A randomized, multicenter phase III Study of once-per-cycle administration of efbemalenograstim alfa (F-627), a novel long-acting rhG-CSF, for prophylaxis of chemotherapy-induced neutropenia in patients with breast cancer.

BACKGROUND: F-627 (efbemalenograstim alfa) is a novel long acting granulocyte colony-stimulating factor (G-CSF) that contains two human G-CSF fused to a human immunoglobulin G2 (hIgG2) -Fc fragment with a peptide linker. This studyevaluated the efficacy and safety of F-627, also known as efbemalenograstim alfa (Ryzneuta®) in reducing neutropenia compared with filgrastim (GRAN®). METHODS: This was a multicenter, randomized, open-label, active-controlled non-inferiority study. Two hundred thirty nine (239) patients were enrolled in thirteen centers and received the chemotherapy with epirubicin (100 mg/m2) and cyclophosphamide (600 mg/m2) on day 1 of each cycle for a maximum of four cycles. Patients were randomized to receive either a single 20 mg subcutaneous (s.c.) injection of F-627 on day 3 of each cycle or daily s.c. injection of filgrastim 5 µg/kg/d starting from day 3 of each cycle. The primary endpoint was the duration of grade 3 or 4 neutropenia in cycle 1. The safety profile was also evaluated. RESULTS: The mean (SD) duration of grade 3 or 4 neutropenia in cycle 1 was 0.68 (1.10) and 0.71 (0.95) days for the F-627 and the filgrastim groups, respectively. The Hodges-Lehmann estimate of the between-group median difference (F-627 vs filgrastim) in the duration of grade 3 or 4 neutropenia in cycle 1 was 0 day and the upper limit of the one-sided 97.5% CI was 0 day, which was within the prespecified non-inferiority margin of 1-day. Results for all efficacy endpoints in cycles 2 - 4 were consistent with the results in cycle 1, however a trend towards a lower incidence and a shorter duration of grade 3 or 4 neutropenia and grade 4 neutropenia was observed in the F-627 group compared with the filgrastim group. The ANC nadir in the F-627 group was significantly higher than that in the filgrastim group in each cycle. A single fixed dose of F-627 was well tolerated and as safe as standard daily filgrastim. CONCLUSIONS: A single fixed dose of 20 mg of F-627 in each cycle was as safe and effective as a daily dose of filgrastim 5 µg/kg/d in reducing neutropenia and its complications in patients who received four cycles of EC. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04174599, on 22/11/2019.

Effect of Mono- and Polysaccharide on the Structure and Property of Soy Protein Isolate during Maillard Reaction.

As a protein extracted from soybeans, soy protein isolate (SPI) may undergo the Maillard reaction (MR) with co-existing saccharides during the processing of soy-containing foods, potentially altering its structural and functional properties. This work aimed to investigate the effect of mono- and polysaccharides on the structure and functional properties of SPI during MR. The study found that compared to oat ß-glucan, the reaction rate between SPI and D-galactose was faster, leading to a higher degree of glycosylation in the SPI-galactose conjugate. D-galactose and oat ß-glucan showed different influences on the secondary structure of SPI and the microenvironment of its hydrophobic amino acids. These structural variations subsequently impact a variety of the properties of the SPI conjugates. The SPI-galactose conjugate exhibited superior solubility, surface hydrophobicity, and viscosity. Meanwhile, the SPI-galactose conjugate possessed better emulsifying stability, capability to produce foam, and stability of foam than the SPI-ß-glucan conjugate. Interestingly, the SPI-ß-glucan conjugate, despite its lower viscosity, showed stronger hypoglycemic activity, potentially due to the inherent activity of oat ß-glucan. The SPI-galactose conjugate exhibited superior antioxidant properties due to its higher content of hydroxyl groups on its molecules. These results showed that the type of saccharides had significant influences on the SPI during MR.

