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.

This study analyzed the variations and evolution characteristics of influenza B Victoria(BV)virus in the Yellow River Delta region of China during 2021-2024.Throat swabs were collected from people with influenza-like illness(ILI)from 2021 to 2024 in Binzhou and Dongying,China.Viral isolation was performed,and 22 representative influenza BV isolates were selected for whole genome sequencing.Phylogenetic analysis of whole-genome sequences was performed in MegAlign and MEGA software.A total of 27 674 samples were obtained,and the overall positivity rate of influenza virus(A/H3N2,A/H1N1,BV)was 11.1％.Our surveillance data indicated that influenza B virus was detected in 2 years,which showed positivity rates of 28.2％and 1.7％,respectively.Statistically significant differences in the positivity rates of influenza BV viruses were observed(x2=3 641.791,P＜0.001).The median pairwise sequence identities ranged from 98.7％to 99.3％for eight segments of 22 viral sequences.The isolates for the monitoring years 2021-2024 were located in clades V1A.3a.1 and V1A.3a.2.Intra-lineage reassortments were discovered in B/shandongbincheng17/2022.The NA gene of one isolate exhibited an increase in the 488NLTV N-glycoproteome site.The K338R mutation occurred in the PA gene.Three locus deletion or insertion mutations occurred in the MP gene.The BV influenza epidemic was prevalent every other year;the intensity ranged from strong to weak;and the duration ranged from long to short in the Yellow River Delta region of China during 2021-2022.Influ-enza B virus formed intra-lineage reassortments,and showed significant mutations in NA gene,PA gene,and MP gene.

The auxin/indole-3-acetic acid (Aux/IAA) gene family is an important regulator for plant growth hormone signaling, involved in plant growth, development, as well as response to environmental stresses. In the present study, we identified SmIAA7 which is potentially associated with Salvia miltiorrhiza leaf development through comparatively analyzed the transcriptome data from different pinnate leaves. SmIAA7 was successfully isolated from S. miltiorrhiza using the specific primers. Then subsequent bioinformatic analysis, prokaryotic expression and purification, subcellular localization, and induction expression analysis under auxin and abiotic stress were performed. The full-length of SmIAA7 contained an ORF of 684 bp encoding a protein of 227 amino acid with a molecular weight of 25.3 kD. Conserved domain analysis showed that SmIAA7 contains the conserved Aux_IAA domain (pfam02309). Sequence analysis and phylogenetic tree analysis results indicated SmIAA7 was phylogenetically close to IAA7 and IAA14 from other plants, suggesting SmIAA7 involved in plant growth and development as well as response to environmental stresses. The prokaryotic expression vector pET28a-SmIAA7 was constructed and SmIAA7 recombinant protein was successfully expressed in E. coli Rosetta (DE3) strain. Subcellular localization experiment demonstrated that SmIAA7 localized in the nucleus of plant cells. Real-time fluorescence quantitative PCR results showed that the expression level of SmIAA7 was upregulated in response to auxin. Drought, low temperature, and salt stress significantly increased the transcript level of SmIAA7 gene. This study lays a foundation for further elucidating the role of SmIAA7 in leaf development, signal transduction, and stress defense in S. miltiorrhiza.

