Method For Diagnosis Research Articles

Contagious Bovine Pleuropneumonia: A Review

Contagious bovine pleuropneumonia (CBPP) is a bacterial disease, the causative agent of which is Mycoplasma mycoides subsp. Mycoides. The CBPP disease is one of important disease to be given attention because it is causing heavy mortality in bovines all over the world with African continent being hardly hit due to vagaries of bacteria. Air droplets is primary source of infection. Colonization in lower respiratory tract, Mmm invades arteries cause vasculitis. Many pathways of Mmm organism to cause destruction in host animal, presence of Gts ABC is one such mechanism. In CBPP we see pulmonary sequestra in chronic cases, severe fibrinous bronchopneumonia and pleural effusion during the acute to subacute phases. For confirmatory diagnosis, laboratory investigation and use of molecular techniques is recommended. Isolation of causative agent is also optimal method for diagnosis. The use of danofloxacin, tetracycline, flufenicol etc have been documented with varying degree of efficiency. Control over movement of bovine species, adequate space (where droplet spread is prevented) for each animal and vaccination are recommendatory steps. Though India has been declared CBPP free in 2007 we still need cautious approach and continue surveillance because north east of India seems very vulnerable to CBPP. Also, the fact that animal husbandry and agriculture drive economy of some of our most populous states it is all the more important that we give due consideration and focus on CBPP.

ARLSSMM: an effective fault diagnosis method for roller bearing under strong noise

ABSTRACT Support matrix machine (SMM) is a matrix classification method that effectively utilises information between rows and columns in matrix-form data. However, the presence of abnormal data in the feature matrix fed into the model can result in model non-convergence and hyperplane shift, greatly affecting classification accuracy. To address this issue, this paper introduces a matrix classification method, called asymmetric robust least squares support matrix machine (ARLSSMM). To construct ARLSSMM, a novel optimisation model based on a matrix with asymmetric constraints is designed to obtain the hyperplane, which can not only use the data set to dynamically determine the position of the hyperplane to obtain the maximum margin space but also reduce the influence of abnormal data on the model classification performance. Two different roller bearing types of test data are used for experimental validation, and the results show that ARLSSMM demonstrates excellent classification performance.

An historical overview of artificial intelligence for diagnosis of major depressive disorder

The Artificial Intelligence (AI) technology holds immense potential in the realm of automated diagnosis for Major Depressive Disorder (MDD), yet it is not without potential shortcomings. This paper systematically reviews the research progresses of integrating AI technology with depression diagnosis and provides a comprehensive analysis of existing research findings. In this context, we observe that the knowledge-driven first-generation of depression diagnosis methods could only address deterministic issues in structured information, with the selection of depression-related features directly influencing identification outcomes. The data-driven second-generation of depression diagnosis methods achieved automatic learning of features but required substantial high-quality clinical data, and the results were often obtained solely from the black-box models which lack sufficient explainability. In an effort to overcome the limitations of the preceding approaches, the third-generation of depression diagnosis methods combined the strengths of knowledge-driven and data-driven approaches. Through the fusion of information, the diagnostic accuracy is greatly enhanced, but the interpretability remains relatively weak. In order to enhance interpretability and introduce diagnostic criteria, this paper offers a new approach using Large Language Models (LLMs) as AI agents for assisting the depression diagnosis. Finally, we also discuss the potential advantages and challenges associated with this approach. This newly proposed innovative approach has the potential to offer new perspectives and solutions in the diagnosis of depression.

Investigation of the efficacy of siRNA-mediated KRAS gene silencing in pancreatic cancer therapy

