Patterns and trends in melanoma mortality in the United States, 1999-2020.

INTRODUCTION: Melanoma, a deadly form of skin cancer, has witnessed a notable increase in incidence over the past decades. Despite advancements in treatment, it remains a significant cause of cancer mortality. Understanding demographic trends and variations in melanoma mortality is crucial for addressing disparities and implementing effective interventions. METHODS: Using the Centers for Disease Control Wide Ranging Online Data for Epidemiologic Research (CDC WONDER) database, we analyzed melanoma mortality data in the United States from 1999 to 2020. Data were stratified by demographic and regional variables, and age-adjusted mortality rates were calculated. Descriptive analysis was performed and Joinpoint regression analysis was employed to identify temporal trends. RESULTS: Between 1999 and 2020, there were 184,416 melanoma-related deaths in the United States Overall, the age-adjusted mortality rate declined from 2.7 to 2.0 per 100,000 people at a rate of -1.3% annually, with significant variations across demographic groups and regions. Men, non-Hispanic White individuals, and those aged > 65 experienced higher mortality rates. Non-Hispanic White individuals noted the steepest decrease in AAMR after 2013 at a rate of -6.1% annually. Disparities were seen by geographic density, with rural populations exhibiting higher mortality compared to their urban and suburban counterparts. CONCLUSION: The study highlights a significant reduction in melanoma mortality in the U.S. since 2013, potentially attributed to advancements in diagnostic techniques such as dermoscopy and the introduction of immune checkpoint inhibitors. Disparities persist, particularly among rural populations. Targeted interventions focusing on increased screening and education are warranted to further mitigate melanoma mortality and address demographic disparities.

Exploring Discussions About Virtual Reality on Twitter to Inform Brain Injury Rehabilitation: Content and Network Analysis.

BACKGROUND: Virtual reality (VR) use in brain injury rehabilitation is emerging. Recommendations for VR development in this field encourage end user engagement to determine the benefits and challenges of VR use; however, existing literature on this topic is limited. Data from social networking sites such as Twitter may further inform development and clinical practice related to the use of VR in brain injury rehabilitation. OBJECTIVE: This study collected and analyzed VR-related tweets to (1) explore the VR tweeting community to determine topics of conversation and network connections, (2) understand user opinions and experiences of VR, and (3) identify tweets related to VR use in health care and brain injury rehabilitation. METHODS: Publicly available tweets containing the hashtags #virtualreality and #VR were collected up to twice weekly during a 6-week period from July 2020 to August 2020 using NCapture (QSR International). The included tweets were analyzed using mixed methods. All tweets were coded using inductive content analysis. Relevant tweets (ie, coded as "VR in health care" or "talking about VR") were further analyzed using Dann's content coding. The biographies of users who sent relevant tweets were examined descriptively. Tweet data networks were visualized using Gephi computational analysis. RESULTS: A total of 260,715 tweets were collected, and 70,051 (26.87%) were analyzed following eligibility screening. The sample comprised 33.68% (23,596/70,051) original tweets and 66.32% (46,455/70,051) retweets. Content analysis generated 10 main categories of original tweets related to VR (ie, advertising and promotion, VR content, talking about VR, VR news, general technology, VR industry, VR live streams, VR in health care, VR events, and VR community). Approximately 4.48% (1056/23,596) of original tweets were related to VR use in health care, whereas 0.19% (45/23,596) referred to VR in brain injury rehabilitation. In total, 14.86% (3506/23,596) of original tweets featured commentary on user opinions and experiences of VR applications, equipment, and software. The VR tweeting community comprised a large network of 26,001 unique Twitter users. Users that posted tweets related to "VR in health care" (2124/26,001, 8.17%) did not form an interconnected VR network, whereas many users "talking about VR" (3752/26,001, 14.43%) were connected within a central network. CONCLUSIONS: This study provides valuable data on community-based experiences and opinions related to VR. Tweets showcased various VR applications, including in health care, and identified important user-based considerations that can be used to inform VR use in brain injury rehabilitation (eg, technical design, accessibility, and VR sickness). Limited discussions and small user networks related to VR in brain injury rehabilitation reflect the paucity of literature on this topic and the potential underuse of this technology. These findings emphasize that further research is required to understand the specific needs and perspectives of people with brain injuries and clinicians regarding VR use in rehabilitation.

