Coronavirus Disease-2019 Conundrum: RAS Blockade and Geriatric-Associated Neuropsychiatric Disorders

Targeting the Renin-Angiotensin System (RAS) for Neuropsychiatric Disorders

BACKGROUND

Neuropsychiatric disorders, such as mood disorders, schizophrenia, and Alzheimer's disease (AD) and related dementias, are associated to significant morbidity and mortality worldwide. The pathophysiological mechanisms of neuropsychiatric disorders remain to be fully elucidated, which has hampered the development of effective therapies. The Renin Angiotensin System (RAS) is classically viewed as a key regulator of cardiovascular and renal homeostasis. The discovery that RAS components are expressed in the brain pointed out a potential role for this system in central nervous system (CNS) pathologies. The understanding of RAS involvement in the pathogenesis of neuropsychiatric disorders may contribute to identifying novel therapeutic targets.

AIMS

We aim to report current experimental and clinical evidence on the role of RAS in physiology and pathophysiology of mood disorders, schizophrenia, AD and related dementias. We also aim to discuss bottlenecks and future perspectives that can foster the development of new related therapeutic strategies.

CONCLUSION

The available evidence supports positive therapeutic effects for neuropsychiatric disorders with the inhibition/antagonism of the ACE/Ang II/AT1 receptor axis or the activation of the ACE2/Ang-(1-7)/Mas receptor axis. Most of this evidence comes from pre-clinical studies and clinical studies lag much behind, hampering a potential translation into clinical practice.

Role of SARS-COV-2 and ACE2 in the pathophysiology of peripheral vascular diseases

The occurrence of a novel coronavirus known as severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), created a serious challenge worldwide. SARS-CoV-2 has high infectivity, the ability to be transmitted even during the asymptomatic phase, and relatively low virulence, which has resulted in rapid transmission. SARS-CoV-2 can invade epithelial cells, hence, many patients infected with SARS-CoV-2 have suffered from vascular diseases (VDs) in addition to pulmonary manifestations. Accordingly, SARS-CoV-2 may can worsen the clinical condition of the patients with pre-existing VDs. Endothelial cells express angiotensin-converting enzyme 2 (ACE2). ACE2 is a biological enzyme that converts angiotensin (Ang)- 2 to Ang-(1-7). SARS-CoV-2 uses ACE2 as a cell receptor for viral entry. Thus, the SARS-CoV-2 virus promotes downregulation of ACE2, Ang-(1-7), and anti-inflammatory cytokines, as well as, an increase in Ang-2, resulting in pro-inflammatory cytokines. SARS-CoV-2 infection can cause hypertension, and endothelial damage, which can lead to intravascular thrombosis. In this review, we have concentrated on the effect of SARS-CoV-2 in peripheral vascular diseases (PVDs) and ACE2 as an enzyme in Renin-angiotensin aldosterone system (RAAS). A comprehensive search was performed on PubMed, Google Scholar, Scopus, using related keywords. Articles focusing on ("SARS-CoV-2", OR "COVID-19"), AND ("Vascular disease", OR "Peripheral vascular disease", OR interested disease name) with regard to MeSH terms, were selected. According to the studies, it is supposed that vascular diseases may increase susceptibility to severe SARS-CoV-2 infection due to increased thrombotic burden and endothelial dysfunction. Understanding SARS-CoV-2 infection mechanism and vascular system pathogenesis is crucial for effective management and treatment in pre-existing vascular diseases.

Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans

COVID pandemic across the world and the emergence of new variants have intensified the need to identify COVID-19 cases quickly and efficiently. In this paper, a novel dual-mode multi-modal approach is presented to detect a covid patient. This has been done using the combination of image of the chest X-ray/CT scan and the clinical notes provided with the scan. Data augmentation techniques are used to extrapolate the dataset. Five different types of image and text models have been employed, including transfer learning. The binary cross entropy loss function and the adam optimizer are used to compile all of these models. The multi-modal is also tried out with existing pre-trained models such as: VGG16, ResNet50, InceptionResNetV2 and MobileNetV2. The final multi-modal gives an accuracy of 97.8% on the testing data. The study provides a different approach to identifying COVID-19 cases using just the scan images and the corresponding notes.

