Diagnostic Framework Research Articles

Narcissistic personality disorder in the ICD-11: Severity and trait profiles of grandiosity and vulnerability.

Modern diagnostic and classification frameworks such as the ICD-11 and DSM-5-AMPD have adopted a dimensional approach to diagnosing personality disorder using a dual "severity" and "trait" model. As narcissistic personality has historically struggled to be adequately captured in dominant diagnostic systems, this study investigated the utility of the new ICD-11 framework in capturing diverse narcissistic expressions. Participants were mental health clinicians (N = 180, 67% female, age = 38.9), who completed ratings of ICD-11 personality severity, trait domains and a clinical reflection for two hypothetical case vignettes reflecting either prototypical "grandiose" or "vulnerable" narcissism. The majority of clinicians (82%) endorsed a diagnosis of personality disorder for both grandiose and vulnerable vignettes. Discriminant elements of personality impairment included rigid, unrealistically positive self-view, low empathy and high conflict with others for grandiosity, and incoherent identity, low self-esteem and hypervigilant, avoidant relations with others for vulnerability. Regarding trait profile, grandiose narcissism was predominately dissocial whereas vulnerable narcissism was primarily associated with negative affectivity and detachment. Qualitative responses highlight distinct clinical themes for each presentation. These findings suggest that clinicians using the ICD-11 framework are able to identify common core elements of personality dysfunction in grandiose and vulnerable narcissism while also recognizing their distinctive differences.

ADHD diagnosis guided by functional brain networks combined with domain knowledge

Utilizing functional magnetic resonance imaging (fMRI) to model functional brain networks (FBNs) is increasingly prominent in attention-deficit/hyperactivity disorder (ADHD) research, revealing neural impact and mechanisms through the exploration of activated brain regions. However, current FBNs-based methods face two major challenges. The primary challenge stems from the limitations of existing modeling methods in accurately capturing both regional correlations and long-distance dependencies (LDDs) within the dynamic brain, thereby affecting the diagnostic accuracy of FBNs as biomarkers. Additionally, limited sample size and class imbalance also pose a challenge to the learning performance of the model. To address the issues, we propose an automated diagnostic framework, which integrates modeling, multimodal fusion, and classification into a unified process. It aims to extract representative FBNs and efficiently incorporate domain knowledge to guide ADHD classification. Our work mainly includes three-fold: 1) A multi-head attention-based region-enhancement module (MAREM) is designed to simultaneously capture regional correlations and LDDs across the entire sequence of brain activity, which facilitates the construction of representative FBNs. 2) The multimodal supplementary learning module (MSLM) is proposed to integrate domain knowledge from phenotype data with FBNs from neuroimaging data, achieving information complementarity and alleviating the problems of insufficient medical data and unbalanced sample categories. 3) An ADHD automatic diagnosis framework guided by FBNs and domain knowledge (ADF-FAD) is proposed to help doctors make more accurate decisions, which is applied to the ADHD-200 dataset to confirm its effectiveness. The results indicate that the FBNs extracted by MAREM perform well in modeling and classification. After with MSLM, the model achieves accuracy of 92.4%, 74.4%, and 80% at NYU, PU, and KKI, respectively, demonstrating its ability to effectively capture crucial information related to ADHD diagnosis. Codes are available at https://github.com/zhuimengxuebao/ADF-FAD.

Architectural learning networks: a study on eliciting the learning agendas of architectural schools

PurposeThe purpose of this paper is to demonstrate how network theory and methods can provide insights into the forces shaping architectural learning agendas and knowledge construction in architectural schools.Design/methodology/approachThe methodology involves conceptualising learning as a constructivist process and the agenda as an interconnected network of actors, concepts and relations. Network analysis techniques, including centrality and brokerage metrics, are used to identify roles and knowledge flows using the data locally collected from Turkish universities as well as from the OpenSyllabus open-source database.FindingsThe analysis reveals the enduring influence of early modernists, signalling imbalanced canon formation in the architectural learning system. However, marginal voices highlight struggles in integrating unconventional perspectives. Limited integration of local figures indicates a consolidation of Eurocentric epistemes. Identifying these hidden forces is vital for reimagining learning agendas and socio-culturally engaged forms of learning. Pioneering figures demonstrate potential for synthesis when situated as brokers, not bifurcated schools.Research limitations/implicationsThe outcomes are limited by the geographical and temporal boundaries of the data and the analysis method employed. Despite limitations, the diagnostic network framework reveals architectural learning as an open, contested ecosystem demanding pluralistic pedagogies concerning not only the global but the local, both canonical and marginal. Further research covering more data could enrich the understanding of qualitative complexities.Practical implicationsThe network perspective prompts critical reflexivity about power, ideology and exclusion in knowledge construction. Strategic inclusion and diversification of voices provide pathways to bridge divides and ground learning locally.Originality/valueThis research offers a methodology model to examine forces and influences shaping architectural education by elucidating hidden and remote roles and knowledge gaps in learning agendas. Extending the techniques more widely can enable strategic interventions toward inclusive, impactful learning across disciplines, time and geographies.

