ObjectiveMicrotube associated protein-2 （MAP-2）， alpha-tubulin （α-tubulin）， and synaptophysin （SYP） are important proteins in neuronal signal communication. This paper observed the effects of modified Zhigancao Tang on the expression of serum α-Synuclein （α-Syn） and its oligomers， MAP-2， α-tubulin， and SYP of patients with liver and kidney deficiency of Parkinson's disease （PD）， analyzed their correlation， and evaluated the therapeutic effect of modified Zhigancao Tang in patients with liver and kidney deficiency of PD based on α-Syn transmission pathway mediated by neuronal communication in vivo. MethodsA total of 60 patients with PD who met the inclusion criteria were randomly divided into a treatment group （30 cases） and a control group （30 cases）. Both groups were treated on the basis of PD medicine， and the treatment group was treated with modified Zhigancao Tang. Both groups were treated for 12 weeks. The changes in UPDRS score， TCM syndrome score， and expression of serum α-Syn and its oligomers， MAP-2， α-tubulin， and SYP were observed before and after 12 weeks of treatment in each group. The correlation between the above-mentioned serum biological indexes and the levels of serum α-Syn and its oligomers was analyzed. ResultsAfter treatment， the TCM syndrome score， UPDRS score， UPDRS-Ⅱ score， and UPDRS-Ⅲ score of the treatment group were significantly decreased （P<0.05， P<0.01）. The UPDRS score， UPDRS-Ⅱ score， and UPDRS-Ⅲ scores in the treatment group were significantly decreased compared with those in the control group after treatment （P<0.05）. After treatment， the total effective rate of the control group was 63.3% （19/30）， and that of the treatment group was 86.7% （26/30）. The clinical effect of the observation group was better than the control group （Z=-2.03， P<0.05）. The total effective rate of the observation group was better than that of the control group， and the difference was statistically significant （χ²=5.136， P<0.05）. After treatment， the oligomer level of serum α-Syn and MAP-2 level in the treatment group were significantly decreased （P<0.05， P<0.01）. The levels of serum α-Syn and its oligomers， as well as α-tubulin in the treatment group， were significantly decreased compared with those in the control group after treatment （P<0.05， P<0.01）. Serum α-Syn was correlated with serum MAP-2 and α-Syn oligomer in patients with PD （P<0.05， P<0.01） but not correlated with serum SYP . Serum α-Syn oligomers of patients with PD were correlated with serum MAP-2 and α-tubulin （P<0.05， P<0.01） but not correlated with serum SYP level. Serum SYP of patients with PD was correlated with serum MAP-2 （P<0.05）. ConclusionModified Zhigancao Tang has a therapeutic effect on patients with liver and kidney deficiency of PD by inhibiting the production of α-Syn oligomers and intervening α-Syn microtubule transport pathway in vivo.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

