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An apical lesion is caused by bacteria invading the tooth apex through caries. Periodontal disease is caused by plaque accumulation. Peri-endo combined lesions include both diseases and significantly affect dental prognosis. The lack of clear symptoms in the early stages of onset makes diagnosis challenging, and delayed treatment can lead to the spread of symptoms. Early infection detection is crucial for preventing complications. PAs used as the database were provided by Chang Gung Memorial Medical Center, Taoyuan, Taiwan, with permission from the Institutional Review Board (IRB): 02002030B0. The tooth apex image enhancement method is a new technology in PA detection. This image enhancement method is used with convolutional neural networks (CNN) to classify apical lesions, peri-endo combined lesions, and asymptomatic cases, and to compare with You Only Look Once-v8-Oriented Bounding Box (YOLOv8-OBB) disease detection results. The contributions lie in the utilization of database augmentation and adaptive histogram equalization on individual tooth images, achieving the highest comprehensive validation accuracy of 95.23% with the ConvNextv2 model. Furthermore, the CNN outperformed YOLOv8 in identifying apical lesions, achieving an F1-Score of 92.45%. For the classification of peri-endo combined lesions, CNN attained the highest F1-Score of 96.49%, whereas YOLOv8 scored 88.49%. [ABSTRACT FROM AUTHOR]

The severity of periodontitis can be analyzed by calculating the loss of alveolar crest (ALC) level and the level of bone loss between the tooth's bone and the cemento-enamel junction (CEJ). However, dentists need to manually mark symptoms on periapical radiographs (PAs) to assess bone loss, a process that is both time-consuming and prone to errors. This study proposes the following new method that contributes to the evaluation of disease and reduces errors. Firstly, innovative periodontitis image enhancement methods are employed to improve PA image quality. Subsequently, single teeth can be accurately extracted from PA images by object detection with a maximum accuracy of 97.01%. An instance segmentation developed in this study accurately extracts regions of interest, enabling the generation of masks for tooth bone and tooth crown with accuracies of 93.48% and 96.95%. Finally, a novel detection algorithm is proposed to automatically mark the CEJ and ALC of symptomatic teeth, facilitating faster accurate assessment of bone loss severity by dentists. The PA image database used in this study, with the IRB number 02002030B0 provided by Chang Gung Medical Center, Taiwan, significantly reduces the time required for dental diagnosis and enhances healthcare quality through the techniques developed in this research. [ABSTRACT FROM AUTHOR]

Variable taxi time prediction is the core of the Airport Collaborative Decision Making (A-CDM) system. An accurate taxi time prediction contributes to enhancing airport operational efficiency, safety and predictability. The deep dynamic spatio-temporal correlation inherent in airport traffic data is critical for taxi time prediction. However, existing machine learning (deep learning) methods have been unable to thoroughly exploit these correlations. To address this issue, we propose a deep learning-based model called the multi-task dynamic spatio-temporal graph attention network (MT-DSTGAN). Our model also predicts future entire airport traffic flow and taxiing segment traffic flow as auxiliary tasks, with the goal of enhancing the accuracy of aircrafts' taxi time prediction. The proposed MT-DSTGAN model is implemented and assessed through a case study of Beijing Capital International Airport with a real-world dataset. The advantage of the proposed model, which shows better performance in various evaluation metrics, is demonstrated in a comparative study with other baseline works. In summary, the proposed MT-DSTGAN exhibits promising capabilities in perceiving the dynamic changes in the taxiing process of aircraft and demonstrates the ability to capture complex spatio-temporal correlations in airport traffic data. [ABSTRACT FROM AUTHOR]

