Pharmacists and pharmacy services in COVID-19 literature: A bibliometirc analysis.

Background: The COVID-19 pandemic had an enormous impact on the global economy and healthcare. Pharmacists were vital members of the healthcare system, and they participated in various strategies to reduce the effect of the pandemic. Numerous papers were published discussing their roles during the pandemic. Bibliometric analysis was used to measure the impact of publications on this topic and assessed them qualitatively and quantitatively over a specific time. Objective: Evaluate published literature pertaining to the roles of pharmacists and pharmacy services during the pandemic and identify gaps. Methods: An electronic search was conducted on PubMed database using a specific query. Eligible publications were published in English between January 2020 and January 2022 and discussed the role of pharmacists, pharmacies, and pharmacy departments during the pandemic. Clinical trials, studies on pharmacy education/training, and conference abstracts were excluded. Results: Of 954 records retrieved, 338 (35.4%) from 67 countries were included. Most papers (n = 113; 33.4%) were from the community pharmacy sector, followed by the clinical pharmacy sector (n = 89; 26.3%). Sixty-one (18%) papers were multinational, mostly involving two countries. The average number of citations of the included papers was 6 times (range 0-89). The most common MeSH terms were 'humans', 'hospitals', and 'telemedicine', where the former frequently co-appeared with the terms 'COVID-19' and 'pharmacists.' Conclusions: Results from this study illustrate the innovative and proactive strategies developed by pharmacists during the pandemic. Pharmacists from around the world are encouraged to share their experiences for stronger healthcare systems to counter future pandemics and environmental disasters.

Road crashes in Adelaide metropolitan region, the consequences of COVID-19.

Background: Many countries instituted lockdown rules as the COVID-19 pandemic progressed, however, the effects of COVID-19 on transportation safety vary widely across countries and regions. In several situations, it has been shown that although the COVID-19 closure has decreased average traffic flow, it has also led to an increase in speeding, which will indeed increase the severity of crashes and the number of fatalities and serious injuries. Methods: At the local level, Generalized linear Mixed (GLM) modelling is used to look at how often road crashes changed in the Adelaide metropolitan area before and after the COVID-19 pandemic. The Geographically Weighted Generalized Linear Model (GWGLM) is also used to explore how the association between the number of crashes and the factors that explain them varies across census blocks. Using both no-spatial and spatial models, the effects of urban structure elements like land use mix, road network design, distance to CBD, and proximity to public transit on the frequency of crashes at the local level were studied. Results: This research showed that lockdown orders led to a mild reduction (approximately 7%) in crash frequency. However, this decrease, which has occurred mostly during the first three months of the lockdown, has not systematically alleviated traffic safety risks in the Greater Adelaide Metropolitan Area. Crash hotspots shifted from areas adjacent to workplaces and education centres to green spaces and city fringes, while crash incidence periods switched from weekdays to weekends and winter to summer. Implications: The outcomes of this research provided insights into the impact of shifting driving behaviour on safety during disorderly catastrophes such as COVID-19.

Personalized predictions of adverse side effects of the COVID-19 vaccines.

Background: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics. Methods: Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Results: The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620-0.686, 0.685-0.716, 0.632-0.727, 0.527-0.598, 0.548-0.655, 0.545-0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777-0.867, 0.795-0.848, 0.857-0.906, 0.788-0.875, 0.683-0.850, and 0.486-0.680, respectively. Conclusions: Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.

Sensitivity to treatment and score bands of the Infants and Toddlers Dermatology Quality of Life questionnaire.

