OBJECTIVE To preliminarily investigate the antiasthmatic effect and mechanism of Ephedrae Herba-Armeniacae Semen Amarum herb pair on the respiratory center. METHODS Male SD rats were randomly divided into blank group， model group， dexamethasone group （positive control）， and Ephedrae Herba-Armeniacae Semen Amarum 2∶1， 1∶1 and 1∶2 groups. Rats in each group were administered different ratios of the herb pair decoction [all at 18 g （crude drug）/kg]， dexamethasone suspension （0.5 mg/kg）， or normal saline intragastrically twice daily for seven consecutive days. Forty minutes after the last administration， medicated cerebrospinal fluid was collected to determine the content of effective components entering the brain. One and a half hours after the last administration， the nucleus tractus solitarius （NTS） was located using a stereotaxic apparatus. Histamine phosphate （1 μL） was injected into the NTS region at a constant rate of 1 μL/min using a 10 μL microsyringe to induce excitation of the respiratory center in rats； the blank group was injected with normal saline. The contents of neurotransmitters [nerve growth factor （NGF）， substance P （SP）， norepinephrine （NA）， 5-hydroxytryptamine （5-HT） and acetylcholine （Ach）] in the medulla oblongata brain tissue were detected. The mRNA expressions of neurokinin-1 receptor （NK-1R）， p38 mitogen-activated protein kinase （MAPK）， and c-fos in the medulla oblongata， as well as the protein expressions of NK-1R， p38 MAPK， and c-fos in the NTS region， were determined. RESULTS The main active components of Ephedrae Herba-Armeniacae Semen Amarum herb pair entering the brain were ephedrine， pseudoephedrine， and methylephedrine. Compared with blank group， the contents of NGF， SP， NA， 5-HT and Ach， and the relative expression levels of NK-1R， p38 MAPK， and c-fos mRNA and protein were significantly increased in the model group （ P ＜0.01）. Compared with model group， Ephedrae Herba-Armeniacae Semen Amarum herb pair groups with different ratios significantly reduced the neurotransmitter contents and the relative expression levels of NK-1R， p38 MAPK， and c-fos mRNA and protein （ P ＜0.01）， with the 2∶1 Ephedrae Herba-Armeniacae Semen Amarum herb pair and 1∶1 mass ratios showing relatively better effects. CONCLUSIONS Ephedrae Herba alkaloids are the main active components in affecting the function of the respiratory center. The herb pair groups with a larger proportion of Ephedrae Herba exhibit stronger effects. Ephedrae Herba-Armeniacae Semen Amarum herb pair can reduce the excitability of the respiratory center by down-regulating the expression of the NK-1R/MAPK/c-fos pathway in the NTS and decreasing the abnormal release of neurotransmitters such as NGF and SP.

Heart rate is a crucial indicator of human health with significant physiological importance. Traditional contact methods for measuring heart rate, such as electrocardiograph or wristbands, may not always meet the need for convenient health monitoring. Remote photoplethysmography (rPPG) provides a non-contact method for measuring heart rate and other physiological indicators by analyzing blood volume pulse signals. This approach is non-invasive, does not require direct contact, and allows for long-term healthcare monitoring. Deep learning has emerged as a powerful tool for processing complex image and video data, and has been increasingly employed to extract heart rate signals remotely. This article reviewed the latest research advancements in rPPG-based heart rate measurement using deep learning, summarized available public datasets, and explored future research directions and potential advancements in non-contact heart rate measurement.
Humans ; Deep Learning ; Heart Rate/physiology* ; Photoplethysmography/methods* ; Video Recording ; Face ; Monitoring, Physiologic/methods* ; Signal Processing, Computer-Assisted

