Closing the Trauma Performance Improvement Loop With In-situ Simulation.

INTRODUCTION: Continuous performance improvement (PI) programs are essential for excellent trauma care. We incorporated PI identified from trauma cases into an in-situ simulation-based medical education curriculum. This is a proof-of-concept study exploring the efficacy of high-fidelity pediatric trauma simulations in improving self-reported provider comfort and knowledge for identified trauma PI issues. METHODS: This study was performed at an American College of Surgeons-verified Level I Pediatric Trauma Center. Several clinical issues were identified during the trauma PI process, including management of elevated intracranial pressure in traumatic brain injury and the use of massive transfusion protocol. These issues were incorporated into a simulation-based medical education curriculum and high-fidelity in-situ trauma mock codes were held. In-depth debriefing sessions were led by a senior faculty member after the simulations. The study participants completed pre- and postsimulation surveys. Univariate statistics are presented. RESULTS: Twenty three providers completed surveys for the pediatric trauma simulations. Self-reported provider confidence Likert scale improved from pre- to postsimulation (P = 0.02) and trauma experience and knowledge scores improved from 82% presimulation to 93% postsimulation (P = 0.02). CONCLUSIONS: High-fidelity pediatric trauma simulations enhance provider comfort, knowledge, and experience in trauma scenarios. By integrating high-fidelity trauma simulations to address clinical issues identified in the trauma PI process, provider education can be reinforced and practiced in a controlled environment to improve trauma care. Future studies evaluating the implementation of clinical pathways and patient outcomes are needed to demonstrate the effectiveness of simulations in PI pathways.

Disparities in child protective services involvement in pediatric traumatic brain injury.

PURPOSE: Traumatic brain injury (TBI) is a leading cause of pediatric death and disability. Abusive head trauma confers greater morbidity and mortality compared with accidental TBI. National trends reveal disproportionate involvement of minority children in the child welfare system. The study investigates socioeconomic disparities in child protective services (CPS) involvement in pediatric TBI. METHODS: Retrospective chart review was conducted for TBI patients (n = 596) admitted to an academic pediatric level I trauma center from 2015 to 2022, where institutional policy dictates automatic CPS referral for TBI patients ≤ 2 years. Analysis of variance, chi-squared, and logistic regressions compared racial and ethnic groups and calculated adjusted odds of CPS case acceptance. RESULTS: Rates of non-accidental trauma, CPS involvement, insurance, and marital status differed across racial and ethnic backgrounds (p < 0.05). Of patients ≤ 2 years, Hispanic patients (OR: 0.38, 95%CI [0.16,0.91]) had decreased odds of CPS involvement compared to non-Hispanic White patients when adjusting for confounders including injury severity, injury type, and socioeconomic status. CONCLUSIONS: We highlight racial and ethnic differences in incidence of pediatric TBI and CPS involvement, even in the setting of an automatic CPS referral policy for pediatric TBI patients ≤ 2 years.

A comparative study for predictive monitoring of COVID-19 pandemic.

COVID-19 pandemic caused by novel coronavirus (SARS-CoV-2) crippled the world economy and engendered irreparable damages to the lives and health of millions. To control the spread of the disease, it is important to make appropriate policy decisions at the right time. This can be facilitated by a robust mathematical model that can forecast the prevalence and incidence of COVID-19 with greater accuracy. This study presents an optimized ARIMA model to forecast COVID-19 cases. The proposed method first obtains a trend of the COVID-19 data using a low-pass Gaussian filter and then predicts/forecasts data using the ARIMA model. We benchmarked the optimized ARIMA model for 7-days and 14-days forecasting against five forecasting strategies used recently on the COVID-19 data. These include the auto-regressive integrated moving average (ARIMA) model, susceptible-infected-removed (SIR) model, composite Gaussian growth model, composite Logistic growth model, and dictionary learning-based model. We have considered the daily infected cases, cumulative death cases, and cumulative recovered cases of the COVID-19 data of the ten most affected countries in the world, including India, USA, UK, Russia, Brazil, Germany, France, Italy, Turkey, and Colombia. The proposed algorithm outperforms the existing models on the data of most of the countries considered in this study.

