Model Of Stage Research Articles

Exploring the role of Cdk5 on striatal synaptic plasticity in a 3-NP-induced model of early stages of Huntington’s disease

Impaired mitochondrial function has been associated with the onset of neurodegenerative diseases. Specifically, certain mitochondrial toxins, such as 3-nitropropionic acid (3-NP), initiate cellular changes within the striatum that closely resemble the pathology observed in Huntington’s disease (HD). Among the pivotal signaling molecules contributing to neurodegeneration, cyclin-dependent kinase 5 (Cdk5) stands out. In particular, Cdk5 has been implicated not only in cellular pathology but also in the modulation of synaptic plasticity. Given its widespread presence in the striatum, this study seeks to elucidate the potential role of Cdk5 in the induction of corticostriatal synaptic plasticity in murine striatal cells subjected to subchronic doses of 3-NP in vivo, aiming to mimic the early stages of HD. Immunostaining analyses revealed an increase in Cdk5 in tissues from animals treated with 3-NP, without a significant change in protein levels. Regarding striatal plasticity, long-term depression (LTD) was induced in both control and 3-NP cells when recorded in voltage clamp mode. The Cdk5 inhibitor roscovitine-reduced LTD in most cells. A minority subset of cells exhibited long-term potentiation (LTP) generation in the presence of roscovitine. The inhibitor of D1 receptors SCH23390 prevented LTP in three of nine cells, implying that MSN cells lacking D1/PKA activation were capable of LTP induction when Cdk5 was also blocked. Nevertheless, the co-administration of H89, a PKA inhibitor, along with roscovitine, prevented the generation of any type of plasticity in all recorded cells. These findings show the impact of 3-NP treatment on striatal plasticity and suggest that Cdk5 during early neurodegeneration may attenuate signaling pathways that lead neurons to increase their activity.

Regional efficiency analysis of fresh food cold chain logistics in China based on three-stage DEA model

PurposeAssessing the efficiency of fresh food cold chain logistics as accurately as possible is essential for industry development planning. This study was designed to analyze the efficiency of fresh food cold chain logistics in China.Design/methodology/approachA three-stage data envelopment analysis (DEA) model was used to analyze the efficiency of fresh food cold chain logistics in 30 provinces of China from 2013 to 2019. The stochastic frontier analysis (SFA) model in the second stage was used to eliminate the influence of external environmental factors and random disturbances on efficiency analysis results.Findings(1) The overall actual efficiency of fresh food cold chain logistics in China is unsatisfactory, with an average technical efficiency of 0.382 over the 7-year period. (2) The national average technical efficiency and average scale efficiency were overestimated by 29.9% and 40.0%, respectively, compared with the actual values. (3) The efficiency of fresh food cold chain logistics does not align with the level of regional economic development. (4) Distinct regional variations exist in the efficiency of fresh food cold chain logistics in China, with higher efficiencies observed in Northwest China and the Central Yangtze River regions, and the lowest efficiencies in the northeast regions.Originality/valueThis study applies a three-stage DEA model to assess the development and regional differences of fresh food cold chain logistics in China, enriching the application of models and empirical analysis in this field. By analyzing the situation in China, it provides ideas and references for other developing countries to develop cold chain logistics.

Numerical simulation of a base metal deposit related to a fossil geothermal system

