Orbit Image Analysis: An open-source whole slide image analysis tool

SlideTiler: A dataset creator software for boosting deep learning on histological whole slide images

The introduction of deep learning caused a significant breakthrough in digital pathology. Thanks to its capability of mining hidden data patterns in digitised histological slides to resolve diagnostic tasks and extract prognostic and predictive information. However, the high performance achieved in classification tasks depends on the availability of large datasets, whose collection and preprocessing are still time-consuming processes. Therefore, strategies to make these steps more efficient are worth investigation. This work introduces SlideTiler, an open-source software with a user-friendly graphical interface. SlideTiler can manage several image preprocessing phases through an intuitive workflow that does not require specific coding skills. The software was designed to provide direct access to virtual slides, allowing custom tiling of specific regions of interest drawn by the user, tile labelling, quality assessment, and direct export to dataset directories. To illustrate the functions and the scalability of SlideTiler, a deep learning-based classifier was implemented to classify 4 different tumour histotypes available in the TCGA repository. The results demonstrate the effectiveness of SlideTiler in facilitating data preprocessing and promoting accessibility to digitised pathology images for research purposes. Considering the increasing interest in deep learning applications of digital pathology, SlideTiler has a positive impact on this field. Moreover, SlideTiler has been conceived as a dynamic tool in constant evolution, and more updated and efficient versions will be released in the future.

[18F]AlF-CBP imaging of type I collagen for non-invasive monitoring of pulmonary fibrosis in preclinical models

PURPOSE

Pulmonary fibrosis is an irreversible scar-forming condition for which there is a lack of non-invasive and specific methods for monitoring its progression and therapy efficacy. However, the disease is known to be accompanied by collagen accumulation. Here, we developed a novel positron emission tomography (PET) probe targeting type I collagen to evaluate its utility for the non-invasive assessment of pulmonary fibrosis.

METHODS

We designed a 18F-labeled PET probe ([18F]AlF-CBP) to target type I collagen and evaluated its binding affinity, specificity and stability in vitro. PET with [18F]AlF-CBP, CT, histopathology, immunofluorescence, and biochemical indice were performed to assess and quantify type I collagen levels and pulmonary fibrosis progression and treatment in murine models. Dynamic PET/CT studies of [18F]AlF-CBP were conducted to assess lung fibrosis in non-human primate models.

RESULTS

[18F]AlF-CBP was successfully prepared, and in vitro and in vivo tests showed high stability (> 95%) and type I collagen specificity (IC50 = 0.36 µM). The lungs of the fibrotic murine model showed more elevated probe uptake and retention compared to the control group, and there was a positive correlation between the radioactivity uptake signals and the degree of fibrosis (CT: R2 = 0.89, P < 0.0001; hydroxyproline levels: R2 = 0.89, P < 0.0001). PET signals also correlated well with mean lung density in non-human primate models of pulmonary fibrosis (R2 = 0.84, P < 0.0001).

CONCLUSION

[18F]AlF-CBP PET imaging is a promising non-invasive method for specific monitoring of lung fibrosis progression and therapy efficacy.

Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology

The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.

Whole cervix imaging of collagen, muscle, and cellularity in term and preterm pregnancy

Cervical softening and dilation are critical for the successful term delivery of a fetus, with premature changes associated with preterm birth. Traditional clinical measures like transvaginal ultrasound and Bishop scores fall short in predicting preterm births and elucidating the cervix's complex microstructural changes. Here, we introduce a magnetic resonance diffusion basis spectrum imaging (DBSI) technique for non-invasive, comprehensive imaging of cervical cellularity, collagen, and muscle fibers. This method is validated through ex vivo DBSI and histological analyses of specimens from total hysterectomies. Subsequently, retrospective in vivo DBSI analysis at 32 weeks of gestation in ten term deliveries and seven preterm deliveries with inflammation-related conditions shows distinct microstructural differences between the groups, alongside significant correlations with delivery timing. These results highlight DBSI's potential to improve understanding of premature cervical remodeling and aid in the evaluation of therapeutic interventions for at-risk pregnancies. Future studies will further assess DBSI's clinical applicability.

BMAL1 sex-specific effects in the neonatal mouse airway exposed to moderate hyperoxia

Supplemental O2 (hyperoxia) is a critical intervention for premature infants (<34 weeks) but consequently is associated with development of bronchial airway hyperreactivity (AHR) and asthma. Clinical practice shifted toward the use of moderate hyperoxia (<60% O2), but risk for subsequent airway disease remains. In mouse models of moderate hyperoxia, neonatal mice have increased AHR with effects on airway smooth muscle (ASM), a cell type involved in airway tone, bronchodilation, and remodeling. Understanding mechanisms by which moderate O2 during the perinatal period initiates sustained airway changes is critical to drive therapeutic advancements toward treating airway diseases. We propose that cellular clock factor BMAL1 is functionally important in developing mouse airways. In adult mice, cellular clocks target pathways highly relevant to asthma pathophysiology and Bmal1 deletion increases inflammatory response, worsens lung function, and impacts survival outcomes. Our understanding of BMAL1 in the developing lung is limited, but our previous findings show functional relevance of clocks in human fetal ASM exposed to O2. Here, we characterize Bmal1 in our established mouse neonatal hyperoxia model. Our data show that Bmal1 KO deleteriously impacts the developing lung in the context of O2 and these data highlight the importance of neonatal sex in understanding airway disease.

Tricaprylin-based drug crystalline suspension for intramuscular long-acting delivery of entecavir with alleviated local inflammation

In order to ensure prolonged pharmacokinetic profile along with local tolerability at the injection site, tricaprylin-based drug crystalline suspension (TS) was designed and its local distribution, pharmacokinetics, and inflammatory response, were evaluated with conventional aqueous suspension (AS). As model drug particles, entecavir 3-palmitate (EV-P), an ester lipidic prodrug for entecavir (EV), was employed. The EV-P-loaded TS was prepared by ultra-sonication method. Prepared TS and conventional AS exhibited comparable morphology (rod or rectangular), median diameter (2.7 and 2.6 μm), crystallinity (melting point of 160-165°C), and in vitro dissolution profile. However, in vivo performances of drug microparticles were markedly different, depending on delivery vehicle. At AS-injected site, drug aggregates of up to 500 μm were formed upon intramuscular injection, and were surrounded with inflammatory cells and fibroblastic bands. In contrast, no distinct particle aggregation and adjacent granulation was observed at TS-injected site, with >4 weeks remaining of the oily vehicle in micro-computed tomographic observation. Surprisingly, TS exhibited markedly alleviated local inflammation compared to AS, endowing markedly lessened necrosis, fibrosis thickness, inflammatory area, and macrophage infiltration. The higher initial systemic exposure was observed with TS compared to AS, but TS provided prolonged delivery of EV for 3 weeks. Therefore, we suggest that the novel TS system can be a promising tool in designing parenteral long-acting delivery, with improved local tolerability.

BiliQML: a supervised machine-learning model to quantify biliary forms from digitized whole slide liver histopathological images

The progress of research focused on cholangiocytes and the biliary tree during development and following injury is hindered by limited available quantitative methodologies. Current techniques include two-dimensional standard histological cell-counting approaches, which are rapidly performed, error prone, and lack architectural context or three-dimensional analysis of the biliary tree in opacified livers, which introduce technical issues along with minimal quantitation. The present study aims to fill these quantitative gaps with a supervised machine-learning model (BiliQML) able to quantify biliary forms in the liver of anti-keratin 19 antibody-stained whole slide images. Training utilized 5,019 researcher-labeled biliary forms, which following feature selection, and algorithm optimization, generated an F score of 0.87. Application of BiliQML on seven separate cholangiopathy models [genetic (Afp-CRE;Pkd1l1null/Fl, Alb-CRE;Rbp-jkfl/fl, and Albumin-CRE;ROSANICD), surgical (bile duct ligation), toxicological (3,5-diethoxycarbonyl-1,4-dihydrocollidine), and therapeutic (Cyp2c70-/- with ileal bile acid transporter inhibition)] allowed for a means to validate the capabilities and utility of this platform. The results from BiliQML quantification revealed biological and pathological differences across these seven diverse models, indicating a highly sensitive, robust, and scalable methodology for the quantification of distinct biliary forms. BiliQML is the first comprehensive machine-learning platform for biliary form analysis, adding much-needed morphologic context to standard immunofluorescence-based histology, and provides clinical and basic science researchers with a novel tool for the characterization of cholangiopathies.NEW & NOTEWORTHY BiliQML is the first comprehensive machine-learning platform for biliary form analysis in whole slide histopathological images. This platform provides clinical and basic science researchers with a novel tool for the improved quantification and characterization of biliary tract disorders.

