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Abstract SPRED3 (Sprouty-related EVH1 domain containing 3) mutants are depicted in various cancers, however, nothing is known about its biofunction in thyroid cancer (THCA). Bioinformatic analyses were conducted to ascertain the level of SPRED3 expression in THCA tissues and its importance in the prognosis of THCA patients. Flag-SPRED3 plasmid and SPRED3-knockout vector were developed to overexpress or deplete the SPRED3 expression in THCA cells. The function of SPRED3 on THCA cell proliferation was examined using the colony formation assay and CCK8 assay. The effect of SPRED3 expression on the transcriptional activity of NF-κB was also examined using luciferase reporter assays. High SPRED3 expression was associated with unfavorable clinical outcomes, advanced tumor characteristics, and traditional molecular markers of papillary thyroid cancer in THCA patients. Genetic analysis revealed differences in mutation rates in key genes between SPRED3-high and SPRED3-low THCA cases. It is also revealed that SPRED3 influenced the immune microenvironment, with increased stromal and immune scores and altered immune cell infiltration. Functionally, SPRED3 overexpression enhanced THCA cell viability and colony formation, while its depletion reduced cell growth and proliferation. In vivo experiments in mice confirmed the inhibitory effect of SPRED3 depletion on tumor growth. Mechanically, we found that SPRED3 activated the NF-κB signaling. For the first time, we found that SPRED3 promotes THCA cell proliferation via the NF-κB signaling pathway. This finding may provide insight into SPRED3’s prognostic potential in thyroid cancer and provide the rationale for SPRED3-targeted druggable interventions.

Abstract Ordered phase transitions are commonly correlated to symmetry breaking, while disordered phase transitions are characterized by symmetry restoration. Nevertheless, this study demonstrates that these correlation relations are not always applicable in chiral polymers under high-pressure Carbon Dioxide. Without racemization, homochiral Poly (lactide acid) can generate two vortex-shaped dendritic crystals with opposite spiral chirality, and snowflake-shaped dendritic crystals without spiral chirality. The transition from homochiral molecules to achiral crystals signifies the chiral symmetry restoration during the ordering process. The primary elements responsible for the various hierarchical transfers of homochiral Poly (lactide acid) are related to chain tilt, surface stress, and frustrated structures of Poly (lactide acid) crystals. Here, we show the entropy impact of Carbon Dioxide can be utilized to programmatically regulate the morphological chirality of crystal superstructure and crystal form of homochiral Poly (lactide acid).

Abstract Background Prior research has highlighted the involvement of a transcriptional complex comprising C-terminal binding protein 2 (CtBP2), histone acetyltransferase p300, and nuclear factor kappa B (NF-κB) in the transactivation of proinflammatory cytokine genes, contributing to inflammation in mice with acute respiratory distress syndrome (ARDS). Nonetheless, it remains uncertain whether the therapeutic targeting of the CtBP2-p300-NF-κB complex holds potential for ARDS suppression. Methods An ARDS mouse model was established using lipopolysaccharide (LPS) exposure. RNA-Sequencing (RNA-Seq) was performed on ARDS mice and LPS-treated cells with CtBP2, p300, and p65 knockdown. Small molecules inhibiting the CtBP2-p300 interaction were identified through AlphaScreen. Gene and protein expression levels were quantified using RT-qPCR and immunoblots. Tissue damage was assessed via histological staining. Key findings We elucidated the specific role of the CtBP2-p300-NF-κB complex in proinflammatory gene regulation. RNA-seq analysis in LPS-challenged ARDS mice and LPS-treated CtBP2-knockdown (CtBP2KD), p300KD, and p65KD cells revealed its significant impact on proinflammatory genes with minimal effects on other NF-κB targets. Commercial inhibitors for CtBP2, p300, or NF-κB exhibited moderate cytotoxicity in vitro and in vivo, affecting both proinflammatory genes and other targets. We identified a potent inhibitor, PNSC928, for the CtBP2-p300 interaction using AlphaScreen. PNSC928 treatment hindered the assembly of the CtBP2-p300-NF-κB complex, substantially downregulating proinflammatory cytokine gene expression without observable cytotoxicity in normal cells. In vivo administration of PNSC928 significantly reduced CtBP2-driven proinflammatory gene expression in ARDS mice, alleviating inflammation and lung injury, ultimately improving ARDS prognosis. Conclusion Our results position PNSC928 as a promising therapeutic candidate to specifically target the CtBP2-p300 interaction and mitigate inflammation in ARDS management.

Yuanbaoshancha (YBSC) is characterized as a new wild tea relative morphologically and phytochemically distinguished from the closest wild tea plants Rongjiangcha (Camellia yungkiangensis, RJC) and Tulecha (Camellia costata, TLC). YBSC young leaves contain higher tea polyphenol and theobromine contents but lower caffeine and theanine as compared with RJC, TLC, and other tea landraces and modern cultivars. The major alkaloid detected in YBSC, TLC, and RJC is theobromine while caffeine is a minor; the primary catechins in YBSC leaves are non-galloylated catechins, significantly different from Camellia sinensis and other low-caffeine tea resources. The unique phytochemical profiles featured YBSC black tea with extremely lower caffeine and higher theobromine, as well as unique flavors and health benefits. This botanical characterization of YBSC and two related low-caffeine wild tea resources lays a foundation for future better utilization for the production of a highly valuable natural low-caffeine/high-theobromine tea. Chemical compounds: Caffeine (PubChem CID: 2519); Theobromine (PubChem CID: 5429); Catechins (PubChem CID: 9064); Epigallocatechin gallate (PubChem CID: 65064); Theanine (PubChem CID: 439378); Jasmone (PubChem CID: 1549018); cis-3-Hexenyl hexanoate (PubChem CID: 5352543); Hexyl 2-methylbutanoate (PubChem CID: 24838).

The pressure and temperature significantly influence precipitable water vapor (PWV) retrieval. Global Navigation Satellite System (GNSS) PWV retrieval is limited because the GNSS stations lack meteorological sensors. First, this article evaluated the accuracy of pressure and temperature in 68 radiosonde stations in China based on ERA5 Reanalysis data from 2015 to 2019 and compared them with GPT3 model. Then, the accuracy of pressure and temperature calculated by ERA5 were estimated in 5 representative IGS stations in China. And the PWV calculated by these meteorological parameters from ERA5 (ERA5-PWV) were analyzed. Finally, the relation between ERA5-PWV and precipitation was deeply explored using wavelet coherence analysis in IGS stations. These results indicate that the accuracy of pressure and temperature of ERA5 is better than the GPT3 model. In radiosonde stations, the mean BIAS and MAE of pressure and temperature in ERA5 are −0.41/1.15 hpa and −0.97/2.12 K. And the mean RMSEs are 1.35 hpa and 2.87 K, which improve 74.77% and 40.58% compared with GPT3 model. The errors of pressure and temperature of ERA5 are smaller than the GPT3 model in bjfs, hksl and wuh2, and the accuracy of ERA5-PWV is improved by 18.77% compared with the GPT3 model. In addition, there is a significant positive correlation between ERA5-PWV and precipitation. And precipitation is always associated with the sharp rise of ERA5-PWV, which provides important references for rainfall prediction.

Defects, boundaries, textures, tensile properties, fractographies, sub-grains, nano-oxide particles, and deformation twins in selective laser melted 316L stainless steel (SLM316LSS) were systematically characterized as functions of laser energy density. The defects, boundaries, textures of the as-built SLM316LSS were shown to depend largely on the applied laser power. The porosity of the SLM316LSS significantly decreased with the increase laser power from 70 to 140 W (corresponding to the laser energy density from 116.7 to 233.3 J/mm3), while exhibiting a slight increase in the laser power range of 140–280 W (the laser energy density of 233.3–466.7 J/mm3). Additionally, the SLM316LSS specimen at a laser power of 140 W reached the hardness of ∼252 HV, the yield strength of ∼605 MPa and the ultimate tensile strength of ∼710 MPa, along with the elongation to fracture of ∼32.5%, respectively, due to the lowest defect concentration and the presence of twin boundaries and textures, combined with SiO2 nano-oxide particles and deformation twinning. However, the crack appeared in the SLM316LSS within the laser power range from 210 to 280 W (the laser energy density from 350.0 to 466.7 J/mm3), and the mean crystallite size was more than 50 μm, which insignificantly affected the mechanical properties of the steel compared with a laser power of 140 W.