Fossil and active geothermal systems that produce ore deposits are sites of complex physicochemical processes and a favorable combination of factors related to the amount of metal-bearing fluid that flows through the system, ore fluid metal concentrations, depositional efficiency, and the duration of ore deposition. Of all these factors, the length of the mineralizing event is one of the least understood aspects of ore genesis.We used fluid inclusion data, chemical compositions of base metal sulfides, and fluid flow rates to constrain a reactive-transport model of a fossil geothermal system - the Patricia Zn-Pb-Ag deposit in northern Chile. The Patricia deposit consists of quartz and base metal sulfide veins of hydrothermal origin with structural control, hosted in a volcanic succession with intense propylitic alteration. The fluid inclusions are liquid-rich, with homogenization temperatures ranging from 250 to 150 °C and salinities between 22 and 1 wt% NaCl equiv., with an early fluid mixing trend and no evidence of boiling in the system. Sulfide mineralogy indicates intermediate sulfidation conditions.To identify the most relevant geochemical and transport parameters controlling the formation of this fossil geothermal system >1000 simulations were performed using the reactive-transport code TOUGHREACT. The paragenesis of the deposit is mimicked by a model of successive stages of fluid circulation consistent with the observed mineral assemblage distribution, the fluid inclusion data, and the estimated resources in the deposit.The entire geothermal activity of the system was modeled considering 10,000 years of fluid-rock interaction, with periods of circulation of metal-barren fluids followed by metal-rich fluids driving the ore formation. In the initial model, base metal solubility with predominant chloride complexing suggests that the most efficient ore-forming mechanism for the Patricia deposit was the result of the interaction of two different fluids, one fluid transporting metals and another fluid transporting reduced sulfur, mixing in a rock volume of high permeability. Mass balance estimations with this model give a period of 3500 to 5000 years for the ore stage duration in which all the ore resources of the Patricia deposit could have been precipitated by fluid mixing.In a second model, the previous estimates for the duration of the main ore stage were used to simulate the fluid-rock interaction during the ore stage for 3500 years. The results indicated the importance of the permeability of the host rock enhanced by fractures to concentrate the volume of the mineralization and the role of the hydrothermal alteration assemblage in controlling the circulating fluid acidity. A higher efficiency in forming sulfide minerals appears to coincide with pH values ranging from 5.1 to 5.3.The results of both models are validated by replicating the system evolution, reproducing the same mineral alteration assemblage, the expected base metal resource distribution, and similar amounts of ore resources to those of the Patricia deposit: a total of 52,602 tons of lead and 157,731 tons of zinc. The models indicate that the hydrothermal event might be two times longer than the ore stage.

Adaptive Learning for Dynamic Features and Noisy Labels.

Applying current machine learning algorithms in complex and open environments remains challenging, especially when different changing elements are coupled and the training data is scarce. For example, in the activity recognition task, the motion sensors may change position or fall off due to the intensity of the activity, leading to changes in feature space and finally resulting in label noise. Learning from such a problem where the dynamic features are coupled with noisy labels is crucial but rarely studied, particularly when the noisy samples in new feature space are limited. In this paper, we tackle the above problem by proposing a novel two-stage algorithm, called Adaptive Learning for Dynamic features and Noisy labels (ALDN). Specifically, optimal transport is firstly modified to map the previously learned heterogeneous model to the prior model of the current stage. Then, to fully reuse the mapped prior model, we add a simple yet efficient regularizer as the consistency constraint to assist both the estimation of the noise transition matrix and the model training in the current stage. Finally, two implementations with direct (ALDN-D) and indirect (ALDN-ID) constraints are illustrated for better investigation. More importantly, we provide theoretical guarantees for risk minimization of ALDN-D and ALDN-ID. Extensive experiments validate the effectiveness of the proposed algorithms.

Cost-utility analysis for sublingual versus intravenous edaravone in the treatment of amyotrophic lateral sclerosis

BackgroundEdaravone has been widely used in amyotrophic lateral sclerosis (ALS) treatment, and a sublingual (SL) tablet has been developed to offer a more convenient alternative for injection. We present a cost-utility analysis to comprehensively evaluate the costs and health outcomes of oral and intravenous edaravone for the treatment of ALS in Chinese medical context.MethodsCost-utility analysis of SL tablets of edaravone versus intravenous edaravone at home was performed by constructing a 20-year Markov model of ALS stage 1–4 and death. The data were extracted from the literature with model assumptions. Typical sensitivity analysis and scenario analysis for administering SL tablets at home versus intravenous tablets at the hospital were performed.ResultsIn the base case analysis, with SL tablets and intravenous injections both at home, the model estimated an additional cost of ¥12,670.04 and an additional 0.034 QALYs over 20 years (life time) of modeling analysis, and the ICER was ¥372,648.24 per QALY. However, in the scenario of intravenous administration at the hospital, SL tablet was demonstrated dominance to intravenous injection.ConclusionsUsing 3 times the GDP per capita of China in 2023 as the threshold, the SL tablet edaravone was not cost-effective in the context of home treatment for both formulationst, but was dominance to intravenous injection in hospital treatment. The results highlighted the importance of treatment context for health economic analysis.

