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Wireless sensor networks (WSNs) are challenged by three main factors: energy usage, delay, and network lifespan. This paper proposes an enhanced marine predator algorithm (EMPA) to effectively determine cluster heads (CHs) in WSNs, aiming to conserve energy, minimize delay, and extend network longevity. EMPA features a Taylor-based neighborhood mechanism (TNM) to improve local exploitation and a dynamic opposition adaptation (DOA) mechanism to avoid stagnation in local optima. These advancements allow much better exploration and exploitation of the search area, optimizing CH selection according to multi-objective parameters, like energy efficacy, distance minimization, and quality of service (QoS). The proposed strategy incorporates exploration and exploitation dynamically to strengthen solutions in terms of robustness and adaptation to different WSN configurations. The experimental evaluation of EMPA proved that this algorithm outperforms other algorithms by achieving incredible convergence speed, longer network lifetime, and communication efficiency. These results confirm that this algorithm is suitable for practical applications requiring energy-aware optimization in large-scale WSNs.

The pivotal role of the tumor microenvironment (TME) in the initiation and advancement of hepatocellular carcinoma (HCC) is widely acknowledged, as it fosters the proliferation and metastasis of HCC cells. Within the intricate TME of HCC, tumor-associated macrophages (TAMs) represent a significant constituent of non-malignant cells. TAMs engage in direct communication with cancer cells in HCC, while also exerting influence on other immune cells to adopt a tumor-supportive phenotype that facilitates tumor progression. Among the multifaceted mechanisms at play, the metabolic reprogramming of both tumor cells and macrophages leads to phenotypic alterations and functional modifications in macrophages. This comprehensive review elucidates the intricate interplay between cellular metabolism and macrophage phenotype/polarization, while also providing an overview of the associated signaling molecules and potential therapeutic strategies for HCC.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a series of obesity-related metabolic liver diseases, ranging from relatively benign hepatic steatosis to metabolic-associated steatohepatitis (MASH). With the changes in lifestyle, its incidence and prevalence have risen to epidemic proportions globally. In recent years, an increasing amount of evidence has indicated that the hepatic microenvironment is involved in the pathophysiological processes of MASH-induced liver fibrosis and the formation of hepatocellular carcinoma (HCC). The hepatic microenvironment is composed of various parenchymal and non-parenchymal cells, which communicate with each other through various factors. In this review, we focus on the changes in hepatocytes, cholangiocytes, liver sinusoidal endothelial cells (LSECs), hepatic stellate cells (HSCs), Kupffer cells (KC), dendritic cells (DC), neutrophils, monocytes, T and B lymphocytes, natural killer cells (NK), natural killer T cells (NKT), mucosal-associated invariant T cells (MAIT), γδT cells, and gut microbiota during the progression of MASLD. Furthermore, we discuss promising therapeutic strategies targeting the microenvironment of MASLD-MASH-HCC.

Production of flavors from natural substrates by microbial transformation has become a growing and expanding field of study over the past decades. Vanillin, a major component of vanilla flavor, is a principal flavoring compound used worldwide. Streptomyces sp. strain V-1 is known to be one of the most promising microbial producers of natural vanillin from ferulic acid. Although identification of the microbial genes involved in the biotransformation of ferulic acid to vanillin has been previously reported, purification and detailed characterization of the corresponding enzymes with important functions have rarely been studied. In this study, we isolated and identified 2 critical genes, fcs and ech, encoding feruloyl-CoA synthetase and enoyl-CoA hydratase/aldolase, respectively, which are involved in the vanillin production from ferulic acid. Both genes were heterologously expressed in Escherichia coli, and the resting cell reactions for converting ferulic acid to vanillin were performed. The corresponding crucial enzymes, Fcs and Ech, were purified for the first time and the enzymatic activity of each purified protein was studied. Furthermore, Fcs was comprehensively characterized, at an optimal pH of 7.0 and temperature of 30°C. Kinetic constants for Fcs revealed the apparent Km, kcat, and Vmax values to be 0.35 mM, 67.7 s(-1), and 78.2 U mg(-1), respectively. The catalytic efficiency (kcat/Km) value of Fcs was 193.4 mM(-1) s(-1) for ferulic acid. The characterization of Fcs and Ech may be helpful for further research in the field of enzymatic engineering and metabolic regulation.

