Predicting Role of Resilience and Meaning in Life in Perceived Stress of Frontline Health Care Workers during COVID-19 Outbreak

This paper investigates the predicting role of resilience and meaning in life on perceived stress of frontline health care workers treating patients with COVID-19. To measure the variables, a set of online questionnaires including Perceived Stress Questionnaire (PSS), Meaning of Life Questionnaire (MLQ), and Resilience Questionnaire (CD_RISC) was prepared. Presence of meaning, search for meaning, notion of personal competence, tolerance and trust in intuition, acceptance and secure relationships, control, and spiritual influences were examined as predictors of perceived stress. Several frontline health care workers were included in the final study. To analyze the data, regression analysis method was used with SPSS-20 software. The results showed that: 1) the regression model of resilience and the presence of meaning in the life of health care workers on their perceived stress was significant (F (6,229)=45.14, p<0.0001); 2) the predictive variables, in total, could explain 53% of the variance of perceived stress; 3) perceived stress correlated negatively with presence of meaning (β = −0.380, p<0.05), with acceptance and secure relationships (β = −0.620, p< 0.05), with control (β = −0.609, p<0.05), and positively correlated with spiritual influences (β = 0.465, p<0.05). Finding and maintaining meaning in life and improving acceptance, secure relationships, and sense of control would reduce perceived stress of frontline health care workers.

Advantages of Machine Learning in Forensic Psychiatric Research—Uncovering the Complexities of Aggressive Behavior in Schizophrenia

Linear statistical methods may not be suited to the understanding of psychiatric phenomena such as aggression due to their complexity and multifactorial origins. Here, the application of machine learning (ML) algorithms offers the possibility of analyzing a large number of influencing factors and their interactions. This study aimed to explore inpatient aggression in offender patients with schizophrenia spectrum disorders (SSDs) using a suitable ML model on a dataset of 370 patients. With a balanced accuracy of 77.6% and an AUC of 0.87, support vector machines (SVM) outperformed all the other ML algorithms. Negative behavior toward other patients, the breaking of ward rules, the PANSS score at admission as well as poor impulse control and impulsivity emerged as the most predictive variables in distinguishing aggressive from non-aggressive patients. The present study serves as an example of the practical use of ML in forensic psychiatric research regarding the complex interplay between the factors contributing to aggressive behavior in SSD. Through its application, it could be shown that mental illness and the antisocial behavior associated with it outweighed other predictors. The fact that SSD is also highly associated with antisocial behavior emphasizes the importance of early detection and sufficient treatment.

Machine Learning Prediction of Progression in FEV1 in the COPDGene Study

Background: The heterogeneous nature of COPD complicates the identification of the predictors of disease progression and consequently the development of effective therapies. We aimed to improve the prediction of disease progression in COPD by using machine learning and incorporating a rich dataset of phenotypic features. Methods: We included 4,496 smokers with available data from their enrollment and 5-year follow-up visits in the Genetic Epidemiology of COPD (COPDGene) study. We constructed supervised random forest models to predict 5-year progression in FEV1 from 46 baseline demographic, clinical, physiologic, and imaging features. Using cross-validation, we randomly partitioned participants into training and testing samples. We also validated the results in the COPDGene 10-year follow-up visit. Results: Predicting the change in FEV1 over time is more challenging than simply predicting the future absolute FEV1 level. Nevertheless, the area under the ROC curves for the prediction of subjects in the top quartile of observed disease progression was 0.70 in the 10-year follow-up data. The model performance accuracy was best for GOLD1-2 subjects and it was harder to achieve accurate prediction in advanced stages of the disease. Predictive variables differed in their relative importance as well as for the predictions by GOLD grade. Conclusion: This state-of-the art approach along with deep phenotyping predicts FEV1 progression with reasonable accuracy. There is significant room for improvement in future models. This prediction model facilitates the identification of smokers at increased risk for rapid disease progression. Such findings may be useful in the selection of patient populations for targeted clinical trials.

