The effect of COVID-19 pandemic on diabetes care indices in Southern Iran: an interrupted time series analysis.

BACKGROUND: Type 2 diabetes mellitus (T2DM) requires a continues bulk of cares. It is very probable COVID-19 pandemic is affected its healthcare coverage. METHODS: The interrupted time series analysis is used to model the trend of diabetes healthcare indices, such as the health worker visits, physician visits, body mass index (MBI), fasting blood sugar (FBS), and hemoglobin A1c (HbA1c), before and after the start of COVID-19 pandemic. The reference of data was the totals of all T2DM patients living in Fars Province, Southern Iran, areas covered by Shiraz University of Medical Science (SUMS), from 2019 to 2020. RESULTS: A significant decrease for visits by the health workers, and physicians was observed by starting COVID-19 pandemic (ß2 = -0.808, P < 0.001, ß2 = -0.560, P < 0.001); Nevertheless, the coverage of these services statistically increased by next months (ß3 = 0.112, P < 0.001, ß3 = 0.053, P < 0.001). A same pattern was observed for the number of BMI, FBS and HbA1c assessments, and number of refer to hospital emergency wards (ß3 = 0.105, P < 0.001; ß3 = 0.076, P < 0.001; ß3 = 0.022, P < 0.001; ß3 = 0.106, P < 0.001). The proportion of T2DM patients with HbA1C < 7%, and controlled hypertension during study period was statistically unchanged. CONCLUSIONS: When the COVID-19 pandemic was announced, T2DM healthcare coverage drastically decreased, but it quickly began to rebound. The health monitoring system could not have any noticeable effects on diabetes outcomes.

The effect of vitamin D and magnesium supplementation on clinical symptoms, and serum inflammatory and oxidative stress markers in patients with COVID-19: A structured summary of a study protocol for a randomized controlled trial (preprint)

Objectives: This study aims to evaluate the effect of vitamin D and magnesium supplementation on clinical symptoms and serum inflammatory and oxidative stress markers in patients with COVID-19. Trial design: This study is a 4-arm randomized, double-blind, placebo-controlled clinical trial with a factorial design and the intervention period is 3 weeks. Participants: This study is conducted on COVID-19 patients admitted to the Shahid Mohammadi hospital in Bandar Abbas, Iran who be eligible for inclusion in the study. Patients are included only if they meet all of the following criteria: 1) aged from 18 to 65 years old, 2) confirmation of COVID-19 by RT-PCR test, 3) completing informed consent, 4) passing less than 48 hours since the patient's hospitalization, 5) no skin or gastrointestinal allergies due to taking multivitamin supplements, vitamin D, and magnesium, 6) having more than 30 breaths per minute and less than 93% oxygen saturation in room air and sea level. Patients are excluded if they have any of the following conditions: 1) pregnancy or lactation, 2) take a daily multivitamin or take a vitamin D or magnesium supplement in the last month, 3) participating in other clinical trials, 4) renal failure or dialysis, severe liver disease or cirrhosis, 5) known diagnosis of hypercalcemia, 6) discharging from the hospital less than 24 hours after the start of the intervention, 7) history of kidney stones in the last year, 8) transfer the patient to the ICU, 9) baseline vitamin D levels above 80 ng/ml, 9) baseline magnesium levels above 2.6 mg/dl. Intervention and comparator: Participants will be randomly allocated to one of the four following groups: A) Vitamin D (two 50,000 IU capsules at the beginning of the study, two 50,000 IU capsules on the 4th day, one 50,000 IU capsule on the 11th day, and one 50,000 IU capsule on the 17th day) and magnesium supplement (300 mg/day). B) Vitamin D capsule and magnesium placebo. C) Magnesium supplement and vitamin D placebo. D) Vitamin D placebo and magnesium placebo. Main outcomes: Clinical symptoms (fever, dry cough, shortness of breath, headache, myalgia, oxygen saturation, and mortality) and laboratory markers (CRP, MDA, TAC, WBC, neutrophils count, lymphocytes count, ratio of neutrophils to lymphocytes, levels of 25 hydroxyvitamin D and magnesium) Randomization: A computer-generated block randomization list is used for randomization. Blinding (masking): Investigators and patients are blinded to group allocation and treatment. A double-blind design is achieved using matched placebos. Numbers to be randomized (sample size): A total of 104 eligible patients are randomized into four groups of 26 subjects (1:1:1:1 allocation ratio). Discussion With the rapid prevalence of COVID-19 in recent years, more attention has been paid to effective dietary supplementation to improve clinical symptoms and biochemical parameters in these patients. To our knowledge, this is the first study to evaluate the effects of vitamin D supplementation in combination with magnesium or alone with respect to this infectious disease. The findings of the current RCT will provide evidence regarding the effectiveness of dietary supplementation strategies to improve COVID-19 outcomes. Trial Status: Ethical approval of the first version of the study protocol was obtained from the medical ethics committee of Hormozgan University of Medical Sciences, Bandar Abbas, Iran on May 30th, 2021 (IR.HUMS.REC.1400.085). Currently, the recruitment phase is ongoing since August 23th, 2021 and is anticipated to be complete by the end of August 2022. Trial registration: The study protocol was registered in the Iranian Registry of Clinical Trials (https://www.irct.ir; IRCT20210702051763N1) on August 14th, 2021. https://www.irct.ir/trial/57413 Full protocol: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol.