Aim Pancreatic carcinoma is an aggressive cancer that progresses without many symptoms. The difficulty of early diagnosis and an inadequate response to traditional treatments also cause the survival rate of pancreatic cancer to be low. Current research is focusing on methods of diagnosis and treatment, such as gene therapy, to increase survival rates. Small interfering ribonucleic acid (siRNA) has emerged as a promising advanced therapeutic strategy for cancer treatment. This study sought to silence the KRAS gene in the human pancreatic carcinoma cell line using a complex of small interfering ribonucleic acid (siRNA) and gold nanoparticles (AuNP). Methods In this study, 25 nM siRNA and gold nanoparticles at 0.5 mg/ml, 0.25 mg/ml, and 0.125 mg/ml concentrations were used to silence the KRAS gene in the CAPAN-1 cell line. Real-time PCR analysis, agarose gel electrophoresis, and double staining were carried out, and xCelligence real-time cell analysis (RTCA) was used to measure proliferation. Results The PCR analysis revealed crossing point (CP) values of actin beta (ACTB) ranging from 33.04 to 35.98, which was in the expected range for all samples. The interaction between the gold nanoparticle/siRNA complex in the double staining analysis revealed that the most effective concentration of gold nanoparticle was 0.125 mg/ml. The WST-1 technique showed that siRNA/AuPEI cells in application groups had a viability rate of over 90%, indicating no toxicity or side effects. The xCELLigence RTCA® showed that at hour 72, there was a significant difference in proliferation in the 0.5 mg/mL PEI/AuNP-siRNA, 0.25 mg/mL PEI/AuNP-siRNA, and 0.125 mg/mL PEI/AuNP-siRNA application groups compared to the control and siRNA groups (p < 0.05). By hour 96, all three groups were statistically different from the control and siRNA groups in terms of proliferation (p < 0.05). Conclusions The results of this analysis suggest that the AuPEI/siRNA complex can be effectively used to silence the target gene, but more studies are needed to verify these results.

A New Method of Intelligent Fault Diagnosis of Ship Dual-Fuel Engine Based on Instantaneous Rotational Speed

Ship engine misfire faults not only pose a serious threat to the safe operation of ships but may also cause major safety accidents or even lead to ship paralysis, which brings huge economic losses. Most traditional fault diagnosis methods rely on manual experience, with limited feature extraction capability, low diagnostic accuracy, and poor adaptability, which make it difficult to meet the demand for high-precision diagnosis. To this end, a fusion intelligent diagnostic model—ResNet–BiLSTM—is proposed based on a residual neural network (ResNet) and a bidirectional long short-term memory network (BiLSTM). Firstly, a multi-scale decomposition of the instantaneous rotational speed signal of a ship’s engine is carried out by using the continuous wavelet transform (CWT), and features containing misfire fault information are extracted. Subsequently, the extracted features are fed into the ResNet–BiLSTM model for learning. Finally, the intelligent diagnosis of ship dual-fuel engine misfire faults is realized by the classifier. The model combines the advantages of ResNet18 in image feature extraction and the capability of BiLSTM in temporal information processing, which can efficiently capture the time-frequency features and dynamic changes in the fault signal. Through comparison experiments with fusion models AlexNet–BiLSTM, VGG–BiLSTM, and the existing AlexNet–LSTM and VGG–LSTM models, the results show that the ResNet–BiLSTM model outperforms the other models in terms of diagnostic accuracy, robustness, and generalization ability. This model provides an effective new method for intelligent diagnosis of ship dual-fuel engine misfire faults to solve the traditional diagnostic methods’ limitations.

Seven-year performance of a clinical metagenomic next-generation sequencing test for diagnosis of central nervous system infections.

Metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) is an agnostic method for broad-based diagnosis of central nervous system (CNS) infections. Here we analyzed the 7-year performance of clinical CSF mNGS testing of 4,828 samples from June 2016 to April 2023 performed by the University of California, San Francisco (UCSF) clinical microbiology laboratory. Overall, mNGS testing detected 797 organisms from 697 (14.4%) of 4,828 samples, consisting of 363 (45.5%) DNA viruses, 211 (26.4%) RNA viruses, 132 (16.6%) bacteria, 68 (8.5%) fungi and 23 (2.9%) parasites. We also extracted clinical and laboratory metadata from a subset of the samples (n = 1,164) from 1,053 UCSF patients. Among the 220 infectious diagnoses in this subset, 48 (21.8%) were identified by mNGS alone. The sensitivity, specificity and accuracy of mNGS testing for CNS infections were 63.1%, 99.6% and 92.9%, respectively. mNGS testing exhibited higher sensitivity (63.1%) than indirect serologic testing (28.8%) and direct detection testing from both CSF (45.9%) and non-CSF (15.0%) samples (P < 0.001 for all three comparisons). When only considering diagnoses made by CSF direct detection testing, the sensitivity of mNGS testing increased to 86%. These results justify the routine use of diagnostic mNGS testing for hospitalized patients with suspected CNS infection.

Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks

Cognitive diagnosis is one of the essential components in intelligent education and aims to diagnose student’s skill or knowledge mastery based on their responses. Recently, with the development of artificial intelligence, some researchers have applied neural network methods to cognitive diagnosis. Although they achieved some success, they seemed to lack a certain basis for designing network structures and could not obtain a unified method for designing network structures. We propose a neural network method for cognitive diagnosis based on Q-matrix constraints, introducing the Q-matrix from traditional cognitive diagnosis to enhance the reliability and interpretability of the network structure. Specifically, our method is highly consistent with generalized deterministic inputs, the noisy “and” gate model (GDINA), and the network structure reflects the direct contribution of skills to answering questions correctly, as well as the indirect contribution of interactions between skills to answering questions correctly. Finally, extensive experiments on both simulated and real datasets demonstrated that our method achieved high accuracy and reliability, with a particularly notable performance on low-quality datasets. As the number of questions and skills increased, our approach exhibited greater robustness compared to the classical methods, highlighting its potential for broad applicability in cognitive diagnosis tasks.

Automatic Watershed Segmentation of Cancerous Lesions in Unsupervised Breast Histology Images

Segmentation of nuclei in histology images is key in analyzing and quantifying morphology changes of nuclei features and tissue structures. Conventional diagnosis, segmenting, and detection methods have relied heavily on the manual-visual inspection of histology images. These methods are only effective on clearly visible cancerous lesions on histology images thus limited in their performance due to the complexity of tissue structures in histology images. Hence, early detection of breast cancer is key for treatment and profits from Computer-Aided-Diagnostic (CAD) systems introduced to efficiently and automatically segment and detect nuclei cells in pathology. This paper proposes, an automatic watershed segmentation method of cancerous lesions in unsupervised human breast histology images. Firstly, this approach pre-processes data through various augmentation methods to increase the size of dataset images, then a stain normalization technique is applied to these augmented images to isolate nuclei features from tissue structures. Secondly, data enhancement techniques namely; erosion, dilation, and distance transform are used to highlight foreground and background pixels while removing unwanted regions from the highlighted nuclei objects on the image. Consequently, the connected components method groups these highlighted pixel components with similar intensity values and, assigns them to their relevant labeled component binary mask. Once all binary masked groups have been determined, a deep-learning recurrent neural network from the Keras architecture uses this information to automatically segment nuclei objects with cancerous lesions and their edges on the image via watershed filling. This segmentation method is evaluated on an unsupervised, augmented human breast cancer histology dataset of 11,151 images. This proposed method produced a significant evaluation result of 98% F1-accuracy score.

Meta-learning-based fault diagnosis method for rolling bearings under cross working-conditions

Abstract Problems such as domain shift and insufficient sample quantity may occur when conducting fault diagnosis under cross-working conditions, decreasing the accuracy and generalization of deep learning algorithms. In this paper, a fault diagnosis framework based on meta-learning and a dual-channel classification feature fusion network combines the advantages of meta-learning and dual-channel classification feature fusion network to enhance the framework's performance in cross-working condition diagnosis and few-shot learning. Firstly, dual-channel network is used to extract the classification features of different domains, and the features are fused. Then, training is conducted under the meta-learning strategy to acquire prior knowledge for fast model learning under cross-working conditions and solve the problem of a few shots. Finally, two public rolling bearing data sets are used to demonstrate the efficacy of the proposed method across different operational conditions. Before that, the appropriate sample length and fusion domain were selected through experiments. The proposed method also has good fault diagnosis accuracy in cross-device tasks. The experimental results verify the effective classification capability and robustness of the proposed method. Furthermore, the superiority of this method over other meta-learning approaches is evident when compared.&amp;#xD;

An attention-based deep network for plant disease classification

Plant disease classification using machine learning in a real agricultural field environment is a difficult task. Often, an automated plant disease diagnosis method might fail to capture and interpret discriminatory information due to small variations among leaf sub-categories. Yet, modern Convolutional Neural Networks (CNNs) have achieved decent success in discriminating various plant diseases using leave images. A few existing methods have applied additional pre-processing modules or sub-networks to tackle this challenge. Sometimes, the feature maps ignore partial information for holistic description by part-mining. A deep CNN that emphasizes integration of partial descriptiveness of leaf regions is proposed in this work. The efficacious attention mechanism is integrated with high-level feature map of a base CNN for enhancing feature representation. The proposed method focuses on important diseased areas in leaves, and employs an attention weighting scheme for utilizing useful neighborhood information. The proposed Attention-based network for Plant Disease Classification (APDC) method has achieved state-of-the-art performances on four public plant datasets containing visual/thermal images. The best top-1 accuracies attained by the proposed APDC are: PlantPathology 97.74%, PaddyCrop 99.62%, PaddyDoctor 99.65%, and PlantVillage 99.97%. These results justify the suitability of proposed method.

