COVID-19 effect on patients with noncommunicable diseases: A narrative review.

Background and Aims: On March 11, 2020, the WHO has declared COVID-19 a global pandemic, affecting our day-to-day lives. Physical distancing and lockdown made significant obstacles to populations, particularly healthcare systems. Most healthcare workers were reallocated to COVID-19 facilities. Noncommunicable disease patients were given low priority and are at a higher risk of severe COVID-19 infection, which disrupted the treatment and disease management of these patients. This review aimed to assess the effect of COVID-19 on different types of noncommunicable diseases and the severity it may cause to patients. Methods: We have conducted a review of the literature on COVID-19 and noncommunicable diseases from December 2019 until January 2022. The search was done in PubMed and Cochrane for relevant articles using variety of searching terms. Data for study variables were extracted. At the end of the selection process, 46 papers were selected for inclusion in the literature review. Result: The result from this review found that the COVID-19 pandemic has affected the efficiency of the patient's treatment indirectly by either delaying or canceling sessions, which solidified the need to rely more on telemedicine, virtual visits, and in-home visits to improve patient education and minimize the risk of exposure to the patients. The major and most common types of noncommunicable diseases are known to be related to the severe outcomes of COVID-19 infection. It is strongly recommended to prioritize these patients for vaccinations against COVID-19 to provide them with the protection that will neutralize the risk imposed by their comorbidities. Conclusion: We recommend conducting more studies with larger population samples to further understand the role of noncommunicable diseases (NCDs) in this pandemic. However, this pandemic has also affected the efficiency of NCDs treatment indirectly by delaying or canceling sessions and others.

Exploiting Signal Joint T Cell Receptor Excision Circle to Investigate the Impact of COVID-19 and Autoimmune Diseases on Age Prediction and Immunosenescence.

Signal joint T cell receptor excision circles (sjTRECs) are a promising marker for age estimation and immunosenescence in different ethnic groups. Several limitations are expected to overshadow their use as accurate markers for age prediction. The current study was conducted to determine the influence of immunologic disorders, such as autoimmune diseases and COVID-19, on the accuracy of sjTRECs as molecular markers for age estimation and immunosenescence among living Egyptians. Peripheral blood sjTRECs level was measured by qPCR in 90 autoimmune patients, 58 COVID-19 patients, and 85 healthy controls. The mean dCt values were significantly (p = 0.0002) different between the three groups, with the highest values in healthy subjects, followed by autoimmune and COVID-19 patients. A significant negative correlation was identified between the sjTRECs levels and ages in all studied cases. There were significant positive correlations between chronological age and predicted age for healthy individuals, autoimmune, and COVID-19 patients with mean absolute deviations (MAD) of 9.40, 11.04, and 9.71, respectively. The two patients' groups exhibited early immunosenescence, which was more noticeable among the young adults with COVID-19 and autoimmune patients of age range (18-49 years). Autoimmunity may represent a critical factor impacting the accuracy of sjTRECs quantitation for age prediction.

COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi.

The diagnosis of COVID-19 is of vital demand. Several studies have been conducted to decide whether the chest X-ray and computed tomography (CT) scans of patients indicate COVID-19. While these efforts resulted in successful classification systems, the design of a portable and cost-effective COVID-19 diagnosis system has not been addressed yet. The memory requirements of the current state-of-the-art COVID-19 diagnosis systems are not suitable for embedded systems due to the required large memory size of these systems (e.g., hundreds of megabytes). Thus, the current work is motivated to design a similar system with minimal memory requirements. In this paper, we propose a diagnosis system using a Raspberry Pi Linux embedded system. First, local features are extracted using local binary pattern (LBP) algorithm. Second, the global features are extracted from the chest X-ray or CT scans using multi-channel fractional-order Legendre-Fourier moments (MFrLFMs). Finally, the most significant features (local and global) are selected. The proposed system steps are integrated to fit the low computational and memory capacities of the embedded system. The proposed method has the smallest computational and memory resources,less than the state-of-the-art methods by two to three orders of magnitude, among existing state-of-the-art deep learning (DL)-based methods.

A novel LC-MS/MS method for determination of the potential antiviral candidate favipiravir for the emergency treatment of SARS-CoV-2 virus in human plasma: Application to a bioequivalence study in Egyptian human volunteers.

A novel, fast and sensitive LC-MS/MS method was developed and validated for the bioanalysis of the antiviral agent favipiravir (FAV); a promising candidate for treatment of SARS-CoV-2 (COVID-19) in human plasma using pyrazinamide as an internal standard (IS). Simple protein precipitation was adopted for plasma sample preparation using methanol. Chromatographic separation was accomplished on Eclipse plus C18 column (50 × 4.6 mm, 3.5 µm) using a mobile phase composed of methanol-0.2 % acetic acid (20:80, v/v) pumped at a flow rate 0.6 mL/min in an isocratic elution mode. The API4500 triple quadrupole tandem mass spectrometer was operated with multiple-reaction monitoring (MRM) in negative electrospray ionization interface for FAV and positive for IS. The MRM function was used for quantification, with the transitions set at m/z 156.00→ 113.00 and m/z 124.80→ 81.00 for FAV and IS. The method was optimized and fully validated in accordance to US-FDA guidelines. Linearity was acquired over a concentration range of 100.0-20000.0 ng/mL by computing using weighted linear regression strategy (1/x2). The proposed method was effectively applied for the pharmacokinetic evaluation of FAV and to demonstrate the bioequivalence of a new FAV formulation (test) and reference product in healthy Egyptian human volunteers.

New machine learning method for image-based diagnosis of COVID-19.

COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively.