The Application of Organoids in the Study of Antiviral Innate Immunity.

Antiviral innate immunity plays a critical role in the defense against viral infections, yet its complex interactions with viruses have been challenging to study using traditional models. Organoids, three-dimensional (3D) tissue-like structures derived from stem cells, have emerged as powerful tools for modeling human tissues and studying the complex interactions between viruses and the host innate immune system. This chapter summarizes relevant applications of organoids in antiviral innate immunity studies and provides detailed information and experimental procedures for using organoids to study antiviral innate immunity.

Application of FTIR two-dimensional correlation spectroscopy (2D-COS) analysis in characterizing environmental behaviors of microplastics: A systematic review.

Microplastics (MPs) are ubiquitous in the environment, continuously undergo aging processes and release toxic chemical substances. Understanding the environmental behaviors of MPs is critical to accurately evaluate their long-term ecological risk. Generalized two-dimensional correlation spectroscopy (2D-COS) is a powerful tool for MPs studies, which can dig more comprehensive information hiding in the conventional one-dimensional spectra, such as infrared (IR) and Raman spectra. The recent applications of 2D-COS in analyzing the behaviors and fates of MPs in the environment, including their aging processes, and interactions with natural organic matter (NOM) or other chemical substances, were summarized systematically. The main requirements and limitations of current approaches for exploring these processes are discussed, and the corresponding strategies to address these limitations and drawbacks are proposed as well. Finally, new trends of 2D-COS are prospected for analyzing the properties and behaviors of MPs in both natural and artificial environmental processes.

A comprehensive review on natural sweeteners: impact on sensory properties, food structure, and new frontiers for their application.

In recent years, the worldwide increase in lifestyle diseases and metabolic disorders has been ascribed to the excessive consumption of sucrose and added sugars. For this reason, many approaches have been developed in order to replace sucrose in food and beverage formulations with alternative sweetening compounds. The raising awareness concerning the synthetic sweeteners due to their negative impact on health, triggered the need to search for alternative substances. Natural sweeteners may be classified in: (i) non-nutritive (e.g., neohesperidine dihydrochalcone, thaumatin, glycyrrhizin mogroside and stevia) and (ii) bulk sweeteners, including both polyols (e.g., maltitol, mannitol, erythritol) and rare sugars (e.g., tagatose and allulose). In this review we discuss the most popular natural sweeteners and their application in the main food sectors (e.g., bakery, dairy, confectionary and beverage), providing a full understanding of their impact on the textural and sensory properties in comparison to sucrose. Furthermore, we analyze the use of natural sweeteners in blends, which in addition to enabling an effective replacement of sugar, in order to complement the merits and limits of individual compounds. Finally, microencapsulation technology is presented as an alternative strategy to solving some issues such as aftertaste, bitterness, unpleasant flavors, but also to enhance their stability and ease of use.

Nueva aplicación móvil para categorización del vértigo en el nuevo milenio / New mobile application for categorization of vertigo in the new millennium

Introducción: El vértigo se define como la distorsión en la sensación de movimiento propio cuando se realiza un movimiento normal de la cabeza, para su estudio y categorización por el médico general, se recomienda el enfoque TiTrATE, que determina el momento de inicio, duración, evolución y los desencadenantes del vértigo. Objetivo: creación de una aplicación web progresiva (PWA) llamada TiTrATEapp y aplicarla en los pacientes que acudan a la emergencia en el mes de febrero 2023. Método: estudio tipo prospectivo, experimental, observacional, de análisis comparativo. Resultado: se creó una aplicación web progresiva (PWA) llamada TiTrATEapp, basada en el algoritmo TiTrATE, comparándose el tiempo de cálculo de la aplicación del algoritmo de forma manual y digital, obteniendo como resultado que la aplicación digital es más rápida y eficaz. Conclusión: Las aplicaciones móviles son una herramienta objetiva que ayuda los profesionales de la salud a diagnosticar y a tomar decisiones clínicas de forma práctica basadas en la evidencia científica, la digitalización del TiTrATE permite a los médicos realizar un diagnóstico acertado de forma rápida y orientan a la referencia del paciente a la especialidad correcta y la eficiencia de recursos. (AU)

