Diagnostic Methods Research Articles

Putative mRNA Biomarkers for the Eradication of Infection in an Equine Experimental Model of Septic Arthritis

Septic arthritis (SA) in horses has long-term health implications. The success of its resolution hinges on the implementation of early, aggressive treatment, which is often sustained over a prolonged period. Common diagnostic methods do not allow for the reliable detection of the eradication of joint infection. A potential alternative is the discovery and characterization of mRNA biomarkers. The purpose of this study was to identify potential mRNA biomarkers for the eradication of joint infection in equine SA and to compare their expression with our previously published proteomics data. In addition, the transcriptomics data were compared to the mRNA biomarker panel, SeptiCyte Lab, used to distinguish sepsis from non-septic shock in humans. A comparative transcriptomics analysis of synovial fluid from the SA joints of five horses with active infection and subsequent post-treatment eradicated infection in the same joints and five horses with non-septic synovitis was performed. Eight novel mRNA transcripts were identified that were significantly upregulated (>3-fold) in horses with active SA compared to horses post-eradication of infection after treatment and horses with non-septic synovitis. Two proteins in our proteomics data corresponded to these mRNA transcripts, but were not statistically different. The transcripts used in the SeptiCyte test were not differentially expressed in our study. Our results suggest that mRNA may be a useful source of biomarkers for the eradication of joint infection in horses and warrants further investigation.

Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer.

Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer. The utilization of deep learning techniques, specifically CNN, enables the extraction of intricate features from medical imaging data, providing a robust and efficient diagnostic model. Additionally, the Fishier Mantis Optimizer, a bio-inspired optimization algorithm inspired by the hunting behavior of the mantis shrimp, is employed to fine-tune the parameters of the CNN, enhancing its convergence speed and performance. This hybrid approach aims to address the limitations of traditional diagnostic methods by leveraging the strengths of both deep learning and nature-inspired optimization to enhance the accuracy and effectiveness of colon cancer diagnosis. The proposed method was evaluated on a comprehensive dataset comprising colon cancer images, and the results demonstrate its superiority over traditional diagnostic approaches. The CNN-Fishier Mantis Optimizer model exhibited high sensitivity, specificity, and overall accuracy in distinguishing between cancer and non-cancer colon tissues. The integration of bio-inspired optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and diagnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features associated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction techniques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1-Score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively.

A combined technique for power transformer fault diagnosis based on k‐means clustering and support vector machine

AbstractThis contribution presents a two‐step hybrid diagnostic approach, combining k‐means clustering for subset formation, followed by subset analysis conducted by human experts. As the feature input vector has a significant influence on the performance of unsupervised machine learning algorithms, seven feature input vectors derived from traditional methods, including Duval pentagon method, Rogers ratio method, three ratios technique, Denkyoken method, ensemble gas characteristics method, Duval triangle method, and Gouda triangle method were explored for the subset formation stage. The seven proposed individual methods, corresponding to the seven feature input vectors, were implemented using a dataset of 595 DGA samples and tested on an additional 254 DGA samples. Furthermore, a combined technique based on a support vector machine was introduced, utilising the diagnostic results of the individual methods as input features. From training and testing, with diagnostic outcomes of 91.09% and 90.94%, the combined technique demonstrated the highest overall diagnostic accuracies. Using the IEC TC10 database, the diagnosis accuracies of the proposed diagnostic methods were compared to existing methods of literature. From the results obtained, the combined technique outperformed the proposed individual methods and existing methods used for comparison.

Alzheimer's Disease and Epilepsy: Exploring Shared Pathways and Promising Biomarkers for Future Treatments.

