Automatic segmentation of pulmonary arteries and veins in CT has great clinical significance. Because the growth range of a single vessel is vast, and the arteries and veins have barely identical intensity values on CT and grow very close to or even interleaved, accurate segmentation of them requires intricate vascular texture information and long-distance vascular trunk information as the basis for artery and vein classification. In order to meet these two requirements simultaneously, we design a residual W-Unit, which concatenated two U-shaped structures. It allows the network to become deeper and improve the receptive field for global information while preserving the detailed features of the vessels. And we design a semantic embedding module using cross-attention, which enhances the expression of bronchial features and assists in further utilizing features. It explicitly leverages the anatomical knowledge of parallel growth between arteries and bronchi. Then we combine RWUs and SEMs to construct a concise network to extract and fuse the features with detailed information from different network depths and receptive fields. Finally, we use a post-processing scheme to reduce spatial inconsistency. We validated our networks on 40 training sets and 17 test sets, and the experimental results show that our networks outperform current segmentation methods.

Pulmonary nocardiosis (PN) is a rare and opportunistic infection. This study aimed to analyze clinical, radiological, and microbiological features, treatment and outcome of PN in southern china. Clinical, laboratory, imaging, treatment and outcome data of PN patients at two tertiary hospitals from January 1, 2018, to January 1, 2024 were collected. Factors associated with clinical outcomes were determined by multivariate logistic regression analysis. 67 PN patients including 53 with clinical improvement and 14 with treatment failure were enrolled. Bronchiectasis was the most common respiratory disease in patients with PN (31.3

Chronic obstructive pulmonary disease (COPD) is a respiratory disease that progresses over time and can significantly affect a person’s quality of life. Our proposed method for assessing the severity of COPD involves using lung computed tomography (CT) scans and a deep learning model called Deformable Dual-Path Networks. This model incorporates a side path that is densely connected to learn as many pathological features as possible. Our study provides a methodological idea for making full use of the results of vascular and airway tree segmentation in the diagnosis and monitoring of COPD patients, which can provide a reference for future researchers. We have created a network that uses chest CT scans to classify patients with COPD. Our method was tested on a dataset of 70 patients, and the results showed that it performs better than existing methods. This approach has the potential to enhance the diagnosis and monitoring of COPD patients. In summary, our method shows promise for improving the accuracy and efficiency of COPD severity assessment through medical imaging.

Pulmonary vessel segmentation from CT images is essential to diagnosis and treatment of lung diseases, particularly in treatment planning and clinical outcome evaluation. The main challenge for pulmonary vessel segmentation is complicated structures of the vascular trees and their similar intensity values with other tissues like the tracheal wall and lung nodules. This paper presents a novel relation extractor U-shaped network combining convolution and self-attention mechanism in an encoder-decoder mode. Particularly, we employ convolution in the shallow layers to extract local information of vessels in a short range and apply self-attention in the deep layers to capture long-range contextual relationship between ancestors and descendants of the vascular tree. We evaluate our proposed method on 50 computer tomography volumes, with the experimental results showing that our method can improve the average coefficient dice and recall to 85.60 and 86.04 respectively.

Pulmonary artery-vein segmentation in computed tomography image is essential to lung disease diagnosis. It is still a challenge to segment small distal vessels, crossover and adhesion of arterioles due to complicated arteriovenous structures and limited computed tomography resolution. This work proposes a new U-shaped architecture of hybrid encoded attention networks that employ stacked hybrid units and 3D resample-free attention gates for automatic pulmonary artery-vein segmentation. Specifically, the hybrid unit uses normal operations, 3D hybrid easy operation, and channel shuffle to extract diverse features while the 3D resample-free attention gate can detect regions of interest such as pulmonary arteries and veins and suppress task-independent responses. We validated our method on 50 computed tomography volumes from LIDC-IDRI, with the experimental results demonstrating that it works more stable and effective than currently available approaches, improving the average dice similarity coefficients of the arteries and veins to (84.32%, 86.41%), respectively.

Pulmonary vessel CT segmentation is important to clinical diagnosis of lung diseases. But it is still a challenge due to limited CT quality and complicated vascular structures. This paper proposes new enhanced U-transformer networks that combine transformers, a contrast enhancement block with a reverse attention block to perform end-to-end vessel segmentation. Specifically, the contrast enhancement block directly augments edge or structural information while the reverse attention block conducts the network paying more attention to blurred boundaries and uncertain regions of vessels, leading to improving the accuracy and smoothness of pulmonary vessel segmentation. We validated our proposed method on 50 CT volumes selected from LIDC-IDRI, with the experimental results demonstrating that it works more effectively and stably than currently available approaches. Particularly, the average dice similarity coefficient and recall were improved from (85.23%, 85.37%) to (86.07%, 86.67%), respectively.

