ER stress protein AGR2 precedes and is involved in the regulation of pancreatic cancer initiation

The work was supported by a grant A12008 from CR-UK (L. Dumartin, N.R. Lemoine and T. Crnogorac-Jurcevic)

Green accounting disclosure and firm market value: evidence from Jordan

Purpose: The main objective of the study was to address the patterns and development in green accounting (GA) made by the industrial companies listed on the Amman Stock Exchange, Jordan, during 2013–2023, with the end goal of revealing the power of the GA disclosure practices on the market value (MV) of those firms. Design/methodology/approach: The corporate annual reports have been examined using content analysis through a disclosure index to recognize the patterns of GA disclosures. OLS regression has been applied to test the hypothesis regarding the impact of GA disclosure on market value. Findings: The multivariate results show that green accounting has a positive and statistically significant impact on MV for the listed manufacturing companies on the ASE. Practical implications: The study has various implications for corporate managers, policymakers, and regulators in developing countries by providing a diagnostic tool for the status quo of green accounting disclosure, showcasing their contribution toward green production and the economy. Therefore, for corporate managers, the finding may draw attention to the role of transparent GA disclosure, which can enhance a company’s reputation, attract investors, and maximize firm value. Policymakers and regulators can utilize the findings to develop regulatory policies that standardize environmental reporting and promote sustainable business practices. Originality/value: The present study is the first comprehensive investigation to collectively address aspects of green accounting disclosures, enriching and extending the GA literature by examining their implications for firm value

Risk Stratification of Penicillin Allergy Labeled Children: A Cross-Sectional Study from Jordan

Jomana W Alsulaiman,1 Khalid A Kheirallah,2 Ahmad Alrawashdeh,3 Tareq Saleh,4 Maha Obeidat,5 Yareen J Alawneh,2 Ziydoun Abu Sanad,5 Wajdi Amayreh,1 Rama J Alawneh2 1Department of Pediatrics, Faculty of Medicine, Yarmouk University, Irbid, Jordan; 2Department of Public Health and Family Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan; 3Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan; 4Department of Pharmacology and Public Health, Faculty of Medicine, The Hashemite University, Zarqa, Jordan; 5Department of Pediatrics, Princess Rahma Teaching Hospital, Irbid, JordanCorrespondence: Jomana W Alsulaiman, Associate Professor of Pediatrics, Department of Pediatrics, Faculty of Medicine, Yarmouk University, Irbid, 21163, Jordan, Tel + 962 07 9941 2277, Email [email protected]: Implementing allergy testing among children with a reported history of penicillin allergy could be challenging, particularly in developing countries with limited resources. This study screened and risk-stratified the likelihood of true penicillin allergy among children labeled with penicillin allergy in Jordan.Methods: A web-based survey, completed by parents, assessed history, type, and severity of penicillin allergic reactions, including age at diagnosis, symptoms, time to the reaction, reaction’s course and resolution, and received medical evaluation/testing. Low-risk allergic symptoms were defined as vomiting, diarrhea, headache, dizziness, itching, rash, cough, or runny nose without evidence of anaphylaxis or severe cutaneous reactions.Results: A total of 530 parents of “penicillin allergy”-labeled children completed the survey. Of these, 86.4% reported allergic reactions to penicillin and 13.6% reported avoidance of penicillin due to family history. Among the former, 52.2% were male, 67.3% were three years old or younger when the reported reaction was established, and 68.3% experienced exclusively low-risk symptoms. Overall, skin rash was the most reported symptom (86.0%). High-risk symptoms were reported in 31.5% of children. About two-thirds (64.0%) of children were reported to have experienced symptoms after the first exposure to penicillin. The most common indication for antibiotic use was a throat infection (63.8%). Asthma comorbidity was significantly higher among high-risk (24.8%) compared low-risk group (11.5%).Conclusion: In Jordan, many parent-reported penicillin allergic reactions seem to be clinically insignificant and unlikely to be verifiable, which can adversely affect patients’ care and antimicrobial stewardship. An appropriate clinical history/evaluation is a key step in identifying true immunoglobulin E-mediated allergic reactions and risk stratifying patients for either de-labeling those with obviously non‐immune–mediated reactions or identifying candidates for direct oral challenge test.Keywords: children, drug hypersensitivity, drug resistance, Jordan, penicillin resistanc

Perineural invasion in pancreatic cancer: proteomic analysis and in vitro modelling.

