Diagnostic Methods Research Articles

Diagnosis and treatment of patients with gastroesophageal reflux disease - a systematic review of cost-effectiveness and economic burden.

This study aims to review the existing knowledge on the cost-effectiveness and item costs related to the diagnosis and treatment of gastroesophageal reflux disease (GERD) patients at different stages. The study adhered to the PRISMA guidelines. The systematic search involved several steps: finding and identifying relevant articles, filtering them according to the set criteria, and examining the final number of selected articles to obtain the primary information. The number of articles published between 2013 and September 2024 in the Web of Science and PubMed databases was considered. The CHEERS checklist was used for the risk of bias assessment. Ultimately, 36 studies were included. Regarding the cost-effectiveness of GERD treatment, Proton pump inhibitors (PPIs) appeared to be the dominant solution for non-refractory patients. However, this might change with the adoption of the novel drug vonoprazan, which is more effective and cheaper. With advancements in emerging technologies, new diagnostic and screening approaches such as Endosheath, Cytosponge, and combined multichannel intraluminal impedance and pH monitoring catheters should be considered, with potential implications for optimal GERD management strategies. The new diagnostic methods are reliable, safe, and more comfortable than standard procedures. PPIs are commonly used as the first line of treatment for GERD. Surgery, such as magnetic sphincter augmentation or laparoscopic fundoplication, is only recommended for patients with treatment-resistant GERD or severe symptoms. Advances in emerging technologies for diagnostics and screening may lead to a shift in the entire GERD treatment model, offering less invasive options and potentially improving patients' quality of life.

Artificial Intelligence in Lung Cancer Detection: Clinical and Economic Assessment of Retrospective CT Analysis Two Years Post-COVID-19 Pandemic

BACKGROUND: The use of chest computed tomography (CT) scans in Krasnoyarsk Krai, Russia, has increased since 2020, during the COVID-19 pandemic. This period also saw a 5.2% decrease in lung cancer (LC) incidence. The potential for missed LC cases has led to the investigation of new diagnostic methods, including the use of artificial intelligence (AI) for analyzing retrospective data. AIM: The study aimed to evaluate the effectiveness of an AI algorithm in identifying patients at high risk for LC using chest CT data from the COVID-19 pandemic. METHODS: A retrospective analysis was conducted on chest CTs from patients diagnosed with COVID-19 in the Krasnoyarsk region, using scans from November 1, 2020, to February 28, 2021. The AI algorithm "Chest-IRA" was applied to detect pulmonary nodules larger than 100 mm³. Radiologists classified the nodules detected by AI into three categories based on LC probability. The economic assessment of the AI algorithm included salary costs and potential savings from early LC treatment. RESULTS: Out of 10,500 CTs, the AI algorithm found nodules in 484 cases. Of these, 192 were highly likely to have LC, 103 showed no signs, and 60 were inconclusive. 112 patients with high or intermediate risk did not seek treatment. The AI confirmed lung cancer in 100 cases, 28.2% of those detected. Early-stage LC was found in 35% of cases, while 65% were at later stages. AI could save about 25.04 months of radiologist work, costing 2.43 million RUB. Early detection savings are estimated at 10.6 to 12.5 million RUB per 10,500 CTs, with a five-year economic impact of 259.4 to 305.1 million RUB. CONCLUSION: AI has proven effective in identifying pulmonary nodules amidst COVID-19, highlighting its potential to improve early detection and diagnostic accuracy, leading to earlier and more precise treatments.

Insights of gut-liver axis in hepatic diseases: Mechanisms, clinical implications, and therapeutic potentials

With the rising prevalence of chronic liver diseases worldwide, there exists a need to diversify our artillery to incorporate a plethora of diagnostic and therapeutic methods to combat this disease. Currently, the most common causes of liver disease are non-alcoholic fatty liver disease, hepatitis, and alcoholic liver disease. Some of these chronic diseases have the potential to transform into hepatocellular carcinoma with advancing fibrosis. In this review, we analyse the relationship between the gut and liver and their significance in liver disease. This two-way relationship has interesting effects on each other in liver diseases. The gut microbiota, through its metabolites, influences the metabolism in numerous ways. Careful manipulation of its composition can lead to the discovery of numerous therapeutic potentials that can be applied in the treatment of various liver diseases. Numerous cohort studies with a pan-omics approach are required to understand the association between the gut microbiome and hepatic disease progression through which we can identify effective ways to deal with this issue.

