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BACKGROUND: Emerging evidence suggests that traumatic brain injury (TBI) is a major risk factor for developing neurodegenerative disease later in life. Quantitative susceptibility mapping (QSM) has been used by an increasing number of studies in investigations of pathophysiological changes in TBI. However, generating artefact-free quantitative susceptibility maps in brains with large focal lesions, as in the case of moderate-to-severe TBI (ms-TBI), is particularly challenging. To address this issue, we utilized a novel two-pass masking technique and reconstruction procedure (two-pass QSM) to generate quantitative susceptibility maps (QSMxT; Stewart et al., 2022, Magn Reson Med.) in combination with the recently developed virtual brain grafting (VBG) procedure for brain repair (Radwan et al., 2021, NeuroImage) to improve automated delineation of brain areas. We used QSMxT and VBG to generate personalised QSM profiles of individual patients with reference to a sample of healthy controls. METHODS: Chronic ms-TBI patients (N = 8) and healthy controls (N = 12) underwent (multi-echo) GRE, and anatomical MRI (MPRAGE) on a 3T Siemens PRISMA scanner. We reconstructed the magnetic susceptibility maps using two-pass QSM from QSMxT. We then extracted values of magnetic susceptibility in grey matter (GM) regions (following brain repair via VBG) across the whole brain and determined if they deviate from a reference healthy control group [Z-score < -3.43 or > 3.43, relative to the control mean], with the aim of obtaining personalised QSM profiles. RESULTS: Using two-pass QSM, we achieved susceptibility maps with a substantial increase in quality and reduction in artefacts irrespective of the presence of large focal lesions, compared to single-pass QSM. In addition, VBG minimised the loss of GM regions and exclusion of patients due to failures in the region delineation step. Our findings revealed deviations in magnetic susceptibility measures from the HC group that differed across

Recently developed small-molecule inhibitors of the lysosomal protease dipeptidyl peptidase 1 (DPP1), also known as cathepsin C (CatC), can suppress suppurative inflammation in vivo by blocking the processing of zymogenic (pro-) forms of neutrophil serine proteases (NSPs), including neutrophil elastase, proteinase 3, and cathepsin G. DPP1 also plays an important role in activating granzyme serine proteases that are expressed by cytotoxic T lymphocytes (CTL) and natural killer (NK) cells. Therefore, it is critical to determine whether DPP1 inhibition can also cause off-target suppression of CTL/NK-cell-mediated killing of virus-infected or malignant cells. Herein, we demonstrate that the processing of human granzymes A and B, transitioning from zymogen to active proteases, is not solely dependent on DPP1. Thus, the killing of target cells by primary human CD8+ T cells, NK cells, and gene-engineered anti-CD19 CAR T cells was not blocked in vitro even after prior exposure to high concentrations of the reversible DPP1 inhibitor brensocatib. Consistent with this observation, the turnover of model granzyme A/B peptide substrates in the human CTL/NK cell lysates was not significantly reduced by brensocatib. In contrast, preincubation with brensocatib almost entirely abolished (>90%) both the cytotoxic activity of mouse CD8+ T cells and granzyme substrate turnover. Overall, our finding that the effects of DPP1 inhibition on human cytotoxic lymphocytes are attenuated in comparison to those of mice indicates that granzyme processing/activation pathways differ between mice and humans. Moreover, the in vitro data suggest that human subjects treated with reversible DPP1 inhibitors, such as brensocatib, are unlikely to experience any appreciable deficits in CTL/NK-cell-mediated immunities.

Perforin is a pore-forming protein whose normal function enables cytotoxic T and natural killer (NK) cells to kill virus-infected and transformed cells. Conversely, unwanted perforin activity can also result in auto-immune attack, graft rejection and aberrant responses to pathogens. Perforin is critical for the function of the granule exocytosis cell death pathway and is therefore a target for drug development. In this study, by screening a fragment library using NMR and surface plasmon resonance, we identified 4,4-diaminodiphenyl sulfone (dapsone) as a perforin ligand. We also found that dapsone has modest (mM) inhibitory activity of perforin lytic activity in a red blood cell lysis assay in vitro. Sequential modification of this lead fragment, guided by structural knowledge of the ligand binding site and binding pose, and supported by SPR and ligand-detected 19F NMR, enabled the design of nanomolar inhibitors of the cytolytic activity of intact NK cells against various tumour cell targets. Interestingly, the ligands we developed were largely inert with respect to direct perforin-mediated red blood cell lysis but were very potent in the context of perforin's action on delivering granzymes in the immune synapse, the context in which it functions physiologically. Our work indicates that a fragment-based, structure-guided drug discovery strategy can be used to identify novel ligands that bind perforin. Moreover, these molecules have superior physicochemical properties and solubility compared to previous generations of perforin ligands.

