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Background Tuberculous spondylitis (TS) is a challenging health care condition requiring spine surgery, and predicting the probabilities of prolonged postoperative length of stay (PLOS) can aid in effective management, especially with the increasing number of aging patients. This study aimed to develop and validate an interpretable nomogram for risk stratification and prediction of prolonged PLOS for TS patients after surgery, utilizing SHapley Additive exPlanations (SHAP) for model interpretation. Methods This retrospective analysis comprised data of 580 TS patients that were hospitalized between January 2016 and December 2022. Prolonged PLOS was defined as hospitalization exceeding the 75th percentile. Factors associated with an increased risk of prolonged PLOS were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) method and incorporated into a multivariable logistic regression model. SHAP values were generated to explain the contribution of each variable in predicting prolonged PLOS. Based on the identified risk variables, a nomogram was constructed using the SHAP values to represent each factor’s contribution to the final outcome. The nomogram’s effectiveness was assessed using calibration plots, discrimination analysis, and decision curve analysis. Results Among 580 patients, 127 had prolonged postoperative length of stay (PLOS > 11 days), while 453 had normal stays (≤ 11 days). The developed nomogram incorporated 7 significant risk factors along with their corresponding SHAP values, which are C-reactive protein (CRP), multiple sections, CT-vertebral destruction, MRI-epidural abscess, transfusions, blood loss and postoperative drainage (Drainage volume on the first day after surgery). The calibration graphs showed that the expected and observed probability of prolonged PLOS was in close agreement. Discrimination analysis yielded an area under the curve (AUC) of 0.867 (95% CI: 0.828–0.908) in the training set, indicating good predictive performance. Decision curve analysis confirmed the clinical utility of the nomogram in risk stratification and treatment decision-making. Conclusions By utilizing SHAP for model interpretation, this study provides an interpretable nomogram for assessing the possibility of extended PLOS in TS patients. The inclusion of SHAP values allows clinicians to understand the contribution of each variable in the prediction, thereby increasing transparency and aiding in the decision-making process. This nomogram has the potential to contribute to improved patient management and optimization of resource allocation. Prospective validation studies are recommended to further evaluate its effectiveness in clinical practice. Clinical trial number Not applicable.

Knee abnormalities, such as meniscus tears and ligament injuries, are common in clinical practice and pose significant diagnostic challenges. While traditional imaging techniques—X-ray, Computed Tomography (CT) scan, and Magnetic Resonance Imaging (MRI)—are vital for assessment. However, X-rays and CT scans often fail to adequately visualize soft tissue injuries, and MRIs can be costly and time-consuming. To overcome these limitations, we developed an innovative AI-driven approach that allows for the detection of soft tissue abnormalities directly from X-ray images—a capability traditionally reserved for MRI or arthroscopy. We conducted a retrospective study with 4,215 patients from two medical centers, utilizing knee X-ray images annotated by orthopedic surgeons. The YOLOv11 model automated knee localization, while five convolutional neural networks—ResNet152, DenseNet121, MobileNetV3, ShuffleNetV2, and VGG19—were adapted for multi-label classification of eight conditions: meniscus tears (MENI), anterior cruciate ligament tears (ACL), posterior cruciate ligament injuries (PCL), medial collateral ligament injuries (MCL), lateral collateral ligament injuries (LCL), joint effusion (EFFU), bone marrow edema or contusion (CONT), and soft tissue injuries (STI). Data preprocessing involved normalization and Region of Interest (ROI) extraction, with training enhanced by spatial augmentations. Performance was assessed using mean average precision (mAP), F1-scores, and area under the curve (AUC). We also developed a Windows-based PyQt application and a Flask Web application for clinical integration, incorporating explainable AI techniques (GradCAM, ScoreCAM) for interpretability. The YOLOv11 model achieved precise knee localization with a mAP@0.5 of 0.995. In classification, ResNet152 outperformed others, recording a mAP of 90.1% in internal testing and AUCs up to 0.863 (EFFU) in external testing. End-to-end performance on the external set yielded a mAP of 86.1% and F1-scores of 84.0% with ResNet152. The Windows and web applications successfully processed imaging data, aligning with MRI and arthroscopic findings in cases like ACL and meniscus tears. Explainable AI visualizations clarified model decisions, highlighting key regions for complex injuries, such as concurrent ligament and soft tissue damage, enhancing clinical trust. This AI-driven model markedly improved the precision and efficiency of knee abnormality detection through X-ray analysis. By accurately identifying multiple coexisting conditions in a single pass, it offered a scalable tool to enhance diagnostic workflows and patient outcomes, especially in resource-constrained areas.