It is crucial to regulate N-methyl-D-aspartate (NMDA) function bivalently depending on the central nervous system (CNS) conditions. CNS disorders with NMDA hyperfunction are involved in the pathogenesis of neurotoxic and/or neurodegenerative disorders with elevated D-serine, one of the NMDA receptor co-agonists. On the contrary, NMDA-enhancing agents have been demonstrated to improve psychotic symptoms and cognition in CNS disorders with NMDA hypofunction. Serine racemase (SR), the enzyme regulating both D- and L-serine levels through both racemization (catalysis from L-serine to D-serine) and β-elimination (degradation of both D- and L-serine), emerges as a promising target for bidirectional regulation of NMDA function. In this study, we explored using dimethyl malonate (DMM), a pro-drug of the SR inhibitor malonate, to modulate NMDA activity in C57BL/6J male mice via intravenous administration. Unexpectedly, 400 mg/kg DMM significantly elevated, rather than decreased (as a racemization inhibitor), D-serine levels in the cerebral cortex and plasma. This outcome prompted us to investigate the regulatory effects of dodecagalloyl-α-D-xylose (α12G), a synthesized tannic acid analog, on SR activity. Our findings showed that α12G enhanced the racemization activity of human SR by about 8-fold. The simulated and fluorescent assay of binding affinity suggested a noncooperative binding close to the catalytic residues, Lys56 and Ser84. Moreover, α12G treatment can improve behaviors associated with major CNS disorders with NMDA hypofunction including hyperactivity, prepulse inhibition deficit, and memory impairment in animal models of positive symptoms and cognitive impairment of psychosis. In sum, our findings suggested α12G is a potential therapeutic for treating CNS disorders with NMDA hypofunction. [ABSTRACT FROM AUTHOR]

A dual biofunctional hydrogel (HQCS-SP) wound dressing, offering antibacterial properties and a biological response, was innovatively designed and developed to repair full-layer skin defects. The HQCS-SP hydrogel creates an artificial matrix that facilitates cell recruitment, extracellular matrix deposition, exhibiting exceptional tissue affinity, robust self-healing, effective hemostatic capabilities and accelerates wound healing. It is synthesized by crosslinking modified chitosan (HQCS) with spirulina protein (SP) and Fe3+. The HQCS provides antibacterial, antioxidant, good tissue affinity and excellent hemostasis performance. The incorporation of SP not only reinforces the antioxidant, antibacterial, anti-inflammatory, and pro-angiogenesis effects but also participates in the regulation of signal pathways and promotes wound healing. Therefore, this study offers a new visual angle for the design of advanced functional trauma dressings with great application potential in the bio-medical field. [ABSTRACT FROM AUTHOR]

This article explores the challenges of continuum and magnetic soft robotics for medical applications, extending from model development to an interdisciplinary perspective. First, we established a unified model framework based on algebra and geometry. The research progress and challenges in principle models, data-driven, and hybrid modeling were then analyzed in depth. Simultaneously, a numerical analysis framework for the principle model was constructed. Furthermore, we expanded the model framework to encompass interdisciplinary research and conducted a comprehensive analysis, including an in-depth case study. Current challenges and the need to address meta-problems were identified through discussion. Overall, this review provides a novel perspective on understanding the challenges and complexities of continuum and magnetic soft robotics in medical applications, paving the way for interdisciplinary researchers to assimilate knowledge in this domain rapidly. [ABSTRACT FROM AUTHOR]