Introduction: Vertigo is defined as the distortion in the sensation of self-movement when a normal movement of the head is made, for its study the TiTrATEapproach is recommended for the general practitioner, which determines the moment of onset, duration, evolution and triggers of vertigo for its study and categorization accurately. Objective: to create a progressive web application (PWA) called TiTrATEapp, Method: Prospective, observational, longitudinal study. Result: creation of a progressive web application (PWA) called TiTrATEapp, based on the TiTrATE algorithm, comparing the calculation time of the application of the algorithm manually and digitally, resulting in the digital application being faster and more efficient. Discussion: Vertigo is a difficult symptom to decipher in the emergency room, due to the subjective description of patients and the wide variety of probable causes, the digitization ofTiTrATE allows to rule out potentially dangerous causes, quickly, accurately. At present there are no studies that measure the effectiveness of mobile applications for the study of vertigo. Conclusion: Mobile applications are an objective tool that helps health professionals diagnose and make clinical decisions in a practical way based on scientific evidence, the digitalization of TiTrATE allows resident doctors to make an accurate diagnosis quickly and guide the patient's referral to the correct specialty and resource efficiency. (AU)

Outbreak detector: a web application to boost disease surveillance systems and timely detection of infectious disease epidemics.

OBJECTIVE: Digital technologies have improved the performance of surveillance systems through early detection of outbreaks and epidemic control. The aim of this study is to introduce an outbreak detection web application called OBDETECTOR (Outbreak Detector), which as a professional web application has the ability to process weekly or daily reported data from disease surveillance systems and facilitates the early detection of disease outbreaks. RESULTS: OBDETECTOR generates a histogram that exhibits the trend of infection within a time range selected by the user. The output comprises red triangles and plus signs, where the former denotes outbreak days determined by the algorithm applied to the data, and the latter represents days identified as outbreaks by the researcher. The graph also displays threshold values and its symbols enable researchers to compute evaluation criteria for outbreak detection algorithms, including sensitivity and specificity. OBDETECTOR allows users to modify algorithm parameters based on their research objectives immediately after loading data. The implementation of automatic web applications results in immediate reporting, precise analysis, and prompt alert notification. Moreover, Public Health authorities and other stakeholders of surveillance can benefit from the widespread accessibility and user-friendliness of these tools, enhancing their knowledge and skills for better engagement in surveillance programs.

[Advances in the Application of Nanozymes in Joint Disease Therapy]. / çº³ç±³é ¶åœ¨å ³èŠ‚ç–¾ç— ä¸­çš„åº”ç”¨è¿›å±•.

Nanozymes are nanoscale materials with enzyme-mimicking catalytic properties. Nanozymes can mimic the mechanism of natural enzyme molecules. By means of advanced chemical synthesis technology, the size, shape, and surface characteristics of nanozymes can be accurately regulated, and their catalytic properties can be customized according to the specific need. Nanozymes can mimic the function of natural enzymes, including catalase (CAT), superoxide dismutase (SOD), and glutathione peroxidase (GPx), to scavenge reactive oxygen species (ROS). Reported findings have shown that nanozymes have the advantages of excellent stability, low cost, and adjustable catalytic activity, thereby showing great potential and broad prospects in the application of disease treatment. Herein, we reviewed the advances in the application of nanozymes in the treatment of joint diseases. The common clinical manifestations of joint diseases include joint pain, swelling, stiffness, and limited mobility. In severe cases, joint diseases may lead to joint destruction, deformity, and functional damage, entailing crippling socioeconomic burdens. ROS is a product of oxidative stress. Increased ROS in the joints can induce macrophage M1 type polarization, which in turn induces and aggravates arthritis. Therefore, the key to the treatment of joint diseases lies in ROS scavenging and increasing oxygen (O2) content. Nanozymes have demonstrated promising application potential in the treatment of joint diseases, including rheumatoid arthritis, osteoarthritis, and gouty arthritis. However, how to ensure their biosafety, reduce the toxicity, and increase enzyme activity remains the main challenge in current research. Precise control of the chemical composition, size, shape, and surface modification of nanomaterials is the main development direction for the future.

Reducing anxiety and enhancing satisfaction in thyroid patients with DietLens application during radioactive iodine therapy: A quasi-experimental study.

