OBJECTIVE: To explore the clinical effect of personalized puncture planning before surgery using Picture Archiving and Communication System (PACS) in the treatment of osteoporotic vertebral compression fractures in the elderly. METHODS: A total of 69 elderly patients with osteoporotic vertebral compression fractures treated by percutaneous vertebroplasty from January 2020 20 to December 2021 with more than 1 year of follow-up were analyzed retrospectively. Thirty-four patients were individualized for preoperative planning with PACS software (observation group), including 8 males and 26 females, with a mean age of (73.30±7.96) years old;and 35 patients were treated with conventional treatment (control group), including 7 males and 28 females, with a mean age of (77.30±7.84) years old. The operation time, the amount of cement injection, cement leakage rate, bone watertight diffusion and refracture within 1 year between two groups were observed and compared. The Cobb's angle, low back pain visual analogue scale(VAS) and the modified Oswsetry disability indexes(ODI) before surgery and 1 day, 1 year after surgery were compared between two groups. RESULTS: Both groups successfully completed the operation without serious surgical complications, 2 refractures occurred in the control group. The operation time in the observation group was(41.9±11.9) min, which was less than that in the control group (52.7±13.6) min (P<0.05). There was no significant difference in the cement injection volume between two groups (P>0.05). Two cases of cement leakage in the observation group was less than 8 in the control group (P<0.05). The bone cement distribution index of two groups had significant difference(P<0.05). There were no significant differences between two groups in Cobb's angle of the injured vertebras and ODI before and 1 day after surgery(P>0.05), however, the comparative differences were statistically significant at 1 year after surgery(P<0.05). There was no significant difference in the VAS between two groups at each time period(P>0.05). CONCLUSION Using the PACS software to plan personalized puncture scheme can reduce the operation time, reduce the cement leakage rate, improve the diffusion of bone cement and longer maintain the postoperative form of vertebral body and the functional state of patients' lumbar back.
Humans ; Male ; Female ; Aged ; Vertebroplasty/methods* ; Fractures, Compression/diagnostic imaging* ; Spinal Fractures/diagnostic imaging* ; Osteoporotic Fractures/diagnostic imaging* ; Aged, 80 and over ; Retrospective Studies ; Radiology Information Systems

OBJECTIVE: To explore clinical efficacy of percutaneous endoscopic discectomy with a lateral approach and dual-channel method in treating highly free lumbar disc herniation(LDH). METHODS: A retrospective analysis was conducted on 54 patients with highly free LDH who were treated with spinal endoscopic techniques from January 2021 to December 2022. Twenty-seven patients were treated with lateral approach dual-channel(lateral approach dual-channel group), including 16 males and 11 females, with an average age of (54.6±10.5) years old. Twenty-seven patients were treated with unilateral biportal endoscopic (UBE group), including 17 males and 10 females, with an average age of (52.9±12.3) years old. The number of intraoperative fluoroscopy, operation time and hospital stay, as well as visual analogue scale (VAS) and Oswestry diability index (ODI) of low back and leg pain between two patients before operation, 1 day, 1, 3, and 12 months after operation, and the efficacy was evaluated by the modified MacNab criteria at 12 mohths after operation. RESULTS: All patients were successfully completed surgical and were followed up, the time raged from 12 to 22 months with an average of (13.57±4.12) months. There was no statistically significant difference in operation time between two groups (P>0.05). The hospital stay of lateral approach dual-channel group was (3.9±1.1) days, which was shorter than that of UBE group (6.5±1.4) days, the number of intraoperative fluoroscopy in lateral approach dual-channel group was (12.7±2.1) times, which was more than that in UBE group (6.6±1.3) times, the differences were statistically significant (t=5.197, -7.532;P<0.05). VAS and ODI for low back pain at 1 day and 1 month after operation, and VAS for leg pain at 1 day after operation of lateral approach dual-channel group were superior to those of UBE group, and the differences were statistically significant (P<0.05). However, there were no statistically significant differences in VAS and ODI for low back and leg pain between two groups before operation and 3 and 12 months after operation (P>0.05). VAS and ODI of low back and leg pain were significantly improved at each time point before and after operation in both groups, and the difference were statistically significant (P<0.05). At 12 months after operation, according to the modified MacNab criteria, the excellent and good rates of therapeutic effects between lateral approach dual-channel group and UBE group were 92.6% (25/27) and 88.9% (24/27), respectively, and the difference was not statistically significant (χ²=0.22, P>0.05). CONCLUSION For patients with highly free lumbar intervertebral disc protrusion, both of lateral approach dual-channel method and UBE endoscopic surgery are safe and effective. Endoscopic surgery with lateral approach and dual-channel method could be performed under local anesthesia, allowing for the removal of the nucleus pulposus under direct vision. It is simpler, more efficient.
Humans ; Male ; Female ; Intervertebral Disc Displacement/surgery* ; Middle Aged ; Diskectomy, Percutaneous/methods* ; Lumbar Vertebrae/surgery* ; Endoscopy/methods* ; Adult ; Retrospective Studies ; Aged

