The theft of drugs from healthcare facilities, also known as drug diversion, occurs frequently but is often undetected. This paper describes a research study to develop and test novel drug diversion detection methods. Improved diversion detection and reduction in diversion improves patient safety, limits harm to the person diverting, reduces the public health impact of substance use disorder, and mitigates significant liability risk to pharmacists and their organizations.Ten acute care inpatient hospitals across 4 independent health systems extracted 2 datasets from various health information technology systems. Both datasets were consolidated, normalized, classified, and sampled to provide a harmonious dataset for analysis. Supervised machine learning methods were iteratively used on the initial sample dataset to train algorithms to classify medication movement transactions as involving a low or high risk of diversion. Thereafter, the resulting machine learning model classified the risk of diversion in a historical dataset capturing 8 to 24 months of history that included 27.9 million medication movement transactions by 19,037 nursing, 1,047 pharmacy, and 712 anesthesia clinicians and that included 22 known, blinded diversion cases to measure when the model would have detected the diversion compared to when the diversion was actually detected by existing methods.n = 22) in blinded data faster than existing detection methods .The machine learning model had 96.3% accuracy, 95.9% specificity, and 96.6% sensitivity in detecting transactions involving a high risk of diversion using the initial sample dataset. In subsequent testing using the much larger historical dataset, the analytics detected known diversion cases (The study showed that (1) consolidated datasets and (2) supervised machine learning can detect known diversion cases faster than existing detection methods. Users of the technology also noted improved investigation efficiency. Drug diversion creates risk for patients, clinicians, and health systems and is a tremendous challenge fueled by the opioid epidemic, the escalating cost of medications, and the growing complexity of health-system medication supply chains and clinical workflows.Most diversion goes undetected by traditional diversion detection methods, such as monthly usage reports, which suffer from low accuracy, specificity, and sensitivity.Machine learning and advanced analytics show tremendous potential for improving early detection and investigative effectiveness and efficiency.1 by healthcare workers (HCWs) is a significant national problem, victimizing patients, HCWs, hospitals, and communities alike,2-6 and can occur anywhere in the drug delivery supply chain—from purchasing through patient administration and/or safe disposal.7 For patients, diversion can lead to substandard care or infections from an addicted HCW whose job function is impaired by drugs.9 While significant advances to improve inpatient safety and reduce drug diversion have occurred over the past 40 years through efforts supported by standards and regulatory bodies, including the American Society of Health-System Pharmacists (ASHP),10 the Joint Commission, the Drug Enforcement Administration, and state licensure boards of pharmacy,11 nursing,12-16 and medicine,17 any innovation or technology that further reduces drug diversion in hospitals provides substantial benefits.18Drug diversion19 Due to the clandestine nature of diversion and the culture in place in many healthcare institutions, diversion is often left undiscovered and/or unreported.20 Numerous investigations and studies have found that roughly 10% of US HCWs abuse controlled substances, a rate that matches abuse within the US population.21-27 The US Substance Abuse and Mental Health Services Administration found most drug diversion by HCWs is not discovered and, when identified, is often not reported or prosecuted.28 A study of 140 facilities conducted by Porter Research in 2017 highlighted many of these challenges and indicated that 65% of respondents believe the majority of diversion goes undetected.29Precise data on drug diversion in individual hospitals is difficult to obtain.31Traditionally, hospitals seek to prevent drug diversion by having strict chain-of-custody policies, such as requiring that 2 clinicians sign off on waste destruction. In most hospitals, current methods of detecting drug diversion rely on historical reporting from automated dispensing cabinets (ADCs), such as monthly anomalous usage reports or daily discrepancy reports. These approaches have several significant challenges. They typically look back historically at data over several months, causing them to be slow to detect diversion. They can fail to detect diversion, particularly when the person diverting takes active steps to hide their anomalous usage. They also often erroneously flag clinicians who are not diverting. As a result, there is growing demand to improve drug diversion detection methods in hospitals and other healthcare facilities in order to detect diversion sooner, with fewer false positives and false negatives.This paper describes a novel innovation to overcome these challenges and improve drug diversion detection utilizing (1) data sources consolidated from multiple information technology (IT) sources and (2) machine learning analytics.This research investigated the effectiveness of (1) building consolidated datasets from multiple health IT systems, (2) training machine learning models to detect drug diversion in small datasets, and finally (3) testing if those models could detect known diversion cases faster than existing methods in large historical datasets.The study, approved by our organizations’ institutional review boards, involved building datasets of clinical data from a total of 10 acute care inpatient hospitals. Hospitals were selected to provide a variety of geographical regions, hospital sizes, IT vendors, and maturity of drug diversion prevention programs. One pediatric hospital was also included. Each hospital participating agreed to provide the necessary datasets to undertake the research study, including their recent known diversion incidents. 32The study was designed to test if the use of artificial intelligence, namely machine learning, could automate currently manual methods for detecting diversion in large datasets and thereby offload time-consuming pharmacy operations tasks, allowing pharmacists to focus on more high-value patient care activities, consistent with ASHP’s statement on the use of artificial intelligence in pharmacy.Interviews with pharmacy and compliance personnel at these hospitals were conducted to document existing methods to detect diversion. All hospitals reported that the work required to detect and investigate diversion largely was the responsibility of the pharmacy department, with occasional support from the compliance department, even though the majority of the diversion cases occurred in other departments, notably nursing and anesthesia.All hospitals reported similar methods for detecting potential diversion leveraging 2 main techniques: (1) reviewing daily discrepancy reports and monthly anomalous usage reports generated from the ADCs and (2) asking clinicians to report impaired behavior by colleagues. Once suspected diversion was detected, all hospitals reported similar investigation methods: First, the investigator manually reconciled ADC and electronic medical record (EMR) records, which they reported required between 4 and 20 hours, depending on the number of transactions by a particular clinician. If that audit showed further evidence of potential diversion, the investigator proceeded with subsequent investigation steps, such as interviewing the clinican suspected of diversion and/or their supervisor.These existing methods had detected a total of 22 cases of confirmed diversion, which were studied in more detail as discussed below.Each participating hospital provided electronic transaction data extracted from the source IT vendor systems shown in 33 Data consolidation also allowed a variety of novel cross-system input features to be tested, which would not have been possible without consolidation. Examples included: (1) reconciling WHL shipments with receipts in the IIS to detect if full or partial shipments had been diverted during shipment from the wholesaler by couriers or during receipt at the hospital by pharmacists; (2) reconciling IIS movement of medications from a central pharmacy to the restocking of an ADC to detect if some or all of the quantity moved had been diverted by pharmacy technicians; (3) reconciling ADC dispenses with EMR documentation of administration and any subsequent ADC wasting to detect if some or all of the quantity dispensed had been diverted by nursing or anesthesia providers; (4) comparing clock-in and clock-out ETC transactions with the times of ADC, IIS, and EMR transactions to detect if clinicians had accessed medications at times they were not working “on the clock”; (5) comparing clinical data in the EMR, such as pain score information, with ADC dispensing patterns to detect unusually high dispensing in relation to episodes of high or low patient pain scores.Data consolidation was essential to allow the analytics to track medication movement transactions from wholesaler purchases through patient administration, along with any waste generated.Significant effort was required to ingest and consolidate the data from these 5 different IT systems because they are typically independent from each other. For example, consolidation required a medication formulary that mapped medication identification numbers in the EMR to medication identification numbers in the WHL, ADC, and IIS systems. In hospitals without single sign-on (SSO) technology, consolidation also required feature creation to enable mapping of username information across the EMR, ADC, IIS, and ETC systems . After the data was consolidated, additional significant effort was required to translate the raw data into meaningful input features for machine learning. For example, to calculate if any dispensed medications were missing, the analytics had to merge the raw ADC dispensed, wasted, returned, and transferred quantities with the raw EMR administered quantities, which in turn required accurately accounting for medication movements across many diverse clinical workflows, such as dispenses from nonprofiled cabinets in procedural areas, and many different dosage forms, such as oral solids, injections, patient-specific infusions, patient controlled analgesia (PCA), and patches. The analytics also were engineered to handle null data elements .Data was initially extracted as a set of text-based flat files and then loaded using manual data loading routines into a single normalized, relational database, thereby merging these disparate IT systems into a single normalized, relational database. The data extraction and ingestion were subsequently automated to append recent data on a daily basis using a variety of extraction, transformation, and loading (ETL) and enterprise application integration (EAI) methods.34-37 A review of these earlier studies yielded many candidate predictors of diversion.  