Follicular (Infundibular-Tricholemmal) Squamous Cell Carcinoma: A New WHO Entity. Clinicopathological Features in 103 Cases, Including Follow-Up and Implications for Patient Management.

AIMS: Cutaneous follicular (infundibular-tricholemmal) squamous cell carcinoma (FSCC) is a new World Health Organization entity. We present the largest series of published cases, summarizing clinical data, diagnostic criteria, differential diagnosis, and implications for patient management. METHODS: Cases were identified from 2004 to 2011. Inclusion criteria included discrete attachment(s) of the tumor to the overlying epidermis via follicular infundibula, tricholemmal keratinization, and cellular pleomorphism. Keratoacanthoma and lesions with adjacent bowenoid epidermal dysplasia were excluded. RESULTS: One hundred three cases of FSCC identified. 48.5% demonstrated completely circumscript borders (in situ for practical purposes), 12.6% uncertain for invasion (overwhelmingly pushing borders), and only 38.8% as clearly invasive. Follicular mucin in acantholytic spaces within tumor epithelium was a distinctive finding in 57.2% of cases. Clinical data indicated predominance in elderly (median 78.5 years) men (70.4%), with preferential head and neck location (81.6%). Many were clinically suspected as squamous cell carcinoma (48.5%). However, a significant minority were clinically diagnosed as basal cell carcinoma (40.8%). This may reflect that FSCC commonly presented as a papule or nodule (51.3%). By contrast, keratoacanthoma was less frequently suggested (17.2%) and still fewer lesions were suspected to be actinic keratosis/Bowen's disease (13.6%). Follow-up in 82 cases (median 26.5 months, range 3-144) identified 5 (6.1%) local recurrences. There was no instance of metastasis in the subgroup of lesions with completely circumscript borders. Three of 45 (6.7%) patients, with follow-up, considered to have tumors with invasive pushing, and/or infiltrative borders developed lymph node metastases. CONCLUSIONS: FSCC is identified as a common skin cancer, incorporating historical entities, such as infundibular carcinoma and tricholemmal carcinoma, with readily identifiable histologic features. Correct diagnosis has implications for patient management; a significant subgroup of lesions show completely circumscript borders that are considered in situ for practical purposes.

Machine and deep learning models for accurate detection of ischemia and scar with myocardial blood flow positron emission tomography imaging.

BACKGROUND: Quantification of myocardial blood flow (MBF) is used for the noninvasive diagnosis of patients with coronary artery disease (CAD). This study compared traditional statistics, machine learning, and deep learning techniques in their ability to diagnose disease using only the rest and stress MBF values. METHODS: This study included 3245 rest and stress rubidium-82 positron emission tomography (PET) studies and matching diagnostic labels from perfusion reports. Standard logistic regression, lasso logistic regression, support vector machine, random forest, multilayer perceptron, and dense U-Net were compared for per-patient detection and per-vessel localization of scars and ischemia. RESULTS: Receiver-operator characteristic area under the curve (AUC) of machine learning models was significantly higher than those of traditional statistics models for per-patient detection of disease (0.92-0.95 vs. 0.87) but not for per-vessel localization of ischemia or scar. Random forest showed the highest AUC = 0.95 among the different models compared. On the final hold-out set for generalizability, random forest showed an AUC of 0.92 for detection and 0.89 for localization of perfusion abnormalities. CONCLUSIONS: For per-vessel localization, simple models trained on segmental data performed similarly to a convolutional neural network trained on polar-map data, highlighting the need to justify the use of complex predictive algorithms through comparison with simpler methods.

Complexity for complexity-How advanced modeling may limit its applicability for decision-makers.

