This study investigated the regulatory role of nerve growth factor (NGF) in sirtuin 1 (SIRT1) expression in cholestatic livers. We evaluated the expression of NGF and its cognate receptors in human livers with hepatolithiasis and the effects of NGF therapy on liver injury and hepatic SIRT1 expression in a bile duct ligation (BDL) mouse model. Histopathological and molecular analyses showed that the hepatocytes of human diseased livers expressed NGF, proNGF (a precursor of NGF), TrkA and p75NTR, whereas only p75NTR was upregulated in hepatolithiasis, compared with non-hepatolithiasis livers. In the BDL model without NGF therapy, p75NTR, but not TrkA antagonism, significantly deteriorated BDL-induced liver injury. By contrast, the hepatoprotective effect of NGF was abrogated only by TrkA and not by p75NTR antagonism in animals receiving NGF therapy. Intriguingly, a positive correlation between hepatic SIRT1 and NGF expression was found in human livers. In vitro studies demonstrated that NGF upregulated SIRT1 expression in mouse livers and human Huh-7 and rodent hepatocytes. Both NGF and proNGF induced protective effects against hydrogen peroxide-induced cytotoxicity in Huh-7 cells, whereas inhibition of TrkA and p75NTR activity prevented oxidative cell death. Mechanistically, NGF, but not proNGF, upregulated SIRT1 expression in human Huh-7 and rodent hepatocytes via nuclear factor (NF)-κB activity, whereas NGF-induced phosphoinositide-3 kinase/Akt, extracellular signal–regulated kinase and NF-κB signaling and SIRT1 activity were involved in its hepatoprotective effects against oxidative injury. These findings suggest that pharmacological manipulation of the NGF/SIRT1 axis might serve as a novel approach for the treatment of cholestatic disease.
Animals ; Bile Ducts ; Cell Death ; Cholestasis ; Hepatocytes ; Humans ; Hydrogen ; In Vitro Techniques ; Ligation ; Liver ; Mice ; Nerve Growth Factor ; Phosphotransferases ; Rodentia ; Sirtuin 1

Objective: Problematic use of social media (PUSM) may affect sleep quality and self-stigma in people with schizophrenia and consequently reduce their quality of life (QoL). This longitudinal study investigated if sleep quality and self-stigma mediated relationships between PUSM and QoL. Methods: One-hundred-and-ninety-three outpatients with schizophrenia were recruited from a psychiatric center in Taiwan from April 2019 to August 2021 and participated in a longitudinal study at intervals of three months between measurements. QoL was assessed using the World Health Organization Quality of Life Questionnaire Brief Version; sleep quality using the Pittsburgh Sleep Quality Index; self-stigma using the Self-Stigma Scale-Short; and PUSM using the Bergen Social Media Addiction Scale. Via SPSS 20.0, general estimating equation models assessed temporal associations between variables. Via R software, mediating effects of self-stigma and sleep quality were examined through Monte Carlo simulations with 20,000 repetitions. Results: Mean scores of physical, psychological, social and environmental QoL ranged from 11.86 to 13.02. Mean scores of sleep quality and self-stigma were 9.1±4.5 and 2.2±0.8, respectively. Sleep quality and self-stigma were directly related to QoL (p<0.001) and mediated indirect relationships between PUSM and all components of QoL with a range of 95% confidence intervals spanning from -0.0591 to -0.0107 for physical QoL; -0.0564 to -0.0095 for psychological QoL; -0.0292 to -0.0035 for social QoL; and -0.0357 to -0.0052 for environmental QoL. Conclusion Sleep quality and self-stigma mediated relationships between PUSM and QoL in people with schizophrenia. Developing interventions targeting PUSM, sleep, and self-stigma may help improve QoL in people with schizophrenia.

OBJECTIVE: The purpose of this study was to quantify lesions on chest radiographs in patients with severe acute respiratory syndrome (SARS) and analyze the severity of the lesions with clinical parameters. MATERIALS AND METHODS: Two experienced radiologists reviewed chest radiographs of 28 patients with SARS. Each lung was divided into upper, middle, and lower zones. A SARS-related lesion in each zone was scored using a four-point scale: zero to three. The mean and maximal radiographic scores were analyzed statistically to determine if the scorings were related to the laboratory data and clinical course. RESULTS: Forward stepwise multiple linear regression showed that the mean radiographic score correlated most significantly with the number of hospitalized days (p < 0.001). The second most significant factor was the absolute lymphocyte count (p < 0.001) and the third most significant factor was the number of days of intubation (p = 0.025). The maximal radiographic score correlated best with the percentage of lymphocytes in a leukocyte count (p < 0.001), while the second most significant factor was the number of hospitalized days (p < 0.001) and the third most significant factor was the absolute lymphocyte count (p = 0.013). The mean radiographic scores of the patients who died, with comorbidities and without a comorbidity were 11.1, 6.3 and 2.9, respectively (p = 0.032). The corresponding value for maximal radiographic scores were 17.7, 9.7 and 6.0, respectively (p = 0.033). CONCLUSION: The severity of abnormalities quantified on chest radiographs in patients with SARS correlates with the clinical parameters.
Adolescent ; Adult ; Aged ; Aged, 80 and over ; Biological Markers/blood ; Blood Gas Analysis/statistics & numerical data ; Female ; Humans ; Intubation, Intratracheal/statistics & numerical data ; Length of Stay ; Lung/*radiography ; Lymphocyte Count/statistics & numerical data ; Male ; Middle Aged ; Observer Variation ; Predictive Value of Tests ; Prognosis ; Retrospective Studies ; Severe Acute Respiratory Syndrome/blood/*diagnosis/mortality ; Severity of Illness Index ; Survival Analysis

BACKGROUND: Extracellular vesicles (EVs) are derived from internal cellular compartments, and have potential as a diagnostic and therapeutic tool in degenerative disease associated with aging. Mesenchymal stem cells (MSCs) have become a promising tool for functional EVs production. This study investigated the efficacy of EVs and its effect on differentiation capacity. METHODS: The characteristics of MSCs were evaluated by flow cytometry and stem cell differentiation analysis, and a production mode of functional EVs was scaled from MSCs. The concentration and size of EVs were quantitated by Nanoparticle Tracking Analysis (NTA). Western blot analysis was used to assess the protein expression of exosomespecific markers. The effects of MSC-derived EVs were assessed by chondrogenic and adipogenic differentiation analyses and histological observation. RESULTS The range of the particle size of adipose-derived stem cells (ADSCs)- and Wharton’s jelly -MSCs-derived EVs were from 130 to 150 nm as measured by NTA, which showed positive expression of exosomal markers. The chondrogenic induction ability was weakened in the absence of EVs in vitro. Interestingly, after EV administration, type II collagen, a major component in the cartilage extracellular matrix, was upregulated compared to the EV-free condition.Moreover, EVs decreased the lipid accumulation rate during adipogenic induction.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.