Contributing risk factors of common psychiatric disorders in the Pakistani population.

With an increasing incidence of psychiatric disorders worldwide, there is a need for a better understanding of the population-specific contributing risk factors that are associated with common psychiatric conditions. This study aimed to assess the correlation between socioeconomic, environmental and clinical features associated with major depression (MDD n = 479), bipolar disorder (BD n = 222) and schizophrenia (SHZ n = 146), in the Pakistani population. Multinomial logistic regression and Pearson's correlation were applied to assess the association and correlation between demographic, socioeconomic, environmental, and clinical features of MDD, BD and SHZ. In the present study, MDD was found to be more prevalent than BD and SHZ. The average age at onset (AAO), was observed to be earlier in females with BD and SHZ, in addition, females with a positive family history of MDD, BD and SHZ also had an earlier AAO. The fitted multinomial logistic regression model indicated a significant association of; aggression, tobacco use, drugs abuse, history of head injuries and family history with BD as compared to MDD, while insomnia and suicidality were significantly associated with MDD. Strong positive correlations were observed mainly between age/AAO, AAO/tobacco use and aggression/insomnia in all three cohorts. In conclusion, the present study identifies possible contributing socio-demographic, biological and environmental factors that are correlated and associated with the psychiatric conditions in the Pakistani population.

Predictive model for early detection of type 2 diabetes using patients' clinical symptoms, demographic features, and knowledge of diabetes.

Background and Aims: With the global rise in type 2 diabetes, predictive modeling has become crucial for early detection, particularly in populations with low routine medical checkup profiles. This study aimed to develop a predictive model for type 2 diabetes using health check-up data focusing on clinical details, demographic features, biochemical markers, and diabetes knowledge. Methods: Data from 444 Nigerian patients were collected and analysed. We used 80% of this data set for training, and the remaining 20% for testing. Multivariable penalized logistic regression was employed to predict the disease onset, incorporating waist-hip ratio (WHR), triglycerides (TG), catalase, and atherogenic indices of plasma (AIP). Results: The predictive model demonstrated high accuracy, with an area under the curve of 99% (95% CI = 97%-100%) for the training set and 94% (95% CI = 89%-99%) for the test set. Notably, an increase in WHR (adjusted odds ratio [AOR] = 70.35; 95% CI = 10.04-493.1, p-value < 0.001) and elevated AIP (AOR = 4.55; 95% CI = 1.48-13.95, p-value = 0.008) levels were significantly associated with a higher risk of type 2 diabetes, while higher catalase levels (AOR = 0.33; 95% CI = 0.22-0.49, p < 0.001) correlated with a decreased risk. In contrast, TG levels (AOR = 1.04; 95% CI = 0.40-2.71, p-value = 0.94) were not associated with the disease. Conclusion: This study emphasizes the importance of using distinct clinical and biochemical markers for early type 2 diabetes detection in Nigeria, reflecting global trends in diabetes modeling, and highlighting the need for context-specific methods. The development of a web application based on these results aims to facilitate the early identification of individuals at risk, potentially reducing health complications, and improving diabetes management strategies in diverse settings.

Monitoring non-parametric profiles using adaptive EWMA control chart.

To monitor the quality of a process in statistical process control (SPC), considering a functional relationship between a dependent variable and one or more independent variables (which is denoted as profile monitoring) is becoming an increasingly common approach. Most of the studies in the SPC literature considered parametric approaches in which the functional relationship has the same form in the in-control (IC) and out-of-control (OC) situations. Non-parametric profiles, which have a different functional relationship in the OC conditions are very common. This paper designs a novel control chart to monitor not only the regression parameters but also the variation of the profiles in Phase II applications using an adaptive approach. Adaptive control charts adjust the final statistic with regard to information of the previous samples. The proposed method considers the relative distance of the chart statistic to the control limits as a tendency index and provides some outcomes about the process condition. The results of Monte Carlo simulations show the superiority of the proposed monitoring scheme in comparison with the common non-parametric control charts.

Response Surface Methodology for Optimization of Operational Parameters To Remove Ciprofloxacin from Contaminated Water in the Presence of a Bacterial Consortium.

Ciprofloxacin (CFX) is a broad-spectrum fluoroquinolone antibiotic that is widely used to treat bacterial infections in humans and other animals. However, its unwanted occurrence in any (eco)system can affect nontarget bacterial communities, which may also impair the performance of the natural or artificially established bioremediation system. The problem could be minimized by optimization of operational parameters via modeling of multifactorial tests. To this end, we used a Box-Behnken design in response surface methodology (RSM) to generate the experimental layout for testing the effect of the CFX biodegradation for four important parameters, that is, temperature (°C), pH, inoculum size (v/v %), and CFX concentration (mg L-1). For inoculation, a consortium of three bacterial strains, namely, Acenitobacter lwofii ACRH76, Bacillus pumilus C2A1, and Mesorihizobium sp. HN3 was used to degrade 26 mg L-1 of CFX. We found maximum degradation of CFX (98.97%; initial concentration of 25 mg L-1) at 2% inoculum size, 7 pH, and 35 °C of temperature in 16 days. However, minimum degradation of CFX (48%; initial concentration of 50 mg L-1) was found at pH 6, temperature 30 °C, and inoculum size 1%. Among different tested parameters, pH appears to be the main limiting factor for CFX degradation. Independent factors attributed 89.37% of variation toward CFX degradation as revealed by the value of the determination coefficient, that is, R 2 = 0.8937. These results were used to formulate a mathematical model in which the computational data strongly correlated with the experimental results. This study showcases the importance of parameter optimization via RSM for any bioremediation studies particularly for antibiotics in an economical, harmless, and eco-friendly manner.

