Iodine-Based Dual-Energy Computed Tomography After Mechanical Thrombectomy Predicts Secondary Neurologic Decline from Cerebral Edema After Severe Stroke.

BACKGROUND: Patients with severe stroke are at high risk of secondary neurologic decline (ND) from the development of malignant cerebral edema (MCE). However, early infarcts are hard to diagnose on conventional head computed tomography (CT). We hypothesize that high-energy (190 keV) virtual monochromatic imaging (VMI) from dual-energy CT (DECT) imaging enables earlier detection of ND from MCE. METHODS: Consecutive patients with severe stroke with National Institute of Health Stroke Scale (NIHSS) scores > 15 and DECT within 10 h of mechanical thrombectomy from May 2020 to March 2022 were included. We excluded patients with parenchymal hematoma type 2 transformation. Retrospective analysis of clinical and novel variables included the VMI Alberta Stroke Program Early CT Score (ASPECTS), total iodine content, and VMI infarct volume. The primary outcome was secondary ND, defined using a composite outcome variable of clinical worsening (increase in NIHSS score ≥ 4 or decrease in Glasgow Coma Scale score > 2) or malignant radiographical edema (midline shift ≥ 5 mm at the level of the septum pellucidum). Fisher's exact test and Wilcoxon's test were used for univariate analysis. Logistic regression was used to develop prediction models for categorical outcomes. RESULTS: Eighty-four patients with severe stroke with a median age of 67.5 (interquartile range [IQR] 57-78) years and an NIHSS score of 22 (IQR 18-25) were included. Twenty-nine patients had ND. The VMI ASPECTS, total iodine content, and VMI infarct volume were associated with ND. The VMI ASPECTS, VMI infarct volume, and total iodine content were predictors of ND after adjusting for age, sex, initial NIHSS score, and tissue plasminogen activator administration, with areas under the receiver operating characteristic curve (AUROC) of 0.691 (95% confidence interval [CI] 0.572-0.810), 0.877 (95% CI 0.800-0.954), and 0.845 (95% CI 0.750-0.940). By including all three predictors, the model achieved an AUROC of 0.903 (95% CI 0.84-0.97) and was cross-validated by the leave one out method, with an AUROC of 0.827. CONCLUSIONS: The VMI ASPECTS and VMI infarct volume from DECT are superior to the conventional CT ASPECTS and are novel predictors for secondary ND due to MCE after severe stroke. Clinical trial registration ClinicalTrials.gov identifier: NCT04189471.

A self-training interpretable cell type annotation framework using specific marker gene.

MOTIVATION: Recent advances in sequencing technology provide opportunities to study biological processes at a higher resolution. Cell type annotation is an important step in scRNA-seq analysis, which often relies on established marker genes. However, most of the previous methods divide the identification of cell types into two stages, clustering and assignment, whose performances are susceptible to the clustering algorithm, and the marker information cannot effectively guide the clustering process. Furthermore, their linear heuristic-based cell assignment process is often insufficient to capture potential dependencies between cells and types. RESULTS: Here, we present Interpretable Cell Type Annotation based on self-training (sICTA), a marker-based cell type annotation method that combines the self-training strategy with pseudo-labeling and the nonlinear association capturing capability of Transformer. In addition, we incorporate biological priori knowledge of genes and pathways into the classifier through an attention mechanism to enhance the transparency of the model. A benchmark analysis on 11 publicly available single-cell datasets demonstrates the superiority of sICTA compared to state-of-the-art methods. The robustness of our method is further validated by evaluating the prediction accuracy of the model on different cell types for each single-cell data. Moreover, ablation studies show that self-training and the ability to capture potential dependencies between cells and cell types, both of which are mutually reinforcing, work together to improve model performance. Finally, we apply sICTA to the pancreatic dataset, exemplifying the interpretable attention matrix captured by sICTA. AVAILABILITY AND IMPLEMENTATION: The source code of sICTA is available in public at https://github.com/nbnbhwyy/sICTA. The processed datasets can be found at https://drive.google.com/drive/folders/1jbqSxacL_IDIZ4uPjq220C9Kv024m9eL. The final version of the model will be permanently available at https://doi.org/10.5281/zenodo.13474010.

