Amphipathic Liponecrosis Impairs Bacterial Clearance and Causes Infection During Sterile Inflammation.

BACKGROUND & AIMS: Although transient bacteremia is common during dental and endoscopic procedures, infections developing during sterile diseases like acute pancreatitis (AP) can have grave consequences. We examined how impaired bacterial clearance may cause this transition. METHODS: Blood samples from patients with AP, normal controls, and rodents with pancreatitis or those administered different nonesterified fatty acids (NEFAs) were analyzed for albumin-unbound NEFAs, microbiome, and inflammatory cell injury. Macrophage uptake of unbound NEFAs using a novel coumarin tracer were done and the downstream effects-NEFA-membrane phospholipid (phosphatidylcholine) interactions-were studied on isothermal titration calorimetry. RESULTS: Patients with infected AP had higher circulating unsaturated NEFAs; unbound NEFAs, including linoleic acid (LA) and oleic acid (OA); higher bacterial 16S DNA; mitochondrial DNA; altered ß-diversity; enrichment in Pseudomonadales; and increased annexin V-positive myeloid (CD14) and CD3-positive T cells on admission. These, and increased circulating dead inflammatory cells, were also noted in rodents with unbound, unsaturated NEFAs. Isothermal titration calorimetry showed progressively stronger unbound LA interactions with aqueous media, phosphatidylcholine, cardiolipin, and albumin. Unbound NEFAs were taken into protein-free membranes, cells, and mitochondria, inducing voltage-dependent anion channel oligomerization, reducing ATP, and impairing phagocytosis. These were reversed by albumin. In vivo, unbound LA and OA increased bacterial loads and impaired phagocytosis, causing infection. LA and OA were more potent for these amphipathic interactions than the hydrophobic palmitic acid. CONCLUSIONS: Release of stored LA and OA can increase their circulating unbound levels and cause amphipathic liponecrosis of immune cells via uptake by membrane phospholipids. This impairs bacterial clearance and causes infection during sterile inflammation.

A large language model-based generative natural language processing framework fine-tuned on clinical notes accurately extracts headache frequency from electronic health records.

OBJECTIVE: To develop a natural language processing (NLP) algorithm that can accurately extract headache frequency from free-text clinical notes. BACKGROUND: Headache frequency, defined as the number of days with any headache in a month (or 4 weeks), remains a key parameter in the evaluation of treatment response to migraine preventive medications. However, due to the variations and inconsistencies in documentation by clinicians, significant challenges exist to accurately extract headache frequency from the electronic health record (EHR) by traditional NLP algorithms. METHODS: This was a retrospective cross-sectional study with patients identified from two tertiary headache referral centers, Mayo Clinic Arizona and Mayo Clinic Rochester. All neurology consultation notes written by 15 specialized clinicians (11 headache specialists and 4 nurse practitioners) between 2012 and 2022 were extracted and 1915 notes were used for model fine-tuning (90%) and testing (10%). We employed four different NLP frameworks: (1) ClinicalBERT (Bidirectional Encoder Representations from Transformers) regression model, (2) Generative Pre-Trained Transformer-2 (GPT-2) Question Answering (QA) model zero-shot, (3) GPT-2 QA model few-shot training fine-tuned on clinical notes, and (4) GPT-2 generative model few-shot training fine-tuned on clinical notes to generate the answer by considering the context of included text. RESULTS: The mean (standard deviation) headache frequency of our training and testing datasets were 13.4 (10.9) and 14.4 (11.2), respectively. The GPT-2 generative model was the best-performing model with an accuracy of 0.92 (0.91, 0.93, 95% confidence interval [CI]) and R2 score of 0.89 (0.87, 0.90, 95% CI), and all GPT-2-based models outperformed the ClinicalBERT model in terms of exact matching accuracy. Although the ClinicalBERT regression model had the lowest accuracy of 0.27 (0.26, 0.28), it demonstrated a high R2 score of 0.88 (0.85, 0.89), suggesting the ClinicalBERT model can reasonably predict the headache frequency within a range of ≤ ± 3 days, and the R2 score was higher than the GPT-2 QA zero-shot model or GPT-2 QA model few-shot training fine-tuned model. CONCLUSION: We developed a robust information extraction model based on a state-of-the-art large language model, a GPT-2 generative model that can extract headache frequency from EHR free-text clinical notes with high accuracy and R2 score. It overcame several challenges related to different ways clinicians document headache frequency that were not easily achieved by traditional NLP models. We also showed that GPT-2-based frameworks outperformed ClinicalBERT in terms of accuracy in extracting headache frequency from clinical notes. To facilitate research in the field, we released the GPT-2 generative model and inference code with open-source license of community use in GitHub. Additional fine-tuning of the algorithm might be required when applied to different health-care systems for various clinical use cases.

