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Although serving as the workhorse, MS/MS cannot fully satisfy the analytical requirements of quantitative sub-metabolome characterization. Because more information intrinsically correlates to more structural and concentration clues, here, efforts were devoted to comprehensively tracing and deciphering MS/MS behaviors through constructing triple three-dimensional (3×3D)-MS/MS spectrum. Ginsenosides-targeted metabolomics of notoginseng, one of the most famous edible medicinal plants, was employed as a proof-of-concept. Serial authentic ginsenosides were deployed to build the correlations between 3×3D-MS/MS spectra and structure/concentration features. Through assaying ginsenosides with progressive concentrations using QTOF-MS to configure 1st 3D spectrum, the generations of MS1 spectral signals, particularly multi-charged multimer anions, e.g., [2M–2H]2– and [2M+2HCOO]2– ions, relied on both concentration and the amount of sugar chains. By programming progressive collision energies to the front collision cell of Qtrap-MS device to gain 2nd 3D spectrum, optimal collision energy (OCE) corresponding to the glycosidic bond fission was primarily correlated with the masses of precursor and fragment ions and partially governed by the glycosidation site. The quantitative relationships between OCEs and masses of precursor and fragment ions were utilized to build large-scale quantitative program for ginsenosides. After applying progressive exciting energies to the back collision chamber to build 3rd 3D spectrum, the fragment ion and the decomposition product anion exhibited identical dissociation trajectories when they shared the same molecular geometry. After ginsenosides-focused quantitative metabolomics, significant differences occurred for sub-metabolome amongst different parts of notoginseng. The differential ginsenosides were confirmatively identified by applying the correlations between 3×3D-MS/MS spectra and structures. Together, 3×3D-MS/MS spectrum covers all MS/MS behaviors and dramatically facilitates sub-metabolome characterization from both quantitative program development and structural identification.

Abstract The aim of this study was to evaluate the prognostic value of peripheral blood inflammation indexes in patients with metastatic Colorectal Cancer (CRC) and to establish a predictive scoring system. A total of 324 CRC patients diagnosed through pathological examination from January 2017 to July 2022 at the Third Affiliated Hospital of Kunming Medical University were included. The prognosis of patients with metastatic CRC was examined, and the correlation between IL-10 expression in pathological tissues and IL-10 expression in serum was analyzed. The results showed that the prognosis of CRC was poorer when metastasis occurred (P

Abstract Chasing refers to the escalation of betting behaviour. It is conventionally seen when losing but can also be seen after wins. Diagnostic and screening items for gambling problems describe chasing as returning ‘another day’ to gamble. However, gamblers may also chase within sessions, and this is particularly relevant in online gambling. This study focused on two expressions of within-session chasing: (1) increasing the bet amount, or (2) a reduced probability of quitting the session, as a function of prior losses or wins. These expressions were examined across five online gambling products: slot machines, probability games, blackjack, video poker, and roulette. Our results showed that gamblers bet more and played longer sessions after immediate losses, but they bet less and played shorter sessions when losing cumulatively. The reversed pattern in the cumulative model may be due to financial constraints. For wins, gamblers bet more after both immediate and cumulative wins, but they also played shorter sessions. Chasing patterns were qualitatively similar by game type—with limited evidence for our hypothesis that chasing would be greatest for slot machines as an established high-risk category. Overall, chasing is multi-faceted, varying across the behavioural expressions, by the immediate or cumulative timeframe of prior outcomes, and by game type.

Abstract Background Previous clinical and basic studies have revealed that ginseng might have cardioprotective properties against anthracycline-induced cardiotoxicity (AIC). However, the underlying mechanism of ginseng action against AIC remains insufficiently understood. The aim of this study was to explore the related targets and pathways of ginseng against AIC using network pharmacology, molecular docking, cellular thermal shift assay (CETSA) and molecular dynamics (MD) simulations. Results Fourteen drug-disease common targets were identified. Enrichment analysis showed that the AGE-RAGE in diabetic complications, fluid shear stress and atherosclerosis, and TNF signaling pathway were potentially involved in the action of ginseng against AIC. Molecular docking demonstrated that the core components including Kaempferol, beta-Sitosterol, and Fumarine had notable binding activity with the three core targets CCNA2, STAT1, and ICAM1. Furthermore, the stable complex of STAT1 and Kaempferol with favorable affinity was further confirmed by CETSA and MD simulation. Conclusions This study suggested that ginseng might exert their protective effects against AIC through the derived effector compounds beta-Sitosterol, Kaempferol and Fumarine by targeting CCNA2, STAT1, and ICAM1, and modulating AGE-RAGE in diabetic complications, fluid shear stress and atherosclerosis, and TNF signaling pathways.

Abstract Background Dysregulation of lipid metabolism and its consequences on growth performance in young ruminants have attracted attention, especially in the context of alternative feeding strategies. This study aims to elucidate the effects of milk replacer (MR) feeding on growth, lipid metabolism, colonic epithelial gene expression, colonic microbiota composition and systemic metabolism in goat kids compared to breast milk (BM) feeding, addressing a critical knowledge gap in early life nutrition. Methods Ten female goat kids were divided into 2 groups: those fed breast milk (BM group) and those fed a milk replacer (MR group). Over a period of 28 d, body weight was monitored and blood and tissue samples were collected for biochemical, transcriptomic and metabolomic analyses. Profiling of the colonial microbiota was performed using 16S rRNA gene sequencing. Intestinal microbiota transplantation (IMT) experiments in gnotobiotic mice were performed to validate causality. Results MR-fed pups exhibited reduced daily body-weight gain due to impaired lipid metabolism as evidenced by lower serum and liver total cholesterol (TC) and non-esterified fatty acid (NEFA) concentrations. Transcriptomic analysis of the colonic epithelium revealed upregulated genes involved in negative regulation of lipid metabolism, concomitant with microbiota shifts characterized by a decrease in Firmicutes and an increase in Actinobacteria. Specifically, genera such as Bifidobacterium and Prevotella were enriched in the MR group, while Clostridium and Faecalibacterium were depleted. Metabolomics analyses confirmed alterations in bile acid and fatty acid metabolic pathways. IMT experiments in mice recapitulated the metabolic phenotype observed in MR-fed goats, confirming the role of the microbiota in modulating host lipid metabolism. Conclusions Milk replacer feeding in goat kids disrupts lipid metabolism and gut microbiota dynamics, resulting in reduced growth rates and metabolic alterations. These findings highlight the importance of early nutritional intervention on metabolic programming and suggest that modulation of the gut microbiota may be a target for improving growth and metabolic health in ruminants. This study contributes to the understanding of nutritional management strategies in livestock and their impact on animal health and productivity.

Abstract Background Continuous glucose monitoring (CGM) devices provide detailed information on daily glucose control and glycemic variability. Yet limited population-based studies have explored the association between CGM metrics and fatty liver. We aimed to investigate the associations of CGM metrics with the degree of hepatic steatosis. Methods This cross-sectional study included 1180 participants from the Guangzhou Nutrition and Health Study. CGM metrics, covering mean glucose level, glycemic variability, and in-range measures, were separately processed for all-day, nighttime, and daytime periods. Hepatic steatosis degree (healthy: n = 698; mild steatosis: n = 242; moderate/severe steatosis: n = 240) was determined by magnetic resonance imaging proton density fat fraction. Multivariate ordinal logistic regression models were conducted to estimate the associations between CGM metrics and steatosis degree. Machine learning models were employed to evaluate the predictive performance of CGM metrics for steatosis degree. Results Mean blood glucose, coefficient of variation (CV) of glucose, mean amplitude of glucose excursions (MAGE), and mean of daily differences (MODD) were positively associated with steatosis degree, with corresponding odds ratios (ORs) and 95% confidence intervals (CIs) of 1.35 (1.17, 1.56), 1.21 (1.06, 1.39), 1.37 (1.19, 1.57), and 1.35 (1.17, 1.56) during all-day period. Notably, lower daytime time in range (TIR) and higher nighttime TIR were associated with higher steatosis degree, with ORs (95% CIs) of 0.83 (0.73, 0.95) and 1.16 (1.00, 1.33), respectively. For moderate/severe steatosis (vs. healthy) prediction, the average area under the receiver operating characteristic curves were higher for the nighttime (0.69) and daytime (0.66) metrics than that of all-day metrics (0.63, P

Abstract The long-term loss of distribution network in the process of distribution network development is caused by the backward management mode of distribution network. The traditional analysis and calculation methods of distribution network loss can not adapt to the current development environment of distribution network. To improve the accuracy of filling missing values in power load data, particle swarm optimization algorithm is proposed to optimize the clustering center of the clustering algorithm. Furthermore, the original isolated forest anomaly recognition algorithm can be used to detect outliers in the load data, and the coefficient of variation of the load data is used to improve the recognition accuracy of the algorithm. Finally, this paper introduces a breadth-first-based method for calculating line loss in the context of big data. An example is provided using the distribution network system of Yuxi City in Yunnan Province, and a simulation experiment is carried out. And the findings revealed that the error of the enhanced fuzzy C-mean clustering algorithm was on average − 6.35, with a standard deviation of 4.015 in the situation of partially missing data. The area under the characteristic curve of the improved isolated forest algorithm subjects in the case of the abnormal sample fuzzy situation was 0.8586, with the smallest decrease, based on the coefficient of variation, and through the refinement of the analysis, it was discovered that the feeder line loss rate is 7.62%. It is confirmed that the suggested technique can carry out distribution network line loss analysis fast and accurately and can serve as a guide for managing distribution network line loss.

