Absolute Method Research Articles

Parameter estimation and global sensitivity analysis of a bacterial-plasmid model with impulsive drug treatment

In this paper, a mathematical model is developed to study bacterial resistance caused by plasmids and mutations in the presence of the impulsive drug treatment. Firstly, the stability of the bacterial-plasmid extinction periodic solution is proved by Floquet theory and the permanence of system is presented while some conditions are satisfied. Secondly, the nontrivial periodic solution bifurcated from the bacterial-plasmid extinction periodic solution is obtained in view of the four-dimensional impulsive control of bifurcation method. Good fitting results are given by the Mean Absolute Percentage Error method for four different cases of bacterial media. Further, the global sensitivity of R0 is analyzed by the Partial Rank Correlation Coefficient method, and the parameters that have important influence on the system are determined. Finally, numerical simulations are performed to validate the accuracy of the theoretical findings.

Prediction of severe hypertriglyceridemia-associated acute pancreatitis using a nomogram based on CT findings and blood biomarkers.

Hypertriglyceridemia is a common cause of acute pancreatitis (AP). Fatty liver, a manifestation of metabolic syndrome, is related to the severity of AP. The present study aimed to construct an accurate predictive model for severe AP (SAP) by combining the fatty liver infiltration on a computerized tomography (CT) scan with a series of blood biomarkers in patients with hypertriglyceridemia-associated AP (HTG-AP). A total of 213 patients diagnosed with HTG-AP were included in the present retrospective study. Clinical information and imageological findings were retrospectively analyzed. The model was constructed from independent risk factors using univariate analysis, the least absolute shrinkage and selection operator method. Subsequently, the data from the training group of 111 patients with HTG-AP was analyzed using logistic regression analysis. The efficacy of the model was verified using an external validation group of 102 patients through the receiver operating characteristic curve (ROC). Independent predictors, including serum calcium, C-reactive protein, lactate dehydrogenase and liver-to-spleen CT attenuation ratio (L/S ratio), were incorporated into the nomogram model for SAP in HTG-AP. The model achieved a sensitivity of 91.3% and a specificity of 88.6% in the training group. Compared with the Ranson model, the established nomogram model exhibited a better discriminative ability in the training group [area under the curve (AUC): 0.957] and external validation group (AUC: 0.930), as well as better calibration and clinical benefits. The present study demonstrates that the constructed nomogram based on CT findings and blood biomarkers is useful for the accurate prediction of SAP in HTG-AP.

Trends and determinants of mental illness in humanitarian migrants resettled in Australia: Analysis of longitudinal data.

Given the frequent exposure of humanitarian migrants to traumatic or stressful circumstances, there exists a potential predisposition to mental illness. Our objective was to pinpoint the trends and determinants of mental illness among humanitarian migrants resettled in Australia. This study considered five waves of longitudinal data involving humanitarian migrants resettled in Australia. Post-traumatic stress disorder (PTSD) and psychological distress were assessed using PTSD-8 and Kessler-6 screening tools. Through a Generalised Linear Mixed model (GLMM), variables displaying a 95% CI that excluded the value of 1.0 for the odds ratio were identified as associated factors for both PTSD and elevated psychological distress. The selection of multivariable covariates was guided by causal loop diagrams and least absolute shrinkage and selection operators methods. At baseline, there were 2399 humanitarian migrants with 1881 retained and at the fifth yearly wave; the response rate was 78.4%. PTSD prevalence decreased from 33.3% (95% CI: 31.4-35.3) at baseline to 28.3% (95% CI: 26.2-30.5) at year 5. Elevated psychological distress persisted across all waves: 17.1% (95% CI: 15.5-18.6) at baseline and 17.1% (95% CI: 15.3-18.9) at year 5. Across the five waves, 34.0% of humanitarian migrants met screening criteria for mental illness, either PTSD or elevated psychological distress. In the multivariate model, factors associated with PTSD were loneliness (AOR 1.5, 95% CI: 1.3-1.8), discrimination (AOR 1.6: 1.2-2.1), temporary housing contract (AOR 3.7: 2.1-6.7), financial hardship (AOR 2.2:1.4-3.6) and chronic health conditions (AOR 1.3: 1.1-1.5), whereas the associated factors for elevated psychological distress were loneliness (AOR 1.8: 1.5-2.2), discrimination (AOR 1.7: 1.3-2.2) and short-term lease housing (AOR 1.6: 1.0-1.7). The prevalence, persistence and consequential burden of mental illness within this demographic underscore the urgent need for targeted social and healthcare policies. These policies should aim to mitigate modifiable risk factors, thereby alleviating the significant impact of mental health challenges on this population.

