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Background: To evaluate the short-term efficacy, lung function, and oxidative stress levels between the robotic-assisted thoracoscopic surgery (RATS) and video-assisted thoracoscopic surgery group (VATS) for non-small cell lung cancer (NSCLC). Methods: We conducted a retrospective cohort study, selecting 248 NSCLC patients who underwent minimally invasive lobectomy at the Department of Thoracic Surgery, Gansu Provincial People’s Hospital, from August 2019 to February 2023. There were 105 patients in the RATS group and 143 patients in the VATS group. The patients in the two groups were subjected to 1:1 propensity score matching analysis (PSM), and the perioperative indicators were recorded. The levels of oxidative stress factors (superoxide dismutase, SOD; malondialdehyde, MDA) and inflammatory factors were measured 1 day before surgery and 3 days after surgery, respectively. Pulmonary function and patient quality of life were measured at 1 day preoperatively and 3 months postoperatively. Results: There are 93 patients in each group after PSM. Compared to the VATS group, the RATS group had shorter operation time, less intraoperative blood loss, greater number and groups of lymph nodes cleared, and shorter postoperative hospital stay. The SOD level in the RATS group was higher and the MDA level was lower than that in the VATS group after surgery. Postoperative inflammatory cytokine levels were less elevated in the RATS group than in the VATS group. At 3 months postoperatively, FVC%, FEV1%, and GQOLI-74 scores were higher in the RATS group than in the VATS group. Conclusion: Compared to VATS lobectomy, RATS lobectomy has the advantages of shorter operative time, lesser bleeding, more lymph node dissection, faster postoperative recovery, and lesser impact on postoperative lung function. It is also capable of reducing the postoperative oxidative stress and inflammatory response, which can improve patients’ quality of life.

Transformers can model global context to enhance the performance of hyperspectral classification. However, the explored global information is generally confined to the spatial neighborhood of target pixels. In order to fully leverage global correlation across broader areas, a deformable graph transformer (DGT) is proposed for hyperspectral classification, in which the global information within an entire image is explored to improve the classification performance. Specifically, DGT layers are designed to adaptively sample virtual nodes at varying distances from an initial graph constructed from an image, by which the global spatial information can be explored using a deformable graph self-attention (DGSA) mechanism. Moreover, a learnable absolute position encoding (LAPE) module is constructed to enhance the spatial context awareness of DGT by integrating positional information into the graph nodes. In addition, graph structure encoding and graph topology encoding are further designed as inductive biases for the graph, by which both local structural information and global topological information of the HSI are captured to enhance the feature extraction capability of the DGT layer. Ultimately, through the stacking of multiple DGT layers, a composite feature fusion learning (CFFL) module is employed to fully utilize the simple low-level and complex abstract high-level features extracted from different layers. Extensive experiments on four datasets demonstrate the superiority and robustness of the proposed DGT over several state-of-the-art (SOTA) methods in terms of various evaluation criteria.

Recent developments in adversarial attack have witnessed the success of background attack against object detectors. However, most existing methods attack detectors by luring targets into background. Therefore, an innovative background attack framework via dual-adversarial-induced error identification (BADEI) is proposed to attack detectors by deceiving background as targets, as well as deceiving targets as background, where the attack performance can be greatly enhanced by these two kinds of induced error identification. Specifically, a mechanism that generates the adversarial background is proposed to result in dual error detection, where the background can conceal the specified targets and cause the misclassification of the adversarial pattern in the background as a specific category. Moreover, an unoccluded training strategy (UTS) that leverages the target mask of an image is introduced to strategically place adaptive adversarial background beneath the targets while optimizing and updating the pixel values of the background outside the target region, which can enhance attack effectiveness for adversarial background, significantly degrade the targets’ average accuracy, and enhance the robustness of background. Finally, a dual deceptive loss function (D2LF) is carefully formulated to generate false negatives (FNs) and false positives (FPs) to achieve untargeted attacks for hiding objects as well as targeted attacks for erroneously recognizing objects. Extensive experiments and comparative analysis of various victim network models on two datasets (including the dataset for object detection in aerial images (DOTA) and RSOD dataset) confirm that the proposed framework exhibits superior performance over the state-of-the-art methods in both digital and physical scenarios.