Pathophysiology and Biomarker Potential of Fatty Acid Ethyl Ester Elevation During Alcoholic Pancreatitis.

BACKGROUND & AIMS: The role of fatty acid ethyl esters (FAEEs) during human alcoholic pancreatitis is unknown. We compared FAEEs levels with their nonesterified fatty acids (NEFAs) precursors during alcohol intoxication and clinical alcoholic pancreatitis. The pathophysiology underlying FAEEs increase and their role as diagnostic biomarkers for alcoholic pancreatitis was investigated. METHODS: A prospective blinded study compared FAEEs, NEFAs, and ethanol blood levels on hospitalization for alcoholic pancreatitis (n = 31), alcohol intoxication (n = 25), and in normal controls (n = 43). Serum FAEEs were measured at admission for nonalcoholic pancreatitis (n = 75). Mechanistic cell and animal studies were done. RESULTS: Median FAEEs were similarly elevated during alcohol intoxication (205 nmol/L; 95% confidence interval [CI], 71.8-515 nmol/L, P < .001) and alcoholic pancreatitis (103.1 nmol/L; 95% CI, 53-689 nmol/L, P < .001) vs controls (1.7 nmol/L; 95% CI, 0.02-4.3 nmol/L) or nonalcoholic pancreatitis (8 nmol/L; 95% CI, 1.1-11.5 nmol/L). Alcoholic pancreatitis increased serum NEFAs (1024 ± 710 µmol/L vs 307 ± 185 µmol/L in controls, P < .05). FAEEs comprised 0.1% to 2% of the parent NEFA concentrations. FAEES correlated strongly with NEFAs independent of ethanol levels in alcoholic pancreatitis but not during alcohol intoxication. On receiver operating characteristic curve analysis for diagnosing alcoholic pancreatitis, the area under the curve for serum FAEEs was 0.87 (95% CI, 0.78-0.95, P < .001). In mice and cells, alcohol administration transiently increased all FAEEs. Oleic acid ethyl ester was the only FAEE with a sustained increase up to 24 hours after intraperitoneal oleic acid plus ethanol administration. CONCLUSIONS: The sustained, alcohol-independent, large (20- to 50-fold) increase in circulating FAEEs during alcoholic pancreatitis results from their visceral release and mirrors the 2- to 4-fold increase in parent NEFA. The large areas under the curve of FAEEs on receiver operating characteristic curve analysis supports their role as alcoholic pancreatitis biomarkers.

Impact of abdominal imaging on the diagnosis of acute pancreatitis in patients with painless lipase elevation.

Abdominal pain is considered a cardinal feature of acute pancreatitis (AP), and abdominal imaging is only required to diagnose AP when the pain is atypical, or serum enzyme elevation does not match the clinical picture. While painless lipase elevation is being increasingly associated with worse outcomes in various diseases, the diagnostic approach to such elevation is so-far unclear. We thus aimed to learn the impact of pain on the diagnosis of AP. METHODS: All patients presenting to the Mayo Clinic Arizona Hospital emergency department with a serum lipase ≥3x upper limit of normal between April 2016 and January 2020 were prospectively followed. Their charts were reviewed for the nature of pain, serum lipase levels on presentation, abdominal imaging, and whether a diagnosis of AP was made. Chronic pancreatitis was excluded. RESULTS: Among 320 patients, 85 (26.5%) had painless lipase elevation. These patients had abdominal imaging less often (56/85, 66%) than in those with abdominal pain (201/235, 83%; p = 0.001). The diagnosis of AP increased overall from 31/63 (49%) without imaging to 198/257 (77%) with imaging (P < 0.001). Imaging increased the diagnosis of AP in patients with painless lipase elevation from 2/29 (7%) without imaging to 16/56 (29%; p = 0.025) among those who were imaged. CONCLUSIONS: Painless lipase elevation >3-fold the upper limit of normal is common in emergency department patients. 1/3 to 1/4 of these may have AP. Abdominal imaging increases the diagnosis of AP in patients with painless lipase elevation. Therefore, abdominal imaging in such patients may help detect AP that otherwise eludes diagnosis.

