Impacts of Evidence-based Nursing on Quality of Life and Anxiety in Patients with Liver Injury.

Background: Plasma exchange is the most commonly applied method for treating severe hepatitis. As a kind of invasive treatment, plasma exchange may have various complications during treatment. Therefore, effective nursing should be implemented during plasma exchange treatment to prevent the incidence of complications. Objective: To compare the effects of traditional nursing methods versus evidence-based nursing practices on the quality of life and anxiety of patients with liver injury. Design: This was a retrospective study. Patient data were obtained from patient records. Setting: This study was carried out in the Department of Gastroenterology, Second Hospital of Hebei Medical University. Participants: One hundred and twenty severe hepatitis patients with 89 cases of early hepatic failure and 31 cases of middle hepatic failure admitted to our department from January 2020 to December 2022 were chosen, followed by randomly separating into a control group and an observation group. Interventions: The control group adopted nursing, while the observation group received evidence-based nursing including psychological nursing, nursing during treatment and post-treatment nursing. Primary Outcome Measures: (1) liver function (2) emotional state assessed by Self-rating Anxiety Scale (SAS) along with Self-rating Depression Scale (SDS) (3) coagulation function, (4) quality of life assessed by Short-Form 36 (SF-36) scale (5) nursing satisfaction, and (6) incidence of complications. Results: In contrast to the control group, the occurrence of complications in the observation group was significantly lower (P < .05). At 1-month review, the quality of life score in the observation group was higher in contrast to the control group (P < .05). In contrast to the control group, the nursing satisfaction of patients in the observation group was better (P < .05), alanine aminotransferase and total bilirubin levels in the observation group were lower, while albumin levels were higher (P < .05), the anxiety and depression scores of the observation group were lessened (P < .05), and the required time of coagulation function indexes in the observation group was shorter (P < .05). Conclusion: The application of evidence-based nursing to artificial liver therapy in patients with liver failure can effectively promote the liver function and coagulation index of patients, help to relieve negative emotions, and promote the quality of life of patients. This study may provide clinical reference for the nursing of artificial liver therapy in patients with liver failure.

CarSitePred: an integrated algorithm for identifying carbonylated sites based on KNDUA-LNDOT resampling technique.

Carbonylated sites are the determining factors for functional changes or deletions in carbonylated proteins, so identifying carbonylated sites is essential for understanding the process of protein carbonylated and exploring the pathogenesis of related diseases. The current wet experimental methods for predicting carbonylated modification sites ae not only expensive and time-consuming, but also have limited protein processing capabilities and cannot meet the needs of researchers. The identification of carbonylated sites using computational methods not only improves the functional characterization of proteins, but also provides researchers with free tools for predicting carbonylated sites. Therefore, it is essential to establish a model using computational methods that can accurately predict protein carbonylated sites. In this study, a prediction model, CarSitePred, is proposed to identify carbonylation sites. In CarSitePred, specific location amino acid hydrophobic hydrophilic, one-to-one numerical conversion of amino acids, and AlexNet convolutional neural networks convert preprocessed carbonylated sequences into valid numerical features. The K-means Normal Distribution-based Undersampling Algorithm (KNDUA) and Localized Normal Distribution Oversampling Technology (LNDOT) were firstly proposed and employed to balance the K, P, R and T carbonylation training dataset. And for the first time, carbonylation modification sites were transformed into the form of images and directly inputted into AlexNet convolutional neural network to extract features for fitting SVM classifiers. The 10-fold cross-validation and independent testing results show that CarSitePred achieves better prediction performance than the best currently available prediction models. Availability: https://github.com/zuoyun123/CarSitePred.Communicated by Ramaswamy H. Sarma.

Laplacian Regularized Sparse Representation Based Classifier for Identifying DNA N4-Methylcytosine Sites via L2,1/2-Matrix Norm.

