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.

Facile synthesis of Fe2O3, Fe2O3@CuO and WO3 nanoparticles: characterization, structure determination and evaluation of their biological activity

Due to their high specific surface area and its characteristic's functionalized nanomaterials have great potential in medical applications specialty, as an anticancer. Herein, functional nanoparticles (NPs) based on iron oxide Fe2O3, iron oxide modified with copper oxide Fe2O3@CuO, and tungsten oxide WO3 were facile synthesized for biomedical applications. The obtained nanomaterials have nanocrystal sizes of 35.5 nm for Fe2O3, 7 nm for Fe2O3@CuO, and 25.5 nm for WO3. In addition to octahedral and square nanoplates for Fe2O3, and WO3; respectively. Results revealed that Fe2O3, Fe2O3@CuO, and WO3 NPs showed remarked anticancer effects versus a safe effect on normal cells through cytotoxicity test using MTT-assay. Notably, synthesized NPs e.g. our result demonstrated that Fe2O3@CuO exhibited the lowest IC50 value on the MCF-7 cancer cell line at about 8.876 µg/ml, compared to Fe2O3 was 12.87 µg/ml and WO3 was 9.211 µg/ml which indicate that the modification NPs Fe2O3@CuO gave the highest antiproliferative effect against breast cancer. However, these NPs showed a safe mode toward the Vero normal cell line, where IC50 were monitored as 40.24 µg/ml for Fe2O3, 21.13 µg/ml for Fe2O3@CuO, and 25.41 µg/ml for WO3 NPs. For further evidence. The antiviral activity using virucidal and viral adsorption mechanisms gave practiced effect by viral adsorption mechanism and prevented the virus from replicating inside the cells. Fe2O3@CuO and WO3 NPs showed a complete reduction in the viral load synergistic effect of combinations between the tested two materials copper oxide instead of iron oxide alone. Interestingly, the antimicrobial efficiency of Fe2O3@CuO NPs, Fe2O3NPs, and WO3NPs was evaluated using E. coli, S. aureus, and C. albicans pathogens. The widest microbial inhibition zone (ca. 38.45 mm) was observed with 250 mg/ml of WO3 NPs against E. coli, whereas using 40 mg/ml of Fe2O3@CuO NPS could form microbial inhibition zone ca. 32.86 mm against S. aureus. Nevertheless, C. albicans was relatively resistant to all examined NPs. The superior biomedical activities of these nanostructures might be due to their unique features and accepted evaluations.

Plasmonic substrates for biochemical applications of surface-enhanced Raman spectroscopy

Due to its great practical importance, the detection and determination of many biomolecules in body fluids and other samples is carried out in a large number of laboratories around the world. One of the most promising analytical techniques now being widely introduced into medical analysis is surface-enhanced Raman scattering (SERS) spectroscopy. SERS is one of the most sensitive analytical methods, and in some cases, a good quality SERS spectrum dominated by the contribution of even a single molecule can be obtained. Highly sensitive SERS measurements can only be carried out on substrates generating a very high SERS enhancement factor and a low Raman spectral background, and so using of right nanomaterials is a key element in the success of SERS biochemical analysis. In this review article, we present progress that has been made in the preparation of nanomaterials used in SERS spectroscopy for detecting various kinds of biomolecules. We describe four groups of nanomaterials used in such measurements: nanoparticles of plasmonic metals and deposits of plasmonic nanoparticles on macroscopic substrates, nanocomposites containing plasmonic and non-plasmonic parts, nanostructured macroscopic plasmonic metals, and nanostructured macroscopic non-plasmonic materials covered by plasmonic films. We also describe selected SERS biochemical analyses that utilize the nanomaterials presented. We hope that this review will be useful for researchers starting work in this fascinating field of ​​science and technology.

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.

Synthesis, characterization and thermoluminescence properties of LiCaPO4 phosphor for ionizing radiation dosimetry

Many minerals and compounds show thermoluminescence (TL) properties but only a few of them can meet the requirements of an ideal dosimeter. Several phosphate materials have been studied for low-dose dosimetryin recent times. Among the various phosphates, ABPO4-type material shows interesting TL properties. In this study, an ABPO4-type (A = Lithium, B=Calcium) phosphor is synthesized using a modified solid-state diffusion method. Temperature is maintained below 800 °C in every step of phosphor preparation to obtain the pure phase of Lithium calcium phosphate (LiCaPO4). The purpose of this work is to synthesize LiCaPO4 using a simple method, examine its structural and luminescence properties in order to gain a deeper understanding of its TL characteristics. The general TL properties, such as TL glow curve, dose linearity, sensitivity, and fading, are investigated. Additionally, this study aims to determine various kinetic parameters through Glow Curve Deconvolution (GCD) method using the Origin Lab software together with the Chen model. XRD analysis confirmed the phase purity of the phosphor with a rhombohedral structure. Lattice parameters, unit cell volume, grain size, dislocated density, and microstrain were also calculated from XRD data. Raman analysis and Fourier Transform Infrared analysis were used to collect information about molecular bonds, vibrations, identity, and structure of the phosphor. To investigate TL properties and associated kinetic parameters, the phosphor was irradiated with 6.0 MV (photon energy) and 6.0 MeV (electron energy) from a linear accelerator for doses ranging from 0.5 Gy to 6.0 Gy. For both photon and electron energy, TL glow curves have two identical peaks near 200 °C and 240 °C.The TL glow curves for 0.5 Gy-6 Gy are deconvoluted, then fitted with the appropriate model and then calculated the kinetic parameters. Kinetic parameters such as geometric factor (μg), order of kinetics, activation energy (E), and frequency factor (s) are obtained from Chen's peak shape method. The dose against the TL intensity curve shows that the response is almost linear in the investigated dose range. For photon and electron energy, the phosphor is found to be the most sensitive at 2.0 Gy and 4.0 Gy, respectively. The phosphor shows a low fading and after 28 days of exposure, it shows a signal loss of better than 3%. The studied TL properties suggest the suitability of LiCaPO4 in radiation dosimetry and associated fields.

