Neuronal Dynamics Regulating Brain and Behavioral State Transitions.

Prolonged behavioral challenges can cause animals to switch from active to passive coping strategies to manage effort-expenditure during stress; such normally adaptive behavioral state transitions can become maladaptive in psychiatric disorders such as depression. The underlying neuronal dynamics and brainwide interactions important for passive coping have remained unclear. Here, we develop a paradigm to study these behavioral state transitions at cellular-resolution across the entire vertebrate brain. Using brainwide imaging in zebrafish, we observed that the transition to passive coping is manifested by progressive activation of neurons in the ventral (lateral) habenula. Activation of these ventral-habenula neurons suppressed downstream neurons in the serotonergic raphe nucleus and caused behavioral passivity, whereas inhibition of these neurons prevented passivity. Data-driven recurrent neural network modeling pointed to altered intra-habenula interactions as a contributory mechanism. These results demonstrate ongoing encoding of experience features in the habenula, which guides recruitment of downstream networks and imposes a passive coping behavioral strategy.

In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images.

Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call "in silico labeling" (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire.

Peripheral TREM1 responses to brain and intestinal immunogens amplify stroke severity.

Stroke is a multiphasic process in which initial cerebral ischemia is followed by secondary injury from immune responses to ischemic brain components. Here we demonstrate that peripheral CD11b+CD45+ myeloid cells magnify stroke injury via activation of triggering receptor expressed on myeloid cells 1 (TREM1), an amplifier of proinflammatory innate immune responses. TREM1 was induced within hours after stroke peripherally in CD11b+CD45+ cells trafficking to ischemic brain. TREM1 inhibition genetically or pharmacologically improved outcome via protective antioxidant and anti-inflammatory mechanisms. Positron electron tomography imaging using radiolabeled antibody recognizing TREM1 revealed elevated TREM1 expression in spleen and, unexpectedly, in intestine. In the lamina propria, noradrenergic-dependent increases in gut permeability induced TREM1 on inflammatory Ly6C+MHCII+ macrophages, further increasing epithelial permeability and facilitating bacterial translocation across the gut barrier. Thus, following stroke, peripheral TREM1 induction amplifies proinflammatory responses to both brain-derived and intestinal-derived immunogenic components. Critically, targeting this specific innate immune pathway reduces cerebral injury.

SPED Light Sheet Microscopy: Fast Mapping of Biological System Structure and Function.

The goal of understanding living nervous systems has driven interest in high-speed and large field-of-view volumetric imaging at cellular resolution. Light sheet microscopy approaches have emerged for cellular-resolution functional brain imaging in small organisms such as larval zebrafish, but remain fundamentally limited in speed. Here, we have developed SPED light sheet microscopy, which combines large volumetric field-of-view via an extended depth of field with the optical sectioning of light sheet microscopy, thereby eliminating the need to physically scan detection objectives for volumetric imaging. SPED enables scanning of thousands of volumes-per-second, limited only by camera acquisition rate, through the harnessing of optical mechanisms that normally result in unwanted spherical aberrations. We demonstrate capabilities of SPED microscopy by performing fast sub-cellular resolution imaging of CLARITY mouse brains and cellular-resolution volumetric Ca(2+) imaging of entire zebrafish nervous systems. Together, SPED light sheet methods enable high-speed cellular-resolution volumetric mapping of biological system structure and function.

Rapid Microbial Profiling through Multimodal Biosensors for Transversal Analysis.

The intricate interactions between host and microbial communities hold significant implications for biology and medicine. However, traditional microbial profiling methods face limitations in processing time, measurement of absolute abundance, detection of low biomass, discrimination between live and dead cells, and functional analysis. This study introduces a rapid multimodal microbial characterization platform, Multimodal Biosensors for Transversal Analysis (MBioTA), for capturing the taxonomy, viability, and functional genes of the microbiota. The platform incorporates single cell biosensors, scalable microwell arrays, and automated image processing for rapid transversal analysis in as few as 2 h. The multimodal biosensors simultaneously characterize the taxon, viability, and functional gene expression of individual cells. By automating the image processing workflow, the single cell analysis techniques enable the quantification of bacteria with sensitivity down to 0.0075%, showcasing its capability in detecting low biomass samples. We illustrate the applicability of the MBioTA platform through the transversal analysis of the gut microbiota composition, viability, and functionality in a familial Alzheimer's disease mouse model. The effectiveness, rapid turnaround, and scalability of the MBioTA platform will facilitate its application from basic research to clinical diagnostics, potentially revolutionizing our understanding and management of diseases associated with microbe-host interactions.

