Effects of Different Defatting Methods of Black Soldier Fly (Hermetia illucens) Larvae Meal on the Metabolic Energy and Nutrient Digestibility in Young Laying Hens.

This study aimed to investigate the effects of different defatting methods of black soldier fly (Hermetia illucens) larvae meal (BSFM) on the metabolic energy and nutrient digestibility in laying hens. Sixty young laying hens (Hy-Line W-36) aged 63 days were randomly divided into two groups (G1 and G2), each with five replicates of six hens housed in individual cages. Group G1 was fed 25% pressed black soldier fly meal (BSFMp) and 75% basal diet, and Group G2 was fed 25% extracted black soldier fly meal (BSFMe) and a 75% basal diet. Both diets included 5 g/kg chromium oxide as an external marker. A 7-day preliminary trial was followed by a 4-day experimental period. The results indicate that pressing and extracting significantly affected the digestibility of crude fat and total energy in BSFM, with BSFMp showing significantly higher crude fat digestibility than BSFMe. Similarly, total energy digestibility was also significantly higher in BSFMp. However, there were no significant differences in dry matter, organic matter, and crude protein digestibility between the two processing methods. The apparent metabolic energy values of BSFMp and BSFMe were 16.34 and 12.41 MJ/kg, respectively, showing a significant difference. The nitrogen-corrected metabolic energy values were 15.89 MJ/kg in BSFMp and 11.93 MJ/kg in BSFMe, indicating a highly significant difference. The digestibility of arginine and leucine in BSFMp was significantly higher than in BSFMe, while differences in lysine, cystine, threonine, tryptophan, and isoleucine were not significant. In conclusion, both defatting methods of BSFM had no adverse effects on the metabolic energy and nutrient digestibility in young laying hens, but BSFMp demonstrated better effects on the digestibility of metabolic energy and nutrients in the feed for young laying hens.

Association between osteoarthritis with Parkinson's disease in the US (NHANES 2011-2020).

Objected: To evaluate the association between osteoarthritis (OA) and Parkinson's disease (PD) in adults in the United States. Methods: Using 2011-2020 NHANES data, a cross-sectional study of 11,117 adults over the age of 40 was conducted. Univariate logistic regression and multivariate logistic regression were used to analyze the relationship between arthritis and PD. In addition, stratified analysis was used to examine whether the relationship between arthritis and PD was interactive with age, gender, race, education, BMI. Results: In this study, a total of 11,117 participants were included, and we found that osteoarthritis was positively correlated with the development of PD compared with non-arthritis patients [1.95 (1.44 ~ 2.62)] (p < 0.001). After adjusting the covariates, the results are still stable. Conclusion: PD patients were positively correlated with OA. Among people with OA, there was a 95% increased risk of PD compared to people without arthritis. Therefore, when treating OA, attention should be paid to the increased risk of PD. In the meantime, further studies are needed to explore the link between OA and PD patients.

Establish TIIC signature score based the machine learning fusion in bladder cancer.

BACKGROUND: Bladder cancer is a prevalent malignant tumor with high heterogeneity. Current treatments, such as transurethral resection of bladder tumor (TURBT) and intravesical Bacillus Calmette-Guérin (BCG) therapy, still have limitations, with approximately 30% of non-muscle-invasive bladder cancer (NMIBC) progressing to muscle-invasive bladder cancer (MIBC), and a substantial number of MIBC patients experiencing recurrence after surgery. Immunotherapy has shown potential benefits, but accurate prediction of its prognostic effects remains challenging. METHODS: We analyzed bladder cancer RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and used various machine learning algorithms to screen for feature RNAs related to tumor-infiltrating immune cells (TIICs) from single-cell data. Based on these RNAs, we established a TIIC signature score and evaluated its relationship with overall survival (OS) and immunotherapy response in bladder cancer patients. RESULTS: The study identified 171 TIIC-RNAs and selected 11 TIIC-RNAs with prognostic value through survival analysis. The TIIC signature score established using a machine learning fusion method was significantly associated with OS and showed good predictive performance in different datasets. Additionally, the signature score was negatively correlated with immunotherapy response, with patients with low TIIC feature scores showing better survival outcomes after immunotherapy. Further biological functional analysis revealed a close association between the TIIC signature score and immune regulation processes, cellular metabolism, and genetic variations. CONCLUSION: This study successfully constructed and validated an RNA signature scoring system based on tumor-infiltrating immune cell (TIIC) features, which can effectively predict OS and the effectiveness of immunotherapy in bladder cancer patients.