Clinical Features Predicting COVID-19 Severity Risk at the Time of Hospitalization.

The global spread of COVID-19 has led to significant mortality and morbidity worldwide. Early identification of COVID-19 patients who are at high risk of developing severe disease can help in improved patient management, care, and treatment, as well as in the effective allocation of hospital resources. The severity prediction at the time of hospitalization can be extremely helpful in deciding the treatment of COVID-19 patients. To this end, this study presents an interpretable artificial intelligence (AI) model, named COVID-19 severity predictor (CoSP) that predicts COVID-19 severity using the clinical features at the time of hospital admission. We utilized a dataset comprising 64 demographic and laboratory features of 7,416 confirmed COVID-19 patients that were collected at the time of hospital admission. The proposed hierarchical CoSP model performs four-class COVID severity risk prediction into asymptomatic, mild, moderate, and severe categories. CoSP yielded better performance with good interpretability, as observed via Shapley analysis on COVID severity prediction compared to the other popular ML methods, with an area under the received operating characteristic curve (AUC-ROC) of 0.95, an area under the precision-recall curve (AUPRC) of 0.91, and a weighted F1-score of 0.83. Out of 64 initial features, 19 features were inferred as predictive of the severity of COVID-19 disease by the CoSP model. Therefore, an AI model predicting COVID-19 severity may be helpful for early intervention, optimizing resource allocation, and guiding personalized treatments, potentially enabling healthcare professionals to save lives and allocate resources effectively in the fight against the pandemic.

Pediatric Traumatic Brain Injury Outcomes and Disparities During the COVID-19 Pandemic.

BACKGROUND: To study the impact of the COVID-19 pandemic on traumatic brain injury (TBI) patient demographic, clinical and trauma related characteristics, and outcomes. METHODS: Retrospective chart review was conducted on pediatric TBI patients admitted to a Level I Pediatric Trauma Center between January 2015 and June 2022. The pre-COVID era was defined as January 1, 2015, through March 12, 2020. The COVID-19 era was defined as March 13, 2020, through June 30, 2022. Bivariate analysis and logistic regression were performed. RESULTS: Four hundred-thirty patients were treated for pediatric TBI in the pre-COVID-19 period, and 166 patients during COVID-19. In bivariate analyses, the racial/ethnic makeup, age, and sex varied significantly across the two time periods (p < 0.05). Unwitnessed TBI events increased during the COVID-19 era. Logistic regression analyses also demonstrated significantly increased odds of death, severe disability, or vegetative state during COVID-19 (AOR 7.23; 95 % CI 1.43, 36.41). CONCLUSION: During the COVID-19 pandemic, patients admitted with pediatric TBI had significantly different demographics with regards to age, sex, and race/ethnicity when compared to patients prior to the pandemic. There was an increase in unwitnessed events. In the COVID period, patients had a higher odds ratio of severe morbidity and mortality despite adjustment for confounding factors. LEVEL OF EVIDENCE AND STUDY TYPE: Level II, Prognosis.

Sprouty proteins are negative regulators of interferon (IFN) signaling and IFN-inducible biological responses.

Interferons (IFNs) have important antiviral and antineoplastic properties, but the precise mechanisms required for generation of these responses remain to be defined. We provide evidence that during engagement of the Type I IFN receptor (IFNR), there is up-regulation of expression of Sprouty (Spry) proteins 1, 2, and 4. Our studies demonstrate that IFN-inducible up-regulation of Spry proteins is Mnk kinase-dependent and results in suppressive effects on the IFN-activated p38 MAP kinase (MAPK), the function of which is required for transcription of interferon-stimulated genes (ISGs). Our data establish that ISG15 mRNA expression and IFN-dependent antiviral responses are enhanced in Spry1,2,4 triple knock-out mouse embryonic fibroblasts, consistent with negative feedback regulatory roles for Spry proteins in IFN-mediated signaling. In other studies, we found that siRNA-mediated knockdown of Spry1, Spry2, or Spry4 promotes IFN-inducible antileukemic effects in vitro and results in enhanced suppressive effects on malignant hematopoietic progenitors from patients with polycythemia vera. Altogether, our findings demonstrate that Spry proteins are potent regulators of Type I IFN signaling and negatively control induction of Type I IFN-mediated biological responses.

