Language：English   Publishing type：Research paper (scientific journal)  

Background: To characterize the clinical significance of vitamin D deficiency (VDD) detected in long COVID, a retrospective observational study was performed for outpatients who visited our clinic during the period from May 2024 to November 2024. Methods: Clinical trends in long COVID patients diagnosed with VDD who showed serum concentrations of 25-hydroxyvitamin D (25-OHD) lower than 20 ng/mL were compared with those in long COVID patients in a non-deficient vitamin D (NDD) group. Results: Of 126 patients with long COVID, 97 patients (female: 50) who had been infected during the Omicron phase were included. Sixty-six patients (68%) were classified in the VDD group. The median serum concentrations of 25-OHD were 14.8 ng/mL in the VDD group and 22.9 ng/mL in the NDD group. There were no significant differences between the two groups in terms of age, gender, BMI, severity of COVID-19, period after infection and vaccination history. Although the levels of serum calcium and phosphate were not significantly different between the two groups, the percentages of patients in the VDD group who complained of dizziness, memory impairment, palpitation and appetite loss were larger than those in the NDD group. Of note, the patients who complained of palpitation showed significantly lower concentrations of serum 25-OHD than those in the patients without palpitation (median: 11.9 vs. 17.3 ng/mL). Moreover, patients in the VDD group had significantly higher scores for physical and mental fatigue as well as higher scores for depressive symptoms. Conclusions: Collectively, VDD is involved in clinical manifestations of long COVID, particularly symptoms of palpitation, fatigue and depression.

DOI： 10.3390/nu17101692

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Background: During the coronavirus disease 2019 (COVID-19) pandemic, people in Japan were urged to stay at home as much as possible, and this resulted in significant changes in lifestyle behavior. The new lifestyle included factors affecting both energy intake and energy consumption, and it is now thought that weight gain during the lockdown was the result of complex effects. The aim of this study was to determine the relationships among lifestyle habits, laboratory data, and body weight gain during the lockdown using medical check-up data. Methods: A total of 3789 individuals who had undergone consecutive medical check-ups during the period from 2018 to 2020 were included in this study. Participants whose body weight had increased by 5% or more were divided into two groups: a before-lockdown group (participants who had gained weight between 2018 and 2019) and an after-lockdown group (participants who had gained weight between 2019 and 2020). Physical measurements, laboratory data, and answers to six questions about lifestyle habits, for which information was obtained from the records from medical check-ups, were compared in the two groups. Results: There was no significant difference between the distribution of weight changes in 2018-2019 before the lockdown and the distribution of weight changes in 2019-2020 after the lockdown. The before-lockdown and after-lockdown groups both included about 7% of the total participants (279 and 273 participants, respectively). Diastolic blood pressure and levels of AST, ALT, and LDL-C were significantly higher in the after-lockdown group than in the before-lockdown group. The percentages of participants with alcohol consumption and exercise habits were significantly higher in the after-lockdown group than in the before-lockdown group, and an analysis by gender showed that the differences were significant for women but not for men. Conclusions: The distributions of weight changes before and during the COVID-19 pandemic were similar. Exercise habits and alcohol consumption might have been unique factors causing weight gain during the COVID-19 pandemic, particularly in women. Our findings suggest that the impact of behavioral restrictions and lifestyle changes during a pandemic may be different in men and women.

DOI： 10.3390/jcm14072242

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Fibroblast growth factor receptor 1 (FGFR1), also known as KAL2, is a tyrosine kinase receptor, and variants of FGFR1 have been detected in patients with Kallmann syndrome (KS), which is a congenital developmental disorder characterized by central hypogonadism and anosmia. Herein, we report an adult case of KS with a novel variant of FGFR1. A middle-aged male was referred for a compression fracture of a lumbar vertebra. It was shown that he had severe osteoporosis, anosmia, gynecomastia, and a past history of operations for cryptorchidism. Endocrine workup using pituitary and gonadal stimulation tests revealed the presence of both primary and central hypogonadism. Genetic testing revealed a novel variant of FGFR1 (c.2197_2199dup, p.Met733dup). To identify the pathogenicity of the novel variant and the clinical significance for the gonads, we investigated the effects of the FGFR1 variant on the downstream signaling of FGFR1 and gonadal steroidogenesis by using human steroidogenic granulosa cells. It was revealed that the transfection of the variant gene significantly impaired FGFR1 signaling, detected through the downregulation of SPRY2, compared with that of the case of the forced expression of wild-type FGFR1, and that the existence of the variant gene apparently altered the expression of key steroidogenic factors, including StAR and aromatase, in the gonad. The results suggested that the novel variant of FGFR1 detected in the patient with KS was linked to the impairment of FGFR1 signaling, as well as the alteration of gonadal steroidogenesis, leading to the pathogenesis of latent primary hypogonadism.

DOI： 10.3390/ijms26062713

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Background: After the start of the COVID-19 pandemic, general fatigue in patients with long COVID and post-COVID-19 conditions (PCC) became a medical issue. Although there is a lack of evidence-based treatments, Kampo medicine (traditional Japanese medicine) has gained attention in Japan. At an outpatient clinic in Japan specializing in long COVID, 24% of all prescriptions were Kampo medicines, and 72% of Kampo medicine prescriptions were hochuekkito. However, there has been no prospective, quantitative study on the course of fatigue in patients with long COVID and PCC who were prescribed hochuekkito. The aim of this study was to clarify the course of fatigue in those patients. Methods: This study included patients aged 18 years or older with general fatigue who visited the long COVID specialized outpatient clinic at Okayama University Hospital and consented to participate after being prescribed hochuekkito. We reviewed the backgrounds of the patients, and we evaluated the patients' fatigue assessment scale in person or online. Results: Twenty patients were enrolled in this study from September to December in 2023. The average age of the patients was 42.9 years (SD: 15.8 years) and 12 patients (60%) were female. After hochuekkito administration, the fatigue assessment scale score decreased from 35.9 (SD: 5.9) at the initial visit to 31.2 (SD: 9.4) after 8 weeks, indicating a trend for improvement in fatigue (difference: 4.7; 95% CI: 0.5-8.9). Conclusions: A trend for improvement in fatigue was observed in patients with long COVID and PCC who were prescribed hochuekkito, indicating a potential benefit of hochuekkito for general fatigue in such patients. General fatigue in patients with long COVID or PCC can be classified as post-infectious fatigue syndrome and is considered a condition of qi deficiency in Kampo medicine, for which hochuekkito is appropriately indicated.

