Revealing the characteristics of SETD2-mutated clear cell renal cell carcinoma through tumor heterogeneity analysis

Background

Renal cell carcinoma (RCC) is the most prevalent type of malignant kidney tumor in adults, with clear cell renal cell carcinoma (ccRCC) comprising about 75% of all cases. The SETD2 gene, which is involved in the modification of histone proteins, is often found to have alterations in ccRCC. Yet, our understanding of how these SETD2 mutations affect ccRCC characteristics and behavior within the tumor microenvironment is still not fully understood.

Methods

We conducted a detailed analysis of single-cell RNA sequencing (scRNA-seq) data from ccRCC. First, the data was preprocessed using the Python package, "scanpy." High variability genes were pinpointed through Pearson's correlation coefficient. Dimensionality reduction and clustering identification were performed using Principal Component Analysis (PCA) and the Leiden algorithm. Malignant cell identification was conducted with the "InferCNV" R package, while cell trajectories and intercellular communication were depicted using the Python packages "VIA" and "cellphoneDB." We then employed the R package "Deseq2" to determine differentially expressed genes (DEGs) between groups. Using high-dimensional weighted gene correlation network analysis (hdWGCNA), co-expression modules were identified. We intersected these modules with DEGs to establish prognostic models through univariate Cox and the least absolute shrinkage and selection operator (LASSO) method.

Results

We identified 69 and 53 distinctive cell clusters, respectively. These were classified further into 12 unique cell types. This analysis highlighted the presence of an abnormal tumor sub-cluster (MT + group), identified by high mitochondrial-encoded protein gene expression and an indication of unfavorable prognosis. Investigation of cellular interactions spotlighted significant interactions between the MT + group and endothelial cells, macrophaes. In addition, we developed a prognostic model based on six characteristic genes. Notably, risk scores derived from these genes correlated significantly with various clinical features. Finally, a nomogram model was established to facilitate more accurate outcome prediction, incorporating four independent risk factors.

Conclusion

Our findings provide insight into the crucial transcriptomic characteristics of ccRCC associated with SETD2 mutation. We discovered that this mutation-induced subcluster could stimulate M2 polarization in macrophages, suggesting a heightened propensity for metastasis. Moreover, our prognostic model demonstrated effectiveness in forecasting overall survival for ccRCC patients, thus presenting a valuable clinical tool.

survivalContour: visualizing predicted survival via colored contour plots

Summary

Advances in survival analysis have facilitated unprecedented flexibility in data modeling, yet there remains a lack of tools for illustrating the influence of continuous covariates on predicted survival outcomes. We propose the utilization of a colored contour plot to depict the predicted survival probabilities over time. Our approach is capable of supporting conventional models, including the Cox and Fine-Gray models. However, its capability shines when coupled with cutting-edge machine learning models such as random survival forests and deep neural networks.

Availability and implementation

We provide a Shiny app at https://biostatistics.mdanderson.org/shinyapps/survivalContour/ and an R package available at https://github.com/YushuShi/survivalContour as implementations of this tool.

Multiple pathogens co-exposure and associated risk factors among cattle reared in a wildlife-livestock interface area in Kenya

Introduction

Understanding multi-pathogen infections/exposures in livestock is critical to inform prevention and control measures against infectious diseases. We investigated the co-exposure of foot-and-mouth disease virus (FMDV), Brucella spp., Leptospira spp., and Coxiella burnetii in cattle in three zones stratified by land use change and with different wildlife-livestock interactions in Narok county, Kenya. We also assessed potential risk factors associated with the transmission of these pathogens in cattle.

Methods

We identified five villages purposively, two each for areas with intensive (zone 1) and moderate wildlife-livestock interactions (zone 2) and one for locations with low wildlife-livestock interactions (zone 3). We sampled 1,170 cattle from 390 herds through a cross-sectional study and tested the serum samples for antibodies against the focal pathogens using enzyme-linked immunosorbent assay (ELISA) kits. A questionnaire was administered to gather epidemiological data on the putative risk factors associated with cattle's exposure to the investigated pathogens. Data were analyzed using the Bayesian hierarchical models with herd number as a random effect to adjust for the within-herd clustering of the various co-exposures among cattle.

Results

Overall, 88.0% (95% CI: 85.0-90.5) of the cattle tested positive for at least one of the targeted pathogens, while 41.7% (95% CI: 37.7-45.8) were seropositive to at least two pathogens. FMDV and Brucella spp. had the highest co-exposure at 33.7% (95% CI: 30.9-36.5), followed by FMDV and Leptospira spp. (21.8%, 95% CI: 19.5-24.4), Leptospira spp. and Brucella spp. (8.8%, 95% CI: 7.2-10.6), FMDV and C. burnetii (1.5%, 95% CI: 0.7-2.8), Brucella spp. and C. burnetii (1.0%, 95% CI: 0.3-2.2), and lowest for Leptospira spp. and C. burnetii (0.3%, 95% CI: 0.0-1.2). Cattle with FMDV and Brucella spp., and Brucella spp. and Leptospira spp. co-exposures and those simultaneously exposed to FMDV, Brucella spp. and Leptospira spp. were significantly higher in zone 1 than in zones 2 and 3. However, FMDV and Leptospira spp. co-exposure was higher in zones 1 and 2 than zone 3.

Discussion/conclusion

We recommend the establishment of a One Health surveillance system in the study area to reduce the morbidity of the targeted zoonotic pathogens in cattle and the risks of transmission to humans.

Incorporating informatively collected laboratory data from EHR in clinical prediction models

BACKGROUND

Electronic Health Records (EHR) are widely used to develop clinical prediction models (CPMs). However, one of the challenges is that there is often a degree of informative missing data. For example, laboratory measures are typically taken when a clinician is concerned that there is a need. When data are the so-called Not Missing at Random (NMAR), analytic strategies based on other missingness mechanisms are inappropriate. In this work, we seek to compare the impact of different strategies for handling missing data on CPMs performance.

METHODS

We considered a predictive model for rapid inpatient deterioration as an exemplar implementation. This model incorporated twelve laboratory measures with varying levels of missingness. Five labs had missingness rate levels around 50%, and the other seven had missingness levels around 90%. We included them based on the belief that their missingness status can be highly informational for the prediction. In our study, we explicitly compared the various missing data strategies: mean imputation, normal-value imputation, conditional imputation, categorical encoding, and missingness embeddings. Some of these were also combined with the last observation carried forward (LOCF). We implemented logistic LASSO regression, multilayer perceptron (MLP), and long short-term memory (LSTM) models as the downstream classifiers. We compared the AUROC of testing data and used bootstrapping to construct 95% confidence intervals.

RESULTS

We had 105,198 inpatient encounters, with 4.7% having experienced the deterioration outcome of interest. LSTM models generally outperformed other cross-sectional models, where embedding approaches and categorical encoding yielded the best results. For the cross-sectional models, normal-value imputation with LOCF generated the best results.

CONCLUSION

Strategies that accounted for the possibility of NMAR missing data yielded better model performance than those did not. The embedding method had an advantage as it did not require prior clinical knowledge. Using LOCF could enhance the performance of cross-sectional models but have countereffects in LSTM models.

Unraveling Crucial Mitochondria-Related Genes in the Transition from Ulcerative Colitis to Colorectal Cancer

Purpose

To clarify the significance of mitochondria-related differentially expressed genes (MTDEGs) in UC carcinogenesis through a bioinformatics analysis and provide potential therapeutic targets for patients with UC associated colorectal cancer.

Methods

Microarray GSE37283 was utilized to investigate differentially expressed genes (DEGs) in UC and UC with neoplasia (UCN). MTDEGs were identified by intersecting DEGs with human mitochondrial genes. Utilizing LASSO and random forest analyses, we identified three crucial genes. Subsequently, using ROC curve to investigate the predictive ability of three key genes. Following, three key genes were confirmed in AOM/DSS mice model by Real-time PCR. Finally, single-sample gene set enrichment analysis (ssGSEA) was employed to explore the correlation between the hub genes and immune cells infiltration in UC carcinogenesis.

Results

The three identified hub MTDEGs (HMGCS2, MAVS, RDH13) may exhibit significant diagnostic specificity in the transition from UC to UCN. Real-time PCR assay further confirmed that the expressions of HMGCS2 and RDH13 were significantly downregulated in UCN mice than that in UC mice. ssGSEA analysis revealed the hub genes were highly associated with CD56dim natural killer cells.

Conclusion

RDH13, HMGCS2, and MAVS may become diagnostic indicators and potential biomarkers for UCN. Our research has the potential to enhance our understanding of the mechanisms underlying carcinogenesis in UC.

Characterize molecular signatures and establish a prognostic signature of gastric cancer by integrating single-cell RNA sequencing and bulk RNA sequencing

Gastric cancer is a significant global health concern with complex molecular underpinnings influencing disease progression and patient outcomes. Various molecular drivers were reported, and these studies offered potential avenues for targeted therapies, biomarker discovery, and the development of precision medicine strategies. However, it was posed that the heterogeneity of the disease and the complexity of the molecular interactions are still challenging. By seamlessly integrating data from single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq), we embarked on characterizing molecular signatures and establishing a prognostic signature for this complex malignancy. We offered a holistic view of gene expression landscapes in gastric cancer, identified 226 candidate marker genes from 3 different dimensions, and unraveled key players' risk stratification and treatment decision-making. The convergence of molecular insights in gastric cancer progression occurs at multiple biological scales simultaneously. The focal point of this study lies in developing a prognostic model, and we amalgamated four molecular signatures (COL4A1, FKBP10, RNASE1, SNCG) and three clinical parameters using advanced machine-learning techniques. The model showed high predictive accuracy, with the potential to revolutionize patient care by using clinical variables. This will strengthen the reliability of the model and enable personalized therapeutic strategies based on each patient's unique molecular profile. In summary, our research sheds light on the molecular underpinnings of gastric cancer, culminating in a powerful prognostic tool for gastric cancer. With a firm foundation in biological insights and clinical implications, our study paves the way for future validations and underscores the potential of integrated molecular analysis in advancing precision oncology.

