Spatial variability and source analysis of soil heavy metals: A case study of the key planting area of special agricultural products in Cangxi County, China.

Heavy metal pollution in farmland soil represents a considerable risk to ecosystems and human health, constituting a global concern. Focusing on a key area for the cultivation of special agricultural products in Cangxi County, we collected 228 surface soil samples. We analyzed the concentration, spatial distribution, and pollution levels of six heavy metals (Cr, Cu, Pb, Ni, Zn, and Hg) in the soil. Moreover, we investigated the sources and contribution rates of these heavy metals using Principal Component Analysis/Absolute Principal Component Scores (PCA/APCS) and Positive Matrix Factorization (PMF) models. Our findings indicate that none of the six metals exceeded the pollution thresholds for farmland soils. However, the mean concentrations of Cr and Ni surpassed the background levels of Sichuan Province. A moderate spatial correlation existed between Pb and Ni, attributable to both natural and anthropogenic factors, whereas Zn, Cu, Hg, and Cr displayed a strong spatial correlation, mainly due to natural factors. The spatial patterns of Cr, Cu, Zn, Pb, and Ni were similar, with higher concentrations in the northern and eastern regions and lower concentrations centrally. Hg's spatial distribution differed, exhibiting a broader range of lower values. The single pollution index evaluation showed that Cr and Ni were low pollution, and the other elements were no pollution. The average value of comprehensive pollution index is 0.994, and the degree of pollution is close to light pollution. Predominantly, higher pollution levels in the northern and eastern regions, lower around reservoirs. The PCA/APCS model identified two main pollution sources: agricultural traffic mixed source (65.2%) and natural parent source (17.2%). The PMF model delineated three sources: agricultural activities (32.59%), transportation (30.64%), and natural parent sources (36.77%). Comparatively, the PMF model proved more accurate and reliable, yielding findings more aligned with the study area's actual conditions.

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity.

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

Serine protease Rv2569c facilitates transmission of Mycobacterium tuberculosis via disrupting the epithelial barrier by cleaving E-cadherin.

Epithelial cells function as the primary line of defense against invading pathogens. However, bacterial pathogens possess the ability to compromise this barrier and facilitate the transmigration of bacteria. Nonetheless, the specific molecular mechanism employed by Mycobacterium tuberculosis (M.tb) in this process is not fully understood. Here, we investigated the role of Rv2569c in M.tb translocation by assessing its ability to cleave E-cadherin, a crucial component of cell-cell adhesion junctions that are disrupted during bacterial invasion. By utilizing recombinant Rv2569c expressed in Escherichia coli and subsequently purified through affinity chromatography, we demonstrated that Rv2569c exhibited cell wall-associated serine protease activity. Furthermore, Rv2569c was capable of degrading a range of protein substrates, including casein, fibrinogen, fibronectin, and E-cadherin. We also determined that the optimal conditions for the protease activity of Rv2569c occurred at a temperature of 37°C and a pH of 9.0, in the presence of MgCl2. To investigate the function of Rv2569c in M.tb, a deletion mutant of Rv2569c and its complemented strains were generated and used to infect A549 cells and mice. The results of the A549-cell infection experiments revealed that Rv2569c had the ability to cleave E-cadherin and facilitate the transmigration of M.tb through polarized A549 epithelial cell layers. Furthermore, in vivo infection assays demonstrated that Rv2569c could disrupt E-cadherin, enhance the colonization of M.tb, and induce pathological damage in the lungs of C57BL/6 mice. Collectively, these results strongly suggest that M.tb employs the serine protease Rv2569c to disrupt epithelial defenses and facilitate its systemic dissemination by crossing the epithelial barrier.

Autoimmune diseases and the risk of bladder cancer: A Mendelian randomization analysis.

