0
selected
-
1.
Risk of flare or relapse in patients with immune-mediated diseases following SARS-CoV-2 vaccination: a systematic review and meta-analysis.
Shabani, M, Shobeiri, P, Nouri, S, Moradi, Z, Amenu, RA, Mehrabi Nejad, MM, Rezaei, N
European journal of medical research. 2024;(1):55
Abstract
BACKGROUND Patients with autoimmune and immune-mediated diseases (AI-IMD) are at greater risk of COVID-19 infection; therefore, they should be prioritized in vaccination programs. However, there are concerns regarding the safety of COVID-19 vaccines in terms of disease relapse, flare, or exacerbation. In this study, we aimed to provide a more precise and reliable vision using systematic review and meta-analysis. METHODS PubMed-MEDLINE, Embase, and Web of Science were searched for original articles reporting the relapse/flare in adult patients with AI-IMD between June 1, 2020 and September 25, 2022. Subgroup analysis and sensitivity analysis were conducted to investigate the sources of heterogeneity. Statistical analysis was performed using R software. RESULTS A total of 134 observations of various AI-IMDs across 74 studies assessed the rate of relapse, flare, or exacerbation in AI-IMD patients. Accordingly, the crude overall prevalence of relapse, flare, or exacerbation was 6.28% (95% CI [4.78%; 7.95%], I2 = 97.6%), changing from 6.28% (I2 = 97.6%) to 6.24% (I2 = 65.1%) after removing the outliers. AI-IMD patients administering mRNA, vector-based, and inactive vaccines showed 8.13% ([5.6%; 11.03%], I2 = 98.1%), 0.32% ([0.0%; 4.03%], I2 = 93.5%), and 3.07% ([1.09%; 5.9%], I2 = 96.2%) relapse, flare, or exacerbation, respectively (p-value = 0.0086). In terms of disease category, nephrologic (26.66%) and hematologic (14.12%) disorders had the highest and dermatologic (4.81%) and neurologic (2.62%) disorders exhibited to have the lowest crude prevalence of relapse, flare, or exacerbation (p-value < 0.0001). CONCLUSION The risk of flare/relapse/exacerbation in AI-IMD patients is found to be minimal, especially with vector-based vaccines. Vaccination against COVID-19 is recommended in this population.
-
2.
Deep learning for tooth identification and numbering on dental radiography: a systematic review and meta-analysis.
Sadr, S, Rokhshad, R, Daghighi, Y, Golkar, M, Tolooie Kheybari, F, Gorjinejad, F, Mataji Kojori, A, Rahimirad, P, Shobeiri, P, Mahdian, M, et al
Dento maxillo facial radiology. 2024;(1):5-21
Abstract
OBJECTIVES Improved tools based on deep learning can be used to accurately number and identify teeth. This study aims to review the use of deep learning in tooth numbering and identification. METHODS An electronic search was performed through October 2023 on PubMed, Scopus, Cochrane, Google Scholar, IEEE, arXiv, and medRxiv. Studies that used deep learning models with segmentation, object detection, or classification tasks for teeth identification and numbering of human dental radiographs were included. For risk of bias assessment, included studies were critically analysed using quality assessment of diagnostic accuracy studies (QUADAS-2). To generate plots for meta-analysis, MetaDiSc and STATA 17 (StataCorp LP, College Station, TX, USA) were used. Pooled outcome diagnostic odds ratios (DORs) were determined through calculation. RESULTS The initial search yielded 1618 studies, of which 29 were eligible based on the inclusion criteria. Five studies were found to have low bias across all domains of the QUADAS-2 tool. Deep learning has been reported to have an accuracy range of 81.8%-99% in tooth identification and numbering and a precision range of 84.5%-99.94%. Furthermore, sensitivity was reported as 82.7%-98% and F1-scores ranged from 87% to 98%. Sensitivity was 75.5%-98% and specificity was 79.9%-99%. Only 6 studies found the deep learning model to be less than 90% accurate. The average DOR of the pooled data set was 1612, the sensitivity was 89%, the specificity was 99%, and the area under the curve was 96%. CONCLUSION Deep learning models successfully can detect, identify, and number teeth on dental radiographs. Deep learning-powered tooth numbering systems can enhance complex automated processes, such as accurately reporting which teeth have caries, thus aiding clinicians in making informed decisions during clinical practice.
