Changes in the power-duration relationship following prolonged exercise: estimation using conventional and all-out protocols and relationship to muscle glycogen

It is not clear how the parameters of the power-duration relationship (critical power (CP) and W′) are influenced by the performance of prolonged endurance exercise. We used severe intensity prediction trials (conventional protocol) and the 3-min all-out test (3MT) to measure CP and W′ following 2 h of heavy-intensity cycling exercise and took muscle biopsies to investigate possible relationships with changes in muscle glycogen concentration ([glycogen]). Fourteen participants completed a rested 3MT to establish end-test power (Control-EP) and work done above EP (Control-WEP). Subsequently, on separate days, immediately following 2 h of heavy-intensity exercise, participants completed a 3MT to establish Fatigued-EP and Fatigued-WEP and three severe-intensity prediction trials to the limit of tolerance (Tlim) to establish Fatigued-CP and Fatigued-W'. A muscle biopsy was collected immediately before and after one of the 2-h exercise bouts. Fatigued-CP (256 ± 41 W) and Fatigued-EP (256 ± 52 W), and Fatigued-Wʹ (15.3 ± 5.0 kJ) and Fatigued-WEP (14.6 ± 5.3 kJ), were not different (P>0.05), but were ~11% and ~20% lower than Control-EP (287 ± 46 W) and Control-WEP (18.7 ± 4.7 kJ), respectively (P<0.05). The change in muscle [glycogen] was not significantly correlated with the changes in either EP (r = 0.19) or WEP (r = 0.07). The power-duration relationship is adversely impacted by prolonged endurance exercise. The 3MT provides valid estimates of CP and W′ following 2 h of heavy-intensity exercise but the changes in these parameters are not primarily determined by changes in muscle [glycogen]

Dynamics of the power-duration relationship during prolonged endurance exercise and influence of carbohydrate ingestion

We tested the hypotheses that the parameters of the power-duration relationship, estimated as the end-test power (EP) and work done above EP (WEP) during a 3-min all-out exercise test (3MT), would be reduced progressively after 40 min, 80 min, and 2 h of heavy-intensity cycling and that carbohydrate (CHO) ingestion would attenuate the reduction in EP and WEP. Sixteen participants completed a 3MT without prior exercise (control), immediately after 40 min, 80 min, and 2 h of heavy-intensity exercise while consuming a placebo beverage, and also after 2 h of heavy-intensity exercise while consuming a CHO supplement (60 g/h CHO). There was no difference in EP measured without prior exercise (260 ± 37 W) compared with EP after 40 min (268 ± 39 W) or 80 min (260 ± 40 W) of heavy-intensity exercise; however, after 2 h EP was 9% lower compared with control (236 ± 47 W; P < 0.05). There was no difference in WEP measured without prior exercise (17.9 ± 3.3 kJ) compared with after 40 min of heavy-intensity exercise (16.1 ± 3.3 kJ), but WEP was lower (P < 0.05) than control after 80 min (14.7 ± 2.9 kJ) and 2 h (13.8 ± 2.7 kJ). Compared with placebo, CHO ingestion negated the reduction of EP following 2 h of heavy-intensity exercise (254 ± 49 W) but had no effect on WEP (13.5 ± 3.4 kJ). These results reveal a different time course for the deterioration of EP and WEP during prolonged endurance exercise and indicate that EP is sensitive to CHO availability

SIAH and EGFR, Two RAS Pathway Biomarkers, are Highly Prognostic in Locally Advanced and Metastatic Breast Cancer

Background: Metastatic breast cancer exhibits diverse and rapidly evolving intra- and inter-tumor heterogeneity. Patients with similar clinical presentations often display distinct tumor responses to standard of care (SOC) therapies. Genome landscape studies indicate that EGFR/HER2/RAS “pathway” activation is highly prevalent in malignant breast cancers. The identification of therapy-responsive and prognostic biomarkers is paramount important to stratify patients and guide therapies in clinical oncology and personalized medicine. Methods: In this study, we analyzed matched pairs of tumor specimens collected from 182 patients who received neoadjuvant systemic therapies (NST). Statistical analyses were conducted to determine whether EGFR/HER2/RAS pathway biomarkers and clinicopathological predictors, alone and in combination, are prognostic in breast cancer. Findings: SIAH and EGFR outperform ER, PR, HER2 and Ki67 as two logical, sensitive and prognostic biomarkers in metastatic breast cancer. We found that increased SIAH and EGFR expression correlated with advanced pathological stage and aggressive molecular subtypes. Both SIAH expression post-NST and NST-induced changes in EGFR expression in invasive mammary tumors are associated with tumor regression and increased survival, whereas ER, PR, and HER2 were not. These results suggest that SIAH and EGFR are two prognostic biomarkers in breast cancer with lymph node metastases. Interpretation: The discovery of incorporating tumor heterogeneity-independent and growth-sensitive RAS pathway biomarkers, SIAH and EGFR, whose altered expression can be used to estimate therapeutic efficacy, detect emergence of resistant clones, forecast tumor regression, differentiate among partial responders, and predict patient survival in the neoadjuvant setting, has a clear clinical implication in personalizing breast cancer therapy. Funding: This work was supported by the Dorothy G. Hoefer Foundation for Breast Cancer Research (A.H. Tang); Center for Innovative Technology (CIT)-Commonwealth Research Commercialization Fund (CRCF) (MF14S-009-LS to A.H. Tang), and National Cancer Institute (CA140550 to A.H. Tang)

