METHODS: We conduct a side-by-side comparison of the machine learning approach with the traditional time series model. The multilayer perceptron model (MLP), a machine learning technique, and the Box-Jenkins methodology, also known as the ARIMA model, are used for classical modeling. Both methods are applied to the Monkeypox cumulative data set and compared using different model selection criteria such as root mean square error, mean square error, mean absolute error, and mean absolute percentage error.
RESULTS: With a root mean square error of 150.78, the monkeypox series follows the ARIMA (7,1,7) model among the other potential models. Comparatively, we use the multilayer perceptron (MLP) model, which employs the sigmoid activation function and has a different number of hidden neurons in a single hidden layer. The root mean square error of the MLP model, which uses a single input and ten hidden neurons, is 54.40, significantly lower than that of the ARIMA model. The actual confirmed cases versus estimated or fitted plots also demonstrate that the multilayer perceptron model has a better fit for the monkeypox data than the ARIMA model.
CONCLUSIONS AND RECOMMENDATION: When it comes to predicting monkeypox, the machine learning method outperforms the traditional time series. A better match can be achieved in future studies by applying the extreme learning machine model (ELM), support vector machine (SVM), and some other methods with various activation functions. It is thus concluded that the selected data provide a real picture of the virus. If the situations remain the same, governments and other stockholders should ensure the follow-up of Standard Operating Procedures (SOPs) among the masses, as the trends will continue rising in the upcoming 10 days. However, governments should take some serious interventions to cope with the virus.
LIMITATION: In the ARIMA models selected for forecasting, we did not incorporate the effect of covariates such as the effect of net migration of monkeypox virus patients, government interventions, etc.
OBJECTIVE: This review was aimed to critically discuss and conceptualize existing evidences related to the pharmaceutical significance and therapeutic feasibility of multi-functionalization of nanomedicines for early diagnosis and efficient treatment of BC.
RESULTS: Though the implication of nanotechnology-based modalities has revolutionised the outcomes of diagnosis and treatment of BC; however, the clinical translation of these nanomedicines is facing grandeur challenges. These challenges include recognition by the reticuloendothelial system (RES), short plasma half-life, non-specific accumulation in the non-cancerous cells, and expulsion of the drug(s) by the efflux pump. To circumvent these challenges, various adaptations such as PEGylation, conjugation of targeting ligand(s), and siteresponsive behaviour (i.e., pH-responsiveness, biochemical, or thermal-responsiveness) have been adapted. Similarly, multi-functionalization of nanomedicines has emerged as an exceptional strategy to improve the pharmacokinetic profile, specific targetability to the tumor microenvironment (active targeting) and efficient internalization, and to alleviate the expulsion of internalized drug contents by silencing-off efflux pump.
CONCLUSION: Critical analysis of the available evidences revealed that multi-functionalization of nanomedicines is a plausible and sustainable adaptation for early diagnosis and treatment of BC with better therapeutic outcomes.
OBJECTIVES: To investigate the incidence, factors, management, and impact of AEs on treatment outcomes in MDR-TB patients.
METHODS: This study reviewed the medical records of 275 MDR-TB patients at Fatimah Jinnah Institute of Chest Diseases in Quetta, Pakistan. Patient information was collected using a designed data collection form. Mann-Whitney U and Kruskal-Wallis tests examined the difference in AEs occurrences based on patients' characteristics. Multiple binary logistic regression identified factors associated with unsuccessful outcomes, with statistical significance set at a p-value 60 years(OR = 23.481), baseline body weight of 31-60 kg(OR = 0.180), urban residence(OR = 0.296), and experiencing ototoxicity (OR = 0.258) and hypothyroidism (OR = 0.136) were significantly associated with unsuccessful treatment outcomes.
CONCLUSION: AEs were highly prevalent but did not negatively impact treatment outcomes. Patients at higher risk of developing AEs and unsuccessful outcomes should receive special attention for its early management.
METHODS: In silico analysis was performed using molecular docking of chemical compounds with vascular endothelial growth factor (VEGF). The different computational tools used were AutoDock Vina, BIOVIA DISCOVERY studio, and PyMOL. Drug likeness and toxicity were analyzed by SWISS ADMET. Cells that were 60-70% confluent were treated with different concentrations of hydrogen peroxide (H2O2) (100-2000 μM) and ascorbic acid (30, 60, 90 μg/mL). The MTT cell proliferation assay was performed to compare the proliferative potential of HepG2 cells treated with H2O2 or ascorbic acid with untreated HepG2 cells using 96-well plates.
RESULTS: The lowest binding energy of VEGF with vitamin C -5.2 kcal/mol and L-ascorbic acid-2 glycoside -4.7 kcal/mol was observed by in silico analysis. Vitamin C was selected because it exhibited a high interaction with VEGF and fulfilled Lipinski's rule, and had better oral viability and pharmacokinetics compared to L-ascorbic acid-2 glycoside. Cell viability assays showed that vitamin C had significant apoptotic effects (P