CT radiomics based on the peritumoral adipose region of gastric adenocarcinoma for preoperative prediction of lymph node metastasis

PurposeTo construct and validate CT radiomics model based on the peritumoral adipose region of gastric adenocarcinoma to preoperatively predict lymph node metastasis (LNM). Methods and Methods293 consecutive gastric adenocarcinoma patients receiving radical gastrectomy with lymph node dissection in two medical institutions were stratified into a development set (from Institution A, n = 237), and an external validation set (from Institution B, n = 56). Volume of interest of peritumoral adipose region was segmented on preoperative portal-phase CT images. The least absolute shrinkage and selection operator method and stepwise logistic regression were used to select features and build radiomics models. Manual classification was performed according to routine CT characteristics. A classifier incorporating the radiomics score and CT characteristics was developed for predicting LNM. Area under the receiver operating characteristic curve (AUC) was used to show discrimination between tumors with and without LNM, and the calibration curves and Brier score were used to evaluate the predictive accuracy. Violin plots were used to show the distribution of radiomics score. ResultsAUC values of radiomics model to predict LNM were 0.938, 0.905, and 0.872 in the training, internal test, and external validation sets, respectively, higher than that of manual classification (0.674, all P values < 0.01). The radiomics score of the positive LNM group were higher than that of the negative group in all sets (both P-values < 0.001). The classifier showed no improved predictive power compared with the radiomics signature alone with AUC values of 0.916 and 0.872 in the development and external validation sets, respectively. Multivariate analysis showed that radiomics score was an independent predictor. ConclusionsRadiomics model based on peritumoral adipose region could be a useful approach for preoperative LNM prediction in gastric adenocarcinoma.

Research on Deployment Process Dynamics and Vibration for Replaceable Interface Mast

The mast of the rover is a device used to carry precision instruments such as cameras on the rover, and its performance directly affects the working quality of these devices. The replaceable interface mast can effectively solve the problems of a single structure and poor maintenance of ordinary masts, so we study the deployment process dynamics properties and vibration for replaceable interface mast. Firstly, we analyzed the spatial motion of the reconfigurable rigid body module by the absolute node coordinate method and the natural coordinate method, and established the dynamic equation of the interface, the reconfigurable module without external constraints. Then, we established a dynamic model of the mast system deployment process. We analyzed the dynamic behavior of the replaceable interface mast and studied and compared the differences in the deployment behavior of the replaceable interface mast under different system configurations, flexible interface geometric parameters, and different driving rules. Finally, we built a model of the mast system and experimentally analyzed the deployment process of the replaceable interface mast. Using numerical solution and experimental verification, we proved that the established dynamic model of the mast system can correctly analyze the deployment behavior dynamics of the replaceable interface mast, and the study can provide a reference for the design and behavior analysis of the mast system.

Bayesian network-based survival prediction model for patients having undergone post-transjugular intrahepatic portosystemic shunt for portal hypertension.

Portal hypertension (PHT), primarily induced by cirrhosis, manifests severe symptoms impacting patient survival. Although transjugular intrahepatic portosystemic shunt (TIPS) is a critical intervention for managing PHT, it carries risks like hepatic encephalopathy, thus affecting patient survival prognosis. To our knowledge, existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes. Consequently, the development of an innovative modeling approach is essential to address this limitation. To develop and validate a Bayesian network (BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS. The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed. Variables were selected using Cox and least absolute shrinkage and selection operator regression methods, and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT. Variable selection revealed the following as key factors impacting survival: age, ascites, hypertension, indications for TIPS, postoperative portal vein pressure (post-PVP), aspartate aminotransferase, alkaline phosphatase, total bilirubin, prealbumin, the Child-Pugh grade, and the model for end-stage liver disease (MELD) score. Based on the above-mentioned variables, a BN-based 2-year survival prognostic prediction model was constructed, which identified the following factors to be directly linked to the survival time: age, ascites, indications for TIPS, concurrent hypertension, post-PVP, the Child-Pugh grade, and the MELD score. The Bayesian information criterion was 3589.04, and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16. The model's accuracy, precision, recall, and F1 score were 0.90, 0.92, 0.97, and 0.95 respectively, with the area under the receiver operating characteristic curve being 0.72. This study successfully developed a BN-based survival prediction model with good predictive capabilities. It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT.

