AIM: To investigate the application of optical coherence tomography angiography(OCTA)in observing the relationship between corneal topographic changes and retinal vessel density and thickness in myopic adolescents after wearing orthokeratology lenses.METHODS:Myopic adolescents who treated and wore orthokeratology lenses in our hospital between January and December 2023 were selected for the study, and their clinical data were retrospectively analyzed. Totally 50 cases(50 eyes)of toric orthokeratology lenses(toric group)and 50 cases(50 eyes)of spherical orthokeratology lenses(spherical group)were selected. Changes in corneal topographic indices, retinal vascular density and retinal thickness were examined in the two groups before and at 3 and 6 mo after wear. The relationship between corneal topographic indexes and retinal vessel density and thickness was analyzed.RESULTS: Compared with the pre-wear, average value of ks and kf(Avek)was significantly lower(all P<0.05), and surface regularity index(SRI)and surface asymmetry index(SAI)were significantly increased(both P<0.05)in the two groups at 3 and 6 mo post-wear. Compared with the pre-wearing period, deep retinal vessel density in whole macular area and superficial retinal vessel density in paracentral retina of macula were significantly higher in the two groups at 3 and 6 mo after wearing(both P<0.05), and the superficial retinal vessel density in macular fovea and paracentral fovea of macula of the toric group was significantly higher(all P<0.05), and the superficial retinal vessel density in paracentral macular fovea of the spherical group was significantly increased(all P<0.05). Compared with the pre-wearing period, the thickness of retina in whole macula and paracentral retina of the macula was significantly increased in the both groups at 3 and 6 mo post-wearing(all P<0.05). After wearing lenses for 3 mo, the Avek of the toric group was negatively correlated with the deep vascular density in whole macular area and paracentral macular fovea, the superficial vascular density in macular fovea and paracentral macular fovea, and the thickness of whole macular and paracentral macular fovea(all P<0.05). The SRI and SAI were positively correlated with the superficial retinal vascular density in macular fovea and paracentral macular fovea, and the retinal thickness in whole macular area and paracentral macular fovea(all P<0.05). The spherical group showed a negative correlation between Avek and the deep retinal vessel density in whole macular area and paracentral macular fovea, as well as the superficial retinal vessel density in paracentral macular fovea after wearing lenses for 3 mo(all P<0.05). SRI and SAI were positively correlated with the deep retinal vessel density in whole macular area, macular fovea and paracentral macular fovea, and the superficial retinal vessel density in paracentral macular fovea(all P<0.05). SRI was positively correlated with the retinal thickness in whole macular area(P<0.05), and SAI was positively correlated with the retinal thickness in paracentral macular fovea(P<0.05).CONCLUSION: After wearing toric and spherical corneal orthokeratology lenses, the corneal topography of myopic adolescents changes, and the retinal blood flow density and retinal thickness increase significantly, improving retinal blood circulation. Moreover, there is a significant correlation between changes in corneal topography and retinal blood flow density and retinal thickness, which has certain guiding significance for the control of myopia.

The diagnosis,treatment,and prognosis evaluation of cervical spondylosis are challenging in clinic.Machine learning(ML)models can improve the accuracy and efficiency of cervical spondylosis diagnosis by processing complex clinical data,assist in selecting more precise treatment plans,and evaluate prognosis.Through the domestic and foreign literature review on the application of ML models in cervical spondylosis in recent years,the study classifies and summarizes the relevant models applied in the diagnosis,treatment,and prognosis evaluation of cervical spondylosis,introduces classic algorithms such as random forest,as well as new algorithms such as convolutional neural networks,deep neural networks and long short-term memory networks,aiming to provide reference ML solutions for various stages of cervical spondylosis diagnosis and treatment.

To explore the inhibitory effect of ginkgolide B (GB) on MH7A human fibroblast-like synoviocytes (FLS) and its potential mechanism. Firstly, 20 μg/L tumor necrosis factor-α (TNF-α) was pretreated with MH7A to establish a cell model of arthritis. After incubation of MH7A cells with various concentrations of GB, CCK-8 assay, Transwell assay, and flow cytometry (FCM) were separately used to detect cell viability, cell invasion, and cell apoptosis rate and cell cycle; Real-time quantitative PCR and Western blot assay were performed to detect the apoptosis- and cycle-related gene transcriptions and protein expressions, respectively. The results showed that compared with the control group, GB dose- and time-dependently suppressed cell viability to a greater extent; GB significantly reduced cell invasive ability and increased cell apoptosis rate and proportion of G0/G1 phase in MH7A cells, along with increased transcription levels of Bcl-2-associated X protein (Bax) and p21 mRNA and decreased transcription levels of Bcl-2, myeloid cell leukemia 1(Mcl-1), protein kinase B (PKB; AKT), IP3K, Cyclin D1 and cyclin-dependent kinase 4 (CDK4) mRNA; GB remarkably increased expression levels of Bax, p21, and cleaved-Caspase 3 protein and decreased expression levels of Bcl-2, Mcl-1, p-AKT, p-PI3K, Cyclin D1, and CDK4 protein, with decreased ratios of p-PI3K/PI3K, p-AKT/AKT, and Bcl-2/Bax. In conclusion, GB blocks the G1-to-S cell cycle transition, suppresses cell viability and cell invasion and induces cell apoptosis of MH7A human RA-FLS via suppressing the PI3K/AKT signaling pathway.

Objective:To explore the risk factors for osteoporosis in postmenopausal patients with rheumatoid arthritis and establish a prediction model for early identification.Methods:A retrospective analysis was conducted on the clinical data of 128 postmenopausal patients with rheumatoid arthritis who visited Northwest Women′s and Children′s Hospital from May 2021 to December 2023. According to their bone mineral density, the patients were divided into the osteoporosis group (T-score ≤-2.5, n=16) and the non-osteoporosis group (T-score >-2.5, n=112). Clinical data of the two groups were compared. Pearson correlation analysis was used to analyze the correlation of various indicators. Multivariate logistic regression analysis was applied to screen the influencing factors for osteoporosis in postmenopausal patients with rheumatoid arthritis, and a Nomogram model for predicting the risk of osteoporosis in such patients was established. The receiver operating characteristic (ROC) curve was drawn to evaluate the predictive efficacy of the model. Results:The age, Disease Activity Score 28 based on Erythrocyte Sedimentation Rate (DAS28-ESR), β-cross-linked C-telopeptide of type I collagen (β-CTX), luteinizing hormone (LH), and follicle-stimulating hormone (FSH) in the osteoporosis group were higher than those in the non-osteoporosis group, while the bone mineral density of lumbar vertebrae t1-4, left hip, right hip, serum calcium, and estradiol (E 2) levels were lower (all P<0.05). Pearson correlation analysis showed that DAS28-ESR was negatively correlated with bone mineral density of lumbar vertebrae t1-4 ( r=-3.026, P=0.002), left hip ( r=-3.409, P<0.001), and right hip ( r=-4.125, P<0.001). Multivariate logistic regression analysis showed that age ( OR=4.697, 95% CI: 2.509-8.795), DAS28-ESR ( OR=4.889, 95% CI: 2.611-9.154), β-CTX ( OR=4.816, 95% CI: 2.572-9.018), LH ( OR=4.998, 95% CI: 2.669-9.357), and FSH ( OR=4.802, 95% CI: 2.565-8.991) were risk factors for osteoporosis in postmenopausal women with rheumatoid arthritis (all P<0.05); bone mineral density of lumbar vertebrae t1-4 ( OR=0.203, 95% CI: 0.108-0.381), left hip ( OR=0.214, 95% CI: 0.114-0.401), right hip ( OR=0.211, 95% CI: 0.113-0.396), serum calcium ( OR=0.199, 95% CI: 0.106-0.372), and E 2 ( OR=0.210, 95% CI: 0.112-0.383) were protective factors (all P<0.05). The Nomogram model for predicting osteoporosis in postmenopausal patients with rheumatoid arthritis had a sensitivity of 0.86(95% CI: 0.73-0.92), a specificity of 0.83(95% CI: 0.72-0.95), and an area under the curve of 0.918(95% CI: 0.865-0.973). Conclusions:Age, DAS28-ESR, β-CTX, LH, FSH, bone mineral density of lumbar vertebrae t1-4, left hip, right hip, serum calcium, and E 2 are related to osteoporosis in postmenopausal patients with rheumatoid arthritis. The established prediction model is helpful for early identification of the risk of osteoporosis.

The diagnosis,treatment,and prognosis evaluation of cervical spondylosis are challenging in clinic.Machine learning(ML)models can improve the accuracy and efficiency of cervical spondylosis diagnosis by processing complex clinical data,assist in selecting more precise treatment plans,and evaluate prognosis.Through the domestic and foreign literature review on the application of ML models in cervical spondylosis in recent years,the study classifies and summarizes the relevant models applied in the diagnosis,treatment,and prognosis evaluation of cervical spondylosis,introduces classic algorithms such as random forest,as well as new algorithms such as convolutional neural networks,deep neural networks and long short-term memory networks,aiming to provide reference ML solutions for various stages of cervical spondylosis diagnosis and treatment.

