Biomass smoke inhalation promotes neuroinflammatory and metabolomic temporal changes in the hippocampus of female mice.

Smoke from wildland fires has been shown to produce neuroinflammation in preclinical models, characterized by neural infiltrations of neutrophils and monocytes, as well as altered neurovascular endothelial phenotypes. To address the longevity of such outcomes, the present study examined the temporal dynamics of neuroinflammation and metabolomics after inhalation exposures from biomass-derived smoke. 2-month-old female C57BL/6 J mice were exposed to wood smoke every other day for 2 weeks at an average exposure concentration of 0.5 mg/m3. Subsequent serial euthanasia occurred at 1-, 3-, 7-, 14-, and 28-day post-exposure. Flow cytometry of right hemispheres revealed two endothelial populations of CD31Hi and CD31Med expressors, with wood smoke inhalation causing an increased proportion of CD31Hi. These populations of CD31Hi and CD31Med were associated with an anti-inflammatory and pro-inflammatory response, respectively, and their inflammatory profiles were largely resolved by the 28-day mark. However, activated microglial populations (CD11b+/CD45low) remained higher in wood smoke-exposed mice than controls at day 28. Infiltrating neutrophil populations decreased to levels below controls by day 28. However, the MHC-II expression of the peripheral immune infiltrate remained high, and the population of neutrophils retained an increased expression of CD45, Ly6C, and MHC-II. Utilizing an unbiased approach examining the metabolomic alterations, we observed notable hippocampal perturbations in neurotransmitter and signaling molecules, such as glutamate, quinolinic acid, and 5-α-dihydroprogesterone. Utilizing a targeted panel designed to explore the aging-associated NAD+ metabolic pathway, wood smoke exposure drove fluctuations and compensations across the 28-day time course, ending with decreased hippocampal NAD+ abundance on day 28. Summarily, these results indicate a highly dynamic neuroinflammatory environment, with potential resolution extending past 28 days, the implications of which may include long-term behavioral changes, systemic and neurological sequalae directly associated with wildfire smoke exposure.

Ethics of AI in Pathology: Current Paradigms and Emerging Issues.

Deep learning has rapidly advanced artificial intelligence (AI) and algorithmic decision-making (ADM) paradigms, affecting many traditional fields of medicine, including pathology, which is a heavily data-centric specialty of medicine. The structured nature of pathology data repositories makes it highly attractive to AI researchers to train deep learning models to improve health care delivery. Additionally, there are enormous financial incentives driving adoption of AI and ADM due to promise of increased efficiency of the health care delivery process. AI, if used unethically, may exacerbate existing inequities of health care, especially if not implemented correctly. There is an urgent need to harness the vast power of AI in an ethically and morally justifiable manner. This review explores the key issues involving AI ethics in pathology. Issues related to ethical design of pathology AI studies and the potential risks associated with implementation of AI and ADM within the pathology workflow are discussed. Three key foundational principles of ethical AI: transparency, accountability, and governance, are described in the context of pathology. The future practice of pathology must be guided by these principles. Pathologists should be aware of the potential of AI to deliver superlative health care and the ethical pitfalls associated with it. Finally, pathologists must have a seat at the table to drive future implementation of ethical AI in the practice of pathology.

Gallbladder cancer: review of a rare orphan gastrointestinal cancer with a focus on populations of New Mexico.

Gallbladder cancer is a rare malignancy of the biliary tract with a poor prognosis, frequently presenting at an advanced stage. While rare in the United States overall, gallbladder cancer has an elevated incidence in geographically distinct locations of the globe including Chile, North India, Korea, Japan and the state of New Mexico in the United States. People with Native American ancestry have a much elevated incidence of gallbladder cancer compared to Hispanic and non-Hispanic white populations of New Mexico. Gallbladder cancer is also one of the few bi-gendered cancers with an elevated female incidence compared to men. Similar to other gastrointestinal cancers, gallbladder cancer etiology is likely multi-factorial involving a combination of genomic, immunological, and environmental factors. Understanding the interplay of these unique epidemiological factors is crucial in improving the prevention, early detection, and treatment of this lethal disease. Previous studies have failed to identify a distinct genomic mutational profile in gallbladder cancers, however, work to identify promising clinically actionable targets is this form of cancer is ongoing. Examples include, interest in the HER2/Neu signaling pathway and the recognition that chronic inflammation plays a crucial role in gallbladder cancer pathogenesis. In this review, we provide a comprehensive overview of gallbladder cancer epidemiology, risk factors, pathogenesis, and treatment with a specific focus on the rural and Native American populations of New Mexico. We conclude this review by discussing future research directions with the goal of improving clinical outcomes for patients of this lethal malignancy.

Influence of TP53 and CDH1 genes in hepatocellular cancer spheroid formation and culture: a model system to understand cancer cell growth mechanics.

BACKGROUND: Spheroid based culture methods are gaining prominence to elucidate the role of the microenvironment in liver carcinogenesis. Additionally, the phenomenon of epithelial-mesenchymal transition also plays an important role in determining the metastatic potential of liver cancer. Tumor spheroids are thus important models to understand the basic biology of liver cancer. METHODS: We cultured, characterized and examined the formation of compact 3-D micro-tumor spheroids in five hepatocellular carcinoma (HCC) cell lines, each with differing TP53 mutational status (wt vs mutant vs null). Spheroid viability and death was systematically measured over a course of a 10 day growth period using various assays. We also examined the TP53 and E-cadherin (CDH1) mRNA and protein expression status in each cell line of the 2-D and 3-D cell models. RESULTS: A novel finding of our study was the identification of variable 3-D spheroid morphology in individual cell lines, ranging from large and compact, to small and unstable spheroid morphologies. The observed morphological differences between the spheroids were robust and consistent over the duration of spheroid culture growth of 10 days in a repeatable manner. Highly variable CDH1 expression was identified depending on the TP53 mutational status of the individual HCC cell line, which may explain the variable spheroid morphology. We observed consistent patterns of TP53 and CDH1 expression in both 2-D and 3-D culture models. CONCLUSIONS: In conclusion, we show that 3-D spheroids are a useful model to determine the morphological growth characteristics of cell lines which are not immediately apparent in routine 2-D culture methods. 3-D culture methods may provide a better alternative to study the process of epithelial-mesenchymal transition (EMT) which is important in the process of liver cancer metastasis.

Evaluating combined effects of chronic, low-dose exposures of cadmium (CLEC) and hyperglycemia on insulin signaling dysfunction in a hepatocellular model.

The pathophysiological effects of chronic heavy metal exposures on human health remains uncertain. In this study, we developed a novel chronic, low-dose exposure of Cadmium (CLEC) model using the hepatocellular cell lines, HepG2 and HUH7. We modulated cell culture conditions to mimic human normoglycemic (5.6 mM) and hyperglycemic (15 mM) states with concomitant cadmium (Cd) exposures for 24 weeks. CLEC cells undergo non-trivial alterations in glucose signaling and metabolic characteristics within our model. We observe elevated baseline reactive oxygen species (ROS) production and decreased 2-NBDG uptake indicative of glucose metabolic dysfunction. Additionally, induction of metallothionein (MT) expression, increased activation of Akt signaling (via phosphorylation) and reduced IRS-2 protein expression are observed in CLEC cells. Cell line specific changes are observed with HepG2 showing a much higher MT gene induction compared to HUH7 cell line which impacts glucose metabolic dysfunction. Hyperglycemic culture conditions (representing type II diabetes) significantly modulate CLEC effects on cells. In conclusion, pathophysiologically relevant models of chronic heavy metal exposures are urgently needed to gain an in-depth, mechanistic understanding of the long-term impacts of toxic metals (e.g., Cd) on human metabolic health.

High Throughput Screening Assessment of Reactive Oxygen Species (ROS) Generation using Dihydroethidium (DHE) Fluorescence Dye.

