Exploring the challenges of taiwanese nurses in the COVID-19 post-pandemic era.

In the wake of the COVID-19 pandemic, the fluctuating nurse resignation rates highlighted an understudied area in healthcare: post-pandemic challenges in clinical settings. This study, conducted from May to November 2023, employed a qualitative inquiry using focus groups to delve into these challenges. Six focus group sessions, involving 33 nurse participants recruited through snowball sampling from various hospital settings were conducted to explore their clinical experiences during and after the pandemic. Thematic analysis revealed two primary themes: the 'Invisibility of Nurses' within the healthcare system and the 'Moral Duty of Nursing Practice'. These findings illuminate a tension between the overlooked role of nurses and their ethical obligations, underscoring a critical need for policy reassessment. The study advocates for systemic changes, particularly in the undervaluation of the nursing profession and the National Health Insurance system, to address the poor working environment and mitigate long-term nursing shortages. This research deepens understanding of post-pandemic nursing workforce challenges in Taiwan, highlighting the need for policy evolution to enhance recognition and support for the nursing industry. It is suggested to provide tangible compensation to acknowledge nurses' daily care and health education for patients. A healthier working environment can be enhanced by collaborative efforts between healthcare institutions and nurses.

NLRC5 modulates phenotypic transition and inflammation of human venous smooth muscle cells by activating Wnt/ß-catenin pathway via TLR4 in varicose veins.

In varicose veins, abnormal phenotypic transition and inflammatory response is commonly found in venous smooth muscle cells (VSMCs). We aimed to explore the potential role and mechanism of NLRC5 exerted on VSMCs phenotypic transition and inflammation. NLRC5 expression was detected in varicose veins and platelet-derived growth factor (PDGF)-induced VSMCs by RT-qPCR and Western bolt assays. A loss-of-function assay was performed to evaluate the effects of NLRC5 knockdown on VSMC proliferation, migration, and phenotypic transition. ELISA was used to detect the contents of pro-inflammatory cytokines in the supernatant. The modulation of NLRC5 on TLR4 expression and Wnt/ß-catenin signaling was also evaluated. We found that the expressions of NLRC5 in varicose veins and PDGF-induced VSMCs were upregulated. NLRC5 knockdown inhibited VSMC proliferation and migration. Extracellular matrix transformation was blocked by downregulating NLRC5 with increasing SM-22α expression and MMP-1/TIMP-1 ratio, as well as decreasing OPN and collagen I expressions. Besides, NLRC5 silencing reduced the contents of inflammatory cytokines. Furthermore, we found that NLRC5 regulated TLR4 expression, as well as subsequently activation of Wnt/ß-catenin pathway and nuclear translocation of ß-catenin, which was involved in NLRC5-mediated phenotypic transition and inflammatory in VSMCs. In conclusion, silencing NLRC5 depressed VSMCs' phenotypic transition and inflammation by modulating Wnt/ß-catenin pathway via TLR4. This may provide a theoretical basis for treatment of varicose veins.

Lgr5 regulates the regeneration of lesioned nasal respiratory epithelium.

Nasal respiratory epithelium is a ciliated pseudostratified columnar epithelium. The cellular components of nasal respiratory epithelium include ciliated cells, goblet cells, and basal cells. Until now, our knowledge in the development of nasal respiratory epithelium is still limited and the cellular mechanism of regeneration is still elusive. In this study, we found that adult stem cell marker leucine-rich repeat-containing G-protein coupled receptor 5 (Lgr5) is expressed in the mice nasal respiratory epithelium. Both immunostaining and lineage tracing analysis indicated Lgr5 positive cells in the nasal respiratory epithelium are proliferative stem/progenitor cells. Using the Rosa-Tdtomato and Rosa26-DTR mice, we elucidated that Lgr5+ cells participate in the regeneration of lesioned nasal respiratory epithelium, and this group of cells is necessary in the process of epithelium recovery. Using the in vitro culture system, we observed the formation of spheres from Lgr5+ cells and these spheres have the capacity to generate other types of cells. Above all, this study reported a group of previously unidentified progenitor/stem cells in nasal respiratory epithelium, unveiling the potential cellular mechanism in nasal respiratory epithelium regeneration.

