The optimal applications of scPDSI and SPEI in characterizing meteorological drought, agricultural drought and terrestrial water availability on a global scale.

The Palmer Drought Severity Index (scPDSI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are two of the most commonly used drought indices. However, scPDSI and SPEI at a specific scale are often used interchangeably to characterize meteorological drought, agricultural drought, or terrestrial water availability, leading to potential inaccuracies in research outcomes. This study thus presents a global-scale assessment of the applications of scPDSI and SPEI at various timescales (SPEIs) in these contexts. Our findings indicate that scPDSI is more suitable for monitoring agricultural drought than meteorological drought, and highlight the effectiveness of SPEI at one month scale (SPEI01) for meteorological drought. Additionally, SPEI at nine months scale (SPEI09) is more appropriate for agricultural drought. Regarding their relationship with vegetation water stress, scPDSI and SPEI09 are more closely associated with root-zone soil moisture, while SPEI01 is most closely linked to vapor pressure deficit. Furthermore, we evaluate the capability of scPDSI and SPEI in representing terrestrial water availability by analyzing the responses of diverse vegetation indicators to them, including the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Solar-Induced Chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP). All four vegetation indicators show the highest sensitivity of negative response to SPEI01 in cold climate regions, suggesting SPEI01 is most applicable in these regions. In drylands, vegetation indicators exhibit higher sensitivity of positive responses to SPEI at six months scale (SPEI06) and SPEI09, indicating SPEI06 and SPEI09 effectively characterize water availability in such areas. These findings enhance the understanding of scPDSI and SPEI, providing clearer guidelines for their global-scale applications in meteorological drought, agricultural drought, and terrestrial water availability.

Analysis of Related Factors Influencing Hypertension Classification among Centenarians in Hainan, China.

Background: As a population ages, blood pressure levels gradually increase, leading to a higher incidence of hypertension and increased cardiovascular diseases risk. This study examines factors affecting hypertension grading among centenarians in the Hainan Province. Methods: Data from 2014 to 2016 were accessed from the cross-sectional database "Hypertension Levels and Epidemiological Characteristics of the Elderly and Centenarians in Hainan province of China". This study included 690 centenarians with hypertension. Hypertension grading was the dependent variable, analyzed against independent variables including demographic information (sex, age, ethnicity, education level, marital status, cohabitation, and regional distribution), lifestyle factors (smoking, alcohol consumption, and physical activity), body mass index (BMI), and comorbid conditions (diabetes and hyperlipidemia). Logistic regression models, adjusted for these factors, were used to assess the determinants of hypertension grading among the participants. Results: Multivariate regression analysis, after adjusting for other variables, revealed significant associations between BMI, low-density lipoprotein (LDL) levels, and hypertension grades. Individuals with BMI below 18.5 kg/m 2 had a 0.614-fold lower risk of developing grade III hypertension (odds ratio [OR]: 0.614, 95% confidence interval [CI]: 0.390-0.966, p = 0.0350) and a 0.586-fold lower risk for grade II hypertension (OR: 0.586, 95% CI: 0.402-0.852, p = 0.0052). Furthermore, individuals with elevated LDL levels had a 6.087-fold greater risk of progressing from grade I to grade III hypertension (OR: 6.087, 95% CI: 1.635-22.660, p = 0.0071) and a 4.356-fold greater risk of progressing from grade II to grade III hypertension (OR: 4.356, 95% CI: 1.052-18.033, p = 0.0423). Additionally, individuals of Li ethnicity had 1.823-fold greater risk of progressing from grade I to grade II hypertension compared to those of Han ethnicity (OR: 1.823, 95% CI: 1.033-3.218, p = 0.0383). Conclusions: A BMI below 18.5 kg/m 2 , elevated LDL, and ethnicity emerged the primary factors associated with hypertension grading in centenarians. To reduce the risk of hypertension, it is crucial for centenarians to maintain a healthy weight, normal LDL levels, and adopt dietary habits including a low-cholesterol and low-fat diet.

Anthraquinones-based photocatalysis: A comprehensive review.

