Automatic cardiothoracic ratio calculation based on lung fields abstracted from chest X-ray images without heart segmentation.

Introduction: The cardiothoracic ratio (CTR) based on postero-anterior chest X-rays (P-A CXR) images is one of the most commonly used cardiac measurement methods and an indicator for initially evaluating cardiac diseases. However, the hearts are not readily observable on P-A CXR images compared to the lung fields. Therefore, radiologists often manually determine the CTR's right and left heart border points of the adjacent left and right lung fields to the heart based on P-A CXR images. Meanwhile, manual CTR measurement based on the P-A CXR image requires experienced radiologists and is time-consuming and laborious. Methods: Based on the above, this article proposes a novel, fully automatic CTR calculation method based on lung fields abstracted from the P-A CXR images using convolutional neural networks (CNNs), overcoming the limitations to heart segmentation and avoiding errors in heart segmentation. First, the lung field mask images are abstracted from the P-A CXR images based on the pre-trained CNNs. Second, a novel localization method of the heart's right and left border points is proposed based on the two-dimensional projection morphology of the lung field mask images using graphics. Results: The results show that the mean distance errors at the x-axis direction of the CTR's four key points in the test sets T1 (21 × 512 × 512 static P-A CXR images) and T2 (13 × 512 × 512 dynamic P-A CXR images) based on various pre-trained CNNs are 4.1161 and 3.2116 pixels, respectively. In addition, the mean CTR errors on the test sets T1 and T2 based on four proposed models are 0.0208 and 0.0180, respectively. Discussion: Our proposed model achieves the equivalent performance of CTR calculation as the previous CardioNet model, overcomes heart segmentation, and takes less time. Therefore, our proposed method is practical and feasible and may become an effective tool for initially evaluating cardiac diseases.

High-Resolution CT Patterns of Anti-PD1 Checkpoint Inhibitor-Related Pneumonitis in Patients With Lung Cancer.

BACKGROUND: Lung cancer has the highest morbidity and mortality in the world, and immunotherapies have been developed for this disease in recent years. However, activation of the immune system can cause immune-related adverse events (irAEs), and checkpoint inhibitor-related pneumonitis (CIP), can be the most severe and fatal. But few reports have systematically examined the spectrum of imaging findings of this condition. Therefore, the objective of this paper is to investigate the high-resolution computed tomography (HRCT) characteristics of CIP in patients with lung cancer. OBJECTIVE: To investigate the HRCT characteristics of CIP in patients with lung cancer. METHODS: HRCT patterns in 41 lung cancer patients who developed CIP after treatment with immune checkpoint inhibitors were retrospectively characterized by interstitial lung disease classification, and their severity was graded. Specific HRCT characteristics related to CIP were identified. RESULTS: There are 4 types of immunotherapy-induce pneumonitis patterns (organizing pneumonia OP 19 cases, nonspecific interstitial pneumonia NSIP 8 cases, acute interstitial pneumonia AIP 7 cases, 7 cases of undetermined type) and image grade (13 cases of grade 1, 17 cases of grade 2, 11 cases of grade 3, 0 cases of grade 4) were identified. Spatial distribution characteristics of these lesions were noted (17 cases predominantly distributed in tumor-containing lobes, 6 cases predominantly distributed in non-tumor-containing lobes, and no specific predilection in 18 cases). Specific CT imaging features found in CIP included, in the order of prevalence, the following: ground glass opacities (38 cases), subpleural/vertical line (37 cases), interstitial thickening around the bronchovascular bundles (36 cases), reticulation (34 cases), fine reticular shadow (31 cases), consolidation (31 cases), small cystic shadow (24 cases, may not having honeycombing), small nodules (17 cases), bronchiectasis (15 cases), honeycombing (11 cases), mosaic sign (11 cases), and pleural effusion (18 cases). CONCLUSION: HRCT of CIP predominantly manifests as ground glass opacities, reticulation, subpleural/vertical line, interstitial thickening around the bronchovascular bundle, and consolidation.

Using Online Memes to Communicate About Health: A Systematic Review.

