Reconstructing the landscape of gut microbial species across 29,000 diverse individuals.

The human gut microbiome has been linked to health and disease. Investigation of the human microbiome has largely employed 16S amplicon sequencing, with limited ability to distinguish microbes at the species level. Herein, we describe the development of Reference-based Exact Mapping (RExMap) of microbial amplicon variants that enables mapping of microbial species from standard 16S sequencing data. RExMap analysis of 16S data captures ∼75% of microbial species identified by whole-genome shotgun sequencing, despite hundreds-fold less sequencing depth. RExMap re-analysis of existing 16S data from 29,349 individuals across 16 regions from around the world reveals a detailed landscape of gut microbial species across populations and geography. Moreover, RExMap identifies a core set of fifteen gut microbes shared by humans. Core microbes are established soon after birth and closely associate with BMI across multiple independent studies. RExMap and the human microbiome dataset are presented as resources with which to explore the role of the human microbiome.

Association of Physical Activity With Bioactive Lipids and Cardiovascular Events.

BACKGROUND: To clarify the mechanisms underlying physical activity (PA)-related cardioprotection, we examined the association of PA with plasma bioactive lipids (BALs) and cardiovascular disease (CVD) events. We additionally performed genome-wide associations. METHODS: PA-bioactive lipid associations were examined in VITAL (VITamin D and OmegA-3 TriaL)-clinical translational science center (REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT01169259; N=1032) and validated in JUPITER (Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin)-NC (REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT00239681; N=589), using linear models adjusted for age, sex, race, low-density lipoprotein-cholesterol, total-C, and smoking. Significant BALs were carried over to examine associations with incident CVD in 2 nested CVD case-control studies: VITAL-CVD (741 case-control pairs) and JUPITER-CVD (415 case-control pairs; validation). RESULTS: We detected 145 PA-bioactive lipid validated associations (false discovery rate <0.1). Annotations were found for 6 of these BALs: 12,13-diHOME, 9,10-diHOME, lysoPC(15:0), oxymorphone-3b-D-glucuronide, cortisone, and oleoyl-glycerol. Genetic analysis within JUPITER-NC showed associations of 32 PA-related BALs with 22 single-nucleotide polymorphisms. From PA-related BALs, 12 are associated with CVD. CONCLUSIONS: We identified a PA-related bioactive lipidome profile out of which 12 BALs also had opposite associations with incident CVD events.

Deepfake Histologic Images for Enhancing Digital Pathology.

A pathologist's optical microscopic examination of thinly cut, stained tissue on glass slides prepared from a formalin-fixed paraffin-embedded tissue blocks is the gold standard for tissue diagnostics. In addition, the diagnostic abilities and expertise of pathologists is dependent on their direct experience with common and rarer variant morphologies. Recently, deep learning approaches have been used to successfully show a high level of accuracy for such tasks. However, obtaining expert-level annotated images is an expensive and time-consuming task, and artificially synthesized histologic images can prove greatly beneficial. In this study, we present an approach to not only generate histologic images that reproduce the diagnostic morphologic features of common disease but also provide a user ability to generate new and rare morphologies. Our approach involves developing a generative adversarial network model that synthesizes pathology images constrained by class labels. We investigated the ability of this framework in synthesizing realistic prostate and colon tissue images and assessed the utility of these images in augmenting the diagnostic ability of machine learning methods and their usability by a panel of experienced anatomic pathologists. Synthetic data generated by our framework performed similar to real data when training a deep learning model for diagnosis. Pathologists were not able to distinguish between real and synthetic images, and their analyses showed a similar level of interobserver agreement for prostate cancer grading. We extended the approach to significantly more complex images from colon biopsies and showed that the morphology of the complex microenvironment in such tissues can be reproduced. Finally, we present the ability for a user to generate deepfake histologic images using a simple markup of sematic labels.

Influence of pretreatment exposure to pediatric dental care using the "Tiny dentist" game on 4-7 years old children's pain and anxiety: a parallel randomised clinical trial.

