Heart failure (HF) is the end-stage of all cardiac diseases, characterized by high prevalence, high mortality, and heavy social and economic burden. Early warning of HF exacerbation is of great value for outpatient management and reducing readmission rates. Currently, remote dynamic monitoring technology, which captures changes in hemodynamic and physiological parameters of HF patients, has become the primary method for early warning and is a hot research topic in clinical studies. This paper systematically reviews the progress in this field, which was categorized into invasive monitoring based on implanted devices, non-invasive monitoring based on wearable devices, and other monitoring technologies based on audio and video. Invasive monitoring primarily involves direct hemodynamic parameters such as left atrial pressure and pulmonary artery pressure, while non-invasive monitoring covers parameters such as thoracic impedance, electrocardiogram, respiration, and activity levels. These parameters exhibit characteristic changes in the early stages of HF exacerbation. Given the clinical heterogeneity of HF patients, multi-source information fusion analysis can significantly improve the prediction accuracy of early warning models. The results of this study suggest that, compared with invasive monitoring, non-invasive monitoring technology, with its advantages of good patient compliance, ease of operation, and cost-effectiveness, combined with AI-driven multimodal data analysis methods, shows significant clinical application potential in establishing an outpatient management system for HF.
Humans ; Heart Failure/physiopathology* ; Monitoring, Physiologic/methods* ; Wearable Electronic Devices ; Remote Sensing Technology ; Early Diagnosis ; Electrocardiography ; Hemodynamics

Objective To analyze the composition characteristics and biological functions of tumor cell subpopulations in glioblastoma(GBM)through multi-transcriptomics sequencing technology,and explore the hypoxia characteristics and spatial localization features of the mesenchymal-like(MES-like)tumor cell subpopulation in GBM and the influence on malignant biological behaviors.Methods Multi-transcriptomics sequencing data,including single-cell RNA sequencing(scRNA-seq)data(18 patients),bulk RNA sequencing(bulk RNA-seq)and spatial transcriptomics(ST)data of GBM,were employed to define cell subpopulations in GBM,and Gene Ontology(GO)and Gene Set Enrichment Analysis(GSEA)were utilized to analyze their functions.The proportions and locations of cell subpopulations in bulk RNA-seq data were evaluated with BayesPrism deconvolution.Immunofluorescence assay was conducted for verification on 12 paraffin samples of GBM from patients who visited the neurosurgical department of our hospital from 2015 to 2023 and met the pathological diagnostic criteria for GBM(10 males and 2 females,at an average age of 53.50 years and a median age of 54.50 years).pySCENIC was applied to predict specific transcription factors of tumor cell subpopulations.Results Tumor cells in GBM were highly heterogeneous,and could be mainly divided into 4 subpopulations:astrocyte-like(AC-like),neural progenitor-like(NPC-like),oligodendrocyte progenitor-like(OPC-like)and MES-like.Differential gene analysis found that the MES-like tumor cells highly expressed vascular endothelial growth factor A(VEGFA),adrenomedullin(ADM),N-myc downstream regulated 1(NDRG1),insulin like growth factor binding protein 5(IGFBP5),and A-kinase anchoring protein 12(AKAP12)(P<0.001).pySCENIC transcription factor prediction found that the high-active transcription factors of the MES-like tumor cells were AT-rich interaction domain 3A(ARID3A),FOS like 2,AP-1 transcription factor subunit(FOSL2),endothelial PAS domain protein 1(EPAS1),CCAAT enhancer binding protein delta(CEBPD),and CCAAT enhancer binding protein beta(CEBPB)(P<0.05).GO and GSEA enrichment analyses found that the MES-like tumor cells were enriched in hypoxia-related pathways,especially the pathway of cell responses to hypoxia levels(NES=2.437,P<0.001).BayesPrism deconvolution showed that the MES-like tumor cells mainly existed in PAN(Pseudopalisading cells around necrosis)and perinecrotic zone.Immunofluorescence assay confirmed CD44+(CD44 antigen)MES-like tumor cells were mainly located in hypoxia areas with highly expression of hypoxia inducible factor 1 subunit alpha(HIF1α)(P<0.01).Multivariate Cox regression analysis indicated that the MES-like tumor cells were significantly correlated with the adverse prognosis of GBM patients(HR=1.71,95%CI:1.38～2.11,P<0.001).Conclusion Tumor cells in GBM are of highly heterogeneity.They could be mainly divided into 4 subpopulations:AC-like,NPC-like,OPC-like and MES-like.MES-like tumor cells,mainly locating in PAN and perinecrotic zone,are characterized by hypoxia,which can promote the malignant progression of GBM.

