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Kansei Engineering (KE) is a user-oriented technology combing customer psychological feelings and engineering for designing and developing products. Conventionally, questionnaire surveys have been extensively applied for understanding customers' affective demands, responses and evaluations. However, the questionnaire is usually time-consuming, labour-intensive and small in data size. Online customer reviews provide trustable, continuously updated and free customers' responses. Existing studies generally focus on the polarity classification of the positivity and negativity of the review texts. This study proposes an opinion mining approach based on KE and machine learning to extract and measure users' affective responses to products from online customer reviews. Five types of machine learning algorithms are applied, including Support Vector Machine (SVM), Support Vector Regression (SVR), Classification and Regression Tree (CART), Multi-Layer Perceptron (MLP) and Ridge Regression (RR). An experiment has been conducted to illustrate the proposed approach. The results show that SVM+SVR is the best performer. It achieved a recall, precision and F 1 score of more than 80% for the classification of the soft-hard attribute with the smallest mean square error. Based on the proposed method, designers and manufacturers can effectively know customers' responses to products through inputting the review texts to facilitate the process of product design. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To elucidate the function of MOTS-c in accelerating bone fracture healing by inducing BMSCs differentiation into osteoblasts, as well as its potential mechanism. MATERIALS AND METHODS: Primary BMSCs were extracted from rats and induced for osteogenesis. The highest dose of MOTS-c that did not affect BMSCs proliferation was determined by CCK-8 assay. After 7-day osteogenesis, the relative levels of ALP, Bglap, and Runx2 in MOTS-c-treated BMSCs influenced by FOXF1 were examined. ALP staining and alizarin red S staining in BMSCs were performed as well. The interaction between FOXF1 and TGF-β was analyzed by ChIP assay. At last, rescue experiments were performed to uncover the role of FOXF1/TGF-β axis in MOTS-c-induced osteogenesis. RESULTS: 1 μM MOTS-c was the highest dose that did not affect BMSCs proliferation. MOTS-c treatment upregulated the relative levels of ALP, Bglap, and Runx2, and stimulated mineralization ability in BMSCs, which were attenuated by the silence of FOXF1. TGF-β was proved to interact with FOXF1, and its level was positively mediated by FOXF1. The silence of FOXF1 attenuated the accelerated osteogenesis and TGF-β upregulation in BMSCs because of MOTS-c induction, and these trends were further reversed by the overexpression of TGF-β. CONCLUSIONS: MOTS-c treatment markedly induces osteogenesis in BMSCs. During MOTS-c-induced osteogenic progression, the upregulated FOXF1 triggers the activation of TGF-β pathway, thus accelerating bone fracture healing. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To investigate whether microRNA-615-3p participates in the development and progression of osteoarthritis by regulating chondrogenic differentiation of bone marrow mesenchymal stem cells. MATERIALS AND METHODS: Bone marrow mesenchymal stem cells (BMSCs) were isolated from rat bone marrow and identified by flow cytometry. After chondrogenic differentiation was induced in BMSCs, expression levels of chondrogenic-specific genes were then detected by quantitate Real-time polymerase chain reaction (qRT-PCR). Expression levels of inflammatory cytokines were detected by enzyme-linked immunosorbent assay (ELISA). Protein expression of SOX9 after overexpression or knockdown of microRNA-615-3p was detected by Western blot, respectively. RESULTS: MicroRNA-615-3p was down-regulated in the process of chondrogenic differentiation of BMSCs. The mRNA expressions of chondrogenic-specific markers, COL2A1, COL10A1, ACAN and MATN3 were decreased after microRNA-615-3p overexpression in BMSCs. Overexpressed microRNA-615-3p down-regulated protein expression of SOX9. Expression levels of inflammatory cytokines, including interleukin-1 (IL-1), interleukin-6 (IL-6), interleukin-α (IL-α) were increased after overexpression of microRNA-615-3p, while inhibition of microRNA-615-3p expression obtained the opposite result. In addition, overexpression of SOX9 rescued the effect induced by microRNA-615-3p on inflammatory cytokines. CONCLUSIONS: MicroRNA-615-3p participates in the development and progression of osteoarthritis by increasing the expressions of inflammatory cytokines and inhibiting chondrogenic differentiation of BMSCs. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To investigate the role of microRNA-337-5p in osteosarcoma (OS) and its underlying mechanism. PATIENTS AND METHODS: The microRNA (microRNA-337-5p) that may be related to OS development was screened out by GEO (Gene Expression Omnibus) database. Survival analysis and ROC curve were performed according to microRNA-337-5p expressions in OA patients. Besides, the correlation between microRNA-337-5p expression and clinical parameters was evaluated by Chi-square analysis. Cox regression analysis was performed to detect the relationship between the overall survival and clinical parameters of OA patients. Subsequently, enriched functions and pathways of microRNA-337-5p were predicted by GESA (gene enrichment sets analysis). MicroRNA-337-5p expression was detected in 65 OS tissue samples and 30 normal tissue samples by qRT-PCR (quantitative Real-Time Polymerase Chain Reaction). For in vitro experiments, after microRNA-337-5p mimics or microRNA-337-5p inhibitor was transfected into OS cells, proliferative and invasive abilities were detected by CCK-8 (Cell Counting Kit-8) and transwell assay, respectively. Finally, Western blot was used to explore the underlying mechanism of microRNA-337-5p in regulating OS. RESULTS: MicroRNA-337-5p was overexpressed in serum and tissue samples of OS patients, which was valuable in diagnosing OS. Besides, microRNA-337-5p expression was correlated with the overall survival and necrosis range of OA patients, whereas not correlated with age and sex. GESA indicated that microRNA-337-5p was enriched in ERBB, MAPK, and VEGF pathways. In vitro experiments indicated elevated proliferative and invasive abilities in MG63 and U2OS cells after microRNA-337-5p overexpression. Furthermore, increased expressions of ERBB2, Erk1/2, and VEGF121 were observed in OS cells after microRNA-337-5p overexpression. CONCLUSIONS: MicroRNA-337-5p is upregulated in OS tissues, which is an independent prognostic factor in OS. Overexpressed microRNA- 337-5p can promote proliferative and invasive abilities of OS cells via activating ERBB, MAPK, and VEGF pathways. [ABSTRACT FROM AUTHOR]

