Objective:To study and establish the UPLC fingerprint and multi-index content determination methods of Hedyotidis chrysotricahae Herba; To provide a reference for the quality control of Hedyotidis chrysotricahae Herba.Methods:The chromatographic column was ACQUITY UPLC HSS T3 (100 mm×2.1 mm,1.8 μm). The mobile phase was acetonitrile-0.1% phosphoric acid solution with gradient elution; the detection wavelength was 254 nm; the flow rate was 0.30 ml/min and column temperature was 35 ℃. The method could determine content and fingerprint of rutin, Kaempferol-3-O-rutinoside, Narcissoside, Neochlorogenic aci, Chlorogenic Acid, Cryptochlorogenic acid and have quality analysis to 17 batches of Hedyotidis chrysotricahae Herba based on the variance of fingerprint, similarity evaluation, clustering analysis along with principal component analysis (PCA) at the same time.Results:The common pattern of UPLC specific chromatogram of Hedyotidis chrysotricahae Herba was established. The 11 common peaks were marked out, among which 7 peaks were identified. 17 batches Hedyotidis chrysotricahae Herba could be divided into 4 categories according to different origins. Quality content of six indicators of Hedyotidis chrysotricahae Herba was in slight difference between different origins, among which the content quality of Hedyotidis chrysotricahae Herba from Duyun in Guizhou Province was the highest.Conclusion:The established UPLC fingerprint and content determination method of 6 indicators from the study can be used for the quality control of Hedyotidis chrysotricahae Herba, which can also provide a theoretical basis for the standard improvement of Hedyotidis chrysotricahae Herba.

The Expert Consensus on Clinical Application of Qinbaohong Zhike Oral Liquid in Treatment of Acute Bronchitis and Acute Attack of Chronic Bronchitis （GS/CACM 337-2023） was released by the China Association of Chinese Medicine on December 13^th， 2023. This expert consensus was developed by experts in methodology， pharmacy， and Chinese medicine in strict accordance with the development requirements of the China Association of Chinese Medicine （CACM） and based on the latest medical evidence and the clinical medication experience of well-known experts in the fields of respiratory medicine （pulmonary diseases） and pediatrics. This expert consensus defines the application of Qinbaohong Zhike oral liquid in the treatment of cough and excessive sputum caused by phlegm-heat obstructing lung， acute bronchitis， and acute attack of chronic bronchitis from the aspects of applicable populations， efficacy evaluation， usage， dosage， drug combination， and safety. It is expected to guide the rational drug use in medical and health institutions， give full play to the unique value of Qinbaohong Zhike oral liquid， and vigorously promote the inheritance and innovation of Chinese patent medicines.

This study explored the potential therapeutic targets and mechanisms of Danggui Shaoyao San(DSS) in the prevention and treatment of Alzheimer's disease(AD) through transcriptomics and metabolomics, combined with animal experiments. Fifty male C57BL/6J mice, aged seven weeks, were randomly divided into the following five groups: control, model, positive drug, low-dose DSS, and high-dose DSS groups. After the intervention, the Morris water maze was used to assess learning and memory abilities of mice, and Nissl staining and hematoxylin-eosin(HE) staining were performed to observe pathological changes in the hippocampal tissue. Transcriptomics and metabolomics were employed to sequence brain tissue and identify differential metabolites, analyzing key genes and metabolites related to disease progression. Reverse transcription-quantitative polymerase chain reaction(RT-qPCR) was employed to validate the expression of key genes. The Morris water maze results indicated that DSS significantly improved learning and cognitive function in scopolamine(SCOP)-induced model mice, with the high-dose DSS group showing the best results. Pathological staining showed that DSS effectively reduced hippocampal neuronal damage, increased Nissl body numbers, and reduced nuclear pyknosis and neuronal loss. Transcriptomics identified seven key genes, including neurexin 1(Nrxn1) and sodium voltage-gated channel α subunit 1(Scn1a), and metabolomics revealed 113 differential metabolites, all of which were closely associated with synaptic function, oxidative stress, and metabolic regulation. RT-qPCR experiments confirmed that the expression of these seven key genes was consistent with the transcriptomics results. This study suggests that DSS significantly improves learning and memory in SCOP model mice and alleviates hippocampal neuronal pathological damage. The mechanisms likely involve the modulation of synaptic function, reduction of oxidative stress, and metabolic balance, with these seven key genes serving as important targets for DSS in the treatment of AD.
Animals ; Alzheimer Disease/genetics* ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Mice, Inbred C57BL ; Metabolomics ; Transcriptome/drug effects* ; Maze Learning/drug effects* ; Hippocampus/metabolism* ; Humans ; Disease Models, Animal ; Memory/drug effects*