Abstract The upper tropospheric water vapor is a key component of Earth's climate. Understanding variations in upper tropospheric water vapor and identifying its influencing factors is crucial for enhancing our comprehension of global climate change. While many studies have shown the impact of El Niño-Southern Oscillation (ENSO) and global warming on water vapor, how they affect the upper tropospheric water vapor remains unclear. Long-term, high-precision ERA5 specific humidity data from the European Centre for Medium-Range Weather Forecasts (ECMWF) provided the data foundation for this study. On this basis, we successfully obtained the patterns of global warming (Independent Component 1, IC1) and ENSO (Independent Component 2, IC2) by employing the strategy of independent component analysis (ICA) combined with non-parametric optimal dimension selection to investigate the upper tropospheric water vapor variations and responses to ENSO and global warming. The results indicate that global warming and ENSO are the primary factors contributing to water vapor variations in the upper troposphere, achieving the significant correlations of 0.87 and 0.61 with water vapor anomalies respectively. Together, they account for 86% of the global interannual variations in water vapor. Consistent with previous studies, our findings also find positive anomalies in upper tropospheric water vapor during El Niño years and negative anomalies during La Niña years. Moreover, the influence extent of ENSO on upper tropospheric water vapor varies with the changing seasons.

Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues. However, the complexity of porous media often limits the effectiveness of individual prediction methods. This study introduces a novel Particle Swarm Optimization-based Permeability Integrated Prediction model (PSO-PIP), which incorporates a particle swarm optimization algorithm enhanced with dynamic clustering and adaptive parameter tuning (KGPSO). The model integrates multi-source data from the Lattice Boltzmann Method (LBM), Pore Network Modeling (PNM), and Finite Difference Method (FDM). By assigning optimal weight coefficients to the outputs of these methods, the model minimizes deviations from actual values and enhances permeability prediction performance. Initially, the computational performances of the LBM, PNM, and FDM are comparatively analyzed on datasets consisting of sphere packings and real rock samples. It is observed that these methods exhibit computational biases in certain permeability ranges. The PSO-PIP model is proposed to combine the strengths of each computational approach and mitigate their limitations. The PSO-PIP model consistently produces predictions that are highly congruent with actual permeability values across all prediction intervals, significantly enhancing prediction accuracy. The outcomes of this study provide a new tool and perspective for the comprehensive, rapid, and accurate prediction of permeability in porous media.

Background: LncRNA HOXB-AS3 are associated with tumor progression in several types of carcinomas, yet, its possibly biological role in gallbladder carcinoma(GBC) remains unclear. Therefore, this study aimed to investigate the biological function of HOXB-AS3 in GBC. Methods: To know the potential function of HOXB-AS3 in gallbladder carcinoma, real-time polymerase chain reaction was used to detected the expression of HOXB-AS3 in gallbladder carcinoma cells. The colony formation assay and cell counting kit-8 assay was performed to measured cell viability. Flow cytometry was to analyse cell apoptosis and cell cycle. Cell invasion and migration were determined by the transwell invasion assay and wound-healing assay. A nude mice xenograft tumor model was performed to investigate the biological function of HOXB-AS3 in vivo. Results: The results indicated that HOXB-AS3 was significantly elevated in gallbladder carcinoma tissues and cell lines. We used siHOXB-AS3 to knockdown the expression levels of HOXB-AS3. And knockdown HOXB-AS3 expression depressed gallbladder cancer cell viability and induced cell apoptosis. In addition, the gallbladder carcinoma cell cycle was obviously arrested at the G1 phase. Cell invasion and migration were markedly suppressed following knockdown HOXB-AS3 expression. Furthermore, the features of siHOXB-AS3 in gallbladder cancer cells could be reversed by the ERK1/2 phosphorylation agonist Ro 67–7476. Finally, we confirmed that HOXB-AS3 promoted the growth of transplanted tumors in vivo. Conclusion: HOXB-AS3 promoted gallbladder carcinoma cell proliferation, invasion and migration by activating the MEK/ERK signaling pathway. HOXB-AS3 contributed to gallbladder cancer tumorigenesis and metastasis, making it a viable therapeutic target for gallbladder cancer treatment.

ObjectiveAedes-borne arboviral diseases were important public health problems in Zhejiang before the coronavirus disease 2019 (COVID-19) pandemic. This study was conducted to investigate the characteristics and change of the epidemiology of Aedes-borne arboviral diseases in the province.MethodsDescriptive analyses were conducted to summarize the epidemiology of Aedes-borne arboviral diseases during 2003–2022.ResultsA total of 3,125 cases, including 1,968 indigenous cases, were reported during 2003–2022. Approximately three-quarters of imported cases were infected from Southeast Asia. The number of annual imported cases increased during 2013–2019 (R2 = 0.801, p = 0.004) and peaked in 2019. When compared with 2003–2012, all prefecture-level cities witnessed an increase in the annual mean incidence of imported cases in 2013–2019 (0.11–0.42 per 100,000 population vs. 0–0.05 per 100,000 population) but a drastic decrease during 2020–2022 (0–0.03 per 100,000 population). The change in geographical distribution was similar, with 33/91 counties during 2003–2012, 86/91 during 2013–2019, and 14/91 during 2020–2022. The annual mean incidence of indigenous cases in 2013–2019 was 7.79 times that in 2003–2012 (0.44 vs. 0.06 per 100,000 population). No indigenous cases were reported between 2020–2022. Geographical extension of indigenous cases was also noted before 2020—from two counties during 2003–2012 to 44 during 2013–2019.ConclusionDengue, chikungunya fever, zika disease, and yellow fever are not endemic in Zhejiang but will be important public health problems for the province in the post-COVID-19 era.

Colorectal cancer (CRC) is the second most common malignant tumor of the gastrointestinal tract with the second highest mortality rate and the third highest incidence rate. Early diagnosis and treatment are important measures to reduce CRC mortality. Small extracellular vesicles (sEVs) have emerged as key mediators that facilitate communication between tumor cells and various other cells, playing a significant role in the growth, invasion, and metastasis of cancer cells. Regulatory RNAs have been identified as potential biomarkers for early diagnosis and prognosis of CRC, serving as crucial factors in promoting CRC cell proliferation, invasion and metastasis, angiogenesis, drug resistance, and immune cell differentiation. This review provides a comprehensive summary of the vital role of sEVs as biomarkers in CRC diagnosis and their potential application in CRC treatment, highlighting their importance as a promising avenue for further research and clinical translation.

Renal cell carcinoma (RCC) represents an extremely challenging disease in terms of both diagnosis and treatment. It poses a significant threat to human health, with incidence rates increasing at a yearly rate of roughly 2%. Extracellular vesicles (EVs) are lipid-based bilayer structures of membranes that are essential for intercellular interaction and have been linked to the advancement of RCC. This review provides an overview of recent studies on the role of EVs in RCC progression, including involvement in the interaction of tumor cells with M2 macrophages, mediating the generation of immune tolerance, and assuming the role of communication messengers in the tumor microenvironment leading to disease progression. Finally, the “ troika “ of EVs in RCC therapy is presented, including engineered sEVs’ or EVs tumor vaccines, mesenchymal stem cell EVs therapy, and reduction of tumor-derived EVs secretion. In this context, we highlight the limitations and challenges of EV-based research and the prospects for future developments in this field. Overall, this review provides a comprehensive summary of the role of EVs in RCC and their potential as a viable pathway for the future treatment of this complex disease.