Objective To investigate the relationship between serum small dense low density lipoprotein cholesterol(sdLDL-C),systemic inflammatory response index(SIRI),monocyte to high density lipoprotein cholesterol ratio(MHR)and coronary artery injury(CAL)and intravenous immunoglobulin(IVIG)treat-ment response in Kawasaki disease(KD)children.Methods From April 2021 to December 2023,121 KD children(KD group)admitted to our hospital were divided into CAL group(42 cases)and NCAL group(79 cases)according to whether CAL occurred.Another 121 healthy children who underwent physical examina-tions in the hospital during the same period were selected as the control group.All the children in the KD group received IVIG treatment and were divided into the IVIG response group(99 cases)and the IVIG non-response group(22 cases)according to the treatment response.The differences in serum sdLDLC,SIRI and MHR levels among each group were compared.Univariate and multivariate Logistic regression models were used to analyze the factors influencing the non-response of children with KD to IVIG treatment,and the re-ceiver operating characteristic(ROC)curve was used to analyze the value of serum sdLDL-C,SIRI,and MHR in predicting the non-response of children with KD to IVIG treatment.Results Serum levels of sdLDL-C,SI-RI and MHR in the KD group were higher than those in the control group(P＜0.05).Serum levels of sdLDL-C,SIRI and MHR in the CAL group were higher than those in the NCAL group(P＜0.05).Serum levels of sdLDL-C,SIRI and MHR in the IVIG non-response group were higher than those in the IVIG response group(P＜0.05).The fever duration in the IVIG non-response group was longer than that in the IVIG response group(P＜0.05),and the combined proportion of CAL,the proportion of neutrophils,white blood cell count,C-reactive protein(CRP),interleukin-6(IL-6),erythrocyte sedimentation rate,and ferritin level were higher than those in the IVIG response group(P＜0.05).High CRP level,high sdLDL-C level,high SIRI and high MHR were risk factors for response to IVIG treatment in children with KD(P＜0.05).The areas under the curve(AUC)of serum sdLDL-C,SIRI,and MHR for predicting response to IVIG treatment in children with KD were 0.773,0.767,and 0.780 respectively.The combined prediction AUC was 0.948,which was higher than the individual predictions of each index.Conclusion The abnormal increase of serum sdLDL-C,SIRI and MHR in KD children is related to the occurrence of non-response to CAL and IVIG treatment.The combina-tion of the three can effectively predict the response to IVIG treatment.

Intelligent medical devices are flourishing with the deep integration of modern information and artificial intelligence technologies into healthcare.Testing is an important means of performance evaluation and quality control for intelligent medical devices.Compared with traditional medical devices,the testing methods and technologies of intelligent medical devices are still immature,and need active research to promote the progress in this area.Intelligent medical devices are classified according to their characteristics as artificial intelligence medical devices in the form of software and medical robots based on a general discussion of their development.The medical-device Internet of Things(IoT)system has also been included due to its close relation to the construction of smart hospitals.For each type of intelligent medical device,testing indexes and testing plans are discussed.It is suggested that specific test rules should be further developed for various specific devices.Besides,the evaluation method of complex intelligent systems should be introduced and real-world data should be used for evaluation.This paper aims to accelerate the development of intelligent medical device testing,laying the foundation for quality control and performance evaluation of intelligent medical devices.

Wearable monitoring, which has the advantages of continuous monitoring for a long time with low physiological and psychological load, represents a future development direction of monitoring technology. Based on wearable physiological monitoring technology, combined with Internet of Things (IoT) and artificial intelligence technology, this paper has developed an intelligent monitoring system, including wearable hardware, ward Internet of Things platform, continuous physiological data analysis algorithm and software. We explored the clinical value of continuous physiological data using this system through a lot of clinical practices. And four value points were given, namely, real-time monitoring, disease assessment, prediction and early warning, and rehabilitation training. Depending on the real clinical environment, we explored the mode of applying wearable technology in general ward monitoring, cardiopulmonary rehabilitation, and integrated monitoring inside and outside the hospital. The research results show that this monitoring system can be effectively used for monitoring of patients in hospital, evaluation and training of patients' cardiopulmonary function, and management of patients outside hospital.
Humans ; Artificial Intelligence ; Internet of Things ; Wearable Electronic Devices ; Monitoring, Physiologic/methods* ; Electrocardiography ; Internet