Anxiety disorder is a major symptom of autism spectrum disorder (ASD) with a comorbidity rate of ~40%. However, the neural mechanisms of the emergence of anxiety in ASD remain unclear. In our study, we found that hyperactivity of basolateral amygdala (BLA) pyramidal neurons (PNs) in Shank3 InsG3680 knock-in (InsG3680^+/+) mice is involved in the development of anxiety. Electrophysiological results also showed increased excitatory input and decreased inhibitory input in BLA PNs. Chemogenetic inhibition of the excitability of PNs in the BLA rescued the anxiety phenotype of InsG3680^+/+ mice. Further study found that the diminished control of the BLA by medial prefrontal cortex (mPFC) and optogenetic activation of the mPFC-BLA pathway also had a rescue effect, which increased the feedforward inhibition of the BLA. Taken together, our results suggest that hyperactivity of the BLA and alteration of the mPFC-BLA circuitry are involved in anxiety in InsG3680^+/+ mice.
Animals ; Prefrontal Cortex/metabolism* ; Basolateral Nuclear Complex/metabolism* ; Mice ; Anxiety/metabolism* ; Nerve Tissue Proteins/genetics* ; Male ; Gene Knock-In Techniques ; Pyramidal Cells/physiology* ; Mice, Transgenic ; Neural Pathways/physiopathology* ; Mice, Inbred C57BL ; Microfilament Proteins

Objective To evaluate the value of pre-ascent 6-minute walking test performed at a high altitude in predicting the incidence of acute mountain sickness(AMS)induced by rapid ascent to a very high altitude.Methods After baseline information was collected,participants completed the 6-minute walking test at a high altitude of 2 900 m.Then,they rapidly ascended to a very high altitude of 5 000 m.The Lake Louise score was recorded to assess AMS.Results The AMS group showed a shorter pre-ascent 6-minute walking distance(6MWD)at the high altitude than the non-AMS group[480.00(450.00,521.75)m vs.546.00(516.50,568.50)m,P=0.006].No difference was observed regarding the pre-ascent heart rate or peripheral oxygen saturation(both P>0.05).The pre-ascent 6MWD at the high altitude was negatively correlated with the Lake Louise score assessed after rapid ascent to the very high altitude(r=-0.497,P=0.012).Logistic regression analysis confirmed that the pre-ascent 6MWD at the high altitude was associated with the risk of AMS induced by rapid ascent to the very high altitude(OR=0.971,95% CI=0.947-0.996,P=0.022).The results indicated that the pre-ascent 6MWD demonstrated ideal prediction performance(area under receiver operating characteristic curve=0.846,P=0.006).Conclusion The pre-ascent 6MWD recorded at the high altitude is a convenient and reliable predictor of the AMS induced by rapid ascent to the very high altitude.
Humans ; Altitude Sickness/diagnosis* ; Male ; Adult ; Female ; Young Adult ; Middle Aged ; Acute Disease ; Walk Test ; Walking ; Altitude ; Exercise Test

