Descriptive Analysis of Common Fusion Mutations in Papillary Thyroid Carcinoma in Hungary.

Thyroid cancer is the most common type of endocrine malignancy. Papillary thyroid carcinoma (PTC) is its predominant subtype, which is responsible for the vast majority of cases. It is true that PTC is a malignant tumor with a very good prognosis due to effective primary therapeutic approaches such as thyroidectomy and radioiodine (RAI) therapy. However, we are often required to indicate second-line treatments to eradicate the tumor properly. In these scenarios, molecular therapies are promising alternatives, especially if specifically targetable mutations are present. Many of these targetable gene alterations originate from gene fusions, which can be found using molecular diagnostics like next-generation sequencing (NGS). Nonetheless, molecular profiling is far from being a routine procedure in the initial phase of PTC diagnostics. As a result, the mutation status, except for BRAF V600E mutation, is not included in risk classification algorithms either. This study aims to provide a comprehensive analysis of fusion mutations in PTC and their associations with clinicopathological variables in order to underscore certain clinical settings when molecular diagnostics should be considered earlier, and to demonstrate yet unknown molecular-clinicopathological connections. We conducted a retrospective fusion mutation screening in formalin-fixed paraffin-embedded (FFPE) PTC tissue samples of 100 patients. After quality evaluation by an expert pathologist, RNA isolation was performed, and then NGS was applied to detect 23 relevant gene fusions in the tumor samples. Clinicopathological data were collected from medical and histological records. To obtain the most associations from the multivariate dataset, we used the d-correlation method for our principal component analysis (PCA). Further statistical analyses, including Chi-square tests and logistic regressions, were performed to identify additional significant correlations within certain subsets of the data. Fusion mutations were identified in 27% of the PTC samples, involving nine distinct genes: RET, NTRK3, CCDC6, ETV6, MET, ALK, NCOA4, EML4, and SQSTM1. RET and CCDC6 fusions were associated with type of thyroidectomy, RAI therapy, smaller tumor size, and history of Hashimoto's disease. NCOA4 fusion correlated with sex, multifocality, microcarcinoma character, history of goiter, and obstructive pulmonary disease. EML4 fusion was also linked with surgical procedure type and smaller tumor size, as well as the history of hypothyroidism. SQSTM1 fusion was associated with multifocality and a medical history of thyroid/parathyroid adenoma. NTRK3 and ETV6 fusions showed significant associations with Hashimoto's disease, and ETV6, also with endometriosis. Moreover, fusion mutations were linked to younger age at the time of diagnosis, particularly the fusion of ETV6. The frequent occurrence of fusion mutations and their associations with certain clinicopathological metrics highlight the importance of integrating molecular profiling into routine PTC management. Early detection of fusion mutations can inform surgical decisions and therapeutic strategies, potentially improving clinical outcomes.

Associations of Wearable Activity Tracker Use With Physical Activity and Health Outcomes in Patients With Cancer: Findings from a Population-Based Survey Study.

BACKGROUND: Physical inactivity is a global issue for cancer survivors. Wearable activity trackers are promising to address physical inactivity by providing real-time feedback on physical activity and offering opportunities for self-monitoring and goal setting. Meta-analysis has reported the effects of interventions that incorporate wearable activity trackers on improved physical inactivity and related health outcomes (eg, BMI, anxiety and depression, and self-rated health status). However, wearable activity trackers were often used as an adjunct to physical activity interventions, and the effectiveness of wearable activity trackers alone is unknown. OBJECTIVE: This study aims to determine the association of wearable activity trackers with physical activity and health outcomes in patients with cancer. METHODS: Data from 957 cancer survivors from the Health Information National Trends Survey-Surveillance, Epidemiology, and End Results (HINTS-SEER) were analyzed. The outcome variables examined were time spent in moderate to vigorous physical activity, weekly frequency of strength training, BMI, anxiety and depression levels, and self-assessed health status. The primary independent variable was whether cancer survivors had used wearable activity trackers within the past 12 months. Design-based linear regression for continuous outcome variables and ordinal logistic regression for ordinal outcome variables were conducted to determine the associations after controlling for sociodemographic, cancer-related, and health-related factors. All data analyses accounted for the complex survey design and sample weights. RESULTS: Only 29% of cancer survivors reported wearable activity tracker use. Bivariate analyses showed that younger age (P<.001), higher education (P=.04), higher income (P<.001), and an employed status (P<.001) were significantly associated with wearable activity tracker use. Wearable activity tracker use was significantly associated with higher time spent in moderate to vigorous physical activity (adjusted =37.94, 95% CI 8.38-67.5; P=.01), more frequent strength training per week (adjusted odds ratio [OR] 1.50, 95% CI 1.09-2.06; P=.01), and better self-rated health status (adjusted OR 1.58, 95% CI 1.09-2.29; P=.01), but not with BMI or anxiety and depression. CONCLUSIONS: This study suggests that the uptake of wearable activity trackers is low and highlights the digital divide among patients with cancer. This study has confirmed the associations of wearable activity tracker use with physical activity and self-rated health, supporting using wearable activity trackers as a promising tool to facilitate physical activity promotion.

