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Abstract Ground settlement prediction for highway subgrades is crucial in related engineering projects. When predicting the ground settlement, sparse sample data are often encountered in practice, which greatly affects the prediction accuracy. However, this has been seldom explored in previous studies. To resolve it, this paper proposes a regression Kriging (RK)—based method for ground settlement prediction with sparse data. Under the framework of RK, the stationarity of sample residual and trend structure are key factors for prediction accuracy. It is found that the use of Box–Cox transformation, which can help to achieve stationarity of sample residual, leads to significant increase of the prediction accuracy with sparse data. Specifically, the various evaluation metrics (i.e., root mean square error (RMSE), mean absolute error (MAE), mean arctangent absolute percent error (MAAPE) and scatter index (SCI)) are significantly decreased when the Box–Cox transformation is incorporated. In addition, the first-order polynomial trend structure is found to be more appropriate than those with higher orders for predicting settlements resulting from primary consolidation. Moreover, comparative study is conducted among the proposed RK method, classical prediction methods and back propagation neural network (BPNN). It is found that the evaluation metrics obtained by the RK method are significantly smaller than those obtained by the other methods, indicating its highest accuracy. By contrast, BPNN has the worst performance among the various methods, because the sparse data are inadequate to establish a satisfactory BPNN model.

Abstract Solid fuel combustion exposure is a leading global health risk factor, yet evidence on its effects, especially on vulnerable children, is sparse. This large-scale, multi-center prospective study aimed to address this gap by involving 9997 schoolchildren across China between 2013 and 2015. Here we show that lung function levels exhibited a marginally significant decline among children exposed to solid fuel usage. Specifically, FVC and FEV1 decreased by 21.2 mL (95% CI: −15.7, 58.1) and 24.1 mL (−8.4, 56.6), respectively. Additionally, PEF, FEF25 and FEF75 decreased by 25.7 mL/s (−46.5, 98.0), 32.7 mL/s (−42.7, 108.2), and 35.4 mL/s (−5.9, 76.7), respectively. Persistent exposure to solid fuel usage in children led to greater lung damage. Children with allergy history were more susceptible to solid fuel exposure. Our study highlights the adverse impact of solid fuel usage on children and the need to promote clean fuel usage for this vulnerable population.

Abstract Background Previous studies have established a positive link between nurse managers’ caring leadership and nurses’ work engagement, but the processes and conditions through which this leadership style influences positive work behaviors remain largely unexplored. To address this gap and contribute to the existing body of knowledge, we developed a chain-mediated effects model to elucidate the impact of caring leadership on nurses’ work engagement and the underlying mechanisms. In this model, we identified professional mission and affective organizational commitment as the mediating variables, offering a novel perspective on the relationship between caring leadership and work engagement. Methods A robust multi-center and large-sample cross-sectional survey was conducted, involving 2502 first-line nurses from six general tertiary hospitals across the eastern, central, and western regions of China. The data collection instruments included a comprehensive questionnaire covering demographic information, the caring leadership scale, the Chinese calling scale, the affective organizational commitment scale, and the Utrecht work engagement scale. Data were meticulously screened and analyzed, employing descriptive analysis to summarize the demographic information, correlation analysis to test the relationship among the variables, stepwise regression analysis to explore the mediating role of calling and affective organization commitment, and the bootstrap method to test the chain mediating effect. This rigorous methodology not only ensures the reliability and validity of research findings but also instills confidence in the robustness of this research. Results The results indicated a positive relationship among caring leadership, calling, affective organizational commitment, and nurses’ work engagement (p

Abstract The impact of the ketogenic diet (KD) on overall mortality and cardiovascular disease (CVD) mortality remains inconclusive.This study enrolled a total of 43,776 adults from the National Health and Nutrition Examination Survey (NHANES) conducted between 2001 and 2018 to investigate the potential association between dietary ketogenic ratio (DKR) and both all-cause mortality as well as cardiovascular disease(CVD) mortality.Three models were established, and Cox proportional hazards regression analysis was employed to examine the correlation. Furthermore, a restricted cubic spline function was utilized to assess the non-linear relationship. In addition, subgroup analysis and sensitivity analysis were performed.In the adjusted Cox proportional hazards regression model, a significant inverse association was observed between DKR and all-cause mortality (HR = 0.76, 95% CI = 0.63–0.9, P = 0.003). However, no significant association with cardiovascular mortality was found (HR = 1.13; CI = 0.79–1.6; P = 0.504). Additionally, a restricted cubic spline(RCS) analysis demonstrated a linear relationship between DKR and all-cause mortality risk. In the adult population of the United States, adherence to a KD exhibits potential in reducing all-cause mortality risk while not posing an increased threat of CVD-related fatalities.

Abstract Background The recent Maternal Immune Activation (MIA) theory suggests maternal systemic inflammation may serve as a mediator in associations between prenatal maternal adversities and neurodevelopmental diseases in offspring. Given the co-exposure to multiple adversities may be experienced by pregnant person, it is unclear whether a quantitative index can be developed to characterize the inflammation related exposure level, and whether this index is associated with neurodevelopmental delays in offspring. Methods Based on Jiangsu Birth Cohort (JBC), a total of 3051 infants were included in the analysis. Inflammation related Prenatal Adversity Index (IPAI) was constructed using maternal data. Neurodevelopmental outcomes were assessed using the Bayley Scales of Infant and Toddler Development, third edition, screening test in one year. Multivariate linear regression and Poisson regression model were performed to analyze the associations between IPAI and neurodevelopment in offspring. Results Compared with “low IPAI” group, offspring with “high IPAI” have lower scores of cognition, receptive communication, expressive communication, and fine motor. The adjusted β were − 0.23 (95%CI: -0.42, -0.04), -0.47 (95%CI: -0.66, -0.28), -0.30 (95%CI: -0.49, -0.11), and − 0.20 (95%CI: -0.33, -0.06). Additionally, the elevated risk for noncompetent development of cognition and receptive communication among “high IPAI” group was observed. The relative risk [RR] and 95% confidence interval [CI] were 1.35 (1.01, 1.69) and 1.37 (1.09, 1.72). Conclusions Our results revealed a significant association between higher IPAI and lower scores across cognition, receptive communication, expressive communication, and fine motor domains, and an increased risk of noncompetent development in the cognition and receptive communication domains.

Abstract Background The intricate interplay between genetics and immunology often dictates the host’s susceptibility to various diseases. This study explored the genetic causal relationship between natural killer (NK) cell-related traits and the risk of infection. Methods Single-nucleotide polymorphisms (SNPs) significantly associated with NK cell-related traits were selected as instrumental variables to estimate their genetic causal effects on infection. SNPs from a genome-wide association study (GWAS) on NK cell-related traits, including absolute cell counts, median fluorescence intensities reflecting surface antigen levels, and relative cell counts, were used as exposure instruments. Summary-level GWAS statistics of four phenotypes of infection were used as the outcome data. The exposure and outcome data were analyzed via the two-sample Mendelian randomization method. Results Each one standard deviation increase in the expression level of human leukocyte antigen (HLA)-DR on HLA-DR+ NK cells was associated with a lower risk of pneumonia (P

Abstract Objective This study aimed to develop and validate a nomogram for assessing the risk of nosocomial infections among obstetric inpatients, providing a valuable reference for predicting and mitigating the risk of postpartum infections. Methods A retrospective observational study was performed on a cohort of 28,608 obstetric patients admitted for childbirth between 2017 and 2022. Data from the year 2022, comprising 4,153 inpatients, were utilized for model validation. Univariable and multivariable stepwise logistic regression analyses were employed to identify the factors influencing nosocomial infections among obstetric inpatients. A nomogram was subsequently developed based on the final predictive model. The receiver operating characteristic (ROC) curve was utilized to calculate the area under the curve (AUC) to evaluate the predictive accuracy of the nomogram in both the training and validation datasets. Results The gestational weeks > = 37, prenatal anemia, prenatal hypoproteinemia, premature rupture of membranes (PROM), cesarean sction, operative delivery, adverse birth outcomes, length of hospitalization (days) > 5, CVC use and catheterization of ureter were included in the ultimate prediction model. The AUC of the nomogram was 0.828 (0.823, 0.833) in the training dataset and 0.855 (0.844, 0.865) in the validation dataset. Conclusion Through a large-scale retrospective study conducted in China, we developed and independently validated a nomogram to enable personalized postpartum infections risk estimates for obstetric inpatients. Its clinical application can facilitate early identification of high-risk groups, enabling timely infection prevention and control measures.