Background: The Infants and Toddlers Dermatology Quality of Life (InToDermQoL) questionnaire is the first dermatology-specific proxy health related QoL instrument for children from birth to 4 years. Score meaning bands and the sensitivity to successful therapeutic intervention are important to interpret the clinical meaning of an instrument. Objective: The aim of the present study was to check the sensitivity to successful therapeutic intervention and establish score bands of the InToDermQoL questionnaire. Methods: Parents or grandparents of 424 children with skin diseases from Spain, Malta, Croatia, Romania, Greece, and Ukraine filled in national language versions of the InToDermQoL questionnaire. Disease severity of children with atopic dermatitis was assessed by SCORAD (Scoring atopic dermatitis). Cohen's d was used to assess the responsiveness of the instrument. Results: The mean total InToDermQoL scores significantly decreased after treatment. Severity grading of the SCORAD scores gave stratification of the InToDermQoL severity grades based on 95% confidence intervals. Scores below a calculated minimal important difference of 2 corresponded to no effect on patient's health related QoL. Limitations: Score banding may be slightly different across patient population and study context. Conclusion: All 3 age-specific versions of the InToDermQoL questionnaire showed sensitivity to treatment. Score bands for the InToDermQoL questionnaire have been established.

Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytes.

SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest to clinicians. To help address this need, we retrieved 269 public RNA-seq human transcriptome samples from GEO that had qualitative disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to calculate gene expression in PBMCs, whole blood, and leukocytes, as well as to predict transcriptional biomarkers in PBMCs and leukocytes. This process involved using Salmon for read mapping, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then performed a random forest machine learning analysis on the read counts data to identify genes that best classified samples based on the COVID-19 severity phenotype. This approach produced a ranked list of leukocyte genes based on their Gini values that includes TGFBI, TTYH2, and CD4, which are associated with both the immune response and inflammation. Our results show that these three genes can potentially classify samples with severe COVID-19 with accuracy of ∼88% and an area under the receiver operating characteristic curve of 92.6--indicating acceptable specificity and sensitivity. We expect that our findings can help contribute to the development of improved diagnostics that may aid in identifying severe COVID-19 cases, guide clinical treatment, and improve mortality rates.

Misdiagnosis of intramural ectopic pregnancy and invasive gestational trophoblastic disease on ultrasound: A challenging case at Tu Du Hospital in Vietnam in COVID-19 pandemic peak and mini-review of literature.

Differentiation between intramural ectopic pregnancy and molar ectopic pregnancy is very difficult because of their exceptional rarity. Herein, we present a misdiagnosed case of intramural pregnancy and invasive trophoblastic disease on ultrasound. A 45-year-old female patient was admitted to our tertiary referral hospital due to abdominal pain and unusual ultrasonography findings. Initially, a diagnosis of intramural ectopic pregnancy was identified based on transvaginal color Doppler ultrasonography, 3-dimensional ultrasound, and serial serum beta-human chorionic gonadotropin, thus the patient underwent laparotomy with hysterectomy. However, the histopathological endpoint showed an invasive trophoblastic disease. Clinically, this pathology should be included in the differential diagnosis of intramural ectopic pregnancy since an imaging scan remains quite unclear.

Sludge-based activated carbon from two municipal sewage sludge precursors for improved secondary wastewater-treatment discharge-effluent

This study investigated the use of sludge-based activated carbon (SBAC) sorbent as an integrated waste-to-resources approach for the removal of contaminants from wastewater. We measured the ability of SBAC sorbents from two types of municipal sewage sludge (SS) precursors (thickened waste SS “TWSS-SBAC” and biosolids “Bio-SBAC”) from a Canadian wastewater treatment plant (WWTP) to stabilise emerging contaminants (ECs) from precursor SS and to remove ECs from the discharged effluent. The ECs were from pharmaceutical and personal care products (PPCPs), including antibiotics, disinfectants, and antibacterial hand-sanitisers and soaps, which were commonly used during the COVID-19 (coronavirus disease of 2019) pandemic. We measured the removal efficacy of Bio-SBAC at two dosages (1g/L and 10g/L) and TWSS-SBAC at one dosage (1g/L) via 30-min batch adsorption tests for eleven PPCPs at mean concentrations of 2–2337ng/L in the discharged effluent, and compared the results with those of other techniques and sorbents reported in literature. At both dosages, Bio-SBAC removed PPCPs, including four blood regulator compounds that have been extensively used since the pandemic outbreak (furosemide, gemfibrozil, glyburide, and warfarin), with their levels decreasing below the detection limit. The percentage removal for ibuprofen, 2-hydroxy-ibuprofen, and naproxen were 91.6–99.8% using 1g/L. The antimicrobial compounds triclosan and triclocarban were completely removed at both dosages. Ninety-nine percentage of bisphenol A was removed at 1g/L dosage and was completely removed at 10g/L. TWSS-SBAC showed similar performance as Bio-SBAC in removing PPCPs from the final effluent to improve the quality of wastewater discharged from a WWTP.

