Objective To investigate the efficacy and safety of transcutaneous electrical acupoint stimulation(TEAS)in reducing the consumption of analgesics after total knee arthroplasty(TKA).Methods Totally 124 patients undergoing unilateral TKA were included and divided into an intervention group and a control group according to random number table method,with 62 cases in each group.Both groups received routine postoperative analgesic protocols,with the intervention group additionally receiving TEAS treatment,30 min per time,twice a day.The additional doses of intravenous patient-controlled analgesia pumps and opioid analgesic consumption in two groups of patients after surgery were analyzed,as well as the adverse events and laboratory test results(WBC,PLT,SCr,BUN,ALT and AST)in both groups.Results The number of additional doses in the intervention group with the patient-controlled analgesia pump and the consumption of opioid analgesic were both lower than those in the control group(P<0.05);the incidence of postoperative nausea symptoms in the intervention group was lower than that in the control group,while other adverse reactions showed no significant difference(P>0.05).There was no significant difference in laboratory test results(WBC,PLT,SCr,BUN,ALT and AST)between the two groups on the day before surgery and on the 1st and 7th days after surgery(P>0.05).Conclusion TEAS can reduce the consumption of analgesics after TKA,decrease some adverse reactions associated with the use of analgesics,and has good safety.

Objective:To develop a preoperative differentiation model for colorectal mucinous adenocarcinoma and non-mucinous adenocarcinoma using a combination of contrast-enhanced CT radiomics and deep learning methods.Methods:This is a retrospective cohort study. Clinical data of colorectal cancer patients confirmed by postoperative pathological examination were retrospectively collected from January 2016 to December 2023 at Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Center 1, n=220) and the First Affiliated Hospital of Bengbu Medical University (Center 2, n=51). Among them, there were 108 patients diagnosed with mucinous adenocarcinoma, including 55 males and 53 females, with an age of (68.4±12.2) years (range: 38 to 96 years); and 163 patients diagnosed with non-mucinous adenocarcinoma, including 96 males and 67 females, with an age of (67.9±11.0) years (range: 43 to 94 years). The cases from Center 1 were divided into a training set ( n=156) and an internal validation set ( n=64) using stratified random sampling in a 7∶3 ratio, and the cases from Center 2 were used as an independent external validation set ( n=51). Three-dimensional tumor volume of interest was manually segmented on venous-phase contrast-enhanced CT images. Radiomics features were extracted using PyRadiomics, and deep learning features were extracted using the ResNet-18 network. The two sets of features were then combined to form a joint feature set. The consistency of manual segmentation was assessed using the intraclass correlation coefficient. Feature dimensionality reduction was performed using the Mann-Whitney U test and the least absolute shrinkage and selection operator regression. Six machine learning algorithms were used to construct models based on radiomics features, deep learning features, and combined features, including support vector machine, logistic regression, random forest, extreme gradient boosting, k-nearest neighbors, and decision tree. The discriminative performance of each model was evaluated using receiver operating characteristic curves, the area under the curve (AUC), DeLong test, and decision curve analysis. Results:After feature selection, 22 features with the most discriminative value were finally retained, among which 12 were traditional radiomics features and 10 were deep learning features. In the internal validation set, the Random Forest algorithm based on the combined features model achieved the best performance (AUC=0.938, 95% CI: 0.875 to 0.984), which was superior to the single-modality radiomics feature model (AUC=0.817, 95% CI: 0.702 to 0.913, P=0.048) and the deep learning feature model (AUC=0.832, 95% CI: 0.727 to 0.926, P=0.087); in the independent external validation set, the Random Forest algorithm with the combined features model maintained the highest discriminative performance (AUC=0.891, 95% CI: 0.791 to 0.969), which was superior to the single-modality radiomics feature model (AUC=0.770, 95% CI: 0.636 to 0.890, P=0.045) and the deep learning feature model (AUC=0.799, 95% CI: 0.652 to 0.911, P=0.169). Conclusion:The combined model based on radiomics and deep learning features from venous-phase enhanced CT demonstrates good performance in the preoperative differentiation of colorectal mucinous from non-mucinous adenocarcinoma.