Factors predicting long-term outcomes following physiotherapy in patients with subacromial pain syndrome: a secondary analysis

BACKGROUND

Although patients with shoulder complaints are frequently referred to physiotherapy, putative predictive factors for outcomes are still unclear. In this regard, only a limited amount of scientific data for patients with subacromial pain syndrome exist, with inconsistent results. An improved knowledge about the ability of baseline variables to predict outcomes could help patients make informed treatment decisions, prevent them from receiving ineffective treatments, and minimize the risk of developing chronic pain.

AIM

The aims of this secondary longitudinal analysis are threefold: First, to investigate baseline differences between patients with and without successful long-term outcomes following physiotherapy. Second, to compare the predictive ability of two sets of putative predictive variables on outcomes, one based on the literature and one based on the data of the original trial. Third, to explore the contribution of short-term follow-up data to predictive models.

METHODS

Differences between responders and nonresponders were calculated. The predictive ability of variables defined through literature and of variables based on the Akaike Information Criterion (AIC) from the original trial dataset on the Shoulder Pain and Disability Index and the Patients' Global Impression of Change at the one-year follow-up were analyzed. To test the robustness of the results, different statistical models were used. To investigate the contribution of follow-up data to prediction, short-term data were included in the analyses.

RESULTS

A sample of 87 patients with subacromial pain syndrome was analyzed. 77% (n = 67) of these participants were classified as responders. Higher expectations and short-term change scores were positive, and higher fear avoidance beliefs, greater baseline disability and pain levels were negative predictors of long-term outcomes in patients with subacromial pain syndrome.

CONCLUSIONS

Although our results are in line with previous research and support the use of clinical factors for prediction, our findings suggest that psychological factors, especially patient expectations and fear avoidance beliefs, also contribute to long-term outcomes and should therefore be considered in the clinical context and further research. However, the hypotheses and recommendations generated from our results need to be confirmed in further studies due to their explorative nature.

TRIAL REGISTRATION

The original trial was registered at Current Controlled Trials under the trial registration number ISRCTN86900354 on March 17, 2010.

A Fast Survival Support Vector Regression Approach to Large Scale Credit Scoring via Safe Screening

Survival models have found wider and wider applications in credit scoring recently due to their ability to estimate the dynamics of risk over time. In this research, we propose a Buckley-James safe sample screening support vector regression (BJS4VR) algorithm to model large-scale survival data by combing the Buckley-James transformation and support vector regression. Different from previous support vector regression survival models, censored samples here are imputed using a censoring unbiased Buckley-James estimator. Safe sample screening is then applied to discard samples that guaranteed to be non-active at the final optimal solution from the original data to improve efficiency. Experimental results on the large-scale real lending club loan data have shown that the proposed BJS4VR model outperforms existing popular survival models such as RSFM, CoxRidge and CoxBoost in terms of both prediction accuracy and time efficiency. Important variables highly correlated with credit risk are also identified with the proposed method.

Co-Expression Network Analysis and Molecular Docking Demonstrate That Diosgenin Inhibits Gastric Cancer Progression via SLC1A5/mTORC1 Pathway

Background

Tumor-Node-Metastasis (TNM) stage of gastric cancer (GC) is one of the main factors affecting clinical outcome. The aim of this study was to explore the targets related to TNM stage of GC, and screening natural bioactive drug.

Methods

RNA sequencing data of the TCGA-STAD cohort were downloaded from UCSC database. Genes associated with TNM staging were identified by weighted gene co-expression network analysis (WGCNA). Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (Xgboost), random forest (RF) and cytohubba plug-in of cytoscope were applied to screen hub genes. Natural bioactive ingredients were available from the HERB database. Molecular docking was used to evaluate the binding activity of active ingredients to the hub protein. CCK-8, flow cytometry, transwell and Western blot assays were used to analyze the effects of diosgenin on GC cells.

Results

898 TNM-related genes were screened out through WGCNA. Three genes associated with GC progression/prognosis were identified, including nuclear receptor subfamily 3 group C member 2 (NR3C2), solute carrier family 1 member 5 (SLC1A5) and FAT atypical cadherin 1 (FAT1) based on the machine learning algorithms and hub co-expression network analysis. Diosgenin had good binding activity with SLC1A5. SLC1A5 was highly expressed in GC and was closely associated with tumor stage, overall survival and immune infiltration of GC patients. Diosgenin could inhibit cell viability and invasive ability, promote apoptosis and induce cell cycle arrest in G0/G1 phase. In addition, diosgenin promoted cleaved caspase 3 expression and inhibited Ki67, cyclin D1, p-S6K1, and SLC1A5 expression levels, while the mTORC1 activator (MHY1485) reversed this phenomenon.

Conclusion

For the first time, this work reports diosgenin may inhibit the activation of mTORC1 signaling through targeting SLC1A5, thereby inhibiting the malignant behaviors of GC cells.

predicTTE: An accessible and optimal tool for time-to-event prediction in neurological diseases

Time-to-event prediction is a key task for biological discovery, experimental medicine, and clinical care. This is particularly true for neurological diseases where development of reliable biomarkers is often limited by difficulty visualising and sampling relevant cell and molecular pathobiology. To date, much work has relied on Cox regression because of ease-of-use, despite evidence that this model includes incorrect assumptions. We have implemented a set of deep learning and spline models for time-to-event modelling within a fully customizable 'app' and accompanying online portal, both of which can be used for any time-to-event analysis in any disease by a non-expert user. Our online portal includes capacity for end-users including patients, Neurology clinicians, and researchers, to access and perform predictions using a trained model, and to contribute new data for model improvement, all within a data-secure environment. We demonstrate a pipeline for use of our app with three use-cases including imputation of missing data, hyperparameter tuning, model training and independent validation. We show that predictions are optimal for use in downstream applications such as genetic discovery, biomarker interpretation, and personalised choice of medication. We demonstrate the efficiency of an ensemble configuration, including focused training of a deep learning model. We have optimised a pipeline for imputation of missing data in combination with time-to-event prediction models. Overall, we provide a powerful and accessible tool to develop, access and share time-to-event prediction models; all software and tutorials are available at www.predictte.org.

Transoceanic pathogen transfer in the age of sail and steam

In the centuries following Christopher Columbus's 1492 voyage to the Americas, transoceanic travel opened unprecedented pathways in global pathogen circulation. Yet no biological transfer is a single, discrete event. We use mathematical modeling to quantify historical risk of shipborne pathogen introduction, exploring the respective contributions of journey time, ship size, population susceptibility, transmission intensity, density dependence, and pathogen biology. We contextualize our results using port arrivals data from San Francisco, 1850 to 1852, and from a selection of historically significant voyages, 1492 to 1918. We offer numerical estimates of introduction risk across historically realistic ranges of journey time and ship population size, and show that both steam travel and shipping regimes that involved frequent, large-scale movement of people substantially increased risk of transoceanic pathogen circulation.

Examining physical and virtual sexual and drug risk venue co-attendance among PrEP-eligible Latinx sexual minority men

Disparities in Latinx sexual minority men (LSMM) pre-exposure prophylaxis (PrEP) initiation and adherence demonstrate the need for innovative strategies to improve outcomes along the PrEP Care Continuum. The purpose of this study was to examine physical and virtual risk venue co-attendance among LSMM of varying stages of PrEP use living in South Florida, an Ending the HIV Epidemic (EHE) priority jurisdiction. Venue co-attendance may be leveraged to support equitable dissemination and implementation (D&I) of HIV and drug use preventive interventions to LSMM. Ninety-four LSMM completed surveys on PrEP use and identified physical and virtual sexual and drug risk venues (where men seek sex partners and/or have sex or access or use drugs). Bivariate analyses examined differences in demographics and venue endorsement by PrEP status. Exponential random graph models examined endorsement of venues by PrEP status. One-mode projection visualizations portray the structure of venue co-attendance networks by PrEP status. Participants identified 49 physical and virtual sexual and drug risk venues. A homophily effect of PrEP status on venue endorsement was identified for both sexual and drug risk venues. Network size and density differed by PrEP status and whether the venue was identified as a sexual or drug risk location. Online dating apps, bars, and saunas had the highest centrality in each network type suggesting their potential role in implementing preventive programming. This study identified specific sexual and drug risk venues that may be leveraged to both disseminate targeted PrEP and harm reduction information and to implement interventions to improve PrEP uptake and adherence among LSMM living in EHE priority jurisdictions.