Monitoring Soybean Soil Moisture Content Based on UAV Multispectral and Thermal-Infrared Remote-Sensing Information Fusion.

By integrating the thermal characteristics from thermal-infrared remote sensing with the physiological and structural information of vegetation revealed by multispectral remote sensing, a more comprehensive assessment of the crop soil-moisture-status response can be achieved. In this study, multispectral and thermal-infrared remote-sensing data, along with soil-moisture-content (SMC) samples (0~20 cm, 20~40 cm, and 40~60 cm soil layers), were collected during the flowering stage of soybean. Data sources included vegetation indices, texture features, texture indices, and thermal-infrared vegetation indices. Spectral parameters with a significant correlation level (p < 0.01) were selected and input into the model as single- and fuse-input variables. Three machine learning methods, eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Genetic Algorithm-optimized Backpropagation Neural Network (GA-BP), were utilized to construct prediction models for soybean SMC based on the fusion of UAV multispectral and thermal-infrared remote-sensing information. The results indicated that among the single-input variables, the vegetation indices (VIs) derived from multispectral sensors had the optimal accuracy for monitoring SMC in different soil layers under soybean cultivation. The prediction accuracy was the lowest when using single-texture information, while the combination of texture feature values into new texture indices significantly improved the performance of estimating SMC. The fusion of vegetation indices (VIs), texture indices (TIs), and thermal-infrared vegetation indices (TVIs) provided a better prediction of soybean SMC. The optimal prediction model for SMC in different soil layers under soybean cultivation was constructed based on the input combination of VIs + TIs + TVIs, and XGBoost was identified as the preferred method for soybean SMC monitoring and modeling, with its R2 = 0.780, RMSE = 0.437%, and MRE = 1.667% in predicting 0~20 cm SMC. In summary, the fusion of UAV multispectral and thermal-infrared remote-sensing information has good application value in predicting SMC in different soil layers under soybean cultivation. This study can provide technical support for precise management of soybean soil moisture status using the UAV platform.

Biomechanics on Ultra-Sensitivity of Venus Flytrap's Micronewton Trigger Hairs.

Numerous plants evolve ingeniously microcantilever-based hairs to ultra-sensitively detect out-of-plane quasi-static tactile loads, providing a natural blueprint for upgrading the industrial static mode microcantilever sensors, but how do the biological sensory hairs work mechanically? Here, the action potential-producing trigger hairs of carnivorous Venus flytraps (Dionaea muscipula) are investigated in detail from biomechanical perspective. Under tiny mechanical stimulation, the deformable trigger hair, composed of distal stiff lever and proximal flexible podium, will lead to rapid trap closure and prey capture. The multiple features determining the sensitivity such as conical morphology, multi-scale functional structures, kidney-shaped sensory cells, and combined deformation under tiny mechanical stimulation are comprehensively researched. Based on materials mechanics, finite element simulation, and bio-inspired original artificial sensors, it is verified that the omnidirectional ultra-sensitivity of trigger hair is attributed to the stiff-flexible coupling of material, the double stress concentration, the circular distribution of sensory cells, and the positive local buckling. Also, the balance strategy of slender hair between sensitivity and structural stability (i.e., avoiding disastrous collapse) is detailed revealed. The unique basic biomechanical mechanism underlying trigger hairs is essential for significantly enhancing the performance of the traditional industrial static mode microcantilever sensors, and ensure the stability of arbitrary load perception.

The functional subclasses of AT1 receptor autoantibody in patients with coronary heart disease.