Abstract Background Small-cell lung cancer (SCLC) is a leading cause of cancer-related death. However, the prognostic value of the tumor shrinkage rate (TSR) after chemotherapy for SCLC is still unknown. Methods We performed a retrospective analysis of 235 patients with SCLC. The TSR cutoff was determined based on receiver-operating characteristic curve analysis. The associations of TSR with progression-free survival (PFS) and overall survival (OS) were assessed using univariate and multivariate Cox proportional hazards models. Survival curves were obtained by the Kaplan–Meier method and compared using the log-rank test. Recurrence patterns after first-line treatment were summarized in a pie chart. A nomogram was constructed to validate the predictive role of the TSR in SCLC. Results The TSR cutoff was identified to be − 6.6%. Median PFS and OS were longer in the group with a TSR − 6.6%. Brain metastasis-free survival was better in the group with a TSR

Abstract Background Nurses' nursing competence and nutritional care literacy directly affect patients' health and safety. Self-directed learning ability was pervasive throughout the entire work process of nursing work and was the basis for improving both. However, there are few studies has explored the mechanism from the perspective of nutritional care literacy. The purpose of this study was to analyze the relationship between self-directed learning ability and nursing competence, and to explore the mediating role of nutritional care literacy between self-directed learning and nursing competence among clinical nurses in China. Methods A cross-sectional survey was conducted among 805 clinical nurses recruited from seven general hospitals in Hunan Province, China, between January 25 and March 6, 2022. The self-directed learning ability, nutritional care literacy and nursing competence of nurses were evaluated through investigation. A total of 799 questionnaires were received, resulting in an response rate of 99.25%.We performed an intermediary modeling to examine the mediating roles of nutritional care literacy on the relationship between self-directed learning ability and nursing competence in clinical nurses. Results Self-directed learning ability was positively correlated with nutritional care literacy (r=0.792, P

Abstract Background Phosphorus is a macronutrient necessary for plant growth and development and its availability and efficient use affect crop yields. Leaves are the largest tissue that uses phosphorus in plants, and membrane phospholipids are the main source of cellular phosphorus usage. Results Here we identify a key process for plant cellular phosphorus recycling mediated by membrane phospholipid hydrolysis during leaf senescence. Our results indicate that over 90% of lipid phosphorus, accounting for more than one-third of total cellular phosphorus, is recycled from senescent leaves before falling off the plants. Nonspecific phospholipase C4 (NPC4) and phospholipase Dζ2 (PLDζ2) are highly induced during leaf senescence, and knockouts of PLDζ2 and NPC4 decrease the loss of membrane phospholipids and delay leaf senescence. Conversely, overexpression of PLDζ2 and NPC4 accelerates the loss of phospholipids and leaf senescence, promoting phosphorus remobilization from senescent leaves to young tissues and plant growth. We also show that this phosphorus recycling process in senescent leaves mediated by membrane phospholipid hydrolysis is conserved in plants. Conclusions These results indicate that PLDζ2- and NPC4-mediated membrane phospholipid hydrolysis promotes phosphorus remobilization from senescent leaves to growing tissues and that the phospholipid hydrolysis-mediated phosphorus recycling improves phosphorus use efficiency in plants.

When 2-chloro-6-(trichloromethyl) pyridine (nitrapyrin) is applied alone, it typically does not significantly increase crop yield. Therefore, we combined gamma-aminobutyric acid (GABA) with nitrapyrin to address the limitations of nitrapyrin in enhancing yield. We conducted indoor incubation experiments and pot experiments in Chernozem and Calcic Kastanozem, respectively. The results demonstrated that GABA exerted an influence on the effectiveness of nitrapyrin by altering its degradation rate. In Chernozem, GABA accelerated nitrapyrin degradation, whereas, in Calcic Kastanozem, the results were the opposite. The pot experiment results showed that the combination of nitrapyrin and GABA increased rice total biomass by 5%, grain yield by 18 ± 2%, and plant nitrogen (N) uptake by 9 ± 1% compared to nitrapyrin applied alone. The increase in yield was attributed to the combined effect of nitrapyrin and GABA, which elevated root biomass and leaf area. In contrast, the effect of GABA on yield through altering the degradation rate of nitrapyrin was weaker. Therefore, the combination of nitrapyrin and GABA combined with urea increases rice yields in Chernozem and Calcic Kastanozem. The aim of this endeavour was to foster the development of a novel fertiliser product that offers both favourable agronomic outcomes and environmental benefits.

Land subsidence is a worldwide geological hazard. Differential land subsidence has posed the major threat to urban infrastructure, linear rail transit and underground space development and utilization, and also restricted the sustainable development of the economy and society. This paper systematically elaborates on the research progresses on land deformation acquisition, evolution mechanism and simulation of land subsidence, and focuses on the land deformation acquisition technology based on InSAR monitoring and multi-source deformation data fusion, as well as the correlation analysis, statistical analysis, machine learning and other methods to analyze the relationship between the evolution of land subsidence and various influencing factors based on geotechnical experiment and long time series observation data. On this basis, the advantages and disadvantages of land subsidence simulation models such as groundwater flow field-land deformation model, mathematical statistical model and machine learning model are explored. It is found that multi-source deformation data fusion can improve the spatiotemporal resolution of land deformation. The differences in geological structure, lithology, groundwater exploitation, and dynamic and static loads are factors contributing to the differential evolution of land subsidence. The difficulty in balancing the computational efficiency and interpretability of mathematical models for land subsidence is the main problem in simulation. According to the literature review, the current researches mainly focus on land subsidence caused by groundwater over-exploitation. This paper further proposes the future research directions for land subsidence, under the background of climate change, new hydrological condition and dataset, and based on the fusion of data through remote sensing and field observations, integrating the latest progress of InSAR, GeoAI, cloud platform and other technologies to reveal the evolution mechanism of land subsidence considering the climate change and anthropic activities and provide technical support for regional land subsidence prevention and urban safety.

Abstract As the node positioning of underwater wireless sensor networks is easily affected by tidal motion, ocean current motion and multipath effect, the node positioning accuracy is low. In order to better improve the positioning accuracy of moving nodes of underwater wireless sensor networks, a method of locating mobile nodes of underwater wireless sensor based on tidal motion model is proposed. Firstly, the Time Difference of Arrival (TDOA) localization optimized by niche genetic algorithm is used to initialize each node. The integration of niche technology can effectively find multiple excellent solutions in the solution space, thus providing more abundant solution choices. This algorithm has excellent performance in multi-modal optimization problems, and can avoid the algorithm falling into local optimal solutions, so as to obtain more comprehensive optimization results. The simulation results show that the proposed algorithm has better positioning accuracy than the traditional Chan algorithm and Taylor algorithm. Then, each node is updated in real time by the optimized tidal movement model formula predicted by Kalman filter algorithm. The prediction algorithm is used to compare the real-time predicted update position of the node with the actual position. The positioning distance error of the prediction algorithm is also enough to meet the practical application requirements.

