Source-free active domain adaptation for diabetic retinopathy grading based on ultra-wide-field fundus images.

Domain adaptation (DA) is commonly employed in diabetic retinopathy (DR) grading using unannotated fundus images, allowing knowledge transfer from labeled color fundus images. Existing DAs often struggle with domain disparities, hindering DR grading performance compared to clinical diagnosis. A source-free active domain adaptation method (SFADA), which generates features of color fundus images by noise, selects valuable ultra-wide-field (UWF) fundus images through local representation matching, and adapts models using DR lesion prototypes, is proposed to upgrade DR diagnostic accuracy. Importantly, SFADA enhances data security and patient privacy by excluding source domain data. It reduces image resolution and boosts model training speed by modeling DR grade relationships directly. Experiments show SFADA significantly improves DR grading performance, increasing accuracy by 20.90% and quadratic weighted kappa by 18.63% over baseline, reaching 85.36% and 92.38%, respectively. This suggests SFADA's promise for real clinical applications.

An ensemble deep learning diagnostic system for determining Clinical Activity Scores in thyroid-associated ophthalmopathy: integrating multi-view multimodal images from anterior segment slit-lamp photographs and facial images.

Background: Thyroid-associated ophthalmopathy (TAO) is the most prevalent autoimmune orbital condition, significantly impacting patients' appearance and quality of life. Early and accurate identification of active TAO along with timely treatment can enhance prognosis and reduce the occurrence of severe cases. Although the Clinical Activity Score (CAS) serves as an effective assessment system for TAO, it is susceptible to assessor experience bias. This study aimed to develop an ensemble deep learning system that combines anterior segment slit-lamp photographs of patients with facial images to simulate expert assessment of TAO. Method: The study included 156 patients with TAO who underwent detailed diagnosis and treatment at Shanxi Eye Hospital Affiliated to Shanxi Medical University from May 2020 to September 2023. Anterior segment slit-lamp photographs and facial images were used as different modalities and analyzed from multiple perspectives. Two ophthalmologists with more than 10 years of clinical experience independently determined the reference CAS for each image. An ensemble deep learning model based on the residual network was constructed under supervised learning to predict five key inflammatory signs (redness of the eyelids and conjunctiva, and swelling of the eyelids, conjunctiva, and caruncle or plica) associated with TAO, and to integrate these objective signs with two subjective symptoms (spontaneous retrobulbar pain and pain on attempted upward or downward gaze) in order to assess TAO activity. Results: The proposed model achieved 0.906 accuracy, 0.833 specificity, 0.906 precision, 0.906 recall, and 0.906 F1-score in active TAO diagnosis, demonstrating advanced performance in predicting CAS and TAO activity signs compared to conventional single-view unimodal approaches. The integration of multiple views and modalities, encompassing both anterior segment slit-lamp photographs and facial images, significantly improved the prediction accuracy of the model for TAO activity and CAS. Conclusion: The ensemble multi-view multimodal deep learning system developed in this study can more accurately assess the clinical activity of TAO than traditional methods that solely rely on facial images. This innovative approach is intended to enhance the efficiency of TAO activity assessment, providing a novel means for its comprehensive, early, and precise evaluation.

A Microwave Pressure Sensor Loaded with Complementary Split Ring Resonator for High-Temperature Applications.

A passive substrate integrated waveguide (SIW) sensor based on the complementary split ring resonator (CSRR) is presented for pressure detection in high-temperature environments. The sensor pressure sensing mechanism is described through circuit analysis and the electromagnetic coupling principle. The pressure sensor is modeled in high frequency structure simulator (HFSS), designed through parameter optimization. According to the optimized parameters, the sensor was customized and fabricated on a high temperature co-fired ceramic (HTCC) substrate using the three-dimensional co-fired technology and screen-printing technology. The pressure sensor was tested in the high-temperature pressure furnace and can work stably in the ambient environment of 25-500 °C and 10-300 kPa. The pressure sensitivity is 139.77 kHz/kPa at 25 °C, and with increasing temperature, the sensitivity increases to 191.97 kHz/kPa at 500 °C. The temperature compensation algorithm is proposed to achieve accurate acquisition of pressure signals in a high-temperature environment.

Relationships between parental responsive feeding and infant appetitive traits: The moderating role of infant temperament.

