Gender differences in plasma S100B levels of patients with major depressive disorder.

BACKGROUND: Low concentrations of S100B have neurotrophic effects and can promote nerve growth and repair, which plays an essential role in the pathophysiological and histopathological alterations of major depressive disorder (MDD) during disease development. Studies have shown that plasma S100B levels are altered in patients with MDD. In this study, we investigated whether the plasma S100B levels in MDD differ between genders. METHODS: We studied 235 healthy controls (HCs) (90 males and 145 females) and 185 MDD patients (65 males and 120 females). Plasma S100B levels were detected via multifactor assay. The Mahalanobis distance method was used to detect the outliers of plasma S100B levels in the HC and MDD groups. The Kolmogorov-Smirnov test was used to test the normality of six groups of S100B samples. The Mann-Whitney test and Scheirer-Ray-Hare test were used for the comparison of S100B between diagnoses and genders, and the presence of a relationship between plasma S100B levels and demographic details or clinical traits was assessed using Spearman correlation analysis. RESULTS: All individuals in the HC group had plasma S100B levels that were significantly greater than those in the MDD group. In the MDD group, males presented significantly higher plasma S100B levels than females. In the male group, the plasma S100B levels in the HC group were significantly higher than those in the MDD group, while in the female group, no significant difference was found between the HC and MDD groups. In the male MDD subgroup, there was a positive correlation between plasma S100B levels and years of education. In the female MDD subgroup, there were negative correlations between plasma S100B levels and age and suicidal ideation. CONCLUSIONS: In summary, plasma S100B levels vary with gender and are decreased in MDD patients, which may be related to pathological alterations in glial cells.

γ-Glutamylcysteine rescues mice from TNBS-driven inflammatory bowel disease through regulating macrophages polarization.

OBJECTIVE: To explore the molecular mechanism of γ-glutamylcysteine (γ-GC) in response to inflammation in vivo and in vitro on regulating the polarization of macrophages. METHODS: The expressions of gene or protein were assessed by qPCR and Western blot assays, respectively. Cell viability was investigated by CCK-8 assay. Eight-week-old male BALB/c mice were established to examine the therapeutic effects of γ-GC in vivo. The release of TNF-α and IL-4 was determined by ELISA assay. Macrophages polarization was identified by flow cytometry assay. RESULTS: Our data showed that γ-GC treatment significantly improved the survival, weight loss, and colon tissue damage of IBD mice. Furthermore, we established M1- and M2-polarized macrophages, respectively, and our findings provided evidence that γ-GC switched M1/M2-polarized macrophages through activating AMPK/SIRT1 axis and inhibiting inflammation-related signaling pathway. CONCLUSION: Collectively, both in vivo and in vitro experiments suggested that γ-GC has the potential to become a promising novel therapeutic dipeptide for the treatment of IBD, which provide new ideas for the treatment of inflammatory diseases in the future.

A Proactive Recognition System for Detecting Commercial Vehicle Driver's Distracted Behavior.

Road traffic accidents regarding commercial vehicles have been demonstrated as an important culprit restricting the steady development of the social economy, which are closely related to the distracted behavior of drivers. However, the existing driver's distracted behavior surveillance systems for monitoring and preventing the distracted behavior of drivers still have some shortcomings such as fewer recognition objects and scenarios. This study aims to provide a more comprehensive methodological framework to demonstrate the significance of enlarging the recognition objects, scenarios and types of the existing driver's distracted behavior recognition systems. The driver's posture characteristics were primarily analyzed to provide the basis of the subsequent modeling. Five CNN sub-models were established for different posture categories and to improve the efficiency of recognition, accompanied by a holistic multi-cascaded CNN framework. To suggest the best model, image data sets of commercial vehicle driver postures including 117,410 daytime images and 60,480 night images were trained and tested. The findings demonstrate that compared to the non-cascaded models, both daytime and night cascaded models show better performance. Besides, the night models exhibit worse accuracy and better speed relative to their daytime model counterparts for both non-cascaded and cascaded models. This study could be used to develop countermeasures to improve driver safety and provide helpful information for the design of the driver's real-time monitoring and warning system as well as the automatic driving system. Future research could be implemented to combine the vehicle state parameters with the driver's microscopic behavior to establish a more comprehensive proactive surveillance system.

