Health system-scale language models are all-purpose prediction engines.

Physicians make critical time-constrained decisions every day. Clinical predictive models can help physicians and administrators make decisions by forecasting clinical and operational events. Existing structured data-based clinical predictive models have limited use in everyday practice owing to complexity in data processing, as well as model development and deployment1-3. Here we show that unstructured clinical notes from the electronic health record can enable the training of clinical language models, which can be used as all-purpose clinical predictive engines with low-resistance development and deployment. Our approach leverages recent advances in natural language processing4,5 to train a large language model for medical language (NYUTron) and subsequently fine-tune it across a wide range of clinical and operational predictive tasks. We evaluated our approach within our health system for five such tasks: 30-day all-cause readmission prediction, in-hospital mortality prediction, comorbidity index prediction, length of stay prediction, and insurance denial prediction. We show that NYUTron has an area under the curve (AUC) of 78.7-94.9%, with an improvement of 5.36-14.7% in the AUC compared with traditional models. We additionally demonstrate the benefits of pretraining with clinical text, the potential for increasing generalizability to different sites through fine-tuning and the full deployment of our system in a prospective, single-arm trial. These results show the potential for using clinical language models in medicine to read alongside physicians and provide guidance at the point of care.

An Hepatozoon americanum-like protozoan in crab-eating (Cerdocyon thous) and grey pampean (Lycalopex gymnocercus) foxes from Uruguay.

In South America, apicomplexan parasites of the genus Hepatozoon have been sporadically detected in mammals. Previous studies in wild canids from Brazil and Argentina demonstrated infections by species genetically related to Hepatozoon americanum. The aim of the present work was to detect the presence of Hepatozoon in road-killed foxes encountered in Uruguayan highways. Blood samples from 45 crab-eating (Cerdocyon thous) and 32 grey pampean (Lycalopex gymnocercus) foxes were analyzed by PCR for Hepatozoon 18S rRNA gene. Eight foxes (10.4%) were found to be infected with an H. americanum-like protozoan, an Hepatozoon closely related to H. americanum. Bayesian and maximum-likelihood phylogenetic analyses revealed that the sequences obtained in this study cluster with H. americanum from the United States, and with an H. americanum-like species from dog and foxes from Brazil and Argentina. In the Unites States, H. americanum causes severe disease in dogs. In addition to this, an increasing habitat overlap between dogs and foxes makes the presence of H. americanum-like protozoan in foxes acquires veterinary relevance. This work represents the first report of L. gymnocercus infected with an H. americanum-like protozoan, and of wild canids infected with Hepatozoon in Uruguay.

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study.

BACKGROUND: There is interest in using convolutional neural networks (CNNs) to analyze medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested that image classification CNNs may not generalize to new data as well as previously believed. We assessed how well CNNs generalized across three hospital systems for a simulated pneumonia screening task. METHODS AND FINDINGS: A cross-sectional design with multiple model training cohorts was used to evaluate model generalizability to external sites using split-sample validation. A total of 158,323 chest radiographs were drawn from three institutions: National Institutes of Health Clinical Center (NIH; 112,120 from 30,805 patients), Mount Sinai Hospital (MSH; 42,396 from 12,904 patients), and Indiana University Network for Patient Care (IU; 3,807 from 3,683 patients). These patient populations had an age mean (SD) of 46.9 years (16.6), 63.2 years (16.5), and 49.6 years (17) with a female percentage of 43.5%, 44.8%, and 57.3%, respectively. We assessed individual models using the area under the receiver operating characteristic curve (AUC) for radiographic findings consistent with pneumonia and compared performance on different test sets with DeLong's test. The prevalence of pneumonia was high enough at MSH (34.2%) relative to NIH and IU (1.2% and 1.0%) that merely sorting by hospital system achieved an AUC of 0.861 (95% CI 0.855-0.866) on the joint MSH-NIH dataset. Models trained on data from either NIH or MSH had equivalent performance on IU (P values 0.580 and 0.273, respectively) and inferior performance on data from each other relative to an internal test set (i.e., new data from within the hospital system used for training data; P values both <0.001). The highest internal performance was achieved by combining training and test data from MSH and NIH (AUC 0.931, 95% CI 0.927-0.936), but this model demonstrated significantly lower external performance at IU (AUC 0.815, 95% CI 0.745-0.885, P = 0.001). To test the effect of pooling data from sites with disparate pneumonia prevalence, we used stratified subsampling to generate MSH-NIH cohorts that only differed in disease prevalence between training data sites. When both training data sites had the same pneumonia prevalence, the model performed consistently on external IU data (P = 0.88). When a 10-fold difference in pneumonia rate was introduced between sites, internal test performance improved compared to the balanced model (10× MSH risk P < 0.001; 10× NIH P = 0.002), but this outperformance failed to generalize to IU (MSH 10× P < 0.001; NIH 10× P = 0.027). CNNs were able to directly detect hospital system of a radiograph for 99.95% NIH (22,050/22,062) and 99.98% MSH (8,386/8,388) radiographs. The primary limitation of our approach and the available public data is that we cannot fully assess what other factors might be contributing to hospital system-specific biases. CONCLUSION: Pneumonia-screening CNNs achieved better internal than external performance in 3 out of 5 natural comparisons. When models were trained on pooled data from sites with different pneumonia prevalence, they performed better on new pooled data from these sites but not on external data. CNNs robustly identified hospital system and department within a hospital, which can have large differences in disease burden and may confound predictions.