Graph convolutional networks (GCNs) have garnered extensive attention in the realm of hyperspectral image (HSI) classification. However, due to the problem of oversmoothing caused by deep GCN, most of the existing GCN-based methods are limited to constructing shallow networks, thus only able to extract superficial features. Moreover, when existing shallow GCNs extend to a more deeper structure, the number of learnable parameters increases linearly, thus leading to poor generalization performance under limited training samples. To address the aforementioned issues, a separable deep GCN integrated with convolutional neural network and prototype learning (SDGCP) is proposed for HSI classification, which can extract effective global structural information of HSI without increasing the number of trainable parameters. Specifically, the spectral and spatial features, adaptively selected by the attention module, are encoded into the structure of a graph by the graph encoder with the assistance of the pixel-to-region mapping obtained from the simple linear iterative clustering (SLIC). Then, a separable deep graph convolution module, composed of feature extraction and deep feature propagation, is adopted to capture the long-range contextual relationships from HSI encoded as graph data, which is combined with locally complementary information extracted by CNN after decoding. Finally, to further boost the performance of classification under limited labeled samples, prototype learning with regularization terms is utilized to enhance the intraclass compactness and interclass separability of feature representations. Extensive experiments on three standard HSI datasets demonstrate the superiority of the proposed SDGCP over the state-of-the-art (SOTA) methods.

The screening of high-quality oxygen carriers is a key focus in the field of chemical looping combustion. However, the existing screening methods have the problems of being high cost and having long material design cycles. Here, a machine learning model has been established which successfully predicted the effect of composition, porosity, specific surface area, and other physicochemical properties on the redox performance. A database consisting of 190 samples was used to train the BP-ANN algorithm and the SVM algorithm. The SVM algorithm triumphs over the BP-ANN algorithm in that the best model by the SVM algorithm makes predictions with a high coefficient of determination (R2= 0.961) and a low root mean square error (RMSE = 0.014). According to the obtained model, the copper ore was estimated to exhibit high reaction performance in terms of 68% CH4conversion and 96% CO conversion at 950 °C. We anticipate the machine learning method can be extended to predict the performance of oxygen carriers for other chemical looping applications.

Orifice reduction strategies for da Vinci robotic surgery have been a hot topic of research in recent years. We retrospectively analyzed the perioperative outcomes of robotic-assisted thoracoscopic surgery (RATS) with two, three, and four-hole approaches in radical lung cancer surgery. Our results revealed that the two-hole group has advantages in terms of operative time, postoperative 3-day drainage, postoperative drainage time, postoperative hospital stay and postoperative day 3 visual analogue scale (VAS) pain scores. There were no significant differences between the three groups in terms of intraoperative bleeding, number of lymph nodes dissected, VAS pain scores on postoperative days 1 and 2, and postoperative complications. In addition, the two-hole group was superior to the three-hole and four-hole groups in terms of C-reactive protein (CRP), procalcitonin (PCT) and interleukin 10 (IL-10). In summary, the RATS two-hole approach has advantages in operation time, rapid recovery after operation and some postoperative inflammatory indicators, and is worth promoting in hospitals that are skilled in three-hole and four-hole da Vinci robot surgery and have conditions.

Cold atmospheric plasma (CAP) has been proved a type of novel and effective anticancer method. However, a current problem lies in the complexity of cancer using the CAP treatment because of the different types and developments of cancers, resulting in the necessity of in-depth investigations on underlying mechanisms. This review will introduce a variety of mechanisms behind the CAP cancer treatment, including apoptosis-related signaling pathways, the ROS/RNS-based “bystander effect,” ferroptosis regulation for lipid peroxidation, genetically DNA damage and cell cycle arrest, amino acid structural modification, calreticulin (CRT) exposure and immune cell maturation, and necrosis. Despite the listed diverse mechanisms, drawbacks and future prospects of CAP treatment are discussed for more in-depth thinking. This review will be of great interest for understanding the current state of the art of cancer treatment of plasma.

Fine particulate matter produced during the rapid industrialization over the past decades can cause significant harm to human health. Twin-fluid atomization technology is an effective means of controlling fine particulate matter pollution. In this paper, the influences of the main parameters on the droplet size, effective atomization range and sound pressure level (SPL) of a twin-fluid nozzle (TFN) are investigated, and in order to improve the atomization performance, a multi-objective synergetic optimization algorithm is presented. A multi-physics coupled acoustic-mechanics model based on the discrete phase model (DPM), large eddy simulation (LES) model, and Ffowcs Williams-Hawkings (FW-H) model is established, and the numerical simulation results of the multi-physics coupled acoustic-mechanics method are verified via experimental comparison. Based on the analysis of the multi-physics coupled acoustic-mechanics numerical simulation results, the effects of the water flow on the characteristics of the atomization flow distribution were obtained. A multi-physics coupled acoustic-mechanics numerical simulation result was employed to establish an orthogonal test database, and a multi-objective synergetic optimization algorithm was adopted to optimize the key parameters of the TFN. The optimal parameters are as follows: A gas flow of 0.94 m3/h, water flow of 0.0237 m3/h, orifice diameter of the self-excited vibrating cavity (SVC) of 1.19 mm, SVC orifice depth of 0.53 mm, distance between SVC and the outlet of nozzle of 5.11 mm, and a nozzle outlet diameter of 3.15 mm. The droplet particle size in the atomization flow field was significantly reduced, the spray distance improved by 71.56%, and the SPL data at each corresponding measurement point decreased by an average of 38.96%. The conclusions of this study offer a references for future TFN research.

