Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model.

BACKGROUND: Clinical notes contain contextualized information beyond structured data related to patients' past and current health status. OBJECTIVE: This study aimed to design a multimodal deep learning approach to improve the evaluation precision of hospital outcomes for heart failure (HF) using admission clinical notes and easily collected tabular data. METHODS: Data for the development and validation of the multimodal model were retrospectively derived from 3 open-access US databases, including the Medical Information Mart for Intensive Care III v1.4 (MIMIC-III) and MIMIC-IV v1.0, collected from a teaching hospital from 2001 to 2019, and the eICU Collaborative Research Database v1.2, collected from 208 hospitals from 2014 to 2015. The study cohorts consisted of all patients with critical HF. The clinical notes, including chief complaint, history of present illness, physical examination, medical history, and admission medication, as well as clinical variables recorded in electronic health records, were analyzed. We developed a deep learning mortality prediction model for in-hospital patients, which underwent complete internal, prospective, and external evaluation. The Integrated Gradients and SHapley Additive exPlanations (SHAP) methods were used to analyze the importance of risk factors. RESULTS: The study included 9989 (16.4%) patients in the development set, 2497 (14.1%) patients in the internal validation set, 1896 (18.3%) in the prospective validation set, and 7432 (15%) patients in the external validation set. The area under the receiver operating characteristic curve of the models was 0.838 (95% CI 0.827-0.851), 0.849 (95% CI 0.841-0.856), and 0.767 (95% CI 0.762-0.772), for the internal, prospective, and external validation sets, respectively. The area under the receiver operating characteristic curve of the multimodal model outperformed that of the unimodal models in all test sets, and tabular data contributed to higher discrimination. The medical history and physical examination were more useful than other factors in early assessments. CONCLUSIONS: The multimodal deep learning model for combining admission notes and clinical tabular data showed promising efficacy as a potentially novel method in evaluating the risk of mortality in patients with HF, providing more accurate and timely decision support.

Association Between Monocyte-to-Lymphocyte Ratio and Hematoma Progression After Cerebral Contusion.

BACKGROUND: The objective of this research was to examine the impact of the monocyte-to-lymphocyte ratio (MLR) on the advancement of hematoma after cerebral contusion. METHODS: The clinical information and laboratory test findings of people with cerebral contusion were retrospectively analyzed. Using the tertiles of MLR, the study participants were categorized into three groups, enabling the evaluation of the correlation between MLR and the advancement of hematoma after cerebral contusion. RESULTS: Among the cohort of patients showing progression, MLR levels were significantly higher compared with the nonprogress group (P < 0.001). The high MLR group had a significantly higher proportion of patients with hematoma progression compared with the medium and low MLR groups. However, the medium MLR group had a lower proportion of patients with hematoma progression compared with the low MLR group. High MLR levels were independently linked to a higher risk of hematoma progression (Odds Ratio 3.546, 95% Confidence Interval 1.187-10.597, P = 0.024). By incorporating factors such as Glasgow Coma Scale score on admission, anticoagulant/antiplatelet therapy, white blood cell count, and MLR into the model, the predictive performance of the model significantly improved (area under the curve 0.754). CONCLUSIONS: Our study suggests that MLR may serve as a potential indicator for predicting the progression of hematoma after cerebral contusion. Further research is necessary to investigate the underlying pathological and physiological mechanisms that contribute to the association between MLR and the progression of hematoma after cerebral contusion and to explore its clinical implications.

[Development of intelligent monitoring system based on Internet of Things and wearable technology and exploration of its clinical application mode].

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.

[Investigation on new paradigm of clinical physiological monitoring by using wearable devices].