3-Bromopyruvate inhibits cell proliferation and induces apoptosis in CD133+ population in human glioma.

The study was aimed to investigate the role of 3-bromopyruvate in inhibition of CD133+ U87 human glioma cell population growth. The results demonstrated that 3-bromopyruvate inhibited the viability of both CD133+ and parental cells derived from U87 human glioma cell line. However, the 3-bromopyruvate-induced inhibition in viability was more prominent in CD133+ cells at 10 µM concentration after 48 h. Treatment of CD133+ cells with 3-bromopyruvate caused reduction in cell population and cell size, membrane bubbling, and degradation of cell membranes. Hoechst 33258 staining showed condensation of chromatin material and fragmentation of DNA in treated CD133+ cells after 48 h. 3-Bromopyruvate inhibited the migration rate of CD133+ cells significantly compared to the parental cells. Flow cytometry revealed that exposure of CD133+ cells to 3-bromopyruvate increased the cell population in S phase from 24.5 to 37.9 % with increase in time from 12 to 48 h. In addition, 3-bromopyruvate significantly enhanced the expression of Bax and cleaved caspase 3 in CD133+ cells compared to the parental cells. Therefore, 3-bromopyruvate is a potent chemotherapeutic agent for the treatment of glioma by targeting stem cells selectively.

A simulated MS/MS library for spectrum-to-spectrum searching in large scale identification of proteins.

Identifying peptides from mass spectrometric fragmentation data (MS/MS spectra) using search strategies that map protein sequences to spectra is computationally expensive. An alternative strategy uses direct spectrum-to-spectrum matching against a reference library of previously observed MS/MS that has the advantage of evaluating matches using fragment ion intensities and other ion types than the simple set normally used. However, this approach is limited by the small sizes of the available peptide MS/MS libraries and the inability to evaluate the rate of false assignments. In this study, we observed good performance of simulated spectra generated by the kinetic model implemented in MassAnalyzer (Zhang, Z. (2004) Prediction of low-energy collision-induced dissociation spectra of peptides. Anal. Chem. 76, 3908-3922; Zhang, Z. (2005) Prediction of low-energy collision-induced dissociation spectra of peptides with three or more charges. Anal. Chem. 77, 6364-6373) as a substitute for the reference libraries used by the spectrum-to-spectrum search programs X!Hunter and BiblioSpec and similar results in comparison with the spectrum-to-sequence program Mascot. We also demonstrate the use of simulated spectra for searching against decoy sequences to estimate false discovery rates. Although we found lower score discrimination with spectrum-to-spectrum searches than with Mascot, particularly for higher charge forms, comparable peptide assignments with low false discovery rate were achieved by examining consensus between X!Hunter and Mascot, filtering results by mass accuracy, and ignoring score thresholds. Protein identification results are comparable to those achieved when evaluating consensus between Sequest and Mascot. Run times with large scale data sets using X!Hunter with the simulated spectral library are 7 times faster than Mascot and 80 times faster than Sequest with the human International Protein Index (IPI) database. We conclude that simulated spectral libraries greatly expand the search space available for spectrum-to-spectrum searching while enabling principled analyses and that the approach can be used in consensus strategies for large scale studies while reducing search times.

Bis(µ-2-{[oxido(phenyl)methylidene]hydrazinylidene}propanoato)bis[dibenzyl(ethanol)tin(IV)].

In the title complex, [Sn(2)(C(7)H(7))(4)(C(10)H(8)N(2)O(3))(2)(C(2)H(5)OH)(2)], the Sn(IV) atom is seven-coordinated in a distorted penta-gonal-bipyramidal geometry by three O atoms and one N atom from the pyruvate benzoyl hydrazone ligand, one ethanol O atom and two axial C atoms from trans-benzyl groups, thus forming a dimeric mol-ecule ([Formula: see text] symmetry) via weak Sn-O inter-actions. The C atoms of one phenyl ring and the ethanol mol-ecule are disordered over two sets of sites with site-occupancy factors of 0.57 (5):0.43 (5) and 0.79 (2):0.21 (2), respectively. Intermolecular O-H⋯O hydrogen bonds stabilize the crystal structure.

MIR100HG: a credible prognostic biomarker and an oncogenic lncRNA in gastric cancer.