Reproducibility and prognostic ability of chronicity parameters in kidney biopsy - Comprehensive evaluation comparing microscopy and artificial intelligence in digital pathology

INTRODUCTION

Semi-quantitative scoring of various parameters in renal biopsy is accepted as an important tool to assess disease activity and prognostication. There are concerns on the impact of interobserver variability in its prognostic utility, generating a need for computerized quantification.

METHODS

We studied 94 patients with renal biopsies, 45 with native diseases and 49 transplant patients with index biopsies for Polyomavirus nephropathy. Chronicity scores were evaluated using two methods. A standard definition diagram was agreed after international consultation and four renal pathologists scored each parameter in a double-blinded manner. Interstitial fibrosis (IF) score was assessed with five different computerized and AI-based algorithms on trichrome and PAS stains.

RESULTS

There was strong prognostic correlation with renal function and graft outcome at a median follow-up ranging from 24 to 42 months respectively, independent of moderate concordance for pathologists scores. IF scores with two of the computerized algorithms showed significant correlation with estimated glomerular filtration rate (eGFR) at biopsy but not at the end of follow-up. There was poor concordance for AI based platforms.

CONCLUSION

Chronicity scores are robust prognostic tools despite interobserver reproducibility. AI-algorithms have absolute precision but are limited by significant variation when different hardware and software algorithms are used for quantification.

High thrombin-activatable fibrinolysis inhibitor expression in thrombi from stroke patients in elevated estrogen states

BACKGROUND

The risk of acute ischemic stroke (AIS) associated with high estrogen states, including pregnant patients and those using oral contraceptives, has been well documented. We described the histological composition of thrombi collected in these cases.

METHODS

From a prospective tissue registry (STRIP registry) of thrombi retrieved during mechanical thrombectomy for AIS, we identified 5 patients with high estrogen states: 1 post-partum patient, 1 undergoing hormone replacement therapy and 3 consuming oral contraceptive pills. Five male control patients were randomly chosen matched by age. Immunohistochemistry for CD42b (platelets), von Willebrand factor (vWF), thrombin-activatable fibrinolysis inhibitor (TAFI), fibrinogen and plasminogen activator inhibitor-1 (PAI-1) was performed. Expression was quantified using Orbit Image Software. Student's t-test was performed as appropriate.

RESULTS

Mean TAFI content for the high estrogen state group was higher than controls (25.6 ± 11.9% versus 9.3 ± 9.0%, p = 0.043*). Mean platelet content for the high estrogen state group was lower than controls (41.7 ± 10.6% versus 61.8 ± 12.9%, p = 0.029*). No significant difference was found in vWF, fibrinogen and PAI-1 expression. Mean time to recanalize was higher in the high estrogen state group compared to the control group (57.8 ± 27.6 versus 22.6 ± 11.4 min, p = 0.0351*). The mean number of passes required was higher in the high estrogen group compared to controls 4.6 versus 1.2, p = 0.0261*).

CONCLUSIONS

TAFI expression, a powerful driver of thrombosis, was significantly higher in stroke thrombi among patients with high estrogen states compared to controls.

Investigating the Role of Brain Natriuretic Peptide (BNP) and N-Terminal-proBNP in Thrombosis and Acute Ischemic Stroke Etiology

The need for biomarkers for acute ischemic stroke (AIS) to understand the mechanisms implicated in pathological clot formation is critical. The levels of the brain natriuretic peptides known as brain natriuretic peptide (BNP) and NT-proBNP have been shown to be increased in patients suffering from heart failure and other heart conditions. We measured their expression in AIS clots of cardioembolic (CE) and large artery atherosclerosis (LAA) etiology, evaluating their location inside the clots, aiming to uncover their possible role in thrombosis. We analyzed 80 thrombi from 80 AIS patients in the RESTORE registry of AIS clots, 40 of which were of CE and 40 of LAA etiology. The localization of BNP and NT-BNP, quantified using immunohistochemistry and immunofluorescence, in AIS-associated white blood cell subtypes was also investigated. We found a statistically significant positive correlation between BNP and NT-proBNP expression levels (Spearman's rho = 0.668 p < 0.0001 *). We did not observe any statistically significant difference between LAA and CE clots in BNP expression (0.66 [0.13-3.54]% vs. 0.53 [0.14-3.07]%, p = 0.923) or in NT-proBNP expression (0.29 [0.11-0.58]% vs. 0.18 [0.05-0.51]%, p = 0.119), although there was a trend of higher NT-proBNP expression in the LAA clots. It was noticeable that BNP was distributed throughout the thrombus and especially within platelet-rich regions. However, NT-proBNP colocalized with neutrophils, macrophages, and T-lymphocytes, suggesting its association with the thrombo-inflammatory process.

Electrical impedance measurements can identify red blood cell-rich content in acute ischemic stroke clots ex vivo associated with first-pass successful recanalization

Background

Electrochemical impedance spectroscopy can determine characteristics such as cell density, size, and shape. The development of an electrical impedance-based medical device to estimate acute ischemic stroke (AIS) clot characteristics could improve stroke patient outcomes by informing clinical decision making.

Objectives

To assess how well electrical impedance combined with machine learning identified red blood cell (RBC)-rich composition of AIS clots ex vivo, which is associated with a successfully modified first-pass effect.

Methods

A total of 253 clots from 231 patients who underwent thrombectomy in 5 hospitals in France, Japan, Serbia, and Spain between February 2021 and October 2023 were analyzed in the Clotbase International Registry. Electrical impedance measurements were taken following clot retrieval by thrombectomy, followed by Martius Scarlet Blue staining. The clot components were quantified via Orbit Image Analysis, and RBC percentages were correlated with the RBC estimations made by the electrical impedance machine learning model.

Results

Quantification by Martius Scarlet Blue staining identified RBCs as the major component in clots (RBCs, 37.6%; white blood cells, 5.7%; fibrin, 25.5%; platelets/other, 30.3%; and collagen, 1%). The impedance-based RBC estimation correlated well with the RBC content determined by histology, with a slope of 0.9 and Spearman's correlation of r = 0.7. Clots removed in 1 pass were significantly richer in RBCs and clots with successful recanalization in 1 pass (modified first-pass effect) were richer in RBCs as assessed using histology and impedance signature.

Conclusion

Electrical impedance estimations of RBC content in AIS clots are consistent with histologic findings and may have potential for clinically relevant parameters.

Quantification of fibrosis extend and airspace availability in lung: A semi-automatic ImageJ/Fiji toolbox

The evaluation of the structural integrity of mechanically dynamic organs such as lungs is critical for the diagnosis of numerous pathologies and the development of therapies. This task is classically performed by histology experts in a qualitative or semi-quantitative manner. Automatic digital image processing methods appeared in the last decades, and although immensely powerful, tools are highly specialized and lack the versatility required in various experimental designs. Here, a set of scripts for the image processing software ImageJ/Fiji to easily quantify fibrosis extend and alveolar airspace availability in Sirius Red or Masson's trichrome stained samples is presented. The toolbox consists in thirteen modules: sample detection, particles filtration (automatic and manual), border definition, air ducts identification, air ducts walls definition, parenchyma extraction, MT-staining specific pre-processing, fibrosis detection, fibrosis particles filtration, airspace detection, and visualizations (tissue only or tissue and airspace). While the process is largely automated, critical parameters are accessible to the user for increased adaptability. The modularity of the protocol allows for its adjustment to alternative experimental settings. Fibrosis and airspace can be combined as an evaluation of the structural integrity of the organ. All settings and intermediate states are saved to ensure reproducibility. These new analysis scripts allow for a rapid quantification of fibrosis and airspace in a large variety of experimental settings.

White blood cell subtypes and neutrophil extracellular traps content as biomarkers for stroke etiology in acute ischemic stroke clots retrieved by mechanical thrombectomy

BACKGROUND

Lymphocytes, macrophages, neutrophils, and neutrophil extracellular traps (NETs) associate with stroke risk factors and form a thrombus through different mechanisms. We investigated the total WBCs, WBC subtypes and NETs composition in acute ischemic stroke (AIS) clots to identify possible etiological differences that could help us further understand the process of thrombosis that leads to AIS.

METHODS

AIS clots from 100 cases each of atherothrombotic (AT), cardioembolic (CE) and cryptogenic stroke etiology were collected per-pass as part of the CÚRAM RESTORE registry of AIS clots. Martius Scarlet Blue stain was used to identify the main histological components of the clots. Immunohistochemical staining was used to identify neutrophils, lymphocytes, macrophages, and NETs patterns. The cellular and histological components were quantified using Orbit Image Analysis software.