Introduction: Between the ages of 6 and 12 months is a crucial stage for children to develop appetitive self-regulation. Evidence suggests that a combination of parental responsive feeding and infant temperament (surgency, effortful control, negative affect) shapes infant appetitive traits (food approach, food avoidance). There is a need for research to explore these relationships, in order to provide guidance for the design of an effective intervention to improve appetitive traits. The objective of the current study was to explore the moderating role of infant temperament in the relationship between parental responsive feeding and infant appetitive traits. Methods: A total of 616 questionnaires, measuring parental responsive feeding, infant appetitive traits, and infant temperament, were collected from parents with infants aged 6-12 months. Results: Results revealed that responsive feeding was associated with both food approach and food avoidance. Furthermore, only lower levels of surgency significantly moderated the relationship between responsive feeding and food approach, while responsive feeding was associated with food avoidance irrespective of infant temperament. Discussion: These findings suggest that a strategy embedding responsive feeding interventions should be adopted to reduce infant food avoidance and low-surgent infant food approach, and interventions that are tailored toward food approach for infants with effortful control, negative affect, or higher levels of surgency should be further sought.

Comparative study on comprehensive quality of Xinhui chenpi by two main plant propagation techniques.

Xinhui chenpi (XHCP), the sun-dried peel of the mandarin orange, Citrus reticulata "Chachi," is the most famous crude drug, as well as a traditional seasoning in Chinese cooking. The main cultivation methods of XHCP are cutting and grafting, but it is generally considered that the quality of XHCP after cutting is superior to that obtained from plants propagated by graftings, which had a negative impact on the marketing of the finished product. In our study, a total of 25 samples of XHCP obtained from plants cultivated by either traditional methods (i.e., from cuttings) or by grafting were collected to compare the contents of four types of metabolites (essential oils, flavonoids, synephrine, and total polysaccharides) as well as antioxidant activity. The results revealed that the quality of XHCP did not decline after cutting, and marked individual differences between XHCP samples, even when prepared from plants grown in the same way. In general, grafting had no significant effect on the most essential oils components, total polysaccharides, synephrine, total flavonoids, total polymethoxylated flavones, hesperidin, nobiletin, tangeretin content, and antioxidant activity. Nevertheless, five volatile compounds can be used as potential chemical markers (p < 0.05) to distinguish between cutting XHCP and grafted XHCP, while four volatile compounds showed high content in grafted XHCP. Our study is expected to provide a theoretical basis for XHCP breeding and cultivation, and thereby further standardize the market of XHCP.

Simultaneous removal of aqueous same ionic type heavy metals and dyes by a magnetic chitosan/polyethyleneimine embedded hydrophobic sodium alginate composite: Performance, interaction and mechanism.

Heavy metals and azo dyes caused huge harm to the aqueous system and human health. A magnetic chitosan/polyethyleneimine embedded hydrophobic sodium alginate composite (MCPS) was designed and prepared to simultaneously remove aqueous same ionic type heavy metals and azo dyes. In mono-polluted system, the optimal pH for Cr(VI), MO (methyl orange), Cu(Ⅱ) and MB (methylene blue) were 3, 2, 6 and 12 with a saturated adsorption capacity of 87.53, 66.41, 351.03 and 286.54 mg/g, respectively. Pseudo-second-order was suitable to describe the adsorption kinetics of them and the adsorption isotherms were more consistent with the Langmuir isotherm model being a spontaneous, endothermic, and entropy-increasing process. In binary-polluted system, MCPS possessed simultaneous adsorption for Cr (Ⅵ)-MO and Cu(Ⅱ)-MB pollutants at their optimal pH, in addition, whether in anionic or cationic solution, the removal of heavy metals were promoted with the add of azo dyes but the removal of azo dyes were suppressed with the add of heavy metals. Both Cr (Ⅵ)-MO and Cu(Ⅱ)-MB pollutants could be effectively adsorbed and desorbed from MCPS by changing the pH of the aqueous solution to realize recyclability. Lastly, removal mechanism was revealed in detail by FT-IR, EDS and XPS.

Selective removal of aqueous Hg2+ by magnetic composites sulfur-containing on the hyper-branched surface: Characterization, performance and mechanism.