N4-methylcytosine (4mC) is one of important epigenetic modifications in DNA sequences. Detecting 4mC sites is time-consuming. The computational method based on machine learning has provided effective help for identifying 4mC. To further improve the performance of prediction, we propose a Laplacian Regularized Sparse Representation based Classifier with L2,1/2-matrix norm (LapRSRC). We also utilize kernel trick to derive the kernel LapRSRC for nonlinear modeling. Matrix factorization technology is employed to solve the sparse representation coefficients of all test samples in the training set. And an efficient iterative algorithm is proposed to solve the objective function. We implement our model on six benchmark datasets of 4mC and eight UCI datasets to evaluate performance. The results show that the performance of our method is better or comparable.

Effect of WeChat Messaging on Improving the Quality of Bowel Preparation for Outpatient Colonoscopy: A Randomized Controlled Study.

With the most active users of any social media platform in China, WeChat has become the preferred platform for public announcements and is widely used in the fields of medicine and nursing (Hong, Zhou, Fang, & Shi, 2017; Zeng, Deng, Wang, & Liu, 2016). The aim of this study was to evaluate the effect of WeChat messaging on bowel preparation for outpatient colonoscopy. A total of 150 outpatients scheduled for colonoscopy in a Grade III level A hospital were randomly assigned to the experimental group (n = 73) or the control group (n = 72). Both groups received routine guidance from the day of the scheduling appointment through the day of colonoscopy. In addition, the experimental group received colonoscopy-related information and individualized guidance daily through WeChat from the day of the appointment. After the colonoscopy, the diet and medication compliance, satisfaction, anxiety, and bowel cleanliness were compared. Post-intervention, there were significant differences in bowel cleanliness, satisfaction, diet and medication compliance, and anxiety between the two groups. WeChat messaging can help improve diet and medication compliance, patient satisfaction, and the success rate and thoroughness of colonoscopy, as well as alleviate the anxiety of patients scheduled for outpatient colonoscopy.

A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data.

Inferring gene regulatory networks (GRNs) based on gene expression profiles is able to provide an insight into a number of cellular phenotypes from the genomic level and reveal the essential laws underlying various life phenomena. Different from the bulk expression data, single-cell transcriptomic data embody cell-to-cell variance and diverse biological information, such as tissue characteristics, transformation of cell types, etc. Inferring GRNs based on such data offers unprecedented advantages for making a profound study of cell phenotypes, revealing gene functions and exploring potential interactions. However, the high sparsity, noise and dropout events of single-cell transcriptomic data pose new challenges for regulation identification. We develop a hybrid deep learning framework for GRN inference from single-cell transcriptomic data, DGRNS, which encodes the raw data and fuses recurrent neural network and convolutional neural network (CNN) to train a model capable of distinguishing related gene pairs from unrelated gene pairs. To overcome the limitations of such datasets, it applies sliding windows to extract valuable features while preserving the direction of regulation. DGRNS is constructed as a deep learning model containing gated recurrent unit network for exploring time-dependent information and CNN for learning spatially related information. Our comprehensive and detailed comparative analysis on the dataset of mouse hematopoietic stem cells illustrates that DGRNS outperforms state-of-the-art methods. The networks inferred by DGRNS are about 16% higher than the area under the receiver operating characteristic curve of other unsupervised methods and 10% higher than the area under the precision recall curve of other supervised methods. Experiments on human datasets show the strong robustness and excellent generalization of DGRNS. By comparing the predictions with standard network, we discover a series of novel interactions which are proved to be true in some specific cell types. Importantly, DGRNS identifies a series of regulatory relationships with high confidence and functional consistency, which have not yet been experimentally confirmed and merit further research.

Reaction-based fluorescence probes for "turn on" sensing fluoride ions.

Introducing a weak covalent bond into an originally highly fluorescent molecule to create a non-fluorescent probe is able to provide a new way to detect some nucleophilic targets with enhanced sensitivity. Herein, this is the first time that a tetraphenylethene (TPE)-based probe (TPEONO2) bearing a p-nitrobenzenesulfonyl moiety for the sensing of F- ions in aqueous solution via a cleavage reaction of the sulfonyl ester bond to induce aggregation-induced emission (AIE) has been reported.

