Research Applications Research Articles

In-Vitro Bioactivity Evaluation of Hydrangenol Extracted from Hydrangea macrophylla (Thunb.) Ser. Leaves

Hydrangea macrophylla plant, native to Japan and Korea, has been attracting scientific attention due to its potential applications in both food science and health-related research. In this investigation, dry Hydrangea leaves were utilized as the source material. Subsequent to comminution and thermal treatment at 70 °C for an 18-hour duration, followed by a 30-minute ultrasonic bath extraction and a 5-minute centrifugation at 5000 rpm, hydrangenol was isolated through preparative HPLC. The investigation involved assessing the antioxidant capacity of hydrangenol, its impact on the activity of α-amylase and α-glucosidase enzymes, and its ability to prevent enzymatic browning. Quantification of antioxidant capacity, determined through TEAC (Trolox Equivalent Antioxidant Capacity), showed values from 1.8 to 3.2 mmol TE/mmol. Likewise, the ORAC (Oxygen Radical Absorbance Capacity) values were in the range of 16.5-27.0 mmol TE/mmol. Total phenolics content (Folin-Ciocalteu test) yielded a range of 7.1-11.2 g GAE (Gallic Acid Equivalents) per 100 g. Examining α-amylase inhibition, hydrangenol demonstrated a 52% inhibition (IC50: 3.6 mg/mL), whereas acarbose (positive control) displayed a higher inhibition of 99 % (IC50: 0.51 mg/mL). Regarding α-glucosidase inhibition, hydrangenol exhibited a 51% inhibition (IC50: 0.97 mg/mL), while acarbose displayed a 46% inhibition (IC50: 2.1 mg/mL). Additionally, the activity of PPO was suppressed by 61% at hydrangenol concentrations of 1 mg/mL and 2 mg/mL, and by 46% at a concentration of 4 mg/mL.

Blockchain-Based Dynamic Consent and its Applications for Patient-Centric Research and Health Information Sharing: Protocol for an Integrative Review.

Blockchain has been proposed as a critical technology to facilitate more patient-centric research and health information sharing. For instance, it can be applied to coordinate and document dynamic informed consent, a procedure that allows individuals to continuously review and renew their consent to the collection, use, or sharing of their private health information. Such has been suggested to facilitate ethical, compliant longitudinal research, and patient engagement. However, blockchain-based dynamic consent is a relatively new concept, and it is not yet clear how well the suggested implementations will work in practice. Efforts to critically evaluate implementations in health research contexts are limited. The objective of this protocol is to guide the identification and critical appraisal of implementations of blockchain-based dynamic consent in health research contexts, thereby facilitating the development of best practices for future research, innovation, and implementation. The protocol describes methods for an integrative review to allow evaluation of a broad range of quantitative and qualitative research designs. The PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) framework guided the review's structure and nature of reporting findings. We developed search strategies and syntax with the help of an academic librarian. Multiple databases were selected to identify pertinent academic literature (CINAHL, Embase, Ovid MEDLINE, PubMed, Scopus, and Web of Science) and gray literature (Electronic Theses Online Service, ProQuest Dissertations and Theses, Open Access Theses and Dissertations, and Google Scholar) for a comprehensive picture of the field's progress. Eligibility criteria were defined based on PROSPERO (International Prospective Register of Systematic Reviews) requirements and a criteria framework for technology readiness. A total of 2 reviewers will independently review and extract data, while a third reviewer will adjudicate discrepancies. Quality appraisal of articles and discussed implementations will proceed based on the validated Mixed Method Appraisal Tool, and themes will be identified through thematic data synthesis. Literature searches were conducted, and after duplicates were removed, 492 articles were eligible for screening. Title and abstract screening allowed the removal of 312 articles, leaving 180 eligible articles for full-text review against inclusion criteria and confirming a sufficient body of literature for project feasibility. Results will synthesize the quality of evidence on blockchain-based dynamic consent for patient-centric research and health information sharing, covering effectiveness, efficiency, satisfaction, regulatory compliance, and methods of managing identity. The review will provide a comprehensive picture of the progress of emerging blockchain-based dynamic consent technologies and the rigor with which implementations are approached. Resulting insights are expected to inform best practices for future research, innovation, and implementation to benefit patient-centric research and health information sharing. PROSPERO CRD42023396983; http://tinyurl.com/cn8a5x7t. DERR1-10.2196/50339.

