Major Depressive Disorder Prediction Based on Sleep-Wake Disorders Symptoms in US Adolescents: A Machine Learning Approach from National Sleep Research Resource.

Background: There is substantial evidence from previous studies that abnormalities in sleep parameters associated with depression are demonstrated in almost all stages of sleep architecture. Patients with symptoms of sleep-wake disorders have a much higher risk of developing major depressive disorders (MDD) compared to those without. Objective: The aim of the present study is to establish and compare the performance of different machine learning models based on sleep-wake disorder symptoms data and to select the optimal model to interpret the importance of sleep-wake disorder symptoms to predict MDD occurrence in adolescents. Methods: We derived data for this work from 2020 to 2021 Assessing Nocturnal Sleep/Wake Effects on Risk of Suicide Phase I Study from National Sleep Research Resource. Using demographic and sleep-wake disorder symptoms data as predictors and the occurrence of MDD measured base on the center for epidemiologic studies depression scale as an outcome, the following six machine learning predictive models were developed: eXtreme Gradient Boosting model (XGBoost), Light Gradient Boosting mode, AdaBoost, Gaussian Naïve Bayes, Complement Naïve Bayes, and multilayer perceptron. The models' performance was assessed using the AUC and other metrics, and the final model's predictor importance ranking was explained. Results: XGBoost is the optimal predictive model in comprehensive performance with the AUC of 0.804 in the test set. All sleep-wake disorder symptoms were significantly positively correlated with the occurrence of adolescent MDD. The insomnia severity was the most important predictor compared with the other predictors in this study. Conclusion: This machine learning predictive model based on sleep-wake disorder symptoms can help to raise the awareness of risk of symptoms between sleep-wake disorders and MDD in adolescents and improve primary care and prevention.

Causal associations between digital device use and suicide risk: A bidirectional Mendelian randomization study.

BACKGROUND: The popularity of digital devices seems to provide a new observational variable for early identification and prevention of suicide with the development of the information technology era. Nevertheless, whether it is the use of digital devices that alters suicide risk or suicide risk manifests itself through change digital device use needs to be further explored. METHODS: Bidirectional Mendelian randomization (MR) analysis was used to explore potential causal relationships in the perspective of genetic prediction. We collected publicly available digital device use and suicide risk summary statistics genome-wide association data from UK Biobank, Neale Lab and FinnGen genetic databases. We used inverse variance weighting methods to assess MR estimates. For robustness of the results, we performed further tests of heterogeneity and pleiotropy. RESULTS: In the Phase 1 results, we did not observe any effect of the length of digital device use on the suicide risk, while the results of Phase 2 suggested a significant positive association between suicide risk and the length of mobile phone use (IVW OR, 1.04; 95%CI, 1.01-1.06; P = 0.002), but this significance disappeared after adjusting for confounders of mental and affective disorders. CONCLUSIONS: In this bidirectional MR analysis, we observed that People at high risk of suicide may be more addicted to digital device use, but more detailed GWAS data and research methods to validate this finding are required.

Narcolepsy and cardiovascular disease: A two-sample Mendelian randomization study.

BACKGROUND: Observational findings suggest that patients with narcolepsy are at higher risk for cardiovascular diseases (CVDs), but the potential causal relationship between narcolepsy and CVDs is unclear. Therefore, Mendelian randomization (MR) was used to explore the association between narcolepsy and CVDs. METHODS: Summary statistics related to narcolepsy, coronary artery disease (CAD), myocardial infarction (MI), heart failure (HF), any stroke (AS), and any ischemic stroke (AIS) were extracted from the public database of relevant published genome-wide association studies (GWAS). Independent single nucleotide polymorphisms were selected as instrumental variables under strict quality control criteria. Inverse variance-weighted (IVW) was the main analytical method to assess causal effects. In addition, we conducted MR pleiotropy residual sum and outlier (MR-PRESSO), weighted median, MR-Egger, and leave-one-out sensitivity analysis to verify the robustness and reliability of the results. RESULTS: The results of the MR study revealed that narcolepsy was significantly associated with an increased risk of HF (OR = 1.714; 95%CI [1.031-2.849]; P = 0.037), CAD (OR = 1.702; 95%CI [1.011-2.864]; P = 0.045). There was no statistically significant causal association between narcolepsy and MI, AS, and AIS. In addition, further sensitivity analysis showed robust results. CONCLUSIONS: The results of the two-sample MR study reveal a potential causal relationship between the increased risk of HF and CAD in narcolepsy. These findings emphasize the importance of early monitoring and assessment of cardiovascular risk in patients with narcolepsy.

The role of robot-assisted training on rehabilitation outcomes in Parkinson's disease: a systematic review and meta-analysis.

PURPOSE: The study aims to assess the efficacy of robot-assisted rehabilitation training on upper and lower limb motor function and fatigue in Parkinson's disease (PD), and to explore the best-acting robotic rehabilitation program. METHODS: We searched studies in seven databases and the search period was from the build to 30 June 2023. Two researchers independently screened studies and assessed the quality of the studies for data extraction. RESULTS: A total of 21 studies were included, 18 studies related to lower limbs rehabilitation and 3 studies related to upper limbs rehabilitation, involving a total of 787 participants. The results showed that robot-assisted rehabilitation significantly improved indicators of lower limb motor function UPDRS Part III (WMD = -3.58, 95% CI = -5.91 to -1.25, p = 0.003) and BBS (WMD = 4.24, 95% CI = 2.88 to 5.54, p < 0.001), as well as non-motor symptoms of fatigue (WMD = -13.39, 95% CI = -17.92 to -8.86, p < 0.001) in PD patients. At the level of upper limb function, there was no statistically significant difference in the outcome measures of PFS (WMD = -0.25, 95% CI = -4.44 to 3.93, p = 0.9) and BBT (WMD = 1.73, 95% CI = -2.85 to 6.33, p = 0.458). CONCLUSION: Robot-assisted rehabilitation significantly improved motor function, fatigue, and balance confidence in PD patients, but current evidence doesn't show that intelligent rehabilitation systems improve upper limb function. In particular, robotics combined with virtual reality worked best.

