Annals of medicine最新文献

Background: Intestinal barrier dysfunction and systemic inflammation are common in obstructive sleep apnoea (OSA). We aimed to investigate the role of melatonin, an anti-inflammatory mediator, in mediating the relationships between OSA, intestinal barrier dysfunction and systemic inflammation.

Methods: Two hundred and thirty-five male participants who complained with sleep problems and underwent whole night polysomnography at our sleep centre between 2017 and 2018 were enrolled. Polysomnographic data, anthropometric measurements and biochemical indicators were collected. Serum melatonin, intestinal barrier function biomarker zonula occludens-1 (ZO-1) and inflammatory biomarkers C-reactive protein (CRP) with lipopolysaccharide (LPS) were detected. Spearman's correlation analysis assessed the correlations between sleep parameters, melatonin and biomarkers (ZO-1, LPS and CRP). Mediation analysis explored the effect of OSA on intestinal barrier dysfunction and systemic inflammation in moderate-severe OSA patients.

Results: As OSA severity increased, serum melatonin decreased, whereas ZO-1, LPS and CRP increased. Spearman's correlation analysis showed that serum melatonin was significantly negatively correlated with ZO-1 (r = -0.19, p < .05) and LPS (r = -0.20, p < .05) in the moderate-OSA group; serum melatonin was significantly negatively correlated with ZO-1 (r = -0.46, p < .01), LPS (r = -0.35, p < .01) and CPR (r = -0.30, p < .05) in the severe-OSA group. Mediation analyses showed melatonin explain 36.12% and 35.38% of the effect of apnoea-hypopnea index (AHI) on ZO-1 and LPS in moderate to severe OSA patients.

Conclusions: Our study revealed that melatonin may be involved in mediating intestinal barrier dysfunction and systemic inflammation in moderate-to-severe OSA patients.

Background: Patients with hip fractures frequently need to receive perioperative transfusions of concentrated red blood cells due to preoperative anemia or surgical blood loss. However, the use of perioperative blood products increases the risk of adverse events, and the shortage of blood products is prompting us to minimize blood transfusion. Our study aimed to construct a machine learning algorithm predictive model to identify patients at high risk for perioperative transfusion early in hospital admission and to manage their patient blood to reduce transfusion requirements.

Methods: This study collected patients hospitalized for hip fractures at a university hospital from May 2016 to November 2022. All patients included in the analysis were randomly divided into a training set and validation set according to 70:30. Eight machine learning algorithms, CART, GBM, KNN, LR, NNet, RF, SVM, and XGBoost, were used to construct the prediction models. The models were evaluated for discrimination, calibration, and clinical utility, and the best prediction model was selected.

Results: A total of 805 patients were included in the study, of whom 306 received transfusions during the perioperative period. We screened eight features used to construct the prediction model: age, fracture time, fracture type, hemoglobin, albumin, creatinine, calcium ion, and activated partial thromboplastin time. After evaluating and comparing the performance of each of the eight models, the model constructed by the XGBoost algorithm had the best performance, with MCC values of 0.828 and 0.939 in the training and validation sets, respectively. In addition, it had good calibration and clinical utility in both the training and validation sets.

Conclusion: The model constructed by the XGBoost algorithm has the best performance, using this model to identify patients at high risk for transfusion early in their admission and promptly incorporating them into a patient blood management plan can help reduce the risk of transfusion.

Background: Racial and ethnic disparities are evident in the accessibility of treatment for opioid use disorder (OUD). Even when medications for OUD (MOUD) are accessible, racially and ethnically minoritized groups have higher attrition rates from treatment. Existing literature has primarily identified the specific racial and ethnic groups affected by these disparities, but has not thoroughly examined interventions to address this gap. Recovery peer navigators (RPNs) have been shown to improve access and overall retention on MOUD.

