A Novel Nutrition-Related Prognostic Biomarker for Predicting Survival in Patients with Colorectal Cancer.

BACKGROUND: Colorectal cancer (CRC) is a prevalent global malignancy with substantial morbidity and mortality. Accurate prognostic evaluation is essential in CRC patient management. This study investigates the prognostic significance of red blood cell count (RBC) and Onodera's prognostic nutritional index (OPNI) in resectable CRC patients. METHODS: A retrospective analysis of 210 CRC patients undergoing radical resection (January 2015-January 2017) assessed clinical and hematological factors, including RBC, albumin, hemoglobin, and OPNI. A novel comprehensive biomarker, R-OPNI, combining preoperative RBC with OPNI, was introduced. Correlations with patient survival were analyzed, and R-OPNI's independent prognostic value was assessed through univariate and multivariate Cox models. Predictive ability was compared to other factors using the receiver operating characteristic (ROC) method. RESULTS: Higher RBC levels (≥ 3.9 × 1012/L) and elevated OPNI were associated with significantly improved overall survival. Lower R-OPNI scores (0 or 1) indicated notably poorer survival. Multivariate analysis confirmed R-OPNI's independent prognostic significance (HR: 0.273, 95% CI: 0.098-0.763, p = 0.013). R-OPNI (AUC = 0.732) demonstrated superior predictive value compared to individual prognostic factors. CONCLUSION: R-OPNI emerges as a robust, independent prognostic predictor for resectable CRC patients, emphasizing the importance of assessing preoperative nutritional status.

Roles of different organizations in implementing patient-reported measures in routine maternity care in Finland.

BACKGROUND: The integration of patient-centered care (PCC) and value-based healthcare (VBHC) principles, emphasizing personalized, responsive care and cost efficiency, is crucial in modern healthcare. Despite advocation from the International Consortium for Health Outcomes Measurement (ICHOM) for the global adoption of these principles through patient-reported measures (PRMs), their implementation, especially the pregnancy and childbirth (PCB) set, remains limited in maternity care. This study focuses on understanding the optimal organizational entity for integrating standard ICHOM-PCB-PRMs into routine maternity care in Finland. It aims to clarify the distribution of tasks among stakeholders and gather Finnish maternity healthcare professionals' perspectives on organizational responsibility in PRM collection. The emphasis was on identifying the optimal organizational framework for managing PRMs in maternity care. RESULTS: A total of 66 maternity healthcare professionals participated in the study, reaching a consensus that public maternity care centers in Finland should be the primary entity responsible for managing PRMs in the maternity sector. Key aspects such as confidence with the role as a mother, maternal confidence with breastfeeding, and satisfaction with the result of care were identified as crucial and should be inquired about in both public maternity care centers and hospital maternity wards. The findings highlight the importance of comprehensive and consistent attention to these PRMs across public maternity care centers and hospital maternity settings to ensure holistic and effective maternal care. CONCLUSIONS: The study highlights the central role of public maternity care centers in the collection and management of PRMs within Finnish maternity care, as agreed upon by the professional consensus. It underscores the importance of a consistent and holistic approach to PRM inquiry across different care settings to enhance the quality and effectiveness of maternity care. This finding is crucial for policymakers and healthcare practitioners, suggesting that reinforcing the collaborative efforts between public maternity care centers and hospital maternity wards is vital for a patient-centric, efficient healthcare system. Aligning with PCC and VBHC principles, this approach aims to improve healthcare outcomes for pregnant and postpartum women in Finland, emphasizing the need for a unified strategy in managing maternity care.

[Differences in Nitrogen Sources and Contributions in Shallow Groundwater in Plateau Lake Area with Different Climate Types].

