Disease Classification Research Articles

DeepPhylo: Phylogeny-Aware Microbial Embeddings Enhanced Predictive Accuracy in Human Microbiome Data Analysis.

Microbial data analysis poses significant challenges due to its high dimensionality, sparsity, and compositionality. Recent advances have shown that integrating abundance and phylogenetic information is an effective strategy for uncovering robust patterns and enhancing the predictive performance in microbiome studies. However, existing methods primarily focus on the hierarchical structure of phylogenetic trees, overlooking the evolutionary distances embedded within them. This study introduces DeepPhylo, a novel method that employs phylogeny-aware amplicon embeddings to effectively integrate abundance and phylogenetic information. DeepPhylo improves both the unsupervised discriminatory power and supervised predictive accuracy of microbiome data analysis. Compared to the existing methods, DeepPhylo demonstrates superiority in informing biologically relevant insights across five real-world microbiome use cases, including clustering of skin microbiomes, prediction of host chronological age and gender, diagnosis of inflammatory bowel disease (IBD) across 15 studies, and multilabel disease classification.

Pest and Disease Video Classification with Convolutional Neural Network and Transfer Learning

The important field crops of agriculture are affected due to attack of various pests and diseases which leads to reduction in crop production. Early classification and identification of pests and diseases in plant helps farmers to take mitigation steps. To address this issue with computer vision based techniques, convolutional neural network (CNN) based deep learning models were studied for classification of pests and diseases videos. Six different CNN models were developed. Two approaches namely from scratch learning and transfer learning were used. Data augmentation techniques such as reflection, scaling, rotation, and translation were also applied to prevent the network from overfitting. The classification accuracy of 99.19%, 99.08% and 98.80% was attained in VGG19, DENSENET201 and CNN 5 Layer model. The results demonstrated that CNN models with good architecture can classify pests and diseases with good performance.

Potato Leaf Disease Detection Based on a Lightweight Deep Learning Model

Traditional methods of agricultural disease detection rely primarily on manual observation, which is not only time-consuming and labor-intensive, but also prone to human error. The advent of deep learning has revolutionized plant disease detection by providing more accurate and efficient solutions. The management of potato diseases is critical to the agricultural industry, as these diseases can lead to substantial losses in crop production. The prompt identification and classification of potato leaf diseases are essential to mitigating such losses. In this paper, we present a novel approach that integrates a lightweight convolutional neural network architecture, RegNetY-400MF, with transfer learning techniques to accurately identify seven different types of potato leaf diseases. The proposed method not only enhances the precision of potato leaf disease detection but also reduces the computational and storage demands, with a mere 0.40 GFLOPs and a model size of 16.8 MB. This makes it well-suited for use on edge devices with limited resources, enabling real-time disease detection in agricultural environments. The experimental results demonstrated that the accuracy of the proposed method in identifying seven potato leaf diseases was 90.68%, providing a comprehensive solution for potato crop management.

Risk of Suicidal Ideation or Attempts in Adolescents With Obesity Treated With GLP1 Receptor Agonists

Glucagon-like peptide 1 receptor agonists (GLP1R) are increasingly being used for the treatment of obesity in adolescents. It is currently unknown whether GLP1R treatment is associated with suicidal ideation or attempts in this population. To investigate the association between GLP1R initiation and suicidal ideation or attempts in adolescents with obesity. Retrospective propensity score-matched cohort study using electronic health records from the TriNetX global federated network between December 2019 and June 2024. The analysis included data from 120 health care organizations, mainly from the USA. Participants were adolescents aged 12 to 18 years with a diagnosis of obesity and evidence of an antiobesity GLP1R prescription or lifestyle intervention without GLP1R within the following year. Cohorts were balanced for baseline demographic characteristics, psychiatric medications and comorbidities, and diagnoses associated with socioeconomic status and health care access using propensity score matching. Initial prescription of GLP1R (study cohort) or lifestyle intervention without GLP1R (control cohort). Incidence of suicidal ideation or attempts based on International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes recorded in patient electronic health records during 12 months of follow-up. Diagnoses of upper respiratory tract infections (URTI) were used as negative control outcomes, and gastrointestinal symptoms (GI) were used as positive control outcomes. A total of 4052 adolescents with obesity and a concomitant antiobesity intervention were identified for the GLP1R cohort and 50 112 were identified for the control cohort. Propensity score matching resulted in 3456 participants in each balanced cohort. Prescription of GLP1R was associated with a 33% reduced risk for suicidal ideation or attempts over 12 months of follow-up (1.45% vs 2.26%; hazard ratio [HR], 0.67; 95% CI, 0.47-0.95; P = .02) and a higher rate of GI symptoms (6.9% vs 5.4%; HR, 1.41; 95% CI, 1.12-1.78; P = .003) but no difference in rates of URTI diagnoses. In this study, adolescents with obesity prescribed a GLP1R had a lower incidence of suicidal ideation or attempts compared with matched patients not prescribed GLP1R who were treated with lifestyle intervention. These results suggest a favorable psychiatric safety profile of GLP1R in adolescents. The detected reduction in HRs for suicidal ideation among adolescents with obesity prescribed GLP1R suggests potential avenues for future research.

