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Neural activity varies dramatically across time. While such neural variability has been associated with cognition, its relationship with pain remains largely unexplored. Here, we systematically investigated the relationship between neural variability and pain, particularly individual differences in pain intensity discriminability, in six large electroencephalography (EEG) datasets (total N  = 633), where healthy volunteers (Datasets 1–5; N  = 606) and postherpetic neuralgia patients (Dataset 6; N  = 27) received painful or nonpainful sensory stimuli. We found robust correlations between neural variability and interindividual pain intensity discriminability. These correlations were replicable in multiple datasets and seemed not to be caused by stimulus-general factors, as no significant correlations were observed in nonpain modalities. Importantly, variability and amplitude of EEG responses were mutually independent and had distinct temporal and oscillatory profiles in encoding pain intensity discriminability. These findings demonstrate that neural variability is a replicable and potentially preferential indicator of individual differences in pain intensity discriminability, thereby enhancing the understanding of neural encoding of pain intensity discriminability and underscoring the value of neural variability in pain studies.

In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system. Further, several emerging challenges, such as cost-effectiveness, security, privacy, accuracy and power consumption, are discussed, and potential solutions for these challenges are also suggested.

Background Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). Machine learning (ML) algorithms can learn from intricate information unbiasedly and facilitate the early identification of clinical outcomes. This study aimed to construct and compare the ability of different ML models to predict DCI after aSAH. Then, we identified and analyzed the essential risk of DCI occurrence by preoperative clinical scores and postoperative laboratory test results. Methods This was a multicenter, retrospective cohort study. A total of 1039 post-operation patients with aSAH were finally included from three hospitals in China. The training group contained 919 patients, and the test group comprised 120 patients. We used five popular machine-learning algorithms to construct the models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, and f1 score were used to evaluate and compare the five models. Finally, we performed a Shapley Additive exPlanations analysis for the model with the best performance and significance analysis for each feature. Results A total of 239 patients with aSAH (23.003%) developed DCI after the operation. Our results showed that in the test cohort, Random Forest (RF) had an AUC of 0.79, which was better than other models. The five most important features for predicting DCI in the RF model were the admitted modified Rankin Scale, D-Dimer, intracranial parenchymal hematoma, neutrophil/lymphocyte ratio, and Fisher score. Interestingly, clamping or embolization for the aneurysm treatment was the fourth button-down risk factor in the ML model. Conclusions In this multicenter study, we compared five ML methods, among which RF performed the best in DCI prediction. In addition, the essential risks were identified to help clinicians monitor the patients at high risk for DCI more precisely and facilitate timely intervention.

This paper presents a solution to satisfy the increasing requirement of real-time secure image transmission over public networks. The main advantage of the proposed cryptosystem is high efficiency. The confusion and diffusion operations are both performed based on a lookup table. Therefore, the time-consuming floating point arithmetic in chaotic map iteration and quantization procedures of traditional chaos-based image cipher can be avoided. Besides, this cryptosystem possesses satisfactory resistance to noise perturbation and loss of cipher data, which are inevitable and unpredictable in real-world channels. The channel disturbance and the deliberate damage from the opponents are both tolerated. The recovered image from the damaged cipher data has satisfactory visual perception. Simulations prove the advantages of the proposed scheme, which render it a good candidate for real-time secure image applications.

Single-target drugs have not achieved satisfactory therapeutic effects for complex diseases involving multiple factors. Instead, innovations in recent drug research and development have revealed the emergence of compound drugs, such as cocktail therapies and ＂polypills＂, as the frontier in new drug development. A traditional Chinese medicine （TCM） prescription that is usually composed of several medicinal herbs can serve a typical representative of compound medicines. Although the traditional compatibility theory of TCM cannot be well expressed using modern scientific language nowadays, the fundamental purpose of TCM compatibility can be understood as promoting efficacy and reducing toxicity. This paper introduces the theory and methods of efficacy-oriented compatibility for developing component-based Chinese medicines.

Background Studies on coronary slow flow are receiving increasing attention, but objective evaluations are still lacking. The purpose of this study was to visualize the current status and research hotspots of coronary slow flow through bibliometric analysis. Methods All relevant publications on coronary slow flow from 2003 to 2022 were extracted from the Web of Science Core Collection database and analyzed by VOSviewer and CiteSpace visualization software. Year of publication, journal, country/region, institution, and first author of each paper, as well as research hotspots were identified. Results A total of 913 publications were retrieved. The journal with the most publications was Coronary Artery Disease. The country/region with the most publications was Turkey, followed by China and the United States. The institution with the largest publication volume was Turkey Specialized Higher Education Research Hospital. The author with the largest publication volume was Chun-Yan Ma from China. Keyword analysis indicated that “treatment and prognosis”, “pathogenesis and risk factors” and “diagnosis” were the clustering centers of coronary slow flow, and the research hotspots gradually changed with time, from pathogenesis to treatment and prognosis. Conclusion Future research will focus on the search for effective and non-invasive detection indicators and treatments of coronary slow flow. Collaboration needs to be enhanced between different institutions or countries/regions, which would improve clinical outcomes for patients with coronary slow flow.

