Final Results Research Articles

Lung cancer screening by low-dose CT scan in France: final results of the DEP KP80 study after three rounds

In prior randomised controlled trials, lung cancer screening using low-dose computed tomography (LDCT) has been shown to reduce lung cancer mortality and overall mortality. Despite these results, organised screening in France remains a challenge. This study assessed the feasibility and efficacy of lung cancer screening within a real-life context in a French administrative territory. DEP KP80 was a single-arm prospective study. Participants aged between 55 and 74 years, smokers or former smokers of ≥30 pack-years, were recruited. An annual LDCT scan was scheduled and three rounds were performed. Subjects were selected by general practitioners or pulmonologists, who checked the inclusion criteria and prescribed the CT scan. Between March 2016 and February 2020, 1254 participants were enrolled. Overall, 945 (75.4%) participants underwent baseline LDCT (T0), 376 (42.8%) completed the first round (T1) and 270 (31%) the second (T2) one. Forty-two lung cancers were diagnosed, 30 cancers (71.4%) were stage I or II and 34 cancers (80.9%) were treated surgically. In this study, the overall positive predictive value for a positive screening was 48% (95% CI 37-59) and the negative predictive value 100% (95% CI 100-100). This study demonstrated the feasibility and efficacy of lung cancer screening in a real-life context with most lung cancers diagnosed at an early stage and surgically removed. Our results also highlighted the importance of participation in each round, underlining the fact that optimising organisation is a major goal. Agence Régionale de Santé de Picardie, La Ligue contre le cancer, le Conseil Départemental de la Somme, and AstraZeneca.

Financial Time Series Forecasting Based on Long-Short Term Memory, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Batch Normalization

Long-short term memory (LSTM) is a state-of-art and widely used model to forecast financial time series. However, primitive LSTM networks do not perform well due to over-fitting problems of the deep learning model and nonlinear and non-stationary characteristics of financial time series data. In addition, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is an outstanding data frequency decomposition technique that can decompose original time series into several intrinsic mode functions and a residue. Thus, this paper proposed a novel hybrid network CEEMDAN-LSTM-BN based on LSTM. Specifically, to avoid over-fitting, the modified LSTM-BN network consists of two LSTM layers, two Batch Normalization (BN) layers following each LSTM layer, and a dropout layer. Each of the intrinsic mode functions and the residue would be processed by CEEMDAN-LSTM-BN and the final prediction results are obtained by reconstructing each predictive series. The advantages of the proposed CEEMDANLSTM-BN networks are verified by comparing them to primitive LSTM, other hybrid models, and some famous machine learning models. Moreover, the robustness of the networks is assessed by numerical experiments on different stock indices datasets.

Single-Channel Sleep EEG Classification Method Based on LSTM and Hidden Markov Model

Background: The single-channel sleep EEG has the advantages of convenient collection, high-cost performance, and easy daily use, and it has been widely used in the classification of sleep stages. Methods: This paper proposes a single-channel sleep EEG classification method based on long short-term memory and a hidden Markov model (LSTM-HMM). First, the single-channel EEG is decomposed using wavelet transform (WT), and multi-domain features are extracted from the component signals to characterize the EEG characteristics fully. Considering the temporal nature of sleep stage changes, this paper uses a multi-step time series as the input for the model. After that, the multi-step time series features are input into the LSTM. Finally, the HMM improves the classification results, and the final prediction results are obtained. Results: A complete experiment was conducted on the Sleep-EDFx dataset. The results show that the proposed method can extract deep information from EEG and make full use of the sleep stage transition rule. The proposed method shows the best performance in single-channel sleep EEG classification; the accuracy, macro average F1 score, and kappa are 82.71%, 0.75, and 0.76, respectively. Conclusions: The proposed method can realize single-channel sleep EEG classification and provide a reference for other EEG classifications.

