Classification Of Disorders Research Articles

Post-marketing safety concerns with montelukast: A pharmacovigilance study based on the FDA adverse event reporting system.

The United States Food and Drug Administration (FDA) has been warning about the psychiatric disorders associated with montelukast (MTK) for years. To study the characteristics of the presence of MTK-associated adverse events (AEs), we obtained data from the FDA Adverse Event Reporting System database and used a case (MTK) vs. non-case (all other drugs) analysis to investigate the safety signals in a disproportionality study. 27,507 reported AEs from January 2004 to December 2022 were analyzed. Disproportionality analysis shows that psychiatric, respiratory, thoracic, and mediastinal disorders as well as social circumstances are the most commonly reported AEs. In addition, our study found several unreported AEs, such as increased systolic blood pressure, diastolic dysfunction, hypothyroidism, obesity, bursitis, and polycystic ovaries. The timing of AE occurrence indicates that MTK-associated AEs are mainly acute effects. Most importantly, we found that 60.1% of patients reporting AEs in the category of psychiatric disorders were younger than 18 years. In summary, we revealed an age-preference pattern of psychiatric AEs in patients prescribed MTK. Our study is helpful for physicians and health professionals to better evaluate the value and risk of MTK and to achieve the goal of optimal patient care.

Retraction Note: Machine learning based identification and classification of disorders in human knee joint– computational approach

Retraction Note: Machine learning based identification and classification of disorders in human knee joint– computational approach

The introduction of the concept of anxiety into China: a thematic historical study

IntroductionThis article presents a thematic historical study on the introduction of “anxiety” to modern China. Based on seminal research conducted previously by European scholars on histories of psychological and psychiatric concepts, this article reviews the itinerary of “anxiety” to China by using multiple types of textual evidence. Alongside a detailed description of how “anxiety” was translated and introduced, a comparison between the notions of “anxiety” in Traditional Chinese Medicine (TCM) and Western psychoanalytic approaches was made. The processes of how “depression” and “anxiety” were introduced to China were also compared.Materials and methodsThree types of textual evidence were meticulously analyzed. First, lexicographical works published since the 19th century were examined to trace the initial introduction and observe the standardized translation of “anxiety” in Chinese. Second, newspaper and periodical articles were analyzed to understand how knowledge of “anxiety” was communicated to the general public. Third, the evolution of psychological and psychiatric terminologies was observed through medical books.ResultsIt was found that “anxiety” was initially translated into various Chinese terms, with “jiaolyu” eventually emerging as the standardized term after 1949. The textual evidence showed that “anxiety” began to be acknowledged as a disease concept in the early 20th century, though it was not formally recognized as a diagnostic category until the late 20th century. This article also highlights the differences between the Western psychoanalytic view on anxiety and the Chinese folk conception of it.Discussion and conclusionsIt was observed that the introduction processes of depression and anxiety to China were similar, though their conceptual histories in the West showed much difference. The fading of neurasthenia marked a new era of psychiatric development in China, characterized by an enhanced specificity in the classification of mental disorders, which was never observed in the discourse about “xin feng,” a corresponding TCM notion of “anxiety.” The article also underscores the need for further exploration of corresponding TCM concepts of “anxiety” and the assimilation of Western psychiatric concepts in China.

The phobic brain: Morphometric features correctly classify individuals with small animal phobia.

Specific phobia represents an anxiety disorder category characterized by intense fear generated by specific stimuli. Among specific phobias, small animal phobia (SAP) denotes a particular condition that has been poorly investigated in the neuroscientific literature. Moreover, the few previous studies on this topic have mostly employed univariate analyses, with limited and unbalanced samples, leading to inconsistent results. To overcome these limitations, and to characterize the neural underpinnings of SAP, this study aims to develop a classification model of individuals with SAP based on gray matter features, by using a machine learning method known as the binary support vector machine. Moreover, the contribution of specific structural macro-networks, such as the default mode, the salience, the executive, and the affective networks, in separating phobic subjects from controls was assessed. Thirty-two subjects with SAP and 90 matched healthy controls were tested to this aim. At a whole-brain level, we found a significant predictive model including brain structures related to emotional regulation, cognitive control, and sensory integration, such as the cerebellum, the temporal pole, the frontal cortex, temporal lobes, the amygdala and the thalamus. Instead, when considering macro-networks analysis, we found the Default, the Affective, and partially the Central Executive and the Sensorimotor networks, to significantly outperform the other networks in classifying SAP individuals. In conclusion, this study expands knowledge about the neural basis of SAP, proposing new research directions and potential diagnostic strategies.

