ObjectiveBased on traditional quality evaluation of Gardeniae Fructus（GF） recorded in historical materia medica， this study systematically compared the quality differences between wild and cultivated GF from morphological characteristics， microscopic features， and contents of primary and secondary metabolites. MethodsVernier calipers and analytical balances were used to measure the length， diameter and individual fruit weight of wild and cultivated GF， and the aspect ratio was calculated. A colorimeter was used to determine the chromaticity value of wild and cultivated GF， and the paraffin sections of them were prepared by safranin-fast green staining and examined under an optical microscope to observe their microstructure. Subsequently， the contents of water-soluble and alcohol-soluble extracts of wild and cultivated GF were detected by hot immersion method under the general rule 2201 in volume Ⅳ of the 2020 edition of the Pharmacopoeia of the People's Republic of China， the starch content was measured by anthrone colorimetric method， the content of total polysaccharides was determined by phenol-sulfuric acid colorimetric method， the sucrose content was determined by high performance liquid chromatography coupled with evaporative light scattering detection（HPLC-ELSD）， and the contents of representative components in them were measured by ultra-performance liquid chromatography（UPLC）. Finally， correlation analysis was conducted between quality traits and phenotypic traits， combined with multivariate statistical analysis methods such as principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）， key differential components between wild and cultivated GF were screened. ResultsIn terms of traits， the wild GF fruits were smaller， exhibiting reddish yellow or brownish red hues with significant variation between batches. While the cultivated GF fruits are larger， displaying deeper orange-red or brownish red. The diameter and individual fruit weight of cultivated GF were significantly greater than those of wild GF， while the blue-yellow value（b^*） of wild GF was significantly higher than that of cultivated GF. In the microstructure， the mesocarp of wild GF contained numerous scattered calcium oxalate cluster crystals， while the endocarp contained stone cell class round， polygonal or tangential prolongation， undeveloped seeds were visible within the fruit. In contrast， the mesocarp of cultivated GF contained few calcium oxalate cluster crystals， or some batches exhibited extremely numerous cluster crystals. The stone cells in the endocarp were predominantly round-like， with the innermost layer arranged in a grid pattern. Seeds were basically mature， and only a few immature seeds existed in some batches. Regarding primary metabolite content， wild GF exhibited significantly higher total polysaccharide level than cultivated GF（P<0.01）. In category-specific component content， wild GF exhibited significantly higher levels of total flavonoids and total polyphenols compared to cultivated GF（P<0.01）. Analysis of 12 secondary metabolites revealed that wild GF exhibited significantly higher levels of Shanzhiside， deacetyl asperulosidic acid methyl ester， gardenoside and chlorogenic acid compared to cultivated GF（P<0.01）. Conversely， the contents of genipin 1-gentiobioside， geniposide and genipin were significantly lower in wild GF（P<0.01）. ConclusionThere are significant differences between wild and cultivated GF in terms of traits， microstructure， and contents of primary and secondary metabolites. At present， the quality evaluation system of cultivated GF remains incomplete， and this study provides a reference for guiding the production of high-quality GF medicinal materials.

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.

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: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

OBJECTIVE: To study the in vitro and in vivo antitumor effects of the polysaccharide of Alocasia cucullata (PAC) and the underlying mechanism. METHODS: B16F10 and 4T1 cells were cultured with PAC of 40 µg/mL, and PAC was withdrawn after 40 days of administration. The cell viability was detected by cell counting kit-8. The expression of Bcl-2 and Caspase-3 proteins were detected by Western blot and the expressions of ERK1/2 mRNA were detected by quantitative real-time polymerase chain reaction (qRT-PCR). A mouse melanoma model was established to study the effect of PAC during long-time administration. Mice were divided into 3 treatment groups: control group treated with saline water, positive control group (LNT group) treated with lentinan at 100 mg/(kg·d), and PAC group treated with PAC at 120 mg/(kg·d). The pathological changes of tumor tissues were observed by hematoxylin-eosin staining. The apoptosis of tumor tissues was detected by TUNEL staining. Bcl-2 and Caspase-3 protein expressions were detected by immunohistochemistry, and the expressions of ERK1/2, JNK1 and p38 mRNA were detected by qRT-PCR. RESULTS: In vitro, no strong inhibitory effects of PAC were found in various tumor cells after 48 or 72 h of administration. Interestingly however, after 40 days of cultivation under PAC, an inhibitory effect on B16F10 cells was found. Correspondingly, the long-time administration of PAC led to downregulation of Bcl-2 protein (P<0.05), up-regulation of Caspase-3 protein (P<0.05) and ERK1 mRNA (P<0.05) in B16F10 cells. The above results were verified by in vivo experiments. In addition, viability of B16F10 cells under long-time administration culture in vitro decreased after drug withdrawal, and similar results were also observed in 4T1 cells. CONCLUSIONS Long-time administration of PAC can significantly inhibit viability and promote apoptosis of tumor cells, and had obvious antitumor effect in tumor-bearing mice.
Mice ; Animals ; Alocasia/metabolism* ; MAP Kinase Signaling System ; Caspase 3/metabolism* ; Apoptosis ; RNA, Messenger/metabolism*

Objective To elucidate the clinical significance of high expression levels of endonuclease meiosis 1(EME1)in the prognosis of hepatocellular carcinoma(HCC).Methods The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO)databases were used to screen and analyze differential gene expression between HCC and non-tumor tissues.A retrospective collection of liver tissue samples from 80 HCC patients who underwent hepatectomy in the Fifth Medical Center of Chinese PLA General Hospital between January 2010 and December 2014 was performed.Immunohistochemistry analysis was employed to detect the EME1 expression levels.Survival analysis was then conducted to assess the impact of EME1 expression on 5-year postoperative survival rate of HCC patients.Additionally,gene enrichment analysis was applied to predict the function of EME1 in HCC.Results A total of 371 HCC tissue samples and 50 non-tumor liver tissue samples from TCGA database were analyzed,revealing significantly higher EME1 expression in HCC tissues.Microarray analysis of 107 samples within the GEO database(70 HCC tissues and 37 non-tumor tissues)confirmed that EME1 mRNA expression was markedly elevated in HCC tissues compared with non-tumor tissues(P<0.05).The 5-year overall survival(OS)rate was notably lower in high EME1 expression group than that in low expression group(44.1%vs.53.0%,P<0.05).Semi-quantitative immunohistochemistry analysis demonstrated that patients with high EME1 expression had a significantly lower OS rate than those with low EME1 expression(32.8%vs.45.0%,P<0.05).Multivariate COX regression analysis identified that high EME1 expression(HR=2.234,95%CI 1.073-4.649,P=0.032)and advanced China liver caner(CNLC)staging(HR=4.317,95%CI 1.799-10.359,P=0.001)were independent risk factors for the 5-year OS of post-operation patients with HCC.Conclusion Elevated EME1 expression in HCC tissues correlates with an adverse prognosis of HCC and suggests that EME1 could serve as a potential therapeutic target for HCC.