Comprehensive profiling of serum glycosphingolipids to discover the diagnostic biomarkers of lung cancer and uncover the variation of glycosphingolipid networks in different lung cancer subtypes.

Glycosphingolipids are glycolipid complexes formed by an oligosaccharide chain covalently linked to a ceramide backbone and play important roles in the occurrence and metastasis of lung cancer. In this study, an UHPLC-HRMS method was developed for the comprehensive profiling of glycosphingolipids, with an in-house library constructed for data interpretation. Serum glycosphingolipids were profiled in 31 healthy controls (HCs) and 92 lung cancer patients with different pathologic subtypes. Over 1700 glycosphingolipids were detected in human serum based on the novel method. A total of 567 differential glycosphingolipids (adjusted P < 0.05, and fold change > 2) were found between lung cancer patients and HCs. Glycosphingolipids can be used as potential biomarkers for lung cancer diagnosis, with sensitivity much higher than that of traditional serum tumor markers. The levels of most glycosphingolipids in squamous cell carcinoma (Squa) were significantly lower than those in small cell lung cancer (SCLC) and adenocarcinoma (Aden). The highest Cer1P abundance in SCLC patients among the three different subtypes of lung cancer was thought to be related to the high malignancy and metastasis of SCLC. An artificial neural network (ANN) model was constructed for the discrimination of the three different subtypes of lung cancer, with accuracy higher than 93%. Beyond providing biomarkers and statistical models for the diagnosis of lung cancer and discrimination of lung cancer subtypes, this study uncovered the variation of glycosphingolipid networks in different subtypes of lung cancer and thereby provided a novel insight to study the pathogenesis of lung cancer and explore therapeutic targets.

Paired comparisons of venetoclax concentration in cerebrospinal fluid, bone marrow, and plasma in acute leukemia patients.

Venetoclax, a small molecule inhibitor of BCL-2, has demonstrated efficacy in treating acute leukemias and has been recommended as one of the first-line anti-leukemia therapies. Although venetoclax has been suggested to probably possess the ability to penetrate the central nervous system (CNS), current data to elucidate the characteristics of venetoclax in cerebrospinal fluid (CSF), bone marrow (BM), and plasma are still lacking. This study investigated the real-world characteristics of venetoclax concentrations in CSF, BM, and plasma in acute leukemia patients. Thirteen acute leukemia patients treated with venetoclax were included, with paired samples of CSF, BM, and plasma collected and venetoclax concentrations measured using LC-MS/MS. With the results, the median venetoclax concentrations were 2030 ng/mL in plasma, 16.7 ng/mL in CSF, and 1390 ng/mL in BM. The percentages of CSF/plasma and BM/plasma were 0.74% and 70.37%, respectively. While no direct correlation was observed between CSF and plasma venetoclax levels, there was a trend toward an improved CSF/plasma percentage over time following the last administration of venetoclax. In contrast, a strong correlation was found between BM and plasma levels. This study demonstrated that venetoclax could reach its effective concentration in most patients, suggesting its potential clinical utility in the management of CNS involvement in acute leukemia.

Association between maternal antibiotic exposure and emotional and behavioural problems in children at four years of age: A biomonitoring-based prospective study.