Cerium oxide nanoparticles in diabetic foot ulcer management: Advances, limitations, and future directions

Diabetic foot ulcer (DFU) is one of the most serious complications of diabetes, potentially resulting in wound infection and amputation under severe circumstances. Oxidative stress and dysbiosis are the primary factors that delay wound healing, posing challenges to effective treatment. Unfortunately, conventional approaches in these aspects have proven satisfactory in achieving curative outcomes. Recent research has increasingly focused on using nanoparticles, leveraging their potential in wound dressing and medication delivery. Their unique physical properties further enhance their therapeutic effectiveness. Among these nanoparticles, cerium oxide nanoparticles (CONPs) have garnered attention due to their notable beneficial effects on oxidative stress and microbial abundance, thus representing a promising therapeutic avenue for DFU. This review comprehensively assesses recent studies on CONPs in treating DFU. Furthermore, we elaborate on the wound healing process, ceria synthesis, and incorporating CONPs with other materials. Crucially, a thorough evaluation of CONPs' toxicity as a novel metallic nanomaterial for therapeutic use must precede their formal clinical application. Additionally, we identify the current challenges CONPs encounter and propose future directions for their development.

The characteristics and medical applications of antler stem cells

Antlers are the only fully regenerable mammalian appendages whose annual renewal is initiated by antler stem cells (ASCs), defined as a specialized type of mesenchymal stem cells (MSCs) with embryonic stem cell properties. ASCs possess the same biological features as MSCs, including the capacity for self-renewal and multidirectional differentiation, immunomodulatory functions, and the maintenance of stem cell characteristics after multiple passages. Several preclinical studies have shown that ASCs exhibit promising potential in wound healing, bone repair, osteoarthritis, anti-tissue fibrosis, anti-aging, and hair regeneration. Medical applications based on ASCs and ASC-derived molecules provide a new source of stem cells and therapeutic modalities for regenerative medicine. This review begins with a brief description of antler regeneration and the role of ASCs. Then, the properties and advantages of ASCs are described. Finally, medical research advances regarding ASCs are summarized, and the prospects and challenges of ASCs are highlighted.

Injectable hydrogels for cartilage and bone tissue regeneration: A review

Annually, millions of patients suffer from irreversible injury owing to the loss or failure of an organ or tissue caused by accident, aging, or disease. The combination of injectable hydrogels and the science of stem cells have emerged to address this persistent issue in society by generating minimally invasive treatments to augment tissue function. Hydrogels are composed of a cross-linked network of polymers that exhibit a high-water retention capacity, thereby mimicking the wet environment of native cells. Due to their inherent mechanical softness, hydrogels can be used as needle-injectable stem cell carrier materials to mend tissue defects. Hydrogels are made of different natural or synthetic polymers, displaying a broad portfolio of eligible properties, which include biocompatibility, low cytotoxicity, shear-thinning properties as well as tunable biological and physicochemical properties. Presently, novel ongoing developments and native-like hydrogels are increasingly being used broadly to improve the quality of life of those with disabling tissue-related diseases. The present review outlines various future and in-vitro applications of injectable hydrogel-based biomaterials, focusing on the newest ongoing developments of in-situ forming injectable hydrogels for bone and cartilage tissue engineering purposes.

Diffusion models in medical imaging: A comprehensive survey

Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward diffusion stage where the input data is gradually perturbed over several steps by adding Gaussian noise and then learns to reverse the diffusion process to retrieve the desired noise-free data from noisy data samples. Diffusion models are widely appreciated for their strong mode coverage and quality of the generated samples in spite of their known computational burdens. Capitalizing on the advances in computer vision, the field of medical imaging has also observed a growing interest in diffusion models. With the aim of helping the researcher navigate this profusion, this survey intends to provide a comprehensive overview of diffusion models in the discipline of medical imaging. Specifically, we start with an introduction to the solid theoretical foundation and fundamental concepts behind diffusion models and the three generic diffusion modeling frameworks, namely, diffusion probabilistic models, noise-conditioned score networks, and stochastic differential equations. Then, we provide a systematic taxonomy of diffusion models in the medical domain and propose a multi-perspective categorization based on their application, imaging modality, organ of interest, and algorithms. To this end, we cover extensive applications of diffusion models in the medical domain, including image-to-image translation, reconstruction, registration, classification, segmentation, denoising, 2/3D generation, anomaly detection, and other medically-related challenges. Furthermore, we emphasize the practical use case of some selected approaches, and then we discuss the limitations of the diffusion models in the medical domain and propose several directions to fulfill the demands of this field. Finally, we gather the overviewed studies with their available open-source implementations at our GitHub.1 We aim to update the relevant latest papers within it regularly.

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.

Improved intelligent water drop-based hybrid feature selection method for microarray data processing

Classifying microarray datasets, which usually contains many noise genes that degrade the performance of classifiers and decrease classification accuracy rate, is a competitive research topic. Feature selection (FS) is one of the most practical ways for finding the most optimal subset of genes that increases classification's accuracy for diagnostic and prognostic prediction of tumor cancer from the microarray datasets. This means that we always need to develop more efficient FS methods, that select only optimal or close-to-optimal subset of features to improve classification performance. In this paper, we propose a hybrid FS method for microarray data processing, that combines an ensemble filter with an Improved Intelligent Water Drop (IIWD) algorithm as a wrapper by adding one of three local search (LS) algorithms: Tabu search (TS), Novel LS algorithm (NLSA), or Hill Climbing (HC) in each iteration from IWD, and using a correlation coefficient filter as a heuristic undesirability (HUD) for next node selection in the original IWD algorithm. The effects of adding three different LS algorithms to the proposed IIWD algorithm have been evaluated through comparing the performance of the proposed ensemble filter-IIWD-based wrapper without adding any LS algorithms named (PHFS-IWD) FS method versus its performance when adding a specific LS algorithm from (TS, NLSA or HC) in FS methods named, (PHFS-IWDTS, PHFS-IWDNLSA, and PHFS-IWDHC), respectively. Naïve Bayes(NB) classifier with five microarray datasets have been deployed for evaluating and comparing the proposed hybrid FS methods. Results show that using LS algorithms in each iteration from the IWD algorithm improves F-score value with an average equal to 5% compared with PHFS-IWD. Also, PHFS-IWDNLSA improves the F-score value with an average of 4.15% over PHFS-IWDTS, and 5.67% over PHFS-IWDHC while PHFS-IWDTS outperformed PHFS-IWDHC with an average of increment equal to 1.6%. On the other hand, the proposed hybrid-based FS methods improve accuracy with an average equal to 8.92% in three out of five datasets and decrease the number of genes with a percentage of 58.5% in all five datasets compared with six of the most recent state-of-the-art FS methods.