Inter-rater Agreement for Movement Disorder Classification in Children with Hyperkinetic Movement Disorders.

Accurate classification is essential for addressing childhood movement disorders (MD), but the common coexistence of multiple MDs complicates this process. The aim was to assess inter-rater agreement on classifying hyperkinetic MDs among pediatric neurologists with expertise in MDs. Five pediatric neurologists were requested to examine 112 videos of 66 pediatric patients. Based on the Movement Disorder-Childhood Rating Scale, 3 queries were posed: (Q1) Is there more than 1 MD? (Q2) What is the (predominant) MD? (Q3) What is the other MD (if present)? The final agreement rates were 57.5% for Q1, 66.6% for Q2, and 43.9% for absolute agreement. All videos with absolute agreement at the first evaluation featured 1 MD, whereas only 2 videos with multiple MDs could totally agree in the final review. This study reveals significant discordance in classification even among pediatric neurologists with expertise in MDs and highlights the necessity for a standardized approach.

Drowning in Medicalization? Commentary on: Dang et al. "Taking a Deeper Dive Into OSFED Subtypes: A Systematic Review and Meta-Analysis".

The study by Dang and colleagues in this issue is a timely reminder of the need for careful consideration when it comes to the inclusion of putative new diagnoses in the diagnostic and statistical manual of mental disorders (DSM). The authors conclude that findings from their systematic review and meta-analysis of recent literature bearing on the DSM-5 other specified feeding and eating disorders (OSFED) category "support the conceptualization of atypical AN, PD and NES as clinically significant EDs with similar severity to full-threshold EDs." This commentary attempts to provide some additional context, historical context in particular, that the author believes may be helpful when considering the potential implications of Dang and colleagues' findings. This is achieved through reference to the construct, well-known in the sociology and feminist literature, of medicalization and by highlighting certain issues relevant to the determination of "clinical significance." I hope that readers approaching Dang and colleagues' research from the currently dominant, medical-model perspective might be persuaded of the importance of considering alternative perspectives when interpreting findings from research of this kind.

Taking a Global View of the OSFED Category From Inside and Outside the DSM-5: Comment on Dang et al. 2024.

This Commentary discusses the findings of Dang et al.'s systematic review and metanalysis on the "Other Specified Feeding or Eating Disorder" (OSFED) category in the context of current conceptualizations and main international diagnostic schemes of classification, the DSM-5 and the ICD-11. The aim to reduce less specified eating disorder categories in these classifications has not been completely achieved and OSFED cases remain prevalent. Different definitions of OSFED contribute to difficulties in study selection and limitation of data aggregation in metanalysis, highlighting the need for improving methodologies for studying OSFED subtypes. Although use of either the DSM-5 or ICD-11 scheme concurs with Dang et al.'s main finding that OSFED comprises categories of similar clinical significance to the recognized eating disorders, the ICD-11 includes more people with anorexia nervosa, bulimia nervosa, or binge-eating disorder who would receive a DSM-5 OSFED diagnosis. This may have impacts for epidemiological studies of distribution as well as for identification and treatment of the individual. We support that before creating new eating disorder categories, consideration be given to also broadening current DSM-5 criteria. This approach may result in fewer OSFED subtypes needing elevation to distinct categories, potentially limiting these to just purging disorder and night eating syndrome.

Characteristics of clinical descriptive variables and polysomnographic findings of catathrenia.

Catathrenia has been classified as a sleep-related breathing disorder variant in the third edition of the International Classification of Sleep Disorders, but its validity remains unverified. We analyzed the clinical descriptive variables and polysomnographic findings of catathrenia and discussed the similarities and differences to those of obstructive sleep apnea (OSA), non-REM parasomnias, and sleep bruxism (SB). A retrospective analysis was conducted on 47 patients diagnosed with nocturnal groaning through polysomnography. We examined sex, body mass index, age at symptom onset, weekly symptom frequency, and presence/absence of comorbidities, including OSA, periodic limb movement disorder, non-REM parasomnia, and SB. The groaning event (GE) index was calculated according to sleep position and sleep stage. The distribution of patients with catathrenia did not show sex difference (male/female = 20:27), body mass index was 20.6 ± 3.0 kg/m2, and age of onset was 18.2 ± 7.4 years. The GE index was higher in stages N1 and R than in stage N3 and in the supine position than in the lateral position. There were no cases complicated with non-REM parasomnia, but the complication of SB was observed in 30% of the participants, and SB events appeared immediately before or during the interictal period of the GE episodes in these cases. Given the clinical background, posture- and sleep stage-dependent appearance of GEs, and the relatively high complication rate of SB, catathrenia pathogenesis may be heterogeneous or comprise elements of different sleep disorders.

