Exploring injury intentionality and mechanism via ICD-10-CM injury codes and self-reported injury in a large, urban emergency department.

Health systems capture injuries using International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Clinical Modification (ICD-10-CM) diagnostic codes and share data with public health to inform injury surveillance. This study analyses provider-assigned ICD-10-CM injury codes among self-reported injuries to determine the effectiveness of ICD-10-CM coding in capturing injury and assault. METHODS: Self-reported injury screen records from an urban, level 1 trauma centre collected between 20 November 2015 and 30 September 2019 were compared with corresponding provider-assigned ICD-10-CM codes discerning the frequency in which intentions are indicated among patients reporting (1) any injury and (2) assault. RESULTS: Of 380 922 patients screened, 32 788 (8.61%) reported any injury and 6763 (1.78%) reported assault. ICD-10-CM codes had a sensitivity of 67.40% (95% CI 66.89% to 67.91%) for any injury and specificity of 89.79% (95% CI 89.69% to 89.89%]). For assault, ICD-10-CM codes had sensitivity of 2.25% (95% CI 1.91% to 2.63%) and specificity of 99.97% (95% CI 99.97% 99.98%). DISCUSSION: This study found provider-assigned ICD-10-CM had limited sensitivity to identify injury and low sensitivity for assault. This study more fully characterises ICD-10-CM coding system effectiveness in identifying assaults.

Studying the Temporal Dynamics of the Gut Microbiota Using Metabolic Stable Isotope Labeling and Metaproteomics.

The gut microbiome and its metabolic processes are dynamic systems. Surprisingly, our understanding of gut microbiome dynamics is limited. Here, we report a metaproteomic workflow that involves protein stable isotope probing (protein-SIP) and identification/quantification of partially labeled peptides. We also developed a package, which we call MetaProfiler, that corrects for false identifications and performs phylogenetic and time series analysis for the study of microbiome dynamics. From the stool sample of five mice that were fed with 15N hydrolysate from Ralstonia eutropha, we identified 12 326 nonredundant unlabeled peptides, of which 8256 of their heavy counterparts were quantified. These peptides revealed incorporation profiles over time that were different between and within taxa, as well as between and within clusters of orthologous groups (COGs). Our study helps unravel the complex dynamics of protein synthesis and bacterial dynamics in the mouse microbiome. MetaProfiler and the bioinformatic pipeline are available at https://github.com/northomics/MetaProfiler.git.

Concussion Care in the Emergency Department: A Prospective Observational Brief Report.

STUDY OBJECTIVE: Emergency physicians are often the initial-and only-clinical providers for patients who have sustained a mild traumatic brain injury. This prospective observational study seeks to examine the practice patterns of clinicians in an academic Level I trauma center as they relate to the evaluation of patients who were presumed to be at high risk for mild traumatic brain injury. Specifically, we describe the frequency of a documented mild traumatic brain injury evaluation, diagnosis, and discharge education. METHODS: This pilot study took place in a single academic Level I trauma and emergency care center during a 4-week period. Patients were identified by triage nurses, who determined whether they responded affirmatively to 2 questions that indicated a potential risk for mild traumatic brain injury. Data were abstracted from emergency department clinician documentation on identified patients to describe the frequency of a documented mild traumatic brain injury evaluation (history and physical examination), diagnosis, and discharge education among those who were identified to be at risk for a mild traumatic brain injury. RESULTS: Ninety-eight subjects were included in the present study. Documentation of a mild traumatic brain injury evaluation was present for less than 50% of patients, a final diagnosis of mild traumatic brain injury was included for 36 (37%; 95% confidence interval 27.8% to 46.7%), and discharge education was provided to 15 (15%; 95% confidence interval 9.2% to 21.4%). Of the 36 patients who received a documented mild traumatic brain injury diagnosis, 15 (41.5%; 95% confidence interval 26.7% to 57.9%) received mild traumatic brain injury-specific discharge education. CONCLUSION: This study suggests that the majority of patients at high risk for mild traumatic brain injury have no documentation of an evaluation for one. Also, patients with a mild traumatic brain injury diagnosis were unlikely to receive appropriate discharge education about it. Education and standardization are needed to ensure that patients at risk for mild traumatic brain injury receive appropriate evaluation and care.

Building capacity for injury prevention: a process evaluation of a replication of the Cardiff Violence Prevention Programme in the Southeastern USA.