Implementing a pilot primary care program: Armenia’s steps toward universal health coverage

Abstract Problem Armenia has struggled to transform its ineffective primary healthcare (PHC) system into one that can address the non-communicable disease burden, which causes 93% of mortality. To address this issue, Armenia aims to establish universal health coverage and invest in high-quality primary care. Description In 2023, the Armenian Ministry of Health signed a multisectoral policy to implement a pilot primary care program to trial a multitude of reforms related to diabetes (DM) and hypertension (HTN). Pilot interventions include pathways for screening/diagnosis, provider training, free medications and diagnostics, and new provider financial incentives. The key question to be answered is if these pilot interventions cause DM and HTN health outcomes to improve. Beginning in 2024, the program is being piloted in a primary care clinic in Dsegh. Results As a result of the 2023 Ministerial policy, multisectoral stakeholders designed and implemented this pilot program. Baseline focus groups found that the pilot addresses the financial barriers that community members noted lead to delays in seeking care and that providers perceive the pilot to be beneficial to their patients. Although final results are not yet available, the pilot has already positively changed features in the national EMR and brought together stakeholders in a way never done before. Lessons The pilot’s innovation comes from it being the first time that hypertension and cholesterol medications are provided by the government for free, provider bonuses are based on outcome indicators, indicator targets are set this high, a point-of-care A1c machine is utilized, and a robust monitoring and evaluation (M&E) plan is implemented, all of which are currently shaping discussions on national policy. For countries similarly working on strengthening primary care, it is recommended to pilot specific reforms and establish mechanisms through M&E systems that allow pilots to inform evidence-based decision making. Key messages • To move towards universal health coverage, pilot programs designed with multi-sectoral stakeholders are needed to trial suggested healthcare reforms. • It is essential to invest in monitoring and evaluations systems to generate the data necessary to develop evidence-based policies.

Global burden of high-risk cardiovascular patients without prior myocardial infarction or stroke: VESALIUS-REAL - preliminary results from Germany

Abstract Background Clinical trial data has shown that low-density lipoprotein cholesterol (LDL-C) lowering with PCSK9-inhibitors reduced cardiovascular (CV) endpoints in patients with established atherosclerotic cardiovascular disease (ASCVD). Intervening early in patients with high CV risk prior to myocardial infarction (MI) or stroke, while perceived to be less urgent, may result in larger public health benefits. The effect of evolocumab in this population is investigated in a phase-3 clinical trial (NCT03872401: Effect of Evolocumab in Patients at High Cardiovascular Risk Without Prior Myocardial Infarction or Stroke [VESALIUS-CV]). This observational study examines the global burden of VESALIUS-CV-like population in the REAL-World ("VESALIUS-REAL") in eight regions (Germany, Hong Kong, Japan, South Korea, Sweden, Taiwan, UK and US) using a unified protocol across multiple databases. The multi-database study is ongoing, and the current analysis focuses on characteristics of VESALIUS-CV like patients in Germany. Methods Eligibility criteria are shown in the Figure. Data were obtained from the German Disease Analyzer (2013-2022), a representative claims database from Germany. Criteria were aligned with the clinical trial, and real-world definitions were created using diagnosis and procedure codes. Results A total of 195,621 patients were included in the present analysis (Table). At baseline, the median age was 71 years (Q1-Q3: 64-79) and 48% of patients were women. Approximately two-thirds of patients had atherosclerosis (48.5% had coronary artery disease, 13.8% had cerebrovascular disease and 13.8% had peripheral artery disease) and 33.7% had high-risk diabetes mellitus (DM), defined as DM with microvascular complications or chronic insulin use. The median baseline LDL-C was 139 (Q1-Q3: 113-166) mg/dL [3.6 (2.9 - 4.3) mmol/L] and 110,867 patients (57%) had an LDL-C ≥130 mg/dL (≥3.4 mmol/L). Overall, 69% of VESALIUS-CV like patients were receiving no background LLT. Conclusions Despite having atherosclerosis and/or high-risk DM and LDL-C exceeding target thresholds, a large proportion of VESALIUS-CV like patients were not on any LLT in Germany. Final results from VESALIUS-REAL will be able to report if this treatment gap is also observed in other countries.

