Theoretical study on intramolecular hydrogen bonds of flavonoid cocrystals.

This study investigates the role of intramolecular hydrogen bonds in the formation of cocrystals involving flavonoid molecules, focusing on three active pharmaceutical ingredients (APIs): chrysin (CHR), isoliquiritigenin (ISO), and kaempferol (KAE). These APIs form cocrystals with different cocrystal formers (CCFs) through intramolecular hydrogen bonding. We found that disruption of these intramolecular hydrogen bonds leads to decreased stability compared to molecules with intact bonds. The extrema of molecular electrostatic potential surfaces (MEPS) show that flavonoid molecules with disrupted intramolecular hydrogen bonds have stronger hydrogen bond donors and acceptors than those with intact bonds. Using the artificial bee colony algorithm, dimeric structures of these flavonoid molecules were explored, representing early-stage structures in cocrystal formation, including API-API, API-CCF, and CCF-CCF dimers. It was observed that the number and strength of dimeric interactions significantly increased, and the types of interactions changed when intramolecular hydrogen bonds were disrupted. These findings suggest that disrupting intramolecular hydrogen bonds generally hinders the formation of cocrystals. This theoretical study provides deeper insight into the role of intramolecular hydrogen bonds in the cocrystal formation of flavonoids.

NBCR-ac4C: A Deep Learning Framework Based on Multivariate BERT for Human mRNA N4-Acetylcytidine Sites Prediction.

N4-acetylcytidine (ac4C) plays a crucial role in regulating cellular biological processes, particularly in gene expression regulation and disease development. However, experiments to identify ac4C in a wet lab are time-consuming and costly, and the learning-based methods struggle to capture the underlying semantic knowledge and relations within sequences. To address this, we propose a deep learning approach called NBCR-ac4C based on pretrained models. Specifically, we employ Nucleotide Transformer and DNABERT2 to construct contextual embedding of nucleotide sequences, which effectively mine and express context relations between different features in the sequence. Convolutional neural network (CNN) and ResNet18 are then applied to further extract shallow and deep knowledge from context embedding. Depending on extensive experiments for the prediction of ac4C sites in nucleotide sequences, we observe that NBCR-ac4C outperforms general learning-based models. It achieves the highest accuracy (ACC) of 83.51% and an Area Under the Receiver Operating Characteristic Curve (AUROC) of 89.58% on an independent test set. Moreover, the proposed model, compared to the current state-of-the-art (SOTA) model LSA-ac4C, demonstrates higher ACC and AUROC by 0.81-3.7% and 0.05-1.58%, respectively. The data set and code are available on https://github.com/2103374200/NBCR to facilitate further discussion on NBCR-ac4C.

ILYCROsite: Identification of lysine crotonylation sites based on FCM-GRNN undersampling technique.

Protein lysine crotonylation is an important post-translational modification that regulates various cellular activities. For example, histone crotonylation affects chromatin structure and promotes histone replacement. Identification and understanding of lysine crotonylation sites is crucial in the field of protein research. However, due to the increasing amount of non-histone crotonylation sites, existing classifiers based on traditional machine learning may encounter performance limitations. In order to address this problem, a novel deep learning-based model for identifying crotonylation sites is presented in this study, given the unique advantages of deep learning techniques for sequence data analysis. In this study, an MLP-Attention-based model was developed for the identification of crotonylation sites. Firstly, three feature extraction strategies, namely Amino Acid Composition, K-mer, and Distance-based residue features extraction strategy, were used to encode crotonylated and non-crotonylated sequences. Then, in order to balance the training dataset, the FCM-GRNN undersampling algorithm combining fuzzy clustering and generalized neural network approaches was introduced. Finally, to improve the effectiveness of crotonylation site identification, we explored various classification algorithms, and based on the relevant experimental performance comparisons, the multilayer perceptron (MLP) combined with the superimposed self-attention mechanism was finally selected to construct the prediction model ILYCROsite. The results obtained from independent testing and five-fold cross-validation demonstrated that the model proposed in this study, ILYCROsite, had excellent performance. Notably, on the independent test set, ILYCROsite achieves an AUC value of 87.93 %, which is significantly better than the existing state-of-the-art models. In addition, SHAP (Shapley Additive exPlanations) values were used to analyze the importance of features and their impact on model predictions. Meanwhile, in order to facilitate researchers to use the prediction model constructed in this study, we developed a prediction program to identify the crotonylation sites in a given protein sequence. The data and code for this program are available at: https://github.com/wmqskr/ILYCROsite.

Study on interaction with high-abundant blood proteins and identification of low-abundant proteins to 5-phenyl-1-(p-tolyl)-1 H-1,2,3-triazole by serum proteomics.

