Abstract: To study the protective effect of asiaticoside(AS) on Isoproterenol Hydrochloride(ISO)-induced myocardial injury in mice. Methods: Sixty male C57BL/6 mice were randomly divided into blank control(CON) group, model group [ISO,ISO 10/(kg·d)], Low dose group [ISO+AS-L,ISO 10 mg/(kg·d)+AS 5 mg/(kg·d)], Medium dose group [ISO+AS-M, ISO 10 mg/(kg·d)+AS 10 mg/(kg·d)], High dose group [ISO+AS-H, ISO 10 mg/(kg·d)+AS 20 mg/(kg·d)]. Heart mass ratio was counted; changes were observed in electrocardiogram; Enzyme linked immunosorbent assay(ELISA) was used to detect the levels of interleukin(IL)-1β and cardiac troponin T(cTn-T) in serum; Masson staining was used to observe the fibrosis of mouse myocardial tissue; Western blot was used to detect the ratio of Bax and Bcl-2 protein expression levels(Bax/Bcl-2) and the expression levels of Caspase-3 and NLRP3 proteins in myocardial tissue; real-time quantitative polymerase chain reaction(qPCR) was used to detect the mRNA expression levels ofANP,BNP,β-MHC,TNF-α, IL-6, Type Ⅰ collagen(COLⅠ), and Type Ⅲ collagen(COLⅢ). Results: Compared with the CON group, the ISO group had an elevated heart-to-mass ratio(P<0.01), a lower heart rate(P<0.05), a prolonged QT interval(P<0.05), elevated expression of myocardial injury markers cTn-T,ANP,BNP, andβ-MHC(P<0.01); increased expression of IL-1β in the serum(P<0.01), increased expression ofTNF-αin the cardiac tissue and increasedIL-6expression(P<0.001), and NLRP3 protein expression was elevated(P<0.05); myocardium showed a large number of collagen fibers bluish staining(P<0.001),COLⅠ,COLⅢmRNA expression levels increased(P<0.001), and Bax/Bcl-2 ratio(P<0.001) and Caspase-3 expression were significantly elevated(P<0.05). Compared with ISO group, heart-to-mass ratio of mice in ISO+AS-L and ISO+AS-M groups decreased(P<0.05), heart rate increased, QT interval was shortened, cTn-T, ANP, BNP and β-MHC decreased(P<0.001), myocardial collagen fiber blue-staining decreased(P<0.01). The mRNA expression levels ofCOLⅠandCOLⅢdecreased(P<0.05). The expression levels of IL-1β and TNF-α decreased(P<0.01). NLRP3, Caspase-3 protein expression and Bax/Bcl-2 ratio decreased(P<0.05). The expression level ofIL-6in ISO+AS-M group decreased(P<0.01). The expression levels ofANP,BNP, andTNF-αmRNA expression were reduced in the ISO+AS-H group(P<0.001); the degree of myocardial fibrosis was improved(P<0.05), and the expression levels ofCOLⅠandCOLⅢmRNA were reduced(P<0.05). Conclusion AS has a protective effect against ISO-induced myocardial injury in mice by ameliorating cardiac fibrosis, inhibiting cardiomyocyte apoptosis and attenuating myocardial tissue inflammatory response.