Reactive oxygen species (ROS) play a key role in the regulation of cellular metabolism in physiological and pathological processes. Physiological ROS production plays a central role in the spatial and temporal modulation of normal cellular functions such as proliferation, signaling, apoptosis, and senescence. In contrast, chronic ROS overproduction is responsible for a wide spectrum of diseases, such as cancer, cardiovascular disease, and diabetes, among others. Quantifying ROS levels in an accurate and reproducible manner is thus essential to understanding normal cellular functionality. Fluorescence imaging-based methods to characterize intra-cellular ROS species is a common approach. Many of the imaging ROS protocols in the literature use 2'-7'-dichlorodihydrofluorescein diacetate (DCFH-DA) dye. However, this dye suffers from significant limitations in its usage and interpretability. The current protocol demonstrates the use of a dihydroethidium (DHE) fluorescent probe as an alternative method to quantify total ROS production in a high-throughput setting. The high throughput imaging platform, CX7 Cellomics, was used to measure and quantify the ROS production. This study was conducted in three hepatocellular cancer cell lines - HepG2, JHH4, and HUH-7. This protocol provides an in-depth description of the various procedures involved in the assessment of ROS within the cells, including - preparation of DHE solution, incubation of cells with DHE solution, and measurement of DHE intensity necessary to characterize the ROS production. This protocol demonstrates that DHE fluorescent dye is a robust and reproducible choice to characterize intracellular ROS production in a high-throughput manner. High throughput approaches to measure ROS production are likely to be helpful in a variety of studies, such as toxicology, drug screening, and cancer biology.

Evaluating Use of Generative Artificial Intelligence in Clinical Pathology Practice: Opportunities and the Way Forward.

CONTEXT.­: Generative artificial intelligence (GAI) technologies are likely to dramatically impact health care workflows in clinical pathology (CP). Applications in CP include education, data mining, decision support, result summaries, and patient trend assessments. OBJECTIVE.­: To review use cases of GAI in CP, with a particular focus on large language models. Specific examples are provided for the applications of GAI in the subspecialties of clinical chemistry, microbiology, hematopathology, and molecular diagnostics. Additionally, the review addresses potential pitfalls of GAI paradigms. DATA SOURCES.­: Current literature on GAI in health care was reviewed broadly. The use case scenarios for each CP subspecialty review common data sources generated in each subspecialty. The potential for utilization of CP data in the GAI context was subsequently assessed, focusing on issues such as future reporting paradigms, impact on quality metrics, and potential for translational research activities. CONCLUSIONS.­: GAI is a powerful tool with the potential to revolutionize health care for patients and practitioners alike. However, GAI must be implemented with much caution considering various shortcomings of the technology such as biases, hallucinations, practical challenges of implementing GAI in existing CP workflows, and end-user acceptance. Human-in-the-loop models of GAI implementation have the potential to revolutionize CP by delivering deeper, meaningful insights into patient outcomes both at an individual and population level.

Harnessing the Power of Generative Artificial Intelligence in Pathology Education.

CONTEXT.­: Generative artificial intelligence (AI) technologies are rapidly transforming numerous fields, including pathology, and hold significant potential to revolutionize educational approaches. OBJECTIVE.­: To explore the application of generative AI, particularly large language models and multimodal tools, for enhancing pathology education. We describe their potential to create personalized learning experiences, streamline content development, expand access to educational resources, and support both learners and educators throughout the training and practice continuum. DATA SOURCES.­: We draw on insights from existing literature on AI in education and the collective expertise of the coauthors within this rapidly evolving field. Case studies highlight practical applications of large language models, demonstrating both the potential benefits and unique challenges associated with implementing these technologies in pathology education. CONCLUSIONS.­: Generative AI presents a powerful tool kit for enriching pathology education, offering opportunities for greater engagement, accessibility, and personalization. Careful consideration of ethical implications, potential risks, and appropriate mitigation strategies is essential for the responsible and effective integration of these technologies. Future success lies in fostering collaborative development between AI experts and medical educators, prioritizing ongoing human oversight and transparency to ensure that generative AI augments, rather than supplants, the vital role of educators in pathology training and practice.