Natural Language-based Machine Learning Models for the Annotation of Clinical Radiology Reports.

Purpose To compare different methods for generating features from radiology reports and to develop a method to automatically identify findings in these reports. Materials and Methods In this study, 96 303 head computed tomography (CT) reports were obtained. The linguistic complexity of these reports was compared with that of alternative corpora. Head CT reports were preprocessed, and machine-analyzable features were constructed by using bag-of-words (BOW), word embedding, and Latent Dirichlet allocation-based approaches. Ultimately, 1004 head CT reports were manually labeled for findings of interest by physicians, and a subset of these were deemed critical findings. Lasso logistic regression was used to train models for physician-assigned labels on 602 of 1004 head CT reports (60%) using the constructed features, and the performance of these models was validated on a held-out 402 of 1004 reports (40%). Models were scored by area under the receiver operating characteristic curve (AUC), and aggregate AUC statistics were reported for (a) all labels, (b) critical labels, and (c) the presence of any critical finding in a report. Sensitivity, specificity, accuracy, and F1 score were reported for the best performing model's (a) predictions of all labels and (b) identification of reports containing critical findings. Results The best-performing model (BOW with unigrams, bigrams, and trigrams plus average word embeddings vector) had a held-out AUC of 0.966 for identifying the presence of any critical head CT finding and an average 0.957 AUC across all head CT findings. Sensitivity and specificity for identifying the presence of any critical finding were 92.59% (175 of 189) and 89.67% (191 of 213), respectively. Average sensitivity and specificity across all findings were 90.25% (1898 of 2103) and 91.72% (18 351 of 20 007), respectively. Simpler BOW methods achieved results competitive with those of more sophisticated approaches, with an average AUC for presence of any critical finding of 0.951 for unigram BOW versus 0.966 for the best-performing model. The Yule I of the head CT corpus was 34, markedly lower than that of the Reuters corpus (at 103) or I2B2 discharge summaries (at 271), indicating lower linguistic complexity. Conclusion Automated methods can be used to identify findings in radiology reports. The success of this approach benefits from the standardized language of these reports. With this method, a large labeled corpus can be generated for applications such as deep learning. © RSNA, 2018 Online supplemental material is available for this article.

Relationship between dynamical entropy and energy dissipation far from thermodynamic equilibrium.

Connections between microscopic dynamical observables and macroscopic nonequilibrium (NE) properties have been pursued in statistical physics since Boltzmann, Gibbs, and Maxwell. The simulations we describe here establish a relationship between the Kolmogorov-Sinai entropy and the energy dissipated as heat from a NE system to its environment. First, we show that the Kolmogorov-Sinai or dynamical entropy can be separated into system and bath components and that the entropy of the system characterizes the dynamics of energy dissipation. Second, we find that the average change in the system dynamical entropy is linearly related to the average change in the energy dissipated to the bath. The constant energy and time scales of the bath fix the dynamical relationship between these two quantities. These results provide a link between microscopic dynamical variables and the macroscopic energetics of NE processes.

