Neutralising reactivity against SARS-CoV-2 Delta and Omicron variants by vaccination and infection history

BACKGROUND

The continuous emergence of SARS-CoV-2 variants of concern (VOC) with immune escape properties, such as Delta (B.1.617.2) and Omicron (B.1.1.529), questions the extent of the antibody-mediated protection against the virus. Here we investigated the long-term antibody persistence in previously infected subjects and the extent of the antibody-mediated protection against B.1, B.1.617.2 and BA.1 variants in unvaccinated subjects previously infected, vaccinated naïve and vaccinated previously infected subjects.

METHODS

Blood samples collected 15 months post-infection from unvaccinated (n=35) and vaccinated (n=41) previously infected subjects (Vo' cohort) were tested for the presence of antibodies against the SARS-CoV-2 spike (S) and nucleocapsid (N) antigens using the Abbott, DiaSorin, and Roche immunoassays. The serum neutralising reactivity was assessed against B.1, B.1.617.2 (Delta), and BA.1 (Omicron) SARS-CoV-2 strains through micro-neutralisation. The antibody titres were compared to those from previous timepoints, performed at 2- and 9-months post-infection on the same individuals. Two groups of naïve subjects were used as controls, one from the same cohort (unvaccinated n=29 and vaccinated n=20) and a group of vaccinated naïve healthcare workers (n=61).

RESULTS

We report on the results of the third serosurvey run in the Vo' cohort. With respect to the 9-month time point, antibodies against the S antigen significantly decreased (P=0.0063) among unvaccinated subjects and increased (P<0.0001) in vaccinated individuals, whereas those against the N antigen decreased in the whole cohort. When compared with control groups (naïve Vo' inhabitants and naïve healthcare workers), vaccinated subjects that were previously infected had higher antibody levels (P<0.0001) than vaccinated naïve subjects. Two doses of vaccine elicited stronger anti-S antibody response than natural infection (P<0.0001). Finally, the neutralising reactivity of sera against B.1.617.2 and BA.1 was 4-fold and 16-fold lower than the reactivity observed against the original B.1 strain.

CONCLUSIONS

These results confirm that vaccination induces strong antibody response in most individuals, and even stronger in previously infected subjects. Neutralising reactivity elicited by natural infection followed by vaccination is increasingly weakened by the recent emergence of VOCs. While immunity is not completely compromised, a change in vaccine development may be required going forward, to generate cross-protective pan-coronavirus immunity in the global population.

SARS-CoV-2 antibody dynamics and transmission from community-wide serological testing in the Italian municipality of Vo'

In February and March 2020, two mass swab testing campaigns were conducted in Vo', Italy. In May 2020, we tested 86% of the Vo' population with three immuno-assays detecting antibodies against the spike and nucleocapsid antigens, a neutralisation assay and Polymerase Chain Reaction (PCR). Subjects testing positive to PCR in February/March or a serological assay in May were tested again in November. Here we report on the results of the analysis of the May and November surveys. We estimate a seroprevalence of 3.5% (95% Credible Interval (CrI): 2.8-4.3%) in May. In November, 98.8% (95% Confidence Interval (CI): 93.7-100.0%) of sera which tested positive in May still reacted against at least one antigen; 18.6% (95% CI: 11.0-28.5%) showed an increase of antibody or neutralisation reactivity from May. Analysis of the serostatus of the members of 1,118 households indicates a 26.0% (95% CrI: 17.2-36.9%) Susceptible-Infectious Transmission Probability. Contact tracing had limited impact on epidemic suppression.

Bayesian mixture modelling of the high-energy photon counts collected by the Fermi Large Area Telescope

Identifying as yet undetected high-energy sources in the [Formula: see text]-ray sky is one of the declared objectives of the Fermi Large Area Telescope (LAT) Collaboration. We develop a Bayesian mixture model which is capable of disentangling the high-energy extra-galactic sources present in a given sky region from the pervasive background radiation. We achieve this by combining two model components. The first component models the emission activity of the single sources and incorporates the instrument response function of the Fermi [Formula: see text]-ray space telescope. The second component reliably reflects the current knowledge of the physical phenomena which underlie the [Formula: see text]-ray background. The model parameters are estimated using a reversible jump MCMC algorithm, which simultaneously returns the number of detected sources, their locations and relative intensities, and the background component. Our proposal is illustrated using a sample of the Fermi LAT data. In the analysed sky region, our model correctly identifies 116 sources out of the 132 present. The detection rate and the estimated directions and intensities of the identified sources are largely unaffected by the number of detected sources.

Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo'

On 21 February 2020, a resident of the municipality of Vo', a small town near Padua (Italy), died of pneumonia due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection¹. This was the first coronavirus disease 19 (COVID-19)-related death detected in Italy since the detection of SARS-CoV-2 in the Chinese city of Wuhan, Hubei province². In response, the regional authorities imposed the lockdown of the whole municipality for 14 days³. Here we collected information on the demography, clinical presentation, hospitalization, contact network and the presence of SARS-CoV-2 infection in nasopharyngeal swabs for 85.9% and 71.5% of the population of Vo' at two consecutive time points. From the first survey, which was conducted around the time the town lockdown started, we found a prevalence of infection of 2.6% (95% confidence interval (CI): 2.1-3.3%). From the second survey, which was conducted at the end of the lockdown, we found a prevalence of 1.2% (95% CI: 0.8-1.8%). Notably, 42.5% (95% CI: 31.5-54.6%) of the confirmed SARS-CoV-2 infections detected across the two surveys were asymptomatic (that is, did not have symptoms at the time of swab testing and did not develop symptoms afterwards). The mean serial interval was 7.2 days (95% CI: 5.9-9.6). We found no statistically significant difference in the viral load of symptomatic versus asymptomatic infections (P = 0.62 and 0.74 for E and RdRp genes, respectively, exact Wilcoxon-Mann-Whitney test). This study sheds light on the frequency of asymptomatic SARS-CoV-2 infection, their infectivity (as measured by the viral load) and provides insights into its transmission dynamics and the efficacy of the implemented control measures.

Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo'

On 21 February 2020, a resident of the municipality of Vo', a small town near Padua (Italy), died of pneumonia due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection¹. This was the first coronavirus disease 19 (COVID-19)-related death detected in Italy since the detection of SARS-CoV-2 in the Chinese city of Wuhan, Hubei province². In response, the regional authorities imposed the lockdown of the whole municipality for 14 days³. Here we collected information on the demography, clinical presentation, hospitalization, contact network and the presence of SARS-CoV-2 infection in nasopharyngeal swabs for 85.9% and 71.5% of the population of Vo' at two consecutive time points. From the first survey, which was conducted around the time the town lockdown started, we found a prevalence of infection of 2.6% (95% confidence interval (CI): 2.1-3.3%). From the second survey, which was conducted at the end of the lockdown, we found a prevalence of 1.2% (95% CI: 0.8-1.8%). Notably, 42.5% (95% CI: 31.5-54.6%) of the confirmed SARS-CoV-2 infections detected across the two surveys were asymptomatic (that is, did not have symptoms at the time of swab testing and did not develop symptoms afterwards). The mean serial interval was 7.2 days (95% CI: 5.9-9.6). We found no statistically significant difference in the viral load of symptomatic versus asymptomatic infections (P = 0.62 and 0.74 for E and RdRp genes, respectively, exact Wilcoxon-Mann-Whitney test). This study sheds light on the frequency of asymptomatic SARS-CoV-2 infection, their infectivity (as measured by the viral load) and provides insights into its transmission dynamics and the efficacy of the implemented control measures.

