Combination of near-infrared spectroscopy with Wasserstein generative adversarial networks for rapidly detecting raw material quality for formula products.

Near-infrared spectroscopy (NIRS) has emerged as a key technique for rapid quality detection owing to its fast, non-destructive, and eco-friendly characteristics. However, its practical implementation within the formulation industry is challenging owing to insufficient data, which renders model fitting difficult. The complexity of acquiring spectra and spectral reference values results in limited spectral data, aggravating the problem of low generalization, which diminishes model performance. To address this problem, we introduce what we believe to be a novel approach combining NIRS with Wasserstein generative adversarial networks (WGANs). Specifically, spectral data are collected from representative samples of raw material provided by a formula enterprise. Then, the WGAN augments the database by generating synthetic data resembling the raw spectral data. Finally, we establish various prediction models using the PLSR, SVR, LightGBM, and XGBoost algorithms. Experimental results show the NIRS-WGAN method significantly improves the performance of prediction models, with R2 and RMSE of 0.949 and 1.415 for the chemical components of sugar, respectively, and 0.922 and 0.243 for nicotine. The proposed framework effectively enhances the predictive capabilities of various models, addressing the issue caused by limited training data in NIRS prediction tasks.

Factors associated with burnout among frontline nurses in the post-COVID-19 epidemic era: a multicenter cross-sectional study.

BACKGROUND: The COVID-19 pandemic has significantly increased the risk of burnout among frontline nurses. However, the prevalence of burnout and its associated factors in the post-pandemic era remain unclear. This research aims to investigate burnout prevalence among frontline nurses in the post-pandemic period and pinpoint associated determinants in China. METHODS: From April to July 2023, a cross-sectional study was carried out across multiple centers, focusing on frontline nurses who had been actively involved in the COVID-19 pandemic. The data collection was done via an online platform. The Maslach Burnout Inventory-Human Services Survey was utilized to evaluate symptoms of burnout. A multivariable logistic regression analysis was used to pinpoint factors associated with burnout. RESULTS: Of the 2210 frontline nurses who participated, 75.38% scored over the cut-off for burnout. Multivariable logistic regression revealed that factors like being female [odds ratio (OR) = 0.41, 95%CI = 0.29-0.58] and exercising 1-2 times weekly[OR = 0.53, 95%CI = 0.42-0.67] were protective factors against burnout. Conversely, having 10 or more night shifts per month[OR = 1.99, 95%CI = 1.39-2.84], holding a master's degree or higher[OR = 2.86, 95% CI = 1.59-5.15], poor health status[OR = 2.43, 95% CI = 1.93-3.08] and [OR = 2.82, 95%CI = 1.80-4.43], under virus infection[OR = 7.12, 95%CI = 2.10-24.17], and elevated work-related stress[OR = 1.53, 95% CI = 1.17-2.00] were all associated with an elevated risk of burnout. CONCLUSION: Our findings indicate that post-pandemic burnout among frontline nurses is influenced by several factors, including gender, monthly night shift frequency, academic qualifications, weekly exercise frequency, health condition, and viral infection history. These insights can inform interventions aimed at safeguarding the mental well-being of frontline nurses in the post-pandemic period.

Integrative analysis of rs717620 polymorphism in therapeutic response to anti-seizure medications.

Background: Previous studies have shown that the rs717620 polymorphism in ABCC2, the gene encoding multidrug resistance protein 2, influences the therapeutic response to anti-seizure medications (ASMs). However, this result is not consistent, and the mechanism by which rs717620 influences ASM responses is unclear. Aims: The present study evaluated the association between rs717620 genotype and ASM efficacy, and examined the potential mechanisms. Main: methods: We conducted a literature search of five electronic databases, Embase, Medline, Web of Science, China National Knowledge Infrastructure, and Wanfang, to identify relevant studies on response to ASM therapy among rs717620 genotypes. Expression quantitative trait loci analysis and drug-gene interaction analysis were also performed to assess the underlying mechanisms. Key findings: The pooled results for 18 studies revealed a significant association between rs717620 genotype and ASM resistance under the recessive model (TT vs. CT + CC: OR = 1.68, 95 % CI = 1.27-2.21, I2 = 3.1 %). A significant association was also found in the Asian population under the recessive model (TT vs. CT + CC: OR = 1.70, 95 % CI = 1.26-2.29, I2 = 29.3 %). Further analysis revealed that rs717620 regulates the expression of ABCC2 in human brain, while drug-gene interaction analysis suggested that ABCC2 interacts with oxcarbazepine and carbamazepine. Significance: The rs717620 polymorphism influences ASM therapeutic responses by altering brain expression levels of ABCC2.