This research builds on promising earlier studies from a variety of experts showing that analytical systems can detect drug diversion much faster than humans while simultaneously cutting down on effort and user errors.38 training models were refined iteratively, over the course of a 12-month period, using various datasets, features, and classification thresholds and algorithms. In every iteration, our supervised machine learning methods used one-hot encoded features and split the dataset: 60% of transactions were used for model training, with the remaining 40% of transactions used to test the accuracy of classification.Supervised machine learningInitial testing used small datasets from Piedmont Healthcare and a pediatric hospital at a health system designated as Health System 3. These tests investigated various classification methods to assign a numerical risk score to every medication movement transaction, thereby defining transactions associated with a high risk of diversion. We surveyed experienced drug diversion investigators at those facilities, asking them to rank the candidate predictors in This initial data analysis allowed these 2 health systems to identify several diversion incidents in the initial sample data, prompting an expansion of the scope of the study to include Scripps Health and a fourth health system.With this expanded dataset, we iteratively tested the accuracy of a variety of machine learning classification algorithms, such as random forest and logistical regression algorithms. To reduce the risk of confirmation bias and overfitting, we introduced K-fold cross-validation and replaced the initial survey results with a heuristic that classified transactions in the 98th percentile as high-risk transactions.  Accuracy was improved by using oversampling of high-risk transactions in the dataset, because most transactions were no-risk and thus oversampling the relatively rare high-risk transactions provided more training data for high-risk transactions. We also investigated model multiclass classification using the logistic regression one-versus-all method, but this did not improve accuracy over the single class classification. As a result of these iterative methods, peak accuracy was reached when the machine learning model’s classification of the high-risk transactions in the initial sample dataset was demonstrated to have 96.3% accuracy, 95.9% specificity, and 96.6% sensitivity, and this model was ready for testing against larger, historical datasets containing known diversion cases.After training of the machine learning model was completed, each health system compiled a list of known drug diversion incidents that had been detected using existing methods prior to the development of the software. These incidents were not used for training the machine learning model but, rather, were used to test the effectiveness of the model, particularly to test (1) if the machine learning models would detect known diversion incidents in the dataset and (2) when that detection would have occurred had the software been available at the time of the diversion.We then loaded a large historical dataset with 8 to 24 months of history that included 27.9 million medication movement transactions by 19,037 nursing, 1,047 pharmacy, and 712 anesthesia clinicians, including 22 nurses who were known to have diverted medications, as shown in Finally, for each of the 22 known diversion incidents, we recorded when the software first flagged high-risk transactions and compared that date to when the diversion had been detected without the use of the software, as described in the next section.One participating health system, Piedmont Healthcare, assembled a list of 9 known diversion incidents during a 24-month period before these analytics had been developed. The medication movement transactions in that dataset were then classified using the machine learning model, and the transactions entered by the 9 clinicians were inspected to determine if the model had classified any of those transactions as high-risk alerts and, if so, the earliest date of the high-risk alert(s).As shown in Scripps Health assembled a list of 7 known diversion incidents during an 8-month period before these analytics were developed, as listed in The software detected all 7 diversion incidents faster than Scripps Health had first detected the diversion , as shown in Health System 4 assembled a list of 6 known diversion incidents during an 18-month period before these analytics were developed, as listed in The software detected all 7 diversion incidents faster than the health system had first detected the diversion , as shown in  Users of the technology also reported that auditing ADC and EMR records required only 10 to 30 minutes with the consolidated dataset; in comparison, auditing required 4 to 20 hours of manual reconciliation using existing methods.Here we report for the first time how hospitals have integrated various existing IT data sources and implemented machine learning analytics to effectively detect drug diversion. To our knowledge, this is the first published study in which multiple hospitals consolidated data from various healthcare IT systems, such as ADCs and EMRs, for the purpose of detecting drug diversion, and we provide here the first evidence-based examples showing how machine learning algorithms have successfully detected known diversion incidents in blinded data.These experimental results showed significant improvements in the effectiveness of drug diversion detection as compared to existing methods. This technology detected all of the 22 known diversion incidents from 4 separate and diverse health systems, which included 10 hospitals, over 20,000 clinicians, and over 25 million medication movement transactions. Moreover, the method detected the diversion incidents much faster than they had been detected using existing methods.39 particularly the system-level controls for (1) automation and technology, (2) monitoring and surveillance, and (3) investigation and reporting. The software is also consistent with best practices published by other healthcare organizations, such as the Joint Commission40 and the Council of State and Territorial Epidemiologists.41Given these results, all participating hospitals expanded their use of the technology, and the National Institute on Drug Abuse funded additional research to accelerate commercialization of the software. This software supports best practices for preventing diversion, as documented in the ASHP guidelines for preventing diversion,42-44 Automated daily feeds are now in place, allowing the original 10 hospitals, and a growing list of additional hospitals, to see alerts of suspected diversion incidents in near real time, enabling them to start investigations much faster compared to their prior use of monthly monitoring reports and to conduct those investigations more efficiently. The software allows each health system to see patterns of high-risk behavior across all care areas over time. Heatmaps and other visualizations illustrate which transactions, and which clinicians, have been flagged by the software as posing a high risk of diversion. Visualizations include the ability to (1) trend behaviors for the nursing unit, (2) trend behavior against other users on the same unit, (3) trend recorded assessments to identify investigation needed versus practice defects, as well as other clinical anomalies, and (4) conduct additional peer-to-peer comparisons.Users of this software report improved effectiveness and efficiency.Users at these hospitals investigate high-risk transactions flagged by the model, assess if an alert was accurate, and enter that assessment into the dataset, thereby providing external validation of the accuracy of the machine learning model as well as providing an expanding dataset for training. This growing dataset also allows us to occasionally revise the input feature set, discarding features that are less relevant and experimenting with new potential features, thereby reducing the risk of model drift/decay.As a secondary outcome, users of the software also reported an improvement in investigation efficiency when suspected diversion is detected, with auditing time dropping from 4 to 20 hours of manual reconciliation using existing methods to 10 to 30 minutes with the consolidated dataset.The combined improvement in both effectiveness and efficiency shows great promise that automating currently manual methods for detecting and investigating diversion using machine learning will offload time-consuming pharmacy operations tasks, allowing pharmacists to focus on more high-value patient care activities.45 Treatment programs are vital for impaired HCWs who seek out help, and these programs have a high overall recovery rate when grouped with intensive inpatient management and follow-up care.46 These components combined create a robust process for diversion deterrence and prevention.Health systems are using this software as one key component of their overall controlled substance diversion prevention program (CSDPP). Hospitals need investigations conducted by trained drug diversion professionals and a leadership team of departmental heads in order to protect innocent HCWs and to confirm the software’s alerts. It is worth noting that once a suspected diversion has been detected, the value of an interview is key, and the Mayo Clinic estimates that approximately 75% of suspected diversion investigations have been brought to closure by a confession on the part of the drug diverter.Moreover, health systems are using this software to expand their CSDPP to not only detect diversion of controlled substances but also to expand their surveillance to include medications that are not controlled substances, particularly medications associated with a high risk of diversion due to their high value. With the increasing cost of medications, as well as the growing complexity of health systems and facilities, all participants in this study recognized the expanding need to monitor their supply chains beyond controlled substances to also include expensive drugs and other frequently diverted substances.n = 22), so the results are based on a fairly small population of diversion incidents. Third, we focused on nursing personnel, and future research will expand to pharmacy, anesthesia, and other clinical areas. For example, in pharmacy we will investigate diversion detection in purchasing, stocking, compounding, repackaging, and returns. Fourth, we tested sensitivity, not specificity . Because this study focused on experiments to confirm that the software could detect previously known drug diversion incidents, additional research is currently underway to (1) confirm that the analytics can detect previously unknown drug diversion incidents; (2) measure specificity in order to develop analytics that will yield fewer alerts than were observed in this study through use of the 98th percentile, and to confirm those analytics produce fewer false positives than existing methods; and (3) improve accuracy over time by engineering new input features and adding new data to the machine learning training set. This research will also allow us to measure and learn from false negatives . That additional research is being conducted in the same hospitals, as well as additional hospitals that have since joined the study, and will be published when completed.There were several limitations to our study. First, we focused on detecting known diversion incidents rather than detecting previously unknown incidents. The latter will be studied in a future phase by asking the same hospitals that participated in this study to investigate and confirm if the analytics successfully detected previously unknown diversion incidents. Second, the number of known diversion incidents in the study was fairly small  reducing the effort required to detect diversion for hospital administrators, nurse managers, and other supervisors.These results provided visibility to the inadequacy of existing detection methods. The key benefits are (1) detecting diversion faster,n = 22) in blinded data faster than existing monitoring systems: a mean of 160 days and a median of 74 days faster . Moreover, users of the software have noted a dramatic improvement in investigation efficiency when suspected diversion is detected.The study showed that (1) a consolidated dataset and (2) supervised machine learning can detect known diversion cases faster than existing detection methods. These analytics detected known diversion cases (