Periodontitis and Hepatocellular Carcinoma: A Two-Sample Mendelian Randomisation Study.

INTRODUCTION AND AIMS: Observational studies have reported conflicting associations between periodontitis (PD) and hepatocellular carcinoma (HCC). To overcome these limitations, we performed a two-sample Mendelian randomization (MR) analysis to investigate the potential association between PD and HCC. METHODS: We used summary data from genome-wide association studies (GWASs) of European ancestry, integrating data from chronic/acute periodontitis (CP/AP) samples (n1 = 34,615; n2 = 277,036; n3 = 410,811) and HCC samples (n1 = 456,348; n2 = 475,638). The inverse variance-weighted (IVW) approach represents our primary analysis method, supplemented by MR-Egger regression, weighted median, weighted-mode, and simple-mode methods. Pleiotropy and heterogeneity tests were also performed. RESULTS: IVW analysis suggested that PD had no effect on HCC (Group 1: odds ratio [OR] = 0.912, 95% confidence interval [CI] = 0.690-1.204, P = .514; Group 2: OR = 1.038, 95% CI = 0.895-1.203, P = .623; Group 3: OR = 0.966, 95% CI = 0.851-1.096, P = .591; Group 4: OR = 1.103, 95% CI = 0.576-2.113, P = .768; Group 5: OR = 1.257, 95% CI = 0.511-1.037, P = .540; Group 6: OR = 0.728, 95% CI = 0.511-1.037, P = .079). Four complementary analyses further support this conclusion. Both the IVW and MR-Egger results indicate that the instrumental variables in each group did not exhibit significant pleiotropy. MR-Egger regression analysis showed no evidence of pleiotropic effects. CONCLUSION: Our MR analysis suggests that PD does not significantly impact the risk of developing HCC. These results provide a new perspective on the relationship between these 2 conditions. CLINICAL RELEVANCE: This MR study suggests no significant genetic causal relationship between PD and HCC, providing a new perspective. It indicates that clinicians may not need to over-intervene in periodontal disease to prevent liver cancer, thereby avoiding unnecessary psychological burden on patients.

ANS-SCMC: A matrix completion method based on adaptive neighbourhood similarity and sparse constraints for predicting microbe-disease associations.

The use of matrix completion methods to predict the association between microbes and diseases can effectively improve treatment efficiency. However, the similarity measures used in the existing methods are often influenced by various factors such as neighbourhood size, choice of similarity metric, or multiple parameters for similarity fusion, making it challenging. Additionally, matrix completion is currently limited by the sparsity of the initial association matrix, which restricts its predictive performance. To address these problems, we propose a matrix completion method based on adaptive neighbourhood similarity and sparse constraints (ANS-SCMC) for predict microbe-disease potential associations. Adaptive neighbourhood similarity learning dynamically uses the decomposition results as effective information for the next learning iteration by simultaneously performing local manifold structure learning and decomposition. This approach effectively preserves fine local structure information and avoids the influence of weight parameters directly involved in similarity measurement. Additionally, the sparse constraint-based matrix completion approach can better handle the sparsity challenge in the association matrix. Finally, the algorithm we proposed has achieved significantly higher predictive performance in the validation compared to several commonly used prediction methods proposed to date. Furthermore, in the case study, the prediction algorithm achieved an accuracy of up to 80% for the top 10 microbes associated with type 1 diabetes and 100% for Crohn's disease respectively.

Exploring pathogenesis, prevalence, and genetic associations in Chiari malformation type 1: a contemporary perspective.