Abstract Background Thinning practices are useful measures in forest management and play an essential role in maintaining ecological stability. However, the effects of thinning on the soil properties and microbial community in large Chinese fir timber plantations remain unknown. The purpose of this study was to investigate the changes in soil physicochemical properties and microbial community composition in topsoil (0–20 cm) under six different intensities (i.e., 300 (R300), 450 (R450), 600 (R600), 750 (R750) and 900 (R900) trees per hectare and 1650 (R1650) as a control) in a large Chinese fir timber plantation. Results Compared with the CK treatment, thinning significantly altered the contents of soil organic carbon (SOC) and its fractions but not in a linear fashion; these indicators were highest in R900. In addition, thinning did not significantly affect the soil microbial community diversity indices but significantly affected the relative abundance of the core microbial community. Proteobacteria, Acidobacteria, and Actinobacteria were the dominant bacterial phyla; the relative abundances of Proteobacteria and Acidobacteria were highest in R900, and that of Actinobacteria was lowest in R900. The dominant fungal phyla were Ascomycota, Basidiomycota and Mucoromycota; the relative abundance of Ascomycota was lowest in R900, and that of Mucoromycota was highest in R900. The fungal microbial community composition was more sensitive than the bacterial community composition. The activity of the carbon-cycling genes was not linearly correlated with thinning, and the abundance of C-cycle genes was highest in R900. Conclusions These findings are important because they show that SOC and its fractions and the abundance of the soil microorganism community in large Chinese fir timber plantations can be significantly altered by thinning, thus affecting the capacity for carbon storage. These results may advance our understanding of how the density of large timber plantations could be modified to promote soil carbon storage.

Abstract Although there is an association between income status and concentration of perfluoroalkyl and polyfluoroalkyl substance (PFAS), the association remains uncertain in patients with hypertension, hyperlipidemia, and comorbidities. Data from the 2013–2016 National Health and Nutrition Examination Survey were analyzed. A total of 2665 adults were included, and the data included participants' serum PFAS (perfluorooctanoic acid [PFOA], perfluorononaic acid, perfluorodecanoic acid, perfluoroundecanoic acid, perfluorohexane sulfonic acid, and perfluorooctane sulfonic acid) levels and selected covariates. Multivariate linear regression models were used to examine the association between the ratio of family income to poverty (PIR) and individual serum PFAS concentrations in the hypertensive and/or hyperlipidemia groups after adjusting for covariates. The potential effects of sex and age on the results were explored using stratified analysis. A mediating effect model was used to explore the mediating effects of body mass index (BMI) and waist circumference on the association results. After adjusting for potential confounders, for hyperlipidemia and comorbidities (hypertension and hyperlipidemia), serum levels of multiple common PFAS increased by 0.09% (95%Confidence interval [CI] 0.02–0.15%) to 0.13% (95%CI 0.08–0.19%) and 0.10% (95%CI 0.02–0.17%) to 0.12% (95%CI 0.06–0.18%), respectively, with each 1% increase in PIR. The covariate model and stratified analyses results suggested the potential effects of different covariates such as age and sex, leading to changes in the statistical significance of the association results. BMI significantly mediated the effect of PIR on PFOA in hyperlipidemia (13%, P

Plant natural products (PNPs) exhibit a wide range of biological activities and have essential applications in various fields such as medicine, agriculture, and flavors. Given their natural limitations, the production of high-value PNPs using microbial cell factories has become an effective alternative in recent years. However, host metabolic burden caused by its massive accumulation has become one of the main challenges for efficient PNP production. Therefore, it is necessary to strengthen the transmembrane transport process of PNPs. This review introduces the discovery and mining of PNP transporters to directly mediate PNP transmembrane transportation both intracellularly and extracellularly. In addition to transporter engineering, this review also summarizes several auxiliary strategies (such as small molecules, environmental changes, and vesicles assisted transport) for strengthening PNP transportation. Finally, this review is concluded with the applications and future perspectives of transportation engineering in the construction and optimization of PNP microbial cell factories.

As climate change worsens, it is becoming increasingly important to effectively harness and store energy from non-fossil fuels to reduce greenhouse gas emissions. The increasing adoption of various rechargeable batteries is being driven by advancements in non-fossil energy, the development of energy storage technology, and electrification in technological fields such as electric vehicles, photovoltaic thermal energy storage, and aerospace technology. However, the compact size of batteries leads to a significant increase in their internal energy density and temperature. Elevated heat flux and temperature not only limit battery performance but also reduce lifespan and pose safety risks to battery systems. The occurrence of thermal runaway under extreme conditions is a concern as it can result in battery combustion or even explosion. Thus, the implementation of efficient battery thermal management systems is essential. Besides traditional methods such as air cooling, phase change materials, and liquid cooling, in recent years, various novel thermal management technologies, including heat pipes, nanoparticle-enhanced phase change materials, and nanofluids, have been extensively explored. Among these emerging technologies, nanofluid battery thermal management systems have garnered attention due to their significant impact, minimal additional energy consumption, and cost-effectiveness compared with alternative approaches. These systems utilize nanofluids, which are liquids with uniformly dispersed nanoparticles. Nanoparticles can contain oxides such as alumina and titanium dioxide, metals such as silver and copper, and carbon-based materials such as carbon nanotube and nanodiamond. The application of nanoparticles results in an improvement in the thermal conductivity of fluids. Moreover, nanoparticles can also supply extra nuclei, improve surface wettability, and delay bubble coalescence while boiling. Thus, nanofluids can enhance heat transfer in both single-phase and two-phase flows. Given the enhancement of heat transfer, nanofluids exhibit significant potential in battery thermal management. Herein, a systematic review of the research progress on nanofluid battery thermal management systems is presented. First, the structure, working principle, and demands of battery thermal management are given. Afterward, the concept and classification of nanofluids are introduced. Subsequently, the fundamental properties of nanofluids, such as stability, viscosity, thermal conductivity, and electrical conductivity, are elucidated. The article further discusses the current research progress on nanofluid-based battery thermal management systems applied in lithium batteries, fuel cells, and photovoltaic batteries. Finally, some notable challenges associated with nanofluid battery thermal management systems and some future development directions on numerical and experimental research and industrial applications are outlined.

Abstract Alzheimer's disease is the predominant form of dementia, and disulfidptosis is the latest reported mode of cell death that impacts various disease processes. This study used bioinformatics to analyze genes associated with disulfidptosis in Alzheimer's disease comprehensively. Based on the public datasets, the differentially expressed genes associated with disulfidptosis were identified, and immune cell infiltration was investigated through correlation analysis. Subsequently, hub genes were determined by a randomforest model. A prediction model was constructed using logistic regression. In addition, the drug-target affinity was predicted by a graph neural network model, and the results were validated by molecular docking. Five hub genes (PPEF1, NEUROD6, VIP, NUPR1, and GEM) were identified. The gene set showed significant enrichment for AD-related pathways. The logistic regression model demonstrated an AUC of 0.952, with AUC values of 0.916 and 0.864 in validated datasets. The immune infiltration analysis revealed significant heterogeneity between the Alzheimer's disease and control groups. High-affinity drugs for hub genes were identified. Through our study, a disease prediction model was constructed using potential biomarkers, and drugs targeting the genes were predicted. These results contribute to further understanding of the molecular mechanisms underlying Alzheimer's disease.

