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Abstract An innovative Chardonnay wine flavored with black tea was prepared under optimal ultrasound conditions at 450.0 W for 22.6 min, and its physicochemical features, antioxidant power, total phenolic content, and volatile composition were systematically examined. A total of 165 phenolic and non-phenolic substances were characterized, including 27 newly formed phenolic substances and 10 non-phenolic substances that were not found in both black tea and Chardonnay wine. Catechin was found to be the phenolic compound with the largest amount detected, which was responsible for the potentiated antioxidant activities. The wine exhibited a profile of 44 volatile compounds, with 13 volatiles as odor-active compounds. Black tea infusion resulted in a decline in fruity, fermented and spicy aromas, but an increase in green and citrus odors, while ultrasound processing further intensified these odors. This study anticipated that ultrasound technology could hold promise for crafting flavored wines with heightened functionalities and appealing flavor profiles. Graphical Abstract Ultrasound-assisted infusion of black tea potentiated the antioxidant activity and enriched the volatile and phenolic profiles of Chardonnay wine.

Developing new high-entropy rare-earth zirconate (HE-RE2Zr2O7) ceramics with low thermal conductivity is essential for thermal barrier coating materials. In this work, the average atomic spacings, interatomic forces, and average atomic masses of 16 rare-earth elements occupying the A site of the cubic A2B2O7 crystal structure were calculated by density functional theory. These three physical qualities, as vectors, characterize the corresponding rare-earth elements. The distance between two vectors quantitatively describes the difference between two rare-earth elements. For greater differences between two rare-earth elements, the disorder degree of HE-RE2Zr2O7 is greater, and therefore, the thermal conductivity is lower. According to the theoretical calculations, the thermal conductivity of the ceramics gradually increases in the order of (Sc0.2Y0.2La0.2Ho0.2Yb0.2)2Zr2O7, (Sc0.2Ce0.2Nd0.2Eu0.2Gd0.2)2Zr2O7, (Sc0.2Y0.2Tm0.2Yb0.2Lu0.2)2Zr2O7, and (Sc0.2Er0.2Tm0.2Yb0.2Lu0.2)2Zr2O7. Using the solution precursor plasma spray method and pressureless sintering method, four types of HE-RE2Zr2O7 powder and bulk samples were prepared. The samples all showed a single defective fluorite structure with a uniform distribution of the elements and a stable phase structure. The thermal conductivities of the sintered HE-RE2Zr2O7 bulk samples ranged from 1.30 to 1.45 W·m−1·K−1 at 1400 °C, and their differences were consistent with the theoretical calculation results. Among the ceramics, (Sc0.2Y0.2La0.2Ho0.2Yb0.2)2Zr2O7 had the lowest thermal conductivity (1.30 W∙m−1∙K−1, 1400 °C), highest thermal expansion coefficient (10.19×10−6 K−1, 200–1400 °C), highest fracture toughness (1.69±0.28 MPa∙m1/2), and smallest brittleness index (3.03 μm−1/2). Therefore, (Sc0.2Y0.2La0.2Ho0.2Yb0.2)2Zr2O7 is considered to be an ideal candidate material for next-generation thermal barrier coating applications.

Abstract Background Gastric cancer (GC) encompasses many different histological and molecular subtypes. It is a major driver of cancer mortality because of poor survival and limited treatment options. Personalised medicine in the form of patient-derived organoids (PDOs) represents a promising approach for improving therapeutic outcomes. The goal of this study was to overcome the limitations of current models by ameliorating organoid cultivation. Methods Organoids derived from cancer tissue were evaluated by haematoxylin and eosin staining, immunohistochemistry, mRNA, and whole-exome sequencing. Three representative chemotherapy drugs, 5-fluorouracil, docetaxel, and oxaliplatin, were compared for their efficacy against different subtypes of gastric organoids by ATP assay and apoptosis staining. In addition, drug sensitivity screening results from two publicly available databases, the Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopaedia, were pooled and applied to organoid lines. Once key targeting genes were confirmed, chemotherapy was used in combination with poly (ADP ribose) polymerase (PARP)-targeted therapy. Results We successfully constructed GC PDOs surgically resected from GC patient tissue. PDOs closely reflected the histopathological and genomic features of the corresponding primary tumours. Whole-exosome sequencing and mRNA analysis revealed that changes to the original tumour genome were maintained during long-term culture. The drugs caused divergent responses in intestinal, poorly differentiated intestinal, and diffuse gastric cancer organoids, which were confirmed in organoid lines. Poorly differentiated intestinal GC patients benefited from a combination of 5-fluorouracil and veliparib. Conclusion The present study demonstrates that combining chemotherapy with PARP targeting may improve the treatment of chemotherapy-resistant tumours.

Several mitochondrial dysfunctions in obesity and diabetes include impaired mitochondrial membrane potential, excessive mitochondrial reactive oxygen species generation, reduced mitochondrial DNA, increased mitochondrial Ca2+ flux, and mitochondrial dynamics disorders. Mitophagy, specialized autophagy, is responsible for clearing dysfunctional mitochondria in physiological and pathological conditions. As a paradox, inhibition and activation of mitophagy have been observed in obesity and diabetes-related heart disorders, with both exerting bidirectional effects. Suppressed mitophagy is beneficial to mitochondrial homeostasis, also known as benign mitophagy. On the contrary, in most cases, excessive mitophagy is harmful to dysfunctional mitochondria elimination and thus is defined as detrimental mitophagy. In obesity and diabetes, two classical pathways appear to regulate mitophagy, including PTEN-induced putative kinase 1 (PINK1)/Parkin-dependent mitophagy and receptors/adapters-dependent mitophagy. After the pharmacologic interventions of mitophagy, mitochondrial morphology and function have been restored, and cell viability has been further improved. Herein, we summarize the mitochondrial dysfunction and mitophagy alterations in obesity and diabetes, as well as the underlying upstream mechanisms, in order to provide novel therapeutic strategies for the obesity and diabetes-related heart disorders.

