Research exploring the development and outcome of COVID-19 infections has led to the need to find better diagnostic and prognostic biomarkers. This cross-sectional study used targeted metabolomics to identify potential COVID-19 biomarkers that predicted the course of the illness by assessing 110 endogenous plasma metabolites from individuals admitted to a local hospital for diagnosis/treatment. Patients were classified into four groups (≈ 40 each) according to standard polymerase chain reaction (PCR) COVID-19 testing and disease course: PCR−/controls (i.e., non-COVID controls), PCR+/not-hospitalized, PCR+/hospitalized, and PCR+/intubated. Blood samples were collected within 2 days of admission/PCR testing. Metabolite concentration data, demographic data and clinical data were used to propose biomarkers and develop optimal regression models for the diagnosis and prognosis of COVID-19. The area under the receiver operating characteristic curve (AUC; 95% CI) was used to assess each models’ predictive value. A panel that included the kynurenine: tryptophan ratio, lysoPC a C26:0, and pyruvic acid discriminated non-COVID controls from PCR+/not-hospitalized (AUC = 0.947; 95% CI 0.931–0.962). A second panel consisting of C10:2, butyric acid, and pyruvic acid distinguished PCR+/not-hospitalized from PCR+/hospitalized and PCR+/intubated (AUC = 0.975; 95% CI 0.968–0.983). Only lysoPC a C28:0 differentiated PCR+/hospitalized from PCR+/intubated patients (AUC = 0.770; 95% CI 0.736–0.803). If additional studies with targeted metabolomics confirm the diagnostic value of these plasma biomarkers, such panels could eventually be of clinical use in medical practice.

We used a structured approach to assess whether active smokers presented with pictorial warnings on cigarette packages (PWCP) had a higher probability of quitting, reducing, and attempting to quit smoking than did unexposed smokers.We identified 21 articles from among nearly 2500 published between 1993 and 2013, prioritizing coverage over relevance or quality because we expected to find only a few studies with behavioral outcomes. We found very large heterogeneity across studies, poor or very poor methodological quality, and generally null or conflicting findings for any explored outcome.The evidence for or against the use of PWCP is insufficient, suggesting that any effect of PWCP on behavior would be modest. Determining the single impact of PWCP on behavior requires studies with strong methodological designs and longer follow-up periods.

COVID-19 infection triggered a global public health crisis during the 2020–2022 period, and it is still evolving. This highly transmissible respiratory disease can cause mild symptoms up to severe pneumonia with potentially fatal respiratory failure. In this cross-sectional study, 41 PCR-positive patients for SARS-CoV-2 and 42 healthy controls were recruited during the first wave of the pandemic in Mexico. The plasmatic expression of five circulating miRNAs involved in inflammatory and pathological host immune responses was assessed using RT-qPCR (Reverse Transcription quantitative Polymerase Chain Reaction). Compared with controls, a significant upregulation of miR-146a, miR-155, and miR-221 was observed; miR-146a had a positive correlation with absolute neutrophil count and levels of brain natriuretic propeptide (proBNP), and miR-221 had a positive correlation with ferritin and a negative correlation with total cholesterol. We found here that CDKN1B gen is a shared target of miR-146a, miR-221-3p, and miR-155-5p, paving the way for therapeutic interventions in severe COVID-19 patients. The ROC curve built with adjusted variables (miR-146a, miR-221-3p, miR-155-5p, age, and male sex) to differentiate individuals with severe COVID-19 showed an AUC of 0.95. The dysregulation of circulating miRNAs provides new insights into the underlying immunological mechanisms, and their possible use as biomarkers to discriminate against patients with severe COVID-19. Functional analysis showed that most enriched pathways were significantly associated with processes related to cell proliferation and immune responses (innate and adaptive). Twelve of the predicted gene targets have been validated in plasma/serum, reflecting their potential use as predictive prognosis biomarkers.

The simultaneous use of several medications is an important risk factor for injurious falls in older people. The aim of this study is to investigate the effect of the number of medications dispensed to elderly persons on fall injuries and to assess whether this relationship is explained by individual demographics, health habits and health status.

Background: Recent evidence points to the relevance of poverty and inequality as factors affecting the spread and mortality of the COVID-19 pandemic in Latin America. This study aimed to determine whether COVID-19 patients living in Mexican municipalities with high levels of poverty have a lower survival compared with those living in municipalities with low levels. Methods: Retrospective cohort study. Secondary data was used to define the exposure (multidimensional poverty level) and outcome (survival time) among patients diagnosed with COVID-19 between 27 February and 1 July 2020. Crude and adjusted hazard ratios (HR) from Cox regression were computed. Results: Nearly 250 000 COVID-19 patients were included. Mortality was 12.3% reaching 59.3% in patients with ≥1 comorbidities. Multivariate survival analyses revealed that individuals living in municipalities with extreme poverty had 9% higher risk of dying at any given time proportionally to those living in municipalities classified as not poor (HR 1.09; 95% CI 1.06–1.12). The survival gap widened with the follow-up time up to the third to fourth weeks after diagnosis. Conclusion: Evidence suggests that the poorest population groups have a lower survival from COVID-19. Thus, combating extreme poverty should be a central preventive strategy.

The knowledge of normal metabolite values for neonates is key to establishing robust cut-off values to diagnose diseases, to predict the occurrence of new diseases, to monitor a neonate’s metabolism, or to assess their general health status. For full term-newborns, many reference biochemical values are available for blood, serum, plasma and cerebrospinal fluid. However, there is a surprising lack of information about normal urine concentration values for a large number of important metabolites in neonates. In the present work, we used targeted tandem mass spectrometry (MS/MS)-based metabolomic assays to identify and quantify 136 metabolites of biomedical interest in the urine from 48 healthy, full-term term neonates, collected in the first 24 h of life. In addition to this experimental study, we performed a literature review (covering the past eight years and over 500 papers) to update the references values in the Human Metabolome Database/Urine Metabolome Database (HMDB/UMDB). Notably, 86 of the experimentally measured urinary metabolites are being reported in neonates/infants for the first time and another 20 metabolites are being reported in human urine for the first time ever. Sex differences were found for 15 metabolites. The literature review allowed us to identify another 78 urinary metabolites with concentration data. As a result, reference concentration values and ranges for 378 neonatal urinary metabolites are now publicly accessible via the HMDB.