Prophylactic Corticosteroid Use with Axicabtagene Ciloleucel (Axi-Cel) in Patients (Pts) with Relapsed/ Refractory Large B-Cell Lymphoma (R/R LBCL): 2-Year Follow-up of Zuma-1 Cohort 6

Background: The ZUMA-1 safety management Cohort 6 (N=40), which evaluated whether prophylactic corticosteroids and earlier corticosteroids and/or tocilizumab could improve safety outcomes, demonstrated an improved safety profile (no Grade >=3 cytokine release syndrome [CRS];15% Grade >=3 neurologic events [NEs]) vs pivotal Cohorts 1+2, without compromising response rate or durability (95% ORR, 80% CR rate, and 53% ongoing response rate with >=1 y of follow-up;Oluwole, et al. ASH 2021. 2832). Here, 2-y updated outcomes are reported. Method(s): Eligible pts with R/R LBCL underwent leukapheresis (followed by optional bridging therapy) and conditioning chemotherapy, then a single axi-cel infusion. Pts received corticosteroid prophylaxis (once-daily oral dexamethasone 10 mg on Days 0 [before axi-cel], 1, and 2) and earlier corticosteroids and/or tocilizumab for CRS and NE management vs Cohorts 1+2 (Oluwole, et al. Br J Haematol. 2021). The primary endpoints were incidence and severity of CRS and NEs. Secondary endpoints included ORR (investigator-assessed), duration of response (DOR), progression-free survival (PFS), overall survival (OS), and chimeric antigen receptor (CAR) T-cell levels in blood. Result(s): As of December 16, 2021, the median follow-up time for the 40 treated pts was 26.9 mo. Since the 1-y analysis, no new CRS events were reported (no pts had Grade >=3 CRS to date). The incidence of Grade >=3 NEs increased from 15% to 18%between the 1-y and 2-y analyses. Two new NEs occurred in 2 pts: 1 pt had Grade 2 dementia (onset on Day 685 and ongoing at time of data cutoff;not related to axi-cel) and 1 had Grade 5 axi-cel-related leukoencephalopathy. Since the 1-y analysis, 6 new infections were reported (Grades 1, 2, and 5 COVID-19 [n=1 each], Grade 3 Pneumocystis jirovecii pneumonia [n=1], Grade 3 unknown infectious episode with inflammatory syndrome [n=1], and Grade 2 herpes zoster [n=1]). In total, 8 deaths occurred since the 1-y analysis (progressive disease [n=5], leukoencephalopathy [n=1], and COVID-19 [n=2]). The ORR was 95% (80% CR), which was unchanged from the 1-y analysis. Median DOR and PFS were since reached (25.9 mo [95% CI, 7.8-not estimable] and 26.8 mo [95% CI, 8.7-not estimable], respectively). Median OS was still not reached. Kaplan- Meier estimates of the 2-y DOR, PFS, and OS rates were 53%, 53%, and 62%, respectively. Of 18 pts (45%) in ongoing response at data cutoff, all achieved CR as the best response. By Month 24, 14/20 pts with evaluable samples (70%) had detectable CAR T cells (vs 23/36 pts [64%] in Cohorts 1+2). Conclusion(s): With 2 y of follow-up, the ZUMA-1 Cohort 6 toxicity management strategy continued to demonstrate an improved long-term safety profile of axi-cel in pts with R/R LBCL. Further, responses remained high, durable, and similar to those observed in Cohorts 1+2 (Locke, et al. Lancet Oncol. 2019).Copyright © 2023 American Society for Transplantation and Cellular Therapy

Lightweight Cost Effective Deep Learning Model for COVID-19 Detection using CXR Images

In the field of medical imaging, deep learning techniques have already proven to be quite a success.The global population's health and well being continue to be severely impacted by the Coronavirus Disease 2019 (COVID-19) pandemic, healthcare systems are unable to examine and diagnose patients as soon as they ought to. The various post COVID complications which may have been dealt in a better way if the virus was detected at an earlier stage and given appropriate clinical support. Chest radiography imaging is essential for detecting and tracking COVID-19 because of its effects on pulmonary tissues. Chest X-Ray(CXR) imaging is even more readily available than chest computed tomography(CT) imaging, especially in developing countries where CT scanners are too costly due to high equipment and maintenance costs. In this work we propose a very lightweight convolutional neural network (CNN), in which the chest X-Ray samples comprising of COVID-19, Non-COVID and Normal cases are analyzed without any human intervention. Our model gives comparable accuracy to other COVID-19 detection models proposed earlier while having significantly fewer parameters than them, which makes our model optimal for deployment on machines with low computing power. © 2022 IEEE.

