Tehran University Medical Journal

One of the main sources of a wide range of biological products as starting material is the human blood. These biological human plasma derived medicines play essential role in prevention and treatment of a variety of life threatening diseases. Mention to the starting material of these medicines which is blood or in another word human plasma, possibility of contamination by blood borne viruses cannot be omitted.
In recent years possibility of contamination by blood borne viruses such as hepatitis B virus (HBV), hepatitis C virus (HCV), and human immunodeficiency virus (HIV) is an important concern. Nowadays most of developed countries the risk is minimum, although in developing countries it is still a challenge. Despite measures for human plasma biological derived medicines safety, such as donor selection, testing of donations, and polymerase chain reaction (PCR) testing on pooled plasma, still more actions are needed to inactivate or remove viruses such as HBV, HCV and HIV. During the process of manufacturing of biological human plasma medicines, there is several production steps which may contribute to viral reduction. These steps consist of precipitation by centrifugation, ethanol, polyethylene glycol, octanoic acid, or ammonium sulphate, chromatographic methods such as immunoaffinity chromatography or ion exchange chromatography, adsorption by aluminum hydroxide, and separation by filtration. All these steps are considered to be weakly effective as viral reduction treatment, and more effective viral inactivation methods are needed to be implemented in line of production of human plasma derived biological medicinal products. These safety measures included virus inactivation by different techniques such as acidic pH, solvent/detergent method, pasteurization and heat treatment, beta-propiolactone plus U/V and also virus removal by nanofiltration, which all these virus inactivation or virus removal methods before implementation in line of production of plasma derived biological medicines, should undergo for validation study.

Results: In this study, from 144 patients with penetrating trauma in the upper extremities, 56 patients were suspected with nerve injuries and 50 patients were suspected with tendon injuries. After evaluation with an ultrasound probe, 21 out of 23 cases suspected of nerve injuries were confirmed by local exploration. Local exploration confirmed that 27 out of 28 cases were suspected of tendon injuries. The accuracy of ultrasound in the assessment of nerve damage was estimated at 99.64% and in tendon damage at 92%, and the sensitivity of ultrasound in the diagnosis of nerve damage was 91.30% and in the diagnosis of tendon damage was 96.42%. Conclusion: By the results of this research it can be concluded, that ultrasound leads to a faster diagnosis. It provides a wider field of view, can reduce possible secondary injuries and increases the accuracy of the diagnosis.

Background: Major Depressive Disorder (MDD) is one of the most prevalent and disabling mental disorders in the world. Due to the life quality decline caused by this disease and its growing nature, timely detection and treatment is of paramount importance. In the present study Electroencephalogram (EEG) signal utilized for the precise detection of MDD using Artificial Intelligence (AI) Methods.
Methods: In this analytic study, which is done in Shahid Beheshti University of medical Sciences in 2023, fifty eight subjects were investigated using an experienced psychiatrist that 30 subjects diagnosed as MDD and 28 determined to be healthy. Nineteen channels EEG signals in resting state with eyes closed situation acquired for five minutes from all of the participants including 36 men and 22 women with the average age of 39.3 years. The EEG signals were preprocessed to remove contaminating signals from brain-originated signals. The EEGLAB package in MATLAB utilized to re-reference channels to the average reference, apply a band-pass filter between 1 and 40 Hz and to remove non-brain components of the signal using Independent Component Analysis (ICA). The cleaned data segmented to the three seconds windows with 50 percent overlapping. These segments were used as the input to the AI models. Deep Learning (DL) models utilized in the present study were EEGNet, ShallowConvNet and DeepConvNet which were developed based on the deep convolutional models for the classification of healthy and MDD brain signals. The main difference between these models laid in the number of specific convolutional layers and the model complexity.

Results: MDD and Healthy signals classification has been done using EEGNet, ShallowConvNet and DeepConvNet models and accuracy of 92.3%, 83.2% and 92.2% were achieved, respectively. Also EEGNet acquired the highest sensitivity of 98.9% and specificity of 79.1%. Conclusion: The detection of MDD patients using EEG signals with high accuracy and generalizability is possible and proposed AI models can be utilized in the clinical settings as assistant tools.

Results: A considerable rise in sperm concentration was noted in both the selenium-fed group and the astaxanthin-fed group when compared to the spinal cord injury group (P=0.001). Supplementation with selenium and astaxanthin improved sperm concentration, progressive motility, and viability, bringing these parameters close to control levels (P<0.001). However, neither of these substances had an effect on abnormal sperm morphology (P>0.05). Both selenium and astaxanthin supplementation enhanced the motor and sensory functions of the injured rats (P<0.001), with selenium showing a greater role in promoting repair compared to astaxanthin (P<0.001). Conclusion: Selenium and astaxanthin supplements improved sperm parameters, except for sperm morphology, and were effective in enhancing motor and sensory functions after spinal cord injury. Selenium was found to be more effective than astaxanthin in promoting spinal cord repair.