Molecular mechanisms and therapeutic application of extracellular vesicles from plants.

Small extracellular vesicles (sEVs) isolated from animal sources are among the most investigated types of cell-free therapeutic tools to cure different diseases. sEVs have been isolated from a variety of sources, ranging from prokaryotes to animals and plants. Human-derived sEVs have many uses in pre- and clinical studies in medicine and drug delivery, while plant-derived EVs, also known as plant-derived nanovesicles (PDNVs), have not been widely investigated until the second decade of the 21st century. For the past five years, there has been a rapid rise in the use of plant EVs as a therapeutic tool due to the ease of massive production with high efficacy and yield of preparation. Plant EVs contain various active biomolecules such as proteins, regulatory RNAs, and secondary metabolites and play a key role in inter-kingdom communications. Many studies have already investigated the potential application of plant EVs in preventing and treating cancer, inflammation, infectious diseases, and tissue regeneration with no sign of toxicity and are therefore considered safe. However, due to a lack of universal markers, the properties of plant EVs have not been extensively studied. Concerns regarding the safety and therapeutic function of plant EVs derived from genetically modified plants have been raised. In this paper, we review the physiological role of EVs in plants. Moreover, we focus on molecular and cellular mechanisms involved in the therapeutic effects of plant EVs on various human diseases. We also provide detailed information on the methodological aspects of plant EV isolation and analysis, which could pave the way for future clinical translation.

Real-time sports injury monitoring system based on the deep learning algorithm.

In response to the low real-time performance and accuracy of traditional sports injury monitoring, this article conducts research on a real-time injury monitoring system using the SVM model as an example. Video detection is performed to capture human movements, followed by human joint detection. Polynomial fitting analysis is used to extract joint motion patterns, and the average of training data is calculated as a reference point. The raw data is then normalized to adjust position and direction, and dimensionality reduction is achieved through singular value decomposition to enhance processing efficiency and model training speed. A support vector machine classifier is used to classify and identify the processed data. The experimental section monitors sports injuries and investigates the accuracy of the system's monitoring. Compared to mainstream models such as Random Forest and Naive Bayes, the SVM utilized demonstrates good performance in accuracy, sensitivity, and specificity, reaching 94.2%, 92.5%, and 96.0% respectively.

Nanomaterials in biology and medicine: a new perspective on its toxicity and applications.

Nanotechnology offers excellent prospects for application in biology and medicine. It is used for detecting biological molecules, imaging, and as therapeutic agents. Due to nano-size (1-100 nm) and high surface-to-volume ratio, nanomaterials possess highly specific and distinct characteristics in the biological environment. Recently, the use of nanomaterials as sensors, theranostic, and drug delivery agents has become popular. The safety of these materials is being questioned because of their biological toxicity, such as inflammatory responses, cardiotoxicity, cytotoxicity, inhalation problems, etc., which can have a negative impact on the environment. This review paper focuses primarily on the toxicological effects of nanomaterials along with the mechanisms involved in cell interactions and the generation of reactive oxygen species by nanoparticles, which is the fundamental source of nanotoxicity. We also emphasize the greener synthesis of nanomaterials in biomedicine, as it is non-hazardous, feasible, and economical. The review articles shed light on the complexities of nanotoxicology in biosystems and the environment.

Usage and attitude of medical students towards mobile medical applications during and after COVID-19 lockdown: repeated cross-sectional study.

BACKGROUND: COVID-19 pandemic has accelerated the shift toward e-learning, particularly in medical education. Mobile medical applications (apps) have become integral tools for e-learning due to the prevalence of smartphones among medical students. Therefore, we aim to assess the usage and attitude of undergraduate Egyptian medical students towards mobile medical apps during and after the COVID-19 lockdown. METHODS: This is a two-phase repeated cross-sectional study using an online, pilot-tested, and self-administered survey conducted at the Faculty of Medicine, Menoufia University, Egypt. Phase 1 was during the academic year 2019-2020 (during lockdown), and phase 2 was during the academic year 2021-2022 (after lockdown). Out of the 4800-target population for each phase, a sample size of 140 medical students was randomly selected from each study year, resulting in a total sample size of 840 students for all six academic and clinical years for each phase. RESULTS: A total of 566 students in phase 1 and 616 students in phase 2 responded to the survey, with response rates of 67.62% and 73.33%, respectively. In phase 1, 55.7% of students reported using medical apps, with no significant difference between males and females (60.1% vs. 53.8%; p = 0.17) or between academic and clinical years (56.3% vs. 54.7%; p = 0.7). In phase 2, the percentage increased to 70.9%, with a significant difference between males and females (80% vs. 62.9%; p < 0.001) and between academic and clinical years (79.8% vs. 63%; p < 0.001). Medical dictionaries were the most commonly used apps, whereas medical calculators were the least common in both phases. Regarding their attitude, most students (65.1% and 73.9% in phases 1 and 2, respectively) expressed confidence in using medical apps, while 81.3% in phase 1 and 88.1% in phase 2 agreed that using medical apps is a flexible method of learning. Lack of knowledge regarding which app to download was the most reported cause of limitation in medical app usage by 37.8% of the students in phase 1 and 40% in phase 2. CONCLUSION: Our study revealed that the COVID-19 lockdown led to a significant increase in the use of mobile medical apps among Egyptian medical students. Despite the positive attitude of students towards these apps, multiple challenges still need to be addressed to ensure their optimal utilization in medical education.