Generative large language models are all-purpose text analytics engines: text-to-text learning is all your need.

OBJECTIVE: To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning. METHODS: We formulated 7 key clinical NLP tasks as text-to-text learning and solved them using one unified generative clinical LLM, GatorTronGPT, developed using GPT-3 architecture and trained with up to 20 billion parameters. We adopted soft prompts (ie, trainable vectors) with frozen LLM, where the LLM parameters were not updated (ie, frozen) and only the vectors of soft prompts were updated, known as prompt tuning. We added additional soft prompts as a prefix to the input layer, which were optimized during the prompt tuning. We evaluated the proposed method using 7 clinical NLP tasks and compared them with previous task-specific solutions based on Transformer models. RESULTS AND CONCLUSION: The proposed approach achieved state-of-the-art performance for 5 out of 7 major clinical NLP tasks using one unified generative LLM. Our approach outperformed previous task-specific transformer models by ∼3% for concept extraction and 7% for relation extraction applied to social determinants of health, 3.4% for clinical concept normalization, 3.4%-10% for clinical abbreviation disambiguation, and 5.5%-9% for natural language inference. Our approach also outperformed a previously developed prompt-based machine reading comprehension (MRC) model, GatorTron-MRC, for clinical concept and relation extraction. The proposed approach can deliver the "one model for all" promise from training to deployment using a unified generative LLM.

nach0: multimodal natural and chemical languages foundation model.

Large Language Models (LLMs) have substantially driven scientific progress in various domains, and many papers have demonstrated their ability to tackle complex problems with creative solutions. Our paper introduces a new foundation model, nach0, capable of solving various chemical and biological tasks: biomedical question answering, named entity recognition, molecular generation, molecular synthesis, attributes prediction, and others. nach0 is a multi-domain and multi-task encoder-decoder LLM pre-trained on unlabeled text from scientific literature, patents, and molecule strings to incorporate a range of chemical and linguistic knowledge. We employed instruction tuning, where specific task-related instructions are utilized to fine-tune nach0 for the final set of tasks. To train nach0 effectively, we leverage the NeMo framework, enabling efficient parallel optimization of both base and large model versions. Extensive experiments demonstrate that our model outperforms state-of-the-art baselines on single-domain and cross-domain tasks. Furthermore, it can generate high-quality outputs in molecular and textual formats, showcasing its effectiveness in multi-domain setups.

Identifying Predictors of Initial Surgical Failure during Minimally Invasive Endoscopic Intracerebral Hemorrhage Evacuation.

Background and Purpose: Intracerebral hemorrhage (ICH) is a common and severe disease with high rates of morbidity and mortality; however, minimally invasive surgical (MIS) hematoma evacuation represents a promising avenue for treatment. In February of 2019, the MISTIE III study found that stereotactic thrombolysis with catheter drainage did not benefit patients with supratentorial spontaneous ICH but that a clinical benefit may be present when no more than 15 mL of hematoma remains at the end of treatment. Intraoperative CT (iCT) imaging has the ability to assess whether or not this surgical goal has been met in real time, allowing for operations to add additional CT-informed 'evacuation periods' (EPs) to achieve the surgical goal. Here, we report on the frequency and predictors of initial surgical failure on at least one iCT requiring additional EPs in a large cohort of patients undergoing endoscopic minimally invasive ICH evacuation with the SCUBA technique. Methods: All patients who underwent minimally invasive endoscopic evacuation of supratentorial spontaneous ICH in a major health system between December 2015 and October 2018 were included in this study. Patient demographics, clinical and radiographic features, procedural details, and outcomes were analyzed retrospectively from a prospectively collected database. Procedures were characterized as initially successful when the first iCT demonstrated that surgical success had been achieved and initially unsuccessful when the surgical goal was not achieved, and additional EPs were performed. The surgical goal was prospectively identified in December of 2015 as leaving no more than 20% of the preoperative hematoma volume at the end of the procedure. Descriptive statistics and regression analyses were performed to identify predictors of initial failure and secondary rescue. Results: Patients (100) underwent minimally invasive endoscopic ICH evacuation in the angiography suite during the study time period. In 14 cases, the surgical goal was not met on the first iCT and multiple Eps were performed; in 10 cases the surgical goal was not met, and no additional EPs were performed. In 14 cases, the surgical goal was never achieved. When additional EPs were performed, a rescue rate of 71.4% (10/14) was seen, bringing the total percentage of cases meeting the surgical goal to 86% across the entire cohort. Cases in which the surgical goal was not achieved were significantly associated with older patients (68 years vs. 60 years; p = 0.0197) and higher rates of intraventricular hemorrhage (34.2% vs. 70.8%; p = 0.0021). Cases in which the surgical goal was rescued from initial failure had similar levels of IVH, suggesting that these additional complexities can be overcome with the use of additional iCT-informed EPs. Conclusions: Initial and ultimate surgical failure occurs in a small percentage of patients undergoing minimally invasive endoscopic ICH evacuation. The use of intraoperative imaging provides an opportunity to evaluate whether or not the surgical goal has been achieved, and to continue the procedure if the surgeon feels that more evacuation is achievable. Now that level-one evidence exists to target a surgical evacuation goal during minimally invasive ICH evacuation, intraoperative imaging, such as iCT, plays an important role in aiding the surgical team to achieve the surgical goal.