Patients with acute heart failure (AHF) often experience dyspnea, and monitoring and quantifying their breathing patterns can provide reference information for disease and prognosis assessment. In this study, 39 AHF patients and 24 healthy subjects were included. Nighttime chest-abdominal respiratory signals were collected using wearable devices, and the differences in nocturnal breathing patterns between the two groups were quantitatively analyzed. Compared with the healthy group, the AHF group showed a higher mean breathing rate (BR_mean) [(21.03 ± 3.84) beat/min vs. (15.95 ± 3.08) beat/min, P < 0.001], and larger R_RSBI_cv [70.96% (54.34%-104.28)% vs. 58.48% (45.34%-65.95)%, P = 0.005], greater AB_ratio_cv [(22.52 ± 7.14)% vs. (17.10 ± 6.83)%, P = 0.004], and smaller SampEn (0.67 ± 0.37 vs. 1.01 ± 0.29, P < 0.001). Additionally, the mean inspiratory time (TI_mean) and expiration time (TE_mean) were shorter, TI_cv and TE_cv were greater. Furthermore, the LBI_cv was greater, while SD1 and SD2 on the Poincare plot were larger in the AHF group, all of which showed statistically significant differences. Logistic regression calibration revealed that the TI_mean reduction was a risk factor for AHF. The BR_ mean demonstrated the strongest ability to distinguish between the two groups, with an area under the curve (AUC) of 0.846. Parameters such as breathing period, amplitude, coordination, and nonlinear parameters effectively quantify abnormal breathing patterns in AHF patients. Specifically, the reduction in TI_mean serves as a risk factor for AHF, while the BR_mean distinguishes between the two groups. These findings have the potential to provide new information for the assessment of AHF patients.
Humans ; Heart Failure/diagnosis* ; Prognosis ; Respiration ; Wearable Electronic Devices ; Acute Disease

Purpose: Osteosarcoma (OS) universally exhibits heterogeneity and cisplatin (CDDP) resistance. Although the Wee1/CDC2 and nuclear factor кB (NF-κB) pathways were reported to show abnormal activation in some tumor cells with CDDP resistance, whether there is any concrete connection is currently unclear. We explored it in human OS cells. Materials and Methods: Multiple OS cell lines were exposed to a Wee1 inhibitor (AZD1775) and CDDP to assess the half-maximal inhibitory concentration values. Western blot, coimmunoprecipitation, confocal immunofluorescence, cell cycle, and Cell Counting Kit-8assays were performed to explore the connection between the Wee1/CDC2 and NF-κB pathways and their subsequent physiological contribution to CDDP resistance. Finally, CDDP-resistant PDX-OS xenograft models were established to confirm that AZD1775 restores the antitumor effects of CDDP. Results: A sensitivity hierarchy of OS cells to CDDP and AZD1775 exists. In the highly CDDP-tolerant cell lines, Wee1 and RelA were physically crosslinked, which resulted in increased abundance of phosphorylated CDC2 (Y15) and RelA (S536) and consequent modulation of cell cycle progression, survival, and proliferation. Wee1 inhibition restored the effects of CDDP on these processes in CDDP-resistant OS cells. In addition, animal experiments with CDDP-resistant PDX-OS cells showed that AZD1775 combined with CDDP not only restored CDDP efficacy but also amplified AZD1775 in inhibiting tumor growth and prolonged the median survival of the mice. Conclusion Simultaneous enrichment of molecules in the Wee1/CDC2 and NF-κB pathways and their consequent coactivation is a new molecular mechanism of CDDP resistance in OS cells. OS with this molecular signature may respond well to Wee1 inhibition as an alternative treatment strategy.

Wearable physiological parameter monitoring devices play an increasingly important role in daily health monitoring and disease diagnosis/treatment due to their continuous dynamic and low physiological/psychological load characteristics. After decades of development, wearable technologies have gradually matured, and research has expanded to clinical applications. This paper reviews the research progress of wearable physiological parameter monitoring technology and its clinical applications. Firstly, it introduces wearable physiological monitoring technology's research progress in terms of sensing technology and data processing and analysis. Then, it analyzes the monitoring physiological parameters and principles of current medical-grade wearable devices and proposes three specific directions of clinical application research: 1) real-time monitoring and predictive warning, 2) disease assessment and differential diagnosis, and 3) rehabilitation training and precision medicine. Finally, the challenges and response strategies of wearable physiological monitoring technology in the biomedical field are discussed, highlighting its clinical application value and clinical application mode to provide helpful reference information for the research of wearable technology-related fields.
Monitoring, Physiologic ; Wearable Electronic Devices