ObjectiveTo investigate the effect of Gelian Tiaotang pills on renal fibrosis in db/db mice based on the nucleotide-binding domain leucine-rich repeat and pyrin domain-containing receptor 3 （NLRP3）/cysteinyl aspartate-specific proteinase （Caspase）-1/gasdermin D （GSDMD） signaling pathway. MethodsForty db/db mice were randomly assigned into model， positive control （0.001 3 g·kg·d^-1 dapagliflozin suspension）， and high-， medium-， and low-dose Gelian Tiaotang pills （3.12， 1.56， and 0.78 g·kg·d^-1 suspension of Gelian Tiaotang pills， respectively） groups， with 8 mice in each group. Eight db/m mice were selected as the normal group. The normal group and model group were given equal volumes of pure water， while the drug interventions groups were administrated with corresponding agents by gavage once a day for 12 consecutive weeks. The general conditions of mice were observed daily. The fasting blood glucose （FBG） and body mass were measured every 4 weeks. Kidneys were weighed after sampling， and the kidney index was calculated. An automatic biochemical analyzer was used to measure the serum levels of triglyceride （TG）， total cholesterol （TC）， serum creatinine （SCr）， and blood urea nitrogen （BUN）. The pathological changes， extracellular matrix deposition， and renal fibrosis degree were examined by hematoxylin-eosin， periodic acid-schiff （PAS）， and Masson staining， respectively. Enzyme-linked immunosorbent assay was employed to measure the levels of interleukin （IL）-1β and IL-18 in the renal tissue. Immunohistochemistry （IHC） was used to detect the localization and expression of fibronectin and collagen Ⅰ in the renal tissue. Western blot was employed to determine the protein levels of NLRP3， Caspase-1， cleaved Caspase-1， GSDMD， and GSDMD-N in the renal tissue. ResultsCompared with the normal group， the model group generally had poor general states and increases in the body mass， kidney weight， kidney index， and levels of FBG， TG， TC， SCr， and BUN （P<0.01）. In addition， glomerular pyknosis， increased matrix， vacuolar degeneration of renal tubular epithelial cells， and interstitial infiltration of inflammatory cells were observed in the model group （P<0.01）， together with rises in the levels of IL-1β and IL-18 in the renal tissue （P<0.01） and up-regulated protein levels of NLRP3， Caspase-1， cleaved Caspase-1， GSDMD， GSDMD-N， fibronectin， and collagen Ⅰ in the renal tissue （P<0.01）. Compared with the model group， 12 weeks of drug interventions reduced the body mass， kidney weight， and kidney index and lowered the levels of FBG， TG， TC， SCr， and BUN in the serum and IL-1β and IL-18 in the renal tissue （P<0.05， P<0.01）. Furthermore， drug interventions ameliorated the renal lesions and down-regulated the protein levels of NLRP3， Caspase-1， cleaved Caspase-1， GSDMD， GSDMD-N， fibronectin， and collagen Ⅰ in the renal tissue （P<0.05， P<0.01）. The high-dose group of Gelian Tiaotang pills had the best effects. ConclusionGelian Tiaotang pills may inhibit pyroptosis and reduce inflammatory responses by regulating the NLRP3/Caspase-1/GSDMD signaling pathway， thus delaying the process of renal fibrosis in diabetes.

OBJECTIVE To evaluate the cost-effectiveness of vitamin D analogue for postmenopausal osteoporotic women in China. METHODS A Markov microsimulation model was developed to analyze the clinical and economic outcomes of eldecalcitol， alfacalcidol and calcitriol for postmenopausal osteoporosis from the healthcare system perspective in China. The clinical parameters required for the model were derived from network meta-analysis， while cost and utility data were primarily obtained from the literature. The cycle length was 1 year ， the time horizon was life time， and the willingness-to-pay threshold was 0.5 times the gross domestic product per capita in China in 2023（44 679 yuan/QALY）. Model uncertainty was tested with one-way sensitivity analysis， probabilistic sensitivity analysis， and scenario analysis. RESULTS The quality-adjusted life years （QALYs） for eldecalcitol were calculated to be 12.03 QALYs， which were higher than those for alfacalcidol （11.94 QALYs） and calcitriol （11.92 QALYs）. Meanwhile， the direct medical cost associated with eldecalcitol treatment amounted to 38 805 yuan， which was lower than that of alfacalcidol （45 173 yuan） and calcitriol （45 821 yuan）. The results of the one-way sensitivity analysis showed that the risk ratio of hip fracture and vertebral fracture had a significant impact on the result. The probabilistic sensitivity analysis and scenario analysis showed the robustness of the findings from the base-case analysis. CONCLUSIONS Compared with alfacalcidol and calcitriol， eldecalcitol is likely to be a cost-effective treatment for postmenopausal osteoporotic women in China.

Objective:To conduct performance tests on medical ultrasound probes repaired by the third party,and explore whether the key parameters of the probes of third-party repair can meet the requirements of clinical use for quality.Methods:A total of 79 ultrasound probes that had been repaired by the third party were selected from different medical institutions.The performance tests were conducted on multiple parameters of ultrasound probes of different models and brands in accordance with national technical standards and relevant industry norms.Then,the test results were analyzed,studied and evaluated.Results:The tested results of the temperature rise and the leakage current of the ultrasound probes,which were repaired by the third party,met the national standards.However,in the test for sound power,26.58%of the probes failed to meet the national standards,which outputted sound intensity that was calculated was higher than the specified value.Conclusion:The general performance of the probes that have been repaired by the third party is well,but the quality of the repair is uneven levels,and some indicators do not meet national standards or industry norms,which might lead to occur risks in ultrasound diagnosis of medical institutions.It is recommended to implement regular test for quality and performance of medical ultrasound equipment,and establish a method and system for quality monitoring and re-evaluation after sale of repair for medical ultrasound,so as to ensure the use and safety of the equipment.