Sustainable synthesis of bionanomaterials using non-native plant extracts for maintaining ecological balance: A computational bibliography analysis.

Biological approaches via biomolecular extracts of bacteria, fungi, or plants have recently been introduced as an alternative approach to synthesizing less or nontoxic nanomaterials, compared to conventional physical and chemical approaches. Among these biological methods, plant-mediated approaches (phytosynthesis) are reported to be highly beneficial for large-scale, nontoxic nanomaterial synthesis. However, plant-mediated synthesis of nanomaterials using native plant extract can lead to bioprospecting issues and deforestation challenges. On the other hand, non-native or invasive plants are non-indigenous to a particular geographic location that can grow and spread rapidly, ultimately disrupting the local and endogenous plant communities or ecosystems. Thus, controlling or eradicating these non-native plants before they damage the ecosystem is necessary. Even though mechanical, chemical, and biological approaches are available to control non-native plants, all these methods possess certain limitations, such as environmental toxicity, disturbance in the nutrient cycle, and loss of genetic integrity. Therefore, non-native plants were recently proposed as a novel sustainable source of phytochemicals for preparing nanomaterials via green chemistry, mainly metallic nanoparticles, as an alternative to native, agriculture-based, or medicinal plants. This work aims to cover a literature gap on plant-mediated bionanomaterial synthesis with an overview and bibliography analysis of non-native plants via novel data mining and advanced visualization tools. In addition, the potential of non-native plants as a sustainable, green chemistry-based alternative for bionanomaterial preparation for maintaining ecological balance, the mechanism of formation via phytochemicals, and their possible applications to promote their control and spread were also discussed. The bibliography analysis revealed that only an average of 4 articles have been published in the last 10 years (2013-2023) on non-native/invasive plants for nanomaterial synthesis, which shows the significance of this article.

Hyaluronan: Sources, Structure, Features and Applications.

Hyaluronan (HA) is a non-sulfated glycosaminoglycan that is present in a variety of body tissues and organs. Hyaluronan has a wide range of biological activities that are frequently influenced by molar mass; however, they also depend greatly on the source, purity, and kind of impurities in hyaluronan. High-molar-mass HA has anti-inflammatory, immunosuppressive, and antiangiogenic properties, while low-molar-mass HA has opposite properties. A number of chemical modifications have been performed to enhance the stability of HA and its applications in medical practice. Hyaluronan is widely applied in medicine, such as viscosupplementation, ophthalmology, otolaryngology, wound healing, cosmetics, and drug delivery. In this review, we summarized several medical applications of polymers based on the hyaluronan backbone.

[Ethical considerations for medical applications of implantable brain-computer interfaces].

Implantable brain-computer interfaces (BCIs) have potentially important clinical applications due to the high spatial resolution and signal-to-noise ratio of electrodes that are closer to or implanted in the cerebral cortex. However, the surgery and electrodes of implantable BCIs carry safety risks of brain tissue damage, and their medical applications face ethical challenges, with little literature to date systematically considering ethical norms for the medical applications of implantable BCIs. In order to promote the clinical translation of this type of BCI, we considered the ethics of practice for the medical application of implantable BCIs, including: reducing the risk of brain tissue damage from implantable BCI surgery and electrodes, providing patients with customized and personalized implantable BCI treatments, ensuring multidisciplinary collaboration in the clinical application of implantable BCIs, and the responsible use of implantable BCIs, among others. It is expected that this article will provide thoughts and references for the research and development of ethics of the medical application of implantable BCI.

[Applications of Naked-Eye 3D Display Technology in Medical Field].