Abstract Background Ice and snow sports, which are inherently high risk due to their physically demanding nature, pose significant challenges in terms of participant safety. These activities increase the likelihood of injuries, largely due to reduced bodily agility and responsiveness in cold, often unpredictable winter environments. The critical need for effective injury prevention in these sports is emphasized by the considerable impact injuries have on the health of participants, alongside the economic and social costs associated with medical and rehabilitative care. In the context of ice and snow sports environments, applying the E principles of injury prevention to evaluate intervention measures can guide the implementation of future sports safety and other health promotion intervention measures in this field. When well executed, this approach can substantially reduce both the frequency and severity of injuries, thereby significantly enhancing the safety and long-term viability of these challenging sports. Objective The objective of this study was to rigorously assess and statistically substantiate the efficacy of diverse injury prevention strategies in ice and snow sports, aiming to bolster future safety measures with solid empirical evidence. Design Systematic review and meta-analysis. Methods The overarching aim of this research was to meticulously aggregate and scrutinize a broad spectrum of scholarly literature, focusing on the quantifiable efficacy of diverse, multicomponent intervention strategies in mitigating the incidence of injuries within the realm of ice and snow sports. This endeavour entailed an exhaustive extraction of data from esteemed academic databases, encompassing publications up to September 30, 2023. In pursuit of methodological excellence and analytical rigor, the study employed advanced bias assessment methodologies, notably the AMSTAR 2 and GRADE approaches, alongside sophisticated random-effects statistical modelling. This comprehensive approach was designed to ensure the utmost validity, reliability, and scholarly integrity of the study’s findings. Results Fifteen papers, including 9 randomized controlled trials, 3 case‒control studies, and 3 cohort studies with 26,123 participants and 4,382 injuries, were analysed. The findings showed a significant reduction in injury rates through various interventions: overall injury prevention (RR = 0.50, 95% CI 0.42–0.63), educational training (RR = 0.50, 95% CI 0.34–0.73), educational videos (RR = 0.53, 95% CI 0.34–0.81), protective equipment (RR = 0.64, 95% CI 0.46–0.87), and policy changes (RR = 0.28, 95% CI 0.16–0.49). Subgroup analysis revealed potential heterogeneity in compliance (p = 0.347). Compared to controls, multicomponent interventions effectively reduced injury rates. Conclusion This systematic review and meta-analysis demonstrated that multicomponent interventions significantly prevent injuries in ice and snow sports. By applying the E principles of injury prevention and constructing a framework for practical injury prevention research in ice and snow sports, we can gradually shift towards a systemic paradigm for a better understanding of the development and prevention of sports injuries. Moreover, sports injury prevention is a complex and dynamic process. Therefore, high-quality experiments in different scenarios are needed in future research to provide more reliable evidence, offer valuable and relevant prevention information for practitioners and participants, and help formulate more effective preventive measures in practice.

Abstract The ESR1 ligand binding domain activating mutations are the most prevalent genetic mechanism of acquired endocrine resistance in metastatic hormone receptor-positive breast cancer. These mutations confer endocrine resistance that remains estrogen receptor (ER) dependent. We hypothesized that in the presence of the ER mutations, continued ER blockade with endocrine therapies that target mutant ER is essential for tumor suppression even with chemotherapy treatment. Here, we conducted comprehensive pre-clinical in vitro and in vivo experiments testing the efficacy of adding fulvestrant to fluorouracil (5FU) and the 5FU pro-drug, capecitabine, in models of wild-type (WT) and mutant ER. Our findings revealed that while this combination had an additive effect in the presence of WT-ER, in the presence of the Y537S ER mutation there was synergy. Notably, these effects were not seen with the combination of 5FU and selective estrogen receptor modulators, such as tamoxifen, or in the absence of intact P53. Likewise, in a patient-derived xenograft (PDX) harboring a Y537S ER mutation the addition of fulvestrant to capecitabine potentiated tumor suppression. Moreover, multiplex immunofluorescence revealed that this effect was due to decreased cell proliferation in all cells expressing ER and was not dependent on the degree of ER expression. Taken together, these results support the clinical investigation of the combination of ER antagonists with capecitabine in patients with metastatic hormone receptor-positive breast cancer who have experienced progression on endocrine therapy and targeted therapies, particularly in the presence of an ESR1 activating mutation.

Articular cartilage defects often involve damage to both the cartilage and subchondral bone, requiring a scaffold that can meet the unique needs of each tissue type and establish an effective barrier between the bone and cartilage. In this study, we used 3D printing technology to fabricate a tri-phasic scaffold composed of PLA/PCL-PLGA/Mg(OH)₂, which includes a cartilage layer, an osteochondral interface, and a bone layer. The scaffold was filled with Velvet antler polypeptides (VAP), and its characterization was assessed using compression testing, XRD, FTIR, SEM, fluorescence microscopy, and EDS. In vitro investigation demonstrated that the scaffold not only supported osteogenesis but also promoted chondrogenic differentiation of fibrocartilage stem cells (FCSCs). n vivo experiments showed that the tri-phasic PLA/PCL-PLGA/Mg(OH)2-VAP scaffold together with FCSC, when transplanted to animal models, increased the recovery of osteochondral defects. Those results demonstrate the promising future of illustrated tri-phasic PLA/PCL-PLGA/Mg(OH)2-VAP scaffold loaded with FCSCs as a new bone and cartilage tissue engineering approach for osteochondral defects treatment.

Abstract Mutations of the human leucine-rich repeat kinase 2 (LRRK2) have been associated with both, idiopathic and familial Parkinson’s disease (PD). Most of these pathogenic mutations are located in the kinase domain (KD) or GTPase domain of LRRK2. In this study we describe a mechanism in which protein kinase activity can be modulated by reversible oxidation or reduction, involving a unique pair of adjacent cysteines, the “CC” motif. Among all human protein kinases, only LRRK2 contains this “CC” motif (C2024 and C2025) in the Activation Segment (AS) of the kinase domain. In an approach combining site-directed mutagenesis, biochemical analyses, cell-based assays, and Gaussian accelerated Molecular Dynamics (GaMD) simulations we could attribute a role for each of those cysteines. We employed reducing and oxidizing agents with potential clinical relevance to investigate effects on kinase activity and microtubule docking. We find that each cysteine gives a distinct contribution: the first cysteine, C2024, is essential for LRRK2 protein kinase activity, while the adjacent cysteine, C2025, contributes significantly to redox sensitivity. Implementing thiolates (R-S-) in GaMD simulations allowed us to analyse how each of the cysteines in the “CC” motif interacts with its surrounding residues depending on its oxidation state. From our studies we conclude that oxidizing agents can downregulate kinase activity of hyperactive LRRK2 PD mutations and may provide promising tools for therapeutic strategies.