Small extracellular vesicles (sEVs) are minute vesicles secreted by various cells that are capable of transporting cargo, including microRNAs, between donor and recipient cells. MicroRNAs (miRNAs), small non-coding RNAs approximately 22 nucleotides in length, have been implicated in a wide array of biological processes, including those involved in tumorigenesis. Emerging evidence highlights the pivotal role of miRNAs encapsulated in sEVs in both the diagnosis and treatment of urological tumors, with potential implications in epithelial-mesenchymal transition, proliferation, metastasis, angiogenesis, tumor microenvironment and drug resistance. This review provides a brief overview of the biogenesis and functional mechanisms of sEVs and miRNAs, followed by a summarization of recent empirical findings on miRNAs encapsulated in sEVs from three archetypal urologic malignancies: prostate cancer, clear cell renal cell carcinoma, and bladder cancer. We conclude by underscoring the potential of sEV-enclosed miRNAs as both biomarkers and therapeutic targets, with a particular focus on their detection and analysis in biological fluids such as urine, plasma, and serum.

BackgroundFew data exist on the immunogenicity and safety of an inactivated enterovirus 71 vaccine (EV71 vaccine) coadministered with trivalent split-virion inactivated influenza vaccine (IIV3) in infants.MethodsThis trial was a phase 4, randomized, controlled trial. Infants aged 6-11 months were eligible, with no history of hand, foot and mouth disease (HFMD) and no history of EV71 vaccine or any influenza vaccine. Eligible infants were randomly assigned to EV71+IIV3 group, EV71 group or IIV3 group. Blood samples were collected on day 0 and 56.ResultsBetween September 2019 and June 2020, 1151 infants met eligibility criteria and 1134 infants were enrolled. 1045 infants were included in the per-protocol population, including 347 in the EV71+IIV3 group, 343 in the EV71 group, and 355 in the IIV3 group. The seroconversion rate (98.56% vs 98.54%; seroconversion rates difference of 0.02% [95% CI: 0.70-0.98]) and GMT (419.05 vs 503.72; GMT ratio of 0.83 [95% CI 0.70 - 0.98]) of EV71 neutralizing antibodies in the EV71+IIV3 group was not inferior to those in the EV71 group. The non-inferiority results for influenza virus antibodies (A/H1N1, A/H3N2 and B) showed that the seroconversion rates and GMTs of the EV71+IIV3 group were non-inferiority to those of the IIV3 group. Systemic and local adverse event rates were similar between groups. None of serious adverse events (SAEs) were related to vaccination.ConclusionsCoadministration of the EV71 vaccine with IIV3 was safe and did not interfere with immunogenicity. These findings support a viable immunization strategy for infants with the EV71 vaccine coadministered with IIV3 in China. This trial is registered with ClinicalTrials.gov, number NCT04091880.

The reliable operation of flexible display devices poses a significant engineering challenge regarding the metrology of high barriers against water vapor. No reliable results have been reported in the range of 10–6 g∙(m2∙d)−1, and there is no standard ultra-barrier for calibration. To detect trace amount of water vapor permeation through an ultra-barrier with extremely high sensitivity and a greatly reduced test period, a predictive instrument was developed by integrating permeation models into high-sensitivity mass spectrometry measurement based on dynamic accumulation, detection, and evacuation of the permeant. Detection reliability was ensured by means of calibration using a standard polymer sample. After calibration, the lower detection limit for water vapor permeation is in the range of 10–7 g∙(m2∙d)−1, which satisfies the ultra-barrier requirement. Predictive permeation models were developed and evaluated using experimental data so that the steady-state permeation rate can be forecasted from non-steady-state results, thus enabling effective measurement of ultra-barrier permeation within a significantly shorter test period.

Abstract Background Essential proteins have great impacts on cell survival and development, and played important roles in disease analysis and new drug design. However, since it is inefficient and costly to identify essential proteins by using biological experiments, then there is an urgent need for automated and accurate detection methods. In recent years, the recognition of essential proteins in protein interaction networks (PPI) has become a research hotspot, and many computational models for predicting essential proteins have been proposed successively. Results In order to achieve higher prediction performance, in this paper, a new prediction model called TGSO is proposed. In TGSO, a protein aggregation degree network is constructed first by adopting the node density measurement method for complex networks. And simultaneously, a protein co-expression interactive network is constructed by combining the gene expression information with the network connectivity, and a protein co-localization interaction network is constructed based on the subcellular localization data. And then, through integrating these three kinds of newly constructed networks, a comprehensive protein–protein interaction network will be obtained. Finally, based on the homology information, scores can be calculated out iteratively for different proteins, which can be utilized to estimate the importance of proteins effectively. Moreover, in order to evaluate the identification performance of TGSO, we have compared TGSO with 13 different latest competitive methods based on three kinds of yeast databases. And experimental results show that TGSO can achieve identification accuracies of 94%, 82% and 72% out of the top 1%, 5% and 10% candidate proteins respectively, which are to some degree superior to these state-of-the-art competitive models. Conclusions We constructed a comprehensive interactive network based on multi-source data to reduce the noise and errors in the initial PPI, and combined with iterative methods to improve the accuracy of necessary protein prediction, and means that TGSO may be conducive to the future development of essential protein recognition as well.

ObjectivesGastric cancer with liver metastasis (GCLM) is highly aggressive and has a poor prognosis. This study aims to evaluate the survival benefit of primary tumor resection (PTR) for gastric cancer with liver metastasis.MethodsData on patients with GCLM was extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. A 1:1 propensity score matching (PSM) analysis was performed to minimize the heterogeneity between the PTR and no-PTR groups. The Kaplan–Meier method and Cox regression analysis were used to assess the impact of primary tumor resection (PTR) on overall survival (OS) and cause-specific survival (CSS).ResultsA total of 3,001 patients with GCLM were included, with 328 patients treated with primary tumor resection (PTR), whereas the other 2,673 patients were not. Patients with PTR had a significantly higher OS and CSS rate than those without PTR in unmatched and PSM cohorts. In an unmatched cohort, the median OS was 12.0 months (95% CI, 10 months to 14 months) for those who underwent PTR and 4 months (95% CI, 4 months to 5 months) for those without PTR; the median CSS for those who underwent PTR was 12.0 months (95% CI, 10 months to14 months) and 4 months (95% CI, 4 months to 5 months) for those without PTR, respectively. After PMS, the median OS was 12.0 months (95% CI, 10 months to 17 months) for those who underwent PTR and 7 months (95% CI, 5 months to 10 months) for those without PTR, respectively; the median CSS for those who underwent PTR was 12.0 months (95% CI, 11 months to 17 months) and 7 months (95% CI, 5 months to 8 months) for those without PTR, respectively. In addition, multivariate Cox analysis in the PSM cohort showed that PTR, age, degree of tumor differentiation, and chemotherapy were independent prognostic factors for OS and CSS in GCLM. Specifically, PTR was a significant protective factor for OS (HR: 0.427; 95% CI, 0.325 to 0.561, P

