Immunogenicity,effectiveness and safety of COVID-19 vaccine in older adults living in nursing homes: A real-life study

IntroductionBNT162b2 (BioNTech and Pfizer) is a nucleoside-modified mRNA vaccine that provides protection against SARS-CoV-2 infection and is generally well tolerated. However, data about its efficacy, immunogenicity and safety in people of old age or with underlying chronic conditions are scarce.PurposeTo describe BNT162b2 (BioNTech and Pfizer) COVID-19 vaccine immunogenicity, effectiveness and reactogenicity after complete vaccination (two doses), and immunogenicity and reactogenicity after one booster, in elders residing in nursing homes (NH) and healthy NH workers in real-life conditions.MethodsObservational, ambispective, multicenter study. Older adults and health workers were recruited from three nursing homes of a private hospital corporation located in three Spanish cities. The primary vaccination was carried out between January and March 2021. The follow-up was 13 months. Humoral immunity, adverse events, SARS-CoV-2 infections, hospitalizations and deaths were evaluated. Cellular immunity was assessed in a participant subset.ResultsA total of 181 residents (mean age 84.1 years; 89.9% females, Charlson index ≥2: 45%) and 148 members of staff (mean age 45.2 years; 70.2% females) were surveyed (n:329). After primary vaccination of 327 participants, vaccine response in both groups was similar; ≈70% of participants, regardless of the group, had an antibody titer above the cut-off considered currently protective (260 BAU/ml). This proportion increased significantly to ≈ 98% after the booster (p < 0.0001 in both groups). Immunogenicity was largely determined by a prior history of COVID-19 infection. Twenty residents and 3 workers were tested for cellular immunity. There was evidence of cellular immunity after primary vaccination and after booster. During the study, one resident was hospitalized for SARS-CoV-2. No SARS-CoV-2-related deaths were reported and most adverse events were mild.ConclusionsOur results suggest that the BNT162b2 mRNA COVID-19 vaccine is immunogenic, effective and safe in elderly NH residents with underlying chronic conditions.     

湖南郴州发现岭南两头蛇

2022年8月,于湖南省郴州市永兴县湘阴渡镇（113°01′18′′E,26°11′87′′N,海拔174 m）采集到两头蛇活体标本1号,通过形态测量比较,发现所采集标本与中国广东省分布的岭南两头蛇（Calamariaarcana）形态特征基本一致;后基于线粒体Cytb基因片段进行分子系统发育分析,结果显示该标本与岭南两头蛇形成高支持率单系群（后验概率PP为1.00,自举值BS为100）,所采集标本与岭南两头蛇的遗传距离为1%。综合形态学和分子生物学证据,最终确定该标本为两头蛇科（Calamariidae）两头蛇属的岭南两头蛇,系湖南省蛇类分布新记录种。     

Enhancing estimation methods for integrating probability and nonprobability survey samples with machine-learning techniques. An application to a Survey on the impact of the COVID-19 pandemic in Spain

Web surveys have replaced Face-to-Face and computer assisted telephone interviewing (CATI) as the main mode of data collection in most countries. This trend was reinforced as a consequence of COVID-19 pandemic-related restrictions. However, this mode still faces significant limitations in obtaining probability-based samples of the general population. For this reason, most web surveys rely on nonprobability survey designs. Whereas probability-based designs continue to be the gold standard in survey sampling, nonprobability web surveys may still prove useful in some situations. For instance, when small subpopulations are the group under study and probability sampling is unlikely to meet sample size requirements, complementing a small probability sample with a larger nonprobability one may improve the efficiency of the estimates. Nonprobability samples may also be designed as a mean for compensating for known biases in probability-based web survey samples by purposely targeting respondent profiles that tend to be underrepresented in these surveys. This is the case in the Survey on the impact of the COVID-19 pandemic in Spain (ESPACOV) that motivates this paper. In this paper, we propose a methodology for combining probability and nonprobability web-based survey samples with the help of machine-learning techniques. We then assess the efficiency of the resulting estimates by comparing them with other strategies that have been used before. Our simulation study and the application of the proposed estimation method to the second wave of the ESPACOV Survey allow us to conclude that this is the best option for reducing the biases observed in our data.     

