Dynamic Changes Research Articles

Multiple Functional Protein-Protein Interaction Interfaces Allosterically Regulate ATP-Binding in Cyclin-Dependent Kinase-1.

The ATP-dependent phosphorylation activity of cyclin-dependent kinase 1 (CDK1), an essential enzyme for cell cycle progression, is regulated by interactions with Cyclin-B, substrate, and Cks proteins. We have recently shown that active site acetylation in CDK1 abrogated binding to Cyclin-B which posits an intriguing long-range communication between the catalytic site and the protein-protein interaction (PPI) interface. Now, we demonstrate a general allosteric link between the CDK1 active site and all three of its PPI interfaces through atomistic molecular dynamics (MD) simulations. Specifically, we examined ATP binding free energies to CDK1 in native nonacetylated (K33wt) and acetylated (K33Ac) forms as well as the acetyl-mimic K33Q and the acetyl-null K33R mutant forms, which are accessible in vitro. In agreement with experiments, ATP binding is stronger in K33wt relative to the other three perturbed states. Free energy decomposition reveals, in addition to expected local changes, significant and selective nonlocal entropic responses to ATP binding/perturbation of K33 from the -helix, activation loop (A-loop), and - H segments in CDK1 which interface with Cyclin-B, substrate, and Cks proteins, respectively. Statistical analysis reveals that while entropic responses of protein segments to active site perturbations are on average correlated with their dynamical changes, such correlations are lost in about 9%-48% of the dataset depending on the segment. Besides proving the bi-directional communication between the active site and the CDK1:Cyclin-B interface, our study uncovers a hitherto unknown mode of ATP binding regulation by multiple PPI interfaces in CDK1.

A novel quantitative double antigen sandwich ELISA for detecting total antibodies against Candida albicans enolase 1.

This study aimed to develop a double antigen sandwich ELISA (DAgS-ELISA) method for more efficient, accurate, and quantitative detection of total antibodies against Candida albicans enolase1 (CaEno1) for diagnosing invasive candidiasis (IC). DAgS-ELISA was developed using recombinant CaEno1 and a monoclonal antibody as the standard. Performance evaluation included limit of detection, accuracy, and repeatability. Dynamic changes in antibody levels against CaEno1 in serum from systemic candidiasis mice were analyzed using DAgS-ELISA. Patient serum samples from IC, Candida colonization, bacterial infections, and healthy controls were analyzed with DAgS-ELISA and indirect ELISA. DAgS-ELISA outperformed indirect ELISA in terms of linear range and test background. In systemic candidiasis mice, a distinctive 'double-peak' pattern in dynamic antibody levels was observed. Additionally, there was a high level of consistency in the positive rates of CaEno1 antibodies detected by both DAgS-ELISA and indirect ELISA. While the positivity rates differed among patient groups, no significant variations in antibody levels were detected among the various positive patient groups. DAgS-ELISA offers a reliable novel approach for IC diagnosis, enabling rapid, accurate, and quantitative detection of CaEno1 antibodies. Further validation and optimization are needed for its clinical application and effectiveness.

Near-Full-Spectrum Emission Realized in a Single Lead Halide Perovskite across the Visible-Light Region.

The engineering of tunable photoluminescence (PL) in single materials with a full-spectrum emission represents a highly coveted objective but poses a formidable challenge. In this context, the realization of near-full-spectrum PL emission, spanning the visible light range from 424 to 620 nm, in a single-component two-dimensional (2D) hybrid lead halide perovskite, (ETA)2PbBr4 (ETA+ = (HO)(CH2)2NH3+), is reported, achieved through high-pressure treatment. A pressure-induced phase transition occurs upon compression, transforming the crystal structure from an orthorhombic phase under ambient conditions to a monoclinic structure at high pressure. This phase transition driven by the adaptive and dynamic configuration changes of organic amine cations enables an effective and continuous narrowing of the bandgap in this halide crystal. The hydrogen bonding interactions between inorganic layers and organic amine cations (N-H…Br and O-H…Br hydrogen bonds) efficiently modulate the organic amine cations penetration and the octahedral distortion. Consequently, this phenomenon induces a phase transition and results in red-shifted PL emissions, leading to the near-full-spectrum emission. This work opens a possibility for achieving wide PL emissions with coverage across the visible light spectrum by employing high pressure in single halide perovskites.

