Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Moringa oleifera, widely utilized in Ayurvedic medicine, is recognized for its leaves, seeds, and velamen possessing traditional effects such as vātahara(wind alleviation), sirovirecaka(brain clearing), and hridya(mental nourishment). This study aims to identify the medicinal part of ■ in the Sārasvata ghee formulation as described in the Bower Manuscript, while investigating the ameliorative effects of different medicinal parts of M. oleifera on learning and memory deficits in mice and elucidating the underlying molecular mechanisms. A total of 144 male ICR mice were randomly assigned to the following groups: control, model(scopolamine hydrobromide, Sco, 2 mg·kg~(-1)), donepezil(donepezil hydrochloride, Don, 3 mg·kg~(-1)), M. oleifera leaf low-, medium-, and high-dose groups(0.5, 1, 2 g·kg~(-1)), M. oleifera seeds low-, medium-, and high-dose groups(0.25, 0.5, 1 g·kg~(-1)), and M. oleifera velamen low-, medium-, and high-dose groups(0.31, 0.62, 1.24 g·kg~(-1)). Learning and memory abilities were assessed using the passive avoidance test and Morris water maze. Nissl and HE staining were employed to examine histopathological changes in the hippocampus. Transcriptomics and targeted metabolomics were used to screen differential genes and metabolites, with MetaboAnalyst 6.0 and O2PLS methods applied to identify key disease-related targets and pathways. RESULTS:: demonstrated that M. oleifera leaf(1 g·kg~(-1)) significantly ameliorated Sco-induced learning and memory deficits, outperforming M. oleifera seeds(0.25 g·kg~(-1)) and M. oleifera velamen(1.24 g·kg~(-1)). This was evidenced by improved behavioral performance, reversal of neuronal damage, and reduced acetylcholinesterase(AChE) activity. Multi-omics analysis revealed that M. oleifera leaf upregulated Tuba1c gene expression through the synaptic vesicle cycle, enhancing glutamate(Glu), dopamine(DA), and acetylcholine(ACh) release via Tuba1c-Glu associations for neuroprotection. M. oleifera seeds targeted the dopaminergic synapse pathway, promoting memory consolidation through Drd2-ACh associations. M. oleifera velamen was associated with the cocaine addiction pathway, modulating dopamine metabolism via Adora2a-DOPAC, with limited relevance to learning and memory. In conclusion, M. oleifera leaf exhibits superior efficacy and mechanistic advantages over M. oleifera seeds and velamen, suggesting that the ■ in the Sārasvata ghee formulation is likely M. oleifera leaf, providing scientific evidence for its identification in ancient texts.
Animals ; Moringa oleifera/chemistry* ; Male ; Mice ; Seeds/chemistry* ; Plant Leaves/chemistry* ; Mice, Inbred ICR ; Memory Disorders/psychology* ; Transcriptome/drug effects* ; Memory/drug effects* ; Learning/drug effects* ; Metabolomics ; Humans ; Drugs, Chinese Herbal/administration & dosage* ; Maze Learning/drug effects*

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

AIM To optimize the processing technology of red Notoginseng Radix et Rhizoma and evaluate its blood tonifying activity.METHODS On the basis of a single factor experiment,with steaming temperature,steaming time,drying temperature,and drying time as influencing factors,the total contents of notoginsenoside R1,ginsenoside Rg1,Rb1,Rk3,Rh4,and 20(R)-ginsenoside Rg3 as evaluation indicators,Box-Behnken response surface method ology was used to optimize the processing technology.Upon the anemic mouse models jointly induced by 1-acetyl-2-phenylhydrazine(APH)and cyclophosphamide(CTX),the investigation of the blood tonifying activity of red Notoginseng Radix et Rhizoma was carried out in contrast to that of the steamed Notoginseng Radix et Rhizoma.RESULTS The optimal conditions,contributing saponin content of 8.326%and RSD of 0.087%,were determined as follows:steaming temperature of 130℃,steaming time of 4 hours,drying temperature of 60℃,and drying time of 48 h.The pharmacological activity revealed that the different processing techniques were responsible for the different blood enriching activity of notoginseng,with red Notoginseng Radix et Rhizoma displaying a better efficacy than that of steamed Notoginseng Radix et Rhizoma.CONCLUSION This stable and feasible method can be used to control the production of red Notoginseng Radix et Rhizoma.