In plant cells, almost all kinds of abiotic stresses are able to raise cytosolic Ca2+ levels, and the spatiotemporal distribution of this molecule in distant cells suggests that Ca2+ may be a universal signal regulating different kinds of abiotic stress. Ca2+ is used to sense and transduce various stress signals through its downstream calcium-binding proteins, thereby inducing a series of biochemical reactions to adapt to or resist various stresses. This review summarizes the roles and molecular mechanisms of cytosolic Ca2+ in response to abiotic stresses such as drought, high salinity, ultraviolet light, heavy metals, waterlogging, extreme temperature and wounding. Furthermore, we focused on the crosstalk between Ca2+ and other signaling molecules in plants suffering from extreme environmental stress.Plants have evolved many strategies for adaptation to extreme environments. Ca CBLs interact with CBL-interacting protein kinases (CIPKs) to form a CBL/CIPK signaling network, which plays a key role in the plant response to abiotic stress. These networks may contain many interactions, with CBLs activating CIPKs and CIPKs phosphorylating CBLs. Phosphorylation is the major mechanism affecting downstream proteins .,20.2+-si2+, acting as a secondary messenger, plays an essential role in the plant response to abiotic stresses; it can not only transmit and recognize various regulatory signals but also participate in gene expression and normal protein functions cyt increase caused by NaCl in the wild-type, hyperaccumulation of cytosolic Ca2+ in PCA1 mutants remained high and did not return to prestimulus [Ca2+]cyt levels. Therefore, Ca2+ pumps contribute to the production of stress-induced Ca2+ signatures [2+]i increases 1 Arabidopsismonocation-induced cyt increases rapidly to induce calcium signaling cyt and cytoplasmic alkalization and activate anion channels in both wild-type and mutant plants, thus causing the stomata to be closed. However, compared with that in wild-type Arabidopsis, exogenous Ca2+ could not induce stomatal closure or activate anion channels on the plasma membrane in attpc1-2 mutants. Taken together, we can conclude that AtTPC1 protein is involved in both stomatal closure and plasma membrane anion channel activation and is regulated by exogenous calcium signals in guard cells; however, it is not regulated by ABA and MeJA cyt when high-temperature stress occurs, while the expression of PhCAM1 and PhCAM2 is not obviously changed after EGTA is added, implying that the Ca2+ signaling system and CAM play a major role in the regulation of resistance to high-temperature stress in Pyropia haitanensis cyt increase. The defense response occurs by catalyzing the elicitor peptide precursors into mature peptides located on the cytoplasmic side of the vacuole membrane; in turn, these peptides are recognized by the cytoplasmic vacuolar membrane-targeted receptor-elicitor peptide receptors cyt increase and ROS accumulation, which may be interdependent cyt. Furthermore, a slightly increased [Ca2+]cyt can not only act on the membrane skeleton and significantly reduce the deformability of cells but is also involved in the lipid redistribution of the membrane and the decline in membrane stability cyt in a precise and complex way so that they can respond to a series of complex upstream signals accurately and exclusively and ensure signal transduction sensitivity and specificity concurrently. Furthermore, because crosstalk between Ca2+ and other signaling molecules is vital for the stress response, the mechanism of stress perception and the system of signal transduction at the biological level should be investigated. Therefore, the next important task for Ca2+ signaling research is to determine which physiological reactions are involved in the various Ca2+-targeted proteins downstream of calcium signaling and which downstream molecules are regulated to affect gene expression. Moreover, with recent advances in techniques and the development of molecular biology, cell biology, genetics and other disciplines, the role of Ca2+ signaling will certainly be elucidated more thoroughly.Although the many molecular mechanisms behind Ca


Ulcerative Colitis (UC) is a chronic inflammatory disease of the intestinal tract for which a definitive etiology is yet unknown. Both genetic and environmental factors have been implicated in the development of UC. Recently, single cell RNA sequencing (scRNA-seq) technology revealed cell subpopulations contributing to the pathogenesis of UC and brought new insight into the pathways that connect genome to pathology. This review describes key scRNA-seq findings in two major studies by Broad Institute and University of Oxford, investigating the transcriptomic landscape of epithelial cells in UC. We focus on five major findings: (1) the identification of BEST4 + cells, (2) colonic microfold (M) cells, (3) detailed comparison of the transcriptomes of goblet cells, and (4) colonocytes and (5) stem cells in health and disease. In analyzing the two studies, we identify the commonalities and differences in methodologies, results, and conclusions, offering possible explanations, and validated several cell cluster markers. In systematizing the results, we hope to offer a framework that the broad scientific GI community and GI clinicians can use to replicate or corroborate the extensive new findings that RNA-seq offers. Ulcerative Colitis (UC) is a chronic and debilitating inflammatory disease of the colon, and is a distinct condition within a broad group of pathologies termed inflammatory bowel disease (IBD), which includes Crohn’s disease (CD) and indeterminate colitis. The etiology of UC remains uncertain, but increasing evidence implicates complex genetic and environmental contributions. Early genomic and immunologic studies were crucial to identifying therapeutic targets and avoiding surgical intervention. Development of infliximab and other monoclonal antibodies against TNFα, and the biologics targeting cytokines (IL-12 or IL-23) that significantly reduce inflammation and induce macroscopic/endoscopic healing, were major breakthroughs in the management of UC .With the development of biologic therapies, the goal of UC treatment extended beyond symptomatic improvement to include both histologic and molecular healing. Several observational studies have shown that histological remission, including epithelial tissue restoration, is associated with lower rates of disease-related complications such as hospitalization, corticosteroid use, and colectomy compared to either resolution of symptoms or endoscopic improvement alone –7. RecogThere are numerous UC-related pathologies that contribute to inadequate epithelial maintenance and regeneration. Cell turnover is increased due to autophagy and apoptosis, yet colonocyte differentiation is reduced –11. BarrSingle cell RNA sequencing (scRNA-seq) allows for in-depth transcriptional characterization of epithelial pathologies in UC and identification of possible pharmacologically fruitful targets. In this review, we discuss findings from two independent scRNA-seq datasets generated by the Broad Institute  and the The heterogeneity of cells that compose the normal colonic epithelium originates from a common intestinal stem cell through the actions of multiple niche factors. As cells migrate from the crypt to surface epithelium, they mature into specialized cells for molecular transport, sensing, secretion, and barrier formation. Each epithelial cell type is critical to sustain organ function, and perturbations in the proportion or phenotype of a cell lineage can contribute to disease. Thus, approaches to study epithelial cell identity and function must distinguish among unique populations within the epithelium.Transcript analyses using mRNA microarrays that preceded RNA-seq analysis allowed for the simultaneous detection of thousands of genes at one time  and was The technical aspects of scRNA-seq are described in great detail in several excellent reviews –30. TypiThe complexity of genetics and environmental factors that fuel UC make it a particularly good candidate for investigation through single cell transcriptomics. scRNA-seq potentially allows for detailed molecular snapshots of the different cell types composing the intestinal mucosa in health and UC. In particular, this technology facilitated the identification of previously unrecognized cell types and their potential transcriptional shift in disease that correlated to disease severity. These new findings could have direct implications in the management of UC. By stratifying patients on the basis of underlying transcriptional and genetic variability, scRNA-seq opens new ways for the development of targeted therapies.+ cells, colonic M-like cells, goblet cells, colonocytes and stem cells.A comparison of parameters and outcomes in the Broad and Oxford scRNA-seq studies is highlighted in +-sensitive chloride channel (BEST4) and proton-conducting ion channel (OTOP2) proposed to regulate luminal pH. These cells were also enriched in the paracrine hormones guanylin (GUCA2A) and uroguanylin (GUCA2B), which can stimulate fluid and electrolyte transport through action on the receptor guanylate cyclase. Among 46 transcripts significantly elevated in healthy BEST4 + cells in the Broad study, 29 were also detected as significantly elevated in the Broad study (Both data sets discovered a novel differentiated absorptive surface colonocyte cluster characterized by expression of a Ca2ad study . However1) of the characteristic transcripts of BEST4 cells which confirmed across both studies that in healthy tissue, BEST4 cell function includes metallothionein expression (HSA-5661231), guanylate cyclase/dehydratase activity (GO:0030250), and proton channel activity (GO:0015252). Surprisingly, proteomic analysis of these BEST4 cells, conducted in the Oxford study, identified several highly upregulated proteins as BAG1 and APOO. These transcripts were not identified as “characteristic” for this cell cluster in Oxford scRNA-seq dataset . These data indicate that further validation at the protein levels are necessary to reliably identify these BEST4 + cells in tissue and better define their role in colonic physiology.We performed STRING interactome analysis  cells are part of an integrated system of immunosurveillance in the intestinal mucosa, with a principal function of transporting luminal antigens to gut-associated lymphoid tissue , 37. ThiAccording to the Broad study, specific markers of colonic M-like cells in healthy tissue were quite different from M cells in ileal Peyer’s patches. Glycoprotein 2 (GP2), the M cell-specific marker that functions as a bacterial uptake receptor in the Peyer’s patches, was not reported in colonic M-like cells, and neither were genes such as PGLYRP2, CLEC7A (Dectin-1), nor JAG1. M cells from Peyer’s patches and colon shared only two transcripts, CCL20 and SPIB, which are known to initiate M cell differentiation . CCL20 iWe analyzed differences between M-like cells in healthy controls and UC . Not onl2FC ≥ 4.0) in both, mature and immature GCs in Broad study were also elevated in this cell type in Oxford study  in relationship to the colonic crypt-surface axis. GCs were sub-classified into immature or mature populations (Broad), or grouped into five transcriptionally distinct clusters (Oxford). Among the 64 major GC markers identified by the Oxford study, 35 were also found elevated in GC clusters in the Broad study. Twelve transcripts highly elevated  cells). MUC12 is listed as elevated in GC cluster 4 in the Oxford study, but is elevated in colonocytes in the Broad study. MUC13 is enriched in mature GCs and in absorptive cells in both studies. MUC17 was not reported in GCs in any of the studies. Surprisingly, gel-forming MUC5B was elevated in mature GCs in the Oxford study but was not listed in GC clusters in the Broad study .The changes in goblet cells in UC through the prism of scRNA-seq are striking. It is widely accepted that number of GCs and the luminal mucus layer are significantly reduced in UC-damaged epithelia . Both stOverall, GC clusters in UC partially lose their secretory identity and are characterized by