Abstract Background APRI and FIB-4 scores are used to exclude clinically significant fibrosis (defined as stage ≥ F2) in patients with chronic viral hepatitis. However, the cut-offs for these scores (generated by Youden indices) vary between different patient cohorts. This study aimed to evaluate whether serum dithiothreitol-oxidizing capacity (DOC), i.e., a surrogate test of quiescin sulfhydryl oxidase-1, which is a matrix remodeling enzyme, could be used to non-invasively identify significant fibrosis in patients with various chronic liver diseases (CLDs). Methods Diagnostic performance of DOC was compared with APRI and FIB-4 for identifying significant fibrosis. ROC curve analyses were undertaken in: a) two chronic hepatitis B (CHB) cohorts, independently established from hospitals in Wenzhou (n = 208) and Hefei (n = 120); b) a MASLD cohort from Wenzhou hospital (n = 122); and c) a cohort with multiple CLD etiologies (except CHB and MASLD; n = 102), which was identified from patients in both hospitals. Cut-offs were calculated using the Youden index. All CLD patients (n = 552) were then stratified by age for ROC curve analyses and cut-off calculations. Results Stratified by CLD etiology or age, ROC curve analyses consistently showed that the DOC test was superior to APRI and FIB-4 for discriminating between clinically significant fibrosis and no fibrosis, when APRI and FIB-4 showed poor/modest diagnostic performance (P 0.05 in 11 cohort comparisons). DOC had a significant advantage over APRI or FIB-4 scores for establishing a uniform cut-off independently of age and CLD etiology (coefficients of variation of DOC, APRI and FIB-4 cut-offs were 1.7%, 22.9% and 47.6% in cohorts stratified by CLD etiology, 2.0%, 26.7% and 29.5% in cohorts stratified by age, respectively). The uniform cut-off was 2.13, yielded from all patients examined. Surprisingly, the uniform cut-off was the same as the DOC upper limit of normal with a specificity of 99%, estimated from 275 healthy control individuals. Hence, the uniform cut-off should possess a high negative predictive value for excluding significant fibrosis in primary care settings. A high DOC cut-off with 97.5% specificity could be used for detecting significant fibrosis (≥ F2) with an acceptable positive predictive value (87.1%). Conclusions This proof-of-concept study suggests that the DOC test may efficiently rule out and rule in significant liver fibrosis, thereby reducing the numbers of unnecessary liver biopsies. Moreover, the DOC test may be helpful for clinicians to exclude significant liver fibrosis in the general population.

Through the creation and characterization of novel poly (L-lactic acid) (PLLA)-based nanocomposites containing graphene oxide (GO), nanocrystalline hydroxyapatite (nano-HAP), and nickel oxide (NiO) nanoparticles, this study seeks to improve the performance of bone tissue scaffolds. Utilizing a casting procedure to create nanocomposites, wet chemical methods were used to synthesize nano-HAP. X-ray diffraction, transmission electron microscopy, and Fourier transform infrared spectroscopy were used to characterize the materials. The nanocomposite comprising 5 % GO + 5 % NiO + 10 % nano-HAP showed a 110 % increase in tensile modulus, a 38 % increase in extension, and a 29 % increase in load-bearing capacity compared to plain PLLA. The results showed a considerable increase in mechanical strength. On the surfaces of the nanocomposite, osteoblast adhesion and proliferation increased by 185 %, according to biological tests. Studies on degradation revealed regulated rates that matched bones' natural mending process while preserving a pH environment that was steady. According to these results, adding nanostructures to PLLA scaffolds improves their mechanical and biological properties considerably, which may lead to therapeutic uses in bone tissue engineering in the future.

Abstract Diabetes mellitus affected more than 500 million of people globally, with an annual mortality of 1.5 million directly attributable to diabetic complications. Oxidative stress, in particularly in post-prandial state, plays a vital role in the pathogenesis of the diabetic complications. However, oxidative status marker is generally poorly characterized and their mechanisms of action are not well understood. In this work, we proposed a new framework for deep characterization of oxidative stress in erythrocytes (and in urine) using home-built micro-scale NMR system. The dynamic of post-prandial oxidative status (against a wide variety of nutritional load) in individual was assessed based on the proposed oxidative status of the red blood cells, with respect to the traditional risk-factors such as urinary isoprostane, reveals new insights into our understanding of diabetes. This new method can be potentially important in drafting guidelines for sub-stratification of diabetes mellitus for clinical care and management.

Abstract Background Previous studies have shown the importance of energy deficiency and malfunctioning mitochondria in the pathophysiology of hypertrophic cardiomyopathy (HCM). There has been a little research into the relationship between plasma free fatty acids (FFA), one of the heart’s main energy sources, and HCM. We evaluated its clinical importance in HCM to see if there was a link between plasma FFA metabolism and HCM. Methods In a single-center retrospective observational study, we investigated 420 HCM patients diagnosed at Beijing Anzhen Hospital between January 1, 2018, and December 31, 2022. Meanwhile, 1372 individuals without HCM (non-HCM) were recruited. 391 non-HCM patients were chosen as controls via a propensity score matching (PSM) study with a 1:1 ratio. Results FFA in HCM patients showed statistically significant correlations with creatinine (r = 0.115, p = 0.023), estimated GFR (r=-0.130, p = 0.010), BNP (r = 0.152, p = 0.007), LVEF (r=-0.227, p

Abstract Background A standard surgical procedure for patients with small early-stage lung adenocarcinomas remains unknown. Hence, we aim in this study to assess the clinical utility of the consolidation-to-tumor ratio (CTR) when treating patients with small (2 cm) early stage lung cancers. Methods A retrospective cohort of 298 sublobar resection and 266 lobar resection recipients for early stage lung adenocarcinoma ≤ 2 cm was assembled from the First Affiliated Hospital of Chongqing Medical University between 2016 and 2019. To compare survival rates among the different groups, Kaplan-Meier curves were calculated, and the log-rank test was used. A multivariate Cox proportional hazard model was constructed utilizing variables that were significant in univariate analysis of survival. Results In the study, 564 patients were included, with 298 patients (52.8%) undergoing sublobar resection and 266 patients (47.2%) undergoing lobar resection. Regarding survival results, there was no significant difference in the 5-year overall survival (OS, P = 0.674) and 5-year recurrence-free survival (RFS, P = 0.253) between the two groups. Cox regression analyses showed that CTR ≥ 0.75(P 56 years (P = 0.007), and sublobar resection(P = 0.001) could predict worse survival. After examining survival results based on CTR categorization, we segmented the individuals into three categories: CTR

Abstract Background Incomplete radiofrequency ablation (iRFA) in hepatocellular carcinoma (HCC) often leads to local recurrence and distant metastasis of the residual tumor. This is closely linked to the development of a tumor immunosuppressive environment (TIME). In this study, underlying mechanisms and potential therapeutic targets involved in the formation of TIME in residual tumors following iRFA were explored. Then, TAK-981-loaded nanocomposite hydrogel was constructed, and its therapeutic effects on residual tumors were investigated. Results This study reveals that the upregulation of small ubiquitin-like modifier 2 (Sumo2) and activated SUMOylation is intricately tied to immunosuppression in residual tumors post-iRFA. Both knockdown of Sumo2 and inhibiting SUMOylation with TAK-981 activate IFN-1 signaling in HCC cells, thereby promoting dendritic cell maturation. Herein, we propose an injectable PDLLA-PEG-PDLLA (PLEL) nanocomposite hydrogel which incorporates self-assembled TAK-981 and BSA nanoparticles for complementary localized treatment of residual tumor after iRFA. The sustained release of TAK-981 from this hydrogel curbs the expansion of residual tumors and notably stimulates the dendritic cell and cytotoxic lymphocyte-mediated antitumor immune response in residual tumors while maintaining biosafety. Furthermore, the treatment with TAK-981 nanocomposite hydrogel resulted in a widespread elevation in PD-L1 levels. Combining TAK-981 nanocomposite hydrogel with PD-L1 blockade therapy synergistically eradicates residual tumors and suppresses distant tumors. Conclusions These findings underscore the potential of the TAK-981-based strategy as an effective therapy to enhance RFA therapy for HCC. Graphic Abstract

Abstract In recent years, single-cell analyses have revealed the heterogeneity of the tumour microenvironment (TME) at the genomic, transcriptomic, and proteomic levels, further improving our understanding of the mechanisms of tumour development. Single-cell RNA sequencing (scRNA-seq) technology allow analysis of the transcriptome at the single-cell level and have unprecedented potential for exploration of the characteristics involved in tumour development and progression. These techniques allow analysis of transcript sequences at higher resolution, thereby increasing our understanding of the diversity of cells found in the tumour microenvironment and how these cells interact in complex tumour tissue. Although scRNA-seq has emerged as an important tool for studying the tumour microenvironment in recent years, it cannot be used to analyse spatial information for cells. In this regard, spatial transcriptomics (ST) approaches allow researchers to understand the functions of individual cells in complex multicellular organisms by understanding their physical location in tissue sections. In particular, in related research on tumour heterogeneity, ST is an excellent complementary approach to scRNA-seq, constituting a new method for further exploration of tumour heterogeneity, and this approach can also provide unprecedented insight into the development of treatments for pancreatic cancer (PC). In this review, based on the methods of scRNA-seq and ST analyses, research progress on the tumour microenvironment and treatment of pancreatic cancer is further explained.