As today's engineering systems have become increasingly sophisticated, assessing the efficacy of their safety-critical systems has become much more challenging. The more classical methods of "failure" analysis by decomposition into components related by logic trees, such as fault and event trees, root cause analysis, and failure mode and effects analysis lead to models that do not necessarily behave like the real systems they are meant to represent. These models need to display similar emergent and unpredictable behaviors to sociotechnical systems in the real world. The question then arises as to whether a return to a simpler whole system model is necessary to understand better the behavior of real systems and to build confidence in the results. This question is more prescient when one considers that the causal chain in many serious accidents is not as deep-rooted as is sometimes claimed. If these more obvious causes are not taken away, why would the more intricate scenarios that emanate from more sophisticated models be acted upon. The paper highlights the advantages of modeling and analyzing these "normal" deviations from ideality, so called weak signals, versus just system failures and near misses as well as catastrophes. In this paper we explore this question.

Aberrant p16, p53 and Ki-67 immunohistochemistry staining patterns can distinguish solitary keratoacanthoma from cutaneous squamous cell carcinoma.

Keratoacanthoma (KA) is widely considered a benign, usually self-resolving, neoplasm distinct from cutaneous squamous cell carcinoma (cSCC), while some consider KA to be indistinguishable from cSCC. Published studies indicate utility for p16, p53, Ki-67 immunostaining and elastic van Gieson (EVG) in the assessment of KA and cSCC. We compared clinical features and staining patterns for p16, p53, Ki-67 and EVG in fully excised KA, cSCC with KA-like features (cSCC-KAL) and other cSCC (cSCC-OTHER). Significant differences between KA, cSCC-KAL and cSCC-OTHER were found for head and neck location (20%, 86%, 84%), and duration <5 months (95%, 63%, 36%). KA shows both a mosaic pattern for p16 (>25-90% of neoplasm area) and peripheral graded pattern for p53 (up to 50% moderate and strong nuclear staining) in 92% compared with 0% of cSCC-KAL and 0% of cSCC-OTHER. In contrast, a highly aberrant pattern (usually null) for one or both p16 and p53, was present in 0% of KA, 83.8% of cSCC-KAL and 90.9% of cSCC-OTHER. Abnormal distribution of Ki-67 beyond the peripheral 1-3 cells was uncommon in KA (4.2%) and common in cSCC-KAL (67.6%) and cSCC-OTHER (88.4%). Moderate to striking entrapment of elastic and collagen fibres was present in the majority of KA (84%), cSCC-KAL (81%) and cSCC-OTHER (65%). KA are clinically distinct neoplasms typically of short duration occurring preferentially outside the head and neck and generally lacking aberrations of p16, p53 and Ki-67, compared with cSCC that have high rates of aberrant or highly aberrant p16, p53 and Ki-67, but EVG lacked specificity.

Computer Vision Analysis of Rheumatoid Arthritis Synovium Reveals Lymphocytic Inflammation Is Associated With Immunoglobulin Skewing in Blood.

OBJECTIVE: We sought to develop computer vision methods to quantify aggregates of cells in synovial tissue and compare these with clinical and gene expression parameters. METHODS: We assembled a computer vision pipeline to quantify five features encompassing synovial cell density and aggregates and compared these with pathologist scores, disease classification, autoantibody status, and RNA expression in a cohort of 156 patients with rheumatoid arthritis (RA) and 149 patients with osteoarthritis (OA). RESULTS: All five features were associated with pathologist scores of synovial lymphocytic inflammation (P < 0.0001). Three features that related to the cells per unit of tissue were significantly increased in patients with both seronegative and seropositive RA compared with those with OA; on the other hand, aggregate features (number and diameter) were significantly increased in seropositive, but not seronegative, RA compared with OA. Aggregate diameter was associated with the gene expression of immunoglobulin heavy-chain genes in the synovial tissue. Compared with blood, synovial immunoglobulin isotypes were skewed from IGHM and IGHD to IGHG3 and IGHG1. Further, patients with RA with high levels of lymphocytic infiltrates in the synovium demonstrated parallel skewing in their blood with a relative decrease in IGHGM (P < 0.002) and IGHD (P < 0.03) and an increase in class-switched immunoglobulin genes IGHG3 (P < 0.03) and IGHG1 (P < 0.002). CONCLUSION: High-resolution automated identification and quantification of synovial immune cell aggregates uncovered skewing in the synovium from naïve IGHD and IGHM to memory IGHG3 and IGHG1 and revealed that this process is reflected in the blood of patients with high inflammatory synovium.