Development of a Recombinase Polymerase Amplification-Coupled CRISPR/Cas12a Platform for Rapid Detection of Antimicrobial-Resistant Genes in Carbapenem-Resistant Enterobacterales

The worldwide spread of carbapenemase-producing Enterobacterales (CPE) represents a significant threat owing to the high mortality and morbidity rates. Traditional diagnostic methods are often too slow and complex for rapid point-of-care testing. Therefore, we developed a recombinase polymerase amplification (RPA)-coupled CRISPR/Cas12a system (RCCS), a rapid, accurate, and simple diagnostic platform for detecting antimicrobial-resistant genes. The RCCS detected carbapenemase genes (blaKPC and blaNDM) within 50 min, including 10 min for DNA extraction and 30–40 min for RCCS reaction (a 20 min RPA reaction with a 10–20-min CRISPR/Cas12a assay). Fluorescence signals obtained from the RCCS platform were visualized using lateral-flow test strips (LFSs) and real-time and endpoint fluorescence. The LFS clearly displayed test lines while detecting carbapenemase genes. Furthermore, the RCCS platform demonstrated high sensitivity by successfully detecting blaKPC and blaNDM at the attomolar and picomolar levels, respectively. The accuracy of the RCCS platform was validated with clinical isolates of Klebsiella pneumoniae and Escherichia coli; a 100% detection accuracy was achieved, which has not been reported when using conventional PCR. Overall, these findings indicate that the RCCS platform is a powerful tool for rapid and reliable detection of carbapenemase-encoding genes, with significant potential for implementation in point-of-care settings and resource-limited environments.

Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review

ObjectiveTo conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection.DesignWe searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided.ResultsSeveral studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT, and the 3D microanatomy characterisation of human tuberculosis lung using μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented.ConclusionThe literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo.

Application of 3D printing in bacterial detection

Diseases caused by bacterial infections, especially pathogen variations and drug-resistant bacteria, are a serious threat to human health globally. Accurate and rapid identification of bacterial pathogens is essential for early diagnosis of infections and timely treatment of disease. At present, the routine diagnostic methods for identifying bacterial pathogens in clinic are bacterial culture, immunological detection, genetic analysis, etc. These methods occupy foundational position for bacterial detection that favors for clinical diagnosis of pathogen infection in daily life. Notably, 3D printing technology, together with 3D bioprinting technology, provides more options for bacterial detection due to its personalized design and manufacturing. Compared with traditional methods, 3D printing provides a more convenient, rapid and accurate bacterial detection platform, thus meeting the urgent needs of clinical and scientific research. Here, we have summarized the research progress and given a comprehensive review of 3D printing technology in bacterial detection, including bacterial detection sensors, microfluidic chips, bioprinting microarray technology, AI and spectroscopy technology, providing a scientific reference and filling in gaps in 3D printing-assistance bacterial detection. At the same time, technical advantages, challenges and future development trends will also be discussed in related healthcare fields.