Coronary stent edge segments as determinant of clinical outcomes: An OCTOBER Trial Substudy

Abstract Background Small lumen dimensions and high plaque burden in stent edge segments are known risk factors for subsequent clinical events after percutaneous coronary intervention (PCI). The evidence using intravascular optical coherence tomography is scarce. Purpose This study aimed to characterize edge segments after PCI and the relation to clinical outcomes in the randomized OCTOBER trial on bifurcation PCI optimized using optical coherence tomography. Methods This is a predefined substudy of the OCTOBER Trial. Edge segments were analysed in detail for degree of stenosis and plaque composition using a dedicated software. Data was planned to be analysed using multi-level cox regression analysis, but descriptive statistics was applied due to very low event rates after OCT-guided PCI. Main endpoints were OCT-detected edge residual stenosis, lipid plaque, calcification, and dissections. Post PCI coronary angiography was analysed for diameter stenosis, edge segment length and minimal lumen diameter. Patients with cardiac events were diagnosed with angiography duringt the two-year follow-up period. Results A total of 597 patients were treated with OCT-guided PCI. Analysable final OCT scans with no additional treatment was available for 505 patients in the main vessel and for the side branch in 289 cases. At deadline for abstract submission 242 scans with 474 edges were analysed and included in preliminary results.Lesions with ≥30% diameter stenosis was found in 20% of proximal edges, 24% of distal edges and 37% of side branch (SB) edges. A plaque burdenOCT ≥180° was found in 74% of proximal edges, 48% of distal edges, and 52% of SB edges. A lipid plaque wafound in 35%, 11% and 14%, dissections in 15%, 6% and 12%, and ≥180° calcium was found in 7%, 4% and 4% of edges respectively. Six patients (1%) of the total OCT-guided population had an edge-related event during the median two-year follow-up. Only two of these 6 patients had post-procedural OCT-scans of the event-related edge, thus 4 of the cases were effectively angiographic guided. Conclusion Only a small fraction of edge segments in the OCTOBER Trial were left with residual edge disease exceeding protocol thresholds. This resulted in historically very low rate of edge related events. No evaluation of a stent edge by OCT in otherwise OCT guided patients appeared to be the main predictor of edge related events. Final results are presented at ESC 2024.

Large-scale proteomic profiling reveals characteristic HIV-specific druggable protein signatures associated with incident heart failure

Abstract Background Compared with the general population, persons with HIV suffer from an augmented risk of both systolic and diastolic heart failure despite effective antiretroviral therapy [1]. While inflammatory and coagulation markers are thought to be involved in this increased risk, other contributing mechanisms, such as pro-inflammatory protein networks, remain to be elucidated. Purpose To identify novel protein predictors of incident heart failure events in the longitudinal Veterans Aging Cohort Study Biomarker Cohort of United States Veterans with HIV (PLWH; n = 1525) and without HIV (PWoH; n = 853) and to assess the functional relatedness of these proteins in PLWH. Methods We characterized the plasma proteome of PLWH and PWoH using an aptamer-based platform and assessed univariable associations with incident heart failure. Druggable proteins were identified using the Therapeutic Target Database [2]. We built a protein interaction network querying significant proteins (q-value <0.05) using Cytoscape for the Retrieval of Interacting Genes/Proteins (STRING) database [3]. To focus on densely connected core proteins, we used the mcl clustering method and identified a subnetwork of approximately 77 proteins. Over-representation analysis was performed for the core network by identifying enriched gene sets using Gene Ontology biological processes and WikiPathways. Final results are filtered based on a false discovery rate (FDR) threshold of 0.05. Results Of 4926 plasma proteins, 441 proteins in all PLWH and 706 in those with well-controlled HIV (viral load <200 copies/mL) were significantly associated with incident heart failure, compared with only 142 among PWoH (Figure 1A). Of the top 50 proteins most significantly associated with heart failure, 21 were druggable in PLWH and 14 in PWoH; only 5 druggable markers were shared, while 16 were unique to HIV (Table 1). Pleiotrophin, a pro-angiogenic, pro-inflammatory cytokine that induces tumor necrosis factor- (TNF) α, interleukin- (IL) 1β, and IL-6 expression and has been implicated in cardiomyocyte apoptosis [4,5], had the highest hazard ratio (HR) for incident heart failure (HR = 4.7; p = 1.6E-20; q = 1.7E-18) in PLWH. The most significant enriched protein pathway included 36 genes involved in chemotaxis (FDR = 1.3E-30); 3 of the 21 druggable targets (pleiotrophin, ephrin-A5, and ephrin type-A receptor 2) were represented in this pathway (Figure 1B). Ephrin signaling pathways, which are involved in cell adhesion, differentiation, and proliferation [6], have not previously been implicated in heart failure, nor in HIV. Subnetwork analysis of druggable proteins demonstrated that ephrin-signaling, IL-15, and TNF pathways were functionally interconnected (Figure 1C). Conclusions Our findings indicate that there may be unique pro-inflammatory pathways characteristic of HIV-associated cardiomyopathy that merit further study as dedicated pharmacologic targets and/or risk markers in this population.

