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 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 examine the content changes of neochlorogenic acid, chlorogenic acid, 1, 5- dicaffeoylquinic acid and total phenolic acids in the water extract from raw and fried Xanthii Fructus. Methods:The stir-frying method was used to process fried Xanthii Fructus from different habitats. The contents of neochlorogenic acid, chlorogenic acid and 1,5-dicaffeoylquinic acid in the wa-ter extract were determined by HPLC, the total phenolic acids content was determined by UV. Results:The contents of neochlorogenic acid, chlorogenic acid, 1,5-dicaffeoylquinic acid and total phenolic acids in the water extract from fried Xanthii Fructus were all in-creased. Conclusion:Fried Xanthii Fructus can increase the contents of effective ingredients in the decoction resulting in the enhanc-ment of clinical curative effect.

OBJECTIVE:To establish the HPLC method for the content determination of paeoniflorin and liquoritin in Jiawei xiaoyao pills. METHODS:It was performed on Hypersil-ODS C18 with the mobile phase of acetonitrile-water(14∶86,V/V)at the flow rate of 1.0 ml/min,the detection wavelength was 230 nm,the column temperature was room temperature and the volume was 10 μl. RESULTS:The linear range was 16.12-80.6 μg/ml for paeoniflorin(r=0.999 6)and 6.08-30.4 μg/ml for liquoritin(r=0.999 8);the RSDs of precision,repeatability and stability tests were all less than 2.0%;the average recovery was respectively 98.51%(RSD=1.94%,n=6)and 98.08%(RSD=1.29%,n=6). CONCLUSIONS:The method is simple,accurate and reliable, and can be used for the content determination of paeoniflorin and liquoritin in Jiawei xiaoyao pills.

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.