Ischemic stroke, a significant threat to human life and health, refers to a class of conditions where brain tissue damage is induced following decreased cerebral blood flow. The incidence of ischemic stroke has been steadily increasing globally, and its disease mechanisms are highly complex and involve a multitude of biological mechanisms at various scales from genes all the way to the human body system that can affect the stroke onset, progression, treatment, and prognosis. To complement conventional experimental research methods, computational systems biology modeling can integrate and describe the pathogenic mechanisms of ischemic stroke across multiple biological scales and help identify emergent modulatory principles that drive disease progression and recovery. In addition, by running virtual experiments and trials in computers, these models can efficiently predict and evaluate outcomes of different treatment methods and thereby assist clinical decision-making. In this review, we summarize the current research and application of systems-level computational modeling in the field of ischemic stroke from the multiscale mechanism-based, physics-based and omics-based perspectives and discuss how modeling-driven research frameworks can deliver insights for future stroke research and drug development.

Parkinson’s disease (PD) is neurodegenerative disease in middle-aged and elderly people with some pathological mechanisms including immune disorder, neuroinflammation, white matter injury and abnormal aggregation of alpha-synuclein, etc. New research suggests that white matter injury may be important in the development of PD, but how inflammation, the immune system, and white matter damage interact to harm dopamine neurons is not yet understood. Therefore, it is particularly important to delve into the crosstalk between immune cells in the central and peripheral nervous system based on the study of white matter damage in PD. This crosstalk could not only exacerbate the pathological process of PD but may also reveal new therapeutic targets. By understanding how immune cells penetrate through the blood–brain barrier and activate inflammatory responses within the central nervous system, we can better grasp the impact of structural destruction of white matter in PD and explore how this process can be modulated to mitigate or combat disease progression. Microglia, astrocytes, oligodendrocytes and peripheral immune cells (especially T cells) play a central role in its pathological process where these immune cells produce and respond to pro-inflammatory cytokines such as tumor necrosis factor (TNF-α), interleukin-1β(IL-1β) and interleukin-6(IL-6), and white matter injury causes microglia to become pro-inflammatory and release inflammatory mediators, which attract more immune cells to the damaged area, increasing the inflammatory response. Moreover, white matter damage also causes dysfunction of blood–brain barrier, allows peripheral immune cells and inflammatory factors to invade the brain further, and enhances microglia activation forming a vicious circle that intensifies neuroinflammation. And these factors collectively promote the neuroinflammatory environment and neurodegeneration changes of PD. Overall, these findings not only deepen our understanding of the complexity of PD, but also provide new targets for the development of therapeutic strategies focused on inflammation and immune regulation mechanisms. In summary, this review provided the theoretical basis for clarifying the pathogenesis of PD, summarized the association between white matter damage and the immune cells in the central and peripheral nervous systems, and then emphasized their potential specific mechanisms of achieving crosstalk with further aggravating the pathological process of PD.

Abstract LRRK2 is one of the most promising drug targets for Parkinson’s disease. Though type I kinase inhibitors of LRRK2 are under clinical trials, alternative strategies like type II inhibitors are being actively pursued due to the potential undesired effects of type I inhibitors. Currently, a robust method for LRRK2–inhibitor structure determination to guide structure-based drug discovery is lacking, and inhibition mechanisms of available compounds are also unclear. Here we present near-atomic-resolution structures of LRRK2 with type I (LRRK2-IN-1 and GNE-7915) and type II (rebastinib, ponatinib, and GZD-824) inhibitors, uncovering the structural basis of LRRK2 inhibition and conformational plasticity of the kinase domain with molecular dynamics (MD) simulations. Type I and II inhibitors bind to LRRK2 in active-like and inactive conformations, so LRRK2–inhibitor complexes further reveal general structural features associated with LRRK2 activation. Our study provides atomic details of LRRK2–inhibitor interactions and a framework for understanding LRRK2 activation and for rational drug design.

Abstract Background The benefits of combining benzoic acid and essential oils (BAO) to mitigate intestinal impairment during the weaning process have been well established, while the detailed underlying mechanism has not been fully elucidated. Previous research has primarily focused on the reparative effects of BAO on intestinal injury, while neglecting its potential in enhancing intestinal stress resistance. Methods In this study, we investigated the pre-protective effect of BAO against LPS-induced stress using a modified experimental procedure. Piglets were pre-supplemented with BAO for 14 d, followed by a challenge with LPS or saline to collect blood and intestinal samples. Results Our findings demonstrated that BAO supplementation led to significant improvements in piglets' final weight, average daily gain, and feed intake/body gain ratio. Additionally, BAO supplementation positively influenced the composition of intestinal microbiota, increasing beneficial Actinobacteriota and Alloprevotella while reducing harmful Desulfobacterota, Prevotella and Oscillospira. Furthermore, BAO supplementation effectively mitigated oxidative disturbances and inflammatory responses induced by acute LPS challenge. This was evidenced by elevated levels of T-AOC, SOD, and GSH, as well as decreased levels of MDA, TNF-α, and IL-6 in the plasma. Moreover, piglets subjected to LPS challenge and pre-supplemented with BAO exhibited significant improvements in intestinal morphological structure and enhanced integrity, as indicated by restored expression levels of Occludin and Claudin-1 compared to the non-supplemented counterparts. Further analysis revealed that BAO supplementation enhanced the jejunal antioxidative capacity by increasing GSH-Px levels and decreasing MDA levels under the LPS challenge and stimulated the activation of the Nrf2 signaling pathway. Additionally, the reduction of TLR4/NF-κB/MAPK signaling pathways activation and proinflammatory factor were also observed in the jejunal of those piglets fed with BAO. Conclusions In summary, our study demonstrates that pre-supplementation of BAO enhances the anti-stress capacity of weaned piglets by improving intestinal microbiota composition, reinforcing the intestinal barrier, and enhancing antioxidative and anti-inflammatory capabilities. These effects are closely associated with the activation of Nrf2 and TLR4/NF-κB/MAPK signaling pathways.

