ObjectiveTo construct and validate a clinical prediction model for inflammatory remission outcomes in rheumatoid arthritis （RA） patients with liver and kidney deficiency syndrome treated with Bushen Zhiwang Decoction （补肾治尪汤， BZD） based on metabolomics. MethodsA prospective cohort study was conducted， enrol-ling 60 RA patients with liver and kidney deficiency syndrome. All patients were treated with BZD and conventional-dose oral conventional synthetic disease-modifying antirheumatic drugs （csDMARDs） for 12 months. Clinical data were collected， and the change in disease activity score in 28 joints （DAS28） after treatment compared with baseline （△DAS28） was used as the primary outcome and grouping criterion. Peripheral blood samples were collected before treatment to analyze plasma metabolites. Differential analysis and least absolute shrinkage and selection operator （LASSO） regression were used to preliminarily screen differential metabolites， followed by machine learning algorithms to further identify a core metabolite combination. Based on the expression levels of the core metabolite combination， a novel metabolite index， namely the metabolomics-based inflammatory remission score （Met-IRS）， was calculated using standar-dized metabolite values， and its clinical applicability was evaluated. A clinical prediction model was constructed by integrating clinical characteristics and Met-IRS， and the model performance was assessed. ResultsAmong the 60 patients， those with △DAS28 ≥ 0.27 were assigned to the high inflammatory remission group， while those with △DAS28 ＜ 0.27 were assigned to the low inflammatory remission group， with 30 cases in each group. Compared to the low inflammatory remission group， the high inflammatory remission group showed a higher frequency of methotrexate use and a lower positive rate of rheumatoid factor （RF） （P＜0.05）. Seven core metabolites were identified as the optimal combination， including mangiferic acid， fatty acid-hydroxy fatty acid ester 40∶6， fatty acid-hydroxy fatty acid ester 18∶0， fatty acid-hydroxy fatty acid ester 36∶1， glucosylceramide， lysophosphatidylcholine 22∶5， and pregnanetriol ketone. The calculated Met-IRS comprehensively reflected the characteristics of differential metabolites and demonstrated clinical applicability. Met-IRS was significantly higher in the high inflammatory remission group than in the low inflammatory remission group， and was positively correlated with high inflammatory remission outcomes （P＜0.05）. Based on the variables Met-IRS， methotrexate use， leflunomide use， and RF positivity， a clinical prediction model for inflammatory remission in RA treatment （Cj-RTRM） was constructed. Model performance evaluation demonstrated that the model had good clinical predictive ability， with an area under the receiver operating characteristic curve （AUC） of 0.880， sensitivity 0.967， specificity 0.700 and Youden's index 0.667. ConclusionThe clinical prediction model Cj-RTRM constructed based on the metabolomics-based inflammatory remission score Met-IRS can effectively predict clinical inflammatory remission outcomes in RA patients treated with BZD and accurately identify the advantageous population for this treatment. This model provides guiding evidence for dynamic inflammation monitoring， targeted management， and identification of populations with advantages in traditional Chinese medicine.

Reninoma is a rare renal secretory tumor，prevalent in the young population. This disease is mostly surgically resected，and the use of microwave ablation to treat reninoma in children is scarce. A case of reninoma in a child was reported in this paper. The patient presented with refractory hypertension，hypokalemia，hyperreninemia and hyperaldosteronemia. Enhanced CT and contrast-enhanced ultrasound showed mass in the lower pole of the right kidney，which was considered as reninoma and microwave ablation was performed. The renin concentration decreased to 68.42 pg/ml at 4 hours after surgery. After 1 year of postoperative follow-up，there was no recurrence of hypertension and hypokalemia，and no signs of tumor recurrence were seen on repeated ultrasound examinations.