The MIR100HG expression was observed to be up-regulated or down-regulated in human cancer tissues depending on tumor types. However, there was no report about the role of MIR100HG in gastric cancer. In our study, we first found levels of MIR100HG expression were increased in gastric cancer cell lines and tissue samples compared with normal gastric epithelial cell line and adjacent normal gastric mucosa tissue samples, respectively. Moreover, high MIR100HG expression was positively associated with clinical stage, tumor invasion, lymph node metastasis, and distant metastasis in gastric cancer patients. Survival analysis showed MIR100HG expression was negative correlated with clinical outcome in gastric cancer patients from The Cancer Genome Atlas (TCGA) database or our study, and high MIR100HG expression served as an independent poor prognostic factor for gastric cancer patient's overall survival. The study in vitro suggested down-regulation of MIR100HG expression inhibits cell proliferation, migration, and invasion in gastric cancer. In conclusion, MIR100HG is a credible prognostic biomarker and functions as an oncogenic lncRNA in gastric cancer.

Improved machine learning method for analysis of gas phase chemistry of peptides.

BACKGROUND: Accurate peptide identification is important to high-throughput proteomics analyses that use mass spectrometry. Search programs compare fragmentation spectra (MS/MS) of peptides from complex digests with theoretically derived spectra from a database of protein sequences. Improved discrimination is achieved with theoretical spectra that are based on simulating gas phase chemistry of the peptides, but the limited understanding of those processes affects the accuracy of predictions from theoretical spectra. RESULTS: We employed a robust data mining strategy using new feature annotation functions of MAE software, which revealed under-prediction of the frequency of occurrence in fragmentation of the second peptide bond. We applied methods of exploratory data analysis to pre-process the information in the MS/MS spectra, including data normalization and attribute selection, to reduce the attributes to a smaller, less correlated set for machine learning studies. We then compared our rule building machine learning program, DataSqueezer, with commonly used association rules and decision tree algorithms. All used machine learning algorithms produced similar results that were consistent with expected properties for a second gas phase mechanism at the second peptide bond. CONCLUSION: The results provide compelling evidence that we have identified underlying chemical properties in the data that suggest the existence of an additional gas phase mechanism for the second peptide bond. Thus, the methods described in this study provide a valuable approach for analyses of this kind in the future.

MiR-422a targets MAPKK6 and regulates cell growth and apoptosis in colorectal cancer cells.

The important role of miR-422a in tumor has been reported in several studies. Recent research discovered that the expression of miR-422a was significantly decreased in colorectal cancer tissues, providing miR-422a as a tumor suppressor in CRC. However, the concrete mechanism of miR-422a regulating CRC cell is still unclear. In this study, we demonstrated that miR-422a could inhibit CRC cell growth and promote cell apoptosis via in vitro analyses. Moreover, computational methods were adopted to identify the targets of miR-422a. We found MAPKK6 was the direct target of miR-422a. Consequently, we further elucidated that miR-422a inhibited CRC cell growth and induced cell apoptosis by inhibiting p38/MAPK pathway. Besides that, we established the tumor xenograft model using nude mice and the inhibitory effects on tumor volumes and weights by miR-422a mimic transfection were also detected. Taken together, these findings demonstrated miR-422a exerted anti-cancer activities on CRC, which could be potentially used for CRC prognosis prediction and treatment.

Elevated Serum Brain-Derived Neurotrophic Factor (BDNF) but not BDNF Gene Val66Met Polymorphism Is Associated with Autism Spectrum Disorders.