Abstract Background Indigenous Chinese cattle have abundant genetic diversity and a long history of artificial selection, giving local breeds advantages in adaptability, forage tolerance and resistance. The detection of selective sweeps and comparative genome analysis of selected breeds and ancestral populations provide a basis for understanding differences among breeds and for the identification and utilization of candidate genes. We investigated genetic diversity, population structure, and signatures of selection using genome-wide sequencing data for a new breed of Qinchuan cattle (QNC, n = 21), ancestral Qinchuan cattle (QCC, n = 20), and Zaosheng cattle (ZSC, n = 19). Results A population structure analysis showed that the ancestry components of QNC and ZSC were similar. In addition, the QNC and ZSC groups showed higher proportions of European taurine ancestry than that of QCC, and this may explain the larger body size of QNC, approaching that of European cattle under long-term domestication and selection. A neighbor-joining tree revealed that QCC individuals were closely related, whereas QNC formed a distinct group. To search for signatures of selection in the QNC genome, we evaluated nucleotide diversity (θπ), the fixation index (F ST) and Tajima’s D. Overlapping selective sweeps were enriched for one KEGG pathway, the apelin signaling pathway, and included five candidate genes (MEF2A, SMAD2, CAMK4, RPS6, and PIK3CG). We performed a comprehensive review of genomic variants in QNC, QCC, and ZSC using whole-genome sequencing data. QCC was rich in novel genetic diversity, while diversity in QNC and ZSC cattle was reduced due to strong artificial selection, with divergence from the original cattle. Conclusions We identified candidate genes associated with production traits. These results support the success of selective breeding and can guide further breeding and resource conservation of Qinchuan cattle.

Abstract Background Ruminant gut microbiota are critical in ecological adaptation, evolution, and nutrition utilization because it regulates energy metabolism, promotes nutrient absorption, and improves immune function. To study the functional roles of key gut microbiota in sheep and goats, it is essential to construct reference microbial gene catalogs and high-quality microbial genomes database. Results A total of 320 fecal samples were collected from 21 different sheep and goat breeds, originating from 32 distinct farms. Metagenomic deep sequencing and binning assembly were utilized to construct a comprehensive microbial genome information database for the gut microbiota. We successfully generated the largest reference gene catalogs for gut microbiota in sheep and goats, containing over 162 million and 82 million nonredundant predicted genes, respectively, with 49 million shared nonredundant predicted genes and 1138 shared species. We found that the rearing environment has a greater impact on microbial composition and function than the host’s species effect. Through subsequent assembly, we obtained 5810 medium- and high-quality metagenome-assembled genomes (MAGs), out of which 2661 were yet unidentified species. Among these MAGs, we identified 91 bacterial taxa that specifically colonize the sheep gut, which encode polysaccharide utilization loci for glycan and mucin degradation. Conclusions By shedding light on the co-symbiotic microbial communities in the gut of small ruminants, our study significantly enhances the understanding of their nutrient degradation and disease susceptibility. Our findings emphasize the vast potential of untapped resources in functional bacterial species within ruminants, further expanding our knowledge of how the ruminant gut microbiota recognizes and processes glycan and mucins. Video Abstract

Abstract Background Factor XI (FXI) deficiency is an autosomal hemorrhagic disorder characterized by reduced plasma FXI levels. Multiple ancestral variants in the F11 gene have been identified in Ashkenazi Jews and other selected European populations. However, there are few reports of predominant variants in Chinese and/or East Asian populations. The aim of this study is to characterize the genotypes and phenotypes of FXI deficiency and identify the predominant variants. Results Of the 41 FXI-deficient patients, 39 exhibited severe FXI defects, considerably more than those with partial defects. The APTT levels showed a negative correlation with FXI activity levels (coefficient=-0.584, P

Abstract Objective This study aims to comprehensively analyze the clinical characteristics and identify the differentially expressed genes associated with drug‐resistant epilepsy (DRE) in patients with focal cortical dysplasia (FCD). Methods A retrospective investigation was conducted from July 2019 to June 2022, involving 40 pediatric cases of DRE linked to FCD. Subsequent follow‐ups were done to assess post‐surgical outcomes. Transcriptomic sequencing and quantitative reverse transcription polymerase chain reaction (qRT‐PCR) were used to examine differential gene expression between the FCD and control groups. Results Among the 40 patients included in the study, focal to bilateral tonic–clonic seizures (13/40, 32.50%) and epileptic spasms (9/40, 22.50%) were the predominant seizure types. Magnetic resonance imaging (MRI) showed frequent involvement of the frontal (22/40, 55%) and temporal lobes (12/40, 30%). In cases with negative MRI results (13/13, 100%), positron emission tomography/computed tomography (PET‐CT) scans revealed hypometabolic lesions. Fused MRI/PET‐CT images demonstrated lesion reduction in 40.74% (11/27) of cases compared with PET‐CT alone, while 59.26% (16/27) yielded results consistent with PET‐CT findings. FCD type II was identified in 26 cases, and FCD type I in 13 cases. At the last follow‐up, 38 patients were prescribed an average of 1.27 ± 1.05 anti‐seizure medications (ASMs), with two patients discontinuing treatment. After a postoperative follow‐up period of 23.50 months, 75% (30/40) of patients achieved Engel class I outcome. Transcriptomic sequencing and qRT‐PCR analysis identified several genes primarily associated with cilia, including CFAP47, CFAP126, JHY, RSPH4A, and SPAG1. Significance This study highlights focal to bilateral tonic–clonic seizures as the most common seizure type in patients with DRE due to FCD. Surgical intervention primarily targeted lesions in the frontal and temporal lobes. Patients with FCD‐related DRE showed a promising prognosis for seizure control post‐surgery. The identified genes, including CFAP47, CFAP126, JHY, RSPH4A, and SPAG1, could serve as potential biomarkers for FCD. Plain Language Summary This study aimed to comprehensively evaluate the clinical data of individuals affected by focal cortical dysplasia and analyze transcriptomic data from brain tissues. We found that focal to bilateral tonic–clonic seizures were the most prevalent seizure type in patients with drug‐resistant epilepsy. In cases treated surgically, the frontal and temporal lobes were the primary sites of the lesions. Moreover, patients with focal cortical dysplasia‐induced drug‐resistant epilepsy exhibited a favorable prognosis for seizure control after surgery. CFAP47, CFAP126, JHY, RSPH4A, and SPAG1 have emerged as potential pathogenic genes for the development of focal cortical dysplasia.

Aspergillus niger is an efficient cell factory for organic acids production, particularly l-malic acid, through genetic manipulation. However, the traditional method of collecting A. niger spores for inoculation is labor-intensive and resource-consuming. In our study, we used the CRISPR-Cas9 system to replace the promoter of brlA, a key gene in Aspergillus conidiation, with a xylose-inducible promoter xylP in l-malic acid-producing A. niger strain RG0095, generating strain brlAxylP. When induced with xylose in submerged liquid culture, brlAxylP exhibited significant upregulation of conidiation-related genes. This induction allowed us to easily collect an abundance of brlAxylP spores (>7.1 × 106/mL) in liquid xylose medium. Significantly, the submerged conidiation approach preserves the substantial potential of A. niger as a foundational cellular platform for the biosynthesis of organic acids, including but not limited to l-malic acid. In summary, our study offers a simplified submerged conidiation strategy to streamline the preparation stage and reduce labor and material costs for industrial organic acid production using Aspergillus species.

