Objective To investigate the feasibility and accuracy of an ultrasound-based transfer learning artificial intelligence model in predicting the malignancy probability of partially cystic thyroid nodules(PCTN).Methods A retrospective analysis was conducted on 246 patients with PCTN who had definitive pathological results and were admitted to Weihai Municipal Hospital,Cheeloo College of Medicine,Shandong University from January 2021 to December 2023.Patients were randomly divided into training and test cohorts at a ratio of 7:3.Ultrasonic image features of PCTN were evaluated,and independent risk factors were identified using multivariate logistic regression analysis,with the area under the curve(AUC)subsequently calculated.Additionally,five different pre-trained models-Inception_v3,EfficientNet,VGG19,ResNet50,and DenseNet121-were selected for transfer learning after data preprocessing using the PyTorch framework in Python.The AUC values of these models were calculated and compared.Results Solid portion greater than 50%,eccentric acute angle,ill-defined margin,spiculated or microlobulated margin,rim calcification,and microcalcification exhibited statistically significant differences(P<0.05)in distinguishing between benign and malignant PCTN.The AUC value derived from these independent risk factors was 0.843.Furthermore,among the five transfer learning models evaluated,the ResNet50 model demonstrated the highest diagnostic efficiency,achieving an AUC value of 0.903 2.Conclusion The ultrasound-based transfer learning artificial intelligence model demonstrated superior performance compared to traditional ultrasound image evaluation methods,enabling accurate prediction of the nature of PCTN and thereby reducing unnecessary ultrasound-guided fine needle biopsies.

Objective To investigate the feasibility and accuracy of an ultrasound-based transfer learning artificial intelligence model in predicting the malignancy probability of partially cystic thyroid nodules(PCTN).Methods A retrospective analysis was conducted on 246 patients with PCTN who had definitive pathological results and were admitted to Weihai Municipal Hospital,Cheeloo College of Medicine,Shandong University from January 2021 to December 2023.Patients were randomly divided into training and test cohorts at a ratio of 7:3.Ultrasonic image features of PCTN were evaluated,and independent risk factors were identified using multivariate logistic regression analysis,with the area under the curve(AUC)subsequently calculated.Additionally,five different pre-trained models-Inception_v3,EfficientNet,VGG19,ResNet50,and DenseNet121-were selected for transfer learning after data preprocessing using the PyTorch framework in Python.The AUC values of these models were calculated and compared.Results Solid portion greater than 50%,eccentric acute angle,ill-defined margin,spiculated or microlobulated margin,rim calcification,and microcalcification exhibited statistically significant differences(P<0.05)in distinguishing between benign and malignant PCTN.The AUC value derived from these independent risk factors was 0.843.Furthermore,among the five transfer learning models evaluated,the ResNet50 model demonstrated the highest diagnostic efficiency,achieving an AUC value of 0.903 2.Conclusion The ultrasound-based transfer learning artificial intelligence model demonstrated superior performance compared to traditional ultrasound image evaluation methods,enabling accurate prediction of the nature of PCTN and thereby reducing unnecessary ultrasound-guided fine needle biopsies.