As a low-load physiological monitoring technology, wearable devices can provide new methods for monitoring, evaluating and managing chronic diseases, which is a direction for the future development of monitoring technology. However, as a new type of monitoring technology, its clinical application mode and value are still unclear and need to be further explored. In this study, a central monitoring system based on wearable devices was built in the general ward (non-ICU ward) of PLA General Hospital, the value points of clinical application of wearable physiological monitoring technology were analyzed, and the system was combined with the treatment process and applied to clinical monitoring. The system is able to effectively collect data such as electrocardiogram, respiration, blood oxygen, pulse rate, and body position/movement to achieve real-time monitoring, prediction and early warning, and condition assessment. And since its operation from March 2018, 1 268 people (657 patients) have undergone wearable continuous physiological monitoring until January 2020, with data from a total of 1 198 people (632 cases) screened for signals through signal quality algorithms and manual interpretation were available for analysis, accounting for 94.48 % (96.19%) of the total. Through continuous physiological data analysis and manual correction, sleep apnea event, nocturnal hypoxemia, tachycardia, and ventricular premature beats were detected in 232 (36.65%), 58 (9.16%), 30 (4.74%), and 42 (6.64%) of the total patients, while the number of these abnormal events recorded in the archives was 4 (0.63%), 0 (0.00%), 24 (3.80%), and 15 (2.37%) cases. The statistical analysis of sleep apnea event outcomes revealed that patients with chronic diseases were more likely to have sleep apnea events than healthy individuals, and the incidence was higher in men (62.93%) than in women (37.07%). The results indicate that wearable physiological monitoring technology can provide a new monitoring mode for inpatients, capturing more abnormal events and provide richer information for clinical diagnosis and treatment through continuous physiological parameter analysis, and can be effectively integrated into existing medical processes. We will continue to explore the applicability of this new monitoring mode in different clinical scenarios to further enrich the clinical application of wearable technology and provide richer tools and methods for the monitoring, evaluation and management of chronic diseases.

Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards.

Physiological signals can contain abundant personalized information and indicate health status and disease deterioration. However, in current medical practice, clinicians working in the general wards are usually lack of plentiful means and tools to continuously monitor the physiological signals of the inpatients. To address this problem, we here presented a medical-grade wireless monitoring system based on wearable and artificial intelligence technology. The system consists of a multi-sensor wearable device, database servers and user interfaces. It can monitor physiological signals such as electrocardiography and respiration and transmit data wirelessly. We highly integrated the system with the existing hospital information system and explored a set of processes of physiological signal acquisition, storage, analysis, and combination with electronic health records. Multi-scale information extracted from physiological signals and related to the deterioration or abnormality of patients could be shown on the user interfaces, while a variety of reports could be provided daily based on time-series signal processing technology and machine learning to make more information accessible to clinicians. Apart from an initial attempt to implement the system in a realistic clinical environment, we also conducted a preliminary validation of the core processes in the workflow. The heart rate veracity validation of 22 patient volunteers showed that the system had a great consistency with ECG Holter, and bias for heart rate was 0.04 (95% confidence interval: -7.34 to 7.42) beats per minute. The Bland-Altman analysis showed that 98.52% of the points were located between Mean ± 1.96SD. This system has been deployed in the general wards of the Hyperbaric Oxygen Department and Respiratory Medicine Department and has collected more than 1000 cases from the clinic. The whole system will continue to be updated based on clinical feedback. It has been demonstrated that this system can provide reliable physiological monitoring for patients in general wards and has the potential to generate more personalized pathophysiological information related to disease diagnosis and treatment from the continuously monitored physiological data.

Impact of renal dysfunction on long-term outcomes of elderly patients with acute coronary syndrome: a longitudinal, prospective observational study.