Biophysical neural adaptation mechanisms enable artificial neural networks to capture dynamic retinal computation

Adaptation is a universal aspect of neural systems that changes circuit computations to match prevailing inputs. These changes facilitate efficient encoding of sensory inputs while avoiding saturation. Conventional artificial neural networks (ANNs) have limited adaptive capabilities, hindering their ability to reliably predict neural output under dynamic input conditions. Can embedding neural adaptive mechanisms in ANNs improve their performance? To answer this question, we develop a new deep learning model of the retina that incorporates the biophysics of photoreceptor adaptation at the front-end of conventional convolutional neural networks (CNNs). These conventional CNNs build on ’Deep Retina,’ a previously developed model of retinal ganglion cell (RGC) activity. CNNs that include this new photoreceptor layer outperform conventional CNN models at predicting male and female primate and rat RGC responses to naturalistic stimuli that include dynamic local intensity changes and large changes in the ambient illumination. These improved predictions result directly from adaptation within the phototransduction cascade. This research underscores the potential of embedding models of neural adaptation in ANNs and using them to determine how neural circuits manage the complexities of encoding natural inputs that are dynamic and span a large range of light levels.

Spatial heterogeneity of immune regulators drives dynamic changes of local immune responses, affecting disease outcomes in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is considered a low immunogenic tumor with "cold" tumor microenvironment (TME) and is mostly unresponsive to immune checkpoint blockade therapies. Here we decipher the impact of intratumoral heterogeneity of immune determinants on antitumor response. We performed spatial proteomic and transcriptomic analyses and multiplexed immunofluorescence on multiple tumor regions, including tumor center (TC) and invasive front (IF), from 220 PDAC-patients, classified according to their transcriptomic immune signaling into high-immunogenic (HI-PDACs, n=54) and low-immunogenic tumors (LI-PDACs, n=166). Spatial compartments (tumor: Pancytokeratin+/CD45- and leukocytes: Pancytokeratin-/CD45+) were defined by fluorescent imaging. HI-PDACs exhibited higher densities of cytotoxic T lymphocytes with upregulation of T-cell priming-associated immune determinants, including CD40, ITGAM, GITR, CXCL10, GZMB, IFNG and HLA-DR, which was significantly more prominent at the IF than the TC. In contrast, LI-PDACs exhibited immune evasive TMEs with downregulation of immune determinants and a negative gradient from TC to IF. Patients with HI-PDACs had significantly better outcomes; however, they showed more frequently exhausted immune phenotypes. Our results indicate strategic differences in the regulation of immune determinants, which lead to different levels of effectiveness of antitumor responses between high- and low-immunogenic tumors and dynamic spatial changes, which affect the evolution of immune evasion and patient outcomes. This supports coevolution of tumor and immune cells and may help define therapeutic vulnerabilities to improve antitumor immunity and harness the responsiveness to immune checkpoint inhibitors in PDAC patients.

Impact of Management Measures on Multiple Ecosystem Function Trade-Offs and Their Dynamics in Subtropic Pinus massoniana Plantations

With the changing demands imposed on forests by human beings, optimizing forest management to fully utilize their multifunctionality has become a priority. Reasonable forest management measures can maintain stable forest ecosystems that fully coordinate the balance between ecological, societal, and economic aspects. As planted forests are the main application scenario of forest management worldwide, it is of great importance to understand the trade-offs between ecosystem functions and their dynamic changes in planted forests. This paper investigates the effects of different management measures on the ecosystem function of Pinus massoniana plantation forests in the subtropics. It examines four different management measures and explores how they impact multiple ecosystem function indexes and the trade-offs between ecosystem functions during forest restoration. The different management measures effectively promoted the studied ecosystem functions, with higher annual growth rates of the integrated functional indices for timber production, carbon sequestration, and biodiversity compared to the control. Over time, the ecosystem function interactions under the different management measures alternated between trade-offs and synergistic. Only the stand with a 65% harvesting intensity and replanting of various native broadleaf species was able to sustain the synergistic relationships among ecosystem functions, and the dominant function trended toward biodiversity. These observations of dynamic changes and interactions in ecosystem functions of Pinus massoniana plantation forests under various management measures will serve as a valuable reference for the sustainable management of these forests in subtropical regions.