In Vivo Tissue Distribution of Polystyrene or Mixed Polymer Microspheres and Metabolomic Analysis after Oral Exposure in Mice.

BACKGROUND: Global plastic use has consistently increased over the past century with several different types of plastics now being produced. Much of these plastics end up in oceans or landfills leading to a substantial accumulation of plastics in the environment. Plastic debris slowly degrades into microplastics (MPs) that can ultimately be inhaled or ingested by both animals and humans. A growing body of evidence indicates that MPs can cross the gut barrier and enter into the lymphatic and systemic circulation leading to accumulation in tissues such as the lungs, liver, kidney, and brain. The impacts of mixed MPs exposure on tissue function through metabolism remains largely unexplored. OBJECTIVES: This study aims to investigate the impacts of polymer microspheres on tissue metabolism in mice by assessing the microspheres ability to translocate across the gut barrier and enter into systemic circulation. Specifically, we wanted to examine microsphere accumulation in different organ systems, identify concentration-dependent metabolic changes, and evaluate the effects of mixed microsphere exposures on health outcomes. METHODS: To investigate the impact of ingested microspheres on target metabolic pathways, mice were exposed to either polystyrene (5µm) microspheres or a mixture of polymer microspheres consisting of polystyrene (5µm), polyethylene (1-4µm), and the biodegradability and biocompatible plastic, poly-(lactic-co-glycolic acid) (5µm). Exposures were performed twice a week for 4 weeks at a concentration of either 0, 2, or 4mg/week via oral gastric gavage. Tissues were collected to examine microsphere ingress and changes in metabolites. RESULTS: In mice that ingested microspheres, we detected polystyrene microspheres in distant tissues including the brain, liver, and kidney. Additionally, we report on the metabolic differences that occurred in the colon, liver, and brain, which showed differential responses that were dependent on concentration and type of microsphere exposure. DISCUSSION: This study uses a mouse model to provide critical insight into the potential health implications of the pervasive issue of plastic pollution. These findings demonstrate that orally consumed polystyrene or mixed polymer microspheres can accumulate in tissues such as the brain, liver, and kidney. Furthermore, this study highlights concentration-dependent and polymer type-specific metabolic changes in the colon, liver, and brain after plastic microsphere exposure. These results underline the mobility within and between biological tissues of MPs after exposure and emphasize the importance of understanding their metabolic impact. https://doi.org/10.1289/EHP13435.

Artificial intelligence and digital pathology: clinical promise and deployment considerations.

Artificial intelligence (AI) presents an opportunity in anatomic pathology to provide quantitative objective support to a traditionally subjective discipline, thereby enhancing clinical workflows and enriching diagnostic capabilities. AI requires access to digitized pathology materials, which, at present, are most commonly generated from the glass slide using whole-slide imaging. Models are developed collaboratively or sourced externally, and best practices suggest validation with internal datasets most closely resembling the data expected in practice. Although an array of AI models that provide operational support for pathology practices or improve diagnostic quality and capabilities has been described, most of them can be categorized into one or more discrete types. However, their function in the pathology workflow can vary, as a single algorithm may be appropriate for screening and triage, diagnostic assistance, virtual second opinion, or other uses depending on how it is implemented and validated. Despite the clinical promise of AI, the barriers to adoption have been numerous, to which inclusion of new stakeholders and expansion of reimbursement opportunities may be among the most impactful solutions.

Neuroinflammatory and Metabolomic Temporal Dynamics Following Wood Smoke Inhalation.