Graph theoretical analysis of the structural connectome has been employed successfully to characterize brain network alterations in patients with traumatic brain injury (TBI). However, heterogeneity in neuropathology is a well-known issue in the TBI population, such that group comparisons of patients against controls are confounded by within-group variability. Recently, novel single-subject profiling approaches have been developed to capture inter-patient heterogeneity. We present a personalized connectomics approach that examines structural brain alterations in five chronic patients with moderate to severe TBI who underwent anatomical and diffusion magnetic resonance imaging. We generated individualized profiles of lesion characteristics and network measures (including personalized graph metric GraphMe plots, and nodal and edge-based brain network alterations) and compared them against healthy reference cases (N = 12) to assess brain damage qualitatively and quantitatively at the individual level. Our findings revealed alterations of brain networks with high variability between patients. With validation and comparison to stratified, normative healthy control comparison cohorts, this approach could be used by clinicians to formulate a neuroscience-guided integrative rehabilitation program for TBI patients, and for designing personalized rehabilitation protocols based on their unique lesion load and connectome.

OBJECTIVE: Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments. METHODS: Twelve emergency departments provided 3 years of retrospective administrative data from Australia (2017-2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020). RESULTS: There were 1 930 609 patient episodes analysed and median site wait times varied from 24 to 54 min. Individual site model prediction median absolute errors varied from±22.6 min (95% CI 22.4 to 22.9) to ±44.0 min (95% CI 43.4 to 44.4). Global model prediction median absolute errors varied from ±33.9 min (95% CI 33.4 to 34.0) to ±43.8 min (95% CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period. CONCLUSIONS: Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.

OBJECTIVE: The World Health Organization declared the COVID-19 pandemic on 11 March 2020. In 2021, several vaccines were provisionally approved to reduce the risk of transmission and hospitalisation of COVID-19 infection. A surge in COVID-19 vaccination was seen between August and October 2021 in Victoria, Australia. We hypothesised this led to an increase in ED presentations. METHODS: Patients in the present study were adults who presented to the ED within 21 days of receiving a dose of a COVID-19 vaccine between 11 August 2021 and 14 November 2021. All cases underwent chart reviews to extract epidemiological features, clinical presentations, ED assessments, investigations and disposition. RESULTS: Notably, 968 patients were included in the study, comprising 6.1% of all ED presentations during the study period. The median age was 31 years. 82.9% of patients were younger than 45 years. 20.1% of patients arrived by ambulance. Chest pain was the most common presenting complaint (43.6%), followed by headache (10.3%) and palpitations (8.2%). The most common investigations were a full blood examination (73.5%), an ECG (63.8%) and serum troponin (49.1%). 64.8% of patients were directly discharged home and 22.1% were sent home after a short stay admission. Only 2.2% of patients were admitted to the hospital. CONCLUSION: A majority of patients who presented to the ED after their COVID vaccinations were young and discharged home after the initial assessment. These presentations have significantly increased the workload in prehospital settings and EDs, contributing to increased investigation usage, ED treatment space occupancy, and increased costs to the health system.

OBJECTIVES: Alcohol is the most widely consumed psychoactive substance in Australia and the consequences of alcohol consumption have enormous personal and social impacts. This study aimed to describe the principal diagnoses of emergency department (ED) presentations involving alcohol use in the previous 12 hours at eight hospitals in Victoria and the Australian Capital Territory, Australia. METHODS: Twelve months' data (1 July 2018 - 30 June 2019) were collected from eight EDs, including demographics, ICD-10 codes, hospital location and self-reported drinking in the preceding 12 hours. The ten most common ICD-10 discharge codes were analysed based on age, sex and hospital geographic area. RESULTS: ICD codes pertaining to mental and behavioural disorders due to alcohol use accounted for the highest proportion in most EDs. Suicide ideation/attempt was in the five highest ICD codes for all but one hospital. It was the second most common alcohol-related presentation for both males and females. CONCLUSIONS: Alcohol plays a major role in a range of presentations, especially in relation to mental health and suicide. IMPLICATIONS FOR PUBLIC HEALTH: The collection of alcohol involvement in ED presentations represents a major step forward in informing the community about the burden of alcohol on their health resources.