A novel bearing weak fault diagnosis method based on rank constrained low-rank and sparse decomposition

Abstract Addressing the challenge of diagnosing incipient bearing faults amidst significant noise, a novel diagnostic approach is introduced, leveraging a Rank Constrained Low-Rank and Sparse Decomposition (RCLRSD) model tailored for weak fault detection in bearings. Initially, we raised the Autocorrelation Function of the Square Envelope in Frequency Domain (AFSEFD) as an innovative method for the estimation of fault frequencies. Subsequently, we constructed a two-dimensional observation matrix, which is formulated independently of predefined assumptions. Then, we examine the configuration and distribution patterns of bearing fault signals, uncovering the low-rank nature of fault characteristics within a designated two-dimensional transform domain. Moreover, we found the noise signal to exhibit sparsity and an approximate Gaussian distribution. Based on this, a rank-constrained low-rank sparse decomposition model is established, and rank-constrained low-rank regular constraints for feature information and sparse regular constraints and Gaussian constraints for interference signals are constructed respectively. Ultimately, we employed the Gray Wolf Optimization (GWO) algorithm to refine the model parameters, and we deduced the model's solver via the Alternating Direction Method of Multipliers (ADMM). The proposed RCLRSD model decomposes bearing fault data into three constituents: low-rank, sparse, and Gaussian components, effectively addressing the challenge of extracting weak fault signatures from bearings. The weak feature extraction capability of the RCLRSD model is verified using a multi-interference simulation model and experimental data of bearing failures under strong noise conditions; the generalization of the model is verified by the classification effect of the Support Vector Machine (SVM) under two bearing failure datasets. Comparison with various algorithms confirms the superiority of the proposed algorithm.

Clinical effect of indirect moxibustion with Taiyi moxa stick in treatment of back myofascitis: a randomized controlled trial

To observe the impact of indirect moxibustion with Taiyi moxa stick on pain severity and the quality of life in the patients with back myofascitis, and evaluate its clinical effect and safety. Seventy-two patients with back myofascitis were randomly divided into an observation group (36 cases, 1 cases dropped out) and a control group (36 cases, 3 cases dropped out). Three to six ashi points were selected using acupoint diagnosis methods. In the observation group, the indirect moxibustion with Taiyi moxa stick was operated at the ashi points, once every other day, three times a week. In the control group, the topical treatment with diclofenac sodium gel was given at ashi points, three times daily. The treatment was delivered consecutively for 2 weeks in the two groups. The scores of visual analogue scale (VAS) and the short-form McGill pain questionnaire (SF-MPQ) were recorded before treatment, after 1 and 2 weeks of treatment, and after 4 weeks of treatment completion (follow-up visit) in the two groups separately. The difference value between VAS score after 2 weeks of treatment and that before treatment was taken as the primary outcome. The SF-36 score was observed before treatment, after 2 weeks of treatment and during the follow-up visit, and the safety was evaluated in the two groups. The difference value of VAS scores was (-4.57±1.50) points and (-2.40±1.31) points in the observation group and the control group, respectively, the reduction of the score in the observation treatment was larger than that of the control group (P<0.001). After 1 and 2 weeks of treatment and during the follow-up visit, VAS and SF-MPQ scores were reduced when compared with those before treatment (P<0.05) in the two groups, and VAS and SF-MPQ scores in the observation group were lower than those in the control group (P<0.05). In the follow-up visit, VAS and SF-MPQ scores in the observation group were dropped in comparison with those after 1 week of treatment (P<0.05), and these scores in the control group were elevated compared with those after 2 weeks of treatment (P < 0.05). After 2 weeks of treatment and during the follow-up visit, the score of each dimension and total score of SF-36 scale were increased in comparison with those before treatment in the observation group (P<0.05); and the scores of the dimensions except that of mental health of SF-36 scale were elevated in the control group (P<0.05). During the follow-up visit, in the control group, the scores of physical functioning, bodily pain, general health and role emotional were elevated when compared with those before treatment (P<0.05). After 2 weeks of treatment and during the follow-up visit, the score of every dimension and total score of SF-36 scale in the observation group were higher than those in the control group (P<0.05). During the follow-up visit, the score of bodily pain was increased when compared with that after 2 weeks of treatment in the observation group (P<0.05), while the scores of bodily pain and vitality were dropped in the control group (P<0.05). No adverse event happened in the two groups. The indirect moxibustion with Taiyi moxa stick effectively reduces pain severity and improves the quality of life in the patients with back myofascitis. This therapy presents the superior and persistent effect compared with topical application of diclofenac sodium gel.

