Microvascular Network Remodeling in the Ischemic Mouse Brain Defined by Light Sheet Microscopy.

BACKGROUND: Until now, the analysis of microvascular networks in the reperfused ischemic brain has been limited due to tissue transparency challenges. METHODS: Using light sheet microscopy, we assessed microvascular network remodeling in the striatum from 3 hours to 56 days post-ischemia in 2 mouse models of transient middle cerebral artery occlusion lasting 20 or 40 minutes, resulting in mild ischemic brain injury or brain infarction, respectively. We also examined the effect of a clinically applicable S1P (sphingosine-1-phosphate) analog, FTY720 (fingolimod), on microvascular network remodeling. RESULTS: Over 56 days, we observed progressive microvascular degeneration in the reperfused striatum, that is, the lesion core, which was followed by robust angiogenesis after mild ischemic injury induced by 20-minute middle cerebral artery occlusion. However, more severe ischemic injury elicited by 40-minute middle cerebral artery occlusion resulted in incomplete microvascular remodeling. In both cases, microvascular networks did not return to their preischemic state but displayed a chronically altered pattern characterized by higher branching point density, shorter branches, higher unconnected branch density, and lower tortuosity, indicating enhanced network connectivity. FTY720 effectively increased microvascular length density, branching point density, and volume density in both models, indicating an angiogenic effect of this drug. CONCLUSIONS: Utilizing light sheet microscopy together with automated image analysis, we characterized microvascular remodeling in the ischemic lesion core in unprecedented detail. This technology will significantly advance our understanding of microvascular restorative processes and pave the way for novel treatment developments in the stroke field.

Slideflow: deep learning for digital histopathology with real-time whole-slide visualization.

Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches typically requires switching software libraries and reprocessing data, reducing the feasibility and practicality of experimenting with new architectures. We developed a flexible deep learning library for histopathology called Slideflow, a package which supports a broad array of deep learning methods for digital pathology and includes a fast whole-slide interface for deploying trained models. Slideflow includes unique tools for whole-slide image data processing, efficient stain normalization and augmentation, weakly-supervised whole-slide classification, uncertainty quantification, feature generation, feature space analysis, and explainability. Whole-slide image processing is highly optimized, enabling whole-slide tile extraction at 40x magnification in 2.5 s per slide. The framework-agnostic data processing pipeline enables rapid experimentation with new methods built with either Tensorflow or PyTorch, and the graphical user interface supports real-time visualization of slides, predictions, heatmaps, and feature space characteristics on a variety of hardware devices, including ARM-based devices such as the Raspberry Pi.

All-optical neuromorphic XOR and XNOR operation utilizing a photonic spiking neuron based on a passive add-drop microring resonator.

We propose a concise hardware architecture supporting efficient exclusive OR (XOR) and exclusive NOR (XNOR) operations, by employing a single photonic spiking neuron based on a passive add-drop microring resonator (ADMRR). The threshold mechanism and inhibitory dynamics of the ADMRR-based spiking neuron are numerically discussed on the basis of the coupled mode theory. It is shown that a precise XOR operation in the ADMRR-based spiking neuron can be implemented by adjusting temporal differences within the inhibitory window. Additionally, within the same framework, the XNOR function can also be carried out by accumulating the input power over time to trigger an excitatory behavior. This work presents a novel, to the best of our knowledge, and pragmatic technique for optical neuromorphic computing and information processing utilizing passive devices.

SERS Detection of Trace Carcinogenic Aromatic Amines Based on Amorphous MoO3 Monolayers.

