Diagnostic Method Research Articles

Novel picture fuzzy distance measure [formula omitted] and its application in medical diagnosis

In this research work, we introduced novel distance measure in picture fuzzy sets and prove all the triangle inequality associated with it. There is an introduction to the use of the distance measure in medical diagnostics. In addition to this, we have shown a comparison between two distance functions ρ and ρ∗ in this research work. Our observation indicates that the novel fuzzy distance measure ρ∗ exhibits greater efficacy in the context of disease diagnosis compared to the conventional measure ρ.

Validation and optimization of the loop-mediated isothermal amplification (LAMP) technique for rapid detection of wheat stripe mosaic virus, a wheat-infecting pathogen

BackgroundWheat stripe mosaic virus (WhSMV) is a significant wheat pathogen that causes substantial yield losses in Brazil and other countries. Although several detection methods are available, reliable and efficient tools for on-site WhSMV detection are currently lacking. In this study, a Loop-Mediated Isothermal Amplification (LAMP) method was developed for rapid and reliable field detection of WhSMV. We designed WhSMV-specific primers for the LAMP assay and optimized reaction conditions for increased sensitivity and specificity using infected plant samples. ResultsWe have developed a diagnostic method utilizing the Loop-Mediated Isothermal Amplification (LAMP) technique capable of rapidly and reliably detecting WhSMV. The LAMP assay has been optimized to enhance sensitivity, specificity, and cost-effectiveness. ConclusionThe LAMP assay described here represents a valuable tool for early WhSMV detection, serving to mitigate the adverse economic and social impacts of this viral pathogen. By enabling swift and accurate identification, this assay can significantly improve the sustainability of cereal production systems, safeguarding crop yields against the detrimental effects of WhSMV.

Artificial Intelligence: A Quick Overview of Speech Recognition System

Abstract: The study of intelligent behaviour is known as artificial intelligence. Wagner said, "We build intelligent machines in science." By this, he implied that the primary goal is to teach these sophisticated devices, such as computers, to examine and comprehend human conduct. numerous believed that human intelligence might be understood through the construction of several programmes, while other researchers believed that numerous basics should be required in order to accomplish the intended objective. We can carry out or replicate any type of task we conduct using a computer. Therefore, computers are the ideal devices for developing artificial intelligence. In the hopes that they will outperform computers in several other areas, some researchers created additional computing devices. Billions of dollars were invested in creating new devices that surpassed the speed of the computer used to simulate software. However, the computers and the code both need to be extremely quick. As you learn about artificial intelligence, you may wonder, what really is intelligence? How is intelligence measured? Turn become something quite significant. In this case, the programme that evaluates its surroundings acts as the intelligent agent. Modern digital computers and human minds are comparable to one other in ways such as symbolic information processing systems. Both use symbolic data as input, change it in accordance with predetermined guidelines, and then use the results to solve issues. In order to mechanically replicate intelligent human activity, artificial intelligence researchers have programmed some algorithms to recognise intelligent human behaviour. Human conduct in language processing, chess, and other games, as well as medical diagnostics, may be used to monitor this activity. Voice recognition is a key component of AI. A certain voice may be recognised using voice recognition technology. The cornerstone for speaker identification is voice signals. Voice targeting is applicable to a wide range of applications, including voice mail, database access, phone banking, and phone purchasing. The ability to enter one's voice for verification is among the most potent uses of voice recognition for security. The fundamental means of interpersonal communication is speech. The technique of translating voice sounds into appropriate text is known as speech recognition. Over the past few years, speech recognition technology has advanced significantly. Nonetheless, there are other significant study obstacles, such as variations in speaker and language, ambient sound, word size, etc. This study aims to give a comprehensive overview of speech acceptance by summarising the several approaches utilised in the standard speech system and describing the numerous processes involved

Lysis-Free Isolation and Direct Amplification of Pathogenic Bacterial DNA Using Diatom Frustules.

