Objective To analyze the correlation between lesion volume,lesion mass,and maximum lesion diameter in the assessment of advanced hepatocarcinoma with lung metastasis,and to evaluate the application value of total volume response and total mass response of lung metastatic lesions in efficacy assessment.Methods A retrospective analysis was conducted on the CT imaging data of 20 patients clinically confirmed with hepatocarcinoma and lung metastases,followed by subsequent follow-up to monitor their survival outcomes.Volume measurement software was used to measure the volume of lesions before and after treatment.We recored lesion diameter,volume measurements and CT values,calculated the mass of the lesions.The correlation between lesion volume,mass and diameter was analyzed,as well as the correlation between the change rates of volume,mass and lesion diameter.Additionally,the total volume and total mass of all lesions were calculated.The correlation between the change rates of total volume/total mass and the change rate of pulmonary lesion diameter under the RECIST 1.1 criteria,as well as the correlation with changes in patients'tumor markers,were analyzed.Furthermore,the overall volume response and overall mass response of lesions were evaluated based on changes in total volume and total mass,and their consistencies with the RECIST 1.1 criteria for efficacy evaluation were analyzed.Finally,univariate Cox regression analysis was performed to explore the association between these variables and patient survival outcomes.Results There was strong correlation between lesion volume,mass and tumor diameter(r=0.771,0.775),between the rate of change in mass and the rate of change in lesion diameter(r=0.846),and between the rates of change in total volume/total mass and the rate of change in pulmonary lesion diameter under the RECIST 1.1 criteria(r=0.800,0.896).The correlation between the rates of change in total volume/total mass and patients'tumor markers was not statistically significant.There was moderate correlation between the rate of change in volume and the rate of change in lesion diameter(r=0.692).The evaluation results of total volume response and total mass response for pulmonary lesions in advanced hepatocarcinoma with lung metastasis were generally consistent with the RECIST 1.1 criteria(Kappa=0.486,0.426).Univariate Cox regression analysis revealed that total lesion volume(P=0.047)and total lesion mass(P=0.049)were independent prognostic factors for survival outcomes.Conclusion Lesion volume,mass,and diameter,as well as their respective change rates,were found to be interrelated.Furthermore,total lesion volume and total lesion mass were identified as independent prognostic factors for survival outcomes.The total volume response and total mass response are promising evaluation methods in evaluating the efficacy of lung metastasis of hepatocarcinoma,which are different from the RECIST 1.1 evaluation criteria.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective:To investigate the clinical diagnosis,treatment and prognosis of traumatic ectopic testis with torsion.Methods:We retrospectively analyzed the clinical data on a case of traumatic ectopic testis with torsion and reviewed relevant litera-ture.Results:After diagnosed with traumatic ectopic testis with torsion,the patient underwent exploratory operation for confirmation of orchiocatabasis,followed by testicular reduction and fixation.Follow-up visit at 1 month after surgery showed good blood supply and no obvious testicular atrophy.Conclusion:Traumatic ectopic testis with torsion is an extremely rare emergency condition,for which color Doppler ultrasonography is an effective means of examination.Once suspected of or confirmed with the problem,the patient should receive exploratory surgery,testicular reduction and fixation within 6 hours,and close postoperative observation.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