Covid-19 Pandemic and its Impact on Orthodontic Patients in Lahore, Pakistan

Objectives: To determine the effect COVID-19 pandemic had on orthodontic patients during the lockdown phase as well as to determine the knowledge of patients regarding the COID-19 disease, its spread, its symptoms, and its prevention. Methodology: This was a cross-sectional descriptive study. It was carried out by distributing a self-administered questionnaire to 300 patients undergoing orthodontic treatment via electronic mail and whatsapp platfortm. The questionnaire assessed the knowledge of the patient regarding the COVID-19 disease, the impact it had on their orthodontic treatment, and their perception of risk and their attitude towards the COVID-19 disease. Result(s): 274 out of 300 patients responded to our questionnaires;Out of them 73% were females and 47.3% were males. A large number of patients were able to correctly identify the cause, spread, symptoms, and necessary measures needed to be taken to contain the disease. Although majority of the patients considered COVID-19 to be severely dangerous and felt highly vulnerable to the disease;54.5% were still willing to continue their treatment during the pandemic. The main reason for their willingness was the fear of increased missed appointments leading to increase in treatment time and cost. Conclusion(s): The pandemic has had an adverse impact on orthodontic treatment regardless. Nevertheless, patients were still inclined to continue their treatment with appropriate infection control and precautionary measures observed both by the patients themselves and the orthodontic clinical staff. Copyright © 2022 Lahore Medical And Dental College. All rights reserved.

Colchicine anti-inflammatory therapy for non-intensive care unit hospitalized COVID-19 patients: results from a pilot open-label, randomized controlled clinical trial.

Systemic inflammation is a hallmark of severe coronavirus disease-19 (COVID-19). Anti-inflammatory therapy is considered crucial to modulate the hyperinflammatory response (cytokine storm) in hospitalized COVID-19 patients. There is currently no specific, conclusively proven, cost-efficient, and worldwide available anti-inflammatory therapy available to treat COVID-19 patients with cytokine storm. The present study aimed to investigate the treatment benefit of oral colchicine for hospitalized COVID-19 patients with suspected cytokine storm. Colchicine is an approved drug and possesses multiple anti-inflammatory mechanisms. This was a pilot, open-label randomized controlled clinical trial comparing standard of care (SOC) plus oral colchicine (colchicine arm) vs. SOC alone (control arm) in non-ICU hospitalized COVID-19 patients with suspected cytokine storm. Colchicine treatment was initiated within first 48 hours of admission delivered at 1.5 mg loading dose, followed by 0.5 mg b.i.d. for next 6 days and 0.5 mg q.d. for the second week. A total of 96 patients were randomly allocated to the colchicine (n=48) and control groups (n=48). Both colchicine and control group patients experienced similar clinical outcomes by day 14 of hospitalization. Treatment outcome by day 14 in colchicine vs control arm: recovered and discharged alive: 36 (75.0%) vs. 37 (77.1%), remain admitted after 14-days: 4 (8.3%) vs. 5 (10.4%), ICU transferred: 4 (8.3%) vs. 3 (6.3%), and mortality: 4 (8.3%) vs. 3 (6.3%). The speed of improvement of COVID-19 acute symptoms including shortness of breath, fever, cough, the need of supplementary oxygen, and oxygen saturation level, was almost identical in the two groups. Length of hospitalization was on average 1.5 day shorter in the colchicine group. There was no evidence for a difference between the two groups in the follow-up serum levels of inflammatory biomarkers including C-reactive protein (CRP), D-dimer, lactate dehydrogenase (LDH), ferritin, interleukin-6 (IL-6), high-sensitivity troponin T (hs-TnT) and N-terminal pro b-type natriuretic peptide (NT pro-BNP). According to the results of our study, oral colchicine does not appear to show clinical benefits in non-ICU hospitalized COVID-19 patients with suspected cytokine storm. It is possible that the anti-inflammatory pathways of colchicine are not crucially involved in the pathogenesis of COVID-19.