RESULTS

AT clots were larger, with more red blood cells and fewer WBCs than CE clots. AT clots had more lymphocytes and cryptogenic clots had fewer macrophages than other etiologies. Most significantly, CE clots showed higher expression of neutrophils and extracellular web-like NETs compared to AT and cryptogenic clots. There was also a significantly higher distribution of web-like NETs around the periphery of the CE clots while a mixed distribution was observed in AT clots.

CONCLUSION

The difference in neutrophil and NETs expression in clots from different etiologies may provide insight into the mechanism of clot formation.

Exogenous hydrogen sulfide attenuates hyperoxia effects on neonatal mouse airways

Supplemental O2 remains a necessary intervention for many premature infants (<34 wk gestation). Even moderate hyperoxia (<60% O2) poses a risk for subsequent airway disease, thereby predisposing premature infants to pediatric asthma involving chronic inflammation, airway hyperresponsiveness (AHR), airway remodeling, and airflow obstruction. Moderate hyperoxia promotes AHR via effects on airway smooth muscle (ASM), a cell type that also contributes to impaired bronchodilation and remodeling (proliferation, altered extracellular matrix). Understanding mechanisms by which O2 initiates long-term airway changes in prematurity is critical for therapeutic advancements for wheezing disorders and asthma in babies and children. Immature or dysfunctional antioxidant systems in the underdeveloped lungs of premature infants thereby heightens susceptibility to oxidative stress from O2. The novel gasotransmitter hydrogen sulfide (H2S) is involved in antioxidant defense and has vasodilatory effects with oxidative stress. We previously showed that exogenous H2S exhibits bronchodilatory effects in human developing airway in the context of hyperoxia exposure. Here, we proposed that exogenous H2S would attenuate effects of O2 on airway contractility, thickness, and remodeling in mice exposed to hyperoxia during the neonatal period. Using functional [flexiVent; precision-cut lung slices (PCLS)] and structural (histology; immunofluorescence) analyses, we show that H2S donors mitigate the effects of O2 on developing airway structure and function, with moderate O2 and H2S effects on developing mouse airways showing a sex difference. Our study demonstrates the potential applicability of low-dose H2S toward alleviating the detrimental effects of hyperoxia on the premature lung.NEW & NOTEWORTHY Chronic airway disease is a short- and long-term consequence of premature birth. Understanding effects of O2 exposure during the perinatal period is key to identify targetable mechanisms that initiate and sustain adverse airway changes. Our findings show a beneficial effect of exogenous H2S on developing mouse airway structure and function with notable sex differences. H2S donors alleviate effects of O2 on airway hyperreactivity, contractility, airway smooth muscle thickness, and extracellular matrix deposition.

Multimodal CT imaging of ischemic stroke thrombi identifies scale-invariant radiomic features that reflect clot biology

BACKGROUND

Biological interpretability of ischemic stroke clot imaging remains challenging.

OBJECTIVE

To carry out paired CT/micro-CT imaging of ischemic stroke clots retrieved by thrombectomy with the aim of identifying interpretable image features that are correlated among pretreatment image modalities and post-treatment histopathology.

METHODS

We performed multimodal CT imaging and histology for 10 stroke clots retrieved by mechanical thrombectomy. Clots were manually segmented from co-registered, pretreatment CT angiography (CTA) and non-contrast CT (NCCT). For the same cases, retrieved clots were iodine-stained, and imaged with a ScanCo micro-CT 100 (4.9 µm resolution). Afterwards, clots were subjected to histological processing (hematoxylin and eosin staining) and whole slide scanned (40X). Clot radiomic features (RFs) (n=93 per modality, 279 total) were extracted using PyRadiomics and histological composition was computed using Orbit Image Analysis. Correlation analysis was used to test associations between micro-CT and CTA (or NCCT) RFs as well as between RFs and histological composition. Statistical significance was considered at R≥0.65 and q<0.05.

RESULTS

From paired RF correlation analysis, we identified 23 scale-invariant RFs with significant correlation between micro-CT and CTA (18), and micro-CT and NCCT (5). Correlation of unpaired RFs identified 377 positively and 36 negatively correlated RFs between micro-CT and CTA, and 168 positively and 41 negatively correlated RFs between micro-CT and NCCT. Scale-invariant RFs computed from CTA and NCCT demonstrated significant correlation with red blood cell and fibrin-platelet components, while micro-CT RFs were found to be correlated with white blood cell percent composition.

CONCLUSION

Multimodal CT, radiomic, and histological analysis of stroke clots can help to bridge the gap between pretreatment imaging and clot pathobiology.

Machine learning implementation strategy in imaging and impedance flow cytometry

Imaging and impedance flow cytometry is a label-free technique that has shown promise as a potential replacement for standard flow cytometry. This is due to its ability to provide rich information and archive high-throughput analysis. Recently, significant efforts have been made to leverage machine learning for processing the abundant data generated by those techniques, enabling rapid and accurate analysis. Harnessing the power of machine learning, imaging and impedance flow cytometry has demonstrated its capability to address various complex phenotyping scenarios. Herein, we present a comprehensive overview of the detailed strategies for implementing machine learning in imaging and impedance flow cytometry. We initiate the discussion by outlining the commonly employed setup to acquire the data (i.e., image or signal) from the cell. Subsequently, we delve into the necessary processes for extracting features from the acquired image or signal data. Finally, we discuss how these features can be utilized for cell phenotyping through the application of machine learning algorithms. Furthermore, we discuss the existing challenges and provide insights for future perspectives of intelligent imaging and impedance flow cytometry.

Functional α7 nicotinic receptors in human airway smooth muscle increase intracellular calcium concentration and contractility in asthmatics

Although nicotinic acetylcholine receptors (nAChRs) are commonly associated with neurons in the brain and periphery, recent data indicate that they are also expressed in non-neuronal tissues. We recently found the alpha7 (α7nAChR) subunit is highly expressed in human airway smooth muscle (hASM) with substantial increase in asthmatics, but their functionality remains unknown. We investigated the location and functional role of α7nAChRs in hASM cells from normal versus mild-moderate asthmatic patients. Immunostaining and protein analyses showed α7nAChR in the plasma membrane including in asthmatics. In asthmatic hASM, patch-clamp recordings revealed significantly higher functional homomeric α7nAChR channels. Real-time fluorescence imaging showed nicotine, via α7nAChR, increases intracellular Ca2+ ([Ca2+]i) independent of ACh effects, particularly in asthmatic hASM, while cellular traction force microscopy showed nicotine-induced contractility including in asthmatics. These results indicate functional homomeric and heteromeric nAChRs that are increased in asthmatic hASM, with pharmacology that likely differ owing to different subunit interfaces that form the orthosteric sites. nAChRs may represent a novel target in alleviating airway hyperresponsiveness in asthma.NEW & NOTEWORTHY Cigarette smoking and vaping exacerbate asthma. Understanding the mechanisms of nicotine effects in asthmatic airways is important. This study demonstrates that functional alpha7 nicotinic acetylcholine receptors (α7nAChRs) are expressed in human airway smooth muscle, including from asthmatics, and enhance intracellular calcium and contractility. Although a7nAChRs are associated with neuronal pathways, α7nAChR in smooth muscle suggests inhaled nicotine (e.g., vaping) can directly influence airway contractility. Targeting α7nAChR may represent a novel approach to alleviating airway hyperresponsiveness in asthma.

Motivation for using data-driven algorithms in research: A review of machine learning solutions for image analysis of micrographs in neuroscience

Machine learning is a powerful tool that is increasingly being used in many research areas, including neuroscience. The recent development of new algorithms and network architectures, especially in the field of deep learning, has made machine learning models more reliable and accurate and useful for the biomedical research sector. By minimizing the effort necessary to extract valuable features from datasets, they can be used to find trends in data automatically and make predictions about future data, thereby improving the reproducibility and efficiency of research. One application is the automatic evaluation of micrograph images, which is of great value in neuroscience research. While the development of novel models has enabled numerous new research applications, the barrier to use these new algorithms has also decreased by the integration of deep learning models into known applications such as microscopy image viewers. For researchers unfamiliar with machine learning algorithms, the steep learning curve can hinder the successful implementation of these methods into their workflows. This review explores the use of machine learning in neuroscience, including its potential applications and limitations, and provides some guidance on how to select a fitting framework to use in real-life research projects.

Fibroblast Activation Protein Expression in Sarcomas

Objectives

Fibroblast activation protein alpha (FAP) is highly expressed by cancer-associated fibroblasts in multiple epithelial cancers. The aim of this study was to characterize FAP expression in sarcomas to explore its potential utility as a diagnostic and therapeutic target and prognostic biomarker in sarcomas.