a mix of transcripts present in both absorptive and secretory lineages. Each GC cluster in UC exhibited highly heterogeneous transcriptional changes, and a consensus for GC markers was not evident. Among 139 transcripts listed in the Oxford study as upregulated in GCs in UC, only 15 (11%) were also found in the Broad study. Additionally, many of these transcripts were also significantly elevated in non-GCs in UC. This significant transcriptional transformation of GCs may explain their functional transformation.In UC-affected epithelium , only SCSTRING interactome analysis of GC-upregulated transcripts in UC from the Oxford study suggest that GCs gain properties for antigen presentation (HSA04612), stress response (GO:0006950), and immune regulation (GO:0002376). Other studies have alluded to this ability of GCs to act as antigen presenting cells, including delivery of antigens to dendritic cells  or microOne of the most striking results from both scRNA-seq datasets is that MUC2 transcript levels were similar between healthy and UC samples, yet MUC2 protein is substantially decreased in patients with severe UC , 44. TheIt was surprising that transcript levels of MUC5AC, a gel-forming mucin that is typically associated with surface gastric epithelia, but can be induced in the intestine during infection , 46, wasIn conclusion, according to both scRNA-seq datasets, goblet cell clusters in UC lost some of their secretory hallmarks and were characterized by a mix of transcripts present in both absorptive and secretory lineages.Colonocytes represent the largest cell pool in colonic epithelial tissue. Numerous studies have characterized the UC inflammation-induced pathologies of colonocytes and suggested that these pathologies must be pharmacologically addressed to achieve a full and sustained remission. Colonocytes can be spatially and functionally divided into three groups: Undifferentiated cells at the bottom of crypts, cells undergoing a transitional differentiation, and fully differentiated surface colonocytes. For the first time, scRNA-seq allows for precise characterization of the stages of cell differentiation and identification of the number of transcriptionally stable transition clusters which traverse from undifferentiated to fully differentiated. We analyzed the cluster specific markers of colonocytes in health and UC disease to gain insights into the molecular-cellular pathways driving UC disease.Both studies identified a cluster of terminally differentiated colonocytes  named Crypt Top Colonocytes (CTC) in the Oxford study or Enterocytes in the Broad study. The CTC cluster included ∼ 264 upregulated transcripts (AUC > 0.7) and Enterocyte cluster included ∼150 significantly upregulated transcripts. Seventeen of these highly elevated transcripts are identified in both studies but neither of these transcripts is cluster-specific . HoweverMany transcripts highly upregulated in differentiated colonocytes in one  or both Only four transcripts were discordant . These tIn UC inflammation, only four highly elevated and specific transcripts for differentiated colonocytes were common ; these mAdditionally, there were significant changes in the composition of claudins in CTC cells; while CLDN7, 23, and 4 were elevated in healthy epithelium, CLDN1 and eight were elevated in disease. Mutations in NOD2, one of the major UC disease susceptibility genes, have been shown to alter the composition of tight junction proteins including upregulation of CLDN1 . scRNA-sIn the Broad study, ∼111 protein transcripts were upregulated in the UC-inflamed Enterocyte cluster with only 16 being cluster-specific . Again, The pool of immature healthy colonocytes is mainly represented by three distinct clusters of Immature Enterocytes 1 and 2, and Enterocyte Progenitors in the Broad study. We searched for specific cluster markers among the substantially elevated protein coding transcripts  transcripts were shared among all three clusters . Howeverctive UC , furtherElevated MUC1 could be considered as a marker of Immature Enterocytes 1 in active UC , while iThe transcriptional comparison of immature colonocytes between two studies is challenging and not straightforward due to differences in transcript clustering algorithms. We speculated that Colonocytes and Absorptive Progenitors could be clusters that represent immature colonocytes in Oxford study. Eight transcripts found to be most elevated in all three undifferentiated colonocyte clusters in the Broad study were also significantly elevated in UC Colonocytes in Oxford study. Four transcripts  were also elevated in Absorptive Progenitors. Again, similar to the results of the Broad study, none of the transcripts were cluster-specific and all were upregulated in most clusters except EECs in the Oxford study. Additionally, both datasets suggest that DOCK4, a membrane-associated cytoplasmic protein, which functions as a guanine nucleotide exchange factor involved in regulation of adherence junctions between the cells, may be a reliable marker of undifferentiated colonocytes in UC disease. Although its role in UC disease is not known, DOCK4 was recently identified as a regulator of goblet cell differentiation and MUC2 production in the gut .Several transcripts identified as markers of healthy differentiated colonocytes in the Broad and Oxford studies , 16 were SC specific, which might substantially broaden the ability to identify and characterize human colonic SC via immunoblotting, immunofluorescence, functional assays and other approaches. OLFM4 is often used as a substitute marker for SC (2FC 2.4) in both SC and TA1 cells. Interestingly, several other highly expressed SC-specific transcripts are lincRNAs (RP11-219E7.4 and RP11-760H22.2) and RP11-84C10.4 antisense, all of which might be involved in SC-specific regulation of CFTR.The SC cluster from healthy tissue was not described in the Oxford study, possibly because only 38 cells contained the stem cell marker LGR5. The Broad study identified LGR5 as the most SC-enriched and specific transcript . Among tr for SC  and is eIn the Broad study, 65 transcripts were upregulated in SC in active UC relative to healthy controls. Although a majority of these transcripts were also upregulated at least in one other cell cluster, eight transcripts were specific to SC in UC . SurprisSTRING analysis of the 65 upregulated SC transcripts found associations to stress response (GO:0006950) and cell communication (GO:0010646) as the most inclusive gene ontology pathways. These pathways also included a large set of molecules involved in immune response. Multiple HLA and IFI transcripts, either SC-specific or shared between SCs and other epithelial cell clusters in UC, were significantly elevated, indicating a shift in SC function toward antigen presentation in UC. HSPA6 and CCL20 are UC GWAS genes, possibly linking genetic predisposition to UC with changes in the SC transcriptome. In UC, the fate of SC markers LGR5, OLFM4, or AXIN2 is not clear, as these transcripts were not listed among those enriched or downregulated in UC in the Oxford study. According to the Broad study, AXIN2 was significantly downregulated in stem cells in UC. In contrast, OLFM4 was slightly upregulated in stem cells and much more elevated in all TA clusters and in Enterocytes in UC. The lack of overlap between the two studies on SC markers in inflamed colonic tissue indicates that it is too soon to hypothesize on the mechanism(s) contributing to long-lasting changes in SCs in active UC.The expression of potential stem cell-enriched transcripts  was not RNA-seq of single cells paves the way for discovery of novel epithelial cell clusters in UC. The major differences between the two studies were in the methods and protocols used to isolate cells and the large difference in the number of analyzed cells. In addition, patient selection is also variable and patients are not comparable across studies . These lBecause parameters vary widely across studies, it is not straightforward to determine whether a particular transcript is specifically changed within a single cluster and thus be a reasonable target for further evaluation in human tissue or organoid models. Our analysis acknowledges this gap and identifies some areas in which non-omics validation is important.scRNA-seq in the Broad and Oxford studies revealed that transcriptional signatures and cell type boundaries become more nebulous in UC. Multiple transcripts were differentially regulated across many clusters, challenging the classical concept of unique cell cluster markers associated with active UC. The lack of clearly separated transcripts reflects a general loss of cell specialization, replaced by cell phenotypes that do not map to healthy tissue. Evaluating markedly altered transcripts in relation to known interactors  adds depth to cluster analysis, but these strategies are not useful if cell type clusters lack discrete boundaries in disease.Study interpretations are limited by the heterogeneity of subjects . The total number of cells acquired and transcripts sequenced also contribute to variability in sensitivity for detecting rare cell types or transcripts. Validation of findings at the protein level is also imperative to advance understanding of the molecular features conserved in UC epithelia.JS, JF-A, and OK researched data, contributed to discussion of content and writing, generated figures and tables, and reviewed/edited the manuscript before submission. WH and JH contributed to discussion of content for the article and reviewed/edited the manuscript before submission. JI researched data for the article and reviewed/edited the manuscript before submission. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Human dental follicle cells (DFCs) as periodontal progenitor cells are used for studies and research in regenerative medicine and not only in dentistry. Even if innovative regenerative therapies in medicine are often considered the main research area for dental stem cells, these cells are also very useful in basic research and here, for example, for the elucidation of molecular processes in the differentiation into mineralizing cells. This article summarizes the molecular mechanisms driving osteogenic differentiation of DFCs. The positive feedback loop of bone morphogenetic protein (BMP) 2 and homeobox protein DLX3 and a signaling pathway associated with protein kinase B (AKT) and protein kinase C (PKC) are presented and further insights related to other signaling pathways such as the WNT signaling pathway are explained. Subsequently, some works are presented that have investigated epigenetic modifications and non-coding ncRNAs and their connection with the osteogenic differentiation of DFCs. In addition, studies are presented that have shown the influence of extracellular matrix molecules or fundamental biological processes such as cellular senescence on osteogenic differentiation. The putative role of factors associated with inflammatory processes, such as interleukin 8, in osteogenic differentiation is also briefly discussed. This article summarizes the most important insights into the mechanisms of osteogenic differentiation in DFCs and is intended to be a small help in the direction of new research projects in this area. Molecular processes during osteogenic differentiation of dental follicle cells (DFCs) have been studied for many years. However, to better understand the greater importance of this very specific subject, a brief look at the role of the tooth in the formation of mineralized tissue in animals is helpful.Teeth probably comprise evolutionarily the earliest mineralized tissue, having been found in the feeding apparatus of extinct jawless fish, the conodonts . NonetheThe tooth germ can initially be divided into two different cell types based on the origin of their germ layers. One cell type is derived from the ectodermal germ layer (the origin of the enamel-forming ameloblasts) and the other—dental mesoderm—is derived