Assessment of the potential protective effects of culture filtrate of Trichoderma harzianum to ameliorate the damaged histoarchitecture of brain in epileptic rats.

The simultaneous hyperexcitability of the neural network is the most well-known manifestation of epilepsy that causes recurrent seizures. The current study was aimed to examine any potential safety benefits of the culture filtrate of Trichoderma harzianum (ThCF) to ameliorate damaged histoarchitecture of the brain in epileptic rats by assessing seizure intensity scale and behavioral impairments and follow up the spontaneous motor seizures during status epilepticus phases in rats. Twenty-four rats were divided into four groups; control (C), epileptic (EP) valproic acid-treated epileptic (EP-VPA), and epileptic treated with T. harzianum cultured filtrate (ThCF). In addition to a seizure intensity score and behavioral tests, routine H&E and Golgi-Copsch histopathology, were used to examine the cell somas, dendrites, axons, and neural spines. ThCF treatment increased activity and recorded movements during grooming, rearing, and ambulation frequency. Brain tissues of epileptic rats exhibited detached meninges, hypercellularity, mild edema in the cortex and markedly degenerated neurons, degenerated glial cells, and microcyst formation in the hippocampus. Moreover, brains of EP-ThCF were noticed with average blood vessels, and increased dendritogenesis. The current data revealed some of negative effects of epileptogenesis brought on by seizure intensity score and retarded histopathological alterations in the hippocampus. Therefore, the study is forecasting to identify novel active components from the metabolites of T. harzianum with a crucial therapeutic role in various disorders.

Association of Epstein-Barr virus (EBV) with nasopharyngeal carcinoma: Experience from a North Indian tertiary care hospital.

INTRODUCTION: Nasopharyngeal carcinoma (NPC), arising from nasopharyngeal epithelium is caused by Epstein-Barr virus (EBV). It is common in South China, South East Asia and North East India. The aim and objectives of this study were to determine the prevalence of EBV in formalin-fixed paraffin-embedded (FFPE) tissue sections of clinically suspected NPC patients, correlate the results of polymerase chain reaction (PCR) with histopathology findings, and to determine the utility of tissue EBV DNA as a diagnostic bio-marker. MATERIALS AND METHODS: 31 FFPE tissue samples were collected from clinically suspected NPC patients from April 2018-December 2019. Histopathological diagnosis was done by examination of Hematoxylin and Eosin stained slides. Presence of EBV was detected by EBNA-1 PCR. IHC was performed using EBV Latent Membrane Protein 1. RESULTS: Of the 31 clinically suspected NPC cases, 15 (48.4 %) were histopathological confirmed NPC. Of these15, 13 (86.6 %) were non-keratinising undifferentiated NPC, and one each were keratinising NPC and non-keratinising differentiated NPC respectively. EBV EBNA1 PCR was positive in 35.5 % (11/31) of clinically suspected NPC cases. Of the 11 PCR positive cases, 9 (81.8 %) were histopathological confirmed NPC. Of the 31 clinically suspected NPC cases, IHC was indicated in 23 biopsies. Of which, 12 (52.2 %) were positive for LMP1 in the abnormal cells. Of the 12 IHC positive samples, 10 were NPC cases. CONCLUSION: EBV DNA as an indicator towards NPC among clinically suspected cases had a sensitivity of 60 % and specificity of 87.5 %. In this study, addition of EBV DNA detection by PCR from FFPE tissue sections could confirm EBV association in 20 % of cases where it was not detected by EBV LMP1 IHC, thus helped in increasing the detection of EBV positivity in NPC cases. Early diagnosis of NPC will improve the cure rate and hence reduce the morbidity and mortality rates.

PathoDuet: Foundation models for pathological slide analysis of H&E and IHC stains.