Characteristics of men and women with medically diagnosed cluster headache in a national integrated healthcare system: A Veterans Health Administration cohort study.

Describe the epidemiology of cluster headache (CH) using Veterans Health Administration (VHA) Electronic Health Record (EHR) data. Epidemiologic studies of CH at the population level are difficult because it has a prevalence of ~0.1%. Hospital system-wide studies are an attractive alternative as they have large numbers of patients and broader populations than headache or neurology clinic-based studies. The VHA is an ideal hospital-based system in which to study CH because it is nationwide, predominantly male, has a strong focus on mood disorders and suicidality, and has accessible individual medical records. Here, we report the first headache study based on an ongoing longitudinal cohort of patients with CH using VHA EHR data. The VHA EHR data were accessed from Fiscal Year 2008 to 2019. Patients with CH consisted of all patients with at least one outpatient visit containing a CH diagnosis code from the International Classification of Diseases (ICD)-9 or -10. We extracted data on demographic features, incidence, and prevalence, as well as pain and psychiatric comorbidities. Of the 1,524,960 distinct patients who presented for headache treatment in the VHA between Fiscal Year 2008-2019, 24,131 had at least one visit with a CH diagnosis. The 1-year period prevalence of a CH diagnosis in the VHA ranges from 0.08% to 0.10% for women and 0.10% to 0.18% for men. A larger proportion of women versus men received a diagnosis of unspecified CH (59.6% [1412/2368] vs. 53.6% [11,663/21,763], p < 0.001). Most patients with CH had both comorbid headache and non-headache pain diagnoses. Headache not-otherwise-specified was the most common comorbid headache disorder at 70.0% (16,885/24,131) and was more common in women (76.1%, 1801/2368) compared to men (69.3%, 15,084/21,763). Other common comorbidities included migraine, depression, tobacco use, and obstructive sleep apnea. Rates of suicidal ideation or attempt were almost 50% higher in women (5-year proportion 9.4%, 222/2368) with CH compared to men (6.6%, 1433/21,763). To our knowledge this is the largest hospital system study of CH to date and reinforces several previous studies. Pain, mental health, and sleep disorders comorbidities are particularly prevalent in this group and were often more common in women compared to men with CH. Future work should examine gender and race stratified prevalence estimates within the VHA and other healthcare systems.

Association between weekend admission and in-hospital mortality for patients with ischemic heart disease upon surgery treatment.

