The Association Between Dietary Insulin Index and Load With Mental Health

Background: Depression, anxiety, and stress are common mental problems in different populations. The aim of this cross-sectional study is to investigate the association between dietary insulin index (DII) and insulin load (DIL) and psychological disorders. Method: Participants (n = 10,000) aged 20-69 were randomly selected from 200 clusters in Yazd from the recruitment phase of Yazd Health Study. Dietary intake of study participants was collected by a reliable and validated food frequency questionnaire (FFQ) consisting of 178 food items. DII and DIL were calculated from the FFQ data using previously published reference values. To assess psychological disorders an Iranian validated short version of a self-reported questionnaire (Depression Anxiety Stress Scales 21 [DASS21]) was used.Results: No significant association was observed between DIL and DII with odds of depression or anxiety in crude and adjusted models. The individuals in the highest quartiles of DIL had the lowest odds of stress (OR: 0.69; 95% CI: 0.48–1.01, P-trend = 0.047). This association remained significant after adjustment for potential confounders in model II (OR: 0.38; 95% CI: 0.16–0.91, P-trend = 0.039) and the third and final model (OR: 0.39; 95% CI: 0.16–0.91, P-trend = 0.041).Conclusion: Overall, consumption of foods with higher II as well as IL was associated with lower feelings of stress; however, no significant relation was observed between DII or DIL and risk of depression and anxiety. Further studies with longitudinal design are needed to confirm these results.

Malay Version of Moral Identity Scale: A Cross-Cultural Validation In Malaysian Youth

Introduction: The Moral Identity Scale (MIS) measures the construct of moral identity – internalization and symbolization. This study aims to translate and validate the original English version of MIS to the Malay language version. Methods: The 10 items of MIS (five items of symbolization and five items of internalization) were translated to the Malay language through forward and backward procedures. A total of 388 youth-aged participants were involved in this online survey. Participants’ age ranged from 18-24 years old. More than half were females. Results: The construct of moral identity was maintained with the re-modeling of internalization. The final model suggested retaining the three internalization items and five symbolization items. Discriminant validity and the construct reliability of the two factors were satisfactory (symbolization=0.81, internalization=0.69). Internal consistencies indicated acceptable Cronbach’s alpha values for both symbolization (0.85) and internalization (0.81). Test-retest Intraclass Correlation Coefficient (ICC) was good and satisfactory (internalization=0.79 and symbolization=0.81). Conclusion: The Malay version of MIS is a reliable tool and free from cultural bias which is useful to be applied in a public health-related program.

Classification of Genuinity in Job Posting Using Machine Learning

To avoid fraudulent Job postings on the internet, we target to minimize the number of such frauds through the Machine Learning approach to predict the chances of a job being fake so that the candidate can stay alert and make informed decisions if required. The model will use NLP to analyze the sentiments and pattern in the job posting and TF-IDF vectorizer for feature extraction. In this model, we are going to use Synthetic Minority Oversampling Technique (SMOTE) to balance the data and for classification, we used Random Forest to predict output with high accuracy, even for the large dataset it runs efficiently, and it enhances the accuracy of the model and prevents the overfitting issue. The final model will take in any relevant job posting data and produce a result determining whether the job is real or fake. Keywords: Natural Language Processing (NLP), Term Frequency-Inverse Document Frequency (TF-IDF), Synthetic Minority Oversampling Technique (SMOTE), Random Forest.

Development of an E-Flipped Classroom Learning Model to Improve Mathematics Students' High Level Thinking Ability

The purpose of this study was to create an e-flipped classroom learning paradigm to help mathematics students enhance their higher order thinking skills. This study is a research development, often known as R&D. In this study, the development model proceeded through the stages of conceptual model, theoretical model, hypothetical model, and final model. The learning devices created in the form of prototypes are restricted to eight disciplines as a consequence of the design of learning devices achieved. The method for building the e-flipped classroom model to increase higher order thinking abilities is separated into three stages: pre-development activities, development activities, and model implementation activities. Validation sheets, observation sheets, questionnaires, and assessment sheets were utilized as study instruments. Validity, practicability, and effectiveness data analysis approaches are employed. The study's findings show that: (1) the e-flipped classroom model was developed to improve the parts that have been obstacles to its use thus far; (2) the flipped classroom model is not only a combination of online and offline but also allows for full online conditions; (3) the model is designed to train students' high-level thinking skills through several supportive activities; and (4) the devices used. Thus, based on the study findings, the e-flipped classroom concept and its learning aids satisfy the requirements for being valid, practical, and successful.