Carboxyl Ester Lipase May Not Mediate Lipotoxic Injury during Severe Acute Pancreatitis.

Acute lipolysis of visceral fat or circulating triglycerides may worsen acute pancreatitis (AP)-associated local and systemic injury. The pancreas expresses pancreatic triacylglycerol lipase (PNLIP), pancreatic lipase-related protein 2 (PNLIPRP2), and carboxyl ester lipase (CEL), which may leak into the visceral fat or systemic circulation during pancreatitis. We, thus, aimed to determine the pancreatic lipase(s) regulating lipotoxicity during AP. For this AP, associated fat necrosis was analyzed using Western blot analysis. Bile acid (using liquid chromatography-tandem mass spectrometry) and fatty acid (using gas chromatography) concentrations were measured in human fat necrosis. The fat necrosis milieu was simulated in vitro using glyceryl trilinoleate because linoleic acid is increased in fat necrosis. Bile acid requirements to effectively hydrolyze glyceryl trilinoleate were studied using exogenous or overexpressed lipases. The renal cell line (HEK 293) was used to study lipotoxic injury. Because dual pancreatic lipase knockouts are lethal, exocrine parotid acini lacking lipases were used to verify the results. PNLIP, PNLIPRP2, and CEL were increased in fat necrosis. Although PNLIP and PNLIPRP2 were equipotent in inducing lipolysis and lipotoxic injury, CEL required bile acid concentrations higher than in human fat necrosis. The high bile acid requirements for effective lipolysis make CEL an unlikely mediator of lipotoxic injury in AP. It remains to be explored whether PNLIP or PNLIPRP2 worsens AP severity in vivo.

A Large Language Model-Based Generative Natural Language Processing Framework Finetuned on Clinical Notes Accurately Extracts Headache Frequency from Electronic Health Records.

Background: Headache frequency, defined as the number of days with any headache in a month (or four weeks), remains a key parameter in the evaluation of treatment response to migraine preventive medications. However, due to the variations and inconsistencies in documentation by clinicians, significant challenges exist to accurately extract headache frequency from the electronic health record (EHR) by traditional natural language processing (NLP) algorithms. Methods: This was a retrospective cross-sectional study with human subjects identified from three tertiary headache referral centers- Mayo Clinic Arizona, Florida, and Rochester. All neurology consultation notes written by more than 10 headache specialists between 2012 to 2022 were extracted and 1915 notes were used for model fine-tuning (90%) and testing (10%). We employed four different NLP frameworks: (1) ClinicalBERT (Bidirectional Encoder Representations from Transformers) regression model (2) Generative Pre-Trained Transformer-2 (GPT-2) Question Answering (QA) Model zero-shot (3) GPT-2 QA model few-shot training fine-tuned on Mayo Clinic notes; and (4) GPT-2 generative model few-shot training fine-tuned on Mayo Clinic notes to generate the answer by considering the context of included text. Results: The GPT-2 generative model was the best-performing model with an accuracy of 0.92[0.91 - 0.93] and R2 score of 0.89[0.87, 0.9], and all GPT2-based models outperformed the ClinicalBERT model in terms of the exact matching accuracy. Although the ClinicalBERT regression model had the lowest accuracy 0.27[0.26 - 0.28], it demonstrated a high R2 score 0.88[0.85, 0.89], suggesting the ClinicalBERT model can reasonably predict the headache frequency within a range of ≤ ± 3 days, and the R2 score was higher than the GPT-2 QA zero-shot model or GPT-2 QA model few-shot training fine-tuned model. Conclusion: We developed a robust model based on a state-of-the-art large language model (LLM)- a GPT-2 generative model that can extract headache frequency from EHR free-text clinical notes with high accuracy and R2 score. It overcame several challenges related to different ways clinicians document headache frequency that were not easily achieved by traditional NLP models. We also showed that GPT2-based frameworks outperformed ClinicalBERT in terms of accuracy in extracting headache frequency from clinical notes. To facilitate research in the field, we released the GPT-2 generative model and inference code with open-source license of community use in GitHub.

Cytokine profiles in intensive care unit delirium.

