How can Psychology inform disaster research?

This paper will set out our current understanding of how psychology can help us to understand and influence preparation for, and responses to disaster. Using four primary research studies, this paper will outline how psychology can inform our knowledge of all stages of a disaster (preparedness, immediate response and long-term consequences). The first study used a questionnaire design to examine factors that influence evacuation behaviours. The second and third studies explored physiological and psychological responses to simulated disaster training. The fourth study explored the consequences of trauma exposure focusing specifically on predictors of post-traumatic stress disorder and post-traumatic growth. The results show that psychology can play a role in our understanding of human behaviour during a disaster. Specifically, study one shows how psychology can inform disaster preparation by identifying barriers to evacuation. The second and third studies show how psychology can help us to explore and predict human behaviour during a disaster. Finally, the fourth study highlights how psychology can help us to understand the longer-term impact of exposure to traumatic events. Overall, the results of these studies show that psychological knowledge can predict and positively influence human behaviour in response to disasters

Maternal trait personality and breastfeeding duration: the importance of confidence and social support

AIM: To explore associations among breastfeeding duration, maternal personality and maternal attitudes and experiences of breastfeeding. BACKGROUND: Understanding influences on breastfeeding initiation and duration is critical to increasing breastfeeding rates and supporting new mothers. Maternal characteristics such as self‐efficacy, knowledge and confidence are known to enable women to breastfeed, but little is known about the influence of maternal trait personality on breastfeeding. DESIGN: An exploratory cross‐sectional survey. METHOD: A total of 602 mothers with an infant aged 6–12 months old completed a self‐report questionnaire examining maternal trait personality, breastfeeding duration and attitudes and experiences of breastfeeding. Data were collected between March–June 2009. RESULTS: Mothers who reported high levels of extraversion, emotional stability and conscientiousness were significantly more likely to initiate and continue breastfeeding for a longer duration. Attitudes and experiences significantly associated with these personality traits such as perceived difficulties and lack of support may explain these patterns. For example, characteristics associated with introversion and anxiety may prevent women from seeking support or challenging negative attitudes of others at this critical time. CONCLUSION: Understanding the influence of maternal personality may thus be a useful tool in antenatal support to recognize women who may need extra, directed support while facilitating discussion of potential barriers to breastfeeding

Mechanical traits of isolated nuclei inspected via force spectroscopy

The larger stiffness of the nuclei when compared to the rest of the cell imposes a key restriction to cell deformability and their capability to traverse interstices. In contrast, cancerous cells have been reported to exhibit larger and poorly-defined nuclear shapes. Upon probing the mechanical properties of these abnormal nuclei, membrane rigidities were found to be below that of normal nuclei. A plausible explanation is an altered distribution of the nuclear chromatin. This argument is in line with the increased migration capabilities of invasive nuclei and their enhanced adaptability to the abnormal forces these cells experiment. As a response to mechanical stresses, the normal function of the nuclei is altered and can induce changes such as gene expression alteration in the cell. Despite the obvious relevance of the nuclear mechanical traits, few works report data directly acquired on nuclear membranes without any participation from the plasma membrane, which is bound to induce alterations that may disrupt results yielded by high sensitivity tests such as those performed using optical tweezers. In the present work, optical tweezers are used alongside force spectroscopy to test the mechanical traits of isolated nuclear membranes. Membranes’ Young moduli and, therefore, stiffnesses are calculated by performing indentation/retraction cycles inducing gentle deformation on the membranes using an optically trapped microbead. Nuclear membrane responses are studied as a function of the frequency with which cycles were performed to highlight possible dependency on the time lapse over which the perturbation is applied. Additionally, drastic pushing of the trapped bead against the membranes and pulling motions were performed to trigger more dramatic mechanical responses from the nuclei. During those perturbations, maximum indentation depth and maximum tension could be measured from simultaneously acquired confocal microscopy images.Ayudas para la recualificación del Sistema Universitario español modalidad Margarita Salas. NextGenerationEU (UE) & Ministerio de Universidades, Gobierno de España. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Modulation of Annexin-Induced Membrane Curvature by Cholesterol and the Anionic Lipid Headgroup during Plasma Membrane Repair

Annexins (ANXAs), calcium-sensitive phospholipid-binding proteins, are pivotal for cellular membrane repair, which is crucial for eukaryotic cell survival under membrane stress. With their unique trimeric arrangements and crystalline arrays on the membrane surface, ANXA4 and ANXA5 induce membrane curvature and rapidly orchestrate plasma membrane resealing. However, the influence of cholesterol and anionic lipid headgroups on annexin-induced membrane curvature remains poorly understood at the molecular level. Using all-atom molecular dynamics simulations, we measured the local curvature-induced underneath ANXA4 and ANXA5 monomers and trimers when they bind to lipid bilayers of distinct lipid compositions: PSPC (20% POPS, 80% POPC), PAPC (20% POPA, 80% POPC), and PSPCCHL (14% POPS, 56% POPC, 30% cholesterol). Laser injury experiments were conducted on MCF7 cells transfected to transiently express fluorescently labeled ANXA4 or ANXA5 to facilitate the examination of protein and lipid accumulation at the damage site. Annexin trimers induce higher curvature than monomers, particularly with cholesterol present. Annexin trimers induce similar curvatures on both PAPC and PSPC membranes. Notably, among monomers, ANXA5 induces the highest curvature on PAPC, suggesting more efficient recruitment of ANXA5 compared with ANXA4 in the early stages of membrane repair near a lesion. Laser injury experiments confirm rapid coaccumulation of phosphatidic acid lipids with ANXA4 and ANXA5 at repair sites, potentially enhancing the accumulation of annexins in the early stages of membrane repair.</p

Can you trust predictive uncertainty under real dataset shifts in digital pathology?

