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Abstract Humans naturally employ muscle coactivation to facilitate a broad range of movements, enhancing joint stability and movement accuracy. However, excessive muscle coactivation can become unfavorable or even detrimental. This phenomenon is often observed in industrial workers who endure repetitive or prolonged joint stress, particularly in areas such as the shoulders. Prolonged stress can result in soft tissue damage and the onset of work-related musculoskeletal disorders (MSDs). In recent years, there have been efforts to mitigate the emergence of work-related MSDs among industrial workers through the implementation of upper-limb occupational exoskeletons (OEs). While previous research has demonstrated their effectiveness in reducing shoulder muscle activation, particularly in static and overhead work activities, there has been a lack of studies examining the impact of upper-limb OEs on muscle coactivation during repetitive arm movements. To bridge this gap in knowledge, our study systematically assesses the influence of a passive exoskeleton’s anti-gravitational support on shoulder muscle coactivation during repetitive arm movements. Results show that peak and mean coactivation levels linearly decrease with the increase of the amount of anti-gravitational support provided by the upper-limb OE, reaching approximately 51% and 54%, respectively. Conversely, the percentage of the movement cycle corresponding to the coactivation peak appears unaffected by the level of assistance. This study marks the first instance in which a passive upper-limb OE has been shown to reduce shoulder muscle coactivations, potentially paving the way for a novel methodology in their evaluation.

Abstract Background Hoarding disorder (HD) is characterized by a compulsion to collect belongings, and to experience significant distress when parting from them. HD is often misdiagnosed for several reasons. These include patient and family lack of recognition that it is a psychiatric disorder and professionals’ lack of relevant expertise with it. This study evaluates the ability of a supervised machine learning (ML) model to match the diagnostic skills of psychiatrists when presented with equivalent information pertinent to symptoms of HD. Methods Five hundred online participants were randomly recruited and completed the Hoarding Rating Scale-Self Report (HRS-SR) and the Generalized Anxiety Disorder 7-item (GAD-7) scale. Responses to the questionnaires were read by an ML model. Responses to the HRS-SR were then converted into anonymized, random-equivalent texts. Each of these individual texts was presented in random order to two experienced psychiatrists who were independently asked for a provisional diagnosis - e.g.; the presence or absence of HD. In case of disagreement between the two assessors, a third psychiatrist broke the tie. A decision tree classification model was employed to predict clinical HD using self-report data from two psychological tests, the HRS-SR and GAD-7. The target variable was whether a participant had clinical HD, while the predictive variables were the continuous scores from the HRS-SR and GAD-7 tests. The model’s performance was evaluated using a confusion matrix, which compared the observed diagnoses with the predicted diagnoses to assess accuracy. Results According to the psychiatrists, approximately 10% of the participants fulfilled DSM-5 diagnostic criteria for HD. 93% of the clinician-identified cases were identified by the ML model based on HRS-SR and GAD-7 scores. A decision tree plot model demonstrated that about 60% of the cases could be detected by the HRS-SR alone while the rest required a combination of HRS-SR and GAD-7 scores. ML evaluation metrics showed satisfactory performance, with a Matthews Correlation Coefficient of 55%; Area Under Curve (AUC), 79%; a Negative Predictive Value of 76%; and a False Negative Rate of 24%. Conclusions Study findings strongly suggest that ML can, in the future, play a significant role in the risk assessment of psychiatric disorders prior to face-to-face consultation. By using AI to scan big data questionnaire responses, wait time for seriously ill patients can be substantially cut, and prognoses substantially improved.

This study used a person-centered approach to identify school readiness profiles in a sample of kindergartners (n=1,826) from a large and diverse school district in the United States. Using latent profile analyses and multilevel modeling, we examined three aims: 1) whether patterns of readiness skills at kindergarten entry could be detected, 2) the extent to which detected patterns predicted gains in academic and social-emotional skills, and 3) whether the quality of teacher-child interactions moderated the associations between profile membership and end of kindergarten outcomes. Based on a comparison of fit indices, a 4-profile solution best represented the data. Eighteen percent of children were in the "High Risk" profile, 34% were in the "Ready" profile, 20% percent of children were in the "Social-Emotional Risk" profile, and 28% of children were in the "High Readiness" profile. For all outcomes, we found that profile membership predicted spring scores, after controlling for fall scores of each skill, suggesting that the constellation of kindergarten readiness skills matters more than any one skill. We also found that the quality of teacher-child interactions moderated associations between profile membership and changes in achievement and problem behavior for children within some groups. Results suggest that children enter kindergarten with unique profiles of skills and that supports for children may need to be individualized based on profiles to maximize social, emotional, and academic development. Results also suggest that high quality teacher-child interactions may serve as an important protective and promotive factor for children with certain clusters of readiness skills at kindergarten entry.