Soil salinization is a significant global threat to sustainable agricultural development, with soil salinity serving as a crucial indicator for evaluating soil salinization. Remote sensing technology enables large-scale inversion of soil salinity, facilitating the monitoring and assessment of soil salinization levels, thus supporting the prevention and management of soil salinization. This study employs multi-source remote sensing data, selecting 8 radar polarization combinations, 10 spectral indices, and 3 topographic factors to form a feature variable dataset. By applying a normalized weighted variable optimization method, highly important feature variables are identified. AdaBoost, LightGBM, and CatBoost machine learning methods are then used to develop soil salinity inversion models and evaluate their performance. The results indicate the following: (1) There is generally a strong correlation between radar polarization combinations and vegetation indices, and a very high correlation between various vegetation indices and the salinity index S3. (2) The top five feature variables, in order of importance, are Aspect, VH2, Normalized Difference Moisture Index (NDMI), VH, and Vegetation Moisture Index (VMI). (3) The method of normalized weighted importance scoring effectively screens important variables, reducing the number of input feature variables while enhancing the model’s inversion accuracy. (4) Among the three machine learning models, CatBoost performs best overall in soil salt content (SSC) prediction. Combined with the top five feature variables, CatBoost achieves the highest prediction accuracy (R2 = 0.831, RMSE = 2.653, MAE = 1.034) in the prediction phase. This study provides insights for the further development and application of methods for collaborative inversion of soil salinity using multi-source remote sensing data.

Resistant starch (RS) has the advantage of reshaping gut microbiota for human metabolism and health, like glycemic control, weight loss, etc. Among them, RS3 prepared from pure starch is green and safe, but it is hard to achieve structural control. Here, we regulate the crystal structure of starch with different chain-length distributions (CLDs) via extrusion at low/high shearing levels. The change in CLDs in extruded starch was obtained, and their effects on the fine structure (Dm, dBragg, dLorentz, degree of order and double helix, degree of crystal) of RS and its physicochemical properties were investigated by SAXS, FTIR, XRD and 13C NMR analyses. The results showed that the RS content under a 250 r/min extrusion condition was the highest at 61.52%. Furthermore, the crystalline system induced by high amylopectin (amylose ≤ 4.78%) and a small amount of amylose (amylose ≥ 27.97%) was favorable for obtaining a high content of RS3-modified products under the extruding environment. The control of the moderate proportion of the A chains (DP 6–12) in the starch matrix was beneficial to the formation of RS.

ABSTRACT: Background: To investigate the clinical distribution and changing trend of antibiotic resistance profiles of multidrug-resistant organisms (MDROs), a retrospective study was undertaken. Methods: The characteristics of MDROs isolated from 2016 to March 2023 were retrospectively analysed. The detection rate of these MDROs was compared prior to COVID-19 (Period 1, 2016–2019), during the COVID-19 pandemic with restrictions (Period 2, 2020–2022), and after the end of zero-policy (Period 3, Jan-March, 2023). Antibiotic-resistant genes were detected. Results: The overall detection rates of CRPA, CRAB, CREC, CRKP, MRSA, and VREfm were 22.6%, 22.6%, 1.3%, 4.0%, 19.5%, and 3.1%, respectively. The detection rate of CRAB was significantly lower in Period 2 and 3 compared with Period 1 (P < 0.0001). The detection rate of CRPA and VREfm was significantly increased in Period 3 compared with Period 1 and 2 (P < 0.0001). The resistance rate to ticarcillin/clavulanic acid (TIM) and piperacillin/tazobactam (TZP) has gradually increased in CRPA since 2018. NDM and KPC were the most common carbapenemase genes identified in CREC (60.0%) and CRKP isolates (47.8%), respectively. All the 10 VREfm isolates carried the vanA gene. Conclusions: The detection rate of CRAB has decreased since 2018, but a significantly increased prevalence of CRPA and VREfm was seen after the end of zero-policy. An increasing resistance rate to TIM and TZP was seen in CRPA. NDM, KPC, and vanA were the common genes harboured by CREC, CRKP, and VREfm, respectively. Ongoing surveillance after the COVID-19 era is suggested.

Abstract Background COVID‐19 has caused severe morbidity and mortality worldwide. After the end of the dynamic zero‐COVID policy in China in December, 2022, concerns regarding reinfection were raised while little was known due to the lack of surveillance data in this country. Aims This study reviews the probability, risk factors, and severity of severe acute respiratory syndrome coronavirus 2 Omicron variant reinfection, as well as the interval between infections, risk of onward transmission by reinfected cases, and the role of booster vaccination against reinfection. Sources References for this review were identified through searches of PubMed and Web of Science up to September 24, 2023. Results The rate of reinfection ranges from 3.1% to 13.0%. Factors associated with a higher risk of reinfection include being female, having comorbidities, and being unvaccinated. Reinfection with the BA.4 or BA.5 variant occurs approximately 180 days after the initial infection. Reinfections are less clinically severe than primary infections, and there is evidence of lower transmissibility. The debate surrounding the effectiveness and feasibility of booster vaccinations in preventing reinfection continues. Conclusions The reinfection rate during the Omicron epidemic is significantly higher than in previous epidemic periods. However, the symptoms and infectivity of reinfection were weaker than those of the prior infection. Medical staff and individuals at high risk of reinfection should be vigilant. The efficacy of booster vaccinations in reducing reinfection is currently under debate.

Studies on the crosstalk between muscle and adipose tissue can provide beneficial help in elucidating the pathogenesis and treatment of obesity-related diseases [1]. In this data article, we performed RNA sequence analysis of mRNA isolated from epididymal adipose tissue and gastrocnemius muscle tissue in obese rats. Twenty-two samples were selected for gene expression analysis. Raw data from the Illumina Hiseq™ platform sequencer was used for differential gene expression analysis using DESeq and deposited in the GEO public repository under accession number GSE237950. With the economic development and the change of people's lifestyle, obesity has become a major public health problem that endangers global health. Obesity is a metabolic disorder caused by excessive accumulation of white adipose tissue, which can further induce metabolic syndrome such as insulin resistance, type 2 diabetes, and cardiovascular and cerebrovascular diseases. Studies have shown that altitude hypoxic exercise can not only improve muscle buffering capacity and body performance, but also reduce body weight and body fat more significantly. In many countries, it has been used as a treatment program for obesity diseases [2]. Hypoxic exercise can improve lipid metabolism, reduce blood lipid levels, inhibit fatty acid synthesis, and promote fatty acid decomposition and oxidation, which is the mechanism of hypoxic exercise to significantly reduce weight and fat. However, the mechanism of the cross-talk between muscle and fat tissue is not well understood under hypoxia exercise and normoxia exercise conditions. The data contained rat's four different states: normoxia quiet, normoxia exercise, hypoxic quiet, and hypoxic exercise. RNA-seq data will provide insights into the cross-talk between muscle and fat, and the mechanisms of fat metabolism. The data of this study have not been published and are hereby published on this platform to study the cross talk between muscle tissue and adipose tissue in rats under different oxygen content and exercise environment.

