Preparation of Biomass Carbon Composites MgO@ZnO@BC and Its Adsorption and Removal of Cu(II) and Pb(II) in Wastewater.

The ternary composite MgO@ZnO@BC was synthesized and characterized for the adsorption of Cu2+, Pb2+ heavy metal ions from wastewater. The results show that the addition of the MgO@ZnO@BC composite results in higher adsorption properties for Cu2+ and Pb2+, with a molar ratio of 5% 0.1 g, and maximum adsorption capacity (50.63 mg/g for Cu2+ and 61.46 mg/g for Pb2+). The Langmuir adsorption isotherm of the adsorption complex and the kinetics of adsorption are secondary kinetics. The adsorption of Cu2+ and Pb2+ was mainly chemisorption, accompanied by physical adsorption. This adsorption method fully conforms to the concepts of clean production and efficient waste utilization, providing a reference for the removal of heavy metal ions from wastewater and waste recycling using ternary composite materials.

Efficacy and safety of anti-interleukin-1 therapeutics in the treatment of knee osteoarthritis: a systematic review and meta-analysis of randomized controlled trials.

OBJECTIVE: We aimed to evaluate the efficacy and safety of anti-interleukin-1 therapeutics, including IL-1 antibodies, interleukin-1 receptor antagonists (IL-1 Ras) and IL-1 inhibitors, for knee osteoarthritis (KOA) treatment. METHODS: Databases (Medline, Embase, Web of Science and CENTRAL) and ClinicalTrials.gov were systematically searched for randomized controlled trials (RCTs) of anti-interleukin-1 therapeutics from inception to August 31, 2022. The outcomes were the mean change in pain and function scores and the risk of adverse effects (AEs). RESULTS: In the 12 studies included, anti-interleukin-1 therapeutics were superior to placebo in terms of pain relief (standardized mean difference [SMD] = - 0.38, 95% confidence interval [CI] = - 1.82 to - 0.40, p < 0.001, I2 = 77%) and functional improvement (SMD = - 1.11, 95% CI = - 1.82 to - 0.40, p = 0.002, I2 = 96%). The incidence of any AE (risk ratio [RR] = 1.02, 95% CI = 0.88-1.18, p < 0.001, I2 = 76%) was higher following treatment with anti-interleukin-1 therapeutics than placebo, while no significant difference was found in the incidence of serious AEs (SAEs) or discontinuations due to AEs. Subgroup analyses showed that IL-1 antibodies and the IL-1 inhibitor provided pain relief (IL-1 antibodies: SMD = - 0.61, 95% CI = - 0.92 to - 0.31, p < 0.001; IL-1 inhibitor: SMD = - 0.39, 95% CI = - 0.72 to - 0.06, p = 0.02, I2 = 74.0%) and functional improvement (IL-1 antibodies: SMD = - 1.75, 95% CI = - 2.10 to - 1.40, p < 0.001; IL-1 inhibitor: SMD = - 0.28, 95% CI = - 0.83 to 0.27, p = 0.31, I2 = 88%) superior to those of placebo, whereas IL-1 Ras did not. However, the IL-1 inhibitor increased the incidence of any AE (RR = 1.35, 95% CI = 0.92-1.98, p < 0.001, I2 = 85%) but not the risk of SAEs or discontinuations due to AEs. IL-1 antibodies and IL-1 Ras showed no difference in safety compared with placebo. CONCLUSIONS: Anti-interleukin-1 therapeutics could relieve OA-related pain and improve function, but is probably associated with an increased risk of adverse events. Specially, IL-1 antibodies and an IL-1 inhibitor could relieve OA-related pain and improve function, whereas IL-1 Ras could not. IL-1 antibodies and IL-1 Ras were relatively safe options, but IL-1 inhibitors were associated with safety concerns.

Rapid detection of phenytoin sodium by partial-least squares and linear regression models combined with surface-enhanced Raman spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) for quantitative analysis is challenging owing to the unstable enhanced effect. However, it can be improved by combining it with chemometrics. In this study, we established a quantitative analysis method for phenytoin sodium (PS) based on partial least-squares (PLS) and linear regression (LR) models combined with SERS. Gold nanoparticles (AuNPs) were optimally enhanced substrates for PS. 180 PS samples in the concentration range of 0.98 - 980 µg mL-1 were used to establish a quantitative prediction model by PLS regression, and an accurate and robust prediction was achieved. Furthermore, we found that SERS peak intensity showed a good linear correlation with the concentration of PS in the concentration range of 1 - 80 µg mL -1. After using P-mercaptobenzoic acid as an internal standard, the accuracy and precision of the LR model were significantly improved compared with that of the model without an internal standard. In general, PLS chemometrics and LR model with internal standard which were combined with SERS in this paper provide new possible analytical methods for analytes to develop a rapid and sensitive quantitative analysis method.