The spilling-breaking waves that appear in chirped wave packets are studied in a two-dimensional wave channel. These waves are produced by superposing waves with gradually decreasing frequencies. The analysis focuses on the nonlinear characteristics, energy variation, and energy transformation during the evolution and breaking of wave groups. Ensemble empirical mode decomposition is used to analyze the non-breaking and breaking energy variations of the intrinsic mode functions (IMFs). It is found that the third-order IMF component is a source of non-breaking energy dissipation and the secondorder IMF, which represents a short wave group with a relatively higher energy content, is a primary source of the energy loss caused by wave breaking. Additionally, the findings reveal that among the three waves preceding the maximum crest, the wave closest to the maximum crest carried most of the energy. When wave breaking occurs, the energy dissipation caused by the wave breaking primarily originates from that wave. After wave breaking, whether it is the first breaker or subsequent breakers, the main energy dissipation occurs in a frequency range higher than the dominant frequency. This energy loss plays a significant role in increasing the energy of free waves. Moreover, a potential link between the number of carrier waves and wave breaking phenomena has been found. As the number of carrier waves increased, both the nonbreaking and breaking energy dissipation rates exhibited an overall increasing trend. The amount of nonbreaking energy dissipation was generally more than twice the breaking energy dissipation rate. For wave groups with more carrier waves, the modulation instability plays a significant role in generating larger waves. Furthermore, an analysis of the dominant frequency variations of the wave group before wave breaking suggests that wave breaking is not a sufficient condition for a frequency downshift in the wave spectra. [ABSTRACT FROM AUTHOR]