Objective To explore the predictive efficacy of several multimodal MRI-based machine learning models for the promoter methylation states of O6-methylguanine-DNA methyltransferase(MGMT)of glioblastoma muliforme(GBM)patients in terms of the GBM heterogeneity and the complexity of the tumor microenvironment.Methods Firstly,the multimodal MRI images of 317 GBM patients from The University of Pennsylvania Glioblastoma(UPENN-GBM)dataset were pre-processed,with four sequences involved in including T1-weighted imaging(T1WI)sequence,T1-weighted contrast-enhanced imaging(T1CE)sequence,T2-weighted imaging(T2WI)sequence and fluid-attenuated inversion recovery(FLAIR)sequence,and the radiomics features were extracted for two regions of interest(ROIs)such as the tumor core region and the tumor edema region.Secondly,the data of the 317 GBM patients were randomly divided into a training set(254 cases)and a test set(63 cases),which underwent normalization with Z-scores and feature selection and dimensionality reduction with Lasso regression.Finally,three models were established respectively with particle swarm optimization-support vector machine(PSO-SVM),C-support vector classification(C-SVC)and adaptive boosting(adaptive boosting(Adaboost)algorithms,and the predictive efficacy of the three models for glioblastoma multiforme MGMT promoter methylation states were evaluated in terms of accuracy and AUC.Results The Adaboost model based on T2WI sequence and radiomics features of the tumor core region had the highest predictive efficacy with accuracy and AUC values of 67％and 0.74,respectively,higher than those of other combinations of sequences,models and regions of interest.Conclusion The multimodal MRI-based machine learning models can be used for the prediction of glioblastoma multiforme MGMT promoter methylation states,which provides powerful support for personalized treatment and prognostic assessment of GBM.[Chinese Medical Equipment Journal,2025,46(6):7-13]

Objective To propose a novel identification algorithm based on ensemble learning for assessing the severity of acute respiratory distress syndrome(ARDS)to achieve continuous monitoring of the disease severity.Methods Firstly,leve-raging the open-source MIMIC-Ⅳ database,a variety of non-invasive physiological parameters of patients were extracted and subjected to preliminary preprocessing.A multivariate feature selection algorithm was employed to rank these parameters and calculate feature importance scores through weighted computation.Secondly,based on the feature importance scores,a subset search algorithm was utilized to identify the subset of features that could yield optimal performance across four machine learning algorithms:neural networks,logistic regression,AdaBoost and XGBoost.Finally,a soft voting ensemble method was designed using a generalized linear regression model to integrate the results of each single machine learning algorithm,and a multivariate ensemble learning algorithm was proposed by combining the optimal feature subsets.The algorithm proposed when used to identify the severity of ADRS was evaluated with MIMIC-Ⅳ database,and compared with the traditional algorithms.Results The sensitivity,specificity,accuracy and AUC of the algorithm were 87.15％,89.23％,88.34％and 0.923 4,respectively,all of which outperformed those of the traditional algorithms.Conclusion The ARDS severity identification algorithm based on ensemble learning is capable of achieving continuous and real-time monitoring of the severity of ARDS,thereby offering robust support for the early identification and warning of ARDS in patients.[Chinese Medical Equipment Journal,2025,46(2):1-9]

Objective This study aimed to provide an effective scientific basis for prevention and control of cockroaches on aircrafts by identifying cockroach-carried pathogens,and assess the insecticidal efficacy of gel bait mediated cockroach control on aircrafts,to provide technical guidance for aircraft disinsection.Methods Cassette-trapping was used to trap cockroaches,and the carried pathogens were detected using bacterial cultivation techniques.The gel bait mediated killing rate was calculated after 1,7,and 30 d by field application of gel bait.Results A total of 411 cockroaches were captured,and all were identified as Blattella germanica.26 strains of pathogenic bacteria were isolated from the trapped cockroaches.The killing rates of cockroaches were 58.8％-96.3％with 1-30 day application of gel bait.Statistically significant differences were observed in cockroach killing rates on different days(χ2=58.95,P<0.01).Conclusions B.germanica carry a large variety of pathogenic bacteria and opportunistic pathogens and are thus important infectious disease carriers.Gel bait agents have proven to be very effective against cockroaches on aircrafts.

