Traumatic cataract induced by improper use of a percussion massage gun.

Purpose: We describe a case of traumatic cataract after improper use of a percussion massage gun over the periorbital area. Observations: A 38-year-old female with a history of high myopia and fibromyalgia presented to the emergency department with painless monocular vision loss OS, noticed two days prior and described as a "white film" over her eye. BCVA was 20/20 OD and 20/600 OS. IOP was normal. Slit lamp examination OS showed a dense posterior subcapsular cataract in a rosette pattern without signs of zonular instability. B-scan ultrasonography showed a clear vitreous cavity without structural globe anomalies. No other abnormalities were apparent. After ruling out other causes, vision loss was attributed to development of a traumatic cataract secondary to percussive massage gun use over the left temple and periorbital area, including directly over the eye, during the past few weeks as an attempt to relieve intractable headaches. Conclusion and importance: Improper use of massage guns can lead to severe ocular side effects including traumatic cataracts that may be difficult to manage. There is a need to educate patients about potential harms as well as require manufacturers to clearly display safety information.

Retrospective Optimization of the Hawkeye Orbital Fracture Prioritization and Evaluation Algorithms for Triaging Ophthalmic Care.

Objectives: Many orbital fracture patients are transferred to tertiary care centers for immediate ophthalmology consultation, though few require urgent ophthalmic evaluation or intervention. This overutilizes limited resources and overburdens patients and the health care system with travel and emergency department (ED) expenses. A simple, easy-to-use, clinical decision-making tool is needed to aid local EDs and triage services in effectively identifying orbital fracture patients who need urgent ophthalmic evaluation. Design: Single center, retrospective cohort study. Subjects: Orbital fracture patients aged ≥ 18 years who presented to the study institution's emergency department and received an ophthalmology consultation. Methods: Ocular injuries that required close monitoring or an intervention within the first few hours after presentation were termed urgent. Two Hawkeye Orbital Fracture Prioritization and Evaluation (HOPE) algorithms were developed to identify orbital fracture patients needing urgent evaluation; including 1 algorithm incorporating computerized tomography (CT) scans interpreted by ophthalmology (HOPE+CT). Algorithms were compared with 3 previously published protocols: the University of Texas Health Science Center at Houston (UTH), the South Texas Orbital Fracture Protocol (STOP), and Massachusetts Eye and Ear (MEE) algorithms. Main Outcome Measures: Correct triage of patients with orbital fractures who have urgent ocular or orbital conditions. Results: In the study institution's ED, 134 adult patients (145 orbits) were seen with orbital fractures in 2019. Eighteen (13.4%) had ocular or orbital conditions categorized as urgent. The HOPE tool resulted in 100% sensitivity and 78.4% specificity. The HOPE+CT tool resulted in 100.0% sensitivity and 94.0% specificity. The UTH algorithm was 91.7% sensitive and 76.5% specific. South Texas Orbital Fracture Protocol and MEE were both 100% sensitive but only 35.1% and 32.8% specific, respectively. Conclusions: The HOPE and HOPE+CT algorithms were superior or equal to the UTH, STOP, and MEE algorithms in terms of specificity while detecting all urgent cases. Implementation of a triage protocol that uses the HOPE or HOPE+CT algorithms could improve resource utilization and reduce health care costs through identification of orbital fracture patients needing urgent evaluation. An online tool that deploys the HOPE+CT algorithm in a user-friendly interface has been developed and is undergoing prospective validation before public dissemination. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

PRAME induces genomic instability in uveal melanoma.

PRAME is a CUL2 ubiquitin ligase subunit that is normally expressed in the testis but becomes aberrantly overexpressed in many cancer types in association with aneuploidy and metastasis. Here, we show that PRAME is expressed predominantly in spermatogonia around the time of meiotic crossing-over in coordination with genes mediating DNA double strand break repair. Expression of PRAME in somatic cells upregulates pathways involved in meiosis, chromosome segregation and DNA repair, and it leads to increased DNA double strand breaks, telomere dysfunction and aneuploidy in neoplastic and non-neoplastic cells. This effect is mediated at least in part by ubiquitination of SMC1A and altered cohesin function. PRAME expression renders cells susceptible to inhibition of PARP1/2, suggesting increased dependence on alternative base excision repair pathways. These findings reveal a distinct oncogenic function of PRAME that can be targeted therapeutically in cancer.

Room-Temperature Magnetic Phase Transition in an Electrically Tuned van der Waals Ferromagnet.