Predicting shock-induced cavitation using machine learning: implications for blast-injury models.

While cavitation has been suspected as a mechanism of blast-induced traumatic brain injury (bTBI) for a number of years, this phenomenon remains difficult to study due to the current inability to measure cavitation in vivo. Therefore, numerical simulations are often implemented to study cavitation in the brain and surrounding fluids after blast exposure. However, these simulations need to be validated with the results from cavitation experiments. Machine learning algorithms have not generally been applied to study blast injury or biological cavitation models. However, such algorithms have concrete measures for optimization using fewer parameters than those of finite element or fluid dynamics models. Thus, machine learning algorithms are a viable option for predicting cavitation behavior from experiments and numerical simulations. This paper compares the ability of two machine learning algorithms, k-nearest neighbor (kNN) and support vector machine (SVM), to predict shock-induced cavitation behavior. The machine learning models were trained and validated with experimental data from a three-dimensional shock tube model, and it has been shown that the algorithms could predict the number of cavitation bubbles produced at a given temperature with good accuracy. This study demonstrates the potential utility of machine learning in studying shock-induced cavitation for applications in blast injury research.

Effects of transcranial magnetic stimulation on the human brain recorded with intracranial electrocorticography.

Transcranial magnetic stimulation (TMS) is increasingly used as a noninvasive technique for neuromodulation in research and clinical applications, yet its mechanisms are not well understood. Here, we present the neurophysiological effects of TMS using intracranial electrocorticography (iEEG) in neurosurgical patients. We first evaluated safety in a gel-based phantom. We then performed TMS-iEEG in 22 neurosurgical participants with no adverse events. We next evaluated intracranial responses to single pulses of TMS to the dorsolateral prefrontal cortex (dlPFC) (N = 10,1414 electrodes). We demonstrate that TMS is capable of inducing evoked potentials both locally within the dlPFC and in downstream regions functionally connected to the dlPFC, including the anterior cingulate and insular cortex. These downstream effects were not observed when stimulating other distant brain regions. Intracranial dlPFC electrical stimulation had similar timing and downstream effects as TMS. These findings support the safety and promise of TMS-iEEG in humans to examine local and network-level effects of TMS with higher spatiotemporal resolution than currently available methods.

Ice Microsphere Optical Cavities

AbstractWhispering gallery mode (WGM) optical microcavities have played an essential role in both fundamental research and practical applications, such as cavity quantum electrodynamics, optomechanics, microlasers, and optical sensors. While microcavities made of various materials (e.g., SiO2, As2S3, polymethylmethacrylate) have been extensively investigated, exploiting new materials for microcavities holds great promise for expanding the functionalities of the microcavities. As one of the most ubiquitous and important solids on earth's surface, ice exhibits intriguing optical properties that render it an excellent material for optical applications. Here, ice microsphere optical cavities are demonstrated for the first time. By rapidly freezing water microdroplets, ice microspheres are batch‐fabricated with relatively smooth surfaces and diameters of 5–50 µm. Excitation of a series of WGMs within ice microsphere cavities is achieved over a broad spectral range from near‐ultraviolet to near‐infrared region (380–1600 nm) with quality factors up to 1.4 × 103. Considering the extremely low absorption coefficient of ice in the ultraviolet, this work provides an exceptional platform for exploring ultraviolet microcavity photonics and its diverse applications.

