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1.
Drug Deliv ; 29(1): 2296-2319, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2187331

ABSTRACT

The emerging cell membrane (CM)-camouflaged poly(lactide-co-glycolide) (PLGA) nanoparticles (NPs) (CM@PLGA NPs) have witnessed tremendous developments since coming to the limelight. Donning a novel membrane coat on traditional PLGA carriers enables combining the strengths of PLGA with cell-like behavior, including inherently interacting with the surrounding environment. Thereby, the in vivo defects of PLGA (such as drug leakage and poor specific distribution) can be overcome, its therapeutic potential can be amplified, and additional novel functions beyond drug delivery can be conferred. To elucidate the development and promote the clinical transformation of CM@PLGA NPs, the commonly used anucleate and eukaryotic CMs have been described first. Then, CM engineering strategies, such as genetic and nongenetic engineering methods and hybrid membrane technology, have been discussed. The reviewed CM engineering technologies are expected to enrich the functions of CM@PLGA for diverse therapeutic purposes. Third, this article highlights the therapeutic and diagnostic applications and action mechanisms of PLGA biomimetic systems for cancer, cardiovascular diseases, virus infection, and eye diseases. Finally, future expectations and challenges are spotlighted in the concept of translational medicine.


Subject(s)
Biomimetics , Nanoparticles , Cell Membrane , Drug Carriers
2.
Int J Environ Res Public Health ; 19(18)2022 Sep 13.
Article in English | MEDLINE | ID: covidwho-2032964

ABSTRACT

This research was carried out to quantify the duration from symptom onset to recovery/death (SOR/SOD) during the first four waves and the Alpha/Delta period of the epidemic in Khyber Pakhtunkhwa, Pakistan, and identify the associated factors. A total of 173,894 COVID-19 cases were admitted between 16 March 2020 and 30 November 2021, including 458 intensive care unit (ICU) cases. The results showed that the case fatality rate (CFR) increased with age, and females had a higher CFR. The median SOR of ICU cases was longer than that of non-ICU cases (27.6 vs. 17.0 days), while the median SOD was much shorter (6.9 vs. 8.4 days). The SOR and SOD in the Delta period were slightly shortened than the Alpha period. Age, cardiovascular diseases, chronic lung disease, diabetes, fever, breathing issues, and ICU admission were risk factors that were significantly associated with SOD (p < 0.001). A control measure, in-home quarantine, was found to be significantly associated with longer SOD (odds ratio = 9.49, p < 0.001). Infected vaccinated individuals had longer SOD than unvaccinated individuals, especially for cases that had received two vaccine doses (p < 0.001). Finally, an advice on getting full-dose vaccination is given specifically to individuals aged 20-59 years.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/epidemiology , Female , Humans , Pakistan/epidemiology , Retrospective Studies , Superoxide Dismutase , Vaccination
3.
J Clin Oncol ; 40(13): 1414-1427, 2022 05 01.
Article in English | MEDLINE | ID: covidwho-1883563

ABSTRACT

PURPOSE: To provide real-world evidence on risks and outcomes of breakthrough COVID-19 infections in vaccinated patients with cancer using the largest national cohort of COVID-19 cases and controls. METHODS: We used the National COVID Cohort Collaborative (N3C) to identify breakthrough infections between December 1, 2020, and May 31, 2021. We included patients partially or fully vaccinated with mRNA COVID-19 vaccines with no prior SARS-CoV-2 infection record. Risks for breakthrough infection and severe outcomes were analyzed using logistic regression. RESULTS: A total of 6,860 breakthrough cases were identified within the N3C-vaccinated population, among whom 1,460 (21.3%) were patients with cancer. Solid tumors and hematologic malignancies had significantly higher risks for breakthrough infection (odds ratios [ORs] = 1.12, 95% CI, 1.01 to 1.23 and 4.64, 95% CI, 3.98 to 5.38) and severe outcomes (ORs = 1.33, 95% CI, 1.09 to 1.62 and 1.45, 95% CI, 1.08 to 1.95) compared with noncancer patients, adjusting for age, sex, race/ethnicity, smoking status, vaccine type, and vaccination date. Compared with solid tumors, hematologic malignancies were at increased risk for breakthrough infections (adjusted OR ranged from 2.07 for lymphoma to 7.25 for lymphoid leukemia). Breakthrough risk was reduced after the second vaccine dose for all cancers (OR = 0.04; 95% CI, 0.04 to 0.05), and for Moderna's mRNA-1273 compared with Pfizer's BNT162b2 vaccine (OR = 0.66; 95% CI, 0.62 to 0.70), particularly in patients with multiple myeloma (OR = 0.35; 95% CI, 0.15 to 0.72). Medications with major immunosuppressive effects and bone marrow transplantation were strongly associated with breakthrough risk among the vaccinated population. CONCLUSION: Real-world evidence shows that patients with cancer, especially hematologic malignancies, are at higher risk for developing breakthrough infections and severe outcomes. Patients with vaccination were at markedly decreased risk for breakthrough infections. Further work is needed to assess boosters and new SARS-CoV-2 variants.


