Impedance cardiography (ICG) plays a crucial role in clinically evaluating cardiac systolic and diastolic functions, along with various other cardiac parameters. However, its accuracy heavily depends on precisely identifying feature points reflecting cardiac function. Moreover, traditional signal processing techniques used to mitigate random noise and breathing artifacts may inadvertently distort the amplitude and temporal characteristics of ICG signals. To address this issue, this study investigates a noise and artifact elimination method based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Particle Swarm Optimization-based Variational Mode Decomposition Algorithm (PSO-VMD). The goal is to preserve the amplitude and temporal features of ICG signals to ensure accurate feature point extraction and computation of associated cardiac parameters. Comparative analysis with signal processing methods employing various wavelet families and Ensemble Empirical Mode Decomposition (EEMD) in ICG signal processing applications reveals that the proposed method achieves superior signal-to-noise ratio (SNR) and lower root-mean-square error (RMSE), while demonstrating enhanced correlation and waveform consistency with the original signal.

BACKGROUND: This study employs systematic review and meta-analysis to explore the incidence and characteristics of spinal cord injury (SCI) between 2000 and 2021, aiming to provide the most recent and comprehensive data support for the prevention, diagnosis, treatment, and care of SCI.
METHODS: Systematic searches were conducted on epidemiological studies of SCI published between January 1, 2000, and March 29, 2024. Meta-analysis, subgroup analysis, meta-regression, publication bias detection, and literature quality assessment were extensively utilized.
RESULTS: The pooled results from 229 studies indicated that the overall incidence rate of SCI was 23.77 (95% CI, 21.50-26.15) per million people, with traumatic spinal cord injuries (TSCI) at a rate of 26.48 (95% CI, 24.15-28.93) per million people, and non-traumatic spinal cord injuries (NTSCI) at a rate of 17.93 (95% CI, 13.30-23.26) per million people. The incidence of TSCI exhibited a marked age-related increase and was significantly higher in community settings compared to hospital and database sources. Males experienced TSCI at a rate 3.2 times higher than females. Between 2000 and 2021, the incidence of TSCI remained consistently high, between 20 and 45 per million people, whereas NTSCI incidence has seen a steady rise since 2007, stabilizing at a high rate of 25-35 per million people. Additionally, the incidence of TSCI in developing countries was notably higher than that in developed countries. There were significant differences in the causes of injury, severity, injury segments, gender, and age distribution among the TSCI and NTSCI populations, but the proportion of male patients was much higher than that of female patients. Moreover, study quality, country type, and SCI type contributed to the heterogeneity in the meta-analysis.
CONCLUSIONS: The incidence rates of different types of SCI remain high, and the demographic distribution of SCI patients is changing, indicating a serious disease burden on healthcare systems and affected populations. These findings underscore the necessity of adopting targeted preventive, therapeutic, and rehabilitative measures based on the incidence and characteristics of SCI.

OBJECTIVE: To determine the cumulative incidence and features of glaucoma in Vogt-Koyanagi-Harada (VKH) syndrome patients compared to non-glaucoma patients.
CONCLUSIONS: Knowing the exact burden of secondary glaucoma in VKH could guide its screening and management in clinical practice as a part of the regular follow-up for VKH patients.
METHODS: The review protocol was pre-registered on PROSPERO [CRD42023462794]. PubMed, Scopus, Web of Science, EBSCOhost, and Google Scholar were searched for studies reporting the cumulative incidence and features of glaucoma presentation in VKH. A manual search was also conducted to supplement the primary search. Subgroup analyses based on glaucoma type, VKH stage, and patients\' age were conducted. All analyses were conducted using STATA. Fixed- and random-effects models were selected according to the observed heterogeneity. Studies\' methodological quality was determined using the NIH tool.
RESULTS: The analysis of 7084 eyes revealed a progressive increase in the cumulative incidence of secondary glaucoma over time. The cumulative incidence was lowest at VKH onset (7%) and highest at 15 years (26%). Open-angle (12%; 95%CI: 9-14%) is more common than angle-closure glaucoma (7%; 95%CI: 3-13%). Glaucoma cumulative incidence is highest in the chronic recurrent stage of VKH (33%; 95%CI: 12-59%) and among children <18 years of age (26%; 95%CI: 16-37%). Features associated with glaucoma occurrence in VKH showed comparable rates to non-glaucoma cases. However, a meta-analysis to determine risk factors of glaucoma development in VKH was not feasible secondary to the lack of adjusted risk measures in included studies. Studies\' quality was questionable in 5 studies. The certainty of evidence was moderate-to-high.
CONCLUSIONS: The cumulative incidence of glaucoma increases throughout VKH\'s course, with a higher tendency in children, chronic recurrent stages, and long-term follow-up. Future research should focus on examining risk factors of glaucoma development in VKH through adjusted multivariable regression models.

