Shahla Faisal

Accurate estimation of parameters during Phase I analysis is a critical step to ensure the effectiveness of the quality control process. Missing values are often encountered in data collection. Ignoring or inappropriate handling of such data can significantly affect the precision of the parameter estimates and the reliability of the control charts. Therefore, before estimating the process parameters, it is important to impute missing values in control charts to ensure a high‐quality input representation. This study presents a comprehensive investigation into the impact of various missing value handling techniques during phase I analysis on the subsequent performance of Exponentially Weighted Moving Average (EWMA) control charts for process dispersion. The robustness of the chart's monitoring capability is evaluated under three common imputation strategies: mean imputation, median imputation, and k ‐nearest neighbors ( k ‐NN) imputation. Furthermore, the common practice of listwise deletion, whereby cases with missing data are simply eliminated from the analysis, is also examined to provide a comparative baseline for the imputation approaches. Performance is compared for different sample sizes with varying percentages of missing values. Our results show that the k ‐NN imputation outperforms the other methods and provides more accurate imputation to detect the out‐of‐control process.

Climate change is thought to be responsible for the spread of various vector-borne diseases. The current study was conducted to evaluate the impact of different temperature and relative humidity regimes on the developmental stages of the yellow fever mosquito, Aedes aegypti (Diptera: Culicidae). The study also evaluated the impact of larval density on the survival of Ae. aegypti. In addition, the association between vector larval abundance, dengue incidence, and climatic factors were elucidated during 2016–2019 in three populated districts of Punjab, Pakistan, i.e., Lahore, Rawalpindi, and Multan. The results of the study revealed that at 10 °C and 35 °C, egg hatching and adult emergence were significantly reduced, regardless of the relative humidity. In contrast, at 20 °C and 30 °C, the rates of egg and adult survival increased with higher relative humidity. In addition, a density-dependent response was observed regarding larval survival of Ae. aegypti. Moreover, larval incidence was positively correlated with the number of dengue patients, Tmax, RH, and precipitation at Lahore (0.55, 0.23, 0.29, and 0.13), Rawalpindi (0.90, 0.30, 0.21, and 0.14), and Multan (0.05, 0.27, and 0.13) respectively, except in Multan, where a negative correlation (−0.09) with precipitation was observed. The inflow of patients had a positive correlation with the occurrence of a larval population, relative humidity, and precipitation at Lahore, Rawalpindi, and Multan districts, with the scale values of 0.55, 0.25, and 0.16; 0.90, 0.22, and 0.03; and 0.05, 0.06, and 0.03, respectively. In addition, a forecast model, ARIMA, predicted that there was a higher rate of larval occurrence in Rawalpindi, followed by Lahore. This study concluded that the role of precipitation > 200 mm prior to a 1–2-month lag, a 20–30 °C temperature range, and an RH exceeding 60% lead to the occurrence of larvae and dengue case spikes. This study will help to reinforce dengue surveillance and control strategies in Pakistan and to establish early management strategies based on changing climatic factors.

Deep learning has been identified as a new technology with the potential to support medical decision-making for a variety of disorders, including localized and diffuse COVID-19 disease. In the medical field, deep learning has gained importance for processing the complexity and amount of imaging data like CT scans and X-ray images. The current study evaluates the detection accuracy of deep learning methods for the detection of COVID-19. A search technique was devised to search three databases Web of Science, PubMed, and Google Scholar, and looked for studies that were published between January 1 and December 15, 2020. The meta-analysis was performed using the selected 22 studies, comprising 4595 chest X-ray images obtained from the patients of COVID-19. The value for pooled sensitivity obtained was 0.91 with a 95% CI of 0.89-0.94 and for pooled specificity was 0.94 with a 95% CI of 0.90-0.96. The value of heterogeneity was obtained I2 = 78%, (p < 0.01). Our findings demonstrate that deep learning models have a great potential for appropriately classifying COVID-19 cases and separating them from patients suffering from other types of pneumonia as well as healthy people. Implementing deep learning-based technologies can help radiologists detect COVID-19 correctly and promptly, and thereby address the COVID-19 pandemic. Health practitioners can use artificial intelligence and deep learning systems to make faster and more efficient decisions.

Background: Breast cancer is the most common malignancy among women worldwide. We aimed to know the past trends of age-specific breast cancer incidence rates in Faisalabad city. Methods: A retrospective study was designed at Allied Hospital Faisalabad (AHF), Pakistan from 2014-2018. Overall, 12742 cancer patients presented throughout these years, out of which 3390 were breast cancer cases. Descriptive statistics were computed and the results were presented as counts and percentage for categorical variables. Means and standard errors were computed for the continuous variables. For testing the association among categorical variables, a chi-square test of independence was used and the p-values less than 0.05 are reported as significant. Results: 84.70% patients were diagnosed with invasive breast carcinoma and 15.30% were all other types reported in the Allied Hospital Faisalabad. The incidence of breast cancer was outrageous in the 40-49 year-old age group (1021 patients, 30.12%) and the mean age is 45 in all years. An increase of 34.86% was observed from 2014 to 2018. The comprehensive four-year data (2015 to 2018) were further analyzed for histology, surgery, staging and grading pattern as 2014 files data was insufficient to discuss. The stage III and grade III were most common throughout the years from 2015 to 2018 with 33.9% and 55.71% respectively. Conclusion: Breast cancer is diagnosed more commonly in women than in any other type of cancers in Faisalabad city. There is a need to upgrade the existing hospital facilities to make the women diagnose the cancer at an earlier stage.