Multiple sarcomatoid carcinomas in the small intestine with perforation: A case report and literature review.

Sarcomatoid carcinoma of the small intestine is an exceedingly rare and aggressive malignancy, often diagnosed at advanced stages with a poor prognosis. This study documents a detailed case of sarcomatoid carcinoma of the small intestine, highlighting the diagnostic challenges and treatment approaches, underscored by a comprehensive review of related literature. Given the rarity of this condition, our report aims to enrich the existing diagnostic and treatment frameworks for this malignancy, emphasizing the necessity for early detection and intervention strategies. By presenting this case in conjunction with a literature review, we seek to shed light on the elusive nature of sarcomatoid carcinoma in the small intestine and propose avenues for improving patient outcomes. Case presentation A 61-year-old male patient initially presented with recurrent abdominal pain and gastrointestinal symptoms. Initial abdominal computed tomography (CT) scans and gastrointestinal endoscopy revealed only inflammatory and hyperplastic changes in the duodenum and jejunum, with a diagnosis of intestinal obstruction. Two years later, due to gastrointestinal perforation, the patient was hospitalized again. CT scans and other examinations revealed small intestinal lesions. Four small intestinal lesions were surgically removed, and pathology and immunohistochemistry confirmed sarcomatoid carcinoma of the small intestine. A short time later, enhanced CT scans revealed metastatic lesions in the hepatic portal and adrenal glands. After surgery, the gastrointestinal function gradually recovered, and the patient was discharged from the hospital on a semiliquid diet. No further treatment such as radiotherapy or chemotherapy was administered postoperatively. Five months after the surgery, the patient died due to brain metastasis. The study outcomes reveal the aggressive nature of sarcomatoid carcinoma of the small intestine, characterized by rapid progression and poor prognosis despite surgical interventions. The patient condition rapidly deteriorated, leading to metastasis and death within 5 months postsurgery. These findings underscore the critical need for early detection and possibly innovative treatment approaches to improve survival rates. This case also highlights the potential for gastrointestinal sarcomatoid carcinoma to metastasize to distant organs, including the brain, suggesting a propensity for hematogenous spread.

Battery degradation diagnosis under normal usage without requiring regular calibration data

Lithium-ion batteries undergo capacity loss and power fade over time. Despite indicating degradation, these changes lack internal insights. Degradation modes group various mechanisms but are challenging to quantify due to aging complexity. In this paper, a noninvasive and comprehensive diagnostic framework is proposed for the accurate estimation of the state of health (SOH) and degradation modes. A large amount of synthetic data is generated from the mechanistic model to cover many typical aging paths without using redundant experimental data. A data-driven multistep diagnosis method is developed by using incremental capacity sequences. This method analyses unique sensitivities to voltage responses from different degradation modes and identifies them sequentially to simplify complex interactions. In addition, overpotential correction is incorporated to estimate battery degradation modes under normal usage. This framework is validated through experimental data under various operating conditions, affirming the 2 % accuracy of SOH estimation and the effective automatic quantification of degradation modes. Furthermore, the method is applicable to common state-of-charge (SOC) windows ranging from 25% to 85 % (3.6 V–4.1 V for the voltage window) and a high current rate up to C/4. The diagnostic framework does not rely on any regular calibration data during model training and thus has high potential for practical application.