MMFGRN: a multi-source multi-model fusion method for gene regulatory network reconstruction.

Lots of biological processes are controlled by gene regulatory networks (GRNs), such as growth and differentiation of cells, occurrence and development of the diseases. Therefore, it is important to persistently concentrate on the research of GRN. The determination of the gene-gene relationships from gene expression data is a complex issue. Since it is difficult to efficiently obtain the regularity behind the gene-gene relationship by only relying on biochemical experimental methods, thus various computational methods have been used to construct GRNs, and some achievements have been made. In this paper, we propose a novel method MMFGRN (for "Multi-source Multi-model Fusion for Gene Regulatory Network reconstruction") to reconstruct the GRN. In order to make full use of the limited datasets and explore the potential regulatory relationships contained in different data types, we construct the MMFGRN model from three perspectives: single time series data model, single steady-data model and time series and steady-data joint model. And, we utilize the weighted fusion strategy to get the final global regulatory link ranking. Finally, MMFGRN model yields the best performance on the DREAM4 InSilico_Size10 data, outperforming other popular inference algorithms, with an overall area under receiver operating characteristic score of 0.909 and area under precision-recall (AUPR) curves score of 0.770 on the 10-gene network. Additionally, as the network scale increases, our method also has certain advantages with an overall AUPR score of 0.335 on the DREAM4 InSilico_Size100 data. These results demonstrate the good robustness of MMFGRN on different scales of networks. At the same time, the integration strategy proposed in this paper provides a new idea for the reconstruction of the biological network model without prior knowledge, which can help researchers to decipher the elusive mechanism of life.

A comprehensive overview and critical evaluation of gene regulatory network inference technologies.

Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to understand the molecular mechanism of organisms and to reveal the essential rules of a large number of biological processes and reactions in organisms. Various outstanding network reconstruction algorithms use specific assumptions that affect prediction accuracy, in order to deal with the uncertainty of processing. In order to study why a certain method is more suitable for specific research problem or experimental data, we conduct research from model-based, information-based and machine learning-based method classifications. There are obviously different types of computational tools that can be generated to distinguish GRNs. Furthermore, we discuss several classical, representative and latest methods in each category to analyze core ideas, general steps, characteristics, etc. We compare the performance of state-of-the-art GRN reconstruction technologies on simulated networks and real networks under different scaling conditions. Through standardized performance metrics and common benchmarks, we quantitatively evaluate the stability of various methods and the sensitivity of the same algorithm applying to different scaling networks. The aim of this study is to explore the most appropriate method for a specific GRN, which helps biologists and medical scientists in discovering potential drug targets and identifying cancer biomarkers.

iEnhancer-KL: A Novel Two-Layer Predictor for Identifying Enhancers by Position Specific of Nucleotide Composition.

An enhancer is a short region of DNA with the ability to recruit transcription factors and their complexes, increasing the likelihood of the transcription of a particular gene. Considering the importance of enhancers, enhancer identification is a prevailing problem in computational biology. In this paper, we propose a novel two-layer enhancer predictor called iEnhancer-KL, using computational biology algorithms to identify enhancers and then classify these enhancers into strong or weak types. Kullback-Leibler (KL) divergence is creatively taken into consideration to improve the feature extraction method PSTNPss. Then, LASSO is used to reduce the dimension of features and finally helps to get better prediction performance. Furthermore, the selected features are tested on several machine learning models, and the SVM algorithm achieves the best performance. The rigorous cross-validation indicates that our predictor is remarkably superior to the existing state-of-the-art methods with an Acc of 84.23 percent and the MCC of 0.6849 for identifying enhancers. Our code and results can be freely downloaded from https://github.com/Not-so-middle/iEnhancer-KL.git.

iPro2L-PSTKNC: A Two-Layer Predictor for Discovering Various Types of Promoters by Position Specific of Nucleotide Composition.