To ensure the safety of buildings, accurate and robust prediction of a reinforced concrete deep beam's shear capacity is necessary to avoid unpredictable accidents caused by brittle failure. However, the failure mechanism of reinforced concrete deep beams is very complicated, has not been fully elucidated, and cannot be accurately described by simple equations. To solve this issue, machine learning techniques have been utilized and corresponding prediction models have been developed. Nevertheless, these models can only provide deterministic prediction results of the scalar type, and the confidence level is uncertain. Thus, these prediction results cannot be used for the design and assessment of deep beams. Therefore, in this paper, a probabilistic prediction approach of the shear strength of reinforced concrete deep beams is proposed based on the natural gradient boosting algorithm trained on a collected database. A database of 267 deep beam experiments was utilized, with 14 key parameters identified as the inputs related to the beam geometry, material properties, and reinforcement details. The proposed NGBoost model was compared to empirical formulas from design codes and other machine learning methods. The results showed that the NGBoost model achieved higher accuracy in mean shear strength prediction, with an R2 of 0.9045 and an RMSE of 38.8 kN, outperforming existing formulas by over 50%. Additionally, the NGBoost model provided probabilistic predictions of shear strength as probability density functions, enabling reliable confidence intervals. This demonstrated the capability of the data-driven NGBoost approach for robust shear strength evaluation of RC deep beams. Overall, the results illustrated that the proposed probabilistic prediction approach dramatically surpassed the current formulas adopted in design codes and machine learning models in both prediction accuracy and robustness. [ABSTRACT FROM AUTHOR]

Furcation defects pose a significant challenge in the diagnosis and treatment planning of periodontal diseases. The accurate detection of furcation involvements (FI) on periapical radiographs (PAs) is crucial for the success of periodontal therapy. This research proposes a deep learning-based approach to furcation defect detection using convolutional neural networks (CNN) with an accuracy rate of 95%. This research has undergone a rigorous review by the Institutional Review Board (IRB) and has received accreditation under number 202002030B0C505. A dataset of 300 periapical radiographs of teeth with and without FI were collected and preprocessed to enhance the quality of the images. The efficient and innovative image masking technique used in this research better enhances the contrast between FI symptoms and other areas. Moreover, this technology highlights the region of interest (ROI) for the subsequent CNN models training with a combination of transfer learning and fine-tuning techniques. The proposed segmentation algorithm demonstrates exceptional performance with an overall accuracy up to 94.97%, surpassing other conventional methods. Moreover, in comparison with existing CNN technology for identifying dental problems, this research proposes an improved adaptive threshold preprocessing technique that produces clearer distinctions between teeth and interdental molars. The proposed model achieves impressive results in detecting FI with identification rates ranging from 92.96% to a remarkable 94.97%. These findings suggest that our deep learning approach holds significant potential for improving the accuracy and efficiency of dental diagnosis. Such AI-assisted dental diagnosis has the potential to improve periodontal diagnosis, treatment planning, and patient outcomes. This research demonstrates the feasibility and effectiveness of using deep learning algorithms for furcation defect detection on periapical radiographs and highlights the potential for AI-assisted dental diagnosis. With the improvement of dental abnormality detection, earlier intervention could be enabled and could ultimately lead to improved patient outcomes. [ABSTRACT FROM AUTHOR]

Zanthoxylum armatum (Z. armatum) leaves represent more than 50% of by-products with promising potential applications, and there are still largely unknown variations in chemical composition and antioxidant activity in its leaves. In the present study, the changes in carbon (C), nitrogen (N) and phosphorous (P), soluble protein (SP), amino acid (AA), soluble sugar (SS), total phenol (TP) and total flavonoids (TF) content as well as antioxidant capacity in Z. armatum leaves from two plant ages between two shoot types and three leaf positions were investigated. Our results showed that there are significant effects of plant age, shoot type and leaf position and their interactions on almost all the tested parameters. Shoot types were the main factor affecting the accumulation of chemical compositions and antioxidant capacity, followed by leaf positions and plant ages. Shoot type and leaf position had stronger effects than plant age. Statistical analysis revealed a series of correlations between antioxidant capacity, TP and chemical elements. Z. armatum leaves, an agricultural by-product, showed significant differences in chemical composition and antioxidant activity in response to plant age, shoot type and leaf position, which can be valuable to incentive its cultivation management strategy and its better utilization for economic and environmental benefits. [ABSTRACT FROM AUTHOR]

