Objective To evaluate the efficacy of Puerariae lobatae radix （PLR） and Anemarrhenae Rhizoma （AR） in preventing and treating Alzheimer’s disease （AD） and explore its potential mechanism of action by LC-MS serum metabolomics strategy. Methods The AD rat model was established by administering aluminum chloride （AlCl₃） and D-galactose （D-gal） for 20 weeks. The traditional Chinese medicine intervention group was given the PLR, AR, and PLR-AR extracts for 8 weeks by gavage. The model effect and efficacy were evaluated by Morris water maze test and biochemical indicators including SOD, NO, and MDA; Metabolomics research based on the UHPLC-Q/TOF-MS method was conducted, and relevant metabolic pathways were analyzed through the MetaboAnalyst online website. Results The learning and memory abilities of AD model rats were significantly decreased compared with the control group, and the levels of oxidative stress and lipid peroxides were significantly increased （P<0.05）, while the SOD content was decreased considerably （P<0.01）. The learning and memory abilities of AD model rats were improved, oxidative stress and lipid peroxidation levels were reversed, and serum SOD content was increased significantly after the intervention of PLR-AR, with better effects than single drugs. Through metabolomics, 70 differential metabolites were identified between the AD model group and the control group, mainly involving 10 pathways, including phenylalanine, tyrosine, and tryptophan biosynthesis, phenylalanine metabolism, and unsaturated fatty acid biosynthesis, et.al. The intervention of PLR-AR could adjust 47 metabolites, with 20 metabolites showing significant differences （P<0.05）. The significantly adjusted metabolites involve 6 pathways, including phenylalanine, tyrosine, and tryptophan biosynthesis, et al. Conclusion The combination of PLR and AR could significantly improve the learning and memory abilities of AD rat models. The mechanism may be related to the improvement of oxidative stress and lipid peroxidation levels, the increase of serum SOD content, and the regulation of phenylalanine, tyrosine, and tryptophan biosynthesis pathways.

Aim To investigate the effect of histamine H, receptor (HjR) on the immune responses in astrocytes induced by lipopolysaccharide (LPS) and the regulatory mechanism of its signaling pathway. Methods LPS was used to establish an in vitro astrocyte inflammation model. Rat primary astrocytes were divided into the control group, LPS group, LPS + Hj R agonist group (2-pyridylethlamine, Pyri), and HjR agonist group. Astrocytes were treated with Pyri 100 p,mol • L~ for 1 h, then stimulated with LPS at 100 p,g • L~ for 24 h. Cell viability was measured using the CCK-8 assay. The expression of GFAP and HjR was detected by immunofluorescence. Glial morphological changes were observed under a microscope. The levels of proinflammatory mediators (TNF-a and IL-6) were detected by ELISA. The protein expressions of p-Akt, Akt, p-NF-KB p65, and NF-KB p65 were detected by Western blot. Results Compared with the control group, more activated astrocytes with fewer cell processes and branches were observed in the LPS group. Besides, LPS enhanced the GFAP expression level, reduced the H,R expression level and stimulated the production of TNF-a and IL-6 from astrocytes. Pre treatment with Pyri for 1 h ameliorated the glial morphological changes stimulated by LPS, inhibited LPS-induced upregulation of GFAP level and the inflammatory factors secretion. In addition, LPS stimulated astrocytes showed a higher phosphorylation of Akt and NF-KB p65, which was also ameliorated by Pyri. Conclusions H, R agonist can inhibit LPS-induced astrocyte activation and inflammatory factor secretion, and the Akt/NF-KB signaling pathway may be an important pathway for the involvement of H,R in immune regulation.

Biosensor analysis technology is a kind of technology with high specificity that can convert biological reactions into optical and electrical signals. In the development of drugs for Alzheimer's disease (AD), according to different disease hypotheses and targets, this technology plays an important role in confirming targets and screening active compounds. This paper briefly describes the pathogenesis of AD and the current situation of therapeutic drugs, introduces three biosensor analysis techniques commonly used in the discovery of AD drugs, such as surface plasmon resonance (SPR), biolayer interferometry (BLI) and fluorescence analysis technology, explains its basic principle and application progress, and summarizes their advantages and limitations respectively.