The adsorbents with recyclable, large adsorption capacity and selective adsorption can effectively remove the pollution and harm of heavy metal ions in water. Therefore, two magnetic composites containing sulfur (MCP-S4 and MCP-S8) on the hyper-branched surface were prepared, furthermore, their structures were characterized and adsorption performance was analyzed by FTIR, XRD, TGA, BET, SEM, TEM, VSM and ICP. The results showed that both MCP-S4 and MCP-S8 had superparamagnetism with saturation susceptibility of 22.10 and 22.26 emu/g, and owned a specific surface area of 11.394 and 11.235 m2/g, respectively. MCP-S4 and MCP-S8 could selectively adsorb Hg2+ with the exist of Fe3+, Cu2+, Co2+, Ni2+, Mn2+, and Al3+ in solution. The adsorption kinetics accorded with pseudo-second-order model and Boyd film diffusion model, and the adsorption isotherm was fitted better with Langmuir isotherm model and D-R model, furthermore, the adsorption was an entropic-increasing and endothermic process. The removal rate of Hg2+ from simulated sewage by the two materials was more than 91%, and the adsorption retention rate was more than 85% after five adsorption-desorption cycles. The adsorption mechanism was analyzed by comparing the changes of FTIR, EDS and XPS spectra before and after adsorption. It was found that functional groups (C-N, CONH, CS, SH) could form stable chelates with Hg2+, which was the main reason why MCP-S4 and MCP-S8 could adsorb Hg2+ selectively, furthermore, S atoms of CS and -SH played a leading role in the process of adsorption. In addition, DFT calculation was also used as an auxiliary means to verify the adsorption mechanism.

Multi-homed abnormal behavior detection algorithm based on fuzzy particle swarm cluster in user and entity behavior analytics.

User and entity behavior analytics (UEBA) is an anomaly detection technique that identifies potential threat events in the enterprise's internal threat analysis and external intrusion detection. One limitation of existing methods in UEBA is that many algorithms use deterministic algorithms only for one category labeling and only compare with other samples within this category. In order to improve the efficiency of potential threat identification, we propose a model to detect multi-homed abnormal behavior based on fuzzy particle swarm clustering. Using the behavior frequency-inverse entities frequency (BF-IEF) technology, the method of measuring the similarity of entity and user behavior is optimized. To improve the iterative speed of the fuzzy clustering algorithm, the particle swarm is introduced into the search process of the category centroid. The entity's nearest neighbor relative anomaly factor (NNRAF) in multiple fuzzy categories is calculated according to the category membership matrix, and it is combined with boxplot to detect outliers. Our model solves the problem that the sample in UEBA is evaluated only in one certain class, and the characteristics of the particle swarm optimization algorithm can avoid clustering results falling into local optimal. The results show that compared with the traditional UEBA approach, the abnormal behavior detection ability of the new method is significantly improved, which can improve the ability of information systems to resist unknown threats in practical applications. In the experiment, the accuracy rate, accuracy rate, recall rate, and F1 score of the new method reach 0.92, 0.96, 0.90, and 0.93 respectively, which is significantly better than the traditional abnormal detections.

Hypermixed Convolutional Neural Network for Retinal Vein Occlusion Classification.

Retinal vein occlusion (RVO) is one of the most common retinal vascular diseases leading to vision loss if not diagnosed and treated in time. RVO can be classified into two types: CRVO (blockage of the main retinal veins) and BRVO (blockage of one of the smaller branch veins). Automated diagnosis of RVO can improve clinical workflow and optimize treatment strategies. However, to the best of our knowledge, there are few reported methods for automated identification of different RVO types. In this study, we propose a new hypermixed convolutional neural network (CNN) model, namely, the VGG-CAM network, that can classify the two types of RVOs based on retinal fundus images and detect lesion areas using an unsupervised learning method. The image data used in this study is collected and labeled by three senior ophthalmologists in Shanxi Eye Hospital, China. The proposed network is validated to accurately classify RVO diseases and detect lesions. It can potentially assist in further investigating the association between RVO and brain vascular diseases and evaluating the optimal treatments for RVO.

Prediction of corneal astigmatism based on corneal tomography after femtosecond laser arcuate keratotomy using a pix2pix conditional generative adversarial network.

