Hokkaido Information University

Post-COVID-19 Vaccination Syndrome (PCVS) refers to persistent, multisystemic symptoms developing after SARS-CoV-2 immunization. We established a nationwide registry across 14 Japanese outpatient clinics and analyzed 179 “clinically definitive” cases from 279 enrollees (December 2020–August 2023). All adverse events (AEs) were coded using MedDRA/J Preferred Terms and System Organ Classes; severity followed NCI-CTCAE V5.0. The cohort (66.5% female; mean age 59 years) experienced 493 AEs (median 2 per patient, range 1–29). Three System Organ Classes—General Disorders (29.2%), Nervous System Disorders (22.3%), and Musculoskeletal Disorders (10.1%)—accounted for 61.7% of events; fatigue, brain fog, dizziness, and extremity pain predominated. While 69.4% of AEs arose within 90 days post-vaccination, 12.4% appeared ≥ 360 days later. Severe AEs (≥ Grade 3) occurred in 14.6% of patients; overall improvement was 65.1%, leaving 29.4% unresolved. A provisional phenotype classification combining symptom patterns, onset timing, and severity identified high-risk subgroups with > 60% non-recovery rates. These findings underscore the need for enhanced post-vaccination surveillance systems and comprehensive care frameworks specifically tailored to address the protean manifestations and persistent nature of PCVS.

Background/Objectives: With increasing life expectancy, interest in healthy aging has grown substantially. Dietary habits are among the key factors that contribute to achieving healthy aging. This study analyzes the relationship between dietary habits and the age-health association in older adults, using the first two years of data from an ongoing annual cohort study conducted in a region of Japan. Methods: We used observational data from approximately 1200 community-dwelling males and females aged 55 to 75 at baseline, drawing on the first two years of a ten-year annual cohort study conducted from 2023 to 2032. First, dietary habits were classified using an ordinal latent block model (OLBM), a model-based clustering approach applied to food frequency questionnaire (FFQ) data. We then examined whether the age-health gradient-measured across 33 indicators-differed significantly across the derived dietary habit types, using random effects models. Results: Dietary habits in the analyzed sample were categorized into six distinct types. Parameter estimates from the model suggest that the extracted patterns represent a continuum ranging from low to high dietary diversity. Regression analyses indicated that, in females, a negative association between age and LDL-C levels was observed among those with highly diverse dietary habits. Conclusions: The data-driven classification of dietary habits based on FFQ responses highlights the potential importance of dietary diversity.

Integrating Big Data, such as large-scale transcriptomic datasets across diseases, continues to be a major challenge. This is in part due to inconsistent preprocessing and the lack of a standardized, reproducible analytical framework. Existing pipelines often rely on manual parameter tuning and fragmented scripts, which limits cross-dataset comparability and downstream interpretability. We developed disint (disease integration and clustering toolkit), an open-source Python framework for standardized cross-dataset expression integration, embedding, and clustering. The pipeline implements housekeeping gene-based normalization, disease-specific log2 fold-change computation, automated Uniform Manifold Approximation and Projection hyperparameter optimization, and adaptive K-means clustering. Building on its outputs, we further implemented a prototype downstream module, disease reposition, which extracts disease-specific gene signatures, evaluates their shared components, and explores potential drug repositioning candidates. The framework was validated on 28 transcriptomic datasets encompassing 34 disease categories and 386 samples, including 255 patient and 131 healthy control samples, covering 194,182 genes in total. These results highlight the reproducibility, scalability, and translational versatility of our proposed framework.

Abstract Thermal infrared imaging by Longwave Infrared Camera (LIR) aboard JAXA's Venus orbiter Akatsuki has revealed horizontal structures of large‐scale topographic gravity waves (mountain waves) and thermal tides in the Venusian atmosphere. For quantitative analysis of these waves, we developed a radiative transfer model for an atmosphere perturbed by a gravity wave, which represents the mountain waves and the thermal tides in the equatorial region. Combining the infrared images with temperature profiles from the Akatsuki radio occultation, the cloud particle scale height, as well as the vertical wavelengths and growth rates of mountain waves and the semidiurnal thermal tide were determined. The cloud particle scale height around the cloud top was estimated to be approximately equal to the atmospheric scale height, indicating a vertically well‐mixed layer of the cloud particles near the cloud top in low latitudes. It was demonstrated that the brightness temperature amplitude observed by LIR is approximately half of the atmospheric temperature amplitude at the cloud top. The waves' impact on the mean flow above the cloud top was assessed by estimating their momentum deposition. The results show that mountain waves can induce an intense but localized deceleration of tens of m s −1 /day or larger above 80 km, while the semidiurnal tide provides a global and persistent deceleration that increases with height to reach the order of 1 m s −1 /day at 80 km. These results reveal two important but distinct mechanisms that contribute to the momentum budget of the Venusian upper atmosphere.

背景．近年，原発性非小細胞肺癌の周術期における免疫チェックポイント阻害薬（immune checkpoint inhibitor）の適応が広がっており，その1つに術前補助化学療法（neoadjuvant chemotherapy）としてニボルマブ＋プラチナ併用療法がある．症例．60歳，女性．原発性肺扁平上皮癌cT3N2M0 stage IIIB（遺伝子変異/転座陰性，programmed cell death ligand 1：80%）の診断となり術前化学免疫療法としてニボルマブ＋プラチナ併用療法を開始した．免疫関連有害事象（immune-related adverse events）のgrade 1の発熱が続き継続困難となり1コースのみの投与となったが，術前のCTで病変の縮小が得られ，その段階で右肺上葉切除＋気管支形成＋ND2a-2を施行した．切除検体の残存生存腫瘍は10%で著明な病理学的奏効を認めた．結論．術前化学免疫療法において免疫関連有害事象の発症に注意し，発症時は早期に診断・治療をした上で，腫瘍への治療効果を見極めながら状況に合わせて手術時期を逸しないことが重要と考える．

