Phase II randomized preoperative window-of-opportunity study of the PI3K inhibitor pictilisib plus anastrozole compared with anastrozole alone in patients with estrogen receptor-positive breast cancer

Purpose: Preclinical data support a key role for the PI3K pathway in estrogen receptor-positive breast cancer and suggest that combining PI3K inhibitors with endocrine therapy may overcome resistance. This preoperative window study assessed whether adding the PI3K inhibitor pictilisib (GDC-0941) can increase the antitumor effects of anastrozole in primary breast cancer and aimed to identify the most appropriate patient population for combination therapy. Patients and Methods: In this randomized, open-label phase II trial, postmenopausal women with newly diagnosed operable estrogen receptor-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancers were recruited. Participants were randomly allocated (2:1, favoring the combination) to 2 weeks of preoperative treatment with anastrozole 1 mg once per day (n = 26) or the combination of anastrozole 1 mg with pictilisib 260 mg once per day (n = 49). The primary end point was inhibition of tumor cell proliferation as measured by change in Ki-67 protein expression between tumor samples taken before and at the end of treatment. Results: There was significantly greater geometric mean Ki-67 suppression of 83.8% (one-sided 95% CI, ≥ 79.0%) for the combination and 66.0% (95% CI, ≤ 75.4%) for anastrozole (geometric mean ratio [combination: anastrozole], 0.48; 95% CI, ≤ 0.72; P = .004). PIK3CA mutations were not predictive of response to pictilisib, but there was significant interaction between response to treatment and molecular subtype (P =.03);for patients with luminal B tumors, the combination:anastrozole geometric mean ratio of Ki-67 suppression was 0.37 (95% CI, ≤ 0.67; P = .008), whereas no significant Ki-67 response was observed for pictilisib in luminal A tumors (1.01; P = .98). Multivariable analysis confirmed Ki-67 response to the combination treatment of patients with luminal B tumors irrespective of progesterone receptor status or baseline Ki-67 expression. Conclusion: Adding pictilisib to anastrozole significantly increases suppression of tumor cell proliferation in luminal B primary breast cancer

White paper: Research Challenges at the Intersection of Energy and Equity in the Energy Transition

Summary report from NSF2026: Conference Workshops to Identify Research Challenges at the Intersection of Energy and Equity in the Energy Transitio

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

Computational Models for Fracture and Degradation of Structures

IASS-IACM 2008 Session: Computational Models for Fracture and Degradation of Structures Session Organizers: Gunther MESCHKE (Ruhr University Bochum), Jan ROTS (TU-Delft) -- "Stepwise softening for concrete and masonry structures" by Jan G. ROTS, Max A.N. HENDRIKS, Matt J. DEJONG (TU-Delft), Beatrice BELLETTI (University of Parma) -- "The multi-scale approach of masonry, paradigm of the clay brick" by Konrad J. KRAKOWIAK, Paulo B. LOURENCO (University of Minho), Franz-J. ULM (MIT) -- "Simplified modeling strategies for non linear dynamic calculations of RC structural walls including soil-structure interaction" by Panagiotis KOTRONIS, J. MAZARS, S. GRANGE, C. GIRY (Grenoble Universites) -- "Modeling mixed-mode crack propagation in reinforced concrete" by Rena C. YU, Gonzalo RUIZ, Jacinto R. CARMONA (University of Castilla-La Mancha) -- "Limit-analysis based identification of fracture and degradation mechanisms in two-phase composite materials" by Josef FUSSL (TU Vienna), Roman LACKNER (TU Munich) -- "Fracture analyses of fiber-reinforced concrete structures" by John BOLANDER (University of California, Davis) -- "Crack-centered enrichment for debonding in two-phase composite applied to textile reinforced concrete" by Rostislav CHUDOBA, Jakub JERABEK, Frank PEIFFER, Joseph HEGGER (RWTH Aachen) -- "Three-dimensional higher order X-FEM model for multifield durability and failure analysis of concrete structures" by Stefan JOX, Christian BECKER, G?nther MESCHKE (Ruhr University Bochum) -- "From multi-scale to multi-grid FE analysis of concrete fracture" by Chris J. PEARCE, Lukasz KACZMARCZYK, Nenad BICANIC (University of Glasgow) -- "Analysis of thin layer ductile concrete as a seismic retrofit for masonry infill walls" by Marios A. KYRIAKIDES, Sarah L. BILLINGTON (Stanford University) -- "Mesoscopic failure simulation of concrete and life-cycle computation of concrete structures" by Kohei NAGAI, Koich MAEKAWA (University of Tokyo

Gut Microbiome and Space Travelers’ Health: State of the Art and Possible Pro/Prebiotic Strategies for Long-Term Space Missions

International audienceThe upcoming exploration missions will imply a much longer duration than any of the missions flown so far. In these missions, physiological adaptation to the new environment leads to changes in different body systems, such as the cardiovascular and musculoskeletal systems, metabolic and neurobehavioral health and immune function. To keep space travelers healthy on their trip to Moon, Mars and beyond and their return to Earth, a variety of countermeasures need to be provided to maintain body functionality. From research on the International Space Station (ISS) we know today, that for instance prescribing an adequate training regime for each individual with the devices available in the respective spacecraft is still a challenge. Nutrient supply is not yet optimal and must be optimized in exploration missions. Food intake is intrinsically linked to changes in the gut microbiome composition. Most of the microbes that inhabit our body supply ecosystem benefit to the host-microbe system, including production of important resources, bioconversion of nutrients, and protection against pathogenic microbes. The gut microbiome has also the ability to signal the host, regulating the processes of energy storage and appetite perception, and influencing immune and neurobehavioral function. The composition and functionality of the microbiome most likely changes during spaceflight. Supporting a healthy microbiome by respective measures in space travelers might maintain their health during the mission but also support rehabilitation when being back on Earth. In this review we are summarizing the changes in the gut microbiome observed in spaceflight and analog models, focusing particularly on the effects on metabolism, the musculoskeletal and immune systems and neurobehavioral disorders. Since space travelers are healthy volunteers, we focus on the potential of countermeasures based on pre- and probiotics supplements

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