Discovery of complex oxides via automated experiments and data science.

The quest to identify materials with tailored properties is increasingly expanding into high-order composition spaces, with a corresponding combinatorial explosion in the number of candidate materials. A key challenge is to discover regions in composition space where materials have novel properties. Traditional predictive models for material properties are not accurate enough to guide the search. Herein, we use high-throughput measurements of optical properties to identify novel regions in three-cation metal oxide composition spaces by identifying compositions whose optical trends cannot be explained by simple phase mixtures. We screen 376,752 distinct compositions from 108 three-cation oxide systems based on the cation elements Mg, Fe, Co, Ni, Cu, Y, In, Sn, Ce, and Ta. Data models for candidate phase diagrams and three-cation compositions with emergent optical properties guide the discovery of materials with complex phase-dependent properties, as demonstrated by the discovery of a Co-Ta-Sn substitutional alloy oxide with tunable transparency, catalytic activity, and stability in strong acid electrolytes. These results required close coupling of data validation to experiment design to generate a reliable end-to-end high-throughput workflow for accelerating scientific discovery.

Microprobe-Capture In-Emitter Elution: An Affinity Capture Technique to Directly Couple a Label-Free Optical Sensing Technology with Mass Spectrometry for Protein Analysis.

Affinity capture of an analyte by a capture agent is one of the most effective sample preparation approaches in mass spectrometry (MS), especially top-down MS. We describe a new affinity capture technique for protein targets, called microprobe-capture in-emitter elution (MPIE), which can directly couple a label-free optical sensing technology (next-generation biolayer interferometry, BLI) with MS. To implement MPIE, an analyte is first captured on the surface of a microprobe and subsequently eluted from the microprobe inside an electrospray emitter. The capture process is monitored in real-time via BLI. When electrospray is established from the emitter to a mass spectrometer, the analyte is immediately ionized via electrospray ionization (ESI) for MS analysis. By this means, BLI and MS are directly coupled in the form of MPIE-ESI-MS. The performance of MPIE-ESI-MS was demonstrated by the analysis of ß-amyloid 1-40 and transferrin using both standard samples and human specimens. In comparison to conventional affinity capture techniques such as bead-based immunoprecipitation, MPIE innovates the affinity capture methodology by introducing real-time process monitoring and providing binding characteristics of analytes, offering more information-rich experiment results. Thus, MPIE is a valuable addition to the top-down MS sample preparation toolbox, and MPIE-ESI-MS can be useful for identification and characterization of targets of interest.

SARS-CoV-2 RNAemia Predicts Clinical Deterioration and Extrapulmonary Complications from COVID-19.

BACKGROUND: The determinants of coronavirus disease 2019 (COVID-19) disease severity and extrapulmonary complications (EPCs) are poorly understood. We characterized relationships between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNAemia and disease severity, clinical deterioration, and specific EPCs. METHODS: We used quantitative and digital polymerase chain reaction (qPCR and dPCR) to quantify SARS-CoV-2 RNA from plasma in 191 patients presenting to the emergency department with COVID-19. We recorded patient symptoms, laboratory markers, and clinical outcomes, with a focus on oxygen requirements over time. We collected longitudinal plasma samples from a subset of patients. We characterized the role of RNAemia in predicting clinical severity and EPCs using elastic net regression. RESULTS: Of SARS-CoV-2-positive patients, 23.0% (44 of 191) had viral RNA detected in plasma by dPCR, compared with 1.4% (2 of 147) by qPCR. Most patients with serial measurements had undetectable RNAemia within 10 days of symptom onset, reached maximum clinical severity within 16 days, and symptom resolution within 33 days. Initially RNAemic patients were more likely to manifest severe disease (odds ratio, 6.72 [95% confidence interval, 2.45-19.79]), worsening of disease severity (2.43 [1.07-5.38]), and EPCs (2.81 [1.26-6.36]). RNA loads were correlated with maximum severity (r = 0.47 [95% confidence interval, .20-.67]). CONCLUSIONS: dPCR is more sensitive than qPCR for the detection of SARS-CoV-2 RNAemia, which is a robust predictor of eventual COVID-19 severity and oxygen requirements, as well as EPCs. Because many COVID-19 therapies are initiated on the basis of oxygen requirements, RNAemia on presentation might serve to direct early initiation of appropriate therapies for the patients most likely to deteriorate.