Large amounts of digitized histopathological data display a promising future for developing pathological foundation models via self-supervised learning methods. Foundation models pretrained with these methods serve as a good basis for downstream tasks. However, the gap between natural and histopathological images hinders the direct application of existing methods. In this work, we present PathoDuet, a series of pretrained models on histopathological images, and a new self-supervised learning framework in histopathology. The framework is featured by a newly-introduced pretext token and later task raisers to explicitly utilize certain relations between images, like multiple magnifications and multiple stains. Based on this, two pretext tasks, cross-scale positioning and cross-stain transferring, are designed to pretrain the model on Hematoxylin and Eosin (H&E) images and transfer the model to immunohistochemistry (IHC) images, respectively. To validate the efficacy of our models, we evaluate the performance over a wide variety of downstream tasks, including patch-level colorectal cancer subtyping and whole slide image (WSI)-level classification in H&E field, together with expression level prediction of IHC marker, tumor identification and slide-level qualitative analysis in IHC field. The experimental results show the superiority of our models over most tasks and the efficacy of proposed pretext tasks. The codes and models are available at https://github.com/openmedlab/PathoDuet.

Transfer learning may explain pigeons' ability to detect cancer in histopathology.

Pigeons' unexpected competence in learning to categorize unseen histopathological images has remained an unexplained discovery for almost a decade (Levensonet al2015PLoS One10e0141357). Could it be that knowledge transferred from their bird's-eye views of the earth's surface gleaned during flight contributes to this ability? Employing a simulation-based verification strategy, we recapitulate this biological phenomenon with a machine-learning analog. We model pigeons' visual experience during flight with the self-supervised pre-training of a deep neural network on BirdsEyeViewNet; our large-scale aerial imagery dataset. As an analog of the differential food reinforcement performed in Levensonet al's study 2015PLoS One10e0141357), we apply transfer learning from this pre-trained model to the same Hematoxylin and Eosin (H&E) histopathology and radiology images and tasks that the pigeons were trained and tested on. The study demonstrates that pre-training neural networks with bird's-eye view data results in close agreement with pigeons' performance. These results support transfer learning as a reasonable computational model of pigeon representation learning. This is further validated with six large-scale downstream classification tasks using H&E stained whole slide image datasets representing diverse cancer types.

Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy.

Introduction: Deep learning (DL) models predicting biomarker expression in images of hematoxylin and eosin (H&E)-stained tissues can improve access to multi-marker immunophenotyping, crucial for therapeutic monitoring, biomarker discovery, and personalized treatment development. Conventionally, these models are trained on ground truth cell labels derived from IHC-stained tissue sections adjacent to H&E-stained ones, which might be less accurate than labels from the same section. Although many such DL models have been developed, the impact of ground truth cell label derivation methods on their performance has not been studied. Methodology: In this study, we assess the impact of cell label derivation on H&E model performance, with CD3+ T-cells in lung cancer tissues as a proof-of-concept. We compare two Pix2Pix generative adversarial network (P2P-GAN)-based virtual staining models: one trained with cell labels obtained from the same tissue section as the H&E-stained section (the 'same-section' model) and one trained on cell labels from an adjacent tissue section (the 'serial-section' model). Results: We show that the same-section model exhibited significantly improved prediction performance compared to the 'serial-section' model. Furthermore, the same-section model outperformed the serial-section model in stratifying lung cancer patients within a public lung cancer cohort based on survival outcomes, demonstrating its potential clinical utility. Discussion: Collectively, our findings suggest that employing ground truth cell labels obtained through the same-section approach boosts immunophenotyping DL solutions.

Dapagliflozin mitigates oxidative stress, inflammatory, and histopathological markers of aging in mice.

Aging, a complex physiological process affecting all living things, is a major area of research, particularly focused on interventions to slow its progression. This study assessed the antiaging efficacy of dapagliflozin (DAPA) on various aging-related parameters in a mouse model artificially induced to age. Forty male Swiss albino mice were randomly divided into four groups of ten animals each. The control group (Group I) received normal saline. The aging model group (Group II) was administered D-galactose orally at 500mg/kg to induce aging. Following the aging induction, the positive control group received Vitamin C supplementation (Group III), while the DAPA group (Group IV) was treated with dapagliflozin. The inflammatory mediators (TNF-α and IL-1ß) showed similar patterns of change. No statistically significant difference was observed between groups III and IV. Both groups had significantly lower values compared to GII, while it was significantly higher compared to GI. Glutathione peroxidase (GSH-Px) showed no statistically significant difference between groups GIII and GIV, but it was higher in GIII compared to GII and significantly lower in GIII compared to GI. The study demonstrated that dapagliflozin exerts a beneficial impact on many indicators of aging in mice. The intervention resulted in a reduction in hypertrophy in cardiomyocytes, an enhancement in skin vitality, a decrease in the presence of inflammatory mediators, and an improvement in the efficacy of antioxidants.