Nonparametric change point estimation for survival distributions with a partially constant hazard rate

We present a new method for estimating a change point in the hazard function of a survival distribution assuming a constant hazard rate after the change point and a decreasing hazard rate before the change point. Our method is based on fitting a stump regression to p values for testing hazard rates in small time intervals. We present three real data examples describing survival patterns of severely ill patients, whose excess mortality rates are known to persist far beyond hospital discharge. For designing survival studies in these patients and for the definition of hospital performance metrics (e.g. mortality), it is essential to define adequate and objective end points. The reliable estimation of a change point will help researchers to identify such end points. By precisely knowing this change point, clinicians can distinguish between the acute phase with high hazard (time elapsed after admission and before the change point was reached), and the chronic phase (time elapsed after the change point) in which hazard is fairly constant. We show in an extensive simulation study that maximum likelihood estimation is not robust in this setting, and we evaluate our new estimation strategy including bootstrap confidence intervals and finite sample bias correction.

G-quadruplex forming sequences in the genome of all known human viruses: A comprehensive guide

G-quadruplexes are non-canonical nucleic-acid structures that control transcription, replication, and recombination in organisms. G-quadruplexes are present in eukaryotes, prokaryotes, and viruses. In the latter, mounting evidence indicates their key biological activity. Since data on viruses are scattered, we here present a comprehensive analysis of potential quadruplex-forming sequences (PQS) in the genome of all known viruses that can infect humans. We show that occurrence and location of PQSs are features characteristic of each virus class and family. Our statistical analysis proves that their presence within the viral genome is orderly arranged, as indicated by the possibility to correctly assign up to two-thirds of viruses to their exact class based on the PQS classification. For each virus we provide: i) the list of all PQS present in the genome (positive and negative strands), ii) their position in the viral genome, iii) the degree of conservation among strains of each PQS in its genome context, iv) the statistical significance of PQS abundance. This information is accessible from a database to allow the easy navigation of the results: http://www.medcomp.medicina.unipd.it/main_site/doku.php?id=g4virus. The availability of these data will greatly expedite research on G-quadruplex in viruses, with the possibility to accelerate finding therapeutic opportunities to numerous and some fearsome human diseases.

Metastatic tumors to the pancreas: a systematic review and meta-analysis

INTRODUCTION

Metastases to the pancreas from other primary tumors are increasingly recognized in clinical practice, but the real role of surgery remains unclear. This study was designated to evaluate by a meta-analytic approach the results of surgical treatment for the most common malignancies metastasizing to the pancreas.

EVIDENCE ACQUISITION

MEDLINE, PubMED, Scopus and Web of Sciences were searched from January 2000 to December 2015. Studies reporting postoperative complications, postoperative mortality, disease-free and overall survival of patients undergoing resection for secondary tumours of the pancreas, were included.

EVIDENCE SYNTHESIS

Fourteen publication with 281 patients met the inclusion criteria and were subjected to the analysis. Operative morbidity and mortality were 34% and 1.3% respectively. Pancreatic resection for renal cell cancer showed better survival compared to other non-renal cell cancer (ratio of mean 1.83; 95% CI: 1.42-2.36, I2=74.52%, P<0.001). Disease-free interval was longer for metastatic renal cell carcinoma patients (mean difference 6.36, 95% CI: 3.803-8.912 years, I2=76:54%, P<0.001). A meta-regression was used to correlate the two endpoints and showed that a longer DFI is associated to a longer survival.

CONCLUSIONS

Pancreatic resection for metastasis should be reserved to patients in good health conditions, with isolated disease from renal cell cancer. For other types of tumor, surgery should be performed only in individual basis. There is a need of studies evaluating the role of chemotherapy in the neoadjuvant setting or the best sequential use of multimodality treatment (targeted therapy, radiotherapy, surgery, etc.).

Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters

Four of the most common limitations of the many available clustering methods are: i) the lack of a proper strategy to deal with outliers; ii) the need for a good a priori estimate of the number of clusters to obtain reasonable results; iii) the lack of a method able to detect when partitioning of a specific data set is not appropriate; and iv) the dependence of the result on the initialization. Here we propose Cross-clustering (CC), a partial clustering algorithm that overcomes these four limitations by combining the principles of two well established hierarchical clustering algorithms: Ward’s minimum variance and Complete-linkage. We validated CC by comparing it with a number of existing clustering methods, including Ward’s and Complete-linkage. We show on both simulated and real datasets, that CC performs better than the other methods in terms of: the identification of the correct number of clusters, the identification of outliers, and the determination of real cluster memberships. We used CC to cluster samples in order to identify disease subtypes, and on gene profiles, in order to determine groups of genes with the same behavior. Results obtained on a non-biological dataset show that the method is general enough to be successfully used in such diverse applications. The algorithm has been implemented in the statistical language R and is freely available from the CRAN contributed packages repository.

