It is well established that periodontal disease (PD) and diabetes mellitus (DM) can have a detrimental effect on each other’s disease course, and that cigarette smoking exacerbates both conditions. However, literature on the periodontal status of smokers with DM is scarce, and the studies conducted to date did not use healthy controls or non-smokers with DM as controls. Consequently, the individual effects of smoking and DM on PD are difficult to untangle and estimate.

A total of 128participants were recruited to this study and their data analyzed. They were assigned to four groups smoking patients with DM (SDM); non-smoking patients with DM (NSDM); smokers without DM (control group, SC) and (4) non-smokers without DM (control group, NSC). Each group consisted of 32 age-matched participants. The periodontal status of the participants was assessed by full oral examination. To express periodontal status, we used the four-stage classification introduced by Fernandes and colleagues (J Periodontol. 80(7)1062-1f even healthy individuals, but the damage is multiplied in a smoker who has DM, even though the effect of DM alone on periodontium health is relatively mild. Our results suggest a synergy between DM and smoking in terms of damage to the periodontal tissues, but the limited sample size of this study does not allow any hard conclusion to be drawn.

Smoking damages the periodontium of even healthy individuals, but the damage is multiplied in a smoker who has DM, even though the effect of DM alone on periodontium health is relatively mild. Our results suggest a synergy between DM and smoking in terms of damage to the periodontal tissues, but the limited sample size of this study does not allow any hard conclusion to be drawn.

Resistance to paclitaxel remains a major challenge in treating breast cancer. Our preclinical study suggested that TEKT4 germline variations in breast cancer are associated with paclitaxel resistance and increase vinorelbine sensitivity. This clinical trial compared the efficacy of paclitaxel and vinorelbine in breast cancer neoadjuvant chemotherapy.

In this open-label, single-center, phase II trial, female patients with human epidermal growth factor receptor 2 (HER2)-negative, stage IIB-IIIC breast cancer harboring TEKT4 germline variations were randomly assigned to the paclitaxel plus epirubicin (PE) or vinorelbine plus epirubicin (NE). The primary endpoint was the pathologic complete response (pCR) rate, and the secondary endpoints were the objective response rate (ORR) and safety. Targeted sequencing of a panel comprising 484 breast-related genes was performed to identify pCR-associated somatic mutations in each group.

91 Patients were assigned to PE (46 patients) or NE (45 patients). NE numerically increased the pCR rate (22.2% versus 8.7%, P = 0.074). The ORRs for NE and PE were 82.2% and 76.1%, respectively. Interestingly, NE (15.4%) showed a significantly higher pCR rate than PE (0%) in the hormone receptor (HR)-positive subgroup (P = 0.044). Both regimens were well tolerated, with grade 3 and 4 toxicities reported at the expected levels. The biomarker analysis showed that UNC13D mutation predicted the pCR rate in NE (P = 0.011).

Although the primary endpoint was not met, NE might bring clinical benefit to HR-positive patients or patients simultaneously carrying UNC13D mutations.

Although the primary endpoint was not met, NE might bring clinical benefit to HR-positive patients or patients simultaneously carrying UNC13D mutations.This study aimed to investigate the rate of passive torque variations of human knee joint in the different velocities of knee flexion and extension movements. Ten healthy men were invited to participate in the tests. All passive torque tests were performed for the knee joint extension and flexion on the sagittal plane in three different angular velocities of 15, 45, and 120°/s; in 5 consecutive cycles; and within 0° to 100° range of motion. The electrical activity of knee joint extensor and flexor muscles was recorded until there was no muscle activity signal. A Three-element Solid Model (SLS) was used to obtain the viscose and elastic coefficients. As the velocity increases, the stretch rate in velocity-independent tissues increases, and the stretch rate in velocity-dependent tissues decreases. By increasing the velocity, the resistance of velocity-dependent parts increases, and the velocity-independent parts are not affected by velocity. Since the first torque that resists the joint movement is passive torque, the elastic and viscous torques should be simultaneously used. It is better to perform the movement at a low velocity so that less energy is lost. The viscoelastic resistance of tissues diminishes. Graphical abstract.Measurement of anatomical structures from ultrasound images requires the expertise of experienced clinicians. Moreover, there are artificial factors that make an automatic measurement complicated. In this paper, we aim to present a novel end-to-end deep learning network to automatically measure the fetal head circumference (HC), biparietal diameter (BPD), and occipitofrontal diameter (OFD) length from 2D ultrasound images. Fully convolutional neural networks (FCNNs) have shown significant improvement in natural image segmentation. this website Therefore, to overcome the potential difficulties in automated segmentation, we present a novelty FCNN and add a regression branch for predicting OFD and BPD in parallel. In the segmentation branch, a feature pyramid inside our network is built from low-level feature layers for a variety of fetal head in ultrasound images, which is different from traditional feature pyramid building methods. In order to select the most useful scale and reduce scale noise, attention mechanism is taken for the feature’s filter. In the regression branch, for the accurate estimation of OFD and BPD length, a new region of interest (ROI) pooling layer is proposed to extract the elliptic feature map. We also evaluate the performance of our method on large dataset HC18. Our experimental results show that our method can achieve better performance than the existing fetal head measurement methods. Graphical Abstract Deep Neural Network for Fetal Head Measurement.