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  • Liao, Shian  (17)
  • Yao, Yuanlin  (17)
  • 1
    Online Resource
    Online Resource
    Pulmonary embolism following the third thoracic tuberculosis surgery: A case report and literature review
    Wiley ; 2022
    In:  Journal of Clinical Laboratory Analysis Vol. 36, No. 3 ( 2022-03)
    Details
    In: Journal of Clinical Laboratory Analysis, Wiley, Vol. 36, No. 3 ( 2022-03)
    Abstract: The study aimed to analyze the clinical effects of pulmonary embolism succeeding a third surgery conducted for multiple recurrences in thoracic tuberculosis (TB). Case report A 74‐year‐old female patient developed thoracic tuberculosis and was subsequently treated in our hospital in March 2019, October 2020, and February 2021. The third surgical intervention included anterolateral thoracic lesion resection, internal fixation, posterior spinal tuberculous sinus resection, and debridement with suture. The operative time was 172 min resulting in a substantial intraoperative blood loss (2321 ml). Postoperative re‐examination of chest CTPA indicated a strip filling defect and pulmonary embolism in the external branch of the right middle lobe of the lung. After completing the active treatment, the D‐dimer quantification, WBC, CRP, and ESR values were 1261 ng/ml, 7.71 × 10 9 /L, 74.66 mg/L, and 63 mm, respectively. Chest CTPA re‐examination after the treatment showed no signs of pulmonary embolism. Conclusion Patients with a long‐term history of multiple operations, high BMI, cerebral infarction, diabetes, and older age group were more likely to develop pulmonary embolism after spinal tuberculosis surgery. Thus, the possibility of postoperative pulmonary embolism should be thoroughly analyzed before any subsequent surgical treatment in patients with recurrent spinal tuberculosis.
    Type of Medium: Online Resource
    ISSN: 0887-8013 , 1098-2825
    URL: Issue
    URL: Article
    DOI: 10.1002/jcla.v36.3
    DOI: 10.1002/jcla.24256
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2001635-9
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  • 2
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    Table1.xlsx
    Frontiers Media SA ; 2023
    In:  Frontiers in Surgery Vol. 9 ( 2023-1-6)
    Details
    In: Frontiers in Surgery, Frontiers Media SA, Vol. 9 ( 2023-1-6)
    Abstract: Tuberculosis (TB) is a chronic infectious disease. Bone and joint TB is a common type of extrapulmonary TB and often occurs secondary to TB infection. In this study, we aimed to find the difference in the blood examination results of patients with bone and joint TB and patients with TB by using machine learning (ML) and establish a diagnostic model to help clinicians better diagnose the disease and allow patients to receive timely treatment. Methods A total of 1,667 patients were finally enrolled in the study. Patients were randomly assigned to the training and validation cohorts. The training cohort included 1,268 patients: 158 patients with bone and joint TB and 1,110 patients with TB. The validation cohort included 399 patients: 48 patients with bone and joint TB and 351 patients with TB. We used three ML methods, namely logistic regression, LASSO regression, and random forest, to screen the differential variables, obtained the most representative variables by intersection to construct the prediction model, and verified the performance of the proposed prediction model in the validation group. Results The results revealed a great difference in the blood examination results of patients with bone and joint TB and those with TB. Infectious markers such as hs-CRP, ESR, WBC, and NEUT were increased in patients with bone and joint TB. Patients with bone and joint TB were found to have higher liver function burden and poorer nutritional status. The factors screened using ML were PDW, LYM, AST/ALT, BUN, and Na, and the nomogram diagnostic model was constructed using these five factors. In the training cohort, the area under the curve (AUC) value of the model was 0.71182, and the C value was 0.712. In the validation cohort, the AUC value of the model was 0.6435779, and the C value was 0.644. Conclusion We used ML methods to screen out the blood-specific factors—PDW, LYM, AST/ALT, BUN, and Na + —of bone and joint TB and constructed a diagnostic model to help clinicians better diagnose the disease in the future.