Rolling Bearing Fault Diagnosis Model Based on External Attention Integrated Convolutional Neural Network under Imbalanced Data Conditions

Abstract Rolling bearings are essential components in numerous mechanical systems, and their failure can result in considerable downtime and expensive repairs. Therefore, accurate and timely fault diagnosis is vital for effective predictive maintenance and overall reliability. Traditional diagnostic methods often struggle with complex and non-stationary signals, compounded by issues of data imbalance in 
 realworld scenarios. A method for diagnosing rolling bearing faults has been developed in this paper utilizing External Attention (EA), Convolutional Neural Networks (CNN), and Continuous Wavelet Transform (CWT), specifically addressing the challenge of imbalanced sample data. This approach offers significant advantages, including a reduction in complexity by eliminating the need for data augmentation and leveraging external attention for enhanced feature extraction from samples. Compared to other attention mechanisms, this method demonstrates outstanding performance on both training and testing sets with imbalanced samples, exhibiting minimal overfitting tendencies. The proposed CWT-EACNN method effectively addresses the challenge of imbalanced sample data in rolling bearing fault diagnosis, demonstrating exceptional performance and reduced complexity.

Structural epilepsy: current state of the problem

This review emphasizes modern conceptions in the fundamental and clinical aspects of structural epilepsy. Highly informative neuroimaging, electrophysiological and pathomorphological diagnostic methods contribution to the study of this problem is noted. Current understanding of the pathogenesis and clinical picture of the most common types of drug resistant epilepsy are presented: hippocampal sclerosis, focal cortical dysplasia and their surgical correction methods. We considered perspective directions of fundamental clinical and experimental research, focusing on the study of epileptogenesis mechanisms and developing effective methods for prevention and treatment.

Lyme borreliosis in Brazil: a critical review on the Baggio-Yoshinari syndrome (Brazilian Lyme-like disease).

SUMMARYLyme borreliosis or Lyme disease is the most frequently reported tick-borne disease in the Northern Hemisphere. In countries of the Southern Hemisphere, such as Brazil, since the early 1990s, some researchers have argued for the existence of an autochthonous Lyme-like borreliosis, known locally as the Baggio-Yoshinari syndrome (BYS), an alleged "Brazilian borreliosis" supposedly caused by a different strain of Borrelia burgdorferi and transmitted by hard ticks. Currently, the existence of BYS in Brazil is still accepted by a large part of the human health care workers, scientists, medical societies, and patients. In fact, this alleged "Brazilian borreliosis" has been the tick-borne zoonotic disease with the greatest number of reported cases and published studies in Brazil during this century, second only to Brazilian spotted fever. In this manuscript, we reviewed all manuscripts directly related to BYS that have been published in Brazil during the last 35 years. This analysis included 199 individual human cases that have been reported in Brazil since 1989, plus multiple studies on ticks, domestic, and wild animals. Our revision aimed to provide a critical opinion on whether the current published works allow healthcare workers, public health agencies, and patients to accept the existence of Lyme disease, BYS, or other Lyme borreliosis-related disease in Brazil. For this purpose, we evaluated the strengths and weaknesses of each published study, considering the diagnostic methods used, such as serological, microbiological, and molecular analyses. Based on these evaluations, we conclude that there is not enough evidence to support the occurrence of Lyme borreliosis in Brazil or that BYS (Brazilian Lyme-like disease) is caused by a bacterium of the genus Borrelia. This assumption is based on the inaccuracy, unreliability, and misinterpretation of the different diagnostic methods that have been used in Brazil. Recognizing the lack of technical evidence for the occurrence of Lyme borreliosis in Brazil has highly relevant implications. For example, it becomes imperative to raise awareness among the country's medical profession, as they have adopted unnecessary and extreme therapies recommended for patients with a supposed borrelial infection, including BYS, in Brazil. Finally, the technical analyses carried out in this study could be applied to other countries in the Southern Hemisphere (e.g., Argentina, South Africa, Australia), where cases classified and alleged as Lyme disease have been reported.

Diabetic Cardiomyopathy: An Update on Emerging Pathological Mechanisms.