The existence and reasons for the weekend effect in patients with ischemic heart disease (IHD) were not yet fully identified. This study aimed to evaluate whether weekend admission was independently associated with in-hospital mortality and the possible mechanisms associated with the IHD patients. The study was a retrospective study, including IHD patients from 2015 to 2023. The International Classification of Diseases, tenth revision (ICD-10) codes were used to identify all admissions with a primary diagnosis of IHD. The sample was divided into weekday and weekend groups. We performed a multivariate logistic regression analysis and a mediation analysis to estimate the effect of weekend admission on hospital mortality. A total of 18,906 IHD patients were included in the study, with an average age of 63.8 ± 12.7. Of these patients, 21.7% (n = 4,102) were admitted over the weekend. The in-hospital 30-days mortality rate was significantly higher among the patients admitted at weekends compared with those admitted at weekdays (2.0% vs. 1.1%). Respectively, the 30-day mortality rate of patients admitted on weekends was higher compared to patients admitted on weekdays among patients with surgical treatment (2.34% vs. 1.06%, OR = 1.75; 95% CI: 1.23-2.42) and with emergency admission (3.48% vs. 2.59%, OR = 1.56; 95% CI: 1.05-2.28). Mediation analyses showed that the surgical scheduling had significant mediated effects on the associations of admission time with mortality risk. IHD patients with a surgical therapy or admitted from emergency department had a significantly higher risk of mortality when admitted on weekends compared to weekdays. These findings have potential implications for resource allocation and redistribution of surgery to weekends in hospitals.

Generative Adversarial Autoencoder Integration Voting Algorithm Based on Mass Spectrometry Data

Mass spectrometry technology is often used in disease prevention and diagnosis and treatment. However, the number of mass spectrometry data features is huge and the features vary greatly between different diseases, making multi-disease diagnosis. The task of judgment is complex and difficult. To address the above problems, this paper proposes a generative adversarial autoencoder integrated voting algorithm msDAGVote based on mass spectrometry data.The generative adversarial network based on dual autoencoders is used as the feature extraction framework of msDAGVote. After inputting mass spectrometry data for training, the generator subnetwork is used for feature extraction.Finally, the constructed features were screened by integrating the voting feature selection algorithm, and the optimal feature subset was used for multi-disease diagnosis.The results were evaluated on mass spectrometry datasets of different disease types. The experimental data showed that the features extracted by msDAGVote outperformed the comparison methods and significantly reduced the classification time.The required number of features and excellent disease classification and diagnosis capabilities are required. The classification AUC exceeds 0.98 on 6 datasets and The set exceeds 0.87.

MobileNet-CAL: Tomato pest and disease classification method based on transfer learning and attention mechanism

Based on the MobileNetV3 model, this paper introduces the Coordinate attention (CA) attention mechanism and transfer learning to propose the MobileNet-CAL model to solve the problems of insufficient attention to model channel information, long classification training time, high training cost, and weak generalization ability. The model combines data enhancement, attention mechanism and transfer learning technology to improve the classification effect of the model, and updates the fully connected layer at the same time. Finally, the model is optimized by combining the Auxiliary loss function and the ASGD optimizer to improve the classification accuracy of the model, speed up the collection speed, and enhance the robustness of the model. Finally, the model is evaluated from multiple angles such as Accuracy, F1-score, Precision, Recall, and Specificity in the tomato pest and disease leaf dataset. Its Accuracy reaches 98.59% an accuracy of, which is improved by, and by, 4.47%respectively, compared with the original model. Experiments show that MobileNet-CAL can improve the classification accuracy to a certain extent, verifying that the model proposed 4.05%、3.43%、16.57% in 4.13%this paper is effective in the classification task of tomato pest and disease leaves.

A CARS-SPA-GA Feature Wavelength Selection Method Based on Hyperspectral Imaging with Potato Leaf Disease Classification.

Early blight and ladybug beetle infestation are important factors threatening potato yields. The current research on disease classification using the spectral differences between the healthy and disease-stressed leaves of plants has achieved good progress in a variety of crops, but less research has been conducted on early blight in potato. This paper proposes a CARS-SPA-GA feature selection method. First, the raw spectral data of potato leaves in the visible/near-infrared light region were preprocessed. Then, the feature wavelengths were selected via competitive adaptive reweighted sampling (CARS) and the successive projection algorithm (SPA), respectively. Then, the two sets of wavelengths were reorganized and duplicates were removed, and secondary feature selection was conducted with genetic algorithm (GA). Finally, the feature wavelengths were fed into different classifiers and the parameters were optimized using a real-coded genetic algorithm (RCGA). The experimental results show that the feature wavelengths selected by the CARS-SPA-GA method accounted only for 9% of the full band, and the classification accuracy of the RCGA-optimized support vector machine (SVM) classification model reached 98.366%. These results show that it is feasible to classify early blight and ladybug beetle infestation in potato using visible/near-infrared spectral data, and the CARS-SPA-GA method can substantially improve the accuracy and detection efficiency of potato pest and disease classification.