Probability-Based Propagation Characteristics from Meteorological to Hydrological Drought and Their Dynamics in the Wei River Basin, China

Understanding the propagation characteristics and driving factors from meteorological drought to hydrological drought is essential for alleviating drought and for early warning systems regarding drought. This study focused on the Weihe River basin (WRB) and its two subregions (the Jinghe River (JRB) and the middle reaches of the Weihe River (MWRB)), utilizing the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) to characterize meteorological and hydrological drought, respectively. Based on Copula theory and conditional probability, a quantification model for the propagation time (PT) of meteorological–hydrological drought was constructed. The dynamic characteristics of PT on annual and seasonal scales were explored. Additionally, the influences of different seasonal meteorological factors and underlying surface factors on the dynamic changes in PT were analyzed. The main conclusions were as follows: (1) The PT of meteorological–hydrological drought was characterized by faster propagation during the hot months (June–September) and slower propagation during the cold months (December to March of the following year); (2) Under the same level of hydrological drought, as the level of meteorological drought increases, the PT of the drought shortens. The propagation thresholds of meteorological to hydrological drought in the WRB, the JRB, and the MWRB are −0.69, −0.81, and −0.78, respectively. (3) In the dynamic changes in PT, the WRB showed a non-significant decrease; however, both the JRB and the MWRB exhibited a significant increase in PT across different drought levels. (4) The influence of the water and heat status during spring, summer, and winter on PT was more pronounced, while in autumn, the impact of the basin’s water storage and discharge status was more significant in the JRB and the MWRB.

A Hybrid Model of Conformer and LSTM for Ocean Wave Height Prediction

This study proposes a hybrid model (Conformer-LSTM) based on Conformer and Long Short-Term Memory networks (LSTM) to overcome the limitations of existing techniques and enhance the accuracy and generalizability of wave height predictions. The model combines the advantages of self-attention mechanisms and convolutional neural networks. It captures global dependencies through multi-head self-attention and utilizes convolutional layers to extract local features, thereby enhancing the model’s adaptability to dynamic changes in time series. The LSTM component handles long-term dependencies, optimizing the coherence and stability of predictions. Additionally, an adaptive feature fusion weight network is introduced to further improve the model’s recognition and utilization efficiency of key features. Experimental data come from the National Oceanic and Atmospheric Administration buoy data, covering wave height, wind speed, and other data from key maritime areas. Evaluation metrics include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2), ensuring a comprehensive assessment of model performance. The results show that the Conformer-LSTM model outperforms traditional LSTM, CNN, and CNN-LSTM models at multiple sites, confirming its potential in wave height prediction.

Flavoromics Analysis of Passion Fruit-Roasted Chicken

Currently, research on the flavor components and their dynamic changes in roasted chicken with a special flavor is rare. In this study, a passion fruit-roasted chicken was prepared, its characteristic flavor components were profiled by flavoromics, and their evolution patterns and precursors were determined. The results showed that the characteristic flavor component with the highest contribution rate was ethyl butyrate (50.44%). In particular, some unique flavor compounds were identified compared with other roasted chicken products available. The main volatile flavor components in all stages of processing were alcohols, esters, and hydrocarbons, 15 to 30 min of roasting is an important stage for establishing the aroma system, and at the end, hydrocarbons were the main volatile compounds. During the 30-day storage period, the characteristic flavor components included ethyl butyrate, ethyl maltol, β-caryophyllene, and guaiacene. In conclusion, passion fruit-roasted chicken contained many characteristic flavor components, which were mainly formed within 15 to 30 min of roasting and were basically stable during the 30-day storage period. In a word, this work prepared a novel roasted chicken and revealed its mechanism of flavor formation at different baking stages and storage periods, which provided references for industrial production.