BACKGROUND:Cervical instability is a common orthopedic disease in young and middle-aged people,and is the early manifestation of cervical spondylosis,which has a great impact on the quality of life of patients.Therefore,early diagnosis of cervical instability to implement early intervention has positive clinical and social significance.OBJECTIVE:The clinical prediction model of cervical instability in young and middle-aged people was constructed based on machine learning to realize early screening of cervical instability in young and middle-aged people before X-ray examination.METHODS:From September 2022 to October 2023,155 young and middle-aged adults with cervical instability and 88 with non-cervical instability recruited through recruitment advertisements and spinal department outpatient of Wangjing Hospital,China Academy of Chinese Medical Sciences were selected as research subjects.The research subjects'general information,living and working habits,discomfort symptoms,visual analog scale score,Neck Disability Index,and 36-ltem Short Form Health Survey were collected on site based on questionnaires.The above information was used as predictive factors.After screening,six machine learning algorithms of Support Vector Machine,LightGBM,RandomForest,Logistic,AdaBoost,and XGBClassifier were used to train the model by ten-fold cross-validation method,and the clinical prediction model of cervical instability was constructed.Area under the curve was used as the main evaluation index.Univariate analysis was performed on the predictors,and SHAP method was used to rank the importance of the predictors.Correlation heat maps were used to show the degree of linear correlation between the predictors and the cervical instability.RESULTS AND CONCLUSION:(1)Among the six machine learning models,RandomForest model was chosen as the final prediction model,including nine predictors,such as age,body mass index,neck circumference/neck length,visual analog scale score,Neck Disability Index,bodily pain,general health,vitality,and mental health,area under the curve=0.725 4,and the calibration degree was good.It could be used as a reference tool for early screening of cervical instability in young and middle-aged people.(2)There were significant differences in age,visual analog scale score,Neck Disability Index,bodily pain,general health,and vitality between the two groups(P＜0.05).(3)The order of importance of predictors was age,Neck Disability Index,visual analog scale score,general health,body mass index,vitality,bodily pain,neck circumference/neck length,mental health,among which age,visual analog scale score,Neck Disability Index were positively correlated with cervical instability,while general health,body mass index,vitality,bodily pain,neck circumference/neck length,and mental health were negatively correlated with cervical instability.

Objective:To verify the prognostic value of perineural invasion and lymphovascular tumor embolus for patients with gastric cancer undergoing gastrectomy and postoperative chemotherapy, and establish a prognostic prediction nomogram model.Methods:According to 7∶3 radio, 781 gastric cancer patients were randomly divided into training cohort and internal validation cohort. One hundred fifty patients were utilized as the external validation cohort. Univariate and multivariate analysis were performed to evaluate the prognostic value of perineural invasion and lymphovascular tumor embolus, and construct the dynamic nomogram. The concordance index (C-index), net reclassification index and integrated discrimination improvement index, receiver operating characteristic curve, calibration curves and decision curve analysis were used to evaluate the nomogram.Results:Perineural invasion ( HR=1.486, 95% CI: 1.150-1.919, P<0.01) and lymphovascular tumor embolus ( HR=1.321, 95% CI: 1.030-1.693, P<0.05) were independent prognostic risk factors for patients with gastric cancer after gastrectomy and postoperative chemotherapy. C-index (training cohort: 0.734, internal validation cohort: 0.755, external validation cohort: 0.715), net reclassification index (training cohort: 0.228 for 3-year and 0.213 for 5-year OS prediction; internal validation cohort: 0.211 for 3-year and 0.279 for 5-year OS prediction; external validation cohort: 0.220 for 3-year and 0.440 for 5-year OS prediction) and integrated discrimination improvement index (training cohort: 0.051 for 3-year and 0.041 for 5-year OS prediction; internal validation cohort: 0.027 for 3-year and 0.036 for 5-year OS prediction; external validation cohort: 0.063 for 3-year and 0.153 for 5-year OS prediction) indicated that the nomogram performed better than the traditional TNM staging system ( P<0.05). Conclusions:Perineural invasion and lymphovascular tumor embolus are independent prognostic risk factors of gastric cancer patients after postoperative chemotherapy. The novel dynamic nomogram model based on perineural invasion and lymphovascular tumor embolus provides better assistance in evaluating prognosis of gastric cancer patients.

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 application of multi-perspective nursing guided by the MPNFS theory in lung cancer patients undergoing chemotherapy.Methods The clinical data of 80 lung cancer patients who underwent chemotherapy in No.971 Hospital of Navy from January 2021 to June 2023 were retrospectively analyzed.The patients were assigned to control group(n=43)and observation group(n=37).The control group received routine nursing interventions,and the observation group received multi-perspective nursing guided by the MPNFS theory in addition to routine nursing.Nutritional status(serum albumin,total protein,and prealbumin),psychological status(assessed by the state-trait anxiety inventory[STAI]),self-perceived burden(assessed by the self-perceived burden scale for cancer patients[SPBS-CP]),cancer-related fatigue(assessed by the Piper fatigue scale[PFS]),and quality of life(assessed by the functional assessment of cancer therapy-lung[FACT-L]scale)were compared between the two groups before and after nursing interventions.Results The serum albumin,total protein,and prealbumin in the observation group were significantly higher than those in the control group after intervention(all P<0.05).The scores of SPBS-CP,PFS and STAI were significantly decreased in the observation group after intervention(all P<0.05),while the scores of FACT-L scale were significantly increased(P<0.05).Conclusion Multi-perspective nursing guided by the MPNFS theory can effectively improve the nutritional status of lung cancer patients undergoing chemotherapy,enhance psychological well-being,reduce self-perceived burden and cancer-related fatigue,and elevate overall quality of life.