Longitudinal assessment of glymphatic changes following mild traumatic brain injury: Insights from perivascular space burden and DTI-ALPS imaging.

Introduction: Traumatic brain injury (TBI) even in the mild form may result in long-lasting post-concussion symptoms. TBI is also a known risk to late-life neurodegeneration. Recent studies suggest that dysfunction in the glymphatic system, responsible for clearing protein waste from the brain, may play a pivotal role in the development of dementia following TBI. Given the diverse nature of TBI, longitudinal investigations are essential to comprehending the dynamic changes in the glymphatic system and its implications for recovery. Methods: In this prospective study, we evaluated two promising glymphatic imaging markers, namely the enlarged perivascular space (ePVS) burden and Diffusion Tensor Imaging-based ALPS index, in 44 patients with mTBI at two early post-injury time points: approximately 14 days (14Day) and 6-12 months (6-12Mon) post-injury, while also examining their associations with post-concussion symptoms. Additionally, 37 controls, comprising both orthopedic patients and healthy individuals, were included for comparative analysis. Results: Our key findings include: (1) White matter ePVS burden (WM-ePVS) and ALPS index exhibit significant correlations with age. (2) Elevated WM-ePVS burden in acute mTBI (14Day) is significantly linked to a higher number of post-concussion symptoms, particularly memory problems. (3) The increase in the ALPS index from acute (14Day) to the chronic (6-12Mon) phases in mTBI patients correlates with improvement in sleep measures. Furthermore, incorporating WM-ePVS burden and the ALPS index from acute phase enhances the prediction of chronic memory problems beyond socio-demographic and basic clinical information. Conclusion: ePVS burden and ALPS index offers distinct values in assessing glymphatic structure and activity. Early evaluation of glymphatic function could be crucial for understanding TBI recovery and developing targeted interventions to improve patient outcomes.

Multiple sclerosis disease activity, a multi-biomarker score of disease activity and response to treatment in multiple sclerosis.

Regular assessment of disease activity in relapsing-remitting multiple sclerosis (RRMS) is required to optimize clinical outcomes. Biomarkers can be a valuable tool for measuring disease activity in multiple sclerosis (MS) if they reflect the pathological processes underlying MS pathogenicity. In this pilot study, we combined multiple biomarkers previously analyzed in RRMS patients into an MS disease activity (MSDA) score to evaluate their ability to predict relapses and treatment response to glatiramer acetate (GA). Response Gene to Complement 32 (RGC-32), FasL, IL-21, SIRT1, phosphorylated SIRT1 (p-SIRT1), and JNK1 p54 levels were used to generate cut-off values for each biomarker. Any value below the cutoff for RGC-32, FasL SIRT1, or p-SIRT1 or above the cutoff for IL-21 or JNK1 p54 was given a +1 value, indicating relapse or lack of response to GA. Any value above the cutoff value for RGC-32, FasL, SIRT1, p-SIRT1 or below that for IL-21 or JNK1 p54 was given a -1 value, indicating clinical stability or response to GA. An MSDA score above +1 indicated a relapse or lack of response to treatment. An MSDA score below -1 indicated clinical stability or response to treatment. Our results showed that the MSDA scores generated using either four or six biomarkers had a higher sensitivity and specificity and significantly correlated with the expanded disability status scale. Although these results suggest that the MSDA test can be useful for monitoring therapeutic response to biologic agents and assessing clinically challenging situations, the present findings need to be confirmed in larger studies.

Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge.

Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular heterogeneity at single-cell resolution. In recent years, deep learning frameworks, such as generative models, have brought great changes to the analysis of transcriptomic data. Nevertheless, relying on the potential space of these generative models alone is insufficient to generate biological explanations. In addition, most of the previous work based on generative models is limited to shallow neural networks with one to three layers of latent variables, which may limit the capabilities of the models. Here, we propose a deep interpretable generative model called d-scIGM for single-cell data analysis. d-scIGM combines sawtooth connectivity techniques and residual networks, thereby constructing a deep generative framework. In addition, d-scIGM incorporates hierarchical prior knowledge of biological domains to enhance the interpretability of the model. We show that d-scIGM achieves excellent performance in a variety of fundamental tasks, including clustering, visualization, and pseudo-temporal inference. Through topic pathway studies, we found that d-scIGM-learned topics are better enriched for biologically meaningful pathways compared to the baseline models. Furthermore, the analysis of drug response data shows that d-scIGM can capture drug response patterns in large-scale experiments, which provides a promising way to elucidate the underlying biological mechanisms. Lastly, in the melanoma dataset, d-scIGM accurately identified different cell types and revealed multiple melanin-related driver genes and key pathways, which are critical for understanding disease mechanisms and drug development.

USP11 promotes prostate cancer progression by up-regulating AR and c-Myc activity.

Androgen receptor (AR) is a main driver for castration-resistant prostate cancer (CRPC). c-Myc is an oncogene underlying prostate tumorigenesis. Here, we find that the deubiquitinase USP11 targets both AR and c-Myc in prostate cancer (PCa). USP11 expression was up-regulated in metastatic PCa and CRPC. USP11 knockdown (KD) significantly inhibited PCa cell growth. Our RNA-seq studies revealed AR and c-Myc as the top transcription factors altered after USP11 KD. ChIP-seq analysis showed that either USP11 KD or replacement of endogenous USP11 with a catalytic-inactive USP11 mutant significantly decreased chromatin binding by AR and c-Myc. We find that USP11 employs two mechanisms to up-regulate AR and c-Myc levels: namely, deubiquitination of AR and c-Myc proteins to increase their stability and deubiquitination of H2A-K119Ub, a repressive histone mark, on promoters of AR and c-Myc genes to increase their transcription. AR and c-Myc reexpression in USP11-KD PCa cells partly rescued cell growth defects. Thus, our studies reveal a tumor-promoting role for USP11 in aggressive PCa through upregulation of AR and c-Myc activities and support USP11 as a potential target against PCa.

Deficient Generation of Spike-Specific Long-Lived Plasma Cells in the Bone Marrow After Severe Acute Respiratory Syndrome Coronavirus 2 Infection.

Generation of a stable long-lived plasma cell (LLPC) population is the sine qua non of durable antibody responses after vaccination or infection. We studied 20 individuals with a prior coronavirus disease 2019 infection and characterized the antibody response using bone marrow aspiration and plasma samples. We noted deficient generation of spike-specific LLPCs in the bone marrow after severe acute respiratory syndrome coronavirus 2 infection. Furthermore, while the regression model explained 98% of the observed variance in anti-tetanus immunoglobulin G levels based on LLPC enzyme-linked immunospot assay, we were unable to fit the same model with anti-spike antibodies, again pointing to the lack of LLPC contribution to circulating anti-spike antibodies.

Longitudinal Assessment of Glymphatic Changes Following Mild Traumatic Brain Injury: Insights from PVS burden and DTI-ALPS Imaging.