Background Tuberculosis spondylitis (TS), commonly known as Pott’s disease, is a severe type of skeletal tuberculosis that typically requires surgical treatment. However, this treatment option has led to an increase in healthcare costs due to prolonged hospital stays (PLOS). Therefore, identifying risk factors associated with extended PLOS is necessary. In this research, we intended to develop an interpretable machine learning model that could predict extended PLOS, which can provide valuable insights for treatments and a web-based application was implemented. Methods We obtained patient data from the spine surgery department at our hospital. Extended postoperative length of stay (PLOS) refers to a hospitalization duration equal to or exceeding the 75th percentile following spine surgery. To identify relevant variables, we employed several approaches, such as the least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) based on support vector machine classification (SVC), correlation analysis, and permutation importance value. Several models using implemented and some of them are ensembled using soft voting techniques. Models were constructed using grid search with nested cross-validation. The performance of each algorithm was assessed through various metrics, including the AUC value (area under the curve of receiver operating characteristics) and the Brier Score. Model interpretation involved utilizing methods such as Shapley additive explanations (SHAP), the Gini Impurity Index, permutation importance, and local interpretable model-agnostic explanations (LIME). Furthermore, to facilitate the practical application of the model, a web-based interface was developed and deployed. Results The study included a cohort of 580 patients and 11 features include (CRP, transfusions, infusion volume, blood loss, X-ray bone bridge, X-ray osteophyte, CT-vertebral destruction, CT-paravertebral abscess, MRI-paravertebral abscess, MRI-epidural abscess, postoperative drainage) were selected. Most of the classifiers showed better performance, where the XGBoost model has a higher AUC value (0.86) and lower Brier Score (0.126). The XGBoost model was chosen as the optimal model. The results obtained from the calibration and decision curve analysis (DCA) plots demonstrate that XGBoost has achieved promising performance. After conducting tenfold cross-validation, the XGBoost model demonstrated a mean AUC of 0.85 ± 0.09. SHAP and LIME were used to display the variables’ contributions to the predicted value. The stacked bar plots indicated that infusion volume was the primary contributor, as determined by Gini, permutation importance (PFI), and the LIME algorithm. Conclusions Our methods not only effectively predicted extended PLOS but also identified risk factors that can be utilized for future treatments. The XGBoost model developed in this study is easily accessible through the deployed web application and can aid in clinical research.

Background: In this study, exosomes were engineered with anti-CD47 antibody and loaded with rifapentine to improve their ability to target macrophages for drug delivery. Methods: Exosomes from RAW264.7 cell supernatant were extracted by differential centrifugation, antibody-modified, and drug-loaded ultrasonically. After co-culturing with macrophages, transcriptomics and proteomics screened differentially expressed genes and proteins. Western Blot identified macrophage polarization, ELISA detected inflammatory indicators, and an apoptosis kit was used for fluorescence staining. Results: Transcriptome sequencing showed that 406 genes in the macrophages changed significantly, with pathways like TNF and NF-κB. Proteomics identified 7478 proteins, 433 with significant differences. Western Blot indicated M1 polarization. Fluorescence staining showed apoptosis in the antiMExo-RIF group. Conclusions: The study provides multi-omics evidence of the immune mechanism of the engineered exosome drug delivery system in inducing macrophage apoptosis, revealing potential molecular mechanisms and the great potential use of engineered exosomes in treating macrophage-related diseases.