Identification of potential crucial genes and therapeutic targets for epilepsy.

BACKGROUND: Epilepsy, a central neurological disorder, has a complex genetic architecture. There is some evidence suggesting that genetic factors play a role in both the occurrence of epilepsy and its treatment. However, the genetic determinants of epilepsy are largely unknown. This study aimed to identify potential therapeutic targets for epilepsy. METHODS: Differentially expressed genes (DEGs) were extracted from the expression profiles of GSE44031 and GSE1834. Gene co-expression analysis was used to confirm the regulatory relationship between newly discovered epilepsy candidate genes and known epilepsy genes. Expression quantitative trait loci analysis was conducted to determine if epilepsy risk single-nucleotide polymorphisms regulate DEGs' expression in human brain tissue. Finally, protein-protein interaction analysis and drug-gene interaction analysis were performed to assess the role of DEGs in epilepsy treatment. RESULTS: The study found that the protein tyrosine phosphatase receptor-type O gene (PTPRO) and the growth arrest and DNA damage inducible alpha gene (GADD45A) were significantly upregulated in epileptic rats compared to controls in both datasets. Gene co-expression analysis revealed that PTPRO was co-expressed with RBP4, NDN, PAK3, FOXG1, IDS, and IDS, and GADD45A was co-expressed with LRRK2 in human brain tissue. Expression quantitative trait loci analysis suggested that epilepsy risk single-nucleotide polymorphisms could be responsible for the altered PTPRO and GADD45A expression in human brain tissue. Moreover, the protein encoded by GADD45A had a direct interaction with approved antiepileptic drug targets, and GADD45A interacts with genistein and cisplatin. CONCLUSIONS: The results of this study highlight PTPRO and GADD45A as potential genes for the diagnosis and treatment of epilepsy.

Anxiety prevalence and associated factors among frontline nurses following the COVID-19 pandemic: a large-scale cross-sectional study.

Introduction: Nurses are more likely to experience anxiety following the coronavirus 2019 epidemic. Anxiety could compromise nurses' work efficiency and diminish their professional commitment. This study aims to investigate nurses' anxiety prevalence and related factors following the pandemic in multiple hospitals across China. Methods: An online survey was conducted from April 16 to July 3, 2023, targeting frontline nurses who had actively participated in China. Anxiety and depression symptoms were assessed using the Self-rating Anxiety Scale and the Self-rating Depression Scale (SDS), respectively. Multivariable logistic regression analysis was employed to identify factors linked with anxiety. Results: A total of 2,210 frontline nurses participated in the study. Overall, 65.07% of participants displayed clinically significant anxiety symptoms. Multivariable logistic regression revealed that nurses living with their families [2.52(95% CI: 1.68-3.77)] and those with higher SDS scores [1.26(95% CI: 1.24-1.29)] faced an elevated risk of anxiety. Conversely, female nurses [0.02(95% CI: 0.00-0.90)] and those who had recovered from infection [0.05(95%CI: 0.07-0.18)] demonstrated lower rates of anxiety. Discussion: This study highlights the association between SDS score, gender, virus infection, living arrangements and anxiety. Frontline nurses need to be provided with emotional support to prevent anxiety. These insights can guide interventions to protect the mental well-being of frontline nurses in the post-pandemic period.

Comprehensive analyses identify potential biomarkers for encephalitis in HIV infection.

