Exogenous harmful residues and endogenous toxic components are the main contents of safety analysis for animal traditional Chinese medicine. This review summarizes the inspection methods for exogenous harmful residues, as well as the research methods for the toxic effects and mechanisms of endogenous toxic components. The strategies for enhancing efficacy and reducing toxicity of toxic animal drugs and quality control, and prospects the development trend of safety analysis for animal drugs were also discussed. In the detection of exogenous harmful residues in animal drugs, traditional methods such as atomic absorption spectrometry and inductively coupled plasma mass spectrometry are widely used, and new methods such as high-resolution mass spectrometry and biochemical analysis are continuously developing. In the study of endogenous toxic components, the toxic components and mechanisms of some animal drugs including cantharidin and toad venom have been revealed through chemical composition analysis, toxicity tests and multiomics technologies, and some strategies for enhancing efficacy and reducing toxicity have been proposed based on this. In the future, it is necessary to strengthen multidisciplinary integration to innovate detection technologies, clarify toxic mechanisms to achieve efficacy enhancement and toxicity reduction, and improve the biosafety research system, so as to enhance the quality and safety of traditional Chinese medicine animal drugs and promote the internationalization process of traditional Chinese medicine.

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 detect the characteristics of whole-brain functional changes in patients with subjective tinnitus(ST)after"frontal-temporal"dual-site repetitive transcranial magnetic stimulation(rTMS)by resting-state functional magnetic resonance imaging(rs-fMRI).Methods A total of 45 ST patients were enrolled,and assessments of tinnitus severity and rs-fMRI scans were performed before and 2 weeks after treatment with"frontal-temporal"dual-site rTMS.Regional homogeneity(ReHo),fractional amplitude of low-frequency fluctuations(fALFF),degree centrality(DC)and seed-based functional connectivity(FC)were analyzed before and after treatment in ST patients.Results Tinnitus handicap inventory(THI)score of ST patients 2 weeks after treatment was significantly decreased compared with that before treatment(P<0.001).ReHo values of the right inferior parietal lobule decreased,fALFF values of the right temporal pole increased,fALFF values of the right superior temporal gyrus decreased,and DC(weighted)and DC(Binarized)values of the right medial temporal gyrus all decreased in ST patients 2 weeks after treatment compared with those before treatment(P<0.05,GRF correction).Using the above differential brain regions as seed points for FC analysis,FC values between right superior temporal gyrus(fALFF)and right middle temporal gyrus reduced,FC values between right middle temporal gyrus[(DC(weighted)]and right superior occipital gyrus reduced,and FC values between right middle temporal gyrus[DC(Binarized)]and right superior occipital gyrus reduced 2 weeks after treatment compared with those before treatment(P<0.05,GRF correction).Conclusion"Frontal-temporal"dual-site rTMS is initially effective for ST patients,and the auditory and non-auditory brain regions of ST patients showed different degrees of regional and interbrain function changes,mainly involving default mode network and visual-auditory network.

Objective To evaluate the transition rules of normal body mass index(BMI),overweight and metabolic syndrome(MetS)in patients with major depressive disorder(MDD).Methods Patients with MDD who had multiple admission records between Jan 2016 and Nov 2021 in Beijing Anding Hospital,Capital Medical University were included.Based on the overweight and metabolic syndrome status assessed at each admission,the patients were categorized into three states:normal BMI,overweight and metabolic syndrome.A multi-state Markov model was used to analyze the transition intensity and transition frequency between three states and the influence of covariates on transitions.Results A total of 892 records of 398 subjects were included,with a median age of 56 years old and 31.4% males.The median follow-up period was 40 months.The multi-state model showed that there were 494 transitions between the three states,of which 5.1% moved from normal BMI to overweight and 5.5% moved from overweight to MetS.The intensity of transition was the highest from overweight to MetS,9.52 times greater than overweight to normal BMI.After 48.53 months,MDD patients with normal BMI began to transition to MetS.For overweight MDD patients,the transition to MetS started after 8.77 months.MDD patients with normal BMI or overweight had 31.4% and 50.4% probabilities of developing Mets after 36 months.For MDD patients comorbid with MetS,the probability of staying at MetS was 51.2% after 36 months.Multivariate analysis showed that being unmarried was a risk factor against developing overweight in normal BMI MDD patients,while a higher level of education was a protective factor against developing MetS in overweight MDD patients.Conclusion MDD patients exhibited a higher intensity and risk of developing MetS,and it is not easy to reverse MetS,suggesting that BMI management and MetS intervention should be strengthened in MDD patients.

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 propose a novel method based on three-dimensional depthwise separable convolution network(3D DSN)for the separation of PET images with dual tracers of 18F-FDG and 18F-FAPI.Methods A total of 120 pairs of 18F-FDG and 18F-FAPI PET images of the same patient scanned separately at different time points were collected,and the dual-tracer PET image was generated through simulation.After the image registration of PET images of two tracers for ensuring spatial position matching,the registered PET images were forward-projected to generate sinogram data,and the sinogram data of two tracers were accumulated to obtain mixed sinogram data.Subsequently,the dual-tracer PET image was reconstructed using maximum likelihood expectation maximization and input into a 3D DSN based network for image separation,thereby obtaining PET images of two single tracers.Results Compared with 3D CNN method,the proposed method increased the structure similarity index measure(SSIM)of the separated 18F-FDG images to the real 18F-FDG images by 0.87%,increased the peak signal-to-noise ratio(PSNR)by 11.8%,and reduced the normalized root mean square error(NRMSE)by 52%.The SSIM of the separated 18F-FAPI images to the real 18F-FAPI images increased by 1.1%,PSNR increased by 17.0%,and NRMSE decreased by 51%.Conclusion The proposed method can be effectively applied to simultaneous PET imaging with dual PET tracers,reducing the number of scans and costs in time and money,and providing clinical doctors more accurate and abundant diagnostic information.