Alzheimer's disease (AD) is a progressive neurodegenerative disease associated with dysfunctions related to thinking, learning, and memory of the brain. AD has multiple pathological characteristics with complicated causes, constructing a suitable pathological model is crucial for the research of AD. Microfluidic chip technology integrates multiple functional units on a chip, which can realize microenvironmental control similar to the physiological environment. It is well applied in the construction of pathological model, early diagnosis as well as drug screening of AD. This paper focuses on the construction of AD microfluidic chips model from the perspective of cell type, culture formats and the chips structure as well as the research progress of microfluidic chips in AD application based on the pathological characteristics of AD, which will provide a reference for further elucidation of AD mechanism and drug development.

Objective To compare artificial intelligence-assisted diagnostic system and conventional manual CT image interpretation for detection of positive pulmonary nodules and differential diagnosis of benign and malignant pulmonary nodules, and to provide a reference for the application of artificial intelligence in clinical screening for lung cancer. Methods Patients who underwent chest CT scans for pulmonary nodules from March 2019 to December 2023 were enrolled. The CT images were subjected to artificial intelligence-based and conventional manual CT image interpretation. The pathological examination results of pulmonary lesions served as a gold standard for comparison of artificial intelligence-based and conventional manual CT image interpretation in detection rate of positive pulmonary nodules and differential diagnosis of benign and malignant pulmonary nodules. Results A total of 327 positive pulmonary nodules were identified in 207 patients. The detection rate of positive pulmonary nodules was significantly higher with artificial intelligence-based CT image interpretation than with conventional manual CT image interpretation (95.72% vs. 86.85%; χ²=16.16, P < 0.01). Moreover, artificial intelligence-based CT image interpretation showed significantly higher detection rates for solid (χ²=7.71, P < 0.01) and ground-glass pulmonary nodules (χ²=5.80, P < 0.05) than conventional manual CT image interpretation. The detection rates for pulmonary nodules with < 1 cm (χ²=4.97, P < 0.05), 1 to < 2 cm (χ²=7.04, P < 0.01), and 2 to < 3 cm (χ²=4.91, P < 0.05) diameters were significantly higher with artificial intelligence-based CT image interpretation than with conventional manual CT image interpretation. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for differential diagnosis of benign and malignant pulmonary nodules were 98.08%, 91.53%, 95.33%, 96.43%, and 95.71% with artificial intelligence-based CT image interpretation and 91.34%, 77.97%, 87.96%, 32.62%, and 86.50% with conventional CT image interpretation. The sensitivity (χ²=4.70, P < 0.05), specificity (χ²=4.20, P < 0.05), negative predictive value (χ²=65.28, P < 0.01), and accuracy (χ²=8.52, P < 0.01) were significantly higher with artificial intelligence-based CT image interpretation than with conventional manual CT image interpretation. However, there was no significant difference in the positive predictive value (χ²=3.80, P > 0.05). Conclusion Compared with conventional manual CT image interpretation, artificial intelligence-assisted diagnostic system for CT image interpretation can significantly increase the detection rate of positive pulmonary nodules and improve the efficiency of differential diagnosis of benign and malignant pulmonary nodules, so it deserves widespread applications in physical examination and early screening for lung cancer.

Humans ; Adolescent ; Acne Vulgaris/therapy* ; Skin Care ; Surveys and Questionnaires ; Cognition ; China ; Skin

