Hasil untuk "Other systems of medicine"

David Croft, Gavin O'Kelly, Guanming Wu et al.

Reactome (http://www.reactome.org) is a collaboration among groups at the Ontario Institute for Cancer Research, Cold Spring Harbor Laboratory, New York University School of Medicine and The European Bioinformatics Institute, to develop an open source curated bioinformatics database of human pathways and reactions. Recently, we developed a new web site with improved tools for pathway browsing and data analysis. The Pathway Browser is an Systems Biology Graphical Notation (SBGN)-based visualization system that supports zooming, scrolling and event highlighting. It exploits PSIQUIC web services to overlay our curated pathways with molecular interaction data from the Reactome Functional Interaction Network and external interaction databases such as IntAct, BioGRID, ChEMBL, iRefIndex, MINT and STRING. Our Pathway and Expression Analysis tools enable ID mapping, pathway assignment and overrepresentation analysis of user-supplied data sets. To support pathway annotation and analysis in other species, we continue to make orthology-based inferences of pathways in non-human species, applying Ensembl Compara to identify orthologs of curated human proteins in each of 20 other species. The resulting inferred pathway sets can be browsed and analyzed with our Species Comparison tool. Collaborations are also underway to create manually curated data sets on the Reactome framework for chicken, Drosophila and rice.

D. Bates, Michael Cohen, L. Leape et al.

A. Gurib-Fakim

Wenting Ma, Olusola O. Adesope, J. Nesbit et al.

Sanket A. Salunkhe, George P. Kontoudis

Multi-robot systems require scalable and federated methods to model complex environments under computational and communication constraints. Gaussian Processes (GPs) offer robust probabilistic modeling, but suffer from cubic computational complexity, limiting their applicability in large-scale deployments. To address this challenge, we introduce the pxpGP, a novel distributed GP framework tailored for both centralized and decentralized large-scale multi-robot networks. Our approach leverages sparse variational inference to generate a local compact pseudo-representation. We introduce a sparse variational optimization scheme that bounds local pseudo-datasets and formulate a global scaled proximal-inexact consensus alternating direction method of multipliers (ADMM) with adaptive parameter updates and warm-start initialization. Experiments on synthetic and real-world datasets demonstrate that pxpGP and its decentralized variant, dec-pxpGP, outperform existing distributed GP methods in hyperparameter estimation and prediction accuracy, particularly in large-scale networks.

Sihle Ngxabi, Avela Sogoni, Nasifu Kerebba et al.

Introduction: Wild cabbage (Trachyandra ciliata) is one of the understudied, wild, edible halophytes from South Africa. Although its edibility has recently been validated, its therapeutic potential was yet to be explored. This research was carried out to profile and characterise the phytochemical content of T. ciliata extracts and evaluate its potential as a therapeutic agent for cancer, acetylcholinesterase (AChE) inhibition and reactive oxygen species (ROS) scavenging in the liver. Methods: Cuttings of T. ciliata were grown under 0, 50, 100, 150, and 200 mM salinity concentrations. The ultra-high-performance liquid chromatography mass spectrometry (UHPLC-MS) was employed to quantify and characterise metabolites in leaves, roots, and flower buds of T. ciliata. The yellow dye 3-(4,5-dimethyltiazol-2yl)-2,5-diphenyl tetrazolium bromide (MTT), Ellman's colourimetric method, and the 2′,7′-dichlorodihydrofluorescein diacetate assay (H2DCF-DA) were respectively employed to evaluate cytotoxicity, AChE antagonism, and ROS scavenging of the extracts of T. ciliata. Results: A total of 71 compounds were observed, which were grouped into flavonoids, anthocyanins, alkaloids, nucleobase, nucleosides/tide, saccharides, fatty acids, amino acids, and coumarins. A concentration of 1 mg/mL of extracts of T. ciliata flower buds prepared from plants grown at 0 mM and 100 mM salinity, showed strong cytotoxicity to cancer cells, however, the extracts also had moderate and weak cytotoxicity to non-cancer cells. All extracts inhibited AChE activity. Moreover, ROS scavenging was mainly observed primarily in the extracts of leaves from plants grown at all salinities, and in the extract of roots from plants grown at 0 mM salinity treatment. Conclusion: A maiden documentation of anticancer, acetylcholinesterase inhibition, and ROS scavenging activity of crude extracts of T. ciliata was achieved in this study. These findings suggest that T. ciliata could be a potential therapeutic agent for the treatment of cancer, Alzheimer's disease, and liver disorders, amidst the quest to develop more plant-based pharmaceuticals for the treatment of chronic diseases.