Anatomical and behavioral correlates of auditory perception in developmental dyslexia

Developmental dyslexia is typically associated with difficulties in basic auditory processing and in manipulating speech sounds. However, the neuroanatomical correlates of auditory difficulties in developmental dyslexia (DD) and their contribution to individual clinical phenotypes are still unknown. Recent intracranial electrocorticography findings associated processing of sound amplitude rises and speech sounds with posterior and middle superior temporal gyrus (STG), respectively. We hypothesize that regional STG anatomy will relate to specific auditory abilities in DD, and that auditory processing abilities will relate to behavioral difficulties with speech and reading. One hundred and ten children (78 DD, 32 typically developing, age 7-15 years) completed amplitude rise time and speech in noise discrimination tasks. They also underwent a battery of cognitive tests. Anatomical MRI scans were used to identify regions in which local cortical gyrification complexity correlated with auditory behavior. Behaviorally, amplitude rise time but not speech in noise performance was impaired in DD. Neurally, amplitude rise time and speech in noise performance correlated with gyrification in posterior and middle STG, respectively. Furthermore, amplitude rise time significantly contributed to reading impairments in DD, while speech in noise only explained variance in phonological awareness. Finally, amplitude rise time and speech in noise performance were not correlated, and each task was correlated with distinct neuropsychological measures, emphasizing their unique contributions to DD. Overall, we provide a direct link between the neurodevelopment of the left STG and individual variability in auditory processing abilities in neurotypical and dyslexic populations.

Cognitive rejuvenation in old rats by hippocampal OSKM gene therapy

Several studies have indicated that interrupted epigenetic reprogramming using Yamanaka transcription factors (OSKM) can rejuvenate cells from old laboratory animals and humans. However, the potential of OSKM-induced rejuvenation in brain tissue has been less explored. Here, we aimed to restore cognitive performance in 25.3-month-old female Sprague-Dawley rats using OSKM gene therapy for 39 days. Their progress was then compared with the cognitive performance of untreated 3.5-month-old rats as well as old control rats treated with a placebo adenovector. The Barnes maze test, used to assess cognitive performance, demonstrated enhanced cognitive abilities in old rats treated with OSKM compared to old control animals. In the treated old rats, there was a noticeable trend towards improved spatial memory relative to the old controls. Further, OSKM gene expression did not lead to any pathological alterations within the 39 days. Analysis of DNA methylation following OSKM treatment yielded three insights. First, epigenetic clocks for rats suggested a marginally significant epigenetic rejuvenation. Second, chromatin state analysis revealed that OSKM treatment rejuvenated the methylome of the hippocampus. Third, an epigenome-wide association analysis indicated that OSKM expression in the hippocampus of old rats partially reversed the age-related increase in methylation. In summary, the administration of Yamanaka genes via viral vectors rejuvenates the functional capabilities and the epigenetic landscape of the rat hippocampus.

MIDASim: a fast and simple simulator for realistic microbiome data

BACKGROUND

Advances in sequencing technology has led to the discovery of associations between the human microbiota and many diseases, conditions, and traits. With the increasing availability of microbiome data, many statistical methods have been developed for studying these associations. The growing number of newly developed methods highlights the need for simple, rapid, and reliable methods to simulate realistic microbiome data, which is essential for validating and evaluating the performance of these methods. However, generating realistic microbiome data is challenging due to the complex nature of microbiome data, which feature correlation between taxa, sparsity, overdispersion, and compositionality. Current methods for simulating microbiome data are deficient in their ability to capture these important features of microbiome data, or can require exorbitant computational time.

METHODS

We develop MIDASim (MIcrobiome DAta Simulator), a fast and simple approach for simulating realistic microbiome data that reproduces the distributional and correlation structure of a template microbiome dataset. MIDASim is a two-step approach. The first step generates correlated binary indicators that represent the presence-absence status of all taxa, and the second step generates relative abundances and counts for the taxa that are considered to be present in step 1, utilizing a Gaussian copula to account for the taxon-taxon correlations. In the second step, MIDASim can operate in both a nonparametric and parametric mode. In the nonparametric mode, the Gaussian copula uses the empirical distribution of relative abundances for the marginal distributions. In the parametric mode, a generalized gamma distribution is used in place of the empirical distribution.

RESULTS

We demonstrate improved performance of MIDASim relative to other existing methods using gut and vaginal data. MIDASim showed superior performance by PERMANOVA and in terms of alpha diversity and beta dispersion in either parametric or nonparametric mode. We also show how MIDASim in parametric mode can be used to assess the performance of methods for finding differentially abundant taxa in a compositional model.

CONCLUSIONS

MIDASim is easy to implement, flexible and suitable for most microbiome data simulation situations. MIDASim has three major advantages. First, MIDASim performs better in reproducing the distributional features of real data compared to other methods, at both the presence-absence level and the relative-abundance level. MIDASim-simulated data are more similar to the template data than competing methods, as quantified using a variety of measures. Second, MIDASim makes few distributional assumptions for the relative abundances, and thus can easily accommodate complex distributional features in real data. Third, MIDASim is computationally efficient and can be used to simulate large microbiome datasets. Video Abstract.

Research on molecular characteristics of ADME-related genes in kidney renal clear cell carcinoma

Genes involved in drug absorption, distribution, metabolism, and excretion (ADME) are named ADME genes. However, the comprehensive role of ADME genes in kidney renal clear cell carcinoma (KIRC) remains unclear. Using the clinical and gene expression data of KIRC patients downloaded from The Cancer Genome Atlas (TCGA), ArrayExpress, and the Gene Expression Omnibus (GEO) databases, we cluster patients into two patterns, and the population with a relatively poor prognosis demonstrated higher level of immunosuppressive cell infiltration and higher proportion of glycolytic subtypes. Then, 17 ADME genes combination identified through the least absolute shrinkage and selection operator algorithm (LASSO, 1000 times) was utilized to calculate the ADME score. The ADME score was found to be an independent predictor of prognosis in KIRC and to be tightly associated with the infiltration level of immune cells, metabolic properties, tumor-related signaling pathways, genetic variation, and responses to chemotherapeutics. Our work revealed the characteristics of ADME in KIRC. Assessing the ADME profiles of individual patients can deepen our comprehension of tumor microenvironment (TME) features in KIRC and can aid in developing more personalized and effective therapeutic strategies.

A longitudinal examination of appraisal, coping, stress, and mental health in students: A cross-lagged panel network analysis

In their seminal Transactional Theory of Stress, Lazarus and Folkman described a complex adaptation mechanism that involves appraisal and coping processes that lead to the subjective experience of stress. In this study, we examined the temporal order of all components of this theory using a cross-lagged panel analysis. A sample of 354 students (62.10% female, mean age = 20.63 y.o.) participated in a 5-week study and completed weekly self-reported measures of prospective appraisal, coping, core burnout, and mental health (i.e. depression, anxiety, and stress). Using cross-lagged panel network analyses, we analyzed how each measured variable is predicted by all variables from the previous measurement moment. Convergent with the transactional stress theory, we found that threat appraisals and negative self-perception predicted future anxiety and depression. Contrary to expectations, core burnout and somatization emerged as significant predictors of poor mental health outcomes, suggesting a self-perpetuating spiral. Furthermore, our study highlighted the importance of recognizing the bidirectional connections between appraisals, coping strategies, and mental well-being.

Desensitizing Anxiety Through Imperceptible Change: Feasibility Study on a Paradigm for Single-Session Exposure Therapy for Fear of Public Speaking

BACKGROUND

Exposure therapy (ET) for anxiety disorders involves introducing the participant to an anxiety-provoking situation over several treatment sessions. Each time, the participant is exposed to a higher anxiety-provoking stimulus; for example, in the case of fear of heights, the participant would successively experience being at a greater height. ET is effective, and its counterpart, virtual reality (VR) exposure therapy (VRET), where VR substitutes real-world exposure, is equally so. However, ET is time-consuming, requiring several sessions.

OBJECTIVE

This study aimed to compare the results of single-session exposure with those of traditional VRET with regard to reducing public speaking anxiety.

METHODS

We introduced a paradigm concerned with public speaking anxiety where the VR exposure occurred in a single session while the participant interacted with a virtual therapist. Over time, the therapist transformed into an entire audience with almost imperceptible changes. We carried out a feasibility study with 45 participants, comparing 3 conditions: single-session exposure (n=16, 36%); conventional multiple-session exposure (n=14, 31%), where the same content was delivered in successive segments over 5 sessions; and a control group (n=15, 33%), who interacted with a single virtual character to talk about everyday matters. A week later, the participants were required to speak on a stage in front of a large audience in VR.

RESULTS

Across most of the series of conventional public speaking anxiety measures, the single-session exposure was at least as effective in reducing anxiety as the multiple-session exposure, and both these conditions were better than the control condition. The 12-item Personal Report of Confidence as a Speaker was used to measure public speaking anxiety levels, where higher values indicated more anxiety. Using a Bayesian model, the posterior probabilities of improvement compared to a high baseline were at least 1.7 times greater for single- and multiple-session exposures compared to the control group. The State Perceived Index of Competence was used as a measure of anticipatory anxiety for speaking on a stage in front of a large audience, where lower values indicated higher anxiety. The probabilities of improvement were just over 4 times greater for single- and multiple-session exposures compared to the control group for a low baseline and 489 (single) and 53 (multiple) times greater for a middle baseline.

CONCLUSIONS

Overall, the results of this feasibility study show that for moderate public speaking anxiety, the paradigm of gradual change in a single session is worth following up with further studies with more severe levels of anxiety and a larger sample size, first with a randomized controlled trial with nonpatients and subsequently, if the outcomes follow those that we have found, with a full clinical trial with patients.

DNA methylation clocks for estimating biological age in Chinese cohorts

Epigenetic clocks are accurate predictors of human chronological age based on the analysis of DNA methylation (DNAm) at specific CpG sites. However, a systematic comparison between DNA methylation data and other omics datasets has not yet been performed. Moreover, available DNAm age predictors are based on datasets with limited ethnic representation. To address these knowledge gaps, we generated and analyzed DNA methylation datasets from two independent Chinese cohorts, revealing age-related DNAm changes. Additionally, a DNA methylation aging clock (iCAS-DNAmAge) and a group of DNAm-based multi-modal clocks for Chinese individuals were developed, with most of them demonstrating strong predictive capabilities for chronological age. The clocks were further employed to predict factors influencing aging rates. The DNAm aging clock, derived from multi-modal aging features (compositeAge-DNAmAge), exhibited a close association with multi-omics changes, lifestyles, and disease status, underscoring its robust potential for precise biological age assessment. Our findings offer novel insights into the regulatory mechanism of age-related DNAm changes and extend the application of the DNAm clock for measuring biological age and aging pace, providing the basis for evaluating aging intervention strategies.