A mechanistic spatio-temporal modeling of COVID-19 data

Understanding the evolution of an epidemic is essential to implement timely and efficient preventive measures. The availability of epidemiological data at a fine spatio-temporal scale is both novel and highly useful in this regard. Indeed, having geocoded data at the case level opens the door to analyze the spread of the disease on an individual basis, allowing the detection of specific outbreaks or, in general, of some interactions between cases that are not observable if aggregated data are used. Point processes are the natural tool to perform such analyses. We analyze a spatio-temporal point pattern of Coronavirus disease 2019 (COVID-19) cases detected in Valencia (Spain) during the first 11 months (February 2020 to January 2021) of the pandemic. In particular, we propose a mechanistic spatio-temporal model for the first-order intensity function of the point process. This model includes separate estimates of the overall temporal and spatial intensities of the model and a spatio-temporal interaction term. For the latter, while similar studies have considered different forms of this term solely based on the physical distances between the events, we have also incorporated mobility data to better capture the characteristics of human populations. The results suggest that there has only been a mild level of spatio-temporal interaction between cases in the study area, which to a large extent corresponds to people living in the same residential location. Extending our proposed model to larger areas could help us gain knowledge on the propagation of COVID-19 across cities with high mobility levels.     

Japanese cancer screening programs during the COVID-19 pandemic: Changes in participation between 2017-2020

BackgroundThe impact of the coronavirus disease 2019 (COVID-19) pandemic on cancer screening participation is a global concern. A national database of screening performance is available in Japan for population-based cancer screening, estimated to cover approximately half of all cancer screenings.MethodsUtilizing the fiscal year (FY) 2017–2020 national database, the number of participants in screenings for gastric cancer (upper gastrointestinal [UGI] series or endoscopy), colorectal cancer (fecal occult blood test), lung cancer (chest X-ray), breast cancer (mammography), and cervical cancer (Pap smear) were identified. The percent change in the number of participants was calculated.ResultsCompared with the pre-pandemic period (FY 2017–2019), in percentage terms FY 2020 recorded the largest decline in gastric cancer UGI series (2.82 million to 1.91 million, percent change was −32.2 %), followed by screening for breast cancer (3.10 million to 2.57 million, percent change was −17.2 %), lung cancer (7.92 million to 6.59 million, percent change was −16.7 %), colorectal cancer (8.42 million to 7.30 million, percent change was −13.4 %), cervical cancer (4.26 million to 3.77 million, percent change was −11.6 %), and gastric cancer via endoscopy (1.02 million to 0.93 million, percent change was −9.0 %).ConclusionThe number of participants in population-based screenings in Japan decreased by approximately 10–30 % during the pandemic. The impact of these declines on cancer detection or mortality should be carefully monitored.     

Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models

ObjectivesWhen the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission.Material and methodsThe sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set.ResultsIn this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%.ConclusionThe findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.     

COVID-19 plasma proteome reveals novel temporal and cell-specific signatures for disease severity and high-precision disease management