GCRS: A hybrid graph convolutional network for risk stratification in multiple myeloma cancer patients.

In this study, we present an efficient Graph Convolutional Network based Risk Stratification system (GCRS) for cancer risk-stage prediction of newly diagnosed multiple myeloma (NDMM) patients. GCRS is a hybrid graph convolutional network consisting of a fusion of multiple connectivity graphs that are used to learn the latent representation of topological structures among patients. This proposed risk stratification system integrates these connectivity graphs prepared from the clinical and laboratory characteristics of NDMM cancer patients for partitioning them into three cancer risk groups: low, intermediate, and high. Extensive experiments demonstrate that GCRS outperforms the existing state-of-the-art methods in terms of C-index and hazard ratio on two publicly available datasets of NDMM patients. We have statistically validated our results using the Cox Proportional-Hazards model, Kaplan-Meier analysis, and log-rank test on progression-free survival (PFS) and overall survival (OS). We have also evaluated the contribution of various clinical parameters as utilized by the GCRS risk stratification system using the SHapley Additive exPlanations (SHAP) analysis, an interpretability algorithm for validating AI methods. Our study reveals the utility of the deep learning approach in building a robust system for cancer risk stage prediction.

COVID-19 image classification using deep learning: Advances, challenges and opportunities.

Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. Among AI methodologies, Deep Learning (DL) algorithms, particularly Convolutional Neural Networks (CNN), have gained significant popularity for the classification of COVID-19. This paper summarizes and reviews a number of significant research publications on the DL-based classification of COVID-19 through CXR and CT images. We also present an outline of the current state-of-the-art advances and a critical discussion of open challenges. We conclude our study by enumerating some future directions of research in COVID-19 imaging classification.

The burden of non-alcoholic steatohepatitis: A systematic review of health-related quality of life and patient-reported outcomes.

Background & Aims: Non-alcoholic steatohepatitis (NASH) is associated with increased mortality and a high clinical burden. NASH adversely impacts patients' health-related quality of life (HRQoL), but published data on the humanistic burden of disease are limited. This review aimed to summarise and critically evaluate studies reporting HRQoL or patient-reported outcomes (PROs) in populations with NASH and identify key gaps for further research. Methods: Medline, EMBASE, the Cochrane Library and PsycINFO were searched for English-language publications published from 2010 to 2021 that reported HRQoL/PRO outcomes of a population or subpopulation with NASH. Results: Twenty-five publications covering 23 unique studies were identified. Overall, the data showed a substantial impact of NASH on HRQoL, particularly in terms of physical functioning and fatigue, with deterioration of physical and mental health as NASH progresses. Prevalent symptoms, including fatigue, abdominal pain, anxiety/depression, cognition problems, and poor sleep quality, adversely impact patients' ability to work and perform activities of daily living and the quality of relationships. However, some patients fail to attribute symptoms to their disease because of a lack of patient awareness and education. NASH is associated with high rates of comorbidities such as obesity and type 2 diabetes, which contribute to reduced HRQoL. Studies were heterogeneous in terms of diagnostic methods, population, outcomes, follow-up time, and measures of HRQoL/utility. Most studies were rated 'moderate' at quality assessment, and all evaluable studies had inadequate control of confounders. Conclusions: NASH is associated with a significant HRQoL burden that begins early in the disease course and increases with disease progression. More robust studies are needed to better understand the humanistic burden of NASH, with adequate adjustment for confounders that could influence outcomes. Lay summary: Non-alcoholic steatohepatitis (NASH) has a significant impact on quality of life, with individuals experiencing worse physical and mental health compared with the general population. NASH and its symptoms, which include tiredness, stomach pain, anxiety, depression, poor focus and memory, and impaired sleep, affect individuals' relationships and ability to work and perform day-to-day tasks. However, not all patients are aware that their symptoms may be related to NASH. Patients would benefit from more education on their disease, and the importance of good social networks for patient health and well-being should be reinforced. More studies are needed to better understand the patient burden of NASH.

Phosphorylation of MCM3 on Ser-112 regulates its incorporation into the MCM2-7 complex.

