Development of a CT-Based comprehensive model combining clinical, radiomics with deep learning for differentiating pulmonary metastases from noncalcified pulmonary hamartomas, a retrospective cohort study.

BACKGROUND: Clinical differentiation between pulmonary metastases and noncalcified pulmonary hamartomas (NCPH) often presents challenges, leading to potential misdiagnosis. However, the efficacy of a comprehensive model that integrates clinical features, radiomics, and deep learning (CRDL) for differential diagnosis of these two diseases remains uncertain. OBJECTIVE: This study evaluated the diagnostic efficacy of a Clinical Features, Radiomics, and Deep Learning (CRDL) model in differentiating pulmonary metastases from noncalcified pulmonary hamartomas (NCPH). METHODS: We retrospectively analyzed the clinical and imaging data of 256 patients from Hospital A and 85 patients from Hospital B, who were pathologically confirmed pulmonary hamartomas or pulmonary metastases after thoracic surgery. Employing Python 3.7 software suites, we extracted radiomic features and deep learning attributes from patient datasets. The cohort was divided into training set, internal validation set, and external validation set. The diagnostic performance of the constructed models was evaluated using receiver operating characteristic (ROC) curve analysis to determine their effectiveness in differentiating between pulmonary metastases and NCPH. RESULTS: Clinical features such as white blood cell count (WBC), platelet count (PLT), history of cancer, carcinoembryonic antigen (CEA) level, tumor marker status, lesion margin characteristics (smooth or blurred) and maximum diameter were found to have diagnostic value in differentiating between the two diseases. In the domains of radiomics and deep learning. Of the 1,130 radiomics features and 512 deep learning features, 24 and 7, respectively, were selected for model development. The area under the ROC curve (AUC) values for the four groups were 0.980, 0.979, 0.999, and 0.985 in the training set, 0.947, 0.816, 0.934, and 0.952 in the internal validation set, and 0.890, 0.904, 0.923, and 0.938 in the external validation set. This demonstrated that the CRDL model showed the greatest efficacy. CONCLUSIONS: The comprehensive model incorporating clinical features, radiomics, and deep learning shows promise for aiding in the differentiation between pulmonary metastases and hamartomas.

An enhanced respiratory mechanics model based on double-exponential and fractional calculus.

We address mathematical modelling of respiratory mechanics and put forward a model based on double-exponential and fractional calculus for parameter estimation, model simulation, and evaluation based on actual data. Our model has been implemented on a publicly available executable code with adjustable parameters, making it suitable for different applications. Our analysis represents the first application of fractional calculus and double-exponential modelling to respiratory mechanics, and allows us to propose a hybrid model fitting experimental data in different ventilation modes. Furthermore, our model can be used to study the mechanical features of the respiratory system, improve the safety of ventilation techniques, reduce ventilation damages, and provide strong support for fast and adaptive determination of ventilation parameters.

Gut mycobiome as a potential non-invasive tool in early detection of lung adenocarcinoma: a cross-sectional study.

BACKGROUND: The gut mycobiome of patients with lung adenocarcinoma (LUAD) remains unexplored. This study aimed to characterize the gut mycobiome in patients with LUAD and evaluate the potential of gut fungi as non-invasive biomarkers for early diagnosis. METHODS: In total, 299 fecal samples from Beijing, Suzhou, and Hainan were collected prospectively. Using internal transcribed spacer 2 sequencing, we profiled the gut mycobiome. Five supervised machine learning algorithms were trained on fungal signatures to build an optimized prediction model for LUAD in a discovery cohort comprising 105 patients with LUAD and 61 healthy controls (HCs) from Beijing. Validation cohorts from Beijing, Suzhou, and Hainan comprising 44, 17, and 15 patients with LUAD and 26, 19, and 12 HCs, respectively, were used to evaluate efficacy. RESULTS: Fungal biodiversity and richness increased in patients with LUAD. At the phylum level, the abundance of Ascomycota decreased, while that of Basidiomycota increased in patients with LUAD. Candida and Saccharomyces were the dominant genera, with a reduction in Candida and an increase in Saccharomyces, Aspergillus, and Apiotrichum in patients with LUAD. Nineteen operational taxonomic unit markers were selected, and excellent performance in predicting LUAD was achieved (area under the curve (AUC) = 0.9350) using a random forest model with outcomes superior to those of four other algorithms. The AUCs of the Beijing, Suzhou, and Hainan validation cohorts were 0.9538, 0.9628, and 0.8833, respectively. CONCLUSIONS: For the first time, the gut fungal profiles of patients with LUAD were shown to represent potential non-invasive biomarkers for early-stage diagnosis.

Study on the Hot Deformation Characterization of Borated Stainless Steel by Hot Isostatic Pressing.

Borated stainless steel (BSS) specimens have a boron content of 1.86 wt%, and are prepared by hot isostatic pressing (HIP) conducted at different temperatures, ranging from 1000 to 1100 °C and a constant true strain rate (0.01, 0.1, 1 and 10 s-1). These tests, with observations and microstructural analysis, have achieved the hot deformation characteristics and mechanisms of BSS. In this research, the activation energy (Q) and Zener-Hollomon parameter (Z) were contrasted against the flow curves: Q = 442.35 kJ/mol. The critical conditions associated with the initiation of dynamic recrystallization (DRX) for BSS were precisely calculated based on the function between the strain hardening rate with the flow stress: at different temperatures from 1000 to 1100 °C: the critical stresses were 146.69-254.77 MPa and the critical strains were 0.022-0.044. The facts show that the boron-containing phase of BSS prevented the onset of DRX, despite the saturated boron in the austenite initiated DRX. The microstructural analysis showed that hot deformation promoted the generation of borides, which differed from the initial microstructure of HIP. The inhomogeneous distribution of elements in the boron-containing phase was caused by hot compression.

Effect of Heat Treatment Temperature on Microstructure and Properties of PM Borated Stainless Steel Prepared by Hot Isostatic Pressing.

Borated stainless steel (BSS) with a boron content of 1.86% was prepared by a powder metallurgy process incorporating atomization and hot isostatic pressing. After solution quenching at 900-1200 °C, the phase composition of the alloy was studied by quantitative X-ray diffraction phase analysis. The microstructure, fracture morphology, and distributions of boron, chromium, and iron in grains of the alloy were analyzed by field-emission scanning electron microscopy with secondary electron and energy-dispersive spectroscopy. After the coupons were heat treated at different temperatures ranging from 900 to 1200 °C, the strength and plasticity were tested, and the fracture surfaces were analyzed. Undergoing heat treatment at different temperatures, the phases of the alloy were austenite and Fe1.1Cr0.9B0.9 phase. Since the diffusion coefficients of Cr, Fe, and B varied at different temperatures, the distribution of elements in the alloy was not uniform. The alloy with good strength and plasticity can be obtained when the heat treatment temperature of alloy ranged from 1000 to 1150 °C while the tensile strength was about 800 MPa, with the elongation standing about 20%.