Discrimination and prediction of Qingzhuan tea storage year using quantitative chemical profile combined with multivariate analysis: Advantages of MRMHR based targeted quantification metabolomics.

The duration of storage significantly influences the quality and market value of Qingzhuan tea (QZT). Herein, a high-resolution multiple reaction monitoring (MRMHR) quantitative method for markers of QZT storage year was developed. Quantitative data alongside multivariate analysis were employed to discriminate and predict the storage year of QZT. Furthermore, the content of the main biochemical ingredients, catechins and alkaloids, and free amino acids (FAA) were assessed for this purpose. The results show that targeted marker-based models exhibited superior discrimination and prediction performance among four datasets. The R2Xcum, R2Ycum and Q2cum of orthogonal projection to latent structure-discriminant analysis discrimination model were close to 1. The correlation coefficient (R2) and the root mean square error of prediction of the QZT storage year prediction model were 0.9906 and 0.63, respectively. This study provides valuable insights into tea storage quality and highlights the potential application of targeted markers in food quality evaluation.

UHPLC-QTOF-MS-based untargeted metabolomic authentication of Chinese red wines according to their grape varieties.

Wine is a very popular alcoholic drink owing to its health benefits of antioxidant effects. However, profits-driven frauds of wine especially false declarations of variety frequently occurred in markets. In this work, an UHPLC-QTOF-MS-based untargeted metabolomics method was developed for metabolite profiling of 119 bottles of Chinese red wines from four varieties (Cabernet Sauvignon, Merlot, Cabernet Gernischt, and Pinot Noir). The metabolites of red wines from different varieties were assessed using orthogonal partial least-squares discriminant analysis (OPLS-DA) and analyzed using KEGG metabolic pathway analysis. Results showed that the differential compounds among different varieties of red wines are mainly flavonoids, phenols, indoles and amino acids. The KEGG metabolic pathway analysis showed that indoles metabolism and flavonoids metabolism are closely related to wine varieties. Based on the differential compounds, OPLS-DA models could identify external validation wine samples with a total correct rate of 90.9 % in positive ionization mode and 100 % in negative ionization mode. This study indicated that the developed untargeted metabolomics method based on UHPLC-QTOF-MS is a potential tool to identify the varieties of Chinese red wines.

Third-order calibration applied to process surfactant-modulated excitation-emission matrix four-way fluorescence data for the direct determination of four polycyclic aromatic hydrocarbons in oilfield produced water.

Fluorescence spectroscopy is a powerful tool to determine polycyclic aromatic hydrocarbons (PAHs) owing to the strong endogenous fluorescence of these compounds. However, the presence of unknown interferences and overlapped spectra hinders the accurate determination of PAHs in oilfield produced water. Moreover, surfactants frequently coexist in oilfield produced water and will seriously affect the fluorescence signals of PAHs. Herein, a new methodology applying third-order calibration to process four-way (4D) fluorescence data was proposed to solve these problems and achieve accurate determination of pyrene, fluorene, phenanthrene, and fluoranthene as an example in oilfield produced water. The methodology is based on excitation-emission matrix fluorescence modulated by different concentrations of sodium dodecyl benzene sulfonate (SDBS) in the analyzed samples. The 4D fluorescence data were processed by third-order calibration methods including four-way parallel factor analysis (4-PARAFAC) and alternating weighted residue constraint quadrilinear decomposition (AWRCQLD), and the results were compared with those of second-order calibration methods. It was proved that third-order calibration was capable of accurately identifying and quantifying PAHs together with SDBS in oilfield produced water, which has better quantitative results and figures of merit compared to second-order calibration. This study provided a new approach to generating 4D fluorescence data and opened up an avenue for the accurate determination of PAHs in complex oilfield produced water with surfactants.

Revealing the chemical differences and their application in the storage year prediction of Qingzhuan tea by SWATH-MS based metabolomics analysis.

