Conceptual Design and Structural Optimization of NASA Environmentally Responsible Aviation (ERA) Hybrid Wing Body Aircraft

Simultaneously achieving the fuel consumption and noise reduction goals set forth by NASA's Environmentally Responsible Aviation (ERA) project requires innovative and unconventional aircraft concepts. In response, advanced hybrid wing body (HWB) aircraft concepts have been proposed and analyzed as a means of meeting these objectives. For the current study, several HWB concepts were analyzed using the Hybrid wing body Conceptual Design and structural optimization (HCDstruct) analysis code. HCDstruct is a medium-fidelity finite element based conceptual design and structural optimization tool developed to fill the critical analysis gap existing between lower order structural sizing approaches and detailed, often finite element based sizing methods for HWB aircraft concepts. Whereas prior versions of the tool used a half-model approach in building the representative finite element model, a full wing-tip-to-wing-tip modeling capability was recently added to HCDstruct, which alleviated the symmetry constraints at the model centerline in place of a free-flying model and allowed for more realistic center body, aft body, and wing loading and trim response. The latest version of HCDstruct was applied to two ERA reference cases, including the Boeing Open Rotor Engine Integration On an HWB (OREIO) concept and the Boeing ERA-0009H1 concept, and results agreed favorably with detailed Boeing design data and related Flight Optimization System (FLOPS) analyses. Following these benchmark cases, HCDstruct was used to size NASA's ERA HWB concepts and to perform a related scaling study

Clinical validation of an artificial intelligence-based decision support system for diagnosis and risk stratification of heart failure (STRATIFYHF): a protocol for a prospective, multicentre longitudinal study

\ua9 Author(s) (or their employer(s)) 2025. Introduction: Heart failure (HF) is a complex clinical syndrome. Accurate risk stratification and early diagnosis of HF are challenging as its signs and symptoms are non-specific. We propose to address this global challenge by developing the STRATIFYHF artificial intelligence-driven decision support system (DSS), which uses novel analytical methods in determining the risk, diagnosis and prognosis of HF. The primary aim of the present study is to collect prospective clinical data to validate the STRATIFYHF DSS (in terms of diagnostic accuracy, sensitivity and specificity) as a tool to predict the risk, diagnosis and progression of HF. The secondary outcomes are the demographic and clinical predictors of risk, diagnosis and progression of HF. Methods and analysis STRATIFYHF is a prospective, multicentre, longitudinal study that will recruit up to 1600 individuals (n=800 suspected/at risk of HF and n=800 diagnosed with HF) aged ≥45 years old, with up to 24 months of follow-up observations. Individuals suspected of HF will be divided into two categories based on current definitions and predefined inclusion criteria. All participants will have their medical history recorded, along with data on physical examination (signs and symptoms), blood tests including serum natriuretic peptides levels, ECG and echocardiogram results, as well as demographic, socioeconomic and lifestyle data, and use of complete novel technologies (cardiac output response to stress test and voice recognition biomarkers). All measurements will be recorded at baseline and at 12-month follow-up, with medical history and hospitalisation also recorded at 24-month follow-up. Cardiovascular MRI assessment will be completed in a subset of participants (n=20-40) from eligible clinical centres only at baseline. Each clinical centre will recruit a subset of participants (n=30) who will complete a 6-month home-based monitoring of clinical characteristics and accelerometry (wrist-worn monitor) to determine the feasibility and acceptability of the STRATIFYHF mobile application. Focus groups and semistructured interviews will be conducted with up to 15 healthcare professionals and up to 20 study participants (10 at risk of HF and 10 diagnosed with HF) to explore the needs of patients and healthcare professionals prior to the development of the STRATIFYHF DSS and to evaluate the acceptability of this mobile application. Ethics and dissemination Ethical approval has been granted by the East Midlands - Leicester Central Research Ethics Committee (24/EM/0101). Dissemination activities will include journal publications and presentations at conferences, as well as development of training materials and delivery of focused training on the STRATIFYHF DSS and mobile application. We will develop and propose policy guidelines for integration of the STRATIFYHF DSS and mobile application into the standard of care in the HF care pathway

Mortality Associated with Surgical Site Infections Following Cardiac Surgery: Insights from the International ID-IRI Study

Objectives: Surgical site infections (SSIs) after cardiac surgery increase morbidity and mortality rates. This multicenter study aimed to identify mortality risk factors associated with SSIs after heart surgery. Methods: Conducted from January to March 2023, this prospective study included 167 patients aged >16 years with post-heart surgery SSIs. The primary focus was the 30-day mortality. Univariate analysis and multivariate logistic regression utilizing the backward elimination method were used to establish the final model. Results: Several factors significantly correlated with mortality. These included urinary catheterization (odds ratio [OR] 14.197; 90% confidence interval [CI] 12.198-91.721]), emergent surgery (OR 8.470 [90% CI 2.028-35.379]), valvular replacement (OR 4.487 [90% CI 1.001-20.627]), higher quick Sequential Organ Failure Assessment scores (OR 3.147 [90% CI 1.450-6.827]), advanced age (OR 1.075 [90% CI 1.020-1.132]), and postoperative re-interventions within 30 days after SSI (OR 14.832 [90% CI 2.684-81.972]). No pathogens were isolated from the wound cultures of 53 (31.7%) patients. A total of 43.1% of SSIs (n = 72) were due to gram-positive microorganisms, whereas 27.5% of cases (n = 46) involved gram-negatives. Among the gram-positive bacteria, Staphylococci (n = 30, 17.9%) were the predominant microorganisms, whereas Klebsiella (n = 16, 9.6%), Escherichia coli (n = 9, 5.4%), and Pseudomonas aeruginosa (n = 7, 4.2%) were the most prevalent. Conclusions: To mitigate mortality after heart surgery, stringent infection control measures and effective surgical antisepsis are crucial, particularly, in the elderly. The clinical progression of the disease is reflected by the quick Sequential Organ Failure Assessment score and patient re-intervention, and effective treatment is another essential component of SSI management