Clarifying the concentration, major sources, and contribution differences of nitrogen in shallow groundwater in plateau lake areas with different climate types can provide a novel direction for the control of nitrate （NO3-） pollution in regional groundwater. Taking the shallow groundwater around Erhai Lake in the subtropical monsoon climate zone and Chenghai Lake in the dry-hot valley area of the Jinsha River as the research objects, using hydrochemical indexes and multi-isotope techniques （δ15N-NO3-, δ18O-NO3-, δ18O-H2O, and δ2H-H2O） combined with the stable isotope （SIAR） model； the differences in nitrogen concentration in shallow groundwater around Erhai Lake and Chenghai Lake were analyzed, the sources of NO3- were identified, and the contribution rates of each pollution source were calculated. The results showed that water quality of more than 33% and 5% of shallow groundwater sampling points around Erhai Lake and Chenghai Lake was worse than the groundwater Class Ⅲ quality requirements （GB/T 14848） of 20 mg·L-1 for nitrate nitrogen （NO3--N）, respectively. The δ18O-H2O and δ2H-H2O in shallow groundwater around Erhai Lake and Chenghai Lake were parallel to the global and Chinese atmospheric precipitation lines, and a large intercept was present, indicating that atmospheric precipitation was not the major recharge source of groundwater in the two regions. The contribution rate of different NO3- sources in shallow groundwater around Erhai Lake was the highest for soil organic nitrogen （53.77%）, followed by nitrogen fertilizer （21.75%） and manure and sewage （21.55%）, and atmospheric deposition nitrogen （2.93%） was the lowest. Denitrification occurred in the transformation process of nitrogen in groundwater. The contribution rate of different NO3- sources in shallow groundwater around Chenghai Lake was manure and sewage （44.88%） > soil organic nitrogen （37.03%） > nitrogen fertilizer （16.17%） > atmospheric deposition nitrogen （1.92%）, and nitrification occurred in the transformation process of nitrogen in groundwater. The climate type significantly affected the shallow groundwater level, altering the migration and transformation process of nitrogen, thereby affecting the nitrogen concentration in groundwater and the contribution of NO3- as the chief source. However, the major source of NO3- was not affected by the climate type； however was more affected by land use, agricultural activities, and manure treatment methods.

Key Factors Influencing Public Attitudes Toward Charities' Involvement in Emergency Management: A Study Based on Online Public Opinion in China.

Charities play a pivotal role in engaging the public in emergency management efforts. They serve to complement governmental restrictions by leveraging social resources to aid in emergency management. The involvement of charities in emergency management is likely to shape public attitudes, thereby influencing their effectiveness in this sphere. Therefore, understanding the factors that influence public attitudes toward charities in emergency management is crucial. This study sought to identify these key factors and offer recommendations for charities to enhance their participation in emergency management. The data for this study were collected from messages and comments on two prominent instant messaging platforms, WeChat Public and Sina Weibo. Content Analysis was employed to categorize the data, and the Apriori algorithm was utilized to uncover association rules and key factors. Based on the key factors, it is recommended that charities focus on collaborating with celebrities and enterprises, prioritize establishing and upholding a positive reputation, and enhance their expertise in emergency management practices.

Single-cell RNA-seq analysis revealed the stemness of a specific cluster of B cells in acute lymphoblastic leukemia progression.

Background: Childhood acute lymphoblastic leukemia (ALL) is a common pediatric cancer. The heterogeneous characterization of B cells in ALL progression poses new challenges to researchers. We used single-cell sequencing to explore the critical role of B cells in regulating the ALL immune microenvironment. Method: We collected the single cell (sc) RNA-seq data of ALL and health sample from the gene expression omnibus (GEO) database, the "Seurat" and "harmony" R package was used for quality control and scRNA-seq analysis, in which the CellMarker2.0 database was used for cell type annotation. Subsequently, the FindAllMarkers function was used to identify the differentially expressed genes (DEGs) among various cell types and the DAVID database was applied for the biological process of DEGs. Then, the "inferCNV" package was used for copy number variation, regulons and cell communication were performed by SCENIC tool and CellChat package. The role of the target gene in regulating ALL progression was assessed using RT-qPCR, Transwell and scratch healing assays. Results: We identified nine mainly cell clusters after scRNA-seq analysis, in which the B cells had higher infiltration proportion in the ALL samples and were sub-clustered into five cell sub-groups. The B cells 1 is closely associated with cell proliferation and stemness (TNFAIP3 and KDM5B), and the significant CNV of amplification occurred on chr6 and chr21 that supported stemness of B cells1. RXRB is a key transcription factor mediated the proliferation of B cells 1, which in turn suppressed hematopoietic stem cells (HSCs) proliferation and promoted cytotoxic NK/T cells activation through diverse cell communication ways. One of the key regulators of B cells is MYC, which promotes the migration and invasive ability of cell line leukemia cell lines. Conclusion: This study reveals the stemness characteristics of B cells and their critical role in ALL progression, a finding that provides new potential directions for the development of targeted therapies against ALL.