In recent years, there has been significant interest in photocatalytic technologies utilizing semiconductors and photosensitizers responsive to solar light, owing to their potential for energy and environmental applications. Current efforts are focused on enhancing existing photocatalysts and developing new ones tailored for environmental uses. Anthraquinones (AQs) serve as redox-active electron transfer mediators and photochemically active organic photosensitizers, effectively addressing common issues such as low light utilization and carrier separation efficiency found in conventional semiconductors. AQs offer advantages such as abundant raw materials, controlled preparation, excellent electron transfer capabilities, and photosensitivity, with applications spanning the energy, medical, and environmental sectors. Despite their utility, comprehensive reviews on AQs-based photocatalytic systems in environmental contexts are lacking. In this review, we thoroughly describe the photochemical properties of AQs and their potential applications in photocatalysis, particularly in addressing key environmental challenges like clean energy production, antibacterial action, and pollutant degradation. However, AQs face limitations in practical photocatalytic applications due to their low electrical conductivity and solubility-related secondary contamination. To mitigate these issues, the design and synthesis of graphene-immobilized AQs are highlighted as a solution to enhance practical photocatalytic applications. Additionally, future research directions are proposed to deepen the understanding of AQs' theoretical mechanisms and to provide practical applications for wastewater treatment. This review aims to facilitate mechanistic studies and practical applications of AQs-based photocatalytic technologies and to improve understanding of these technologies.

Biogeographic patterns and community assembly mechanisms of bacterial community in the upper seawater of seamounts and non-seamounts in the Eastern Indian Ocean.

Seamounts are widespread underwater topographic features in the ocean that exert an influential role in shaping the microbial biogeographic distribution. Nevertheless, research on the differences in microbial biogeographic distribution between seamount and non-seamount upper water column is still lacking, particularly in the Indian Ocean where studies are limited. In the present study, a total of 45 seawater samples were collected from the water column (5-200 m) of seamounts (HS) and non-seamounts (E87 transect) regions in the Eastern Indian Ocean (EIO) for the analysis of microbial biogeographic patterns and community assembly processes. The results indicated that bacterial community diversity did not differ significantly between the HS and E87 transect regions; however, the community composition was significantly different. Additionally, bacterial community diversity, composition, as well as structure were more affected by depth than by region. Community diversity tended to increase with depth in E87 transect region, while it tended to decrease in HS region. A distance decay analysis also demonstrated that bacterial communities were more influenced by environmental and depth distances than geographic distances. In the assembly of bacterial communities on HS and E87 transect regions, as well as at different depths, stochastic processes, particularly dispersal limitation, were found to be predominant. These findings enhance our comprehension of bacterial community characteristics in the upper seawater of seamounts and non-seamounts regions in the EIO and offer insights into the assembly processes shaping microbial communities at varying depths. IMPORTANCE: By comparing the bacterial diversity, composition, and structure in the upper seawater of seamount and non-seamount areas, we provide valuable insights into the influential role of seamounts in shaping microbial biogeography. The finding that the depth had a more significant impact on bacterial community characteristics than region underscores the importance of considering vertical stratification when examining microbial distributions. Moreover, the dominance of stochastic processes, particularly dispersal limitation, in governing community assembly across both seamount and non-seamount areas offers critical implications for the mechanisms underlying microbial biogeographic patterns in these dynamic ocean environments. This study expands the current knowledge and lays the groundwork for further investigations into the complex interactions between oceanographic features, environmental gradients, and microbial community dynamics in the Indian Ocean.

Leveraging cfDNA fragmentomic features in a stacked ensemble model for early detection of esophageal squamous cell carcinoma.

In this study, we develop a stacked ensemble model that utilizes cell-free DNA (cfDNA) fragmentomics for the early detection of esophageal squamous cell carcinoma (ESCC). This model incorporates four distinct fragmentomics features derived from whole-genome sequencing (WGS) and advanced machine learning algorithms for robust analysis. It is validated across both an independent validation cohort and an external cohort to ensure its generalizability and effectiveness. Notably, the model maintains its robustness in low-coverage sequencing environments, demonstrating its potentials in clinical settings with limited sequencing resources. With its remarkable sensitivity and specificity, this approach promises to significantly improve the early diagnosis and management of ESCC. This study represents a substantial step forward in the application of cfDNA fragmentomics in cancer diagnostics, emphasizing the need for further research to fully establish its clinical efficacy.

5-Hydroxymethylcytosines in circulating cell-free DNA as a diagnostic biomarker for nasopharyngeal carcinoma.