Chiari malformation type 1 (CM 1) entails a structural defect in the cerebellum, involving the herniation of cerebellar tonsils toward the foramen magnum. The symptomatic or asymptomatic nature of CM 1 is contingent upon the condition of malformation in the spinal cord. This review presents an updated perspective on the prevalence of CM 1, its pathogenesis, genetic associations, and treatment. CM 1 exhibits a higher prevalence in adult females than males. Despite the incomplete understanding of the exact cause of CM 1, recent research suggests the involvement of both genetic and environmental factors in its development. One of the reasons for the occurrence of CM 1 in individuals is the smaller posterior cranial fossa, which manifests as typical morphological features. Additionally, environmental factors can potentially interact with genetic factors, modifying the observable characteristics of the disease and affecting the symptoms, severity, and development of the condition. Notably, headaches, neck pain, dizziness, and neurological deficits may be exhibited by individuals with CM 1, highlighting the importance of early diagnosis. Magnetic resonance imaging (MRI) serves as an alternative diagnostic technique for monitoring the symptoms of CM 1. Multiple genetic factors are likely to contribute to a cascade of abnormalities in CM 1. Early studies provided evidence, including clustering within families, bone development, and co-segregation with known genetic syndromes, establishing CM 1's association with a genetic basis. Furthermore, surgery is the only available treatment option to alleviate symptoms or hinder the progression of damage to the central nervous system (CNS) in CM 1 cases.

SPLHRNMTF: robust orthogonal non-negative matrix tri-factorization with self-paced learning and dual hypergraph regularization for predicting miRNA-disease associations.

MicroRNAs (miRNAs) have been demonstrated to be closely related to human diseases. Studying the potential associations between miRNAs and diseases contributes to our understanding of disease pathogenic mechanisms. As traditional biological experiments are costly and time-consuming, computational models can be considered as effective complementary tools. In this study, we propose a novel model of robust orthogonal non-negative matrix tri-factorization (NMTF) with self-paced learning and dual hypergraph regularization, named SPLHRNMTF, to predict miRNA-disease associations. More specifically, SPLHRNMTF first uses a non-linear fusion method to obtain miRNA and disease comprehensive similarity. Subsequently, the improved miRNA-disease association matrix is reformulated based on weighted k-nearest neighbor profiles to correct false-negative associations. In addition, we utilize L 2 , 1 norm to replace Frobenius norm to calculate residual error, alleviating the impact of noise and outliers on prediction performance. Then, we integrate self-paced learning into NMTF to alleviate the model from falling into bad local optimal solutions by gradually including samples from easy to complex. Finally, hypergraph regularization is introduced to capture high-order complex relations from hypergraphs related to miRNAs and diseases. In 5-fold cross-validation five times experiments, SPLHRNMTF obtains higher average AUC values than other baseline models. Moreover, the case studies on breast neoplasms and lung neoplasms further demonstrate the accuracy of SPLHRNMTF. Meanwhile, the potential associations discovered are of biological significance.

Consistent Nursing Presence Through Questionably Unprecedented Times.

Election season. Those words evoke sighs, spur eye rolls, emblazon debates, strain relationships, spark activism, and fan hope. The Clinical Journal of Oncology Nursing team works months ahead of each issue. By publication, A.

The joint associations of physical activity and ultra-processed food consumption with depression: A cohort study in the UK Biobank.

BACKGROUND: Despite substantial evidence regarding independent associations between physical activity (PA) and ultra-processed foods (UPF) consumption with depression, the joint effects of these two factors remain unknown. METHODS: This study included 99,126 participants without depression in the UK Biobank at baseline. A 24-h recall method was used to assess UPF consumption, and self-reported total physical activity (TPA), moderate-to-vigorous physical activity (MVPA), and vigorous physical activity (VPA) were assessed by metabolic equivalent task (MET). A series of Cox proportional hazard regression models were used to explore the independent and joint effects of TPA, MVPA, VPA and UPF consumption on depression. RESULTS: The incidence rate of depression was 1.94 % [95 % confidence interval (CI): 1.80 %-2.10 %] per 1000 person-years after an average follow-up of 12.10 years. We found that MVPA and UPF consumption had additive interactions on depression risk (p < 0.05). Participants in Q1 of TPA and Q4 of UPF consumption (HR: 1.83, 95%CI: 1.45-2.31) showed a higher risk for depression than those in Q4 of TPA and Q1 of UPF consumption. Compared with the participants with WHO guideline-recommended MVPA and the lowest UPF consumption, those below recommended MVPA (HR: 1.51, 95%CI: 1.20-1.89) or above recommended MVPA (HR: 1.40, 95%CI: 1.10-1.78) and with the highest UPF consumption had a higher risk for depression. LIMITATIONS: Study limitations include use of self-reported data, observational study and concerns regarding generalizability. CONCLUSION: Higher UPF consumption, accompanied by lower PA levels regardless of TPA, MVPA, and VPA, is associated with a higher risk of depression. Our study offers insights on public health priorities to decrease the risk of depression in the population by addressing both PA and UPF consumption together.

