Efficacy of supplemented Er-xian decoction combined with acupoint application for poor ovarian response.

This study aims to observe the efficacy of supplemented Er-xian decoction combined with acupoint application in treating poor ovarian response (POR). This study was a randomized controlled trial. A total of 80 patients, who were treated in the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine from January 2016 to December 2017, were divided into two groups by tables of random numbers: experimental group (n = 40), and control group (n = 40). In the experimental group, patients orally received supplemented Er-xian decoction with acupoint application. In the control group, a Kuntai capsule was administered according to the course of treatment. The therapeutic effects in the two groups were observed and compared. In the experimental group, the total effective rate was 90%, the cure rate was 15% (six patients), the markedly effective rate was 35% (14 patients), the effective rate was 40% (16 patients), and the ineffective rate was 10% (four patients). In the control group, the total effective rate was 50%, the cure rate was 5% (two patients), the markedly effective rate was 15% (six patients), the effective rate was 30% (12 patients), and the ineffective rate was 50% (20 patients). The differences were statistically significant (P > 0.05). Definite efficacy was observed when a poor ovarian response was treated by supplemented Er-xian decoction combined with acupoint application. Improvements in perimenopausal symptoms, menstruation conditions, hormone levels, inhibin B (INHB), and antral follicle count (AFC) were markedly better in the experimental group than in the control group. In addition, the treatment was safe and had few side effects.

Recognizing basal cell carcinoma on smartphone-captured digital histopathology images with a deep neural network.

BACKGROUND: Pioneering effort has been made to facilitate the recognition of pathology in malignancies based on whole-slide images (WSIs) through deep learning approaches. It remains unclear whether we can accurately detect and locate basal cell carcinoma (BCC) using smartphone-captured images. OBJECTIVES: To develop deep neural network frameworks for accurate BCC recognition and segmentation based on smartphone-captured microscopic ocular images (MOIs). METHODS: We collected a total of 8046 MOIs, 6610 of which had binary classification labels and the other 1436 had pixelwise annotations. Meanwhile, 128 WSIs were collected for comparison. Two deep learning frameworks were created. The 'cascade' framework had a classification model for identifying hard cases (images with low prediction confidence) and a segmentation model for further in-depth analysis of the hard cases. The 'segmentation' framework directly segmented and classified all images. Sensitivity, specificity and area under the curve (AUC) were used to evaluate the overall performance of BCC recognition. RESULTS: The MOI- and WSI-based models achieved comparable AUCs around 0·95. The 'cascade' framework achieved 0·93 sensitivity and 0·91 specificity. The 'segmentation' framework was more accurate but required more computational resources, achieving 0·97 sensitivity, 0·94 specificity and 0·987 AUC. The runtime of the 'segmentation' framework was 15·3 ± 3·9 s per image, whereas the 'cascade' framework took 4·1 ± 1·4 s. Additionally, the 'segmentation' framework achieved 0·863 mean intersection over union. CONCLUSIONS: Based on the accessible MOIs via smartphone photography, we developed two deep learning frameworks for recognizing BCC pathology with high sensitivity and specificity. This work opens a new avenue for automatic BCC diagnosis in different clinical scenarios. What's already known about this topic? The diagnosis of basal cell carcinoma (BCC) is labour intensive due to the large number of images to be examined, especially when consecutive slide reading is needed in Mohs surgery. Deep learning approaches have demonstrated promising results on pathological image-related diagnostic tasks. Previous studies have focused on whole-slide images (WSIs) and leveraged classification on image patches for detecting and localizing breast cancer metastases. What does this study add? Instead of WSIs, microscopic ocular images (MOIs) photographed from microscope eyepieces using smartphone cameras were used to develop neural network models for recognizing BCC automatically. The MOI- and WSI-based models achieved comparable areas under the curve around 0·95. Two deep learning frameworks for recognizing BCC pathology were developed with high sensitivity and specificity. Recognizing BCC through a smartphone could be considered a future clinical choice.