Automatic speaker diarization for natural conversation analysis in autism clinical trials.

Challenges in social communication is one of the core symptom domains in autism spectrum disorder (ASD). Novel therapies are under development to help individuals with these challenges, however the ability to show a benefit is dependent on a sensitive and reliable measure of treatment effect. Currently, measuring these deficits requires the use of time-consuming and subjective techniques. Objective measures extracted from natural conversations could be more ecologically relevant, and administered more frequently-perhaps giving them added sensitivity to change. While several studies have used automated analysis methods to study autistic speech, they require manual transcriptions. In order to bypass this time-consuming process, an automated speaker diarization algorithm must first be applied. In this paper, we are testing whether a speaker diarization algorithm can be applied to natural conversations between autistic individuals and their conversational partner in a natural setting at home over the course of a clinical trial. We calculated the average duration that a participant would speak for within their turn. We found a significant correlation between this feature and the Vineland Adaptive Behaviour Scales (VABS) expressive communication score (r = 0.51, p = 7 × 10-5). Our results show that natural conversations can be used to obtain measures of talkativeness, and that this measure can be derived automatically, thus showing the promise of objectively evaluating communication challenges in ASD.

Machine learning identification of thresholds to discriminate osteoarthritis and rheumatoid arthritis synovial inflammation.

BACKGROUND: We sought to identify features that distinguish osteoarthritis (OA) and rheumatoid arthritis (RA) hematoxylin and eosin (H&E)-stained synovial tissue samples. METHODS: We compared fourteen pathologist-scored histology features and computer vision-quantified cell density (147 OA and 60 RA patients) in H&E-stained synovial tissue samples from total knee replacement (TKR) explants. A random forest model was trained using disease state (OA vs RA) as a classifier and histology features and/or computer vision-quantified cell density as inputs. RESULTS: Synovium from OA patients had increased mast cells and fibrosis (p < 0.001), while synovium from RA patients exhibited increased lymphocytic inflammation, lining hyperplasia, neutrophils, detritus, plasma cells, binucleate plasma cells, sub-lining giant cells, fibrin (all p < 0.001), Russell bodies (p = 0.019), and synovial lining giant cells (p = 0.003). Fourteen pathologist-scored features allowed for discrimination between OA and RA, producing a micro-averaged area under the receiver operating curve (micro-AUC) of 0.85±0.06. This discriminatory ability was comparable to that of computer vision cell density alone (micro-AUC = 0.87±0.04). Combining the pathologist scores with the cell density metric improved the discriminatory power of the model (micro-AUC = 0.92±0.06). The optimal cell density threshold to distinguish OA from RA synovium was 3400 cells/mm2, which yielded a sensitivity of 0.82 and specificity of 0.82. CONCLUSIONS: H&E-stained images of TKR explant synovium can be correctly classified as OA or RA in 82% of samples. Cell density greater than 3400 cells/mm2 and the presence of mast cells and fibrosis are the most important features for making this distinction.

Mapping Processes in the Emergency Department Using the Functional Resonance Analysis Method.

Emergency departments (EDs) are dynamic, complex, and demanding environments. Introducing changes that lead to improvements in EDs can be challenging owing to the high staff turnover and mix, high patient volume with different needs, and being the front door to the hospital for the sickest patients. Quality improvement is a methodology applied routinely in EDs to instigate change to improve several outcomes such as waiting times, time to definitive treatment, and patient safety. Introducing the changes needed to transform the system in this way is seldom straightforward with the risk of "not seeing the forest for the trees" when attempting to change the system. In this article, we demonstrate how the functional resonance analysis method can be used to capture the experiences and perceptions of frontline staff to identify the key functions in the system (the trees), to understand the interactions and dependencies between them to make up the ED ecosystem ("the forest") and to support quality improvement planning, identifying priorities and patient safety risks.

