Alterations of Lipid Profile in COVID-19: A Narrative Review

The COVID-19 pandemic has led to over 100 million infections and over 3 million deaths worldwide. Understanding its pathogenesis is crucial to guide prognostic and therapeutic implications. Viral infections are known to alter the lipid profile and metabolism of their host cells, similar to the case with MERS and SARS-CoV-2002. Since lipids play various metabolic roles, studying lipid profile alterations in COVID-19 is an inevitable step as an attempt to achieve better therapeutic strategies, as well as a potential prognostic factor in the course of this disease. Several studies have reported changes in lipid profile associated with COVID-19. The most frequently reported changes are a decline in serum cholesterol and ApoA1 levels and elevated triglycerides. The hyper-inflammatory state mediated by the Cytokine storm disturbs several fundamental lipid biosynthesis pathways. Virus replication is a process that drastically changes the host cell's lipid metabolism program and overuses cell lipid resources. Lower HDL-C and ApoA1 levels are associated with higher severity and mortality rates and with higher levels of inflammatory markers. Studies suggest that arachidonic acid omega-3 derivatives might help modulate hyper-inflammation and cytokine storm resulting from pulmonary involvement. Also, statins have been shown to be beneficial when administered after COVID-19 diagnosis via unclear mechanisms probably associated with anti-inflammatory effects and HDL-C rising effects

Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches

The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood coastal aquifer placed in the northern part of Iran. Six hydrological GALDIT parameters (i.e., G groundwater occurrence, A aquifer hydraulic conductivity, L level of groundwater above sea level, D distance from the shore, I impact of the existing status of seawater intrusion in the region, and T thickness of the aquifer) were considered as inputs for each model. In the training step, the values of GALDIT’s vulnerability index were conditioned by using the values of TDS concentration in order to obtain the conditioned vulnerability index (CVI). The CVI was considered as the target for each model. After training the models, each model was tested using a separate TDS dataset. The results indicated that the ANN and ANFIS algorithms performed better than the SVM algorithm. The values of correlation were obtained as 88, 87, and 80% for ANN, ANFIS, and SVM models, respectively. In the testing step of the SCMAI model, the values of RMSE, R2, and r were obtained as 6.4, 0.95, and 97%, respectively. Overall, SCMAI model outperformed other models to spatially predicting vulnerable zones. The result of the SCMAI model confirmed that the western zones along the shoreline had the highest vulnerability to seawater intrusion; therefore, it seems critical to consider emergency protection plans for study area

Recurrent pleural effusion from ovarian hemangioma: A rare pseudo‐Meigs syndrome presentation

Abstract Pleural effusion is a common condition related to various diseases such as heart failure, malignancies, and pneumonia. Ovarian hemangioma is a rare type of female genital tumour and can rarely cause pleural effusion. In this case, we present a 48‐year‐old female with repeated episodes of recurrent right‐sided pleural effusion over 1 year with no clear aetiology. Abdominal computed tomography revealed a large left ovarian mass. After surgical removal of the mass, the repeated pleural effusion episodes ceased, and histopathology analysis reported a rare ovarian hemangioma. Pseudo Meigs' syndrome is a triad of an ovarian tumour, ascites, and hydrothorax that rarely presents with ovarian hemangioma; both effusions are eradicated after removing the tumour

Corrigendum to “Reducing Cardiac Steatosis: Interventions to Improve Diastolic Function: A Narrative Review”. [Current Problems in Cardiology volume 48 (2023) 1–2]

The authors regret the name of one of the authors of this paper (Razieh Ziaei MD) has been removed from the original paper. The authors would like to apologize for any inconvenience caused

Reducing Cardiac Steatosis: Interventions to Improve Diastolic Function: A Narrative Review