Estrogens play a significant role in endocrinology and oncology. Although separation methods coupled with mass spectrometry (MS) have emerged as a powerful tool for studying estrogens, imaging the spatial distributions of estrogens is crucial but remains challenging due to its low endogenous concentration and poor ionization efficiency. Charge-generation derivatization, such as N-alkylpyridinium quaternization and S-methyl thioetherification, represents a method wherein neutral molecules involving analytes and derivatization reagents undergo chemical reactions to establish permanent charges directly onto the analytes to improve detection sensitivity. Here, we developed a novel derivatization reagent, thianthrene (TT), which enabled oxidization to radical cations ([TT]•+) using an electrochemical method and completed the online charge-generation derivatization of estrogens on a mass spectrometry imaging platform. In this strategy, [TT]•+can efficiently and selectively derivatize estrogens via an electrophilic aromatic substitution reaction. Results indicated that derivatization with [TT]•+can significantly enhance imaging sensitivity (3 orders of magnitude), enabling the visualization of estrogen and its metabolites in ovarian and breast tissues. Furthermore, a higher mass intensity of these estrogens was captured in breast para-cancerous tissues than in cancerous tissues, which might provide estrogens spatial dimension information for further research on the initiation and progression of breast cancer.

Chemical looping is a revolutionary energy conversion method aimed at the low-carbon transformation of fossil fuels. The development of this technology primarily involves the screening of oxygen carriers, the design of reactors, and the optimization of process flows, typically requiring extensive experimental trials and time consumption. Machine learning, with its high-precision predictive capabilities, can optimize the development of chemical looping technology. This review comprehensively summarizes the methods and recent advances in the application of machine learning in chemical looping technology. This review outlined the typical machine learning process involving database construction, model analysis, and application of interpretable algorithms. Then, recent advances in oxygen carrier screening, reactor design, and process flow optimization through machine learning are explored. To address the challenges found in these research developments, potential solutions and future application perspectives are proposed. We hope that this review can offer inspiration for researchers in this field and promote the advancement of chemical looping technology.

Full‐range therapeutic regimens for osteoarthritis (OA) should consider organs (joints)‐tissues (cartilage)‐cells (chondrocytes)‐organelles cascade, of which the subcellular mitochondria dominate eukaryotic cells' fate, and thus causally influence OA progression. However, the dynamic regulation of mitochondrial rise and demise in impaired chondrocytes and the exact role of mitochondrial metronome sirtuins 3 (SIRT3) is not clarified. Herein, chondrocytes are treated with SIRT3 natural agonist dihydromyricetin (DMY) or chemical antagonist 3‐TYP, respectively, to demonstrate the positive action of SIRT3 on preserving cartilage extracellular matrix (ECM). Molecular mechanical investigations disclose that SIRT3‐induced chondroprotection depended on the repression of mitochondrial apoptosis (mtApoptosis) and the activation of mitophagy. Inspired by the high‐level matrix proteinases and reactive oxygen species (ROS) in the OA environment, by anchoring gelatin methacrylate (GelMA) and benzenediboronic acid (PBA) to hyaluronic acid methacrylate (HAMA) with microfluidic technology, a dual‐responsive hydrogel microsphere laden with DMY is tactfully fabricated and named as DMY@HAMA‐GelMA‐PBA (DMY@HGP). In vivo injection of DMY@HGP ameliorated cartilage abrasion and subchondral bone sclerosis, as well as promoted motor function recovery in post‐traumatic OA (PTOA) model via recouping endogenous mtApoptosis and mitophagy balance. Overall, this study unveils a novel mitochondrial dynamic‐oriented strategy, holding great promise for the precision treatment of OA. Starting from the mitochondria in chondrocytes, activation of SIRT3 suppressed mtApoptosis via BCL2/BAX‐cytochrome c release‐apoptosome arrest but promoted mitophagy via the PINK1/Parkin‐LC3B‐mitophagosomal complex, thus impeding ECM degradation. DMY@HGP hydrogel microspheres, responding to OA‐specific ROS and degrading micro‐milieu, is able to ameliorate joint degeneration and improve functional motion by full‐range mitochondria‐chondrocytes‐cartilage precision modulation.

