No abstract

Prostate cancer (PCa) is one of the most frequently diagnosed cancers and a leading cause of death among men worldwide. Despite extensive efforts in biomarker discovery during the last years, currently used clinical biomarkers are still lacking enough specificity and sensitivity for PCa early detection, patient prognosis, and monitoring. Therefore, more precise biomarkers are required to improve the clinical management of PCa patients. In this context, metabolomics has shown to be a promising and powerful tool to identify novel PCa biomarkers in biofluids. Thus, changes in polyamines, tricarboxylic acid (TCA) cycle, amino acids, and fatty acids metabolism have been reported in different studies analyzing PCa patients’ biofluids. The review provides an up-to-date summary of the main metabolic alterations that have been described in biofluid-based studies of PCa patients, as well as a discussion regarding their potential to improve clinical PCa diagnosis and prognosis. Furthermore, a summary of the most significant findings reported in these studies and the connections and interactions between the different metabolic changes described has also been included, aiming to better describe the specific metabolic signature associated to PCa.

Background Despite the increasing incidence of anaphylaxis, its underlying molecular mechanisms and biomarkers for appropriate diagnosis remain undetermined. The rapid onset and potentially fatal outcome in the absence of managed treatment prevent its study. Up today, there are still no known biomarkers that allow an unequivocal diagnosis. Therefore, the aim of this study was to explore metabolic changes in patients suffering anaphylactic reactions depending on the trigger (food and/or drug) and severity (moderate and severe) in a real‐life set‐up. Methods Eighteen episodes of anaphylaxis, one per patient, were analysed. Sera were collected during the acute phase (T1), the recovery phase (T2) and around 2–3 months after the anaphylactic reaction (T0: basal state). Reactions were classified following an exhaustive allergological evaluation for severity and trigger. Sera samples were analysed using untargeted metabolomics combining liquid chromatography coupled to mass spectrometry (LC‐MS) and proton nuclear magnetic resonance spectroscopy (1H‐NMR). Results ‘Food T1 vs T2’ and ‘moderate T1 vs T2’ anaphylaxis comparisons showed clear metabolic patterns during the onset of an anaphylactic reaction, which differed from those induced by drugs, food + drug or severe anaphylaxis. Moreover, the model of food anaphylaxis was able to distinguish the well‐characterized IgE (antibiotics) from non‐IgE‐mediated anaphylaxis (nonsteroidal anti‐inflammatory drugs), suggesting a differential metabolic pathway associated with the mechanism of action. Metabolic differences between ‘moderate vs severe’ at the acute phase T1 and at basal state T0 were studied. Among the altered metabolites, glucose, lipids, cortisol, betaine and oleamide were observed altered. Conclusions The results of this exploratory study provide the first evidence that different anaphylactic triggers or severity induce differential metabolic changes along time or at specific time‐point, respectively. Besides, the basal status T0 might identify high‐risk patients, thus opening new ways to understand, diagnose and treat anaphylaxis.

Background After a national lockdown during the first wave of the COVID-19 pandemic in Spain, regional governments implemented different non-pharmaceutical interventions (NPIs) during the second wave. Aim To analyse which implemented NPIs significantly impacted effective reproduction number (Rt) in seven Spanish provinces during 30 August 2020–31 January 2021. Methods We coded each NPI and levels of stringency with a ‘severity index’ (SI) and computed a global SI (mean of SIs per six included interventions). We performed a Bayesian change point analysis on the Rt curve of each province to identify possible associations with global SI variations. We fitted and compared several generalised additive models using multimodel inference, to quantify the statistical effect on Rt of the global SI (stringency) and the individual SIs (separate effect of NPIs). Results The global SI had a significant lowering effect on the Rt (mean: 0.16 ± 0.05 units for full stringency). Mandatory closing times for non-essential businesses, limited gatherings, and restricted outdoors seating capacities (negative) as well as curfews (positive) were the only NPIs with a significant effect. Regional mobility restrictions and limited indoors seating capacity showed no effect. Our results were consistent with a 1- to 3-week-delayed Rt as a response variable. Conclusion While response measures implemented during the second COVID-19 wave contributed substantially to a decreased reproduction number, the effectiveness of measures varied considerably. Our findings should be considered for future interventions, as social and economic consequences could be minimised by considering only measures proven effective.

