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Introduction: Deviant sexual interest for children (pedophilia, hebephilia) is associated with a higher risk of sexual offending against children (CSA) and consuming child sexual abuse images (CSAI). There is a general shortage of therapeutic programs for individuals who feel sexually attracted to juvenile bodies and are concerned about their sexual behaviour. Efforts to establish regional centres throughout Germany offering preventive support led to the prevention network "Don't become an offender" ("Kein Täter werden"). Objectives: To identify dynamic risk factors (DRFs) and evaluate a treatment programme aiming to reduce CSA and CSAI among potential or existing pedosexual offenders (who have not been legally charged). In addition, changes in the course of therapy are examined to provide information about the accessibility and motivation of the target group and its therapeutic responsiveness. Methods: Participants undergo standardized diagnostic and treatment procedures. Therapy comprises an outpatient psychotherapy program (group therapy) over the course of approx. 48 weekly sessions, optional individual and partner/relative including sessions, as well as additional pharmaceutical treatment. Assessments are carried out through self- and other-reported psychometric test batteries pre-, during and post-treatment up to a 3.5 year follow-up. The test battery includes clinical questionnaires (WHO-5, CTQ-SF), personality questionnaires (ISK-K, NEO-FFI), sexuality questionnaires (EKK-R, KV-M, MSI, HBI-19) and risk assessment procedures (VRAG-R, STATIC-99, VRS:SO). Main outcome measures are self- and externally-reported DRF changes well as offending behaviour characteristics. Results: By September 20, 2023, N=12 individuals were enrolled in the treatment program. All individuals had a deviant sexual preference (exclusive/non-exclusive pedo-/hebephilia). Nine individuals reported past and/or current use of CSAI. Of these, two individuals reported at least one CSA in the past. Three had no previous use of CSAI or CSA history. In the first treatment group (N=6), preliminary results show reduction in dynamic risk factors (e.g., Cognitive Bias, Sexual Compulsivity, Impulsivity) after the first 12 weeks of treatment. The evaluation of additional clinical data is pending. Conclusions: To date, therapy for individuals with pedophilia or hebephilia has been insufficient – particularly when not offending. Ongoing evaluation of the therapy program should provide further insight into responsiveness and therapeutic motivation of this target group. In particular, the impact of therapy on changing dynamic risk factors for CSA and CSAI remains to be examined. Disclosure of Interest: None Declared [ABSTRACT FROM AUTHOR]

In this study two approaches are presented to model Dominant Runoff production Processes (DRP) with respect to regionalization. The approaches have been developed in the micro-scale experimental Zemmer basin (Germany). The first approach combines the permeability of the substratum, land-use and slope of the basin in a GIS-based analysis. The second approach makes use of discriminant analysis of the physiographic characteristics of the basin and links it to the GIS analysis. The net results were two maps indicating modelled DRP for the Zemmer basin, which were then compared to an existing DRP map of the Zemmer basin. Both approaches provided satisfactory results when compared to this existing DRP map. The first approach was strongly linked to the geological conditions of the basin while the second approach revealed a strong dependence on the topography. Therefore, impermeability of the substratum and the topography of the basin were used as suitable parameters for modelling dominant runoff processes. [ABSTRACT FROM AUTHOR]

Background and Objectives: Prediction of patient outcomes after severe traumatic brain injury (sTBI) is limited with current clinical tools. This study aimed to improve such prognostication by combining clinical data and serum inflammatory and neuronal proteins in patients with sTBI to develop predictive models for post-traumatic vasospasm (PTV) and mortality., Methods: Fifty-three adult civilian patients were prospectively enrolled in the sTBI arm of the Surgical Critical Care Initiative (SC2i). Clinical, serum inflammatory, and neuronal protein data were combined using the parsimonious machine learning methods of least absolute shrinkage and selection operator (LASSO) and classification and regression trees (CART) to construct parsimonious models for predicting development of PTV and mortality., Results: Thirty-six (67.9%) patients developed vasospasm and 10 (18.9%) died. The mean age was 39.2 years; 22.6% were women. CART identified lower IL9, lower presentation pulse rate, and higher eotaxin as predictors of vasospasm development (full data area under curve (AUC) = 0.89, mean cross-validated AUC = 0.47). LASSO identified higher Rotterdam computed tomography score and lower age as risk factors for vasospasm development (full data AUC 0.94, sensitivity 0.86, and specificity 0.94; cross-validation AUC 0.87, sensitivity 0.79, and specificity 0.93). CART identified high levels of eotaxin as most predictive of mortality (AUC 0.74, cross-validation AUC 0.57). LASSO identified higher serum IL6, lower IL12, and higher glucose as predictive of mortality (full data AUC 0.9, sensitivity 1.0, and specificity 0.72; cross-validation AUC 0.8, sensitivity 0.85, and specificity 0.79)., Conclusion: Inflammatory cytokine levels after sTBI may have predictive value that exceeds conventional clinical variables for certain outcomes. IL-9, pulse rate, and eotaxin as well as Rotterdam score and age predict development of PTV. Eotaxin, IL-6, IL-12, and glucose were predictive of mortality. These results warrant validation in a prospective cohort., (Copyright © 2024 Written work prepared by employees of the Federal Government as part of their official duties is, under the U.S. Copyright Act, a “work of the United States Government” for which copyright protection under Title 17 of the United States Code is not available. As such, copyright does not extend to the contributions of employees of the Federal Government.)