Multi-modal EEG NEO-FFI with Trained Attention Layer (MENTAL) for mental disorder prediction.

Early detection and accurate diagnosis of mental disorders is difficult due to the complexity of the diagnostic process, resulting in conditions being left undiagnosed or misdiagnosed. Previous studies have demonstrated that features of Electroencephalogram (EEG) data, such as Power Spectral Density (PSD), are useful biomarkers for indicating the onset of various mental disorders. Existing models using EEG data are typically trained to distinguish between healthy and afflicted individuals, and they are unable to distinguish between individuals with different disorders. We propose MENTAL (Multi-modal EEG NEO-FFI with Trained Attention Layer) to predict an individual's mental state using both EEG and Neo-Five Factor Inventory (NEO-FFI) personality data. We include an attention layer that captures the interactions between personality traits and PSD features, and emphasizes the important PSD features. MENTAL features a Recurrent Neural Network (RNN) to model the temporal nature of EEG data. We train our model with the Two Decades Brainclinics Research Archive for Insights in Neuroscience (TDBRAIN) dataset, which consists of 1274 healthy and psychiatric individuals including over 30 different diagnoses. MENTAL is able to achieve 93.3% accuracy when trained to classify between healthy individuals and those with ADHD. When trained to identify individuals with ADHD from among 33 disorder classes, MENTAL improves accuracy from 70.5 to 81.7%. MENTAL also demonstrates over 20% improvement in predictive accuracy when trained for MDD prediction. For the multi-class classification task of three classes, MENTAL improves accuracy by 4%, and for five classes, by nearly 8%. MENTAL is the first multi-modal model that utilizes both EEG and NEO-FFI data for the task of mental disorder prediction. We are one of the first groups to utilize TDBRAIN for automated disorder classification. MENTAL is accessible and cost-effective, as EEG is more affordable than other neuroimaging methods such as MRI, and the NEO-FFI is a self- reported survey. Our model can lead to acceptance and support for individuals living with mental health challenges and improve quality of life in our society.

The Symptoms-Varices-Pathophysiology Classification of Pelvic Venous Disorders: A Report of the American Vein & Lymphatic Society International Working Group on Pelvic Venous Disorders

The Symptoms-Varices-Pathophysiology Classification of Pelvic Venous Disorders: A Report of the American Vein & Lymphatic Society International Working Group on Pelvic Venous Disorders

Multiclass classification of Autism Spectrum Disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis.

Neurodevelopmental conditions, such as Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), present unique challenges due to overlapping symptoms, making an accurate diagnosis and targeted intervention difficult. Our study employs advanced machine learning techniques to analyze functional magnetic resonance imaging (fMRI) data from individuals with ASD, ADHD, and typically developed (TD) controls, totaling 120 subjects in the study. Leveraging multiclass classification (ML) algorithms, we achieve superior accuracy in distinguishing between ASD, ADHD, and TD groups, surpassing existing benchmarks with an area under the ROC curve near 98%. Our analysis reveals distinct neural signatures associated with ASD and ADHD: individuals with ADHD exhibit altered connectivity patterns of regions involved in attention and impulse control, whereas those with ASD show disruptions in brain regions critical for social and cognitive functions. The observed connectivity patterns, on which the ML classification rests, agree with established diagnostic approaches based on clinical symptoms. Furthermore, complex network analyses highlight differences in brain network integration and segregation among the three groups. Our findings pave the way for refined, ML-enhanced diagnostics in accordance with established practices, offering a promising avenue for developing trustworthy clinical decision-support systems.

Classification of Mental Disorders Using Modified Balanced Random Forest And Feature Selection

Classification of Mental Disorders Using Modified Balanced Random Forest And Feature Selection

Multimodal Fusion of EEG and Audio Spectrogram for Major Depressive Disorder Recognition Using Modified DenseNet121.

This study investigates the classification of Major Depressive Disorder (MDD) using electroencephalography (EEG) Short-Time Fourier-Transform (STFT) spectrograms and audio Mel-spectrogram data of 52 subjects. The objective is to develop a multimodal classification model that integrates audio and EEG data to accurately identify depressive tendencies. We utilized the Multimodal open dataset for Mental Disorder Analysis (MODMA) and trained a pre-trained Densenet121 model using transfer learning. Features from both the EEG and audio modalities were extracted and concatenated before being passed through the final classification layer. Additionally, an ablation study was conducted on both datasets separately. The proposed multimodal classification model demonstrated superior performance compared to existing methods, achieving an Accuracy of 97.53%, Precision of 98.20%, F1 Score of 97.76%, and Recall of 97.32%. A confusion matrix was also used to evaluate the model's effectiveness. The paper presents a robust multimodal classification approach that outperforms state-of-the-art methods with potential application in clinical diagnostics for depression assessment.