Indoprofen exerts a potent therapeutic effect against sepsis by alleviating high mobility group box 1-mediated inflammatory responses.

Indoprofen is a non-steroidal anti-inflammatory drug, and has provided insights into treatment of spinal muscular atrophies; however, the treatment effect of indoprofen on sepsis and the precise underlying mechanism remain to be elucidated. This study was carried out to examine the inhibitory effect of indoprofen on high mobility group box 1 (HMGB1)-mediated inflammatory responses in vivo and in vitro. Intraperitoneal injection of indoprofen (20 or 40 mg/kg) at 8 h post-sepsis markedly improved the survival of BALB/c mice and ameliorated multiple-organ injury by blocking the inflammatory responses. In addition, indoprofen partially reduced the HMGB1 level in the serum and in the lung, as well as ameliorated pulmonary edema. Mechanistically, indoprofen potently inhibited the release of HMGB1 following stimulation by lipopolysaccharide (LPS) or polyinosinic:polycytidylic acid (poly I:C), and suppressed recombinant human HMGB1(rhHMGB1)-induced inflammatory responses. It was also found that indoprofen has both cyclooxygenase 2-dependent and -independent inhibitory effects on the proinflammatory effect of HMGB1 in THP-1 cells. Further, the drug reduced rhHMGB1-induced cell surface levels of toll-like receptor 2, toll-like receptor 4, and receptor of advanced glycation end-products in a concentration-dependent manner. Collectively, these data demonstrated that the anti-inflammatory effect of indoprofen in sepsis was associated with HMGB1-mediated inflammatory responses, thus offering a favorable mechanistic basis to support the therapeutic potential of indoprofen for the treatment of lethal sepsis or other inflammatory diseases.

Atovaquone enhances doxorubicin's efficacy via inhibiting mitochondrial respiration and STAT3 in aggressive thyroid cancer.

The clinical management of anaplastic thyroid carcinoma and follicular thyroid carcinoma is challenging and requires an alternative therapeutic strategy. Although atovaquone is an FDA-approved anti-malarial drug, studies has recently demonstrated its anti-cancer activities. In line with these efforts, our study shows that atovaquone is an attractive candidate for thyroid cancer treatment. We show that atovaquone significantly inhibits growth, migration and survival in a concentration-dependent manner in 8505C and FTC113 cells. Mechanistically, atovaquone inhibits mitochondrial complex III activity, leading to mitochondrial respiration inhibition and reduction of ATP production in thyroid cancer cells. The inhibitory effects of atovaquone is reversed in mitochondrial respiration-deficient 8505C ρ0 cells, confirming mitochondrial respiration as the mechanism of atovaquone's action in thyroid cancer. In addition, atovaquone suppresses phosphorylation of STAT3 in thyroid cancer wildype but not ρ0 cells, demonstrating that STAT3 phosphorylation inhibition by atovaquone is a consequence of mitochondrial respiration inhibition. Notably, we further demonstrate that atovaquone significantly augments doxorubicin's inhibitory effects via suppressing mitochondrial respiration and STAT3. Our findings suggest that atovaquone can be repurposed for thyroid cancer treatment. Our work also highlights that targeting mitochondrial respiration may represent potential therapeutic strategy in thyroid cancer.

An investigation of heterogeneous impact, temporal stability, and aggregate shift in factors affecting the driver injury severity in single-vehicle rollover crashes.