Adaptive model training strategy for continuous classification of time series

The classification of time series is essential in many real-world applications like healthcare. The class of a time series is usually labeled at the final time, but more and more time-sensitive applications require classifying time series continuously. For example, the outcome of a critical patient is only determined at the end, but he should be diagnosed at all times for timely treatment. For this demand, we propose a new concept, Continuous Classification of Time Series (CCTS). Different from the existing single-shot classification, the key of CCTS is to model multiple distributions simultaneously due to the dynamic evolution of time series. But the deep learning model will encounter intertwined problems of catastrophic forgetting and over-fitting when learning multi-distribution. In this work, we found that the well-designed distribution division and replay strategies in the model training process can help to solve the problems. We propose a novel Adaptive model training strategy for CCTS (ACCTS). Its adaptability represents two aspects: (1) Adaptive multi-distribution extraction policy. Instead of the fixed rules and the prior knowledge, ACCTS extracts data distributions adaptive to the time series evolution and the model change; (2) Adaptive importance-based replay policy. Instead of reviewing all old distributions, ACCTS only replays important samples adaptive to their contribution to the model. Experiments on four real-world datasets show that our method outperforms all baselines.

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.

Functionally graded additive manufacturing for orthopedic applications

Background

Additive Manufacturing due to its benefits in developing parts with complex geometries and shapes, has evolved as an alternate manufacturing process to develop implants with desired properties. The structure of human bones being anisotropic in nature is biologically functionally graded i,e. The structure possesses different properties in different directions. Therefore, various orthopedic implants such as knee, hip and other bone plates, if functionally graded can perform better. In this context, the development of functionally graded (FG) parts for orthopedic application with tailored anisotropic properties has become easier through the use of additive manufacturing (AM).

Objectives

and Rationale: The current paper aims to study the various aspects of additively manufactured FG parts for orthopedic applications. It presents the details of various orthopedic implants such as knee, hip and other bone plates in a structured manner. A systematic literature review is conducted to study the various material and functional aspects of functionally graded parts for orthopedic applications. A section is also dedicated to discuss the mechanical properties of functionally graded parts.

Conclusion

The literature revealed that additive manufacturing can provide lot of opportunities for development of functionally graded orthopedic implants with improved properties and durability. Further, the effect of various FG parameters on the mechanical behavior of these implants needs to be studied in detail. Also, with the advent of various AM technologies, the functional grading can be achieved by various means e.g. density, porosity, microstructure, composition, etc. By varying the AM parameters. However, the current limitations of cost and material biocompatibility prevent the widespread exploitation of AM technologies for various orthopedic applications.

Carbon nanostructures: a comprehensive review of potential applications and toxic effects

There is no doubt that nanotechnology has revolutionized our life since the 1970s when it was first introduced. Nanomaterials have helped us to improve the current products and services we use. Among the different types of nanomaterials, the application of carbon-based nanomaterials in every aspect of our lives has rapidly grown over recent decades. This review discusses recent advances of those applications in distinct categories, including medical, industrial, and environmental applications. The first main section introduces nanomaterials, especially carbon-based nanomaterials. In the first section, we discussed medical applications, including medical biosensors, drug and gene delivery, cell and tissue labeling and imaging, tissue engineering, and the fight against bacterial and fungal infections. The next section discusses industrial applications, including agriculture, plastic, electronic, energy, and food industries. In addition, the environmental applications, including detection of air and water pollutions and removal of environmental pollutants, were vastly reviewed in the last section. In the conclusion section, we discussed challenges and future perspectives.

Gender differences regarding intention to use mHealth applications in the Dutch elderly population: a cross-sectional study

BACKGROUND

In light of the increasing demands in health care, a call has been made for the development of new strategies. One of these strategies is placing a higher emphasis on individuals, who are expected to better manage their own health and illness. mHealth applications could increase this self-management behaviour among older adults. However, it is crucial to know the intention to use mHealth of older adults before implementing these services. Even less is known regarding differences between genders on factors influencing this intention to use mHealth applications.

OBJECTIVE

The aim of this study was to study the gender differences regarding the relationship between technology acceptance factors and the intention to use mHealth applications in the Dutch elderly population.

METHODS

We conducted a quantitative cross-sectional study using questionnaires. The participants were 65 years or older, lived independently or in a senior living facility, without cognitive impairment. Logistic regression with interaction terms was done to determine gender differences in the relationship between the intention to use mHealth applications and technology acceptance factors.

RESULTS

While we found that half of the studied population had intention to use medical applications (50.3%) a notable difference was observed within gender groups which showed more men had intention to use medical applications rather than women (59.4% vs. 43.4% respectively). Adjusted logistic regression analysis per factor on the male and female part of the study population respectively showed that the factors Perceived usefulness (OR 21,69 and 2,39, resp.), Perceived ease of use (OR 7,21 and 2,74), Attitude toward use (OR 24,61 and 4,94), Sense of control (OR 4,12 and 2,67), Personal innovativeness (OR 2,54 and 1,58), Self-perceived effectiveness (OR 3,21 and 2,34), Service availability (OR 4,38 and 2,51) and Facilitating circumstances (OR 3,04 and 2,18) had a statistically significant influence on intention to use in both models. Logistic regression with interaction terms showed that two of the technology acceptance factors differed statistically significant in their relationship with intention to use when comparing females to males, namely Perceived usefulness (OR 0,11) and Attitude toward use (OR 0.24). Both factors were more strongly associated with intention to use for men compared to women.