OBJECTIVE: To investigate the association between autoimmune diseases (AIDs) and bladder cancer (BC) at the genetic level using Mendelian randomization (MR). METHODS: Single nucleotide polymorphisms (SNPs) associated with the seven AIDs were extracted from the IEU GWAS database, and the SNPs were quality-controlled using strict screening criteria. The association between AIDs and BC risk was assessed by inverse-variance weighted (IVW), MR-Egger regression and Weighted median method. The heterogeneity of SNPs was evaluated by Cochran Q test. MR-Egger intercept test and MR-PRESSO global test were used to test the horizontal pleiotropy of SNPs. Both sides with potential causal associations were validated using the validation set. RESULTS: Our result showed that genetically predicted RA was significantly associated with an increased risk of BC (IVW OR = 1.214, 95 % CI = 1.062-1.388, P = 0.005). MS nominally increased the risk of BC (IVW OR = 1.095, 95 % CI = 1.005-1.193, P = 0.037), consistent with the results of the MR analysis of the BC validation cohort. However SLE, T1D, UC, CD, and MG were not causally associated with BC risk (P > 0.05). The sensitivity analyses showed that there was no heterogeneity or horizontal pleiotropy in our findings. CONCLUSION: This study provides evidence of a causal relationship between AIDs and BC risk at the genetic level, confirming a causal relationship between RA and MS in increasing the risk of BC.

Durable and Robust Antibacterial Polypropylene Hernia Mesh for Abdominal Wall Defect Repair.

Polypropylene (PP) mesh is commonly used in repairing abdominal wall hernia (AWH). However, the use of synthetic prosthesis comes with the risk of developing a prosthetic infection, resulting in delayed healing, secondary surgery, and potentially increased mortality. To address these issues, a facile surface functionalization strategy for PP mesh based on phytic acid (PA) and polyhexamethylene guanidine (PHMG) was constructed through a one-step co-deposition process, referred to as the PA/PHMG coating. The development of PA/PHMG coating is mainly attributed to the surface affinity of PA and the electrostatic interactions between PA and PHMG. The PA/PHMG coating could be completed within 4 h under mild conditions. The prepared PA/PHMG coatings on PP mesh surfaces exhibited desirable biocompatibility toward mammalian cells and excellent antibacterial properties against the notorious "superbug" methicillin-resistant Staphylococcus aureus (MRSA) and tetracycline-resistant Escherichia coli (TRE). The PA/PHMG-coated PP meshes showed killing ratios of over 99% against MRSA in an infected abdominal wall hernia repair model. Furthermore, histological and immunohistochemical analysis revealed a significantly attenuated degree of neutrophil infiltration in the PA/PHMG coating group, attributed to the decreased bacterial numbers alleviating the inflammatory response at the implant sites. Meanwhile, the pristine PP and PA/PHMG-coated meshes showed effective tissue repair, with the PA/PHMG coating group exhibiting enhanced angiogenesis compared with pristine PP meshes, suggesting superior tissue restoration. Additionally, PP meshes with the highest PHMG weight ratio (PA/PHMG(3)) exhibited excellent long-term robustness under phosphate-buffered saline (PBS) immersion with a killing ratio against MRSA still exceeding 95% after 60 days of PBS immersion. The present work provides a facile and promising approach for developing antibacterial implants.

A blind image super-resolution network guided by kernel estimation and structural prior knowledge.

The goal of blind image super-resolution (BISR) is to recover the corresponding high-resolution image from a given low-resolution image with unknown degradation. Prior related research has primarily focused effectively on utilizing the kernel as prior knowledge to recover the high-frequency components of image. However, they overlooked the function of structural prior information within the same image, which resulted in unsatisfactory recovery performance for textures with strong self-similarity. To address this issue, we propose a two stage blind super-resolution network that is based on kernel estimation strategy and is capable of integrating structural texture as prior knowledge. In the first stage, we utilize a dynamic kernel estimator to achieve degradation presentation embedding. Then, we propose a triple path attention groups consists of triple path attention blocks and a global feature fusion block to extract structural prior information to assist the recovery of details within images. The quantitative and qualitative results on standard benchmarks with various degradation settings, including Gaussian8 and DIV2KRK, validate that our proposed method outperforms the state-of-the-art methods in terms of fidelity and recovery of clear details. The relevant code is made available on this link as open source.

Effects of variable-temperature drying on the qualities and sweet-substance profile of Zizyphus jujuba Mill. cv. Junzao.