Burst-like transcription of MYH7, allelic and contractile imbalance already in early stage hiPSC-cardiomyocytes indicate these as pathogenic factors in Hypertrophic Cardiomyopathy

Abstract Background Hypertrophic Cardiomyopathy (HCM) is caused by heterozygous mutations in sarcomeric proteins, which can cause hyper- or hypocontractiliy of cardiomyocytes. Previously we also observed highly variable force generation (contractile imbalance) among individual cardiomyocytes of HCM-patients with myosin (MYH7) or myosin-binding-protein-C mutations (MYBPC3), ranging from normal to highly altered values. Contractile imbalance is presumably caused by stochastic, burst-like transcription of mutant and wildtype allele, which induces unequal ratios of mutant vs. wildtype mRNA among individual cardiomyocytes. Variable force generation most likely is the result of also unequal fractions of mutated protein from cell to cell. Aim With this study we aimed to examine whether in addition to the effects of the mutation on sarcomere function, also allelic and contractile imbalance already exist at the beginning of disease development or whether both result from disease-related alterations in symptomatic HCM-patients. Methods and Results We studied this in patient-derived human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) with MYH7-mutation R723G as model for an early disease stage. These early, fetal-like cardiomyocytes developed typical HCM-related features like hypertrophy and increased myofibrillar disarray compared to WT-hiPSC-CMs. Calcium sensitivity was reduced, twitch kinetics were slowed down. To test whether MYH7 in these early-stage hiPSC-CMs also is transcribed in bursts or continuously, we performed RNA-fluorescence in situ hybridization (RNA-FISH). Nuclei without, with one and with two or more active transcription sites were observed which indicates stochastic, burst-like and independent transcription of the two MYH7-alleles. Ratios of mRNA from both alleles were quantified in individual cardiomyocytes by allele-specific single cell RT-PCR. We found highly variable ratios of mutated vs. wildtype mRNA among individual cardiomyocytes, similar to HCM-patient’s cardiac tissue, indicating unequal expression of mutant and wildtype mRNA from cell-to-cell. Furthermore, functional measurements on myofibrils from R723G-hiPSC-CMs revealed substantial variability in force generation at physiological calcium, compared to much smaller heterogeneity among WT-hiPSC-CMs. In addition, variability of twitch kinetics of intact hiPSC-CMs was also substantially increased among R723G- compared to WT-hiPSC-CMs. Altogether, this suggests that functional heterogeneity results from burst-like transcription in heterozygous HCM cardiomyocytes. Conclusions Our results in HCM-patient derived hiPSC-cardiomyocytes with a fetal-like developmental stage suggest that allelic and contractile imbalance among individual cardiomyocytes together with the mutation are present at the beginning of the disease. Thus, they most likely are exacerbating factors that contribute development of HCM hallmarks.

A multi-phase mission success evaluation approach for maritime autonomous surface ships considering equipment performance degradation and system composition changes

With the development of Maritime Autonomous Surface Ships (MASSs), the mission success assessment problem cannot be ignored because of potential security issues of MASSs. This paper addresses the issue of performance degradation at different stages, the problem of state dependence in multi-stage missions of MASSs, and the correlation problem between missions of stages. Based on the coupling of the reliability model of each single-stage mission system, a multi-stage mission success evaluation method for MASSs is proposed. By leveraging the ability of the conditional probability tables in Bayesian networks to express the complex relationships between the nodes, the dynamic Bayesian network model of stages is constructed based on the fault tree. Based on the Markov process, the problem of state dependence on shared equipment between stages is solved. Considering the complex relationship between multi-stage missions, the virtual node is introduced, and the multi-state Bayesian network is combined to realize the coupling of the reliability evaluation results of each single-stage mission. It is applied to the multi-stage mission success evaluation of the MASS to obtain the success probability of MASSs and the key equipment of each stage. The results show that evaluation results are more suitable for engineering practice.