Traumatic brain injury (TBI) even in the mild form may result in long-lasting post-concussion symptoms. TBI is also a known risk to late-life neurodegeneration. Recent studies suggest that dysfunction in the glymphatic system, responsible for clearing protein waste from the brain, may play a pivotal role in the development of dementia following TBI. Given the diverse nature of TBI, longitudinal investigations are essential to comprehending the dynamic changes in the glymphatic system and its implications for recovery. In this prospective study, we evaluated two promising glymphatic imaging markers, namely the enlarged perivascular space (ePVS) burden and Diffusion Tensor Imaging-based ALPS index, in 44 patients with mTBI at two early post-injury time points: approximately 14 days (14Day) and 6-12 months (6-12Mon) post-injury, while also examining their associations with post-concussion symptoms. Additionally, 37 controls, comprising both orthopedic patients and healthy individuals, were included for comparative analysis. Our key findings include: 1) White matter ePVS burden (WM-ePVS) and ALPS index exhibit significant correlations with age. 2) Elevated WM-ePVS burden in acute mTBI (14Day) is significantly linked to a higher number of post-concussion symptoms, particularly memory problems. 3) The increase in the ALPS index from acute (14Day) to the chronic (6-12Mon) phases in mTBI patients correlates with improvement in sleep measures. Furthermore, incorporating WM-ePVS burden and the ALPS index from acute phase enhances the prediction of chronic memory problems beyond socio-demographic and basic clinical information, highlighting their distinct roles in assessing glymphatic structure and activity. Early evaluation of glymphatic function could be crucial for understanding TBI recovery and developing targeted interventions to improve patient outcomes.

Utilizing ultra-early continuous physiologic data to develop automated measures of clinical severity in a traumatic brain injury population.

Determination of prognosis in the triage process after traumatic brain injury (TBI) is difficult to achieve. Current severity measures like the Trauma and injury severity score (TRISS) and revised trauma score (RTS) rely on additional information from the Glasgow Coma Scale (GCS) and the Injury Severity Score (ISS) which may be inaccurate or delayed, limiting their usefulness in the rapid triage setting. We hypothesized that machine learning based estimations of GCS and ISS obtained through modeling of continuous vital sign features could be used to rapidly derive an automated RTS and TRISS. We derived variables from electrocardiograms (ECG), photoplethysmography (PPG), and blood pressure using continuous data obtained in the first 15 min of admission to build machine learning models of GCS and ISS (ML-GCS and ML-ISS). We compared the TRISS and RTS using ML-ISS and ML-GCS and its value using the actual ISS and GCS in predicting in-hospital mortality. Models were tested in TBI with systemic injury (head abbreviated injury scale (AIS) ≥ 1), and isolated TBI (head AIS ≥ 1 and other AIS ≤ 1). The area under the receiver operating characteristic curve (AUROC) was used to evaluate model performance. A total of 21,077 cases (2009-2015) were in the training set. 6057 cases from 2016 to 2017 were used for testing, with 472 (7.8%) severe TBI (GCS 3-8), 223 (3.7%) moderate TBI (GCS 9-12), and 5913 (88.5%) mild TBI (GCS 13-15). In the TBI with systemic injury group, ML-TRISS had similar AUROC (0.963) to TRISS (0.965) in predicting mortality. ML-RTS had AUROC (0.823) and RTS had AUROC 0.928. In the isolated TBI group, ML-TRISS had AUROC 0.977, and TRISS had AUROC 0.983. ML-RTS had AUROC 0.790 and RTS had AUROC 0.957. Estimation of ISS and GCS from machine learning based modeling of vital sign features can be utilized to provide accurate assessments of the RTS and TRISS in a population of TBI patients. Automation of these scores could be utilized to enhance triage and resource allocation during the ultra-early phase of resuscitation.

Hyperventilation During Manual Ventilation Can Be Reduced Using a Novel Ventilator but Not With Education Interventions.