A study of generative large language model for medical research and healthcare.

There are enormous enthusiasm and concerns in applying large language models (LLMs) to healthcare. Yet current assumptions are based on general-purpose LLMs such as ChatGPT, which are not developed for medical use. This study develops a generative clinical LLM, GatorTronGPT, using 277 billion words of text including (1) 82 billion words of clinical text from 126 clinical departments and approximately 2 million patients at the University of Florida Health and (2) 195 billion words of diverse general English text. We train GatorTronGPT using a GPT-3 architecture with up to 20 billion parameters and evaluate its utility for biomedical natural language processing (NLP) and healthcare text generation. GatorTronGPT improves biomedical natural language processing. We apply GatorTronGPT to generate 20 billion words of synthetic text. Synthetic NLP models trained using synthetic text generated by GatorTronGPT outperform models trained using real-world clinical text. Physicians' Turing test using 1 (worst) to 9 (best) scale shows that there are no significant differences in linguistic readability (p = 0.22; 6.57 of GatorTronGPT compared with 6.93 of human) and clinical relevance (p = 0.91; 7.0 of GatorTronGPT compared with 6.97 of human) and that physicians cannot differentiate them (p < 0.001). This study provides insights into the opportunities and challenges of LLMs for medical research and healthcare.

Population scale latent space cohort matching for the improved use and exploration of observational trial data.

A significant amount of clinical research is observational by nature and derived from medical records, clinical trials, and large-scale registries. While there is no substitute for randomized, controlled experimentation, such experiments or trials are often costly, time consuming, and even ethically or practically impossible to execute. Combining classical regression and structural equation modeling with matching techniques can leverage the value of observational data. Nevertheless, identifying variables of greatest interest in high-dimensional data is frequently challenging, even with application of classical dimensionality reduction and/or propensity scoring techniques. Here, we demonstrate that projecting high-dimensional medical data onto a lower-dimensional manifold using deep autoencoders and post-hoc generation of treatment/control cohorts based on proximity in the lower-dimensional space results in better matching of confounding variables compared to classical propensity score matching (PSM) in the original high-dimensional space (P<0.0001) and performs similarly to PSM models constructed by experts with prior knowledge of the underlying pathology when evaluated on predicting risk ratios from real-world clinical data. Thus, in cases when the underlying problem is poorly understood and the data is high-dimensional in nature, matching in the autoencoder latent space might be of particular benefit.

Development and Clinical Validation of a Finite Element Method Model Mapping Focal Intracranial Cooling.

Therapeutic hypothermia (TH) is a common and effective technique to reduce inflammation and induce neuroprotection across a variety of diseases. Focal TH of the brain can avoid the side effects of systemic cooling. The degree and extent of focal TH are a function of cooling probe design and local brain thermoregulation processes. To refine focal TH probe design, with application-specific optimization, we develop precise computational models of brain thermodynamics under intense local cooling. Here, we present a novel multiphysics in silico model that can accurately predict brain response to focal cooling. The model was parameterized from previously described values of metabolic activity, thermal conductivity, and temperature-dependent cerebral perfusion. The model was validated experimentally using data from clinical cases where local cooling was induced intracranially and brain temperatures monitored in real-time with MR thermometry. The validated model was then used to identify optimal design probe parameters to maximize volumetric TH, including considering three stratifications of cooling (mild, moderate, and profound) to produce Volume of Tissue Cooled (VOTC) maps. We report cooling radius increases in a nearly linear fashion with probe length and decreasing probe surface temperature.

