Objective:To evaluate the efficacy of defocus incorporated multiple segments (DIMS) on spherical equivalent (SE) and axial length (AL) in myopic children.Methods:Clinical studies on the progress of DIMS lens intervention in myopia reported in CNKI, Wanfang Data, VIP, China Biomedical Literature Database, PubMed, Embase, Cochrane Library, and Web of Science from the establishment of the database to June 2023 were searched.The experimental group and control group used DIMS lens and single vision lens (SVL) for intervention, respectively.Two researchers independently conducted the literature search, screening and data extraction based on the inclusion and exclusion criteria.Literature quality and risk of bias were evaluated in accordance with the requirements of the Cochrane Manual.Meta-analysis was performed using Revman 5.4 software.Results:A total of 7 clinical studies using DIMS lenses to intervene in the development of myopia were included .Seven studies provided complete SE data for analysis, with sample sizes of 1 164 eyes and 1 140 eyes for the DIMS group and SVL group, respectively.Five studies provided complete AL data for analysis, with sample sizes of 339 eyes and 315 eyes for the DIMS group and SVL group, respectively.There were statistically significant differences in SE and AL between DIMS group and SVL group (SE: WMD=0.40, 95% CI: 0.32-0.47, P<0.001; AL: WMD=-0.14, 95% CI: -0.17--0.11, P<0.001).DIMS lens could effectively delay SE progression and AL growth in myopic patients. Conclusions:DIMS lenses could delay the increase of SE and AL to a certain extent.

Objective:To construct a diagnostic prediction model for childhood asthma and conduct a preliminary evaluation based on the test results of specific IgE (sIgE) for airborne allergens and in combination with clinical data.Methods:This study is a case-control study. A total of 4 338 cases that completed the sIgE test for airborne allergens in the Allergy Department of Beijing Children′s Hospital Affiliated to Capital Medical University from January to December 2023 were selected as the research subjects. They were divided into the asthma group and the non-asthma group based on the diagnostic information. Age, gender, cough and wheezing symptoms, and the classification results of sIgE concentrations of 15 airborne allergens were collected as the predictor variables of the asthma diagnostic prediction model. Differential analysis and LASSO regression were employed for the screening of predictor variables. The multivariate logistic regression method was applied to construct the nomogram prediction model. The data set was randomly split at a ratio of 7∶3 into a training set (3 036 cases) for constructing the prediction model and a validation set (1 302 cases) for testing the predictive efficacy of the model. The area under the receiver operating characteristic (ROC) curve (AUC), the Hosmer-Lemeshow calibration curve were utilized to assess the discrimination and goodness of fit of the model, and the clinical decision curve (DCA) was adopted to evaluate the clinical application value of the model.Results:Among 4 338 pediatric cases, children aged 0 to <3 years accounted for 10.17% (441 cases), those aged 3 to <6 years accounted for 36.49% (1 583 cases), those aged 6 to <12 years accounted for 46.98% (2 038 cases), and those aged 12 to 18 years accounted for 6.36% (276 cases). Males constituted 65.17% (2 827 cases), and females 34.83% (1 511 cases). The proportion of children without wheezing symptoms was 41.47% (1 799 cases), while those with wheezing symptoms was 58.53% (2 539 cases). The asthma group accounted for 41.77% (1 812 cases), and the non-asthma group for 58.23% (2 526 cases). Statistically significant differences were observed between the asthma group and the non-asthma group in 18 predictive variables including age, gender, wheezing symptoms, d1, d2, e1, e5, g2, g6, m6, t11, t3, t6, w1, w22, w6, wx5, and m3 ( P<0.05). LASSO regression analysis identified six predictor variables: age (calculated in months), cough and wheezing symptoms, and sIgE of four airborne allergens, namely, Dermatophagoides pteronyssinus (d1), Canis familiaris dander (e5), Aspergillus fumigatus (m3), and Artemisia vulgaris pollen (w6).Multifactorial regression analysis revealed that the contribution degrees of the above-mentioned predictor variables to the asthma diagnosis prediction model were ranked as follows: cough and wheezing symptoms ( OR=24.37, P<0.001), m3 ( OR=1.34, P<0.001), d1 ( OR=1.22, P<0.001), e5 ( OR=1.12, P=0.028), w6 ( OR=1.11, P<0.001), and age ( OR=1.01, P<0.001).The AUCs of the nomogram prediction model for the training set and the validation set were 0.853 (95% CI: 0.840-0.866) and 0.838 (95% CI: 0.817-0.860), respectively. The Hosmer-Lemeshow calibration curve indicated a good fit ( P=0.215 for the training set; P=0.352 for the validation set). The DCA of the validation set demonstrated that when the probability threshold for predicting the occurrence of childhood asthma was 8%-92%, the model had the best applicability. Conclusion:By combining age, cough and wheezing symptoms, and sIgE of the four airborne allergens (d1, e5, m3, and w6) selected from 15 airborne allergens, a childhood asthma diagnosis prediction model with good predictive performance and clinical practicability was constructed. It can serve as a simple and convenient tool for accurately identifying asthma and provides a practical basis for the application of artificial intelligence big data analysis models in the prevention, treatment, and management of childhood asthma.

