Zhihai Su

Background: Lumbar disc herniation (LDH) is the most common etiological cause of low back pain (LBP). Objective and precise pain evaluation is of significant clinical value. Functional near-infrared spectroscopy (fNIRS) as a noninvasive neuroimaging modality, has been increasingly validated to reflect subjective pain perception through hemodynamic correlates. This study aimed to analyze the fNIRS changes in patients with LDH about to receive Unilateral Biportal Endoscopy and to further explore the feasibility of fNIRS as an objective biomarkers for clinical assessment of LDH. Methods: Resting-state fNIRS data were acquired from 67 preoperative LDH patients and 20 healthy controls (HC). Brain functional maps—including z-standardized fractional amplitude of low-frequency fluctuations (zfALFF) and seed-based functional connectivity (FC)—were extracted and quantified. Group-level comparisons were performed between LDH and HC groups across four predefined regions of interest; additionally, correlation analyses were conducted between fNIRS metrics and clinical assessment scores within the LDH cohort. Results: Compared with HC, LDH patients exhibited significantly altered zfALFF in the medial prefrontal cortex (mPFC): decreased amplitude at channel CH12 (t = −2.031, p = 0.045) and increased amplitude at CH21 (t = 2.462, p = 0.016). Whole-brain FC analysis further revealed widespread changes—particularly between the parietal somatosensory cortex and prefrontal regions. Among all tested FC–clinical indicator associations, 56 reached statistical significance after FDR correction (q < 0.05). VAS_ lumbar and SF-36_SF exhibited the highest number of significant connections. Conclusions: LDH patients with LBP exhibit notable alterations in prefrontal resting-state ALFF and FC between the parietal somatosensory cortex and prefrontal cortex relative to HC. Importantly, these neural alterations exhibit significant associations with both pain severity (VAS) and long-term health-related quality of life (SF-36), thereby strengthening their candidacy as neural correlates meriting prospective validation as objective, mechanism-informed biomarkers for clinical evaluation of lumbar disc herniation (LDH). Moreover, these findings highlight candidate neural targets for future longitudinal studies investigating early prognostic prediction and treatment response monitoring in LDH.

Ankylosing spondylitis (AS) is a chronic inflammatory disease characterized by progressive spinal fusion and systemic inflammation. Recent studies suggest that gut microbiota plays a crucial role in the pathogenesis of AS. This study investigated the therapeutic effects of Limosilactobacillus reuteri (L. reuteri) on AS progression and its underlying mechanisms using a proteoglycan (PG)-induced mouse model. Female BALB/c mice (n = 10/group) were randomized into control group, PG group and PG + L. reuteri group. Disease severity was assessed via arthritis scores, Micro-CT images, and histopathology. Serum cytokines (IL-1β, IL-18, IL-17A, IL-23) were measured by ELISA. Intestinal barrier integrity was evaluated using FITC-dextran permeability, immunofluorescence (ZO-1, occludin), and colon histology. Gut microbiota (16S rRNA sequencing) and fecal metabolites (untargeted metabolomics) were analyzed. AhR/NLRP3 pathway activity was assessed via qRT-PCR (AhR, CYP1A1, CYP1B1, and NLRP3). Our findings demonstrated that L. reuteri significantly alleviated AS progression, as evidenced by reduced joint swelling and erythema, alongside a decreased arthritis index and paw thickness. Furthermore, treatment with L. reuteri resulted in a marked reduction in serum levels of pro-inflammatory cytokines, including IL-1β, IL-18, IL-17A, and IL-23, indicating its potential to modulate systemic inflammation. Additionally, L. reuteri enhanced intestinal mucosal barrier function, as demonstrated by improved histopathological integrity, reduced intestinal permeability, and restored expression of tight junction proteins ZO-1 and occludin. Moreover, L. reuteri treatment restored gut microbiota composition and metabolite profiles, aligning them more closely with control groups. Notably, L. reuteri may exert its effects partially through the AhR/NLRP3 pathway, as evidenced by increased mRNA levels of AhR, CYP1A1, and CYP1B1, along with reduced NLRP3 expression. In conclusion, L. reuteri effectively prevents the progression of AS in mice by restoring gut microbiota-metabolism homeostasis and modulating inflammatory pathways, highlighting its potential as a therapeutic agent for AS.