A Short-term PV Power Prediction and Uncertainty Analysis Model Based on CEEMDAN and AHA-BP

The stochastic and intermittent nature of photovoltaic (PV) generation brings a series of scheduling problems to the power system. An effective prediction of PV power is essential to minimize the impact of uncertainty. Therefore, this paper presents an integrated prediction model with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the artificial hummingbird algorithm (AHA), and the BP neural network (BPNN) for predicting power generation from PV power plants, and a methodology for uncertainty analysis by using the nonparametric kernel density estimation (NPKDE). First, one month of PV power is decomposed into an array of components using CEEMDAN. Then, the weights and thresholds of the BPNN are optimized by using AHA. These components are trained using the BPNN. Finally, the final prediction results are obtained by superimposing the components, and NPKDE is employed to compute the probability density and confidence interval of the prediction error. The proposed prediction method demonstrates superior predictive performance in comparison with other models. Also, the NPKDE approach better describes the accuracy of the probability density distribution.

QC Constellation: a cutting-edge solution for risk and patient-based quality control in clinical laboratories.

Clinical laboratories face limitations in implementing advanced quality control (QC) methods with existing systems. This study aimed to develop a web-based application to addresses this gap, and improve QC practices. QC Constellation, a web application built using Python 3.11, integrates various statistical QC modules. These include Levey-Jennings charts with Westgard rules, sigma-metric calculations, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, and method decision charts. Additionally, it offers a risk-based QC section and a patient-based QC module aligning with modern QC practices. The codes and the web application links for QC Constellation were shared at https://github.com/hikmetc/QC_Constellation, and http://qcconstellation.com, respectively. Using synthetic data, QC Constellation demonstrated effective implementation of Levey-Jennings charts with user-friendly features like checkboxes for Westgard rules and customizable moving averages graphs. Sigma-metric calculations for hypothetical performance values of serum total cholesterol were successfully performed using allowable total error and maximum allowable measurement uncertainty goals, and displayed on method decision charts. The utility of the risk-based QC module was exemplified by assessing QC plans for serum total cholesterol, showcasing the application's capability in calculating risk-based QC parameters including maximum unreliable final patient results, risk management index, and maximum run size andoffering risk-based QC recommendations. Similarly, the patient-based QC and optimization modules were demonstrated using simulated sodium results. In conclusion, QC Constellation emerges as a pivotal tool for laboratory professionals, streamlining the management of quality control and analytical performance monitoring, while enhancing patient safety through optimized QC processes.

England RCT of air filtration to prevent respiratory infections (including covid-19) in care homes

Abstract Context There is evidence that portable high-efficiency-particulate-air (HEPA) filtration units remove microbial particles from the air, but it is unclear whether this is sufficient to reduce infections in care home residents. Objectives To investigate the effectiveness of portable HEPA filtration units for winter respiratory infection episode reduction in care home residents. Study design and analysis: Cluster controlled trial with intention-to-treat analysis. Setting: 90 residential care homes for older adults in England. Population: Residents expected to reside in the care home for ≥1 month. Intervention care homes: Five HEPA filtration units in communal rooms and one HEPA filtration unit in 10 to 16 bedrooms. Control care homes: Usual care. Outcome measures: The primary outcome is the number of symptomatic winter respiratory infection episodes recorded by care home staff. Secondary outcomes: other infection measures; falls /near falls; laboratory confirmed infections; hospitalisations; and staff sickness. Process evaluation to assess intervention acceptability and implementation. Results Care homes were randomised between January 2022 and February 2024, 43 to intervention and 47 to usual care. Respectively, in intervention and control group care homes: 43% and 45% provided nursing care; 87% and 87% dementia care; and median (IQR) number of communal rooms were 4 (3-5) in both groups. 1126 residents were consented, of whom 31 (3%) moved away; 151 (13%) died; and 5 stopped participating. Primary outcome data is 98% complete with 175,318 'resident days' symptom data collected. Comparing intervention and control group residents: median (IQR) age was 88 (81-92) and 88 (82-92) years; 33% and 43% were receiving nursing care; 58% and 57% had dementia; frailty scores were 6 (4-7) and 6 (5-7); 94% and 94% received influenza; and 97% and 95% COVID vaccines. Conclusions Final analytic results of this world-first RCT will be available for the conference. Key messages • This world-first RCT using HEPA filters in care homes will report the evidence of effectiveness of respiratory infection episode reduction in care home residents. • The trial will inform policy makers about the effectiveness, acceptability and implementation of HEPA filters in care homes.