It's generally believed that the longer the storage, the better the quality of dark tea, but the chemical differences of Qingzhuan tea (QZT) with different storage years is still unclear. Herein, in this work, an untargeted metabolomic approach based on SWATH-MS was established to investigate the differential compounds of QZT with 0-9 years' storage time. These QZT samples were roughly divided into two categories by principal component analysis (PCA). After orthogonal projections to latent structures discriminant analysis (OPLS-DA), 18 differential compounds were putatively identified as chemical markers for the storage year variation of QZT. Heatmap visualization showed that the contents of catechins, fatty acids, and some phenolic acids significantly reduced, flavonoid glycosides, triterpenoids, and 8-C N-ethyl-2-pyrrolidinone-substituted flavan-3-ols (EPSFs) increased with the increase of storage time. Furthermore, these chemical markers were verified by the peak areas corresponding to MS2 ions from SWATH-MS. Based on the extraction chromatographic peak areas of MS and MS2 ions, a duration time prediction model was built for QZT with correlation coefficient R2 of 0.9080 and 0.9701, and RMSEP value of 0.85 and 1.24, respectively. This study reveals the chemical differences of QZT with different storage years and provides a theoretical basis for the quality evaluation of stored dark tea.

[Progress in the application of novel nano-materials to the safety analysis of agricultural products].

The quality and safety of agricultural products are strongly related to human livelihood. Thus, the government and consumers have recently paid increased attention to the quality and safety of agricultural products. The development of efficient, rapid, and sensitive analytical methods for detecting pesticides, veterinary drugs, heavy metals, mycotoxins, and environmental pollutants in agricultural products is of great significance. Owing to the complexity of many sample matrices and the low concentration of pollutants in a typical sample, appropriate sample pretreatment steps are necessary to enrich pollutants in agricultural products. Solid-phase extraction (SPE) is the most widely used sample pretreatment technology; in this technique, the adsorbent generally determines the selectivity and efficiency of the extraction process. An increasing number of novel materials have been used as SPE adsorbents. The extraction efficiency, extraction selectivity, and analytical throughput of SPE could be greatly improved by combining these novel materials with various extraction modes (e. g., solid-phase microextraction, dispersed SPE, and magnetic SPE (MSPE)) during sample preparation. Because of their large specific surface area and high affinity toward target analytes, nanomaterials are often used as SPE adsorbents, thereby greatly improving the selectivity and sensitivity of the analytical technology. More importantly, these materials have become a priority area of research on preconcentration technologies for trace compounds in agricultural products. This paper summarizes the adsorption characteristics of several new nanomaterials, including magnetic materials, carbon-based materials, metal nanomaterials (MNs), metal oxide nanomaterials (MONs), metal organic frameworks (MOFs), and covalent organic frameworks (COFs). These nanomaterials present numerous advantages, such as large specific surface areas, high adsorption capacities, and tailorable structural designs. MSPE employs magnetic materials as sorbents to afford fast dispersion and efficient recycling when applied to complex sample matrices under an external magnetic field. The use of MSPE can avoid several typical problems associated with SPE such as poor adsorbent packing and high pressure, thereby greatly simplifying the pretreatment process and providing a high flux for sample analysis. Carbon-based materials are powdered or bulk nonmetallic solid materials with carbon as the main component; carbon and nitrogen materials, mesoporous carbon, carbon nanotubes, and graphene are some examples of these materials. These materials provide large specific surface areas, abundant pore structures, good thermal stability, high mechanical strength and adsorption capacity, and controllable morphology. Pure and modified carbon nanomaterials have been successfully used to purify target analytes from agricultural products. Given their unique physical and chemical properties, MNs and MONs have attracted significant interest for use in sample preparation. MNs and MONs with excellent thermal and mechanical stabilities show good resistance to a wide pH range and diverse organic solvents, which is crucial in adsorbent-based extraction methods. The surface of these materials can be easily modified with various ligands to improve their selectivity. MOFs and COFs present many advantages such as large specific surface areas, high porosity, adjustable pore performance, and good thermal stability. Several methods that employ novel adsorbent materials to analyze pollutants in a variety of agricultural products, such as chromatography, spectroscopy, mass spectrometry, and other detection technologies, have been established. This paper also reviews the application of adsorbent materials in the analysis of agricultural product quality and safety, and discusses the future development trends of these sorbents in sample preparation for the safety analysis of agricultural products.