Stability Analysis of Construction Factors for Partially Cable-Stayed Bridges with Multiple Towers and High Piers

Partially cable-stayed bridges have the characteristics of continuous rigid-frame bridges and cable-stayed bridges, making them a novel composite bridge system. This study focuses on the construction project of a multi-tower high-pier curved partially cable-stayed bridge to investigate the bridge’s stability during construction. The Midas/Civil software was used to establish a model for key construction stages of the bridge, considering structural linear elasticity and geometric nonlinearity. The study examines the impact of static wind loads, asymmetric construction of the main girder, closure sequence, and the load and detachment of the hanging basket on the bridge’s stability during construction. The results indicate that static wind loads have a significant impact on structural geometric nonlinearity, with a maximum reduction of 4.99%. Asymmetric construction at both ends of the main girder can cause structural instability and should be avoided. The geometric nonlinearity stability coefficient for the hanging basket load decreased by 10.83% during the maximum no-cable stage and by 7.84% during the cable stage, significantly affecting the stability during construction. A bridge closure sequence of side-span, secondary midspan, and midspan provides the most stable condition during the construction phase. The results of this study can inform the construction of similar partially cable-stayed bridges.

Numerical simulation of the main stage of a lightning

We present a numerical model of the main stage of a lightning discharge. Within the framework of the developed model, evolution of parameters of the current channel upon the return stroke (the lightning main stage) is described by the system of equations governing conservation of mass, momentum, total energy, along with the transmission-line equations for determining the electric potential and the total current in each channel cross section. The main characteristics of lightning at the stage of the return stroke detectable experimentally, such as gas heating in the channel to temperatures in the range of 10–40 kK, the fundamental possibility of propagation of the potential-gradient wave at a speed varying from several hundredth to several tenths of the speed of light, and the possibility of the return-stroke wave propagating a relatively long distance without substantial attenuation, are demonstrated numerically. The conclusion that the developed physical and numerical model of the lightning discharge describes physical processes that occur under real conditions qualitatively correctly can be drawn based on the results on simulation of lightning discharges of various intensity.

Study on the creep constitutive model of layered rockconsidering anisotropic and damage factors after hightemperature exposure

The failure of layered rock after high temperature exposure is a major concern in deep underground engineering projects. This paper proposes an improved Nishihara creep constitutive model that considers damage factors and the bedding angle, which overcomes the shortcomings of the deviation in the description of the conventional Nishihara model in the acceleration stage. The constitutive model is verified by the conventional triaxial creepiest. The theoretical curve has a high degree of fitting with the experimental curve. The experimental results show that a temperature of [Formula: see text] has an obvious influence on the steady creep rate and the creep strain of layered sandstone, and [Formula: see text] can be regarded as the temperature threshold for the long-term strength and change from anisotropic to isotropic of layered sandstone. The irreversible melting mixing phenomenon at the boundary of mineral particles with increasing temperature is the mechanism by which different treatment temperatures affect the anisotropy degree of layered rock.

Integrative prognostic modeling for stage III lung adenosquamous carcinoma post-tumor resection: machine learning insights and web-based implementation.

The prognostic landscape of stage III Lung Adenosquamous Carcinoma (ASC) following primary tumor resection remains underexplored. A thoughtfully developed prognostic model has the potential to guide clinicians in patient counseling and the formulation of effective therapeutic strategies. Utilizing data from the Surveillance, Epidemiology, and End Results database spanning 2000 to 2018, this study identified independent prognostic factors influencing Overall Survival (OS) in ASC using Boruta analysis. Employing Gradient Boosting, Random Forest, and Neural Network algorithms, predictive models were constructed. Model performance was assessed through key metrics, including Area Under the Receiver Operating Characteristic Curve (AUC), calibration plot, Brier score, and Decision Curve Analysis (DCA). Among 241 eligible patients, seven clinical parameters-age, sex, primary tumor size, N stage, primary tumor site, chemotherapy, and systemic therapy-were identified as significant predictors of OS. Advanced age, male gender, larger tumor size, absence of chemotherapy, and lack of systemic therapy were associated with poorer survival. The Random Forest model outperformed others, achieving 3- and 5-year AUCs of 0.80/0.79 (training) and 0.74/0.65 (validation). It also demonstrated better calibration, lower Brier scores (training: 0.189/0.171; validation: 0.207/0.199), and more favorable DCA. SHAP values enhanced model interpretability by highlighting the impact of each parameter on survival predictions. To facilitate clinical application, the Random Forest model was deployed on a web-based server for accessible prognostic assessments. This study presents a robust machine learning model and a web-based tool that assist healthcare practitioners in personalized clinical decision-making and treatment optimization for ASC patients following primary tumor resection.