Exploring procedure duration and risk for serious adverse events during congenital cardiac catheterization.

Objectives: While procedure length is considered an important metric for cardiothoracic surgical procedures, the relationship between procedure length and adverse events (AEs) in congenital cardiac catheterizations has little published data available. Furthermore, most existing congenital cardiac catheterization risk prediction models are built on logistic regression models. This study aimed to characterize the relationship between case length and AE occurrence in congenital cardiac catheterization while adjusting for known risk factors and to investigate the potential role of non-linear analysis in risk modeling. Design: Age, case type, and procedure duration were evaluated for relationships with the primary outcome using logistic regression. Non-linearity of the associations with continuous risk factors was assessed using restricted cubic spline transformations. Setting and participants: All diagnostic and interventional congenital cardiac catheterization cases performed at Boston Children's Hospital between January 1, 2014 and October 31, 2019 were analyzed. Main outcome measure: The primary outcome was defined as the occurrence of any clinically significant (level 3/4/5) AE. Results: A total of 7011 catheterization cases met inclusion criteria, with interventional procedures accounting for 68% of cases. Median case duration was 97 min. A multivariable model including age, procedure type, and case duration showed a significant relationship between case duration and AE occurrence (OR 1.07 per 10 min increase, 95% CI 1.06 to 1.09, p<0.001). Conclusions: This study demonstrated the importance of procedure duration as a potential frontier for procedure risk management. Better understanding of the role of procedure duration in cardiac catheterizations may provide opportunities for quality improvement in patient safety and resource planning.

The ethical dilemmas of risky decisions.

Even in a pandemic there seem to be inherent conflicts of interest between the individual and societal consequences of remedial actions and strategies. Actions taken in the sole interests of patients, as required by the Hippocratic oath, can have broadly inconvenient economic implications for the State. ("Average" benefits for a population can impose individual inconveniences for the vulnerable.). Understandably these decisions are not normally made explicitly and transparently by governments. This leads to seemingly illogical and inhumane strategies which are not understood and hence mistrusted and often ignored by the public. Vaccination sentiments on social media are often an unwanted symptom of this dilemma. This article outlines and discusses a number of examples of such situations with a focus on ethical aspects. It concludes that each case must be considered individually as to the issues that need to be weighed in these difficult decisions; and that there are no clear and universally acceptable ethical solutions. What can be learned from the COVID-19 crisis is that short term utilitarianism has consequences that in the eyes of the population are unacceptable. This lesson seems equally valid for cost benefit evaluations regarding other risks, such as from hazardous industries, flood defenses, and air transport. Decisionmakers and politicians can learn that persuasion only goes so far. In the end the people appear to prioritize in terms of deontology.

Rheumatoid Arthritis Synovial Inflammation Quantification Using Computer Vision.

OBJECTIVE: We quantified inflammatory burden in rheumatoid arthritis (RA) synovial tissue by using computer vision to automate the process of counting individual nuclei in hematoxylin and eosin images. METHODS: We adapted and applied computer vision algorithms to quantify nuclei density (count of nuclei per unit area of tissue) on synovial tissue from arthroplasty samples. A pathologist validated algorithm results by labeling nuclei in synovial images that were mislabeled or missed by the algorithm. Nuclei density was compared with other measures of RA inflammation such as semiquantitative histology scores, gene-expression data, and clinical measures of disease activity. RESULTS: The algorithm detected a median of 112,657 (range 8,160-821,717) nuclei per synovial sample. Based on pathologist-validated results, the sensitivity and specificity of the algorithm was 97% and 100%, respectively. The mean nuclei density calculated by the algorithm was significantly higher (P < 0.05) in synovium with increased histology scores for lymphocytic inflammation, plasma cells, and lining hyperplasia. Analysis of RNA sequencing identified 915 significantly differentially expressed genes in correlation with nuclei density (false discovery rate is less than 0.05). Mean nuclei density was significantly higher (P < 0.05) in patients with elevated levels of C-reactive protein, erythrocyte sedimentation rate, rheumatoid factor, and cyclized citrullinated protein antibody. CONCLUSION: Nuclei density is a robust measurement of inflammatory burden in RA and correlates with multiple orthogonal measurements of inflammation.

