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To retrospectively assess the effectiveness of deep learning (DL) model, based on breast magnetic resonance imaging (MRI), in predicting preoperative lymphovascular invasion (LVI) status in patients diagnosed with invasive breast cancer who have negative axillary lymph nodes (LNs). Data was gathered from 280 patients, including 148 with LVI-positive and 141 with LVI-negative lesions. These patients had undergone preoperative breast MRI and were histopathologically confirmed to have invasive breast cancer without axillary LN metastasis. The cohort was randomly split into training and validation groups in a 7:3 ratio. Radiomics features for each lesion were extracted from the first post-contrast dynamic contrast-enhanced (DCE)-MRI. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method and logistic regression analyses were employed to identify significant radiomic features and clinicoradiological variables. These models were established using four machine learning (ML) algorithms and one DL algorithm. The predictive performance of the models (radiomics, clinicoradiological, and combination) was assessed through discrimination and compared using the DeLong test. Four clinicoradiological parameters and 10 radiomic features were selected by LASSO for model development. The Multilayer Perceptron (MLP) model, constructed using both radiomic and clinicoradiological features, demonstrated excellent performance in predicting LVI, achieving a high area under the curve (AUC) of 0.835 for validation. The DL model (MLP-radiomic) achieved the highest accuracy (AUC = 0.896), followed by DL model (MLP-combination) with an AUC of 0.835. Both DL models were significantly superior to the ML model (RF-clinical) with an AUC of 0.720. The DL model (MLP), which integrates radiomic features with clinicoradiological information, effectively aids in the preoperative determination of LVI status in patients with invasive breast cancer and negative axillary LNs. This is beneficial for making informed clinical decisions. [ABSTRACT FROM AUTHOR]

Abstract: An easy method is described for the synthesis of a mesostructured Ni/ordered mesoporous carbon (OMC) composite with a highly ordered cubic structure (space group Im3m). This synthesis was carried out by the carbonization of the F127/[Ni(H2O)6](NO3)2/RF (resorcinol-formaldehyde) composite self-assembled in an alkaline medium. The effects of nickel loading content and carbonization temperature on the morphologies, pore features, structures and magnetic properties of these Ni/OMC composites were investigated using the thermogravimetric analysis, X-ray diffraction, nitrogen sorption, transmission electron microscopy and vibrating-sample magnetometer measurements. It was found that Ni2+ was captured by the network of F127/RF and further reduced into metallic Ni nanoparticles during the carbonization. The nickel nanoparticles were well-dispersed in the ordered mesoporous carbon walls. The Ni/OMC composites exhibit the soft ferromagnetic behavior and the magnetization parameters can be adjusted by the content of nickel and the carbonization temperatures. The excellent acid-resistant property of the magnetic materials makes them useful in magnetic separation. [Copyright &y& Elsevier]

Atomic layer deposition of high quality Al2O3 thin films onto Au electrodes was enabled by surface modification with a self-assembled monolayer of -OH groups that react with a monolayer of trimethylaluminum gas source. Ar ion milling was then used to expose the edge of the Au/insulator/Au structure for molecular electrode contacts. The junctions are characterized by atomic force microscope and tunnel current properties. The Au/self-assembled monolayer/Al2O3/Au tunnel junction, with a very thin oxide insulator layer (15.4 Å), is stable and has a small tunneling current density of about 0.20-0.75 A/cm2 at 0.5 V. [ABSTRACT FROM AUTHOR]

We report a 4.3 µm mid-infrared (mid-IR) high-power amplified spontaneous emission (ASE) fiber source based on CO 2 -filled nested hollow-core anti-resonant fiber (Nested HC-ARF). The pump source is a homemade hundred-watt-level wavelength-tunable 2 µm single-frequency fiber laser. A 5.7 m long 8-tube Nested HC-ARF is used as the gas cell, with a core diameter of 110 µm and cladding diameter of 400 µm, which exhibits transmission loss of 0.1 dB/m at 2 µm and 0.24 dB/m at 4.3 µm respectively. To improve the coupling efficiency of the high-power pump laser and reduce the influence of the thermal effect at the input end of the hollow-core fiber, the fiber is designed for multimode transmission at the pump wavelength. A continuous wave output power of 6.6 W at 4.3 µm is achieved, and the slope efficiency is 17.05%. To the best of our knowledge, it is the highest output power for such gas-filled HC-ARF ASE sources in 4∼5 µm. This work demonstrates the great potential of gas-filled HC-ARF generating high-power mid-IR emission.