    Type of Medium: Online Resource
    ISSN: 2296-875X
    URL: Article
    URL: Article
    DOI: 10.3389/fsurg.2022.1031105
    DOI: 10.3389/fsurg.2022.1031105.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2773823-1
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  • 3
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    Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram
    Springer Science and Business Media LLC ; 2022
    In:  BMC Musculoskeletal Disorders Vol. 23, No. 1 ( 2022-12)
    Details
    In: BMC Musculoskeletal Disorders, Springer Science and Business Media LLC, Vol. 23, No. 1 ( 2022-12)
    Abstract: The present study attempted to predict blood transfusion risk in spinal tuberculosis surgery by using a novel predictive nomogram. Methods The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion group) who underwent spinal tuberculosis surgery in our hospital from June 2012 to June 2021. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analyses were used to screen out statistically significant parameters, which were included to establish a novel predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration curves, C-index, and decision curve analysis (DCA) were used to evaluate the model. Finally, the nomogram was further assessed through internal validation. Results The C-index of the nomogram was 0.787 (95% confidence interval: 74.6%–.82.8%). The C-value calculated by internal validation was 0.763. The area under the curve (AUC) of the predictive nomogram was 0.785, and the DCA was 0.01–0.79. Conclusion A nomogram with high accuracy, clinical validity, and reliability was established to predict blood transfusion risk in spinal tuberculosis surgery. Surgeons must prepare preoperative surgical strategies and ensure adequate availability of blood before surgery.
    Type of Medium: Online Resource
    ISSN: 1471-2474
    URL: Article
    DOI: 10.1186/s12891-022-05132-z
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2041355-5
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  • 4
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    Development and Validation of a Nomogram for Predicting Albumin Transfusion After Spinal Tuberculosis Surgery: Based on Propensity Score Matching Analysis
    Elsevier BV ; 2022
    In:  World Neurosurgery Vol. 157 ( 2022-01), p. e374-e389
    Details
    In: World Neurosurgery, Elsevier BV, Vol. 157 ( 2022-01), p. e374-e389
    Type of Medium: Online Resource
    ISSN: 1878-8750
    URL: Article
    DOI: 10.1016/j.wneu.2021.10.102
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 2530041-6
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  • 5
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    Mechanism of COVID-19-Related Proteins in Spinal Tuberculosis: Immune Dysregulation
    Frontiers Media SA ; 2022
    In:  Frontiers in Immunology Vol. 13 ( 2022-6-2)
    Details
    In: Frontiers in Immunology, Frontiers Media SA, Vol. 13 ( 2022-6-2)
    Abstract: The purpose of this article was to investigate the mechanism of immune dysregulation of COVID-19-related proteins in spinal tuberculosis (STB). Methods Clinical data were collected to construct a nomogram model. C-index, calibration curve, ROC curve, and DCA curve were used to assess the predictive ability and accuracy of the model. Additionally, 10 intervertebral disc samples were collected for protein identification. Bioinformatics was used to analyze differentially expressed proteins (DEPs), including immune cells analysis, Gene Ontology (GO) and KEGG pathway enrichment analysis, and protein-protein interaction networks (PPI). Results The nomogram predicted risk of STB ranging from 0.01 to 0.994. The C-index and AUC in the training set were 0.872 and 0.862, respectively. The results in the external validation set were consistent with the training set. Immune cells scores indicated that B cells naive in STB tissues were significantly lower than non-TB spinal tissues. Hub proteins were calculated by Degree, Closeness, and MCC methods. The main KEGG pathway included Coronavirus disease-COVID-19. There were 9 key proteins in the intersection of COVID-19-related proteins and hub proteins. There was a negative correlation between B cells naive and RPL19. COVID-19-related proteins were associated with immune genes. Conclusion Lymphocytes were predictive factors for the diagnosis of STB. Immune cells showed low expression in STB. Nine COVID-19-related proteins were involved in STB mechanisms. These nine key proteins may suppress the immune mechanism of STB by regulating the expression of immune genes.