Objective To investigate the reasons for the inconsistent results between the vertical auto profile(VAP)method and bio-chemical homogeneous phase(BHP)method in detecting plasma low-density lipoprotein cholesterol(LDL-C),and provide experimen-tal basis for the accurate and quantitative detection of plasma LDL-C levels.Methods A total of 360 plasma samples from diabetes mellitus patients combined with carotid plaque admitted to the Department of Endocrinology of Ningbo Yinzhou Hospital of Traditional Chinese Medicine during January,2022 and January,2023 were collected.The LDL-C levels of these samples were detected by the VAP method and BHP method,respectively.The VAP method uses software to automatically calculate the area under the LDL-C curve after centrifugation of the sample as the LDL-C level(LDL-CVAP)and the BHP method directly detects the LDL-C level(LDL-CBHP)by the special surfactant method.360 samples were divided into the consistent group(group A)and inconsistent group(group B)ac-cording to the relative deviation between the LDL-CBHP and LDL-CVAP methods.Group B was further divided into the LDL-CBHP on the high side group(Group B1)and LDL-CBHP on the low side group(Group B2).Groups B1 and B2 were divided into B1-1,B1-2,B1-3 and B2-1 groups based on the degree of relative deviation.The percentages of samples and levels of lipoprotein a cholesterol[Lp(a)-C],intermediate-density lipoprotein cholesterol(IDL-C),Lp(a)-C and IDL-C[Lp(a)-C+IDL-C],very low-density lipo-protein cholesterol(VLDL-C),total cholesterol(TC)and total triglyceride(TG)in each group were compared.Results The LDL-CBHP levels of 360 samples were significantly higher than that of LDL-CVAP(P＜0.01).The percentage of samples in group B was significantly higher than that in group A,and that of group B1 was significantly higher than that of group B2(P＜0.05).The levels of Lp(a)-C,IDL-C and Lp(a)-C+IDL-C in groups B1-1,B1-2,and B1-3 were significantly higher than those in group A(P＜0.01).The relative deviation between LDL-CBHP and LDL-CVAP in 360 samples was significantly positively correlated with the levels of Lp(a)-C,IDL-C,and Lp(a)-C+IDL-C(P＜0.01).The maximum correlation coefficient was found in Lp(a)-C+IDL-C.Conclusion The results of plasma LDL-C in diabetes mellitus patients combined with carotid plaque detected by the BHP method are significantly different from those detected by the VAP method,which mainly shows that the results of the BHP method are on the high side.The higher the level of plasma Lp(a)-C+IDL-C,the greater the relative deviation between the BHP method and VAP method.The reason for the high results of LDL-C detected by the BHP method may be related to the fact that LDL-CBHP contains irremovable Lp(a)-C and cholesterol carried by IDL-C.The VAP method can be used as an accurate method for detecting real LDL-C without Lp(a)-C and IDL-C.

Solenopsis invicta is a kind of invasive pest that causes serious damage to local agriculture, environment, and human health. They attack mainly with venom within stingers. Those who are allergic to the venom would suffer a systemic anaphylaxis, even fatal shock, after being stinged by these ants. Many studies reveal that their venom is mainly composed by water, insoluble alkaloids and trace proteins, within which alkaloids are the main cause of burning sensation and blisters, while allergic reactions are caused by proteins or peptides. The research progress of toxic substances in the venom of Solenopsis invicta as well as the roles and functions of each component were reviewed in this paper.

Objective:To explore the clinical value of peripheral remnant lipoproteins (RLP), low density lipoprotein cholesterol particle (LDL-P) and sdLDL particle (sdLDL-P) measurement in the diagnosis of carotid plaque, so as to provide practical basis for the accurate diagnosis of carotid plaque and the control of carotid plaque related cardiovascular and cerebrovascular diseases.Methods:People who underwent carotid plaque ultrasound examination in Xingtai Third Hospital , from January 2020 to June 2021 were selected as the research object. According to the ultrasound results, they were divided into carotid plaque group ( n=146) and control group without carotid plaque ( n=149). The fasting RLP, LDL-P and sdLDL-P of the two groups were measured by vertical auto profile (VAP) centrifugal separation phase, and the fasting TG and LDL-C were detected by routine mixed phase method. The indexes were compared between the two groups and the true positive rate, true negative rate, false positive rate and false negative rate of the diagnosis of carotid plaque were analyzed. The receiver operating characteristic curve of each test index was drawn, and AUC was used to evaluate the clinical diagnostic value of each test index for carotid plaque. Results:The levels of RLP, LDL-P and sdLDL-P in carotid plaque group were significantly higher than those in non-carotid plaque group ([1.07±0.36] mmol/L vs [0.59±0.17] mmol/L,[1 300±370] nmol/L vs [781±215] nmol/L,[435±139] nmol/L vs [156±59] nmol/L, all P<0.01). The true positive rate (78.08% [114/146],81.51% [119/146]) and true negative rate (84.56% [126/149], 86.58%[129/149]) of serum RLP and LDL-P for the diagnosis of carotid plaque were significantly higher than TG (58.90%[86/146], 43.62%[65/149]) and LDL-C (59.59% [87/146], 46.98% [70/149]), and the false positive rate (15.44% [23/149], 13.42% [20/149]) and false negative rate (21.92% [32/146], 18.49% [27/146]) were significantly lower than TG (56.38% [84/149], 41.10% [60/146]) and LDL-C (53.02% [79/149], 40.41% [59/146], all P<0.01). The AUC of the ROC curve of RLP (0.890), LDL-P (0.902) and sdLDL-P (0.973) for the diagnosis of carotid plaque was higher than TG (0.682) and LDL-C (0.712). The AUC of ROC curve of the RLP combined with sdLDL-P (0.977) for the diagnosis of carotid plaque was higher than the RLP and sdLDL-P (all P<0.01). Conclusion:The serum RLP, LDL-P and sdLDL-P can be used as indicators of carotid plaque, and their clinical diagnostic value are superior to TG and LDL-C; the combined diagnostic effect of lipoprotein subclass is better than that of single index alone.