Background: Alzheimer's disease (AD) and epilepsy represent two complex neurological disorders with distinct clinical manifestations, yet recent research has highlighted their intricate interplay. This review examines the association between AD and epilepsy, with particular emphasis on late-onset epilepsy of unknown etiology, increasingly acknowledged as a prodrome of AD. It delves into epidemiology, pathogenic mechanisms, clinical features, diagnostic characteristics, treatment strategies, and emerging biomarkers to provide a comprehensive understanding of this relationship. Methods: A comprehensive literature search was conducted, identifying 128 relevant articles published between 2018 and 2024. Results: Findings underscore a bidirectional relationship between AD and epilepsy, indicating shared pathogenic pathways that extend beyond traditional amyloid-beta and Tau protein pathology. These pathways encompass neuroinflammation, synaptic dysfunction, structural and network alterations, as well as molecular mechanisms. Notably, epileptic activity in AD patients may exacerbate cognitive decline, necessitating prompt detection and treatment. Novel biomarkers, such as subclinical epileptiform activity detected via advanced electroencephalographic techniques, offer promise for early diagnosis and targeted interventions. Furthermore, emerging therapeutic approaches targeting shared pathogenic mechanisms hold potential for disease modification in both AD and epilepsy. Conclusions: This review highlights the importance of understanding the relationship between AD and epilepsy, providing insights into future research directions. Clinical data and diagnostic methods are also reviewed, enabling clinicians to implement more effective treatment strategies.

Micro- and Macroscopic Analysis of Fatigue Wear of Gear Wheel Top Layer-An Impact Analysis of Thermochemical Treatment.

Today, there are many diagnostic methods and advanced measurement techniques enabling the correct diagnosis and assessment of the type and degree of wear of cogwheels (gears, pumps, etc.). The present study presents an analysis of the surface defects of a cogwheel of an oil pump prototype (3PW-BPF-24). The test object operated for a certain number of hours under controlled operating and environmental parameters. The damage to the surface layer was caused by fatigue phenomena and previous thermo-chemical treatment. On the basis of the significant percentage share (~30%) of residual austenite in the volume of the diffusion layer, a hypothetical conclusion was drawn about the suboptimal parameters of the thermo-chemical treatment process (in relation to the chemical composition of the analyzed pinion). A large number of research studies indicate that the significant presence of residual austenite causes a decrease in tooth surface hardness, the initiation of brittle cracks, a sharp decrease in fatigue strength, an increase in brittleness and a tendency to develop surface layer cracks during operation. High-resolution 3D scans of randomly selected pitting defects were used in the detailed study of the present work. It was indicated that the analysis of the morphology of surface defects allowed some degree of verification of the quality of the heat/chemical treatment. The martensitic transformation of residual austenite under controlled (optimum) repeated heat treatment conditions could significantly improve the durability of the pinion (cogwheel). In the case analyzed, the preferred treatment was the low-temperature treatment. The paper concludes with detailed conclusions based on the microscopic and macroscopic investigations carried out.

Multiple sclerosis: new insights into molecular pathogenesis and novel platforms for disease treatment.

Multiple sclerosis (MS), a chronic inflammatory disorder, affects the central nervous system via myelin degradation. The cause of MS is not fully known, but during recent years, our knowledge has deepened significantly regarding the different aspects of MS, including etiology, molecular pathophysiology, diagnosis and therapeutic options. Myelin basic protein (MBP) is the main myelin protein that accounts for maintaining the stability of the myelin sheath. Recent evidence has revealed that MBP citrullination or deamination, which is catalyzed by Ca2+ dependent peptidyl arginine deiminase (PAD) enzyme leads to the reduction of positive charge, and subsequently proteolytic cleavage of MBP. The overexpression of PAD2 in the brains of MS patients plays an essential role in new epitope formation and progression of the autoimmune disorder. Some drugs have recently entered phase III clinical trials with promising efficacy and will probably obtain approval in the near future. As different therapeutic platforms develop, finding an optimal treatment for each individual patient will be more challenging. This review provides a comprehensive insight into MS with a focus on its pathogenesis and recent advances in diagnostic methods and its present and upcoming treatment modalities. MS therapy alters quickly as research findings and therapeutic options surrounding MS expand. McDonald's guidelines have created different criteria for MS diagnosis. In recent years, ever-growing interest in the development of PAD inhibitors has led to the generation of many reversible and irreversible PAD inhibitors against the disease with satisfactory therapeutic outcomes.