Perineural invasion (PNI) is a common and characteristic feature of pancreatic ductal adenocarcinoma (PDAC) that is associated with poor prognosis, tumor recurrence, and generation of pain. However, the molecular alterations in cancer cells and nerves within PNI have not previously been comprehensively analysed. Here, we describe our proteomic analysis of the molecular changes underlying neuro-epithelial interactions in PNI using liquid chromatography-mass spectrometry (LC-MS/MS) in microdissected PNI and non-PNI cancer, as well as invaded and non-invaded nerves from formalin-fixed, paraffin-embedded PDAC tissues. In addition, an in vitro model of PNI was developed using a co-culture system comprising PDAC cell lines and PC12 cells as the neuronal element. The overall proteomic profiles of PNI and non-PNI cancer appeared largely similar. In contrast, upon invasion by cancer cells, nerves demonstrated widespread plasticity with a pattern consistent with neuronal injury. The upregulation of SCG2 (secretogranin II) and neurosecretory protein VGF (non-acronymic) in invaded nerves in PDAC tissues was further validated using immunohistochemistry. The tested PDAC cell lines were found to be able to induce neuronal plasticity in PC12 cells in our in vitro established co-culture model. Changes in expression levels of VGF, as well as of two additional proteins previously reported to be overexpressed in PNI, Nestin and Neuromodulin (GAP43), closely recapitulated our proteomic findings in PDAC tissues. Furthermore, induction of VGF, while not necessary for PC12 survival, mediated neurite extension induced by PDAC cell lines. In summary, here we report the proteomic alterations underlying PNI in PDAC and confirm that PDAC cells are able to induce neuronal plasticity. In addition, we describe a novel, simple, and easily adaptable co-culture model for in vitro study of neuro-epithelial interactions

Machine Learning for ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury

UCHL1-dependent control of hypoxia-inducible factor transcriptional activity during liver fibrosis

\ua9 2024 The Author(s)Liver fibrosis is the excessive accumulation of extracellular matrix proteins that occurs in most types of chronic liver disease. At the cellular level, liver fibrosis is associated with the activation of hepatic stellate cells (HSCs) which transdifferentiate into a myofibroblast-like phenotype that is contractile, proliferative and profibrogenic. HSC transdifferentiation induces genome-wide changes in gene expression that enable the cell to adopt its profibrogenic functions. We have previously identified that the deubiquitinase ubiquitin C-terminal hydrolase 1 (UCHL1) is highly induced following HSC activation; however, the cellular targets of its deubiquitinating activity are poorly defined. Here, we describe a role for UCHL1 in regulating the levels and activity of hypoxia-inducible factor 1 (HIF1), an oxygen-sensitive transcription factor, during HSC activation and liver fibrosis. HIF1 is elevated during HSC activation and promotes the expression of profibrotic mediator HIF target genes. Increased HIF1α expression correlated with induction of UCHL1 mRNA and protein with HSC activation. Genetic deletion or chemical inhibition of UCHL1 impaired HIF activity through reduction of HIF1α levels. Furthermore, our mechanistic studies have shown that UCHL1 elevates HIF activity through specific cleavage of degradative ubiquitin chains, elevates levels of pro-fibrotic gene expression and increases proliferation rates. As we also show that UCHL1 inhibition blunts fibrogenesis in a pre-clinical 3D human liver slice model of fibrosis, these results demonstrate how small molecule inhibitors of DUBs can exert therapeutic effects through modulation of HIF transcription factors in liver disease. Furthermore, inhibition of HIF activity using UCHL1 inhibitors may represent a therapeutic opportunity with other HIF-related pathologies

Spatial lipidomics reveals sphingolipid metabolism as anti-fibrotic target in the liver