Objective:To investigate the feasibility of deep learning based on computed tomography(CT)radiomics in improving diagnostic accuracy for pulmonary nodules.Methods:A total of 500 patients with pulmonary nodules who admitted to our hospital from January 2023 to January 2024 were selected as study subjects,and they were randomly divided into a training set(350 patients)and a test set(150 patients)as 7:3 ratio.All patients underwent CT examination,and pathological diagnosis was used as gold standard to record pulmonary nodules that were judged by clinical judgment.The radiomics features were screened from the CT images of the patients,and these features were used to construct multiple machine learning models.The predictive value of different models in diagnosing pulmonary nodules was analyzed through confusion matrices and receiver operating characteristic(ROC)curve.Results:A total of 1,594 radiomics features,including 1,195 texture features(74.97%)that was the largest ratio,334 first-order histograms(20.95%),and 65 second-order histograms(4.08%),were extracted in this study.After least absolute shrinkage and selection operator(LASSO)regression analysis and ten-fold cross-validation processing,a total of six radiomics features were screened out.The screened radiomics features were incorporated respectively into four assembled models with machine learning,including ResNet50,DenseNet121,Inception_V3 and VGG19.The constructed models were evaluated respectively using the training set and the test set.The results showed that the assembled model had the highest accuracies in both training set and the test set(96.57%and 95.33%),which area under curve(AUC)values were 0.934 and 0.923,and specificities were 81.64%and 80.52%,and sensitivities were 90.25%and 88.71%,respectively.The results of consistency test indicated that the assembled model had the best classification consistency(Kappa=0.856,P<0.001)in the constructed diagnostic model for pulmonary nodule,which was the best-performing model.Conclusion:The deep learning technique based on CT radiomics has a certain feasibility in improving the diagnostic accuracy for pulmonary nodules,and the machine learning model that is included in this study has favorable predictive value in diagnosing pulmonary nodules.In them,the assembled model that is constructed on the basis of ResNet50,DenseNet121,Inception_V3,and VGG19 has better classification ability.

Aging is an inevitable biological process in organisms,accompanied by the decline of multiple physiological functions and increased risk of diseases.With the intensification of global aging,the research associated with mechanisms and the development of anti-aging drugs have become critical topics in the biomedical field.Aging animal models are pivotal tools for investigating aging mechanisms and developing anti-aging interventions.Model organisms commonly used in aging research include nematodes(Caenorhabditis elegans),fruit flies(Drosophila melanogaster),mice(Mus musculus),rats(Rattus norvegicus),naked-mole-rats(e.g.,Heterocephalus glaber),and rhesus macaques(Macaca mulatta).Considering experimental costs and time constraints,mice represent the most extensively employed mammalian model.Under standard housing conditions,mice develop aging phenotypes at approximately 18 months of age,Resultsing in lengthy and costly experimental timelines.To accelerate research,scientists have established diverse progeroid mouse models through genetic,pharmacological,and environmental interventions.Given the tissue-specific heterogeneity of aging,distinct progeria models are required to investigate aging mechanisms across different organ systems.Notably,each model exhibits unique advantages and limitations in mimicking human aging phenotypes,screening therapeutic targets,and evaluating anti-aging compounds.This review comprehensively examines morphological,physiological,and pathological variations among established progeria models,delineates their context-dependent applications and inherent constraints,and provides a systematic framework for model selection in fundamental aging research and translational geroscience,with perspectives on future method ological developments.

