METHODS: This community-based cross-sectional survey was conducted in Karachi, Pakistan, from January 2022 to August 2022. A total of 1065 healthy individuals aged 25-80 years of any gender were consecutively included. MetS was assessed using the National Cholesterol Education Program for Adult Treatment Panel (NCEP-ATP) III guidelines, International Diabetes Federation (IDF), and modified NCEP-ATP III.
RESULTS: The prevalence of MetS was highest with the modified NCEP-ATP III definition at 33.9% (95% CI: 31-36), followed by the IDF definition at 32.2% (95% CI: 29-35). In contrast, the prevalence was lower at 22.4% (95% CI: 19-25) when using the NCEP ATP III definition. The risk of MetS significantly increases with higher BMI, as defined by the IDF criteria (adjusted OR [ORadj] 1.13, 95% CI 1.09-2.43), NCEP-ATP III criteria (ORadj 1.15, 95% CI 1.11-1.19), and modified NCEP-ATP III criteria (ORadj 1.16, 95% CI 1.12-1.20). Current smokers had significantly higher odds of MetS according to the IDF (ORadj 2.72, 95% CI 1.84-4.03), NCEP-ATP III (ORadj 3.93, 95% CI 2.55-6.06), and modified NCEP-ATP III (ORadj 0.62, 95% CI 0.43-0.88). Areca nut use was associated with higher odds of MetS according to both IDF (ORadj 1.71, 95% CI 1.19-2.47) and modified NCEP-ATP III criteria (ORadj 1.58, 95% CI 1.10-2.72). Furthermore, low physical activity had significantly higher odds of MetS according to the NCEP-ATP III (ORadj 1.36, 95% CI 1.01-1.84) and modified NCEP-ATP III criteria (ORadj 1.56, 95% CI 1.08-2.26).
CONCLUSION: One-third of the healthy individuals were diagnosed with MetS based on IDF, NCEP-ATP III, and modified NCEP-ATP III criteria. A higher BMI, current smoking, areca nut use, and low physical activity were significant factors.
Methods: Six different polymers were used to prepare FLU nanopolymeric particles: hydroxyl propyl methylcellulose (HPMC), poly (vinylpyrrolidone) (PVP), poly (vinyl alcohol) (PVA), ethyl cellulose (EC), Eudragit (EUD), and Pluronics®. A low-energy method, nanoprecipitation, was used to prepare the polymeric nanoparticles.
Results and conclusion: The combination of HPMC-PVP and EUD-PVP was found most effective to produce stable FLU nanoparticles, with particle sizes of 250 nm ±2.0 and 280 nm ±4.2 and polydispersity indices of 0.15 nm ±0.01 and 0.25 nm ±0.03, respectively. The molecular modeling studies endorsed the same results, showing highest polymer drug binding free energies for HPMC-PVP-FLU (-35.22 kcal/mol ±0.79) and EUD-PVP-FLU (-25.17 kcal/mol ±1.12). In addition, it was observed that Ethocel® favored a wrapping mechanism around the drug molecules rather than a linear conformation that was witnessed for other individual polymers. The stability studies conducted for 90 days demonstrated that HPMC-PVP-FLU nanoparticles stored at 2°C-8°C and 25°C were more stable. Crystallinity of the processed FLU nanoparticles was confirmed using differential scanning calorimetry, powder X-ray diffraction analysis and TEM. The Fourier transform infrared spectroscopy (FTIR) studies showed that there was no chemical interaction between the drug and chosen polymer system. The HPMC-PVP-FLU nanoparticles also showed enhanced dissolution rate (P<0.05) compared to the unprocessed counterpart. The in vitro antibacterial studies showed that HPMC-PVP-FLU nanoparticles displayed superior effect against gram-positive bacteria compared to the unprocessed FLU and positive control.
Methods: A cross-sectional study was conducted for three months, in patients with type 2 diabetes who visited three community pharmacies located in Khobar, Saudi Arabia. Patients' disease knowledge and their adherence to medications were documented using Arabic versions of the Michigan Diabetes Knowledge Test and the General Medication Adherence Scale respectively. Data were analyzed through SPSS version 23. Chi-square test was used to report association of demographics with adherence. Spearman's rank correlation was employed to report the relationship among HbA1c values, disease knowledge and adherence. Logistic regression model was utilized to report the determinants of medication adherence and their corresponding adjusted odds ratio. Study was approved by concerned ethical committee (IRB-UGS-2019-05-001).
Results: A total of 318 patients consented to participate in the study. Mean HbA1c value was 8.1%. A third of patients (N = 105, 33%) had high adherence and half of patients (N = 162, 50.9%) had disease knowledge between 51% - 75%. A significantly weak-to-moderate and positive correlation (ρ = 0.221, p < 0.01) between medication adherence and disease knowledge was reported. Patients with >50% correct answers in the diabetes knowledge test questionnaire were more likely to be adherent to their medications (AOR 4.46, p < 0.01).