OBJECTIVE: We conducted a systematic review of the literature on online health memes to (a) detect and describe the available research, (b) identify key findings, and (c) delineate future research needs/opportunities. DATA SOURCE: Databases, communication and public health journals, and ancestry search. STUDY INCLUSION AND EXCLUSION CRITERIA: Empirical studies, in English, published in peer-reviewed academic journals, and focused on memes to discuss health-related topics. DATA EXTRACTION: We scrutinized 357 empirical articles and included 35. DATA SYNTHESIS: Descriptive summary of the locations where studies were conducted, the health topics addressed, theories and methods used, features of memes analyzed, study outcomes, and researchers' challenges. RESULTS: Most studies were conducted in the USA (n = 14); focused on COVID-19 (n = 15); were a-theoretical (n = 19); and quantitative (n = 19). Most explored the themes and use of the memes available online (n = 19); compared meme variations (n = 12) by health issue, content, or participants' group; examined meme' effects on health outcomes (n = 1); or evaluated memes as part of a health campaign (n = 3). We summarized the studies' limitations and key findings. CONCLUSIONS: Memes are a promising message strategy for health promotion and education, but more research is necessary. Considering the information retrieved, we provide five specific recommendations for future research directions when studying health memes, including suggestions on the multiple health issues to address and promising theories to adopt and expand.

Hemi-diaphragm detection of chest X-ray images based on convolutional neural network and graphics.

BACKGROUND: Chest X-rays (CXR) are widely used to facilitate the diagnosis and treatment of critically ill and emergency patients in clinical practice. Accurate hemi-diaphragm detection based on postero-anterior (P-A) CXR images is crucial for the diaphragm function assessment of critically ill and emergency patients to provide precision healthcare for these vulnerable populations. OBJECTIVE: Therefore, an effective and accurate hemi-diaphragm detection method for P-A CXR images is urgently developed to assess these vulnerable populations' diaphragm function. METHODS: Based on the above, this paper proposes an effective hemi-diaphragm detection method for P-A CXR images based on the convolutional neural network (CNN) and graphics. First, we develop a robust and standard CNN model of pathological lungs trained by human P-A CXR images of normal and abnormal cases with multiple lung diseases to extract lung fields from P-A CXR images. Second, we propose a novel localization method of the cardiophrenic angle based on the two-dimensional projection morphology of the left and right lungs by graphics for detecting the hemi-diaphragm. RESULTS: The mean errors of the four key hemi-diaphragm points in the lung field mask images abstracted from static P-A CXR images based on five different segmentation models are 9.05, 7.19, 7.92, 7.27, and 6.73 pixels, respectively. Besides, the results also show that the mean errors of these four key hemi-diaphragm points in the lung field mask images abstracted from dynamic P-A CXR images based on these segmentation models are 5.50, 7.07, 4.43, 4.74, and 6.24 pixels,respectively. CONCLUSION: Our proposed hemi-diaphragm detection method can effectively perform hemi-diaphragm detection and may become an effective tool to assess these vulnerable populations' diaphragm function for precision healthcare.

Computed tomography (CT) quantitative assessment of single lobe emphysema correlates with chronic obstructive pulmonary disease (COPD) severity: a cross-sectional study with retrospective data collection.