Smoke from wildland fires has been shown to produce neuroinflammation in preclinical models, characterized by neural infiltrations of neutrophils and monocytes, as well as altered neurovascular endothelial phenotypes. To address the longevity of such outcomes, the present study examined the neuroinflammatory and metabolomic temporal dynamics after inhalation exposures from biomass-derived smoke. 2-month-old female C57BL/6J mice were exposed to wood smoke every other day for two weeks at an average exposure concentration of 0.5mg/m 3 . Subsequent serial euthanasia occurred at 1-, 3-, 7-, 14-, and 28-days post-exposure. Flow cytometry of right hemispheres revealed two endothelial populations of PECAM (CD31), high and medium expressors, with wood smoke inhalation causing an increased proportion of PECAM Hi . These populations of PECAM Hi and PECAM Med were associated with an anti-inflammatory and pro-inflammatory response, respectively, and their inflammatory profiles were largely resolved by the 28-day mark. However, activated microglial populations (CD11b + /CD45 low ) remained higher in wood smoke-exposed mice than controls at day 28. Infiltrating neutrophil populations decreased to levels below controls by day 28. However, the MHC-II expression of the peripheral immune infiltrate remained high, and the population of neutrophils retained an increased expression of CD45, Ly6C, and MHC-II. Utilizing an unbiased approach examining the metabolomic alterations, we observed notable hippocampal perturbations in neurotransmitter and signaling molecules like glutamate, quinolinic acid, and 5-α-dihydroprogesterone. Utilizing a targeted panel designed to explore the aging-associated NAD + metabolic pathway, wood smoke exposure drove fluctuations and compensations across the 28-day time course, ending with decreased hippocampal NAD + abundance at day 28. Summarily, these results indicate a highly dynamic neuroinflammatory environment, with potential resolution extending past 28 days, the implications of which may include long-term behavioral changes, systemic and neurological sequalae directly associated wtith wildfire smoke exposure.

Pathology Education Powered by Virtual and Digital Transformation: Now and the Future.

CONTEXT.­: Myriad forces are changing teaching and learning strategies throughout all stages and types of pathology education. Pathology educators and learners face the challenge of adapting to and adopting new methods and tools. The digital pathology transformation and the associated educational ecosystem are major factors in this setting of change. OBJECTIVE.­: To identify and collect resources, tools, and examples of educational innovations involving digital pathology that are valuable to pathology learners and teachers at each phase of professional development. DATA SOURCES.­: Sources were a literature review and the personal experience of authors and educators. CONCLUSIONS.­: High-quality digital pathology tools and resources have permeated all the major niches within anatomic pathology and are increasingly well applied to clinical pathology for learners at all levels. Coupled with other virtual tools, the training landscape in pathology is highly enriched and much more accessible than in the past. Digital pathology is well suited to the demands of peer-to-peer education, such as in the introduction of new testing, grading, or other standardized practices. We found that digital pathology was well adapted to apply our current understanding of optimal teaching strategies and was effective at the undergraduate, graduate, postgraduate, and peer-to-peer levels. We curated and tabulated many existing resources within some segments of pathology. We identified several best practices for each training or educational stage based on current materials and proposed high-priority areas for potential future development.

In Vivo Tissue Distribution of Microplastics and Systemic Metabolomic Alterations After Gastrointestinal Exposure.

Global plastic use has consistently increased over the past century with several different types of plastics now being produced. Much of these plastics end up in oceans or landfills leading to a substantial accumulation of plastics in the environment. Plastic debris slowly degrades into microplastics (MPs) that can ultimately be inhaled or ingested by both animals and humans. A growing body of evidence indicates that MPs can cross the gut barrier and enter into the lymphatic and systemic circulation leading to accumulation in tissues such as the lungs, liver, kidney, and brain. The impacts of mixed MPs exposure on tissue function through metabolism remains largely unexplored. To investigate the impact of ingested MPs on target metabolomic pathways, mice were subjected to either polystyrene microspheres or a mixed plastics (5 µm) exposure consisting of polystyrene, polyethylene and the biodegradability and biocompatible plastic, poly-(lactic-co-glycolic acid). Exposures were performed twice a week for four weeks at a dose of either 0, 2, or 4 mg/week via oral gastric gavage. Our findings demonstrate that, in mice, ingested MPs can pass through the gut barrier, be translocated through the systemic circulation, and accumulate in distant tissues including the brain, liver, and kidney. Additionally, we report on the metabolomic changes that occur in the colon, liver and brain which show differential responses that are dependent on dose and type of MPs exposure. Lastly, our study provides proof of concept for identifying metabolomic alterations associated with MPs exposure and adds insight into the potential health risks that mixed MPs contamination may pose to humans.

