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BackgroundColorectal cancer (CRC) is a leading cause of morbidity and mortality, and timely diagnosis and intervention are crucial for cancer patients. Transfer RNA-derived fragments (tRFs) play a noncoding regulatory role in organisms. Serum EV(extracellular vesicles), as an integral mediator of intercellular transmission of genetic information vesicles in Transfer RNA-derived fragment (tRF RNA), are expected to be minimally invasive diagnostic and predictive biologic factors of CRC.MethodsCollect serum samples from 205 CRC patients, and then isolate extracellular vesicles from the serum. Captured the physical morphology of EV through transmission electron microscopy. The particle size was detected by particle size assay, and protein expression on the surface of EV was verified by Western blot. Gene microarrays were screened for differentially expressed tRF-RNA. TRF RNAs were verified by qPCR for differential expression in 205 CRC patients and 201 healthy donors, assessing the CRC diagnostic efficiency by area under the curve (AUC).ResultsCompared with 201 healthy donors, CRC patients experienced significantly down-regulated serum EV 3’tRF-ThrCGT while significantly up-regulated 3’tRF-mtlleGAT. Serum EV 3’tRF-ThrCGT and 3’tRF-mtlleGAT predictive diagnostic efficiency: 0.669 and 0.656, and the combination of CEA and CA724 predictive diagnostic efficiency was 0.938.ConclusionThe study data showed that 3’tRF-ThrCGT and 3’tRF-mtlelGAT can be minimally invasive diagnostic CRC indicators. The combination of tumor markers CEA and CA724 has important diagnostic significance.

In this paper, F-augmented closure spaces are generalized to F-closure spaces, and the concept of F-closed sets are introduced. Properties of their ordered structures are investigated. Representations of various algebraic domains such as algebraic lattices, algebraic L-domains, BF-domains via F-closure spaces are considered. As applications of these methods, more direct approaches to representing various algebraic domains via classical closure space are given, respectively. F-relations between F-closure spaces are defined and properties of them are examined. It is also proved that the category of algebraic domains with Scott continuous maps is equivalent to that of F-closure spaces with F-relations.

Abstract Whether in the field of medical care, or in people’s daily life and health protection, the importance of masks has been paid more and more attention. Acne, the most common complication after wearing masks, which is also called maskne, has been successfully introduced into the common language as a common topic of dermatologist consultations. This study aims to study the changes of microflora in maskne patients and healthy controls before and after wearing masks. In the summer of 2023, we collected a total of 50 samples from 15 maskne patients and 10 healthy controls before and after wearing surgical masks for a long time. 16 S ribosomal DNA sequencing and identification technology with V3-V4 variable region were adopted to explore the microbiome changes caused by mask wearing, analyze the changes in microbial diversity, and make interaction network. LDA effect size analysis was used to identify which bacteria showed significant changes in their relative abundance from phylum to genus. After wearing a mask, the microbiome of the maskne patients changed significantly more than that of the healthy controls, with both α diversity and β diversity lower than those of maskne patients before wearing masks and those of healthy controls after wearing masks. Co-occurrence network analysis showed that compared with other groups, the network of maskne patients after wearing masks for a long time had the lowest connectivity and complexity, but the highest clustering property, while the opposite was true for healthy controls. Many microbes that are potentially beneficial to the skin decreased significantly after wearing a mask. There was almost no difference in healthy controls before and after wearing a mask.

The diffusion of chloride ions in reinforced concrete under the seawater environment will affect the physical or mechanical properties and durability. Sulfate ions also influence the diffusion of chloride ions by generating expansion products through physical and chemical reactions. Further research on the effects of chloride binding and sulfate ion attack on chloride ion diffusion will deepen the understanding of concrete failure mechanism. In this paper, a quaternary blend based on calcium sulfoaluminate (CSA) cement was prepared and laboratory immersion tests were conducted in artificial seawater with varying salt concentrations. Initially, chloride ion concentration measurements and XRD tests were conducted at different corrosion ages and salt concentrations. Subsequently, the chloride binding properties of CSA-based materials was quantified through the analysis of chloride ion concentration and the phases of hydration products. Meanwhile, the corrosive effect of sulfate ions on chloride ion diffusion was determined corresponds to the content of expansive hydration products. Furthermore, numerical simulations were employed to evaluate the impact of sulfate ion corrosion, chloride binding, and their combined effects on chloride ion diffusion, and the results were compared with free chloride ion concentration (Cf). The results indicate that the total chloride ion concentration (Ct) exhibits a linear relationship with increasing salt concentrations of seawater in CSA-based materials. Meanwhile, the bound chloride ion concentration (Cb) follows a logarithmic growth pattern. The chloride binding rate experiences a linear decline with the increase of chloride ion concentration in the artificial seawater. Additionally, the relationship between Cf and Cb or Ct can be described using different isothermal adsorption models for the CSA-based materials. Among these models, the Langmuir model is suitable for characterizing the relationship between Cf and Cb, while the Freundlich model is suitable for describing the relationship between Cf and Ct. Furthermore, it is noteworthy that sulfate ion attack and chloride binding exert nearly equivalent effects on the diffusion of chloride ions in CSA-based materials at short corrosion age and low chloride ion concentration. However, the corrosive impact of sulfate ions gradually evolves into the dominant factor in the progression of corrosion over time and the increase in seawater salt concentration.

ABSTRACT Current microbiome signatures for chronic diseases such as diabetic kidney disease (DKD) are mainly based on low-resolution taxa such as genus or phyla and are often inconsistent among studies. In microbial ecosystems, bacterial functions are strain specific, and taxonomically different bacteria tend to form co-abundance functional groups called guilds. Here, we identified guild-level signatures for DKD by performing in-depth metagenomic sequencing and conducting genome-centric and guild-based analysis on fecal samples from 116 DKD patients and 91 healthy subjects. Redundancy analysis on 1,543 high-quality metagenome-assembled genomes (HQMAGs) identified 54 HQMAGs that were differentially distributed among the young healthy control group, elderly healthy control group, early-stage DKD patients (EDG), and late-stage DKD patients (LDG). Co-abundance network analysis classified the 54 HQMAGs into two guilds. Compared to guild 2, guild 1 contained more short-chain fatty acid biosynthesis genes and fewer genes encoding uremic toxin indole biosynthesis, antibiotic resistance, and virulence factors. Guild indices, derived from the total abundance of guild members and their diversity, delineated DKD patients from healthy subjects and between different severities of DKD. Age-adjusted partial Spearman correlation analysis showed that the guild indices were correlated with DKD disease progression and with risk indicators of poor prognosis. We further validated that the random forest classification model established with the 54 HQMAGs was also applicable for classifying patients with end-stage renal disease and healthy subjects in an independent data set. Therefore, this genome-level, guild-based microbial analysis strategy may identify DKD patients with different severity at an earlier stage to guide clinical interventions.IMPORTANCETraditionally, microbiome research has been constrained by the reliance on taxonomic classifications that may not reflect the functional dynamics or the ecological interactions within microbial communities. By transcending these limitations with a genome-centric and guild-based analysis, our study sheds light on the intricate and specific interactions between microbial strains and diabetic kidney disease (DKD). We have unveiled two distinct microbial guilds with opposite influences on host health, which may redefine our understanding of microbial contributions to disease progression. The implications of our findings extend beyond mere association, providing potential pathways for intervention and opening new avenues for patient stratification in clinical settings. This work paves the way for a paradigm shift in microbiome research in DKD and potentially other chronic kidney diseases, from a focus on taxonomy to a more nuanced view of microbial ecology and function that is more closely aligned with clinical outcomes.