Nonsolvating Fluoroaromatic Cosolvent Enabled Long-Term Cycling of High-Voltage Lithium-Ion Batteries with Organosulfur Electrolytes.

The structure-activity relationships of nonsolvating cosolvents for organosulfur-based electrolyte systems were revealed. The performance of nonsolvating dilutant fluorobenzene (FB) was compared to various fluorinated ether dilutants in high-voltage electrolytes containing a concentration of 1.2 M LiPF6 dissolved in fluoroethylene carbonate (FEC), ethyl methyl sulfone (EMS), and the dilutant. In a high-voltage and high-loading LiNi0.8Mn0.1Co0.1O2 (NMC811) full cell configuration, the organosulfur-based electrolyte containing FB dilutant enabled superior electrochemical performance compared to the electrolytes using other nonsolvating fluorinated ether formulations. Moreover, the FB-containing electrolyte exhibited the highest ionic conductivity and lowest viscosity among all organosulfur-based electrolytes containing nonsolvating dilutant. These improvements are attributed to the enhanced physical properties of electrolyte and lithium-ion mobility. Furthermore, by employing first-principles simulations, the observed suppression of side reactions at high voltage is linked to FB's lower reactivity toward singlet dioxygen, which is likely produced at the NMC interface. Overall, FB is considered an excellent diluent that does not impede cell operation by mass decomposition at the cathode.

Tertiary lymphoid structures and their therapeutic implications in cancer.

Tertiary lymphoid structures (TLSs) are ectopic lymphoid aggregates formed by the structured accumulation of immune cells such as B cells and T cells in non-lymphoid tissues induced by infection, inflammation, and tumors. They play a crucial role in the immune response, particularly in association with tumor development, where they primarily exert anti-tumor immune functions during tumorigenesis. Current research suggests that TLSs inhibit tumor growth by facilitating immune cell infiltration and are correlated with favorable prognosis in various solid tumors, serving as an indicator of immunotherapy effectiveness to some extent. Therefore, TLSs hold great promise as a valuable biomarker. Most importantly, immunotherapies aimed to prompting TLSs formation are anticipated to be potent adjuncts to current cancer treatment. This review focuses on the formation process of TLSs and their potential applications in cancer therapy.

Pyrimidine compounds BY4003 and BY4008 inhibit glioblastoma cells growth via modulating JAK3/STAT3 signaling pathway.

Glioblastoma (GBM) is a brain tumor characterized by its aggressive and invasive properties. It is found that STAT3 is abnormally activated in GBM, and inhibiting STAT3 signaling can effectively suppress tumor progression. In this study, novel pyrimidine compounds, BY4003 and BY4008, were synthesized to target the JAK3/STAT3 signaling pathway, and their therapeutic efficacy and mechanisms of action were evaluated and compared with Tofacitinib in U251, A172, LN428 and patient-derived glioblastoma cells. The ADP-Glo™ kinase assay was utilized to assessed the inhibitory effects of BY4003 and BY4008 on JAK3, a crucial member of the JAK family. The results showed that both compounds significantly inhibited JAK3 enzyme activity, with IC50 values in the nanomolar range. The antiproliferative effects of BY4003, BY4008, and Tofacitinib on GBM and patient-derived glioblastoma cells were evaluated by MTT and H&E assays. The impact of BY4003 and BY4008 on GBM cell migration and apoptosis induction was assessed through wound healing, transwell, and TUNEL assays. STAT3-regulated protein expression and relative mRNA levels were analyzed by western blotting, immunocytochemistry, immunofluorescence, and qRT-PCR. It was found that BY4003, BY4008 and Tofacitinib could inhibit U251, A172, LN428 and patient-derived glioblastoma cells growth and proliferation. Results showed decreased expression of STAT3-associated proteins, including p-STAT3, CyclinD1, and Bcl-2, and increased expression of Bax, a pro-apoptotic protein, as well as significant down-regulation of STAT3 and STAT3-related genes. These findings suggested that BY4003 and BY4008 could inhibit GBM growth by suppressing the JAK3/STAT3 signaling pathway, providing valuable insights into the therapeutic development of GBM.