The changes in the qualities and sweet-substance levels of Junzao jujube during variable-temperature drying (VTD) were investigated. The results showed that VTD retains the original color of jujube, reduces its hardness and chewiness, and decreases its wrinkling while shortening the drying time by 13.2% compared with that of constant temperature drying (CTD). "Electronic-tongue" taste analysis showed that the sweetness of VTD jujube is significantly higher than that for CTD. This is shown to be related to the contents of sucrose, fructose, and glucose, as well as the activities of invertase and sucrose synthase enzymes. In addition, the content trends for sweet amino acids are correlated with the temperature gradient used in VTD. Thus, the present study elucidates the factors governing the transformation of sugar substances in jujube during VTD, as well as providing a practical reference for the application of VTD in the jujube industry.

Unravelling immune microenvironment features underlying tumor progression in the single-cell era.

The relationship between the immune cell and tumor occurrence and progression remains unclear. Profiling alterations in the tumor immune microenvironment (TIME) at high resolution is crucial to identify factors influencing cancer progression and enhance the effectiveness of immunotherapy. However, traditional sequencing methods, including bulk RNA sequencing, exhibit varying degrees of masking the cellular heterogeneity and immunophenotypic changes observed in early and late-stage tumors. Single-cell RNA sequencing (scRNA-seq) has provided significant and precise TIME landscapes. Consequently, this review has highlighted TIME cellular and molecular changes in tumorigenesis and progression elucidated through recent scRNA-seq studies. Specifically, we have summarized the cellular heterogeneity of TIME at different stages, including early, late, and metastatic stages. Moreover, we have outlined the related variations that may promote tumor occurrence and metastasis in the single-cell era. The widespread applications of scRNA-seq in TIME will comprehensively redefine the understanding of tumor biology and furnish more effective immunotherapy strategies.

Swift Covalent Gelation Coupled with Robust Wet Adhesive Powder: A Novel Approach for Acute Massive Hemorrhage Control in Dynamic and High-Pressure Wound Environments.

The quest for efficient hemostatic agents in emergency medicine is critical, particularly for managing massive hemorrhages in dynamic and high-pressure wound environments. Traditional self-gelling powders, while beneficial due to their ease of application and rapid action, fall short in such challenging conditions. To bridge this gap, the research introduces a novel self-gelling powder that combines ultrafast covalent gelation and robust wet adhesion, presenting a significant advancement in acute hemorrhage control. This ternary system comprises ε-polylysine (ε-PLL) and 4-arm polyethylene glycol succinyl succinate (4-arm-PEG-NHS) forming the hydrogel framework. Na2HPO4 functions as the "H+ sucker" to expedite the amidation reaction, slashing gelation time to under 10 s, crucial for immediate blood loss restriction. Moreover, PEG chains' hydrophilicity facilitates efficient absorption of interfacial blood, increasing the generated hydrogel's cross-linking density and strengthens its tissue bonding, thereby resulting in excellent mechanical and wet adhesion properties. In vitro experiments reveal the optimized formulation's exceptional tissue compliance, procoagulant activity, biocompatibility and antibacterial efficacy. In porcine models of heart injuries and arterial punctures, it outperforms commercial hemostatic agent Celox, confirming its rapid and effective hemostasis. Conclusively, this study presents a transformative approach to hemostasis, offering a reliable and potent solution for the emergency management of massive hemorrhage.

Frailty as an Effect Modifier in Randomized Controlled Trials: A Systematic Review.