Objective:This study applies an integrated SWOT-CLPV framework combined with stakeholder analysis to systematically assess the strengths,weaknesses,opportunities,and threats of China's disease control inspector system,while identifying its control factors,leverage points,key problems,and vulnerabilities.Methods:Drawing on literature review,policy document analysis,and expert interviews with seven public health professionals,we extracted and categorized SWOT elements.A CLPV interaction analysis was conducted alongside stakeholder mapping to evaluate internal dynamics and systemic risks.Results:The inspector system demonstrates strengths in policy innovation and medical-public health integration,with external opportunities stemming from rising public health awareness and digital health advancements.However,the system faces weak endogenous momentum,limited leverage,and prominent control constraints and problem-prone areas,especially among grassroots institutions and inspectors themselves.Cross-sectoral coordination barriers and uneven local implementation contribute to significant institutional vulnerabilities.Conclusion:To enhance implementation and resilience,the system requires capacity building for key actors,improved governance structures,incentive and evaluation reforms,and strengthened coordination mechanisms to support the sustained and adaptive development of public health supervision.

Monkeypox is a viral zoonotic disease,and there is currently a lack of safe and effective vac-cines against the monkeypox virus.Therefore,screening and developing vaccine candidates is of signifi-cant practical importance.With the rapid advancement of molecular biology and plant genetic engineer-ing,plant bioreactors offer promising potential for producing vaccine proteins due to their advantages,in-cluding safety,cost-effectiveness,and scalability.In this study,we focused on the monkeypox protein B6R.The recombinant expression plasmid pFolia40108-B6R-Fer was successfully constructed using am-plification,enzyme digestion,and flexible linker tandem ferritin technology.A complete transient expres-sion system in Nicotiana benthamiana and a purification system for the recombinant monkeypox protein were established.The optimal expression time was determined to be 12-14 days,with a final purified pro-tein concentration of approximately 1 mg/mL and a yield of 0.85 mg/kg fresh weight.The purified B6R-Fer recombinant protein self-assembled into spherical virus-like particles(VLPs)with an average particle size of 24 nm.The B6R-Fer recombinant protein from this study shows promising potential for use in the development and screening of plant-derived monkeypox vaccine candidates.

Numerous studies have shown that depression is main-ly associated with the abnormal expression of connexin 43(Cx43)in astrocytes(Astro)and its mediated dysfunction of gap junction(GJ).However,the molecular mechanism of post-translational modifications targeting Cx43 to regulate neuroin-flammation-associated depression is still unclear.Post-transla-tional modifications of Cx43 mainly include phosphorylation of specific amino acid sites by PKC,PKA,PKG,MAPK and PTK,and protein degradation of Cx43 through the K48/K63 polyubiq-uitylation and deubiquitination pathways,which ultimately lead to protein degradation through K48/K63 polyubiquitination and deubiquitination.These modifications are ultimately involved in the regulation of neuroinflammatory responses through the associ-ation of GJ function.In this paper,we systematically review the role of Cx43 post-translational modifications in neuroinflamma-tion,with the aim of further exploring the potential application of targeting these modifications to modulate the inflammatory re-sponse mechanism in improving depressive symptoms.

Objective To detect the characteristics of whole-brain functional changes in patients with subjective tinnitus(ST)after"frontal-temporal"dual-site repetitive transcranial magnetic stimulation(rTMS)by resting-state functional magnetic resonance imaging(rs-fMRI).Methods A total of 45 ST patients were enrolled,and assessments of tinnitus severity and rs-fMRI scans were performed before and 2 weeks after treatment with"frontal-temporal"dual-site rTMS.Regional homogeneity(ReHo),fractional amplitude of low-frequency fluctuations(fALFF),degree centrality(DC)and seed-based functional connectivity(FC)were analyzed before and after treatment in ST patients.Results Tinnitus handicap inventory(THI)score of ST patients 2 weeks after treatment was significantly decreased compared with that before treatment(P<0.001).ReHo values of the right inferior parietal lobule decreased,fALFF values of the right temporal pole increased,fALFF values of the right superior temporal gyrus decreased,and DC(weighted)and DC(Binarized)values of the right medial temporal gyrus all decreased in ST patients 2 weeks after treatment compared with those before treatment(P<0.05,GRF correction).Using the above differential brain regions as seed points for FC analysis,FC values between right superior temporal gyrus(fALFF)and right middle temporal gyrus reduced,FC values between right middle temporal gyrus[(DC(weighted)]and right superior occipital gyrus reduced,and FC values between right middle temporal gyrus[DC(Binarized)]and right superior occipital gyrus reduced 2 weeks after treatment compared with those before treatment(P<0.05,GRF correction).Conclusion"Frontal-temporal"dual-site rTMS is initially effective for ST patients,and the auditory and non-auditory brain regions of ST patients showed different degrees of regional and interbrain function changes,mainly involving default mode network and visual-auditory network.