A Bayesian Network Approach to Lung Cancer Screening: Assessing the Impact of Data Quantity, Quality, and the Combination of Data from Danish Electronic Health Records.

: Lung cancer (LC) is the leading cause of cancer mortality, making early diagnosis essential. While LC screening trials are underway globally, optimal prediction models and inclusion criteria are still lacking. This study aimed to develop and evaluate Bayesian Network (BN) models for LC risk prediction using a decade of data from Denmark.

Bivalent boosters and risk of post-acute sequelae following vaccine-breakthrough SARS-CoV-2 Omicron infection: a cohort study.

Background: Vaccination has been shown to attenuate the risk of post-acute sequelae following SARS-CoV-2 infection. However, no prior population-based studies have evaluated if updated bivalent boosters reduce risk of post-acute sequelae following Omicron-variant infection, versus ancestral vaccines.

Methods: National databases were utilised to construct a population-based cohort of adult individuals infected during Omicron-predominant transmission.

Actionability of Synthetic Data in a Heterogeneous and Rare Health Care Demographic: Adolescents and Young Adults With Cancer.

Purpose: Research on rare diseases and atypical health care demographics is often slowed by high interparticipant heterogeneity and overall scarcity of data. Synthetic data (SD) have been proposed as means for data sharing, enlargement, and diversification, by artificially generating real phenomena while obscuring the real patient data. The utility of SD is actively scrutinized in health care research, but the role of sample size for actionability of SD is insufficiently explored.

Artificial intelligence research in radiation oncology: a practical guide for the clinician on concepts and methods.

The use of artificial intelligence (AI) holds great promise for radiation oncology, with many applications being reported in the literature, including some of which are already in clinical use. These are mainly in areas where AI provides benefits in efficiency (such as automatic segmentation and treatment planning). Prediction models that directly impact patient decision-making are far less mature in terms of their application in clinical practice.

Skeletal muscle is independently associated with grade 3-4 toxicity in advanced stage pancreatic ductal adenocarcinoma patients receiving chemotherapy.

Background: Patients with advanced-stage pancreatic ductal adenocarcinoma (PDAC) are regularly treated with FOLFIRINOX, a chemotherapy regimen based on 5-fluorouracil, irinotecan and oxaliplatin, which is associated with high toxicity. Dosing of FOLFIRINOX is based on body surface area, risking under- or overdosing caused by altered pharmacokinetics due to interindividual differences in body composition. This study aimed to investigate the relationship between body composition and treatment toxicity in advanced stage PDAC patients treated with FOLFIRINOX.

Shwachman-Diamond Syndrome Presenting as Neonatal Ichthyosis.

Shwachman-Diamond syndrome (SDS) is a rare inherited bone marrow failure syndrome characterized by the triad of exocrine pancreatic dysfunction, cytopenia, and skeletal abnormalities. We report a 5-month-old boy with SDS who presented with generalized ichthyosis in the neonatal period that evolved into more eczematous skin eruptions, accompanied by severe failure to thrive. This report highlights the importance of including SDS as a differential diagnosis in patients who present with early ichthyosis, failure-to-thrive, gastrointestinal symptoms and cytopenia.

Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters.

Background: Accurate prognostication of overall survival (OS) for non-small cell lung cancer (NSCLC) patients receiving definitive radiotherapy (RT) is crucial for developing personalized treatment strategies. This study aims to construct an interpretable prognostic model that combines radiomic features extracted from normal lung and from primary tumor with clinical parameters. Our model aimed to clarify the complex, nonlinear interactions between these variables and enhance prognostic accuracy.

Improving communication during damage control surgery: a survey of adult major trauma centres in England.

Introduction: Deficiencies in non-technical skills can severely impede the functioning of teams in high-intensity scenarios, such as in damage control surgery for the critically injured trauma patient. Truncated preoperative checklists, modified from the standard World Health Organization preoperative checklist, and situational reporting at intervals during surgery are long-established practices in the military, and are recommended in the National Health Service guidelines on major incidents. These tools allow the multiprofessional team to create a shared mental model of the anaesthetic and operative plan, thereby improving team efficiency.