Translation specifics of English language comics into Ukrainian

The article addresses the issue of translating comics, a special type of text characterized by a high degree of visuality and textuality. The research is aimed at studying the linguistic aspects of translating multimodal comic narratives, particularly English language comics, into Ukrainian in order to preserve theircomic potential and effective communication with the audience. Having analyzed numerous approaches to the translation of comic speech, the authors identify key factors that influence the preservation of the pragmatic effect and emotional sphere of the original. Additionally, they focus on the main linguistic and stylistic tools used in multimodal comic narratives, analyze various approaches to the translation of multimodal comic narratives, determine the impact of applied linguistic and parasemic tools on the perception of emotional and informative effect, and explore the specifics in translating various genres of comics. There was employed a combined methodological approach, which includes the analysis of linguistic and stylistic aspects of multimodal comic narratives, a comparative analysis of translations, as well as experimental studies on the audience’s perception of the emotional and informative effect. Overall, the article helps deepen the understanding of the translation process and approaches to preserving the emotional and informative effect in comics, contributing to further research and practical application in the field of stylistics and translation studies.

A Combinational Therapy for Preventing and Delaying the Onset of Alzheimer's Disease: A Focus on Probiotic and Vitamin Co-Supplementation.

Alzheimer's disease is a progressive neurodegenerative disorder with a complex etiology, and effective interventions to prevent or delay its onset remain a global health challenge. In recent years, there has been growing interest in the potential role of probiotic and vitamin supplementation as complementary strategies for Alzheimer's disease prevention. This review paper explores the current scientific literature on the use of probiotics and vitamins, particularly vitamin A, D, E, K, and B-complex vitamins, in the context of Alzheimer's disease prevention and management. We delve into the mechanisms through which probiotics may modulate gut-brain interactions and neuroinflammation while vitamins play crucial roles in neuronal health and cognitive function. The paper also examines the collective impact of this combinational therapy on reducing the risk factors associated with Alzheimer's disease, such as oxidative stress, inflammation, and gut dysbiosis. By providing a comprehensive overview of the existing evidence and potential mechanisms, this review aims to shed light on the promise of probiotic and vitamin co-supplementation as a multifaceted approach to combat Alzheimer's disease, offering insights into possible avenues for future research and clinical application.

The application of operation research in the quality management engineering

The integration of Operations Research (OR) principles into Quality Management Engineering constitutes a powerful strategy for enhancing product and service quality throughout the entire lifecycle. This synergistic relationship unfolds within the context of OR, a discipline rooted in mathematical models, analytical methods, and decision-making tools, originally developed during World War II. Quality Management Engineering, built upon principles like Total Quality Management (TQM) and Six Sigma methodologies, systematically combines engineering principles with quality management techniques to ensure consistent high-quality outcomes. This interdisciplinary approach proves particularly critical in industries emphasizing precision, reliability, and efficiency. The application of OR in Quality Management Engineering encompasses diverse facets, including process optimization, supply chain management, quality control, root cause analysis, resource allocation, and risk management. This integration leverages mathematical modelling, statistical analysis, and optimization techniques to systematically improve and sustain product and process quality. Emphasizing continuous improvement, the collaboration integrates Lean principles, waste reduction strategies, and a commitment to ongoing assessment. The partnership between OR and Quality Management Engineering remains adaptable to changing business landscapes, addressing human factors, environmental considerations, and the era of data-driven decision-making. This abstract highlights the evolution of OR and its contribution to creating a structured, data-driven, and analytical approach to decision-making in quality management. Ultimately, this integration shapes efficient, resilient, and quality-focused organizations, positioning them for success and competitiveness in dynamic industries.