Screening and Structure-Activity Relationship of Potential Compounds against Proposed Targets of COVID-19 Infection

Background: With reference to COVID-19 pandemic prevailing across the globe, chloroquine and hydroxychloroquine were reported as effective against the disease to some extent. This effectiveness can be attributed to the glycosylation interruption of the Angiotensin-converting enzyme 2 (ACE2) recep-tor, which is a known target for SARS-CoV-2 entery. On the other hand, studies suggest that the inhibition of ACE2 can be lethal in certain cases, thereby causing cardiovascular disorders, especially in patients already suffering from heart-related diseases. Methods: In this study, the most probable targets (other than ACE2) have been proposed for the treatment of COVID-19 infection by taking chloroquine and hydroxychloroquine as reference drugs. Swiss Tar-getPrediction and PASSonline tools were used in order to achieve this objective. Known drugs against each target possessing close relation to either viral infections or lung disorders were assessed from the DrugBank database, and simultaneous efficacy of these drugs towards other proposed targets has been analyzed. By taking the most effective drugs as a reference, similar compounds were screened from the ChEMBL library by using the Swiss Similarity tool. Finally, molecular docking studies were performed through MOE software by using screened compounds against proposed targets. Results: Four most probable targets have been proposed, which include chemokine receptors (CCRs), dipeptidyl peptidase 4 (DPP4), muscarinic acetylcholine receptors (CHRMs), and histamine N-methyltransferase (HNMT). Furthermore, it has been evaluated that quinacrine and vildagliptin are effective against most of the proposed targets. By taking vildagliptin as well as quinacrine as reference drugs, further eight compounds with similar effectiveness against these targets have been screened from the ChEMBL library. Molecular docking studies with CCR5, DPP4, and CHRM5 suggest that the quinacrine and its analogue (ChEMBL1782742) as well as vildagliptin and its analogue (ChEMBL511785) are the most suitable compounds as HITs for these targets. Conclusion: It has been established that the quinacrine, ChEMBL1782742, vildagliptin, ChEMBL511785, mavorixafor, atropine, and N-(2-aminoethyl)-1-aziridineethanamine in descending order can be considered as effective drugs for the treatment of COVID-19 infection.

COMPARISON OF SARS-COV2 GENOME SEQUENCES FROM PAKISTAN WITH GENOME SEQUENCES FROM OTHER COUNTRIES

OBJECTIVE: To analyze SARS-CoV-2 whole genome reported of Pakistan and compare them with other viral strains collected from other world region to better understand the origin and genetic characterization of the virus. METHODS: All the available genomic information of SARS-CoV-2 including Pakistani strains were collected from various online sources. Phylogenetic analysis of 131 sequences from 11 countries (Brazil, China, India, Italy, Nepal, Pakistan, Spain, Sweden, Taiwan, USA and Viet-Nam) were performed and compared with other related coronaviruses to find the evolution of virus and its origin. Individual SARS-CoV-2 gene, spike (S) glycoprotein and the receptor binding domain (RBD) with closely related coronaviruses were compared to further explore genetic variations and the likely RBD properties of the virus. RESULTS: The analysis shows that genome of all analyzed 131 SARS-CoV-2 strains collected from different geographical area were extremely similar, exhibiting >99% sequence identity. Notably, genome of the SARS-CoV-2 has high similarity (89.1% sequence identity) with the two bat-derived severe acute respiratory syndrome (SARS) like betacoronaviruses, bat-SL-CoVZC45 and bat-SL-CoVZXC21 but was sufficiently divergent from SARS-CoV (82.3% similarity) and MERS-CoV (50% similarity). Phylogenetic analysis shows that the SARS-CoV-2 has relatively similar spike glycoprotein with bat-SL-CoVZC45, however, the RBD was more like that of SARS-CoVGZ02. CONCLUSION: Using different bioinformatics tools, we determined that SARS-CoV-2 has high similarities to bat-derived SARS like betacoronaviruses than SARS-CoV at the whole genome level, however, the RBD was more like that of SARS-CoVGZ02, which shows that they use similar ACE2 as a cell receptor.

Assessing the impact of COVID-19 on corporate investment behavior

The current wave of COVID-19 outbreak has created new strategical challenges for policy officials of the industrial sector across the world. The effect of COVID-19 is more in developing economies where industrial sector is already struggling for its stability. This study introduces the impact of COVID-19 on the corporate investment behavior of non-financial publicly listed firms of Pakistan. To achieve the objective, we employ the panel data ranging from 2010 to 2020 and apply the difference-in-differences (DID) model to quantifies the empirical relationship. The outcomes of DID model suggest that the pandemic period and treatment have a significant and negative impact on corporate capital investment behavior. During pandemic spread period, the enterprises have limited their investment into fixed assets due to less productive use of such assets. Similarly, industries that exist in high-impact areas face a negative investment growth rate due to quarantine policy, fewer social movements, and high installing cost of new machinery. However, this negative effect diminishes across those firms that have a quick cash inflow rate and more availability of bank loans. These two factors serve as a financial setback against the adversities of pandemic. By drawing upon the empirical reasoning on the effect of COVID-19, this study also presents possible solutions to alienate unfavorable impacts of this pandemic. Current analysis can be considered as an early attempt towards investigating the consequences of COVID-19 on investment decisions of industrial sector. © 2021 by the authors. Licensee ESJ, Italy.

