A bcl-2/bax ratio greater than 1 and p53 gene mutation were closely associated with early relapse in patients with superficial transitional cell carcinoma of the bladder during intravesical chemotherapy after resection. This finding suggests that the relative levels of bcl-2 and bax, and p53 gene status may contribute to drug sensitivity and progression, and help to predict recurrence. Moreover, bcl-2/bax correlated with p53 status, which implies that cross talk among bcl-2, bax and p53 has a role in influencing drug induced apoptosis and regulating resistance to chemotherapy.

Electrical remodeling of atrial fibrillation may account for the increase in atrial defibrillation thresholds over time. The aim of this study was to examine the time course of electrical remodeling and the benefit of early defibrillation on the defibrillation threshold. Twenty-six mongrel dogs weighing 27.6 +/- 3.3 kg were induced into AF by repeated high output burst atrial pacing. Eight dogs were paced for multiple time periods of 5, 20, 40, and 60 minutes. Five dogs each had burst pacingfor 4 hours and 8 hours, and eight dogs were paced at a high rate (640 beats/min) for 48 hours. Biphasic atrial defibrillation shocks with a pulse width of 3/3 ms synchronized to the left apical electrogram were delivered to coil electrode catheters positioned in the lateral left and right atria. Defibrillation voltage was increased from 50 V in 20- to 30-V steps until defibrillation was successful. As the pacing period increased, a decrease in atrial fibrillation cycle lengths and atrial effective refractory period was not observed before 8 hours. Similarly, the defibrillation threshold did not change significantly until the 8-hour pacing period was reached. The defibrillation thresholds were 69 +/- 28 V for 5 minutes, 64 +/- 20 V for 20 minutes, 99 +/- 85 V for 40 minutes, 78 +/- 51 V for 60 minutes, 78 +/- 38 V for 4 hours, 124 +/- 33 V for 8 hours, and 133 +/- 32 V for 48 hours (mean +/- SD) (P < 0.05). Atrial electrical remodeling in a rapid atrial pacing canine model is not observed until after 4 hours of burst atrial pacing. The atrial defibrillation threshold increases with increasing duration of burst atrial pacing, and follows a similar time course to other parameters of electrical remodeling.

Background The safety and efficacy of rotational atherectomy (RA) in acute coronary syndrome (ACS) patients treated with different rotational speeds remain unclear.Methods This was an observational retrospective registry. Between February 2017 and January 2022, a total of 283 ACS patients were treated with RA. The patients were divided into two groups: the low-speed group (130,000-150,000rpm,182 cases) and the high-speed group (160,000-220,000rpm,101 cases) according to the maximum RA speed. The primary outcome was the occurrence of hypotension, vasospasm, dissection, slow flow, perforation, bradyarrhythmia, burr entrapment, rotawire fracture during RA, as well as the incidence of heart failure, stent thrombosis, and cardiac death during hospitalization.Results Patients in the low-speed RA group experienced a higher incidence of vasospasm during RA operation (15.4% versus 6.9%, p = 0.040), whereas the incidence of slow blood flow was higher in the high-speed RA group (16.5% versus 27.7%, p = 0.031). There was no significant difference in other complications between the two groups. Moreover, logistic regression analysis identified rotational speed (160,000-220,000rpm) as a predictor of slow flow during RA operation (OR = 1.900, 95%CI:1.006–3.588, p = 0.048). For every 10,000 rpm increase in rotational speed, the risk of slow flow increased by 27% (OR = 1.273, 95% CI: 1.047–1.547, p = 0.015).Conclusion ACS patients treated with a lower RA speed (130,000-150,000 rpm) had a higher risk of vasospasm, while in patients where higher speeds were used (160,000-220,000 rpm ), a higher incidence of slow flow was identified. High rotational speed (160,000-220,000 rpm) is an independent risk factor for slow flow during RA in ACS patients.

Recently, extensive studies have been performed for crack detection and segmentation using deep learning and computer vision techniques to accomplish autonomous bridge inspection. These deep network models are frequently trained with a large volume of parameters to ensure good performance. However, the robust applications under real-world situations of actual bridge inspection still face significant challenges. For example, false-positive recognitions of complex background disturbances excluded in the training sets are inevitable to exist. Besides, the real-time requirement for deploying large-volume deep networks in edge computing equipment is still challenging to achieve. This study establishes a lightweight semantic segmentation model for complex concrete crack segmentation in actual bridge inspection. First, the DeepLabv3+ model is adopted as the baseline, and the backbone module is replaced by MobileNetV2 instead of ResNet101. Second, the depthwise separable convolution, atrous convolution pyramid, and inverted residual modules are utilized to reduce convolutional parameters, expand receptive fields, and alleviate gradient vanishing, respectively. Third, the dataset is enhanced with negative disturbance examples, including straight-line-like structural edges and exposed rebars, to improve the model performance against false positives without additional labeling workload. Original images with different resolutions are first collected from actual bridges, and negative samples are further added to the dataset. A total of 4303 patches in 512 × 512 are generated by a sliding window, where 3443, 430, and 430 are randomly selected for training, validation, and test. Ablation experiments demonstrate the necessity and effectiveness of using MobileNetV2 instead of ResNet101 as the backbone and adding negative examples into the dataset. The results show that the mean intersection-over-union (mIoU) for crack segmentation in various real-world scenarios reaches 0.759. The recognition rate of false positives for complex background disturbances is effectually suppressed by introducing straight-line-like structural edges and exposed rebars into the dataset. Furthermore, the average time cost gains a significant reduction of 35.1% using the established lightweight crack segmentation model with only a slight drop on IoU of 0.017.

No abstract