BACKGROUND: Maternal exposure to multiple antibiotics exposure during pregnancy has attracted extensive attention, but biomonitoring studies linking prenatal antibiotic exposure to emotional and behavioural problems in children are limited. METHODS: A total of 2475 pregnant women from the Ma'anshan Birth Cohort were included, and the Strengths and Difficulties Questionnaire was completed when their children turned four years of age. The levels of 41 maternal urinary antibiotics and two metabolites were measured during the first, second and third trimesters. Generalized estimating equations and binary logistic regression models were applied to analyse the associations between maternal antibiotic exposure and emotional and behavioural problems in children and to determine the sensitive period, respectively. A quantile-based g-computation (QGC) approach was employed to examine the combined effects of multiple antibiotics on emotional and behavioural problems in children. RESULTS: Overall, florfenicol and preferred-as-veterinary antibiotic (PVA) exposure during pregnancy increased the risk of emotional problems in children, and ofloxacin exposure increased the risk of hyperactivity-inattention. Maternal exposure to trimethoprime, ciprofloxacin, florfenicol, other antibiotics and PVA exposure during the first trimester was positively associated with emotional problems in children. Second-trimester trimethoprime concentrations and third-trimester ciprofloxacin concentrations were positively associated with hyperactivity-inattention. Third-trimester veterinary antibiotic (VA) exposure was negatively associated with hyperactivity-inattention, and second-trimester VA and PVA exposure was negatively associated with peer problems. The QGC model revealed that mixed antibiotic exposure in the first trimester exacerbated the risk of childhood emotional problems (the contribution of ciprofloxacin is prominent), and mixed antibiotic exposure in the second trimester increased the risk of hyperactivity-inattention (the contribution of trimethoprime is prominent). CONCLUSION: Maternal mixed antibiotic exposure during the first and second trimesters increases the risk of emotional problems and hyperactivity-inattention in children at four years of age.

A comparative study on urban waterlogging susceptibility assessment based on multiple data-driven models.

Accurate identification of urban waterlogging areas and assessing waterlogging susceptibility are crucial for preventing and controlling hazards. Data-driven models are utilized to forecast waterlogging areas by establishing intricate relationships between explanatory variables and waterlogging states. This approach tackles the constraints of mechanistic models, which are frequently complex and unable to incorporate socio-economic factors. Previous research predominantly employed single-type data-driven models to predict waterlogging locations and evaluation of their effectiveness. There is a scarcity of comprehensive performance comparisons and uncertainty analyses of different types of models, as well as a lack of interpretability analysis. The chosen study area was the central area of Beijing, which is prone to waterlogging. Given the high manpower, time, and economic costs associated with collecting waterlogging information, the waterlogging point distribution map released by the Beijing Water Affairs Bureau was selected as labeled samples. Twelve factors affecting waterlogging susceptibility were chosen as explanatory variables to construct Random Forest (RF), Support Vector Machine with Radial Basis Function (SVM-RBF), Particle Swarm Optimization-Weakly Labeled Support Vector Machine (PSO-WELLSVM), and Maximum Entropy (MaxEnt). The utilization of diverse single evaluation indicators (such as F-score, Kappa, AUC, etc.) to assess the model performance may yield conflicting results. The Distance between Indices of Simulation and Observation (DISO) was chosen as a comprehensive measure to assess the model's performance in predicting waterlogging points. PSO-WELLSVM exhibited the highest performance with a DISOtest value of 0.63, outperforming MaxEnt (0.78), which excelled in identifying areas highly susceptible to waterlogging, including extremely high susceptibility zones. The SVM-RBF and RF models demonstrated suboptimal performance and exhibited overfitting. The examination of waterlogging susceptibility distribution maps predicted by the four models revealed significant spatial differences due to variations in computational principles and input parameter complexities. The integration of four WSAMs based on logistic regression has been shown to significantly decrease the uncertainty of a single data-driven model and identify the most flood-prone areas. To improve the interpretability of the data model, a geographical detector was incorporated to demonstrate the explanatory capacity of 12 variables and the process of waterlogging. Building Density (BD) exhibits the highest explanatory power in relation to explain waterlogging susceptibility (Q value = 0.202), followed by Distance to Road, Frequency of Heavy Rainstorms (FHR), DEM, etc. The interaction between BD and FHR results in a nonlinear increase in the explanatory power of waterlogging susceptibility. The presence of waterlogging susceptibility risk in the research area can be attributed to the interactions of multiple factors.

Study on the relationship between the changes in polyamine content and seedless grapes embryo rescue breeding.