\ua9 2025 The Authors. Background and aims: Steatotic liver disease (SLD), which encompasses various causes of fat accumulation in the liver, is a major cause of liver fibrosis. Understanding the specific mechanisms of lipotoxicity, dysregulated lipid metabolism, and the role of different hepatic cell types involved in fibrogenesis is crucial for therapy development. Methods: We analysed liver tissue from SLD patients and 3 mouse models. We combined bulk/spatial lipidomics, transcriptomics, imaging mass cytometry (IMC) and analysis of published spatial and single-cell RNA sequencing (scRNA-seq) data to explore the metabolic microenvironment in fibrosis. Pharmacological inhibition of sphingolipid metabolism with myriocin, fumonisin B1, miglustat and D-PDMP was carried out in hepatic stellate cells (HSCs) and human precision cut liver slices (hPCLSs). Results: Bulk lipidomics revealed increased glycosphingolipids, ether lipids and saturated phosphatidylcholines in fibrotic samples. Spatial lipidomics detected >40 lipid species enriched within fibrotic regions, notably sphingomyelin (SM) 34:1. Using bulk transcriptomics (mouse) and analysis of published spatial transcriptomics data (human) we found that sphingolipid metabolism was also dysregulated in fibrosis at transcriptome level, with increased gene expression for ceramide and glycosphingolipid synthesis. Analysis of human scRNA-seq data showed that sphingolipid-related genes were widely expressed in non-parenchymal cells. By integrating spatial lipidomics with IMC of hepatic cell markers, we found excellent spatial correlation between sphingolipids, such as SM(34:1), and myofibroblasts. Inhibiting sphingolipid metabolism resulted in anti-fibrotic effects in HSCs and hPCLSs. Conclusions: Our spatial multi-omics approach suggests cell type-specific mechanisms of fibrogenesis involving sphingolipid metabolism. Importantly, sphingolipid metabolic pathways are modifiable targets, which may have potential as an anti-fibrotic therapeutic strategy

Development and Validation of a Comprehensive Model to Estimate Early Allograft Failure among Patients Requiring Early Liver Retransplant

Importance: Expansion of donor acceptance criteria for liver transplant increased the risk for early allograft failure (EAF), and although EAF prediction is pivotal to optimize transplant outcomes, there is no consensus on specific EAF indicators or timing to evaluate EAF. Recently, the Liver Graft Assessment Following Transplantation (L-GrAFT) algorithm, based on aspartate transaminase, bilirubin, platelet, and international normalized ratio kinetics, was developed from a single-center database gathered from 2002 to 2015. Objective: To develop and validate a simplified comprehensive model estimating at day 10 after liver transplant the EAF risk at day 90 (the Early Allograft Failure Simplified Estimation [EASE] score) and, secondarily, to identify early those patients with unsustainable EAF risk who are suitable for retransplant. Design, Setting, and Participants: This multicenter cohort study was designed to develop a score capturing a continuum from normal graft function to nonfunction after transplant. Both parenchymal and vascular factors, which provide an indication to list for retransplant, were included among the EAF determinants. The L-GrAFT kinetic approach was adopted and modified with fewer data entries and novel variables. The population included 1609 patients in Italy for the derivation set and 538 patients in the UK for the validation set; all were patients who underwent transplant in 2016 and 2017. Main Outcomes and Measures: Early allograft failure was defined as graft failure (codified by retransplant or death) for any reason within 90 days after transplant. Results: At day 90 after transplant, the incidence of EAF was 110 of 1609 patients (6.8%) in the derivation set and 41 of 538 patients (7.6%) in the external validation set. Median (interquartile range) ages were 57 (51-62) years in the derivation data set and 56 (49-62) years in the validation data set. The EASE score was developed through 17 entries derived from 8 variables, including the Model for End-stage Liver Disease score, blood transfusion, early thrombosis of hepatic vessels, and kinetic parameters of transaminases, platelet count, and bilirubin. Donor parameters (age, donation after cardiac death, and machine perfusion) were not associated with EAF risk. Results were adjusted for transplant center volume. In receiver operating characteristic curve analyses, the EASE score outperformed L-GrAFT, Model for Early Allograft Function, Early Allograft Dysfunction, Eurotransplant Donor Risk Index, donor age × Model for End-stage Liver Disease, and Donor Risk Index scores, estimating day 90 EAF in 87% (95% CI, 83%-91%) of cases in both the derivation data set and the internal validation data set. Patients could be stratified in 5 classes, with those in the highest class exhibiting unsustainable EAF risk. Conclusions and Relevance: This study found that the developed EASE score reliably estimated EAF risk. Knowledge of contributing factors may help clinicians to mitigate risk factors and guide them through the challenging clinical decision to allocate patients to early liver retransplant. The EASE score may be used in translational research across transplant centers

Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury