Search Results (2,814)

Arginine kinase is a key phosphagen kinase in invertebrates that facilitates rapid ATP regeneration by reversibly transferring phosphate groups between phosphoarginine and ADP. Structural studies have shown that the enzyme adopts distinct conformations in its ligand-free and ligand-bound states, known as the “open” and “closed” forms, respectively. These conformational changes are crucial for catalytic activity, enabling precise positioning of active-site residues and loop closure during phosphoryl transfer. Transition-state analog complexes have provided additional insights by mimicking intermediate states of catalysis, supporting the functional relevance of the open/closed structural model. Furthermore, studies across multiple species reveal how monomeric and dimeric forms of arginine kinase contribute to its allosteric regulation and substrate specificity. Beyond its metabolic role, arginine kinase is also recognized as a major allergen in crustaceans. Its structural uniqueness and absence in vertebrates make it a promising candidate for selective drug targeting. By integrating crystallographic data with functional context, this review highlights conserved features and species-specific variations of arginine kinase that may inform the design of inhibitors. Such molecules have the potential to serve both as antiparasitic agents and as novel therapeutics to manage crustacean-related allergic responses in humans. Full article

This study presents an algorithm for non-invasive blood pressure (BP) estimation in neonates using photoplethysmography (PPG), suitable for resource-constrained neonatal telecare platforms. Using the Windkessel model, the algorithm processes PPG signals from a MAX 30102 sensor, (Analog Devices (formerly Maxim Integrated), based in San Jose, CA, USA) filtering motion noise and extracting cardiac cycle time and systolic time (ST). These parameters inform a derived blood flow signal, the input for the Windkessel model. Calibration utilizes average parameters based on the newborn’s post-conceptional age, weight, and gestational age. Performance was validated against readings from a standard non-invasive BP cuff at SES Hospital Universitario de Caldas. Two parameter estimation methods were evaluated. The first yielded root mean square errors (RMSEs) of 24.14 mmHg for systolic and 19.13 mmHg for diastolic BP. The second method significantly improved accuracy, achieving RMSEs of 2.31 mmHg and 5.13 mmHg, respectively. The successful adaptation of the Windkessel model to single PPG signals allows for BP calculation alongside other physiological variables within the telecare program. A device analysis was conducted to determine the appropriate device based on computational capacity, availability of programming tools, and ease of integration within an Internet of Things environment. This study paves the way for future research that focuses on parameter variations due to cardiovascular changes in newborns during their first month of life. Full article

Cancer-related cognitive impairment (CRCI)—encompassing anxiety, depression, and memory deficits—significantly diminishes the quality of life in patients with cancer, yet remains underrecognized in clinical practice. In this study, we investigated the therapeutic potential of tabernanthalog (TBG), a non-hallucinogenic analog of psychedelic compounds, as a novel intervention for CRCI using a Lewis lung carcinoma (3LL) mouse model. Behavioral assessments revealed heightened anxiety-like behavior and memory impairment following 3LL cell transplantation. Biochemical analysis revealed reduced tryptophan levels in both blood and hippocampal tissue, accompanied by the downregulation of serotonergic receptor genes and upregulation of pro-inflammatory cytokine genes in the hippocampus of tumor-bearing mice. Additionally, microglial density and morphological activation were markedly elevated. TBG treatment reversed these behavioral deficits, improving both anxiety-related behavior and memory performance. These effects were associated with the normalization of microglial density and morphology, as well as the restoration of serotonergic receptor and cytokine gene expression. In vitro, TBG partially suppressed neuroinflammatory gene expression in BV-2 microglial cells exposed to conditioned medium from 3LL cells. Collectively, these findings suggest that TBG alleviates CRCI-like symptoms by modulating neuroinflammation and microglial activation. This study highlights TBG as a promising therapeutic candidate for improving cognitive and emotional functioning in patients with cancer. Full article

Hormone-dependent breast cancer, particularly in its treatment-resistant forms, remains a significant therapeutic challenge. In this study, we applied a fully computational strategy to design steroid-based compounds capable of simultaneously targeting three key receptors involved in disease progression: progesterone receptor (PR), estrogen receptor alpha (ER-α), and HER2. Using a robust 3D-QSAR model (R² = 0.86; Q²_LOO = 0.86) built from 52 steroidal structures, we identified molecular features associated with high anticancer potential, specifically increased polarizability and reduced electronegativity. From a virtual library of 271 DFT-optimized analogs, 31 compounds were selected based on predicted potency (pIC₅₀ > 7.0) and screened via molecular docking against PR (PDB 2W8Y), HER2 (PDB 7JXH), and ER-α (PDB 6VJD). Seven candidates showed strong binding affinities (ΔG ≤ −9 kcal/mol for at least two targets), with Estero-255 emerging as the most promising. This compound demonstrated excellent conformational stability, a robust hydrogen-bonding network, and consistent multitarget engagement. Molecular dynamics simulations over 100 nanoseconds confirmed the structural integrity of the top ligands, with low RMSD values, compact radii of gyration, and stable binding energy profiles. Key interactions included hydrophobic contacts, π–π stacking, halogen–π interactions, and classical hydrogen bonds with conserved residues across all three targets. These findings highlight Estero-255, alongside Estero-261 and Estero-264, as strong multitarget candidates for further development. By potentially disrupting the PI3K/AKT/mTOR signaling pathway, these compounds offer a promising strategy for overcoming resistance in hormone-driven breast cancer. Experimental validation, including cytotoxicity assays and ADME/Tox profiling, is recommended to confirm their therapeutic potential. Full article

Background/Objectives: Neck pain caused by myofascial pain syndrome (MPS) is a highly prevalent musculoskeletal condition. Repetitive peripheral magnetic stimulation (rPMS) is a promising treatment option; however, its therapeutic effect and optimal treatment frequency remain unclear. This study aimed to investigate the therapeutic effect and duration of effect of rPMS in patients with MPS of the neck. Methods: In this randomized, sham-controlled, crossover trial, 27 patients with neck MPS and baseline visual analog scale (VAS) scores ≥ 40 were enrolled. The mean age was 43.8 ± 9.1 years, and 63% were female. Participants were randomly assigned to receive either an initial rPMS treatment (a 10 min session delivering 3900 pulses at 5–10 Hz) or sham stimulation. After 7 days, groups crossed over. Pain intensity (VAS), disability (Neck Disability Index; NDI), and analgesic use were recorded daily for seven consecutive days. A linear mixed-effects model was used for analysis. Results: At baseline, the VAS and NDI scores were 61.8 ± 10.5 and 26.0 ± 6.3, respectively. rPMS produced a significantly greater reduction in both VAS and NDI scores, with the greatest differences observed on Day 4: the differences were −24.1 points in VAS and −8.5 points in NDI compared to the sham group. There was no significant difference in analgesic use between the two groups. Conclusions: A single rPMS session provides short-term improvement in pain and disability in neck MPS. Based on the observed therapeutic window, more frequent sessions (e.g., twice weekly) may provide sustained benefit and should be explored in future studies. Full article