Background: In the past, many researchers have studied the correlation between quantitative parameters of computed tomography (CT) and parameters of pulmonary function test (PFT) in patients with chronic obstructive pulmonary disease (COPD) with good results. Most of these studies have focused on the whole-lung level. In this study, we analyzed the biphasic CT lung volume parameters and the percentage of emphysema volume in different lobes of the lungs of patients with different grades of COPD and assessed their relationship with different lung function indices. Methods: We retrospectively collected patients who underwent PFTs at The First Affiliated Hospital of Guangzhou Medical University from 1 July 2019 to 27 January 2020, and underwent chest respiratory dual-phase CT scans within 1 week, including 112 non-COPD patients and 297 COPD patients. We quantified the biphasic CT lung volume parameters and the percentage of emphysema volume in different lobes using a pulmonary image analysis tool. One-way analysis of variance (ANOVA) and Kruskal-Wallis H method were used to compare the quantitative CT parameters of each lung lobe in different groups. The correlation between quantitative CT parameters of different lung lobes and lung function indices was assessed using multiple linear regression. Results: Among the 3 biphasic CT lung volume parameters, only volume change/inspiratory lung volume (∆LV/LVin) in the non-COPD control, mildly to moderately severe, and severe to extremely severe groups had statistical differences in each lobe level (all P<0.05). Correlation was significant between LVin and different lung function indices and between low attenuation areas percent below the threshold of -950 in the inspiratory phase [low attenuation area below -950 in the inspiratory phase (%LAA-950in)] and lung function indices in the left lower lobe (all P<0.05). There was statistically significant correlation between expiratory lung volume and ∆LV/LVin and lung function indices in the right lower lung (all P≤0.001). In the remaining lobes, LVin, expiratory lung volume, ∆LV/LVin, and %LAA-950in correlated with only some of the lung function indices. Conclusions: The percentage of emphysema volume did not differ between lobes in the non-COPD control and severe to extremely severe COPD populations. LVin and %LAA-950in in the left upper lobe, expiratory lung volume and ∆LV/LVin in the right lower lobe were more reflective of the changes in lung function indices of the patients, whereas the correlation of the 3 biphasic CT lung volume parameters and the percentage of emphysema volume in the upper lobes of both lungs and the right middle lung with lung function indices was unclear.

Predicting postacute phase anaemia after aneurysmal subarachnoid haemorrhage: nomogram development and validation.

BACKGROUND: Anaemia is a severe and common complication in patients with aneurysmal subarachnoid haemorrhage (aSAH). Early intervention for at-risk patients before anaemia occurs is indicated as potentially beneficial, but no validated method synthesises patients' complicated clinical features into an instrument. The purpose of the current study was to develop and externally validate a nomogram that predicted postacute phase anaemia after aSAH. METHODS: We developed a novel nomogram for aSAH patients to predict postacute phase anaemia (3 days after occurrence of aSAH, prior to discharge) on the basis of demographic information, imaging, type of treatment, aneurysm features, blood tests and clinical characteristics. We designed the model from a development cohort and tested the nomogram in external and prospective validation cohorts. We included 456 aSAH patients from The First Affiliated Hospital for the development, 220 from Sanmen People's Hospital for external validation and a prospective validation cohort that included 13 patients from Hangzhou Red Cross Hospital. We assessed the performance of the nomogram via concordance statistics and evaluated the calibration of predicted anaemia outcome with observed anaemia occurrence. RESULTS: Variables included in the nomogram were age, treatment method (open surgery or endovascular therapy), baseline haemoglobin level, fasting blood glucose level, systemic inflammatory response syndrome score on admission, Glasgow Coma Scale score, aneurysm size, prothrombin time and heart rate. In the validation cohort, the model for prediction of postacute phase anaemia had a c-statistic of 0.910, with satisfactory calibration (judged by eye) for the predicted and reported anaemia outcome. Among forward-looking forecasts, our predictive model achieved an 84% success rate, which showed that it has some clinical practicability. CONCLUSIONS: The developed and validated nomogram can be used to calculate individualised anaemia risk and has the potential to serve as a practical tool for clinicians in devising improved treatment strategies for aSAH.

Injectable cross-linked hyaluronic acid hydrogels with epigallocatechin gallate loading as vitreous substitutes.

Hyaluronic acid (HA) serves as a vitreous substitute owing to its ability to mimic the physical functions of native vitreous humor. However, pure HA hydrogels alone do not provide sufficient protection against potential inflammatory risks following vitrectomy. In this study, HA was crosslinked with 1,4-butanediol diglycidyl ether (BDDE) to form HA hydrogels (HB). Subsequently, the anti-inflammatory agent epigallocatechin gallate (EGCG) was added to the hydrogel (HBE) for ophthalmic applications as a vitreous substitute. The characterization results indicated the successful preparation of HB with transparency, refractive index, and osmolality similar to those of native vitreous humor, and with good injectability. The anti-inflammatory ability of HBE was also confirmed by the reduced expression of inflammatory genes in retinal pigment epithelial cells treated with HBE compared with those treated with HB. In a New Zealand white rabbit model undergoing vitreous substitution treatment, HBE 50 (EGCG 50 µM addition) exhibited positive results at 28 days post-surgery. These outcomes included restored intraocular pressure, improved electroretinogram responses, minimal increase in corneal thickness, and no inflammation during histological examination. This study demonstrated the potential of an injectable HA-BDDE cross-linked hydrogel containing EGCG as a vitreous substitute for vitrectomy applications, offering prolonged degradation time and anti-inflammatory effects postoperatively.