BACKGROUND: This study investigated the impact of renal dysfunction (RD) on long-term outcomes in elderly patients with acute coronary syndrome (ACS), and evaluated prognostic factors in elderly patients with ACS and RD. METHODS: This longitudinal prospective study included 184 consecutive patients who were admitted with ACS between January 2009 and January 2010 and also had RD. Patients were divided into five groups according to their estimated glomerular filtration rate (eGFR): 1) eGFR ≥ 90 mL/min/1.73 m2 with evidence of kidney damage, 2) 60 ≤ eGFR <90 mL/min/1.73 m2, 3) 30 ≤ eGFR <60 mL/min/1.73 m2, 4) 15 ≤ eGFR <30 mL/min/1.73 m2, and 5) eGFR <15 mL/min/1.73 m2. The primary endpoints were death and complications during hospitalization. The secondary endpoint was any major adverse cardiac event (MACE) during follow-up. RESULTS: The mean follow-up period was 502.2 ± 203.6 days. The mean patient age was 73.7 ± 9.4 years, and 61.4% of the patients were men. Severe RD (eGFR <30 mL/min/1.73 m2) was an independent predictor of MACE. Severe RD was associated with a low hemoglobin level, low left ventricular ejection fraction, and high levels of high-sensitivity C-reactive protein, N-terminal pro-B-type natriuretic peptide, and cystatin C. Survival was significantly poorer in patients with severe RD than in patients with mild RD. CONCLUSIONS: Among patients with ACS, severe RD was associated with advanced age, diabetes, hypertension, and cardiac dysfunction. Severe RD was an independent risk factor for MACE, and was associated with poor prognosis.

Applications of Engineered Skin Tissue for Cosmetic Component and Toxicology Detection.

The scale of the cosmetic market is increasing every day. There are many safety risks to cosmetics, but they benefit people at the same time. The skin can become red, swollen, itchy, chronically toxic, and senescent due to the misuse of cosmetics, triggering skin injuries, with contact dermatitis being the most common. Therefore, there is an urgent need for a system that can scientifically and rationally detect the composition and perform a toxicological assessment of cosmetic products. Traditional detection methods rely on instrumentation and method selection, which are less sensitive and more complex to perform. Engineered skin tissue has emerged with the advent of tissue engineering technology as an emerging bioengineering technology. The ideal engineered skin tissue is the basis for building good in vitro structures and physiological functions in this field. This review introduces the existing cosmetic testing and toxicological evaluation methods, the current development status, and the types and characteristics of engineered skin tissue. The application of engineered skin tissue in the field of cosmetic composition detection and toxicological evaluation, as well as the different types of tissue engineering scaffold materials and three-dimensional (3D) organoid preparation approaches, is highlighted in this review to provide methods and ideas for constructing the next engineered skin tissue for cosmetic raw material component analysis and toxicological evaluation.

Major Adverse Cardiovascular Events and Prognosis in Patients With Coronary Slow Flow.

This study aimed to investigate its clinical implications, risk factors, prognosis, and overall long-term outcomes. Demographic profiles, various clinical characteristics, and clinical outcomes were compared between 614 patients with coronary slow flow (CSF) and 428 patients with normal coronary artery. The incidence of CSF was found to be 2.65%. Significant differences were observed between patients with CSF and control subjects in terms of sex, chest tightness, hyperlipidemia, smoking history, alcohol consumption, age, height, weight, body mass index, diastolic blood pressure, heart rate, and body surface area (P < 0.05). CSF (hazard ratio: 1.531; 95% confidence interval: 1.064-2.202; p = 0.022) proved to be independent prognostic predictors of major adverse cardiovascular events (MACEs). Kaplan-Meier survival evaluations for MACEs presented a worser outcome for patients with CSF. Patients with CSF are at high risk for cardiovascular events and experience generally poor clinical outcomes.

Establishment and verification of a nomogram that predicts the risk for coronary slow flow.

Background: Coronary slow flow (CSF) has gained significance as a chronic coronary artery disease, but few studies have integrated both biological and anatomical factors for CSF assessment. This study aimed to develop and validate a simple-to-use nomogram for predicting CSF risk by combining biological and anatomical factors. Methods: In this retrospective case-control study, 1042 patients (614 CSF cases and 428 controls) were randomly assigned to the development and validation cohorts at a 7:3 ratio. Potential predictive factors were identified using least absolute shrinkage and selection operator regression and subsequently utilized in multivariate logistic regression to construct the nomogram. Validation of the nomogram was assessed by discrimination and calibration. Results: N-terminal pro brain natriuretic peptide, high density lipoprotein cholesterol, hemoglobin, left anterior descending artery diameter, left circumflex artery diameter, and right coronary artery diameter were independent predictors of CSF. The model displayed high discrimination in the development and validation cohorts (C-index 0.771, 95% CI: 0.737-0.805 and 0.805, 95% CI: 0.757-0.853, respectively). The calibration curves for both cohorts showed close alignment between predicted and actual risk estimates, demonstrating improved model calibration. Decision curve analysis suggested high clinical utility for the predictive nomogram. Conclusion: The constructed nomogram accurately and individually predicts the risk of CSF for patients with suspected CSF and may be considered for use in clinical care.