A Comprehensive Review on the Excitation-Emission Matrix Fluorescence Spectroscopic Characterization of Petroleum-Containing Substances: Principles, Methods, and Applications.

Petroleum-containing substance (PCS) is a general term used for petroleum and its derivatives. A comprehensive characterization of PCSs is crucial for resource exploitation, economic development and environmental protection. Fluorescence spectroscopy, especially excitation-emission matrix fluorescence (EEMF) spectroscopy, has been proved to be a powerful tool to characterize PCSs since its remarkable sensitivity, selectivity, simplicity and high efficiency. However, there is a lack of systematic review focusing on this field in the literature. This paper reviews the fundamental principles and measurements of EEMF for characterizing PCSs, and makes a systematic introduction to various information mining methods including basic peak information extraction, spectral parameterization and some commonly used chemometric methods. In addition, recent advances in applying EEMF to characterize PCSs during the whole life-cycle process of petroleum are also revisited. Furthermore, the current limitations of EEMF in the measurement and characterization of PCSs are discussed and corresponding solutions are provided. For promoting the future development of this field, the urgent need to build a relatively complete EEMF fingerprint library to trace PCSs, not only pollutants but also crude oil and petroleum products, is proposed. Finally, the extensions of EEMF to high-dimensional chemometrics and deep learning are prospected, with the expectation of solving more complex systems and problems.

UHPLC-QTOF-MS-based untargeted metabolomics and mineral element analysis insight into the geographical differences of Chrysanthemum morifolium Ramat cv. "Hangbaiju" from different origins.

Chrysanthemum morifolium Ramat cv. "Hangbaiju" (HBJ), known as one of the "eight flavors of Zhejiang", is commonly used as a classical tea material for both food and medicine over three thousand years in China. The quality of HBJ is closely related to its geographical origins. However, the mechanism underlying the geographical differences of HBJ remains to be elucidated. In this study, an untargeted metabolomic strategy based on UHPLC-QTOF-MS was established to discover the differential metabolites in HBJ samples from four different origins and explore the possible relationship with mineral elements in planting soils by chemometric analysis. Eight compounds were screened and identified as the key differential metabolites in HBJ samples from different origins. Among them, four important pharmacodynamic compounds including L-arginine, rutin, chlorogenic acid and apigenin-7-O-glucoside are the most abundant in HBJ samples from Tongxiang region, which suggests that HBJ planted in Tongxiang has higher medicinal values. Pearson correlation analysis revealed that the contents of soil mineral elements are positively correlated with those of chlorogenic acid, rutin, apigenin-7-O-glucoside in HBJ samples. Furthermore, an interrelationship model based on random forest algorithm was established to successfully predict the contents of differential metabolites in HBJ samples by soil mineral elements. All these results indicated that the contents of differential metabolites in HBJ samples seemed to be affected by soil mineral elements and therefore resulted in the geographical differences of HBJ.

Discrimination of the harvesting season of green tea by alcohol/salt-based aqueous two-phase systems combined with chemometric analysis.

The flavor and aroma quality of green tea are closely related to the harvest season. The aim of this study was to identify the harvesting season of green tea by alcohol/salt-based aqueous two-phase system (ATPS) combined with chemometric analysis. In this paper, the single factor experiments (SFM) and response surface methodology (RSM) optimization were designed to investigate and select the optimal ATPS. A total of 180 green tea samples were studied in this work, including 86 spring tea and 94 autumn tea. After the active components in green tea samples were extracted by the optimal ethanol/(NH4)2SO4 ATPS, the qualitative and quantitative analysis was realized based on HPLC-DAD combined with alternating trilinear decomposition-assisted multivariate curve resolution (ATLD-MCR) algorithm, with satisfactory spiked recoveries (86.00 %-112.45 %). The quantitative results obtained from ATLD-MCR model were subjected to chemometric pattern recognition analysis. The constructed partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) models showed better results than the principal component analysis (PCA) model, and the R2Xcum values (>0.835) and R2Ycum (>0.937) were close to 1, the Q2cum values were greater than 0.75 (>0.933), and the differences between R2Ycum and Q2cum were not larger than 0.2, indicating excellent cross-validation prediction performance of the models. Furthermore, the classification results based on the hierarchical clustering analysis (HCA) were consistent with the PCA, PLS-DA and OPLS-DA results, establishing a good correlation between tea active components and the harvesting seasons of green tea. Overall, the combination of ATPS and chemometric methods is accurate, sensitive, fast and reliable for the qualitative and quantitative determination of tea active components, providing guidance for the quality control of green tea.