A Deep Learning Convolutional Neural Network Can Differentiate Between Helicobacter Pylori Gastritis and Autoimmune Gastritis With Results Comparable to Gastrointestinal Pathologists.

CONTEXT.­: Pathology studies using convolutional neural networks (CNNs) have focused on neoplasms, while studies in inflammatory pathology are rare. We previously demonstrated a CNN that differentiates reactive gastropathy, Helicobacter pylori gastritis (HPG), and normal gastric mucosa. OBJECTIVE.­: To determine whether a CNN can differentiate the following 2 gastric inflammatory patterns: autoimmune gastritis (AG) and HPG. DESIGN.­: Gold standard diagnoses were blindly established by 2 gastrointestinal (GI) pathologists. One hundred eighty-seven cases were scanned for analysis by HALO-AI. All levels and tissue fragments per slide were included for analysis. The cases were randomized, 112 (60%; 60 HPG, 52 AG) in the training set and 75 (40%; 40 HPG, 35 AG) in the test set. A HALO-AI correct area distribution (AD) cutoff of 50% or more was required to credit the CNN with the correct diagnosis. The test set was blindly reviewed by pathologists with different levels of GI pathology expertise as follows: 2 GI pathologists, 2 general surgical pathologists, and 2 residents. Each pathologist rendered their preferred diagnosis, HPG or AG. RESULTS.­: At the HALO-AI AD percentage cutoff of 50% or more, the CNN results were 100% concordant with the gold standard diagnoses. On average, autoimmune gastritis cases had 84.7% HALO-AI autoimmune gastritis AD and HP cases had 87.3% HALO-AI HP AD. The GI pathologists, general anatomic pathologists, and residents were on average, 100%, 86%, and 57% concordant with the gold standard diagnoses, respectively. CONCLUSIONS.­: A CNN can distinguish between cases of HPG and autoimmune gastritis with accuracy equal to GI pathologists.

Individuals with Metabolic Syndrome Show Altered Fecal Lipidomic Profiles with No Signs of Intestinal Inflammation or Increased Intestinal Permeability.

BACKGROUND: Metabolic Syndrome (MetS) is a clinical diagnosis where patients exhibit three out of the five risk factors: hypertriglyceridemia, low high-density lipoprotein (HDL) cholesterol, hyperglycemia, elevated blood pressure, or increased abdominal obesity. MetS arises due to dysregulated metabolic pathways that culminate with insulin resistance and put individuals at risk to develop various comorbidities with far-reaching medical consequences such as non-alcoholic fatty liver disease (NAFLD) and cardiovascular disease. As it stands, the exact pathogenesis of MetS as well as the involvement of the gastrointestinal tract in MetS is not fully understood. Our study aimed to evaluate intestinal health in human subjects with MetS. METHODS: We examined MetS risk factors in individuals through body measurements and clinical and biochemical blood analysis. To evaluate intestinal health, gut inflammation was measured by fecal calprotectin, intestinal permeability through the lactulose-mannitol test, and utilized fecal metabolomics to examine alterations in the host-microbiota gut metabolism. RESULTS: No signs of intestinal inflammation or increased intestinal permeability were observed in the MetS group compared to our control group. However, we found a significant increase in 417 lipid features of the gut lipidome in our MetS cohort. An identified fecal lipid, diacyl-glycerophosphocholine, showed a strong correlation with several MetS risk factors. Although our MetS cohort showed no signs of intestinal inflammation, they presented with increased levels of serum TNFα that also correlated with increasing triglyceride and fecal diacyl-glycerophosphocholine levels and decreasing HDL cholesterol levels. CONCLUSION: Taken together, our main results show that MetS subjects showed major alterations in fecal lipid profiles suggesting alterations in the intestinal host-microbiota metabolism that may arise before concrete signs of gut inflammation or intestinal permeability become apparent. Lastly, we posit that fecal metabolomics could serve as a non-invasive, accurate screening method for both MetS and NAFLD.