Development and integration of VGG and dense transfer-learning systems supported with diverse lung images for discovery of the Coronavirus identity.

The contagious SARS-CoV-2 has had a tremendous impact on the life and health of many communities. It was first rampant in early 2019 and so far, 539 million cases of COVID-19 have been reported worldwide. This is reminiscent of the 1918 influenza pandemic. However, we can detect the infected cases of COVID-19 by analysing either X-rays or CT, which are presumably considered the least expensive methods. In the existence of state-of-the-art convolutional neural networks (CNNs), which integrate image pre-processing techniques with fully connected layers, we can develop a sophisticated AI system contingent on various pre-trained models. Each pre-trained model we involved in our study assumed its role in extracting some specific features from different chest image datasets in many verified sources, such as (Mendeley, Kaggle, and GitHub). First, for CXR datasets associated with the CNN trained model from the beginning, whereby is comprised of four layers beginning with the Conv2D layer, which comprises 32 filters, followed by the MaxPooling and afterwards, we reiterated similarly. We used two techniques to avoid overgeneralization, the early stopping and the Dropout techniques. After all, the output was one neuron to classify both cases of 0 or 1, followed by a sigmoid function; in addition, we used the Adam optimizer owing to the more improved outcomes than what other optimizers conducted; ultimately, we referred to our findings by using a confusion matrix, classification report (Recall & Precision), sensitivity and specificity; in this approach, we achieved a classification accuracy of 96%. Our three integrated pre-trained models (VGG16, DenseNet201, and DenseNet121) yielded a remarkable test accuracy of 98.81%. Besides, our merged models (VGG16, DenseNet201) trained on CT images with the utmost effort; this model held an accurate test of 99.73% for binary classification with the (Normal/Covid-19) scenario. Comparing our results with related studies shows that our proposed models were superior to the previous CNN machine learning models in terms of various performance metrics. Our pre-trained model associated with the CT dataset achieved 100% of the F1score and the loss value was approximately 0.00268.

Feasibility of home-based ELISA capillary blood self-testing for anti-SARS-CoV-2 antibodies.

Objectives: Serological assays for the presence of anti-SARS-CoV-2 antibodies are crucially needed for research and monitoring of the SARS-CoV-2 pandemic. Antibodies are reliability detected in capillary blood, a minimally invasive and cost-effective alternative to venous blood testing. However, there is a limited knowledge on feasibility of capillary blood self-sampling. This study compared the feasibility of capillary blood self-testing in people aged less than 65 vs. people aged 65 or more. A secondary aim was to investigate the performance of the Hem-Col® (no additive) device compared to venous blood testing. Design and methods: Data were collected in a prospective study in Switzerland (n = 106). Capillary blood was collected using the Hem-Col® (no additive) device. Feasibility was assessed using 1) collecting the recommended amount of capillary blood and 2) achieving all steps of capillary blood collection. A sample of 5 ml of venous blood was also collected. Results: For the primary objective, 86.2%/62.1% of patients aged less than 65 collected the recommended amount of capillary blood/achieved all steps vs. 62.5%/39.6% of patients aged 65 or more (p = .006/p = .022). For the secondary objective, the correlation between capillary and venous blood was r = 0.992 and kappa = 1. Conclusions: Capillary blood self-testing appeared as a feasible and reliable alternative to venous blood testing. Such alternative would improve access to serological testing and spare health care resources. However, the difference between age groups should be considered when using self-sampling devices. Help should be developed for older people, such as phone counseling or encouraging asking younger family members for help.

