CAM3.0: determining cell type composition and expression from bulk tissues with fully unsupervised deconvolution.

MOTIVATION: Complex tissues are dynamic ecosystems consisting of molecularly distinct yet interacting cell types. Computational deconvolution aims to dissect bulk tissue data into cell type compositions and cell-specific expressions. With few exceptions, most existing deconvolution tools exploit supervised approaches requiring various types of references that may be unreliable or even unavailable for specific tissue microenvironments. RESULTS: We previously developed a fully unsupervised deconvolution method-Convex Analysis of Mixtures (CAM), that enables estimation of cell type composition and expression from bulk tissues. We now introduce CAM3.0 tool that improves this framework with three new and highly efficient algorithms, namely, radius-fixed clustering to identify reliable markers, linear programming to detect an initial scatter simplex, and a smart floating search for the optimum latent variable model. The comparative experimental results obtained from both realistic simulations and case studies show that the CAM3.0 tool can help biologists more accurately identify known or novel cell markers, determine cell proportions, and estimate cell-specific expressions, complementing the existing tools particularly when study- or datatype-specific references are unreliable or unavailable. AVAILABILITY AND IMPLEMENTATION: The open-source R Scripts of CAM3.0 is freely available at https://github.com/ChiungTingWu/CAM3/(https://github.com/Bioconductor/Contributions/issues/3205). A user's guide and a vignette are provided.

In vitro-in silico correlation of three-dimensional turbulent flows in an idealized mouth-throat model.

There exists an ongoing need to improve the validity and accuracy of computational fluid dynamics (CFD) simulations of turbulent airflows in the extra-thoracic and upper airways. Yet, a knowledge gap remains in providing experimentally-resolved 3D flow benchmarks with sufficient data density and completeness for useful comparison with widely-employed numerical schemes. Motivated by such shortcomings, the present work details to the best of our knowledge the first attempt to deliver in vitro-in silico correlations of 3D respiratory airflows in a generalized mouth-throat model and thereby assess the performance of Large Eddy Simulations (LES) and Reynolds-Averaged Numerical Simulations (RANS). Numerical predictions are compared against 3D volumetric flow measurements using Tomographic Particle Image Velocimetry (TPIV) at three steady inhalation flowrates varying from shallow to deep inhalation conditions. We find that a RANS k-ω SST model adequately predicts velocity flow patterns for Reynolds numbers spanning 1'500 to 7'000, supporting results in close proximity to a more computationally-expensive LES model. Yet, RANS significantly underestimates turbulent kinetic energy (TKE), thus underlining the advantages of LES as a higher-order turbulence modeling scheme. In an effort to bridge future endevours across respiratory research disciplines, we provide end users with the present in vitro-in silico correlation data for improved predictive CFD models towards inhalation therapy and therapeutic or toxic dosimetry endpoints.

Quantitative morphology of femtosecond laser-written point-by-point optical fiber Bragg gratings.

We investigate the morphology of femtosecond laser, single pulse-inscribed, point-by-point (PbP) fiber Bragg gratings. Direct measurement of a PbP grating's refractive index profile was carried out with micro-reflectivity analysis. PbP gratings were imaged at sub-micrometer scale with scanning electron microscopy, Raman and photoluminescence studies were performed to probe the structural and electronic changes. Comparison of results from different characterisation techniques suggests that the creation of an increased refractive index region around the micro-void is due to contributions from both densification and the formation of highly polarizable non-bridging oxygen bonds.

Acute coronary syndrome in young males after a prolonged stay at high altitude.

The Indian population is predisposed to acute coronary syndrome at a younger age, but very few cases are reported at high altitude. Acute coronary syndrome is frequently associated with multiple cardiovascular risk factors. During management of seven young patients with acute coronary syndrome, it was found that none of them had conventional cardiovascular risk factors including recent physical exertion. It is a known fact that the risk of vascular thrombosis increases by 30 times in Indian soldiers after a long stay at high altitude. Therefore, it is necessary to carry out the tests for procoagulant markers to know whether the acute coronary syndrome was because of the prothrombotic state, and if yes, was high altitude responsible for the procoagulant state or whether the person per se had a procoagulant syndrome. With the absence of these tests at hospitals at high-altitude areas, it becomes difficult to ascertain the exact cause of acute coronary syndrome. This study highlights the importance of aggressively testing for procoagulant markers in young patients presenting with chest pain at high altitude, even in the absence of traditional risk factors.

