BackgroundTo isolate and characterization of human spermatogonial stem cells from stem spermatogonium.MethodsThe disassociation of spermatogonial stem cells (SSCs) were performed using enzymatic digestion of type I collagenase and trypsin. The SSCs were isolated by using Percoll density gradient centrifugation, followed by differential surface-attachment method. Octamer-4(OCT4)-positive SSC cells were further identified using immunofluorescence staining and flow cytometry technques. The purity of the human SSCs was also determined, and a co-culture system for SSCs and Sertoli cells was established.ResultsThe cell viability was 91.07% for the suspension of human spermatogonial stem cells dissociated using a two-step enzymatic digestion process. The cells isolated from Percoll density gradient coupled with differential surface-attachement purification were OCT4 positive, indicating the cells were human spermatogonial stem cells. The purity of isolated human spermatogonial stem cells was 86.7% as assessed by flow cytometry. The isolated SSCs were shown to form stable human spermatogonial stem cell colonies on the feeder layer of the Sertoli cells.ConclusionsThe two-step enzyme digestion (by type I collagenase and trypsin) process is an economical, simple and reproducible technique for isolating human spermatogonial stem cells. With little contamination and less cell damage, this method facilitates isolated human spermatogonial stem cells to form a stable cell colony on the supporting cell layer.
Background. Many complications occur after surgery in patients with spinal tuberculosis (STB); however, the severity varies in different patients. The complications’ severity is evaluated from grades I to V by the Clavien–Dindo classification (CDC), and grade V is the most severe. Most complications are mild, and only severe complications are life threatening, and thus, it is important to identify severe complications in patients with STB. The purpose of this study was to identify the risk factors of postoperative complication severity in patients with STB. Methods. Between January 2012 and May 2021, a retrospective study included 188 patients that underwent STB debridement surgery. The patients were divided into three groups based on postoperative complication severity. Clinical characteristics measured included age, sex, body mass index (BMI), comorbidities of diabetes mellitus and pulmonary tuberculosis, alcohol use and smoking history, course of disease, preoperative hemoglobin, preoperative serum albumin, preoperative lymphocytes, preoperative erythrocyte sedimentation rate (ESR), preoperative C-reactive protein (CRP), surgical approach, operating time, blood loss during surgery, postoperative hemoglobin, and postoperative serum albumin. The clinical characteristics of patients with STB who developed postoperative complications were evaluated using logistic regression analysis. Results. 188 patients suffered at least one postoperative complication; 77, 91, and 20 patients experienced grade I, II, and III-IV complications, respectively. In the univariate analysis, sex, diabetes mellitus, postoperative hemoglobin, and postoperative albumin are statistically significant. In the multivariable analysis, postoperative albumin (adjusted odds ratio OR = 0.861 , P < 0.001 ) was an independent risk factor of the postoperative complication severity in patients with STB. Receiver operating characteristic (ROC) analysis showed that the optimal cutoff values for postoperative albumin were 32 g/L (sensitivity: 0.571, specificity: 0.714, area under the ROC curve: 0.680) and 30 g/L (sensitivity: 0.649, specificity: 0.800, area under the ROC curve: 0.697) for grade II and grade III-IV complications, respectively. Conclusions. Postoperative albumin is an independent risk factor for postoperative complication severity in patients with STB. The improvement of postoperative albumin levels may reduce the risk of severe complications in patients with STB.
Objective: This study aimed to find out the risk factors of postoperative moderate anemia (PMA) to develop a scoring scale for predicting the occurrence of PMA and to determine the recommended preoperative hemoglobin level in spinal tuberculosis (STB) patients.Methods: A total of 223 STB patients who underwent focus debridement from January 2012 to March 2020 were enrolled in the study. The study cohort was divided into two groups owing to the occurrence of PMA. Moderate anemia was defined as a hemoglobin level of < 90 g/L. The clinical characteristics of STB patients who developed PMA were evaluated, and a scale was developed by logistic regression analysis. The performance of this scoring scale is prevalidated.Results: Of the 223 patients, 76 developed PMA. Multivariate binary logistic regression analysis showed that body mass index, diabetes, low preoperative hemoglobin level, long operation time, and posterior approach were independent risk factors for PMA in STB patients. These significant items were assigned scores to create a scoring scale as to predicting PMA, and receiver operating characteristic (ROC) curve analysis implicated that the optimal cutoff score was 4 points. On the basis of the scoring scale, patients with scores within 0–3 points were defined as the low-risk group; those with scores within 4–6 points were defined as the moderate-risk group; and those with scores within 7–10 points were defined as the high-risk group. The perioperative decrease in hemoglobin level was 20.07 ± 10.47 g/L in the low-risk group, 24.44 ± 12.67 g/L in the moderate-risk group, and 29.18 ± 10.34 g/L in the high-risk group.Conclusion: According to the scoring scale, patients with STB with a score of 0–3 points have a low risk of PMA, those with a score of 4–6 have a moderate risk, and those with a score of 7–10 have a high risk. The recommended preoperative hemoglobin levels for the low-, moderate-, and high-risk groups are 110, 115, and 120 g/L, respectively.
BackgroundInterbody cage subsidence is a common complication after instrumented posterior lumbar fusion surgery, several previous studies have shown that cage subsidence is related to multiple factors. But the current research has not combined these factors to predict the subsidence, there is a lack of an individualized and comprehensive evaluation of the risk of cage subsidence following the surgery. So we attempt to identify potential risk factors and develop a risk prediction model that can predict the possibility of subsidence by providing a Cage Subsidence Score (CSS) after surgery, and evaluate whether machine learning-related techniques can effectively predict the subsidence.MethodsThis study reviewed 59 patients who underwent posterior lumbar fusion in our hospital from 2014 to 2019. They were divided into a subsidence group and a non-subsidence group according to whether the interbody fusion cage subsidence occurred during follow-up. Data were collected on the patient, including age, sex, cage segment, number of fusion segments, preoperative space height, postoperative space height, preoperative L4 lordosis Angle, postoperative L4 lordosis Angle, preoperative L5 lordosis Angle, postoperative PT, postoperative SS, postoperative PI. The conventional statistical analysis method was used to find potential risk factors that can lead to subsidence, then the results were incorporated into stepwise regression and machine learning algorithms, respectively, to build a model that could predict the subsidence. Finally the diagnostic efficiency of prediction is verified.ResultsUnivariate analysis showed significant differences in pre−/postoperative intervertebral disc height, postoperative L4 segment lordosis, postoperative PT, and postoperative SS between the subsidence group and the non-subsidence group (p < 0.05). The CSS was trained by stepwise regression: 2 points for postoperative disc height > 14.68 mm, 3 points for postoperative L4 segment lordosis angle >16.91°, and 4 points for postoperative PT > 22.69°. If the total score is larger than 0.5, it is the high-risk subsidence group, while less than 0.5 is low-risk. The score obtains the area under the curve (AUC) of 0.857 and 0.806 in the development and validation set, respectively. The AUC of the GBM model based on the machine learning algorithm to predict the risk in the training set is 0.971 and the validation set is 0.889. The AUC of the avNNet model reached 0.931 in the training set and 0.868 in the validation set, respectively.ConclusionThe machine learning algorithm has advantages in some indicators, and we have preliminarily established a CSS that can predict the risk of postoperative subsidence after lumbar fusion and confirmed the important application prospect of machine learning in solving practical clinical problems.
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