The application of operation research in the design of on-site construction organization

This article explores the integration of Operations Research (OR) principles in the design and management of on-site construction organizations. The first section introduces Operations Research as a multidisciplinary field that employs mathematical modelling, statistical analysis, and optimization techniques to enhance decision-making processes across various sectors. Key aspects such as mathematical modelling, optimization, decision analysis, simulation, queueing theory, linear programming, and game theory are highlighted. The second section focuses on the design of on-site construction organizations, emphasizing the structure and organization of resources, personnel, and processes essential for efficient project execution. Various key elements, including project management, site personnel, project support teams, engineering and design teams, health and safety personnel, procurement and supply chain, communication and coordination, equipment and maintenance, and environmental compliance, are discussed. The third section delves into the application of Operations Research techniques in the design and management of on-site construction organizations. It outlines how OR principles can be employed for resource allocation and optimization, project scheduling and planning, inventory management, equipment utilization, project costing and budgeting, risk management, supply chain management, human resource management, facility layout and design, and the development of decision support systems. The conclusion highlights that the strategic application of Operations Research methodologies contributes to enhanced efficiency, optimized resources, and improved decision-making throughout the construction project lifecycle. The integration of OR principles not only enhance the overall effectiveness of on-site construction organizations but also leads to cost savings, timely project completion, and improved project outcomes. As construction projects continue to grow in scale and complexity, the systematic and analytical approach provided by Operations Research becomes increasingly valuable in addressing the intricate challenges faced by construction stakeholders.

Spontaneous Topological (Anti)meron Chains in the Domain Walls of Centrosymmetric Rare‐Earth Magnet

AbstractExploration and manipulation of topological protected magnetic swirls, such as skyrmion, antiskyrmion, meron, and vortex, holds significance for fundamental research and practical applications in high‐density magnetic information storage and spintronics because of their high storage density and low driven current. This study unveils the existence of field‐free spontaneous (anti)meron chains with a topological charge of ±1/2 in the centrosymmetric rare‐earth magnet Tb6Co2.17Si2.5 via in situ real‐space observation. The spin reorientation transition from in‐plane to uniaxial anisotropy contributes to the spontaneous transformation from straight domain walls to topological (anti)meron chains and to stripe domains along the [110] zone axis during in situ cooling. The study further confirms the critical role of the noncollinear magnetic structure of Tb atoms in the formation of topological (anti)meron chains via real‐space observations, first‐principles calculations, and micromagnetic simulations. The spontaneous topological magnetic texture is strongly correlated with the 4f electrons of rare‐earth atoms, enriching and stimulating alternative generation mechanisms of topological spin textures from emerging rare‐earth magnets, and further applications in spintronics.

The Fitting of the OJ Phase of Chlorophyll Fluorescence Induction Based on an Analytical Solution and Its Application in Urban Heat Island Research

Chlorophyll (Chl) fluorescence induction (FI) upon a dark–light transition has been widely analyzed to derive information on initial events of energy conversion and electron transfer in photosystem II (PSII). However, currently, there is no analytical solution to the differential equation of QA reduction kinetics, raising a doubt about the fitting of FI by numerical iteration solution. We derived an analytical solution to fit the OJ phase of FI, thereby yielding estimates of three parameters: the functional absorption cross-section of PSII (σPSII), a probability parameter that describes the connectivity among PSII complexes (p), and the rate coefficient for QA− oxidation (kox). We found that σPSII, p, and kox exhibited dynamic changes during the transition from O to J. We postulated that in high excitation light, some other energy dissipation pathways may vastly outcompete against excitation energy transfer from a closed PSII trap to an open PSII, thereby giving the impression that connectivity seemingly does not exist. We also conducted a case study on the urban heat island effect on the heat stability of PSII using our method and showed that higher-temperature-acclimated leaves had a greater σPSII, lower kox, and a tendency of lower p towards more shade-type characteristics.

