Murine CD103+ dendritic cells protect against steatosis progression towards steatohepatitis.

BACKGROUND & AIMS: Non-alcoholic fatty liver (NAFL) is the hepatic consequence of metabolic syndrome and can progress to non-alcoholic steatohepatitis (NASH). The identification of molecular and cellular factors that determine the progression of NASH and lead to irreversible hepatocellular damage are crucial. Dendritic cells (DCs) represent a heterogeneous cell population among which CD103+ DCs play a significant role in immunity and tolerance. We aimed to clarify the role of this DC subset in the pathomechanism of NASH. METHODS: Steatosis progression towards steatohepatitis was analysed using multicolor FACS analyses, cytokine and qPCR array in high sucrose diet (HSD) and methionine and choline deficient diet (MCD) fed wild-type and basic leucine zipper transcription factor, ATF-Like-3 (Batf3) deficient animals, which lack CD103+ DCs (classical type-1 DC, cDC1s). RESULTS: Metabolic challenge of Batf3-/- animals resulted in the progression of steatosis towards steatohepatitis, manifesting by an increased influx of inflammatory cells into the liver and elevated inflammatory cytokine production of myeloid cells upon innate stimuli. However, the lack of cDC1s did not affect cellular apoptosis and fibrosis progression but altered genes involved in lipid metabolism. The adoptive transfer of CD103+ cDC1s to Batf3 deficient animals reversed these observed changes and more importantly could attenuate cellular damage and inflammation in established murine steatohepatitis. CONCLUSION: Here, we have identified the murine CD103+ cDC1s as a protective DC subtype that influences the pro-anti-inflammatory balance and protects the liver from metabolic damage. As guardians of liver integrity, they play a key role in the inflammatory process during the development of steatohepatitis in mice. LAY SUMMARY: Non-alcoholic fatty liver (NAFL) is the hepatic consequence of metabolic syndrome and can lead to non-alcoholic steatohepatitis (NASH). The current study demonstrated that a specific murine dendritic cell subtype possesses a potent regulatory role to influence the inflammatory milieu of the liver in this process.

Podoplanin discriminates distinct stromal cell populations and a novel progenitor subset in the liver.

Podoplanin/gp38(+) stromal cells present in lymphoid organs play a central role in the formation and reorganization of the extracellular matrix and in the functional regulation of immune responses. Gp38(+) cells are present during embryogenesis and in human livers of primary biliary cirrhosis. Since little is known about their function, we studied gp38(+) cells during chronic liver inflammation in models of biliary and parenchymal liver fibrosis and steatohepatitis. Gp38(+) cells were analyzed using flow cytometry and confocal microscopy, and the expression of their steady state and inflammation-associated genes was evaluated from healthy and inflamed livers. Gp38(+) cells significantly expanded in all three models of liver injury and returned to baseline levels during regression of inflammation. Based on CD133 and gp38 expression in the CD45(-)CD31(-)Asgpr1(-) liver cell fraction, numerous subsets could be identified that were negative for CD133 (gp38(hi)CD133(-), gp38(low)CD133(-), and gp38(-)CD133(-)). Moreover, among the CD133(+) cells, previously identified as progenitor population in injured liver, two subpopulations could be distinguished based on their gp38 expression (gp38(-)CD133(+) and CD133(+)gp38(+)). Importantly, the distribution of the identified subsets in inflammation illustrated injury-specific changes. Moreover, the gp38(+)CD133(+) cells exhibited liver progenitor cell characteristics similar to the gp38(-)CD133(+) population, thus representing a novel subset within the classical progenitor cell niche. Additionally, these cells expressed distinct sets of inflammatory genes during liver injury. Our study illuminates a novel classification of the stromal/progenitor cell compartment in the liver and pinpoints a hitherto unrecognized injury-related alteration in progenitor subset composition in chronic liver inflammation and fibrosis.

The Tanta University risk model could help identify patients with acute poisoning who would require intensive care unit level of care.

OBJECTIVE: To independently validate the negative predictive value of the Tanta University risk model for intensive care requirements in poison center telephone consultations with other physicians. METHODS: This study included 400 consecutive patients with acute poisoning. Clinical and laboratory parameters were recorded during the initial consultation with the poison center. Patients who were already ventilated or on vasopressors at the time of consultation were excluded. The Tanta University risk model score was calculated from the data according to the following equation: Tanta University risk model score = 1.966*Glasgow Coma Scale + 0.329*oxygen saturation (percent) + 0.212*diastolic blood pressure (mmHg) - 0.27*respiratory rate (breaths/minute) + 0.33*standard bicarbonate (mmol/L). Twenty-four hours later, the patients' courses were followed up by telephone. The Tanta University risk model was then compared to a composite endpoint indicating the requirement for admission to an intensive care unit (vasopressors, need for intubation, or death). RESULTS: Four hundred patients with acute poisoning were included. Thirty-seven patients had a complicated clinical course as defined by the composite endpoint. Receiver operating characteristic analysis revealed the area under the curve to be 0.87 (95 percent confidence interval 0.83-0.90). An unfavorable Tanta University risk model score was defined as less than 73.46, using a cut-off derived from a previous study of an unrelated series of patients with acute poisoning admitted to our service. Thirty-one of 37 patients with complicated courses had an unfavorable Tanta University risk model score compared to six patients with complicated courses among 306 patients with a favorable Tanta University risk model score (P < 0.0002, Fisher's exact test). Sixty-three patients had an unfavorable Tanta University risk model score but an uneventful course. The negative predictive value of the Tanta University risk model was 0.98 (95 percent confidence interval 0.96-0.99), sensitivity was 0.84, and specificity 0.83. CONCLUSIONS: In the present study of poison center telephone consultations, the Tanta University risk model was significantly related to the outcomes in patients with acute poisoning. Patients with a favorable Tanta University risk model score (greater than or equal to 73.46) were unlikely to need intensive care unit level of care.

