SARS-Cov2-Induced Cytokine Storm and Schizophrenia, Could There be a Connection?

Today, a new coronavirus (2019-nCoV, later named SARS-CoV-2) has become known as a pandemic with over 3,949,200 cases and 271,782 deaths. It has been considered that most of the deaths in infected patients stem from comorbidity conditions. Therefore, understanding at-risk populations are currently under the focus of investigations. This object has highly driven attention to put patients with a higher potential of death related to SARS-CoV2 infection at priority. For instance, this can happen in Schizophrenia owing to ambiguous immunology attributes, including elevated levels of pro-inflammatory cytokines and stress-related immune disability. Given that, the hyper-inflammatory responses are the significant cause of the pathophysiology of the SARS-CoV2-related mortality. Moreover, SARS-CoV2 can prompt the risk of developing Schizophrenia in the future. This review punctuates that prenatal/perinatal infection could be associated with increased Schizophrenia risk; on the flip side, the potential risk of ongoing medication can worsen mentally disabled patients, and healthy people are at risk

Which came first, the risk of migraine or the risk of asthma? A systematic review

Objectives We conducted this review to systematically assess the association and risk of the migraine in the patient with asthma and vice versa. Methods We systematically searched publishes articles indexed in PubMed, Scopus, Cochrane library, PsycINFO, CINAHL, ISI Web of Science, Science Direct from inception, and Embase databases until June 2017. The quality assessment of the involved studies was done using the Newcastle-Ottawa Scale (NOS). Results Eight studies with 389,573 participants were reviewed and selected for data extraction. Among the selected studies, 5 were reported the association between migraine with asthma risk, and the rest three studies reported the risk of asthma in patient with migraine compared to non-moraine individuals. Odds ratio (OR) of migraine for patient with asthma as compared with non-asthmatic individuals was 1.62 (95% CI 1.43–1.82). Data pooling using a random-effect model showed that migraine was associated with a significant increased risk of asthma (relative risk (RR): 1.56; 95% CI: 1.51–1.60; p < .00001). Besides, sub-group and sensitivity analyses supported the positive association between asthma and migraine, and risk of asthma in migraine patients. Conclusion Now it is unknown if control of the asthma will impact the severity of migraines or vice versa, but it is necessary to perform more research to further explain the mechanisms through which asthma increases the frequency of migraine or vice versa. If two conditions linked, once an individual undergo better control of asthma symptoms, might the excruciating migraine ease, too

Metabolite changes in the posterior cingulate cortex could be a signature for early detection of Alzheimer’s disease: a systematic review and meta-analysis study based on 1H-NMR

Abstract Background Posterior cingulate cortex (PCC) is a paralimbic cortical structure with a fundamental role in integrative functions of the default mode network (DMN). PCC activation and deactivation of interconnected structures within the medial temporal lobe is essential in memory recall. Aim Assessing the metabolomics content changes in PCC of the patients with Alzheimer’s disease (AD) compared to healthy controls (HC) to find a new method for early AD detection was the primary goal of this study. Methods We performed a comprehensive search through eight international indexing databases. Searches were done using the medical subject headings (Mesh) keywords. Outcome measures included Population (HC/AD), Age (y), Gender (Male/Female), MRI equipment, Tesla (T), MMSE (mean ± SD), absolute and ratio absolutes metabolites in the PCC. All meta-analyses were performed using STATA V.14 tools to provide pooled figures. Results Studies published from 1980 to 2019 using the 1H-NMR technique of 3,067 screened studies, 18 studies comprising 1647 people (658 males and 941 females, 921 HC and 678 AD cases) were included. The results revealed a significant increase in mI content and a substantial decrease in NAA, Glu, and Glx levels of the PCC in AD patients compared to HC. Conclusions Our meta-analysis showed that microstructural disruptions in the PCC could be used as a marker for early AD detection. Although NAA, mI, Glu, and (NAA, Cho, and mI)/Cr biomarkers are substantial metabolites for diagnosis and are most sensitive for diagnosis. Trial registration PROSPERO Registration: CRD42018099325