Objective:To explore the risk factors for osteoporosis in postmenopausal patients with rheumatoid arthritis and establish a prediction model for early identification.Methods:A retrospective analysis was conducted on the clinical data of 128 postmenopausal patients with rheumatoid arthritis who visited Northwest Women′s and Children′s Hospital from May 2021 to December 2023. According to their bone mineral density, the patients were divided into the osteoporosis group (T-score ≤-2.5, n=16) and the non-osteoporosis group (T-score >-2.5, n=112). Clinical data of the two groups were compared. Pearson correlation analysis was used to analyze the correlation of various indicators. Multivariate logistic regression analysis was applied to screen the influencing factors for osteoporosis in postmenopausal patients with rheumatoid arthritis, and a Nomogram model for predicting the risk of osteoporosis in such patients was established. The receiver operating characteristic (ROC) curve was drawn to evaluate the predictive efficacy of the model. Results:The age, Disease Activity Score 28 based on Erythrocyte Sedimentation Rate (DAS28-ESR), β-cross-linked C-telopeptide of type I collagen (β-CTX), luteinizing hormone (LH), and follicle-stimulating hormone (FSH) in the osteoporosis group were higher than those in the non-osteoporosis group, while the bone mineral density of lumbar vertebrae t1-4, left hip, right hip, serum calcium, and estradiol (E 2) levels were lower (all P<0.05). Pearson correlation analysis showed that DAS28-ESR was negatively correlated with bone mineral density of lumbar vertebrae t1-4 ( r=-3.026, P=0.002), left hip ( r=-3.409, P<0.001), and right hip ( r=-4.125, P<0.001). Multivariate logistic regression analysis showed that age ( OR=4.697, 95% CI: 2.509-8.795), DAS28-ESR ( OR=4.889, 95% CI: 2.611-9.154), β-CTX ( OR=4.816, 95% CI: 2.572-9.018), LH ( OR=4.998, 95% CI: 2.669-9.357), and FSH ( OR=4.802, 95% CI: 2.565-8.991) were risk factors for osteoporosis in postmenopausal women with rheumatoid arthritis (all P<0.05); bone mineral density of lumbar vertebrae t1-4 ( OR=0.203, 95% CI: 0.108-0.381), left hip ( OR=0.214, 95% CI: 0.114-0.401), right hip ( OR=0.211, 95% CI: 0.113-0.396), serum calcium ( OR=0.199, 95% CI: 0.106-0.372), and E 2 ( OR=0.210, 95% CI: 0.112-0.383) were protective factors (all P<0.05). The Nomogram model for predicting osteoporosis in postmenopausal patients with rheumatoid arthritis had a sensitivity of 0.86(95% CI: 0.73-0.92), a specificity of 0.83(95% CI: 0.72-0.95), and an area under the curve of 0.918(95% CI: 0.865-0.973). Conclusions:Age, DAS28-ESR, β-CTX, LH, FSH, bone mineral density of lumbar vertebrae t1-4, left hip, right hip, serum calcium, and E 2 are related to osteoporosis in postmenopausal patients with rheumatoid arthritis. The established prediction model is helpful for early identification of the risk of osteoporosis.

Objective To investigate the radioactivity levels of gross α and gross β in foods around Zhangzhou nuclear power plant, China before operation. Methods Forty-nine samples from 33 kinds of foods in 5 categories of daily food around Zhangzhou nuclear power plant were collected, pretreated, dried, and ashed. The radioactivity levels of gross α and gross β were measured by the low-background α and β measuring instrument. The atomic absorption technique was employed to measure the level of potassium (K), and the radioactivity level of gross β (subtracting ⁴⁰K) was calculated with K concentrations in different foods consulted from the nutritional dietary system. Results The radioactivity levels of gross α in vegetables and fruits, grain, poultry and livestock, aquatic products, and tea around Zhangzhou nuclear power plant were < minimum detectable level (MDL)-7.97, < MDL-6.82, < MDL, < MDL-20.76, and 11.90-23.08 Bq/kg, respectively; the radioactivity levels of gross β were 34.56-122.81, 13.05-188.96, 56.00-108.34, 17.86-169.01, and 123.74-171.63 Bq/kg, respectively; the radioactivity levels of gross β (subtracting ⁴⁰K) were not detected (ND)-14.27, ND-27.86, ND-48.72, ND-45.85, and 6.69-13.79 Bq/kg, respectively. Conclusion The radioactivity of gross α and gross β in foods around Zhangzhou nuclear power plant before operation is basically at the same level as that in other areas of China.