Background::B-cell maturation antigen (BCMA)-directed chimeric antigen receptor T (CAR-T) therapy yield remarkable responses in patients with relapsed/refractory multiple myeloma (R/RMM). Circulating tumor DNA (ctDNA) reportedly exhibits distinct advantages in addressing the challenges posed by tumor heterogeneity in the distribution and genetic variations in R/RMM.Methods::Herein, the ctDNA of 108 peripheral blood plasma samples from patients with R/RMM at the First Affiliated Hospital, School of Medicine, Zhejiang University was thoroughly investigated before administration of anti-BCMA CAR-T therapy to establish its predictive potential. Flow cytometry is used primarily to detect subgroups of T cells or CAR-T cells.Results::In this study, several tumor and T cell effector-mediated factors were considered to be related to treatment failure by an integrat analysis, including higher percentages of multiple myeloma (MM) cells in the bone marrow ( P = 0.0125), lower percentages of CAR-T cells in the peripheral blood at peak ( P = 0.0375), and higher percentages of CD8 + T cells ( P = 0.0340). Furthermore, there is a substantial correlation between high ctDNA level (>143 ng/mL) and shorter progression-free survival (PFS) ( P = 0.007). Multivariate Cox regression analysis showed that high levels of ctDNA (>143 ng/mL), MM-driven high-risk mutations (including IGLL5 [ P = 0.004], IRF4 [ P = 0.024], and CREBBP [ P = 0.041]), number of multisite mutations, and resistance-related mutation ( ERBB4, P = 0.040) were independent risk factors for PFS. Conclusion::Finally, a ctDNA-based risk model was built based on the above independent risk factors, which serves as an adjunct non-invasive measure of substantial tumor burden and a prognostic genetic feature that can assist in predicting the response to anti-BCMA CAR-T therapy.

Objective:To study the distribution characteristics of current patients with Kashin-Beck disease (KBD) in Molidawa Daur Autonomous Banner (referred to as Morin Banner), and provide suggestions for service management.Methods:Information of KBD current patients in Morin Banner was collected from January 1, 2018 to June 30, 2024 using the "KBD Current Patient Survey System" provided by the Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention. A descriptive study method was used to analyze the basic information and clinical data of current patients.Results:As of June 30, 2024, a total of 6 223 KBD current patients were reported in Morin Banner, and the patients were distributed in 15 townships (towns). There was a statistically significant difference in the prevalence rate of KBD among different townships (towns, χ 2 = 3 069.01, P < 0.001). The minimum age of the KBD current patients was 27 years old, and the maximum was 98 years old, mainly concentrated in the age range of 45 - 74 years old, accounting for 95.7% (5 954/6 223). There was a significant difference in the prevalence rate of KBD among different age groups (χ 2 = 5 912.76, P < 0.001). The male to female ratio was 1.00∶1.14 (2 910 ∶ 3 313), and there was a statistically significant difference in prevalence rate of KBD between genders(χ 2 = 44.38, P < 0.001). The KBD current patients mainly had a primary school education, married, and farmers, accounting for 59.2% (3 685/6 223), 89.8% (5 590/6 223), 93.2% (5 802/6 223), respectively; and the clinical grading of patients is mainly degree Ⅰ. There was a statistically significant difference in the rate of limb disability among patients with different clinical grades (χ 2 = 64.26, P < 0.001). The rate of limb disability in males was higher than that in females (χ 2 = 10.36, P = 0.001). Conclusions:The KBD current patients in Morin Banner are distributed in various township (town), with middle-aged and elderly famers being the main ones. It is necessary to strengthen monitoring of KBD, and pay attention to personalized treatment and management of KBD current patients.