Naked-eye 3D display technology has excellent 3D visual effects and does not require wearable devices assistance. It can present the depth, position and complex structure information of 3D medical images, allowing viewers to obtain information about tissues and organs from different points, reducing cognitive load, contributing to medical teaching and opening up innovative methods for planning and diagnosis. Naked-eye 3D augmented reality display can display medical images in real 3D space, achieving virtual and real vision. It helps a lot to medical research. The applications of naked-eye 3D display technology in three major aspects of medical diagnosis, clinical surgery and rehabilitation training is reviewed in the study. It provides the direction for the subsequent research in medical field, thus assisting medical research and improving medical practice.

Textual emotion detection in health: Advances and applications.

Textual Emotion Detection (TED) is a rapidly growing area in Natural Language Processing (NLP) that aims to detect emotions expressed through text. In this paper, we provide a review of the latest research and development in TED as applied in health and medicine. We focus on medical and non-medical data types, use cases, and methods where TED has been integral in supporting decision-making. The application of NLP technologies in health, and particularly TED, requires high confidence that these technologies and technology-aided treatment will first, do no harm. Therefore, this review also aims to assess the accuracy of TED systems and provide an update on the state of the technology. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were used in this review. With a specific focus on the identification of different human emotions in text, the more general sentiment analysis studies that only recognize the polarity of text were excluded. A total of 66 papers met the inclusion criteria. This review found that TED in health and medicine is mainly used in the detection of depression, suicidal ideation, and the mental status of patients with asthma, Alzheimer's disease, cancer, and diabetes with major data sources of social media, healthcare services, and counseling centers. Approximately, 44% of the research in the domain is related to COVID-19, investigating the public health response to vaccinations and the emotional response of the public. In most cases, deep learning-based NLP techniques were found to be preferred over other methods due to their superior performance. Developing methods for implementing and evaluating dimensional emotional models, resolving annotation challenges by utilizing health-related lexicons, and using deep learning techniques for multi-faceted and real-time applications were found to be among the main avenues for further development of TED applications in health.

Miniaturized Wirelessly Powered and Controlled Implants for Multisite Stimulation.

This paper presents a miniaturized implant with a diameter of only 14 mm, which houses a novel System on Chip (SoC) enabling two voltage level stimulation of up to 16 implants using a single Tx coil. Each implant can operate at a distance of 80 mm in the air through the inductive resonant link. The SoC consumes only 27 µW static power and enables two channels with stimulation amplitudes of 1.8 V and 3.3 V and timing resolution of 100 µs. The SoC is implemented in the standard 180 nm complementary metal oxide semiconductor (CMOS) technology and has an area of 0.75 mm × 1.6 mm. The SoC comprises an RF rectifier, low drop-out regulator (LDO), error detection block, clock data recovery, finite state machine (FSM), and output stage. Each implant has a PCB-defined passcode, which enables the individual addressability of the implants for synchronized therapies. The implantable device weighs only 80 mg and sizes 20.1 mm3. Tolerance of up to 70° to angular misalignment was measured at a distance of 50 mm. The efficacy of bilateral stimulation was further verified by implanting two devices on two sides of a pig's neck and performing bilateral vagus nerve stimulation (VNS), while monitoring the heart rate.

A vision of 14 T MR for fundamental and clinical science.

OBJECTIVE: We outline our vision for a 14 Tesla MR system. This comprises a novel whole-body magnet design utilizing high temperature superconductor; a console and associated electronic equipment; an optimized radiofrequency coil setup for proton measurement in the brain, which also has a local shim capability; and a high-performance gradient set. RESEARCH FIELDS: The 14 Tesla system can be considered a 'mesocope': a device capable of measuring on biologically relevant scales. In neuroscience the increased spatial resolution will anatomically resolve all layers of the cortex, cerebellum, subcortical structures, and inner nuclei. Spectroscopic imaging will simultaneously measure excitatory and inhibitory activity, characterizing the excitation/inhibition balance of neural circuits. In medical research (including brain disorders) we will visualize fine-grained patterns of structural abnormalities and relate these changes to functional and molecular changes. The significantly increased spectral resolution will make it possible to detect (dynamic changes in) individual metabolites associated with pathological pathways including molecular interactions and dynamic disease processes. CONCLUSIONS: The 14 Tesla system will offer new perspectives in neuroscience and fundamental research. We anticipate that this initiative will usher in a new era of ultra-high-field MR.