Objective:To explore the construction and validation of a nomogram model for predicting poor survival prognosis in patients with clear cell renal cell carcinoma（ccRCC）based on deep learning of pathological images.Methods:This study was an observational cohort study. The original pathological images and clinicopathological data（TCGA cohort）of 378 patients with ccRCC were obtained from the Cancer Genome Atlas Database（TCGA）for model training. A total of 301 patients with ccRCC who underwent surgical treatment at Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2010 to December 2020（Renji cohort）and 214 patients with ccRCC who underwent surgical treatment at the First People’s Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2012 to December 2018（General cohort）were included for model validation. Their original pathological images and clinical pathological data were collected. A clustering-constrained attention and multi-instance learning method was used to accurately identify sub-regions of the images to classify and extract features of the pathological images. A deep learning-based disease-free survival prognosis prediction model（DL-DFS）was constructed through a weakly supervised learning strategy. The clinical pathological features and DL-DFS were further combined to construct a nomogram model for the clinical prognosis of ccRCC patients. Univariate and multivariate Cox regression analyses were employed to evaluate the independent risk factors for disease-free survival（DFS）. The efficacy of the predictive model were evaluated by the receiver operating characteristic curve（ROC）with area under the curve（AUC），respectively. Survival analysis was conducted using the Kaplan-Meier curve.Results:DL-DFS could accurately predict the DFS status of ccRCC patients in 5 years after surgery. Through ROC analysis in the training cohort，the AUC value reached 0.75（ P < 0.001）. In the Renji cohort and the General cohort，the AUC values were 0.65（ P < 0.001）and 0.81（ P < 0.001），respectively. Through Kaplan-Meier survival analysis，we found that DL-DFS could identify ccRCC patients with high survival risks. The hazard ratio in the training cohort was 3.86（95% CI 2.36-6.30， P < 0.001）. The hazard ratio in the Renji cohort and General cohort were 1.97（95% CI 1.03-3.80， P = 0.009）and 4.66（95% CI 1.80-12.06， P = 0.008），respectively. Univariate and multivariate Cox regression analyses indicated that DL-DFS risk score，tumor grade，and tumor stage could act as prognostic risk factors for patients with ccRCC（ P < 0.05）. Considering that age was a common prognostic risk factor for patients with renal cancer，a nomogram model was constructed by combining the DL-DFS risk score with patient age，tumor grade，and tumor stage. The AUC of this model for predicting the 5-year DFS of ccRCC patients after surgery was 0.87，which was significantly higher than that of DL-DFS（AUC = 0.74），tumor stage（AUC = 0.84），tumor grade（AUC = 0.72），and patient age（AUC = 0.56）in the TCGA cohort（all P<0.05）. In the Renji cohort and the General cohort，the AUC of the nomogram model were 0.78 and 0.86 respectively，which was significantly higher than that of DL-DFS（0.65 and 0.81），tumor stage（0.72 and 0.69），tumor grade（0.64 and 0.77），and patient age（0.56 and 0.63）. Conclusions:In this study a DL-DFS for ccRCC patients was constructed. Then a nomogram model was constructed by combining the DL-DFS risk value with patient age，tumor grade，and tumor stage. This nomogram model demonstrated superior predictive performance compared to DL-DFS alone in evaluating the DFS prognosis of ccRCC patients，which still needs to be further verified in prospective clinical studies.

OBJECTIVE: Rhodiolae Crenulatae Radix et Rhizoma (Hongjingtian in Chinese, RCRR), the roots and rhizomes of Rhodiola crenulata and its application in the medicinal market is very chaotic. In this study, DNA barcoding database and identification engine of Rhodiola species were established, decoction pieces from the medicinal market were identified, and the application and challenges of DNA barcoding in the rapid radiation of Rhodiola species were analyzed. This study provides reference for the protection, rational development, and utilization of endangered resources within Rhodiola species. METHODS: A total of 50 original plant samples from 20 species of the genus Rhodiola from Hebei, Xinjiang, Tibet, Jilin, and other major production areas were collected. Theses samples cover the typical distribution area (Qinghai-Tibetan Platea) of Rhodiola species and other scattered alpine regions (Changbai Mountain, Taibai Mountain, Lushan Mountain, etc.), it encompasses all Rhodiola species with thick rhizomes in China. ITS2 and psbA-trnH barcode of Rhodiola database (BORD) were established and an identification engine named Rhodiola-IDE was developed. The stability and accuracy of the standard DNA barcoding database were evaluated using two datasets. Rhodiola-IDE identified 31 decoction pieces of RCRR from the medicinal material market. RESULTS: The BORD containing 1 532 sequences of 88 Rhodiola species has been established, and the identification efficiency results showed good accuracy and stability. According to the Chinese Pharmacopoeia (2020 edition), 23 samples (74.2%) were identified as authentic R. crenulata, while the rest of the marketed varieties were R. kirilowii, R. dumulosa, and R. fastigiata. The product label "Larger flower, Hongjingtian" was identified as R. crenulata. Samples labeled as "Smaller flower, Hongjingtian" were identified as R. crenulata, R. kirilowii, and R. fastigiata. CONCLUSION ITS2 and psbA-trnH barcodes can identify monophyletic groups represented by R. crenulata. However, for non-monophyletic species, it is necessary to collect as many samples as possible and combine them with multiple markers for joint identification. This study discussed the application and challenges of DNA barcodes in Rhodiola under rapid radiation conditions, providing a scientific basis for the rational development and utilization of Rhodiola varieties.