Comprehensive Review of Li-Rich Mn-Based Layered Oxide Cathode Materials for Lithium-Ion Batteries: Theories, Challenges, Strategies and Perspectives.

Lithium-rich manganese-based layered oxide cathode materials (LLOs) have always been considered as the most promising cathode materials for achieving high energy density lithium-ion batteries (LIBs). However, in practical applications, LLOs often face some key problems, such as low initial coulombic efficiency, capacity/voltage decay, poor rate performance and poor cycle stability. It seriously shortens the lifespan of lithium-ion batteries and hinder the large-scale commercial application of LLOs. Herein, firstly, the basic theories of LLOs were systematically reviewed, including the structural characteristics, the working mechanism of LLOs, the preparation methods of LLOs (liquid phase co-precipitate method, sol-gel method, hydrothermal synthesis method, solid phase method, low heat solid-phase method, high temperature solid-state method etc.), and electrochemical characteristics of LLOs (first charge discharge characteristics and reversible efficiency, cycling performance, high and low temperature performance and thermal stability etc.). Then, key challenges faced by LLOs were systematically discussed. Finally, the LLOs modification strategies used to address these challenges (element doping, surface modification, defect engineering, structural and morphological control etc.) were elaborated in detail. This important review provides potential insights and directions for further improving the electrochemical performance of LLOs, and provides a necessary theoretical basis for accelerating the large-scale commercial application of LLOs. It possesses important scientific research value and far-reaching social significance.

3D Vessel Segmentation With Limited Guidance of 2D Structure-Agnostic Vessel Annotations.

Delineating 3D blood vessels of various anatomical structures is essential for clinical diagnosis and treatment, however, is challenging due to complex structure variations and varied imaging conditions. Although recent supervised deep learning models have demonstrated their superior capacity in automatic 3D vessel segmentation, the reliance on expensive 3D manual annotations and limited capacity for annotation reuse among different vascular structures hinder their clinical applications. To avoid the repetitive and costly annotating process for each vascular structure and make full use of existing annotations, this paper proposes a novel 3D shape-guided local discrimination (3D-SLD) model for 3D vascular segmentation under limited guidance from public 2D vessel annotations. The primary hypothesis is that 3D vessels are composed of semantically similar voxels and often exhibit tree-shaped morphology. Accordingly, the 3D region discrimination loss is firstly proposed to learn the discriminative representation measuring voxel-wise similarities and cluster semantically consistent voxels to form the candidate 3D vascular segmentation in unlabeled images. Secondly, the shape distribution from existing 2D structure-agnostic vessel annotations is introduced to guide the 3D vessels with the tree-shaped morphology by the adversarial shape constraint loss. Thirdly, to enhance training stability and prediction credibility, the highlighting-reviewing-summarizing (HRS) mechanism is proposed. This mechanism involves summarizing historical models to maintain temporal consistency and identifying credible pseudo labels as reliable supervision signals. Only guided by public 2D coronary artery annotations, our method achieves results comparable to SOTA barely-supervised methods in 3D cerebrovascular segmentation, and the best DSC in 3D hepatic vessel segmentation, demonstrating the effectiveness of our method.

Trends in Empiric Broad-Spectrum Antibiotic Use for Suspected Community-Onset Sepsis in US Hospitals.