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[Objective] The primary objective of this study is to tackle the manifold challenges associated with identifying coal gangue during coal mining, including but not limited to the inefficiencies, high operational complexity, and substantial costs inherent in traditional identification methods. By integrating and refining advanced deep learning technologies, particularly the YOLOv8-based object detection model, we seek to develop an intelligent system capable of automatically, rapidly, and precisely identifying coal gangue. This intelligent system will not only enhance the efficiency and quality of coal mining and minimize human errors and cost inefficiencies but also foster the sustainable utilization of coal resources, mitigate environmental pollution, and provide technical support for the green transition of the coal industry. [Methods] To achieve the aforementioned research objectives, this study adopted a systematic research method. First, we conducted an in-depth analysis of the advantages and disadvantages of the YOLOv8 model and proposed the YOLOv8-ER model based on this. This model deeply optimized the backbone network of YOLOv8 by introducing an efficient channel attention network, enabling the model to focus more effectively on key features in the image and improve the accuracy and efficiency of feature extraction. At the same time, we applied structural reparameterization technology to simplify the network architecture of the model, reduce the computational complexity and resource consumption, and improve the inference speed and stability of the model. In the model training phase, we used a large-scale and diverse dataset of coal gangue images and continuously optimized the model parameters through multiple iterations of training to ensure the accuracy and robustness of the model in practical applications. [Results] This study has achieved multiple innovative results in the field of coal gangue identification. First, the YOLOv8-ER model has achieved significant improvements in both processing speed and detection accuracy. The high-speed detection capability of the YOLOv8-ER model (i.e., 240.8 frames/second) can meet the high real-time requirements of application scenarios, with a performance rate of up to 92.3% mAP@0.5, which ensures the accuracy and reliability of recognition. Second, compared with the traditional YOLOv8 model, YOLOv8-ER has made breakthroughs in multiple key indicators, such as an accuracy improvement of 3.2%, mAP@0.5 improved by 4.4%, and PR balance rate improved by 2.6%. These improvements make the model more competitive in practical applications. In addition, by optimizing the network structure and applying reparameterization techniques, we have successfully reduced the computational complexity and resource consumption of the model, improving its operational efficiency and economy. [Conclusions] In summary, this study successfully developed an efficient and accurate coal gangue identification technology by introducing and optimizing the YOLOv8-ER model. This technology not only overcomes the limitations of traditional recognition methods and improves the efficiency and quality of coal mining but also provides strong support for the intelligent transformation of the coal industry. In the future, with the continuous advancement of technology and the expansion of application scenarios, the YOLOv8-ER model is expected to play an important role in more fields, promoting the sustainable development and innovation of related industries. At the same time, we will continue to conduct in-depth research on the optimization and extended application of this model to meet the needs of coal gangue recognition in different scenarios. [ABSTRACT FROM AUTHOR]

Efficient cutting of float glass can be achieved by using 532 nm nanosecond laser with the bottom-up spiral scanning method. Due to the obvious thermal effect when the nanosecond laser acts on the glass, it will cause glass edge chipping, white spots on the side wall surface, surface micro cracks and voids and other defect behaviors. Focusing on this problem, the single-factor experimental method was used to explore 532 nm laser hole cutting defect behavior. The results show that the laser pulse frequency is the key factor affecting the defect shape. Improper pulse frequency and pulse energy will seriously increase the glass edge chipping and the surface roughness of hole wall, and also produce uneven regional white spots. Increasing the scanning speed will reduce the surface roughness of hole wall. Smaller spiral overlap rate will cause the corrugated surface morphology of glass hole wall. When the hole wall quality is poor, the micro cracks and voids are more obvious. [ABSTRACT FROM AUTHOR]

