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Colon adenocarcinoma (COAD) is a common malignant tumor of digestive tract and lactate metabolism has been linked to tumor development and progression. In this study, we sought to investigate the influence of lactate metabolism-related genes (LRGs) prognosis. We also aimed to identify distinct LRG-related clusters and develop a risk signature for assessing patient prognosis, immunological characteristics, and response to therapy. We analyzed data from The Cancer Genome Atlas (TCGA) to reveal the expression and mutational features of LRGs in COAD patients. In the integrated TCGA and GSE39582 cohort, consensus clustering analysis was employed to classify patients into two distinct LRG-related clusters. Using differentially expressed genes (DRGs) from these two clusters, we established a LRG-related gene cluster and prognostic signature which was used to classify patients into high-risk and low-risk groups. An validation cohort was used to validate the predictive ability of risk signature and expression of 6 candidate LRGs was confirmed through quantitative real-time PCR (qRT-PCR). Nomograms were created to visually represent the clinical value of LRG-related signature. Furthermore, we extensively examined differences in immune cell infiltration, tumor mutational load (TMB), microsatellite instability (MSI) and drug sensitivity between two risk groups. Analysis of the integrated TCGA and GSE39582 cohorts revealed two distinct LRG-related clusters and gene clusters with significant differences in overall survival (OS) and tumor microenvironment. We developed a LRG-related signature comprising 6 candidate LRGs that reliably predicted OS and qRT-PCR validation confirmed the expression of LRGs. Based on the median risk score, patients were divided into low-risk and high-risk groups, with low-risk group showing better survival. Furthermore, patients in high-risk group were more sensitive to chemotherapy and associated with higher TMB, higher proportion of MSI-H. Our study provides a valuable method for guiding clinical management and personalized treatment of COAD patients, which offers new insights into individualized treatment strategies, ultimately improving patient outcomes.

To develop and validate a demethylation-driven gene signature for predicting HCC prognosis, immune microenvironment features, and therapeutic vulnerabilities. We integrated transcriptomic data from TCGA-LIHC ( n  = 346 tumors) and GEO-GSE112790 ( n  = 183 tumors) with a curated list of 3,743 demethylation-related genes (DRGs). Tumor-associated DRGs were identified via differential expression analysis and WGCNA. A prognostic six-gene signature was derived using univariate Cox and LASSO regression, and a risk score model was constructed by multivariate Cox analysis. Patients were stratified into high- and low-risk groups. Model performance was evaluated using Kaplan–Meier and time-dependent ROC analyses. Immune infiltration was assessed by ssGSEA, somatic mutations were profiled, and drug sensitivity was predicted via the GDSC database. G6PD expression was validated using immunohistochemistry. A six-gene prognostic signature (CEP41, SUB1, CDC20, G6PD, VPS72, SPINDOC) was established. The risk score significantly stratified patients into high- and low-risk groups with distinct overall survival ( p  < 0.001). The model showed strong predictive accuracy with AUCs ≥ 0.70 at 1, 3, and 5 years. High-risk patients exhibited enrichment in cell cycle, DNA repair, and metabolic pathways, along with an immunosuppressive microenvironment marked by regulatory T cells and myeloid-derived suppressor cells. Somatic mutation analysis revealed differential TP53 mutation frequencies between risk groups. Drug sensitivity prediction indicated that high-risk patients may respond better to agents such as Tozasertib and Navitoclax. IHC confirmed significant upregulation of G6PD in HCC tissues, supporting its role in metabolic reprogramming. This study establishes a robust, demethylation-driven six-gene signature that effectively stratifies HCC patients into distinct prognostic groups. The model integrates multi-omic insights into tumor biology and therapeutic vulnerability, providing a clinically actionable framework for personalized risk assessment and treatment planning in hepatocellular carcinoma.

In general, customers may perceive that qualities and reliabilities of new and remanufactured products are different, so the activities of taking back and remanufacturing used products by OEMs (Original Equipment Manufacturers) have a negative effect on themselves. Thus, these activities are executed by third-party remanufacturers, but which will lead to the competition between the new and remanufactured products. However, the customers' points to new and remanufactured products are different. For example, customers who are interested in the new product are mainly sensitive to the price, whereas those who prefer the remanufactured product are more sensitive to the quality and reliability. Based on the idea, a differentiation competition between the new and remanufactured products is considered in this paper. We consider the decentralized decisions framework with one manufacturer, one remanufacturer, and one retailer. In this framework, the manufacturer decides the wholesale price of new products, and the remanufacturer determines the warranty period of remanufactured products, and they sell their products to the retailer who decides the retail prices of two products. We provide the properties of the equilibrium decisions, and investigate the effects of parameters on the equilibrium decisions by using theoretical and numerical methods.

