Diagnostic Results Research Articles

Smart Machine Learning Analysis and Blockchain Healthcare Platform

The Smart Machine Learning Medical Analysis and Blockchain Healthcare Platform (SMLMAP) represents a transformative approach to medical diagnostics and data management by integrating advanced machine learning algorithms with blockchain technology. This platform is designed to accurately diagnose seven critical diseases, achieving impressive diagnostic outcomes across various conditions, including diabetes, breast cancer, heart disease, kidney disease, liver disease, malaria, and pneumonia. SMLMAP employs Random Forest algorithms for text data analysis and Convolutional Neural Networks (CNNs) for image data, ensuring rapid and precise diagnostic results. In addition to enhancing diagnostic capabilities, SMLMAP addresses significant privacy and security concerns inherent in healthcare data management. The integration of blockchain technology facilitates secure, transparent, and tamper-proof storage of patient records, empowering individuals with greater control over their medical information. This dual approach not only improves the accuracy of diagnoses but also fosters trust between patients and healthcare providers by safeguarding sensitive data against unauthorized access. Key functionalities of SMLMAP include the ability to schedule doctor appointments conveniently through the platform, ensuring that patients can receive timely medical attention. Additionally, the application provides users with a weekly healthcare magazine, delivering valuable information on health tips, disease management, and wellness advice to promote informed health decisions. By leveraging the strengths of machine learning and blockchain, SMLMAP paves the way for a more efficient, accurate, and secure healthcare ecosystem. This innovative platform not only enhances the quality of medical diagnostics but also represents a significant step forward in addressing the critical challenges of data privacy and security in the healthcare sector. The findings underscore the potential of SMLMAP to revolutionize patient care and data management in the evolving landscape of healthcare technology.

The value and sensitivity of contrast-enhanced ultrasonography combined with fine-needle aspiration biopsy in the diagnosis of thyroid nodules

To investigate the value and sensitivity of contrast-enhanced ultrasonography (CEUS) combined with fine-needle aspiration biopsy (FNAB) in the diagnosis of thyroid nodules. This study enrolled 120 patients with thyroid nodules who visited our hospital between July 2018 and January 2021. All patients underwent CEU and CEUS-guided FNAB. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of CEUS-guided FNAB for the qualitative diagnosis of thyroid nodules were significantly higher compared with CEUS only (P < 0.05). The consistency in the diagnostic results was high between CEU-guided FNAB and surgical pathology and moderate between CEU and surgical pathology. The diagnostic coincidence rates of CEUS-guided FNAB were significantly higher than those of CEU only for papillary thyroid carcinoma, follicular carcinoma, undifferentiated carcinoma, and medullary carcinoma (P < 0.05). The diagnostic coincidence rates of CEUS-guided FNAB for stages I, II, and III thyroid cancer were significantly higher compared with CEU only (P < 0.05). Contrast-enhanced ultrasonography combined with FNAB improved the sensitivity and accuracy of making a qualitative diagnosis for thyroid nodules. This outcome will not only help to distinguish thyroid cancer from benign ones but can also assist in determining the pathological type and tumor stage of thyroid cancer, thereby presenting good diagnostic value.

Optimized imaging for suspected acute organ bleeding

Contrast-enhanced computed tomography (CT) is the standard procedure for diagnosing acute organ bleeding. It provides rapid, precise and comprehensive visualization of the bleeding situation, which is crucial in emergency situations. The examination is usually conducted in three phases: native, arterial and venous. These phases enable the precise identification and localization of the source of bleeding. The CT imaging is not only used to confirm the diagnosis but also aids in planning further treatment, facilitating targeted preparation for interventional radiologists, surgeons and endoscopists. The possibilities for use of the technique in CT have significantly advanced in recent years. High-resolution CT scanners provide detailed images, which are particularly useful for detecting small bleedings and traumatic injuries. Multi-energy CT provides additional advantages by enabling virtual native examinations and mono-energetic image reconstructions, which enhance the visibility of iodine-based contrast agents. These technologies improve the sensitivity for detecting bleeding and enable a better characterization of the source of bleeding. To optimize the CT diagnostics for bleeding, the use of ahighly concentrated iodine contrast agent with afast injection rate is recommended. Additionally, imaging data should be reconstructed with aslice thickness of 1-3 mm to achieve optimal diagnostic results. Multiplanar reconstructions are essential in all cases to ensure comprehensive imaging information. The combination of these advanced techniques and settings leads to improved diagnostics and treatment of acute organ bleeding.

