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.

For middle-aged and elderly patients with conditions such as spinal fractures and degenerative spinal diseases, spinal internal fixation is a core surgical procedure for reconstructing spinal stability, heavily relying on the biomechanical stability provided by pedicle screw systems. Whereas, these patients are often complicated by osteoporosis that can significantly compromise the stability of the bone-pedicle screw interface, leading to a marked increase in pedicle screw loosening and surgical failure rates. The bone cement-augmented pedicle screw technique, which involves injecting bone cement into the vertebral body or screw trajectory to optimize the mechanical properties of the bone-pedicle screw composite, has been proven to significantly enhance fixation strength and effectively prevent screw-related failures, thereby reducing the incidence of internal fixation failure in high-risk populations undergoing spinal fusion. However, the widespread clinical application of this technique has faced challenges such as inaccurate clinical decision-making (indication and contraindication selection), non-standardized operative practices, and insufficient awareness of complication prevention, resulting in considerable variability in clinical outcomes and even severe complications. To address this, Prof. Luo Fei from First Affiliated Hospital of Army Medical University initiated the project and the Chinese Association Orthopaedic Surgeons organized relevant experts to develop the Evidence-based clinical practice guideline for bone cement-augmented pedicle screw technique ( version 2025), based on current evidence. The guidelines put forward 8 recommendations regarding the clinical value, scope of application, and operational standards of the technique, aiming to provide evidence-based medical support and technical standardization for clinical decision-making.

For middle-aged and elderly patients with conditions such as spinal fractures and degenerative spinal diseases, spinal internal fixation is a core surgical procedure for reconstructing spinal stability, heavily relying on the biomechanical stability provided by pedicle screw systems. Whereas, these patients are often complicated by osteoporosis that can significantly compromise the stability of the bone-pedicle screw interface, leading to a marked increase in pedicle screw loosening and surgical failure rates. The bone cement-augmented pedicle screw technique, which involves injecting bone cement into the vertebral body or screw trajectory to optimize the mechanical properties of the bone-pedicle screw composite, has been proven to significantly enhance fixation strength and effectively prevent screw-related failures, thereby reducing the incidence of internal fixation failure in high-risk populations undergoing spinal fusion. However, the widespread clinical application of this technique has faced challenges such as inaccurate clinical decision-making (indication and contraindication selection), non-standardized operative practices, and insufficient awareness of complication prevention, resulting in considerable variability in clinical outcomes and even severe complications. To address this, Prof. Luo Fei from First Affiliated Hospital of Army Medical University initiated the project and the Chinese Association Orthopaedic Surgeons organized relevant experts to develop the Evidence-based clinical practice guideline for bone cement-augmented pedicle screw technique ( version 2025), based on current evidence. The guidelines put forward 8 recommendations regarding the clinical value, scope of application, and operational standards of the technique, aiming to provide evidence-based medical support and technical standardization for clinical decision-making.

AIM To establish a rapid quantitative analysis model for saponins in Paris polyphylla var.yunnanensis(PPY)by near infrared spectroscopy.METHODS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ and there total content in PPY were determined by HPLC,while spectral data within the range of 10 000 to 4 000 cm-1 were collected.A quantitative analysis model was established by combining these data with partial least squares regression(PLSR).Multivariate scatter correction(MSC)and vector normalization(SNV)were applied prior to further preprocessing the spectra with original,first-order derivative(1stD),or second-order derivative(2ndD)treatments.Lastly,the model was optimized through non-smoothing(NS),Norris Derivative filtering(Nd),and Savitzky-Golay filtering(S-G)method.Model stability was evaluated based on correlation coefficients and variance.The predicted contents of each saponin component in the validation set samples were calculated.RESULTS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ were 0.42-17.98,0.46-10.44,0.23-3.86 mg/g,respectively.The total content ranged from 2.91 to 22.1 mg/g.The optimal parameters of three saponins were achieved when selecting the MSC+2ndD+S-G pretreatment method.The corresponding ratio of line segment length to segment gap was 13∶5,15∶5,11∶5,with correlation coefficients of 0.982,0.930,0.958,respectively.The root mean square errors of calibration(RMSEC)were 0.702,0.797,0.238,and the root mean square errors of prediction(RMSEP)were 1.120,0.835,0.304,respectively.The optimal parameters for the total content were obtained when selecting the MSC+2ndD+NS pretreatment method,with a correlation coefficient of 0.970,a RMSEC of 1.090,and a RMSEP of 1.740.CONCLUSION This accurate and rapid method can be used for detection of saponin contents in P.Polyphylla.