ObjectiveTo analyze the distribution characteristics of traditional Chinese medicine （TCM） syndrome elements in different risk populations of heart failure complicated with type 2 diabetes. MethodsClinical data of 675 type 2 diabetes patients were retrospectively collected. Lasso-multivariate Logistic regression was used to construct a clinical prediction nomogram model. Based on this， 441 non-heart failure patients were divided into a low-risk group （325 cases） and a high-risk group （116 cases） according to the median risk score of heart failure complicated with type 2 diabetes. TCM diagnostic information （four diagnostic methods） was collected for both groups， and factor analysis was applied to summarize the distribution of TCM syndrome elements in different risk populations. ResultsLasso-multivariate Logistic regression analysis identified age， disease duration， coronary heart disease， old myocardial infarction， arrhythmia， absolute neutrophil count， activated partial thromboplastin time， and α-hydroxybutyrate dehydrogenase as independent risk factors for heart failure complicated with type 2 diabetes. These were used as final predictive factors to construct the nomogram model. Model validation results showed that the area under the curve （AUC） of the receiver operating characteristic （ROC） curve for the modeling group and validation group were 0.934 and 0.935， respectively. The Hosmer-Lemeshow test （modeling group P = 0.996， validation group P = 0.121） indicated good model discrimination. Decision curve analysis showed that the curves for All and None crossed in the upper right corner， indicating high clinical utility. The low-risk and high-risk groups each obtained 14 common factors. Preliminary analysis revealed that the main disease elements in the low-risk group were qi deficiency （175 cases， 53.85%）， dampness （118 cases， 36.31%）， and heat （118 cases， 36.31%）， with the primary locations in the spleen （125 cases， 38.46%） and lungs （99 cases， 30.46%）. In the high-risk group， the main disease elements were yang deficiency （73 cases， 62.93%）， blood stasis （68 cases， 58.62%）， and heat （49 cases， 42.24%）， with the primary locations in the kidney （84 cases， 72.41%） and heart （70 cases， 60.34%）. ConclusionThe overall disease characteristics in different risk populations of type 2 diabetes patients with heart failure are a combination of deficiency and excess， with deficiency being predominant. Deficiency and heat are present throughout. The low-risk population mainly shows qi deficiency with dampness and heat， related to the spleen and lungs. The high-risk population shows yang deficiency with blood stasis and heat， related to the kidneys and heart.

The unique geography,climate,and natural conditions of plateaus pose significant challenges to the physical health and combat effectiveness of stationed troops.In high-altitude regions,maladaptation can lead to acute mountain sickness,such as high-altitude pulmonary edema and high-altitude cerebral edema.Previous studies have shown that gradual acclimatization is an effective strategy for preventing and mitigating altitude sickness.Additionally,non-pharmacological strategies such as intermittent hypoxia and pre-simulated altitude training have also been proven to effectively promote altitude acclimatization.Conventional acclimatization strategies take longer time and are not conducive to rapid deployment of military personnel.In this article,we review various acclimatization training for individuals entering high-altitude areas,in order to provide a reference for the research and application of scientific and efficient altitude acclimatization training strategies.

Objective:Middle ear cholesteatoma is a common otolaryngological disease, and traditional diagnostic methods have certain limitations. This study aims to construct a computer-aided diagnosis model for middle ear cholesteatoma based on integrated convolutional neural networks (CNNs) to improve diagnostic accuracy and efficiency.Methods:Firstly, Data were collected from patients who visited the Department of Otorhinolaryngology Head and Neck Surgery at the First People′s Hospital of Yinchuan between January 2020 and December 2021. 8 000 temporal bone CT images were collected, including 5 000 images diagnosed pathologically as middle ear cholesteatoma and 3 000 normal images. A five-fold cross-validation method was used to divide the dataset into training and testing sets. Next, a transfer learning approach was used to initialize model parameters, and the AlexNet, GoogleNet, and ResNet networks were pre-trained to extract deep features from the images. Then, the Softmax classification algorithm was applied to classify the features, resulting in three independent classifiers. These classifiers were combined using an ensemble learning method with a weighted voting approach to obtain the final diagnostic results. Finally, the model was evaluated by comparing the ensemble classifier with individual classifiers to assess its accuracy, precision, sensitivity, specificity, and diagnostic time, and a comparison with low-mid-and high-experience physician groups was conducted to comprehensively evaluate the model′s diagnostic performance.Results:The experimental results showed that the model achieved an accuracy of 88.8%(178/200), precision of 92.9%,(112/120) sensitivity of 89.8%(108/120), and specificity of 88.1%(70/80). The average diagnostic time for individual patient temporal bone CT images was reduced to 2-3 seconds. Compared to the diagnostic results from low-mid-and high-experience physician groups, the model demonstrated significant advantages and effectively assisted clinicians in making rapid and accurate middle ear cholesteatoma diagnoses.Conclusion:The proposed middle ear cholesteatoma diagnostic model based on integrated convolutional neural networks exhibits high recognition accuracy and rapid diagnostic speed, significantly improving clinical diagnostic efficiency, especially in early screening and auxiliary diagnosis, making it of considerable value in clinical practice.