Objective To investigate the efficacy and safety of transcutaneous electrical acupoint stimulation(TEAS)in reducing the consumption of analgesics after total knee arthroplasty(TKA).Methods Totally 124 patients undergoing unilateral TKA were included and divided into an intervention group and a control group according to random number table method,with 62 cases in each group.Both groups received routine postoperative analgesic protocols,with the intervention group additionally receiving TEAS treatment,30 min per time,twice a day.The additional doses of intravenous patient-controlled analgesia pumps and opioid analgesic consumption in two groups of patients after surgery were analyzed,as well as the adverse events and laboratory test results(WBC,PLT,SCr,BUN,ALT and AST)in both groups.Results The number of additional doses in the intervention group with the patient-controlled analgesia pump and the consumption of opioid analgesic were both lower than those in the control group(P<0.05);the incidence of postoperative nausea symptoms in the intervention group was lower than that in the control group,while other adverse reactions showed no significant difference(P>0.05).There was no significant difference in laboratory test results(WBC,PLT,SCr,BUN,ALT and AST)between the two groups on the day before surgery and on the 1st and 7th days after surgery(P>0.05).Conclusion TEAS can reduce the consumption of analgesics after TKA,decrease some adverse reactions associated with the use of analgesics,and has good safety.

Objective:To develop a preoperative differentiation model for colorectal mucinous adenocarcinoma and non-mucinous adenocarcinoma using a combination of contrast-enhanced CT radiomics and deep learning methods.Methods:This is a retrospective cohort study. Clinical data of colorectal cancer patients confirmed by postoperative pathological examination were retrospectively collected from January 2016 to December 2023 at Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Center 1, n=220) and the First Affiliated Hospital of Bengbu Medical University (Center 2, n=51). Among them, there were 108 patients diagnosed with mucinous adenocarcinoma, including 55 males and 53 females, with an age of (68.4±12.2) years (range: 38 to 96 years); and 163 patients diagnosed with non-mucinous adenocarcinoma, including 96 males and 67 females, with an age of (67.9±11.0) years (range: 43 to 94 years). The cases from Center 1 were divided into a training set ( n=156) and an internal validation set ( n=64) using stratified random sampling in a 7∶3 ratio, and the cases from Center 2 were used as an independent external validation set ( n=51). Three-dimensional tumor volume of interest was manually segmented on venous-phase contrast-enhanced CT images. Radiomics features were extracted using PyRadiomics, and deep learning features were extracted using the ResNet-18 network. The two sets of features were then combined to form a joint feature set. The consistency of manual segmentation was assessed using the intraclass correlation coefficient. Feature dimensionality reduction was performed using the Mann-Whitney U test and the least absolute shrinkage and selection operator regression. Six machine learning algorithms were used to construct models based on radiomics features, deep learning features, and combined features, including support vector machine, logistic regression, random forest, extreme gradient boosting, k-nearest neighbors, and decision tree. The discriminative performance of each model was evaluated using receiver operating characteristic curves, the area under the curve (AUC), DeLong test, and decision curve analysis. Results:After feature selection, 22 features with the most discriminative value were finally retained, among which 12 were traditional radiomics features and 10 were deep learning features. In the internal validation set, the Random Forest algorithm based on the combined features model achieved the best performance (AUC=0.938, 95% CI: 0.875 to 0.984), which was superior to the single-modality radiomics feature model (AUC=0.817, 95% CI: 0.702 to 0.913, P=0.048) and the deep learning feature model (AUC=0.832, 95% CI: 0.727 to 0.926, P=0.087); in the independent external validation set, the Random Forest algorithm with the combined features model maintained the highest discriminative performance (AUC=0.891, 95% CI: 0.791 to 0.969), which was superior to the single-modality radiomics feature model (AUC=0.770, 95% CI: 0.636 to 0.890, P=0.045) and the deep learning feature model (AUC=0.799, 95% CI: 0.652 to 0.911, P=0.169). Conclusion:The combined model based on radiomics and deep learning features from venous-phase enhanced CT demonstrates good performance in the preoperative differentiation of colorectal mucinous from non-mucinous adenocarcinoma.