Enhanced external counterpulsation modulates the heartbeat evoked potential.

Introduction: Accumulating evidence suggests that enhanced external counterpulsation (EECP) influences cardiac functions, hemodynamic characteristics and cerebral blood flow. However, little is known about whether or how the EECP affects the brain-heart coupling to produce these physiological and functional changes. We aimed to determine if the brain-heart coupling is altered during or after EECP intervention by assessing the heartbeat evoked potential (HEP) in healthy adults. Methods: Based on a random sham-controlled design, simultaneous electroencephalography and electrocardiography signals as well as blood pressure and flow status data were recorded before, during and after two consecutive 30-min EECP in 40 healthy adults (female/male: 17/23; age: 23.1 ± 2.3 years). HEP amplitude, frequency domain heart rate variability, electroencephalographic power and hemodynamic measurements of 21 subjects (female/male: 10/11; age: 22.7 ± 2.1 years) receiving active EECP were calculated and compared with those of 19 sham control subjects (female/male: 7/12; age: 23.6 ± 2.5 years). Results: EECP intervention caused immediate obvious fluctuations of HEP from 100 to 400 ms after T-peak and increased HEP amplitudes in the (155-169) ms, (354-389) ms and (367-387) ms time windows after T-peak in the region of the frontal pole lobe. The modifications in HEP amplitude were not associated with changes in the analyzed significant physiological measurements and hemodynamic variables. Discussion: Our study provides evidence that the HEP is modulated by immediate EECP stimuli. We speculate that the increased HEP induced by EECP may be a marker of enhanced brain-heart coupling. HEP may serve as a candidate biomarker for the effects and responsiveness to EECP.

A study on the immediate effects of enhanced external counterpulsation on physiological coupling.

Introduction: Enhanced external counterpulsation (EECP) is a non-invasive assisted circulation technique for its clinical application in the rehabilitation and management of ischemic cardiovascular and cerebrovascular diseases, which has complex physiological and hemodynamic effects. However, the effects of EECP on the coupling of physiological systems are still unclear. We aimed to investigate the immediate effects of EECP on the coupling between integrated physiological systems such as cardiorespiratory and cardiovascular systems. Methods: Based on a random sham-controlled design, simultaneous electrocardiography, photoplethysmography, bio-electrical impedance, and continuous hemodynamic data were recorded before, during and after two consecutive 30 min EECP in 41 healthy adults. Physiological coupling strength quantified by phase synchronization indexes (PSI), hemodynamic measurements and heart rate variability indices of 22 subjects (female/male: 10/12; age: 22.6 ± 2.1 years) receiving active EECP were calculated and compared with those of 19 sham control subjects (female/male: 7/12; age: 23.6 ± 2.5 years). Results: Immediately after the two consecutive EECP interventions, the physiological coupling between respiratory and cardiovascular systems PSIRES-PTT (0.34 ± 0.14 vs. 0.49 ± 0.17, P = 0.002), the physiological coupling between cardiac and cardiovascular systems PSIIBI-PTT (0.41 ± 0.14 vs. 0.52 ± 0.16, P = 0.006) and the total physiological coupling PSItotal (1.21 ± 0.35 vs. 1.57 ± 0.49, P = 0.005) in the EECP group were significantly lower than those before the EECP intervention, while the physiological coupling indexes in the control group did not change significantly (P > 0.05). Conclusion: Our study provides evidence that the PSI is altered by immediate EECP intervention. We speculate that the reduced PSI induced by EECP may be a marker of disturbed physiological coupling. This study provides a new method for exploring the mechanism of EECP action and may help to further optimize the EECP technique.