The regulation of glial cell activation is a critical step for the treatment or prevention of neuroinflammation‐based brain diseases. However, the development of therapeutic drugs that pass the blood–brain barrier (BBB) and inhibit the glia cell activation remains a significant challenge. Herein, an ultrasmall 2D vanadium carbide quantum dots (V2C QDs) that are capable of crossing the BBB are prepared, and the admirable anti‐neuroinflammatory effects are presented. The prepared 2D V2C QDs with an average size of 2.54 nm show good hydrophilicity, physiological stability, and effective BBB‐crossing ability. The biological effect of V2C QDs on inflammatory reactions demonstrates fascinating results in preventing the impairment of learning and memory in BALB/c mice stimulated by lipopolysaccharide. Investigation of molecular mechanism reveals that V2C QDs not only inhibit the toll‐like receptor 4/myeloid differentiation factor 88‐mediated nuclear factor kappa B and mitogen‐activated protein kinase pathways, but also prevent eukaryotic translation initiation factor 2α/activating transcription factor 4/C/EBP homologous protein‐signaling pathway and reduce oxidative stress via activating the NF‐E2‐related factor‐2/heme oxygenase‐1‐signaling pathway, leading to greatly inhibited activation of microglia and astrocytes and weakened production of inflammatory cytokines. In summary, V2C QDs exert potent anti‐inflammatory effects through multiple pathways, thus offer great potential for the treatment of neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The photoinduced decarboxylative radical cascade alkylation/cyclization strategy of diverse benzimidazoles containing unactivated alkene moieties towards the synthesis of polycyclic benzimidazoles via ligand-to-metal-charge-transfer (LMCT) has been developed. Specifically, the reaction initiates the decarboxylation of alkyl carboxylic acids through light-induced charge transfer from the ligand to iron, resulting in the formation of alkyl radical centers. Moreover, the protocol has prominent features, such as a broad substrate scope, high regioselectivity, mild reaction conditions, operational simplicity and readily available air acting as the sole oxidant. [ABSTRACT FROM AUTHOR]

During last decades, significant advances have been made in iron-based spin crossover (SCO) complexes, with a particular emphasis on achieving reversible and reproducible thermal hysteresis at room temperature (RT). This pursuit represents a pivotal goal within the field of molecular magnetism, aiming to create molecular devices capable of operating in ambient conditions. Here, we summarize the recent progress of iron complexes with spin transition near RT based on nitrogen ligands containing aromatic rings from molecular design to functional devices. Specifically, we discuss the various factors, including supramolecular interactions, crystal packing, guest molecules and pressure effects, that could influence its cooperativity and the spin transition temperature. Furthermore, the most recent advances in their implementation as mechanical actuators, switching/memories, sensors, and other devices, have been introduced as well. Finally, we give a perspective on current challenges and future directions in SCO community. [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Cereals, Oils & Foods is the property of Science & Technology of Cereals, Oils & Foods Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: The study aims to shed light on the concepts most addressed in scientific research, which blockchain topics are of most interest, how relevant are these tools for academia, and how relevant are they? Design/methodology/approach: The authors have developed a bibliometric study of scientific publications on blockchain made since 2016. For the analysis, the VOSViewer software version 1.6.19 has been used, which allows a statistical analysis of scientific publications on the subject. Findings: The study manifest the relevance of Initial Coin Offering, growth of research interest in this field and the relevance of blockchain technology in the development of entrepreneurial projects. Originality/value: This study provides a complete and updated picture of the scientific research on blockchain for the subsequent transfer of knowledge to the business world. [ABSTRACT FROM AUTHOR]

Copyright of Acta Prataculturae Sinica is the property of Acta Prataculturae Sinica Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Spectrum sensing describes, whether the spectrum is occupied or empty. Main objective of cognitive radio network (CRN) is to increase probability of detection (P d ) and reduce probability of error (P e ) for energy consumption. To reduce energy consumption, probability of detection should be increased. In cooperative spectrum sensing (CSS), all secondary users (SU) transmit their data to fusion center (FC) for final measurement according to the status of primary user (PU). Cluster should be used to overcome this problem and improve performance. In the clustering technique, all SUs are grouped into clusters on the basis of their similarity. In cluster technique, SU transfers their data to cluster head (CH) and CH transfers their combined data to FC. This paper proposes the detection performance optimization of CRN with a machine learning-based metaheuristic algorithm using clustering CSS technique. This article presents a hybrid support vector machine (SVM) and Red Deer Algorithm (RDA) algorithm named Hybrid SVM-RDA to identify spectrum gaps. Algorithm proposed in this work outperforms the computational complexity, an issue reported with various conventional cluster techniques. The proposed algorithm increases the probability of detection (up to 99%) and decreases the probability of error (up to 1%) at different parameters., (© 2023. Springer Nature Limited.)

