JMA journal最新文献

On December 2, 2023, Japan's Ministry of Health, Labour and Welfare (MHLW) announced an ordinance regulating the possession, consumption, and distribution of hexahydrocannabihexol (HHCH) except for medical purposes. HHCH, a synthetic cannabinoid, has been linked to central nervous system symptoms, including nausea, dizziness, and numbness, presumably due to its structural similarity to tetrahydrocannabinol. This regulatory action reflects Japan's historical drug regulation approach, which has evolved to address synthetic substances not covered by earlier laws. The emergence of new psychoactive substances has led to increased poisoning cases and necessitated Japan to introduce a generic scheduling system and collectively regulate these compounds. Despite the reduction in designer drug-related arrests following system implementation, recent trends have shown a resurgence in arrests, partly because of the increased online accessibility of these substances. The persistence of HHCH gummy manufacturers highlights the limitations of current regulations. Thus, enhancing health literacy and social responsibility among consumers and proactive measures by healthcare professionals are essential to mitigate the public health risks associated with these emerging substances. Regulatory frameworks should prioritize public health over economic benefits.

Introduction: Obstetric hemorrhage is a leading cause of pregnancy-related mortality. Our hospital protocol states that patients with obstetric hemorrhage undergo initial imaging with contrast-enhanced dynamic computed tomography (CE-dCT) to ascertain the presence and location of active bleeding, followed by tailored therapeutic interventions. Herein, we aimed to elucidate the prevailing status and clinical outcomes of obstetric hemorrhage cases at our institution, which are characterized by a distinctive, methodical treatment approach.

Methods: This retrospective observational study included 150 patients with obstetric hemorrhage. Clinical information, including bleeding volume, hemorrhage etiology, therapeutic intervention, transfusion quantity, patient outcome, and CE-dCT findings, were explored.

Results: The leading cause of obstetric hemorrhage was atonic bleeding (55%), followed by vaginal hematoma (13%) and retained placenta (11%). The median amount of bleeding was 2,803 mL, and the median volume of red blood cells (RBC) and fresh frozen plasma (FFP) required was 6 units. Blood loss and transfusion volume were similar regardless of the cause of obstetric hemorrhage. Conservative management, such as uterotonics or balloon tamponade, achieved hemostasis in 57% of patients, whereas 43% required invasive interventions, such as transcatheter arterial embolization. CE-dCT was performed on 85% of patients, and extravasation was detected in 53%. Moreover, "PRACE," characterized by Postpartum hemorrhage, Resistance to treatment, and Arterial Contrast Extravasation on CE-dCT scans, potentially requires massive blood transfusions and invasive treatment.

Conclusions: Although obstetric hemorrhage encompasses a diverse array of pathologies, medical practitioners must recognize that approximately 3,000 mL of blood is lost and at least 6 units of RBC and FFP are required, irrespective of the cause. CE-dCT plays a pivotal role in elucidating the etiology of obstetric hemorrhage and guiding therapeutic interventions.

Down syndrome (DS) is a risk factor for severe food protein-induced enterocolitis syndrome (FPIES), with DS patients tending to have multiple-food FPIES. This is the first case where a DS infant with a history of severe chronic FPIES to milk and soy could effectively be introduced with some untested high-risk foods through hospital-based oral food challenges (OFCs). The infant is a 20-month-old girl with DS, who was diagnosed with milk- and soy-induced FPIES. Considering her history of intensive care unit care for severe FPIES reactions, we considered that introducing other high-risk foods, such as wheat and hen's egg (white and yolk), at home was not appropriate for her. We offered hospital-based OFCs effectively and safely by introducing wheat and hen's egg as high-risk foods against FPIES to the 20-month-old infant. As a result, she tolerated soy-based seasoning, wheat, and egg whites without any symptoms, but she developed frequent vomiting after ingesting egg yolk. We did a prompt intervention with intravenous fluid replacement to prevent severe adverse conditions. After discharge, she exhibited an FPIES symptom as a consequence of ingesting green peas and miso; hence, we recommended the elimination of peas, in addition to soy, milk, and egg yolk, from her diet. She remained symptom-free since adhering to this dietary regimen. In severe FPIES children, it is encouraged to introduce unconsumed high-risk foods in the hospital safely to avoid severe reactions at home and prevent unnecessary food eliminations.

Introduction: Despite a dramatic increase in the incidence of mild-cognitive impairment (MCI) and early dementia, accessible and engaging screening methods for older adults are lacking. Gamification has gained attention in the self-management of various health conditions, making it a promising avenue for dementia screening. This study aimed to evaluate a gamified mobile application for the early detection of cognitive impairment associated with dementia.