There have been no experiments on interactive modelling through computer games, although there have been a few on modelling a pre-exposure method for managing anxiety among preschoolers. The impact of partaking in a dental simulation game prior to the dental treatment on pain and anxiety in kids aged 4 to7 years during their first appointment was studied. A total of 156 kids who required unilateral pulp therapy and preformed crowns on their mandibular primary molars were enrolled in this double-blind, randomized clinical trial. They were then randomly assigned to intervention and control groups. The intervention group engaged in the game three times/day for 07 days prior to the anticipated appointment. The Wong-Baker Faces Rating Scale (WBFRS) was used to record their pre- and post-operative pain experienced during the dental procedure. Additionally, a finger pulse oximeter was used to record heart rate (HR) at each of the six treatment phases: baseline (the first session, two weeks prior to treatment) and stages 2-6. Playing video games considerably lowered the heart rate. Playing and the treatment period interacted in a major way. On comparing the groups at every time point, the intervention group displayed lower HR during injection, tooth preparation with an air-rotor and biomechanical preparation with endodontic rotary files.The results suggest that engaging in specific dental simulation games prior to the 1st dentist visit could help preschoolers feel less anxious during routine dental operations.

Deep Neural Networks for Classification of LC-MS Spectral Peaks.

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics has emerged as a valuable tool for biological discovery, capable of assaying thousands of diverse chemical entities in a single biospecimen. Processing of nontargeted LC-MS spectral data requires identification and isolation of true spectral features from the random, false noise peaks that comprise a significant portion of total signals, using inexact peak selection algorithms and time-consuming visual inspection of data. To increase the fidelity and speed of data processing, herein we establish, optimize, and evaluate a machine learning pipeline employing deep neural networks as well as a simpler multiple logistic regression model for classification of spectral features from nontargeted LC-MS metabolomics data. Machine learning-based approaches were found to remove up to 90% of false peaks from complex nontargeted LC-MS data sets without reducing true positive signals and exhibit excellent reproducibility across multiple data sets. Application of machine learning for nontargeted LC-MS-based peak selection provides for robust and scalable peak classification and data filtering, enabling handling and processing of large scale, complex metabolomics data sets.

PSIP1/p75 promotes tumorigenicity in breast cancer cells by promoting the transcription of cell cycle genes.

Breast cancer (BC) is a highly heterogeneous disease, both at the pathological and molecular level, and several chromatin-associated proteins play crucial roles in BC initiation and progression. Here, we demonstrate the role of PSIP1 (PC4 and SF2 interacting protein)/p75 (LEDGF) in BC progression. PSIP1/p75, previously identified as a chromatin-adaptor protein, is found to be upregulated in basal-like/triple negative breast cancer (TNBC) patient samples and cell lines. Immunohistochemistry in tissue arrays showed elevated levels of PSIP1 in metastatic invasive ductal carcinoma. Survival data analyses revealed that the levels of PSIP1 showed a negative association with TNBC patient survival. Depletion of PSIP1/p75 significantly reduced the tumorigenicity and metastatic properties of TNBC cell lines while its over-expression promoted tumorigenicity. Further, gene expression studies revealed that PSIP1 regulates the expression of genes controlling cell-cycle progression, cell migration and invasion. Finally, by interacting with RNA polymerase II, PSIP1/p75 facilitates the association of RNA pol II to the promoter of cell cycle genes and thereby regulates their transcription. Our findings demonstrate an important role of PSIP1/p75 in TNBC tumorigenicity by promoting the expression of genes that control the cell cycle and tumor metastasis.

Towards Translation of Discrete Frequency Infrared Spectroscopic Imaging for Digital Histopathology of Clinical Biopsy Samples.

Fourier transform infrared (FT-IR) spectroscopic imaging has been widely tested as a tool for stainless digital histology of biomedical specimens, including for the identification of infiltration and fibrosis in endomyocardial biopsy samples to assess transplant rejection. A major barrier in clinical translation has been the slow speed of imaging. To address this need, we tested and report here the viability of using high speed discrete frequency infrared (DFIR) imaging to obtain stain-free biochemical imaging in cardiovascular samples collected from patients. Images obtained by this method were classified with high accuracy by a Bayesian classification algorithm trained on FT-IR imaging data as well as on DFIR data. A single spectral feature correlated with instances of fibrosis, as identified by the pathologist, highlights the advantage of the DFIR imaging approach for rapid detection. The speed of digital pathologic recognition was at least 16 times faster than the fastest FT-IR imaging instrument. These results indicate that a fast, on-site identification of fibrosis using IR imaging has potential for real time assistance during surgeries. Further, the work describes development and applications of supervised classifiers on DFIR imaging data, comparing classifiers developed on FT-IR and DFIR imaging modalities and identifying specific spectral features for accurate identification of fibrosis. This addresses a topic of much debate on the use of training data and cross-modality validity of IR measurements. Together, the work is a step toward addressing a clinical diagnostic need at acquisition time scales that make IR imaging technology practical for medical use.