30Cr3 is a novel ultra-high strength steel (UHSS) with excellent strength and plastic toughness, which has been mainly used in aerospace shells and stressed parts. Currently, there is still a lack of systematic research and comprehensive evaluation on the laser cleaning of this particular material. In this study, a nanosecond pulsed laser was employed for the cleaning process. First, the energy distribution model was derived, the influence of the spatial and temporal distribution of laser energy on the cleaning quality was discussed, and the cleaning experiment was conducted. The results showed that a laser energy density of 15 J/cm2, combined with the beam overlap rate 52% in the x-direction and 61% in the y-direction, can effectively remove the oxide layer and thoroughly expose the natural metal matrix. Then, under the optimum parameters, the oxygen content decreased from 22.3% to 3.4%, the Fe content increased from 54.5 to 83.4%, the Fe3+ peak disappeared in the X-ray photoelectron spectroscopy (XPS), a remelted layer of only 1 μm was observed. Last, the roughness was slightly reduced from 3.573 μm to 2.985 μm, the hardness was restored to the original hardness level of the substrate (∼260 HV), and the electron back scatter diffraction (EBSD) showed that the structure hardly changed after cleaning, the grain size was comparable to that of the matrix. These findings indicated that laser cleaning can effectively remove the oxide layer of 30Cr3 UHSS without damaging the properties of the base material, which has promising application in the cleaning of UHSS.

The present study designed and prepared a novel platinum alloy system Pt-xAu-5Ni (x = 0, 2, 5, wt.%) with high strength and good electrical resistivity. The mechanical, electrical properties and related microstructure during the cold deformation and subsequent aging process were investigated. The cold-deformed Pt–5Ni alloy obtained the yield strength of 581 MPa, the ultimate tensile strength of 828 MPa, the hardness of 283 HV, and the electrical resistivity of 23.9 μΩ⋅cm. The subsequent aging process and the Au addition into Pt–5Ni alloy were found to both enhance the mechanical properties. Therefore, with the dual strengthening effect of the subsequent aging and the Au addition, Pt–5Au–5Ni alloy achieved the high yield strength of 889 MPa, the high ultimate tensile strength of 1117 MPa, the high hardness of 361 HV with the electrical resistivity of 26.7 μΩ⋅cm. The precipitation of L12-type Pt3Ni nanoparticles and L11-type CSRO dominated the age-strengthening behavior of Pt–2Au–5Ni and Pt–5Au–5Ni alloys after cold deformation. The Au addition refined the grains via the experimental observations, and predictably favored the L12-type Pt3Ni nanoprecipitation based on the thermodynamical calculations, thus the Au addition effectively strengthened Pt–5Ni alloy. This study provides a novel platinum alloy system with excellent mechanical and electrical properties, which has potential application in the electrical contact field.

Vegetation restoration and microbial reclamation of mining subsidence area is the key spot to construct green mine.The effects of Arbuscular Mycorrhizal Fungi (AMF) and re-vegetation types on the ecological chemometric characteristics of the plant-litter-soil continuum and leaf nutrient resorption are still unclear, but are important for illuminating nutrient cycling and sustainable development after ecological restoration in arid coal mining subsidence areas in Northwest China. Five types of restored vegetation and the 0～10 cm soil of the corresponding vegetation were selected for study in the reclamation of 10 years of inoculation with AMF and the control area—Amorpha fruticosa, Hippophae rhamnoides, Xan-thoceras sorbifolium, Cerasus humilis and Cerasus szechuanica.The intention was to explore the influences of inoculum and re-vegetation type on C, N and P contents and ecological stoichiometric ratios, nutrient recovery rates, and nutrient limitation within the plant-litter-soil continuum. Compared with the national level, the average nutrient content of the five restored vegetation organs showed the characteristics of higher C, lower P and lower N. The decomposition of the litter and plant growth were limited by the deficiency of soil N.Redundancy analysis indicates that the main soil factors affecting plant ecological stoichiometry were soil organic carbon, P, easily extracta-ble-glomalin-related soil protein and positive correlation between soil nutrients and leaf nutrient resorption mediated by mycorrhizae.AMF inoculation enhanced plant and soil nutrient content, N and P recovery rates and C:N, reduced vegetation N:P, which in turn alleviated plant N limitation, increased plant growth rate supporting the restoration of revegetation and promoting nutrient cycling along the continuum.On the other hand, the nutrient content, stability and growth rate of the inoculated nitrogen-fixing vegeta-tion in different artificially reconstructed vegetation of Amorpha fruticose and Hippophae rhamnoides were high. Ecological stoichiometry and nutrient recovery characteristics of the plant-litter-soil continuum pre-dicted soil nitrogen-limited sustainable ecological restoration in subsidence areas, while inoculation with AMF promoted the potential of sustainable ecological restoration in subsidence areas.

Abstract The construction of acoustic emission criterion system is crucial for monitoring and providing early warning of geological hazards. In the current soil acoustic emission monitoring methods, the signal generated by soil deformation and failure is weak and experiences high attenuation, resulting in a low level of the monitored signal. One approach to enhance the quality of monitoring data is by utilizing the active waveguide model. However, the current research on the active waveguide model system is not extensive. To address these issues, a set of active waveguide system was designed to improve the data quality of acoustic emission monitoring and early warning. The deformation and failure process of loess monitored by acoustic emission was divided into stages, and the precursor information of acoustic emission for geological disasters in loess areas was comprehensively deconstructed. The data quality advantage of the active waveguide model was verified through comparative experiments of with the passive waveguide model. This study investigates the AE signal characteristics of the active waveguide model. It explores various aspects such as the AE waveform parameter characteristics, the discrimination method for failure mode based on RA–AF value, the AE r-value characteristics, the AE b-value characteristics, and the frequency-amplitude characteristics. The study reveals the evolution law of AE signals in the active waveguide model, including early warning signs and failure morphological characteristics. Furthermore, it constructs a warning criterion for the active waveguide system. The development of this criterion system is of great importance in guiding the monitoring and early warning of geological disasters in loess areas.

Abstract Background Reintroduction represents an effective strategy for the conservation of endangered wildlife, yet it might inadvertently impact the native ecosystems. This investigation assesses the impact of reintroducing endangered Przewalski's horses into the desert grassland ecosystem of the Kalamaili Nature Reserve (KNR), particularly its effect on the spatial distribution of ticks. In a 25 km2 core area of Przewalski's horse distribution, we set up 441 tick sampling sites across diverse habitats, including water sources, donkey trails, and grasslands, recording horse feces and characteristics to analyze the occurrence rate of ticks. Additionally, we gathered the data of 669 fresh feces of horses. To evaluate the spatial dynamics between these feces and ticks, we used methods such as Fixed Kernel Estimation (FKE), Moran’s I spatial autocorrelation index, and Generalized Linear Models (GLM). Results The dominant species of ticks collected in the core area were adult Hyalomma asiaticum (91.36%). Their occurrence rate was higher near donkey trails (65.99%) and water sources (55.81%), particularly in areas with the fresh feces of Przewalski's horses. The ticks’ three risk areas, as defined by FKE, showed significant overlap and positive correlation with the distribution of Przewalski's horses, with respective overlap rates being 90.25% in high risk, 33.79% in medium risk, and 23.09% in low risk areas. Moran's I analysis revealed a clustering trend of the fresh feces of Przewalski's horses in these areas. The GLM confirmed a positive correlation between the distribution of H. asiaticum and the presence of horse fresh feces, alongside a negative correlation with the proximity to water sources and donkey trails. Conclusions This study reveals the strong spatial correlation between Przewalski's horses and H. asiaticum in desert grasslands, underlining the need to consider interspecific interactions in wildlife reintroductions. The findings are crucial for shaping effective strategies of wildlife conservation and maintaining ecological balance.