Electrocoagulation (EC) has gained increasing attention as an effective and environmentally friendly technique for purifying water and wastewater. This review provides a comprehensive analysis of the recent literature on EC and identifies new trends and potentials for further research. Initially, the nature of EC and its operating parameters are discussed, while the research trends are analyzed using the Scopus database and VOSviewer software. From 1977 to 2022, 2691 research articles and review papers on EC for water/wastewater treatment were published, with the number of publications increasing from 2 in 1977 to 293 in 2022. In the past five years, most studies focused on treatment performance and the mechanism of EC systems. However, recent emphasis has been placed on combining EC with other treatment processes and addressing emerging pollutants. The innovative applications of EC are highlighted, including the removal of microplastics and per/polyfluoroalkyl substances, the power supply of EC via microbial fuel cells (MFCs) and electro-wetlands (EWs), and the application of power management systems in EC. The review concludes with suggestions for further research to enhance the technology and expand its scope of applications. [ABSTRACT FROM AUTHOR]

Ectodysplasin A (EDA), a ligand of the TNF family, plays an important role in maintaining the homeostasis of the ocular surface. EDA is necessary for the development of the meibomian gland, the lacrimal gland, as well as the proliferation and barrier function of the corneal epithelium. The mutation of EDA can induce the destruction of the ocular surface resulting in keratopathy, abnormality of the meibomian gland and maturation of the lacrimal gland. Experimental animal studies showed that a prenatal ultrasound-guided intra-amniotic injection or postnatal intravenous administration of soluble recombinant EDA protein can efficiently prevent the development of ocular surface abnormalities in EDA mutant animals. Furthermore, local application of EDA could restore the damaged ocular surface to some extent. Hence, a recombinant EDA-based therapy may serve as a novel paradigm to treat ocular surface disorders, such as meibomian gland dysfunction and corneal epithelium abnormalities. [ABSTRACT FROM AUTHOR]

With the development of modern electromagnetic stealth technology and ARM, traditional active radar detection cannot accomplish its detection mission, limited by its ability. Relying on such superior advantages such as imperceptibility, anti-electromagnetic interference and electromagnetic stealth, passive transducers are playing an indispensable and significant role in situation awareness. While, in addition to different passive transducer localization modes and solutions of target's location, the reasonable planning and optimal layout of passive transducers' location are other major factors affecting the precision of localization. Planning an optimal airspace for passive transducers is the key problem to improve the monitoring efficiency. This paper proposes the optimal layout algorithm for the cooperative platform in the space based on the geometrical relationship of cooperative localization. For example, the principle of direction location in traditional methods is simple: only two passive sensors can work, but the location accuracy of long-distance targets is low. At the same time, TDOA (Time Difference Of Arrival) location has high accuracy and good stability, but it needs at least three passive sensors to work together, which requires the most resources. In this paper, a platform optimization layout algorithm based on direction and TDOA hybrid positioning is proposed. Compared with direction positioning, it improves the long-distance positioning accuracy, reduces the number of sensors required for TDOA positioning, and reduces the resource occupancy rate. However, the TDOA positioning data mixed with direction positioning data inevitably leads to the decline of overall accuracy. In order to solve these difficulties, the weighted least square method is used to optimize the accuracy. The simulation shows that, within the designated target airspace, the optimal action airspace can be generated automatically based on the platforms' cooperation mode. If there is no resource limitation, the airspace planning based on TDOA positioning has the highest accuracy for the target. However, in practical application, considering the resource limitation, the hybrid positioning of direction and TDOA can also meet the requirements of high accuracy and high stability. The average error is reduced by more than 45% compared with direction positioning, and the airspace occupancy is reduced by more than 30% compared with TDOA positioning. The goal of minimizing the scope of platform airspace planning is realized. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for automatic segmentation of dental X-ray images into single tooth sections and for placing every segmented tooth onto a precise corresponding position table. Moreover, the proposed method automatically determines the tooth's position in a panoramic X-ray film. The image-processing step incorporates a variety of image-enhancement techniques, including sharpening, histogram equalization, and flat-field correction. Moreover, image processing was implemented iteratively to achieve higher pixel value contrast between the teeth and cavity. The next image-enhancement step is aimed at detecting the teeth cavity and involves determining the segment and points separating the upper and lower jaw, using the difference in pixel values to cut the image into several equal sections and then connecting each cavity feature point to extend a curve that completes the description of the separated jaw. The curve is shifted up and down to look for the gap between the teeth, to identify and address missing teeth and overlapping. Under FDI World Dental Federation notation, the left and right sides receive eight-code sequences to mark each tooth, which provides improved convenience in clinical use. According to the literature, X-ray film cannot be marked correctly when a tooth is missing. This paper utilizes artificial center positioning and sets the teeth gap feature points to have the same count. Then, the gap feature points are connected as a curve with the curve of the jaw to illustrate the dental segmentation. In addition, we incorporate different image-processing methods to sequentially strengthen the X-ray film. The proposed procedure had an 89.95% accuracy rate for tooth positioning. As for the tooth cutting, where the edge of the cutting box is used to determine the position of each tooth number, the accuracy of the tooth positioning method in this proposed study is 92.78%. [ABSTRACT FROM AUTHOR]