BACKGROUND: Neuroinflammation causing disruption of the blood-brain barrier and immune cell extravasation into the brain parenchyma may cause delirium; however, knowledge of the exact pathophysiologic mechanism remains incomplete. The purpose of our study was to determine whether cytokine profiles differ depending on whether delirium occurs in the setting of sepsis, coronavirus disease 2019 (COVID-19), or recent surgery. METHODS: This prospective observational cohort study involved 119 critically ill patients admitted to a multidisciplinary intensive care unit (ICU) during 2019 and 2020. Delirium was identified using the validated confusion assessment method for the ICU. Multiple delirium risk factors were collected daily including clinical characteristics, hospital course, lab values, vital signs, surgical exposure, drug exposure, and COVID-19 characteristics. Serums samples were collected within 12 hours of ICU admission and cytokine levels were measured. RESULTS: The following proinflammatory cytokines were elevated in our delirium population: tumor necrosis factor (TNF)-α, interleukin (IL)-6, IL-18, C-C motif ligand (CCL) 2, CCL3, C-X-C motif chemokine ligand (CXCL)1, CXCL10, IL-8, IL-1 receptor antagonist, and IL-10. Analysis of relative cytokine levels in those patients that developed delirium in the setting of sepsis, COVID-19, and recent surgery showed elevations of CCL2, CXCL10, and TNF-α in both the sepsis and COVID-19 group in comparison to the postsurgical population. In the postsurgical group, granulocyte colony-stimulating factor was elevated and CXCL10 was decreased relative to the opposing groups. CONCLUSIONS: We identify several cytokines and precipitating factors known to be associated with delirium. However, our study suggests that the cytokine profile associated with delirium is variable and contingent upon delirium precipitating factors.

Chemokines in ICU Delirium: An Exploratory Study.

OBJECTIVES: The pathophysiology of delirium is complex and incompletely understood. Inflammation is hypothesized to be integral to its development due to effects on blood brain barrier integrity, facilitation of leukocyte extravasation into brain parenchyma, and propagation of neuroinflammation. Septic shock is the prototypical condition associated with ICU delirium; however, the relative contribution of resultant hypotension and systemic inflammation to the development of delirium is unknown. DESIGN: This was a prospective exploratory study. SETTING: A multidisciplinary ICU at an academic medical center in Phoenix, AZ. PATIENTS: Critically ill patients older than or equal to 18 years old admitted to the ICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Screening for delirium was performed using the Confusion Assessment Method for the ICU tool. The levels of C-C motif ligand 2 (CCL2), C-C motif ligand 3, C-X-C motif chemokine ligand 1, C-X-C motif chemokine ligand 10, and interleukin-8 were measured in serum samples obtained within 12 hours of ICU admission. Univariate and multivariate analyses were performed to assess the association of delirium with patient data pertaining to hospital course, laboratory values, vital signs, medication administration, and levels of the aforementioned chemokines. Forty-one of 119 patients (34.5%) in the study cohort developed ICU delirium. Each chemokine studied was associated with delirium on univariate analyses; however, CCL2 was the only chemokine found to be independently associated with the development of delirium on multivariable analysis. The association of increased CCL2 levels with delirium remained robust in various models controlling for age, presence of shock, Sequential Organ Failure Assessment score, Acute Physiology and Chronic Health Evaluation IV score, mean arterial pressure at presentation, lowest mean arterial pressure, and total opioid, midazolam, propofol, and dexmedetomidine exposure. CONCLUSIONS: The demonstrated relationship between CCL2 and delirium suggests this chemokine may play a role in the development of delirium and warrants further investigation.

Evidence showing lipotoxicity worsens outcomes in covid-19 patients and insights about the underlying mechanisms.

We compared three hospitalized patient cohorts and conducted mechanistic studies to determine if lipotoxicity worsens COVID-19. Cohort-1 (n = 30) compared COVID-19 patients dismissed home to those requiring intensive-care unit (ICU) transfer. Cohort-2 (n = 116) compared critically ill ICU patients with and without COVID-19. Cohort-3 (n = 3969) studied hypoalbuminemia and hypocalcemia's impact on COVID-19 mortality. Patients requiring ICU transfer had higher serum albumin unbound linoleic acid (LA). Unbound fatty acids and LA were elevated in ICU transfers, COVID-19 ICU patients and ICU non-survivors. COVID-19 ICU patients (cohort-2) had greater serum lipase, damage-associated molecular patterns (DAMPs), cytokines, hypocalcemia, hypoalbuminemia, organ failure and thrombotic events. Hypocalcemia and hypoalbuminemia independently associated with COVID-19 mortality in cohort-3. Experimentally, LA reacted with albumin, calcium and induced hypocalcemia, hypoalbuminemia in mice. Endothelial cells took up unbound LA, which depolarized their mitochondria. In mice, unbound LA increased DAMPs, cytokines, causing endothelial injury, organ failure and thrombosis. Therefore, excessive unbound LA in the circulation may worsen COVID-19 outcomes.