DNA Polymerase Theta (Pol θ) is a conserved an A-family polymerase that plays an essential role in repairing double strand breaks, through micro-homology end joining, and bypassing DNA lesions, through translesion synthesis, to protect genome integrity. Despite its essential role in DNA repair, Pol θ is inherently error-prone. Recently, key loop regions were identified to play an important role in key functions of Pol θ. Here we present a comparative structure-function study of the polymerase domain of zebrafish and human Pol θ. We show that these two proteins share a large amount of sequence and structural homology. However, we identify differences in the amino acid composition within the key loop areas shown to drive characteristic Pol θ functions. Despite these differences zebrafish Pol θ still displays characteristics identify in human Pol θ, including DNA template extension in the presence of different divalent metals, microhomology-mediated end joining, and translesion synthesis. These results will support future studies looking to gain insight into Pol θ function on the basis of evolutionarily conserved features.

Background: Thoracic injury can cause impairment of lung function leading to respiratory complications such as pneumonia (PNA). There is increasing evidence that central memory T cells of the adaptive immune system play a key role in pulmonary immunity. We sought to explore whether assessment of cell phenotypes using flow cytometry (FCM) could be used to identify pulmonary infection after thoracic trauma., Methods: We prospectively studied trauma patients with thoracic injuries who survived >48 hours at a Level 1 trauma center from 2014 to 2020. Clinical and FCM data from serum samples collected within 24 hours of admission were considered as potential variables. Random forest and logistic regression models were developed to estimate the risk of hospital-acquired and ventilator-associated PNA. Variables were selected using backwards elimination, and models were internally validated with leave-one-out., Results: Seventy patients with thoracic injuries were included (median age, 35 years [interquartile range (IQR), 25.25-51 years]; 62.9% [44 of 70] male, 61.4% [42 of 70] blunt trauma). The most common injuries included rib fractures (52 of 70 [74.3%]) and pulmonary contusions (26 of 70 [37%]). The incidence of PNA was 14 of 70 (20%). Median Injury Severity Score was similar for patients with and without PNA (30.5 [IQR, 22.6-39.3] vs. 26.5 [IQR, 21.6-33.3]). The final random forest model selected three variables (Acute Physiology and Chronic Health Evaluation score, highest pulse rate in first 24 hours, and frequency of CD4 + central memory cells) that identified PNA with an area under the curve of 0.93, sensitivity of 0.91, and specificity of 0.88. A logistic regression with the same features had an area under the curve of 0.86, sensitivity of 0.76, and specificity of 0.85., Conclusion: Clinical and FCM data have diagnostic utility in the early identification of patients at risk of nosocomial PNA following thoracic injury. Signs of physiologic stress and lower frequency of central memory cells appear to be associated with higher rates of PNA after thoracic trauma., Level of Evidence: Diagnostic Test/Criteria; Level IV., (Copyright © 2023 Written work prepared by employees of the Federal Government as part of their official duties is, under the U.S. Copyright Act, a "œwork of the United States Government"œ for which copyright protection under Title 17 of the United States Code is not available. As such, copyright does not extend to the contributions of employees of the Federal Government.)