Promoters are DNA regulatory elements located proximal to the transcription start site, which are in charge of the initiation of specific gene transcription. In Escherichia coli, promoters can be recognized by σ factors that have multiple families based on distinct function and structure, such as σ24, σ28, σ32, σ38, σ54 and σ70. At present, biological methods are mainly used to identify these promoters. However, because it is time-consuming and material-consuming to do biological experiments, computational biology algorithm has emerged as a more effective way to predict the classification. In this study, we develop a novel two-layer seamless predictor called iPro2L-PSTKNC to identify the promoters of the E. coli genome, which based on the feature extraction model we newly proposed that is named as the position specific tendencies of k-mer nucleotide composition (PSTKNC). On the first layer, it is a binary classification predicting whether a sequence is promoter or not. And the second layer is a multiple classification identifying which type the identified promoter belongs to. The ensemble classification SVM performsbest comparing with other algorithms, which gets a promising accuracy and the Matthews correlation coefficient (MCC) at [Formula: see text] and [Formula: see text]. Our data and code are available at https://github.com/lyuyinuo/iPro2L-PSTKNC.

The study on the interactions of two 1,2,3-triazoles with several biological macromolecules by multiple spectroscopic methodologies and molecular docking.

1-(4-chlorophenyl)-5-phenyl-1H-1,2,3-triazole (CPTC) and 5-(3-chlorophenyl) -1-phenyl-1H-1,2,3-triazole (PCTA) are two new derivatives of 1,2,3-triazole. Their structural and spectral properties were characterized by density functional theory calculations (DFT). The binding properties of CPTC or PCTA with several typical biomacromolecules such as human serum albumin (HSA), bovine hemoglobin (BHb), human immunoglobulin (HIgG) or DNA were investigated by molecular docking and multiple spectroscopic methodologies. The different parameters including binding constants and thermodynamic parameters for CPTC/PCTA-HSA/BHb/HIgG/DNA systems were obtained based on various fluorescence enhancement or quenching mechanisms. The results of binding constants indicated that there were the strong interactions between two triazoles and four biological macromolecules due to the higher order of magnitude between 103 and 105. The values of thermodynamic parameters revealed that the binding forces for these systems are mainly hydrophobic interactions, electrostatic force, or hydrogen bond, respectively, which are in agreement with the results of molecular docking to a certain extent. Moreover, the information from synchronous, 3D fluorescence and UV-Vis spectroscopies proved that two compounds CPTC and PCTA could affect the microenvironment of amino acids residues of three kinds of proteins. Based on the above experimental results, a comparison of the interaction mechanisms for CPTC/PCTA-proteins/DNA systems have been performed in view of their different molecular structures, which is beneficial for the further research in order to design them as the novel drugs.

A Combined Analysis of Genetically Correlated Traits Identifies Genes and Brain Regions for Insomnia.

AIMS: Previous studies have inferred that there is a strong genetic component in insomnia. However, the etiology of insomnia is still unclear. This study systematically analyzed multiple genome-wide association study (GWAS) data sets with core human pathways and functional networks to detect potential gene pathways and networks associated with insomnia. METHODS: We used a novel method, multitrait analysis of genome-wide association studies (MTAG), to combine 3 large GWASs of insomnia symptoms/complaints and sleep duration. The i-Gsea4GwasV2 and Reactome FI programs were used to analyze data from the result of MTAG analysis and the nominally significant pathways, respectively. RESULTS: Through analyzing data sets using the MTAG program, our sample size increased from 113,006 subjects to 163,188 subjects. A total of 17 of 1,816 Reactome pathways were identified and showed to be associated with insomnia. We further revealed 11 interconnected functional and topologically interacting clusters (Clusters 0 to 10) that were associated with insomnia. Based on the brain transcriptome data, it was found that the genes in Cluster 4 were enriched for the transcriptional coexpression profile in the prenatal dorsolateral prefrontal cortex (P = 7 × 10-5), inferolateral temporal cortex (P = 0.02), medial prefrontal cortex (P < 1 × 10-5), and amygdala (P < 1 × 10-5), and detected RPA2, ORC6, PIAS3, and PRIM2 as core nodes in these 4 brain regions. CONCLUSIONS: The findings provided new genes, pathways, and brain regions to understand the pathology of insomnia.