from neural crest cells. Tooth development generally represents an interaction between these ectodermal and mesodermal cell layers that form the three dental germ tissues: enamel organ, dental papilla  and dental follicle ,6. DurinThe dental follicle, but not the developing tooth crown, actively participates in tooth eruption as it allows the eruption of an encapsulated metal crown replacing a tooth crown at an early stage of tooth development . While tThis article can be subdivided. First, it briefly discusses the isolation of human dental follicle cells. Then, it describes molecular mechanisms driving osteogenic differentiation of DFCs and ends with a short conclusion.For the beginning of the endeavor of the isolation of undifferentiated cells from the dental follicle two properties were actually suggested: these cells have the potential to differentiate into functional tissue cells such as alveolar osteoblasts and they have the ability to self-renew (to become a new undifferentiated cell again). These undifferentiated cells like other somatic stem cells have the ability to maintain their undifferentiated state even after numerous cell division cycles. They can renew themselves with symmetrical as well as asymmetrical cell division so that they are potentially immortal without having the typical characteristics of tumor cells ,17,18,19While these factors remained elusive for protocols for the isolation of undifferentiated cells, the first isolation of DFCs adopted a simple method used decades ago by osteologist Friedenstein to isolate bone marrow stromal cells (BMSCs) . Here, aHowever, additional cell types were isolated from the dental follicle . RecentlInterestingly, moreover, subpopulations of murine dental follicle cells were discovered in murine tooth germs. Ono and colleagues showed that murine dental follicles contain different subpopulations that can be distinguished by expressing specific genes such as the osteogenic transcription factor Osterix, PTHrP, and Gli1, which are involved in the Hedgehog signaling pathway, which contributes to both tooth eruption and periodontal development ,8,30. HoIsolated undifferentiated stem/progenitor cells are particularly suitable, among other things, for studying developmental processes under in vitro conditions. Molecular processes during osteogenic differentiation are only one example. Molecular studies with different osteogenic progenitor cells revealed which pathways are involved in the induction and in the regulation of the osteogenic differentiation, which is difficult to obtain with primary alveolar osteoblasts or with immortalized tumor cell lines. Here, the integration of the Bone Morphogenetic Protein (BMP) signaling pathway, the canonical WNT (Wingless-Type MMTV Integration Site Family Member) signaling pathway, the Mitogen-Activated Protein Kinase (MAPK) signaling pathway and the NOTCH signaling pathway is crucial for the osteogenic differentiation process ,32,33. MThis part of the article begins with the positive feedback loop of bone morphogenetic protein (BMP)2 and homeobox protein DLX3 and continues with a signaling pathway associated with protein kinase B (AKT) and protein kinase C (PKC). These two signaling pathways are presented in a larger context. Then short summaries on the role of non-coding RNAs and epigenetic modifications during differentiation, followed by first insights into the influence of extracellular matrix proteins and biological processes, such as, e.g., B. cellular senescence, on the induction of osteogenic differentiation. Thereafter, work examining the role of pro- and anti-inflammatory factors in osteogenic differentiation is discussed.Downstream and sometimes also upstream of the above-mentioned signaling pathways, specific transcription factors are involved in the differentiation of osteogenic progenitor cells . InteresWhile comparisons of gene and protein expression profiles between DFCs before and after the osteogenic differentiation revealed that only a small percentage of the regulated genes are associated with the biological process of skeletal development , the BMPAnalyses of phosphoproteomes of DFCs after induction of osteogenic differentiation revealed that BMP2 induced hedgehog “off” state phosphoproteins . FurtherIn addition, the NOTCH signaling pathway also regulates the osteogenic differentiation of DFCs . The famFurthermore, the osteogenic differentiation of DFCs involving the BMP2/DLX3 pathway also depends on parts of the canonical WNT/β-catenin signaling pathway . The β-cIn summary, the BMP2/DLX3 positive feedback loop is involved in the osteogenic differentiation of DFCs and is controlled by PTHrP, PKA, and β-catenin, among others. In addition to the BMP2/DLX3 pathway described above, there are other signaling pathways that are involved in differentiation in conjunction with it, but also independently of it. The transcription factor EGR1, which is involved in many biological processes, supports craniofacial development and osteogenic differentiation of DFCs ,63. ReumNew results also suggest that a complex of signaling pathways and crucial cellular processes directs the osteogenic differentiation. This was for example indicated by the sensitivity of osteogenic differentiation to changes in AMP-activated protein kinase (AMPK) and autophagic activity ,71. BothWhile signaling pathways guide the progress of osteogenic differentiation, this network of signaling pathways must be controlled not only by interactions such as feedback loops but also by additional cellular mechanisms. Therefore, the expression of osteogenic differentiation markers and also of signaling pathways can also be modulated by non-coding (nc) RNAs . One groThe second group of ncRNAs is the so-called long non-coding (lnc) RNAs. These nucleic acid molecules are at least 200 nt in size and they have diverse molecular functions during osteogenic differentiation, including regulation of gene expression at the transcriptional and post-transcriptional levels. Recently, a number of lncRNAs were discovered that promote or inhibit the differentiation of osteogenic progenitor cells ,79. One These examples with ncRNAs already indicate that epigenetic modifications of both genomic DNA and histone proteins are involved in osteogenic differentiation. Besides the ncRNA, the ubiquitin-like SUMO system is another factor involved in the methylation of nuclear histone proteins and the regulation of gene expression. The SUMO-specific isopeptidase SENP3 controls the methylation of the histone protein H3K4 by regulating the histone-modifying SET1/MLL complex, which consists of histone methyltransferase and the regulatory components WDR5, RbBP5, Ash2L and DPY-30. MLL1/MLL2 complexes are particularly important for the activation of homeobox genes such as DLX3 . MoreoveRecent studies also identified factors directly affecting epigenetics and osteogenic differentiation of DFCs. Chromodomain-helicase-DNA-binding protein 7 (CHD7), for example, recognizes epigenetic modifications such as histone protein methylation and regulates the assembly and organization of nucleosomes and promotes the expression of genes such as the receptor of PTHrP (PTH1R) . AnotherDuring tooth development, DFCs come into contact with various tissues and the various extracellular matrix (ECM) proteins, polysaccharides, glycoproteins and proteoglycans that are important for cell behavior. A modification of the surface of a cell culture dish with ECM proteins or polysaccharides such as agarose for example influences whether DFCs are cultivated as adherent cells or as spheroids in a 3D cell culture system ,88. TissECM proteins activate intracellular signaling pathways such as the Extracellular-signal Regulated Kinases (ERK) pathway or the Focal Adhesion Kinase (FAK) pathway for the induction of the osteogenic differentiation ,94,95. LThe same intracellular signaling pathway is activated in DFCs by collagen type I, which makes up most of the mineralized tissues . HoweverA decellularized mineralized dentin matrix can also be obtained from extracted teeth and this dentin matrix was successfully used together with DFCs for tooth root regeneration and differentiation induction experiments ,102,103.As mentioned above, recent data have led us to believe that biological processes related to cell viability should receive a new focus for studies of osteogenic differentiation mechanisms in DFC. A process strongly associated with cell viability is cellular senescence. It is also known that differentiation in osteogenic progenitor cells is impaired after induction of cellular senescence ,108,109.Recent studies suggest that mechanisms involved in the induction of cellular senescence are also related to the inhibition of osteogenic differentiation. Kampron et al. showed for example that uremic toxins not only inhibit osteogenic differentiation but also induce senescence . It is vEscherichia coli. LPS from E. coli did not only induce the expression of pro-inflammatory cytokines such as interleukin 6 but also the activity of alkaline phosphatase, which is an early marker of the osteogenic differentiation [P. gingivalis did not induce the expression of proinflammatory cytokines. However, do DFCs have strong immunomodulatory properties and can they also target macrophages in this direction? And how do these properties relate to the osteogenic differentiation potential of DFCs? It is obvious that the same cells of the dental follicle cannot contribute to the bone formation during the formation of the periodontium and bone resorption during tooth eruption at the same time. However, something is known about the immunomodulatory properties of DFCs. Chen et al. showed that DFCs reprogrammed macrophages into the anti-inflammatory M2 phenotype by secreting the paracrine factors TGF-β3 and TSP-1, alleviating LPS-induced inflammation [+ T lymphocytes derived from asthma patients by increasing the amount of CD4+CD25+FoxP3+ regulatory T cells. They conclude that DFCs suppressed allergen-induced polarization of T-helper 2 cells while promoting differentiation of T lymphocytes into T-helper 1 cells [The dental stem cells are said to have immune-modulating properties, which, in addition to their regenerative potential, can also be used for immunotherapies in the future . Howeverntiation . In contammation . In addi 1 cells ,127. Int 1 cells . A recen 1 cells . How theDFCs are dental mesodermal cells, which are involved in both bone destruction during tooth eruption and bone formation during periodontal tissue development. These cells are interesting for regenerative dentistry and a number of works have started to elucidate how DFCs promote osteoclast maturation or osteogenic differentiation. This article summarized works that discovered molecular mechanisms in DFCs. Most of these articles concentrate on mechanisms of osteogenic differentiation. Here, different signaling pathways, epigenetic modifications and ncRNAs could be elucidated. However, these numerous works on specific molecular mechanisms, which are sometimes contradictory, cannot explain the entire process of osteogenic differentiation. For that reason, recent works also focus on crucial biological processes such as reaction to oxidative stress, cell viability, metabolic processes and cellular senescence. This later work is of great interest for the future because it helps us to better understand the whole process of osteogenic differentiation, which may give us some fruitful clues on how, for example, to manipulate biological processes to enhance alveolar bone formation. However, scientific work should also consider the properties of the developing organ, i.e., the developing periodontal ligament or alveolar bone, as a whole. For example, it may be important to consider conflicting effects of soft tissue formation or tissue mineralization on molecular processes in DFCs at the same time. To make more progress, scientists need to think for themselves and not just feed a computer big data.