Patients and methods: In this retrospective cohort study, we evaluate the role of RPNs on patient retention in clinical care at an outpatient program in a racially and ethnically diverse urban community. Charts were reviewed of new patients seen from January 1, 2019 through December 31, 2019. Sociodemographic and clinical visit data, including which providers and services were utilized, were collected, and the primary outcome of interest was continuous retention in care. Bivariate analysis was done to test for statistically significant associations between variables by racial/ethnic group and continuous retention in care using Student's t-test or Pearson's chi-square test. Variables with p value ≤0.10 were included in a multivariable regression model.

Results: A total of 131 new patients were included in the study. RPNs improved continuous retention in all-group analysis (27.6% pre-RPN compared to 80.2% post-RPN). Improvements in continuous retention were observed in all racial/ethnic subgroups but were statistically significant in the non-Hispanic Black (NHB) group (p < 0.001). Among NHB, increases in continuous retention were observed post-RPN in patients with male sex (p < 0.001), public health insurance (p < 0.001), additional substance use (p < 0.001), medical comorbidities (p < 0.001), psychiatric comorbidities (p = 0.001), and unstable housing (p = 0.005). Multivariate logistic regression demonstrated that patients who lacked insurance had lower odds of continuous retention compared to patients with public insurance (aOR = 0.17, 95% CI 0.039-0.70, p = 0.015).

Conclusions: RPNs can improve clinical retention for patients with OUD, particularly for individuals experiencing several sociodemographic and clinical factors that are typically correlated with discontinuation of care.

Aim: The use of tobacco is responsible for many preventable diseases and deaths worldwide. Digital interventions have greatly improved patient health and clinical care and have proven to be effective for quitting smoking in the general population due to their flexibility and potential for personalization. However, there is limited evidence on the effectiveness of digital interventions for smoking cessation in Asian countries.

Methods: Three major databases - Web of Science (WOS), Scopus, and PubMed - for relevant studies published between 1 January 2010 and 12 February 2023 were searched for studies evaluating the effectiveness of digital intervention for smoking cessation in Asian countries.

Results: A total of 25 studies of varying designs were eligible for this study collectively involving a total of n = 22,005 participants from 9 countries. Among different digital tools for smoking cessation, the highest abstinence rate (70%) was reported with cognitive behavioural theory (CBT)-based smoking cessation intervention via Facebook followed by smartphone app (60%), WhatsApp (59.9%), and Pharmacist counselling with Quit US smartphone app (58.4%). However, WhatsApp was preferred over Facebook intervention due to lower rates of relapse. WeChat was responsible for 15.6% and 41.8% 7-day point prevalence abstinence. For telephone/text messaging abstinence rate ranged from 8-44.3% and quit rates from 6.3% to 16.8%. Whereas, no significant impact of media/multimedia messages and web-based learning on smoking cessation was observed in this study.

Conclusion: Based on the study findings the use of digital tools can be considered an alternative and cost-effective smoking cessation intervention as compared to traditional smoking cessation interventions.

Background: A monkeypox (MPOX) outbreak occurred in May 2022. On June 3, 2022, the WHO Blueprint organized a consultation on MPOX research knowledge gaps and priority research questions because the engagement of health care providers (HCPs) in providing accurate information and the public's motivation to adapt protective behaviour were crucial. Thus, we conducted this study to explore the knowledge issues, animal patterns, and interactions of HCPs in the context of MPOX and COVID-19 during the MPOX outbreak.

Methods: We conducted a cross-sectional web-based survey among 816 HCPs working in governmental health facilities from many countries, mainly Syria, Egypt, Saudi Arabia, and Cameroon, in September 2022.

Results: Four hundred and sixty (56.37%) were aged between 18 and less than 35 years old. About 34.44% were physicians, while only 37.25% worked on the frontlines with patients. 37.99% and 5.88% received vaccinations against chickenpox and MPOX, respectively. In the meantime, 55.39% had taken courses or training programmes regarding COVID-19. Regarding knowledge-seeking behaviours (KSBs) about COVID-19, 38.73% were through passive attention, while only 28.8% got their information through active search. Most of the participants (56.86%) had a moderate level of knowledge regarding COVID-19. Only 8.82% had courses or training programmes regarding MPOX. Regarding KSB about MPOX, 50.86% were obtained through passive attention, while only 18.01% and 23.04% got their information through active and passive search, respectively. Most of the participants (57.60%) had a poor level of knowledge regarding MPOX. The regression analysis of the MPOX knowledge score revealed that individuals working on the frontlines with patients and those who had training programmes or courses were shown to have a higher score by 1.25 and 3.18 points, respectively.