Optimizing the Use of Severe Acute Respiratory Syndrome Coronavirus 2 Antigen Rapid Diagnostic Tests for the Timely Detection of and Response to COVID-19 in Schools and Markets in Uganda.

The early detection and management of infections is crucial to control epidemics. We evaluated the feasibility and utility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen rapid diagnostic tests (Ag-RDTs) for the timely detection of and response to coronavirus disease 2019 in high-risk border communities in Uganda. Between May and September 2022, monthly cross-sectional surveys were conducted in 11 schools and two markets in two border districts. Only baseline and end-line testing were also performed in matched control communities. Antigen rapid diagnostic test results and demographic and clinical data were collected, and contacts of patients were traced and tested. All patients were advised to self-isolate, and compliance was assessed on day 5. We enrolled 10,406 participants out of 10,472 screened individuals. The participants had a 1.3% test positivity rate, with schools recording higher, but non-significant, positivity rates than markets (1.4% versus 0.9%; P = 0.149). We tracked 556 contacts, and 536 (96.4%) agreed to test. The test positivity rate was significantly higher among contacts than the index participants (8.8% versus 1.3%; P <0.001). Only 55 (29.7%) of the index participants self-isolated effectively. Settings that received monthly testing had lower end-line positivity rates than controls (0.3% versus 1.4%; P = 0.001). Repeated SARS-CoV-2 Ag-RDT testing is feasible and could reduce SARS-CoV-2 infections. However, the participation in testing may have been enhanced by the compensation provided. Also, isolation was limited, which may reduce the impact of the intervention when rolled out on a large scale. Innovative strategies to increase the isolation of patients could improve the utility of early testing for transmission reduction during epidemics.

Diagnostic and Therapeutic Value of Endoscopic Retrograde Cholangiopancreatography in Acute Cholangitis: A Study From Kashmir Valley, India.

Background Cholangitis, or bile duct infection, can present in two primary forms, namely, acute ascending cholangitis (the milder form) and acute fulminant cholangitis (the more severe variety). In all types of cholangitis, bile duct obstruction occurs, with choledocholithiasis (the presence of gallstones in the bile duct) being the leading cause of this blockage. Escherichia coli is the most commonly isolated pathogen in these infections. If the biliary duct infection becomes suppurative, sepsis is a common complication. Cholangitis can lead to significant morbidity and mortality, which may persist even with prompt treatment or drainage. Endoscopic retrograde cholangiopancreatography (ERCP) remains a crucial diagnostic and therapeutic tool for managing acute cholangitis. Despite its importance in managing cholangitis, there is limited research on the characteristics and outcomes of ERCP in patients with acute cholangitis from Kashmir in northern India. This study was conducted to explore the role and effectiveness of ERCP in the diagnosis and treatment of acute cholangitis. Methodology This observational study was conducted in the Department of Gastroenterology at a tertiary care hospital in North India over a period of two years, from January 1, 2018, to December 31, 2019. The study included 48 consecutive patients diagnosed with acute cholangitis who underwent ERCP for both diagnostic and therapeutic purposes. Results The median age of the patients included in the study was 55 years and the male-to-female ratio in the study cohort was 1.6:1, with 30 (62.5%) males and 18 (37.5%) females. Among the patients with comorbid conditions, hypertension was the most common, present in 12.5% of cases. Choledocholithiasis was the most frequent diagnosis, identified in 70.83% of patients. Ultrasonography of the abdomen revealed choledocholithiasis in 34 (70.83%) cases, the most common radiological finding. A comparison of clinical symptoms and diagnostic test results before and after 72 hours of ERCP demonstrated a statistically significant improvement in patient outcomes following the procedure. Conclusions In contrast to other diagnostic approaches for evaluating biliary duct obstruction, timely ERCP remains a reliable and effective option for both diagnosis and therapeutic intervention in acute cholangitis. It is associated with improved diagnostic accuracy, enhanced therapeutic outcomes, and reduced morbidity and mortality rates, making it a critical tool in managing this potentially life-threatening condition.