During late M and early G(1), MCM2-7 assembles and is loaded onto chromatin in the final step of prereplicative complex (pre-RC) formation. However, the regulation of MCM assembly remains poorly understood. Cyclin-dependent kinase (CDK)-dependent phosphorylation contributes to DNA replication by initially activating pre-RCs and subsequently inhibiting refiring of origins during S and M phases, thus limiting DNA replication to a single round. Although the precise roles of specific MCM phosphorylation events are poorly characterized, we now demonstrate that CDK1 phosphorylates MCM3 at Ser-112, Ser-611, and Thr-719. In vivo, CDK1-dependent phosphorylation of Ser-112 triggers the assembly of MCM3 with the remaining MCM subunits and subsequent chromatin loading of MCMs. Strikingly, loss of MCM3 triggers the destabilization of other MCM proteins, suggesting that phosphorylation-dependent assembly is essential for stable accumulation of MCM proteins. These data reveal that CDK-dependent MCM3 phosphorylation contributes to the regulated formation of the MCM2-7 complex.

Robust Brain State Decoding using Bidirectional Long Short Term Memory Networks in functional MRI.

Decoding brain states of the underlying cognitive processes via learning discriminative feature representations has recently gained a lot of interest in brain imaging studies. Particularly, there has been an impetus to encode the dynamics of brain functioning by analyzing temporal information available in the fMRI data. Long-short term memory (LSTM), a class of machine learning model possessing a "memory" component, to retain previously seen temporal information, is increasingly being observed to perform well in various applications with dynamic temporal behavior, including brain state decoding. Because of the dynamics and inherent latency in fMRI BOLD responses, future temporal context is crucial. However, it is neither encoded nor captured by the conventional LSTM model. This paper performs robust brain state decoding via information encapsulation from both the past and future instances of fMRI data via bi-directional LSTM. This allows for explicitly modeling the dynamics of BOLD response without any delay adjustment. To this end, we utilize a bidirectional LSTM, wherein, the input sequence is fed in normal time-order for one LSTM network, and in the reverse time-order, for another. The two hidden activations of forward and reverse directions in bi-LSTM are collated to build the "memory" of the model and are used to robustly predict the brain states at every time instance. Working memory data from the Human Connectome Project (HCP) is utilized for validation and was observed to perform 18% better than it's unidirectional counterpart in terms of accuracy in predicting the brain states.

Multivariate graph learning for detecting aberrant connectivity of dynamic brain networks in autism.

Alterations in static functional brain networks have previously been reported in Autistic Spectrum Disorder (ASD). Although functional brain networks are known to be time-varying, alterations in time-varying or dynamic brain networks in ASD is largely unknown. Hence, in this study, we analyze resting-state fMRI data of ASD group versus Typically Developing Control (TDC) group to understand alterations in dynamic functional brain networks in ASD vis-à-vis healthy controls. We introduce a new framework for extracting overlapping dynamic functional brain networks to study these alterations. We utilize sliding window approach along with the recent Multivariate Vector Regression-based Connectivity (MVRC) method to construct functional connectivity (FC) matrices in each time-window. Further, we build three-mode subject-summarized spatio-temporal tensor in both ASD and TDC groups. This tensor is utilized to determine a set of overlapping dynamic functional brain networks and their temporal profiles. This helps us in studying alterations in dynamic brain networks in ASD subjects at the group-level. The proposed framework is tested on two publicly available resting-state fMRI dataset of ASD and normal controls. Our analyses on resting-state fMRI data indicate that dynamic functional brain networks of ASD subjects are altered compared to the TDC group. Two-sample t-test is used to establish the statistical significance of the differences observed in network strengths of the two groups. Compared to the TDC subjects, autistic subjects showed alterations in multiple functional brain networks including cognitive control, subcortical, auditory, visual, bilateral limbic, and default mode network. The proposed methodology is able to provide information on alterations in dynamic functional brain networks in ASD and may provide potential biomarkers for studying human brain disorders.

Low rank and sparsity constrained method for identifying overlapping functional brain networks.