[Expression of eosinophilic cationic protein in neutrophils and its effect on objective evaluation of nasal inflammation].

Objective:The aim of this study is to explore the expression of ECP in the neutrophils and its impact on the evaluation of nasal inflammation.Method:Neutrophils and eosinophils in nasal secretions were collected and stained with ECP immunohistochemistry to observe the staining of ECP in different cells. The concentration of ECP and MPO in nasal secretion were detected of 32 patients with allergic rhinitis (AR group), 29 patients with chronic rhinosinusitis without nasal polyps and allergic rhinitis (CRSsNP group), and 21 healthy people (control group). The percentage of neutrophils and eosinophils were calculated and analyzed as well.Result:ECP could be found in both eosinophils and neutrophils with immunohistochemical staining. The expression of ECP is much stronger in eosinophils than that in neutrophils. The ECP and MPO concentration in the nasal secretions of AR group and CRSsNP group were significantly higher than that in control group (P<0.000 1), and the ECP concentration in AR group and CRSsNP group had no difference. The expression of ECP in the AR group was not different from that in CRSsNP group, but the expression of MPO was significantly lower than that in CRSsNP group(P<0.000 1).Conclusion:ECP is expressed in neutrophils, and which is likely to have influence on the objective evaluation to nasal inflammation. Combining with the expression of ECP and MPO, we can make a more accurate judgment of local inflammation.

[The diagnostic value of eosinophilic and eosinophilic cationic proteins in blood and nasal secretions for allergic rhinitis].

Objective:To compare the diagnostic value of eosinophil and eosinophil cationin protein（ECP） in blood and nasal secretions for allergic rhinitis（AR）. Method:Collecting the blood samples of 33 patients with AR （AR group） and 40 healthy people（control group）, and test the concentration of ECP in serum and the percentage of eosinophil in blood. At the same time, collecting the nasal secretions samples of 33 patients with AR and 24 healthy people, and test the concentration of ECP and the percentage of eosinophils in nasal secretions. Using receiver operating characteristic curve（ROC） analysis, calculate the area under the curve（AUC） for each parameter and determine their predictive capabilities, then analyzing the correlation between each indicator and clinical symptom scores. Result:Compared with the healthy control group, the concentration of ECP and the percentage of eosinophil in blood and nasal secretion increased significantly（P<0.001） in patients with AR. The AUG of ECP concentration in nasal secretions was 0.965 9, when the cut-off value was 3.634, 100% sensitivity and 88% specificity were obtained; the AUG of eosinophil percentage in blood was 0.9087, and when its cut-off value was 4.6, 95% sensitivity and 73% specificity were obtained; the ECP concentration in serum had an AUG of 0.903, and when the cut-off value was 0.866, 90% sensitivity and 76% specificity were obtained; the AUG of nasal secretion eosinophil's percentage was 0.863 6, when its cut-off value is 0.72, 100% sensitivity and 73% specificity were obtained. Conclusion:For allergic rhinitis, ECP concentrate in nasal secretions is the ideal auxiliary diagnosis marker, and has the best predictive capability.

Transmembrane grasshopper Semaphorin I promotes axon outgrowth in vivo.

Members of the Semaphorin family of glycoproteins play an important role in axonal pathfinding by functioning as inhibitory guidance cues. Here we provide evidence that a transmembrane form of Semaphorin (Semaphorin I), which is expressed by bands of epithelial cells in the developing grasshopper limb bud, functions as an attractive/permissive cue for the growth cones of the subgenual organ. In addition, we demonstrate that Semaphorin I is needed for initial axonal outgrowth from the subgenual organ. These results are consistent with an alternative function for a transmembrane form of Semaphorin and may explain the previously reported arrest of the proximal extension of the subgenual organ growth cones in the absence of the Ti1 pioneer pathway.