Abstract Background Reactive astrocytes participate in various pathophysiology after subarachnoid hemorrhage (SAH), including neuroinflammation, glymphatic–lymphatic system dysfunction, brain edema, BBB disruption, and cell death. Astrocytes transform into two new reactive phenotypes with changed morphology, altered gene expression, and secretion profiles, termed detrimental A1 and beneficial A2. This study investigates the effect of 67LR activation by PEDF-34, a PEDF peptide, on neuroinflammation and astrocyte polarization after the experimental SAH. Methods A total of 318 male adult Sprague-Dawley rats were used in experiments in vivo, of which 272 rats were subjected to the endovascular perforation model of SAH and 46 rats underwent sham surgery. 67LR agonist (PEDF-34) was administrated intranasally 1 h after SAH. 67LR-specific inhibitor (NSC-47924) and STAT1 transcriptional activator (2-NP) were injected intracerebroventricularly 48 h before SAH. Short- and long-term neurological tests, brain water content, immunostaining, Nissl staining, western blot, and ELISA assay were performed. In experiments in vitro, primary astrocyte culture with hemoglobin (Hb) stimulation was used to mimic SAH. The expression of the PEDF-34/67LR signaling pathway and neuro-inflammatory cytokines were assessed using Western blot, ELISA, and immunohistochemistry assays both in vivo and in vitro. Results Endogenous PEDF and 67LR expressions were significantly reduced at 6 h after SAH. 67LR was expressed in astrocytes and neurons. Intranasal administration of PEDF-34 significantly reduced brain water content, pro-inflammatory cytokines, and short-term and long-term neurological deficits after SAH. The ratio of p-JNK/JNK and p-STAT1/STAT1 and the expression of CFB and C3 (A1 astrocytes marker), significantly decreased after PEDF-34 treatment, along with fewer expression of TNF-α and IL-1β at 24 h after SAH. However, 2-NP (STAT1 transcriptional activator) and NSC-47924 (67LR inhibitor) reversed the protective effects of PEDF-34 in vivo and in vitro by promoting A1 astrocyte polarization with increased inflammatory cytokines. Conclusion PEDF-34 activated 67LR, attenuating neuroinflammation and inhibiting astrocyte A1 polarization partly via the JNK/STAT1 pathway, suggesting that PEDF-34 might be a potential treatment for SAH patients. Graphical Abstract

Abstract Background Systemic inflammation markers have recently been identified as being associated with cardiac disorders. However, limited research has been conducted to estimate the pre-diagnostic associations between these markers and paroxysmal atrial fibrillation (PAF). Our aim is to identify potential biomarkers for early detection of PAF. Methods 91 participants in the PAF group and 97 participants in the non-PAF group were included in this study. We investigated the correlations between three systemic inflammation markers, namely the systemic immune inflammation index (SII), system inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI), and PAF. Results The proportion of patients with PAF gradually increased with increasing logSII, logSIRI, and logAISI tertiles. Compared to those in the lowest tertiles, the PAF risks in the highest logSII and logSIRI tertiles were 3.2-fold and 2.9-fold, respectively. Conversely, there was no significant correlation observed between logAISI and PAF risk within the highest tertile of logAISI. The restricted cubic splines (RCS) analysis revealed a non-linear relationship between the elevation of systemic inflammation markers and PAF risk. Specifically, the incidence of PAF is respectively increased by 56%, 95%, and 150% for each standard deviation increase in these variables. The ROC curve analysis of logSII, logSIRI and logAISI showed that they had AUC of 0.6, 0.7 and 0.6, respectively. It also demonstrated favorable sensitivity and specificity of these systemic inflammation markers in detecting the presence of PAF. Conclusions In conclusion, our study reveals significant positive correlations between SII, SIRI, and AISI with the incidence of PAF.

Abstract Excessive phosphorus (P) levels can disrupt nutrient balance in plants, adversely affecting growth. The molecular responses of Pennisetum species to high phosphorus stress remain poorly understood. This study examined two Pennisetum species, Pennisetum americanum × Pennisetum purpureum and Pennisetum americanum, under varying P concentrations (200, 600 and 1000 µmol·L− 1 KH2PO4) to elucidate transcriptomic alterations under high-P conditions. Our findings revealed that P. americanum exhibited stronger adaption to high-P stress compared to P. americanum× P. purpureum. Both species showed an increase in plant height and leaf P content under elevated P levels, with P. americanum demonstrating greater height and higher P content than P. americanum× P. purpureum. Transcriptomic analysis identified significant up- and down-regulation of key genes (e.g. SAUR, GH3, AHP, PIF4, PYL, GST, GPX, GSR, CAT, SOD1, CHS, ANR, P5CS and PsbO) involved in plant hormone signal transduction, glutathione metabolism, peroxisomes, flavonoid biosynthesis, amino acid biosynthesis and photosynthesis pathways. Compared with P. americanum× P. purpureum, P. americanum has more key genes in the KEGG pathway, and some genes have higher expression levels. These results contribute valuable insights into the molecular mechanisms governing high-P stress in Pennisetum species and offer implications for broader plant stress research.

Abstract While sanguinarine has gained recognition for antimicrobial and antineoplastic activities, its complex conjugated structure and low abundance in plants impede broad applications. Here, we demonstrate the complete biosynthesis of sanguinarine and halogenated derivatives using highly engineered yeast strains. To overcome sanguinarine cytotoxicity, we establish a splicing intein-mediated temperature-responsive gene expression system (SIMTeGES), a simple strategy that decouples cell growth from product synthesis without sacrificing protein activity. To debottleneck sanguinarine biosynthesis, we identify two reticuline oxidases and facilitated functional expression of flavoproteins and cytochrome P450 enzymes via protein molecular engineering. After comprehensive metabolic engineering, we report the production of sanguinarine at a titer of 448.64 mg L−1. Additionally, our engineered strain enables the biosynthesis of fluorinated sanguinarine, showcasing the biotransformation of halogenated derivatives through more than 15 biocatalytic steps. This work serves as a blueprint for utilizing yeast as a scalable platform for biomanufacturing diverse benzylisoquinoline alkaloids and derivatives.

The assessment of ecological functions, such as those of forest structure zoning and carbon sinks, heavily relies on forest age classification. Therefore, monitoring forest age is a crucial element of forest resource surveys. With the increased availability of high-quality open-access satellite data and advancements in Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) technology, remote sensing has emerged as an essential method for acquiring accurate forest age information. In this study, Sentinel-2 remote sensing data, UAV-LiDAR data, and combined Sentinel-2 and LiDAR data are used as data sources. Three machine learning algorithms, Adaptive Boosting (AdaBoost), Random Forest (RF), and Extreme Random Tree (ERT), are used to predict forest age in a Masson pine (Pinus massoniana Lamb.) forest. The optimal model is used to predict the forest age and simulate the spatial age distribution. The machine learning models based on separate Sentinel-2 and LiDAR data accurately predict the age of the Masson pine forest. Nevertheless, the accuracy of the RF model with combined data was higher than that in other cases, with an accuracy R value of 0.81. The model displayed good stability, and the spatial uncertainty of age estimation was low. Compared with the RF model using only Sentinel-2 data (R = 0.43), the RF model with combined LiDAR and Sentinel-2 data achieved the highest accuracy, with R values 88.37% higher. In addition, the forest canopy structure parameters, such as the average height of the forest stand extracted from UAV-LiDAR data, had a significant impact on the estimation of forest age. Thus, when the combined Sentinel-2 and LiDAR data were used to establish these parameters, the highest accuracy in the estimation of Masson pine was obtained. The findings of this study provide new insights for forest age estimation based on multi-source remote sensing data.

Despite the existence of many interventions to mitigate or adapt to the health effects of climate change, their effectiveness remains unclear. Here, we introduce the Comprehensive Evaluation Framework for Intervention on Health Effects of Ambient Temperature to evaluate study designs and effects of intervention studies. The framework comprises three types of interventions: proactive, indirect, and direct, and four categories of indicators: classification, methods, scope, and effects. We trialed the framework by an evaluation of existing intervention studies. The evaluation revealed that each intervention has its own applicable characteristics in terms of effectiveness, feasibility, and generalizability scores. We expanded the framework's potential by offering a list of intervention recommendations in different scenarios. Future applications are then explored to establish models of the relationship between study designs and intervention effects, facilitating effective interventions to address the health effects of ambient temperature under climate change.