In silico screening of hundred phytocompounds of ten medicinal plants as potential inhibitors of nucleocapsid phosphoprotein of COVID-19: an approach to prevent virus assembly.

Currently, there is no specific treatment to cure COVID-19. Many medicinal plants have antiviral, antioxidant, antibacterial, antifungal, anticancer, wound healing etc. Therefore, the aim of the current study was to screen for potent inhibitors of N-terminal domain (NTD) of nucleocapsid phosphoprotein of SARS-CoV-2. The structure of NTD of RNA binding domain of nucleocapsid phosphoprotein of SARS coronavirus 2 was retrieved from the Protein Data Bank (PDB 6VYO) and the structures of 100 different phytocompounds were retrieved from Pubchem. The receptor protein and ligands were prepared using Schrodinger's Protein Preparation Wizard. Molecular docking was done by using the Schrodinger's maestro 12.0 software. Drug likeness and toxicity of active phytocompounds was predicted by using Swiss adme, admetSAR and protox II online servers. Molecular dynamic simulation of the best three protein- ligand complexes (alizarin, aloe-emodin and anthrarufin) was performed to study the interaction stability. We have identified three potential active sites (named as A, B, C) on receptor protein for efficient binding of the phytocompounds. We found that, among 100 phytocompounds, emodin, aloe-emodin, anthrarufin, alizarine, and dantron of Rheum emodi showed good binding affinity at all the three active sites of RNA binding domain of nucleocapsid phosphoprotein of COVID-19.The binding energies of emodin, aloe-emodin, anthrarufin, alizarine, and dantron were -8.299, -8.508, -8.456, -8.441, and -8.322 Kcal mol-1 respectively (site A), -7.714, -6.433, -6.354, -6.598, and -6.99 Kcal mol-1 respectively (site B), and -8.299, 8.508, 8.538, 8.841, and 8.322 Kcal mol-1 respectively (site C). All the active phytocompounds follows the drug likeness properties, non-carcinogenic, and non-toxic. Theses phytocompounds (alone or in combination) could be developed into effective therapy against COVID-19. From MD simulation data, we found that all three complexes of 6VYO with alizarin, aloe-emodin and anthrarufin were stable up to 50 ns. These phytocompounds can be tested further for in vitro or in vivo and used as a potential drug to cure SARS-CoV-2 infection.Communicated by Ramaswamy H. Sarma.

Pancreatic triglyceride lipase mediates lipotoxic systemic inflammation.

Visceral adipose tissue plays a critical role in numerous diseases. Although imaging studies often show adipose involvement in abdominal diseases, their outcomes may vary from being a mild self-limited illness to one with systemic inflammation and organ failure. We therefore compared the pattern of visceral adipose injury during acute pancreatitis and acute diverticulitis to determine its role in organ failure. Acute pancreatitis-associated adipose tissue had ongoing lipolysis in the absence of adipocyte triglyceride lipase (ATGL). Pancreatic lipase injected into mouse visceral adipose tissue hydrolyzed adipose triglyceride and generated excess nonesterified fatty acids (NEFAs), which caused organ failure in the absence of acute pancreatitis. Pancreatic triglyceride lipase (PNLIP) increased in adipose tissue during pancreatitis and entered adipocytes by multiple mechanisms, hydrolyzing adipose triglyceride and generating excess NEFAs. During pancreatitis, obese PNLIP-knockout mice, unlike obese adipocyte-specific ATGL knockouts, had lower visceral adipose tissue lipolysis, milder inflammation, less severe organ failure, and improved survival. PNLIP-knockout mice, unlike ATGL knockouts, were protected from adipocyte-induced pancreatic acinar injury without affecting NEFA signaling or acute pancreatitis induction. Therefore, during pancreatitis, unlike diverticulitis, PNLIP leaking into visceral adipose tissue can cause excessive visceral adipose tissue lipolysis independently of adipocyte-autonomous ATGL, and thereby worsen organ failure.