Achieving precise estimates of battery cycle life is a formidable challenge due to the nonlinear nature of battery degradation. This study explores an approach using machine learning (ML) methods to predict the cycle life of lithium-metal-based rechargeable batteries with high mass loading LiNi0.8Mn0.1Co0.1O2 electrode, which exhibits more complicated and electrochemical profile during battery operating conditions than typically studied LiFePO₄/graphite based rechargeable batteries. Extracting diverse features from discharge, charge, and relaxation processes, the intricacies of cell behavior without relying on specific degradation mechanisms are navigated. The best-performing ML model, after feature selection, achieves an R2 of 0.89, showcasing the application of ML in accurately forecasting cycle life. Feature importance analysis unveils the logarithm of the minimum value of discharge capacity difference between 100 and 10 cycle (Log(|min(ΔDQ 100-10(V))|)) as the most important feature. Despite the inherent challenges, this model demonstrates a remarkable 6.6% test error on unseen data, underscoring its robustness and potential for transformative advancements in battery management systems. This study contributes to the successful application of ML in the realm of cycle life prediction for lithium-metal-based rechargeable batteries with practically high energy density design.

BACKGROUND: Leveraging free smartphone apps can help expand the availability and use of evidence-based smoking cessation interventions. However, there is a need for additional research investigating how the use of different features within such apps impacts their effectiveness.
OBJECTIVE: We used observational data collected from an experiment of a publicly available smoking cessation app to develop supervised machine learning (SML) algorithms intended to distinguish the app features that promote successful smoking cessation. We then assessed the extent to which patterns of app feature use accounted for variance in cessation that could not be explained by other known predictors of cessation (eg, tobacco use behaviors).
METHODS: Data came from an experiment (ClinicalTrials.gov NCT04623736) testing the impacts of incentivizing ecological momentary assessments within the National Cancer Institute\'s quitSTART app. Participants\' (N=133) app activity, including every action they took within the app and its corresponding time stamp, was recorded. Demographic and baseline tobacco use characteristics were measured at the start of the experiment, and short-term smoking cessation (7-day point prevalence abstinence) was measured at 4 weeks after baseline. Logistic regression SML modeling was used to estimate participants\' probability of cessation from 28 variables reflecting participants\' use of different app features, assigned experimental conditions, and phone type (iPhone [Apple Inc] or Android [Google]). The SML model was first fit in a training set (n=100) and then its accuracy was assessed in a held-aside test set (n=33). Within the test set, a likelihood ratio test (n=30) assessed whether adding individuals\' SML-predicted probabilities of cessation to a logistic regression model that included demographic and tobacco use (eg, polyuse) variables explained additional variance in 4-week cessation.
RESULTS: The SML model\'s sensitivity (0.67) and specificity (0.67) in the held-aside test set indicated that individuals\' patterns of using different app features predicted cessation with reasonable accuracy. The likelihood ratio test showed that the logistic regression, which included the SML model-predicted probabilities, was statistically equivalent to the model that only included the demographic and tobacco use variables (P=.16).
CONCLUSIONS: Harnessing user data through SML could help determine the features of smoking cessation apps that are most useful. This methodological approach could be applied in future research focusing on smoking cessation app features to inform the development and improvement of smoking cessation apps.
BACKGROUND: ClinicalTrials.gov NCT04623736; https://clinicaltrials.gov/study/NCT04623736.

BACKGROUND: Machine learning techniques are starting to be used in various health care data sets to identify frail persons who may benefit from interventions. However, evidence about the performance of machine learning techniques compared to conventional regression is mixed. It is also unclear what methodological and database factors are associated with performance.
OBJECTIVE: This study aimed to compare the mortality prediction accuracy of various machine learning classifiers for identifying frail older adults in different scenarios.
METHODS: We used deidentified data collected from older adults (65 years of age and older) assessed with interRAI-Home Care instrument in New Zealand between January 1, 2012, and December 31, 2016. A total of 138 interRAI assessment items were used to predict 6-month and 12-month mortality, using 3 machine learning classifiers (random forest [RF], extreme gradient boosting [XGBoost], and multilayer perceptron [MLP]) and regularized logistic regression. We conducted a simulation study comparing the performance of machine learning models with logistic regression and interRAI Home Care Frailty Scale and examined the effects of sample sizes, the number of features, and train-test split ratios.
RESULTS: A total of 95,042 older adults (median age 82.66 years, IQR 77.92-88.76; n=37,462, 39.42% male) receiving home care were analyzed. The average area under the curve (AUC) and sensitivities of 6-month mortality prediction showed that machine learning classifiers did not outperform regularized logistic regressions. In terms of AUC, regularized logistic regression had better performance than XGBoost, MLP, and RF when the number of features was ≤80 and the sample size ≤16,000; MLP outperformed regularized logistic regression in terms of sensitivities when the number of features was ≥40 and the sample size ≥4000. Conversely, RF and XGBoost demonstrated higher specificities than regularized logistic regression in all scenarios.
CONCLUSIONS: The study revealed that machine learning models exhibited significant variation in prediction performance when evaluated using different metrics. Regularized logistic regression was an effective model for identifying frail older adults receiving home care, as indicated by the AUC, particularly when the number of features and sample sizes were not excessively large. Conversely, MLP displayed superior sensitivity, while RF exhibited superior specificity when the number of features and sample sizes were large.