Inflammasomes are multimeric protein complexes activated in response to a wide range of pathogen- and damage-associated molecular patterns that induce auto-processing of the proenzyme caspase-1. Activation of caspase-1 results in pyroptotic cell death, and the processing and release of the inflammatory cytokines, IL-1β and IL-18, primarily through gasdermin D (GSDMD) pores. A diverse range of sensors, triggers, and regulators makes inflammasome activation a tightly controlled process that is central to health and disease. Dysregulation of these processes is associated with various inflammatory disorders, including sepsis, cancer, cardiovascular diseases, and even neurological disorders.This Research Topic includes studies identifying and discussing the recent advances in the regulation and function of inflammasome and associated molecules in modulating immunity.Andina et al. reviewed the role of inflammasomes in myeloid malignancies including myelodysplastic syndromes, myeloproliferative neoplasms, and acute myeloid leukemia which are heterogeneous disorders originating from genomic mutations in hematopoietic stem and progenitor cells. While the pro-inflammatory environment resulting from Inflammasome activation was shown to be involved in disease initiation and clonal progression, contrary data also showed that inflammasomes can counteract leukemia by mediating cell death in malignant clones. These effects of inflammasome activation were additionally dependent on the mutational context. In myeloid malignancies, controversial roles of inflammasomes were also described in cancer incidence, progression, and response to treatment Saleh et al. described how inflammasomes and more particularly IL-1β or IL-18 promote cancer cell survival, proliferation, and invasion. They can also participate in tissue repair, fibrosis, and immunosuppression. Contrarily, inflammasomes were also shown to maintain tissue homeostasis and to induce anti-tumor immunity. Inflammasome activation can also induce anti-tumor effects in cancer cells by inhibiting their proliferation and promoting their differentiation or pyroptosis, a kind of immunogenic cell death. These studies and the outcomes of trials targeting inflammasomes in cancer highlight that a deeper contextual understanding of the impact of inflammasome activation in the type and the stage of cancers is required to harness the potential of this promising therapeutic target.Two papers reviewed the impact of inflammasomes within immune and cancerous cells during tumor development, and how the role of inflammasome is a function of the cell type undergoing inflammasome activation. Opoku et al. show that GSDMD mediates inflammation-induced inhibition of RCT and promotes atherosclerosis. Using Gsdmd−/− mice, the authors demonstrate that RCT inhibition in response to NLRP3 activation is severely impaired in the absence of GsdmD. This effect is mediated by a secretory factor (such as IL-1β) released through GSDMD pores, as conditioned medium from Gsdmd−/− cells is ineffective at inhibiting both RCT and foam cell formation, while WT conditioned medium can inhibit RCT and promote foam cell formation both on WT and Gsdmd−/− macrophages. In vivo, GSDMD is cleaved in aortic root plaques and participates in RCT suppression. Another impact of GSDMD is to inhibit apoptotic cell death, as Gsdmd−/− macrophages seem to be more susceptible to this non-inflammatory cell death. These studies shed light on the various processes modulated by inflammasome activation during the development of atherosclerosis.Inflammasomes also modulate the incidence or severity of other diseases such as atherosclerosis, bacterial infections, and neurological disorders, such as multiple sclerosis. The role of inflammasomes (NLRP3 or AIM2) and IL-1β in atherosclerosis are well known. Lipid engulfment by macrophages leads to their transformation into foam cells which accumulate in the arterial wall to form atherosclerotic plaques. Removal of excess cholesterol from these cells via Reverse Cholesterol Transport (RCT) may reverse disease progression. While inflammasomes were described to participate in the continued influx of immune cells into the plaques and in the inhibition of RCT, the direct role of GSDMD in atherosclerosis is not fully clear. In their paper, Guo et al. In this study, the authors used two strains of Francisella novicida, the virulent WT (U112) and the 4′-phosphatase-deficient avirulent strain (XWK4) that has an altered lipid A structure. The mutant strain induces higher caspase-1 activation and IL-1β production, through activation of both AIM2 and NLRP3 inflammasomes. However, while inflammasome activation promotes clearance of the WT-U112 Francisella novicida strain, it significantly worsens clearance and pathology induced by the mutant XWK4. Further, while the inflammasome activation induced by U112 is dependent on type I Interferons (IFN), XWK4 induced inflammasome activation relies on type II IFN signaling. This work highlights how the engagement of distinct pattern recognition receptors (PRRs), and cell death pathways by pathogens can alter host inflammatory response and outcomes in infectious diseases. Understanding these processes is critical to targeting these complexes to fight bacterial infections.Inflammasomes drive pyroptotic cell death and inflammatory cytokine release in response to microbial infections. The amplitude and function of this response depend on the identity of the pathogen. This dichotomous impact of inflammasome activation is illustrated in the study by Li et al. identified manoalide as a new small molecule inhibitor specific to the NLRP3 inflammasome. It does not impact the proximal processes of potassium and chloride efflux or mitochondrial dysfunction involved in NLRP3 activation. Instead, it covalently binds to Lys 377 of the NLRP3 NACHT domain and inhibits its interaction with NEK7. Experimental Autoimmune Encephalomyelitis pathogenesis (EAE) is a murine model of multiple sclerosis characterized by CD4 T cell-mediated inflammation and demyelination and is dependent on NLRP3 mediated IL-1β production. Manoalide attenuated clinical, pathological, and inflammatory features of EAE in mice, suggesting that manoalide can be used in pathologies associated with overt NLRP3 activation.In addition to understanding the function and context of inflammasome activation, novel and specific inhibitors of the inflammasome pathway are required to modulate these responses in distinct pathological contexts. To address this, We thank the authors who contributed to this Research Topic wherein we highlight the function and significance of inflammasomes in immune system-related diseases. As exemplified by these studies, the pathological context defines the function of and therefore can predict the impact of therapies that target inflammasome activation. Thus, the function of inflammasome activation should be clearly defined for these therapeutic interventions to be effective. Further research is needed to find new inflammasome modulators and markers to help clinicians decide whether inhibition or activation of inflammasomes and downstream mediators would improve patient recovery.