Recently, the identification of autoantibodies (AT1-AA) targeting the second extracellular loop of angiotensin II type 1 receptor (AT1R-ECII) in patients with coronary heart disease (CHD) offers a novel perspective on the interplay between immunity and cardiovascular disease. However, much remains unknown regarding the functional diversity of AT1-AA. In this study, we measured the levels of AT1-AA in the sera of 306 CHD patients and purified AT1-AA from patient's sera (n = 127). The subclasses of AT1-AA were categorized based on their impact on intracellular calcium ([Ca2+]i) levels in mouse arterial smooth muscle cells (MASMCs). Our findings revealed 4 distinct [Ca2+]i response patterns indicating the existence of 4 functional subclasses named H1-, H2-, H3-, and H4-AT1-AA. The correlation analysis demonstrated a positive association between H1-AT1-AA and endogenous coagulation, as well as between H2-AT1-AA and exogenous coagulation; no significant correlation was observed between H3-AT1-AA and the indicators we analyzed. Conversely, H4-AT1-AA exhibited a negative correlation with both leukocyte number and bile acid levels. Logistic regression analysis showed that H2-AT1-AA possessed predictive value for severe CHD. Furthermore, in vitro experiments indicated that both H1- and H2-AT1-AA exerted cytotoxic effects on MASMCs, while H4-AT1-AA increased cell viability. Additionally, an AT1-AA-positive rat model was established by subcutaneously injecting with AT1R-ECII peptide, which produced four similar functional subclasses of rat AT1-AA upon active immunization. This study suggested that classifying different functional subclasses of AT1-AAs can facilitate more accurate evaluation of the condition and prognosis in patients with CHD, thereby providing a novel basis for clinical diagnosis and treatment.

Global research landscape of Peutz-Jeghers syndrome and successful endoscopic management of intestinal intussusception in patients with recurrent laparotomies.

BACKGROUND: Peutz-Jeghers syndrome (PJS) has brought significant physical, psychological and economic burdens on the patients and their families due to its early onset, diagnostic and therapeutic challenges and increased recurrence risk. AIM: To explore the current research status and emerging hotspots of PJS. METHODS: Studies on PJS published during 1994-2023 were gathered based on Web of Science Core Collection. Additionally, a case of PJS-induced intestinal intussusception, successfully treated with endoscopic methods despite three laparotomies, was highlighted. Comprehensive bibliometric and visual analysis were conducted with VOSviewer, R and CiteSpace. RESULTS: Altogether 1760 studies were identified, indicating a steady increase in the publication number. The United States had the highest influence, whereas the University of Helsinki emerged as the leading institution, and Aaltonen LA from the University of Helsinki was the most prolific author. Cancer Research, Oncogene and Endoscopy were the top three journals based on H-index. Keyword burst direction analysis revealed that "cancer risk", "management", "surveillance" and "familial pancreatic cancer" were the potential hotspots for investigation. Additionally, "early detection", "capsule endoscopy", "clinical management", "double-balloon endoscopy", "familial pancreatic cancer" and "molecular genetic basis" were identified as the key clusters of co-cited references. Endoscopic polypectomy remained effective on resolving intestinal intussusception in patients who underwent three previous laparotomies. CONCLUSION: In the last three decades, global publications related to PJS show a steadily increasing trend in number. Endoscopic management is currently a research hotspot.

Unloading and successful treatment with bioresorbable stents during percutaneous coronary intervention: A case report.

BACKGROUND: With the development of percutaneous coronary intervention (PCI), the number of interventional procedures without implantation, such as bioresorbable stents (BRS) and drug-coated balloons, has increased annually. Metal drug-eluting stent unloading is one of the most common clinical complications. Comparatively, BRS detachment is more concealed and harmful, but has yet to be reported in clinical research. In this study, we report a case of BRS unloading and successful rescue. CASE SUMMARY: This is a case of a 59-year-old male with the following medical history: "Type 2 diabetes mellitus" for 2 years, maintained with metformin extended-release tablets, 1 g PO BID; "hypertension" for 20 years, with long-term use of metoprolol sustained-release tablets, 47.5 mg PO QD; "hyperlipidemia" for 20 years, without regular medication. He was admitted to the emergency department of our hospital due to intermittent chest pain lasting 18 hours, on February 20, 2022 at 15: 35. Electrocardiogram results showed sinus rhythm, ST-segment elevation in leads I and avL, and poor R-wave progression in leads V1-3. High-sensitivity troponin I level was 4.59 ng/mL, indicating an acute high lateral wall myocardial infarction. The patient's family requested treatment with BRS, without implantation. During PCI, the BRS became unloaded but was successfully rescued. The patient was followed up for 2 years; he had no episodes of angina pectoris and was in generally good condition. CONCLUSION: We describe a case of a 59-year-old male experienced BRS unloading and successful rescue. By analyzing images, the causes of BRS unloading and the treatment plan are discussed to provide insights for BRS release operations. We discuss preventive measures for BRS unloading.