Influence of increasing BMI on insulin sensitivity and secretion in normotolerant men and women of a wide age span

The impact of sex and age on glucose metabolism in the development of overweight/obesity is a matter of debate. We hypothesized that insulin sensitivity (IS) and β-cell function (BF) in a normal white population will differ between males and females and aimed to evaluate the possible effects of BMI and age on metabolic parameters of both sexes. This study is a cross-sectional analysis of the general community. IS was measured with quantitative insulin sensitivity check index (QUICKI) and oral glucose insulin sensitivity (OGIS) and BF with the insulinogenic index during 75-g 2-h oral glucose-tolerance tests (OGTTs). We studied 611 females and 361 males with normal glycemia according to both fasting and 2-h glucose (85 ± 0.3 mg/dl (means ± SE) in females and 89 ± 0.4 in males (P < 0.0001), and 93 ± 1 in females and 89 ± 1 in males (P = 0.005), respectively). Females were younger (37 ± 1 years) than males (40 ± 1, P < 0.0001), but no difference was found in mean BMI (BMI = 25.8 ± 0.2 kg/m(2) in both). Student's two-sample t-test was used for simple comparison between and within genders, multiple linear regressions to account for covariates. During the OGTT, females had lower glucose (area under the curve (AUC) 133 ± 1 mg/ml·2 h vs. 148 ± 2; P < 0.00001), while insulinemia was comparable (AUC 5.3 ± 0.1 mU/ml·2 h vs. 5.7 ± 0.2, P = 0.15). IS remained higher in females (473 ± 3 ml/min/m(2) vs. 454 ± 3, P < 0.0001) also after having accounted for age and BMI (P = 0.015). No difference was observed in fasting insulin or BF. However, BF increased by 46% with BMI and when accounting for age and BMI, BF of females was significantly higher (P < 0.0001). Because IS and BF are higher in females than in males, sex should be considered in metabolic studies and overweight/obese populations.

Free fatty acid kinetics during long-term treatment with pioglitazone added to sulfonylurea or metformin in Type 2 diabetes

BACKGROUND

Free fatty acids (FFAs) are linked to impaired insulin action, but their role in mediating long-term insulin sensitization during diabetes treatment is unclear.

OBJECTIVES

To examine the effect of pioglitazone addition to existing therapy on FFA dynamics and insulin action.

DESIGN

Two 2-year, randomized, parallel-group, double-blind, double-dummy, clinical trials.

SETTING

One hundred and seventy-one centres in Europe, Australia and Canada.

SUBJECTS

Male and female patients with Type 2 diabetes inadequately managed with metformin or sulfonylurea.

INTERVENTIONS

Patients were randomized to pioglitazone (15-45 mg day(-1); n=319) or metformin (850-2550 mg day(-1); n=320) as add-on therapy to gliclazide or pioglitazone (n=317) versus gliclazide (80-320 mg day(-1); n=313) as add-on therapy to metformin.

OUTCOME MEASURE

Plasma FFA profiles during oral glucose tolerance tests in selected centres before and during treatment (n=588).

RESULTS

At Week 104, pioglitazone treatment decreased fasting FFAs by 0.08 mmol L(-1) when added to sulfonylurea and by 0.11 mmol L(-1) when added to metformin versus the respective sulfonylurea + metformin groups (0.03 mmol L(-1), P=0.05 and 0.04 mmol L(-1), P<0.05), and this was accompanied by significant improvements in fasting adipose tissue insulin sensitivity. Changes in postchallenge FFAs were similar between groups and not related to changes in liver transaminases, insulin action and secretion. However, the sensitivity of FFA to insulin was affected by treatment (P<0.001) and visit (P<0.05). Insulin sensitivity of FFA rose when pioglitazone was added to sulfonylurea (P<0.05), but decreased for gliclazide + metformin (P<0.05).