Purpose: This study aimed to develop a deep learning model to generate a postoperative corneal axial curvature map of femtosecond laser arcuate keratotomy (FLAK) based on corneal tomography using a pix2pix conditional generative adversarial network (pix2pix cGAN) for surgical planning. Methods: A total of 451 eyes of 318 nonconsecutive patients were subjected to FLAK for corneal astigmatism correction during cataract surgery. Paired or single anterior penetrating FLAKs were performed at an 8.0-mm optical zone with a depth of 90% using a femtosecond laser (LenSx laser, Alcon Laboratories, Inc.). Corneal tomography images were acquired from Oculus Pentacam HR (Optikgeräte GmbH, Wetzlar, Germany) before and 3 months after the surgery. The raw data required for analysis consisted of the anterior corneal curvature for a range of ± 3.5 mm around the corneal apex in 0.1-mm steps, which the pseudo-color corneal curvature map synthesized was based on. The deep learning model used was a pix2pix conditional generative adversarial network. The prediction accuracy of synthetic postoperative corneal astigmatism in zones of different diameters centered on the corneal apex was assessed using vector analysis. The synthetic postoperative corneal axial curvature maps were compared with the real postoperative corneal axial curvature maps using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). Results: A total of 386 pairs of preoperative and postoperative corneal tomography data were included in the training set, whereas 65 preoperative data were retrospectively included in the test set. The correlation coefficient between synthetic and real postoperative astigmatism (difference vector) in the 3-mm zone was 0.89, and that between surgically induced astigmatism (SIA) was 0.93. The mean absolute errors of SIA for real and synthetic postoperative corneal axial curvature maps in the 1-, 3-, and 5-mm zone were 0.20 ± 0.25, 0.12 ± 0.17, and 0.09 ± 0.13 diopters, respectively. The average SSIM and PSNR of the 3-mm zone were 0.86 ± 0.04 and 18.24 ± 5.78, respectively. Conclusion: Our results showed that the application of pix2pix cGAN can synthesize plausible postoperative corneal tomography for FLAK, showing the possibility of using GAN to predict corneal tomography, with the potential of applying artificial intelligence to construct surgical planning models.

Synthesis of Polycarboxylate Viscosity Reducer and the Effect of Different Chain Lengths of Polyether on Viscosity Reduction of Heavy Oil.

Since there are not many studies on the application of polymeric surfactants in viscosity reduction emulsification of heavy oil, a series of polyether carboxylic acid-sulfonate polymeric surfactants were synthesized. The viscosity reduction performance and the effect of different chain lengths on the viscosity reduction effect were also investigated. The viscosity reduction, emulsification, wetting, and foaming performance tests showed that the viscosity reduction performance of this series of polymeric surfactants was excellent, with the viscosity reduction rate exceeding 95%, and the viscosity was reduced to 97 mPa·s by the polymeric surfactant with a molecular weight of 600 polyethers. It was also concluded that among the three surfactants with different side chains, the polymeric surfactant with a polyether molecular weight of 600, which is the medium side-chain length, had the best viscosity reduction performance. The study showed that the polyether carboxylic acid-sulfonate polymer surfactant had a promising application in the viscosity reduction of heavy oil.

Multi-Model Domain Adaptation for Diabetic Retinopathy Classification.

Diabetic retinopathy (DR) is one of the most threatening complications in diabetic patients, leading to permanent blindness without timely treatment. However, DR screening is not only a time-consuming task that requires experienced ophthalmologists but also easy to produce misdiagnosis. In recent years, deep learning techniques based on convolutional neural networks have attracted increasing research attention in medical image analysis, especially for DR diagnosis. However, dataset labeling is expensive work and it is necessary for existing deep-learning-based DR detection models. For this study, a novel domain adaptation method (multi-model domain adaptation) is developed for unsupervised DR classification in unlabeled retinal images. At the same time, it only exploits discriminative information from multiple source models without access to any data. In detail, we integrate a weight mechanism into the multi-model-based domain adaptation by measuring the importance of each source domain in a novel way, and a weighted pseudo-labeling strategy is attached to the source feature extractors for training the target DR classification model. Extensive experiments are performed on four source datasets (DDR, IDRiD, Messidor, and Messidor-2) to a target domain APTOS 2019, showing that MMDA produces competitive performance for present state-of-the-art methods for DR classification. As a novel DR detection approach, this article presents a new domain adaptation solution for medical image analysis when the source data is unavailable.