The Kekeya oilfield is located at the southwestern edge of the Tarim Basin, in the southern margin of the Yecheng depression, at the western end of the second structural belt of the northern foothills of the Kunlun Mountains. It is one of the important oil and gas fields in western China, with significant oil and gas resource potential in the X4-X5 section of the Xihepu Formation. This study focuses on the edge of the alluvial fan depositional system, employing various techniques, including core data and well logging data, to precisely characterize the sand body architecture and comprehensively analyze the reservoir architecture in the study area. First, the regional geological background of the area is analyzed, clarifying the sedimentary environment and evolutionary process of the Xihepu Formation. Based on the sedimentary environment and microfacies classification, the sedimentary features of the region are revealed. On this basis, using reservoir architecture element analysis, the interfaces of the reservoir architecture are finely subdivided. The spatial distribution characteristics of the planar architecture are discussed, and the spatial distribution and internal architecture of individual sand body units are analyzed. The study focuses on the spatial combination of microfacies units along the profile and their internal distribution patterns. Additionally, a quantitative analysis of the sizes of various types of sand bodies is conducted, constructing the sedimentary model for the region and revealing the control mechanisms of different sedimentary architectures on reservoir properties and oil and gas accumulation patterns. This study pioneers a quantitative model for alluvial fan fringe in gentle-slope basins, featuring the following: (1) lobe width-thickness ratios (avg. 128), (2) four base-level-sensitive boundary markers, and (3) a retrogradational stacking mechanism. The findings directly inform reservoir development in analogous arid-climate systems. This research not only provides a scientific basis for the exploration and development of the Kekeya oilfield but also serves as an important reference for reservoir architecture studies in similar geological contexts. Full article

It is demonstrated that neuromorphic materials designed for computational tasks can be effectively implemented by drawing an analogy with trigger-based systems built upon classical binary elements. Among the most promising approaches in this context are systems that perform computations based on the Residue Number System (RNS). A specific implementation of a trigger-based adder employing the proposed methodology is presented and tested through simulation modeling. This adder utilizes the representation of natural numbers as elements of a subtraction ring modulo P, where P is the product of Mersenne prime numbers. This configuration enables component-wise, independent execution of arithmetic operations. It is further shown that analogous trigger-based systems can be realized using recurrent neural network analogs, particularly those implemented with neuromorphic materials. The study emphasizes that it is possible to construct a neural network, especially one based on neuromorphic substrates, that can perform logical operations equivalent to those executed by conventional binary circuitry. A key challenge in the proposed approach lies in implementing an operation analogous to the carry mechanism employed in classical binary adders. An algorithm addressing this issue is proposed, based on the transition from computations modulo P to computations modulo 2P. Full article

To advance the development of protein-rich plant-based foods, a novel binder system for vegan sausage alternatives without the requirement of heat application was investigated. This enables long-term ripening of plant-based analogs similar to traditional fermented meat or dairy products, allowing for refined flavor and texture development. This was achieved by using a poorly water-soluble calcium source (calcium carbonate) to introduce calcium ions into a low-ester pectin—gluten matrix susceptible to crosslinking via divalent ions. The gelling reaction of pectin–gluten dispersions with Ca²⁺ ions was time-delayed due to the gradual production of lactic acid during fermentation. Firm, sliceable matrices were formed, in which particulate substances such as texturized proteins and solid vegetable fat could be integrated, hence forming an unheated raw-fermented plant-based salami-type sausage model matrix which remained safe for consumption over 21 days of ripening. Gluten as well as pectin had a significant influence on the functional properties of the matrices, especially water holding capacity (increasing with higher pectin or gluten content), hardness (increasing with higher pectin or gluten content), tensile strength (increasing with higher pectin or gluten content) and cohesiveness (decreasing with higher pectin or gluten content). A combination of three simultaneously occurring effects was observed, modulating the properties of the matrices, namely, (a) an increase in gel strength due to increased pectin concentration forming more brittle gels, (b) an increase in gel strength with increasing gluten content forming more elastic gels and (c) interactions of low-ester pectin with the gluten network, with pectin addition causing increased aggregation of gluten, leading to strengthened networks. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) is the predominant form of liver cancer and currently is the second-leading cause of cancer-related mortality globally. Current front-line systemic therapies for advanced HCC offer only modest improvements in patient overall survival. HCC is a sexually dimorphic disease, and cancer progression is driven in part by AR activity. Here, we present novel niclosamide pro-drugs for use in advanced HCC based upon niclosamide’s known anti-AR activity and additional anti-cancer pathway efficacy. Methods: Niclosamide analogs were evaluated for their impacts on the AR protein in two HCC cell lines with different AR phenotypes. Amino acid conjugates of niclosamide were developed, and pharmacokinetic (PK) analyses were conducted to determine improvements in clearance and oral exposure. Finally, niclosamide analogs and amino acid conjugates were evaluated in an in vivo model of HCC. Results: Niclosamide analogs maintained anti-AR properties in HCC. Valine-conjugated niclosamide showed improved oral exposure, positioning it as a potential therapeutic in advanced HCC. Conclusions: Valine–niclosamide improves upon niclosamide’s poor solubility and oral bioavailability, increasing its utility for a variety of therapeutic uses. Further study of valine–niclosamide in advanced HCC and in other cancers or diseases is warranted. Full article