M. Kiranmayee, K. Riazunnisa

Background: Numerous natural substances with various pharmacological properties can be discovered in medicinal plants. Pithecellobium dulce (Roxb.) Benth is a traditional medicinal plant, belonging to the family Leguminosae. The various parts of the P. dulce plant are traditionally used to treat a wide range of health disorders in numerous countries. Purpose: The aim of this investigation is to demonstrate the phytochemical profiling of methanol and acetonitrile leaf extracts of P. dulce and evaluate their antioxidant activity and antibacterial potential against pathogenic bacteria. Materials and methods: The methanol and acetonitrile extracts of P. dulce leaf were extracted and characterized by using GCMS– and FTIR. Further evaluation was done for an understanding of the biomedical uses of P. dulce. The antibacterial activity of the extracts was monitored against Escherichia coli, Bacillus subtilis, and Staphylococcus aureus. The antioxidant activity of extracts was conducted by using DPPH and hydrogen peroxide scavenging activity, and total antioxidant activity was performed by phosphomolybdenum assay. Results: A total of twenty-three compounds were tentatively identified through GC–MS; five of these were associated with the methanolic extracts, while eighteen were with the acetonitrile extract. The tentatively identified compounds present in acetonitrile leaf extract were 2-nonynoic acid; pentane, 2,4-dimethyl-; lupeo; phytol; propiolic acid; bicyclo(2.2.1), and heptane-5-(ethyl-1-amine). Propanamide; N-methyl-2-amino-; ethane, 1‑chloro-1-fluoro; nitrous oxide; oxirane, (ethoxymethyl)- and 2,5-cyclooctadien-1-ol were the compounds found in methanol extract. FTIR analysis identified various functional groups in the leaf extracts, indicating the presence of alcohols, phenols, unsaturated and saturated hydrocarbons, amines, flavonoids, and halogenated compounds. Acetonitrile and methanolic P. dulce leaf extracts exhibited significant antioxidant and antibacterial properties. Methanolic and acetonitrile leaf extracts exhibited the highest zone of inhibition against E. coli (21 ± 1.1 mm) and B. subtilis (20.6 ± 0.5 mm). Conclusion: This study highlighted the presence of different phytochemicals that showed anti-bacterial and antioxidant activities. Therefore, P. dulce leaf extracts may play a significant role in inhibiting human infections.

Eun-Byul Cho, Jae-Chang Lee, Eun-Jung Kim et al.

Objectives: The present study aimed to analyze the status and needs of Korean Medicine doctors’ (KMDs) continuing professional development (CPD) and establish future improvement directions. Methods : A cross-sectional survey targeting all KMDs was conducted between October 23 to November 5, 2024, obtaining 624 responses. The questionnaire was developed to assess satisfaction with continuing education and residency training, perceived efficacy on competency development, and improvement needs. Results : While current CPD programs effectively enhanced optimal patient care competencies (highest-rated domain), approximately 40% of respondents indicated minimal improvement in social accountability and clinical management competencies. The most requested improvements were the expansion of practical training opportunities (63.0%) and curriculum improvement (37.0%). For residency training, respondents prioritized developing competency-centered curricula (65.0%) and standardized educational programs across training hospitals (63.3%). Conclusion : Findings indicate that KMDs desire increased practical training opportunities, competency-based standardized curricula, and expanded scope of practice through institutional support. Establishing competency frameworks across clinical departments and strengthening competencies required in modern healthcare environments are essential for advancing Korean Medicine practice.

Nader Motee, Qiyu Sun

This paper presents Carleman-Fourier linearization for analyzing nonlinear real dynamical systems with periodic vector fields. Using Fourier basis functions, this novel framework transforms such dynamical systems into equivalent infinite-dimensional linear dynamical systems. In this paper, we establish the exponential convergence of the primary block in the finite-section approximation of this linearized system to the state vector of the original nonlinear system. To showcase the efficacy of our approach, we apply it to the Kuramoto model, a prominent model for coupled oscillators. The results demonstrate promising accuracy in approximating the original system's behavior.