Conclusion: Disease knowledge in most patients was average and half of patients had high-to-good adherence. Patients with better knowledge were 4 to 5 times more likely to have high adherence. This highlights the importance of patient education and awareness regarding medication adherence in managing diabetes.
Methods: This is a descriptive clinical study. A combination of self-reporting questionnaires and data extraction tools were used to collect information during baseline tests, interviews, and follow-ups. Patients' medical, clinical, and socioeconomic history were recorded. Participants were recruited using random sampling from multiple centers.
Results: Out of 1034 COPD patients, heroin smokers represented the vast majority of addiction cases (n = 133). Heroin smokers were leaner than non-addicts (19.78 ± 4.07 and 24.01 ± 5.6, respectively). The most common type of comorbidities among heroin smokers was emphysema (27%). Both the forced expiratory volume (FEV1)/forced vital capacity ratio and FEV1% predicted were lower among heroin smokers than non-addicts (52.79 ± 12.71 and 48.54 ± 14.38, respectively). The majority of heroin smokers (55%) had advanced COPD, and at least 15% of heroin smokers suffered from frequent respiratory failure. The mean ± SD for COPD onset age among heroin smokers was 44.23 ± 5.72, and it showed a statistically significant correlation (P < 0.001).
Conclusion: Heroin smoking might be linked to the onset of COPD. Heroin smokers showed a significantrespiratory impairment compared to tobacco smokers of the same age group.
OBJECTIVES: The current study aims to explore the role of oligodendrocyte-specific transcription factors (OLIG2 and MYT1L) under suitable media composition to facilitate human umbilical-cord-derived mesenchymal stem cells (hUC-MSCs) differentiation toward oligodendrocyte for their potential use to treat demyelinating disorders.
METHODOLOGY: hUC-MSCs were isolated, cultured, and characterized based on their morphological and phenotypic characteristics. hUC-MSCs were transfected with OLIG2 and MYT1L transcription factors individually and in synergistic (OLIG2 + MYT1L) groups using a lipofectamine-based transfection method and incubated under two different media compositions (normal and oligo induction media). Transfected hUC-MSCs were assessed for lineage specification and differentiation using qPCR. Differentiation was also analyzed via immunocytochemistry by determining the expression of oligodendrocyte-specific proteins.
RESULTS: All the transfected groups showed significant upregulation of GFAP and OLIG2 with downregulation of NES, demonstrating the MSC commitment toward the glial lineage. Transfected groups also presented significant overexpression of oligodendrocyte-specific markers (SOX10, NKX2.2, GALC, CNP, CSPG4, MBP, and PLP1). Immunocytochemical analysis showed intense expression of OLIG2, MYT1L, and NG2 proteins in both normal and oligo induction media after 3 and 7 days.
CONCLUSIONS: The study concludes that OLIG2 and MYT1L have the potential to differentiate hUC-MSCs into oligodendrocyte-like cells, which is greatly facilitated by the oligo induction medium. The study may serve as a promising cell-based therapeutic strategy against demyelination-induced neuronal degeneration.
METHOD: The model was formulated by integrating the Caputo fractional derivative with the previous cancer treatment model. Thereafter, the linear-quadratic with the repopulation model was coupled into the model to account for the cells' population decay due to radiation. The treatment process was then simulated with numerical variables, numerical parameters, and radiation parameters. The numerical parameters which included the proliferation coefficients of the cells, competition coefficients of the cells, and the perturbation constant of the normal cells were obtained from previous literature. The radiation and numerical parameters were obtained from reported clinical data of six patients treated with radiotherapy. The patients had tumor volumes of 24.1cm3, 17.4cm3, 28.4cm3, 18.8cm3, 30.6cm3, and 12.6cm3 with fractionated doses of 2 Gy for the first two patients and 1.8 Gy for the other four. The initial tumor volumes were used to obtain initial populations of cells after which the treatment process was simulated in MATLAB. Subsequently, a global sensitivity analysis was done to corroborate the model with clinical data. Finally, 96 radiation protocols were simulated by using the biologically effective dose formula. These protocols were used to obtain a regression equation connecting the value of the Caputo fractional derivative with the fractionated dose.
RESULTS: The final tumor volumes, from the results of the simulations, were 3.58cm3, 8.61cm3, 5.68cm3, 4.36cm3, 5.75cm3, and 6.12cm3, while those of the normal cells were 23.87cm3, 17.29cm3, 28.17cm3, 18.68cm3, 30.33cm3, and 12.55cm3. The sensitivity analysis showed that the most sensitive model factors were the value of the Caputo fractional derivative and the proliferation coefficient of the cancer cells. Lastly, the obtained regression equation accounted for 99.14% of the prediction.
CONCLUSION: The model can simulate a cancer treatment process and predict the results of other radiation protocols.