Hypoxic culture of umbilical cord mesenchymal stem cell-derived sEVs prompts peripheral nerve injury repair.

Introduction: Repair and regeneration of the peripheral nerve are important for the treatment of peripheral nerve injury (PNI) caused by mechanical tears, external compression injuries and traction injuries. Pharmacological treatment can promote the proliferation of fibroblasts and Schwann cells (SCs), which longitudinally fill the endoneurial canal and form Bungner's band, helping the repair of peripheral nerves. Therefore, the development of new drugs for the treatment of PNI has become a top priority in recent years. Methods: Here, we report that small extracellular vesicles (sEVs) produced from umbilical cord mesenchymal stem cells (MSC-sEVs) cultured under hypoxia promote repair and regeneration of the peripheral nerve in PNI and may be a new therapeutic drug candidate. Results: The results showed that the amount of secreted sEVs was significantly increased in UC-MSCs compared with control cells after 48 h of culture at 3% oxygen partial pressure in a serum-free culture system. The identified MSC-sEVs could be taken up by SCs in vitro, promoting the growth and migration of SCs. In a spared nerve injury (SNI) mouse model, MSC-sEVs accelerated the recruitment of SCs at the site of PNI and promoted peripheral nerve repair and regeneration. Notably, repair and regeneration in the SNI mouse model were enhanced by treatment with hypoxic cultured UC-MSC-derived sEVs. Discussion: Therefore, we conclude that hypoxic cultured UC-MSC-derived sEVs may be a promising candidate drug for repair and regeneration in PNI.

Surveillance of emerging infectious diseases for biosecurity.

Emerging infectious diseases, such as COVID-19, continue to pose significant threats to human beings and their surroundings. In addition, biological warfare, bioterrorism, biological accidents, and harmful consequences arising from dual-use biotechnology also pose a challenge for global biosecurity. Improving the early surveillance capabilities is necessary for building a common biosecurity shield for the global community of health for all. Furthermore, surveillance could provide early warning and situational awareness of biosecurity risks. However, current surveillance systems face enormous challenges, including technical shortages, fragmented management, and limited international cooperation. Detecting emerging biological risks caused by unknown or novel pathogens is of particular concern. Surveillance systems must be enhanced to effectively mitigate biosecurity risks. Thus, a global strategy of meaningful cooperation based on efficient integration of surveillance at all levels, including interdisciplinary integration of techniques and interdepartmental integration for effective management, is urgently needed. In this paper, we review the biosecurity risks by analyzing potential factors at all levels globally. In addition to describing biosecurity risks and their impact on global security, we also focus on analyzing the challenges to traditional surveillance and propose suggestions on how to integrate current technologies and resources to conduct effective global surveillance.

Assessing Electrocardiogram and Respiratory Signal Quality of a Wearable Device (SensEcho): Semisupervised Machine Learning-Based Validation Study.