Non-invasive diagnosis of papillary thyroid microcarcinoma using a novel metabolomics analysis of urine.

The severity of thyroid cancer is judged on the basis of histologic and clinical features. A limited number of studies have considered urinary metabolite signatures for its diagnosis, and no reliable urinary metabolite biomarkers have been proposed. This diagnostic method would be particularly valuable because of its non-invasive nature. A nuclear magnetic resonance (NMR)-based metabolomics approach was used as the analytical platform to study the urine samples of patients with PTMC. Urine samples collected from 41 PTMC patients, 52 healthy subjects, and 13 patients with benign tumors were analyzed using 1H-NMR spectroscopy to identify metabolic changes. PLS-DA, or partial least squares discriminant analysis, was used to analyze the NMR spectra. A double cross-validation method and randomization tests were used to validate PLS-DA models. Clear discriminations between PTMC patients and healthy controls, as well as between PTMC patients and patients with benign tumors were obtained. Collectively, pi-methyhistidine, trimethylamine, myo-inositol, acetate, suberate, azelate, mannitol, tau-methylhistine, ascorbate, 3-aminoisobutyric acid, 2-oxoglutarate, and methanol contributed to the discrimination. Apart from myo-inositol and methanol, all of these metabolites exhibited increased levels in the urine samples of PTMC patients as compared to that of patients with benign tumors. The application of this NMR-based metabolomics approach allowed the detection of anomalous metabolic traits directly connected PTMC, potentially yielding a more sensitive and comprehensive diagnostic results for PTMC.

LLM-TSFD: An industrial time series human-in-the-loop fault diagnosis method based on a large language model

Industrial time series data provides real-time information about the operational status of equipment and helps identify anomalies. Data-driven and knowledge-guided methods have become predominant in this field. However, these methods depend on industrial domain knowledge and high-quality industrial data which can lead to issues such as unclear diagnostic results and lengthy development cycles. This paper introduces a novel human-in-the-loop task-driven approach to reduce reliance on manually annotated data and improve the interpretability of diagnostic outcomes. This approach utilises a large language model for fault detection, fostering process autonomy and enhancing human–machine collaboration. Furthermore, this paper explores four key roles of the large language model: managing the data pipeline, correcting causality, controlling model management, and making decisions about diagnostic results. Additionally, it presents a prompt structure designed for fault diagnosis of time series data, enabling the large language model to realize task-driven. Finally, the paper validates the proposed framework through a case study in the context of steel metallurgy.

Optimization of Hybrid Pulse Power Characterization (HPPC) Protocol for Lithium-Ion Battery Diagnostics

The importance of accurate diagnostics in lithium-ion battery applications has received increasing emphasis, especially after the formation step to assess the quality of the solid-electrolyte interphase layer and before second-life usage to investigate whether the recycled battery meets safety and performance requirements [1, 2]. Among diagnostic methods, hybrid pulse power characterization (HPPC) tests are promising as they extract relatively large information from high-rate perturbations from a near-equilibrium state by undergoing a sufficiently long rest step before applying the pulses [3]. However, traditional HPPC protocols (that is, using fixed magnitudes of charge/discharge pulses at uniformly distributed states of charges) provide superficial information about the battery health by only capturing the convoluted symptoms instead of investigating the physical cause of the degradation [3]. To overcome this limitation, a novel diagnostic method of using voltage pulses for HPPC protocols (i.e., smart HPPC) was proposed to successfully separate the impact of the individual degradation mechanisms on current responses [3].While smart HPPC has shown decent performance in identifying individual degradation mechanisms such as film resistance, surface blockage, and electrolyte loss, this work aims to make the protocol even smarter by (1) designing reaction model/electrode material-specific HPPC protocols, (2) performing a quantitative identifiability analysis by considering the uncertainty of estimated degradation parameters, (3) validating the robustness of designed HPPC protocols by performing simulations in various hypothetical situations, and (4) incorporating information from the previous diagnostic results to improve the next round of diagnostics. In this work, a novel framework for designing a set of optimal HPPC protocols is described which is composed of three steps: (1) formulating the parametric uncertainty using a model-based design of experiment (DOE) method and the diagnostic time using scaling analysis, (2) constructing a Pareto front by solving a two-objective optimization, and (3) evaluating the robustness of the designed HPPC protocol by performing Markov Chain Monte Carlo (MCMC) simulations with various degradation states. This work demonstrates the application to a Nickel-Cobalt-Aluminum (NCA)-graphite electrode full-cell with the coupled ion-electron transfer (CIET) reaction model. Our framework could reduce the total diagnostic time by up to 45% of the traditional HPPC protocol while retaining accuracy for identifying all degradation mechanisms.The information dependency on the state of charge (SOC) is investigated in our work through simulation results from overpotential-balanced full cells, showing that the pulses performed with the lower filling fraction of the overpotential dominant electrode are more informative than the pulses with the higher filling fraction. This result allows us to design an innovative shallow-depth HPPC protocol that only explores the SOC regions with high information, which dramatically reduces the total diagnostic time.