DNA has been implicated as an important biomarker for the diagnosis of bacterial infections. Herein, we developed a streamlined methodology that uses diatom frustules (DFs) to liberate and capture bacterial DNA and allows direct downstream amplification tests without any lysis, washing, or elution steps. Unlike most conventional DNA isolation methods that rely on cell lysis to release bacterial DNA, DFs can trigger the oxidative stress response of bacterial cells to promote bacterial membrane vesicle formation and DNA release by generating reactive oxygen species in aqueous solutions. Due to the hierarchical porous structure, DFs provided high DNA capture efficiency exceeding 80% over a wide range of DNA amounts from 10 pg to 10 ng, making only 10 μg DFs sufficient for each test. Since laborious liquid handling steps are not required, the entire DNA sample preparation process using DFs can be completed within 3 min. The diagnostic use of this DF-based methodology was illustrated, which showed that the DNA of the pathogenic bacteria in serum samples was isolated by DFs and directly detected using polymerase chain reaction (PCR) at concentrations as low as 102 CFU/mL, outperforming the most used approaches based on solid-phase DNA extraction. Furthermore, most of the bacterial cells were still alive after DNA isolation using DFs, providing the possibility of recycling samples for storage and further diagnosis. The proposed DF-based methodology is anticipated to simplify bacterial infection diagnosis and be broadly applied to various medical diagnoses and biological research.

CD62-L down-regulation after L18-MDP stimulation as a complementary flow cytometry functional assay for the diagnosis of XIAP deficiency.

X-linked inhibitor of apoptosis (XIAP) deficiency is an infrequent inborn error of immunity caused by mutations in XIAP gene. Most cases present with absence of XIAP protein which can be detected by flow cytometry (FC), representing a rapid diagnostic method. However, since some genetic defects may not preclude protein expression, it is important to include a complementary functional test in the laboratory workup of these patients. L-selectin (CD62-L) is a molecule that is cleaved from the surface membrane of leukocytes upon stimulation of different receptors such as toll like receptors (TLRs) and nucleotide-binding oligomerization domain-like receptors (NLRs), including NOD2. Considering that XIAP deficiency impairs NOD2 signaling, we decided to assess CD62-L down-regulation by FC post-stimulation of neutrophils and monocytes with L18-muramyl Di-Peptide (L18-MDP), a NOD2 specific agonist, in order to develop a novel assay for the functional evaluation of patients with suspicion of XIAP defects. Whole blood samples from 20 healthy controls (HC) and four patients with confirmed molecular diagnosis of XIAP deficiency were stimulated with 200 ng/mL of L18-MDP for 2 h. Stimulation with 100 ng/mL of lipopolysaccharide (LPS) was carried out in parallel as a positive control of CD62-L shedding. CD62-L expression was evaluated by FC using an anti CD62-L- antibody and down-regulation was assessed by calculating the difference in CD62-L expression before and after stimulation, both in terms of percentage of CD62-L expressing cells (Δ%CD62-L) and median fluorescence intensity (ΔMFI%). Neutrophils and monocytes from XIAP deficient patients displayed a significantly diminished response to L18-MDP stimulation compared with HC (p < 0.0001), indicating a severely altered mechanism of CD62-L down-regulation following activation of NOD2-XIAP axis. On the other hand, the response to LPS stimulation was comparable between patients and heathy controls, suggesting preserved CD62-L shedding with a different stimulus. FC detection of CD62-L down-regulation in monocytes and neutrophils after whole blood stimulation with L18-MDP results in an effective and rapid functional test for the identification of XIAP deficient patients.

Educating patients on osteoporosis and bone health: Can "ChatGPT" provide high-quality content?