Using machine learning for chemical-free histological tissue staining.

Hematoxylin and eosin staining can be hazardous, expensive, and prone to error and variability. To circumvent these issues, artificial intelligence/machine learning models such as generative adversarial networks (GANs), are being used to 'virtually' stain unstained tissue images indistinguishable from chemically stained tissue. Frameworks such as deep convolutional GANs (DCGAN) and conditional GANs (CGANs) have successfully generated highly reproducible 'stained' images. However, their utility may be limited by requiring registered, paired images which can be difficult to obtain. To avoid these dataset requirements, we attempted to use an unsupervised CycleGAN pix2pix model(5,6) to turn unpaired, unstained bright-field images into pathologist-approved digitally 'stained' images. Using formalin-fixed-paraffin-embedded liver samples, 5µm section images (20x) were obtained before and after staining to create "stained" an "unstained" datasets. Model implementation was conducted using Ubuntu 20.04.4 LTS, 32 GB RAM, Intel Core i7-9750 CPU @2.6 GHz, Nvidia GeForce RTX 2070 Mobile, Python 3.7.11 and Tensorflow 2.9.1. The CycleGAN framework utilized a u-net-based generator and discriminator from pix2pix, a CGAN. The CycleGAN used a modified loss function, cycle consistent loss that assumed unpaired images, so loss was measured twice. To our knowledge, this is the first documented application of this architecture using unpaired bright-field images. Results and suggested improvements are discussed.

Exploring Radiomics Features Based on H&E Images as Potential Biomarkers for Evaluating Muscle Atrophy: A Preliminary Study.

Radiomics features have been widely used as novel biomarkers in the diagnosis of various diseases, but whether radiomics features derived from hematoxylin and eosin (H&E) images can evaluate muscle atrophy has not been studied. Therefore, this study aims to establish a new biomarker based on H&E images using radiomics methods to quantitatively analyze H&E images, which is crucial for improving the accuracy of muscle atrophy assessment. Firstly, a weightless muscle atrophy model was established by laying macaques in bed, and H&E images of the shank muscle fibers of the control and bed rest (BR) macaques were collected. Muscle fibers were accurately segmented by designing a semi-supervised segmentation framework based on contrastive learning. Then, 77 radiomics features were extracted from the segmented muscle fibers, and a stable subset of features was selected through the LASSO method. Finally, the correlation between radiomics features and muscle atrophy was analyzed using a support vector machine (SVM) classifier. The semi-supervised segmentation results show that the proposed method had an average Spearman's and intra-class correlation coefficient (ICC) of 88% and 86% compared to manually extracted features, respectively. Radiomics analysis showed that the AUC of the muscle atrophy evaluation model based on H&E images was 96.87%. For individual features, GLSZM_SZE outperformed other features in terms of AUC (91.5%) and ACC (84.4%). In summary, the feature extraction based on the semi-supervised segmentation method is feasible and reliable for subsequent radiomics research. Texture features have greater advantages in evaluating muscle atrophy compared to other features. This study provides important biomarkers for accurate diagnosis of muscle atrophy.

Differential cytokine expression in gastric tissues highlights helicobacter pylori's role in gastritis.

Helicobacter pylori (H. pylori), known for causing gastric inflammation, gastritis and gastric cancer, prompted our study to investigate the differential expression of cytokines in gastric tissues, which is crucial for understanding H. pylori infection and its potential progression to gastric cancer. Focusing on Il-1ß, IL-6, IL-8, IL-12, IL-18, and TNF-α, we analysed gene and protein levels to differentiate between H. pylori-infected and non-infected gastritis. We utilised real-time quantitative polymerase chain reaction (RT-qPCR) for gene quantification, immunohistochemical staining, and ELISA for protein measurement. Gastric samples from patients with gastritis were divided into three groups: (1) non-gastritis (N-group) group, (2) gastritis without H. pylori infection (G-group), and (3) gastritis with H. pylori infection (GH-group), each consisting of 8 samples. Our findings revealed a statistically significant variation in cytokine expression. Generally, cytokine levels were higher in gastritis, but in H. pylori-infected gastritis, IL-1ß, IL-6, and IL-8 levels were lower compared to H. pylori-independent gastritis, while IL-12, IL-18, and TNF-α levels were higher. This distinct cytokine expression pattern in H. pylori-infected gastritis underscores a unique inflammatory response, providing deeper insights into its pathogenesis.

