The sepsis and sepsis-induce lung inju-ry threats seriously human health.Dexmedetomi-dine(DEX),a sedative drug,plays an active role in preventing sepsis-induced lung injury during the ba-sic and clinical practice.The current article reviews the role and mechanism of DEX dexmedetomidine alleviating sepsis-induced lung injury from the as-pects of inflammation,oxidative stress,apoptosis,mitochondrial dynamics,autophagy,vascular per-meability,neuro-regulation,targeting miR-128-3p/MAPK14 and DNA methylation,etc.This review looks forward to deepen the understanding the ap-plication of DEX in the field of critical care medi-cine,expand the pharmacological effect of DEX and provide a new idea for the prevention and treat-ment of sepsis from the sedation approach.

Objective To present an assessment method based on oscillometric upper-arm cuff pressure wave for realizing home-based detection of arteriosclerosis.Methods An upper-arm cuff pressure wave acquisition device based on an electronic blood pressure monitor was designed to implement data collection for cuff inflation and deflation,as well as the 15 s pressure holding phase.Following preprocessing of the data collected during this holding phase,individual pulse waves were extracted for pulse decomposition analysis(PDA).A novel model that superimposed a logarithmic normal function with two Gaussian functions,and an optimization method that combined template matching and particle swarm optimization were proposed to efficiently and accurately extract PDA features.Gaussian process regression was employed for multifeature fusion analysis,leading to the proposal of a new predictive indicator for arteriosclerosis.Results The mean absolute error of PDA was less than 1.2%,and the average processing time for a single pulse wave was less than 0.2 s.The regression determination coefficient R2 between the obtained indicators and baPWV was 0.8178.Conclusion The analysis on upper-arm cuff pressure wave provides a new reference for non-invasive arteriosclerosis detection,and it can be applied to electronic blood pressure monitors for home-based monitoring,offering significant practical value.

Objective To present an assessment method based on oscillometric upper-arm cuff pressure wave for realizing home-based detection of arteriosclerosis.Methods An upper-arm cuff pressure wave acquisition device based on an electronic blood pressure monitor was designed to implement data collection for cuff inflation and deflation,as well as the 15 s pressure holding phase.Following preprocessing of the data collected during this holding phase,individual pulse waves were extracted for pulse decomposition analysis(PDA).A novel model that superimposed a logarithmic normal function with two Gaussian functions,and an optimization method that combined template matching and particle swarm optimization were proposed to efficiently and accurately extract PDA features.Gaussian process regression was employed for multifeature fusion analysis,leading to the proposal of a new predictive indicator for arteriosclerosis.Results The mean absolute error of PDA was less than 1.2%,and the average processing time for a single pulse wave was less than 0.2 s.The regression determination coefficient R2 between the obtained indicators and baPWV was 0.8178.Conclusion The analysis on upper-arm cuff pressure wave provides a new reference for non-invasive arteriosclerosis detection,and it can be applied to electronic blood pressure monitors for home-based monitoring,offering significant practical value.

Depression,as a severe psychological and psychiatric disorder,significantly impairs the long-term physical and mental health of patients.Current depression detection methods are plagued by strong subjectivity,limited techniques,and inadequate intelligence.Previous studies have mostly relied on single-modal signal analysis,making it difficult to comprehensively reflect the multidimensional characteristics of depression.Based on the independently developed intelligent depression detection system,wearable devices are used to collect prefrontal dual-lead EEG signals,PPG signals,and single-lead ECG signals.Data from 30 patients with depression and 40 healthy controls are collected and analyzed.A multimodal depression recognition model named RBLF-Net is proposed,which integrates spatiotemporal features,weighted attention,and random forests to utilize the multi-signal features for depression recognition.The model exhibits superior performance in the five-fold cross-validation,achieving a classification accuracy of 81.43%,a precision of 81.02%,and a recall rate of 81.25%,outperforming other comparative models,and thus providing an intelligent analysis approach for depression recognition from the perspective of multi-modal fusion.

