Development and validation of a machine learning-based postoperative prognostic model for plasma cell neoplasia with spinal lesions as initial clinical manifestations: a single-center cohort study.

BACKGROUND: Spinal multiple myeloma (MM) and solitary plasmacytoma of bone (SPB), both plasma cell neoplasms, greatly affect patients' quality of life due to spinal involvement. Accurate prediction of surgical outcomes is crucial for personalized patient care, but systematic treatment guidelines and predictive models are lacking. OBJECTIVE: This study aimed to develop and validate a machine learning (ML)-based model to predict postoperative outcomes and identify prognostic factors for patients with spinal MM and SPB. METHODS: A retrospective analysis was conducted on patients diagnosed with MM or SPB from 2011 to 2015, followed by prospective data collection from 2016 to 2017. Patient demographics, tumor characteristics, clinical treatments, and laboratory results were analyzed as input features. Four types of ML algorithms were employed for model development. The performance was assessed using discrimination and calibration measures, and the Shapley Additive exPlanations (SHAP) method was applied for model interpretation. RESULTS: A total of 169 patients were included, with 119 for model training and 50 for validation. The Gaussian Naïve Bayes (GNB) model exhibited superior predictive accuracy and stability. Prospective validation on the 50 patients revealed an area under the curve (AUC) of 0.863, effectively distinguishing between 5-year survivors and non-survivors. Key prognostic factors identified included International Staging System (ISS) stage, Durie-Salmon (DS) stage, targeted therapy, and age. CONCLUSIONS: The GNB model has the best performance and high reliability in predicting postoperative outcomes. Variables such as ISS stage and DS stage were significant in influencing patient prognosis. This study enhances the ability to identify patients at risk of poor outcomes, thereby aiding clinical decision-making.

Mesenchymal stem cell-derived exosomes as a new drug carrier for the treatment of spinal cord injury: A review.

Spinal cord injury (SCI) is a devastating traumatic disease seriously impairing the quality of life in patients. Expectations to allow the hopeless central nervous system to repair itself after injury are unfeasible. Developing new approaches to regenerate the central nervous system is still the priority. Exosomes derived from mesenchymal stem cells (MSC-Exo) have been proven to robustly quench the inflammatory response or oxidative stress and curb neuronal apoptosis and autophagy following SCI, which are the key processes to rescue damaged spinal cord neurons and restore their functions. Nonetheless, MSC-Exo in SCI received scant attention. In this review, we reviewed our previous work and other studies to summarize the roles of MSC-Exo in SCI and its underlying mechanisms. Furthermore, we also focus on the application of exosomes as drug carrier in SCI. In particular, it combs the advantages of exosomes as a drug carrier for SCI, imaging advantages, drug types, loading methods, etc., which provides the latest progress for exosomes in the treatment of SCI, especially drug carrier.

ε-Poly-l-lysine-hydroxyphenyl propionic acid/IL-4 composite hydrogels with inflammation regulation and antibacterial activity for improving integration stability of soft tissues and orthopedic implants.

The failure of orthopedic implants is usually caused by inflammation, poor tissue integration, and infection, which can lead to pain, limited mobility, dysfunction of patients. This may require additional surgical interventions, such as removal, replacement, or repair of implants, as well as related treatment measures such as antibiotic therapy, physical therapy. Here, an injectable hydrogel carrier was developed for the steady release of inflammatory regulators to reduce the surface tissue inflammatory response of orthopedic implants and induce soft tissue regeneration, ultimately achieving the promotion of implants stability. The hydrogels carrier was prepared by hydroxyphenyl propionic acid-modified ε-Poly-l-lysine (EPA), hydrogen peroxide and horseradish peroxidase, which showed antibacterial bioactive and stable factor release ability. Due to the introduction of IL-4, EPA@IL-4 hydrogels showed good inflammatory regulation. EPA@IL-4 hydrogels regulated the differentiation of macrophages into M2 in inflammatory environment in vitro, and promoted endothelial cells to show a more obvious trend of tube formation. The composite hydrogels reduced the inflammation on the surface of the implants in vivo, induced local endothelial cell angiogenesis, and had more collagen deposition and new granulation tissue. Therefore, EPA hydrogels based on IL-4 release are promising candidates for promoting of implants surface anti-inflammatory, soft tissue regeneration, and anti-infection.

Generalizable, Reproducible, and Neuroscientifically Interpretable Imaging Biomarkers for Alzheimer's Disease.

Precision medicine for Alzheimer's disease (AD) necessitates the development of personalized, reproducible, and neuroscientifically interpretable biomarkers, yet despite remarkable advances, few such biomarkers are available. Also, a comprehensive evaluation of the neurobiological basis and generalizability of the end-to-end machine learning system should be given the highest priority. For this reason, a deep learning model (3D attention network, 3DAN) that can simultaneously capture candidate imaging biomarkers with an attention mechanism module and advance the diagnosis of AD based on structural magnetic resonance imaging is proposed. The generalizability and reproducibility are evaluated using cross-validation on in-house, multicenter (n = 716), and public (n = 1116) databases with an accuracy up to 92%. Significant associations between the classification output and clinical characteristics of AD and mild cognitive impairment (MCI, a middle stage of dementia) groups provide solid neurobiological support for the 3DAN model. The effectiveness of the 3DAN model is further validated by its good performance in predicting the MCI subjects who progress to AD with an accuracy of 72%. Collectively, the findings highlight the potential for structural brain imaging to provide a generalizable, and neuroscientifically interpretable imaging biomarker that can support clinicians in the early diagnosis of AD.

BRANT: A Versatile and Extendable Resting-State fMRI Toolkit.

Data processing toolboxes for resting-state functional MRI (rs-fMRI) have provided us with a variety of functions and user friendly graphic user interfaces (GUIs). However, many toolboxes only cover a certain range of functions, and use exclusively designed GUIs. To facilitate data processing and alleviate the burden of manually drawing GUIs, we have developed a versatile and extendable MATLAB-based toolbox, BRANT (BRAinNetome fmri Toolkit), with a wide range of rs-fMRI data processing functions and code-generated GUIs. During the implementation, we have also empowered the toolbox with parallel computing techniques, efficient file handling methods for compressed file format, and one-line scripting. In BRANT, users can find rs-fMRI batch processing functions for preprocessing, brain spontaneous activity analysis, functional connectivity analysis, complex network analysis, statistical analysis, and results visualization, while developers can quickly publish scripts with code-generated GUIs.