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Pharmacological evaluation of poly(3-methylthiophene) and its titanium(IV)phosphate nanocomposite: DNA interaction, molecular docking, and cytotoxic activity

Baig U, Gondal MA, Alam MF, Wani WA, Younus H

J. Photochem. Photobiol. B, Biol., 2016 Nov;164:244-255.
PMID: 27710872 DOI: 10.1016/j.jphotobiol.2016.09.034

Cancer and pathogenic microbial diseases have terribly affected human health over a longer period of time. In response to the increasing casualties due to cancer and microbial diseases, unique poly(3-methylthiophene) and poly(3-methylthiophene)-titanium(IV)phosphate composite were prepared via in-situ oxidative chemical polymerization in this work. The poly(3-methylthiophene) and poly(3-methylthiophene)-titanium(IV)phosphate composite were well characterized by Fourier transform infrared spectroscopy and field emission scanning electron microscopy. DNA binding studies by UV-Visible and fluorescence spectroscopic investigations indicated strong binding affinities of poly(3-methylthiophene) and poly(3-methylthiophene)-titanium(IV)phosphate nanocomposite; leading to structural damage of DNA. Poly(3-methylthiophene)-titanium(IV)phosphate nanocomposite showed stronger interactions with DNA as compared to poly(3-methylthiophene) and from dye displacement assay it was confirmed that mode of binding of both the formulations was intercalative. The antimicrobial screening revealed that polymer and its composite displayed stronger antibacterial effects than ampicillin against Bacillus subtilis, Staphylococcus aureus, Pseudomonas aeruginosa and Salmonella typhimurium. Besides, the poly(3-methylthiophene) and poly(3-methylthiophene)-titanium(IV)phosphate nanocomposite showed dose dependent effects towards estrogen receptor positive breast cancer (MCF-7) and estrogen receptor negative breast cancer (MDA-MB-231) cell lines; with poly(3-methylthiophene)-titanium(IV)phosphate nanocomposite showing better activities against both cell lines. In all in-vitro biological investigations, poly(3-methylthiophene)-titanium(IV)phosphate composite showed superior properties to that of the pure poly(3-methylthiophene), which encouraged us to suggest its potential as future therapeutic gear in drug delivery and other allied fields.
Fulltext Cost-Effectiveness and Cost-Utility of Palbociclib versus Ribociclib in Women with Stage IV Breast Cancer: A Real-World Data Evaluation

Al-Ziftawi NH, Alam MF, Elazzazy S, Shafie AA, Hamad A, Mohamed Ibrahim MI

Int J Environ Res Public Health, 2022 Dec 28;20(1).
PMID: 36612831 DOI: 10.3390/ijerph20010512

Palbociclib and ribociclib are indicated in the first-line treatment of hormonal-receptor-positive HER-2 negative (HR+/HER-2 negative) advanced breast cancer. Despite their clinical benefit, they can increase healthcare expenditure. Yet, there are no comparative pharmacoeconomic evaluations for them in developing countries, the Middle East, or Gulf countries. This study compared the cost-effectiveness of palbociclib and ribociclib in Qatar. A 10-year within-cycle-corrected Markov's model was developed using TreeAge Pro® software. The model consisted of three main health states: progression-free (PFS), progressed-disease (PD), and death. Costs were obtained from the actual hospital settings, transition probabilities were calculated from individual-patient data, and utilities were summarized from the published literature. The incremental cost-effectiveness ratio (ICER) and the incremental cost-utility ratio (ICUR) were calculated and compared to three gross-domestic-products per capita. Deterministic and probabilistic sensitivity analyses were performed. Ribociclib dominated palbociclib in terms of costs, life-years gained, and quality-adjusted life-years gained. The conclusions remained robust in the different cases of the deterministic sensitivity analyses. Taking all combined uncertainties into account, the confidence in the base-case conclusion was approximately 60%. Therefore, in HR+/HER-2 negative stage IV breast cancer patients, the use of ribociclib is considered cost-saving compared to palbociclib.
Fulltext The effectiveness and safety of palbociclib and ribociclib in stage IV HR+/HER-2 negative breast cancer: a nationwide real world comparative retrospective cohort study

Al-Ziftawi NH, Elazzazy S, Alam MF, Shafie A, Hamad A, Bbujassoum S, et al.