Robust erythroid differentiation system for rhesus hematopoietic progenitor cells allowing preclinical screening of genetic treatment strategies for the hemoglobinopathies.

BACKGROUND AIMS: γ-globin expression can be induced by various gene modification strategies, which could be beneficial for hemoglobin (Hb) disorders. To translate promising ideas into clinics, large animal models have proven valuable to evaluate safety and efficacy of the approaches; however, in vitro erythroid differentiation methods have not been established to determine whether they can be modeled in nonhuman primates. METHODS: We optimized erythroid differentiation culture to produce high-level adult Hb from rhesus hematopoietic progenitor cells by using low (LC) or high cytokine concentration (HC) protocols with or without feeder cells. In addition, we established rhesus globin protein analysis using reverse-phase high performance liquid chromatography and mass spectrometry. RESULTS: Robust adult Hb production at protein levels was observed in the LC protocol when feeder cells were used, whereas the HC protocol resulted in higher baseline fetal Hb levels (P < 0.01). We then compared lentiviral transduction of rhesus cells between serum-containing LC media and serum-free StemSpan-based differentiation media, revealing 100-fold more efficient transduction in serum-free differentiation media (P < 0.01). Finally, rhesus CD34+ cells were transduced with lentiviral vectors encoding artificial zinc finger proteins (ZF-Ldb1), which can reactivate γ-globin expression via tethering the transcriptional co-regulator Ldb1 to γ-globin promoters, and were differentiated in the optimized erythroid differentiation method. This resulted in marked increases of γ-globin levels compared with control groups (P < 0.01). DISCUSSION: In conclusion, we developed an efficient rhesus erythroid differentiation protocol from hematopoietic progenitor cells with low fetal and high adult Hb production. Further studies are warranted to optimize gene modification and transplantation of rhesus hematopoietic progenitor cells.

Primary Tubercular Granulomatous Thyroiditis, Presenting as Thyroid Nodule with Hyperthyroidism, Pyrexia of Unknown Origin and Severe Anemia.

Tuberculosis of Thyroid gland is a rare entity even in countries with high prevalence of tuberculosis. The patients present with a broad spectrum of manifestations ranging from an isolated painful nodule to frank hyperthyroidism. We report an interesting case of primary tubercular granulomatous thyroiditis, presenting as a thyroid nodule with pyrexia of unknown origin, hyperthyroidism and severe anaemia which responded to anti tubercular and anti-inflammatory treatment with complete recovery, as evidenced by blood reports and CT scan reports. Thus, proper diagnosis may avoid unnecessary surgical interventions, that were a trend in the past.

ABDS: a bioinformatics tool suite for analyzing biologically diverse samples.

Bioinformatics software tools are essential to identify informative molecular features that define different phenotypic sample groups. Among the most fundamental and interrelated tasks are missing value imputation, signature gene detection, and differential pattern visualization. However, many commonly used analytics tools can be problematic when handling biologically diverse samples if either informative missingness possess high missing rates with mixed missing mechanisms, or multiple sample groups are compared and visualized in parallel. We developed the ABDS tool suite specifically for analyzing biologically diverse samples. Collectively, a mechanism-integrated group-wise pre-imputation scheme is proposed to retain informative missingness associated with signature genes, a cosine-based one-sample test is extended to detect group-silenced signature genes, and a unified heatmap is designed to display multiple sample groups. We describe the methodological principles and demonstrate the effectiveness of three analytics tools under targeted scenarios, supported by comparative evaluations and biomedical showcases. As an open-source R package, ABDS tool suite complements rather than replaces existing tools and will allow biologists to more accurately detect interpretable molecular signals among phenotypically diverse sample groups.

Information set supported deep learning architectures for improving noisy image classification.