Nextflow pipeline for Visium and H&E data from patient-derived xenograft samples.

We designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole-slide image (WSI), optimized for patient-derived xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genome input and stand-alone image analysis. We show the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of H&E imaging features reveal similar patterns arising from the two data modalities.

DeepCG: A cell graph model for predicting prognosis in lung adenocarcinoma.

Lung cancer is the first leading cause of cancer-related death in the United States, with lung adenocarcinoma as the major subtype accounting for 40% of all cases. To improve patient survival, image-based prognostic models were developed due to the ready availability of pathological images at diagnosis. However, the application of these models is hampered by two main challenges: the lack of publicly available image datasets with high-quality survival information and the poor interpretability of conventional convolutional neural network models. Here, we integrated matched transcriptomic and H&E staining data from TCGA (The Cancer Genome Atlas) to develop an image-based prognostic model, termed Deep-learning based Cell Graph (DeepCG) model. Instead of survival data, we used a gene signature to predict patient prognostic risks, which was then used as labels for training DeepCG. Importantly, by employing graph structures to capture cell patterns, DeepCG can provide cell-level interpretation, which was more biologically relevant than previous region-level insights. We validated the prognostic values of DeepCG in independent datasets and demonstrated its ability to identify prognostically informative cells in images.

"The Design and Acceptability of a Hip Hop Themed Integrated Nutrition Math Curriculum for Minoritized 5th Grade Students Using the Multisensory Multilevel Health Education Model".

Digital technology creates new opportunities to design multisensory learning experiences. Evidence suggests that digital innovation can greatly benefit health education, including nutrition programs. The COVID-19 pandemic disrupted the education sector, forcing schools to modify standard practices from exclusively in-person delivery to online or blended learning. Digitalized curriculums became particularly useful as an Emergency Remote Teaching tool. This article focuses on developing and implementing a multimedia, multisensory, and scalable Hip-Hop Healthy Eating and Living in Schools (H.E.A.L.S.) Nutrition-Math Curriculum (NMC). NMC comprises 20 lessons-music-based multimedia resources used in the classroom or at home. Fourteen lessons represent self-directed online modules (asynchronous learning) hosted on a Learning Management System (LMS) called "Gooru." The remaining six lessons are teacher-facilitated (in person or using Zoom) review sessions (synchronous learning). The article discusses (1) the development of NMC through the lens of the Multisensory Multilevel Health Education Model (MMHEM), (2) the high acceptability of NMC evaluated using a mixed-methods design among minoritized fifth-grade students attending an after-school program, and (3) the students' completion and mastery rates of the NMC modules based on LMS data. Multimedia nutrition education programs integrated with common core curriculum content, such as NMC, may be a promising avenue for disseminating health education to minoritized children living in New York City and similar high fast-food density cities.

Clearing Properties Between Coconut Oil and Xylene in Histological Tissue Processing.

Xylene is the commonest clearing agent even though it is hazardous and costly. This study evaluated the clearing properties of coconut oil as an alternative cost-effective clearing agent for histological processes. Ten (10) prostate samples fixed in formalin were taken and each one was cut into 4 before randomly separating them into four groups (A, B, C and D). Tissues were subjected to ascending grades of alcohol for dehydration. Group A was cleared in xylene and Groups B, C, and D were cleared at varying times of 1hr 30mins, 3hrs, and 4hrs in coconut oil respectively before embedding, sectioning, and staining were carried out. Gross and histological features were compared. Results indicated a significant shrinkage in coconut oil-treated specimen compared with the xylene-treated specimen and only the tissues cleared in coconut oil for 4hrs were as rigid as the tissues cleared in xylene (p > 0.05). No significant difference was found in either of the sections when checked for cellular details and staining quality (p > 0.999). Coconut oil is an efficient substitute for xylene in prostate tissues with a minimum clearing time of 4hrs, as it is environmentally friendly and less expensive, but causes significant shrinkage to prostate tissue.