Aging is an inevitable biological process in organisms,accompanied by the decline of multiple physiological functions and increased risk of diseases.With the intensification of global aging,the research associated with mechanisms and the development of anti-aging drugs have become critical topics in the biomedical field.Aging animal models are pivotal tools for investigating aging mechanisms and developing anti-aging interventions.Model organisms commonly used in aging research include nematodes(Caenorhabditis elegans),fruit flies(Drosophila melanogaster),mice(Mus musculus),rats(Rattus norvegicus),naked-mole-rats(e.g.,Heterocephalus glaber),and rhesus macaques(Macaca mulatta).Considering experimental costs and time constraints,mice represent the most extensively employed mammalian model.Under standard housing conditions,mice develop aging phenotypes at approximately 18 months of age,Resultsing in lengthy and costly experimental timelines.To accelerate research,scientists have established diverse progeroid mouse models through genetic,pharmacological,and environmental interventions.Given the tissue-specific heterogeneity of aging,distinct progeria models are required to investigate aging mechanisms across different organ systems.Notably,each model exhibits unique advantages and limitations in mimicking human aging phenotypes,screening therapeutic targets,and evaluating anti-aging compounds.This review comprehensively examines morphological,physiological,and pathological variations among established progeria models,delineates their context-dependent applications and inherent constraints,and provides a systematic framework for model selection in fundamental aging research and translational geroscience,with perspectives on future method ological developments.

Objective:To conduct performance tests on medical ultrasound probes repaired by the third party,and explore whether the key parameters of the probes of third-party repair can meet the requirements of clinical use for quality.Methods:A total of 79 ultrasound probes that had been repaired by the third party were selected from different medical institutions.The performance tests were conducted on multiple parameters of ultrasound probes of different models and brands in accordance with national technical standards and relevant industry norms.Then,the test results were analyzed,studied and evaluated.Results:The tested results of the temperature rise and the leakage current of the ultrasound probes,which were repaired by the third party,met the national standards.However,in the test for sound power,26.58%of the probes failed to meet the national standards,which outputted sound intensity that was calculated was higher than the specified value.Conclusion:The general performance of the probes that have been repaired by the third party is well,but the quality of the repair is uneven levels,and some indicators do not meet national standards or industry norms,which might lead to occur risks in ultrasound diagnosis of medical institutions.It is recommended to implement regular test for quality and performance of medical ultrasound equipment,and establish a method and system for quality monitoring and re-evaluation after sale of repair for medical ultrasound,so as to ensure the use and safety of the equipment.

Objective:To investigate the feasibility of deep learning based on computed tomography(CT)radiomics in improving diagnostic accuracy for pulmonary nodules.Methods:A total of 500 patients with pulmonary nodules who admitted to our hospital from January 2023 to January 2024 were selected as study subjects,and they were randomly divided into a training set(350 patients)and a test set(150 patients)as 7:3 ratio.All patients underwent CT examination,and pathological diagnosis was used as gold standard to record pulmonary nodules that were judged by clinical judgment.The radiomics features were screened from the CT images of the patients,and these features were used to construct multiple machine learning models.The predictive value of different models in diagnosing pulmonary nodules was analyzed through confusion matrices and receiver operating characteristic(ROC)curve.Results:A total of 1,594 radiomics features,including 1,195 texture features(74.97%)that was the largest ratio,334 first-order histograms(20.95%),and 65 second-order histograms(4.08%),were extracted in this study.After least absolute shrinkage and selection operator(LASSO)regression analysis and ten-fold cross-validation processing,a total of six radiomics features were screened out.The screened radiomics features were incorporated respectively into four assembled models with machine learning,including ResNet50,DenseNet121,Inception_V3 and VGG19.The constructed models were evaluated respectively using the training set and the test set.The results showed that the assembled model had the highest accuracies in both training set and the test set(96.57%and 95.33%),which area under curve(AUC)values were 0.934 and 0.923,and specificities were 81.64%and 80.52%,and sensitivities were 90.25%and 88.71%,respectively.The results of consistency test indicated that the assembled model had the best classification consistency(Kappa=0.856,P<0.001)in the constructed diagnostic model for pulmonary nodule,which was the best-performing model.Conclusion:The deep learning technique based on CT radiomics has a certain feasibility in improving the diagnostic accuracy for pulmonary nodules,and the machine learning model that is included in this study has favorable predictive value in diagnosing pulmonary nodules.In them,the assembled model that is constructed on the basis of ResNet50,DenseNet121,Inception_V3,and VGG19 has better classification ability.