Psoriasis is a common and chronic autoimmune skin disease which affects 2 to 3% of the world population. Abnormal proliferation of human keratinocytes is an important feature of psoriasis, along with local hypoxia and vascular abnormal growth. To leverage recent molecular findings into the personalized treatment of psoriasis, we need a strategy that integrates clinical stratification with molecular phenotyping. MicroRNAs (miRNAs) are a large family of small non-coding RNA which regulates diverse biological process, including cell proliferation, by modulating gene expression at the posttranscriptional level. In the present study, we indicated that miR-150 specifically down-regulated expressed in psoriatic skin lesions, and could inhibit HaCaT cells and primary adult human keratinocytes (HKCs)' proliferation in either normal or hypoxia conditions; by direct targeting, miR-150 could also regulate the expression of hypoxia-inducible factor-1α (HIF-1α) and vascular endothelial growth factor A (VEGFA). In addition, we found that HIF-1α and VEGFA were highly expressed in the lesional psoriatic skin compared with the non-lesional psoriatic skin, and negatively correlated with miR-150 expression. Taken together, we indicated miR-150 regulates human keratinocytes' proliferation in hypoxic conditions through targeting HIF-1α and VEGFA in psoriasis for the first time, and provide diagnostic markers and a novel target for psoriasis treatment.

Most of current crowd simulation methods have considered the impact of interindividual emotion on the agent’s behavior pattern during emergency evacuations. However, the emotion contagion is not only at the individual level, but also at the contagion in groups. Psychological researches show that the third-party authority also has an impact on emotion contagion. For example, security guards can guide individuals to find exits and calms them; and teachers can lead their students safely evacuate from multi-layer teaching buildings, etc. In this paper, we propose a unified framework to simulate the emergency evacuations in virtual environment. This framework considers four kinds of agents: third-party authority agents, group leaders, members and isolated agents. Firstly, we randomly assign each agent a specific personality and initialize its emotion. Secondly, the emotion contagion in this paper is considered with three aspects: intra-group contagion, inter-group contagion and third-party authority based emotion contagion. We simulate inter-group aggregated behaviors by improving the ASCRIBE and provide a threshold model to simulate inter-group switching behaviors. The third-party authorities exhibit a calm effect on the agents during an evacuation, and we set their negative emotion very low and keep unchanged. Meanwhile, we perform high-level path planning to explore the environments and obtain a cognitive map for navigation purposes. Through quantitative and qualitative experiments, simulation results demonstrate that our proposed model can simulate the emotion contagion in groups during emergency evacuations, and our model outperforms some existing works.

Smart clothing that can measure electrocardiogram (ECG) signals and monitor the health status of people meets the needs of our increasingly aging society. However, the conventional measurement of ECG signals is complicated and its electrodes can cause irritation to the skin, which makes the conventional measurement method unsuitable for applications in smart clothing. In this paper, a novel wearable measurement of ECG signals is proposed. There are only three ECG textile electrodes knitted into the fabric of smart clothing. The acquired ECG signals can be transmitted to a smartphone via Bluetooth, and they can also be sent out to a PC terminal by a smartphone via WiFi or Internet. To get more significant ECG signals, the ECG differential signal between two electrodes is calculated based on a spherical volume conductor model, and the best positions on the surface of a human body for two textile electrodes to measure ECG signals are simulated by using the body-surface potential mapping (BSPM) data. The results show that position 12 in the lower right and position 11 in the upper left of the human body are the best for the two electrodes to measure ECG signals, and the presented wearable measurement can obtain good performance when one person is under the conditions of sleeping and jogging.