DCNN-FuzzyWOA: Artificial Intelligence Solution for Automatic Detection of COVID-19 Using X-Ray Images

Artificial intelligence (AI) techniques have been considered effective technologies in diagnosing and breaking the transmission chain of COVID-19 disease. Recent research uses the deep convolution neural network (DCNN) as the discoverer or classifier of COVID-19 X-ray images. The most challenging part of neural networks is the subject of their training. Descent-based (GDB) algorithms have long been used to train fullymconnected layer (FCL) at DCNN. Despite the ability of GDBs to run and converge quickly in some applications, their disadvantage is the manual adjustment of many parameters. Therefore, it is not easy to parallelize them with graphics processing units (GPUs). Therefore, in this paper, the whale optimization algorithm (WOA) evolved by a fuzzy system called FuzzyWOA is proposed for DCNN training. With accurate and appropriate tuning of WOA's control parameters, the fuzzy system defines the boundary between the exploration and extraction phases in the search space. It causes the development and upgrade of WOA. To evaluate the performance and capability of the proposed DCNN-FuzzyWOA model, a publicly available database called COVID-Xray-5k is used. DCNN-PSO, DCNN-GA, and LeNet-5 benchmark models are used for fair comparisons. Comparative parameters include accuracy, processing time, standard deviation (STD), curves of ROC and precision-recall, and F1-Score. The results showed that the FuzzyWOA training algorithm with 20 epochs was able to achieve 100% accuracy, at a processing time of 880.44 s with an F1-Score equal to 100%. Structurally, the i-6c-2s-12c-2s model achieved better results than the i-8c-2s-16c-2s model. However, the results of using FuzzyWOA for both models have been very encouraging compared to particle swarm optimization, genetic algorithm, and LeNet-5 methods.

COVID-19 vaccines breakthrough infection and adverse reactions in medical students: A nationwide survey in Iran (preprint)

Purpose This study aimed to investigate the rate of COVID-19 breakthrough infection and adverse events in medical students.Methods Iranian medical students receiving two doses of COVID-19 vaccines were included in this retrospective cohort study. The medical team gathered the demographic characteristics, comorbidities, type of vaccine, adverse events following vaccination, and history of COVID-19 infection data through a phone interview. The frequency of adverse events and breakthrough infection was stratified by vaccine type (ChAdOx1-S, Gam-COVID-Vac, and BIBP-CorV).Results A total of 3591 medical students enrolled in this study, of which 57.02% were females, with a mean age of 23.31 + 4.87. A PCR-confirmed and suspicious-for-COVID-19 breakthrough infection rate of 4.51% and 7.02% was detected, respectively. There was no significant relation between breakthrough infection and gender, BMI, blood groups, and comorbidities. However, there was a significant difference in breakthrough infection rate among different types of vaccines (P = 0.001) and history of COVID-19 infection (P = 0.001). A total of 16 participants were hospitalized for COVID-19 infection, and no severe infection or death was observed in the studied population.Conclusion Vaccination prevented severe COVID-19 infection, although a high breakthrough infection rate was evident among Iran medical students during the Delta variant’s peak. Vaccine effectiveness may be fragile during emerging new variants and in high-exposure settings. Moreover, adverse events are rare, and the benefits of vaccination outweigh the side effects. However, many limitations challenged this study, and the results should be cautious.

Delayed covid-19 induced cytokine storm after root canal therapy with favorable response to plasmapheresis, tocilizumab, and methylprednisolone pulses therapy: a case report (preprint)

COVID-19 showed different characteristics and many cases showed clinical manifestations that could not be attributed to other conditions. We present a 22-year-old female had an uneventful recovery from COVID-19 and after that, she developed a cytokine storm and a worsening clinical condition two days after dental root canal therapy.

Medical Imaging and Computational Image Analysis in COVID-19 Diagnosis: A Review (preprint)

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. Sometimes the symptoms of the disease increase so much they lead to the death of the patients. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. Many studies have tried to use medical imaging for early diagnosis of COVID-19. This study attempts to review papers on automatic methods for medical image analysis and diagnosis of COVID-19. For this purpose, PubMed, Google Scholar, arXiv and medRxiv were searched to find related studies by the end of April 2020, and the essential points of the collected studies were summarised. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis based on the accuracy and the method used, 4) to express the research limitations in this field and the methods used to overcome them. COVID-19 reveals signs in medical images can be used for early diagnosis of the disease even in asymptomatic patients. Using automated machine learning-based methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.