CONCLUSION

Policymakers and interventions aiming to stimulate the uptake of mHealth applications should acknowledge gender differences. Interventions based on improving the Perceived usefulness and Attitude toward use among female users could be a means to stimulate the full potential of medical applications and improve the uptake.

Fabrication strategies and biomedical applications of three-dimensional bacterial cellulose-based scaffolds: A review

Bacterial cellulose (BC), an extracellular polysaccharide, is a versatile biopolymer due to its intrinsic physicochemical properties, broad-spectrum applications, and remarkable achievements in different fields, especially in the biomedical field. Presently, the focus of BC-related research is on the development of scaffolds containing other materials for in-vitro and in-vivo biomedical applications. To this end, prime research objectives concern the biocompatibility of BC and the development of three-dimensional (3D) BC-based scaffolds. This review summarizes the techniques used to develop 3D BC scaffolds and discusses their potential merits and limitations. In addition, we discuss the various biomedical applications of BC-based scaffolds for which the 3D BC matrix confers desired structural and conformational features. Overall, this review provides comprehensive coverage of the idea, requirements, synthetic strategies, and current and prospective applications of 3D BC scaffolds, and thus, should be useful for researchers working with polysaccharides, biopolymers, or composite materials.

Machine learning in medical applications: A review of state-of-the-art methods

Applications of machine learning (ML) methods have been used extensively to solve various complex challenges in recent years in various application areas, such as medical, financial, environmental, marketing, security, and industrial applications. ML methods are characterized by their ability to examine many data and discover exciting relationships, provide interpretation, and identify patterns. ML can help enhance the reliability, performance, predictability, and accuracy of diagnostic systems for many diseases. This survey provides a comprehensive review of the use of ML in the medical field highlighting standard technologies and how they affect medical diagnosis. Five major medical applications are deeply discussed, focusing on adapting the ML models to solve the problems in cancer, medical chemistry, brain, medical imaging, and wearable sensors. Finally, this survey provides valuable references and guidance for researchers, practitioners, and decision-makers framing future research and development directions.

Sensitive and real-time detection of IgG using interferometric reflecting imaging sensor system

Considering the limitations of well-known traditional detection techniques, innovative research studies have focused on the development of new sensors to offer label-free, highly sensitive, real-time, low-cost, and rapid detection for biomolecular interactions. In this study, we demonstrate immunoglobulin G (IgG) detection in aqueous solutions by using real-time and label-free kinetic measurements of the Interferometric Reflectance Imaging Sensor (IRIS) system. By performing kinetic characterization experiments, the sensor's performance is comprehensively evaluated and a high correlation coefficient value (>0.94) is obtained in the IgG concentration range of 1-50 μg/mL with a low detection limit (0.25 μg/mL or 1.67 nM). Moreover, the highly sensitive imaging system ensures accurate quantification and reliable validation of recorded binding events, offering new perspectives in terms of direct biomarker detection for clinical applications.

Analyzing the use of artificial intelligence for the management of chronic obstructive pulmonary disease (COPD)

OBJECTIVE

Chronic obstructive pulmonary disease (COPD) is a disease that causes airflow limitation to the lungs and has a high morbidity around the world. The objective of this study was to evaluate how artificial intelligence (AI) is being applied for the management of the disease, analyzing the objectives that are raised, the algorithms that are used and what results they offer.

METHODS

We conducted a scoping review following the Arksey and O'Malley (2005) and Levac et al. (2010) guidelines. Two reviewers independently searched, analyzed and extracted data from papers of five databases: Web of Science, PubMed, Scopus, Cinahl and Cochrane. To be included, the studies had to apply some AI techniques for the management of at least one stage of the COPD clinical process. In the event of any discrepancy between both reviewers, the criterion of a third reviewer prevailed.

RESULTS

380 papers were identified through database searches. After applying the exclusion criteria, 67 papers were included in the study. The studies were of a different nature and pursued a wide range of objectives, highlighting mainly those focused on the identification, classification and prevention of the disease. Neural nets, support vector machines and decision trees were the AI algorithms most commonly used. The mean and median values of all the performance metrics evaluated were between 80% and 90%.

CONCLUSIONS

The results obtained show a growing interest in the development of medical applications that manage the different phases of the COPD clinical process, especially predictive models. According to the performance shown, these models could be a useful complementary tool in the decision-making by health specialists, although more high-quality ML studies are needed to endorse the findings of this study.

Herbal medicine derived carbon dots: synthesis and applications in therapeutics, bioimaging and sensing

Since the number of raw material selections for the synthesis of carbon dots (CDs) has grown extensively, herbal medicine as a precursor receives an increasing amount of attention. Compared with other biomass precursors, CDs derived from herbal medicine (HM-CDs) have become the most recent incomer in the family of CDs. In recent ten years, a great many studies have revealed that HM-CDs tend to be good at theranostics without drug loading. However, the relevant development and research results are not systematically reviewed. Herein, the origin and history of HM-CDs are outlined, especially their functional performances in medical diagnosis and treatment. Besides, we sort out the herbal medicine precursors, and analyze the primary synthetic methods and the key characteristics. In terms of the applications of HM-CDs, medical therapeutics, ion and molecular detection, bioimaging, as well as pH sensing are summarized. Finally, we discuss the crucial challenges and future prospects.

