Objective:To explore the value of predicting new-onset heart failure events in patients with hypertrophic cardiomyopathy (HCM) using clinical and cardiac magnetic resonance (CMR) features based on machine learning algorithms.Methods:The study was a retrospective cohort study. Patients with a confirmed diagnosis of HCM who underwent CMR examinations at Beijing Anzhen Hospital from May 2017 to March 2021 were selected and randomly divided into the training set and the validation set in a ratio of 7∶3. Clinical data and CMR parameters (including conventional parameters and radiomics features) were collected. The endpoint events were heart failure hospitalization and heart failure death, with follow-up ending in January 2023. Features with high stability and P value<0.05 in univariate Cox regression analysis were selected. Subsequently, three machine learning algorithms—random forest, decision tree, and XGBoost—were used to build heart failure event prediction models in the training set. The model performance was then evaluated using the independent validation set, with the performance assessed based on the concordance index. Results:A total of 462 patients were included, with a median age of 51 (39, 62) years, of whom 332 (71.9%) were male. There were 323 patients in the training set and 139 in the validation set. The median follow-up time was 42 (28, 52) months. A total of 44 patients (9.5% (44/462)) experienced endpoint events (8 cases of heart failure death and 36 cases of heart failure hospitalization), with 31 events in the training set and 13 in the validation set. Univariate Cox regression analysis identified 39 radiomic features, 4 conventional CMR parameters (left ventricular end-diastolic volume index, left ventricular end-systolic volume index, left ventricular ejection fraction, and late gadolinium enhancement ratio), and 1 clinical feature (history of non-sustained ventricular tachycardia) that could be included in the machine learning model. In the prediction models built with the training set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.966 (95% CI 0.813-0.995), 0.956 (95% CI 0.796-0.992), and 0.973 (95% CI 0.823-0.996), respectively. In the validation set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.854 (95% CI 0.557-0.964), 0.706 (95% CI 0.399-0.896), and 0.703 (95%CI 0.408-0.890), respectively. Conclusion:Integrating clinical and CMR features of HCM patients through machine learning aids in predicting heart failure events, with the random forest model showing superior performance.

Alzheimer's disease(AD)is a severe threat to human health and one of the three major causes of human death.Double-stranded RNA-dependent protein kinase(PKR)is an interferon-induced protein kinase involved in innate immunity.In the occurrence and development of AD,PKR is upregulated and continu-ously activated.On the one hand,the activation of PKR triggers an integrated stress response in brain cells.On the other hand,it indirectly upregulates the expression of 3-site amyloid precursor protein cleaving enzyme 1 and facilitates the accumulation of amyloid-β protein(Aβ),which could activate PKR activator to further activate PKR,thus forming a sustained accumulation cycle of Aβ.In addition,PKR can promote Tau phosphorylation,thereby reducing microtubule stability in nerve cells.Inflammation in brain tissue,neurotoxicity resulted from Aβaccumulation,and disruption of microtubule stability led to the progression of AD and the declines of memory and cognitive function.Therefore,PKR is a key molecule in the development and progression of AD.Effective PKR detection can aid in the diagnosis and prediction of AD progression and provide opportunities for clinical treat-ment.The inhibitors targeting PKR are expected to control the activity of PKR,thereby controlling the progression of AD.Therefore,PKR could be a target for the development of therapeutic drugs for AD.

Objective To construct a TCM Zhengsu differentiation model for type 2 diabetes based on deep learning and multimodal fusion,thus providing algorithmic support for full intelligence in TCM Zhengsu differentiation.Methods A total of 2585 patients with type 2 diabetes were recruited.Three experts were invited to perform the Zhengsu differentiation separately.Deep fully connected neural networks,U2-Net and ResNet34 networks were applied to construct the symptom-based differentiation model(S-Model)and the tongue image-based differentiation model(T-Model),respectively,while multimodal fusion techniques were employed to build the multimodal fusion model(TS-Model)with the above two as co-inputs.Finally,the prediction performance of the above models was compared by F1 value,accuracy,and recall.Results The predicted F1 values of the T-Model fluctuated from 0.000%to 86.726%,while those in the S-Model and TS-Model fluctuated from 0.000%to 97.826%and from 55.556%to 99.065%,respectively.A stable and high F1 value was found in the TS-Model.Conclusion The multimodal fusion technique was demonstrated to be applicable in the TCM Zhengsu differentiation model,which provided methodological support for developingof a fully intelligent Zhengsu differentiation model with high objective four diagnostic information.

