iDVEIP: A computer-aided approach for the prediction of viral entry inhibitory peptides.

With the notable surge in therapeutic peptide development, various peptides have emerged as potential agents against virus-induced diseases. Viral entry inhibitory peptides (VEIPs), a subset of antiviral peptides (AVPs), offer a promising avenue as entry inhibitors (EIs) with distinct advantages over chemical counterparts. Despite this, a comprehensive analytical platform for characterizing these peptides and their effectiveness in blocking viral entry remains lacking. In this study, we introduce a groundbreaking in silico approach that leverages bioinformatics analysis and machine learning to characterize and identify novel VEIPs. Cross-validation results demonstrate the efficacy of a model combining sequence-based features in predicting VEIPs with high accuracy, validated through independent testing. Additionally, an EI type model has been developed to distinguish peptides specifically acting as Eis from AVPs with alternative activities. Notably, we present iDVEIP, a web-based tool accessible at http://mer.hc.mmh.org.tw/iDVEIP/, designed for automatic analysis and prediction of VEIPs. Emphasizing its capabilities, the tool facilitates comprehensive analyses of peptide characteristics, providing detailed amino acid composition data for each prediction. Furthermore, we showcase the tool's utility in identifying EIs against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).

iDVIP: identification and characterization of viral integrase inhibitory peptides.

Antiretroviral peptides are a kind of bioactive peptides that present inhibitory activity against retroviruses through various mechanisms. Among them, viral integrase inhibitory peptides (VINIPs) are a class of antiretroviral peptides that have the ability to block the action of integrase proteins, which is essential for retroviral replication. As the number of experimentally verified bioactive peptides has increased significantly, the lack of in silico machine learning approaches can effectively predict the peptides with the integrase inhibitory activity. Here, we have developed the first prediction model for identifying the novel VINIPs using the sequence characteristics, and the hybrid feature set was considered to improve the predictive ability. The performance was evaluated by 5-fold cross-validation based on the training dataset, and the result indicates the proposed model is capable of predicting the VINIPs, with a sensitivity of 85.82%, a specificity of 88.81%, an accuracy of 88.37%, a balanced accuracy of 87.32% and a Matthews correlation coefficient value of 0.64. Most importantly, the model also consistently provides effective performance in independent testing. To sum up, we propose the first computational approach for identifying and characterizing the VINIPs, which can be considered novel antiretroviral therapy agents. Ultimately, to facilitate further research and development, iDVIP, an automatic computational tool that predicts the VINIPs has been developed, which is now freely available at http://mer.hc.mmh.org.tw/iDVIP/.

Anticancer peptide Q7 suppresses the growth and migration of human endometrial cancer by inhibiting DHCR24 expression and modulating the AKT-mediated pathway.

Endometrial cancer is one of the most common malignancy affecting women in developed countries. Resection uterus or lesion area is usually the first option for a simple and efficient therapy. Therefore, it is necessary to find a new therapeutic drug to reduce surgery areas to preserve fertility. Anticancer peptides (ACP) are bioactive amino acids with lower toxicity and higher specificity than chemical drugs. This study is to address an ACP, herein named Q7, which could downregulate 24-Dehydrocholesterol Reductase (DHCR24) to disrupt lipid rafts formation, and sequentially affect the AKT signal pathway of HEC-1-A cells to suppress their tumorigenicity such as proliferation and migration. Moreover, lipo-PEI-PEG-complex (LPPC) was used to enhance Q7 anticancer activity in vitro and efficiently show its effects on HEC-1-A cells. Furthermore, LPPC-Q7 exhibited a synergistic effect in combination with doxorubicin or paclitaxel. To summarize, Q7 was firstly proved to exhibit an anticancer effect on endometrial cancer cells and combined with LPPC efficiently improved the cytotoxicity of Q7.

Microbiome analysis of maternal and neonatal microbial communities associated with the different delivery modes based on 16S rRNA gene amplicon sequencing.