Diet affects inflammatory arthritis: a Mendelian randomization study of 30 dietary patterns causally associated with inflammatory arthritis.

Background: The causal associations between dietary intake and the risk and severity of Inflammatory Arthritis (IA) are currently unknown. Objective: In this study, we aimed to investigate the causal relationship between nine dietary categories (30 types of diet) and IA using Mendelian randomization (MR). Methods: We analyzed data from 30 diets and IA in a genome-wide association study (GWAS). Single nucleotide polymorphisms (SNPs) that could influence the results of MR analyses were screened out through the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test. SNPs were analyzed through two-sample bidirectional MR using inverse variance weighting, MR-Egger regression, and weighted median method. The multiplicity and heterogeneity of SNPs were assessed using MR-Egger intercept term tests and Cochran's Q tests. FDR correction was used to correct the p-values. Results: IVW results showed that Beef intake [Odds ratio (OR) = 2.862; 95% confidence interval (CI), 1.360-6.021, p = 0.006, p_fdr < 0.05] was positively associated with rheumatoid arthritis(RA); Dried fruit intake (OR = 0.522; 95% CI, 0.349-0.781, p = 0.002, p_fdr < 0.05), and Iron intake (OR = 0.864; 95%CI, 0.777-0.960, p = 0.007, p_fdr < 0.05) were negatively associated with RA, all of which were evidence of significance. Fresh fruit intake (OR = 2.528. 95% CI, 1.063-6.011, p = 0.036, p_fdr > 0.05) was positively associated with psoriatic arthritis (PsA); Cheese intake (OR = 0.579; 95% CI, 0.367-0.914, p = 0.019, p_fdr > 0.05) was negatively associated with PsA; both were suggestive evidence. Processed meat intake (OR = 0.238; 95% CI, 0.100-0.565, p = 0.001, p_fdr < 0.05) was negatively associated with reactive arthritis (ReA), a protective factor, and significant evidence. All exposure data passed the heterogeneity check (Cochrane's Q test p > 0.05) and no directional pleiotropy was detected. Leave-one-out analyses demonstrated the robustness of the causal relationship in the positive results. Conclusion: Our study presents genetic evidence supporting a causal relationship between diet and an increased risk of IA. It also identifies a causal relationship between various dietary modalities and different types of IA. These findings have significant implications for the prevention and management of IA through dietary modifications.

An ensemble approach for circular RNA-disease association prediction using variational autoencoder and genetic algorithm.

Circular RNAs (circRNAs) are endogenous non-coding RNAs with a covalently closed loop structure. They have many biological functions, mainly regulatory ones. They have been proven to modulate protein-coding genes in the human genome. CircRNAs are linked to various diseases like Alzheimer's disease, diabetes, atherosclerosis, Parkinson's disease and cancer. Identifying the associations between circular RNAs and diseases is essential for disease diagnosis, prevention, and treatment. The proposed model, based on the variational autoencoder and genetic algorithm circular RNA disease association (VAGA-CDA), predicts novel circRNA-disease associations. First, the experimentally verified circRNA-disease associations are augmented with the synthetic minority oversampling technique (SMOTE) and regenerated using a variational autoencoder, and feature selection is applied to these vectors by a genetic algorithm (GA). The variational autoencoder effectively extracts features from the augmented samples. The optimized feature selection of the genetic algorithm effectively carried out dimensionality reduction. The sophisticated feature vectors extracted are then given to a Random Forest classifier to predict new circRNA-disease associations. The proposed model yields an AUC value of 0.9644 and 0.9628 under 5-fold and 10-fold cross-validations, respectively. The results of the case studies indicate the robustness of the proposed model.

