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Aims: Surgical approaches to cervical ossification of the posterior longitudinal ligament (OPLL) remain controversial. The purpose of the present study was to analyze and compare the long-term neurological recovery following anterior decompression with fusion (ADF) and posterior laminectomy and fusion with bone graft and internal fixation (PLF) based on > ten-year follow-up outcomes in a single centre. Methods: Included in this retrospective cohort study were 48 patients (12 females; mean age 55.79 years (SD 8.94)) who were diagnosed with cervical OPLL, received treatment in our centre, and were followed up for 10.22 to 15.25 years. Of them, 24 patients (six females; mean age 52.88 years (SD 8.79)) received ADF, and the other 24 patients (five females; mean age 56.25 years (SD 9.44)) received PLF. Clinical data including age, sex, and the OPLL canal-occupying ratio were analyzed and compared. The primary outcome was Japanese Orthopaedic Association (JOA) score, and the secondary outcome was visual analogue scale neck pain. Results: Compared with the baseline, neurological function improved significantly after surgery in all patients of both groups (p < 0.001). The JOA recovery rate in the ADF group was significantly higher than that in the PLF group (p < 0.001). There was no significant difference in postoperative cervical pain between the two groups (p = 0.387). The operating time was longer and intraoperative blood loss was greater in the PLF group than the ADF group. More complications were observed in the ADF group than in the PLF group, although the difference was not statistically significant. Conclusion: Long-term neurological function improved significantly after surgery in both groups, with the improvement more pronounced in the ADF group. There was no significant difference in postoperative neck pain between the two groups. The operating time was shorter and intraoperative blood loss was lower in the ADF group; however, the incidence of perioperative complications was higher.
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BACKGROUND: Parkinson's disease (PD) ranks as the second most prevalent neurodegenerative disorder globally, and its incidence is rapidly rising. The diagnosis of PD relies on clinical characteristics. Although current treatments aim to alleviate symptoms, they do not effectively halt the disease's progression. Early detection and intervention hold immense importance. This study aimed to establish a new PD diagnostic model. METHODS: Data from a public database were adopted for the construction and validation of a PD diagnostic model with random forest and artificial neural network models. The CIBERSORT platform was applied for the evaluation of immune cell infiltration in PD. Quantitative real-time PCR was performed to verify the accuracy and reliability of the bioinformatics analysis results. RESULTS: Leveraging existing gene expression data from the Gene Expression Omnibus (GEO) database, we sifted through differentially expressed genes (DEGs) in PD and identified 30 crucial genes through a random forest classifier. Furthermore, we successfully designed a novel PD diagnostic model using an artificial neural network and verified its diagnostic efficacy using publicly available datasets. Our research also suggests that mast cells may play a significant role in the onset and progression of PD. CONCLUSION: This work developed a new PD diagnostic model with machine learning techniques and suggested the immune cells as a potential target for PD therapy.