Objective:To investigate the efficacy and safety of daratumumab (Dara) - combination regimens for newly diagnosed multiple myeloma (NDMM).Methods:A retrospective case series study was conducted. The clinical data of 34 patients with NDMM receiving treatment regimen including Dara from Qilu Hospital of Shandong University, Yantai Yuhuangding Hospital, Huangdao Branch of Affiliated Hospital of Qingdao University and Taian City Central Hospital between April 2020 and March 2022 were retrospectively collected. The efficacy, survival and adverse reactions of patients were analyzed. Cox proportional risk model was used to analyze the factors affecting overall survival (OS) and minimal residual disease (MRD) turning negative.Results:Among 34 patients with NDMM, there were 19 males and 15 females, with 21 cases aged < 65 years and 13 cases aged ≥65 years. The median follow-up duration [ M ( Q1, Q3)] was 22 months (19 months, 26 months), the median of Dara treatment cycles was 7 (5, 11), and the overall response rate (ORR) reached 97.1% (33/34). There were statistically significant differences in the optimal efficacy of patients stratified by receiving hematopoietic stem cell transplantation or not and receiving different treatment cycles (all P ≤ 0.05), while there were no statistically significant differences in patients stratified by other clinical features (all P > 0.05). The 1-year progression-free survival rate was 79.4% and the 1-year OS rate was 94.1%. Multivariate Cox regression analysis showed that the cycle number of treatment regimens containing Dara was an independent influencing factor of MRD turning negative (6 cycles vs. 2 cycles, HR = 0.267, 95% CI: 0.076-0.935, P = 0.039); age ≥ 65 years was an independent risk factor for OS ( HR = 35.313, 95% CI: 1.709-729.669, P = 0.021). The incidence of hematological adverse reactions grade 3 or above was 20.6% (7/34), and the non-hematological adverse reactions primarily included infection [44.1% (15/34)] and edema of extremity and trunk [41.2% (14/34)]. Conclusions:The Dara-based regimens for NDMM exhibit a high ORR. The remission depth accelerated with the increasing number of treatment cycle, and the adverse reactions are mild.

Objective To analyze the effects of three sterilization methods,namely,pressure steam,low-temperature plasma and ethylene oxide,on the magnetic flux of magnetic surgical devices and their sterilization costs.Methods A total of 234 magnetic surgical devices of different specifications and models(magnetic rings)were randomly divided into Group A,Group B and Group C after the paired number was labelled,and each group consisted of 78 pieces(39 pairs).After packaging each pair of devices according to sterilization specifications,Group A was sterilized by pressure steam,Group B was sterilized by low-temperature plasma,and Group C was sterilized by ethylene oxide.We measured the magnetic flux of three sets of magnetic rings before and after sterilization,and comparatively analyzed the sterilization cost and sterilization time of the single package.Results There was no statistically significant difference in the impact of the three sterilization methods on the magnetic flux of the magnetic surgical devices(P＞0.05),but there was a significant difference in the magnetic flux before and after sterilization for each sterilization method(P＜0.001);the sterilization cost was(1.96±0.16)yuan for Group A,(23.17±0.32)yuan for Group B,and(8.16±0.18)yuan for Group C,showing statistically significant differences among the three groups(P＜0.01).The sterilization time was(65.21±3.36)min for Group A,(45.46±1.39)min for Group B,and(1020.38±12.21)min for Group C,with statistically significant differences among the three groups(P＜0.01).Conclusion None of the three sterilization methods affects the magnetic flux of the magnetic surgical devices.Pressure steam method shows the lowest cost of single package,low-temperature plasma method shows the highest cost of single package,while ethylene oxide method shows the highest sterilization time.Pressure steam should be the preferred sterilization method for magnetic surgical devices.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