Diabetic Cardiomyopathy (DCM) is a notable consequence of diabetes mellitus, distinguished by cardiac dysfunction that occurs separately from coronary artery disease or hypertension. A recent study has revealed an intricate interaction of pathogenic processes that contribute to DCM. Important aspects involve the dysregulation of glucose metabolism, resulting in heightened oxidative stress and impaired mitochondrial function. In addition, persistent high blood sugar levels stimulate inflammatory pathways, which contribute to the development of heart fibrosis and remodelling. Additionally, changes in the way calcium is managed and the presence of insulin resistance are crucial factors in the formation and advancement of DCM. This may be due to the involvement of many molecular mechanistic pathways such as NLRP3, NF- κB, PKC, and MAPK with their downstream associated signaling pathways. Gaining a comprehensive understanding of these newly identified pathogenic pathways is crucial in order to design precise therapy approaches that can enhance the results for individuals suffering from diabetes. In addition, this review offers an in-depth review of not just pathogenic pathways and molecular mechanistic pathways but also diagnostic methods, treatment options, and clinical trials.

Determinants of Doctor–Patient Communication in Terms of Patient Rights During the COVID-19 Pandemic

Background. Today, the public does not want to be just a passive consumer of health services. Patients often expect to be informed and involved in decisions about their health. Objective. With better doctor–patient communication, patients are more likely to follow treatment recommendations. Material and methods. The study was conducted using a face-to-face survey method on a group of 203 adult, independent patients from 2021 to 2022 at a medical facility, i.e., a rehabilitation clinic. The purpose of this study was to assess the determinants of doctor–patient communication in terms of patient rights. One of patients’ rights is the right to information about their health condition and treatment methods and the right to ask questions when the doctor does not provide details about the treatment or diagnosis or when it is not understandable. Doctors providing information to the patient and the opportunity for the patient to ask questions are key elements in the process of making informed decisions regarding further medical treatment. Therefore, patients were divided into two groups: active (+) and passive in communication (−) with doctors. Results. Patients who were active in communication (33%) wanted to ask questions or had the opportunity to ask the doctor questions, and thus, they were able to take an active part in the discussion with the doctor. In contrast, patients who were passive in communication (67%) did not want to ask questions or did not have the opportunity to ask the doctor questions, and therefore, their active participation in the discussion and thus their right to ask questions may have been limited. The authors’ survey shows that respondents with active communication were significantly more likely than patients with passive communication (almost 100% vs. 86%) to obtain information about their condition (p = 0.002), diagnostic methods (p = 0.003), therapeutic methods (p = 0.00007), treatment results, and prognosis (p = 0.0008). Moreover, almost all respondents with active communication as opposed to respondents with passive communication (95% vs. 52%) rated communication with doctors highest (on a scale from 0 to 5), including credible and professional approach to patients (p < 0.0001), providing information in clear and simple language (p < 0.0001), answering questions asked by patients (p < 0.0001), openness and kindness (p < 0.0001), maintaining professional confidentiality (p < 0.0001), or emotional support (p < 0.0001). Conclusions. Hence, the primary key element of the medical consultation is appropriate amount and content of information given to the patient, providing explanations and answering questions. Also importantly, according to the results, active communication between patients and doctors was significantly influenced by female gender, higher education, and a positive evaluation of communication with doctors.

Development and Validation of Concept of Innovative Method of Computer-Aided Monitoring and Diagnostics of Machine Components

The monitoring and diagnostic system has been suggested as a non-destructive diagnostic method. The structure and operation of the suggested system can be described by the concept of digital shadow (DS). One of the main DS subsystems is a set of sensors properly placed on the monitored object and coupled with a discrete data processing model created in Matlab/Simulink. The discrete model, as another important DS subsystem of the monitored facility, transfers information about its technical condition to the operator based on data recorded by the sensor system. The digital monitoring model processes the recorded data in the form of the object’s response to actions caused by its operating conditions. This work formalized a mathematical model determining the coupling of the digital model with the sensors placed on the monitored object. The formulated method using DS, due to its sensitivity, enables the detection of the damage in the object at an early stage. The tests allowed for detecting the regularities enabling the determination of the area of damage to the shaft and its size.