Evaluating green data centers is a multi-attribute decision problem. To enhance the rigor and precision of green data center assessment, this study verifies the weighting of the national green data center evaluation index system using the Analytic Hierarchy Process (AHP) with the participation of 19 domain experts from various data center sectors. The aim is to gain an in-depth understanding of the perspectives and priorities of different types of institutions regarding evaluation indicators and to investigate the underlying reasons for these perspectives and priorities. Through an analysis of expert sample distribution, this paper reveals the preferences of financial, internet, research, and design, as well as technical consulting service institutions, regarding indicators such as energy-efficient utilization, computational resource utilization, green low-carbon development, scientific layout, and intensive construction. Specifically, financial institutions tend to place a relatively lower emphasis on energy efficiency due to their focus on transaction speed and security. In contrast, internet companies prioritize efficient utilization of computational resources. Research and design institutions consider scientific layout and intensive construction more crucial, while technical consulting service institutions emphasize green and low-carbon development. Meanwhile, we identified substantial discrepancies among experts in determining the weights of specific indicators, suggesting a lack of consensus within the industry about the correlation between these indicators and green data centers. To propel the sustainable development of green data centers, future assessments should refine evaluation dimensions, consider disparities such as data center types and embrace regional differences, actively adopt novel technologies and innovative practices, and establish mechanisms for long-term monitoring and evaluation.

Functional magnetic resonance imaging (fMRI) in anesthetized-animals is critical in studying the mechanisms of fMRI and investigating animal models of various diseases. Medetomidine was recently introduced for independent anesthesia for longitudinal (survival) fMRI studies in rats. Since stimulation-induced fMRI signal is anesthesia-dependent and its characteristics in rats under medetomidine are not fully elucidated, the blood oxygenation level dependent (BOLD) fMRI response to electrical forepaw stimulation under medetomidine was systematically investigated at 9.4 T. Robust activations in contralateral primary somatosensory cortex (SI) and thalamus were observed and peaked at the stimulus frequency of 9 Hz. The response in SI saturates at the stimulus strength of 4 mA while that in thalamus monotonically increases. In addition to fMRI data acquired with the forepaw stimulation, data were also acquired during the resting-state to investigate the synchronization of low frequency fluctuations (LFF) in the BOLD signal (< 0.08 Hz) in different brain regions. LFF during resting-state have been observed to be synchronized between functionally related brain regions in human subjects while its origin is not fully understood. LFF have not been extensively studied or widely reported in anesthetized-animals. In our data, synchronized LFF of BOLD signals are found in clustered, bilaterally symmetric regions, including SI and caudate–putamen and the magnitude of the LFF is ∼ 1.5%, comparable to the stimulation-induced BOLD signals. Similar to resting-state data reported in human subjects, LFF in rats under medetomidine likely reflect functional connectivity of these brain regions.

To investigate the diagnostic value of intraoperative cytology and rapid immunocytochemistry in primary central nervous system lymphoma. 254 cases of lymphoma and 82 cases of non-lymphoma were collected from 2010 to 2023. Frozen section(FS) was using alone in 44 cases during 2010–2014, FS and intraoperative cytology(IC) were using in 251 cases during 2015 to 2022. Rapid immunocytochemical(RICC, CD20, GFAP) were using with FS + IC in 41 cases during 2021 to 2023. Method One: According to the results of archives, statistic the diagnostic accuracy of lymphoma during three time periods. Method Two: All cases were randomly renumbered, 4 neuropathologists compared the accuracy of independent histology and that of combining cytology. The archives showed the diagnostic accuracy of FS in PCNSL was 77.27%, FS + IC was 86.06%, FS + IC + RICC was 92.68%. The retrospective study demonstrated the diagnostic accuracy of FS was 79.76%, FS + IC was 87.33% and FS + IC + RICC was 92.68%. The positive predictive value, negative predictive value, sensitivity, specificity and accuracy of CD20 were 100%, 76.92%, 90.32%, 100% and 92.68%, respectively. The results of the paired χ 2  test was no statistically significant difference (0.05 <  P  < 0.1) between FS + IC + RICC and immunohistochemical (IHC) diagnosis of paraffin sections. The integration of IC + RICC + FS diagnosis can significantly enhance the intraoperative diagnostic accuracy of PCNSL and rectify potential errors that may occurred when relying solely on FS diagnosis.