Objective:To observe the effects of Qifu Lizhong Enema Prescription on ulcerative colitis rats with yang deficiency of spleen and kidney syndrome; To discuss its mechanism.Methods:Totally 70 male SD rats were randomly divided into blank group, model group, mesalazine group, Qifu Lizhong Guanchang Prescription high-, medium- and low-dosage groups; blank group ( n=10), other groups ( n=12). Except for the blank group, the other groups used bitter cold purgative therapy (Dahuang Decoction) by gavage, and combined with trinitrobenzen sulfonic acid (TNBS) +55% ethanol compound method to induce UC rat model. After successful modeling, the blank group and model group were given 1 ml normal saline enema daily, Qifu Lizhong Enema Prescription groups were given Qifu Lizhong Enema Prescription 3.00, 1.50, 0.75 g/kg enema daily, and the mesalazine group was given mesalazine 0.03 g/kg enema daily, once a day for consecutive 14 days. After 14 days, Disease Activity Index (DAI) score was performed, and hematoxylin-eosin staining (HE) was used to observe the pathological tissues of the colon. The expressions of Occludin and adhesion molecules A (JAM-A) protein in colon tissue were detected by immunohistochemistry and Western blot. Results:HE results showed that the mucosal structure was damaged, inflammatory cells were infiltrated, edema and ulcer foci were observed in model group. The mucosal structure of mesalazine group and Qifu Lizhong Enema Prescription groups were intact, and inflammatory infiltration, edema and ulcer of neoepithelial were improved. Compared with model group, the DAI scores of Qifu Lizhong Enema Prescription groups decreased ( P<0.01), the expressions of Occludin and JAM-A in Qifu Lizhong Guanchang Prescription high- and medium-dosage groups significantly increased ( P<0.05). Conclusion:Qifu Lizhong Enema Prescription can significantly relieve the symptoms and pathological morphology of UC rats, and the mechanism of repairing intestinal mucosal barrier may be related to up-regulating the expressions of Occludin and JAM-A proteins.

This article discusses the overview of virtual reality (VR) technology and chronic pain kinesiophobia, elucidating the principles by which VR technology reduces chronic pain kinesiophobia and its effectiveness in the treatment and rehabilitation training of patients with this condition. The advantages and limitations of VR technology are summarized, aiming to provide references for clinical practitioners to better apply VR technology in the treatment and rehabilitation management of chronic pain kinesiophobia. The goal is to improve patients' fear of movement, fear-avoidance beliefs, pain related to movement injuries, physical function, motivation for training, and patient satisfaction.

Objectives:Fenofibric acid is extracted from the widely used hypolipemic fenofibrate,nowadays being approved for marketing around numerous nations and regions,nonetheless not in China.Present trial evaluated the efficacy and safety in the Chinese hypertriglyceridemia population. Methods:This is a multi-center,randomized,double-blind,placebo-controlled phase Ⅲ clinical trial.Patients from 3 different cohorts,including severe hypertriglyceridemia(HTG),moderate HTG and mixed-dyslipidemia(MD),were randomized at 1:1 ratio to receive fenofibric acid 135 mg or placebo daily for 12 weeks.The primary endpoint was the percentage change of triglyceridemia(TG)from baseline at week 12.Secondary endpoints were the percentage changes of other blood lipid indexes.At the same time,the incidence of medical adverse events was observed. Results:Among the three cohorts of patients with severe HTG(n=52),moderate HTG(n=23)and MD(n=52),the TG levels in the fenofibric acid-treated group decreased by(49.12±29.19)%,(49.95±25.19)%and(49.79±19.28)%,respectively from baseline to 12 weeks,while the corresponding placebo groups decreased by(18.88±40.69)%,(8.11±29.86)%and increased by(10.42±73.04)%,respectively from baseline to 12 weeks.The differences between treatment and placebo groups were statistically significant(P<0.017 for severe HTG cohort,P<0.05 for moderate and MD cohort).The high-density lipoprotein cholesterol(HDL-C)in the fenofibric acid-treated group increased by(25.51±21.45)%,(24.55±24.73)%,and(23.60±27.38)%,and the placebo group increased by(1.91±20.42)%,(2.40±9.32)%and(7.13±19.12)%,respectively,the differences between the two groups were statistically significant(all P<0.05).In the fenofibric acid group,adverse events with incidence>5%included upper respiratory tract infection(10.9%),abdominal pain(6.3%),and increased serum creatinine levels(6.3%),rates of adverse events were similar between the two groups(P>0.05). Conclusions:Fenofibric acid can significantly reduce triglycerides and elevate HDL-C levels safely in Chinese patients with severe to moderate HTG without statin or MD patients on top of statin therapy.