Green Synthesis of MnO Nanoparticles Using Abutilon indicum Leaf Extract for Biological, Photocatalytic, and Adsorption Activities.

We report the synthesis of MnO nanoparticles (AI-MnO NAPs) using biological molecules of Abutilon indicum leaf extract. Further, they were evaluated for antibacterial and cytotoxicity activity against different pathogenic microbes (Escherichia coli, Bordetella bronchiseptica, Staphylococcus aureus, and Bacillus subtilis) and HeLa cancerous cells. Synthesized NAPs were also investigated for photocatalytic dye degradation potential against methylene blue (MB), and adsorption activity against Cr(VI) was also determined. Results from Scanning electron microscope (SEM), X-ray powder diffraction (XRD), Energy-dispersive X-ray (EDX), and Fourier-transform infrared spectroscopy (FTIR) confirmed the successful synthesis of NAPs with spherical morphology and crystalline nature. Biological activity results demonstrated that synthesized AI-MnO NAPs exhibited significant antibacterial and cytotoxicity propensities against pathogenic microbes and cancerous cells, respectively, compared with plant extract. Moreover, synthesized AI-MnO NAPs demonstrated the comparable biological activities results to standard drugs. These excellent biological activities results are attributed to the existence of the plant's biological molecules on their surfaces and small particle size (synergetic effect). Synthesized NAPs displayed better MB-photocatalyzing properties under sunlight than an ultraviolet lamp. The Cr(VI) adsorption result showed that synthesized NAPs efficiently adsorbed more Cr(VI) at higher acidic pH than at basic pH. Hence, the current findings suggest that Abutilon indicum is a valuable source for tailoring the potential of NAPs toward various enhanced biological, photocatalytic, and adsorption activities. Consequently, the plant's biological molecule-mediated synthesized AI-MnO NAPs could be excellent contenders for future therapeutic applications.

An enhanced nonparametric EWMA sign control chart using sequential mechanism.

Control charts play a significant role to monitor the performance of a process. Nonparametric control charts are helpful when the probability model of the process output is not known. In such cases, the sampling mechanism becomes very important for picking a suitable sample for process monitoring. This study proposes a nonparametric arcsine exponentially weighted moving average sign chart by using an efficient scheme, namely, sequential sampling scheme. The proposal intends to enhance the detection ability of the arcsine exponentially weighted moving average sign chart, particularly for the detection of small shifts. The performance of the proposal is assessed, and compared with its counterparts, by using some popular run length properties including average, median and standard deviation run lengths. The proposed chart shows efficient shift detection ability as compared to the other charts, considered in this study. A real-life application based on the smartphone accelerometer data-set, for the implementation of the proposed scheme, is also presented.

Analysis of factors affecting employee satisfaction: A case study from Pakistan.

BACKGROUND: Employee job satisfaction has been a research focal point throughout the world. It is a key factor when measuring the performance of an organization and individuals. A leading engineering goods manufacturing enterprise in Pakistan, has been used in this case study. In Pakistan, very limited research has been done with respect to factors affecting job satisfaction. Some research has been done in medical institutions, banks, universities and the information technology sector but large public sector organizations in Pakistan have not been studied. A theoretical foundation for researching factors affecting job satisfaction in large organizations is outlined. OBJECTIVE: The objective of this research is to analyze various demographic, financial and non-financial factors affecting the satisfaction level of employees and to study the effects across different employee groups. DESIGN/METHODOLOGY: This study is based on quantitative data analysis. The employees of the organization under study have been divided into 10 homogeneous groups based on their departments. Information on job related factors (affecting the satisfaction level) have been collected from subsamples of each group using a self-administered questionnaire. An overall sample of 250 (out of total 1100) employees has been selected. Before conducting the survey, reliability of the questionnaire was measured using Cronbach's alpha. The normality of data was also examined using the Kolmogorov Smirnov test. Hypotheses devised to address the research questions were tested by using non-parametric Spearman correlation and Kruskal-Wallis tests. RESULTS: The response rate was 73.2%. Research findings indicated the significant factors that affect the satisfaction level of employees. Median group differences existed between responses based on age, work experience, salary and designation (i.e. job position/rank) of employees. Job satisfaction was also positively and significantly associated with job related factors such as pay, promotion, relation with employees, relation with supervisor, work stress and job security. CONCLUSIONS: Job satisfaction of employees was significantly affected by demographic, financial and non-financial factors. Employees who are older than 50 years, with greater experience, and in higher management positions were more satisfied with pay, promotion, work stress, work condition and working environment. The employees' departments did not play any significant role in affecting satisfaction levels.