Characterization of Corrosion-Induced Fracture in Reinforced Concrete Beams Using Electrical Potential, Ultrasound and Low-Frequency Vibration

The paper deals with the non-destructive experimental testing of the reinforced concrete beams under progressive corrosion. A series of experiments using electrical potential, ultrasound and low-frequency vibrations techniques are reported. Electrical potential and natural frequencies were used to characterise and monitor the corrosion process at its initial state. The P-wave velocity measurements were proved to be effective in quantitative assessment of the level of corrosion as it progresses. The possibility of early detection of damage using a proposed damage index and diagnostic framework is promising for possible applications in the non-invasive diagnostics of reinforced concrete elements.

SMOTE-based Deep LSTM System with GridSearchCV Optimization for Intelligent Diabetes Diagnosis

Diabetes is a metabolic illness initiated by either inadequate insulin creation by the pancreas or the body's reduced responsiveness to insulin. It is characterised by consistently high levels of blood sugar and symptoms such as frequent urination, thirst, and increased appetite. Untreated diabetes can result in significant problems that impact crucial organs, presenting potentially fatal dangers. In order to address the need for accurate diagnosis, researchers have utilised artificial intelligence to develop the G-LSTM system. This system employs a novel method that combines SMOTE-based deep LSTM and GridSearchCV optimization to classify diabetes. This technique effectively tackles the issue of class imbalance in diabetes datasets, demonstrating an exceptional level of prediction accuracy. When tested on the PIMA dataset, G-LSTM demonstrated exceptional performance with an accuracy of 97.12%. Additionally, it produced high precision, recall, F1-score, AUC, and MCC values of 97.12%, 0.963, 0.954, 0.887, 0.989, and 0.882, respectively. The results highlight the higher performance of the G-LSTM method compared to other techniques, suggesting its use for clinical investigation of diabetes patients. This innovative intelligent diagnostic framework not only demonstrates the potential of artificial intelligence in healthcare, but also highlights its crucial role in enhancing the precision and effectiveness of diabetes diagnosis and treatment.

Enhancing Disease Diagnosis through Transfer Learning: Improving Accuracy and Generalization

This study aims to investigate the effectiveness of transfer learning in the context of disease diagnosis. By leveraging pre-trained deep learning models on large-scale datasets, the objective is to enhance the accuracy and generalization of disease diagnosis models. This research explores the potential of transfer learning to improve diagnostic performance, particularly in cases where labeled data is limited. The study also examines the transferability of learned features across different diseases, considering the benefits of knowledge transfer from related domains. The goal is to develop a robust and efficient diagnostic framework that demonstrates improved accuracy and generalization in disease classification tasks.

Assessment of symptom severity in psychotic disorder patients based on heart rate variability and accelerometer mobility data

Background:Advancement in mental health care requires easily accessible, efficient diagnostic and treatment assessment tools. Viable biomarkers could enable objectification and automation of the diagnostic and treatment process, currently dependent on a psychiatric interview. Available wearable technology and computational methods make it possible to incorporate heart rate variability (HRV), an indicator of autonomic nervous system (ANS) activity, into potential diagnostic and treatment assessment frameworks as a biomarker of disease severity in mental disorders, including schizophrenia and bipolar disorder (BD). Method:We used a commercially available electrocardiography (ECG) chest strap with a built-in accelerometer, i.e. Polar H10, to record R-R intervals and physical activity of 30 hospitalized schizophrenia or BD patients and 30 control participants through ca. 1.5-2 h time periods. We validated a novel approach to data acquisition based on a flexible, patient-friendly and cost-effective setting. We analyzed the relationship between HRV and the Positive and Negative Syndrome Scale (PANSS) test scores, as well as the HRV and mobility coefficient. We also proposed a method of rest period selection based on R-R intervals and mobility data. The source code for reproducing all experiments is available on GitHub, while the dataset is published on Zenodo. Results:Mean HRV values were lower in the patient compared to the control group and negatively correlated with the results of the PANSS general subcategory. For the control group, we also discovered the inversely proportional dependency between the mobility coefficient, based on accelerometer data, and HRV. This relationship was less pronounced for the treatment group. Conclusions:HRV value itself, as well as the relationship between HRV and mobility, may be promising biomarkers in disease diagnostics. These findings can be used to develop a flexible monitoring system for symptom severity assessment.

An Automated Diagnostic Framework for Multiple Oscillations With Median Complementary EEMD

An Automated Diagnostic Framework for Multiple Oscillations With Median Complementary EEMD

From Consensus Statement to Pills to Pixels: New Innovations in Attention-Deficit/Hyperactivity Disorder Care.

Objectives: This review aims to present recent innovations and advancements in attention-deficit/hyperactivity disorder (ADHD) care, encompassing international consensus statement, new medication formulations, digital therapeutics, and neurostimulation devices. Methods: A comprehensive literature search of relevant articles published in the past five years was conducted, emphasizing the evidence base, efficacy, safety, and practical implications of these advancements. Results: The World Federation of ADHD Consensus Statement offers an updated diagnostic and treatment framework rooted in global scientific evidence. There are several newer ADHD medication formulations, including a nonstimulant (Viloxazine extended release) and the first transdermal amphetamine patch approved to treat ADHD. These options offer some unique benefits to personalize treatment based on symptom profile, lifestyle, preferences, and response. Digital tools offer additional means to restructure environments for individuals with ADHD, reducing impairment and reliance on others. In addition, digital therapeutics enhance access, affordability, personalization, and feasibility of ADHD care, complementing or augmenting existing interventions. Trigeminal nerve stimulation emerges as a well-tolerated nonpharmacological, device-based treatment for pediatric ADHD, with initial trials indicating effect sizes comparable to nonstimulant medications. Conclusions: These innovations in ADHD care represent clinically significant new treatment options and opportunities for personalized care. Health care professionals should integrate these developments into clinical practice, mindful of individual patient and family needs and preferences. Future research should assess long-term outcomes, cost-effectiveness, and acceptability of these innovations.

Detection and isolation of faults in a lithium-ion battery pack using a switched architecture of equivalent cell diagnosers

Lithium-ion battery packs are typically built as a series network of Parallel Cell Modules (PCM). A fault can occur within a specific cell of a PCM, in the sensors, or the numerous connection joints and bus conductors. This paper presents a method of detecting a single occurrence of various common faults in a Lithium-ion battery pack and isolating the fault to the faulty PCM, its connecting conductors, and joints, or to the sensor in the pack using a Diagnostic Automata of configurable Equivalent Cell Diagnosers. This is achieved by activating a sequence of diagnosers that generate residuals for various sub-networks of the pack and test hypotheses on them to search for the smallest sub-network and the associated sensors that contain the source of the fault. The sequence of activation of the diagnosers follows the state transition of the Diagnostic Automata triggered by the outcomes of the hypotheses tests. Once the faulty PCM sub-network is identified, the fault type is classified based on selected features of the residuals. For robust detection, events arising from hypothesis tests in the diagnosers and the classifiers are used to compute the probability of occurrence of a specific fault in a specific PCM. The performance of the diagnostic framework is tested offline with experimentally obtained data from an automotive battery pack. Finally, a Hardware-in-the-Loop simulation test demonstrates that the proposed method can be implemented on standard Battery Management System hardware to avoid extensive damage to the pack, enhance safety, and facilitate quick cell-level replacements to repair expensive Lithium-ion battery packs.

The split ladder of participation: A literature review and dynamic path forward

Participation of people in decision making and tackling complex problems where there is lack of consensus on the science and relevant values continues to be an important research topic. The 2015 Split Ladder of Participation offered a diagnostic and methodological framework cited in 162 papers. This paper addresses the question: What does a literature review of the Split Ladder of Participation reveal about engaging people in policy problems, effecting transformational change, and how can this diagnostic and strategic tool be improved? A systematic literature review revealed papers that disclose transformational change, or triple loop learning requires: strong social science; social and natural interdisciplinary science; and considerations of uncertainty in environmental science together with uncertainty of values and considerations of power. Policy problems with low levels of trust offer opportunities to engage interest and participation in their resolution. Governments over-utilizing methods limiting participation, may lead to lock-in. Focusing on complex, interconnected problems through participation creates an enduring policy and science, interdisciplinary innovation space. Recognizing participation that is plural, amorphous, and fluid draws attention to power, multiple stakeholder framings of complex issues, advances social learning changing values, norms, power, and the very ethics of science (where social and natural/physical scientists acknowledge and share their power with people), and ultimately advances environmental justice.

Clinical Characteristics, Investigations and Treatment in Children with Chronic Urticaria: An Observational Study.