ABSTRACT Sulfadoxine-pyrimethamine (SP) resistance impairs the efficacy of antimalarial drugs. Monitoring molecular markers in exported malaria infections provides an efficient way to trace the emergence of drug resistance in countries where malaria is endemic. Molecular markers in Pfdhfr and Pfdhps of 237 Plasmodium falciparum infections imported from central Africa between 2016 and 2021 were detected. The spatial and temporal distributions of Pfdhfr and Pfdhps mutations were analyzed. A high prevalence of Pfdhfr single-nucleotide polymorphisms (SNPs) (~92.34% to 99.10%) and a high frequency of the triple mutation haplotype I51R59N108 were observed. Cameroon, Equatorial Guinea, and Gabon showed a higher frequency (~96.61% to 100.00%) of I51R59N108 than other countries (~71.11% to 88.10%). The prevalence of C59R and I51R59N108 increased while that of other SNPs or haplotypes did not fluctuate greatly from 2016 to 2021. Large proportions of Pfdhps SNPs (A437G and K540E) were demonstrated. The SNP distribution of Pfdhps differed between countries, with S436A dominating in northern countries and A437G dominating in others. The proportions of I431V, A437G, and the triple mutant haplotype declined between 2016 and 2021, whereas the prevalence of the single mutant haplotype rose from 61.60% to 73.68%. Combinations of Pfdhfr-Pfdhps alleles conferring partial resistance, full resistance, and superresistance to SP, as defined in the text, were detected in 63.64%, 8.64%, and 0.91% of the samples, respectively. The octuple Pfdhfr-Pfdhps allele (I51R59N108-V431A436G437K540G581S613) was seen in 5.00% of the samples. We demonstrated the wide geographic spread and increasing trends in highly SP-resistant Pfdhfr genes and varying spatial patterns of Pfdhps mutants across countries in central Africa. The high prevalences of partially resistant, fully resistant, and superresistant Pfdhfr-Pfdhps combinations observed here indicated impaired SP efficacy. Increased molecular surveillance is required to monitor the changing status of the Pfdhfr and Pfdhps genes. IMPORTANCE Monitoring drug resistance is important for malaria control because its early detection enables timely action to prevent its spread and mitigate its impact. The wide geographic spread and the increasing trend of highly resistant Pfdhfr genes between 2016 and 2021 found in our study are worrisome and emphasize the urgency to monitor their updated status in central Africa. This study also illustrated the wide spread of the novel mutant Pfdhps I431V as well as the high prevalence of “partially resistant,” “fully resistant,” and “superresistant” Pfdhfr-Pfdhps combinations, indicating the urgent concern for SP efficacy in central Africa. These findings are alarming in central African countries where malaria is endemic, where SP was is widely used for the intermittent preventive treatment of malaria in pregnancy (IPTp) and the intermittent preventive treatment of malaria in infants below 5 years of age (IPTi), and urge enhanced molecular surveillance and responses to the threat of drug resistance.

BackgroundClinical target delineation is a primary focus in the field of radiotherapy. This study aimed to investigate whether high-risk clinical target volume can be removed in nasopharyngeal carcinoma patients with different T stages.Materials and methodsWe designed a test plan without the high-risk clinical target volume for 111 nasopharyngeal carcinoma patients and further compared the test plans with the treatment plans in the parameters of planning target volumes and the radiation dose to normal organs.ResultsOur data showed that when high-risk clinical target volume was abnegated, target coverage, conformity indices, and homogeneity indices of planning target volumes and doses of normal organs were not influenced in the T4 nasopharyngeal carcinoma patients, and more than 95% of the high-risk planning target volume area could still be covered by the 60 Gy dose line. However, only some T1–3 patients achieved the ideal dose coverage, and even fewer after induction chemotherapy (62.8% vs. 41.2%, p = 0.018). Gross tumor volume was positively correlated with the target coverage of the original high-risk planning target volume in the test-plan (p = 0.0001). Gross tumor volume can be used to predict whether the target coverage of high-risk planning target volume is more than 95% (area under the curve = 0.868).ConclusionOmitting high risk clinical target volume can be considered in patients with T4 nasopharyngeal carcinoma according to physical evaluations. However, this approach is only suitable for a specific subset of T1–3 patients.

A thin-walled steel cylindrical shell is a common engineering structure that has an efficient load-carrying capacity. This structure is more easily subjected to partial axial compression loads in application, and buckling is the main failure mode. However, there are few available design methods for partial axially compressed steel cylindrical shells. Motivated by this, a design method called the localized perturbation load approach (LPLA) is proposed in this paper. The finite element framework for the application of LPLA is established. The location and number of perturbation loads are determined by considering the imperfection sensitivity and the buckling failure mode of partial axial compressed cylinders. A series of buckling experiments are carried out to validate the LPLA method. In addition, the reliability of LPLA for the design of cylindrical shells with different imperfection locations and dimensions is also verified. The results show that LPLA can give conservative and reliable lower-bound buckling loads. Therefore, LPLA can be used as a design method for thin-walled steel cylindrical shell structures under partial axial compression in actual engineering.

Abstract Background Non-prescription antibiotic use at community is a main driver of antimicrobial resistance. Cough is a common condition and prevalent in all townships, including China. This study aims to investigate the non-prescription antibiotic use for cough in China and explore to which extent antibiotic use knowledge was correctly instructed in communities. Methods A probability-proportionate-to-size sampling method was adopted to survey from all 14 townships in Yiwu city, China. All participants were investigated by face-to-face interview on Portable Android Devices. The continuous variables were presented by mean and standard deviation or medium and inter-quartile range (IQR). The categorical variables were presented using percentage or constituent ratio. Chi-square test for univariate analysis and logistic regression for multivariate analysis were conducted to assess the odds ratios and 95% confidence intervals, respectively. Results A total of 3034 respondents across all the 14 townships and the 50 natural villages/streets completed all key items of the questionnaire. Of 2400 (79.10%) respondents stated that they experienced cough in the past 12 months with the medium age of 36.5 (IQR: 26–49) and 12.21% (293/2400) respondents had the non-prescription antibiotic use behavior. Among those 293 respondents, the proportion of non-prescription antibiotic use for cough peaked at around 16% among people aged 30–39 years old. The major sources of antibiotics were pharmacy (77.70%) and/or family storage (43.92%). Conclusions Non-prescription antibiotics use for cough is prevalent in the community, especially among people in their thirties. Strengthened drug purchase regulation and well-trained professional pharmacists would be promising alternatives to ameliorate AMR. Moreover, penetrating antibiotics knowledge to common citizens and is an urgent task to alleviate antimicrobial resistance. Therefore, proactive policies and regulations should be made to improve current situations.

Binary TiO2-Al2O3 serial composites(TA-x) and the NiMo supported catalysts(NiMo/TA-x) were successfully synthesized at different solvent evaporation temperatures,. The materials were characterized by several advanced characterization techniques, and the catalytic hydrodesulfurization (HDS) performances were evaluated. The results revealed that the synthesized TA-x serial composites are amorphous mixtures of TiO2 and Al2O3 with highly ordered two-dimensional hexagonal mesopores. Both the mesostructures and the acidities of the synthesized TA-x composites changed at different evaporation temperature of the solvent. The metal-support interaction(MSI), the existence states and the coordination states of the Mo precursors can be modulated by the changes in the physicochemical property of the TA-x composites caused by the solvent evaporating temperature. A moderate solvent evaporation temperature is favorable for the doping of Ni species into the MoS2 slab and Mo sulfidation degree, thus enhancing the proportion of the NiMoS active phases. Catalyst NiMo/TA-60 exhibits superior activity and the highest direct desulfurization(DDS) pathway selectivity due to the excellent acidity and pore structure property, the highest dispersion and the highest Mo sulfidation degree, a moderate MSI, the highest efficiency in the formation of NiMoS phase and the highest proportion of corner Mo atoms. Moreover, the rate constants of the specific HDS pathways can be correlated with the specific types of active sites: the DDS pathway (kDDS) is closely related to the proportion of corner active sites and the hydrogenation desulfurization pathway (kHYDS) is closely related to the proportion of brim sites. The activity of a single corner active site is found to be approximately 100 times that of a single edge active site. These findings are promising in the design of HDS catalysts for inferior distillates.

Abstract Since the outbreak of COVID-19 in December 2019 in Wuhan, Zhejiang has become the province with the largest number of cases. The aim of this article is to present Zhejiang province’s experience of establishing an accurate and smart control mechanism for epidemic prevention and control and resumption of work and production using a ‘five-colour epidemic chart’. The number of confirmed cases, proportion of local cases, and occurrence of clustered outbreaks were used as evaluation indicators to calculate the county-level epidemic risk and were assigned different weight coefficients; the absence of cases for 3 and 7 consecutive days was used as the adjustment index. When the first chart was published on February 9, there were 1 very-high-risk, 12 high-risk, and 12 low-risk counties. Under the five-colour chart, Zhejiang began to adopt precise measures to prevent and control the epidemic and resume work and production. By February 24, the low-risk counties had expanded to 82, with no high-risk and very-high-risk counties. The epidemic situation in Zhejiang province has been effectively controlled. The experience of epidemic prevention and control in Zhejiang is worthy to be emulated and learned by other countries and regions.