Conclusions: The studied HCPs had poorer knowledge about the MPOX virus than they did about the SARS-CoV-2 virus. Training programmes and education courses had an impact on their knowledge.

Background: Animal and human studies have shown that exposure to hypoxia can increase brain-derived neurotrophic factor (BDNF) protein transcription and reduce systematic inflammatory cytokine response. Therefore, the aim of this study was to investigate the acute and chronic effects of intermittent hypoxic-hyperoxic exposure (IHHE) prior to aerobic exercise on BDNF, interleukin-6 (IL-6), and C-reactive protein (CRP) blood levels in geriatric patients.

Patients and methods: Twenty-five geriatric patients (83.1 ± 5.0 yrs, 71.1 ± 10.0 kg, 1.8 ± 0.9 m) participated in a placebo-controlled, single-blinded trial and were randomly assigned to either an intervention (IG) or control group (CG) performing an aerobic cycling training (17 sessions, 20 min·session^-1, 3 sessions·week^-1). Prior to aerobic cycling exercise, the IG was additionally exposed to IHHE for 30 min, whereas the CG received continuous normoxic air. Blood samples were taken immediately before (pre-exercise) and 10 min (post-exercise) after the first session as well as 48 h (post-training) after the last session to determine serum (BDNF_S) and plasma BDNF (BDNF_P), IL-6, and CRP levels. Intervention effects were analyzed using a 2 x 2 analysis of covariance with repeated measures. Results were interpreted based on effect sizes with a medium effect considered as meaningful (η_p² ≥ 0.06, d ≥ 0.5).

Results: CRP was moderately higher (d = 0.51) in the CG compared to the IG at baseline. IHHE had no acute effect on BDNF_S (η_p² = 0.01), BDNF_P (η_p² < 0.01), BDNF serum/plasma-ratio (η_p² < 0.01), IL-6 (η_p² < 0.01), or CRP (η_p² = 0.04). After the 6-week intervention, an interaction was found for BDNF serum/plasma-ratio (η_p² = 0.06) but not for BDNF_S (η_p² = 0.04), BDNF_P (η_p² < 0.01), IL-6 (η_p² < 0.01), or CRP (η_p² < 0.01). BDNF serum/plasma-ratio increased from pre-exercise to post-training (d = 0.67) in the CG compared to the IG (d = 0.51). A main effect of time was found for BDNF_P (η_p² = 0.09) but not for BDNF_S (η_p² = 0.02). Within-group post-hoc analyses revealed a training-related reduction in BDNF_P in the IG and CG by 46.1% (d = 0.73) and 24.7% (d = 0.57), respectively.

Conclusion: The addition of 30 min IHHE prior to 20 min aerobic cycling seems not to be effective to increase BDNF_S and BDNF_P or to reduce IL-6 and CRP levels in geriatric patients after a 6-week intervention.The study was retrospectively registered at drks.de (DRKS-ID: DRKS00025130).

Background: The incidence of mortality is considerable after ST-elevation myocardial infarction (STEMI) hospitalization; risk assessment is needed to guide postdischarge management. Age shock index (SI) and age modified shock index (MSI) were described as useful prognosis instruments; nevertheless, their predictive effect on short and long-term postdischarge mortality has not yet been sufficiently confirmed.

Methods: This analysis included 3389 prospective patients enrolled from 2016 to 2018. Endpoints were postdischarge mortality within 30 days and from 30 days to 1 year. Hazard ratios (HRs) were evaluated by Cox proportional-hazards regression. Predictive performances were assessed by area under the curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI) and decision curve analysis (DCA) and compared with TIMI risk score and GRACE score.