Assault is the leading preventable cause of death, traumatic brain injury (TBI), and associated mental health problems. The COVID-19 pandemic has had a profound impact on patterns of interpersonal violence across the world. In this retrospective cross-sectional study, we analysed medical records of 1232 assault victims (domestic violence: 111, random assault: 900, prison assault: 221) with head injuries who presented to the emergency department (ED) at St Vincent's Hospital in Melbourne, Australia, a city with one of the longest and most severe COVID-19 restrictions worldwide. We examined changes in prevalence in the assault group overall and in domestic violence, random assault, and prison assault victims, comparing data from 19.5 months before and after the first day of COVID-19 restrictions in Melbourne. Moreover, we investigated differences driven by demographic factors (Who: age group, sex, and nationality) and clinical variables (Where: assault location, and When: time of arrival to the ED and time from moment of injury until presentation at ED). Descriptive statistics and chi-square analyses were performed. We found the COVID-19 pandemic significantly affected the Where of assault-related TBI, with a shift in the location of assaults from the street to the home, and the increase at home being driven by random assaults on middle-aged adults. Overall, we observed that 86% of the random assault cases were males, whereas 74% of the domestic assault cases were females. Meanwhile, nearly half (44%) of the random assault victims reported alcohol consumption versus a fifth (20%) of domestic violence victims. These findings will have direct implications for developing screening tools and better preventive and ameliorative interventions to manage the sequelae of assault TBI, particularly in the context of future large-scale health crises or emergencies.

OBJECTIVE: To quantify the attitude ED clinicians hold towards patients presenting with different medical conditions, including a novel pandemic condition. METHODS: A cross-sectional study of emergency doctors and nurses utilising the Medical Condition Regard Scale (MCRS); a validated tool used to capture the bias and emotions of clinicians towards individual medical conditions. The five conditions presented to participants each represent a classical medical, complex medical, psychiatric/substance use, somatoform and a novel medical condition. RESULTS: One hundred and ninety-six clinicians were included in the study including 116 nurses and 80 doctors. Concerning each condition, both medical and nursing staff demonstrated the highest regard for a classical medical condition (58 ± 5 and 57 ± 6, respectively). Significantly different from the classical medical condition, the lowest MCRS scores were for the somatoform condition (36 ± 10) for emergency doctors and the substance use condition (39 ± 11) for emergency nurses. Regard for a novel condition (i.e., COVID-19 infection) was comparably high among both cohorts. CONCLUSION: Emergency doctors and nurses generally hold lower regard for complex medical conditions with behavioural components, including substance use disorders and somatoform conditions.

New drugs that precisely target the immune mechanisms critical for cytotoxic T lymphocyte (CTL) and natural killer (NK) cell driven pathologies are desperately needed. In this perspective, we explore the cytolytic protein perforin as a target for therapeutic intervention. Perforin plays an indispensable role in CTL/NK killing and controls a range of immune pathologies, while being encoded by a single copy gene with no redundancy of function. An immunosuppressant targeting this protein would provide the first-ever therapy focused specifically on one of the principal cell death pathways contributing to allotransplant rejection and underpinning multiple autoimmune and postinfectious diseases. No drugs that selectively block perforin-dependent cell death are currently in clinical use, so this perspective will review published novel small molecule inhibitors, concluding with in vivo proof-of-concept experiments performed in mouse models of perforin-mediated immune pathologies that provide a potential pathway toward a clinically useful therapeutic agent.

The COVID-19 pandemic has imposed significant mental health burdens upon the general population worldwide, either directly owing to the disease or indirectly through aggressive public health measures to control spread of the virus that causes COVID-19. In this narrative review, we used a systematic approach to summarize the impact of restrictive lockdown measures on the general mental health of people living in Victoria, Australia during 2020 and to identify the groups with an increased risk of adverse mental health outcomes. A systematic database search (Ovid Medline, PsycINFO, Embase) for articles examining the mental health of Victorians in the context of the COVID-19 pandemic during 2020 yielded 88 articles, of which 15 articles were finally included in this review. We found that the general mental health of Victorians was negatively affected by COVID-19 restrictions during 2020. Although studies reported heterogeneous mental health outcomes, we found that the general population consistently used coping strategies and demonstrated mental health help-seeking behaviors in response to the restrictions. Women, children, young people, carers, people who became unemployed owing to the pandemic, and those with pre-existing psychiatric conditions had a higher risk of adverse mental health consequences during the COVID-19 pandemic in 2020.