Neuroprotective potential of nuclear factor erythroid 2-related factor 2 (Nrf2)/antioxidant response element signaling modulator cucurbitacin I upon glucose and oxygen deprivation/reperfusion (OGD/RP).

This study aimed to investigate the inhibitory effect and mechanism of Cucurbitacin I (Cu I) on apoptosis, oxidative stress, and mitophagy in PC12 cells with glucose and oxygen deprivation/reperfusion (OGD/RP) injury. OGD/RP cell injury model was established by gas anoxic cell incubator and glucose-free medium. The cells were divided into the control group, OGD/RP group, OGD/RP + Cu I group, and OGD/RP + Cu I + 2 µM nuclear factor erythroid 2-related factor 2 (Nrf2) inhibitor ML385 group. The results showed that apoptotic rate and reactive oxygen species (ROS) production were significantly increased in OGD/RP group, which were reversed by Cu I pretreatment. Meanwhile, western blot analysis proved that Cu I inhibited OGD/RP-induced mitophagy, manifested as the decreased expression of PTEN-induced kinase 1 (PINK1) and parkin RBR E3 ubiquitin-protein ligase (Parkin), and light chain 3 (LC3) Ⅱ∕LC3 I, as well as the increased expression of P62. Furthermore, immunofluorescence (IF) staining showed that Cu I reduced the co-localized puncta of LC3 with TOM20 in OGD/RP-induced PC12 cells. Similarly, transmission electron microscope finding is consistent with the IF results. Mechanically, after Cu I and OGD/RP treatments, nuclear Nrf2 expression and the levels of downstream target genes were significantly upregulated compared with OGD/RP alone treatment. Nrf2 inhibition reversed the protective effects of Cu I on OGD/RP-induced injury in PC12 cells. The present study provides evidence of the neuroprotective effect of Cu I unraveling its potential as a potential therapeutic candidate for the treatment of ischemic stroke.

Development of 1,3,4-Oxadiazole Derived Antifungal Agents and Their Application in Maize Diseases Control.

Maize is an important food crop and its fungal disease has become a limiting factor to improve the yield and quality of maize. In the control of plant pathogens, commercial fungicides have no obvious effect on corn diseases due to the emergence of drug resistance. Therefore, it is of great significance to develop new fungicides with novel structure, high efficiency, and low toxicity to control maize diseases. In this paper, a series of 1,3,4-oxadiazole derivatives were designed and synthesized from benzoyl hydrazine and aromatic aldehydes through condensation and oxidation cyclization reaction. The antifungal activity of oxadiazole derivatives against three maize disease pathogens, such as Rhizoctonia solani (R. solani), Gibberella zeae (G. zeae), and Exserohilum turcicum (E. turcicum), were evaluated by mycelium growth rate method in vitro. The results indicated that most of the synthesized derivatives exhibited positive antifungal activities. Especially against E. turcicum, several compounds demonstrated significant antifungal activities and their EC 50 values were lower than positive control carbendazim. The EC 50 values of compounds 4k, 5e, and 5k were 50.48, 47.56, 32.25 µg/ml, respectively, and the carbendazim was 102.83 µg/ml. The effects of active compounds on E. turcicum microstructure were observed by scanning electron microscopy (SEM). The results showed that compounds 4k, 5e, and 5k could induce the hyphae of E. turcicum to shrink and collapse obviously. In order to elucidate the preliminary mechanism of oxadiazole derivatives, the target compounds 5e and 5k were docked with the theoretical active site of succinate dehydrogenase (SDH). Compounds 5e and 5k could bind to amino acid residues through hydrophobic contact and hydrogen bonds, which explained the possible mechanism of binding between the inhibitor and target protein. In addition, the compounds with antifungal activities had almost no cytotoxicity to MCF-7. This study showed that 1,3,4-oxadiazole derivatives were worthy for further attention as potential antifungal agents for the control of maize diseases.

Diabetic Retinopathy Grading by Deep Graph Correlation Network on Retinal Images Without Manual Annotations.