A systems analysis of the UK COVID 19 pandemic response: Part 2 - work as imagined vs work as done.

Modelling complex sociotechnical systems to try to understand how observed behaviours emerge from a network of interacting, interdependent and interrelated functions is a major challenge. Woods et al (Branlat, 2010), have pointed out that it is difficult to find a satisfactory current methodology. They suggested that perhaps the Functional Resonance Analysis Methodology developed by Hollnagel (Hollnagel, 2012) could be an appropriate approach to try. In Part 1 this approach was employed to build an overall model of the UK's COVID 19 response management system, which was constructed as a framework against which, a series of more detailed analyses of specific health care responses could be examined. This meant being conscious of the bigger picture of simultaneous activities and the dynamic emergence of unexpected developments. At that stage, it was of necessity a model of "work as imagined" from careful official and speculative media sources. Although, a full rigorous application will require a more authenticated, official (peer reviewed?), set of data, reports and evidence statements, which doubtless will be available eventually from the inevitable Public Inquiry, it seems a pity to delay gaining and applying any insights and adaptations from "work as (actually) done", that have been observed to date. The recent select committee evidence from people at the heart of the system, (Committee, 2021), which may, or may not be corroborated later, nevertheless now provides a provisional database, which can be utilised to test whether the FRAM model can produce such insights from the actual performance of this highly complex system. This paper has thus attempted a trial run and has found that it can produce a plausible set of insights, which can explain how the system behaved in practice. With such a serious challenge to Government systems worldwide, in all their advisory, operation and decision-making functions, such insights although provisional, could usefully be incorporated and formalised in the current systems rather than waiting for inquiry endorsed recommendations to be formally considered perhaps some years into the future. The paper thus sets out a set of conclusions and recommendations, caveated by emphasising the lack of fully authenticated public domain data on which it is based. The main conclusion however is that the current system appears not to have included any of the functions which could have provided the adaptability and resilience required by fast moving emergencies, such as pandemics. The exception noted was the establishment of a parallel, independent vaccine development and delivery function and it is hoped that at least this lesson from the work as done could be recognised as providing the type of adaptability required and incorporated without delay.

A systems analysis of the COVID-19 pandemic response in the United Kingdom - Part 1 - The overall context.

The most common reaction to suggesting that we could learn valuable lessons from the way the current pandemic has been/ is being handled, is to discourage the attempt; as it is suggested that it can all be done more accurately and authoritatively after the inevitable Public Inquiry (Slater, 2019). On the other hand, a more constructive approach, is to capture and understand the work that was actually done.This would include normal activities, as well as positive adaptations to challenges and failures that may have occurred. Such an approach aimed at improving what worked, rather than blaming people for what went wrong, has the potential to contribute more successfully to controlling the consequences of the current crisis. Such an approach should thus be aimed at detecting and feeding back lessons from emerging and probably unexpected behaviours and helping to design the system to adapt better to counter the effects. The science and discipline of Human Factors (HF) promotes system resilience. This can be defined as an organisation's ability to adjust its functioning before, during or after significant disturbances (such as a pandemic), enabling adaptation and operation under both anticipated and unanticipated circumstances. A "functional" approach methodology enables the identification of where the system and its various interdependent functions (an activity or set of activities that are required to give a certain output), could be improved and strengthened; if not immediately, at least for the future. Along these lines, suggestions for adding key resilience functions are additionally identified and outlined. The application and insights gained from this functional approach to the 2015 MERS-Cov pandemic in South Korea has been seen as contributing substantially to the effective response to the current crisis in that country (Min, submitted for publication). In this paper, we present an overarching framework for a series of projects that are planned to carry out focussed systems-based analysis to generate learning from key aspects of the COVID-19 pandemic response in the United Kingdom.

