Early management of acute severe UC in the biologics era: development and international validation of a prognostic clinical index to predict steroid response

Objectives We aimed to determine whether changes in acute severe colitis (ASC) management have translated to improved outcomes and to develop a simple model predicting steroid non-response on admission. Design Outcomes of 131 adult ASC admissions (117 patients) in Oxford, UK between 2015 and 2019 were compared with data from 1992 to 1993. All patients received standard treatment with intravenous corticosteroids and endoscopic disease activity scoring (Ulcerative Colitis Endoscopic Index of Severity (UCEIS)). Steroid non-response was defined as receiving medical rescue therapy or surgery. A predictive model developed in the Oxford cohort was validated in Australia and India (Gold Coast University Hospital 2015–2020, n=110; All India Institute of Medical Sciences, New Delhi 2018–2020, n=62). Results In the 2015–2019 Oxford cohort, 15% required colectomy during admission vs 29% in 1992–1993 (p=0.033), while 71 (54%) patients received medical rescue therapy (27% ciclosporin, 27% anti-tumour necrosis factor, compared with 27% ciclosporin in 1992–1993 (p=0.0015). Admission C reactive protein (CRP) (false discovery rate, p=0.00066), albumin (0.0066) and UCEIS scores (0.015) predicted steroid non-response. A four-point model was developed involving CRP of ≥100 mg/L (one point), albumin of ≤25 g/L (one point), and UCEIS score of ≥4 (1 point) or ≥7 (2 points). Patients scoring 0, 1, 2, 3 and 4 in the validation cohorts had steroid response rates of 100, 75.0%, 54.9%, 18.2% and 0%, respectively. Scoring of ≥3 was 84% (95% CI 0.70 to 0.98) predictive of steroid failure (OR 11.9, 95% CI 10.8 to 13.0). Colectomy rates in the validation cohorts were were 8%–11%. Conclusions Emergency colectomy rates for ASC have halved in 25 years to 8%–15% worldwide. Patients who will not respond to corticosteroids are readily identified on admission and may be prioritised for early intensification of therapy

The CMS Statistical Analysis and Combination Tool: Combine

Metrics: https://link.springer.com/article/10.1007/s41781-024-00121-4/metricsThis paper describes the Combine software package used for statistical analyses by the CMS Collaboration. The package, originally designed to perform searches for a Higgs boson and the combined analysis of those searches, has evolved to become the statistical analysis tool presently used in the majority of measurements and searches performed by the CMS Collaboration. It is not specific to the CMS experiment, and this paper is intended to serve as a reference for users outside of the CMS Collaboration, providing an outline of the most salient features and capabilities. Readers are provided with the possibility to run Combine and reproduce examples provided in this paper using a publicly available container image. Since the package is constantly evolving to meet the demands of ever-increasing data sets and analysis sophistication, this paper cannot cover all details of Combine. However, the online documentation referenced within this paper provides an up-to-date and complete user guide.CERN (European Organization for Nuclear Research)STFC (United Kingdom)Marie-Curie programme and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundatio

Integrated optimization of mechanical alloying parameters for nanostructured Ti-Mg-Zr alloy using desirability function, educational competition, and grey wolf algorithms

The present study aims to develop a novel nanostructured ternary Ti-Mg-Zr alloy using a mechanical alloying (MA) process designed for biomedical applications. The Ti-Mg-Zr alloy was designed with the desirable characteristics of Titanium offering the highest biocompatibility (among all metallic materials), Magnesium (Mg) possessing lightweight characteristics with improved strength and elastic compatibility with bone, Zirconium (Zr) minimizes the magnetic resonance imaging (MRI) artefacts and improved corrosion resistance. Synthesizing and retaining nanostructure in alloy development requires appropriate control of MA parameters such as milling speed (MS), milling time (MT), and ball-to-powder-weight ratio (BPR). Experiments are designed with simultaneously varying parameters and levels to collect the developed alloy's overall performance (crystallite size, CS, and lattice strain, LS). MS showed a dominant effect, followed by MT and BPR towards CS and LS. The model produced a better coefficient of determination (0.9908 for CS and 0.9803 for LS), resulting in the average absolute percent deviation in predicting ten random experimental cases equal to 3.88 % for CS and 3.34 % for LS. The best-fit curve drawn with 1000 data points resulted in the CS establishing a strong linear inverse relationship (correlation coefficient of 0.9821) with LS. The complex interplay (BPR exhibited non-linear behaviour, while MS and MT demonstrated linear relationships with both CS and LS) among MA parameters require solving conflicting responses (simultaneously minimizing CS and maximizing LS). Educational competition optimizer (ECO) and grey wolf optimizer (GWO) integrated with desirability function approach (DFA) determined single optimal conditions (MS: 400 rpm; MT: 30 h, and BPR: 10) resulted in the highest composite desirability value (say 1) with reduced CS of 26.73 nm, and higher LS of 0.816 %. The experimental value of CS and LS corresponding to optimal conditions is found to be equal to 28.6 nm and 0.812 %, respectively. GWO converges marginally faster than ECO in locating the highest composite desirability value. Morphological analysis of powdered samples was carried out using XRD, SEM and TEM. The minimum CS and maximum LS ensure nanostructure in synthesized Ti-Mg-Zr alloy and are treated as promising alloys for permanent implants that require frequent MRI assessments

Custom design of protein particles as multifunctional biomaterials

Assembled protein particles, as emerging biomaterials, have broad applications ranging from vaccines and drug delivery to biocatalysis and particle tracking, but to date these require trial-and-error rational design experimentation and/or intensive computational methods to generate. Here, the authors describe an easy-to-implement engineering strategy to generate customized protein particles as multifunctional biomaterials. They utilize protein–peptide modules to generate functional nanoparticles whose assembly and size is controlled by the addition of mild stimuli. The protein assembling method is versatile, as exemplified through particle formation with 7 distinct protein modules, using a variety of assembly conditions tailored by the chemistries of 3 peptide partners. They have generated customized protein particles using enzymes, binding and reporter proteins, and their functions and utilities are demonstrated using biocatalysis, sensing, and labelling applications, respectively. Furthermore, co-assembly with two functional proteins within one particle has been successfully achieved and demonstrated. Physical insights into the kinetics and molecular mechanisms of particle formation are revealed by small angle X-ray scattering and mass photometry, providing fundamental knowledge to guide design and manufacture these interesting biomaterials in future. Their protein assembling strategy is a reliable method for fabricating a protein particle to deliver new functionalities on-demand