Methods

Available tissue samples from patients with bone or soft tissue tumors were identified at the University of California, Los Angeles. FAP expression was evaluated via immunohistochemistry (IHC) in tumor samples (n = 63), adjacent normal tissues (n = 30), and positive controls (n = 2) using semiquantitative systems for intensity (0 = negative; 1 = weak; 2 = moderate; and 3 = strong) and density (none, <25%, 25-75%; >75%) in stromal and tumor/nonstromal cells and using a qualitative overall score (not detected, low, medium, and high). Additionally, RNA sequencing data in publicly available databases were utilized to compare FAP expression in samples (n = 10,626) from various cancer types and evaluate the association between FAP expression and overall survival (OS) in sarcoma (n = 168).

Results

The majority of tumor samples had FAP IHC intensity scores ≥2 and density scores ≥25% for stromal cells (77.7%) and tumor cells (50.7%). All desmoid fibromatosis, myxofibrosarcoma, solitary fibrous tumor, and undifferentiated pleomorphic sarcoma samples had medium or high FAP overall scores. Sarcomas were among cancer types with the highest mean FAP expression by RNA sequencing. There was no significant difference in OS in patients with sarcoma with low versus high FAP expression.

Conclusion

The majority of the sarcoma samples showed FAP expression by both stromal and tumor/nonstromal cells. Further investigation of FAP as a potential diagnostic and therapeutic target in sarcomas is warranted.

A review of open-source image analysis tools for mammalian cell culture: algorithms, features and implementations

Cell culture is undeniably important for multiple scientific applications, including pharmaceuticals, transplants, and cosmetics. However, cell culture involves multiple manual steps, such as regularly analyzing cell images for their health and morphology. Computer scientists have developed algorithms to automate cell imaging analysis, but they are not widely adopted by biologists, especially those lacking an interactive platform. To address the issue, we compile and review existing open-source cell image processing tools that provide interactive interfaces for management and prediction tasks. We highlight the prediction tools that can detect, segment, and track different mammalian cell morphologies across various image modalities and present a comparison of algorithms and unique features of these tools, whether they work locally or in the cloud. This would guide non-experts to determine which is best suited for their purposes and, developers to acknowledge what is worth further expansion. In addition, we provide a general discussion on potential implementations of the tools for a more extensive scope, which guides the reader to not restrict them to prediction tasks only. Finally, we conclude the article by stating new considerations for the development of interactive cell imaging tools and suggesting new directions for future research.

Identifying ex vivo acute ischemic stroke thrombus composition using electrochemical impedance spectroscopy

BACKGROUND

Intra-procedural characterization of stroke thromboemboli might guide mechanical thrombectomy (MT) device choice to improve recanalization rates. Electrochemical impedance spectroscopy (EIS) has been used to characterize various biological tissues in real time but has not been used in thrombus.

OBJECTIVE

To perform a feasibility study of EIS analysis of thrombi retrieved by MT to evaluate: (1) the ability of EIS and machine learning to predict red blood cell (RBC) percentage content of thrombi and (2) to classify the thrombi as "RBC-rich" or "RBC-poor" based on a range of cutoff values of RBC.

METHODS

ClotbasePilot was a multicentric, international, prospective feasibility study. Retrieved thrombi underwent histological analysis to identify proportions of RBC and other components. EIS results were analyzed with machine learning. Linear regression was used to evaluate the correlation between the histology and EIS. Sensitivity and specificity of the model to classify the thrombus as RBC-rich or RBC-poor were also evaluated.

RESULTS

Among 514 MT,179 thrombi were included for EIS and histological analysis. The mean composition in RBC of the thrombi was 36% ± 24. Good correlation between the impedance-based prediction and histology was achieved (slope of 0.9, R²  =  0.53, Pearson coefficient  =  0.72). Depending on the chosen cutoff, ranging from 20 to 60% of RBC, the calculated sensitivity for classification of thrombi ranged from 77 to 85% and the specificity from 72 to 88%.

CONCLUSION

Combination of EIS and machine learning can reliably predict the RBC composition of retrieved ex vivo AIS thrombi and then classify them into groups according to their RBC composition with good sensitivity and specificity.

Histologically interpretable clot radiomic features predict treatment outcomes of mechanical thrombectomy for ischemic stroke

PURPOSE

Radiomics features (RFs) extracted from CT images may provide valuable information on the biological structure of ischemic stroke blood clots and mechanical thrombectomy outcome. Here, we aimed to identify RFs predictive of thrombectomy outcomes and use clot histomics to explore the biology and structure related to these RFs.

METHODS

We extracted 293 RFs from co-registered non-contrast CT and CTA. RFs predictive of revascularization outcomes defined by first-pass effect (FPE, near to complete clot removal in one thrombectomy pass), were selected. We then trained and cross-validated a balanced logistic regression model fivefold, to assess the RFs in outcome prediction. On a subset of cases, we performed digital histopathology on the clots and computed 227 histomic features from their whole slide images as a means to interpret the biology behind significant RF.

RESULTS

We identified 6 significantly-associated RFs. RFs reflective of continuity in lower intensities, scattered higher intensities, and intensities with abrupt changes in texture were associated with successful revascularization outcome. For FPE prediction, the multi-variate model had high performance, with AUC = 0.832 ± 0.031 and accuracy = 0.760 ± 0.059 in training, and AUC = 0.787 ± 0.115 and accuracy = 0.787 ± 0.127 in cross-validation testing. Each of the 6 RFs was related to clot component organization in terms of red blood cell and fibrin/platelet distribution. Clots with more diversity of components, with varying sizes of red blood cells and fibrin/platelet regions in the section, were associated with RFs predictive of FPE.

CONCLUSION

Upon future validation in larger datasets, clot RFs on CT imaging are potential candidate markers for FPE prediction.

Mouse strain-dependent variation in metabolic associated fatty liver disease (MAFLD): a comprehensive resource tool for pre-clinical studies

Non-alcoholic steatohepatitis (NASH), characterized as the joint presence of steatosis, hepatocellular ballooning and lobular inflammation, and liver fibrosis are strong contributors to liver-related and overall mortality. Despite the high global prevalence of NASH and the substantial healthcare burden, there are currently no FDA-approved therapies for preventing or reversing NASH and/or liver fibrosis. Importantly, despite nearly 200 pharmacotherapies in different phases of pre-clinical and clinical assessment, most therapeutic approaches that succeed from pre-clinical rodent models to the clinical stage fail in subsequent Phase I-III trials. In this respect, one major weakness is the lack of adequate mouse models of NASH that also show metabolic comorbidities commonly observed in NASH patients, including obesity, type 2 diabetes and dyslipidaemia. This study provides an in-depth comparison of NASH pathology and deep metabolic profiling in eight common inbred mouse strains (A/J, BALB/c, C3H/HeJ, C57BL/6J, CBA/CaH, DBA/2J, FVB/N and NOD/ShiLtJ) fed a western-style diet enriched in fat, sucrose, fructose and cholesterol for eight months. Combined analysis of histopathology and hepatic lipid metabolism, as well as measures of obesity, glycaemic control and insulin sensitivity, dyslipidaemia, adipose tissue lipolysis, systemic inflammation and whole-body energy metabolism points to the FVB/N mouse strain as the most adequate diet-induced mouse model for the recapitulation of metabolic (dysfunction) associated fatty liver disease (MAFLD) and NASH. With efforts in the pharmaceutical industry now focussed on developing multi-faceted therapies; that is, therapies that improve NASH and/or liver fibrosis, and concomitantly treat other metabolic comorbidities, this mouse model is ideally suited for such pre-clinical use.

Lung cancer-kidney cross talk induces kidney injury, interstitial fibrosis, and enhances cisplatin-induced nephrotoxicity

Patients with cancer represent a unique patient population with increased susceptibility to kidney disease. Drug-induced acute kidney injury (AKI) in patients with cancer is a common problem. Cisplatin is a highly effective treatment used in many solid-organ cancers and causes AKI in 30% of patients, increasing the risk of chronic kidney disease development. Most preclinical cisplatin toxicity studies have been completed in mice without cancer. We believe that the physiology of patients with cancer is not adequately represented in preclinical models, and the objective of this study was to determine how lung cancer will alter the nephrotoxicity of cisplatin. A genetically engineered mouse model and a syngeneic xenograft model of lung cancer were used. Mice were divided into the following four groups: 1) noncancer/vehicle, 2) noncancer/cisplatin, 3) cancer/vehicle, and 4) cancer/cisplatin. Mice were administered cisplatin via intraperitoneal injection once a week for 4 wk. Animals were euthanized 72 h following their final cisplatin injection. Mice with lung cancer had increased renal toxicity, injury, and fibrosis following repeated low doses of cisplatin. In addition, lung cancer alone induced kidney injury and fibrosis in the kidney before cisplatin treatment. In conclusion, this is the first study that we are aware of that assesses the impact of cancer on the kidney in conjunction with the nephrotoxicity of cisplatin. We believe that cancer is providing the first hit to the kidney and the subsequent damage from repeated doses of cisplatin becomes unsurmountable, leading to AKI and progression to chronic kidney disease.NEW & NOTEWORTHY Patients with cancer have impaired kidney function and increased susceptibility to nephrotoxic agents. Cisplatin is a commonly used chemotherapeutic with nephrotoxicity as the dose-limiting side effect. Cisplatin nephrotoxicity is almost exclusively studied in mice without cancer. Our current preclinical models do not adequately represent the complexity of patients with cancer. This study demonstrates increased renal toxicity, injury, and fibrosis in mice with lung cancer, which is exacerbated with cisplatin treatment. These results highlight the necessity of using preclinical models that more accurately capture the altered physiology of patients with cancer treated with cisplatin.

