Analysing Deviation-From-Target Data: Applying the Correct Force in Sellick’s Maneuver

The problem of comparing the deviation from a target of two or more treatments or procedures arises now and again in medicine. Practitioners usually carry out a t-test on a loss function such as absolute error. We have adapted and developed statistical methods to give a normative methodology for deviation-from-target problems and exemplify them by evaluating the performance of a tactile feedback device. Parametric and nonparametric analyses are compared and contrasted. We recommend nonparametric methods for inference about loss functions such as absolute error, with a permutation test for testing the hypothesis that the two methods perform identically, and the nonparametric bootstrap for deriving standard errors and confidence intervals on loss function ratios. We develop a new permutation test that can be used when the practitioner is unwilling to decide which loss function should be used. We recommend parametric analysis when more insight into how one method is superior is desired, or there are covariates, and discuss the complications. The results for our example are that the tactile sensing device reduces an upward bias in applied force, and more importantly reduces the spread (variance) of the applied force. It performs significantly better than manual force application.     

Seasonal influenza vaccination policies in the Eastern Mediterranean Region: Current status and the way forward

Background

The World Health Organization recommends annual influenza vaccination, especially in high-risk groups. Little is known about the adoption and implementation of influenza vaccination policies in the Eastern Mediterranean Region.

Frameshift Sequence Variants in the Human Lipase‐H Gene Causing Hypotrichosis

Hypotrichosis is a condition of abnormal hair pattern characterized by sparse to absent hair on different parts of the body, including the scalp. The condition is often characterized by tightly curled woolly hairs, discoloration of hair, and development of multiple keratin filled cysts or papules on the body. Sequence analysis of the lipase H (LIPH) gene, mapped on chromosome 3q27.3, led to the identification of a novel frameshift deletion variant (c.932delC, p.Pro311Leufs*3) in one family and previously reported 2‐bp deletion (c.659_660delTA) in five other families, inherited hypotrichosis, and woolly hair in an autosomal recessive pattern. The study further extends the body of evidence that sequence variants in the LIPH gene result in hypotrichosis and woolly hair phenotype.     

Safety of pregabalin among hemodialysis patients suffering from uremic pruritus

Objectives: The aim of this study was to assess the safety and probability of adverse events associated with the use of 75 mg pregabalin post hemodialysis (pHD) among patients with UP. Methods: A cross-sectional study done among the hemodialysis patients suffering from uremic pruritus (UP) Aljaber Kidney Center (AJKC), Al-Ahsa, Eastern Province, Saudi Arabia. Assessment for the safety profile of pregabalin was done using Naranjo’s algorithm. A predictive model was developed using binary multiple logistic regression to explore association of patients’ demographics and risk factors with the occurrence of AEs. Throughout statistical significance level was considered significant at 0.05. Key findings: Assessment of safety of pregabalin revealed that somnolence and dizziness were the two frequent adverse events followed by constipation, weight gain and edema. However, it was noticed that female patients aged less than 50 years were found to be at a higher risk in comparison with men. Moreover, those patients having one comorbid complication (i.e. hypertension or diabetes mellitus alone) were at a higher risk of somnolence, weight gain and dry mouth. Conclusion: Naranjo’s quantification for the possibility and probability of adverse events reflect that all the events were probable. Age, gender and comorbid medical conditions are some of the factors that might have clinical association with the occurrence of the AEs.     

A Longitudinal Survey for Genome-based Identification of SARS-CoV-2 in Sewage Water in Selected Lockdown Areas of Lahore City,Pakistan: A Potential Approach for Future Smart Lockdown Strategy

In 2019, the newly emerged SARS-CoV-2 virus caused pneumonia-like illness. The disease rapidly spread globally, leading to a worldwide outbreak referred to as the COVID-19 pandemic. The affected patients show symptoms of fever, dry cough, respiratory distress, myalgia, and gastrointestinal disturbance. As of April 5, 2021, 132,083,022 people worldwide were affected by COVID-19, while 2,868,454 people died due to the disease[1]. SARS-CoV-2-positive patients may remain asymptomatic or start showing symptoms in 2?14 days after exposure to the virus[2]. The viral infection can be diagnosed from nasopharyngeal, throat, alveolar lavage, lacrimal, blood, and stool samples. The patient starts shedding the virus in stool regardless of being symptomatic or asymptomatic, which makes sewage-based detection of the virus to be more beneficial in the early infection stage.     

Comparative Study of Supervised Machine Learning Algorithms for Predicting the Compressive Strength of Concrete at High Temperature

High temperature severely affects the nature of the ingredients used to produce concrete, which in turn reduces the strength properties of the concrete. It is a difficult and time-consuming task to achieve the desired compressive strength of concrete. However, the application of supervised machine learning (ML) approaches makes it possible to initially predict the targeted result with high accuracy. This study presents the use of a decision tree (DT), an artificial neural network (ANN), bagging, and gradient boosting (GB) to forecast the compressive strength of concrete at high temperatures on the basis of 207 data points. Python coding in Anaconda navigator software was used to run the selected models. The software requires information regarding both the input variables and the output parameter. A total of nine input parameters (water, cement, coarse aggregate, fine aggregate, fly ash, superplasticizers, silica fume, nano silica, and temperature) were incorporated as the input, while one variable (compressive strength) was selected as the output. The performance of the employed ML algorithms was evaluated with regards to statistical indicators, including the coefficient correlation (R²), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual models using DT and ANN gave R² equal to 0.83 and 0.82, respectively, while the use of the ensemble algorithm and gradient boosting gave R² of 0.90 and 0.88, respectively. This indicates a strong correlation between the actual and predicted outcomes. The k-fold cross-validation, coefficient correlation (R²), and lesser errors (MAE, MSE, and RMSE) showed better performance than the ensemble algorithms. Sensitivity analyses were also conducted in order to check the contribution of each input variable. It has been shown that the use of the ensemble machine learning algorithm would enhance the performance level of the model.