To investigate the clinical efficacy of percutaneous kyphoplasty (PKP) with a unilateral versus bilateral approach in the treatment of osteoporotic vertebral compression fractures (OVCFs). We retrospectively analyzed a total of 147 patients (unilateral group: 79, bilateral group: 68) with OVCFs treated with PKP at the Department of Spine Surgery, Wuhan Fourth Hospital between August 2020 and January 2022. Patients’ personal information, operation time, bone cement injection volume, as well as pre- and post-operative visual analogue scale (VAS), Oswestry disability index (ODI), anterior vertebral body height and Cobb angle were recorded. All 147 patients were successfully treated with PKP and were followed up for at least 6 months. Our results showed that the operative time was significantly shorter in the unilateral group (41.60±5.64) minutes than in the bilateral group (66.53±9.40) minutes, and the volume of bone cement injected was also significantly less in the unilateral group (5.27±0.73) mL than in the bilateral group (6.87±0.93) mL (P<0.01). The VAS score, ODI index, vertebral height and Cobb angle at postoperative follow-up were significantly improved in both groups compared to the preoperative period (P<0.01); However, the difference between the two groups was not statistically significant (P>0.05). Repeat thoracic and lumbar radiographs showed cement leakage in seven cases (8.86%) in the unilateral group and five cases (7.35%) in the bilateral group, but all were asymptomatic and required no further management. During our entire follow-up period, there were five adjacent vertebral fractures in the unilateral group (6.33%) and four in the bilateral group (5.88%). There was no significant difference between the two groups in terms of improvement in VAS score, ODI index, restoration of vertebral body height, and posterior convexity deformity, but unilateral puncture had the advantage of shorter operative time and less cement injection.

Lobelia nummularia Lam. is a traditional medicinal herb of which the anticancer mechanisms remain largely unexplored. Here, we demonstrated that its ethanolic extract (LNE) exerts potent anti-breast cancer activity by inducing ROS-dependent mitochondrial apoptosis in MDA-MB-231 cells, a mechanism confirmed via rescue experiments with the antioxidant N-acetylcysteine (NAC). This pro-apoptotic program is driven by a dual mechanism: potent suppression of the pro-survival EGFR/PI3K/AKT signaling pathway and simultaneous activation of the TP53-mediated apoptotic cascade, culminating in the cleavage of executor caspase-3. Phytochemical analysis identified numerous flavonoids, and quantitative HPLC confirmed that key bioactive compounds, including luteolin and apigenin, are substantially present in the extract. These mechanisms translated to significant in vivo efficacy, where LNE administration suppressed primary tumor growth and lung metastasis in a 4T1 orthotopic model in BALB/c mice. Furthermore, a validated molecular docking protocol provided a plausible structural basis for these multi-target interactions. Collectively, this study provides a comprehensive, multi-layered validation of LNE’s therapeutic potential, establishing it as a mechanistically well-defined candidate for natural product-based anticancer drug discovery.

Background: The diagnosis and prognostic assessment of bone tumors represent a complex and clinically significant challenge. In recent years, the rise of artificial intelligence (AI), particularly deep learning (DL) and classical machine learning (ML), has emerged as a promising tool in this field. This study systematically reviews the applications of AI in bone tumor diagnosis, prognosis, segmentation, and treatment response, with a focus on model performance, emerging trends, and current limitations. Methods: This systematic review follows to the PRISMA guidelines and conducted a comprehensive search of four major databases (PubMed, Web of Science, Scopus, and Cochrane Library) to identify studies published between January 2019 and May 2025 on the application of AI in bone tumors. Relevant original articles were identified based on predefined inclusion and exclusion criteria, and research data such as basic information, algorithms, models, performance metrics, and clinical tasks, were systematically extracted and analyzed. And the performance of DL and ML methods in bone tumors was comparatively analyzed. Results: The review included 70 studies involving 53,149 cases, of which 45.83% were malignant bone tumors. DL was used in 77.63% of the studies and classical ML in 22.37%. Diagnostic tasks dominated the research focus (81.94%), followed by survival prediction (11.11%) and treatment response evaluation (6.94%). Performance metrics indicated that DL models exhibited higher weighted averages in accuracy (0.87), AUC (0.89), sensitivity (0.84), specificity (0.88), precision (0.81), and F-score (0.84), while classical ML models achieved the highest precision (0.90). Although DL demonstrated a performance advantage in image-based tasks, classical ML maintained greater stability in structured datasets. No significant performance differences were observed between large-sample and small-sample studies, reflecting the robustness of both model types. Additionally, a recent shift in research focus was observed, from diagnostic applications toward disease prediction. Conclusion: Artificial intelligence has demonstrated strong performance and potential in bone tumor research. DL often demonstrates more balanced performance in image-based bone tumor tasks, while classical ML remains competitive and may hold advantages in structured, small-sample datasets, precision-prioritized settings. However, we did not observe statistically significant differences, so these findings should be interpreted as performance tendencies in specific contexts rather than universally validated superiority. Future research should focus on optimizing DL and classical ML models, developing fusion algorithms in bone tumors can improve the generalization performance, accuracy, and ability to adapt to complex data scenarios. At the same time, fostering interdisciplinary and multicenter collaborations between computer scientists and clinicians, improving data-sharing frameworks, and addressing ethical and privacy concerns will be essential to fully harness the significant potential of AI in bone tumor research and clinical applications.