GUSignal: An Informatics Tool to Analyze Glucuronidase Gene Expression in Arabidopsis Thaliana Roots

The uidA gene codifies for a glucuronidase (GUS) enzyme which has been used as a biotechnological tool during the last years. When uidA gene is fused to a gene's promotor region, it is possible to evaluate the activity of this one in response to a stimulus. Arabidopsis thaliana has served as the biological platform to elucidate molecular and regulatory signaling responses in plants. Transgenic lines of A. thaliana, tagged with the uidA gene, have allowed explaining how plants modify their hormonal pathways depending on the environmental conditions. Although the information extracted from microscopic images of these transgenic plants is often qualitative and in many publications is not subjected to quantification, in this paper we report the development of an informatics tool focused on computer vision for processing and analysis of digital images in order to analyze the expression of the GUS signal in A. thaliana roots, which is strongly correlated with the intensity of the grayscale images. This means that the presence of the GUS-induced color indicates where the gene has been actively expressed, such as our statistical analysis has demonstrated after treatment of A. thaliana DR5::GUS with naphtalen-acetic acid (0.0001 mM and 1 mM). GUSignal is a free informatics tool that aims to be fast and systematic during the image analysis since it executes specific and ordered instructions, to offer a segmented analysis by areas or regions of interest, providing quantitative results of the image intensity levels.

SWI/SNF chromatin remodeling subunit Smarca4/BRG1 is essential for female fertility†

Mammalian folliculogenesis is a complex process that involves the regulation of chromatin structure for gene expression and oocyte meiotic resumption. The SWI/SNF complex is a chromatin remodeler using either Brahma-regulated gene 1 (BRG1) or BRM (encoded by Smarca4 and Smarca2, respectively) as its catalytic subunit. SMARCA4 loss of expression is associated with a rare type of ovarian cancer; however, its function during folliculogenesis remains poorly understood. In this study, we describe the phenotype of BRG1 mutant mice to better understand its role in female fertility. Although no tumor emerged from BRG1 mutant mice, conditional depletion of Brg1 in the granulosa cells (GCs) of Brg1fl/fl;Amhr2-Cre mice caused sterility, whereas conditional depletion of Brg1 in the oocytes of Brg1fl/fl;Gdf9-Cre mice resulted in subfertility. Recovery of cumulus-oocyte complexes after natural mating or superovulation showed no significant difference in the Brg1fl/fl;Amhr2-Cre mutant mice and significantly fewer oocytes in the Brg1fl/fl;Gdf9-Cre mutant mice compared with controls, which may account for the subfertility. Interestingly, the evaluation of oocyte developmental competence by in vitro culture of retrieved two-cell embryos indicated that oocytes originating from the Brg1fl/fl;Amhr2-Cre mice did not reach the blastocyst stage and had higher rates of mitotic defects, including micronuclei. Together, these results indicate that BRG1 plays an important role in female fertility by regulating granulosa and oocyte functions during follicle growth and is needed for the acquisition of oocyte developmental competence.

Framing Apache Spark in life sciences

Advances in high-throughput and digital technologies have required the adoption of big data for handling complex tasks in life sciences. However, the drift to big data led researchers to face technical and infrastructural challenges for storing, sharing, and analysing them. In fact, this kind of tasks requires distributed computing systems and algorithms able to ensure efficient processing. Cutting edge distributed programming frameworks allow to implement flexible algorithms able to adapt the computation to the data over on-premise HPC clusters or cloud architectures. In this context, Apache Spark is a very powerful HPC engine for large-scale data processing on clusters. Also thanks to specialised libraries for working with structured and relational data, it allows to support machine learning, graph-based computation, and stream processing. This review article is aimed at helping life sciences researchers to ascertain the features of Apache Spark and to assess whether it can be successfully used in their research activities.

S100b in acute ischemic stroke clots is a biomarker for post-thrombectomy intracranial hemorrhages

Background and purpose

Post-thrombectomy intracranial hemorrhages (PTIH) are dangerous complications of acute ischemic stroke (AIS) following mechanical thrombectomy. We aimed to investigate if S100b levels in AIS clots removed by mechanical thrombectomy correlated to increased risk of PTIH.

Methods

We analyzed 122 thrombi from 80 AIS patients in the RESTORE Registry of AIS clots, selecting an equal number of patients having been pre-treated or not with rtPA (40 each group). Within each subgroup, 20 patients had developed PTIH and 20 patients showed no signs of hemorrhage. Gross photos of each clot were taken and extracted clot area (ECA) was measured using ImageJ. Immunohistochemistry for S100b was performed and Orbit Image Analysis was used for quantification. Immunofluorescence was performed to investigate co-localization between S100b and T-lymphocytes, neutrophils and macrophages. Chi-square or Kruskal-Wallis test were used for statistical analysis.

Results

PTIH was associated with higher S100b levels in clots (0.33 [0.08-0.85] vs. 0.07 [0.02-0.27] mm², H1 = 6.021, P = 0.014^*), but S100b levels were not significantly affected by acute thrombolytic treatment (P = 0.386). PTIH was also associated with patients having higher NIHSS at admission (20.0 [17.0-23.0] vs. 14.0 [10.5-19.0], H1 = 8.006, P = 0.005) and higher number of passes during thrombectomy (2 [1-4] vs. 1 [1-2.5], H1 = 5.995, P = 0.014^*). S100b co-localized with neutrophils, macrophages and with T-lymphocytes in the clots.

Conclusions

Higher S100b expression in AIS clots, higher NIHSS at admission and higher number of passes during thrombectomy are all associated with PTIH. Further investigation of S100b expression in AIS clots by neutrophils, macrophages and T-lymphocytes could provide insight into the role of S100b in thromboinflammation.

Comprehensive Analysis of Fibroblast Activation Protein Expression in Interstitial Lung Diseases

Rationale: Sustained activation of lung fibroblasts and the resulting oversynthesis of the extracellular matrix are detrimental events for patients with interstitial lung diseases (ILDs). Lung biopsy is a primary evaluation technique for the fibrotic status of ILDs, and is also a major risk factor for triggering acute deterioration. Fibroblast activation protein (FAP) is a long-known surface biomarker of activated fibroblasts, but its expression pattern and diagnostic implications in ILDs are poorly defined. Objectives: The present study aims to comprehensively investigate whether the expression intensity of FAP could be used as a potential readout to estimate or measure the amounts of activated fibroblasts in ILD lungs quantitatively. Methods: FAP expression in human primary lung fibroblasts as well as in clinical lung specimens was first tested using multiple experimental methods, including real-time quantitative PCR (qPCR), Western blot, immunofluorescence staining, deep learning measurement of whole slide immunohistochemistry, as well as single-cell sequencing. In addition, FAP-targeted positron emission tomography/computed tomography imaging PET/CT was applied to various types of patients with ILD, and the correlation between the uptake of FAP tracer and pulmonary function parameters was analyzed. Measurements and Main Results: Here, it was revealed, for the first time, FAP expression was upregulated significantly in the early phase of lung fibroblast activation event in response to a low dose of profibrotic cytokine. Single-cell sequencing data further indicate that nearly all FAP-positive cells in ILD lungs were collagen-producing fibroblasts. Immunohistochemical analysis validated that FAP expression level was closely correlated with the abundance of fibroblastic foci on human lung biopsy sections from patients with ILDs. We found that the total standard uptake value (SUV) of FAP inhibitor (FAPI) PET (SUVtotal) was significantly related to lung function decline in patients with ILD. Conclusions: Our results strongly support that in vitro and in vivo detection of FAP can assess the profibrotic activity of ILDs, which may aid in early diagnosis and the selection of an appropriate therapeutic window.

Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and Researchers

Training machine learning models for artificial intelligence (AI) applications in pathology often requires extensive annotation by human experts, but there is little guidance on the subject. In this work, we aimed to describe our experience and provide a simple, useful, and practical guide addressing annotation strategies for AI development in computational pathology. Annotation methodology will vary significantly depending on the specific study's objectives, but common difficulties will be present across different settings. We summarize key aspects and issue guiding principles regarding team interaction, ground-truth quality assessment, different annotation types, and available software and hardware options and address common difficulties while annotating. This guide was specifically designed for pathology annotation, intending to help pathologists, other researchers, and AI developers with this process.

Graphene Oxide Nanoscale Platform Enhances the Anti-Cancer Properties of Bortezomib in Glioblastoma Models

Graphene-based 2D nanomaterials possess unique physicochemical characteristics which can be utilized in various biomedical applications, including the transport and presentation of chemotherapeutic agents. In glioblastoma multiforme (GBM), intratumorally administered thin graphene oxide (GO) nanosheets demonstrate a widespread distribution throughout the tumor volume without impact on tumor growth, nor spread into normal brain tissue. Such intratumoral localization and distribution can offer multiple opportunities for treatment and modulation of the GBM microenvironment. Here, the kinetics of GO nanosheet distribution in orthotopic GBM mouse models is described and a novel nano-chemotherapeutic approach utilizing thin GO sheets as platforms to non-covalently complex a proteasome inhibitor, bortezomib (BTZ), is rationally designed. Through the characterization of the GO:BTZ complexes, a high loading capacity of the small molecule on the GO surface with sustained BTZ biological activity in vitro is demonstrated. In vivo, a single low-volume intratumoral administration of GO:BTZ complex shows an enhanced cytotoxic effect compared to free drug in two orthotopic GBM mouse models. This study provides evidence of the potential that thin and small GO sheets hold as flat nanoscale platforms for GBM treatment by increasing the bioavailable drug concentration locally, leading to an enhanced therapeutic effect.

Hemodynamic Analysis Shows High Wall Shear Stress Is Associated with Intraoperatively Observed Thin Wall Regions of Intracranial Aneurysms

BACKGROUND

Studying the relationship between hemodynamics and local intracranial aneurysm (IA) pathobiology can help us understand the natural history of IA. We characterized the relationship between the IA wall appearance, using intraoperative imaging, and the hemodynamics from CFD simulations.

METHODS

Three-dimensional geometries of 15 IAs were constructed and used for CFD. Two-dimensional intraoperative images were subjected to wall classification using a machine learning approach, after which the wall type was mapped onto the 3D surface. IA wall regions included thick (white), normal (purple-crimson), and thin/translucent (red) regions. IA-wide and local statistical analyses were performed to assess the relationship between hemodynamics and wall type.

RESULTS

Thin regions of the IA sac had significantly higher WSS, Normalized WSS, WSS Divergence and Transverse WSS, compared to both normal and thick regions. Thicker regions tended to co-locate with significantly higher RRT than thin regions. These trends were observed on a local scale as well. Regression analysis showed a significant positive correlation between WSS and thin regions and a significant negative correlation between WSSD and thick regions.

CONCLUSION

Hemodynamic simulation results were associated with the intraoperatively observed IA wall type. We consistently found that elevated WSS and WSS_Norm were associated with thin regions of the IA wall rather than thick and normal regions.

microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation

In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.

Deep learning-based framework for slide-based histopathological image analysis

Digital pathology coupled with advanced machine learning (e.g., deep learning) has been changing the paradigm of whole-slide histopathological images (WSIs) analysis. Major applications in digital pathology using machine learning include automatic cancer classification, survival analysis, and subtyping from pathological images. While most pathological image analyses are based on patch-wise processing due to the extremely large size of histopathology images, there are several applications that predict a single clinical outcome or perform pathological diagnosis per slide (e.g., cancer classification, survival analysis). However, current slide-based analyses are task-dependent, and a general framework of slide-based analysis in WSI has been seldom investigated. We propose a novel slide-based histopathology analysis framework that creates a WSI representation map, called HipoMap, that can be applied to any slide-based problems, coupled with convolutional neural networks. HipoMap converts a WSI of various shapes and sizes to structured image-type representation. Our proposed HipoMap outperformed existing methods in intensive experiments with various settings and datasets. HipoMap showed the Area Under the Curve (AUC) of 0.96±0.026 (5% improved) in the experiments for lung cancer classification, and c-index of 0.787±0.013 (3.5% improved) and coefficient of determination ([Formula: see text]) of 0.978±0.032 (24% improved) in survival analysis and survival prediction with TCGA lung cancer data respectively, as a general framework of slide-based analysis with a flexible capability. The results showed significant improvement comparing to the current state-of-the-art methods on each task. We further discussed experimental results of HipoMap as pathological viewpoints and verified the performance using publicly available TCGA datasets. A Python package is available at https://pypi.org/project/hipomap , and the package can be easily installed using Python PIP. The open-source codes in Python are available at: https://github.com/datax-lab/HipoMap .

An integrative web-based software tool for multi-dimensional pathology whole-slide image analytics

Objective.In the era of precision medicine, human tumor atlas-oriented studies have been significantly facilitated by high-resolution, multi-modal tissue based microscopic pathology image analytics. To better support such tissue-based investigations, we have developed Digital Pathology Laboratory (DPLab), a publicly available web-based platform, to assist biomedical research groups, non-technical end users, and clinicians for pathology whole-slide image visualization, annotation, analysis, and sharing via web browsers.Approach.A major advancement of this work is the easy-to-follow methods to reconstruct three-dimension (3D) tissue image volumes by registering two-dimension (2D) whole-slide pathology images of serial tissue sections stained by hematoxylin and eosin (H&E), and immunohistochemistry (IHC). The integration of these serial slides stained by different methods provides cellular phenotype and pathophysiologic states in the context of a 3D tissue micro-environment. DPLab is hosted on a publicly accessible server and connected to a backend computational cluster for intensive image analysis computations, with results visualized, downloaded, and shared via a web interface.Main results.Equipped with an analysis toolbox of numerous image processing algorithms, DPLab supports continued integration of community-contributed algorithms and presents an effective solution to improve the accessibility and dissemination of image analysis algorithms by research communities.Significance.DPLab represents the first step in making next generation tissue investigation tools widely available to the research community, enabling and facilitating discovery of clinically relevant disease mechanisms in a digital 3D tissue space.

Cellular senescence is increased in airway smooth muscle cells of elderly persons with asthma

Senescent cells can drive age-related tissue dysfunction partially via a senescence-associated secretory phenotype (SASP) involving proinflammatory and profibrotic factors. Cellular senescence has been associated with a structural and functional decline during normal lung aging and age-related diseases such as chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF). Asthma in the elderly (AIE) represents a major healthcare burden. AIE is associated with bronchial airway hyperresponsiveness and remodeling, which involves increased cell proliferation and higher rates of fibrosis, and resistant to standard therapy. Airway smooth muscle (ASM) cells play a major role in asthma such as remodeling via modulation of inflammation and the extracellular matrix (ECM) environment. Whether senescent ASM cells accumulate in AIE and contribute to airway structural or functional changes is unknown. Lung tissues from elderly persons with asthma showed greater airway fibrosis compared with age-matched elderly persons with nonasthma and young age controls. Lung tissue or isolated ASM cells from elderly persons with asthma showed increased expression of multiple senescent markers including phospho-p53, p21, telomere-associated foci (TAF), as well as multiple SASP components. Senescence and SASP components were also increased with aging per se. These data highlight the presence of cellular senescence in AIE that may contribute to airway remodeling.

Easing batch image processing from OMERO: a new toolbox for ImageJ

The Open Microscopy Environment Remote Objects (OMERO) is an open-source image manager used by many biologists to store, organize, view, and share microscopy images, while the open-source software ImageJ/Fiji is a very popular program used to analyse them. However, there is a lack of an easy-to-use generic tool to run a workflow on a batch of images without having to download them to local computers, and to automatically organize the results in OMERO. To offer this functionality, we have built (i) a library in Java: “Simple OMERO Client”, to communicate with an OMERO database from Java software, (ii) an ImageJ/Fiji plugin to run a macro-program on a batch of images from OMERO and (iii) a new set of Macro Functions, “OMERO Macro extensions“, dedicated to interact with OMERO in macro-programming. The latter is intended for developers, with additional possibilities using tag criteria, while the “Batch OMERO plugin” is more geared towards non-IT scientists and has a very easy to use interface. Each tool is illustrated with a use case.