Hyperthyroidism and thyroid cancer significantly impact health, and often require Radioactive Iodine (RAI) therapy. Anxiety is common in patients undergoing RAI, particularly related to dietary compliance. This study aimed to assess the effectiveness of the mobile health application, DietLens in reducing anxiety and increasing satisfaction in patients preparing for RAI therapy, focusing on low-iodine diet (LID). A quasi-experimental study was conducted in a Singapore tertiary hospital outpatient department from March 13, 2019 to March 27, 2020, involving patients scheduled for their first RAI treatment. Participants were divided into a control group receiving standard care and an intervention group using DietLens alongside standard care. Anxiety levels were assessed using the Zung Self-Rating Anxiety Scale, and satisfaction levels were measured through self-reported questionnaires. In the study, 56 participants were initially divided into control (n = 28) and intervention (n = 28) groups. After accounting for dropouts, 50 participants finished the study, with each group comprising 25 individuals. Anxiety levels were similar between groups pre-intervention. Post-intervention, the intervention group displayed a significant decrease in anxiety levels compared to the control group (independent t-test: t (48) = 2.50, p = 0.02). The multivariate linear regression analysis indicated that being in the intervention group was significantly associated with a decrease in post-intervention anxiety score (ß = -4.03, 95 % CI: -7.33 to -0.72, p = 0.02). Fisher's Exact Test revealed a borderline significant difference in satisfaction with educational materials and the overall treatment process, with 100 % of the intervention group expressing satisfaction compared to 80 % in the control group, resulting in a p-value of 0.052 in both instances. DietLens was effective in reducing anxiety and enhancing satisfaction related to RAI therapy preparation, particularly in managing a LID, highlighting a beneficial role for digital interventions in healthcare settings.

Novel digital-based approach for evaluating wine components' intake: A deep learning model to determine red wine volume in a glass from single-view images.

Estimation of wine components' intake (polyphenols, alcohol, etc.) through Food Frequency Questionnaires (FFQs) may be particularly inaccurate. This paper reports the development of a deep learning (DL) method to determine red wine volume from single-view images, along with its application in a consumer study developed via a web service. The DL model demonstrated satisfactory performance not only in a daily lifelike images dataset (mean absolute error = 10 mL), but also in a real images dataset that was generated through the consumer study (mean absolute error = 26 mL). Based on the data reported by the participants in the consumer study (n = 38), average red wine volume in a glass was 114 ± 33 mL, which represents an intake of 137-342 mg of total polyphenols, 11.2 g of alcohol, 0.342 g of sugars, among other components. Therefore, the proposed method constitutes a diet-monitoring tool of substantial utility in the accurate assessment of wine components' intake.

Biomimetic strategies for the deputization of proteoglycan functions.

Proteoglycans (PGs), which have glycosaminoglycan chains attached to their protein cores, are essential for maintaining the morphology and function of healthy body tissues. Extracellular PGs perform various functions, classified into the following four categories: i) the modulation of tissue mechanical properties; ii) the regulation and protection of the extracellular matrix; iii) protein sequestration; and iv) the regulation of cell signaling. The depletion of PGs may significantly impair tissue function, encompassing compromised mechanical characteristics and unregulated inflammatory responses. Since PGs play critical roles in the function of healthy tissues and their synthesis is complex, the development of PG mimetic molecules that recapitulate PG functions for tissue engineering and therapeutic applications has attracted the interest of researchers for more than 20 years. These approaches have ranged from semisynthetic graft copolymers to recombinant PG domains produced by cells that have undergone genetic modifications. This review discusses some essential extracellular PG functions and approaches to mimicking these functions.

Advancing Diabetes Self-Management: A Novel Smartphone Application Featuring a Scoring Algorithm for Tailored User Engagement.