Objective To explore the correlation between psoas major index(PMI)in patients with diffuse large B-cell lymphoma(DLBCL)and prognosis after rituximab,cyclophosphamide,doxorubicin,vincristine,prednisone(R-CHOP)chemotherapy.Methods Totally 148 DLBCL patients who received R-CHOP chemotherapy were retrospectively enrolled.Based on axis CT image at L3 vertebral transverse process,bilateral psoas muscles were delineated,and PMI was calculated.Then the patients were divided into muscle reduction group(≤lower quartile,n=37)and control group(＞lower quartile,n=111)according to the lower quartile of PMI.Clinical and imaging data were compared between groups,and the correlations of PMI with the above indexes were analyzed.The impact factors of prognosis of DLBCL were observed.Results Significant differences of body mass index(BMI),Eastern Cooperative Oncology Group(ECOG)score,international prognostic index(IPI),the proportion of B symptoms,mortality rate,3-year survival rate,β2-microglobulin,hemoglobin,albumin and PMI were found between groups(all P＜0.05).PMI of DLBCL patient was negatively correlated with age,ECOG score,IPI,β2-microglobulin and lactate dehydrogenase(LDH)(r=—0.126,-0.225,—0.262,—0.232,—0.229,all P＜0.05),but positively correlated with BMI and hemoglobin(r=0.282,0.376,both P＜0.05).The age,ECOG score,IPI and PMI were all independent impact factors of overall survival in DLBCL patients(all P＜0.05).Conclusion PMI was an independent impact factor of prognosis of DLBCL after R-CHOP chemotherapy.

Objective To explore the correlation between psoas major index(PMI)in patients with diffuse large B-cell lymphoma(DLBCL)and prognosis after rituximab,cyclophosphamide,doxorubicin,vincristine,prednisone(R-CHOP)chemotherapy.Methods Totally 148 DLBCL patients who received R-CHOP chemotherapy were retrospectively enrolled.Based on axis CT image at L3 vertebral transverse process,bilateral psoas muscles were delineated,and PMI was calculated.Then the patients were divided into muscle reduction group(≤lower quartile,n=37)and control group(＞lower quartile,n=111)according to the lower quartile of PMI.Clinical and imaging data were compared between groups,and the correlations of PMI with the above indexes were analyzed.The impact factors of prognosis of DLBCL were observed.Results Significant differences of body mass index(BMI),Eastern Cooperative Oncology Group(ECOG)score,international prognostic index(IPI),the proportion of B symptoms,mortality rate,3-year survival rate,β2-microglobulin,hemoglobin,albumin and PMI were found between groups(all P＜0.05).PMI of DLBCL patient was negatively correlated with age,ECOG score,IPI,β2-microglobulin and lactate dehydrogenase(LDH)(r=—0.126,-0.225,—0.262,—0.232,—0.229,all P＜0.05),but positively correlated with BMI and hemoglobin(r=0.282,0.376,both P＜0.05).The age,ECOG score,IPI and PMI were all independent impact factors of overall survival in DLBCL patients(all P＜0.05).Conclusion PMI was an independent impact factor of prognosis of DLBCL after R-CHOP chemotherapy.