Nafion Modified Titanium Nitride pH Sensor for Future Biomedical Applications.

pH sensors are increasingly being utilized in the biomedical field and have been implicated in health applications that aim to improve the monitoring and treatment of patients. In this work, a previously developed Titanium Nitride (TiN) solid-state pH sensor is further enhanced, with the potential to be used for pH regulation inside the human body and for other biomedical, industrial, and environmental applications. One of the main limitations of existing solid-state pH sensors is their reduced performance in high redox mediums. The potential shift E0 value of the previously developed TiN pH electrode in the presence of oxidizing or reducing agents is 30 mV. To minimize this redox shift, a Nafion-modified TiN electrode was developed, tested, and evaluated in various mediums. The Nafion-modified electrode has been shown to shift the E0 value by only 2 mV, providing increased accuracy in highly redox samples while maintaining acceptable reaction times. Overcoming the redox interference for pH measurement enables several advantages of the Nafion-modified TiN electrode over the standard pH glass electrode, implicating its use in medical diagnosis, real-time health monitoring, and further development of miniaturized smart sensors.

Human Activity Recognition with an HMM-Based Generative Model.

Human activity recognition (HAR) has become an interesting topic in healthcare. This application is important in various domains, such as health monitoring, supporting elders, and disease diagnosis. Considering the increasing improvements in smart devices, large amounts of data are generated in our daily lives. In this work, we propose unsupervised, scaled, Dirichlet-based hidden Markov models to analyze human activities. Our motivation is that human activities have sequential patterns and hidden Markov models (HMMs) are some of the strongest statistical models used for modeling data with continuous flow. In this paper, we assume that emission probabilities in HMM follow a bounded-scaled Dirichlet distribution, which is a proper choice in modeling proportional data. To learn our model, we applied the variational inference approach. We used a publicly available dataset to evaluate the performance of our proposed model.

Fabrication and Optimization of Nafion as a Protective Membrane for TiN-Based pH Sensors.

In this study, a solid-state modified pH sensor with RF magnetron sputtering technology was developed. The sensor consists of an active electrode consisting of a titanium nitride (TiN) film with a protective membrane of Nafion and a reference glass electrode of Ag/AgCl. The sensitivity of the pH sensor was investigated. Results show a sensor with excellent characteristics: sensitivity of 58.6 mV/pH for pH values from 2 to 12, very short response time of approximately 12 s in neutral pH solutions, and stability of less than 0.9 mV in 10 min duration. Further improvement in the performance of the TiN sensor was studied by application of a Nafion protective membrane. Nafion improves the sensor sensitivity close to Nernstian by maintaining a linear response. This paves the way to implement TiN with Nafion protection to block any interference species during real time applications in biosensing and medical diagnostic pH sensors.

Automatic Video-Oculography System for Detection of Minimal Hepatic Encephalopathy Using Machine Learning Tools.

This article presents an automatic gaze-tracker system to assist in the detection of minimal hepatic encephalopathy by analyzing eye movements with machine learning tools. To record eye movements, we used video-oculography technology and developed automatic feature-extraction software as well as a machine learning algorithm to assist clinicians in the diagnosis. In order to validate the procedure, we selected a sample (n=47) of cirrhotic patients. Approximately half of them were diagnosed with minimal hepatic encephalopathy (MHE), a common neurological impairment in patients with liver disease. By using the actual gold standard, the Psychometric Hepatic Encephalopathy Score battery, PHES, patients were classified into two groups: cirrhotic patients with MHE and those without MHE. Eye movement tests were carried out on all participants. Using classical statistical concepts, we analyzed the significance of 150 eye movement features, and the most relevant (p-values ≤ 0.05) were selected for training machine learning algorithms. To summarize, while the PHES battery is a time-consuming exploration (between 25-40 min per patient), requiring expert training and not amenable to longitudinal analysis, the automatic video oculography is a simple test that takes between 7 and 10 min per patient and has a sensitivity and a specificity of 93%.

Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review.

Human gesture detection, obstacle detection, collision avoidance, parking aids, automotive driving, medical, meteorological, industrial, agriculture, defense, space, and other relevant fields have all benefited from recent advancements in mmWave radar sensor technology. A mmWave radar has several advantages that set it apart from other types of sensors. A mmWave radar can operate in bright, dazzling, or no-light conditions. A mmWave radar has better antenna miniaturization than other traditional radars, and it has better range resolution. However, as more data sets have been made available, there has been a significant increase in the potential for incorporating radar data into different machine learning methods for various applications. This review focuses on key performance metrics in mmWave-radar-based sensing, detailed applications, and machine learning techniques used with mmWave radar for a variety of tasks. This article starts out with a discussion of the various working bands of mmWave radars, then moves on to various types of mmWave radars and their key specifications, mmWave radar data interpretation, vast applications in various domains, and, in the end, a discussion of machine learning algorithms applied with radar data for various applications. Our review serves as a practical reference for beginners developing mmWave-radar-based applications by utilizing machine learning techniques.