Application Of The Classroom Coexistence Program "Let's Be Friends" For Social Skills In Ecuadorian Adolescents

In adolescence there are a series of physical, psychological and social challenges, this stage is the beginning of adulthood so that coexistence with others helps the constitution of the personality, in this sense the objective was to determine the influence of the classroom coexistence program "Let's be friends" in the improvement of social skills in Ecuadorian adolescents, the results determine that the experimental group presented higher values compared to the control group in the post test situation for social skills, also the p value between groups is 0.009 < 0.05. The application of the program was effective, so the application of further research oriented to psychological intervention specifically in social skills training, expression of emotions and relationship with adults is discussed.

Reprogramming Pancreatic Ductal Adenocarcinoma to Pluripotency.

The generation of induced pluripotent stem cells (iPSCs) using transcription factors has been achieved from almost any differentiated cell type and has proved highly valuable for research and clinical applications. Interestingly, iPSC reprogramming of cancer cells, such as pancreatic ductal adenocarcinoma (PDAC), has been shown to revert the invasive PDAC phenotype and override the cancer epigenome. The differentiation of PDAC-derived iPSCs can recapitulate PDAC progression from its early pancreatic intraepithelial neoplasia (PanIN) precursor, revealing the molecular and cellular changes that occur early during PDAC progression. Therefore, PDAC-derived iPSCs can be used to model the earliest stages of PDAC for the discovery of early-detection diagnostic markers. This is particularly important for PDAC patients, who are typically diagnosed at the late metastatic stages due to a lack of reliable biomarkers for the earlier PanIN stages. However, reprogramming cancer cell lines, including PDAC, into pluripotency remains challenging, labor-intensive, and highly variable between different lines. Here, we describe a more consistent protocol for generating iPSCs from various human PDAC cell lines using bicistronic lentiviral vectors. The resulting iPSC lines are stable, showing no dependence on the exogenous expression of reprogramming factors or inducible drugs. Overall, this protocol facilitates the generation of a wide range of PDAC-derived iPSCs, which is essential for discovering early biomarkers that are more specific and representative of PDAC cases.

A survey of sustainable development of intelligent transportation system based on urban travel demand

<p>This paper provides a comprehensive exploration of urban travel demand forecasting and its implications for intelligent transportation systems, emphasizing the crucial role of intelligent transportation systems in promoting sustainable urban development. With the increasing challenges posed by traffic congestion, environmental pollution, and diverse travel needs, accurate prediction of urban travel demand becomes essential for optimizing transportation systems, fostering sustainable travel methods, and creating opportunities for business development. However, achieving this goal involves overcoming challenges such as data collection and processing, privacy protection, and information security. To address these challenges, the paper proposes a set of strategic measures, including advancing intelligent transportation technology, integrating intelligent transportation systems with urban planning, enforcing policy guidance and market supervision, promoting sustainable travel methods, and adopting intelligent transportation technology and green energy solutions. Additionally, the study highlights the role of intelligent transportation systems in mitigating traffic congestion and environmental impact through intelligent road condition monitoring, prediction, and traffic optimization. Looking ahead, the paper foresees an increasingly pivotal role for intelligent transportation systems in the future, leveraging advancements in deep learning and information technology to more accurately collect and analyze urban travel-related data for better predictive modeling. By combining data analysis, public transportation promotion, shared travel modes, intelligent transportation technology, and green energy adoption, cities can build more efficient, environmentally friendly transportation systems, enhancing residents’ travel experiences while reducing congestion and pollution to promote sustainable urban development. Furthermore, the study anticipates that intelligent transportation systems will be intricately integrated with urban public services and management, facilitating efficient and coordinated urban functions. Ultimately, the paper envisions intelligent transportation systems playing a vital role in supporting urban traffic management and enhancing the overall well-being of urban construction and residents’ lives. In conclusion, this research not only enhances our understanding of urban travel demand forecasting and the evolving landscape of intelligent transportation systems but also provides valuable insights for future research and practical applications in related fields. The study encourages greater attention and investment from scholars and practitioners in the research and practice of intelligent transportation systems to collectively advance the progress of urban transportation and sustainable development.<strong></strong></p>

Advancing breastfeeding promotion: leveraging integrative natural galactagogues and unveiling their potential roles-Insights from a 19-year Taiwan nationwide registry to address lactation insufficiency in postpartum women.