Human immunodeficiency virus encephalitis (HIVE) is a severe neurological complication after HIV infection. Evidence shows that genetic factors play an important role in HIVE. The aim of the present study was to identify new potential therapeutic targets for HIVE. Differentially expressed gene (DEG), functional annotation and pathway, and protein-protein interaction analyses were performed to identify the hub genes associated with HIVE. Gene co-expression analysis was carried out to confirm the association between the hub genes and HIVE. Finally, the role of the hub genes in HIVE therapy was evaluated by conducting drug-gene interaction analysis. A total of 20 overlapping DEGs closely related to HIVE were identified. Functional annotation and pathway enrichment analysis indicated that the markedly enriched DEG terms included ion transport, type II interferon signaling, and synaptic signaling. Moreover, protein-protein interaction analysis revealed that 10 key HIVE-related genes were hub genes, including SCN8A, CDK5R2, GRM5, SCN2B, IFI44L, STAT1, SLC17A7, ISG15, FGF12, and FGF13. Furthermore, six hub genes were co-expressed with HIVE-associated host genes in human brain tissue. Finally, three hub genes (STAT1, ISG15, and SCN2B) interacted with several inflammation-associated drugs. These findings suggested that SCN8A, CDK5R2, GRM5, SCN2B, IFI44L, STAT1, SLC17A7, ISG15, FGF12, and FGF13 may be new targets for diagnosis and therapy of HIVE.

Intelligent large-scale flue-cured tobacco grading based on deep densely convolutional network.

Flue-cured tobacco grading plays a crucial role in tobacco leaf purchase and the formulation of tobacco leaf groups. However, the traditional flue-cured tobacco grading mode is usually manual, which is time-consuming, laborious, and subjective. Hence, it is essential to research more efficient and intelligent flue-cured tobacco grading methods. Most existing methods suffer from the more classes less accuracy problem. Meanwhile, limited by different industry applications, the flue-cured tobacco datasets are hard to be obtained publicly. The existing methods employ relatively small and lower resolution tobacco data that are hard to apply in practice. Therefore, aiming at the insufficiency of feature extraction ability and the inadaptability to multiple flue-cured tobacco grades, we collected the largest and highest resolution dataset and proposed an efficient flue-cured tobacco grading method based on deep densely convolutional network (DenseNet). Diverging from other approaches, our method has a unique connectivity pattern of convolutional neural network that concatenates preceding tobacco feature data. This mode connects all previous layers to the subsequent layer directly for tobacco feature transmission. This idea can better extract depth tobacco image information features and transmit each layer's data, thereby reducing the information loss and encouraging tobacco feature reuse. Then, we designed the whole data pre-processing process and experimented with traditional and deep learning algorithms to verify our dataset usability. The experimental results showed that DenseNet could be easily adapted by changing the output of the fully connected layers. With an accuracy of 0.997, significantly higher than the other intelligent tobacco grading methods, DenseNet came to the best model for solving our flue-cured tobacco grading problem.

Preparation of gastrodin-modified dendrimer-entrapped gold nanoparticles as a drug delivery system for cerebral ischemia-reperfusion injury.