A correction is presented to the article the article "MOTS-c accelerates bone fracture healing by stimulating osteogenesis of bone marrow mesenchymal stem cells via positively regulating FOXF1 to activate the TGF-β pathway.

Copyright of Food & Fermentation Industries is the property of Food & Fermentation Industries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A major design challenge for a grid-integrated photovoltaic power plant is to generate maximum power under varying loads, irradiance, and outdoor climatic conditions using competitive algorithm-based controllers. The objective of this study is to review experimentally validated advanced maximum power point tracking algorithms for enhancing power generation. A comprehensive analysis of 14 of the most advanced metaheuristics and 17 hybrid homogeneous and heterogeneous metaheuristic techniques is carried out, along with a comparison of algorithm complexity, maximum power point tracking capability, tracking frequency, accuracy, and maximum power extracted from PV systems. The results show that maximum power point tracking controllers mostly use conventional algorithms; however, metaheuristic algorithms and their hybrid variants are found to be superior to conventional techniques under varying environmental conditions. The Grey Wolf Optimization, in combination with Perturb & Observe, and Jaya-Differential Evolution, is found to be the most competitive technique. The study shows that standard testing and evaluation procedures can be further developed for comparing metaheuristic algorithms and their hybrid variants for developing advanced maximum power point tracking controllers. The identified algorithms are found to enhance power generation by grid-integrated commercial solar power plants. The results are of importance to the solar industry and researchers worldwide. [ABSTRACT FROM AUTHOR]

The preparation of Fe(II) complexes combining monodentate NCX− (X = S or Se) and the tetradentate Schiff base chiral ligands RR-L1 and SS-L1 = (RR- or SS-L1 = 1R,2R or 1S,2S)-N1,N2-bis(pyridin-2-ylmethylen)cyclohexane-1,2-diamine in acetone results in an unexpected reaction. Thus, four enantiomerically pure compounds of formulas [Fe(RR-S-L2)(NCX)2] and [Fe(SS-R-L2)(NCX)2] (X = S or Se) are formed by the new asymmetrical ligand L2. In L2, one acetone solvent molecule is incorporated into the ligand forming a bond with the C atom of one of the two CN imine groups of L1, which is transformed into an amine (Mannich reaction). This reaction is diastereoselective as the incorporation of acetone leads to an asymmetric C adjacent to the NH group with opposite chirality S- or R- to that of the cyclohexane carbons (RR- or SS-, respectively). Therefore, L2 contains three C chiral centers. Structural and magnetic characterization of these compounds demonstrates that they show in the bulk a gradual spin-crossover behavior and LIESST effect. Interestingly, the presence of an intramolecular hydrogen bond between the integrated acetone molecule and the NH group can trigger a secondary stimuli-responsive behavior in the system. Therefore, by changing the solvent polarity, the color of the complex in solution can be easily tuned. [ABSTRACT FROM AUTHOR]

Magnetic induction tomography (MIT) is an emerging imaging technology holding significant promise in the field of cerebral hemorrhage monitoring. The commonly employed imaging method in MIT is time-difference imaging. However, this approach relies on magnetic field signals preceding cerebral hemorrhage, which are often challenging to obtain. Multiple bioelectrical impedance information with different frequencies is added to this study on the basis of single-frequency information, and the collected signals with different frequencies are identified to obtain the magnetic field signal generated by single-layer heterogeneous tissue. The Stacked Autoencoder (SAE) neural network algorithm is used to reconstruct the images of head multi-layer tissues. Both numerical simulation and phantom experiments are carried out. The results indicate that the relative error of the multi-frequency SAE reconstruction is only 7. 82°/o, outperforming traditional algorithms. Moreover, under a noise level of 40 dB, the anti-interference capability of the MIT algorithm based on frequency identification and SAE is superior to traditional algorithms. This research explores a novel approach for the dynamic monitoring of cerebral hemorrhage and demonstrates the potential advantages of MIT in non-invasive monitoring. [ABSTRACT FROM AUTHOR]