One of the most commonly replicated results in the research area of recidivism risk assessment is the superiority of structured and standardized prediction methods in comparison to unstructured, subjective, intuitive, or impressionistic clinical judgments. However, the quality of evidence supporting this conclusion is partly still controversially discussed because studies including direct comparisons of the predictive accuracy of unstructured and structured risk assessment methods have been relatively rarely conducted. Therefore, we examined in the present study retrospectively N = 416 expert witness reports written about individuals convicted of violent and/or sexual offenses in Germany between 1999 and 2015. The predictive accuracy of different methodological approaches of risk assessment (subjective clinical [i.e., unstructured clinical judgment; UCJ], structured professional judgment [SPJ], actuarial risk assessment instruments [ARAIs], and combinations of ARAIs-/SPJ-based risk assessments) was compared by analyzing the actual reoffenses according to the Federal Central Register (average follow-up period M = 7.08 years). In accordance with previously published results, the results indicated a higher predictive accuracy for structured compared to unstructured risk assessment approaches for the prediction of general, violent, and sexual recidivism. Taken together, the findings underline the limited accuracy of UCJs and provided further support for the use of structured and standardized risk assessment procedures in the area of crime and delinquency. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Background: An emerging body of literature supports the role of individualized prognostic tools to guide the management of patients after trauma. The aim of this study was to develop advanced modeling tools from multidimensional data sources, including immunological analytes and clinical and administrative data, to predict outcomes in trauma patients., Methods: This was a prospective study of trauma patients at Level 1 centers from 2015 to 2019. Clinical, flow cytometry, and serum cytokine data were collected within 48 hours of admission. Sparse logistic regression models were developed, jointly selecting predictors and estimating the risk of ventilator-associated pneumonia, acute kidney injury, complicated disposition (death, rehabilitation, or nursing facility), and return to the operating room. Model parameters (regularization controlling model sparsity) and performance estimation were obtained via nested leave-one-out cross-validation., Results: A total of 179 patients were included. The incidences of ventilator-associated pneumonia, acute kidney injury, complicated disposition, and return to the operating room were 17.7%, 28.8%, 22.5%, and 12.3%, respectively. Regarding extensive resource use, 30.7% of patients had prolonged intensive care unit stay, 73.2% had prolonged length of stay, and 23.5% had need for prolonged ventilatory support. The models were developed and cross-validated for ventilator-associated pneumonia, acute kidney injury, complicated dispositions, and return to the operating room, yielding predictive areas under the curve from 0.70 to 0.91. Each model derived its optimal predictive value by combining clinical, administrative, and immunological analyte data., Conclusion: Clinical, immunological, and administrative data can be combined to predict post-traumatic outcomes and resource use. Multidimensional machine learning modeling can identify trauma patients with complicated clinical trajectories and high resource needs., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: This review describes the research and development process of gantenerumab, a fully human anti-amyloid monoclonal antibody in development to treat early symptomatic and asymptomatic Alzheimer's disease (AD). Anti-amyloid monoclonal antibodies can substantially reverse amyloid plaque pathology and may modify the course of the disease by slowing or stopping its clinical progression. Several molecules targeting amyloid have failed in clinical development due to drug-related factors (e.g., treatment-limiting adverse events, low potency, poor brain penetration), study design/methodological issues (e.g., disease stage, lack of AD pathology confirmation), and other factors. The US Food and Drug Administration's approval of aducanumab, an anti-amyloid monoclonal antibody as the first potential disease-modifying therapy for AD, signaled the value of more than 20 years of drug development, adding to the available therapies the first nominal success since cholinesterase inhibitors and memantine were approved. BODY: Here, we review over 2 decades of gantenerumab development in the context of scientific discoveries in the broader AD field. Key learnings from the field were incorporated into the gantenerumab phase 3 program, including confirmed amyloid positivity as an entry criterion, an enriched clinical trial population to ensure measurable clinical decline, data-driven exposure-response models to inform a safe and efficacious dosing regimen, and the use of several blood-based biomarkers. Subcutaneous formulation for more pragmatic implementation was prioritized as a key feature from the beginning of the gantenerumab development program., Conclusion: The results from the gantenerumab phase 3 programs are expected by the end of 2022 and will add critical information to the collective knowledge on the search for effective AD treatments., (© 2022. The Author(s).)

The current research paradigm for Huntington's disease is based on participants with overt clinical phenotypes and does not address its pathophysiology nor the biomarker changes that can precede by decades the functional decline. We have generated a new research framework to standardise clinical research and enable interventional studies earlier in the disease course. The Huntington's Disease Integrated Staging System (HD-ISS) comprises a biological research definition and evidence-based staging centred on biological, clinical, and functional assessments. We used a formal consensus method that involved representatives from academia, industry, and non-profit organisations. The HD-ISS characterises individuals for research purposes from birth, starting at Stage 0 (ie, individuals with the Huntington's disease genetic mutation without any detectable pathological change) by using a genetic definition of Huntington's disease. Huntington's disease progression is then marked