Objective:To explore the effect of dynamic changes of cerebral oxygen saturation before and after treatment on the progression of infarction in patients with acute ischemic stroke (AIS).Methods:Totally 39 patients with first onset AIS within 24 hours in Tianjin First Central Hospital and Shanghai Fourth People′s Hospital Affiliated to Tongji University from May 2018 to July 2020 were enrolled retrospectively. All patients underwent multi-modal MR at admission (baseline) and within 2 weeks after standardized treatment, including diffusion weighted imaging (DWI), susceptibility weighted imaging (SWI), and dynamic magnetic sensitive contrast-enhanced magnetic resonance perfusion imaging (DSC-PWI). The degree of asymmetrically prominent cortical vein (APCV) at admission was observed on SWI, and the venous oxygen saturation (SvO 2) of APCV on the infarcted cerebral hemisphere was calculated in all patients before and after treatment. The original DWI and DSC-PWI images obtained from two MR scans were imported into the software to obtain the delayed perfusion volume [peak time (T max)>6 s] and the infarct core volume (apparent diffusion coeffivient value<620×10 -6 mm 2/s). According to the comparison of baseline infarct core (DWI-ASPECT) score and follow-up (FUP-ASPECT) score, all patients were divided into infarct progression group (27 cases) and non-infarct progression group (12 cases). Two independent sample t-test or Mann Whitney U-test were used to compare the differences of baseline infarct core volume, baseline SvO 2, SvO 2 change, baseline hypoperfusion volume and hypoperfusion volume change between the two groups. Univariate analysis and multivariate logistic regression analysis were used to obtain independent predictors of infarct progression. Pearson correlation analysis was used to evaluate the correlation between SvO 2 change, hypoperfusion volume change and infarct change score respectively. Results:Difference in baseline infarct core volume, baseline SvO 2, hypoperfusion volume and hypoperfusion volume change between infarct progression group and non-progression group had no statistical significance ( P>0.05). There was significant difference in the change of SvO 2 between the infarct progression group and non-infarct progression group after treatment [(27±11)%, (35±6)% respectively, t=-2.56, P=0.015]. Univariate logistic regression analysis showed that the change value of SvO 2 was the influencing factor of infarction progression of AIS (OR=0.872, 95%CI 0.773-0.984, P=0.026). Multivariate logistic regression analysis showed that the baseline NIHSS score (OR=1.248, 95%CI 1.042-1.494, P=0.016) was an independent predictor of infarction progression in AIS, and the change value of SvO 2 (OR=0.814, 95%CI 0.688-0.964, P=0.017) was an independent protective factor. The change of SvO 2 was positively correlated with the score of infarct change ( r=0.425, P=0.007). Conclusions:The change of SvO 2 after AIS treatment can independently predict the progress of acute infarction. Improvement of SvO 2 after treatment is conducive to delay the progress of infarction.