A two-hit model of sepsis plus hyperoxia causes lung permeability and inflammation

Mouse models of acute lung injury (ALI) have been instrumental for studies of the biologic underpinnings of lung inflammation and permeability, but murine models of sepsis generate minimal lung injury. Our goal was to create a murine sepsis model of ALI that reflects the inflammation, lung edema, histologic abnormalities and physiologic dysfunction that characterize ALI. Using a cecal slurry (CS) model of polymicrobial abdominal sepsis and exposure to hyperoxia (95%), we systematically varied timing and dose of the CS injection, fluids and antibiotics and dose of hyperoxia. We found that CS alone had a high mortality rate that was improved with the addition of antibiotics and fluids. Despite this, we did not see evidence of ALI as measured by bronchoalveolar lavage (BAL) cell count, total protein, CXCL-1 or by lung wet:dry weight ratio. Addition of hyperoxia (95% FiO2) to CS immediately after CS injection increased BAL cell counts and CXCL-1 and lung wet:dry weight ratio but was associated with 40% mortality. Splitting the hyperoxia treatment into two 12 hour exposures (0-12 hours24-36 hours) after CS injection increased survival to 75% and caused significant lung injury compared to CS alone as measured by increased BAL total cell count (92500 vs 240000, p=0.0004), BAL protein (71 vs 103 ug/ml, p=0.0030, and lung wet:dry weight ratio (4.5 vs 5.5 p=0.0005), and compared to sham as measured by increased BAL CXCL-1 (20 vs 2372 pg/ml, p<0.0001), and histologic lung injury score (1.9 vs 4.2, p=0.0077). Additionally, our final model showed evidence of lung epithelial (increased BAL and plasma RAGE) and endothelial (increased Syndecan-1 and sulfated glycosaminoglycans) injury. In conclusion, we have developed a clinically relevant mouse model of sepsis-induced ALI using IP injection of CS, antibiotics and fluids, and hyperoxia. This clinically relevant model can be used for future studies of sepsis-induced ALI.

ILeukin10Pred: A Computational Approach for Predicting IL-10-Inducing Immunosuppressive Peptides Using Combinations of Amino Acid Global Features

Interleukin (IL)-10 is a homodimer cytokine that plays a crucial role in suppressing inflammatory responses and regulating the growth or differentiation of various immune cells. However, the molecular mechanism of IL-10 regulation is only partially understood because its regulation is environment or cell type-specific. In this study, we developed a computational approach, ILeukin10Pred (interleukin-10 prediction), by employing amino acid sequence-based features to predict and identify potential immunosuppressive IL-10-inducing peptides. The dataset comprises 394 experimentally validated IL-10-inducing and 848 non-inducing peptides. Furthermore, we split the dataset into a training set (80%) and a test set (20%). To train and validate the model, we applied a stratified five-fold cross-validation method. The final model was later evaluated using the holdout set. An extra tree classifier (ETC)-based model achieved an accuracy of 87.5% and Matthew’s correlation coefficient (MCC) of 0.755 on the hybrid feature types. It outperformed an existing state-of-the-art method based on dipeptide compositions that achieved an accuracy of 81.24% and an MCC value of 0.59. Our experimental results showed that the combination of various features achieved better predictive performance..

