Goal-Focused Emotion-Regulation Therapy (GET) for young adult survivors of testicular cancer: a pilot randomized controlled trial of a biobehavioral intervention protocol.

BackgroundTesticular cancer diagnosis and treatment, especially given its threat to sexuality and reproductive health, can be distressing in the formative period of young adulthood and the majority of young survivors experience impairing, distressing, and modifiable adverse outcomes that can persist long after medical treatment. These include psychological distress, impairment in pursuit of life goals, persistent physical side effects, elevated risk of secondary malignancies and chronic illness, and biobehavioral burden (e.g., enhanced inflammation, dysregulated diurnal stress hormones). However, few targeted interventions exist to assist young survivors in renegotiating life goals and regulating cancer-related emotions, and none focus on reducing the burden of morbidity via biobehavioral mechanisms. This paper describes the methodology of a randomized controlled biobehavioral trial designed to investigate the feasibility and preliminary impact of a novel intervention, Goal-focused Emotion-Regulation Therapy (GET), aimed at improving distress symptoms, emotion regulation, goal navigation skills, and stress-sensitive biomarkers in young adult testicular cancer patients.MethodsParticipants will be randomized to receive six sessions of GET or Individual Supportive Therapy (ISP) delivered over 8 weeks. In addition to indicators of intervention feasibility, we will measure primary (depressive and anxiety symptoms) and secondary (emotion regulation and goal navigation skills, career confusion) psychological outcomes prior to (T0), immediately after (T1), and 12 weeks after (T2) intervention. Additionally, identified biomarkers will be measured at baseline and at T2.DiscussionGET may have the potential to improve self-regulation across biobehavioral domains, improve overall cancer adjustment, and address the need for targeted supportive care interventions for young adult cancer survivors.Trial registrationClinicaltrials.gov, NCT04150848. Registered on 28 October 2019

Molecular Adaptation of Borrelia burgdorferi in the Murine Host

An analysis of expression of 137 lipoprotein genes on the course of murine infection revealed a two-step molecular adaptation by Borrelia burgdorferi, the Lyme disease spirochete. For the first step, regardless whether the initial inocula of B. burgdorferi expressed either all (cultured spirochetes) or less than 40 (host-adapted spirochetes) of the 137 lipoprotein genes, the spirochetes were modulated to transcribe 116 of the genes within 10 d after being introduced to the murine host. This step of adaptation was induced by the microenvironment of the host tissue. During the second step, which was forced by host immune selection pressure and occurred between 17 and 30 d after infection, B. burgdorferi down-regulated most of the lipoprotein genes and expressed less than 40 of the 137 genes. This novel adaptation mechanism could be a critical step for B. burgdorferi to proceed to chronic infection, as the pathogen would be cleared at the early stage of infection if the spirochetes failed to undergo this process

Spiky oscillations in NF-kB signalling

The NF-kB signalling system is involved in a variety of cellular processes including immune response, inflammation, and apoptosis. Recent experiments have found oscillations in the nuclear-cytoplasmic translocation of the NF-kB transcription factor. How the cell uses the oscillations to differentiate input conditions and send specific signals to downstream genes is an open problem. We shed light on this issue by examining the small core network driving the oscillations, which, we show, is designed to produce periodic spikes in nuclear NF-kB concentration. The oscillations can be used to regulate downstream genes in a variety of ways. In particular, we show that genes to whose operator sites NF-kB binds and dissociates fast can respond very sensitively to changes in the input signal, with effective Hill coefficients in excess of 20.Comment: 11 pages, 13 figure

Where does stress happen? Ecological momentary assessment of daily stressors using a mobile phone app.

Despite the importance of daily stress to individuals' health and wellbeing, few studies have explored where stress happens in real time. As such, stress interventions rarely account for the environment in which stress occurs. We used ecological momentary assessment (EMA) to collect daily stress data. Thirty-three participants utilized a mobile phone-based EMA app to record stressors as they went about their daily lives. GPS coordinates were automatically collected with each stress report. Data from thematic and geographic information system (GIS) analysis were used in a chi-square analysis of stressors by location (home, work, work from home, and other) to determine if certain stressors were more prevalent in certain environments. The study found that nine daily stressors significantly differed by location. Work-related stress was reported more often at work but was also commonly experienced at home. In contrast, pets, household chores, sleep and media related stressors were reported most at home, but not experienced as often in other locations. Physical illnesses, vehicles or driving, and law and order stressors occurred most often in the 'work from home' condition. Traffic-related stress was experienced more common in 'other' environments. Study findings: 1) expand the understanding of environments in which specific stressors occur; 2) extend the nomological network of cognitive appraisal theory to include stress experienced in free-living conditions; 3) provide baseline data for potential targeted 'just-in-time' stress interventions, tailored to specific stressors in certain environments; 4) provide findings related to the 'work from home' phenomenon, increasingly popular during and after the COVID-19 pandemic

A data-driven conceptual framework for understanding the nature of hazards in railway accidents

Hazards threaten railway safety by their potential to trigger railway accidents. Whilst there are a considerable number of prior works investigating railway hazards, few offer a holistic view of hazards across jurisdictions and time and demonstrate policy implementation due to the inability to analyse a large amount of safety-related textual data. The conceptual framework HazardMap is developed to overcome this gap, employing open-sourced Natural Language Processing topic model BERTopic for the automated analysis of textual data from Rail Accident Investigation Branch (RAIB), Australian Transport Safety Bureau (ATSB), National Transportation Safety Board (NTSB) and Transportation Safety Board of Canada (TSB) railway accident reports. The topic modelling depicts the relationships between hazards, railway accidents and investigator recommendations and is further extended and integrated with the existing risk theory and epidemiological accident models. Results show that each hazard in the railway system has different aspects and could trigger a railway accident when combined with other hazards. Each aspect can be partially or fully addressed by implementing hazard mitigation policies such as introducing new technologies or regulations. A case study of the application to the risk at level crossings is provided to illustrate how HazardMap works with real-world data. This demonstrates a high degree of coverage within the existing risk management system, indicating the capability of helping policymaking for managing risks with adequate accuracy. The primary contributions of the framework proposed are to enable a huge amount of knowledge accumulated for an intuitive policymaking process to be summarised, and to allow other railway investigators to leverage lessons learnt across jurisdictions and time with limited human intervention. Future research could incorporate data from road, aviation or maritime accidents

RecoMap - a semi-automated tool for analysing railway accident recommendations across jurisdictions and over time

To maintain a safer railway operational environment, recommendations are proposed by independent investigators after accidents. Despite a considerable number of (sometimes similar) recommendations made across jurisdictions, practitioners suffer from a lack of synthesised recommendations made across jurisdictions and time due to the high complexity of analysing textual data. To fulfil the gap, an auto mated tool for the analysis of accident report recommendations is developed, allowing the railway industry to learn from other countries. The Structural Topic Model (STM) is used to extract critical insights from recommendations to depict how independent railway accident investigators mitigate risks observed. Empirical data is retrieved from official railway accident reports published by Rail Accident Investigation Branch (RAIB), Australian Transport Safety Bureau (ATSB), National Transportation Safety Board (NTSB) and Transportation Safety Board of Canada (TSB). The resulting RecoMap is developed as a framework to help practitioners learn across jurisdictions and time. The study also identifies a transition from making interfering recommendations addressing operational issues to making supportive recommendations addressing organisational issues in the railway industry across countries. Additionally, the concept of triple-loop learning is insufficient in the railway industry of the investigated jurisdictions, implying that current practices might result in railway accidents that could have been prevented by learning from other jurisdictions and implementing corresponding mitigation measures in advance