Xanthatin inhibits non-small cell lung cancer proliferation by breaking the redox balance.

Lung cancer is the cancer with the highest mortality, and non-small cell lung cancer (NSCLC) accounts for more than 80%. Tumor cells often have high reactive oxygen species (ROS) and antioxidant capacity. Redox balance is very important for tumor. The decline of antioxidant capacity and excessive ROS will induce the death of tumor cells. Destroying the redox balance of tumor cells is a promising tumor treatment strategy. Xanthatin is an active sesquiterpene lactone isolated from Xanthium strumarium L. We observed that xanthatin induced the up regulation of mitochondrial ROS and mitochondrial damage. Meanwhile, our results showed that xanthatin could inhibit system xc - and reduce glutathione (GSH) synthesis. Antioxidant GSH and N-acetyl- l-cysteine (NAC) significantly reversed cell proliferation inhibition and apoptosis induced by xanthatin. ß-Mercaptoethanol (ß-ME) which can avoid inhibition of system xc -  can also reverse the inhibition of cell proliferation induced by xanthatin, si-SLC7A11 was the opposite. Based on these results, we believe that the inhibition of xanthatin on the proliferation of NSCLC cells may be related to breaking the intracellular redox balance. Our data suggest that xanthatin is a promising antitumor candidate for the treatment of NSCLC.

Type 2 diabetes mellitus potentiates acute acrylonitrile toxicity: Potentiation reduction by phenethyl isothiocyanate.

Acrylonitrile (AN) is a known animal carcinogen and suspected human carcinogen. Recently, occupational exposure to AN has considerably increased. Previously, we demonstrated that streptozotocin-induced diabetes potentiates AN-induced acute toxicity in rats and that the induced cytochrome P450 2E1 (CYP2E1) is responsible for this effect. In the present study, we examined whether induction of CYP2E1 is also the underlying mechanism for the potentiation of AN-induced acute toxicity in type 2 diabetes in db/db mice. The effect of phenethyl isothiocyanate (PEITC) in reducing potentiation was also investigated. The mice were randomly divided into the normal control, diabetic control, AN, diabetes + AN, PEITC + AN, and diabetes + PEITC + AN groups. PEITC (40 mg/kg) was orally administered to rats for 3 days, and 1 h after the last PEITC gavage, 45 mg/kg AN was intraperitoneally injected. Time to death was observed. The CYP2E1 level and enzymatic activity, cytochrome c oxidase (CCO) activity, and reactive oxygen species (ROS) levels were measured. The survival rate was decreased in AN-treated db/db mice compared with that in AN-treated wild-type mice. The hepatic CYP2E1 level and enzymatic activity remained unaltered in db/db mice. Phenethyl isothiocyanate alleviated AN-induced acute toxicity in db/db mice as evident in the increased survival rate, restored CCO activity, and decreased ROS level in both the liver and brain. The study results suggested that CYP2E1 may not be responsible for the sensitivity to AN-induced acute toxicity in db/db mice and that PEITC reduced the potentiation of AN-induced acute toxicity in db/db mice.

[Research progress on the role of HIFs-PHDs oxygen-sensing pathway in cellular ferroptosis].

Hypoxia-inducible factors (HIFs) are one of the primary transcription factors regulating oxygen balance, and their stability is determined by the hydroxylation state of the prolyl hydroxylase domain (PHD) that is sensitive to oxygen. In recent years, studies have shown that HIFs-prolyl hydroxylases (PHDs) oxygen-sensing pathway is involved in the process of cellular ferroptosis. Ferroptosis, a new type of cell death, different from necrosis, apoptosis, necrotizing apoptosis, and pyroptosis, is essentially a programmed death caused by the accumulation of iron-dependent lipid peroxides in cells. This paper focuses on the role and mechanism of the HIFs-PHDs oxygen-sensing pathway in cellular ferroptosis involved in nerve diseases, tumors, lung injury, and chemical nerve damage from three aspects of iron metabolism, lipid metabolism, and glutathione (GSH) synthesis/metabolism. This review will provide a theoretical basis and new ideas for the development of novel drugs targeting the HIFs-PHDs oxygen-sensing pathway and capable of regulating ferroptosis for the treatment of diseases related to ferroptosis such as nervous system diseases and tumors.

Solver-informed neural networks for spectrum reconstruction of colloidal quantum dot spectrometers.