To determine the feasibility of coating resorbable filter device made up of polydioxanone (PDDO) with gold nanoparticles as a radiopaque, computed tomography (CT)-visible inferior vena cava (IVC) filter device.

Polyethylene terephthalate (PET) has emerged as a focal point in addressing global pollution and a critical environmental issue due to its potential health hazards. However, its role in the pathogenesis of osteosarcoma (OS) and the underlying molecular mechanisms remain largely unexplored, further highlighting the necessity of assessing its molecular toxicity. This study integrated network toxicology, machine learning, molecular docking, and CIBERSORT-based immune infiltration analysis to systematically investigate the potential impact of PET exposure on contracting OS, elucidating its biological functions, signaling mechanisms, and immune microenvironment. Molecular docking was further applied to characterize the binding properties of PET with hub proteins, and potential therapeutic agents for OS were predicted. We identified 12 potential key targets of OS associated with PET exposure and, through machine learning models, selected six hub genes (i.e., BCAT1, CDK4, CSF1R, CXCR4, MYB, and PRTN3). Gene Ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analyses were conducted to elucidate the roles of these genes in biological processes, cellular components, molecular functions, and signaling pathways. Molecular docking results revealed that PET exhibits high specificity in binding to these hub genes, particularly by interacting with CSF1R (−8.312 kcal/mol), potentially activating the PI3K-Akt signaling pathway and modulating the OS immune microenvironment to promote tumor progression through multiple mechanisms. Furthermore, drug prediction analysis identified p-Benzoquinone and JNK-9L as potential therapeutic candidates for OS. This study reveals that PET may play a critical role in OS development by regulating hub genes and signaling pathways. Molecular docking analysis demonstrates that PET can tightly bind to specific target proteins, suggesting a potential molecular mechanism underlying OS progression. These findings provide a scientific basis for further evaluating PET-related health risks and offer theoretical support for the development of future prevention and treatment strategies.

Objective: To compare the clinical and radiographic outcomes of percutaneous kyphoplasty (PKP) and short-segment fixation with vertebroplasty (SSF+VP) in patients with stage III Kümmell disease (KD) without neurological deficits, and to evaluate whether the preoperative local Cobb angle can guide risk stratification for surgical decision-making. Methods: This two-center retrospective study included 117 patients with stage III KD without neurological deficits who underwent surgery between January 2019 and December 2024. Among them, 71 underwent PKP and 46 underwent SSF+VP. Perioperative parameters, VAS, ODI, local Cobb angle, Cobb angle loss, cement distribution, and cement leakage were compared. In this exploratory analysis, residual mild pain was defined as a final follow-up VAS ≥2. In the PKP group, the association between Cobb angle loss and final follow-up VAS was analyzed. ROC analysis was performed to assess the predictive value of the preoperative Cobb angle for residual mild pain after PKP. Patients were further stratified according to the optimal preoperative Cobb angle cut-off, and 2,000 bootstrap resamples were used for internal validation. Results: Baseline characteristics were comparable. PKP was associated with shorter operative time, less blood loss, smaller cement volume, shorter hospital stay, and greater early VAS and ODI improvement than SSF+VP. However, at final follow-up, SSF+VP showed lower VAS, while ODI was comparable. Radiographically, SSF+VP achieved lower postoperative and final Cobb angles, less Cobb angle loss, more diffuse cement distribution, and less cement leakage. In the PKP group, Cobb angle loss correlated positively with final VAS (rho=0.542, p < 0.001). The preoperative Cobb angle predicted residual pain after PKP, with an optimal cut-off of 25.95°. After stratification, final VAS did not differ between procedures in the mild-moderate kyphosis subgroup, whereas SSF+VP showed better intermediate-term pain outcomes in the severe kyphosis subgroup. Bootstrap validation supported the stability of this risk-stratified finding. Conclusion: Both PKP and SSF+VP improved pain and function in stage III KD without neurological deficits. PKP enabled faster early recovery, whereas SSF+VP provided better correction maintenance and intermediate-term pain outcomes in patients with severe preoperative kyphosis. Preoperative Cobb angle-based stratification may help guide individualized surgical selection.