CONCLUSION

Long-term improvements in adipose tissue insulin sensitivity and reduction in fasting FFAs with pioglitazone may help to reduce lipotoxicity in Type 2 diabetes.

A simulation-based comparison of techniques to correct for measurement error in matched case-control studies

The presence of measurement errors affecting the covariates in regression models is a relevant topic in many scientific areas, as, for example, in epidemiology. An example is given by an epidemiological population-based matched case-control study on the aetiology of childhood malignancies, which is currently under completion in Italy. This study was aimed at evaluating the effects of childhood exposure to extremely low electromagnetic fields on the risk of disease occurrence by taking into account the possibility of erroneous measures of the exposure. Within this framework, we focus on the application of likelihood methods to correct for measurement error. This approach, which has received less attention in literature with respect to alternatives, is compared with commonly used methods such as regression calibration and SIMEX. The comparison is performed by simulation, under a broad range of measurement error structures.

Cost-effective experimental design to support modeling of concentration-response functions

Modeling concentration-response function became extremely popular in ecotoxicology during the last decade. Indeed, modeling allows determining the total response pattern of a given substance. However, reliable modeling is consuming in term of data, which is in contradiction with the current trend in ecotoxicology, which aims to reduce, for cost and ethical reasons, the number of data produced during an experiment. It is therefore crucial to determine experimental design in a cost-effective manner. In this paper, we propose to use the theory of locally D-optimal designs to determine the set of concentrations to be tested so that the parameters of the concentration-response function can be estimated with high precision. We illustrated this approach by determining the locally D-optimal designs to estimate the toxicity of the herbicide dinoseb on daphnids and algae. The results show that the number of concentrations to be tested is often equal to the number of parameters and often related to the their meaning, i.e. they are located close to the parameters. Furthermore, the results show that the locally D-optimal design often has the minimal number of support points and is not much sensitive to small changes in nominal values of the parameters. In order to reduce the experimental cost and the use of test organisms, especially in case of long-term studies, reliable nominal values may therefore be fixed based on prior knowledge and literature research instead of on preliminary experiments.

Quantitative indices for the assessment of the repeatability of distortion product otoacoustic emissions in laboratory animals

Distortion product otoacoustic emissions (DPOAE) can be used to study cochlear function in an objective and non-invasive manner. One practical and essential aspect of any investigating measure is the consistency of its results upon repeated testing of the same individual/animal (i.e., its test/retest repeatability). The goal of the present work is to propose two indices to quantitatively assess the repeatability of DPOAE in laboratory animals. The methodology is here illustrated using two data sets which consist of DPOAE subsequently collected from Sprague-Dawley rats. The results of these experiments showed that the proposed indices are capable of estimating both the repeatability of the true emission level and the inconsistencies associated with measurement error. These indices could be a significantly useful tool to identify real and even small changes in the cochlear function exerted by potential ototoxic agents.

Modeling the concentration-response function of the herbicide dinoseb on Daphnia magna (survival time, reproduction) and Pseudokirchneriella subcapitata (growth rate)

Models describing dose-response relationships are becoming increasingly popular in ecotoxicology. They allow simple and thorough evaluations of toxicity test results, including inter- and extrapolations to concentrations or exposure times other than those tested. Simple parametric regression models are of particular interest because their parameters may be attributed mechanistic meanings and they can be applied without sophisticated mathematical and computational support. We recently proposed a four-parameter logistic regression model to fit the survival data of Daphnia magna under dinoseb stress. The model parameters are the maximum survival time, the minimum time required for an individual to die, effect concentration, EC(50), and a curve shape parameter. This model has now been applied to compare the lethality and reproduction toxicity of D. magna and the growth inhibition of Pseudokirchneriella subcapitata under dinoseb stress. It can be fitted adequately to all the measured data and the parameters can be attributed biological meanings in any of the three endpoints. A comparison of the modeled concentration-response functions of all three endpoints for dinoseb toxicity shows that the range of ECs with respect to both D. magna and algae is steep (a decrease of between 0.1 and 0.6 mg/L). The survival and reproduction of D. magna exhibit similar characteristic concentration-response functions and toxicities. The statistical no-effect concentration (SNEC) is 0.14 (survival) and 0.11 (reproduction)mg/L, respectively. On the other hand, algae seem to be less sensitive to dinoseb than D. magna (SNEC: 0.48 mg/L). However, further investigations of individual algae may lead to a more suitable comparison. We speculate that the four parameters of the model function can be related to specific properties of chemicals and organisms. Characterization of these properties would allow simple and appropriate estimation of the toxic effects of these chemicals.

