Preoperative prediction of hepatocellular carcinoma microvascular invasion based on magnetic resonance imaging feature extraction artificial neural network.

BACKGROUND: Hepatocellular carcinoma (HCC) recurrence is highly correlated with increased mortality. Microvascular invasion (MVI) is indicative of aggressive tumor biology in HCC. AIM: To construct an artificial neural network (ANN) capable of accurately predicting MVI presence in HCC using magnetic resonance imaging. METHODS: This study included 255 patients with HCC with tumors < 3 cm. Radiologists annotated the tumors on the T1-weighted plain MR images. Subsequently, a three-layer ANN was constructed using image features as inputs to predict MVI status in patients with HCC. Postoperative pathological examination is considered the gold standard for determining MVI. Receiver operating characteristic analysis was used to evaluate the effectiveness of the algorithm. RESULTS: Using the bagging strategy to vote for 50 classifier classification results, a prediction model yielded an area under the curve (AUC) of 0.79. Moreover, correlation analysis revealed that alpha-fetoprotein values and tumor volume were not significantly correlated with the occurrence of MVI, whereas tumor sphericity was significantly correlated with MVI (P < 0.01). CONCLUSION: Analysis of variable correlations regarding MVI in tumors with diameters < 3 cm should prioritize tumor sphericity. The ANN model demonstrated strong predictive MVI for patients with HCC (AUC = 0.79).

Electrochemical Behaviors of Sulfurized-Polyacrylonitrile with Synthesized Polyacrylonitrile Precursors Based on the Radical Polymerization Through Monomer Acrylonitrile.

Polyacrylonitrile (PAN) precursors have been polymerized at different radical polymerization temperatures for preparing sulfurized-polyacrylonitrile (S-PAN) composite cathodes in rechargeable lithium sulfur battery. The physical properties of these composites have been investigated using X-ray diffraction, Fourier transform infrared spectrometry, Raman spectroscopy, Brunner-Emmet-Teller measurement and Gel permeation chromatography analysis. The electrochemical performance of the S-PAN composite cathodes made from the PAN precursor was investigated. The results showed that the molecular weight distribution of the PAN precursors affected the electrochemical performance of the S-PAN made from the PAN precursor. S-PAN composites derived from PAN with a narrower molecular weight distribution at 65 °C were exhibit the best electrochemical performance in lithium-sulfur battery.

MIL-68 (In) nano-rods for the removal of Congo red dye from aqueous solution.

MIL-68 (In) nano-rods were prepared by a facile solvothermal synthesis using NaOAc as modulator agent at 100°C for 30 min. The BET test showed that the specific surface area and pore volume of MIL-68 (In) nanorods were 1252 m(2) g(-1) and 0.80 cm(3) g(-1), respectively. The as-prepared MIL-68 (In) nanorods showed excellent adsorption capacity and rapid adsorption rate for removal of Congo red (CR) dye from water. The maximum adsorption capacity of MIL-68 (In) nanorods toward CR reached 1204 mg g(-1), much higher than MIL-68 (In) microrods and most of the previously reported adsorbents. The adsorption process of CR by MIL-68 (In) nano-rods was investigated and found to be obeying the Langmuir adsorption model in addition to pseudo-second-order rate equation. Moreover, the MIL-68 (In) nanorods showed an acceptable reusability after regeneration with ethanol. All information gives an indication that the as-prepared MIL-68 (In) nanorods show their potential as the adsorbent for highly efficient removal of CR in wastewater.