Aromatic amines are important commercial chemicals, but their carcinogenicity poses a threat to humans and other organisms, making their rapid quantitative detection increasingly urgent. Here, amorphous MoO3 (a-MoO3) monolayers with localized surface plasmon resonance (LSPR) effect in the visible region are designed for the trace detection of carcinogenic aromatic amine molecules. The hot-electron fast decay component of a-MoO3 decreases from 301 fs to 150 fs after absorption with methyl orange (MO) molecules, indicating the plasmon-induced hot-electron transfer (PIHET) process from a-MoO3 to MO. Therefore, a-MoO3 monolayers present high SERS performance due to the synergistic effect of electromagnetic enhancement (EM) and PIHET, proposing the EM-PIHET synergistic mechanism in a-MoO3. In addition, a-MoO3 possesses higher electron delocalization and electronic state density than crystal MoO3 (c-MoO3), which is conducive to the PIHET. The limit of detection (LOD) for o-aminoazotoluene (o-AAT) is 10-9 M with good uniformity, acid resistance, and thermal stability. In this work, trace detection and identification of various carcinogenic aromatic amines based on a-MoO3 monolayers is realized, which is of great significance for reducing cancer infection rates.

Aggregating intrinsic information to enhance BCI performance through federated learning.

Insufficient data is a long-standing challenge for Brain-Computer Interface (BCI) to build a high-performance deep learning model. Though numerous research groups and institutes collect a multitude of EEG datasets for the same BCI task, sharing EEG data from multiple sites is still challenging due to the heterogeneity of devices. The significance of this challenge cannot be overstated, given the critical role of data diversity in fostering model robustness. However, existing works rarely discuss this issue, predominantly centering their attention on model training within a single dataset, often in the context of inter-subject or inter-session settings. In this work, we propose a hierarchical personalized Federated Learning EEG decoding (FLEEG) framework to surmount this challenge. This innovative framework heralds a new learning paradigm for BCI, enabling datasets with disparate data formats to collaborate in the model training process. Each client is assigned a specific dataset and trains a hierarchical personalized model to manage diverse data formats and facilitate information exchange. Meanwhile, the server coordinates the training procedure to harness knowledge gleaned from all datasets, thus elevating overall performance. The framework has been evaluated in Motor Imagery (MI) classification with nine EEG datasets collected by different devices but implementing the same MI task. Results demonstrate that the proposed framework can boost classification performance up to 8.4% by enabling knowledge sharing between multiple datasets, especially for smaller datasets. Visualization results also indicate that the proposed framework can empower the local models to put a stable focus on task-related areas, yielding better performance. To the best of our knowledge, this is the first end-to-end solution to address this important challenge.

Federated Graph Neural Networks: Overview, Techniques, and Challenges.

Graph neural networks (GNNs) have attracted extensive research attention in recent years due to their capability to progress with graph data and have been widely used in practical applications. As societies become increasingly concerned with the need for data privacy protection, GNNs face the need to adapt to this new normal. Besides, as clients in federated learning (FL) may have relationships, more powerful tools are required to utilize such implicit information to boost performance. This has led to the rapid development of the emerging research field of federated GNNs (FedGNNs). This promising interdisciplinary field is highly challenging for interested researchers to grasp. The lack of an insightful survey on this topic further exacerbates the entry difficulty. In this article, we bridge this gap by offering a comprehensive survey of this emerging field. We propose a 2-D taxonomy of the FedGNN literature: 1) the main taxonomy provides a clear perspective on the integration of GNNs and FL by analyzing how GNNs enhance FL training as well as how FL assists GNN training and 2) the auxiliary taxonomy provides a view on how FedGNNs deal with heterogeneity across FL clients. Through discussions of key ideas, challenges, and limitations of existing works, we envision future research directions that can help build more robust, explainable, efficient, fair, inductive, and comprehensive FedGNNs.

Fabrication of microfibrillated cellulose from biomass by use of carbon nitride with high nitrogen/carbon ratio.

Microfibrillated cellulose (MFC) as an attractive green bio-based material has attracted widespread attention in recent years due to its non-toxicity, degradability, excellent performance, and high aspect ratio. In this study, the g-C3N5 with a high nitrogen/carbon ratio was prepared as a catalyst through the self-polymerization of a nitrogen-rich precursor. The triazole groups at the edges of g-C3N5 were proven to exhibit strong adsorption to biomass and strong alkalinity. In a low-acidic aqueous system with g-C3N5, MFC with diameters of 100-200 nm and lengths up to 100 µm was fabricated from various biomasses within 5 min under microwave radiation. The ultimate yield of the MFC produced from viscose reached 90 %. Young's modulus of the MFC reaches 3.7 GPa. This work provides a particular method with high efficiency to prepare MFC with excellent properties from biomass by chemical method.