Objective: Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments. Methods: Twelve emergency departments provided 3 years of retrospective administrative data from Australia (2017–2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020). Results: There were 1 930 609 patient episodes analysed and median site wait times varied from 24 to 54 min. Individual site model prediction median absolute errors varied from±22.6 min (95% CI 22.4 to 22.9) to ±44.0 min (95% CI 43.4 to 44.4). Global model prediction median absolute errors varied from ±33.9 min (95% CI 33.4 to 34.0) to ±43.8 min (95% CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period. Conclusions: Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.

OBJECTIVE: Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments. METHOD(S): Twelve emergency departments provided 3years of retrospective administrative data from Australia (2017-2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020). RESULT(S): There were 1930609 patient episodes analysed and median site wait times varied from 24 to 54min. Individual site model prediction median absolute errors varied from+/-22.6min (95%CI 22.4 to 22.9) to +/-44.0min (95%CI 43.4 to 44.4). Global model prediction median absolute errors varied from +/-33.9min (95%CI 33.4 to 34.0) to +/-43.8min (95%CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period. CONCLUSION(S): Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.Copyright © Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.

OBJECTIVE: The aim of this study was to describe the characteristics and outcomes of patients presenting to Australian emergency departments (EDs) with suspected and confirmed COVID-19 during 2020, and to determine the predictors of in-hospital death for SARS-CoV-2 positive patients. METHOD(S): This analysis from the COVED Project presents data from twelve sites across four Australian states for the period from 1 April to 30 November 2020. All adult patients who met local criteria for suspected COVID-19 and underwent testing for SARS-CoV-2 in the ED were eligible for inclusion. Study outcomes were mechanical ventilation and in-hospital mortality. RESULT(S): Among 24,405 eligible ED presentations over the whole study period, 423 tested positive for SARS-CoV-2. During the "second wave" from 1 July to 30 September 2020, 26 (6%) of 406 SARS-CoV-2 patients received invasive mechanical ventilation, compared to 175 (2%) of the 9,024 SARS-CoV-2 negative patients (OR 3.5; 95% CI: 2.3-5.2, p<0.001), and 41 (10%) SARS-CoV-2 positive patients died in hospital compared to 312 (3%) SARS-CoV-2 negative patients (OR 3.2; 95% CI: 2.2-4.4, p=0.001). For SARS-CoV-2 positive patients, the strongest independent predictors of hospital death were age (OR 1.1; 95% CI: 1.1-1.1, p<0.001), higher triage category (OR 3.5; 95% CI 1.3-9.4, p=0.012), obesity (OR 4.2; 95% CI: 1.2-14.3, p=0.024) and receiving immunosuppressive treatment (OR 8.2; 95% CI: 1.8-36.7, p=0.006). CONCLUSION(S): ED patients who tested positive for SARS-CoV-2 had higher odds of mechanical ventilation and death in hospital. The strongest predictors of death were age, a higher triage category, obesity and receiving immunosuppressive treatment. This article is protected by copyright. All rights reserved.

Study objective: To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door-to-off-stretcher wait times that are applicable to a wide variety of emergency departments. Method(s): Nine emergency departments provided 3 years (2017 to 2019) of retrospective administrative data from Australia. Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine-learning models were developed to predict wait times at each site and were internally and externally validated. Result(s): There were 421,894 episodes analyzed, and median site off-load times varied from 13 (interquartile range [IQR], 9 to 20) to 29 (IQR, 16 to 48) minutes. The global site prediction model median absolute errors were 11.7 minutes (95% confidence interval [CI], 11.7 to 11.8) using linear regression and 12.8 minutes (95% CI, 12.7 to 12.9) using elastic net. The individual site model prediction median absolute errors varied from the most accurate at 6.3 minutes (95% CI, 6.2 to 6.4) to the least accurate at 16.1 minutes (95% CI, 15.8 to 16.3). The model technique performance was the same for linear regression, random forests, elastic net, and rolling average. The important variables were the last k-patient average waits, triage category, and patient age. The global model performed at the lower end of the accuracy range compared with models for the individual sites but was within tolerable limits. Conclusion(s): Electronic emergency demographic and flow information can be used to estimate emergency ambulance patient off-stretcher times. Models can be built with reasonable accuracy for multiple hospitals using a small number of point-of-care variables.Copyright © 2021 American College of Emergency Physicians