Discovery of Clinical Candidate 2‑((2<i>S</i>,6<i>S</i>)‑2-Phenyl-6-hydroxyadamantan-2-yl)-1-(3′-hydroxyazetidin-1-yl)ethanone [BMS-816336], an Orally Active Novel Selective 11β-Hydroxysteroid Dehydrogenase Type 1 Inhibitor

BMS-816336 (<b>6n-2</b>), a hydroxy-substituted adamantyl acetamide, has been identified as a novel, potent inhibitor against human 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) enzyme (IC₅₀ 3.0 nM) with >10000-fold selectivity over human 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2). <b>6n-2</b> exhibits a robust acute pharmacodynamic effect in cynomolgus monkeys (ED₅₀ 0.12 mg/kg) and in DIO mice. It is orally bioavailable (%<i>F</i> ranges from 20 to 72% in preclinical species) and has a predicted pharmacokinetic profile of a high peak to trough ratio and short half-life in humans. This ADME profile met our selection criteria for once daily administration, targeting robust inhibition of 11β-HSD1 enzyme for the first 12 h period after dosing followed by an “inhibition holiday” so that the potential for hypothalamic–pituitary–adrenal (HPA) axis activation might be mitigated. <b>6n-2</b> was found to be well-tolerated in phase 1 clinical studies and represents a potential new treatment for type 2 diabetes, metabolic syndrome, and other human diseases modulated by glucocorticoid control

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Efficacy and safety of two neutralising monoclonal antibody therapies, sotrovimab and BRII-196 plus BRII-198, for adults hospitalised with COVID-19 (TICO): a randomised controlled trial

We aimed to assess the efficacy and safety of two neutralising monoclonal antibody therapies (sotrovimab [Vir Biotechnology and GlaxoSmithKline] and BRII-196 plus BRII-198 [Brii Biosciences]) for adults admitted to hospital for COVID-19 (hereafter referred to as hospitalised) with COVID-19. In this multinational, double-blind, randomised, placebo-controlled, clinical trial (Therapeutics for Inpatients with COVID-19 [TICO]), adults (aged ≥18 years) hospitalised with COVID-19 at 43 hospitals in the USA, Denmark, Switzerland, and Poland were recruited. Patients were eligible if they had laboratory-confirmed SARS-CoV-2 infection and COVID-19 symptoms for up to 12 days. Using a web-based application, participants were randomly assigned (2:1:2:1), stratified by trial site pharmacy, to sotrovimab 500 mg, matching placebo for sotrovimab, BRII-196 1000 mg plus BRII-198 1000 mg, or matching placebo for BRII-196 plus BRII-198, in addition to standard of care. Each study product was administered as a single dose given intravenously over 60 min. The concurrent placebo groups were pooled for analyses. The primary outcome was time to sustained clinical recovery, defined as discharge from the hospital to home and remaining at home for 14 consecutive days, up to day 90 after randomisation. Interim futility analyses were based on two seven-category ordinal outcome scales on day 5 that measured pulmonary status and extrapulmonary complications of COVID-19. The safety outcome was a composite of death, serious adverse events, incident organ failure, and serious coinfection up to day 90 after randomisation. Efficacy and safety outcomes were assessed in the modified intention-to-treat population, defined as all patients randomly assigned to treatment who started the study infusion. This study is registered with ClinicalTrials.gov, NCT04501978. Between Dec 16, 2020, and March 1, 2021, 546 patients were enrolled and randomly assigned to sotrovimab (n=184), BRII-196 plus BRII-198 (n=183), or placebo (n=179), of whom 536 received part or all of their assigned study drug (sotrovimab n=182, BRII-196 plus BRII-198 n=176, or placebo n=178; median age of 60 years [IQR 50–72], 228 [43%] patients were female and 308 [57%] were male). At this point, enrolment was halted on the basis of the interim futility analysis. At day 5, neither the sotrovimab group nor the BRII-196 plus BRII-198 group had significantly higher odds of more favourable outcomes than the placebo group on either the pulmonary scale (adjusted odds ratio sotrovimab 1·07 [95% CI 0·74–1·56]; BRII-196 plus BRII-198 0·98 [95% CI 0·67–1·43]) or the pulmonary-plus complications scale (sotrovimab 1·08 [0·74–1·58]; BRII-196 plus BRII-198 1·00 [0·68–1·46]). By day 90, sustained clinical recovery was seen in 151 (85%) patients in the placebo group compared with 160 (88%) in the sotrovimab group (adjusted rate ratio 1·12 [95% CI 0·91–1·37]) and 155 (88%) in the BRII-196 plus BRII-198 group (1·08 [0·88–1·32]). The composite safety outcome up to day 90 was met by 48 (27%) patients in the placebo group, 42 (23%) in the sotrovimab group, and 45 (26%) in the BRII-196 plus BRII-198 group. 13 (7%) patients in the placebo group, 14 (8%) in the sotrovimab group, and 15 (9%) in the BRII-196 plus BRII-198 group died up to day 90. Neither sotrovimab nor BRII-196 plus BRII-198 showed efficacy for improving clinical outcomes among adults hospitalised with COVID-19. US National Institutes of Health and Operation Warp Spee