Relevant factors for policy concerning comprehensive genomic profiling in oncology: stakeholder perspectives

BackgroundComprehensive genomic profiling (CGP) can identify targets beyond standard of care, potentially revolutionizing personalized cancer management. However, conducting well designed studies in this rapidly evolving field is complex and demands time and investments. Consequently, the total added value of CGP remains uncertain. Clinical benefit and costs often are driving factors in coverage decisions. Recently, five additional factors were identified in the literature that can influence the choice for targeted profiling vs. CGP, specifically: “feasibility”, “test journey patient/physician”, “wider implications of diagnostic results”, “organization of laboratories”, and “scientific spillover”. The objective of the current study is to examine the role and importance assigned to these five additional factors for a comprehensive technology assessment by different stakeholders.MethodsPurposive sampling was used to identify respondents from 4 stakeholder groups (i.e., medical specialists, molecular specialists, patient representatives, and policymakers) from different regions and hospital types (academic vs. non-academic) in the Netherlands. In semi-structured interviews, respondents scored the importance to decision-making of the five factors on a 0 (not important) to 5 (essential) scale. Reasoning behind the scores were elicited using open-ended follow-up questions. Transcripts were independently double-coded by two researchers using thematic analysis.ResultsNineteen stakeholders (100% response rate; medical specialists (n = 7), molecular specialists (n = 7), patient representatives (n = 2), and policymakers (n = 3)) were interviewed. We observed differences between stakeholders in the relative importance assigned to the factors (range of median importance scores: 2–5). Overall, “wider implications of diagnostic results”, primarily CGP’s potential to identify additional treatment options, was deemed the most important factor alongside clinical benefit and costs in decision-making about CGP (median range: 3–5). While the “organization of laboratories” was considered less important (median range: 3–4), opposing arguments and preferences regarding the organization of laboratories were identified, with participants from academic centers preferring a centralized approach whilst non-academics preferred a decentralized approach.ConclusionsStakeholders deemed “wider implications of diagnostic results”, “feasibility”, and “test journey” the most important considerations for decision-making about targeted profiling vs. CGP alongside clinical benefit and costs. For policy decision-making, it is important to understand the arguments behind the heterogeneous opinions, often related to the setting they originate from.

The diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learning

This work is to investigate the diagnostic value of a deep learning-based magnetic resonance imaging (MRI) image segmentation (IS) technique for shoulder joint injuries (SJIs) in swimmers. A novel multi-scale feature fusion network (MSFFN) is developed by optimizing and integrating the AlexNet and U-Net algorithms for the segmentation of MRI images of the shoulder joint. The model is evaluated using metrics such as the Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity (SE). A cohort of 52 swimmers with SJIs from Guangzhou Hospital serve as the subjects for this study, wherein the accuracy of the developed shoulder joint MRI IS model in diagnosing swimmers’ SJIs is analyzed. The results reveal that the DSC for segmenting joint bones in MRI images based on the MSFFN algorithm is 92.65%, with PPV of 95.83% and SE of 96.30%. Similarly, the DSC for segmenting humerus bones in MRI images is 92.93%, with PPV of 95.56% and SE of 92.78%. The MRI IS algorithm exhibits an accuracy of 86.54% in diagnosing types of SJIs in swimmers, surpassing the conventional diagnostic accuracy of 71.15%. The consistency between the diagnostic results of complete tear, superior surface tear, inferior surface tear, and intratendinous tear of SJIs in swimmers and arthroscopic diagnostic results yield a Kappa value of 0.785 and an accuracy of 87.89%. These findings underscore the significant diagnostic value and potential of the MRI IS technique based on the MSFFN algorithm in diagnosing SJIs in swimmers.