OBJECTIVE: This study sought to evaluate the feasibility of multifunctional gastrodin (GAS)-containing nano-drug carrier system against cerebral ischemia-reperfusion injury (CIRI). METHODS: The drug-loaded nanocomposite (Au-G5.NHAc-PS/GAS) with certain encapsulation efficiency (EE) was prepared by physical adsorption method using different proportions of GAS and drug-carrying system (Au-G5.NHAc-PS). High-performance liquid chromatography was used to determine the drug loading and EE. Cultured rat astrocytes and hypothalamic neurons were assigned into four groups: PBS, Au-G5.NHAc-PS, Au-G5.NHAc-PS/GAS, and GAS. CCK-8 assay, flow cytometry, and quantitative real-time PCR were performed to examine the cell viability, apoptosis, and the expression of tumor necrosis factor-α (TNF-α), IL-1ß, and IL-6 in the astrocytes and hypothalamic neurons, respectively. Cellular uptake of GAS and Au-G5.NHAc-PS/GAS was analyzed by using Hoechst 33342 staining. The animal model with focal cerebral ischemia was generated by middle cerebral artery occlusion (MCAO) in healthy male Sprague Dawley (SD) rats, and pathological changes of brain tissue and major organs in the rats were identified by hematoxylin and eosin (HE) staining. Apoptosis in rat astrocytes and hypothalamic neurons was detected by TUNEL staining and flow cytometry. RESULTS: Au-G5.NHAc-PS had a spherical shape with a uniform size of 157.3 nm. Among the nanoparticles, Au-G5.NHAc-PS/GAS with an EE of 70.3% displayed the best release delay effect. Moreover, we observed that in vitro cytotoxicity and cellular uptake of Au-G5.NHAc-PS/GAS were higher than those of GAS, whereas the expression of TNF-α, IL-1ß, and IL-6 was significantly downregulated in Au-G5.NHAc-PS/GAS group as compared to G5.NHAc-PS group. Notably, HE staining revealed that although Au-G5.NHAc-PS/GAS had no toxic and side effects on the main organs of rats, it alleviated the damage of brain tissue in the MCAO rats. Besides, Au-G5.NHAc/GAS markedly reduced MCAO-induced apoptosis. CONCLUSION: Au-G5.NHAc-PS showed favorable surface morphology, sustained drug release ability, no measurable toxicity, and good biocompatibility, indicating that GAS exerts anti-inflammatory and antiapoptotic effects on CIRI.

Integrative Analyses Identify KCNJ15 as a Candidate Gene in Patients with Epilepsy.

INTRODUCTION: Although there is accumulating evidence that genetic factors play a vital role in the pathogenesis of epilepsy, few epilepsy-associated genes have been identified. Additionally, the role of KCNJ15 in epilepsy has not been evaluated so far. METHODS: Here, we performed differentially expressed gene analysis, expression quantitative trait loci analysis, gene co-expression analysis, and protein-protein interaction analysis to evaluate the role of KCNJ15 in epilepsy. RESULTS: Analysis of gene expression and expression quantitative trait loci data revealed that KCNJ15 was significantly downregulated in patients with epilepsy (adjusted P = 0.0146 and log2 Fold change = - 1.0025), and an epilepsy-associated polymorphism (rs2833098) was linked to altered KCNJ15 expression level in human temporal lobe brain tissue (P = 0.0036). Gene co-expression analysis revealed that KCNJ15 was co-expressed with genes that have been reported to be associated with epilepsy in human brain tissue. Furthermore, protein-protein interaction analysis revealed strong supportive evidence for the role of KCNJ15 in epilepsy. CONCLUSION: Our results show that KCNJ15 may be a candidate target for epilepsy. Functional analysis of KCNJ15 may provide novel insights for epilepsy treatment.

Electroacupuncture promotes the recovery of rats with spinal cord injury by suppressing the Notch signaling pathway via the H19/EZH2 axis.

BACKGROUND: Spinal cord injury (SCI) is a life-changing event with an extremely poor prognosis. In our preliminary studies, electroacupuncture (EA) was found to promote the repair of SCI, which was closely related to the Notch signaling pathway. Therefore, in the present study, we hypothesized that EA protects against SCI by inhibiting the Notch signaling pathway and sought to investigate the underlying molecular mechanisms. METHODS: Rat and cell models of SCI were established. The expression of long non-coding RNA H19 was measured by real-time quantitative polymerase chain reaction. The expression levels of EZH2, Notch1, Notch3, Notch4, Hes1, and PS1 protein were measured by western blot. Cell apoptosis and viability were analyzed using flow cytometry and Cell Counting Kit-8 assays, respectively. The expressions of glial fibrillary acidic protein (GFAP) and nestin were detected by immunofluorescence staining. RESULTS: The expressions of H19, EZH2, and GFAP were significantly increased after SCI but were inhibited by EA; in contrast, nestin expression was significantly decreased by SCI but was restored by EA. Moreover, oxygen-glucose deprivation (OGD) treatment elevated the expression of H19, EZH2, and Notch-related factors as well as apoptosis in PC-12 cells, while suppressing cell viability. Suppressing H19 alleviated the effects of OGD on cell viability and apoptosis, and inhibited the expression of EZH2 and Notch-related factors expression; these effects were reversed by EZH2 overexpression. Finally, EA promoted the recovery of SCI rats and neural stem cell (NSC) proliferation by inhibiting the Notch signaling pathway, which was reversed by H19 overexpression. CONCLUSIONS: Our results demonstrated that EA promotes the recovery of SCI rats and increases the proliferation and differentiation of NSCs by suppressing the Notch signaling pathway via modulating the H19/EZH2 axis.