Background: We developed and evaluated an intelligent diabetes assistant application (Diabetter) for the self-management of diabetes. It suggested that increasing the patient's interest and participation in using smartphone apps is important for the effectiveness of diabetes management apps. Methods: After evaluating all-encompassing features for diabetes management, we divided the selected factors into sub-factors for use in the application. Then, we created the first high-fidelity prototype using related programs and conducted early user testing to validate and improve Diabetter. To handle the user transaction time and keep them motivated, we designed and implemented a scoring system based on the nudge theory rules within the app. Results: To evaluate Diabetter's impact on diabetes self-management, we measured HbA1c levels after a prolonged period. The Diabetter prototype was developed and modified in a revised version for better user interaction with the app. The scoring system increased the input of users' information, which resulted in more analysis and recommendations to users. Clinical studies showed that as a result of continuous input of information from users who had been using the application for a longer period of time, their HbA1c levels were within the healthy range. Conclusions: The results demonstrate that the Diabetter application has been able to play an effective role in diabetes self-management by increasing users' app usage time. However, future study is needed to provide a better interpretation.

Strategies for improving cathode electrolyte interphase in high-performance dual-ion batteries.

Dual-ion batteries (DIBs) offer high energy density due to the ability to intercalate both anions and cations, thereby increasing the cutoff voltage and battery capacity. Graphite, with its ordered layered structure and cost-effectiveness, is commonly employed as the cathode material for DIBs. However, the discharge capacity of graphite cathodes is relatively low, and their cycling stability is poor, limiting the practical applications of DIBs. The formation of cathode electrolyte interphase (CEI) on the graphite cathode surface is closely related to anion behavior. Constructing a stable cathode electrolyte interface is crucial for improving the stability of anion storage. Therefore, we introduce a series of strategies to enhance the quality of the CEI layer, including additives, binders, main salts or solvents, high-concentration electrolytes, doping elements, artificial CEI, and graphite surface modifications. These strategies improve the CEI by enhancing anion transport rates, increasing anion solvation capabilities, and improving the structural stability of graphite cathodes, which is of profound significance for increasing the capacity and stability of DIBs. This review provides inspiration for future CEI research, encouraging further exploration of resources of CEI components and improvement strategies to further promote the development of DIBs technology.

Enhanced Fenton catalytic degradation of methylene blue by the synergistic effect of Fe and Ce in chitosan-supported mixed-metal MOFs (Fe/Ce-BDC@CS).

Methylene blue (MB) is a refractory organic pollutant that poses a potential threat to the aquatic environment. Fenton reaction is considered a primrose strategy to treat MB. However, the traditional Fenton process is plagued by narrow pH application range, poor stability, and secondary pollution. To solve these problems, many Fenton-like catalysts including metal-organic frameworks (MOFs) have been prepared. Herein, a novel bimetallic MOF (Fe/Ce-BDC@CS) was prepared through simple adsorption for the effective removal of MB, where chitosan (CS) was used as the carrier. The degradation performance of Fe/Ce-BDC@CS (100 % within 20 min) was better than that of most reported monometallic MOFs. Moreover, Fe/Ce-BDC@CS exhibited good repeatability and its anti-interference performance of some inorganic ions was also remarkable. Column loading experiments showed that the removal efficiency of MB was still about 50 % over 155 h with a flowing speed of 0.30 L/h. Comparative analysis indicated that such excellent performances could be attributed to the synergistic effect between Fe and Ce. Furthermore, the results of quenching tests indicate that OH, O2-, and 1O2 contributed to MB degradation. In brief, Fe/Ce-BDC@CS has promising prospects in MB treatment, which can provide scientific references for the design and application of bimetallic MOFs.

An artificial intelligence tool to assess the risk of severe mental distress among college students in terms of demographics, eating habits, lifestyles, and sport habits: an externally validated study using machine learning.