Low-Abundance Protein Enrichment for Medical Applications: The Involvement of Combinatorial Peptide Library Technique.

The discovery of low- and very low-abundance proteins in medical applications is considered a key success factor in various important domains. To reach this category of proteins, it is essential to adopt procedures consisting of the selective enrichment of species that are present at extremely low concentrations. In the past few years pathways towards this objective have been proposed. In this review, a general landscape of the enrichment technology situation is made first with the presentation and the use of combinatorial peptide libraries. Then, a description of this peculiar technology for the identification of early-stage biomarkers for well-known pathologies with concrete examples is given. In another field of medical applications, the determination of host cell protein traces potentially present in recombinant therapeutic proteins, such as antibodies, is discussed along with their potentially deleterious effects on the health of patients on the one hand, and on the stability of these biodrugs on the other hand. Various additional applications of medical interest are disclosed for biological fluids investigations where the target proteins are present at very low concentrations (e.g., protein allergens).

[Support of opioid rotation using online apps : Evaluation of applicability and comparison to the LONTS guidelines]. / Unterstützung der Opioidrotation mithilfe von Online-Apps : Evaluation der Anwendbarkeit und Vergleich mit der LONTS-Leitlinie.

BACKGROUND: Opioid rotation can be indicated due to drug side effects, drug interactions or inadequate effect of treatment with opioids. For the determination of the oral morphine equivalence, a practice tool has been published with the long-term use of opioids in chronic nontumor-related pain (LONTS) guidelines. In contrast, several apps are available that have not yet been evaluated. MATERIAL AND METHODS: Apps and web applications for opioid conversion were searched using Google Play Store®, iOS App Store® and the Google® search engine. German and English language apps with calculator functions were included. Using the apps, 16 test cases from clinical practice were calculated and the deviation from the recommendation of the LONTS guidelines was calculated. RESULTS: A total of 17 apps were identified, 11 named the origin of the algorithm and 3 of them defined the literature sources. None of the apps and web applications had a quality seal, and none could solve all cases. Deviations of the resulting oral morphine equivalents of up to 179% from the guideline-compliant conversion were identified and 4 apps warned for overdosing. CONCLUSION: Although the apps and web applications simplify conversion between opioids, there is high variance in conversion factors and sometimes a relevant deviation from evidence-based tables. Overall, there is a high risk of false opioid dosing.

Non-Thermal Plasma Application in Medicine-Focus on Reactive Species Involvement.

Non-thermal plasma (NTP) application in medicine is a dynamically developing interdisciplinary field. Despite the fact that basics of the plasma phenomenon have been known since the 19th century, growing scientific attention has been paid in recent years to the use of plasma in medicine. Three most important plasma-based effects are pivotal for medical applications: (i) inactivation of a broad spectrum of microorganisms, (ii) stimulation of cell proliferation and angiogenesis with lower plasma treatment intensity, and (iii) inactivation of cells by initialization of cell death with higher plasma intensity. In this review, we explain the underlying chemical processes and reactive species involvement during NTP in human (or animal) tissues, as well as in bacteria inactivation, which leads to sterilization and indirectly supports wound healing. In addition, plasma-mediated modifications of medical surfaces, such as surgical instruments or implants, are described. This review focuses on the existing knowledge on NTP-based in vitro and in vivo studies and highlights potential opportunities for the development of novel therapeutic methods. A full understanding of the NTP mechanisms of action is urgently needed for the further development of modern plasma-based medicine.

Chitosan-Based Antibacterial Films for Biomedical and Food Applications.

Antibacterial chitosan films, versatile and eco-friendly materials, have garnered significant attention in both the food industry and medicine due to their unique properties, including biodegradability, biocompatibility, and antimicrobial activity. This review delves into the various types of chitosan films and their distinct applications. The categories of films discussed span from pure chitosan films to those enhanced with additives such as metal nanoparticles, metal oxide nanoparticles, graphene, fullerene and its derivatives, and plant extracts. Each type of film is examined in terms of its synthesis methods and unique properties, establishing a clear understanding of its potential utility. In the food industry, these films have shown promise in extending shelf life and maintaining food quality. In the medical field, they have been utilized for wound dressings, drug delivery systems, and as antibacterial coatings for medical devices. The review further suggests that the incorporation of different additives can significantly enhance the antibacterial properties of chitosan films. While the potential of antibacterial chitosan films is vast, the review underscores the need for future research focused on optimizing synthesis methods, understanding structure-property relationships, and rigorous evaluation of safety, biocompatibility, and long-term stability in real-world applications.