Artificial Intelligence-Clinical Decision Support System in Infectious Disease Control: Combatting Multidrug-Resistant Klebsiella pneumoniae with Machine Learning.

The World Health Organization has identified Klebsiella pneumoniae (KP) as a significant threat to global public health. The rising threat of carbapenem-resistant Klebsiella pneumoniae (CRKP) leads to prolonged hospital stays and higher medical costs, necessitating faster diagnostic methods. Traditional antibiotic susceptibility testing (AST) methods demand at least 4 days, requiring 3 days on average for culturing and isolating the bacteria and identifying the species using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), plus an extra day for interpreting AST results. This lengthy process makes traditional methods too slow for urgent clinical situations requiring rapid decision-making, potentially hindering prompt treatment decisions, especially for fast-spreading infections such as those caused by CRKP. This research leverages a cutting-edge diagnostic method that utilizes an artificial intelligence-clinical decision support system (AI-CDSS). It incorporates machine learning algorithms for the swift and precise detection of carbapenem-resistant and colistin-resistant strains. We selected 4307 KP samples out of a total of 52,827 bacterial samples due to concerns about multi-drug resistance using MALDI-TOF MS and Vitek-2 systems for AST. It involved thorough data preprocessing, feature extraction, and machine learning model training fine-tuned with GridSearchCV and 5-fold cross-validation, resulting in high predictive accuracy, as demonstrated by the receiver operating characteristic and area under the curve (AUC) scores, laying the groundwork for our AI-CDSS. MALDI-TOF MS analysis revealed distinct intensity profiles differentiating CRKP and susceptible strains, as well as colistin-resistant Klebsiella pneumoniae (CoRKP) and susceptible strains. The Random Forest Classifier demonstrated superior discriminatory power, with an AUC of 0.96 for detecting CRKP and 0.98 for detecting CoRKP. Integrating MALDI-TOF MS with machine learning in an AI-CDSS has greatly expedited the detection of KP resistance by approximately 1 day. This system offers timely guidance, potentially enhancing clinical decision-making and improving treatment outcomes for KP infections.

Acoustic frequency-based method for high-speed aircraft combustion analysis and hybrid artificial intelligence diagnostics

In the paper, a method of high-speed continuous combustion monitoring for jet aircrafts, based on acoustic data processing, is proposed. As part of the research, acoustic signals corresponding to the specific phases of the combustible mixture formation and its combustion were separated. They were assigned to the generated process energy value. In the result, parametric functions were obtained in multidimensional spaces of states and processes. Parameters and acoustic characteristics in the amplitude, frequency and JTFA domains constituted information about particular symptoms in the jet engine, thanks to which reference waveforms of signals and forms for specific malfunctions were mapped. The above graphic characteristics have been parameterized to determine their diagnostic reliability factors.The method is verified by empirical signals obtained from turbojet propulsion system in time and power dependent conditions. Appropriate exothermic combustion phases were assigned for the determined acoustic spectra, taking into account the obtained power. Then, representative diagnostic parameters transformed in the process values domain were calculated. Finally, monitoring functions and algorithms of combustion efficiency with determination of malfunctions sources (at dynamic conditions) are proposed.As a result, it is possible to identify first outbreaks of design and process failures during fuel injection and combustion in aircrafts, taking into account artificial intelligence methods. The obtained results indicate it is possible to adapt the acoustic characteristics of the spectra and their discrete representatives, for appropriate states and failures, to detect first dangerous symptoms in the aircraft during flight conditions (supporting OBD with the acoustic adaptive diagnostic procedures).

Raman spectroscopy in lung cancer diagnostics: Can an in vivo setup compete with ex vivo applications?