DOI： 10.3390/jcm14041391

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Objectives: Evidence for the usefulness of biomarkers that aid in diagnosis, assessment of severity, and prediction of prognosis in patients with long COVID is limited. The aim of this study was to clarify the characteristics of brain natriuretic peptide (BNP) in long COVID. Methods: We conducted a retrospective observational study of patients who visited the COVID-19 aftercare outpatient clinic at Okayama University Hospital from February 2021 to April 2024. Results: A total of 428 patients were enrolled in this study, and the patients were divided into a group with normal BNP (n = 314, ≤18.4 pg/mL) and a group with increased BNP (n = 114, >18.4 pg/mL). The long COVID group with increased BNP had a higher proportion of females (44.3% vs. 73.7%, p < 0.01) and an older median age (38 vs. 51 years, p < 0.01). Fatigue and brain fog were commonly manifested in both groups, while dyspnea was a more frequent complaint in the group with increased BNP. Various symptoms including fatigue, palpitations, and taste and/or olfactory disorders were associated with elevated BNP (23 to 24 pg/mL). Memory impairment was also linked to higher BNP (OR: 2.36, p = 0.05). In long COVID patients, plasma BNP elevation appears to be more pronounced in females and is often related to cardiogenic factors, in which inflammatory responses are also involved. Conclusions: Plasma BNP measurement may be useful for evaluating the severity of long COVID, especially in female patients and those with respiratory symptoms and/or memory impairment.

DOI： 10.3390/jcm14030817

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

OBJECTIVES:  The purposes of this study were to identify reflective processes from patients' points of view for difficult patient-doctor interactions and learn how the processes made medical students and residents aware of their own medical practice.  These processes were compared in two countries (US and Japan). METHODS:  The study was a descriptive qualitative study utilizing semi-structured interviews.  Participants were from US and Japan.  We analyzed the data using reflective thematic analysis of the implementation of reflections (from a patient's point of view) into medical clinical education and training from a constructivism paradigm. RESULTS:  We included twenty participants each from US and Japan by convenience sampling.  The participants were medical students during clinical clerkship and post graduate year-1, 2, and 3 medical residents.  Medical students and residents realized four cognitions (different expectations of patients and doctors, language communication barrier, time restriction and healthcare system challenges) from reflections from patients' perspectives.  Subsequently, medical students and residents identified three types of awareness (appropriate communication, empathy, and patient-doctor relationship of trust).  During these reflections from patients' perspectives, the medical students and residents had a willingness to change future behavior. CONCLUSIONS:  This study revealed one aspect of medical students' and residents' cognitions and awareness of clinical experiences through reflection from patients' perspectives.  This cognitive process aligns the ideas of medical students and residents more closely with the patients' perceptions and influences their willingness to change their behavior. This process enables comprehensive realization in person-centered care.

DOI： 10.5116/ijme.6741.f16c

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PURPOSE: To elucidate the impact of long COVID on menstruation and mental health, medical records of patients with long COVID were evaluated. METHODS: Symptoms of long COVID, QOL, mental health, and related endocrine data were compared between two groups with and without menstrual disturbances. RESULTS: Of 349 female patients who visited our clinic between February 2021 and March 2023, 223 patients with long COVID (aged 18-50 years) were included. Forty-four (19.7%) of the patients had menstrual symptoms associated with long COVID. The patients with menstrual symptoms were older than those without menstrual symptoms (42.5 vs. 38 years). The percentage of patients with menstrual symptoms was higher during the Omicron phase (24%) than during the Preceding (13%) and Delta (12%) phases. Cycle irregularity was the most frequent (in 63.6% of the patients), followed by severe pain (25%), heavy bleeding (20.5%), perimenopausal symptoms (18.2%), and premenstrual syndrome (15.9%). Fatigue and depression were the most frequent complications. Scores for fatigue and for QOL were significantly worse in long COVID patients with menstrual symptoms. Results of endocrine examinations showed significantly increased cortisol levels in patients with menstrual complaints. CONCLUSION: Long COVID has an impact on menstrual conditions and on QOL related to menstrual conditions.

DOI： 10.1080/0167482X.2024.2305899

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：JMIR Publications Inc.  

Background

Generative artificial intelligence (AI), particularly in the form of large language models, has rapidly developed. The LLaMA series are popular and recently updated from LLaMA2 to LLaMA3. However, the impacts of the update on diagnostic performance have not been well documented.

Objective

We conducted a comparative evaluation of the diagnostic performance in differential diagnosis lists generated by LLaMA3 and LLaMA2 for case reports.

Methods

We analyzed case reports published in the American Journal of Case Reports from 2022 to 2023. After excluding nondiagnostic and pediatric cases, we input the remaining cases into LLaMA3 and LLaMA2 using the same prompt and the same adjustable parameters. Diagnostic performance was defined by whether the differential diagnosis lists included the final diagnosis. Multiple physicians independently evaluated whether the final diagnosis was included in the top 10 differentials generated by LLaMA3 and LLaMA2.

Results

In our comparative evaluation of the diagnostic performance between LLaMA3 and LLaMA2, we analyzed differential diagnosis lists for 392 case reports. The final diagnosis was included in the top 10 differentials generated by LLaMA3 in 79.6% (312/392) of the cases, compared to 49.7% (195/392) for LLaMA2, indicating a statistically significant improvement (P&lt;.001). Additionally, LLaMA3 showed higher performance in including the final diagnosis in the top 5 differentials, observed in 63% (247/392) of cases, compared to LLaMA2’s 38% (149/392, P&lt;.001). Furthermore, the top diagnosis was accurately identified by LLaMA3 in 33.9% (133/392) of cases, significantly higher than the 22.7% (89/392) achieved by LLaMA2 (P&lt;.001). The analysis across various medical specialties revealed variations in diagnostic performance with LLaMA3 consistently outperforming LLaMA2.

Conclusions

The results reveal that the LLaMA3 model significantly outperforms LLaMA2 per diagnostic performance, with a higher percentage of case reports having the final diagnosis listed within the top 10, top 5, and as the top diagnosis. Overall diagnostic performance improved almost 1.5 times from LLaMA2 to LLaMA3. These findings support the rapid development and continuous refinement of generative AI systems to enhance diagnostic processes in medicine. However, these findings should be carefully interpreted for clinical application, as generative AI, including the LLaMA series, has not been approved for medical applications such as AI-enhanced diagnostics.