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion. [ABSTRACT FROM AUTHOR]

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu's threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film. [ABSTRACT FROM AUTHOR]

The performance requirements for Global Navigation Satellite Systems (GNSS) are becoming more demanding as the range of mission-critical vehicular applications, including the Unmanned Aerial Vehicle (UAV) and ground vehicle-based applications, increases. However, the accuracy and reliability of GNSS in some environments, such as in urban areas, are often affected by non-line-of-sight (NLOS) signals and multipath effects. It is therefore essential to develop an effective fault detection scheme that can be applied to GNSS observations so as to ensure that the vehicle positioning can be calculated with a high accuracy. In this paper, we propose an online dataset based faulty GNSS measurement detection and exclusion algorithm for vehicle positioning that takes account of the NLOS/multipath affected scenarios. The proposed algorithm enables a real-time online dataset based fault detection and exclusion scheme, which makes it possible to detect multiple faults in different satellites simultaneously and accurately, thereby allowing real-time quality control of GNSS measurements in dynamic urban positioning applications. The algorithm was tested with simulated/artificial step errors in various scenarios in the measured pseudoranges from a dataset acquired from a UAV in an open area. Furthermore, a real-world test was also conducted with a ground-vehicle driving in a dense urban environment to validate the practical efficiency of the proposed algorithm. The UAV based simulation exhibits a fault detection rate of 100% for both single and multi-satellite fault scenarios, with the horizontal positioning accuracy improved to about 1 metre from tens of metres after fault detection and exclusion. The ground vehicle-based real test shows an overall improvement of 26.1% in 3D positioning accuracy in an urban area compared to the traditional least square method. [ABSTRACT FROM AUTHOR]

Gait stability has been measured by using many entropy-based methods. However, the relation between the entropy values and gait stability is worth further investigation. A research reported that average entropy (AE), a measure of disorder, could measure the static standing postural stability better than multiscale entropy and entropy of entropy (EoE), two measures of complexity. This study tested the validity of AE in gait stability measurement from the viewpoint of the disorder. For comparison, another five disorders, the EoE, and two traditional metrics methods were, respectively, used to measure the degrees of disorder and complexity of 10 step interval (SPI) and 79 stride interval (SI) time series, individually. As a result, every one of the 10 participants exhibited a relatively high AE value of the SPI when walking with eyes closed and a relatively low AE value when walking with eyes open. Most of the AE values of the SI of the 53 diseased subjects were greater than those of the 26 healthy subjects. A maximal overall accuracy of AE in differentiating the healthy from the diseased was 91.1%. Similar features also exists on those 5 disorder measurements but do not exist on the EoE values. Nevertheless, the EoE versus AE plot of the SI also exhibits an inverted U relation, consistent with the hypothesis for physiologic signals. [ABSTRACT FROM AUTHOR]