Tianyi Huang, Richard Xiong

This paper introduces a cost-effective robotic handwriting system designed to replicate human-like handwriting with high precision. Combining a Raspberry Pi Pico microcontroller, 3D-printed components, and a machine learning-based handwriting generation model implemented via TensorFlow, the system converts user-supplied text into realistic stroke trajectories. By leveraging lightweight 3D-printed materials and efficient mechanical designs, the system achieves a total hardware cost of approximately \$56, significantly undercutting commercial alternatives. Experimental evaluations demonstrate handwriting precision within $\pm$0.3 millimeters and a writing speed of approximately 200 mm/min, positioning the system as a viable solution for educational, research, and assistive applications. This study seeks to lower the barriers to personalized handwriting technologies, making them accessible to a broader audience.

Masoud Mortezazadeh, Mehdi Karimi, Mohsen Esfandbod et al.

BackgroundLatent tuberculosis (TB) can reactivate in immunocompromised individuals, such as cancer patients undergoing chemotherapy, leading to severe complications. Understanding the prevalence of latent TB in this high-risk group is crucial, especially in regions with moderate to high TB burdens.AimThis study aims to determine the prevalence of latent tuberculosis in cancer patients before chemotherapy and immunotherapy to guide preventive interventions and reduce the risk of TB reactivation.MethodsThis case-control study was conducted at Sina Hospital in Tehran, Iran, from 2012 to 2022. A total of 392, including 107 newly diagnosed cancer (case) and 285 non-cancer (control) patients, were enrolled in this study. All patients had received the Bacillus Calmette-Guérin (BCG) vaccine at the age of one. They underwent a thorough clinical examination and were screened using the tuberculin skin test (TST) to detect latent TB. Any active TB cases were identified through acid-fast smear tests. The data collected from the study participants was then analyzed.ResultsThe results showed no significant difference in the size of TST between cancer and non-cancer patients (cases: median = 2 mm, IQR: 1–12; controls: median = 2 mm, IQR: 1–5; p = 0.09). The prevalence of latent TB was 27.1% in cancer patients and 20.7% in non-cancer patients, with no significant association identified between latent TB and malignancies (P-value = 0.176). Over a median follow-up of 4 years, mortality was significantly higher in cancer patients compared to controls (42.1% vs 1.8%; P< 0.001, OR = 40.64). Additionally, deceased patients exhibited a greater prevalence of latent TB (44% vs 19.3% in survivors; P< 0.001, OR = 3.28), and increased size of TST was associated with higher mortality risk among cancer patients.ConclusionIn conclusion, this study emphasizes the need for vigilant latent TB screening in cancer patients, given the association between larger TST sizes and increased mortality risk. While no direct link between cancer type and latent TB was found, proactive TB management remains crucial, particularly for those undergoing immunosuppressive therapy.

M. S. Ganachari

Nicolae BACINSCHI, Anna DONICA, Elena TUDOR

Tratamentul tuberculozei necesită utilizarea preparatelor antituberculoase pe termen lung cu un risc înalt de reacții adverse. În ultimele decenii, concomitent cu reacțiile adverse frecvente și detaliat studiate (simptomele gastrointestinale, hepatotoxicitate, neurotoxicitate, reacții alergice), sunt descrise dereglări hematologice și de coagulabilitate la administrarea medicamentelor antituberculoase. Manifestările principale ale acestora pot fi: anemia feriprivă, anemia hemolitică, anemia megaloblastică și sideroblastică, trombocitopenia, purpura trombocitopenică, leucopenie, eozinofilie, agranulocitoză, tromboza venoasă profundă, tromboembolia, coagulopatia intravenoasă diseminată etc. Deși aceste reacții adverse sunt rare necesită o atenție deosebită deoarece se pot manifesta mai frecvent în cazul tratamentul intermitent sau întrerupt și prezintă dificultăți de diagnostic.

Wei-fang Song, Rui-jun Wang, Rui-xin Yao et al.