BACKGROUND: The effect of clinical interventions may vary by patients' frailty status. Understanding treatment effect heterogeneity by frailty could lead to frailty-guided treatment strategies and reduce overtreatment and undertreatment. This systematic review aimed to examine the effect modification by frailty in randomized controlled trials (RCTs) that evaluate pharmacological, non-pharmacological, and multicomponent interventions. METHODS: We searched PubMed, Web of Science, EMBASE, and ClinicalTrial.gov, from their inception to 8 December 2023. Two reviewers independently extracted trial data and examined the study quality with senior authors. RESULTS: Sixty-one RCTs that evaluated the interaction between frailty and treatment effects in older adults were included. Frailty was evaluated using different tools such as the deficit accumulation frailty index, frailty phenotype, and other methods. The effect of several pharmacological interventions (e.g., edoxaban, sacubitril/valsartan, prasugrel, and chemotherapy) varied according to the degree of frailty, whereas other treatments (e.g., antihypertensives, vaccinations, osteoporosis medications, and androgen medications) demonstrated consistent benefits across different frailty levels. Some non-pharmacological interventions had greater benefits in patients with higher (e.g., chair yoga, functional walking, physical rehabilitation, and higher dose exercise program) or lower (e.g., intensive lifestyle intervention, psychosocial intervention) levels of frailty, while others (e.g., resistance-type exercise training, moderate-intensive physical activity, walking and nutrition or walking) produced similar intervention effects. Specific combined interventions (e.g., hospital-based disease management programs) demonstrated inconsistent effects across different frailty levels. DISCUSSION: The efficacy of clinical interventions often varied by frailty levels, suggesting that frailty is an important factor to consider in recommending clinical interventions in older adults. REGISTRATION: PROSPERO registration number CRD42021283051.

Exact results for gene-expression models with general waiting-time distributions.

Complex molecular details of transcriptional regulation can be coarse-grained by assuming that reaction waiting times for promoter-state transitions, the mRNA synthesis, and the mRNA degradation follow general distributions. However, how such a generalized two-state model is analytically solved is a long-standing issue. Here we first present analytical formulas of burst-size distributions for this model. Then, we derive an iterative equation for the mRNA moment-generating function, by which mRNA raw and binomial moments of any order can be conveniently calculated. The analytical results obtained in the special cases of phase-type waiting-time distributions not only provide insights into the mechanisms of complex transcriptional regulations but also bring conveniences for experimental data-based statistical inferences.

Effect of Preoperative Lifestyle Management and Prehabilitation on Postoperative Capability of Colorectal Cancer Patients: A Systematic Review and Meta-Analysis.

BACKGROUND: The surgical intervention serves as the paramount and prevalent remedy for individuals afflicted with colorectal malignancies, with the significance of perioperative stewardship and convalescence being indisputable. Prehabilitation coupled with preoperative lifestyle modulation has demonstrated efficacy in patients subjected to certain classifications of abdominal procedures. However, the evidence pertaining to its impact on those battling colorectal cancer remains equivocal. METHODS: A meta-analysis, grounded in pairwise contrast, of randomized controlled trials (RCTs) was orchestrated, coupled with a systematic review, to probe the efficacy of preoperative lifestyle modulation and prehabilitation on patients' postoperative functionality and recuperation. An exhaustive exploration of 8 electronic databases and trial registries was undertaken to encompass all pertinent RCTs disseminated in English or Chinese from January 2012 through December 2022. Employing a random-effects model, we evaluated parameters such as the 6-minute walk test (6 MWT), complications, quality of life (QoL), aggregate and postoperative duration of hospitalization (tLHS and postLHS), and healthcare expenditure (HExp) for postoperative patients. RESULTS: A total of 28 RCTs were incorporated into the systematic review and meta-analysis. Relative to conventional preoperative care, rehabilitation or preoperative lifestyle management was found to enhance postoperative 6MWT (SMD 1.30, 95% CI 0.30 to 2.29) and diminish the complication rate (OR 0.53, 95% CI 0.40 to 0.69). Nonetheless, no significant discrepancies were observed in QoL (SMD 1.81, 95% CI -0.26 to 3.87), tLHS (SMD -0.26, 95% CI -0.68 to 0.15), and postLHS (SMD -1.46, 95% CI -3.12 to 0.20) between the groups. HExp could not be evaluated due to a lack of sufficient data for synthesis. Most pooled outcomes exhibited significant heterogeneity, urging a cautious interpretation. Subgroup analysis revealed that nutritional interventions could mitigate the incidence of complications, and preoperative exercise could improve tLHS and postLHS. A combined approach of physical, nutritional, and psychological intervention or prehabilitation proved superior to any single intervention in enhancing postoperative capabilities. CONCLUSION: This meta-analysis delineated the efficacy of preoperative interventions on postoperative capabilities in patients with colorectal cancer, thereby offering evidence for clinical practice. It was concluded that preoperative interventions are unequivocally beneficial for postoperative functional recovery and the reduction of complication rates in patients with colorectal cancer. Nonetheless, the acquisition of more high-level evidence is still necessitated to further ascertain the effectiveness of this strategy for other patient groups and to establish its best practices. The heterogeneity in the pooled outcomes underlines the need for future studies to be more uniform in their design and reporting, which would facilitate more robust and reliable meta-analyses.

