Theophylline‐7‐acetic acid (acefylline) (3) and its derivatives are pharmacologically active compounds and generally recognized as bronchodilators for the treatment of respiratory diseases like acute asthma for over 70 years. In this article, synthesis of 2‐((5‐((1,3‐dimethyl‐2,6‐dioxo‐2,3‐dihydro‐1H‐purin‐7(6H)‐yl)methyl)‐1,3,4‐oxadiazol‐2‐yl)thio)‐N‐arylacetamides (10a‐j) has been reported. All the synthesized derivatives (10a‐j) were structurally verified by FT‐IR, 1H NMR, 13C NMR and evaluated for their anti‐cancer (using MTT assay), hemolytic and thrombolytic potential. N‐(4‐Chlorophenyl)‐2‐(5‐((1,3‐dimethyl‐2,6‐dioxo‐2,3‐dihydro‐1H‐purin‐7(6H)‐yl)methyl)‐1,3,4‐oxadiazol‐2‐ylthio)acetamide (10g) was found to be the most active against human liver cancer cell lines (Huh7) having cell viability 53.58 ± 1.28 using 100 μg/mL concentration of compound which was further in‐silico modelled to describe the possible mechanistic insights for its anti‐proliferative activity. The results of hemolytic and thrombolytic activities indicated that these derivatives were less toxic and hold considerable potential as a drug candidate. 2‐(5‐((1,3‐Dimethyl‐2,6‐dioxo‐2,3‐dihydro‐1H‐purin‐7(6H)‐yl)methyl)‐1,3,4‐oxadiazol‐2‐ylthio)‐N‐(2‐fluorophenyl)acetamide (10c) of the series was found to be least toxic with 0.1% hemolysis relative to ABTS (95.5%) as positive control. 2‐(5‐((1,3‐Dimethyl‐2,6‐dioxo‐2,3‐dihydro‐1H‐purin‐7(6H)‐yl)methyl)‐1,3,4‐oxadiazol‐2‐ylthio)‐N‐(tetrahydro‐2H‐pyran‐4‐yl)acetamide (10j) exhibited potent clot lysis activity (90%) as compared to negative control DMSO (0.57%).

Objectives The objective of this study was to build and assess the performance of survival prediction models using the gender-specific informative risk factors for patients with left ventricular systolic dysfunction. Methods A lasso approach was used to decide the informative predictors for building semi-parametric proportional hazards Cox model. Separate models were built for all patients [N = 299], male patients [N male = 194 (64.88%)], and female patients [N female = 105 (35.12%)], to observe the risk factors associated with the individual’s risk of death. The likelihood- ratio test was used to test the goodness of fit of the selected model, and the C-index was used to assess the predictive performance of the selected model(s) with respect to the overall model with all observed risk factors. Results The survival prediction model for females is notably different from that for males. For males, smoking, diabetes, and anaemia, whereas for females, ejection fraction, sodium, and platelets count are non-informative with zero regression coefficients. The goodness of fit of the selected models with respect to the general model with all observed risk factors is tested using the likelihood-ratio test. The results are in favor of the selected models with p-values 0.51,0.61, and 0.70 for all patients, male patients, and female patients, respectively. The same values of C-index for the full model and the selected models for overall data, for males, and for females (0.72, 0.73, and 0.77 for overall data, male data, and female data, respectively) indicate that the selected models are as good as the corresponding overall models regarding their predictive performance. Conclusion There is a substantial difference in the survival prediction models for heart failure (HF) of male and female patients in this study. More studies are needed in Pakistan for confirming this striking male-female difference regarding the potential risk factors to predict survival with heart failure.

No abstract

Breast cancer is one of the most commonly diagnosed female disorders globally. Numerous studies have been conducted to predict survival markers, although the majority of these analyses were conducted using simple statistical techniques. In lieu of that, this research employed machine learning approaches to develop models for identifying and visualizing relevant prognostic indications of breast cancer survival rates. A comprehensive hospital-based breast cancer dataset was collected from the National Cancer Institute’s SEER Program’s November 2017 update, which offers population-based cancer statistics. The dataset included female patients diagnosed between 2006 and 2010 with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3). The dataset included nine predictor factors and one predictor variable that were linked to the patients’ survival status (alive or dead). To identify important prognostic markers associated with breast cancer survival rates, prediction models were constructed using K -nearest neighbor (K-NN), decision tree (DT), gradient boosting (GB), random forest (RF), AdaBoost, logistic regression (LR), voting classifier, and support vector machine (SVM). All methods yielded close results in terms of model accuracy and calibration measures, with the lowest achieved from logistic regression ( accuracy = 80.57   percent ) and the greatest acquired from the random forest ( accuracy = 94.64   percent ). Notably, the multiple machine learning algorithms utilized in this research achieved high accuracy, suggesting that these approaches might be used as alternative prognostic tools in breast cancer survival studies, especially in the Asian area.