Lactation insufficiency is a prevalent challenge for nursing mothers globally. There is a growing interest in the use of herbal galactagogues for enhancing lactation, but their therapeutic efficacy and underlying mechanisms need thorough investigation. This study aims to investigate the efficacy and mechanisms of action of herbal galactagogues in addressing lactation insufficiency by utilizing real-world data and employing a network analysis approach. Our retrospective study used Taiwan's Longitudinal Health Insurance Database 2000 (LHID2000) to identify 490 patients diagnosed with lactation insufficiency from 2000 to 2018. We analyzed demographic characteristics, co-existing diseases, and prescription patterns for both users and non-users of Chinese herbal products (CHP). Additionally, we utilized a network analysis approach to explore potential compounds and targets in the most frequently used CHP, the Wang Bu Liu Xing and Lu Lu Tong herb pair (WLHP) combination. Out of 490 patients, 81% were CHP users. There were no significant differences in demographic characteristics between CHP users and non-users, but we observed a notable divergence in the prevalence of co-existing diseases. A detailed examination of CHP prescriptions revealed the predominance of WLHP, prompting further investigation. Comprehensive analysis identified 29 major compounds in WLHP, which were associated with 215 unique targets. Intersection analysis revealed 101 overlapping targets between WLHP and lactation, suggesting their potential as therapeutic targets for lactation insufficiency treatment. Topological analysis of the protein-protein interaction (PPI) network identified 13 hub genes potentially crucial for the therapeutic effect of WLHP. Functional enrichment analysis showed that these targets were involved in critical lactation regulation pathways, including the PI3K-Akt signaling pathway, prolactin signaling pathway, estrogen signaling pathway, and AMPK signaling pathway. This study emphasizes the potential of CHP, specifically the WLHP combination, in managing lactation insufficiency. The multi-compound, multi-target approach of WLHP and its interaction with key biological processes and signaling pathways offer valuable insights into the underlying mechanisms of its therapeutic effects. These findings warrant further experimental validation and can guide future research and clinical applications of CHP in lactation insufficiency treatment.

The use of digital technologies in peri-implant soft tissue augmentation - A narrative review on planning, measurements, monitoring and aesthetics.

To identify the different uses and modalities of digital technologies to diagnose, plan and monitor peri-implant soft tissue conditions and aesthetics. A comprehensive narrative review of pertinent literature was conducted, critically appraising key digital technologies that may assist peri-implant soft tissue augmentation and assessment. An electronic search on four databases including studies published prior to 1st July 2023 was performed and supplemented by a manual search. Predominantly, tools such as cone beam computed tomography (CBCT), intraoral scanning (iOS), intraoral ultrasonography and digital spectrophotometry were commonly to assess and monitor peri-implant soft tissues. The main clinical and research applications included: (i) initial assessment of mucosal thickness, supra-crestal tissue height and keratinized mucosa width, (ii) evaluation of peri-implant soft tissue health and inflammation, (iii) monitoring profilometric changes and midfacial mucosal margin stability over time and (iv) aesthetic evaluation through colour assessment. While evidence for some digital tools may be limited, the integration of digital technologies into peri-implant soft tissue management holds great promise. These technologies offer improved precision, comfort and speed in assessment, benefiting both patients and clinicians. As digital technologies progress, their full potential in peri-implant soft tissue augmentation and their value will become more evident with ongoing research. Embracing these innovations and their potential benefits is recommended to ensure that during progress in implant dentistry, patient care is not hindered.