Rate Coefficient and Branching Ratio for the Formation of Criegee Intermediate Syn-/Anti-CH3CHOO from CH3CHI + O2 and the Self-Reaction of Syn-/Anti-CH3CHOO Determined with Simultaneous IR/UV Probes.

A flow reactor coupled with a light-emitting diode at 286 nm, an infrared quantum-cascade laser near 11 µm, and an ultraviolet laser at 335 nm was implemented to probe the precursor CH3CHI2, syn-CH3CHOO, and anti/syn-CH3CHOO, respectively, in the reaction of CH3CHI + O2. The branching between syn- and anti-CH3CHOO was determined to be ≈80:20 from two methods. The concentration temporal profiles of anti-CH3CHOO, derived on comparison of infrared and ultraviolet profiles, yielded the rate coefficient for the self-reaction of anti-CH3CHOO, kself anti = (6 ± 2) × 10-10 cm3 molecule-1 s-1, ∼4 times the corresponding value, kself syn = (1.4 ± 0.3) × 10-10 cm3 molecule-1 s-1, for syn-CH3CHOO; the rate coefficient for the cross-reaction between syn-CH3CHOO and anti-CH3CHOO was estimated to be (2.1 ± 0.6) × 10-10 cm3 molecule-1 s-1. With determined concentrations of syn-CH3CHOO and self-reaction rate coefficients, the rate coefficient for the formation of CH3CHOO from CH3CHI + O2 was determined to be kform = (3.8 ± 0.7) × 10-12 cm3 molecule-1 s-1 at 298 K, ∼45% of previous reports.

Development of Machine Learning Model for Predicting Prolonged Operation Time in Lumbar Stenosis undergoing Posterior Lumbar Interbody Fusion: A Multi-center study.

BACKGROUND CONTEXT: Longer posterior lumbar interbody fusion (PLIF) surgeries for individuals with lumbar spinal stenosis are linked to more complications and negatively affect recovery after the operation. Therefore, there is a critical need for a method to accurately predict patients who are at risk for prolonged operation times. PURPOSE: This research aimed to develop a clinical model to predict prolonged operation time for patients undergoing PLIF procedures. STUDY DESIGN/SETTING: This study employs a machine-learning approach to analyze data retrospectively collected. PATIENT SAMPLE: 3233 patients diagnosed with lumbar spinal stenosis (LSS) had posterior lumbar interbody fusion (PLIF) at 22 hospitals in China from January 2015 to December 2022. OUTCOME MEASURES: The primary outcome was operation time. Prolonged operation time defined as exceeded 75% of the overall surgical duration, which mean exceeding 240 minutes. METHODS: A total of 3233 patients who underwent PLIF surgery with lumbar spinal stenosis (LSS) were divided into one training group and four test groups based on different district areas. The training group included 1569 patients, while Test1 had 541, Test2 had 403, Test3 had 351, and Test4 had 369 patients. Variables consisted of demographics, perioperative details, preoperative laboratory examinations and other Additional factors. Six algorithms were employed for variable screening, and variables identified by more than two screening methods were incorporated into the final model. In the training cohort, a 10-fold cross-validation (CV) and Bayesian hyperparameter optimization techniques were utilized to construct a model using eleven machine learning algorithms. Following this, the model was evaluated using four separate external test sets, and the mean Area Under the Curve (AUC) was computed to determine the best-performing model. Further performance metrics of the best model were evaluated, and SHapley Additive exPlanations(SHAP) were used for interpretability analysis to enhance decision-making transparency. Ultimately, an online calculator was created. RESULTS: Among the various machine learning models, the Random Forest achieved the highest performance in the validation set, with AUROC scores of 0.832 in Test1, 0.834 in Test2, 0.816 inTest3, 0.822 in Test4) compared with other machine learning models. The top contributing variables were number of levels fusion, pre-APTT, weight and age. The predictive model was further refined by developing a web-based calculator for clinical application. (https://wenle.shinyapps.io/PPOT_LSS/) CONCLUSIONS: This predictive model can facilitate identification of risk for prolonged operation time following PLIF surgery. Predictive calculators are expected to improve preoperative planning, identify patients with high risk factors, and help clinicians facilitating the improvement of treatment plans and the implementation of clinical intervention.