Responsive Microspheres In article 2302475by Huilin Yang, Yong Xu, Yijian Zhang, Xuesong Zhu, and co‐workers, a microenvironment responsive hydrogel microsphere is developed to rebalance BCL2/BAX‐cytochrome c (mtApoptosis) and PINK1/Parkin‐LC3B‐P62 (mitophagy) through activation of the mitochondrial guardian SIRT3. Restoring mitochondrial homeostasis proves effective in rescuing extracellular matrix disintegration and restraining articular cartilage degeneration, thereby providing a novel organelle‐oriented strategy for treating osteoarthritis.

Cell therapy has been shown to be a key clinical therapeutic option for central nervous system diseases or damage. Standardization of clinical cell therapy procedures is an important task for professional associations devoted to cell therapy. The Chinese Branch of the International Association of Neurorestoratology (IANR) completed the first set of guidelines governing the clinical application of neurorestoration in 2011. The IANR and the Chinese Association of Neurorestoratology (CANR) collaborated to propose the current version “Clinical Cell Therapy Guidelines for Neurorestoration (IANR/CANR 2017)”. The IANR council board members and CANR committee members approved this proposal on September 1, 2016, and recommend it to clinical practitioners of cellular therapy. These guidelines include items of cell type nomenclature, cell quality control, minimal suggested cell doses, patient-informed consent, indications for undergoing cell therapy, contraindications for undergoing cell therapy, documentation of procedure and therapy, safety evaluation, efficacy evaluation, policy of repeated treatments, do not charge patients for unproven therapies, basic principles of cell therapy, and publishing responsibility.

Background: Uncoupled extracellular matrix (ECM) causes cartilage degeneration and osteoarthritis (OA) by suppressing the synthesis and activating the degradation of ECM components. Gingko bilobais a natural Chinese herb with a variety of biological functions; however, the extent to which it can protect against OA and the mechanisms involved are unknown. Methods: In our study, using bioinformatics tools, we were able to identify an important lactone, bilobalide (BB), from Gingko biloba. In vitro experiments were performed to evaluate the potential therapeutic effects of BB on ECM homeostasis. In vivo experiments were conducted to assess the protection of systemic administration of BB on cartilage degeneration. Molecular mechanisms underlying BB-regulated anti-arthritic role were further explored. Results: In interleukin-1β-incubated human chondrocytes, in vitro treatment with BB increased the expression of cartilage anabolic proteins, while inhibiting the activities of ECM degrading enzymes. In a mice model, systemic administration of BB, in vivo, prevented post-traumatic cartilage erosion and attenuated the formation of abnormal osteophytes in the subchondral bone. Mechanistically, the activation of the adenosine 5′-monophosphate-activated protein kinase (AMPK)-sirtuin 1 (SIRT1) signaling pathway was involved in the anti-arthritic effects of BB. In vitro, blocking BB’s chondroprotection with the AMPK-specific inhibitor Compound C abrogated it. Conclusions: These results demonstrated that BB extracted from Gingko bilobaregulates ECM balance to prevent OA by activating the AMPK-SIRT1 signaling pathway. This study proposed the monomer BB, a traditional Chinese medicine, as a de novotherapeutic insight for OA. Graphical Abstract: Schematic representation of the experimental design. Based on the bioinformatic analysis, bilobalide (BB), a natural herb Gingko biloba-derived ingredient, was identified as a candidate for treating osteoarthritis. In vitro, BB treatment not only facilitates cartilage extracellular matrix synthesis but also inhibits proteolytic enzyme activities. In vivo intraperitoneal injection of BB improves cartilage degeneration and subchondral bone sclerosis. BB, in particular, had anti-arthritic effects by activating the AMPK-SIRT1 signaling pathway.

Mass spectrometry imaging has become a hot research field owing to its ability to reflect the distribution of multiple metabolites in tissue. However, not all kinds of metabolites have great ionization efficiency in mass spectrometry imaging. The mass signals of low polar metabolites like monoglycerides and diglycerides may be seriously suppressed. Many strategies have been proposed to fix the problem, such as on-tissue derivatization and online derivatization. Also, some challenges were encountered when implementing these approaches. Herein, a platform coupled online quaternized derivatization and laser ablation carbon fiber ionization mass spectrometry imaging has been developed. The mass signals of monoglycerides and diglycerides were drastically increased in the platform, and high-quality mass images of these metabolites could be acquired readily. In the platform, metabolites were first desorbed by a laser and then reacted online with a derivatization reagent transmitted by carbon fiber ionization, which also undertook the postionization of derivatization products. Pyridine acted as the main derivatization reagent to target metabolites with hydroxyl groups. Remarkably, the derivatization reaction proceeded rapidly without any catalyst owing to the high energy provided by the laser. The mass images of eight monoglycerides and 21 diglycerides were achieved after applying the platform into human ovarian cancer tissues. Notably, a higher mass intensity of these glycerides was captured in cancerous tissues than in para-cancerous tissues, which might infer aberrations in glyceride metabolisms of cancerous tissues.