SARS-CoV-2 IgG Seropositivity and Acute Asymptomatic Infection Rate among Firefighter First Responders in an Early Outbreak County in California.

Objective: Firefighter first responders and other emergency medical services (EMS) personnel have been among the highest risk healthcare workers for illness during the SARS-CoV-2 pandemic. We sought to determine the rate of seropositivity for SARS-CoV-2 IgG antibodies and of acute asymptomatic infection among firefighter first responders in a single county with early exposure in the pandemic. Methods: We conducted a cross-sectional study of clinically active firefighters cross-trained as paramedics or EMTs in the fire departments of Santa Clara County, California. Firefighters without current symptoms were tested between June and August 2020. Our primary outcomes were rates of SARS-CoV-2 IgG antibody seropositivity and SARS-CoV-2 RT-PCR swab positivity for acute infection. We report cumulative incidence, participant characteristics with frequencies and proportions, and proportion positive and associated relative risk (with 95% confidence intervals). Results: We enrolled 983 out of 1339 eligible participants (response rate: 73.4%). Twenty-five participants (2.54%, 95% CI 1.65-3.73) tested positive for IgG antibodies and 9 (0.92%, 95% CI 0.42-1.73) tested positive for SARS-CoV-2 by RT-PCR. Our cumulative incidence, inclusive of self-reported prior positive PCR tests, was 34 (3.46%, 95% CI 2.41-4.80). Conclusion: In a county with one of the earliest outbreaks in the United States, the seroprevalence among firefighter first responders was lower than that reported by other studies of frontline health care workers, while the cumulative incidence remained higher than that seen in the surrounding community.

Characterization of High Molecular Weight Multi-Arm Functionalized PEG-Maleimide for Protein Conjugation by Charge-Reduction Mass Spectrometry Coupled to Two-Dimensional Liquid Chromatography.

A current trend in drug development involves the use of high molecular weight, branched, and functionalized polymers for protein conjugation and drug delivery. Accurately characterizing these polymers is critical to control the product quality, to monitor the stability, and ultimately to ensure the drug efficacy and patient safety. However, due to the heterogeneity in size, the multiplicity of functional groups, and the highly convoluted charge-distribution profile in mass spectra, the characterization of these polymers is highly challenging from both chromatography and mass spectrometry perspectives. To overcome these challenges, we developed a strategy utilizing charge-reduction mass spectrometry (CRMS) coupled with two-dimensional HPLC (2D-LC). We then applied the workflow to characterize a 40 kDa 8-arm polyethylene glycol (PEG) functionalized with a maleimide terminal group for protein conjugation. The development was carried out in stages, where first we focused on the development of a CRMS method to simplify the charge profile of the polymers and then coupled it to HPLC to obtain discernible mass spectra of key impurities and degradants. Second, the CRMS method was applied to an investigation of the size-variant impurity resolved by reversed-phase size-exclusion 2D-LC. Finally, a separate size-exclusion reversed-phase 2D-LC-CRMS method was developed to capture a wider range of process-related impurities and reaction intermediates from the PEG-maleimide polymers throughout the conjugation process. The combination of these experiments using the 2D-LC-CRMS strategy enables the sensitive characterization of the entire impurity profile of the high molecular weight multifunctionalized PEG-maleimide conjugation intermediate.

A Novel Platform Using RNA Signatures To Accelerate Antimicrobial Susceptibility Testing in Neisseria gonorrhoeae.

The rise of antimicrobial-resistant pathogens can be attributed to the lack of a rapid pathogen identification (ID) or antimicrobial susceptibility testing (AST), resulting in delayed therapeutic decisions at the point of care. Gonorrhea is usually empirically treated, with no AST results available before treatment, thus contributing to the rapid rise in drug resistance. Here, we present a rapid AST platform using RNA signatures for Neisseria gonorrhoeae Transcriptome sequencing (RNA-seq) followed by bioinformatic tools was applied to explore potential markers in the transcriptome profile of N. gonorrhoeae upon minutes of azithromycin exposure. Validation of candidate markers using quantitative real-time PCR (qRT-PCR) showed that two markers (arsR [NGO1562] and rpsO) can deliver accurate AST results across 14 tested isolates. Further validation of our susceptibility threshold in comparison to MIC across 64 more isolates confirmed the reliability of our platform. Our RNA markers combined with emerging molecular point-of-care systems has the potential to greatly accelerate both ID and AST to inform treatment.