Deep learning-based algorithms have shown great promise for assisting pathologists in detecting lymph node metastases when evaluated based on their predictive accuracy. However, for clinical adoption, we need to know what happens when the test set dramatically changes from the training distribution. In such settings, we should estimate the uncertainty of the predictions, so we know when to trust the model (and when not to). Here, we i) investigate current popular methods for improving the calibration of predictive uncertainty, and ii) compare the performance and calibration of the methods under clinically relevant in-distribution dataset shifts. Furthermore, we iii) evaluate their performance on the task of out-of-distribution detection of a different histological cancer type not seen during training. Of the investigated methods, we show that deep ensembles are more robust in respect of both performance and calibration for in-distribution dataset shifts and allows us to better detect incorrect predictions. Our results also demonstrate that current methods for uncertainty quantification are not necessarily able to detect all dataset shifts, and we emphasize the importance of monitoring and controlling the input distribution when deploying deep learning for digital pathology.</p

Annexin A7 mediates lysosome repair independently of ESCRT-III

Lysosomes are crucial organelles essential for various cellular processes, and any damage to them can severely compromise cell viability. This study uncovers a previously unrecognized function of the calcium- and phospholipid-binding protein Annexin A7 in lysosome repair, which operates independently of the Endosomal Sorting Complex Required for Transport (ESCRT) machinery. Our research reveals that Annexin A7 plays a role in repairing damaged lysosomes, different from its role in repairing the plasma membrane, where it facilitates repair through the recruitment of ESCRT-III components. Notably, our findings strongly suggest that Annexin A7, like the ESCRT machinery, is dispensable for membrane contact site formation within the newly discovered phosphoinositide-initiated membrane tethering and lipid transport (PITT) pathway. Instead, we speculate that Annexin A7 is recruited to damaged lysosomes and promotes repair through its membrane curvature and cross-linking capabilities. Our findings provide new insights into the diverse mechanisms underlying lysosomal membrane repair and highlight the multifunctional role of Annexin A7 in membrane repair.</p

SSX addiction in melanoma propagates tumor growth and metastasis

Cancer/testis antigens are receiving attention as targets for cancer therapy due to their germ- and cancer cell-restricted expression. However, many of these antigens are inconsistently expressed among cancer types and individual tumors. Here, we show that members of the SSX cancer/testis antigen family comprise attractive targets in the majority of melanoma patients, as SSX is expressed in more than 90% of primary melanomas and metastases and plays a critical role in metastatic progression. Accordingly, SSX silencing in melanoma mouse xenograft models reduced tumor growth and completely abolished the formation of metastatic lesions in lungs and livers. Mechanistically, we demonstrate that silencing SSX in melanoma cells induces cell cycle S-phase stalling, leading to proliferative arrest and enhanced apoptosis, which elucidates the inhibitory effect of SSX loss on tumor growth and colonization capacity. Silencing SSX further compromised the capacity of melanoma cells to migrate and invade, influencing these cells’ capability to spread and colonize. Taken together, these studies highlight SSX proteins as pivotal targets in melanoma with implications for blocking metastatic progression.</p

Investigating the possible causal association of smoking with depression and anxiety using Mendelian randomisation meta-analysis: the CARTA consortium

Objectives: To investigate whether associations of smoking with depression and anxiety are likely to be causal, using a Mendelian randomisation approach. Design: Mendelian randomisation meta-analyses using a genetic variant (rs16969968/rs1051730) as a proxy for smoking heaviness, and observational meta-analyses of the associations of smoking status and smoking heaviness with depression, anxiety and psychological distress. Participants: Current, former and never smokers of European ancestry aged ≥16 years from 25 studies in the Consortium for Causal Analysis Research in Tobacco and Alcohol (CARTA). Primary outcome measures: Binary definitions of depression, anxiety and psychological distress assessed by clinical interview, symptom scales or self-reported recall of clinician diagnosis. Results: The analytic sample included up to 58 176 never smokers, 37 428 former smokers and 32 028 current smokers (total N=127 632). In observational analyses, current smokers had 1.85 times greater odds of depression (95% CI 1.65 to 2.07), 1.71 times greater odds of anxiety (95% CI 1.54 to 1.90) and 1.69 times greater odds of psychological distress (95% CI 1.56 to 1.83) than never smokers. Former smokers also had greater odds of depression, anxiety and psychological distress than never smokers. There was evidence for positive associations of smoking heaviness with depression, anxiety and psychological distress (ORs per cigarette per day: 1.03 (95% CI 1.02 to 1.04), 1.03 (95% CI 1.02 to 1.04) and 1.02 (95% CI 1.02 to 1.03) respectively). In Mendelian randomisation analyses, there was no strong evidence that the minor allele of rs16969968/rs1051730 was associated with depression (OR=1.00, 95% CI 0.95 to 1.05), anxiety (OR=1.02, 95% CI 0.97 to 1.07) or psychological distress (OR=1.02, 95% CI 0.98 to 1.06) in current smokers. Results were similar for former smokers. Conclusions: Findings from Mendelian randomisation analyses do not support a causal role of smoking heaviness in the development of depression and anxiety.This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland