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Journal of Drug Delivery and Therapeutics ; 12(4-s):101-111, 2022.
Article in English | CAB Abstracts | ID: covidwho-2056786


In-silico Computer-Aided Drug Design (CADD) often comprehends virtual screening (VS) of datasets of natural pharmaco-active compounds for drug discovery protocols. Plant Based Natural Products (PBNPs) still, remains to be a prime source of pharmaco-active compounds due to their unique chemical structural scaffolds and functionalities with distinct chemical characteristic feature from natural source that are much acquiescent to drug metabolism and kinetics. In the Post-COVID-Era number of publications pertaining to PBNPs and publicly accessible plant based natural product databases (PBNPDBs) has significantly increased. Moreover, PBNPs are important sources of inspiration or starting points to develop novel therapeutic agents. However, a well-structured, indepth ADME/Tox profile of PBNPs has been limited or lacking for many of such compounds, this hampers the successful exploitation of PBNPs by pharma industries. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties play key roles in the discovery/ development of drugs, pesticides, food additives, consumer products, and industrial chemicals. In the present study, ADMET-informatics of Tetradecanoic Acid (Myristic Acid) from ethyl acetate fraction of Moringa oleifera leaves to predict drug metabolism and pharmacokinetics (DMPK) outcomes has been taken up. This work contributes to the deeper understanding of Myristic acid as major source of drug from commonly available medicinal plant - Moringa oleifera with immense therapeutic potential. The data generated herein could be useful for NP based lead generation programs.

International Journal of Performability Engineering ; 18(8):598, 2022.
Article in English | ProQuest Central | ID: covidwho-2026292


The increasing and global spread of Coronavirus (COVID-19) has made facemasks imperative and valuable. It established new norms to our way of life with regulations that are necessary for survival. This study portrays the methodological significance of image processing using Deep Learning: MobileNet-v2 cascade for detection of the masked face and spawning face embedding. It achieves the best results for larger datasets as MobileNet-V2 is a convolutional semantic network with a depth of about 53 layers, meanwhile, the application of similar methods on smaller datasets proves challenging. This paper paves a path of exploring detection on the basis of the Single Shot Detector (SSD) algorithm that introduces a channel attention mechanism to improve the ability of the model to express salient features while simultaneously utilizing information of different feature levels optimizing the function loss. It also sheds light on the resultant output, which creates a large chunk of data categorized as big data. The algorithm shows final experimental results predicting the goal of face recognition and mask detention as successful and effective with an accuracy of the results ranging between 90-95%.

Blood ; 138:3891, 2021.
Article in English | EMBASE | ID: covidwho-1582255


BACKGROUND Cellular therapies (allogeneic hematopoietic cell transplantation, allo-HCT, autologous hematopoietic cell transplantation, auto-HCT, and chimeric antigen receptor T cell therapy, CAR T) render patients severely immunocompromised for extended periods post-therapy. Emerging data suggest reduced immune responses to COVID-19 vaccines among patients with hematologic malignancies, but data for cellular therapy recipients are sparse. We therefore assessed immune responses to mRNA COVID-19 vaccines among patients who underwent cellular therapies at our center to identify predictors of response. PATIENT AND METHODS In this observational prospective study, anti-SARS-CoV-2 spike IgG antibody titers and circulating neutralizing antibodies were measured at 1 and 3 months after the 1 st dose of vaccination. CD4, CD19, mitogen, and IgG levels from patient samples collected prior to initiation of vaccination in a subset of patients were used to assess immune recovery and association with response. A concurrent healthy donor (HD) cohort provided control response rates. RESULTS Allo-HCT (N=149), auto HCT (N=61), and CAR T (N=7) patients vaccinated between 12/22/2020- 2/28/2021 with mRNA vaccines and 69 HD participated in this study. At 3 months, 188 pts (87%) had a positive anti-SARS-CoV-2 spike IgG levels (median 5,379 AU/mL, IQR 451-15,750), and 139 (77%) had a positive neutralization Ab assay (median 93%, IQR 36-96%). All HD (100%) had a positive anti-SARS-CoV-2 spike IgG and a positive neutralization Ab assay with median levels of 8,011 AU/mL (IQR 4573-11,159) and 96% (IQR 78- 96%), respectively. Time from vaccination to cellular therapy was associated with response;67% of patients vaccinated in the first 12 months post-cellular therapy (N=42) mounted a serologic response, compared with patients vaccinated between 12-24 (89%) (N=45), 24-36 (91%) (N=32) and >36 (93%) (N=98) months post-treatment, p= 0.001 (figure 1). Patients with immune parameters below the recommended threshold for vaccinations post-cellular therapies were also less likely to mount a response (figure 2): CD4+ T-cell count < 200 vs >200 cells/μL, 66% vs 87% (p=0.012);CD19+ B-cell count <50 vs >50 cells/μL;33% vs 95% (p<0.001), phytohemagglutinin mitogen response <40% vs >40%, 42% vs 89% (p<0.001), and IgG <500 vs >500 mg/dl, 71% vs 91% (p=0.003). Patient age, gender, prior COVID-19 infection, treatment with IVIG, and type of mRNA COVID-19 vaccine were not associated with the likelihood of serologic response. CONCLUSION This largest cohort to date, demonstrates that COVID-19 vaccine responses of cellular therapy recipients are reduced compared to healthy control and response varies based on time interval from cellular therapy and immune function at the time of vaccination, underscoring the importance of monitoring immune status parameters, as well as qualitative measures (neutralizing Ab) of vaccine response, in informing clinical decisions, including the indication for booster vaccines. [Formula presented] Disclosures: Politikos: Merck: Research Funding;ExcellThera, Inc: Other: Member of DSMB - Uncompensated. Vardhana: Immunai: Membership on an entity's Board of Directors or advisory committees. Perales: Equilium: Honoraria;Cidara: Honoraria;Sellas Life Sciences: Honoraria;Miltenyi Biotec: Honoraria, Other;Celgene: Honoraria;MorphoSys: Honoraria;Takeda: Honoraria;Incyte: Honoraria, Other;Karyopharm: Honoraria;Kite/Gilead: Honoraria, Other;Merck: Honoraria;NexImmune: Honoraria;Novartis: Honoraria, Other;Medigene: Honoraria;Omeros: Honoraria;Servier: Honoraria;Bristol-Myers Squibb: Honoraria;Nektar Therapeutics: Honoraria, Other. Shah: Amgen: Research Funding;Janssen Pharmaceutica: Research Funding.