Therapeutic hypothermia for intracerebral hemorrhage: Systematic review and meta-analysis of the experimental and clinical literature.

BACKGROUND: Intracerebral hemorrhage remains the deadliest form of stroke worldwide, inducing neuronal death through a wide variety of pathways. Therapeutic hypothermia is a robust and well-studied neuroprotectant widely used across a variety of specialties. AIMS: This review summarizes results from preclinical and clinical studies to highlight the overall effectiveness of therapeutic hypothermia to improve long-term intracerebral hemorrhage outcomes while also elucidating optimal protocol regimens to maximize therapeutic effect. SUMMARY OF REVIEW: A systematic review was conducted across three databases to identify trials investigating the use of therapeutic hypothermia to treat intracerebral hemorrhage. A random-effects meta-analysis was conducted on preclinical studies, looking at neurobehavioral outcomes, blood brain barrier breakdown, cerebral edema, hematoma volume, and tissue loss. Several mixed-methods meta-regression models were also performed to adjust for variance and variations in hypothermia induction procedures. Twwenty-one preclinical studies and five human studies were identified. The meta-analysis of preclinical studies demonstrated a significant benefit in behavioral scores (ES = -0.43, p = 0.02), cerebral edema (ES = 1.32, p = 0.0001), and blood brain barrier (ES = 2.73, p ≤ 0.00001). Therapeutic hypothermia was not found to significantly affect hematoma expansion (ES = -0.24, p = 0.12) or tissue loss (ES = 0.06, p = 0.68). Clinical study outcome reporting was heterogeneous; however, there was recurring evidence of therapeutic hypothermia-induced edema reduction. CONCLUSIONS: The combined preclinical evidence demonstrates that therapeutic hypothermia reduced multiple cell death mechanisms initiated by intracerebral hemorrhage; yet, there is no definitive evidence in clinical studies. The cooling strategies employed in both preclinical and clinical studies were highly diverse, and focused refinement of cooling protocols should be developed in future preclinical studies. The current data for therapeutic hypothermia in intracerebral hemorrhage remains questionable despite the highly promising indications in preclinical studies. Definitive randomized controlled studies are still required to answer this therapeutic question.

Deploying deep learning models on unseen medical imaging using adversarial domain adaptation.

The fundamental challenge in machine learning is ensuring that trained models generalize well to unseen data. We developed a general technique for ameliorating the effect of dataset shift using generative adversarial networks (GANs) on a dataset of 149,298 handwritten digits and dataset of 868,549 chest radiographs obtained from four academic medical centers. Efficacy was assessed by comparing area under the curve (AUC) pre- and post-adaptation. On the digit recognition task, the baseline CNN achieved an average internal test AUC of 99.87% (95% CI, 99.87-99.87%), which decreased to an average external test AUC of 91.85% (95% CI, 91.82-91.88%), with an average salvage of 35% from baseline upon adaptation. On the lung pathology classification task, the baseline CNN achieved an average internal test AUC of 78.07% (95% CI, 77.97-78.17%) and an average external test AUC of 71.43% (95% CI, 71.32-71.60%), with a salvage of 25% from baseline upon adaptation. Adversarial domain adaptation leads to improved model performance on radiographic data derived from multiple out-of-sample healthcare populations. This work can be applied to other medical imaging domains to help shape the deployment toolkit of machine learning in medicine.

A large language model for electronic health records.

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language model-GatorTron-using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on five clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA). We examine how (1) scaling up the number of parameters and (2) scaling up the size of the training data could benefit these NLP tasks. GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve five clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery. The GatorTron models are publicly available at: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og .

Multivariate statistical identification of human bladder carcinomas using ambient ionization imaging mass spectrometry.