Abstract The Eurasian Bronze Age (BA) has been described as a period of substantial human migrations, the emergence of pastoralism, horse domestication, and development of metallurgy. This study focuses on two north Eurasian sites sharing Siberian genetic ancestry. One of the sites, Rostovka, is associated with the Seima-Turbino (ST) phenomenon (~2200-1900 BCE) that is characterized by elaborate metallurgical objects found throughout Northern Eurasia. The genetic profiles of Rostovka individuals vary widely along the forest-tundra Siberian genetic cline represented by many modern Uralic-speaking populations, and the genetic heterogeneity observed is consistent with the current understanding of the ST being a transcultural phenomenon. Individuals from the second site, Bolshoy Oleni Ostrov in Kola, in comparison form a tighter cluster on the Siberian ancestry cline. We further explore this Siberian ancestry profile and assess the role of the ST phenomenon and other contemporaneous BA cultures in the spread of Uralic languages and Siberian ancestry.

This study aimed to develop a medium-low glycemic index (GI) cereal meal replacement powder suitable for diabetic patients using selenium-enriched germinated brown rice, red adzuki beans, barley, tartary buckwheat, bean dregs, and mulberry leaf extracts as raw materials. The baking processing technology was optimized through response surface experiments using sensory scores and GI value-weighted scores as indicators. At the same time, the effects of the two processing methods of baking and extrusion on the microstructure, crystal properties, gelatinization properties, gelatinization degree, brewability, caking rate and other physicochemical properties of selenium-enriched germinated brown rice meal replacement powder were discussed. The results showed that the GI value was 62.75, the sensory score was 91.17, and the highest weighted comprehensive score was 60.69 under the optimal conditions of the baking time of 35 min, the baking temperature of 140 ℃ and the liquid-to-material ratio of 1:1 (L/kg). Compared with unprocessed meal replacement powder, the relative crystallinity of baking optimal meal replacement powder and extruded meal replacement powder decreased to 15.15 and 4.28 respectively, and the B-type and V-type crystals increased. The round starch granules in the microstructure of the other two meal replacement powders were reduced and irregular granules were increased. In the electronic nose comprehensive evaluation equation, the comprehensive odor score from high to low was baked optimal meal replacement powder>extruded meal replacement powder>unprocessed meal replacement powder. The experimental conclusion showed that the optimal GI value of baking meal replacement powder was between 55 and 70, which belonged to the medium-low GI range and was suitable for people with diabetes. It can be recommended as the preferred processing parameter for the development of meal replacement powder with a medium and low glycemic index.

Articular cartilage has limited self-healing ability, resulting in injuries often evolving into osteoarthritis (OA), which poses a significant challenge in the medical field. Although some treatments exist to reduce pain and damage, there is a lack of effective means to promote cartilage regeneration. Reactive Oxygen Species (ROS) have been found to increase significantly in the OA micro-environment. They play a key role in biological systems by participating in cell signaling and maintaining cellular homeostasis. Abnormal ROS expression, caused by internal and external stimuli and tissue damage, leads to elevated levels of oxidative stress, inflammatory responses, cell damage, and impaired tissue repair. To prevent excessive ROS accumulation at injury sites, biological materials can be engineered to respond to the damaged microenvironment, release active components in an orderly manner, regulate ROS levels, reduce oxidative stress, and promote tissue regeneration. Hydrogels have garnered significant attention due to their excellent biocompatibility, tunable physicochemical properties, and drug delivery capabilities. Numerous antioxidant hydrogels have been developed and proven effective in alleviating oxidative stress. This paper discusses a comprehensive treatment strategy that combines antioxidant hydrogels with existing treatments for OA and explores the potential applications of antioxidant hydrogels in cartilage tissue engineering.

Chimeric antigen receptor T (CAR-T) cell therapy is a new treatment for cancers, but reports on curing immune-related skin diseases are limited. We report a case of successful CAR-T-cell therapy in a patient with refractory/relapsed diffuse large B-cell lymphoma (R/R DLBCL) who was incidentally cured of chronic generalized plaque psoriasis. The patient, a 65-year-old male who had a known history of psoriasis for 45 years, did not receive immunotherapy for psoriasis during this period. Imaging, molecular biology and immunology diagnostics confirmed DLBCL. After several weeks of standard-dose R-CHOP chemotherapy, the patient achieved partial remission, but according to CT, the patient relapsed, and there was no significant improvement in her psoriasis symptoms. Subsequently, the patient was enrolled in the CD19 CAR-T-cell therapy group. Four weeks after CAR-T-cell infusion, the patient’s abdominal pain disappeared, and there was a significant improvement in overall skin lesions. One year later, follow-up results indicated complete remission of R/R DLBCL (confirmed by PET-CT), with only minimal residual psoriatic skin lesions limited to the patient’s neck. The results of using CAR-T-cell therapy to achieve an incidental cure for psoriasis highlight the potential for exploring cell-based therapies for complex autoinflammatory skin diseases.

Gibberellin 20-oxidases (GA20oxs) are multifunctional enzymes involved in regulating gibberellin (GA) biosynthesis and controlling plant growth. We identified and characterized the GA20ox1 gene in a plant height mutant of Sophora davidii, referred to as SdGA20ox1. This gene was expressed in root, stem, and leaf tissues of the adult S. davidii plant height mutant, with the highest expression observed in the stem. The expression of SdGA20ox1 was regulated by various exogenous hormones. Overexpression of SdGA20ox1 in Arabidopsis resulted in significant elongation of hypocotyl and root length in seedlings, earlier flowering, smaller leaves, reduced leaf chlorophyll content, lighter leaf color, a significant increase in adult plant height, and other phenotypes. Additionally, transgenic plants exhibited a substantial increase in biologically active endogenous GAs (GA1, GA3, and GA4) content, indicating that overexpression of SdGA20ox1 accelerates plant growth and development. Using a yeast two-hybrid (Y2H) screen, we identified two SdGA20ox1-interacting proteins: the ethylene receptor EIN4 (11430582) and the rbcS (11416005) protein. These interactions suggest a potential regulatory mechanism for S. davidii growth. Our findings provide new insights into the role of SdGA20ox1 and its interacting proteins in regulating the growth and development of S. davidii.

Soil nematodes are the most abundant soil metazoans, occupying multiple trophic levels in the soil food web and playing an important role in soil function. Research on the biogeographic distribution patterns of soil nematode communities and their drivers has received greater attention. However, the distribution characteristics of soil nematode communities along the elevational gradient in the arid and semi-arid regions of Northwest China remain unclear. In this study, four elevational gradients (1750–1900, 1900–2100, 2100–2350 and 2350–2560 m) were established on Luoshan Mountain, Ningxia, an arid and semi-arid region in Northwest China, and soil nematodes in the soil layers of 0–10, 10–20 and 20–40 cm were investigated using the improved Baermann funnel method. The results revealed a monotonically decreasing trend in the total number of soil nematodes along the elevational gradient and soil layer depth, decreasing by 63.32% to 79.94% and 73.59% to 86.90%, respectively, while the interactions were not obvious. A total of 1487 soil nematodes belonging to 27 families and 32 genera were identified across the elevational gradient, with Helicotylenchus as the dominant genus, accounting for 10.43% of the total number of nematodes, and bacterivore nematodes as the main trophic groups, accounting for 32.39% to 52.55% of the relative abundance at each elevation, which increased with increasing elevation. Soil nematode community diversity, richness and maturity indices were relatively low at high elevation and decreased by 44.62%, 48% and 54.74%, respectively, with increasing soil layer depth at high elevations. Compared to low elevations, high-elevation soils experienced greater disturbance, reduced structural complexity and nutrient enrichment of the soil food web, and a shift in soil organic matter decomposition from bacterial to fungal pathways as elevation increased. Finally, redundancy analysis showed that soil pH, bulk density, soil moisture, soil organic carbon, available nitrogen, available phosphorus and available potassium were the main soil factors affecting the composition of soil nematode communities, which well explained the differences in nematode communities at different elevations and soil depths. This study can be used as basic information for further research on soil biota in this mountainous region, expanding our further understanding of the spatial ecology of soil nematodes in the arid and semi-arid mountain ecosystems.

The wound healing process involves communication among growth factors, cytokines, signaling pathways, and cells in the extracellular matrix, with growth factors acting as key regulators. Although stem cells can promote wound healing by secreting diverse growth factors, their therapeutic potential is hindered by poor survival and engraftment. Mimicking the stem cell-matrix interactions can improve stem cell survival, regulate their fate, and even enhance their paracrine effects. This study investigated the use of composite RGDmix hydrogel, which can support the survival and proliferation of human amniotic mesenchymal stem cells (hAMSCs), and effectively increase the expression of various growth factors, thereby promoting wound re-epithelialization, angiogenesis, and epidermal maturation. At last, the specific role of integrin αv and PI3K/AKT signaling pathways in the secretion of growth factors were examined by silencing them in vitro and in vivo. Results suggested that the RGDmix hydrogel improved the secretion of growth factors by hAMSCs through the RGDSP/integrin αv/PI3K/AKT axis, thereby enhancing the therapeutic effect in wound healing.

BackgroundAging, a significant public health issue, is associated with multiple concurrent chronic diseases and aging-related conditions (geriatric syndromes). ObjectiveThis study aims to investigate the impact of age and chronic conditions on geriatric syndromes and the intercorrelations between multiple geriatric syndromes and quality of life (QoL) in older adults (aged ≥65 years) at the population level. MethodsA large representative sample was randomly selected from a county in China, Feidong, with 17 towns and 811,867 residents. Multiple chronic conditions, geriatric syndromes (frailty, polypharmacy, and malnutrition), and QoL were assessed and compared. Associations of demographic information and chronic conditions with geriatric conditions and QoL in older adults were assessed using multivariable-adjusted logistic regression. Intercorrelations between age, multiple geriatric syndromes, and QoL were investigated using both correlation analysis and restricted cubic splines–based multivariable-adjusted dose-response analysis. ResultsOlder adults comprised 43.42% (3668/8447) of the entire study population. The prevalence of frailty, premalnutrition or malnutrition, polypharmacy, and impaired QoL (median age 73, IQR 69-78 years; 1871/3668, 51% men) was 8.26% (303/3668), 15.59% (572/3668), 3.22% (118/3668), and 10.8% (396/3668), respectively. Different age and sex subgroups mostly had similar prevalence of geriatric syndromes (except that frailty occurred more often with older age). Premalnutrition or malnutrition were associated with a lower frequency of obesity and a higher frequency of constipation, polypharmacy with a higher frequency of diabetes and constipation, frailty with a higher frequency of constipation and hernia, and impaired QoL with a higher frequency of hypertension, diabetes, physical disability, and constipation. Mini Nutritional Assessment–Short Form, Groningen Frailty Indicator, and EQ-5D-5L scores, as well as the number of medications used, mostly predicted each other and QoL. Impaired QoL was associated with a higher frequency of frailty, premalnutrition or malnutrition, and polypharmacy, and frailty with a higher frequency of premalnutrition or malnutrition and polypharmacy. At a 1.5-year follow-up, impaired QoL was linked to polypharmacy and frailty at baseline, premalnutrition or malnutrition and polypharmacy were associated with frailty at baseline, and frailty was linked to both premalnutrition or malnutrition and polypharmacy at baseline. Causal mediation analyses showed that frailty mediated the link between polypharmacy and worse QoL and that polypharmacy mediated the link between frailty and worse QoL. ConclusionsIn this large population-based study of older adults, multiple chronic conditions were associated with ≥1 of the investigated geriatric syndromes. Geriatric syndromes were mostly intercorrelated with, and well predictive of, each other and QoL; and causal relationships existed between geriatric syndromes and QoL, with other geriatric syndromes being mediators. The findings might be biased by residual confounding factors. It is important to perform personalized geriatric syndrome assessments stratified by chronic condition; active prevention of, or intervention for, any syndrome might help to reduce the others and improve QoL.

BackgroundAn increasing number of studies have turned their lens to the application of Artificial Intelligence (AI) in the diagnosis and treatment of colorectal cancer (CRC).ObjectiveTo clarify and visualize the basic situation, research hotspots, and development trends of AI in the diagnosis and treatment of CRC, and provide clues for research in the future.MethodsOn January 31, 2024, the Web of Science Core Collection (WoSCC) database was searched to screen and export the relevant research published during 2004-2023, and Cite Space, VoSviewer, Bibliometrix were used to visualize the number of publications, countries (regions), institutions, journals, authors, citations, keywords, etc.ResultsA total of 2715 pieces of literature were included. The number of publications grew slowly until the end of 2016, but rapidly after 2017, till to the peak of 798 in 2023. A total of 92 countries, 3997 organizations, and 15,667 authors were involved in this research. Chinese scholars released the highest number of publications, and the U.S. contributed the highest number of total citations. As to authors, MORI, YUICHI had the highest number of publications, and WANG, PU had the highest number of total citations. According to the analysis of citations and keywords, the current research hotspots are mainly related to “Colonoscopy”, “Polyp Segmentation”, “Digital Pathology”, “Radiomics”, “prognosis”.ConclusionResearch on the application of AI in the diagnosis and treatment of CRC has made significant progress and is flourishing across the world. Current research hotspots include AI-assisted early screening and diagnosis, pathology, and staging, and prognosis assessment, and future research is predicted to put weight on multimodal data fusion, personalized treatment, and drug development.

BackgroundWe conducted a large-scale epidemiological analysis to investigate the associations between systemic inflammation markers and heart failure (HF). Our aim is to identify potential biomarkers for early detection of HF.MethodsA cross-sectional study was conducted using data from the National Health and Nutrition Examination Survey. We investigated the associations between five systemic inflammation markers (neutrophil to lymphocyte ratio [NLR], platelet to lymphocyte ratio [PLR], systemic immune inflammation index [SII], system inflammation response index [SIRI], and aggregate index of systemic inflammation [AISI]) and the risk of HF.ResultsThe prevalence rates of HF exhibited a gradual increase across increasing logNLR, logPLR, logSII, logSIRI, and logAISI tertiles. Compared to those in the highest tertiles of logNLR, logSII, logSIRI, and logAISI had a 1.579-fold, 1.341-fold, 1.956-fold, and 1.499-fold increased risk of HF compared to those in the lowest tertile respectively. Conversely, there was no significant correlation between logPLR and HF risk among subjects in the highest tertile. The restricted cubic splines (RCS) analysis revealed a non-linear relationship between the elevation of systemic inflammation markers and HF prevalence. Specifically, a per standard deviation increase in any of these variables is associated with a respective 45%, 29%, 28%, 44% and 29% increase in HF prevalence. The receiver operating characteristic (ROC) analysis demonstrated favorable sensitivity and specificity of these systemic inflammation markers in detecting the presence of HF.ConclusionOur cross-sectional study demonstrates significant positive correlations between the NLR, PLR, SII, SIRI, and AISI with the incidence of HF.

Introduction Accurate identification of Aged Care residents accessing hospital services is challenging due to multiple hospital Electronic Health Record (EHR) systems and entry points resulting in inconsistent addresses and coding. Improving accuracy and completeness of addresses is crucial for cohort identification and data linkage. Objective Develop methods to validate cross-sectoral data linkages to improve identification of aged care residents within EHR systems. Approach Among individuals aged ≥65 years (or ≥50 identifying as Aboriginal or Torres Strait Islander) between January 2013 to December 2023 within our Data Platform, we first used data items mandated by the Department of Health to identify aged care residency. Data quality was enhanced using a residential address index derived from Australian Geocoded National Address File (G-NAF). We evaluated sensitivity, specificity, and Positive Predictive Values (PPVs) of recorded addresses against the Australian Institute of Health and Welfare (AIHW) aged care services list. Results Among 409,677 addresses extracted, 55.4% (raw) had sufficient data for validation. This increased to 89.9% (enhanced) with G-NAF. Among 79,028 admitted and 88,876 emergency patients, 22.7% and 18.4% respectively were aged care residents. We compared performance of raw vs enhanced comparisons were: sensitivity (admission: 34.1% vs 73.8%), (emergency: 42.6% vs 66.2%); specificity (admission: 96.2% vs 90.9%), emergency (96.8% vs 95.2%); Positive Predictive Value (admission: 61.3% vs 58.8%), (emergency: 64.2% vs 65.3%). Conclusions and Implications Our approach significantly increased sensitivity without sacrificing specificity, enhancing Aged Care resident identification in EHR data. It also improved linkage with external datasets and Statistical Area Level 1 (SA1) mapping.

Rationale and objectives: This study aims to evaluate the effectiveness of integrating clinical data and quantitative CT parameters with machine learning techniques in forecasting the short-term outcomes of severe COVID-19 in elderly patients. Materials and methods: In this retrospective study, we analyzed the clinical profiles and chest quantitative CT parameters of 239 elderly patients with severe COVID-19 admitted for treatment. The cohort included 61 deceased patients (death group) and 178 who recovered and were discharged (survival group). The participants were randomly assigned into a training group (n = 167) and a validation group (n = 72). Quantitative CT parameters were measured using the 3D-Slicer software. Univariate and multivariate logistic regression analyses identified independent risk factors for mortality. Predictive models were developed employing four machine learning algorithms: Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM). Results: Both univariate and multivariate logistic regression analyses revealed age, hypersensitive C-reactive protein (hs-CRP), and solid organ volume percentage (SOV%) as independent predictors of mortality. The Area Under the Curve (AUC) values for the LR, RF, DT, and SVM models in the training group were 0.795, 0.726, 0.854, and 0.589, respectively; for the validation group, they were 0.817, 0.634, 0.869, and 0.754, respectively. The DT algorithm outperformed other models in both the training and validation groups, emerging as the most effective predictive model in this study. Conclusion: The combination of clinical data and quantitative CT parameters with machine learning approaches is highly valuable in predicting the short-term prognosis of severe COVID-19 in the elderly. Among the various models tested, the Decision Tree algorithm-based model proved to be the most accurate and reliable in this context.

The Above-Ground Carbon (AGC) is an important indicator reflecting the carbon sink function of forest ecosystems. The area of coniferous forests in China accounts for about 50 % of the national forest area. Accurately estimating the AGC of coniferous forests plays an important role in evaluating forest carbon sink function. This study takes coniferous forests in Chun’an, Zhejiang Province as an example, using Landsat 8 OLI as the remote sensing data source, and innovatively proposes a multi-scale Geographically Weighted Regression model (MGWR) combined with Co-Kriging (COK), namely MGWR-COK and Ordinary Kriging (OK), namely MGWR-OK, for AGC estimation. This method first processes Landsat8 OLI remote sensing data, extracts and screens variables, and then constructs an MGWR model with AGC survey samples to estimate AGC and calculate residuals; Afterwards, two models, COK and OK, were used to spatially estimate the residuals; Finally, the AGC estimation results are overlaid with the residual estimation results to obtain the spatial distribution of AGC in coniferous forests. The study indicates that: (1) The selected remote sensing feature variables such as Optimized Soil-Adjusted Vegetation Index (OSAVI), Normalized Difference Vegetation Index (NDVI), Entropy (B3_ent_1 and B2_ent_2), Correlation (B4_corr and B5_corr_2) of texture information, B754 with band combination and elevation have a significant impact on AGC estimation. (2) The AGC accuracy R2 estimated by MGWR-OK and MGWR-COK are 0.837 and 0.857, respectively, which are 6 % and 8 % higher than the MGWR model, and the root mean square error is also reduced by 10 % and 12 %, respectively. This indicates that the combination of MGWR and Kriging interpolation can effectively reduce its spatial estimation error. (3) The range of AGC values estimated by the MGWR-COK model is 0.189–62.591 Mg ∙ hm−2, with a mean of 28.795 Mg ∙ hm−2. The spatial distribution shows a characteristic of high in the southeast and low in the northwest, which is consistent with the actual situation in the study area.

Background: Korean red ginseng extract (KRGE) (Family: Araliaceae) is one of the most widely used traditional herbs in Asia. Multiple studies have shown that KRGE has anti-inflammation, anti-fatigue, anti-obesity, anti-oxidant, and anti-cancer effects. Methods: Sprague-Dawley rats were divided into five groups for PTU-induced hypothyroidism and six groups for LT4-induced hyperthyroidism. At the experiment's conclusion, rats were sacrificed, and blood, thyroid gland, and liver samples were collected. Body weight was recorded weekly, and serum hormone levels were assessed using enzyme-linked immunoassay. Thyroid gland and liver tissues were stained with hematoxylin and eosin. KRGE was prepared in 0.5% CMC and stored at 4 °C before administration. Results: In the LT4-induced hyperthyroidism model, KRGE prevented decreases in body weight, thyroid gland weight, liver weight, serum glucose, and thyroid hormone levels compared to the PTU group. It also reduced increases in T3, T4, and serum aspartate aminotransferase levels after LT4 treatment. Additionally, KRGE improved thyroid gland and liver histopathology, effects not observed in the PTU-induced hypothyroidism model. Conclusion: All things considered, our research points to KRGE's potential protective role in rat hyperthyroidism caused by LT4 by lowering thyroid hormone production.

In recent years, one of the most prevalent matters in population ecology has been the study of predator-prey relationships. In this context, this paper investigated the dynamic behavior of a delayed predator-prey model considering square root type functional response and stage structure for predators. First, we obtained positivity and boundedness of the solutions and existence of equilibrium points. Second, by applying the stability theory of delay differential equations and the Hopf bifurcation theorem, we discussed the system's local stability and the existence of a Hopf bifurcation at the positive equilibrium point. Moreover, the properties of the Hopf bifurcation were deduced by using the central manifold theorem and normal form method. Analytical results showed that when the time delay was less than the critical value, the two populations will coexist, otherwise the ecological balance will be disrupted. Finally, some numerical simulations were also included to verify the theoretical results.

Abstract Choroid plexus tumors (CPT) are rare and highly vascularized neoplasms that have three histologically confirmed diagnoses, including choroid plexus papilloma, atypical choroid plexus papilloma, and choroid plexus carcinoma (CPC). This study aimed to determine the epidemiology and survival of patients with CPTs and develop a nomogram to quantify the prognosis of the patients with CPT. Data of 808 patients who were diagnosed as CPT between 2000 and 2020 was obtained from the surveillance, epidemiology, and end results database. Descriptive analysis was used to assess the distribution and tumor-related characteristics of the patients with CPT. Independent prognostic factors for patients with CPT were identified by univariate and multivariate Cox regression analysis. The nomogram was established and evaluated by receiver operating characteristic curve, and decision curve analysis (DCA), calibration curves. The independent prognostic factors for patients with CPT are age, tumor size, surgery, chemotherapy, tumor number, pathologies, and race. For the prognostic nomogram, the area under the curve (AUC) of 60-, 120-, and 180-months were 0.855, 0.869 and 0.857 in the training set and 0.836, 0.864 and 0.922 in the test set. The DCA and calibration curve indicated the good performance of the nomogram. Patients with CPTs can be diagnosed at any age. Among the three histopathological tumors, patients with CPC had the worst prognosis. The nomogram was established to predict the prognosis of patients with CPT, which had satisfactory accuracy, and clinical utility may benefit for clinical decision-making.

Abstract Background TG103, a glucagon-like peptide-1 analog, is being investigated as an option for weight management. We aimed to determine the safety, tolerability, pharmacokinetics, and pharmacodynamics of TG103 injection in participants who are overweight or obese without diabetes. Methods In this randomized, double-blind, placebo-controlled, multiple-dose phase 1b study, participants aged 18–75 years with a body-mass index (BMI) ≥ 26.0 kg/m2 and body weight ≥ 60 kg were enrolled from three centers in China. The study included three cohorts, and in each cohort, eligible participants were randomly assigned (3:1) to one of three once-weekly subcutaneous TG103 groups (15.0, 22.5 and 30.0 mg) or matched placebo, without lifestyle interventions. In each cohort, the doses of TG103 were escalated in 1-week intervals to the desired dose over 1 to 4 weeks. Then participants were treated at the target dose until week 12 and then followed up for 2 weeks. The primary endpoint was safety and tolerability assessed by the incidence and severity of adverse events (AEs) from baseline to the end of the follow-up period. Secondary endpoints included pharmacokinetic and pharmacodynamic profiles of TG103 and the occurrence of anti-drug antibodies to TG103. Results A total of 147 participants were screened, and 48 participants were randomly assigned to TG103 (15.0, 22.5 and 30.0 mg groups, n = 12 per group) or placebo (n = 12). The mean (standard deviation, SD) age of the participants was 33.9 (10.0) years; the mean bodyweight was 81.65 (10.50) kg, and the mean BMI was 29.8 (2.5) kg/m2. A total of 466 AEs occurred in 45 of the 48 participants, with 35 (97.2%) in the TG103 group and 10 (83.3%) in the pooled placebo group. Most AEs were grade 1 or 2 in severity, and there were no serious adverse events (SAEs), AEs leading to death, or AEs leading to discontinuation of treatment. The steady-state exposure of TG103 increased with increasing dose and was proportional to Cmax,ss, AUCss, AUC0-t and AUC0-inf. The mean values of Cmax,ss ranged from 951 to 1690 ng/mL, AUC0-t ranged from 150 to 321 μg*h/mL, and AUC0-inf ranged from 159 to 340 μg*h/mL. TG103 had a half-life of 110–116 h, with a median Tmax of 36–48 h. After treatment for 12 weeks, the mean (SD) values of weight loss from baseline in the TG103 15.0 mg, 22.5 mg and 30.0 mg groups were 5.65 (3.30) kg, 5.35 (3.39) kg and 5.13 (2.56) kg, respectively, and that in the placebo group was 1.37 (2.13) kg. The least square mean percent weight loss from baseline to D85 in all the TG103 groups was more than 5% with p