OBJECTIVES: Violence is a major public health problem in the USA. In 2016, more than 1.6 million assault-related injuries were treated in US emergency departments (EDs). Unfortunately, information about the magnitude and patterns of violent incidents is often incomplete and underreported to law enforcement (LE). In an effort to identify more complete information on violence for the development of prevention programme, a cross-sectoral Cardiff Violence Prevention Programme (Cardiff Model) partnership was established at a large, urban ED with a level I trauma designation and local metropolitan LE agency in the Atlanta, Georgia metropolitan area. The Cardiff Model is a promising violence prevention approach that promotes combining injury data from hospitals and LE. The objective was to describe the Cardiff Model implementation and collaboration between hospital and LE partners. METHODS: The Cardiff Model was replicated in the USA. A process evaluation was conducted by reviewing project materials, nurse surveys and interviews and ED-LE records. RESULTS: Cardiff Model replication centred around four activities: (1) collaboration between the hospital and LE to form a community safety partnership locally called the US Injury Prevention Partnership; (2) building hospital capacity for data collection; (3) data aggregation and analysis and (4) developing and implementing violence prevention interventions based on the data. CONCLUSIONS: The Cardiff Model can be implemented in the USA for sustainable violent injury data surveillance and sharing. Key components include building a strong ED-LE partnership, communicating with each other and hospital staff, engaging in capacity building and sustainability planning.

Evaluating in Vitro Culture Medium of Gut Microbiome with Orthogonal Experimental Design and a Metaproteomics Approach.

In vitro culture based approaches are time- and cost-effective solutions for rapidly evaluating the effects of drugs or natural compounds against microbiomes. The nutritional composition of the culture medium is an important determinant for effectively maintaining the gut microbiome in vitro. This study combines orthogonal experimental design and a metaproteomics approach to obtaining functional insights into the effects of different medium components on the microbiome. Our results show that the metaproteomic profile respond differently to medium components, including inorganic salts, bile salts, mucin, and short-chain fatty acids. Multifactor analysis of variance further revealed significant main and interaction effects of inorganic salts, bile salts, and mucin on the different functional groups of gut microbial proteins. While a broad regulating effect was observed on basic metabolic pathways, different medium components also showed significant modulations on cell wall, membrane, and envelope biogenesis and cell motility related functions. In particular, flagellar assembly related proteins were significantly responsive to the presence of mucin. This study provides information on the functional influences of medium components on the in vitro growth of microbiome communities and gives insight on the key components that must be considered when selecting and optimizing media for culturing ex vivo microbiotas.

Meaning in life in psychotherapy: The perspective of experienced psychotherapists.

OBJECTIVE: Our goal was to explore the meaning experienced psychotherapists derive from providing psychotherapy, their beliefs about the role of meaning in life (MIL) in psychotherapy, how they worked with MIL with a client who explicitly presented concerns about MIL, and how they worked with a different client for whom MIL was a secondary and more implicit concern. METHOD: Thirteen experienced psychotherapists were interviewed and data were analyzed using consensual qualitative research. RESULTS: Therapists derived self-oriented meaning (e.g., feeling gratified, fulfilled, connected) and other-oriented meaning (helping others, making the world a better place) from providing psychotherapy. They believed that MIL is fundamental and underlies all human concerns, including those brought to therapy. In contrast to the clients who had implicit MIL concerns, clients who explicitly presented MIL concerns were reported to have more interpersonal problems and physical problems, but about the same amount of psychological distress and loss/grief. Therapists used insight-oriented interventions, support, action-oriented interventions, and exploratory interventions to work with MIL with both types of clients, but used more exploratory interventions with implicit than explicit MIL clients. CONCLUSIONS: MIL is a salient topic for experienced, existentially oriented psychotherapists; they work with MIL extensively with some clients in psychotherapy. We recommend that therapists receive training to work with MIL in therapy, and that they pay attention to MIL concerns when they conduct psychotherapy. We also recommend additional research on MIL in psychotherapy.

Visual Functions Are Associated with Biomarker Changes in Alzheimer's Disease.