Lung carcinoma remains the leading cause of cancer death worldwide. The tactic to change this unfortunate rate may be a timely and rapid diagnostic, which may in many cases improve patient prognosis.In our study, we focus on the comparison of two novel methods of rapid lung carcinoma diagnostics, label-free in vivo and ex vivo Raman spectroscopy of the epithelial tissue, and assess their feasibility in clinical practice. As these techniques are sensitive not only to the basic molecular composition of the analyzed sample but also to the secondary structure of large biomolecules, such as tissue proteins, they represent suitable candidate methods for epithelial cancer diagnostics.During routine bronchoscopy, we collected 78 in vivo Raman spectra of normal and cancerous lung tissue and 37 samples of endobronchial pathologies, which were subsequently analyzed ex vivo. Using machine learning techniques, namely principal component analysis (PCA) and support vector machines (SVM), we were able to reach 87.2% (95% CI, 79.8–94.6%) and 100.0% (95% CI, 92.1–100.0%) of diagnostic accuracy for in vivo and ex vivo setup, respectively. Although the ex vivo approach provided superior results, the rapidity of in vivo Raman spectroscopy might become unmatchable in the acceleration of the diagnostic process.

Complex clinical and cytological diagnosis of eye adnexa epithelial tumors

Purpose. Studying clinical and cytological signs of malignant epithelial genesis tumors of eye adnexa. Material and methods. In 2015–2020, 308 patients aged from 23 to 87 (58.0 ± 6.7) years with suspected malignant neoplasms of eyelid skin and conjunctiva were cytologically examined. The material for cytological examination was obtained by scraping (n = 271; 88 %), imprinting (n = 27; 8.8 %), and fine needle aspiration biopsy (n = 10; 3.2 %) if the neoplasm was more than 10 mm thick. Preparations were processed and fixed according to the standard technique using panchromic Pappenheim staining. Results. The cytological analysis confirmed the clinical diagnosis and the epithelial genesis of the malignant neoplasm in 273 (88.7 %) patients. The benign nature of the tumor was confirmed in 17 patients (5.5 %) and the non-tumor nature of the process was confirmed in 11 patients (3.6 %). In the group of malignant tumors, basal cell cancer prevailed, affecting 240 patients (87.9 % of all malignant tumors). 27 patients (9.9% ) had squamous cell cancer, 4 patients had meibomian gland cancer in 4 patients, and 2 patients had undifferentiated cancer. Benign tumors included papillomas (n = 11), while tumor-like lesions included atheromas (n = 3) and granulation polyps (n = 3). Non-tumor lesions involved productive inflammation of the chalazion type (n = 7), or chronic inflammation of a reactive nature (n = 4). 22 patients had histological examination to check the diagnosis, in all cases the histological outcome coincided with the cytological diagnosis. Conclusion. The comprehensive clinical and cytological study allowed us to specify cytomorphological characteristics of epithelial tumors of the eye adnexa: the basal cell cancer and its varieties according to the clinical forms of the tumor, squamous cell cancer and meibomian gland cancer, and papilloma. The complex clinical and cytological method of diagnostics is easily applicable in out-patient conditions, allowing us to specify the diagnosis during the initial visit, which considerably reduces the time required for full patient’s examination.

Metagenomic profiling of nasopharyngeal samples from adults with acute respiratory infection

Diagnosis of acute respiratory infections (ARIs) is challenging due to the broad diversity of potential microbial causes. We used metagenomic next-generation sequencing (mNGS) to analyze the nasopharyngeal virome of ARI patients, who had undergone testing with a clinical multiplex PCR panel (Amplisens ARVI-screen-FRT). We collected nasopharyngeal swabs from 49 outpatient adults, 32 of whom had ARI symptoms and were PCR-positive, and 4 asymptomatic controls in Kazakhstan during Spring 2021. We assessed the biodiversity of the mNGS-derived virome and concordance with PCR results. PCR identified common ARI viruses in 65% of the symptomatic cases. mNGS revealed viral taxa consisting of human, non-human eukaryotic and bacteriophage groups, comprising 15, 11 and 28 genera, respectively. Notable ARI-associated human viruses included rhinovirus (16.3%), betaherpesvirus 7 (14.3%) and Epstein-Barr virus (8.16%). The primary phage hosts were Streptococcus spp. (32.7%), Pseudomonas aeruginosa (24.5%) and Burkholderia spp. (20.4%). In total, 47% of ARIs were linked solely to bacterial pathogens, a third to viral-bacterial co-infections, and less than 10% to only viral infections by mNGS. PCR showed low concordance with mNGS, except for rhinovirus. These results underscore the importance of broad diagnostic methods and question the effectiveness of commonly used PCR panels in ARI diagnosis.