CAD-PsorNet: deep transfer learning for computer-assisted diagnosis of skin psoriasis

Psoriasis, being a chronic, inflammatory, lifelong skin disorder, has become a major threat to the human population. The precise and effective diagnosis of psoriasis continues to be difficult for clinicians due to its varied nature. In northern India, the prevalence of psoriasis among adult population ranges from 0.44 to 2.8%. Chronic plaque psoriasis accounts for over 90% of cases. This study utilized a dataset of 325 raw images collected from a reputable local hospital using a digital camera under uniform lighting conditions. These images were processed to generate 496 image patches (both diseased and normal), which were then normalized and resized for model training. An automated psoriasis image recognition framework was developed using four state-of-the-art deep transfer learning models: VGG16, VGG19, MobileNetV1, and ResNet-50. The convolutional layers adopted various edge, shape, and color filters to generate the feature map for psoriasis detection. Each pre-trained model was adapted with two dense layers, one dropout layer, and one output layer to classify input images. Among these models, MobileNetV1 achieved the best performance, with 94.84% sensitivity, 89.37% specificity, and 97.24% overall accuracy. Hyper-parameter tuning was performed using grid search to optimize learning rates, batch sizes, and dropout rates. The AdaGrad (Adaptive gradient)) optimizer was chosen for its adaptive learning rate capabilities, facilitating quicker convergence in model performance. Consequently, the methodology’s performance improved to 94.25% sensitivity, 96.42% specificity, and 99.13% overall accuracy. The model’s performance was also compared with non-machine learning-based diagnostic methods, yielding a Dice coefficient of 0.98. However, the model’s effectiveness is dependent upon high-quality input images, as poor image conditions may affect accuracy, and it may not generalize well across diverse demographics or psoriasis variations, highlighting the need for varied training datasets for robustness.

Detecting Plant Infections: Prospects for Chlorophyll Fluorescence Imaging

Phytopathogens are a significant challenge to agriculture and food security. In this regard, methods for the early diagnosis of plant diseases, including optical methods, are being actively developed. This review focuses on one of the optical diagnostic methods, chlorophyll fluorescence (ChlF) imaging. ChlF reflects the activity of photosynthetic processes and responds subtly to environmental factors, which makes it an excellent tool for the early detection of stressors, including the detection of pathogens at a pre-symptomatic stage of disease. In this review, we analyze the peculiarities of changes in ChlF parameters depending on the type of pathogen (viral, bacterial, or fungal infection), the terms of disease progression, and its severity. The main mechanisms responsible for the changes in ChlF parameters during the interaction between pathogen and host plant are also summarized. We discuss the advantages and limitations of ChlF imaging in pathogen detection compared to other optical methods and ways to improve the sensitivity of ChlF imaging in the early detection of pathogens.

The study of Alzheimer's disease risk diagnosis based on natural killer cell marker genes in the multi-omics data.

Alzheimer's disease (AD) is a common neurodegenerative disorder, currently lacking effective early diagnostic methods. However, natural killer (NK) cells may play a potential role in AD pathogenesis. This study aims to identify AD-related feature genes from NK cell markers to construct a diagnostic model and explore their potential biological mechanisms in AD. Single-cell RNA sequencing data was used to identify NK cell markers. A novel feature selection algorithm, adaptive dynamic graph convolutional network (ADGCN), was proposed to extract AD-related feature genes and construct a diagnostic model. Differential, correlation and enrichment analyses were performed to understand the biological mechanisms of these genes. Immune infiltration analysis compared the immune microenvironment between AD and controls. Two regulatory networks explored interactions between feature genes, transcription factors and microRNAs. The association between SNPs and feature genes' expression was examined through expression quantitative trait loci analysis. Differential CpG sites were identified to analyze their association with the NK cell markers' expression. We developed an optimal diagnostic model (ADGCN-XGBoost) with 17 feature genes, demonstrating high diagnostic effectiveness across datasets. These genes were primarily related to macromolecule biosynthesis, cytoplasmic translation biological processes and ribosome pathway, and potentially modulated immune infiltration of AD patients. We predicted 27 target miRNAs and 21 transcription factors influencing these genes. Multimodal analysis identified 57 significant SNP-gene associations and seven CpG-gene pairs. This study proposed a novel feature selection algorithm and developed a diagnostic model based on 17 feature genes, providing new potential biomarkers for AD diagnosis.