OBJECTIVE: To evaluate the diagnostic value of 5-hydroxymethylcytosines (5hmC) in circulating cell-free DNA (cfDNA) for nasopharyngeal carcinoma (NPC) and to develop a diagnostic model. METHODS: Genome-wide 5hmC profiles in cfDNA from 174 NPC patients and 146 non-cancer individuals were analyzed using the 5hmC-Seal technique. A cfDNA 5hmC-based diagnostic model to identify NPC patients was developed using least absolute shrinkage and selection operator (LASSO) logistic regression, and performance was evaluated with receiver operating characteristic (ROC) curves and confusion matrices. RESULTS: The 5hmC-Seal data from patients with NPC showed a different genome-wide distribution than non-tumor samples. Our initial analysis revealed a 12-gene-based 5hmC marker panel to be an accurate diagnostic model effectively distinguishing between NPC samples and non-cancerous samples (training set: area under curve (AUC)= 0.97 [95 % CI: 0.94-0.99]; and test set: AUC= 0.93 [95 % CI: 0.88-0.98]) superior to EBV DNA testing. The diagnostic score performed well in differentiating the non-cancer subjects from early-stage NPC (training set: AUC=0.99 [95 % CI: 0.98-1]; test set: AUC=0.98 [95 % CI: 0.95-1]), and advanced-stage NPC (training set: AUC=0.96 [95 % CI: 0.93-0.99]; test set: AUC=0.93 [95 % CI: 0.88-0.98]). Notably, in EBV-negative patients, the diagnostic scores showed excellent capacity for distinguishing EBV-negative patients with NPC from non-cancer subjects in both the training set (AUC= 0.94 [95 % CI: 0.88-1]) and test set (AUC=0.91 [95 % CI: 0.81-1]). CONCLUSION: 5hmC modifications in cfDNA are promising noninvasive biomarkers for NPC, offering high sensitivity and specificity, particularly for early-stage and EBV-negative NPC.

Novel machine-learning prediction tools for overall survival of patients with chondrosarcoma: Based on recursive partitioning analysis.

BACKGROUND: Chondrosarcoma (CHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple prognostic factors to deliver personalized survival predictions for individual patients. This study aimed to develop a novel prediction tool based on recursive partitioning analysis (RPA) to improve the estimation of overall survival for patients with CHS. METHODS: Data from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed, including demographic, clinical, and treatment details of patients diagnosed between 2000 and 2018. Using C5.0 algorithm, decision trees were created to predict survival probabilities at 12, 24, 60, and 120 months. The performance of the models was assessed through confusion scatter plot, accuracy rate, receiver operator characteristic (ROC) curve, and area under ROC curve (AUC). RESULTS: The study identified tumor histology, surgery, age, visceral (brain/liver/lung) metastasis, chemotherapy, tumor grade, and sex as critical predictors. Decision trees revealed distinct patterns for survival prediction at each time point. The models showed high accuracy (82.40%-89.09% in training group, and 82.16%-88.74% in test group) and discriminatory power (AUC: 0.806-0.894 in training group, and 0.808-0.882 in test group) in both training and testing datasets. An interactive web-based shiny APP (URL: https://yangxg1209.shinyapps.io/chondrosarcoma_survival_prediction/) was developed, simplifying the survival prediction process for clinicians. CONCLUSIONS: This study successfully employed RPA to develop a user-friendly tool for personalized survival predictions in CHS. The decision tree models demonstrated robust predictive capabilities, with the interactive application facilitating clinical decision-making. Future prospective studies are recommended to validate these findings and further refine the predictive model.

Generation and evaluation of Salmonella entericaserovar Choleraesuis mutant strains as a potential live-attenuated vaccine.

BACKGROUND: Salmonella entericaserovar Choleraesuis (S.C) is a swine enteric pathogen causing paratyphoid fever, enterocolitis, and septicemia in piglets. S. C is mainly transmitted through the fecal-oral route. Vaccination is an effective strategy for preventing and controlling Salmonella infection. RESULTS: Herein, we used CRISPR-Cas9 technology to knockout the virulence regulatory genes, rpoS, and slyA of S. C and constructed the ∆rpoS, ∆slyA, and ∆rpoS ∆slyA strains. The phenotypic characteristics of the mutant strains remained unchanged compared with the parental wild-type strain. In vivo study, unlike the wild-type strain, the ∆slyA and ∆rpoS ∆slyA strains alleviated splenomegaly, colon atrophy, and lower bacterial loads in the spleen, liver, ileum, and colon. These mutant strains survived in Peyer's patches (PPs) and mesenteric lymph nodes (MLN) for up to 15 days post-infection. Furthermore, the immunization of the ∆rpoS ∆slyA strain induced robust humoral and cellular immune responses. CONCLUSIONS: Consequently, vaccination with ∆rpoS ∆slyA conferred a high percentage of protection against lethal invasive Salmonella, specifically S. C, in mice. This study provided novel insights into the development of live-attenuated vaccines against the infection of S. C.