Er/Zr and Sc/Zr were added into 5xxx series alloy to prepare welding wires, and the welding wires with the different compositions were used to weld 6xxx extruded alloy. The effects of different alloying additions on the microstructure and properties of welded joints were investigated. The results show that Al3(Sc1-x, Zrx) particles in the Al–Mg-Sc-Zr alloy welding wire can be used as heterogeneous nucleation site, which can effectively refine the grain structure and reduce the flow of low melting point elements to the PMZ of the joint. Compared with Al–Mg and Al–Mg–Er–Zr welding wire, the grain refinement in WZ is improved by 20.5 % and 13.8 %, and that in FZ is improved by 35.0 % and 8.1 %. At the same time, the refined grain structure can effectively eliminate the stress concentration of the joint and reduce the probability of liquation cracks. The joint welded by Al–Mg-Sc-Zr wire possesses the highest ultimate tensile strength of 249 MPa, while the joints welded by Al–Mg-Sc-Zr wire and Al–Mg wire possess ultimate tensile strength of 159 MPa and 128 MPa, respectively.

Abstract Background Oligoribonuclease (orn) of P. aeruginosa is a highly conserved exonuclease, which can regulate the global gene expression levels of bacteria through regulation of both the nanoRNA and c-di-GMP. NanoRNA can regulate the expression of the bacterial global genome as a transcription initiator, and c-di-GMP is the most widely second messenger in bacterial cells. Objective This study seeks to elucidate on the regulation by orn on pathogenicity of P. aeruginosa. Methods P. aeruginosa with orn deletion was constructed by suicide plasmid homologous recombination method. The possible regulatory process of orn was analyzed by TMT quantitative labeling proteomics. Then experiments were conducted to verify the changes of Δorn on bacterial motility, virulence and biofilm formation. Bacterial pathogenicity was further detected in cell and animal skin trauma models. ELISA detection c-di-GMP concentration and colony aggregation and biofilm formation were observed by scanning electron microscope. Results orn deletion changed the global metabolism of P. aeruginosa and reduced intracellular energy metabolism. It leads to the disorder of the quorum sensing system, the reduction of bacterial motility and virulence factors pyocyanin and rhamnolipids. But, orn deletion enhanced pathogenicity in vitro and in vivo, a high level of c-di-GMP and biofilm development of P. aeruginosa. Conclusion orn regulates the ability of P. aeruginosa to adapt to the external environment.

To ensure the detection quality of the domestic model ionospheric digisonde adopted by the Phase II of Chinese Meridian Project, prototype testing including technique testing and a data quality comparison was performed. The test was carried out in the National Space Weather Observation Station in Danzhou, Hainan. After the prototype digisonde testing process was introduced and completed, the data quality evaluation results were analyzed in detail. A thorough comparison demonstrated that the prototype reached the overall standard established by the mature-type DSP-4D in the aspects of quality of ionograms, accuracy of ionospheric parameters (h'F2, hmF2, foF2, auto-scaled or manual-scaled), and drift velocity. According to the testing results of technical indices, the peak power of the two transmitting channels is no more than 686 W, and the voltage standing wave ratio is no more than 2.23. The sensitivity of the receiving channel reached −125 dBm，and the dynamic range reached 105 dB. The baseband bandwidth of the digital receiver is 70 kHz, and the suppression of outband interference is no less than 101 dB. The results of the data quality evaluation showed that the average differences between the prototype digisonde parameters of Fof2, h'F2, and hmF2 obtained using auto-scaling and manual scaling are 0.02 MHz, 28.39 km, and 0.39 km, respectively. The average differences between the three parameters of the prototype digisonde and those of the DPS-4D obtained using auto-scaling are −0.0 MHz, −2.61 km, and −9.12 km, respectively. The average differences between the three parameters of the prototype digisonde and those of the DPS-4D obtained using manual scaling are −0.13 MHz, 14.7 km, and 3.21 km, respectively. The prototype testing clarified the status of the technique of the domestic instrument, greatly contributing to its design optimization and finalization. It also established a reference for digisonde data quality assessment.

ObjectivesCardiogenic cerebral embolism (CCE) poses a significant health risk; however, there is a dearth of published prognostic prediction models addressing this issue. Our objective is to establish prognostic prediction models (PM) for predicting poor functional outcomes at 3 months in patients with acute CCE associated with non-valvular atrial fibrillation (NVAF) and perform both internal and external validations.MethodsWe included a total of 730 CCE patients in the development cohort. The external regional validation cohort comprised 118 patients, while the external time-sequential validation cohort included 63 patients. Multiple imputation by chained equations (MICE) was utilized to address missing values and the least absolute shrink and selection operator (LASSO) regression was implemented through the glmnet package, to screen variables.ResultsThe 3-month prediction model for poor functional outcomes, denoted as N-ABCD2, was established using the following variables: NIHSS score at admission (N), Age (A), Brain natriuretic peptide (BNP), C-reactive protein (CRP), D-dimer polymers (D), and discharge with antithrombotic medication (D). The model’s Akaike information criterion (AIC) was 637.98, and the area under Curve (AUC) for the development cohort, external regional, and time-sequential cohorts were 0.878 (95% CI, 0.854–0.902), 0.918 (95% CI, 0.857–0.979), and 0.839 (95% CI, 0.744–0.934), respectively.ConclusionThe N-ABCD2 model can accurately predict poor outcomes at 3 months for CCE patients with NVAF, demonstrating strong prediction abilities. Moreover, the model relies on objective variables that are readily obtainable in clinical practice, enhancing its convenience and applicability in clinical settings.

Monkeypox has been spreading worldwide since May 2022, when the World Health Organization (WHO) declared the outbreak a “public health emergency of international concern.” The spread of monkeypox has posed a serious threat to the health of people around the world, but few studies have been conducted, and the molecular mechanism of monkeypox after infection remains unclear. We therefore implemented a transcriptome analysis to identify signaling pathways and biomarkers in monkeypox-infected cells to help understand monkeypox-host cell interactions. In this study, datasets GSE36854 and GSE11234 were obtained from GEO. Of these, 84 significantly different genes were identified in the dataset GSE36854, followed by KEGG, GO analysis protein-protein interaction (PPI) construction, and Hub gene extraction. We also analyzed the expression regulation of hub genes and screened for drugs targeting hub genes. The results showed that monkeypox-infected cells significantly activated the cellular immune response. The top 10 hub genes are IER3, IFIT2, IL11, ZC3H12A, EREG, IER2, NFKBIE, FST, IFIT1 and AREG. AP-26113 and itraconazole can be used to counteract the inhibitory effect of monkeypox on IFIT1 and IFIT2 and serve as candidate drugs for the treatment of monkeypox virus infection. IRF1 may also be a transcription factor of IFIT. Our results provide a new entry point for understanding how monkeypox virus interacts with its host.

Objective Tunnel cracks seriously damage the corresponding life time and traffic safety. However, traditional manual detections cannot efficiently and accurately identify a large number of cracks in long tunnels.This paper proposes a real-time detection algorithm for tunnel surface cracks. Methods It innovatively applies the Gate Recurrent Unit (GRU) model for text learning and signal analysis to image classification, improving detection speed and ensuring detection accuracy of tunnel cracks. To enhance training efficiency, the cracks are preprocessed and converted into the frequency domain to extract the key information of tunnel cracks, and the matrix is reconstructed into one-dimensional vectors. Then, one-dimensional convolutional neural network is used to extract the vector depth feature, and recurrent neural networks can learn corresponding sequential dependencies to realize tunnel cracks detection. Results Test results show that this model can reduce the number of training parameters and hardware configuration requirements. At the same time, the detection accuracy can reach 98.8%, and the detection speed for single image can reach in 2.1 s. Comparing with the mainstream classification detection algorithms, its accuracy remains unchanged, with significantly improvements of both training efficiency and prediction rate respectively. Conclusion Finally, a detection framework is developed for large-scale tunnel cracks to extract corresponding crack information effectively.

Abstract. Background:. Emergence delirium (ED) is a kind of delirium that occured in the immediate post-anesthesia period. Lower body temperature on post-anesthesia care unit (PACU) admission was an independent risk factor of ED. The present study was designed to investigate the association between intraoperative body temperature and ED in elderly patients undergoing non-cardiac surgery. Methods:. This study was a secondary analysis of a prospective observational study. Taking baseline body temperature as a reference, intraoperative absolute and relative temperature changes were calculated. The relative change was defined as the amplitude between intraoperative lowest/highest temperature and baseline reference. ED was assessed with the confusion assessment method for intensive care unit at 10 and 30 min after PACU admission and before PACU discharge. Results:. A total of 874 patients were analyzed with a mean age of 71.8 ± 5.3 years. The incidence of ED was 38.4% (336/874). When taking 36.0°C, 35.5°C, and 35.0°C as thresholds, the incidences of absolute hypothermia were 76.7% (670/874), 38.4% (336/874), and 17.5% (153/874), respectively. In multivariable logistic regression analysis, absolute hypothermia (lowest value 1.0°C from baseline) and its cumulative duration were also associated with an increased risk of ED, respectively. When taking the relative increment >0.5°C as a threshold, the incidence of relative hyperthermia was 21.7% (190/874) and it was associated with a decreased risk of ED after adjusting above confounders. Conclusions:. In the present study, we found that intraoperative hypothermia, defined as either absolute or relative hypothermia, was associated with an increased risk of ED in elderly patients after non-cardiac surgery. Relative hyperthermia, but not absolute hyperthermia, was associated with a decreased risk of ED. Registration:. Chinese Clinical Trial Registry (No. ChiCTR-OOC-17012734).

Abstract We aimed to explore excessive daytime sleepiness (EDS) and correlates of clinical characteristics by using cross‐sectional and longitudinal analyses from the Parkinson's Progression Markers Initiative database. Four hundred twenty‐three patients with Parkinson's disease (PD; EDS: non‐EDS = 357:66) and 195 healthy controls (HCs; EDS: nEDS = 171:24) were enrolled in our study at baseline. Multiple linear and linear mixed‐effects models were used to research the relationships between EDS/daytime sleepiness severity and clinical characteristics. Relationships between the change rates of clinical characteristics and daytime sleepiness severity were further investigated through multiple linear regression models. Mediating effect analysis was used to determine whether autonomic dysfunction was the mediator between cognition assessments and daytime sleepiness severity. Patients with PD with EDS and greater daytime sleepiness severity presented faster cognitive decline, high possibility for rapid eye movement sleep behavior disorder, autonomic dysfunction, depression, and anxiety from cross‐sectional and longitudinal analyses. Furthermore, HC individuals with EDS showed a higher striatal binding ratio of the right putamen, right striatum, and mean striatum. Autonomic dysfunction may act as a mediator between PD and cognitive decline. In early PD, EDS and daytime sleepiness severity were related to several clinical variables, suggesting that EDS might play an essential role in regulating PD progression.

In the evolving realm of ergonomics, there is a growing demand for enhanced comfortability, visibility, and accessibility in the operation of engineering machinery. This study introduces an innovative approach to assess the ergonomics of a driller’s cabin by utilizing a digital human. Through the utilization of inertial motion capture sensors, the method enables the operation of a virtual driller animated by real human movements, thereby producing more precise and realistic human–machine interaction data. Additionally, this study develops a simplified model for the human upper limbs, facilitating the calculation of joint forces and torques. An ergonomic analysis platform, encompassing a virtual driller’s cabin and a digital human model, is constructed using Unity 3D. This platform enables the quantitative evaluation of comfortability, visibility, and accessibility. Its versatility extends beyond the current scope, offering substantial support for product development and enhancement.

Local ice pressure refers to the ice pressure exerted on a very small area of a structure during ice failure. The existence of high-pressure zones may lead to local deformation and damage to ice-resistant structures, posing a serious threat to the overall structural stability. This study simulates the interaction between sea ice and structures through model tests, analyzing the timing of extreme local ice pressures. The results show that at low loading speeds, there is a 50% probability that the extreme local ice pressure occurs at the peak of the global ice force, while at high loading speeds, this probability drops to around 25%. Further investigation into the relationship between the global ice force peak, ice thickness, loading speed, and local area with local ice pressure amplitude reveals that the local ice pressure amplitude decreases with increasing loading speed and increases with ice thickness. Based on the area averaging method for square regions, the relationship between local ice pressure amplitude and local area is studied, showing that ice thickness, local width, and loading speed all influence the pressure–area relationship. Based on the square area averaging method, the relationship between the local ice pressure amplitude and the local area was studied. It was found that a linear relationship exists between the power function coefficient of local ice pressure–area and the thickness-to-width ratio. Compared to brittle failure, the local ice pressure amplitude under ductile failure of the ice sheet is more significantly affected by ice thickness. This study provides a foundation and reference for the analysis of ice-resistant performance and structural design of polar marine engineering structures.

Low self-esteem has been identified as a risk factor for problematic mobile phone use (PMPU). However, the magnitude of self-esteem’s effect on PMPU varied across different studies. Drawing on the Interaction of Person-Affect-Cognition-Execution model and the response styles theory, this study developed a moderated mediation model to investigate the mediating role of fear of missing out (FoMO) and the moderating roles of ruminative subtypes (i.e., brooding and reflection) in the relationship between self-esteem and PMPU. We conducted a cross-sectional survey among 806 undergraduate students (Mage = 19.35 years, SD = 1.18) using the convenience sampling method. Results showed that self-esteem was negatively associated with PMPU. Mediation analysis revealed that the association between self-esteem and PMPU was mediated by FoMO. Furthermore, moderated mediation analyses revealed that the mediating effect of FoMO was moderated by both brooding and reflection, such that the indirect effect became stronger for individuals with higher levels of brooding/reflection. These findings add to previous research by shedding light on how (i.e., mediation) and under what conditions (i.e., moderation) self-esteem is associated with PMPU and have implications for early prevention and intervention of individual PMPU.

Objective With the development of drilling technology for deep strata, the study of the dynamic characteristics and parametric response law of drill bits and deep complex strata during drilling has become a key and difficult point in research. Methods In this paper, a combination of numerical simulation and response surface analysis was used to study the dynamic characteristics of PDC bits and typical complex strata in the drilling process, and parametric analysis was carried out. In this paper, soft-hard-staggered strata with dip angles as a typical complex formation were studied, the dynamic damage finite element simulation model between the PDC drill bit and the soft-hard-staggered strata with the full real size was established based on Abaqus, and the dynamic characteristics of the drill bit subjected to the reaction force of the strata during drilling were derived. Numerical simulation experiments were also conducted by orthogonal tests to investigate the dynamic characteristics of the PDC bit under different formation parameters and drilling parameters. Results The single factor analysis and response surface analysis were used to model and identify the relationship between drilling parameters and stratigraphic parameters and the reaction force on the drill bit by the stratum and to obtain the changing trend and regression model of the reaction force on the drill bit by the stratum under multiple parameters. The results showed that the higher the inclination angle of soft-hard-staggered strata, the higher the average value and peak impact value of the bit subjected to the stratigraphic reaction force. The larger the ratio of softness to the hardness of the strata is, the larger the average and peak reaction forces on the drill bit. Within a certain range, increasing the rotational speed will reduce the impact of the reaction force on the drill bit. Conclusion The research results provide a basis for predicting the bit reaction force during drilling in deep and complex formations and guide the control of the drilling trajectory in deep and complex formations, which is of great significance for deep geological drilling.

Abstract Introduction Rheumatoid arthritis (RA) is a chronic inflammatory illness that mostly affects the joints of the hands and feet and can reduce life expectancy by an average of 3 to 10 years. Although tremendous progress has been achieved in the treatment of RA, a large minority of patients continue to respond poorly to existing medications, owing in part to a lack of appropriate therapeutic targets. Methods To find therapeutic targets for RA, a Mendelian randomization (MR) was performed. Cis-expression quantitative trait loci (cis-eQTL, exposure) data were obtained from the eQTLGen Consortium (sample size 31,684). Summary statistics for RA (outcome) were obtained from two largest independent cohorts: sample sizes of 97,173 (22,350 cases and 74,823 controls) and 269,377 (8279 cases and 261,098), respectively. Colocalisation analysis was used to test whether RA risk and gene expression were driven by common SNPs. Drug prediction and molecular docking was further used to validate the medicinal value of drug targets. Results Seven drug targets were significant in both cohorts in MR analysis and supported by localization. PheWAS at the gene level showed only ATP2A1 associated with other traits. These genes are strongly associated with immune function in terms of biological significance. Molecular docking showed excellent binding for drugs and proteins with available structural data. Conclusion This study identifies seven potential drug targets for RA. Drugs designed to target these genes have a higher chance of success in clinical trials and is expected to help prioritise RA drug development and save on drug development costs.