Comparative evaluation of simple insulin sensitivity methods based on the oral glucose tolerance test

AIMS/HYPOTHESIS

We compared five surrogate insulin sensitivity (IS) methods against the euglycaemic-hyperinsulinaemic clamp. These methods were the homeostasis model assessment (HOMA) and four methods based on the OGTT (OGIS, MCRest, ISIcomp, SIORAL).

METHODS

We compared these IS methods against the clamp (0.28 nmol.min(-1).m(-2) insulin infusion) M value in 147 women (58-61 years; BMI 19-38 kg/m2; 116 NGT, 25 IFG/IGT, six type 2 diabetic), by evaluating the correlation coefficient with M. We also tested the ability to reproduce the relationships between IS and typical IS correlates (BMI, fasting insulin, insulin to glucose OGTT area ratio and fasting, 2 h and mean glucose) by means of the "discrepancy index" D, in which (1) D=0 if the correlation between IS and the variable of interest is as with the clamp, (2) D is smaller than 0 if the correlation is overestimated, and (3) D is greater than 0 if underestimated.

RESULTS

All IS methods correlated with M (r=0.57-0.83, p<0.0001); for MCRest the relationship was markedly curvilinear. All IS measures correlated with the considered variables (r=0.29-0.94, p<0.0005); however, no method had D approximately 0 for all variables. The best surrogates of M were OGIS (one D not =0) and MCRest (two D not =0); the other methods either under- or overestimated the degree of correlation (three or more D not =0), in particular with fasting insulin (HOMA: D=-57%; ISIcomp: D=-36%) and BMI (HOMA: D=-14%; ISIcomp: D=-14%; SiORAL: D=-11%).

CONCLUSIONS/INTERPRETATION

All IS methods were correlated with M. OGIS and MCRest were preferable to the other methods and in particular to HOMA for reproducing relationships with the independent variables.

Effects of dinoseb on the life cycle of Daphnia magna: modeling survival time and a proposal for an alternative to the no-observed-effect concentration

Risk assessment is in urgent need of more accurate toxic effect endpoints than those currently in use, especially for low concentrations. Often such endpoints are estimated by analysis of variance, linear interpolation, or smoothing. As these statistical methods are not always satisfactory, some authors have proposed to describe the entire dose-response curves by fully formalized parametric regression models whose parameters have toxicological meaning. These models allow a better evaluation of pollutant effects, including inter- and extrapolation to any other than the measured effect values. Following this line, a four-parameter logistic regression model (standard model) was fitted to survival data of Daphnia magna under pesticide (dinoseb) stress. The heterogeneity of the variance was taken into account with a both-sides logarithmic transformation. Besides the standard model, a hormesis and a threshold model were tested too. These two others models have been described in the literature and might better represent the dose-response function we are looking for. All three models showed a good fit to our data, and the statistics gave no hints as to which model is the most appropriate. As no evidence was seen for hormesis or for the existence of a threshold concentration, we used the simplest, namely, the standard model, for most of our calculations. Model calculations allow the quantification of the effects on individuals' longevity as well as on mean survival time of the population. We used them to define a no-effect value, the statistical-no-effect concentration (SNEC). The SNEC is based on the confidence bands of the modeled regression and represents the highest value for which an effect is statistically not different from the control. The SNEC is an alternative to classical endpoints, like the no-observed-effect concentration (NOEC) or the low-effect concentrations (e.g., EC10, EC5, EC1).