Untargeted metabolomic analysis of Chinese red wines for geographical origin traceability by UPLC-QTOF-MS coupled with chemometrics.

Identifying geographical origins of red wines made in specific regions is of significance since the false claim of geographical origins has been frequently exposed in China's wine industry. In this work, an untargeted metabolomic approach based on UPLC-QTOF-MS was established to discriminate geographical origins of Chinese red wines. The principal component analysis (PCA) showed significant differences between wine samples from three famous geographical origins in China. The metabolites contributing to the differentiation were screened by orthogonal partial least squares-discriminant analysis (OPLS-DA) with pairwise modeling. 40 and 46 differential metabolites in positive and negative ionization modes were putatively identified as chemical markers. Furthermore, heatmap visualization and OPLS-DA models were constructed based on these identified markers and external verification wine samples from different regions were successfully discriminated, with recognition rate up to 96.7%. This study indicated that UPLC-QTOF-MS-based untargeted metabolomics has great potential for the geographical origin traceability of Chinese red wines.

Highly fluorescent N doped C-dots as sensor for selective detection of Hg2+ in beverages.

In this work, nitrogen doped carbon dots (NCDs) were synthesized through one step hydrothermal reaction using citric acid and ethylenediamine as precursors. The prepared NCDs exhibit high quantum yield of 67.4%, good stability, excellent selectivity and sensitivity. It was found that the NCDs have potential as a fluorescence sensor for the detection of Hg2+. Under optimal conditions, good linearity between the change in NCDs fluorescence intensity and Hg2+ concentration was obtained in the range of 0.3 to 2.0 µM with a detection limit at 0.24 µM. The possible detection mechanism was static quenching of NCDs by Hg2+. The established method was successfully applied to the determination of Hg2+ in beverage samples. The results indicated that the NCDs-based sensor has potential for detection of Hg2+ in real beverage sample.

The type 2 diabetes mellitus susceptibility gene CDKAL1 polymorphism is associated with depressive symptom in first-episode drug-naive schizophrenic patients.

BACKGROUND: Patients with schizophrenia have an increased prevalence of type 2 diabetes mellitus that has shown a significant association with the rs7754840 polymorphism in the gene encoding the cyclin-dependent kinase 5 (CDK5) regulatory subunit-associated protein 1-like 1 (CDKAL1). OBJECTIVE: To examine whether this polymorphism was involved in the susceptibility in first-episode drug-naive schizophrenic patients (FDSP), and further influenced their clinical symptoms. METHODS: This polymorphism was genotyped in 239 FDSP and 368 healthy controls. The clinical symptoms in FDSP were assessed using the Positive and Negative Syndrome Scale (PANSS) five-factor models. RESULTS: There was no significant difference in the allelic and genotypic frequencies of this polymorphism between two groups (both p > 0.05) after adjusting for covariates. However, the PANSS depressive score significantly differed by genotype in FDSP after adjusting for covariates (F = 5.25, p = 0.006). This significant difference also persisted after Bonferroni correction (p < 0.05). FDSP with C/C genotype had significantly higher PANSS depressive score than those with C/G genotype (p = 0.007) and those with G/G genotype (p = 0.005). Moreover, further stepwise multivariate regression analysis showed the significant association between the rs7754840 polymorphism and PANSS depressive score in FDSP (ß = -1.07, t = -2.75, p = 0.007). CONCLUSIONS: Our findings demonstrated that although the CDKAL1 rs7754840 polymorphism did not contribute to the susceptibility to FDSP, it might be implicated in depressive symptoms in this patient group.