Abstract The incidence of metabolic syndrome is significantly higher in patients with irritable bowel syndrome (IBS), but the mechanisms involved remain unclear. Gut microbiota is causatively linked with the development of both metabolic dysfunctions and gastrointestinal disorders, thus gut dysbiosis in IBS may contribute to the development of metabolic syndrome. Here, we show that human gut bacterium Ruminococcus gnavus-derived tryptamine and phenethylamine play a pathogenic role in gut dysbiosis-induced insulin resistance in type 2 diabetes (T2D) and IBS. We show levels of R. gnavus, tryptamine, and phenethylamine are positively associated with insulin resistance in T2D patients and IBS patients. Monoassociation of R. gnavus impairs insulin sensitivity and glucose control in germ-free mice. Mechanistically, treatment of R. gnavus-derived metabolites tryptamine and phenethylamine directly impair insulin signaling in major metabolic tissues of healthy mice and monkeys and this effect is mediated by the trace amine-associated receptor 1 (TAAR1)-extracellular signal-regulated kinase (ERK) signaling axis. Our findings suggest a causal role for tryptamine/phenethylamine-producers in the development of insulin resistance, provide molecular mechanisms for the increased prevalence of metabolic syndrome in IBS, and highlight the TAAR1 signaling axis as a potential therapeutic target for the management of metabolic syndrome induced by gut dysbiosis.

The value of Extracellular vesicles (EVs) diagnostic markers is widely recognized. However, current research on EV DNA remains limited. This study investigates the biological properties, preprocessing factors, and diagnostic potential of EV DNA. We found that DNA positive vesicles account for 23.3% ± 6.7% of the urine total EV, with a large amount of DNA attached to the outside. EV DNA fragments are large, there is no significant effect on uEV DNA when store urine less than 6 h at 4°C. In addition, the influence of different EV extraction methods on methylation detection is also minor. More importantly, RASSF1A methylation in urine total EV DNA can distinguish between PCa and BPH, with an AUC of 0.874. Our results suggest the potential of urine EV DNA as a novel marker for PCa diagnosis. This provides a new idea for the study of urinary tumor markers.

Abstract Objective: To determine the prevalence of overweight and obesity in patients with severe mental disorders (SMD) and the factors associated with their socio-demographic and disease characteristics in a cross-sectional population-based study. Design: This analysis examined the prevalence of overweight and obesity in 14 868 managed SMD patients in an urban area of Shenzhen city based on data from the health information monitoring system in 2021. Multivariate logistic regression were used to identify the factors associated with the prevalence of overweight and obesity in patients with SMD. Setting: China. Participants: 14 868 patients with SMD. Results: The prevalence of overweight and obesity in patients with SMD in this study was 32·6 % and 16·1 %, respectively. In multivariate analysis, married status, Shenzhen household registration, management durations of 5–10 years and >10 years, participation in family physician services, taking clozapine or aripiprazole, FPG > 6·1 mmol/l, hypertension, TC ≥ 5·2 mmol/l, TG ≥ 1·7 mmol/l, and more frequent follow-ups in the past year were associated with higher odds of overweight and obesity. Compared to their respective reference categories, living with parents, spouse and children, taking risperidone, aripiprazole, amisulpride and perphenazine, FPG > 6·1 mmol/l, hypertension, TC ≥ 5·2 mmol/l, TG ≥ 1·7 mmol/l, and more frequent follow-ups in the past year were associated with higher odds of obesity. Conclusion: We observed a high prevalence of overweight and obesity in patients with SMD in this study. The findings highlight the need for integrated management of overweight and obesity risk factors among patients with SMD.

BackgroundInterpersonal violence (IPV) against young women, including physical and sexual violence, poses a major threat to public health. We analyzed global, regional and national trends in violence against females aged 10–24 years from 1990 to 2019.MethodsWe extracted age-standardized prevalence rates (ASPRs) of physical violence by firearm (PVF), physical violence by other means (PVOM), physical violence by sharp object (PVSO), and sexual violence (SV) from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019. Joinpoint regression analysis calculated annual and average annual percentage changes (AAPCs) in ASPRs.ResultsGlobally, the ASPRs of the four measures of IPV decreased between 1990 and 2019, with the steepest declines between 2000 and 2009, except for SV, which increased slightly. However, the ASPRs of PVF and PVOM increased slightly between 2010 and 2019. Regionally, PVF prevalence declined most in East Asia (−0.9505, −1.0011 to −0.8975), South Asia (−0.277, −0.3089 to −0.244) and Latin America but PVOM prevalence increased in Oceania (0.6275, 0.6036 to 0.6498) and SV prevalence increased in Caribbean (0.4267, 0.4069 to 0.4495). Nationally, PVF prevalence decreased most in Thailand (−2.4031, −2.4634 to −2.3328) but increased most in Libya (6.8143, 6.6194 to 7.0113). SV prevalence increased most in Oman (0.4561, 0.4338 to 0.478) and the largest increase in Disability-adjusted life years (DALYs) from PVOM was observed in Botswana (6.2725, 6.0951 to 6.4082). DALYs showed similar trends.ConclusionWhile global declines over 30 years are encouraging, IPV against young women persists. Urgent, tailored approaches across sectors are critical to curb drivers of violence against young women, including poverty, inequality and sociocultural attitudes. High-quality data and in-depth analyses can inform locally-relevant solutions. Overall, intensified political will and resource investment are needed to overcome this pervasive human rights violation.

The aim of this study was to understand the molecular epidemiological characteristics and drug resistance mechanism of carbapenem-resistant Pseudomonas aeruginosa (CRPA) and to provide a theoretical basis for the prevention and treatment of CRPA infection in hospitals. A total of 34 CRPA strains were isolated, and resistance to 13 commonly used antibiotics was detected using the TDR-300B Plus VitEK-2 compact automatic bacterial identification instrument. Then, carbapenemase production was detected using the Carbe NP test. RT-qPCR was used to detect the expression of efflux pump MexA and outer membrane protein OprD, and PCR amplification and sequence analysis were used to detect class Ⅰ integrons carried by drug resistance genes. Our results showed that of the 34 CRPAs, 22 were multi-drug resistant (MDR), and five were extensively drug-resistant (XDR). Sequencing analysis showed that class Ⅰ integron mainly carried aminoglycosides or quinolones resistance genes. Multiple mechanisms play important roles in the formation and development of MDR or XDR.

The functional tea CFT-1 has been introduced into China as a nutraceutical beverage according to the “Healthy China” national project. The effects on human hepatocellular carcinoma (HCC) cells remain unclear and were investigated with the functional tea extract (purity > 98%). The morphological changes in the cells were observed with microscopes. Cell proliferation, migration, cycle distribution, and apoptotic effects were assessed by MTT, Transwell assays, and flow cytometry, respectively, while telomerase inhibition was evaluated with telomerase PCR ELISA assay kits. The CFT-1 treatment resulted in cell shrinkage, nuclear pyknosis, and chromatin condensation. CFT-1 suppressed the growth of Hep3B cells with IC50 of 143 μg/mL by inducing apoptosis and G0/G1 arrest in Hep3B cells. As for the molecular mechanism, CFT-1 treatment can effectively reduce the telomerase activity. The functional tea extract inhibits cell growth in human HCC by inducing apoptosis and G0/G1 arrest, possibly through a reduction in telomerase activity. These results indicate that CFT-1 extract exhibited in vitro anticancer activities and provided insights into the future development and utilization of CFT-1 as functional foods to inhibit the proliferation of HCC cells.

