Diagnosis Process Research Articles

Клинико-эпидемиологические особенности системной красной волчанки у детей: данные наблюдений ГБУЗ “Морозовская ДГКБ ДЗМ”

Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease of unknown etiology characterized by hyperproduction of autoantibodies. The problems of timely diagnosis and identification of predictors of an unfavorable outcome of SLE are relevant due to the variable clinical picture and a high risk of 5-year mortality in the absence of treatment. The purposes of this research were to assess the frequency of various variants of SLE onset, to identify trigger factors contributing to the development of the disease and to analyze the degree of disease activity in children. Materials and methods used: a single-center cohort retrospective study was conducted involving patients aged 9 to 17 y/o who had undergone inpatient treatment at the rheumatological department of the Morozov Children’s City Clinical Hospital (Moscow, Russia) in 2020-2023, specifying age, gender, assessing the frequency of observed symptoms of SLE onset, disease activity according to the SLEDAI scale, laboratory parameters and drug treatment. Results: total of 45 records were analyzed: 75.5% (34) girls/24.5% (11) boys; median age 15 [13; 15] y/o; most frequent clinical syndrome was cutaneous (75.5%); kidney damage was noted in 40% of cases. 86.6% had hematological disorders as follows: thrombocytopenia and leukopenia (48.9%), anemia (46.6%): Coombs-positive anemia (71.4%), lymphopenia (37.7%) and increased ESR (66.6%). Antinuclear factor (ANF Hep2) was detected in all cases. According to the SLEDAI-2K scale, the median disease activity was 11 [7; 16] points. Low disease activity (less than 6 points) was observed in 4.4% (2), average (6 to 10 points) in 44.4% (20), high (11 to 19 points) in 37.7% (17), above average (over 20 points) in 13.3% (6). Moreover, disease activity was higher in girls (p=0.940) and a relationship was found between the time required for diagnosis and disease activity according to the SLEDAI-2K scale (p=0.206). Drug therapy: glucocorticoids (GCS) orally in all cases, intravenous pulse GCS therapy in 71.0%. Hydroxychloroquine therapy was prescribed in 100.0%, mycophenolate mofetil in 57.7%, intravenous immunoglobulin in 37.7%, cyclophosphamide in 13.3%, methotrexate in 6.6%, biological therapy in 31.0%, the drug of choice was rituximab. Conclusion: updating the data on the frequency of various variants of SLE debut as well as identifying the new trigger factors contributing to the development of the disease are necessary steps aiming to increasing the awareness of the SLE course with its debut in childhood, optimizing the diagnosis process and reducing the risk for complications.

“I thought I was Probably Going to Die due to People Looking at me with Pity”: A Phenomenological Qualitative Study on the Lived Cancer Stigma Experiences of Breast Cancer Patients

It has been stated that there is a need for more recognition of the "stigma concept," which negatively affects the lives of patients with breast cancer during the diagnosis and treatment process. There are no recent studies on the experiences of Turkish women with breast cancer about stigmatization that employ qualitative methods. This study aims to examine in depth the lived stigma experiences of women with breast cancer during the diagnosis and treatment process. In total, 18 phenomenological semistructured in-depth individual interviews were conducted with patients with breast cancer between December 2023 and March 2024. Data were analyzed using Collaizzi's 7-step descriptive analysis approach. Four main themes and 8 subthemes developed from the phenomenological analysis of the interviews: (1) Cancer Metaphors: "A dark journey," "A bomb falling on the house!" and "Cancer = death!"; (2) Social Stigma: "Cancer = an infectious disease," "Negative and Disturbing Attitudes/Approaches of People"; (3) Internalized Stigma, and (4) Coping: "Acceptance" and "Social support." It was determined that the patients experienced social stigmatization due to negative attitudes of people toward cancer, perceiving it as infectious and approaching them with pity or demonstrating disturbing expressions. In addition, their social interactions were negatively affected due to the internalized stigma, and they were in an effort to cope with all the problems they experienced. The study highlighted the psychological needs of patients with breast cancer related to "stigmatization." Cancer nurses should monitor the stigmatization experiences of women with breast cancer, reveal their perceptions of internalized stigma, and implement programs to cope with stigma. It is also recommended that interventional studies, including coping programs with "stigma" for patients with breast cancer, should be conducted in future research.