Radiobiological effects and medical applications of non-ionizing radiation

Radiation is used in medicine to diagnose and treat diseases but it can also cause harm to the body by burning or mutation. This depends on whether the radiation is ionizing or nonionizing. Despite its vast applications in surgery, dermatology and cosmetics, little is taught and thus known about non-ionizing radiation. This review article discusses the fundamentals of non-ionizing electromagnetic radiations. The main aim is to extensively explain the different types of non-ionizing radiation. This will equip students and medical personnel with knowledge on different medical applications and expose them to a variety of specializations in medicine that utilize non-ionizing radiation. The article discusses the physics, hazard, means of protection and medical application of each type of radiation: ultraviolet radiation, light (both visible light and LASER), infrared radiation, microwaves and extremely low frequency radiation separately. It presents these terms in a simple manner that avoids rigors mathematics and physics, which makes them comprehensible for medical students. The development of new diagnostic and therapeutic approaches could also lead to increased hazards to the body unless they are treated with precaution. If not adequately monitored, a significant health risk may be posed to potentially exposed employees. Hence proper dosage should be used for non-ionizing radiation. This is only possible through understanding of the risks/benefits of these radiations by studying the physics and radiobiological effects of each individual radiation.

Privacy-preserving contact tracing in 5G-integrated and blockchain-based medical applications

The current pandemic situation due to COVID-19 is seriously affecting our daily work and life. To block the propagation of infectious diseases, an effective contact tracing mechanism needs to be implemented. Unfortunately, existing schemes have severe privacy issues that jeopardize the identity-privacy and location-privacy for both users and patients. Although some privacy-preserving systems have been proposed, there remain several issues caused by centralization. To mitigate this issues, we propose a Privacy-preserving contact Tracing scheme in 5G-integrated and Blockchain-based Medical applications, named PTBM. In PTBM, the 5G-integrated network is leveraged as the underlying infrastructure where everyone can perform location checking with his mobile phones or even wearable devices connected to 5G network to find whether they have been in possible contact with a diagnosed patient without violating their privacy. A trusted medical center can effectively trace the patients and their corresponding close contacts. Thorough security and performance analysis show that the proposed PTBM scheme achieves privacy protection, traceability, reliability, and authentication, with high computation & communication efficiency and low latency.

Design Considerations for Interacting and Navigating with 2 Dimensional and 3 Dimensional Medical Images in Virtual, Augmented and Mixed Reality Medical Applications

The extended realities, including virtual, augmented, and mixed realities (VAMR) have recently experienced significant hardware improvement resulting in an expansion in medical applications. These applications can be classified by the target end user (for instance, classifying applications as patient-centric, physician-centric, or both) or by use case (for instance educational, diagnostic tools, therapeutic tools, or some combination). When developing medical applications in VAMR, careful consideration of both the target end user and use case must heavily influence design considerations, particularly methods and tools for interaction and navigation. Medical imaging consists of both 2-dimensional and 3-dimensional medical imaging which impacts design, interaction, and navigation. Additionally, medical applications need to comply with regulatory considerations which will also influence interaction and design considerations. In this manuscript, the authors explore these considerations using three VAMR tools being developed for cardiac electrophysiology procedures.

Requirements and reliability of AI in the medical context

The digital information age has been a catalyst in creating a renewed interest in Artificial Intelligence (AI) approaches, especially the subclass of computer algorithms that are popularly grouped into Machine Learning (ML). These methods have allowed one to go beyond limited human cognitive ability into understanding the complexity in the high dimensional data. Medical sciences have seen a steady use of these methods but have been slow in adoption to improve patient care. There are some significant impediments that have diluted this effort, which include availability of curated diverse data sets for model building, reliable human-level interpretation of these models, and reliable reproducibility of these methods for routine clinical use. Each of these aspects has several limiting conditions that need to be balanced out, considering the data/model building efforts, clinical implementation, integration cost to translational effort with minimal patient level harm, which may directly impact future clinical adoption. In this review paper, we will assess each aspect of the problem in the context of reliable use of the ML methods in oncology, as a representative study case, with the goal to safeguard utility and improve patient care in medicine in general.

Development and validation of an analytical model allowing accurate predictions of gamma and electron beam dose distributions in a water medical phantom

The aim of this work was to study photon and electron dose distributions in a phantom filled with water using the Monte Carlo Geant4 tool for electron energies ranging from 1 to 21 MeV and for photon energies ranging from 1.25 MeV to 25 MeV, corresponding to conventional radiotherapy Linac energies. The results of the Geant4 calculations were validated based on the relevant experimental data previously published. The results obtained were fitted and analytical models of dose distributions were developed for gamma radiation and electrons. For each of these models, one-dimensional (including dose depth profiles as a function of the depth inside the phantom) and two-dimensional (including the dose distribution as a function of depth and lateral position inside the phantom) dose distributions have been considered. Results are presented for photons and electrons of various energies. The coefficient of determination [Formula: see text] illustrates an excellent match between the developed analytical model and the Geant4 results. It is demonstrated that the analytical models developed in the present study can be applied in various fields such as those used for calibration applications and radiation therapy. It is concluded that the analytical models developed allow for quick, easy and reliable clinical dose estimates and offer promising alternatives to the standard tools and methods used in radiotherapy for treatment planning.

FCOD: Fast COVID-19 Detector based on deep learning techniques

The sudden COVID-19 pandemic has caused a serious global concern due to infections and mortality rates. It is a hazardous disease that has recently become the biggest crisis in the modern era. Due to the limitation of test kits and the need for screening and rapid diagnosis of patients, it is essential to perform a self-operating detection model as a fast recognition system to detect COVID-19 infection and prevent the spread among the people. In this paper, we propose a novel technique called Fast COVID-19 Detector (FCOD) to have a fast detection of COVID-19 using X-ray images. The FCOD is a deep learning model based on the Inception architecture that uses 17 depthwise separable convolution layers to detect COVID-19. Depthwise separable convolution layers decrease the computation costs, time, and they can have a reducing role in the number of parameters compared to the standard convolution layers. To evaluate FCOD, we used covid-chestxray-dataset, which contains 940 publicly available typical chest X-ray images. Our results show that FCOD can provide accuracy, F1-score, and AUC of 96%, 96%, and 0.95%, respectively in classifying COVID-19 during 0.014 s for each case. The proposed model can be employed as a supportive decision-making system to assist radiologists in clinics and hospitals to screen patients immediately.