Targeted information sources include radar and ADS (Automatic Dependent Surveillance) for civil ATM (Air Traffic Management) systems, and the new navigation system based on satellites has the capability of global coverage. In order to solve the surveillance problem in mid-and-high altitude airspace and approaching airspace, this paper proposes a filter-based covariance matrix weighting method, measurement variance weighting method, and measurement-first weighted fusion method weighting integration algorithm to improve the efficiency of data integration calculation under fixed accuracy. Besides this, this paper focuses on the technology of the integration of a multi-radar surveillance system and automated related surveillance system in the ATM system and analyzes the constructional method of a multigeneration surveillance data integration system, as well as establishing the targeted model of sensors and the target track and designing the logical structure of multi-radar and ADS data integration. [ABSTRACT FROM AUTHOR]

Hetero-nanomaterials constructed by plasmonic metals and functional semiconductors show enormous potential in photocatalytic applications, such as in hydrogen production, CO2 reduction, and treatment of pollutants. Their photocatalytic performances can be better regulated through adjusting structure, composition, and components' arrangement. Therefore, the reasonable design and synthesis of metal/semiconductor hetero-nanostructures is of vital significance. In this mini-review, we laconically summarize the recent progress in efficiently establishing metal/semiconductor nanomaterials for improved photocatalysis. The defined photocatalysts mainly include traditional binary hybrids, ternary multi-metals/semiconductor, and metal/multi-semiconductors heterojunctions. The underlying physical mechanism for the enhanced photocatalysis of the established photocatalysts is highlighted. In the end, a brief summary and possible future perspectives for further development in this field are demonstrated. [ABSTRACT FROM AUTHOR]

Stems are the important residues of Trapa quadrispinosa Roxb., which are abundant in phenolic compounds. Ultrasonic-assisted enzymatic extraction (UAEE) is confirmed as a novel extraction technology with main advantages of enhancing extraction yield and physiological activities of the extracts from various plants. In this study, UAEE was applied to obtain the highest yield of phenolic content, strongest antioxidant, and antitumor activities and to optimize the extraction conditions using response surface methodology (RSM). The extracts from the stems of T. quadrispinosa were characterized by determination of their antioxidant activities through 2,2-azinobis(3-ethylbenzthiazoline)-6-sulfonic acid (ABTS), 1,1-Diphenyl-2-picrylhydrazxyl (DPPH) radical scavenging, total antioxidant capacity (TAC), ferric reducing antioxidant capacity (FRAC) methods and of their antitumor activity by MTT method. The selected key independent variables were cellulase concentration ( X ₁: 1.5%-2.5%), extraction time ( X ₂: 20-30 min) and extraction temperature ( X ₃: 40-60 °C). The optimal extraction conditions for total phenolic content (TPC) value of the extracts were determined as 1.74% cellulase concentration, 25.5 min ultrasonic extraction time and 49.0 °C ultrasonic temperature. Under these conditions, the highest TPC value of 53.6 ± 2.2 mg Gallic acid equivalent (GAE)/g dry weight (DW) was obtained, which agreed well with the predicted value (52.596 mg GAE/g·DW. Furthermore, the extracts obtained from UAEE presented highest antioxidant activities through ABTS, DPPH, TAC and FRAC methods were of 1.54 ± 0.09 mmol Trolox equivalent (TE)/g·DW; 1.45 ± 0.07 mmol·TE/g·DW; 45.2 ± 2.2 mg·GAE/g·DW; 50.4 ± 2.6 μmol FeSO₄ equivalent/g·DW and lowest IC 50 values of 160.4 ± 11.6 μg/mL, 126.1 ± 10.8 μg/mL, and 178.3 ± 13.1 μg/mL against Hela, HepG-2 and U251 tumor cells, respectively. The results indicated that the UAEE was an efficient alternative to improve extraction yield and enhance the antioxidant and antitumor activities of the extracts. The phenolic extracts from the stems of T. quadrispinosa had significant antioxidant and antitumor activities, which could be used as a source of potential antioxidant and antitumor agents.