AIM To establish a rapid quantitative analysis model for saponins in Paris polyphylla var.yunnanensis(PPY)by near infrared spectroscopy.METHODS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ and there total content in PPY were determined by HPLC,while spectral data within the range of 10 000 to 4 000 cm-1 were collected.A quantitative analysis model was established by combining these data with partial least squares regression(PLSR).Multivariate scatter correction(MSC)and vector normalization(SNV)were applied prior to further preprocessing the spectra with original,first-order derivative(1stD),or second-order derivative(2ndD)treatments.Lastly,the model was optimized through non-smoothing(NS),Norris Derivative filtering(Nd),and Savitzky-Golay filtering(S-G)method.Model stability was evaluated based on correlation coefficients and variance.The predicted contents of each saponin component in the validation set samples were calculated.RESULTS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ were 0.42-17.98,0.46-10.44,0.23-3.86 mg/g,respectively.The total content ranged from 2.91 to 22.1 mg/g.The optimal parameters of three saponins were achieved when selecting the MSC+2ndD+S-G pretreatment method.The corresponding ratio of line segment length to segment gap was 13∶5,15∶5,11∶5,with correlation coefficients of 0.982,0.930,0.958,respectively.The root mean square errors of calibration(RMSEC)were 0.702,0.797,0.238,and the root mean square errors of prediction(RMSEP)were 1.120,0.835,0.304,respectively.The optimal parameters for the total content were obtained when selecting the MSC+2ndD+NS pretreatment method,with a correlation coefficient of 0.970,a RMSEC of 1.090,and a RMSEP of 1.740.CONCLUSION This accurate and rapid method can be used for detection of saponin contents in P.Polyphylla.

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 expression of disulfidptosis-related genes in hepatocellular carcinoma(HCC)and the prognostic value of disulfidptosis in HCC,to construct a prognostic model,and to analyze its impact on the biological processes of HCC and sorafenib resistance.Methods The TCGA-LIHC database was used to collect the mRNA expression profiles and corresponding clinical data of HCC patients,and the LASSO-Cox regression algorithm was used to construct a four-gene predictive model for prognosis in the TCGA cohort.The external datasets ICGC and GSE14520 were used to validate the prognostic efficacy of the model,and the Cancer Drug Sensitivity Genomics(GDSC)data were used to investigate the value of the disulfidptosis model in predicting sorafenib treatment response,and gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)analyses were used to investigate the biological functions of disulfidptosis-related genes.The independent-samples t test was used for comparison of continuous data between two groups,and the chi-square test was used for comparison of categorical data between two groups.The Kaplan-Meier curve and the log-rank test were used to evaluate the difference in prognosis,and univariate and multivariate Cox regression analyses were used to investigate whether risk score was an independent influencing factor for patient prognosis.Results The univariate Cox regression analysis in the TCGA cohort showed that seven known disulfidptosis-related genes were significantly associated with overall survival(OS)in HCC(all P<0.05).The LASSO-Cox regression analysis was used to construct a prognostic model based on disulfidptosis-related genes(DRG),and the risk score RS-DRG was calculated as RS-DRG=(0.061 6)×GYS1 expression level+(0.152 8)×LRPPRC expression level+(0.268 3)×RPN1 expression level+(0.183 5)×SLC7A11 expression level.The log-rank test showed that the patients with a high risk score based on the disulfidptosis model had a significantly lower OS than those with a low risk score(P<0.001).Based on the results of the multivariate Cox regression analysis,risk score was an independent predictive factor for OS in both TCGA and ICGC cohorts(TCGA:hazard ratio[HR]=1.869,P=0.002;ICGC:HR=3.469,P=0.004).The Spearman correlation analysis showed that RS-DRG was significantly positively correlated with the infiltration level of various immune cells(including B lymphocytes,CD4+T lymphocytes,neutrophils,macrophages,and dendritic cells)in tumor microenvironment(all P<0.05).The patients in the high-risk score group had a significantly lower IC50 value of sorafenib and were more sensitive to sorafenib(P<0.001).The KEGG/GO enrichment analysis showed that the differentially expressed disulfidptosis-related genes were significantly enriched in various mitosis-related molecular functions.Conclusion This study constructed a novel prognostic model based on disulfidptosis-related genes,which has a potential clinical value in predicting the prognosis of HCC,and targeting disulfidptosis-related genes may provide a promising approach for HCC treatment.