Protein corona dynamicity contributes to biological destiny disparities of nanoparticles.

Extracellular protein coronas (exPCs), which have been identified in various biofluids, are recognized for their pivotal role in mediating the interaction between nanoparticles and the cytomembrane. However, it is still unclear whether various exPCs can induce different levels of intracellular proteostasis, which is of utmost importance in preserving cellular function, and eliciting distinct intracellular biological behaviors. To investigate this, two types of exPC-coated iron oxide nanoparticles (IONPs) are prepared and used to investigate the influence of exPCs on extracellular and intracellular biological outcomes. The results demonstrate that the formation of exPCs promotes the colloidal stability of IONPs, and the discrepancies in the components of the two exPCs, including opsonin, dysopsonin, and lipoprotein, are responsible for the disparities in cellular uptake and endocytic pathways. Moreover, the differential evolution of the two exPCs during cellular internalization leads to distinct autophagy and glycolysis activities, which can be attributed to the altered depletion of angiopoietin 1 during the formation of intracellular protein coronas, which ultimately impacts the PI3K/AKT-mTOR signaling. These findings offer valuable insights into the dynamic characteristics of exPCs during cellular internalization, and their consequential implications for cellular internalization and intracellular metabolism activity, which may facilitate the comprehension of PCs on biological effects of NPs and expedite the design and application of biomedical nanoparticles.

Exploring the emotional mechanism of consumer satisfaction in new energy vehicles: a dual-path model of intelligent and eco-friendly experiences.

New energy vehicles (NEVs) are considered a crucial means of reducing travel costs, enhancing consumer experiences, and innovating services. This paper aims to categorize the functional experiences of NEVs into two types: intelligent experience and eco-friendly experience, using a dual-path model. By analyzing 118,648 text data from automotive information and service platforms, the relevant factors influencing consumer satisfaction are explored. The research findings reveal that intelligent experience has a significantly positive impact on consumer satisfaction, whereas eco-friendly experience has a significantly negative impact on consumer satisfaction. This suggests that new energy vehicle companies need technological innovation in infrastructure and range to enhance consumer satisfaction. Furthermore, the research confirms that, when facing the intelligent experience of new energy vehicles, consumers' "rational" thinking plays a dominant role, and efficacy is an intermediate variable to enhance consumer satisfaction. On the other hand, when facing the eco-friendly experience of new energy vehicles, consumers' "emotional" thinking predominates, and identity is an intermediate variable to enhance consumer satisfaction. Additionally, the moderating effect of perceived usability is examined. When faced with the intelligent experience of NEVs, consumers with high perceived usability are more likely to generate a sense of efficiency. In the case of the eco-friendly experience of NEVs, consumers with low perceived usability are more likely to experience a sense of identity.

Participation in Multidisciplinary Teams Among Healthcare Professionals: A Discrete Choice Experiment in Tertiary Public Hospitals in China.