Analysis of functional magnetic resonance imaging (fMRI) data has revealed that brain regions can be grouped into functional brain networks (fBNs) or communities. A community in fMRI analysis signifies a group of brain regions coupled functionally with one another. In neuroimaging, functional connectivity (FC) measure can be utilized to quantify such functionally connected regions for disease diagnosis and hence, signifies the need of devising novel FC estimation methods. In this paper, we propose a novel method of learning FC by constraining its rank and the sum of non-zero coefficients. The underlying idea is that fBNs are sparse and can be embedded in a relatively lower dimension space. In addition, we propose to extract overlapping networks. In many instances, communities are characterized as combinations of disjoint brain regions, although recent studies indicate that brain regions may participate in more than one community. In this paper, large-scale overlapping fBNs are identified on resting state fMRI data by employing non-negative matrix factorization. Our findings support the existence of overlapping brain networks.

Double temporal sparsity based accelerated reconstruction of compressively sensed resting-state fMRI.

A number of reconstruction methods have been proposed recently for accelerated functional Magnetic Resonance Imaging (fMRI) data collection. However, existing methods suffer with the challenge of greater artifacts at high acceleration factors. This paper addresses the issue of accelerating fMRI collection via undersampled k-space measurements combined with the proposed method based on l1-l1 norm constraints, wherein we impose first l1-norm sparsity on the voxel time series (temporal data) in the transformed domain and the second l1-norm sparsity on the successive difference of the same temporal data. Hence, we name the proposed method as Double Temporal Sparsity based Reconstruction (DTSR) method. The robustness of the proposed DTSR method has been thoroughly evaluated both at the subject level and at the group level on real fMRI data. Results are presented at various acceleration factors. Quantitative analysis in terms of Peak Signal-to-Noise Ratio (PSNR) and other metrics, and qualitative analysis in terms of reproducibility of brain Resting State Networks (RSNs) demonstrate that the proposed method is accurate and robust. In addition, the proposed DTSR method preserves brain networks that are important for studying fMRI data. Compared to the existing methods, the DTSR method shows promising potential with an improvement of 10-12 dB in PSNR with acceleration factors upto 3.5 on resting state fMRI data. Simulation results on real data demonstrate that DTSR method can be used to acquire accelerated fMRI with accurate detection of RSNs.

Multivariate brain network graph identification in functional MRI.

Motivated by recent interest in identification of functional brain networks, we develop a new multivariate approach for functional brain network identification and name it as Multivariate Vector Regression-based Connectivity (MVRC). The proposed MVRC method regresses time series of all regions to those of other regions simultaneously and estimates pairwise association between two regions with consideration of influence of other regions and builds the adjacency matrix. Next, modularity method is applied on the adjacency matrix to detect communities or functional brain networks. We compare the proposed MVRC method with existing methods ranging from simple Pearson correlation to advanced Multivariate Adaptive Sparse Representation (ASR) methods. Experimental results on simulated and real fMRI dataset demonstrate that MVRC is able to extract functional brain networks that are consistent with the literature. Also, the proposed MVRC method is 650-750 times faster compared to the existing ASR method on 90 node network.

Optshrink LR + S: accelerated fMRI reconstruction using non-convex optimal singular value shrinkage.

This paper presents a new accelerated fMRI reconstruction method, namely, OptShrink LR + S method that reconstructs undersampled fMRI data using a linear combination of low-rank and sparse components. The low-rank component has been estimated using non-convex optimal singular value shrinkage algorithm, while the sparse component has been estimated using convex l 1 minimization. The performance of the proposed method is compared with the existing state-of-the-art algorithms on real fMRI dataset. The proposed OptShrink LR + S method yields good qualitative and quantitative results.

Augmentation of HIV-1 subtype C vaccine constructs induced immune response in mice by CpG motif 1826-ODN.