Objective To compare artificial intelligence-assisted diagnostic system and conventional manual CT image interpretation for detection of positive pulmonary nodules and differential diagnosis of benign and malignant pulmonary nodules, and to provide a reference for the application of artificial intelligence in clinical screening for lung cancer. Methods Patients who underwent chest CT scans for pulmonary nodules from March 2019 to December 2023 were enrolled. The CT images were subjected to artificial intelligence-based and conventional manual CT image interpretation. The pathological examination results of pulmonary lesions served as a gold standard for comparison of artificial intelligence-based and conventional manual CT image interpretation in detection rate of positive pulmonary nodules and differential diagnosis of benign and malignant pulmonary nodules. Results A total of 327 positive pulmonary nodules were identified in 207 patients. The detection rate of positive pulmonary nodules was significantly higher with artificial intelligence-based CT image interpretation than with conventional manual CT image interpretation (95.72% vs. 86.85%; χ²=16.16, P < 0.01). Moreover, artificial intelligence-based CT image interpretation showed significantly higher detection rates for solid (χ²=7.71, P < 0.01) and ground-glass pulmonary nodules (χ²=5.80, P < 0.05) than conventional manual CT image interpretation. The detection rates for pulmonary nodules with < 1 cm (χ²=4.97, P < 0.05), 1 to < 2 cm (χ²=7.04, P < 0.01), and 2 to < 3 cm (χ²=4.91, P < 0.05) diameters were significantly higher with artificial intelligence-based CT image interpretation than with conventional manual CT image interpretation. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for differential diagnosis of benign and malignant pulmonary nodules were 98.08%, 91.53%, 95.33%, 96.43%, and 95.71% with artificial intelligence-based CT image interpretation and 91.34%, 77.97%, 87.96%, 32.62%, and 86.50% with conventional CT image interpretation. The sensitivity (χ²=4.70, P < 0.05), specificity (χ²=4.20, P < 0.05), negative predictive value (χ²=65.28, P < 0.01), and accuracy (χ²=8.52, P < 0.01) were significantly higher with artificial intelligence-based CT image interpretation than with conventional manual CT image interpretation. However, there was no significant difference in the positive predictive value (χ²=3.80, P > 0.05). Conclusion Compared with conventional manual CT image interpretation, artificial intelligence-assisted diagnostic system for CT image interpretation can significantly increase the detection rate of positive pulmonary nodules and improve the efficiency of differential diagnosis of benign and malignant pulmonary nodules, so it deserves widespread applications in physical examination and early screening for lung cancer.

Objective To observe the clinical effect of acupuncture at the core muscle group combined with rehabilitation training in the treatment of chronic non-specific low back pain.Methods A total of 96 patients with chronic non-specific low back pain were randomly divided into observation group and control group,48 cases in each group.The control group was given rehabilitation training,and the observation group was given acupuncture at core muscle group training on the basis of the control group.Patients in both groups were treated continuously for two weeks.After two weeks of treatment,the clinical efficacy of the two groups was evaluated.The changes of root mean square(RMS)values of transverse abdominal muscle and multifidus muscle were observed before and after treatment,as well as the scores of Oswestry Dysfunction Index,Roland-Morris Dysfunction Questionnaire and Quebec Low Back Pain Scale.The changes of serum β-endorphin,serum substance P and serum prostaglandin E2 levels were compared before and after treatment between the two groups.The patients were followed up for five months after treatment,and the recurrence of the two groups was evaluated.Results(1)The total effective rate was 93.75%(45/48)in the observation group and 81.25%(39/48)in the control group.The curative effect of the observation group was superior to that of the control group,and the difference was statistically significant(P＜0.05).(2)After treatment,the Oswestry Dysfunction Index score,Roland-Morris Dysfunction Questionnaire score and Quebec Low Back Pain Disorder Scale score of the two groups were significantly improved(P＜0.05),and the improvement of Oswestry Dysfunction Index score,Roland-Morris Dysfunction Questionnaire score and Quebec Low Back Pain Disorder Scale score in the observation group was significantly superior to that in the control group,the difference was statistically significant(P＜0.05).(3)After treatment,the RMS values of transverse abdominal muscle and multifidus muscle in the two groups were significantly improved(P＜0.05),and the observation group was significantly superior to the control group in improving the RMS values of transverse abdominal muscle and multifidus muscle,the difference being significant(P＜0.05).(4)After treatment,the levels of β-endorphin,substance P and prostaglandin E2 in the two groups were significantly improved(P＜0.05),and the observation group was significantly superior to the control group in improving the levels of β-endorphin,substance P and prostaglandin E2,the differences were statistically significant(P＜0.05).(5)After treatment,the recurrence rate of the observation group was 6.25%(3/48),and that of the control group was 20.83%(10/48).The recurrence rate of the observation group was significantly lower than that of the control group,and the difference was statistically significant(P＜0.05).Conclusion Acupuncture at the core muscle group combined with rehabilitation training in the treatment of chronic non-specific low back pain can significantly improve the clinical symptoms of patients,improve the levels of β-endorphin,substance P and prostaglandin E2,and the clinical effect is significant.