Time to surgery and outcomes following open reduction and internal fixation of both-bone forearm fractures.

PURPOSE: To determine the effect of time to surgery on outcomes following open reduction and internal fixation (ORIF) of both-bone forearm fractures (BBFFs). METHODS: Ninety-nine patients who underwent ORIF of BBFFs in a single academic medical center over a 16-year time period were retrospectively reviewed. Demographic and clinical data including age, sex, current smoking status, time from injury to surgery (tsurg), presence of open injury, polytrauma status, and complications were obtained. Radiographs of the affected extremity were reviewed for fracture morphology, reduction quality, and time to union (or presence of nonunion). In addition to descriptive statistics, Chi-square and Wilcoxon-Mann-Whitney tests were used to compare categorical and interval, respectively, with a significance level of 0.05. RESULTS: A tsurg > 48 h was associated with increased rate of delayed unions (tsurg < 48 h: 25% vs tsurg > 48 h: 59%, p = 0.03), but not complications (tsurg < 48 h: 44% vs tsurg > 48 h: 47%, p = 0.79). Open BBFFs were not associated with increased rates of delayed unions (closed: 16% vs open: 19%, p = 0.77) or complications (closed: 42% vs open: 53%, p = 0.29). A trend toward increased time to union with tsurg > 48 h was also seen, but did not reach significance (tsurg < 48 h: 13.5 weeks vs tsurg > 48 h: 15.7 weeks, p = 0.11). CONCLUSION: A tsurg > 48 h is associated with an increased rate of delayed union, but not complications, after ORIF of BBFFs. LEVEL OF EVIDENCE: Therapeutic Level III (Retrospective Cohort).

MmCMS: mouse models' consensus molecular subtypes of colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) primary tumours are molecularly classified into four consensus molecular subtypes (CMS1-4). Genetically engineered mouse models aim to faithfully mimic the complexity of human cancers and, when appropriately aligned, represent ideal pre-clinical systems to test new drug treatments. Despite its importance, dual-species classification has been limited by the lack of a reliable approach. Here we utilise, develop and test a set of options for human-to-mouse CMS classifications of CRC tissue. METHODS: Using transcriptional data from established collections of CRC tumours, including human (TCGA cohort; n = 577) and mouse (n = 57 across n = 8 genotypes) tumours with combinations of random forest and nearest template prediction algorithms, alongside gene ontology collections, we comprehensively assess the performance of a suite of new dual-species classifiers. RESULTS: We developed three approaches: MmCMS-A; a gene-level classifier, MmCMS-B; an ontology-level approach and MmCMS-C; a combined pathway system encompassing multiple biological and histological signalling cascades. Although all options could identify tumours associated with stromal-rich CMS4-like biology, MmCMS-A was unable to accurately classify the biology underpinning epithelial-like subtypes (CMS2/3) in mouse tumours. CONCLUSIONS: When applying human-based transcriptional classifiers to mouse tumour data, a pathway-level classifier, rather than an individual gene-level system, is optimal. Our R package enables researchers to select suitable mouse models of human CRC subtype for their experimental testing.

The evolution of the COVID-19 pandemic in paediatric patients with sickle cell disease: From Alpha to Omicron.

We compare the impact of SARS-CoV-2 variants on healthcare utilization and clinical presentation in paediatric patients with sickle cell disease (SCD). One hundred and ninety-one unique patients with SCD and positive SARS-CoV-2 polymerase chain reactions were identified between March 2020 and January 2022. Hospitalizations, which accounted for 42% (N = 81) of cases, were highest during the Delta dominant era (48%) and lowest during Omicron (36%) (p = 0.285). The most common SCD-related complication was vaso-occlusive pain (37%, N = 71), which accounted for 51% of all hospital admissions (N = 41), and acute chest was highest in the Alpha variant era (N = 15). Overall, COVID-19 remained mild in clinical severity within most paediatric SCD patients.