Purpose: Healthcare professionals' participation is crucial for the efficient implementation of multidisciplinary team (MDT) collaboration models. We identified the key factors influencing healthcare professionals' preference to participate in MDTs in tertiary hospitals. Methods: To clarify the attributes and levels of the discrete choice experiment (DCE), we conducted a targeted literature review and conducted in-depth interviews with MDT service providers. Following this, a DCE was designed to evaluate healthcare professionals' preferences for MDT participation, and the influence of factors such as salary subsidies, leadership attention, patient participation, quality assessment, working intensity, and case complexity. A conditional logit model estimated the utility of each attribute. Willingness-to-pay estimates were derived by taking the negative ratio of the coefficients of non-economic and economic attributes. A series of policy simulation analyses were conducted. Results: Two hundred healthcare professionals completed the questionnaire, with 180 valid responses used for analysis. All attributes were statistically significant. Leadership attention and working intensity were the primary factors influencing staff willingness to participate in MDTs, followed by quality assessment and salary subsidies. Significant preference differences were observed between respondents; compared with mid-level staff, senior-level healthcare professionals believed patient engagement would be more helpful in boosting participation. The policy simulation showed that changing leadership attention from "neglect" to "emphasis" would increase the probability of staff choosing to participate in MDTs from 24.4% to 66.98%. Conclusion: Leadership attention was the primary concern for healthcare professionals in MDTs. To effectively motivate staff participation in MDTs, policymakers should adopt a holistic approach that considers work motivation and individual backgrounds, including competitive salary packages and a positive work environment. They should concurrently introduce MDT case complexity measurement tools to optimize resource allocation. Addressing staff members' unique needs and career aspirations by creating targeted training programs, pathways for advancement, and personalized career development plans are also crucial.

Combinatorial accumulation, stress response, detoxification and synaptic transmission effects of cadmium and selenium in clams Ruditapes philippinarum.

This study investigated the toxicological effects and mechanisms of cadmium (Cd) (5 and 50 µg/L) and selenium (Se) (3 and 30 µg/L) at environmentally relevant concentrations on the gills and digestive glands of clams Ruditapes philippinarum. Results indicated that Cd and Se could tissue-specifically impact osmoregulation, energy metabolism, and synaptic transmission in the gills and digestive glands of clams. After exposure to 50 µg/L Cd, the digestive glands of clams up-regulated the expression of methionine-gamma-lyase and metallothionein for detoxification. Clam digestive glands exposed to 3 µg/L Se up-regulated the expression of catalase and glutathione peroxidase to alleviate oxidative stress, and down-regulated the expression of selenide-water dikinase to reduce the conversion of inorganic Se. Additionally, the interaction mode between Cd and Se largely depended on their molar ratio, with a ratio of 11.71 (50 µg/L Cd + 3 µg/L Se) demonstrated to be particularly harmful, as manifested by significantly more lesions, oxidative stress, and detoxification demand in clams than those exposed to Cd or Se alone. Collectively, this study revealed the complex interaction patterns and mechanisms of Cd and Se on clams, providing a reference for exploring their single and combined toxicity.

Diurnal variation of postoperative delirium in elderly patients undergoing esketamine anesthesia for elective noncardiac surgery: a randomized clinical trial.

BACKGROUND: Postoperative delirium (POD) is a serious and common complication. The aim of present study is to investigate the diurnal variation of POD and the effects of esketamine in elderly patients. METHODS: A randomized, double-blind, placebo-controlled clinical trial with factorial design was conducted. Patients (aged 65 to 85 years) with normal Mini-Mental State Examination (MMSE) score were stratified by age (≤70 vs. >70) and American Society of Anesthesiologists physical status classification (Ⅱ vs. Ⅲ), then randomly assigned to either morning (08:00-12:00) or afternoon (14:00-18:00) noncardiac operation under general anesthesia with or without esketamine administration (0.2 mg/kg). The primary outcome was the incidence of POD (3-Minute Diagnostic Interview for Confusion Assessment Method-defined Delirium, 3D-CAM) on postoperative days 1, 3, and 7. The secondary outcomes were the scores of MMSE and Hospital Anxiety and Depression Scale. The intention-to-treat analysis of the outcomes were performed by generalized estimating equation. RESULTS: Six patients who did not receive an intervention because of canceled operation were excluded after randomization. The datasets containing 426 cases were analyzed following the intention-to-treat principle after handling missing data via multiple imputation method. The incidence of POD declined from about 55% on postoperative day 1 to 31 and 18% on postoperative days 3 and 7, respectively. Afternoon operation [B=-0.583, OR (95% CI) 0.558 (0.319-0.976); P=0.041], but not esketamine, significantly decreased the incidence of POD. Both esketamine and operation time failed to significantly affect MMSE, HAD, and NRS score. There was no interaction among operation time, esketamine, and follow up time. CONCLUSION: Elderly patients undergoing elective noncardiac surgery in the afternoon displayed lower POD incidence than those operated in the morning. A single low-dose of esketamine before general anesthesia induction failed to significantly decrease the risk of POD but decrease the risk of intraoperative hypotension and emergence agitation.