Abstract Background Understanding the molecular mechanisms of Alzheimer’s disease (AD) has important clinical implications for guiding therapy. Impaired amyloid beta (Aβ) clearance is critical in the pathogenesis of sporadic AD, and blood monocytes play an important role in Aβ clearance in the periphery. However, the mechanism underlying the defective phagocytosis of Aβ by monocytes in AD remains unclear. Methods Initially, we collected whole blood samples from sporadic AD patients and isolated the monocytes for RNA sequencing analysis. By establishing APP/PS1 transgenic model mice with monocyte-specific cystatin F overexpression, we assessed the influence of monocyte-derived cystatin F on AD development. We further used a nondenaturing gel to identify the structure of the secreted cystatin F in plasma. Flow cytometry, enzyme-linked immunosorbent assays and laser scanning confocal microscopy were used to analyse the internalization of Aβ by monocytes. Pull down assays, bimolecular fluorescence complementation assays and total internal reflection fluorescence microscopy were used to determine the interactions and potential interactional amino acids between the cystatin F protein and Aβ. Finally, the cystatin F protein was purified and injected via the tail vein into 5XFAD mice to assess AD pathology. Results Our results demonstrated that the expression of the cystatin F protein was specifically increased in the monocytes of AD patients. Monocyte-derived cystatin F increased Aβ deposition and exacerbated cognitive deficits in APP/PS1 mice. Furthermore, secreted cystatin F in the plasma of AD patients has a dimeric structure that is closely related to clinical signs of AD. Moreover, we noted that the cystatin F dimer blocks the phagocytosis of Aβ by monocytes. Mechanistically, the cystatin F dimer physically interacts with Aβ to inhibit its recognition and internalization by monocytes through certain amino acid interactions between the cystatin F dimer and Aβ. We found that high levels of the cystatin F dimer protein in blood contributed to amyloid pathology and cognitive deficits as a risk factor in 5XFAD mice. Conclusions Our findings highlight that the cystatin F dimer plays a crucial role in regulating Aβ metabolism via its peripheral clearance pathway, providing us with a potential biomarker for diagnosis and potential target for therapeutic intervention.

Prostate cancer is a prevalent malignancy and leading cause of male mortality worldwide, thus highlighting the need for precision medicine and a combined single-cell and bulk transcriptome-based diagnostic assessment model. First, we used single-cell RNA sequencing data and the high dimensional weighted gene co-expression network analysis (hdWGCNA) method to identify specific cell types in tumor tissues. We identified higher proportions of epithelial cells and increased intra-tissue heterogeneity in prostate cancer; most of these epithelial cells were closely associated with metal ion transport functions. Lasso regression identified diagnostic-related genes that were incorporated into a sample evaluation model using multiple machine learning algorithms. Thus, we established a diagnostic model that combined immune cells, diagnostic genes and deep learning methods. The Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) models exhibited the highest diagnostic efficacy for Lasso genes. Neural network models, combining immune microenvironment assessment and diagnostic gene expression, also showed good diagnostic efficacy. Our study highlights the potential of machine learning and convolutional neural networks for the diagnostic assessment of prostate cancer, thus providing support for personalized treatment decisions, and identifying areas for future research.

A two-year field experiment was conducted to measure the effects of densification methods on photosynthesis and yield of densely planted wheat. Inter-plant and inter-row distances were used to define rate-fixed pattern (RR) and row-fixed pattern (RS) density treatments. Meanwhile, four nitrogen (N) rates (0, 144, 192, and 240 kg N ha−1, termed N0, N144, N192, and N240) were applied with three densities (225, 292.5, and 360 × 104 plants ha−1, termed D225, D292.5, and D360). The wheat canopy was clipped into three equal vertical layers (top, middle, and bottom layers), and their chlorophyll density (ChD) and photosynthetically active radiation interception (FIPAR) were measured. Results showed that the response of ChD and FIPAR to N rate, density, and pattern varied with different layers. N rate, density, and pattern had significant interaction effects on ChD. The maximum values of whole-canopy ChD in the two seasons appeared in N240 combined with D292.5 and D360 under RR, respectively. Across two growing seasons, FIPAR values of RR were higher than those of RS by 29.37% for the top layer and 5.68% for the middle layer, while lower than those of RS by 20.62% for the bottom layer on average. With a low N supply (N0), grain yield was not significantly affected by density for both patterns. At N240, increasing density significantly increased yield under RR, but D360 of RS significantly decreased yield by 3.72% and 9.00% versus D225 in two seasons, respectively. With an appropriate and sufficient N application, RR increased the yield of densely planted wheat more than RS. Additionally, the maximum yield in two seasons appeared in the combination of D360 with N144 or N192 rather than of D225 with N240 under both patterns, suggesting that dense planting combined with an appropriate N-reduction application is feasible to increase photosynthesis capacity and yield.

Abstract Background As the largest group of healthcare professionals, nurses play an indispensable and crucial role in disaster response. The enhancement of nurses’ disaster literacy is imperative for effective disaster emergency management. However, there is currently a lack of knowledge regarding nurses’ disaster literacy. This study represents the first attempt to explore the key components and characteristics of disaster literacy among nurses. Methods A qualitative descriptive design was employed, and the reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines (refer to File S1). The purposive sampling method was utilized. Thirty-one rescue nurses from 31 medical institutions across 25 provinces and regions in China were recruited to participate in the study. The respondents were requested to share their experiences and insights regarding disaster rescue operations. Inductive content analysis was employed for data examination. Results The results indicated that rescue nurses universally recognized that there was a pressing need to enhance the level of disaster literacy among nurses. The disaster literacy of nurses encompasses nine dimensions: physical and mental quality, disaster rescue general knowledge, professional and technical competence, professional ethics, teamwork, emotional ability, information literacy, leadership, and knowledge transformation. Conclusions To ensure national sustainability, government departments, healthcare organizations, and hospital administrators can accurately evaluate the disaster literacy of individual clinical nurses, groups, and the workforce as a whole through nine dimensions, which also can provide evidence to support the development of precision strategies to strengthen the disaster literacy of nurses.

Abstract Background As one of the most famous natural products, salvianolic acid A (SAA) is undergoing clinical trials for the treatments of angina pectoris and coronary heart disorders. However, the in vivo metabolites of SAA have only been tentatively identified, leading to a barrier for precise therapeutical drug monitoring. Methods Ultra-high performance liquid chromatography coupled with quadrupole time of flight tandem mass spectrometry (UPLC–Qtof-MS/MS) was firstly employed to acquire high-resolution MS1 and MS2 spectra for all metabolites. Through paying special attention onto the features of ester bond dissociation, metabolism sites were restricted at certain regions. To further determine the metabolism site, such as the monomethylated products (M23, M25, and M26), post collision-induced dissociation energy-resolved mass spectrometry (post-CID ER-MS) was proposed through programming progressive exciting energies to the second collision chamber of hybrid triple quadrupole-linear ion trap mass spectrometry (Qtrap-MS) device. Results After SAA oral administration, 29 metabolites (M1–M29), including five, thirteen, and sixteen ones in rat plasma, urine, and feces, respectively, were detected in rats. The metabolism route was initially determined by applying well-defined mass fragmentation pathways to those HR-m/z values of precursor and fragment ions. Metabolism site was limited to SAF- or DSS-unit based on the fragmentation patterns of ester functional group. Through matching the dissociation trajectories of concerned 1st-generation fragment ions with expected decomposition product anions using post-CID ER-MS strategy, M23 and M25 were unequivocally assigned as 3'-methyl-SAA and 3''-methyl-SAA, and M26 was identified as 2-methyl-SAA or 3-methyl-SAA. Hydrolysis, methylation, glucuronidation, sulfation, and oxidation were the primary metabolism channels being responsible for the metabolites' generation. Conclusion Together, the metabolism regions and sites of SAA metabolites were sequentially identified based on the ester bond dissociation features and post-CID ER-MS strategy. Importantly, the present study provided a promising way to elevate the structural identification confidence of natural products and metabolites. Graphical abstract

Abstract Objective Cancer-related fatigue (CRF) has been considered the biggest influencing factor for cancer patients after surgery. This study aimed to develop and validate a nomogram for severe cancer-related fatigue (CRF) patients with cervical cancer (CC). Methods A cross-sectional study was conducted to develop and validate a nomogram (building set = 196; validation set = 88) in the Department of Obstetrics and Gynecology of a Class III hospital in Shenyang, Liaoning Province. We adopted the questionnaire method, including the Cancer Fatigue Scale (CFS), Medical Uncertainty in Illness Scale (MUIS), Medical Coping Modes Questionnaire (MCMQ), Multidimensional Scale of Perceived Social Support (MSPSS), and Sense of Coherence-13 (SOC-13). Binary logistic regression was used to test the risk factors of CRF. The R4.1.2 software was used to develop and validate the nomogram, including Bootstrap resampling method, the ability of Area Under Curve (AUC), Concordance Index (C-Index), Hosmer Lemeshow goodness of fit test, Receiver Operating Characteristic (ROC) curve, Calibration calibration curve, and Decision Curve Analysis curve (DCA). Results The regression equation was Logit(P) = 1.276–0.947 Monthly income + 0.989 Long-term passive smoking − 0.952 Physical exercise + 1.512 Diagnosis type + 1.040 Coping style − 0.726 Perceived Social Support − 2.350 Sense of Coherence. The C-Index of the nomogram was 0.921 (95% CI: 0.877 $$ \sim $$ 0.958). The ROC curve showed the sensitivity of the nomogram was 0.821, the specificity was 0.900, and the accuracy was 0.857. AUC was 0.916 (95% CI: 0.876 $$ \sim $$ 0.957). The calibration showed that the predicted probability of the nomogram fitted well with the actual probability. The DCA curve showed when the prediction probability was greater than about 10%, the benefit of the nomogram was positive. The results in the validation group were similar. Conclusion This nomogram had good identifiability, accuracy and clinical practicality, and could be used as a prediction and evaluation tool for severe cases of clinical patients with CC.