The rise of artificial intelligence (AI) models like ChatGPT offers potential for varied applications, including patient education in healthcare. With gaps in osteoporosis and bone health knowledge and adherence to prevention and treatment, this study aims to evaluate the accuracy of ChatGPT in delivering evidence-based information related to osteoporosis. Twenty of the most common frequently asked questions (FAQs) related to osteoporosis were subcategorized into diagnosis, diagnostic method, risk factors, and treatment and prevention. These FAQs were sourced online and inputted into ChatGPT-3.5. Three orthopedic surgeons and one advanced practice provider who routinely treat patients with fragility fractures independently reviewed the ChatGPT-generated answers, grading them on a scale from 0 (harmful) to 4 (excellent). Mean response accuracy scores were calculated. To compare the variance of the means across the four categories, a one-way analysis of variance (ANOVA) was used. ChatGPT displayed an overall mean accuracy score of 91%. Its responses were graded as "accurate requiring minimal clarification" or "excellent," with a mean response score ranging from 3.25 to 4. No answers were deemed inaccurate or harmful. No significant difference was observed in the means of responses across the defined categories. ChatGPT-3.5 provided high-quality educational content. It showcased a high degree of accuracy in addressing osteoporosis-related questions, aligning closely with expert opinions and current literature, with structured and inclusive answers. However, while AI models can enhance patient information accessibility, they should be used as an adjunct rather than a substitute for human expertise and clinical judgment.

Analysis of gesture recognition applications based on deep learning

With the advent of artificial intelligence, deep learning has been continuously evolving. It has progressed from its initial stages, exemplified by AlphaGo, to the development of related technologies exclusively centred around deep learning for the game of Go. Subsequently, deep learning has found applications in an increasing number of domains, including healthcare, sports, and more. This paper delves into the profound influence and development of deep learning as the foundation for analysing its impact on gesture recognition. Deep learning has brought about significant advancements in this field, enabling more precise and versatile gesture recognition systems. Furthermore, we explore specific use cases and contexts where deep learning has been harnessed, such as in medical diagnostics and sports performance analysis. While scrutinizing these applications, a promising future for deep learning becomes evident, with the potential to revolutionize various industries and enhance technology interaction through the development of more intuitive and sophisticated gesture recognition systems. The continuous growth and evolution of deep learning offer a bright prospect for the future of human-machine interfaces and artificial intelligence-driven solutions.

IMPLEMENTING ADVANCED X-RAY TECHNOLOGIES AT THE THIRD CHERKASY CITY EMERGENCY HOSPITAL

Introduction: In recent decades, the world has seen a transition from traditional to digital radiography. Ukraine has also joined this process, replacing outdated equipment with modern digital systems that have become especially important during the Covid-19 epidemic and the Russian-Ukrainian war. The Third Cherkasy City Emergency Hospital implements modern digital technologies of X-ray diagnostics in order to improve the provision of medical care and diagnostics. This study aims at investigating and evaluating the experience of implementing digital X-ray technologies in The Third Cherkasy City Emergency Hospital. Materials and methods: This study involved analyzing medical records from The Third Cherkasy City Emergency Hospital and conducting individual interviews with eight healthcare professionals, including radiologists, other physicians, and a nurse from the hospital. Results. Beginning in 2017, due to the decentralization policy and changes in the Cherkassy city administration, there has been a gradual transition of the medical sector, particularly The Third Cherkassy City Emergency Hospital, to modern digital equipment. This transition has significantly enhanced the quality of medical services and streamlined the process of diagnosing and treating patients. The integration of digital X-ray technologies into the hospital's medical practice has expedited the acquisition and processing of X-ray images, reducing labor and time previously spent on film installation and processing. This reduction in personnel and equipment needs has led to cost savings and process optimization. The implementation of modern X-ray technology has notably improved the efficiency and workflow of hospital staff, facilitating faster and more accurate processing and analysis of X-ray images. The absence of chemical reagents and the utilization of high-quality, clear images have reduced physical and emotional stress among medical personnel. Furthermore, the technology has enabled more effective detection of various diseases at early stages and streamlined emergency care for injured patients. Additionally, the impact of X-ray radiation on both patients and medical staff has decreased. However, the adoption of digital technologies has brought forth new challenges, such as organizing storage and access to digital archives. Addressing these challenges necessitates additional investments in server equipment and the development of specialized programs. Ensuring the reliability and security of medical information storage is crucial to prevent potential data loss or corruption.