Ultrasensitive detection of pathogenic viruses with electrochemical biosensor: State of the art

Last few decades, viruses are a real menace to human safety. Therefore, the rapid identification of viruses should be one of the best ways to prevent an outbreak and important implications for medical healthcare. The recent outbreak of coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus which belongs to the single-stranded, positive-strand RNA viruses. The pandemic dimension spread of COVID-19 poses a severe threat to the health and lives of seven billion people worldwide. There is a growing urgency worldwide to establish a point-of-care device for the rapid detection of COVID-19 to prevent subsequent secondary spread. Therefore, the need for sensitive, selective, and rapid diagnostic devices plays a vital role in selecting appropriate treatments and to prevent the epidemics. During the last decade, electrochemical biosensors have emerged as reliable analytical devices and represent a new promising tool for the detection of different pathogenic viruses. This review summarizes the state of the art of different virus detection with currently available electrochemical detection methods. Moreover, this review discusses different fabrication techniques, detection principles, and applications of various virus biosensors. Future research also looks at the use of electrochemical biosensors regarding a potential detection kit for the rapid identification of the COVID-19.

Smartphone and medical application use among dentists in China

Background

With the development of information technology, an increasing number of healthcare professionals are using smartphones and mobile medical applications (apps) in their clinical practice. The objective of this study was to survey the use of smartphone-based medical apps among dentists in China and determine dentists’ perceptions of such apps.

Methods

All data were collected using anonymous questionnaires. The questionnaires for this cross-sectional study were randomly sent to dentists by email, and 379 dentists responded. Dentists’ demographics and perceptions of WeChat, QQ (the most popular social media apps in China) and other medical apps were assessed; the questionnaire including questions on the purpose, frequency, daily use, and opinion of the apps they used. Questions were answered using a Likert scale (1 = strongly agree, 2 = agree, 3 = not sure, 4 = disagree, and 5 = strongly disagree).

Results

A total of 379 valid responses were received; the respondents had a median age of 33.6 years old (63.3% female). All subjects (100%) owned a smartphone, and all of them installed and used WeChat or QQ in their clinical practice. Only 76% of subjects installed medical apps (other than WeChat and QQ) on their smartphones. Male dentists were more likely to install medical apps than female dentists (p < 0.05). With increasing age, the percentage of dentists who installed medical apps decreased (p < 0.001). The frequency and daily use were higher for WeChat and QQ than for medical apps. Medical apps were positively perceived, with dentists reporting that they recommend these medical apps to their peers (Likert score: 1.67 ± 0.68).

Conclusion

Medical apps were perceived to have a positive impact on clinical practice, education and patient care in dentistry by providing relevant medical information. However, there will still be much room for improvement in the future.

Impact of mobile health and medical applications on clinical practice in gastroenterology

Mobile health apps (MHAs) and medical apps (MAs) are becoming increasingly popular as digital interventions in a wide range of health-related applications in almost all sectors of healthcare. The surge in demand for digital medical solutions has been accelerated by the need for new diagnostic and therapeutic methods in the current coronavirus disease 2019 pandemic. This also applies to clinical practice in gastroenterology, which has, in many respects, undergone a recent digital transformation with numerous consequences that will impact patients and health care professionals in the near future. MHAs and MAs are considered to have great potential, especially for chronic diseases, as they can support the self-management of patients in many ways. Despite the great potential associated with the application of MHAs and MAs in gastroenterology and health care in general, there are numerous challenges to be met in the future, including both the ethical and legal aspects of applying this technology. The aim of this article is to provide an overview of the current status of MHA and MA use in the field of gastroenterology, describe the future perspectives in this field and point out some of the challenges that need to be addressed.

The role of virtual and augmented reality in orthopedic trauma surgery: From diagnosis to rehabilitation

Virtual and augmented reality have been used to assist and improve human capabilities in many fields. Most recent advances allow the usage of these technologies for personal and professional purposes. In particular, they have been progressively introduced in many medical procedures since the last century. Thanks to immersive training systems and a better comprehension of the ongoing procedure, their main objectives are to increase patient safety and decrease recovery time. The current and future possibilities of virtual and augmented reality in the context of bone fracture reduction are the main focus of this review. This medical procedure requires meticulous planning and a complex intervention in many cases, hence becoming a promising candidate to be benefited from this kind of technology. In this paper, we exhaustively analyze the impact of virtual and augmented reality to bone fracture healing, detailing each task from diagnosis to rehabilitation. Our primary goal is to introduce novel researchers to current trends applied to orthopedic trauma surgery, proposing new lines of research. To that end, we propose and evaluate a set of qualitative metrics to highlight the most promising challenges of virtual and augmented reality technologies in this context.

Biochemical functionality of magnetic particles as nanosensors: how far away are we to implement them into clinical practice?

Magnetic nanosensors have become attractive instruments for the diagnosis and treatment of different diseases. They represent an efficient carrier system in drug delivery or in transporting contrast agents. For such purposes, magnetic nanosensors are used in vivo (intracorporeal application). To remove specific compounds from blood, magnetic nanosensors act as elimination system, which represents an extracorporeal approach. This review discusses principles, advantages and risks on recent advances in the field of magnetic nanosensors. First, synthesis methods for magnetic nanosensors and possibilities for enhancement of biocompatibility with different coating materials are addressed. Then, attention is devoted to clinical applications, in which nanosensors are or may be used as carrier- and elimination systems in the near future. Finally, risk considerations and possible effects of nanomaterials are discussed when working towards clinical applications with magnetic nanosensors.