Detection and Classification of Bronchiectasis Based on Improved Mask-RCNN.

Bronchiectasis is defined as a permanent dilation of the bronchi that can cause pulmonary ventilation dysfunction. CT examination is an important means of diagnosing bronchiectasis. It can also be used in severity scoring. Current studies on bronchiectasis have focused on high-resolution CT (HRCT), ignoring the more common low-dose CT (LDCT). Methodologically, existing studies have not adopted an authoritative standard to classify the severity of bronchiectasis. In effect, the accuracy of detection and classification needs to be improved for practical application. In this paper, the ACER image enhancement method, RDU-Net lung lobe segmentation method and HDC Mask R-CNN model were proposed to detect and classify bronchiectasis. Moreover, a Python-based system was developed: after inputing an LDCT image of a patient's lung, it can automatically perform a series of processing, then call on the trained deep learning model for detection and classification, and automatically obtain the patient's bronchiectasis final score according to the Reiff and BRICS scoring criteria. In this paper, the mapping relationship between original lung CT image data and bronchiectasis scoring system was established. The accuracy of the method proposed in this paper was 91.4%; the IOU, sensitivity and specificity were 88.8%, 88.6% and 85.4%, respectively; and the recognition speed of one picture was about 1 s. Compared to a human doctor, the system can process large amounts of data simultaneously, quickly and efficiently, with the same judgment accuracy as a human doctor. Doctors only need to judge the uncertain cases, which significantly reduces the burden of doctors and provides a useful reference for doctors to diagnose the disease.

Pharmacokinetics and tissue distribution study of hupehenenine in rats: A novel isosteroid alkaloid isolated from Bulbus Hupehensis Fritillariae.

Hupehenenine is a novel isosteroid alkaloid that was first isolated from Bulbus Hupehensis Fritillariae. The inhibitory proliferation effect of hupehenenine and its three related alkaloid derivatives, including o-caproyl-hupehenenine, o-(2-furanoyl)-hupehenenine, and Δ5(6) -isopeimine on human lung cancer cell line, human chronic myeloid leukemia cell line, and human thyroid duct cancer cell line in vitro, has been identified. This study first developed a sensitive HPLC-MS/MS method for the simultaneous quantification of hupehenenine and three alkaloid derivatives in rat plasma and tissues. The developed method was validated, and it was linear over the concentration range of 1-800 ng/mL for all analytes with R2 ≥ 0.9939 and 0.9972, respectively, in rat plasma and rat liver homogenate. The lower limit of quantitation was 1 ng/mL for all analytes. The intra-day and inter-day precision and accuracy were satisfactory. This validated method was successfully applied to investigate the pharmacokinetics and tissue distribution of hupehenenine in rats. In pharmacokinetic study, the maximum plasma concentration of rats exists gender difference. Tissue distribution study showed that hupehenenine has good affinity for multiple tissues but is unable to cross the blood-brain barrier. These results may provide a useful reference for further research of hupehenenine and its three related alkaloid derivatives.

Three new diterpenoid alkaloids isolated from Aconitum brevicalcaratum.

In the present study, three new aconitine-type diterpenoid alkaloids brochyponines A-C (1-3) were isolated from the roots of Aconitum brevicalcaratum. Their structures were established on the basis of extensive spectroscopic analyses (IR, HR-ESI-MS, and 1D and 2D NMR). The NMR data of salt form for compound 1 in CDCl3 were also measured.

Exploiting ensemble learning for automatic cataract detection and grading.

Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is one of the most common causes of visual impairment worldwide. Early diagnosis demands the expertise of trained healthcare professionals, which may present a barrier to early intervention due to underlying costs. To date, studies reported in the literature utilize a single learning model for retinal image classification in grading cataract severity. We present an ensemble learning based approach as a means to improving diagnostic accuracy. Three independent feature sets, i.e., wavelet-, sketch-, and texture-based features, are extracted from each fundus image. For each feature set, two base learning models, i.e., Support Vector Machine and Back Propagation Neural Network, are built. Then, the ensemble methods, majority voting and stacking, are investigated to combine the multiple base learning models for final fundus image classification. Empirical experiments are conducted for cataract detection (two-class task, i.e., cataract or non-cataractous) and cataract grading (four-class task, i.e., non-cataractous, mild, moderate or severe) tasks. The best performance of the ensemble classifier is 93.2% and 84.5% in terms of the correct classification rates for cataract detection and grading tasks, respectively. The results demonstrate that the ensemble classifier outperforms the single learning model significantly, which also illustrates the effectiveness of the proposed approach.