Low-Cost, Accurate, Effective Treatment of Hypertensive Cerebral Hemorrhage With Three-Dimensional Printing Technology.

Background: Hypertensive intracerebral hemorrhage (HICH) is an acute, severe neurosurgical disease. Puncture drainage of the hematoma has gradually been accepted as a surgical treatment for HICH because of its minimally invasive nature. The precision of the puncture is extremely high because of particular physiological functions. This study was performed to explore the effect of a navigation mold created by three-dimensional printing (3DP) technology in the surgical treatment of HICH. Material and methods: We conducted a retrospective analysis of all consecutive patients with ICH treated with minimally invasive surgery using 3DP navigation or craniotomy to remove the hematoma through a small bone window at the Binzhou Medical University Hospital from June 2017 to March 2019. In total, 61 patients were treated with minimally invasive surgery using 3DP navigation (3DP group), and 67 patients were treated with craniotomy to remove the hematoma through a small bone window (craniotomy group). A comparative study of the two groups was conducted to assess the preoperative and postoperative conditions. Results: The duration of the surgery was significantly longer in the craniotomy group than in the 3DP group (3.27 ± 1.14 h vs. 1.52 ± 0.23 h). Postoperative complication rates were significantly lower in the 3DP group than in the craniotomy group (18.0 vs. 34.3%). Moreover, the rate of patients with a Glasgow Outcome Scale score ≥4 points was not statistically significantly different in the two groups. Conclusion: Minimally invasive surgery assisted by 3DP navigation to treat patients with HICH appears to be safe and effective. The 3DP technique may improve the individualization and accuracy of the surgery.

CircRNAs as potential biomarkers for the clinicopathology and prognosis of glioma patients: a meta-analysis.

BACKGROUND: An increasing number of studies have reported circular RNAs (circRNAs) as new potential biomarkers for the prognosis of gliomas. However, the overall prognostic value of circRNAs for glioma remains unclear. Therefore, this study is the first comprehensive evaluation of the clinicopathological and prognostic value of dysregulated circRNAs in the treatment of glioma patients. METHODS: We systematically reviewed the online databases of PubMed, Web of Science, EMBASE, and Cochrane Library to identify studies that explored the relationship between circRNA expression and clinicopathological and prognostic factors in glioma through April 11, 2020. The quality of the included studies was evaluated by the Newcastle-Ottawa Scale (NOS) checklists. Clinicopathological features were assessed by pooled odds ratios (ORs) and 95% confidence intervals (CIs), and overall survival (OS) was assessed by hazard ratios (HRs) and 95% CIs. RESULTS: Twenty-four eligible studies, including 22 studies of clinicopathological features, 1 diagnostic study, and 18 studies of prognosis, that included a total of 1390 patients were ultimately included in this study. Meta-analysis showed that highly expressed oncogenic circRNAs were significantly related to poor clinicopathological features (age: P = 0.026; tumor size: P ≤ 0.001; tumor grade: P ≤ 0.001; KPS: P = 0.012) and worse overall survival (OS) (HR = 2.01, 95% CI: 1.61-2.50, P ≤ 0.001). Moreover, we found that highly expressed tumor-suppressor circRNAs were related to better clinicopathological features (gender: P = 0.042; age: P = 0.014; tumor size: P = 0.022; tumor grade: P ≤ 0.001) and longer OS (HR = 2.70, 95% CI: 1.82-3.99, P ≤ 0.001). CONCLUSIONS: In conclusion, there is a significant correlation between the dysregulated expression of circRNAs and the clinicopathology and prognosis of glioma patients.

Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks.

The origin of tobacco is the most important factor in determining the style characteristics and intrinsic quality of tobacco. There are many applications for the identification of tobacco origin by near-infrared spectroscopy. In order to improve the accuracy of the tobacco origin classification, a near-infrared spectrum (NIRS) identification method based on multimodal convolutional neural networks (CNN) was proposed, taking advantage of the strong feature extraction ability of the CNN. Firstly, the one-dimensional convolutional neural network (1-D CNN) is used to extract and combine the pattern features of one-dimensional NIRS data, and then the extracted features are used for classification. Secondly, the one-dimensional NIRS data are converted into two-dimensional spectral images, and the structure features are extracted from two-dimensional spectral images by the two-dimensional convolutional neural network (2-D CNN) method. The classification is performed by the combination of global and local training features. Finally, the influences of different network structure parameters on model identification performance are studied, and the optimal CNN models are selected and compared. The multimodal NIR-CNN identification models of tobacco origin were established by using NIRS of 5,200 tobacco samples from 10 major tobacco producing provinces in China and 3 foreign countries. The classification accuracy of 1-D CNN and 2-D CNN models was 93.15% and 93.05%, respectively, which was better than the traditional PLS-DA method. The experimental results show that the application of 1-D CNN and 2-D CNN can accurately and reliably distinguish the NIRS data, and it can be developed into a new rapid identification method of tobacco origin, which has an important promotion value.

A Sensitive and Selective Fluorescent Coumarin-Based Probe for Detection of Hypochlorite ion and its Application to Cellular Imaging.

Selective, sensitive fluorescent probes for ClO(-) are desirable due to the importance of ClO(-) in biological processes. Herein, a readily available turn-off fluorescent probe for ClO(-) is reported, which displays highly selectivity and sensitivity over other common anions and reactive oxygen/nitrogen. Moreover, it is able to detect ClO(-) in Ramos cells via cellular imaging.

[Study on an Algorithm for Near Infrared Singular Sample Identification Based on Strong Influence Degree].

Correcting sample selection and elimination of singular sample is very important for the quantitative and qualitative modeling of near infrared spectroscopy. However, methods for identification of singular sample available are generally based on data center estimates which require an experience decision threshold, this largely limit its recognition accuracy and practicability. Aiming at the low accuracy of the existing methods of singular sample recognition problem, this paper improves the existing metric-Leverage value and presents a new algorithm for near infrared singular sample identification based on strong influence degree. This metric reduces the dependence on the data center to a certain extent, so that the normal samples become more aggregation, and the distance between the singular samples and the normal samples is opened; at the same time, in order to avoid artificial setting threshold unreasonably according to experience, this paper introduces the concept of the jump degree in the field of statistics, and proposes an automatic threshold setting method to distinguish singular samples. In order to verify the validity of our algorithm, abnormal samples of 200 representative samples were eliminated in the calibration set with using Mahalanobis distance, Leverage-Spectral residual method and the algorithm presented in this paper respectively; then through partial least squares (PLS), the rest of the calibration samples were made quantitative modelings (took Nicotine as index), and the results of quantitative modelings were made a comparative analysis; besides, 60 representative testing samples were made a prediction through the modelings; at last, all the algorithms above were made a comparison with took Root Mean Square Error of Cross Validation (RMSECV), Correlation Coefficient (r) and Root Mean Square Error of Prediction (RMSEP) as evaluation Index. The experimental results demonstrate that the algorithm for near infrared singular sample identification based on strong influence degree significantly improves the accuracy of singular sample identificition over existing methods. With lower RMSECV (0.104), RMSEP (0.112) and higher r (0.983), it also contribute to boost the stability and prediction ability of the model.