Methods: The gamified app and the Mini-Mental State Examination (MMSE) were administered to 138 participants. The game, based on the N-back working memory task, simulates a restaurant scenario where players cook curries with hidden ingredients to fulfill customer orders, with the difficulty increasing in each round. The correlations between MMSE scores and game metrics were analyzed, and the game metrics were compared between the normal and impaired groups.

Results: Among the 138 older adult participants, the game metrics such as level reached, accuracy, response times, tap times, and swipe times exhibited significant correlations with scores on the MMSE, a standard cognitive screening tool (r = 0.42, 0.419, -0.575, -0.484, and -0.667, respectively; P < 0.05 for all). The participants were divided into the normal (≥28) and impaired (<28) groups based on the MMSE cutoff values. The impaired group had significantly worse performance on all game metrics. After multivariate adjustment, average swipe time emerged as the strongest predictor, achieving 70.8% sensitivity and 80.6% specificity in detecting impairment using a 3.31-s cutoff (area under the curve = 0.820).

Conclusions: This classification accuracy was comparable to standard dementia screening tests. These results indicate the potential use of gamification with joyous experience for older adults to enable scalable cognitive screening beyond conventional testing paradigms.

In recent years, every aspect of the society has rapidly transformed because of the emergence of artificial intelligence (AI) technologies. AI excels not only in image and voice recognition and analysis but also in achieving near-natural conversations through the development of large language models. These technological innovations are steadily being integrated into healthcare settings and can significantly change the way physicians work in clinics in the near future. Patient interviews will predominantly be performed by AI. Physicians will discuss the findings of traditional tests like electrocardiograms and chest X-rays with AI, providing beyond-human interpretation. Additionally, AI is changing areas that have seen little development for a long time, such as auscultation and phonocardiography, and the recognition and quantification of previously challenging observations like the gait analysis. Although barriers to real-world implementation exist, in the near future, a majority of physicians will collaborate with AIs supporting various aspects of clinical practice, consequently enabling more accurate and appropriate diagnosis and treatment of cardiovascular diseases, including ischemic and valvular heart diseases, arrhythmias, and heart failure. This review focuses on AI application in the field of cardiology, specifically on how it can improve the workflow in clinical settings. We examine various examples of AI integration in cardiology to demonstrate how these technologies can lead to more accurate and efficient patient care. Understanding the advancements in AI can lead to more appropriate and streamlined medical practices, which will ultimately benefit both healthcare providers and patients.

CD4⁺ T cells, the so-called T helper cells, are one of the main players in the human immune system, which can regulate acquired immunity. Dysfunction of the acquired immune system induces various chronic inflammatory diseases such as malignancies and autoimmune diseases. IgG4-related disease (IgG4-RD) is also a chronic inflammatory disease that is characterized by elevated serum IgG4 concentration and infiltration of IgG4-positive plasma cells in affected tissues. Despite that remarkable advances in understanding the pathogenesis of IgG4-RD have been on the rise, the detailed mechanisms by which IgG4-RD develops are still unknown. In fact, CD4⁺ T cells abundantly infiltrate at lesions of IgG4-RD, and they are also associated with the pathogenesis of other refractory chronic inflammatory diseases. Therefore, our focus was on CD4⁺ T cells, and we previously reported the roles of their subsets including regulatory T cells, CD4 cytotoxic T lymphocytes, T follicular helper (Tfh) cells, T follicular regulatory cells, and T peripheral helper (Tph) cells in IgG4-RD. Among the subsets, Tph cells play an important role in generating ectopic lymphoid structures at inflammatory sites. Moreover, we found that circulating Tph cells are increased in IgG4-RD patients. Unlike Tfh cells, Tph cells express high levels of chemokine receptors and cytotoxic molecules. Thus, they can infiltrate affected tissues and exert a cytotoxic function. Additionally, our latest observations demonstrated that Tph cells interact with extrafollicular B cells in affected tissues. Hence, Tph cells may collaborate with a specific B-cell subset, and they play a role in the maintenance of persistent fibroinflammation in lesions of IgG4-RD. Tph cells may have an important role to play in the pathogenesis of not only IgG4-RD but also other chronic inflammatory diseases. This review summarizes and discusses the possible pathologic roles of CD4⁺ T cell subsets including Tph cells in IgG4-RD.