Return visit rates after an emergency department discharge for children with sickle cell pain episodes.

BACKGROUND: High return visit rates after hospitalization for people with sickle cell disease (SCD) have been previously established. Due to a lack of multicenter emergency department (ED) return visit rate data, the return visit rate following ED discharge for pediatric SCD pain treatment is currently unknown. PROCEDURE: A seven-site retrospective cohort study of discharged ED visits for pain by children with SCD was conducted using the Pediatric Emergency Care Applied Research Network Registry. Visits between January 2017 and November 2021 were identified using previously validated criteria. The primary outcome was the 14-day return visit rate, with 3- and 7-day rates also calculated. Modified Poisson regression was used to analyze associations for age, sex, initial hospitalization rate, and a visit during the COVID-19 pandemic with return visit rates. RESULTS: Of 2548 eligible ED visits, approximately 52% were patients less than 12 years old, 50% were female, and over 95% were non-Hispanic Black. The overall 14-day return visit rate was 29.1% (95% confidence interval [CI]: 27.4%-30.9%; site range 22.7%-31.7%); the 7- and 3-day return visit rates were 23.0% (95% CI: 21.3%-24.6%) and 16.7% (95% CI: 15.3%-18.2%), respectively. Younger children had slightly lower 14-day return visit rates (27.3% vs. 31.1%); there were no associations for site hospitalization rate, sex, and a visit occurring during the pandemic with 14-day returns. CONCLUSION: Nearly 30% of ED discharged visits after SCD pain treatment had a return visit within 14 days. Increased efforts are needed to identify causes for high ED return visit rates and ensure optimal ED and post-ED care.

Consensus definition of essential, optimal, and suggested components of a pediatric sickle cell disease center.

Sickle cell disease (SCD) requires coordinated, specialized medical care for optimal outcomes. There are no United States (US) guidelines that define a pediatric comprehensive SCD program. We report a modified Delphi consensus-seeking process to determine essential, optimal, and suggested elements of a comprehensive pediatric SCD center. Nineteen pediatric SCD specialists participated from the US. Consensus was predefined as 2/3 agreement on each element's categorization. Twenty-six elements were considered essential (required for guideline-based SCD care), 10 were optimal (recommended but not required), and five were suggested. This work lays the foundation for a formal recognition process of pediatric comprehensive SCD centers.

Genome-wide in vivo screen identifies novel host regulators of metastatic colonization.

Metastasis is the leading cause of death for cancer patients. This multi-stage process requires tumour cells to survive in the circulation, extravasate at distant sites, then proliferate; it involves contributions from both the tumour cell and tumour microenvironment ('host', which includes stromal cells and the immune system). Studies suggest the early steps of the metastatic process are relatively efficient, with the post-extravasation regulation of tumour growth ('colonization') being critical in determining metastatic outcome. Here we show the results of screening 810 mutant mouse lines using an in vivo assay to identify microenvironmental regulators of metastatic colonization. We identify 23 genes that, when disrupted in mouse, modify the ability of tumour cells to establish metastatic foci, with 19 of these genes not previously demonstrated to play a role in host control of metastasis. The largest reduction in pulmonary metastasis was observed in sphingosine-1-phosphate (S1P) transporter spinster homologue 2 (Spns2)-deficient mice. We demonstrate a novel outcome of S1P-mediated regulation of lymphocyte trafficking, whereby deletion of Spns2, either globally or in a lymphatic endothelial-specific manner, creates a circulating lymphopenia and a higher percentage of effector T cells and natural killer (NK) cells present in the lung. This allows for potent tumour cell killing, and an overall decreased metastatic burden.

The Great Masquerader: Mycotoxins.

No Abstract Available.

Providing Self-Led Mental Health Support Through an Artificial Intelligence-Powered Chat Bot (Leora) to Meet the Demand of Mental Health Care.