Background: While various biomarkers of Alzheimer's disease (AD) have been associated with general cognitive function, their association to visual-perceptive function across the AD spectrum warrant more attention due to its significant impact on quality of life. Thus, this study explores how AD biomarkers are associated with decline in this cognitive domain. Objective: To explore associations between various fluid and imaging biomarkers and visual-based cognitive assessments in participants across the AD spectrum. Methods: Data from participants (N = 1,460) in the Alzheimer's Disease Neuroimaging Initiative were analyzed, including fluid and imaging biomarkers. Along with the Mini-Mental State Examination (MMSE), three specific visual-based cognitive tests were investigated: Trail Making Test (TMT) A and TMT B, and the Boston Naming Test (BNT). Locally estimated scatterplot smoothing curves and Pearson correlation coefficients were used to examine associations. Results: MMSE showed the strongest correlations with most biomarkers, followed by TMT-B. The p-tau181/Aß1-42 ratio, along with the volume of the hippocampus and entorhinal cortex, had the strongest associations among the biomarkers. Conclusions: Several biomarkers are associated with visual processing across the disease spectrum, emphasizing their potential in assessing disease severity and contributing to progression models of visual function and cognition.

HarmonyTM: multi-center data harmonization applied to distributed learning for Parkinson's disease classification.

Purpose: Distributed learning is widely used to comply with data-sharing regulations and access diverse datasets for training machine learning (ML) models. The traveling model (TM) is a distributed learning approach that sequentially trains with data from one center at a time, which is especially advantageous when dealing with limited local datasets. However, a critical concern emerges when centers utilize different scanners for data acquisition, which could potentially lead models to exploit these differences as shortcuts. Although data harmonization can mitigate this issue, current methods typically rely on large or paired datasets, which can be impractical to obtain in distributed setups. Approach: We introduced HarmonyTM, a data harmonization method tailored for the TM. HarmonyTM effectively mitigates bias in the model's feature representation while retaining crucial disease-related information, all without requiring extensive datasets. Specifically, we employed adversarial training to "unlearn" bias from the features used in the model for classifying Parkinson's disease (PD). We evaluated HarmonyTM using multi-center three-dimensional (3D) neuroimaging datasets from 83 centers using 23 different scanners. Results: Our results show that HarmonyTM improved PD classification accuracy from 72% to 76% and reduced (unwanted) scanner classification accuracy from 53% to 30% in the TM setup. Conclusion: HarmonyTM is a method tailored for harmonizing 3D neuroimaging data within the TM approach, aiming to minimize shortcut learning in distributed setups. This prevents the disease classifier from leveraging scanner-specific details to classify patients with or without PD-a key aspect for deploying ML models for clinical applications.

Multimodal imaging measures in the prediction of clinical response to deep brain stimulation for refractory depression: A machine learning approach.

OBJECTIVES: This study compared machine learning models using unimodal imaging measures and combined multi-modal imaging measures for deep brain stimulation (DBS) outcome prediction in treatment resistant depression (TRD). METHODS: Regional brain glucose metabolism (CMRGlu), cerebral blood flow (CBF), and grey matter volume (GMV) were measured at baseline using 18F-fluorodeoxy glucose (18F-FDG) positron emission tomography (PET), arterial spin labelling (ASL) magnetic resonance imaging (MRI), and T1-weighted MRI, respectively, in 19 patients with TRD receiving subcallosal cingulate (SCC)-DBS. Responders (n = 9) were defined by a 50% reduction in HAMD-17 at 6 months from the baseline. Using an atlas-based approach, values of each measure were determined for pre-selected brain regions. OneR feature selection algorithm and the naïve Bayes model was used for classification. Leave-out-one cross validation was used for classifier evaluation. RESULTS: The performance accuracy of the CMRGlu classification model (84%) was greater than CBF (74%) or GMV (74%) models. The classification model using the three image modalities together led to a similar accuracy (84%0 compared to the CMRGlu classification model. CONCLUSIONS: CMRGlu imaging measures may be useful for the development of multivariate prediction models for SCC-DBS studies for TRD. The future of multivariate methods for multimodal imaging may rest on the selection of complementing features and the developing better models.Clinical Trial Registration: ClinicalTrials.gov (#NCT01983904).

Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System.

Although current research aims to improve deep learning networks by applying knowledge about the healthy human brain and vice versa, the potential of using such networks to model and study neurodegenerative diseases remains largely unexplored. In this work, we present an in-depth feasibility study modeling progressive dementia in silico with deep convolutional neural networks. Therefore, networks were trained to perform visual object recognition and then progressively injured by applying neuronal as well as synaptic injury. After each iteration of injury, network object recognition accuracy, saliency map similarity between the intact and injured networks, and internal activations of the degenerating models were evaluated. The evaluation revealed that cognitive function of the network progressively decreased with increasing injury load whereas this effect was much more pronounced for synaptic damage. The effects of neurodegeneration found for the in silico model are especially similar to the loss of visual cognition seen in patients with posterior cortical atrophy.