Non-autophagy Role of Atg5 and NBR1 in Unconventional Secretion of IL-12 Prevents Gut Dysbiosis and Inflammation.

Intestinal myeloid cells play a critical role in balancing intestinal homeostasis and inflammation. Here, we report that expression of the autophagy-related 5 [Atg5] protein in myeloid cells prevents dysbiosis and excessive intestinal inflammation by limiting IL-12 production. Mice with a selective genetic deletion of Atg5 in myeloid cells [Atg5ΔMye] showed signs of dysbiosis preceding colitis, and exhibited severe intestinal inflammation upon colitis induction that was characterised by increased IFNγ production. The exacerbated colitis was linked to excess IL-12 secretion from Atg5-deficient myeloid cells and gut dysbiosis. Restoration of the intestinal microbiota or genetic deletion of IL-12 in Atg5ΔMye mice attenuated the intestinal inflammation in Atg5ΔMye mice. Additionally, Atg5 functions to limit IL-12 secretion through modulation of late endosome [LE] acidity. Last, the autophagy cargo receptor NBR1, which accumulates in Atg5-deficient cells, played a role by delivering IL-12 to LE. In summary, Atg5 expression in intestinal myeloid cells acts as an anti-inflammatory brake to regulate IL-12, thus preventing dysbiosis and uncontrolled IFNγ-driven intestinal inflammation.

Evaluation of Commercial Next-Generation Sequencing Bioinformatics Software Solutions.

Next-generation sequencing (NGS) diagnostics continue to expand rapidly in clinical medicine. An ever-expanding menu of molecular biomarkers is deemed important for diagnostic, prognostic, and therapeutic assessment in patients. The increasing role of NGS in the clinic is driven mainly by the falling costs of sequencing. However, the data-intensive nature of NGS makes bioinformatic analysis a major challenge to many clinical laboratories. Critically needed NGS bioinformatics personnel are hard to recruit and retain in small- to mid-size clinical laboratories. Also, NGS software often lacks the scalability necessary for expanded clinical laboratory testing volumes. Commercial software solutions aim to bridge the bioinformatics barrier via turnkey informatics solutions tailored specifically for the clinical workplace. Yet, there has been no systematic assessment of these software solutions thus far. This article presents an end-to-end vendor evaluation experience of commercial NGS bioinformatics solutions. Six different commercial vendor solutions were assessed systematically. Key metrics of NGS software evaluation to aid in the robust assessment of software solutions are described. Comprehensive feedback, provided by the TriCore Reference Laboratories molecular pathology team, enabled the final vendor selection. Many key lessons were learned during the software evaluation process, which are described herein. This article aims to provide a detailed road map for small- to mid-size clinical laboratories interested in evaluating commercial bioinformatics solutions available in the marketplace.

Cadmium exposure activates Akt/ERK Signaling and pro-inflammatory COX-2 expression in human gallbladder epithelial cells via a ROS dependent mechanism.