Breathing pattern parameters refer to the characteristic pattern parameters of respiratory movements, including the breathing amplitude and cycle, chest and abdomen contribution, coordination, etc. It is of great importance to analyze the breathing pattern parameters quantificationally when exploring the pathophysiological variations of breathing and providing instructions on pulmonary rehabilitation training. Our study provided detailed method to quantify breathing pattern parameters including respiratory rate, inspiratory time, expiratory time, inspiratory time proportion, tidal volume, chest respiratory contribution ratio, thoracoabdominal phase difference and peak inspiratory flow. We also brought in "respiratory signal quality index" to deal with the quality evaluation and quantification analysis of long-term thoracic-abdominal respiratory movement signal recorded, and proposed the way of analyzing the variance of breathing pattern parameters. On this basis, we collected chest and abdomen respiratory movement signals in 23 chronic obstructive pulmonary disease (COPD) patients and 22 normal pulmonary function subjects under spontaneous state in a 15 minute-interval using portable cardio-pulmonary monitoring system. We then quantified subjects' breathing pattern parameters and variability. The results showed great difference between the COPD patients and the controls in terms of respiratory rate, inspiratory time, expiratory time, thoracoabdominal phase difference and peak inspiratory flow. COPD patients also showed greater variance of breathing pattern parameters than the controls, and unsynchronized thoracic-abdominal movements were even observed among several patients. Therefore, the quantification and analyzing method of breathing pattern parameters based on the portable cardiopulmonary parameters monitoring system might assist the diagnosis and assessment of respiratory system diseases and hopefully provide new parameters and indexes for monitoring the physical status of patients with cardiopulmonary disease.
Humans ; Lung ; Pulmonary Disease, Chronic Obstructive ; Respiration ; Tidal Volume ; Wearable Electronic Devices

Objective:To investigate the effect of sevoflurane anesthesia on electroencephalographic (EEG) seizures and long-term behavior and possible mechanism in neonatal rats.Methods:A total of 141 postnatal days 4-6 Sprague-Dawley rats (66 male, 75 female) were divided into 3 groups ( n=47 in each group) according to random number table method: control group, sevoflurane group, and NKCC1 inhibitor group, with 22 males and 25 females in each group. Rats in the control group were fed in normal cage without anesthesia; rats in the sevoflurane group were anesthetized with 2.1% sevoflurane for 6 hours; rats in the NKCC1 blocker group received intraperitoneal injection of 1.82 mg / kg bumetanide 30 minutes before anesthesia with 2.1% sevoflurane. The rats in the control group and sevoflurane group were injected subcutaneously with the same dose of DMSO at the same time when the NKCC1 blocker group received the drug intervention, so as to eliminate the influence caused by the solvent. The rats were observed for 30 minutes after recovery from anesthesia and then continued to breastfeed normally. Some of the new born rats received EEG monitoring from 9 to 11 days after being raised; the other rats received EPM and PPI respectively at 60 and 70 days after being raised. Results:The results of EEG showed that, compared with the control group, the number of epileptic waves((0.429±0.787), (1.571±0.787), t=2.753, P<0.01), the average duration of single epileptic wave ((1.575±2.349), (6.392±3.374), t=3.880, P< 0.01), the total duration increased significantly ((1.800±3.617), (10.957±6.028), t= 3.929, P<0.01) were all increased, the differences were statistically significant. Compared with sevoflurane group, the number of epileptic waves in EEG of male rats in NKCC1 blocker group decreased, the average duration of single epileptic wave decreased, and the total duration of epileptic wave shortened significantly, with statistical significance ((0.286±0.756), (0.925±1.733), (1.043±2.759), t=3.097, 4.404, 4.254, all P<0.01). There were no significant differences in female rat among the three groups (all P>0.05). Compared with male rats, the average duration of female rats in sevoflurane group decreased ((6.392±3.374), (2.515±2.992), t=3.044, P<0.01), the total duration shortened ((10.957±6.028), (3.270±5.883), t=2.626, P<0.01), the difference was statistically significant.The behavioral results showed that, compared with the control group, the open arm dwell time of male rats in sevoflurane group was significantly shorter ( P<0.05), and the panic response in PPI group was significantly lower ( P<0.05), the difference was statistically significant.Compared with the sevoflurane group, the open arm dwell time in NKCC1 blocker group was significantly longer ( P<0.05), and the panic response in PPI group was significantly increased.The difference was statistically significant ( P<0.05). The change trend in female rats of each group was similar to that of male rats, but there was no significant difference (all P>0.05). Comparison between male and female rats: compared with male rats in sevoflurane group, the female rats in sevoflurane group had a longer open arm stay time in EPM experiment ( P<0.05), the difference was statistically significant. Conclusion:Sevoflurane anesthesia for 6 hours can significantly increase the generation of epileptic waves in EEG of male newborn rats, and cause behavioral abnormalities in adult male rats, which may be related to NKCC1.And male rats are more vulnerable to the negative effects of sevoflurane anesthesia on brain nerve development.