Development of a generalized pseudo-probabilistic approach for characterizing ecological conditions in estuaries using secondary data

Under the best circumstances, achieving or sustaining optimum ecological conditions in estuaries is challenging. Persistent information gaps in estuarine data make it difficult to differentiate natural variability from potential regime shifts. Long-term monitoring is critical for tracking ecological change over time. In the United States (US), many resource management programs are working at maximum capacity to address existing state and federal water quality mandates (e.g., pollutant load limits, climate impact mitigation, and fisheries management) and have little room to expand routine sampling efforts to conduct periodic ecological baseline assessments, especially at state and local scales. Alternative design, monitoring, and assessment approaches are needed to help offset the burden of addressing additional data needs to increase understanding about estuarine system resilience when existing monitoring data are sparse or spatially limited. Research presented here offers a pseudo-probabilistic approach that allows for the use of found or secondary data, such as data on hand and other acquired data, to generate statistically robust characterizations of ecological conditions in estuaries. Our approach uses a generalized pseudo-probabilistic framework to synthesize data from different contributors to inform probabilistic-like baseline assessments. The methodology relies on simple geospatial techniques and existing tools (R package functions) developed for the US Environmental Protection Agency to support ecological monitoring and assessment programs like the National Coastal Condition Assessment. Using secondary estuarine water quality data collected in the Northwest Florida (US) estuaries, demonstrations suggest that the pseudo-probabilistic approach produces estuarine condition assessment results with reasonable statistical confidence, improved spatial representativeness, and value-added information. While the pseudo-probabilistic framework is not a substitute for fully evolved monitoring, it offers a scalable alternative to bridge the gap between limitations in resource management capability and optimal monitoring strategies to track ecological baselines in estuaries over time.

Explainable AI-prioritized plasma and fecal metabolites in inflammatory bowel disease and their dietary associations

Fecal metabolites effectively discriminate inflammatory bowel disease (IBD) and show differential associations with diet. Metabolomics and AI-based models, including explainable AI (XAI), play crucial roles in understanding IBD. Using datasets from the UK Biobank and the Human Microbiome Project Phase II IBD Multi'omics Database (HMP2 IBDMDB), this study uses multiple machine learning (ML) classifiers and Shapley additive explanations (SHAP)-based XAI to prioritize plasma and fecal metabolites and analyze their diet correlations. Key findings include the identification of discriminative metabolites like glycoprotein acetyl and albumin in plasma, as well as nicotinic acid metabolites andurobilin in feces. Fecal metabolites provided a more robust disease predictor model (AUC [95%]: 0.93 [0.87-0.99]) compared to plasma metabolites (AUC [95%]: 0.74 [0.69-0.79]), with stronger and more group-differential diet-metabolite associations in feces. The study validates known metabolite associations and highlights the impact of IBD on the interplay between gut microbial metabolites and diet.

Emotion brain network topology in healthy subjects following passive listening to different auditory stimuli

A large body of research establishes the efficacy of musical intervention in many aspects of physical, cognitive, communication, social, and emotional rehabilitation. However, the underlying neural mechanisms for musical therapy remain elusive. This study aimed to investigate the potential neural correlates of musical therapy, focusing on the changes in the topology of emotion brain network. To this end, a Bayesian statistical approach and a cross-over experimental design were employed together with two resting-state magnetoencephalography (MEG) as controls. MEG recordings of 30 healthy subjects were acquired while listening to five auditory stimuli in random order. Two resting-state MEG recordings of each subject were obtained, one prior to the first stimulus (pre) and one after the final stimulus (post). Time series at the level of brain regions were estimated using depth-weighted minimum norm estimation (wMNE) source reconstruction method and the functional connectivity between these regions were computed. The resultant connectivity matrices were used to derive two topological network measures: transitivity and global efficiency which are important in gauging the functional segregation and integration of brain network respectively. The differences in these measures between pre- and post-stimuli resting MEG were set as the equivalence regions. We found that the network measures under all auditory stimuli were equivalent to the resting state network measures in all frequency bands, indicating that the topology of the functional brain network associated with emotional regulation in healthy subjects remains unchanged following these auditory stimuli. This suggests that changes in the emotion network topology may not be the underlying neural mechanism of musical therapy. Nonetheless, further studies are required to explore the neural mechanisms of musical interventions especially in the populations with neuropsychiatric disorders.

Natural grazing by horses and cattle promotes bird diversity in a restored European alluvial grassland

Context

A challenge in grassland conservation is to maintain both the openness and the heterogeneity of the habitat to support the diversity of their animal communities, including birds-a taxon that is known to be sensitive to disturbance. An increasingly used management tool in European grassland conservation, especially in rewilding projects, is grazing by large herbivores such as horses and cattle. These grazers are believed to create and maintain patchy landscapes that promote diversity and richness of other species, but their influence on birds is often debated by conservationists, who raise concerns about the impact of disturbance by the grazers.

Objectives

Our aim was to examine the relationship between the abundance and species richness of birds across four foraging guilds and the area utilization patterns of Highland cattle and Konik horses in an alluvial grassland in France. We also aimed to examine the influence of land cover and season on the spatial distribution, including abundance and species richness, of different bird guilds present in the grazed area.

Methods

We used GPS-collars on all grazers and recorded their positions on an hourly basis over a study period of 1.5 years, assessing patterns of area usage. We counted birds weekly along three transects to describe their distribution within the grazed area and carried out land-cover surveys to describe the habitat. To assess how species richness and abundance of birds of different guilds were related to grazer density, season, and habitat characteristics, we used GAMM models in a spatially explicit framework. We also compared bird numbers at our main study site with a nearby non-grazed control area.

Results

The number of birds in the grazed area was about twice the number in the non-grazed control area. Within the grazed area, the abundance of open-area foraging birds increased with increasing grazer density. The number of woodland-foraging birds was also positively correlated with grazer density but less so than open-area foraging birds. The number of individuals in the aerial and wetland bird guilds was not correlated with the density of grazers. Most bird species and individuals were observed on open landscapes scattered with woody patches and waterbodies, and on areas with moderate grazer density.

Conclusions

Low-intensity grazing represents a potentially important management tool in creating heterogeneity in alluvial grasslands, thereby promoting suitable habitat for a diverse assemblage of bird species.

Targeted co-expression networks for the study of traits

Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used approach for the generation of gene co-expression networks. However, networks generated with this tool usually create large modules with a large set of functional annotations hard to decipher. We have developed TGCN, a new method to create Targeted Gene Co-expression Networks. This method identifies the transcripts that best predict the trait of interest based on gene expression using a refinement of the LASSO regression. Then, it builds the co-expression modules around those transcripts. Algorithm properties were characterized using the expression of 13 brain regions from the Genotype-Tissue Expression project. When comparing our method with WGCNA, TGCN networks lead to more precise modules that have more specific and yet rich biological meaning. Then, we illustrate its applicability by creating an APP-TGCN on The Religious Orders Study and Memory and Aging Project dataset, aiming to identify the molecular pathways specifically associated with APP role in Alzheimer's disease. Main biological findings were further validated in two independent cohorts. In conclusion, we provide a new framework that serves to create targeted networks that are smaller, biologically relevant and useful in high throughput hypothesis driven research. The TGCN R package is available on Github: https://github.com/aliciagp/TGCN .

Reactivating cue approached positive personality traits during sleep promotes positive self-referential processing

People preferentially endorse positive personality traits as more self-descriptive than negative ones, a positivity self-referential bias. Here, we investigated how to enhance positive self-referential processing, integrating wakeful cue-approach training task (CAT) and sleep-based targeted memory reactivation (TMR). In the CAT, participants gave speeded motor responses to cued positive personality traits. In a subsequent nap, we unobtrusively re-played half of the trained positive traits during slow-wave sleep (TMR). Upon awakening, CAT+TMR facilitated participants' speed in endorsing positive traits in immediate tests, and rendered participants endorse more positive traits as self-descriptive after one week. Notably, these enhancements were associated with the directionality of cue-related 1-4 Hz slow traveling waves (STW) that propagate across brain regions. Specifically, anterior-to-posterior backward STW was positively associated with these benefits, whereas forward STW showed negative associations. These findings demonstrate the potential benefits of integrated wakeful cue-approach training and sleep-based memory reactivation in strengthening positive self-referential processing.

Prognostic significance of migrasomes in neuroblastoma through machine learning and multi-omics

This study explores migrasomes' role in neuroblastoma, a common malignant tumor in children, and their potential impact on tumor formation. We analyzed neuroblastoma RNA-seq datasets from public databases, including GSE62564, GSE181559, target, and fwr144. Through data normalization and unsupervised classification using migrasome-specific molecular markers, Differentially Expressed Genes were identified, followed by functional enrichment analysis. Our novel migrasome-associated machine learning model, MigScore, was developed using ten algorithms and 101 combinations, validated on two single-cell datasets. This enabled immune infiltration assessment and drug compatibility prediction, highlighting the utility of MS275, a histone deacetylase inhibitor. Results showed a significant inverse relationship between MigScore and favorable clinical outcomes, elucidating the link between migrasome pathways and tumor immunogenicity. These findings suggest that migrasomes are crucial in neuroblastoma prognosis, leading to the possibility of personalized treatment strategies and improved outcomes.