OBJECTIVE: An intelligent moxibustion instrument based on electromyography was designed to evaluate the real-time therapeutic effect of moxibustion. METHODS: Taking Shenshu (BL23) as the subject, surface electromyography (sEMG) at the center and equidistant points of Shenshu (BL23) were collected. The characteristic parameters, integrated electromyography (iEMG) and root mean square (RMS) were calculated before and after moxibustion. After analyzing the effect of moxibustion, a function algorithm for the end-of-moxibustion was obtained. Using this algorithm and combined with STM32 technology, the control system of moxibustion instrument and the upper computer software were designed to achieve the precise control during moxibustion delivery. Finally, the function, stability and safety of the moxibustion instrument were verified through clinical trials to ensure its effectiveness in practical application. RESULTS: During one cycle of moxibustion at the center of Shenshu (BL23), the iEMG of sEMG decreased over time, meaning the decrease in muscle fatigue degree, and after one cycle of moxibustion, it elevated over time, showing the increase in muscle fatigue degree. RMS increased by 1.90% before and after moxibustion at the equidistant points of Shenshu (BL23), and the system indicated the end of moxibustion when RMS increased by 0.15%, and decreased by 0.13% at the center of Shenshu (BL23). The intelligent moxibustion instrument designed based on this algorithm can realize the function of mild moxibustion, and the effect of moxibustion can be evaluated by the real-time monitoring of RMS changes through the upper computer. During the operation of moxibustion instrument, moxa stick was fixed stably, remained a safe distance of 3 cm to 4 cm away from the skin surface. When the length of moxa stick was less than 5 cm left after ignited and the skin temperature exceeded the preset safety threshold of 48 ℃, the system was alarmed automatically. CONCLUSION The intelligent moxibustion instrument designed in the research can effectively evaluate the effect of moxibustion, and ensure the safety and stability during moxibustion delivery.
Humans ; Moxibustion/methods* ; Electromyography/instrumentation* ; Adult ; Male ; Female ; Young Adult ; Acupuncture Points ; Algorithms ; Middle Aged