Defined Host-Guest Chemistry on Nanocarbon for Sustained Inhibition of Cancer.

Signal transducer and activator of transcription factor 3 (STAT-3) is known to be overexpressed in cancer stem cells. Poor solubility and variable drug absorption are linked to low bioavailability and decreased efficacy. Many of the drugs regulating STAT-3 expression lack aqueous solubility; hence hindering efficient bioavailability. A theranostics nanoplatform based on luminescent carbon particles decorated with cucurbit[6]uril is introduced for enhancing the solubility of niclosamide, a STAT-3 inhibitor. The host-guest chemistry between cucurbit[6]uril and niclosamide makes the delivery of the hydrophobic drug feasible while carbon nanoparticles enhance cellular internalization. Extensive physicochemical characterizations confirm successful synthesis. Subsequently, the host-guest chemistry of niclosamide and cucurbit[6]uril is studied experimentally and computationally. In vitro assessments in human breast cancer cells indicate approximately twofold enhancement in IC50 of drug. Fourier transform infrared and fluorescence imaging demonstrate efficient cellular internalization. Furthermore, the catalytic biodegradation of the nanoplatforms occur upon exposure to human myeloperoxidase in short time. In vivo studies on athymic mice with MCF-7 xenograft indicate the size of tumor in the treatment group is half of the controls after 40 d. Immunohistochemistry corroborates the downregulation of STAT-3 phosphorylation. Overall, the host-guest chemistry on nanocarbon acts as a novel arsenal for STAT-3 inhibition.

Tunable Luminescent Carbon Nanospheres with Well-Defined Nanoscale Chemistry for Synchronized Imaging and Therapy.

In this work, we demonstrate the significance of defined surface chemistry in synthesizing luminescent carbon nanomaterials (LCN) with the capability to perform dual functions (i.e., diagnostic imaging and therapy). The surface chemistry of LCN has been tailored to achieve two different varieties: one that has a thermoresponsive polymer and aids in the controlled delivery of drugs, and the other that has fluorescence emission both in the visible and near-infrared (NIR) region and can be explored for advanced diagnostic modes. Although these particles are synthesized using simple, yet scalable hydrothermal methods, they exhibit remarkable stability, photoluminescence and biocompatibility. The photoluminescence properties of these materials are tunable through careful choice of surface-passivating agents and can be exploited for both visible and NIR imaging. Here the synthetic strategy demonstrates the possibility to incorporate a potent antimetastatic agent for inhibiting melanomas in vitro. Since both particles are Raman active, their dispersion on skin surface is reported with Raman imaging and utilizing photoluminescence, their depth penetration is analysed using fluorescence 3D imaging. Our results indicate a new generation of tunable carbon-based probes for diagnosis, therapy or both.

Extracting knowledge from chemical imaging data using computational algorithms for digital cancer diagnosis.

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.

Animal metaphors in oral pathology-A review.

Oral pathology is a challenging yet intriguing subject. By comparing pathological traits to things we typically encounter, it is possible to reduce the difficulty of memorising these qualities. Hence, the usage of various analogical methods, like the usage of metaphors, conveys likeliness or similarities between two items and can help the students. A lot of pathological features have a resemblance to animals we see on a frequent basis. Students might find comparing, examining and comprehending oral pathology easier when the information is compared with familiar animals. This article is an attempt to compile animal metaphors related to oral pathology.

Circulating dimethylguanidino valeric acid, dietary factors, and risk of coronary heart disease.