Objective:To explore more objective detection method for the performance of diagnostic equipment with ultrasound,so as to demonstrate that the subjectivity of the interpretation criteria will affect the results of resolution parameters of detection performance of ultrasonic equipment.Methods:A total of 77 conventional probes of 33 ultrasound equipment of Jiangsu Provincial Hospital were collected and sorted out.The resolution parameters of their performance were tested twice from 2022 to 2023 by the technical personnel of our institute and the tester of Jiangsu Institute of Metrology.The data was described by using the quartiles method.The resolution parameters of performances of ultrasound equipment and probes were paired to conduct Wilcoxon rank sum test,so as to compare the differences in same institute between different years,and in same year between different institute in testing the resolution parameters of performances of ultrasound equipment and probes.Results:For linear array probes,as long as the performance was tested in accordance with the standard method,there was no statistically significant difference in the resolution parameters measured between different institutes and between different times(P>0.05).For the convex array probe,in 2023,the lateral resolution[2(2,2)]of the 70mm target group,and the axial and lateral resolutions[1(1,1)and 3(3,3.25)]of the 120mm target group of our institute were different with the lateral resolution[2(1,2)]of the 70mm target group,and the axial and lateral resolutions[2(2,3)and 2(2,3)]of the 120mm target group of Jiangsu Institute of Metrology,and the differences of them between our institute and Jiangsu Institute of Metrology were significant(Z=-2.887,-2.53,-3.69,P<0.05),respectively.In our institute,the axial and lateral resolutions[1(1,2)and 2.5(2,3)]of the 70mm target group,and the axial resolution[1(1,2)]of the 120mm target group in 2022 were different with the axial and lateral resolutions[1(1,1)and 2(2,2)]of the 70mm target group,and the axial resolution[1(1,1)]of the 120mm target group in 2023,and the differences of them between two years were significant(Z=-3.317,-3.419,-2.982,P<0.05).In Jiangsu Institute of Metrology,the lateral resolutions of the 70mm target group,and the axial resolution[1(1,1)]of the 120mm target group in 2022 year were different with those in 2023 year,and the differences of them were significant(Z=-2.333,-3.051,P<0.05),respectively.Conclusion:The interpretation for lateral resolution of convex array probe in target group with high depth,the difference of subjective interpretation criteria will cause the essential difference of final results.This paper suggested that the key image should be retained when the resolution of the image of target group with high depth of the convex array probe is interpreted.At the later stage,artificial intelligence(AI)-assisted quality control can also be introduced to conduct objective interpretation by computers.

BACKGROUND:Cervical instability is a common orthopedic disease in young and middle-aged people,and is the early manifestation of cervical spondylosis,which has a great impact on the quality of life of patients.Therefore,early diagnosis of cervical instability to implement early intervention has positive clinical and social significance.OBJECTIVE:The clinical prediction model of cervical instability in young and middle-aged people was constructed based on machine learning to realize early screening of cervical instability in young and middle-aged people before X-ray examination.METHODS:From September 2022 to October 2023,155 young and middle-aged adults with cervical instability and 88 with non-cervical instability recruited through recruitment advertisements and spinal department outpatient of Wangjing Hospital,China Academy of Chinese Medical Sciences were selected as research subjects.The research subjects'general information,living and working habits,discomfort symptoms,visual analog scale score,Neck Disability Index,and 36-ltem Short Form Health Survey were collected on site based on questionnaires.The above information was used as predictive factors.After screening,six machine learning algorithms of Support Vector Machine,LightGBM,RandomForest,Logistic,AdaBoost,and XGBClassifier were used to train the model by ten-fold cross-validation method,and the clinical prediction model of cervical instability was constructed.Area under the curve was used as the main evaluation index.Univariate analysis was performed on the predictors,and SHAP method was used to rank the importance of the predictors.Correlation heat maps were used to show the degree of linear correlation between the predictors and the cervical instability.RESULTS AND CONCLUSION:(1)Among the six machine learning models,RandomForest model was chosen as the final prediction model,including nine predictors,such as age,body mass index,neck circumference/neck length,visual analog scale score,Neck Disability Index,bodily pain,general health,vitality,and mental health,area under the curve=0.725 4,and the calibration degree was good.It could be used as a reference tool for early screening of cervical instability in young and middle-aged people.(2)There were significant differences in age,visual analog scale score,Neck Disability Index,bodily pain,general health,and vitality between the two groups(P＜0.05).(3)The order of importance of predictors was age,Neck Disability Index,visual analog scale score,general health,body mass index,vitality,bodily pain,neck circumference/neck length,mental health,among which age,visual analog scale score,Neck Disability Index were positively correlated with cervical instability,while general health,body mass index,vitality,bodily pain,neck circumference/neck length,and mental health were negatively correlated with cervical instability.