Whole transcriptome sequencing revealed the gene regulatory network of hypoxic response in yak Sertoli cells.

Yaks live in the Qinghai-Tibet Plateau for a long time where oxygen is scarce, but can ensure the smooth development of testis and spermatogenesis. The key lies in the functional regulation of the Sertoli cells under hypoxia. In this study, we sequenced yak Sertoli cells cultured in normal oxygen concentration (Normoxia) and treated with low oxygen concentration (Hypoxia) by whole transcriptomics, and screened out 194 differentially expressed mRNAs (DEmRNAs), 934 differentially expressed LncRNAs (DELncRNAs) and 129 differentially expressed miRNAs (DEmiRNAs). GO and KEGG analysis showed that these differential genes were mainly concentrated in PI3K-AKT, MAPK, RAS, and other signaling pathways, and were associated with glucose metabolism, tight junction, steroid hormone synthesis, cell fusion, and immunity of yak Sertoli cells. We constructed the gene interaction network of yak Sertoli cells in hypoxia and screened out the relationship pairs related to glucose metabolism and tight junction. The results suggested that the changes in energy metabolism, tight junction, and immune regulation of yak Sertoli cells under hypoxia might provide favorable conditions for spermatogenesis. This study provides data for further study on the role of non-coding RNA in testis development and spermatogenesis of yak.

Increased neutrophil extracellular trap formation in oligoarticular, polyarticular juvenile idiopathic arthritis and enthesitis-related arthritis: biomarkers for diagnosis and disease activity.

Objective: Neutrophil extracellular traps (NETs) are important factors in initiating and perpetuating inflammation. However, the role of NETs in different subtypes of juvenile idiopathic arthritis (JIA) has been rarely studied. Therefore, we aimed to explore the ability of JIA-derived neutrophils to release NETs and the effect of TNF-α (tumor necrosis factor-alpha) inhibitors on NET formation both in vitro and in vivo, and evaluate the associations of NET-derived products with clinical and immune-related parameters. Methods: The ability of neutrophils to release NETs and the effect of adalimumab on NET formation was assessed via in vitro stimulation and inhibition studies. Plasma NET-derived products were detected to assess the incidence of NET formation in vivo. Furthermore, flow cytometry and western blotting were used to detect NET-associated signaling components in neutrophils. Results: Compared to those derived from HCs, neutrophils derived from patients with oligoarticular-JIA, polyarticular-JIA and enthesitis-related arthritis were more prone to generate NETs spontaneously and in response to TNF-α or PMA in vitro. Excessive NET formation existed in peripheral circulation of JIA patients, and elevated plasma levels of NET-derived products (cell-free DNA and MPO-DNA complexes) could accurately distinguish JIA patients from HCs and were positively correlated with disease activity. Multiple linear regression analysis showed that erythrocyte sedimentation rate and TNF-α levels were independent variables and were positively correlated with cell-free DNA concentration. Notably, TNF-α inhibitors could effectively prevent NET formation both in vitro and in vivo. Moreover, the phosphorylation levels of NET-associated kinases in JIA-derived neutrophils were markedly increased. Conclusion: Our data suggest that NETs might play pathogenic roles and may be involved in TNF-α-mediated inflammation in JIA. Circulating NET-derived products possess potential diagnostic and disease monitoring value. Furthermore, the preliminary results related to the molecular mechanisms of NET formation in JIA patients provide a theoretical basis for NET-targeted therapy.

Multi-Dimensional Fragmentomics Enables Early and Accurate Detection of Colorectal Cancer.