Drivers and inhibitors of entrepreneurship in Europe's Outermost Regions: Implications for entrepreneurship education

Governments of peripheral regions often seek to encourage entrepreneurship as a means of bolstering employment, typically charging higher education institutions with the task of delivering this mission through their entrepreneurship education programmes. This study investigates the drivers and inhibitors of entrepreneurial intentions among young people in Madeira, a semi-autonomous outlying region of Portugal. Data were collected from 352 final-year undergraduate students on management, economics and tourism courses. The adaptive Least Absolute Shrinkage and Selection Operator method was then applied to select the best predictors from among a large pool of potential covariates. The results found that students with less access to start-up finance and a greater fear of failure tended to have the least entrepreneurial intentions. Children of entrepreneurs had significantly stronger intentions to become entrepreneurs themselves. Entrepreneurial intention also increased significantly with the student's age. The paper concludes that entrepreneurial education providers in island economies firstly need to change the narrative that young people in peripheral regions receive about becoming entrepreneurs, particularly with regard to the greater vulnerability to business risks (the ‘island penalty factor’), and secondly should provide practical support to students who do not have access to family business networks (a possible ‘island bonus factor’).

An analytical approach for shock response of fractional viscoelastic beams with tip mass

This paper specifically analyzes the behavior of a beam with fractional viscoelastic properties and a tip mass under shock. The shock is applied longitudinally to the beam, and a fractional Kelvin–Voigt model is employed to describe the viscoelastic properties. Additionally, the paper examines the fractional viscoelastic power as a function of time during the shock. The equation of motion for the fractional viscoelastic beam with a tip mass is derived using Hamilton’s principle. In order to ensure accurate analysis of the results, the graphs are carefully analyzed in both the time and frequency domains. To solve the equations, the special technique provided for differential equations of variable order (VO) is used. Two distinct methods, namely, absolute method and square root of the sum of squares method, are thoroughly examined to calculate the absolute acceleration. Subsequently, the most suitable method is selected based on the evaluation results. The study’s results demonstrate that the damping parameters of the viscoelastic beam have a significant impact on shock transmissibility. Additionally, the findings demonstrate that the ability to transfer shocks can be manipulated by modifying the system’s geometry and mass.

A novel method for multiple targets localization based on normalized cross-correlation adaptive variable step-size dynamic template matching

The template matching method has been widely utilized in the defect detection of wafer surfaces. However, the traditional matching approaches are limited by illumination, noise, and deformation, which cannot meet the requirements of accuracy and robustness. In this paper, a novel multiple targets localization method, named Normalized Cross-correlation Adaptive Variable Step-Size Dynamic Template (NCC-AVSSDT) matching, is proposed to improve the accuracy and efficiency of image localization, which combines the advantages of NCC and AVSSDT. The AVSSDT method is utilized to dynamically adjust the scanning step size based on the NCC matching coefficients. This approach optimizes the scanning process, accelerating convergence toward the optimal matching position. Experimental results verify the accuracy and robustness of the proposed method under different conditions, especially when dealing with rotational variations and variations in noise textures. Therefore, NCC-AVSSDT can be used to perform multiple targets localization of chip image in nearly real-time. Three experiment types were used for comprehensive evaluations, including multiple targets, noise, and rotation angles. Experimental results show that NCC-AVSSDT is much better than the sequential similarity detection algorithm and mean absolute deviation methods in terms of multiple targets (0.667 vs 0.811 s, 0.832 s) and success rate (100% vs 35%, 20%).

