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Exploring the associations between the presence, characteristics, and biopsychosocial covariates of pain and lifetime depression in adolescents: A cross-sectional ABCD study analysis
No full textINTRODUCTION: Depression and pain co-occur, even during adolescence. However, there is limited knowledge on the association between pain and lifetime depression, and which biopsychosocial measures are associated with this co-occurrence. METHODS: Cross-sectional analysis of the Adolescent Brain and Cognitive Development (ABCD) two-year follow-up. We explored associations between the presence and characteristics of past month pain (intensity, duration, activity limitations, and number of pain sites) and lifetime depression using logistic regression. We explored associations of brain structure, physical, behavioural, emotional, social, and cognitive measures with lifetime depression and past month pain compared to having had one or neither condition using multinomial logistic regression. RESULTS: A total of 5211 adolescents (mean age = 12.0 years) who had: (1) no lifetime mental ill-health and no pain (n = 3327); (2) pain only (n = 1407); (3) lifetime depressive disorder but no pain (n = 272); and (4) lifetime depressive disorder and pain (n = 205) were included. Pain presence was associated with lifetime depression (OR[95%CI]: 1.76 [1.45, 2.13], p < 0.001). Pain-related activity limitations (1.13 [1.06, 1.21], p < 0.001) and the number of pain sites (1.06 [1.02, 1.09], p < 0.001) were associated with lifetime depression. Various behavioural, emotional, social, and cognitive, but not brain structure or physical measures, were associated with lifetime depression and past month pain. LIMITATIONS: Longitudinal analyses should validate prognostic markers for predicting co-occurring depression and pain. CONCLUSIONS: Results support an association between the presence and characteristics of pain and lifetime depression during adolescence and could indicate the need for more integrated recognition and clinical care of youth experiencing both depression and pain
Understanding the regulation of the BH3-only proteins PUMA and BIM by TRP53 dependent and TRP53 independent processes to enhance the efficacy of BH3-mimetic drugs and other anti-cancer agents in cancer therapy
No full text© 2025 Deeksha KaloniApoptosis is regulated by the BCL-2 family. The pro-apoptotic BH3-only proteins like PUMA and BIM are critical for killing cancer cells. PUMA and BIM can trigger apoptosis via both TP53-dependent and independent pathways. We generated reporter mouse models to study the regulation of PUMA and BIM in blood cancers. Whole-genome CRISPR/Cas9 knockout screens in reporter tumour lines revealed novel regulators of Puma and Bim expression. These findings highlight potential strategies to boost the expression of these BH3-only proteins and improve cancer therapy
Gaussian process regression on multiple drivers and attributes for rapid prediction of maximum flood inundation extent and depth
No full textTraditional high-resolution flood models are too slow for real-time predictions. The most common industry practice is to use a lookup table or interpolation algorithm to derive flood extents from a pre-generated library of flood maps. For the library interpolation approach to be effective, the input flood data need to closely match those in the map library. To effectively emulate complex and dynamic flood behaviour, the interpolation approach should be able to account for multiple flood drivers (such as rivers, tributaries and tides) and attributes (such as shape and timing of the hydrographs). However, a simple extension of existing interpolation algorithms would make them overly complex and need a very large map library to deal with these drivers and attributes. To address this challenge, this study investigates the capability of a Gaussian Process (GP) modelling approach to accommodate the complex influence of multiple flood drivers and attributes, and thus to provide robust, accurate and fast predictions. By training the GP model, it learns the underlying relationships between flood depths and multiple flood drivers and attributes. Model accuracy and speed in predicting maximum flood extents and depths are examined. In a case study of a floodplain in Port Fairy, Australia, the GP model is found to generally outperform the library interpolation approach in accuracy, particularly for complex floods, while being similar efficient. The GP model is a promising approach for real-time flood predictions
Polyphenol rich sugarcane extract restricts select respiratory viruses depending on their mode of entry