INTRODUCTION: Traumatic brain injury (TBI) is the leading cause of combat casualties in modern war with an estimated 20% of casualties experiencing head injury. Since the release of the Brain Trauma Foundation's Guidelines for the Management of Severe Traumatic Brain Injury in 1995, recommendations for management of TBI have included the avoidance of routine hyperventilation. However, both published and anecdotal data suggest that many patients with TBI are inappropriately ventilated during transport, thereby increasing the risk of morbidity and mortality from secondary brain injury. MATERIALS AND METHODS: Enlisted Air Force personnel with prior emergency medical technician training completing a 3-week trauma course were evaluated on their ability to provide manual ventilation. Participants provided manual ventilation using either an in-situ endotracheal tube (ETT) or standard face mask on a standardized simulated patient manikin with TBI on the first and last days of the course. Manual ventilation was provided via a standard manual ventilator and a novel manual ventilator designed to limit tidal volume (VT) and respiratory rate (RR). Participants were given didactic and hands-on training on the third day of the course. Half of the participants were given simulator feedback during the hands-on training. All students provided 2 minutes of manual ventilation with each respirator. Data were collected on the breath-to-breath RR, VT, and peak airway pressures generated by the participant for each trial and were averaged for each trial. A minute ventilation (MV) was then derived from the calculated RR and VT. RESULTS: One hundred fifty-six personnel in the trauma course were evaluated in this study. Significant differences were found in the participant's performance with manual ventilation with the novel compared to the traditional ventilator. Before training, MV with the novel ventilator was less than with the traditional ventilator by 2.1 ± 0.4 L/min (P = .0003) and 1.6 ± 0.5 L/min (P = .0489) via ETT and face mask, respectively. This effect persisted after training with a difference between the devices of 1.8 ± 0.4 L/min (P = .0069) via ETT. Both traditional education interventions (didactics with hands-on training) and simulator-based feedback did not make a significant difference in participant's performance in delivering MV. CONCLUSIONS: The use of a novel ventilator that limits RR and VT may be useful in preventing hyperventilation in TBI patients. Didactic education and simulator-based feedback training may not have significant impact on improving ventilation practices in prehospital providers.

Results of a randomized, double-blind, placebo-controlled trial of lorcaserin in cocaine use disorder.

BACKGROUND: Cocaine use disorder (CUD) is a major public health problem for which there is no approved pharmacotherapy. The primary purpose of this study was to evaluate the ability of lorcaserin, a 5-hydroxytryptamine2 C (5-HT2 C) receptor agonist, to facilitate abstinence in individuals seeking treatment for CUD. METHODS: This was a 12-site, randomized, parallel arm study with a 13-week Treatment Phase that included a 1-week, single-blind run-in period when all participants received twice daily 15mg acetazolamide capsules (a medication adherence marker), followed by randomization to either twice daily 10mg lorcaserin or placebo capsules for the remaining 12 weeks. Pre-randomization data were utilized in an enrichment strategy aimed at achieving high levels of medication adherence and low placebo response rates in a subgroup of participants that qualified for the "efficacy population." For lorcaserin vs. placebo, the primary efficacy endpoint was the proportion of participants in the efficacy population achieving abstinence during the last three weeks of treatment, as evidenced by self-report of no cocaine use, confirmed by urine testing. RESULTS: Within the efficacy population, 1.1% of 91 participants receiving lorcaserin and 4.3% of 92 receiving placebo achieved abstinence during the last 3 weeks of treatment. Among all randomized participants, 2.5% of 118 receiving lorcaserin and 5.6% of 124 receiving placebo achieved similar abstinence. Study participants receiving lorcaserin exhibited significantly greater reductions in body weight and BMI, indicating that medication adherence was sufficient to produce a pharmacological effect. CONCLUSIONS: Twice daily 10mg lorcaserin failed to demonstrate efficacy in the treatment of CUD.

Iodine-based dual-energy CT predicts early neurological decline from cerebral edema after large hemispheric infarction.