BackgroundHER2-positive molecular breast cancer subtypes are characterized by high aggressiveness and malignancy, and their metastasis and mortality rates are among the highest of all types of breast cancer. The use of anti-HER2-targeted agents in neoadjuvant therapy has significantly improved the prognosis of patients with HER2-positive breast cancer. In this study, we investigated the efficacy and safety of a neoadjuvant Chinese THP regimen (docetaxel, trastuzumab biosimilar TQB211 plus the pertuzumab biosimilar TQB2440 or pertuzumab) for ER/PR-negative and HER2-positive breast cancer in China.MethodAll enrolled patients received the THP regimen (T: docetaxel 75 mg/m2 per cycle; H: trastuzumab biosimilar TQB211 8 mg/kg in the first cycle and 6 mg/kg maintenance dose in cycles 2 to 4; P: pertuzumab biosimilar TQB2440 or pertuzumab 840 mg in the first cycle, maintenance dose 420 mg in cycles 2 to 4) every 3 weeks for 4 cycles. The biosimilar TQB2440 pertuzumab and pertuzumab were randomly assigned to patients. Docetaxel, TQB211, and TQB2440 were all developed by Chiatai Tianqing. The primary endpoint was the complete pathological response (pCR) in the breast, and the secondary endpoint was cardiac safety. ResultsOf the 28 eligible patients, 19 (67.9%) achieved tpCR. The tpCR rate was higher than in the NeoSphere trial (pCR63.2%) and the PEONY study (tpCR52.5%). The adverse events that occurred most frequently were leukopenia and neutropenia, with incidence rates of 82.1% and 75.0%, respectively. Of these, grade 3 leukopenia and neutropenia occupied 46.4% and 35.7%. Other grade 3 or higher adverse events were bone marrow suppression (7.1%), lymphopenia (3.6%), and anemia (3.6%). There were no events of heart failure in patients and no patient died during the neoadjuvant phase. ConclusionDomestic dual-target HP has a more satisfactory efficacy and safety in the neoadjuvant phase of treatment.Clinical trial registrationhttps://clinicaltrials.gov/study/NCT05985187, NCT05985187.

Despite mounting evidence for dietary protease benefits, the mechanisms beyond enhanced protein degradation are poorly understood. This study aims to thoroughly investigate the impact of protease addition on the growth performance, intestinal function, and microbial composition of weaned piglets. Ninety 28-day-old weaned pigs were randomly assigned to the following three experimental diets based on their initial body weight for a 28-day experiment: (1) control (CC), a basic diet with composite enzymes without protease; (2) negative control (NC), a diet with no enzymes; and (3) dietary protease (PR), a control diet with protease. The results show that dietary proteases significantly enhanced growth performance and boosted antioxidant capacity, increasing the total antioxidant capacity (T-AOC) levels (p < 0.05) while reducing malonaldehyde levels (p < 0.05). Additionally, protease addition reduced serum levels of inflammatory markers TNF-α, IL-1β, and IL-6 (p < 0.05), suppressed mRNA expression of pro-inflammatory factors in the jejunum (p < 0.01), and inhibited MAPK and NF-κB signaling pathways. Moreover, protease-supplemented diets improved intestinal morphology and barrier integrity, including zonula occludens protein 1(ZO-1), Occludin, and Claudin-1 (p < 0.05). Microbiota compositions were also significantly altered by protease addition with increased abundance of beneficial bacteria (Lachnospiraceae_AC2044_group and Prevotellaceae_UCG-001) (p < 0.05) and reduced harmful Terrisporobacter (p < 0.05). Further correlation analysis revealed a positive link between beneficial bacteria and growth performance and a negative association with inflammatory factors and intestinal permeability. In summary, dietary protease addition enhanced growth performance in weaned piglets, beneficial effects which were associated with improved intestinal barrier integrity, immunological response, and microbiota composition.

Road transport and traffic congestion significantly contribute to dust pollution, which negatively impacts the growth of roadside plants in urban areas. This study aims to quantify the air pollution tolerance index (APTI) and analyze the impacts of dust deposition on different plant species and trees planted along a busy urban roadside in Lahore, Pakistan by considering seasonal variations. The APTI of each species is determined based on inputs of various biochemical parameters (leaf extract pH, ascorbic acid content, relative water content, and total chlorophyll levels), including dust deposition. In this study, laboratory analysis techniques are employed to assess these factors in selected plant species such as Mangifera indica, Saraca asoca, Cassia fistula, and Syzygium cumini. A statistical analysis is conducted to understand the pairwise correlation between various parameters and the APTI at significant and non-significant levels. Additionally, uncertainties in the inputs and APTI are addressed through a probabilistic analysis using the Monte Carlo simulation method. This study unveils seasonal variations in key parameters among selected plant species. Almost all biochemical parameters exhibit higher averages during the rainy season, followed by the summer and winter. Conversely, dust deposition on plants follows an inverse trend, with values ranging from 0.19 to 4.8 g/cm2, peaking during winter, notably in Mangifera indica. APTI values, ranging from 9.39 to 14.75, indicate varying sensitivity levels across species, from sensitive (Syzygium cumini) to intermediate tolerance (Mangifera indica). Interestingly, plants display increased tolerance during regular traffic hours, reflecting a 0.9 to 5% difference between the APTI at peak and regular traffic hours. Moreover, a significant negative correlation (−0.86 at p < 0.05 level) between APTI values and dust deposition suggests a heightened sensitivity to pollutants during the winter. These insights into the relationship between dust pollution and plant susceptibility will help decision makers in the selection of resilient plants for urban areas and improve air quality.