Digital mental health services are becoming increasingly valuable for addressing the global public health burden of mental ill-health. There is significant demand for scalable and effective web-based mental health services. Artificial intelligence (AI) has the potential to improve mental health through the deployment of chatbots. These chatbots can provide round-the-clock support and triage individuals who are reluctant to access traditional health care due to stigma. The aim of this viewpoint paper is to consider the feasibility of AI-powered platforms to support mental well-being. The Leora model is considered a model with the potential to provide mental health support. Leora is a conversational agent that uses AI to engage in conversations with users about their mental health and provide support for minimal-to-mild symptoms of anxiety and depression. The tool is designed to be accessible, personalized, and discreet, offering strategies for promoting well-being and acting as a web-based self-care coach. Across all AI-powered mental health services, there are several challenges in the ethical development and deployment of AI in mental health treatment, including trust and transparency, bias and health inequity, and the potential for negative consequences. To ensure the effective and ethical use of AI in mental health care, researchers must carefully consider these challenges and engage with key stakeholders to provide high-quality mental health support. Validation of the Leora platform through rigorous user testing will be the next step in ensuring the model is effective.

Activation of innate-adaptive immune machinery by poly(I:C) exposes a therapeutic vulnerability to prevent relapse in stroma-rich colon cancer.

OBJECTIVE: Stroma-rich tumours represent a poor prognostic subtype in stage II/III colon cancer (CC), with high relapse rates and limited response to standard adjuvant chemotherapy. DESIGN: To address the lack of efficacious therapeutic options for patients with stroma-rich CC, we stratified our human tumour cohorts according to stromal content, enabling identification of the biology underpinning relapse and potential therapeutic vulnerabilities specifically within stroma-rich tumours that could be exploited clinically. Following human tumour-based discovery and independent clinical validation, we use a series of in vitro and stroma-rich in vivo models to test and validate the therapeutic potential of elevating the biology associated with reduced relapse in human tumours. RESULTS: By performing our analyses specifically within the stroma-rich/high-fibroblast (HiFi) subtype of CC, we identify and validate the clinical value of a HiFi-specific prognostic signature (HPS), which stratifies tumours based on STAT1-related signalling (High-HPS v Low-HPS=HR 0.093, CI 0.019 to 0.466). Using in silico, in vitro and in vivo models, we demonstrate that the HPS is associated with antigen processing and presentation within discrete immune lineages in stroma-rich CC, downstream of double-stranded RNA and viral response signalling. Treatment with the TLR3 agonist poly(I:C) elevated the HPS signalling and antigen processing phenotype across in vitro and in vivo models. In an in vivo model of stroma-rich CC, poly(I:C) treatment significantly increased systemic cytotoxic T cell activity (p<0.05) and reduced liver metastases (p<0.0002). CONCLUSION: This study reveals new biological insight that offers a novel therapeutic option to reduce relapse rates in patients with the worst prognosis CC.

Amino acid signature during sickle cell pain crisis shows significant alterations related to nitric oxide and energy metabolism.

Nitric oxide depletion secondary to arginase induced arginine deficiency has been shown to be important in the pathophysiology of vaso-occlusion in sickle cell pain crisis. Our objective of this study was to perform a comprehensive amino acid evaluation during sickle cell pain crisis. In a total of 58 subjects (29 in steady-state sickle cell disease and 29 with sickle cell pain crisis), the amino acids related to nitric oxide pathway was significantly decreased during sickle cell pain crisis compared to steady-state sickle cell disease: arginine (p = 0.001), citrulline (p = 0.012), and ornithine (p = 0.03). In addition, the amino acids related to energy metabolism was significantly decreased during a pain crisis: asparagine (p < 0.001), serine (p = 0.002), histidine (p = 0.017), alanine (p = 0.004), tyrosine (p = 0.012), methionine (p = 0.007), cystine (p = 0.016), isoleucine (p = 0.016) and lysine (p = 0.006). The amino acid related to oxidative stress were significantly higher during a sickle cell pain crisis (glutamic acid (p < 0.001). Furthermore, multivariate analysis with partial least squares-discriminant analysis (PLS-DA) showed that deficiencies of the amino acids arginine, asparagine, citrulline, methionine and alanine were the most important related to sickle cell pain crisis.