Transversal dose distribution optimization for laser-accelerated proton beam medical applications by means of Geant4

The main purpose of this paper is to quantitatively study the possibility of delivering dose distributions of clinical relevance with laser-driven proton beams. A Monte Carlo application has been developed with the Geant4 toolkit, simulating the ELIMED (MEDical and multidisciplinary application at ELI-Beamlines) transport and dosimetry beam line which is being currently installed at the ELI-Beamlines in Prague (CZ). The beam line will be used to perform irradiations for multidisciplinary studies, with the purpose of demonstrating the possible use of optically accelerated ion beams for therapeutic purposes. The ELIMED Geant4-based application, already validated against reference transport codes, accurately simulates each single element of the beam line, necessary to collect the accelerated beams and to select them in energy. Transversal dose distributions at the irradiation point have been studied and optimized to try to quantitatively answer the question if such kind of beam lines, and specifically the systems developed for ELIMED in Prague, will be actually able to transport ion beams not only for multidisciplinary applications, such as pitcher-catcher nuclear reactions (e.g. neutrons), PIXE analysis for cultural heritage and space radiation, but also for delivering dose patterns of clinical relevance in a future perspective of possible medical applications.

Medical apps used during advanced pharmacy practice experiences

BACKGROUND AND PURPOSE

The purpose of this study was to determine what type of medical applications (apps) are being used by fourth-year students during advanced pharmacy practice experiences (APPEs).

EDUCATIONAL ACTIVITY AND SETTING

Recent doctor of pharmacy school graduates were surveyed pertaining to their use of medical apps on APPEs, frequency of use for select apps, willingness to buy apps and at what cost, and their perceived importance on using medical apps in their pharmacy practice. The survey concluded by inquiring if medical apps should be taught in the pharmacy curriculum.

FINDINGS

Ninety of 132 recent graduates responded to the survey and over 97% of the students used medical apps on their APPEs. Lexicomp^®, UpToDate^® and the Atherosclerotic Cardiovascular Disease (ASCVD) Risk calculator were the most frequently used applications during APPEs. Of those surveyed, 83% believe that medical apps should be taught in pharmacy school.

DISCUSSION AND SUMMARY

Medical apps are frequently used on APPEs. It may be prudent to teach students how to use and evaluate medical apps before going on experiences so that students and patients can get the most benefit from these tools.

Smartphones in clinical practice: doctors' experience at two Dublin paediatric teaching hospitals

INTRODUCTION

We conducted a study at two main centres for paediatric surgery in the Republic of Ireland, Temple Street Children's University Hospital (TSCUH) and Our Lady's Children's Hospital Crumlin (OLCHC).

AIM

The aim of the study was to assess doctors' opinions, frequency and methods of smartphone use in clinical practice.

METHODS

Doctors (n = 265) at the two hospitals were invited to complete a 12-item survey. Descriptive statistics were calculated accordingly.

RESULTS

Almost 50% (132/256) responded; 98.4% owned a smartphone and 76% owned an iPhone. Seventy-nine percent used medical apps and 59% used the OLCHC formulary. Ninety-seven percent used smartphones for inter-team communication with 73.5% (n = 97) using WhatsApp. Smartphone camera was used by 57% for clinical photos and 41% for photos of radiological imaging. Only 42% reported obtaining consent for smartphone camera use from patients or guardians. Ninety-two percent either 'strongly agreed' or 'agreed' that smartphones positively impact their practice.

CONCLUSION

Smartphone usage is universal among healthcare professionals and their influence is growing in patient care.

Magnetic Resonance Spectroscopy and its Clinical Applications: A Review

In vivo NMR spectroscopy is known as magnetic resonance spectroscopy (MRS). MRS has been applied as both a research and a clinical tool in order to detect visible or nonvisible abnormalities. The adaptability of MRS allows a technique that can probe a wide variety of metabolic uses across different tissues. Although MRS is mostly applied for brain tissue, it can be used for detection, localization, staging, tumour aggressiveness evaluation, and tumour response assessment of breast, prostate, hepatic, and other cancers. In this article, the medical applications of MRS in the brain, including tumours, neural and psychiatric disorder studies, breast, prostate, hepatic, gastrointestinal, and genitourinary investigations have been reviewed.

Measurement of activation cross-section of long-lived products in deuteron induced nuclear reactions on palladium in the 30-50MeV energy range

Excitation functions were measured in the 31-49.2MeV energy range for the ^natPd(d,xn)^{111,110m,106m,105,104g,103}Ag, ^natPd(d,x) ^{111m,109,101,100}Pd, ^natPd(d,x), ^{105,102m,102g,101m,101g,100,99m,99g}Rh and ^natPd(d,x)^103,97Ru nuclear reactions by using the stacked foil irradiation technique. The experimental results are compared with our previous results and with the theoretical predictions calculated with the ALICE-D, EMPIRE-D and TALYS (TENDL libraries) codes.

Smartphone, the New Learning Aid amongst Medical Students

INTRODUCTION

The use of smartphone is increasing day by day for personal as well as professional purpose. They are becoming a more suitable tool for advancing education in developing countries. Mobile access to information and many applications are successfully harnessed in health care. Smartphones are also becoming popular as an effective educational tool.

AIM

The present study was conducted to evaluate the use of smartphones as an educational tool amongst the medical students. The study also aimed at identifying the common medical application used by the students.

MATERIALS AND METHODS

It was an observational cross-sectional study carried out amongst medical students of private medical institute in India. A validated 16 point, structured, open-ended, questionnaire regarding ownership and use of smart phones was self-administered to 446 medical students. Data were analysed using SPSS and open ended questions were analysed by summative content analysis.

RESULTS

Among the study population, 96% owned a smartphone -Android based 72.4%, i phone 13.0%, Windows based Nokia phones 7% and Blackberry 3.6%. Common medical applications used by the students were Anatomy and Medical Dictionary in First MBBS; Medical Dictionary, Medscape and Google/Wikipedia in Second MBBS; and Medscape, Google/Wikipedia and Prognosis/Diagnosis in Third MBBS. More than 90% students, reported to have technological skills to use smartphones, for medical education, communication and instant access during bedside teaching. Advertently, 37.2% students felt if smartphones are used for clinical purposes, they will need to spend less time with patients. Almost 79.4% felt that smartphones should be introduced in MBBS course.

CONCLUSION

Smartphone use amongst medical students as learning aid for various medical applications is rapidly advancing. But it will be worthwhile to study whether use of smartphones has any impact on the grades of the students before introducing them in medical schools.

