COVID-associated pernio: Mechanistic insights to an abortive, seronegative SARS-CoV-2 infection

Introduction: SARS-CoV-2 replicates primarily in the airways but generates a systemic immune response mediated by Type I interferons (IFN-I). Pernio is a rare skin manifestation of disorders characterized by excessive IFN-I signalling. Although pernio increased in incidence during the pandemic, the relationship to SARS-CoV-2 remains controversial. Because of the pivotal nature of interferons in COVID-19 outcomes, pernio offers a window to investigate the biology underlying host resiliency to SARS-CoV-2 infection. Method(s): To further assess COVID-associated pernio, we characterized clinical samples from affected patients across 4 waves of the pandemic and investigated mechanistic feasibility in a rodent model. Patients were followed longitudinally with banking of blood and tissue. Golden hamsters were mock-treated or intra-nasally infected with SARS-CoV-2 and harvested at 3-and 30-days post-infection. Result(s): In affected tissue, immunophenotyping utilizing multiplex immunohistochemistry profiled a robust IFN-1 signature characterized by plasmacytoid dendritic cell activation. Viral RNA was detectable in a subset of cases using in situ hybridization for the SARS-CoV-2 S gene transcript. Profiling of the systemic immune response did not reveal a durable type 1 interferon signature. Consistent with previous literature, antibody and T-cell specific responses to SARS-CoV-2 were not detected. Nasopharyngeal SARS-CoV-2 inoculation in hamsters resulted in rapid dissemination of viral RNA and the generation of an IFN-I response that were both detectable in the paws of infected animals. Conclusion(s): Our data support a durable local IFN signature, with direct evidence of viral SARS-CoV-2 RNA in acral skin and suggest that COVID-associated pernio results from an abortive, seronegative SARS-CoV-2 infection.

Psychotic-like Experiences during COVID-19 Outbreak: A survey from Pakistan

Introduction: Despite the fact that adolescents have been at higher risk of distress during the COVID-19 pandemic, the effect of pandemic on psychotic-like experiences (PLEs) is not well described. Objective(s): The study's objective is to evaluate if PLEs are induced in young individuals aged 18-24 during the pandemic. Method(s):A total of 201 college students from Pakistan (ages 18-24) were recruited for a cross-sectional research. We investigated the incidence of PLEs in Pakistan during the pandemic, their links to socio-demographic factors, COVID-19-related characteristics, depression, anxiety, and sleep difficulties. Community Assessment of Psychic Experience's positive symptom component (CAPE), Patient Health Questionnaire, Generalized Anxiety Disorder Scale, and IBM SPSS 25 were used. Result(s): CAPE-Frequency and CAPE-stress were positively associated with PHQ total (p<0.0010);GAD total (p<0.001);time spent indoors due to COVID-19 (p<0.001). Psychiatric disorder other than bipolar disorder or psychosis (p<0.001 for CAPE-frequency and stress), family history of psychiatric disorders (p<0.001 for CAPE-frequency and stress), chronic medical disease (p=0.021 CAPE-frequency and p=0.026 CAPE-stress), illegal drug usage (p<0.001 for CAPE-frequency and stress) were associated with CAPE-Frequency and CAPE-stress. In linear stepwise regression analysis, the best model predicted CAPE-Frequency explained 77.4% of variance with the following variables: PHQ total (B=0.552, SE= 0.08, t=6.909, p<0.001), GAD total (p<0.001), duration at home (p<0.001), and psychiatric disorder in family (p<0.001). Conclusion(s): PLEs have been linked with anxiety and depression during the pandemic. Individuals with a mental condition, family history of psychiatric disorder, chronic medical illness, illicit drug use, and increased time spent at home experienced more PLEs and stress.

COVID-19 and tuberculosis: the double whammy of respiratory pathogens.