STUDY OBJECTIVE: To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door-to-off-stretcher wait times that are applicable to a wide variety of emergency departments. METHODS: Nine emergency departments provided 3 years (2017 to 2019) of retrospective administrative data from Australia. Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine-learning models were developed to predict wait times at each site and were internally and externally validated. RESULTS: There were 421,894 episodes analyzed, and median site off-load times varied from 13 (interquartile range [IQR], 9 to 20) to 29 (IQR, 16 to 48) minutes. The global site prediction model median absolute errors were 11.7 minutes (95% confidence interval [CI], 11.7 to 11.8) using linear regression and 12.8 minutes (95% CI, 12.7 to 12.9) using elastic net. The individual site model prediction median absolute errors varied from the most accurate at 6.3 minutes (95% CI, 6.2 to 6.4) to the least accurate at 16.1 minutes (95% CI, 15.8 to 16.3). The model technique performance was the same for linear regression, random forests, elastic net, and rolling average. The important variables were the last k-patient average waits, triage category, and patient age. The global model performed at the lower end of the accuracy range compared with models for the individual sites but was within tolerable limits. CONCLUSION: Electronic emergency demographic and flow information can be used to estimate emergency ambulance patient off-stretcher times. Models can be built with reasonable accuracy for multiple hospitals using a small number of point-of-care variables.

OBJECTIVE: The aim of the present study was to describe the epidemiology and clinical features of patients presenting to the ED with suspected and confirmed COVID-19. METHODS: The COVID-19 ED (COVED) Project is an ongoing prospective cohort study in Australian EDs. This analysis presents data from eight sites across Victoria and Tasmania for July 2020 (during Australia's 'second wave'). All adult patients who met criteria for 'suspected COVID-19' and underwent testing for SARS-CoV-2 in the ED were eligible for inclusion. Study outcomes included a positive SARS-CoV-2 test result and mechanical ventilation. RESULTS: In the period 1 July to 31 July 2020, there were 30 378 presentations to the participating EDs and 2917 (9.6%; 95% confidence interval 9.3-9.9) underwent testing for SARS-CoV-2. Of these, 50 (2%) patients returned a positive result. Among positive cases, two (4%) received mechanical ventilation during their hospital admission compared to 45 (2%) of the SARS-CoV-2 negative patients (odds ratio 1.7, 95% confidence interval 0.4-7.3; P = 0.47). Two (4%) SARS-CoV-2 positive patients died in hospital compared to 46 (2%) of the SARS-CoV-2 negative patients (odds ratio 1.7, 95% confidence interval 0.4-7.1; P = 0.49). Strong clinical predictors of a positive SARS-CoV-2 result included self-reported fever, non-smoking status, bilateral infiltrates on chest X-ray and absence of a leucocytosis on first ED blood tests (P < 0.05). CONCLUSION: In this prospective multi-site study from July 2020, a substantial proportion of ED patients required SARS-CoV-2 testing, isolation and enhanced infection prevention and control precautions. Presence of SARS-CoV-2 on nasopharyngeal swab was not associated with death or mechanical ventilation.

OBJECTIVE: To demonstrate the potential of machine learning and capability of natural language processing (NLP) to predict disposition of patients based on triage notes in the ED. METHODS: A retrospective cohort of ED triage notes from St Vincent's Hospital (Melbourne) was used to develop a deep-learning algorithm that predicts patient disposition. Bidirectional Encoder Representations from Transformers, a recent language representation model developed by Google, was utilised for NLP. Eighty percent of the dataset was used for training the model and 20% was used to test the algorithm performance. Ktrain library, a wrapper for TensorFlow Keras, was employed to develop the model. RESULTS: The accuracy of the algorithm was 83% and the area under the curve was 0.88. Sensitivity, specificity, precision and F1-score of the algorithm were 72%, 86%, 56% and 63%, respectively. CONCLUSION: Machine learning and NLP can be together applied to the ED triage note to predict patient disposition with a high level of accuracy. The algorithm can potentially assist ED clinicians in early identification of patients requiring admission by mitigating the cognitive load, thus optimises resource allocation in EDs.