BackgroundStudies on the relationships of environmental noise exposure with negative emotions and sleep quality have long been reported. Self-perceived noise intensity is not only related to environmental noise exposure, but also reflects an individual's susceptibility to noise; however, few studies on self-perceived noise intensity, negative emotions, and sleep quality have been reported, and it is not clear whether negative emotions play a mediating role in the relationship between self-perceived noise intensity and sleep quality. ObjectiveTo analyze the mediating role of negative emotions (anxiety, depression, and stress) in the relationship between self-perceived noise intensity and sleep quality in noise-exposed workers, and to provide a scientific basis for addressing psychological problems and sleep quality induced by noise exposure. MethodsStratified cluster sampling was used to select noise-exposed workers from a large equipment manufacturing plant in Chengdu from May to June 2023, and demographic characteristics were investigated using a self-designed general information questionnaire; self-perceived noise intensity was reported by the study subjects as the noise intensity of their workplaces; sleep quality was synthesized from three indicators: night sleep duration, self-perceived sleep quality, and sleep-related symptoms; the Anxiety-Depression-Stress Scale (DASS-21 scale) was used to evaluate anxiety, depression, and stress of a worker. Structural equation modeling was used to analyze the pathways of negative emotions (anxiety, depression, and stress) between self-perceived noise intensity and sleep quality. ResultsThe positive rates of negative emotions were 33.1% (anxiety symptoms, 215/649), 26.0% (depression symptoms, 169/649), and 14.0% (stress symptoms, 91/649), respectively in a total of 649 noise-exposed workers. Self-perceived noise intensity was positively correlated with sleep quality score (r=0.218, P

Mountain glaciers are sensitive to climate variability and can be of great importance for downstream populations due to their hydrological significance. Synthetic Aperture Radar (SAR) images are often used to monitor the characteristics of glaciers based on the backscattering coefficient. However, the influence of satellite orbit and polarization when collecting images for wide regions has not been well considered. This study focuses on the extraction of wet snow in summer and firn in winter in West Kunlun Shan and the Tibet Interior Mountains by using Sentinel-1 C-band SAR data. The investigated regions have different climate patterns. We compare backscatter coefficient distributions for wet snow and firn, derived from maximum likelihood classification under various polarizations, alongside their respective ratios and show that polarization has a minor impact on the identification and monitoring of both wet snow and firn. However, a comparison of the wet snow ratios at different satellite orbits reveals notable differences between ascending and descending orbits in summer. We furthermore show, by analyzing weather stations on glaciers, that such effect can be related to the different acquisition time and different temperatures in the morning and afternoon and therefore to the orbit. In contrast, firn ratios across different orbits show less variation in winter, and the monitoring results consistently align with the patterns of ablation and accumulation typical under both climatic influences. These findings demonstrate glaciers’ sensitivity to temperature fluctuations and the radar wave’s responsiveness to surface characteristics. Consequently, when employing SAR for glacier monitoring, it is crucial to consider the influence of orbit and polarization, in combination with temperature variations, and whether the season is winter or summer.

Abstract Background T-cell exhaustion (TEX), a condition characterized by impaired T-cell function, has been implicated in numerous pathological conditions, but its role in acute myocardial Infarction (AMI) remains largely unexplored. This research aims to identify and characterize all TEX-related genes for AMI diagnosis. Methods By integrating gene expression profiles, differential expression analysis, gene set enrichment analysis, protein-protein interaction networks, and machine learning algorithms, we were able to decipher the molecular mechanisms underlying TEX and its significant association with AMI. In addition, we investigated the diagnostic validity of the leading TEX-related genes and their interactions with immune cell profiles. Different types of candidate small molecule compounds were ultimately matched with TEX-featured genes in the “DrugBank” database to serve as potential therapeutic medications for future TEX-AMI basic research. Results We screened 1725 differentially expressed genes (DEGs) from 80 AMI samples and 71 control samples, identifying 39 differential TEX-related transcripts in total. Functional enrichment analysis identified potential biological functions and signaling pathways associated with the aforementioned genes. We constructed a TEX signature containing five hub genes with favorable prognostic performance using machine learning algorithms. In addition, the prognostic performance of the nomogram of these five hub genes was adequate (AUC between 0.815 and 0.995). Several dysregulated immune cells were also observed. Finally, six small molecule compounds which could be the future therapeutic for TEX in AMI were discovered. Conclusion Five TEX diagnostic feature genes, CD48, CD247, FCER1G, TNFAIP3, and FCGRA, were screened in AMI. Combining these genes may aid in the early diagnosis and risk prediction of AMI, as well as the evaluation of immune cell infiltration and the discovery of new therapeutics.

Abstract Background Prospective observational studies have demonstrated that the machine learning (ML) -guided noninvasive chromosome screening (NICS) grading system, which we called the noninvasive chromosome screening-artificial intelligence (NICS-AI) grading system, can be used embryo selection. The current prospective interventional clinical study was conducted to investigate whether this NICS-AI grading system can be used as a powerful tool for embryo selection. Methods Patients who visited our centre between October 2018 and December 2021 were recruited. Grade A and B embryos with a high probability of euploidy were transferred in the NICS group. The patients in the control group selected the embryos according to the traditional morphological grading. Finally, 90 patients in the NICS group and 161 patients in the control group were compared statistically for their clinical outcomes. Results In the NICS group, the clinical pregnancy rate (70.0% vs. 54.0%, p

Abstract Background High-flow nasal cannula (HFNC) has emerged as a promising noninvasive method for delivering oxygen to critically ill patients, particularly those with sepsis and acute lung injury. However, uncertainties persist regarding its therapeutic benefits in this specific patient population. Methods This retrospective study utilized a propensity score-matched cohort from the Medical Information Mart in Intensive Care-IV (MIMIC-IV) database to explore the correlation between HFNC utilization and mortality in patients with sepsis-induced acute lung injury. The primary outcome was 28-day all-cause mortality. Results In the propensity score-matched cohort, the 28-day all-cause mortality rate was 18.63% (95 out of 510) in the HFNC use group, compared to 31.18% (159 out of 510) in the non-HFNC group. The use of HFNC was associated with a lower 28-day all-cause mortality rate (hazard ratio [HR] = 0.53; 95% confidence interval [CI] = 0.41–0.69; P

Abstract Construction waste is unavoidable in the process of urban development, causing serious environmental pollution. Accurate assessment of municipal construction waste generation requires building construction waste identification models using deep learning technology. However, this process requires high-quality public datasets for model training and validation. This study utilizes Google Earth and GF-2 images as the data source to construct a specific dataset of construction waste landfills in the Changping and Daxing districts of Beijing, China. This dataset contains 3,653 samples of the original image areas and provides mask-labeled images in the semantic segmentation domains. Each pixel within a construction waste landfill is classified into 4 categories of the image areas, including background area, vacant landfillable area, engineering facility area, and waste dumping area. The dataset contains 237,115,531 pixels of construction waste and 49,724,513 pixels of engineering facilities. The pixel-level semantic segmentation labels are provided to quantify the construction waste yield, which can serve as the basic data for construction waste extraction and yield estimation both for academic and industrial research.