BACKGROUND: Open-system electronic cigarette (EC) product features, such as battery capacity, maximum output wattage, and so forth, are major components that drive product costs and may influence use patterns. Moreover, continued innovation and monitoring of product features and prices will provide critical information for designing appropriate taxation policies and product regulations.
OBJECTIVE: This study will examine how product features are associated with the prices of devices sold in web-based vape shops.
METHODS: We draw samples from 5 popular, US-based, web-based vape shops from April to August 2022 to examine starter kits, device-only products, and e-liquid container-only products. We implemented a linear regression model with a store-fixed effect to examine the association between device attributes and prices.
RESULTS: EC starter kits or devices vary significantly by type, with mod prices being much higher than pod and vape pen prices. The prices of mod starter kits were even lower than those of mod devices, suggesting that mod starter kits are discounted in web-based vape shops. The price of mod kits, mod device-only products, and pod kits increased as the battery capacity and output wattage increased. For vape pens, the price was positively associated with the volume size of the e-liquid container. On the other hand, the price of pod kits was positively associated with the number of containers.
CONCLUSIONS: A unit-based specific tax, therefore, will impose a higher tax burden on lower-priced devices such as vape pens or pod systems and a lower tax burden on mod devices. A volume- or capacity-based specific tax on devices will impose a higher tax burden on vape pens with a larger container size. Meanwhile, ad valorem taxes pegged to wholesale or retail prices would apply evenly across device types, meaning those with advanced features such as higher battery capacities and output wattage would face higher rates. Therefore, policy makers could manipulate tax rates by device type to discourage the use of certain device products.

Human judgments of similarity and difference are sometimes asymmetrical, with the former being more sensitive than the latter to relational overlap, but the theoretical basis for this asymmetry remains unclear. We test an explanation based on the type of information used to make these judgments (relations versus features) and the comparison process itself (similarity versus difference). We propose that asymmetries arise from two aspects of cognitive complexity that impact judgments of similarity and difference: processing relations between entities is more cognitively demanding than processing features of individual entities, and comparisons assessing difference are more cognitively complex than those assessing similarity. In Experiment 1 we tested this hypothesis for both verbal comparisons between word pairs, and visual comparisons between sets of geometric shapes. Participants were asked to select one of two options that was either more similar to or more different from a standard. On unambiguous trials, one option was unambiguously more similar to the standard; on ambiguous trials, one option was more featurally similar to the standard, whereas the other was more relationally similar. Given the higher cognitive complexity of processing relations and of assessing difference, we predicted that detecting relational difference would be particularly demanding. We found that participants (1) had more difficulty detecting relational difference than they did relational similarity on unambiguous trials, and (2) tended to emphasize relational information more when judging similarity than when judging difference on ambiguous trials. The latter finding was replicated using more complex story stimuli (Experiment 2). We showed that this pattern can be captured by a computational model of comparison that weights relational information more heavily for similarity than for difference judgments.

Despite the noteworthy advancements and the introduction of new technologies in diagnostic tools for cardiovascular disorders, the electrocardiogram (ECG) remains a reliable, easily accessible, and affordable tool to use. In addition to its crucial role in cardiac emergencies, ECG can be considered a very useful ancillary tool for the diagnosis of many non-cardiac diseases as well. In this narrative review, we aimed to explore the potential contributions of ECG for the diagnosis of non-cardiac diseases such as stroke, migraine, pancreatitis, Kounis syndrome, hypothermia, esophageal disorders, pulmonary embolism, pulmonary diseases, electrolyte disturbances, anemia, coronavirus disease 2019, different intoxications and pregnancy.

暂无摘要。