This study was envisaged to investigate the physiological reasons affecting the embryo development and abortion of seedless grapes on the basis of the previous embryo rescue breeding techniques of seedless grapes. Specifically, the relationship between the embryo rescue breeding of seedless grapes and the change of polyamine content was evaluated, in order to provide hybrid germplasm in the breeding of new seedless grape cultivars. Four ovules of 4 naturally pollinated Eurasian seedless grape cultivars, including 'Thompson Seedless' grape (hereinafter referred to as 'Seedless White' grape), 'Flame Seedless' grape, 'Heshi Seedless' grape and 'Ruby Seedless' grape were employed for the study. Changes in the endogenous polyamine content, exogenous polyamine content, and the suitable combination of exogenous polyamines in the seedless grape berries and isolated ovules were determined during the best embryo rescue period. Furthermore, the effect of different exogenous polyamine contents on the germination and seedling rate of different seedless grape embryos was analyzed. In the best embryo rescue period, the number of ovules had different effects on the content of polyamines. For seedless grape cultivars with 4 ovules, a high content of polyamines was found to be more beneficial in the embryonic development. The existence of embryos had different effects on the development of embryos. In the ovules with embryo, an increase in the content of polyamine was beneficial to the growth and development of the ovule. Different ratios of exogenous polyamines had varying effects on the embryonic development. Putrescine (Put) exhibited the greatest effect on the embryonic development. Further, correlation analysis showed that different combinations of exogenous polyamines had varying effects on the embryonic development. A maximal ovule development was observed in the combination of exogenous polyamines of putrescine2+spermidine2+spermine1. For maximal embryo germination and seeding formation, the optimal combination was putrescine2+spermidine2+spermine2. Irrespective to the number of ovules or the existence of embryos, the results indicated that a high content of endogenous polyamines promoted the growth and development of embryos. The embryo rescue efficiency of different exogenous polyamines was different, and the appropriate combination of exogenous polyamines was beneficial to the growth and development of ovules, with a high development rate of the ovule and seedling.

Machine learning reveals serum myristic acid, palmitic acid and heptanoylcarnitine as biomarkers of coronary artery disease risk in patients with type 2 diabetes mellitus.

BACKGROUND: Coronary heart disease (CHD) is the most important complication of type 2 diabetes mellitus (T2DM) and the leading cause of death. Identifying the risk of CHD in T2DM patients is important for early clinical intervention. METHODS: A total of 213 participants, including 81 healthy controls (HCs), 69 T2DM patients and 63 T2DM patients complicated with CHD were recruited in this study. Serum metabolomics were conducted by using ultra-high performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS). Demographic information and clinical laboratory test results were also collected. RESULTS: Metabolic phenotypes were significantly altered among HC, T2DM and T2DM-CHD. Acylcarnitines were the most disturbed metabolites between T2DM patients and HCs. Lower levels of bile acids and higher levels of fatty acids in serum were closely associated with CHD risk in T2DM patients. Artificial neural network model was constructed for the discrimination of T2DM and T2DM complicated with CHD based on myristic acid, palmitic acid and heptanoylcarnitine, with accuracy larger than 0.95 in both training set and testing set. CONCLUSION: Altogether, these findings suggest that myristic acid, palmitic acid and heptanoylcarnitine have a good prospect for the warning of CHD complications in T2DM patients, and are superior to traditional lipid, blood glucose and blood pressure indicators.

Trimester-specific effect of maternal co-exposure to organophosphate esters and phthalates on preschooler cognitive development: The moderating role of gestational vitamin D status.