AIMS: Circulating dimethylguanidino valeric acid (DMGV) was identified as a novel metabolite related to cardiorespiratory fitness and cardiometabolic abnormalities. Circulating DMGV levels are subjective to dietary modulation; however, studies on its associations with intakes of coronary heart disease (CHD)-related foods/nutrients are limited. We investigated whether plasma DMGV was related to risk of incident CHD. We tested associations of DMGV with CHD-related dietary intakes measured by 7-day dietary records and estimated corresponding disease risk. METHODS AND RESULTS: This nested case-control study on the incidence of CHD included 1520 women (760 incident cases of fatal CHD and nonfatal myocardial infarction and 760 controls) from the Nurses' Health Study. Separately, plasma DMGV and CHD-related dietary intakes and cardiometabolic abnormalities were assessed in the Women's Lifestyle Validation Study (WLVS; n=724). Higher plasma DMGV was related to a greater risk of CHD (relative risk [RR] per 1-SD: 1.26 [95% CI: 1.13, 1.40]; P-for-linearity=0.006). Greater intakes of sodium, energy dense-foods, and processed/red meat were related to higher DMGV levels; every 1-SD intake of sodium was associated with ß 0.13 (SE 0.05; p=0.007) for DMGV-Z-scores, which corresponded to a RR of 1.031 [1.016, 1.046] for CHD. High DMGV (the top quartile, Q4) showed a significant RR of 1.60 [1.17, 2.18] after adjusting for diet and lifestyle factors; the RR further adjusting for obesity and hypertension was 1.29 [0.93, 1.79] as compared with the lowest quartile. In both cohorts, greater adiposity and adverse cardiometabolic factor status were significantly related to higher DMGV levels. CONCLUSION: Higher levels of plasma DMGV, a metabolite reflecting unfavorable CHD-related dietary intakes, were associated with an increased risk of CHD. The unfavorable association was attenuated by cardiometabolic risk factor status. Our study underscores the potential importance of plasma DMGV as an early biomarker associated with diet and the long-term risk of CHD among women.

Plasma Levels of Polyols Erythritol, Mannitol, and Sorbitol and Incident Coronary Heart Disease Among Women.

BACKGROUND AND AIMS: Erythritol, a sugar alcohol (polyol), has recently been linked to the risks of major adverse cardiovascular events. We investigated whether plasma erythritol and other polyols (mannitol/sorbitol) were associated with the risk of incident coronary heart disease (CHD). METHODS: This prospective nested case-control study included 762 incident cases of CHD and 762 controls from the Nurses' Health Study. Plasma concentrations of polyols were measured at baseline (1989-90 or 2000-02). Associations of erythritol with cardiometabolic risk factors were also analyzed in the Women's Lifestyle Validation Study (n=728; blood collected in 2010-12). RESULTS: Higher erythritol levels were related to more adverse cardiometabolic risk factor status. The relative risk (RR) for CHD per 1-SD increment was 1.15 [95% CI: 1.04, 1.28] for erythritol and 1.16 [1.05, 1.28] for mannitol/sorbitol, after adjusting for diet quality, lifestyles, and adiposity. Compared with women in the lowest quartile, those in the highest quartile (Q4) of erythritol had a RR 1.55 [1.13, 2.14] for CHD. The RR in Q4 of erythritol was 1.61 [1.15, 2.24; p=0.006] when hypertension and dyslipidemia were further added to the model; the RR was 1.21 [0.86, 1.70] after adjustment for diabetes. For mannitol/sorbitol, the RR in the Q4 was 1.42 [1.05, 1.91; p=0.022] for CHD in the multivariable-adjusted model including diabetes. CONCLUSIONS: Higher plasma erythritol and mannitol/sorbitol were related to elevated risks of CHD even after adjustment for diet, lifestyles, adiposity, and other risk factors. The unfavorable association of mannitol/sorbitol, but not erythritol, with CHD risk remained significant independently of diabetes/hyperglycemia.

The present study shows unfavorable associations of circulating erythritol and mannitol/sorbitol with long-term coronary heart disease (CHD) risk even after adjustments for overall diet quality, lifestyle factors, and several other traditional CHD risk factors among women at usual risk. In contrast to mannitol/sorbitol, the association between high erythritol levels and increased CHD risk was no longer significant upon additional inclusion of diabetes in the multivariable-adjusted model. Our findings from the two independent study populations of women without prior CHD suggest endogenous and exogenous erythritol levels are related to unfavorable cardiometabolic risk factor status.

