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  1. Roy P, Abdulsalam FI, Pandey DK, Bhattacharjee A, Eruvaram NR, Malik T
    Pharmacognosy Res, 2015 Jun;7(Suppl 1):S57-62.
    PMID: 26109789 DOI: 10.4103/0974-8490.157997
    Swertia cordata and Swertia chirayita are temperate Himalayan medicinal plants used as potent herbal drugs in Indian traditional systems of medicine (Ayurvedic, Unani and Siddha).
  2. Malik A, Hussain M, Uddin F, Raza W, Hussain S, Habiba UE, et al.
    Water Environ Res, 2021 Sep 27.
    PMID: 34570384 DOI: 10.1002/wer.1639
    In this current work, the performance of an aerobic granular sludge (AGS) for real textile wastewater was investigated based on system operational parameters evaluation. The study was performed for 90 days, and sampling was done once a week in which textile dyeing effluent from the textile mill was collected and subjected to laboratory-scale treatment. The samples from the inlet, the outlet of the wastewater plant, and within the bioreactor were collected at various concentrations of MLSS, and hydraulic retention remained the same in the investigated period of 53 hours. The objective of this study was to analyze the AGS system performance assessment by evaluating the effect of different MLSS concentrations on chemical oxygen demand (COD), TSS, and oil/grease removal from real-based textile water. The results showed that removal of organic material from the process water increases with an increase in MLSS concentration in the bioreactor and gradually shifts removal of COD from 91.2% to 94.5%. As the concentration of microorganisms in the reactor (aeration tank) increases, the degradation of waste organics in the wastewater increases as well. Moreover, the % removal of total suspended solids (83.5 to 98 %) and removal of oil/grease (62.5 to 76.4%) were also increased. These results ultimately suggest that the utilization of an activated sludge system can effectively treat complex and highly polluted denim textile wastewater to avoid secondary pollution posed by this industry.
  3. Bhagat SK, Tiyasha T, Kumar A, Malik T, Jawad AH, Khedher KM, et al.
    J Environ Manage, 2022 Feb 16;309:114711.
    PMID: 35182982 DOI: 10.1016/j.jenvman.2022.114711
    Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay's ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (Tmin, Tmax and TavgoC), rainfall (Rn mm) and their interactions with the other batch HMs, are hypothesized to have high impact for the decision-making strategies to minimize the impacts of Pb. Three feature selection (FS) algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia. These FS algorithms were statistically evaluated using principal component analysis (PCA) Biplot along with the correlation metrics describing the statistical characteristics that exist in the input and output parameter space of the models. To ensure a high accuracy attained by the applied predictive artificial intelligence (AI) models i.e., XGBoost, support vector machine (SVM) and random forest (RF), an auto-hyper-parameter tuning process using a Grid-search approach was also implemented. Cu, Ni, Ce, and Fe were selected by all the three applied FS algorithms whereas the Tavg and Rn inputs remained the essential parameters identified by GA and Boruta. The order of the FS outcome was XGBoost > GA > Boruta based on the applied statistical examination and the PCA Biplot results and the order of applied AI predictive models was XGBoost-SVM > GA-SVM > Boruta-SVM, where the SVM model remained at the top performance among the other statistical metrics. Based on the Taylor diagram for model evaluation, the RF model was reflected only marginally different so overall, the proposed integrative AI model provided an evidence a robust and reliable predictive technique used for coastal sediment Pb prediction.
  4. Qureshi S, Iqbal M, Rafiq A, Ahmed H, Malik T, Kalam MN, et al.
    AIMS Public Health, 2023;10(3):553-567.
    PMID: 37842276 DOI: 10.3934/publichealth.2023039
    BACKGROUND: Childhood malnutrition remains a significant public health problem impacting the physical and mental growth if school aged children, particularly in limited-resource countries.

    OBJECTIVE: The study objective was to assess levels of physical activity, patterns of screen time (S.T.), the relationship between physical activity and screen time patterns, and how these factors affect growth status (adjusting for socioeconomic status).

    METHODOLOGY: A cross-sectional study included 3,834 children between 6-14 years attending pre-selected schools. Teachers, students, and parents were invited to fill out a standardized questionnaire, and Body Mass Index (BMI) was calculated using Center for disease control (CDC) centile charts. A Chi-square was performed to see the possible association between any height and weight abnormalities and all possible risk factors. Multivariate logistic regression was applied to see the effect of variables significantly associated with univariate analysis.

    RESULTS: Approximately 2,447 (63.8%) children were between 11-14 years old and 1,387 (36.2%) were between 4-10 years old. The mean height was 143.71 ± 16.51 centimetres, the mean weight was 36.5 ± 12.9 kilogram, and the mean BMI was 17.16 ± 3.52. Multivariate logistic regression status and junk food combined affected stunting socioeconomic status was significantly associated with being underweight p = 0.001.

    CONCLUSION: Childhood obesity and stunting remain significant problems in Pakistani school-going children. These are significantly associated with poverty, a lack of physical activity opportunities, and available food quality.

  5. Butt MD, Ong SC, Rafiq A, Malik T, Sajjad A, Batool N, et al.
    Sci Rep, 2023 Dec 27;13(1):23037.
    PMID: 38155289 DOI: 10.1038/s41598-023-50517-2
    In 2021, the International Diabetes Federation (IDF) reported that the prevalence of diabetes in Pakistan was 9.6%, higher than the global average. However, adherence to treatment guidelines, e.g., American Diabetes Association and Pakistan Endocrine Society and prescription patterns for Oral anti-diabetes (OAD), is poorly understood in Pakistan. Therefore, this study aimed to examine the prescribing practices of anti-diabetic medications, an association of lifestyle modification with drugs prescribed, and their effectiveness in preserving ideal glycemic levels in diabetic patients undergoing treatment in tertiary care teaching hospitals in rural and urban Pakistan. In this cross-sectional study, data were collected from prescriptions of outpatient diabetic patients from different rural and urban tertiary care hospitals between October 2021 and February 2022. 388 participants were enrolled in the study for a detailed interview on prescription evaluation and glycemic control. The coinvestigators conducted an interview with the patient and used a pre-validated questionnaire to collect the data. The relationship between following treatment guidelines and clinical and demographic factors was found using chi-square tests for bivariate analyses. The study reported that out of 388, the mean ages of the patients were 48 ± 12.4, and the majority were female. It was observed that 60.1% and 66.5% have uncontrolled fasting and random blood glucose, respectively. The education level of the study participants was also below par to have a complete understanding of the medical condition and self-management therapy. Even though they were taking the right medications-an average prescription regimen included 5.08 medications-52.1% of the studied people had glycated haemoglobin (HbA1c) levels higher than the therapeutic threshold set by the International Diabetes Federation. In this modern era, it was observed that the prescribing trend was still focused on traditional therapeutic options Biguanides, sulfonylureas, and dipeptidyl peptidase-4 inhibitors were prescribed in 64.6% of the patients. A significant association was found between glycemic control and body mass index, adherence to lifestyle modifications, and the number of medications prescribed (p-value 
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