In the context of curriculum ideological and political education reform, ideological and political education entering acupuncture professional courses has great advantages and development prospects. By introducing the cases of famous and veteran traditional Chinese medicine (TCM) doctors and their life stories into acupuncture teaching, the ideological and political elements related to caring for patients, loving the country, innovation and development, cultural confidence etc. were deeply explored. Through optimizing the course design in acupuncture teaching, the integration of acupuncture teaching and ideological and political education is better achieved. While interpreting the life stories and superb medical skills of famous TCM experts, students are encouraged to establish good medical ethics and values, build their cultural confidence and innovation consciousness, and achieve the teaching goal of educating students through the course.
在课程思政教学改革的大环境下，思政教育进入针灸专业课有着很大的优势和发展前景。通过在针灸教学中引入名老中医医案及名老中医生平，深入挖掘名老中医案例中与爱护患者、热爱祖国、创新发展、文化自信等有关的思政元素，在针灸教学过程中优化课程设计，使针灸教学和思政教育更好融合。在解读名医大家的生平事迹和高超医术的同时，使学生树立良好的医德和价值观，激发学生的文化自信和创新意识，实现课程育人的教学目标。.

UNASSIGNED: To explore a definition of healthspan that based on actual situation of veterans is of significance for improving their health status and life quality.
UNASSIGNED: This was a retrospective study. Based on the medical data of veterans from the Chinese PLA General Hospital. Total of 1,421 subjects were enrolled to this study, among which 441 deceased cases were further analyzed. The indicators of healthspan of the subjects was calculated from four dimensions (the status of chronic diseases, physical function, social function and psychological function). The risk factors for death were analyzed in a population cohort from 2008 to 2021 (including 763 subjects, among which 372 were deceased).
UNASSIGNED: The average lifespan and adjusted healthspan of the subjects were 93.3 years and 75.1 years, respectively. The four dimensions of healthspan were: adjusted healthspan without chronic diseases was 76.3 years, social function-related healthspan was 88.8 years, physical function-related healthspan was 91.5 years, and psychological function-related healthspan was 92.7 years. By analyzing the cohort in 2008, it was inferred that the main risk factors for the death of veterans were poor nutritional status, renal function injury, high blood pressure, high blood sugar, and aging.
UNASSIGNED: This study proposed four dimensions related to \"healthspan\" for Chinese veterans (adjusted healthspan without chronic diseases, physical function-related healthspan, social function-related healthspan, and psychological function-related healthspan). Besides, poor nutritional status, renal function injury, and high blood pressure were the most important risk factors affecting the death of veterans.

OBJECTIVE: Moderate-to-severe traumatic brain injuries (TBI) have been reported to increase the risk of Alzheimer disease (AD). Whether mild TBI (mTBI) in veterans confers a similar increased risk of AD is less known. This study investigated early AD changes using CSF biomarkers in veterans with blast mTBI.
METHODS: This was a cross-sectional case-control study of veterans with mTBI and non-mTBI veterans and civilians from 2 study sources. Blast-mTBI veterans had at least 1 war zone blast or combined blast/impact mTBI meeting Veterans Affairs (VA) and Department of Defense (DoD) criteria for mTBI. Non-mTBI participants had no lifetime history of TBI. All participants underwent standardized clinical and neuropsychological assessments and lumbar puncture for collection of the CSF. CSF biomarkers were measured using MesoScale Discovery assays for Aβ40 and Aβ42 and INNOTEST ELISAs for phosphorylated tau181 (p-tau181) and total tau (t-tau).
RESULTS: Our sample comprised 51 participants with mTBI and 85 non-mTBI participants with mean (SD) ages 34.0 (10.1) and 33.5 years (8.9), respectively. All participants but 1 (99%) were male. Differences in CSF AD biomarkers between mTBI and non-mTBI groups were age dependent and most pronounced at older ages (omnibus test p ≤ 0.08). At age 50 years, the mTBI group had lower mean [95% CI] CSF Aβ42 and Aβ40 than the non-mTBI group by 154 [-12 to 319] and 1864 [610-3,118] pg/mL, respectively. By contrast, CSF p-tau181 and t-tau mean levels remained relatively constant with age in participants with mTBI, while tending to be higher at older ages for the non-mTBI group. The mTBI group also demonstrated poorer cognitive performance at older ages (omnibus p < 0.08): at age 50 years, the mean TMT-B time was higher by 34 seconds [10-58] and the mean CVLT-II short-delay recall was lower by 4.2 points [1.9-6.6]. Poorer verbal memory and verbal fluency performance were associated with lower CSF Aβ42 (p ≤ 0.05) in older participants.
CONCLUSIONS: CSF Aβ levels decreased in middle-aged veterans with blast-related mTBI. These data suggest that chronic neuropathologic processes associated with blast mTBI share properties in common with pathogenic processes known to portend AD onset, thus raising concern that veterans with blast-related mTBI may develop a dementing disorder later in life.