Single-vehicle rollover crashes have been acknowledged as a predominant highway crash type resulting in serious casualties. To investigate the heterogeneous impact of factors determining different injury severity levels in single-vehicle rollover crashes, the random parameters logit model with unobserved heterogeneity in means and variances was employed in this paper. A five-year dataset on single-vehicle rollover crashes, gathered in California from January 1, 2013, to December 31, 2017, was utilized. Driver injury severities that were determined to be outcome variables include no injury, minor injury, and severe injury. Characteristics pertaining to the crash, driver, temporal, vehicle, roadway, and environment were acknowledged as potential determinants. The results showed that the gender indicator specified to minor injury was consistently identified as a significant random parameter in four years' models and the joint five-year model, excluding the 2016 crash model where the night indicator associated with no injury was observed to produce the random effect. Additionally, two series of likelihood ratio tests were conducted to assess the year-to-year and aggregate-to-component temporal stability of model estimation results. Marginal effects of explanatory variables were also calculated and compared to analyze the temporal stability and interpret the results. The findings revealed an overall temporal instability of model specifications across individual years, while there is no significant aggregate-to-component variation. Injury severities were observed to be stably affected by several variables, including improper turn indicator, under the influence of alcohol indicator, old driver indicator, seatbelt indicator, insurance indicator, and airbag indicator. Furthermore, the year-to-year and aggregate-to-component shift was quantified and characterized by calculating the differences in probabilities between within-sample observations and out-of-sample predictions. The overall results imply that continuing to expand and refine the model to incorporate more comprehensive datasets can result in more robust and stable injury severity prediction, thus benefiting in mitigating the associated driver injury severity.

Driving risk identification of urban arterial and collector roads based on multi-scale data.

Urban arterial and collector roads, while interconnected within the urban transportation network, serve distinct purposes, leading to different driving risk profiles. Investigating these differences using advanced methods is of paramount significance. This study aims to achieve this by primarily collecting and processing relevant vehicle trajectory data alongside driver-vehicle-road-environment data. A comprehensive risk assessment matrix is constructed to assess driving risks, incorporating multiple conflict and traffic flow indicators with statistically temporal stability. The Entropy weight-TOPSIS method and the K-means algorithm are employed to determine the risk scores and levels of the target arterial and collector roads. Using risk levels as the outcome variables and multi-scale features as the explanatory variables, random parameters models with heterogeneity in means and variances are developed to identify the determinants of driving risks at different levels. Likelihood ratio tests and comparisons of out-of-sample and within-sample prediction are conducted. Results reveal significant statistical differences in the risk profiles between arterial and collector roads. The marginal effects of significant parameters are then calculated separately for arterial and collector roads, indicating that several factors have different impacts on the probability of risk levels for arterial and collector roads, such as the number of movable elements in road landscape pictures, the standard deviation of the vehicle's lateral acceleration, the average standard deviation of speed for all vehicles on the road segment, and the number of one-way lanes on the road segment. Some practical implications are provided based on the findings. Future research can be implemented by expanding the collected data to different regions and cities over longer periods.

Prediction of driving stress on high-altitude expressway using driving environment features: A naturalistic driving study in Tibet.

OBJECTIVE: Owing to the harsh environment in high-altitude areas, drivers experience significant driving stress. Compared with urban roads or expressways in low-altitude areas, the driving environment in high-altitude areas has distinct features, including mountainous environments and a higher proportion of trucks and buses. This study aims to investigate the feasibility of predicting stress levels through elements in the driving environment. METHODS: Naturalistic driving tests were conducted on an expressway in Tibet. Driving stress was assessed using heart rate variability (HRV)-based indicators and classified using K-means clustering. A DeepLabv3 model was built to conduct semantic segmentation and extract environment elements from the driving scenarios recorded through a camera next to the driver's eyes. A decision tree and 4 other ensemble learning models based on decision trees were built to predict driving stress levels using the environment elements. RESULTS: Fifty-six indicators were extracted from the driving environment. Results of the prediction models demonstrate that extreme gradient boosting has the best overall performance with the F1 score (harmonic mean of the precision and recall) and G-mean (geometric mean of sensitivity and specificity) reaching 0.855 and 0.890, respectively. Indicators based on the variation rate of trucks and buses have high feature importance and exhibit positive effects on driving stress. Indicators reflecting the proportion of mountain, road, and sky features negatively affect the expected levels of driving stress. Additionally, the mountain feature demonstrates multidimensional effects, because driving stress is positively affected by indicators of the variation rate for mountain elements. CONCLUSIONS: This study validates the prediction of driving stress using environment elements in the driver's field of view and extends its application to high-altitude expressways with distinct environmental characteristics. This method provides a real-time, less intrusive, and safer method of driving stress assessment and prediction and also enhances the understanding of the environmental determinants of driving stress. The results hold promising applications, including the development of a driving state assessment and warning module as well as the identification of high-risk road sections and implementation of control measures.

