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In tuberculosis (TB), chest radiography (CXR) patterns are highly variable, mimicking pneumonia and many other diseases. This study aims to evaluate the efficacy of Google teachable machine, a deep neural network-based image classification tool, to develop algorithm for predicting TB probability of CXRs. The training dataset included 348 TB CXRs and 3806 normal CXRs for training TB detection. We also collected 1150 abnormal CXRs and 627 normal CXRs for training abnormality detection. For external validation, we collected 250 CXRs from our hospital. We also compared the accuracy of the algorithm to five pulmonologists and radiological reports. In external validation, the AI algorithm showed areas under the curve (AUC) of 0.951 and 0.975 in validation dataset 1 and 2. The accuracy of the pulmonologists on validation dataset 2 showed AUC range of 0.936–0.995. When abnormal CXRs other than TB were added, AUC decreased in both human readers (0.843–0.888) and AI algorithm (0.828). When combine human readers with AI algorithm, the AUC further increased to 0.862–0.885. The TB CXR AI algorithm developed by using Google teachable machine in this study is effective, with the accuracy close to experienced clinical physicians, and may be helpful for detecting tuberculosis by CXR.

Cell membranes are an intricate yet fragile interface that requires substrate support for stabilization. Upon cell death, disassembly of the cytoskeletal network deprives plasma membranes of mechanical support and leads to membrane rupture and disintegration. By assembling a network of synthetic hydrogel polymers inside the intracellular compartment using photo-activated crosslinking chemistry, we show that the fluid cell membrane can be preserved, resulting in intracellularly gelated cells with robust stability. Upon assessing several types of adherent and suspension cells over a range of hydrogel crosslinking densities, we validate retention of surface properties, membrane lipid fluidity, lipid order, and protein mobility on the gelated cells. Preservation of cell surface functions is further demonstrated with gelated antigen presenting cells, which engage with antigen-specific T lymphocytes and effectively promote cell expansion ex vivo and in vivo. The intracellular hydrogelation technique presents a versatile cell fixation approach adaptable for biomembrane studies and biomedical device construction. Cell membrane interface is mostly studied using synthetic bilayers and reconstituted cell membranes. Here the authors present a new cell fixation method in which the cytoskeleton is replaced by a synthetic hydrogel polymer network assembled inside the cell, thereby preserving the fluid membrane properties after cell death.

Derailed transmembrane receptor trafficking could be a hallmark of tumorigenesis and increased tumor invasiveness, but receptor dynamics have not been used to differentiate metastatic cancer cells from less invasive ones. Using single-particle tracking techniques, we  developed a phenotyping asssay named T ransmembrane Re ceptor D ynamics (TReD), studied the dynamics of epidermal growth factor receptor (EGFR) in seven breast epithelial cell lines and developed a phenotyping assay named T ransmembrane Re ceptor D ynamics (TReD). Here we show a clear evidence that increased EGFR diffusivity and enlarged EGFR confinement size in the plasma membrane (PM) are correlated with the enhanced metastatic potential in these cell lines. By comparing the TReD results with the gene expression profiles, we found a clear negative correlation between the EGFR diffusivities and the breast cancer luminal differentiation scores (r = −0.75). Upon the induction of epithelial-mesenchymal transition (EMT), EGFR diffusivity significantly increased for the non-tumorigenic MCF10A (99%) and the non-invasive MCF7 (56%) cells, but not for the highly metastatic MDA-MB-231 cell. We believe that the reorganization of actin filaments during EMT modified the PM structures, causing the receptor dynamics to change. TReD can thus serve as a new biophysical marker to probe the metastatic potential of cancer cells and even to monitor the transition of metastasis.

Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective activity represented in neural signals. Here we introduce a neurobiological-driven deep learning model, termed m ulti s cale neural dy namics n eural o rdinary d ifferential e quation (msDyNODE), to describe multiscale brain communications governing cognition and behavior. We demonstrate that msDyNODE successfully captures multiscale activity using both simulations and electrophysiological experiments. The msDyNODE-derived causal interactions between recording locations and scales not only aligned well with the abstraction of the hierarchical neuroanatomy of the mammalian central nervous system but also exhibited behavioral dependences. This work offers a new approach for mechanistic multiscale studies of neural processes.

Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied at the micro- to meso-scale to reveal the connections between the dynamical patterns and the behaviors, investigations of neural population dynamics are mainly limited to single-scale analysis. Our goal is to develop a cross-scale dynamical model for the collective activity of neuronal populations. Here we introduce a bio-inspired deep learning approach, termed NeuroBondGraph Network (NBGNet), to capture cross-scale dynamics that can infer and map the neural data from multiple scales. Our model not only exhibits more than an 11-fold improvement in reconstruction accuracy, but also predicts synchronous neural activity and preserves correlated low-dimensional latent dynamics. We also show that the NBGNet robustly predicts held-out data across a long time scale (2 weeks) without retraining. We further validate the effective connectivity defined from our model by demonstrating that neural connectivity during motor behaviour agrees with the established neuroanatomical hierarchy of motor control in the literature. The NBGNet approach opens the door to revealing a comprehensive understanding of brain computation, where network mechanisms of multi-scale activity are critical.

The extracellular matrix (ECM) is a dynamic and complex microenvironment that modulates cell behavior and cell fate. Changes in ECM composition and architecture have been correlated with development, differentiation, and disease progression in various pathologies, including breast cancer [ 1 ]. Studies have shown that aligned fibers drive a pro-metastatic microenvironment, promoting the transformation of mammary epithelial cells into invasive ductal carcinoma via the epithelial-to-mesenchymal transition (EMT) [ 2 ]. The impact of ECM orientation on breast cancer metabolism, however, is largely unknown. Here, we employ two non-invasive imaging techniques, fluorescence-lifetime imaging microscopy (FLIM) and intensity-based multiphoton microscopy, to assess the metabolic states of cancer cells cultured on ECM-mimicking nanofibers in a random and aligned orientation. By tracking the changes in the intrinsic fluorescence of nicotinamide adenine dinucleotide and flavin adenine dinucleotide, as well as expression levels of metastatic markers, we reveal how ECM fiber orientation alters cancer metabolism and EMT progression. Our study indicates that aligned cellular microenvironments play a key role in promoting metastatic phenotypes of breast cancer as evidenced by a more glycolytic metabolic signature on nanofiber scaffolds of aligned orientation compared to scaffolds of random orientation. This finding is particularly relevant for subsets of breast cancer marked by high levels of collagen remodeling (e.g. pregnancy associated breast cancer), and may serve as a platform for predicting clinical outcomes within these subsets [ 3 , 4 , 5 – 6 ].

Background Despite the fact that an increasing number of studies have focused on developing therapies for acute lung injury, managing acute respiratory distress syndrome (ARDS) remains a challenge in intensive care medicine. Whether the pathology of animal models with acute lung injury in prior studies differed from clinical symptoms of ARDS, resulting in questionable management for human ARDS. To evaluate precisely the therapeutic effect of transplanted stem cells or medications on acute lung injury, we developed an animal model of severe ARDS with lower lung function, capable of keeping the experimental animals survive with consistent reproducibility. Establishing this animal model could help develop the treatment of ARDS with higher efficiency. Results In this approach, we intratracheally delivered bleomycin (BLM, 5 mg/rat) into rats’ left trachea via a needle connected with polyethylene tube, and simultaneously rotated the rats to the left side by 60 degrees. Within seven days after the injury, we found that arterial blood oxygen saturation (SpO 2 ) significantly decreased to 83.7%, partial pressure of arterial oxygen (PaO 2 ) markedly reduced to 65.3 mmHg, partial pressure of arterial carbon dioxide (PaCO 2 ) amplified to 49.2 mmHg, and the respiratory rate increased over time. Morphologically, the surface of the left lung appeared uneven on Day 1, the alveoli of the left lung disappeared on Day 2, and the left lung shrank on Day 7. A histological examination revealed that considerable cell infiltration began on Day 1 and lasted until Day 7, with a larger area of cell infiltration. Serum levels of IL-5, IL-6, IFN-γ, MCP-1, MIP-2, G-CSF, and TNF-α substantially rose on Day 7. Conclusions This modified approach for BLM-induced lung injury provided a severe, stable, and one-sided (left-lobe) ARDS animal model with consistent reproducibility. The physiological symptoms observed in this severe ARDS animal model are entirely consistent with the characteristics of clinical ARDS. The establishment of this ARDS animal model could help develop treatment for ARDS.

Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study molecular states in complex cellular environment as the lifetime readings are not biased by fluorophore concentration or excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termed flimGANE ( f luorescence l ifetime im aging based on G enerative A dversarial N etwork E stimation) that can rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. We demonstrated our model is up to 2,800 times faster than the gold standard time-domain maximum likelihood estimation ( TD_MLE ) and that flimGANE provides a more accurate analysis of low-photon-count histograms in barcode identification, cellular structure visualization, Förster resonance energy transfer characterization, and metabolic state analysis in live cells. With its advantages in speed and reliability, flimGANE is particularly useful in fundamental biological research and clinical applications, where high-speed analysis is critical. In this study, Chen et al. introduced a new deep learning-based method termed flimGANE to rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. flimGANE is particularly useful in fundamental biological research and clinical applications.

Background While the triggers for the metastatic transformation of breast cancer (BC) cells remain unknown, recent evidence suggests that intrinsic cellular metabolism could be a crucial driver of migratory disposition and chemoresistance. Aiming to decipher the molecular mechanisms involved in BC cell metabolic maneuvering, we study how a ketomimetic (ketone body-rich, low glucose) nutrient medium can engineer the glycocalyx and metabolic signature of BC cells, to further maneuver their response to therapy. Methods Doxorubicin (DOX) has been used as a model chemotherapeutic in this study. Bioorthogonal imaging was used to assess the degree of sialylation of the glycocalyx along with measurements of drug-induced cytotoxicity and drug internalization. Single cell label-free metabolic imaging has been performed, coupled with measurement of cellular proliferative and migratory abilities, and MS-based metabolomic screens. Transcriptomic analysis of crucial enzymes was performed using total RNA extraction and rt-qPCR. Results We found an inverse correlation of glycocalyx sialylation with drug-induced cytotoxicity and drug internalization, where ketomimetic media enhanced sialylation and protected BC cells from DOX. These hypersialylated cells proliferated slower and migrated faster as compared to their counterparts receiving a high glucose media, while exhibiting a preference for glycolysis. These cells also showed pronounced lipid droplet accumulation coupled with an inversion in their metabolomic profile. Enzymatic removal of sialic acid moieties at the glycocalyx revealed for the first time, a direct role of sialic acids as defense guards, blocking DOX entry at the cellular membrane to curtail internalization. Interestingly, the non-cancerous mammary epithelial cells exhibited opposite trends and this differential pattern in cancer vs. normal cells was traced to its biochemical roots, i.e. the expression levels of key enzymes involved in sialylation and fatty acid synthesis. Conclusions Our findings revealed that a ketomimetic medium enhances chemoresistance and invasive disposition of BC cells via two main oncogenic pathways: hypersialylation and lipid synthesis. We propose that the crosstalk between these pathways, juxtaposed at the synthesis of the glycan precursor UDP-GlcNAc, furthers advancement of a metastatic phenotype in BC cells under ketomimetic conditions. Non-cancerous cells lack this dual defense machinery and end up being sensitized to DOX under ketomimetic conditions.

Fluorescence resonance energy transfer (FRET) reporters are commonly used in the final stages of nucleic acid amplification tests to indicate the presence of nucleic acid targets, where fluorescence is restored by nucleases that cleave the FRET reporters. However, the need for dual labelling and purification during manufacturing contributes to the high cost of FRET reporters. Here we demonstrate a low-cost silver nanocluster reporter that does not rely on FRET as the on/off switching mechanism, but rather on a cluster transformation process that leads to fluorescence color change upon nuclease digestion. Notably, a 90 nm red shift in emission is observed upon reporter cleavage, a result unattainable by a simple donor-quencher FRET reporter. Electrospray ionization–mass spectrometry results suggest that the stoichiometric change of the silver nanoclusters from Ag 13 (in the intact DNA host) to Ag 10 (in the fragments) is probably responsible for the emission colour change observed after reporter digestion. Our results demonstrate that DNA-templated silver nanocluster probes can be versatile reporters for detecting nuclease activities and provide insights into the interactions between nucleases and metallo-DNA nanomaterials. Here the authors present a non-FRET DNA-templated silver nanocluster probe that exhibits a distinct colour switch from green to red upon nuclease digestion, visible under UV excitation, offering a low-cost, effective alternative to fluorescent reporters for detecting nuclease activities.