This paper investigates the optimal private health insurance contract design problem, considering the joint interests of a policyholder and an insurer. Both the policyholder and the insurer jointly determine the premium of private health insurance. In order to better reflect reality, the illness expenditure is modelled by an extended compound Poisson process depending on health status. Under the mean–variance criterion and by applying dynamic programming, control theory, and leader–follower game techniques, analytically time-consistent private health insurance strategies are derived, optimal private health insurance contracts are designed, and their implications toward insurance are analysed. Finally, we perform numerical experiments assuming that the policyholder and the insurer calculate their wealth every year and they deposit their disposable income into the Bank of China with the interest rate being r=0.021. The values of other model parameters are set by referring to the data in the related literature. We find that the worse the policyholder’s health, the higher the premium that they pay for private health insurance, and buying private health insurance can effectively reduce the policyholder’s economic losses caused by illnesses.

Type-II superlattices (T2SLs) are emerging as next-generation materials for infrared detectors. The epitaxial quality of T2SLs is of great importance to the performance of infrared detectors such as dark current and detectivity. Herein, we explore the effect of the native GaSb oxide layer on the surface morphology and crystal quality of InAs/GaSb T2SLs grown with molecular beam epitaxy. The experimental results demonstrate that the thickness of the oxidation layer on GaSb substrates gradually increases over time and is saturated at around 73 Å in the natural oxidation condition. Moreover, the oxidation process is sensitive to humidity. As the thickness of the GaSb oxide layer increases from 18.79 Å to 61.54 Å, the full width at half maximum of the first satellite peak increases from 38.44 to 61.34 arcsec in X-ray diffraction measurements, and the root mean square roughness increases from 0.116 nm to 0.171 nm in atomic force microscopy measurements. Our results suggest that the thickness of the GaSb oxide layer should be less than 55 Å to obtain smooth buffer layers and qualified superlattices. The work provides an optimized direction for achieving high-quality superlattices for infrared optoelectronic devices.

Following the rollout of a booster campaign to promote immunity against COVID-19 in China, this study aimed to assess booster hesitancy among adults who were fully vaccinated with primary doses across Zhejiang Province. Firstly, the modified 5C scale developed by a German research team was assessed for reliability and validity via a pre-survey in Zhejiang Province. Then, a 30-item questionnaire was established to conduct online and offline surveys during 10 November to 15 December 2021. Demographic characteristics and information on previous vaccination experience, vaccine type of primary doses, attitudes towards booster doses and awareness of SARS-CoV-2 infection were collected. Chi-square tests, pairwise comparison and multivariate logistic regression were performed in data analysis. In total, 4039 valid questionnaires were analyzed, with booster hesitancy of 14.81%. Dissatisfaction with previous vaccination experience of primary doses (ORs = 1.771~8.025), less confidence in COVID-19 vaccines (OR = 3.511, 95%CI: 2.874~4.310), younger age compared to the elderly aged 51–60 years old (2.382, 1.274~4.545), lower education level (ORs = 1.707~2.100), weaker awareness of social responsibility of prevention and control of COVID-19 (1.587, 1.353~1.859), inconvenience of booster vaccination (1.539, 1.302~1.821), complacency regarding vaccine efficacy as well as self-health status (1.224, 1.056~1.415) and excessive trade-offs before vaccination (1.184, 1.005~1.398) were positively associated with booster hesitancy. Therefore, intelligent means should be strengthened to optimize vaccination services. More influential experts and other significant figures should be supported to promote timely evidence-based information via various media platforms to reduce public hesitancy and increase booster uptake.

Background: We aimed to evaluate the therapeutic effect of additional ursodeoxycholic acid (UDCA) with mesalazine, compared to mesalazine alone in patients with ulcerative colitis (UC). The mechanism was evaluated by monitoring the changes of IL-23-IL-17 axis and the intestinal microflora. Methods: In this prospective, single center study, patients with UC were randomly assigned to the Mesalazine group (n=20) or the UDCA + Mesalazine group (n=20). Mayo score and Inflammatory Bowel Disease Questionnaire (IBDQ), and fecal samples for 16S rRNA sequencing and blood samples for IL-23 and IL-17 ELISA were collected for analysis. Results: Mayo scores and IBDQ score of the UDCA + Mesalazine group were significantly better than those of the Mesalazine group (P = 0.015 and P < 0.001, respectively). At post-treatment week 4, IL-23 and IL-17 levels were significantly lower in the UDCA + Mesalazine group compared to those in the Mesalazine group (both P < 0.038). In patients with UC after treatment, Firmicutes in the UDCA + Mesalazine group was higher than those in the Mesalazine group (P < 0.001). The UDCA + Mesalazine group showed lower percentage of Proteobacteria compared to those in the Mesalazine group (P < 0.001). Conclusion: Additional UDCA could provide better therapeutic effects than mesalazine alone, possibly due to the change of IL-23 and IL-17 and the proportional distribution of intestinal microflora.

Hand, foot and mouth disease (HFMD) is a common infectious disease in western Asia area and the full range of the long-term sequelae of HFMD remains poorly described. We conducted a retrospective hospital-based cohort study of HFMD patients with central nervous system (CNS) complications caused by EV-A71 or CV-A16 between 2010 and 2016. Patients were classified into three groups, including CNS only, autonomic nervous system (ANS) dysregulation, and cardiorespiratory failure. Neurologic examination, neurodevelopmental assessments, Magnetic Resonance Imaging (MRI) and lung function, were performed at follow up. Of the 176 patients followed up, 24 suffered CNS only, 133 ANS dysregulation, and 19 cardiorespiratory failure. Median follow-up period was 4.3 years (range [1.4–8.3]). The rate of neurological abnormalities was 25% (43 of 171) at discharge and 10% (17 of 171) at follow-up. The rates of poor outcome were significantly different between the three groups of complications in motor (28%, 38%, 71%) domain (p=0.020), but not for cognitive (20%, 24%, 35%), language (25%, 36%, 41%) and adaptive (24%, 16%, 26%) domains (p = 0.537, p = 0.551, p = 0.403). For children with ventilated during hospitalization, 41% patients (14 of 34) had an obstructive ventilatory defect, and one patient with scoliosis had mixed ventilatory dysfunction. Persistent abnormalities on brain MRI were 0% (0 of 7), 9% (2 of 23) and 57% (4 of 7) in CNS, ANS and cardiorespiratory failure group separately. Patients with HFMD may have abnormalities in neurological, motor, language, cognition, adaptive behaviour and respiratory function. Long-term follow-up programmes for children’s neurodevelopmental and respiratory function may be warranted.

Abstract Background Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well. Results In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (fivefold CV), 10-Fold Cross Validation (tenfold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in fivefold CV, tenfold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA. Conclusion The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

BackgroundHand-foot-mouth disease (HFMD) is caused by a group of enteroviruses (EVs) and has a high incidence in children; some subtypes had high mortalities in children. The subtypes of HFMD had a different incidence across seasons. Thereby, we suspect that the infection of HFMD is varied by meteorological factors. However, studies examining serotype-specific associations between meteorological factors and HFMD incidence were rare.MethodsWe obtained all HFMD cases that occurred from 1 January 2010 to 31 December 2018 in Zhejiang province from the China Information System for Disease Control and Prevention (CISDCP). Daily meteorological data for Zhejiang province were provided by the China Methodological Data Sharing Service System and linked to HFMD cases based on residential addresses and dates of onset. The associations between meteorological factors and HFMDs were examined using distributed lag non-linear models (DLNMs) for each serotype.ResultsOverall, the incidences of all HFMD cases were increasing in study years, while the number of severe and fatality cases were decreasing. The dominant serotypes varied by study year. The association between temperature and incidence of both CVA16 and EV71 serotypes showed an inverted U shape. The risk ratio for CVA16 was increasing when temperature is 11–25°C, reaching the maximum RR at 18°C and humidity above 77% can promote the occurrence with CVA16, and temperature between 11 and 32°C with the maximum RR at 21°C and relative humidity above 77% are risk conditions of the occurrence of HFMD associated with EV71. For other enteroviruses causing HFMD, temperature above 11°C and humidity above 76% have a risk effect. CVA16, EV71, and all enteroviruses of HFMD have a maximum effect on lag day 0, and temperature is 35, 34, and 33°C respectively, while the enteroviruses of HFMD other than EV71 and CVA16 has a maximum effect when the temperature is 33°C and the lag time is 7 days.ConclusionThis study shows that meteorological factors have an effect on the occurrence of different HFMD serotypes. Local control strategies for public health should be taken in time to prevent and reduce the risk of HFMD while the weather is getting warmer and wetter.