Deep learning models have been widely used in many supervised learning applications. However, these models suffer from overfitting due to various types of uncertainty with deteriorating performance when facing data biases, class imbalance, or noise propagation. The Information-Set Deep learning (ISDL) architectures with four variants are developed by integrating information set theory and deep learning principles to address the critical problem of the absence of robust deep learning models. There is a description of the ISDL architectures, learning algorithms, and analytic workflows. The performance of the ISDL models and standard architectures is evaluated using a noise-corrupted benchmark dataset. The experimental results show that the ISDL models can efficiently handle noise-dominated uncertainty and outperform peer architectures.

Exploring the role of electrostatic deposition on inhaled aerosols in alveolated microchannels.

Large amounts of net electrical charge are known to accumulate on inhaled aerosols during their generation using commonly-available inhalers. This effect often leads to superfluous deposition in the extra-thoracic airways at the cost of more efficient inhalation therapy. Since the electrostatic force is inversely proportional to the square of the distance between an aerosol and the airway wall, its role has long been recognized as potentially significant in the deep lungs. Yet, with the complexity of exploring such phenomenon directly at the acinar scales, in vitro experiments have been largely limited to upper airways models. Here, we devise a microfluidic alveolated airway channel coated with conductive material to quantify in vitro the significance of electrostatic effects on inhaled aerosol deposition. Specifically, our aerosol exposure assays showcase inhaled spherical particles of 0.2, 0.5, and 1.1 µm that are recognized to reach the acinar regions, whereby deposition is typically attributed to the leading roles of diffusion and sedimentation. In our experiments, electrostatic effects are observed to largely prevent aerosols from depositing inside alveolar cavities. Rather, deposition is overwhelmingly biased along the inter-alveolar septal spaces, even when aerosols are charged with only a few elementary charges. Our observations give new insight into the role of electrostatics at the acinar scales and emphasize how charged particles under 2 µm may rapidly overshadow the traditionally accepted dominance of diffusion or sedimentation when considering aerosol deposition phenomena in the deep lungs.

Modeling Flow in an In Vitro Anatomical Cerebrovascular Model with Experimental Validation.

Acute ischemic stroke (AIS) is a leading cause of mortality that occurs when an embolus becomes lodged in the cerebral vasculature and obstructs blood flow in the brain. The severity of AIS is determined by the location and how extensively emboli become lodged, which are dictated in large part by the cerebral flow and the dynamics of embolus migration which are difficult to measure in vivo in AIS patients. Computational fluid dynamics (CFD) can be used to predict the patient-specific hemodynamics and embolus migration and lodging in the cerebral vasculature to better understand the underlying mechanics of AIS. To be relied upon, however, the computational simulations must be verified and validated. In this study, a realistic in vitro experimental model and a corresponding computational model of the cerebral vasculature are established that can be used to investigate flow and embolus migration and lodging in the brain. First, the in vitro anatomical model is described, including how the flow distribution in the model is tuned to match physiological measurements from the literature. Measurements of pressure and flow rate for both normal and stroke conditions were acquired and corresponding CFD simulations were performed and compared with the experiments to validate the flow predictions. Overall, the CFD simulations were in relatively close agreement with the experiments, to within ±7% of the mean experimental data with many of the CFD predictions within the uncertainty of the experimental measurement. This work provides an in vitro benchmark data set for flow in a realistic cerebrovascular model and is a first step towards validating a computational model of AIS.

Modeling flow in an in vitro anatomical cerebrovascular model with experimental validation.

Acute ischemic stroke (AIS) is a leading cause of mortality that occurs when an embolus becomes lodged in the cerebral vasculature and obstructs blood flow in the brain. The severity of AIS is determined by the location and how extensively emboli become lodged, which are dictated in large part by the cerebral flow and the dynamics of embolus migration which are difficult to measure in vivo in AIS patients. Computational fluid dynamics (CFD) can be used to predict the patient-specific hemodynamics and embolus migration and lodging in the cerebral vasculature to better understand the underlying mechanics of AIS. To be relied upon, however, the computational simulations must be verified and validated. In this study, a realistic in vitro experimental model and a corresponding computational model of the cerebral vasculature are established that can be used to investigate flow and embolus migration and lodging in the brain. First, the in vitro anatomical model is described, including how the flow distribution in the model is tuned to match physiological measurements from the literature. Measurements of pressure and flow rate for both normal and stroke conditions were acquired and corresponding CFD simulations were performed and compared with the experiments to validate the flow predictions. Overall, the CFD simulations were in relatively close agreement with the experiments, to within ±7% of the mean experimental data with many of the CFD predictions within the uncertainty of the experimental measurement. This work provides an in vitro benchmark data set for flow in a realistic cerebrovascular model and is a first step towards validating a computational model of AIS.

