METHODS: The algorithm utilises the weighted support vector machine method for efficient classification of heart disease based on a binary response that indicates the presence or absence of heart disease as the result of an angiographic test. The optimal values of the support vector machine and the Radial Basis Function kernel parameters for the heart disease classification were determined via a 10-fold cross-validation method. The heart disease data was partitioned into training and testing sets using different percentages of the splitting ratio. Each of the training sets was used in training the classification method while the predictive power of the method was evaluated on each of the test sets using the Monte-Carlo cross-validation resampling technique. The effect of different percentages of the splitting ratio on the method was also observed.
RESULTS: The misclassification error rate was used to compare the performance of the method with three selected machine learning methods and was observed that the proposed method performs best over others in all cases considered.
CONCLUSION: Finally, the results illustrate that the classification algorithm presented can effectively predict the heart disease status of an individual based on the results of an angiographic test.
Methods: A cross-sectional study including 193 patients diagnosed with appendicitis was conducted at four hospitals in Pahang, Malaysia. Those who presented between 1 February 2020 and 17 March 2020 were included in the pre-MCO group and those between 18 March 2020 and 30 April 2020 in the MCO group. The definitions of simple and complicated appendicitis were based on the Sunshine Appendicitis Grading Score. The primary outcome was the incidence of complicated appendicitis, and the secondary outcomes were length of stay, a composite of surgical morbidities and a composite of organ failure.
Results: A total of 105 patients in the pre-MCO group and 88 in the MCO group were analysed. The incidence of complicated appendicitis was 33% and it was higher in the MCO than in the pre-MCO group (44% versus 23%, P = 0.002). The MCO period was independently associated with complicated appendicitis in the logistic regression (P = 0.001). It was also associated with prolonged length of stay (3.5 days versus 2.4 days, P < 0.001) and higher overall surgical morbidity (19% versus 5%, P = 0.002).
Conclusion: The MCO imposed during the COVID-19 pandemic was associated with a higher incidence of complicated appendicitis and surgical morbidity.
Methods: A cross-sectional study was conducted at the Obstetrics and Gynecology Department of Sandeman Provincial Hospital Quetta city, Pakistan. The respondents were asked to answer the Urdu (lingua franca of Pakistan) version of the Quality of Life Questionnaire for Physiological Pregnancy. Data were coded and analyzed by SPPS v 21. The Kolmogorov-Smirnov test was used to establish normality of the data and non-parametric tests were used accordingly. Quality of Life was assessed as proposed by the developers. The Chi-square test was used to identify significant associations and linear regression was used to identify the predictors of QoL. For all analyses, p < 0.05 was taken significantly.
Results: Four hundred and three pregnant women participated in the study with a response rate of 98%. The mean QoL score was 19.85 ± 4.89 indicating very good QoL in the current cohort. The Chi-Square analysis reported a significant association between age, education, occupation, income, marital status, and trimester. Education was reported as a positive predictor for QoL (p = 0.006, β = 2.157). On the other hand, trimester was reported as a negative predictor of QoL (p = 0.013, β = -1.123).
Conclusion: Improving the QoL among pregnant women requires better identification of their difficulties and guidance. The current study highlighted educational status and trimester as the predictors of QoL in pregnant women. Health care professionals and policymakers should consider the identified factors while designing therapeutic plans and interventions for pregnant women.
OBJECTIVE: The purpose of this study was to examine the association of gestational cardiovascular health-formally characterized by a combination of 5 metrics-with adverse maternal and newborn outcomes.
STUDY DESIGN: We analyzed data from the Hyperglycemia and Adverse Pregnancy Outcome study, including 2304 mother-newborn dyads from 6 countries. Maternal cardiovascular health was defined by the combination of the following 5 metrics measured at a mean of 28 (24-32) weeks' gestation: body mass index, blood pressure, lipids, glucose, and smoking. Levels of each metric were categorized using pregnancy guidelines, and the total cardiovascular health was scored (0-10 points, where 10 was the most favorable). Cord blood was collected at delivery, newborn anthropometrics were measured within 72 hours, and medical records were abstracted for obstetrical outcomes. Modified Poisson and multinomial logistic regression were used to test the associations of gestational cardiovascular health with pregnancy outcomes, adjusted for center and maternal and newborn characteristics.
RESULTS: The average age of women at study exam was 29.6 years old, and they delivered at a mean gestational age of 39.8 weeks. The mean total gestational cardiovascular health score was 8.6 (of 10); 36.3% had all ideal metrics and 7.5% had 2+ poor metrics. In fully adjusted models, each 1 point higher (more favorable) cardiovascular health score was associated with lower risks for preeclampsia (relative risk, 0.67 [95% confidence interval, 0.61-0.73]), unplanned primary cesarean delivery (0.88 [0.82-0.95]), newborn birthweight >90th percentile (0.81 [0.75-0.87]), sum of skinfolds >90th percentile (0.84 [0.77-0.92]), and insulin sensitivity <10th percentile (0.83 [0.77-0.90]). Cardiovascular health categories demonstrated graded associations with outcomes; for example, relative risks (95% confidence intervals) for preeclampsia were 3.13 (1.39-7.06), 5.34 (2.44-11.70), and 9.30 (3.95-21.86) for women with ≥1 intermediate, 1 poor, or ≥2 poor (vs all ideal) metrics, respectively.
CONCLUSION: More favorable cardiovascular health at 24 to 32 weeks' gestation was associated with lower risks for several adverse pregnancy outcomes in a multinational cohort.
Materials and Methods: A library of 120 phytochemical ligands was prepared, from which 5 were selected considering their absorption, distribution, metabolism, and excretion (ADMET) and quantitative structure-activity relationship (QSAR) profiles. The protein active sites and belonging quantum tunnels were defined to conduct supramolecular docking of the aforementioned ligands. The hydrogen bond formation and hydrophobic interactions between the ligand-receptor complexes were studied following the molecular docking steps. A comprehensive molecular dynamic simulation (MDS) was conducted for each of the ligand-receptor complexes to figure out the values - root mean square deviation (RMSD) (Å), root mean square fluctuation (RMSF) (Å), H-bonds, Cα, solvent accessible surface area (SASA) (Å2), molecular surface area (MolSA) (Å2), Rg (nm), and polar surface area (PSA) (Å). Finally, computational programming and algorithms were used to interpret the dynamic simulation outputs into their graphical quantitative forms.
Results: ADMET and QSAR profiles revealed that the most active candidates from the library to be used were apigenin, isovitexin, piperolactam A, and quercetin as test ligands, whereas serpentine as the control. Based on the binding affinities of supramolecular docking and the parameters of molecular dynamic simulation, the strength of the test ligands can be classified as isovitexin > quercetin > piperolactam A > apigenin when complexed with the hACE2 receptor. Surprisingly, serpentine showed lower affinity (-8.6 kcal/mol) than that of isovitexin (-9.9 kcal/mol) and quercetin (-8.9 kcal/mol). The MDS analysis revealed all ligands except isovitexin having a value lower than 2.5 Ǻ. All the test ligands exhibited acceptable fluctuation ranges of RMSD (Å), RMSF (Å), H-bonds, Cα, SASA (Å2), MolSA (Å2), Rg (nm), and PSA (Å) values.
Conclusion: Considering each of the parameters of molecular optimization, docking, and dynamic simulation interventions, all of the test ligands can be suggested as potential targeted drugs in blocking the hACE2 receptor.