Identification of sanguinarine as a novel antagonist for perfluorooctanoate/perfluorooctane sulfonate-induced senescence of hepatocytes: An integrated computational and experimental analysis.

Perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS), two prominent per- and polyfluoroalkyl substances (PFASs), are potentially harmful to many human organs. However, there only exist limited methods to mitigate their health hazards. The aim of this study is to combine a bioinformatics analysis with in vitro experiments to discover small molecules that can alleviate liver damage caused by PFOA/PFOS. We identified 192 and 82 key genes related to hepatocytes exposed to PFOA and PFOS, respectively. The functional enrichment analysis of key genes suggested cellular senescence may be important in PFOA/PFOS-induced hepatotoxicity. The in vitro models revealed that PFOA/PFOS led to hepatocyte senescence by increasing the activity of SA-ß-gal, inducing mitochondrial dysfunction, impacting cell cycle arrest, and elevating the expressions of p21, p53, IL-1ß, and SASP-related cytokines. The drug-target gene set enrichment analysis method was employed to compare the transcriptome data from the Gene Expression Omnibus database (GEO), Comparative Toxicogenomics Database (CTD), and the high-throughput experiment- and reference-guided database (HERB), and 21 traditional Chinese medicines (TCMs) were identified that may alleviate PFOA/PFOS-induced liver aging. The experimental results of co-exposure to PFOA/PFOS and TCMs showed that sanguinarine has particular promise in alleviating cellular senescence caused by PFOA/PFOS. Further investigations revealed that the mTOR-p53 signaling pathway was involved in PFOA/PFOS-mediated hepatic senescence and can be blocked using sanguinarine.

Magnetic Activation: A Novel Approach to Enhance Hydrogen Evolution Activity of Co0.85Se@CNTs Heterostructured Catalyst.

The heterostructure strategy is currently an effective method for enhancing the catalytic activity of materials. However, the challenge that is how to further improve their catalytic performance, based on the principles of material modification is must addressed. Herein, a strategy is introduced for magnetically regulating the catalytic activity to further enhance the hydrogen evolution reaction (HER) activity for Co0.85Se@CNTs heterostructured catalyst. Building on heterostructure modulation, an external alternating magnetic field (AMF) is introduced to enhance the electronic localization at the active sites, which significantly boosts catalytic performance (71 to 43 mV at 10 mA cm-2). To elucidate the catalytic mechanism, especially under the influence of the AMF, in situ Raman spectroscopy is innovatively applied to monitor the HER process of Co0.85Se@CNTs, comparing conditions with and without the AMF. This study demonstrates that introducing the AMF does not induce a change in the true active site. Importantly, it shows that the Lorentz force generated by the AMF enhances HER activity by promoting water molecule adsorption and O─H bond cleavage, with the Stark tuning rate indicating increased water interaction and bond cleavage efficiency. Theoretical calculations further support that the AMF optimizes energy barriers for key reaction intermediates (steps of *H2O-TS and *H+*1/2H2).

Uniform Nanocrystal Spatial Distribution-Enhanced SnO2-based Sensor for High-Sensitivity Hydrogen Detection.