Multispectral thermometry is based on the law of blackbody radiation and is widely used in engineering practice today. Temperature values can be inferred from radiation intensity and multiple sets of wavelengths. Multispectral thermometry eliminates the requirements for single-spectral and spectral similarity, which are associated with two-colour thermometry. In the process of multispectral temperature inversion, the solution of spectral emissivity and multispectral data processing can be seen as the keys to accurate thermometry. At present, spectral emissivity is most commonly estimated using assumption models. When an assumption model closely matches an actual situation, the inversion of the temperature and the accuracy of spectral emissivity are both very high; however, when the two are not closely matched, the inversion result is very different from the actual situation. Assumption models of spectral emissivity exhibit drawbacks when used for thermometry of a complex material, or any material whose properties dynamically change during a combustion process. To address the above problems, in the present study, we developed a multispectral thermometry method based on optimisation ideas. This method involves analysing connections between measured temperatures of each channel in a multispectral temperature inversion process; it also makes use of correlations between multispectral signals at different temperatures. In short, we established a multivariate temperature difference correlation function based on the principles of multispectral radiometric thermometry, using information correlations between data for each channel in a temperature inversion process. We then established a high-precision thermometry model by optimising the correlation function and correcting any measurement errors. This method simplifies the modelling process so that it becomes an optimisation problem of the temperature difference function. This also removes the need to assume the relationships between spectral emissivity and other physical quantities, simplifying the process of multispectral thermometry. Finally, this involves correction of the spectral data so that any impact of measurement error on the thermometry is reduced. In order to verify the feasibility and reliability of the method, a simple eight-channel multispectral thermometry device was used for experimental validation, in which the temperature emitted from a blackbody furnace was identified as the standard value. In addition, spectral data from the 468–603 nm band were calibrated within a temperature range of 1923.15–2273.15 K, resulting in multispectral thermometry based on optimisation principles with an error rate of around 0.3% and a temperature calculation time of less than 3 s. The achieved level of inversion accuracy was better than that obtained using either a secondary measurement method (SMM) or a neural network method, and the calculation speed achieved was considerably faster than that obtained using the SMM method.

Biomedical and food analysis has always been an important topic that closely relates to health [...]

Smooth bromegrass (Bromus inermis) is a perennial, high-quality forage grass. However, its seed yield is influenced by agronomic practices, climatic conditions, and the growing year. The rapid and effective prediction of seed yield can assist growers in making informed production decisions and reducing agricultural risks. Our field trial design followed a completely randomized block design with four blocks and three nitrogen levels (0, 100, and 200 kg·N·ha−1) during 2022 and 2023. Data on the remote vegetation index (RVI), the normalized difference vegetation index (NDVI), the leaf nitrogen content (LNC), and the leaf area index (LAI) were collected at heading, anthesis, and milk stages. Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) regression models were utilized to predict seed yield. In 2022, the results indicated that nitrogen application provided a sufficiently large range of variation of seed yield (ranging from 45.79 to 379.45 kg ha⁻¹). Correlation analysis showed that the indices of the RVI, the NDVI, the LNC, and the LAI in 2022 presented significant positive correlation with seed yield, and the highest correlation coefficient was observed at the heading stage. The data from 2022 were utilized to formulate a predictive model for seed yield. The results suggested that utilizing data from the heading stage produced the best prediction performance. SVM and RF outperformed MLR in prediction, with RF demonstrating the highest performance (R2 = 0.75, RMSE = 51.93 kg ha−1, MAE = 29.43 kg ha−1, and MAPE = 0.17). Notably, the accuracy of predicting seed yield for the year 2023 using this model had decreased. Feature importance analysis of the RF model revealed that LNC was a crucial indicator for predicting smooth bromegrass seed yield. Further studies with an expanded dataset and integration of weather data are needed to improve the accuracy and generalizability of the model and adaptability for the growing year.

The increasing demand for hydroxyapatite (HA) coatings with enhanced functionality have led to novel materials with compositions that are more closely resembling those of natural bone apatites. In the present study, selenium-substituted (Se–HA) powders with various Se quantities were prepared by chemical precipitation. By applying suspension plasma spray (SPS), the surface of Ti substrates was coated with corresponding Se-HA coatings. The microstructural and physicochemical properties of the powders and the coatings were studied. Analysis by x-ray diffraction indicates that SeO _3 ^2− replaced the PO _4 ^3− groups, affecting the crystal structure and crystallinity of HA. As the extent of Se substitution increases, the lattice volume expands and the crystallinity decreases. The synthesized Se-HA powders were confirmed to be calcium-deficient apatite through energy-dispersive x-ray spectroscopy. However, the high temperature of SPS accelerated volatilization of P, increasing the Ca/(P + Se) ratio of the coatings, whereas the chemical valence state of Se remained unchanged. The Se–HA coating exhibited a high bonding strength of >33 MPa, fully meeting the requirement of 15 MPa as coating implant materials. Dissolution behavior tests indicate that the Se–HA coatings had high solubility in comparison to the HA coating. As the substitution degree of Se increased, the dissolution rate also increased, which greatly enhanced the capacity to generate a bone-like apatite layer. In vitro cell experiments indicate that the Se–HA coatings exhibited excellent biocompatibility, and facilitated adhesion and proliferation of osteoblast MC3T3-E1, even in the presence of fluorine. Furthermore, antibacterial properties of Se–HA coatings were also indicated by inhibition of Staphylococcus aureus , which is the main cause of most infections after orthopedic surgeries. Thus, the Se–HA coatings have potential as implant coating materials for orthopedic applications.

Natural speech plays a pivotal role in communication and interactions between human beings. The prosody of natural speech, due to its high ecological validity and sensitivity, has been acoustically analyzed and more recently utilized in machine learning to identify individuals with autism spectrum disorders (ASDs). In this meta-analysis, we evaluated the findings of empirical studies on acoustic analysis and machine learning techniques to provide statistically supporting evidence for adopting natural speech prosody for ASD detection. Using a random-effects model, the results observed moderate-to-large pooled effect sizes for pitch-related parameters in distinguishing individuals with ASD from their typically developing (TD) counterparts. Specifically, the standardized mean difference (SMD) values for pitch mean, pitch range, pitch standard deviation, and pitch variability were 0.3528, 0.6744, 0.5735, and 0.5137, respectively. However, the differences between the two groups in temporal features could be unreliable, as the SMD values for duration and speech rate were only 0.0738 and −0.0547. Moderator analysis indicated task types were unlikely to influence the final results, whereas age groups showed a moderating role in pooling pitch range differences. Furthermore, promising accuracy rates on ASD identification were shown in our analysis of multivariate machine learning studies, indicating averaged sensitivity and specificity of 75.51% and 80.31%, respectively. In conclusion, these findings shed light on the efficacy of natural prosody in identifying ASD and offer insights for future investigations in this line of research.

