjusted HRs (95% CIs) in the fourth quartile of the whole population were 1.28 (0.94-1.75), 1.78 (0.91-3.48), 1.20 (0.82-1.77), and 1.13 (0.50-2.54), as compared to those in the fourth quartile of the healthy BMI population, which were 1.87 (1.24-2.83), 3.06 (1.21-7.74), 1.79 (1.05-3.05), and 1.29 (0.49-3.41) for all patients with all stroke, intracerebral hemorrhage, cerebral infarction, and subarachnoid hemorrhage, respectively. CONCLUSION: Increased TG/HDL-C correlated with a significant increase in stroke risk only in the healthy BMI population and not the whole population. Furthermore, it was primarily associated with increased intracerebral hemorrhage and cerebral infarction risk.

Corrigendum: Curcumin ß-D-Glucuronide Modulates an Autoimmune Model of Multiple Sclerosis with Altered Gut Microbiota in the Ileum and Feces.

[This corrects the article DOI: 10.3389/fcimb.2021.772962.].

Curdlan, a Microbial ß-Glucan, Has Contrasting Effects on Autoimmune and Viral Models of Multiple Sclerosis.

Multiple sclerosis (MS) is an immune-mediated disease characterized by inflammatory demyelination and axonal degeneration in the central nervous system (CNS). Bacterial and fungal infections have been associated with the development of MS; microbial components that are present in several microbes could contribute to MS pathogenesis. Among such components, curdlan is a microbial 1,3-ß-glucan that can stimulate dendritic cells, and enhances T helper (Th) 17 responses. We determined whether curdlan administration could affect two animal models for MS: an autoimmune model, experimental autoimmune encephalomyelitis (EAE), and a viral model, Theiler's murine encephalomyelitis virus (TMEV)-induced demyelinating disease (TMEV-IDD). We induced relapsing-remitting EAE by sensitizing SJL/J mice with the myelin proteolipid protein (PLP)139-151 peptide and found that curdlan treatment prior to PLP sensitization converted the clinical course of EAE into hyperacute EAE, in which the mice developed a progressive motor paralysis and died within 2 weeks. Curdlan-treated EAE mice had massive infiltration of T cells and neutrophils in the CNS with higher levels of Th17 and Th1 responses, compared with the control EAE mice. On the other hand, in TMEV-IDD, we found that curdlan treatment reduced the clinical scores and axonal degeneration without changes in inflammation or viral persistence in the CNS. In summary, although curdlan administration exacerbated the autoimmune MS model by enhancing inflammatory demyelination, it suppressed the viral MS model with reduced axonal degeneration. Therefore, microbial infections may play contrasting roles in MS depending on its etiology: autoimmunity versus viral infection.

Hemoglobin Concentration and the Incidence of Stroke in the General Japanese Population: The Jichi Medical School Cohort Study.

BACKGROUND: Several studies have described an association between hemoglobin concentration and stroke; however, the influence of hemoglobin on stroke incidence has not been fully revealed. Our objective was to elucidate the association between hemoglobin concentration and stroke incidence in Japanese community residents. METHODS: In the present study, we collected the data of 12,490 subjects who were enrolled between April 1992 and July 1995 in the Jichi Medical School (JMS) Cohort Study. We excluded the subjects with a history of stroke. Hemoglobin concentrations were grouped in quartiles, and quartile 2 (Q2) was used as the reference category. A Cox proportional-hazards model was used to examine hazard ratios (HRs) and the stroke incidence rates with 95% confidence intervals (CIs). RESULTS: During 10.8 years of follow-up, 409 participants (212 men and 197 women) experienced a new stroke, including 97 intracerebral hemorrhages, 259 cerebral infarctions, and 52 subarachnoid hemorrhages (SAH). In sex-specific hemoglobin quartiles, the multivariate-adjusted HR was statistically significantly higher in Q1 than in Q2, and a relationship similar to a J shape was observed between all strokes (HR in Q2 vs Q1, 1.36; 95% CI, 1.02-1.83; Q3, 1.20; 95% CI, 0.87-1.64; and Q4, 1.16; 95% CI, 0.84-1.60). Furthermore, the analysis of stroke subtypes showed a statistically significantly higher multivariate-adjusted HR in Q1 than in Q2 for SAH (HR 2.61; 95% CI, 1.08-6.27). CONCLUSIONS: A low hemoglobin concentration was associated with an increased risk of stroke, which was strongly influenced by the incidence of SAH.

Stroke Risk Due to Smoking Characterized by Sex Differences in Japan: The Jichi Medical School Cohort Study.

OBJECTIVES: Smoking is a risk factor for stroke. The relationship between smoking and the risk of different subtypes of stroke has not been fully elucidated. We investigated the relationship between smoking and the incidence of stroke in the Japanese population. MATERIALS AND METHODS: This prospective, population-based cohort study included 11,324 participants (4447 men; 6877 women) from 12 districts in Japan, between April 1992 and July 1995. Participants were stratified according to smoking status (non-smoker [never smoked]/ex-smoker/current smoker). Male current smokers were further stratified according to the number of cigarettes smoked per day (1-14, 15-29, or ≥ 30). The non-smoking group was used as a reference. Cox proportional hazards analysis was used to determine the risk of stroke due to smoking. RESULTS: Four hundred and seventeen new stroke events (212 men; 205 women) were recorded during a mean follow-up of 10.7 years, including 95 intracerebral hemorrhages (48 men; 47 women), 267 cerebral infarctions (152 men; 115 women), and 54 subarachnoid hemorrhages (12 men; 42 women). In multivariable analysis, the hazard ratios (95% confidence intervals) for male current smokers (≥ 30 cigarettes/day) were 1.89 (1.08-3.31) and 3.41 (1.22-9.57) for all strokes and intracerebral hemorrhages, respectively; those for female current smokers were 2.78 (1.62-4.74), 3.14 (1.51-6.54), and 4.03 (1.64-9.93) for all strokes, cerebral infarctions, and subarachnoid hemorrhages, respectively. CONCLUSIONS: Smoking ≥ 30 cigarettes/day is a risk factor for stroke, especially intracerebral hemorrhage in men. Furthermore, smoking increases the risk of cerebral infarction and subarachnoid hemorrhage in women.