Importance: Little is known about the degree to which suspected sepsis drives broad-spectrum antibiotic use in hospitals, what proportion of antibiotic courses are unnecessarily broad in retrospect, and whether these patterns are changing over time. Objective: To describe trends in empiric broad-spectrum antibiotic use for suspected community-onset sepsis. Design, Setting, and Participants: This cross-sectional study used clinical data from adults admitted to 241 US hospitals in the PINC AI Healthcare Database. Eligible participants were aged 18 years or more and were admitted between 2017 and 2021 with suspected community-onset sepsis, defined by a blood culture draw, lactate measurement, and intravenous antibiotic administration on admission. Exposures: Empiric anti-methicillin-resistant Staphylococcus aureus (MRSA) and/or antipseudomonal ß-lactam agent use. Main Outcomes and Measures: Annual rates of empiric anti-MRSA and/or antipseudomonal ß-lactam agent use and the proportion that were likely unnecessary in retrospect based on the absence of ß-lactam resistant gram-positive or ceftriaxone-resistant gram-negative pathogens from clinical cultures obtained through hospital day 4. Annual trends were calculated using mixed-effects logistic regression models, adjusting for patient and hospital characteristics. Results: Among 6 272 538 hospitalizations (median [IQR] age, 66 [53-78] years; 443 465 male [49.6%]; 106 095 Black [11.9%], 65 763 Hispanic [7.4%], 653 907 White [73.1%]), 894 724 (14.3%) had suspected community-onset sepsis, of whom 582 585 (65.1%) received either empiric anti-MRSA (379 987 [42.5%]) or antipseudomonal ß-lactam therapy (513 811 [57.4%]); 311 213 (34.8%) received both. Patients with suspected community-onset sepsis accounted for 1 573 673 of 3 141 300 (50.1%) of total inpatient anti-MRSA antibiotic days and 2 569 518 of 5 211 745 (49.3%) of total antipseudomonal ß-lactam days. Between 2017 and 2021, the proportion of patients with suspected sepsis administered anti-MRSA or antipseudomonal therapy increased from 63.0% (82 731 of 131 275 patients) to 66.7% (101 003 of 151 435 patients) (adjusted OR [aOR] per year, 1.03; 95% CI, 1.03-1.04). However, resistant organisms were isolated in only 65 434 cases (7.3%) (30 617 gram-positive [3.4%], 38 844 gram-negative [4.3%]) and the proportion of patients who had any resistant organism decreased from 9.6% to 7.3% (aOR per year, 0.87; 95% CI, 0.87-0.88). Most patients with suspected sepsis treated with empiric anti-MRSA and/or antipseudomonal therapy had no resistant organisms (527 356 of 582 585 patients [90.5%]); this proportion increased from 88.0% in 2017 to 91.6% in 2021 (aOR per year, 1.12; 95% CI, 1.11-1.13). Conclusions and Relevance: In this cross-sectional study of adults admitted to 241 US hospitals, empiric broad-spectrum antibiotic use for suspected community-onset sepsis accounted for half of all anti-MRSA or antipseudomonal therapy; the use of these types of antibiotics increased between 2017 and 2021 despite resistant organisms being isolated in less than 10% of patients treated with broad-spectrum agents.

Diagnosis and therapies for patients with cerebral palsy over the past 30 years: a bibliometric analysis.

Background: Currently, the incidence of cerebral palsy is high in newborns. However, the current methods for diagnosing and treating patients with cerebral palsy are complex and poorly targeted. Moreover, these studies lack the support of bibliometric analysis results. Objective: Our study focused on a bibliometric analysis of published papers on the diagnosis and treatment of patients with cerebral palsy. This study identified the primary authors, institutions, and countries involved in analyzing the status and trends of research on the diagnosis and treatment of patients with cerebral palsy. Additionally, the study also involved screening pathways related to cerebral palsy. Methods: The PubMed database was searched for publications on the diagnosis and treatment of patients with cerebral palsy between 1990 and 2023. R v4.2.2 and VOSviewer v1.6.18 software tools were utilized to perform bibliometric analysis and visualization. Results: There were 1,965 publications on cerebral palsy diagnosis and 5,418 articles on the qualified treatment strategies, and the annual number of publications also increased. The United States dominated in this field of research. Gregory Y.H. Lip and Patrizio Lancellotti published the most number of papers. The Cleveland Clinic published the most number of papers in the field. According to the analysis of the co-occurrence of keywords, we found that the main research directions were age, sex, disease diagnosis, and treatment. Newly emerging research has focused mainly on heart failure, which is related to valvular heart disease. Conclusion: The findings presented in this study offer valuable insights into ongoing research and potential future directions pertaining to cerebral palsy. These insights can assist researchers in identifying suitable collaborators and enhancing their investigations aimed at identifying the underlying molecular mechanisms associated with cerebral palsy, encompassing its etiology, preventive measures, and therapeutic interventions.