Osteoporosis is characterized by low bone mineral density leading to enhanced bone fragility and a consequent increase in fracture risk. The focus of this case‐control study was to identify significant socioeconomic risk factors of osteoporosis in Pakistani women and examine how the risk increases for different levels of risk factors. A case‐control study was conducted from November 2018 to August 2019 in two main hospitals in Faisalabad, Pakistan. Multiple logistic regression was used to explore the significant risk factors of osteoporosis and how the risk increases in cases (cases = 120) as compared to the control group (controls = 120) in the presence of these risk factors. The mean age ± standard deviation for cases and controls was 59.62 ± 10.75 and 54.27 ± 10.09, respectively. The minimum and maximum ages were 36 and 80 years, respectively. In addition to age, bone fracture, family history, regular physical activity, family size, use of meat, type of birth, breastfeeding, premature menopause, loss of appetite, and use of anticoagulants were significant risk factors with p‐values less than 0.05. The risk prediction model with significant risk factors was a good fit with a p‐value of 0.28, corresponding to the Hosmer–Lemeshow test value (χ2 = 9.78). This parsimonious model with Cox–Snell R2 = 0.50 (with a maximum value = 0.75) and Nagelkerke R2 = 0.66 showed an AUC of 0.924 as compared to the full model with all risk factors under study that exhibited an AUC of 0.949.

Immunization is a precautionary measure that helps to stop diseases before their occurrence. Vaccine-preventable diseases are a primary cause of death among children under the age of five in many developing nations. The purpose of this study is to investigate the immunization status and associated demographic characteristics among children aged 1223 months in Punjab, Pakistan. The study used the data from the Multiple Indicator Cluster Survey (MICS) for Punjab, Pakistan. Data were collected from caregivers using interviewer-administered questionnaires. To summarize the data, descriptive statistics are computed, and logistic regression is used to identify the significant factors that are responsible for complete immunization among the children in Punjab. Odds ratios, 95 CI, and Chi-square statistics were computed to identify the factors associated with no or partial immunization. The prevalence of complete immunization coverage was 89.1. Women in the rich wealth quantile had the highest odds of completing the immunization for their children (AOR=2.314; 95 CI: 1.6423.261) compared to those who are poor. Those in rural areas were more likely to fully vaccinate their children (AOR=1.54; 95 CI: 1.2321.925) compared to those in urban areas. Those in the highest level of the educational group (AOR=2.639; 95 CI: 1.8003.87) are more likely to complete vaccination for their children compared to those with no formal education. However, female children are less likely to complete immunization compared to male children (AOR=0.813; 95 CI: 0.6870.963). The immunization status of children shows a significant association with maternal education, wealth status, and area of residence.

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 -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 ( ) and the greatest acquired from the random forest ( ). 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.

Multiple Imputation (MI) is always challenging in high dimensional settings. The imputation model with some selected number of predictors can be incompatible with the analysis model leading to inconsistent and biased estimates. Although compatibility in such cases may not be achieved, but one can obtain consistent and unbiased estimates using a semi-compatible imputation model. We propose to relax the lasso penalty for selecting a large set of variables (at most n). The substantive model that also uses some formal variable selection procedure in high-dimensional structures is then expected to be nested in this imputation model. The resulting imputation model will be semi-compatible with high probability. The likelihood estimates can be unstable and can face the convergence issues as the number of variables becomes nearly as large as the sample size. To address these issues, we further propose to use a ridge penalty for obtaining the posterior distribution of the parameters based on the observed data. The proposed technique is compared with the standard MI software and MI techniques available for high-dimensional data in simulation studies and a real life dataset. Our results exhibit the superiority of the proposed approach to the existing MI approaches while addressing the compatibility issue.

Rabbit meat is a good source of protein as compared to beef and chicken. The processing of rabbit meat gives attractive texture with a strong flavor. Charcoal grilling is a well-known cooking method which results in the formation of polycyclic aromatic hydrocarbons. Therefore, to determine the concentration of PAHs, rabbit meat samples were prepared with and without additives and analyzed for six PAHs (naphthalene, fluorene, phenanthrene, anthracene, fluoranthene, and pyrene) using gas chromatography-mass spectrometry (GC-MS). Naphthalene was dominant in all the samples and the highest naphthalene concentration (2.61 µg/g) was found in the foreleg sample (without additives) due to the presence of higher fat molecules. PAHs concentration was found to be significantly (p ≤ 0.05) lower by the addition of antioxidants. Lowest naphthalene concentration was observed in the back sample of seekh kebab (0.77 µg/g) with 1 h marinating duration. The content of pyrene was only noticed in foreleg (0.01 µg/g) and hind leg (0.03 µg/g) samples from reshmi kebab. The content of fluorene was not observed in all the samples.