Measuring mortality has been a challenge during the COVID-19 pandemic. Here, we compared the results from the Spanish daily mortality surveillance system (MoMo) of excess mortality estimates, using a time series analysis, with those obtained for the confirmed COVID-19 deaths reported to the National Epidemiological Surveillance Network (RENAVE). The excess mortality estimated at the beginning of March 2020 was much greater than what has been observed in previous years, and clustered in a very short time. The cumulated excess mortality increased with age. In the first epidemic wave, the excess mortality estimated by MoMo was 1.5 times higher than the confirmed COVID-19 deaths reported to RENAVE, but both estimates were similar in the following pandemic waves. Estimated excess mortality and confirmed COVID-19 mortality rates were geographically distributed in a very heterogeneous way. The greatest increase in mortality that has taken place in Spain in recent years was detected early by MoMo, coinciding with the spread of the COVID-19 pandemic. MoMo is able to identify risk situations for public health in a timely manner, relying on mortality in general as an indirect indicator of various important public health problems.

Background Single-dose vaccination was widely recommended in the pre-Omicron era for persons with previous SARS-CoV-2 infection. The effectiveness of a second vaccine dose in this group in the Omicron era is unknown. Methods We linked nationwide population registries in Spain to identify community-dwelling individuals aged 18-64, with a positive SARS-CoV-2 test before single-dose mRNA vaccination (mRNA-1273 or BNT162b2). Every day between January 3 and February 6, 2022 we matched 1:1 individuals receiving a second mRNA vaccine-dose and controls on sex, age, province, first dose type and time, month of primary infection and number of previous tests. We then estimated Kaplan-Meier risks of confirmed SARS-CoV-2 reinfection. We performed a similar analysis in a Delta-dominant period, between July 19 and November 30, 2021. Results In the Omicron period, estimated effectiveness (95% confidence interval) of a second dose was 62.2% (58.2, 66.4) 7 to 34 days after administration, similar across groups defined by age, sex, type of first vaccine and time since the first dose. Estimated effectiveness was 65.4% (61.1, 69.9) for mRNA-1273 and 52.0% (41.8, 63.1) for BNT162b2. Estimated effectiveness was 78.5% (67.4, 89.9), 66.1% (54.9, 77.5), and 60.2% (55.5, 64.8) when primary infection had occurred in the Delta, Alpha, and pre-Alpha periods, respectively. In the Delta period, the estimated effectiveness of a second dose was 8.8% (-55.3, 81.1). Conclusions Our results suggest that, over a month after administration, a second dose of mRNA vaccine increases protection against SARS-CoV-2 reinfection with the Omicron variant among individuals with single-dose vaccination and previously infected with another variant.

Background A unique policy of perimeter closures of Basic Health Zones (small administrative health units) was implemented in the Autonomous Community of Madrid from September 21st 2020 to May 23rd 2021 to face the COVID-19 pandemic. Aim To assess the impact of local perimeter confinements on the 14-days cumulative incidence of SARS-CoV-2 during the second wave of the pandemic in Madrid, Spain. Methods We compare the errors in estimation of two families of mathematical models: ones that include the perimeter closures as explanatory covariables and ones that do not, in search of a significant improvement in estimation of one family over the other. We incorporate leave-one-out cross-validation, and at each step of this process we select the best model in AIC score from a family of 15 differently tuned ones. Results The two families of models provided very similar estimations, for a 1- to 3-weeks delay in observed cumulative incidence, and also when restricting the analysis to only those Basic Health Zones that were subject to at least one closure during the time under study. In all cases the correlation between the errors yielded by both families of models was higher than 0.98 (±10− 3 95% CI), and the average difference of estimated 14-days cumulative incidence was smaller than 1.49 (±0.33 95% CI). Conclusion Our analysis suggests that the perimeter closures by Basic Health Zone did not have a significant effect on the epidemic curve in Madrid.

No abstract