[Route to the Adoption of Quantitative NMR in the Japanese Pharmacopoeia].

Quantitative NMR (qNMR) is employed to determine the purity of reagents used as standards for HPLC quantification in the Japanese Pharmacopoeia (JP) and has become recognized as a new absolute quantification method in various fields such as pharmaceuticals, foods, and food additives. This report outlines how and why qNMR has been adopted as an official method in the JP and introduces its progression from JP16 to JP18. The results of a survey of companies in the Japan Pharmaceutical Manufacturers' Association regarding how and when to use qNMR from development to manufacturing stages are introduced. The issues involved in the expansion of the use of qNMR in the field of chemical pharmaceuticals in 2017 are discussed and how these were resolved.

A Retrospective Study of Biological Risk Factors Associated with Primary Knee Osteoarthritis and the Development of a Nomogram Model.

A high percentage of the elderly suffer from knee osteoarthritis (KOA), which imposes a certain economic burden on them and on society as a whole. The purpose of this study is to examine the risk of KOA and to develop a KOA nomogram model that can timely intervene in this disease to decrease patient psychological burdens. Data was collected from patients with KOA and without KOA at our hospital from February 2021 to February 2023. Initially, a comparison was conducted between the variables, identifying statistical differences between the two groups. Subsequently, the risk of KOA was evaluated using the Least Absolute Shrinkage and Selection Operator method and multivariate logistic regression to determine the most effective predictive index and develop a prediction model. The examination of the disease risk prediction model in KOA includes the corresponding nomogram, which encompasses various potential predictors. The assessment of disease risk entails the application of various metrics, including the consistency index (C index), the area under the curve (AUC) of the receiver operating characteristic curve, the calibration chart, the GiViTi calibration band, and the model for predicting KOA. Furthermore, the potential clinical significance of the model is explored through decision curve analysis (DCA) and clinical influence curve analysis. The study included a total of 582 patients, consisting of 392 patients with KOA and 190 patients without KOA. The nomogram utilized age, haematocrit, platelet count, apolipoprotein a1, potassium, magnesium, hydroxybutyrate dehydrogenase, creatine kinase, and estimated glomerular filtration rate as predictors. The C index, AUC, calibration plot, Giviti calibration band, DCA and clinical influence KOA indicated the ability of nomogram model to differentiate KOA. Using nomogram based on disease risk, high-risk KOA can be identified directly without imaging.

[Quantitative 31P-NMR for Purity Determination of Organophosphorus Compounds (Pharmaceuticals)].

Quantitative NMR (qNMR), particularly 1H-qNMR, is useful for determining the absolute purity of organic molecules. However, identifying the target signal(s) for quantification is difficult, because of the overlap and complexity of organic molecules. Therefore, we focused on the 31P nucleus, owing to the simplicity of its signals, and investigated the 31P-qNMR absolute determination method by using organophosphorus drugs, water-soluble cyclophosphamide hydrate (CP), and water-insoluble sofosbuvir (SOF). The optimized and reproducible 31P-qNMR conditions, such as qNMR sample preparation [i.e., selecting suitable deuterated solvents and a reference standard (RS) for 31P-qNMR], hygroscopicity and solution stability of the analyte and RS, and qNMR measurements-such as acquisition time, relaxation delay time, and spectral width-were examined. The CP purities determined using 31P-qNMR agreed well with those for the established 1H-qNMR method in D2O. In contrast, the SOF purity determined using 31P-qNMR was 1.6% higher than that for 1H-qNMR in the protic solvent CD3OD. Therefore, using a protic solvent, such as CD3OD, was not suitable for 31P-qNMR; the deuterium exchange with the RS for 31P-qNMR (i.e., phosphonoacetic acid) resulted in a small integrated intensity. Consequently, the aprotic solvent DMSO-d6 was employed to determine the SOF purity. The data revealed that the SOF purities determined using 31P-qNMR agreed well with the established 1H-qNMR values, indicating that the absolute quantification of SOF using both 31P-qNMR and 1H-qNMR is possible in DMSO-d6. Thus, we established an optimized and reproducible 31P-qNMR method in validation study across multiple laboratories.