A systematic review of the application of machine learning techniques to ultrasound tongue imaging analysis.

B-mode ultrasound has emerged as a prevalent tool for observing tongue motion in speech production, gaining traction in speech therapy applications. However, the effective analysis of ultrasound tongue image frame sequences (UTIFs) encounters many challenges, such as the presence of high levels of speckle noise and obscured views. Recently, the application of machine learning, especially deep learning techniques, to UTIF interpretation has shown promise in overcoming these hurdles. This paper presents a thorough examination of the existing literature, focusing on UTIF analysis. The scope of our work encompasses four key areas: a foundational introduction to deep learning principles, an exploration of motion tracking methodologies, a discussion of feature extraction techniques, and an examination of cross-modality mapping. The paper concludes with a detailed discussion of insights gleaned from the comprehensive literature review, outlining potential trends and challenges that lie ahead in the field.

Cyano-determined mercury (II) ion selective fluorescence assay over polymeric carbon nitride.

Selective response is the key index to evaluate the performance of polymeric carbon nitride (PCN)-based heavy metal ion fluorescence sensors. Herein, to explore the role of cyano groups on selectivity, four kinds of PCN, including PCN-Cl, PCN-Ac, PCN-B and PCN-K were prepared by the molten salt method of sodium chloride and sodium acetate, the reduction method of sodium borohydride and the etching method of potassium hydroxide, respectively. These PCNs exhibited different surface cyano characteristics, but all of them had significant blue emission under ultraviolet excitation. It is proved that the assistant of sodium chloride or potassium hydroxide is an effective method to prepare PCNs with abundant surface cyano group. A series of fluorescence quenching experiments of metal ions showed that the cyano-rich degree of PCN is closely related to its selective response to mercury (II) ions. PCN-Cl and PCN-K emerged good selective quenching of mercury (II) ions, which may be related to the soft acid-soft base strong interaction between mercury (II) ions and cyano groups. Both PCN-Cl and PCN-K fluorescent probes for mercury (II) ions had a linear range of 5 âˆ¼ 50 µmol L-1, and PCN-Cl exhibited a lower detection limit of 0.38 µmol L-1. This work confirmed the selective fluorescence response of cyano-rich PCN to mercury (II) ions, proposed the mechanism of selective fluorescence quenching response of mercury (II) ions, and provided a new idea for the design of efficient and accurate PCN-based fluorescence probes.

Artificial intelligence for geoscience: Progress, challenges, and perspectives.

This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth's complexities and uncertainties pose challenges in optimization and real-world applicability. In contrast, contemporary data-driven models, particularly those utilizing machine learning (ML) and deep learning (DL), leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge. ML techniques have shown promise in addressing Earth science-related questions. Nevertheless, challenges such as data scarcity, computational demands, data privacy concerns, and the "black-box" nature of AI models hinder their seamless integration into geoscience. The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm. These models, which incorporate domain knowledge to guide AI methodologies, demonstrate enhanced efficiency and performance with reduced training data requirements. This review provides a comprehensive overview of geoscientific research paradigms, emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience. It examines major methodologies, showcases advances in large-scale models, and discusses the challenges and prospects that will shape the future landscape of AI in geoscience. The paper outlines a dynamic field ripe with possibilities, poised to unlock new understandings of Earth's complexities and further advance geoscience exploration.