Emerging Biohybrids of Aptamer-Based Nano-Biosensing Technologies for Effective Early Cancer Detection.

Cancer is a leading global cause of mortality, which underscores the imperative of early detection for improved patient outcomes. Biorecognition molecules, especially aptamers, have emerged as highly effective tools for early and accurate cancer cell identification. Aptamers, with superior versatility in synthesis and modification, offer enhanced binding specificity and stability compared with conventional antibodies. Hence, this article reviews diagnostic strategies employing aptamer-based biohybrid nano-biosensing technologies, focusing on their utility in detecting cancer biomarkers and abnormal cells. Recent developments include the synthesis of nano-aptamers using diverse nanomaterials, such as metallic nanoparticles, metal oxide nanoparticles, carbon-derived substances, and biohybrid nanostructures. The integration of these nanomaterials with aptamers significantly enhances sensitivity and specificity, promising innovative and efficient approaches for cancer diagnosis. This convergence of nanotechnology with aptamer research holds the potential to revolutionize cancer treatment through rapid, accurate, and non-invasive diagnostic methods.

Real-time Application of ITS and D1-D3 Nanopore Amplicon Metagenomic Sequencing in Fungal Infections: Enhancing Fungal Infection Diagnostics

While fungal infections cause considerable morbidity and mortality, the performance of the current diagnostic tests for fungal infection is low. Even though fungal metagenomics or targeted next-generation sequencing have been investigated for various clinical samples, the real-time clinical utility of these methods still needs to be elucidated. In this study, we used internal transcribed spacer (ITS) and D1-D3 ribosomal DNA nanopore amplicon metagenomic sequencing to assess its utility in patients with fungal infections. Eighty-four samples from seventy-three patients were included and categorized into ‘Fungal infection,’ ‘Fungal colonization,’ and ‘Fungal contamination’ groups based on the judgement of infectious disease specialists. In the ‘Fungal infection’ group, forty-seven initial samples were obtained from forty-seven patients. Three fungal cases detected not by the sequencing but by conventional fungal assays were excluded from the analysis. In the remaining cases, the conventional fungal assay-negative/sequencing-positive group (n=11) and conventional fungal assay-positive/sequencing-positive group (n=33) were compared. Non-Candida and non-Aspergillus fungi infections were more frequent in the conventional-negative/sequencing-positive group (p-value = 0.031). We demonstrated the presence of rare human pathogens, such as Trichosporon asahii and Phycomyces blakesleeanus. In the ‘Fungal infection’ group and ‘Fungal colonization’ group, sequencing was faster than culturing (mean difference = 4.92 days, p-value < 0.001/ mean difference = 4.67, p-value <0.001). Compared to the conventional diagnostic methods including culture, nanopore amplicon sequencing showed a shorter turnaround time and a higher detection rate for uncommon fungal pathogens.

A novel rapid visual nucleic acid detection technique for tick-borne encephalitis virus by combining RT-recombinase-aided amplification and CRISPR/Cas13a coupled with a lateral flow dipstick

Tick-borne encephalitis virus (TBEV), a zoonotic pathogen, can cause severe neurological complications and fatal outcomes in humans. Early diagnosis of TBEV infection is crucial for clinical practice. Although serological assays are frequently employed for detection, the lack of antibodies in the early stages of infection and the cross-reactivity of antibodies limit their efficacy. Conventional molecular diagnostic methods such as RT-qPCR can achieve early and accurate identification but require specialized instrumentation and professionals, hindering their application in resource-limited areas. Our study developed a rapid and visual TBEV molecular detection method by combining RT-recombinase-aided amplification, the CRISPR/Cas13a system, and lateral flow dipsticks. The diagnostic sensitivity of this method is 50 CFU/ml, with no cross-reactivity with a variety of viruses. The detection can be carried out within 1 h at a temperature between 37 and 42 °C, and the results can be visually determined without the need for complex instruments and professionals. Subsequently, this assay was used to analyze clinical samples from 15 patients suspected of TBEV infection and 10 healthy volunteers, and its sensitivity and specificity reached 100 %, which was consistent with the results of RT-qPCR. These results indicate that this new method can be a promising point-of-care test for the diagnosis of tick-borne encephalitis.