Objective To explore the effects of transcutaneous electrical acupoint stimulation(TEAS)combined with multi-model analgesia on infrared thermal imaging characteristics and pain after total knee arthroplasty(TKA).Methods A total of 74 patients with TKA were divided into the treatment group and the control group according to random number table method,with 37 cases in each group.The control group was treated with multi-model analgesia,and the treatment group was treated with TEAS on the basis of multi-model analgesia for 30 min,once in the morning and afternoon before the patient's rehabilitation exercise 1-7 days after surgery.The infrared thermal imaging data,visual analogue scale(VAS)score,Western Ontario and McMaster Universities Osteoarthritis Index(WOMAC)score and pain threshold of the two groups were recorded and analyzed before and at different time points after operation.Results There were 2 cases dropped out in each group.Compared with before surgery,both groups had an increase in knee joint temperature on the surgical side 14 days after surgery(P<0.05),and the temperature in the treatment group was lower than that in the control group(P<0.05).The self knee temperature difference in the treatment group was lower than that in the control group 14 days after surgery(P<0.05).Compared with before surgery,the VAS score of the treatment group decreased 3 and 7 days after surgery(P<0.05),while the VAS score of the control group decreased 7 days after surgery(P<0.05);compared with the control group at 3 and 7 days after surgey,the VAS score of the treatment group was lower than that of the control group.Compared with before surgery,the WOMAC scores of both groups increased 7 days after surgery(P<0.05);after 7 days of surgery,the WOMAC score of the treatment group was lower than that of the control group(P<0.05).Compared with before surgery,the pain threshold values of both groups increased 7 days after surgery(P<0.05);compared with the control group at 3 and 7 days after surgery,the pain threshold values of the treatment group was higher than that of the control group(P<0.05).Conclusion TEAS combined with multi-model analgesia can reduce the temperature of the knee joint,relieve the pain of the operated limb,and promote the early functional recovery of the knee joint after TKA.Infrared thermal imaging technology has a certain application value in indirectly assessing the degree of postoperative pain and detecting early infection of the affected limb.

Objective To investigate the effects of transcutaneous electrical acupoint stimulation(TEAS)combined with multimodal analgesia on short-term and long-term joint function after total knee arthroplasty(TKA).Methods Totally 110 patients with TKA were divided into the treatment group and the control group according to random number table method,with 55 cases in each group.The control group was treated with multimodal analgesia,and the treatment group was treated with TEAS on the basis of multimodal analgesia.The VAS score,pain threshold value,WOMAC score and SF-12 score of the two groups before and at different time points after operation were recorded and analyzed.Results The VAS score of the treatment group was lower than that of the control group on the 3rd and 7th day after operation(P<0.05).On the 7th day after operation,the knee pain threshold in the treatment group was higher than that in the control group(P<0.05).The WOMAC score of the treatment group was lower than that of the control group at the 1st,2nd,4th and 12th week after operation(P<0.05).At the 4th and 12th week after operation,the SF-12 score of the treatment group was higher than that of the control group(P<0.05).Conclusion TEAS combined with multimodal analgesia can relieve the pain after TKA,and promote the recovery of joint function in the short term and long term,and the improvement effect in the short term is more obvious than that in the long term.

Objective To investigate the diagnostic value of the T2WI gray scale ratio for Hashimoto's thyroiditis(HT).Methods The T2WI-iterative decomposition of water and fat with echo asymmetry and least square estimation(IDEAL)quantitation sequence water images of 22 HT cases were analyzed retrospectively.The gray scale ratio of the thyroid,sternocleidomastoid muscle,trachea cavity,and subcutaneous fat at the same layer were measured on the picture archiving and communication systems(PACS).The gray scale ratios of thyroid/sternocleidomastoid muscle(T/M),thyroid/trachea cavity(T/Tr),and thyroid/lipid(T/L)were calculated.The intraclass correlation coefficient(ICC)was used to evaluate the consistency among the measurements,and the optimal threshold for distinguishing HT from non-HT was determined via the receiver operating characteristic(ROC)curve.The Spearman correlation analysis was used to analyze the correlation between T/M,T/Tr,T/L ratios,and titers of thyroid peroxidase antibody(TPO-Ab)and thyroglobulin antibody(Tg-Ab),respectively.Results On the T2WI-IDEAL quantitation sequence water images,the(x)±s of T/M,T/Tr,T/L ratios for HT and non-HT were 2.17±0.47 and 1.62±0.21(t=14.90,P<0.001),9.40±3.24 and 4.87±2.93(t=11.42,P<0.001),1.66±0.32 and 1.21±0.31(t=7.51,P<0.001),respectively.The area under the curve(AUC)of T/M,T/Tr,and T/L ratios for diagnosing HT were 0.89,0.86,and 0.85,respectively;the optimal thresholds were 1.90,3.50,and 1.36,and the sensitivity and specificity were 72.7%and 100%,100%and 40.5%,95.5%and 29.7%,respectively.The T/M ratio had a moderate correlation with TPO-Ab(r=0.513,P<0.05),and T/Tr,T/L ratios had a mild correlation with TPO-Ab,respectively.Conclusion The T/M ratio in the T2WI gray scale ratio can quantitatively and objectively distinguish HT from non-HT to some extent and is correlated with TPO-Ab.It has extremely high specificity and holds promise as a non-invasive imaging method for the diagnosis of incidental HT.