Diagnosis of human bladder cancer in untreated tissue sections is achieved by using imaging data from desorption electrospray ionization mass spectrometry (DESI-MS) combined with multivariate statistical analysis. We use the distinctive DESI-MS glycerophospholipid (GP) mass spectral profiles to visually characterize and formally classify twenty pairs (40 tissue samples) of human cancerous and adjacent normal bladder tissue samples. The individual ion images derived from the acquired profiles correlate with standard histological hematoxylin and eosin (H&E)-stained serial sections. The profiles allow us to classify the disease status of the tissue samples with high accuracy as judged by reference histological data. To achieve this, the data from the twenty pairs were divided into a training set and a validation set. Spectra from the tumor and normal regions of each of the tissue sections in the training set were used for orthogonal projection to latent structures (O-PLS) treated partial least-square discriminate analysis (PLS-DA). This predictive model was then validated by using the validation set and showed a 5% error rate for classification and a misclassification rate of 12%. It was also used to create synthetic images of the tissue sections showing pixel-by-pixel disease classification of the tissue and these data agreed well with the independent classification that uses histological data by a certified pathologist. This represents the first application of multivariate statistical methods for classification by ambient ionization although these methods have been applied previously to other MS imaging methods. The results are encouraging in terms of the development of a method that could be utilized in a clinical setting through visualization and diagnosis of intact tissue.

Direct detection of fatty acid ethyl esters using low temperature plasma (LTP) ambient ionization mass spectrometry for rapid bacterial differentiation.

Low temperature plasma mass spectrometry (LTP-MS) was employed to detect fatty acid ethyl esters (FAEE) from bacterial samples directly. Positive ion mode FAEE mass spectrometric profiles of sixteen different bacterial samples were obtained without extraction or other sample preparation. In the range m/z 200-300, LTP mass spectra show highly reproducible and characteristic patterns. To identify the FAEE's associated with the characteristic peaks, accurate masses were recorded in the full scan mode using an LTQ/Orbitrap instrument, and tandem mass spectrometry was performed. Data were examined by principal component analysis (PCA) to determine the degree of differentiation possible amongst different bacterial species. Gram-positive and gram-negative bacteria are readily distinguished, and 11 out of 13 Salmonella strains show distinctive patterns. Growth media effects are observed but do not interfere with species recognition based on the PCA results.

Data quality in tissue analysis using desorption electrospray ionization.

There has been a recent surge in applications of mass spectrometry (MS) to tissue analysis, particularly lipid-based tissue imaging using ambient ionization techniques. This recent growth highlights the need to examine the effects of sample handling, storage conditions, and experimental protocols on the quality of the data obtained. Variables such as time before freezing after organ removal, storage time at -80 °C, time stored at room temperature, heating, and freeze/thaw cycles were investigated for their effect on the data quality obtained in desorption electrospray ionization (DESI)-MS using mouse brain. In addition, analytical variables such as tissue thickness, drying times, and instrumental conditions were also examined for their impact on DESI-MS data. While no immediate changes were noted in the DESI-MS lipid profiles of the mouse brain tissue after spending 1 h at room temperature when compared to being frozen immediately following removal, minor changes were noted between the tissue samples after 7 months of storage at -80 °C. In tissue sections stored at room temperature, degradation was noted in 24 h by the appearance of fatty acid dimers, which are indicative of high fatty acid concentrations, while in contrast, those sections stored at -80 °C for 7 months showed no significant degradation. Tissue sections were also subjected to up to six freeze/thaw cycles and showed increasing degradation following each cycle. In addition, tissue pieces were subjected to 50 °C temperatures and analyzed at specific time points. In as little as 2 h, degradation was observed in the form of increased fatty acid dimer formation, indicating that enzymatic processes forming free fatty acids were still active in the tissue. We have associated these dimers with high concentrations of free fatty acids present in the tissue during DESI-MS experiments. Analytical variables such as tissue thickness and time left to dry under nitrogen were also investigated, with no change in the resulting profiles at thickness from 10 to 25 µm and with optimal signal obtained after just 20 min in the dessicator. Experimental conditions such as source parameters, spray solvents, and sample surfaces are all shown to impact the quality of the data. Inter-section (relative standard deviation (%RSD), 0.44-7.2%) and intra-sample (%RSD, 4.0-8.0%) reproducibility data show the high quality information DESI-MS provides. Overall, the many variables investigated here showed DESI-MS to be a robust technique, with sample storage conditions having the most effect on the data obtained, and with unacceptable sample degradation occurring during room temperature storage.