OBJECTIVE: To evaluate the effectiveness of correcting post-traumatic equinovarus deformity using Ilizarov external fixation technique combined with limited osteotomy. METHODS: A retrospective analysis was conducted on clinical data from 29 patients with post-traumatic equinovarus deformity treated between July 2018 and March 2023. The cohort included 18 males and 11 females, with ages ranging from 15 to 57 years (mean, 24.3 years). All patients exhibited ankylosed ankle joints with equinovarus deformity. During surgery, external fixators were installed according to Ilizarov pinning principles, and minimally invasive osteotomy was performed at the ankle joint. Concurrently, soft tissue release was achieved via minimally invasive Achilles tendon lengthening. Postoperatively, multiplanar deformity correction was accomplished through gradual adjustment of the external fixator. The fixator was removed after bony union at the osteotomy site, followed by bracing. The surgical duration, intraoperative blood loss, fixator wear time, and complications were recorded. Postoperative outcomes included assessment of deformity correction and bony union at the osteotomy site. Functional improvement and pain relief were evaluated using pre- and post-operative scores from the American Orthopaedic Foot & Ankle Society (AOFAS) ankle-hindfoot score and visual analogue scale (VAS) score. RESULTS: All 29 patients were followed up 12-24 months (mean, 18 months). The mean surgical duration was 85.6 minutes, with a mean intraoperative blood loss of 110 mL. Full deformity correction was achieved within 26-80 days (mean, 40.7 days) through progressive fixator adjustments. At correction completion, all ankles restored to a neutral or 5°-10° dorsiflexed position with plantigrade foot function. Superficial pin tract infections occurred in 3 patients (10.3%), resolved with local wound care, enhanced nursing, and oral antibiotics. No deep or systemic infections was observed. One patient sustained a calcaneal half-pin fracture due to a fall during fixator wear, but no bone fragment displacement occurred. No vascular or neurological complication was reported. Complete bony union was achieved at all osteotomy sites without nonunion. At last follow-up, the AOFAS ankle-hindfoot score improved from preoperative 42.7±8.7 to postoperative 65.7±9.3, and the VAS score decreased from preoperative 4.5±1.3 to postoperative 2.5±1.1, with significant differences ( P<0.05). Functional outcomes were rated as excellent in 14 cases, good in 13 cases, fair in 1 case, and poor in 1 case, with an excellent and good rate of 93.1%. CONCLUSION The progressive correction strategy combining Ilizarov external fixation technique with limited foot osteotomy effectively corrects post-traumatic equinovarus deformity while preserving soft tissue integrity. This method is associated with minimal, largely controllable complications and achieves alignment stability and fusion outcomes comparable to traditional open surgery, making it an effective treatment for complex foot and ankle deformities.
Humans ; Male ; Female ; Osteotomy/methods* ; Adult ; Retrospective Studies ; Ilizarov Technique ; Middle Aged ; Adolescent ; External Fixators ; Young Adult ; Treatment Outcome ; Ankle Joint/surgery* ; Clubfoot/etiology* ; Minimally Invasive Surgical Procedures/methods*

Objective To conduct preoperative simulations of three different osteotomy line design schemes under centric occlusion based on two distinct material assignment methods,evaluate biomechanical properties of the models,and explore which osteotomy line design schemes are more suitable for different types of mandibles.Methods Three types of mandibles were selected,and CT images were obtained for three-dimensional(3D)reconstruction.Material assignment was completed using the cortical/cancellous bone assignment method and the gray value assignment method.Osteotomy was simulated according to the three osteotomy line design schemes,followed by finite element analysis.Results In all simulation results of the mandibles,the maximum stress was 81.10 MPa,the maximum strain was 0.035 52,and the maximum displacement was 432.4 μm.The stress distributions obtained by the cortical/cancellous bone assignment method showed a larger stress distribution range than that that by the gray value assignment methods,but the maximum stress,strain,and displacement were generally lower.For the outflare type and common type mandibles,Scheme 1 showed lower maximum stress,strain,and displacement under both material assignment methods,but no clearly suitable scheme was found for the retracted type.Conclusions The outflare type and common type mandibles are more suitable for adopting the osteotomy line design scheme of Scheme 1.For the retracted type,other mandibular angle osteotomy plastic surgery methods may be considered to ensure better biomechanical characteristics.Whether choosing the osteotomy line design scheme or the modeling material assignment method,it is necessary to make the final decision based on the specific analysis objective and resource conditions.

Reninoma is a rare renal secretory tumor，prevalent in the young population. This disease is mostly surgically resected，and the use of microwave ablation to treat reninoma in children is scarce. A case of reninoma in a child was reported in this paper. The patient presented with refractory hypertension，hypokalemia，hyperreninemia and hyperaldosteronemia. Enhanced CT and contrast-enhanced ultrasound showed mass in the lower pole of the right kidney，which was considered as reninoma and microwave ablation was performed. The renin concentration decreased to 68.42 pg/ml at 4 hours after surgery. After 1 year of postoperative follow-up，there was no recurrence of hypertension and hypokalemia，and no signs of tumor recurrence were seen on repeated ultrasound examinations.