Circulating extracellular vesicles in sera of chronic patients as a method for determining active parasitism in Chagas disease.

Chagas disease, once restricted mainly to the Americas, Chagas disease has become a global health problem due to migration from endemic to non-endemic areas. In non-endemic regions, transmission is limited to vertical transmission from infected mothers to newborns or through blood and organ donations. A major challenge in the management of the disease lies in the diagnosis of chronic cases, as blood-borne parasites are often absent and antibodies persist for life, complicating the evaluation of treatment. This study investigates whether detection of circulating extracellular vesicles (EVs) or their immunocomplexes with host IgGs in the serum of chronic patients with Chagas disease could serve as diagnostic tools and biomarkers of the active presence of the parasite. This method may prove valuable in cases where parasitaemia and other diagnostic tests are inconclusive, especially for assessing treatment efficacy and confirming mother-to-child transmission. Together with exovesicle purification by ultracentrifugation, which is the 'gold standard', an affordable and simplified method for the isolation of EVs or immunocomplexes was tested for use in less well-equipped diagnostic laboratories. EV detection was performed by enzyme-linked immunosorbent assay (ELISA) targeting Trypanosoma cruzi antigens. Positive results were demonstrated in Bolivian patients in Spain, covering asymptomatic and symptomatic cases (cardiac, gastrointestinal or both). The study also examined infected mothers and their newborns. These findings were further confirmed in Panamanian patients with inconclusive diagnostic results. Moreover, host IgG isotypes that formed immunocomplexes with parasite exovsicles were identified, with IgG2 and IgG4 being predominant. Our results confirm the usefulness of circulating EVs and their immunocomplexes as markers of metabolically active T. cruzi in chronic infections without detectable parasitaemia, as well as their efficacy in confirming vertical transmission and in cases of inconclusive diagnostic tests.

Fast Processing of Electron Microscopic Specimen Preserved Ultrastructure of Glomeruli and Electron-Dense Deposits in Diagnostic Renal Biopsies: A Prospective and Retrospective Comparative Study

Optimization of electron microscopy (EM) tissue processing protocols is essential to handle the global increase in the number of renal biopsies requiring EM for accurate diagnosis. The conventional EM processing method (CEM) is the standard method used by >95% of laboratories worldwide and it takes at least 48 to 52 hours for completion. In contrast, a fast-processing EM (FEM) method using microwave irradiation can be completed in 8 hours, allowing EM findings to be reported within 24 hours for most cases. There is widespread concern about the suboptimal quality of the FEM that may compromise its diagnostic roles; however, qualitative and quantitative data supporting the noninferiority of FEM compared with CEM has not been reported. We performed both prospective and retrospective studies. The prospective analysis compares FEM and CEM images from the same biopsy samples. For each case, the tissue was divided into 2 pieces: 1 piece for FEM processing and the second for CEM processing. The retrospective study compares the EM images of renal cases with electron-dense deposits from our archives that were processed either by FEM or CEM. The prospective analysis included 4 cases: lupus membranous nephropathy, IgA nephropathy, immune complex–mediated glomerulonephritis, and acute tubular injury. Both FEM and CEM methods obtained high-resolution images with comparable quality. A quantitative morphometric analysis of the glomerular basement membrane (GBM) in the IgA nephropathy case showed similar GBM thickness when processed by the FEM and the CEM, suggesting that FEM did not affect GBM thickness. The retrospective study of 42 cases with electron-dense deposits showed that the ultrastructural features of electron-dense deposits were indistinguishable between the FEM and the CEM. This included microtubular substructures in immunotactoid glomerulonephritis, the "fingerprint" deposits in cryoglobulinemic glomerulonephritis, fibril deposits in the light chain amyloidosis as well as fibrillary glomerulonephritis, with comparable morphometric measurements of the deposits. The FEM is efficient, consistent, reproducible, and delivers comparable high-quality sections and images for diagnostic assessment of renal biopsies, compared with those attained by the CEM while decreasing turnaround time significantly, making it possible to provide faster and accurate diagnostic results.