Colorectal cancer (CRC) is frequently diagnosed in advanced stages, highlighting the need for developing approaches for early detection. Liquid biopsy using cell-free DNA (cfDNA) fragmentomics is a promising approach, but the clinical application is hindered by complexity and cost. This study aimed to develop an integrated model using cfDNA fragmentomics for accurate, cost-effective early-stage CRC detection. Plasma cfDNA was extracted and sequenced from a training cohort of 360 participants, including 176 CRC patients and 184 healthy controls. An ensemble stacked model comprising five machine learning models was employed to distinguish CRC patients from healthy controls using five cfDNA fragmentomic features. The model was validated in an independent cohort of 236 participants (117 CRC patients and 119 controls) and a prospective cohort of 242 participants (129 CRC patients and 113 controls). The ensemble stacked model showed remarkable discriminatory power between CRC patients and controls, outperforming all base models and achieving a high area under the ROC curve (AUC) of 0.986 in the validation cohort. It reached 94.88% sensitivity and 98% specificity for detecting CRC in the validation cohort, with sensitivity increasing as cancer progressed. The model also demonstrated consistently high accuracy in within-run and between-run tests and across various conditions in healthy individuals. In the prospective cohort, it achieved 91.47% sensitivity and 95.58% specificity. This integrated model capitalizes on the multiplex nature of cfDNA fragmentomics to achieve high sensitivity and robustness, offering significant promise for early CRC detection and broad patient benefit.

Effect and mechanism of Qingre Huashi decoction on drug-resistant Helicobacter pylori.

BACKGROUND: Helicobacter pylori (HP), the most common pathogenic microorganism in the stomach, can induce inflammatory reactions in the gastric mucosa, causing chronic gastritis and even gastric cancer. HP infection affects over 4.4 billion people globally, with a worldwide infection rate of up to 50%. The multidrug resistance of HP poses a serious challenge to eradication. It has been de-monstrated that compared to bismuth quadruple therapy, Qingre Huashi decoction (QHD) combined with triple therapy exhibits comparable eradication rates but with a lower incidence of adverse reactions; in addition, QHD can directly inhibit and kill HP in vitro. AIM: To explore the effect and mechanism of QHD on clinically multidrug-resistant and strong biofilm-forming HP. METHODS: In this study, 12 HP strains were isolated in vitro after biopsy during gastroscopy of HP-infected patients. In vitro, the minimum inhibitory concentration (MIC) values for clinical HP strains and biofilm quantification were determined through the E-test method and crystal violet staining, respectively. The most robust biofilm-forming strain of HP was selected, and QHD was evaluated for its inhibitory and bactericidal effects on the strain with strong biofilm formation. This assessment was performed using agar dilution, E-test, killing dynamics, and transmission electron microscopy (TEM). The study also explored the impact of QHD on antibiotic resistance in these HP strains with strong biofilm formation. Crystalline violet method, scanning electron microscopy, laser confocal scanning microscopy, and (p)ppGpp chromatographic identification were employed to evaluate the effect of QHD on biofilm in strong biofilm-forming HP strains. The effect of QHD on biofilm and efflux pump-related gene expression was evaluated by quantitative polymerase chain reaction. Non-targeted metabolomics with UHPLC-MS/MS was used to identify potential metabolic pathways and biomarkers which were different between the NC and QHD groups. RESULTS: HP could form biofilms of different degrees in vitro, and the intensity of formation was associated with the drug resistance of the strain. QHD had strong bacteriostatic and bactericidal effects on HP, with MICs of 32-64 mg/mL. QHD could inhibit the biofilm formation of the strong biofilm-forming HP strains, disrupt the biofilm structure, lower the accumulation of (p)ppGpp, decrease the expression of biofilm-related genes including LuxS, Spot, glup (HP1174), NapA, and CagE, and reduce the expression of efflux pump-related genes such as HP0605, HP0971, HP1327, and HP1489. Based on metabolomic analysis, QHD induced oxidative stress in HP, enhanced metabolism, and potentially inhibited relevant signaling pathways by upregulating adenosine monophosphate (AMP), thereby affecting HP growth, metabolism, and protein synthesis. CONCLUSION: QHD exerts bacteriostatic and bactericidal effects on HP, and reduces HP drug resistance by inhibiting HP biofilm formation, destroying its biofilm structure, inhibiting the expression of biofilm-related genes and efflux pump-related genes, enhancing HP metabolism, and activating AMP in HP.

Aging-induced short-chain acyl-CoA dehydrogenase promotes age-related hepatic steatosis by suppressing lipophagy.