Pain sensitivity variations represent a critical frontier in pain neuroscience, where advanced neuroimaging has mapped cerebral correlates of nociception for decades, yet conventional brain-centric models persistently overlook the spinal cord’s hub role in pain gating and amplification. Here we show that a corticospinal pain sensitivity signature, a pattern of functional connectivity from simultaneous corticospinal magnetic resonance imaging, predicts individual pain sensitivity and clinical pain. Trained on resting-state data and validated across independent healthy (n = 723) and patient cohorts ( n  = 46), the model generalized to new datasets, distinguished pain from non-pain, and outperformed brain-centric models. Crucially, transcranial magnetic stimulation perturbation revealed a causal axis where enhanced motor cortex-spinal connectivity directly changes pain perception (r = 0.55). These results indicate a previously unknown corticospinal biomarker that bridges laboratory pain measures and patient symptoms, providing insights into translating pain mechanisms from healthy individuals to clinical populations and informing neuromodulation approaches. In this study, the authors present an fMRI‑based signature of corticospinal connections, which predicts individual pain sensitivity, generalizes to patient cohorts, and tracks changes after brain stimulation, suggesting a biomarker to guide personalized pain care.

Background The purpose of this study was to explore the relationship between quantitative epicardial adipose tissue (EAT) based on coronary computed tomography angiography (CCTA) and coronary slow flow (CSF). Methods A total of 85 patients with < 40% coronary stenosis on diagnostic coronary angiography were included in this retrospective study between January 2020 and December 2021. A semi-automatic method was developed for EAT quantification on CCTA images. According to the thrombolysis in myocardial infarction flow grade, the patients were divided into CSF group (n = 39) and normal coronary flow group (n = 46). Multivariate logistic regression was used to explore the relationship between EAT and CSF. Receiver operating characteristic (ROC) curve was plotted to evaluate the diagnostic value of EAT in CSF. Results EAT volume in the CSF group was significantly higher than that of the normal coronary flow group (128.83± 21.59 mL vs. 101.87± 18.56 mL, P < 0.001). There was no significant difference in epicardial fat attenuation index between the two groups (P > 0.05). Multivariate logistic regression analysis showed that EAT volume was independently related to CSF [odds ratio (OR) = 4.82, 95% confidence interval (CI): 3.06–7.27, P < 0.001]. The area under ROC curve for EAT volume in identifying CSF was 0.86 (95% CI: 0.77–0.95). The optimal cutoff value of 118.46 mL yielded a sensitivity of 0.80 and a specificity of 0.94. Conclusions Increased EAT volume based on CCTA is strongly associated with CSF. This preliminary finding paves the way for future and larger studies aimed to definitively recognize the diagnostic value of EAT in CSF.

Background Stapokibart/CM310, a humanized monoclonal antibody targeting the interleukin-4 receptor α chain, has shown promising treatment benefits in patients with moderate-to-severe atopic dermatitis in previous phase II clinical trials. Objective We aimed to evaluate the long-term efficacy and safety of stapokibart in adults with moderate-to-severe atopic dermatitis. Methods Enrolled patients who previously completed parent trials of stapokibart received a subcutaneous stapokibart 600-mg loading dose, then 300 mg every 2 weeks up to 52 weeks. Efficacy outcomes included the proportions of patients with ≥ 50%/75%/90% improvements from baseline of parent trials in the Eczema Area and Severity Index, Investigator’s Global Assessment, and weekly average of the daily Peak Pruritus Numerical Rating Scale. Results In total, 127 patients were enrolled, and 110 (86.6%) completed the study. At week 52, the Eczema Area and Severity Index-50/75/90 response rates were 96.3%, 87.9%, and 71.0%, respectively. An Investigator’s Global Assessment 0/1 with a ≥ 2-point reduction was achieved in 39.3% of patients at week 16, increasing to 58.9% at week 52. The proportions of patients with ≥ 3-point and ≥ 4-point reductions in the weekly average of daily Peak Pruritus Numerical Rating Scale scores were 80.2% and 62.2%, respectively, at week 52. Improvement in patients’ quality of life was sustained over a 52-week treatment period. Treatment-emergent adverse events occurred in 88.2% of patients, with an exposure-adjusted event rate of 299.2 events/100 patient-years. Coronavirus disease 2019, upper respiratory tract infection, and conjunctivitis were the most common treatment-emergent adverse events. Conclusions Long-term treatment with stapokibart for 52 weeks showed high efficacy and good safety profiles, supporting its use as a continuous long-term treatment option for atopic dermatitis. Clinical Trial Registration ClinicalTrials.gov identifier: NCT04893707 (15 May, 2021).

Background Coronary inflammation induces changes in pericoronary adipose tissue (PCAT) can be detected by coronary computed tomography angiography (CCTA). Our aim was to investigate whether different PCAT radiomics model based on CCTA could improve the prediction of major adverse cardiovascular events (MACE) within 3 years. Methods This retrospective study included 141 consecutive patients with MACE and matched to patients with non-MACE ( n  = 141). Patients were randomly assigned into training and test datasets at a ratio of 8:2. After the robust radiomics features were selected by using the Spearman correlation analysis and the least absolute shrinkage and selection operator, radiomics models were built based on different machine learning algorithms. The clinical model was then calculated according to independent clinical risk factors. Finally, an overall model was established using the radiomics features and the clinical factors. Performance of the models was evaluated for discrimination degree, calibration degree, and clinical usefulness. Results The diagnostic performance of the PCAT model was superior to that of the RCA-model, LAD-model, and LCX-model alone, with AUCs of 0.723, 0.675, 0.664, and 0.623, respectively. The overall model showed superior diagnostic performance than that of the PCAT-model and Cli-model, with AUCs of 0.797, 0.723, and 0.706, respectively. Calibration curve showed good fitness of the overall model, and decision curve analyze demonstrated that the model provides greater clinical benefit. Conclusion The CCTA-based PCAT radiomics features of three major coronary arteries have the potential to be used as a predictor for MACE. The overall model incorporating the radiomics features and clinical factors offered significantly higher discrimination ability for MACE than using radiomics or clinical factors alone.