Abstract Clustering algorithm is the primary technology used in target clustering and group status analysis which are key features of the Unmanned Aerial Vehicles (UAVs) control system. Due to variable application environment, the stability of the algorithm in the UAVs control system needs to be considered. K‐means clustering is a widely used method in intelligent systems. However, K‐means algorithm is susceptible to the local optimum due to the influence of the initial centroid. For this problem, the predecessors have proposed various effective solutions. These algorithms perform better on real and large‐scale datasets, but they are unable to achieve optimum results with unbalanced datasets. Herein, a simpler and more effective algorithm for seed initialization is proposed, it has a better accuracy rate than the alternative algorithms.Moreover, after running tests multiple times with each algorithm independently, it has the highest stability and the lowest overall volatility. With unbalanced datasets, the proposed algorithm performs significantly better than several other algorithms and therefore can solve the problems that other algorithms have with unbalanced datasets.

Abstract Solid tumors are complex ecosystems with heterogeneous 3D structures, but the spatial intra-tumor heterogeneity (sITH) at the macroscopic (i.e., whole tumor) level is under-explored. Using a phylogeographic approach, we sequence genomes and transcriptomes from 235 spatially informed sectors across 13 hepatocellular carcinomas (HCC), generating one of the largest datasets for studying sITH. We find that tumor heterogeneity in HCC segregates into spatially variegated blocks with large genotypic and phenotypic differences. By dissecting the transcriptomic heterogeneity, we discover that 30% of patients had a “spatially competing distribution” (SCD), where different spatial blocks have distinct transcriptomic subtypes co-existing within a tumor, capturing the critical transition period in disease progression. Interestingly, the tumor regions with more advanced transcriptomic subtypes (e.g., higher cell cycle) often take clonal dominance with a wider geographic range, rejecting neutral evolution for SCD patients. Extending the statistical tests for detecting natural selection to many non-SCD patients reveal varying levels of selective signal across different tumors, implying that many evolutionary forces including natural selection and geographic isolation can influence the overall pattern of sITH. Taken together, tumor phylogeography unravels a dynamic landscape of sITH, pinpointing important evolutionary and clinical consequences of spatial heterogeneity in cancer.

Abstract Class I KNOTTED-like homeobox (KNOXI) genes are parts of the regulatory network that control the evolutionary diversification of leaf morphology. Their specific spatiotemporal expression patterns in developing leaves correlate with the degrees of leaf complexity between simple-leafed and compound-leafed species. However, KNOXI genes are not involved in compound leaf formation in several legume species. Here, we identify a pathway for dual repression of MtKNOXI function in Medicago truncatula. PINNATE-LIKE PENTAFOLIATA1 (PINNA1) represses the expression of MtKNOXI, while PINNA1 interacts with MtKNOXI and sequesters it to the cytoplasm. Further investigations reveal that UNUSUAL FLORAL ORGANS (MtUFO) is the direct target of MtKNOXI, and mediates the transition from trifoliate to pinnate-like pentafoliate leaves. These data suggest a new layer of regulation for morphological diversity in compound-leafed species, in which the conserved regulators of floral development, MtUFO, and leaf development, MtKNOXI, are involved in variation of pinnate-like compound leaves in M. truncatula.

Abstract Background Urinary incontinence (UI) is significantly link to abdominal obesity. This study aimed to assess the association between anthropometric indices of abdominal obesity, including body roundness index (BRI), conicity index (CI), and waist-to-height ratio (WHtR), and UI risk in adult females. Methods We analyzed data from 10, 317 adult females in the National Health and Nutrition Examination Survey (NHANES) database (2005–2018). Weighted multivariable-adjusted regression analysis was conducted to determine the odds ratio (OR) and 95% confidence intervals (CI) for the association between BRI, CI, WHtR, and UI. Stratified analyses revealed the association based on the population type. Receiver operating characteristic curve (ROC) analyses were used to assess the predictive value of UI. Results All indices of abdominal obesity investigated were positively and independently associated with the prevalence and severity of three types of UI. After adjusting for all relevant confounding variables, a significantly positive association between BRI and the prevalence of UI were observed (OR quartile 4 vs. quartile 1: urge UI (UUI): 1.93, 95% CI 1.61–2.30; stress UI (SUI): 2.29, 95% CI 1.94–2.70; mixed UI (MUI): 2.26, 95% CI 1.82–2.82; all P

IntroductionOsteoporosis-induced changes in bone structure and composition significantly reduce bone strength, particularly in the human proximal femur. This study examines how these changes affect the mechanical performance of trabecular bone to enhance diagnosis, prevention, and treatment strategies.MethodsA proximal femur sample was scanned using micro-CT at 40 μm resolution. Five regions of interest were selected within the femoral head, femoral neck, and greater trochanter. Structural models simulating various stages of osteoporosis were created using image processing software. Micro-finite element analysis evaluated the mechanical properties of trabecular bone under different conditions of structural deterioration and tissue-level elastic modulus variations. The combined effects of structural deterioration and tissue-level mechanical properties on trabecular bone mechanical performance were further analyzed.ResultsThe mechanical performance of trabecular bone generally follows a power-law relationship with its microstructural characteristics. However, in any specific region, the apparent mechanical properties linearly decrease with structural deterioration. The femoral neck and greater trochanter are more sensitive to structural deterioration than the femoral head. A 5% bone mass loss in the femoral head led to a 7% reduction in mechanical performance, while the femoral neck experienced a 12% loss. Increasing tissue-level elastic modulus improved mechanical performance, partially offsetting bone mass reduction effects.ConclusionTrabecular bone in low bone mass regions is more affected by bone mass loss. Structural deterioration primarily reduces bone strength, but improvements in tissue-level properties can mitigate this effect, especially in early osteoporosis. Targeted assessments and interventions are crucial for effective management. Future research should explore heterogeneous deterioration models to better understand osteoporosis progression.

Lung cancer remains a leading cause of cancer-related deaths globally, with its incidence steadily rising each year, representing a significant threat to human health. Early detection, diagnosis, and timely treatment play a crucial role in improving survival rates and reducing mortality. In recent years, significant and rapid advancements in artificial intelligence (AI) technology have found successful applications in various clinical areas, especially in the diagnosis and treatment of lung cancer. AI not only improves the efficiency and accuracy of physician diagnosis but also aids in patient treatment and management. This comprehensive review presents an overview of fundamental AI-related algorithms and highlights their clinical applications in lung nodule detection, lung cancer pathology classification, gene mutation prediction, treatment strategies, and prognosis. Additionally, the rapidly advancing field of AI-based three-dimensional (3D) reconstruction in lung cancer surgical resection is discussed. Lastly, the limitations of AI and future prospects are addressed.