Traditionally, multiple courses of RAI are given until the patient either achieves cure or reaches a maximum lifetime 131I exposure, arbitrarily set at a cumulative dose of 600 mCi (22.2 GBq). However, despite repeated courses of 131I, a complete response is found in only 0%-22% of children with pulmonary metastatic disease.6 Furthermore, RAI can be associated with side effects such as salivary gland damage, pulmonary fibrosis, and an increased risk of second primary malignancies.14 Fortunately, stage II PTC is indolent in most pediatric patients and long-term survival is the norm.2Management of pediatric stage II PTC includes thyroidectomy and compartment-focused neck dissection by a high-volume thyroid cancer surgeon followed by treatment with radioactive iodine (RAI).Because of its low cure rate and the potential for late effects arising from repeated courses of high-dose RAI, stage II PTC in children is a challenging disease to treat. Better therapeutic approaches are needed that might enhance the cure rates while minimizing the potential consequences of repeated radioiodine doses. Herein, we present an adolescent with a TRK fusion–positive PTC metastatic to the lungs who was treated using a neoadjuvant systemic approach with the selective TRK inhibitor larotrectinib before the initial dose of RAI.TPM3-NTRK1 fusion gene, a known oncogenic driver identified in about 8% of TRK fusion–positive thyroid cancers.15 The patient proceeded to a hypothyroid 123I thyroid scan while following a low iodine diet and with a documented low random urine iodine level  of the neck and chest identified bulky central and right-sided cervical disease and evidence for bilateral micro- and macronodular pulmonary metastatic diseases. She was treated with total thyroidectomy and comprehensive central and right lateral neck dissections. Final American Joint Committee on Cancer, eighth edition stage group was II (T4aN1bM1). DNA and RNA next-generation sequencing identified a 131I therapy. Unexpectedly, the recombinant human TSH-stimulated 131I thyroid scan showed significantly increased uptake in all the pulmonary metastatic disease, including the previous noniodine-avid disease  disease.23 Ideally, despite the challenges of recruitment and the issues surrounding the assessment of response in nonmeasurable disease, which is typical in young patients with stage II PTC, well-designed clinical trials should be considered to answer these important questions.Although the current case is an exciting one that suggests a potential shift in the traditional management of distantly metastatic PTC, caution must be undertaken as we learn more about the possible long-term side effects of systemic TRK inhibition, particularly in the context of the concomitant use of RAI. It is also important to better understand the optimal duration of therapy and if it is safe to administer RAI while actively receiving larotrectinib; in the current case, we stopped the medication the day before RAI was administered. It also remains unknown if such an approach would apply to the other commercially available TRK inhibitor entrectinib or to the selective RET inhibitors, selpercatinib and pralsetinib, although recent publications would suggest a similar therapeutic effect with selpercatinib.131I, the patient's tumor also demonstrated an unexpected and significant increase in RAI avidity (redifferentiation), which we believe improved the therapeutic outcome and permitted the use of a lower administered dose of 131I. The time may be upon us to reconsider our treatment paradigm and incorporate selective fusion gene inhibitors such as larotrectinib earlier in the treatment course to maximize the tumor response to RAI in advanced PTC.In conclusion, to our knowledge, we report the first case of a patient who achieved clinical benefit from initiation of the selective TRK inhibitor larotrectinib in a neoadjuvant fashion before the first dose of RAI. Although the initial goal of therapy was to medically debulk her tumor to allow more effective delivery of


Cumulative evidence has shown that mechanical and frictional forces exert distinct effects in the multi-cellular aortic layers and play a significant role in the development of abdominal aortic aneurysms (AAA). These mechanical cues collectively trigger signaling cascades relying on mechanosensory cellular hubs that regulate vascular remodeling programs leading to the exaggerated degradation of the extracellular matrix (ECM), culminating in lethal aortic rupture. In this review, we provide an update and summarize the current understanding of the mechanotransduction networks in different cell types during AAA development. We focus on different mechanosensors and stressors that accumulate in the AAA sac and the mechanotransduction cascades that contribute to inflammation, oxidative stress, remodeling, and ECM degradation. We provide perspectives on manipulating this mechano-machinery as a new direction for future research in AAA. Although the advancement of surgical approaches as curative management of abdominal aortic aneurysm (AAA) has become more sophisticated in recent years, there is still a shortage of non-interventional treatments available to curb the growth of aneurysm sacs, thereby reducing the risk of life-threatening aortic rupture . This liWithin the vascular wall, the cells and composite extracellular matrix (ECM) are constantly exposed to the toned physical forces and mechanical stimuli from the luminal and adventitial microenvironment . Endothe2) generated by blood flow, which regulates local oxidative stress, reinforces the inflammatory barrier capacity and protects the overall segment against pathological remodeling (2), which over-activate the ECs, thereby reprogramming their function to create oxidative stress and attract immune cells circulating in the blood  in AAA when shear stress increases  is a calcium-permeable channel commonly expressed by ECs and SMCs, which can regulate calcium influx ncreases –41. Calcncreases  demonstrormation .in vivo studies have been conducted in rodents with AAA modeled by calcium chloride (CaCl2) or calcium phosphate (CaPO4) treatment. These models are based on vasodilation, calcification, oxidative stress, and SMCs apoptosis , widely regarded as the pathological culprits that exaggeratedly degrade the ECM during AAA. Hence, strategies to manipulate Ca2+ levels are desirable to curb AAA. However, it is worth noting that the use of broad-spectrum Ca2+ blockers did not show beneficial effects in treating thoracic aneurysm progression associated with Marfan syndrome , and vascular endothelial growth factor-2 (VEGFR2), through conformational changes and specific interactions of the α and β subunits which can drive focal adhesion kinase (FAK) activation  and AT1R (angiotensin II type I receptor), are present in ECs and SMCs, regulating an array of functions including vascular tone, angiogenesis, and cell proliferation. AT1R has also been demonstrated to act as a mechanosensor in ECs and can be activated under excess mechanical stretch , all of which have been detected in increased amounts in AAA samples and positively associated with aneurysm size and mortality risk of patients  in AAA induced by injured lungs, suggesting the pathological role of RIPK3 to trigger ROS production  formation is known to contribute to the pro-inflammatory and prothrombotic backdrop of AAA –90. It wSurprisingly, thrombus formation is also observed in regions exposed to low , 96 and It is clear that mechanical disturbance in the aortic environment and the changes in mechanotransduction within aortic tissue are progressive events during AAA development; hence, monitoring these events in a clinical setting could be beneficial for improved surveillance and therapeutic purpose.in vitro and in vivo models to examine shear stress in AAA. Accordingly, porcine pancreatic elastase (PPE) infusion, along with partial ligation of the iliac artery in murine and rodent models, can increase mechanical stretch and shear stress and alter the morphology of AAA phenotypes , which allows the measurement of aortic stiffness, were reported as a promising imaging readout to monitor disease progression not only in mice but also for post-surgical prognosis of patients with AAA –119. HowThe Nobel-awarded study of David Julius and Ardem Patapoutian in 2021  for the in vivo study performed in Marfan mice treated with calcium channel blockers targeted extracellular AT1R-mediated ERK1/2 activation and resulted in deleterious effect on aortic SMCs, therefore, accelerating aneurysmal growth in the ascending aorta (Other mechanical targets such as AT1R blocker, Losartan and Telmisartan, were shown to slow AAA growth in hypertensive and Marfan syndrome mice , 72. Howng aorta .–/– mice and elastase-induced rat AAA models (The availability of compounds targeting ROS and inflammation is another attractive future target for AAA treatment. Alpha-ketoglutarate, a pleitropic antioxidant, has been shown to reduce ROS generation in C57BL mice challenged with pancreatic elastase . ExogenoA models –132. Howin vitro and in vivo models. The interrelated pathways between mechanosensors and other critical pathways in AAA may also hint that poly-pharmaceutical agents that can target both mechanosignaling and intrinsic cellular pathways are a more practical approach to prevent and limit AAA growth.As the road to target mechanotransduction signaling pathways for therapeutic intervention remains an underdeveloped field, future works need to primarily address the exact contribution of key mechanosignaling pathways using available high-throughput systems that can replicate the microenvironment of the human aneurysm and precisely record the dynamic changes in both Mechanosensory and mechanotransduction machinery are typifying features inherent to AAA development. The formation of AAA is influenced by altered mechanical stress that amplifies pathological cellular signaling through the interaction between the stresses and mechanosensors present at the cell membrane of ECs and SMCs. In addition, increased ROS levels generally coincide with mechanotransduction during AAA, acting as critical second messengers that modulate several signaling pathways that participate in vascular remodeling, inflammation, and apoptosis. Further mapping of the mechano-behaviors in human AAA capable of capturing the dynamics of mechanical perturbation of the complex human aorta at critical phases of AAA development using sophisticated imaging and high-throughput technologies is still warranted. Further translational research is needed to test the relevance of the observations of defective mechanosensitive signals from murine to human disease. One critical step would be the use of large animal models such as porcine AAA models to test whether locally modulating mechanical signals during AAA onset would be of beneficial clinical interest. Importantly, strategies to intervene on these mechanical signals spatiotemporally are important factors to consider as most AAA are detected beyond their initial phase, bypassing an interventional window during the stages of AAA onset. Therefore, there is a dire need to discover novel druggable targets focused on altering mechanical signaling pathways during the progressing phases of AAA as the future of independent non-interventional therapies or in conjunction with current surgical approaches.BR and MP conceptually developed and supervised the project and co-wrote the manuscript. AR, CL, PK, and MP did the literature search and wrote the specific sections. CR, RH, and JV edited the text, provided the feedback, and discussion. All authors contributed to the article and approved the submitted version.