Adaptations to practice and resilience in a paediatric major trauma centre during a mass casualty incident.

BACKGROUND: Innovation and human adaptation in the face of unfolding catastrophe is the cornerstone of an effective systemwide response. Capturing, analysing, and disseminating this is fundamental in developing resilience for future events. The aim of this study was to understand the characteristics of adaptations to practice early in a paediatric major trauma centre during a mass casualty incident. METHODS: A qualitative interview study of 40 healthcare staff at a paediatric major trauma centre in the immediate aftermath of a terrorist bombing was conducted. An inductive thematic analysis approach was used, followed by a deductive analysis of the identified adaptations informed by constructs of resilience engineering. RESULTS: Five themes of adaptations to practice that enhanced the resilient performance of the hospital were identified: teamworking; psychologically supporting patients, families, and staff; reconfiguring infrastructure; working around the hospital electronic systems; and maintaining hospital safety. Examples of resilience potential in terms of respond, monitor, anticipate, and learn are presented. CONCLUSIONS: Our study shows how adaptations to practice sustained the resilient performance of a paediatric major trauma centre during a mass casualty incident. Rapid, early capture of these data during a mass casualty incident provides key insights into enhancing future emergency preparedness, response, and resilience planning.

Early life experience sets hard limits on motor learning as evidenced from artificial arm use.

The study of artificial arms provides a unique opportunity to address long-standing questions on sensorimotor plasticity and development. Learning to use an artificial arm arguably depends on fundamental building blocks of body representation and would therefore be impacted by early life experience. We tested artificial arm motor-control in two adult populations with upper-limb deficiencies: a congenital group-individuals who were born with a partial arm, and an acquired group-who lost their arm following amputation in adulthood. Brain plasticity research teaches us that the earlier we train to acquire new skills (or use a new technology) the better we benefit from this practice as adults. Instead, we found that although the congenital group started using an artificial arm as toddlers, they produced increased error noise and directional errors when reaching to visual targets, relative to the acquired group who performed similarly to controls. However, the earlier an individual with a congenital limb difference was fitted with an artificial arm, the better their motor control was. Since we found no group differences when reaching without visual feedback, we suggest that the ability to perform efficient visual-based corrective movements is highly dependent on either biological or artificial arm experience at a very young age. Subsequently, opportunities for sensorimotor plasticity become more limited.

Self-Reported and Objective Sleep Measures in Stroke Survivors With Incomplete Motor Recovery at the Chronic Stage.

Background. Stroke survivors commonly complain of difficulty sleeping. Poor sleep is associated with reduced quality of life and more understanding of long-term consequences of stroke on sleep is needed. Objective. The primary aims were to (1) compare sleep measures between chronic stroke survivors and healthy controls and (2) test for a relationship between motor impairment, time since stroke and sleep. Secondary aims were to explore mood and inactivity as potential correlates of sleep and test the correlation between self-reported and objective sleep measures. Methods. Cross-sectional sleep measures were obtained for 69 chronic stroke survivors (mean 65 months post-stroke, 63 years old, 24 female) and 63 healthy controls (mean 61 years old, 27 female). Self-reported sleep was assessed with the sleep condition indicator (SCI) and sleep diary ratings, objective sleep with 7-nights actigraphy and mood with the Hospital Anxiety and Depression Scale. Upper extremity motor impairment was assessed with the Fugl-Meyer assessment. Results. Stroke survivors had significantly poorer SCI score (P < .001) and higher wake after sleep onset (P = .005) than controls. Neither motor impairment, nor time since stroke, explained significant variance in sleep measures for the stroke group. For all participants together, greater depression was associated with poorer SCI score (R2adj = .197, P < .001) and higher age with more fragmented sleep (R2adj = .108, P < .001). There were weak correlations between nightly sleep ratings and actigraphy sleep measures (rs = .15-.24). Conclusions. Sleep disturbance is present long-term after stroke. Depressive symptoms may present a modifiable factor which should be investigated alongside techniques to improve sleep in this population.