BACKGROUND: Precisely estimating the probability of mental health challenges among college students is pivotal for facilitating timely intervention and preventative measures. However, to date, no specific artificial intelligence (AI) models have been reported to effectively forecast severe mental distress. This study aimed to develop and validate an advanced AI tool for predicting the likelihood of severe mental distress in college students. METHODS: A total of 2088 college students from five universities were enrolled in this study. Participants were randomly divided into a training group (80%) and a validation group (20%). Various machine learning models, including logistic regression (LR), extreme gradient boosting machine (eXGBM), decision tree (DT), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), were employed and trained in this study. Model performance was evaluated using 11 metrics, and the highest scoring model was selected. In addition, external validation was conducted on 751 participants from three universities. The AI tool was then deployed as a web-based AI application. RESULTS: Among the models developed, the eXGBM model achieved the highest area under the curve (AUC) value of 0.932 (95% CI: 0.911-0.949), closely followed by RF with an AUC of 0.927 (95% CI: 0.905-0.943). The eXGBM model demonstrated superior performance in accuracy (0.850), precision (0.824), recall (0.890), specificity (0.810), F1 score (0.856), Brier score (0.103), log loss (0.326), and discrimination slope (0.598). The eXGBM model also received the highest score of 60 based on the evaluation scoring system, while RF achieved a score of 49. The scores of LR, DT, and SVM were only 19, 32, and 36, respectively. External validation yielded an impressive AUC value of 0.918. CONCLUSIONS: The AI tool demonstrates promising predictive performance for identifying college students at risk of severe mental distress. It has the potential to guide intervention strategies and support early identification and preventive measures.

Haxe as a Swiss knife for bioinformatic applications: the SeqPHASE case story.

Haxe is a general purpose, object-oriented programming language supporting syntactic macros. The Haxe compiler is well known for its ability to translate the source code of Haxe programs into the source code of a variety of other programming languages including Java, C++, JavaScript, and Python. Although Haxe is more and more used for a variety of purposes, including games, it has not yet attracted much attention from bioinformaticians. This is surprising, as Haxe allows generating different versions of the same program (e.g. a graphical user interface version in JavaScript running in a web browser for beginners and a command-line version in C++ or Python for increased performance) while maintaining a single code, a feature that should be of interest for many bioinformatic applications. To demonstrate the usefulness of Haxe in bioinformatics, we present here the case story of the program SeqPHASE, written originally in Perl (with a CGI version running on a server) and published in 2010. As Perl+CGI is not desirable anymore for security purposes, we decided to rewrite the SeqPHASE program in Haxe and to host it at Github Pages (https://eeg-ebe.github.io/SeqPHASE), thereby alleviating the need to configure and maintain a dedicated server. Using SeqPHASE as an example, we discuss the advantages and disadvantages of Haxe's source code conversion functionality when it comes to implementing bioinformatic software.

Machine Learning Assists in the Design and Application of Microneedles.

Microneedles (MNs), characterized by their micron-sized sharp tips, can painlessly penetrate the skin and have shown significant potential in disease treatment and biosensing. With the development of artificial intelligence (AI), the design and application of MNs have experienced substantial innovation aided by machine learning (ML). This review begins with a brief introduction to the concept of ML and its current stage of development. Subsequently, the design principles and fabrication methods of MNs are explored, demonstrating the critical role of ML in optimizing their design and preparation. Integration between ML and the applications of MNs in therapy and sensing were further discussed. Finally, we outline the challenges and prospects of machine learning-assisted MN technology, aiming to advance its practical application and development in the field of smart diagnosis and treatment.

Exploring Teacher Awareness of Artificial Intelligence in Education: A Case Study from Northern Cyprus.

This study investigates the level of awareness among teachers regarding the use of artificial intelligence (AI) in education, focusing on whether this awareness varies according to socio-demographic characteristics, access to technology, and specific knowledge and beliefs about AI. Conducted in Northern Cyprus during the 2023-2024 academic year, this study employed a survey model with purposive and snowball sampling methods, involving 164 teachers. Teachers at different levels, namely, primary school, secondary school, high school, and university, were included in this study. The "Artificial Intelligence Awareness Scale", developed by Ferikoglu and Akgün (2022), was used to measure AI awareness. Data normality was verified through skewness and kurtosis values, allowing for parametric statistical tests such as t-tests, one-way ANOVA, logistic regression, and chi-square analysis. This study explored the distribution of AI use across different school types and educational levels and assessed the impact of sub-dimensions of AI awareness on its application in teaching. Findings revealed no significant influence of teacher demographics (age, gender, education level, type of school, institution level, and monthly income) on AI awareness. However, usage patterns indicated that university lecturers were more likely to incorporate AI in their teaching, followed by primary and high school teachers, with secondary school teachers using it the least. A Multilayer Neural Network Analysis identified practical knowledge as the most critical factor influencing the use of AI in teaching (importance weight of 0.450), followed by beliefs and attitudes (0.298), relatability (0.148), and theoretical knowledge (0.104). These results highlight the importance of practical knowledge for fostering AI integration in educational practices, underscoring significant implications for teacher training and professional development programs.