Study objective: To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door–to–off-stretcher wait times that are applicable to a wide variety of emergency departments. Methods: Nine emergency departments provided 3 years (2017 to 2019) of retrospective administrative data from Australia. Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine-learning models were developed to predict wait times at each site and were internally and externally validated. Results: There were 421,894 episodes analyzed, and median site off-load times varied from 13 (interquartile range [IQR], 9 to 20) to 29 (IQR, 16 to 48) minutes. The global site prediction model median absolute errors were 11.7 minutes (95% confidence interval [CI], 11.7 to 11.8) using linear regression and 12.8 minutes (95% CI, 12.7 to 12.9) using elastic net. The individual site model prediction median absolute errors varied from the most accurate at 6.3 minutes (95% CI, 6.2 to 6.4) to the least accurate at 16.1 minutes (95% CI, 15.8 to 16.3). The model technique performance was the same for linear regression, random forests, elastic net, and rolling average. The important variables were the last k-patient average waits, triage category, and patient age. The global model performed at the lower end of the accuracy range compared with models for the individual sites but was within tolerable limits. Conclusion: Electronic emergency demographic and flow information can be used to estimate emergency ambulance patient off-stretcher times. Models can be built with reasonable accuracy for multiple hospitals using a small number of point-of-care variables.

Objective: To demonstrate the potential of machine learning and capability of natural language processing (NLP) to predict disposition of patients based on triage notes in the ED. Method(s): A retrospective cohort of ED triage notes from St Vincent's Hospital (Melbourne) was used to develop a deep-learning algorithm that predicts patient disposition. Bidirectional Encoder Representations from Transformers, a recent language representation model developed by Google, was utilised for NLP. Eighty percent of the dataset was used for training the model and 20% was used to test the algorithm performance. Ktrain library, a wrapper for TensorFlow Keras, was employed to develop the model. Result(s): The accuracy of the algorithm was 83% and the area under the curve was 0.88. Sensitivity, specificity, precision and F1-score of the algorithm were 72%, 86%, 56% and 63%, respectively. Conclusion(s): Machine learning and NLP can be together applied to the ED triage note to predict patient disposition with a high level of accuracy. The algorithm can potentially assist ED clinicians in early identification of patients requiring admission by mitigating the cognitive load, thus optimises resource allocation in EDs.Copyright © 2020 Australasian College for Emergency Medicine

OBJECTIVE: Acute migraine is associated with significant personal, economic and work-related disability. Management guidelines advise the use of simple analgesia, triptans, chlorpromazine and anti-emetics based on severity, with avoidance of opioids. We aimed to determine consistency of prescribing patterns in our ED with national guidelines. METHODS: We performed a retrospective cohort analysis of migraine presentations (ICD-10-AM G439) between 2012 and 2016. Exclusion criteria included migraine without headache, other primary headaches and secondary headaches. Demographic and prescribing data were extracted from medical records. Results have been reported as proportions. RESULTS: Of 4769 headache presentations, the application of exclusion criteria led to a total of 744 patients who received a migraine diagnosis (G439). Most were female (558/744, 75%), young (mean age 36.4 years) and had a self-reported migraine history (558/744, 75%). There were 54 different medications prescribed. Paracetamol was more frequently prescribed (385/744, 52%) than aspirin (134/744, 18%). Opioid prescription occurred in nearly half of all presentations (345/744, 46%). Similar opioid prescriptions were also observed in those with a documented history of migraines (253/558, 45%). A minority of patients received triptans (51/744, 7%). Overall, a quarter of patients (189/744, 25%) received no guideline-recommended medications. CONCLUSION: We observed considerable polypharmacy in ED migraine management with inconsistent prescribing patterns. Recommended medications were infrequently used and opioid use was common. Factors influencing prescribing patterns require further investigation in order to improve rates of guideline recommended treatment.

BACKGROUND: Auto-immune mediated anti-NMDA receptor encephalitis is a very common delayed diagnosed encephalitis which predominately affecting young population. OBJECTIVES: This encephalitis is relatively unknown amongst emergency physicians and a majority of patients are admitted to psychiatric wards before their diagnosis is confirmed and appropriate treatments are commenced. We reported a case of a 22-year-old female presented to our emergency department with acute psychiatric symptoms. She was initially diagnosed with first presentation of acute psychosis and was hospitalised under mental health act. further assessment in the emergency department identified possible an organic cause for her acute psychosis and she was later admitted under medical team after her mental health assessment order was revoke. Several days later, her CSF result was positive with anti-NMDA receptor anti-bodies. Appropriate treatments were instituted leading to her full recovery. CONCLUSION: This case was the first confirmed anti-NMDA receptor encephalitis in our emergency department. It highlights the importance of thorough assessment of psychiatric presentations to emergency departments and consideration of auto-immune medicated encephalitis as one of the differential diagnosis in young patients presenting with first acute psychotic episode.