HHOMR: a hybrid high-order moment residual model for miRNA-disease association prediction.

Numerous studies have demonstrated that microRNAs (miRNAs) are critically important for the prediction, diagnosis, and characterization of diseases. However, identifying miRNA-disease associations through traditional biological experiments is both costly and time-consuming. To further explore these associations, we proposed a model based on hybrid high-order moments combined with element-level attention mechanisms (HHOMR). This model innovatively fused hybrid higher-order statistical information along with structural and community information. Specifically, we first constructed a heterogeneous graph based on existing associations between miRNAs and diseases. HHOMR employs a structural fusion layer to capture structure-level embeddings and leverages a hybrid high-order moments encoder layer to enhance features. Element-level attention mechanisms are then used to adaptively integrate the features of these hybrid moments. Finally, a multi-layer perceptron is utilized to calculate the association scores between miRNAs and diseases. Through five-fold cross-validation on HMDD v2.0, we achieved a mean AUC of 93.28%. Compared with four state-of-the-art models, HHOMR exhibited superior performance. Additionally, case studies on three diseases-esophageal neoplasms, lymphoma, and prostate neoplasms-were conducted. Among the top 50 miRNAs with high disease association scores, 46, 47, and 45 associated with these diseases were confirmed by the dbDEMC and miR2Disease databases, respectively. Our results demonstrate that HHOMR not only outperforms existing models but also shows significant potential in predicting miRNA-disease associations.

Investigating the Variables Associated with Physical Exercise Status among United States Adults with Arthritis.

Background/Objectives: Arthritis is a chronic, debilitating condition affecting millions of United States (US) adults. Regular physical exercise is particularly important for adults with arthritis. This study aimed to investigate the characteristics associated with regular physical exercise in US adults with arthritis. Methods: This cross-sectional database study used 2021 Medical Expenditure Panel Survey data and included US adults (age ≥ 18) alive with arthritis. A multivariable logistic regression model was developed to test the association of the following variables with regular physical exercise (defined as moderate-vigorous intensity exercise for ≥30 min ≥5 times weekly; yes, no): age, sex, Hispanic, race, census region, marriage status, schooling, employment, health insurance, household income, mental health, general health, smoking status, chronic conditions, pain, and functional limitations. Results: Overall, 5091 people (regular physical exercise n = 2331, no regular physical exercise n = 2760) were involved in this analysis. Most were female, non-Hispanic, white, married, had schooling beyond high school, were unemployed, had private health insurance, had mid-high household income, had good mental health, had good general health, were non-smokers, had two or more chronic conditions, had little/moderate pain, and did not have a functional limitation. In multivariable logistic regression analysis, male vs. female sex (odds ratio [OR] = 1.440, 95% confidence interval [CI] = 1.185-1.749), employed vs. unemployed (OR = 1.277, 95% CI = 1.005-1.624), good vs. poor general health (OR = 2.174, 95% CI = 1.673-2.824), little/moderate vs. quite a bit/extreme pain (OR = 1.418, 95% CI = 1.109-1.818), and no functional limitation (OR = 1.592, 95% CI = 1.282-1.980) were associated with higher odds of reporting regular physical exercise, while Midwest vs. West census region (OR = 0.698, 95% CI = 0.521-0.935) was associated with lower odds of reporting regular physical exercise. Conclusions: This study identified variables associated with regular physical exercise among US adults with arthritis. Further work is needed to develop interventions for characteristics that may help increase exercise and, subsequently, health outcomes in this population.

Association between serum cotinine levels and urinary incontinence in adults in the United States: a population-based cross-sectional analysis.