This study analyzed the variations and evolution characteristics of influenza B Victoria(BV)virus in the Yellow River Delta region of China during 2021-2024.Throat swabs were collected from people with influenza-like illness(ILI)from 2021 to 2024 in Binzhou and Dongying,China.Viral isolation was performed,and 22 representative influenza BV isolates were selected for whole genome sequencing.Phylogenetic analysis of whole-genome sequences was performed in MegAlign and MEGA software.A total of 27 674 samples were obtained,and the overall positivity rate of influenza virus(A/H3N2,A/H1N1,BV)was 11.1％.Our surveillance data indicated that influenza B virus was detected in 2 years,which showed positivity rates of 28.2％and 1.7％,respectively.Statistically significant differences in the positivity rates of influenza BV viruses were observed(x2=3 641.791,P＜0.001).The median pairwise sequence identities ranged from 98.7％to 99.3％for eight segments of 22 viral sequences.The isolates for the monitoring years 2021-2024 were located in clades V1A.3a.1 and V1A.3a.2.Intra-lineage reassortments were discovered in B/shandongbincheng17/2022.The NA gene of one isolate exhibited an increase in the 488NLTV N-glycoproteome site.The K338R mutation occurred in the PA gene.Three locus deletion or insertion mutations occurred in the MP gene.The BV influenza epidemic was prevalent every other year;the intensity ranged from strong to weak;and the duration ranged from long to short in the Yellow River Delta region of China during 2021-2022.Influ-enza B virus formed intra-lineage reassortments,and showed significant mutations in NA gene,PA gene,and MP gene.

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.

Objective To investigate the expression of arginase Ⅱ(ArgⅡ)in kidney tissue of rats with diabetic nephropathy(DN)and its significance in the development of DN.Methods A total of 10 male SD rats were randomly divided into the control group and the model group,with 5 rats in each group.An rat model of DN was developed by feeding with high-sugar and high-fat diet combined with intra-peritoneal injection of low-dose streptozotocin(45 mg/kg),and they were sacrificed after 11 weeks of continued feeding.The body weight,and biochemical indexes of blood and urine of rats were determined.The right kidney was weighed and histopathological examination was performed.The pathological changes of kidney tissues and protein expression of ArgⅡ and CD68+were observed,and the immunofluores-cence double staining was used to observe the distribution and expression of ArgⅡand a marker of renal macrophage activation CD68+;the protein expression of ArgⅡ,NF-κB,TNF-α and IL-6 in kidney tissues was determined by Western blot.Results Compared with the control group,the ratio of kidney weight to body weight,24-hour urine volume,24-hour urine protein,fasting blood glucose,urea nitrogen and insulin level in the model group were significantly increased(P<0.05).The renal histopathology showed that the mesangial cells of the renal glomerular were necrotic with vascular dilatation,and the renal tubular epithelial cells were steatosis and congestion.Compared with the control group,the protein expression of ArgⅡ,CD68+,NF-κB,TNF-α and IL-6 in the kidney tissues of the model group were significantly increased(P<0.05).Immunofluorescence double staining demonstrated the co-expression of ArgⅡ and CD68+in renal tissue,and the fluorescence intensities of both ArgⅡ and CD68+in the model group were significantly stronger than those in the control group(P<0.01).Conclusion The expression of ArgⅡ is increased in DN,which may be participated in the occurrence of inflammatory lesions in DN.

Objective To investigate the expression of arginase Ⅱ(ArgⅡ)in kidney tissue of rats with diabetic nephropathy(DN)and its significance in the development of DN.Methods A total of 10 male SD rats were randomly divided into the control group and the model group,with 5 rats in each group.An rat model of DN was developed by feeding with high-sugar and high-fat diet combined with intra-peritoneal injection of low-dose streptozotocin(45 mg/kg),and they were sacrificed after 11 weeks of continued feeding.The body weight,and biochemical indexes of blood and urine of rats were determined.The right kidney was weighed and histopathological examination was performed.The pathological changes of kidney tissues and protein expression of ArgⅡ and CD68+were observed,and the immunofluores-cence double staining was used to observe the distribution and expression of ArgⅡand a marker of renal macrophage activation CD68+;the protein expression of ArgⅡ,NF-κB,TNF-α and IL-6 in kidney tissues was determined by Western blot.Results Compared with the control group,the ratio of kidney weight to body weight,24-hour urine volume,24-hour urine protein,fasting blood glucose,urea nitrogen and insulin level in the model group were significantly increased(P<0.05).The renal histopathology showed that the mesangial cells of the renal glomerular were necrotic with vascular dilatation,and the renal tubular epithelial cells were steatosis and congestion.Compared with the control group,the protein expression of ArgⅡ,CD68+,NF-κB,TNF-α and IL-6 in the kidney tissues of the model group were significantly increased(P<0.05).Immunofluorescence double staining demonstrated the co-expression of ArgⅡ and CD68+in renal tissue,and the fluorescence intensities of both ArgⅡ and CD68+in the model group were significantly stronger than those in the control group(P<0.01).Conclusion The expression of ArgⅡ is increased in DN,which may be participated in the occurrence of inflammatory lesions in DN.