A Novel Trans-Tracheostomal Retrograde Inhalation Technique Increases Subglottic Drug Deposition Compared to Traditional Trans-Oral Inhalation.

Subglottic stenosis represents a challenging clinical condition in otolaryngology. Although patients often experience improvement following endoscopic surgery, recurrence rates remain high. Pursuing measures to maintain surgical results and prevent recurrence is thus necessary. Steroids therapy is considered effective in preventing restenosis. Currently, however, the ability of trans-oral steroid inhalation to reach and affect the stenotic subglottic area in a tracheotomized patient is largely negligible. In the present study, we describe a novel trans-tracheostomal retrograde inhalation technique to increase corticosteroid deposition in the subglottic area. We detail our preliminary clinical outcomes in four patients treated with trans-tracheostomal corticosteroid inhalation via a metered dose inhaler (MDI) following surgery. Concurrently, we leverage computational fluid-particle dynamics (CFPD) simulations in an extra-thoracic 3D airway model to gain insight on possible advantages of such a technique over traditional trans-oral inhalation in augmenting aerosol deposition in the stenotic subglottic region. Our numerical simulations show that for an arbitrary inhaled dose (aerosols spanning 1-12 µm), the deposition (mass) fraction in the subglottis is over 30 times higher in the retrograde trans-tracheostomal technique compared to the trans-oral inhalation technique (3.63% vs. 0.11%). Importantly, while a major portion of inhaled aerosols (66.43%) in the trans-oral inhalation maneuver are transported distally past the trachea, the vast majority of aerosols (85.10%) exit through the mouth during trans-tracheostomal inhalation, thereby avoiding undesired deposition in the broader lungs. Overall, the proposed trans-tracheostomal retrograde inhalation technique increases aerosol deposition rates in the subglottis with minor lower-airway deposition compared to the trans-oral inhalation technique. This novel technique could play an important role in preventing restenosis of the subglottis.

Predicting benzodiazepine prescriptions: A proof-of-concept machine learning approach.

Introduction: Benzodiazepines are the most commonly prescribed psychotropic medications, but they may place users at risk of serious adverse effects. Developing a method to predict benzodiazepine prescriptions could assist in prevention efforts. Methods: The present study applies machine learning methods to de-identified electronic health record data, in order to develop algorithms for predicting benzodiazepine prescription receipt (yes/no) and number of benzodiazepine prescriptions (0, 1, 2+) at a given encounter. Support-vector machine (SVM) and random forest (RF) approaches were applied to outpatient psychiatry, family medicine, and geriatric medicine data from a large academic medical center. The training sample comprised encounters taking place between January 2020 and December 2021 (N = 204,723 encounters); the testing sample comprised data from encounters taking place between January and March 2022 (N = 28,631 encounters). The following empirically-supported features were evaluated: anxiety and sleep disorders (primary anxiety diagnosis, any anxiety diagnosis, primary sleep diagnosis, any sleep diagnosis), demographic characteristics (age, gender, race), medications (opioid prescription, number of opioid prescriptions, antidepressant prescription, antipsychotic prescription), other clinical variables (mood disorder, psychotic disorder, neurocognitive disorder, prescriber specialty), and insurance status (any insurance, type of insurance). We took a step-wise approach to developing a prediction model, wherein Model 1 included only anxiety and sleep diagnoses, and each subsequent model included an additional group of features. Results: For predicting benzodiazepine prescription receipt (yes/no), all models showed good to excellent overall accuracy and area under the receiver operating characteristic curve (AUC) for both SVM (Accuracy = 0.868-0.883; AUC = 0.864-0.924) and RF (Accuracy = 0.860-0.887; AUC = 0.877-0.953). Overall accuracy was also high for predicting number of benzodiazepine prescriptions (0, 1, 2+) for both SVM (Accuracy = 0.861-0.877) and RF (Accuracy = 0.846-0.878). Discussion: Results suggest SVM and RF algorithms can accurately classify individuals who receive a benzodiazepine prescription and can separate patients by the number of benzodiazepine prescriptions received at a given encounter. If replicated, these predictive models could inform system-level interventions to reduce the public health burden of benzodiazepines.

ABDS: tool suite for analyzing biologically diverse samples.

Motivation: Analytics tools are essential to identify informative molecular features about different phenotypic groups. Among the most fundamental tasks are missing value imputation, signature gene detection, and expression pattern visualization. However, most commonly used analytics tools may be problematic for characterizing biologically diverse samples when either signature genes possess uneven missing rates across different groups yet involving complex missing mechanisms, or multiple biological groups are simultaneously compared and visualized. Results: We develop ABDS tool suite tailored specifically to analyzing biologically diverse samples. Mechanism-integrated group-wise imputation is developed to recruit signature genes involving informative missingness, cosine-based one-sample test is extended to detect enumerated signature genes, and unified heatmap is designed to comparably display complex expression patterns. We discuss the methodological principles and demonstrate the conceptual advantages of the three software tools. We also showcase the biomedical applications of these individual tools. Implemented in open-source R scripts, ABDS tool suite complements rather than replaces the existing tools and will allow biologists to more accurately detect interpretable molecular signals among diverse phenotypic samples. Availability and implementation: The R Scripts of ABDS tool suite is freely available at https://github.com/niccolodpdu/ABDS.

Forced enhancer-promoter rewiring to alter gene expression in animal models.

Transcriptional enhancers can be in physical proximity of their target genes via chromatin looping. The enhancer at the ß-globin locus (locus control region [LCR]) contacts the fetal-type (HBG) and adult-type (HBB) ß-globin genes during corresponding developmental stages. We have demonstrated previously that forcing proximity between the LCR and HBG genes in cultured adult-stage erythroid cells can activate HBG transcription. Activation of HBG expression in erythroid cells is of benefit to patients with sickle cell disease. Here, using the ß-globin locus as a model, we provide proof of concept at the organismal level that forced enhancer rewiring might present a strategy to alter gene expression for therapeutic purposes. Hematopoietic stem and progenitor cells (HSPCs) from mice bearing human ß-globin genes were transduced with lentiviral vectors expressing a synthetic transcription factor (ZF-Ldb1) that fosters LCR-HBG contacts. When engrafted into host animals, HSPCs gave rise to adult-type erythroid cells with elevated HBG expression. Vectors containing ZF-Ldb1 were optimized for activity in cultured human and rhesus macaque erythroid cells. Upon transplantation into rhesus macaques, erythroid cells from HSPCs expressing ZF-Ldb1 displayed elevated HBG production. These findings in two animal models suggest that forced redirection of gene-regulatory elements may be used to alter gene expression to treat disease.

Fate of inhaled aerosols under the influence of glottal motion in a realistic in silico human tracheobronchial tree model.

Despite the prevalence of inhalation therapy in the treatment of various respiratory diseases, predicting and optimizing lung deposition fractions of inhaled drugs for maximal efficacy remains challenging due to the complex anatomical structures of the extra-thoracic airways, notably the glottal region. One of the widespread speculations in current insilico simulations lies in assuming a static glottis during inhalation, while in reality inhalation leads to significant glottis cross-sectional area expansion. The present work attempts to explore, insilico, the influence of transient movement of the glottal structures on inhalation therapy outcomes. To this end, we adopted a CT-based realistic human tracheobronchial tree (TB) model and explored transient airflows and deposition outcomes for a broad range of inhaled aerosols (i.e., dp=1-12 µm) under a dry powder inhaler (DPI) maneuver. Three different glottal expansion ratios, spanning static to 40 percent cross-sectional area expansion have been considered for the analysis. Our findings point to the tangible impact of glottal motion on airflow and particle deposition along the respiratory tract for a DPI maneuver, where a static glottis underpredicts the total particle deposition in the TB model for lower sized particles (dp≤ 3 µm) compared to predictions for all dynamic glottal motions. In contrast, for larger size particles (i.e., 5 ≤dp≤ 10 µm), a static glottis yields lower total deposition in the TB model compared with dynamic glottal motions. Our study also underlines that regional deposition of smaller micron-sized particles is most affected by glottal deformation in the conducting airways.