Safety and efficacy of voxelotor in pediatric patients with sickle cell disease aged 4 to 11 years.

BACKGROUND: Sickle cell disease (SCD) is a devastating, multisystemic disorder that affects millions of people worldwide. The earliest clinical manifestations of SCD can affect infants as young as 6 months of age, and pediatric patients are at risk for acute and life-threatening complications. Early intervention with treatments that target the underlying pathophysiological mechanism of SCD, sickle hemoglobin (HbS) polymerization, are expected to slow disease progression and circumvent disease-associated morbidity and mortality. PROCEDURE: The HOPE-KIDS 1 trial (NCT02850406) is an ongoing four-part, phase 2a, open-label, single- and multiple-dose study to evaluate the pharmacokinetics, efficacy, and safety of voxelotor-a first-in-class HbS polymerization inhibitor-in patients aged 6 months to 17 years with SCD. Initial findings from a cohort of 45 patients aged 4 to 11 years who received voxelotor treatment for up to 48 weeks are reported. RESULTS: Hemoglobin (Hb) response, defined as a >1.0 g/dl increase from baseline, was achieved at week 24 by 47% (n = 16/34) of patients with Hb measurements at baseline and week 24. At week 24, 35% (n = 12/34) and 21% (n = 7/34) of patients had a >1.5 g/dl increase and a >2.0 g/dl increase from baseline in Hb concentration, respectively. Concurrent improvements in hemolytic markers were observed. Voxelotor was well tolerated in this young cohort, with no newly emerging safety signals. CONCLUSIONS: Based on its mechanism as an HbS polymerization inhibitor, voxelotor improves Hb levels and markers of hemolysis and has the potential to mitigate SCD-related complications; these results support its use in patients aged ≥4 years.

Fully automated detection of formal thought disorder with Time-series Augmented Representations for Detection of Incoherent Speech (TARDIS).

Formal thought disorder (ThD) is a clinical sign of schizophrenia amongst other serious mental health conditions. ThD can be recognized by observing incoherent speech - speech in which it is difficult to perceive connections between successive utterances and lacks a clear global theme. Automated assessment of the coherence of speech in patients with schizophrenia has been an active area of research for over a decade, in an effort to develop an objective and reliable instrument through which to quantify ThD. However, this work has largely been conducted in controlled settings using structured interviews and depended upon manual transcription services to render audio recordings amenable to computational analysis. In this paper, we present an evaluation of such automated methods in the context of a fully automated system using Automated Speech Recognition (ASR) in place of a manual transcription service, with "audio diaries" collected in naturalistic settings from participants experiencing Auditory Verbal Hallucinations (AVH). We show that performance lost due to ASR errors can often be restored through the application of Time-Series Augmented Representations for Detection of Incoherent Speech (TARDIS), a novel approach that involves treating the sequence of coherence scores from a transcript as a time-series, providing features for machine learning. With ASR, TARDIS improves average AUC across coherence metrics for detection of severe ThD by 0.09; average correlation with human-labeled derailment scores by 0.10; and average correlation between coherence estimates from manual and ASR-derived transcripts by 0.29. In addition, TARDIS improves the agreement between coherence estimates from manual transcripts and human judgment and correlation with self-reported estimates of AVH symptom severity. As such, TARDIS eliminates a fundamental barrier to the deployment of automated methods to detect linguistic indicators of ThD to monitor and improve clinical care in serious mental illness.

Pain Burden in the CASiRe International Cohort of Sickle Cell Patients: United States and Ghana.

