Mental health analysis of international students using machine learning techniques.

International students' mental health has become an increasing concern in recent years, as more students leave their country for better education. They experience a wide range of challenges while studying abroad that have an impact on their psychological well-being. These challenges can include language obstacles, cultural differences, homesickness, financial issues and other elements that could severely impact the mental health of international students. Given the limited research on the demographic, cultural, and psychosocial variables that influence international students' mental health, and the scarcity of studies on the use of machine learning algorithms in this area, this study aimed to analyse data to understand the demographic, cultural factors, and psychosocial factors that impact mental health of international students. Additionally, this paper aimed to build a machine learning-based model for predicting depression among international students in the United Kingdom. This study utilized both primary data gathered through an online survey questionnaire targeted at international students and secondary data was sourced from the 'A Dataset of Students' Mental Health and Help-Seeking Behaviors in a Multicultural Environment,' focusing exclusively on international student data within this dataset. We conducted data analysis on the primary data and constructed models using the secondary data for predicting depression among international students. The secondary dataset is divided into training (70%) and testing (30%) sets for analysis, employing four machine learning models: Logistic Regression, Decision Tree, Random Forest, and K Nearest Neighbor. To assess each algorithm's performance, we considered metrics such as Accuracy, Sensitivity, Specificity, Precision and AU-ROC curve. This study identifies significant demographic variables (e.g., loan status, gender, age, marital status) and psychosocial factors (financial difficulties, academic stress, homesickness, loneliness) contributing to international students' mental health. Among the machine learning models, the Random Forest model demonstrated the highest accuracy, achieving an 80% accuracy rate in predicting depression.

Biologic use in psoriatic arthritis and ankylosing spondylitis patients: a descriptive epidemiological study using linked, routine data in Wales, UK.

OBJECTIVES: PsA and AS are chronic diseases associated with significant morbidities. National and international management guidelines include treatment with biologic therapies to improve outcomes and quality of life. There are limited real-world data on the patients' journey from symptom onset to diagnosis and treatment in the UK. We use real-life, linked health data to explore patient pathways and the impact of biologics on patient outcomes. METHODS: Data from the Secure Anonymised Information Linkage databank in Wales were used to assess diagnosis and treatment of patients ≥18 years of age with at least one International Classification of Diseases, Tenth Revision code present for PsA/AS in rheumatology clinic data and at least one Read code present in primary care records. We investigated the use of biologics while exploring demographics, comorbidities and surgical procedures of 641 AS patients and 1312 PsA patients. RESULTS: AS patients were significantly younger at diagnosis and were predominantly male. The average time from presenting symptoms to diagnosis of AS and PsA was 7.9 (s.d. 5.5) and 9.3 (s.d. 5.5) years, respectively. The proportion of patients receiving biologic treatment was significantly higher in AS (46%) compared with PsA patients (28.8%); of these, 23.1% of AS and 22.2% of PsA patients stopped/switched a biologic. There was a significant reduction in primary care involvement, sick notes and disability living allowance for both AS and PsA patients following biologic initiation. CONCLUSION: This real-world descriptive study confirms that patients treated with biologics have reduced disability and time off work despite being initiated ∼13 years after the first symptoms and 6 years after diagnosis.

Copper Nanoparticles as Growth Promoter, Antioxidant and Anti-Bacterial Agents in Poultry Nutrition: Prospects and Future Implications.

Copper (Cu) is a vital trace mineral involved in many physiological functions of the body. In the poultry industry, copper sulfate is being used as a major source of Cu. Copper in the bulk form is less available in the body, and much of its amount excreted out with feces causing environmental pollution and economic loss. The application of nanotechnology offers promise to address these issues by making nanoparticles. Copper nanoparticles (Cu-NP) are relatively more bioavailable due to their small size and high surface to volume ratio. Although, there is limited research on the use of Cu-NP in the poultry industry. Some researchers have pointed out the importance of Cu-NP as an effective alternative of chemical, anti-bacterial agents, and growth promoters. The effect of Cu-NP depends on their size, dose rate and the synthesis method. Apart from there, high bioavailability Cu-NP exhibited positive effects on the immunity of the birds. However, some toxic effects of Cu-NP have also been reported. Further investigations are essentially required to provide mechanistic insights into the role of Cu-NP in the avian physiology and their toxicological properties. This review aims to highlight the potential effects of Cu-NP on growth, immune system, antioxidant status, nutrient digestibility, and feed conversion ratio in poultry. Moreover, we have also discussed the future implications of Cu-NP as a growth promoter and alternative anti-bacterial agents in the poultry industry.

No increased rate of acute myocardial infarction or stroke among patients with ankylosing spondylitis-a retrospective cohort study using routine data.

OBJECTIVES: To examine if people with ankylosing spondylitis (AS) are at higher risk of acute myocardial infarction (MI) or stroke compared to those without AS. METHODS: Primary care records were linked with all hospital admissions and deaths caused by MI or stroke in Wales for the years 1999-2010. The linked data were then stratified by AS diagnosis and survival analysis was used to obtain the incidence rate of MI and separately cerebrovascular disease (CVD)/stroke. Cox regression was used to adjust for gender and age. Logistic regression was used to examine prevalence of diabetes, hypertension, or hyperlipidemia for those with AS compared to those without. RESULTS: There were 1686 AS patients (75.9% male, average age 46.1 years) compared to 1,206,621 controls (48.9% male, average age 35.9 years). Age- and gender-adjusted hazard ratios for MI were 1.28 (95% CI: 0.93 to 1.74) P = 0.12, and for CVD/stroke 1.0 (95% CI: 0.73 to 1.39) P = 0.9, in AS compared to controls. The prevalence of diabetes and hypertension, but not hyperlipidemia/hypercholesterolemia, was higher in AS. CONCLUSIONS: There is no increase in the MI or CVD/stroke rates in patients with AS compared to those without AS, despite higher rates of hypertension, which may be related to nonsteroidal anti-inflammatory drug use.