BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

The innate immune sensor NLRP3 inflammasome overactivation is involved in the pathogenesis of ulcerative colitis. PGAM5 is a mitochondrial phosphatase involved in NLRP3 inflammasome activation in macrophages. However, the role of PGAM5 in ulcerative colitis and the mechanisms underlying PGAM5 regulating NLRP3 activity remain unknown. Here, we show that PGAM5 deficiency ameliorates dextran sodium sulfate (DSS)-induced colitis in mice via suppressing NLRP3 inflammasome activation. By combining APEX2-based proximity labeling focused on PGAM5 with quantitative proteomics, we identify NEK7 as the new binding partner of PGAM5 to promote NLRP3 inflammasome assembly and activation in a PGAM5 phosphatase activity-independent manner upon inflammasome induction. Interfering with PGAM5-NEK7 interaction by punicalagin inhibits the activation of the NLRP3 inflammasome in macrophages and ameliorates DSS-induced colitis in mice. Altogether, our data demonstrate the PGAM5-NEK7 interaction in macrophages for NLRP3 inflammasome activation and further provide a promising therapeutic strategy for ulcerative colitis by blocking the PGAM5-NEK7 interaction.

With the intensification of population aging, type 2 diabetes has emerged as a significant global public health concern, particularly in developing countries.Epidemiological data indicate that the elderly population faces a higher risk of diabetes, accompanied by an increasing incidence rate.Due to the unique pathological characteristics and comorbid chronic diseases prevalent among elderly diabetes patients, polypharmacy is both common and often unavoidable.Inappropriate polypharmacy poses heightened health risks for patients and complicates clinical management, underscoring the urgent need to optimize intervention strategies.Effective approaches include regular medication reviews, adjustments to blood glucose control targets, and the development of personalized deprescribing plans informed by comprehensive geriatric assessments.While some interventions have demonstrated positive effects in reducing potentially inappropriate medications, addressing prescription omissions, and enhancing medication adherence, their capacity to yield significant clinical improvements requires further validation.Future research should prioritize the identification of the most effective interventions for high-risk populations.

Objective To analyze the antiviral effect of molnupiravir against influenza virus in vitro and in vivo.Methods The antiviral ability of molnupiravir against influenza virus strain H1N1 PR8 was detected in vitro and in vivo.H uman non-small cell lung cancer cell line(A549 cells)was used as an in vitro model of PR8 infection.The antiviral effects of molnupiravir on virus replication,protein synthesis,and viral particle formation were analyzed using real-time fluorescence quantitative polymerase chain reaction(qRT-PCR),Western blot(WB)assay,and plaque assay.PR8 nose-dripping infected C57BL/6 female mice were used as an in vivo infection model.The antivi-ral ability of molnupiravir was evaluated by detecting viral load,pathological changes,and immunohistochemistry in the control group,administration group,inoculation group,and inoculation-administration group.Results In vitro test results showed that molnupiravir had no significant inhibitory effect on influenza virus replication,protein syn-thesis,and virus particle formation(all P＞0.05).In vivo test results showed that compared with mice infected with H1N1 alone,the viral load in the lung tissue of mice treated with molnupiravir declined significantly,and the extent of pathological changes was milder.Immunohistochemical detection showed a significant weakening in nuclear protein(NP)antigen signal,and the expression levels of interferon(IFN)-α,interleukin(IL)-4,and IL-6 in the lungs were lower(all P＜0.01).Conclusion As a precursor with antiviral activity,molnupiravir can not exert anti-viral activity in lung-derived cells cultured in vitro,but can be transformed into an active form in the host,which significantly decreases the ability of H1N1 influenza virus to proliferate in the lungs and thereby alleviates the da-mage of virus to lung tissue.

Objective To analyze the antiviral effect of molnupiravir against influenza virus in vitro and in vivo.Methods The antiviral ability of molnupiravir against influenza virus strain H1N1 PR8 was detected in vitro and in vivo.H uman non-small cell lung cancer cell line(A549 cells)was used as an in vitro model of PR8 infection.The antiviral effects of molnupiravir on virus replication,protein synthesis,and viral particle formation were analyzed using real-time fluorescence quantitative polymerase chain reaction(qRT-PCR),Western blot(WB)assay,and plaque assay.PR8 nose-dripping infected C57BL/6 female mice were used as an in vivo infection model.The antivi-ral ability of molnupiravir was evaluated by detecting viral load,pathological changes,and immunohistochemistry in the control group,administration group,inoculation group,and inoculation-administration group.Results In vitro test results showed that molnupiravir had no significant inhibitory effect on influenza virus replication,protein syn-thesis,and virus particle formation(all P＞0.05).In vivo test results showed that compared with mice infected with H1N1 alone,the viral load in the lung tissue of mice treated with molnupiravir declined significantly,and the extent of pathological changes was milder.Immunohistochemical detection showed a significant weakening in nuclear protein(NP)antigen signal,and the expression levels of interferon(IFN)-α,interleukin(IL)-4,and IL-6 in the lungs were lower(all P＜0.01).Conclusion As a precursor with antiviral activity,molnupiravir can not exert anti-viral activity in lung-derived cells cultured in vitro,but can be transformed into an active form in the host,which significantly decreases the ability of H1N1 influenza virus to proliferate in the lungs and thereby alleviates the da-mage of virus to lung tissue.