IRight4Me: individualisation of P2Y12 inihibitors in the realworld practice - an interim report

Abstract Background The uptake of CYP2C19 testing has been limited, as clinicians opined majority of patients with acute coronary syndrome (ACS) undergoing percutaneous coronary interventions (PCI) should receive Ticagrelor per international guidelines. Remaining minority with high bleeding risk (HBR) would be on Clopidogrel from the outset. We set out to study how clinicians’ knowledge of CYP2C19 phenotype affects choice of P2Y12 inhibitors, and thus real-world clinical outcomes for ACS patients. We report the interim findings of bleeding at 6 months in the Ticagrelor-loaded subgroup. Methods Patients eligible for escalation or de-escalation were recruited into the study and tested for CYP2C19 polymorphism. CYP2C19 phenotype results and recommendations were revealed to outpatient clinicians, giving them the choice to keep or switch P2Y12 inhibitors. Bleeding outcomes were prospectively tracked and adjudicated by independent study team members. Results A total of 740 patients who have completed 6 months from index admission for ACS were used for this analysis. Overall adjudicated MACCE and bleeding rates were 3.46% (n = 27) and 12.7% (n=95) respectively. CYP2C19 Non-LoF (Loss-of-Function) patients who were de-escalated to Clopidogrel were least likely to bleed (Fig 1). Ticagrelor-only patients experienced almost twice the risk of bleeding than those who were de-escalated to Clopidogrel, regardless of phenotype status (15.1% vs 7.8%, Cox HR, p = 0.042). High bleeding risk (HBR) factors did not explain for differences in bleeding. The non-LoF patients on Ticagrelor (n = 173) experienced more than 2-fold risk of bleeding compared to those de-escalated to Clopidogrel (n = 71), adjusted HR 0.389, p = 0.059. MACCE rates were similar between these two groups at 2.41% for Non-LoF continued on Ticagrelor (n = 164) and 2.44% for Non-LoF switched to Clopidogrel (n = 81) respectively. Conclusion Our interim analysis shows that Ticagrelor is associated with significantly higher rates of bleeding which could be mitigated by de-escalation to Clopidogrel. Non-LoF patients who have been de-escalated to Clopidogrel could be at much lower risk of bleeding, without losing out on MACCE outcomes. CYP2C19 phenotype status has the potential to add more precision to decision-making. We expect to report final results of 1000 patients at the end of 12 months to fully assess the impact of phenotype-guided de-escalation on bleeding and MACCE.