Size-dependent responses of colony-founding bumblebee (Bombus impatiens) queens to exposure to pesticide residues in soil during hibernation

Bumblebees and other key pollinators are experiencing global declines, a phenomenon driven by multiple environmental stressors, including pesticide exposure. While bumblebee queens spend most of their life hibernating underground, no study to date has examined how exposure to pesticide-contaminated soils might affect bumblebee queens during this solitary phase of their lifecycle. We exposed Bombus impatiens queens (n = 303) to soil treated with field-realistic concentrations of two diamide insecticides (chlorantraniliprole and cyantraniliprole) and two fungicides (boscalid and difenoconazole), alone or combined, during a 30-week hibernation period. We found that exposure to boscalid residues in soil doubled the likelihood of queens surviving through the colony initiation period (after successful hibernation) and laying eggs. Our data also revealed complex interactions between pesticide exposure and queen body mass on aspects of colony founding. Among others, exposure to cyantraniliprole led to lethal and sublethal post-hibernation effects that were dependent on queen size, with larger queens showing higher mortality rates, delayed emergence of their first brood, and producing smaller workers. Our results show that effects of pesticide exposure depend on intrinsic traits of bumblebee queen physiology and challenge our understanding of how bees respond to pesticides under environmentally realistic exposure scenarios.

Prediction of future visceral adiposity and application to cancer research: The Multiethnic Cohort Study

BACKGROUND

We previously developed a prediction score for MRI-quantified abdominal visceral adipose tissue (VAT) based on concurrent measurements of height, body mass index (BMI), and nine blood biomarkers, for optimal performance in five racial/ethnic groups. Here we evaluated the VAT score for prediction of future VAT and examined if enhancement with additional biomarkers, lifestyle behavior information, and medical history improves the prediction.

METHODS

We examined 500 participants from the Multiethnic Cohort (MEC) with detailed data (age 50-66) collected 10 years prior to their MRI assessment of VAT. We generated three forecasted VAT prediction models: first by applying the original VAT equation to the past data on the predictors ("original"), second by refitting the past data on anthropometry and biomarkers ("refit"), and third by building a new prediction model based on the past data enhanced with lifestyle and medical history ("enhanced"). We compared the forecasted prediction scores to future VAT using the coefficient of determination (R2). In independent nested case-control data in MEC, we applied the concurrent and forecasted VAT models to assess association of the scores with subsequent incident breast cancer (950 pairs) and colorectal cancer (831 pairs).

RESULTS

Compared to the VAT prediction by the concurrent VAT score (R2 = 0.70 in men, 0.68 in women), the forecasted original VAT score (R2 = 0.54, 0.48) performed better than past anthropometry alone (R2 = 0.47, 0.40) or two published scores (VAI, METS-VF). The forecasted refit (R2 = 0.61, 0.51) and enhanced (R2 = 0.62, 0.55) VAT scores each showed slight improvements. Similar to the concurrent VAT score, the forecasted VAT scores were associated with breast cancer, but not colorectal cancer. Both the refit score (adjusted OR for tertile 3 vs. 1 = 1.27; 95% CI: 1.00-1.62) and enhanced score (1.27; 0.99-1.62) were associated with breast cancer independently of BMI.

CONCLUSIONS

Predicted VAT from midlife data can be used as a surrogate to assess the effect of VAT on incident diseases associated with obesity, as illustrated for postmenopausal breast cancer.

Case Report: Benefits of LSVT LOUD in a multilingual patient with hypokinetic-hyperkinetic dysarthria and suspected progressive supranuclear palsy

Purpose

This case study measured how well the Lee Silverman Voice Treatment (LSVT) improved vocal features, intelligibility, and communicative effectiveness for a multilingual participant with hypokinetic-hyperkinetic dysarthria secondary to suspected progressive supranuclear palsy. LSVT treatment was chosen for the participant due to the strengths and deficits he presented with prior to treatment, and for the anticipated challenges in treatment that may arise from the presence of multilingualism and impaired cognitive functioning.

Methods

A multilingual patient in their 60's (English, Spanish, and French) with hypokinetic-hyperkinetic dysarthria secondary to suspected progressive supranuclear palsy completed the standard treatment sessions for LSVT. Assessment measures were taken at baseline, immediately post-treatment, and three-months post-treatment.

Results

Improvements were measured in vocal quality, vocal loudness, intelligibility, and communicative effectiveness immediately post-treatment. Three months post-treatment, improvements in vocal quality and intelligibility were maintained.

Conclusion

This case study illustrates that LSVT may be a beneficial treatment for complex clients who are multilingual and present with complex comorbidities and cognitive deficits. LSVT resulted in some meaningful changes in vocal quality, intelligibility, and communicative effectiveness for this individual. Clinicians who work with complex patients may wish to consider the theoretical underpinnings of LSVT, client profile, areas of client need, and ability and desire to complete an intensive treatment program to determine if trialing LSVT is appropriate. The use of LSVT with complex clients may yield positive outcomes.

Machine Learning Analysis in Diffusion Kurtosis Imaging for Discriminating Pediatric Posterior Fossa Tumors: A Repeatability and Accuracy Pilot Study

Background and purpose: Differentiating pediatric posterior fossa (PF) tumors such as medulloblastoma (MB), ependymoma (EP), and pilocytic astrocytoma (PA) remains relevant, because of important treatment and prognostic implications. Diffusion kurtosis imaging (DKI) has not yet been investigated for discrimination of pediatric PF tumors. Estimating diffusion values from whole-tumor-based (VOI) segmentations may improve diffusion measurement repeatability compared to conventional region-of-interest (ROI) approaches. Our purpose was to compare repeatability between ROI and VOI DKI-derived diffusion measurements and assess DKI accuracy in discriminating among pediatric PF tumors. Materials and methods: We retrospectively analyzed 34 children (M, F, mean age 7.48 years) with PF tumors who underwent preoperative examination on a 3 Tesla magnet, including DKI. For each patient, two neuroradiologists independently segmented the whole solid tumor, the ROI of the area of maximum tumor diameter, and a small 5 mm ROI. The automated analysis pipeline included inter-observer variability, statistical, and machine learning (ML) analyses. We evaluated inter-observer variability with coefficient of variation (COV) and Bland-Altman plots. We estimated DKI metrics accuracy in discriminating among tumor histology with MANOVA analysis. In order to account for class imbalances, we applied SMOTE to balance the dataset. Finally, we performed a Random Forest (RF) machine learning classification analysis based on all DKI metrics from the SMOTE dataset by partitioning 70/30 the training and testing cohort. Results: Tumor histology included medulloblastoma (15), pilocytic astrocytoma (14), and ependymoma (5). VOI-based measurements presented lower variability than ROI-based measurements across all DKI metrics and were used for the analysis. DKI-derived metrics could accurately discriminate between tumor subtypes (Pillai's trace: p < 0.001). SMOTE generated 11 synthetic observations (10 EP and 1 PA), resulting in a balanced dataset with 45 instances (34 original and 11 synthetic). ML analysis yielded an accuracy of 0.928, which correctly predicted all but one lesion in the testing set. Conclusions: VOI-based measurements presented improved repeatability compared to ROI-based measurements across all diffusion metrics. An ML classification algorithm resulted accurate in discriminating PF tumors on a SMOTE-generated dataset. ML techniques based on DKI-derived metrics are useful for the discrimination of pediatric PF tumors.

Experimental evidence of climate change extinction risk in Neotropical montane epiphytes

Climate change is conjectured to endanger tropical species, particularly in biodiverse montane regions, but accurate estimates of extinction risk are limited by a lack of empirical data demonstrating tropical species' sensitivity to climate. To fill this gap, studies could match high-quality distribution data with multi-year transplant experiments. Here, we conduct field surveys of epiphyte distributions on three mountains in Central America and perform reciprocal transplant experiments on one mountain across sites that varied in elevation, temperature and aridity. We find that most species are unable to survive outside of their narrow elevational distributions. Additionally, our findings suggest starkly different outcomes from temperature conditions expected by 2100 under different climate change scenarios. Under temperatures associated with low-emission scenarios, most tropical montane epiphyte species will survive, but under emission scenarios that are moderately high, 5-36% of our study species may go extinct and 10-55% of populations may be lost. Using a test of tropical species' climate tolerances from a large field experiment, paired with detailed species distribution data across multiple mountains, our work strengthens earlier conjecture about risks of wide-spread extinctions from climate change in tropical montane ecosystems.

Image-based identification and isolation of micronucleated cells to dissect cellular consequences

Recent advances in isolating cells based on visual phenotypes have transformed our ability to identify the mechanisms and consequences of complex traits. Micronucleus (MN) formation is a frequent outcome of genome instability, triggers extensive disease-associated changes in genome structure and signaling coincident with MN rupture, and is almost exclusively defined by visual analysis. Automated MN detection in microscopy images has proved extremely challenging, limiting unbiased discovery of the mechanisms and consequences of MN formation and rupture. In this study we describe two new MN segmentation modules: a rapid and precise model for classifying micronucleated cells and their rupture status (VCS MN), and a robust model for accurate MN segmentation (MNFinder) from a broad range of microscopy images. As a proof-of-concept, we define the transcriptome of non-transformed human cells with intact or ruptured MN after inducing chromosome missegregation by combining VCS MN with photoactivation-based cell isolation and RNASeq. Surprisingly, we find that neither MN formation nor rupture triggers a unique transcriptional response. Instead, transcriptional changes are correlated with increased aneuploidy in these cell classes. Our MN segmentation modules overcome a significant challenge to reproducible MN quantification, and, joined with visual cell sorting, enable the application of powerful functional genomics assays, including pooled CRISPR screens and time-resolved analyses of cellular and genetic consequences, to a wide-range of questions in MN biology.