Beacon-Based Remote Measurement of Social Behavior in ASD Clinical Trials: A Technical Feasibility Assessment.

In this work, we propose a Bluetooth low energy (BLE) beacon-based algorithm to enable remote measurement of the social behavior of the participants of an observational Autism Spectrum Disorder (ASD) clinical trial (NCT03611075). We have developed a mobile application for a smartphone and a smartwatch to collect beacon signals from BLE beacon sensors as well as to store information about the participants' household rooms. Our goal is to collect beacon information about the time the participants spent in different rooms of their household to infer sociability information. We applied the same technology and setup in an internal experiment with healthy volunteers to evaluate the accuracy of the proposed algorithm in 10 different home setups, and we observed an average accuracy of 97.2%. Moreover, we show that it is feasible for the clinical study participants/caregivers to set up the BLE beacon sensors in their homes without any technical help, with 96% of them setting up the technology on the first day of data collection. Next, we present results from one-week location data from study participants collected through the proposed technology. Finally, we provide a list of good practice guidelines for optimally applying beacon technology for indoor location monitoring. The proposed algorithm enables us to estimate time spent in different rooms of a household that can pave the development of objective sociability features and eventually support decisions regarding drug efficacy in ASD.

A novel approach to explore Safety-I and Safety-II perspectives in in situ simulations-the structured what if functional resonance analysis methodology.

OBJECTIVES: With ever increasingly complex healthcare settings, technology enhanced simulation (TES) is well positioned to explore all perspectives to enhance patient safety and patient outcomes. Analysis from a Safety-II stance requires identification of human adjustments in daily work that are key to maintaining safety. The aim of this paper is to describe an approach to explore the consequences of human variability from a Safety-II perspective and describe the added value of this to TES. METHODS: The reader is guided through a novel application of functional resonance analysis methodology (FRAM), a method to analyse how a system or activity is affected by human variability, to explore human adaptations observed in in situ simulations (ISS). The structured applicability of this novel approach to TES is described by application to empirical data from the standardised ISS management of paediatric time critical head injuries (TCHI). RESULTS: A case series is presented to illustrate the step-wise observation of key timings during ISSs, the construction of FRAM models and the visualisation of the propagation of human adaptations through the FRAM models. The key functions/actions that ensure the propagation are visible, as are the sequelae of the adaptations. CONCLUSIONS: The approach as described in this paper is a first step to illuminating how to explore, analyse and observe the consequences of positive and negative human adaptations within simulated complex systems. This provides TES with a structured methodology to visualise and reflect upon both Safety-I and Safety-II perspectives to enhance patient safety and patient outcomes.

Intronic TP53 Polymorphisms Are Associated with Increased Δ133TP53 Transcript, Immune Infiltration and Cancer Risk.

We investigated the influence of selected TP53 SNPs in exon 4 and intron 4 on cancer risk, clinicopathological features and expression of TP53 isoforms. The intron 4 SNPs were significantly over-represented in cohorts of mixed cancers compared to three ethnically matched controls, suggesting they confer increased cancer risk. Further analysis showed that heterozygosity at rs1042522(GC) and either of the two intronic SNPs rs9895829(TC) and rs2909430(AG) confer a 2.34-5.35-fold greater risk of developing cancer. These SNP combinations were found to be associated with shorter patient survival for glioblastoma and prostate cancer. Additionally, these SNPs were associated with tumor-promoting inflammation as evidenced by high levels of infiltrating immune cells and expression of the Δ133TP53 and TP53ß transcripts. We propose that these SNP combinations allow increased expression of the Δ133p53 isoforms to promote the recruitment of immune cells that create an immunosuppressive environment leading to cancer progression.