Bilateral chronic cavitary pulmonary aspergillomas in an adult patient with recurrent tuberculosis: a case report and literature review

BackgroundAspergillomas are globular growths of Aspergillus fumigatus, a benign aspergillosis of the lungs. It usually affects patients who are immunocompromised and have anatomically defective lung structures. The majority of aspergilloma cases are asymptomatic, despite the fact that 10% of cases spontaneously resolve. Most patients do not have any symptoms from their lesions. Direct serological or microbiological evidence of an Aspergillus species along with radiologic evidence is required for the diagnosis of an aspergilloma.CaseWe describe a 35-year-old adult Oromo male patient who had been experiencing night sweats, an intermittent productive cough with sparse whitish sputum, loss of appetite, and easy fatigability for 3 months. At 5 years prior, he received treatment for pulmonary tuberculosis that was smear-positive and was subsequently certified healed. Objectively, he was tachypneic and had intercostal, subcostal, and supraclavicular retractions with symmetric chest movement. A high-resolution computed tomography scan revealed bilateral apical cavitary lesions with core soft tissue attenuating spherical masses and an air crescentic sign suggestive of aspergillomas, which were confirmed by sputum light microscopic examination. The patient was managed with antibiotics and antifungals.ConclusionAspergilloma is a symptom of chronic pulmonary aspergillosis, a category of lung disorders caused by a persistent Aspergillus infection. Primary aspergillomas are uncommon and frequently occur in people with compromised immune systems. A prolonged cough, fever, chest pain, and hemoptysis are all symptoms of pulmonary aspergillomas. The majority of the time, pulmonary aspergillosis is difficult to identify. Despite high mortality and morbidity rates, surgery is still the most effective treatment for pulmonary aspergilloma.

Classification of amyloidosis and protein misfolding disorders in animals 2024: A review on pathology and diagnosis.

Amyloidosis is a group of diseases in which proteins become amyloid, an insoluble fibrillar aggregate, resulting in organ dysfunction. Amyloid deposition has been reported in various animal species. To diagnose and understand the pathogenesis of amyloidosis, it is important to identify the amyloid precursor protein involved in each disease. Although 42 amyloid precursor proteins have been reported in humans, little is known about amyloidosis in animals, except for a few well-described amyloid proteins, including amyloid A (AA), amyloid light chain (AL), amyloid β (Aβ), and islet amyloid polypeptide-derived amyloid. Recently, several types of novel amyloidosis have been identified in animals using immunohistochemistry and mass spectrometry-based proteomic analysis. Certain species are predisposed to specific types of amyloidosis, suggesting a genetic background for its pathogenesis. Age-related amyloidosis has also emerged due to the increased longevity of captive animals. In addition, experimental studies have shown that some amyloids may be transmissible. Accurate diagnosis and understanding of animal amyloidosis are necessary for appropriate therapeutic intervention and comparative pathological studies. This review provides an updated classification of animal amyloidosis, including associated protein misfolding disorders of the central nervous system, and the current understanding of their pathogenesis. Pathologic features are presented together with state-of-the-art diagnostic methods that can be applied for routine diagnosis and identification of novel amyloid proteins in animals.

HAGCN: A hybrid-order brain network-based graph convolution learning framework with multi-head attention for brain disorder classification

Brain networks provide essential insights in diagnosing many brain disorders, such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). However, current works often fail to fully capture the potential interaction characteristics of inter-regional functional connectivity within the brain network. To address this limitation, we propose a Hybrid-Attention Graph Convolution Network (HAGCN) for brain disorder classification. The learning framework designs a hybrid-order effective network (graph) for representing the lower-order and higher-order spatial interaction relations between brain regions, proposes a prior graph based on statistical tests to eliminate the noisy edges effectively, presents a dual-channel graph convolution module for simultaneously capturing information flow features of each brain region in out-degree and in-degree directions, and implement a multi-head attention module to focus and discern the significant features from the multidimensional interaction relations for improving the expressiveness of the model. Experiments on ADHD-200 and ABIDE-I datasets show the effectiveness and advantages of our proposed method, achieving the highest average accuracy values of 77.95 % and 78.17 %, respectively. The results demonstrate that integrating multidimensional and directional interaction characteristics can enhance the network’s capacity and improve learning efficiency. Additionally, our approach identifies and analyzes several key brain regions linked to brain disorders.

