BackgroundThe COSMIN checklist is a standardized tool for assessing the methodological quality of studies on measurement properties. It contains 9 boxes, each dealing with one measurement property, with 5–18 items per box about design aspects and statistical methods. Our aim was to develop a scoring system for the COSMIN checklist to calculate quality scores per measurement property when using the checklist in systematic reviews of measurement properties.MethodsThe scoring system was developed based on discussions among experts and testing of the scoring system on 46 articles from a systematic review. Four response options were defined for each COSMIN item (excellent, good, fair, and poor). A quality score per measurement property is obtained by taking the lowest rating of any item in a box (“worst score counts”).ResultsSpecific criteria for excellent, good, fair, and poor quality for each COSMIN item are described. In defining the criteria, the “worst score counts” algorithm was taken into consideration. This means that only fatal flaws were defined as poor quality. The scores of the 46 articles show how the scoring system can be used to provide an overview of the methodological quality of studies included in a systematic review of measurement properties.ConclusionsBased on experience in testing this scoring system on 46 articles, the COSMIN checklist with the proposed scoring system seems to be a useful tool for assessing the methodological quality of studies included in systematic reviews of measurement properties.
Ghenwa Chamouni, Filippo Lococo, Carolina Sassorossi
et al.
IntroductionArtificial intelligence (AI) is increasingly integrating into the healthcare field, particularly in lung cancer care, including screening, diagnosis, treatment, and prognosis. While these applications offer promising advancements, they also raise complex challenges that must be addressed to ensure responsible implementation in clinical practice. This scoping review explores the ethical and legal aspects of AI applications in lung cancer.MethodsA search was conducted across PubMed, Scopus, Web of Science, Cochrane Library, PROSPERO, OAIster, and CABI. A total of 581 records were initially retrieved, of which 20 met the eligibility criteria and were included in the review. The PRISMA guidelines were followed.ResultsThe most frequently reported ethical concern was data privacy. Other recurrent issues included informed consent, no harm to patients, algorithmic bias and fairness, transparency, equity in AI access and use, and trust. The most frequently raised legal concerns were data protection and privacy, although issues relating to cybersecurity, liability, safety and effectiveness, the lack of appropriate regulation, and intellectual property law were also noted. Solutions proposed ranged from technical approaches to calls for regulatory and policy development. However, many studies lacked comprehensive legal analysis, and most included papers originated from high-income countries. This highlights the need for a broader global perspective.DiscussionThis review found that data privacy and protection are the most prominent ethical and legal concerns in AI applications for lung cancer care. Deep Learning (DL) applications, especially in diagnostic imaging, are closely tied to data privacy, lack of transparency, and algorithmic bias. Hybrid and multimodal AI systems raise additional concerns regarding informed consent and the lack of proper regulations. Ethical issues were more frequently addressed than legal ones, with limited consideration for global applicability, particularly in low- and lower middle-income countries. Although technical and policy solutions have been proposed, these remain largely unvalidated and fragmented, with limited real-world feasibility or scalability.
AbstractThis chapter examines the progressive property school’s attempts to address property’s shortcomings, as it is one of the more recent critiques of the ownership model to have gained traction. The main characteristics of progressive property are described, and the contributions of prominent scholars are summarised in relation to virtue ethics, public trust and the common heritage of mankind. While noting that this school emerged in the specific cultural context of the US, and that its parameters are continuing to evolve, the chapter nevertheless outlines some conceptual limitations in progressive property thinking that have implications for developing a spatially just approach to property. The chapter concludes by reinforcing the importance of a legal geographical perspective when examining the law’s relationship with land.
ObjectivesConsidering the high incidence rates of denture stomatitis, research that providing dental biomaterials with antifungal property are essential for clinical dentistry. The objectives of the present study were to investigate the effect of zinc dimethacrylate (ZDMA) modification on the antifungal and cytotoxic properties, as well as the variance in surface characteristics and other physicochemical properties of polymethyl methacrylate (PMMA) denture base resin.MethodsPMMA with various mass fraction of ZDMA (1 wt%, 2.5 wt% and 5 wt%) were prepared for experimental groups, and unmodified PMMA for the control. Fourier-transform infrared spectroscopy (FTIR) was applied for characterization. Thermogravimetric analysis, atomic force microscopy and water contact angle were performed to investigate the thermal stability and surface characteristics (n=5). Antifungal capacities and cytocompatibility were evaluated with Candida albicans (C. albicans) and human oral fibroblasts (HGFs), respectively. Colony-forming unit counting, crystal violet assay, live/dead biofilm staining and scanning electron microscopy observation were performed to assess antifungal effects, and the detection of intracellular reactive oxygen species production was applied to explore the possible antimicrobial mechanism. Finally, the cytotoxicity of ZDMA modified PMMA resin was evaluated by the 3-(4,5-dimethyl-thiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT) assay and live/dead double staining.ResultsThe FTIR analyses confirmed some variation in chemical bonding and physical blend of the composites. Incorporation of ZDMA significantly enhanced the thermal stability and hydrophilicity compared with unmodified PMMA (p < 0.05). The surface roughness increased with the addition of ZDMA while remained below the suggested threshold (≤ 0.2 µm). The antifungal activity significantly improved with ZDMA incorporation, and cytocompatibility assays indicated no obvious cytotoxicity on HGFs.ConclusionsIn the present study, the ZDMA mass fraction up to 5 wt% in PMMA performed better thermal stability, and an increase in surface roughness and hydrophilicity without enhancing microbial adhesion. Moreover, the ZDMA modified PMMA showed effective antifungal activity without inducing any cellular side effects.
Lucia Verrillo, Rosita Di Palma, Alberto de Bellis
et al.
Neuroplasticity is a crucial property of the central nervous system to change its activity in response to intrinsic or extrinsic stimuli. This is mainly achieved through the promotion of changes in the epigenome. One of the epi-drivers priming this process is suberoylanilide hydroxamic acid (SAHA or Vorinostat), a pan-histone deacetylase inhibitor that modulates and promotes neuroplasticity in healthy and disease conditions. Knowledge of the specific molecular changes induced by this epidrug is an important area of neuro-epigenetics for the identification of new compounds to treat cognition impairment and/or epilepsy. In this review, we summarize the findings obtained in cellular and animal models of various brain disorders, highlighting the multiple mechanisms activated by SAHA, such as improvement of memory, learning and behavior, and correction of faulty neuronal functioning. Supporting this evidence, <i>in vitro</i> and <i>in vivo</i> data underline how SAHA positively regulates the expression of neuronal genes and microtubule dynamics, induces neurite outgrowth and spine density, and enhances synaptic transmission and potentiation. In particular, we outline studies regarding neurodevelopmental disorders with pharmaco-resistant seizures and/or severe cognitive impairment that to date lack effective drug treatments in which SAHA could ameliorate defective neuroplasticity.
In this paper, we develop a novel soft-clipping discrete beta GARCH (ScDBGARCH) model that provides an available method to model bounded time series with under-dispersion, equi-dispersion or over-dispersion. The new model not only allows positive dependence, but also negative dependence. The stochastic properties of the models are established, and these results are, in turn, used in the analysis of the asymptotic properties of the conditional maximum likelihood (CML) estimator of the new model. In addition, we apply the new model to measles infection to show its improved performance.
If you have a new creative or innovative idea then you have the right to benefit from it. That right can be bought, sold, hired or licenced like any other property. It is important that you are aware of what these IP rights are, how they are protected and, in due course, how to benefit from them. For example, composers receive royalties (money) when their music is played live or receives 'airplay' on radio. Similarly, fashion designers frequently receive royalties when their designs are used by clothing chains.
We here have developed an S(O)<sub>2</sub>–N coupling between phenylsulfinic acid derivatives and aryl azides by dual copper and visible light catalysis. In this efficient and mild pathway, the reaction produces sulfonamide compounds under redox-neutral condition, which is mechanistically different from the nitrogen nucleophilic substitution reactions. Significantly, this transformation intends to utilize the property of visible light-induced azides to generate triplet nitrene and followed coupling with sulfonyl radicals in situ to achieve structurally diverse benzenesulfinamides in good yields.
Designing materials for targeted materials properties is the key to tackle the demands for personalized consumer products. The deficiency in the existing linear and nonlinear correlation methods attributed to simplifying assumptions and idealizations, nondeterministic simulations, and limited experimental data due to heavy computational time and cost, necessitates a design method that provides sufficient confidence to designers in decision making. To address this requirement, we propose, in this paper, an inverse goal-oriented materials design method supported by the design space exploration framework (DSEF). Keeping in view the accuracy and precision in the prediction confidence of machine learning-based methods, we developed an Artificial Neural Network based prediction model that supports DSEF. The proposed method for materials design can help designers to (1) explore PSPP spaces starting from end property requirements, (2) adjust the errors being propagated in the PSPP chain as well as in the predictions made by the model, and (3) timely adjust model parameters of the prediction model for accurate predictions. The efficacy of the method is illustrated for the hot stamping process to produce structural components from ultrahigh-strength steels (UHSS). The proposed method and prediction model are generic and applicable to any sequential manufacturing process to realize an end product.
Rama Jayasundar, Somenath Ghatak, Dushyant Kumar
et al.
Background: Ayurveda, the indigenous medical system of India, has chemosensory property (rasa) as one of its major pharmacological metric. Medicinal plants have been classified in Ayurveda under six rasas/tastes—sweet, sour, saline, pungent, bitter and astringent. This study has explored for the first time, the use of Electronic tongue for studies of rasa-based classification of medicinal plants.Methods: Seventy-eight medicinal plants, belonging to five taste categories (sweet, sour, pungent, bitter, astringent) were studied along with the reference taste standards (citric acid, hydrochloric acid, caffeine, quinine, L-alanine, glycine, β-glucose, sucrose, D-galactose, cellobiose, arabinose, maltose, mannose, lactose, xylose). The studies were carried out with the potentiometry-based Electronic tongue and the data was analysed using Principle Component Analysis, Discriminant Function Analysis, Taste Discrimination Analysis and Soft Independent Modeling of Class Analogy.Results: Chemosensory similarities were observed between taste standards and the plant samples–citric acid with sour group plants, sweet category plants with sucrose, glycine, β-glucose and D-galactose. The multivariate analyses could discriminate the sweet and sour, sweet and bitter, sweet and pungent, sour and pungent plant groups. Chemosensory category of plant (classified as unknown) could also be identified.Conclusion: This preliminary study has indicated the possibility of fingerprinting the chemosensory-based ayurvedic classification of medicinal plants using E-tongue coupled with multivariate statistical analysis.