Recently, the miniature spectrometer based on the optical filter array has received much attention due to its versatility. Among many open challenges, designing efficient and stable algorithms to recover the input spectrum from the raw measurements is the key to success. Of many existing spectrum reconstruction algorithms, regularization-based algorithms have emerged as practical approaches to the spectrum reconstruction problem, but the reconstruction is still challenging due to ill-posedness of the problem. To alleviate this issue, we propose a novel reconstruction method based on a solver-informed neural network (NN). This approach consists of two components: (1) an existing spectrum reconstruction solver to extract the spectral feature from the raw measurements (2) a multilayer perceptron to build a map from the input feature to the spectrum. We investigate the reconstruction performance of the proposed method on a synthetic dataset and a real dataset collected by the colloidal quantum dot (CQD) spectrometer. The results demonstrate the reconstruction accuracy and robustness of the solver-informed NN. In conclusion, the proposed reconstruction method shows excellent potential for spectral recovery of filter-based miniature spectrometers.

Hierarchical Multi-Scale Convolutional Neural Networks for Hyperspectral Image Classification.

Deep learning models combining spectral and spatial features have been proven to be effective for hyperspectral image (HSI) classification. However, most spatial feature integration methods only consider a single input spatial scale regardless of various shapes and sizes of objects over the image plane, leading to missing scale-dependent information. In this paper, we propose a hierarchical multi-scale convolutional neural networks (CNNs) with auxiliary classifiers (HMCNN-AC) to learn hierarchical multi-scale spectral-spatial features for HSI classification. First, to better exploit the spatial information, multi-scale image patches for each pixel are generated at different spatial scales. These multi-scale patches are all centered at the same central spectrum but with shrunken spatial scales. Then, we apply multi-scale CNNs to extract spectral-spatial features from each scale patch. The obtained multi-scale convolutional features are considered as structured sequential data with spectral-spatial dependency, and a bidirectional LSTM is proposed to capture the correlation and extract a hierarchical representation for each pixel. To better train the whole network, weighted auxiliary classifiers are employed for the multi-scale CNNs and optimized together with the main loss function. Experimental results on three public HSI datasets demonstrate the superiority of our proposed framework over some state-of-the-art methods.

The platform business model selection of online ride-hailing giants based on the aggregation model.

Pure self-management model, pure aggregation business model and Self-support + aggregation model are three commonly used business modes on ride-hailing platforms. We use an analytical model to study these three business models and give the optimal business model decision of the platform. The research shows that the heterogeneity ratio of drivers, the cost of the platform under the Self-support model, the franchise fee received by the platform under the aggregation model and the dissatisfaction of the original users on the platform play a key role in the selection of the platform's business model. When the difference between the franchise fee under the aggregation mode and the platform cost under the Self-support mode fails to generate positive feedback on the platform profit, the platform should choose the pure Self-support mode. When riders are more sensitive to the heterogeneity of service quality of the platform and user stickiness can be ensured, the platform should choose the pure aggregation business model. When user stickiness can be guaranteed and the cost of the platform under the self-run model is controllable, the platform should choose the Self-support + aggregation business model.

Dibromoacetonitrile induced autophagy by mediating the PERK signalling pathway and ROS interaction in HT22 cell.

Dibromoacetonitrile (DBAN) is a high-risk haloacetonitrile (HAN) generated as a byproduct of chloramine disinfection in drinking water. DBAN-induced neurotoxicity in mouse hippocampal neuronal cells (HT22) and mammals was observed to be related to reactive oxygen species (ROS). ROS, endoplasmic reticulum stress (ERS) and autophagy play crucial roles in regulating a variety of cellular processes. However, whether ERS and autophagy are associated with HAN-responsive apoptosis remains unclear. This study indicated that DBAN (10 µM, 24 h) activated the ERS protein kinase like endoplasmic reticulum kinase (PERK) signaling pathway. The ERS inhibitor 4-phenylbutyric acid (4-PBA) reversed DBAN-inhibited cell viability and alleviated DBAN-induced apoptosis in HT22 cell, indicating that activation of the ERS PERK pathway mediates DBAN induced cytotoxicity. Moreover, DBAN activated autophagy. The autophagy inhibitor 3-methyladenine(3-MA) reversed DBAN-inhibited cell viability and alleviated DBAN-induced apoptosis in HT22 cell, suggesting that autophagy activation mediates DBAN-induced cell toxicity. Notably, the results showed that 4-PBA inhibited DBAN-activated autophagy, demonstrating that ERS-PERK promotes DBAN-induced cellular autophagy. Pretreatment with antioxidant N-acetylcysteine (NAC) inhibited the increase in ROS production and the activation of ERS, and protected cells from toxicity. Furthermore, 4-PBA pretreatment reduced the increase in ROS production, indicating that the ROS and PERK promote each other and form a positive feedback loop. ROS also promoted DBAN-induced autophagy. In summary, our findings indicate that DBAN induced autophagy by mediating the PERK signalling pathway and ROS interaction, leading to HT22 cell damage. Accordingly, targeting these pathogenic mechanisms may provide a potential target and theoretical basis for preventing and improving HAN-induced neurotoxicity.