Subject(s)
COVID-19 , Hematologic Neoplasms , BNT162 Vaccine , COVID-19/complications , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19 Vaccines/adverse effects , Hematologic Neoplasms/complications , Hematologic Neoplasms/epidemiology , Hematologic Neoplasms/therapy , Humans , SARS-CoV-2
4.
J Clin Oncol ; 39(35): 3997-3998, 2021 12 10.
Article in English | MEDLINE | ID: covidwho-1581947
5.
J Clin Oncol ; 39(20): 2232-2246, 2021 07 10.
Article in English | MEDLINE | ID: covidwho-1484813

ABSTRACT

PURPOSE: Variation in risk of adverse clinical outcomes in patients with cancer and COVID-19 has been reported from relatively small cohorts. The NCATS' National COVID Cohort Collaborative (N3C) is a centralized data resource representing the largest multicenter cohort of COVID-19 cases and controls nationwide. We aimed to construct and characterize the cancer cohort within N3C and identify risk factors for all-cause mortality from COVID-19. METHODS: We used 4,382,085 patients from 50 US medical centers to construct a cohort of patients with cancer. We restricted analyses to adults ≥ 18 years old with a COVID-19-positive or COVID-19-negative diagnosis between January 1, 2020, and March 25, 2021. We followed N3C selection of an index encounter per patient for analyses. All analyses were performed in the N3C Data Enclave Palantir platform. RESULTS: A total of 398,579 adult patients with cancer were identified from the N3C cohort; 63,413 (15.9%) were COVID-19-positive. Most common represented cancers were skin (13.8%), breast (13.7%), prostate (10.6%), hematologic (10.5%), and GI cancers (10%). COVID-19 positivity was significantly associated with increased risk of all-cause mortality (hazard ratio, 1.20; 95% CI, 1.15 to 1.24). Among COVID-19-positive patients, age ≥ 65 years, male gender, Southern or Western US residence, an adjusted Charlson Comorbidity Index score ≥ 4, hematologic malignancy, multitumor sites, and recent cytotoxic therapy were associated with increased risk of all-cause mortality. Patients who received recent immunotherapies or targeted therapies did not have higher risk of overall mortality. CONCLUSION: Using N3C, we assembled the largest nationally representative cohort of patients with cancer and COVID-19 to date. We identified demographic and clinical factors associated with increased all-cause mortality in patients with cancer. Full characterization of the cohort will provide further insights into the effects of COVID-19 on cancer outcomes and the ability to continue specific cancer treatments.


Subject(s)
COVID-19/therapy , Neoplasms/mortality , Adolescent , Adult , Aged , COVID-19/diagnosis , COVID-19/mortality , Case-Control Studies , Cause of Death , Electronic Health Records , Female , Humans , Male , Middle Aged , Neoplasms/diagnosis , Neoplasms/therapy , Prognosis , Registries , Risk Assessment , Risk Factors , Time Factors , United States , Young Adult
6.
Euro Surveill ; 25(40)2020 10.
Article in English | MEDLINE | ID: covidwho-841040

ABSTRACT

BackgroundThe natural history of disease in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remained obscure during the early pandemic.AimOur objective was to estimate epidemiological parameters of coronavirus disease (COVID-19) and assess the relative infectivity of the incubation period.MethodsWe estimated the distributions of four epidemiological parameters of SARS-CoV-2 transmission using a large database of COVID-19 cases and potential transmission pairs of cases, and assessed their heterogeneity by demographics, epidemic phase and geographical region. We further calculated the time of peak infectivity and quantified the proportion of secondary infections during the incubation period.ResultsThe median incubation period was 7.2 (95% confidence interval (CI): 6.9‒7.5) days. The median serial and generation intervals were similar, 4.7 (95% CI: 4.2‒5.3) and 4.6 (95% CI: 4.2‒5.1) days, respectively. Paediatric cases < 18 years had a longer incubation period than adult age groups (p = 0.007). The median incubation period increased from 4.4 days before 25 January to 11.5 days after 31 January (p < 0.001), whereas the median serial (generation) interval contracted from 5.9 (4.8) days before 25 January to 3.4 (3.7) days after. The median time from symptom onset to discharge was also shortened from 18.3 before 22 January to 14.1 days after. Peak infectivity occurred 1 day before symptom onset on average, and the incubation period accounted for 70% of transmission.ConclusionThe high infectivity during the incubation period led to short generation and serial intervals, necessitating aggressive control measures such as early case finding and quarantine of close contacts.


Subject(s)
Coronavirus Infections/transmission , Coronavirus/pathogenicity , Infectious Disease Incubation Period , Pneumonia, Viral/transmission , Adolescent , Adult , Age Distribution , Aged , Aged, 80 and over , Betacoronavirus , COVID-19 , Child , Child, Preschool , China/epidemiology , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Epidemiologic Studies , Female , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , SARS-CoV-2 , Young Adult
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