Objective:To evaluate the clinical efficacy of"Shibao Decoction"in the management of late-onset hypogonadism(LOH)caused by deficiency of kidney essence.Methods:Sixty male patients with late-onset hypogonadism of kidney essence defi-ciency type were randomly assigned to the treatment group and the control group,each with 30 cases.The patients in treatment group were treated with oral Shibao Decoction,while the control group was treated with oral Testosterone Undecanoate Capsules.The patients in both groups were treated for 12 weeks.The PADAM symptom score,TCM syndrome score,serum total testosterone(TT),serum free testosterone(FT),sex hormone binding globulin(SHBG),body mass index(BMI),total skeletal muscle mass index(SMI),appendicular skeletal muscle mass index(ASMI),FBG,FINS,and insulin resistance index(HOMA-IR)levels were compared be-tween the two groups.Results:After treatment,PADAM scores for each item and TCM symptoms score decreased,TT and FT in-creased in both groups,all with statistically significant differences from those of pre-treatment(P＜0.05).The level of SHBG in the control group decreased(P＜0.05),which had not changed significantly in the treatment group(P＞0.05).After treatment,SMI and ASMI increased in both groups significantly(P＜0.05).BMI decreased in the control group(P＜0.05),which had not changed significantly in the treatment group(P＞0.05).The level of FINS decreased in the control group(P＜0.05),which had not changed significantly in the treatment group(P＞0.05).FPG had not changed significantly in both groups(P＞0.05),and the insulin resist-ance index(HOMA-IR)had significantly improved in both groups,all with statistically significant differences from those of pre-treat-ment(P＜0.05).After treatment,the total effective rates of PADAM score and TCM syndrome score in the treatment group were 73.3％and 86.6％respectively,and the total effective rates in the control group were 66.7％and 76.6％respectively.The total ef-fective rates of the two scores in the treatment group were slightly higher than those in the control group(P＞0.05).There was no sig-nificant difference in the indicators between the two groups after treatment,and the treatment group is generally comparable with the control group in the therapeutic effects(P＞0.05).And no adverse reactions occurred during treatment in both groups.Conclu-sion:The"Shibao Decoction"has a remarkable therapeutic effect on late-onset hypogonadism caused by deficiency of kidney essence and has good safety.It can be used as an alternative to testosterone undecanoate and is worthy of clinical promotion and application.

Objective To explore the predictive efficacy of several multimodal MRI-based machine learning models for the promoter methylation states of O6-methylguanine-DNA methyltransferase(MGMT)of glioblastoma muliforme(GBM)patients in terms of the GBM heterogeneity and the complexity of the tumor microenvironment.Methods Firstly,the multimodal MRI images of 317 GBM patients from The University of Pennsylvania Glioblastoma(UPENN-GBM)dataset were pre-processed,with four sequences involved in including T1-weighted imaging(T1WI)sequence,T1-weighted contrast-enhanced imaging(T1CE)sequence,T2-weighted imaging(T2WI)sequence and fluid-attenuated inversion recovery(FLAIR)sequence,and the radiomics features were extracted for two regions of interest(ROIs)such as the tumor core region and the tumor edema region.Secondly,the data of the 317 GBM patients were randomly divided into a training set(254 cases)and a test set(63 cases),which underwent normalization with Z-scores and feature selection and dimensionality reduction with Lasso regression.Finally,three models were established respectively with particle swarm optimization-support vector machine(PSO-SVM),C-support vector classification(C-SVC)and adaptive boosting(adaptive boosting(Adaboost)algorithms,and the predictive efficacy of the three models for glioblastoma multiforme MGMT promoter methylation states were evaluated in terms of accuracy and AUC.Results The Adaboost model based on T2WI sequence and radiomics features of the tumor core region had the highest predictive efficacy with accuracy and AUC values of 67％and 0.74,respectively,higher than those of other combinations of sequences,models and regions of interest.Conclusion The multimodal MRI-based machine learning models can be used for the prediction of glioblastoma multiforme MGMT promoter methylation states,which provides powerful support for personalized treatment and prognostic assessment of GBM.[Chinese Medical Equipment Journal,2025,46(6):7-13]