Objective To investigate the role of P-I-R classification and Laennec grading in evaluating histological changes in patients with hepatitis B cirrhosis after receiving antiviral therapy, as well as the association of these two evaluation systems with clinical prognosis. Methods A total of 218 patients from 14 centers were consecutively screened from October 2013 to October 2014, and these patients were diagnosed with liver cirrhosis based on pathology (Ishak score ≥5), received antiviral therapy for 72 weeks, completed two liver biopsies, and met the P-I-R classification criteria. The 218 patients were divided into non-hepatocellular carcinoma (HCC) group with 186 patients and HCC group with 32 patients. The chi-square test and the Fisher's exact test were used for comparison of categorical data between groups. For the comparison of HCC after antiviral therapy, the non-parametric Mann-Whitney U test was used for continuous variables, and for the comparison of P-I-R classification and Laennec grading, the non-parametric Kruskal-Wallis H test was used for continuous variables. Univariate and multivariate Cox regression analyses were used to calculate hazard ratio ( HR ) and 95% confidence interval ( CI ), and the Kaplan-Meier method was used to calculate the cumulative incidence rate of HCC. Results After 72 weeks of antiviral therapy, there was a significant difference in P-I-R classification between the non-HCC group and the HCC group ( P < 0.001). There were significant differences in the distribution of Laennec grading and P-I-R classification before and after antiviral therapy ( P < 0.001). After antiviral therapy, the 218 patients were divided into 4A group with 33 patients, 4B group with 71 patients, and 4C group with 114 patients according to Laennec grading, and there were significant differences between these three groups in platelet count (PLT) ( H =36.429, P < 0.001), liver stiffness measurement (LSM) ( H =13.983, P =0.004), Ishak score ( χ 2 =23.060, P < 0.001), and HAI score ( P < 0.001). After antiviral therapy, the 218 patients were divided into R group with 70 patients, I group with 52 patients, and P group with 96 patients according to P-I-R classification, and there were significant differences between these three groups in PLT ( H =7.193, P =0.028), LSM ( H =6.238, P =0.045), Ishak score ( χ 2 =7.986, P < 0.001), HAI score ( P =0.002), and HCC ( P < 0.001). There was a significant difference in the incidence rate of HCC between the P and R groups based on P-I-R classification ( HR =24.21, 95% CI : 0.46-177.99, P =0.002). After adjustment for other confounding factors, P-I-R classification was an independent predictive factor for HCC ( HR =12.69, 95% CI : 4.63-34.80, P =0.002). Conclusion Both P-I-R classification and Laennec grading can reflect the features and changes of fibrosis before and after antiviral therapy, and P-I-R classification is more sensitive to fibrosis changes after antiviral therapy. P-I-R classification (after treatment) can be used to assess the risk of HCC in patients after antiviral therapy.