Impact of a Multifaceted Pharmacist-Led Intervention on Antimicrobial Stewardship in a Gastroenterology Ward: A Segmented Regression Analysis.

BACKGROUND: Irrational use of antimicrobial agents for gastrointestinal diseases deserves attention, but corresponding antimicrobial stewardship programs (ASPs) are generally not a priority for managers. We conducted this study to evaluate the effectiveness of multifaceted pharmacist-led (MPL) interventions in the gastroenterology ward (GW) to provide evidence for the efficacy of ASPs in a non-priority department. METHODS: This was an interventional, retrospective study implemented in China. The MPL intervention lasting 1.5 years involved daily ward rounds with physicians, regular review of medical orders, monthly indicator feedback, frequent physician training, and necessary patient education. Data on all hospitalized adults receiving antibiotics was extracted from the hospital information system over a 36-month period from January 2016 to December 2018. Segmented regression analysis of interrupted time series was performed to evaluate the effect of the MPL interventions (started in July 2017) on antibiotic use and length of hospital stay, which was calculated monthly as analytical units. RESULTS: A total of 1763 patients receiving antibiotics were enrolled. Segmented regression models showed descending trends from the baseline in the intensity of antibiotic consumption (coefficient = -0.88, p = 0.01), including a significant decline in the level of change of the proportion of patients receiving combined antibiotics (coefficient = -9.91, p = 0.03) and average length of hospital stay (coefficient = -1.79, p = 0.00), after MPL interventions. The MPL interventions led to a temporary increase in the proportion of patients receiving antibiotics (coefficient = 4.95, p = 0.038), but this was part of a declining secular trend (coefficient = -0.45, p = 0.05). CONCLUSION: The MPL interventions led a statistically significant decline in the number of patients receiving antibiotics, the antibiotic consumption, and the average hospital stay post-intervention compared to the pre-intervention phase of the study. Health policymakers should actively practice MPL interventions by clinical pharmacists in ASPs in those departments that are not included in priority management.

A new 1,2,3-triazole and its rhodamine B derivatives as a fluorescence probe for mercury ions.

A newly synthesized compound, 5-methyl-1-phenyl-1H-1,2,3-triazole-4- carboxylic acid (MPC) was analyzed for its quantum chemical parameters and theoretical spectrum by computational chemistry. The calculated spectrum was in accord with the experimental measurements in a great degree. Then MPC was successfully designed and synthesized to a novel rhodamine B derivative RMPC. The RMPC exhibited about a 4000-fold increase in fluorescence intensity in the presence of Hg2+ ions over most other competitive metal ions. The triazole appended colorless chemodosimeter RMPC turns to pink upon the complex formation only with Hg2+ ions as a 1: 2 M ratio and enables naked-eye detection. The coordination mechanism of turning on/off fluorescence for Hg2+ ions were well proposed by explaining Hg2+ inducing the ring-opened rhodamine B moiety. The fluorescence imaging experiments of Hg2+ in HeLa cell demonstrated that the probe was labeled and it could be used in biological systems.

The psychometric properties of the Chinese version internet gaming disorder scale.