Background and Objectives: The guidelines for chronic urticaria in children contain recommendations that are often based on adult studies. The diagnostic pathway has not been standardized and the effectiveness of anti-H1, omalizumab, montelukast, and systemic glucocorticoids is rarely reported in the pediatric population. There is a wide variation in the rate of remission of chronic urticaria between studies. The aim of this study is to enhance our understanding of pediatric chronic urticaria. Materials and Methods: This study enrolled 37 children with chronic urticaria aged from 0 to 18 years. Demographic parameters, medical history, clinical features, laboratory data and treatment information were collected. Children were treated with the recommended dosage of second-generation H1-antihistamines, which was increased by up to twofold. Omalizumab was added for refractory anti-H1 patients. A three-day course with systemic glucocorticoids was administered for severe exacerbations. Montelukast was administered to some children. Results: Wheals without angioedema were common. Chronic urticaria was spontaneous in 32 children (86.48%), inducible in 2 (5.41%), induced by a parasite in 1 and vasculitic in 2. Treatment of the potential causes of chronic urticaria was of no benefit, except for eradication of Dientamoeba fragilis. Chronic urticaria was resolved within three years in 45.9% of cases. Allergic diseases were present in nine children (24.32%) and autoimmune diseases were present in three (8.11%). All children were treated with anti-H1 at the licensed dose or at a higher dose. A partial or complete response to anti-H1 was observed in 29 (78.38%) patients. Montelukast showed no benefit. All children treated with omalizumab responded. Systemic glucocorticoids were successfully used to treat exacerbations. Conclusions: Our findings indicate that laboratory tests should not be routinely performed in children with chronic urticaria without clinical suspicion. However, comorbidities such as thyroid autoimmune disease and coeliac disease are suggested to be monitored over the chronic urticaria course. These clinical conditions could be diagnosed from the diagnostic framework of chronic urticaria. Increasing the dosage of anti-H1 and omalizumab was effective in children resistant to standard treatment but we still need further studies to generate a standard patient-centered treatment.

A novel diagnostic framework based on vibration image encoding and multi-scale neural network

Intelligent fault diagnosis employing CNN-based techniques has demonstrated promising results in rotating machinery maintenance and management. However, most approaches that rely on time-series vibration signals fail to incorporate the physical knowledge of faults into feature learning, and are subject to obtaining the correlation between input and output characteristics. Furthermore, it is challenging for the neural network to improve feature extraction due to the rigid convolution kernel and the limited receptive field. To address these issues, an intelligent fault diagnosis framework is developed based on vibration image encoding and multi-scale neural networks. Firstly, a novel image encoding rule is designed to convert time-series signals into vibration images, which incorporate the impact characteristics of faults into the model inputs to enhance fault information. Secondly, a multi-scale feature fusion network with dilatated and irregular sampling convolution block is constructed for feature extraction and classification. In addition, a new regularization term is designed to avoid training overfitting. Finally, Grad-CAM is introduced to visualize the region of interest of the model. The proposed method is verified on the datasets with different fault types from our laboratory and DIRG bearing dataset with different fault severities from Politecnico di Torino. The experimental results demonstrate that our diagnostic framework exhibits good generalization and noise robustness, and can achieve high-precision fault diagnosis.

Raman Spectra of Blood Serum as Holistic Biomarker for Differential Auxiliary Diagnoses of Attention Deficit and Hyperactivity Disorder (ADHD) in Adults