Malignant Pleural Effusion: Diagnosis and Treatment—Up-to-Date Perspective

Malignant pleural effusion is the presence of malignant cells within the pleural fluid, representing the second most common cause of pleural exudate. Although diagnostic methods and management techniques for malignant pleural effusion have dramatically improved over the decades, the current treatment is still palliative, aiming to remove pleural fluid, possibly prevent its recurrence, and alleviate symptoms through a wide range of available procedures. Treatment should be tailored to the individual patient, considering comorbidities, size of the effusion, rate of fluid accumulation, underlying cardiac or respiratory conditions, rate of recurrence, presence of loculations or trapped lung, tumor characteristics, cancer type, and patient preferences. This manuscript aims to review the available literature and to present the latest evidence on malignant pleural effusion management in order to provide an updated perspective on its diagnosis and treatment.

Balanço nutricional em mangueiras no semiárido brasileiro utilizando métodos diagnósticos nutricionais

ABSTRACT Nutritional assessment of mango trees based on diagnostic methods considering nutritional balance is recommended. This study aimed to establish optimum nutritional ranges using diagnostic methods, compare them, identify the most efficient diagnostic method, and select the nutrients responding best to the application of the diagnostic method. The study was conducted in commercial mango orchards in the São Francisco Valley. Nutritional content was calculated using the diagnosis and recommendation integrated system (DRIS-Beaufils, DRIS-Jones), modified DRIS (M-DRIS-Beaufils; M-DRIS-Jones), compositional nutrient diagnosis (CND), and mathematical chance (ChM) methods and compared using the chi-square test. Principal component analysis was applied to select the most efficient diagnostic method and the nutrients responsible for the greatest variability. The DRIS-Beaufils, M-DRIS-Beaufils, DRIS-Jones, M-DRIS-Jones, CND, and ChM methods generated nutritional sufficiency ranges for the evaluated cultivars. The nutritional diagnoses of the DRIS-Beaufils and M-DRIS-Beaufils methods were similar and discordant with those of DRIS-Jones, M-DRIS-Jones, and CND. The DRIS-Beaufils method, updated by Maia, proved to be more consistent for the nutritional assessment of mango trees. The nutrients N, P, K, Mg, and S in the Tommy Atkins cultivar; N, P, Mg, S, B, Mn, Zn, Mo, and Cl in the Kent cultivar; and N, P, K, Ca, S, B, Cu, Fe, Zn, Mo, and Cl in the Keitt cultivar showed significant responses to the application of the DRIS-Beaufils method updated by Maia.

Diagnostic significance of the method of computerized joint vibration analysis in studying the functional state of temporomandibular joints

polyetiology, progressive course and high frequency of relapses is one of the urgent problems of modern dentistry. Different conceptual approaches to the analysis of etiological and pathogenetic aspects of the development of dysfunctional TMJ disorders predetermine the increased interest in the search for highly informative diagnostic methods, especially at the stage of preclinical manifestations.Goal. Evaluation of possibilities of computerized joint vibration analysis method in diagnostics of TMJ functional state.Materials and methods. The study involved 42 people of both sexes with physiologic types of bite, preserved tooth rows, no signs of functional disorders in TMJ and masticatory muscles. The patients underwent clinical dental examination, «Hamburg test», selective grinding of teeth, and measurement of mouth opening amplitude. Joint vibration analysis was performed on the hardware-software complex «BioPAK» with registration of TMJ noise oscillations according to the following indices: «Total Integral», «High Integral», «Low Integral», «Ratio». A flow chart (Ishigaki S. et al., 1993) was used to interpret the numerical values. The functional state of TMJ was de-termined according to the flowchart using numerical parameters in accordance with the classification of TMJ disorders (Wilkes С.Н., 1989).Results. In patients with physiologic occlusion, according to joint vibration analysis, Me «Total Integral» of the right TMJ was 11.7 PaHz, left TMJ – 12.6 PaHz, [Q-25; Q-75] – [9.3; 15.1] PaHz and [10.5; 18.3] PaHz respectively, Me «High Integral» of the right TMJ – 1.3 PaHz, left TMJ – 1.4 PaHz, [Q-25; Q-75] – [1.0; 1.8] PaHz and [1.2; 2.1] PaHz respectively, with no statistically significant (p≤0.05) differences in the studied parameters between the right and left TMJ.Conclusions. The diagnosis of «structurally intact» TMJ, corresponding to stage I of the American classification of TMJ disorders (Wilkes S.N., 1989) in people with physiologic types of bite, is confirmed by the numerical values [Q-25; Q-75] of «Total Integral», «High Integral» (block diagram of S. Ishigaki et al., 1993), the degree of maximum mouth opening [43; 50] mm in the absence of pronounced signs of facial asymmetry.