Objective:To explore the changes of large-scale functional brain networks and network topological properties in patients with major depressive disorder (MDD) whose diagnosis had not changed after 5 years of follow-up.Methods:Totally 521 cases of hospitalized MDD patients were recruited from January 2012 to August 2018, and another 204 cases of gender- and age-matched healthy controls were recruited. All participants completed resting-state functional magnetic resonance scanning and clinical assessment. Their diagnosis were reviewed 5 years after discharge.A total of 258 participants whose diagnosis had not changed were counted into the MDD group for analysis. The differences in large-scale brain network connectivity between the two groups were analyzed by constructing a whole-brain functional network, on the basis of which the altered topological properties of the sensorimotor network (SMN), visual network (VN) and default mode network (DMN) were further analyzed between the two groups.The SPSS 24.0 software was used for data analysis and the independent sample t-test and χ2 test were used for the data comparison of the two groups. Results:Compared with the healthy controls, the MDD group had significantly decreased network clustering, mainly involving the SMN, VN and DMN (edge P<0.001, cluster P<0.05). The MDD group had decreased functional connectivity(FC) strength within the SMN, VN and DMN networks, the FC strength between the SMN and VN networks, between the frontoparietal network (FPN) and the DAN networks were decreased(all P<0.05, FDR corrected). Graph-theory analysis showed that local efficiency, clustering coefficient, and normalized shortest path length were decreased in the MDD group, node efficiency was decreased in the left ventral medial prefrontal cortex and the middle of the bilateral insula, node centrality was decreased in the middle of the bilateral insula and occipital lobe, and the betweenness was decreased in the middle of the right insula (all P<0.05, FDR corrected). Conclusion:MDD exhibits abnormal network functional connectivity, disruption of network topological properties, diminished optimal information processing, and to some extent reflects the severity of depressive symptoms. The decreased ability of information transfer flow in the insula plays an important role for the functional abnormality of the network.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

Objective:To study the prevalence and distribution of adult thyroid diseases in urban and rural areas of Hebei Province.Methods:A multi-stage stratified cluster sampling method was used to select Renqiu City and Licun Town, Luquan City of Hebei Province as the urban and rural survey sites, respectively. Questionnaire survey, physical examination and thyroid B ultrasound examination were conducted on local permanent residents (≥ 5 years of residencies) over 18 years old. The fasting venous blood sample was collected to determine the serum thyroid function indicaters.Results:A total of 2 650 adults were surveyed, including 1 393 urban residents and 1 257 rural residents (1 357 males and 1 293 females). A total of 435 patients with thyroid diseases were diagnosed, the detection rate was 16.42%. There were seven thyroid diseases, including subclinical hypothyroidism (60.92%, 265/435), Hashimoto's thyroiditis (34.02%, 148/435), hypothyroidism (4.83%, 21/435), simple goiter (3.22%, 14/435), hyperthyroidism (2.53%, 11/435), subclinical hyperthyroidism (2.53%, 11/435), and thyroid cancer (1.84%, 8/435). The detection rates of thyroid diseases in urban and rural areas were 21.18% (295/1 393) and 11.14% (140/1 257), respectively. The detection rates of thyroid diseases in males and females were 11.42% (155/1 357) and 21.66% (280/1 293), respectively. The detection rates of thyroid diseases in 18-< 30, 30-< 40, 40-< 50, 50-< 60 and ≥60 years old were 13.46% (91/676), 14.81% (81/547), 15.42% (89/577), 20.94% (85/406) and 20.05% (89/444), respectively. There were statistically significant differences between different areas, gender and age groups (χ 2 = 48.54, 50.53, 14.68, P < 0.05). Conclusions:The detection rate of subclinical hypothyroidism in adults in urban and rural areas of Hebei Province is relatively high, followed by Hashimoto's thyroiditis. Attention should be paid to the screening, evaluation, and intervention of thyroid function among urban female populations.

With the rapid development of healthcare big data and artificial intelligence technology, how to utilize the massive medical data generated based on clinical diagnosis and treatment has become an important issue to be solved in the field of clinical research. Clinical diagnosis and treatment data is an essential part of healthcare big data, and also the main field of healthcare big data research. With the continuous deepening and extensive development of informatization, hospitals have accumulated a large number of patient-centered clinical diagnosis and treatment data. Deeply mining and analyzing these data through big data technology can provide reference for precise diagnosis and treatment, and standardized prevention and control of diseases. However, conducting relevant research still faces many difficulties and blockages, such as the increased risk of data leakage or abuse, and the difficulty in implementing informed consent. To safely, legally and efficiently utilize clinical diagnosis and treatment data to conduct clinical research and fully tap into the value of these precious medical resources, a tertiary hospital in Beijing has built a research big data platform and developed relevant systems to effectively solve the problems of blockages and difficulties in the application of rich clinical resources to clinical research, and improve the service quality of medical institutions and the conversion rate of scientific research achievements. By introducing the key points and management methods in the implementation of clinical research based on the scientific research big data platform, analyzing and exploring the existing problems and improvement measures, this paper aimed to provide theoretical basis and system reference for high-quality and efficient health and medical big data clinical research, inspire and promote the continuous improvement of medical research management, and promote the development of medical and health science and technology innovation.