Objective: To understand the situation of school health staff and the allocation of health equipment in primary and secondary schools in Jinan. Methods: A questionnaire survey was conducted in CDC and public primary and secondary schools to describe the situation of the school health staff and health equipment in primary and secondary schools. Results: There were 23 school health staffs in CDC, among which 26.1% were full time. The total staffing ratio of school doctors was 13.9%, with the compliance rate of 19.8%. The total allocation rate of health care teachers was 74.7%, 92.2% of them were part time. The total staffing ratio of school doctors in boarding schools was 20.0%, with a staffing compliance rate of 12.5%. The total allocation rate of school doctors and health care teachers in boarding schools was 66.7%. 16.8% of public primary and secondary schools and 23.3% of public boarding primary and secondary schools did not have any health staffs. 27.6% of public primary and secondary schools are equipped with infirmary, 70.8% with health rooms, and 10.3% without any. The configuration of necessary equipment in infirmary and health care rooms were less than 100%. Conclusion There is a serious shortage of school health staffs and health equipment in primary and secondary schools in Jinan. Attention and investment in school health should be strengthened, and resources should be rationally allocated to ensure the health of primary and secondary school students.

Metformin, a first-line drug for type 2 diabetes mellitus, has been recognized as a potential anti-tumor agent in recent years. Epigallocatechin-3-gallate (EGCG), as the dominant catechin in green tea, is another promising adjuvant agent for tumor prevention. In the present work, the potential effect of EGCG on the anti-tumor efficacy of metformin in a mouse melanoma cell line (B16F10) was investigated. Results indicated that EGCG and metformin exhibited a synergistic effect on cell viability, migration, and proliferation, as well as signal transducer and activator of transcription 3/nuclear factor-κB (STAT3/NF-κB) pathway signaling and the production of inflammation cytokines. Meanwhile, the combination showed an antagonistic effect on cell apoptosis and oxidative stress levels. The combination of EGCG and metformin also differentially affected the nucleus (synergism) and cytoplasm (antagonism) of B16F10 cells. Our findings provide new insight into the potential effects of EGCG on the anti-tumor efficacy of metformin in melanoma cells.

Objective:The association between leptin receptor (LEPR) Q223R (Gln>Arg) gene polymorphism and systemic lupus erythematosus (SLE) remains controversial.In this study, a meta-analysis was used to comprehensively evaluate the association between LEPR Q223R gene polymorphism and SLE susceptibility.Methods:Case control studies on the relationship between LEPR Q223R gene polymorphism and SLE susceptibility were comprehensively searched by Medline (PubMed), Web of Science, CNKI, Wanfang digital journal full-text database, etc., and the search time was up to April 2020.The data of A/G allele frequency and AA/AG/GG genotype in SLE patients and healthy controls were extracted, the odds ratio ( OR) value and 95% confidence interval ( CI) were used as the combined effect-size indicators to analyze the correlation between allele, genotype and SLE risk.The heterogeneity among studies was analyzed quantitatively, and the publication bias was evaluated by Begg and Egger’s test. Results:A total of 7 case-control studies from 4 studies were retrieved.A total of 9 052 patients with SLE and 8 146 healthy controls were included in the meta-analysis.The results showed that there was no significant association between LEPR Q223R A/G gene polymorphism and SLE susceptibility, and the OR of A allele in LEPR Q223R gene locus associated with SLE risk was 1.03(95% CI: 0.92-1.14). The dominant (AA+ AG vs GG) and recessive (AA vs AG+ GG) models both suggested that LEPR Q223R A/G gene polymorphism was not associated with SLE, and the combined OR (95% CI) was 0.88(0.15-5.37) and 1.13(0.37-3.49), respectively.The results also showed that the distribution of LEPR Q223R genotype was different among different populations, and the inter-study heterogeneity was large. Conclusion:The existing evidence is insufficient to indicate that there is an association between LEPR Q223R A/G gene polymorphism and SLE susceptibility, which needs to be confirmed by further studies.