Organophosphate esters (OPEs) and phthalate acid esters (PAEs) are prevalent endocrine-disrupting chemicals (EDCs). Humans are often exposed to OPEs and PAEs simultaneously through multiple routes. Given that fetal stage is a critical period for neurodevelopment, it is necessary to know whether gestational co-exposure to OPEs and PAEs affects fetal neurodevelopment. However, accessible epidemiological studies are limited. The present study included 2, 120 pregnant women from the Ma'anshan Birth Cohort (MABC) study. The concentrations of tris (2-chloroethyl) phosphate (TCEP), 6 OPE metabolites and 7 PAE metabolites were measured in the first, second and third trimester using ultra-performance liquid chromatography-tandem mass spectrometry (LC-MS). Cognitive development of preschooler was assessed based on the Wechsler Preschool and Primary Scale of Intelligence-Fourth Edition (WPPSI-IV) of the Chinese version. Generalized estimating equations (GEEs), restricted cubic spline (RCS) and generalized additive models (GAMs) were employed to explore the associations between individual OPE exposure and preschooler cognitive development. The quantile-based g-computation (QGC) method was used to estimate the joint effect of PAEs and OPEs exposure on cognitive development. GEEs revealed significant adverse associations between diphenyl phosphate (DPHP) (ß: -0.58, 95% CI: -1.14, -0.01), bis (2-butoxyethyl) phosphate(BBOEP) (ß: -0.44, 95% CI: -0.85, -0.02), bis(1-chloro-2-propyl) phosphate (BCIPP) (ß: -0.81, 95%CI: -1.43, -0.20) and full-scale intelligence quotient (FSIQ) in the first trimester; additionally, TCEP and bis(2-ethylhexyl) phosphate (BEHP) in the second trimester, as well as DPHP in the third trimester, were negatively associated with cognitive development. Through the QGC analyses, mixture exposure in the first trimester was negatively associated with FSIQ scores (ß: -1.70, 95% CI: -3.06, -0.34), mono-butyl phthalate (MBP), BCIPP, and DPHP might be the dominant contributors after controlling for other OPEs and PAEs congeners. Additionally, the effect of OPEs and PAEs mixture on cognitive development might be driven by vitamin D deficiency.

Lumbar and neck injuries of occupants in different reclining postures.

PURPOSE: With the increasing level of automation in automobiles, the advent of autonomous vehicles has reduced the tendency of drivers and passengers to focus on the task of driving. The increasing automation in automobiles reduced the drivers' and passengers' focus on driving, which allowed occupants to choose a more relaxed and comfortable sitting position. Meanwhile, the occupant's sitting position went from a frontal, upright position to a more relaxed and reclined one, which resulted in the existing restraint systems cannot to keep occupants safe and secure. This study aimed to determine the effects of different reclining states on occupants' lumbar and neck injuries. METHODS: This is an original research on the field of automotive safety engineering. Occupants in different initial sitting positions (25°, 35°, 45°, and 55°) were adapted to changes in seat back angle and restraint systems and placed in the same frontal impact environment. Neck injury indexes, lumbar axial compression force and acceleration, as well as occupant dynamic response during the impact, were compared in different sitting positions. The injury response and kinematic characteristics of occupants in different reclining positions were analyzed by the control variable method. RESULTS: As the sitting angle increased, the occupant's head acceleration decreased, and the forward-lean angle decreased. Occupants in the standard sitting position had the greatest neck injury, with an Nij of 0.95, and were susceptible to abbreviated injury scale 2+ cervical medullary injuries. As the seatback angle increased, the geometric position of the lumbar spine tended to be horizontal, and the impact load transmitted greater forces to the lumbar spine. The occupant's lumbar injury was greatest in the lying position, with a peak axial compression force on the lumbar region of 5.5 KN, which was 2.3 KN greater than in the standard sitting position. CONCLUSION: The study of occupant lumbar and neck injuries based on different recline states can provide a theoretical basis for optimizing lumbar evaluation indexes, which is conducive to the understanding of the lumbar injury mechanism and the comprehensive consideration of occupant safety protection.

SB431542 partially inhibits high glucose-induced EMT by restoring mitochondrial homeostasis in RPE cells.