Diabetic Retinopathy Severity Level Detection Using Convolution Neural Network

Diabetic retinopathy is a common complication of diabetes mellitus, leading to damage and blockage of retinal blood vessels. Early and accurate detection of diabetic retinopathy severity levels is crucial for timely treatment and prevention of blindness. Diagnostic methods rely on manual examination and human interpretation, resulting in slower and less efficient treatment processes. As a branch of artificial intelligence, computer vision offers a potential solution to analyze retinal images quickly and accurately. The developed system employs image processing techniques and a CNN-based classification model to detect and classify the severity levels of diabetic retinopathy. By providing an automated and efficient approach, the system aims to assist doctors and optometrists in making informed decisions and reducing subjectivity in diagnosis. Early detection through this system can facilitate prompt treatment and improve patient outcomes. The developed system achieves promising results through experimentation and testing with various datasets, with accuracy ranging from 80% to 97%. This project's integration of artificial intelligence, machine learning, and image processing technologies demonstrates their potential in healthcare applications, particularly in diabetic retinopathy diagnosis.

СПОСОБЫ ВЫПОЛНЕНИЯ МЕСТНОГО ПРОВОДНИКОВОГО ОБЕЗБОЛИВАНИЯ НА ВЕРХНЕЙ ЧЕЛЮСТИ (ЛИТЕРАТУРНЫЙ ОБЗОР)

Currently, both classical and improved methods of performing local conduction anesthesia on the upper jaw in the area of the infraorbital foramen are used in dental practice. Classical techniques for performing infraorbital anesthesia do not provide a wide area of anesthesia and the palatal surface of the alveolar process is not anesthetized, so an intraosseous method of administering an anesthetic in the area of the “crow’s foot” projection was proposed, but this method has the disadvantage of traumatizing the cortical plate of the alveolar process of the upper jaw in areas of intraosseous injection of local anesthetic. The Ushnitsky-Chakhov apparatus is used to perform infraorbital anesthesia using the extraoral method, but its use does not provide individualization of all parameters and structural features of the infraorbital canal, which often opens on the anterior surface of the body of the upper jaw with not one hole, but two or three, and therefore the clinical effectiveness of this type of pain relief in certain clinical cases is low. The variability of the anatomical and topographic parameters of the location of the infraorbital foramen, the structural features of the facial skeleton of each patient necessitates the search for new personalized methods of performing infraorbital anesthesia through individualization and the process of performing local conduction anesthesia by using a combination of x-ray and digital diagnostic methods, as well as the use of an individual navigation template that allows ensure the accuracy, safety and high clinical effectiveness of this method of conduction anesthesia when performing dental and other types of medical manipulations on the upper jaw.

A contrast-enhanced computed tomography-based radiomics nomogram for preoperative differentiation between benign and malignant cystic renal lesions.