Objective·To investigate the efficacy and safety of a dose-reduced,all-oral lenalidomide/melphalan/prednisone acetate(RMP)regimen in elderly and frail patients with newly diagnosed multiple myeloma(NDMM).Methods·Elderly and frail NDMM patients who visited the Department of Hematology of Ruijin Hospital,Shanghai Jiao Tong University School of Medicine,and the Third People's Hospital of Kunshan from April 2018 to March 2024 were retrospectively included.Clinical data and laboratory indicators were collected,and all patients were treated with the RMP regimen.SPSS 27.0 and R software were used for statistical analysis.Independent t-test was applied to normally distributed quantitative data,Mann-Whitney U test to non-normally distributed quantitative data,and x2 test and Fisher's exact probability method to qualitative data.Kaplan-Meier survival curves and Log-rank test were used for survival analysis.Results·Among the 22 elderly and frail NDMM patients treated with RMP,the median age was 76.3(68.4,95.0)years,and the median follow-up time was 25.5 months.The overall response rate(ORR)was 68.2%,and the rate of≥very good partial response(VGPR)was 36.4%.The median progression-free survival(PFS)was 20.53 months.The median PFS in the≤75-year-old group was 25.23(95%CI 12.95?37.52)months,while in the>75-year-old group it was 18.23(95%CI 14.86?21.61)months.There was no significant difference between the two groups.The median PFS in the≥partial response(PR)group was 20.67(95%CI 13.57?27.76)months,and in the

Depression,as a severe psychological and psychiatric disorder,significantly impairs the long-term physical and mental health of patients.Current depression detection methods are plagued by strong subjectivity,limited techniques,and inadequate intelligence.Previous studies have mostly relied on single-modal signal analysis,making it difficult to comprehensively reflect the multidimensional characteristics of depression.Based on the independently developed intelligent depression detection system,wearable devices are used to collect prefrontal dual-lead EEG signals,PPG signals,and single-lead ECG signals.Data from 30 patients with depression and 40 healthy controls are collected and analyzed.A multimodal depression recognition model named RBLF-Net is proposed,which integrates spatiotemporal features,weighted attention,and random forests to utilize the multi-signal features for depression recognition.The model exhibits superior performance in the five-fold cross-validation,achieving a classification accuracy of 81.43%,a precision of 81.02%,and a recall rate of 81.25%,outperforming other comparative models,and thus providing an intelligent analysis approach for depression recognition from the perspective of multi-modal fusion.

Cardiovascular diseases and psychological disorders represent two major threats to human physical and mental health. Research on electrocardiogram (ECG) signals offers valuable opportunities to address these issues. However, existing methods are constrained by limitations in understanding ECG features and transferring knowledge across tasks. To address these challenges, this study developed a multi-resolution feature encoding network based on residual networks, which effectively extracted local morphological features and global rhythm features of ECG signals, thereby enhancing feature representation. Furthermore, a model compression-based continual learning method was proposed, enabling the structured transfer of knowledge from simpler tasks to more complex ones, resulting in improved performance in downstream tasks. The multi-resolution learning model demonstrated superior or comparable performance to state-of-the-art algorithms across five datasets, including tasks such as ECG QRS complex detection, arrhythmia classification, and emotion classification. The continual learning method achieved significant improvements over conventional training approaches in cross-domain, cross-task, and incremental data scenarios. These results highlight the potential of the proposed method for effective cross-task knowledge transfer in ECG analysis and offer a new perspective for multi-task learning using ECG signals.
Humans ; Electrocardiography/methods* ; Mental Health ; Algorithms ; Signal Processing, Computer-Assisted ; Machine Learning ; Arrhythmias, Cardiac/diagnosis* ; Cardiovascular Diseases ; Neural Networks, Computer ; Mental Disorders

ObjectiveTo evaluate the clinical efficacy and safety of Huaban Jiedu Formulation （化斑解毒方， HJF） in treating psoriasis vulgaris with blood-heat syndrome. MethodsA randomized， double-blind， placebo-controlled study was conducted with 60 patients diagnosed with psoriasis vulgaris of blood-heat syndrome. Patients were randomly assigned to either a treatment group or a control group， with 30 cases in each. The treatment group received HJF granules orally， one dose a day， combined with topical Qingshi Zhiyang Ointment （青石止痒软膏）， while the control group received placebo granules， one dose a day， combined with the same topical ointment. Both groups were topically treated twice daily of 28 days treatment cours. Psoriasis area and severity index （PASI）， visual analogue scale for pruritus （VAS）， traditional Chinese medicine （TCM） syndrome scores， dermatology life quality index （DLQI）， and psoriasis life stress inventory （PLSI） were assessed before treatment and on day 14 and day 28. Response rates for PASI 50 （≥50% reduction） and PASI 75 （≥75% reduction）， as well as overall clinical efficacy， were compared between groups. Serum levels of interleukin-6 （IL-6） and interleukin-17 （IL-17） were measured before and after 28 days of treatment. Adverse reactions during treatment were recorded. ResultsAfter 28 days of treatment， both groups showed significant reductions in PASI total score， lesion area score， erythema， scaling， and infiltration scores， pruritus VAS score， TCM syndrome score， DLQI， PLSI， and serum IL-6 and IL-17 levels （P＜0.05）. Compared to the control group， the treatment group had significantly greater improvements in PASI total score and erythema score， TCM syndrome score， serum IL-6 and IL-17 levels， and PASI 50 response rate after 28 days （P＜0.05）. Between-group comparisons of score differences before and after 28-day treatment revealed that the treatment group showed significantly better improvements in PASI total， lesion area score， erythema score， TCM syndrome score， DLQI， PLSI， and inflammatory markers （P＜0.05 or P＜0.01）. The total effective rate on day 14 and day 28 was 40.00% （12/30） and 83.33% （25/30） in the treatment group， versus 6.90% （2/29） and 41.38% （12/29） in the control group， respectively. The clinical efficacy in the treatment group was significantly superior to that in the control group （P＜0.05）. Mild gastric discomfort occurred in 3 patients in the treatment group and 1 in the control group. ConclusionHJF can effectively improve skin lesions and TCM symptoms relieve pruritus， enhance quality of life， and reduce inflammatory markers IL-6 and IL-17， in patients with blood-heat syndrome of psoriasis vulgaris， with a good safety profile.