RNA markers for ultra-rapid molecular antimicrobial susceptibility testing in fluoroquinolone-treated Klebsiella pneumoniae.

OBJECTIVES: Traditional antimicrobial susceptibility testing (AST) is growth dependent and time-consuming. With rising rates of drug-resistant infections, a novel diagnostic method is critically needed that can rapidly reveal a pathogen's antimicrobial susceptibility to guide appropriate treatment. Recently, RNA sequencing has been identified as a powerful diagnostic tool to explore transcriptional gene expression and improve AST. METHODS: RNA sequencing was used to investigate the potential of RNA markers for rapid molecular AST using Klebsiella pneumoniae and ciprofloxacin as a model. Downstream bioinformatic analysis was applied for optimal marker selection. Further validation on 11 more isolates of K. pneumoniae was performed using quantitative real-time PCR. RESULTS: From RNA sequencing, we identified RNA signatures that were induced or suppressed following exposure to ciprofloxacin. Significant shifts at the transcript level were observed as early as 10 min after antibiotic exposure. Lastly, we confirmed marker expression profiles with concordant MIC results from traditional culture-based AST and validated across 11 K. pneumoniae isolates. recA, coaA and metN transcripts harbour the most sensitive susceptibility information and were selected as our top markers. CONCLUSIONS: Our results suggest that RNA signature is a promising approach to AST development, resulting in faster clinical diagnosis and treatment of infectious disease. This approach is potentially applicable in other models including other pathogens exposed to different classes of antibiotics.

Pathogenic variants in NUBPL result in failure to assemble the matrix arm of complex I and cause a complex leukoencephalopathy with thalamic involvement.

Disorders of the white matter are genetically very heterogeneous including several genes involved in mitochondrial bioenergetics. Diagnosis of the underlying cause is aided by pattern recognition on neuroimaging and by next-generation sequencing. Recently, genetic changes in the complex I assembly factor NUBPL have been characterized by a consistent recognizable pattern of leukoencephalopathy affecting deep white matter including the corpus callosum and cerebellum. Here, we report twin boys with biallelic variants in NUBPL, an unreported c.351 G > A; p.(Met117Ile) and a previously reported pathological variant c. 693 + 1 G > A. Brain magnetic resonance imaging showed abnormal T2 hyperintense signal involving the periventricular white matter, external capsule, corpus callosum, and, prominently, the bilateral thalami. The neuroimaging pattern evolved over 18 months with marked diffuse white matter signal abnormality, volume loss, and new areas of signal abnormality in the cerebellar folia and vermis. Magnetic resonance spectroscopy showed elevated lactate. Functional studies in cultured fibroblasts confirmed pathogenicity of the genetic variants. Complex I activity of the respiratory chain was deficient spectrophotometrically and on blue native gel with in-gel activity staining. There was absent assembly and loss of proteins of the matrix arm of complex I when traced with an antibody to NDUFS2, and incomplete assembly of the membrane arm when traced with an NDUFB6 antibody. There was decreased NUBPL protein on Western blot in patient fibroblasts compared to controls. Compromised NUBPL activity impairs assembly of the matrix arm of complex I and produces a severe, rapidly-progressive leukoencephalopathy with thalamic involvement on MRI, further expanding the neuroimaging phenotype.

Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach.

BACKGROUND: Deep learning models have attracted significant interest from health care researchers during the last few decades. There have been many studies that apply deep learning to medical applications and achieve promising results. However, there are three limitations to the existing models: (1) most clinicians are unable to interpret the results from the existing models, (2) existing models cannot incorporate complicated medical domain knowledge (eg, a disease causes another disease), and (3) most existing models lack visual exploration and interaction. Both the electronic health record (EHR) data set and the deep model results are complex and abstract, which impedes clinicians from exploring and communicating with the model directly. OBJECTIVE: The objective of this study is to develop an interpretable and accurate risk prediction model as well as an interactive clinical prediction system to support EHR data exploration, knowledge graph demonstration, and model interpretation. METHODS: A domain-knowledge-guided recurrent neural network (DG-RNN) model is proposed to predict clinical risks. The model takes medical event sequences as input and incorporates medical domain knowledge by attending to a subgraph of the whole medical knowledge graph. A global pooling operation and a fully connected layer are used to output the clinical outcomes. The middle results and the parameters of the fully connected layer are helpful in identifying which medical events cause clinical risks. DG-Viz is also designed to support EHR data exploration, knowledge graph demonstration, and model interpretation. RESULTS: We conducted both risk prediction experiments and a case study on a real-world data set. A total of 554 patients with heart failure and 1662 control patients without heart failure were selected from the data set. The experimental results show that the proposed DG-RNN outperforms the state-of-the-art approaches by approximately 1.5%. The case study demonstrates how our medical physician collaborator can effectively explore the data and interpret the prediction results using DG-Viz. CONCLUSIONS: In this study, we present DG-Viz, an interactive clinical prediction system, which brings together the power of deep learning (ie, a DG-RNN-based model) and visual analytics to predict clinical risks and visually interpret the EHR prediction results. Experimental results and a case study on heart failure risk prediction tasks demonstrate the effectiveness and usefulness of the DG-Viz system. This study will pave the way for interactive, interpretable, and accurate clinical risk predictions.