Model of the Floating Bearing Bushing Movement in an External Gear Pump and the Relation to Its Parameterization

This article presents the modeling, simulation and experimental validation of the movement of the floating bearing bushing in an external gear pump. As a starting point, a complete pump parameterization was carried out through standard tests, and these parameters were used in a first bond graph model in order to simulate the gear pump behavior. This model was experimentally validated under working conditions in field tests. Then, a sophisticated bond graph model of the movement of the floating bushing was developed from the equations that define its lubrication. Finally, as a result, both models were merged by integrating the dynamics of the floating bushing bearing with the variation of the characteristic parameters (loss coefficients). Finally, the final model was experimentally validated both in laboratory and field tests by assembling the pump in a drilling machine to drive the auxiliary movements. The novelty of this article is the conception and construction of a simple and experimentally validated tool for the study of a gear pump, which relates its macroscopic behavior as a black box (defined by the loss coefficients) to the internal changes of the unit (defined by its internal lubrication).

Measuring the Post-Impact of Programming MOOCs: Development and Validation of an Instrument

Massive open online courses (MOOCs) can potentially affect the lives of millions of people, but there is little research on how a programming MOOC could affect participants’ lives after participation. In Estonia, we have organised several programming MOOCs over the years, attended by over 14,000 people. This inspired us to develop and validate a scale for measuring the impact of programming MOOC on participants’ lives. We analysed data from 1261 MOOC participants who had completed at least one of our programming MOOCs. Confirmatory factor analysis (CFA) was used to validate the developed scale. The final model fitted quite well to the data. We found that the factors that influence learners’ lives after a MOOC include acquired learning skills, interest in continuing computer science related studies, learning experience gained from the MOOC, the MOOC’s impact on work and personal life, and new contacts that are established with other participants and organisers. We also compared the means of the factors. The highest-rated factor was related to the learning experience from the MOOC and the lowest-rated was related to finding new contacts through the MOOC.

Development and Validation of a Dermoscopic Severity Score for Female Pattern Hair Loss

<b><i>Introduction:</i></b> Despite the high prevalence and impact on quality of life, there are no objective methods to estimate the severity of female pattern hair loss (FPHL). Here, we aimed to develop a dermoscopic severity score for FPHL. <b><i>Methods:</i></b> Cross-sectional study involving 76 women with FPHL and 12 controls. Standardized dermoscopic photos of the scalp were taken to evaluate the main findings of FPHL. The variable selection and their scores in the final model were defined by multivariate methods. Twenty participants were retested to assess the reliability, and 10 participants were tested before and after treatment for estimating its sensibility to change after 6 months. <b><i>Results:</i></b> Eight patients (11%) presented the Sinclair clinical scale grade I, 40 (53%) presented at grade II, 19 (25%) presented at grade III, and 9 (12%) presented at grades IV and V. In the multivariate exploration, the following variables were considered significant: total terminal hairs, total miniaturized hairs, brown peripilar sign, scalp honeycomb pigmentation, white peripilar sign, and yellow dots. The final model resulted in a high correlation (rho = 0.89) with the ranked clinical assessment. <b><i>Conclusion:</i></b> An objective and reliable severity score of FPHL was developed and validated, allowing its use as an additional outcome in therapeutic trials.

Race/Ethnicity Is Not Independently Associated with Risk of Adverse Waitlist Removal among Patients with HCC Exception Points

(1) Background: Since 2015, exception points have been awarded to appropriate candidates after six months of waitlist time to allow more equitable access to liver transplants regardless of hepatocellular carcinoma status. However, it remains unknown whether racial disparities in outcomes among waitlisted patients remain after the introduction of a 6-month waiting period for exception points. (2) Methods: Using the United Network for Organ Sharing database, we identified 2311 patients diagnosed with hepatocellular carcinoma listed for liver transplant who received exception points from 2015 to 2019. The outcome of interest was waitlist survival defined as the composite outcome of death or removal for clinical deterioration. Competing risk analysis was used to identify factors associated with death or removal for clinical deterioration. The final model adjusted for age, sex, race/ethnicity, blood type, diabetes, obesity, laboratory MELD score, tumor size, AFP, locoregional therapies, UNOS region, and college education. (3) Results: No difference was found in the risk of adverse waitlist removal among ethnic/racial groups.