Accurate pulmonary nodule segmentation in computed tomography (CT) images is of great importance for early diagnosis and analysis of lung diseases. Although deep convolutional networks driven medical image analysis methods have been reported for this segmentation task, it is still a challenge to precisely extract them from CT images due to various types and shapes of lung nodules. This work proposes an effective and efficient deep learning framework called enhanced square U-Net (ESUN) for accurate pulmonary nodule segmentation. We trained and tested our proposed method on publicly available data LUNA16. The experimental results showing that our proposed method can achieve Dice coefficient of 0.6896 better than other approaches with high computational efficiency, as well as reduce the network parameters significantly from 44.09M to 7.36M.

Background: As a novel pathophysiological characteristic of obstructive sleep apnea, intermittent hypoxia (IH) contributes to human renal tubular epithelial cells impairment. The underlying pathological mechanisms remain unrevealed. The present study aimed to evaluate the influence of Bcl-2 19-kDa interacting protein 3 (BNIP3)-mediated mi-tophagy on IH-induced renal tubular epithelial cell impairment. Material/Methods: Human kidney proximal tubular (HK-2) cells were exposed to IH condition. IH cycles were as follows: 21% oxy-gen for 25 min, 21% descended to 1% for 35 min, 1% oxygen sustaining for 35 min, and 1% ascended to 21% for 25 min. The IH exposure lasted 24 h with 12 cycles of hypoxia and re-oxygenation. Both the siBNIP3 and BNIP3 vector were transfected to cells. Cell viability and apoptosis, mitochondrial morphology and function, and mitophagy were detected by cell counting kit-8, flow cytometry and TUNEL staining, transmission electron microscopy, western blotting, and immunofluorescence, respectively. Results: In the IH-induced HK-2 cells, inhibition of BNIP3 further aggravated mitochondrial structure damage, and de-creased mitophagy level, leading to increased cell apoptosis and decreased cell viability. While overexpression of BNIP3 enhanced mitophagy, which protected mitochondrial structure, it can decrease cell death in HK-2 cells exposed to IH. Conclusions: The present study showed that BNIP3-mediated mitophagy plays a protective role against IH-induced renal tu-bular epithelial cell impairment.

Coronavirus Disease-2019 (COVID-19) has been a major public health issue all over the world, placing a significant burden on available healthcare resources. The most common types of COVID-19 are the mild and common forms. Although the proportion of the severe-critical types is smaller, the rate of death is significantly higher and the medical resources required tend to be greater. Thus, a variety of scores based on other disease and COVID-19 were used to assess the risk of poor prognosis on the COVID-19, including the common scores for community-acquired pneumonia, sepsis and viral pneumonia. Unfortunately, the above scores often lacked an adequate description of the applicable population or were at high risk of bias with unknown applicability. Therefore, the article summarized the existing scores, aiming to provide a reference for clinical prognostic risk assessment.

肠道微生物谱的构成与呼吸系统疾病存在密切关系，影响肺部的免疫反应强度，肠道微生态失衡及肠源性内毒素的释放，均能直接或间接地促进呼吸系统疾病的发生发展。目前，肠菌移植在呼吸系统疾病方面的研究相对较少。本文总结肠道微生态、肠菌移植与呼吸系统疾病关系的研究进展并提出新思路。肠菌移植在呼吸系统疾病治疗上具有一定的应用前景。

A 53-year-old female patient with advanced lung cancer, who was negative for all types of driving genes, received apatinib mesylate tablets(apatinib) 500 mg/d orally after 2 cycles of ineffective chemotherapy of pemetrexed disodium plus cisplatin (21 days as 1 cycle). After 22 days of medication, apatinib was reduced to 250 mg/d because of the hand-foot skin reactions. After 7 months of treatment with apatinib, the pulmonary neoplasms shrank, but the patient developed shortness of breath. CT examination showed bilateral pulmonary interstitial fibrosis. Apatinib was discontinued and hormone therapy was given. The patient′s symptoms were progressively aggravated. On day 32 of drug discontinuation, CT scan showed that the pulmonary interstitial fibrosis was aggravated and the patient developed typeⅠrespiratory failure. An IV infusion of methylprednisolone 500 mg/d and oxygen inhalation were given. However, the patient′s condition was still deteriorating. On day 37 of drug discontinuation, the patient died. Key words: Protein kinase inhibitors; Apatinib; Adenocarcinoma of lung; Lung diseases, interstitial