Pivotal research led by Louis Harold Gray in the 1950s suggested that oxygen plays a vital role during radiotherapy. By proving that tumours have large necrotic cores due to hypoxia and that hypoxic cells require significantly larger doses of ionising radiation to achieve the same cell kill, Thomlinson and Gray inspired the subsequent decades of research into better defining the mechanistic role of molecular oxygen at the time of radiation. Ultimately, the work pioneered by Thomlinson and Gray led to numerous elegant studies which demonstrated that tumour hypoxia predicts for poor patient outcomes. Furthermore, this subsequently resulted in investigations into markers and measurement of hypoxia, as well as modification strategies. However, despite an abundance of pre-clinical data supporting hypoxia-targeted treatments, there is limited widespread application of hypoxia-targeted therapies routinely used in clinical practice. Significant contributing factors underpinning disappointing clinical trial results include the use of model systems which are more hypoxic than human tumours and a failure to stratify patients based on levels of hypoxia. However, translating the original findings of Thomlinson and Gray remains a research priority with the potential to significantly improve patient outcomes and specifically those receiving radiotherapy. The origins of radiotherapy date back to 1896 when Emil Herman Grubbé utilised X-rays, less than 60 days after their discovery by Wilhelm Conrad Röntgen, to treat a case of advanced ulcerative breast cancer in Chicago with little knowledge of their physical properties or biological effects , 3.In 1955, Gray published a research paper in collaboration with Raymond Hugh Thomlinson titled “The histological structure of some human lung cancers and the possible implications for radiotherapy” in the British Journal of Cancer . At thisThese seminal findings from Thomlinson and Gray in 1955 built upon the prior pivotal research of Gray et al., which demonstrated that tumour cells experiencing anoxia at the time of irradiation are ~2.5–3 times more radioresistant to a given dose of X-rays or γ-rays than oxygenated tumour cells [In addition, Thomlinson and Gray noted that a given dose of radiation, sufficient to cause cell death in the outer radiosensitive cells, will increase the supply of oxygen and other nutrients to the central necrotic cells, allowing the cells to continue proliferation if they have retained the capacity to replicate following the period of oxygen deprivation . Today, At the time of this pivotal research, the influence of oxygen tension at the time of irradiation on the biological response to radiotherapy had not been mechanistically evaluated . HoweverAs previously, the pioneering work of Thomlinson and Gray highlighted central cores of necrotic cells as the first indicator of tumour hypoxia. Necrosis of tumour cells occurs as a result of chronic or diffusion-limited hypoxia at distances >180 μM from functional blood vessels , 9. As p18F-fluoromisonidazole (FMISO) is one of several 18F-labelled tracers more recently utilised in oncology to specifically detect hypoxic tumour regions. Lipophilic FMISO diffuses into a cell where its nitro group is reversibly reduced via the addition of two electrons by nitroreductase enzymes [2) [After Thomlinson and Gray highlighted that hypoxia could be visualised through necrosis, significant effort was invested in developing probes for hypoxia in both the laboratory and clinical settings. For example, the 2-nitroimidazole compounds Pimonidazole and EF5 act as predictive biomarkers for hypoxia by being reduced and covalently binding intracellular macromolecules specifically at low oxygen concentrations <10 mmHg , 14. Pim enzymes . In oxyg enzymes . However enzymes   . FMISO PConversely, endogenous markers of tumour hypoxia are genes or proteins whose expression is selectively induced in conditions of hypoxia. The most studied are the hypoxia-inducible factors (HIFs), the master orchestrator of the biological response to physiological hypoxia, whose transcriptional activity is highly dependent on cellular oxygen concentration , 20. As More recently, it has become apparent that not only does hypoxia exist as a gradient, but the biological response to hypoxia differs in an oxygen-dependent manner , 26. ForAs a hallmark characteristic of solid tumours, the clinical importance of tumour hypoxia as a strong predictive biomarker indicating a likely poor response to both chemotherapy and radiotherapy treatments is well-established, with the latter being first revealed as significant by Gray et al. in 1953 . In addiOver the recent decades, several hypoxia modification strategies have been developed and investigated in an attempt to improve patient outcomes , howeveIn addition, the first proposed some decades ago was hypoxia modification through the development of hypoxia-activated prodrugs (HAPs). HAPs are compounds that are selectively reduced by reductase enzymes exclusively in regions of hypoxia to yield cytotoxic agents. Examples include tirapazamine , 45, TH-A more recent strategy to eliminate tumour hypoxia by correcting the imbalance between oxygen supply and demand is via decreased oxygen consumption resulting in increased availability of oxygen to hypoxic cells . This stToday, tumour hypoxia is well-established as a critical characteristic of the majority of solid tumours and is closely associated with negative treatment outcomes in a clinical setting. Data generated in the 1950s, including that published by Thomlinson and Gray, suggested a critical role of oxygen in radiation therapy efficacy and identified regions of necrosis as a consequence of tumour hypoxia. Importantly, both of these key findings have since been mechanistically validated.Inspired by this pivotal research, extensive studies have been undertaken to better define hypoxic regions, develop measurement and quantification methods, as well as propose targeted modification strategies . Issues


Endocrine resistance is a major hurdle in the treatment of estrogen receptor (ER)-positive breast cancer. When abnormally regulated, molecular signals responsible for cellular proliferation, as well as ER itself, allow for cellular evasion of ER-dependent treatments. Therefore, pharmacological treatments that target these evasion mechanisms are beneficial for the treatment of endocrine-resistant breast cancers. This review summarizes currently understood molecular signals that contribute to endocrine resistance and their crosstalk that stem from mitogen-activated protein kinase (MAPK), phosphoinositol-3 kinase/protein kinase B (PI3K/AKT), mechanistic target of rapamycin (mTOR), cyclin-dependent kinases 4 and 6 (CDK4/6) and aberrant ER function. Recent clinical trials that target these molecular signals as a treatment strategy for endocrine-resistant breast cancer are also highlighted. The connection between the function of endocrine organs and breast cancer was described as early as 1896 in a case series by Beatson  and its The ER is a transcription factor that contains 6 functional domains termed by letters A–F that represent relate to specific functions of ER. Most notably, the C domain contains the DNA binding domain (DBD) that allows for ER’s nuclear action, the D domain contains nuclear localization signal (NLS) that allows translocation of ER to the nucleus, and the E domain contains the ligand binding domain (LBD) that allows interaction of ER with its ligand—estrogen  5]. The. The5]. Effects from ERα signaling can follow canonical pathways, whereby the ERα effect is nuclear and acts either directly or through intermediate activators and promoters on genetic response elements. Alternatively, the effect can be non-canonical, and exhibit immediate cytoplasmic response in cellular signaling cascades such as through phosphorylation of extracellular signal-regulated kinase (ERK) or protein kinase B (AKT) . TranscrTreatments targeting estrogen signaling either through a reduction in estrogen production or direct action on the receptor are known as endocrine therapies and have been a part of the core strategy of treating ER-positive breast cancer. SERDs and SERMs both act directly on ER and reduce signaling with slight variations in their mechanisms of action. While SERMs compete with estrogen for receptor binding, SERDs bind with ER and mark ER for degradation . DespiteThe first Food and Drug Administration (FDA)-approved endocrine therapy was in the 1970s when tamoxifen, a SERM, has become the mainstay therapy in the treatment of ER-positive breast cancer and has been shown to reduce mortality and recurrence risk for 15 years after 5 years of treatment . DespiteDespite having similar mechanisms of action to SERMs, SERDs bind to ERα with a stronger affinity than tamoxifen  and wereSimilar to SERDs, AIs were found to provide a better clinical response rate in postmenopausal women when compared to women treated with tamoxifen . In partIn an attempt to identify alternative targets for treating breast cancers, exploratory studies looked for hormone receptors beyond the classic three targets in breast cancer. A receptor that was identified to be present in at least half of their samples was luteinizing hormone-releasing hormone (LHRH) and was even identified in triple-negative breast cancer samples, that are notoriously resistant to traditional targeted hormone treatments , 33. LeuDespite the three classes of endocrine therapies’ action on estrogen signaling, the difference between the therapies in the patient population they benefit emphasizes the importance of tailoring treatments to the disease. In the case of hormone therapy treatment resistance, switching to different hormone therapies is a strategy that is implemented in an attempt to overcome resistance. Unfortunately, response rates to treatment after switching over are not always as impressive as they are with initial hormone treatments, and require different treatment strategies . For exaESR1) mutations leading to ER hyperactivation or to activation of ER-independent pathways responsible for cell growth and survival.Resistance to endocrine therapies can be acquired as a result of long-term endocrine therapy or be a result of an intrinsic mechanism at play before the initiation of treatment. Both of these mechanisms can stem from direct actions on ER, such as the case of estrogen receptor 1  and mTORC2 , 94. mTOEvidence of crosstalk between the PI3K/AKT and MAPK and involvement of other tumorigenic pathways, as was seen with mTOR, has been identified in numerous studies. This crosstalk can sometimes be missed in patients as was seen with the case of acquired HER2 activation with endocrine resistance despite an initial negative HER2 status. Interestingly, HER2 activation was previously described in acquired endocrine resistance despite initial low expression and is understood to be a result of crosstalk between multiple pathways including MAPK and PI3K/AKT . This crFurther downstream of the receptor tyrosine kinase signaling and of ER, the activation of CDKs contributes to tumor proliferation and cell cycle progression as a result of transcriptional upregulation of cell cycle regulators, these include CDK2 and CDK4 . SignaliTaking into account the molecular pathways involved in endocrine resistance and the dynamic nature of disease progression, clinical trials investigating treatment options in endocrine-resistant breast cancers turn to combinatory treatments and crossover to different therapies in response to shifts in molecular signaling. Combinatory treatments targeting pathways downstream of receptor tyrosine kinases, mTOR, and CDK4/6 have shown promise in clinical trials.Recent trials investigating the clinical benefit of inhibition of CDK4/6 yielded favorable results . ResultsPIK3CA mutation which is found in nearly 40% of ER-positive breast cancers [PIK3CA mutation who were treated alpelisib and fulvesterant when compared to patients treated with fulvesterant and placebo [Evidence of the involvement of receptor tyrosine kinases and their downstream serine-threonine kinases in endocrine resistance prompted further investigations and eventually led to the characterization of the  cancers , 117. Th placebo . This coAnother receptor tyrosine kinase implicated in endocrine resistance that is currently investigated is the HER2 receptor. In endocrine-resistant breast cancer with low HER2 expression, treatment with an antibody-drug conjugate has shown promising results in clinical and preclinical studies . SuggestThe multitude of molecular subtypes of breast cancers highlights the heterogeneity of the disease and the need for disease classification and the development of tailored therapies. Furthermore, molecular changes throughout treatment as a result of genetic changes and crosstalk between multiple signaling cascades elucidates the dynamic nature of breast cancer progression and the value of polytherapy to simultaneously target multiple pathways to circumvent the effects of future shifts in molecular signaling. In response to this heterogeneity and dynamic molecular shifts technologies to assay tumor types have been developed to improve tumor classification and implement more targeted treatments . The res