AN IN-DEPTH EXPLORATION OF ARTIFICIAL INTELLIGENCE IN THE CONTEXT OF CONTEMPORARY DATA CHALLENGES; DIFFERENCES BETWEEN HUMAN AND MACHINE LEARNING

Machine learning and artificial intelligence produce algorithms that appear to be able to make "intelligent" decisions similar to those of humans but function differently from human thinking. To make decisions based on machine suggestions, humans should be able to understand the background of these suggestions. However, since humans are oriented to understand human intelligence, it is not yet fully clear whether humans can truly understand the "thinking" generated by machine learning, or whether they merely transfer human-like cognitive processes to machines. In addition, media representations of artificial intelligence show higher capabilities and greater human likeness than they currently have. In our daily lives, we increasingly encounter assistance systems that are designed to facilitate human tasks and decisions based on intelligent algorithms. These algorithms are predominantly based on machine learning technologies, which make it possible to discover previously unknown correlations and patterns by analyzing large amounts of data. One example is the machine analysis of thousands of X-ray images of sick and healthy people. This requires identifying the patterns by which images labeled as "healthy" can be distinguished from those labeled as "sick" and to find an algorithm that identifies the latter. In the meantime, "trained" algorithms created in this way are used in various fields of application, not only for medical diagnoses but also in the pre-selection of applicants for a job advertisement or in communication with the help of voice assistants. These voice assistants are enabled by intelligent algorithms to offer internet services through short commands. Harald Lesch, referring to his book Unpredictable, written together with Thomas Schwarz, says the development of artificial intelligence can be compared to bringing aliens to Earth. With machine learning, a previously unknown form of non-human intelligence has been created. This chapter discusses whether forms of artificial intelligence, as they are currently being publicly discussed, differ substantially from human thinking. Furthermore, it will be discussed to what extent humans can comprehend the functioning of artificial intelligence that has been created through machine learning when interacting with them. Finally, the risks and opportunities will be weighed and discussed..

Evaluation of Ultrasound Screening Method and Prevalence for Developmental Hip Dysplasia In The Central Anatolia

Objectives:This study aimed to assess the frequency and subsequent results of developmental dysplasia of the hip (DDH) in newborns in Central Anatolia who were admitted to Konya City Hospital and evaluate the efficiency of the existing screening and treatment methods in detecting and managing DDH in infants. Methods: Total of 10,650 infants were evaluated who had Graf USG screening in Konya City Hospital. İnfants born at or after 38 weeks of gestation were included, while premature infants were excluded. Additionally, we included infants who underwent USG screening between 30 and 90 days after birth, and whose hip development physiology was adequately visualized and assessed during the USG examination. Results: The study examined 8,695 term infants (52.5% male, 47.5% female) who underwent USG screening between 2020 and 2022. Participants mean gestational age at the time of the first USG examination was 7.94±2.07 weeks. Graf type 1 was higher in males (%97-96,5) and graf type 2 was higher in females (%7,2-7,8). Radiologists prefer to re-examine after 1 month for type 2a(%84,49-82,02) but orthopaedic consultation for type Type-2B, 2C, and type-3. The majority of infants (93.6%) underwent only one USG screening. No patients who continued regular USG control and treatment required surgical treatment requiring osteotomy. Conclusion: USG is an early diagnostic method for developmental hip dysplasia (DDH), which allows for simple treatment options and prevention of complications. It is a simple, inexpensive, and non-invasive method. Our study supports that regular USG screenings in infants eliminate the need for surgical procedures requiring osteotomy.