Attention deficit and hyperactivity disorder (ADHD) is a prevalent neurodevelopmental condition, impacting approximately 10% of children globally. A significant proportion, around 30–50%, of those diagnosed during childhood continue to manifest ADHD symptoms into adulthood, with 2–5% of adults experiencing the condition. The existing diagnostic framework for ADHD relies on clinical assessments and interviews conducted by healthcare professionals. This diagnostic process is complicated by the disorder’s overlap in symptoms and frequent comorbidities with other neurodevelopmental conditions, particularly bipolar disorder during its manic phase, adding complexity to achieving accurate and timely diagnoses. Despite extensive efforts to identify reliable biomarkers that could enhance the clinical diagnosis, this objective remains elusive. In this study, Raman spectroscopy, combined with multivariate statistical methods, was employed to construct a model based on the analysis of blood serum samples. The developed partial least-squares discriminant analysis (PLS-DA) model demonstrated an ability to differentiate between individuals with ADHD, healthy individuals, and those diagnosed with bipolar disorder in the manic phase, with a total accuracy of 97.4%. The innovative approach in this model involves utilizing the entire Raman spectrum, within the 450–1720 cm−1 range, as a comprehensive representation of the biochemical blood serum setting, thus serving as a holistic spectroscopic biomarker. This method circumvents the necessity to pinpoint specific chemical substances associated with the disorders, eliminating the reliance on specific molecular biomarkers. Moreover, the developed model relies on a sensitive and reliable technique that is cost-effective and rapid, presenting itself as a promising complementary diagnostic tool for clinical settings. The potential for Raman spectroscopy to contribute to the diagnostic process suggests a step forward in addressing the challenges associated with accurately identifying and distinguishing ADHD from other related conditions.

Detection of dental periapical lesions using retinex based image enhancement and lightweight deep learning model

Dental periapical lesions, commonly associated with inflammation around the tooth apex, pose a significant challenge in early diagnosis and treatment. This study introduces a novel approach for the detection of dental periapical lesions through the integration of Retinex-based image enhancement techniques and a lightweight deep learning model. The Retinex algorithm is employed to enhance the radiographic images, addressing issues related to inconsistent illumination and contrast. Subsequently, a tailored lightweight deep learning model is designed to efficiently extract relevant features from the enhanced images. Present methodology leverages a dataset of dental radiographs to train and evaluate the deep learning model, incorporating a diverse range of periapical lesion cases. The model is optimized for computational efficiency while maintaining high accuracy, making it suitable for deployment in resource-constrained environment. To enhance precision in lesion detection, the U-Net segmentation technique has been incorporated, providing a sophisticated approach to delineate and analyze specific areas of interest within the radiographic images. This addition further refines our diagnostic framework, contributing to the robustness of lesion identification. Experimental results demonstrate the effectiveness of the Retinex-based image enhancement in improving the visibility of periapical lesions. The lightweight deep learning model exhibits promising performance in accurately detecting and classifying dental periapical lesions, showcasing its potential for early and efficient diagnosis. The results obtained from the present model are compared with those from Convolutional Neural Network (CNN) as well as with diagnosis of expert practitioners and the model is observed to perform very well. The study contributes to the advancement of computer-aided diagnostic tools in dentistry, offering a scalable and accessible solution for the identification of dental periapical lesions through the fusion of image enhancement and lightweight deep learning techniques.

A component diagnostic and prognostic framework for pump bearings based on deep learning with data augmentation

To support the mission of providing safe electricity generation with a high capacity factor, a Predictive Maintenance (PdM) framework using Machine Learning Models (MLM) to optimize component maintenance operations is developed. Using sensor measurements to better predict the true component’s Remaining Useful Life (RUL), the PdM framework has the potential to optimize maintenance costs by performing maintenance only when necessary. The PdM framework to pump bearings, the framework consists of a Convolutional Neural Network Autoencoder (CNN-AE) to detect component deviations from normality, a CNN to characterize component fault modes, and a Bayesian Neural Network (BNN) to estimate the component RUL with uncertainty. To increase the number of training samples, a synthetic data generation procedure was developed and includes procedures to recreate the fault-specific characteristic frequencies for diagnostics and the degradation trends for prognostics. The MLMs trained on the synthetic data are tested on the Center for Intelligent Maintenance Systems (IMS) dataset to showcase how well the synthetic data replicates measurement data. Utilizing this framework, the PdM was found to delay maintenance on average by a total of 8.92 years over 40 years and decrease the unexpected component failure rate from 10% to 0% when compared to traditional maintenance philosophies.