Coronavirus disease 2019 (COVID-19) is a systemic inflammatory condition with high mortality that may benefit from personalized medicine and high-precision approaches. COVID-19 patient plasma was analysed with targeted proteomics of 1161 proteins. Patients were monitored from Days 1 to 10 of their intensive care unit (ICU) stay. Age- and gender-matched COVID-19-negative sepsis ICU patients and healthy subjects were examined as controls. Proteomic data were resolved using both cell-specific annotation and deep-analysis for functional enrichment. COVID-19 caused extensive remodelling of the plasma microenvironment associated with a relative immunosuppressive milieu between ICU Days 3–7, and characterized by extensive organ damage. COVID-19 resulted in (1) reduced antigen presentation and B/T-cell function, (2) increased repurposed neutrophils and M1-type macrophages, (3) relatively immature or disrupted endothelia and fibroblasts with a defined secretome, and (4) reactive myeloid lines. Extracellular matrix changes identified in COVID-19 plasma could represent impaired immune cell homing and programmed cell death. The major functional modules disrupted in COVID-19 were exaggerated in patients with fatal outcome. Taken together, these findings provide systems-level insight into the mechanisms of COVID-19 inflammation and identify potential prognostic biomarkers. Therapeutic strategies could be tailored to the immune response of severely ill patients.     

Two New C19-Diterpenoid Alkaloids from Delphinium shawurense

Shawurenine C ( 1a ) and D ( 1b ), a new pair of regioisomeric C₁₉-diterpenoid alkaloids, and five known C₁₉-diterpenoid alkaloids ( 2 – 6 ) were isolated from the aerial part of Delphinium shawurense W. T. Wang. The chemical structures of new compounds were established based on spectroscopic analyses: HR-ESI-MS, and 1D, 2D NMR spectroscopic data. The anti-inflammatory and cytotoxic activities of these diterpenoid alkaloids were also evaluated.     

Four New C19-Diterpenoid Alkaloids from Aconitum nagarum Stapf

Four new aconitine-type C₁₉-diterpenoid alkaloids, were isolated from the roots of Aconitum nagarum Stapf which were named as nagarutines A–D ( 1–4 ), together with eleven known compounds ( 5–15 ). The structures of the compounds were identified by IR, HR-ESI-MS, 1D and 2D NMR spectra. All compounds were tested for the inhibitory effect on LPS induced NO production in RAW 264.7 macrophages, compound 7 showed moderate anti-inflammatory activity effect and Inhibition rate is about 44.50%.     

Prevalence and trends of common mental disorders from 2007-2009 to 2019-2022: results from the Netherlands Mental Health Survey and Incidence Studies (NEMESIS), including comparison of prevalence rates before vs. during the COVID-19 pandemic

Up-to-date information on the prevalence and trends of common mental disorders is relevant to health care policy and planning, owing to the high burden associated with these disorders. In the first wave of the third Netherlands Mental Health Survey and Incidence Study (NEMESIS-3), a nationally representative sample was interviewed face-to-face from November 2019 to March 2022 (6,194 subjects; 1,576 interviewed before and 4,618 during the COVID-19 pandemic; age range: 18-75 years). A slightly modified version of the Composite International Diagnostic Interview 3.0 was used to assess DSM-IV and DSM-5 diagnoses. Trends in 12-month prevalence rates of DSM-IV mental disorders were examined by comparing these rates between NEMESIS-3 and NEMESIS-2 (6,646 subjects; age range: 18-64 years; interviewed from November 2007 to July 2009). Lifetime DSM-5 prevalence estimates in NEMESIS-3 were 28.6% for anxiety disorders, 27.6% for mood disorders, 16.7% for substance use disorders, and 3.6% for attention-deficit/hyperactivity disorder. Over the last 12 months, prevalence rates were 15.2%, 9.8%, 7.1%, and 3.2%, respectively. No differences in 12-month prevalence rates before vs. during the COVID-19 pandemic were found (26.7% pre-pandemic vs. 25.7% during the pandemic), even after controlling for differences in socio-demographic characteristics of the respondents interviewed in these two periods. This was the case for all four disorder categories. From 2007-2009 to 2019-2022, the 12-month prevalence rate of any DSM-IV disorder significantly increased from 17.4% to 26.1%. A stronger increase in prevalence was found for students, younger adults (18-34 years) and city dwellers. These data suggest that the prevalence of mental disorders has increased in the past decade, but this is not explained by the COVID-19 pandemic. The already high mental disorder risk of young adults has particularly further increased in recent years.