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Genomic characterization of dengue virus serotype 2 during dengue outbreak and endemics in Hangzhou, Zhejiang (2017-2019)

Sun H, Yao W, Siddique A, He F, Yue M

Front Microbiol, 2023;14:1245416.
PMID: 37692383 DOI: 10.3389/fmicb.2023.1245416

INTRODUCTION: Dengue fever (DF) is a mosquito-borne viral disease caused by the dengue virus (DENV). In recent years, Hangzhou has undergone a DF epidemic, particularly in 2017, with an outbreak of 1,128 patients. The study aimed to investigate the genetic diversity and molecular evolution among the DF clinical isolates during and after the outbreak to aid in mapping its spread.
METHODS: To understand the genetic diversity, 74 DENV-2 strains were isolated from DF epidemic cases between 2017 and 2019. Combining whole genome sequencing (WGS) technology, additional phylogenetic, haplotype, amino acid (AA) substitution, and recombination analyses were performed.
RESULTS: The results revealed that strains from 2017 were closely related to those from Singapore, Malaysia, and Thailand, indicating an imported international transmission. Local strains from 2018 were clustered with those recovered from 2019 and were closely associated with Guangzhou isolates, suggesting a within-country transmission after the significant outbreak in 2017. Compared to DENV-2 virus P14337 (Thailand/0168/1979), a total of 20 AA substitutions were detected. Notably, V431I, T2881I, and K3291T mutations only occurred in indigenous cases from 2017, and A1402T, V1457I, Q2777E, R3189K, and Q3310R mutations were exclusively found in imported cases from 2018 to 2019. The recombination analysis indicated that a total of 14 recombination events were observed.
CONCLUSION: This study may improve our understanding of DENV transmission in Hangzhou and provide further insight into DENV-2 transmission and the local vaccine choice.
Fulltext Electronic Science Games Used to Enhance Cognitive Ability: Opinion of Design From Personalization and Adaptation

Wen D, Yao W, Xu J, Wang S, Zhong Y, Chen H, et al.

Front Aging Neurosci, 2021;13:789547.
PMID: 34867306 DOI: 10.3389/fnagi.2021.789547
Ultrasound-assisted fabrication of chitosan-hydroxypropyl methylcellulose nanoemulsions loaded with thymol and cinnamaldehyde: Physicochemical properties, stability, and antifungal activity

Phyo HM, Al-Maqtari QA, Mi S, Du Y, Khalid MU, Yao W

Int J Biol Macromol, 2024 Nov;281(Pt 1):136278.
PMID: 39368575 DOI: 10.1016/j.ijbiomac.2024.136278

This study investigated the influence of chitosan (CH) and hydroxypropyl methylcellulose (H), along with ultrasound power, on the physicochemical properties, antifungal activity, and stability of oil-in-water (O/W) nanoemulsions containing thymol and cinnamaldehyde in a 7:3 (v/v) ratio. Eight O/W formulations were prepared using CH, H, and a 1:1 (v/v) blend of CH and H, both with and without ultrasonication (U). Compared to untreated samples, U-treated nanoemulsions had lower droplet sizes (433-301 nm), polydispersity index (0.42-0.47), and zeta potential (-0.42-0.77 mV). The U treatment decreased L* and b* values, increased a* color attribute values, and increased apparent viscosity (0.26-2.17) at the same shear rate. After 28 days, microbiological testing of nanoemulsions treated with U showed counts below the detection limits (< 2 log CFU mL-1). The U-treated nanoemulsions exhibited stronger antifungal effects against R. stolonifer, with the NE/CH-U and NE/CH-H-U formulations demonstrating the lowest minimum inhibitory and fungicidal concentrations, measured at 0.12 and 0.24 μL/mL, respectively. On day 28, U-treated nanoemulsions demonstrated higher ionic, thermal, and physical stability than untreated samples. These findings suggest that the stability and antifungal efficacy of polysaccharide-based nanoemulsions may be improved by ultrasonic treatment. This study paves the way for innovative, highly stable nanoemulsions.
Classification of ERP signal from amnestic mild cognitive impairment with type 2 diabetes mellitus using single-scale multi-input convolution neural network

Wen D, Cheng Z, Li J, Zheng X, Yao W, Dong X, et al.

J Neurosci Methods, 2021 Nov 01;363:109353.
PMID: 34492241 DOI: 10.1016/j.jneumeth.2021.109353