Endometriosis is a common estrogen-dependent disease in women of childbearing age,often leading to chronic pelvic pain,infertility,ovarian cancer,and other serious complications,and jeopardizing the health of women.The pathogenesis of endometriosis is complex and involves the alteration of multiple signaling pathways mediated by hormones,immunity,genetics,and the environment,and their interactions.Wnt/β-catenin signaling is involved in the regulation of embryonic development and tissue homeostasis,and it has recently been implicated in the pathogenesis of endometriosis via multiple pathways.This review considers the biological characteristics of the Wnt/β-catenin signaling pathway and summarizes the main mechanisms and signaling pathways involved in the pathogenesis of endometriosis,as well as the curr-ent research status of the regulation of this signaling pathway by traditional Chinese medicine for the treatment of endometriosis.We aim to clarify how the Wnt/β-catenin signaling pathway affects the development of endometriosis,and suggest that future studies should focus on exploring its potential role as an indicator for the clinical prediction and early diagnosis of endometriosis,thus providing theoretical support for the early diagnosis of this condition and the development of targeted drugs.

Endometriosis is a common estrogen-dependent disease in women of childbearing age,often leading to chronic pelvic pain,infertility,ovarian cancer,and other serious complications,and jeopardizing the health of women.The pathogenesis of endometriosis is complex and involves the alteration of multiple signaling pathways mediated by hormones,immunity,genetics,and the environment,and their interactions.Wnt/β-catenin signaling is involved in the regulation of embryonic development and tissue homeostasis,and it has recently been implicated in the pathogenesis of endometriosis via multiple pathways.This review considers the biological characteristics of the Wnt/β-catenin signaling pathway and summarizes the main mechanisms and signaling pathways involved in the pathogenesis of endometriosis,as well as the curr-ent research status of the regulation of this signaling pathway by traditional Chinese medicine for the treatment of endometriosis.We aim to clarify how the Wnt/β-catenin signaling pathway affects the development of endometriosis,and suggest that future studies should focus on exploring its potential role as an indicator for the clinical prediction and early diagnosis of endometriosis,thus providing theoretical support for the early diagnosis of this condition and the development of targeted drugs.

AIM:To analyze the clinical value of predicting drug resistance in pulmonary tuberculo-sis patients based on improved machine learning models,and to build a visualization system for veri-fication.METHODS:Retrospectively selected 1 025 pulmonary tuberculosis patients hospitalized in Zhuhai Sixth People's Hospital from March 2019 to March 2024 with drug sensitivity test results as the research object.According to the definition of drug-resistant tuberculosis,the patients were divided in-to 631 sensitive groups(drug sensitivity test results showed no drug resistance),271 RR/MDR groups(meeting the definition of rifampicin resistant tu-berculosis or multi drug resistant tuberculosis,but no drug resistance to any kind of fluoroquino-lones),and 123 pre XDR groups(on the basis of multi drug resistant tuberculosis,and at the same time,drug resistance to any kind of fluoroquino-lones).Analyze clinical data based on the improved machine learning model,help build a drug resistant tuberculosis prediction model,synchronously com-plete feature screening,conduct value analysis on the screened features,and build a visual system for verification.RESULTS:Three groups of patients with baseline data comparison shows:Age,Body mass index(BMI),basic treatment of classification,lung diseases,haemoptysis,second-line drug use history,damage to lung,with empty in all statisti-cally significant difference between the three groups(P<0.05);Based on the modified ma-chine learning model,8 variables were screened,which were history of second-line drug use,BMI,treatment classification,destructive lung,underly-ing lung diseases,cavitation,hemoptysis,and age.The modified machine learning model had the high-est prediction accuracy compared with the tradi-tional model,with AUC values of 0.9322(RR/MDR prediction was positive class)and 0.9545(pre-XDR prediction was positive class).CONCLUSION:The application of the improved machine learning mod-el can help predict the occurrence of drug-resistant tuberculosis and assist the clinical formulation of more effective treatment plans.