Case of buprenorphine-associated central sleep apnea resolving with dose reduction.

Aside from respiratory suppression in overdose, full opioid agonist agents are known to cause sleep-disordered breathing (SDB). The increasing rates of opioid overdose in the United States have led to increasing use of medication-assisted treatments for opioid use disorders. Dose-dependent increase in SDB has been documented with methadone. There is emerging literature in the form of case reports providing evidence of buprenorphine and buprenorphine-naloxone contributing to sleep apnea. We report an additional case of a female patient developing central sleep apnea during initiation of buprenorphine-naloxone treatment. The condition resolved with dose reduction.

Ventilation-induced epithelial injury drives biological onset of lung trauma in vitro and is mitigated with prophylactic anti-inflammatory therapeutics.

Mortality rates among patients suffering from acute respiratory failure remain perplexingly high despite the maintenance of blood oxygen homeostasis during ventilatory support. The biotrauma hypothesis advocates that mechanical forces from invasive ventilation trigger immunological mediators that spread systemically. Yet, how these forces elicit an immune response remains unclear. Here, a biomimetic in vitro three-dimensional (3D) upper airways model allows to recapitulate lung injury and immune responses induced during invasive mechanical ventilation in neonates. Under such ventilatory support, flow-induced stresses injure the bronchial epithelium of the intubated airways model and directly modulate epithelial cell inflammatory cytokine secretion associated with pulmonary injury. Fluorescence microscopy and biochemical analyses reveal site-specific susceptibility to epithelial erosion in airways from jet-flow impaction and are linked to increases in cell apoptosis and modulated secretions of cytokines IL-6, -8, and -10. In an effort to mitigate the onset of biotrauma, prophylactic pharmacological treatment with Montelukast, a leukotriene receptor antagonist, reduces apoptosis and pro-inflammatory signaling during invasive ventilation of the in vitro model. This 3D airway platform points to a previously overlooked origin of lung injury and showcases translational opportunities in preclinical pulmonary research toward protective therapies and improved protocols for patient care.

Human Multi-Compartment Airways-on-Chip Platform for Emulating Respiratory Airborne Transmission: From Nose to Pulmonary Acini.

The past decade has witnessed tremendous endeavors to deliver novel preclinical in vitro lung models for pulmonary research endpoints, including foremost with the advent of organ- and lung-on-chips. With growing interest in aerosol transmission and infection of respiratory viruses within a host, most notably the SARS-CoV-2 virus amidst the global COVID-19 pandemic, the importance of crosstalk between the different lung regions (i.e., extra-thoracic, conductive and respiratory), with distinct cellular makeups and physiology, are acknowledged to play an important role in the progression of the disease from the initial onset of infection. In the present Methods article, we designed and fabricated to the best of our knowledge the first multi-compartment human airway-on-chip platform to serve as a preclinical in vitro benchmark underlining regional lung crosstalk for viral infection pathways. Combining microfabrication and 3D printing techniques, our platform mimics key elements of the respiratory system spanning (i) nasal passages that serve as the alleged origin of infections, (ii) the mid-bronchial airway region and (iii) the deep acinar region, distinct with alveolated airways. Crosstalk between the three components was exemplified in various assays. First, viral-load (including SARS-CoV-2) injected into the apical partition of the nasal compartment was detected in distal bronchial and acinar components upon applying physiological airflow across the connected compartment models. Secondly, nebulized viral-like dsRNA, poly I:C aerosols were administered to the nasal apical compartment, transmitted to downstream compartments via respiratory airflows and leading to an elevation in inflammatory cytokine levels secreted by distinct epithelial cells in each respective compartment. Overall, our assays establish an in vitro methodology that supports the hypothesis for viral-laden airflow mediated transmission through the respiratory system cellular landscape. With a keen eye for broader end user applications, we share detailed methodologies for fabricating, assembling, calibrating, and using our multi-compartment platform, including open-source fabrication files. Our platform serves as an early proof-of-concept that can be readily designed and adapted to specific preclinical pulmonary research endpoints.