DOI： 10.2196/64844

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

Abstract

Background

Multi‐institutional faculty development programs for chief residents were lacking in Japan. This study evaluated the effectiveness of the program for enhancing the knowledge and behaviors of chief residents at the national level.

Methods

Six participants completed self‐assessment questionnaires at three points: before, immediately after, and 6 months post‐program. Knowledge and behavior changes were compared using the Mann–Whitney U test.

Results

Significant improvements in knowledge were observed and maintained at 6 months. However, behavioral changes were limited, with significant improvements only in counseling skills.

Conclusion

The program effectively increased knowledge but faced challenges in translating it into sustained behavioral change.

DOI： 10.1002/jgf2.744

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：JMIR Publications Inc.  

Background

Generative artificial intelligence (GAI) systems by Google have recently been updated from Bard to Gemini and Gemini Advanced as of December 2023. Gemini is a basic, free-to-use model after a user’s login, while Gemini Advanced operates on a more advanced model requiring a fee-based subscription. These systems have the potential to enhance medical diagnostics. However, the impact of these updates on comprehensive diagnostic accuracy remains unknown.

Objective

This study aimed to compare the accuracy of the differential diagnosis lists generated by Gemini Advanced, Gemini, and Bard across comprehensive medical fields using case report series.

Methods

We identified a case report series with relevant final diagnoses published in the American Journal Case Reports from January 2022 to March 2023. After excluding nondiagnostic cases and patients aged 10 years and younger, we included the remaining case reports. After refining the case parts as case descriptions, we input the same case descriptions into Gemini Advanced, Gemini, and Bard to generate the top 10 differential diagnosis lists. In total, 2 expert physicians independently evaluated whether the final diagnosis was included in the lists and its ranking. Any discrepancies were resolved by another expert physician. Bonferroni correction was applied to adjust the P values for the number of comparisons among 3 GAI systems, setting the corrected significance level at P value &lt;.02.

Results

In total, 392 case reports were included. The inclusion rates of the final diagnosis within the top 10 differential diagnosis lists were 73% (286/392) for Gemini Advanced, 76.5% (300/392) for Gemini, and 68.6% (269/392) for Bard. The top diagnoses matched the final diagnoses in 31.6% (124/392) for Gemini Advanced, 42.6% (167/392) for Gemini, and 31.4% (123/392) for Bard. Gemini demonstrated higher diagnostic accuracy than Bard both within the top 10 differential diagnosis lists (P=.02) and as the top diagnosis (P=.001). In addition, Gemini Advanced achieved significantly lower accuracy than Gemini in identifying the most probable diagnosis (P=.002).

Conclusions

The results of this study suggest that Gemini outperformed Bard in diagnostic accuracy following the model update. However, Gemini Advanced requires further refinement to optimize its performance for future artificial intelligence–enhanced diagnostics. These findings should be interpreted cautiously and considered primarily for research purposes, as these GAI systems have not been adjusted for medical diagnostics nor approved for clinical use.

DOI： 10.2196/63010

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/healthcare12161621

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Language：English   Publishing type：Research paper (scientific journal)  

Orthostatic intolerance (OI) is a key symptom of long COVID; however, the pathophysiology remains unknown. Among 688 long COVID patients who visited our clinic during the period from February 2021 to April 2023, 86 patients who were suspected of having OI and who underwent an active standing test (ST) were investigated to elucidate the clinical characteristics of OI in patients with long COVID. Of the 86 patients, 33 patients (38%) were ST-positive. Nausea and tachycardia in daily life were frequent complaints in the ST-positive group. The increase in heart rate (HR) during the ST was significantly greater during a 10-min period after standing in the ST-positive group (+ 30 bpm) than in the ST-negative group (+ 16 bpm). The initial increase in diastolic blood pressure (DBP) just after standing was significantly greater in the ST-positive group (+ 14 mmHg) than in the ST-negative group (+ 9 mmHg). Serum cortisol levels in the ST-positive patients aged over 20 years were higher and growth hormone levels in the patients under 20 years of age were lower than those in the ST-negative group. Autonomous nervous symptoms, transient DBP rise with increasing HR after standing, and endocrine dysfunctions are helpful for detecting OI related to long COVID.

DOI： 10.1038/s41598-024-67815-y

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Objective General fatigue is one of the most frequent chief complaints in primary care, and an accurate assessment of fatigue has a direct impact on a patient's quality of life and treatment decisions. The Fatigue Assessment Scale (FAS), a measure of general fatigue, is useful for assessing fatigue in diverse cultures and diseases. However, there has been no study showing the reliability and validity of the scale in the Japanese context. The present study assessed the reliability and validity of the Japanese version of the FAS. Methods This study was conducted on 649 patients with long COVID who had a high frequency of general fatigue. To test the structural validity of the FAS, the patients were randomly divided into two groups: one in which an exploratory factor analysis (EFA) was conducted and one in which a confirmatory factor analysis (CFA) was conducted. Cronbach's alpha was calculated to assess internal consistency reliability. Results As 58 patients had missing values, we analyzed the data of 591 patients. The EFA led to an FAS comprising two factors. The CFA showed an acceptable fit for this two-factor model. The internal consistency was found to be good (Cronbach's alpha =0.89). Conclusion This study verified the structural validity and internal consistency and reliability of the Japanese version of the FAS. The results indicate that the Japanese version of the FAS is useful for assessing general fatigue in patients with long COVID in Japan.