Finding tunable van der Waals (vdW) ferromagnets that operate at above room temperature is an important research focus in physics and materials science. Most vdW magnets are only intrinsically magnetic far below room temperature and magnetism with square-shaped hysteresis at room temperature has yet to be observed. Here, we report magnetism in a quasi-2D magnet Cr_{1.2}Te_{2} observed at room temperature (290 K). This magnetism was tuned via a protonic gate with an electron doping concentration up to 3.8×10^{21} cm^{-3}. We observed nonmonotonic evolutions in both coercivity and anomalous Hall resistivity. Under increased electron doping, the coercivities and anomalous Hall effects (AHEs) vanished, indicating a doping-induced magnetic phase transition. This occurred up to room temperature. DFT calculations showed the formation of an antiferromagnetic (AFM) phase caused by the intercalation of protons which induced significant electron doping in the Cr_{1.2}Te_{2}. The tunability of the magnetic properties and phase in room temperature magnetic vdW Cr_{1.2}Te_{2} is a significant step towards practical spintronic devices.

Inter-hospital variation in data collection, radiotherapy treatment, and survival in patients with head and neck cancer: A multisite study.

BACKGROUND AND PURPOSE: Inter-hospital inequalities in head and neck cancer (HNC) survival may exist due to variation in radiotherapy treatment-related factors. This study investigated inter-hospital variation in data collection, primary radiotherapy treatment, and survival in HNC patients from an Australian setting. MATERIALS AND METHODS: Data collected in oncology information systems (OIS) from seven Australian hospitals was extracted for 3,182 adults treated with curative radiotherapy, with or without surgery or chemotherapy, for primary, non-metastatic squamous cell carcinoma of the head and neck (2000-2017). Death data was sourced from the National Death Index using record linkage. Multivariable Cox regression was used to assess the association between survival and hospital. RESULTS: Inter-hospital variation in data collection, primary radiotherapy dose, and five-year HNC-related death was detected. Completion of eleven fields ranged from 66%-98%. Primary radiotherapy treated Tis-T1N0 glottic and any stage oral cavity and oropharynx cancers received significantly different time-corrected biologically equivalent dose in two gray fractions (EQD2T) by hospital, with observed deviation from Australian radiotherapy guidelines. Increased EQD2T dose was associated with a reduced risk of five-year HNC-related death in all patients and those treated with primary radiotherapy. Hospital, tumour site, and T and N classification were also identified as independent prognostic factors for five-year HNC-related death in all patients treated with radiotherapy. CONCLUSION: Unexplained variation exists in HNC-related death in patients treated at Australian hospitals. Available routinely collected data in OIS are insufficient to explain variation in survival. Innovative data collection, extraction, and classification practices are needed to inform clinical practice.

Effect of experimental hookworm infection on insulin resistance in people at risk of type 2 diabetes.

The reduced prevalence of insulin resistance and type 2 diabetes in countries with endemic parasitic worm infections suggests a protective role for worms against metabolic disorders, however clinical evidence has been non-existent. This 2-year randomised, double-blinded clinical trial in Australia of hookworm infection in 40 male and female adults at risk of type 2 diabetes assessed the safety and potential metabolic benefits of treatment with either 20 (n = 14) or 40 (n = 13) Necator americanus larvae (L3) or Placebo (n = 13) (Registration ACTRN12617000818336). Primary outcome was safety defined by adverse events and completion rate. Homoeostatic model assessment of insulin resistance, fasting blood glucose and body mass were key secondary outcomes. Adverse events were more frequent in hookworm-treated participants, where 44% experienced expected gastrointestinal symptoms, but completion rates were comparable to Placebo. Fasting glucose and insulin resistance were lowered in both hookworm-treated groups at 1 year, and body mass was reduced after L3-20 treatment at 2 years. This study suggests hookworm infection is safe in people at risk of type 2 diabetes and associated with improved insulin resistance, warranting further exploration of the benefits of hookworms on metabolic health.

Rift-induced disruption of cratonic keels drives kimberlite volcanism.