Nanoarray Digital Polymerase Chain Reaction with High-Resolution Melt for Enabling Broad Bacteria Identification and Pheno-Molecular Antimicrobial Susceptibility Test.

Toward combating infectious diseases caused by pathogenic bacteria, there remains an unmet need for diagnostic tools that can broadly identify the causative bacteria and determine their antimicrobial susceptibilities from complex and even polymicrobial samples in a timely manner. To address this need, a microfluidic and machine-learning-based platform that performs broad bacteria identification (ID) and rapid yet reliable antimicrobial susceptibility testing (AST) is developed. Specifically, this platform builds on "pheno-molecular AST", a strategy that transforms nucleic acid amplification tests (NAATs) into phenotypic AST through quantitative detection of bacterial genomic replication, and utilizes digital polymerase chain reaction (PCR) and digital high-resolution melt (HRM) to quantify and identify bacterial DNA molecules. Bacterial species are identified using integrated experiment-machine learning algorithm via HRM profiles. Digital DNA quantification allows for rapid growth measurement that reflects susceptibility profiles of each bacterial species within only 30 min of antibiotic exposure. As a demonstration, multiple bacterial species and their susceptibility profiles in a spiked-in polymicrobial urine specimen were correctly identified with a total turnaround time of ∼4 h. With further development and clinical validation, this platform holds the potential for improving clinical diagnostics and enabling targeted antibiotic treatments.

Simultaneous fast measurement of circuit dynamics at multiple sites across the mammalian brain.

Real-time activity measurements from multiple specific cell populations and projections are likely to be important for understanding the brain as a dynamical system. Here we developed frame-projected independent-fiber photometry (FIP), which we used to record fluorescence activity signals from many brain regions simultaneously in freely behaving mice. We explored the versatility of the FIP microscope by quantifying real-time activity relationships among many brain regions during social behavior, simultaneously recording activity along multiple axonal pathways during sensory experience, performing simultaneous two-color activity recording, and applying optical perturbation tuned to elicit dynamics that match naturally occurring patterns observed during behavior.

Transverse relaxation-based assessment of mammographic density and breast tissue composition by single-sided portable NMR.

PURPOSE: Elevated mammographic density (MD) is an independent risk factor for breast cancer (BC) as well as a source of masking in X-ray mammography. High-frequency longitudinal monitoring of MD could also be beneficial in hormonal BC prevention, where early MD changes herald the treatment's success. We present a novel approach to quantification of MD in breast tissue using single-sided portable NMR. Its development was motivated by the low cost of portable-NMR instrumentation, the suitability for measurements in vivo, and the absence of ionizing radiation. METHODS: Five breast slices were obtained from three patients undergoing prophylactic mastectomy or breast reduction surgery. Carr-Purcell-Meiboom-Gill (CPMG) relaxation curves were measured from (1) regions of high and low MD (HMD and LMD, respectively) in the full breast slices; (2) the same regions excised from the full slices; and (3) excised samples after H2 O-D2 O replacement. T2 distributions were reconstructed from the CPMG decays using inverse Laplace transform. RESULTS: Two major peaks, identified as fat and water, were consistently observed in the T2 distributions of HMD regions. The LMD T2 distributions were dominated by the fat peak. The relative areas of the two peaks exhibited statistically significant (P < .005) differences between HMD and LMD regions, enabling their classification as HMD or LMD. The relative-area distributions exhibited no statistically significant differences between full slices and excised samples. CONCLUSION: T2 -based portable-NMR analysis is a novel approach to MD quantification. The ability to quantify tissue composition, combined with the low cost of instrumentation, make this approach promising for clinical applications.

Factors Associated With Influenza in an Emergency Department Setting.