DOI： 10.2169/internalmedicine.4101-24

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Purpose: The present study aimed to clarify the effects of a hyperglycemic condition on the clinical consequences of long COVID. Methods: Among 643 patients who visited the outpatient clinic of our hospital from February 2021 to September 2023, long COVID patients were classified into a hyperglycemic (HG) group with casual blood glucose levels above 140 mg/dL and a normoglycemic (NG) group. The patients' backgrounds, clinical symptoms, health status including the QOL evaluation scale (EQ-5D-5L), self-rating depression scale (SDS), and F-scale questionnaire (FSSG), blood test data, and recovery periods were analyzed. Results: The NG group included 607 patients with long COVID and the HG group included 36 patients with long COVID. Patients in the HG group were older than those in the NG group (55 vs. 41 years; p < 0.001) and included a larger percentage of males (67% vs. 44%; p = 0.009). The HG group had a larger percentage of patients with moderate-to-severe conditions in the acute infection phase (28% vs. 12%; p = 0.008), a higher BMI (25 vs. 22 kg/m2; p < 0.001), higher blood pressure (138/81 vs. 122/72 mmHg; p < 0.001), and a larger percentage of patients with an alcohol drinking habit (53% vs. 34%; p = 0.031). Long COVID symptoms and self-rated scales were not differed between the two groups; however, the laboratory data showed that liver and renal functions and metabolic data were significantly worse in the HG group. Although there was no apparent difference between the two groups in duration from the infection to the first visit, the HG group had a significantly longer period of recovery from long COVID (median period of 421 vs. 294 days; p = 0.019). Conclusion: A hyperglycemic state associated with other lifestyle-related diseases is associated with the prolongation of recovery from long COVID.

DOI： 10.3390/jcm13144099

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Purpose: The present study aimed to uncover the impact of long COVID on the working situations of Japanese patients. Methods: Changes in the working situations of the patients who visited our long COVID clinic were evaluated from medical records for the aspects of physical status, quality of life (QOL), and mental conditions. Results: Of 846 long COVID patients who visited our clinic from February 2021 to December 2023, 545 employed patients aged between 18 and 65 years were included in this study. A total of 295 patients (54.1%) with long COVID (median age: 43 years, female: 55.6%) experienced changes in their working status. Those patients included 220 patients (40.4%) who took a leave of absence, 53 patients (9.7%) who retired, and 22 patients (4%) with reduced working hours. Most of the patients (93.2%) with changes in working conditions had mild disease severity in the acute phase of COVID-19. The majority of those patients with mild disease severity (58.8%) were infected in the Omicron-variant phase and included 65.3% of the female patients. The major symptoms in long COVID patients who had changes in their working situations were fatigue, insomnia, headache, and dyspnea. Scores indicating fatigue and QOL were worsened in long COVID patients who had changes in their working situations. In addition, 63.7% of the long COVID patients with changes in their working situations had decreases in their incomes. Conclusions: Changes in the working situation of long COVID patients who were employed had a negative impact on the maintenance of their QOL.

DOI： 10.3390/jcm13133809

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：JMIR Publications Inc.  

Background

The potential of artificial intelligence (AI) chatbots, particularly ChatGPT with GPT-4 (OpenAI), in assisting with medical diagnosis is an emerging research area. However, it is not yet clear how well AI chatbots can evaluate whether the final diagnosis is included in differential diagnosis lists.

Objective

This study aims to assess the capability of GPT-4 in identifying the final diagnosis from differential-diagnosis lists and to compare its performance with that of physicians for case report series.

Methods

We used a database of differential-diagnosis lists from case reports in the American Journal of Case Reports, corresponding to final diagnoses. These lists were generated by 3 AI systems: GPT-4, Google Bard (currently Google Gemini), and Large Language Models by Meta AI 2 (LLaMA2). The primary outcome was focused on whether GPT-4’s evaluations identified the final diagnosis within these lists. None of these AIs received additional medical training or reinforcement. For comparison, 2 independent physicians also evaluated the lists, with any inconsistencies resolved by another physician.

Results

The 3 AIs generated a total of 1176 differential diagnosis lists from 392 case descriptions. GPT-4’s evaluations concurred with those of the physicians in 966 out of 1176 lists (82.1%). The Cohen κ coefficient was 0.63 (95% CI 0.56-0.69), indicating a fair to good agreement between GPT-4 and the physicians’ evaluations.

Conclusions

GPT-4 demonstrated a fair to good agreement in identifying the final diagnosis from differential-diagnosis lists, comparable to physicians for case report series. Its ability to compare differential diagnosis lists with final diagnoses suggests its potential to aid clinical decision-making support through diagnostic feedback. While GPT-4 showed a fair to good agreement for evaluation, its application in real-world scenarios and further validation in diverse clinical environments are essential to fully understand its utility in the diagnostic process.

DOI： 10.2196/59267

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Language：Japanese   Publisher：(一社)日本病院総合診療医学会  

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Determination of long COVID requires ruling out alternative diagnoses, but there has been no report on the features of alternative diagnoses. This study was a single-center retrospective study of outpatients who visited our clinic between February 2021 and June 2023 that was carried out to determine the characteristics of alternative diagnoses in patients with post-COVID-19 symptoms. In a total of 731 patients, 50 patients (6.8%) were newly diagnosed with 52 diseases requiring medical intervention, and 16 (32%) of those 50 patients (2.2% of the total) were considered to have priority for treatment of the newly diagnosed disorders over long COVID treatment. The proportion of patients with a new diagnosis increased with advance of age, with 15.7% of the patients aged 60 years or older having a new diagnosis. Endocrine and metabolic diseases and hematological and respiratory diseases were the most common, being detected in eight patients (16%) each. Although 35 of the 52 diseases (67%) were related to their symptoms, endocrine and metabolic diseases were the least associated with specific symptoms. Other disorders that require attention were found especially in elderly patients with symptomatic long COVID. Thus, appropriate assessment and differentiation from alternative diagnoses are necessary for managing long COVID.

DOI： 10.1038/s41598-024-55526-3

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Language：English   Publishing type：Research paper (scientific journal)  

INTRODUCTION: This study aimed to elucidate the prevalence and clinical characteristics of patients with long COVID (coronavirus disease 2019), especially focusing on 50% hemolytic complement activity (CH50). METHODS: This retrospective observational study focused on patients who visited Okayama University Hospital (Japan) for the treatment of long COVID between February 2021 and March 2023. CH50 levels were measured using liposome immunometric assay (Autokit CH50 Assay, FUJIFILM Wako Pure Chemical Corporation, Japan); high CH50 was defined as ≥59 U/mL. Univariate analyses assessed differences in the clinical background, long COVID symptoms, inflammatory markers, and clinical scores of patients with normal and high CH50. Logistic regression model investigated the association between high CH50 levels and these factors. RESULTS: Of 659 patients who visited our hospital, 478 patients were included. Of these, 284 (59.4%) patients had high CH50 levels. Poor concentration was significantly more frequent in the high CH50 group (7.2% vs. 13.7%), whereas no differences were observed in other subjective symptoms (fatigue, headache, insomnia, dyspnea, tiredness, and brain fog). Multivariate analysis was performed on factors that could be associated with poor concentration, suggesting a significant relationship to high CH50 levels (adjusted odds ratio [aOR], 2.70; 95% confidence interval [CI], 1.33-5.49). Also, high CH50 was significantly associated with brain fog (aOR, 1.66; 95% CI, 1.04-2.66). CONCLUSIONS: High CH50 levels were frequently reported in individuals with long COVID, indicating a relationship with brain fog. Future in-depth research should examine the pathological role and causal link between complement immunity and the development of long COVID.