MethNet: a robust approach to identify regulatory hubs and their distal targets from cancer data

Aberrations in the capacity of DNA/chromatin modifiers and transcription factors to bind non-coding regions can lead to changes in gene regulation and impact disease phenotypes. However, identifying distal regulatory elements and connecting them with their target genes remains challenging. Here, we present MethNet, a pipeline that integrates large-scale DNA methylation and gene expression data across multiple cancers, to uncover cis regulatory elements (CREs) in a 1 Mb region around every promoter in the genome. MethNet identifies clusters of highly ranked CREs, referred to as 'hubs', which contribute to the regulation of multiple genes and significantly affect patient survival. Promoter-capture Hi-C confirmed that highly ranked associations involve physical interactions between CREs and their gene targets, and CRISPR interference based single-cell RNA Perturb-seq validated the functional impact of CREs. Thus, MethNet-identified CREs represent a valuable resource for unraveling complex mechanisms underlying gene expression, and for prioritizing the verification of predicted non-coding disease hotspots.

Maternal preconceptional and prenatal exposure to El Niño Southern Oscillation levels and child mortality: a multi-country study

El Niño Southern Oscillation (ENSO) has been shown to relate to the epidemiology of childhood infectious diseases, but evidence for whether they increase child deaths is limited. Here, we investigate the impact of mothers' ENSO exposure during and prior to delivery on child mortality by constructing a retrospective cohort study in 38 low- and middle-income countries. We find that high levels of ENSO indices cumulated over 0-12 lagged months before delivery are associated with significant increases in risks of under-five mortality; with the hazard ratio ranging from 1.33 (95% confidence interval [CI], 1.26, 1.40) to 1.89 (95% CI, 1.78, 2.00). Child mortality risks are particularly related to maternal exposure to El Niño-like conditions in the 0th-1st and 6th-12th lagged months. The El Niño effects are larger in rural populations and those with unsafe sources of drinking water and less education. Thus, preventive interventions are particularly warranted for the socio-economically disadvantaged.

Influence of degree of learning on rate of forgetting of tonal sequences

Initial performance is frequently equated in studies that compare forgetting rates across groups. However, since the encoding capacity of different groups can be different, some procedures to match initial degree of learning need to be implemented, adding confounding variables such as longer exposures to the material, which would create memories of a different age. Slamecka and McElree Journal of Experimental Psychology: Learning, Memory, and Cognition, 9, 384-397, (1983) and our previous work found that the rate of forgetting was independent from initial degree of learning using verbal material. The present study seeks to determine whether this pattern holds true when undertaken with nonverbal material. In two experiments, we manipulate initial degree of learning by varying the number of presentations of the material and studying the effect on the forgetting rates. A set of 30 tonal sequences were presented to young, healthy participants either once or three times. Forgetting was evaluated in a yes/no recognition paradigm immediately and 1 hour or 24 hours after the study phase. A different subset of 10 sequences was tested along with 10 nontargets at each retention interval. The results of these experiments showed that initial acquisition was modulated by the number of repetitions. However, the forgetting rates were independent of initial degree of learning. These results are in keeping with the pattern found by Slamecka and McElree, and in our own previous studies. They suggest that the pattern of parallel forgetting after different levels of initial learning is not limited to verbal material.

Host-derived protein profiles of human neonatal meconium across gestational ages

Meconium, a non-invasive biomaterial reflecting prenatal substance accumulation, could provide valuable insights into neonatal health. However, the comprehensive protein profile of meconium across gestational ages remains unclear. Here, we conducted an extensive proteomic analysis of first meconium from 259 newborns across varied gestational ages to delineate protein composition and elucidate its relevance to neonatal diseases. The first meconium samples were collected, with the majority obtained before feeding, and the mean time for the first meconium passage from the anus was 11.9 ± 9.47 h. Our analysis revealed 5370 host-derived meconium proteins, which varied depending on sex and gestational age. Specifically, meconium from preterm infants exhibited elevated concentrations of proteins associated with the extracellular matrix. Additionally, the protein profiles of meconium also exhibited unique variations depending on both specific diseases, including gastrointestinal diseases, congenital heart diseases, and maternal conditions. Furthermore, we developed a machine learning model to predict gestational ages using meconium proteins. Our model suggests that newborns with gastrointestinal diseases and congenital heart diseases may have immature gastrointestinal systems. These findings highlight the intricate relationship between clinical parameters and meconium protein composition, offering potential for a novel approach to assess neonatal gastrointestinal health.

Identification of a pro-protein synthesis osteosarcoma subtype for predicting prognosis and treatment

Osteosarcoma (OS) is a heterogeneous malignant spindle cell tumor that is aggressive and has a poor prognosis. Although combining surgery and chemotherapy has significantly improved patient outcomes, the prognosis for OS patients with metastatic or recurrent OS has remained unsatisfactory. Therefore, it is imperative to gain a fresh perspective on OS development mechanisms and treatment strategies. After studying single-cell RNA sequencing (scRNA-seq) data in public databases, we identified seven OS subclonal types based on intra-tumor heterogeneity. Subsequently, we constructed a prognostic model based on pro-protein synthesis osteosarcoma (PPS-OS)-associated genes. Correlation analysis showed that the prognostic model performs extremely well in predicting OS patient prognosis. We also demonstrated that the independent risk factors for the prognosis of OS patients were tumor primary site, metastatic status, and risk score. Based on these factors, nomograms were constructed for predicting the 3- and 5-year survival rates. Afterward, the investigation of the tumor immune microenvironment (TIME) revealed the vital roles of γδ T-cell and B-cell activation. Drug sensitivity analysis and immune checkpoint analysis identified drugs that have potential application value in OS. Finally, the jumping translocation breakpoint (JTB) gene was selected for experimental validation. JTB silencing suppressed the proliferation, migration, and invasion of OS cells. Therefore, our research suggests that PPS-OS-related genes facilitate the malignant progression of OS and may be employed as prognostic indicators and therapeutic targets in OS.

Patterns of brain volume and metabolism predict clinical features in the progressive supranuclear palsy spectrum

Progressive supranuclear palsy (PSP) is a neurodegenerative tauopathy that presents with highly heterogenous clinical syndromes. We perform cross-sectional data-driven discovery of independent patterns of brain atrophy and hypometabolism across the entire PSP spectrum. We then use these patterns to predict specific clinical features and to assess their relationship to phenotypic heterogeneity. We included 111 patients with PSP (60 with Richardson syndrome and 51 with cortical and subcortical variant subtypes). Ninety-one were used as the training set and 20 as a test set. The presence and severity of granular clinical variables such as postural instability, parkinsonism, apraxia and supranuclear gaze palsy were noted. Domains of akinesia, ocular motor impairment, postural instability and cognitive dysfunction as defined by the Movement Disorders Society criteria for PSP were also recorded. Non-negative matrix factorization was used on cross-sectional MRI and fluorodeoxyglucose-positron emission tomography (FDG-PET) scans. Independent models for each as well as a combined model for MRI and FDG-PET were developed and used to predict the granular clinical variables. Both MRI and FDG-PET were better at predicting presence of a symptom than severity, suggesting identification of disease state may be more robust than disease stage. FDG-PET predicted predominantly cortical abnormalities better than MRI such as ideomotor apraxia, apraxia of speech and frontal dysexecutive syndrome. MRI demonstrated prediction of cortical and more so sub-cortical abnormalities, such as parkinsonism. Distinct neuroanatomical foci were predictive in MRI- and FDG-PET-based models. For example, vertical gaze palsy was predicted by midbrain atrophy on MRI, but frontal eye field hypometabolism on FDG-PET. Findings also differed by scale or instrument used. For example, prediction of ocular motor abnormalities using the PSP Saccadic Impairment Scale was stronger than with the Movement Disorders Society Diagnostic criteria for PSP oculomotor impairment designation. Combination of MRI and FDG-PET demonstrated enhanced detection of parkinsonism and frontal syndrome presence and apraxia, cognitive impairment and bradykinesia severity. Both MRI and FDG-PET patterns were able to predict some measures in the test set; however, prediction of global cognition measured by Montreal Cognitive Assessment was the strongest. MRI predictions generalized more robustly to the test set. PSP leads to neurodegeneration in motor, cognitive and ocular motor networks at cortical and subcortical foci, leading to diverse yet overlapping clinical syndromes. To advance understanding of phenotypic heterogeneity in PSP, it is essential to consider data-driven approaches to clinical neuroimaging analyses.

Dual-task training is as effective as functional training on the functional fitness of older women: a randomized clinical trial

BACKGROUND

The interest in approaches that improve older individuals' functional fitness and autonomy is increasing. However, the effects of dual-task training on older women's functional fitness and the comparison with the functional training approach are unclear. Therefore, we compared dual-task and functional training on the functional fitness of older women and the effects of three months of detraining.

METHODS

Sixty-one women performed 16 weeks of dual-task training or functional training. The functional fitness was measured pre-, post-training, and post-detraining, based on the ability to put on and take off a t-shirt, evaluating the mobility of the upper limb, standing-up from the prone position measuring the global functionality, five times sit-to-stand test to assess the lower limbs muscle power, timed up and go to measure the dynamic balance and agility, gallon-jug shelf-transfer to evaluate the global functionality emphasizing the upper limbs and 10 m walk test to analyze the gait ability.