Objective: This study aims to apply different deep learning convolutional neural network algorithms to assess the grading of cervical spinal stenosis and to evaluate their consistency with clinician grading results as well as clinical manifestations of patients. Methods: We retrospectively enrolled 954 patients with cervical spine magnetic resonance imaging (MRI) data and medical records from the Fifth Affiliated Hospital of Sun-Yat Sen University. The Kang grading method for sagittal MR images of the cervical spine and the spinal cord compression ratio for horizontal MR images of the cervical spine were adopted for cervical spinal canal stenosis grading. The collected data were randomly divided into training/validation and test sets. The training/validation sets were processed by various image preprocessing and annotation methods, in which deep learning convolutional networks, including classification, target detection, and key point localization models, were applied. The predictive grading of the test set by the model was finally contrasted with the grading results of the clinicians, and correlation analysis was performed with the clinical manifestations of the patients. Result: The EfficientNet_B5 model achieved a five-fold cross-validated accuracy of 79.45% and near-perfect agreement with clinician grading on the test set (κ= 0.848, 0.822), surpassing resident–clinician consistency (κ = 0.732, 0.702). The model-derived compression ratio (0.45 ± 0.07) did not differ significantly from manual measurements (0.46 ± 0.07). Correlation analysis showed moderate associations between model outputs and clinical symptoms: EfficientNet_B5 grades (r = 0.526) were comparable to clinician assessments (r = 0.517, 0.503) and higher than those of residents (r = 0.457, 0.448). Conclusion: CNN models demonstrate strong performance in the objective, consistent, and efficient grading of cervical spinal stenosis severity, offering potential clinical value in automated diagnostic support.

Background: Ankylosing spondylitis (AS) is a chronic inflammatory and autoimmune disease that primarily affects the sacroiliac joints and axial skeleton. While the exact pathogenetic mechanism of AS remains unclear, previous reports have highlighted the involvement of genetic factors, immune responses, and gut microbiota dysregulation in the development of this condition. Short-chain fatty acids (SCFAs), which are microbial fermentation products derived from sugar, protein, and dietary fibers, play a role in maintaining the intestinal barrier function and reducing inflammatory responses. The aim of this study was to investigate the therapeutic potential of butyric acid (BA), an important SCFA, in the treatment of AS. Methods: To evaluate the anti-inflammatory and anti-bone loss effects of BA, a murine AS model was established using proteoglycan and dimethyl dioctadecyl ammonium (DDA) adjuvants. Various techniques, including an enzyme-linked immunosorbent assay (ELISA), magnetic resonance imaging (MRI), micro-CT, histology, quantitative PCR (qPCR) for intestinal tight junction protein expression, and 16S rDNA sequencing to analyze gut microbiota abundance, were employed to assess the inflammation and bone health in the target tissues. Results: The results indicated that BA demonstrated potential in alleviating the inflammatory response in the peripheral joints and the axial spine affected by AS, as evidenced by the reductions in inflammatory infiltration, synovial hyperplasia, and endplate erosion. Furthermore, BA was found to impact the intestinal barrier function positively. Notably, BA was associated with the downregulation of harmful inflammatory factors and the reversal of bone loss, suggesting its protective effects against AS. Conclusions: These beneficial effects were attributed to the modulation of gut microbiota, anti-inflammatory properties, and the maintenance of skeletal metabolic homeostasis. This study contributes new evidence supporting the relationship between gut microbiota and bone health.

(1) Background: This study aims to develop a deep learning model based on a 3D Deeplab V3+ network to automatically segment multiple structures from magnetic resonance (MR) images at the L4/5 level. (2) Methods: After data preprocessing, the modified 3D Deeplab V3+ network of the deep learning model was used for the automatic segmentation of multiple structures from MR images at the L4/5 level. We performed five-fold cross-validation to evaluate the performance of the deep learning model. Subsequently, the Dice Similarity Coefficient (DSC), precision, and recall were also used to assess the deep learning model’s performance. Pearson’s correlation coefficient analysis and the Wilcoxon signed-rank test were employed to compare the morphometric measurements of 3D reconstruction models generated by manual and automatic segmentation. (3) Results: The deep learning model obtained an overall average DSC of 0.886, an average precision of 0.899, and an average recall of 0.881 on the test sets. Furthermore, all morphometry-related measurements of 3D reconstruction models revealed no significant difference between ground truth and automatic segmentation. Strong linear relationships and correlations were also obtained in the morphometry-related measurements of 3D reconstruction models between ground truth and automated segmentation. (4) Conclusions: We found it feasible to perform automated segmentation of multiple structures from MR images, which would facilitate lumbar surgical evaluation by establishing 3D reconstruction models at the L4/5 level.