The diagnostic challenge of differentiating Tumefactive Multiple Sclerosis (TMS) from other brain lesions: a case report and literature review on a rare subtype of MS

Introduction and Importance: This case report is a clinical diagnosis walk through of a rare subtype of Multiple sclerosis (MS). It gives an overview of how Tumefactive multiple sclerosis (TMS) is systematically narrowed down as the definitive diagnosis. Case Presentation: Our 29 year old male patient presented to the emergency department. He collapsed after experiencing pain over his right frontotemporal region followed by a seizure witnessed by his family. Magnetic Resonance Imaging of the brain displayed a diffuse enlargement and abnormal T2 weighted and FLAIR hyperintense signals in the diagnostic impressions described by the radiologist of the right temporoparietal region. Clinical Discussion: Liquefactive Multiple Sclerosis, also known as tumefactive multiple sclerosis or Marburg-type multiple sclerosis, is a rare subtype of the neurological disorder that can be difficult to diagnose. Unlike the traditional form of Multiple sclerosis (MS), Tumefactive multiple sclerosis (TMS) can present as a brain tumor and must be diagnosed with a biopsy rather than via magnetic resonance imaging (MRI) and clinical findings alone. Patients can typically present with headache, cognitive abnormalities, mental confusion, aphasia, apraxia, seizures, and weakness. Here, we discuss the presentation, disease diagnosis process and patient management. Conclusion: The patient was stabilized and discharged with a referral to the neurosurgery and neurology departments for outpatient consultation for future clinical management and treatment of their condition.

Diagnostic process of cancer in a health system without gatekeeping: a single centre survey analysis.

It is essential to increase the rates of early diagnosis in cancer control, and the diagnostic process needs to be improved to achieve this goal. Previous studies showed that in countries where there is a gatekeeping system, there might be a delay in cancer diagnosis. Our aim is to examine the process of cancer diagnosis in a healthcare system without gatekeeping. A quantitative descriptive study has been conducted in various outpatient clinics of Pendik Training and Research Hospital, between 1 February and 31 May 2019, with individuals aged over 18 and diagnosed with cancer in the last six months. The data was collected through a questionnaire filled in by face-to-face interview method. Patient's socio-economic characteristics, their symptoms at the time of the diagnosis and the diagnosis process were questioned. The median diagnostic interval was 30 days (min-max 1-365), and the median patient interval was 60 (1-600) days. Patients pointed out that the diagnostic tests, especially the pathology reporting process, caused the diagnostic interval to be prolonged. Of the patients, 84% (n 135) stated that they did not consider their symptoms as a sign of serious illness. The patient interval was shortest with symptoms of haematuria and haematochezia and longest with dysuria and change in bladder habit. The study examined the diagnosis process in our health system, where patients can apply for health services at any stage. The results showed that there were no superior outcomes to those observed in primary care-led health systems. Patients reported that waiting times for medical tests led to prolongation of the diagnosis time. Cancer awareness of patients should also be increased to shorten patient admission times.

Endothelium-targeted NF-κB siRNA nanogel for magnetic resonance imaging and visualized-anti-inflammation treatment of atherosclerosis

Atherosclerosis-induced lethal cardiovascular disease remains a severe healthcare threat due to the limited drug efficiency and untimely prediction of high-risk events caused by inadequate target specificity of medications, incapable recognition of insensitive patients, and variable morphology of vulnerable plaques. Therefore, it is necessary to develop efficient strategies to improve the diagnosis accuracy and achieve visualized treatment of atherosclerosis. Herein, we establish an inflamed endothelium-targeted three-in-one nucleic acid nanogel system that can reverse the inflammatory state of endothelial cells (ECs) in plaques and simultaneously achieve real-time monitoring of the therapy process for efficient atherosclerosis diagnosis and treatment. For this purpose, contrast agent (Gd-DOTA) and VCAM-1-targeted peptide (VP) are first covalently conjugated onto DNA strands by click reaction respectively, which could self-assemble into Y-shaped structures (Gd-Y1 and VP-Y2 motifs) with magnetic resonance (MR) imaging and endothelium targeting capacities. Thereafter, NF-κB subunit p65-targeting siRNA (siNF-κB) is crosslinked with Gd-Y1 and VP-Y2 motifs to construct the endothelium-targeting nanogel platform. With contrast agents inside, the nanogel enables MR-based diagnosis and visualized therapy of atherosclerosis, providing accurate prognostic analysis and indications for treatment results, which ensures timely disclosure of insensitive individuals and avoids acute lethal events. By delivering siNF-κB to inflammatory endothelium, the nanogel significantly regresses plaques in both the aorta and carotid artery with reduced inflammation cytokines, collagens, macrophages, and apoptotic cells, providing a potential anti-inflammation strategy to treat atherosclerosis and avoid acute cardiovascular disease.