COVID-19 in Older Individuals Requiring Hospitalization

Older individuals have an increased risk for severe coronavirus disease 2019 (COVID-19) and a higher risk for complications and death. The aim of this study was to investigate the clinical characteristics of older patients admitted with COVID-19 and describe their outcomes. This was a retrospective cohort study of patients older than 65 years admitted to the COVID-19 Department of the University Hospital of Heraklion. Data recorded and evaluated included age, gender, Infectious Diseases Society of America (IDSA) severity score, Charlson comorbidity index (CCI), high-flow nasal oxygen (HFNO) use, admission to the Intensive Care Unit (ICU), laboratory exams, treatment administered, and outcome. In total, 224 patients were evaluated in the present study. The median age was 75 years and 105 (46.9%) were female. In 50 patients (22.7%), HFNO was used and 23 (10.3%) were admitted to the ICU. Mortality was 13.4% (30 patients). Patients that died had higher age, were more likely to be male, had an IDSA severity score of 3, had prior HFNO use, had been admitted to the ICU, and were also more likely to have a higher white blood cell (WBC) count, CRP, ferritin, procalcitonin, d-dimers, and troponin. A multivariate logistic regression analysis identified age and the need for HFNO use to be independently positively associated with mortality. To conclude, COVID-19 carries significant mortality in hospitalized older patients, which increases with age, while the need for HFNO also increased the likelihood of worse outcomes. Clinicians caring for patients with COVID-19 should bear in mind these two factors. Future studies could elaborate on the effect of new variants on the dynamics of mortality in older patients.

EpistoNet: an ensemble of Epistocracy-optimized mixture of experts for detecting COVID-19 on chest X-ray images

The Coronavirus has spread across the world and infected millions of people, causing devastating damage to the public health and global economies. To mitigate the impact of the coronavirus a reliable, fast, and accurate diagnostic system should be promptly implemented. In this study, we propose EpistoNet, a decision tree-based ensemble model using two mixtures of discriminative experts to classify COVID-19 lung infection from chest X-ray images. To optimize the architecture and hyper-parameters of the designed neural networks, we employed Epistocracy algorithm, a recently proposed hyper-heuristic evolutionary method. Using 2500 chest X-ray images consisting of 1250 COVID-19 and 1250 non-COVID-19 cases, we left out 500 images for testing and partitioned the remaining 2000 images into 5 different clusters using K-means clustering algorithm. We trained multiple deep convolutional neural networks on each cluster to help build a mixture of strong discriminative experts from the top-performing models supervised by a gating network. The final ensemble model obtained 95% accuracy on COVID-19 images and 93% accuracy on non-COVID-19. The experimental results show that EpistoNet can accurately, and reliably be used to detect COVID-19 infection in the chest X-ray images, and Epistocracy algorithm can be effectively used to optimize the hyper-parameters of the proposed models.

A Review of the Current Status of Clinical Management of COVID-19 in the Elderly

The high infectivity and severity of SARS-CoV-2 infection (COVID-19), and our limited understanding of the biology of the novel coronavirus, as well as the lack of an effective treatment for COVID-19, have created a global pandemic. Those most likely to become seriously ill with COVID-19 are adults, especially the elderly and those who are already weak or sick. At present, a specific drug for treatment of COVID-19 has not been developed. This, combined with the typical coexistence of a variety of chronic diseases in elderly patients, makes treatment challenging at present. In addition, for elderly patients, COVID-19 isolation measures during the epidemic can easily lead to psychological problems. Thus, how to manage elderly patients has become a focus of social attention in the current circumstances. This article reviews the effects of COVID-19 and makes management suggestions for elderly patients during this epidemic period. In addition to the elderly, critically ill people are also highly susceptible to this novel coronavirus. For elderly COVID-19 patients, antiviral therapy, immune regulation, and even auxiliary respiratory therapy can be given after a comprehensive evaluation of the disease. With the approval and use of COVID-19 vaccines, it is reasonable to expect that we can conquer SARS-CoV-2.