BACKGROUND: With the development and promotion of wearable devices and their mobile health (mHealth) apps, physiological signals have become a research hotspot. However, noise is complex in signals obtained from daily lives, making it difficult to analyze the signals automatically and resulting in a high false alarm rate. At present, screening out the high-quality segments of the signals from huge-volume data with few labels remains a problem. Signal quality assessment (SQA) is essential and is able to advance the valuable information mining of signals. OBJECTIVE: The aims of this study were to design an SQA algorithm based on the unsupervised isolation forest model to classify the signal quality into 3 grades: good, acceptable, and unacceptable; validate the algorithm on labeled data sets; and apply the algorithm on real-world data to evaluate its efficacy. METHODS: Data used in this study were collected by a wearable device (SensEcho) from healthy individuals and patients. The observation windows for electrocardiogram (ECG) and respiratory signals were 10 and 30 seconds, respectively. In the experimental procedure, the unlabeled training set was used to train the models. The validation and test sets were labeled according to preset criteria and used to evaluate the classification performance quantitatively. The validation set consisted of 3460 and 2086 windows of ECG and respiratory signals, respectively, whereas the test set was made up of 4686 and 3341 windows of signals, respectively. The algorithm was also compared with self-organizing maps (SOMs) and 4 classic supervised models (logistic regression, random forest, support vector machine, and extreme gradient boosting). One case validation was illustrated to show the application effect. The algorithm was then applied to 1144 cases of ECG signals collected from patients and the detected arrhythmia false alarms were calculated. RESULTS: The quantitative results showed that the ECG SQA model achieved 94.97% and 95.58% accuracy on the validation and test sets, respectively, whereas the respiratory SQA model achieved 81.06% and 86.20% accuracy on the validation and test sets, respectively. The algorithm was superior to SOM and achieved moderate performance when compared with the supervised models. The example case showed that the algorithm was able to correctly classify the signal quality even when there were complex pathological changes in the signals. The algorithm application results indicated that some specific types of arrhythmia false alarms such as tachycardia, atrial premature beat, and ventricular premature beat could be significantly reduced with the help of the algorithm. CONCLUSIONS: This study verified the feasibility of applying the anomaly detection unsupervised model to SQA. The application scenarios include reducing the false alarm rate of the device and selecting signal segments that can be used for further research.

Longitudinal Changes and Recovery in Heart Rate Variability of Young Healthy Subjects When Exposure to a Hypobaric Hypoxic Environment.

Background: The autonomic nervous system (ANS) is crucial for acclimatization. Investigating the responses of acute exposure to a hypoxic environment may provide some knowledge of the cardiopulmonary system's adjustment mechanism. Objective: The present study investigates the longitudinal changes and recovery in heart rate variability (HRV) in a young healthy population when exposed to a simulated plateau environment. Methods: The study followed a strict experimental paradigm in which physiological signals were collected from 33 healthy college students (26 ± 2 years, 171 cm ± 7 cm, 64 ± 11 kg) using a medical-grade wearable device. The subjects were asked to sit in normoxic (approximately 101 kPa) and hypoxic (4,000 m above sea level, about 62 kPa) environments. The whole experimental process was divided into four stable resting measurement segments in chronological order to analyze the longitudinal changes of physical stress and recovery phases. Seventy-six time-domain, frequency-domain, and non-linear indicators characterizing rhythm variability were analyzed in the four groups. Results: Compared to normobaric normoxia, participants in hypobaric hypoxia had significantly lower HRV time-domain metrics, such as RMSSD, MeanNN, and MedianNN (p < 0.01), substantially higher frequency domain metrics such as LF/HF ratio (p < 0.05), significantly lower Poincaré plot parameters such as SD1/SD2 ratio and other Poincaré plot parameters are reduced considerably (p < 0.01), and Refined Composite Multi-Scale Entropy (RCMSE) curves are reduced significantly (p < 0.01). Conclusion: The present study shows that elevated heart rates, sympathetic activation, and reduced overall complexity were observed in healthy subjects exposed to a hypobaric and hypoxic environment. Moreover, the results indicated that Multiscale Entropy (MSE) analysis of RR interval series could characterize the degree of minor physiological changes. This novel index of HRV can better explain changes in the human ANS.

Systemic risk factors correlated with hyperhomocysteinemia for specific MTHFR C677T genotypes and sex in the Chinese population.