Robot-assisted rehabilitation significantly improves motor symptoms, lower limb motor function, fatigue, and balance confidence in Parkinson's disease (PD) patients.Robotics combined with virtual reality is the most effective application and should be encouraged.In the robotic rehabilitation of PD patients, the focus needs to be on the duration of the training and the long-term benefits it provides.

Global comparison of the effect of non-pharmacological interventions on glycemic control in patients with type 2 diabetes: A network meta-analysis from 107 randomized controlled trials.

OBJECTIVE: To investigate the effectiveness of non-pharmacological interventions (NPIs) on glycemic control in patients with type 2 diabetes (T2D) and to provide guidance for clinical healthcare-giver. DESIGN: Network meta-analysis (NMA). SETTING AND PARTICIPANTS: Randomized controlled trials comparing the effect of NPIs with usual care, waitlist, or other NPIs on glycemic control in patients with T2D. METHODS: This NMA was guided by frequentist framework. PubMed, Embase, the Cochrane Library Central Register of Controlled Trials, Cumulated Index to Nursing and Allied Health Literature, and Web of Science were searched from their inception until January 2023. The primary outcome was HbA1c and secondary outcomes were cardiovascular risk scores and related psychosocial scores. Mean differences and standardized mean differences were pooled using NMA. Study quality was assessed with the Confidence in Network Meta-analysis. RESULTS: A total of 107 studies (10,496 participants) were included. The median sample size of the included studies was 64 (range, 10-563) and the median duration was 3 months (range, 1-24). Compared to usual care, all NPIs except acupuncture (MD: -0.28; 95 % CI: -1.02, 0.26) and psychological therapy (MD: -0.29; 95 % CI: -0.66, 0.08) showed significantly differences in improving glycemic control in patients with T2D. And according to the results of surface under the cumulative ranking analysis and Cluster ranking, meditation therapy was considered to the best choice when balancing the efficacy of glycemic control with self-efficacy and diabetes related problems, while nutrition therapy was considered to the best choice when balancing quality of life with risk of cardiovascular complications. CONCLUSIONS: These findings validate the efficacy of NPIs for glycemic control in patients with T2D and suggest that healthcare-giver should consider both the efficacy of interventions and the psychosocial needs of patients when developing NPIs programs.

Effectiveness of Wearable Device-based Intervention on Glycemic Control in Patients with Type 2 Diabetes: A System Review and Meta-Analysis.

With the development of flexible electronics and chip technology, the application value of wearable devices in lifelong treatment of chronic diseases is increasing. In view of its rapid development and diversified forms, wearable device-based intervention seems to provide a promising option to solve the problems of long-term glycemic control in type 2 diabetes (T2D). However, to date, it is unclear whether the intervention based on wearable device is effective on glycemic control in patients with T2D. In order to explore whether this choice is effective in glycemic control in patients with T2D, after database search and study screening, 6 studies and 1001 patients were selected from 181studies for this meta-analysis. The results guided that the wearable device-based intervention may be more effective than usual care on glycemic control in patients with T2D. Subgroup analysis showed that when the duration of intervention was equal to or less than 12 weeks, the effect of wearable device-based intervention was significantly different from that of usual care, but when the intervention duration greater than 12 weeks, the effect was not significantly different. The intervention effect of wearable devices with goal-setting or encouragement functions was significantly better than that of usual care, and there was no significant difference between automatic drug delivery wearable devices and usual care. In conclusion, the wearable device-based intervention is effective on glycemic control in patients with T2D. In general, this choice of wearable devices for patients with T2D may be effective to some extent.

Dissociative ionization of the 1-propanol dimer in a supersonic expansion under tunable synchrotron VUV radiation.

Photoionization and dissociation of the 1-propanol dimer and subsequent fragmentations have been investigated by synchrotron vacuum ultraviolet (VUV) photoionization mass spectrometry and theoretical calculations. Besides the protonated monomer cation (C3H7OH)·H(+) (m/z = 61) and Cα-Cß bond cleavage fragment CH2O·(C3H7OH)H(+) (m/z = 91), the measured mass spectrum at an incident photon energy of 13 eV suggests a new dissociation channel resulting in the formation of the (C3H7OH)·H(+)·(C2H5OH) (m/z = 107) fragment. The appearance energies of the fragments (C3H7OH)·H(+), CH2O·(C3H7OH)H(+) and (C3H7OH)·H(+)·(C2H5OH) are measured at 10.05 ± 0.05 eV, 9.48 ± 0.05 eV, and 12.8 ± 0.1 eV, respectively, by scanning photoionization efficiency (PIE) spectra. The 1-propanol ion fragments as a function of VUV photon energy were interpreted with the aid of theoretical calculations. In addition to O-H and Cα-Cß bond cleavage, a new dissociation channel related to Cß-Cγ bond cleavage opens. In this channel, molecular rearrangement (proton transfer and hydrogen transfer after surmounting an energy barrier) gives rise to the generated complex, which then dissociates to produce the mixed propanol/ethanol proton bound cation (C3H7OH)·H(+)·(C2H5OH). This new dissociation channel has not been reported in previous studies of ethanol and acetic acid dimers. The photoionization and dissociation processes of the 1-propanol dimer are described in the photon energy range of 9-15 eV.