by measurable indicators of underlying pathophysiology (Stage 1), a detectable clinical phenotype (Stage 2), and then decline in function (Stage 3). Individuals can be precisely classified into stages based on thresholds of stage-specific landmark assessments. We also demonstrated the internal validity of this system. The adoption of the HD-ISS could facilitate the design of clinical trials targeting populations before clinical motor diagnosis and enable data standardisation across ongoing and future studies., Competing Interests: Declaration of interests SJT reports personal fees from F Hoffmann La Roche, Annexon, PTC Therapeutics, Takeda Pharmaceuticals, Vertex Pharmaceuticals, Alnylam Pharmaceuticals, Alphasights, Genentech, LoQus23 Therapeutics, Triplet Therapeutics, Novartis, Atalanta, Spark Therapeutics, Horama, University College Irvine, and Guidepoint; a patent application (2105484.6) and structural analogues licensed to Adrestia Therapeutics; funding from the CHDI Foundation, the UK Dementia Research Institute that receives its funding from DRI, the UK Medical Research Council, Alzheimer's Society, and Alzheimer's Research UK, and the Wellcome Trust (200181/Z/15/Z). SSc is a full-time employee of F Hoffmann La Roch. ECG and CS are employees and receive salaries from CHDI Management. CS has received consultancy honorariums (unrelated to Huntington's disease) from Pfizer, Kyowa Kirin, vTv Therapeutics, GW pharmaceuticals, Neuraly, Neuroderm, Green Valley Pharmaceuticals, and Pinteon Pharmaceuticals. AM is a consultant to CHDI Management. BB is an employee of Novartis Pharmaceuticals. PK is Chief Scientific Officer and cofounder of VectorY. TAM has received speaker honorariums from Abbvie and the International Parkinson and Movement Disorder Society; consultancy fees from CHDI Foundation and CHDI Management, Sunovion, Valeo Pharma, Roche, nQ Medical, and Merz; advisory board fees from Abbvie, Biogen, Sunovion, Medtronic; and research funding from the EU Joint Programme—Neurodegenerative Disease Research, uOBMRI, Roche, Ontario Research Fund, CIHR, MJFF, Parkinson Canada, PDF/PSG, LesLois Foundation, PSI Foundation, Parkinson Research Consortium, and Brain Canada. JP and APM are full-time employees of Wave Life Sciences. CAR reports grants from CHDI Foundation, outside the submitted work; consultancy fees from HSG, Annexon, Mitoconix, NeuBase, NeuExcell, Roche/Genentech, Sage, Spark, Teva, uniQure, and Wave. MZ is an employee of Vaccinex and has a patent issued. KR, SSi, and ECT declare competing interests. JDL reports grants from CHDI; personal fees from F Hoffmann La Roche, uniQure biopharma BV, Triplet Therapeutics, PTC Therapeutics, Remix, Vaccinex, and Wave Life Sciences USA., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Introduction: Venous thromboembolism (VTE) is a frequent complication of trauma associated with high mortality and morbidity. Clinicians lack appropriate tools for stratifying trauma patients for VTE, thus have yet to be able to predict when to intervene. We aimed to compare random forest (RF) and logistic regression (LR) predictive modelling for VTE using (1) clinical measures alone, (2) serum biomarkers alone and (3) clinical measures plus serum biomarkers., Methods: Data were collected from 73 military casualties with at least one extremity wound and prospectively enrolled in an observational study between 2007 and 2012. Clinical and serum cytokine data were collected. Modelling was performed with RF and LR based on the presence or absence of deep vein thrombosis (DVT) and/or pulmonary embolism (PE). For comparison, LR was also performed on the final variables from the RF model. Sensitivity/specificity and area under the curve (AUC) were reported., Results: Of the 73 patients (median Injury Severity Score=16), nine (12.3%) developed VTE, four (5.5%) with DVT, four (5.5%) with PE, and one (1.4%) with both DVT and PE. In all sets of predictive models, RF outperformed LR. The best RF model generated with clinical and serum biomarkers included five variables (interleukin-15, monokine induced by gamma, vascular endothelial growth factor, total blood products at resuscitation and presence of soft tissue injury) and had an AUC of 0.946, sensitivity of 0.992 and specificity of 0.838., Conclusions: VTE may be predicted by clinical and molecular biomarkers in trauma patients. This will allow the development of clinical decision support tools which can help inform the management of high-risk patients for VTE., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Improper or delayed activation of a massive transfusion protocol may have consequences to individuals and institutions. We designed a complex predictive algorithm that was packaged within a smartphone application. We hypothesized it would accurately assess the need for massive transfusion protocol activation and assist clinicians in that decision., Methods: We prospectively enrolled patients at an urban, level I trauma center. The application recorded the surgeon's initial opinion for activation and then prompted inputs for the model. The application provided a prediction and recorded the surgeon's final decision on activation., Results: Three hundred and twenty-one patients were enrolled (83% male; 59% penetrating; median Injury Severity Score 9; mean base deficit -4.11). Of 36 massive transfusion protocol activations, 26 had an app prediction of "high" or "moderate" probability. Of these, 4 (15%) patients received <10 u blood as a result of early hemorrhage control. Two hundred and eighty-five patients did not have massive transfusion protocol activated by the surgeon with 27 (9%) patients having "moderate" or "high" likelihood predicted by the application. Twenty-four of these did not require massive transfusion, and all patients had acidosis that unrelated to hemorrhagic shock. For 13 (50%) of the patients with "high" probability, the surgeon correctly altered their initial decision based on this information. The algorithm demonstrated an adjusted accuracy of 0.96 (95% confidence interval [0.93-0.98); P ≤ .001]), sensitivity = 0.99, specificity 0.72, positive predictive value 0.96, negative predictive value 0.99, and area under the receiver operating curve = 0.86., Conclusion: A smartphone-based clinical decision tools can aid surgeons in the decision to active massive transfusion protocol in real time, although it does not completely replace clinician judgment., (Copyright © 2021 Elsevier Inc. All rights reserved.)