Neuromusculoskeletal model-informed machine learning-based control of a knee exoskeleton with uncertainties quantification.

Introduction: Research interest in exoskeleton assistance strategies that incorporate the user's torque capacity is growing rapidly. However, the predicted torque capacity from users often includes uncertainty from various sources, which can have a significant impact on the safety of the exoskeleton-user interface. Methods: To address this challenge, this paper proposes an adaptive control framework for a knee exoskeleton that uses muscle electromyography (EMG) signals and joint kinematics. The framework predicted the user's knee flexion/extension torque with confidence bounds to quantify the uncertainty based on a neuromusculoskeletal (NMS) solver-informed Bayesian Neural Network (NMS-BNN). The predicted torque, with a specified confidence level, controlled the assistive torque provided by the exoskeleton through a TCP/IP stream. The performance of the NMS-BNN model was also compared to that of the Gaussian process (NMS-GP) model. Results: Our findings showed that both the NMS-BNN and NMS-GP models accurately predicted knee joint torque with low error, surpassing traditional NMS models. High uncertainties were observed at the beginning of each movement, and at terminal stance and terminal swing in self-selected speed walking in both NMS-BNN and NMS-GP models. The knee exoskeleton provided the desired assistive torque with a low error, although lower torque was observed during terminal stance of fast walking compared to self-selected walking speed. Discussion: The framework developed in this study was able to predict knee flexion/extension torque with quantifiable uncertainty and to provide adaptive assistive torque to the user. This holds significant potential for the development of exoskeletons that provide assistance as needed, with a focus on the safety of the exoskeleton-user interface.

Multiple-Channel Information Encryption Based on Quantum Dot Absorption Spectra.

Large-capacity information encryption has attracted significant interest in the information age. The diversity and controllability of spectra have positioned them to be widely applied for information encryption. Current spectra-based information encryption methods commonly rely on either spectral alteration induced by external stimuli or the utilization of narrowband channels within spectra. However, these methods encounter a common challenge in attaining both high security and large capacity simultaneously. To address these issues, we propose a multiple-channel information encryption system based on quantum dot (QD) absorption spectra. The diversity of QD absorption spectra and their broadband features ensure that the encrypted spectra can hardly be decrypted without knowing the correct channel matrix. Meanwhile, the large capacity is realized through the combination of multiple QD spectral channels with a theoretical maximum capacity of 24.0 bits in a single spectrum. In order to optimize the performance of our proposed system, the selection principle of the channel matrix is established to achieve the rapid identification of the optimal channel matrix in several milliseconds. The additivity of QD spectral channels and the consistency of QD spectra are also explored to minimize the impact of errors on information decryption. Furthermore, two spectral encryption scenarios of spatial pattern and spectral pattern are applied to demonstrate the feasibility, showcasing their ability to achieve both a high level of security and large capacity. Owing to the advantages offered by QD spectra, the QD spectra-based information system exhibits excellent potential for broader applications in information storage, authentication, and computing.

Ankle Joint Torque Prediction Using an NMS Solver Informed-ANN Model and Transfer Learning.