Revealing disease subtypes and heterogeneity in common variable immunodeficiency through transcriptomic analysis

Common Variable Immunodeficiency (CVID) is a primary immunodeficiency characterized by reduced levels of specific immunoglobulins, resulting in frequent infections, autoimmune disorders, increased cancer risk, and diminished antibody production despite an adequate B cell count. With its clinical manifestations being highly variable, the classification of CVID, including the widely recognized Freiburg classification, is primarily based on clinical symptoms and genetic variations. Our study aims to refine the classification of CVID by analyzing transcriptomics data to identify distinct disease subtypes. We utilized the GSE51405 dataset, examining transcriptomic profiles from 30 CVID patients without complications. Employing a combination of clustering techniques—KMeans, hierarchical agglomerative clustering, spectral clustering, and Gaussian Mixture models—and differential gene expression analysis with R’s limma package, we integrated molecular findings with demographic data (age and gender) through correlation analysis and identified common genes among clusters. Three distinct clusters of CVID patients were identified using KMeans, Agglomerative Clustering, and Gaussian Mixture Models, highlighting the disease’s heterogeneity. Differential expression analysis unveiled 31 genes with variable expression levels across these clusters. Notably, nine genes (EIF5A, RPL21, ANP32A, DTX3L, NCF2, CDC42EP3, CHP1, FOLR3, and DEFA4) exhibited consistent differential expression across all clusters, independent of demographic factors. The study recommends categorizing patients based on the four genes, NCF2, CHP1, FOLR3, and DEFA4—as they may assist in prognostic prediction. Transcriptomic analysis of common variable immunodeficiency (CVID) patients identified three distinct clusters based on gene expression, independent of age and gender. Nine differentially expressed genes were identified across these clusters, suggesting potential biomarkers for CVID subtype classification. These findings highlight the genetic heterogeneity of CVID and provide novel insights into disease classification and potential personalized treatment approaches.

All-cause mortality and factors associated with it in Finnish patients with type 1 diabetes

AimsTo assess the effect of comorbidities, risk classification for chronic kidney disease (CKD) according to albuminuria and eGFR, HbA1c and LDL-cholesterol levels on all-cause mortality in patients with type 1 diabetes (DM1). MethodsThe study included all 45,801 DM1 patients from the Finnish Diabetes Registry during 2018–2022. Mortality of patients with DM1 was compared with mortality in non-diabetic population in Finland by estimating standardized mortality rates (SMRs). Poisson regression model was used to estimate the effect of risk factors on the SMR. ResultsA total of 2469 patients died during follow-up. SMR for the total cohort was 1.84 (95 % CI 1.77–1.92) peaking at the age of 30–49 years. The coverage of HbA1c values was 98 %, that of LDL-cholesterol 94 %, and U-ACR and eGFR 80 %. In a multivariate analysis, assessing the effect on mortality, the rate ratio for end-stage renal disease was 2.66, cardiovascular diseases 1.92, mental and behavioural disorders 1.64, foot complications 1.51, high or very high risk for CKD 3.64, LDL-cholesterol ≥2.6 mmol/l 1.33, and HbA1c ≥8 % (64 mmol/mol) 1.27. ConclusionsThere's substantial excess mortality due to DM1 in Finland. Interventions should focus on addressing both renal and cardiovascular risk factors but also pay more attention to mental health.

Polycystic ovary syndrome underdiagnosis patterns by individual-level and spatial social vulnerability measures.