The aim of our study was to illuminate the potential role of brain-derived neurotrophic factor (BDNF) in autism spectrum disorder (ASD). We measured the circulating levels of BDNF in serum and BDNF gene (Val66Met) polymorphisms, in which two indicators were then compared between ASD and normal controls. A total of 82 drug-naïve ASD children and 82 age- and gender-matched normal controls were enrolled in the study. Their serum BDNF levels were detected by the ELISA. BDNF Val66Met polymorphism genotyping was conducted as according to the laboratory's standard protocol in laboratory. The ASD severity assessment was mainly determined by the score of the Childhood Autism Rating Scale (CARS). ELISA assay showed that the mean serum BDNF level of children with ASD was significantly (P < 0.0001) higher than that of the control cases (17.75 ± 5.43 vs. 11.49 ± 2.85 ng/ml; t = 9.236). Besides, the serum BDNF levels and CARS scores (P < 0.0001) were positively related. And, the BDNF genotyping results showed that there was no difference between the ASD cases and the control. Among the children with ASD, the mean serum BDNF level of Met/Met group was lower than other groups. According to the ROC curve generated from our clinical data, the optimal cutoff value of serum BDNF levels, an indicator for diagnosis of ASD, was projected to be 12.50 ng/ml. Thus, it yielded a corresponding sensitivity of 81.7 % and the specificity of 66.9 %. Accordingly, area value under the curve was 0.836 (95 % CI, 0.774-0.897); the positive predictive value (PPV) and the negative predictive value (NPV) were 70.1 and 79.1 %, respectively. These results suggested that rather than Val66Met polymorphism, BDNF was more possible to impact the pathogenesis of ASD.

MiR-595 targeting regulation of SOX7 expression promoted cell proliferation of human glioblastoma.

Increasing evidence indicated that dysregulation of microRNAs (miRNAs) were involved with human disease including cancer. Recently, miR-595 was reported as a tumor promoter in malignant mesothelioma. However, the underlying mechanism of miR-595 in human glioblastoma (GBM) cells have not been well elucidated. Therefore, in this study, we investigated the biological functions and molecular mechanisms of miR-595 in human GBM. MiR-595 expression was significantly upregulated in GBM tissues and cells. We modified miR-595 levels in GBM cells and investigated their effects on the cell proliferation by MTT, colony formation and anchorage-independent growth assays. We found that miR-595 significantly increased GBM cell proliferation. Bioinformatic analysis predicted that miR-595 may target the 3'-UTR of SOX7and suppressed its translation, and further confirmed by luciferase assay. In sum, these observations together indicated that miR-595 played a critical role in carcinogenesis by suppression of SOX7, and may serve as a therapeutic target for the treatment of GBM.

Exploration of CO2-Philicity of Poly(vinyl acetate-co-alkyl vinyl ether) through Molecular Modeling and Dissolution Behavior Measurement.

Hydrocarbon CO2-philes are of great interest for use in expanding CO2 applications as a green solvent. In this work, multiscale molecular modeling and dissolution behavior measurement were both applied to explore CO2-philicity of the poly(vinyl acetate) (PVAc)-based copolymer. Introduction of a favorable comonomer, i.e., vinyl ethyl ether (VEE), could significantly reduce the polymer-polymer interaction on the premise that the polymer-CO2 interaction was not weakened but enhanced. The ab initio calculated interaction of the model molecules with CO2 demonstrated that the ether group in VEE or VBE was the suitable CO2-philic segment. From the molecular dynamics (MD) simulations of polymer/CO2 systems, the interaction energy and Flory-Huggins parameter (χ12) of poly(VAc-alt-VEE)/CO2 supported that poly(VAc-alt-VEE) possessed better CO2-philicity than PVAc. The dissolution behaviors of the synthesized poly(VAc-co-alkyl vinyl ether) copolymers in CO2 showed the best CO2-phile had the VEE content of about 34 mol %. The MD simulations also indicated that the interaction of random poly(VAc-co-VEE) containing about 30 mol % VEE with CO2 was the strongest and the χ12 was the smallest in these polymer/CO2 systems. Not only could the VEE monomer reduce the polymer-polymer interaction, but it could also enhance the polymer-CO2 interaction with an optimized composition. Introducing a suitable comonomer with a certain composition might be a promising strategy to form the synergistic effect of polymer-polymer interaction and polymer-CO2 interaction for screening the hydrocarbon CO2-philes.

Evaluation of CO2-philicity of poly(vinyl acetate) and poly(vinyl acetate-alt-maleate) copolymers through molecular modeling and dissolution behavior measurement.