Mastering tracheostomy care: Refresher programme for tracheostomy training for nurses: Comparison of two training methods based on hands-on simulation-based training alone versus additional complementary self-directed e-learning.

Background: Effective clinical education is essential for managing tracheostomy patients safely and efficiently. Simulation-based training has shown greater efficacy than traditional methods in various clinical settings. Our internal training programme, called the Tracheostomy Refresher Program (TRP) was used to enhance nurses' skills in tracheostomy care.

External validation of a deep learning model for automatic segmentation of skeletal muscle and adipose tissue on abdominal CT images.

Objectives: Body composition assessment using CT images at the L3-level is increasingly applied in cancer research and has been shown to be strongly associated with long-term survival. Robust high-throughput automated segmentation is key to assess large patient cohorts and to support implementation of body composition analysis into routine clinical practice. We trained and externally validated a deep learning neural network (DLNN) to automatically segment L3-CT images.

Nurse-Led Interventions in Managing Cancer-Related Fatigue: A Systematic Review.

Background: Cancer-related fatigue (CRF) is common among patients with cancer. Various interventions have been reported to reduce fatigue, and some evidence suggests that nurse-led interventions may be effective in the management of CRF. A systematic review was conducted to assess the effectiveness of nurse-led interventions for CRF in patients with cancer and survivors.

Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study.

Objectives: Although neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy response image biomarkers.

Methods: This study retrospectively obtained non-contrast enhanced and contrast enhancedbubu CT scans of patients with NSCLC who underwent surgery after receiving neoadjuvant immunochemotherapy at multiple centers between August 2019 and February 2023.

Nirmatrelvir/ritonavir treatment and risk for postacute sequelae of COVID-19 in older Singaporeans.

Objectives: Significant heterogeneity has been reported in cohort studies evaluating the impact of early oral antiviral treatment on preventing postacute sequelae after COVID-19. We evaluated the impact of early nirmatrelvir/ritonavir on risk of postacute cardiovascular, neurological, respiratory, and autoimmune diagnoses, as well as postacute symptoms amongst older Singaporeans.

Methods: National COVID-19 registries and healthcare claims databases were used to construct a retrospective population-based cohort enrolling all Singaporeans aged ≥60 years diagnosed with SARS-CoV-2 infection in primary care during Omicron transmission (18 March 2022-4 August 2023).

Autoimmune Sequelae After Delta or Omicron Variant SARS-CoV-2 Infection in a Highly Vaccinated Cohort.

Importance: Studies have reported increased risk of autoimmune sequelae after SARS-CoV-2 infection. However, risk may potentially be attenuated by milder Omicron (B.1.

Incomplete transcriptional dosage compensation of chicken and platypus sex chromosomes is balanced by post-transcriptional compensation.

Heteromorphic sex chromosomes (XY or ZW) present problems of gene dosage imbalance between sexes and with autosomes. A need for dosage compensation has long been thought to be critical in vertebrates. However, this was questioned by findings of unequal mRNA abundance measurements in monotreme mammals and birds.

Improving Diagnosis and Care for Patients With Sarcoma: Do Real-World General Practitioners Data and Prospective Data Collections Have a Place Next to Clinical Trials?

There has been growing interest in the use of real-world data (RWD) to address clinically and policy-relevant (research) questions that cannot be answered with data from randomized controlled trials (RCTs) alone. This is, for example, the case in rare malignancies such as sarcomas as limited patient numbers pose challenges in conducting RCTs within feasible timeliness, a manageable number of collaborators, and statistical power. This narrative review explores the potential of RWD to generate real-world evidence (RWE) in sarcoma research, elucidating its application across different phases of the patient journey, from prediagnosis to the follow-up/survivorship phase.

Risks of SARS-CoV-2 JN.1 Infection and COVID-19-Associated Emergency Department Visits/Hospitalizations Following Updated Boosters and Prior Infection: A Population-Based Cohort Study.

Background: Data on protection afforded by updated coronavirus disease 2019 (COVID-19) vaccines (bivalent/XBB 1.5 monovalent) against the emergent JN.1 variant remain limited.