OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

A new method was developed for simultaneous and efficient determination of linear perfluorooctanoic acid(n-PFOA)and linear perfluorooctane sulfonate(n-PFOS),and their typical branched isomers in seawater by online solid phase extraction-liquid chromatography-tandem mass spectrometry(Online SPE-LC-MS/MS).Only centrifugation of the seawater sample was required to remove the particulate matter,and then the seawater sample was directly injected and analyzed by online SPE-LC-MS/MS.An Eclipse Plus-C18 guard column was selected as SPE column for online enrichment of linear and branched isomers,and a F5 PFP column(150 mm×2.1 mm,2.7 μm)was used as the analytical column.Under the optimized experimental conditions,the separation and detection of all PFOA and PFOS linear and branched isomers could be completed within 20 min.The spiked recoveries of various target compounds ranged from 82.9%to 107.7%with detection limits and limits of quantification of 0.10-1.05 ng/L and 0.30-2.11 ng/L,respectively.The method was characterized by good precision(RSD≤9.10%)and linearity(R2≥0.990).Subsequently,linear and branched isomers of PFOA and PFOS in surface and bottom seawater samples collected from the Laizhou Bay of China were determined.The results showed that the detection rate of all the four branched PFOA isomers were 100%,with the highest average concentration of 25.85 ng/L found for 6m-PFOA,which accounted for 11.79%of the∑PFOA.For the five branched isomers of PFOS,the highest detection rate of 90.84%was found for 5m-PFOS.The highest average concentration of 0.64 ng/L was observed for 3m-PFOS,accounting for 19.88%of ∑PFOS.The proposed method provided an effective detection tool for qualitative and quantitative detection of PFOA and PFOS isomers in the marine aquatic environment.

Objective To explore the application value of physical examination information annotation combined with artificial intelli-gence (AI) in CT diagnosis of rib fractures. Methods The clinical data of 100 patients with chest trauma who underwent rib CT examina-tion with physical examination information annotation were collected. The images were analyzed by two physicians in the department of radiology with different seniorities using four methods[diagnosed by physicians independently (group A),diagnosed by physicians combined with physical examination information annotation (group B),diagnosed by physicians under the assistance of AI (group C),and diagnosed by physicians combined with physical examination information annotation under the assistance of AI (group D)]. The diagnostic efficacy and diagnostic time of two radiologists using different methods for rib fractures were compared. Results The sensitivities of two radiologists with different seniorities in the diagnosis of rib fracture in the group A were lower than those in the groups B,C and D (P<0.05),but there was no significant difference in the sensitivity of rib fracture among groups B,C and D (P>0.05). The diagnostic sensitivity of resident physician in the group A was lower than that of the attending physicians (P<0.05),and there was no significant difference in the diagnostic sensitivity of rib fracture in the other groups between the two physicians (P>0.05). There was no statistically significant difference in the false-positive rate of rib fractures among groups between two physicians (P>0.05). There was statistically significant difference in the diagnostic time among groups between two physicians (P<0.05),among which group A took the longest diagnosis time and group C took the shortest. Conclusion The assistance of AI and conbinatin of physical examination information annotation can increase the sensitivity of the physician in the diagnosis of rib fractures,shorten the diagnostic time and improve the work efficiency.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective To explore the application value of physical examination information annotation combined with artificial intelli-gence (AI) in CT diagnosis of rib fractures. Methods The clinical data of 100 patients with chest trauma who underwent rib CT examina-tion with physical examination information annotation were collected. The images were analyzed by two physicians in the department of radiology with different seniorities using four methods[diagnosed by physicians independently (group A),diagnosed by physicians combined with physical examination information annotation (group B),diagnosed by physicians under the assistance of AI (group C),and diagnosed by physicians combined with physical examination information annotation under the assistance of AI (group D)]. The diagnostic efficacy and diagnostic time of two radiologists using different methods for rib fractures were compared. Results The sensitivities of two radiologists with different seniorities in the diagnosis of rib fracture in the group A were lower than those in the groups B,C and D (P<0.05),but there was no significant difference in the sensitivity of rib fracture among groups B,C and D (P>0.05). The diagnostic sensitivity of resident physician in the group A was lower than that of the attending physicians (P<0.05),and there was no significant difference in the diagnostic sensitivity of rib fracture in the other groups between the two physicians (P>0.05). There was no statistically significant difference in the false-positive rate of rib fractures among groups between two physicians (P>0.05). There was statistically significant difference in the diagnostic time among groups between two physicians (P<0.05),among which group A took the longest diagnosis time and group C took the shortest. Conclusion The assistance of AI and conbinatin of physical examination information annotation can increase the sensitivity of the physician in the diagnosis of rib fractures,shorten the diagnostic time and improve the work efficiency.

Objective To investigate the diagnostic efficiency of artificial intelligence(AI)in rib frac-ture under the computed tomography(CT)images with different reconstruction slice thickness.Methods The first CT images of 100 patients with rib fractures were selected,and the interval-free recon-struction was carried out with the thickness of 0.625 mm,1.250 mm,2.500 mm and 5.000 mm,respectively.The rib fracture screening function of AI was used to automatically detect the CT images of four groups,and the diagnostic efficiency of AI for rib fracture under different reconstruction thickness conditions was com-pared.Results The sensitivity of AI in the diagnosis of rib fracture at 0.625 mm,1.250 mm,2.500 mm and 5.000 mm thickness was 99.32%(436/439),98.41%(432/439),89.52%(393/439)and 83.60%(367/439),respectively.The false positive rate was 4.80%(22/458),0.92%(4/436),0.76%(3/396)and 0.27%(1/368).The diagnostic sensitivity of AI in 0.625 mm and 1.250 mm thickness was higher than that in 2.500 mm and 5.000 mm,and the difference was statistically significant(P<0.05),while there was no significant difference in the thickness of 0.625 mm and 1.250 mm.The false positive rate of AI in the diagnosis of 0.625 mm slice thickness was higher than that of 1.250 mm,2.500 mm and 5.000 mm,and the difference was sta-tistically significant(P<0.05),while there was no significant difference in the thickness of 1.250 mm,2.500 mm and 5.000 mm(P>0.05).Conclusion The diagnostic efficiency of AI in 1.250 mm CT images is better than that in 0.625 mm,2.500 mm and 5.000 mm CT images.

A new method for simultaneous determination of 23 kinds of per-and polyfluoroalkyl substances(PFASs)(13 kinds of perfluoro carboxylic acids,4 kinds of perfluoro sulfonic acids,and 6 kinds of new substitutes)in plant leaf tissue by ultra-high performance liquid chromatography-tandem mass spectrometry(UHPLC-MS/MS)using automatic online solid phase extraction(SPE)to remove the matrix interference components in plant crude extracts was developed.The plant leaf samples were extracted twice with 1%formic acid-methanol solution,then evaporated to dry,redissolved with 70%methanol solution,and directly injected for analysis.After 23 kinds of target PFASs were purified automatically by online SPE with a WAX column,the six-way valve was switched to rinse PFASs onto an alkaline mobile phase system-compatible C18 analytical column.Then,the 23 kinds of target PFASs were separated within 16 min by gradient elution using a binary mobile phase system of methanol/water(Containing 0.4%ammonium hydroxide).Tandem mass spectrometry was performed in multiple reaction monitoring(MRM)mode for online detection of various PFASs,and quantification was carried out by internal standard method.The results of the method validation showed that satisfactory average recoveries of 23 kinds of PFASs in plant leaf samples(64.2%-125.5%),precision(relative standard deviations(RSDs)of 0.7%-12.8%),linearity(R2＞0.990),and sensitivity(the detection limits(S/N=3)were in the range of 0.02-0.50 μg/kg)were achieved.Finally,this method was used to detect PFASs in the marine green tide algae(Enteromorpha prolifera)and several tree leaves,and a total of 6 kinds of PFASs were detected,in which PFBA was the main contaminant.Compared with the reported offline SPE methods,the proposed online SPE technique significantly simplified the sample pretreatment process and provided an automatic,simple,and environment-friendly method for the routine monitoring of legacy and emerging PFASs in plant tissues.

Objective: The effect on fat infiltration (FI) of paraspinal muscles in degenerative lumbar spinal diseases has been demonstrated except for spinopelvic parameters. The present study is to identify the effect of spinopelvic parameters on FI of paraspinal muscle (PSM) and psoas major muscle (PMM) in patients with degenerative lumbar spondylolisthesis. Methods: A single-center, retrospective cross-sectional study of 160 patients with degenerative lumbar spondylolisthesis (DLS) and lumbar stenosis (LSS) who had lateral full-spine x-ray and lumbar spine magnetic resonance imaging was conducted. PSM and PMM FIs were defined as the ratio of fat to its muscle cross-sectional area. The FIs were compared among patients with different pelvic tilt (PT) and pelvic incidence (PI), respectively. Results: The PSM FI correlated significantly with pelvic parameters in DLS patients, but not in LSS patients. The PSM FI in pelvic retroversion (PT > 25°) was 0.54 ± 0.13, which was significantly higher in DLS patients than in normal pelvis (0.41 ± 0.14) and pelvic anteversion (PT < 5°) (0.34 ± 0.12). The PSM FI of DLS patients with large PI ( > 60°) was 0.50 ± 0.13, which was higher than those with small ( < 45°) and normal PI (0.37 ± 0.11 and 0.36 ± 0.13). However, the PSM FI of LSS patients didn’t change significantly with PT or PI. Moreover, the PMM FI was about 0.10–0.15, which was significantly lower than the PSM FI, and changed with PT and PI in a similar way of PSM FI with much less in magnitude. Conclusion FI of the PSMs increased with greater pelvic retroversion or larger pelvic incidence in DLS patients, but not in LSS patients.

Objective: The effect on fat infiltration (FI) of paraspinal muscles in degenerative lumbar spinal diseases has been demonstrated except for spinopelvic parameters. The present study is to identify the effect of spinopelvic parameters on FI of paraspinal muscle (PSM) and psoas major muscle (PMM) in patients with degenerative lumbar spondylolisthesis. Methods: A single-center, retrospective cross-sectional study of 160 patients with degenerative lumbar spondylolisthesis (DLS) and lumbar stenosis (LSS) who had lateral full-spine x-ray and lumbar spine magnetic resonance imaging was conducted. PSM and PMM FIs were defined as the ratio of fat to its muscle cross-sectional area. The FIs were compared among patients with different pelvic tilt (PT) and pelvic incidence (PI), respectively. Results: The PSM FI correlated significantly with pelvic parameters in DLS patients, but not in LSS patients. The PSM FI in pelvic retroversion (PT > 25°) was 0.54 ± 0.13, which was significantly higher in DLS patients than in normal pelvis (0.41 ± 0.14) and pelvic anteversion (PT < 5°) (0.34 ± 0.12). The PSM FI of DLS patients with large PI ( > 60°) was 0.50 ± 0.13, which was higher than those with small ( < 45°) and normal PI (0.37 ± 0.11 and 0.36 ± 0.13). However, the PSM FI of LSS patients didn’t change significantly with PT or PI. Moreover, the PMM FI was about 0.10–0.15, which was significantly lower than the PSM FI, and changed with PT and PI in a similar way of PSM FI with much less in magnitude. Conclusion FI of the PSMs increased with greater pelvic retroversion or larger pelvic incidence in DLS patients, but not in LSS patients.