In combat casualty care, tranexamic acid (TXA) is administered as part of initial resuscitation effort; however, conflicting data exist as to whether TXA contributes to increased risk of venous thromboembolism (VTE). The purpose of this study is to determine what factors increase risk of pulmonary embolism after combat-related orthopaedic trauma and whether administration of TXA is an independent risk factor for major thromboembolic events., Setting: United States Military Trauma Centers., Patients: Combat casualties with orthopaedic injuries treated at any US military trauma center for traumatic injuries sustained from January 2011 through December 2015. In total, 493 patients were identified., Intervention: None., Main Outcome Measures: Occurrence of major thromboembolic events, defined as segmental or greater pulmonary embolism or thromboembolism-associated pulseless electrical activity., Results: Regression analysis revealed TXA administration, traumatic amputation, acute kidney failure, and hypertension to be associated with the development of a major thromboembolic event for all models. Injury characteristics independently associated with risk of major VTE were Injury Severity Score 23 or greater, traumatic amputation, and vertebral fracture. The best performing model utilized had an area under curve  = 0.84, a sensitivity=0.72, and a specificity=0.84., Conclusions: TXA is an independent risk factor for major VTE after combat-related Orthopaedic injury. Injury factors including severe trauma, major extremity amputation, and vertebral fracture should prompt suspicion for increased risk of major thromboembolic events and increased threshold for TXA use if no major hemorrhage is present., Level of Evidence: III, Prognostic Study., Competing Interests: Each author certifies that neither he or she, nor any member of his or her immediate family, has funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc) that might pose a conflict of interest in connection with the submitted article. The authors have no conflicts of interest to disclose., (Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Orthopaedic Trauma Association.)

Background: Appendicitis is one of the most common surgically treated diseases in the world. CT scans are often over-utilized and ordered before a surgeon has evaluated the patient. Our aim was to develop a tool using machine learning (ML) algorithms that would help determine if there would be benefit in obtaining a CT scan prior to surgeon consultation., Methods: Retrospective chart review of 100 randomly selected cases who underwent appendectomy and 100 randomly selected controls was completed. Variables included components of the patient's history, laboratory values, CT readings, and pathology. Pathology was used as the gold standard for appendicitis diagnosis. All variables were then used to build the ML algorithms. Random Forest (RF), Support Vector Machine (SVM), and Bayesian Network Classifiers (BNC) models with and without CT scan results were trained and compared to CT scan results alone and the Alvarado score using area under the Receiver Operator Curve (ROC), sensitivity, and specificity measures as well as calibration indices from 500 bootstrapped samples., Results: Among the cases that underwent appendectomy, 88% had pathology-confirmed appendicitis. All the ML algorithms had better sensitivity, specificity, and ROC than the Alvarado score. SVM with and without CT had the best indices and could predict if imaging would aid in appendicitis diagnosis., Conclusion: This study demonstrated that SVM with and without CT results can be used for selective imaging in the diagnosis of appendicitis. This study serves as the initial step and proof-of-concept to externally validate these results with larger and more diverse patient population., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Background: Flow cytometry (FCM) is a rapid diagnostic tool for monitoring immune cell function. We sought to determine if assessment of cell phenotypes using standardized FCM could be used to identify nosocomial infection after trauma., Methods: Prospective study of trauma patients at a Level I center from 2014 to 2018. Clinical and FCM data were collected within 24 hours of admission. Random forest (RF) models were developed to estimate the risk of severe sepsis (SS), organ space infection (OSI), and ventilator-associated pneumonia (VAP). Variables were selected using backward elimination and models were validated with leave-one-out., Results: One hundred and thirty-eight patients were included (median age, 30 years [23-44 years]; median Injury Severity Score, 20 (14-29); 76% (105/138) Black; 60% (83/138) gunshots). The incidence of SS was 8.7% (12/138), OSI 16.7% (23/138), and VAP 18% (25/138). The final RF SS model resulted in five variables (RBCs transfused in first 24 hours; absolute counts of CD56- CD16+ lymphocytes, CD4+ T cells, and CD56 bright natural killer [NK] cells; percentage of CD16+ CD56+ NK cells) that identified SS with an AUC of 0.89, sensitivity of 0.98, and specificity of 0.78. The final RF OSI model resulted in four variables (RBC in first 24 hours, shock index, absolute CD16+ CD56+ NK cell counts, percentage of CD56 bright NK cells) that identified OSI with an AUC of 0.76, sensitivity of 0.68, and specificity of 0.82. The RF VAP model resulted in six variables (Sequential [Sepsis-related] Organ Failure Assessment score: Injury Severity Score; CD4- CD8- T cell counts; percentages of CD16- CD56- NK cells, CD16- CD56+ NK cells, and CD19+ B lymphocytes) that identified VAP with AUC of 0.86, sensitivity of 0.86, and specificity of 0.83., Conclusions: Combined clinical and FCM data can assist with early identification of posttraumatic infections. The presence of NK