Background Cerebral alveolar echinococcosis (CAE) and brain metastases (BM) share similar in locations and imaging appearance. However, they require distinct treatment approaches, with CAE typically treated with chemotherapy and surgery, while BM is managed with radiotherapy and targeted therapy for the primary malignancy. Accurate diagnosis is crucial due to the divergent treatment strategies. Purpose This study aims to evaluate the effectiveness of radiomics and machine learning techniques based on magnetic resonance imaging (MRI) to differentiate between CAE and BM. Methods We retrospectively analyzed MRI images of 130 patients (30 CAE and 100 BM) from Xinjiang Medical University First Affiliated Hospital and The First People's Hospital of Kashi Prefecture, between January 2014 and December 2022. The dataset was divided into training (91 cases) and testing (39 cases) sets. Three dimensional tumors were segmented by radiologists from contrast-enhanced T1WI images on open resources software 3D Slicer. Features were extracted on Pyradiomics, further feature reduction was carried out using univariate analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO). Finally, we built five machine learning models, support vector machine, logistic regression, linear discrimination analysis, k-nearest neighbors classifier, and Gaussian naïve bias and evaluated their performance via several metrics including sensitivity (recall), specificity, positive predictive value (precision), negative predictive value, accuracy and the area under the curve (AUC). Results The area under curve (AUC) of support vector classifier (SVC), linear discrimination analysis (LDA), k-nearest neighbors (KNN), and gaussian naïve bias (NB) algorithms in training (testing) sets are 0.99 (0.94), 1.00 (0.87), 0.98 (0.92), 0.97 (0.97), and 0.98 (0.93), respectively. Nested cross-validation demonstrated the robustness and generalizability of the models. Additionally, the calibration plot and decision curve analysis demonstrated the practical usefulness of these models in clinical practice, with lower bias toward different subgroups during decision-making. Conclusion The combination of radiomics and machine learning approach based on contrast enhanced T1WI images could well distinguish CAE and BM. This approach holds promise in assisting doctors with accurate diagnosis and clinical decision-making.

Background Diabetic foot complications, including infections and osteomyelitis, pose significant health risks, with high prevalence and amputation rates. Differentiating diabetic foot infection (DFI) from osteomyelitis (OM) is challenging due to overlapping symptoms and limitations of current diagnostic methods. This study aimed to develop and validate an explainable machine learning (ML) model using routine blood biomarkers to improve differential diagnosis and provide a clinically accessible tool. Methods This retrospective, two-center study included 3,612 patients diagnosed with either DFI ( n  = 1,699) or OM ( n  = 1,913). Data from Center 1 ( n  = 3271) were used for model development (75% training, 25% internal validation), and data from Center 2 ( n  = 341) served as an independent external validation cohort. A robust feature selection pipeline identified the most predictive routine biomarkers. Multiple machine learning classifiers were trained and evaluated, with the top-performing model selected based on the area under the receiver operating characteristic curve (AUC), Brier score, and other key metrics. Explainable AI (XAI) techniques (SHAP, LIME) were used to ensure model transparency. A web-based calculator was developed for clinical translation. Results A LightGBM model using only six biomarkers—Age, HbA1c, Creatinine, Albumin, ESR, and Sodium—was selected as the final model. It achieved an AUC of 0.879 (95% CI 0.854–0.902) in internal validation and demonstrated excellent, generalizable performance in the external cohort with an AUC of 0.942 (95% CI 0.936–0.950). The model was well-calibrated and showed significant clinical utility in decision curve analysis. SHAP analysis quantified the specific contribution of each biomarker to individual predictions, enhancing interpretability. The final model was deployed as a user-friendly, publicly accessible web calculator. Conclusions An externally validated machine learning model based on six routine blood biomarkers can accurately and reliably differentiate DFI from OM. The model demonstrated high discriminative performance and clinical utility. Deployed as a transparent web calculator with integrated explainable AI, this low-cost tool has the potential to aid clinicians in diagnostic decision-making, particularly in resource-limited settings. Clinical trial number Not applicable.