DOI： 10.1016/j.jiac.2023.10.016

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Background: Functional hyperthermia (FH) is characterized by hyperthermia resulting from sympathetic hyperactivity rather than inflammation, and it is frequently overlooked by medical practitioners due to the absence of abnormalities in a medical examination. Although FH is an important differential diagnosis for fever of unknown origin (FUO), the literature on FUO cases in Japan lacks information on FH. In this study, we aimed to uncover the population of FH patients hidden in FUO cases. Methods: An outpatient clinic for FUO was established at Okayama University Hospital, and 132 patients were examined during the period from May 2019 to February 2022. Results: A diagnosis of FH was made in 31.1% of the FUO cases, and FH predominantly affected individuals in their third and fourth decades of life with a higher incidence in females (68.3%). The frequency of a history of psychiatric illness was higher in patients with FH than in patients with other febrile illnesses. Although the C-reactive protein (CRP) is generally negative in FH cases, some obese patients, with a body mass index ≥ 25 had slightly elevated levels of CRP but were diagnosed with FH. Conclusions: The results showed the importance of identifying FH when encountering patients with FUO without any organic etiology.

DOI： 10.3390/jcm13030889

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SAGE Publications  

Background

The utility of a clinical decision support system using a machine learning (ML) model for simultaneous cardiac and pulmonary auscultation is unknown.

Objective

This study aimed to develop and evaluate an ML system's utility for cardiopulmonary auscultation.

Methods

First, we developed an ML system for cardiopulmonary auscultation, using cardiopulmonary sound files from our previous study. The technique involved pre-processing, feature extraction, and classification through several neural network layers. After integration, the output class was categorized as “normal,” “abnormal,” or “undetermined.” Second, we evaluated the ML system with 24 junior residents in an open-label randomized controlled trial at a university hospital. Participants were randomly assigned to the ML system group (intervention) or conventional auscultation group (control). During training, participants listened to four cardiac and four pulmonary sounds, all of which were correctly classified. Then, participants classified a series of 16 simultaneous cardiopulmonary sounds. The control group auscultated the sounds using noise-cancelling headphones, while the intervention group did so by watching recommendations from the ML system.

Results

The total scores for correctly identified normal or abnormal cardiopulmonary sounds in the intervention group were significantly higher than those in the control group (366/384 [95.3%] vs. 343/384 [89.3%], P = 0.003). The cardiac test score in the intervention group was better (111/192 [57.8%] vs. 90/192 [46.9%], P = 0.04); there was no significant difference in pulmonary auscultation.

Conclusions

The ML-based system improved the accuracy of cardiopulmonary auscultation for junior residents. This result suggests that the system can assist early-career physicians in accurate screening.

DOI： 10.1177/20552076241233689

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Other Link： http://journals.sagepub.com/doi/full-xml/10.1177/20552076241233689

Language：English   Publishing type：Research paper (scientific journal)   Publisher：SAGE Publications  

Background

Diagnostic performance of generative artificial intelligences (AIs) using large language models (LLMs) across comprehensive medical specialties is still unknown.

Objective

We aimed to evaluate the diagnostic performance of generative AIs using LLMs in complex case series across comprehensive medical fields.

Methods

We analyzed published case reports from the American Journal of Case Reports from January 2022 to March 2023. We excluded pediatric cases and those primarily focused on management. We utilized three generative AIs to generate the top 10 differential-diagnosis (DDx) lists from case descriptions: the fourth-generation chat generative pre-trained transformer (ChatGPT-4), Google Gemini (previously Bard), and LLM Meta AI 2 (LLaMA2) chatbot. Two independent physicians assessed the inclusion of the final diagnosis in the lists generated by the AIs.

Results

Out of 557 consecutive case reports, 392 were included. The inclusion rates of the final diagnosis within top 10 DDx lists were 86.7% (340/392) for ChatGPT-4, 68.6% (269/392) for Google Gemini, and 54.6% (214/392) for LLaMA2 chatbot. The top diagnoses matched the final diagnoses in 54.6% (214/392) for ChatGPT-4, 31.4% (123/392) for Google Gemini, and 23.0% (90/392) for LLaMA2 chatbot. ChatGPT-4 showed higher diagnostic accuracy than Google Gemini ( P &lt; 0.001) and LLaMA2 chatbot ( P &lt; 0.001). Additionally, Google Gemini outperformed LLaMA2 chatbot within the top 10 DDx lists ( P &lt; 0.001) and as the top diagnosis ( P = 0.010).

Conclusions

This study demonstrated the diagnostic performance of generative AIs including ChatGPT-4, Google Gemini, and LLaMA2 chatbot. ChatGPT-4 exhibited higher diagnostic accuracy than the other platforms. These findings suggest the importance of understanding the differences in diagnostic performance among generative AIs, especially in complex case series across comprehensive medical fields, like general medicine.

DOI： 10.1177/20552076241265215

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BACKGROUND: The characteristics of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) related to COVID-19 have remained uncertain. To elucidate the clinical trend of ME/CFS induced by long COVID, we examined data for patients who visited our outpatient clinic established in a university hospital during the period from Feb 2021 to July 2023. METHODS: Long COVID patients were classified into two groups, an ME/CFS group and a non-ME/CFS group, based on three diagnostic criteria. RESULTS: The prevalence of ME/CFS in the long COVID patients was 8.4% (62 of 739 cases; female: 51.6%) and factors related to ME/CFS were severe illness, smoking and alcohol drinking habits, and fewer vaccinations. The frequency of ME/CFS decreased from 23.9% in the Preceding period to 13.7% in the Delta-dominant period and to 3.3% in the Omicron-dominant period. Fatigue and headache were commonly frequent complaints in the ME/CFS group, and the frequency of poor concentration in the ME/CFS group was higher in the Omicron period. Serum ferritin levels were significantly higher in female patients in the ME/CFS group infected in the Preceding period. In the ME/CFS group, the proportion of patients complaining of brain fog significantly increased from 22.2% in the Preceding period to 47.9% in the Delta period and to 81.3% in the Omicron period. The percentage of patients who had received vaccination was lower in the ME/CFS group than the non-ME/CFS group over the study period, whereas there were no differences in the vaccination rate between the groups in each period. CONCLUSION: The proportion of long COVID patients who developed ME/CFS strictly diagnosed by three criteria was lower among patients infected in the Omicron phase than among patients infected in the other phases, while the proportion of patients with brain fog inversely increased. Attention should be paid to the variant-dependent trends of ME/CFS triggered by long COVID (300 words).

