مجله مطالعات علوم پزشکی
سال سی و سوم شماره 11 (بهمن 1401)

Background & Aim:

Genu varus is one of the most common injuries of the lower limb, which affects posture control by creating internal torque on the ankle and foot joints. The complication of Genu varus is accompanied by the biomechanical change of the lower limb during walking and running. The aim of the present study was to evaluate the effect of using agility shoes on running kinetics in individuals with genu varus before and after fatigue protocol.

Methods & Methods:

This study was a case-control type. In this study, 14 individuals with genu varus and 15 healthy ones as control aged 20-30 years participate. Using the Bertec Force platform, ground reaction forces were recorded during running before and after the fatigue protocol. The normality of data distribution was confirmed using the Shapiro Wilk test. Two way analysis of variance with repeated measures was used for statistical analysis. All the analyzes were performed at a significance level of 0.05 using SPSS 25 software.

In both groups, the fxto values during the pre-test was larger than that post-test (p=0.000). Also, the fyhc values in the pretest was larger than the posttest (p=0.000). Moreover, the fypo values in the pretest were larger than the posttest (p=0.000).

In general, running kinetics were similar in individuals with genu varus compared with healthy controls before and after fatigue protocol while using agility shoes.

Background & Aims:

Polycystic ovary syndrome (PCOS) is the most common female endocrine disease and often causes infertility in the women of reproductive age. This syndrome includes different genes and proteins, multiple pathways, and complex processes of hormone secretion. Therefore, a single factor cannot explain the pathogenesis of PCOS. Using computational biology and omicses including genomics, transcriptomics, proteomics, and metabolomics, can provide faster and more effective methods for studying the pathogenesis of complex diseases such as PCOS.

Materials & Methods: 

In this study, to find the related studies, PubMed, Google Scholar, and Science Direct databases were searched without time constraints for 3 years using the keywords of "Polycystic Ovary Syndrome, Computational Biology, protein-protein interaction, Network Biology, and Pathways analysis".

Various databases have been designed and made available to the public to repository data, Protein-Protein interactions, networks, and biological pathways related to humans. Three databases PCOSBase, PCOSKB, and PCOSDB have been explicitly developed for polycystic ovary syndrome.

Since the molecular mechanisms of polycystic ovary syndrome are still not completely understood, to realize this syndrome, besides experimental results using omics platforms, computational biology, and bioinformatics tools, it is necessary to identify the interaction between proteins and the pathways involved in it.

Background & Aims:

Regdanumab is a monoclonal antibody that is widely used to treat the patients with COVID-19. The aim of this study was to investigate the clinical effectiveness of Regdanumab in the patients with COVID-19.

Materials & Methods:

A systematic review of the most important major databases including; PubMed, Cochrane Library, Scopus and Web of Science were conducted up to August 2022. Also, a manual search was performed in the sources of primary review and key studies. No restrictions were considered for the language and year of publication of the studies. Evaluation of the quality of studies was performed using the Newcastle checklist. Efficacy outcomes included reduction in mortality, length of hospital stay, disease progression, and need for oxygen therapy. Meta-analysis was performed using Comprehensive Meta-Analysis version 3 software.

Out of 208 articles, seven studies met the inclusion criteria. The results of the meta-analysis showed that the risk ratio (RR) in the Regdanumab group was 0.23 (CI95% 0.05 - 1.02), for the duration of hospitalization was -1.15 (CI95% -0.49 - 1.80), and the need for oxygen therapy was 0.43 (CI95% 0.34-0.56). However, no significant effect was observed for the outcomes of mortality rate, mechanical ventilation and clinical improvement (P˃0.05).

The results of the meta-analysis showed that Regdanumab reduces mortality and length of hospitalization in the patients with COVID-19; although it had no effect on reducing the need for mechanical ventilation and clinical improvement in patients.

Background & Aims:

Frozen shoulder or adhesive capsulitis is one of the most common causes of shoulder pain and disability, which is characterized by pain and limited range of motion of the glenohumeral joint. The purpose of this study was to compare the effect of scapular stabilization and mobilization techniques on pain and range of motion glenohumeral joint in the women with frozen shoulder.

Materials & Methods:

In this semi-experimental research, 45 women suffering from frozen shoulder with an age range of 40-60 years, referred to Urmia city clinics were selected and divided into three groups of scapular stabilization exercises (age: 48.19±2.52 years), shoulder mobilization (age: 51.73±3.85 years), and control (age: 49.33±2.49 years) groups. The range of motion of abduction, flexion, and internal and external rotation of the shoulder were measured using a goniometer. Shoulder pain was measured using a visual analog scale (VAS) before and after the interventions. Data analysis was done using SPSS software at a significant level of P≤0.05. ANCOVA and Sidak post hoc test were used to compare the mean of the variables.

There was no significant difference between the two types of therapeutic intervention on pain and range of motion. However, a significant difference was observed between the two scapular stabilization and mobilization programs in order of reduction of pain and improvement of the range of motion of abduction, flexion, internal rotation, and external rotation compared to the control group.

Carrying out scapular stabilization and mobilization exercises has a significant effect on reducing pain and improving the range of motion of the shoulder of patients. Therefore, the mentioned programs can be used to reduce pain and increase the range of motion of the shoulder of patients with frozen shoulder.

Background & Aim:

Determining the sex is the first and most important step in determining biological and anthropological characteristics of everyone. Sometimes, Forensic medicine and cognitive archeology encounter cadavers that have several parts lost or unusable due to environmental conditions or corruption. Therefore, in order to determine the sex and some other characteristics, the parts of the body skeleton like sternum that are more durable and resistant are selected and examined. The purpose of this study was to review and determine the correlation between sternum and its parameters with the characteristics of individuals like age.

Materials & Methods:

 This applied study was performed on 61 three-dimensional CT-Scan images of the sternum of 38 male (62%) and 23 female (38%) cadavers. All tissues, fats and other bones except the sternum were removed. Eight parameters of sternum were measured with electronic caliper and then the obtained data were analyzed statistically. For data analysis, SPSS version 23 software and linear regression were used for statistical analyses.

We found in this study that sternal body width (R =0.373) and manobrium width (R=0.334) had the most correlation with people's age.

According to the results of this study, we can find the relationship between the parameters of the sternum with the age of individuals and use these findings in forensic medicine and anthropology.

Background & Aims:

 Early diagnosis of liver diseases has a significant effect on the prevention of its complications as well as control and treatment of the disease. “Machine learning” is one of the branches of artificial intelligence that has many applications in the field of medical diagnosis. This study aimed to provide a model with high accuracy and reliability for diagnosing liver diseases using machine learning methods that can help physicians in the early diagnosis and control of liver diseases.

Materials & Methods:

 This applied-developmental study used the dataset of 583 liver patients. In order to more accurately diagnose of the people with liver diseases, the results of the three classifiers including: Random Forest, Support Vector Machine, and Artificial Neural Network were combined using Dempster-Shafer theory. Weka data mining tool and Python programming language were used to implement the model. The k-fold cross-validation method was applied to evaluate efficiency of the model.

The results showed that accuracy, sensitivity, and specificity in the proposed model were 91.47%, 89.52%, and 93.03%, respectively, which had a better performance than similar studies.

The proposed model in the studied statistical population has a better performance in diagnosing liver diseases, and can help physicians in early diagnosis of the disease and appropriate treatment of it in the early stages of it and thus prevent development of the disease.