Background: Diabetic retinopathy, as a severe public health problem associated with vision loss, should be diagnosed early using an accurate screening tool. While many previous deep learning models have been proposed for this disease, they need sufficient professional annotation data to train the model, requiring more expensive and time-consuming screening skills. Method: This study aims to economize manual power and proposes a deep graph correlation network (DGCN) to develop automated diabetic retinopathy grading without any professional annotations. DGCN involves the novel deep learning algorithm of a graph convolutional network to exploit inherent correlations from independent retinal image features learned by a convolutional neural network. Three designed loss functions of graph-center, pseudo-contrastive, and transformation-invariant constrain the optimisation and application of the DGCN model in an automated diabetic retinopathy grading task. Results: To evaluate the DGCN model, this study employed EyePACS-1 and Messidor-2 sets to perform grading results. It achieved an accuracy of 89.9% (91.8%), sensitivity of 88.2% (90.2%), and specificity of 91.3% (93.0%) on EyePACS-1 (Messidor-2) data set with a confidence index of 95% and commendable effectiveness on receiver operating characteristic (ROC) curve and t-SNE plots. Conclusion: The grading capability of this study is close to that of retina specialists, but superior to that of trained graders, which demonstrates that the proposed DGCN provides an innovative route for automated diabetic retinopathy grading and other computer-aided diagnostic systems.

Selective adsorption of heavy metals from water by a hyper-branched magnetic composite material: Characterization, performance, and mechanism.

The development of adsorbents to remove heavy metal ions from water with recyclable, high adsorption capacity, strong selectivity, safe, and economic performances has always been the focus and challenge of current research. A hyper-branched magnetic composite material (Fe3O4@SiO2-S4) was fabricated by a method combining "grafting,", "branching," and "modification,", and the structure was characterized by FTIR, XRD, SEM, TEM, SAED, VSM, TGA, and BET. In addition, the adsorption performance and mechanism for heavy metal ions in water were studied. The as-prepared composite material had excellent selective absorbability for Hg2+, Cd2+, and Ag+ in the presence of Fe3+, Fe2+, Cu2+, Mn2+, CO2+, Zn2+, and Ni2+, and when pH = 6, T = 30 °C, t = 4 h, it reached a saturated adsorption capacity of 2.42, 2.18, and 1.94 mmol/g to Hg2+, Cd2+, and Ag+, respectively. The adsorption isotherm was consistent with the Langmuir isotherm adsorption model, and the Dubinin Redushcke (D-R) model identified that the adsorption was chemical adsorption in nature. The adsorption kinetic followed the pseudo-second-order model and Boyd film diffusion models. The adsorption capacity of as-prepared material remained about 83% after five elutions. The adsorption mechanism and selective adsorption were revealed by FTIR, EDS, XPS, and DFT calculation. N atoms and O atoms of the active functional groups complexed with metal ions to form stable 2 heptachate chelates and 1 tridentate chelate to achieve the effect of adsorption; furthermore, the adsorption was mainly governed by N atoms of Schiff base groups. This work not only explored an innovative method for the construction of adsorbing materials but also provided a promising adsorbent to selectively remove heavy metal ions in water with potential application.

Augmentation-Consistent Clustering Network for Diabetic Retinopathy Grading with Fewer Annotations.

Diabetic retinopathy (DR) is currently one of the severe complications leading to blindness, and computer-aided, diagnosis technology-assisted DR grading has become a popular research trend especially for the development of deep learning methods. However, most deep learning-based DR grading models require a large number of annotations to provide data guidance, and it is laborious for experts to find subtle lesion areas from fundus images, making accurate annotation more expensive than other vision tasks. In contrast, large-scale unlabeled data are easily accessible, becoming a potential solution to reduce the annotating workload in DR grading. Thus, this paper explores the internal correlations from unknown fundus images assisted by limited labeled fundus images to solve the semisupervised DR grading problem and proposes an augmentation-consistent clustering network (ACCN) to address the above-mentioned challenges. Specifically, the augmentation provides an efficient cue for the similarity information of unlabeled fundus images, assisting the supervision from the labeled data. By mining the consistent correlations from augmentation and raw images, the ACCN can discover subtle lesion features by clustering with fewer annotations. Experiments on Messidor and APTOS 2019 datasets show that the ACCN surpasses many state-of-the-art methods in a semisupervised manner.