No full textWe previously showed that Polyphenol rich sugarcane extract (PRSE) displayed significant inhibitory effect against influenza A virus (IAV). In this study, we investigated the mechanism of action (MOA) of PRSE against respiratory viruses in human-derived cells. We showed that PRSE treatment does not promote an antiviral state via expression of interferon stimulated genes (ISGs). We subsequently investigated any potential perturbation on the viral entry process and observed that PRSE treatment did not affect caveolin-mediated endocytosis but led to a significant attenuation in clathrin-mediated endocytosis. We confirmed this inhibitory effect on IAV entry, as infection was unaffected by PRSE when IAV fusion was induced at the plasma membrane, instead of endosomal membranes. Based on these findings we observed significant inhibitory effect of PRSE against respiratory syncytial virus and human metapneumovirus, which utilise clathrin-mediated endocytosis, but not human parainfluenza virus type 3, which fuses at the plasma membrane. In conclusion, we show that PRSE has broad antiviral activity and potentially perturbs virus entry via clathrin-mediated endocytosis to inhibit viral replication in vitro
Imposed and Self-Imposed Isolation Among Children and Young People Who Have Grown Up in Domestic Abuse
No full textABSTRACT
Research about the effects of isolation on women living with domestic abuse (DA) shows that women's mental health and well‐being is negatively affected by the consequences of isolation while enduring DA. Less attention has been given to the effects of isolation for children's health and well‐being when experiencing DA. Based on the voices of young people who grew up in DA, the qualitative study that is the basis for this article asked young people about aspects of their childhood and what helped them to cope despite DA in their family of origin. The study found that methods of control used by the abuser led to children being isolated. At other times, children living with DA reported isolating themselves due to the effects of living with DA. Utilizing Vygotsky's sociocultural theory, children's isolation caused by DA is examined. The effects on children's social, emotional and educational well‐being are explored, and potential social work roles in helping children overcome such isolation are presented. We focus particularly on young people's call for connection with peers from similar backgrounds
Causal effect of molar incisor hypomineralisation on oral health-related quality of life of Australian children aged 7–16 years
No full textPURPOSE: Molar incisor hypomineralisation (MIH) is a qualitative defect of enamel characterised by demarcated opacities. Aesthetic and functional sequelae of MIH may manifest as reduced oral health related quality of life (OHRQoL). This study aims to investigate the impact of the presence and severity of MIH on children's OHRQoL. METHODS: This cross-sectional study recruited children aged 7-16 years-of-age attending specialist paediatric dental clinics in Melbourne, Australia. Clinical examination utilised the modified European Academy of Paediatric Dentistry index to quantify the presence and severity of MIH. OHRQoL data was collected via the Child Perception Questionnaire, Parent-Caregiver Perception Questionnaire and Family Impact Statement. Causal analysis used quantile regression and included poor medical health as a confounding variable. Sensitivity analysis used the same model and different strata of MIH lesion location and severity. RESULTS: 131 participants with complete self-reported OHRQoL data were included in the causal analysis. The estimated average causal effect after adjusting for poor medical health showed the estimated difference in medians of child-reported OHRQoL was 6 (CI = 2.62, 12.25, p = 0.02) in the MIH group compared to the unaffected group. The estimated difference in medians of self-reported OHRQoL after adjusting for poor medical health was 7 (CI = 1.87, 11.99, p = 0.01) for severe MIH group and - 1 (CI = - 5.16, 3.62, p = 0.63) for the mild group compared to those unaffected. The estimated difference in medians of self-reported OHRQoL after adjusting for poor medical health was 5.16 (CI = - 2.42, 10.99, p = 0.15) for participants with MIH-affected incisors compared to the rest of the cohort. CONCLUSIONS: MIH impacts children's OHRQoL as reported by self and parent/caregiver
The role of the risk gene CACNA1C in neuroinflammation and peripheral immunity in autism spectrum disorder
