The purpose of this study was to show how continuous exercise affects the basal values of biochemical and hematological parameters in elite athletes. A total of 14,010 samples (male = 8452 and female = 5558 (March 2011–March 2020)) from 3588 elite athletes (male = 2258 and female = 1330, mean age 24.9 ± 6.9 vs. 24.1 ± 5.5 years, respectively) from 32 sport modalities, were studied over 9 years to check the variation of basal biochemical and hematological parameter values. There were differences seen in the basal values of creatine kinase (CK), urea, creatinine, aspartate transaminase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LDH), potassium, total bilirubin, and eosinophil percentage compared to reference population data. However, other analytes showed narrow ranges of variation like glucose, total protein, albumin, sodium, hemoglobin, mean cell volume (MCV), and platelet count. Exercise produces changes in biochemical and hematological basal values of athletes compared to the general population, with the greatest variation in CK, but AST, ALT, LDH, potassium, and total bilirubin (TBil) show high values in serum, only with a wider distribution of values. The data here reflects the effect of exercise on biochemical and hematological parameter baseline ranges in elite athletes. As clinical laboratories use reference intervals to validate clinical reports, these “pseudo” reference intervals should be used when validating laboratory reports.

Fetal echocardiography has limited prognostic ability in the evaluation of left-sided congenital heart defects (left heart defects). Cord blood cardiovascular biomarkers could improve the prognostic evaluation of left heart defects. A multicenter prospective cohort (2013–2019) including fetuses with left heart defects (aortic coarctation, aortic stenosis, hypoplastic left heart, and multilevel obstruction (complex left heart defects) subdivided according to their outcome (favorable vs. poor), and control fetuses were evaluated in the third trimester of pregnancy at three referral centers in Spain. Poor outcome was defined as univentricular palliation, heart transplant, or death. Cord blood concentrations of N-terminal precursor of B-type natriuretic peptide, Troponin I, transforming growth factor β, placental growth factor, and soluble fms-like tyrosine kinase-1 were determined. A total of 45 fetuses with left heart defects (29 favorable and 16 poor outcomes) and 35 normal fetuses were included, with a median follow-up of 3.1 years (interquartile range 1.4–3.9). Left heart defects with favorable outcome showed markedly increased cord blood transforming growth factor β (normal heart median 15.5 ng/mL (6.8–21.4) vs. favorable outcome 51.7 ng/mL (13.8–73.9) vs. poor outcome 25.1 ng/mL (6.9–39.0), p = 0.001) and decreased placental growth factor concentrations (normal heart 17.9 pg/mL (13.8–23.9) vs. favorable outcome 12.8 pg/mL (11.7–13.6) vs. poor outcome 11.0 pg/mL (8.8–15.4), p < 0.001). Poor outcome left heart defects had higher N-terminal precursor of B-type natriuretic peptide (normal heart 508.0 pg/mL (287.5–776.3) vs. favorable outcome 617.0 pg/mL (389.8–1087.8) vs. poor outcome 1450.0 pg/mL (919.0–1645.0), p = 0.001) and drastically reduced soluble fms-like tyrosine kinase-1 concentrations (normal heart 1929.7 pg/mL (1364.3–2715.8) vs. favorable outcome (1848.3 pg/mL (646.9–2313.6) vs. poor outcome 259.0 pg/mL (182.0–606.0), p < 0.001). Results showed that fetuses with left heart defects present a distinct cord blood biomarker profile according to their outcome.

The synovial fluid (SF) analysis involves a series of chemical and physical studies that allow opportune diagnosing of septic, inflammatory, non-inflammatory, and other pathologies in joints. Among the variety of analyses to be performed on the synovial fluid, the study of viscosity can help distinguish between these conditions, since this property is affected in pathological cases. The problem with viscosity measurement is that it usually requires a large sample volume, or the necessary instrumentation is bulky and expensive. This study compares the viscosity of normal synovial fluid samples with samples with infectious and inflammatory pathologies and classifies them using an ANN (Artificial Neural Network). For this purpose, a low-cost, portable QCR-based sensor (10 MHz) was used to measure the viscous responses of the samples by obtaining three parameters: $\Delta f$, $\Delta \mathrm{\Gamma }$ (parameters associated with the viscoelastic properties of the fluid), and viscosity calculation. These values were used to train the algorithm. Different versions of the ANN were compared, along with other models, such as SVM and random forest. Thirty-three samples of SF were analyzed. Our study suggests that the viscosity characterized by our sensor can help distinguish infectious synovial fluid, and that implementation of ANN improves the accuracy of synovial fluid classification.