Customization of 2D Atomic-Molecular Heterojunction with Manipulatable Charge-Transfer and Band Structure.

Manipulating the properties of 2D materials through meticulously engineered artificial heterojunctions holds great promise for novel device applications. However, existing research on the crucial charge-transfer interactions and energy profile regulation is predominantly focused on 2D van der Waals structures formed via weak van der Waals forces, limiting regulatory efficiency at high costs. Herein, a refined atomic-molecular heterojunction strategy featuring strong covalent bonds between organic molecule and 2D violet phosphorus (VP) atomic crystal is developed, which enables enhanced charge-transfer dynamics and customizable band structure regulation at the molecular level. Both experimentally and theoretically, it is demonstrated that grafting efficiency, charge redistribution, and energy gap regulation critically depend on organic electronegativity, providing a low-cost yet high-efficiency regulatory effect on a large scale. As a proof of concept, the novel VP-molecular heterojunctions exhibit optimized performance in diverse application domains, presenting a general platform for future high-performance device applications.

Clinical performance evaluation of an HIV Duo assay: From HIV screening to acute and non-acute HIV infection detection.

BACKGROUND: Evaluating the clinical performance of Elecsys HIV Duo assay for primary human immunodeficiency virus (HIV) screening and acute HIV infection detection. METHODS: This study was conducted from April 2022 to April 2023 and involved two distinct populations. For the HIV screening population, three HIV Duo results [HIV Duo, HIV antigen (Ag), and HIV antibody (Ab)] in primary screening were obtained (January 2021 to June 2021). In the diagnosed HIV population, retrospective samples from November 2016 to March 2023 were measured. RESULTS: The HIV screening population included 111,383 samples from a real-world screening program. The assay demonstrated a specificity of 99.91 % (95 % CI: 99.89 %, 99.93 %) and a PPV of 0.8516 (95 % CI: 0.8225, 0.8776). Regarding the diagnosed HIV population, 836 HIV patients were enrolled, including 14 acute HIV infectious patients with only HIV Ag + and a Western Blot (WB) confirmation rate of 0 %. The median (IQR) of the numeric cut-off index (COI) ratios of HIV Duo Ab and Ag significantly differed among the Ag + Ab-, Ag-Ab+, and Ag + Ab + subgroups. CONCLUSION: The Elecsys HIV Duo assay is suitable for primary HIV screening and can be integrated into a novel laboratory HIV testing algorithm to improve acute HIV detection in Chinese clinical practice. ABBREVIATIONS: HIV, Human immunodeficiency virus; AIDS, acquired immunodeficiency syndrome; Ag, antigen; Ab, antibody; WB, Western Blot; COI, numeric cut-off index; CI, confidence interval; NAT, nucleic acid tests; EDC, electronic data capture systems; CDC, Chinese Centers for Disease Control and Prevention; IQR, interquartile range; PPV, positive predictive value; HCV, hepatitis C virus; HBV, hepatitis B virus; CI, confidence interval; ND, not able to define; F, female; M, male.

Explainable machine learning and online calculators to predict heart failure mortality in intensive care units.