Design of Montelukast Nanocrystalline Suspension for Parenteral Prolonged Delivery

Background

Montelukast (MTK), a representative leukotriene receptor antagonist, is currently being investigated as a potential candidate for treating Alzheimer’s disease. For potent and effective dosing in elderly patients, a parenteral prolonged delivery system is favored, with improved medication adherence with reduced dosage frequency.

Purpose

This study aimed to design a nanocrystalline suspension (NS)-based MTK prolonged delivery system and evaluate its pharmacokinetics profile and local tolerability following subcutaneous administration.

Methods

To decelerate the dissolution rate, the amorphous MTK raw material was transformed into a crystalline state using a solvent-mediated transformation method and subsequently formulated into NS using a bead-milling technique. The MTK NSs were characterized by morphology, particle size, crystallinity, and in vitro dissolution profiles. The pharmacokinetic profile and local tolerability at the injection site following subcutaneous injection of MTK suspension were evaluated in rats.

Results

Microscopic and physical characterization revealed that the amorphous MTK powder was lucratively transformed into a crystalline form in acidic media (pH 4). MTK crystalline suspensions with different diameters (200 nm, 500 nm, and 3 μm) were uniformly prepared using bead-milling technology, employing polysorbate 80 as suspending agent. Prepared crystalline suspensions exhibited analogous crystallinity (melting point, 150°C) and size-dependent in vitro dissolution profiles. MTK NSs with particle sizes of 200 nm and 500 nm provided a protracted pharmacokinetic profile for up to 4 weeks in rats, with a higher maximum drug concentration in plasma than the 3 μm-sized injectable suspensions. Histopathological examination revealed that MTK NS caused chronic granulomatous inflammation at the injection site, which resolved after 4 weeks.

Conclusion

The MTK parenteral NS delivery system is expected to be a valuable tool for treating Alzheimer’s disease with extended dose intervals.

Histology of Cerebral Clots in Cryptogenic Stroke Varies According to the Presence of a Patent Foramen Ovale

Although a pathophysiological impact remains difficult to prove in individual patient care, a patent foramen ovale (PFO) is currently considered of high relevance for secondary prophylaxis in selected patients with cryptogenic ischemic stroke. By quantification of histological clot composition, we aimed to enhance pathophysiological understanding of PFO attributable ischemic strokes. Retrospectively, we evaluated all cerebral clots retrieved by mechanical thrombectomy for acute ischemic stroke treatment between 2011 and 2021 at our comprehensive stroke care center. Inclusion criteria applied were cryptogenic stroke, age (≤60 years), and PFO status according to transesophageal echocardiography, resulting in a study population of 58 patients. Relative clot composition was calculated using orbit image analysis to define the ratio of main histologic components (fibrin/platelets (F/P), red blood cell count (RBC), leukocytes). Cryptogenic stroke patients with PFO (PFO+, n = 20) displayed a significantly higher percentage of RBC (0.57 ± 0.17; p = 0.002) and lower percentage of F/P (0.38 ± 0.15; p = 0.003) compared to patients without PFO (PFO–, n = 38) (RBC: 0.41 ± 0.21; F/P: 0.52 ± 0.20). In conclusion, histologic clot composition in cryptogenic stroke varies depending on the presence of a PFO. Our findings histologically support the concept that a PFO may be of pathophysiological relevance in cryptogenic ischemic stroke.

Computational pathology in ovarian cancer

Histopathologic evaluations of tissue sections are key to diagnosing and managing ovarian cancer. Pathologists empirically assess and integrate visual information, such as cellular density, nuclear atypia, mitotic figures, architectural growth patterns, and higher-order patterns, to determine the tumor type and grade, which guides oncologists in selecting appropriate treatment options. Latent data embedded in pathology slides can be extracted using computational imaging. Computers can analyze digital slide images to simultaneously quantify thousands of features, some of which are visible with a manual microscope, such as nuclear size and shape, while others, such as entropy, eccentricity, and fractal dimensions, are quantitatively beyond the grasp of the human mind. Applications of artificial intelligence and machine learning tools to interpret digital image data provide new opportunities to explore and quantify the spatial organization of tissues, cells, and subcellular structures. In comparison to genomic, epigenomic, transcriptomic, and proteomic patterns, morphologic and spatial patterns are expected to be more informative as quantitative biomarkers of complex and dynamic tumor biology. As computational pathology is not limited to visual data, nuanced subvisual alterations that occur in the seemingly “normal” pre-cancer microenvironment could facilitate research in early cancer detection and prevention. Currently, efforts to maximize the utility of computational pathology are focused on integrating image data with other -omics platforms that lack spatial information, thereby providing a new way to relate the molecular, spatial, and microenvironmental characteristics of cancer. Despite a dire need for improvements in ovarian cancer prevention, early detection, and treatment, the ovarian cancer field has lagged behind other cancers in the application of computational pathology. The intent of this review is to encourage ovarian cancer research teams to apply existing and/or develop additional tools in computational pathology for ovarian cancer and actively contribute to advancing this important field.

Deep Learning Approaches for the Segmentation of Glomeruli in Kidney Histopathological Images

Deep learning is widely applied in bioinformatics and biomedical imaging, due to its ability to perform various clinical tasks automatically and accurately. In particular, the application of deep learning techniques for the automatic identification of glomeruli in histopathological kidney images can play a fundamental role, offering a valid decision support system tool for the automatic evaluation of the Karpinski metric. This will help clinicians in detecting the presence of sclerotic glomeruli in order to decide whether the kidney is transplantable or not. In this work, we implemented a deep learning framework to identify and segment sclerotic and non-sclerotic glomeruli from scanned Whole Slide Images (WSIs) of human kidney biopsies. The experiments were conducted on a new dataset collected by both the Siena and Trieste hospitals. The images were segmented using the DeepLab V2 model, with a pre-trained ResNet101 encoder, applied to 512 × 512 patches extracted from the original WSIs. The results obtained are promising and show a good performance in the segmentation task and a good generalization capacity, despite the different coloring and typology of the histopathological images. Moreover, we present a novel use of the CD10 staining procedure, which gives promising results when applied to the segmentation of sclerotic glomeruli in kidney tissues.

Software tools and platforms in Digital Pathology: a review for clinicians and computer scientists

At the end of the twentieth century, a new technology was developed that allowed an entire tissue section to be scanned on an objective slide. Originally called virtual microscopy, this technology is now known as Whole Slide Imaging (WSI). WSI presents new challenges for reading, visualization, storage, and analysis. For this reason, several technologies have been developed to facilitate the handling of these images. In this paper, we analyze the most widely used technologies in the field of digital pathology, ranging from specialized libraries for the reading of these images to complete platforms that allow reading, visualization, and analysis. Our aim is to provide the reader, whether a pathologist or a computational scientist, with the knowledge to choose the technologies to use for new studies, development, or research.

Correlation of von Willebrand factor and platelets with acute ischemic stroke etiology and revascularization outcome: an immunohistochemical study

BACKGROUND

Platelets and von Willebrand factor (vWF) are key components of acute ischemic stroke (AIS) emboli. We aimed to investigate the CD42b (platelets)/vWF expression, its association with stroke etiology and the impact these components may have on the clinical/procedural parameters.

METHODS

CD42b/vWF immunostaining was performed on 288 emboli collected as part of the multicenter STRIP Registry. CD42b/VWF expression and distribution were evaluated. Student's t-test and χ² test were performed as appropriate.

RESULTS

The mean CD42b and VWF content in clots was 44.3% and 21.9%, respectively. There was a positive correlation between platelets and vWF (r=0.64, p<0.001**). We found a significantly higher vWF level in the other determined etiology (p=0.016*) and cryptogenic (p=0.049*) groups compared with cardioembolic etiology. No significant difference in CD42b content was found across the etiology subtypes. CD42b/vWF patterns were significantly associated with stroke etiology (p=0.006*). The peripheral pattern was predominant in atherosclerotic clots (36.4%) while the clustering (patchy) pattern was significantly associated with cardioembolic and cryptogenic origin (66.7% and 49.8%, respectively). The clots corresponding to other determined etiology showed mainly a diffuse pattern (28.1%). Two types of platelets were distinguished within the CD42b-positive clusters in all emboli: vWF-positive platelets were observed at the center, surrounded by vWF-negative platelets. Thrombolysis correlated with a high platelet content (p=0.03*). vWF-poor and peripheral CD42b/vWF pattern correlated with first pass effect (p=0.03* and p=0.04*, respectively).

CONCLUSIONS

The vWF level and CD42b/vWF distribution pattern in emboli were correlated with AIS etiology and revascularization outcome. Platelet content was associated with response to thrombolysis.

