ObjectiveTo explore the value of ultrasound real-time tissue elastography in the differential diagnosis between benign and malignant breast lesions.Methods A total of 131 cases of patients with breast lesions who underwent ultrasound examination in the People’s Hospital of Guangan City between December 2010 and December 2015 were enrolled as the research object. The patients took conventional color Doppler ultrasound diagnosis firstly, and then took ultrasound real-time tissue elastography diagnosis. The lesions were scored with improved 5-scoring system respectively. By the strain ratio measure method equipped with the ultrasonic machine, strain ratio of the lesion was calculated, with 3.08 as the cut-off pont. The results were campared with the pathologic diagnosis.ResultsThere were 182 breast lumps in the 131 patients. The conventional ultrasound examination detected 128 benign lesions and 54 malignant lesions. By ultrasound real-time tissue elastography examination, there were 121 benign tumors and 61 malignant tumors. For the benign tumors, the elasticity imaging score was 1.74±0.81, and the elastic strain rate ratio was 1.83±1.22; for the malignant tumors, the elasticity imaging score was 4.45±0.59, and the elastic strain rate ratio was 8.68±5.58. The 182 breast lumps were all removed by surgical resection, and the pathologic examination showed there were 121 benign lesions and 61 malignant lesions. The accuracy, sensitivity and specificity of conventional ultrasonic diagnosis of breast malignant lesions was 76.4%, 59.0% and 85.1%, respectively; while the indexes of ultrasound real-time tissue elastography diagnosis of breast malignant lesions was 96.7%, 95.1% and 97.5%, respectively, and the differences were statistically significant (P<0.05).ConclusionReal-time tissue elastography is helpful in the differential diagnosis between malignant and benign breast lesions.
Objective To investigate the risk factors and preventions of functional delayed gastric emptying (FDGE) after pylorus-preserving pancreatoduodenectomy (PPPD). Methods The clinical data of 41 patients after undergoing PPPD between 2003 and 2009 in this hospital were analyzed retrospectively.Results In all 41 cases, postoperative complications developed in 13 patients (31.7%), in which 7 patients developed FDGE (17.1%). The complications excluding FDGE (P=0.010) and diabetes (P=0.024) had remarkable relations with the FDGE in the univariate analysis; Compared with the non-FDGE patients, the albumin was declined obviously (P=0.020) while the serum direct bilirubin increased significantly (P=0.036) in the FDGE patients, while the development of FDGE had relation only with the albumin (P=0.039) and the complication of diabete (P=0.047) by the binary logistic regression analysis. Conclusion In the patients undergoing PPPD, preoperative control of the blood glucose, preoperative correction of hypoproteinemia and hyperbilirubinemia, and centralizing PPPD in high-volume have possibly positive significance for the prevention of FDGE.
Patients with acute heart failure (AHF) often experience dyspnea, and monitoring and quantifying their breathing patterns can provide reference information for disease and prognosis assessment. In this study, 39 AHF patients and 24 healthy subjects were included. Nighttime chest-abdominal respiratory signals were collected using wearable devices, and the differences in nocturnal breathing patterns between the two groups were quantitatively analyzed. Compared with the healthy group, the AHF group showed a higher mean breathing rate (BR_mean) [(21.03 ± 3.84) beat/min vs. (15.95 ± 3.08) beat/min, P < 0.001], and larger R_RSBI_cv [70.96% (54.34%–104.28)% vs. 58.48% (45.34%–65.95)%, P = 0.005], greater AB_ratio_cv [(22.52 ± 7.14)% vs. (17.10 ± 6.83)%, P = 0.004], and smaller SampEn (0.67 ± 0.37 vs. 1.01 ± 0.29, P < 0.001). Additionally, the mean inspiratory time (TI_mean) and expiration time (TE_mean) were shorter, TI_cv and TE_cv were greater. Furthermore, the LBI_cv was greater, while SD1 and SD2 on the Poincare plot were larger in the AHF group, all of which showed statistically significant differences. Logistic regression calibration revealed that the TI_mean reduction was a risk factor for AHF. The BR_ mean demonstrated the strongest ability to distinguish between the two groups, with an area under the curve (AUC) of 0.846. Parameters such as breathing period, amplitude, coordination, and nonlinear parameters effectively quantify abnormal breathing patterns in AHF patients. Specifically, the reduction in TI_mean serves as a risk factor for AHF, while the BR_mean distinguishes between the two groups. These findings have the potential to provide new information for the assessment of AHF patients.