Association between stereopsis deficits and attention decline in patients with major depressive disorder.

Cognitive and sensory deficits were considered a core feature of major depressive disorder (MDD). However, few studies investigated stereopsis integrity in patients with MDD. Thus, the objectives of this study investigated stereopsis integrity and its correlations with cognitive function and depressive symptom in patients with MDD. 90 patients with MDD and 116 healthy controls (HCs) were enrolled in this study. Their stereoacuity was evaluated using the Titmus Stereopsis Test as well as assessing their cognitive function and depressive symptom by the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and Hamilton Depression Scale (HAMD). Log seconds of arc was significantly higher in patients than HCs (1.92 ± 0.41 versus 1.67 ± 0.16, t = 5.35, p < 0.0001). The percentage of patients with correct stereopsis detection was markedly declined in 400 (z = 3.06, p = 0.002), 200 (z = 3.84, p < 0.001), 140 (z = 4.73, p < 0.001), 100 (z = 4.58, p < 0.001), 80 (z = 5.06, p < 0.001), 60 (z = 4.72, p < 0.001), 50 (z = 4.24, p < 0.001), and 40 (z = 4.85, p < 0.001) seconds of arc compared with HCs. Log seconds of arc was significantly correlated with the RBANS total score (r = -0.38, p < 0.0001), subscores of attention (r = -0.49, p < 0.0001) and language (r = -0.33, p = 0.001) rather than HAMD score (r = 0.03, p = 0.78) in MDD patients. In addition, log seconds of arc was significantly related to the RBANS total score (r = -0.58, p < 0.0001) and language score (r = -0.45, p = 0.006) rather than attention score (r = -0.30, p = 0.07) in HCs. Further stepwise multivariate regression analyses showed the negative correlation of log seconds of arc with attention score (ß = -0.80, t = -3.95, p < 0.0001) rather than HAMD score (ß = -0.008, t = -0.09, p = 0.93) in MDD patients. However, there was no relationship between log seconds of arc and attention score in HCs (ß = 1.52, t = 1.19, p = 0.24). Our results identified the marked deficits of stereopsis in MDD patients that were tightly correlated with their attention functioning rather than depressive symptom.

HPLC-DAD fingerprints combined with chemometric techniques for the authentication of plucking seasons of Laoshan green tea.

Laoshan green teas plucked in summer and autumn were measured by high performance liquid chromatography-diode array detector (HPLC-DAD). After baseline correction, the fingerprints data were resolved by multivariate curve resolution-alternating least squares (MCR-ALS) and a total of 57 components were acquired. Relative concentrations of these components were afterwards applied to distinguish plucking seasons using principal component analysis (PCA), support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA). For both SVM and PLS-DA models, the total recognition rates of training set, cross-validation and testing set were 100%, 91.3% and 100%, respectively. Besides, three variable selection methods were employed to determine characteristic components for the authentication of summer and autumn teas. Results showed that PLS-DA model based on three characteristic components selected by VIP possesses identical predictive ability as the original model. This study demonstrated that our proposed strategy is competent for the authentication of plucking seasons of Laoshan green tea.

The role of lipoprotein profile in depression and cognitive performance: a network analysis.

Lipid profile (total cholesterol and lipoprotein fractions) has been found to correlate with depression and cognitive impairment across the lifespan. However, the role of lipid levels in self-rated depressive state and cognitive impairment remains unclear. In this study, we examined the relationship between lipid profile (total cholesterol, triglycerides, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol) and cognition in adults with and without self-rated depression. Four hundred and thirty-eight healthy participants completed the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), the Self-Rating Depression Scale (SDS), and a serum lipoprotein test. Using multivariate ANOVA, partial correlation and network analysis, a network linking lipoprotein profile, depressive state and cognition was constructed. A significant difference in serum lipid profile between the high and low depressive groups was detected. Depressive state had a strong negative correlation with cognitive performance. Of the lipid profile, only high-density lipoprotein was positively correlated with depressive symptom severity, whereas the other three indices showed negative correlation with both depressive state and cognitive performance. Our results suggest that serum lipid profile may be directly linked to self-rated depression and cognitive performance. Further studies recruiting larger clinical samples are needed to elucidate the specific effect of lipoprotein on cognitive impairment in mood disorder.