    Type of Medium: Online Resource
    ISSN: 1664-3224
    URL: Article
    DOI: 10.3389/fimmu.2022.882651
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2606827-8
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  • 6
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    Comprehensive AI-assisted tool for ankylosing spondylitis based on multicenter research outperforms human experts
    Frontiers Media SA ; 2023
    In:  Frontiers in Public Health Vol. 11 ( 2023-2-9)
    Details
    In: Frontiers in Public Health, Frontiers Media SA, Vol. 11 ( 2023-2-9)
    Abstract: The diagnosis and treatment of ankylosing spondylitis (AS) is a difficult task, especially in less developed countries without access to experts. To address this issue, a comprehensive artificial intelligence (AI) tool was created to help diagnose and predict the course of AS. Methods In this retrospective study, a dataset of 5389 pelvic radiographs (PXRs) from patients treated at a single medical center between March 2014 and April 2022 was used to create an ensemble deep learning (DL) model for diagnosing AS. The model was then tested on an additional 583 images from three other medical centers, and its performance was evaluated using the area under the receiver operating characteristic curve analysis, accuracy, precision, recall, and F1 scores. Furthermore, clinical prediction models for identifying high-risk patients and triaging patients were developed and validated using clinical data from 356 patients. Results The ensemble DL model demonstrated impressive performance in a multicenter external test set, with precision, recall, and area under the receiver operating characteristic curve values of 0.90, 0.89, and 0.96, respectively. This performance surpassed that of human experts, and the model also significantly improved the experts' diagnostic accuracy. Furthermore, the model's diagnosis results based on smartphone-captured images were comparable to those of human experts. Additionally, a clinical prediction model was established that accurately categorizes patients with AS into high-and low-risk groups with distinct clinical trajectories. This provides a strong foundation for individualized care. Discussion In this study, an exceptionally comprehensive AI tool was developed for the diagnosis and management of AS in complex clinical scenarios, especially in underdeveloped or rural areas that lack access to experts. This tool is highly beneficial in providing an efficient and effective system of diagnosis and management.
    Type of Medium: Online Resource
    ISSN: 2296-2565
    URL: Article
    DOI: 10.3389/fpubh.2023.1063633
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2711781-9
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  • 7
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    Monocyte-to-Lymphocyte Ratio Was an Independent Factor of the Severity of Spinal Tuberculosis
    Hindawi Limited ; 2022
    In:  Oxidative Medicine and Cellular Longevity Vol. 2022 ( 2022-5-20), p. 1-18
    Details
    In: Oxidative Medicine and Cellular Longevity, Hindawi Limited, Vol. 2022 ( 2022-5-20), p. 1-18
    Abstract: Purpose. The purpose was to explore the relationship between monocyte-to-lymphocyte ratio (MLR) and the severity of spinal tuberculosis. Methods. A total of 1,000 clinical cases were collected, including 496 cases of spinal tuberculosis and 504 cases of nonspinal tuberculosis. Laboratory blood results were collected, including C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), white blood cells (WBC), hemoglobin (HGB), platelets (PLT), neutrophil count, percentage of neutrophils, lymphocyte count, percentage of lymphocytes, monocyte count, percentage of monocytes, MLR, platelets -to- monocyte ratio (PMR), platelets -to- lymphocyte ratio (PLR), neutrophil -to- lymphocyte ratio (NLR), and platelets -to- neutrophil ratio (PNR). The statistical parameters analyzed by the Least Absolute Shrinkage and Selection Operator (LASSO) and receiver-operating characteristic (ROC) curves were used to construct the nomogram. The nomogram was assessed by C-index, calibration curve, ROC curve, and decision curve analysis (DCA) curve. Results. The C-index of the nomogram in the training set and external validation set was 0.801 and 0.861, respectively. Similarly, AUC was 0.801 in the former and 0.861 in the latter. The net benefit of the former nomogram ranged from 0.1 to 0.95 and 0.02 to 0.99 in the latter nomogram. Furthermore, there was a correlation between MLR and the severity of spinal tuberculosis. Conclusion. MLR was an independent factor in the diagnosis of spinal tuberculosis and was associated with the severity of spinal tuberculosis. Additionally, MLR may be a predictor of active spinal tuberculosis.