AIMS: This study aims to develop explainable machine learning models and clinical tools for predicting mortality in patients in the intensive care unit (ICU) with heart failure (HF). METHODS: Patients diagnosed with HF who experienced their first ICU stay lasting between 24 h and 28 days were selected from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The primary outcome was all-cause mortality within 28 days. Data analysis was performed using Python and R, with feature selection conducted via least absolute shrinkage and selection operator (LASSO) regression. Fifteen models were evaluated, and the most effective model was rendered explainable through the Shapley additive explanations (SHAP) approach. A nomogram was developed based on logistic regression to facilitate interpretation. For external validation, the eICU database was utilized. RESULTS: After selection, the study included 2343 records, with 1808 surviving and 535 deceased patients. The median age of the study population was 70.00, with ~3/5 males (60.31%). The median length of stay in the ICU was 6.00 days. The median age of the survival group was younger than the non-survival group (69.00 vs. 73.00), and non-survival patients spent longer time in the ICU. Seventy-five features were initially selected, including basic information, vital signs, laboratory tests, haemodynamics and oxygen status. LASSO regression determined the shrinkage parameter α = 0.020, and 44 features were chosen for model construction. The linear discriminant analysis (LDA) model showed the best performance, and the accuracy reached 0.8354 in the training cohort and 0.8563 in the testing cohort. It showed satisfying area under the curve (AUC), recall, precision, F1 score, Cohen's kappa score and Matthew's correlation coefficient. The concordance index (c-index) reached 0.7972 in the training cohort and 0.8125 in the testing cohort. In external validation, the LDA model achieved approximately 0.9 in accuracy, precision, recall and F1 score, with an AUC of 0.79. Univariable analysis was performed in the training cohort. Features that differed significantly between the survival and non-survival groups were subjected to multiple logistic regression. The nomogram built on multiple logistic regression included 14 features and demonstrated excellent performance. The AUC of the nomogram is 0.852 in the training cohort, 0.855 in the internal validation cohort and 0.770 in the external validation cohort. The calibration curve showed good consistency. CONCLUSIONS: The study developed an LDA and a nomogram model for predicting mortality in HF patients in the ICU. The SHAP approach was employed to elucidate the LDA model, enhancing its utility for clinicians. These models were made accessible online for clinical application.

An improved approach to generate IL-15+/+/TGFßR2-/- iPSC-derived natural killer cells using TALEN.

We present a TALEN-based workflow to generate and maintain dual-edited (IL-15+/+/TGFßR2-/-) iPSCs that produce enhanced iPSC-derived natural killer (iNK) cells for cancer immunotherapy. It involves using a cell lineage promoter for knocking in (KI) gene(s) to minimize the potential effects of expression of any exogenous genes on iPSCs. As a proof-of-principle, we KI IL-15 under the endogenous B2M promoter and show that it results in high expression of the sIL-15 in iNK cells but minimal expression in iPSCs. Furthermore, given that it is known that knockout (KO) of TGFßR2 in immune cells can enhance resistance to the suppressive TGF-ß signaling in the tumor microenvironment, we develop a customized medium containing Nodal that can maintain the pluripotency of iPSCs with TGFßR2 KO, enabling banking of these iPSC clones. Ultimately, we show that the dual-edited IL-15+/+/TGFßR2-/- iPSCs can be efficiently differentiated into NK cells that show enhanced autonomous growth and are resistant to the suppressive TGF-ß signaling.

Clinical Features and Vaccination Effects among Children with Post-Acute Sequelae of COVID-19 in Taiwan.

BACKGROUND: Post-acute sequelae of SARS-CoV-2 infection (PASC) affects patients after recovering from acute coronavirus disease 2019 (COVID-19). This study investigates the impact of SARS-CoV-2 vaccination on PASC symptoms in children in Taiwan during the Omicron pandemic. METHODS: We enrolled children under 18 years with PASC symptoms persisting for more than 4 weeks. Data collected included demographics, clinical information, vaccination status, and symptom persistence. We used logistic regression models to compare symptoms in the acute and post-COVID-19 phases and to assess the association between vaccination and these symptoms. RESULTS: Among 500 PASC children, 292 (58.4%) were vaccinated, 282 (52.8%) were male, and the mean (SD) age was 7.6 (4.6) years. Vaccinated individuals exhibited higher odds of experiencing symptoms in the previous acute phase, such as cough (adjusted odds ratio [AOR] = 1.57; 95% confidence interval [CI]: 1.02-2.42), rhinorrhea/nasal congestion (AOR = 1.74; 95% CI: 1.13-2.67), sneezing (AOR = 1.68; 95% CI: 1.02-2.76), sputum production (AOR = 1.91; 95% CI: 1.15-3.19), headache/dizziness (AOR = 1.73; 95% CI: 1.04-2.87), and muscle soreness (AOR = 2.33; 95% CI: 1.13-4.80). In contrast, there were lower odds of experiencing abdominal pain (AOR = 0.49; 95% CI: 0.25-0.94) and diarrhea (AOR = 0.37; 95% CI: 0.17-0.78) in children who had received vaccination during the post-COVID-19 phase. CONCLUSIONS: This study revealed clinical features and vaccination effects in PASC children in Taiwan. Vaccination may reduce some gastrointestinal symptoms in the post-COVID-19 phase.