Purpose: This study builds upon an established effective training method to investigate the advantages of high variability phonetic identification training for enhancing lexical tone perception and production in Mandarin-speaking pediatric cochlear implant (CI) recipients, who typically face ongoing challenges in these areas. Method: Thirty-two Mandarin-speaking children with CIs were quasirandomly assigned into the training group (TG) and the control group (CG). The 16 TG participants received five sessions of high variability phonetic training (HVPT) within a period of 3 weeks. The CG participants did not receive the training. Perception and production of Mandarin tones were administered before (pretest) and immediately after (posttest) the completion of HVPT via lexical tone recognition task and picture naming task. Both groups participated in the identical pretest and posttest with the same time frame between the two test sessions. Results: TG showed significant improvement from pretest to posttest in identifying Mandarin tones for both trained and untrained speech stimuli. Moreover, perceptual learning of HVPT significantly facilitated trainees' production of T1 and T2 as rated by a cohort of 10 Mandarin-speaking adults with normal hearing, which was corroborated by acoustic analyses revealing improved fundamental frequency (F0) median for T1 and T2 production and enlarged F0 movement for T2 production. In contrast, TG children's production of T3 and T4 showed nonsignificant changes across two test sessions. Meanwhile, CG did not exhibit significant changes in either perception or production. Conclusions: The results suggest a limited and inconsistent transfer of perceptual learning to lexical tone production in children with CIs, which challenges the notion of a robust transfer and highlights the complexity of the interaction between perceptual training and production outcomes. Further research on individual differences with a longitudinal design is needed to optimize the training protocol or tailor interventions to better meet the diverse needs of learners.

Passive radiant cooling is a potentially sustainable thermal management strategy amid escalating global climate change. However, petrochemical-derived cooling materials often face efficiency challenges owing to the absorption of sunlight. We present an intrinsic photoluminescent biomass aerogel, which has a visible light reflectance exceeding 100%, that yields a large cooling effect. We discovered that DNA and gelatin aggregation into an ordered layered aerogel achieves a solar-weighted reflectance of 104.0% in visible light regions through fluorescence and phosphorescence. The cooling effect can reduce ambient temperatures by 16.0°C under high solar irradiance. In addition, the aerogel, efficiently produced at scale through water-welding, displays high reparability, recyclability, and biodegradability, completing an environmentally conscious life cycle. This biomass photoluminescence material is another tool for designing next-generation sustainable cooling materials. [ABSTRACT FROM AUTHOR]

Purpose: This study builds upon an established effective training method to investigate the advantages of high variability phonetic identification training for enhancing lexical tone perception and production in Mandarin-speaking pediatric cochlear implant (CI) recipients, who typically face ongoing challenges in these areas. Method: Thirty-two Mandarin-speaking children with CIs were quasirandomly assigned into the training group (TG) and the control group (CG). The 16 TG participants received five sessions of high variability phonetic training (HVPT) within a period of 3 weeks. The CG participants did not receive the training. Perception and production of Mandarin tones were administered before (pretest) and immediately after (posttest) the completion of HVPT via lexical tone recognition task and picture naming task. Both groups participated in the identical pretest and posttest with the same time frame between the two test sessions. Results: TG showed significant improvement from pretest to posttest in identifying Mandarin tones for both trained and untrained speech stimuli. Moreover, perceptual learning of HVPT significantly facilitated trainees’ production of T1 and T2 as rated by a cohort of 10 Mandarin-speaking adults with normal hearing, which was corroborated by acoustic analyses revealing improved fundamental frequency (F0) median for T1 and T2 production and enlarged F0 movement for T2 production. In contrast, TG children’s production of T3 and T4 showed nonsignificant changes across two test sessions. Meanwhile, CG did not exhibit significant changes in either perception or production. Conclusions: The results suggest a limited and inconsistent transfer of perceptual learning to lexical tone production in children with CIs, which challenges the notion of a robust transfer and highlights the complexity of the interaction between perceptual training and production outcomes. Further research on individual differences with a longitudinal design is needed to optimize the training protocol or tailor interventions to better meet the diverse needs of learners. [ABSTRACT FROM AUTHOR]

Leucine-rich repeat protein kinase 2 (LRRK2) is a multi-domain protein encompassing two of biology’s most critical molecular switches, a kinase and a GTPase, and mutations in LRRK2 are key players in the pathogenesis of Parkinson’s disease (PD). The availability of multiple structures (full-length and truncated) has opened doors to explore intradomain cross-talk in LRRK2. A helix extending from the WD40 domain and stably docking onto the kinase domain is common in all available structures. This C-terminal (Ct) helix is a hub of phosphorylation and organelle-localization motifs and thus serves as a multi-functional protein : protein interaction module. To examine its intra-domain interactions, we have recombinantly expressed a stable Ct motif (residues 2480–2527) and used peptide arrays to identify specific binding sites. We have identified a potential interaction site between the Ct helix and a loop in the CORB domain (CORB loop) using a combination of Gaussian accelerated molecular dynamics simulations and peptide arrays. This Ct-Motif contains two auto-phosphorylation sites (T2483 and T2524), and T2524 is a 14-3-3 binding site. The Ct helix, CORB loop, and the CORB-kinase linker together form a part of a dynamic ‘CAP’ that regulates the N-lobe of the kinase domain. We hypothesize that in inactive, full-length LRRK2, the Ct-helix will also mediate interactions with the N-terminal armadillo, ankyrin, and LRR domains (NTDs) and that binding of Rab substrates, PD mutations, or kinase inhibitors will unleash the NTDs. [ABSTRACT FROM AUTHOR]