Previous research rarely identified the synergistic effects of climate change and human activities on hydrological regimes, thus leading to an incomplete attribution of hydrological regime changes. To address this issue, this paper presents a complete approach for accurately quantifying these impacts. The framework integrates various methodologies, including the runoff abrupt change point judgment method for distinguishing pre- and post-impact periods, The Soil and Water Assessment Tool (SWAT) is used to isolate runoff processes influenced solely by climate change, the linear separation method for identifying runoff processes affected solely by human activities, and the Indicator of Hydrologic Alteration (IHA) for quantifying changes in hydrological regimes. Application of this framework to the Ganjiang River Basin (GRB) unveils significant alterations in hydrological regimes. This has resulted in a significant decline in aquatic organism species compared to the prior-impact period levels. Notably, climate change and human activities exert opposing effects on minimum flow indicators and flow pulse indicators in the GRB. Moreover, these factors exhibit a substantial synergistic effect on numerous indicators, resulting in hydrological regimes combined influenced by climate change and human activities being considerably different with the cumulative impact on hydrological regimes solely influenced by climate change and those solely influenced by human activities. Consequently, in future water management, it is crucial to recognize the positive role of prudent human activities in mitigating the adverse impacts of climate change.

Cd ions are absorbed and transported from the soil by crop roots, which are the first organ to be exposed to Cd. This results in an increase in cadmium ions in crops, significantly affecting crop growth and yield. Exogenous melatonin (MT) can help reduce cadmium (Cd) stress in cotton, but the specific contribution of roots to this process remains unclear. In order to address this knowledge gap, an in-situ root phenotyping study was conducted to investigate the the phenotype and lifespan of roots under cadmium stress (Cd) and melatonin treatment (Cd + MT). The results showed that MT alleviated the decreases in plant height, leaf area, SPAD value, stem diameter, stomatal conductance and net photosynthetic rate under Cd stress, which further promoted the biomass accumulation in various cotton organs. What is more, the Cd + MT treatment increased root volume, surface area, and length under Cd stress by 25.63 %, 10.58 %, and 21.89 %, respectively, compared with Cd treatment. Interestingly, compared to Cd treatment, Cd + MT treatment also significantly extended the lifespan of roots and root hairs by 6.68 days and 2.18 days, respectively. In addition, Cd + MT treatment reduced the transport of Cd from roots to shoots, particularly to bolls, and decreased the Cd bioconcentration factor in bolls by 61.17 %, compared to Cd treatment. In conclusion, these findings show that applying MT externally helps reduce Cd stress by delaying root senescence, promoting root development and regulating Cd transport. This method can be an effective approach to managing Cd stress in cotton.

Accurate location information is essential for the effective deployment of Wireless Sensor Networks (WSNs) applications. Recently, researchers have developed numerous localization algorithms, with multi-hop methods being particularly attractive due to their cost-effectiveness and robustness, but their accuracy is still unsatisfactory. For better localization accuracy, this paper presented a distance correction range-free localization algorithm (DCRA). Firstly, the proposed algorithm estimates the distance between neighboring nodes based on neighbor connectivity to improve the accuracy of single-hop distance estimation. Secondly, during the multi-hop distance estimation phase, path similarity metrics are employed to ascertain the optimal path correction coefficients, thereby minimizing multi-hop distance estimation errors. Finally, based on the estimated distances, the node localization is converted into an optimization problem, which is then solved using an enhanced particle swarm algorithm to obtain the node coordinates. Simulation results show that the distance estimation accuracy of the DCRA algorithm in different node density scenarios is improved by more than 31 % and 12 % compared to the DV-Hop and DV-RND algorithms, respectively; and in terms of localization accuracy the DCRA algorithm is improved by more than 58 % and 35 % compared to the DV-Hop and DV-RND algorithms, respectively.

IntroductionSalinity negatively affects maize productivity. However, calcium lignosulfonate (CLS) could improve soil properties and maize productivity.MethodsIn this study, we evaluated the effects of CLS application on soil chemical properties, plant physiology and grain quality of maize under salinity stress. Thus, this experiment was conducted using three CLS application rates, CLS0, CLS5, and CLS10, corresponding to 0%, 5%, and 10% of soil mass, for three irrigation water salinity (WS) levels WS0.5, WS2.5, and WS5.5 corresponding to 0.5 and 2.5 and 5.5 dS/m, respectively.Results and discussionResults show that the WS0.5 × CLS10 combination increased potassium (K 0.167 g/kg), and calcium (Ca, 0.39 g/kg) values while reducing the sodium (Na, 0.23 g/kg) content in soil. However, the treatment WS5.5 × CLS0 decreased K (0.120 g/kg), and Ca (0.15 g/kg) values while increasing Na (0.75 g/kg) content in soil. The root activity was larger in WS0.5 × CLS10 than in WS5.5 × CLS0, as the former combination enlarged K and Ca contents in the root while the latter decreased their values. The leaf glutamine synthetase (953.9 µmol/(g.h)) and nitrate reductase (40.39 µg/(g.h)) were higher in WS0.5 × CLS10 than in WS5.5 × CLS0 at 573.4 µmol/(g.h) and 20.76 µg/(g.h), leading to the improvement in cell progression cycle, as revealed by lower malonaldehyde level (6.57 µmol/g). The K and Ca contents in the leaf (881, 278 mg/plant), stem (1314, 731 mg/plant), and grains (1330, 1117 mg/plant) were greater in WS0.5 × CLS10 than in WS5.5 × CLS0 at (146, 21 mg/plant), (201, 159 mg/plant) and (206, 157 mg/plant), respectively. Therefore, the maize was more resistance to salt stress under the CLS10 level, as a 7.34% decline in yield was noticed when salinity surpassed the threshold value (5.96 dS/m). The protein (13.6 %) and starch (89.2 %) contents were greater in WS0.5 × CLS10 than in WS5.5 × CLS0 (6.1 %) and (67.0 %), respectively. This study reveals that CLS addition can alleviate the adverse impacts of salinity on soil quality and maize productivity. Thus, CLS application could be used as an effective soil amendment when irrigating with saline water for sustainable maize production.

BackgroundUniversity emergencies, garnering significant public attention and shaping network opinions, pose a crucial challenge to universities’ management and societal stability. Hence, network public opinion on university emergencies is a vital issue. Nevertheless, the underlying mechanism has not been fully explored and cannot be efficiently controlled. This study aimed to explore the formation pattern of network public opinion on university emergencies, analyze its causes, and provide scientific governance strategies for coping with this issue.MethodsBased on a sample set of 204 cases from the Zhiwei Data Sharing Platform, this study classifies network public opinion on university emergencies into six types and visually analyzes their characteristics: time distribution, subject, duration, and emotion. By integrating the theory of the network public opinion field, this study develops a network public opinion field model of university emergencies to reveal its formation pattern. Furthermore, it analyzes the causes of network public opinion on university emergencies from the perspective of the public opinion lifecycle and proposes corresponding governance strategies.ResultsThe sample consisted of 304 cases of real-life public opinion, and the visualization results show that public opinion on mental health and teacher–student safety constitutes the predominant types, accounting for 83.3%. High-occurrence subjects are public universities (88.24%) and students (48%). The most frequent months are July and December. 90.20% of the public opinions have a lifespan of less than 19 days, with an impact index ranging from 40 to 80. The public’s emotional response to different types of public opinion varies, with negative emotions dominating.ConclusionThis study provides novel insights for understanding their formation and dissemination. It also provides practical implications for relevant departments to govern network public opinion on university emergencies.

IntroductionAccurate prediction of line losses in distribution networks is crucial for optimizing power system planning and network restructuring, as these losses significantly impact grid operation quality. This paper proposes a novel approach that combines advanced feature selection techniques with Stacking ensemble learning to enhance the effectiveness of distribution network loss analysis and assessment.MethodsUtilizing data from 44 substations over an 18-month period, we integrated a Stacking ensemble learning model with multiple feature selection methods, including correlation coefficient, maximum information coefficient, and tree-based techniques. These methods were employed to identify the key predictors of power loss in the distribution network.ResultsThe proposed model achieved a Mean Absolute Percentage Error (MAPE) of 3.78% and a Root Mean Square Error (RMSE) of 1.53, demonstrating a substantial improvement over traditional linear regression-based prediction methods. The analysis revealed that historical line loss and line active power were the most influential predictive variables, while the inclusion of time-related features further refined the model's performance.DiscussionThis study highlights the efficacy of combining multiple feature selection methods with Stacking ensemble learning for predicting power loss in 10 kV distribution networks. The enhanced accuracy and reliability of the proposed model offer valuable insights for electrical engineering applications, potentially contributing to more efficient and sustainable energy distribution systems. Future research could explore the applicability of this approach to other distribution network voltage levels and investigate the incorporation of additional environmental and network-specific factors to further improve power loss prediction.