Impact of diverse background interferences on the alternating trilinear decomposition modeling of excitation-emission matrix fluorescence data acquired from different sample sources.

Alternating trilinear decomposition (ATLD) method enables the qualitative and quantitative analysis of excitation-emission matrix fluorescence (EEMF) data acquired from complex samples. However, the impact of diverse background interferences from different sample sources on the performances of ATLD method has never been lucubrated. In this work, simulated and real EEMF data sets from different sample sources with diverse background interferences were collected and subjected to ATLD analysis. The performances of ATLD modeling individual and global EEMF data sets were comprehensively compared in terms of the resolved spectral profiles and quantitative results. It was found that ATLD method can use the same set of calibration samples to resolve and quantify multiple components of interest in multiple complex systems with diverse background interferences, regardless of individual or global modeling. The results revealed that the qualitative and quantitative results provided by ATLD method were affected neither by diversity of background interferences nor by data merging as long as the acquired EEMF data sets conform to the trilinear component model. This property of ATLD method can enrich the "second-order advantage", i.e. the term "unknown interferences" in the concept of "second-order advantage" refers to not only constant background interferences but also diverse background interferences, which will be certain to further expand the practicality of ATLD method in complex sample analysis, especially in the field of fluorescence spectroscopy.

Prevalence, social-demographic and cognitive correlates of depression in Chinese psychiatric medical staff.

BACKGROUND: The high prevalence of depression in general population was related to its social-demographics and cognitive performance. However, no studies investigated the prevalence of depression, its social-demographic and cognitive correlates in psychiatric medical staff. Thus, the aims of this study investigated the prevalence, social-demographic and cognitive correlates of depression in Chinese psychiatric medical staff. METHODS: 186 Chinese psychiatric medical staff were enrolled in Wenzhou Kangning Hospital. Depressive symptom score was assessed by the Self-rating Depression Scale (SDS). Cognition was assessed by the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). RESULTS: The prevalence of depression was 17.74% in these medical staff. The RBANS total score in participants with depressive symptom was significantly lower than that in participants with not depressive symptom after controlling for the confounding variables. The Person correlation analysis found that the normal SDS score in these medical staff was significantly related to age, education, occupations, RBANS total score and subscale scores. Stepwise multivariate regression analysis further identified that age and RBANS total score were significantly associated with the normal SDS score in these medical staff. LIMITATIONS: The limitations included cross-sectional study design, the small sample size, and the self-rating scale of depression. CONCLUSIONS: The prevalence of depression in Chinese psychiatric medical staff was higher in comparison with Chinese general population, but lower in comparison with Chinese medical staff. Cognitive deficits might be considered a core feather of depression that should be a valuable target for future interventions. Age influenced depressive symptom in these medical staff .

Increasing of FKBP9 can predict poor prognosis in patients with prostate cancer.

BACKGROUND: FK506 binding protein 9 (FKBP9) has been reported and identified for a long time, but its relationship with cancer is rarely studied. For example, the role of FK506 binding protein 9 in prostate cancer (PCa) is still unclear. Therefore, we decided to detect the expression level of FKBP9 in PCa and explore its clinical significance. METHODS: The expression level of FKBP9 protein was detected by immunohistochemistry. In addition, it was demonstrated by high-throughput sequencing of mRNA levels in the TCGA (cancer genome atlas) dataset of 499 patients. Kaplan-meier analysis and Cox proportional hazard regression model were used to evaluate the relationship between FKBP9 expression and survival in prostate cancer patients. RESULTS: The expression of FKBP9 was localized in the cytoplasm, which in normal prostate tissues was obviously lower than that in PCa tissues (P = 0.001). High expression of FKBP9 was related with lymph node metastasis (P = 0.022) and distant metastasis (P = 0.028). Kaplan-Meier survival analysis revealed that the BCR-free survival of PCa patients with high FKBP9 level was significantly shortened (P=0.041). CONCLUSIONS: FKBP9 may be a cancer promoter that enhances PCa progression, and the level of FKBP9 may be used as an independent precursor of PCa patients.