A machine learning approach to predicting vascular calcification risk of type 2 diabetes: A retrospective study.

Recently, patients with type 2 diabetes mellitus (T2DM) have experienced a higher incidence and severer degree of vascular calcification (VC), which leads to an increase in the incidence and mortality of vascular complications in patients with T2DM. To construct and validate prediction models for the risk of VC in patients with T2DM. Twenty-three baseline demographic and clinical characteristics were extracted from the electronic medical record system. Tenclinical features were screened with least absolute shrinkage and selection operator method and were used to develop prediction models based on eight machine learning (ML) algorithms (k-nearest neighbor [k-NN], light gradient boosting machine, logistic regression [LR], multilayer perception [(MLP], Naive Bayes [NB], random forest [RF], support vector machine [SVM], XGBoost[XGB]). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, and precision. A total of 1407 and 352 patients were retrospectively collected in the training and test sets, respectively. Among the eight models, the AUC value in the NB model was higher than the other models (NB: 0.753, LGB: 0.719, LR: 0.749, MLP: 0.715, RF: 0.722, SVM: 0.689, XGB:0.707, p < .05 for all). The k-NN model achieved the highest sensitivity of 0.75 (95% confidence interval [CI]: 0.633-0.857), the MLP model achieved the highest accuracy of 0.81 (95%CI: 0.767-0.852) and specificity of 0.875 (95%CI: 0.836-0.912). This study developed a predictive model of VC based on MLand clinical features in type 2 diabetic patients. The NB model is a tool with potential to facilitate clinicians in identifying VC in high-risk patients.

A proposed numerical method for absolute efficiency calibration of α-spectrometers and its application for activity calculation

In this work, an absolute method to calibrate an α-spectrometer is proposed taking into account the Source-to-Detector, and lateral distances due to eccentric source distribution. An analytical formula to calibrate an α-spectrometer is derived and the Simpson's integration method was utilized to solve these equations in its integral form numerically using a written C computer code. The general Monte Carlo N-particle code, MCNP as well as experimental measurements for some standard α-emitters are used to benchmark the proposed method. An agreement was found between the efficiency results calculated by MC and the proposed method with a maximum relative difference of about 0.5%. While, experimental measurement of α-emitters activity employing the proposed method differs by about 1.65% from the certified values. Accounting for the man made error allows to accurately quantify the assayed sample. Therefore, the inaccuracy in the efficiency results due to non-accurate inputs pertained to the source, and detector radii, Source–Detector distances, and eccentric source distribution are investigated in the Source-to-Detector distance range of (4 to 44 mm). The results show that a difference of ±1% in the detector radius, and Source-to-Detector distance than the normal values yields a relative difference of about ±2%, while a difference of ±50% in the source radius or source lateral distance from detector symmetry axis could only yields inaccuracy of less than ±3% in the efficiency results.

A CT-based radiomics nomogram involving the cystic fluid area for differentiating appendiceal mucinous neoplasms from appendicitis with intraluminal fluid

ObjectiveTo develop and validate a radiomics nomogram based on computed tomography (CT) to distinguish appendiceal mucinous neoplasms (AMNs) from appendicitis with intraluminal fluid (AWIF).MethodA total of 211 patients from two medical institutions were retrospectively analysed, of which 109 were pathologically confirmed as having appendicitis with concomitant CT signs of intraluminal fluid and 102 as having AMN. All patients were randomly assigned to a training (147 patients) or validation cohort (64 patients) at a 7:3 ratio. Radiomics features of the cystic fluid area of the appendiceal lesions were extracted from nonenhanced CT images using 3D Slicer software. Minimum redundancy maximum relevance and least absolute shrinkage and selection operator regression methods were employed to screen the radiomics features and develop a radiomics model. Combined radiomics nomogram and clinical-CT models were further developed based on the corresponding features selected after multivariate analysis. Lastly, receiver operating characteristic curves, and decision curve analysis (DCA) were used to assess the models’ performances in the training and validation cohorts.ResultsA total of 851 radiomics features were acquired from the nonenhanced CT images. Subsequently, a radiomics model consisting of eight selected features was developed. The combined radiomics nomogram model comprised rad-score, age, and mural calcification, while the clinical-CT model contained age and mural calcification. The combined model achieved area under the curves (AUCs) of 0.945 (95% confidence interval [CI]: 0.895, 0.976) and 0.933 (95% CI: 0.841, 0.980) in the training and validation cohorts, respectively, which were larger than those obtained by the radiomics (training cohort: AUC, 0.915 [95% CI: 0.865, 0.964]; validation cohort: AUC, 0.912 [95% CI: 0.843, 0.981]) and clinical-CT models (training cohort: AUC, 0.884 [95% CI: 0.820, 0.931]; validation cohort: AUC, 0.767 [95% CI: 0.644, 0.863]). Finally, DCA showed that the clinical utility of the combined model was superior to that of the clinical CT and radiomics models.ConclusionOur combined radiomics nomogram model constituting radiomics, clinical, and CT features exhibited good performance for differentiating AMN from AWIF, indicating its potential application in clinical decision-making.