In this work, we predicted ankle joint torque by combining a neuromusculoskeletal (NMS) solver-informed artificial neural network (hybrid-ANN) model with transfer learning based on joint angle and muscle electromyography signals. The hybrid-ANN is an ANN augmented with two kinds of features: 1) experimental measurements - muscle signals and joint angles, and 2) informative physical features extracted from the underlying NMS solver, such as individual muscle force and joint torque. The hybrid-ANN model accuracy in torque prediction was studied in both intra- and inter-subject tests, and compared to the baseline models (NMS and standard-ANN). For each prediction model, seven different cases were studied using data from gait at different speeds and from isokinetic ankle dorsi/plantarflexion motion. Additionally, we integrated a transfer learning method in inter-subject models to improve joint torque prediction accuracy by transferring the learned knowledge from previous participants to a new participant, which could be useful when training data is limited. Our results indicated that better accuracy could be obtained by integrating informative NMS features into a standard ANN model, especially in inter-subject cases; overall, the hybrid-ANN model predicted joint torque with higher accuracy than the baseline models, most notably in inter-subject prediction after adopting the transfer learning technique. We demonstrated the potential of combining physics-based NMS and standard-ANN models with a transfer learning technique in different prediction scenarios. This procedure holds great promise in applications such as assistance-as-needed exoskeleton control strategy design by incorporating the physiological joint torque of the users.

Enhanced Carrier Separation in Visible-Light-Responsive Polyoxometalate-Based Metal-Organic Frameworks for Highly Efficient Oxidative Coupling of Amines.

Photocatalytic technology is widely studied, while it comes with drawbacks such as low sunlight utilization efficiency and high carrier recombination rates. Herein, for the first time, we present two crystalline polyoxometalate (POM)-based metal-organic frameworks (POMOFs), {[Cd(DMF)2Ru(bpy)2(dcbpy)]2(POMs)(DMF)2} xDMF (PMo-1, POMs = [PMoVI11MoVO40]4-, x = 5; SiW-2, POMs = [SiW12O40]4-, x = 4) through assembling the photosensitizer [Ru(bpy)2(H2dcbpy)]Cl2 and POMs into a single framework. The assembly not only enhances light absorption in the visible light regime but also improves carrier separation efficiency; atop of that, both POMOFs demonstrate activities in the photocatalytic oxidative coupling of amines. Particularly, PMo-1 enables the quantitative completion of oxidative coupling of benzylamine reaction within 30 min (yield = 99.6%) with a high turnover frequency (TOF = 6631.6 h-1). To our knowledge, the PMo-1 catalyst outperforms any other photocatalysts previously reported in similar use cases where TOF values were usually obtained <2000 h-1.

Oxidative injury induced by drinking water disinfection by-products dibromoacetonitrile and dichloroacetonitrile in mouse hippocampal neuronal cells: The protective effect of N-acetyl-L-cysteine.

Dibromoacetonitrile (DBAN) and dichloroacetonitrile (DCAN) are haloacetonitriles (HANs) produced as by-products of chloramine disinfection of drinking water and can cause neurotoxicity. The molecular pathways leading to HAN-induced neuronal cell death remain unclear. The nuclear factor erythroid 2-related factor 2 (Nrf2) is an important regulator of oxidation reactions. We explored the role of the sequestosome 1 (p62)-Kelch-like ECH-associated protein 1 (Keap1)-Nrf2 pathway in DBAN- and DCAN-induced mouse hippocampal neuronal (HT22) cell injury. DBAN and DCAN reduced cell viability, increased lactate dehydrogenase release rate, and promoted apoptosis. Over the same treatment time, DBAN at lower concentrations caused cell injury, suggesting that DBAN is more cytotoxic than DCAN. DBAN and DCAN triggered oxidative stress by reducing intracellular glutathione and increasing reactive oxygen species concentrations. DBAN and DCAN activated the Nrf2 pathway. Furthermore, Nrf2 inhibitors (all-trans retinoic acid) attenuated DBAN- and DCAN-induced toxicity, whereas Nrf2 activators (tert-Butylhydroquinone) achieved the opposite effect. This indicates that activation of the Nrf2 pathway mediates DBAN- and DCAN-induced cell injury. Notably, the expression of p62, a noncanonical pathway that mediates Nrf2 activation, increased, whereas the expression of Keap1, another regulator of Nrf2, decreased. We noted that high p62 expression activated the Nrf2 pathway, and p62 was regulated through Nrf2, forming a positive feedback loop. N-acetyl-L-cysteine, a mercaptan substance, protected against DBAN- and DCAN-induced toxicity and inhibited the Nrf2 pathway. In summary, Nrf2 pathway inhibition and mercaptan supplementation prevent DBAN- and DCAN-induced HT22 cell injury, accordingly, targeting them is a potential approach to preventing HAN-induced neurotoxicity.