Kombucha - An ancient fermented beverage with desired bioactivities: A narrowed review.

Kombucha, originated in China 2000  years ago, is a sour and sweet-tasted drink, prepared traditionally through fermentation of black tea. During the fermentation of kombucha, consisting of mainly acidic compounds, microorganisms, and a tiny amount of alcohol, a biofilm called SCOBY forms. The bacteria in kombucha has been generally identified as Acetobacteraceae. Kombucha is a noteworthy source of B complex vitamins, polyphenols, and organic acids (mainly acetic acid). Nowadays, kombucha is tended to be prepared with some other plant species, which, therefore, lead to variations in its composition. Pre-clinical studies conducted on kombucha revealed that it has desired bioactivities such as antimicrobial, antioxidant, hepatoprotective, anti-hypercholestorelomic, anticancer, anti-inflammatory, etc. Only a few clinical studies have been also reported. In the current review, we aimed to overhaul pre-clinical bioactivities reported on kombucha as well as its brief compositional chemistry. The literature data indicate that kombucha has valuable biological effects on human health.

Using artificial intelligence to risk stratify COVID-19 patients based on chest X-ray findings.

BACKGROUND: Deep learning-based radiological image analysis could facilitate use of chest x-rays as a triaging tool for COVID-19 diagnosis in resource-limited settings. This study sought to determine whether a modified commercially available deep learning algorithm (M-qXR) could risk stratify patients with suspected COVID-19 infections. METHODS: A dual track clinical validation study was designed to assess the clinical accuracy of M-qXR. The algorithm evaluated all Chest-X-rays (CXRs) performed during the study period for abnormal findings and assigned a COVID-19 risk score. Four independent radiologists served as radiological ground truth. The M-qXR algorithm output was compared against radiological ground truth and summary statistics for prediction accuracy were calculated. In addition, patients who underwent both PCR testing and CXR for suspected COVID-19 infection were included in a co-occurrence matrix to assess the sensitivity and specificity of the M-qXR algorithm. RESULTS: 625 CXRs were included in the clinical validation study. 98% of total interpretations made by M-qXR agreed with ground truth (p = 0.25). M-qXR correctly identified the presence or absence of pulmonary opacities in 94% of CXR interpretations. M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary opacities were 94%, 95%, 99%, and 88% respectively. M-qXR correctly identified the presence or absence of pulmonary consolidation in 88% of CXR interpretations (p = 0.48). M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary consolidation were 91%, 84%, 89%, and 86% respectively. Furthermore, 113 PCR-confirmed COVID-19 cases were used to create a co-occurrence matrix between M-qXR's COVID-19 risk score and COVID-19 PCR test results. The PPV and NPV of a medium to high COVID-19 risk score assigned by M-qXR yielding a positive COVID-19 PCR test result was estimated to be 89.7% and 80.4% respectively. CONCLUSION: M-qXR was found to have comparable accuracy to radiological ground truth in detecting radiographic abnormalities on CXR suggestive of COVID-19.

Educational impact of early COVID-19 operating room restrictions on neurosurgery resident training in the United States: A multicenter study.