Abstract Background Pulsatilla chinensis (PC) is a traditional Chinese medicine (TCM) known for its beneficial activities. It has been historically used to treat dysentery, vaginal trichomoniasis, bacterial infections, and malignant tumors. The therapeutic potential of PC in the management of hypercholesterolemia remains largely unexplored. Methods A high-throughput screening based on high-throughput sequencing was conducted in HepG2 cells to construct gene expression profiles for several hundred TCMs. In vivo evaluation of the efficacy of PC was performed using rats with hypercholesterolemia. Transcriptome analysis was carried out on PC-treated rat livers and HepG2 cells to investigate the mechanism of action of PC in vitro. The findings were further validated using RT-qPCR and western blot techniques. Results PC was identified as similar to Rhizoma Coptidis based on signature genes related to metabolism. Administration of PC via gavage in rats with hypercholesterolemia for 11 weeks resulted in substantially reduced serum total cholesterol and low-density lipoprotein (LDL) cholesterol and ameliorated fatty liver. Transcriptome analysis revealed that PC regulated various pathways associated with lipid metabolism. The LDL receptor (LDLR), a key player in cholesterol metabolism, was upregulated by PC both in vivo and in vitro. It was discovered that PC achieved this upregulation by activating extracellular regulated protein kinase (ERK) signaling in HepG2 cells. To uncover the major bioactive components responsible for the anti- hypercholesterolemia effect of PC, two major saponins, named Pulsatilla saponin D (PCD) and PC anemoside B4 (PCB4), were assessed. PCD, but not PCB4, was identified as the active ingredient responsible for the upregulation of LDLR by PC. Conclusion These findings demonstrated that PC acts as an antihypercholesterolemic agent by upregulating LDLR in an ERK-dependent manner and holds potential in the treatment of hypercholesterolemia.

Mostafaali Ayubirad, Hamid R. Ossareh

The prediction-based nonlinear reference governor (PRG) is an add-on algorithm to enforce constraints on pre-stabilized nonlinear systems by modifying, whenever necessary, the reference signal. The implementation of PRG carries a heavy computational burden, as it may require multiple numerical simulations of the plant model at each sample time. To this end, this paper proposes an alternative approach based on machine learning, where we first use a regression neural network (NN) to approximate the input-output map of the PRG from a set of training data. During the real-time operation, at each sample time, we use the trained NN to compute a nominal reference command, which may not be constraint admissible due to training errors and limited data. We adopt a novel sensitivity-based approach to minimally adjust the nominal reference while ensuring constraint enforcement. We thus refer to the resulting control strategy as the modified neural network reference governor (MNN-RG), which is significantly more computationally efficient than the PRG. The computational and theoretical properties of MNN-RG are presented. Finally, the effectiveness and limitations of the proposed method are studied by applying it as a load governor for constraint management in automotive fuel cell systems through simulation-based case studies.

Moh. Kamalul Wafi, Milad Siami

This paper addresses the challenge of network synchronization under limited communication, involving heterogeneous agents with different dynamics and various network topologies, to achieve consensus. We investigate the distributed adaptive control for interconnected unknown linear subsystems with a leader and followers, in the presence of input-output disturbance. We enhance the communication within multi-agent systems to achieve consensus under the leadership's guidance. While the measured variable is similar among the followers, the incoming measurements are weighted and constructed based on their proximity to the leader. We also explore the convergence rates across various balanced topologies (Star-like, Cyclic-like, Path, Random), featuring different numbers of agents, using three distributed algorithms, ranging from first- to high-order tuners to effectively address time-varying regressors. The mathematical foundation is rigorously presented from the network designs of the unknown agents following a leader, to the distributed methods. Moreover, we conduct several numerical simulations across various networks, agents and tuners to evaluate the effects of sparsity in the interaction between subsystems using the $L_2-$norm and $L_\infty-$norm. Some networks exhibit a trend where an increasing number of agents results in smaller errors, although this is not universally the case. Additionally, patterns observed at initial times may not reliably predict overall performance across different networks. Finally, we demonstrate that the proposed modified high-order tuner outperforms its counterparts, and we provide related insights along with our conclusions.

Ahmad Mohammad Saber, Amr Youssef, Davor Svetinovic et al.