Association between decreased HDL levels and cognitive deficits in patients with bipolar disorder: a pilot study.

BACKGROUND: Cognitive deficits are common in patients with bipolar disorder (BD). Abnormal high density lipoprotein (HDL) levels have been implicated in cognitive deficits associated with ageing and neurodegenerative disorders. The present study aimed to investigate serum HDL levels, cognitive deficits and their association in patients with BD. METHODS: Thirty-seven patients with BD and 37 gender- and age-matched healthy controls (HCs) were recruited in a case-control study. Cognition was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), and serum HDL levels were measured using enzymatic colourimetry. RESULTS: There was no difference in serum HDL levels between patients with BD and HCs after adjusting for gender, age, education and body mass index (BMI). Cognitive test scores in patients with BD were significantly lower than those in HCs except for the visuospatial/constructional index after adjusting for confounding variables. Serum HDL levels were positively correlated with RBANS total score and language score in patients with BD. Stepwise multiple regression analysis showed that serum HDL levels were significantly correlated with RBANS total score and subscale scores on immediate memory and language in patients with BD after adjusting for confounding factors. CONCLUSIONS: Our findings suggest that patients with BD had poorer cognitive performance than HCs except for the visuospatial/constructional domain, and decreased serum HDL levels were correlated with cognitive deficits, especially in immediate memory and language domains in patients with BD.

Association between decreased serum TBIL concentration and immediate memory impairment in schizophrenia patients.

Cognitive impairment is a core feature of schizophrenia (SCH). In addition to the toxic effect of Bilirubin (BIL), it has antioxidant properties that were associated with the psychopathology and cognitive impairment of psychiatric disorders. The aim of this study was to examine the correlation of serum total BIL (TBIL) concentration with cognitive impairment in SCH patients. We recruited 34 SCH patients and 119 healthy controls (HCs) in this case-control design. Cognition was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Serum TBIL concentration was measured using the immunoturbidimetric method. Serum TBIL concentration was significantly decreased in SCH patients compared to HCs after adjusting for age, gender, and education. Serum TBIL concentration in SCH patients was also positively correlated with the RBANS immediate memory score. Further stepwise multiple regression analysis confirmed the positive association between serum TBIL concentration and immediate memory score in SCH patients. Our findings supported that the decline in serum TBIL concentration was associated with the immediate memory impairment and psychopathology of SCH.

Dealing with overlapped and unaligned chromatographic peaks by second-order multivariate calibration for complex sample analysis: Fast and green quantification of eight selected preservatives in facial masks.

The quantification of preservatives in cosmetics has attracted great attentions for their controversial and widespread use. HPLC is a prevailing method for preservatives determination among various analytical methods. However, it takes long time to fully separate these compounds because of the complexity of cosmetic matrices. In this study, a fast and green HPLC-DAD strategy assisted with second-order multivariate calibration methods based on alternating trilinear decomposition (ATLD) and multivariate curve resolution-alternating least squares (MCR-ALS) was developed for the simultaneous determination of eight selected preservatives in complex facial mask samples. This appealing strategy proved to be a useful tool for eliminating unknown interferences in complex matrices without complete separation, which benefited from the "second-order advantages" and thus made the determination of the eight analytes in facial mask samples shorten to 8.2 min under a fast elution program. In particular, for the first time, we focused on the applicability of ATLD method for modeling of HPLC-DAD data with severe signal overlapping and slight time shifts. The spiked recovery values were in the range of 71.4-124.6%, and the RMSEP and REP values ranged from 0.07 to 2.4 µg mL-1 and 1.3-14.5%, respectively, indicating that the ATLD method could provide satisfactory prediction. The resolved spectral profiles and concentration values were compared with those obtained by the MCR-ALS method, an excellent tool for modeling of data deviating from trilinearity. Both qualitative and quantitative results from the two methods were consistent with each other, which evidenced the competence of ATLD method in handling HPLC-DAD data with severe signal overlapping and slight time shifts.