Damage-associated molecular patterns (DAMPs) are endogenous danger molecules that are released from damaged or dying cells and activate the innate immune system by interacting with pattern recognition receptors (PRRs). Although DAMPs contribute to the host's defense, they promote pathological inflammatory responses. Recent studies have suggested that various DAMPs, such as high-mobility group box 1 (HMGB1), S100 proteins, and heat shock proteins (HSPs), are increased and considered to have a pathogenic role in inflammatory diseases. Here, we review current research on the role of DAMPs in inflammatory diseases, including rheumatoid arthritis, systemic lupus erythematosus, osteoarthritis, atherosclerosis, Alzheimer's disease, Parkinson's disease, and cancer. We also discuss the possibility of DAMPs as biomarkers and therapeutic targets for these diseases.

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Our previous studies demonstrated that peroxisome proliferator-activated receptor α (PPARα) activation reduces weight gain and improves insulin sensitivity in obese mice. Since excess lipid accumulation in non-adipose tissues is suggested to be responsible for the development of insulin resistance, this study was undertaken to examine whether the lemon balm extract ALS-L1023 regulates hepatic lipid accumulation, obesity, and insulin resistance and to determine whether its mechanism of action involves PPARα. Administration of ALS-L1023 to high-fat-diet-induced obese mice caused reductions in body weight gain, visceral fat mass, and visceral adipocyte size without changes of food consumption profiles. ALS-L1023 improved hyperglycemia, hyperinsulinemia, glucose and insulin tolerance, and normalized insulin-positive β-cell area in obese mice. ALS-L1023 decreased hepatic lipid accumulation and concomitantly increased the expression of PPARα target genes responsible for fatty acid β-oxidation in livers. In accordance with the in vivo data, ALS-L1023 reduced lipid accumulation and stimulated PPARα reporter gene expression in HepG2 cells. These effects of ALS-L1023 were comparable to those of the PPARα ligand fenofibrate, while the PPARα antagonist GW6471 inhibited the actions of ALS-L1023 on lipid accumulation and PPARα luciferase activity in HepG2 cells. Higher phosphorylated protein kinase B (pAkt)/Akt ratios and lower expression of gluconeogenesis genes were observed in the livers of ALS-L1023-treated mice. These results indicate that ALS-L1023 may inhibit obesity and improve insulin sensitivity in part through inhibition of hepatic lipid accumulation via hepatic PPARα activation.

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Predicting long-term outcomes after sepsis is important when caring for patients with this condition. The purpose of the present study was to develop models predicting long-term mortality of patients with sepsis, including septic shock.Retrospective data from 446 patients with sepsis (60.8% men; median age, 71 years) treated at a single university-affiliated tertiary care hospital over 3 years were reviewed. Binary logistic regression was used to identify factors predicting mortality at 180 and 365 days after arrival at the emergency department. Long-term prognosis scores for the 180- and 365-day models were calculated by assigning points to variables according to their β coefficients.The 180- and 365-day mortality rates were 40.6% and 47.8%, respectively. Multivariate analysis identified the following factors for inclusion in the 180- and 365-day models: age ≥65 years, body mass index ≤18.5 kg/m2, hemato-oncologic diseases as comorbidities, and ventilator care. Patients with scores of 0 to ≥3 had 180-day survival rates of 83.8%, 70.8%, 42.3%, and 25.0%, respectively, and 365-day survival rates of 72.1%, 64.6%, 36.2%, and 15.9%, respectively (all differences P < .001; log-rank test). The areas under the receiver operating characteristic curves of the 180- and 365-day models were 0.713 (95% confidence interval [CI] 0.668–0.756, P < .001) and 0.697 (95% CI 0.650–0.740, P < .001), respectively.These long-term prognosis models based on baseline patient characteristics and treatments are useful for predicting the 6- and 12-month mortality rates of patients with sepsis.

The revised World Health Organization guidelines on multidrug-resistant tuberculosis include linezolid in the core drugs group. Consequently, the use of linezolid for patients with multidrug-resistant tuberculosis is increasing. Common adverse events of long-term linezolid use include bone marrow suppression and neuropathies. However, there is limited information on a rare adverse event, black hairy tongue. Here, we report a case of linezolid-induced black hairy tongue in a patient with multidrug-resistant tuberculosis. The etiology, pathogenesis, diagnosis, and treatment of black hairy tongue are also discussed.

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BackgroundThe purpose of this study was to determine whether components of the ProVent model can predict the high medical costs in Korean patients requiring at least 21 days of mechanical ventilation (prolonged mechanical ventilation [PMV]).MethodsRetrospective data from 302 patients (61.6% male; median age, 63.0 years) who had received PMV in the past 5 years were analyzed. To determine the relationship between medical cost per patient and components of the ProVent model, we collected the following data on day 21 of mechanical ventilation (MV): age, blood platelet count, requirement for hemodialysis, and requirement for vasopressors.ResultsThe mortality rate in the intensive care unit (ICU) was 31.5%. The average medical costs per patient during ICU and total hospital (ICU and general ward) stay were 35,105 and 41,110 US dollars (USD), respectively. The following components of the ProVent model were associated with higher medical costs during ICU stay: age <50 years (average 42,731 USD vs. 33,710 USD, p=0.001), thrombocytopenia on day 21 of MV (36,237 USD vs. 34,783 USD, p=0.009), and requirement for hemodialysis on day 21 of MV (57,864 USD vs. 33,509 USD, p<0.001). As the number of these three components increased, a positive correlation was found betweeen medical costs and ICU stay based on the Pearson's correlation coefficient (γ) (γ=0.367, p<0.001).ConclusionThe ProVent model can be used to predict high medical costs in PMV patients during ICU stay. The highest medical costs were for patients who required hemodialysis on day 21 of MV.