BACKGROUND: The epithelial-mesenchymal transition (EMT) of retinal pigment epithelial (RPE) cells participated in the development of retinal fibrosis. SB431542 is a small molecule inhibitor with inhibitory effects on the ALK4, ALK5 and ALK7. Our study aimed to explore the effect of SB431542 on the EMT of RPE cells and to provide new ideas for the treatment of retinal fibrosis. METHODS: We performed fundus fluorescein angiography, optical coherence tomography and hematoxylin-eosin staining in vivo to observe the effect of SB431542 on choroidal neovascularization (CNV)-induced retinopathy. The proliferation, migration, cytoskeleton, adhesion, reactive oxygen species (ROS), mitochondrial morphology and membrane potential of RPE cells were observed in vitro through fluorescein diacetate staining, Cell Counting Kit-8 experiment, wound healing assay, phalloidin staining, immunofluorescence, MitoSOX, DCFH-DA, MitoTracker and JC-10 staining. Western blot, reverse transcription quantitative and immunofluorescence were used to detect the expression of EMT-related markers, pERK1/2, pGSK3ß and ß-catenin. RESULTS: SB431542 significantly alleviated retinopathy in the CNV model. The proliferation, migration and adhesion in RPE cells decreased to a certain extent in SB431542 treatment. SB431542 partially normalized the structure of RPE cells. The expression levels of E-cadherin increased, while the expression levels of laminin and N-cadherin decreased with SB431542 treatment. SB431542 reduced the production of total ROS, mitochondrial SOX and recovered the mitochondrial membrane potential to a certain degree. In addition, our study showed that SB431542 downregulated the phosphorylation of ERK1/2, GSK3ß and the expression of ß-catenin. CONCLUSION: SB431542 improved EMT in RPE cells by maintaining mitochondrial homeostasis via the ERK1/2 and GSK3ß/ß-catenin pathways. Video Abstract SB431542 inhibits EMT in RPE cells under high glucose conditions.

Study on spatiotemporal dynamic characteristics of precipitation and causes of waterlogging based on a data-driven framework.

The discernible alterations in regional precipitation patterns, influenced by the intersecting factors of urbanization and climate change, exert a substantial impact on urban flood disasters. Based on multi-source precipitation data, a data-driven model fusion framework was constructed to analyze the spatial and temporal dynamic distribution characteristics of precipitation in Beijing. Wavelet analysis method was used to reveal the periodic variation characteristics and multi-scale effects of precipitation, and the machine learning method was used to characterize the spatiotemporal dynamic change pattern of precipitation. Finally, geographical detector was used to explore the causes of waterlogging in Beijing. The research outcomes reveal a disparate distribution of precipitation across the year, with 78 % of the total precipitation occurring during the flood season. The principal periodic cycles observed in annual cumulative precipitation (ACP) were identified at 21, 13, and 9-year intervals. Spatially, while a decreasing trend in precipitation was observed in most areas of Beijing, 63.4 % of the region exhibited an escalating concentration trend, thereby heightening the risk of urban waterlogging. Machine learning model clustering elucidated three predominant spatial dynamic distribution patterns of precipitation in Beijing. The utilization of web crawler technology to acquire water accumulation data addressed challenges in obtaining urban waterlogging data, and validation through Landsat8 images enhanced data reliability and authenticity. Factor detection shows that road network density, topography, and precipitation were the main factors affecting urban waterlogging. These findings hold significant implications for informing flood control strategies and emergency management protocols in urban areas across China.

Multi-Omic blood analysis reveals differences in innate inflammatory sensitivity between species.

Vertebrates differ greatly in responses to pro-inflammatory agonists such as bacterial lipopolysaccharide (LPS), complicating use of animal models to study human sepsis or inflammatory disorders. We compared transcriptomes of resting and LPS-exposed blood from six LPS-sensitive species (rabbit, pig, sheep, cow, chimpanzee, human) and four LPS-resilient species (mice, rats, baboon, rhesus), as well as plasma proteomes and lipidomes. Unexpectedly, at baseline, sensitive species already had enhanced expression of LPS-responsive genes relative to resilient species. After LPS stimulation, maximally different genes in resilient species included genes that detoxify LPS, diminish bacterial growth, discriminate sepsis from SIRS, and play roles in autophagy and apoptosis. The findings reveal the molecular landscape of species differences in inflammation, and may inform better selection of species for pre-clinical models.