Prior to coronavirus disease 2019 (COVID-19), tuberculosis (TB) was the worst killer among infectious diseases. The union of these two obnoxious respiratory diseases can be devastating, with severe public health implications. The COVID-19 pandemic has affected all TB-elimination programmes due to the severe burden on healthcare systems and the diversion of funds and attention towards controlling the pandemic. The emerging data show that the COVID-19 pandemic caused a marked decrease in case notifications and bacille Calmette-Guérin immunisations, ultimately promoting disease transmission and increasing the susceptible population. The similarity between the clinical characteristics of TB and COVID-19 adds to the public health complications, with evidence of immune dysregulation in both cases leading to severe consequences. Clinical evidence suggests that severe acute respiratory syndrome coronavirus 2 infection predisposes patients to TB infection or may lead to reactivation of latent disease. Similarly, underlying TB disease can worsen COVID-19. Treatment options are limited in COVID-19; therefore, using immunosuppressive and immunomodulatory regimens that can modulate the concomitant bacterial infection and interaction with anti-TB drugs requires caution. Thus, considering the synergistic impact of these two respiratory diseases, it is crucial to manage both diseases to combat the syndemic of TB and COVID-19.

Efficient Security and Authentication for Edge-Based Internet of Medical Things.

Internet of Medical Things (IoMT)-driven smart health and emotional care is revolutionizing the healthcare industry by embracing several technologies related to multimodal physiological data collection, communication, intelligent automation, and efficient manufacturing. The authentication and secure exchange of electronic health records (EHRs), comprising of patient data collected using wearable sensors and laboratory investigations, is of paramount importance. In this article, we present a novel high payload and reversible EHR embedding framework to secure the patient information successfully and authenticate the received content. The proposed approach is based on novel left data mapping (LDM), pixel repetition method (PRM), RC4 encryption, and checksum computation. The input image of size [Formula: see text] is upscaled by using PRM that guarantees reversibility with lesser computational complexity. The binary secret data are encrypted using the RC4 encryption algorithm and then the encrypted data are grouped into 3-bit chunks and converted into decimal equivalents. Before embedding, these decimal digits are encoded by LDM. To embed the shifted data, the cover image is divided into [Formula: see text] blocks and then in each block, two digits are embedded into the counter diagonal pixels. For tamper detection and localization, a checksum digit computed from the block is embedded into one of the main diagonal pixels. A fragile logo is embedded into the cover images in addition to EHR to facilitate early tamper detection. The average peak signal to noise ratio (PSNR) of the stego-images obtained is 41.95 dB for a very high embedding capacity of 2.25 bits per pixel. Furthermore, the embedding time is less than 0.2 s. Experimental results reveal that our approach outperforms many state-of-the-art techniques in terms of payload, imperceptibility, computational complexity, and capability to detect and localize tamper. All the attributes affirm that the proposed scheme is a potential candidate for providing better security and authentication solutions for IoMT-based smart health.

Effectiveness of Noninvasive Positive Pressure Ventilation in COVID-19 Patients with Severe Acute Respiratory Distress Syndrome

Results of invasive positive pressure ventilation (IPPV) in COVID-19 patients with Severe Acute Respiratory Distress Syndrome are discouraging despite its prompt use. However noninvasive positive pressure ventilation (NIPPV) is yet not a common practice internationally because of lack of global evidence advocating its effective use in severe cases of ARDS as well as dreadful concern about aerosol generation especially in patients having COVID-19 infection. Objective: To determine whether, NIPPV application is effective and safe in COVID-19 Patients. Methods: One hundred and thirty hemodynamically stable patients with severe CARDS as per Berlin definition (PaO2/FiO2 ratio <= 100mm Hg), having GCS > 13, respiratory breathing index (RBI) < 105, and without any systemic complication were selected. They were managed with NIPPV in Corona Intensive Care Unit of Mayo Hospital/King Edward Medical University Lahore. A little innovation was done with the application of a specific orofacial interface, fitted with heat and moist exchanger (HME) at the interface and viral/bacterial filters at the expiratory limb of ventilatory circuit. Favorable outcome has been observed in 64% of treated cases in terms of improvement in PaO2/FiO2 ratio, thus abating severity of ARDS from severe to mild category, in an average time span of 6 days. Remaining 36% of patients progressed to IPPV with definitive airway. During study period, 4.2% of healthcare workers (HCW) got infected with COVID-19. Associated complications of NIPPV application were claustrophobia (13.8%), nasal crusting (6.9%), aspiration (6.1%) and barotrauma (0.7%). Conclusion: In carefully selected patients, use of noninvasive positive pressure ventilation with the application of HME and viral/bacterial filters is an effective, preferable and safe modality of choice to provide respiratory support, thus obviating the need for IPPV. However further larger studies are needed to confirm our recommendations.