Deep Learning-Based construction of a Drug-Like compound database and its application in virtual screening of HsDHODH inhibitors.

The process of virtual screening relies heavily on the databases, but it is disadvantageous to conduct virtual screening based on commercial databases with patent-protected compounds, high compound toxicity and side effects. Therefore, this paper utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells to learn the properties of drug compounds in the DrugBank, aiming to obtain a new and virtual screening compounds database with drug-like properties. Ultimately, a compounds database consisting of 26,316 compounds is obtained by this method. To evaluate the potential of this compounds database, a series of tests are performed, including chemical space, ADME properties, compound fragmentation, and synthesizability analysis. As a result, it is proved that the database is equipped with good drug-like properties and a relatively new backbone, its potential in virtual screening is further tested. Finally, a series of seedling compounds with completely new backbones are obtained through docking and binding free energy calculations.

Uric acid levels and heart failure: A mendelian randomization study.

BACKGROUND AND AIMS: Uric acid, the end-product of purine metabolism within the human body, has been the subject of studies exploring its potential association with cardiovascular and cerebrovascular diseases. However, the precise relationship between uric acid levels and heart failure remains elusive. METHODS AND RESULTS: In this particular study, aggregated data from genome-wide association studies on uric acid and heart failure were utilized to perform a two-sample Mendelian randomization (MR) analysis utilizing R software. The aim was to uncover any causal link between these variables. The primary outcome was assessed using inverse variance weighted (IVW) methodology, while sensitivity analyses employed MR-Egger, weighted median (WME), and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) techniques. IVW results revealed a possible causal relationship between elevated uric acid levels and an increased risk of heart failure (OR: 1.09, 95 % CI: 1.01-1.17, P < 0.05). Encouragingly, the directions provided by MR-Egger and WME aligned with IVW findings, and no anomalies were detected in the remaining sensitivity analyses. CONCLUSION: These outcomes indicate the stability of the results of the study, thereby suggesting that heightened uric acid levels may contribute to an augmented risk of heart failure.

Inhibition of valve mesenchymal stromal cell calcium deposition by bFGF through alternative polyadenylation regulation of the CAT gene.

OBJECTIVE: Calcific aortic valve disease (CAVD) is the leading cause of angina, heart failure, and death from aortic stenosis. However, the molecular mechanisms of its progression, especially the complex disease-related transcriptional regulatory mechanisms, remain to be further elucidated. METHODS: This study used porcine valvular interstitial cells (PVIC) as a model. We used osteogenic induced medium (OIM) to induce calcium deposition in PVICs to calcify them, followed by basic fibroblast growth factor (bFGF) treatment to inhibit calcium deposition. Transcriptome sequencing was used to study the mRNA expression profile of PVICs and its related transcriptional regulation. We used DaPars to further examine alternative polyadenylation (APA) between different treatment groups. RESULTS: We successfully induced calcium deposition of PVICs through OIM. Subsequently, mRNA-seq was used to identify differentially expressed mRNAs for three different treatments: control, OIM-induced and OIM-induced bFGF treatment. Global APA events were identified in the OIM and bFGF treatment groups by bioinformatics analysis. Finally, it was discovered and proven that catalase (CAT) is one of the potential targets of bFGF-induced APA regulation. CONCLUSION: We described a global APA change in a calcium deposition model related to CAVD. We revealed that transcriptional regulation of the CAT gene may contribute to bFGF-induced calcium deposition inhibition.

Distribution and prognostic value of high-sensitivity cardiac troponin T and I across glycemic status: a population-based study.