Challenges and Opportunities in Transitioning Autistic Individuals Into Adulthood.

Approximately 1.2 million autistic youth will reach the legal age of adulthood over the next decade. Given dynamic changes in the socioecological context of the transition to adulthood, we produced an updated transitions research agenda reflecting the perspectives of autistic young adults, care partners (parents), and professionals who use autism research. We conducted 9 focus groups: 4 young adult, 4 care partner, and 1 professional, with 59 total participants. Young adults were grouped by self-reported identity: (1) racial and ethnic minority, (2) lesbian, gay, bisexual, transgender, queer, intersex, asexual, and other identities, and (3) nonspeaking, in addition to a general group. We selected care partners who supported youth with varying intensity of support needs. We used rapid qualitative inquiry methods to extract key points from answers to semistructured interview questions. Delays in diagnosis and transition processes, and placement on wait lists resulted in significant obstacles to successful transitions. Parents assumed a dominant role by coordinating services, navigating systems and identifying opportunities for community participation, and providing direct supports. There was an overarching need for inclusion of autistic people in transitions research and consideration of cultural differences in priorities and values. Participants prioritized investigation of variation in transitions among autistic youth with intersecting identities, navigation support (eg, peer mentors), efficacy of services and supports (eg, transition and employment services), differences in available services by location, and problems within specific benefits programs. Research should focus on understanding population-level factors of system performance on outcomes and support needs, service delivery among marginalized groups, and transformation of complex service ecosystems.

Thoracoscopic minimally invasive surgical treatment with the same incisions in a patient with uremia complicated with large thymoma and right upper lobe lung cancer: a case report

A 41 year old female with stage 5 chronic kidney disease undergoing hemodialysis was admitted to the hospital. Chest CT scan revealed a large mass lesion of approximately 6.0 × 3.5x4.9 cm in size in the anterior superior mediastinum and a ground glass nodule in the upper lobe of the right lung, which increased in size from 9 × 7 mm 1 year and 9 months ago to 11mmx9mm before surgery. We designed a localization method to accurately locate the pulmonary nodule and successfully performed thoracoscopic minimally invasive resection of both thymoma and lung cancer through a subxiphoid approach with the same incision for this patient. With the support of perioperative hemodialysis, the patient's outcome is good. The pathological diagnosis of the anterior mediastinal mass is thymoma (b1 type), and the pathological diagnosis of the right upper lobe nodule is invasive lung adenocarcinoma (acinar type). This report describes the diagnosis and treatment process of the case.

The Potential Benefit of a Novel Urine Biosensor Platform for Lung Cancer Detection in the Decision-Making Process: From the Bench to the Bedside

Background: Lung cancer is the leading cause of cancer-related mortality worldwide. Lung cancer screening and early detection resulted in a decrease in cancer-specific mortality; however, it introduced additional dilemmas and adherence barriers for patients and providers. Methods: Innovations such as biomolecular diagnosis and biosensor-based technology improve the detection and stratification of high-risk patients and might assist in overcoming adherence barriers, hence providing new horizons for better selection of screened populations. Conclusions: In the present manuscript, we discuss some of the dilemmas clinicians are currently facing during the diagnosis and treatment processes. We further highlight the potential benefits of a novel biosensor platform for lung cancer detection during the decision making process surrounding lung cancer.

A Framework for the Human-Centered Design of Service Processes Enabled by Medical Devices: A Case Study of Wearable Devices for Parkinson’s Disease

The successful introduction of medical devices (MDs) in real-world settings hinges on designing service processes that cater to stakeholders’ needs. While human-centered design (HCD) approaches have been widely applied to service process innovation, the literature lacks a methodology that leverages MDs’ key features to design service processes that meet stakeholders’ needs. This study aims to fill this gap by developing a framework for the HCD of service processes enabled by MDs. The proposed framework mixes and adapts methodological elements from HCD and technology-enabled design approaches and proposes four new tools. The five-phase framework was applied to the design of a new Parkinson’s disease diagnosis and treatment process (PD-DTP) enabled by two wearable MDs for the detection of motor symptoms. The case study lasted five months and involved 42 stakeholders in 21 meetings (interviews, focus groups, etc.). Thanks to the case study, the framework was tested, and a new PD-DTP that could benefit all stakeholders involved was identified. This study provides a framework that, in addition to contributing to theory, could assist MDs developers and healthcare managers in designing service processes that cater to stakeholders’ needs by leveraging MDs’ key features.