Gallbladder cancer (GBC) is the commonest biliary tract cancer with an ill-defined etiology. We examined the role of Cd+2 exposures in a primary human gallbladder (GB) cell line model in this study. Cd+2 exposures induced decreased cell viability, reactive oxygen species (ROS) generation, altered Akt/ERK signaling pathway activation, PGE2 and COX-2 expression in a human primary gallbladder epithelial cell model. Pharmacological inhibitors were used to determine the key drivers of elevated COX-2 expression due to Cd+2 exposure. Our results show Cd+2 causes a dose-dependent reduction in GB cell viability (EC50 value - 18.6 µM). Dose-dependent activation of phospho-Akt and phospho-ERK signaling pathways via increased phosphoprotein expression was observed due to Cd+2. Signaling activation of Akt and ERK was prevented by 5 mM N-Acetyl Cysteine (NAC), establishing the role of ROS as a key driver in the activation process. Importantly, we observed Cd+2 also caused a dose dependent change in the COX-2 and PGE2 expression levels. PI3K-Akt and NF-kB signaling pathways play a key role in Cd+2 exposure induced COX-2 activation in the gallbladder epithelial cells. In conclusion, our study measures the toxicological effects of Cd+2 exposures on human GB epithelial cells for the first time and establishes the role of Cd+2 as a possible driver of the Akt/ERK pathway overactivity and chronic inflammation in gallbladder carcinogenesis.

A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology.

CONTEXT.­: Most deep learning (DL) studies have focused on neoplastic pathology, with the realm of inflammatory pathology remaining largely untouched. OBJECTIVE.­: To investigate the use of DL for nonneoplastic gastric biopsies. DESIGN.­: Gold standard diagnoses were blindly established by 2 gastrointestinal pathologists. For phase 1, 300 classic cases (100 normal, 100 Helicobacter pylori, 100 reactive gastropathy) that best displayed the desired pathology were scanned and annotated for DL analysis. A total of 70% of the cases for each group were selected for the training set, and 30% were included in the test set. The software assigned colored labels to the test biopsies, which corresponded to the area of the tissue assigned a diagnosis by the DL algorithm, termed area distribution (AD). For Phase 2, an additional 106 consecutive nonclassical gastric biopsies from our archives were tested in the same fashion. RESULTS.­: For Phase 1, receiver operating curves showed near perfect agreement with the gold standard diagnoses at an AD percentage cutoff of 50% for normal (area under the curve [AUC] = 99.7%) and H pylori (AUC = 100%), and 40% for reactive gastropathy (AUC = 99.9%). Sensitivity/specificity pairings were as follows: normal (96.7%, 86.7%), H pylori (100%, 98.3%), and reactive gastropathy (96.7%, 96.7%). For phase 2, receiver operating curves were slightly less discriminatory, with optimal AD cutoffs reduced to 40% across diagnostic groups. The AUCs were 91.9% for normal, 100% for H pylori, and 94.0% for reactive gastropathy. Sensitivity/specificity parings were as follows: normal (73.7%, 79.6%), H pylori (95.7%, 100%), reactive gastropathy (100%, 62.5%). CONCLUSIONS.­: A convolutional neural network can serve as an effective screening tool/diagnostic aid for H pylori gastritis.

Site-Specific Genomic Alterations in a Well-Differentiated Pancreatic Neuroendocrine Tumor With High-Grade Progression.

The major categories of pancreatic neuroendocrine tumor (PanNET) are well-differentiated NET and poorly differentiated neuroendocrine carcinoma. Sequencing of these tumors has identified multiple important genes in the pathogenesis of PanNETs, such as DAXX/ATRX, MEN1, TP53, RB, and mTOR pathway genes. We identified a case of well-differentiated PanNET with high-grade progression with simultaneous low- and high-grade histologic regions containing variable genomic profiles. We performed tumor microdissection and analyzed both regions using a 409-gene comprehensive cancer panel using next-generation sequencing in addition to immunohistochemical and morphologic studies. The low-grade region showed a change in the DAXX gene as a copy number variant (CNV) deletion. The high-grade region showed CNV deletion changes in the DAXX gene as well as the MEN1 gene. We observed additional mutational changes in the PTEN gene and SMAD4 gene in the high-grade region. Our data support that high-grade progression in PanNETs may be the result of the progressive accumulation of genetic changes (CNVs and point mutational changes) within the body of the tumor. Next generation sequencing may provide pathologists and clinicians with ancillary information to accurately characterize and treat these tumors.