BACKGROUND: The coronavirus (COVID-19) pandemic has caused unprecedented suspensions of neurosurgical elective surgeries, a large proportion of which involve spine procedures. The goal of this study is to report granular data on the impact of early COVID-19 pandemic operating room restrictions upon neurosurgical case volume in academic institutions, with attention to its secondary impact upon neurosurgery resident training. This is the first multicenter quantitative study examining these early effects upon neurosurgery residents caseloads. METHODS: A retrospective review of neurosurgical caseloads among seven residency programs between March 2019 and April 2020 was conducted. Cases were grouped by ACGME Neurosurgery Case Categories, subspecialty, and urgency (elective vs. emergent). Residents caseloads were stratified into junior (PGY1-3) and senior (PGY4-7) levels. Descriptive statistics are reported for individual programs and pooled across institutions. RESULTS: When pooling across programs, the 2019 monthly mean (SD) case volume was 214 (123) cases compared to 217 (129) in January 2020, 210 (115) in February 2020, 157 (81), in March 2020 and 82 (39) cases April 2020. There was a 60% reduction in caseload between April 2019 (207 [101]) and April 2020 (82 [39]). Adult spine cases were impacted the most in the pooled analysis, with a 66% decrease in the mean number of cases between March 2020 and April 2020. Both junior and senior residents experienced a similar steady decrease in caseloads, with the largest decreases occurring between March and April 2020 (48% downtrend). CONCLUSIONS: Results from our multicenter study reveal considerable decreases in caseloads in the neurosurgical specialty with elective adult spine cases experiencing the most severe decline. Both junior and senior neurosurgical residents experienced dramatic decreases in case volumes during this period. With the steep decline in elective spine cases, it is possible that fellowship directors may see a disproportionate increase in spine fellowships in the coming years. In the face of the emerging Delta and Omicron variants, programs should pay attention toward identifying institution-specific deficiencies and developing plans to mitigate the negative educational effects secondary to such caseloads reduction.

RNAi-mediated siRNA sequences to combat the COVID-19 pandemic with the inhibition of SARS-CoV2.

The outbreak of the COVID-19 pandemic has cost five million lives to date, and was caused by a positive-sense RNA virus named SARS-CoV2. The lack of drugs specific to SARS-CoV2, leads us to search for an effective and specific therapeutic approach. Small interfering RNA (siRNA) is able to activate the RNA interference (RNAi) pathway to silence the specific targeted gene and inhibit the viral replication, and it has not yet attracted enough attention as a SARS-CoV2 antiviral agent. It could be a potential weapon to combat this pandemic until the completion of full scale, effective mass vaccination. For this study, specific siRNAs were designed using a web-based bioinformatics tool (siDirect2.0) against 14 target sequences. These might have a high probability of silencing the essential proteins of SARS-CoV2. such as: 3CLpro/Mpro/nsp5, nsp7, Rd-Rp/nsp12, ZD, NTPase/HEL or nsp13, PLpro/nsp3, envelope protein (E), spike glycoprotein (S), nucleocapsid phosphoprotein (N), membrane glycoprotein (M), ORF8, ORF3a, nsp2, and its respective 5' and 3'-UTR. Among these potential drug targets, the majority of them contain highly conserved sequences; the rest are chosen on the basis of their role in viral replication and survival. The traditional vaccine development technology using SARS-CoV2 protein takes 6-8 months; meanwhile the virus undergoes several mutations in the candidate protein chosen for vaccine development. By the time the protein-based vaccine reaches the market, the virus would have undergone several mutations, such that the antibodies against the viral sequence may not be effective in restricting the newly mutated viruses. However, siRNA technology can make sequences based on real time viral mutation status. This has the potential for suppressing SARS-CoV2 viral replication, through RNAi technology.

COVID-19 associated vasculitis: A systematic review of case reports and case series.

Vasculitis is one of the complications of COVID-19. We conducted a systematic review analysing the association of COVID-19 with vasculitis. We searched Google Scholar and PubMed from December 1, 2019, to October 11, 2021. The review included 8 studies (7 case reports and 1 case series) reporting 9 cases of vasculitis secondary to COVID-19. The mean age was 29.17 ± 28.2 years, ranging from 6 months to 83 years. The male to female ratio was 4:5. Maculopapular, violaceous, papular and erythematous rash were common. Heparin(n = 2), corticosteroids (n = 6) (methylprednisolone) and intravenous immunoglobulin (n = 4) were prescribed in these patients. Significant clinical improvement was observed in 8 out of 9 patients. One person died during treatment. Our study discusses vasculitis as one of the complications of COVID-19. Furthermore, the pathophysiology, clinical presentation, and management of COVID-19 associated vasculitis is discussed.