BACKGROUND:Intervertebral disk degeneration is a pathological change caused by a series of complex molecular mechanisms that result in the aging and damage of intervertebral discs,ultimately leading to severe clinical symptoms.Traditional Chinese medicine has unique advantages in the treatment of intervertebral disk degeneration due to its low cost,non-addictive nature,multi-target effects,minimally toxic and side effects,and high patient acceptance. OBJECTIVE:To review the latest research results of traditional Chinese medicine monomer intervention-related signaling pathways in the treatment of intervertebral disk degeneration,describe and analyze the action mechanism of traditional Chinese medicine monomer on intervertebral disk degeneration,and provide a new approach and theoretical basis for future basic research and clinical treatment. METHODS:The first author searched for relevant literature from January 2018 to February 2023 in CNKI,PubMed,VIP,and WanFang using the search terms"intervertebral disc,signal pathway".The articles that did not meet the criteria were excluded after preliminary screening of the titles and abstracts.Finally,72 articles were selected for review and analysis. RESULTS AND CONCLUSION:Traditional Chinese medicine monomers can regulate multiple classical signaling pathways such as Wnt/β-catenin,PI3K/Akt,mTOR,NF-κB,and MAPK.They achieve this by regulating oxidative stress,adjusting the expression of pro/anti-apoptotic proteins in cells,stimulating cellular autophagy function,reducing stimulation of cell inflammatory factors,increasing the expression of extracellular matrix markers,reducing the production of matrix-degrading enzymes,maintaining the synthesis and stability of extracellular matrix,inducing differentiation of mesenchymal stem cells in the nucleus pulposus into nucleus pulposus cells,promoting endogenous repair and reconstruction,controlling apoptosis and aging of nucleus pulposus cells,and increasing the activity of nucleus pulposus cells.These actions improve the microenvironment within the intervertebral disc,maintain the normal physiological function of the intervertebral disc,and delay intervertebral disc degeneration.

Objective:To explore the application value of real-time shear wave elastography(SWE)and ultrafast pulse wave velocity(UFPWV)techniques in assessing carotid artery stiffness of patients with gouty arthritis(GA).Methods:A total of 80 GA patients admitted to the First People's Hospital of Yibin from August 2022 to July 2023 were selected as GA group.Among of them,24 cases combined with bone erosion who were confirmed by imaging examination,and 56 cases did not combine with bone erosion.In addition,a total of 40 healthy individuals,who underwent physical examinations in our hospital during the same period,were selected as the healthy control group.SWE and UFPWV were used to detect the maximum,minimum and mean values of Young's modulus values of the vessel wall of common carotid artery,and the pulse wave velocity at the beginning of systole(PWVBS)and pulse wave velocity at the end of systole(PWVES)of the vessel wall of common carotid artery of all subjects,and to assess the hardness of circumferential and longitudinal vessel wall of common carotid artery of them.The differences in Young's modulus values,PWVBS and PWVES between two groups were compared.And then,the correlations between bone erosion and Young's modulus values,PWVBS,PWVES of GA patients were further analyzed.Results:The maximum,minimum,mean values,PWVBS and PWVES in the GA group were respectively(78.80±12.38)kPa,(57.83±7.42)kPa,(67.67±8.38)kPa,(7.06±0.59)m/s and(9.50±0.63)m/s,which were significantly higher than those in the healthy control group(t=11.066,8.216,11.751,14.159,19.118,P＜0.05).The maximum,minimum,mean values,PWVBS and PWVES of GA patients with bone erosion were respectively(82.95±6.37)kPa,(59.59±5.29)kPa,(72.18±7.62)kPa,(7.37±0.43)m/s,(10.43±0.47)m/s,which were significantly higher than those[(77.01±5.32)kPa,(57.08±4.73)kPa,(65.73±4.54)kPa,(6.92±0.39)m/s and(9.10±0.41)m/s]of GA patients without bone erosion,the differences were significant(t=4.309,2.099,4.699,4.586,12.720,P＜0.05).Spearman correlation analysis showed that there was significant correlations between maximum,minimum,mean values,PWVBS,PWVES and the combined bone erosion in GA patients(r=0.602,0.421,0.682,0.647,0.732,P＜0.05).Conclusion:SWE and UFPEV techniques can evaluate the hardness of vascular wall of GA patients,and the hardness of circumferential and longitudinal vessel wall of common carotid artery of GA patient is higher than that of healthy person,and the hardness of circumferential and longitudinal vessel wall of common carotid artery of GA patient with bone erosion is higher.

BACKGROUND: Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts. METHODS: We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models. RESULTS: The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749). CONCLUSIONS: The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population. TRIAL REGISTRATION Chictr.org, ChiCTR 1800018895.
Humans ; Aged ; Sarcopenia/diagnosis* ; Deep Learning ; Aging ; Algorithms ; Biomarkers