The greatest biomedical challenge of this century is to develop a preventive vaccine against Human Immunodeficiency Virus (HIV-1). For an HIV vaccine to be effective, it appears logical to develop new strategies that enhance the level of the immune response as well as steer it towards the desirable cellular type. In view of this, there is a need for rational inclusion of biological adjuvants into the HIV-1 vaccination strategies that could potentiate the immune responses both qualitatively and quantitatively. The adjuvant may include the immunostimulatory oligonucleotides containing CpG motifs, whose immunomodulatory characters are well established and represent the basis for an effective vaccine adjuvant. In our study, we investigated the use of an immunostimulatory oligonucleotide (or CpG motif), 1826-ODN to augment the immune response elicited by plasmid DNA vaccine constructs containing Indian subtype C HIV-1 envelope gp120 and gag-protease genes in Balb/c mouse model system. A dose of 2-microg CpG motifs/mouse was found to be optimum when co-administered with the DNA vaccine constructs with the peak level of humoral and cell mediated immune responses at 6 weeks post immunization. Murine IFN-gamma ELISpot assay demonstrated that the use of 1826-ODN led to a broad based and long term recognition of the subtype C envelope and gag peptides. The use of CpG motifs has been effective in augmenting the immune responses generated by the DNA constructs. Taken together, these results are an important advancement towards the design of future preclinical and clinical trials of these vaccine constructs.

Adjuvant action of murine IL-2/Ig plasmid after intramuscular immunization with Indian HIV-1 subtype C recombinant env.gp 120 construct.

The human immunodeficiency virus (HIV) epidemic is probably the greatest scourge to affect mankind in the 20th century. Containment of the acquired immunodeficiency syndrome (AIDS) epidemic will require an effective vaccine. Of various vaccine approaches, immunization with DNA plasmids containing HIV-1 structural genes is the most popular approach. However, an important limitation of DNA immunization is that these responses are relatively weak and are often only transient in their nature. The use of immunologic adjuvants together with DNA vaccines is a promising way to enhance and to optimize DNA-derived immunity. Cytokines have been widely used to enhance the immune responses of DNA vaccines. In the present investigation, we studied the in vivo immunomodulation of HIV-1 Indian subtype C plasmid construct (pJWSK3, encoding envgp120 gene) by plasmid-based murine IL-2/Ig construct. Subcloning of mIL-2/Ig gene from pVRCmIL-2/Ig construct into pJW4304 vector was done followed by its in vitro expression study on the COS-7 cell line. Co-immunization of the recombinant HIV-1 env-gp120 construct with the IL-2/Ig construct in the female Balb/c mice by the intramuscular route resulted in induction of significantly higher levels of both HIV-1-specific antibody response and cell mediated immune response than by DNA plasmid construct alone (p < 0.001 and p < 0.05, respectively). The induced HIV-1-specific murine IFN-gamma response was robust, broad based, and seen even at the end of 6 months after immunization. Taken together these results indicate that the strategy of using IL-2/Ig plasmid can be highly effective when used along with recombinant DNA constructs and serve as the potential tool for the development of more rationally designed vaccines against HIV-1.

Scorpion envenomation causing hemiparesis.

We report a case of scorpion sting who presented with right hemiparesis and deranged level of consciousness. Prolonged BT/CT, decreased platelet counts, prolongation of PT and APTT with positive fibrinogen degradation products (FDP) D-dimer and CT scan findings of multiple cerebral haemorrhagic infarct indicating the presence of disseminated intravascular coagulation (DIC) as a cause of hemiparesis. Patient was managed with fresh blood transfusion and conventional treatment with favourable outcome.

Nuclear cyclin D1/CDK4 kinase regulates CUL4 expression and triggers neoplastic growth via activation of the PRMT5 methyltransferase.

Cyclin D1 elicits transcriptional effects through inactivation of the retinoblastoma protein and direct association with transcriptional regulators. The current work reveals a molecular relationship between cyclin D1/CDK4 kinase and protein arginine methyltransferase 5 (PRMT5), an enzyme associated with histone methylation and transcriptional repression. Primary tumors of a mouse lymphoma model exhibit increased PRMT5 methyltransferase activity and histone arginine methylation. Analyses demonstrate that MEP50, a PRMT5 coregulatory factor, is a CDK4 substrate, and phosphorylation increases PRMT5/MEP50 activity. Increased PRMT5 activity mediates key events associated with cyclin D1-dependent neoplastic growth, including CUL4 repression, CDT1 overexpression, and DNA rereplication. Importantly, human cancers harboring mutations in Fbx4, the cyclin D1 E3 ligase, exhibit nuclear cyclin D1 accumulation and increased PRMT5 activity.