Research on biomechanics integrated Bayesian network mental health diagnosis system

With the rapid economic development of various countries around the world and the acceleration of global networking, countries are striving to promote their own urbanization and industrialization progress. The side effect is that social pressure leads to the concentrated outbreak of various social contradictions. The main psychological health testing and evaluation method in society is still conducted through dialogue with psychologists. Doctors obtain information through dialogue and communication with patients, and diagnose their psychological status based on this information. Affected by factors such as communication style and the patient’s own mental state. The information obtained may result in omissions and biases, leading to inaccurate diagnostic results. Bayesian network is a probabilistic graphical model that derives the results through information calculation. It can analyze and calculate the finite and incomplete conditions, carry out corresponding reasoning, and obtain more rigorous results. This article applied the naive Bayesian algorithm to the research of mental health diagnosis systems, and compared it with mental health diagnosis systems that do not use algorithms. According to the mental health index of contemporary people, the algorithm achievement test experiment of mental health diagnosis system was carried out. After research and comparison, it was found that for the collected data, the maximum accuracy of the Naive Bayesian algorithm within a hundred calculations reached 99%, with a mean of 96.5%. The traditional paper-based psychological diagnosis method had a maximum accuracy of 89%, a minimum of 70%, and an average accuracy of 80.5%. Therefore, the application of naive Bayesian network to the development and research of mental health diagnosis system can effectively improve the efficiency, accuracy and diagnostic effect of mental diagnosis.

The study on ultrasound image classification using a dual-branch model based on Resnet50 guided by U-net segmentation results

In recent years, the incidence of nodular thyroid diseases has been increasing annually. Ultrasonography has become a routine diagnostic tool for thyroid nodules due to its high real-time capabilities and low invasiveness. However, thyroid images obtained from current ultrasound tests often have low resolution and are plagued by significant noise interference. Regional differences in medical conditions and varying levels of physician experience can impact the accuracy and efficiency of diagnostic results. With the advancement of deep learning technology, deep learning models are used to identify whether a nodule in a thyroid ultrasound image is benign or malignant. This helps to close the gap between doctors’ experience and equipment differences, improving the accuracy of the initial diagnosis of thyroid nodules. To cope with the problem that thyroid ultrasound images contain complex background and noise as well as poorly defined local features. in this paper, we first construct an improved ResNet50 classification model that uses a two-branch input and incorporates a global attention lightening module. This model is used to improve the accuracy of benign and malignant nodule classification in thyroid ultrasound images and to reduce the computational effort due to the two-branch structure.We constructed a U-net segmentation model incorporating our proposed ACR module, which uses hollow convolution with different dilation rates to capture multi-scale contextual information for feature extraction of nodules in thyroid ultrasound images and uses the results of the segmentation task as an auxiliary branch for the classification task to guide the classification model to focus on the lesion region more efficiently in the case of weak local features. The classification model is guided to focus on the lesion region more efficiently, and the classification and segmentation sub-networks are respectively improved specifically for this study, which is used to improve the accuracy of classifying the benign and malignant nature of the nodules in thyroid ultrasound images. The experimental results show that the four evaluation metrics of accuracy, precision, recall, and f1 of the improved model are 96.01%, 93.3%, 98.8%, and 96.0%, respectively. The improvements were 5.7%, 1.6%, 13.1%, and 7.4%, respectively, compared with the baseline classification model.

PRESERVING PERSONAL ECOLOGY IN THE CONTEXT OF SUPPORT OF HUMAN PSYCHOLOGICAL HEALTH

The problem of supporting individual psychological health under current conditions is relevant and signifi cant to consider. It is necessary to take into account the determinants of subjective well-being, resources to support the optimal state of personality ecology. The methodological and theoretical basis for the analysis of genesis, structure, factors for preserving human ecology and psychological health was the ideas and principles of the system-activity approach, as well as foreign and Russian theories of psychological health, revealing the essence, content and structure of this concept, functions and specifi cs of the development of this state. The structural components of the phenomenon of psychological health (axiological, instrumental, need-motivational) and the diagnosed signs of each of them (self-esteem, social tolerance, sense of personal security; stress resistance, social adaptation, psychological adaptability; acceptance of the values of a self-actualizing person, the need for self-realization, active social contacts) are described. Testing, analysis, methods of mathematical statistics were used as methods.  students of Armavir State Pedagogical University took part in the diagnostic examination. According to the diagnostic results, the following data were obtained. The level of self-esteem: overestimated – % ( people), adequate – % ( people). The results of the diagnostics showed the tendency of students to overestimate their abilities and overestimate themselves. The level of sociability: high – % ( people), average – % ( people), low – % ( people). A high level of sociability is one of the professionally important qualities of a teacher’s personality and one of the parameters for a successful adaptation process to the new social environment at a university. The results of the study expand scientifi c ideas on the indicators of human psychological health, form the theoretical basis for managing the processes of its preservation and strengthening.