Environmental tobacco smoke (ETS) exposure has been shown to be associated with a variety of diseases, but evidence regarding the association between it and urinary incontinence (UI) is limited. Cotinine, a metabolite of nicotine in the human body, can more accurately quantify the level of human exposure to tobacco smoke. The study utilized data from seven survey cycles (2007-March 2020 Pre-pandemic) of the National Health and Nutrition Examination Survey (NHANES) program. Weighted multivariable logistic regression analysis, subgroup analysis, interaction tests, smooth curve fitting, and threshold effect models were used to analyze the relationship between serum cotinine and UI. Additionally, a 1:1 nearest neighbor propensity score matching (PSM) method was employed to minimize the impact of confounding factors. Before and after PSM, serum cotinine levels were higher in individuals with UI than those without (P < 0.05). Both before and after PSM, UI was positively correlated with serum cotinine levels, with a significantly increased risk of urinary incontinence when serum cotinine levels were in the Q3 range (before PSM: OR = 1.89, 95% CI = 1.59-2.24; after PSM: OR = 1.60, 95% CI = 1.28-2.00). Smooth curve fitting before and after PSM showed an approximate J-shaped non-linear dose-response relationship between log-transformed serum cotinine levels and UI. This study indicates that among American adults, there is a positive relationship between serum cotinine levels and UI, which is also significant in self-reported non-smoking populations. Therefore, reducing exposure to environmental tobacco smoke (e.g., avoiding second-hand smoke) in work and daily life may help alleviate the occurrence of UI, and serum cotinine levels have the potential to be a tool for predicting the degree of risk of developing UI.

RepurposeDrugs: an interactive web-portal and predictive platform for repurposing mono- and combination therapies.

RepurposeDrugs (https://repurposedrugs.org/) is a comprehensive web-portal that combines a unique drug indication database with a machine learning (ML) predictor to discover new drug-indication associations for approved as well as investigational mono and combination therapies. The platform provides detailed information on treatment status, disease indications and clinical trials across 25 indication categories, including neoplasms and cardiovascular conditions. The current version comprises 4314 compounds (approved, terminated or investigational) and 161 drug combinations linked to 1756 indications/conditions, totaling 28 148 drug-disease pairs. By leveraging data on both approved and failed indications, RepurposeDrugs provides ML-based predictions for the approval potential of new drug-disease indications, both for mono- and combinatorial therapies, demonstrating high predictive accuracy in cross-validation. The validity of the ML predictor is validated through a number of real-world case studies, demonstrating its predictive power to accurately identify repurposing candidates with a high likelihood of future approval. To our knowledge, RepurposeDrugs web-portal is the first integrative database and ML-based predictor for interactive exploration and prediction of both single-drug and combination approval likelihood across indications. Given its broad coverage of indication areas and therapeutic options, we expect it accelerates many future drug repurposing projects.

Cuarenta años: un canto a la transformación y la trayectoria de la Terapia Ocupacional / Forty years: An ode to the transformation and trajectory of Occupational Therapy / Quarenta anos: um canto à transformação e à trajetória da Terapia Ocupacional

La Revista Ocupación Humana ha sido un importante medio de divulgación del conocimiento en Terapia Ocupacional y en los estudios sobre ocupación humana. Su primera edición, emitida el 24 de octubre de 1984, significó un hito para Colombia y para Latinoamérica, al constituirse en referente para el ejercicio profesional y el desarrollo disciplinar. Inicialmente, se orientó a comprender las áreas del desempeño profesional, a socializar investigaciones, a promover la discusión y la proyección de la profesión en ciencia y tecnología, y a conocer y difundir la emergente participación en escenarios de incidencia comunitaria, social y política. Después de una pausa editorial de cinco años, en el año 2013, se retoma la publicación. Desde entonces, se ha trabajado en su fortalecimiento, visibilidad y apertura, proceso en el que se ha contado con la participación de colegas de Colombia y otros países hermanos. En este aniversario, el Comité Editorial desea que la revista siga consolidándose como medio fundamental para que las y los terapeutas ocupacionales sigan escuchándose, conociéndose, conectándose con los debates profesionales y fortaleciéndose como gremio. Por eso, celebra e invita a celebrar con un canto estos cuarenta años de retos y a dar la bienvenida a los que vendrán

The Revista Ocupación Humana has been an essential medium in disseminating knowledge in Occupational Therapy and studies on human occupation. Its first edition, released on 24 October 1984, marked a milestone for Colombia and Latin America, becoming a benchmark for professional practice and disciplinary development. Initially, it focused on understanding areas of professional performance, disseminating research, promoting discussion, and projecting the profession in science and technology, and understanding and spreading the emerging participation in community, social, and political-impact scenarios. After a five-year editorial break, in 2013, the publication resumed. Since then, work has been undertaken to strengthen its visibility and openness, with the participation of colleagues from Colombia and other sister countries. On this anniversary, the Editorial Board hopes that the magazine will continue to consolidate itself as a key means for occupational therapists to continue listening to each other, get to know each other, engage in professional debates, and strengthen themselves as a body. As such, it celebrates and invites you to celebrate these forty years of challenges and welcome those that will come.