Geographical origin authentication of agrifood products is essential for ensuring their quality, preventing fraud, and maintaining consumers’ trust. In this study, we used proton nuclear magnetic resonance (¹H NMR) and excitation–emission matrix (EEM) fluorescence spectroscopy combined with chemometric methods for the geographical origin characterization of olive drupes and leaves from different Tuscany subregions, where olive oil production is relevant. Single-block approaches were implemented for individual datasets, using principal component analysis (PCA) for data visualization and Soft Independent Modeling of Class Analogy (SIMCA) for sample classification. ¹H NMR spectroscopy provided detailed metabolomic profiles, identifying key compounds such as polyphenols and organic acids that contribute to geographical differentiation. EEM fluorescence spectroscopy, in combination with Parallel Factor Analysis (PARAFAC), revealed distinctive fluorescence signatures associated with polyphenolic content. A mid-level data fusion strategy, integrating the common dimensions (ComDim) method, was explored to improve the models’ performance. The results demonstrated that both spectroscopic techniques independently provided valuable insights in terms of geographical characterization, while data fusion further improved the model performances, particularly for olive drupes. Notably, this study represents the first attempt to apply EEM fluorescence for the geographical classification of olive drupes and leaves, highlighting its potential as a complementary tool in geographic origin authentication. The integration of advanced spectroscopic and chemometric methods offers a reliable approach for the differentiation of samples from closely related areas at a subregional level. Full article

A chemical investigation of the EtOAc fraction from soft coral Clavularia viridis resulted in the isolation of 12 undescribed dolabellane-type diterpenoids, namely clavirolides W–Z (1–4), clavularols A–H (5–12), and three known analogs (13–15). Their structures were characterized by an extensive analysis of spectroscopic data, including X-ray diffraction and ECD calculations for the assignment of absolute configurations. The structures of 2 and 4–6 are feathered as peroxyl-substituted derivatives, while compounds 7–12 possess additional oxidative cyclization, including epoxide or furan that are rare in the dolabellane family. All these compounds were evaluated for activities on cytotoxic and anti-inflammatory models. Compound 10 exhibited most potential against NO production in the BV2 cell induced by LPS with an IC₅₀ value of 18.3 μM. Full article

The characterization of Large Reasoning Models (LRMs) as exhibiting an “illusion of thinking” has recently emerged in the literature, sparking widespread public discourse. Some have suggested these manifestations represent bugs requiring fixes. I challenge this interpretation by reframing LRM behavior through dual-process theory from cognitive psychology. I draw on more than half a century of research on human cognitive effort and disengagement. The observed patterns include performance collapse at high complexity and counterintuitive reduction in reasoning effort. These appear to align with human cognitive phenomena, particularly System 2 engagement and disengagement under cognitive load. Rather than representing technical limitations, these behaviors likely manifest computational processes analogous to human cognitive constraints. In other words, they represent not a bug but a feature of bounded rational systems. I propose empirically testable hypotheses comparing LRM token patterns with human pupillometry data. I suggest that computational “rest” periods may restore reasoning performance, paralleling human cognitive recovery mechanisms. This reframing indicates that LRM limitations may reflect bounded rationality rather than fundamental reasoning failures. Accordingly, this article is presented as a hypothesis paper: it collates six decades of cognitive effort research and invites the scientific community to subject the dual-process predictions to empirical tests through coordinated human–AI experiments. Full article