Gut-Microbiota-Related Metabolite Phenylacetylglutamine and Risk of Incident Coronary Heart Disease among Women.

CONTEXT: Phenylacetylglutamine (PAGln) is a novel metabolite derived from gut microbial metabolism of dietary proteins, specifically phenylalanine, which may be linked to risks of adverse cardiovascular events. OBJECTIVE: We investigated whether higher plasma levels of PAGln were associated with a greater risk of incident coronary heart disease (CHD) and tested whether adherence to a plant-based diet, which characterizes habitual dietary patterns of animal and plant food intake, modified the associations. METHODS: We examined associations between plasma PAGln and risk of incident CHD over 11-16 years in a nested case-control study of 1520 women (760 incident cases and 760 controls) from the Nurses' Health Study. Separately, we analyzed relations between PAGln and dietary intakes measured through dietary records in the Women's Lifestyle Validation Study (n=725). RESULTS: Higher PAGln levels were related to a greater risk of CHD (p <0.05 for dose-response relationship). Higher PAGln was associated with greater red/processed meat intake and lower vegetable intake (p <0.05 for all). We found a significant interaction between PAGln and adherence to plant-based diet index (PDI) on CHD (Pinteraction=0.008); higher PAGln levels were associated with an increased risk of CHD (relative risk per 1 SD: 1.22 [95% CI: 1.05, 1.41]) among women with low PDI but not among those with high PDI. CONCLUSION: Higher PAGln was associated with higher risk of CHD, particularly in women with dietary patterns of eating more animal foods and fewer plant-based foods. Adherence to plant-based diets might attenuate unfavorable associations between a novel microbial metabolite and CHD risk.

Linoleoyl-lysophosphatidylcholine suppresses immune-related adverse events due to immune checkpoint blockade.

Immune related adverse events (irAEs) after immune checkpoint blockade (ICB) therapy occur in a significant proportion of cancer patients. To date, the circulating mediators of ICB-irAEs remain poorly understood. Using non-targeted mass spectrometry, here we identify the circulating bio-active lipid linoleoyl-lysophosphatidylcholine (LPC 18:2) as a modulator of ICB-irAEs. In three independent human studies of ICB treatment for solid tumor, loss of circulating LPC 18:2 preceded the development of severe irAEs across multiple organ systems. In both healthy humans and severe ICB-irAE patients, low LPC 18:2 was found to correlate with high blood neutrophilia. Reduced LPC 18:2 biosynthesis was confirmed in preclinical ICB-irAE models, and LPC 18:2 supplementation in vivo suppressed neutrophilia and tissue inflammation without impacting ICB anti-tumor response. Results indicate that circulating LPC 18:2 suppresses human ICB-irAEs, and LPC 18:2 supplementation may improve ICB outcomes by preventing severe inflammation while maintaining anti-tumor immunity.

Colon Cancer Grading Using Infrared Spectroscopic Imaging-Based Deep Learning.

Tumor grade assessment is critical to the treatment of cancers. A pathologist typically evaluates grade by examining morphologic organization in tissue using hematoxylin and eosin (H&E) stained tissue sections. Fourier transform infrared spectroscopic (FT-IR) imaging provides an alternate view of tissue in which spatially specific molecular information from unstained tissue can be utilized. Here, we examine the potential of IR imaging for grading colon cancer in biopsy samples. We used a 148-patient cohort to develop a deep learning classifier to estimate the tumor grade using IR absorption. We demonstrate that FT-IR imaging can be a viable tool to determine colorectal cancer grades, which we validated on an independent cohort of surgical resections. This work demonstrates that harnessing molecular information from FT-IR imaging and coupling it with morphometry is a potential path to develop clinically relevant grade prediction models.

INFORM: INFrared-based ORganizational Measurements of tumor and its microenvironment to predict patient survival.

The structure and organization of a tumor and its microenvironment are often associated with cancer outcomes due to spatially varying molecular composition and signaling. A persistent challenge is to use this physical and chemical spatial organization to understand cancer progression. Here, we present a high-definition infrared imaging-based organizational measurement framework (INFORM) that leverages intrinsic chemical contrast of tissue to label unique components of the tumor and its microenvironment. Using objective and automated computational methods, further, we determine organization characteristics important for prediction. We show that the tumor spatial organization assessed with this framework is predictive of overall survival in colon cancer that adds to capability from clinical variables such as stage and grade, approximately doubling the risk of death in high-risk individuals. Our results open an all-digital avenue for measuring and studying the association between tumor spatial organization and disease progression.