A Revista Ocupación Humana tem sido um importante meio de divulgação do conhecimento em Terapia Ocupacional e em estudos sobre ocupação humana. Sua primeira edição, emitida em 24 de outubro de 1984, significou um marco para a Colômbia e para a América Latina, ao se constituir como referência para a prática profissional e o desenvolvimento disciplinar. Inicialmente, dedicou-se a compreender as áreas do desempenho profissional, a socializar pesquisas, a promover a discussão e a projeção da profissão em ciência e tecnologia, a conhecer e difundir a emergente participação em cenários de incidência comunitária, social e política. Após uma pausa editorial de cinco anos, em 2013, retomou-se a publicação. Desde então, tem-se trabalhado no seu fortalecimento, visibilidade e abertura, processo no qual se conta com a participação de colegas da Colômbia e de outros países irmãos. Neste aniversário, o Comitê Editorial deseja que a revista continue a se consolidar como meio fundamental para que as e os terapeutas ocupacionais continuem ouvindo-se, conhecendo-se, conectando-se com os debates profissionais e fortalecendo-se no coletivo. Por isso, celebra e convida a celebrar com um canto estes quarenta anos de desafios, bem como a dar as boas-vindas aos que virão.

NSAID medication mediates the causal effect of genetically predicted major depressive disorder on falls: Evidence from a Mendelian randomization study.

BACKGROUND: Increasing evidence supports that depression including major depressive disorder (MDD) is associated with an increased risk of falls. However, some studies suggest no association between MDD and falls. Therefore, the specific causal relationship whereby MDD affects the risk of falls remains elusive, and the potential mediators are unclear. METHODS: Summary-level data for MDD and falls were collected from the Genome-wide association studies (GWAS) in this study. Mendelian randomization (MR) and multivariable MR (MVMR) analyses were performed to evaluate the causal associations between MDD and falls. A Two-step MR analysis was employed to analyze the mediating effect of nonsteroidal anti-inflammatory drugs (NSAIDs) on the causal association between MDD and the risk of falls. RESULTS: Using the inverse-variance weighted (IVW) method, genetically predicted MDD was associated with an increased risk of falls (ß = 0.15, SE = 0.034; P = 1.61E-5). MVMR and two-step MR analyses demonstrated that MDD was a causal determinant of increased falls independent of body mass index (BMI), smoking initiation, and alcohol consumption and that this causal relationship was mediated by NSAID medication. LIMITATIONS: Extracted GWAS summary statistics are from European ancestry. Stratified analyses by sex and age were not included in our study. Therefore, it is unclear whether the results are the same for other ethnic groups, genders, and ages. CONCLUSIONS: Our results demonstrate that MDD is independently associated with an increased risk of falls, in which NSAIDs mediate the association. This study suggests that avoiding the use of NSAIDs may reduce the risk of falls in patients diagnosed with MDD.

PGCNMDA: Learning node representations along paths with graph convolutional network for predicting miRNA-disease associations.

Identifying miRNA-disease associations (MDAs) is crucial for improving the diagnosis and treatment of various diseases. However, biological experiments can be time-consuming and expensive. To overcome these challenges, computational approaches have been developed, with Graph Convolutional Network (GCN) showing promising results in MDA prediction. The success of GCN-based methods relies on learning a meaningful spatial operator to extract effective node feature representations. To enhance the inference of MDAs, we propose a novel method called PGCNMDA, which employs graph convolutional networks with a learning graph spatial operator from paths. This approach enables the generation of meaningful spatial convolutions from paths in GCN, leading to improved prediction performance. On HMDD v2.0, PGCNMDA obtains a mean AUC of 0.9229 and an AUPRC of 0.9206 under 5-fold cross-validation (5-CV), and a mean AUC of 0.9235 and an AUPRC of 0.9212 under 10-fold cross-validation (10-CV), respectively. Additionally, the AUC of PGCNMDA also reaches 0.9238 under global leave-one-out cross-validation (GLOOCV). On HMDD v3.2, PGCNMDA obtains a mean AUC of 0.9413 and an AUPRC of 0.9417 under 5-CV, and a mean AUC of 0.9419 and an AUPRC of 0.9425 under 10-CV, respectively. Furthermore, the AUC of PGCNMDA also reaches 0.9415 under GLOOCV. The results show that PGCNMDA is superior to other compared methods. In addition, the case studies on pancreatic neoplasms, thyroid neoplasms and leukemia show that 50, 50 and 48 of the top 50 predicted miRNAs linked to these diseases are confirmed, respectively. It further validates the effectiveness and feasibility of PGCNMDA in practical applications.