A temporal analysis of crash injury severities in multivehicle crashes involving distracted and non-distracted driving on tollways.

Distracted driving is a prominent cause of traffic crashes and may increase the severity of collisions. Due to the larger speeds on toll ways, distracted driving crashes are more severe than on other types of roads, making it worthwhile to investigate. This study examined the variation in the influence of factors affecting injury severity in crashes involving distracted and non-distracted driving, as well as the change over time, using crash data from Florida toll ways from the 2017 to 2019. A series of random parameters logit models with heterogeneity in the means and variances were developed to analyze different driver-injury severities (no injury, minor injury, and severe injury) in crashes involving distracted and non-distracted driving. In addition, likelihood ratio tests were conducted to determine whether model parameters differed between different driver behaviors (distracted/non-distracted driving) and among years. Several factors potentially impacting injury severities were studied, including driver, crash, vehicle, roadway, environment, temporal, and others. Significant disparities were observed between the contributing factors of the severity of crashes involving distracted and non-distracted driving. Results showed that considerable differences were also observed in the severity of injuries caused by two types of crashes and distracted driving resulted in more serious crashes than non-distracted driving. Despite model results indicated that factors influencing injury severity altered over time, several factors, such as motorcycle involvement and commercial car involvement, still exhibited relative temporal stability in non-distracted driving crashes over the three years. Temporal instability and non-transferability were also captured by out-of-sample predictions to verify the temporal shifts of contributing variables from year to year. This study is useful for distinguishing the influence mechanisms between the two types of crashes involving distracted and non-distracted driving, and the results can be applied for safety countermeasures development.

Phosphorylated Hsp27 promotes adriamycin resistance in breast cancer cells through regulating dual phosphorylation of c-Myc.

Chemotherapy resistance of breast cancer cells is one of the major factors affecting patient survival rate. Heat shock protein 27 (Hsp27) is a member of the small heat shock protein family that has been reported to be associated with chemotherapy resistance in tumor cells, but the exact mechanism is not fully understood. Here, we explored the regulation of Hsp27 in adriamycin-resistant pathological conditions of breast cancer in vitro and in vivo. We found that overexpression of Hsp27 in MCF-7 breast cancer cells reversed DNA damage induced by adriamycin, and thereby reduced subsequent cell apoptosis. Non-phosphorylated Hsp27 accelerated ubiquitin-mediated degradation of c-Myc under normal physiological conditions. After stimulation with adriamycin, Hsp27 was phosphorylated and translocated from the cytoplasm into the nucleus, where phosphorylated Hsp27 upregulated c-Myc and Nijmegen breakage syndrome 1 (NBS1) protein levels thus leading to ATM activation. We further showed that phosphorylated Hsp27 promoted c-Myc nuclear import and stabilization by regulating T58/S62 phosphorylation of c-Myc through a protein phosphatase 2A (PP2A)-dependent mechanism. Collectively, the data presented in this study demonstrate that Hsp27, in its phosphorylation state, plays a critical role in adriamycin-resistant pathological conditions of breast cancer cells.

Hainanxylogranolides A-F: New Limonoids isolated from the seeds of Hainan mangrove plant Xylocarpus granatum.