With the intensification of population aging, type 2 diabetes has emerged as a significant global public health concern, particularly in developing countries.Epidemiological data indicate that the elderly population faces a higher risk of diabetes, accompanied by an increasing incidence rate.Due to the unique pathological characteristics and comorbid chronic diseases prevalent among elderly diabetes patients, polypharmacy is both common and often unavoidable.Inappropriate polypharmacy poses heightened health risks for patients and complicates clinical management, underscoring the urgent need to optimize intervention strategies.Effective approaches include regular medication reviews, adjustments to blood glucose control targets, and the development of personalized deprescribing plans informed by comprehensive geriatric assessments.While some interventions have demonstrated positive effects in reducing potentially inappropriate medications, addressing prescription omissions, and enhancing medication adherence, their capacity to yield significant clinical improvements requires further validation.Future research should prioritize the identification of the most effective interventions for high-risk populations.

Objective:To analyze the general chapter for lotions 0127 of the Chinese Pharmacopoeia 2020 Vol-ume Ⅳ,and discuss how to improve the general technical requirements of lotions 0127 in the Chinese Pharma-copoeia.Methods:By comparing the general chapter for lotions in domestic and international pharmacopoe-ias,the definition,classification,process,storage and corresponding inspection requirements were analyzed.Results and Conclusions:The general chapter for lotions 0127 of the Chinese Pharmacopoeia should be revised,including improvement of the definition,increasement of forms of preparations,and expansion of included varieties,so as to promote scientific regulation for drugs and exhibit a guiding role of the Chinese pharmacopoeia in drug control.

Studies have suggested that the nucleus accumbens (NAc) is implicated in the pathophysiology of major depression; however, the regulatory strategy that targets the NAc to achieve an exclusive and outstanding anti-depression benefit has not been elucidated. Here, we identified a specific reduction of cyclic adenosine monophosphate (cAMP) in the subset of dopamine D1 receptor medium spiny neurons (D1-MSNs) in the NAc that promoted stress susceptibility, while the stimulation of cAMP production in NAc D1-MSNs efficiently rescued depression-like behaviors. Ketamine treatment enhanced cAMP both in D1-MSNs and dopamine D2 receptor medium spiny neurons (D2-MSNs) of depressed mice, however, the rapid antidepressant effect of ketamine solely depended on elevating cAMP in NAc D1-MSNs. We discovered that a higher dose of crocin markedly increased cAMP in the NAc and consistently relieved depression 24 h after oral administration, but not a lower dose. The fast onset property of crocin was verified through multicenter studies. Moreover, crocin specifically targeted at D1-MSN cAMP signaling in the NAc to relieve depression and had no effect on D2-MSN. These findings characterize a new strategy to achieve an exclusive and outstanding anti-depression benefit by elevating cAMP in D1-MSNs in the NAc, and provide a potential rapid antidepressant drug candidate, crocin.

Objective To develop a high performance liquid chromatography-tandem mass spectrometry(HPLC-MS/MS)method for simultaneous determination of five flavonoids in Ganmao'an granules(GMA).Methods Chromatographic separation was achieved on a Kromasil C18 Column(150 mm×4.6 mm,5 μm,100 ?),which was eluted with methanol(A)-0.1％formic acid(B)at the flow rate of 1.0 ml/min.The gradient condition was as follows:0-20 min,35％A,and 20-40 min,45％A.The column temperature was 25 ℃.Analytes were detected using a triple quadrupole tandem mass spectrometer equipped with an electrospray ionization source in the negative ion scanning.The multiple reaction monitoring mode was used for qualitative analysis.For each flavonoid,two precursor ion/product ion transitions were chosen:lutin m/z 609.1→300.1,hyperin and isoquercitrin m/z 463.0→300.1,quercetin m/z 301.0→151.0,luteolin m/z 285.0→132.9.Results Five flavonoids showed the good relationships within their own concentration ranges(correlation coefficient r>0.999 1),whose average recoveries were in the range of 100.63％-102.81％with RSDs of 0.67％-2.07％.The content results of rutin,hyperoside,isoquercetin,quercetin,and luteolin in 10 batches of GMA were 32.23-479.83,0.291-1.825,11.44-20.54,6.32-18.41,3.46-6.51 μg/g,respectively.Conclusion The results indicated that the developed method was sensitive,accurate and could provide excellent specificity for simultaneous determination of five flavonoids in GMA.