Objective:Middle ear cholesteatoma is a common otolaryngological disease, and traditional diagnostic methods have certain limitations. This study aims to construct a computer-aided diagnosis model for middle ear cholesteatoma based on integrated convolutional neural networks (CNNs) to improve diagnostic accuracy and efficiency.Methods:Firstly, Data were collected from patients who visited the Department of Otorhinolaryngology Head and Neck Surgery at the First People′s Hospital of Yinchuan between January 2020 and December 2021. 8 000 temporal bone CT images were collected, including 5 000 images diagnosed pathologically as middle ear cholesteatoma and 3 000 normal images. A five-fold cross-validation method was used to divide the dataset into training and testing sets. Next, a transfer learning approach was used to initialize model parameters, and the AlexNet, GoogleNet, and ResNet networks were pre-trained to extract deep features from the images. Then, the Softmax classification algorithm was applied to classify the features, resulting in three independent classifiers. These classifiers were combined using an ensemble learning method with a weighted voting approach to obtain the final diagnostic results. Finally, the model was evaluated by comparing the ensemble classifier with individual classifiers to assess its accuracy, precision, sensitivity, specificity, and diagnostic time, and a comparison with low-mid-and high-experience physician groups was conducted to comprehensively evaluate the model′s diagnostic performance.Results:The experimental results showed that the model achieved an accuracy of 88.8%(178/200), precision of 92.9%,(112/120) sensitivity of 89.8%(108/120), and specificity of 88.1%(70/80). The average diagnostic time for individual patient temporal bone CT images was reduced to 2-3 seconds. Compared to the diagnostic results from low-mid-and high-experience physician groups, the model demonstrated significant advantages and effectively assisted clinicians in making rapid and accurate middle ear cholesteatoma diagnoses.Conclusion:The proposed middle ear cholesteatoma diagnostic model based on integrated convolutional neural networks exhibits high recognition accuracy and rapid diagnostic speed, significantly improving clinical diagnostic efficiency, especially in early screening and auxiliary diagnosis, making it of considerable value in clinical practice.

[This corrects the article DOI: 10.1016/j.apsb.2022.06.016.].

BACKGROUND:Mesenchymal stem cell-derived exosomes may play a crucial role in tissue damage repair,and miRNA is an important component of exosomes for therapeutic effects.Among them,miR-29b-3p has the effect of reducing cell apoptosis,promoting axonal regeneration,and angiogenesis. OBJECTIVE:To study the protective effect of adipose-derived mesenchymal stem cell-derived exosome via miR-29b-3p on a neural cell injury model simulated by H2O2-treated PC12 cells,and explore the relevant mechanisms. METHODS:(1)First,the collagenase digestion method was used to extract rat adipose-derived mesenchymal stem cells.Adipose-derived mesenchymal stem cells were transfected with miR-29b-3p mimics and inhibitors.Exosomes were extracted from the culture supernatant by ultracentrifugation and identified so as to construct adipose-derived mesenchymal stem cell-derived exosomes with high expression and knockdown miR-29b-3p.(2)By constructing a neural cell injury model simulated by PC12 cells treated with H2O2,the relevant mechanisms of the protective effect of adipose-derived mesenchymal stem cell-derived exosome via miR-29b-3p on the simulated neuronal cell injury model were studied. RESULTS AND CONCLUSION:(1)Adipose-derived mesenchymal stem cell-derived exosome had a typical cup-shaped shape and a diameter distribution in the range of 50-140 nm,expressed membrane proteins Alix,CD63,and TSG101,which were specific markers on the surface of exosomes,and could be successfully ingested by PC12 cells.(2)Adipose-derived mesenchymal stem cell-derived exosome pretreatment could reduce cell apoptosis induced by H2O2 treatment in PC12 cells,and this protective effect was enhanced with the increase of miR-29b-3p expression in the exosomes and weakened with the decrease of miR-29b-3p expression in the exosomes.The mechanism of its effect was related to adipose-derived mesenchymal stem cell-derived exosome via miR-29b-3p promoting the expression of anti-apoptotic protein Bcl-2 and inhibiting the expression of apoptotic protein Bax.