There is lack of discrimination as to traditional imaging diagnostic methods of cystic renal lesions (CRLs). This study aimed to evaluate the value of machine learning models based on clinical data and contrast-enhanced computed tomography (CECT) radiomics features in the differential diagnosis of benign and malignant CRL. There were 192 patients with CRL (Bosniak class ≥ II) enrolled through histopathological examination, including 144 benign cystic renal lesions (BCRLs) and 48 malignant cystic renal lesions (MCRLs). Radiomics features were extracted from CECT images taken during the medullary phase. Using the light gradient boosting machine (LightGBM) algorithm, the clinical, radiomics and combined models were constructed. A comprehensive nomogram was developed by integrating the radiomics score (Rad-score) with independent clinical factors. Receiver operating characteristic (ROC) curves were plotted. The corresponding area under the curve (AUC) value was worked out to quantify the discrimination performance of the three models in training and validation cohorts. Calibration curves were worked out to assess the accuracy of the probability values predicted by the models. Decision curve analysis (DCA) was worked out to assess the performance of models at different thresholds. Maximum diameter and Bosniak class were independent risk factors of patients with MCRL in the clinical model. Twenty-one radiomics features were extracted to work out a Rad-score. The performance of the clinical model in the training cohort was AUC =0.948, 95% confidence interval (CI): 0.917-0.980, and the performance in the validation cohort was AUC =0.936, 95% CI: 0.859-1.000 (P<0.05). The performance of the radiomics model in the training cohort was AUC =0.990, 95% CI: 0.979-1.000, and the performance in the validation cohort was AUC =0.959, 95% CI: 0.903-1.000 (P<0.05). Compared with the above models, the combined radiomics nomogram had an AUC of 0.989 (95% CI: 0.977-1.000) in the training cohort and an AUC of 0.962 (95% CI: 0.905-1.000) in the validation cohort (P<0.05), showing the best diagnostic efficacy. The radiomics nomogram integrating clinical independent risk factors and radiomics signature improved the diagnostic accuracy in differentiating between BCRL and MCRL, which can provide a reference for clinical decision-making and help clinicians develop individualized treatment strategies for patients.

A contrast-enhanced computed tomography-based radiomics nomogram for preoperative differentiation between benign and malignant cystic renal lesions.

There is lack of discrimination as to traditional imaging diagnostic methods of cystic renal lesions (CRLs). This study aimed to evaluate the value of machine learning models based on clinical data and contrast-enhanced computed tomography (CECT) radiomics features in the differential diagnosis of benign and malignant CRL. There were 192 patients with CRL (Bosniak class ≥ II) enrolled through histopathological examination, including 144 benign cystic renal lesions (BCRLs) and 48 malignant cystic renal lesions (MCRLs). Radiomics features were extracted from CECT images taken during the medullary phase. Using the light gradient boosting machine (LightGBM) algorithm, the clinical, radiomics and combined models were constructed. A comprehensive nomogram was developed by integrating the radiomics score (Rad-score) with independent clinical factors. Receiver operating characteristic (ROC) curves were plotted. The corresponding area under the curve (AUC) value was worked out to quantify the discrimination performance of the three models in training and validation cohorts. Calibration curves were worked out to assess the accuracy of the probability values predicted by the models. Decision curve analysis (DCA) was worked out to assess the performance of models at different thresholds. Maximum diameter and Bosniak class were independent risk factors of patients with MCRL in the clinical model. Twenty-one radiomics features were extracted to work out a Rad-score. The performance of the clinical model in the training cohort was AUC =0.948, 95% confidence interval (CI): 0.917-0.980, and the performance in the validation cohort was AUC =0.936, 95% CI: 0.859-1.000 (P<0.05). The performance of the radiomics model in the training cohort was AUC =0.990, 95% CI: 0.979-1.000, and the performance in the validation cohort was AUC =0.959, 95% CI: 0.903-1.000 (P<0.05). Compared with the above models, the combined radiomics nomogram had an AUC of 0.989 (95% CI: 0.977-1.000) in the training cohort and an AUC of 0.962 (95% CI: 0.905-1.000) in the validation cohort (P<0.05), showing the best diagnostic efficacy. The radiomics nomogram integrating clinical independent risk factors and radiomics signature improved the diagnostic accuracy in differentiating between BCRL and MCRL, which can provide a reference for clinical decision-making and help clinicians develop individualized treatment strategies for patients.