To use electronic health records (EHR) data at Boston Medical Center (BMC) to identify individual-level and spatial predictors of missed diagnosis, among those who meet diagnostic criteria for PCOS. The BMC Clinical Data Warehouse was used to source patients who presented between October 1, 2003 and September 30, 2015 for any of the following: androgen blood tests, hirsutism, evaluation of menstrual regularity, pelvic ultrasound for any reason, or PCOS. Algorithm PCOS cases were identified as those with International Classification of disease (ICD) codes for irregular menstruation and either an ICD code for hirsutism, elevated testosterone lab, or polycystic ovarian morphology as identified using natural language processing on pelvic ultrasounds. Logistic regression models were used to estimate odds ratios (ORs) of missed PCOS diagnosis by age, race/ethnicity, education, primary language, body mass index (BMI), insurance type and social vulnerability index (SVI) score. In the 2003-2015 BMC-EHR PCOS at-risk cohort (n=23,786), there were 1,199 physician-diagnosed PCOS cases and 730 algorithm PCOS cases. In logistic regression models controlling for age, year, education, and SVI scores, Black/African American patients were more likely to have missed a PCOS diagnosis (OR = 1.69 [95% CI, 1.28, 2.24]) compared to non-Hispanic White patients, and relying on Medicaid or charity for insurance was associated with an increased odds of missed diagnosis when compared to private insurance (OR = 1.90 [95% CI, 1.47, 2.46], OR = 1.90 [95% CI, 1.41, 2.56], respectively). Higher SVI scores were associated with increased odds of missed diagnosis in univariate models. We observed individual-level and spatial disparities within the PCOS diagnosis. Further research should explore drivers of disparities for earlier intervention.

Utility Assessment of Follow-up Evaluation for Commotio Retinae

Purpose: To perform an outcome-and-cost analysis of follow-up visits and associated imaging in patients presenting to the Massachusetts Eye and Ear (MEE) emergency department with commotio retinae. Methods: This retrospective consecutive case series comprised consecutive patients presenting to the emergency department from January 1, 2020, to January 1, 2022, who were diagnosed with contusion or other injuries of the eye or orbit (International Classification of Diseases, 10th edition, codes S05.1 and S05.8). Cost analysis was performed using Medicare fee data and previously described methods. Costs considered included follow-up examinations, optical coherence tomography (OCT), and fundus photography. Results: Of the 490 patients presenting to the emergency department, 201 had findings of commotio retinae on examination and were included in this study. Of the 201 patients with initial commotio retinae, 142 (70.6%) returned for follow-up. OCT was performed in 34% of patients and fundus photography in 33%. A total of 254 follow-up visits were conducted. Four patients (2.8%) had retinal tears on follow-up, with only 1 (0.7%) developing a retinal detachment. Complications requiring intervention were always heralded by worsening symptoms. The total cost of follow-up was $60,436 for the cohort and $425.60 per patient. Conclusions: Patients with commotio retinae infrequently develop retinal complications and have a high rate of loss to follow-up. Significant resources are dedicated to following up and imaging patients with commotio retinae. Our results underscore the low utility in multiple follow-up visits among asymptomatic patients.

Intelligent agricultural robotic detection system for greenhouse tomato leaf diseases using soft computing techniques and deep learning

The development of soft computing methods has had a significant influence on the subject of autonomous intelligent agriculture. This paper offers a system for autonomous greenhouse navigation that employs a fuzzy control algorithm and a deep learning-based disease classification model for tomato plants, identifying illnesses using photos of tomato leaves. The primary novelty in this study is the introduction of an upgraded Deep Convolutional Generative Adversarial Network (DCGAN) that creates augmented pictures of disease tomato leaves from original genuine samples, considerably enhancing the training dataset. To find the optimum training model, four deep learning networks (VGG19, Inception-v3, DenseNet-201, and ResNet-152) were carefully compared on a dataset of nine tomato leaf disease classes. These models have validation accuracy of 92.32%, 90.83%, 96.61%, and 97.07%, respectively, when using the original PlantVillage dataset. The system then uses an enhanced dataset with ResNet-152 network design to achieve a high accuracy of 99.69%, as compared to the original dataset with ResNet-152’s accuracy of 97.07%. This improvement indicates the use of the proposed DCGAN in improving the performance of the deep learning model for greenhouse plant monitoring and disease detection. Furthermore, the proposed approach may have a broader use in various agricultural scenarios, potentially altering the field of autonomous intelligent agriculture.