CONTINUOUS SCORING OF AUTISM SPECTRUM DISORDER PATIENTS BY ANALYZING THEIR EEG SIGNALS

Clinical manifestations and standard psychological tests have been widely used to diagnose autism spectrum disorder (ASD) patients and evaluate their severity level. The gold-standard criterion to diagnose ASD patients is the childhood autism rating scale (CARS), which is a qualitative questionnaire that is filled out through a systematic interview while no physiological test/record is performed to determine this score. To make the diagnosis process quantitative, electroencephalography (EEG) signals have been repeatedly analyzed to differentiate healthy subjects from ASD patients. However, the precise relationship between the abnormal behavior of EEG signals and different ASD severity levels is not well investigated. Here, we use CARS to qualitatively determine the severity level of 14 autistic children, who voluntarily enrolled in our study. We recorded their EEG signals from 19 scalp channels when they were awake in the idle state and elicited three informative features including approximation entropy, multiscale entropy and sample entropy in successive time frames. Among the three measures of entropy, the last one exhibits the highest sensitivity, where its correlation coefficient (CC) exceeds 0.7, on the electrode positions T6 (CC [Formula: see text] 0.74), P4 (CC [Formula: see text] 0.76) and Cz (CC [Formula: see text] 0.75). Results of sample entropy in channels Cz-versus-Pz and P4-versus-FP2 show that a simple K-nearest neighbor classifier can provide 93% classification accuracy among patients with mild, moderate and severe ASD levels. Comparing the proposed method to the conventional ones, we also extracted power spectral density features from the channels but they failed to identify the ASD severity level with an acceptable accuracy.

A New Diagnosis of Intestinal Metaplasia in Gastric Mucosa: Convolutional Neural Network Based on Visual Auxiliary Method

Abstract Introduction/Objective Intestinal metaplasia (IM) is a common lesion observed in chronic gastric disease and is thought to be associated with the development of gastric cancer. One of the characteristics of IM is the presence of goblet cells. However, differentiating these inconspicuous goblet cells from glandular cavities and gastric mucus cells using traditional algorithms is inefficient. This paper proposes an interpretable auxiliary diagnosis method for gastric mucosal intestinal metaplasia based on a convolutional neural network(CNN). Methods/Case Report The overall flow chart of the algorithm is shown in Figure 1A. The self-constructed gastric mucosa dataset is utilized in this paper for IM. To improve performance, some modifications are applied to the U-Net. The auxiliary diagnosis process incorporates R34W and R34U for segmenting the glands and IM regions. The R34W represents a dual-resolution input network structure (Figure 1B). For the initial stage of the auxiliary diagnosis, the convolutional neural network calculates patches with both large and small fields for gland region segmentation. Then, the IM glands segmentation network is used to classify regions with and without IM lesions. Results (if a Case Study enter NA) The result of the WSI level mask generated after post-processing is shown in Figure 2A. R34W Net offers significant advantages in differentiating open epithelial structures and proper glands. This network effectively differentiates between proper glands and epithelial regions while maintaining precise segmentation, leveraging high-resolution features in a small visual field. The quantitative indexes of gland segmentation networks are shown in Figure 2B. As part of auxiliary diagnosis, the classification of proper glands with intestinal metaplasia achieves the highest accuracy of 0.95 and AUC of 0.975 in the test datasets (Figure 2C). The area proportion of IM regions generated by CNN is shown in Figure 2D. Conclusion This paper provides a visual auxiliary method based on a CNN for the pathological diagnosis of intestinal metaplasia. The goal is to enhance the speed and accuracy of pathologists in performing this diagnosis. Additionally, we propose a CNN with a dual-resolution input structure, which is utilized for segmenting gastric proper glands.