Simulation of neuroplasticity in a CNN-based in-silico model of neurodegeneration of the visual system.

The aim of this work was to enhance the biological feasibility of a deep convolutional neural network-based in-silico model of neurodegeneration of the visual system by equipping it with a mechanism to simulate neuroplasticity. Therefore, deep convolutional networks of multiple sizes were trained for object recognition tasks and progressively lesioned to simulate neurodegeneration of the visual cortex. More specifically, the injured parts of the network remained injured while we investigated how the added retraining steps were able to recover some of the model's object recognition baseline performance. The results showed with retraining, model object recognition abilities are subject to a smoother and more gradual decline with increasing injury levels than without retraining and, therefore, more similar to the longitudinal cognition impairments of patients diagnosed with Alzheimer's disease (AD). Moreover, with retraining, the injured model exhibits internal activation patterns similar to those of the healthy baseline model when compared to the injured model without retraining. Furthermore, we conducted this analysis on a network that had been extensively pruned, resulting in an optimized number of parameters or synapses. Our findings show that this network exhibited remarkably similar capability to recover task performance with decreasingly viable pathways through the network. In conclusion, adding a retraining step to the in-silico setup that simulates neuroplasticity improves the model's biological feasibility considerably and could prove valuable to test different rehabilitation approaches in-silico.

Modeling Neurodegeneration in silico With Deep Learning.

Deep neural networks, inspired by information processing in the brain, can achieve human-like performance for various tasks. However, research efforts to use these networks as models of the brain have primarily focused on modeling healthy brain function so far. In this work, we propose a paradigm for modeling neural diseases in silico with deep learning and demonstrate its use in modeling posterior cortical atrophy (PCA), an atypical form of Alzheimer's disease affecting the visual cortex. We simulated PCA in deep convolutional neural networks (DCNNs) trained for visual object recognition by randomly injuring connections between artificial neurons. Results showed that injured networks progressively lost their object recognition capability. Simulated PCA impacted learned representations hierarchically, as networks lost object-level representations before category-level representations. Incorporating this paradigm in computational neuroscience will be essential for developing in silico models of the brain and neurological diseases. The paradigm can be expanded to incorporate elements of neural plasticity and to other cognitive domains such as motor control, auditory cognition, language processing, and decision making.

Reimagining Perinatal Mental Health: An Expansive Vision For Structural Change.

Diagnoses of depression, anxiety, or other mental illness capture just one aspect of the psychosocial elements of the perinatal period. Perinatal loss; trauma; unstable, unsafe, or inhumane work environments; structural racism and gendered oppression in health care and society; and the lack of a social safety net threaten the overall well-being of birthing people, their families, and communities. Developing relevant policies for perinatal mental health thus requires attending to the intersecting effects of racism, poverty, lack of child care, inadequate postpartum support, and other structural violence on health. To fully understand and address this issue, we use a human rights framework to articulate how and why policy makers must take progressive action toward this goal. This commentary, written by an interdisciplinary and intergenerational team, employs personal and professional expertise to disrupt underlying assumptions about psychosocial aspects of the perinatal experience and reimagines a new way forward to facilitate well-being in the perinatal period.

Supervised machine learning tools: a tutorial for clinicians.

In an increasingly data-driven world, artificial intelligence is expected to be a key tool for converting big data into tangible benefits and the healthcare domain is no exception to this. Machine learning aims to identify complex patterns in multi-dimensional data and use these uncovered patterns to classify new unseen cases or make data-driven predictions. In recent years, deep neural networks have shown to be capable of producing results that considerably exceed those of conventional machine learning methods for various classification and regression tasks. In this paper, we provide an accessible tutorial of the most important supervised machine learning concepts and methods, including deep learning, which are potentially the most relevant for the medical domain. We aim to take some of the mystery out of machine learning and depict how machine learning models can be useful for medical applications. Finally, this tutorial provides a few practical suggestions for how to properly design a machine learning model for a generic medical problem.

Ability of crime, demographic and business data to forecast areas of increased violence.