Results: The AUCs were 0.753, 0.746 for age SI and 0.755, 0.755 for age MSI for short- and long-term postdischarge mortality. No significant AUC differences and NRI were observed compared with the classic scores; decreased IDI was observed especially for long-term postdischarge mortality. Multivariate analysis revealed significantly higher short- and long-term postdischarge mortality for patients with high age SI (HR: 5.44 (2.73-10.85), 5.34(3.18-8.96)), high age MSI (HR: 4.17(1.78-9.79), 5.75(3.20-10.31)) compared to counterparts with low indices. DCA observed comparable clinical usefulness for predicting short-term postdischarge mortality. Furthermore, age SI and age MSI were not significantly associated with postdischarge prognosis for patients who received fibrinolysis.

Conclusions: Age SI and age MSI were valuable instruments to identify high postdischarge mortality with comparable predictive ability compared with the classic scores, especially for events within 30 days after hospitalization.

Background: The beneficial effects of a plant-based diet on gut microbiota diversity are well documented, however, its impact on clinical bowel health and defecation patterns are less well understood. Vegetarian diets have been associated with a higher bowel movement (BM) frequency as well as softer stools in cross-sectional studies. The effects of the de-novo adoption of a vegan diet on bowel health, however, have never been investigated in a randomized-controlled trial.

Materials and methods: The present study examined bowel health and defecation patterns in relation to diet and nutrient intake in a young and healthy sample of n = 65 physically-active German university students who were randomly assigned to either a vegan or a meat-rich diet for eight weeks. Bowel health assessment included the Bristol Stool Form Scale (BSFS), the Gastrointestinal Quality of Life Index (GIQLI) and the Cleveland Clinic Fecal Incontinence Score (CCFIS). Nutrient intake was assessed using weighed food diaries. The study was prospectively registered at the German Clinical Trial Register (DRKS00031541).

Results: Weekly BM frequency slightly increased in vegans, whereas it remained unaltered in participants assigned to a meat-rich diet. Fiber intake increased significantly in vegans (34.89 (18.46) g/d) whereas it decreased in those assigned to the meat-rich group (22.79 (12.5) g/d). No significant intergroup differences in BSFS and CCFIS patterns were observed. Adoption of a vegan diet neither resulted in a transient increase in abdominal discomfort nor in a decreased gastrointestinal quality of life, which was comparable across the diet groups.

Conclusions: The short-term de-novo adoption of a vegan diet did not negatively affect markers of bowel health in this study.

Objective: Breast cancer is the most common cancer in women, threatening both physical and mental health. The epidemiological evidence for association between sleep duration, depression and breast cancer is inconsistent. The aim of this study was to determine the association between them and build machine-learning algorithms to predict breast cancer.

Methods: A total of 1,789 participants from the National Health and Nutrition Examination Survey (NHANES) were included in the study, and 263 breast cancer patients were identified. Sleep duration was collected using a standardized questionnaire, and the Nine-item Patient Health Questionnaire (PHQ-9) was used to assess depression. Logistic regression yielded multivariable-adjusted breast cancer odds ratios (OR) and 95% confidence intervals (CI) for sleep duration and depression. Then, six machine learning algorithms, including AdaBoost, random forest, Boost tree, artificial neural network, limit gradient enhancement and support vector machine, were used to predict the development of breast cancer and find out the best algorithm.

Results: Body mass index (BMI), race and smoking were statistically different between breast cancer and non-breast cancer groups. Participants with depression were associated with breast cancer (OR = 1.99, 95%CI: 1.55-3.51). Compared with 7-9h of sleep, the ORs for <7 and >9 h of sleep were 1.25 (95% CI: 0.85-1.37) and 1.05 (95% CI: 0.95-1.15), respectively. The AdaBoost model outperformed other machine learning algorithms and predicted well for breast cancer, with an area under curve (AUC) of 0.84 (95%CI: 0.81-0.87).

Conclusions: No significant association was observed between sleep duration and breast cancer, and participants with depression were associated with an increased risk for breast cancer. This finding provides new clues into the relationship between breast cancer and depression and sleep duration, and provides potential evidence for subsequent studies of pathological mechanisms.