AIM To investigate the effects of Yiqi Juanbi Formula on chondrocyte pyroptosis in rat models of collagen-induced arthritis(CIA).METHODS Fifty rats were subcutaneously injected at the tail base with an emulsion containing equal volumes of bovine type Ⅱ collagen and incomplete Freund's adjuvant(IFA)to establish the CIA models.These rats were then randomly assigned to the model group,the methotrexate group(0.35 mg/kg),and the low-dose,medium-dose,and high-dose Yiqi Juanbi Formula groups(9.4,18.7,37.4 g/kg),in contrast to the ten intact rats serving in the normal control group.Following four weeks of intragastric administration,the rats had their general conditions observed;their joint swelling and arthritis indices measured;their ankle joint pathology assessed by HE staining;their serum levels of IL-1β,IL-18 and TNF-ɑ detected by ELISA;their mRNA expressions of NLRP3,Caspase-1,GSDMD,IL-1β,IL-18 and TNF-ɑ in ankle cartilage quantified by RT-qPCR;their protein expressions of NF-κB,NLRP3 and Caspase-1 in ankle cartilage analyzed by Western blot;and their NLRP3 and GSDMD positive expressions in ankle cartilage examined by immunohistochemistry.RESULTS Compared to the control group,the model group showed significantly increased joint swelling and arthritis indices(P＜0.01);elevated serum levels of IL-1 β,IL-18 and TNF-ɑ(P＜0.01);pathological changes including cartilage surface defects,reduced cell count,altered cellular morphology,irregular cell arrangement,and significant inflammatory cell infiltration in synovial tissue;upregulated mRNA expressions of NF-κB,NLRP3,Caspase-1,GSDMD,IL-1β,IL-18 and TNF-ɑ(P＜0.01)and increased protein expressions of NF-κB,NLRP3 and Caspase-1(P＜0.01)in ankle cartilage;enhanced positive expressions of NLRP3 and GSDMD in ankle cartilage(P＜0.01).Compared to the model group,the groups intervened with methotrexate or medium-or high-dose Yiqi Juanbi Formula exhibited reduced joint swelling and arthritis indices(P＜0.01);alleviated pathological damage in ankle joints;decreased serum levels of IL-1β,IL-18 and TNF-ɑ(P＜0.01);downregulated mRNA expressions of NF-κB,NLRP3,Caspase-1,GSDMD,IL-1β,IL-18 and TNF-ɑ(P＜0.05,P＜0.01),and reduced protein expressions of NF-κB,NLRP3 and Caspase-1(P＜0.05,P＜0.01)in ankle cartilage;and diminished positive expressions of NLRP3 and GSDMD in ankle cartilage(P＜0.01).CONCLUSION Yiqi Juanbi Formula alleviates inflammation in CIA rats,potentially by inhibiting the activation of the NF-κB/NLRP3/Caspase-1 signaling pathway,thereby suppressing chondrocyte pyroptosis.

Objective To investigate the practical effects of pediatric rapid sequence intubation(RSI)nursing coordination training based on the LSPPDM(learn,see,practice,prove,do,maintain)framework in order to provide evidence for optimizing pediatric RSI nursing training programs.Methods Nurses from the intensive care unit(ICU)of Children's Hospital,Fudan University during Feb 2023 and Jan 2024 were divided into the experimental group(n=35)and the control group(n=35)by block randomization.The experimental group received LSPPDM framework-based training,while the control group underwent conventional training with theoretical lectures and procedural demonstrations.Outcomes included training satisfaction,theoretical knowledge and procedural skill assessment scores,team collaboration compliance and RSI procedure time were compared between the two groups.Results The experimental group demonstrated significantly higher training satisfaction(123.80±2.04 vs.117.26±9.82,P<0.05),superior post-training theoretical knowledge and procedural skills(P<0.05),enhanced team collaboration compliance(P<0.05),and shorter RSI completion time(P<0.05)compared with the control group.Conclusion Pediatric RSI nursing coordination training based on the LSPPDM framework can effectively increase training satisfaction,promote theoretical and procedural skills and reduce completion time in nurses.

Peri-implant keratinized mucosa (PIKM) augmentation refers to surgical procedures aimed at increasing the width of PIKM. Consensus reports emphasize the necessity of maintaining a minimum width of PIKM to ensure long-term peri-implant health. Currently, several surgical techniques have been validated for their effectiveness in increasing PIKM. However, the selection and application of PIKM augmentation methods may present challenges for dental practitioners due to heterogeneity in surgical techniques, variations in clinical scenarios, and anatomical differences. Therefore, clear guidelines and considerations for PIKM augmentation are needed. This expert consensus focuses on the commonly employed surgical techniques for PIKM augmentation and the factors influencing their selection at second-stage surgery. It aims to establish a standardized framework for assessing, planning, and executing PIKM augmentation procedures, with the goal of offering evidence-based guidance to enhance the predictability and success of PIKM augmentation.
Humans ; Consensus ; Dental Implants ; Mouth Mucosa/surgery* ; Keratins

Polygonati Rhizoma demonstrates significant potential for addressing both chronic and hidden hunger. The supply of high-quality seedlings is a primary factor influencing the development of the Polygonati Rhizoma industry. Warm water soaking is often used in agriculture to promote the rapid germination of seeds, while its application and molecular mechanism in Polygonati Rhizoma have not been reported. To rapidly obtain high-quality seedlings, this study treated Polygonatum kingianum var. grandifolium seeds with sand storage at low temperatures, warm water soaking, and cultivation temperature gradients. The results showed that the culture at 25 ℃ or sand storage at 4 ℃ for 2 months rapidly broke the seed dormancy of P. kingianum var. grandifolium, while the culture at 20 ℃ or sand storage at 4 ℃ for 1 month failed to break the seed dormancy. Soaking seeds in 60 ℃ warm water further increased the germination rate, germination potential, and germination index. Specifically, the seeds soaked at 60 ℃ and cultured at 25 ℃ without sand storage treatment(Aa25) achieved a germination rate of 78. 67%±1. 53% on day 42 and 83. 40%±4. 63% on day 77. The seeds pretreated with sand storage at 4 ℃ for 2 months, soaked in 60 ℃ water, and then cultured at 25 ℃ achieved a germination rate comparable to that of Aa25 on day 77. Transcriptomic analysis indicated that warm water soaking might promote germination by triggering reactive oxygen species( ROS), inducing the expression of heat shock factors( HSFs) and heat shock proteins( HSPs), which accelerated DNA replication, transcript maturation, translation, and processing, thereby facilitating the accumulation and turnover of genetic materials. According to the results of indoor controlled experiments and field practices, maintaining a germination and seedling cultivation environment at approximately 25 ℃ was crucial for the one-year seedling cultivation of P. kingianum var. grandifolium.
Germination ; Seedlings/genetics* ; Water/metabolism* ; Seeds/metabolism* ; Polygonatum/genetics* ; Temperature ; Plant Proteins/genetics* ; Plant Dormancy