Origin of chiral selectivity in gas-phase serine tetramers.

Serine "magic-number" clusters have attracted substantial experimental and theoretical interest since their discovery. Serine undergoes marked chiral enrichment upon sublimation, which has been associated with the homochiral selectivity of the octamer. This process has been implicated in one possible mechanism leading to the origin of biological homochirality. While the octamer is the best known of the serine clusters, here we focus on the tetramer, the smallest serine cluster known to exhibit homochiral preference. This choice is based on its greater simplicity and tractability with accessible computational resources. Basin-hopping molecular dynamics simulations coupled to density functional theory calculations yield a "structural landscape" for low-lying configurations on the potential energy surface. The full range of enantiomeric compositions and charge states is investigated. Global energy minimum serine tetramers consist of a cage structure bonded by zwitterionic terminal groups. The participation of the serine hydroxyl side chains in hydrogen bonds with adjacent monomers drives the homochiral selectivity of serine tetramers. The configuration of the hydrogen bonding network is strongly dependent on enantiomeric composition and charge state. Smaller cations are incorporated into the center of the tetramer cage and effectively disable all side chain hydrogen bonding, while larger cations appear not to incorporate into the tetramer cage and are stabilized outside only in the homochiral case. The current theoretical data requires the introduction of a kinetic barrier to complete the model, limiting rearrangement from the basic cage configuration in some cases, which is discussed and probed directly by doubly-nudged elastic band transition state searches. These calculations elucidate a large barrier for reorganization of the cage, completing the theoretical understanding of the tetramers.

Stereoscopic virtual reality does not improve knowledge acquisition of congenital heart disease.

Advances in virtual reality have made it possible for clinicians and trainees to interact with 3D renderings of hearts with congenital heart disease in 3D stereoscopic vision. No study to date has assessed whether this technology improved instruction compared to standard 2D interfaces. The purpose of this study was to assess whether stereoscopic virtual reality improves congenital heart disease anatomy education. Subjects in a prospective, blinded, randomized trial completed a pre-test assessing factual and visuospatial knowledge of common atrioventricular canal and were randomized to an intervention or control group based on their score. The intervention group used a 3D virtual reality (VR) headset to visualize a lecture with 3D heart models while the control group used a desktop (DT) computer interface with the same models. Subjects took a post-test and provided subjective feedback. 51 subjects were enrolled, 24 in the VR group & 27 in the DT group. The median score difference for VR subjects was 12 (IQR 9-13.3), compared to 10 (IQR 7.5-12) in the DT group. No difference in score improvement was found (p = 0.11). VR subjects' impression of the ease of use of their interface was higher than DT subjects (median 8 vs 7, respectively, p = 0.01). VR subjects' impression of their understanding of the subject matter was higher than desktop subjects (median 7 vs 5, respectively, p = 0.01). There was no statistically significant difference in the knowledge acquisition observed between the stereoscopic virtual reality group and the monoscopic desktop-based group. Participants in virtual reality reported a better learning experience and self-assessment suggesting virtual reality may increase learner engagement in understanding congenital heart disease.

Stimulating the Facial Nerve to Treat Ischemic Stroke: A Systematic Review.

Acute ischemic stroke (AIS) is a common devastating disease that has increased yearly in absolute number of cases since 1990. While mechanical thrombectomy and tissue plasminogen activator (tPA) have proven to be effective treatments, their window-of-efficacy time is very short, leaving many patients with no viable treatment option. Over recent years there has been a growing interest in stimulating the facial nerves or ganglions to treat AIS. Pre-clinical studies have consistently demonstrated an increase in collateral blood flow (CBF) following ganglion stimulation, with positive indications in infarct size and neurological scores. Extensive human trials have focused on trans-oral electrical stimulation of the sphenopalatine ganglion, but have suffered from operational limitations and non-significant clinical findings. Regardless, the potential of ganglion stimulation to treat AIS or elongate the window-of-efficacy for current stroke treatments remains extremely promising. This review aims to summarize results from recent trial publications, highlight current innovations, and discuss future directions for the field. Importantly, this review comes after the release of four important clinical trials that were published in mid 2019.