Treated obstructive sleep apnoea and incident arrhythmias

Abstract Background Obstructive sleep apnoea (OSA) is prevalent in approximately 15% of western populations. Cardiovascular disease and arrhythmia are extremely common in this group with significant morbidity overlap. Whilst treatment of OSA with continuous positive airways pressure (CPAP) appears to reduce hypertension, bradycardia and recurrence of atrial fibrillation (AF), large randomised control trials and meta-analyses do not demonstrate a significant reduction of incident tachyarrhythmias such as atrial fibrillation or sudden cardiac death. Some smaller studies suggest that AF may have up to 20% incidence. Analysis of these studies suggest that there may be significant heterogeneity in both CPAP usage and OSA phenotype, confounding the results. Furthermore, standard arrhythmia detection often underestimates paroxysmal arrhythmias. This on-going trial investigates a treated OSA population using CPAP monitoring and implantable loop recorders (ILR). Purpose This study aims to identify the incidence of arrhythmia in a treated OSA population with comprehensive arrhythmia monitoring and CPAP usage. It also explores the mechanisms linking OSA and arrhythmia such as heart rate variability and turbulence and cardiac biomarkers in order to identify at risk sub populations. Methods Continuous moderate-to-severe OSA patients were recruited from a major UK sleep centre after ECG and medical history confirmed the absence of arrhythmia. Each patient was then randomised to receive either standard-care or standard-care with an ILR with 24-hour heart rate variability/turbulence measurements, echocardiography (where possible) and cardiac biomarkers at baseline and 12 months. All patients had remote CPAP usage monitoring (where possible). The trial ended at 36 months after enrolment. Results 200 patients were recruited to the trial. Current mean average follow-up is 28 months in this on-going trial. Mean age and apnoea hypopnea index at enrolment was 53 years and 43.8 respectively. To date we have diagnosed atrial fibrillation in 15% of the ILR group compared to 2% in the standard care group. Other arrhythmias detected in the ILR group include two with supraventricular tachycardia and 2 with significant pauses but not requiring further treatment. Final analysis of arrhythmia incidence with CPAP usage and heart rate variability/turbulence, and cardiac biomarkers is ongoing with final results due by the time of final submission. Conclusion Arrhythmias, particularly AF, are highly incident in OSA despite CPAP treatment. This study may determine if heterogenous CPAP usage confounds the above observation and whether there may be predictors of arrhythmia that may indicate a need for arrhythmia screening in the OSA population.

Development of an Index System for Public Toilets Based on HRWS-PS Theory and Improvement Strategies

As an integral part of the urban public health infrastructure, public toilets play a significant role in shaping a city’s image. With the advancement of the economy, people have increasingly higher expectations for public toilets. While existing standards primarily focus on physical indicators, there is still room to incorporate measures that prioritize humanistic care. Therefore, based on the Human Rights to Water and Sanitation in Public Spaces (HRWS-PS) standards, this study develops a comprehensive index system for public toilets with a specific emphasis on humanistic care, utilizing expert questionnaires and employing methods such as the analytic hierarchy process (AHP) and entropy weight, and highlights safety and quality as paramount concerns for users, followed by usability and accessibility as secondary needs. Acceptability, which encompasses additional amenities, depends on meeting these secondary needs through solid basic design and construction practices. Affordability, related to consumer costs, is comparatively less demanding. By applying this framework, an analysis of the current state of upgrading urban public toilets in Shijiazhuang was conducted, leading to recommendations for improvements across four key areas: environmental sanitation, architectural design, infrastructure development, and urban planning. The final results provide practical insights for evaluating and suggesting enhancements to ongoing toilet revolution initiatives and urban basic service facilities while also informing urban renewal efforts and designs.

Geometric optimization to improve the performance of a new design of concentration‐based solar distiller using particle swarm optimization algorithm

AbstractSolar distillers are one of the oldest and simplest technologies for desalinating salt water using solar energy. The main problem is the low freshwater production compared to the amount of energy input from the sun. To overcome this problem, a solar distiller was developed using a parabolic trough concentrator to increase the freshwater yield. The geometry optimization of various parameters of the developed system was also studied using the particle swarm optimization (PSO) algorithm to obtain the optimal design and give the improved freshwater yield. The simulation is carried out based on the algorithm using the climatic condition data of Rabat City, Morocco. Numerical resolution of the energy balance equations was performed using MATLAB code. In terms of flexibility, speed, and global convergence, this method's final results were satisfactory. The maximum optimized freshwater yield using the PSO approach is 12.83 kg/m2 and the maximum optimized energy and exergy efficiency were about 204.7% and 34.3%, respectively. Furthermore, the results showed that the absorber basin accounts for the highest exergy destruction, 88.7% of the total optimized exergy destruction was found from exergy analysis.