To determine the optimal cultivation duration and harvest period for cultivated Notopterygium incisum and promote its industrial development, this study established a characteristic chromatographic profile of cultivated N. incisum and employed chemometrics combined with entropy-weighted grey correlation analysis to assess differences in agronomic traits and quality indicators across different cultivation years and harvest periods. By comparing with reference substances, ten common peaks were identified, including chlorogenic acid, p-coumaric acid, ferulic acid, marmesinin, nodakenin, isochlorogenic acid B, notopterol, phenethyl ferulate, isoimperatorin, and falcarindiol. The similarity between the characteristic chromatographic profiles of N. incisum at different cultivation years and the reference profile was all above 0.932. Principal component analysis(PCA) and orthogonal partial least squares discriminant analysis(OPLS-DA) revealed that the quality of 1-to 3-year-old cultivated N. incisum was highly dispersed and unstable, whereas the quality of 4-year-old cultivated N. incisum remained relatively stable across different harvest periods. This suggests that the accumulation of relevant compounds in the medicinal material had reached a plateau, confirming that the optimal cultivation period for N. incisum is four years. Entropy-weighted grey correlation analysis indicated that the quality of 4-year-old cultivated N. incisum across different harvest periods ranked from highest to lowest as follows: November, December, October, August, July, and September, demonstrating that November is the optimal harvest time. The findings of this study establish the suitable cultivation duration and optimal harvest period for N. incisum, providing a scientific basis for cultivation guidance and quality standardization.
Chromatography, High Pressure Liquid/methods* ; Apiaceae/chemistry* ; Entropy ; Chemometrics/methods* ; Drugs, Chinese Herbal/chemistry* ; Principal Component Analysis ; Quality Control

The therapeutic effects of Yueju Pills on depression and cardiovascular diseases have been widely recognized. Previous studies have shown that the drug can significantly improve depressive-like behaviors induced by chronic unpredictable mild stress(CUMS) combined with atherosclerosis(AS). Given the complex pathogenesis of psychocardiac diseases, this study integrated metabolomics and network pharmacology to systematically elucidate the mechanism of Yueju Pills in alleviating depressive symptoms in psychocardiac diseases. The results demonstrate that, after Yueju Pill intervention, the levels of 9 abnormal metabolites in the hippocampus restore to normal ranges, primarily involving key pathways or signaling pathways, including the cyclic adenosine monophosphate(cAMP), mammalian target of rapamycin(mTOR), glycine/serine/threonine metabolism, and aminoacyl-tRNA biosynthesis. In a high-fat diet-induced CUMS ApoE~(-/-) mouse model, Yueju Pills significantly increases adenosine monophosphate(AMP) levels and decreases L-alanine and D-glyceric acid levels in the hippocampus. In conclusion, Yueju Pills exert antidepressant effects by regulating multiple metabolic axes, including glycine/serine/threonine metabolism and the cAMP, mTOR signaling pathways. Network pharmacology predictions reveal that the treatment of CUMS combined with AS by its core active components may be realized through modulating pathways concerning neuroinflammation and synaptic plasticity, including serine/threonine-protein kinase 1(AKT1), mitogen-activated protein kinase 1(MAPK1), and prostaglandin-endoperoxide synthase 2(PTGS2). This study provides a theoretical reference for the clinical application of Yueju Pills in alleviating the depressive symptoms of psychocardiac diseases.
Animals ; Network Pharmacology ; Mice ; Drugs, Chinese Herbal/administration & dosage* ; Metabolomics ; Male ; Depression/genetics* ; Humans ; Hippocampus/drug effects* ; Mice, Inbred C57BL ; Signal Transduction/drug effects*