R.Graph: A new risk-based causal reasoning and its application to COVID-19 risk analysis.

Various unexpected, low-probability events can have short or long-term effects on organizations and the global economy. Hence there is a need for appropriate risk management practices within organizations to increase their readiness and resiliency, especially if an event may lead to a series of irreversible consequences. One of the main aspects of risk management is to analyze the levels of change and risk in critical variables which the organization's survival depends on. In these cases, an awareness of risks provides a practical plan for organizational managers to reduce/avoid them. Various risk analysis methods aim at analyzing the interactions of multiple risk factors within a specific problem. This paper develops a new method of variability and risk analysis, termed R.Graph, to examine the effects of a chain of possible risk factors on multiple variables. Additionally, different configurations of risk analysis are modeled, including acceptable risk, analysis of maximum and minimum risks, factor importance, and sensitivity analysis. This new method's effectiveness is evaluated via a practical analysis of the economic consequences of new Coronavirus in the electricity industry.

All hands on deck: SARS-CoV-2 proteins that block early anti-viral interferon responses.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is responsible for the current pandemic coronavirus disease of 2019 (COVID-19). Like other pathogens, SARS-CoV-2 infection can elicit production of the type I and III interferon (IFN) cytokines by the innate immune response. A rapid and robust type I and III IFN response can curb viral replication and improve clinical outcomes of SARS-CoV-2 infection. To effectively replicate in the host, SARS-CoV-2 has evolved mechanisms for evasion of this innate immune response, which could also modulate COVID-19 pathogenesis. In this review, we discuss studies that have reported the identification and characterization of SARS-CoV-2 proteins that inhibit type I IFNs. We focus especially on the mechanisms of nsp1 and ORF6, which are the two most potent and best studied SARS-CoV-2 type I IFN inhibitors. We also discuss naturally occurring mutations in these SARS-CoV-2 IFN antagonists and the impact of these mutations in vitro and on clinical presentation. As SARS-CoV-2 continues to spread and evolve, researchers will have the opportunity to study natural mutations in IFN antagonists and assess their role in disease. Additional studies that look more closely at previously identified antagonists and newly arising mutants may inform future therapeutic interventions for COVID-19.

Invasive Pulmonary Aspergillosis and Tuberculosis Complicated by Hemophagocytic Lymphohistiocytosis - Sequelae of COVID-19 in a Liver Transplant Recipient.

Liver transplant recipients are at an increased risk of opportunistic infections due to the use of immunosuppression. Coronavirus disease of 2019 (COVID-19) increases the risk of these infections further due to associated immune dysfunction and the use of high-dose steroids. We present a case of a liver transplant recipient who developed disseminated tuberculosis and invasive pulmonary aspergillosis complicated by acquired hemophagocytic lymphohistiocytosis after recovering from severe COVID-19.

Herbal Immune Booster-Induced Liver Injury in the COVID-19 Pandemic - A Case Series.