Narrative online guides for the interpretation of digital-pathology images and tissue-atlas data

Multiplexed tissue imaging facilitates the diagnosis and understanding of complex disease traits. However, the analysis of such digital images heavily relies on the experience of anatomical pathologists for the review, annotation and description of tissue features. In addition, the wider use of data from tissue atlases in basic and translational research and in classrooms would benefit from software that facilitates the easy visualization and sharing of the images and the results of their analyses. In this Perspective, we describe the ecosystem of software available for the analysis of tissue images and discuss the need for interactive online guides that help histopathologists make complex images comprehensible to non-specialists. We illustrate this idea via a software interface (Minerva), accessible via web browsers, that integrates multi-omic and tissue-atlas features. We argue that such interactive narrative guides can effectively disseminate digital histology data and aid their interpretation.

Developing image analysis methods for digital pathology

The potential to use quantitative image analysis and artificial intelligence is one of the driving forces behind digital pathology. However, despite novel image analysis methods for pathology being described across many publications, few become widely adopted and many are not applied in more than a single study. The explanation is often straightforward: software implementing the method is simply not available, or is too complex, incomplete, or dataset‐dependent for others to use. The result is a disconnect between what seems already possible in digital pathology based upon the literature, and what actually is possible for anyone wishing to apply it using currently available software. This review begins by introducing the main approaches and techniques involved in analysing pathology images. I then examine the practical challenges inherent in taking algorithms beyond proof‐of‐concept, from both a user and developer perspective. I describe the need for a collaborative and multidisciplinary approach to developing and validating meaningful new algorithms, and argue that openness, implementation, and usability deserve more attention among digital pathology researchers. The review ends with a discussion about how digital pathology could benefit from interacting with and learning from the wider bioimage analysis community, particularly with regard to sharing data, software, and ideas. © 2022 The Author. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Differential Response of Candida Species Morphologies and Isolates to Fluconazole and Boric Acid

Candida albicans is the most prevalent cause of vulvovaginal candidiasis ("yeast infection" or VVC) and recurrent vulvovaginal candidiasis (RVVC), although the incidence of non-albicans yeast species is increasing. The azole fluconazole is the primary antifungal drug used to treat RVVC, yet isolates from some species have intrinsic resistance to fluconazole, and recurrent infection can occur even with fluconazole-susceptible populations. The second-line broad-spectrum antimicrobial drug, boric acid, is an alternative treatment that has been found to successfully treat complicated VVC infections. Far less is known about how boric acid inhibits growth of yeast isolates in different morphologies compared to fluconazole. We found significant differences in drug resistance and drug tolerance (the ability of a subpopulation to grow slowly in high levels of drug) between C. albicans, Candida glabrata, and Candida parapsilosis isolates, with the specific relationships dependent on both drug and phenotype. Population-level variation for both susceptibility and tolerance was broader for fluconazole than boric acid in all species. Unlike fluconazole, which neither prevented hyphal formation nor disrupted mature biofilms, boric acid inhibited C. albicans hyphal formation and reduced mature biofilm biomass and metabolic activity in all isolates in a dose-dependent manner. Variation in planktonic response did not generally predict biofilm phenotypes for either drug. Overall, our findings illustrate that boric acid is broadly effective at inhibiting growth across many isolates and morphologies, which could explain why it is an effective treatment for RVVC.

Easing batch image processing from OMERO: a new toolbox for ImageJ

The Open Microscopy Environment Remote Objects (OMERO) is an open source image manager used by many biologists to store, organize, view and share microscopy images, while the open source software ImageJ/Fiji is a very popular program used to analyse them. However, there is a lack of an easy-to-use generic tool to run a workflow on a batch of images without having to download them to local computers; and to automatically organize the results in OMERO. To offer this functionality, we have built three tools in Java language: “Simple OMERO Client”, a library to communicate with an OMERO database from Java softwares ; an ImageJ/Fiji plugin to run a macro-program on a batch of images from OMERO and “OMERO Macro extensions“, a dedicated vocabulary to interact with OMERO in macro-programming. The latter is intended for developers, with additional possibilities using tag criteria, while the “Batch OMERO plugin” is more geared towards non-IT scientists and has a very easy to use interface. Both tools are illustrated with a use case.

Effects of diabetes mellitus complicated by admission hyperglycemia on clot histological composition and ultrastructure in patients with acute ischemic stroke

Background

Type 2 diabetes mellitus (T2DM) affects the occurrence and prognosis of acute ischemic stroke (AIS). However, the impact of diabetes on thrombus characteristics is unclear. The relationship between the composition and ultrastructure of clots and DM with admission hyperglycemia was investigated.

Methods

Consecutive patients with AIS who underwent endovascular thrombus retrieval between June 2017 and May 2021 were recruited. The thrombus composition and ultrastructure were evaluated using Martius scarlet blue stain and scanning electron microscopy. Clot perviousness was evaluated via thrombus attenuation increase on computed tomography angiography (CTA) versus non-contrast CT. Patients with admission hyperglycemia DM (ahDM) and those without DM (nonDM) were compared in terms of thrombus composition, ultrastructure, and perviousness.

Results

On admission, higher NIHSS scores (17 vs. 12, respectively, p = 0.015) was evident in ahDM patients. After the 90-day follow-up, the rates of excellent outcomes (mRS 0–1) were lower in patients with ahDM (16.6%, p = 0.038), but functional independence (mRS 0–2) and handicapped (mRS 3–5) were comparable between patients with ahDM and nonDM. The outcome of mortality was higher in patients with ahDM (33.3%, p = 0.046) than in nonDM patients. Clots in patients with ahDM had more fibrin (39.4% vs. 25.0%, respectively, p = 0.007), fewer erythrocyte components (21.2% vs. 41.5%, respectively, p = 0.043), equivalent platelet fraction (27.7% vs. 24.6%, respectively, p = 0.587), and higher WBC counts (4.6% vs. 3.3%, respectively, p = 0.004) than in nonDM patients. The percentage of polyhedral erythrocytes in thrombi was significantly higher in ahDM patients than in nonDM patients (68.9% vs. 45.6%, respectively, p = 0.007). The proportion of pervious clots was higher in patients nonDM than in patients with ahDM (82.61% vs. 40%, respectively, p = 0.026).

Conclusion

Patients with ahDM presented with greater stroke severity on admission and poorer functional outcomes after 3 months. Clots in patients with ahDM had more fibrin, leucocytes, and fewer erythrocyte components than in patients nonDM. The content of polyhedral erythrocytes and impervious clots proportion were significantly higher in thrombi of patients with AIS and ahDM. Further research is required to validate these findings.

Does prior administration of rtPA influence acute ischemic stroke clot composition? Findings from the analysis of clots retrieved with mechanical thrombectomy from the RESTORE registry

BACKGROUND AND PURPOSE

There is still much debate whether bridging-therapy [intravenous thrombolysis (IVT) prior to mechanical thrombectomy (MT)] might be beneficial compared to MT alone. We investigated the effect of IVT on size and histological composition of the clots retrieved from patients undergoing bridging-therapy or MT alone.

METHODS

We collected mechanically extracted thrombi from 1000 acute ischemic stroke (AIS) patients included in RESTORE registry. Patients were grouped according to the administration (or not) of IVT before thrombectomy. Gross photos of each clot were taken and Extracted Clot Area (ECA) was measured using ImageJ software. Martius Scarlett Blue stain was used to characterize the main histological clot components [red blood cells (RBCs), fibrin (FIB), platelets/other (PTL)] and Orbit Image Analysis was used for quantification. Additionally, we calculated the area of each main component by multiplying the component percent by ECA. Chi-squared and Kruskal-Wallis tests were used for statistical analysis.

RESULTS

451 patients (45%) were treated with bridging-therapy while 549 (55%) underwent MT alone. When considering only percent histological composition, we did not find any difference in RBC% (P = 0.895), FIB% (P = 0.458) and PTL% (P = 0.905). However, bridging-therapy clots were significantly smaller than MT-alone clots [32.7 (14.8-64.9) versus 36.8 (20.1-79.8) mm2, N = 1000, H1 = 7.679, P = 0.006*]. A further analysis expressing components per clot area showed that clots retrieved from bridging-therapy cases contained less RBCs [13.25 (4.29-32.06) versus 14.97 (4.93-39.80) mm2, H1 = 3.637, P = 0.056] and significantly less fibrin [9.10 (4.62-17.98) versus 10.54 (5.57-22.48) mm2, H1 = 7.920, P = 0.005*] and platelets/other [5.04 (2.26-11.32) versus 6.54 (2.94-13.79) mm2, H1 = 9.380, P = 0.002*] than MT-alone clots.

CONCLUSIONS

Our results suggest that previous IVT administration significantly reduces thrombus size, proportionally releasing all the main histological components.

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.