Gouty arthritis and kidney function outcomes and serum uric acid level variations in obese patients following bariatric surgery

Background Obesity is an independent risk factor for chronic kidney disease (CKD) and is the strongest known modifiable risk factor for hyperuricemia and gout. We aimed to discover the outcome of serum uric acid (SUA), gouty arthritis, and kidney function in obese patients after bariatric surgery and possible links with BMI variations. Methods Retrospective study has been performed in National Hospital in Riyadh, KSA, between Jan. 2018 to Jan. 2020. We studied only 98 patients who met our inclusion criteria. Patients followed-up at 1 month (for gouty attack only) postoperative, 3 months postoperative, and 6 months postoperative for body mass index (BMI), serum creatinine, dipstick urinalysis, SUA, and estimated glomerular filtration rate (eGFR). Radiological studies, medical history, follow up radiological studies, and clinical follow up were obtained from the hospital data system. Results A total of 98 patients with mean eGFR were 90.65 ± 29.34 ml/min/1.73 m2, mean SUA 5.56 ± 1.84 mg/dl, and mean BMI was 45.28 ± 7.25 kg/m2, at surgery. Mean BMI had decreased significantly to 38.52 ± 6.05 kg/m2 at 3 months and to 34.61 ± 5.35 kg/m2 at 6 months (P < 0.001). The mean GFR had improved significantly (99.14 ± 23.32 ml/min/1.73 m2) at 6 months (P < 0.001). Interestingly, proteinuria had resolved in 17 patients out of 23 patients at 6 months. Number of gouty attacks was decreased during the first month post-surgery (P < 0.001). SUA level was significantly decreased (4.32 ± 1.27 mg/dl) (P < 0.001). SUA showed significant negative correlations with eGFR at 3 months and positively significant correlations with BMI at 3 and 6 months. By multinomial logistic regression, BMI and initial eGFR were the independent predictive variables for the outcome of eGFR at 6 months, while male gender and initial SUA were the independent predictive variables on the outcome of SUA at 6 months. Postoperatively in gouty arthritis patients, the number of joints affected, patient global VAS assessment, and number of gouty attacks were significantly reduced (P < 0.001). Conclusion Bariatric surgery has been associated with reduction of BMI and subsequently reduction of SUA levels, gouty attacks, and improvement of eGFR.

Machine learning applied to lentic habitat use by spawning walleye demonstrates the benefits of considering multiple spatial scales in aquatic research

Scale and hierarchy have received less attention in aquatic systems compared to terrestrial. Walleye Sander vitreus spawning habitat offers an opportunity to investigate scale’s importance. We estimated lake-, transect-, and quadrat-scale influences on nearshore walleye egg deposition in 28 Minnesota lakes from 2016-2018. Random forest models (RFM) estimated importance of predictive variables to walleye egg deposition. Predictive accuracies of a multi-scale classification tree (CT) and a quadrat-scale CT were compared. RFM results suggested that five of our variables were unimportant when predicting egg deposition. The multi-scale CT was more accurate than the quadrat-scale CT when predicting egg deposition. Both model results suggest that in-lake egg deposition by walleye is regulated by hierarchical abiotic processes and that silt/clay abundance at the transect-scale (reef-scale) is more important than abundance at the quadrat-scale (within-reef). Our results show machine learning can be used for scale-optimization and potentially to determine cross-scale interactions. Further incorporation of scale and hierarchy into studies of aquatic systems will increase our understanding of species-habitat relationships, especially in lentic systems where multi-scale approaches are rarely used.

Determinants of financial performance in Albanian economic entities, case of construction industry in Albania

Financial performance mainly reflects the overall financial health of the business sector over a period of time. It shows how well an entity is using its resources to maximize shareholder’s wealth. Although a thorough assessment of a firm's financial performance takes into account many other measures, the most common performance measurement used in the area of finance are financial ratios. This paper provides a comprehensive study of the financial performance measurement literature related to the construction sector in Albania. The literature covers studies from Albania, Iran, India and Pakistan, but some international evidence has also been presented. The construction sector is chosen because of its impact on economic growth in Albania, it represents the second main sector according to its share effect on Albanian GDP. The financial ratios used to measure the financial performance of the construction sector are the debt ratio, the liquidity ratio and the profitability ratio from the period 2018-2020 for 100 construction companies in Albania. Return on Assets (ROA) is taken as the predictor variable and three financial ratios are taken as the predictive variables. This research reveals that the financial ratios have positive correlation with the dependent variable whereas the leverage ratio has negative correlation. To overcome the limitations of the forthcoming studies, the considered number of years need to be increased and other models such as Market Value Added, Capital Asset Pricing Model and Economic Value Added can be used to be tested for research to analyze other factors that may affect financial performance.