Hydrogen (H2) is colorless, odorless, and has a wide explosive concentration range (4-75 vol %), making rapid and accurate detection of hydrogen leaks essential. This paper demonstrates a method to modify the spatial distribution of nanocrystals (NCs) by adding surfactants to improve the sensing performance. In order to explore its potential for H2 gas-sensing applications, SnO2, containing different mass percentages of PdCu NCs, was dispersed. The results show that the 0.1 wt % PdCu-SnO2 sensor based on surfactant dispersion performs well, with a response to 0.1 vol % H2 that is 18 times higher than that of the undispersed 0.1 wt % PdCu-SnO2 sensor. The enhanced gas-sensing ability after dispersion can be attributed to the fact that the uniform distribution of NCs generates higher quantum efficiency and exposes more active sites on the carrier surface compared to nonuniform distribution. This study provides a simple, novel, and effective method to improve the sensor response.

Design and synthesis of dual functional NBD-fluorophore-incorporated naphthalene diimide derivatives as G-quadruplex ligands.

Nitrobenzoxadiazole (NBD)-incorporated naphthalene diimide derivatives were designed and synthesized as candidates of antitumor agents with cytotoxicity against human pancreatic cancer cell MIA PaCa-2. Among these, compounds 1NND and 3NND exhibited fluorescent "turn-off" property toward human telomeric G-quadruplex (G4), which allows the direct measurement of dissociation constant (Kd) of ligands against G4 by fluorescence titration method. Notably, the compound 1NND not only exhibited great cytotoxic activity against MIA PaCa-2 with a half maximal inhibitory concentration (IC50) of 77.9 nM, but also exhibited high affinity against G4 with Kd of 1.72 µM. Furthermore, the target binding properties were investigated by circular dichroism (CD) spectra and further studied by molecular docking methods.

Machine learning-based integration develops a mitophagy-related lncRNA signature for predicting the progression of prostate cancer: a bioinformatic analysis.

Prostate cancer remains a complex and challenging disease, necessitating innovative approaches for prognosis and therapeutic guidance. This study integrates machine learning techniques to develop a novel mitophagy-related long non-coding RNA (lncRNA) signature for predicting the progression of prostate cancer. Leveraging the TCGA-PRAD dataset, we identify a set of four key lncRNAs and formulate a riskscore, revealing its potential as a prognostic indicator. Subsequent analyses unravel the intricate connections between riskscore, immune cell infiltration, mutational landscapes, and treatment outcomes. Notably, the pan-cancer exploration of YEATS2-AS1 highlights its pervasive impact, demonstrating elevated expression across various malignancies. Furthermore, drug sensitivity predictions based on riskscore guide personalized chemotherapy strategies, with drugs like Carmustine and Entinostat showing distinct suitability for high and low-risk group patients. Regression analysis exposes significant correlations between the mitophagy-related lncRNAs, riskscore, and key mitophagy-related genes. Molecular docking analyses reveal promising interactions between Cyclophosphamide and proteins encoded by these genes, suggesting potential therapeutic avenues. This comprehensive study not only introduces a robust prognostic tool but also provides valuable insights into the molecular intricacies and potential therapeutic interventions in prostate cancer, paving the way for more personalized and effective clinical approaches.

DrugMetric: quantitative drug-likeness scoring based on chemical space distance.

The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.

Amino acid metabolic reprogramming in the tumor microenvironment and its implication for cancer therapy.

Amino acids are essential building blocks for proteins, crucial energy sources for cell survival, and key signaling molecules supporting the resistant growth of tumor cells. In tumor cells, amino acid metabolic reprogramming is characterized by the enhanced uptake of amino acids as well as their aberrant synthesis, breakdown, and transport, leading to immune evasion and malignant progression of tumor cells. This article reviews the altered amino acid metabolism in tumor cells and its impact on tumor microenvironment, and also provides an overview of the current clinical applications of amino acid metabolism. Innovative drugs targeting amino acid metabolism hold great promise for precision and personalized cancer therapy.