Objective To investigate the risk factors for bronchopulmonary dysplasia(BPD)in twin preterm infants with a gestational age of<34 weeks,and to provide a basis for early identification of BPD in twin preterm infants in clinical practice.Methods A retrospective analysis was performed for the twin preterm infants with a gestational age of<34 weeks who were admitted to 22 hospitals nationwide from January 2018 to December 2020.According to their conditions,they were divided into group A(both twins had BPD),group B(only one twin had BPD),and group C(neither twin had BPD).The risk factors for BPD in twin preterm infants were analyzed.Further analysis was conducted on group B to investigate the postnatal risk factors for BPD within twins.Results A total of 904 pairs of twins with a gestational age of<34 weeks were included in this study.The multivariate logistic regression analysis showed that compared with group C,birth weight discordance of>25%between the twins was an independent risk factor for BPD in one of the twins(OR=3.370,95%CI:1.500-7.568,P<0.05),and high gestational age at birth was a protective factor against BPD(P<0.05).The conditional logistic regression analysis of group B showed that small-for-gestational-age(SGA)birth was an independent risk factor for BPD in individual twins(OR=5.017,95%CI:1.040-24.190,P<0.05).Conclusions The development of BPD in twin preterm infants is associated with gestational age,birth weight discordance between the twins,and SGA birth.

Objective To evaluate the management and indicators of pathogen detection before antimicrobial treat-ment for inpatients in second level and above medical institutions(MIs)in Guangdong Province,and provide direc-tion and decision-making basis for the improvement of pathogen detection quality in the region.Methods The ma-nagement status,information system functions,and pathogen detection rate indicators of secondary and above MIs in 21 cities in Guangdong Province was surveyed through online questionnaire surveys and system submission.A baseline survey on sentinel monitoring MIs was conducted from July 15th to August 8th,2023.From November 7th to 30th,a baseline survey on non-sentinel monitoring MIs was launched.Surveys on indicator information of all MIs were completed from January 15th to 30th,2024.Results A total of 265 MIs were surveyed,and the proportions of establishing special working groups(83.98％),developing special action improvement plans(79.01％),estab-lishing pathogen detection rate management systems(91.71％),and developing management assessment plans(76.80％)of tertiary MIs were all higher than that of secondary MIs,differences were all statistically significant(all P＜0.05).The proportion of tertiary MIs with various information system functions was higher than that of secondary MIs(all P＜0.05).The pathogen detection rate(61.07％)before antimicrobial treatment and health-care-associated infection(HAI)diagnosis-related pathogen detection rate(88.00％)of inpatients in tertiary MIs were both higher than those in secondary MIs(both P＜0.05).Among different types of MIs,pathogen detection rate before antimicrobial treatment of inpatients in maternal and child health MIs was higher than that in other types of MIs.HAI diagnosis-related pathogen detection rate in other specialized hospitals was the highest,and pathogen detection rate before combined use of key antimicrobial treatment in traditional Chinese medicine hospitals was the lowest,differences were all statistically significant(all P＜0.05).Conclusion Tertiary MIs have more advantages in management strategies and information technology construction than secondary MIs,secondary MIs need more guidance and support.Monitoring and analysis of pathogen detection rate indicators in MIs of different levels and types should be strengthened through special actions.