cells supports the innate immune response that occurs during acute inflammation. Further research is needed to determine the functional role of these innate cell phenotypes and their value in predictive models immediately after injury., Level of Evidence: Prognostic, level III., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Background: The immunologic pathways activated during snakebite envenoming (SBE) are poorly described, and their association with recovery is unclear. The immunologic response in SBE could inform a prognostic model to predict recovery. The purpose of this study was to develop pre- and post-antivenom prognostic models comprised of clinical features and immunologic cytokine data that are associated with recovery from SBE., Materials and Methods: We performed a prospective cohort study in an academic medical center emergency department. We enrolled consecutive patients with Crotalinae SBE and obtained serum samples based on previously described criteria for the Surgical Critical Care Initiative (SC2i)(ClinicalTrials.gov Identifier: NCT02182180). We assessed a standard set of clinical variables and measured 35 unique cytokines using Luminex Cytokine 35-Plex Human Panel pre- and post-antivenom administration. The Patient-Specific Functional Scale (PSFS), a well-validated patient-reported outcome of functional recovery, was assessed at 0, 7, 14, 21 and 28 days and the area under the patient curve (PSFS AUPC) determined. We performed Bayesian Belief Network (BBN) modeling to represent relationships with a diagram composed of nodes and arcs. Each node represents a cytokine or clinical feature and each arc represents a joint-probability distribution (JPD)., Results: Twenty-eight SBE patients were enrolled. Preliminary results from 24 patients with clinical data, 9 patients with pre-antivenom and 11 patients with post-antivenom cytokine data are presented. The group was mostly female (82%) with a mean age of 38.1 (SD ± 9.8) years. In the pre-antivenom model, the variables most closely associated with the PSFS AUPC are predominantly clinical features. In the post-antivenom model, cytokines are more fully incorporated into the model. The variables most closely associated with the PSFS AUPC are age, antihistamines, white blood cell count (WBC), HGF, CCL5 and VEGF. The most influential variables are age, antihistamines and EGF. Both the pre- and post-antivenom models perform well with AUCs of 0.87 and 0.90 respectively., Discussion: Pre- and post-antivenom networks of cytokines and clinical features were associated with functional recovery measured by the PSFS AUPC over 28 days. With additional data, we can identify prognostic models using immunologic and clinical variables to predict recovery from SBE., Competing Interests: ES was employed by DecisionQ. CG receives grant funding from BTG Specialty Pharmaceuticals. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Gerardo, Silvius, Schobel, Eppensteiner, McGowan, Elster, Kirk and Limkakeng.)

The longitudinal dynamics of the most promising biofluid biomarker candidates for Huntington's disease (HD)-mutant huntingtin (mHTT) and neurofilament light (NfL)-are incompletely defined. Characterizing changes in these candidates during disease progression could increase our understanding of disease pathophysiology and help the identification of effective therapies. In an 80-participant cohort over 24 months, mHTT in cerebrospinal fluid (CSF), as well as NfL in CSF and blood, had distinct longitudinal trajectories in HD mutation carriers compared with controls. Baseline analyte values predicted clinical disease status, subsequent clinical progression, and brain atrophy, better than did the rate of change in analytes. Overall, NfL was a stronger monitoring and prognostic biomarker for HD than mHTT. Nonetheless, mHTT has prognostic value and might be a valuable pharmacodynamic marker for huntingtin-lowering trials., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Background: Tools to assist clinicians in predicting pneumonia could lead to a significant decline in morbidity. Therefore, we sought to develop a model in combat trauma patients for identifying those at highest risk of pneumonia., Methods: This was a retrospective study of 73 primarily blast-injured casualties with combat extremity wounds. Binary classification models for pneumonia prediction were developed with measurements of injury severity from the Abbreviated Injury Scale (AIS), transfusion blood products received before arrival at Walter Reed National Military Medical Center (WRNMMC), and serum protein levels. Predictive models were generated with leave-one-out-cross-validation using the variable selection method of backward elimination (BE) and the machine learning algorithms of random forests (RF) and logistic regression (LR). BE was attempted with two predictor sets: (1) all variables and (2) serum proteins alone., Results: Incidence of pneumonia was 12% (n = 9). Different variable sets were produced by BE when considering all variables and just serum proteins alone. BE selected the variables ISS, AIS chest, and cryoprecipitate within the first 24 h following injury for the first predictor set 1 and FGF-basic, IL-2R, and IL-6 for predictor set 2. Using both variable sets, a RF was generated with AUCs of 0.95 and 0.87-both higher than LR algorithms., Conclusion: Advanced modeling allowed for the identification of clinical and biomarker data predictive of pneumonia in a cohort of predominantly blast-injured combat trauma patients. The generalizability of the models developed here will require an external validation dataset.

In the original article, the units indicated on the y-axes of Fig. 3 are incorrectly labelled. The correct label is pg/mL. Following is the corrected Fig. 3.