Biomarkers for Alzheimer's Disease – An Overview

AbstractAlzheimer's disease (AD) is a complex neurodegenerative disorder with a significant global impact on public health. The emergence of atypical clinical phenotypes challenges traditional diagnostic approaches, necessitating a deeper exploration of biomarkers for accurate identification. The US Food and Drug Administration (FDA) classification of biomarkers and their integration into different stages of AD provide a structured framework for their application in research and clinical settings. Within the context of AD drug development, biomarkers are essential for participant selection, target engagement evaluation, and assessment of pathological hallmarks, including Aβ and tau protein abnormalities. The incorporation of nanoparticles with a biodegradable approach introduces innovative strategies to address the complexities of AD. This paper extensively discusses biomarkers associated with synaptic dysfunction, neuroinflammation, and glial activation, recognizing their significance in elucidating disease mechanisms. Common pathologies such as synuclein and TDP‐43 further underscore the multifaceted nature of AD. Current biomarkers for AD diagnosis, encompassing cerebral spinal fluid (CSF) biomarkers and various imaging modalities, reflect the ongoing efforts to enhance early detection and monitoring. Intriguingly, novel biomarkers continue to emerge, offering promising avenues for improved understanding and intervention. Current review provides a comprehensive survey of biomarkers for AD, elucidating their diverse roles across different aspects of the disease. By highlighting their contributions to diagnosis, drug development, and mechanistic insights, this overview underscores the importance of biomarker research in the pursuit of effective AD management and treatment strategies.

In vitro evolution of diagnostic antibodies targeting native antigens in plasma by sandwich flow cytometry.

Monoclonal antibodies (mAbs) that recognize and bind to specific antigens (Ags) have a wide range of applications in research, therapy, and diagnostics. However, many of these antibodies cannot bind well to the native Ags. In this study, based on the Chinese hamster ovary (CHO) cell display platform developed previously in our lab, we reported a novel artificial evolution procedure to improve the affinity of mAb against the native Ag directly using the plasma samples without purification of the native Ag. In this procedure, a pair of antibodies able to bind the Ag in sandwich manner are first confirmed (Ab1/Ab2) and the antibody (Ab) to be affinity-improved (Ab1) is displayed on CHO cells for Ab mutation. Then the cells were detected and sorted with flow cytometry in the form of Ab1-Ag-fluorescence labeled Ab2, which we named sandwich flow cytometry. Here, we used soluble isoform of suppression of tumorigenicity 2 (sST2) protein as model Ag, carried out "sandwich" maturation directly using the plasma samples containing the native sST2 protein and optimized a pair of antibodies with significantly improved sensitivity in the detection of the native sST2 in plasma. This method could be very useful in optimization of the diagnostic Ab pairs working in a "sandwich" manner if more antibodies were also successfully affinity-matured with this method.

Review of Scientific Instruments New Products

In order to supplement manufacturers’ information, this department will welcome the submission by our readers of brief communications reporting measurements on the physical properties of materials that supersede earlier data or suggest new research applications.

Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways.

Natural language processing (NLP), a technology that translates human language into machine-readable data, is revolutionizing numerous sectors, including cancer care. This review outlines the evolution of NLP and its potential for crafting personalized treatment pathways for cancer patients. Leveraging NLP's ability to transform unstructured medical data into structured learnable formats, researchers can tap into the potential of big data for clinical and research applications. Significant advancements in NLP have spurred interest in developing tools that automate information extraction from clinical text, potentially transforming medical research and clinical practices in radiation oncology. Applications discussed include symptom and toxicity monitoring, identification of social determinants of health, improving patient-physician communication, patient education, and predictive modeling. However, several challenges impede the full realization of NLP's benefits, such as privacy and security concerns, biases in NLP models, and the interpretability and generalizability of these models. Overcoming these challenges necessitates a collaborative effort between computer scientists and the radiation oncology community. This paper serves as a comprehensive guide to understanding the intricacies of NLP algorithms, their performance assessment, past research contributions, and the future of NLP in radiation oncology research and clinics.