Objective To observe the therapeutic effect and mechanism of Guhuaisi Kangfu Pills on rats with steroid-induced osteonecrosis of the femoral head(SONFH).Methods Sixty rats were randomly divided into blank group,model group,Xianling Gubao Capsules group and Guhuaisi Kangfu Pills low-,medium-and high-dose groups,10 rats in each group.Except for the blank group,the SONFH model was established by lipopolysaccharide combined with Glucocorticoid induction method in all other groups of rats.At the end of the intervention,for the femoral head,blood vessel radiography was performed to observe the microvascular changes in the bone marrow,and hematoxylin-eosin(HE)staining and calculation of the empty bone trap rate,Micro-CT scanning analysis,and compression experiments were carried out,and the real-time quantitative polymerase chain reaction(RT-qPCR)was used to detect the gene expressions of phosphatidylinositol 3-kinase(PI3K),protein kinase B(Akt)1,vascular endothelial growth factor(VEGF)and platelet endothelial cell adhesion molecule 1(CD31)in whole blood.Results Compared with the blank group,the blood supply in the femoral head medullary cavity of the model group was poor,the empty bone lacuna rate was increased(P＜0.05),the bone mineral density and bone volume fraction were significantly decreased(P＜0.05),the maximum load and elastic modulus of the femoral head were decreased(P＜0.05),and the mRNA expression levels of Akt1,PI3K,VEGF and CD31 in whole blood were decreased(P＜0.05).Compared with the model group,the blood supply in the femoral head medullary cavity was relatively good,the empty bone lacuna rate was decreased(P＜0.05),the bone mineral density,bone volume fraction,trabecular number and trabecular thickness were significantly increased(P＜0.05),the trabecular separation was significantly decreased(P＜0.05),the maximum load and elastic modulus of the femoral head were increased(P＜0.05),and the mRNA expression levels of Akt1,PI3K,VEGF and CD31 in the whole blood were increased(P＜0.05)in the high-dose group of Guhuaisi Kangfu Pills and Xianling Gubao Capsules group.There was no significant difference in the above indexes between the high-dose group of Guhuaisi Kangfu Pills and the Xianling Gubao Capsules group(P＞0.05).Conclusion Guhuaisi Kangfu Pills improves SONFH in rats,and its mechanism is related to the promotion of VEGF/PI3K/Akt pathway gene expression,thereby promoting angiogenesis.

Objective To establish a dynamic prediction model of fatal massive hemorrhage in trauma based on the vital signs time series data and machine learning algorithms.Methods Retrospectively analyze the vital signs time series data of 7522 patients with trauma in the Medical Information Mart for Intensive Care-Ⅳ(MIMIC-Ⅳ)database from 2008 to 2019.According to the occurrence of posttraumatic fatal massive hemorrhage,the patients were divided into two groups:fatal massive hemorrhage group(n=283)and non-fatal massive hemorrhage group(n=7239).Six machine learning algorithms,including logistic regression(LR),support vector machine(SVM),random forests(RF),adaptive boosting(AdaBoost),gated recurrent unit(GRU),and GRU-D were used to develop a dynamic prediction models of fatal massive hemorrhage in trauma.The probability of fatal massive hemorrhage in the following 1,2,and 3 h was dynamically predicted.The performance of the models was evaluated by accuracy,sensitivity,specificity,positive predictive value,negative predictive value,Youden index,and area under receiver operating characteristic curve(AUC).The models were externally validated based on the trauma database of the Chinese PLA General Hospital.Results In the MIMIC-Ⅳ database,the set of dynamic prediction models based on the GRU-D algorithm was the best.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.946±0.029,0.940±0.032,and 0.943±0.034,respectively,and there was no significant difference(P=0.905).In the trauma dataset,GRU-D model achieved the best external validation effect.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.779±0.013,0.780±0.008,and 0.778±0.009,respectively,and there was no significant difference(P=0.181).This set of models was deployed in a public web calculator and hospital emergency department information system,which is convenient for the public and medical staff to use and validate the model.Conclusion A set of dynamic prediction models has been successfully developed and validated,which is greatly significant for the early diagnosis and dynamic prediction of fatal massive hemorrhage in trauma.