Due to the unique high-altitude geological conditions of the railway in the cold region, the problem of high ground stress in the construction process is very prominent. In constructing high ground stress tunnels, accurately evaluating the surrounding rock grades is important in rock mass engineering. Based on this, based on a plateau tunnel under construction, this paper selects the classification index of the surrounding rock, which can accurately reflect the geological characteristics of high ground stress tunnel around the geological environment elements of the surrounding rock of high ground stress tunnel. Based on the rapid classification method of surrounding rock of the BP neural network, the classification method of the surrounding rock suitable for high ground stress tunnel is constructed, and the tunnel engineering data is introduced into the BP neural network classification method of surrounding rock for training and testing. It is found that the classification results of surrounding rock obtained by the classification method of surrounding rock of high ground stress tunnel are in good agreement with the actual situation, which provides an important guarantee for the accurate and rapid determination of the surrounding rock grade of high ground stress tunnel and the safe and efficient construction of the tunnel. [ABSTRACT FROM AUTHOR]

T cells play a pivotal role in the immune system by distinguishing between various harmful pathogens and cancerous cells within the human body and initiating an immune response. Within the tumor microenvironment (TME), immune effector T cells encounter both immunosuppressive cells and factors that hinder their functionality. Additionally, they endure robust and persistent antigenic stimulation, often leading to exhaustion and apoptosis. However, the stemness of T cells, characterized by their ability to survive and self-renew over extended periods, represents a primary target in immune checkpoint therapies such as anti-PD-1 therapy. T cell stemness encompasses specific memory T cell subsets and progenitor-exhausted T cells with stem cell-like properties. Therefore, understanding the impact of the TME on T cell stemness, including factors like K+, lactate, and H+, holds significant importance and can facilitate the mitigation of terminal T-cell depletion, the identification of potential resilient biomarkers or therapeutic targets resistant to immune checkpoint therapies, and ultimately lead to sustained anti-tumor effects. Thus, it offers a novel perspective for advancing tumor immunotherapy. [ABSTRACT FROM AUTHOR]

Abstract Background This study aimed to investigate the differences in the microbiota composition of serum exosomes from patients with acute and chronic cholecystitis. Method Exosomes were isolated from the serum of cholecystitis patients through centrifugation and identified and characterized using transmission electron microscopy and nano-flow cytometry. Microbiota analysis was performed using 16S rRNA sequencing. Results Compared to patients with chronic cholecystitis, those with acute cholecystitis exhibited lower richness and diversity. Beta diversity analysis revealed significant differences in the microbiota composition between patients with acute and chronic cholecystitis. The relative abundance of Proteobacteria was significantly higher in exosomes from patients with acute cholecystitis, whereas Actinobacteria, Bacteroidetes, and Firmicutes were significantly more abundant in exosomes from patients with chronic cholecystitis. Furthermore, functional predictions of microbial communities using Tax4Fun analysis revealed significant differences in metabolic pathways such as amino acid metabolism, carbohydrate metabolism, and membrane transport between the two patient groups. Conclusions This study confirmed the differences in the microbiota composition within serum exosomes of patients with acute and chronic cholecystitis. Serum exosomes could serve as diagnostic indicators for distinguishing acute and chronic cholecystitis.

The utilization of antibiotics is prevalent among lactating mothers. Hence, the rapid determination of trace amounts of antibiotics in human milk is crucial for ensuring the healthy development of infants. In this study, we constructed a human milk system containing residual doxycycline (DXC) and/or tetracycline (TC). Machine learning models and clustering algorithms were applied to classify and predict deficient concentrations of single and mixed antibiotics via label-free SERS spectra. The experimental results demonstrate that the CNN model has a recognition accuracy of 98.85% across optimal hyperparameter combinations. Furthermore, we employed Independent Component Analysis (ICA) and the pseudo-Siamese Convolutional Neural Network (pSCNN) to quantify the ratios of individual antibiotics in mixed human milk samples. Integrating the SERS technique with machine learning algorithms shows significant potential for rapid discrimination and precise quantification of single and mixed antibiotics at deficient concentrations in human milk.

Ophiocordyceps sinensis is a genus of ascomycete fungi that has been widely used as a valuable tonic or medicine. However, due to over-exploitation and the destruction of natural ecosystems, the shortage of wild O. sinensis resources has led to an increase in artificially cultivated O. sinensis. To rapidly and accurately identify the molecular differences between cultivated and wild O. sinensis, this study employs surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms to distinguish the two O. sinensis categories. Specifically, we collected SERS spectra for wild and cultivated O. sinensis and validated the metabolic profiles of SERS spectra using Ultra-Performance Liquid Chromatography coupled with Orbitrap High-Resolution Mass Spectrometry (UPLC-Orbitrap-HRMS). Subsequently, we constructed machine learning classifiers to mine potential information from the spectral data, and the spectral feature importance map is determined through an optimized algorithm. The results indicate that the representative characteristic peaks in the SERS spectra are consistent with the metabolites identified through metabolomics analysis, confirming the feasibility of the SERS method. The optimized support vector machine (SVM) model achieved the most accurate and efficient capacity in discriminating between wild and cultivated O. sinensis (accuracy = 98.95%, 5-fold cross-validation = 98.38%, time = 0.89s). The spectral feature importance map revealed subtle compositional differences between wild and cultivated O. sinensis. Taken together, these results are expected to enable the application of SERS in the quality control of O. sinensis raw materials, providing a foundation for the efficient and rapid identification of their quality and origin.

Objectives: Patients with epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma (LUAD) can benefit from individualized targeted therapy. This study aims to develop, compare, analyse prediction models based on dual-energy spectral computed tomography (DESCT) and CT-based radiomic features to non-invasively predict EGFR mutation status in LUAD. Materials and methods: Patients with LUAD (n = 175), including 111 patients with and 64 patients without EGFR mutations, were enrolled in the current study. All patients were randomly divided into a training dataset (122 cases) and validation dataset (53 cases) at a ratio of 7:3. After extracting CT-based radiomic, DESCT and clinical features, we built seven prediction models and a nomogram of the best prediction. Receiver operating characteristic (ROC) curves and the mean area under the curve (AUC) values were used for comparisons among the models to obtain the best prediction model for predicting EGFR mutations. Results: The best distinguishing ability is the combined model incorporating radiomic, DESCT and clinical features for predicting the EGFR mutation status with an AUC of 0.86 (95 % CI: 0.79–0.92) in the training group and an AUC value of 0.83 (95 % CI: 0.73, 0.96) in the validation group. Conclusions: Our study provides a predictive nomogram non-invasively with a combination of CT-based radiomic, DESCT and clinical features, which can provide image-based biological information for targeted therapy of LUAD with EGFR mutations.