A Model-Driven Approach to Extract Multi-Source Fault Features of a Screw Pump

Screw pumps’ faulty working conditions affect the stability of oil production. At project sites, different sensors are used simultaneously to collect multi-dimensional signals; the data fault labels and location are not clear, and how to comprehensively use multi-source information in effective fault feature extraction has become an urgent issue. Existing diagnostic methods use a single signal or part of a signal and do not fully utilize the acquired signal, which makes it difficult to achieve the required accuracy of diagnostic results. This paper focuses on the model-driven approach to extract multi-source fault features of screw pumps. Firstly, it constructs a fault data model (FDM) by analyzing the fault mechanism of the screw pump. Secondly, it uses the FDM to select an effective data set. Thirdly, it constructs a multi-dimensional fault feature extraction model (MDFEM) to extract featured signal features and data features, for which we also comprehensively used multi-source signals in effective fault feature extraction, while other traditional methods only use one or two signals. Finally, after feature selection, unsupervised fault diagnosis was achieved by using the k-means method. After experimental verification, the method can comprehensively use multi-source information to construct an effective data set and extract multi-dimensional, effective fault features for screw pump fault diagnosis.

Rolling element bearing fault diagnosis under time-varying speed conditions based on improved wavelet packet transform and envelope order tracking

Abstract Rolling element bearings (REBs) are crucial components of rotating machinery and play an important role in industrial production. Failure of REBs can lead to significant economic losses and catastrophic accidents. In order to ensure their safe and stable operation, we propose a novel approach called EOT-IWPT which combines the envelope order tracking (EOT) with an improved wavelet packet transform algorithm (IWPT) for diagnosing failures in REBs under time-varying speed conditions. First, the time- domain vibration signal is resampled at constant angle intervels according to the phase of the rotating shaft to obtain the angle-domian signal. Next, the resampled signal is decomposed to sub-band signals using IWPT. Third, the squared envelope spectra (also called envelope order spectra) of the last layer wavelet packet coefficients are obtained by the Hilbert transform and the Fourier transform. Finally, the diagnostic results can be easily determined based on the squared envelope spectra. The procedure and performance of the proposed approach are illustrated and evaluated through simulation analysis, proving that the proposed method can effectively extract the fault features. Furthermore, experimental analysis results indicate that the proposed method outperforms both EOT-WPT and SK-EOT-BPF.

Diagnostic comparison of bedside lung ultrasound and chest radiography in the intensive care unit

Bedside lung ultrasound (POCUS) offers advantages over chest X-ray, including better cost-effectiveness for diagnosing certain pulmonary pathologies. This study compares the diagnostic concordance between portable chest X-rays and bedside lung ultrasounds in the intensive care unit (ICU). Adult ICU patients were included. POCUS was performed using the abbreviated BLUE protocol. Diagnostic results from POCUS and chest radiographies were compared using the intensivist clinical diagnosis - based on clinical examinations and lung ultrasounds - as the reference. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the ultrasounds were calculated. A total of 100 patients were included, 71 with pulmonary pathologies. The average time to perform the ultrasound was 308 seconds. Ultrasound identified pathology in 20 patients with a normal chest radiographs. Diagnostic discrepancies occurred in 30 patients, highlighting ultrasound´s superior sensitivity in detecting atelectasis, pleural effusions, and pulmonary edema. Ultrasound demonstrated sensitivity (S) of 85%, specificity (E) of 100%, positive predictive value (PPV) of 100%, and negative predictive value (NPV) of 55%. Lung point-of-care ultrasound at ICU admission detects more pathologies and does not miss significant abnormalities seen on chest X-rays. It also shows good diagnostic accuracy. These findings suggest that pulmonary POCUS, using an abbreviated protocol, could be a viable alternative to chest radiography for initial evaluation and follow-up of pulmonary pathologies in critically ill patients, potentially improving care quality and management.