Heart failure is one of the main causes of morbidity and mortality around the globe. Heart failure with preserved ejection fraction is primarily caused by diastolic dysfunction. Adipose tissue deposition in the heart has been previously explained in the pathogenesis of diastolic dysfunction. In this article, we aim to discuss the potential interventions that can reduce the risk of diastolic dysfunction by reducing cardiac adipose tissue. A healthy diet with reduced dietary fat content can reduce visceral adiposity and improve diastolic function. Aerobic and resistance exercises also reduce visceral and epicardial fat and ameliorate diastolic dysfunction. Some medications, include metformin, glucagon-like peptide-1 analogues, dipeptidyl peptidase-4 inhibitors, thiazolidinediones, sodium-glucose co-transporter-2, inhibitors, statins, ACE-Is, and ARBs, have shown different degrees of effectiveness in improving cardiac steatosis and diastolic function. Bariatric surgery has also shown promising results in this field

Performance evaluation of sentinel-2 and landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms

With the development of remote sensing algorithms and increased access to satellite data, generating up-to-date, accurate land use/land cover (LULC) maps has become increasingly feasible for evaluating and managing changes in land cover as created by changes to ecosystem and land use. The main objective of our study is to evaluate the performance of Support Vector Machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), Minimum Distance (MD), and Mahalanobis (MH) algorithms and compare them in order to generate a LULC map using data from Sentinel 2 and Landsat 8 satellites. Further, we also investigate the effect of a penalty parameter on SVM results. Our study uses different kernel functions and hidden layers for SVM and ANN algorithms, respectively. We generated the training and validation datasets from Google Earth images and GPS data prior to pre-processing satellite data. In the next phase, we classified the images using training data and algorithms. Ultimately, to evaluate outcomes, we used the validation data to generate a confusion matrix of the classified images. Our results showed that with optimal tuning parameters, the SVM classifier yielded the highest overall accuracy (OA) of 94%, performing better for both satellite data compared to other methods. In addition, for our scenes, Sentinel 2 date was slightly more accurate compared to Landsat 8. The parametric algorithms MD and MLC provided the lowest accuracy of 80.85% and 74.68% for the data from Sentinel 2 and Landsat 8. In contrast, our evaluation using the SVM tuning parameters showed that the linear kernel with the penalty parameter 150 for Sentinel 2 and the penalty parameter 200 for Landsat 8 yielded the highest accuracies. Further, ANN classification showed that increasing the hidden layers drastically reduces classification accuracy for both datasets, reducing zero for three hidden layers

Cancer recurrence or aggravation following COVID-19 vaccination

COVID-19 infection has been a global health issue in the past recent years and numerous topics are studied in order to discover its pathophysiology and potential side effects. The potential for disease recurrence following the administration of the COVID-19 vaccine is one of the issues that has recently attracted attention. Several studies have revealed that the COVID-19 vaccines, like other vaccines, may have side effects and, in some cases, they may even deteriorate the underlying illnesses, such as rheumatic diseases, autoimmune diseases, and cancers. The effectiveness and safety of the COVID-19 vaccine for patients with malignancies are one of the factors that are considered regarding this vaccine. Lymph node involvement, disease recurrence, and potential paraclinical changes after receiving the COVID-19 vaccine are some of the concerns of patients with malignancy. In this mini-review, we attempted to investigate cases of cancer recurrence or recovery as well as lymphadenopathy following vaccination

IgA vasculitis nephritis (Schönlein-Henoch purpura with nephritis) following COVID-19 vaccination

IgA vasculitis nephritis (Schönlein-Henoch purpura nephritis) is an autoimmune circumstance characterized by palpable purpura involving the lower limbs, arthralgia, abdominal pain and kidney involvement. It is possible that a cytokine storm following coronavirus disease 2019 (COVID-19) could lead to an immunological dysregulation responsible for IgA vasculitis nephritis in these cases. Reactivation or first onset of IgA vasculitis nephritis is uncommon; however, there have been increasing reports of this disease, as a complication of COVID-19 vaccination. It is possible that COVID-19 mRNA vaccination may trigger several auto-inflammatory and autoimmune cascades. Previous research has shown that Toll-like receptors play a role in the development of IgA vasculitis nephritis. Following injection of a COVID-19 mRNA vaccine, the uptake of double-stranded RNA by-products will trigger Toll-like receptors, leading to a series of intracellular cascades starting an innate immunity-driven process of cell-mediated and humoral- mediated immunity