Background: Scoliosis is a spinal deformity in which one or more spinal segments bend to the side or show vertebral rotation. Some artificial intelligence (AI) apps have already been developed for measuring the Cobb angle in patients with scoliosis. These apps still require doctors to perform certain measurements, which can lead to interobserver variability. The AI app (cobbAngle pro) in this study will eliminate the need for doctor measurements, achieving complete automation. Objective: We aimed to evaluate the reliability and accuracy of our new AI app that is based on deep learning to automatically measure the Cobb angle in patients with scoliosis. Methods: A retrospective analysis was conducted on the clinical data of children with scoliosis who were treated at the Pediatric Orthopedics Department of the Children’s Hospital affiliated with Fudan University from July 2019 to July 2022. Three measurers used the Picture Archiving and Communication System (PACS) to measure the coronal main curve Cobb angle in 802 full-length anteroposterior and lateral spine X-rays of 601 children with scoliosis, and recorded the results of each measurement. After an interval of 2 weeks, the mobile AI app was used to remeasure the Cobb angle once. The Cobb angle measurements from the PACS were used as the reference standard, and the accuracy of the Cobb angle measurements by the app was analyzed through the Bland-Altman test. The intraclass correlation coefficient (ICC) was used to compare the repeatability within measurers and the consistency between measurers. Results: Among 601 children with scoliosis, 89 were male and 512 were female (age range: 10-17 years), and 802 full-length spinal X-rays were analyzed. Two functionalities of the app (photography and photo upload) were compared with the PACS for measuring the Cobb angle. The consistency was found to be excellent. The average absolute errors of the Cobb angle measured by the photography and upload methods were 2.00 and 2.08, respectively. Using a clinical allowance maximum error of 5°, the 95% limits of agreement (LoAs) for Cobb angle measurements by the photography and upload methods were –4.7° to 4.9° and –4.9° to 4.9°, respectively. For the photography and upload methods, the 95% LoAs for measuring Cobb angles were –4.3° to 4.6° and –4.4° to 4.7°, respectively, in mild scoliosis patients; –4.9° to 5.2° and –5.1° to 5.1°, respectively, in moderate scoliosis patients; and –5.2° to 5.0° and –6.0° to 4.8°, respectively, in severe scoliosis patients. The Cobb angle measured by the 3 observers twice before and after using the photography method had good repeatability (P<.001). The consistency between the observers was excellent (P<.001). Conclusions: The new AI platform is accurate and repeatable in the automatic measurement of the Cobb angle of the main curvature in patients with scoliosis.

Cancer‐associated fibroblasts (CAFs) play a pivotal role in the metabolic symbiosis that drives tumor proliferation and immune evasion. Paradoxically, eliminating CAFs can disrupt tissue homeostasis and potentially accelerate tumor spread. To address this challenge, this study proposes a novel strategy to reprogram CAFs, transforming them into “anchors” and “fuel stations” for anti‐tumor immune cells, thereby enhancing the immune response against tumors. Utilizing a fibroblast‐specific lipid nanoparticle (LNP) delivery system, key metabolic genes in CAFs are targeted and downregulated, specifically hexokinase 2 (HK2) and mitochondrial cytochrome c oxidase I (MTCO1). The dual inhibition of glycolysis and mitochondrial respiration in CAFs consequently results in glucose overload and mitochondrial dysfunction. As a result, these energy‐deprived CAFs exhibit high expression of MHC II molecules and inflammatory cytokines, promoting immune cell infiltration and providing essential fuel for subsequent activation and proliferation. Furthermore, this metabolic reprogramming results in reduced angiogenesis and an immune microenvironment characterized by M1 macrophage polarization and enhanced lymphocyte infiltration. Consequently, this approach improves the efficacy of immune checkpoint inhibitors (ICIs) in castration‐resistant prostate cancer (CRPC). Thus, the reprogramming of CAFs into immune cell allies offers a promising strategy to overcome the limitations of current ICIs therapies.

Background: The precise location of unlar osteotomy remains a subject of ongoing controversy in chronic Monteggia fracture (CMF). The purpose of this study was to analyze the influence of different levels of ulnar osteotomy on redislocation in CMF. Methods: We retrospectively reviewed 18 children following our previous series. Except the baseline characteristics and radiographic parameters such as ulnar osteotomy angle, maximum interosseous distance (MID) and proportional ulnar length (PUL), we introduced a new parameter: proportional ulnar osteotomy (PUO) which represents the quantitative level of proximal ulnar osteotomy. Based on the value of PUO, we divided it into two intervals: appropriate PUO range (1/5< PUO 1/3; PUO <1/5). The relationship between these indexes and redislocation was analyzed. Results: According to the reduction state of radial head, patients were divided in two groups: reduced (15/18) and redislocation (3/18). Only PUO range differed significantly (P=0.043) between the two groups, with a notably higher number of patients showed an osteotomy between 1/5 and 1/3 of ulna in reduced group. Combining PUO range with radiographic parameters (osteotomy angle and post-PUL) improved the accuracy and specificity over using osteotomy angle and post-PUL (accuracy, 94.44% vs. 83.33%) (specificity, 93.33% vs. 86.67%, P=0.008). This combination further enhanced the predictive capability for detecting the risk of redislocation in CMFs. Conclusions: Ulnar osteotomy between the proximal 1/3 and 1/5 appears to provide a much safer and more stable radiocapitellar joint in CMF.

Background: The chemosensitivity of osteosarcoma patients to MTX is closely related to prognosis. There is currently a lack of advance prediction methods for MTX sensitivity. Objective: We proposed novel peri-osteosarcoma fat parameters based on computed tomography (CT) to evaluate the chemotherapy response preoperatively and calculate the correlation between image characteristics and methotrexate (MTX) blood concentration and systemic inflammation. Materials and methods: Pediatric patients with osteosarcoma (OS) who were treated with high-dose MTX were retrospectively studied and grouped according to postoperative Huvos classification. Clinical data were collected and reviewed. Image characteristics including peri-osteosarcoma fat volume and fat attenuation index were measured using the threshold method based on CT images. Statistical significance, correlation and prediction performance were performed. Results: Eighteen patients (good response (GR) group/poor response (PR) group: 10/8) was enrolled. MTX peak value at 6 h differed significantly between the two groups which was significantly higher in GR group (745.1 μmol/L vs 529.0 μmol/L p = 0.001). Peri-osteosarcoma fat attenuation index was significantly lower in GR group compared with that in PR group (− 104.90 vs. − 97.19, p < 0.0001). MTX blood concentration at 6 h negatively correlated with peri-osteosarcoma fat attenuation index (R = − 0.519, p = 0.027). In addition, 6 h MTX blood concentration (OR 0.974; 95% CI 0.951–0.998, p = 0.037) and FAI (OR 2.108; 95% CI 1.047–4.243, p = 0.037) were, respectively, independently related to good response to chemotherapy. The prediction performance on chemotherapy response of peri-osteosarcoma fat attenuation index and 6 h MTX blood concentration were both good with the comparable area under the ROC curve (0.950, 95% CI 0.856–1.000 and 0.963, 95% CI 0.878–1.00). Conclusions: Peri-osteosarcoma fat parameters based on CT were associated with the chemotherapy response and the MTX blood concentration, but not with the systemic inflammation. Combined with the requirement of current clinical practice, peri-osteosarcoma fat parameters may have the potential to be valuable image characteristics for monitoring chemotherapy response in OS pediatric patients.