The intersection between menopause and depression: overview of research using animal models

Menopausal women may experience symptoms of depression, sometimes even progressing clinical depression requiring treatment to improve quality of life. While varying levels of estrogen in perimenopause may contribute to an increased biological vulnerability to mood disturbances, the effectiveness of estrogen replacement therapy (ERT) in the relief of depressive symptoms remains controversial. Menopausal depression has a complex, multifactorial etiology, that has limited the identification of optimal treatment strategies for the management of this psychiatric complaint. Nevertheless, clinical evidence increasingly supports the notion that estrogen exerts neuroprotective effects on brain structures related to mood regulation. Indeed, research using preclinical animal models continues to improve our understanding of menopause and the effectiveness of ERT and other substances at treating depression-like behaviors. However, questions regarding the efficacy of ERT in perimenopause have been raised. These questions may be answered by further investigation using specific animal models of reduced ovarian function. This review compares and discusses the advantages and pitfalls of different models emulating the menopausal stages and their relationship with the onset of depressive-like signs, as well as the efficacy and mechanisms of conventional and novel ERTs in treating depressive-like behavior. Ovariectomized young rats, middle-to-old aged intact rats, and females treated with reprotoxics have all been used as models of menopause, with stages ranging from surgical menopause to perimenopause. Additionally, this manuscript discusses the impact of organistic and therapeutic variables that may improve or reduce the antidepressant response of females to ERT. Findings from these models have revealed the complexity of the dynamic changes occurring in brain function during menopausal transition, reinforcing the idea that the best approach is timely intervention considering the opportunity window, in addition to the careful selection of treatment according to the presence or absence of reproductive tissue. Additionally, data from animal models has yielded evidence to support new promising estrogens that could be considered as ERTs with antidepressant properties and actions in endocrine situations in which traditional ERTs are not effective.

Associations between the keratinized mucosa width and the underlying alveolar bone dimensions at partial edentulous molar sites: a retrospective cross-sectional study

BackgroundThe assessment of hard and soft tissue at edentulous sites is important for subsequent implant treatment design. The aim of the present study was to explore the associations between the keratinized mucosa width (KMW) and the underlying alveolar bone dimensions at partial edentulous molar sites.MethodsIn this retrospective study, a total of 110 patients with at least one missing molar were selected. The buccal KMW of the edentulous molar sites was evaluated. Cone-beam computed tomography scans were collected, and the height discrepancy between the alveolar crest and the buccal bone plate (HC−B) as well as the alveolar bone height (ABH) were measured. The KMW was compared among the HC−B and ABH groups at both maxillary and mandibular sites. Linear regression and generalized estimation equations (GEEs) were used to explore the associations between the KMW and alveolar bone dimensions at α = 0.05.ResultsAmong the 110 patients, 158 edentulous molar sites were analyzed. The average HC−B and ABH were significantly lower at the maxillary sites (1.26 ± 1.62 mm, 11.62 ± 3.94 mm) than at the mandibular sites (3.67 ± 2.85 mm, 14.91 ± 3.01 mm, p < 0.001). The KMW was significantly lower at sites with HC−B > 2 mm than at sites with HC−B ≤ 2 mm both in the maxilla and mandible (p < 0.001). No significant differences were found between the KMW at sites with ABH < 10 mm and sites with ABH ≥ 10 mm (p > 0.05). Linear regression and GEEs analyses revealed that the HC−B was significantly associated with the KMW (B = -0.339, p < 0.001), while the association between the KMW and the ABH was not statistically significant (B = -0.046, p = 0.352).ConclusionsThe buccal KMW at edentulous molar sites was significantly associated with the HC−B. Alveolar ridges presenting with a sloped configuration were more prone to possess a narrower band of keratinized mucosa. Both hard and soft tissue augmentation should be considered for implant treatment at these sites. The correlations of dynamic changes between the KMW and alveolar bone dimensions after tooth extraction should be further investigated.