BACKGROUND: Whether distributions and prognostic values of high-sensitivity cardiac troponin (hs-cTn) T and I are different across normoglycemic, prediabetic, and diabetic populations is unknown. METHODS: 10127 adult participants from the National Health and Nutrition Examination Survey 1999-2004 with determined glycemic status and measurement of at least one of hs-cTn assays were included, from whom healthy participants and presumably healthy diabetic and prediabetic participants were selected to investigate pure impacts of glycemic status on distributions of hs-cTn. The nonparametric method and bootstrapping were used to derive the 99th upper reference limits of hs-cTn and 95% CI. Participants with available follow-up and hs-cTn concentrations of all 4 assays were included in prognostic analyses. Associations of hs-cTn with all-cause and cardiac-specific mortality were modeled by Cox proportional hazard regression under the complex survey design. The incremental value of hs-cTn to an established risk score in predicting cardiac-specific mortality was assessed by the 10-year area under time-dependent receiver operating characteristic curve (AUC) using the Fine-Grey competing risk model. RESULTS: Among 9714 participants included in prognostic analyses, 5946 (61.2%) were normoglycemic, 2172 (22.4%) prediabetic, and 1596 (16.4%) diabetic. Hyperglycemic populations were older than the normoglycemic population but sex and race/ethnicity were similar. During the median follow-up of 16.8 years, hs-cTnT and hs-cTnI were independently associated with all-cause and cardiac-specific mortality across glycemic status. In the diabetic population, adjusted hazard ratios per 1-standard deviation increase of log-transformed hs-cTnT and hs-cTnI (Abbott) concentrations were 1.77 (95% CI 1.48-2.12; P < .001) and 1.83 (95% CI 1.33-2.53; P < .001), respectively, regarding cardiac-specific mortality. In the diabetic but not the normoglycemic population, adding either hs-cTnT (difference in AUC: 0.062; 95% CI 0.038-0.086; P < 0.001) or hs-cTnI (Abbott) (difference in AUC: 0.071; 95% CI 0.046-0.097; P < 0.001) would significantly increase the discriminative ability of the risk score; AUC of the score combined with hs-cTnT would be further improved by incorporating hs-cTnI (0.018; 95%CI 0.006-0.029; P = 0.002). The 99th percentile of hs-cTnT of the presumably healthy diabetic population was higher than the healthy population and had no overlap in 95% CIs, however, for hs-cTnI 99th percentiles of the two populations were very close and 95% CIs extensively overlapped. CONCLUSIONS: Hs-cTnT and hs-cTnI demonstrated consistent prognostic associations across glycemic status but incremental predictive values in hyperglycemic populations only. The susceptibility of hs-cTnT 99th percentiles to diabetes plus the additive value of hs-cTnI to hs-cTnT in diabetic cardiovascular risk stratification suggested hs-cTnI and hs-cTnT may be differentially associated with glycemic status, but further research is needed to illustrate the interaction between hyperglycemia and hs-cTn.

Power-law behavior of transcriptional bursting regulated by enhancer-promoter communication.

Revealing how transcriptional bursting kinetics are genomically encoded is challenging because genome structures are stochastic at the organization level and are suggestively linked to gene transcription. To address this challenge, we develop a generic theoretical framework that integrates chromatin dynamics, enhancer-promoter (E-P) communication, and gene-state switching to study transcriptional bursting. The theory predicts that power law can be a general rule to quantitatively describe bursting modulations by E-P spatial communication. Specifically, burst frequency and burst size are up-regulated by E-P communication strength, following power laws with positive exponents. Analysis of the scaling exponents further reveals that burst frequency is preferentially regulated. Bursting kinetics are down-regulated by E-P genomic distance with negative power-law exponents, and this negative modulation desensitizes at large distances. The mutual information between burst frequency (or burst size) and E-P spatial distance further reveals essential characteristics of the information transfer from E-P communication to transcriptional bursting kinetics. These findings, which are in agreement with experimental observations, not only reveal fundamental principles of E-P communication in transcriptional bursting but also are essential for understanding cellular decision-making.

Prognostic and predictive value of super-enhancer-derived signatures for survival and lung metastasis in osteosarcoma.