Hepatic steatosis, the first step in the development of nonalcoholic fatty liver disease (NAFLD), is frequently observed in the aging population. However, the underlying molecular mechanism remains largely unknown. In this study, we first employed GSEA enrichment analysis to identify short-chain acyl-CoA dehydrogenase (SCAD), which participates in the mitochondrial ß-oxidation of fatty acids and may be associated with hepatic steatosis in elderly individuals. Subsequently, we examined SCAD expression and hepatic triglyceride content in various aged humans and mice and found that triglycerides were markedly increased and that SCAD was upregulated in aged livers. Our further evidence in SCAD-ablated mice suggested that SCAD deletion was able to slow liver aging and ameliorate aging-associated fatty liver. Examination of the molecular pathways by which the deletion of SCAD attenuates steatosis revealed that the autophagic degradation of lipid droplets, which was not detected in elderly wild-type mice, was maintained in SCAD-deficient old mice. This was due to the decrease in the production of acetyl-coenzyme A (acetyl-CoA), which is abundant in the livers of old wild-type mice. In conclusion, our findings demonstrate that the suppression of SCAD may prevent age-associated hepatic steatosis by promoting lipophagy and that SCAD could be a promising therapeutic target for liver aging and associated steatosis.

Novel decision tree models predict the overall survival of patients with submandibular gland cancer.

BACKGROUND: While the accurate prediction of the overall survival (OS) in patients with submandibular gland cancer (SGC) is paramount for informed therapeutic planning, the development of reliable survival prediction models has been hindered by the rarity of SGC cases. The purpose of this study is to identify key prognostic factors for OS in SGC patients using a large database and construct decision tree models to aid the prediction of survival probabilities in 12, 24, 60 and 120 months. MATERIALS AND METHODS: We performed a retrospective cohort study using the Surveillance, Epidemiology and End Result (SEER) program. Demographic and peri-operative predictor variables were identified. The outcome variables overall survival at 12-, 24-, 60, and 120 months. The C5.0 algorithm was utilized to establish the dichotomous decision tree models, with the depth of tree limited within 4 layers. To evaluate the performances of the novel models, the receiver operator characteristic (ROC) curves were generated, and the metrics such as accuracy rate, and area under ROC curve (AUC) were calculated. RESULTS: A total of 1,705, 1,666, 1,543, and 1,413 SGC patients with a follow up of 12, 24, 60 and 120 months and exact survival status were identified from the SEER database. Predictor variables of age, sex, surgery, radiation, chemotherapy, tumor histology, summary stage, metastasis to distant lymph node, and marital status exerted substantial influence on overall survival. Decision tree models were then developed, incorporating these vital prognostic indicators. Favorable consistency was presented between the predicted and actual survival statuses. For the training dataset, the accuracy rates for the 12-, 24-, 60- and 120-month survival models were 0.866, 0.767, 0.737 and 0.797. Correspondingly, the AUC values were 0.841, 0.756, 0.725, and 0.774 for the same time points. CONCLUSIONS: Based on the most important predictor variables identified using the large, SEER database, decision tree models were established that predict OS of SGC patients. The models offer a more exhaustive evaluation of mortality risk and may lead to more personalized treatment strategies.

Multi-dimensional cell-free DNA-based liquid biopsy for sensitive early detection of gastric cancer.

BACKGROUND: Gastric cancer is the fifth most common cancer type. Most patients are diagnosed at advanced stages with poor prognosis. A non-invasive assay for the detection of early-stage gastric cancer is highly desirable for reducing associated mortality. METHODS: We collected a prospective study cohort of 110 stage I-II gastric cancer patients and 139 non-cancer individuals. We performed whole-genome sequencing with plasma samples and profiled four types of cell-free DNA (cfDNA) characteristics, fragment size pattern, copy number variation, nucleosome coverage pattern, and single nucleotide substitution. With these differential profiles, we developed an ensemble model to detect gastric cancer signals. Further, we validated the assay in an in-house first validation cohort of 73 gastric cancer patients and 94 non-cancer individuals and an independent second validation cohort of 47 gastric cancer patients and 49 non-cancer individuals. Additionally, we evaluated the assay in a hypothetical 100,000 screening population by Monte Carlo simulation. RESULTS: Our cfDNA-based assay could distinguish early-stage gastric cancer from non-cancer at an AUROC of 0.962 (95% CI: 0.942-0.982) in the study cohort, 0.972 (95% CI: 0.953-0.992) in the first validation cohort and 0.937 (95% CI: 0.890-0.983) in the second validation cohort. The model reached a specificity of 92.1% (128/139) and a sensitivity of 88.2% (97/110) in the study cohort. In the first validation cohort, 91.5% (86/94) of non-cancer individuals and 91.8% (67/73) of gastric cancer patients were correctly identified. In the second validation cohort, 89.8% (44/49) of non-cancer individuals and 87.2% (41/47) of gastric cancer patients were accurately classified. CONCLUSIONS: We introduced a liquid biopsy assay using multiple dimensions of cfDNA characteristics that could accurately identify early-stage gastric cancer from non-cancerous conditions. As a cost-effective non-invasive approach, it may provide population-wide benefits for the early detection of gastric cancer. TRIAL REGISTRATION: This study was registered on ClinicalTrials.gov under the identifier NCT05269056 on March 7, 2022.