Construction of Energy Consumption Model in Asphalt Mixture Production Stage Based on Field Measurements

To better construct an energy consumption model for the asphalt mixture production stage, this study divides the production process into four phases: aggregate dust removal, aggregate drying, asphalt heating, and mixture blending. Utilizing measured data from various regions in Anhui Province, the model considers factors such as season, mixture type, and energy consumption type. The study quantitatively compares and analyzes the energy consumption results obtained from three calculation methods: the theoretical method, the quota method, and the energy consumption model method. The results indicate that the total energy consumption shares of the aggregate dust removal, aggregate drying, asphalt heating, and mixture blending phases are 4.23%, 69.50%, 17.75%, and 8.52%, respectively. The primary energy consumption during the asphalt mixture production stage is concentrated in the aggregate drying and asphalt heating phases. The energy consumption model based on on-site measurements effectively reflects the actual energy consumption levels during the asphalt mixture production stage. Moreover, the total energy consumption calculated by the three methods follows the order quota method > energy consumption model method > theoretical method. By constructing an energy consumption model based on measured data, it is possible to more accurately assess the energy usage during the asphalt mixture production process. This helps optimize production techniques, reduce energy consumption and carbon emissions, and provide a scientific basis for sustainable road construction.

Integrating CLSP and GLSP models for optimizing production batch sizes in vulcanized component manufacturing

This study addresses the complex issue of optimizing production batch sizes within a company specializing in manufacturing rubber, thermoplastic, and polyurethane components, all of which undergo vulcanization prior to sale. Through meticulous analysis of the production planning process, it was observed that certain activities could be logically grouped into two sequential stages without compromising information integrity or detail. The initial stage encompasses press activities involving vulcanization and curing, while the next one involves greenhouse operations for post-curing. The commencement of the second stage is contingent upon the completion of all activities in the first stage. Consequently, an effective production plan must concurrently consider both stages. To tackle this, a mathematical model is proposed to address the batch sizing problem within the company, integrating both stages and drawing inspiration from the CLSP (Capacitated Lot Sizing Problem) for the first stage and the subperiod approach derived from GLSP (General Lot-sizing and Scheduling Problem) models for the second stage. The model is solved utilizing the CPLEX solver version 12.8. It incorporates 74 items, spans 20 periods, each with specific demands, and delineates 16 sub-periods within each planning horizon period. Experimentation involving a hypothetical example and subsequent tests utilizing real data from the case study company demonstrate the successful resolution of the problem, yielding enhanced production plans, particularly in terms of cost reduction objectives.

Field test of internal visualization of deep-water pile foundation using ultrasonic waves

Early-age internal visualization is critical for timely assessing the quality of pile foundations, especially as super cross-sea bridges increase the diameter and length of pile foundations beyond the capabilities of traditional cross-hole sonic logging (CSL). This article introduces two innovations. First, ultrasonic instruments are specially developed to transmit waves through a concrete pile with a diameter of 4 m, a length of 92 m, and a water depth of 63 m, before the full hardening of concrete. Second, an imaging method is proposed to upgrade traditional CSL from 1D detection to 3D visualization. Both large-scale experiments and field tests are tested in the first 7 days after the concrete casting. The experiments successfully visualize typical construction defects, achieving a detection resolution of 18 cm for layered defects and 15 cm for void defects. During the field test, a low-pixel region is observed at the pile head, extending approximately 2.1 m deep, attributed to mud floating up during concrete casting into the steel casing. These developments in instrument technology and visualization methods provide essential tools for the quality evaluation of super pile foundations. Furthermore, the visualization data can support digital twin modeling in subsequent stages of construction and maintenance planning.

Construction and validation of a nomogram model for lymph node metastasis of stage II-III gastric cancer based on machine learning algorithms.