Kimberlites are volatile-rich, occasionally diamond-bearing magmas that have erupted explosively at Earth's surface in the geologic past1-3. These enigmatic magmas, originating from depths exceeding 150 km in Earth's mantle1, occur in stable cratons and in pulses broadly synchronous with supercontinent cyclicity4. Whether their mobilization is driven by mantle plumes5 or by mechanical weakening of cratonic lithosphere4,6 remains unclear. Here we show that most kimberlites spanning the past billion years erupted about 30 million years (Myr) after continental breakup, suggesting an association with rifting processes. Our dynamical and analytical models show that physically steep lithosphere-asthenosphere boundaries (LABs) formed during rifting generate convective instabilities in the asthenosphere that slowly migrate many hundreds to thousands of kilometres inboard of rift zones. These instabilities endure many tens of millions of years after continental breakup and destabilize the basal tens of kilometres of the cratonic lithosphere, or keel. Displaced keel is replaced by a hot, upwelling mixture of asthenosphere and recycled volatile-rich keel in the return flow, causing decompressional partial melting. Our calculations show that this process can generate small-volume, low-degree, volatile-rich melts, closely matching the characteristics expected of kimberlites1-3. Together, these results provide a quantitative and mechanistic link between kimberlite episodicity and supercontinent cycles through progressive disruption of cratonic keels.

PRAME induces genomic instability in uveal melanoma.

PRAME is a CUL2 ubiquitin ligase subunit that is normally expressed in the testis but becomes aberrantly overexpressed in many cancer types in association with aneuploidy and metastasis. Here, we show that PRAME is expressed predominantly in spermatogonia around the time of meiotic crossing-over in coordination with genes mediating DNA double strand break repair. Expression of PRAME in somatic cells upregulates pathways involved in meiosis, chromosome segregation and DNA repair, and it leads to increased DNA double strand breaks, telomere dysfunction and aneuploidy in neoplastic and non-neoplastic cells. This effect is mediated at least in part by ubiquitination of SMC1A and altered cohesin function. PRAME expression renders cells susceptible to inhibition of PARP1/2, suggesting increased dependence on alternative base excision repair pathways. These findings reveal a distinct oncogenic function of PRAME than can be targeted therapeutically in cancer.

Systematic Review of Tumor Segmentation Strategies for Bone Metastases.

PURPOSE: To investigate the segmentation approaches for bone metastases in differentiating benign from malignant bone lesions and characterizing malignant bone lesions. METHOD: The literature search was conducted in Scopus, PubMed, IEEE and MedLine, and Web of Science electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 77 original articles, 24 review articles, and 1 comparison paper published between January 2010 and March 2022 were included in the review. RESULTS: The results showed that most studies used neural network-based approaches (58.44%) and CT-based imaging (50.65%) out of 77 original articles. However, the review highlights the lack of a gold standard for tumor boundaries and the need for manual correction of the segmentation output, which largely explains the absence of clinical translation studies. Moreover, only 19 studies (24.67%) specifically mentioned the feasibility of their proposed methods for use in clinical practice. CONCLUSION: Development of tumor segmentation techniques that combine anatomical information and metabolic activities is encouraging despite not having an optimal tumor segmentation method for all applications or can compensate for all the difficulties built into data limitations.

Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach.

In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume.

The Utility of Oncology Information Systems for Prognostic Modelling in Head and Neck Cancer.

Cancer centres rely on electronic information in oncology information systems (OIS) to guide patient care. We investigated the completeness and accuracy of routinely collected head and neck cancer (HNC) data sourced from an OIS for suitability in prognostic modelling and other research. Three hundred and fifty-three adults diagnosed from 2000 to 2017 with head and neck squamous cell carcinoma, treated with radiotherapy, were eligible. Thirteen clinically relevant variables in HNC prognosis were extracted from a single-centre OIS and compared to that compiled separately in a research dataset. These two datasets were compared for agreement using Cohen's kappa coefficient for categorical variables, and intraclass correlation coefficients for continuous variables. Research data was 96% complete compared to 84% for OIS data. Agreement was perfect for gender (κ = 1.000), high for age (κ = 0.993), site (κ = 0.992), T (κ = 0.851) and N (κ = 0.812) stage, radiotherapy dose (κ = 0.889), fractions (κ = 0.856), and duration (κ = 0.818), and chemotherapy treatment (κ = 0.871), substantial for overall stage (κ = 0.791) and vital status (κ = 0.689), moderate for grade (κ = 0.547), and poor for performance status (κ = 0.110). Thirty-one other variables were poorly captured and could not be statistically compared. Documentation of clinical information within the OIS for HNC patients is routine practice; however, OIS data was less correct and complete than data collected for research purposes. Substandard collection of routine data may hinder advancements in patient care. Improved data entry, integration with clinical activities and workflows, system usability, data dictionaries, and training are necessary for OIS data to generate robust research. Data mining from clinical documents may supplement structured data collection.

Machine Learning and Nomogram Prognostic Modeling for 2-Year Head and Neck Cancer-Specific Survival Using Electronic Health Record Data: A Multisite Study.