Background The differentiation of brucellar spondylitis (BS) from tuberculous spondylitis (TS) on magnetic resonance imaging (MRI) is a critical clinical challenge. While deep learning holds promise, the optimal architectural strategy for integrating information from multi-sequence MRI remains unclear. This study systematically compared distinct deep learning architectures to identify a valid and effective integration strategy for this diagnostic problem. Methods In this retrospective, single-center diagnostic study, we included 235 patients with surgically and pathologically confirmed BS ( n  = 82) or TS ( n  = 153) from January 2014 to December 2024. We systematically evaluated four distinct architectural strategies for processing sagittal T1-weighted, T2-weighted, and fat-suppressed MRI sequences: (1) baseline models trained on single sequences; (2) a single-branch model that fused sequences as input channels; (3) a heterogeneous multi-branch model using different backbones for each sequence; and (4) a homogeneous multi-branch model using identical backbones. Models were developed on patient-level data splits for training (70%), validation (15%), and internal testing (15%). The primary performance metric was the area under the receiver operating characteristic curve (AUC) on the test set. Statistical significance of performance differences between models was assessed using the DeLong test, with P values adjusted for multiple comparisons using the Benjamini–Hochberg procedure. Results The single-branch fusion model, which treated the three sequences as channels in a single input, failed to learn, yielding performance equivalent to random chance (test AUC range: 0.474–0.538). In stark contrast, both the single-sequence and multi-branch architectures proved to be effective. The best single-sequence model achieved a test AUC of 0.765 (95% CI 0.759–0.771). The optimal multi-branch model, which successfully integrated all three sequences, achieved a comparable test AUC of 0.764 (95% CI 0.757–0.770). Conclusions The choice of architecture for integrating multi-sequence MRI data is a critical determinant of model viability. Our findings demonstrate that naive channel wise fusion is an invalid strategy for this task. In contrast, both processing a single MRI sequence and utilizing a multi-branch parallel-processing architecture are valid and effective strategies, achieving comparable diagnostic performance. This study clarifies the architectural principles required for successfully applying deep learning to this multi-modal diagnostic challenge.

Tuberculosis, caused by Mycobacterium tuberculosis (Mtb), remains one of the leading causes of death among infectious diseases. Enhancing the ability of anti-tuberculosis drugs to eradicate Mycobacterium tuberculosis within host cells remains a significant challenge. A mannosamine-modified nanoparticle delivery system was developed using poly(lactic-co-glycolic acid) (PLGA) copolymers to enhance the targeted delivery of rifapentine (RPT) to macrophages. D-mannosamine was conjugated to PLGA-polyethylene glycol (PLGA-PEG) copolymers through EDC/NHS coupling chemistry, and the resultant RPT-MAN-PLGA-PEG nanoparticles (NPs) were prepared through a combination of phacoemulsification and solvent evaporation methods. The physicochemical properties, toxicity, in vitro drug release profiles, stability, cellular uptake, and anti-TB efficacy of the NPs were systematically evaluated. The RPT-MAN-PLGA-PEG NPs had a mean particle size of 108.2 ± 7.2 nm, with encapsulation efficiency and drug loading rates of 81.2 ± 6.3% and 13.7 ± 0.7%, respectively. RPT release from the NPs was sustained for over 60 hours. Notably, the phagocytic uptake of the MAN-PLGA NPs by macrophages was significantly higher compared to PLGA-PEG NPs. Both NPs improved pharmacokinetic parameters without inducing significant organ toxicity. The minimum inhibitory concentration for the NPs was 0.047 μg/mL, compared to 0.2 μg/mL for free RPT. The engineered RPT-MAN-PLGA-PEG NPs effectively enhanced macrophage uptake in vitro and facilitated the intracellular clearance of Mtb. This nanoparticle-based delivery system offers a promising approach for improving the precision of anti-TB therapy, extending drug release, optimizing pharmacokinetic profiles, augmenting antimicrobial efficacy, and mitigating drug-related toxicities.