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Clusters, an aggregation of several to thousands of atoms, molecules, or ions, are the building blocks of novel functional materials by atomic manufacturing and exhibit excellent applications in catalysis, quantum information, and nanomedicine. The evolution of cluster structures has been studied for many years. Many effective structural search methods, such as genetic algorithm, basin‐hopping, and so on, have been developed. However, the efficient execution of these methods relies on precise energy calculators, such as density functional theory (DFT) calculations. Up to now, limited by computational methods and capabilities, the researches mainly focus on free‐standing clusters, which are different from clusters in practical applications. Recently, the rapid development of big data‐driven machine learning is expected to replace DFT for high‐precision large‐scale computing. In this review, the present cluster search methods and challenges currently faced have been summarized. It is proposed that the development of artificial intelligence has the potential to solve some practical problems including the structural and properties evolution of clusters in complex environment, causing revolutionary developments in the fields of catalysis, quantum information, and nanomedicine based on clusters. [ABSTRACT FROM AUTHOR]

With remarkable learning capabilities and swift operational speeds, artificial intelligence (AI) can assist researchers in swiftly extracting valuable patterns, trends, and associations from subjective information. Tribological behaviors are characterized by dependence on systems, evolution with time, and multidisciplinary coupling. The friction process involves a variety of phenomena, including mechanics, thermology, electricity, optics, magnetics, and so on. Hence, tribological information possesses the distinct characteristics of being multidisciplinary, multilevel, and multiscale, so that the application of AI in tribology is highly extensive. To delineate the scope, classification, and recent trends of AI implementation in tribology, this review embarks on exploration of the tribology research domain. It comprehensively outlines the utilization of AI in basic theory of tribology, intelligent tribology, component tribology, extreme tribology, bio-tribology, green tribology, and other fields. Finally, considering the emergence of "tribo-informatics" as a novel interdisciplinary field, which combines tribology with informatics, this review elucidates the future directions and research framework of "AI for tribology". In this paper, tribo-system information is divided into 5 categories: input information (I), system intrinsic information (S), output information (O), tribological state information (Ts), and derived state information (Ds). Then, a fusion method among 5 types of tribo-system information and different AI technologies (regression, classification, clustering, and dimension reduction) has been proposed, which enables tribo-informatics methods to solve common problems such as tribological behavior state monitoring, behavior prediction, and system optimization. The purpose of this review is to offer a systematic comprehension of tribo-informatics and to inspire new research ideas of tribo-informatics. Ultimately, it aspires to enhance the efficiency of problem-solving in tribology. [ABSTRACT FROM AUTHOR]

This study introduces a method to predict the remaining useful life (RUL) of plain bearings operating under stationary, wear-critical conditions. In this method, the transient wear data of a coupled elastohydrodynamic lubrication (mixed-EHL) and wear simulation approach is used to parametrize a statistical, linear degradation model. The method incorporates Bayesian inference to update the linear degradation model throughout the runtime and thereby consider the transient, system-dependent wear progression within the RUL prediction. A case study is used to show the suitability of the proposed method. The results show that the method can be applied to three distinct types of post-wearing-in behavior: wearing-in with subsequent hydrodynamic, stationary wear, and progressive wear operation. While hydrodynamic operation leads to an infinite lifetime, the method is successfully applied to predict RUL in cases with stationary and progressive wear. [ABSTRACT FROM AUTHOR]

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Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hematologic malignancies (HMs), also referred to as hematological or blood cancers, pose significant threats to patients as they impact the blood, bone marrow, and lymphatic system. Despite significant clinical strategies using chemotherapy, radiotherapy, stem cell transplantation, targeted molecular therapy, or immunotherapy, the five-year overall survival of patients with HMs is still low. Fortunately, recent studies demonstrate that the nanodrug delivery system holds the potential to address these challenges and foster effective anti-HMs with precise treatment. In particular, cell membrane camouflaged nanodrug offers enhanced drug targeting, reduced toxicity and side effects, and/or improved immune response to HMs. This review firstly introduces the merits and demerits of clinical strategies in HMs treatment, and then summarizes the types, advantages, and disadvantages of current nanocarriers helping drug delivery in HMs treatment. Furthermore, the types, functions, and mechanisms of cell membrane fragments that help nanodrugs specifically targeted to and accumulate in HM lesions are introduced in detail. Finally, suggestions are given about their clinical translation and future designs on the surface of nanodrugs with multiple functions to improve therapeutic efficiency for cancers. [ABSTRACT FROM AUTHOR]