ObjectiveAutochthonous transmission of the dengue virus (DENV) occurred each year from 2014 to 2018 in Zhejiang province, and became an emerging public health problem. We characterized the autochthonous transmission of the DENV and traced the source of infection for further control and prevention of dengue.MethodsDescriptive and spatiotemporal cluster analyses were conducted to characterize the epidemiology of autochthonous transmission of the DENV. Molecular epidemiology was used to identify the infection source.ResultsIn total, 1,654 indigenous cases and 12 outbreaks, with no deaths, were reported during 2004–2018. Before 2017, all outbreaks occurred in suburban areas. During 2017–2018, five out of eight outbreaks occurred in urban areas. The median duration of outbreaks (28 days) in 2017–2018 was shortened significantly (P = 0.028) in comparison with that in 2004–2016 (71 days). The median onset-visiting time, visiting-confirmation time, and onset-confirmation time was 1, 3, and 4 days, respectively. The DENV serotypes responsible for autochthonous transmission in Zhejiang Province were DENV 1, DENV 2, and DENV 3, with DENV 1 being the most frequently reported. Southeast Asia was the predominant source of indigenous infection.ConclusionsZhejiang Province witnessed an increase in the frequency, incidence, and geographic expansion of indigenous Dengue cases in recent years. The more developed coastal and central region of Zhejiang Province was impacted the most.

Introduction Pertussis is one of the top 10 diseases of children under 10 years of age, and the few vaccine-preventable diseases who is on a rise in China in recent years; however, the true burden of pertussis, including age-stratified incidence and risk factors of severe sequelae, are under-recognised. We aim to estimate the health burden of laboratory-confirmed pertussis by age groups, considering the setting of illness onset (ie, in community, outpatient and inpatient), in a Chinese population (~2.23 million in total) at two sites.Methods and analysis This paper describes the study design of a 1-year, prospective, age-stratified and population-based case–control study, including site selection, study population, case registry, ascertainment and enrolment, control recruitment, follow-up of case, microbiological methods, data collection, quality control activities and statistical methods used to generate incidence estimates. During June 2021 through May 2022, registry of suspected pertussis cases (namely chronic/persistent cough) will be conducted in several participating hospitals (SHs) at the two sites, which are selected based on Healthcare Utilisation and Attitudes Surveys (HUAS) carried out before study initiation. A case–control study will be conducted in the SHs and we aim to enrol a total of 1000 suspected pertussis cases (ie, all hospital admissions and the first 1–3 outpatient visits each week each hospital) and 2000 frequency matched healthy controls in community. Our primary study outcome, the laboratory-confirmed Bordetella pertussis infection, will be determined by a comprehensive laboratory methods and procedures (ie, culture, PCR and serological tests) in both cases and controls at enrolment and during 60-day’s follow-up visits. Finally, data from HUAS (ie, population size), case registry (ie, the total number of suspected pertussis cases) and case–control study (ie, the prevalence or population attributable fraction of Bordetella pertussis) will be combined to calculate incidence and its 95% CI through bootstrap method. Epidemiological analyses will be conducted to determine the risk factors associated with severe sequelae of pertussis.Ethics and dissemination This study has been approved by Chinese Centre for Disease Control and Prevention’s Institutional Review Board (no. ICDC-202110). Results will be disseminated via academic presentations and publication in peer-reviewed journals, and will provide valuable scientific data and some new insights into the incidence, aetiology and risk factors for severe sequelae of pertussis to academic societies and the public health authorities who is currently struggling and fighting against this burdensome disease worldwide.

Background The high cost and insufficient supply of HPV vaccines have substantially slowed their implementation in lower-income countries. This study aimed to assess the incremental cost-effectiveness of two doses of human papillomavirus (HPV) vaccination (bivalent 16/18 vaccine; 2vHPV) compared to a no-vaccination scenario and a three-dose scenario in one province in China. Methods A static Markov model was used to model a lifetime cohort of 100,000 girls aged 12 years at the start of vaccination. A two-dose vaccination schedule was assumed to be non-inferior to a three-dose schedule in terms of vaccine efficacy, and both vaccination schemes were assumed to provide lifelong protection. Incremental costs, health effects and incremental cost-effectiveness ratios were used to measure the outcomes when comparing the different strategies. Results Compared to no vaccination, the incremental cost-effectiveness ratio (Chinese yuan per quality-adjusted life year) of the two-dose vaccination strategy is 12,472, and the 2-dose strategy is calculated to be cost saving relative to the 3-dose vaccination strategy. Conclusions Introducing the 2vHPV vaccine would be highly cost effective at a per-dose vaccine price of CNY 500, which has implications for cervical cancer control in China and other resource-limited countries.

Essential proteins play significant roles in cell survive. In current years, some Protein-Protein Interaction (PPI) data have been discovered in saccharomyces cerevisiae. Due to the high costs of biological experiments, a growing number of computational models are adopted to predict essential proteins. However, the identification accuracy of these computational models still has broad space for improvement. In this paper, a novel prediction model called NPRI is proposed to infer potential essential proteins based on the PageRank algorithm. In NPRI, a new heterogeneous Protein-Domain network will be constructed by integrating three kinds of networks such as the weighted PPI network, the Domain-Domain network and the initial Protein-Domain network first. Here, these three kinds of networks are established in accordance with gene expression data, original PPI network and known Protein-Domain network respectively. Next, based on the newly constructed heterogeneous Protein-Domain network, we will extract functional features and topological characteristics for each protein to further construct a novel distribution rate network. And then, an improved iteration method based on the PageRank algorithm will be implemented on the novel distribution rate network to infer essential proteins. Finally, in order to evaluate the performance of NPRI, we will compare NPRI with other state-of-the-art prediction models, and simulation results show that NPRI can achieve reliable identification accuracies of 90%, 84.5% and 79% in top 100, 200 and 300 predicted candidate essential proteins separately, which outperform these competitive models remarkably, and means that NPRI is a promising framework for identifying essential proteins as well.

Identification of essential proteins is important for understanding cell survival and development, because even if only one of these proteins is missing, organisms cannot survive or develop. Since traditional methods for identifying essential proteins based on biological experiments are costly and inefficient, more and more computational models are proposed for predicting essential proteins in recent years. In this paper, a novel computational model called BSPM is proposed, in which, an original PPI network will be built based on known protein-protein associations first, and then topology information of the original PPI network will be adopted to measure the similarities between proteins based on the SimRank algorithm. Thereafter, a weighted PPI network can be obtained based on the similarities between proteins and the original PPI network. Finally, based on the weighted PPI network, the PageRank algorithm will be used to infer potential essential proteins. Moreover, in order to evaluate the performance of BSPM, we have compared the performance of BSPM with 14 classical prediction models in the field based on two different databases, and experimental results show that BSPM can achieve prediction accuracies of 92%, 81% and 76% out of the top 100, 200 and 300 candidate proteins separately, which not only are significantly better than those 14 competitive classical prediction models, but also means that BSPM can be used as an effective model for identifying essential proteins in the future.