Deep Learning‐Based Anomaly Diagnosis Method for Capacitive Voltage Transformer Measurement Error in PIoT

ABSTRACTIn the era of power Internet of Things (PIoT), the accuracy of capacitive voltage transformers (CVTs) is crucial for maintaining the reliability of voltage measurement and protection systems in smart grids, thereby contributing to overall grid stability and efficiency. Accurate and timely detection of anomalies in CVT measurement errors is essential for preventing equipment failures, reducing maintenance costs, and improving overall system reliability. However, existing anomaly diagnosis methods often rely on statistical analysis and rule‐based approaches, which have limitations in capturing complex patterns and adapting to evolving anomaly types. This paper proposes a novel deep learning‐based anomaly diagnosis method for CVT measurement error in PIoT, called LSTM‐CVT. The proposed method leverages a long short‐term memory (LSTM) neural network architecture with three key strategies: bidirectional temporal dependency capture, hierarchical feature learning, and joint anomaly diagnosis and error estimation. The experimental results demonstrate the superior performance of LSTM‐CVT compared to state‐of‐the‐art baseline methods.

INNV-35. DATA-DRIVEN MODELING OF EMERGING TRENDS AND COLLABORATIVE NETWORKS ACROSS BRAIN METASTASES RESEARCH: THE PAST DECADE OF INNOVATION

Abstract With recent significant growth in brain metastases literature output, more comprehensive research is necessary to understand emerging trends and novel methods of diagnosis and treatment. We aimed to address this by providing a contemporary systematic global analysis of emerging brain metastases research in the last decade while visually depicting the international collaborative environment among its researchers. Data mining was performed on all brain metastases publications across the last decade indexed in Elsevier’s Scopus database. Our Scopus search revealed 6,362 publications from 1,184 sources from 2013-2022. Of those, 252 were identified as high-impact articles with an annual citation rate (CR) &amp;gt; 10. The compounded annual growth rate (CAGR) of brain metastases publications was 7.2% per year since 2013. There was an acceleration of scientific production, from 5.9% CAGR across 2013-2018 to 9.6% across 2019-2022. Authors per document increased from 7.2% to 8.5% while international co-authorships increased from 16.6% to 18.3% in the same periods, respectively. Recent trending topics were noted to be artificial intelligence, understanding the brain microenvironment, exosomes, novel mechanisms of diagnosis (such as circulating tumor cells), and targeted treatments. High-impact articles tended to revolve around specific therapies or biomarkers as opposed to general synopses. Clustering analysis revealed three separate pivotal collaborative communities centered around Brown PD/Sahgal A, Ahluwalia MS, and Wang Y, with Brown PD as most notable for high impact research. While the top institutional collaborative group centered around Mayo Clinic, the highest impact research centered around the Dana-Farber Cancer Institute. Sub-analyses revealed that studies involving multiple countries tended to have a higher impact. The median overall survival of brain metastases, by primary source, was illustrated across the last five decades. Overall, significant growth in scientific production is driven by the integration of novel technologies (such as artificial intelligence), interdisciplinary approaches, and collaboration across global communities.