The COVID-19 pandemic has resulted in widespread use of complementary and alternative medicines. Tinospora cordifolia is a widely grown shrub which has been commonly used in India's traditional system of Ayurveda for its immune booster properties and has been extensively used as prophylaxis against COVID-19. Six patients (4 women, 2 men) with a median (IQR) age of 55 years (45-56) and with an history of Tinospora cordifolia consumption presented with symptoms of acute hepatitis during the study period of 4 months in the COVID-19 pandemic. The median (IQR) duration of Tinospora cordifolia consumption was 90 days (21-210). The median (IQR) peak bilirubin and AST were 17.5 mg/dl (12.2-24.9) and 1350 IU/ml (1099-1773), respectively. The patients had either a definite (n = 4) or probable (n = 2) revised autoimmune hepatitis score with an autoimmune pattern of drug-induced liver injury on biopsy. Four of these patients (all women) had underlying silent chronic liver disease of possible autoimmune etiology associated with other autoimmune diseases - hypothyroidism and type 2 diabetes mellitus. One of the three patients treated with steroids decompensated on steroid tapering. The other five patients had resolution of symptoms, liver profile, and autoimmune serological markers on drug withdrawal/continuing steroid treatment. The median (IQR) time to resolution from discontinuing the herb was 86.5 days (53-111). Tinospora cordifolia consumption seems to induce an autoimmune-like hepatitis or unmask an underlying autoimmune chronic liver disease, which may support its immune stimulant mechanism. However, the same mechanism can cause significant liver toxicity, and we recommend that caution be exercised in the use of this herb, especially in those predisposed to autoimmune disorders. Besides, in patients presenting with acute hepatitis, even in the presence of autoimmune markers, a detailed complementary and alternative medicine history needs to be elicited.

Case report of familial COVID-19 cluster associated with High prevalence of anosmia, ageusia, and gastrointestinal symptoms.

BACKGROUND: Patients with COVID-19 most commonly report respiratory symptoms, with a minority reporting gastrointestinal (GI) symptoms in currently available reports. Additionally, little is known about the symptoms of anosmia/hyposmia, ageusia, and dysgeusia anecdotally seen in COVID-19 patients, which may potentially be considered both GI and sensory/neurological manifestations of infection. We hope to clarify the prevalence of these symptoms and patterns of transmission within a family cluster. CASE PRESENTATION: We interviewed 7 patients via oral inquiries and a questionnaire, collecting data on subject symptoms and their durations. Reverse transcriptase-polymerase chain reaction (RT-PCR) was used to confirm 2 of these cases. We report a familial cluster of 5 presumed and 2 confirmed COVID-19 cases, all of whom reported one or more GI symptoms and 5 of whom reported sensory symptoms of anosmia/hyposmia, ageusia/hypogeusia, and/or dysgeusia. CONCLUSIONS: This frequency of GI symptoms is high relative to currently available epidemiological reports, which also infrequently report on sensory symptoms. COVID-19 exhibits wide variation in duration, severity, and progression of symptoms, even within a familial cluster.

Collateral damage caused by COVID-19: Change in volume and spectrum of neurosurgery patients.

BACKGROUND: There has been a dramatic change in the pattern of patients being seen in hospitals and surgeries performed during the ongoing COVID-19 pandemic. The objective of this study is to study the change in the volume and spectrum of surgeries performed during the ongoing COVID-19 pandemic compared to pre-COVID-19 era. METHODS: Details of all patients who were operated under department of neurosurgery at our institute since the onset of COVID-19 pandemic in India were collected and compared to the same time period last year. The demographic profile, diagnosis, surgery performed, type of surgery (routine/emergency, cranial/spinal and major/minor) in these two groups were compared. They were further categorized into various categories [neuro-oncology (brain and spine tumors), neuro-trauma (head injury and spinal trauma), congenital cases, degenerative spine, neuro-vascular, CSF diversion procedures, etc.] and compared between the two groups. RESULTS: Our study showed a drastic fall (52.2%) in the number of surgeries performed during the pandemic compared to pre-COVID era. 11.3% of patients operated during COVID-19 pandemic were non-emergent surgeries compared to 57.7% earlier (p = 0.000). There was increase in proportion of minor cases from 28.8% to 41.5% (p = 0.106). The proportion of spinal cases decreased from 27.9% to 11.3% during the COVID-19 pandemic (p = 0.043). CONCLUSIONS: The drastic decrease in the number of surgeries performed will result in large backlog of patients waiting for 'elective' surgery. There is a risk of these patients presenting at a later stage with progressed disease and the best way forward would be to resume work with necessary precautions and universal effective COVID-19 testing.