[Research on pretreatment methods for cystine determination in milk powder].

OBJECTIVE: To establish a rapid, accurate and safe pretreatment method for the determination of cystine in milk powder. METHODS: Samples were oxidized at 50 ℃ for 10 min after adding performic acid, and then the reaction was terminated with ethanol as oxidation terminator. The reaction solution was concentrate to dryness by vacuum centrifugation at 50 ℃. The residue was hydrolyzed at 145 ℃ for 4 h after adding 15 mL hydrochloric acid(6 mol/L). Quantitatively transfer the hydrolysate to 25 mL volumetric flask, rinsing with water, and dilute to volume. A proper amount of hydrolysate was concentrated to dryness by vacuum centrifugation at 50 ℃, dissolved in sodium citrate buffer, filtered, and determined by amino acid analyzer. RESULTS: A rapid detection method of cystine in milk powder was realized in this study. Cysteic acid were linearly correlated within the set range, and the correlation coefficients were more than 0.999. This method was applied to the determination of milk powder reference material SRM1849A. The result was within the range of values, and the precision was all less than 4%; The detection limit of cystine was 0.001%, and the limit of quantitation was 0.003%. The applicability of this approach was validated by a comparison study with milk powder reference material(SRM 1869). To explore the scalability of the method, fish meal and soybean meal were measured for six times under the above conditions according to the classification of animals and plants. Meanwhile, milk powder, fish meal and soybean meal were also measured according to GB/T 15399-2018 as reference value. The relative deviation of the average values measured by both method were all less than 6%. CONCLUSION: This method is simple and rapid, with good repeatability and high accuracy, and less harm to the operators and experimental equipment. It can be used for the rapid detection of cystine in milk powder, and it also has applicability to other complex samples.

Past and projected future patterns of fractional vegetation coverage in China.

With the intensifying climate change and the strengthening ecosystem management, quantifying the past and predicting the future influence of these two factors on vegetation change patterns in China need to be analyzed urgently. By constructing a framework model to accurately identify fractional vegetation coverage (FVC) change patterns, we found that FVC in China from 1982 to 2018 mainly showed linear increase (29.5 %) or Gaussian decrease (27.4 %). FVC variation was mainly affected by soil moisture in the Qi-North region and by vapor pressure deficit in other regions. The influence of environmental change on FVC, except for Yang-Qi region in the southwest (-2.0 %), played a positive role, and weakened from the middle (Hu-Yang region: 2.7 %) to the northwest (Qi-North region: 2.4 %) to the east (Hu-East region: 0.8 %). Based on five machine learning algorithms, it was predicted that under four Shared Socioeconomic Pathways (SSPs, including SSP126、SSP245、SSP370、SSP585) from 2019 to 2060, FVC would maintain an upward trend, except for the east, where FVC would rapidly decline after 2039. FVC in the eastern region experienced a transition from past growth to future decline, suggesting that the focus of future ecosystem management should be on this region.

Self-reported and digital-trace measures of computer science students' self-regulated learning in blended course designs.