BACKGROUND:Metformin is currently considered the first-line medication for the treatment of type 2 diabetes.Metformin may delay the progression of osteoarthritis,but its specific mechanism of action remains unclear.OBJECTIVE:To evaluate the therapeutic effects and the related action mechanisms of metformin on osteoarthritis in rats.METHODS:(1)Network pharmacology:Potential common targets for metformin,osteoarthritis,and ferroptosis were screened using the CTD,SwissTargetPrediction,GeneCards,and OMIM databases.After importing the targets into the STRING database,protein-protein interaction analysis was conducted to identify the key targets for metformin,osteoarthritis,and ferroptosis.(2)Molecular docking:P53 and its downstream factor SLC7A11 protein structures in PDB format were downloaded from the PDB database.The 2D structure of metformin was converted to a 3D structure,and molecular docking of metformin with the proteins was performed using Discovery Studio 2019 Client.(3)In vivo experiments:Thirty male SD rats were randomly divided into three groups(n=10).The blank group did not receive surgery.The osteoarthritis model was established using the modified Hulth method for the model and metformin groups.One day after the surgery,rats in the metformin group were gavaged with metformin 200 mg/kg per day,while the blank and model groups were gavaged with physiological saline.Treatment continued for 4 weeks.Hematoxylin-eosin staining and Safranin O-fast green staining were used to observe the pathological morphology and structure of the knee cartilage,and Mankin scoring was performed.ELISA was used to measure the levels of tumor necrosis factor-α and interleukin-6 in the serum.The microplate method was used to measure serum ferroptosis-related indicators,including glutathione,malondialdehyde,and Fe2+.Immunofluorescence staining,western blot assay,and real-time qPCR were used to detect the protein and mRNA expression of P53,SLC7A11,glutathione peroxidase 4,proteoglycans,and matrix metalloproteinase 13 in the cartilage tissue of the rats.RESULTS AND CONCLUSION:(1)A total of 96 intersecting targets among metformin,osteoarthritis,and ferroptosis were identified.After protein-protein interaction analysis,77 potential targets were found.Further screening identified the core targets as TP53,AKT1,JUN,interleukin-6,MYC,interleukin-1β,and tumor necrosis factor-α,among others.(2)Docking analysis results showed that metformin bound strongly and stably with P53 and its downstream factor SLC7A11.(3)In the model group,the knee cartilage surface was irregular,with cartilage tissue defects and reduced chondrocyte numbers.Compared to the model group,the knee cartilage structure damage in the metformin group was significantly improved,with a smoother cartilage surface and increased chondrocyte numbers.The Mankin score in the model group was significantly higher than that in the blank group,while the Mankin score in the intervention group was significantly lower than that in the model group.(4)Compared with the model group,the metformin group had significantly lower levels of tumor necrosis factor-α,interleukin-6,malondialdehyde,and Fe2+,and significantly higher glutathione levels.(5)Compared to the model group,the metformin group had significantly increased protein and mRNA expression of SLC7A11,glutathione peroxidase 4,and proteoglycans,and significantly decreased protein and mRNA expression of P53 and matrix metalloproteinase 13 in their cartilage tissue.(6)The results indicate that metformin can effectively improve cartilage damage in osteoarthritis rats and alleviate chondrocyte ferroptosis by inhibiting the aberrantly activated P53/SLC7A11/glutathione peroxidase 4 signaling pathway.This improvement in chondrocyte iron metabolism and lipid peroxidation response further reduces cartilage matrix degradation and prevents further cartilage damage and inflammatory response.

Objective:To evaluate the efficacy of defocus incorporated multiple segments (DIMS) on spherical equivalent (SE) and axial length (AL) in myopic children.Methods:Clinical studies on the progress of DIMS lens intervention in myopia reported in CNKI, Wanfang Data, VIP, China Biomedical Literature Database, PubMed, Embase, Cochrane Library, and Web of Science from the establishment of the database to June 2023 were searched.The experimental group and control group used DIMS lens and single vision lens (SVL) for intervention, respectively.Two researchers independently conducted the literature search, screening and data extraction based on the inclusion and exclusion criteria.Literature quality and risk of bias were evaluated in accordance with the requirements of the Cochrane Manual.Meta-analysis was performed using Revman 5.4 software.Results:A total of 7 clinical studies using DIMS lenses to intervene in the development of myopia were included .Seven studies provided complete SE data for analysis, with sample sizes of 1 164 eyes and 1 140 eyes for the DIMS group and SVL group, respectively.Five studies provided complete AL data for analysis, with sample sizes of 339 eyes and 315 eyes for the DIMS group and SVL group, respectively.There were statistically significant differences in SE and AL between DIMS group and SVL group (SE: WMD=0.40, 95% CI: 0.32-0.47, P<0.001; AL: WMD=-0.14, 95% CI: -0.17--0.11, P<0.001).DIMS lens could effectively delay SE progression and AL growth in myopic patients. Conclusions:DIMS lenses could delay the increase of SE and AL to a certain extent.