IVACS: Intelligent Voice Assistant for Coronavirus Disease (COVID-19) Self-Assessment

At the time of writing this paper, the world has around eleven million cases of COVID-19, scientifically known as severe acute respiratory syndrome corona-virus 2 (SARS-COV-2). One of the popular critical steps various health organizations are advocating to prevent the spread of this contagious disease is self-assessment of symptoms. Multiple organizations have already pioneered mobile and web-based applications for self-assessment of COVID-19 to reduce the spread of this global pandemic. We propose an intelligent voice-based assistant for COVID-19 self-assessment (IVACS). This interactive assistant has been built to diagnose the symptoms related to COVID-19 using the guidelines provided by the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO). The empirical testing of the application has been performed with 22 human subjects, all volunteers, using the NASA Task Load Index (TLX), and subjects' performance accuracy has been measured. The results indicate that the IVACS is beneficial to users. However, it still needs additional research and development to promote its widespread application.

Violence against Healthcare Workers during the COVID-19 Pandemic: A Review of Incidents from a Lower-Middle-Income Country.

Background: Healthcare workers (HCWs) across the globe have met tremendous challenges during the COVID-19 (coronavirus disease 2019) pandemic, such as shortages of personal protective equipment, extensive work hours, and constant fear of catching the virus or transmitting it to loved ones. Adding on to the already existing burnout, an increase in incidents of violence and aggression against HCWs was seen in Pakistan and globally. Objectives: Primarily to review cases of violence against HCWs in Pakistan, highlighting and comparing the instigating factors seen within the country and globally. Secondly, to enlist possible interventions to counter workplace violence in healthcare during a pandemic and in general. Methods: Incidents of violence towards HCWs in Pakistan during the COVID-19 pandemic occurring between April 7, 2020, and August 7, 2020, were included. The incidents reported from local newspapers were reviewed. Findings and Conclusion: A total of 29 incidents were identified, with perpetrators of violence most commonly being relatives of COVID-19 patients. Most frequent reasons included mistrust in HCWs, belief in conspiracy theories, hospitals' refusal to admit COVID-19 patients due to limited space, COVID-19 hospital policies, and the death of the COVID-19 patients. Protests by doctors and other HCWs for provision of adequate PPE, better quarantine conditions for doctors with suspected COVID-19, and better compensation for doctors on COVID-19 patient duty resulted in police violence towards HCWs. To avoid such incidents in the future, institutions, healthcare policymakers, media organisations, and law enforcement agencies must work together for widespread public awareness to counter misconceptions and to exhibit responsible journalism. In hospitals, measures such as de-escalation training and increased security must be implemented. Furthermore, law enforcement agencies must be trained in non-violent methods of crowd dispersal and control to manage peaceful protests by HCWs over legitimate issues.

IVACS: I ntelligent v oice A ssistant for C oronavirus Disease (COVID-19) S elf-Assessment

At the time of writing this paper, the world has around eleven million cases of COVID-19, scientifically known as severe acute respiratory syndrome corona-virus 2 (SARS-COV-2). One of the popular critical steps various health organizations are advocating to prevent the spread of this contagious disease is self-assessment of symptoms. Multiple organizations have already pioneered mobile and web-based applications for self-assessment of COVID-19 to reduce the spread of this global pandemic. We propose an intelligent voice-based assistant for COVID-19 self-assessment (IVACS). This interactive assistant has been built to diagnose the symptoms related to COVID-19 using the guidelines provided by the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO). The empirical testing of the application has been performed with 22 human subjects, all volunteers, using the NASA Task Load Index (TLX), and subjects' performance accuracy has been measured. The results indicate that the IVACS is beneficial to users. However, it still needs additional research and development to promote its widespread application. © 2020 IEEE.