Beauty's Betrayal: Mycobacterium abscessus Case Series Following Aesthetic Procedures in the Brazilian Amazon.

The Brazilian market holds the second position globally in the beauty sector, poised to surpass the USD 50 billion mark in the upcoming years. Aesthetic procedures encompass a spectrum, ranging from non-invasive ones, such as drainage, radiofrequency, ultrasound, and cryolipolysis, to more invasive techniques, including fillers, botulinum toxin, microneedling, micropigmentation, carboxytherapy, and enzyme application. This wide array of treatments has yielded satisfactory cosmetic results for individuals who opt out of surgical procedures. However, despite being categorized as having low complexity, they still carry inherent risks. These risks are often exacerbated by the breach of the skin barrier, the exposure of organs and spaces, or the presence of implantable devices. Among the bacteria most isolated concerning this matter are non-tuberculous Mycobacteria. This study presents descriptive case reports involving three patients under the care of the Infectious Diseases Department at General Hospital of Roraima (HGR). These patients were diagnosed with Mycobacterium abscessus infections subsequent to undergoing enzyme application procedures. Although these cases involve the same microorganism, they exhibit varying degrees of severity, ranging from the development of locally nodular formations to a progression towards sepsis. These cases provide an opportunity to delve into the diagnostic subtleties and clinical implications of these infections while also prompting a critical evaluation of therapeutic strategies. Additionally, the report underscores the potential risks associated with routine aesthetic procedures.

Leveraging Feedback From Families of Children With Autism to Create Digital Support for Service Navigation: Descriptive Study.

BACKGROUND: It is difficult for families to navigate and access services for their children with autism. Barriers to service access are compounded among families from low-resourced backgrounds. OBJECTIVE: The purpose of our study was to explore the development of an app to facilitate access to services among families of children with autism from low-resourced backgrounds. Our specific aims were to explore feedback from an advisory board about the app and to explore feedback from navigators about the app. METHODS: Via a multistage codevelopment process, we elicited feedback from 5 key parties: the research team, a community organization, the app development team, the advisory board, and family navigators. Collectively, 36 individuals provided feedback about the development of the app via individual interviews, focus groups, observations, and surveys. The key features of the app included a dashboard showing the service needs of the family and related resources, a messaging feature between the family, the navigator, and the supervisor, and a fidelity checklist and evaluation feature. RESULTS: The advisory board provided feedback about the app to increase its user-friendliness, include the ability to develop an action plan, improve the identification of needed services, and add information about service providers. Navigators suggested that the app should connect navigators to one another, have a clearer purpose for the notes section, and reflect an easier log-in process. Navigators also wanted training to role-play using the app. After participating in a role play using the app, navigators reported significantly more satisfaction with the app and greater usefulness (P<.001). CONCLUSIONS: Our work sheds light on the importance of eliciting feedback from end users, especially users who are often overlooked by the research community and app developers. Further, it is important to elicit feedback in multiple ways to improve the app.

The LOD paradox: When lower isn't always better in biosensor research and development.

Biosensor research has long focused on achieving the lowest possible Limits of Detection (LOD), driving significant advances in sensitivity and opening up new possibilities in analysis. However, this intense focus on low LODs may not always meet the practical needs or suit the actual uses of these devices. While technological improvements are impressive, they can sometimes overlook important factors such as detection range, ease of use, and market readiness, which are vital for biosensors to be effective in real-world applications. This review advocates for a balanced approach to biosensor development, emphasizing the need to align technological advancements with practical utility. We delve into various applications, including the detection of cancer biomarkers, pathology-related biomarkers, and illicit drugs, illustrating the critical role of LOD within these contexts. By considering clinical needs and broader design aspects like cost-effectiveness, sustainability, and regulatory compliance, we argue that integrating technical progress with practicality will enhance the impact of biosensors. Such an approach ensures that biosensors are not only technically sound but also widely useable and beneficial in real-world applications. Addressing the diverse analytical parameters alongside user expectations and market demands will likely maximize the real-world impact of biosensors.