RESULTS

Dual-task training and functional training generally provided significant small to moderate magnitude performance increases in the put on and take off a t-shirt (dual-task training: d = 0.35 / functional training: d = 0.49), five times sit-to-stand test (dual-task training: d = 0.41 / functional training: d = 0.77), timed up and go (dual-task training: d = 0.34 / functional training: d = 0.78), and gallon-jug shelf-transfer (dual-task training: d = 0.76 / functional training: d = 0.82). Only the functional training improved the 10 m walk test (d = 0.32; p = 0.013), and both groups did not change the standing-up from the prone position performance. After the detraining period, both groups kept the adaptations for the gallon-jug shelf-transfer and five times sit-to-stand test. At the same time, only the dual-task training maintained the adaptations for the put on and take off a t-shirt and the functional training for the timed up and go.

CONCLUSION

Sixteen weeks of dual-task and functional training are similarly effective in improving older women's functional fitness, maintaining their benefits even after three months of detraining.

TRIAL REGISTRATION

RBR-10ny848z ( https://ensaiosclinicos.gov.br/rg/RBR-10ny848z ).

Deep Learning Auto-Segmentation Network for Pediatric Computed Tomography Data Sets: Can We Extrapolate From Adults?

PURPOSE

Artificial intelligence (AI)-based auto-segmentation models hold promise for enhanced efficiency and consistency in organ contouring for adaptive radiation therapy and radiation therapy planning. However, their performance on pediatric computed tomography (CT) data and cross-scanner compatibility remain unclear. This study aimed to evaluate the performance of AI-based auto-segmentation models trained on adult CT data when applied to pediatric data sets and explore the improvement in performance gained by including pediatric training data. It also examined their ability to accurately segment CT data acquired from different scanners.

METHODS AND MATERIALS

Using the nnU-Net framework, segmentation models were trained on data sets of adult, pediatric, and combined CT scans for 7 pelvic/thoracic organs. Each model was trained on 290 to 300 cases per category and organ. Training data sets included a combination of clinical data and several open repositories. The study incorporated a database of 459 pediatric (0-16 years) CT scans and 950 adults (>18 years), ensuring all scans had human expert ground-truth contours of the selected organs. Performance was evaluated based on Dice similarity coefficients (DSC) of the model-generated contours.

RESULTS

AI models trained exclusively on adult data underperformed on pediatric data, especially for the 0 to 2 age group: mean DSC was below 0.5 for the bladder and spleen. The addition of pediatric training data demonstrated significant improvement for all age groups, achieving a mean DSC of above 0.85 for all organs in every age group. Larger organs like the liver and kidneys maintained consistent performance for all models across age groups. No significant difference emerged in the cross-scanner performance evaluation, suggesting robust cross-scanner generalization.

CONCLUSIONS

For optimal segmentation across age groups, it is important to include pediatric data in the training of segmentation models. The successful cross-scanner generalization also supports the real-world clinical applicability of these AI models. This study emphasizes the significance of data set diversity in training robust AI systems for medical image interpretation tasks.

Development of a novel prognostic signature for colorectal cancer based on angiogenesis-related genes

Background

Colorectal cancer (CRC) is the third most common malignant tumor worldwide. Angiogenesis is closely related to tumor metastasis, which is the main cause of cancer death. Although several angiogenesis signatures have been proposed in some cancer types, no angiogenic signature has been developed to predict the prognosis and efficacy of antiangiogenic bevacizumab in CRC patients.

Methods

We developed a novel CRC angiogenic signature by refining seven publicly available angiogenic gene sets using least absolute shrinkage and selection operator (LASSO). Immune and stromal cells within the tumor microenvironment were compared between the high- and low-risk groups in more than 1000 CRC samples classified by calculating the risk score based on the customized angiogenic signature. The correlation of this new gene set with the efficacy of bevacizumab was also compared.

Results

A new prognostic-associated angiogenesis signature gene set was constructed that can divide CRC patients into two high- and low-risk groups. The high-risk angiogenic group was significantly associated with extracellular matrix organization, epithelial-mesenchymal transition (EMT), and myogenesis. In addition, the high-risk group had higher infiltration of stromal and immune cells and was more resistant to bevacizumab than the low-risk group.

Conclusion

Briefly, we constructed a novel angiogenic signature that can predict the prognosis of CRC patients and the efficacy of bevacizumab in treating CRC. Our results provide new insights into the relationships among angiogenesis, metastasis, and medication for CRC.

Histone lactylation-related genes correlate with the molecular patterns and functions of cancer-associated fibroblasts and have significant clinical implications in clear cell renal cell carcinoma

Recent research emphasised the indispensable role of histone lactylation in the activation of hepatic stellate cells. The VHL mutation is extremely common in clear cell renal cell carcinoma, which normally causes a metabolic shift in cancer cells and increases lactate production, eventually creating a lactate-enriched tumour microenvironment. Cancer-associated fibroblasts (CAFs) promote tumour progression, which is also vital in clear cell renal cell carcinoma. Therefore, this study investigated histone lactylation in CAFs and its impact on patient survival. Multiomics technology was employed to determine the role of histone lactylation-related genes in the evolution of CAFs which correlated with the function and molecular signatures of CAFs. The results suggested that TIMP1 was the hub gene of histone lactylation-related genes in clear cell renal cell carcinoma.

Loss of Airway Phylogenetic Diversity Is Associated with Clinical and Pathobiological Markers of Disease Development in Chronic Obstructive Pulmonary Disease

Rationale: The airway microbiome has the potential to shape chronic obstructive pulmonary disease (COPD) pathogenesis, but its relationship to outcomes in milder disease is unestablished. Objectives: To identify sputum microbiome characteristics associated with markers of COPD in participants of the Subpopulations and Intermediate Outcome Measures of COPD Study (SPIROMICS). Methods: Sputum DNA from 877 participants was analyzed using 16S ribosomal RNA gene sequencing. Relationships between baseline airway microbiota composition and clinical, radiographic, and mucoinflammatory markers, including longitudinal lung function trajectory, were examined. Measurements and Main Results: Participant data represented predominantly milder disease (Global Initiative for Chronic Obstructive Lung Disease stage 0-2 obstruction in 732 of 877 participants). Phylogenetic diversity (i.e., range of different species within a sample) correlated positively with baseline lung function, decreased with higher Global Initiative for Chronic Obstructive Lung Disease stage, and correlated negatively with symptom burden, radiographic markers of airway disease, and total mucin concentrations (P < 0.001). In covariate-adjusted regression models, organisms robustly associated with better lung function included Alloprevotella, Oribacterium, and Veillonella species. Conversely, lower lung function, greater symptoms, and radiographic measures of small airway disease were associated with enrichment in members of Streptococcus, Actinobacillus, Actinomyces, and other genera. Baseline sputum microbiota features were also associated with lung function trajectory during SPIROMICS follow-up (stable/improved, decline, or rapid decline groups). The stable/improved group (slope of FEV1 regression ⩾66th percentile) had greater bacterial diversity at baseline associated with enrichment in Prevotella, Leptotrichia, and Neisseria species. In contrast, the rapid decline group (FEV1 slope ⩽33rd percentile) had significantly lower baseline diversity associated with enrichment in Streptococcus species. Conclusions: In SPIROMICS, baseline airway microbiota features demonstrate divergent associations with better or worse COPD-related outcomes.

Egg protein exposure estimation in risk assessment for Japanese food allergy labeling

To assess the risk of food allergies in foods processed under the Japanese food labeling system, estimating exposure to hidden allergens is necessary. We assessed exposure to egg protein in foods processed according to the Japanese food labeling system. First, we estimated the concentration distribution of egg protein by Bayesian methods using data from the literature and the measurement of food products with precautional declarations in the labeling margin. We then estimated the food-intake portion-size distribution under two scenarios: soft drink consumption as an example of single, high-intake consumption, and confections, which are frequently consumed by children, as a realistic example of low-intake consumption. Finally, we estimated the distribution of unexpected intake of egg proteins in the form of single consumption. The mean exposure to egg protein under the high-intake scenario was estimated to be 0.0164 mg for 1-15-year-olds, 0.0171 mg for 4-15-year-olds, 0.0181 mg for 7-15-year-olds, and ≥0.0188 mg for 16-year-olds. The mean exposure to egg protein under the low-intake scenario was estimated to be 0.0018 mg for 1-15-year-olds, 0.0019 mg for 4-15-year-olds, 0.0020 mg for 7-15-year-olds, and ≥0.0022 mg for 16-year-olds. Compared to the reference dose of 2.0 mg proposed by the Joint the Food and Agriculture Organization (FAO)/World Health Organization (WHO) Expert Committee, the risk of onset of food allergies due to egg protein contamination from foods without egg labeling is considered to be extremely low under the current Japanese food labeling system.

Model-based estimation of individual-level social determinants of health and its applications in All of Us

OBJECTIVES

We introduce a widely applicable model-based approach for estimating individual-level Social Determinants of Health (SDoH) and evaluate its effectiveness using the All of Us Research Program.

MATERIALS AND METHODS

Our approach utilizes aggregated SDoH datasets to estimate individual-level SDoH, demonstrated with examples of no high school diploma (NOHSDP) and no health insurance (UNINSUR) variables. Models are estimated using American Community Survey data and applied to derive individual-level estimates for All of Us participants. We assess concordance between model-based SDoH estimates and self-reported SDoHs in All of Us and examine associations with undiagnosed hypertension and diabetes.