Isaric 4c Mortality Score As A Predictor Of In-Hospital Mortality In Covid-19 Patients Admitted In Ayub Teaching Hospital During First Wave Of The Pandemic

BACKGROUND: Many factors have been identified which can predict severe outcomes and mortality in hospitalized patients of COVID-19. This study was conducted with the objective of finding out the association of various clinical and laboratory parameters as used by International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO)- ISARIC/WHO 4C Mortality score in predicting high risk patients of COVID-19. Ascertaining the parameters would help in triage of patients of severe disease at the outset, and shall prove beneficial in improving the standard of care. METHODS: This cross-sectional study was carried out in COVID-19 Department of Ayub Teaching Hospital, Abbottabad. All COVID-19 patients admitted from 15th April to 15th July 2020 were included. RESULTS: A total of 347 patients were included in the study. The mean age was 56.46±15.44 years. Male patients were 225 (65%) and female 122 (35%). Diabetes (36%) was the most common co-morbidity, followed by hypertension (30.8%). Two hundred & six (63.8%) patients recovered and 117 (36.2%) patients died. Shortness of breath (80%), fever (79%) and cough (65%) were the most common presenting symptoms. Patients admitted with a 4C Mortality score of 0-3 (Low Risk Category), the patients who recovered were 36 (90%) and those who died were 4 (10.0%). In patients admitted with a 4C Mortality score of more than 14 (Very High-Risk Category), the number of patients who recovered was 1 (20%), and those who died were 4 (80%). The difference in mortality among the categories was statistically significant (p<0.001). Hypertension was a risk factor for death in patients of COVID-19 (Odds ratio=1.24, 95% CI [0.76-2.01]). Lymphopenia was not associated with statistically significant increased risk for mortality. CONCLUSIONS: The ISARIC 4C mortality score can be used for stratifying and predicting mortality in COVID-19 patients on arrival in hospital. We propose that it should be used in every patient of COVID-19 presenting to the hospital. Those falling in Low and Intermediate Risk Category should be managed in ward level. Those falling in High and Very High Category should be admitted in HDU/ICU with aggressive treatment from the start.

Clinical Characteristics, Mortality and Associated risk factors in COVID-19 patients reported in ten major hospitals of Khyber Pakhtunkhwa, Pakistan

Background: COVID-19 is an ongoing public health issue across the world. Several risk factors associated with mortality in COVID-19 have been reported. The present study aims to describe clinical and epidemiological characteristics and predictors of mortality in hospitalized patients from Khyber Pakhtunkhwa, a province in Pakistan with highest COVID-19 associated case fatality rate. Methods: This multicentre, retrospective study was conducted in hospitalized COVID-19 patients who died or discharged alive until 1st May 2020. Data about sociodemographic characteristics, clinical and laboratory findings, treatment and outcome were obtained from hospital records and compared between survivors and non-survivors. Statistical tests were applied to determine the risk factors associated with mortality in hospitalized patients. Results: Of the total 179 patients from the 10 designated hospitals, 127 (70.9%) were discharged alive while 52 (29.1%) died in the hospital. Overall, 109 (60.9%) patients had an underlying comorbidity with hypertension being the commonest. Multivariate logistics regression analysis showed significantly higher odds of in-hospital death from COVID-19 in patients with multiple morbidities (OR 3.2, 95% CI 1.1, 9.1, p-value=0.03), length of hospital stay (OR 0.8, 95% CI 0.7, 0.9, p-value <0.001), those presenting with dyspnoea (OR 4.0, 95% CI 1.1, 14.0, p-value=0.03) and oxygen saturation below 90 (OR 9.6, 95% CI: 3.1, 29.2, p-value <0.001). Conclusion: Comorbidity, oxygen saturation and dyspnoea on arrival and length of stay in hospital (late admission) are associated with COVID-19 mortality. The demographic, clinical and lab characteristics could potentially help clinician and policy makers before potential second wave in the country.