Line Current Differential Relays (LCDRs) are high-speed relays progressively used to protect critical transmission lines. However, LCDRs are vulnerable to cyberattacks. Fault-Masking Attacks (FMAs) are stealthy cyberattacks performed by manipulating the remote measurements of the targeted LCDR to disguise faults on the protected line. Hence, they remain undetected by this LCDR. In this paper, we propose a two-module framework to detect FMAs. The first module is a Mismatch Index (MI) developed from the protected transmission line's equivalent physical model. The MI is triggered only if there is a significant mismatch in the LCDR's local and remote measurements while the LCDR itself is untriggered, which indicates an FMA. After the MI is triggered, the second module, a neural network-based classifier, promptly confirms that the triggering event is a physical fault that lies on the line protected by the LCDR before declaring the occurrence of an FMA. The proposed framework is tested using the IEEE 39-bus benchmark system. Our simulation results confirm that the proposed framework can accurately detect FMAs on LCDRs and is not affected by normal system disturbances, variations, or measurement noise. Our experimental results using OPAL-RT's real-time simulator confirm the proposed solution's real-time performance capability.

Vittal Srinivasan, Nak-seung Patrick Hyun

In nature, different species of smaller animals produce ultra-fast movements to aid in their locomotion or protect themselves against predators. These ultra-fast impulsive motions are possible, as often times, there exist a small latch in the organism that could hold the potential energy of the system, and once released, generate an impulsive motion. These types of systems are classified as Latch Mediated Spring Actuated (LaMSA) systems, a multi-dimensional, multi-mode hybrid system that switches between a latched and an unlatched state. The LaMSA mechanism has been studied extensively in the field of biology and is observed in a wide range of animal species, such as the mantis shrimp, grasshoppers, and trap-jaw ants. In recent years, research has been done in mathematically modeling the LaMSA behavior with physical implementations of the mechanism. A significant focus is given to mimicking the physiological behavior of the species and following an end-to-end trajectory of impulsive motion. This paper introduces a foundational analysis of the theoretical dynamics of the contact latch-based LaMSA mechanism. The authors answer the question on what makes these small-scale systems impulsive, with a focus on the intrinsic properties of the system using bifurcations. Necessary and sufficient conditions are derived for the existence of the saddle fixed points. The authors propose a mathematical explanation for mediating the latch when a saddle node exists, and the impulsive behavior after the bifurcation happens.

Hyunki Seong, Chanyoung Chung, David Hyunchul Shim

In this letter, we propose a model parameter identification method via a hyperparameter optimization scheme (MI-HPO). Our method adopts an efficient explore-exploit strategy to identify the parameters of dynamic models in a data-driven optimization manner. We utilize our method for model parameter identification of the AV-21, a full-scaled autonomous race vehicle. We then incorporate the optimized parameters for the design of model-based planning and control systems of our platform. In experiments, MI-HPO exhibits more than 13 times faster convergence than traditional parameter identification methods. Furthermore, the parametric models learned via MI-HPO demonstrate good fitness to the given datasets and show generalization ability in unseen dynamic scenarios. We further conduct extensive field tests to validate our model-based system, demonstrating stable obstacle avoidance and high-speed driving up to 217 km/h at the Indianapolis Motor Speedway and Las Vegas Motor Speedway. The source code for our work and videos of the tests are available at https://github.com/hynkis/MI-HPO.

Yu Zheng, Qingxuan Xie, Hong Wang et al.

Plant polysaccharides with multiple biological activities and health benefit effects are usually considered as natural active macromolecules in food and medicine dual purposes plant. Nerveless, there are still some problems with plant polysaccharides, such as the lack of concentration in the range of action, poor stability, rapid blood clearance and poor targeting, which affect the bioavailability and clinical application of plant polysaccharides. Over the last decade, researchers have increasingly turned their attention toward understanding the role of plant polysaccharides in normal cellular function and in disease, while opening up new research fronts in designing and developing nanomaterial delivery systems for the treatment of cancer, immune diseases and other diseases using plant polysaccharides as a carrier or object drug. However, deficiencies of NDDS research limits the application of polysaccharides in disease treatment. Herein, advances in the application of plant polysaccharides in nano-based drug delivery systems are reviewed. In addition, the further research on plant polysaccharides in nanotechnology was prospected.