Background & Purpose: Ischemia affecting two thirds of the MCA territory predicts development of malignant cerebral edema. However, early infarcts are hard to diagnose on conventional head CT. We hypothesize that high-energy (190keV) virtual monochromatic images (VMI) from dual-energy CT (DECT) imaging enables earlier detection of secondary injury from malignant cerebral edema (MCE). Methods: Consecutive LHI patients with NIHSS ≥ 15 and DECT within 10 hours of reperfusion from May 2020 to March 2022 were included. We excluded patients with parenchymal hematoma-type 2 transformation. Retrospective analysis of clinical and novel variables included VMI Alberta Stroke Program Early CT Score (ASPECTS), total iodine content, and VMI infarct volume. Primary outcome was early neurological decline (END). Secondary outcomes included hemorrhagic transformation, decompressive craniectomy (DC), and medical treatment of MCE. Fisher's exact test and Wilcoxon test were used for univariate analysis. Logistic regression was used to develop prediction models for categorical outcomes. Results: Eighty-four LHI patients with a median age of 67.5 [IQR 57,78] years and NIHSS 22 [IQR 18,25] were included. Twenty-nine patients had END. VMI ASPECTS, total iodine content, and VMI infarct volume were associated with END. VMI ASPECTS, VMI infarct volume, and total iodine content were predictors of END after adjusting for age, sex, initial NIHSS, and tPA administration, with a AUROC of 0.691 [0.572,0.810], 0.877 [0.800, 0.954], and 0.845 [0.750, 0.940]. By including all three predictors, the model achieved AUROC of 0.903 [0.84,0.97] and was cross validated by leave one out method with AUROC of 0.827. Conclusion: DECT with high-energy VMI and iodine quantification is superior to conventional CT ASPECTS and is a novel predictor for early neurological decline due to malignant cerebral edema after large hemispheric infarction.

Elastomeric half-mask respirator disinfection practices among healthcare personnel.

BACKGROUND: Disposable N95 respirator shortages during the COVID-19 and 2009 H1N1 influenza pandemics highlighted the need for reusable alternatives, such as elastomeric half-mask respirators (EHMRs). Two US medical organizations deployed reusable EHMRs during the COVID-19 response. In addition to wipe-based disinfection following patient care episodes expected per local policies at both organizations, postshift centralized cleaning and disinfection (C&D) was expected at one site (A), permitting shared-pool EHMR use, and optional at the other (Site B), where EHMRs were issued to individuals. Using a survey, we evaluated disinfection practices reported by EHMR users and predictors of disinfection behaviors and perceptions. METHODS: Surveys assessed EHMR disinfection practices, occupational characteristics, EHMR use frequency, training, and individual-issue versus shared-pool EHMR use. RESULTS: Of 1080 EHMR users completing the survey, 76% reported that they disinfect the EHMR after each patient encounter, which was the expected practice at both sites. Increasing EHMR use, recall of disinfection training, and work in intensive care or emergency settings significantly influenced higher reporting of this practice. 36% of respondents reported using centralized C&D, although reporting was higher at the site (A) where this was expected (53%). Confidence in cleanliness of the EHMR following centralized C&D was not influenced by individual versus shared-pool EHMR issue. CONCLUSIONS: Most EHMR users reported adherence with expected post-care individual-based disinfection of EHMRs but did not necessarily use standardized, centralized C&D. Future efforts to limit reliance on behavior related to respirator disinfection may improve EHMR implementation in healthcare to avert dependence on single-use, disposable N95 respirators.

Replacement of Antiretroviral Therapy with HIV Broadly Neutralizing Antibodies to Maximize the Effectiveness of Chemotherapy in HIV Patients with Lung Cancer.