Objective To explore the feasibility and safety of ultrasound-guided percutaneous microwave ablation for unresectable pancreatic cancer.Methods Totally 84 patients who underwent ultrasound-guided percutaneous microwave ablation for unresectable pancreatic cancer were enrolled,and the technical success rate,complete ablation rate,complication rate,pain relief rate and survival time,etc.were observed.Results The median age of 84 cases was 61.5 years.Totally 86 tumors,including 44.19％(38/86)at the head/neck and 55.81％(48/86)at the body/tail of pancreas were detected,and a total of 85 ablation sessions were performed with the median ablation energy applied per tumor of 9.90(1.08,21.60)kJ and the complete ablation rate of 42.86％(36/84).The technical success rate was 100％(85/85).Thirty-nine complication events occurred in 25 cases,no ablation-related death.Among 34 patients underwent ablation mainly for pain symptoms,the pain score decreased from(6.22±1.12)points before treatment to(1.94±1.64)points after treatment(P＜0.001).During 6.8(3.3,12.9)months' follow-up,the mean survival time was(8.5±6.7)months,and all 47 patients died due to tumor progression.Conclusion Ultrasound-guided percutaneous microwave ablation was safe and feasible for unresectable pancreatic cancer.

Objective:To construct a diagnostic prediction model for childhood asthma and conduct a preliminary evaluation based on the test results of specific IgE (sIgE) for airborne allergens and in combination with clinical data.Methods:This study is a case-control study. A total of 4 338 cases that completed the sIgE test for airborne allergens in the Allergy Department of Beijing Children′s Hospital Affiliated to Capital Medical University from January to December 2023 were selected as the research subjects. They were divided into the asthma group and the non-asthma group based on the diagnostic information. Age, gender, cough and wheezing symptoms, and the classification results of sIgE concentrations of 15 airborne allergens were collected as the predictor variables of the asthma diagnostic prediction model. Differential analysis and LASSO regression were employed for the screening of predictor variables. The multivariate logistic regression method was applied to construct the nomogram prediction model. The data set was randomly split at a ratio of 7∶3 into a training set (3 036 cases) for constructing the prediction model and a validation set (1 302 cases) for testing the predictive efficacy of the model. The area under the receiver operating characteristic (ROC) curve (AUC), the Hosmer-Lemeshow calibration curve were utilized to assess the discrimination and goodness of fit of the model, and the clinical decision curve (DCA) was adopted to evaluate the clinical application value of the model.Results:Among 4 338 pediatric cases, children aged 0 to <3 years accounted for 10.17% (441 cases), those aged 3 to <6 years accounted for 36.49% (1 583 cases), those aged 6 to <12 years accounted for 46.98% (2 038 cases), and those aged 12 to 18 years accounted for 6.36% (276 cases). Males constituted 65.17% (2 827 cases), and females 34.83% (1 511 cases). The proportion of children without wheezing symptoms was 41.47% (1 799 cases), while those with wheezing symptoms was 58.53% (2 539 cases). The asthma group accounted for 41.77% (1 812 cases), and the non-asthma group for 58.23% (2 526 cases). Statistically significant differences were observed between the asthma group and the non-asthma group in 18 predictive variables including age, gender, wheezing symptoms, d1, d2, e1, e5, g2, g6, m6, t11, t3, t6, w1, w22, w6, wx5, and m3 ( P<0.05). LASSO regression analysis identified six predictor variables: age (calculated in months), cough and wheezing symptoms, and sIgE of four airborne allergens, namely, Dermatophagoides pteronyssinus (d1), Canis familiaris dander (e5), Aspergillus fumigatus (m3), and Artemisia vulgaris pollen (w6).Multifactorial regression analysis revealed that the contribution degrees of the above-mentioned predictor variables to the asthma diagnosis prediction model were ranked as follows: cough and wheezing symptoms ( OR=24.37, P<0.001), m3 ( OR=1.34, P<0.001), d1 ( OR=1.22, P<0.001), e5 ( OR=1.12, P=0.028), w6 ( OR=1.11, P<0.001), and age ( OR=1.01, P<0.001).The AUCs of the nomogram prediction model for the training set and the validation set were 0.853 (95% CI: 0.840-0.866) and 0.838 (95% CI: 0.817-0.860), respectively. The Hosmer-Lemeshow calibration curve indicated a good fit ( P=0.215 for the training set; P=0.352 for the validation set). The DCA of the validation set demonstrated that when the probability threshold for predicting the occurrence of childhood asthma was 8%-92%, the model had the best applicability. Conclusion:By combining age, cough and wheezing symptoms, and sIgE of the four airborne allergens (d1, e5, m3, and w6) selected from 15 airborne allergens, a childhood asthma diagnosis prediction model with good predictive performance and clinical practicability was constructed. It can serve as a simple and convenient tool for accurately identifying asthma and provides a practical basis for the application of artificial intelligence big data analysis models in the prevention, treatment, and management of childhood asthma.