This study focuses on the microstructures and soft-magnetic properties of the Al1.5Fe3Co3Cr1 multi-principal-element alloy (MPEA) in different states. The MPEA was prepared using arc melting and suction-casting, followed by various heat treatments. The crystal structures were analyzed using X-ray diffraction (XRD), while the microstructures were characterized by means of transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The results reveal that the MPEA consists mainly of coherent body-centered cubic (BCC) and B2 phases, with a moderate lattice misfit (ε = 0.14~0.21%) between them. The homogenized alloy shows the presence of coarse equiaxed grains and micro-scale cells, and it has good soft-magnetic properties with MS = 127 emu/g and HC = 143.3 A/m (1.8 Oe). The thermal stability of the alloy is found to be optimal after aging at 873 K, as there are no significant changes in microstructures and soft-magnetic properties. However, when the aging temperature increases to 973 K, the BCC nanoprecipitates are coarsened, leading to a decrease in the soft-magnetic properties.

The sea ice failure mode and ice force amplitude depend on the structural form at the point of interaction, but the impacts of ice load when interacting with marine engineering structures with additional attachments are not yet clear. This study conducts numerical simulations using the discrete element method to investigate the interaction between sea ice and cable pipes attached to offshore substation structures. Various operating conditions such as ice velocity, ice thickness, and ice attack angle are selected to simulate the interaction between sea ice and such structures, clarifying the variations in the sea ice failure mode and ice force amplitude. The results indicate that crushing failure mainly occurs when sea ice interacts with such structures, and the presence of cable pipes does not alter the sea ice failure mode at the legs of offshore substation structures. The preliminary action of sea ice with cable pipes effectively reduces the ice load on the structure, and the minimum ice force amplitude occurs at an ice attack angle of 90°, with the ice force amplitude increasing with the ice thickness but showing no clear correlation with the ice velocity. The findings of this study provide a reference for the ice-resistant design of offshore substation structures in cold regions.

BackgroundSome observational studies have shown that immune thrombocytopenia (ITP) is highly associated with the alteration-composition of gut microbiota. However, the causality of gut microbiota on ITP has not yet been determined.MethodsBased on accessible summary statistics of the genome-wide union, the latent connection between ITP and gut microbiota was estimated using bi-directional Mendelian randomization (MR) and multivariable MR (MVMR) analyses. Inverse variance weighted (IVW), weighted median analyses, and MR-Egger regression methods were performed to examine the causal correlation between ITP and the gut microbiota. Several sensitivity analyses verified the MR results. The strength of causal relationships was evaluated using the MR-Steiger test. MVMR analysis was undertaken to test the independent causal effect. MR analyses of reverse direction were made to exclude the potential of reverse correlations. Finally, GO enrichment analyses were carried out to explore the biological functions.ResultsAfter FDR adjustment, two microbial taxa were identified to be causally associated with ITP (PFDR < 0.10), namely Alcaligenaceae (PFDR = 7.31 × 10–2) and Methanobacteriaceae (PFDR = 7.31 × 10–2). In addition, eight microbial taxa were considered as potentially causal features under the nominal significance (P < 0.05): Actinobacteria, Lachnospiraceae, Methanobacteria, Bacillales, Methanobacteriales, Coprococcus2, Gordonibacter, and Veillonella. According to the reverse-direction MR study findings, the gut microbiota was not significantly affected by ITP. There was no discernible horizontal pleiotropy or instrument heterogeneity. Finally, GO enrichment analyses showed how the identified microbial taxa participate in ITP through their underlying biological mechanisms.ConclusionSeveral microbial taxa were discovered to be causally linked to ITP in this MR investigation. The findings improve our understanding of the gut microbiome in the risk of ITP.

The extraordinary success of deep learning is made possible due to the availability of crowd-sourced large-scale training datasets. Mostly, these datasets contain personal and confidential information, thus, have great potential of being misused, raising privacy concerns. Consequently, privacy-preserving deep learning has become a primary research interest nowadays. One of the prominent approaches adopted to prevent the leakage of sensitive information about the training data is by implementing differential privacy during training for their differentially private training, which aims to preserve the privacy of deep learning models. Though these models are claimed to be a safeguard against privacy attacks targeting sensitive information, however, least amount of work is found in the literature to practically evaluate their capability by performing a sophisticated attack model on them. Recently, DP-BCD is proposed as an alternative to state-of-the-art DP-SGD, to preserve the privacy of deep-learning models, having low privacy cost and fast convergence speed with highly accurate prediction results. To check its practical capability, in this article, we analytically evaluate the impact of a sophisticated privacy attack called the membership inference attack against it in both black box as well as white box settings. More precisely, we inspect how much information can be inferred from a differentially private deep model’s training data. We evaluate our experiments on benchmark datasets using AUC, attacker advantage, precision, recall, and F1-score performance metrics. The experimental results exhibit that DP-BCD keeps its promise to preserve privacy against strong adversaries while providing acceptable model utility compared to state-of-the-art techniques.

The key equipment to prevent precision machinery such as lithography from being influenced by air temperature fluctuation is a high-precision air temperature control (ATC) system. The ATC system developed for a lithography immersion testing platform suffers from the difficulties of multiple disturbances, large inertia and time delay. However, seldom have control methods can solve these problems systematically. A two-degree-of-freedom (2DOF) controller is proposed in this paper for this system, where model predictive control (MPC) is developed to overcome large inertia and time delay in the main control loop, and the measurable disturbance from the inlet air is compensated; based on the loop shaping approach, the controller in the disturbance rejection control loop is designed to attenuate the load disturbance; a Kalman filter is designed to reduce the influence of noise having no stable frequency characteristic. The experimental results show that the MPC controller and Kalman filter can effectively avoid the system oscillation, and the disturbance rejection control loop can significantly attenuate the load disturbance in the frequency range from 0.01 to 0.02 rad/s. Therefore, the 1σ precision is increased by 3.3 mK to 8.7 mK by the 2DOF controller when the system is influenced by the load disturbance with different fluctuation amplitudes.

Abstract Background Dysbiotic gut microbiome, genetically predisposed or chemically disrupted, has been linked with insulin-dependent diabetes (IDD) including autoimmune type 1 diabetes (T1D) in both humans and animal models. However, specific IDD-inducing gut bacteria remain to be identified and their casual role in disease development demonstrated via experiments that can fulfill Koch’s postulates. Results Here, we show that novel gut pathobionts in the Muribaculaceae family, enriched by a low-dose dextran sulfate sodium (DSS) treatment, translocated to the pancreas and caused local inflammation, beta cell destruction and IDD in C57BL/6 mice. Antibiotic removal and transplantation of gut microbiota showed that this low DSS disrupted gut microbiota was both necessary and sufficient to induce IDD. Reduced butyrate content in the gut and decreased gene expression levels of an antimicrobial peptide in the pancreas allowed for the enrichment of selective members in the Muribaculaceae family in the gut and their translocation to the pancreas. Pure isolate of one such members induced IDD in wildtype germ-free mice on normal diet either alone or in combination with normal gut microbiome after gavaged into stomach and translocated to pancreas. Potential human relevance of this finding was shown by the induction of pancreatic inflammation, beta cell destruction and IDD development in antibiotic-treated wildtype mice via transplantation of gut microbiome from patients with IDD including autoimmune T1D. Conclusion The pathobionts that are chemically enriched in dysbiotic gut microbiota are sufficient to induce insulin-dependent diabetes after translocation to the pancreas. This indicates that IDD can be mainly a microbiome-dependent disease, inspiring the need to search for novel pathobionts for IDD development in humans. Video Abstract

Parkinson’s Disease (PD) in the set of neuro-degenerative disorders stimulates due to the loss of dopaminergic neurons from the substantia nigra. Electroencephalogram (EEG) signals are being extensively utilized for diagnosing PD. The existing approaches extract the features using various frequency transformations that lose valuable signal information. An optimized Deep Convolutional Neural Network (CNN) inspired by the encoder part of U-Net architecture is proposed for classifying PD incorporating the resting electroencephalogram (EEG) signal dataset. The proposed model follows the U-Net architecture for extracting the features from the signals. The EEG recordings are taken from two datasets: the University of Mexico (UNM) EEGs and the University of California San Diego (UCSD) resting state dataset. The EEGs are pre-processed with a basic pre-processing pipeline, then separated into single channels, plotting each channel as a simple graph. These graphs are then fed to the proposed 23-layered convolutional neural network (CNN) for classifying PD from the normal control. Consequently, the model achieved maximum values of 93.10%, 93.18%, 93.09%, and 0.9313 of accuracy, precision, recall, and F1-score respectively for the UNM dataset, whereas, 97.90%, 98%, 97.87% and 0.9794 of accuracy, precision, recall, and F1-score respectively for UCSD dataset. The results show improved scores compared to the individual Machine Learning and CNN models applied on the same datasets.