Spatially Resolved Defect Characterization and Fidelity Assessment for Complex and Arbitrary Irregular 3D Printing Based on 3D P-OCT and GCode.

To address the challenges associated with achieving high-fidelity printing of complex 3D bionic models, this paper proposes a method for spatially resolved defect characterization and fidelity assessment. This approach is based on 3D printer-associated optical coherence tomography (3D P-OCT) and GCode information. This method generates a defect characterization map by comparing and analyzing the target model map from GCode information and the reconstructed model map from 3D P-OCT. The defect characterization map enables the detection of defects such as material accumulation, filament breakage and under-extrusion within the print path, as well as stringing outside the print path. The defect characterization map is also used for defect visualization, fidelity assessment and filament breakage repair during secondary printing. Finally, the proposed method is validated on different bionic models, printing paths and materials. The fidelity of the multilayer HAP scaffold with gradient spacing increased from 0.8398 to 0.9048 after the repair of filament breakage defects. At the same time, the over-extrusion defects on the nostril and along the high-curvature contours of the nose model were effectively detected. In addition, the finite element analysis results verified that the 60-degree filling model is superior to the 90-degree filling model in terms of mechanical strength, which is consistent with the defect detection results. The results confirm that the proposed method based on 3D P-OCT and GCode can achieve spatially resolved defect characterization and fidelity assessment in situ, facilitating defect visualization and filament breakage repair. Ultimately, this enables high-fidelity printing, encompassing both shape and function.

Understanding the Ecological Robustness and Adaptability of the Gut Microbiome in Plastic-Degrading Superworms (Zophobas atratus) in Response to Microplastics and Antibiotics.

Recent discoveries indicate that several insect larvae are capable of ingesting and biodegrading plastics rapidly and symbiotically, but the ecological adaptability of the larval gut microbiome to microplastics (MPs) remains unclear. Here, we described the gut microbiome assemblage and MP biodegradation of superworms (Zophobas atratus larvae) fed MPs of five major petroleum-based polymers (polyethylene, polypropylene, polystyrene, polyvinyl chloride, and polyethylene terephthalate) and antibiotics. The shift of molecular weight distribution, characteristic peaks of C═O, and metabolic intermediates of residual polymers in egested frass proved depolymerization and biodegradation of all MPs tested in the larval intestines, even under antibiotic suppression. Superworms showed a wide adaptation to the digestion of the five polymer MPs. Antibiotic suppression negatively influenced the survival rate and plastic depolymerization patterns. The larval gut microbiomes differed from those fed MPs and antibiotics, indicating that antibiotic supplementation substantially shaped the gut microbiome composition. The larval gut microbiomes fed MPs had higher network complexity and stability than those fed MPs and antibiotics, suggesting that the ecological robustness of the gut microbiomes ensured the functional adaptability of larvae to different MPs. In addition, Mantel's test indicated that the gut microbiome assemblage was obviously related to the polymer type, the plastic degradability, antibiotic stress, and larval survival rate. This finding provided novel insights into the self-adaptation of the gut microbiome of superworms in response to different MPs.

Prognostic factors and novel prediction models for overall survival of patients with submandibular gland cancer: A population-based retrospective cohort study.