BACKGROUND: The application of deep learning models to electroencephalogram (EEG) signal classification has recently become a popular research topic. Several deep learning models have been proposed to classify EEG signals in patients with various neurological diseases. However, no effective deep learning model for event-related potential (ERP) signal classification is yet available for amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM).
METHOD: This study proposed a single-scale multi-input convolutional neural network (SSMICNN) method to classify ERP signals between aMCI patients with T2DM and the control group. Firstly, the 18-electrode ERP signal on alpha, beta, and theta frequency bands was extracted by using the fast Fourier transform, and then the mean, sum of squares, and absolute value feature of each frequency band were calculated. Finally, these three features are converted into multispectral images respectively and used as the input of the SSMICNN network to realize the classification task.
RESULTS: The results show that the SSMICNN can fuse MSI formed by different features, SSMICNN enriches the feature quantity of the neural network input layer and has excellent robustness, and the errors of SSMICNN can be simultaneously transmitted to the three convolution channels in the back-propagation phase. Comparison with Existing Method(s): SSMICNN could more effectively identify ERP signals from aMCI with T2DM from the control group compared to existing classification methods, including convolution neural network, support vector machine, and logistic regression.
CONCLUSIONS: The combination of SSMICNN and MSI can be used as an effective biological marker to distinguish aMCI patients with T2DM from the control group.
Supercritical fluid extraction of four aromatic herbs and assessment of the volatile compositions, bioactive compounds, antibacterial, and anti-biofilm activity

Al-Maqtari QA, Al-Ansi W, Mahdi AA, Al-Gheethi AAS, Mushtaq BS, Al-Adeeb A, et al.

Environ Sci Pollut Res Int, 2021 May;28(20):25479-25492.
PMID: 33462691 DOI: 10.1007/s11356-021-12346-6

Artemisia arborescens, Artemisia abyssinica, Pulicaria jaubertii, and Pulicaria petiolaris are fragrant herbs traditionally used in medication and as a food seasoning. To date, there are no studies on the use of supercritical fluids extraction with carbon dioxide (SFE-CO2) on these plants. This study evaluates and compares total phenolic content (TPC), antioxidant activity by DPPH• and ABTS•+, antibacterial, and anti-biofilm activities of SFE-CO2 extracts. Extraction was done by SFE-CO2 with 10% ethanol as a co-solvent. A. abyssinica extract had the highest extraction yield (8.9% ± 0.41). The GC/MS analysis of volatile compounds identified 307, 265, 213, and 201compounds in A. abyssinica, A. arborescens, P. jaubertii, and P. petiolaris, respectively. The P. jaubertii extract had the highest TPC (662.46 ± 50.93 mg gallic acid equivalent/g dry extract), antioxidant activity (58.98% ± 0.20), and antioxidant capacity (71.78 ± 1.84 mg Trolox equivalent/g dry extract). The A. abyssinica and P. jaubertii extracts had significantly higher antimicrobial activity and were more effective against Gram-positive bacteria. B. subtilis was the most sensitive bacterium. P. aeruginosa was the most resistant bacterium. P. jaubertii extract had the optimum MIC and MBC (0.4 mg/ml) against B. subtilis. All SFE-CO2 extracts were effective as an anti-biofilm formation for all tested bacteria at 1/2 MIC. Meanwhile, P. jaubertii and P. petiolaris extracts were effective anti-biofilm for most tested bacteria at 1/16 MIC. Overall, the results indicated that the SFE-CO2 extracts of these plants are good sources of TPC, antioxidants, and antibacterial, and they have promising applications in the industrial fields.
Evidence for the Higgs Boson Decay to a Z Boson and a Photon at the LHC

Aad G, Abbott B, Abeling K, Abicht NJ, Abidi SH, Aboulhorma A, et al.

Phys Rev Lett, 2024 Jan 12;132(2):021803.
PMID: 38277607 DOI: 10.1103/PhysRevLett.132.021803

The first evidence for the Higgs boson decay to a Z boson and a photon is presented, with a statistical significance of 3.4 standard deviations. The result is derived from a combined analysis of the searches performed by the ATLAS and CMS Collaborations with proton-proton collision datasets collected at the CERN Large Hadron Collider (LHC) from 2015 to 2018. These correspond to integrated luminosities of around 140 fb^{-1} for each experiment, at a center-of-mass energy of 13 TeV. The measured signal yield is 2.2±0.7 times the standard model prediction, and agrees with the theoretical expectation within 1.9 standard deviations.
Combination of Measurements of the Top Quark Mass from Data Collected by the ATLAS and CMS Experiments at sqrt[s]=7 and 8 TeV

Hayrapetyan A, Tumasyan A, Adam W, Andrejkovic JW, Bergauer T, Chatterjee S, et al.

Phys Rev Lett, 2024 Jun 28;132(26):261902.
PMID: 38996325 DOI: 10.1103/PhysRevLett.132.261902

A combination of fifteen top quark mass measurements performed by the ATLAS and CMS experiments at the LHC is presented. The datasets used correspond to an integrated luminosity of up to 5 and 20 fb^{-1} of proton-proton collisions at center-of-mass energies of 7 and 8 TeV, respectively. The combination includes measurements in top quark pair events that exploit both the semileptonic and hadronic decays of the top quark, and a measurement using events enriched in single top quark production via the electroweak t channel. The combination accounts for the correlations between measurements and achieves an improvement in the total uncertainty of 31% relative to the most precise input measurement. The result is m_{t}=172.52±0.14(stat)±0.30(syst) GeV, with a total uncertainty of 0.33 GeV.