Bridging the mental health gap: unveiling and mitigating the hidden toll of workplace behaviors on diverse populations.

This study investigates the critical issue of mental health disparities within diverse populations in modern workplaces, a concern that significantly affects both individuals and organizational structures. By focusing on how prevailing workplace behaviors, including implicit biases, microaggressions, and the scarcity of diversity in leadership, exacerbate these disparities, the research highlights the urgent need for attention and action in this area. The mental health gap-disparities in conditions and access to care among different workplace groups-emerges from systemic inequalities and stigmatization, deeply influencing employee productivity, creativity, collaboration, and retention. Our research underscores the disproportionate impact of this gap on diverse populations, characterized by varying ethnicity, gender, age, socio-economic status, and other unique identity attributes. The paper articulates the substantial economic repercussions for organizations, manifesting as reduced productivity, increased absenteeism, and higher turnover rates. Recommendations include the implementation of cultural competency training, promotion of inclusive leadership, investment in tailored mental health resources and fostering open dialog about mental health. These strategies are pivotal in creating an inclusive, resilient, and harmonious work environment. Our findings aim to catalyze a shift in organizational practices toward mental well-being, advocating for comprehensive strategies to bridge the mental health divide in workplaces, thereby enhancing overall organizational health and cohesion.

Current and Expected Trends for the Marine Chitin/Chitosan and Collagen Value Chains.

Chitin/chitosan and collagen are two of the most important bioactive compounds, with applications in the pharmaceutical, veterinary, nutraceutical, cosmetic, biomaterials, and other industries. When extracted from non-edible parts of fish and shellfish, by-catches, and invasive species, their use contributes to a more sustainable and circular economy. The present article reviews the scientific knowledge and publication trends along the marine chitin/chitosan and collagen value chains and assesses how researchers, industry players, and end-users can bridge the gap between scientific understanding and industrial applications. Overall, research on chitin/chitosan remains focused on the compound itself rather than its market applications. Still, chitin/chitosan use is expected to increase in food and biomedical applications, while that of collagen is expected to increase in biomedical, cosmetic, pharmaceutical, and nutritional applications. Sustainable practices, such as the reuse of waste materials, contribute to strengthen both value chains; the identified weaknesses include the lack of studies considering market trends, social sustainability, and profitability, as well as insufficient examination of intellectual property rights. Government regulations, market demand, consumer preferences, technological advancements, environmental challenges, and legal frameworks play significant roles in shaping both value chains. Addressing these factors is crucial for seizing opportunities, fostering sustainability, complying with regulations, and maintaining competitiveness in these constantly evolving value chains.

Development and assessment of machine learning models for predicting recurrence risk after endovascular treatment in patients with intracranial aneurysms.

Intracranial aneurysms (IAs) remain a major public health concern and endovascular treatment (EVT) has become a major tool for managing IAs. However, the recurrence rate of IAs after EVT is relatively high, which may lead to the risk for aneurysm re-rupture and re-bleed. Thus, we aimed to develop and assess prediction models based on machine learning (ML) algorithms to predict recurrence risk among patients with IAs after EVT in 6 months. Patient population included patients with IAs after EVT between January 2016 and August 2019 in Hunan Provincial People's Hospital, and an adaptive synthetic (ADASYN) sampling approach was applied for the entire imbalanced dataset. We developed five ML models and assessed the models. In addition, we used SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. A total of 425 IAs were enrolled into this study, and 66 (15.5%) of which recurred in 6 months. Among the five ML models, gradient boosting decision tree (GBDT) model performed best. The area under curve (AUC) of the GBDT model on the testing set was 0.842 (sensitivity: 81.2%; specificity: 70.4%). Our study firstly demonstrated that ML-based models can serve as a reliable tool for predicting recurrence risk in patients with IAs after EVT in 6 months, and the GBDT model showed the optimal prediction performance.

Stretchable, Self-Healing, and Skin-Mounted Active Sensor for Multipoint Muscle Function Assessment.

Assessment of muscle function is an essential indicator for estimating elderly health, evaluating motor function, and instructing rehabilitation training, which also sets urgent requirements for mechanical sensors with superior quantification, accuracy, and reliability. To overcome the rigidity and vulnerability of traditional metallic electrodes, we synthesize an ionic hydrogel with large deformation tolerance and fast self-healing ability. And we propose a stretchable, self-healing, and skin-mounted (Triple S) active sensor (TSAS) based on the principles of electrostatic induction and electrostatic coupling. The skin modulus-matched TSAS provides outstanding sensing properties: maximum output voltage of 78.44 V, minimal detection limit of 0.2 mN, fast response time of 1.03 ms, high signal-to-noise ratio and excellent long-term service stability. In training of arm muscle, the functional signals of biceps and triceps brachii muscles as well as the joint dexterity of bending angle can be acquired simultaneously through TSAS. The signal can also be sent wirelessly to a terminal for analysis. With the characteristics of high sensitivity, reliability, convenience, and low-cost, TSAS shows its potential to be the next-generation procedure for real-time assessment of muscle function and rehabilitation training.