The ability of cytotoxic lymphocytes (CL) to eliminate virus-infected or cancerous target cells through the granule exocytosis death pathway is critical to immune homeostasis. Congenital loss of CL function due to bi-allelic mutations in PRF1, UNC13D, STX11, or STXBP2 leads to a potentially fatal immune dysregulation, familial haemophagocytic lymphohistiocytosis (FHL). This occurs due to the failure of CLs to release functional pore-forming protein perforin and, therefore, inability to kill the target cell. Bi-allelic mutations in partner proteins STXBP2 or STX11 impair CL cytotoxicity due to failed docking/fusion of cytotoxic secretory granules with the plasma membrane. One unique feature of STXBP2- and STX11-deficient patient CLs is that their short-term in vitro treatment with a low concentration of IL-2 partially or completely restores natural killer (NK) cell degranulation and cytotoxicity, suggesting the existence of a secondary, yet unknown, pathway for secretory granule exocytosis. In the current report, we studied NK and T-cell function in an individual with late presentation of FHL due to hypomorphic bi-allelic mutations in STXBP2. Intriguingly, in addition to the expected alterations in the STXBP2 and STX11 proteins, we also observed a concomitant significant reduction in the expression of homologous STXBP1 protein and its partner STX1, which had never been implicated in CL function. Further analysis of human NK and T cells demonstrated a functional role for the STXBP1/STX1 axis in NK and CD8+ T-cell cytotoxicity, where it appears to be responsible for as much as 50% of their cytotoxic activity. This discovery suggests a unique and previously unappreciated interplay between STXBP/Munc proteins regulating the same essential granule exocytosis pathway.

CD8 T cells protect the liver against viral infection, but can also cause severe liver damage that may even lead to organ failure. Given the lack of mechanistic insights and specific treatment options in patients with acute fulminant hepatitis, we develop a mouse model reflecting a severe acute virus-induced CD8 T cell-mediated hepatitis. Here we show that antigen-specific CD8 T cells induce liver damage in a perforin-dependent manner, yet liver failure is not caused by effector responses targeting virus-infected hepatocytes alone. Additionally, CD8 T cell mediated elimination of cross-presenting liver sinusoidal endothelial cells causes endothelial damage that leads to a dramatically impaired sinusoidal perfusion and indirectly to hepatocyte death. With the identification of perforin-mediated killing as a critical pathophysiologic mechanism of liver failure and the protective function of a new class of perforin inhibitor, our study opens new potential therapeutic angles for fulminant viral hepatitis.

The structure-activity relationships for a series of arylsulphonamide-based inhibitors of the pore-forming protein perforin have been explored. Perforin is a key component of the human immune response, however inappropriate activity has also been implicated in certain auto-immune and therapy-induced conditions such as allograft rejection and graft versus host disease. Since perforin is expressed exclusively by cells of the immune system, inhibition of this protein would be a highly selective strategy for the immunosuppressive treatment of these disorders. Compounds from this series were demonstrated to be potent inhibitors of the lytic action of both isolated recombinant perforin and perforin secreted by natural killer cells in vitro. Several potent and soluble examples were assessed for in vivo pharmacokinetic properties and found to be suitable for progression to an in vivo model of transplant rejection.

The pore-forming protein perforin is a key component of mammalian cell-mediated immunity and essential to the pathway that allows elimination of virus-infected and transformed cells. Perforin activity has also been implicated in certain auto-immune conditions and therapy-induced conditions such as allograft rejection and graft versus host disease. An inhibitor of perforin activity could be used as a highly specific immunosuppressive treatment for these conditions, with reduced side-effects compared to currently accepted therapies. Previously identified first-in-class inhibitors based on a 2-thioxoimidazolidin-4-one core show suboptimal physicochemical properties and toxicity toward the natural killer (NK) cells that secrete perforin in vivo. The current benzenesulphonamide-based series delivers a non-toxic bioisosteric replacement possessing improved solubility.