Accumulating studies have indicated that essential proteins play critical roles in numerous biological processes. With the rapid development of high-throughput technologies, a large number of Protein-Protein Interaction (PPI) data have been found in Saccharomyces cerevisiae, which facilitate the formation of PPI networks. Up to now, a series of computational methods for predicting essential proteins from PPI networks have been proposed successively. However, the prediction accuracy of these computational methods is still not quite satisfactory. In this paper, a novel prediction method called CVIM is proposed to infer potential essential proteins. In CVIM, original PPI networks will be first transferred into weighted PPI networks by implementing PCC (Pearson Correlation Coefficient) on protein gene expression data. And then, based on weighted PPI networks and information of orthologous proteins, some critical network topological features and protein functional features will be extracted for each protein in the weighted PPI network. Finally, based on these newly extracted topological and functional features of proteins, an iterative algorithm will be designed to predict essential proteins. In order to evaluate the identification performance of CVIM, we have compared CVIM with 13 kinds of state-of-the-art prediction methods. Experimental results show that CVIM can achieve prediction accuracies of 92%, 80% and 71% out of the top 1%, 5% and 10% candidate proteins separately, which significantly outperform the prediction accuracies achieved by those state-of-the-art prediction methods. We have demonstrated that the prediction accuracy of essential proteins can be effectively improved by integrating the functional and network topological characteristics of proteins, which means that the novel method CVIM may be an excellent addition to the protein researches in the future.

With rapid development of high-throughput technique in the field of life science, LncRNAs are found to be inextricably linked to many diseases that seriously endanger human health. However, traditional experiment-based methods for inferring unknown diseases-LncRNA associations are time-consuming and laborious, therefore, it has become an effective way to adopt computational models to predict potential LncRNA-disease associations in recent years. In this article, a novel prediction model called LHNHLDA has been proposed. In LHNHLDA, based on known LncRNA-disease associations downloaded from benchmark databases, a heterogeneous LncRNA-disease network is built first by integrating LncRNA-LncRNA similarities with disease-disease similarities. And then, through adopting the LHN-2 algorithm, the path-based similarities between different nodes in the newly constructed heterogeneous network are obtained, which can be utilized to infer potential associations between LncRNAs and diseases. Finally, in order to evaluate the performance of LHNHLDA, intensive experiments have been done, and experimental results show that LHNHLDA can achieve reliable AUCs of 0.8155, 0.8281 and 0.8569 under the frameworks of 2-Fold CV, 5-Fold CV and LOO-CV respectively. Furthermore, case studies on lung cancer and leukemia illustrate that there are 10 and 7 potential LncRNAs out of the top 10 related LncRNAs of leukemia and lung cancer predicted by LHNHLDA having been confirmed to be linked to these two kinds of diseases by latest studies separately. Hence, due to the satisfactory prediction performance achieved by LHNHLDA, it is obvious that LHNHLDA may be a useful tool for future researches in the field of bioinformatics.

In recent years, with the development of high-throughput technologies, lots of computational methods for predicting essential proteins based on protein-protein interaction (PPI) networks and biological information of proteins have been proposed successively. However, due to the incompleteness of PPI networks, the prediction accuracy achieved by these methods is still unsatisfactory, and it remains to be a challenging work to design effective computational models to identify essential proteins. In this manuscript, a novel Prediction Model based on the Non-negative Matrix Factorization (PMNMF for abbreviation) is proposed. In PMNMF, an original PPI network will be constructed first based on PPIs downloaded from any given benchmark database. And then, based on topological features of protein nodes, the original PPI network will be further converted to a weighted PPI network. Moreover, in order to overcome the incompleteness of PPI networks, the NMF (Non-negative Matrix Factorization) method will be implemented on the weighted PPI network to obtain a transition probability matrix. And then, by integrating biological information including the gene expression information, homologous information and subcellular localization information of proteins, a unique initial score will be calculated and assigned to each protein node in the weighed PPI network, based on which, an improved Page-Rank algorithm will be designed to infer potential essential proteins. Finally, in order to evaluate the performance of PMNMF, it will be compared with 14 state-of-the-art prediction models, and experimental results show that PMNMF can achieve the best identification accuracy.

Growing evidence have demonstrated that many biological processes are inseparable from the participation of key proteins. In this paper, a novel iterative method called linear neighborhood similarity-based protein multifeatures fusion (LNSPF) is proposed to identify potential key proteins based on multifeature fusion. In LNSPF, an original protein-protein interaction (PPI) network will be constructed first based on known protein-protein interaction data downloaded from benchmark databases, based on which, topological features will be further extracted. Next, gene expression data of proteins will be adopted to transfer the original PPI network to a weighted PPI network based on the linear neighborhood similarity. After that, subcellular localization and homologous information of proteins will be integrated to extract functional features for proteins, and based on both functional and topological features obtained above. And then, an iterative method will be designed and carried out to predict potential key proteins. At last, for evaluating the predictive performance of LNSPF, extensive experiments have been done, and compare results between LNPSF and 15 state-of-the-art competitive methods have demonstrated that LNSPF can achieve satisfactory recognition accuracy, which is markedly better than that achieved by each competing method.

Background: To evaluate the value of locoregional radiotherapy (LRRT) in de novo metastatic nasopharyngeal carcinoma (dmNPC) and identify predictive factors for additional LRRT after palliative chemotherapy (PCT). Methods: Overall survival (OS) was the primary endpoint. Patients who underwent PCT and LRRT were categorized as the PCT+LRRT group; patients who only received palliative chemotherapy were categorized as the PCT group. Oligometastatic diseases (OMD) was defined as ≤5 metastatic lesions and ≤2 metastatic organs. Results: A total of 168 patients were included for this study. The median OS of patients in the PCT+LRRT group was significantly higher than those in the PCT group (57 months vs. 22 months, P

Accumulating studies have shown that microbes are closely related to human diseases. In this paper, a novel method called MSBMFHMDA was designed to predict potential microbe–disease associations by adopting multi-similarities bilinear matrix factorization. In MSBMFHMDA, a microbe multiple similarities matrix was constructed first based on the Gaussian interaction profile kernel similarity and cosine similarity for microbes. Then, we use the Gaussian interaction profile kernel similarity, cosine similarity, and symptom similarity for diseases to compose the disease multiple similarities matrix. Finally, we integrate these two similarity matrices and the microbe-disease association matrix into our model to predict potential associations. The results indicate that our method can achieve reliable AUCs of 0.9186 and 0.9043 ± 0.0048 in the framework of leave-one-out cross validation (LOOCV) and fivefold cross validation, respectively. What is more, experimental results indicated that there are 10, 10, and 8 out of the top 10 related microbes for asthma, inflammatory bowel disease, and type 2 diabetes mellitus, respectively, which were confirmed by experiments and literatures. Therefore, our model has favorable performance in predicting potential microbe–disease associations.

Considering that traditional biological experiments are expensive and time consuming, it is important to develop effective computational models to infer potential essential proteins. In this manuscript, a novel collaborative filtering model-based method called CFMM was proposed, in which, an updated protein–domain interaction (PDI) network was constructed first by applying collaborative filtering algorithm on the original PDI network, and then, through integrating topological features of PDI networks with biological features of proteins, a calculative method was designed to infer potential essential proteins based on an improved PageRank algorithm. The novelties of CFMM lie in construction of an updated PDI network, application of the commodity-customer-based collaborative filtering algorithm, and introduction of the calculation method based on an improved PageRank algorithm, which ensured that CFMM can be applied to predict essential proteins without relying entirely on known protein–domain associations. Simulation results showed that CFMM can achieve reliable prediction accuracies of 92.16, 83.14, 71.37, 63.87, 55.84, and 52.43% in the top 1, 5, 10, 15, 20, and 25% predicted candidate key proteins based on the DIP database, which are remarkably higher than 14 competitive state-of-the-art predictive models as a whole, and in addition, CFMM can achieve satisfactory predictive performances based on different databases with various evaluation measurements, which further indicated that CFMM may be a useful tool for the identification of essential proteins in the future.