Objective To establish the energy expenditure(EE)prediction models of health Qigong Wuqinxi based on heart rate combined with accelerometer counts in different body parts,so to provide a reference for monitoring EE of Wuqinxi.Methods Seventy-four healthy college students aged 18-30 were selected as the research objects.They were divided into a skilled group of 39(21 males and 18 females)and a primary group of 35(17 males and 18 females)according to their level of practice,with 55 in the model group and 19 in the validation group.When performing a set of Wuqinxi,all subjects were recorded heart rate(HR),accelerometer counts[the average counts of X-axis,Y-axis,Z-axis and vector magnitude(VM)],and EE-related indicators by wearing the Polar heart rate moni-tor,ActiGraph-GT3X+accelerometers(9 parts:waist,both arms,both wrists,both thighs,and both ankles)and CORTEX Meta Max3B-R2 portable indirect calorimeter.Based on the accurate EE mea-sured by the gas metabolism meter,linear regression models of only HR,only accelerometer counts,or HR combined accelerometer counts were established,and their accuracy was analyzed and com-pared.Results The EE of Wuqinxi was significantly correlated with gender,height,weight,muscle mass,HR-related indicators,and accelerometer counts in different parts(X-axis counts of the waist,VM values of the left thigh and counts of the X-axis and Y-axis,VM values of the right thigh and the Y-axis,Z-axis count value,left ankle VM value and Y-axis count value,right ankle VM value and Y-axis,Z-axis count value)(P<0.05).Moreover,the adjusted R2 of the prediction model of only HR,only accelerometer counts,and the both were 0.582,0.508 and 0.678,respectively(P<0.05).The correlation between the predicted and measured values ranged between 0.706 and 0.817.Accord-ing to the Bland-Altman analysis,for each model,all except one error value fell outside the 95%confidence interval.That is,all models had an excellent fitting effect and high accuracy.Among them,the combined model was of the best prediction effect:EE(kcal)=-20.089+0.279×body weight(kg)+0.243×ΔHR(exercise HR-quiet HR,bpm)+0.001×Right thigh Y-axis count value+0.181×exer-cise HR(bpm)-4.202×gender(male=0,female=1).Conclusion The EE prediction model of Wuqinxi es-tablished on HR combined with accelerometer counts has the best effect and can calculate the EE more accurately.

OBJECTIVE To investigate the improvement mechanism of caudatin on liver injury of rats. METHODS SD rats were randomly divided into blank group， model group， caudatin low-dose and high-dose groups （25， 50 mg/kg）， with 6 rats in each group. Diethylnitrosamine （DEN） was injected intraperitoneally three times per week for eight weeks to establish liver injury model of rats. At 5th week of modeling， the rats received relevant medicine or 0.5% sodium carboxymethylcellulose intragastrically for 4 weeks. The levels of liver function indexes [alanine transaminase （ALT）， aspartate transaminase （AST）， total protein （TP） and total bilirubin （TBI）] and inflammatory factors [interleukin （IL-6）， tumor necrosis factor α （TNF-α）， IL-1β] in serum were detected； the histopathological morphological changes of rat liver were observed； the positive protein expressions of nuclear factor κB （NF-κB） and 78 kDa glucose regulatory protein （Grp78） in liver tissue were also determined； the expressions of endoplasmic reticulum stress-related protein Grp78， C/EBP homologous protein （CHOP）， activating transcription factor 6 （ATF6） and inositol requiring enzyme 1α （IRE1α） and the level of protein kinase R-like endoplasmic reticulum kinase robertluoyi@126.com （PERK） in liver tissue were detected. RESULTS Compared with blank group， serum levels of ALT， AST， TBI， IL-6， TNF-α and IL-1β and positive expressions of NF-κB and Grp78 in liver tissue as well as protein expressions of Grp78， CHOP， ATF6 and IRE1α， PERK protein phosphorylation level were all increased significantly in model group （P＜0.05）， while the serum level of TP was decreased significantly （P＜0.05）. The disordered structure of liver lobule， swollen liver cells， unclear intercellular boundary were observed and accompanied by inflammatory cell infiltration. Compared with model group， most of the above indexes were significantly reversed in caudatin groups （P＜0.05）； the structure of hepatic lobule was relatively complete and clear， the cells were arranged orderly， and the infiltration of inflammatory cells was also reduced. CONCLUSIONS Caudatin has a significant improvement effect against DEN-induced liver injury in rats， the mechanism of which may be associated with inhibiting endoplasmic reticulum stress and inflammatory reaction.

