COVID-19: The experience from Iran

The Comparison of Clinical Epidemiology of Hospitalized Patients with COVID-19 during the Third and Fourth Waves of the Pandemic in Gorgan

Background

COVID-19 has turned into a global public health crisis. This study intended to compare demographic characteristics, disease severity, treatment methods, and clinical outcomes in hospitalized patients with COVID-19 during the third and fourth waves of the pandemic in Golestan Province, Iran.

Methods

In this cross-sectional study, the clinical epidemiology of all COVID-19 patients, who were hospitalized in two educational hospitals in Golestan Province for 30 days from the start of the third and fourth waves of the coronavirus pandemic in 2021-2022, was assessed. Their electronic medical records were used to collect their epidemiological, demographic, laboratory, and clinical information and treatment outcome.

Results

In all, 930 electronic medical records of the hospitalized patients (351 in the third wave and 579 in the fourth wave) were studied. In the third and fourth waves, 29.06% and 13.13% of the patients had severe COVID-19, respectively (P = 0.001). The number of deaths in the third wave was larger compared to the fourth wave (P = 0.015). The mean duration of hospitalization was longer in the third wave than in the fourth wave (P = 0.001). The drugs administered most in these two waves were remdesivir, dexamethasone, and heparin, and the patients who received these drugs were compared in the third and fourth waves (P = 0.001).

Conclusion

The reduced rate of mortality in the fourth wave was compared to the third wave. This reduction can be attributed to the change in the national strategy adopted in terms of hospitalization criteria and treatment protocols taking into account the acquired experience, earlier hospitalization, and start of drug therapy.

Adverse outcome and severity of COVID-19 in patients with autoimmune bullous diseases: A historical cohort study

The ongoing COVID-19 pandemic has raised concerns regarding the outcome of this infection in patients with autoimmune bullous dermatoses (AIBDs) due to effect of drugs used to treat these disorders. This investigation was performed from the onset of the pandemic to June 1, 2021. Patients with AIBDs who contracted COVID-19 were evaluated. A generalized linear model was employed to find the predictors of severe COVID-19 among patients with AIBDs. Ninety-three patients with AIBDs with a mean age of 50.3 years were evaluated. The most COVID-19 related symptoms were tiredness (76.3%) myalgia (69%), and cough (63.4%). During follow-up, the rate of hospitalization and death were 45.2% and 4.3%, respectively. Previous comorbidities (β = 0.61) and mean prednisolone dosage above 10 mg/day in the last 3 months (β = 1.10) significantly increased COVID-19 severity. Also, vaccination against SARS-CoV-2 (β = -1.50) and each passing month from the last rituximab dose decreased severity (β = -0.02). Notably, 19.3% of the patients developed AIBD flare-ups following COVID-19 infection. Higher prednisone dose and the shorter interval from the last rituximab infusion were determinants of severe COVID-19. Physicians should assess the risk versus the benefits when prescribing the medications. Moreover, vaccination could successfully attenuate COVID-19 severity.

COVID-19 and urban rivers: Effects of lockdown period on surface water pollution and quality- A case study of the Zarjoub River, north of Iran

Due to the spreading of the coronavirus (COVID-19) in Iran, restrictions and lockdown were announced to control the infection. In order to determine the effects of the lockdown period on the status of the water quality and pollution, the concentrations of Al, As, Ba, Cr, Cu, Mo, Ni, Pb, Se, and Zn, together with Na+, Mg2+, Ca2+ and electrical conductivity (EC), were measured in the Zarjoub River, north of Iran, in both pre-lockdown and post-lockdown periods. The results indicated that water pollution and associated human health risk reduced by an average of 30% and 39%, respectively, during the lockdown period. In addition, the multi-purpose water quality index also improved by an average of 34%. However, the water salinity and alkalinity increased during the lockdown period due to the increase of municipal wastewater and the use of disinfectants. The major sources of pollution were identified as weathering, municipal wastewater, industrial and agricultural effluents, solid waste, and vehicular pollution. PCA-MLR receptor model showed that the contribution of mixed sources of weathering and municipal wastewater in water pollution increased from 23 to 50% during the lockdown period. However, the contribution of mixed sources of industrial effluents and solid wastes reduced from 64 to 45%. Likewise, the contribution of traffic-related sources exhibited a reduction from 13% in the pre-lockdown period to 5% together with agricultural effluent in the post-lockdown period. Overall, although the lockdown period resulted in positive impacts on diminishing the level of water pollution caused by industrial and vehicular contaminants, the increase of municipal waste and wastewater is a negative consequence of the lockdown period.

Proposing a novel deep network for detecting COVID-19 based on chest images

The rapid outbreak of coronavirus threatens humans' life all around the world. Due to the insufficient diagnostic infrastructures, developing an accurate, efficient, inexpensive, and quick diagnostic tool is of great importance. To date, researchers have proposed several detection models based on chest imaging analysis, primarily based on deep neural networks; however, none of which could achieve a reliable and highly sensitive performance yet. Therefore, the nature of this study is primary epidemiological research that aims to overcome the limitations mentioned above by proposing a large-scale publicly available dataset of chest computed tomography scan (CT-scan) images consisting of more than 13k samples. Secondly, we propose a more sensitive deep neural networks model for CT-scan images of the lungs, providing a pixel-wise attention layer on top of the high-level features extracted from the network. Moreover, the proposed model is extended through a transfer learning approach for being applicable in the case of chest X-Ray (CXR) images. The proposed model and its extension have been trained and evaluated through several experiments. The inclusion criteria were patients with suspected PE and positive real-time reverse-transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2. The exclusion criteria were negative or inconclusive RT-PCR and other chest CT indications. Our model achieves an AUC score of 0.886, significantly better than its closest competitor, whose AUC is 0.843. Moreover, the obtained results on another commonly-used benchmark show an AUC of 0.899, outperforming related models. Additionally, the sensitivity of our model is 0.858, while that of its closest competitor is 0.81, explaining the efficiency of pixel-wise attention strategy in detecting coronavirus. Our promising results and the efficiency of the models imply that the proposed models can be considered reliable tools for assisting doctors in detecting coronavirus.

Consecutive Waves of COVID-19 in Iran: Various Dimensions and Probable Causes

OBJECTIVE

The coronavirus disease (COVID-19) pandemic has somehow affected the lives of 80% of the world's population. Iran has also experienced numerous outbreaks of this disease. The fifth wave having occurred in August 2021 was one of the most agonizing incidences of the pandemic in the country.

METHOD

We reviewed all of publications and govermental statistics about COVID-19 pandemic In Iran between 2019 to 2021.

RESULTS

The current study discusses the possible dimensions and causes of successive waves of COVID-19 in Iran, namely, the consequences of a significant delay in vaccination administration in due time, the collective overwhelming fallacy toward immunization, the polypharmacy controversy, inadequate community-based participation in risk reduction, and noticeable decrease in the public's resilience.

CONCLUSION

A variety of strategies have been recommended in the article to modify the principal challenges in order to help control the pandemic in the country.

Teledermatology During COVID-19: An Updated Review

The coronavirus disease 2019 (COVID-19) pandemic has fundamentally transformed the landscape of providing dermatologic care. In an age of lockdowns and social distancing, teledermatology (TD) has emerged as a powerful tool to deliver remote care. Here, we review literature on TD use during the pandemic to evaluate the positives and negatives of TD implementation. We especially consider the reception of TD in underserved communities and the developing world as well as the ethico-legal challenges wrought by the burgeoning utilization of this new paradigm of care. The potential of TD to occupy a more prominent role in dermatologic care in a post-COVID-19 world is also discussed.