Introduction: Most countries are dependent on nonpharmaceutical public health interventions such as social distancing, contact tracing, and case isolation to mitigate COVID-19 spread until medicines or vaccines widely available. Minimal research has been performed on the independent and combined impact of each of these interventions based on empirical case data. Methods: We obtained data from all confirmed COVID-19 cases from January 7th to February 22nd 2020 in Zhejiang Province, China, to fit an age-stratified compartmental model using human contact information before and during the outbreak. The effectiveness of social distancing, contact tracing, and case isolation was studied and compared in simulation. We also simulated a two-phase reopening scenario to assess whether various strategies combining nonpharmaceutical interventions are likely to achieve population-level control of a second-wave epidemic. Results: Our study sample included 1,218 symptomatic cases with COVID-19, of which 664 had no inter-province travel history. Results suggest that 36.5 % (95 % CI, 12.8–57.1) of contacts were quarantined, and approximately five days (95 % CI, 2.2–11.0) were needed to detect and isolate a case. As contact networks would increase after societal and economic reopening, avoiding a second wave without strengthening nonpharmaceutical interventions compared to the first wave it would be exceedingly difficult. Conclusions: Continuous attention and further improvement of nonpharmaceutical interventions are needed in second-wave prevention. Specifically, contact tracing merits further attention.

To understand the activity of gravity waves (GWs) over the Tibetan Plateau (TP) is of great significance for improving global climate models. Considering that the lower stratosphere is the main level of GWs activity, this paper first established a 14-year 2° × 2° longitude–latitude monthly mean GWs model in the lower stratosphere (18~20 km) of the TP by combining post-processed dry temperature profiles provided by the multi-Global Navigation Satellite System (GNSS) radio occultation (RO) missions: The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) and the Meteorological Operational (METOP) series polar-orbiting meteorological satellites (METOP-A, METOP-B, and METOP-C) from August 2006 to September 2020. Based on this model, this paper analyzed the characteristics of GWs activity around TP and the effects of topography, background wind, and zonal wind on GWs activity and summarized the general process of topographic wave excitation and upward propagation around TP. The spatial distribution of the lower stratospheric GW Ep is highly correlated with the spatial distribution of background wind and the topography of TP during GWs excitation. The GW Ep is obviously filtered by the zero-speed wind. The change in GW Ep is strongly correlated with the change in topography. These phenomena indicate that the GWs of TP are mainly topographic waves. Moreover, the lower stratospheric GW Ep of TP shows that periodic changes are mainly affected by the periodic background wind, and the GW Ep value is larger in February and smaller in August. The large GW Ep in the lower stratosphere of TP is not only related to the GWs strongly generated by the interaction between the strong background wind and the large elevation or large topographic changes but also related to the strong zonal westerly winds that promote the propagation of GWs upward. Multivariable linear regression models were used to reconstruct the lower stratospheric GW Ep over TP based on the background wind and the zonal wind and a goodness of fit of 81.1% was achieved. It indicates that the GW Ep is dominated by the topographic wave over TP in the lower stratosphere and the background wind has a greater influence on the GWs than the zonal wind.

In recent years, many computational models have been designed to detect essential proteins based on protein-protein interaction (PPI) networks. However, due to the incompleteness of PPI networks, the prediction accuracy of these models is still not satisfactory. In this manuscript, a novel key target convergence sets based prediction model (KTCSPM) is proposed to identify essential proteins. In KTCSPM, a weighted PPI network and a weighted (Domain-Domain Interaction) network are constructed first based on known PPIs and PDIs downloaded from benchmark databases. And then, by integrating these two kinds of networks, a novel weighted PDI network is built. Next, through assigning a unique key target convergence set (KTCS) for each node in the weighted PDI network, an improved method based on the random walk with restart is designed to identify essential proteins. Finally, in order to evaluate the predictive effects of KTCSPM, it is compared with 12 competitive state-of-the-art models, and experimental results show that KTCSPM can achieve better prediction accuracy. Considering the satisfactory predictive performance achieved by KTCSPM, it indicates that KTCSPM might be a good supplement to the future research on prediction of essential proteins.

In recent years, due to low accuracy and high costs of traditional biological experiments, more and more computational models have been proposed successively to infer potential essential proteins. In this paper, a novel prediction method called KFPM is proposed, in which, a novel protein-domain heterogeneous network is established first by combining known protein-protein interactions with known associations between proteins and domains. Next, based on key topological characteristics extracted from the newly constructed protein-domain network and functional characteristics extracted from multiple biological information of proteins, a new computational method is designed to effectively integrate multiple biological features to infer potential essential proteins based on an improved PageRank algorithm. Finally, in order to evaluate the performance of KFPM, we compared it with 13 state-of-the-art prediction methods, experimental results show that, among the top 1, 5, and 10% of candidate proteins predicted by KFPM, the prediction accuracy can achieve 96.08, 83.14, and 70.59%, respectively, which significantly outperform all these 13 competitive methods. It means that KFPM may be a meaningful tool for prediction of potential essential proteins in the future.

Objective: To develop and validate the Thai version of the functional assessment of cancer therapy with nasopharyngeal cancer subscale (FACT-NP) with the abbreviated version of the World Health Organization Quality of Life-BREF instrument (WHOQOL-BREF). Material and Methods: A cross-sectional study was performed in the Radiology Department between January 2014 and October 2016. Inclusion criteria: nasopharyngeal carcinoma patients aged greater than 18 years. Exclusion criteria: no comprehension of the Thai language, had other cancers (except for skin cancer and diagnosed with impaired cognition and/or overt psychosis), major depression or delirium. After signing the consent form, participants were interviewed and self-completed FACT-NP and WHOQOL-BREF. The structure of the FACT-NP was determined with exploratory factors analysis. The internal reliability of identified domains was evaluated using Cronbach’s alpha. The correlation between domains of the modified FACT-NP and the domains of WHOQOL-BREF were examined with Spearman’s correlation. Known-group validity was determined by comparing patients with different sociodemographic and clinical characteristics using the T-test, Ranksum test and analysis of variance. A p-value of less than 0.05 was considered significant. Results: This study was terminated early due to a long accrual period. Of the 230 patients included in the study, only 220 completed both FACT-NP and WHOQOL-BREF. Exploratory factor analysis showed an accumulative variance of 0.56 with 4 factors. The internal reliability of modified FACT-NP was 0.92. There was moderate correlation between modified FACT-NP and WHOQOL-BREF. Both the modified FACT-NP and WHOQOL-BREF could identify differences between the groups. Conclusion: The Thai modified FACT-NP was found to be both valid and reliable for measuring the quality of life in Thai nasopharyngeal cancer patients.

In recent years a number of calculative models based on protein-protein interaction (PPI) networks have been proposed successively. However, due to false positives, false negatives, and the incompleteness of PPI networks, there are still many challenges affecting the design of computational models with satisfactory predictive accuracy when inferring key proteins. This study proposes a prediction model called WPDINM for detecting key proteins based on a novel weighted protein-domain interaction (PDI) network. In WPDINM, a weighted PPI network is constructed first by combining the gene expression data of proteins with topological information extracted from the original PPI network. Simultaneously, a weighted domain-domain interaction (DDI) network is constructed based on the original PDI network. Next, through integrating the newly obtained weighted PPI network and weighted DDI network with the original PDI network, a weighted PDI network is further constructed. Then, based on topological features and biological information, including the subcellular localization and orthologous information of proteins, a novel PageRank-based iterative algorithm is designed and implemented on the newly constructed weighted PDI network to estimate the criticality of proteins. Finally, to assess the prediction performance of WPDINM, we compared it with 12 kinds of competitive measures. Experimental results show that WPDINM can achieve a predictive accuracy rate of 90.19, 81.96, 70.72, 62.04, 55.83, and 51.13% in the top 1%, top 5%, top 10%, top 15%, top 20%, and top 25% separately, which exceeds the prediction accuracy achieved by traditional state-of-the-art competing measures. Owing to the satisfactory identification effect, the WPDINM measure may contribute to the further development of key protein identification.

Renal cell carcinoma (RCC), with high morbidity and mortality, is one of the top ten serious cancers. Due to limited therapies and little knowledge about the mechanism underlying RCC, overall survival of RCC patients is poor. UBE2C is a member of ubiquitin modification system and promotes carcinogenesis in cancer, but its role in RCC is unknown. Based on the TCGA (The Cancer Genome Atlas) data, UBE2C was over-expressed in a total of 525 RCC tissues and displayed higher expression in advanced tissues (stage IV vs stage I, p