Diabetes mellitus and idiopathic pulmonary fibrosis: a Mendelian randomization study

BackgroundThe question as to whether or not diabetes mellitus increases the risk of idiopathic pulmonary fibrosis (IPF) remains controversial. This study aimed to investigate the causal association between type 1 diabetes (T1D), type 2 diabetes (T2D), and IPF using Mendelian randomization (MR) analysis.MethodsWe used two-sample univariate and multivariate MR (MVMR) analyses to investigate the causal relationship between T1D or T2D and IPF. We obtained genome-wide association study (GWAS) data for T1D and T2D from the European Bioinformatics Institute, comprising 29,652 T1D samples and 101,101 T1D single nucleotide polymorphisms (SNPs) and 655,666 T2D samples and 5,030,727 T2D SNPs. We also used IPF GWAS data from the FinnGen Biobank comprising 198,014 IPF samples and 16,380,413 IPF SNPs. All cases and controls in these datasets were derived exclusively from European populations. In the univariate MR analysis, we employed inverse variance-weighted (IVW), weighted median (WM), and MR-Egger regression methods. For the MVMR analysis, we used the multivariate IVW method primarily, and supplemented it with multivariate MR-Egger and multivariate MR- least absolute shrinkage and selection operator methods. Heterogeneity tests were conducted using the MR-IVW and MR-Egger regression methods, whereas pleiotropic effects were assessed using the MR-Egger intercept. The results of MR and sensitivity analyses were visualized using forest, scatter, leave-one-out, and funnel plots.ResultsUnivariate MR revealed a significant causal relationship between T1D and IPF (OR = 1.118, 95% CI = 1.021–1.225, P = 0.016); however, no significant causal relationship was found between T2D and IPF (OR = 0.911, 95% CI = 0.796–1.043, P = 0.178). MVMR analysis further confirmed a causal association between T1D and IPF (OR = 1.133, 95% CI = 1.011–1.270, P = 0.032), but no causal relationship between T2D and IPF (OR = 1.009, 95% CI = 0.790–1.288, P = 0.950). Sensitivity analysis results validated the stability and reliability of our findings.ConclusionUnivariate and multivariate analyses demonstrated a causal relationship between T1D and IPF, whereas no evidence was found to support a causal relationship between T2D and IPF. Therefore, in clinical practice, patients with T1D should undergo lung imaging for early detection of IPF.

Estimating Hazard Function through Reliability Function and Empirical Methods

In this research, the reliability functions are applied to estimate the hazard function of four used car components (tires, brakes, lights, engine), which are inspected by aperiodic vehicles inspection (PVI) established in Erbil city, as a specialized company that conducts the annual technical inspection of vehicles to detect the failure component, that either require repair or replace it with a new one. For our purpose, the data about the failure components of a sample of size (50000) cars are obtained from the Erbil traffic directorate, which are annually inspected for 11 years (2010 – 2020) by a (PVI) company. From the available data the reliability function, hazard function, and probability density function of failure time of each component are found by the non-parametric method and the estimated Rayleigh distribution since the failure rates of the components are the linear functions of time, also the comparison between their reliability values have made by the mean absolute error method.