BACKGROUND: Risk stratification and personalized care are crucial in managing osteosarcoma due to its complexity and heterogeneity. However, current prognostic prediction using clinical variables has limited accuracy. Thus, this study aimed to explore potential molecular biomarkers to improve prognostic assessment. METHODS: High-throughput inhibitor screening of 150 compounds with broad targeting properties was performed and indicated a direction towards super-enhancers (SEs). Bulk RNA-seq, scRNA-seq, and immunohistochemistry (IHC) were used to investigate SE-associated gene expression profiles in osteosarcoma cells and patient tissue specimens. Data of 212 osteosarcoma patients who received standard treatment were collected and randomized into training and validation groups for retrospective analysis. Prognostic signatures and nomograms for overall survival (OS) and lung metastasis-free survival (LMFS) were developed using Cox regression analyses. The discriminatory power, calibration, and clinical value of nomograms were evaluated. RESULTS: High-throughput inhibitor screening showed that SEs significantly contribute to the oncogenic transcriptional output in osteosarcoma. Based on this finding, focus was given to 10 SE-associated genes with distinct characteristics and potential oncogenic function. With multi-omics approaches, the hyperexpression of these genes was observed in tumor cell subclusters of patient specimens, which were consistently correlated with poor outcomes and rapid metastasis, and the majority of these identified SE-associated genes were confirmed as independent risk factors for poor outcomes. Two molecular signatures were then developed to predict survival and occurrence of lung metastasis: the SE-derived OS-signature (comprising LACTB, CEP55, SRSF3, TCF7L2, and FOXP1) and the SE-derived LMFS-signature (comprising SRSF3, TCF7L2, FOXP1, and APOLD1). Both signatures significantly improved prognostic accuracy beyond conventional clinical factors. CONCLUSIONS: Oncogenic transcription driven by SEs exhibit strong associations with osteosarcoma outcomes. The SE-derived signatures developed in this study hold promise as prognostic biomarkers for predicting OS and LMFS in patients undergoing standard treatments. Integrative prognostic models that combine conventional clinical factors with these SE-derived signatures demonstrate substantially improved accuracy, and have the potential to facilitate patient counseling and individualized management.

Phase separation reduces cell-to-cell variability of transcriptional bursting.

Gene expression is a stochastic and noisy process often occurring in "bursts". Experiments have shown that the compartmentalization of proteins by liquid-liquid phase separation is conducive to reducing the noise of gene expression. Therefore, an important goal is to explore the role of bursts in phase separation noise reduction processes. We propose a coupled model that includes phase separation and a two-state gene expression process. Using the timescale separation method, we obtain approximate solutions for the expectation, variance, and noise strength of the dilute phase. We find that a higher burst frequency weakens the ability of noise reduction by phase separation, but as the burst size increases, this ability first increases and then decreases. This study provides a deeper understanding of phase separation to reduce noise in the stochastic gene expression with burst kinetics.

The effect of aperiodic components in distinguishing Alzheimer's disease from frontotemporal dementia.

Distinguishing between Alzheimer's disease (AD) and frontotemporal dementia (FTD) presents a clinical challenge. Inexpensive and accessible techniques such as electroencephalography (EEG) are increasingly being used to address this challenge. In particular, the potential relevance between aperiodic components of EEG activity and these disorders has gained interest as our understanding evolves. This study aims to determine the differences in aperiodic activity between AD and FTD and evaluate its potential for distinguishing between the two disorders. A total of 88 participants, including 36 patients with AD, 23 patients with FTD, and 29 healthy controls (CN) underwent cognitive assessment and scalp EEG acquisition. Neuronal power spectra were parameterized to decompose the EEG spectrum, enabling comparison of group differences in different components. A support vector machine was employed to assess the impact of aperiodic parameters on the differential diagnosis. Compared with the CN group, both the AD and FTD groups showed varying degrees of increased alpha power (both periodic and raw power) and theta alpha power ratio. At the channel level, theta power (both periodic and raw power) in the frontal regions was higher in the AD group compared to the FTD group, and aperiodic parameters (both exponents and offsets) in the frontal, temporal, central, and parietal regions were higher in the AD group than in the FTD group. Importantly, the inclusion of aperiodic parameters led to improved performance in distinguishing between the two disorders. These findings highlight the significance of aperiodic components in discriminating dementia-related diseases.