Background: Accurately predicting the survival rate of submandibular gland cancer (SGC) is of significant importance for guiding treatment decision-making and improving patient outcomes. This study was aimed to identify the independent prognostic factors of overall survival (OS) in SGC patients, and develop novel prediction models to aid clinicians in predicting the survival probability. Materials and methods: Patients diagnosed with primary SGC after the year 2010 were extracted from SEER database and then randomly allocated into training and test samples in a 7:3 ratio. Uni- and multi-variable COX analyses were employed using the training sample to ascertain independent prognostic factors for OS. Subsequently, graphic and online dynamic nomograms were established basing on the independent prognostic factors. We utilized C-index, calibration curve, receiver operating characteristic (ROC) curve, and area under ROC curve (AUC) value to evaluate the discrimination capacity and the consistency between predicted and actual survival. Results: A total of 527 SGC patients were included (369 assigned to training group and 158 assigned to test group). The multivariable COX analysis showed that age, sex, marital status, tumor histology, summary stage, metastases to bone, and tumor size were independently associated with OS. Novel graphical and online dynamic (URL: https://yangxg1209.shinyapps.io/overall_survival_submandibular_gland_tumor/) nomograms were established. The C-indices (training: 0.77, 95%CI 0.71-0.84; test: 0.77, 95%CI 0.68-0.85) indicate favorable discrimination ability of the model, and the calibration curves demonstrated favorable consistency between the predicted and actual survival rates. Conclusions: Our study identified the independent prognostic factors influencing OS in patients with SGC, and successfully established and validated novel nomograms, which provide accurate prediction of survival rates and allows for personalized risk assessment.

Porcine lung tissue slices: a culture model for PRCV infection and innate immune response investigations.

Respiratory coronaviruses (RCoVs) significantly threaten human health, necessitating the development of an ex vivo respiratory culture system for investigating RCoVs infection. Here, we successfully generated a porcine precision-cut lung slices (PCLSs) culture system, containing all resident lung cell types in their natural arrangement. Next, this culture system was inoculated with a porcine respiratory coronavirus (PRCV), exhibiting clinical features akin to humans who were infected by SARS-CoV-2. The results demonstrated that PRCV efficiently infected and replicated within PCLSs, targeting ciliated cells in the bronchioles, terminal bronchioles, respiratory bronchioles, and pulmonary alveoli. Additionally, through RNA-Seq analysis of the innate immune response in PCLSs following PRCV infection, expression levels of interferons, inflammatory cytokines and IFN stimulated genes were significantly upregulated. This ex vivo model may not only offer new insights into PRCV infection in the porcine respiratory tract but also serve as a valuable tool for studying human respiratory CoVs infection.

Insight into effect of polyethylene microplastic on nitrogen removal in moving bed biofilm reactor: Focusing on microbial community and species interactions.

Microplastics (MPs) pollution has emerged as a global concern, and wastewater treatment plants (WWTPs) are one of the potential sources of MPs in the environment. However, the effect of polyethylene MPs (PE) on nitrogen (N) removal in moving bed biofilm reactor (MBBR) remains unclear. We hypothesized that PE would affect N removal in MBBR by influencing its microbial community. In this study, we investigated the impacts of different PE concentrations (100, 500, and 1000 µg/L) on N removal, enzyme activities, and microbial community in MBBR. Folin-phenol and anthrone colorimetric methods, oxidative stress and enzyme activity tests, and high-throughput sequencing combined with bioinformation analysis were used to decipher the potential mechanisms. The results demonstrated that 1000 µg/L PE had the greatest effect on NH4+-N and TN removal, with a decrease of 33.5 % and 35.2 %, and nitrifying and denitrifying enzyme activities were restrained by 29.5-39.6 % and 24.6-47.4 %. Polysaccharide and protein contents were enhanced by PE, except for 1000 µg/L PE, which decreased protein content by 65.4 mg/g VSS. The positive links of species interactions under 1000 µg/L PE exposure was 52.07 %, higher than under 500 µg/L (51.05 %) and 100 µg/L PE (50.35 %). Relative abundance of some metabolism pathways like carbohydrate metabolism and energy metabolism were restrained by 0.07-0.11 % and 0.27-0.4 %. Moreover, the total abundance of nitrification and denitrification genes both decreased under PE exposure. Overall, PE reduced N removal by affecting microbial community structure and species interactions, inhibiting some key metabolic pathways, and suppressing key enzyme activity and functional gene abundance. This paper provides new insights into assessing the risk of MPs to WWTPs, contributing to ensuring the health of aquatic ecosystems.