OBJECTIVE: With the rising number of cases of non-vaginal delivery worldwide, scientists have been concerned about the influence of the different delivery modes on maternal and neonatal microbiomes. Although the birth rate trend is decreasing rapidly in Taiwan, more than 30 percent of newborns are delivered by caesarean section every year. However, it remains unclear whether the different delivery modes could have a certain impact on the postpartum maternal microbiome and whether it affects the mother-to-newborn vertical transmission of bacteria at birth. MATERIALS AND METHODS: To address this, we recruited 30 mother-newborn pairs to participate in this study, including 23 pairs of vaginal delivery (VD) and seven pairs of caesarean section (CS). We here investigate the development of the maternal prenatal and postnatal microbiomes across multiple body habitats. Moreover, we also explore the early acquisition of neonatal gut microbiome through a vertical multi-body site microbiome analysis. RESULTS AND CONCLUSION: The results indicate that no matter the delivery mode, it only slightly affects the maternal microbiome in multiple body habitats from pregnancy to postpartum. On the other hand, about 95% of species in the meconium microbiome were derived from one of the maternal body habitats; notably, the infants born by caesarean section acquire bacterial communities resembling their mother's oral microbiome. Consequently, the delivery modes play a crucial role in the initial colonization of the neonatal gut microbiome, potentially impacting children's health and development.

Source Apportionment of Volatile Organic Compounds (VOCs) by Positive Matrix Factorization (PMF) supported by Model Simulation and Source Markers - Using Petrochemical Emissions as a Showcase.

This study demonstrates the use of positive matrix factorization (PMF) in a region with a major Petrochemical Complex, a prominent source of volatile organic compounds (VOCs), as a showcase of PMF applications. The PMF analysis fully exploited the quality and quantity of the observation data, sufficed by a cluster of 9 monitoring sites within a 20 km radius of the petro-complex. Each site provided continuous data of 54 speciated VOCs and meteorological variables. Wind characteristics were highly seasonal and played a decisive role in the source-receptor relationship, hence the dataset was divided into three sub-sets in accordance with the prevailing wind flows. A full year of real-time data were analyzed by PMF to resolve into various distinct source types including petrochemical, urban, evaporative, long-range air parcels, etc., with some sites receiving more petro-influence than others. To minimize subjectivity in the assignment of the PMF source factors, as commonly seen in some PMF works, this study attempted to solidify PMF results by supporting with two tools of spatially/temporally resolved air-quality model simulations and observation data. By exploiting the two supporting tools, the dynamic process of individual sources to a receptor were rationalized. Percent contributions from these sources to the receptor sites were calculated by summing over the occurrence of different source types. Interestingly, although the Petro-complex is the single largest local VOC source in the 20 km radius study domain, all monitoring sites in the region received far less influence from the Petro-complex than from other emission types within or outside the region, which together add up to more than 70% of the total VOC abundance.

A novel method for determination of low molecular weight dicarboxylic acids in background atmospheric aerosol using ion chromatography.

This paper describes a novel gradient elution ion chromatographic method using a Dionex AS11 system for the determination of low molecular weight dicarboxylic acids (low-M(w) DCAs) in background atmospheric aerosol. Interference with the oxalic acid peak from sulfate in background PM(2.5) aerosol, 15.8 times the oxalic acid concentration, was remedied by removing sulfate using a barium cartridge, whilst interference with the malonic acid peak from carbonate was reduced by using a carbonate removal device. An alternative remedy to sulfate interference was use of an AS14 system using isocratic eluent, and this produced good resolution of oxalic acid from a high sulfate peak. In both the AS11 and the AS14 system, linear correlation coefficients were at all times >0.9990 with excellent linear range, the recoveries ranged from 92.8 to 106%, with relative standard deviation of 3.67-6.30%, whilst method detection limits (MDLs) ranged from 0.36microgL(-1) for malic acid to 3.87microgL(-1) for maleic acid. These data indicate that the analytical methods developed herein produce excellent separation efficiency and good determination of low-M(w) DCAs with satisfactory accuracy, recoveries, and MDLs. Samples left at room temperature (20 degrees C) for 300min in a simulation of the 'waiting time' involved in the proposed IC analysis decayed to between 86% (oxalic acid) and 39% (succinic and malonic acids) of their original concentration, whilst at 4 degrees C concentrations remained at 96-101% of original, indicating that maintaining samples at a low temperature prior to injection into the IC analyzer is vital for obtaining accurate results when analyzing low-M(w) DCAs. Oxalic acid was found to be the most prevalent low-M(w) DCA in background aerosol, comprising 57% of the total low-M(w) DCAs and 0.959% of the PM(2.5) aerosol mass, followed by succinic acid and malonic acid.