Abstract Strategies to improve T cell therapy efficacy in solid tumors such as hepatocellular carcinoma (HCC) are urgently needed. The common cytokine receptor γ chain (γc) family cytokines such as IL-2, IL-7, IL-15 and IL-21 play fundamental roles in T cell development, differentiation and effector phases. This study aims to determine the combination effects of IL-21 in T cell therapy against HCC and investigate optimized strategies to utilize the effect of IL-21 signal in T cell therapy. The antitumor function of AFP-specific T cell receptor-engineered T cells (TCR-T) was augmented by exogenous IL-21 in vitro and in vivo. IL-21 enhanced proliferation capacity, promoted memory differentiation, downregulated PD-1 expression and alleviated apoptosis in TCR-T after activation. A novel engineered IL-21 receptor was established, and TCR-T armed with the novel engineered IL-21 receptors (IL-21R-TCR-T) showed upregulated phosphorylated STAT3 expression without exogenous IL-21 ligand. IL-21R-TCR-T showed better proliferation upon activation and superior antitumor function in vitro and in vivo. IL-21R-TCR-T exhibited a less differentiated, exhausted and apoptotic phenotype than conventional TCR-T upon repetitive tumor antigen stimulation. The novel IL-21 receptor in our study programs powerful TCR-T and can avoid side effects induced by IL-21 systemic utilization. The novel IL-21 receptor creates new opportunities for next-generation TCR-T against HCC.

Abstract As a critical component of space-air-ground integrated IoT, the aerial network provides highly reliable, low-latency and ubiquitous information services to ground users by virtue of their high mobility, easy deployment and low cost. However, the current computation and resource management model of air-ground integrated networks are insufficient to meet the latency demanding of emerging intelligent services such as autonomous systems, extended reality and haptic feedback. To tackle these challenges, we propose a computation offloading and optimization method based on potential game. First, we construct an cloud-edge collaborative computing model. Secondly, we construct Offloading Decision Objective Functions (ODOF) with the objective of minimum task processing latency and energy consumption. ODOF is proved to be a Mixed Inferior Nonlinear Programming (MINLP) problem, which is hard to solve. ODOF is converted to be a full potential game, and the Nash equilibrium solution exists. Then, a computational resource allocation algorithm based on Karush–Kuhn–Tucker (KKT) conditions is proposed to solve resource allocation problem. On this basis, a distributed game-based computational offloading algorithm is proposed to minimize the offloading cost. Extensive simulation results demonstrate that the convergence performance of the proposed algorithm is reduced by 50%, the convergence time is reduced by 13.3% and the average task processing delay is reduced by 10%.

Abstract Background Idiopathic pulmonary fibrosis is a persistent disease of the lung interstitium for which there is no efficacious pharmacological therapy. Protodioscin, a steroidal saponin, possesses diverse pharmacological properties; however, its function in pulmonary fibrosis is yet to be established. Hence, in this investigation, it was attempted to figure out the anti-pulmonary fibrosis influences of protodioscin and its pharmacological properties related to oxidative stress. Methods A mouse lung fibrosis model was generated using tracheal injections of bleomycin, followed by intraperitoneal injection of different concentrations of protodioscin, and the levels of oxidative stress and fibrosis were detected in the lungs. Multiple fibroblasts were treated with TGF-β to induce their transition to myofibroblasts. It was attempted to quantify myofibroblast markers’ expression levels and reactive oxygen species levels as well as Nrf2 activation after co-incubation of TGF-β with fibroblasts and different concentrations of protodioscin. The influence of protodioscin on the expression and phosphorylation of p62, which is associated with Nrf2 activation, were detected, and p62 related genes were predicted by STRING database. The effects of Nrf2 inhibitor or silencing of the Nrf2, p62 and NBR1 genes, respectively, on the activation of Nrf2 by protodioscin were examined. The associations between p62, NBR1, and Keap1 in the activation of Nrf2 by protodioscin was demonstrated using a co-IP assay. Nrf2 inhibitor were used when protodioscin was treated in mice with pulmonary fibrosis and lung tissue fibrosis and oxidative stress levels were detected. Results In vivo, protodioscin decreased the levels of fibrosis markers and oxidative stress markers and activated Nrf2 in mice with pulmonary fibrosis, and these effects were inhibited by Nrf2 inhibitor. In vitro, protodioscin decreased the levels of myofibroblast markers and oxidative stress markers during myofibroblast transition and promoted Nrf2 downstream gene expression, with reversal of these effects after Nrf2, p62 and NBR1 genes were silenced or Nrf2 inhibitors were used, respectively. Protodioscin promoted the binding of NBR1 to p62 and Keap1, thereby reducing Keap1-Nrf2 binding. Conclusion The NBR1-p62-Nrf2 axis is targeted by protodioscin to reduce oxidative stress and inhibit pulmonary fibrosis. Graphical Abstract

Abstract Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (Mpro) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity.

Recurrent opportunistic infections (OIs) in patients with severely immunosuppressed AIDS remain an unresolved medical challenge despite advancements in antiretroviral therapy (ART). To address this gap, we developed an HLA-mismatched allogeneic adoptive immune therapy (AAIT) specifically targeting this patient population. The safety and efficacy of this novel therapeutic approach were preliminarily confirmed in our phase 1 trial. Subsequently, a multicenter, open-label, controlled, phase 2a trial was conducted to evaluate the efficacy of AAIT in combination with ART compared with the conventional ART-only regimen. No difference in the incidence of adverse events (AEs) was observed between the two groups at the 96-week follow-up. AAIT treatment improved CD4+ T cell recovery at weeks 72 (P = 0.048) and 96 (P = 0.024) compared to the Control Group. Additionally, stratified analysis of patients in the AAIT Group showed that donor/recipient sex mismatch was significantly associated with the likelihood of patients achieving an immunological response (OR = 8.667; 95% CI, 2.010–37.377; P = 0.004). These findings suggest that AAIT serves as a promising adjunct therapy for improving the outcomes of patients with severely immunosuppressed AIDS. Further studies are needed to elucidate the immunological mechanisms underlying AAIT and identify the subpopulations that respond optimally to this therapeutic approach. This trial is registered at www.clinicaltrials.gov (NCT04098770).Trial registration: ClinicalTrials.gov identifier: NCT04098770.Trial registration: ClinicalTrials.gov identifier: NCT02651376.

ABSTRACTIn the past decades, the environment has changed drastically in China with rapid economic development. Lack of data, especially data with spatiotemporal information, has however hindered evaluating progress of environmental indicators in the global Sustainable Development Goal (SDG) framework. Here, we evaluate and explore geospatial information of 20 environmental SDG indicators from six SDGs in China from 2010 to 2020 using big data. We show China has improved rapidly in most of the 20 indicators, except for indicator 13.2.2 (CO2 emission). 63.5% of China’s land mass showed improved states for the SDG indicators. By 2020, four of the indicators were found to have achieved their 2030 targets. It is predicted from the existing data that 10 of the 20 indicators can achieve their targets smoothly by 2030, but two indicators will lag behind. We quantitatively show China’s progress towards the environmental SDG indicators, and put forward timelines and suggestions for policy makers.

This paper is concerned with a three-component quasilinear chemotaxis system with nonlinear diffusion and singular sensitivity for alopecia areata ut=∇D1(u)∇u−χ1∇uw∇w+w−μ1uγ1,(x,t)∈Ω×(0,∞),vt=∇D2(v)∇v−χ2∇vw∇w+w+ruv−μ2vγ2,(x,t)∈Ω×(0,∞),wt=Δw+u+v−w,(x,t)∈Ω×(0,∞),∂u∂ν=∂v∂ν=∂w∂ν=0,(x,t)∈∂Ω×(0,∞),u(x,0)=u0(x),v(x,0)=v0(x),w(x,0)=w0(x),x∈Ω,associated with homogeneous Neumann boundary conditions in a convex smooth bounded domain Ω⊂R2. For i=1,2, the parameters χi,μi,r are positive and γi≥2. The nonlinear diffusion functions Di(s)∈C2 satisfy Di(s)⩾(s+1)αi for all s≥0. We delve into analyzing the global existence and boundedness of classical solutions for the aforementioned system under specific conditions. Additionally, in the scenario where γi=2, we develop a Lyapunov functional and scrutinize its temporal evolution to ascertain the asymptotic stability of the coexistence state.