直接作用型経口抗凝固薬（DOAC）導入後の深部静脈血栓症（DVT）治療の現状と変化を検討するため，日本静脈学会会員を対象にアンケート調査を実施した．2024年6–8月に学会会員約1,343名へ案内し，103名から有効回答を得た．結果，内服治療の増加，外来治療へのシフト，服薬アドヒアランスの改善，および血栓消失転帰の向上が示唆され，従来のワルファリン中心の治療からDOACを用いた治療への転換が臨床現場における安全性と有効性の向上に寄与していることが明らかとなった．また，紹介元から抗凝固療法が実施されるケースの増加など，複数の診療科で抗凝固療法が行われるようになった点から，DVT治療における診療体制の多様化と連携強化が進んでいることが示唆された．そのうえで，抗凝固療法以外の治療オプションの啓蒙など，新たな課題も考えられた．本調査は，DOAC導入後の治療現場の実態を示すとともに，今後の治療方針の議論の一助となることが期待される．

In this short communication, we reconceptualize autonomy in medical education, particularly within the context of professional identity formation (PIF). Traditionally, autonomy has been framed as independence and achievement based on ‘fixed norms’. However, identity is also shaped through ‘relational responsiveness’, which emphasizes mutual engagement and co-construction. Drawing on Asian cultural understandings of selfhood, we propose viewing autonomy as integrity enacted within a network of mutual responsibility. We argue that both fixed norms and relational responsiveness are essential, and we introduce a preliminary framework that highlights their complementary roles. While responsiveness fosters adaptability and connectedness, it can lead to identity diffusion if not grounded in internal standards. A blended model of both modes offers a more inclusive, culturally sensitive, and resilient approach to PIF in today’s complex medical landscape.

Abstract The spectrum of school absenteeism, futoko in Japanese, encompasses various behaviors, from psychological school avoidance motivation to the initiation and persistence of absentee behaviors. This study determined if the progression from ideation to action in absenteeism is attributable to the intensification of ideation (i.e., the spectrum hypothesis) or the lowering of the behavioral hurdle that cannot be explained solely by ideation (i.e., the actualization hurdle hypothesis). Retrospective data from 156 students who had experienced futoko were used to examine their school avoidance motivation and absentee behavior at four time points before the onset of futoko. The analysis delineated a trajectory wherein students, 2 years before ceasing attendance, showed adaptation comparable to that of their attending peers, followed by progression into futoko, marked by an escalation in avoidance motivation over the following 2 years. This study found no substantial reduction in behavioral actualization hurdles, indicating that the emergence of such behaviors is linked to escalating school avoidance motivation. These findings underscore the importance of monitoring changes in ideation, specifically school avoidance motivation, to prevent absenteeism.

ObjectiveEndovascular aneurysm repair (EVAR) has become a preferred method for treating abdominal aortic aneurysms (AAA) due to its minimally invasive approach. However, identifying factors that influence long-term patient outcomes is crucial for improving prognosis. This study investigates whether machine learning (ML)-based decision tree analysis (DTA) can predict long-term survival (over 5 years postoperatively) by uncovering complex patterns in patient data.MethodsWe retrospectively analyzed data from 142 patients who underwent elective EVAR for AAA at Tokyo Medical University Hospital between October 2013 and July 2018. The dataset comprised 24 variables, including age, gender, nutritional status, comorbidities, and surgical details. The decision tree classifier was developed and validated using Python 3.7 and the scikit-learn toolkit.ResultsDTA identified poor nutritional status as the most significant predictor, followed by compromised immunity, active cancer, octogenarians, chronic kidney disease, and chronic obstructive pulmonary disease. The decision tree identified 9 terminal nodes with probabilities of long-term survival. Four of these terminal nodes represented groups of patients with a high probability of long-term survival: 100%, 84%, 77%, and 60%, whereas the other 5 terminal nodes represented groups of patients with a low probability of long-term survival: 17%, 25%, 30%, 45%, and 47%. The model achieved a moderately high accuracy of 76.1%, specificity of 72.4%, sensitivity of 81.8%, precision of 65.2%, and area under the receiver operating characteristic curve of 0.84.ConclusionML-based DTA effectively predicts long-term survival after EVAR, highlighting the importance of comprehensive preoperative assessments and personalized management strategies to improve patient outcomes.

Objectives: Thoracic endovascular aneurysm repair (TEVAR) has revolutionized the treatment of thoracic aortic aneurysms (TAA) by providing a less invasive alternative to open surgery. This study aims to identify risk factors for early mortality following TEVAR for degenerative TAA using a machine learning-based decision tree analysis (DTA). Methods: This retrospective observational study analyzed 79 patients who underwent elective TEVAR to identify predictors of early mortality (within 2 years) using decision tree analysis. The dataset included 36 variables, covering age, sex, nutritional status, comorbidities, inflammation, immune status, and surgical details. The decision tree classifier was developed and validated using Python 3.7 with the scikit-learn toolkit. Results: DTA identified octogenarian status as the strongest predictor of early mortality, followed by poor nutritional status, debranching procedures, and compromised immunity. The model identified 7 terminal nodes, with early mortality risk ranging from 0% to 77.7%. It demonstrated moderate accuracy (65.8%) and high sensitivity (81.0%) but had relatively low specificity (60.3%), effectively identifying high-risk patients. Conclusions: Machine learning-based DTA identified key predictors of early mortality following TEVAR, including octogenarian status, poor nutritional status, compromised immunity, and debranching procedures. The model provides an interpretable risk stratification tool, but its clinical applicability requires further validation.