Background: The timing of coverage of an open wound is based on heavily on clinical gestalt. DoD's Surgical Critical Care Initiative created a clinical decision support tool that predicts wound closure success using clinical and biomarker data. The military uses a regimented protocol consisting of serial washouts and debridements. While decisions around wound closure in civilian centers are subject to the same clinical parameters, preclosure wound management is, generally, much more variable. We hypothesized that the variability in management would affect local biomarker expression within these patients., Methods: We compared data from 116 wounds in 73 military patients (MP) to similar data from 88 wounds in 78 civilian patients (CP). We used Wilcoxon rank-sum tests to assess concentrations of 32 individual biomarkers taken from wound effluent. Along with differences in the debridement frequency, we focused on these local biomarkers in MP and CP at both the first washout and the washout performed just prior to attempted closure., Results: On average, CP waited longer from the time of injury to closure (21.9 days, vs. 11.6 days, p < 0.0001) but had a similar number of washouts (3.86 vs. 3.44, p = 0.52). When comparing the wound effluent between the two populations, they had marked biochemical differences both when comparing the results at the first washout and at the time of closure. However, in a subset of civilian patients whose average number of days between washouts was never more than 72 hours, these differences ceased to be significant for most variables., Conclusion: There were significant differences in the baseline biochemical makeup of wounds in the CP and MP. These differences could be eliminated if both were treated under similar wound care paradigms. Variations in therapy affect not only outcomes but also the actual biochemical makeup of wounds., Level of Evidence: Therapeutic, level IV.

Introduction: Clinical decision support tools capable of predicting which patients are at highest risk for venous thromboembolism (VTE) can assist in guiding surveillance and prophylaxis decisions. The Trauma Embolic Scoring System (TESS) has been shown to model VTE risk in civilian trauma patients. No such support tools have yet been described in combat casualties, who have a high incidence of VTE. The purpose of this study was to evaluate the utility of TESS in predicting VTE in military trauma patients., Methods: A retrospective cohort study of 549 combat casualties from October 2010 to November 2012 admitted to a military treatment facility in the USA was performed. TESS scores were calculated through data obtained from the Department of Defense Trauma Registry and chart reviews. Univariate analysis and multivariate logistic regression were performed to evaluate risk factors for VTE. Receiver operating characteristic (ROC) curve analysis of TESS in military trauma patients was also performed., Results: The incidence of VTE was 21.7% (119/549). The median TESS for patients without VTE was 8 (IQR 4-9), and the median TESS for those with VTE was 10 (IQR 9-11). On multivariate analysis, Injury Severity Score (ISS) (OR 1.03, p=0.007), ventilator days (OR 1.05, p=0.02), and administration of tranexamic acid (TXA) (OR 1.89, p=0.03) were found to be independent risk factors for development of VTE. On ROC analysis, an optimal high-risk cut-off value for TESS was ≥7 with a sensitivity of 0.92 and a specificity of 0.53 (area under the curve 0.76, 95% CI 0.72 to 0.80, p<0.0001)., Conclusions: When used to predict VTE in military trauma, TESS shows moderate discrimination and is well calibrated. An optimal high-risk cut-off value of ≥7 demonstrates high sensitivity in predicting VTE. In addition to ISS and ventilator days, TXA administration is an independent risk factor for VTE development., Level of Evidence: Level III., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Identifying clinical and biomarker profiles of trauma patients may facilitate the creation of models that predict postoperative complications. We sought to determine the utility of modeling for predicting severe sepsis (SS) and organ space infections (OSI) following laparotomy for abdominal trauma., Methods: Clinical and molecular biomarker data were collected prospectively from patients undergoing exploratory laparotomy for abdominal trauma at a Level I trauma center between 2014 and 2017. Machine learning algorithms were used to develop models predicting SS and OSI. Random forest (RF) was performed, and features were selected using backward elimination. The SS model was trained on 117 records and validated using the leave-one-out method on the remaining 15 records. The OSI model was trained on 113 records and validated on the remaining 19. Models were assessed using areas under the curve., Results: One hundred thirty-two patients were included (median age, 30 years [23-42 years], 68.9% penetrating injury, median Injury Severity Score of 18 [10-27]). Of these, 10.6% (14 of 132) developed SS and 13.6% (18 of 132) developed OSI. The final RF model resulted in five variables for SS (Penetrating Abdominal Trauma Index, serum epidermal growth factor, monocyte chemoattractant protein-1, interleukin-6, and eotaxin) and four variables for OSI (Penetrating Abdominal Trauma Index, serum epidermal growth factor, monocyte chemoattractant protein-1, and interleukin-8). The RF models predicted SS and OSI with areas under the curve of 0.798 and 0.774, respectively., Conclusion: Random forests with RFE can help identify clinical and biomarker profiles predictive of SS and OSI after trauma laparotomy. Once validated, these models could be used as clinical decision support tools for earlier detection and treatment of infectious complications following injury., Level of Evidence: Prognostic, level III.

