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BackgroundDespite chronic obstructive pulmonary disease’s (COPD)'s prevalence in the general populace, its incidence in heart failure (HF) patients is understudied. This study aimed to assess COPD prevalence and clinical associations in Chinese HF patients.MethodsFrom the Chinese Heart Failure Study, demographic and clinical details of 2008 HF patients were analyzed. Divided into 233 COPD cases and 1775 non-COPD controls, a multivariable logistic regression identified factors linked to COPD onset in HF, with thorough examination of intergroup clinical differences.ResultsThe incidence of COPD in HF individuals was 11.60% (233/2008). The COPD subgroup featured a higher ratio of individuals over 60 and males, alongside lower systolic blood pressure (SBP), body mass index (BMI), higher Charlson Comorbidity Index (CCI) scores, and increased PaCO₂ levels (p

ObjectiveThe aim of this research was to examine the association between alcohol consumption and depressive symptoms in the Wa ethnic minority of China, a population where this relationship has been insufficiently explored.MethodsA cross-sectional analysis was conducted on a sample of 317 current drinkers from the Wa ethnic minority, a “direct fast-forward” group in Cangyuan County, between December 1, 2022, and February 28, 2023. Utilizing a multi-stage cluster sampling approach, participants were selected, each of whom exhibited an Alcohol Use Disorder Identification Test (AUDIT) score exceeding 0. Data were collected via face-to-face interviews employing a uniform questionnaire. Quantification of alcohol use was achieved through the application of the AUDIT, while the Patient Health Questionnaire (PHQ)-9 was employed to gauge depressive symptoms. The relationship between the severity of alcohol consumption and depressive symptoms was evaluated using a multivariable logistic regression model. Subsequently, potential non-linear associations were scrutinized through the application of a smoothing plot.ResultsThe study included 317 current drinkers (196 males [61.83%]; mean age 41.78 ± 12.91 years), of which 94 (29.65%) exhibited comorbid depressive symptoms. Multivariable logistic regression analysis, adjusting for confounders, revealed that higher AUDIT scores were significantly associated with an increased probability of depressive symptoms (OR = 1.09, 95% CI: 1.02–1.18, p = 0.008). The risk of depressive symptoms was notably greater in the group with alcohol dependent range in contrast the group at low-risk range (OR = 7.01, 95% CI: 1.66–29.62, p = 0.008). The smoothing plot indicated a J-shaped non-linear relationship with an inflection point at an AUDIT score of 15. To the left of this inflection point, no significant relationship was observed, whereas to the right, a positive correlation emerged (OR = 1.39, 95% CI: 1.11–1.74, p

ObjectiveThe risk of suicide can be decreased by accurately identifying high-risk suicide groups and implementing the right interventions. The aim of this study was to develop a nomogram for suicide attempts (SA) in patients with first-episode drug-naïve (FEDN) major depressive disorder (MDD).MethodsThis study undertook a cross-sectional analysis of 1,718 patients diagnosed with FEDN MDD, providing comprehensive clinical data from September 2016 to December 2018. Data on anthropometric and sociodemographic factors were gathered, and the severity of depression and anxiety was evaluated using the 17-item Hamilton Depression Scale (HAMD-17) and the Hamilton Anxiety Scale (HAMA), respectively. Additionally, thyroid hormone levels, lipid profile parameters, and fasting blood glucose (FBG) were measured. Suicide attempt (SA) history was verified based on an amalgamation of medical records, patient interviews, and family interviews. Participants were randomly divided into a training group (70%, n = 1,204) and a validation group (30%, n = 514). In the training group, LASSO analysis and multivariate regression were used to identify variables associated with SA. A nomogram was then constructed using the identified risk factors to estimate the likelihood of SA within the training group. To assess the accuracy, the area under the receiver operating characteristic curve (AUC) was utilized, and calibration plots were employed to evaluate calibration. Additionally, decision curve analysis (DCA) was performed to assess the precision of the model. Finally, internal validation was carried out using the validation group.ResultsA practical nomogram has been successfully constructed, incorporating HAMD, HAMA, thyroid stimulating hormone (TSH), thyroid peroxidase antibody (TPOAb), and systolic blood pressure (SBP) parameters, to estimate the probability of SA in Chinese patients diagnosed with FEDN MDD. The pooled area under the ROC for SA risk in both the training and validation groups was found to be 0.802 (95% CI: 0.771 to 0.832) and 0.821 (95% CI: 0.774 to 0.868), respectively. Calibration analysis revealed a satisfactory correlation between the nomogram probabilities and the actual observed probabilities. The clinical applicability of the nomogram was confirmed through decision curve analysis. To enhance accessibility for clinicians and researchers, an online version of the nomogram can be accessed at https://doctorjunjunliu.shinyapps.io/dynnomapp/.ConclusionsWe constructed and validated a nomogram for the early detection of FEDN MDD patients with a high risk of SA, thereby contributing to the implementation of effective suicide prevention programs.