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Jiuwei Xifeng Granules have become a Chinese patent medicine in the market. Because the formula contains Os Draconis, a top-level protected fossil of ancient organisms, the formula was to be improved by replacing Os Draconis with Ostreae Concha. To evaluate whether the improved formula has the same effectiveness and safety as the original formula, a randomized, double-blind, parallel-controlled, equivalence clinical trial was conducted. This study enrolled 288 tic disorder(TD) of children and assigned them into two groups in 1∶1. The treatment group and control group took the modified formula and original formula, respectively. The treatment lasted for 6 weeks, and follow-up visits were conducted at weeks 2, 4, and 6. The primary efficacy endpoint was the difference in Yale global tic severity scale(YGTSS)-total tic severity(TTS) score from baseline after 6 weeks of treatment. The results showed that after 6 weeks of treatment, the declines in YGTSS-TSS score showed no statistically significant difference between the two groups. The difference in YGTSS-TSS score(treatment group-control group) and the 95%CI of the full analysis set(FAS) were-0.17[-1.42, 1.08] and those of per-protocol set(PPS) were 0.29[-0.97, 1.56], which were within the equivalence boundary [-3, 3]. The equivalence test was therefore concluded. The two groups showed no significant differences in the secondary efficacy endpoints of effective rate for TD, total score and factor scores of YGTSS, clinical global impressions-severity(CGI-S) score, traditional Chinese medicine(TCM) response rate, or symptom disappearance rate, and thus a complete evidence chain with the primary outcome was formed. A total of 6 adverse reactions were reported, including 4(2.82%) cases in the treatment group and 2(1.41%) cases in the control group, which showed no statistically significant difference between the two groups. No serious suspected unexpected adverse reactions were reported, and no laboratory test results indicated serious clinically significant abnormalities. The results support the replacement of Os Draconis by Ostreae Concha in the original formula, and the efficacy and safety of the modified formula are consistent with those of the original formula.
Adolescent ; Child ; Child, Preschool ; Female ; Humans ; Male ; Double-Blind Method ; Drugs, Chinese Herbal/therapeutic use* ; Tic Disorders/drug therapy* ; Treatment Outcome

Emodin is a hydroxyanthraquinone compound that is widely distributed and has multiple pharmacological activities, including anti-diarrheal, anti-inflammatory, and liver-protective effects. Research indicates that emodin may be one of the main components responsible for inducing hepatotoxicity. However, studies on the mechanisms of liver injury are relatively limited, particularly those related to bile acids(BAs) metabolism. This study aims to systematically investigate the effects of different dosages of emodin on BAs metabolism, providing a basis for the safe clinical use of traditional Chinese medicine(TCM)containing emodin. First, this study evaluated the safety of repeated administration of different dosages of emodin over a 5-week period, with a particular focus on its impact on the liver. Next, the composition and content of BAs in serum and liver were analyzed. Subsequently, qRT-PCR was used to detect the mRNA expression of nuclear receptors and transporters related to BAs metabolism. The results showed that 1 g·kg~(-1) emodin induced hepatic damage, with bile duct hyperplasia as the primary pathological manifestation. It significantly increased the levels of various BAs in the serum and primary BAs(including taurine-conjugated and free BAs) in the liver. Additionally, it downregulated the mRNA expression of farnesoid X receptor(FXR), retinoid X receptor(RXR), and sodium taurocholate cotransporting polypeptide(NTCP), and upregulated the mRNA expression of cholesterol 7α-hydroxylase(CYP7A1) in the liver. Although 0.01 g·kg~(-1) and 0.03 g·kg~(-1) emodin did not induce obvious liver injury, they significantly increased the level of taurine-conjugated BAs in the liver, suggesting a potential interference with BAs homeostasis. In conclusion, 1 g·kg~(-1) emodin may promote the production of primary BAs in the liver by affecting the FXR-RXR-CYP7A1 pathway, inhibit NTCP expression, and reduce BA reabsorption in the liver, resulting in BA accumulation in the peripheral blood. This disruption of BA homeostasis leads to liver injury. Even doses of emodin close to the clinical dose can also have a certain effect on the homeostasis of BAs. Therefore, when using traditional Chinese medicine or formulas containing emodin in clinical practice, it is necessary to regularly monitor liver function indicators and closely monitor the risk of drug-induced liver injury.
Emodin/administration & dosage* ; Bile Acids and Salts/metabolism* ; Animals ; Male ; Liver/injuries* ; Chemical and Drug Induced Liver Injury/genetics* ; Drugs, Chinese Herbal/adverse effects* ; Humans ; Rats, Sprague-Dawley ; Mice ; Rats