Non-small cell lung cancer (NSCLC) is the most fatal non-AIDS defining cancer in people living with HIV (PWH) on antiretroviral therapy (ART). Treatment of malignancies in PWH requires concomitant cancer therapy and ART, which can lead to potential drug-drug interactions (DDIs) and overlapping toxicities. In this study, we hypothesize that replacement of ART with HIV broadly neutralizing antibodies (bNAbs) during cancer chemotherapy (chemo) may maintain HIV suppression and tumor inhibition while minimizing DDIs and overlapping toxicities. We compared HIV suppression, tumor inhibition, and toxicity between conventional treatment (ART plus chemo) and a new modality (bNAbs plus chemo) in humanized mice. Humanized mice infected with HIVYU2 and xenografted with human NSCLC A549 cells were treated with NSCLC chemo (cisplatin and gemcitabine) and first-line ART (dolutegravir, tenofovir disoproxil difumarate, and emtricitabine) or bNAbs (N49P9.6-FR and PGT 121) at human equivalent drug doses. We monitored plasma HIV RNA, tumor volume, and toxicities over five cycles of chemo. We found that chemo plus ART or bNAbs were equally effective at maintaining suppression of HIV viremia and tumor growth. Comparative analysis showed that mice on ART and chemo had significant reductions in body weight and significant increases in plasma creatinine concentrations compared with mice on bNAbs and chemo, which suggests that a combination of bNAbs and chemo produces less renal toxicity than an ART and chemo combination. These data suggest that bNAb therapy during concomitant chemo may be an improved treatment option over ART for PWH and NSCLC, and possibly other cancers, because bNAbs maintain HIV suppression while minimizing DDIs and toxicities.

SMAD3 promotes expression and activity of the androgen receptor in prostate cancer.

Overexpression of androgen receptor (AR) is the primary cause of castration-resistant prostate cancer, although mechanisms upregulating AR transcription in this context are not well understood. Our RNA-seq studies revealed that SMAD3 knockdown decreased levels of AR and AR target genes, whereas SMAD4 or SMAD2 knockdown had little or no effect. ChIP-seq analysis showed that SMAD3 knockdown decreased global binding of AR to chromatin. Mechanistically, we show that SMAD3 binds to intron 3 of the AR gene to promote AR expression. Targeting these binding sites by CRISPRi reduced transcript levels of AR and AR targets. In addition, ∼50% of AR and SMAD3 ChIP-seq peaks overlapped, and SMAD3 may also cooperate with or co-activate AR for AR target expression. Functionally, AR re-expression in SMAD3-knockdown cells partially rescued AR target expression and cell growth defects. The SMAD3 peak in AR intron 3 overlapped with H3K27ac ChIP-seq and ATAC-seq peaks in datasets of prostate cancer. AR and SMAD3 mRNAs were upregulated in datasets of metastatic prostate cancer and CRPC compared with primary prostate cancer. A SMAD3 PROTAC inhibitor reduced levels of AR, AR-V7 and AR targets in prostate cancer cells. This study suggests that SMAD3 could be targeted to inhibit AR in prostate cancer.

Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approach.

Secondary neurologic decline (ND) after traumatic brain injury (TBI) is independently associated with outcome, but robust predictors of ND are lacking. In this retrospective analysis of consecutive isolated TBI admissions to the R. Adams Cowley Shock Trauma Center between November 2015 and June 2018, we aimed to develop a triage decision support tool to quantify risk for early ND. Three machine learning models based on clinical, physiologic, or combined characteristics from the first hour of hospital resuscitation were created. Among 905 TBI cases, 165 (18%) experienced one or more ND events (130 clinical, 51 neurosurgical, and 54 radiographic) within 48 h of presentation. In the prediction of ND, the clinical plus physiologic data model performed similarly to the physiologic only model, with concordance indices of 0.85 (0.824-0.877) and 0.84 (0.812-0.868), respectively. Both outperformed the clinical only model, which had a concordance index of 0.72 (0.688-0.759). This preliminary work suggests that a data-driven approach utilizing physiologic and basic clinical data from the first hour of resuscitation after TBI has the potential to serve as a decision support tool for clinicians seeking to identify patients at high or low risk for ND.

A Drug Combination Prediction Framework Based on Graph Convolutional Network and Heterogeneous Information.