Spleen enlargement is often detected in patients with liver cirrhosis, but the precise pathogenetic mechanisms behind the phenomenon have not been clearly elucidated. We investigated the pathogenetic mechanisms of splenomegaly in both portal hypertensive patients and rats, and tried to identify the possible therapy for this disease. Spleen samples were collected from portal hypertensive patients after splenectomy. Rat models of portal hypertension were induced by common bile duct ligation and partial portal vein ligation. Spleen samples from patients and rats were used to study the characteristics of splenomegaly by histological, immunohistochemical, and western blot analyses. Rapamycin or vehicle was administered to rats to determine the contribution of mTOR signaling pathway in the development of splenomegaly. We found that not only spleen congestion, but also increasing angiogenesis, fibrogenesis, inflammation and proliferation of splenic lymphoid tissue contributed to the development of splenomegaly in portal hypertensive patients and rats. Intriguingly, splenomegaly developed time-dependently in portal hypertensive rat that accompanied with progressive activation of mTOR signaling pathway. mTOR blockade by rapamycin profoundly ameliorated splenomegaly by limiting lymphocytes proliferation, angiogenesis, fibrogenesis and inflammation as well as decreasing portal pressure. This study provides compelling evidence indicating that mTOR signaling activation pathway plays a key role in the pathogenesis of splenomegaly in both portal hypertensive patients and rats. Therapeutic intervention targeting mTOR could be a promising strategy for patients with portal hypertension and splenomegaly.

The key to damage detection is whether fault features can be extracted effectively from raw signals. Hence, we propose an approach based on the refined time-shift multiscale dispersion Lempel-Ziv complexity (RTSMDLZC) to effectively extract fault features. First, the time-shift multiscale sequence constructed from the raw time series can obtain more fault information more effectively. Then, the refined method addresses the lacking of sizeable numerical fluctuation on a large scale and enhances the algorithm’s stability. Simulation signals and two experimental cases verify the effectiveness and applicability of the RTSMDLZC. The results indicate that compared with other classic methods, the RTSMDLZC can extract bearing fault features more accurately and has better identification accuracy.

As a clean energy technology, air source heat pump (ASHP) technology has garnered increasing attention nowadays; however, the sharp decline in energy efficiency under low-temperature conditions remains a significant challenge impeding their widespread adoption. In this paper, in response to the demand under the Australian standard, mathematical modeling was carried out to explore the technical strategy for improving the performance of an ASHP; then, these strategies were evaluated by simulation calculation and experiment. The results show that, using frequency conversion, the coefficient of performance of the heat pump system (COP) reaches a maximum value of 2.48 when the frequency is 50 Hz, and the COP of the heat pump can reach a maximum value of 2.51 when the opening of the expansion valve is about 40%. Then, a new type of mixed refrigerant (R134a:R600a:R290) was proposed; the COP of the system reaches 2.502 when the mixture ratio is 1:6:3, which is an improvement of 6.61% compared to the use of only R134a. Finally, an analysis of the annual energy consumption under Australian weather conditions was conducted; the average COP is about 3.894, which is 6.3% higher than the result of using R134a. These results provide a theoretical basis for the development of a heat pump system that meets Australian standards.

Psoriasis is one of the most frequent inflammatory skin conditions and could be treated via tele-dermatology, provided that the current lack of reliable tools for objective severity assessments is overcome. Psoriasis Area and Severity Index (PASI) has a prominent level of subjectivity and is rarely used in real practice, although it is the most widely accepted metric for measuring psoriasis severity currently. This study aimed to develop an image-artificial intelligence (AI)-based validated system for severity assessment with the explicit intention of facilitating long-term management of patients with psoriasis. A deep learning system was trained to estimate the PASI score by using 14,096 images from 2367 patients with psoriasis. We used 1962 patients from January 2015 to April 2021 to train the model and the other 405 patients from May 2021 to July 2021 to validate it. A multiview feature enhancement block was designed to combine vision features from different perspectives to better simulate the visual diagnostic method in clinical practice. A classification header along with a regression header was simultaneously applied to generate PASI scores, and an extra cross-teacher header after these 2 headers was designed to revise their output. The mean average error (MAE) was used as the metric to evaluate the accuracy of the predicted PASI score. By making the model minimize the MAE value, the model becomes closer to the target value. Then, the proposed model was compared with 43 experienced dermatologists. Finally, the proposed model was deployed into an app named SkinTeller on the WeChat platform. The proposed image-AI-based PASI-estimating model outperformed the average performance of 43 experienced dermatologists with a 33.2% performance gain in the overall PASI score. The model achieved the smallest MAE of 2.05 at 3 input images by the ablation experiment. In other words, for the task of psoriasis severity assessment, the severity score predicted by our model was close to the PASI score diagnosed by experienced dermatologists. The SkinTeller app has been used 3369 times for PASI scoring in 1497 patients from 18 hospitals, and its excellent performance was confirmed by a feedback survey of 43 dermatologist users. An image-AI-based psoriasis severity assessment model has been proposed to automatically calculate PASI scores in an efficient, objective, and accurate manner. The SkinTeller app may be a promising alternative for dermatologists' accurate assessment in the real world and chronic disease self-management in patients with psoriasis.