Outcomes of surgical repair of Retinoschisis-associated retinal detachment compared to Rhegmatogenous retinal detachment

Background The aim of this study is to compare outcomes of primary retinal detachment (RD) repair in retinoschisis-associated RD (RSRD) and rhegmatogenous RD (RRD). Methods This is a retrospective observational cohort study. Charts of 2247 consecutive patients operated for RD repair at the Centre hospitalier universitaire de Québec – Université Laval between 2014 and 2018 were reviewed. Patients with RSRD and RRD were included to compare the visual and anatomical outcomes of both groups. Results There were 41 patients (1.8%) with RSRD and 1661 patients (74%) with RRD. RSRD patients had more primary repair failures (n = 9, 22%, vs. n = 166, 10%; p = 0.013). The primary anatomical success rates for pars plana vitrectomy with and without scleral buckle (PPV-SB vs. PPV) as primary repair method were similar in both RSRD patients (n = 11/14, 79% vs. n = 20/25, 80%; p = 0.92) and RRD patients (n = 751/827, 91% vs. n = 641/721, 89%; p = 0.21). At final follow-up, best corrected visual acuity (VA) in logarithm of the minimum angle of resolution (logMAR) was 0.30 [0.10, 0.88] and 0.18 [0.10, 0.40] (p = 0.03) in RSRD patients and RRD patients, respectively. Presence of retinoschisis was associated with worse final VA (β 0.082, p < 0.001). Other predictive variables included female sex, macula-off presentation, number of RD quadrants involved, longer symptoms duration, worse baseline VA, and primary repair failure. The greatest predictors were worse baseline VA, primary repair failure, and macula-off status at presentation. Presence of retinoschisis did not significantly increase risk of primary repair failure in multivariable analysis (OR 1.45, 95% CI: 0.50–4.17; p = 0.49). Symptoms duration was the greatest effect factor associated with for primary repair failure (OR 1.37, 95% CI: 1.12–1.69; p = 0.003). Conclusions RSRD is associated with more primary repair failure in univariate analysis, but not in multivariate analysis after adjusting for symptoms duration. It is however associated with worse final VA even after adjusting for primary repair failure. Both PPV and PPV-SB are valid repair methods for RSRD. However, RSRD remains a challenge to treat.

International experiences and the development of intercultural sensitivity among university students

International cultural immersion experiences are deemed one of the most effective ways to prepare multicultural and global citizens. The purpose of the study was to determine: (1) first-year and final-year university students’ levels of Intercultural Sensitivity (henceforth IS); (2) if there was a relationship between IS and experiences of intercultural contact; and (3) the variables that might predict the development of IS. A longitudinal method and a correlational-predictive design was used. The sample comprised 1645 (52.5% women and 47.5% men) undergraduate students from 8 public universities and one private university in Andalusia (Spain) with a mean age of 23.29 (SD = 4.99). The Intercultural Development Inventory (IDI) was used to measure IS, and the Intercultural Experiences Inventory (IEI) was administered to explore students' intercultural experiences. Findings highlighted that there is little change in the IDI scores among first-year and final-year students, and most students were found to be in the intermediate stages of intercultural development (the minimization stage according to Bennett’s model, characterized by the widespread belief that everyone is quite similar). The lack of development found in the students’ IS could have something to do with the lack of opportunity to reflect and think about cultural differences. Positive correlations were found between IS and mobility experiences and intercultural friendships, and negative correlations were found with the number of intercultural interactions and language knowledge. Having mobility experiences, being female, and having friends from other cultures were predictive variables of IS. Finally, the educational implications are discussed.