In this study, a comprehensive investigation was undertaken to elucidate a simple triazole compound, 5-phenyl-1-(p-tolyl)-1 H-1,2,3-triazole (PPTT), its interactions with high-abundant proteins and identification of low-abundant proteins by serum proteomics. Employing a combination of spectroscopic techniques and computational chemistry, the interactions between PPTT and three high-abundance blood globular proteins, namely human serum albumin (HSA), human immunoglobulin G (HIgG), and hemoglobin (BHb), were explored, thereby ascertaining their binding constants and thermodynamic parameters at the molecular level. Subsequently, based on the differential proteomics, utilizing two-dimensional gel electrophoresis (2-DE) in conjunction with matrix-assisted laser desorption time-of-flight mass spectrometry (MALDI-TOF-MS), the research team isolated and identified differentially expressed low-abundance proteins in human blood serum samples following exposure to PPTT. The results showed that there were twenty highly expressed proteins identified from blood serum samples intervened by PPTT. Combining bioinformatics techniques, these proteins were classified, providing preliminary insights like preproprotein or precursors inhibiting the activity of elastase, defending and regulating the immune system, carrying lipid, and other functions into their biological functionalities. One of the differential proteins, apolipoprotein A-1 (ApoA-1) protein, was selected as a possible target to explore the mechanism of action of PPTT intervention on the related signaling pathways involved in human hepatocellular carcinomas(Hep G2) cells. These research findings offer scientifically sound guidance for further in-depth exploration, development, and application of the 1,2,3-triazole compound.

Genetic Evidence of SpGH9A3 in Leaf Morphology Variation of Spathiphyllum 'Mojo'.

Leaves play a crucial role as ornamental organs in Spathiphyllum, exhibiting distinct differences across various Spathiphyllum varieties. Leaf development is intricately linked to processes of cell proliferation and expansion, with cell morphology often regulated by plant cell walls, primarily composed of cellulose. Alterations in cellulose content can impact cell morphology, subsequently influencing the overall shape of plant organs. Although cellulases have been shown to affect cellulose levels in plant cells, genetic evidence linking them to the regulation of leaf shape remains limited. This study took the leaves of Spathiphyllum 'Mojo' and its somatic variants as the research objects. We screened four cellulase gene family members from the transcriptome and then measured the leaf cellulose content, cellulase activity, and expression levels of cellulase-related genes. Correlation analysis pinpointed the gene SpGH9A3 as closely associated with leaf shape variations in the mutant. Green fluorescent fusion protein assays revealed that the SpGH9A3 protein was localized to the cell membrane. Notably, the expression of the SpGH9A3 gene in mutant leaves peaked during the early spread stage, resulting in smaller overall leaf size and reduced cellulose content upon overexpression in Arabidopsis.

Glypred: Lysine Glycation Site Prediction via CCU-LightGBM-BiLSTM Framework with Multi-Head Attention Mechanism.

Glycation, a type of posttranslational modification, preferentially occurs on lysine and arginine residues, impairing protein functionality and altering characteristics. This process is linked to diseases such as Alzheimer's, diabetes, and atherosclerosis. Traditional wet lab experiments are time-consuming, whereas machine learning has significantly streamlined the prediction of protein glycation sites. Despite promising results, challenges remain, including data imbalance, feature redundancy, and suboptimal classifier performance. This research introduces Glypred, a lysine glycation site prediction model combining ClusterCentroids Undersampling (CCU), LightGBM, and bidirectional long short-term memory network (BiLSTM) methodologies, with an additional multihead attention mechanism integrated into the BiLSTM. To achieve this, the study undertakes several key steps: selecting diverse feature types to capture comprehensive protein information, employing a cluster-based undersampling strategy to balance the data set, using LightGBM for feature selection to enhance model performance, and implementing a bidirectional LSTM network for accurate classification. Together, these approaches ensure that Glypred effectively identifies glycation sites with high accuracy and robustness. For feature encoding, five distinct feature types─AAC, KMER, DR, PWAA, and EBGW─were selected to capture a broad spectrum of protein sequence and biological information. These encoded features were integrated and validated to ensure comprehensive protein information acquisition. To address the issue of highly imbalanced positive and negative samples, various undersampling algorithms, including random undersampling, NearMiss, edited nearest neighbor rule, and CCU, were evaluated. CCU was ultimately chosen to remove redundant nonglycated training data, establishing a balanced data set that enhances the model's accuracy and robustness. For feature selection, the LightGBM ensemble learning algorithm was employed to reduce feature dimensionality by identifying the most significant features. This approach accelerates model training, enhances generalization capabilities, and ensures good transferability of the model. Finally, a bidirectional long short-term memory network was used as the classifier, with a network structure designed to capture glycation modification site features from both forward and backward directions. To prevent overfitting, appropriate regularization parameters and dropout rates were introduced, achieving efficient classification. Experimental results show that Glypred achieved optimal performance. This model provides new insights for bioinformatics and encourages the application of similar strategies in other fields. A lysine glycation site prediction software tool was also developed using the PyQt5 library, offering researchers an auxiliary screening tool to reduce workload and improve efficiency. The software and data sets are available on GitHub: https://github.com/ZBYnb/Glypred.

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.

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.

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.

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.