BACKGROUND:Metformin is currently considered the first-line medication for the treatment of type 2 diabetes.Metformin may delay the progression of osteoarthritis,but its specific mechanism of action remains unclear.OBJECTIVE:To evaluate the therapeutic effects and the related action mechanisms of metformin on osteoarthritis in rats.METHODS:(1)Network pharmacology:Potential common targets for metformin,osteoarthritis,and ferroptosis were screened using the CTD,SwissTargetPrediction,GeneCards,and OMIM databases.After importing the targets into the STRING database,protein-protein interaction analysis was conducted to identify the key targets for metformin,osteoarthritis,and ferroptosis.(2)Molecular docking:P53 and its downstream factor SLC7A11 protein structures in PDB format were downloaded from the PDB database.The 2D structure of metformin was converted to a 3D structure,and molecular docking of metformin with the proteins was performed using Discovery Studio 2019 Client.(3)In vivo experiments:Thirty male SD rats were randomly divided into three groups(n=10).The blank group did not receive surgery.The osteoarthritis model was established using the modified Hulth method for the model and metformin groups.One day after the surgery,rats in the metformin group were gavaged with metformin 200 mg/kg per day,while the blank and model groups were gavaged with physiological saline.Treatment continued for 4 weeks.Hematoxylin-eosin staining and Safranin O-fast green staining were used to observe the pathological morphology and structure of the knee cartilage,and Mankin scoring was performed.ELISA was used to measure the levels of tumor necrosis factor-α and interleukin-6 in the serum.The microplate method was used to measure serum ferroptosis-related indicators,including glutathione,malondialdehyde,and Fe2+.Immunofluorescence staining,western blot assay,and real-time qPCR were used to detect the protein and mRNA expression of P53,SLC7A11,glutathione peroxidase 4,proteoglycans,and matrix metalloproteinase 13 in the cartilage tissue of the rats.RESULTS AND CONCLUSION:(1)A total of 96 intersecting targets among metformin,osteoarthritis,and ferroptosis were identified.After protein-protein interaction analysis,77 potential targets were found.Further screening identified the core targets as TP53,AKT1,JUN,interleukin-6,MYC,interleukin-1β,and tumor necrosis factor-α,among others.(2)Docking analysis results showed that metformin bound strongly and stably with P53 and its downstream factor SLC7A11.(3)In the model group,the knee cartilage surface was irregular,with cartilage tissue defects and reduced chondrocyte numbers.Compared to the model group,the knee cartilage structure damage in the metformin group was significantly improved,with a smoother cartilage surface and increased chondrocyte numbers.The Mankin score in the model group was significantly higher than that in the blank group,while the Mankin score in the intervention group was significantly lower than that in the model group.(4)Compared with the model group,the metformin group had significantly lower levels of tumor necrosis factor-α,interleukin-6,malondialdehyde,and Fe2+,and significantly higher glutathione levels.(5)Compared to the model group,the metformin group had significantly increased protein and mRNA expression of SLC7A11,glutathione peroxidase 4,and proteoglycans,and significantly decreased protein and mRNA expression of P53 and matrix metalloproteinase 13 in their cartilage tissue.(6)The results indicate that metformin can effectively improve cartilage damage in osteoarthritis rats and alleviate chondrocyte ferroptosis by inhibiting the aberrantly activated P53/SLC7A11/glutathione peroxidase 4 signaling pathway.This improvement in chondrocyte iron metabolism and lipid peroxidation response further reduces cartilage matrix degradation and prevents further cartilage damage and inflammatory response.