Infrared Spectroscopic Imaging Visualizes a Prognostic Extracellular Matrix-Related Signature in Breast Cancer.

Molecular analysis techniques such as gene expression analysis and proteomics have contributed greatly to our understanding of cancer heterogeneity. In prior studies, gene expression analysis was shown to stratify patient outcome on the basis of tumor-microenvironment associated genes. A specific gene expression profile, referred to as ECM3 (Extracellular Matrix Cluster 3), indicated poorer survival in patients with grade III tumors. In this work, we aimed to visualize the downstream effects of this gene expression profile onto the tissue, thus providing a spatial context to altered gene expression profiles. Using infrared spectroscopic imaging, we identified spectral patterns specific to the ECM3 gene expression profile, achieving a high spectral classification performance of 0.87 as measured by the area under the curve of the receiver operating characteristic curve. On a patient level, we correctly identified 20 out of 22 ECM3 group patients and 19 out of 20 non-ECM3 group patients by using this spectroscopic imaging-based classifier. By comparing pixels that were identified as ECM3 or non-ECM3 with H&E and IHC images, we were also able to observe an association between tissue morphology and the gene expression clusters, showing the ability of our method to capture broad outcome associated features from infrared images.

Multi-functionality Redefined with Colloidal Carotene Carbon Nanoparticles for Synchronized Chemical Imaging, Enriched Cellular Uptake and Therapy.

Typically, multiplexing high nanoparticle uptake, imaging, and therapy requires careful integration of three different functions of a multiscale molecular-particle assembly. Here, we present a simpler approach to multiplexing by utilizing one component of the system for multiple functions. Specifically, we successfully synthesized and characterized colloidal carotene carbon nanoparticle (C(3)-NP), in which a single functional molecule served a threefold purpose. First, the presence of carotene moieties promoted the passage of the particle through the cell membrane and into the cells. Second, the ligand acted as a potent detrimental moiety for cancer cells and, finally, the ligands produced optical contrast for robust microscopic detection in complex cellular environments. In comparative tests, C(3)-NP were found to provide effective intracellular delivery that enables both robust detection at cellular and tissue level and presents significant therapeutic potential without altering the mechanism of intracellular action of ß-carotene. Surface coating of C(3) with phospholipid was used to generate C(3)-Lipocoat nanoparticles with further improved function and biocompatibility, paving the path to eventual in vivo studies.

Computational chemical imaging for cardiovascular pathology: chemical microscopic imaging accurately determines cardiac transplant rejection.

Rejection is a common problem after cardiac transplants leading to significant number of adverse events and deaths, particularly in the first year of transplantation. The gold standard to identify rejection is endomyocardial biopsy. This technique is complex, cumbersome and requires a lot of expertise in the correct interpretation of stained biopsy sections. Traditional histopathology cannot be used actively or quickly during cardiac interventions or surgery. Our objective was to develop a stain-less approach using an emerging technology, Fourier transform infrared (FT-IR) spectroscopic imaging to identify different components of cardiac tissue by their chemical and molecular basis aided by computer recognition, rather than by visual examination using optical microscopy. We studied this technique in assessment of cardiac transplant rejection to evaluate efficacy in an example of complex cardiovascular pathology. We recorded data from human cardiac transplant patients' biopsies, used a Bayesian classification protocol and developed a visualization scheme to observe chemical differences without the need of stains or human supervision. Using receiver operating characteristic curves, we observed probabilities of detection greater than 95% for four out of five histological classes at 10% probability of false alarm at the cellular level while correctly identifying samples with the hallmarks of the immune response in all cases. The efficacy of manual examination can be significantly increased by observing the inherent biochemical changes in tissues, which enables us to achieve greater diagnostic confidence in an automated, label-free manner. We developed a computational pathology system that gives high contrast images and seems superior to traditional staining procedures. This study is a prelude to the development of real time in situ imaging systems, which can assist interventionists and surgeons actively during procedures.