DOI： 10.1371/journal.pone.0315385

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：JMIR Publications Inc.  

BACKGROUND: In the evolving field of health care, multimodal generative artificial intelligence (AI) systems, such as ChatGPT-4 with vision (ChatGPT-4V), represent a significant advancement, as they integrate visual data with text data. This integration has the potential to revolutionize clinical diagnostics by offering more comprehensive analysis capabilities. However, the impact on diagnostic accuracy of using image data to augment ChatGPT-4 remains unclear. OBJECTIVE: This study aims to assess the impact of adding image data on ChatGPT-4's diagnostic accuracy and provide insights into how image data integration can enhance the accuracy of multimodal AI in medical diagnostics. Specifically, this study endeavored to compare the diagnostic accuracy between ChatGPT-4V, which processed both text and image data, and its counterpart, ChatGPT-4, which only uses text data. METHODS: We identified a total of 557 case reports published in the American Journal of Case Reports from January 2022 to March 2023. After excluding cases that were nondiagnostic, pediatric, and lacking image data, we included 363 case descriptions with their final diagnoses and associated images. We compared the diagnostic accuracy of ChatGPT-4V and ChatGPT-4 without vision based on their ability to include the final diagnoses within differential diagnosis lists. Two independent physicians evaluated their accuracy, with a third resolving any discrepancies, ensuring a rigorous and objective analysis. RESULTS: The integration of image data into ChatGPT-4V did not significantly enhance diagnostic accuracy, showing that final diagnoses were included in the top 10 differential diagnosis lists at a rate of 85.1% (n=309), comparable to the rate of 87.9% (n=319) for the text-only version (P=.33). Notably, ChatGPT-4V's performance in correctly identifying the top diagnosis was inferior, at 44.4% (n=161), compared with 55.9% (n=203) for the text-only version (P=.002, χ2 test). Additionally, ChatGPT-4's self-reports showed that image data accounted for 30% of the weight in developing the differential diagnosis lists in more than half of cases. CONCLUSIONS: Our findings reveal that currently, ChatGPT-4V predominantly relies on textual data, limiting its ability to fully use the diagnostic potential of visual information. This study underscores the need for further development of multimodal generative AI systems to effectively integrate and use clinical image data. Enhancing the diagnostic performance of such AI systems through improved multimodal data integration could significantly benefit patient care by providing more accurate and comprehensive diagnostic insights. Future research should focus on overcoming these limitations, paving the way for the practical application of advanced AI in medicine.

DOI： 10.2196/55627

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Background: Although many adverse reactions after SARS-CoV-2 vaccination have been reported, there have been few comprehensive studies on persistent symptoms after SARS-CoV-2 vaccination. The aim of this study was to determine the clinical characteristics of patients with various persistent symptoms after SARS-CoV-2 vaccination. Methods: A retrospective descriptive study was performed for patients who visited a specialized clinic established at Okayama University Hospital to evaluate adverse events after SARS-CoV-2 vaccination during the period from April 2021 to March 2023. Results: Descriptive analysis was performed for 121 of 127 patients who visited the clinic during the study period, and separate analysis was performed for the other 6 patients who had serious complications, who required treatment with prednisolone, and who had persistent symptoms. The median [interquartile range] age of the patients was 48 years [31–64 years], and the patients included 44 males (36.4%) and 77 females (63.6%). The most frequent symptoms were sensory impairment (34 patients, 28.1%), general fatigue (30 patients, 24.8%), fever/low-grade fever (21 patients, 17.4%), and headache (21 patients, 17.4%). Serious complications included myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), sarcoidosis, aseptic meningitis, neuromyelitis optica spectrum disorders (NMOSDs), tendon adhesions, and idiopathic thrombocytopenia. Conclusions: Although causal relationships were not determined, 15 persistent symptoms after SARS-CoV-2 vaccination were characterized. All of the symptoms had onset from 12 hours to one week after vaccination, with 10 symptoms persisting for 6 months or longer. The most frequent symptom was sensory impairment.

DOI： 10.3390/vaccines11111661

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BACKGROUND: The diagnostic accuracy of differential diagnoses generated by artificial intelligence chatbots, including ChatGPT models, for complex clinical vignettes derived from general internal medicine (GIM) department case reports is unknown. OBJECTIVE: This study aims to evaluate the accuracy of the differential diagnosis lists generated by both third-generation ChatGPT (ChatGPT-3.5) and fourth-generation ChatGPT (ChatGPT-4) by using case vignettes from case reports published by the Department of GIM of Dokkyo Medical University Hospital, Japan. METHODS: We searched PubMed for case reports. Upon identification, physicians selected diagnostic cases, determined the final diagnosis, and displayed them into clinical vignettes. Physicians typed the determined text with the clinical vignettes in the ChatGPT-3.5 and ChatGPT-4 prompts to generate the top 10 differential diagnoses. The ChatGPT models were not specially trained or further reinforced for this task. Three GIM physicians from other medical institutions created differential diagnosis lists by reading the same clinical vignettes. We measured the rate of correct diagnosis within the top 10 differential diagnosis lists, top 5 differential diagnosis lists, and the top diagnosis. RESULTS: In total, 52 case reports were analyzed. The rates of correct diagnosis by ChatGPT-4 within the top 10 differential diagnosis lists, top 5 differential diagnosis lists, and top diagnosis were 83% (43/52), 81% (42/52), and 60% (31/52), respectively. The rates of correct diagnosis by ChatGPT-3.5 within the top 10 differential diagnosis lists, top 5 differential diagnosis lists, and top diagnosis were 73% (38/52), 65% (34/52), and 42% (22/52), respectively. The rates of correct diagnosis by ChatGPT-4 were comparable to those by physicians within the top 10 (43/52, 83% vs 39/52, 75%, respectively; P=.47) and within the top 5 (42/52, 81% vs 35/52, 67%, respectively; P=.18) differential diagnosis lists and top diagnosis (31/52, 60% vs 26/52, 50%, respectively; P=.43) although the difference was not significant. The ChatGPT models' diagnostic accuracy did not significantly vary based on open access status or the publication date (before 2011 vs 2022). CONCLUSIONS: This study demonstrates the potential diagnostic accuracy of differential diagnosis lists generated using ChatGPT-3.5 and ChatGPT-4 for complex clinical vignettes from case reports published by the GIM department. The rate of correct diagnoses within the top 10 and top 5 differential diagnosis lists generated by ChatGPT-4 exceeds 80%. Although derived from a limited data set of case reports from a single department, our findings highlight the potential utility of ChatGPT-4 as a supplementary tool for physicians, particularly for those affiliated with the GIM department. Further investigations should explore the diagnostic accuracy of ChatGPT by using distinct case materials beyond its training data. Such efforts will provide a comprehensive insight into the role of artificial intelligence in enhancing clinical decision-making.