OBJECTIVES: Sickle Cell Disease (SCD) is a genetic blood disorder affecting over 1 million people globally. The aim of this analysis is to explore the pain burden of patients with SCD in two countries: the United States and Ghana. METHODS: The Consortium for the Advancement of Sickle Cell Research (CASiRe) was created to better understand the clinical severity of patients with SCD worldwide. Data regarding gender, SCD genotype, prior medical diagnoses, and validated pain burden measures were analyzed from the CASiRe database. The Sickle Cell Pain Burden Interview (SCPBI) was used to assess pain burden, the impact of pain on physical, emotional, and social function. RESULTS: Most subjects identified as Black/African American (n = 298, 97.0%). Patient ages ranged from 6 to 73 years. 35.9% resided in the United States, 64.1% resided in Ghana, 40.9% were men, and 58.7% were women. The mean SCPBI score for US SCD patients was 6.53(±5.89) vs 4.04(±5.10) for Ghanaian patients, P <0.001. Pain burden was higher in US men vs Ghanaian men (6.74(±5.68) vs 3.54(±4.46), P = .003) and in US women vs Ghanaian women (6.37 ± 6.06 vs 4.44(±5.54), P = .032). Pain burden was higher in US patients than Ghanaian patients for both the Hb SC/SBeta+ genotype (5.40(±5.29) vs 2.82(±4.86), P = .054) and Hb SS/SBeta0 genotype (6.79(±6.01) vs 4.49(±5.13), P = .003). Pain burden was significantly higher in SCD patients with comorbid conditions independent of geographic origin including stroke, cholecystectomy, gallstones, depression, and headache. DISCUSSION: US patients with SCD have a higher pain burden than Ghanaian patients. Further studies should investigate underlying contributors to pain burden in these populations and further explore the etiology of geographic differences in pain.

Molds, Mycotoxins, the Brain, the Gut and Misconceptions.

Molds are present in homes and other indoor places that are water damaged and they produce mycotoxins. One mold species can produce several different mycotoxins and one mycotoxin can come from several different molds. Even small amounts of mold growth in an air conditioner or in ducts will result in the occupants being chronically exposed, constantly breathing mold spores and mycotoxins, causing illness.

Social connectedness, mindfulness, and coping as protective factors during the COVID-19 pandemic.

The COVID-19 pandemic has had an unprecedented psychological impact, revealing immense emotional disturbances among the general population. This study examined the extent to which social connectedness, dispositional mindfulness, and coping moderate symptoms of anxiety and depression in 1242 adults under the same government-issued COVID-19 stay-at-home mandate. Participants completed measures of anxiety, depression, dispositional mindfulness, social connectedness, and coping, and regression analyses were used to examine associations and interaction effects. Results indicated that social connectedness and dispositional mindfulness were associated with reduced symptoms. For individuals living with a partner, decreased mindfulness and avoidant coping were associated with anxious symptoms. In households with children, overutilization of approach coping served to increase symptoms of depression. Results indicate the importance of considering social connectedness, mindfulness, and coping in counseling to enhance factors serving to protect clients during a public health crisis. Implications for professional counselors and areas of future research are discussed.

MCL1 Is Required for Maintenance of Intestinal Homeostasis and Prevention of Carcinogenesis in Mice.