INTRODUCTION: The study tested the psychometric properties of two Chinese version Internet Gaming Disorder scales (IGDSs): a dichotomous IGDS with yes/no type of response and a polytomous IGDS with a 0-5 Likert-type response. METHODS: The reliability and validity of two scales were tested separately, among two population-based samples of Chinese adolescents and adults (351 for dichotomous IGDS and 378 for polytomous IGDS). The diagnostic accuracy of the dichotomous IGDS was assessed in an independent sample of 114 gamers (56 disordered gamers and 58 average gamers) using Receiver Operating Characteristic (ROC) analysis. RESULTS: The results demonstrated good internal consistency (αdichotomous = 0.80 and αpolytomous = 0.89) and test-retest reliability (rdichotomous = 0.83 and rpolytomous = 0.84) for both scales. Both scales showed sound validity, as indicated by significant correlations with measurements of internet addiction, aggression, impulsivity, craving for gaming and time spent playing games. Factor analysis demonstrated that both Chinese IGDSs have a similar single-component structure to the original scales. The ROC analysis indicated an excellent diagnostic accuracy of the dichotomous IGDS. When apply the five or more cutoff points, the prevalence of IGD was 7.41% in the population-based sample. CONCLUSION: This study demonstrated robust psychometric properties of the Chinese version dichotomous IGDS and polytomous IGDS, and suggests that these scales are valid tools that suitable for clinical and research purposes.

Identification of Highest-Affinity Binding Sites of Yeast Transcription Factor Families.

Transcription factors (TFs) play a crucial role in controlling key cellular processes and responding to the environment. Yeast is a single-cell fungal organism that is a vital biological model organism for studying transcription and translation in basic biology. The transcriptional control process of yeast cells has been extensively calculated and studied using traditional methods and high-throughput technologies. However, the identities of transcription factors that regulate major functional categories of genes remain unknown. Due to the avalanche of biological data in the post-genomic era, it is an urgent need to develop automated computational methods to enable accurate identification of efficient transcription factor binding sites from the large number of candidates. In this paper, we analyzed high-resolution DNA-binding profiles and motifs for TFs, covering all possible contiguous 8-mers. First, we divided all 8-mer motifs into 16 various categories and selected all sorts of samples from each category by setting the threshold of E-score. Then, we employed five feature representation methods. Also, we adopted a total of four feature selection methods to filter out useless features. Finally, we used Extreme Gradient Boosting (XGBoost) as our base classifier and then utilized the one-vs-rest tactics to build 16 binary classifiers to solve this multiclassification problem. In the experiment, our method achieved the best performance with an overall accuracy of 79.72% and Mathew's correlation coefficient of 0.77. We found the similarity relationship among each category from different TF families and obtained sequence motif schematic diagrams via multiple sequence alignment. The complexity of DNA recognition may act as an important role in the evolution of gene regulation. Source codes are available at https://github.com/guofei-tju/tfbs.

The computational and experimental studies on a 1, 2, 3-triazole compound and its special binding to three kinds of blood proteins.

A newly synthesized compound, ethyl 5-phenyl-2-(p-tolyl)-2H-1, 2, 3-triazole-4-carboxylate (EPPC) may be considered as a drug candidate and was exploited to study the structural and spectral properties by using quantum chemical calculation and multiple spectroscopic techniques. The results on theoretical spectrum of EPPC were consistent with experimental spectrum in great degree. In addition, EPPC has been as a special probe and investigated on the interactions with three kinds of blood proteins including human serum albumin (HSA), human immunoglobulin (HIgG) and bovine hemoglobin (BHb) by using UV-Vis, fluorescence spectroscopy and molecular modeling, respectively. Changes in various fluorescence and UV-Vis spectra were observed upon ligand binding along with a remarkable degree of fluorescence enhancement on complex formation under physiological condition with binding constant about 105 order of magnitudes, which caused the variations of conformation and microenvironment of these proteins in aqueous solution. The obtained results from the thermodynamic parameters calculated according to the van't Hoff equation indicated that the entropy change ΔS° and enthalpy change ΔH° were found to be 0.168 KJ/mol K and 22.154 KJ/mol for EPPC-HSA system, 0.284 KJ/mol K and 54.408 KJ/mol for EPPC-HIgG system, and 0.228 KJ/mol K and 37.548 KJ/mol for EPPC-BHb system, respectively, which demonstrated that the primary binding pattern is determined by hydrophobic interaction. The results of docking and molecular dynamics simulation using three proteins crystal models revealed that EPPC could bind to three proteins well into hydrophobic cavity, which showed good consistence with the spectroscopic measurements.Communicated by Ramaswamy H. Sarma.