To study the dynamic characteristics of the fluid-filled ship piping system with flanges and to optimize the design, and based on the transfer matrix methods (TMMs), this paper proposes two modeling methods for flat-welded flanges and weld-neck flanges. Method 1 employs a lumped mass equivalent flange. Method 2, based on the finite element and analogy ideas, equates the flange to pipe sections with a larger wall thickness. By comparing with the finite element method (FEM) results, it is found that for both flat-weld flanges and weld-neck flanges, the accuracy of Method 2 proposed in this paper is superior to that of Method 1. Meanwhile, experimental verification is carried out, and the experimental results are generally consistent with those obtained using Method 2. Furthermore, the multi-objective particle swarm optimization (MOPSO) algorithm is further introduced for the dynamic design of a branch pipeline system. The goal is to avoid resonance by adjusting the natural frequency of the system. Through the comparison of the FEM results, it has been confirmed that this optimization method is both efficient and accurate in optimizing the natural frequency. The method proposed in this paper has a specific reference value for engineering practice. Full article

This article presents a comprehensive modeling and simulation framework for a capacitive MEMS accelerometer integrated with a sigma-delta analog-to-digital converter (ADC), with a focus on applications in wearable health and motion monitoring devices. The accelerometer used in the system is connected to a smartphone equipped with dedicated software and will be used to assess the risk of falling, which is crucial for patients with balance disorders. The authors designed the accelerometer with special attention paid to the specification required in a system, where the acceleration is ±2 g and the frequency is 100 Hz. They investigated the sensor’s behavior in the DC, AC, and time domains, capturing both the mechanical response of the proof mass and the resulting changes in output capacitance due to external acceleration. A key component of the simulation is the implementation of a second-order sigma-delta modulator designed to digitize the small capacitance variations generated by the sensor. The Simulink model includes the complete signal path from analog input to quantization, filtering, decimation, and digital-to-analog reconstruction. By combining MEMS+ modeling with MATLAB-based system-level simulations, the workflow offers a fast and flexible alternative to traditional finite element methods and facilitates early-stage design optimization for MEMS sensor systems intended for real-world deployment. Full article

The increasing complexity and scale of electronic health records (EHRs) demand advanced tools for efficient data retrieval, summarization, and comparative analysis in clinical practice. MERA (Medical Electronic Records Assistant) is a Retrieval-Augmented Generation (RAG)-based AI system that addresses these needs by integrating domain-specific retrieval with large language models (LLMs) to deliver robust question answering, similarity search, and report summarization functionalities. MERA is designed to overcome key limitations of conventional LLMs in healthcare, such as hallucinations, outdated knowledge, and limited explainability. To ensure both privacy compliance and model robustness, we constructed a large synthetic dataset using state-of-the-art LLMs, including Mistral v0.3, Qwen 2.5, and Llama 3, and further validated MERA on de-identified real-world EHRs from the MIMIC-IV-Note dataset. Comprehensive evaluation demonstrates MERA’s high accuracy in medical question answering (correctness: 0.91; relevance: 0.98; groundedness: 0.89; retrieval relevance: 0.92), strong summarization performance (ROUGE-1 F1-score: 0.70; Jaccard similarity: 0.73), and effective similarity search (METEOR: 0.7–1.0 across diagnoses), with consistent results on real EHRs. The similarity search module empowers clinicians to efficiently identify and compare analogous patient cases, supporting differential diagnosis and personalized treatment planning. By generating concise, contextually relevant, and explainable insights, MERA reduces clinician workload and enhances decision-making. To our knowledge, this is the first system to integrate clinical question answering, summarization, and similarity search within a unified RAG-based framework. Full article