Evaluation of large language models as a diagnostic aid for complex medical cases.

Background: The use of large language models (LLM) has recently gained popularity in diverse areas, including answering questions posted by patients as well as medical professionals. Objective: To evaluate the performance and limitations of LLMs in providing the correct diagnosis for a complex clinical case. Design: Seventy-five consecutive clinical cases were selected from the Massachusetts General Hospital Case Records, and differential diagnoses were generated by OpenAI's GPT3.5 and 4 models. Results: The mean number of diagnoses provided by the Massachusetts General Hospital case discussants was 16.77, by GPT3.5 30 and by GPT4 15.45 (p < 0.0001). GPT4 was more frequently able to list the correct diagnosis as first (22% versus 20% with GPT3.5, p = 0.86), provide the correct diagnosis among the top three generated diagnoses (42% versus 24%, p = 0.075). GPT4 was better at providing the correct diagnosis, when the different diagnoses were classified into groups according to the medical specialty and include the correct diagnosis at any point in the differential list (68% versus 48%, p = 0.0063). GPT4 provided a differential list that was more similar to the list provided by the case discussants than GPT3.5 (Jaccard Similarity Index 0.22 versus 0.12, p = 0.001). Inclusion of the correct diagnosis in the generated differential was correlated with PubMed articles matching the diagnosis (OR 1.40, 95% CI 1.25-1.56 for GPT3.5, OR 1.25, 95% CI 1.13-1.40 for GPT4), but not with disease incidence. Conclusions and relevance: The GPT4 model was able to generate a differential diagnosis list with the correct diagnosis in approximately two thirds of cases, but the most likely diagnosis was often incorrect for both models. In its current state, this tool can at most be used as an aid to expand on potential diagnostic considerations for a case, and future LLMs should be trained which account for the discrepancy between disease incidence and availability in the literature.

Characterizing the Increase in Artificial Intelligence Content Detection in Oncology Scientific Abstracts From 2021 to 2023.

PURPOSE: Artificial intelligence (AI) models can generate scientific abstracts that are difficult to distinguish from the work of human authors. The use of AI in scientific writing and performance of AI detection tools are poorly characterized. METHODS: We extracted text from published scientific abstracts from the ASCO 2021-2023 Annual Meetings. Likelihood of AI content was evaluated by three detectors: GPTZero, Originality.ai, and Sapling. Optimal thresholds for AI content detection were selected using 100 abstracts from before 2020 as negative controls, and 100 produced by OpenAI's GPT-3 and GPT-4 models as positive controls. Logistic regression was used to evaluate the association of predicted AI content with submission year and abstract characteristics, and adjusted odds ratios (aORs) were computed. RESULTS: Fifteen thousand five hundred and fifty-three abstracts met inclusion criteria. Across detectors, abstracts submitted in 2023 were significantly more likely to contain AI content than those in 2021 (aOR range from 1.79 with Originality to 2.37 with Sapling). Online-only publication and lack of clinical trial number were consistently associated with AI content. With optimal thresholds, 99.5%, 96%, and 97% of GPT-3/4-generated abstracts were identified by GPTZero, Originality, and Sapling respectively, and no sampled abstracts from before 2020 were classified as AI generated by the GPTZero and Originality detectors. Correlation between detectors was low to moderate, with Spearman correlation coefficient ranging from 0.14 for Originality and Sapling to 0.47 for Sapling and GPTZero. CONCLUSION: There is an increasing signal of AI content in ASCO abstracts, coinciding with the growing popularity of generative AI models.

Enhanced Chemoradiotherapy for MRSA-Infected Osteomyelitis Using Immunomodulatory Polymer-Reinforced Nanotherapeutics.