Artificial Intelligence Revolutionizes Esophageal Squamous Cell Carcinoma Management

<span lang="ES">The application of artificial intelligence in esophageal tumor pathology highlights the ongoing and significant transformative impact AI has in medicine. This article provides a comprehensive overview of AI technologies' current use in diagnosing and treating esophageal squamous cell carcinoma (ESCC). Through our research, we identified and meticulously analyzed 33 relevant academic papers and studies that contribute to this field. These papers encompass a broad spectrum of AI applications related to the management of esophageal squamous cell carcinoma. AI integration and deployment in both the diagnosis process and therapeutic interventions for tumors offer highly promising prospects, for example, in endoscopic procedures AI algorithm can process endoscopic images in real-time to identify abnormalities that may be missed by the human eye, it can highlight subtle changes such as color variations, small growths or tissue anomalies, which might go unnoticed due to fatigue, distraction or human limitations in perceiving fine details. AI technologies enhance medical practice precision and effectiveness by significantly reducing the likelihood of human errors and offering practical, innovative solutions. Consequently, AI confluence with medical practice not only increases diagnosis accuracy but also improves the overall efficiency of treating esophageal squamous cell carcinoma.</span>

Adaptive fusion graph convolutional network based interpretable fault diagnosis method for HVAC systems enhanced by unlabeled data

Fault diagnosis is critical in maintaining the operational stability and reliability of Heating, Ventilation, and Air Conditioning (HVAC) systems, which are crucial for ensuring indoor environmental quality and energy efficiency in buildings. However, traditional fault diagnosis methodologies face substantial challenges in accurately capturing the dynamic interactions within these systems and effectively utilizing unlabeled data. To overcome these limitations, an innovative fault diagnosis approach utilizing the Adaptive Fusion Graph Convolution Network (AFGCN) is proposed in this paper. This method significantly enhances the model's learning and inference abilities, particularly in scenarios with limited labeled data, by adaptively integrating the associative graph features of both unlabeled and labeled data. Furthermore, to augment the transparency and trustworthiness of the diagnostic outcomes, this paper introduces an interpretability analysis module. This module is designed to quantify the contribution of each sensor node in the fault diagnosis process. Experimental evaluations using the ASHRAE RP-1043, actual building operating chillers datasets, and LBNL FDD Data Sets_SDAHU indicate that this method offers substantial performance improvements in diagnosing faults in HVAC systems.

Recurrent Visceral Leishmaniasis in a Case With Interleukin-12 Receptor Beta-1 Deficiency.

In this study, we present the case of a children who was followed up for recurrent visceral leishmaniasis and diagnosed with IL-12Rβ1 deficiency. A female patient who received Bacille Calmette-Guérin (BCG) vaccine 2 months after birth and developed visceral leishmaniasis at the age of 91 months was subsequently diagnosed with IL-12Rβ1 deficiency. The patient's diagnosis and treatment process were examined retrospectively. IL-12Rβ1 deficiency is an autosomal recessive disease characterized by susceptibility to recurrent and/or severe infections caused by weakly pathogenic mycobacteria and salmonella. Infections with other intramacrophagic organisms may also occur, although rarely. Based on this information, it is believed that the mutation in the IFN-γ/IL-12 axis in our patient predisposed her to recurrent Leishmania infections. This study adds to the limited literature on IL12RB1 deficiency as a cause of VL. Patients diagnosed with VL should be evaluated immunologically, as recurrent Leishmania infections may occur in those with IL-12Rβ1 defects.

BIF-Net: Boundary information fusion network for abdominal aortic aneurysm segmentation

The accurate abdominal aortic aneurysm (AAA) segmentation is significant for assisting clinicians in diagnosis and treatment planning. However, existing segmentation methods exhibit a low utilization rate for the semantic information of vessel boundaries, which is disadvantageous for segmenting AAA with significant scale variability of vessel diameter (diameter ranges from 4 mm to 85 mm). To tackle this problem, we introduce a boundary information fusion network (BIF-Net) specially designed for AAA segmentation. BIF-Net initially constructs convolutional kernel functions based on Gabor and Sobel operators, enriching the global semantic features and localization information through the Gabor and Sobel dilated convolution (GSDC) module. Additionally, BIF-Net supplements lost boundary feature information during the sampling process through the guided filtering feature supplementation (GFFS) module and the channel-spatial attention module (CSAM), enhancing the ability to capture targets with shape diversity and boundary features. Finally, we introduce a boundary feature loss function to alleviate the impact of the imbalance between positive and negative samples. The results demonstrate that BIF-Net outperforms current state-of-the-art methods across multiple evaluation metrics, achieving the highest Dice similarity coefficient (DSC) accuracies of 93.29 % and 91.01 % on the preoperative and postoperative datasets, respectively. Compared to the state-of-the-art methods, BIF-Net improves DSC by 6.86 % and 3.85 %. Due to the powerful boundary feature extraction ability, the proposed BIF-Net is a competitive AAA segmentation method exhibiting significant potential for application in diagnosis and treatment processes of AAA.