Exploring the Regulatory Effect of LPJZ-658 on Copper Deficiency Combined with Sugar-Induced MASLD in Middle-Aged Mice Based on Multi-Omics Analysis.

Globally, metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed nonalcoholic fatty liver disease (NAFLD), is one of the most common liver disorders and is strongly associated with copper deficiency. To explore the potential effects and mechanisms of Lactiplantibacillus plantarum LPJZ-658, copper deficiency combined with a high-sugar diet-induced MASLD mouse model was utilized in this study. We fed 40-week-old (middle-aged) male C57BL/6 mice a copper-deficient and high-sugar diet for 16 weeks (CuDS), with supplementary LPJZ-658 for the last 6 weeks (CuDS + LPJZ-658). In this study, we measured body weight, liver weight, and serum biochemical markers. Lipid accumulation, histology, lipidomics, and sphingolipid metabolism-related enzyme expression were investigated to analyze liver function. Untargeted metabolomics was used to analyze the serum and the composition and abundance of intestinal flora. In addition, the correlation between differential liver lipid profiles, serum metabolites, and gut flora at the genus level was measured. The results show that LPJZ-658 significantly improves abnormal liver function and hepatic steatosis. The lipidomics analyses and metabolic pathway analysis identified sphingolipid, retinol, and glycerophospholipid metabolism as the most relevant metabolic pathways that characterized liver lipid dysregulation in the CuDS group. Consistently, RT-qPCR analyses revealed that the enzymes catalyzing sphingolipid metabolism that were significantly upregulated in the CuDS group were downregulated by the LPJZ-658 treatment. In addition, the serum metabolomics results indicated that the linoleic acid, taurine and hypotaurine, and ascorbate and aldarate metabolism pathways were associated with CuDS-induced MASLD. Notably, we found that treatment with LPJZ-658 partially reversed the changes in the differential serum metabolites. Finally, LPJZ-658 effectively regulated intestinal flora abnormalities and was significantly correlated with differential hepatic lipid species and serum metabolites. In conclusion, we elucidated the function and potential mechanisms of LPJZ-658 in alleviating copper deficiency combined with sugar-induced middle-aged MASLD and hope this will provide possible treatment strategies for improving MASLD.

Artificial Channels Based on Bottlebrush Polymers: Enhanced Ion Transport Through Polymer Topology Control.

Synthetic structures mimicking the transport function of natural ion channel proteins have a wide range of applications, including therapeutic treatments, separation membranes, sensing, and biotechnologies. However, the development of polymer-based artificial channels has been hampered due to the limitation on available models. In this study, we demonstrate the great potential of bottlebrush polymers as accessible and versatile molecular scaffolds for developing efficient artificial ion channels. Adopting the bottlebrush configuration enhanced ion transport activity of the channels compared to their linear analogs. Matching the structure of lipid bilayers, the bottlebrush channel with a hydrophilic-hydrophobic-hydrophilic triblock architecture exhibited the highest activity among the series. Functionalized with urea groups, these channels displayed high anion selectivity. Additionally, we illustrated that the transport properties could be fine-tuned by modifying the chemistry of ion binding sites. This work not only highlights the importance of polymer topology control in channel design, but also reveals the great potential for further developing bottlebrush channels with customized features and diverse functionalities.

Organic acids released by submerged macrophytes with damaged leaves alter the denitrification microbial community in rhizosphere.