Attributes and Predictors of Opinion Leaders on Twitter: COVID-19 Childhood Vaccination Campaign.

Identifying attributes of the COVID-19 childhood vaccine message disseminators is beneficial to health campaign research. This study examines the We Can Do This COVID-19 childhood vaccination health campaign message disseminators on Twitter during 2021-2022. Guided by the diffusion of innovation theory and the theory of the two-step flow communication, we classify the disseminators (n=823) and examine their influence over the two-year period. A manual content analysis and social network analysis were conducted to measure the impact of disseminators on their networks through in-degree centrality assessment. Results revealed that experts in medical fields, industrial professionals, and social media influencers were the top three most common career titles disclosed in Twitter bios among individual accounts, whereas local nonprofit organizations, governments, and companies were the most common organizational accounts. Mann-Whitney U Tests indicated that in both years message disseminators with clinical medicine expertise were more influential than those who did not reveal such expertise. However, only in 2021 were campaign message disseminators with parental expertise more influential within the social network. We discuss the implications of disseminator classification as well as the significance of enhancing health campaigns by identifying who discusses them on social media.

Global responses of wetland methane emissions to extreme temperature and precipitation.

As global warming continues, events of extreme heat or heavy precipitation will become more frequent, while events of extreme cold will become less so. How wetlands around the globe will react to these extreme events is unclear yet critical, because they are among the greatest natural sources of methane(CH4). Here we use seven indices of extreme climate and the rate of methane emission from global wetlands(WME) during 2000-2019 simulated by 12 published models as input data. Our analyses suggest that extreme cold (particularly extreme low temperatures) inhibits WME, whereas extreme heat (particularly extreme high temperatures) accelerates WME. Our results also suggest that daily precipitation >10 mm accelerates WME, while much higher daily precipitation levels can slow WME. The correlation of extreme high temperature and precipitation with rate of WME became stronger during the study period, while the correlation between extreme low temperature and WME rate became weaker.

Imaging features and deep learning for prediction of pulmonary epithelioid hemangioendothelioma in CT images.

Background: Pulmonary epithelioid hemangioendothelioma (PEH) is a rare vascular tumour, and its early diagnosis remains challenging. This study aims to comprehensively analyse the imaging features of PEH and develop a model for predicting PEH. Methods: Retrospective and pooled analyses of imaging findings were performed in PEH patients at our center (n=25) and in published cases (n=71), respectively. Relevant computed tomography (CT) images were extracted and used to build a deep learning model for PEH identification and differentiation from other diseases. Results: In this study, bilateral multiple nodules/masses (n=19) appeared to be more common with most nodules less than 2 cm. In addition to the common types and features, the pattern of mixed type (n=4) and isolated nodules (n=4), punctate calcifications (5/25) and lymph node enlargement were also observed (10/25). The presence of pleural effusion is associated with a poor prognosis in PEH. The deep learning model, with an area under the receiver operating characteristic curve (AUC) of 0.71 [95% confidence interval (CI): 0.69-0.72], has a differentiation accuracy of 100% and 74% for the training and test sets respectively. Conclusions: This study confirmed the heterogeneity of the imaging findings in PEH and showed several previously undescribed types and features. The current deep learning model based on CT has potential for clinical application and needs to be further explored in the future.

An optimized water table depth detected for mitigating global warming potential of greenhouse gas emissions in wetland of Qinghai-Tibetan Plateau.