Microfluidic Synthesis of Multifunctional Micro-/Nanomaterials from Process Intensification: Structural Engineering to High Electrochemical Energy Storage.

Multifunctional micro-/nanomaterials featuring functional superiority and high value-added physicochemical nature have received immense attention in electrochemical energy storage. Microfluidic synthesis has become an emergent technology for massively producing multifunctional micro-/nanomaterials with tunable microstructure and morphology due to its rapid mass/heat transfer and precise fluid controllability. In this review, the latest progresses and achievements in microfluidic-synthesized multifunctional micro-/nanomaterials are summarized via reaction process intensification, multifunctional micro-/nanostructural engineering and electrochemical energy storage applications. The reaction process intensification mechanisms of various micro-/nanomaterials, including quantum dots (QDs), metal materials, conducting polymers, metallic oxides, polyanionic compounds, metal-organic frameworks (MOFs) and two-dimensional (2D) materials, are discussed. Especially, the multifunctional structural engineering principles of as-fabricated micro-/nanomaterials, such as vertically aligned structure, heterostructure, core-shell structure, and tunable microsphere, are introduced. Subsequently, the electrochemical energy storage application of as-prepared multifunctional micro-/nanomaterials is clarified in supercapacitors, lithium-ion batteries, sodium-ion batteries, all-vanadium redox flow batteries, and dielectric capacitors. Finally, the current problems and future forecasts are illustrated.

[Shallow Groundwater Around Plateau Lakes： Spatiotemporal Distributions of Dissolved Carbon and Its Driving Factors].

Dissolved carbon in groundwater plays an important role in carbon cycling and ecological function maintenance, and its concentration level affects the migration and transformation of pollutants in groundwater. To understand the spatiotemporal variation characteristics of dissolved carbon and its driving factors in shallow groundwater around plateau lakes, variations in the concentrations of dissolved organic carbon （DOC）, inorganic carbon （DIC）, and total carbon （DTC） and their driving factors in shallow groundwater （n = 404） around eight plateau lakes were analyzed. The results indicated that the average values of ρ（DOC）, ρ（DIC）, and ρ（DTC） in shallow groundwater around plateau lakes were 8.23, 49.01, and 57.84 mg·L-1, respectively, with the ρ（DOC） in 79.0% of shallow groundwater samples exceeding 5 mg·L-1. There were no significant differences in the DOC, DIC, and DTC concentrations between rainy and dry seasons, whereas the change in dissolved carbon concentrations in shallow groundwater were strongly affected by the intensity of agricultural intensification and the depth of groundwater table； the DOC, DIC, and DTC concentrations in shallow groundwater from facility agricultural regions （SFAR）, cropland fallow agricultural regions （CFAR）, and intensive agricultural regions with deeper groundwater tables （DIAR） were significantly reduced by 25.8% - 56.6%, 14.0% - 32.9%, and 16.6% - 36.7%, respectively, compared with those in intensive agricultural regions with shallower groundwater tables （SIAR）. Additionally, the dissolved carbon concentrations in shallow groundwater from DIAR were significantly lower than those of SFAR and CFAR. RDA revealed that physicochemical factors in water and soil significantly explained the changes in the dissolved carbon concentrations. Moreover, the dissolved carbon concentrations in shallow groundwater around Yilong Lake were significantly higher than those of other lakes, whereas that of Chenghai Lake was significantly lower than that of other lakes. Our study highlights that agricultural intensification intensity and groundwater table depth jointly drove the variations in dissolved carbon concentrations in shallow groundwater around plateau lakes. The study results are expected to provide a scientific basis for understanding the carbon cycle in plateau lake areas with underground runoff flowing into lakes and evaluating the attenuation of pollutants by dissolved carbon in shallow groundwater.