Six new limonoids, named hainanxylogranolides A-F (1-6), together with nineteen known ones (7-25) were isolated from the seeds of a Hainan mangrove Xylocarpus granatum. The structures of the new compounds were established by extensive NMR spectroscopic data combined with the DFT and TDDFT calculated electronic circular dichroism spectra. Hainanxylogranolide A (1) is the aromatic B-ring limonoid containing a central pyridine ring and a C-17 substituted γ(21)-hydroxybutenolide moiety. Hainanxylogranolide B (2) belongs to the small group of mexicanolides containing a C3-O-C8 bridge, whereas hainanxylogranolides C and D (3 and 4) are mexicanolides comprising a C1-O-C8 bridge. Compounds 9 and 25 posed obvious inhibition effect on the tube formation of HUVECs. There are only about 25% tube-like structures were observed at the concentration of 40.0 µM of compound 25. The antiviral activities of the isolates against herpes simplex virus-1 (HSV-1) and severe fever with thrombocytopenia syndrome virus (SFTSV) were tested in vitro. Compound 23 exhibited moderate anti-SFTSV activity with the IC50 value of 29.58 ± 0.73 µM. This is the first report of anti-angiogenic effect and anti-SFTSV activity of limonoids from the genus Xylocarpus.

Gender differences in plasma glial cell line-derived neurotrophic factor levels of patients with bipolar disorder.

BACKGROUND: The glial cell line-derived neurotrophic factor (GDNF) has an important role in neurons and is closely associated with psychiatric disorders. The development of bipolar disorder (BD) may differ between genders. Existing studies have shown that plasma GDNF levels are altered in patients with BD. In this study, we investigate whether the GDNF levels in patients with BD differ in terms of gender. METHODS: Participants were divided into the BD group (n = 76, with 26 males and 50 females) and healthy control (HC) group (n = 89, with 35 males and 54 females). Plasma GDNF levels were detected via multifactor assay. Clinical symptoms of patients with BD were collected and assessed using the Hamilton Depression-17 Inventory, Hamilton Anxiety-17 Inventory, Young's Mania Rating Scale, and Brief Psychiatric Rating Scale. RESULTS: The GDNF levels were significantly higher in all participants in the HC group (F = 4.262, p < 0.05) compared with those in the BD group. In the HC group, the males (t = 4.814, p < 0.001) presented significantly higher levels than the females. The plasma GDNF levels in males in the BD group (t = 3.022, p < 0.05) were significantly lower than those in males in the HC group. CONCLUSION: Differences in plasma GDNF levels are associated with the gender of patients with BD.

Gray matter volume reduction in orbitofrontal cortex correlated with plasma glial cell line-derived neurotrophic factor (GDNF) levels within major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a severe mental disorder characterized by reduced gray matter volume (GMV). To date, the pathogenesis of MDD remains unclear, but neurotrophic factors play an essential role in the pathophysiological alterations of MDD during disease development. In particular, plasma glial cell line-derived neurotrophic factor (GDNF) has been suggested as a potential biomarker that may be associated with disease activity and neurological progression in MDD. Our study investigated whether plasma GDNF levels in MDD patients and healthy controls (HCs) are correlated with GMV alterations. METHODS: We studied 54 MDD patients and 48 HCs. The effect of different diagnoses on whole-brain GMV was investigated using ANOVA (Analysis of Variance). The threshold of significance was p < 0.05, and Gaussian random-field (GRF) correction for error was used. All analyses were controlled for covariates such as ethnicity, handedness, age, and gender that could affect GMV. RESULT: Compared with the HC group, the GMV in the MDD group was significantly reduced in the right inferior orbitofrontal cortex (OFC), and plasma GDNF levels were significantly higher in the MDD group than in the HC group. In the right inferior OFC, the GDNF levels were positively correlated with GMV reduction in the MDD group, whereas in the HC group, a negative correlation was observed between GDNF levels and GMV reduction. CONCLUSION: Although increased production of GDNF in MDD may help repair neural damage in brain regions associated with brain disease, its repairing effects may be interfered with and hindered by underlying neuroinflammatory processes.