BACKGROUND: Methyltetrahydrofolate reductase (MTHFR) is a main regulatory enzyme in homocysteine (Hcy) metabolism. A common C677T mutation in the MTHFR gene results in decreased enzyme activity, which contributes to hyperhomocysteinemia (HHcy). Previous studies have shown that HHcy was correlated with various systemic diseases, such as cardiovascular disease, stroke, cancer, renal failure and so on. However, we hypothesized that HHcy in different genotype and sex groups may have different risk factors, which would lead to various pathologic states. Therefore, the aim of this study was to explore systemic information that are correlated with HHcy for specific MTHFR C677T genotypes and sex, which might be useful for predicting and preventing systemic diseases. METHODS: This cross-sectional study was performed through November 2017 to July 2019. A total of 4,534 adults aged 20-75 y were selected for this study. All the participants underwent a physical examination, blood tests and MTHFRC677T genotyping. Multivariable linear regression was performed to explore the risk factors for HHcy for each sex and genotype. RESULTS: The average of Hcy level is higher in the TT genotype than CC and CT genotypes (P=0.000). Multiple linear regression analysis identified the common protective factors (folate and Vit B12) and risk factor (Cr) for HHcy. Besides that, each group has its specific risk factors-female-CT (age, SBP, and Hb), female-TT (SBP and AST); male-CC (age, AST and Hb), male-CT (age and AST) and male-TT (SBP, AST, and Hb). CONCLUSIONS: HHcy was associated with different risk factors for each specific sex and genotype. These risk factors might be useful for predicting and preventing systemic diseases.

Mechanism of aquaporin 4 (AQP 4) up-regulation in rat cerebral edema under hypobaric hypoxia and the preventative effect of puerarin.

AIM: We aim to investigate the mechanism of aquaporin 4 (AQP 4) up-regulation during high-altitude cerebral edema (HACE) in rats under hypobaric hypoxia and preventative effect of puerarin. METHODS: Rats were exposed to a hypobaric chamber with or without the preventative treatment of puerarin or dexamethasone. Morriz water maze was used to evaluate the spatial memory injury. HE staining and W/D ratio were used to evaluate edema injury. Rat astrocytes and microglia were co-cultured under the condition of hypoxia with the administration of p38 inhibitor, NF-κB inhibitor or puerarin. Interleukin 6 (IL-6) and tumor necrosis factor α (TNF α) of cortex and culture supernatant were measured with ELISA. AQP4, phosphorylation of MAPKs, NF-κB pathway of cortex and astrocytes were measured by Western blot. KEY FINDINGS: Weakened spatial memory and cerebral edema were observed after hypobaric hypoxia exposure. AQP4, phosphorylation of NF-κB and MAPK signal pathway of cortex increased after hypoxia exposure and decreased with preventative treatment of puerarin. Hypoxia increased TNF-α and IL-6 levels in cortex and microglia and puerarin could prevent the increase of them. AQP4 of astrocytes increased after co-cultured with microglia when both were exposed to hypoxia. AQP4 showed a decrease after administered with p38 inhibitor, NF-κB inhibitor or puerarin. SIGNIFICANCE: Hypoxia triggers inflammatory response, during which AQP4 of astrocytes can be up regulated through the release of TNF-α and IL-6 from microglia. Puerarin can exert a preventative effect on the increase of AQP4 through inhibiting the release of TNF-α and phosphorylation of NF-κB, MAPK pathway.

[The relationship between TIMI (thrombolysis in myocardial infarction) risk score and efficacy of conservative or interventional strategy in patients with non-ST-segment elevation acute coronary syndromes].

OBJECTIVE: To investigate the relationship between thrombolysis in myocardial infarction (TIMI) risk score and efficacy of different treatment strategies in patients with non-ST-segment elevation acute coronary syndromes (NSTE-ACS). METHODS: From Oct. 2001 to Oct. 2003, 545 consecutive patients with NSTE-ACS were randomly assigned to early conservative strategy (n = 284) or early invasive strategy group (n = 261). The combined cardiovascular events (a combination of cardiac death, nonfatal myocardial infarction, nonfatal heart failure and re-hospital admission due to recurrent ischemia angina) within 30 days and 6 months were analyzed and related to the TIMI risk score at admission. RESULTS: Rehospitalization due to recurrent ischemia angina of 30 days and the combined cardiovascular events of 30 days and 6 months were significantly lower in early invasive strategy group (3.5%, 10.0%, 21.1%) compared with early conservative strategy group (8.1%, 16.9%, 28.2%, all P < 0.05). Subgroup analysis indicated early invasive strategy could significantly decrease the 30 d incidence of the combined end point events in patients with high TIMI risk score and the 6 months incidence of the combined end point events in patients with moderate and high TIMI risk score (all P < 0.01), but the incidence was similar between the two different strategies in patients with low TIMI risk score. CONCLUSIONS: Early invasive strategy may significantly reduce combined cardiovascular events in NSTE-ACS patients with moderate and high TIMI risk score compared with early conservative strategy.