The quality of traditional Chinese medicine (TCM) prescriptions (TCMPs) is critical to clinical efficacy; however, evaluating their consistency and identifying sources of variability remain challenging. This study proposes an integrated strategy to assess the quality of 100 widely sold TCMPs. A "one-for-all" chromatographic method was employed to analyze 645 sample batches. This large-scale data collection enabled statistical evaluations, such as hierarchical cluster analysis (HCA) and similarity heatmap, to identify quality inconsistencies. The introduction of a TCM-specific mass spectrometry (MS) database allowed for rapid, automated annotation of chemicals across 100 prescriptions and facilitated the tracing of raw material sources. Results indicate that 19% of prescriptions exhibited chemical inconsistencies, which are associated with high market value, low pricing, and substantial price disparities. The MS database allowed rapid annotation of 761 and 673 compounds in positive and negative modes, respectively, in 100 TCMPs, with 73 prescriptions reported for the first time. The tracing efforts succeeded in identifying >40% of the raw material sources for 51 prescriptions. P93 (Yinianjin (YNJ)) is a case in which the chromatographic profiles from three manufacturers displayed inconsistencies. Analysis using the database traced divergent peaks to Rhei Radix et R hizoma (RRER). Verification with self-prepared samples confirmed that manufacturers utilized three distinct botanical sources. This integrated strategy provides a scalable framework for quality control in TCMPs.

OBJECTIVE To analyze the influential factors of drug-induced cholestatic liver disease （DIC） related to tigecycline （TGC）， and establish a prediction model for the risk of this adverse reaction. METHODS Data of 707 hospitalized patients who received TGC treatment in our hospital from August 2022 to August 2024 were collected and randomly divided into training set （n＝566） and test set （n＝141） at a ratio of 8∶2. Prediction variables were screened using the least absolute shrinkage and selection operator regression analysis. Multivariate Logistic regression analysis was used to screen the independent risk factors for TGC-related DIC， and a nomogram prediction model was drawn based on the above factors. The prediction performance of the model was evaluated by the receiver operator characteristic curve （ROC curve） and its area under the curve （AUC）. The accuracy of the model was assessed by the Hosmer-Lemeshow goodness-of-fit test and calibration curves. The clinical net benefit of the prediction model were evaluated by decision curve analysis. RESULTS Among the 707 patients， 93 patients developed DIC， with an incidence rate of 13.15%. Gender， age， high-dose administration of TGC， intensive care unit （ICU） admission， duration of medication of TGC， and concurrent use of antifungal drug voriconazole were independent risk factors for the occurrence of TGC-related DIC （P＜0.05）. The AUC of the training set model was 0.745 （95%CI： 0.687-0.801）， with a sensitivity of 76.6% and a specificity of 60.3%. The AUC of ROC curve of the test set model was 0.762 （95%CI： 0.650-0.900）， with a sensitivity of 81.3% and a specificity of 72.0%. The Hosmer-Lemeshow goodness-of-fit test for the training set， the χ 2 value was 5.187 and P was 0.737； and for the test set， the χ 2 value was 9.980 and P was 0.266. The mean absolute error of the calibration curve for the training set was 0.012， and for the test set， it was 0.038. The risk threshold range for the training set was 4%-45%， and for the test set， it was 4%-28%. CONCLUSIONS Age， gender， high-dose administration of TGC， ICU admission， duration of medication of TGC， and concurrent use of antifungal drug voriconazole are independent risk factors for TGC-related DIC. The established TGC-related DIC risk prediction model has good prediction performance and accuracy.