Objective To analyze the risk factors of permanent hypoparathyroidism(pHPP)after total thyroidectomy in patients with thyroid cancer and establish a nomogram prediction model.Methods A total of 245 patients with thyroid cancer who received total thyroidectomy in our hospital were enrolled between January 2020 and January 2024.According to presence or absence of postoperative pHPP,patients were divided into the pHPP group and the non-pHPP group.The influencing factors of postoperative pHPP in patients with thyroid cancer were analyzed by univariate and multivariate Logistic regression analysis.The nomogram prediction model for postoperative pHPP in patients with thyroid cancer was constructed and varified,and efficiency of the model was evaluated.Results In 245 patients with thyroid cancer,the incidence of pHPP within 6 months after surgery was 10.20%(25/245).Univariate analysis showed that there were significant differences in tumor size,surgical method,central lymph node dissection,use of nano carbon tracer,envelope invasion,parathyroid excision by mistake,Hashimoto thyroiditis,serum calcium and parathyroid hormone at 1 d after surgery between the two groups(P＜0.05),but there were no significant differences in gender,age,smoking,drinking,extraglandular invasion,parathyroid autologous transplantation,preoperative vitamin D or serum phosphorus at 1 d after surgery between the two groups(P＞0.05).Multivariate analysis showed that maximum tumor diameter≥4 cm,routine and open total thyroidectomy,central lymph node dissection,no use of nano carbon tracer and parathyroid excision by mistake were all independent risk factors for postoperative pHPP in patients with thyroid cancer(P＜0.05).Results of nomogram prediction model showed that C-index was 0.921,the corrected curve was close to ideal curve,and AUC of nomogram model for predicting postoperative pHPP was 0.926(95%CI:0.871-0.981).Conclusion The nomogram prediction model constructed based on independent risk factors of postoperative pHPP has good predictive efficiency in patients with thyroid cancer.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective To analyze the risk factors of permanent hypoparathyroidism(pHPP)after total thyroidectomy in patients with thyroid cancer and establish a nomogram prediction model.Methods A total of 245 patients with thyroid cancer who received total thyroidectomy in our hospital were enrolled between January 2020 and January 2024.According to presence or absence of postoperative pHPP,patients were divided into the pHPP group and the non-pHPP group.The influencing factors of postoperative pHPP in patients with thyroid cancer were analyzed by univariate and multivariate Logistic regression analysis.The nomogram prediction model for postoperative pHPP in patients with thyroid cancer was constructed and varified,and efficiency of the model was evaluated.Results In 245 patients with thyroid cancer,the incidence of pHPP within 6 months after surgery was 10.20%(25/245).Univariate analysis showed that there were significant differences in tumor size,surgical method,central lymph node dissection,use of nano carbon tracer,envelope invasion,parathyroid excision by mistake,Hashimoto thyroiditis,serum calcium and parathyroid hormone at 1 d after surgery between the two groups(P＜0.05),but there were no significant differences in gender,age,smoking,drinking,extraglandular invasion,parathyroid autologous transplantation,preoperative vitamin D or serum phosphorus at 1 d after surgery between the two groups(P＞0.05).Multivariate analysis showed that maximum tumor diameter≥4 cm,routine and open total thyroidectomy,central lymph node dissection,no use of nano carbon tracer and parathyroid excision by mistake were all independent risk factors for postoperative pHPP in patients with thyroid cancer(P＜0.05).Results of nomogram prediction model showed that C-index was 0.921,the corrected curve was close to ideal curve,and AUC of nomogram model for predicting postoperative pHPP was 0.926(95%CI:0.871-0.981).Conclusion The nomogram prediction model constructed based on independent risk factors of postoperative pHPP has good predictive efficiency in patients with thyroid cancer.

Objective:To investigate the nutritional risk and prevalence of malnutrition in patients with terminal stage gastrointestinal malignant tumors in a tertiary hospital in Changsha.Methods:Cluster sampling was used to conduct a cross-sectional survey of inpatients from Departments of Gastroenterology, Gastrointestinal Surgery, Hepatobiliary Surgery and Oncology in Hunan Provincial People's Hospital from January 2019 to July 2020. Nutritional Risk Screening 2002 (NRS 2002) was used to assess the prevalence of nutritional risk with malnutrition defined as concurrent presence of BMI < 18.5 kg/m 2, poor general condition and NRS 2002 nutritional impairment score of 3. Step 2 of Global Leadership Initiative on Malnutrition (GLIM) diagnostic criteria (without whole body muscle mass) was adopted to diagnose malnutrition. Step 3 of GLIM criteria was used to evaluate the prevalence of severe malnutrition. Results:A total of 802 patients registered in the 4 departments were selected for screening via cluster sampling and 514 were enrolled according to the inclusion/exclusion criteria. The prevalence of nutritional risk in patients with terminal stage gastrointestinal cancer was 49.8% (256/514). The prevalence of malnutrition and severe malnutrition per GLIM criteria were 41.6% (214/514) and 18.3% (94/514), respectively.Conclusions:Although nutritional support therapy is not recommended for patients with end-stage cancer. This paper suggests that the prevalence of nutritional risk and malnutrition in patients with end-stage gastrointestinal cancer is not as high as described in some articles.

OBJECTIVE:To prepare Sanzi capsules and establish the Standard of its quality.METHODS:Water decocting method was applied to extract physic liquor,thin-layer chromatography(TLC)was used for qualitative identification,and high efficiency liquid chromatography(HPLC)was used to determine the content of Jasminoidin in the preparation.RESULTS:Feature spots of Fructus Gardeniae,Fructus Chebulae,Fructus Toosendan were identified by TLC,with no sensible interference seen in the negative control.The linear range for Jasminoidin was 3.0～ 30? g? mL-1(r=0.999 9)with average recovery rate at 100.06%(RSD=1.17%).CONCLUSION:The preparation method is well-grounded,highly-specific and reproducible in property identification,accurate and reliable in content determination,and can be used for the quality control of Sanzi capsules.