Applications for Electrical Impedance Tomography (EIT) and Electrical Properties of the Human Body

Electrical Impedance Tomography (EIT) is a promising application that displays changes in conductivity within a body. The basic principle of the method is the repeated measurement of surface voltages of a body, which are a result of rolling injection of known and small-volume sinusoidal AC current to the body through the electrodes attached to its surface. This method finds application in biomedicine, biology and geology. The objective of this paper is to present the applications of Electrical Impedance Tomography, along with the method's capabilities and limitations due to the electrical properties of the human body. For this purpose, investigation of existing literature has been conducted, using electronic databases, PubMed, Google Scholar and IEEE Xplore. In addition, there was a secondary research phase, using paper citations found during the first research phase. It should be noted that Electrical Impedance Tomography finds use in a plethora of medical applications, as the different tissues of the body have different conductivities and dielectric constants. Main applications of EIT include imaging of lung function, diagnosis of pulmonary embolism, detection of tumors in the chest area and diagnosis and distinction of ischemic and hemorrhagic stroke. EIT advantages include portability, low cost and safety, which the method provide, since it is a noninvasive imaging method that does not cause damage to the body. The main disadvantage of the method, which blocks its wider spread, appears in the image composition from the voltage measurements, which are conducted by electrodes placed on the periphery of the body, because the injected currents are affected nonlinearly by the general distribution of the electrical properties of the body. Furthermore, the complex impedance of the skin-electrode interface can be modelled by using a capacitor and two resistor, as a result of skin properties. In conclusion, Electrical Impedance Tomography is a promising method for the development of noninvasive diagnostic medicine, since it is able to provide imaging of the interior of the human body in real time without causing harm or putting the human body in risk.

Hydrogen generation by reaction of Si nanopowder with neutral water

Si and its oxide are nonpoisonous materials, and thus, it can be taken for medical effects. We have developed a method of generation of hydrogen by use of reactions of Si nanopowder with water in the neutral pH region. Si nanopowder is fabricated by the simple bead milling method. Si nanopowder reacts with water to generate hydrogen even in cases where pH is set at the neutral region between 7.0 and 8.6. The hydrogen generation rate strongly depends on pH and in the case of pH 8.0, ∼55 ml/g hydrogen which corresponds to that contained in approximately 3 L saturated hydrogen-rich water is generated in 1 h. The reaction rate for hydrogen generation greatly increases with pH, indicating that the reacting species is hydroxide ions. The change of pH after the hydrogen generation reaction is negligibly low compared with that estimated assuming that hydroxide ions are consumed by the reaction. From these results, we conclude the following reaction mechanism: Si nanopowder reacts with hydroxide ions in the rate-determining reaction to form hydrogen molecules, SiO₂, and electrons in the conduction band. Then, generated electrons are accepted by water molecules, resulting in production of hydrogen molecules and hydroxide ions. The hydrogen generation rate strongly depends on the crystallite size of Si nanopowder, but not on the size of aggregates of Si nanopowder. The present study shows a possibility to use Si nanopowder for hydrogen generation in the body in order to eliminate hydroxyl radicals which cause various diseases.

In vitro importance of probiotic Lactobacillus plantarum related to medical field

Lactobacillus plantarum is a Gram positive lactic acid bacterium commonly found in fermented food and in the gastro intestinal tract and is commonly used in the food industry as a potential starter probiotic. Recently, the consumption of food together with probiotics has tremendously increased. Among the lactic acid bacteria, L. plantarum attracted many researchers because of its wide applications in the medical field with antioxidant, anticancer, anti-inflammatory, antiproliferative, anti-obesity and antidiabetic properties. The present study aimed to investigate the in vitro importance of L. plantarum toward medical applications. Moreover, this report short listed various reports related to the applications of this promising strain. In conclusion, this study would attract the researchers in commercializing this strain toward the welfare of humans related to medical needs.

Transformation of antimicrobial stewardship programs through technology and informatics

The successful integration of technology in antimicrobial stewardship programs has made it possible for clinicians to function more efficiently. With government endorsement of electronic health records (EHRs), EHRs and clinical decision support systems (CDSSs) are being used as decision support tools to aid clinicians in efforts to improve antibiotic use. Likewise, medical applications (apps) have provided educational tools easily accessible to clinicians through their mobile devices. In this article, the impact that informatics and technology have had on promoting antibiotic stewardship is described, focusing on EHRs and CDSSs, apps, electronic resources, and social media.

Particle Detector Applications in Medicine

Selected particle detectors are described which find an application in medicine and have been the topic of presentations at the 2013 Vienna Conference of Instrumentation (VCI).

The Meaning of Information Technology (IT) Mobile Devices to Me, the Infectious Disease Physician

The devolopment of mobile devices such as smartphone or tablet has revolutionized our quality and way of life. As a specialist of infectious diseases, various aspects of information technology on our professional fields were reviewed and lots of useful medical applications were also presented in this review.

Enabling the use of hereditary information from pedigree tools in medical knowledge-based systems

The use of family information is a key issue to deal with inheritance illnesses. This kind of information use to come in the form of pedigree files, which contain structured information as tree or graphs, which explains the family relationships. Knowledge-based systems should incorporate the information gathered by pedigree tools to assess medical decision making. In this paper, we propose a method to achieve such a goal, which consists on the definition of new indicators, and methods and rules to compute them from family trees. The method is illustrated with several case studies. We provide information about its implementation and integration on a case-based reasoning tool. The method has been experimentally tested with breast cancer diagnosis data. The results show the feasibility of our methodology.

BioMEMS -Advancing the Frontiers of Medicine

Biological and medical application of micro-electro-mechanical-systems (MEMS) is currently seen as an area of high potential impact. Integration of biology and microtechnology has resulted in the development of a number of platforms for improving biomedical and pharmaceutical technologies. This review provides a general overview of the applications and the opportunities presented by MEMS in medicine by classifying these platforms according to their applications in the medical field.