BACKGROUND: Emergency departments (EDs) become more overcrowded during peak respiratory virus season. Distinguishing influenza from other viruses is crucial to implement social distancing practices, early treatment, and prompt disposition. OBJECTIVES: We sought to determine factors associated with influenza among a prospective cohort of consecutive ED patients with acute respiratory illness (ARI). METHODS: Between December 2016 and March 2017, trained research assistants screened consecutive ED patients with ARI symptoms. ARI criteria included measured fever at home or in the ED >38°C and a cough, sore throat, or rhinorrhea with a duration of symptoms >12 hours and <1 week. After consent, research assistants collected demographics and clinical history using a standardized data form, and patients had a polymerase chain reaction-based assay that is nearly 100% sensitive for influenza. Univariate analysis was conducted on all predictor variables. Significant variables were entered into a multivariate logistic regression model to find factors that were independently associated with influenza. RESULTS: One hundred nineteen patients consented to enrollment and 31% were found to be positive for influenza. Myalgia, the absence of gastrointestinal symptoms (no diarrhea or vomiting), sore throat, chills, headache, and oxygen saturation ≥97% were significant on univariate analysis and were entered into the multivariate model. Myalgia (adjusted odds ratio [AOR] 3.9), the absence of gastrointestinal symptoms (AOR 4.7), and oxygen saturation ≥97% (AOR 2.8) were significant independent factors of influenza. CONCLUSION: The presence of myalgia, the absence of gastrointestinal symptoms, and oxygen saturation ≥97% are factors that can help distinguish influenza from other acute respiratory illnesses in the ambulatory ED population.

Assessing microscope image focus quality with deep learning.

BACKGROUND: Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality. RESULTS: We present a deep neural network model capable of predicting an absolute measure of image focus on a single image in isolation, without any user-specified parameters. The model operates at the image-patch level, and also outputs a measure of prediction certainty, enabling interpretable predictions. The model was trained on only 384 in-focus Hoechst (nuclei) stain images of U2OS cells, which were synthetically defocused to one of 11 absolute defocus levels during training. The trained model can generalize on previously unseen real Hoechst stain images, identifying the absolute image focus to within one defocus level (approximately 3 pixel blur diameter difference) with 95% accuracy. On a simpler binary in/out-of-focus classification task, the trained model outperforms previous approaches on both Hoechst and Phalloidin (actin) stain images (F-scores of 0.89 and 0.86, respectively over 0.84 and 0.83), despite only having been presented Hoechst stain images during training. Lastly, we observe qualitatively that the model generalizes to two additional stains, Hoechst and Tubulin, of an unseen cell type (Human MCF-7) acquired on a different instrument. CONCLUSIONS: Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of synthetically defocused images precludes the need for a manually annotated training dataset. The model also generalizes to different image and cell types. The framework for model training and image prediction is available as a free software library and the pre-trained model is available for immediate use in Fiji (ImageJ) and CellProfiler.

A "Culture" Shift: Broad Bacterial Detection, Identification, and Antimicrobial Susceptibility Testing Directly from Whole Blood.

BACKGROUND: The time required for bloodstream pathogen detection, identification (ID), and antimicrobial susceptibility testing (AST) does not satisfy the acute needs of disease management. Conventional methods take up to 3 days for ID and AST. Molecular diagnostics have reduced times for ID, but their promise to supplant culture is unmet because AST times remain slow. We developed a combined quantitative PCR (qPCR)-based ID+AST assay with sequential detection, ID, and AST of leading nosocomial bacterial pathogens. METHODS: ID+AST was performed on whole blood samples by (a) removing blood cells, (b) brief bacterial enrichment, (c) bacterial detection and ID, and (d) species-specific antimicrobial treatment. Broad-spectrum qPCR of the internal transcribed spacer between the 16S and 23S was amplified for detection. High-resolution melting identified the species with a curve classifier. AST was enabled by Ct differences between treated and untreated samples. RESULTS: A detection limit of 1 CFU/mL was achieved for Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus. All species were accurately identified by unique melting curves. Antimicrobial minimum inhibitory concentrations were identified with Ct differences of ≥1 cycle. Using an RNA target allowed reduction of AST incubation time from 60 min to 5 min. Rapid-cycle amplification reduced qPCR times by 83% to 30 min. CONCLUSIONS: Combined, sequential ID+AST protocols allow rapid and reliable detection, ID, and AST for the diagnosis of bloodstream infections, enabling conversion of empiric to targeted therapy by the second dose of antimicrobials.