Currently, many companies and institutions use centralized or distributed databases to store massive amounts of data. However, the use of untrusted centralized third-party auditors can result in security issues because these auditors may be malicious and tamper with or delete user data. This poses a significant challenge for ensuring the reliability of the data verification results. Although introducing a third-party auditor can help address this issue, it may also be untrustworthy and collude with the database service provider to forge false data verification results. In this study, we propose a data integrity verification scheme using smart contracts (DIV-SC) to address this challenge in a centralized database environment. Our approach utilizes blockchain technology as a decentralized third-party auditor, ensuring that the information stored on the blockchain is immutable and cannot be tampered with maliciously. In addition, smart contracts deployed on the blockchain can ensure that the verification procedures are correct and are not affected by any malicious party. We also leverage game theory to improve the reliability of multiple verifications, reduce verification time and improve overall performance. Our proposed scheme reduces the total verification time consumption by up to 53.44% while increasing the number of verifiable times by nearly 3.8 times, compared to conventional data integrity verification schemes.

Deep learning models have revolutionized AI tasks by producing accurate predictions. These models′ success largely depends on precise training using large-scale datasets, mostly crowdsourced from the target population. The training datasets may contain sensitive personal information, and the model parameters can encode this information on internal wirings of hidden layers, thus bearing the risk of a privacy breach. The modern trend of sharing trained models has grown the privacy breach risk manifold. The performance of existing privacy-preserving deep learning models trying to address this issue is unsatisfactory. Consequently, only a marginal proportion of these privacy-preserved models have been adopted in the industry. Therefore, we developed the first differentially private version of the block coordinate descent (BCD) algorithm. Our proposed mechanism considerably cuts the privacy cost by injecting an appropriate amount of noise in block variables. It achieves high accuracy comparable to its non-private equivalents. It improves the convergence speed and provides a provable privacy guarantee by performing privacy accounting using advanced composition and moments accountant methods. We empirically evaluate the robustness of our proposed mechanism on benchmark datasets. The results demonstrate the competitive performance in terms of both privacy cost reduction and speedy convergence against the state-of-the-art differential privacy-based mechanisms.

Profile matching is a key feature in mobile social networking applications, where friends are recommended to requesters by comparing the similarity of attributes between them.However, users’ personal information is exposed to the risk of privacy disclosure in the process of profile matching.The existing solutions exist some issues such as inaccurate matching results or inability to meet users’ requirements for multi-dimensional privacy protection.Baesed on this, a privacy-preserving precise profile matching (P3M) scheme was proposed, which allowed users to flexibly set the matching range of attributes and distances according to their requirements.The Paillier encryption was utilized to ensure data security of users, and a secure dot product protocol was designed to achieve secure ciphertext comparison of user attributes and query ranges.The P3M realized multi-dimensional privacy-preserving of users including user feature attributes and location attributes.Finally, the correctness and security of P3M scheme were analyzed and proved in detail, and extensive experimental results verified the effectiveness and efficiency of P3M scheme.

Forkhead box transcription factor, FOXQ1 is reported to promote epithelial-mesenchymal transition (EMT) and cancer metastasis. Here the authors show that FOXQ1 recruits the KMT2/MLL histone methyltransferase complex as a transcriptional coactivator to activate EMT programme in breast cancer.

Commercial insurance is an important risk management tool whose demand is affected by psychological factors. Music can promote consumption by evoking non-random emotional and behavioral responses in consumers . Using data from the China Comprehensive Social Survey, we examined the impact of music on commercial insurance purchase behavior using a probit model, an instrumental variable, propensity score matching, and other methods. The results indicate that music can significantly increase the probability of residents purchasing commercial medical insurance and endowment insurance by 2.569% and 1.869%, respectively. Regarding the mechanism, music encourages residents to purchase insurance by increasing their general cognition, risk cognition, insurance cognition, and ability to manage risk. Furthermore, an analysis of heterogeneity shows that music plays a greater role in promoting the aforementioned behavior of residents with lower levels of education, lower frequencies of social interaction, internet usage, an agricultural household registration, and of residents with jobs.

Background: Castration-resistant prostate cancer (CRPC) is a deadly malignancy without effective therapeutics. Cyclovirobuxine (CVB) can play an anticancer role by inhibiting mitochondrial function, regulating tumor cell apoptosis, dysregulating autophagy, and other mechanisms. This study aimed to examine the function and mechanism of CVB in CRPC to provide new insights into CRPC treatment. Methods: The effect of CVB on PC3 and C4-2 cell viability was determined using a CCK8 assay. Core therapeutic targets of CVB in CRPC cells were identified using RNA sequencing, online database, and PPI network analyses. Western blotting, RT–qPCR and molecular docking were performed to evaluate the regulation of core targets by CVB. Utilizing GO and KEGG enrichment analyses, the probable anti-CRPC mechanism of CVB was investigated. Immunofluorescence, flow cytometry and colony formation assays were used to verify the potential phenotypic regulatory role of CVB in CRPC. Results: CVB inhibited CRPC cell activity in a concentration-dependent manner. Mechanistically, it primarily regulated BRCA1-, POLD1-, BLM-, MSH2-, MSH6- and PCNA-mediated mismatch repair, homologous recombination repair, base excision repair, Fanconi anemia repair, and nucleotide excision repair pathways. Immunofluorescence, Western blot, flow cytometry and colony formation experiments showed that CVB induced DNA damage accumulation, cell apoptosis, and cell cycle arrest and inhibited CRPC cell proliferation. Conclusion: CVB can induce DNA damage accumulation in CRPC cells by targeting DNA repair pathways and then induce cell apoptosis and cell cycle arrest, eventually leading to inhibition of the long-term proliferation of CRPC cells.

Abstract As the world is now fighting against rampant virus COVID‐19, the development of vaccines on a large scale and making it reach millions of people to be immunised has become quintessential. So far 40.9% of the world got vaccinated. Still, there are more to get vaccinated. Those who got vaccinated have the chance of getting the vaccine certificate as proof to move, work, etc., based on their daily requirements. But others create their own forged vaccine certificate using advanced software and digital tools which will create complex problems where we cannot distinguish between real and fake vaccine certificates. Also, it will create immense pressure on the government and as well as healthcare workers as they have been trying to save people from day 1, but parallelly people who have fake vaccine certificates roam around even if they are COVID/Non‐COVID patients. So, to avoid this huge problem, this paper focuses on detecting fake vaccine certificates using a bot powered by Artificial Intelligence and neurologically powered by Deep Learning in which the following are the stages: a) Data Collection, b) Preprocessing to remove noise from the data, and convert to grayscale and normalised, c) Error level analysis, d) Texture‐based feature extraction for extracting logo, symbol and for the signature we extract Crest‐Trough parameter, and e) Classification using DenseNet201 and thereby giving the results as fake/real certificate. The evaluation of the model is taken over performance measures like accuracy, specificity, sensitivity, detection rate, recall, f1‐score, and computation time over state‐of‐art models such as SVM, RNN, VGG16, Alexnet, and CNN in which the proposed model (D201‐LBP) outperforms with an accuracy of 0.94.