O57: Tailored risk assessment and forecasting in intermittent claudication using machine learning (TRAFIC-ML)

Abstract Introduction Guidelines recommend cardiovascular risk reduction and supervised exercise therapy as the initial treatment for patients with intermittent claudication(IC). However, implementation challenges and poor patient compliance lead to significant variation in management and therefore outcomes. We propose a precise machine learning derived decision support system that aims to provide personalised outcome predictions across different management strategies. Methods Feature selection was performed using the least absolute shrinkage and selection operator method. The model was developed using a bootstrapped sample of 10,000 patients based on 255 patients from our vascular centre. The model considered 27 baseline characteristics, compliance to best medical therapy/smoking cessation and treatment strategy. The model was validated using a separate dataset of 254 patients. This model was then used to build a prototype interactive decision support system which was evaluated using calibration curves, decision curve analyses and area under the receiver operator characteristic (AUROC) curves. Results The AUROC curves demonstrated excellent discrimination for the risk of progression to chronic limb threatening ischaemia at two years(0.892) and five years(0.866) and likelihood of major adverse cardiovascular events(0.836), major adverse limb events(0.891) and revascularisation(0.896)within 5 years, regardless of the treatment strategy. Calibration curves demonstrated good consistency between predicted and actual outcomes and decision curve analyses confirmed clinical utility. The tool maintained an accuracy of &amp;gt;80% and an effect size of &amp;gt;0.5. Conclusion Our decision support system successfully predicts outcomes in IC regardless of treatment strategy, offering potential for improved risk stratification and patient outcomes.

Identification of a disulfidptosis-related lncRNA signature for the prognostic and immune landscape prediction in head and neck squamous cell carcinoma

PurposeDisulfidptosis, a newly identified form of cell death, is triggered by disulfide stress. Herein, a unique signature was developed based on disulfidptosis-related lncRNAs (DRlncRNAs) for the prognostic and immune landscape prediction of head and neck squamous cell carcinoma (HNSCC).MethodsTranscriptome, somatic mutation, and clinical data were acquired at The Cancer Genome Atlas database. Individuals were partitioned into training and test cohorts at a 1:1 ratio to facilitate the development of a DRlncRNA signature using the least absolute shrinkage and selection operation method. Based on the median risk score, all HNSCC individuals were stratified into the high-risk group (HRG) and low-risk group (LRG). Kaplan–Meier survival and time-dependent receiver operating characteristic (ROC) analyses were used to estimate the prognostic value, and a nomogram was generated for survival prediction. To provide a more comprehensive assessment, the tumor microenvironment, functional enrichment, immune cell infiltration, and immunotherapeutic sensitivity were explored between LRG and HRG.ResultsA DRlncRNA signature was established with 10 DRlncRNAs. The corresponding values of areas under the ROC curves for 1–, 3–, and 5–year overall survival were 0.710, 0.692, and 0.640. A more favorable prognosis was noted in the patients with lower risk, along with higher immune scores, increased immune-related functions, and immune cell infiltration, as well as improved response to the immunotherapeutic intervention in comparison with individuals at higher risk.ConclusionThese findings demonstrate that the developed DRlncRNA signature holds promise as a reliable prognostic marker and predictor of immunotherapy response in HNSCC patients.

An integrated chronological study on the Quaternary sedimentary sequences of the Yangtze River delta, China

The sedimentary sequences of the Yangtze River delta comprise important Quaternary records of tectonics, sea-level oscillation, and climatic change; however, analysis is hampered by a lack of a reliable chronological framework. In this paper, we present an integrated chronology for two sedimentary sequences (core ZKW2 and ZKW4) on the southern side of the Yangtze River delta, based on magnetostratigraphy and absolute methods (OSL, 14C, and ESR dating). The basal ages of the core ZKW2 and ZKW4 are ca. 2.25 Ma and 2.96 Ma, respectively. The integrated chronology indicates significant increases in the sedimentary accumulation rate at ∼0.15 Ma, rather than the Matuyama-Brunhes boundary as indicated by magnetostratigraphy alone. The observed sedimentary transition is possibly controlled by regional subsidence of the Zhe-Min Uplift, and climate evolution during the last interglacial period.