The role of genes affected by human evolution marker GNA13 in schizophrenia.

Numerous variants associated with increased risk for SCZ have undergone positive selection and were associated with human brain development, but which brain regions and developmental stages were influenced by the positive selection for SCZ risk alleles are unclear. We analyzed SCZ using summary statistics from a genome-wide association study (GWAS) from the Psychiatric Genomics Consortium (PGC). Machine-learning scores were used to investigate two natural-selection scenarios: complete selection (loci where a selected allele has reached fixation) and incomplete selection (loci where a selected allele has not yet reached fixation). Based on the p value of single nucleotide polymorphisms (SNPs) with selection scores in the top 5%, we formed five subgroups: p < 0.0001, 0.001, 0.01, 0.05, or 0.1. We found that 48 and 29 genes (p < 0.0001) in complete and incomplete selection, respectively, were enrichedfor the transcriptionalco-expressionprofilein theprenatal dorsolateral prefrontal cortex (DFC), inferior parietal cortex (IPC), and ventrolateral prefrontal cortex (VFC). Core genes (GNA13, TBC1D19, and ZMYM4) involved in regulating early brain development were identified in these three brain regions. RNA sequencing for primary cortical neurons that were transfected Gna13 overexpressed lentivirus demonstrated that 135 gene expression levels changed in the Gna13 overexpressed groups compared with the controls. Gene-set analysis identified important associations among common variants of these 13 genes, which were associated with neurodevelopment and putamen volume [p = 0.031; family-wise error correction (FWEC)], SCZ (p = 0.022; FWEC). The study indicate that certain SCZ risk alleles were likely to undergo positive selection during human evolution due to their involvement in the development of prenatal DFC, IPC and VFC, and suggest that SCZ is related to abnormal neurodevelopment.

The specific binding of a new 1,2,3-triazole to three blood proteins and it's appended rhodamine complex for selective detection of Hg2.

A synthesized compound, ethyl 2,5-diphenyl-2H-1,2,3-triazole-4- carboxylate (EDTC) was investigated on its physiochemical parameters and structural properties by using the quantum-chemical method. The results on the theoretical spectrum of EDTC were line with experimental fluorescence and absorption spectrum in large degree. Then EDTC was successfully synthesized to a novel rhodamine-based fluorescent probe (REDTC), which showed a distinct fluorescence enhancement upon the presence of Hg2+ in dimethyl formamide-water (DMF-H2O) buffer solution (pH 7.4). Meanwhile, the triazole appended colorless compound turns to pink upon complex formation with Hg2+ ions as 1:2 molar ratios and enables naked-eye detection. The chromogenic mechanism was determined by using spectroscopic measurements and TD DFT calculation. The fluorescence imaging experiments of Hg2+ in HeLa cell revealed that the probe REDTC could be labeled and it could be used in biological systems. Also, the intermediate EDTC was developed as a sensitive fluorescent probe for specific studies on the interactions to three kinds of blood proteins including human serum albumin (HSA), human immunoglobulin (HIg) and bovine hemoglobin (BHb) by using a series of spectroscopic methods and molecular docking under the simulative physiological conditions. The interactions between EDTC and these proteins led to the distinct fluorescence enhancement. The thermodynamic measured results further suggested that hydrophobic forces play a dominating role in stabilizing the complexes, which are in correspondence with the results from molecular docking. The UV-visible, synchronous, and three-dimensional (3D) fluorescence measurements demonstrated that EDTC influences the conformational of proteins and the microenvironment surrounding HSA, HIg, or BHb in aqueous solution.