RESULTS

Compared to self-reported SDoHs, the area under the curve for NOHSDP is 0.727 (95% CI, 0.724-0.730) and for UNINSUR is 0.730 (95% CI, 0.727-0.733) among the 329 074 All of Us participants, both significantly higher than aggregated SDoHs. The association between model-based NOHSDP and undiagnosed hypertension is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.649. Similarly, the association between model-based NOHSDP and undiagnosed diabetes is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.900.

DISCUSSION AND CONCLUSION

The model-based SDoH estimation method offers a scalable and easily standardized approach for estimating individual-level SDoHs. Using the All of Us dataset, we demonstrate reasonable concordance between model-based SDoH estimates and self-reported SDoHs, along with consistent associations with health outcomes. Our findings also underscore the critical role of geographic contexts in SDoH estimation and in evaluating the association between SDoHs and health outcomes.

Combination of an enzyme-linked immunosorbent assay and an indirect fluorescence assay for a nationwide sero-survey of Toxoplasma gondii infection in pig herds in Taiwan

Toxoplasma gondii is a zoonotic pathogen that can infect farm animals, companion animals, and humans, sometimes causing public health issues. In Taiwan, the pig industry is a vital agricultural industry, with a self-sufficiency rate of 91 %, and pigs are also food-producing animal reservoirs of Toxoplasma gondii. Infected pigs are usually asymptomatic, and abortions and death may occur in severe cases. We combined an enzyme-linked immunosorbent assay (ELISA) and an indirect fluorescence assay (IFA) to investigate the seroprevalence of Toxoplasma gondii among pig populations in Taiwan. A stratified sampling approach to determine the number of sample farms proportional to the number of pig farms in each county was employed, with 15 blood samples collected at each farm between July and September 2017. With the tested results, empirical Bayesian smoothing was utilized to assess the proportion of Toxoplasma-positive farms at the county level. Bayesian mixed-effects logistic regression models, incorporating farm and county as random effects, were employed to investigate associations between Toxoplasma test results and potential risk factors. A total of 930 serum samples from 62 pig farms were collected and tested. An overall herd prevalence of 27.4 % was shown with the seroprevalence in northern Taiwan being greater than that in southern Taiwan. The sampling month and companion dog density in 2017 were significantly associated with Toxoplasma infections in pigs. With every increase in the number of companion dogs per km² at the county level, the odds of Toxoplasma infection in pigs increased by 4.7 % (95 % CI: 1.7-8.9 %). This study demonstrated that combining ELISA for screening with IFA for confirmation is a cost-effective and time-saving method for conducting a large-scale sample investigation. This was also the first nationwide, cross-sectional study in Taiwanese pig herds to investigate Toxoplasma gondii infection.

Exploration of ferroptosis and necroptosis-related genes and potential molecular mechanisms in psoriasis and atherosclerosis

Objective

Ferroptosis and necroptosis are two recently identified forms of non-apoptotic cell death. Their dysregulation plays a critical role in the development and progression of Psoriasis (PsD) and Atherosclerosis (AS). This study explores shared Ferroptosis and necroptosis-related genes and elucidates their molecular mechanisms in PsD and AS through the analysis of public databases.

Methods

Data sets for PsD (GSE30999) and AS (GSE28829) were retrieved from the GEO database. Differential gene expression (DEG) and weighted gene co-expression network analysis (WGCNA) were performed. Machine learning algorithms identified candidate biomarkers, whose diagnostic values were assessed using Receiver Operating Characteristic (ROC) curve analysis. Additionally, the expression levels of these biomarkers in cell models of AS and PsD were quantitatively measured using Western Blot (WB) and real-time quantitative PCR (RT-qPCR). Furthermore, CIBERSORT evaluated immune cell infiltration in PsD and AS tissues, highlighting the correlation between characteristic genes and immune cells. Predictive analysis for candidate drugs targeting characteristic genes was conducted using the DGIdb database, and an lncRNA-miRNA-mRNA network related to these genes was constructed.

Results

We identified 44 differentially expressed ferroptosis-related genes (DE-FRGs) and 30 differentially expressed necroptosis-related genes (DE-NRGs). GO and KEGG enrichment analyses revealed significant enrichment of these genes in immune-related and inflammatory pathways, especially in NOD-like receptor and TNF signaling pathways. Two ferroptosis-related genes (NAMPT, ZFP36) and eight necroptosis-related genes (C7, CARD6, CASP1, CTSD, HMOX1, NOD2, PYCARD, TNFRSF21) showed high sensitivity and specificity in ROC curve analysis. These findings were corroborated in external validation datasets and cell models. Immune infiltration analysis revealed increased levels of T cells gamma delta, Macrophages M0, and Macrophages M2 in PsD and AS samples. Additionally, we identified 43 drugs targeting 5 characteristic genes. Notably, the XIST-miR-93-5p-ZFP36/HMOX1 and NEAT1-miR-93-5p-ZFP36/HMOX1 pathways have been identified as promising RNA regulatory pathways in AS and PsD.

Conclusion

The two ferroptosis-related genes (NAMPT, ZFP36) and eight necroptosis-related genes (C7, CARD6, CASP1, CTSD, HMOX1, NOD2, PYCARD, TNFRSF21) are potential key biomarkers for PsD and AS. These genes significantly influence the pathogenesis of PsD and AS by modulating macrophage activity, participating in immune regulation, and mediating inflammatory responses.

An automated protocol to construct flexibility parameters for classical forcefields: applications to metal-organic frameworks

In this work, forcefield flexibility parameters were constructed and validated for more than 100 metal-organic frameworks (MOFs). We used atom typing to identify bond types, angle types, and dihedral types associated with bond stretches, angle bends, dihedral torsions, and other flexibility interactions. Our work used Manz's angle-bending and dihedral-torsion model potentials. For a crystal structure containing N atoms in its unit cell, the number of independent flexibility interactions is 3(N atoms - 1). Because the number of bonds, angles, and dihedrals is normally much larger than 3(N atoms - 1), these internal coordinates are redundant. To reduce (but not eliminate) this redundancy, our protocol prunes dihedral types in a way that preserves symmetry equivalency. Next, each dihedral type is classified as non-rotatable, hindered, rotatable, or linear. We introduce a smart selection method that identifies which particular torsion modes are important for each rotatable dihedral type. Then, we computed the force constants for all flexibility interactions together via LASSO regression (i.e., regularized linear least-squares fitting) of the training dataset. LASSO automatically identifies and removes unimportant forcefield interactions. For each MOF, the reference dataset was quantum-mechanically-computed in VASP via DFT with dispersion and included: (i) finite-displacement calculations along every independent atom translation mode, (ii) geometries randomly sampled via ab initio molecular dynamics (AIMD), (iii) the optimized ground-state geometry using experimental lattice parameters, and (iv) rigid torsion scans for each rotatable dihedral type. After training, the flexibility model was validated across geometries that were not part of the training dataset. For each MOF, we computed the goodness of fit (R-squared value) and the root-mean-squared error (RMSE) separately for the training and validation datasets. We compared flexibility models with and without bond-bond cross terms. Even without cross terms, the model yielded R-squared values of 0.910 (avg across all MOFs) ± 0.018 (st. dev.) for atom-in-material forces in the validation datasets. Our SAVESTEPS protocol should find widespread applications to parameterize flexible forcefields for material datasets. We performed molecular dynamics simulations using these flexibility parameters to compute heat capacities and thermal expansion coefficients for two MOFs.

Neural reinforcement learning signals predict recovery from impulse control disorder symptoms in Parkinson's disease

BACKGROUND

Impulse control disorders (ICD) in Parkinson's disease (PD) are associated with a heavy burden on patients and caretakers. While recovery can occur, ICD persists in many patients despite optimal management. The basis for this inter-individual variability in recovery is unclear and poses a major challenge to personalized health care.

METHODS

We adopt a computational psychiatry approach and leverage the longitudinal, prospective Personalized Parkinson Project (N=136 persons with PD, within 5 years of diagnosis) to combine dopaminergic learning theory-informed fMRI with machine learning (at baseline) to predict ICD symptom recovery after two years of follow-up. We focused on a change in QUIP-rs across the entire cohort, regardless of an ICD diagnosis.

RESULTS

Greater reinforcement learning signals during gain trials but not loss trials at baseline, including those in the ventral striatum, medial prefrontal cortex and the behavioral accuracy score measured while ON medication were associated with greater recovery from impulse control symptoms two years later. These signals accounted for a unique proportion of the relevant variability over and above that explained by other known factors, such as decreases in dopamine agonist use.

CONCLUSIONS

Our results provide a proof of principle for combining generative model-based inference of latent learning processes with machine learning-based predictive modeling of variability in clinical symptom recovery trajectories. Hence, we showed that RL modelling parameters predict recovery from ICD symptoms in PD.

Frailty trajectories and determinants in Chinese older adults: A longitudinal study

OBJECTIVES

This study aimed to enrich the research on frailty trajectories by using FRAIL scale and frailty index (FI), and analyze the determinants of the different trajectories in older Chinese.

METHODS

2268 older adults from the Chinese Longitudinal Healthy Longevity Survey were included. The FRAIL scale was constructed from 5 items and FI was constructed from 39 deficits. Latent Class Trajectory Model was used to depict frailty trajectories. Lasso - logistic model was applied to exploration of influencing factors.

RESULTS

Four FRAIL trajectories and three FI trajectories were identified. Women, smoking, illiteracy, more than two chronic diseases, and poor instrumental activities of daily living (all p < 0.05) were associated with frailty trajectories, regardless of the frailty instrument employed.

CONCLUSIONS

Frailty trajectories of older Chinese adults are diverse and they are influenced by different frailty measurement tools. Long-term assessment and management of frailty are recommended as routine care in community healthcare centers.