HRGCNLDA: Forecasting of lncRNA-disease association based on hierarchical refinement graph convolutional neural network.

Long non-coding RNA (lncRNA) is considered to be a crucial regulator involved in various human biological processes, including the regulation of tumor immune checkpoint proteins. It has great potential as both a cancer biomolecular biomarker and therapeutic target. Nevertheless, conventional biological experimental techniques are both resource-intensive and laborious, making it essential to develop an accurate and efficient computational method to facilitate the discovery of potential links between lncRNAs and diseases. In this study, we proposed HRGCNLDA, a computational approach utilizing hierarchical refinement of graph convolutional neural networks for forecasting lncRNA-disease potential associations. This approach effectively addresses the over-smoothing problem that arises from stacking multiple layers of graph convolutional neural networks. Specifically, HRGCNLDA enhances the layer representation during message propagation and node updates, thereby amplifying the contribution of hidden layers that resemble the ego layer while reducing discrepancies. The results of the experiments showed that HRGCNLDA achieved the highest AUC-ROC (area under the receiver operating characteristic curve, AUC for short) and AUC-PR (area under the precision versus recall curve, AUPR for short) values compared to other methods. Finally, to further demonstrate the reliability and efficacy of our approach, we performed case studies on the case of three prevalent human diseases, namely, breast cancer, lung cancer and gastric cancer.

Associations with other cancer-related biomarkers might contribute to poor outcomes in RAS-altered, younger patients with colorectal cancer.

Colorectal cancer (CRC) is a common cancer in younger adults. In patients undergoing liver resection with RAS-altered CRCs, there is evidence suggesting younger patients have worse outcomes than older patients. To explain this pattern, differences in associations between RAS status and other cancer-related biomarkers in tumors from younger versus older patients with CRC were evaluated in a cohort of 925 patients with CRC, 277 (30.0%) of whom were ≤50 years old, and 454 (49.1%) who had RAS-altered tumors. For 3 biomarkers, RNF43, APC, and microsatellite instability (MSI), the association with RAS status was significantly modified by age after adjustment for multiple testing. Specifically, younger patients with RAS-altered tumors were more likely to be MSI-high, RNF43 mutated, and APC wild type. These differences might contribute to the observed pattern of diminished survival in younger versus older patients with CRC with RAS-mutated tumors undergoing liver metastasis resection.

EMCMDA: predicting miRNA-disease associations via efficient matrix completion.

Abundant researches have consistently illustrated the crucial role of microRNAs (miRNAs) in a wide array of essential biological processes. Furthermore, miRNAs have been validated as promising therapeutic targets for addressing complex diseases. Given the costly and time-consuming nature of traditional biological experimental validation methods, it is imperative to develop computational methods. In the work, we developed a novel approach named efficient matrix completion (EMCMDA) for predicting miRNA-disease associations. First, we calculated the similarities across multiple sources for miRNA/disease pairs and combined this information to create a holistic miRNA/disease similarity measure. Second, we utilized this biological information to create a heterogeneous network and established a target matrix derived from this network. Lastly, we framed the miRNA-disease association prediction issue as a low-rank matrix-complete issue that was addressed via minimizing matrix truncated schatten p-norm. Notably, we improved the conventional singular value contraction algorithm through using a weighted singular value contraction technique. This technique dynamically adjusts the degree of contraction based on the significance of each singular value, ensuring that the physical meaning of these singular values is fully considered. We evaluated the performance of EMCMDA by applying two distinct cross-validation experiments on two diverse databases, and the outcomes were statistically significant. In addition, we executed comprehensive case studies on two prevalent human diseases, namely lung cancer and breast cancer. Following prediction and multiple validations, it was evident that EMCMDA proficiently forecasts previously undisclosed disease-related miRNAs. These results underscore the robustness and efficacy of EMCMDA in miRNA-disease association prediction.