Systemic sclerosis (SSc) is a chronic autoimmune disease characterized by fibrosis. The challenge of early diagnosis, along with the lack of effective treatments for fibrosis, contribute to poor therapeutic outcomes and high mortality of SSc. Therefore, there is an urgent need to identify suitable biomarkers for early diagnosis of SSc. Three skin gene expression datasets of SSc patients and healthy controls were downloaded from Gene Expression Omnibus (GEO) database (GSE130955, GSE58095, and GSE181549). GSE130955 (48 early diffuse cutaneous SSc and 33 controls) were utilized to screen differentially expressed genes (DEGs) between SSc and normal skin samples. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) were performed to identify diagnostic genes and construct a diagnostic prediction model. The results were further validated in GSE58095 (61 SSc and 36 controls) and GSE181549 (113 SSc and 44 controls) datasets. Receiver operating characteristic (ROC) curves were applied for assessing the level of diagnostic ability. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to verify the diagnostic genes in skin tissues of out cohort (10 SSc and 5 controls). Immune infiltration analysis were performed using CIBERSORT algorithm. A total of 200 DEGs were identified between SSc and normal skin samples. Functional enrichment analysis revealed that these DEGs may be involved in the pathogenesis of SSc, such as extracellular matrix remodeling, cell-cell interactions, and metabolism. Subsequently, two critical genes (ENHO and NOX4) were identified by LASSO and SVM-RFE. ENHO was found down-regulated while NOX4 was up-regulated in skin of SSc patients and their expression levels were validated by above three datasets and our cohort. Notably, these differential expressions were more pronounced in patients with diffuse cutaneous SSc than in those with limited cutaneous SSc. Next, we developed a novel diagnostic model for SSc using ENHO and NOX4, which demonstrated strong predictive power in above three cohorts and in our own cohort. Furthermore, immune infiltration analysis revealed dysregulated levels of various immune cell subtypes within early SSc skin specimens, and a negative correlation was observed between the levels of ENHO and Macrophages M1 and M2, while a positive correlation was observed between the levels of NOX4 and Macrophages M1 and M2. This study identified ENHO and NOX4 as novel biomarkers that can be serve as a diagnostic prediction model for early detection of SSc and play a potential role in the pathogenesis of the disease.

Systemic sclerosis (SSc) is a chronic systemic disease characterized by immune dysregulation and fibrosis for which there is no effective treatment. Animal models are crucial for advancing SSc research. Tree shrews are genetically, anatomically, and immunologically closer to humans than rodents. Thus, the tree shrew model provides a unique opportunity for translational research in SSc. In this study, a SSc tree shrew model was constructed by subcutaneous injection of different doses of bleomycin (BLM) for 21 days. We assessed the degree of inflammation and fibrosis in the skin and internal organs, and antibodies in serum. Furthermore, RNA sequencing and a series of bioinformatics analyses were performed to analyze the transcriptome changes, hub genes and immune infiltration in the skin tissues of BLM induced SSc tree shrew models. Multiple sequence alignment was utilized to analyze the conservation of selected target genes across multiple species. Subcutaneous injection of BLM successfully induced a SSc model in tree shrew. This model exhibited inflammation and fibrosis in skin and lung, and some developed esophageal fibrosis and secrum autoantibodies including antinuclear antibodies and anti-scleroderma-70 antibody. Using RNA sequencing, we compiled skin transcriptome profiles in SSc tree shrew models. 90 differentially expressed genes (DEGs) were identified, which were mainly enriched in the PPAR signaling pathway, tyrosine metabolic pathway, p53 signaling pathway, ECM receptor interaction and glutathione metabolism, all of which are closely associated with SSc. Immune infiltration analysis identified 20 different types of immune cells infiltrating the skin of the BLM-induced SSc tree shrew models and correlations between those immune cells. By constructing a protein-protein interaction (PPI) network, we identified 10 hub genes that were significantly highly expressed in the skin of the SSc models compared to controls. Furthermore, these genes were confirmed to be highly conserved in tree shrews, humans and mice. This study for the first time comfirmed that tree shrew model of SSc can be used as a novel and promising experimental animal model to study the pathogenesis and translational research in SSc.

This study is aimed at exploring the potential mechanism of the PPAR signaling pathway in breast cancer (BRCA) and constructing a novel prognostic-related risk model. We used various bioinformatics methods and databases to complete our exploration in this research. Based on TCGA database, we use multiple extension packages based on the R language for data conversion, processing, and statistics. We use LASSO regression analysis to establish a prognostic-related risk model in BRCA. And we combined the data of multiple online websites, including GEPIA, ImmuCellAI, TIMER, GDSC, and the Human Protein Atlas database to conduct a more in-depth exploration of the risk model. Based on the mRNA data in TCGA database, we conducted a preliminary screening of genes related to the PPAR signaling pathway through univariate Cox analysis, then used LASSO regression analysis to conduct a second screening, and successfully established a risk model consisting of ten genes in BRCA. The results of ROC curve analysis show that the risk model has good prediction accuracy. We can successfully divide breast cancer patients into high- and low-risk groups with significant prognostic differences (P=1.92e−05) based on this risk model. Combined with the clinical data in TCGA database, there is a correlation between the risk model and the patient’s N, T, gender, and fustat. The results of multivariate Cox regression show that the risk score of this risk model can be used as an independent risk factor for BRCA patients. In particular, we draw a nomogram that can predict the 5-, 7-, and 10-year survival rates of BRCA patients. Subsequently, we conducted a series of pancancer analyses of CNV, SNV, OS, methylation, and immune infiltration for this risk model gene and used GDSC data to investigate drug sensitivity. Finally, to gain insight into the predictive value and protein expression of these risk model genes in breast cancer, we used GEO and HPA databases for validation. This study provides valuable clues for future research on the PPAR signaling pathway in BRCA.