Aim To establish NCI-H446/EP for small cell lung cancer resistant cells resistant to cisplatin and etoposide, and to evaluate their biological characteristics and multidrug resistance. Methods Nude mice were subcutaneously inoculated with NCI-H446 cells of SCLC to construct an in vivo model of xenograft tumor, and were given first-line EP regimen treatment for SCLC, inducing drug resistance in vivo, and stripping tumor tissue in vitro culture to obtain drug-resistant cells. The resistance coefficient, cell doubling time, cell cycle distribution, expression of multidrug resistance gene (MDR1), and drug resistance-related protein were detected in vitro, and the drug resistance to cisplatin and etoposide in vivo were verified. Results Mice with NCI-H446 tumors acquired resistance after eight weeks' EP regimen treatment, and the drug-resistant cell line NCI-H446/EP was obtained by isolation and culture in vitro. The resistance factors of this cell line to cisplatin, etoposide, SN38 and doxorubicin were 12.01, 18.36, 65.4 and 10.12, respectively. Compared with parental cells, the proportion of NCIH446/EP cells in Q

Chinese medicinal resources are the material basis for the survival and development of traditional Chinese medicine(TCM)and the sustainable development of Chinese medicinal resources is also an important project for the modernization of TCM in China. With the increasing demand for Chinese medicinal resources in China, over-exploitation has destroyed Chinese medicinal resources, resulting in a shortage of many natural medicinal resources in China and making the sustainable development of TCM in trouble. The introduced new foreign medicinal resources have become effective supplement and replacement for Chinese medicinal resources to some extent. However, the development and utilization of new foreign medicinal resources in China are different. To fully understand the development of new foreign medicinal resources in China, this paper, taking 43 new foreign medicinal resources such as Acacia nilotica as objects, sorted out the introduction forms and policies of new foreign medicinal resources, overviewed its current development status in China, summarized the application experience of new foreign medicinal resources in the place of origin, as well as the research progress and problems of new foreign medicinal resources in China and abroad, and analyzed the research situation, which can enrich Chinese medicinal resources and other uses, promote the sustainable development of Chinese medicinal resources, and provide ideas for further development and research of new foreign medicinal resources.
Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional ; Conservation of Natural Resources ; Sustainable Development ; Internationality ; China

The modernization and development of traditional Chinese medicine has led to higher standards for the quality of traditional Chinese medicine products. The extraction process is a crucial component of traditional Chinese medicine production, and it directly impacts the final quality of the product. However, the currently relied upon methods for quality assurance of the extraction process, such as simple wet chemical analysis, have several limitations, including time consumption and labor intensity, and do not offer precise control of the extraction process. As a result, there is significant value in incorporating near-infrared spectroscopy (NIRS) in the production process of traditional Chinese medicine to improve the quality control of the final products. In this study, we focused on the extraction process of Xiao'er Xiaoji Zhike oral liquid (XXZOL), using near-infrared spectra collected by both a Fourier transform near-infrared spectrometer and a portable near-infrared spectrometer. We used the concentration of synephrine, a quality control index component specified by the pharmacopoeia, to achieve rapid and accurate detection in the extraction process. Moreover, we developed a model transfer method to facilitate the transfer of models between the two types of near-infrared spectrometers (analytical grade and portable), thus resolving the low resolution, poor performance, and insufficient prediction accuracy issues of portable instruments. Our findings enable the rapid screening and quality analysis of XXZOL onsite, which is significant for quality monitoring during the traditional Chinese medicine production process.

Aim To compare the effects of different time sequence interventions on virus infected mice by using oseltamivir (Tamiflu) as a "tool drug" in view of the current situation of the too early the administration time of antiviral in vivo experiment, so as to provide a basis for selecting a reasonable model intervention time point for antiviral drug research. Methods Balb/c mice were randomly divided into six groups. The virus infection model was established by intranasal infection with influenza virus (0.25 TCID50). Tamiflu-1 group and Tamiflu-2 group were administered orally on 1st and 4th day after exposure. The body mass, survival rate, organ index, viral load and inflammatory factor content were measured. Results Compared with the blank control group, the body weight of the mice in the model group decreased and the lung index increased significantly (P < 0.05). The expression levels of 13 inflammatory factors in model 2 group were significantly different ( P < 0.05). Compared with the model-1 group ,the lung index and spleen index of the Tamiflu-1 group decreased significantly (P < 0.05). Compared with the mode-2 group,the lung index in the Tamiflu-2 group was significantly lower (P <0.05) ,and the thy-mus index was significantly higher (P<0.05). The viral load was 0. 03 times that of the model-2 group. The expression levels of 13 inflammatory factors were significantly different (P < 0. 05). Conclusions The symptoms of the mice in Scheme 2 are more obvious and stable after exposure. After administration, the lung inflammation damage is alleviated. Considering the latency, drug intervention is in line with the drug indications when the model animals show symptoms. It will be more reasonable and accurate whether in the model evaluation or drug evaluation.

