A novel approach for longitudinal analysis of serum biomarkers of joint metabolism and knee injury in military officers

by Liubov Arbeeva, Virginia B. Kraus, Amanda E. Nelson, Maryalice Nocera, Leigh F. Callahan, Richard F. Loeser, Kenneth L. Cameron, Jesse R. Trump, Stephen W. Marshall, Yvonne M. Golightly

Purpose

To investigate the longitudinal relationships between serum biomarkers of joint metabolism, knee injury, and Knee Injury and Osteoarthritis Outcome Score (KOOS) using novel methodologies.

Methods

Data were collected from military officers who enrolled as cadets between 2004–2009, with follow-up conducted between 2015–2017. Analyses included 234 officers who had no history of knee ligament/meniscal injury at the time of military academy matriculation, had serum biomarker measurements at matriculation and graduation, demographic data, and KOOS assessment at follow-up. Biomarkers included Collagen Type II (C2C) and Type I and II (C1,2C) collagenase-generated cleavage epitopes, C-terminal propeptide of Type II collagen (CPII), and C- and N-terminal telopeptides of type I collagen (CTX and NTX). Angle-based Joint and Individual Variation Explained (AJIVE) was used to determine demographic determinants of biomarker levels and individual modes of variation specific to biomarker levels at matriculation and graduation, stratified by sex.

Results

We confirmed known associations of joint metabolism biomarkers with age in both sexes and with smoking in males. Matriculation biomarker data in males suggested a protective biomarker profile characterized by high cartilage synthesis and low cleavage of type I and II collagen in association with healthy KOOS scores at follow-up. CPII measured at matriculation was negatively associated with incident injuries after adjustment for smoking status (p = 0.03, logistic regression), confirming results from AJIVE.

Conclusion

These exploratory analyses suggest that CPII alone, or in combination with other joint metabolism biomarkers, may help identify individual risk of knee injury.

Predicting the frequency of positive laboratory submissions for porcine reproductive and respiratory syndrome in Ontario, Canada, using autoregressive integrated moving average, exponential smoothing, random forest, and recurrent neural network

by Tatiana Petukhova, Maria Spinato, Tanya Rossi, Michele T. Guerin, Cathy A. Bauman, Pauline Nelson-Smikle, Davor Ojkic, Zvonimir Poljak

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) is endemic in many pig-producing countries and poses significant health and economic challenges. Enhanced surveillance strategies are essential for effective disease management. This study aimed to evaluate and compare the performance of different time-series modeling techniques to predict weekly PRRSV-positive laboratory submissions in Ontario, Canada. Ten years of PRRSV diagnostic data were obtained from the Animal Health Laboratory at the University of Guelph and were processed into a weekly time series. The dataset was analyzed with autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), random forest (RF), and recurrent neural network (RNN) models. Two validation strategies were employed: a traditional train-test split and a simulated prospective rolling forecast. Model accuracy was evaluated using common predictive error metrics. Descriptive analysis indicated a gradual increase in PRRSV positive submissions over time, with no consistent seasonal pattern. ARIMA and ETS models generally overpredict case counts, while RF and RNN tended to underpredict them. Among the evaluated models, the RF regression model most accurately captured the underlying time-series dynamics and produced the lowest prediction errors across both validation approaches. Despite outperforming other models, the RF model’s high relative prediction errors limit its suitability for accurate forecasting of PRRSV-positive submissions in Ontario’s routine surveillance system. Further data refinement and algorithm improvements are warranted.

Adaptation and evaluation of a digital dialectical behaviour therapy for youth at clinical high risk for psychosis: A protocol for a feasibility randomized controlled trial

by Thea Lynne Hedemann, Yun Lu, Sofia Campitelli, Lisa D. Hawke, Nelson Shen, Sarah Saperia, Brett D. M. Jones, Gillian Strudwick, Chelsey R. Wilks, Wei Wang, Marco Solmi, Michael Grossman, Muhammad Ishrat Husain, Nicole Kozloff, George Foussias, Muhammad Omair Husain

Background

Youth at clinical high risk (CHR) for psychosis often experience emotional dysregulation, psychiatric symptoms, substance use, suicidality, and functional impairment. Dialectical behaviour therapy (DBT) is an evidence-based intervention that improves emotion regulation, clinical outcomes, and functioning across psychiatric populations. Digital adaptations (d-DBT) may enhance accessibility and engagement for CHR youth, but acceptability and potential benefits in this group are unknown.

Objective

To adapt d-DBT for CHR youth and evaluate the acceptability of delivering it to this population, as well as the feasibility of a larger-scale clinical trial.

Methods

This mixed-methods clinical trial has two phases. In Phase 1, d-DBT will be adapted for CHR youth in collaboration with a lived-experience youth advisory group. In Phase 2, an assessor-masked randomized controlled trial will compare d-DBT (n = 30) with treatment as usual (n = 30). The intervention consists of eight weekly modules, with primary outcomes assessing acceptability, usability, and trial feasibility. Secondary outcomes include changes in emotional dysregulation, psychiatric symptoms, substance use, suicidality, and functioning.

Conclusions

We anticipate that d-DBT will be acceptable to CHR youth and that conducting a larger trial will be feasible. Preliminary findings may demonstrate improvements in emotion regulation, psychiatric symptoms, suicidality, and functioning. Results will guide further refinement of the intervention and inform the design of a confirmatory clinical trial.

Trial registration

ClinicalTrials.gov #NCT06928935

Anti-inflammatory activity of <i>Acanthospermum australe</i>: Insights from network pharmacology, chemical analysis, and <i>in vitro</i> assays

by Andrea Salinas, Christa Burgos, Aaron Rodríguez-Ramos, Alberto Burgos-Edwards, Nelson Alvarenga, Pablo H. Sotelo, Patricia Langjahr

Inflammation plays a crucial role in homeostasis and defense responses; however, exaggerated and chronic inflammation contribute to the development and worsening of various diseases. Acanthospermum australe (Loefl.) Kuntze (A. australe) is a medicinal plant traditionally used to alleviate inflammation. However, the anti-inflammatory activity of this plant has not yet been explored. This study aimed to evaluate the immunomodulatory activity of this species using network pharmacology, UPLC-ESI-MS/MS analysis, and in vitro assays. Network pharmacology analysis revealed the involvement of immune system processes, and among the main targets of A. australe related to inflammation were innate immune responses, toll-like receptors (TLRs), and T cell receptor signaling pathways. A methanolic extract was prepared and analyzed using UPLC-ESI-MS/MS, and 15 compounds were detected. Additionally, the potential targets of A. australe predicted by network pharmacology analysis were validated in vitro using monocytic THP-1 cells and splenocytes. The RT-qPCR analysis indicated that A. australe significantly inhibited the production of pro-inflammatory cytokines IL-1β, IL-6, and TNF-α, as well as chemokine CCL-2, in lipopolysaccharide (LPS)-stimulated cells. Finally, the extract significantly decreased concanavalin A (ConA)-induced T cell proliferation. Overall, our study provides evidence for the anti-inflammatory effects of this species and highlights its mechanisms of action.