Objective:To explore the construction and validation of a nomogram model for predicting poor survival prognosis in patients with clear cell renal cell carcinoma（ccRCC）based on deep learning of pathological images.Methods:This study was an observational cohort study. The original pathological images and clinicopathological data（TCGA cohort）of 378 patients with ccRCC were obtained from the Cancer Genome Atlas Database（TCGA）for model training. A total of 301 patients with ccRCC who underwent surgical treatment at Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2010 to December 2020（Renji cohort）and 214 patients with ccRCC who underwent surgical treatment at the First People’s Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2012 to December 2018（General cohort）were included for model validation. Their original pathological images and clinical pathological data were collected. A clustering-constrained attention and multi-instance learning method was used to accurately identify sub-regions of the images to classify and extract features of the pathological images. A deep learning-based disease-free survival prognosis prediction model（DL-DFS）was constructed through a weakly supervised learning strategy. The clinical pathological features and DL-DFS were further combined to construct a nomogram model for the clinical prognosis of ccRCC patients. Univariate and multivariate Cox regression analyses were employed to evaluate the independent risk factors for disease-free survival（DFS）. The efficacy of the predictive model were evaluated by the receiver operating characteristic curve（ROC）with area under the curve（AUC），respectively. Survival analysis was conducted using the Kaplan-Meier curve.Results:DL-DFS could accurately predict the DFS status of ccRCC patients in 5 years after surgery. Through ROC analysis in the training cohort，the AUC value reached 0.75（ P < 0.001）. In the Renji cohort and the General cohort，the AUC values were 0.65（ P < 0.001）and 0.81（ P < 0.001），respectively. Through Kaplan-Meier survival analysis，we found that DL-DFS could identify ccRCC patients with high survival risks. The hazard ratio in the training cohort was 3.86（95% CI 2.36-6.30， P < 0.001）. The hazard ratio in the Renji cohort and General cohort were 1.97（95% CI 1.03-3.80， P = 0.009）and 4.66（95% CI 1.80-12.06， P = 0.008），respectively. Univariate and multivariate Cox regression analyses indicated that DL-DFS risk score，tumor grade，and tumor stage could act as prognostic risk factors for patients with ccRCC（ P < 0.05）. Considering that age was a common prognostic risk factor for patients with renal cancer，a nomogram model was constructed by combining the DL-DFS risk score with patient age，tumor grade，and tumor stage. The AUC of this model for predicting the 5-year DFS of ccRCC patients after surgery was 0.87，which was significantly higher than that of DL-DFS（AUC = 0.74），tumor stage（AUC = 0.84），tumor grade（AUC = 0.72），and patient age（AUC = 0.56）in the TCGA cohort（all P<0.05）. In the Renji cohort and the General cohort，the AUC of the nomogram model were 0.78 and 0.86 respectively，which was significantly higher than that of DL-DFS（0.65 and 0.81），tumor stage（0.72 and 0.69），tumor grade（0.64 and 0.77），and patient age（0.56 and 0.63）. Conclusions:In this study a DL-DFS for ccRCC patients was constructed. Then a nomogram model was constructed by combining the DL-DFS risk value with patient age，tumor grade，and tumor stage. This nomogram model demonstrated superior predictive performance compared to DL-DFS alone in evaluating the DFS prognosis of ccRCC patients，which still needs to be further verified in prospective clinical studies.

Objective To conduct preoperative simulations of three different osteotomy line design schemes under centric occlusion based on two distinct material assignment methods,evaluate biomechanical properties of the models,and explore which osteotomy line design schemes are more suitable for different types of mandibles.Methods Three types of mandibles were selected,and CT images were obtained for three-dimensional(3D)reconstruction.Material assignment was completed using the cortical/cancellous bone assignment method and the gray value assignment method.Osteotomy was simulated according to the three osteotomy line design schemes,followed by finite element analysis.Results In all simulation results of the mandibles,the maximum stress was 81.10 MPa,the maximum strain was 0.035 52,and the maximum displacement was 432.4 μm.The stress distributions obtained by the cortical/cancellous bone assignment method showed a larger stress distribution range than that that by the gray value assignment methods,but the maximum stress,strain,and displacement were generally lower.For the outflare type and common type mandibles,Scheme 1 showed lower maximum stress,strain,and displacement under both material assignment methods,but no clearly suitable scheme was found for the retracted type.Conclusions The outflare type and common type mandibles are more suitable for adopting the osteotomy line design scheme of Scheme 1.For the retracted type,other mandibular angle osteotomy plastic surgery methods may be considered to ensure better biomechanical characteristics.Whether choosing the osteotomy line design scheme or the modeling material assignment method,it is necessary to make the final decision based on the specific analysis objective and resource conditions.