Independent validation of the Tanta University Risk Model for intensive care requirement in acutely poisoned adults.

OBJECTIVE: To independently validate the predictive value of the Tanta University Risk Model for intensive care requirement in unselected poisoned patients. METHOD: Retrospective chart review of 293 poisoned patients. The Tanta University Risk Model was calculated as follows: Tanta University Risk Model = -1.966*Glasgow Coma Scale - 0.329*oxygen saturation - 0.212*diastolic blood pressure + 0.27*respiratory rate - 0.33*standard bicarbonate. It was then compared to a composite endpoint indicating an intensive care unit requirement (death in hospital, vasopressors, need for intubation). RESULTS: Nineteen of 293 patients had a complicated clinical course as defined by meeting the primary endpoint definition. Receiver operating characteristic analysis revealed the area under the curve to be 0.79 (95% confidence interval 0.73-0.83). A positive Tanta University Risk Model was defined >-73.46. Fifteen out of 84 patients with a positive Tanta University Risk Model had a complicated course, while four of 209 patients with a negative Tanta University risk model had a complicated course (P<0.0001, Fisher's exact test). The negative predictive value of the Tanta University Risk Model was 0.98 (95% confidence interval 0.95-0.99), the sensitivity was 0.79 and that specificity was 0.75. CONCLUSION: Poisoned patients with a negative Tanta University Risk Model score are unlikely to need an intensive care unit level of care.

ToxNet: an artificial intelligence designed for decision support for toxin prediction.

BACKGROUND: Artificial intelligences (AIs) are emerging in the field of medical informatics in many areas. They are mostly used for diagnosis support in medical imaging but have potential uses in many other fields of medicine where large datasets are available. AIM: To develop an artificial intelligence (AI) "ToxNet", a machine-learning based computer-aided diagnosis (CADx) system, which aims to predict poisons based on patient's symptoms and metadata from our Poison Control Center (PCC) data. To prove its accuracy and compare it against medical doctors (MDs). METHODS: The CADx system was developed and trained using data from 781,278 calls recorded in our PCC database from 2001 to 2019. All cases were mono-intoxications. Patient symptoms and meta-information (e.g., age group, sex, etiology, toxin point of entry, weekday, etc.) were provided. In the pilot phase, the AI was trained on 10 substances, the AI's prediction was compared to naïve matching, literature matching, a multi-layer perceptron (MLP), and the graph attention network (GAT). The trained AI's accuracy was then compared to 10 medical doctors in an individual and in an identical dataset. The dataset was then expanded to 28 substances and the predictions and comparisons repeated. RESULTS: In the pilot, the prediction performance in a set of 8995 patients with 10 substances was 0.66 ± 0.01 (F1 micro score). Our CADx system was significantly superior to naïve matching, literature matching, MLP, and GAT (p < 0.005). It outperformed our physicians experienced in clinical toxicology in the individual and identical dataset. In the extended dataset, our CADx system was able to predict the correct toxin in a set of 36,033 patients with 28 substances with an overall performance of 0.27 ± 0.01 (F1 micro score), also significantly superior to naïve matching, literature matching, MLP, and GAT. It also outperformed our MDs. CONCLUSION: Our AI trained on a large PCC database works well for poison prediction in these experiments. With further research, it might become a valuable aid for physicians in predicting unknown substances and might be the first step into AI use in PCCs.

Clinical effect of ethanol co-use in patients with acute drug toxicity involving the use of central nervous system depressant recreational drugs.

BACKGROUND AND IMPORTANCE: Patients who use recreational drugs frequently co-ingest ethanol, which is considered a central nervous system (CNS) depressant. The clinical relevance of this in acute toxicity involving other CNS depressants is not well described. OBJECTIVE: To assess the clinical impact of ethanol co-use in patients presenting to the emergency department (ED) with acute toxicity involving the use of CNS depressant drugs. DESIGN, SETTINGS AND PARTICIPANTS: A retrospective multicentre study using data from the Euro-DEN Plus database from January 2014 to December 2019. OUTCOMES MEASURE AND ANALYSIS: Comparison of epidemiologic and clinical characteristics, ED and hospital management of patients with CNS depressant intoxication with or without ethanol co-use. MAIN RESULTS: Although 7644 (17.5%) of the 43 633 presentations were included, ethanol was co-ingested in 3811 (49.9%). In total 53.3% required medical treatment, 14 patients died. Patients with ethanol co-use more frequently presented with a Glasgow Coma Scale (GCS) ≤8 (34.1% vs. 22.4%; P < 0.001), vomiting (8.1% vs. 4.6%; P < 0.001), anxiety (12 % vs. 6.4%; P < 0.001), agitation/aggression (22% vs. 14.7%; P < 0.001), seizures (3.8% vs. 2.4%; P < 0.001) and hypotension (7.5% vs. 4.6%; P < 0.001). They more often required ambulance transport (85.5% vs. 76.5%; P < 0.001), medical treatment (57.3% vs. 48.0%; P < 0.001), hospitalization (27.7% vs. 18.9%; P < 0.001), and admission to intensive care (12.2% vs. 4.0%; P < 0.001). Subgroup analysis showed that GCS ≤8 was particularly common in patients who combined ethanol with opioids or gamma-hydroxybutyrate (GHB)/gamma-butyrolactone (GBL). CONCLUSION: Co-use of ethanol with CNS-depressant drugs appears to increase the risk of adverse effects and is associated with a higher need for medical treatment, especially when ethanol is combined with opioids or GHB/GBL.