Identifying geographic areas and time periods of increased violence is of considerable importance in prevention planning. This study compared the performance of multiple data sources to prospectively forecast areas of increased interpersonal violence. We used 2011-2014 data from a large metropolitan county on interpersonal violence (homicide, assault, rape and robbery) and forecasted violence at the level of census block-groups and over a one-month moving time window. Inputs to a Random Forest model included historical crime records from the police department, demographic data from the US Census Bureau, and administrative data on licensed businesses. Among 279 block groups, a model utilizing all data sources was found to prospectively improve the identification of the top 5% most violent block-group months (positive predictive value = 52.1%; negative predictive value = 97.5%; sensitivity = 43.4%; specificity = 98.2%). Predictive modelling with simple inputs can help communities more efficiently focus violence prevention resources geographically.

MetaPro-IQ: a universal metaproteomic approach to studying human and mouse gut microbiota.

BACKGROUND: The gut microbiota has been shown to be closely associated with human health and disease. While next-generation sequencing can be readily used to profile the microbiota taxonomy and metabolic potential, metaproteomics is better suited for deciphering microbial biological activities. However, the application of gut metaproteomics has largely been limited due to the low efficiency of protein identification. Thus, a high-performance and easy-to-implement gut metaproteomic approach is required. RESULTS: In this study, we developed a high-performance and universal workflow for gut metaproteome identification and quantification (named MetaPro-IQ) by using the close-to-complete human or mouse gut microbial gene catalog as database and an iterative database search strategy. An average of 38 and 33 % of the acquired tandem mass spectrometry (MS) spectra was confidently identified for the studied mouse stool and human mucosal-luminal interface samples, respectively. In total, we accurately quantified 30,749 protein groups for the mouse metaproteome and 19,011 protein groups for the human metaproteome. Moreover, the MetaPro-IQ approach enabled comparable identifications with the matched metagenome database search strategy that is widely used but needs prior metagenomic sequencing. The response of gut microbiota to high-fat diet in mice was then assessed, which showed distinct metaproteome patterns for high-fat-fed mice and identified 849 proteins as significant responders to high-fat feeding in comparison to low-fat feeding. CONCLUSIONS: We present MetaPro-IQ, a metaproteomic approach for highly efficient intestinal microbial protein identification and quantification, which functions as a universal workflow for metaproteomic studies, and will thus facilitate the application of metaproteomics for better understanding the functions of gut microbiota in health and disease.

ß-Estradiol results in a proprotein convertase subtilisin/kexin type 9-dependent increase in low-density lipoprotein receptor levels in human hepatic HuH7 cells.

The lower risk of coronary artery disease in premenopausal women than in men and postmenopausal women implicates sex steroids in cardioprotective processes. ß-Estradiol upregulates liver low-density lipoprotein receptor (LDLR), which, in turn, decreases circulating levels of low-density lipoprotein, which is a risk factor for coronary artery disease. Conversely, LDLR protein is negatively regulated by proprotein convertase subtilisin/kexin type 9 (PCSK9). Herein, we investigated PCSK9 regulation by ß-estradiol and its impact on LDLR in human hepatocarcinoma HuH7 cells grown in the presence or absence of ß-estradiol. Immunoblot analysis showed upregulation of LDLR at 3 µm ß-estradiol (140%), and the upregulation reached 220% at 10 µm ß-estradiol; only at the latter dose was an increase in LDLR mRNA detected by qPCR, suggesting post-translational regulation of LDLR. No changes in PCSK9 mRNA or secreted protein levels were detected by qPCR or ELISA, respectively. ß-estradiol-conditioned medium devoid of PCSK9 failed to upregulate LDLR. Similarly, PCSK9 knockdown cells showed no upregulation of LDLR by ß-estradiol. Together, these results indicate a requirement for PCSK9 in the ß-estradiol-induced upregulation of LDLR. A radiolabeling assay showed a significant, dose-dependent decrease in the ratio of secreted phosphoPCSK9 to total secreted PCSK9 with increasing ß-estradiol levels, suggesting a change in the functional state of PCSK9 in the presence of ß-estradiol. Our results indicate that the protein upregulation of LDLR at subtranscriptionally effective doses of ß-estradiol, and its supratranscriptional upregulation at 10 µm ß-estradiol, occur through an extracellular PCSK9-dependent mechanism.