Generalized nonlinear hybrid-norm parallel sparse filtering for bearing fault diagnosis under complex interference

Abstract Considerable attention has been garnered by the bearing fault diagnostic method, founded on sparse feature learning, for its exceptional robustness and adaptability to noise. Being an effective sparse learning method, parallel sparse filtering (PSF) has found successful applications in intelligent fault diagnosis. However, influenced by mechanical structural collisions and complex transmission paths, the obtained bearing signals come with abnormal impacts and noise, resulting in a reduced reliability of the PSF. Additionally, PSF constrains features and samples with l_(1⁄2) norm, which is significantly disturbed by noise. To address these issues, generalized nonlinear hybrid-norm parallel sparse filtering (GNHNPSF) is proposed. Firstly, the Sigmoid nonlinear function is examined to suppress the amplitude of the anomalous shock. Subsequently, the hybrid-norm constraints, which involve the application of a l_(1⁄2) norm to the column features of the input signal and a l_(1⁄3) norm to the row features, can be employed to effectively extract distinctive information from the original data. The GNHNPSF is demonstrated to improve the accuracy of bearing fault diagnosis under complex interference, as evidenced by the results of simulation and experiments.

INDEX-BASED ASSESSMENT ALGORITHM FOR ORAL HYGIENE IN WEARERS OF FIXED DENTAL BRIDGES

Introduction: The study focuses on the importance of maintaining oral hygiene, especially in wearers of fixed dental bridge. The impact of inadequate oral hygiene care on the development of dental diseases and the necessity for developing effective methods for its assessment are examined. Research objective: The aim of this study is to refine the fluorescent diagnostic method in orthopedic dentistry and to develop an index for assessing the hygienic status of natural teeth and fixed dental prostheses through an in vivo experiment. This method is intended to improve the diagnosis of oral conditions in wearers of fixed prostheses, ultimately enhancing the quality of their orthopedic treatment. Materials and methods: The study is based on the application of fluorescent diagnostics using Rhodamine B dye, which, when exposed to ultraviolet light, allows for the detection of dental deposits. The technique involves treating the surfaces of teeth and prostheses with a special solution that enhances the visibility of dental plaque under ultraviolet illumination. Research results: The experimental results demonstrated the high efficiency of the fluorescent method in detecting dental deposits both on the enamel of natural teeth and on the ceramic surface of fixed dentures. The digital analysis method allows for obtaining specific graphical indicators of the biofilm area, enhancing the relevance of an individual approach. Conclusions: Conclusions: The experimental study confirmed the high efficiency of the refined fluorescent method for assessing the hygienic status of the oral cavity, applicable to both natural teeth and fixed orthopedic constructions. This technique enables accurate assessment of hygiene status, facilitating effective prevention of dental diseases and optimization of treatment procedures. The method is convenient to use, and the software is accessible for clinicians. Further observations on the outcomes of this technique in patients with various orthopedic profiles are planned to expand its applicability.

Bright and durable scintillation from colloidal quantum shells

Efficient, fast, and robust scintillators for ionizing radiation detection are crucial in various fields, including medical diagnostics, defense, and particle physics. However, traditional scintillator technologies face challenges in simultaneously achieving optimal performance and high-speed operation. Herein we introduce colloidal quantum shell heterostructures as X-ray and electron scintillators, combining efficiency, speed, and durability. Quantum shells exhibit light yields up to 70,000 photons MeV−1 at room temperature, enabled by their high multiexciton radiative efficiency thanks to long Auger-Meitner lifetimes (>10 ns). Radioluminescence is fast, with lifetimes of 2.5 ns and sub-100 ps rise times. Additionally, quantum shells do not exhibit afterglow and maintain stable scintillation even under high X-ray doses (>109 Gy). Furthermore, we showcase quantum shells for X-ray imaging achieving a spatial resolution as high as 28 line pairs per millimeter. Overall, efficient, fast, and durable scintillation make quantum shells appealing in applications ranging from ultrafast radiation detection to high-resolution imaging.

Pharmacotherapy for alcohol use disorder among adults with medical disorders in Sweden

BackgroundAlcohol-attributable medical disorders are prevalent among individuals with alcohol use disorder (AUD). However, there is a lack of research on prescriptions of pharmacological treatment for AUD in those with comorbid conditions. This study aims to investigate the utilization of pharmacological treatment (acamprosate, disulfiram and naltrexone) in specialist care among patients with AUD and comorbid medical diagnoses.MethodsThis was a descriptive register-based Swedish national cohort study including 132,728 adults diagnosed with AUD (N = 270,933) between 2007 and 2015. The exposure was alcohol-attributable categories of comorbid medical diagnoses. Odds ratios (OR) were calculated using mixed-effect logistic regression analyses for any filled prescription of acamprosate, disulfiram or oral naltrexone within 12 months post AUD diagnosis.ResultsIndividuals with comorbid alcohol-attributable medical diagnoses had lower odds of filling prescriptions for any type of AUD pharmacotherapy compared to those without such comorbidities. Cardiovascular (OR = 0.41 [95% CI: 0.39–0.43]), neurological (OR = 0.52 [95% CI: 0.48–0.56]) and gastrointestinal (OR = 0.57 [95% CI: 0.54–0.60]) diseases were associated with the lowest rates of prescription receipt. The presence of diagnoses which are contraindications to AUD pharmacotherapy did not fully explain the low prescription rate.ConclusionThere is a substantial underutilization of AUD pharmacotherapy in patients with AUD and comorbid medical disorders in specialist care. Increasing the provision of pharmacotherapy to this group of patients is essential and may prevent morbidity and mortality. There is a need to further understand barriers to medical treatment both from the patient and prescriber perspective.

Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning

NMR is widely considered the gold standard for organic compound structure determination. As such, NMR is routinely used in organic compound identification, drug metabolite characterization, natural product discovery, and the deconvolution of metabolite mixtures in biofluids (metabolomics and exposomics). In many cases, compound identification by NMR is achieved by matching measured NMR spectra to experimentally collected NMR spectral reference libraries. Unfortunately, the number of available experimental NMR reference spectra, especially for metabolomics, medical diagnostics, or drug-related studies, is quite small. This experimental gap could be filled by predicting NMR chemical shifts for known compounds using computational methods such as machine learning (ML). Here, we describe how a deep learning algorithm that is trained on a high-quality, “solvent-aware” experimental dataset can be used to predict 1H chemical shifts more accurately than any other known method. The new program, called PROSPRE (PROton Shift PREdictor) can accurately (mean absolute error of &lt;0.10 ppm) predict 1H chemical shifts in water (at neutral pH), chloroform, dimethyl sulfoxide, and methanol from a user-submitted chemical structure. PROSPRE (pronounced “prosper”) has also been used to predict 1H chemical shifts for &gt;600,000 molecules in many popular metabolomic, drug, and natural product databases.

Alzheimer's disease early screening and staged detection with plasma proteome using machine learning and convolutional neural network.

Alzheimer's disease (AD) stands as the prevalent progressive neurodegenerative disease, precipitating cognitive impairment and even memory loss. Amyloid biomarkers have been extensively used in the diagnosis of AD. However, amyloid proteins offer limited information about the disease process and accurate diagnosis depends on the presence of a substantial accumulation of amyloid deposition which significantly impedes the early screening of AD. In this study, we have combined plasma proteomics with an ensemble learning model (CatBoost) to develop a cost-effective and non-invasive diagnostic method for AD. A longitudinal panel has been identified that can serve as reliable biomarkers across the entire progression of AD. Simultaneously, we have developed a neural network algorithm that utilizes plasma proteins to detect stages of Alzheimer's disease. Based on the developed longitudinal panel, the CatBoost model achieved an area under the operating curve of at least 0.90 in distinguishing mild cognitive impairment from cognitively normal. The neural network model was utilized for the detection of three stages of AD, and the results demonstrated that the neural network model exhibited an accuracy as high as 0.83, surpassing that of the traditional machine learning model.