Integration of inorganic sulfate into biological molecules plays an important role in biological systems and is directly involved in the instigation of diseases. Protein tyrosine sulfation (PTS) is a common post-translational modification that was first reported in the literature fifty years ago. However, the significance of PTS under physiological conditions and its link to diseases have just begun to be appreciated in recent years. PTS is catalyzed by tyrosylprotein sulfotransferase (TPST) through transfer of an activated sulfate from 3'-phosphoadenosine-5'-phosphosulfate to tyrosine in a variety of proteins and peptides. Currently, only a small fraction of sulfated proteins is known and the understanding of the biological sulfation mechanisms is still in progress. In this review, we give an introductory and selective brief review of PTS and then summarize the basic biochemical information including the activity and the preparation of TPST, methods for the determination of PTS, and kinetics and reaction mechanism of TPST. This information is fundamental for the further exploration of the function of PTS that induces protein-protein interactions and the subsequent biochemical and physiological reactions.

A new biflavonol glycoside, quercetin-3-O-β-D-glucopyranoside-(3'→O-3''')-quercetin-3-O-β-D-galactopyranoside (9), together with eight known compounds was isolated for the first time from the leaves of Machilus zuihoensis Hayata (Lauraceae). The structure of compound 9 was elucidated by various types of spectroscopic data analysis. Analysis of the biological activity assay found that compound 9 showed significant superoxide anion scavenging activity (IC₅₀ is 30.4 μM) and markedly suppressed LPS-induced high mobility group box 1 (HMGB-1) protein secretion in RAW264.7 cells. In addition, the HMGB-1 protein secretion was also inhibited by quercitrin (3), ethyl caffeate (6), and ethyl 3-O-caffeoylquinate (7) treatment. In the LPS-stimulated inducible nitric oxide synthase (iNOS) activation analysis, two known compounds, quercetin (1) and ethyl caffeate (6), were found to markedly suppress nitric oxide (NO) production (IC₅₀ value, 27.6 and 42.9 μM, respectively) in RAW264.7 cells. Additionally, it was determined that ethyl caffeate (6) down-regulated mRNA expressions of iNOS, IL-1β, and IL-10 in the LPS-treatment of RAW264.7 cells via a suppressed NF-kB pathway. These results suggested for the first time that the new compound 9 and other constituents isolated from M. zuihoensis have potential anti-inflammatory and superoxide anion scavenging effects. These constituents may be useful for treating various inflammatory diseases.

Many diseases occur when the immune system is weakened. Intracellular signals activate immuno-responsive cells to produce cytokines that modulate the immune response. Schisandra chinensis has been used traditionally to treat general fatigue, neurasthenia, and spontaneous sweating. In the present study, the effect of constituents of S. chinensis on cytokine release by human monocytic leukemia cells (THP-1) was tested using microparticle-based flow cytometric analysis. Two major lignans, schizandrin (Sch) and gomisin A (Gom A), were identified and shown to induce interleukin (IL)-8, macrophage inflammatory protein-1β (MIP-1β), and granulocyte-macrophage-colony stimulating factor (GM-CSF) release by THP-1 cells. By reverse transcription polymerase chain reaction (RT-PCR) or quantitative real-time PCR, there was a dose-dependent increase of IL-8, MIP-1β and GM-CSF mRNA levels. Thus, Sch and Gom A from S. chinensis enhance cytokine release by THP-1 cells and this effect occurs through mRNA upregulation. Upregulation of MIP-1β and GM-CSF in particular may have clinical applications. Therefore, S. chinensis may be therapeutically beneficial by promoting humoral and cell-mediated immune responses.