Evaluation of Standard Wire Loop Method in Determination of Significant Bacteriuria among Pregnant Women in Ekpoma

Standard wire loop method is a time-tested method for UTI diagnosis. The term "bacteriuria" means the presence of bacteria in urine. It may result from contamination during or after collection of urine or it may indicate the presence of bacteria in the bladder urine. There are several cultural quantitative techniques for determining bacteriuria but standard wire loop method is the most popular method though relatively simple and cheap. This study was carried out to evaluate the standard wire loop method for determination of significant bacteriuria among pregnant women in Ekpoma. A total of one hundred (100) early morning mid-stream urine samples from pregnant women attending antenatal clinics in Ekpoma and environs were collected aseptically using sterile, wide-mouthed, leak-proof universal bottles. Microscopic examination was carried out to detect the presence of pus cells, epithelial cells, red blood cells, yeast cells, crystals, parasites, bacteria and casts. Undiluted urine, 1:10and 1:1000 diluted urine samples were aseptically inoculated into blood agar and MacConkey agar using the Standard Loop method and compared with Miles and Mirsa method, it was incubated at 370C aerobically for 24 hours. After incubation colonies were counted and isolates identified by standard biochemical methods. Colony count of ≥105 CFU per ml were considered as significant growth. In vitro antibiotics susceptibility of bacterial isolates was determined using disc diffusion technique. The data generated was analyzed statistically using the chi-squared statistics to ascertain the significance of the results. Out of 100 urine samples collected 39(39%) samples yielded significant bacterial growth, while 61(61%) of the samples had no significant bacterial growth or no growth with higher prevalence in age group &lt;25 years which is a sexually active group than in other age group ≤35 years which had incidence rate of 12 (25%). Standard loop method gave false positive rates of 4/35 (11.4%) and false negative rates of 4/36 (11.1%). The standard loop was found to have a sensitivity of 100%, specificity of 88.9% and identical results in 76.3% of the samples. To reduce bacteriuria and consequently prevent UTIs, it is necessary to take the precautions by doing regular routine check-ups and appropriate treatment. For large scale urine surveys the method chosen should be reasonably accurate, fast and simple to use. The materials required should be minimal, stable and easily transported. It is recommended that the method of choice for most laboratories is the standard loop method.

Endoscopic resection for esophageal granular cell tumors: report of 62 cases.

To analyze the clinical manifestations, endoscopic features, pathological features, endoscopic resection, and prognosis of esophageal granular cell tumors (GCTs). The present study retrospectively analyzed and followed up the clinical data of 62 patients diagnosed with esophageal GCTs who underwent endoscopic resection at Zhongshan Hospital of Fudan University between July 2007 and March 2022. The clinicopathological features, endoscopic diagnosis, and treatment experience of esophageal GCT patients were summarized. Among the 62 patients with esophageal GCT, there were 34 males and 28 females, with an average age of 49.3 ± 11.7 years. Only 11 patients had symptoms, such as epigastric discomfort, regurgitation or dysphagia. One patient had multiple lesions, and the rest had single lesions, totaling 63 lesions. Most lesions (53/63) were located in the median and lower esophagus, the diameters ranged from 3 to 22mm. The endoscopic morphology of the GCTs was molar, flat, hemispherical, or irregular submucosal protuberance. Endoscopic ultrasound (EUS) was performed in 38 cases, most cases (31/38) were hypoechoic, and 32 cases were appeared as homogeneous lesions. There were no complications during or after the endoscopic operations, and the en bloc resection rate was 100%. The negative rate of microscopic incisional margin was 63.5% (40/63). No patients developed recurrence during the follow-up period. The follow-up duration was 21-197 months (100.5 months for average). Esophageal GCT is a rare disease with no obvious symptoms and a good prognosis. Endoscopic resection is a safe and effective method of diagnosis and treatment for esophageal GCTs. A microscopic positive tumor margin may not increase the rate of recurrence.

A Systematic Review Evaluating Methods for Diagnosis, Treatment and Prevention of Dental Erosions.

The aim of this systematic review was to investigate the quality of evidence for methods used to diagnose, restore and prevent dental erosions, by evaluating and summarising clinical controlled studies and insitu studies covering this field. Three databases, PubMed, Embase and Cochrane database, were searched for studies published until October 2023. Studies fulfilling the selection criteria were selected, and the risk of bias of the included studies was assessed. Two authors independently reviewed the studies, evaluated their risk of bias and extracted data from the included studies. The certainty of evidence of the findings in the studies was determined using GRADE. The literature search identified 7176 records. Out of these, 19 studies were included in this review, three revolving diagnostics, three revolving treatment and 13 revolving prevention. However, due to the fact that only few studies with a small number of participants have investigated the same methods on the same outcome, the studies do not constitute any body of evidence. This systematic review highlights the shortage of data on methods used for diagnostics, restorative treatment and prevention of dental erosions. More knowledge is needed to strengthen the scientific evidence regarding methods used for dental erosions.