Gastric cancer, a pervasive malignancy globally, often presents with regional lymph node metastasis (LNM), profoundly impacting prognosis and treatment options. Existing clinical methods for determining the presence of LNM are not precise enough, necessitating the development of an accurate risk prediction model. Our primary objective was to employ machine learning algorithms to identify risk factors for LNM and establish a precise prediction model for stage II-III gastric cancer. A study was conducted at Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine between May 2010 and December 2022. This retrospective study analyzed 1147 surgeries for gastric cancer and explored the clinicopathological differences between LNM and non-LNM cohorts. Utilizing univariate logistic regression and two machine learning methodologies-Least absolute shrinkage and selection operator (LASSO) and random forest (RF)-we identified vascular invasion, maximum tumor diameter, percentage of monocytes, hematocrit (HCT), and lymphocyte-monocyte ratio (LMR) as salient factors and consolidated them into a nomogram model. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curves were used to evaluate the test efficacy of the nomogram. Shapley Additive Explanation (SHAP) values were utilized to illustrate the predictive impact of each feature on the model's output. Significant differences in tumor characteristics were discerned between LNM and non-LNM cohorts through appropriate statistical methods. A nomogram, incorporating vascular invasion, maximum tumor diameter, percentage of monocytes, HCT, and LMR, was developed and exhibited satisfactory predictive capabilities with an AUC of 0.787 (95% CI: 0.749-0.824) in the training set and 0.753 (95% CI: 0.694-0.812) in the validation set. Calibration curves and decision curves affirmed the nomogram's predictive accuracy. In conclusion, leveraging machine learning algorithms, we devised a nomogram for precise LNM risk prognostication in stage II-III gastric cancer, offering a valuable tool for tailored risk assessment in clinical decision-making.

Vector Field Model of CKD Stage and Its Directional Derivative Mathematically Enable Accurate Kidney Prognosis Prediction

Vector Field Model of CKD Stage and Its Directional Derivative Mathematically Enable Accurate Kidney Prognosis Prediction

A two-stage adaptive consensus reaching process with improved automatic strategy for multi-attribute large group emergency decision-making

The multi-attribute large group emergency decision-making (MALGEDM) problem has gained increasing attention and becomes an important topic in the field of decision-making. The existing consensus models, however, rarely consider the effective balance between experts’ willingness and time consumption. To address this problem, we design a two-stage adaptive consensus reaching process that combines two models of the minimum adjustment amount and the minimum iteration number in the MALGEDM context. The process can achieve consensus with an improved automatic strategy that considers experts’ willingness. Firstly, the self-organizing map algorithm is employed to classify large group heterogeneous experts into multiple subgroups for the dual decision of clustering and expert categories. Secondly, we classify different degrees of consensus into four scenarios (low, medium, medium-high, and high). On this basis, we have constructed a two-stage adaptive consensus model that incorporates the novel possibility degree. The minimum adjustment amount of the adaptive feedback process can be obtained by the optimization model in the first stage, which is used as a constraint for considering the experts’ willingness in the second stage. Finally, the practicality and timeliness of the proposed model is verified through an illustrative example of emergency decision-making. Moreover, comparative analyses are conducted to highlight the superiority of the proposed model.

Cellular fibronectin-targeted fluorescent aptamer probes for early detection and staging of liver fibrosis

Liver fibrosis is a key process in the progression of chronic liver disease to cirrhosis. Currently, early diagnosis and precise staging of liver fibrosis remain great challenges. Extracellular matrix (ECM) molecules expressed specifically during liver fibrosis are ideal targets for bioimaging and detection of liver fibrosis. Here, we report that fluorescent probes based on a nucleic acid aptamer (ZY-1) targeting cellular fibronectin (cFN), a critical ECM molecule significantly accumulating during liver fibrosis, are promising bioimaging agents for the staging of liver fibrosis. In the work, the outstanding binding affinity of ZY-1 to cFN was validated through an in vitro model of human-derived hepatic stellate cells (HSCs). Subsequently, we constructed different ZY-1-based fluorescent probes and explored the real-time imaging performance of these fluorescent probes in CCl4-induced mouse models of different liver fibrosis stages. The ZY-1-based fluorescent probes, for the first time, effectively identified and distinguished early-stage liver fibrosis (stage 3 of Ishak 6) from advanced liver fibrosis (stage 5 of Ishak 6). The proof-of-concept study provides compelling evidences that ZY-1-based probes are a promising tool for the early diagnosis and staging of liver fibrosis and paves the way for further development of clinical-related diagnosis strategies for fibrotic diseases of the liver and other organs. Statement of significanceCurrently, early diagnosis and accurate staging of liver fibrosis continue to present significant challenges. This study demonstrates that fluorescent probes based on the nucleic acid aptamer ZY-1, which targets cellular fibronectin (cFN)—a crucial extracellular matrix (ECM) molecule that significantly accumulates during liver fibrosis—are promising bioimaging agents for staging liver fibrosis. The ZY-1-based fluorescent probes effectively identified and differentiated early-stage liver fibrosis from advanced liver fibrosis. This proof-of-concept study not only provides compelling evidence that ZY-1-based probes show promise for the early diagnosis and staging of liver fibrosis but also paves the way for further investigations into the use of ZY-1 in detecting other diseases associated with cFN.

