Polyethylene glycol crosslinked modified chitosan/halloysite nanotube composite aerogel microspheres for efficient adsorption of melanoidin.

Melanoidins are widely present in molasses wastewater and are dark-colored macromolecules that are hazardous to the environment. Currently, adsorption methods can effectively remove melanoidins from wastewater. However, existing adsorbents have shown unsatisfactory removal efficiency for melanoidins, making practical application challenging. Polyethylene glycol crosslinked modified chitosan/halloysite nanotube composite aerogel microspheres (PCAM@HNTs) were developed as a highly efficient adsorbent for melanoidins. The removal rate of PCAM@HNTs for melanoidins was 98.53 % at adsorbent dosage 0.4 mg/mL, pH 7, temperature 303 K and 450 mg/L initial melanoidins concentration, and the corresponding equilibrium adsorption capacity was 1108.49 mg/g. The analysis results indicate that the adsorption of melanoidins by PCAM@HNTs is a spontaneous and endothermic process. It fits well with pseudo-second-order kinetic models and the Freundlich isotherm equation. The adsorption of PCAM@HNT on melanoidins is primarily attributed to electrostatic and hydrogen bonding interactions. Furthermore, PCAM@HNTs exhibit excellent biocompatibility and are nonhazardous. Therefore, PCAM@HNTs proved to be an ideal adsorbent for the decolorization of molasses wastewater.

Adsorption of cationic/anionic dyes and endocrine disruptors by yeast/cyclodextrin polymer composites.

Factory and natural wastewaters contain a wide range of organic pollutants. Therefore, multifunctional adsorbents must be developed that can purify wastewater. Phytic acid-cross-linked Baker's yeast cyclodextrin polymer composites (IBY-PA-CDP) were prepared using a one-pot method. IBY-PA-CDP was used to adsorb methylene blue (MB), bisphenol A (BPA), and methyl orange (MO). Studies on the ionic strength and strongly acidic ion salts confirmed that IBY-PA-CDP adsorbs MO through hydrophobic interactions. This also shows that Na+ was the direct cause of the increased MO removal. Adsorption studies on binary systems showed that MB/MO inhibited the adsorption of BPA by IBY-PA-CDP. The presence of MB increased the removal rate of MO by IBY-PA-CDP due to the bridging effect. The Langmuir isotherm model calculated the maximum adsorption capacities for MB and BPA to be 630.96 and 83.31 mg g-1, respectively. However, the Freundlich model is more suitable for fitting the experimental data for MO adsorption. To understand the rate-limiting stage of adsorption, a mass-transfer mechanism model was employed. The fitting results show that adsorption onto the active sites is the rate-determining step. After five regeneration cycles, IBY-PA-CDP could be reused with good stability and recyclability.

Application of deep learning to predict underestimation in ductal carcinoma in situ of the breast with ultrasound.

BACKGROUND: To develop an ultrasound-based deep learning model to predict postoperative upgrading of pure ductal carcinoma in situ (DCIS) diagnosed by core needle biopsy (CNB) before surgery. METHODS: Of the 360 patients with DCIS diagnosed by CNB and identified retrospectively, 180 had lesions upstaged to ductal carcinoma in situ with microinvasion (DCISM) or invasive ductal carcinoma (IDC) postoperatively. Ultrasound images obtained from the hospital database were divided into a training set (n=240) and validation set (n=120), with a ratio of 2:1 in chronological order. Four deep learning models, based on the ResNet and VggNet structures, were established to classify the ultrasound images into postoperative upgrade and pure DCIS. We obtained the area under the receiver operating characteristic curve (AUROC), specificity, sensitivity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) to estimate the performance of the predictive models. The robustness of the models was evaluated by a 3-fold cross-validation. RESULTS: Clinical features were not significantly different between the training set and the test set (P value >0.05). The area under the receiver operating characteristic curve of our models ranged from 0.724 to 0.804. The sensitivity, specificity, and accuracy of the optimal model were 0.733, 0.750, and 0.742, respectively. The three-fold cross-validation results showed that the model was very robust. CONCLUSIONS: The ultrasound-based deep learning prediction model is effective in predicting DCIS that will be upgraded postoperatively.