Objective: In various cancers, migration and invasion inhibitory protein (MIIP) is expressed at low level and is involved in cancer pathogenesis. Herein, we sought to explore the function of MIIP in clear cell renal cell carcinoma (ccRCC). Methods: CCK-8, colony formation, cell cycle, and endothelial cell tube formation assays were performed to evaluate the roles of MIIP in ccRCC proliferation and angiogenesis. To explore the underlying mechanism, we conducted RNA-sequencing, GSEA, qRT-PCR, Western blot, ELISA, cell transfection, coimmunoprecipitation, and ubiquitination assays in ccRCC cell lines. Furthermore, xenograft tumor growth in nude mice, and Ki-67 and CD31 staining in xenograft tissues were examined. Finally, the association of MIIP expression with clinical pathology and the expression status of HIF-2α and cysteine-rich 61 (CYR61) were further analyzed in human RCC tissues through Western blot and immunohistochemistry. Results: Both in vitro and in vivo functional experiments indicated that forced expression of MIIP inhibited ccRCC proliferation and angiogenesis, whereas silencing MIIP either in normal HK-2 cells or in ccRCC cells had the opposite effect (P < 0.05). Mechanistically, CYR61 was identified as a gene significantly downregulated by MIIP overexpression, and was required for the suppressive role of MIIP in ccRCC. MIIP was found to promote HSP90 acetylation and thus impair its chaperone function toward HIF-2α. Consequently, RACK1 binds HIF-2α and causes its ubiquitination and proteasomal degradation, thus decreasing the transcription of its target, CYR61. Finally, analyses of clinical samples demonstrated that MIIP is significantly downregulated in cancer vs. normal tissues in RCC cases, and its expression is negatively associated with histological grade, metastasis, the prognosis of patients with RCC, and the expression of HIF-2α and CYR61 (P < 0.05). Conclusions: MIIP is a novel tumor suppressor in ccRCC via negative regulation of HIF-2α-CYR61 axis.

Today, in the field of malware detection, the expanding limitations of traditional detection methods and the increasing accuracy of detection methods designed on the basis of artificial intelligence algorithms are driving research findings in this area in favour of the latter. Therefore, we propose a novel malware detection model in this paper. This model combines a grey-scale image representation of malware with an autoencoder network in a deep learning model, analyses the feasibility of the grey-scale image approach of malware based on the reconstruction error of the autoencoder, and uses the dimensionality reduction features of the autoencoder to achieve the classification of malware from benign software. The proposed detection model achieved an accuracy of 96% and a stable F-score of about 96% by using the Android-side dataset we collected, which outperformed some traditional machine learning detection algorithms.

ABSTRACT Coronavirus disease 2019 (COVID-19) severity has been associated with alterations of the gut microbiota. However, the relationship between gut microbiome alterations and COVID-19 prognosis remains elusive. Here, we performed a genome-resolved metagenomic analysis on fecal samples from 300 in-hospital COVID-19 patients, collected at the time of admission. Among the 2,568 high quality metagenome-assembled genomes (HQMAGs), redundancy analysis identified 33 HQMAGs which showed differential distribution among mild, moderate, and severe/critical severity groups. Co-abundance network analysis determined that the 33 HQMAGs were organized as two competing guilds. Guild 1 harbored more genes for short-chain fatty acid biosynthesis, and fewer genes for virulence and antibiotic resistance, compared with Guild 2. Based on average abundance difference between the two guilds, the guild-level microbiome index (GMI) classified patients from different severity groups (average AUROC [area under the receiver operating curve] = 0.83). Moreover, age-adjusted partial Spearman’s correlation showed that GMIs at admission were correlated with 8 clinical parameters, which are predictors for COVID-19 prognosis, on day 7 in hospital. In addition, GMI at admission was associated with death/discharge outcome of the critical patients. We further validated that GMI was able to consistently classify patients with different COVID-19 symptom severities in different countries and differentiated COVID-19 patients from healthy subjects and pneumonia controls in four independent data sets. Thus, this genome-based guild-level signature may facilitate early identification of hospitalized COVID-19 patients with high risk of more severe outcomes at time of admission. IMPORTANCE Previous reports on the associations between COVID-19 and gut microbiome have been constrained by taxonomic-level analysis and overlook the interaction between microbes. By applying a genome-resolved, reference-free, guild-based metagenomic analysis, we demonstrated that the relationship between gut microbiota and COVID-19 is genome-specific instead of taxon-specific or even species-specific. Moreover, the COVID-19-associated genomes were not independent but formed two competing guilds, with Guild 1 potentially beneficial and Guild 2 potentially more detrimental to the host based on comparative genomic analysis. The dominance of Guild 2 over Guild 1 at time of admission was associated with hospitalized COVID-19 patients at high risk for more severe outcomes. Moreover, the guild-level microbiome signature is not only correlated with the symptom severity of COVID-19 patients, but also differentiates COVID-19 patients from pneumonia controls and healthy subjects across different studies. Here, we showed the possibility of using genome-resolved and guild-level microbiome signatures to identify hospitalized COVID-19 patients with a high risk of more severe outcomes at the time of admission.

Fluorescence technology is an effective tool for detecting polycyclic aromatic hydrocarbons (PAHs) in water. However, the accuracy of fluorescence detection is reduced by the spectral overlap of different PAHs and coexisting colored dissolved organic matter (CDOM). In this study, a single-excitation interval selection method based on an excitation-emission matrix is proposed to quantify four PAHs: fluorene, pyrene, phenanthrene, and benzo(a)pyrene under CDOM interference. The optimal excitation wavelength for each PAH was obtained by stability analysis, based on which the optimal emission interval was obtained by chaotic particle swarm optimization. The partial least squares (PLS) prediction models of four PAHs under interference were established. On comparing with other modeling methods, the results show that the models with interval selection have better prediction accuracy (mean relative error

BackgroundThe trend towards low fertility and low mortality is prominant worldwide. The accelerating ageing and the pressure on public pensions are making the “dependent pattern of old-age support”, which relies on family and government, unsustainable. It is urgent for people to change their mindset about ageing and to develop a sense of “relying on themselves for oldage support”.MethodsThis study incorporates the commercial pension insurance, formal care and the attitude towards independent old-age support pattern into the framework of “independent pattern of old-age support”, using the probit regression model and instrumental variables approach to examine the impact of internet use on old-age support patterns of middleaged and older people based on the CGSS 2012–2018 five-period data.ResultsThe more frequent internet use increased the likelihood of purchasing commercial pension insurance, accepting formal care, and endorsing independence in later life. The internet can promote the acceptance of independent pattern of old-age support by delivering information and facilitating social interaction.DiscussionConsistent with previous research, this study finds that internet use can promote the purchase of commercial pension insurance. There are no relevant studies on the impact of internet use on formal care and attitude towards independent old-age support pattern. Our finding provides important empirical and theoretical references for ageing countries to further transform old-age support patterns.

In our previous study, a gut microbiota-targeted dietary intervention with a high-fiber diet improved the immune status of both genetically obese (Prader-Willi Syndrome, PWS) and simple obese (SO) children. However, PWS children had higher inflammation levels than SO children throughout the trial, the gut microbiota of the two cohorts was similar. As some virulence factors (VFs) produced by the gut microbiota play a role in triggering host inflammation, this study compared the characteristics and changes of gut microbial VF genes of the two cohorts before and after the intervention using a fecal metagenomic dataset. We found that in both cohorts, the high-fiber diet reduced the abundance of VF, and particularly pathogen-specific, genes. The composition of VF genes was also modulated, especially for offensive and defensive VF genes. Furthermore, genes belonging to invasion, T3SS (type III secretion system), and adherence classes were suppressed. Co-occurrence network analysis detected VF gene clusters closely related to host inflammation in each cohort. Though these cohort-specific clusters varied in VF gene combinations and cascade reactions affecting inflammation, they mainly contained VFs belonging to iron uptake, T3SS, and invasion classes. The PWS group had a lower abundance of VF genes before the trial, which suggested that other factors could also be responsible for the increased inflammation in this cohort. This study provides insight into the modulation of VF gene structure in the gut microbiota by a high-fiber diet, with respect to reduced inflammation in obese children, and differences in VF genes between these two cohorts.