Uncertainty-driven dynamic ensemble framework for rotating machinery fault diagnosis under time-varying working conditions

Multi-scale ensemble learning combines different scales of feature resolution, thereby improving fault diagnostic accuracy. However, the effectiveness of different information scales in characterizing fault features under time-varying speed conditions varies with speed. It is difficult for existing ensemble strategies to ensure the effectiveness of feature information when ensemble multi-scale feature information is involved. Accordingly, we propose an uncertainty-driven dynamic ensemble Bayesian convolutional neural network (DEBCNN) framework. The uncertainty of the results of different scale models was used to dynamically determine their weights in the ensemble framework, which reduced the influence of irrelevant features on the diagnostic results. By employing the proposed dynamic ensemble strategy, the ensemble framework can utilize fault feature information corresponding to different rotational speeds in the final diagnostic results. Experiments on motor and bearing datasets illustrate the superiority of this strategy over other techniques. This study provides useful insights for further research in the field of fault diagnosis of rotating machinery at time-varying speeds.

Assessment of the Choked Flow Model of RELAP5 for Application of Inverse Quantification Methods

In the framework of deterministic safety analysis, best estimate plus uncertainty methodologies can provide a more informative detailed analysis of transients than conservative approaches. However, one important step in the application of such methodologies is derivation of uncertainty of the physical models. Probability density functions of the physical model parameters are obtained by applying inverse uncertainty quantification (IUQ) methods to experimental data. The present work deals with evaluation of the experimental database and assessment of the simulation model, which are required steps prior to the application of IUQ methods. The U.S. Nuclear Regulatory Commission system code RELAP5 has been applied for simulation of three experimental databases on choked/critical flow: Sozzi-Sutherland, Super MobyDick, and Marviken Critical Flow Tests. First, the adequacy of the experimental data to the specific target domain is carried out, to then proceed to calibration of the input model parameters by combining the use of Gaussian Process metamodels and optimization schemes. The assessment of the simulation models is performed by evaluating independently accuracy, precision, and consistency through four statistical indicators, including a novel indicator to evaluate the consistency of the results. The final results indicate that the model is capable of reproducing the selected experimental database and overall average accuracy, precision, and consistency below the 10% threshold and can be used for application of IUQ methods.

The value of a multimodal approach combining radical surgery and intraoperative radiotherapy in the recurrence treatment of gynecological malignancies - analysis of a large patient cohort in a tertiary care center

BackgroundRecurrent and locally advanced gynecological malignancies have a poor prognosis. In particularly, pelvic local recurrence after previous radiotherapy and/or positive resection margins during surgical treatment for recurrent disease result in low survival rates. Consequently, locoregional control is of utmost importance in this cohort of patients. The aim of this study was to analyze treatment outcomes and determine prognostic factors for patients treated with surgery and intraoperative radiotherapy (IORT) for recurrent and locally advanced gynecological malignancies.Methods40 patients who underwent surgical treatment and IORT between 2010 and 2022 were eligible for inclusion. The median follow-up time was 22 months. The outcomes measured were locoregional control (LRC), overall survival (OS), and survival without distant metastases (DMFS). The Cox proportional hazards model was used for univariate and multivariate analysis to assess the impact of patient variables and treatment factors on the endpoints mentioned. The following variables were analyzed: age at surgical treatment and IORT and initial diagnosis (< 65 vs. ≥65 years, each), disease-free interval (DFI) between initial diagnosis and first recurrence, DFI to surgical treatment and IORT, grading, histology, IORT dose (≤ 13 vs. >13 Gy) and technique (high dose radiotherapy (HDR) vs. IORT using electrons, (IOERT)). Survival curves were generated using the Kaplan-Meier method.ResultsThe mean IORT dose was 13.8 Gy (range 10–18 Gy). Cervical carcinoma was most frequently found in 27.5% of patients followed by endometrial carcinoma and vulvar carcinoma in 25% respectively. The final histopathologic results after surgery with IORT showed no residual tumour in 24 patients (60%), microscopic residual disease in 5 patients (12.5%), resection status could not be evaluated in three patients (7.5%) and the resection status was unknown in eight patients (20%). Subsequently, 27.5% of patients also received adjuvant radiotherapy of the local recurrence bed. However, after IORT, 65% of the women suffered a recurrence. Of these, the recurrences were localized: in-field 32.5%, out-of-field 22.5% and margin-of-field 12.5%. The 3- and 5-year OS was 69% and 55% respectively. The 3- and 5-year LRC was 56% respectively. The 3- and 5-year DMFS was 66% and 49%. Whereas the comparison between groups by IORT dose level (≤ 13 vs. >13 Gy) showed a non-significant trend in favor of the higher dose only for OS (p = 0.094), but not in LRC and DMFS (p > 0.05). OS and DMFS, but not LRC, differed significantly between the HDR-IORT and IOERT groups (p = 0.06 and p = 0.03,) in favor of the HDR-IORT technique. For HDR-IORT technique a trend towards superior OS and LRC was observed in the univariate analysis: HR 3.76, CI 95%: 0.95–14.881, p = 0.059 and HR 2.165 CI 95%: 0.916–5.114, p = 0.078ConclusionsThe survival rate for pelvic recurrence in gynecological malignancies remains poor and comparable with historical data from the last two decades. Particularly HDR-IORT, appears to provide a long-term oncological benefit in carefully selected patients.