The eradication of osteomyelitis caused by methicillin-resistant Staphylococcus aureus (MRSA) poses a significant challenge due to its development of biofilm-induced antibiotic resistance and impaired innate immunity, which often leads to frequent surgical failure. Here, the design, synthesis, and performance of X-ray-activated polymer-reinforced nanotherapeutics that modulate the immunological properties of infectious microenvironments to enhance chemoradiotherapy against multidrug-resistant bacterial deep-tissue infections are reported. Upon X-ray radiation, the proposed polymer-reinforced nanotherapeutic generates reactive oxygen species and reactive nitrogen species. To robustly eradicate MRSA biofilms at deep infection sites, these species can specifically bind to MRSA and penetrate biofilms for enhanced chemoradiotherapy treatment. X-ray-activated nanotherapeutics modulate the innate immunity of macrophages to prevent the recurrence of osteomyelitis. The remarkable anti-infection effects of these nanotherapeutics are validated using a rat osteomyelitis model. This study demonstrates the significant potential of a synergistic chemoradiotherapy and immunotherapy method for treating MRSA biofilm-infected osteomyelitis.

Multiple valence states of Fe boosting SERS activity of Fe3O4 nanoparticles and enabling effective SERS-MRI bimodal cancer imaging.

Developing novel nanoparticle-based bioprobes utilized in clinical settings with imaging resolutions ranging from cell to tissue levels is a major challenge for tumor diagnosis and treatment. Herein, an optimized strategy for designing a Fe3O4-based bioprobe for dual-modal cancer imaging based on surface-enhanced Raman scattering (SERS) and magnetic resonance imaging (MRI) is introduced. Excellent SERS activity of ultrasmall Fe3O4 nanoparticles (NPs) was discovered, and a 5 × 10-9 M limit of detection for crystal violet molecules was successfully obtained. The high-efficiency interfacial photon-induced charge transfer in Fe3O4 NPs was promoted by multiple electronic energy levels ascribed to the multiple valence states of Fe, which was observed using ultraviolet-visible diffuse reflectance spectroscopy. Density functional theory calculations were utilized to reveal that the narrow band gap and high electron density of states of ultrasmall Fe3O4 NPs significantly boosted the vibronic coupling resonances in the SERS system upon illumination. The subtypes of cancer cells were accurately recognized via high-resolution SERS imaging in vitro using the prepared Fe3O4-based bioprobe with high sensitivity and good specificity. Notably, Fe3O4-based bioprobes simultaneously exhibited T1 -weighted MRI contrast enhancement with an active targeting capability for tumors in vivo. To the best of our knowledge, this is the first report on the use of pure semiconductor-based SERS-MRI dual-modal nanoprobes in tumor imaging in vivo and in vitro, which has been previously realized only using semiconductor-metal complex materials. The non-metallic materials with SERS-MRI dual-modal imaging established in this report are a promising cancer diagnostic platform, which not only showed excellent performance in early tumor diagnosis but also possesses great potential for image-guided tumor treatment.

Arterial specification precedes microvascular restitution in the peri-infarct cortex that is driven by small microvessels.

Evaluation of microvascular networks was impeded until recently by the need of histological tissue sectioning, which precluded 3D analyses. Using light-sheet microscopy, we investigated microvascular network characteristics in the peri-infarct cortex of mice 3-56 days after transient middle cerebral artery occlusion. In animal subgroups, the sphingosine-1-phosphate analog FTY720 (Fingolimod) was administered starting 24 hours post-ischemia. Light-sheet microscopy revealed a striking pattern of microvascular changes in the peri-infarct cortex, that is, a loss of microvessels, which was most prominent after 7 days and followed by the reappearance of microvessels over 56 days which revealed an increased branching point density and shortened branches. Using a novel AI-based image analysis algorithm we found that the length density of microvessels expressing the arterial specification marker α-smooth muscle actin markedly increased in the peri-infarct cortex already at 7 days post-ischemia. The length and branch density of small microvessels, but not of intermediate or large microvessels increased above pre-ischemic levels within 14-56 days. FTY720 increased the length and branch density of small microvessels. This study demonstrates long-term alterations of microvascular architecture post-ischemia indicative of increased collateralization most notably of small microvessels. Light-sheet microscopy will greatly advance the assessment of microvascular responses to restorative stroke therapies.