UNASSIGNED: ICD-11 complex post-traumatic stress disorder is a more severe condition than post-traumatic stress disorder, and recent studies indicate it is more prevalent among military samples. In this study, we tested the psychometric properties of the International Trauma Questionnaire, assessed the relative prevalence rates of post-traumatic stress disorder and complex post-traumatic stress disorder in the sample population and explored relationships between complex post-traumatic stress disorder and post-traumatic stress disorder and a range of risk factors.
UNASSIGNED: Survey participants (N = 189) were mental health support-seeking former-serving veterans of the Australian Defence Force (ADF) recruited from primary care. Confirmatory factor analysis was used to test the factorial validity of the International Trauma Questionnaire.
UNASSIGNED: The latent structure of the International Trauma Questionnaire was best represented by a two-factor second-order model consistent with the ICD-11 model of complex post-traumatic stress disorder. The International Trauma Questionnaire scale scores demonstrated excellent internal reliability. Overall, 9.1% (95% confidence interval = [4.8%, 13.5%]) met diagnostic requirements for post-traumatic stress disorder and an additional 51.4% (95% confidence interval = [44.0%, 58.9%]) met requirements for complex post-traumatic stress disorder. Those meeting diagnostic requirements for complex post-traumatic stress disorder were more likely to have served in the military for 15 years or longer, had a history of more traumatic life events and had the highest levels of depression, anxiety and stress symptoms.
UNASSIGNED: The International Trauma Questionnaire can effectively distinguish between post-traumatic stress disorder and complex post-traumatic stress disorder within primary care samples of Australian Defence Force veterans. A significantly greater proportion of Australian Defence Force veterans met criteria for complex post-traumatic stress disorder than post-traumatic stress disorder. Australian military mental health services should adopt the International Trauma Questionnaire to routinely screen for complex post-traumatic stress disorder and develop complex post-traumatic stress disorder specific interventions to promote recovery in Australian Defence Force veterans with complex post-traumatic stress disorder.

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Despite recognition in the West that military veterans experience more mental health issues than the general population, little research has focused on this subject in China. This study examined the associations between male veterans\' military experience and depression in China.
A sample of 12,914 men including 669 veterans was included in the final analysis and propensity score matching, multivariable regressions and fixed effect model were used.
The military experience was associated with a lower likelihood of depression in male veterans. In the subgroup analysis, military experience was associated with a lower likelihood of depression among married and urban male veterans. Military experience was also associated with a lower likelihood of depression in both \"junior college and above\" and \"below junior college\" groups. In contrast, evidence was lacking regarding the associations between military experience and depression for unmarried and rural veterans.
Individual characteristics could influence the relationship between military experience and depression in male veterans, and the mental health of veterans should be paid more attention and guaranteed.

Faith-based organizations (FBOs) are often \"gatekeepers\" to mental health care for congregants at risk of mental illness and suicide, especially U.S. military Veterans, but data to inform better collaboration are needed. We conducted focus groups with clergy in Los Angeles County to understand the mental health support FBOs provide and barriers to collaboration with the mental healthcare system. Clergy detailed strategies used to support the mental health of Veteran congregants. Barriers included stigma, limits in clergy training, and incomplete knowledge about community and VA mental health resources. Results suggest strategies to improve collaboration between FBOs and the mental healthcare system in Los Angeles County.

Depression is a common mental health problem among veterans, with high mortality. Despite the numerous conducted investigations, the prediction and identification of risk factors for depression are still severely limited. This study used a deep learning algorithm to identify depression in veterans and its factors associated with clinical manifestations.
Our data originated from the National Health and Nutrition Examination Survey (2005-2018). A dataset of 2,546 veterans was identified using deep learning and five traditional machine learning algorithms with 10-fold cross-validation. Model performance was assessed by examining the area under the subject operating characteristic curve (AUC), accuracy, recall, specificity, precision, and F1 score.
Deep learning had the highest AUC (0.891, 95%CI 0.869-0.914) and specificity (0.906) in identifying depression in veterans. Further study on depression among veterans of different ages showed that the AUC values for deep learning were 0.929 (95%CI 0.904-0.955) in the middle-aged group and 0.924(95%CI 0.900-0.948) in the older age group. In addition to general health conditions, sleep difficulties, memory impairment, work incapacity, income, BMI, and chronic diseases, factors such as vitamins E and C, and palmitic acid were also identified as important influencing factors.
Compared with traditional machine learning methods, deep learning algorithms achieved optimal performance, making it conducive for identifying depression and its risk factors among veterans.