Abstract Recently, in Hepatocellular carcinoma (HCC) setting, the use of metformin has been associated to a trend toward worse response rate, overall survival and progression free survival in patients who received immunotherapy. The study population included individuals from both Eastern and Western regions with a confirmed diagnosis of HCC and receiving first line treatment with Atezolizumab plus bevacizumab or Lenvatinib. Univariate and multivariate analyses were performed by Cox proportional. For the analysis, patients were stratified based on their use of concomitant medication or not. At the time of database lock, 319 deaths were observed: 209 in the Lenvatinib cohort, 110 in the Atezolizumab plus bevacizumab cohort. In the Atezolizumab plus Bevacizumab arm, 50 (16.5%) patients were on chronic metformin use. At the univariate analysis for OS, patients who used metformin showed significantly shorter OS compared to patients who did not use metformin (HR 1.9, 95% CI 1.1–3.2). Multivariate analysis confirmed that patients in metformin group had significantly shorter OS compared to patients in no-metformin group (HR 1.9; 95% CI 1.1–3.1). At the univariate analysis for PFS, patients in metformin group had significantly shorter PFS compared to patients in no-metformin group (HR 1.6, 95% CI 1.0–2.6). Multivariate analysis confirmed that patients in metformin group had significantly shorter PFS compared to patients in no-metformin group (HR 1.7; 95% CI 1.1–2.7; p = 0.0147). No differences were reported in terms of ORR and DCR between patients in metformin group and those in no-metformin group. In the Lenvatinib cohort, 65 (15%) patients were recorded to chronically use metformin. No statistically significant differences in terms of both OS and PFS were found between patients in metformin group and patients in no-metformin group. This analysis unveils a negative prognostic role associated with metformin use specifically within the Atezolizumab plus Bevacizumab group.

Abstract The concept of a healthy lifestyle is receiving increasing attention. This study sought to identify an optimal healthy lifestyle profile associated with sleep health in general population of China. An online cross-sectional survey was conducted from June to July 2022. Six healthy lifestyle factors were assessed: healthy diet, regular physical exercise, never smoking, never drinking alcohol, low sedentary behavior, and normal weight. Participants were categorized into the healthy lifestyle (5-6 factors), average (3-4 factors), and unhealthy lifestyle groups (0-2 factors). The study’s primary outcome was sleep health, which included sleep quality, duration, pattern, and the presence of any sleep disorder or disturbance, including insomnia, excessive daytime sleepiness, obstructive apnea syndrome, and narcolepsy. Multivariable logistic regression analysis was applied to explore lifestyles associated with the selected sleep health outcomes. 41,061 individuals were included, forming 18.8% healthy, 63.8% average, and 17.4% unhealthy lifestyle groups. After adjusting for covariates, participants with healthy lifestyle were associated with a higher likelihood of good sleep quality (OR = 1.56, 95% CI = 1.46–1.68), normal sleep duration (OR = 1.60, 95% CI = 1.49–1.72), healthy sleep pattern (OR = 2.15, 95% CI = 2.00–2.31), and lower risks of insomnia (OR = 0.66, 95% CI = 0.61–0.71), excessive daytime sleepiness (OR = 0.66, 95% CI = 0.60–0.73), and obstructive apnea syndrome (OR = 0.40, 95% CI = 0.37–0.43), but not narcolepsy (OR = 0.92, 95% CI = 0.83–1.03), compared to those with unhealthy lifestyle. This large cross-sectional study is the first to our knowledge to quantify the associations of a healthy lifestyle with specific aspects of sleep health. The findings offer support for efforts to improve sleep health by modulating lifestyle.

Abstract Optical resonators are fundamental building blocks of photonic systems, enabling meta-surfaces, sensors, and transmission filters to be developed for a range of applications. Sub-wavelength size (

Cancer represents a major global health burden, prompting continuous research for effective therapeutic strategies. Natural compounds derived from plants have emerged as potential strategies for preventing cancer and treatment because of their inherent pharmacological properties. This comprehensive review aimed to evaluate the therapeutic potential of five key natural compounds: apigenin, quercetin, piperine, curcumin, and resveratrol in cancer prevention and therapy. By examining their molecular mechanisms and preclinical evidence, this review seeks to elucidate their role as potential adjuvants or stand-alone therapies in cancer management. The exploration of natural compounds as cancer therapeutics offers several advantages, including low toxicity, wide availability, and compatibility with conventional chemotherapeutic agents. We highlighted the current understanding of their anticancer mechanisms and clinical applications for advancing personalized cancer care to improve patient outcomes. We discussed the empirical findings from in vitro, in vivo, and clinical studies reporting biological activity and therapeutic efficacy in antioxidant, immunomodulatory, anti-carcinogenic, and chemo-sensitizing modes. Innovative delivery systems and personalized treatment approaches may further enhance their bioavailability and therapeutic utility in a synergistic approach with chemo- and radiotherapeutic disease management. This review underscores the importance of natural compounds in cancer prevention and treatment, promoting a multidisciplinary approach to the development of innovative therapeutic strategies.