-
3.
Post-Exposure Prophylaxis for COVID-19: A Systematic Review.
SeyedAlinaghi, S, Karimi, A, Pashaei, Z, Shobeiri, P, Janfaza, N, Behnezhad, F, Ghasemzadeh, A, Barzegary, A, Arjmand, G, Noroozi, A, et al
Infectious disorders drug targets. 2023;(5):e130423215723
Abstract
INTRODUCTION SARS-CoV-2 cause pneumonia can spread across the lung and lead to acute respiratory distress syndrome (ARDS) in severe cases. Post-exposure prophylaxis has shown great potential to prevent the transmission of some viral infections; however, such results for COVID-19 are still inconclusive. METHODS Therefore, the aim of this study was to systematically review the resources that utilized postexposure prophylaxis (PEP) for COVID-19 and the possible clinical benefits of such drugs. An organized search of relevant literature was done using the keywords and search queries on public databases of Cochrane, PubMed, Web of Science and Scopus from December 2019 to August 23, 2021. Original resources that had the inclusion criteria were included after two-phase title/abstract and full-text screenings. This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta- Analysis (PRISMA) statement. RESULTS Out of 841 retrieved records 17 resources were appropriate to include in the systematic review. Hydroxychloroquine with a daily dose of 400-800 mg and a duration of 5-14 days was the most frequently used agent for PEP. Chloroquine was recommended to use to control treatment in patients with mild to severe COVID-19 pneumonia. Other agents like Lopinavir-ritonavir (LPV/r), angiotensinconverting enzyme inhibitors (ACEIs), Angiotensin receptor blockers (ARBs), Vitamin D, arbidol, thymosin drugs, and Xin guan no.1 (XG.1, a Chinese formula medicine) have also been applied in some studies. CONCLUSION Current evidence demonstrated no established clinical benefits of any drug as PEP in individuals with COVID-19. However, scarce indication occurs for the beneficial effects of some agents, but more studies are needed to explore such effects.
-
4.
Novel Systemic Inflammation Markers to Predict COVID-19 Prognosis.
Karimi, A, Shobeiri, P, Kulasinghe, A, Rezaei, N
Frontiers in immunology. 2021;:741061
Abstract
Coronavirus disease 2019 (COVID-19) has resulted in a global pandemic, challenging both the medical and scientific community for the development of novel vaccines and a greater understanding of the effects of the SARS-CoV-2 virus. COVID-19 has been associated with a pronounced and out-of-control inflammatory response. Studies have sought to understand the effects of inflammatory response markers to prognosticate the disease. Herein, we aimed to review the evidence of 11 groups of systemic inflammatory markers for risk-stratifying patients and prognosticating outcomes related to COVID-19. Numerous studies have demonstrated the effectiveness of neutrophil to lymphocyte ratio (NLR) in prognosticating patient outcomes, including but not limited to severe disease, hospitalization, intensive care unit (ICU) admission, intubation, and death. A few markers outperformed NLR in predicting outcomes, including 1) systemic immune-inflammation index (SII), 2) prognostic nutritional index (PNI), 3) C-reactive protein (CRP) to albumin ratio (CAR) and high-sensitivity CAR (hsCAR), and 4) CRP to prealbumin ratio (CPAR) and high-sensitivity CPAR (hsCPAR). However, there are a limited number of studies comparing NLR with these markers, and such conclusions require larger validation studies. Overall, the evidence suggests that most of the studied markers are able to predict COVID-19 prognosis, however NLR seems to be the most robust marker.