Carbon storage-driven multi-objective spatial planning: simulation and practice at the management unit level in Northeast China

In recent years, global attention to forest ecological environment, carbon, wood yield, and biodiversity has been increasing, which requires forest managers to make reasonable forest planning in time and space to meet the needs of multi-objectives and multi-resources. In this study, the resources of business units in Northeast China were analyzed, and the FPS-ATLAS model was established. A multi-objective decision supporting spatial forest planning scheme for 16629.53 ha of forest in Jingouling forest farm of Wangqing Forestry Bureau in Jilin Province was proposed. In this paper, the volume growth model of six forest types was established, and the carbon storage model was introduced to show the total carbon, biomass carbon, dissolved organic carbon, and total soil carbon of the ecosystem. The dynamic change in carbon during the whole planning period has achieved good results. After 20 time periods, the total soil carbon increased to 7,627,208 Mg (+6.63%), the total ecosystem carbon increased to 14,889,876 Mg (+27.51%), the biomass carbon increased to 5,362,779 Mg (+59.70%), and the dissolved organic carbon increased to 9,531,906 Mg (+14.59%). The purpose of this article is to achieve dynamic management of forest resources, meet multiple constraint settings, achieve multiple management objectives, harvest wood products, and ensure the ecological and social functions of forests so that forests ultimately achieve the ideal state of sustainability.

Dynamic changes of gut microbiota in mouse models of metabolic dysfunction-associated steatohepatitis and its transition to hepatocellular carcinoma.

Dysbiosis of gut microbiota may account for pathobiology in simple fatty liver (SFL), metabolic dysfunction-associated steatohepatitis (MASH), fibrotic progression, and transformation to MASH-associated hepatocellular carcinoma (MASH-HCC). The aim of the present study is to investigate gut dysbiosis in this progression. Fecal microbial rRNA-16S sequencing, absolute quantification, histopathologic, and biochemical tests were performed in mice fed high fat/calorie diet plus high fructose and glucose in drinking water (HFCD-HF/G) or control diet (CD) for 2, 16 weeks, or 14 months. Histopathologic examination verified an early stage of SFL, MASH, fibrotic, or MASH-HCC progression with disturbance of lipid metabolism, liver injury, and impaired gut mucosal barrier as indicated by loss of occludin in ileum mucosa. Gut dysbiosis occurred as early as 2 weeks with reduced α diversity, expansion of Kineothrix, Lactococcus, Akkermansia; and shrinkage in Bifidobacterium, Lactobacillus, etc., at a genus level. Dysbiosis was found as early as MAHS initiation, and was much more profound through the MASH-fibrotic and oncogenic progression. Moreover, the expansion of specific species, such as Lactobacillus johnsonii and Kineothrix alysoides, was confirmed by an optimized method for absolute quantification. Dynamic alterations of gut microbiota were characterized in three stages of early SFL, MASH, and its HCC transformation. The findings suggest that the extent of dysbiosis was accompanied with MASH progression and its transformation to HCC, and the shrinking or emerging of specific microbial species may account at least in part for pathologic, metabolic, and immunologic alterations in fibrogenic progression and malignant transition in the liver.

A Dynamic Spatiotemporal Understanding of Changes in Social Vulnerability to Wildfires at Local Scale

Research on wildfires and social vulnerability has gained significant importance due to the increasing frequency and severity of wildfires around the world. This study investigates the dynamic changes in social vulnerability to wildfires over a decade in Idaho, USA, utilizing GIS-based tools and a quasi-experimental design. We assess the evolving nature of social vulnerability at a local scale, emphasizing both spatial and temporal dynamics. Initially, we identified social vulnerability trends in relation to varying levels of wildfire risk. The research then employs propensity score matching to contrast areas affected by wildfires in 2012 with similar non-affected regions, thereby quantifying the short-term shifts in social vulnerability post-wildfires. The results indicate that regions with a high wildfire risk may display elevated vulnerability, characterized by an increase in unemployment rates and a reduction in high-income households. These findings tentatively demonstrate the compounded effect of wildfires on already vulnerable populations, highlighting the critical need for targeted interventions. Ultimately, this study underscores the importance of integrating dynamic social vulnerability assessments into wildfire management and planning, aiming to enhance community resilience and equitable resource distribution in the face of escalating wildfire threats.