The dentate nucleus (DN) is the major relay station for neural connection between the cerebellum and cerebrum via the thalamus, and is a significant component of the neuropathological profile of Friedreich ataxia (FRDA). We have previously shown that the size of the superior cerebellar peduncle (SCP), which links the DN to cortical and subcortical structures via the thalamus, is significantly reduced in individuals with FRDA compared to control participants. This study used magnetization transfer imaging (MTI) to examine and contrast the integrity of white matter (WM) in the SCP and the corpus callosum (CC) (control region) in ten individuals with FRDA and ten controls. Individuals with FRDA demonstrated a significant reduction in the magnetization transfer ratio (MTR) in the SCP compared to control participants. However, there was no significant difference between groups in MTR in the CC. When comparing regions within groups, there was a significant reduction in MTR in the SCP compared to CC in participants with FRDA only. We suggest that the reduction in MTR in the SCP may be indicative of lack of myelin secondary to axonal loss and oligodendroglial dysfunction in WM tracts in individuals with FRDA. © 2014 Elsevier B.V.

Brain pathology in Friedreich ataxia is characterized by progressive degeneration of nervous tissue in the brainstem, cerebellum and cerebellar peduncles. Evidence of cerebral involvement is however equivocal. This brain imaging study investigates cerebello-cerebral white matter connectivity in Friedreich ataxia with diffusion MRI and tractography performed in 13 individuals homozygous for a GAA expansion in intron one of the frataxin gene and 14 age- and gender-matched control participants. New evidence is presented for disrupted cerebello-cerebral connectivity in the disease, leading to secondary effects in distant cortical and subcortical regions. Remote regions affected by primary cerebellar and brainstem pathology include the supplementary motor area, cingulate cortex, frontal cortices, putamen and other subcortical nuclei. The connectivity disruptions identified provide an explanation for some of the non-ataxic symptoms observed in the disease and support the notion of reverse cerebellar diaschisis. This is the first study to comprehensively map white matter connectivity disruptions in Friedreich ataxia using tractography, connectomic techniques and super-resolution track density imaging.

Atrophy of the dentate nucleus is one of the major neuropathological changes in Friedreich ataxia (FRDA). Neuroimaging studies demonstrated white matter (WM) degeneration in FRDA. In this study, we used advanced tractography techniques to quantitatively measure WM changes in the dentato-thalamic and dentato-rubral tracts, and correlated these changes with cognitive profiles of FRDA. We also analysed diffusivity changes of the thalamo-cortical tract to assess whether neurological degeneration of WM extends beyond the primary site of involvement in FRDA. Twelve genetically proven individuals with FRDA and 14 controls were recruited. Sixty directions diffusion tensor images were acquired. The WM bundles from the dentate nucleus were estimated using a constrained spherical deconvolution method and the diffusivity characteristics measured. The Simon task was used to assess cognitive profile of FRDA. The dentato-rubral, dentato-thalamic and thalamo-cortical tracts manifested significantly lower fractional anisotropy, higher mean diffusivity and increased radial diffusivity in FRDA compared with controls. There was no difference in axial diffusivity between the two groups. The mean and radial diffusivity of the dentato-rubral tract was positively correlated with choice reaction time, congruent reaction time, incongruent reaction time and Simon effect reaction time and negatively with the larger GAA repeat. Significant changes in diffusivity characteristics were observed in the dentato-thalamic and thalamo-cortical tracts, suggesting extensive WM degeneration and affected WM structures in FRDA. Correlation of WM changes in the dentato-rubral tract with the cognitive assessment suggested that this tract is an important contributor to cognitive disturbances in FRDA.

Friedreich ataxia (FRDA) is the most common of the genetically inherited ataxias. We recently demonstrated that people with FRDA have impairment in motor planning - most likely because of pathology affecting the cerebral cortex and/or cerebello-cortical projections. We used the Simon interference task to examine how effective 13 individuals with FRDA were at inhibiting inappropriate automatic responses associated with stimulus-response incompatibility in comparison with control participants. Participants had to respond to arrow targets according to two features which were either congruent or incongruent. We found that individuals with FRDA were differentially affected in reaction time to incongruent, compared with congruent stimuli, when compared with control participants. There was a significant negative correlation between age of onset and the incongruency effect, suggesting an impact of FRDA on the developmental unfolding of motor cognition, independent of the effect of disease duration. Future neuroimaging studies will be required to establish whether this dysfunction is due to cerebellar impairment disrupting cerebro-ponto-cerebello-thalamo-cerebral loops (and thus cortical function), direct primary cortical pathology, or a possible combination of the two. © 2011 Elsevier Inc.