Determining nine fasciolicides and three metabolite residues in milk and infant formula using solid-phase extraction and liquid chromatography-tandem mass spectrometry.

A new sensitive method of liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis for nine fasciolicides (closantel, rafoxanide, oxyclozanide, niclosamide, nitroxinil, ioxynil, 4-nitro-3-(trifluoromethyl)phenol, salicylanilide, and triclabendazole) and three metabolite residues (ketotriclabnedazole, triclabendazole sulfone, and triclabendazole sulfoxide) in milk and infant formula was established. The samples were extracted and purified through solid-phase extraction and analyzed using LC-MS/MS. The proposed method demonstrated high accuracy (the average recoveries ranged from 70.5 % to 107.4 %) and high sensitivity (the limits of quantification ranged from 1.0 to 25.0 µg/kg). This method was successfully applied to determine nine fasciolicides and three metabolite residues in 45 milk and infant formula, providing technical support for the safety and quality evaluation of dairy products.

Self-Healable Sandfish Scale-Inspired Scalable Triboelectric Layer for Hybrid Energy Harvesting in Desert Environment.

In deserts, sedimentation from frequent dust activities on solar cells poses a substantial technical challenge, reducing efficiency and necessitating advanced cost-inefficient cleaning mechanisms. Herein, a novel sandfish scale-inspired self-healing fluorinated copolymer-based triboelectric layer is directly incorporated on top of the polysilicon solar cell for sustained hybrid energy harvesting. The transparent biomimetic layer, with distinctive saw-tooth microstructured morphology, exhibits ultra-low sand adhesion and high abrasion-resistant properties, inhibits sedimentation deposition on solar cells, and concurrently harvests kinetic energy from wind-driven sand particles through triboelectric nanogenerator (TENG). The film exhibits a low friction coefficient (0.149), minimal sand adhesion force (27 nN), and a small wear area (327 µm2). In addition, over 2 months, a solar cell with the sandfish scale-inspired structure demonstrates only a 16% decline in maximum power output compared to the bare solar cell, which experiences a 60% decline. Further, the sandfish scale-based TENG device's electrical output is fully restored to its original value after a 6-h self-healing cycle and maintains consistent stable outputs. These results highlight the exceptional advantages of employing biomimetic self-healing materials as robust triboelectric layers, showcasing sustained device stability and durability for prolonged use in harsh desert environments, ultimately contributing to a low cost-of-electricity generation paradigm.

Exploring the impact of women-specific reproductive factors on phenotypic aging and the role of life's essential 8.

BACKGROUND: Aging is an inevitable biological process. Accelerated aging renders adults more susceptible to chronic diseases and increases their mortality rates. Previous studies have reported the relationship between lifestyle factors and phenotypic aging. However, the relationship between intrinsic factors, such as reproductive factors, and phenotypic aging remains unclear. METHODS: This study utilized data from the National Health and Nutrition Examination Survey (NHANES), spanning from 1999 to 2010 and 2015-2018, with 14,736 adult women. Random forest imputation was used to handle missing covariate values in the final cohort. Weighted linear regression was utilized to analyze the relationship between women-specific reproductive factors and PhenoAgeAccel. Considering the potential impact of menopausal status on the results, additional analyses were conducted on premenopausal and postmenopausal participants. Additionally, the Life's Essential 8 (LE8) was used to investigate the impact of healthy lifestyle and other factors on the relationship between women-specific reproductive factors and PhenoAgeAccel. Stratified analyses were conducted based on significant interaction p-values. RESULTS: In the fully adjusted models, delayed menarche and gynecological surgery were associated with increased PhenoAgeAccel, whereas pregnancy history were associated with a decrease. Additionally, early or late ages of menopause, first live birth, and last live birth can all negatively impact PhenoAgeAccel. The relationship between women-specific reproductive factors and PhenoAgeAccel differs between premenopausal and postmenopausal women. High LE8 scores positively impacted the relationship between certain reproductive factors (age at menarche, age at menopause, age at first live birth, and age at last live birth) and phenotypic age acceleration. Stratified analysis showed significant interactions for the following variables: BMI with age at menarche, pregnancy history, and age at menopause; ethnicity with age at menopause, age at first live birth, and parity; smoking status with use of contraceptive pills and gynecologic surgery; hypertension with use of contraceptive pills, pregnancy history, and age at menopause. CONCLUSION: Delayed menarche, gynecological surgery, and early or late ages of menopause, first live birth, and last live birth are associated with accelerated phenotypic aging. High LE8 score may alleviate the adverse effects of reproductive factors on phenotypic aging.