Assessment of the impact of physical and chemical factors of the production environment on the morphological state of target organs in an animal experiment

The study of the morphological state of target organs of outbred (nonlinear) laboratory mice ICR (CD-1), which were under prolonged dynamic influence of physical and chemical factors of the production environment under model conditions, is of wide research interest, as it allows to assess the impact of these factors at the tissue and cellular level and to identify the presence of pathological changes in relation to the group control that was not exposed to the studied factors of the production environment during the 90-day experiment, which has clinical and practical significance. When assessing the 90-day dynamics (from 0 days – 6 months (background) to 30–60–90 days – 7–8–9 months) of the impact of environmental factors on the morphological state of tissues and organs of outbred (nonlinear) laboratory mice ICR (CD-1), generally accepted and unified methods were used. Animals (weighing 45–50 gr., sexually mature age – 6 months) were divided into 3 experimental groups (vibration exposure, noise exposure, chemical exposure) of 30 individuals each (15 females and 15 males) and 1 control group (15 females and 15 males), kept in standard comfortable conditions, and not exposed to the factors of the production environment. The experiment was conducted in accordance with the rules adopted by the European Convention for the Protection of Animals Used for Experimental Scientific Purposes (Strasbourg, 1986), after approval by the Ethical commission of the Novosibirsk Research Institute of Hygiene of Rospotrebnadzor. Statistical processing of the research materials was carried out using standard Statistica 10.0 application programs. The morphological analysis of the target organs of experimental animals revealed the presence of a complex of pathological changes in the 90-day dynamics of the impact of factors of the production environment, compared with the morphological state of the organs in the control group. The results obtained have clinical and practical significance, as they allow us to justify preventive measures aimed at preventing the negative impact of long-term production factors on the labor contingent. Further study of the morphological state of target organs of ICR laboratory mice exposed to prolonged dynamic exposure to physical and chemical factors of the production environment under model conditions will contribute to the establishment of mechanisms for the development of occupational diseases and the development of scientifically based medical and hygienic measures aimed at preserving the health of the labor contingent in the management of labor longevity.

Brain state dynamics differ between eyes open and eyes closed rest.

The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.

Discovery of a terpene synthase synthesizing a nearly non-flexible eunicellane reveals the basis of flexibility

Eunicellane diterpenoids, containing a typical 6,10-bicycle, are bioactive compounds widely present in marine corals, but rarely found in bacteria and plants. The intrinsic macrocycle exhibits innate structural flexibility resulting in dynamic conformational changes. However, the mechanisms controlling flexibility remain unknown. The discovery of a terpene synthase, MicA, that is responsible for the biosynthesis of a nearly non-flexible eunicellane skeleton, enable us to propose a feasible theory about the flexibility in eunicellane structures. Parallel studies of all eunicellane synthases in nature discovered to date, including 2Z-geranylgeranyl diphosphate incubations and density functional theory-based Boltzmann population computations, reveale that a trans-fused bicycle with a 2Z-configuration alkene restricts conformational flexibility resulting in a nearly non-flexible eunicellane skeleton. The catalytic route and the enzymatic mechanism of MicA are also elucidated by labeling experiments, density functional theory calculations, structural analysis of the artificial intelligence-based MicA model, and mutational studies.

Selection of Various Indications of Tempering Lubricant Concentration Reuse in the Wet Rolling Process

ABSTRACTDiscarded tempering lubricants retained significant reuse potential, making their recycling a vital step in reducing resource wastage and wastewater treatment costs in the strip steel industry. Hence, developing an accurate, rapid evaluation indicator for recycled fluid concentration was essential for facilitating this process. Research showed that among common evaluation indicators for metal fluids, three—electrical conductivity, refractive index and total base number (TBN)—due to their high linear correlation with tempering lubricant concentration (R2 &gt; 0.995), could be utilised to monitor the dynamic changes in the concentration of tempering lubricants. Subsequent experiments on reused tempering lubricants revealed that electrical conductivity, significantly altered by iron powder (7%–24% variance), and refractive index, impacted by hydraulic oil (3% deviation), highlighted contaminant challenges; yet, filtration effectively mitigated iron powder's effect on TBN. Finally, A 17‐day reused tempering lubricants simulation demonstrated consistent effectiveness of the three indicators in monitoring the need to update tempering lubricant concentration. However, in terms of sensitivity, precision, and particularly stability and relative mean deviation, the TBN concentration evaluation indicator outperformed, with TBN (3.38%) &lt; Refractive Index (7.92%) &lt; Electrical Conductivity (11.05%). This indicates the TBN method's superior stability over conductivity and refractive index methods, with its accuracy deviation below 2%, making it a stable, simple and reliable metric worthy of broader adoption.