Submerged macrophytes have important impacts on the denitrification and anaerobic ammonia-oxidizing (anammox) processes. Leaf damage in these plants probably changes the rhizosphere environment, affecting organic acid release and denitrifying bacteria. However, there is a lack of comprehensive understanding of the specific changes. This study investigated these changes in the rhizosphere of Potamogeton crispus with four degrees of leaf excision. When 0 %, 30 %, 50 % and 70 % of leaves were excised, the concentrations of total organic acid were 31.45, 32.67, 38.26, and 35.16 mg/L, respectively. The abundances of nirS-type denitrifying bacteria were 2.10 × 1010, 1.59 × 1010, 2.54 × 1010, and 4.67 × 1010 copies/g dry sediment, respectively. The abundances of anammox bacteria were 7.58 × 109, 4.59 × 109, 3.81 × 109, and 3.90 × 109 copies/g dry sediment, respectively. The concentration of total organic acids and the abundance of two denitrification microorganisms in the rhizosphere zone were higher than those in the root zone and non-rhizosphere zone. With increasing leaf damage, the number of OTUs in the Pseudomonas genus of nirS-type denitrifying bacteria first increased and then decreased, while that of the Thauera genus was relatively stable. The overall increase in the OTU number of anammox bacteria indicated that leaf damage promotes root exudates release, thereby leading to an increase in their diversity. The co-occurrence network revealed that the two denitrification microorganisms had about 60.52 % positive connections in rhizosphere while 64.73 % negative connections in non-rhizosphere. The abundance and community composition of both denitrification microorganisms were positively correlated with the concentrations of various substances such as oxalic acid, succinic acid, total organic acids and NO2--N. These findings demonstrate that submerged plant damage has significantly impacts on the structure of denitrification microbial community in the rhizosphere, which may alter the nitrogen cycling process in the deposit sediment. SYNOPSIS: This study reveals leaf damage of macrophyte changed the rhizosphere denitrification microbial community, which is helpful to further understand the process of nitrogen cycle in water.

Exploring the nexus between green finance and energy efficiency: Unravelling the impact through green technology innovation and energy structure.

This study delves into the intricate relationship between green finance and energy efficiency, focusing on how green technology innovation and energy structure transformations contribute to this dynamic. Utilizing panel data from China's provinces over the period 2015-2022, the research aims to uncover the nuances of how green finance can serve as a catalyst for enhancing energy efficiency across different regions. The objective is to quantify the impact of green finance on energy efficiency, considering the mediating roles of green technology innovation and shifts in energy structure. The analysis employs a sophisticated panel entropy weighting technique to analyze the data, ensuring a robust examination of the relationships between these variables. The results reveal a significant positive impact of green finance on energy efficiency, mediated by advances in green technology and modifications in the energy structure towards more sustainable forms. Specifically, regions with higher engagement in green finance initiatives demonstrated marked improvements in energy efficiency, attributed to substantial investments in green technologies and a gradual shift away from traditional, inefficient energy sources. These findings underscore the pivotal role of green finance in driving the transition towards a more energy-efficient and sustainable economic model. Policy implications drawn from this study suggest that targeted financial policies promoting green investments can significantly bolster energy efficiency.

Characterization of PANoptosis-related genes and the immune landscape in moyamoya disease.

Moyamoya disease (MMD) is a cerebrovascular narrowing and occlusive condition characterized by progressive stenosis of the terminal portion of the internal carotid artery and the formation of an abnormal network of dilated, fragile perforators at the base of the brain. However, the role of PANoptosis, an apoptotic mechanism associated with vascular disease, has not been elucidated in MMD. In our study, a total of 40 patients' genetic data were included, and a total of 815 MMD-related differential genes were screened, including 215 upregulated genes and 600 downregulated genes. Among them, DNAJA3, ESR1, H19, KRT18 and STK3 were five key genes. These five key genes were associated with a variety of immune cells and immune factors. Moreover, GSEA (gene set enrichment analysis) and GSVA (gene set variation analysis) showed that the different expression levels of the five key genes affected multiple signaling pathways associated with MMD. In addition, they were associated with the expression of MMD-related genes. Then, based on the five key genes, a transcription factor regulatory network was constructed. In addition, targeted therapeutic drugs against MMD-related genes were obtained by the Cmap drug prediction method: MST-312, bisacodyl, indirubin, and tropanyl-3,5-dimethylbenzoate. These results suggest that the PANoptosis-related genes may contribute to the pathogenesis of MMD through multiple mechanisms.