Recent advancements in battery thermal management system (BTMS): A review of performance enhancement techniques with an emphasis on nano-enhanced phase change materials.

Because of their numerous benefits such as high charge cycle count, low self-discharge rate, low maintenance requirements, and tiny footprint, Li-batteries have been extensively employed in recent times. However, mostly Li-batteries have a limited lifespan of up to three years after production, may catch fire if the separator is damaged, and cannot be recharged when they are fully depleted. Due to the significant heat generation that li-batteries produce while they are operating, the temperature difference inside the battery module rises. This reduces the operating safety of battery and limits its life. Therefore, maintaining safe battery temperatures requires efficient thermal management using both active and passive. Thermal optimization may be achieved battery thermal management system (BTMS) that employs phase change materials (PCMs). However, PCM's shortcomings in secondary heat dissipation and restricted thermal conductivity still require development in the design, structure, and materials used in BTMS. We summarize new methods to control temperature of batteries using Nano-Enhanced Phase Change Materials (NEPCMs), air cooling, metallic fin intensification, and enhanced composite materials using nanoparticles which work well to boost their performance. To the scientific community, the idea of nano-enhancing PCMs is new and very appealing. Hybrid and ternary battery modules are already receiving attention for the li-battery life span enhancement ultimately facilitating their broader adoption across various applications, from portable electronics to electric vehicles and beyond.

Assessing a machine learning-based downscaling framework for obtaining 1km daily precipitation from GPM data.

Hydro-meteorological monitoring through satellites in arid and semi-arid regions is constrained by the coarse spatial resolution of precipitation data, which impedes detailed analyses. The objective of this study is to evaluate various machine learning techniques for developing a downscaling framework that generates high spatio-temporal resolution precipitation products. Focusing on the Hai River Basin, we evaluated three machine learning approaches-Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Back Propagation (BP) neural networks. These methods integrate environmental variables including land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), Precipitable Water Vapor (PWV), and albedo, to downscale the 0.1° spatial resolution Global Precipitation Measurement (GPM) product to a 1 km resolution. We further refined the results with residual correction and calibration using terrestrial rain gauge data. Subsequently, utilizing the 1 km annual precipitation, we employed the moving average window method to derive monthly and daily precipitation. The results demonstrated that the XGBoost method, calibrated with Geographical Difference Analysis (GDA) and Kriging spatial interpolation, proved to be the most accurate, achieving a Mean Absolute Error (MAE) of 58.40 mm for the annual product, representing a 14 % improvement over the original data. The monthly and daily products achieved MAE values of 11.61 mm and 1.79 mm, respectively, thus enhancing spatial resolution while maintaining accuracy comparable to the original product. In the Hai River Basin, key factors including longitude, latitude, DEM, LST_night, and PWV demonstrated greater importance and stability than other factors, thereby enhancing the model's precipitation prediction capabilities. This study provides a comprehensive assessment of the annual, monthly, and daily high-temporal and high-spatial resolution downscaling processes of precipitation, serving as an important reference for hydrology and related fields.

Diagnostic value of combined detection of AKP, TSGF, and LDH for pediatric osteosarcoma: a case-control study.