A multi-class fundus disease classification system based on an adaptive scale discriminator and hybrid loss

Fundus images are crucial in the observation and detection of ophthalmic diseases. However, detecting multiple ophthalmic diseases from fundus images using deep learning techniques is a complex and challenging task One challenge is the complexity of fundus disease structures, which leads to low detection accuracy. Another challenge is the class imbalance problem common in multi-label image classification, which increases the difficulty of algorithm training and evaluation. To address these issues, this study leverages deep learning to propose an ophthalmic disease classification system. We first employ ResNet50 as the backbone network to extract image features, and then use our designed multi-dimensional attention module and adaptive scale discriminator to enhance the network's ability to detect disease features. During training, we innovatively propose a hybrid loss function method to improve the detection capability on imbalanced data. Finally, we conducted experiments on the ODRI-5K dataset with the proposed classification system. In the test set, our method achieved an AUC of 98.53 and an F1-score of 89.73. This result fully demonstrates the excellent disease classification capability of our method. In summary, the multi-label fundus image disease classification system we proposed exhibits outstanding recognition capability, providing an effective solution for the diagnosis of multi-label fundus image diseases.

Association Between Body Mass Index and Risk of Aortic Stenosis in Women in the Swedish Medical Birth Registry.

Overweight and obesity are increasing globally with aging, as are life expectancy and aging-associated disorders, including calcific aortic stenosis (AS). Studies investigating the correlation between high body mass index (BMI) and AS are contradictory and inconclusive. This study examines a potential association between BMI and AS in women. By linking the Swedish Medical Birth Register and the Swedish National Patient Register, we included women aged 18 to 55 years with a first childbirth from 1981 to 2020. Diagnosis of AS and comorbidities were defined according to the International Classification of Diseases (ICD) codes. The women were divided into groups on the basis of BMI. Cox proportional hazards regression models were used to investigate the difference in the risk of being diagnosed with AS, with reference BMI 20 to <22.5 kg/m2. Among the 1 722 625 included women, the mean age was 28 years, and mean BMI was 24 kg/m2, with 21% being overweight (BMI 25 to <30 kg/m2) and 8.5% obese (BMI ≥30 kg/m2). During median follow-up of 19.5 years, 2488 women (0.14%) were diagnosed with AS. The age-adjusted risk of being diagnosed with AS increased with higher BMI to 2.82 (95% CI, 2.44-3.25) times higher in women with BMI 30 to <35 kg/m2, and to 3.72 (95% CI, 2.95-4.70) times higher in those with BMI ≥35 kg/m2. Similar results were found after excluding AS of rheumatic pathogenesis. An increase in BMI from its upper normal range was consistently and independently associated with the risk of developing AS in women.

Comparison of heart failure risk assessment tools among cancer survivors

BackgroundCancer survivors have an increased risk of incident heart failure (HF) attributable to shared risk factors and cancer treatment-induced cardiac dysfunction. Selection for HF screening depends on risk assessment, but the optimal means of assessing risk is undefined. We undertook a comparison of HF risk calculators among survivors.MethodsIn this study from the UK Biobank, cancer and HF diagnoses were determined based on the International Classification of Diseases (ICD)-10 code and non-cancer participants were included as controls. Participants’ risk of incident HF was determined using the Heart Failure Association-International Cardio-oncology Society (HFA-ICOS), the Atherosclerosis Risk in Communities (ARIC-HF) and the Pooled Cohort Equations to Prevent Heart Failure (PCP-HF). The predictive performances of each were compared using the area under the curve (AUC).ResultsAfter propensity matching with age and sex, 9,232 survivors from breast cancer or lymphoma (mean age 59.9 years, 87.8% female), and 23,800 survivors from other cancer types (mean age 59.1 years, 85.8% female) were included in the analysis. The discriminative value for HFA-ICOS (AUC 0.753 [95%CI: 0.739–0.766]) and ARIC-HF (0.757 [95%CI: 0.744–0.770]) were similar, and superior to PCP-HF (0.717 [95%CI: 0.702–0.732]). The overall performance for each risk score was better among participants in other cancer types than those with breast cancer and lymphoma.ConclusionsHFA-ICOS and ARIC-HF outperformed the PCP-HF among cancer- and non-cancer cohort, although all showed modest discrimination for incident HF to be applied to clinical practice. A cancer-specific HF prediction tool could facilitate HF prevention among survivors.