AHP SO06 - A case report of the nutritional and multi-disciplinary team (MDT) management of an emergency oesophageal perforation associated with Eosinophilic Oesophagitis (EOE)

Abstract Background EOE once considered rare, is a chronic inflammatory condition of the oesophagus presenting as dysphagia and food impaction in children and adults. Awareness and management of EOE is variable despite an increasing prevalence and the condition deemed the most common cause of oesophageal perforation in adults and children by the British Society of Gastroenterology (BSG). This case review aims to increase the awareness of EOE, highlight the clinical presentation of EOE, discuss considerations for the nutritional management and the importance of an MDT approach to the care of this condition in relation to the BSG consensus guidelines. Method The patient was admitted to a specialist oesophago-gastric surgical centre in the Northwest of England. A retrospective review of the patient’s case notes was completed. This included exploration of all inpatient and outpatient contacts stored in the electronic patient records system. Results 18-year-old underweight male with IgE-mediated nut allergy and odynophagia, developed complex multi-site oesophageal perforations following OGD. Management included 40-day endoluminal vacuum therapy and extensive nutritional support. Although initial biopsies lacked diagnostic quality and conclusive results, EOE was subsequently confirmed. Dietetic evaluation included nutritional status, atopic history, symptoms, and dietary intolerances. MDT discussions influenced care continuity and patient compliance. Feeding delays attributable to frequent theatre visits impacted continued weight loss; addressed with allergen-free enteral and parenteral feeding, and dietary reintroduction, following six-food elimination once perforations had healed. Healthy BMI was achieved through patient’s preferred long-term management of normal diet and Budesonide. Conclusion Increased awareness of EOE is paramount for patients presenting with unexplained oesophageal perforation. Obtaining biopsy samples as per BSG guidance supports earlier diagnosis and management plan. A thorough clinical history including childhood eating behaviours, atopic history, dietary intolerances and symptom presentation from the patient and relatives gathers collateral information to support diagnosis and treatment planning. Joint discussions regarding pharmaceutical management, dietary elimination and reintroduction are important to achieve remission. The case highlights the importance of collaborative MDT working with the surgical, medical and dietetics whilst empowering the patient to make informed decisions regarding the nutritional and medical management of condition.

End-point diagnostics of Giardia duodenalis assemblages A and B by combining RPA with CRISPR/Cas12a from human fecal samples

BackgroundGiardia duodenalis is a common enteric protozoan parasite that is categorized into eight assemblages (A–H). In particular, assemblages A and B are zoonotic, capable of infecting both humans and animals worldwide, resulting in significant economic losses and public health challenges in epidemic regions. Thus, the development of rapid, accurate and non-laboratory-based diagnostic methods for infected animals is crucial for the effective prevention and control of giardiasis. Recent advancements in clustered, regularly interspaced, short palindromic repeats (CRISPR) and CRISPR-associated (Cas) protein (Cas12a) systems allow promising avenues for nucleic acid detection, characterized by their high flexibility, sensitivity and specificity.MethodsCombined recombinase polymerase amplification and CRISPR/Cas12a systems were combined and used as end-point diagnostic methods (termed REPORT) to detect G. duodenalis assemblage A and B. The diagnostic results can be observed by fluorescence readouts with the naked eye under blue light or colorimetric signals using a lateral flow strip (LFS).ResultsThe limit of detection (LOD) of the REPORT‑based G. duodenalis assemblage A detection was 2.04 CFU/ml and 10 trophozoites per gram (TPG), and the LOD of assemblage B was 1.1 CFU/ml and 10 cysts per gram (CPG). The REPORT‑based G. duodenalis assemblage A and assemblage B detection methods have strong specificity and no cross-reactivity with other assemblages of G. duodenalis or common enteric parasitic protozoa and have excellent performance in clinical sample detection.ConclusionsThis study presents a novel strategy for the direct identification of G. duodenalis assemblages A and B, requiring neither highly trained personnel nor costly specialized equipment.Graphical