Modeling the temporal relationship between contributing factors and injury severities in rural single-vehicle alcohol-impaired driving crashes: Insights from random parameters logit models in the means and variances.

OBJECTIVES: Alcohol-impaired driving (A-ID) crashes have been acknowledged as fatality-concentrated while there is a limited understanding of how contributors relating to A-ID influence crash severity and lead to more severe injuries in rural areas. The current paper utilized North Carolina crash data to investigate the unobserved heterogeneity and temporal stability of the rural single-vehicle A-ID crash injury-severity determinants over a five-year period from 2014-2018. METHODS: Crash injury severities were estimated using a group of random parameters logit models in the means and variances with three categories of injury-severity determined as outcome variables including no injury, minor injury, and severe injury. Explanatory variables were selected across multiple factors that could be classified as roadway characteristics, environmental characteristics, crash characteristics, temporal characteristics, vehicle characteristics and driver characteristics. The temporal stability of the models was examined through a series of likelihood ratio tests. Marginal effects were also adopted to analyze the temporal stability of the explanatory variables. RESULTS: The result uncovers an overall temporal instability. Some contributors present relatively temporal stability such as female, turning, passenger car, motorcycle, vehicle age (5-9 years old), speed limit (<45 mph), curved segment, dry road surface, animal collision and overturned collision. Curved segment and dry road surface are found to consistently increase the possibility of severe injuries in rural alcohol-involved crashes. CONCLUSIONS: This paper can provide insights into preventing single-vehicle A-ID crashes and could potentially facilitate the development of single-vehicle A-ID crash injury mitigation policies in rural areas. More studies could be conducted adopting the advanced data-driven methods for A-ID crash prediction.

Temporal-spatial evolution analysis of severe traffic violations using three functional forms of models considering the diurnal variation of meteorology.

Traffic violations and crashes are inherently associated. Analysis of traffic violation frequency is a prerequisite for improvements in crash prevention and corresponding countermeasures. One of the essential works in the field of traffic violations relates to the exploration of the correlations between a certain violation type (e.g., speeding or safety belt use) and its causal factors (e.g., demographics and road types). Till now, the effects of spatiotemporal and meteorological factors on severe traffic violations, a general term for dangerous driving behaviors, have not been fully considered. Using the dataset consisting of daily severe traffic violations and meteorological conditions during 12 months in Jiangsu Province, China, violation performance functions were developed for three violation types (total violations, driving under the influence, and speeding) based on three models (Poisson regression, zero-inflated Poisson regression, and negative binomial model). The findings indicate that the negative binomial model has a better performance for traffic violation frequency estimation. Additionally, elastic analysis for three violation types relying on the negative binomial model was conducted to present the relationships between the explanatory variables and the expected violation frequency. The effects of spatiotemporal factors have revealed that the violation situations are significantly different in varying cities and the frequency of drunk driving shows a significant time instability. It is also found that rainy days will generate a decrease in the possibility of violation occurrence. With regard to temperature, a significant negative effect is found and the decrease in temperature will bring about an increase in violation frequency. Besides, traffic violation frequency is significantly increased during holidays with comfortable weather conditions. The conclusion of this study can provide insightful suggestions for the department of traffic enforcement to adjust the patrol plans according to the specified periods (weeks, months, or holidays) and weather conditions. Special rectification actions and targeted educational activities are also advised to be put forward simultaneously.

FusionM4Net: A multi-stage multi-modal learning algorithm for multi-label skin lesion classification.