Detection of centroblast cells in H&E stained whole slide image based on object detection.

Introduction: Detection and counting of Centroblast cells (CB) in hematoxylin & eosin (H&E) stained whole slide image (WSI) is an important workflow in grading Lymphoma. Each high power field (HPF) patch of a WSI is inspected for the number of CB cells and compared with the World Health Organization (WHO) guideline that organizes lymphoma into 3 grades. Spotting and counting CBs is time-consuming and labor intensive. Moreover, there is often disagreement between different readers, and even a single reader may not be able to perform consistently due to many factors. Method: We propose an artificial intelligence system that can scan patches from a WSI and detect CBs automatically. The AI system works on the principle of object detection, where the CB is the single class of object of interest. We trained the AI model on 1,669 example instances of CBs that originate from WSI of 5 different patients. The data was split 80%/20% for training and validation respectively. Result: The best performance was from YOLOv5x6 model that used the preprocessed CB dataset achieved precision of 0.808, recall of 0.776, mAP at 0.5 IoU of 0.800 and overall mAP of 0.647. Discussion: The results show that centroblast cells can be detected in WSI with relatively high precision and recall.

Treated Primary Cutaneous Malignant Melanoma With Later Metastasis Found in Clinical Presentation of Left Axilla Lymphadenopathy: A Case Report.

This case report details the rare instance of metastatic spread of cutaneous malignant melanoma to the breast in a 50-year-old female. The patient presented with a palpable axillary mass confirmed to be metastasis despite excision and closure of the primary malignancy. The mass seen in clinical and radiological presentations presented with features of complicated differentiation from a primary breast tumor. Biopsy and staining with immunohistochemical markers S100 and Sox10 played a critical role in confirming the melanocytic origin of this metastatic lesion. Breast metastases are associated with poor prognosis, and this case emphasizes the importance of in-depth evaluations for patients with a history of malignant melanoma and the need for ongoing clinical awareness in this field.

OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification.

Early diagnosis of potentially malignant disorders, such as oral epithelial dysplasia, is the most reliable way to prevent oral cancer. Computational algorithms have been used as an auxiliary tool to aid specialists in this process. Usually, experiments are performed on private data, making it difficult to reproduce the results. There are several public datasets of histological images, but studies focused on oral dysplasia images use inaccessible datasets. This prevents the improvement of algorithms aimed at this lesion. This study introduces an annotated public dataset of oral epithelial dysplasia tissue images. The dataset includes 456 images acquired from 30 mouse tongues. The images were categorized among the lesion grades, with nuclear structures manually marked by a trained specialist and validated by a pathologist. Also, experiments were carried out in order to illustrate the potential of the proposed dataset in classification and segmentation processes commonly explored in the literature. Convolutional neural network (CNN) models for semantic and instance segmentation were employed on the images, which were pre-processed with stain normalization methods. Then, the segmented and non-segmented images were classified with CNN architectures and machine learning algorithms. The data obtained through these processes is available in the dataset. The segmentation stage showed the F1-score value of 0.83, obtained with the U-Net model using the ResNet-50 as a backbone. At the classification stage, the most expressive result was achieved with the Random Forest method, with an accuracy value of 94.22%. The results show that the segmentation contributed to the classification results, but studies are needed for the improvement of these stages of automated diagnosis. The original, gold standard, normalized, and segmented images are publicly available and may be used for the improvement of clinical applications of CAD methods on oral epithelial dysplasia tissue images.

Automatic data augmentation to improve generalization of deep learning in H&E stained histopathology.

In histopathology practice, scanners, tissue processing, staining, and image acquisition protocols vary from center to center, resulting in subtle variations in images. Vanilla convolutional neural networks are sensitive to such domain shifts. Data augmentation is a popular way to improve domain generalization. Currently, state-of-the-art domain generalization in computational pathology is achieved using a manually curated set of augmentation transforms. However, manual tuning of augmentation parameters is time-consuming and can lead to sub-optimal generalization performance. Meta-learning frameworks can provide efficient ways to find optimal training hyper-parameters, including data augmentation. In this study, we hypothesize that an automated search of augmentation hyper-parameters can provide superior generalization performance and reduce experimental optimization time. We select four state-of-the-art automatic augmentation methods from general computer vision and investigate their capacity to improve domain generalization in histopathology. We analyze their performance on data from 25 centers across two different tasks: tumor metastasis detection in lymph nodes and breast cancer tissue type classification. On tumor metastasis detection, most automatic augmentation methods achieve comparable performance to state-of-the-art manual augmentation. On breast cancer tissue type classification, the leading automatic augmentation method significantly outperforms state-of-the-art manual data augmentation.