Harnessing AI, IoT and Quantum Resonance Magnetic Analysis for Digital Diagnosis and Treatment

This research presents the development and implementation of a novel healthcare ecosystem leveraging Internet of Things (IoT), Artificial Intelligence (AI), Deep Learning and Quantum Magnetic Resonance Analysis (QMRA) technology. The aim is to streamline medical diagnostics and treatment processes by integrating a hardware device capable of conducting comprehensive body scans in minutes, coupled with an Android application for data collection and presentation. The hardware device utilizes Quantum Magnetic Resonance Analyzer for rapid and accurate body scanning, enabling users to record symptoms through voice clips and capture images of wounds or other concerns. The collected data is securely transmitted to a backend database hosted on Firebase for storage and retrieval. The Android app facilitates seamless access to patient profiles by registered doctors, empowering them to analyze scan reports, review recorded symptoms, and view images for informed diagnosis and treatment planning. By combining IoT, AI, and Quantum Magnetic Resonance Analysis, this healthcare ecosystem offers a transformative approach to medical care, improving efficiency, accessibility, and accuracy in diagnosis and treatment.

Label-free voltammetric screening of human blood serum

The current study presents a comprehensive voltammetric investigation into the direct analysis of untreated human blood serum in a phosphate buffer at an unmodified, graphite electrode by means of voltammetry. By employing advanced square-wave voltammetry at an edge plane pyrolytic graphite electrode (EPPGE), the basic principles were established for developing a sensitive, fast, simple, and label-free method for the simultaneous screening of uric acid, bilirubin, and albumin analytes that are present in human blood serum and are quite essential for rapid medical diagnostics. The electrochemical protocol utilizes the specific structural patterns of the EPPGE, the inherent redox and adsorption properties of the analysed analytes, and the sensitivity and rapidity of the employed advanced voltammetric technique. The methodology has been successfully applied for quantification of the considered analytes in a series of samples of human blood serum and was compared with the standard methods used in a clinical biochemical laboratory. This novel method represents a significant advancement towards the development of point-of-care devices aimed at swiftly and simultaneously quantifying uric acid, bilirubin, and albumin levels in human serum.

Intelligent Magnetic Microrobots with Fluorescent Internal Memory for Monitoring Intragastric Acidity

AbstractThis study investigates the dynamic fluctuations of pH caused by gastric acid secretion, a process of both biological and clinical significance, with microrobots. Abnormal patterns of acidity often indicate gastrointestinal diseases, underlying the importance of precise intragastric pH monitoring. Traditional methods using fluorescent probes face challenges due to their faint solid‐state fluorescence, limited target specificity, and accuracy. To overcome these obstacles, pH‐responsive fluorescent organic microparticles decorated with magnetite (Fe3O4) nanoparticles are engineered. These microrobots exhibit a unique fluorescence switching capability at a critical pH, enabling the monitoring of gastric acidity. The magnetic part of these microrobots ensures magnetic maneuverability to enable targeted navigation. The microrobots’ fluorescence switching mechanism is elucidated through comprehensive spectroscopy, microscopy, and X‐ray diffraction analyses, revealing molecular‐level structural transformations upon interaction with gastric acid and antacids. These transformations, specifically protonation and deprotonation of the microrobots’ fluorescent components, prompt a distinct fluorescence response correlating with pH shifts. In vitro and ex vivo experiments, simulating stomach conditions, confirm the microrobots’ efficacy in pH‐responsive imaging. The results showcase the promising diagnostic potential of microrobots for gastrointestinal tract diseases, marking a significant advancement in imaging‐based medical diagnostics at targeted locations.

THE ANALYSIS OF FURNISHING AND DESIGNING OF DIAGNOSTIC CENTERS WITHIN SPORTS OBJECTS

The increasing development of mankind can be seen in the progress in the design of sports facilities which must satisfy certain high standards of construction and equipment and which need to offer a maximum number of services in their environment in order to meet the needs of customers. One of the major positive influences on users includes opening diagnostic centers within sports objects which must be furnished properly and functionally. When furnishing and designing the interior, it is necessary to pay attention to the choice of colours, floor materials, lighting, and most importantly – the equipment that will be used in the premises of the center. They can be separated into zones: medical rooms, laboratory, and diagnostics and training area. Each of these zones has different requirements for equipment with special attention to the privacy of the patients being tested, their safety and keeping the space clean.