BACKGROUND & AIMS: Intestinal epithelial homeostasis depends on a tightly regulated balance between intestinal epithelial cell (IEC) death and proliferation. While the disruption of several IEC death regulating factors result in intestinal inflammation, the loss of the anti-apoptotic BCL2 family members BCL2 and BCL2L1 has no effect on intestinal homeostasis in mice. We investigated the functions of the antiapoptotic protein MCL1, another member of the BCL2 family, in intestinal homeostasis in mice. METHODS: We generated mice with IEC-specific disruption of Mcl1 (Mcl1ΔIEC mice) or tamoxifen-inducible IEC-specific disruption of Mcl1 (i-Mcl1ΔIEC mice); these mice and mice with full-length Mcl1 (controls) were raised under normal or germ-free conditions. Mice were analyzed by endoscopy and for intestinal epithelial barrier permeability. Intestinal tissues were analyzed by histology, in situ hybridization, proliferation assays, and immunoblots. Levels of calprotectin, a marker of intestinal inflammation, were measured in intestinal tissues and feces. RESULTS: Mcl1ΔIEC mice spontaneously developed apoptotic enterocolopathy, characterized by increased IEC apoptosis, hyperproliferative crypts, epithelial barrier dysfunction, and chronic inflammation. Loss of MCL1 retained intestinal crypts in a hyperproliferated state and prevented the differentiation of intestinal stem cells. Proliferation of intestinal stem cells in MCL1-deficient mice required WNT signaling and was associated with DNA damage accumulation. By 1 year of age, Mcl1ΔIEC mice developed intestinal tumors with morphologic and genetic features of human adenomas and carcinomas. Germ-free housing of Mcl1ΔIEC mice reduced markers of microbiota-induced intestinal inflammation but not tumor development. CONCLUSION: The antiapoptotic protein MCL1, a member of the BCL2 family, is required for maintenance of intestinal homeostasis and prevention of carcinogenesis in mice. Loss of MCL1 results in development of intestinal carcinomas, even under germ-free conditions, and therefore does not involve microbe-induced chronic inflammation. Mcl1ΔIEC mice might be used to study apoptotic enterocolopathy and inflammatory bowel diseases.

Deep Learning-Based Annotation Transfer between Molecular Imaging Modalities: An Automated Workflow for Multimodal Data Integration.

An ever-increasing array of imaging technologies are being used in the study of complex biological samples, each of which provides complementary, occasionally overlapping information at different length scales and spatial resolutions. It is important to understand the information provided by one technique in the context of the other to achieve a more holistic overview of such complex samples. One way to achieve this is to use annotations from one modality to investigate additional modalities. For microscopy-based techniques, these annotations could be manually generated using digital pathology software or automatically generated by machine learning (including deep learning) methods. Here, we present a generic method for using annotations from one microscopy modality to extract information from complementary modalities. We also present a fast, general, multimodal registration workflow [evaluated on multiple mass spectrometry imaging (MSI) modalities, matrix-assisted laser desorption/ionization, desorption electrospray ionization, and rapid evaporative ionization mass spectrometry] for automatic alignment of complex data sets, demonstrating an order of magnitude speed-up compared to previously published work. To demonstrate the power of the annotation transfer and multimodal registration workflows, we combine MSI, histological staining (such as hematoxylin and eosin), and deep learning (automatic annotation of histology images) to investigate a pancreatic cancer mouse model. Neoplastic pancreatic tissue regions, which were histologically indistinguishable from one another, were observed to be metabolically different. We demonstrate the use of the proposed methods to better understand tumor heterogeneity and the tumor microenvironment by transferring machine learning results freely between the two modalities.

Implications of Peak Selection in the Interpretation of Unsupervised Mass Spectrometry Imaging Data Analyses.

Mass spectrometry imaging can produce large amounts of complex spectral and spatial data. Such data sets are often analyzed with unsupervised machine learning approaches, which aim at reducing their complexity and facilitating their interpretation. However, choices made during data processing can impact the overall interpretation of these analyses. This work investigates the impact of the choices made at the peak selection step, which often occurs early in the data processing pipeline. The discussion is done in terms of visualization and interpretation of the results of two commonly used unsupervised approaches: t-distributed stochastic neighbor embedding and k-means clustering, which differ in nature and complexity. Criteria considered for peak selection include those based on hypotheses (exemplified herein in the analysis of metabolic alterations in genetically engineered mouse models of human colorectal cancer), particular molecular classes, and ion intensity. The results suggest that the choices made at the peak selection step have a significant impact in the visual interpretation of the results of either dimensionality reduction or clustering techniques and consequently in any downstream analysis that relies on these. Of particular significance, the results of this work show that while using the most abundant ions can result in interesting structure-related segmentation patterns that correlate well with histological features, using a smaller number of ions specifically selected based on prior knowledge about the biochemistry of the tissues under investigation can result in an easier-to-interpret, potentially more valuable, hypothesis-confirming result. Findings presented will help researchers understand and better utilize unsupervised machine learning approaches to mine high-dimensionality data.