Multiscale molecular modeling and dissolution behavior measurement were both used to evaluate the factors conclusive on the CO2-philicity of poly(vinyl acetate) (PVAc) homopolymer and poly(vinyl acetate-alt-maleate) copolymers. The ab initio calculated interaction energies of the candidate CO2-philic molecule models with CO2, including vinyl acetate dimer (VAc), dimethyl maleate (DMM), diethyl maleate (DEM), and dibutyl maleate (DBM), showed that VAc was the most CO2-philc segment. However, the cohesive energy density, solubility parameter, Flory-Huggins parameter, and radial distribution functions calculated by using the molecular dynamics simulations for the four polymer and polymer-CO2 systems indicated that poly(VAc-alt-DBM) had the most CO2-philicity. The corresponding polymers were synthesized by using free radical polymerization. The measurement of cloud point pressures of the four polymers in CO2 also demonstrated that poly(VAc-alt-DBM) had the most CO2-philicity. Although copolymerization of maleate, such as DEM or DBM, with PVAc reduced the polymer-CO2 interactions, the weakened polymer-polymer interaction increased the CO2-philicity of the copolymers. The polymer-polymer interaction had a significant influence on the CO2-philicity of the polymer. Reduction of the polymer-polymer interaction might be a promising strategy to prepare the high CO2-philic polymers on the premise that the strong polymer-CO2 interaction could be maintained.

Improved validation of peptide MS/MS assignments using spectral intensity prediction.

A major limitation in identifying peptides from complex mixtures by shotgun proteomics is the ability of search programs to accurately assign peptide sequences using mass spectrometric fragmentation spectra (MS/MS spectra). Manual analysis is used to assess borderline identifications; however, it is error-prone and time-consuming, and criteria for acceptance or rejection are not well defined. Here we report a Manual Analysis Emulator (MAE) program that evaluates results from search programs by implementing two commonly used criteria: 1) consistency of fragment ion intensities with predicted gas phase chemistry and 2) whether a high proportion of the ion intensity (proportion of ion current (PIC)) in the MS/MS spectra can be derived from the peptide sequence. To evaluate chemical plausibility, MAE utilizes similarity (Sim) scoring against theoretical spectra simulated by MassAnalyzer software (Zhang, Z. (2004) Prediction of low-energy collision-induced dissociation spectra of peptides. Anal. Chem. 76, 3908-3922) using known gas phase chemical mechanisms. The results show that Sim scores provide significantly greater discrimination between correct and incorrect search results than achieved by Sequest XCorr scoring or Mascot Mowse scoring, allowing reliable automated validation of borderline cases. To evaluate PIC, MAE simplifies the DTA text files summarizing the MS/MS spectra and applies heuristic rules to classify the fragment ions. MAE output also provides data mining functions, which are illustrated by using PIC to identify spectral chimeras, where two or more peptide ions were sequenced together, as well as cases where fragmentation chemistry is not well predicted.

Improving sensitivity in shotgun proteomics using a peptide-centric database with reduced complexity: protease cleavage and SCX elution rules from data mining of MS/MS spectra.

Correct identification of a peptide sequence from MS/MS data is still a challenging research problem, particularly in proteomic analyses of higher eukaryotes where protein databases are large. The scoring methods of search programs often generate cases where incorrect peptide sequences score higher than correct peptide sequences (referred to as distraction). Because smaller databases yield less distraction and better discrimination between correct and incorrect assignments, we developed a method for editing a peptide-centric database (PC-DB) to remove unlikely sequences and strategies for enabling search programs to utilize this peptide database. Rules for unlikely missed cleavage and nontryptic proteolysis products were identified by data mining 11 849 high-confidence peptide assignments. We also evaluated ion exchange chromatographic behavior as an editing criterion to generate subset databases. When used to search a well-annotated test data set of MS/MS spectra, we found no loss of critical information using PC-DBs, validating the methods for generating and searching against the databases. On the other hand, improved confidence in peptide assignments was achieved for tryptic peptides, measured by changes in DeltaCN and RSP. Decreased distraction was also achieved, consistent with the 3-9-fold decrease in database size. Data mining identified a major class of common nonspecific proteolytic products corresponding to leucine aminopeptidase (LAP) cleavages. Large improvements in identifying LAP products were achieved using the PC-DB approach when compared with conventional searches against protein databases. These results demonstrate that peptide properties can be used to reduce database size, yielding improved accuracy and information capture due to reduced distraction, but with little loss of information compared to conventional protein database searches.