Sepsis presents challenges in clinical treatment due to its high incidence,difficult treatment,and high fatality rate.The intestine has long been considered the"motor"of multiple organ dysfunction syndrome(MODS).Intestinal dysfunction is one of the common complications of sepsis,which is often neglected due to its hidden occurrence and poor clinical efficacy,leading to poor prognosis.Therefore,it is of great significance to explore the pathogenesis and treatment of intestinal dysfunction in sepsis.As an effective supplement for the clinical treatment of intestinal dysfunction in sepsis,Traditional Chinese medicine has been paid more and more attention by clinicians.This article summarizes the research progress on the pathogenesis of sepsis-induced intestinal dysfunction and the clinical application of traditional Chinese medicine(TCM),aiming to provide more ideas and references for the clinical treatment of sepsis.

In this study, the current status for breast diseases in a region with high-incidence of cervical cancer were epidemiologically investigated. From March to August, 2009, 17618 women, from Wufeng area of Hubei province, China, were recruited to screen breast diseases by using breast infrared diagnostic apparatus. Other diagnostic methods, such as B-mode ultrasound, X-ray mammography, needle biopsy and pathological examination were, if necessary, used to further confirm the diagnosis. The screening showed that 5990 of 17618 cases (34.00%) had breast diseases, 5843 (33.16%) had mammary gland hyperplasia, 48 (0.27%) had breast fibroadenoma, 11 (0.06%) had breast carcinoma, and 88 (0.50%) had other breast diseases. The peak morbidity of breast cancer was found in the women aged 50-60 ages. The morbidity of breast cancer was significantly increased in women elder than or equal to 50 years old (n=8, 0.157%) in comparison with that in the subjects younger than 50 years old (n=3, 0.024%) (u=2.327, P<0.05). It was shown that the occurrence of breast diseases was concentrated in women aged 20-40 years, while the total morbidity reached its peak at the age of 30 years and then decreased sharply after age of 40. Compared with the patients elder than or equal to 40 years old (n=3289, 27.46%), the morbidity rate of breast diseases was significantly increased in women less than 40 years old (2648 cases, 47.18%; P<0.001). However, there was no significant difference in the morbidity of breast diseases between the age group of 20-29 years and that of 30-39 years (P=0.453), and both of them were high. There was no significant association between the morbidity of breast diseases and cervical cancer. Since the morbidity of breast diseases was higher among young women, more attention should be paid to the screening of breast diseases among young women for early diagnosis.

A semi-supervised learning method based on multi-scale consistency and regional reliability perception is proposed to combine unlabeled data with a small amount of labeled data to achieve high-performance lung tumor segmentation tasks.A multi-scale consistency mean teacher framework is used to construct a multi-scale consistency loss and constrain the outputs in the mean teacher network to be consistent across multiple scales,so that the model learns richer consistency knowledge.In addition,a regional reliability perception scheme is adopted to make the knowledge exchange between consistency learning more efficient,enabling the model to learn more valid and reliable knowledge from unlabeled data.The evaluation on the lung tumor dataset in the Medical Segmentation Decathlon shows superior performance of the proposed method over current state-of-the-art semi-supervised learning methods,validating its effectiveness.

An airway segmentation method based on coordinate information and multi-scale parallel network is proposed to solve the problem of insufficient accuracy of airway model in surgical navigation.Airway features at different scales are learned separately by a parallel network to address the feature conflict arising from airways of different sizes.Then,a coordinate guided up-sampling module is designed to utilize coordinate information from shallow features for guiding reconstruction of deeper features,thus restricting the spatial location of the target and improving the model accuracy.Finally,a channel guided multi-scale feature aggregation module is constructed to capture semantic details across multiple scales and investigate channel relationships between features at different scales.The proposed method and other models are trained and tested on two public datasets,namely LIDC-IDRI and EXACT'09.Experimental results show that the proposed method achieves an average Dice coefficient of 93.20%which is 2.61%higher than 3D U-Net,a false positive rate of only 0.012%,a tree length detection rate of 88.59%,and a branch detection rate of 97.42%,demonstrating that the method can be applied to lung disease diagnosis or navigation bronchoscopy.