Association of Prenatal Congenital Lung Malformation Volume Ratio to Postnatal Computed Tomography Characteristics.

INTRODUCTION: Congenital lung malformations (CLMs) are diverse and readily diagnosed on prenatal ultrasound. Postnatal computed tomography (CT) characteristics, including volume, are used in centers for the clinical decision-making of asymptomatic CLM. We aim to evaluate the relationship of prenatal CLM volume ratio (CVR) to postnatal CT characteristics by suspected prenatal diagnosis and postnatal radiological diagnosis. METHODS: We performed a single-center retrospective review of all patients evaluated with prenatally diagnosed CLM (May 2015-December 2022). Demographics, prenatal imaging findings at initial evaluation, and postnatal radiological diagnosis/imaging findings were analyzed with descriptive statistics. Pairwise correlation coefficient tests were performed to analyze the correlation between prenatal CVR and postnatal CT lesion size stratified by diagnosis. RESULTS: Of 123 patients referred and evaluated, suspected prenatal diagnoses consisted of 68 bronchial atresia (BA), 20 intralobar bronchopulmonary sequestration (iBPS), 20 extralobar bronchopulmonary sequestration (eBPS), and nine congenital pulmonary airway malformation (CPAM). Postnatal radiological diagnoses consisted of 53 BA, 22 iBPS, 14 eBPS, and 20 CPAM. Overall correlation coefficient of prenatal CVR to postnatal CT lesion size volume was 0.56. By suspected prenatal diagnosis, correlation coefficients were 0.61 (BA), 0.59 (iBPS), 0.29 (eBPS), and 0.51 (CPAM). For postnatal radiological diagnosis, correlation coefficients were 0.58 (BA), 0.56 (iBPS), 0.33 (eBPS), and 0.62 (CPAM). CONCLUSIONS: Our results indicate that initial CVR is overall consistent with the postnatal CT lesion size. This correlation is present in suspected prenatal diagnoses of BA and iBPS and postnatal radiological diagnoses of BA, iBPS, and CPAM. Additional studies analyzing long-term follow-up should be conducted to specify the safety of patients who undergo observation rather than surgical intervention.

Unlocking Potential of Pyrochlore in Energy Systems via Soft Voting Ensemble Learning.

In traditional machine learning (ML)-based material design, the defects of low prediction accuracy, overfitting and low generalization ability are mainly caused by the training of a single ML model. Here, a Soft Voting Ensemble Learning (SVEL) approach is proposed to solve the above issues by integrating multiple ML models in the same scene, thus pursuing more stable and reliable prediction. As a case study, SVEL is applied to develop the broad chemical space of novel pyrochlore electrocatalysts with the molecular formula of A2B2O7, to explore promising pyrochlore oxides and accelerate predictions of unknown pyrochlore in the periodic table. The model successfully established the structure-property relationship of pyrochlore, and selected six cost-effective pyrochlore from the periodic table with a high prediction accuracy of 91.7%, all of which showed good electrocatalytic performance. SVEL not only effectively avoids the high costs of experimentation and lengthy computations, but also addresses biases arising from data scarcity in single models. Furthermore, it has significantly reduced the research cycle of pyrochlore by ≈ 22 years, offering broad prospects for accelerating the development of materials genomics. SVEL method is intended to integrate multiple AI models to provide broader model training clues for the AI material design community.