Public health education is gaining significance globally, and it is important for managing health risks. This study empirically analyzed the effect of public health education on people's demand for commercial health insurance. And we used the fixed effects and the mediating effect models, and instrumental variables regression in our research based on panel data of 31 provinces (including municipalities and autonomous regions) in China from year 2009 to 2019. The findings show that public health education significantly increases people's demand for commercial health insurance, and this effect remains significant when considering endogeneity and robustness. We further analyzed and found that the increased demand for commercial health insurance is caused by health literacy, health risk perceptions and health risk attitudes. Through heterogeneity analysis, we found that there were significant differences in the effects of public health education in regions with different demographic and socioeconomic characteristics. We found that the effect of health education on promoting people's demand for commercial health insurance is more obvious in regions with high levels of urbanization, proportion of men, education, economic development, medical resources, and social medical insurance coverage. Governments are supposed to take further measures to enhance the effectiveness of public health education, develop high-quality commercial health insurance, and continuously improve health risk coverage.

ABSTRACT Invertebrates, particularly sponges, have been a dominant source of new marine natural products. For example, lasonolide A (LSA) is a potential anticancer molecule isolated from the marine sponge Forcepia sp., with nanomolar growth inhibitory activity and a unique cytotoxicity profile against the National Cancer Institute 60-cell-line screen. Here, we identified the putative biosynthetic pathway for LSA. Genomic binning of the Forcepia sponge metagenome revealed a Gram-negative bacterium belonging to the phylum Verrucomicrobia as the candidate producer of LSA. Phylogenetic analysis showed that this bacterium, here named “Candidatus Thermopylae lasonolidus,” only has 88.78% 16S rRNA identity with the closest relative, Pedosphaera parvula Ellin514, indicating that it represents a new genus. The lasonolide A (las) biosynthetic gene cluster (BGC) was identified as a trans-acyltransferase (AT) polyketide synthase (PKS) pathway. Compared with its host genome, the las BGC exhibits a significantly different GC content and pentanucleotide frequency, suggesting a potential horizontal acquisition of the gene cluster. Furthermore, three copies of the putative las pathway were identified in the candidate producer genome. Differences between the three las repeats were observed, including the presence of three insertions, two single-nucleotide polymorphisms, and the absence of a stand-alone acyl carrier protein in one of the repeats. Even though the verrucomicrobial producer shows signs of genome reduction, its genome size is still fairly large (about 5 Mbp), and, compared to its closest free-living relative, it contains most of the primary metabolic pathways, suggesting that it is in the early stages of reduction. IMPORTANCE While sponges are valuable sources of bioactive natural products, a majority of these compounds are produced in small quantities by uncultured symbionts, hampering the study and clinical development of these unique compounds. Lasonolide A (LSA), isolated from marine sponge Forcepia sp., is a cytotoxic molecule active at nanomolar concentrations, which causes premature chromosome condensation, blebbing, cell contraction, and loss of cell adhesion, indicating a novel mechanism of action and making it a potential anticancer drug lead. However, its limited supply hampers progression to clinical trials. We investigated the microbiome of Forcepia sp. using culture-independent DNA sequencing, identified genes likely responsible for LSA synthesis in an uncultured bacterium, and assembled the symbiont’s genome. These insights provide future opportunities for heterologous expression and cultivation efforts that may minimize LSA’s supply problem.

ABSTRACT Stillbirths account for half of all perinatal mortality, but the underlying cause of a significant portion of the cases remains unknown. We set out to test the potential role and extent of microbial infection in stillbirth through a case-control analysis of fetal cord blood collected from the multisite Stillbirth Collaborative Research Network. Cases (n = 60) were defined as stillbirths at >20 weeks of gestation, and controls (n = 176) were live births. The bacterial presence, abundance, and composition were analyzed by endpoint PCR of full-length 16S rRNA and the V4 amplicon sequence variants (ASVs). The results demonstrate that bacterial prevalence and abundance were both significantly increased in stillbirth, even after adjusting for maternal age, race, body mass index, number of pregnancies, gestational age, and multiple gestations. Composition of bacterial communities in the cord blood also differed significantly. Using a group of 25 ASVs differentially abundant between the two groups, a Random Forest classification model achieved an accuracy score of 0.76 differentiating stillbirth and live birth, with Group B Streptococcus as the most enriched species in stillbirth. Positive PCR was also significantly associated with early preterm birth. A group of oral anaerobes, including Actinomyces, Campylobacter, Fusobacterium, Peptostreptococcus, Porphyromonas, and Prevotella, were enriched in live early preterm birth, suggesting possible oral origin of infection. Our ASV-based microbiome analysis revealed specific candidate pathogens associated with infections in stillbirth and early preterm birth. The cord blood microbial signatures may be markers of adverse pregnancy outcomes. Our study will help identify possible mechanism of infection and improve our ability to prevent stillbirth and early preterm birth. IMPORTANCE Stillbirth accounts for half of all perinatal mortality, but the underlying cause of a substantial portion of all cases remains elusive. We examined the umbilical cord blood microbiome in stillbirths (n = 60) and live births (n = 176) and discovered that the bacterial prevalence and abundance were significantly higher in stillbirths than live births. The microbial compositions also differed significantly. Group B Streptococcus was the most prevalent species detected in stillbirth. In addition, pathogens previously unknown to be associated with stillbirth were identified. A group of oral anaerobes including Fusobacterium nucleatum were found to be specifically enriched in the cord blood in early preterm live birth. This is by far the most comprehensive study to examine the microbial signatures in umbilical cord blood. Cord blood microbial signatures may be markers for adverse birth outcomes. Detection of key microbial signatures will help identify individuals at risk and develop effective preventative strategies.

ABSTRACT The rapid and accurate diagnostic methods to identify carbapenemase-producing organisms (CPO) is of great importance for controlling the CPO infection. Herein, we have developed a microfluidic chip-based technique to detect CPO and assessed its clinical value in detecting CPO directly from blood cultures (BCs). The detection performance of the microfluidic chip-based LAMP amplification method was analyzed retrospectively on a collection of 192 isolates including molecularly characterized 108 CPO and 84 non-CPO and prospectively on a collection of 133 positive BCs with or without CPO suspicion, respectively. In the retrospective study, the microfluidic chip-based LAMP amplification method exhibited 87.5% accuracy (95% CI [82.0–91.5]), 97.7% sensitivity (95% CI [91.2–99.6]), 78.8% specificity (95% CI [69.5–86.0]), 79.6% positive predictive value (PPV) (95% CI [70.6–86.5]) and 97.6% negative predictive value (NPV) (95% CI [90.9–99.6]). Among the 192 isolates, 22 (11.5%) false-positives (FP) and 2 (1.0%) false negatives (FN) were observed. In the prospective study, the 133 routine isolates of positive BCs including 18 meropenem-resistant CPO and 115 non-CPO were assessed, and 4 FP were observed in non-CPO and CPO, respectively. The current method showed a total detection performance of 94.0% accuracy (95% CI [88.4–97.1]), 100.0% sensitivity (95% CI [73.2–100.0]), 93.2% specificity (95% CI [86.7–96.8]), 63.6% PPV (95% CI [40.8–82.0]) and 100.0% NPV (95% CI [95.8–100.0]). In summary, the microfluidic chip-based LAMP amplification method is reliable for the rapid screening and detection of CPO with high accuracy, sensitivity, and specificity, and could easily be implemented in clinical microbiology laboratories. IMPORTANCE Rapid and accurate identification of CPO may reduce the genetic exchanges among bacteria and prevent further dissemination of carbapenemases to non-CPO. The current method had designed microfluidic chip-based LAMP amplification method for multiplex detection of carbapenemase genes and evaluated the detection performance of the newly method. The current method can rapidly screen and detect CPO with high accuracy, sensitivity, and specificity, and could easily be implemented in clinical microbiology laboratories, as this will reduce the carbapenem resistance issues worldwide.