Objective To monitor the cumulative terrestrial γ radiation dose around Shidaowan nuclear power plant, Shandong, China before operation, to analyze the dose levels and influencing factors, and to estimate the annual effective dose to local residents. Methods Fifty-six monitoring sites were selected within 30 km around the nuclear power plant. The environmental γ radiation dose was measured by the thermoluminescence dosimeter monitoring method. The γ radiation dose levels were investigated for 369 days in four monitoring periods (January 16 to April 14, April 15 to July 20, July 21 to October 21, 2021, and October 22, 2021 to January 20, 2022 for periods I to IV, respectively). Relations between γ radiation and monitoring time, altitude, distance from the nuclear power plant were analyzed, and the annual effective dose of terrestrial γ radiation to residents was estimated to reflect the background terrestrial γ radiation level in the area. Results The average values of terrestrial γ radiation dose rate in the four monitoring periods in the area were (76.196 ± 3.366), (81.773 ± 6.144), (93.554 ± 7.449), and (97.604 ± 9.396) nGy/h, respectively, and the terrestrial γ radiation dose rate in the whole year was (87.282 ± 6.589) nGy/h. The effective dose to residents was 0.428 mSv. The terrestrial γ radiation level was high from July 2021 to January 2022. There was no significant difference in the γ radiation dose rate at the monitoring sites with different distance from the nuclear power plant. No impact upon the terrestrial γ radiation dose by the altitude was observed in this study. Conclusion The terrestrial γ radiation level around Shidaowan nuclear power plant in 2021 was at the background level.

Objectives:To investigate the prevalence,clinical characteristics and risk factors of coronavirus disease-2019(COVID-19)in patients with pulmonary hypertension(PH). Methods:A questionnaire survey was conducted from December 30,2022 to January 6,2023 through the WeChat official account of the PH Patients Mutual Aid Organization.PH patients aged≥18 years from 26 province(municipality/autonomous region)were recruited to fill in the electronic survey questionnaire. Results:A total of 293 valid questionnaires were collected from PH patients.The mean age of patients was(40.6±12.7)years,and 226 patients(77.1%)of them were female.The vaccination rate was 59.7%(175/293),117 patients(39.9%)received three or more doses of vaccine,145 patients(49.5%)received inactivated vaccine.242 patients(82.6%)had COVID-19.The most common symptoms during infection were fever(85.5%),cough(77.7%),and fatigue(66.5%).10.7%of the patients had severe or critical COVID-19.Age(OR =1.057,95%CI:1.027-1.087,P<0.001)and comorbid pulmonary disease(OR=3.341,95%CI:1.215-9.184,P=0.019)were associated with severe or critical COVID-19.After adjusting for confounding factors,age was an independent risk factor for severe or critical COVID-19(OR=1.049,95%CI:1.019-1.080,P=0.001).Severe or critical COVID-19 was an independent risk factor for worsening heart failure in PH patients during COVID-19 pandemic(OR=10.522,95%CI:4.311-25.682,P<0.001). Conclusions:The immunization coverage of PH patients is insufficient.PH patients have a higher risk of developing severe or critical COVID-19 than general population.Ageing is an independent risk factor for severe or critical COVID-19,and the risk of worsening heart failure in PH patients with severe or critical COVID-19 is significantly increased during COVID-19 pandemic.

Humans ; Endovascular Aneurysm Repair ; Endovascular Procedures ; Blood Vessel Prosthesis Implantation ; Aortic Aneurysm, Abdominal/surgery* ; Treatment Outcome ; Postoperative Complications ; Risk Factors ; Aortic Rupture/surgery* ; Retrospective Studies

Glioblastoma is carcinogenesis of glial cells in central nervous system and has the highest incidence among primary brain tumors. Brain metastasis, such as breast cancer and lung cancer, also leads to high mortality. The available medicines are limited due to blood-brain barrier. Abnormal activation of phosphatidylinositol 3-kinases (PI3K) signaling pathway is prevalent in glioblastoma and metastatic tumors. Here, we characterized a 2-amino-4-methylquinazoline derivative XH30 as a potent PI3K inhibitor with excellent anti-tumor activity against human glioblastoma. XH30 significantly repressed the proliferation of various brain cancer cells and decreased the phosphorylation of key proteins of PI3K signaling pathway, induced cell cycle arrest in G1 phase as well. Additionally, XH30 inhibited the migration of glioma cells and blocked the activation of PI3K pathway by interleukin-17A (IL-17A), which increased the migration of U87MG. Oral administration of XH30 significantly suppressed the tumor growth in both subcutaneous and orthotopic tumor models. XH30 also repressed tumor growth in brain metastasis models of lung cancers. Moreover, XH30 reduced IL-17A and its receptor IL-17RA in vivo. These results indicate that XH30 might be a potential therapeutic drug candidate for glioblastoma migration and brain metastasis.

Objective The experiment project was designed to explore the variation of NaI (Tl) gamma spectrometer channels with environmental temperature. ⁶⁰Co and ¹⁵²Eu were used to verify the reliability of the correction methods. Methods Two correction methods were applicated, which were curve fitting correction method and known measurement peak correction method. Results The experimental results showed that temperature changes had an effect on NaI (Tl) measured spectra peak. The relative peak will shift to the right at 5℃ by 9.6%, and to the left at 60℃ by 16%, with the reference temperature set at 25℃. The two methods are based on the channel change due to temperature changes, and they could effectively correct the temperature peak-drift. Conclusion In order to make the measured spectrum information accurate and reliable in field monitoring, it is suggested to monitor the environmental temperature so as to correct the measured data.