Background Lumber spinal stenosis (LSS) is the increasingly reason for spine surgery for elder patients since China is facing the fastest-growing aging population. The aim of this research was to create a model to predict the probabilities of requiring a prolonged postoperative length of stay (PLOS) for lumbar spinal stenosis patients, minimizing the healthcare burden. Methods A total of 540 LSS patients were enrolled in this project. The outcome was a prolonged PLOS after spine surgery, defined as hospitalizations ≥ 75th percentile for PLOS, including the day of discharge. The least absolute shrinkage and selection operator (LASSO) was used to identify independent risk variables related to prolonged PLOS. Multivariable logistic regression analysis was utilized to generate a prediction model utilizing the variables employed in the LASSO approach. The receiver operating characteristic (ROC) curve’s area under the curve (AUC) and the calibration curve’s respective curves were used to further validate the model’s calibration with predictability and discriminative capabilities. By using decision curve analysis, the resulting model’s clinical effectiveness was assessed. Results Among 540 individuals, 344 had PLOS that was within the usual range of P75 (8 days), according to the interquartile range of PLOS, and 196 had PLOS that was above the normal range of P75 (prolonged PLOS). Four variables were incorporated into the predictive model, named: transfusion, operation duration, blood loss and involved spine segments. A great difference in clinical scores can be found between the two groups ( P  < 0.001). In the development set, the model’s AUC for predicting prolonged PLOS was 0.812 (95% CI: 0.768–0.859), while in the validation set, it was 0.830 (95% CI: 0.753–0.881). The calibration plots for the probability showed coherence between the expected probability and the actual probability both in the development set and validation set respectively. When intervention was chosen at the potential threshold of 2%, analysis of the decision curve revealed that the model was more clinically effective. Conclusions The individualized prediction nomogram incorporating five common clinical features for LSS patients undergoing surgery can be suitably used to smooth early identification and improve screening of patients at higher risk of prolonged PLOS and minimize health care.

Tuberculosis spondylitis (TS), also known as Pott's disease, is the most common destructive form of musculoskeletal tuberculosis and poses significant clinical challenges, particularly when complicated by osteoporosis. Osteoporosis exacerbates surgical outcomes and increases the risk of complications, making its accurate prediction crucial for effective patient management. This retrospective study included 906 TS patients from two medical centers between January 2016 and November 2022. We collected demographic information and blood test data from routine examinations. To address class imbalance, the synthetic minority oversampling technique (SMOTE) was applied. Feature selection was performed using LASSO, Boruta, and Recursive Feature Elimination (RFE) to identify key predictors of osteoporosis. Multiple machine learning (ML) algorithms, including logistic regression, random forest, and XGBoost, were trained and optimized using nested cross-validation and hyperparameter tuning. The optimal model was further refined through threshold tuning to enhance performance metrics. Model interpretability was achieved using SHapley Additive exPlanations (SHAP), and an online web application was developed for real-time clinical use. Out of 906 patients, 60 were diagnosed with osteoporosis based on Dual-energy X-ray absorptiometry (DXA) measurements. Feature selection identified hemoglobin (HB), estimated glomerular filtration rate (eGFR), and cystatin C (CYS_C) as significant predictors. The logistic regression model exhibited the highest performance with an area under the receiver operating characteristic curve (AUC) of 0.826, which was externally validated with an AUC of 0.796. Threshold tuning optimized the decision threshold to 0.32, improving the F1-score and balancing sensitivity and specificity. SHAP analysis highlighted the critical roles of HB, eGFR, and CYS_C in osteoporosis prediction. The developed web application facilitates the model's integration into clinical workflows, enabling healthcare professionals to make informed decisions at the bedside. This study successfully developed and validated an ML-based tool for predicting osteoporosis in TS patients using readily available clinical data. The model demonstrated robust predictive performance and was effectively integrated into a user-friendly online application, offering a practical solution to enhance surgical decision-making and improve patient outcomes in real-time clinical settings.