Preparation and characterization of Schiff-base modified Fe3O4 hybrid material and its selective adsorption for aqueous Hg2.

A Fe3O4 hybrid material (Fe3O4@SiO2-S2) modified by Schiff-base was prepared by grafting methyl acrylate (MA), ethylenediamine (EDA), and salicylaldehyde (SA) to the prepared magnetic hybrid material (Fe3O4@SiO2-NH2) successively; what's more, the structure was characterized by FTIR, XRD, SEM, TEM, and VSM. The results showed that the Fe3O4@SiO2-S2 has an obvious core-shell structure and a saturation magnetization of 45.9 emu/g. Study on the adsorption of aqueous heavy metal ions showed that Fe3O4@SiO2-S2 posed selective adsorption for Hg2+ with the saturated adsorption capacity of 362 mg/g (1.12 mmol/g), which was superior to Fe3O4@SiO2-NH2, Fe3O4@SiO2-HO-S, and other adsorbents, at the condition of pH = 6, 45℃, the adsorption capacity remained 89% after 5 cycles of adsorption-desorption; what is more, adsorption equilibrium was reached at about 300 min, and the adsorption isotherm conformed to the Langmuir isotherm adsorption model; in addition, pseudo-second-order model could be well described the adsorption kinetic process of Fe3O4@SiO2-S2 to Hg2+. The adsorption mechanism demonstrated that the N atoms of Schiff-base were mainly contributed to the adsorption of Hg2+; what is more, the N atom of tertiary amine and the O atoms of hydroxy and carbonyl also help to the adsorption of Hg2+.

Prediction of repeated-dose intravenous ketamine response in major depressive disorder using the GWAS-based machine learning approach.

Major depressive disorder (MDD) is one of the most common psychiatric disorders. Various clinical studies have shown that the N-methyl-D-aspartate (NMDA) receptor antagonist ketamine has rapid, robust, and sustained antidepressant effects. However, given the concerns about the adverse effects of ketamine on patients, it would be important to identify a set of biomarkers that could be used to predict clinical outcomes for its treatment. A total of 83 MDD patients received treatment with six ketamine infusions for up to 2 weeks and were classified into "responders" or "non-responders" based on an average change in the HAMD score >50% from baseline. A nested cross-validation approach was applied to prevent information leakage and overestimation of model performance. The initial dataset was divided randomly into training and test sets in a nested six-fold cross-validation. We first performed genome-wide logistic regression to find potentially significant variants related to treatment response and then selected the top SNPs based on the genetic association results using the random forests algorithm. Subsequently, six machine learning models were employed to construct prediction models by using ten-fold cross-validation. A series of model comparisons showed that the best performing fold was characterized by accuracy of 0.85, precision of 0.75, and a sensitivity of 1.00 with the support vector machine algorithm. Together, these findings demonstrated that the machine learning approach can predict the treatment outcomes of multiple ketamine infusions on the basis of the genotyping information of each participant.

Immobilization properties and adsorption mechanism of nickel(II) in soil by biochar combined with humic acid-wood vinegar.

Biochar (BC) combined with humic acid (HA) and wood vinegar (WV) was designed and prepared as an inexpensive, effective, and environmentally friendly immobilization material (BHW) for metal-polluted soil. The influences of the wood vinegar and humic acid on the immobilization properties and adsorption mechanism of this new material were also investigated. The remediation performance was evaluated using a laboratory-made, nickel-contaminated soil with a Ni2+ concentration of 200 mg per kg surface soil (top 20 cm from agricultural land). The results indicated that the immobilization ratio sequence of nickel (II) in the soil was BC< BH< BHW. The maximum adsorption capacity increased in the same order: BC< BH< BHW. All three adsorption isotherms were better fitted by the Freundlich model, which were consistent with the surface heterogeneity of the remediation materials. The cause of this surface heterogeneous migration may be due to the increase in oxygen-containing groups in the BC introduced by the HA and WV. The WV can increase the number of the oxygen-containing groups in the BC combined with HA, which enhanced the adsorption and immobilization of Ni2+ ions. The results suggested that BHW is recommended for the remediation of metal-contaminated soils, because of its high efficacy, economic feasibility, environmental and food safety.