PURPOSE: There is limited knowledge of the prediction of 2-year cancer-specific survival (CSS) in the head and neck cancer (HNC) population. The aim of this study is to develop and validate machine learning models and a nomogram for the prediction of 2-year CSS in patients with HNC using real-world data collected by major teaching and tertiary referral hospitals in New South Wales (NSW), Australia. MATERIALS AND METHODS: Data collected in oncology information systems at multiple NSW Cancer Centres were extracted for 2,953 eligible adults diagnosed between 2000 and 2017 with squamous cell carcinoma of the head and neck. Death data were sourced from the National Death Index using record linkage. Machine learning and Cox regression/nomogram models were developed and internally validated in Python and R, respectively. RESULTS: Machine learning models demonstrated highest performance (C-index) in the larynx and nasopharynx cohorts (0.82), followed by the oropharynx (0.79) and the hypopharynx and oral cavity cohorts (0.73). In the whole HNC population, C-indexes of 0.79 and 0.70 and Brier scores of 0.10 and 0.27 were reported for the machine learning and nomogram model, respectively. Cox regression analysis identified age, T and N classification, and time-corrected biologic equivalent dose in two gray fractions as independent prognostic factors for 2-year CSS. N classification was the most important feature used for prediction in the machine learning model followed by age. CONCLUSION: Machine learning and nomogram analysis predicted 2-year CSS with high performance using routinely collected and complete clinical information extracted from oncology information systems. These models function as visual decision-making tools to guide radiotherapy treatment decisions and provide insight into the prediction of survival outcomes in patients with HNC.

Empirical comparison of routinely collected electronic health record data for head and neck cancer-specific survival in machine-learnt prognostic models.

BACKGROUND: Knowledge of the prognostic factors and performance of machine learning predictive models for 2-year cancer-specific survival (CSS) is limited in the head and neck cancer (HNC) population. METHODS: Data from our facilities' oncology information system (OIS) collected for routine practice (OIS dataset, n = 430 patients) and research purposes (research dataset, n = 529 patients) were extracted on adults diagnosed between 2000 and 2017 with squamous cell carcinoma of the head and neck. RESULTS: Machine learning demonstrated excellent performance (area under the curve, AUC) in the whole cohort (AUC = 0.97, research dataset), larynx cohort (AUC = 0.98, both datasets), and oropharynx cohort (AUC = 0.99, both datasets). Tumor site and T classification were identified as predictors of 2-year CSS in both datasets. Hypothyroidism and fitness for operation were further identified in the research dataset. CONCLUSIONS: Datasets extracted from an OIS for routine clinical practice and research purposes demonstrated high utility for informing 2-year head and neck CSS.

Larynx cancer survival model developed through open-source federated learning.

INTRODUCTION: Federated learning has the potential to perfrom analysis on decentralised data; however, there are some obstacles to survival analyses as there is a risk of data leakage. This study demonstrates how to perform a stratified Cox regression survival analysis specifically designed to avoid data leakage using federated learning on larynx cancer patients from centres in three different countries. METHODS: Data were obtained from 1821 larynx cancer patients treated with radiotherapy in three centres. Tumour volume was available for all 786 of the included patients. Parameter selection among eleven clinical and radiotherapy parameters were performed using best subset selection and cross-validation through the federated learning system, AusCAT. After parameter selection, ß regression coefficients were estimated using bootstrap. Calibration plots were generated at 2 and 5-years survival, and inner and outer risk groups' Kaplan-Meier curves were compared to the Cox model prediction. RESULTS: The best performing Cox model included log(GTV), performance status, age, smoking, haemoglobin and N-classification; however, the simplest model with similar statistical prediction power included log(GTV) and performance status only. The Harrell C-indices for the simplest model were for Odense, Christie and Liverpool 0.75[0.71-0.78], 0.65[0.59-0.71], and 0.69[0.59-0.77], respectively. The values are slightly higher for the full model with C-index 0.77[0.74-0.80], 0.67[0.62-0.73] and 0.71[0.61-0.80], respectively. Smoking during treatment has the same hazard as a ten-years older nonsmoking patient. CONCLUSION: Without any patient-specific data leaving the hospitals, a stratified Cox regression model based on data from centres in three countries was developed without data leakage risks. The overall survival model is primarily driven by tumour volume and performance status.

Evaluation of an automated Presidio anonymisation model for unstructured radiation oncology electronic medical records in an Australian setting.

BACKGROUND: Electronic medical records (EMRs) contain valuable information for clinical research, however, the presence of personally identifying information (PII) restricts their use. Anonymisation of PII from EMRs enables clinical information to be shared for research purposes. Since there is limited research relating to the anonymisation of Australian EMRs, the performance of Microsoft Presidio with customisation on clinical documents from an Australian radiation oncology information system (OIS) was evaluated. METHODS: A random sample of 300 unstructured free-text clinical documents were extracted from the Prince of Wales Cancer Centre OIS on patients diagnosed with cancer of the head and neck between 2000 and 2017. Anonymisation of clinical text was performed using Microsoft Presidio, implemented in Python programming language. Each clinical document was manually compared pre- and post-anonymisation for the identification and redaction of 13 PII. Model performance was evaluated using three classification criteria; correct, partial, and missed classification, to determine recall, precision, and F1-score. These three metrics were performed under relaxed conditions, where partial classifications were considered correct, and under strict conditions, where only correct classifications were considered correct. RESULTS: A total of 8,713 PII were identified, of which 7,026 (81%) were classified as correct, 850 (10%) as partial, and 837 (9%) as missed. There were 245 instances of incorrect classifications. Evaluation of the model demonstrated an average precision of 0.8921, recall (strict) of 0.8064, F1-score (strict) of 0.8471, recall (relaxed) of 0.9039, and F1-score (relaxed) of 0.8980. CONCLUSION: This is the first example of an open-source anonymisation model to be customised and tested on clinical documents from an Australian radiation oncology EMR. These findings support the use of Presidio for the safe use and sharing of cancer data within Australia for certain PII, however, additional checks are required to ensure person names are successfully anonymised.

Peptides derived from hookworm anti-inflammatory proteins suppress inducible colitis in mice and inflammatory cytokine production by human cells.

A decline in the prevalence of parasites such as hookworms appears to be correlated with the rise in non-communicable inflammatory conditions in people from high- and middle-income countries. This correlation has led to studies that have identified proteins produced by hookworms that can suppress inflammatory bowel disease (IBD) and asthma in animal models. Hookworms secrete a family of abundant netrin-domain containing proteins referred to as AIPs (Anti-Inflammatory Proteins), but there is no information on the structure-function relationships. Here we have applied a downsizing approach to the hookworm AIPs to derive peptides of 20 residues or less, some of which display anti-inflammatory effects when co-cultured with human peripheral blood mononuclear cells and oral therapeutic activity in a chemically induced mouse model of acute colitis. Our results indicate that a conserved helical region is responsible, at least in part, for the anti-inflammatory effects. This helical region has potential in the design of improved leads for treating IBD and possibly other inflammatory conditions.

Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer.

INTRODUCTION: Emerging evidence suggests that data-driven support tools have found their way into clinical decision-making in a number of areas, including cancer care. Improving them and widening their scope of availability in various differing clinical scenarios, including for prognostic models derived from retrospective data, requires co-ordinated data sharing between clinical centres, secondary analyses of large multi-institutional clinical trial data, or distributed (federated) learning infrastructures. A systematic approach to utilizing routinely collected data across cancer care clinics remains a significant challenge due to privacy, administrative and political barriers. METHODS: An information technology infrastructure and web service software was developed and implemented which uses machine learning to construct clinical decision support systems in a privacy-preserving manner across datasets geographically distributed in different hospitals. The infrastructure was deployed in a network of Australian hospitals. A harmonized, international ontology-linked, set of lung cancer databases were built with the routine clinical and imaging data at each centre. The infrastructure was demonstrated with the development of logistic regression models to predict major cardiovascular events following radiation therapy. RESULTS: The infrastructure implemented forms the basis of the Australian computer-assisted theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning. Four radiation oncology departments (across seven hospitals) in New South Wales (NSW) participated in this demonstration study. Infrastructure was deployed at each centre and used to develop a model predicting for cardiovascular admission within a year of receiving curative radiotherapy for non-small cell lung cancer. A total of 10,417 lung cancer patients were identified with 802 being eligible for the model. Twenty features were chosen for analysis from the clinical record and linked registries. After selection, 8 features were included and a logistic regression model achieved an area under the receiver operating characteristic (AUROC) curve of 0.70 and C-index of 0.65 on out-of-sample data. CONCLUSION: The infrastructure developed was demonstrated to be usable in practice between clinical centres to harmonize routinely collected oncology data and develop models with federated learning. It provides a promising approach to enable further research studies in radiation oncology using real world clinical data.