Safety Profiles Related to Dosing Errors of Rapid-Acting Insulin Analogs: A Comparative Analysis Using the EudraVigilance Database.

Diabetes is a widespread condition that can lead to multiple and severe complications. Rapid-acting insulin analogs (RAIAs) and long-acting insulin analogs are prescribed for the effective management of diabetes. RAIAs are expected to be associated with a higher number of dosing errors because of their rapid onset, short duration of action, and the need for frequent dosing, compared to other insulin analogs. There are three approved RAIAs on the market: insulin lispro (LIS), insulin aspart (ASP), and insulin glulisine (GLU). The aim of this study is to evaluate the real-world evidence on dosing errors reported for RAIAs in EudraVigilance (EV), an established pharmacovigilance database, in comparison to other insulin analogs and human insulins. A descriptive analysis and a disproportionality analysis were conducted. ASP and LIS were associated with high percentages of adverse drug reactions (ADRs) (22% and 17%, respectively), with over 70% of the reports involving serious ADRs. A higher frequency of cardiac and eye disorder ADRs was observed for LIS compared with ASP and GLU. GLU showed a higher frequency of ADRs in the skin and subcutaneous tissue disorders category. LIS dosing errors accounted for 5% of the total number of cases, while dosing errors for ASP and GLU were less than 3%. The most frequently reported dosing errors involved improper dosing (49%). Although there were fewer dosing errors of RAIAs in comparison to other insulins, the severity of the potential outcome highlights the importance of precise dosing and timing. Improved the monitoring and reporting of these dosing errors could enhance diabetes patient care. Additionally, smart medical devices could improve therapeutic outcomes.

Gender bias of antisocial and borderline personality disorders among psychiatrists.

Antisocial Personality Disorder (ASPD) and Borderline Personality Disorder (BPD) challenge mental health professionals with similar maladaptive behaviors. However, these two disorders differ regarding available evidence-based treatments. The Diagnostic and Statistical Manual of Mental Disorders (DSM) has been criticized as being gender-biased diagnostic construct. The present study aimed to determine the gender bias of ASPD and BPD among Turkish psychiatrists. Three case vignettes were randomly presented as male or female to the psychiatrists online. The first case was a patient with schizophrenia case to confirm the participant's ability to diagnose. The second case was a patient with ASPD, and the third one was a patient with BPD. Two hundred fifty participants diagnosed the first case correctly (n = 250). The results with statistical significance demonstrated that a female case with ASPD was 5.1 times more likely to get misdiagnosed than a male case with ASPD (p˂0.0001). Categorical classification of personality disorders in DSM leads to gender bias in in the diagnosis of ASPD and BPD. The present study shows that female cases with ASPD are misdiagnosed as BPD which may result in treatment attempts without evidence.

Dissecting the Mechanisms of Intestinal Immune Homeostasis by Analyzing T-Cell Immune Response in Crohn's Disease and Colorectal Cancer.

Crohn's disease (CD) and colorectal cancer (CRC) represent a group of intestinal disorders characterized by intricate pathogenic mechanisms linked to the disruption of intestinal immune homeostasis. Therefore, comprehending the immune response mechanisms in both categories of intestinal disorders is of paramount significance in the prevention and treatment of these debilitating intestinal ailments. IIn this study, we conducted single-cell analysis on paired samples obtained from primary colorectal tumors and individuals with Crohn's disease, which was aimed at deciphering the factors influencing the composition of the intestinal immune microenvironment. By aligning T cells across different tissues, we identified various T cell subtypes, such as γδ T cell, NK T cell, and regulatory T (Treg) cell, which maintained immune system homeostasis and were confirmed in enrichment analyses. Subsequently, we generated pseudo-time trajectories for subclusters of T cells in both syndromes to delineate their differentiation patterns and identify key driver genes Result: Furthermore, cellular communication and transcription factor regulatory networks are all essential components of the intricate web of mechanisms that regulate intestinal immune homeostasis. The identified complex cellular interaction suggested potential T-lineage immunotherapeutic targets against epithelial cells with high copy number variation (CNV) levels in CD and CRC. Finally, the analysis of regulon networks revealed several promising candidates for cell-specific transcription factors (TFs). This study focused on the immune molecular mechanism under intestinal diseases. It contributed to the novel insight of depicting a detailed immune landscape and revealing T-cell responding mechanisms in CD and CRC.

Classification of lung disorders in chest multi-modal images using hyper-Parameter tuning and modified ResNet50

Classification of lung disorders in chest multi-modal images using hyper-Parameter tuning and modified ResNet50