This study investigated the extent to which self-report and digital-trace measures of students' self-regulated learning in blended course designs align with each other amongst 145 first-year computer science students in a blended "computer systems" course. A self-reported Motivated Strategies for Learning Questionnaire was used to measure students' self-efficacy, intrinsic motivation, test anxiety, and use of self-regulated learning strategies. Frequencies of interactions with six different online learning activities were digital-trace measures of students' online learning interactions. Students' course marks were used to represent their academic performance. SPSS 28 was used to analyse the data. A hierarchical cluster analysis using self-reported measures categorized students as better or poorer self-regulated learners; whereas a hierarchical cluster analysis using digital-trace measures clustered students as more active or less active online learners. One-way ANOVAs showed that: 1) better self-regulated learners had higher frequencies of interactions with three out of six online learning activities than poorer self-regulated learners. 2) More active online learners reported higher self-efficacy, higher intrinsic motivation, and more frequent use of positive self-regulated learning strategies, than less active online learners. Furthermore, a cross-tabulation showed significant (p < .01) but weak association between student clusters identified by self-reported and digital-trace measures, demonstrating self-reported and digital-trace descriptions of students' self-regulated learning experiences were consistent to a limited extent. To help poorer self-regulated learners improve their learning experiences in blended course designs, teachers may invite better self-regulated learners to share how they approach learning in class.

Level of consistency between students' self-reported and observed study approaches in flipped classroom courses: How does it influence students' academic learning outcomes?

Using Student Approaches to Learning research as a theoretical framework, the present study used both self-reported and observational log data to understand students' study approaches in a flipped classroom course amongst 143 computer science undergraduate students. Specifically, it aimed to examine: 1) to what extent students' study approaches identified by self-reported and observational log data are consistent with each other; and 2) to what extent students' academic learning outcomes differ between students who showed consistent and inconsistent study approaches by self-reported and observational log data. Using The Revised Study Process Questionnaire, students were clustered as reporting either a Deep or a Surface Study Approach. Using frequencies of students' participation in five online learning activities, they were classified as adopting either an Active or a Passive Study Approach. A 2 x 2 cross-tabulation showed a positive and moderate association between clusters of students' study approaches resulted from two types of data. Amongst students who self-reported a Deep Study Approach, the proportion of students who adopted an Active Study Approach (80.7%) was significantly higher than those who adopted a Passive Study Approach (19.3%). In contrast, of the students who self-reported a Surface Study Approach, the proportion of students who used a Passive Study Approach (51.2%) was significantly higher than those who used an Active Study Approach (48.8%). Furthermore, students who had good study approaches by both self-report and observation did not differ from students who adopted an Active study approach by observation but reported a Surface Study Approach on course grades. Likewise, there was no significant difference in terms of academic learning outcomes between those who had poor study approaches by both self-report and observation and those who adopted Passive study approach by observation but reported a Deep Study Approach. Future studies may consider incorporating some qualitative methods in order to find out possible reasons behind the inconsistencies between self-reported and observed study approaches.

Learning experience of domestic and international students in blended courses: An ecological perspective.

Adopting an ecological perspective on student learning, this study compares the cognitive, social, and material elements of the learning experience of 193 domestic and 120 international students in blended courses. For cognitive elements, domestic students adopted relatively more deep approaches to learning while international students held more positive perceptions towards the blended learning environment. In terms of the social elements, domestic group had a higher proportion of students who chose to collaborate than international group. The domestic and international students also differed with regard to their experience of material elements that international students were more likely to select the option of showing the answer for solving multiple choice questions. International students particularly need help in the improvement of their collaborative learning experience, which may be enhanced by strategies, such as mixing domestic and international students in group work and training international students' intercultural communication strategies.

Simultaneous determination of four antibiotics in human plasma by high-performance LC-MS/MS: application to therapeutic drug monitoring.

Background: Aimed to simultaneously measure linezolid, voriconazole, cefoperazone and fluconazole in human plasma suitable for therapeutic drug monitoring applications, a robust, rapid and easy-to-use HPLC-MS/MS approach was developed and validated. Materials & methods: Protein precipitation was used to prepare analytes from 100 µl plasma. HPLC was employed for analyte separation, and quantification was conducted via multiple-reaction monitoring in positive ion mode. The methodology was fully validated. Results & conclusion: All four antibiotics were found to be stable under the tested conditions, and accuracy values ranged from 90.96 to 113.25% and CV values were <14.0%. This HPLC-MS/MS method can be used for routine clinical therapeutic drug monitoring of linezolid, voriconazole, cefoperazone and fluconazole simultaneously.

