Prominent genetic variants and epigenetic changes in post-traumatic stress disorder among combat veterans.

Post-traumatic stress disorder (PTSD) is one of the most widespread and disabling psychiatric disorders among combat veterans. Substantial interindividual variability in susceptibility to PTSD suggests the presence of different risk factors for this disorder. Twin and family studies confirm genetic factors as important risk factors for PTSD. In addition to genetic factors, epigenetic factors, especially DNA methylation, can be considered as a potential mechanism in changing the risk of PTSD. So far, many genetic and epigenetic association studies have been conducted in relation to PTSD. In genetic studies, many single nucleotide polymorphisms have been identified as PTSD risk factors. Meanwhile, the variations in catecholamines-related genes, serotonin transporter and receptors, brain-derived neurotrophic factor, inflammatory factors, and apolipoprotein E are the most prominent candidates. CpG methylation in the upstream regions of many genes is also considered a PTSD risk factor. Accurate identification of genetic and epigenetic changes associated with PTSD can lead to the presentation of suitable biomarkers for susceptible individuals to this disorder. This study aimed to delineate prominent genetic variations and epigenetic changes associated with post-traumatic stress disorder in military veterans who have experienced combat, focusing on genetic and epigenetic association studies.

Blue light emitting graphene quantum dots/ Rhodamine B doped gold nanostars for ratiometric detection of methotrexate.

In this work, an innovative ratiometric sensing platform was developed for the determination of methotrexate (MTX), an antifolate drug, a chemotherapy agent, and an immune system suppressant based on blue emission graphene quantum dots/Rhodamine B doped gold nanostars (B-GQDs/Au NSt-RB). The developed sensor was a dual-emission fluorescent probe with two major emission peaks at 440 nm (B-GQDs) and 580 nm (Au NSt-RB) by exciting at 330 nm. Based on the inhibiting effect of MTX on the system's fluorescence density, the stable ratiometric fluorescent probe was used for the rapid determination of MTX in aquatic solutions and spiked human serum samples. The results indicated good linear correlations over the logarithmic concentration range of 0.3 nM-50.0 µM. In addition, B-GQDs/Au NSt-RB can further realize highly sensitive detection of MTX with a low LOD value of 2.28 × 10-10 M. The RSD% values obtained for the intra-day and inter-day precision were 0.63-3.86 %. With recoveries of 98.2-100.1 % and 98.7-100.5 %, respectively. The short-term temperature and freeze-thaw tests confirmed the higher stability of the developed sensor. In addition, the calculated recoveries for MTX recognition in real samples were in the range of 98-102 %. These findings suggested the excellent potential of the ratiometric fluorescence B-GQDs/Au NSt-RB sensor for detecting MTX in real plasma samples.

Artificial intelligence used to diagnose osteoporosis from risk factors in clinical data and proposing sports protocols.

Osteoporosis (OP) is characterized by diminished bone mass and deteriorating bone structure that increases the chance of fractures in the spine, hips, and wrists. In this paper, a novel data processing method of artificial intelligence (AI) is used for evaluating, predicting, and classifying OP risk factors in clinical data of men and women separately. Additionally, artificial intelligence was used to suggest the most appropriate sports programs for treatment. Data was obtained from dual-energy x-ray absorption scanning center of Ayatollah Kashani, Milad, and Khatam al-Anbia hospitals in Tehran, Iran. The subjects included 1224 men and women. Models were developed using decision tree, random forest (RF), k-nearest neighbor, support vector machine, gradient boosting (GB), Extra trees, Ada Boost (AB), and artificial neural network multilayer perceptron analysis to predict osteoporosis and to recommend sports programs. Data was divided into training (80%) and test dataset (20%). The results were obtained on a 20% test dataset. Area under receiver operating characteristic curve (AUROC) was used to compare the performance of the models. To predict healthy individuals, osteopenia and osteoporosis, the FR algorithm with AUROC 0.91 performed best in men and the GB algorithm with AUROC 0.95 performed best in women compared to other classification algorithms. Prediction of RF algorithm in women and men with AUROC 0.96 and 0.99, respectively, showed the highest performance in diagnosing the type of exercise for healthy individuals and those with osteopenia and OP. Eight AI algorithms were developed and compared to accurately predict osteoporosis risk factors and classify individuals into three categories: healthy, osteopenia, and OP. In addition, the AI algorithms were developed to recommend the most appropriate sports programs as part of treatment. Applying the AI algorithms in a clinical setting could help primary care providers classify patients with osteoporosis and improve treatment by recommending appropriate exercise programs.

Influenza Vaccine and COVID-19 Pandemic: Could This Vaccine Help Limit the Potential Adverse Consequences of SARS-CoV-2?

The COVID-19 pandemic has prompted researchers to find treatments and vaccines to control SARS-CoV-2. There are some hypotheses about the benefit of respiratory virus vaccines, like MMR, for COVID-19 pneumonia severity, morbidity, and mortality. The influenza vaccine is one of the most frequently used respiratory virus vaccines covered by one of the Iranian insurance institutes. We have a symmetrical group of participants that have received this vaccine that could be compared with each other. We compared 3,379 persons aged 20 - 75 years for the effect of the influenza vaccine on COVID-19 mortality. We ultimately found that it does not affect mortality caused by COVID-19 pneumonia, but it can decrease the hospitalization cost in people over 65 years with a history of chronic disease.