Ex vivo confocal microscopy features of common benign lesions that mimic non-melanoma skin cancers: Towards clinical integration.

Ex vivo confocal microscope (EVCM) rapidly images freshly excised tissue at a histopathological resolution. EVCM features of keratinocyte skin cancers are well-established, but those of benign clinical mimickers remain scarce. We describe EVCM features of common benign lesions and compare them with their malignant differentials. EVCM was used to image 14 benign and 3 cancer tissues. We compared EVCM features of benign lesions with corresponding histopathology and with those of keratinocyte cancers. Key features of benign lesions were identified and differentiated from malignant lesions. Elastin and fat appeared prominent in EVCM; while koilocytes and melanin were difficult to identify. Visualization of entire epidermis was challenging due to difficulty of tissue flattening during imaging. Benign lesions can be differentiated from keratinocyte cancers with EVCM. Using EVCM, a rapid, bedside diagnosis and management of skin neoplasms is possible, especially in a remote location without a histopathology lab.

Comparing Accuracy of Helicobacter pylori Identification Using Traditional Hematoxylin and Eosin-Stained Glass Slides With Digital Whole Slide Imaging.

With advancements in the field of digital pathology, there has been a growing need to compare the diagnostic abilities of pathologists using digitized whole slide images against those when using traditional hematoxylin and eosin (H&E)-stained glass slides for primary diagnosis. One of the most common specimens received in pathology practices is an endoscopic gastric biopsy with a request to rule out Helicobacter pylori (H. pylori) infection. The current standard of care is the identification of the organisms on H&E-stained slides. Immunohistochemical or histochemical stains are used selectively. However, due to their small size (2-4 µm in length by 0.5-1 µm in width), visualization of the organisms can present a diagnostic challenge. The goal of the study was to compare the ability of pathologists to identify H. pylori on H&E slides using a digital platform against the gold standard of H&E glass slides using routine light microscopy. Diagnostic accuracy rates using glass slides vs digital slides were 81% vs 72% (P = .0142) based on H&E slides alone. When H. pylori immunohistochemical slides were provided, the diagnostic accuracy was significantly improved to comparable rates (96% glass vs 99% digital, P = 0.2199). Furthermore, differences in practice settings (academic/subspecialized vs community/general) and the duration of sign-out experience did not significantly impact the accuracy of detecting H. pylori on digital slides. We concluded that digital whole slide images, although amenable in different practice settings and teaching environments, does present some shortcomings in accuracy and precision, especially in certain circumstances and thus is not yet fully capable of completely replacing glass slide review for identification of H. pylori. We specifically recommend reviewing glass slides and/or performing ancillary stains, especially when there is a discrepancy between the degree of inflammation and the presence of microorganisms on digital images.

A multimodal pipeline for image correction and registration of mass spectrometry imaging with microscopy.

The integration of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) and histology plays a pivotal role in advancing our understanding of complex heterogeneous tissues, which provides a comprehensive description of biological tissue with both wide molecule coverage and high lateral resolution. Herein, we proposed a novel strategy for the correction and registration of MALDI MSI data with hematoxylin & eosin (H&E) staining images. To overcome the challenges of discrepancies in spatial resolution towards the unification of the two imaging modalities, a deep learning-based interpolation algorithm for MALDI MSI data was constructed, which enables spatial coherence and the following orientation matching between images. Coupled with the affine transformation (AT) and the subsequent moving least squares algorithm, the two types of images from one rat brain tissue section were aligned automatically with high accuracy. Moreover, we demonstrated the practicality of the developed pipeline by projecting it to a rat cerebral ischemia-reperfusion injury model, which would help decipher the link between molecular metabolism and pathological interpretation towards microregion. This new approach offers the chance for other types of bioimaging to boost the field of multimodal image fusion.