DOI： 10.2196/48808

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DOI： 10.1507/endocrj.EJ23-0266

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OBJECTIVE: The most common symptom of post-acute coronavirus disease 2019 (COVID-19) is fatigue, and it potentially leads to myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS); however, a specific prognosticator is lacking. We aimed to elucidate the clinical characteristics of patients who developed ME/CFS after COVID-19. METHODS: In this retrospective observational study, patients who visited Okayama University Hospital for long COVID between February 2021 and March 2022 were investigated. RESULTS: Of the 234 patients, 139 (59.4%) had fatigue symptoms. Fifty patients with fatigue symptoms (21.4%) met the criteria for ME/CFS (ME/CFS group), while the other 89 patients did not (non-ME/CFS group); 95 patients had no fatigue complaints (no-fatigue group). Although the patients' backgrounds were not significantly different between the three groups, the ME/CFS group presented the highest scores on the self-rating symptom scales, including the Fatigue Assessment Scale (FAS), EuroQol, and the Self-Rating Depression Scale (SDS). Furthermore, serum ferritin levels, which were correlated with FAS and SDS scores, were significantly higher in the ME/CFS group (193.0 μg/L, interquartile range (IQR): 58.8-353.8) than in the non-ME/CFS group (98.2 μg/L, 40.4-251.5) and no-fatigue group (86.7 μg/L, 37.5-209.0), and a high serum ferritin level was prominent in female patients. Endocrine workup further showed that the ME/CFS group had higher thyrotropin levels but lower growth hormone levels in serum and that insulin-like growth factor-I levels were inversely correlated with ferritin levels (R = -0.328, p < 0.05). CONCLUSIONS: Serum ferritin level is a possible predictor of the development of ME/CFS related to long COVID, especially in female patients.

DOI： 10.3390/jcm12144737

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Here we report a case of sarcoidosis which demonstrated with bilateral ankle arthritis. We usually do not consider sarcoidosis routinely for the differential diagnosis of acute arthritis, however, up to 15 to 25% of sarcoidosis complicate arthritis, typically in ankle joints. Sarcoid arthritis is reported to occur mainly in young northern Europe women, whereas this case intimate that we should take sarcoidosis into account as the differential diagnosis of ankle arthralgia also among Asian men especially when combining upper respiratory symptoms.

DOI： 10.1002/jgf2.621

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Objectives: The present study aimed to elucidate the characteristics of long COVID patients with headaches. Methods: A single-center retrospective observational study was performed for long COVID outpatients who visited our hospital from 12 February 2021 to 30 November 2022. Results: A total of 482 long COVID patients, after excluding 6, were divided into two groups: the Headache group of patients with complaints of headache (113 patients: 23.4%) and the remaining Headache-free group. Patients in the Headache group were younger (median age: 37 years) than patients in the Headache-free group (42 years), while the ratio of females (56%) in the Headache group was nearly the same as that in the Headache-free group (54%). The proportion of patients in the Headache group who were infected in the Omicron-dominant phase (61%) was larger than the proportions of patients infected in the Delta (24%) and preceding (15%) phases, and that trend was significantly different from the trend in the Headache-free group. The duration before the first visit for long COVID was shorter in the Headache group (71 days) than in the Headache-free group (84 days). The proportions of patients in the Headache group with comorbid symptoms, including general fatigue (76.1%), insomnia (36.3%), dizziness (16.8%), fever (9.7%), and chest pain (5.3%) were larger than the proportions of patients in the Headache-free group, whereas blood biochemical data were not significantly different between the two groups. Interestingly, patients in the Headache group had significant deteriorations of scores indicating depression and scores for quality of life and general fatigue. In multivariate analysis, headache, insomnia, dizziness, lethargy, and numbness were shown to be involved in the quality of life (QOL) of long COVID patients. Conclusions: The manifestation of headaches related to long COVID was found to have a significant impact on social and psychological activities. Alleviation of headaches should be a priority for the effective treatment of long COVID.

DOI： 10.3390/jcm12103533

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OBJECTIVES: The aim of this study was to determine the characteristics of hypozincemia in long COVID patients. METHODS: This study was a single-center retrospective observational study for outpatients who visited the long COVID clinic established in a university hospital during the period from 15 February 2021 to 28 February 2022. Characteristics of patients with a serum zinc concentration lower than 70 μg/dL (10.7 μmol/L) were compared with characteristics of patients with normozincemia. RESULTS: In a total of 194 patients with long COVID after excluding 32 patients, hypozincemia was detected in 43 patients (22.2%) including 16 male patients (37.2%) and 27 female patients (62.8%). Among various parameters including the background characteristics of the patients and medical histories, the patients with hypozincemia were significantly older than the patients with normozincemia (median age: 50 vs. 39 years). A significant negative correlation was found between serum zinc concentrations and age in male patients (R = -0.39; p < 0.01) but not in female patients. In addition, there was no significant correlation between serum zinc levels and inflammatory markers. General fatigue was the most frequent symptom in both male patients with hypozincemia (9 out of 16: 56.3%) and female patients with hypozincemia (8 out of 27: 29.6%). Patients with severe hypozincemia (serum zinc level lower than 60 μg/dL) had major complaints of dysosmia and dysgeusia, which were more frequent complaints than general fatigue. CONCLUSIONS: The most frequent symptom in long COVID patients with hypozincemia was general fatigue. Serum zinc levels should be measured in long COVID patients with general fatigue, particularly in male patients.