    Type of Medium: Online Resource
    ISSN: 1942-0994 , 1942-0900
    URL: Article
    DOI: 10.1155/2022/7340330
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2455981-7
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  • 8
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    Application of machine learning in prediction of bone cement leakage during single-level thoracolumbar percutaneous vertebroplasty
    Springer Science and Business Media LLC ; 2023
    In:  BMC Surgery Vol. 23, No. 1 ( 2023-03-23)
    Details
    In: BMC Surgery, Springer Science and Business Media LLC, Vol. 23, No. 1 ( 2023-03-23)
    Abstract: In the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery. Methods The clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables using four ML methods and used the intersection to generate the prediction model. In addition, predictive models were used in the validation cohort. Results The ML method was used to select five factors to create a Nomogram diagnostic model. The nomogram model's AUC was 0.646667, and its C value was 0.647. The calibration curves revealed a consistent relationship between nomogram predictions and actual probabilities. In 91 randomized samples, the AUC of this nomogram model was 0.7555116. Conclusion In this study, we invented a prediction model for bone cement leakage in single-segment PVP surgery, which can help doctors in performing better surgery with reduced risk.
    Type of Medium: Online Resource
    ISSN: 1471-2482
    URL: Article
    DOI: 10.1186/s12893-023-01959-y
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2050442-1
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  • 9
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    To infer the probability of cervical ossification of the posterior longitudinal ligament and explore its impact on cervical surgery
    Springer Science and Business Media LLC ; 2023
    In:  Scientific Reports Vol. 13, No. 1 ( 2023-06-17)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2023-06-17)
    Abstract: The ossification of the posterior longitudinal ligament (OPLL) in the cervical spine is commonly observed in degenerative changes of the cervical spine. Early detection of cervical OPLL and prevention of postoperative complications are of utmost importance. We gathered data from 775 patients who underwent cervical spine surgery at the First Affiliated Hospital of Guangxi Medical University, collecting a total of 84 variables. Among these patients, 144 had cervical OPLL, while 631 did not. They were randomly divided into a training cohort and a validation cohort. Multiple machine learning (ML) methods were employed to screen the variables and ultimately develop a diagnostic model. Subsequently, we compared the postoperative outcomes of patients with positive and negative cervical OPLL. Initially, we compared the advantages and disadvantages of various ML methods. Seven variables, namely Age, Gender, OPLL, AST, UA, BMI, and CHD, exhibited significant differences and were used to construct a diagnostic nomogram model. The area under the curve (AUC) values of this model in the training and validation groups were 0.76 and 0.728, respectively. Our findings revealed that 69.2% of patients who underwent cervical OPLL surgery eventually required elective anterior surgery, in contrast to 86.8% of patients who did not have cervical OPLL. Patients with cervical OPLL had significantly longer operation times and higher postoperative drainage volumes compared to those without cervical OPLL. Interestingly, preoperative cervical OPLL patients demonstrated significant increases in mean UA, age, and BMI. Furthermore, 27.1% of patients with cervical anterior longitudinal ligament ossification (OALL) also exhibited cervical OPLL, whereas this occurrence was only observed in 6.9% of patients without cervical OALL. We developed a diagnostic model for cervical OPLL using the ML method. Our findings indicate that patients with cervical OPLL are more likely to undergo posterior cervical surgery, and they exhibit elevated UA levels, higher BMI, and increased age. The prevalence of cervical anterior longitudinal ligament ossification was also significantly higher among patients with cervical OPLL.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-023-36992-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2615211-3
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  • 10
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    A seven-gene signature and the C–C motif chemokine receptor family genes are the sarcoma-related immune genes
    Informa UK Limited ; 2021
    In:  Bioengineered Vol. 12, No. 1 ( 2021-01-01), p. 7616-7630
    Details
    In: Bioengineered, Informa UK Limited, Vol. 12, No. 1 ( 2021-01-01), p. 7616-7630
    Type of Medium: Online Resource
    ISSN: 2165-5979 , 2165-5987
    URL: Article
    DOI: 10.1080/21655979.2021.1981797
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2737830-5
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