OBJECTIVE: To evaluate the diagnostic value of serum alkaline phosphatase (AKP), tumor-supplied growth factor group (TSGF), and lactate dehydrogenase (LDH) for pediatric osteosarcoma. METHODS: A retrospective analysis of clinical data from 81 pediatric osteosarcoma patients (osteosarcoma group) and 63 patients with benign bone tumors (benign bone tumor group) admitted to Yantaishan Hospital from February 2023 to November 2023 was conducted. Basic and clinical data differences between the two groups of children were compared. A multivariate regression model was established to determine predictive factors for pediatric osteosarcoma, and the diagnostic value of identified indicators for pediatric osteosarcoma was evaluated. RESULTS: Osteosarcoma group demonstrated significantly higher serum AKP (375.76±73.47 vs 286.12±76.50 U/L), TSGF (69.01±16.30 vs 53.57±16.37 U/mL), and LDH (269.55±66.96 vs 207.46±59.20 U/L) levels as compared to the benign bone tumor group. Correlation analysis suggested significant positive correlations between AKP (rho=0.505), TSGF (rho=406), LDH (rho=0.449) and pediatric osteosarcoma. Multivariate regression analysis showed serum AKP, TSGF, and LDH were independent predictive factor for pediatric osteosarcoma. The AUC value for AKP was 0.794, with a Youden index of 0.459; the AUC value for TSGF was 0.736, with a Youden index of 0.406; and the AUC value for LDH was 0.761, with a Youden index of 0.462. The combined use of these three biomarkers yielded an AUC of 0.886. CONCLUSION: The combined detection of serum AKP, TSGF, and LDH can enhance the diagnostic accuracy of pediatric osteosarcoma, providing important evidence for clinical treatment.

Cardiovascular health and cancer mortality: evidence from US NHANES and UK Biobank cohort studies.

BACKGROUND: The American Heart Association recently introduced a novel cardiovascular health (CVH) metric, Life's Essential 8 (LE8), for health promotion. However, the relationship between LE8 and cancer mortality risk remains uncertain. METHODS: We investigated 17,076 participants from US National Health and Nutrition Examination Survey (US NHANES) and 272,727 participants from UK Biobank, all free of cancer at baseline. The CVH score, based on LE8 metrics, incorporates four health behaviors (diet, physical activity, smoking, and sleep) and four health factors (body mass index, lipid, blood glucose, and blood pressure). Self-reported questionnaires assessed health behaviors. Primary outcomes were mortality rates for total cancer and its subtypes. The association between CVH score (continuous and categorical variable) and outcomes was examined using Cox model with adjustments. Cancer subtypes-related polygenic risk score (PRS) was constructed to evaluate its interactions with CVH on cancer death risk. RESULTS: Over 141,526 person-years in US NHANES, 424 cancer-related deaths occurred, and in UK Biobank, 8,872 cancer deaths were documented during 3,690,893 person-years. High CVH was associated with reduced overall cancer mortality compared to low CVH (HR 0.58, 95% CI 0.37-0.91 in US NHANES; 0.51, 0.46-0.57 in UK Biobank). Each one-standard deviation increase in CVH score was linked to a 19% decrease in cancer mortality (HR: 0.81; 95% CI: 0.73-0.91) in US NHANES and a 19% decrease (HR: 0.81; 95% CI: 0.79-0.83) in UK Biobank. Adhering to ideal CVH was linearly associated with decreased risks of death from lung, bladder, liver, kidney, esophageal, breast, colorectal, pancreatic, and gastric cancers in UK Biobank. Furthermore, integrating genetic data revealed individuals with low PRS and high CVH exhibited the lowest mortality from eight cancers (HRs ranged from 0.36 to 0.57) compared to those with high PRS and low CVH. No significant modification of the association between CVH and mortality risk for eight cancers by genetic predisposition was observed. Subgroup analyses showed a more pronounced protective association for overall cancer mortality among younger participants and those with lower socio-economic status. CONCLUSIONS: Maintaining optimal CVH is associated with a substantial reduction in the risk of overall cancer mortality. Adherence to ideal CVH correlates linearly with decreased mortality risk across multiple cancer subtypes. Individuals with both ideal CVH and high genetic predisposition demonstrated significant health benefits. These findings support adopting ideal CVH as an intervention strategy to mitigate cancer mortality risk and promote healthy aging.