Climate change and human activities have intensified variations of water table depth (WTD) in wetlands around the world, which may strongly affect greenhouse gas emissions. Here, we analyzed how emissions of CO2, CH4, and N2O from the Zoige wetland on the Qinghai-Tibetan Plateau (QTP) vary with the WTD. Our data indicate that the wetland shows net positive global warming potential (11.72 tCO2-e ha-1 yr-1), and its emissions of greenhouse gases are driven primarily by WTD. Our analysis suggests that an optimal WTD exists, which at our study site was approximately 18 cm, for mitigating increases in global warming potential from the wetland. Our study provides insights into how climate change and human acitivies affect greenhouse gas emissions from alpine wetlands, and they suggest that water table management may be effective at mitigating future increases in emissions.

Development and validation of a deep learning signature for predicting lymphovascular invasion and survival outcomes in clinical stage IA lung adenocarcinoma: A multicenter retrospective cohort study.

PURPOSE: The presence of lymphovascular invasion (LVI) influences the management and outcomes of patients with clinical stage IA lung adenocarcinoma. The objective was the development of a deep learning (DL) signature for the prediction of LVI and stratification of prognosis. METHODS: A total of 2077 patients from three centers were retrospectively enrolled and divided into a training set (n = 1515), an internal validation set (n = 381), and an external set (n = 181). A -three-dimensional residual neural network was used to extract the DL signature and three models, namely, the clinical, DL, and combined models, were developed. Diagnostic efficiency was assessed by ROC curves and AUC values. Kaplan-Meier curves and Cox proportional hazards regression analyses were conducted to evaluate links between various factors and disease-free survival. RESULTS: The DL model could effectively predict LVI, shown by AUC values of 0.72 (95 %CI: 0.68-0.76) and 0.63 (0.54-0.73) in the internal and external validation sets, respectively. The incorporation of DL signature and clinical-radiological factors increased the AUC to 0.74 (0.71-0.78) and 0.77 (0.70-0.84) in comparison with the DL and clinical models (AUC of 0.71 [0.68-0.75], 0.71 [0.61-0.81]) in the internal and external validation sets, respectively. Pathologic LVI, LVI predicted by both DL and combined models were associated with unfavorable prognosis (all p < 0.05). CONCLUSION: The effectiveness of the DL signature in the diagnosis of LVI and prognosis prediction in patients with clinical stage IA lung adenocarcinoma was demonstrated. These findings suggest the potential of the model in clinical decision-making.

Imaging and Histopathological Features Of Primary Thymic Neuroendocrine Tumor.

OBJECTIVES: To investigate CT, MRI, and PET/CT features with histopathological findings of primary thymic neuroendocrine tumor. MATERIALS AND METHODS: All 9 cases with pathologically proven primary thymic neuroendocrine tumors were reviewed retrospectively. Among them, 7 underwent enhanced CT, 1 with MRI (enhanced) and another with PET/CT scan. Multiple characters were examined, including tumor location, contour, CT attenuation, enhancement pattern, involvement of surrounding structure and lymphadenopathy. RESULTS: Among 9 patients studied, 7 (77%) masses were located in the anterior superior mediastinum, 1 in the anterior superior-middle mediastinum, and 1 in the anterior and middle mediastinum. The maximum diameter (longitudinal) ranged from 4.2 to 23 cm (mean ± standard deviation, 9.5 cm ± 2.8). Four masses had irregular, 3 had lobulated, and 2 had smooth contours, while 8 masses had clear margins and 1 had an ill-defined margin. Six masses showed heterogeneous attenuation with necrotic/cystic component (n=5), calcification (n=2) and hemorrhage(n=1), and 3 showed homogeneous attenuation on the non-enhanced image. After contrast administration, 8 masses showed heterogeneous attenuation, and 1 showed homogeneous attenuation with tumor vessels visible in 4 masses. Among all, 8 masses showed strong enhancement, and 1 showed moderate enhancement in comparison to muscles in the anterior thoracic wall on enhanced images. Involvement of adjacent mediastinal structures was observed in 5 cases. Immunohistochemical analysis showed that the tumor cells were positive for CgA, Syn, CK, CD56 and EMA. CONCLUSION: Primary NETs are large masses located anterior superior mediastinum, irregular in contour, showing heterogeneous attenuation with necrotic/cystic component and strong heterogeneous enhancement with tumor vessels, compressing local mediastinal structures. In addition, immunohistochemical examination is required in such a diagnosis.