Skin disease is one of the most common diseases in the world. Deep learning-based methods have achieved excellent skin lesion recognition performance, most of which are based on only dermoscopy images. In recent works that use multi-modality data (patient's meta-data, clinical images, and dermoscopy images), the methods adopt a one-stage fusion approach and only optimize the information fusion at the feature level. These methods do not use information fusion at the decision level and thus cannot fully use the data of all modalities. This work proposes a novel two-stage multi-modal learning algorithm (FusionM4Net) for multi-label skin diseases classification. At the first stage, we construct a FusionNet, which exploits and integrates the representation of clinical and dermoscopy images at the feature level, and then uses a Fusion Scheme 1 to conduct the information fusion at the decision level. At the second stage, to further incorporate the patient's meta-data, we propose a Fusion Scheme 2, which integrates the multi-label predictive information from the first stage and patient's meta-data information to train an SVM cluster. The final diagnosis is formed by the fusion of the predictions from the first and second stages. Our algorithm was evaluated on the seven-point checklist dataset, a well-established multi-modality multi-label skin disease dataset. Without using the patient's meta-data, the proposed FusionM4Net's first stage (FusionM4Net-FS) achieved an average accuracy of 75.7% for multi-classification tasks and 74.9% for diagnostic tasks, which is more accurate than other state-of-the-art methods. By further fusing the patient's meta-data at FusionM4Net's second stage (FusionM4Net-SS), the entire FusionM4Net finally boosts the average accuracy to 77.0% and the diagnostic accuracy to 78.5%, which indicates its robust and excellent classification performance on the label-imbalanced dataset. The corresponding code is available at: https://github.com/pixixiaonaogou/MLSDR.

L-theanine prevents progression of nonalcoholic hepatic steatosis by regulating hepatocyte lipid metabolic pathways via the CaMKKß-AMPK signaling pathway.

BACKGROUND: L-theanine, a non-protein amino acid was found principally in the green tea, has been previously shown to exhibit potent anti-obesity property and hepatoprotective effect. Herein, we investigated the effects of L-theanine on alleviating nonalcoholic hepatic steatosis in vitro and in vivo, and explored the underlying molecular mechanism. METHODS: In vitro, HepG2 and AML12 cells were treated with 500 µM oleic acid (OA) or treated with OA accompanied by L-theanine. In vivo, C57BL/6J mice were fed with normal control diet (NCD), high-fat diet (HFD), or HFD along with L-theanine for 16 weeks. The levels of triglycerides (TG), accumulation of lipid droplets and the expression of genes related to hepatocyte lipid metabolic pathways were detected in vitro and in vivo. RESULTS: Our data indicated that, in vivo, L-theanine significantly reduced body weight, hepatic steatosis, serum levels of alanine transaminase (ALT), aspartate transaminase (AST), TG and LDL cholesterol (LDL-C) in HFD-induced nonalcoholic fatty liver disease (NAFLD) mice. In vitro, L-theanine also significantly alleviated OA induced hepatocytes steatosis. Mechanic studies showed that L-theanine significantly inhibited the nucleus translocation of sterol regulatory element binding protein 1c (SREBP-1c) through AMPK-mTOR signaling pathway, thereby contributing to the reduction of fatty acid synthesis. We also identified that L-theanine enhanced fatty acid ß-oxidation by increasing the expression of peroxisome proliferator-activated receptor α (PPARα) and carnitine palmitoyltransferase-1 A (CPT1A) through AMP-activated protein kinase (AMPK). Furthermore, our study indicated that L-theanine can active AMPK through its upstream kinase Calmodulin-dependent protein kinase kinase-ß (CaMKKß). CONCLUSIONS: Taken together, our findings suggested that L-theanine alleviates nonalcoholic hepatic steatosis by regulating hepatocyte lipid metabolic pathways via the CaMKKß-AMPK signaling pathway.

γ-Glutamylcysteine alleviates ethanol-induced hepatotoxicity via suppressing oxidative stress, apoptosis, and inflammation.