Plastic bronchitis associated with human bocavirus 1 infection in children.

Plastic bronchitis (PB) is a clinical-pathological syndrome characterized by the abnormal accumulation of endogenous substances in the bronchial airways, causing partial or complete obstruction and resulting in impaired lung ventilation. In this retrospective analysis, we aim to summarize the clinical manifestations, imaging characteristics, diagnostic methods, and treatment approaches to enhance clinicians' ability to detect children who are infected withhuman bocavirus 1(hBoV 1) and develop PB. In the period from January 2021 to January 2024, a total of six hBoV 1 infection children were diagnosed with PB through bronchoscopy. The onset of the condition was mainly concentrated between June and December. The detection methods used included metagenomic next-generation sequencing for pathogen identification (three cases) and respiratory pathogen nucleic acid 13-plex detection (oropharyngeal swab) (three cases), both of which confirmed the presence of hBoV 1. Out of the six children with PB, two were girls and four were boys. Their ages ranged from 10 months to 4 years old. Common symptoms reported by all patients included fever, cough,and wheezing. Chest high-resolution computed tomography scans revealed atelectasis in six cases, in addition to pneumonia. After the removal of the plastic bronchi via bronchoscopy, the airway obstruction symptoms in the children were relieved, and no recurrence was observed during the follow-up period. Pathological findings indicated cellulose exudation and inflammatory cell infiltration, consistent with nonlymphatic PB. When children infected with hBoV 1 exhibit persistent or worsening symptoms such as cough, fever, and wheezing despite treatment, clinicians should remain highly vigilant for the potential occurrence of PB. Bronchoscopy plays a crucial role not only in diagnosing the presence of a plastic bronchus but also in effectively treating PB.

Advances in molecular imaging of kidney diseases.

Background Diagnosing and monitoring kidney diseases traditionally rely on blood and urine analyses and invasive procedures such as kidney biopsies, the latter offering limited possibilities for longitudinal monitoring and a comprehensive understanding of disease dynamics. Current non-invasive methods lack specificity in capturing intrarenal molecular processes, hindering patient stratification and patient monitoring in clinical practice and clinical trials. Summary Molecular imaging enables non-invasive and quantitative assessment of physiological and pathological molecular processes. By using specific molecular probes and imaging technologies, e.g., magnetic resonance imaging (MRI), positron emission tomography (PET), single-photon emission computed tomography (SPECT), or ultrasound (US), molecular imaging allows the detection and longitudinal monitoring of disease activity with spatial and temporal resolution of different kidney diseases and disease-specific pathways. Several approaches have already shown promising results in kidneys and exploratory clinical studies, and validation is needed before implementation in clinical practice. Key messages Molecular imaging offers a non-invasive assessment of intrarenal molecular processes, overcoming the limitations of current diagnostic methods. It has the potential to serve as companion diagnostics, not only in clinical trials, aiding in patient stratification and treatment response assessment. By guiding therapeutic interventions, molecular imaging might contribute to the development of targeted therapies for kidney diseases.