Blood leukocytes as a non-invasive diagnostic tool for thyroid nodules: a prospective cohort study

BackgroundThyroid nodule (TN) patients in China are subject to overdiagnosis and overtreatment. The implementation of existing technologies such as thyroid ultrasonography has indeed contributed to the improved diagnostic accuracy of TNs. However, a significant issue persists, where many patients undergo unnecessary biopsies, and patients with malignant thyroid nodules (MTNs) are advised to undergo surgery therapy.MethodsThis study included a total of 293 patients diagnosed with TNs. Differential methylation haplotype blocks (MHBs) in blood leukocytes between MTNs and benign thyroid nodules (BTNs) were detected using reduced representation bisulfite sequencing (RRBS). Subsequently, an artificial intelligence blood leukocyte DNA methylation (BLDM) model was designed to optimize the management and treatment of patients with TNs for more effective outcomes.ResultsThe DNA methylation profiles of peripheral blood leukocytes exhibited distinctions between MTNs and BTNs. The BLDM model we developed for diagnosing TNs achieved an area under the curve (AUC) of 0.858 in the validation cohort and 0.863 in the independent test cohort. Its specificity reached 90.91% and 88.68% in the validation and independent test cohorts, respectively, outperforming the specificity of ultrasonography (43.64% in the validation cohort and 47.17% in the independent test cohort), albeit with a slightly lower sensitivity (83.33% in the validation cohort and 82.86% in the independent test cohort) compared to ultrasonography (97.62% in the validation cohort and 100.00% in the independent test cohort). The BLDM model could correctly identify 89.83% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. In micronodules, the model displayed higher specificity (93.33% in the validation cohort and 92.00% in the independent test cohort) and accuracy (88.24% in the validation cohort and 87.50% in the independent test cohort) for diagnosing TNs. This performance surpassed the specificity and accuracy observed with ultrasonography. A TN diagnostic and treatment framework that prioritizes patients is provided, with fine-needle aspiration (FNA) biopsy performed only on patients with indications of MTNs in both BLDM and ultrasonography results, thus avoiding unnecessary biopsies.ConclusionsThis is the first study to demonstrate the potential of non-invasive blood leukocytes in diagnosing TNs, thereby making TN diagnosis and treatment more efficient in China.

The Reductions in Phenomenology - A Comparison Across Main Authors

IntroductionPhenomenology is one of the fundamental tools in the clinical practice of psychiatrists, constituting one of the touchstones regarding the diagnostic framework in which clinicians navigate.For Husserl, Phenomenology provided access to the structure of pure consciousness, experience and existence. These are conditions of possibility for the object of Psychiatry, ontologically prior to it. Thus, clarification of the object and method of Phenomenology is preliminary to understanding the object of Psychiatry.Phenomenology, being a direct tributary of Philosophy, evolves dialectically, constantly dialoguing with its predecessors. While it is taken as a philosophical current, it is also considered a method. It is precisely as a method that we can see how the methodology changes in different phenomenological traditions.ObjectivesTo compare how the main phenomenological traditions operate.Methods Comparative analysis between the phenomenological reductions in key figures of the phenomenological tradition, resorting to the corpus of the Husserliana, Being and Time, Phenomenology of Perception and General Psychopathology. Additionally, a non-systematic literature review of papers on the database Philpapers, using the keywords “critical phenomenology”, “eidetic reduction”, “phenomenological reduction”.ResultsWhile there is a multiplicity of ways of taxonomizing phenomenological currents, we divide it in: pure, existential, embodied, jasperian, psychopathological, and critical.Husserl’s pure phenomenology uses the free variation in phantasy and epoché as operators, starting from the natural attitude.Heidegger’s existential phenomenology makes no reference to a reduction of any kind. For him, it is necessary to take a step back, to a more primordial mode of being through which we can access Being, where the world is given and constituted.Embodied phenomenology, of Merleaupontinian provenance, recognizes the reduction, but cannot be fully achieve it.Jasperian phenomenology uses empathy and co-experience as its operators, through which it gains access to the subjective states of the other, with the aim of systematizing and taxonomizing subjective phenomena.Phenomenological psychopathology tentatively uses Husserlian reductions to identify the a priori structures of the human, be it Biswanger’s forms of manifestation of failed human existence or Blakenburg’s anthropological disproportions.Critical phenomenology uses a historical-transcendental analysis of experience as its operator, through which it accesses transcendental intersubjectivity.ConclusionsAt a time when the DSM and ICD are increasingly seen as inadequate, limited and dogmatic, the resurgence of interest in Phenomenology is evident. It is important to avoid falling back on new presuppositions without constant revision and questioning, at the risk of simply mutating dogmas and missing the original legacy of pure phenomenology, the suspension of presuppositions.Disclosure of InterestNone Declared