DNA methylation analysis of the SDC2, SEPT9 and VIM genes in fecal DNA for colorectal cancer diagnosis.

Colorectal cancer is one of the most common cancers worldwide. DNA methylation sites may serve as a new gene signature for colorectal cancer diagnosis. The search for representative DNA methylation sites is urgently needed. This study aimed to systematically identify a methylation gene panel for colorectal cancer diagnosis via tissue and fecal samples. A total of 181 fecal and 50 tumor tissue samples were collected. They were obtained from 83 colorectal cancer patients and 98 healthy subjects. These samples were evaluated for DNA methylation of 9 target genes via quantitative bisulfite next-generation sequencing. We employed the rank-sum test to screen the colorectal cancer-specific methylation sites in the tissue and fecal cohorts. A data model was subsequently constructed and validated via the dedicated validation dataset. Compared with the fecal and negative control samples, the colorectal cancer tissue samples presented significantly higher methylation rates for all the selected gene sites. The methylation rates of the tissue and preoperative fecal samples showed the same high and low rates at the same sites. After screening, a panel of 29 loci in the SDC2, SEPT9, and VIM genes proved to be reliable biomarkers for colorectal cancer diagnosis in fecal samples. Logistic regression models were then constructed and validated using this panel. The sensitivity of the model was 91.43% (95% CI = [89.69, 93.17]), the specificity was 100% (95% CI = [100,100]), and the AUC value is 99.31% (95% CI = [99,99.62]). The diagnostic accuracy of the model for stage I and stage II colorectal cancer was 100% (11/11) and 91.3% (21/23), respectively. Overall, this study confirms that the gene locus panel and the model can be used to diagnose colorectal cancer effectively through feces. Our study identified a set of key methylation sites for colorectal cancer diagnosis from fecal samples, highlighting the importance of using tissue and fecal samples to accurately assess DNA methylation levels to screen for methylation sites, and developing an effective diagnostic model for colorectal cancer.

Deep Learning for Pediatric Sleep Staging from Photoplethysmography: A Transfer Learning Approach from Adults to Children.

Sleep staging is critical for diagnosing sleep disorders. Traditional methods in clinical settings involve time-intensive scoring procedures. Recent advancements in data-driven algorithms using photoplethysmogram (PPG) time series have shown promise in automating sleep staging in adults. However, for children, algorithm development is hindered by the limited availability of datasets, with the Childhood Adenotonsillectomy Trial (CHAT) being the only substantial source, comprising recordings from children aged 5-10. This limitation constrains the evaluation of algorithmic generalization performance. We employed a deep learning model for sleep staging from PPG, initially trained using a large dataset of adult sleep recordings, and fine-tuned it on 80% of the CHAT dataset (CHAT-train) for the task of three-class sleep staging (wake, REM, non-REM). The resulting algorithm performance was compared to the same model architecture but trained from scratch on CHAT-train (benchmark). The algorithms are evaluated on the local test set, denoted CHAT-test, as well as on a newly introduced independent dataset. Our deep learning algorithm achieved a Cohen's Kappa of 0.88 on CHAT-test (versus 0.65), and demonstrated generalization capabilities with a Kappa of 0.72 on the external Ichilov dataset for children above 5 years old (versus 0.64) and 0.64 for those below 5 (versus 0.53). This research establishes a new state-of-the-art performance for the task of sleep staging in children using raw PPG. The findings underscore the value of transfer learning from the adults to children domain. However, the reduced performance in children under 5 suggests the need for further research and additional datasets covering a broader pediatric age range to fully address generalization limitations.