Alcohol abuse is a major cause of alcoholic liver disease (ALD) and can result in fibrosis and cirrhosis. γ-glutamylcysteine (γ-GC) is a precursor of glutathione (GSH) with antioxidant and anti-inflammatory properties. Our research aimed to explore the protective impact of γ-GC on ALD and its potential mechanisms of efficiency in vitro and in vivo. L02 cells were pretreated with γ-GC (20, 40, and 80 µM) for 2 h and exposed to ethanol for 24 h. Cell viability, apoptosis, oxidative stress, and inflammatory levels were measured. The expression of protein cleaved caspase-3 and cleaved PARP and flow cytometry results indicated that γ-GC decreases apoptosis on L02 cells after ethanol treatment. Moreover, γ-GC also attenuated oxidative stress and mitochondrial damage in hepatocytes caused by ethanol via increasing cellular GSH, SOD activity, and mitochondrial membrane potential. In vivo experiments, γ-GC effectively reduced the AST, ALT, and TG levels in mice. The inflammation of ALD was alleviated by γ-GC both in vivo and in vitro. Additionally, histopathological examination demonstrated that γ-GC treatment lessened lipid droplet formation and inflammatory damage. In conclusion, these results showed that γ-GC has anti-inflammatory and anti-apoptotic effects on ALD because it could help hepatocytes maintain sufficient GSH levels to combat the excess reactive oxygen species (ROS) generated during ethanol metabolism. PRACTICAL APPLICATIONS: Alcohol intake is the fifth highest risk factor for premature death and disability among all risk variables. However, few medicines are both safe and effective for the treatment of ALD. As a direct precursor of GSH, γ-GC has a broad variety of potential antioxidant and anti-inflammatory applications for the treatment of numerous medical conditions. In conclusion, these results showed that γ-GC could protect cells from ALD via suppressing oxidative stress, alleviating inflammation, and preventing apoptosis.

γ-Glutamylcysteine ameliorates D-gal-induced senescence in PC12 cells and mice via activating AMPK and SIRT1.

Aging is a natural process accompanied by inflammation and oxidative stress and is closely associated with age-related diseases. As a direct precursor of glutathione, γ-glutamylcysteine (γ-GC) possesses antioxidant and anti-inflammatory properties; however, whether γ-GC plays an important role in anti-aging remains unknown. Here, we investigated the protective effects and mechanisms of γ-GC in D-galactose (D-gal)-induced senescence in PC12 cells and aging mice. Our results showed that γ-GC treatment significantly reduced the percentage of senescence-associated-ß-galactosidase (SA-ß-Gal)-positive cells and inhibited D-gal-induced cell cycle arrest in PC12 cells. The results of Nissl and hematoxylin and eosin (H&E) staining in mouse brain showed that γ-GC treatment markedly reversed the damage in the hippocampus of D-gal-induced aging mice. Moreover, γ-GC increased the phosphorylation of AMP-activated protein kinase (AMPK) to promote the nuclear translocation of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) while inhibiting the nuclear translocation of deleted in breast cancer 1 (DBC1), which leads to the activation of sirtuin 1 (SIRT1) and deacetylation of p53 in the nucleus. Therefore, γ-GC may be a potential therapeutic candidate compound for the prevention and treatment of age-related diseases.

Assessing emission reduction effects from shifts of urban passenger transport modes by implementing targeted emission tax considering the whole fuel cycle.

Controlling the emission of urban passenger transport modes has become one of the most important tasks of governing urban air pollution. Most strategies only focused on carbon emission, whereas neglecting the influences of other pollutants (CO, HC, NOx, PM2.5), especially for upstream emissions from electricity generation caused by the electricity consumed during the operation of electrified transport modes. Based on the multinomial logit model (MNL), this study firstly calculated and evaluated the emission reduction effects brought about by the implementation of targeted emission taxes on different transport modes from the perspective of whole fuel cycle. Taking Jiangning District as an example, our research found that the policy implementing targeted emission tax for different transport modes can not only bring reduce 13.104 tons of CO, 0.327 tons of HC, 0.568 tons of NOx, and 0.140 tons of PM2.5, but also 26,726.82 (euro) of eco-environmental benefits for the treatment of air pollution. Our study can provide useful insights for shifting the structure of urban passenger transport modes, especially promoting the transfer of private cars to the urban green transport systems, to alleviate urban air pollution by formulating effective emission reduction strategies.