Simultaneous Fixed and Random Effects Selection in Order-restricted Mixed Effects Models

When dealing with longitudinal data, if we directly select a specific model for modeling without any prior information about the existence of significant random effects before utilizing the mixed model, it may result in the misuse of the model, thereby affecting the final estimation results. This paper investigates a variable selection method that can jointly select both fixed and random effects in Bayesian mixed model under order constraints. This method can effectively prevent model misuse. A computationally feasible Gibbs algorithm is proposed for posterior inference. The performance of our proposal is evaluated by simulated data and two real applications related to Blood lead levels and Ramus bone heights. Results show that the proposed approaches perform very well in various situations.

Best supportive care for patients with primary progressive multiple sclerosis (PPMS) in Germany prior to ocrelizumab treatment: Final results from the RETRO PPMS study.

Best supportive care (BSC) measures are an essential component for the management of primary progressive multiple sclerosis (PPMS). RETRO PPMS (ML39631) is the first study to systematically analyze the therapeutic journey and standard of BSC of patients with PPMS in Germany. This multicenter, non-interventional study retrospectively analyzed patient charts. Methods: Data were recorded up until the first infusion of ocrelizumab (July 2018 to October 2021). Medical history, disease status, disease activity and treatments were assessed from 12 months before PPMS diagnosis until study start. Acute interventions, BSC parameters and rehabilitation measures from the past 27 months were assessed. The core analysis population (N = 462) had a mean age (range) of 57.4 (27-85) years and mean disease duration of 13.7 (0.3-55.2) years. The most frequently reported symptoms were muscle spasticity, bladder disorder, ataxia, gait disturbance and fatigue. The most commonly used treatment was physical/occupational therapy (66.5% of patients); 47.2% received off-label treatment with corticosteroids/disease-modifying therapies. BSC measures for many symptoms were strikingly rare - especially for fatigue and cognitive impairment. This analysis uncovers severe BSC deficits for many debilitating PPMS symptoms. There is still a large unmet need for innovative multidisciplinary care concepts and improvements in neurological primary and secondary care.

Using Continuous Online Assessment of Learning Outcomes for Grading: A Case Study

In recent years, the problem of the number of students enrolled in STEM courses falling below the minimum required to carry out the courses has worsened. This situation, which used to occur primarily in degree programs at the end of their life cycle, is increasingly happening in active STEM degree programs as well. The declining attractiveness of traditional STEM degree programs and negative demographic trends are seen as the main causes of this problem. To assist students and their teachers in this situation, a novel approach to grading student work based on the level at which students achieve learning outcomes in an online system was explored. Since the focus was on STEM students, math-based tasks were used to assess student knowledge within the online system. In addition to the accuracy of the final results of the tasks, the accuracy of the intermediate results was also monitored. Each result was linked to the corresponding concepts, which in turn were linked to the corresponding learning outcomes. By entering correct intermediate and final results, the online system was able to monitor student success in mastering concepts and learning outcomes. The implemented online system utilizes fuzzy inference to calculate the levels at which students have achieved the learning outcomes. These levels are continuously recalculated during the semester and presented to the students through generated recommendations after each solved task. The proposed approach has positively impacted students’ motivation to learn during the semester, as confirmed by an anonymous questionnaire. Additionally, the results have shown that the final level at which students have achieved the learning outcomes within the online system, calculated at the end of the semester, can be used for grading and is comparable to the grades students would achieve in traditional midterm and final exams, thus helping teachers with their workload.