The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis

Artificial intelligence (AI) systems have become critical in support of decision-making. This systematic review summarizes all the data currently available on the AI-assisted CT-Scan prediction accuracy for COVID-19. The ISI Web of Science, Cochrane Library, PubMed, Scopus, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched. We used the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess all included studies' quality and potential bias. A hierarchical receiver-operating characteristic summary (HSROC) curve and a summary receiver operating characteristic (SROC) curve have been implemented. The area under the curve (AUC) was computed to determine the diagnostic accuracy. Finally, 36 studies (a total of 39,246 image data) were selected for inclusion into the final meta-analysis. The pooled sensitivity for AI was 0.90 (95% CI, 0.90–0.91), specificity was 0.91 (95% CI, 0.90–0.92) and the AUC was 0.96 (95% CI, 0.91–0.98). For deep learning (DL) method, the pooled sensitivity was 0.90 (95% CI, 0.90–0.91), specificity was 0.88 (95% CI, 0.87–0.88) and the AUC was 0.96 (95% CI, 0.93–0.97). In case of machine learning (ML), the pooled sensitivity was 0.90 (95% CI, 0.90–0.91), specificity was 0.95 (95% CI, 0.94–0.95) and the AUC was 0.97 (95% CI, 0.96–0.99). AI in COVID-19 patients is useful in identifying symptoms of lung involvement. More prospective real-time trials are required to confirm AI's role for high and quick COVID-19 diagnosis due to the possible selection bias and retrospective existence of currently available studies

A convolution neural network for rapid and accurate staging of breast cancer based on mammography

Introduction: Breast cancer (BC) has been one of the main reasons for women's deaths in the recent decade. This study hypothesizes that the deep features resulting from mammography staging can be used to determine tumor staging and lymph nodes of the sentinel lymph node (SLN) before surgery in BC patients. Methods: A retrospective study of female BC patients being treated. Pathological variables were collected from medical records, including age, tumor location, staging based on the American Joint Committee on Cancer (AJCC), pathological type, human epidermal growth factor receptor 2 (HER2) status, Progesterone Receptor (PR) status, and Estrogen Receptor (ER) status. The contrast enhancement technique with Contrast-Limited Adaptive Histogram Equalization (CLAHE) was applied to the studied data using MATLAB software. The images were first changed from color to gray in the preprocessing process, and then the images were changed from gray to color again. The regions of interest (ROI) of the primary tumor were isolated from other areas of breast tissue by two expert radiologists. Therefore, a proposed convolution neural network (CNN) model was applied. Results: In this study, 390 patients' information was used to analyze the data. In total, out of 390 patients, 300 had metastatic SLN involvement, and 390 had a tumor size greater than 1 mm. Performance evaluation of the proposed CNN staging was AUC 0.919 (95 % CI: 0.852,0.971) for the training group and AUC 0.877 (95 % CI: 0.805,0.949) for the validation group. Conclusions: This study proposes a deep learning model based on CNN based on mammographic images, which has a suitable function for determining tumor staging and SLN metastasis

Short-Term Effects of Cell Phone Radiation on Fertility and Testosterone Hormone in Male Rats

Aim: Given the increasing usage of cell phones (6.9 billion subscriptions globally) and heterogeneous reports, this study aimed to determine the cell phone effect as non-ionizing radiation on the level of testosterone hormone and sperm parameters in male rats. Material and Methods: Twenty-five matured male Wistar rats were randomly allocated to five groups with the same body weights. Radiofrequency radiation for the exposed groups was 1 h/day call, 2 h/day call, and 50 missed calls/day in 30 days. The other two groups were control (out of any radiation) and positive control (exposed to ?-radiation) groups. Sperm parameters (motility, morphology, viability, counting), histopathology, and serum level of testosterone were measured and analyzed. Results: According to the results, the sperm viability significantly decreased compared to the control group (p<0.001). Also, the findings revealed that the sperm motility in all groups except missed call group (p=0.475). For sperm count and morphology only in Group C (2 h/day call) and Group D (positive control), there were significant reductions compared to the control group (p<0.001). The level of testosterone was not statistically significantly different between the groups (p=0.451). Conclusion: This study suggests that cell phone hazard to infertility was mild to moderate, and cell phone usage might have long-term effects on infertility. However, the cell phone cannot significantly affect the serum testosterone level

Amyloid PET scan diagnosis of Alzheimer’s disease in patients with multiple sclerosis: a scoping review study

Highlights The years after the first diagnosis and progressive or non-progressive MS are crucial factors in increasing the risk of early AD. The florbetapir-based radio traces in helping to diagnose early AD. Logical to use an age-specific cutoff in MS patients for early AD diagnosis