Headache is a common clinical complication of ischemic stroke. As a precursor of stroke, headache occurs repeatedly in the convalescent period of ischemic stroke, leading to secondary stroke and seriously hindering patients' rehabilitation. Currently, it is believed that the pathogenesis of ischemic stroke-related headache is associated with the abnormal release of vasoactive substances, high platelet aggregation, and stimulation of intracranial pain-sensitive structures. The active ingredients in traditional Chinese medicines(TCM) with the effects of activating blood to resolve stasis and clearing heat to release exterior can protect brain tissue and relieve headache by reducing the release of inflammatory cytokines, alleviating antioxidant stress, inhibiting neuronal apoptosis and so on. This paper introduces the research progress in the potential mechanism and TCM treatment of ischemic stroke-related headache, aiming to provide reference for further research and drug development of this complication.
Humans ; Ischemic Stroke/drug therapy* ; Brain Ischemia/drug therapy* ; Medicine, Chinese Traditional ; Stroke/drug therapy* ; Headache/drug therapy* ; Drugs, Chinese Herbal/therapeutic use*

OBJECTIVE: To evaluate the existing randomized controlled trials (RCTs) for evidence of the efficacy and safety of head acupuncture (HA) plus Schuell's language rehabilitation (SLR) in post-stroke aphasia. METHODS: Seven databases including Embase, PubMed, Cochrane Library, Technology Periodical Database, the China National Knowledge Infrastructure, SinoMed and Wanfang Data Information Site were searched for RCTs published from database inception until November 14, 2021. RCTs that compared HA plus SLR with sham (or blank) control, acupuncture therapy alone, certain language rehabilitation therapy alone or other therapies for post-stroke aphasia were included. Data were extracted and assessed, and the quality of RCTs was evaluated. Fixed-effects model was used, with meta-inflfluence analysis, meta-regression, and regression-based sub-group analyses applied for exploration of heterogeneity. Publication bias was estimated by funnel plots and Egger's tests. RESULTS: A total of 32 RCTs with 1,968 patients were included and 51 comparisons were conducted classified as types of strokes and aphasia. (1) For patients with aphasia after ischemic stroke, HA plus PSA showed significantly higher accumulative markedly effective rate [relative risk (RR)=1.55, 95% confidence interval (CI): 1.19-2.02, I²=0%] and accumulative effective rate (RR=1.22, 95% CI: 1.09-1.36, I²=0%). (2) For patients with comprehensive types of stroke, HA plus PSA was more effective in increasing recovery rate (RR=1.89, 95% CI: 1.39-2.56, I²=0%), accumulative markedly effective rate (RR=1.53, 95% CI: 1.36-1.72, I²=9%) and accumulative effective rate (RR=1.14, 95% CI: 1.09-1.19, I²=34%). (3) For patients with aphasia after stroke, HA plus PSA was superior to PSA alone with statistical significance in increasing recovery rate (RR=2.08, 95% CI: 1.24-3.46, I²=0%), accumulative markedly effective rate (RR=1.49, 95% CI: 1.24-1.78, I²=0%) and accumulative effective rate (RR=1.15, 95% CI: 1.06-1.24, I²=39%). (4) For patients with multiple types of aphasia, HA plus PSA also demonstrated significantly higher recovery rate (RR=1.86, 95% CI: 1.28-2.72, I²=0%), accumulative markedly effective rate (RR=1.55, 95% CI: 1.35-1.78, I²=22%), and accumulative effective rate (RR=1.17, 95% CI: 1.11-1.23, I²=41%). (5) For patients with motor aphasia after ischemic stroke, compared with PSA alone, HA plus PSA showed significantly higher accumulative markedly effective rate (RR=1.38, 95% CI: 1.06-1.79, I²=0%) and accumulative effective rate (RR=1.20, 95% CI: 1.05-1.37, I²=0%). Meta-regression analyses were performed without significant difference, and publication bias was found in some comparisons. CONCLUSION HA plus SLR was significantly associated with better language ability and higher effective rate for patients with post-stroke aphasia, and HA should be operated cautiously especially during acupuncture at eye and neck. (Registration No. CRD42020154475).
Acupuncture Therapy ; Aphasia/rehabilitation* ; Humans ; Ischemic Stroke ; Language ; Prostate-Specific Antigen ; Randomized Controlled Trials as Topic ; Stroke/therapy*