Ultrasound (US) imaging is a widely used tool in oncology because of its noninvasiveness and real-time performance. However, its diagnostic accuracy can be limited by the skills of the examiner when performing manual scanning and by the presence of acoustic shadows that degrade image quality. Artificial intelligence (AI) technologies can support examiners in cancer screening and diagnosis by addressing these limitations. Here, we examine recent advances in AI research and development for US imaging in oncology. Breast cancer has been the most extensively studied cancer, with research predominantly focusing on tumor detection, differentiation between benign and malignant lesions, and prediction of lymph node metastasis. The American College of Radiology developed a medical imaging reporting and data system for various cancers that is often used to evaluate the accuracy of AI models. We will also explore the application of AI in clinical settings for US imaging in oncology. Despite progress, the number of approved AI-equipped software as medical devices for US imaging remains limited in Japan, the United States, and Europe. Practical issues that need to be addressed for clinical application include domain shifts, black boxes, and acoustic shadows. To address these issues, advances in image quality control, AI explainability, and preprocessing of acoustic shadows are essential.

Introduction: This study aimed to evaluate the characteristics of private long-term care (LTC) service users provided by a company independent from public LTC insurance (LTCI) and to analyze the usage patterns across different types of services.

Methods: We utilized data from 8,046 consultations from the administration data of a private LTC service in Suginami Ward, Tokyo, Japan. We focused on older adults enrolled from February 2016 to October 2019 with follow-up until June 2020. The descriptions included users' demographics, LTCI-certified care levels, living situations, and reasons for choosing private LTC services. Furthermore, we examined the frequencies and minutes of each type of service used, such as shopping, meal, cleaning, outing, and social participation assistance, stratified by solitary living and LTCI certification.

Results: The study included 51 older adults, including 35 (69%) women, 28 (55%) solitary living individuals, 23 (45%) public LTCI-certified individuals, and 45 (88%) participants residing in detached houses. The primary motive for private service use was the absence of informal caregiving in 55% of the participants. Cleaning assistance was the most frequently used. Solitary living residents used various types of assistance, not only cleaning, and LTCI-certified individuals more frequently used meal and outing assistance than those without LTCI certification.

Conclusions: These findings indicate that older adults using private LTC services predominantly lived alone, lived in detached houses, or had no informal care support. Our findings provide an opportunity to examine the appropriateness of the complementary relationship between public and private LTC services.

Artificial intelligence (AI), empowered by advances in deep learning technology, has demonstrated its capabilities in the medical field to automate tedious tasks that are otherwise performed by humans or to detect or predict diseases with higher accuracy compared with experts. Given the ability to take complex multidimensional data as input, AI models have primarily been applied to complex medical imaging and time-series data. Another prominent strength of AI applications is its large scalability. The field of cardiovascular medicine uses various noninvasive and accessible metrics that produce a large amount of complex multidimensional data, such as electrocardiograms (ECGs) and echocardiograms. AI models can increase the utility of such modalities. Simple automation of conventional tasks using AI models provides significant opportunities for cost reduction and capacity expansion. The ability to improve disease detection or prediction at scale may provide novel opportunities for disease screening, enabling early intervention in asymptomatic patients. For example, AI-enabled pipelines can accurately identify cardiomyopathies and congenital heart diseases from a single ECG or echocardiogram recording. The detection of these diseases using the conventional approach usually requires complicated diagnostic strategies or expensive tests. Therefore, underdiagnosis is a huge problem. Using AI models to screen these diseases will provide opportunities for reducing missed cases. The utility of AI models in the medical field is not limited to the development of clinically useful models. Recent research has shown the promise of AI models in mechanism research by combining them with genetic and structural analyses. In this review, we provide an update on the current achievements of the innovative AI application for ECG and echocardiogram and provide insights into the future direction of AI in cardiovascular care and research settings.

Chronic kidney disease (CKD) is a complex disease that is related not only to dialysis but also to the onset of cardiovascular disease and life prognosis. As renal function declines with age and depending on lifestyle, the number of patients with CKD is rapidly increasing in Japan. Accurate prognosis prediction for patients with CKD in clinical settings is important for selecting treatment methods and screening patients with high-risk. In recent years, big databases on CKD and dialysis have been constructed through the use of data science technology, and the pathology of CKD is being elucidated. Therefore, we developed an artificial intelligence (AI) system that can accurately predict the prognosis of CKD such as its progression, the timing of dialysis introduction, and death. Aiming for its social implementation, the prognosis prediction system developed for patients with CKD was released on the website. We then developed a clinical practice guideline creation support system called Doctor K as an AI system. When creating clinical practice guidelines, huge amounts of manpower and time are required to conduct a systematic review of thousands of papers. Therefore, we developed a natural language processing (NLP) AI system to significantly improve work efficiency. Doctor K was used in the preparation of the guidelines of the Japanese Society of Nephrology. Furthermore, by comparing and analyzing the medical word virtual space constructed by the NLP AI system based on patient big data, we proved using the latest mathematical theory (category theory) that this system reflects the pathology of CKD. This suggests the possibility that the NLP AI system can predict patient prognosis. We hope that, through these studies, the use of AI based on big data will lead to the development of new treatments and improvement in patient prognosis.