Decoding the intricacies: a comprehensive analysis of microRNAs in the pathogenesis, diagnosis, prognosis and therapeutic strategies for COVID-19.

The pandemic of coronavirus disease-19 (COVID-19), provoked by the appearance of a novel coronavirus named severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), required a worldwide healthcare emergency. This has elicited an immediate need for accelerated research into its mechanisms of disease, criteria for diagnosis, methods for forecasting outcomes, and treatment approaches. microRNAs (miRNAs), are diminutive RNA molecules, that are non-coding and participate in gene expression regulation post-transcriptionally, having an important participation in regulating immune processes. miRNAs have granted substantial interest in their impact on viral replication, cell proliferation, and modulation of how the host's immune system responds. This narrative review delves into host miRNAs' multifaceted roles within the COVID-19 context, highlighting their involvement in disease progression, diagnostics, and prognostics aspects, given their stability in biological fluids and varied expression profiles when responding to an infection. Additionally, we discuss complicated interactions between SARS-CoV-2 and host cellular machinery facilitated by host miRNAs revealing how dysregulation of host miRNA expression profiles advances viral replication, immune evasion, and inflammatory responses. Furthermore, it investigates the potential of host miRNAs as therapeutic agents, whether synthetic or naturally occurring, which could be harnessed to either mitigate harmful inflammation or enhance antiviral responses. However, searching more deeply is needed to clarify how host's miRNAs are involved in pathogenesis of COVID-19, its diagnosis processes, prognostic assessments, and treatment approaches for patients.

Utilizing Multi-layer Perceptron for Esophageal Cancer Classification Through Machine Learning Methods

Aims This research paper aims to check the effectiveness of a variety of machine learning models in classifying esophageal cancer through MRI scans. The current study encompasses Convolutional Neural Network (CNN), K-Nearest Neighbor (KNN), Recurrent Neural Network (RNN), and Visual Geometry Group 16 (VGG16), among others which are elaborated in this paper. This paper aims to identify the most accurate model to facilitate increased, improved diagnostic accuracy to revolutionize early detection methods for this dreadful disease. The ultimate goal is, therefore, to improve the clinical practice performance and its results with advanced machine learning techniques in medical diagnosis. Background Esophageal cancer poses a critical problem for medical oncologists since its pathology is quite complex, and the death rate is exceptionally high. Proper early detection is essential for effective treatment and improved survival. The results are positive, but the conventional diagnostic methods are not sensitive and have low specificity. Recent progress in machine learning methods brings a new possibility to high sensitivity and specificity in the diagnosis. This paper explores the potentiality of different machine-learning models in classifying esophageal cancer through MRI scans to complement the constraints of the traditional diagnostics approach. Objective This study is aimed at verifying whether CNN, KNN, RNN, and VGG16, amongst other advanced machine learning models, are effective in correctly classifying esophageal cancer from MRI scans. This review aims at establishing the diagnostic accuracy of all these models, with the best among all. It plays a role in developing early detection mechanisms that increase patient outcome confidence in the clinical setting. Methods This study applies the approach of comparative analysis by using four unique machine learning models to classify esophageal cancer from MRI scans. This was made possible through the intensive training and validation of the model using a standardized set of MRI data. The model’s effectiveness was assessed using performance evaluation metrics, which included accuracy, precision, recall, and F1 score. Results In classifying esophageal cancers from MRI scans, the current study found VGG16 to be an adequate model, with a high accuracy of 96.66%. CNN took the second position, with an accuracy of 94.5%, showing efficient results for spatial pattern recognition. The model of KNN and RNN also showed commendable performance, with accuracies of 91.44% and 88.97%, respectively, portraying their strengths in proximity-based learning and handling sequential data. These findings underline the potential to add significant value to the processes of esophageal cancer diagnosis using machine learning models. Conclusion The study concluded that machine learning techniques, mainly VGG16 and CNN, had a high potential for escalated diagnostic precision in classifying esophageal cancer from MRI imaging. VGG16 showed great accuracy, while CNN displayed advanced spatial detection, followed by KNN and RNN. Thus, the results set new opportunities for introducing advanced computational models to the clinics, which might transform strategies for early detection to improve patient-centered outcomes in oncology.