Objective: This cross-sectional investigation aimed to determine the incidence, clinical characteristics, prognosis, and related risk factors of olfactory and gustatory dysfunctions related to infection with the SARS-CoV-2 Omicron strain in mainland China. Methods: Data of patients with SARS-CoV-2 from December 28, 2022, to February 21, 2023, were collected through online and offline questionnaires from 45 tertiary hospitals and one center for disease control and prevention in mainland China. The questionnaire included demographic information, previous health history, smoking and alcohol drinking, SARS-CoV-2 vaccination, olfactory and gustatory function before and after infection, other symptoms after infection, as well as the duration and improvement of olfactory and gustatory dysfunction. The self-reported olfactory and gustatory functions of patients were evaluated using the Olfactory VAS scale and Gustatory VAS scale. Results: A total of 35 566 valid questionnaires were obtained, revealing a high incidence of olfactory and taste dysfunctions related to infection with the SARS-CoV-2 Omicron strain (67.75%). Females(χ2=367.013, P<0.001) and young people(χ2=120.210, P<0.001) were more likely to develop these dysfunctions. Gender(OR=1.564, 95%CI: 1.487-1.645), SARS-CoV-2 vaccination status (OR=1.334, 95%CI: 1.164-1.530), oral health status (OR=0.881, 95%CI: 0.839-0.926), smoking history (OR=1.152, 95%CI=1.080-1.229), and drinking history (OR=0.854, 95%CI: 0.785-0.928) were correlated with the occurrence of olfactory and taste dysfunctions related to SARS-CoV-2(above P<0.001). 44.62% (4 391/9 840) of the patients who had not recovered their sense of smell and taste also suffered from nasal congestion, runny nose, and 32.62% (3 210/9 840) suffered from dry mouth and sore throat. The improvement of olfactory and taste functions was correlated with the persistence of accompanying symptoms(χ2=10.873, P=0.001). The average score of olfactory and taste VAS scale was 8.41 and 8.51 respectively before SARS-CoV-2 infection, but decreased to3.69 and 4.29 respectively after SARS-CoV-2 infection, and recovered to 5.83and 6.55 respectively at the time of the survey. The median duration of olfactory and gustatory dysfunctions was 15 days and 12 days, respectively, with 0.5% (121/24 096) of patients experiencing these dysfunctions for more than 28 days. The overall self-reported improvement rate of smell and taste dysfunctions was 59.16% (14 256/24 096). Gender(OR=0.893, 95%CI: 0.839-0.951), SARS-CoV-2 vaccination status (OR=1.334, 95%CI: 1.164-1.530), history of head and facial trauma(OR=1.180, 95%CI: 1.036-1.344, P=0.013), nose (OR=1.104, 95%CI: 1.042-1.171, P=0.001) and oral (OR=1.162, 95%CI: 1.096-1.233) health status, smoking history(OR=0.765, 95%CI: 0.709-0.825), and the persistence of accompanying symptoms (OR=0.359, 95%CI: 0.332-0.388) were correlated with the recovery of olfactory and taste dysfunctions related to SARS-CoV-2 (above P<0.001 except for the indicated values). Conclusion: The incidence of olfactory and taste dysfunctions related to infection with the SARS-CoV-2 Omicron strain is high in mainland China, with females and young people more likely to develop these dysfunctions. Active and effective intervention measures may be required for cases that persist for a long time. The recovery of olfactory and taste functions is influenced by several factors, including gender, SARS-CoV-2 vaccination status, history of head and facial trauma, nasal and oral health status, smoking history, and persistence of accompanying symptoms.
Female ; Humans ; Adolescent ; SARS-CoV-2 ; Smell ; COVID-19/complications* ; Cross-Sectional Studies ; COVID-19 Vaccines ; Incidence ; Olfaction Disorders/etiology* ; Taste Disorders/etiology* ; Prognosis

italic>Artemisia argyi (A. argyi) is a Chinese herbal medicine in China. The main active components are volatile oils, flavonoids, and other compounds, which have various pharmacological activities. Methoxylated flavonoids are the main active ingredients in A. argyi. Flavonoid O-methyltransferase (FOMT) is a key enzyme in the O-methylation of flavonoids. In order to further understand the function and characteristics of FOMT proteins, this paper carried out the whole genome mining and identification of FOMT genes in A. argyi and performed phylogenetic, chromosomal localization, gene sequence characterization, subcellular localization prediction, protein structure, gene structure analysis, and expression pattern analysis. The results showed that a total of 83 FOMT genes were identified in the genome of A. argyi. The phylogenetic tree shows that FOMT genes are divided into two subgroups, CCoAOMT (caffeoyl CoA O-methyltransferase) subfamily (32 genes) and COMT (caffeic acid O-methyltransferase) subfamily (51 genes). Gene sequence analysis showed that the number of amino acids encoded by FOMT was 70-734 aa, the molecular weight was 25 296.55-34 241.3 Da, and the isoelectric point was 4.51-9.99. Compared with 32 members of the CCoAOMT subfamily, nearly 1/3 of the 51 members of the COMT subfamily were hydrophobic proteins and 2/3 were hydrophilic proteins. Subcellular localization prediction showed that more than 80% of CCoAOMT subfamily members were located in the cytoplasm, and 96% of COMT subfamily members were located in the chloroplast. COMT subfamily members have more motifs than CCoAOMT subfamily members. The N-terminal motifs of COMT subfamily proteins are relatively variable, while the C-terminal motifs are relatively conserved. Expression pattern analysis showed that CCoAOMT subfamily members were mainly expressed in roots, while COMT members were mainly expressed in leaves. Some FOMTs showed the tissue expression specificity by real-time quantitative PCR analysis, especially in leaves. In this study, we identified and analyzed the FOMT gene family in A. argyi, and provided a theoretical basis for further research on the function of FOMTs and the biosynthesis of methylated flavonoids in A. argyi.