Objective:To investigate the feasibility of predicting lateral cervical lymph node metastasis (LLNM) in patients with papillary thyroid carcinoma (PTC) based on the nomogram constructed by dual-energy CT data.Methods:In total 417 patients with PTC confirmed by pathology in Tianjin First Central Hospital from January 2015 to December 2018 were retrospectively analyzed as a training group. Internal validation was conducted, including 139 patients in the LLNM group and 278 patients in the non-LLNM group. A total of 169 PTC patients from January 2019 to June 2020 were included as an external validation group, including 58 patients in the LLNM group and 111 patients in the non-LLNM group. The morphological characteristics of the primary thyroid lesions on dual-energy CT iodine maps were analyzed, including tumor location, maximum diameter, calcification, and extrathyroidal extension (ETE). Iodine concentration (IC) of the PTC parenchyma and the internal carotid artery on the same level in the arterial and venous phases were measured, and normalized iodine concentration (NIC) was calculated. The independent risk factors for LLNM were obtained by univariate and multivariate logistic regression analysis. Base on the results, a prediction model was constructed and expressed in the form of a nomogram. The internal and external validation of the model was carried out using ROC curve.Results:Multivariate binary logistic regression analysis showed that the lesion location in the upper polar of the thyroid, the presence of ETE, IC in arterial phase>2.9 mg/ml, IC in the venous phase>3.2 mg/ml, and NIC in the arterial phase>0.21 were independent risk factors for LLNM prediction. The nomogram based on the above factors was constructed with an area under the ROC curve (AUC) of 0.895 (95%CI 0.862-0.923). With a cut-off value of 0.79, the sensitivity and specificity were 86.3% and 75.2%, respectively. As for the external validation group, the AUC of the model was 0.887 (95%CI 0.830-0.931), with the sensitivity of 82.8%, and the specificity of 81.1%.Conclusion:The application values of the nomogram model based on dual-energy CT data in preoperative evaluation of the possibility of LLNM of PTC patients has been verified. The model constructed in this study might be helpful with the individualized treatment in a certain degree.

Objective To evaluate the effect of transitional care intervention on quality of life for patients with chronic obstructive pulmonary disease (COPD). Methods The Cochrane library, PubMed, Embase, Chinese biomedical literature database(CBM), Chinese scientific journals full-text database, Wanfang data were searched up to June 2015 to identify randomized controlled trials(RCTs) comparing continuity of patient care with conventional treatment.The data were analyzed using Revman 5.2 software. Results 26 RCTs involving 2 443 participants were included.The results of Meta-analysis showed continuity of care couldreduce the scores of The COPD Assessment Test [ SMD=-2.24, 95%CI=(-3.52--0.96), P<0.01],and St George′s Respiratory Questionnaire [SMD=-0.26, 95%CI(-0.50--0.02), P≤0.05], and could improve the scores of Chronic Respiratory Disease Questionnaires [SMD=0.46, 95%CI(0.08-0.84), P≤0.05]. Conclusions Transitional care can improve the quality of life of patients with COPD.

Objective To investigate the health behavior and the exposure rate of risk factors of rural population with high risk of chronic obstructive pulmonary disease (COPD).Methods A total of 176 cold region rural COPD high-risk persons were recruited and investigated with Health-Promoting Lifestyle Profile Ⅱ (HPLP Ⅱ),Health Behaviour Self-efficacv Scale,general information questionnaire.Results The total score of HPLP was (92.70±11.22) points in the rural area of COPD,and the scores of the nutrition and interpersonal relationship were the highest,which were (20.06±2.75) points,(19.30±3.16) points,health responsibility and sports dimension were lower,which were respectively (13.74±2.60) points,(9.85±2.80) points.Health behavior self-efficacy scored (71.75±7.68) points,which showed significant correlation with HPLP Ⅱ score (P ＜ 0.01).Conclusions COPD high-risk population of the cold area health promoting lifestyle and health behavior self-efficacy level are not high.Improving mral population at higher risk of COPD health behavior and self-efficacy level can form good healthy lifestyle and behavior.

Objective To understand the self-efficacy for health behaviors and its influencing factors among investigation of rural chronic obstructive pulmonary disease (COPD) high-risk groups.Methods November 2014 using the general questionnaire,Health Behavior Self-efficacy Scale for 176 over 40 yearsold of high-risk populations in rural COPD self-efficacy and health behavior factors research.Results In rural areas at high risk of health behavior self-efficacy scored (71.75±7.68) points,including nutrition and psychological comfortable highest scores which were (14.86±2.03) points and (19.75±3.03) points.Exercise and health responsibilities lower scored 18.90±2.87 and 18.42±2.52.Multiple regression analysis showed that marital status,education level and income were the main factors affecting rural health behavior at high risk of COPD self-efficacy (β=0.384,0.297,0.236,P ＜0.05).Conclusions COPD risk populations in rural areas at the middle level of health behavior self-efficacy,nurses should be given a lower level of education and income were more concerned to help them improve the health behavior self-efficacy.