Prevalence, risk factors and genotype distribution of Toxoplasma gondii DNA in soil in China.

In the present study, we performed a cross-sectional survey to determine the occurrence and genotype distribution of T. gondii DNA in soil samples collected from different sources from six geographic regions in China. Between March 2015 and June 2017, 2100 soil samples were collected from schools, parks, farms and coastal beaches, and examined for T. gondii DNA using three PCR assays targeting 529-bp repeat element (RE) sequence, B1 gene and ITS-1 gene sequences. Also, we investigated whether geographic region, soil source and type, and sampling season can influence the prevalence of T. gondii DNA in the soil. Soil samples collected from farms and parks had the highest prevalence, whereas samples collected from school playgrounds and coastal beaches had the lowest prevalence. PCR assays targeting 529-bp RE and ITS-1 gene sequences were more sensitive than the B1 gene-based assay. Positive PCR products were genotyped using multi-locus PCR-RFLP, and ToxoDB #9 was the predominant genotype found in the contaminated soil samples. Multiple logistic regression identified factors correlated significantly with the presence of T. gondii DNA in the soil to be the source of the soil, including farms (odds ratio 3.10; 95% confidence interval [CI], 1.52 to 6.29; p = 0.002) and parks (2.59; 95% CI 1.28 to 5.27; p = 0.009). These results show that Chinese soil hosts T. gondii of the most prevalent genotype in China (ToxoDB#9) and that the soil type influences infection patterns.

Systematic estimation of BMI: A novel insight into predicting overweight/obesity in undergraduates.

The prevalence of overweight-obesity has increased sharply among undergraduates worldwide. In 2016, approximately 52% of adults were overweight-obese. This cross-sectional study aimed to investigate the prevalence of overweight-obesity and explore in depth the connection between eating habits and overweight-obesity among Chinese undergraduates.The study population included 536 undergraduates recruited in Shijiazhuang, China, in 2017. They were administered questionnaires for assessing demographic and daily lifestyle characteristics, including sex, region, eating speed, number of meals per day, and sweetmeat habit. Anthropometric status was assessed by calculating the body mass index (BMI). The determinants of overweight-obesity were investigated by the Pearson χ test, Spearman rho test, multivariable linear regression, univariate/multivariate logistic regression, and receiver operating characteristic curve analysis.The prevalence of undergraduate overweight-obesity was 13.6%. Sex [male vs female, odds ratio (OR): 1.903; 95% confidence interval (95% CI): 1.147-3.156], region (urban vs rural, OR: 1.953; 95% CI: 1.178-3.240), number of meals per day (3 vs 2, OR: 0.290; 95% CI: 0.137-0.612), and sweetmeat habit (every day vs never, OR: 4.167; 95% CI: 1.090-15.933) were significantly associated with overweight-obesity. Eating very fast was positively associated with overweight-obesity and showed the highest OR (vs very slow/slow, OR: 5.486; 95% CI: 1.622-18.553). However, the results of multivariate logistic regression analysis indicated that only higher eating speed is a significant independent risk factor for overweight/obesity (OR: 17.392; 95% CI, 1.614-187.363; P = .019).Scoremeng = 1.402 × scoresex + 1.269 × scoreregion + 19.004 × scoreeatin speed + 2.546 × scorenumber of meals per day + 1.626 × scoresweetmeat habit and BMI = 0.253 × Scoremeng + 18.592. These 2 formulas can help estimate the weight status of undergraduates and predict whether they will be overweight or obese.

A robust statistical procedure to discover expression biomarkers using microarray genomic expression data.

Microarray has become increasingly popular biotechnology in biological and medical researches, and has been widely applied in classification of treatment subtypes using expression patterns of biomarkers. We developed a statistical procedure to identify expression biomarkers for treatment subtype classification by constructing an F-statistic based on Henderson method III. Monte Carlo simulations were conducted to examine the robustness and efficiency of the proposed method. Simulation results showed that our method could provide satisfying power of identifying differentially expressed genes (DEGs) with false discovery rate (FDR) lower than the given type I error rate. In addition, we analyzed a leukemia dataset collected from 38 leukemia patients with 27 samples diagnosed as acute lymphoblastic leukemia (ALL) and 11 samples as acute myeloid leukemia (AML). We compared our results with those from the methods of significance analysis of microarray (SAM) and microarray analysis of variance (MAANOVA). Among these three methods, only expression biomarkers identified by our method can precisely identify the three human acute leukemia subtypes.