Differentiation of dissociative psychosis, delirious mania and unspecified dissociative disorder in an adolescent case: a case report

Dissociative psychosis is, it is a condition that usually occurs after a trauma, when psychotic and dissociative symptoms occur together.In this process, disorganized behaviors increase, insight is lost, and hallucinations become worse.It usually results in sudden improvement over a period of several days to several weeks and most patients remain amnesic into the dissociative psychosis phase. A sixteen-year-old female adolescent was brought in with severe manic and psychotic complaints that started suddenly a week ago.In-mental-status-examination; her associations, thought content and perception examination were particularly striking.During-the-hospitalization-process, passivity, delusional delusions of misidentification, and disorganized speech and behavior were also observed.The adolescent, whose complaints regressed in the fourth week, was amnesic during the episode.This study aims to present the differential diagnosis and treatment process of dissociative psychosis, delirious mania, and unspecified dissociative disorder in an adolescent female.

Deep Learning-Enabled Dynamic Model for Nutrient Status Detection of Aquaponically Grown Plants

Developing models to assess the nutrient status of plants at various growth stages is challenging due to the dynamic nature of plant development. Hence, this study encoded spatiotemporal information of plants within a single time-series model to precisely assess the nutrient status of aquaponically cultivated lettuce. In particular, the long short-term memory (LSTM) and deep autoencoder (DAE) approaches were combined to classify aquaponically grown lettuce plants according to their nutrient status. The proposed approach was validated using extensive sequential hyperspectral reflectance measurements acquired from lettuce leaves at different growth stages across the growing season. A DAE was used to extract distinct features from each sequential spectral dataset time step. These features were used as input to an LSTM model to classify lettuce grown across a gradient of nutrient levels. The results demonstrated that the LSTM outperformed the convolutional neural network (CNN) and multi-class support vector machine (MCSVM) approaches. Also, features selected by the DAE showed better performance compared to features extracted using both genetic algorithms (GAs) and sequential forward selection (SFS). The hybridization of deep autoencoder and long short-term memory (DAE-LSTM) obtained the highest overall classification accuracy of 94%. The suggested methodology presents a pathway to automating the process of nutrient status diagnosis throughout the entire plant life cycle, with the LSTM technique poised to assume a pivotal role in forthcoming time-series analyses for precision agriculture.

Research on fault diagnosis for three-phase rectifier device in direct current transmission system based on continuous wavelet transform and vision transformer

Abstract As a core component of photovoltaic power generation systems, the three-phase rectifier device plays a crucial role, and its failure can potentially reduce energy conversion efficiency and output quality. Presently, the performance of fault time-domain signal diagnosis methods based on three-phase rectifier circuits is challenging to enhance. This paper proposes a novel fault detection method for three-phase rectifier devices based on Vision Transformer, referred to as CWT-ViT, to address this issue. This method transforms time-domain fault signals into images through Continuous Wavelet Transform (CWT), subsequently inputting these images into a Vision Transformer model. Relying on its powerful self-attention mechanism and fully connected layers, it realizes the extraction and learning of rectifier device image features. A Simulink simulation model of a three-phase bridge controllable rectifier circuit is established for fault injection to collect fault signals. Fault diagnosis experiments demonstrate that the proposed diagnostic method achieves a prediction accuracy of 98.6%, maintaining a relatively high precision level. In comparison to four excellent classification models currently available: AlexNet, RepVGG, ResNet, and GoogLeNet, the proposed method demonstrates superior diagnostic performance. Additionally, this paper conducts ablation experiments to meticulously analyze the impact of each module in the fault diagnosis process. This research achieves more precise and efficient fault diagnosis in photovoltaic power generation systems, thereby reducing downtime and maintenance costs for actual equipment and enhancing the stability of photovoltaic power generation systems. This research provides an innovative, intelligent solution for the intelligent operations and maintenance of photovoltaic power generation.