A medical disease assisted diagnosis method based on lightweight fuzzy SZGWO-ELM neural network model.

The application of neural network model in intelligent diagnosis usually encounters challenges such as continuous adjustment of network parameters and significant cost in training the network facing numerous complex physiological data. To address this challenge, this paper introduces a fuzzy SZGWO-ELM neural network model for medical disease aid diagnosis with fuzzy membership function and ELM network to refine the improved Gray Wolf optimization algorithm. Firstly, the Z-type membership function is introduced as the inertia weight to get a balance for the grey wolf in seeking the optimal solution globally and locally and ensuring fast convergence. Secondly, the S-type membership function is utilized as the adaptive weight to flexibly adjust the grey wolf search step size to facilitate a quick approximation of the optimal solution. Finally, the improved Gray Wolf optimization algorithm is used to optimize the parameters of the ELM neural network model, termed as SZGWO-ELM. This method can eliminate the need for extensive network parameter adjustments and quickly locate the optimal solution to the problem using a lightweight neural network. The performance of the SZGWO is assessed by using metrics like convergence, mean, and standard deviation. Multiple experiments reveal that this method shows superior performance. Furthermore, five publicly accessible medical disease datasets from UCI were conducted to evaluate the performance of SZGWO-ELM network model comparing with different classify model, and the results in terms of precision, sensitivity, specificity and accuracy can achieve 99.52%, 94.14%, 99.26% and 96.08%, respectively, which illustrate that the proposed SZGWO-ELM neural network significantly enhance the model's accuracy, providing better support for doctors in disease diagnosis.

Optimization of upper respiratory tract cancer diagnosis method based on exhaled breath gas analysis

Introduction. Head and neck tumors comprise about 7 % of all malignant neoplasms. In the head and neck area, tumors are usually located on the tongue (25–40 %) and floor of mouth (15–20 %). In the majority of cases, diagnosis, especially at early disease stages, is based on clinical and histopathological evaluation of tumor process. However, recently development and implementation of non-invasive techniques of early diagnosis of upper respiratory tract tumors through detection of pathognomonic volatile tumor markers in the exhaled air has become topical.Aim. To evaluate diagnostic accuracy of sensory gas analysis device and artificial neural network for examination of exhaled gas samples from patients with oropharyngeal, laryngeal, laryngopharyngeal cancer and to establish the optimal conditions for sampling.Materials and methods. The study included 28 patients with oropharyngeal, laryngeal, laryngopharyngeal cancers and 25 healthy volunteers. The proposed technique is based on analysis of samples of exhaled gas from the studied individuals using a diagnostic device developed by the authors. The device detects volatile compounds in the exhaled air using a set of semiconductor sensors with subsequent analysis by a neural network. The exhaled air was sampled using two methods: in the morning in the fasted state before daily hygienic procedures and physical activity (prepared samples) and in the context of everyday life, nutrition and hygiene without restrictions before sampling (non-prepared samples).Results. Based on the signals from the sensors, the neural network classified and detected patients with malignant tumors. Accuracy of the prepared samples from healthy volunteers and patients with oropharyngeal, laryngeal, laryngopharyngeal cancers was 79.17 %, of non-prepared – 84.09 %.Conclusion. High diagnostic accuracy of the developed technique of non-invasive diagnosis of malignant tumors of the oropharyngeal, laryngeal, laryngopharyngeal areas using exhaled air samples which does not require special preparation of the studied samples was demonstrated.

Development of a methodology for diagnosing faults in bearings operating under variable operating conditions based on self-supervised learning

Predictive maintenance plays a crucial role in ensuring the efficiency and availability of industrial assets. Bearings, essential components in rotating machinery, are subject to diverse and complex operating conditions, necessitating advanced fault diagnosis methods. Traditional diagnostic approaches often rely on supervised learning, which requires extensive labeled datasets, a process that is both costly and impractical under varying conditions. This work proposes a novel methodology for diagnosing bearing faults using self-supervised learning, which leverages unlabeled data to generate useful representations for fault detection. The proposed approach aims to develop an end-to-end system that processes raw vibration signals to accurately diagnose the current state of bearings, including fault detection, localization, and severity assessment. The methodology is validated using experimental data from a test rig simulating various fault conditions and will be further tested on real industrial machinery. This research contributes to the development of more efficient and generalizable diagnostic tools for rotating machinery, particularly under variable operational conditions.