Comparison of various chemometric methods on visible and near-infrared spectral analysis for wood density prediction among different tree species and geographical origins.

Visible and near-infrared (Vis-NIR) spectroscopy has been widely applied in many fields for the qualitative and quantitative analysis. Chemometric techniques including pre-processing, variable selection, and multivariate calibration models play an important role to better extract useful information from spectral data. In this study, a new de-noising method (lifting wavelet transform, LWT), four variable selection methods, as well as two non-linear machine learning models were simultaneously analyzed to compare the impact of chemometric approaches on wood density determination among various tree species and geographical locations. In addition, fruit fly optimization algorithm (FOA) and response surface methodology (RSM) were employed to optimize the parameters of generalized regression neural network (GRNN) and particle swarm optimization-support vector machine (PSO-SVM), respectively. As for various chemometric methods, the optimal chemometric method was different for the same tree species collected from different locations. FOA-GRNN model combined with LWT and CARS deliver the best performance for Chinese white poplar of Heilongjiang province. In contrast, PLS model showed a good performance for Chinese white poplar collected from Jilin province based on raw spectra. However, for other tree species, RSM-PSO-SVM models can improve the performance of wood density prediction compared to traditional linear and FOA-GRNN models. Especially for Acer mono Maxim, when compared to linear models, the coefficient of determination of prediction set ( R p 2 ) and relative prediction deviation (RPD) were increased by 47.70% and 44.48%, respectively. And the dimensionality of Vis-NIR spectral data was decreased from 2048 to 20. Therefore, the appropriate chemometric technique should be selected before building calibration models.

ETNPPL modulates hyperinsulinemia-induced insulin resistance through the SIK1/ROS-mediated inactivation of the PI3K/AKT signaling pathway in hepatocytes.

Hyperinsulinemia is a critical risk factor for the pathogenesis of insulin resistance (IR) in metabolic tissues, including the liver. Ethanolamine phosphate phospholyase (ETNPPL), a newly discovered metabolic enzyme that converts phosphoethanolamine (PEA) to ammonia, inorganic phosphate, and acetaldehyde, is abundantly expressed in liver tissue. Whether it plays a role in the regulation of hyperinsulinemia-induced IR in hepatocytes remains elusive. Here, we established an in vitro hyperinsulinemia-induced IR model in the HepG2 human liver cancer cell line and primary mouse hepatocyte via a high dose of insulin treatment. Next, we overexpressed ETNPPL by using lentivirus-mediated ectopic to investigate the effects of ETNPPL per se on IR without insulin stimulation. To explore the underlying mechanism of ETNPPL mediating hyperinsulinemia-induced IR in HepG2, we performed genome-wide transcriptional analysis using RNA sequencing (RNA-seq) to identify the downstream target gene of ETNPPL. The results showed that ETNPPL expression levels in both mRNA and protein were significantly upregulated in hyperinsulinemia-induced IR in HepG2 and primary mouse hepatocytes. Upon silencing ETNPPL, hyperinsulinemia-induced IR was ameliorated. Under normal conditions without IR in hepatocytes, overexpressing ETNPPL promotes IR, reactive oxygen species (ROS) generation, and AKT inactivation. Transcriptome analysis revealed that salt-inducible kinase 1 (SIK1) is markedly downregulated in the ETNPPL knockdown HepG2 cells. Moreover, disrupting SIK1 prevents ETNPPL-induced ROS accumulation, damage to the PI3K/AKT pathway and IR. Our study reveals that ETNPPL mediates hyperinsulinemia-induced IR through the SIK1/ROS-mediated inactivation of the PI3K/AKT signaling pathway in hepatocyte cells. Targeting ETNPPL may present a potential strategy for hyperinsulinemia-associated metabolic disorders such as type 2 diabetes.