The three‐dimensional (3D) anatomy of Kambin's triangle is crucial for surgical planning in minimally invasive spine surgery via the transforaminal approach. Few pieces of research have, however, used image segmentation to explore the 3D reconstruction of Kambin's triangle. This study aimed to develop a new method of 3D reconstruction of Kambin's triangle based on automated magnetic resonance image (MRI) segmentation of the lumbar spinal structures. An experienced (>5 years) “ground truth” spinal pain physician meticulously segmented and labeled spinal structures (e.g., bones, dura mater, discs, and nerve roots) on MRI. Subsequently, a 3D U‐Net algorithm was developed for automatically segmenting lumbar spinal structures for the 3D reconstruction of Kambin's triangle. The Dice similarity coefficient (DSC), precision, recall, and the area of Kambin's triangle were used to assess anatomical performance. The automatic segmentation of all spinal structures at the L4/L5 levels and L5/S1 levels resulted in good performance: DSC = 0.878/0.883, precision = 0.889/0.890, recall = 0.873/0.882. Furthermore, the area measurements of Kambin's triangle revealed no significant difference between ground truth and automatic segmentation (p = 0.333 at the L4/L5 level, p = 0.302 at the L5/S1 level). The 3D U‐Net model used in this study performed well in terms of simultaneous segmentation of multi‐class spinal structures (including bones, dura mater, discs, and nerve roots) on MRI, allowing for accurate 3D reconstruction of Kambin's triangle.

Automatic estimation of axial spine indices is clinically desired for various spine computer aided procedures, such as disease diagnosis, therapeutic evaluation, pathophysiological understanding, risk assessment, and biomechanical modeling. Currently, the spine indices are manually measured by physicians, which is time-consuming and laborious. Even worse, the tedious manual procedure might result in inaccurate measurement. To deal with this problem, in this paper, we aim at developing an automatic method to estimate multiple indices from axial spine images. Inspired by the success of deep learning for regression problems and the densely connected network for image classification, we propose a dense enhancing network (DE-Net) which uses the dense enhancing blocks (DEBs) as its main body, where a feature enhancing layer is added to each of the bypass in a dense block. The DEB is designed to enhance discriminative feature embedding from the intervertebral disc and the dural sac areas. In addition, the cross-space distance-preserving regularization (CSDPR), which enforces consistent inter-sample distances between the output and the label spaces, is proposed to regularize the loss function of the DE-Net. To train and validate the proposed method, we collected 895 axial spine MRI images from 143 subjects and manually measured the indices as the ground truth. The results show that all deep learning models obtain very small prediction errors, and the proposed DE-Net with CSDPR acquires the smallest error among all methods, indicating that our method has great potential for spine computer aided procedures.

Ankylosing spondylitis is a chronic, progressive disease, and its treatment is relevant to the gut microbiota. Anti‐tumor necrosis factor‐alpha (anti‐TNF‐α) therapy alters the gut microbiota in many diseases, including inflammatory bowel disease. However, little is known about the effect of TNF‐α blocker treatment on the gut microbiota in ankylosing spondylitis. Herein, the effect of a TNF‐α blocker on the gut microbiota in proteoglycan‐induced arthritis was investigated. Proteoglycan‐induced mice were treated with an rhTNFR:Fc solution of etanercept (5 µg/g) for 4 weeks. rhTNFR:Fc treatment attenuated the arthritis incidence and severity of arthritis in the proteoglycan‐induced mice and decreased inflammation in the ankle joints and ameliorated ileal tissue destruction. Moreover, high gut permeability occurred, and zonula occludens‐1 and occludin protein levels were reduced in proteoglycan‐induced mice. These levels were significantly restored by the administration of rhTNFR:Fc. The serum TNF‐α and IL‐17 levels were also decreased. In addition, flora analysis via 16S rDNA high‐throughput sequencing revealed that rhTNFR:Fc treatment restored the gut microbiota composition to a composition similar to that in control mice. In conclusion, anti‐TNF‐α therapy attenuated proteoglycan‐induced arthritis progression and modulated the gut microbiota and intestinal barrier function. These results provide new insights for anti‐TNF‐α therapy strategies via regulating the gut microbiota in ankylosing spondylitis.