СПОСОБЫ ВЫПОЛНЕНИЯ МЕСТНОГО ПРОВОДНИКОВОГО ОБЕЗБОЛИВАНИЯ НА ВЕРХНЕЙ ЧЕЛЮСТИ (ЛИТЕРАТУРНЫЙ ОБЗОР)

Currently, both classical and improved methods of performing local conduction anesthesia on the upper jaw in the area of the infraorbital foramen are used in dental practice. Classical techniques for performing infraorbital anesthesia do not provide a wide area of anesthesia and the palatal surface of the alveolar process is not anesthetized, so an intraosseous method of administering an anesthetic in the area of the “crow’s foot” projection was proposed, but this method has the disadvantage of traumatizing the cortical plate of the alveolar process of the upper jaw in areas of intraosseous injection of local anesthetic. The Ushnitsky-Chakhov apparatus is used to perform infraorbital anesthesia using the extraoral method, but its use does not provide individualization of all parameters and structural features of the infraorbital canal, which often opens on the anterior surface of the body of the upper jaw with not one hole, but two or three, and therefore the clinical effectiveness of this type of pain relief in certain clinical cases is low. The variability of the anatomical and topographic parameters of the location of the infraorbital foramen, the structural features of the facial skeleton of each patient necessitates the search for new personalized methods of performing infraorbital anesthesia through individualization and the process of performing local conduction anesthesia by using a combination of x-ray and digital diagnostic methods, as well as the use of an individual navigation template that allows ensure the accuracy, safety and high clinical effectiveness of this method of conduction anesthesia when performing dental and other types of medical manipulations on the upper jaw.

Diagnostic value and method of soluble transferrin receptor for suspected coronary artery disease: a case-control study.

Many studies have pointed out that iron overload in the body is a risk factor for coronary atherosclerosis (AS), while there are also studies that show that iron deficiency is associated with coronary AS. There is still no consensus on how iron metabolism affects coronary artery disease (CAD). This study aimed to analyze the relationship between iron metabolism indexes and CAD, investigate the diagnostic value of soluble transferrin receptor (sTfR) in suspected CAD, and establish a diagnostic model. This was a retrospective study. A total of 268 people with CAD-like symptoms who underwent coronary angiography in the Department of Cardiovascular Medicine, The Second Affiliated Hospital of Anhui Medical University from September 2022 to May 2023 without other chronic diseases or related medication history were included in the study and formed a continuous series including 188 CAD patients and 80 control subjects. Each iron metabolism index was divided into a grade variable according to tertile. The comparison of CAD morbidity between the tertiles and nonlinear correlation test was conducted to investigate the relationship between iron metabolism indexes and CAD risk. We used restricted cubic spline (RCS) to plot the relationship curve between sTfR and CAD risk and to determine the sTfR value corresponding to the minimal odds, according to which we divided the total sample into the "sTfR low level" subgroup and the "sTfR high level" subgroup. Logistic regression analyses were used to establish diagnostic models in both subgroups. The diagnostic efficiency of the indexes and models was compared by receiver operating characteristic (ROC) analysis. There is a "J" shape correlation between sTfR and CAD risk. Age/sTfR ratio [area under the curve (AUC) =0.690, 95% confidence interval (CI): 0.598-0.782, specificity 0.488 and sensitivity 0.842] has the best diagnostic efficiency in the "sTfR low level" subgroup. The diagnostic efficiency of sTfR (AUC =0.701, 95% CI: 0.598-0.803, specificity 0.541 and sensitivity 0.797) in the "sTfR high level" subgroup was higher than that of cardiac troponin I (cTnI) (AUC =0.674, 95% CI: 0.564-0.784, specificity 0.719 and sensitivity 0.653). The specific diagnostic methods were as follows: (I) When sTfR ≤1.087 mg/L, calculate the age/sTfR ratio, which indicates the diagnosis of CAD when the result is >58.595; (II) We can directly make a preliminary clinical diagnosis of CAD when sTfR >1.205 mg/L. Except for the above 2 cases, we can initially rule out a diagnosis of CAD. The iron metabolism index sTfR correlates with CAD morbidity in a "J" shape. With superior diagnostic efficacy than cTnI, sTfR can assist in diagnosing CAD in patients with CAD-like symptoms. In addition, sTfR can provide guidance for the management of body iron levels in CAD patients.