A meta-analysis of survival of patients with second primary non-small cell lung cancer after pulmonary lobectomy and partial pulmonary lobectomy.

OBJECTIVE: A meta-analysis of the screened data was performed to investigate the survival status of patients with second primary non-small cell lung cancer (SPNSCLC) after pulmonary lobectomy and partial pulmonary lobectomy, and assess the applicability of such option in the treatment of such disease. METHODS: Based on the databases of PubMed, Ovid, and CNKI, relevant reports on postoperative survival of patients with SPNSCLC were obtained, and the information, including authors, publication year, research type, tumor staging, tumor diameter, surgical options, reasons for resection, and overall survival (OS), was extracted. The data were analyzed using Review Manager 5.3. RESULTS: A total of 11 studies published from 2010 to 2017 were collected. Patients with SPNSCLC were mainly in stage Ia and Ib, 51.4% of whom underwent pulmonary lobectomy and 34.7% of whom underwent partial pulmonary lobectomy. The analysis results showed that after the second lobectomy, the mortality rate of the patients undergoing intensive surgery was 8%, the incidence of postoperative complications was 29%, and the 5-year OS rate was 53% after the second lobectomy for patients with SPNSCLC. CONCLUSION: The survival rate of patients with SPNSCLC is lower than that of patients with early lung cancer. In view of this, surgical resection is feasible. There are remarkable differences between pulmonary lobectomy and partial pulmonary lobectomy.

Explainable liver tumor delineation in surgical specimens using hyperspectral imaging and deep learning.

Surgical removal is the primary treatment for liver cancer, but frequent recurrence caused by residual malignant tissue remains an important challenge, as recurrence leads to high mortality. It is unreliable to distinguish tumors from normal tissues merely under visual inspection. Hyperspectral imaging (HSI) has been proved to be a promising technology for intra-operative use by capturing the spatial and spectral information of tissue in a fast, non-contact and label-free manner. In this work, we investigated the feasibility of HSI for liver tumor delineation on surgical specimens using a multi-task U-Net framework. Measurements are performed on 19 patients and a dataset of 36 specimens was collected with corresponding pathological results serving as the ground truth. The developed framework can achieve an overall sensitivity of 94.48% and a specificity of 87.22%, outperforming the baseline SVM method by a large margin. In particular, we propose to add explanations on the well-trained model from the spatial and spectral dimensions to show the contribution of pixels and spectral channels explicitly. On that basis, a novel saliency-weighted channel selection method is further proposed to select a small subset of 5 spectral channels which provide essentially as much information as using all 224 channels. According to the dominant channels, the absorption difference of hemoglobin and bile content in the normal and malignant tissues seems to be promising markers that could be further exploited.

[HPLC fingerprint of Euodiae Fructus].

OBJECTIVE: To establish a HPLC fingerprint for quality evaluation of fructus Euodiae rutaecarpa, and to perform qualitative and quantitative analysis of its main constituents. METHOD: The 10 batches of samples were analyzed by HPLC linear gradient elution method and evaluated by similarity evaluation and system cluster analysis. The common peaks in chromatographic fingerprint were identified by LC-DAD-MS. RESULT: The HPLC fingerprint of Euodiae Fructus showed 32 characteristic peaks, among them 13 peaks were identified, and 2 constituents were quantified. CONCLUSION: The HPLC fingerprint established could reflect the main constituents of Euodiae Fructus, and could combine with content determination to take an overall control of Euodiae Fructus.

Experimental study on a high conversion efficiency, low threshold, high-repetition-rate periodically poled lithium niobate optical parametric generator.

A high-conversion-efficiency, low-threshold, quasi-continuous-wave optical parametric generator (OPG) based on a periodically poled lithium niobate (PPLN) crystal is presented. Pumped by an acousto-optically Q-switched 1064 nm Nd:YAG laser with a power output of 848 mW, the OPG generated an output power of 452 mW for the signal and the idle waves, achieving an internal conversion efficiency of 62.7% and a slope efficiency of 75.6%. To the best of our knowledge, this is the highest efficiency ever reported for single-pass, quasi-continuous-wave OPGs by using periodically poled crystals.