Development and Validation of Dimensional Clinical Personality Inventory 2 (IDCP-2) Algorithms to Assess ICD-11 Personality Trait Domain Qualifiers.

With the advent of the new diagnostic model for personality disorders in the 11th revision of the International Classification of Diseases (ICD-11), researchers and practitioners in World Health Organization signatory countries are urged to implement it. This study aims to develop a brief, reliable, and valid scale for assessing maladaptive personality traits according to the ICD-11 model, using the item pool of the Dimensional Clinical Personality Inventory (IDCP-2). Quantitative and qualitative criteria for item selection were applied to a sample of 251 Brazilian adults. As a result, the 25 items (five items per factor) were selected, demonstrating promising evidence of validity based on the internal structure with a database of 1,659 Brazilian adults. In addition, we found good evidence of validity based on relationships with external variables, particularly those related to personality pathology, in a sample of 617 Brazilian adults. The implications of these findings are discussed.

Association of allostatic load with depression, anxiety, and suicide: a prospective cohort study

BackgroundAllostatic load (AL) is an objective measure of the biological components of chronic stress within clinical practice, potentially influencing depression, anxiety, and suicide. This study investigates the association between AL and these mental disorders. MethodsIn this cohort study of 333,017 adults, participants without prior diagnoses of depression, anxiety, or suicide were observed from March 13, 2006, to October 31, 2022. AL was estimated using 10 biomarkers reflecting metabolic, cardiovascular, and inflammatory dysregulation. Diagnoses were based on the International Classification of Diseases 10th Revision (ICD-10). We performed Cox proportional hazards models to assess the relationship between AL and these mental disorders. Additionally, we conducted subgroup analyses for sex, age, and Townsend Deprivation Index (TDI), along with sensitivity analyses. ResultsThe median follow-up period was 12.99 years. Over the follow-up period, 13,441 (4.04%) participants developed depression, 13,903 (4.17%) developed anxiety and 796 (0.24%) committed suicide. In fully adjusted model, individuals with high AL had an increased risk of depression (HR = 1.389, P = 8.38 ×10-27), anxiety (HR = 1.304, P = 5.82 ×10-19) and suicide (HR = 1.433, P = 4.46 ×10-3). Women and younger individuals with high AL were vulnerable to depression and anxiety, while moderate AL levels were significantly associated with suicide in men and younger participants. Moreover, individuals with middle and high AL had an elevated risk of comorbid depression and anxiety. ConclusionsHigh AL is positively associated with increased risks of depression, anxiety, and suicide, highlighting its potential as a predictive tool in mental health.

Comparison of traditional and next-generation oral anticoagulants in the etiology of epistaxis

Background/Aim: There is a dearth of studies addressing the effects of next-generation anticoagulants on epistaxis. The aim of this investigation was to determine whether there are any differences between traditional and next-generation anticoagulants in the etiology of epistaxis. Methods: This retrospective cohort study focused on a total of 7,110 individuals (3,278 females (46.1%) and 3,832 males (53.9%)) diagnosed with epistaxis between 2018 and 2022; the mean age of the patients was 37.7 years. Patient data (age, gender, outpatient and inpatient treatments, relevant laboratory parameters, and treatment evidence) were retrospectively reviewed from a hospital database. The severity of epistaxis was assessed based on treatment notes. Patients with hypertension and those undergoing antiaggregant therapy were excluded from the study. International Classification of Diseases (ICD) codes from the automated system were examined retrospectively. The data were used to establish three patient groups: the first group consisted of individuals taking next-generation oral anticoagulants, the second group consisted of individuals taking traditional oral anticoagulants, and the third group consisted of healthy controls. Results: We found statistically significant differences among the groups in terms of age, the severity of epistaxis, the treatment modality, and laboratory findings (P&lt;0.001); no statistically significant difference was found in terms of gender (P=0.954). Group 2 contained the largest number of hospitalized patients and patients with severe active nosebleeds. Conclusion: Next-generation anticoagulants are more reliable than traditional anticoagulants in terms of the severity of epistaxis, the need for hospitalization, and laboratory results.