Structure and Functions of Endophytic Bacterial Communities Associated with Sphagnum Mosses and Their Drivers in Two Different Nutrient Types of Peatlands.

Sphagnum mosses are keystone plant species in the peatland ecosystems that play a crucial role in the formation of peat, which shelters a broad diversity of endophytic bacteria with important ecological functions. In particular, methanotrophic and nitrogen-fixing endophytic bacteria benefit Sphagnum moss hosts by providing both carbon and nitrogen. However, the composition and abundance of endophytic bacteria from different species of Sphagnum moss in peatlands of different nutrient statuses and their drivers remain unclear. This study used 16S rRNA gene amplicon sequencing to examine endophytic bacterial communities in Sphagnum mosses and measured the activity of methanotrophic microbial by the 13C-CH4 oxidation rate. According to the results, the endophytic bacterial community structure varied among Sphagnum moss species and Sphagnum capillifolium had the highest endophytic bacterial alpha diversity. Moreover, chlorophyll, phenol oxidase, carbon contents, and water retention capacity strongly shaped the communities of endophytic bacteria. Finally, Sphagnum palustre in Hani (SP) had a higher methane oxidation rate than S. palustre in Taishanmiao. This result is associated with the higher average relative abundance of Methyloferula an obligate methanotroph in SP. In summary, this work highlights the effects of Sphagnum moss characteristics on the endophytic bacteriome. The endophytic bacteriome is important for Sphagnum moss productivity, as well as for carbon and nitrogen cycles in Sphagnum moss peatlands.

More enhanced non-growing season methane exchanges under warming on the Qinghai-Tibetan Plateau.

Uncertainty in methane (CH4) exchanges across wetlands and grasslands in the Qinghai-Tibetan Plateau (QTP) is projected to increase due to continuous permafrost degradation and asymmetrical seasonal warming. Temperature plays a vital role in regulating CH4 exchange, yet the seasonal patterns of temperature dependencies for CH4 fluxes over the wetlands and grasslands on the QTP remain poorly understood. Here, we demonstrated a stronger warming response of CH4 exchanges during the non-growing season compared to the growing season on the QTP. Analyzing 9745 daily observations and employing four methods -regression fitting of temperature-CH4 flux, temperature dependence calculations, field-based and model-based control experiments-we found that warming intensified CH4 emissions in wetlands and uptakes in grasslands. Specifically, the average reaction intensity in the non-growing season surpasses that in the growing season by 1.89 and 4.80 times, respectively. This stronger warming response of CH4 exchanges during the non-growing season significantly increases the regional CH4 exchange on the QTP. Our research reveals that CH4 exchanges in the QTP have a higher warming sensitivity in non-growing seasons, which meanwhile are dominated by a larger warming rate than the annual average. The combined effects of these two factors will significantly alter the CH4 source/sink on the QTP. Neglecting these impacts would lead to inaccurate estimations of CH4 source/sink over the QTP under climate warming.

A CT-based deep learning model: visceral pleural invasion and survival prediction in clinical stage IA lung adenocarcinoma.

Pathologic visceral pleural invasion (VPI) in patients with early-stage lung cancer can result in the upstaging of T1 to T2, in addition to having implications for surgical resection and prognostic outcomes. This study was designed with the goal of establishing and validating a CT-based deep learning (DL) model capable of predicting VPI status and stratifying patients based on their prognostic outcomes. In total, 2077 patients from three centers with pathologically confirmed clinical stage IA lung adenocarcinoma were enrolled. DL signatures were extracted with a 3D residual neural network. DL model was able to effectively predict VPI status. VPI predicted by the DL models, as well as pathologic VPI, was associated with shorter disease-free survival. The established deep learning signature provides a tool capable of aiding the accurate prediction of VPI in patients with clinical stage IA lung adenocarcinoma, thus enabling prognostic stratification.