Prognostic value of immunotherapy in advanced non-small cell lung cancer based on baseline and dynamic changes in hemoglobin, albumin, and platelets

Immune checkpoint inhibitors enhance the tumor-killing ability of T-cells in non-small cell lung cancer (NSCLC), thereby boosting overall survival (OS) and transforming treatment for advanced stages. However, challenges persist, including low response rates and the absence of effective markers for candidate selection. This study evaluated the impact of hemoglobin, albumin, and platelet (HALP), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) on immunotherapy efficacy and survival in advanced NSCLC. Furthermore, the study aimed to develop a nomogram based on these parameters. Clinical and hematological data from patients diagnosed with NSCLC who received immunotherapy were analyzed. Efficacy was assessed using the immune Response Evaluation Criteria in Solid Tumors (iRECIST), and progression-free survival (PFS) and OS were analyzed. Prediction models were based on baseline and post-treatment HALP, NLR, and PLR. The 203 included patients had a median follow-up of 16 months, a median PFS (mPFS) of 7 months (6.0 – 8.0), while the median OS (mOS) was not available (24.0 – not available). The PLR before treatment (PLR0) was linked to a higher disease control rate (DCR) (odds ratio [OR] = 0.258), while initial immunotherapy and NLR after four cycles of treatment (NLR4C) significantly boosted the objective response rate (ORR). Cox regression showed that HALP before treatment (HALP0), HALP after four cycles of treatment (HALP4C), and NLR before treatment (NLR0) significantly influenced PFS. Additionally, HALP0, NLR0, and PLR after four cycles of treatment (PLR4C) were associated with OS. The C-indices for PFS and OS were 0.823 and 0.878, respectively, indicating good prediction accuracy. HALP, NLR, and PLR at various time points effectively predicted immunotherapy response in advanced NSCLC patients. Low HALP with high NLR and PLR indicated a poor prognosis. The findings can provide the basis for stratified randomized controlled trials (RCTs) in the future.

In situ Raman reveals the critical role of Pd in electrocatalytic CO2 reduction to CH4 on Cu-based catalysts.

Electrocatalytic CO2 reduction reaction (CO2RR) for CH4 production presents a promising strategy to address carbon neutrality, and the incorporation of a second metal has been proven effective in enhancing catalyst performance. Nevertheless, there remains limited comprehension regarding the fundamental factors responsible for the improved performance. Herein, the critical role of Pd in electrocatalytic CO2 reduction to CH4 on Cu-based catalysts has been revealed at a molecular level using in situ surface-enhanced Raman spectroscopy (SERS). A "borrowing" SERS strategy has been developed by depositing Cu-Pd overlayers on plasmonic Au nanoparticles to achieve the in situ monitoring of the dynamic change of the intermediate during CO2RR. Electrochemical tests demonstrate that Pd incorporation significantly enhances selectivity toward CH4 production, and the Faradaic efficiency (FE) of CH4 is more than two times higher than that for the catalysts without Pd. The key intermediates, including *CO2-, *CO, and *OH, have been directly identified under CO2RR conditions, and their evolution with the electrochemical environments has been determined. It is found that Pd incorporation promotes the activation of both CO2 and H2O molecules and accelerates the formation of abundant active *CO and hydrogen species, thus enhancing the CH4 selectivity. This work offers fundamental insights into the understanding of the molecular mechanism of CO2RR and opens up possibilities for designing more efficient electrocatalysts.