The incidence of inflammatory bowel disease (IBD) and the associated risk of colon cancer are increasing globally. Traditional Chinese medicine (TCM) treatment has unique advantages. The Sishen Pill, a common Chinese patented drug used to treat abdominal pain and diarrhea, consists mainly of Psoraleae Fructus, Myristicae Semen, Euodiae Fructus, and Schisandra Chinensis. Modern research has confirmed that Sishen Pill and its active secondary metabolites, such as psoralen, myristicin, evodiamine, and schisandrin, can improve intestinal inflammation and exert antitumor pharmacological effects. Common mechanisms in treating IBD and colon cancer mainly include regulating inflammation-related signaling pathways such as nuclear factor-kappa B, mitogen-activated protein kinase, phosphatidylinositol 3-kinase, NOD-like receptor heat protein domain-related protein 3, and wingless-type MMTV integration site family; NF-E2-related factor 2 and hypoxia-inducible factor 1α to inhibit oxidative stress; mitochondrial autophagy and endoplasmic reticulum stress; intestinal immune cell differentiation and function through the Janus kinase/signal transducer and activator of transcription pathway; and improving the gut microbiota and intestinal barrier. Overall, existing evidence suggests the potential of the Sishen pill to improve IBD and suppress inflammation-to-cancer transformation. However, large-scale randomized controlled clinical studies and research on the safety of these clinical applications are urgently required. [ABSTRACT FROM AUTHOR]

Mitochondria-derived peptides (MDPs) are a new class of bioactive peptides encoded by small open reading frames (sORFs) within known mitochondrial DNA (mtDNA) genes. MDPs may affect the expression of nuclear genes and play cytoprotective roles against chronic and age-related diseases by maintaining mitochondrial function and cell viability in the face of metabolic stress and cytotoxic insults. In this review, we summarize clinical and experimental findings indicating that MDPs act as local and systemic regulators of glucose homeostasis, immune and inflammatory responses, mitochondrial function, and adaptive stress responses, and focus on evidence supporting the protective effects of MDPs against myocardial infarction. These insights into MDPs actions suggest their potential in the treatment of cardiovascular diseases and should encourage further research in this field. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Northwest A & F University - Natural Science Edition is the property of Editorial Department of Journal of Northwest A&F University (Natural Science Edition) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Vibration, Measurement & Diagnosis is the property of Nanjing Hangkong Daxue and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food & Fermentation Industries is the property of Food & Fermentation Industries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Acknowledging the neurological symptoms of COVID‐19 and the long‐lasting neurological damage even after the epidemic ends are common, necessitating ongoing vigilance. Initial investigations suggest that extracellular vesicles (EVs), which assist in the evasion of the host's immune response and achieve immune evasion in SARS‐CoV‐2 systemic spreading, contribute to the virus's attack on the central nervous system (CNS). The pro‐inflammatory, pro‐coagulant, and immunomodulatory properties of EVs contents may directly drive neuroinflammation and cerebral thrombosis in COVID‐19. Additionally, EVs have attracted attention as potential candidates for targeted therapy in COVID‐19 due to their innate homing properties, low immunogenicity, and ability to cross the blood‐brain barrier (BBB) freely. Mesenchymal stromal/stem cell (MSCs) secreted EVs are widely applied and evaluated in patients with COVID‐19 for their therapeutic effect, considering the limited antiviral treatment. This review summarizes the involvement of EVs in COVID‐19 neuropathology as carriers of SARS‐CoV‐2 or other pathogenic contents, as predictors of COVID‐19 neuropathology by transporting brain‐derived substances, and as therapeutic agents by delivering biotherapeutic substances or drugs. Understanding the diverse roles of EVs in the neuropathological aspects of COVID‐19 provides a comprehensive framework for developing, treating, and preventing central neuropathology and the severe consequences associated with the disease. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Cancer Biotherapy is the property of Editorial Office of Chinese Journal of Cancer Biotherapy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)