Background: Early life acute respiratory infection (ARI) with respiratory syncytial virus (RSV) has been strongly associated with the development of childhood wheezing illnesses, but the pathways underlying this association are poorly understood., Objective: To examine the role of the nasopharyngeal microbiome in the development of childhood wheezing illnesses following RSV ARI in infancy., Methods: We conducted a nested cohort study of 118 previously healthy, term infants with confirmed RSV ARI by RT-PCR. We used next-generation sequencing of the V4 region of the 16S ribosomal RNA gene to characterize the nasopharyngeal microbiome during RSV ARI. Our main outcome of interest was 2-year subsequent wheeze., Results: Of the 118 infants, 113 (95.8%) had 2-year outcome data. Of these, 46 (40.7%) had parental report of subsequent wheeze. There was no association between the overall taxonomic composition, diversity, and richness of the nasopharyngeal microbiome during RSV ARI with the development of subsequent wheeze. However, the nasopharyngeal detection and abundance of Lactobacillus was consistently higher in infants who did not develop this outcome. Lactobacillus also ranked first among the different genera in a model distinguishing infants with and without subsequent wheeze., Conclusions: The nasopharyngeal detection and increased abundance of Lactobacillus during RSV ARI in infancy are associated with a reduced risk of childhood wheezing illnesses at age 2 years., (Copyright © 2018 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Huntington's disease (HD) is a genetic progressive neurodegenerative disorder, caused by a mutation in the HTT gene, for which there is currently no cure. The identification of sensitive indicators of disease progression and therapeutic outcome could help the development of effective strategies for treating HD. We assessed mutant huntingtin (mHTT) and neurofilament light (NfL) protein concentrations in cerebrospinal fluid (CSF) and blood in parallel with clinical evaluation and magnetic resonance imaging in premanifest and manifest HD mutation carriers. Among HD mutation carriers, NfL concentrations in plasma and CSF correlated with all nonbiofluid measures more closely than did CSF mHTT concentration. Longitudinal analysis over 4 to 8 weeks showed that CSF mHTT, CSF NfL, and plasma NfL concentrations were highly stable within individuals. In our cohort, concentration of CSF mHTT accurately distinguished between controls and HD mutation carriers, whereas NfL concentration, in both CSF and plasma, was able to segregate premanifest from manifest HD. In silico modeling indicated that mHTT and NfL concentrations in biofluids might be among the earliest detectable alterations in HD, and sample size prediction suggested that low participant numbers would be needed to incorporate these measures into clinical trials. These findings provide evidence that biofluid concentrations of mHTT and NfL have potential for early and sensitive detection of alterations in HD and could be integrated into both clinical trials and the clinic., (Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Application of metabolic magnetic resonance imaging measures such as cerebral blood flow in translational medicine is limited by the unknown link of observed alterations to specific neurophysiological processes. In particular, the sensitivity of cerebral blood flow to activity changes in specific neurotransmitter systems remains unclear. We address this question by probing cerebral blood flow in healthy volunteers using seven established drugs with known dopaminergic, serotonergic, glutamatergic and GABAergic mechanisms of action. We use a novel framework aimed at disentangling the observed effects to contribution from underlying neurotransmitter systems. We find for all evaluated compounds a reliable spatial link of respective cerebral blood flow changes with underlying neurotransmitter receptor densities corresponding to their primary mechanisms of action. The strength of these associations with receptor density is mediated by respective drug affinities. These findings suggest that cerebral blood flow is a sensitive brain-wide in-vivo assay of metabolic demands across a variety of neurotransmitter systems in humans.

Aim: The objectives of this first-in-human study were to evaluate the safety and tolerability, pharmacokinetics and pharmacodynamics, and maximum tolerated dose (MTD) of single ascending oral doses of RG7342, a positive allosteric modulator (PAM) of the metabotropic glutamate receptor 5 (mGlu5) for the treatment of schizophrenia, in healthy male subjects., Methods: This was a single-centre, randomized, double-blind, adaptive study of 37 subjects receiving single ascending oral doses of RG7342 (ranging from 0.06-1.2 mg, n = 27) or placebo (n = 10). A modified continual reassessment method, with control for the probability of overdosing based on the occurrence of dose-limiting events (DLEs), was applied to inform the subsequent dose decisions for RG7342., Results: DLEs consisted of dizziness, nausea and vomiting, and the incidence and severity of these adverse events increased in a concentration-dependent manner. RG7342 doses of 1.2 mg under fasting conditions, which reached a mean maximum plasma concentration (C max ) of 10.2 ng ml -1 , were not tolerated (four out of six subjects experienced DLEs). RG7342 showed dose-proportional pharmacokinetics, with rapid absorption and a biphasic decline, and a mean terminal half-life estimated to be >1000 h., Conclusions: Single oral doses of RG7342 were generally tolerated up to 0.6 mg under fasting and 0.9 mg under fed conditions in healthy subjects. Bayesian adaptive methods describing the probability of DLEs were applied effectively to support dose escalation. MTDs (fasting, fed) were associated with a C max of 6.5 ng ml -1 . The development of RG7342 was discontinued owing to the potential challenges associated with a long half-life in context of the observed adverse events., (© 2017 The British Pharmacological Society.)