No full textAutism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by persistent deficits in social communication and interaction, restricted and repetitive behaviors and interests, with the severity of symptoms varying greatly among individuals. The pathogenesis of ASD is influenced by the complex interaction of genetic and environmental factors. Increasing evidence suggests that dysregulated immune processes represent a crucial aspect in ASD pathology. The CACNA1C gene, which encodes the pore-forming α1C subunit of the L-type calcium channel (LTCC) CaV1.2, is a major genetic risk factor for ASD. CaV1.2 channels modulate neuronal excitability, synaptic plasticity, and neurotransmitter release in the central nervous system (CNS), all of which are essential for brain development and function. CaV1.2 channels are also expressed in generally non-excitable immune cells, including CNS microglia and peripheral immune cells, where they influence activation, differentiation, and cytokine release. These immune functions may contribute to ASD pathogenesis; however, the specific role of CaV1.2 in immune regulation and neuroinflammation in ASD is yet to be elucidated. Here, we will review recent research on the role of CACNA1C in immune mechanisms relevant to ASD. We will summarize current knowledge on the function of CaV1.2 in brain microglia and peripheral immune cells such as T cells, B cells, and dendritic cells that contribute to immune dysfunction in ASD. In addition, we will discuss the therapeutic prospects of targeting CaV1.2 channels in immune cells to manage both behavioral and inflammatory conditions associated with ASD
The palaeoenvironmental and biological significance of marine carbonate depositional surfaces
No full textCarbonate rocks form a substantial part of the ancient geological record, yet their reliability as archives of past environments and ecosystems has been questioned because of diagenetic alteration. This review investigates the potential of carbonate depositional surfaces – irregular or planar interfaces formed at the sediment–water or sediment–air boundary during deposition – to serve as records of ancient environments and ecosystems. These surfaces, commonly preserved in carbonate strata, provide valuable insights into sedimentary processes and ecosystems over varying timescales. Some carbonate depositional surfaces have the same features as siliciclastic examples, such as desiccation cracks, ripples, dunes and surfaces with trace and body fossils. Surfaces offer a glimpse into ordinary day-to-day deposition, such as ripples moving with the tide or mudcracks forming on an exposed tidal flat. Other depositional surfaces are unique to carbonate sediment, including hardgrounds, tepees, beachrock, reefs and microbialites. These surfaces represent cemented and/or condensed intervals of deposition, preserving significant differences in the duration of exposure and environmental conditions. Sedimentary factors, original mineralogy, biological and ocean chemical changes over Earth history and tectonic/burial history all impact the likelihood of preserving carbonate depositional surfaces
Development of a Machine Learning Model for Predicting Treatment-Related Amenorrhea in Young Women with Breast Cancer
No full textTreatment-induced ovarian function loss is a significant concern for many young patients with breast cancer. Accurately predicting this risk is crucial for counselling young patients and informing their fertility-related decision-making. However, current risk prediction models for treatment-related ovarian function loss have limitations. To provide a broader representation of patient cohorts and improve feature selection, we combined retrospective data from six datasets within the FoRECAsT (Infertility after Cancer Predictor) databank, including 2679 pre-menopausal women diagnosed with breast cancer. This combined dataset presented notable missingness, prompting us to employ cross imputation using the k-nearest neighbours (KNN) machine learning (ML) algorithm. Employing Lasso regression, we developed an ML model to forecast the risk of treatment-related amenorrhea as a surrogate marker of ovarian function loss at 12 months after starting chemotherapy. Our model identified 20 variables significantly associated with risk of developing amenorrhea. Internal validation resulted in an area under the receiver operating characteristic curve (AUC) of 0.820 (95% CI: 0.817–0.823), while external validation with another dataset demonstrated an AUC of 0.743 (95% CI: 0.666–0.818). A cutoff of 0.20 was chosen to achieve higher sensitivity in validation, as false negatives—patients incorrectly classified as likely to regain menses—could miss timely opportunities for fertility preservation if desired. At this threshold, internal validation yielded sensitivity and precision rates of 91.3% and 61.7%, respectively, while external validation showed 92.9% and 60.0%. Leveraging ML methodologies, we not only devised a model for personalised risk prediction of amenorrhea, demonstrating substantial enhancements over existing models but also showcased a robust framework for maximally harnessing available data sources