DOI： 10.3390/jcm12052062

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Background: Since the start of the global pandemic of coronavirus disease 2019 (COVID-19), not only adults but also many children have suffered from it. However, the clinical characteristics of long COVID in children remain unclear. Methods: In this retrospective observational study conducted in a single facility, we reviewed the medical records of all long COVID patients who visited Okayama University Hospital from February 2021 to October 2022, and we compared the clinical characteristics of long COVID in teenagers (11 to 18 years of age) with those in adults. Results: Data for 452 long COVID patients including 54 teenagers (11.9%) were analyzed. Fatigue was the most frequent symptom in teenagers (55.6% of the patients) and also in adults. On the other hand, the percentage of teenagers who complained of headache, which was the second most frequent complaint, was significantly higher than the percentage of adults (35.2% vs. 21.9%, p < 0.05). A comparison of the frequencies of symptoms depending on the viral variant showed that fatigue and headache were predominant symptoms in the Omicron variant phase. Of the 50 teenagers who were enrolled in schools, 28 (56.0%) could not attend school due to long COVID symptoms. The most common symptoms as reasons for absence from school were fatigue (85.7% of the patients), headache (42.9%), and insomnia (32.1%). Conclusions: Attention should be paid to the symptoms of fatigue and headache in teenagers with long COVID.

DOI： 10.3390/medicina59020261

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

BACKGROUND: Excessive psychological stress in medical students affects their mental health and causes problems such as burnout and depression. Furthermore, changes in the learning environment to online learning due to the COVID-19 pandemic have had a psychological effect on medical students. However, the relationships between medical students' perceived stress and different methods of communication, including in-person and online communication, remain unclear. The purpose of this study was to investigate the differences in stress perception of medical students depending on in-person communication and online communication during the COVID-19 pandemic. METHODS: This study was a cross-sectional study conducted from September to October in 2020. All of the students of Okayama University School of Medicine were asked to participate in a questionnaire survey. The explanatory variables were the frequency and length of communications with others (by in-person or online communication), empathy, and lifestyle. The main outcome measure was perceived stress. Subgroup analysis was conducted for students who preferred to be by themselves and students who preferred to study together and interact with other people. Univariate analysis and multivariate multiple regression analysis were conducted. Gender and grade, which have been shown to be associated with stress in previous studies, were used as covariates for multiple regression analysis. RESULTS: Valid responses to the questionnaire survey were received from 211 (29.4%) of the 717 students. There was no significant association between perceived stress and online communication, but the number of people with which students had in-person communication (1-2 people compared to 0 as a control, regression coefficient [B] = -4.4, 95% confidence interval [CI]; -7.8, -1.1, more than 10 people, B = -12, 95% CI: -18, -5.8) and the length of communication (more than 120 min, B = -4.5, 95% CI: -8.1, -0.92) were associated with a reduction in perceived stress. In subgroup analysis, the number of people with in-person communication and the length of communication had significant associations with stress reduction even in the group of students who had a preference for being by themselves. CONCLUSION: In-person communications rather than online communications were associated with a lower level of perceived stress. In subgroup analysis, this trend was statistically significant in the group of students who had a preference for being by themselves.

DOI： 10.3390/ijerph20021579

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The recruitment and training of early-career researchers are important for the development of science, especially in countries with low birth rates, such as Japan. In several academic societies for social medicine, early-career researchers have formed associations for the purposes of networking and career development. However, to date, little information about the activities of these associations has been shared. Therefore, we organized a symposium at the 93rd Annual Meeting of the Japanese Society for Hygiene (March 4, 2023) to introduce the early-career researcher associations that have been formed within five academic societies namely the Japanese Society for Hygiene, Japan Epidemiological Association, Japan Society for Occupational Health, Japan Society for Medical Education, and Japan Society for Healthcare Administration. In this paper, we summarize the activities, challenges, and future prospects of each association and their strategies for future development and collaboration on the basis of presentations and discussions at the symposium.

DOI： 10.1265/jjh.23005

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OBJECTIVES: The objective of this study was to determine the clinical and endocrinological features of sleep disturbance in patients with long COVID. METHODS: This study was a single-center retrospective observational study for patients who visited the COVID-19 aftercare outpatient clinic (CAC) established in Okayama University Hospital in Japan during the period from 15 February 2021 to 29 July 2022. The long COVID patients were divided into two groups based on the presence or absence of sleep disturbance, and the clinical and laboratory characteristics of the patients were analyzed. RESULTS: Out of 363 patients with long COVID, after excluding 6 patients, 60 patients (16.5%) (55% males, median age of 38 years) complaining of sleep disturbance were compared with 303 patients (83.5%) (43% males, median age of 40 years) without sleep-related symptoms. Although there were no significant differences in clinical backgrounds and severities of COVID-19 between the two groups by the multivariate analysis, the percentage of long COVID patients with sleep disturbance was significantly increased among patients infected in the Omicron-dominant phase. In addition, the prevalence rate of sleep disturbance in patients when infected in the Omicron phase (24.8%) was two-times higher than that in patients infected in the Delta phase (12.8%). Of note, the percentages of patients with sleep disturbance who also complained of general fatigue, headache, concentration loss, anxiety, low-grade fever, and brain fog symptoms were higher than the percentages of patients without sleep disturbance who had the same complaints. Among the types of sleep disturbance, the percentage of patients who complained of loss of sleep induction (75%) was much higher than the percentage of patients with early-awakening sleep disturbance (6.7%), and many of the patients with mid-awakening types of insomnia had brain fog symptoms. Endocrine examinations revealed that long COVID patients with sleep disturbance had significantly higher levels of plasma adrenocorticotropin and lower levels of serum growth hormone, suggesting the presence of hypothalamic-pituitary stress. CONCLUSION: The prevalence of sleep disturbance has been increasing in long COVID patients infected in the Omicron phase with a certain clinical and endocrine trend.

DOI： 10.3390/jcm11247332

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DOI： 10.1507/endocrj.EJ22-0093

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DOI： 10.3390/jcm11071955

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