Combination therapy, which can improve therapeutic efficacy and reduce side effects, plays an important role in the treatment of complex diseases. Yet, a large number of possible combinations among candidate compounds limits our ability to identify effective combinations. Though many studies have focused on predicting potential drug combinations, the existing methods are not entirely satisfactory in terms of performance and scalability. In this study, we propose a new computational pipeline, called DCMGCN, which integrates diverse drug-related information, to predict novel drug combinations. Specifically, DCMGCN first learns low-dimensional representations of drugs from the drug attributes and similarity networks. Then, by quantifying the degree of the nodes in the known drug-drug network and the similarity between connected nodes, we found the drug-drug network has heterophily and sparseness, which may limit the effectiveness of the graph convolutional network (GCN). Therefore, we introduce two designs to modify GCN. Finally, the drug representations are optimized using modified GCN (MGCN) and used to predict drug combinations. The tests on multiple drug combination datasets show that DCMGCN achieved substantial improvements over state-of-the-art methods. Importantly, our model may embed the mechanism of ground-truth drug pairs into the low-dimensional representation of each drug, which may help to further clarify the understanding of mechanisms of drug action.

Tolerability, user acceptance and preference for a novel reusable respirator among health care workers.

BACKGROUND: The CleanSpace Technology Halo respirator combines a clear face mask and a powered air supply, without belts or hoses. Although providing higher protection than other respirators used in health care, user acceptance of this device has not been assessed with validated tools. METHODS: We surveyed healthcare workers (HCWs) within a US medical system using Halo respirators in 2021. Subjects completed 3 surveys over 8 weeks, which included the Respirator Comfort, Wearing Experience, and Function Instrument (R-COMFI), a validated tool to assess respirator tolerability. The survey included additional questions about user acceptability and respirator preference. Responses were evaluated for change over time and for significant predictors. RESULTS: Of 113 HCWs who completed the initial survey (29% response rate), mean ± SD R-COMFI score was 9.1± 5.1, (scale 0-47, lower = more tolerable) and did not change over time (P = .42). Fewer years in healthcare significantly predicted better R-COMFI score (P = .01). Many users preferred Halo in both usual care (45%-52%) and care of patients with COVID-19 (60%-64%). DISCUSSION: Halo respirators received favorable tolerability scores by HCWs, who often preferred them, especially during care of patients with COVID-19. CONCLUSIONS: Given demand for respirator use in health care, the innovative design provides higher protection than other respirators with a favorable user experience.

Association between Sleep Disturbances at Subacute Stage of Mild Traumatic Brain Injury and Long-Term Outcomes.

Mild (mTBI) traumatic brain injury (TBI) accounts for the majority of all TBI cases. Evidence has suggested that patients with mTBI can suffer from long-lasting cognitive deficits, persistent symptoms, and decreased quality of life. Sleep disorders are commonly observed after TBI, with the prevalence rate of sleep disturbances in persons with TBI being much higher than that in the general population. Poor sleep quality can impair cognitive functions in the general population. This effect of sleep disturbances may impede the recovery processes in the population with TBI. The objective of this study is to add to our understanding of the relationship between self-reported sleep problems and other post-concussion symptoms and look at the association between early sleep problems and long-term outcomes in mTBI. Post-concussion symptoms, neurocognitive functions, level of global outcomes, and rating of satisfaction of life were assessed in 64 patients with mTBI. The results revealed that the presence of sleep disturbances co-occur with an increased level of overall post-concussion symptoms at the subacute stage of mTBI, particularly with symptoms including poor concentration, memory problems, and irritability. In addition, sleep disturbance at the subacute stage is associated with persistent poor concentration and memory problems, as well as worse neurocognitive function, slower overall recovery, and lower satisfactory of life at the long term. Our findings suggest that sleep disturbance can be a prognostic factor of long-term outcomes after mTBI. Early interventions to improve sleep quality can have potential benefits to facilitate the recovery process from mTBI.