Performance of Chest CT-Based Artificial Intelligence Models in Distinguishing Pulmonary Mucormycosis, Invasive Pulmonary Aspergillosis, and Pulmonary Tuberculosis.

In clinical practice, differentiating among pulmonary mucormycosis (PM), invasive pulmonary aspergillosis (IPA), and pulmonary tuberculosis (PTB) can be challenging. This study aimed to evaluate the performance of chest CT-based artificial intelligence (AI) models in distinguishing among these three diseases. Patients with confirmed PM, IPA, or PTB were retrospectively recruited from three tertiary hospitals.

Strategies for creation of data reserve and stress testing of medical AI products.

With the fast development of artificial intelligence (AI) and its applications in medicine, it is often said that the time for intelligent medicine is arriving, if not already have arrived. While there is no doubt that AI-centred intelligent medicine will transform current healthcare, it is necessary to test and re-test medical AI (MAI) products before they are implemented in the real world. From the perspective of ensuring safety, accuracy and efficiency, it is imperative that MAIs undergo stress tests in a systematic and comprehensive manner where stress tests subject MAIs to workloads and environments beyond tests carried out by their developers.

Nanocatalysis in cathode pre-lithiation for lithium-ion batteries: progress and challenges.

Pre-lithiation, which is capable of supplying additional active lithium sources to lithium-ion batteries, has been widely accepted as one of the most promising approaches for addressing the issue of active lithium loss during the entire process of initial charging and subsequent cycling. In comparison with anode pre-lithiation, cathode pre-lithiation exhibits a facile operating procedure and good compatibility with current lithium-ion battery production processes. However, cathode pre-lithiation additives suffer from high decomposition voltage and low decomposition efficiency.

Controlled synthesis of silver/silicon hybrid nanostructures enables enhanced photocatalytic CO reduction.

Localized surface plasmon resonances in plasmonic nanoparticles enable effective photon harvesting and generate energetic electrons and holes. However, fast charge recombination and surface contamination due to reactant-involved synthesis can significantly hinder the activity. Herein, we report a facile, solution-processed synthesis of hybrid uncoated silver nanoparticles combined with silicon nanocrystals to address these issues.

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.

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.

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.

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.

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.

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.

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.

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.

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(CH). 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.

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.

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.

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).

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 CO, CH, and NO 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.

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

Uncertainty in methane (CH) 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 CH exchange, yet the seasonal patterns of temperature dependencies for CH fluxes over the wetlands and grasslands on the QTP remain poorly understood. Here, we demonstrated a stronger warming response of CH exchanges during the non-growing season compared to the growing season on the QTP.

Supervised training models with or without manual lesion delineation outperform clinicians in distinguishing pulmonary cryptococcosis from lung adenocarcinoma on chest CT.

Background: The role of artificial intelligence (AI) in the discrimination between pulmonary cryptococcosis (PC) and lung adenocarcinoma (LA) warrants further research.

Objectives: To compare the performances of AI models with clinicians in distinguishing PC from LA on chest CT.

Methods: Patients diagnosed with confirmed PC or LA were retrospectively recruited from three tertiary hospitals in Guangzhou.

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.

An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau.

Intense grazing may lead to grassland degradation on the Qinghai-Tibetan Plateau, but it is difficult to predict where this will occur and to quantify it. Based on a process-based ecosystem model, we define a productivity-based stocking rate threshold that induces extreme grassland degradation to assess whether and where the current grazing activity in the region is sustainable. We find that the current stocking rate is below the threshold in ~80% of grassland areas, but in 55% of these grasslands the stocking rate exceeds half the threshold.