Front Oncol, 2023;13:1203684.
PMID: 38162489 DOI: 10.3389/fonc.2023.1203684

INTRODUCTION: Palbociclib and ribociclib are indicated in the first-line treatment of hormonal receptor-positive HER-2 negative (HR+/HER2- negative) advanced breast cancer. Although randomized-controlled trials (RCTs) proved their clinical efficacy, there are no observational studies yet to validate the clinical findings in the real-world. Therefore, this study aimed to evaluate and compare the clinical effectiveness and safety profiles of palbociclib and ribociclib in Qatar.
MATERIALS AND METHODS: A retrospective observational study was conducted on HR+/HER-2-negative stage-IV breast cancer patients receiving palbociclib or ribociclib in the state of Qatar. Clinical data were collected from the National Center for Cancer Care and Research (NCCCR) in Qatar using Cerner®. Primary outcomes were progression-free-survival (PFS) and overall-survival (OS) generated by Kaplan-Meier curves. Moreover, safety profiles of both two treatments were evaluated.
RESULTS: The data from 108 patients were included in the final analysis. There was no statistically significant difference in PFS between the palbociclib and ribociclib groups; PFS was 17.85 versus 13.55 months, respectively(p> 0.05). Similarly, there was no statistically significant difference in OS between the two medications, 29.82 versus 31.72 months, respectively(p>0.05). Adverse events were similar between the two groups. Neutropenia was the most common side effect in the study population accounting for 59.3% of the patients.
CONCLUSIONS: Therefore, both treatments have similar efficacy and safety profiles. Further research on a larger-scale population and longer follow-up period is recommeneded.
Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3

Rahman MS, Islam KR, Prithula J, Kumar J, Mahmud M, Alam MF, et al.

BMC Med Inform Decis Mak, 2024 Sep 09;24(1):249.
PMID: 39251962 DOI: 10.1186/s12911-024-02655-4

BACKGROUND: Sepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Sepsis is crucial for improving survival rates. Machine learning techniques offer advantages over traditional methods for predicting outcomes. This study aimed to develop a prognostic model using a Stacking-based Meta-Classifier to predict 30-day mortality risks in Sepsis-3 patients from the MIMIC-III database.
METHODS: A cohort of 4,240 Sepsis-3 patients was analyzed, with 783 experiencing 30-day mortality and 3,457 surviving. Fifteen biomarkers were selected using feature ranking methods, including Extreme Gradient Boosting (XGBoost), Random Forest, and Extra Tree, and the Logistic Regression (LR) model was used to assess their individual predictability with a fivefold cross-validation approach for the validation of the prediction. The dataset was balanced using the SMOTE-TOMEK LINK technique, and a stacking-based meta-classifier was used for 30-day mortality prediction. The SHapley Additive explanations analysis was performed to explain the model's prediction.
RESULTS: Using the LR classifier, the model achieved an area under the curve or AUC score of 0.99. A nomogram provided clinical insights into the biomarkers' significance. The stacked meta-learner, LR classifier exhibited the best performance with 95.52% accuracy, 95.79% precision, 95.52% recall, 93.65% specificity, and a 95.60% F1-score.
CONCLUSIONS: In conjunction with the nomogram, the proposed stacking classifier model effectively predicted 30-day mortality in Sepsis patients. This approach holds promise for early intervention and improved outcomes in treating Sepsis cases.
Fulltext QCovSML: A reliable COVID-19 detection system using CBC biomarkers by a stacking machine learning model

Rahman T, Khandakar A, Abir FF, Faisal MAA, Hossain MS, Podder KK, et al.

Comput Biol Med, 2022 Apr;143:105284.
PMID: 35180500 DOI: 10.1016/j.compbiomed.2022.105284

The reverse transcription-polymerase chain reaction (RT-PCR) test is considered the current gold standard for the detection of coronavirus disease (COVID-19), although it suffers from some shortcomings, namely comparatively longer turnaround time, higher false-negative rates around 20-25%, and higher cost equipment. Therefore, finding an efficient, robust, accurate, and widely available, and accessible alternative to RT-PCR for COVID-19 diagnosis is a matter of utmost importance. This study proposes a complete blood count (CBC) biomarkers-based COVID-19 detection system using a stacking machine learning (SML) model, which could be a fast and less expensive alternative. This study used seven different publicly available datasets, where the largest one consisting of fifteen CBC biomarkers collected from 1624 patients (52% COVID-19 positive) admitted at San Raphael Hospital, Italy from February to May 2020 was used to train and validate the proposed model. White blood cell count, monocytes (%), lymphocyte (%), and age parameters collected from the patients during hospital admission were found to be important biomarkers for COVID-19 disease prediction using five different feature selection techniques. Our stacking model produced the best performance with weighted precision, sensitivity, specificity, overall accuracy, and F1-score of 91.44%, 91.44%, 91.44%, 91.45%, and 91.45%, respectively. The stacking machine learning model improved the performance in comparison to other state-of-the-art machine learning classifiers. Finally, a nomogram-based scoring system (QCovSML) was constructed using this stacking approach to predict the COVID-19 patients. The cut-off value of the QCovSML system for classifying COVID-19 and Non-COVID patients was 4.8. Six datasets from three different countries were used to externally validate the proposed model to evaluate its generalizability and robustness. The nomogram demonstrated good calibration and discrimination with the area under the curve (AUC) of 0.961 for the internal cohort and average AUC of 0.967 for all external validation cohort, respectively. The external validation shows an average weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 92.02%, 95.59%, 93.73%, 90.54%, and 93.34%, respectively.