Influence Of Environmental Conditions On Performance And Heart Rate Responses To The 30-15 Incremental Fitness Test In Rugby Union Athletes

The purpose of this study was to examine the differences in performance and heart rate responses between a high heat outdoor condition (34.0°C, 64.1% humidity) and a temperate indoor condition (22.0°C, 50.0% humidity) during the 30-15 intermittent fitness test (30-15IFT). Eight highly trained Rugby Union players (28.1 +/- 1.5 years, 181.4 +/- 8.8 cm, 88.4 +/- 13.3kg) completed the 30-15IFT in two different temperature conditions. Dependant variables recorded and analysed included; final running speed of the 30-15IFT, heart rate (HR) at rest (HR rest), maximum HR (Max HR), HR recovery (HRR), average HR (HR ave) and sub-maximal HR corresponding to 25%, 50% and 75% of final test speed (HR 25%, HR 50% and HR 75%) and HR at 13 km[middle dot]h-1 (HR 13 km[middle dot]h-1). Greater running speeds were achieved when the test was conducted indoors (19.4 +/- 0.7 km[middle dot]h-1 vs. 18.6 +/- 0.6 km·h-1, p = 0.002, d = 1.67). HR ave and HR 13 km·h-1 were greater when the test was conducted outdoors (p 0.85). Large effect sizes were observed for the greater HR at submaximal intensities (d > 0.90). The results of this study highlight the influence of temperature on 30-15IFT performance and cardiac responses. It is recommended that prescription of training based on 30-15IFT results reflects the temperature that the training will be performed in and that practitioners acknowledge that a meaningful change in assessment results can be the result of seasonal temperature change rather than training induced change

Effects of an active warm-up on variation in bench press and back squat (upper and lower body measures).

The present study investigated the magnitude of diurnal variation in back squat and bench press using the MuscleLab linear encoder over three different loads and assessed the benefit of an active warm-up to establish whether diurnal variation could be negated. Ten resistance-trained males underwent (mean ± SD: age 21.0 ± 1.3 years, height 1.77 ± 0.06 m, and body mass 82.8 ± 14.9 kg) three sessions. These included control morning (M, 07:30 h) and evening (E, 17:30 h) sessions (5-min standardized warm-up at 150 W, on a cycle ergometer), and one further session consisting of an extended active warm-up morning trial (ME, 07:30 h) until rectal temperature (Trec) reached previously recorded resting evening levels (at 150 W, on a cycle ergometer). All sessions included handgrip, followed by a defined program of bench press (at 20, 40, and 60 kg) and back squat (at 30, 50, and 70 kg) exercises. A linear encoder was attached to an Olympic bar used for the exercises and average force (AF), peak velocity (PV), and time to peak velocity (tPV) were measured (MuscleLab software; MuscleLab Technology, Langesund, Norway) during the concentric phase of the movements. Values for Trec were higher in the E session compared to values in the M session (Δ0.53 °C, P  0.05) to increase from M to E levels. Therefore, MuscleLab linear encoder could detect meaningful differences between the morning and evening for all variables. However, the diurnal variation in bench press and back squat (measures of lower and upper body force and power output) is not explained by time-of-day oscillations in Trec

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Performance of non-invasive tests and histology for the prediction of clinical outcomes in patients with non-alcoholic fatty liver disease: an individual participant data meta-analysis

BackgroundHistologically assessed liver fibrosis stage has prognostic significance in patients with non-alcoholic fatty liver disease (NAFLD) and is accepted as a surrogate endpoint in clinical trials for non-cirrhotic NAFLD. Our aim was to compare the prognostic performance of non-invasive tests with liver histology in patients with NAFLD.MethodsThis was an individual participant data meta-analysis of the prognostic performance of histologically assessed fibrosis stage (F0–4), liver stiffness measured by vibration-controlled transient elastography (LSM-VCTE), fibrosis-4 index (FIB-4), and NAFLD fibrosis score (NFS) in patients with NAFLD. The literature was searched for a previously published systematic review on the diagnostic accuracy of imaging and simple non-invasive tests and updated to Jan 12, 2022 for this study. Studies were identified through PubMed/MEDLINE, EMBASE, and CENTRAL, and authors were contacted for individual participant data, including outcome data, with a minimum of 12 months of follow-up. The primary outcome was a composite endpoint of all-cause mortality, hepatocellular carcinoma, liver transplantation, or cirrhosis complications (ie, ascites, variceal bleeding, hepatic encephalopathy, or progression to a MELD score ≥15). We calculated aggregated survival curves for trichotomised groups and compared them using stratified log-rank tests (histology: F0–2 vs F3 vs F4; LSM: 2·67; NFS: 0·676), calculated areas under the time-dependent receiver operating characteristic curves (tAUC), and performed Cox proportional-hazards regression to adjust for confounding. This study was registered with PROSPERO, CRD42022312226.FindingsOf 65 eligible studies, we included data on 2518 patients with biopsy-proven NAFLD from 25 studies (1126 [44·7%] were female, median age was 54 years [IQR 44–63), and 1161 [46·1%] had type 2 diabetes). After a median follow-up of 57 months [IQR 33–91], the composite endpoint was observed in 145 (5·8%) patients. Stratified log-rank tests showed significant differences between the trichotomised patient groups (p<0·0001 for all comparisons). The tAUC at 5 years were 0·72 (95% CI 0·62–0·81) for histology, 0·76 (0·70–0·83) for LSM-VCTE, 0·74 (0·64–0·82) for FIB-4, and 0·70 (0·63–0·80) for NFS. All index tests were significant predictors of the primary outcome after adjustment for confounders in the Cox regression.InterpretationSimple non-invasive tests performed as well as histologically assessed fibrosis in predicting clinical outcomes in patients with NAFLD and could be considered as alternatives to liver biopsy in some cases

Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study

Background and aims: Detecting NASH remains challenging, while at-risk NASH (steatohepatitis and F≥ 2) tends to progress and is of interest for drug development and clinical application. We developed prediction models by supervised machine learning techniques, with clinical data and biomarkers to stage and grade patients with NAFLD. Approach and results: Learning data were collected in the Liver Investigation: Testing Marker Utility in Steatohepatitis metacohort (966 biopsy-proven NAFLD adults), staged and graded according to NASH CRN. Conditions of interest were the clinical trial definition of NASH (NAS ≥ 4;53%), at-risk NASH (NASH with F ≥ 2;35%), significant (F ≥ 2;47%), and advanced fibrosis (F ≥ 3;28%). Thirty-five predictors were included. Missing data were handled by multiple imputations. Data were randomly split into training/validation (75/25) sets. A gradient boosting machine was applied to develop 2 models for each condition: clinical versus extended (clinical and biomarkers). Two variants of the NASH and at-risk NASH models were constructed: direct and composite models.Clinical gradient boosting machine models for steatosis/inflammation/ballooning had AUCs of 0.94/0.79/0.72. There were no improvements when biomarkers were included. The direct NASH model produced AUCs (clinical/extended) of 0.61/0.65. The composite NASH model performed significantly better (0.71) for both variants. The composite at-risk NASH model had an AUC of 0.83 (clinical and extended), an improvement over the direct model. Significant fibrosis models had AUCs (clinical/extended) of 0.76/0.78. The extended advanced fibrosis model (0.86) performed significantly better than the clinical version (0.82). Conclusions: Detection of NASH and at-risk NASH can be improved by constructing independent machine learning models for each component, using only clinical predictors. Adding biomarkers only improved the accuracy of fibrosis