ObjectiveMajor depressive disorder (MDD) sufferers frequently have psychotic symptoms, yet the underlying triggers remain elusive. Prior research suggests a link between insulin resistance (IR) and increased occurrence of psychotic symptoms. Hence, this study sought to investigate the potential association between psychotic symptoms in Chinese patients experiencing their first-episode drug-naïve (FEDN) MDD and the triglyceride glucose (TyG) index, an alternative measure of insulin resistance (IR).MethodsBetween September 2016 and December 2018, 1,718 FEDN MDD patients with an average age of 34.9 ± 12.4 years were recruited for this cross-sectional study at the First Hospital of Shanxi Medical University in China. The study collected clinical and demographic data and included assessments of anxiety, depression, and psychotic symptoms using the 14-item Hamilton Anxiety Rating Scale (HAMA), the 17-item Hamilton Depression Rating Scale (HAMD-17), and the positive subscales of the Positive and Negative Syndrome Scale (PANSS), respectively. Measurements of metabolic parameters, fasting blood glucose (FBG), and thyroid hormones were also gathered. To assess the correlation between the TyG index and the likelihood of psychotic symptoms, the study used multivariable binary logistic regression analysis. Additionally, two-segmented linear regression models were employed to investigate possible threshold effects in case non-linearity relationships were identified.ResultsAmong the patients, 9.95% (171 out of 1,718) exhibited psychotic symptoms. Multivariable logistic regression analysis showed a positive correlation between the TyG index and the likelihood of psychotic symptoms (OR = 2.12, 95% CI: 1.21-3.74, P = 0.01) after adjusting for confounding variables. Moreover, smoothed plots revealed a nonlinear relationship with the TyG index, revealing an inflection point at 8.42. Interestingly, no significant link was observed to the left of the inflection point (OR = 0.50, 95% CI: 0.04-6.64, P = 0.60), whereas beyond this point, a positive correlation emerged between the TyG index and psychotic symptoms (OR = 2.42, 95% CI: 1.31-4.48, P = 0.01). Particularly, a considerable 142% rise in the probability of experiencing psychotic symptoms was found with each incremental elevation in the TyG index.ConclusionsUnderstanding the non-linear link between the TyG index and the risk of psychotic symptoms in Chinese patients with FEDN MDD highlights the potential for targeted therapeutic approaches. By acknowledging the threshold effect observed, there is an opportunity to mitigate risk factors associated with IR-related psychiatric comorbidities through tailored interventions. These preliminary results stress the need for further longitudinal research to solidify these insights and contribute to more effective therapeutic strategies.

We used unmanned aerial vehicles (UAVs) to carry out a relatively complete population census of large wild herbivores in Maduo County on the Tibetan Plateau in the spring of 2017. The effective area covered by aerial surveys was 326.6 km2, and 23,784 images were acquired. Interpretation tag libraries for UAV images were created for wild animals, including Kiang (Equus kiang), Tibetan gazelle (Procapra picticaudata), and blue sheep (Pseudois nayaur), as well as livestock, including yaks and Tibetan sheep. Large wild herbivores in the survey transect were identified through manual imagery interpretation. Densities ranged from 1.15/km2 for Kiang, 0.61/km2 for Tibetan gazelle, 0.62/km2 for blue sheep, 4.12/km2 for domestic yak, and 7.34/km2 for domestic sheep. A method based on meadows in the cold and warm seasons was used for estimating the densities and numbers of large wild herbivores and livestock, and was verified against records of livestock numbers. Population estimates for Kiang, Tibetan gazelle, blue sheep, domestic yak, and domestic sheep were 17,109, 15,961, 9324, 70,846, and 102,194, respectively. Based on published consumption estimates, the results suggest that domestic stock consume 4.5 times the amount of vegetation of large wild herbivores. Compared with traditional ground survey methods, performance of UAV remote sensing surveys of large wild herbivore populations was fast, economical and reliable, providing an effective future method for surveying wild animals.

Ice cover on pavement may reduce the road adhesion coefficient and increase the crash risks, which might result in more traffic crashes. The primary factor utilized to assess whether the wet pavement is icy or not is the pavement temperature. Therefore, forecasting pavement temperature is an effective method to judge road conditions and improve traffic safety. This paper proposes a combination model based on the extreme gradient boosting (XGBoost) model and long- and short-term time-series network (LSTNet) model to predict pavement temperature. Pavement temperature and meteorological data were collected for the cities along the Shandong part of the Beijing-Taipei Expressway (G3). In this study, nine meteorological variables were used. Subsequently, after correlation analysis, five variables, including air temperature, dew point temperature, relative humidity, evaporation, and potential evaporation, were selected for prediction. The method proposed in this study comprises the following steps. First, the XGBoost and the LSTNet models are respectively formulated based on the time-varying characteristics of pavement temperatures. Then, using the preset weight of the variable, the XGBoost model is used for preliminary prediction to add features. Finally, the experimental analysis is performed on the Qihe data set after the two models have been integrated using the inverse variance method. As revealed by the experimental results, the mean absolute error (MAE) and root-mean-square error (RMSE) of the proposed XGBoost-LSTNet model are 0.8235 and 1.2412, respectively. Compared with the long short-term memory (LSTM) model, random forest (RF) model, XGBoost model, and LSTNet model, the XGBoost-LSTNet model proposed in this paper has higher accuracy. The study’s findings can successfully increase wintertime expressway traffic safety and serve as a guide for managing maintenance and preventing icing-related accidents. [ABSTRACT FROM AUTHOR]