Protective effects of puerarin on acute lung and cerebrum injury induced by hypobaric hypoxia via the regulation of aquaporin (AQP) via NF-κB signaling pathway.

OBJECTIVE: Hypobaric hypoxia, frequently encountered at high altitude, may lead to lung and cerebrum injury. Our study aimed to investigate whether puerarin could exert ameliorative effects on rats exposed to hypobaric hypoxia via regulation of aquaporin (AQP) and NF-κB signaling pathway in lung and cerebrum. MATERIALS AND METHODS: 40 Sprague Dawley rats were divided into four groups (normal control group, hypobaric hypoxia group, puerarin group and dexamethasone group). Wet/dry ratio, blood gas, pathological changes of lung and cerebrum and spatial memory were observed in each group. Inflammatory cytokines in bronchoalveolar lavage fluid (BALF) were determined with ELISA and expression of AQP1, AQP4, NF-κB signaling pathway in lung and cerebrum with western blot RESULTS: Puerarin showed significant preventative effects on tissue injury and behavioral changes, as evidenced by histopathological findings and Morris water maze. In addition, levels of inflammatory cytokines in BALF decreased in the two preventative groups compared with those of hypobaric hypoxia group. AQP in lung and cerebrum increased under the condition of hypobaric hypoxia while was down regulated in both two preventative groups. NF-κB and IκB was also inhibited by puerarin. CONCLUSION: Our study suggested that lung and cerebrum injury, increased inflammatory cytokines in BALF and increased AQP1, AQP4 and NF-κB signaling pathway occurred under the condition of hypobaric hypoxia. Moreover, puerarin could prevent lung and cerebrum injury of rats exposed to hypobaric hypoxia via down-regulation of inflammatory cytokines, AQP1 and AQP4 expression and NF-κB signaling pathway.

[The changes of electrocardiogram and impact of early invasive strategy in patients with acute coronary syndromes without ST-segment elevation].

OBJECTIVE: To investigate the changes of electrocardiogram (ECG) and impact of early invasive strategy in patients with acute coronary syndrome (ACS) without ST-segment elevation. METHODS: Five hundred and forty-five consecutive ACS patients without ST-segment elevation were randomly assigned to early conservative treatment group and early invasive treatment group. The combined cardiovascular events, including cardiac death, nonfatal myocardial infarction, nonfatal heart failure, and re-hospitalization due to recurrent ischemia angina, within 30 days and 6 months were analyzed and the effects of varied ECG changes and different intervention strategies on outcomes of patients were evaluated. RESULTS: The incidences of each and combined cardiovascular events were higher in the patients with ST-segment depression than in those without ST-segment depression. ST-segment depression was one of independent predictive factors for an increase in cardiovascular events within 6 months (OR 3.864, 95% CI: 1.668 approximately 9.451, P < 0.001). Early invasive strategy was associated with a lower rate of re-hospitalization due to recurrent ischemia angina within 30 days and a decreased incidence of combined cardiovascular events within 30 days and 6 months in comparison with the early conservative treatment group (all P < 0.05). Subgroup analysis implied that incidences of combined cardiovascular events within 30 days and 6 months decreased significantly only in patients with ST-segment depression treated with early invasive strategy, and no such benefit was seen in the patients without ST-segment depression. CONCLUSION: ST-segment depression is an effective indicator for identifying those patients with non-ST segment elevation ACS most likely to benefit from early invasive strategy. Early invasive strategy markedly decreases the cardiovascular events in ACS patients with ST-segment depression than early conservative strategy.