Developmental pleiotropy of SDP1 from seedling to mature stages in B. napus.

Rapeseed, an important oil crop, relies on robust seedling emergence for optimal yields. Seedling emergence in the field is vulnerable to various factors, among which inadequate self-supply of energy is crucial to limiting seedling growth in early stage. SUGAR-DEPENDENT1 (SDP1) initiates triacylglycerol (TAG) degradation, yet its detailed function has not been determined in B. napus. Here, we focused on the effects of plant growth during whole growth stages and energy mobilization during seedling establishment by mutation in BnSDP1. Protein sequence alignment and haplotypic analysis revealed the conservation of SDP1 among species, with a favorable haplotype enhancing oil content. Investigation of agronomic traits indicated bnsdp1 had a minor impact on vegetative growth and no obvious developmental defects when compared with wild type (WT) across growth stages. The seed oil content was improved by 2.0-2.37% in bnsdp1 lines, with slight reductions in silique length and seed number per silique. Furthermore, bnsdp1 resulted in lower seedling emergence, characterized by a shrunken hypocotyl and poor photosynthetic capacity in the early stages. Additionally, impaired seedling growth, especially in yellow seedlings, was not fully rescued in medium supplemented with exogenous sucrose. The limited lipid turnover in bnsdp1 was accompanied by induced amino acid degradation and PPDK-dependent gluconeogenesis pathway. Analysis of the metabolites in cotyledons revealed active amino acid metabolism and suppressed lipid degradation, consistent with the RNA-seq results. Finally, we proposed strategies for applying BnSDP1 in molecular breeding. Our study provides theoretical guidance for understanding trade-off between oil accumulation and seedling energy mobilization in B. napus.

Enhancing Oxygen Activation Ability by Composite Interface Construction over a 2D Co3O4-Based Monolithic Catalyst for Toluene Oxidation.

Developing robust metal-based monolithic catalysts with efficient oxygen activation capacity is crucial for thermal catalytic treatment of volatile organic compound (VOC) pollution. Two-dimensional (2D) metal oxides are alternative thermal catalysts, but their traditional loading strategies on carriers still face challenges in practical applications. Herein, we propose a novel in situ molten salt-loading strategy that synchronously enables the construction of 2D Co3O4 and its growth on Fe foam for the first time to yield a unique monolithic catalyst named Co3O4/Fe-S. Compared to the Co3O4 nanocube-loaded Fe foam, Co3O4/Fe-S exhibits a significantly improved catalytic performance with a temperature reduction of 44 °C at 90% toluene conversion. Aberration-corrected scanning transmission electron microscopy and theoretical calculation suggest that Co3O4/Fe-S possesses abundant 2D Co3O4/Fe3O4 composite interfaces, which promote the construction of active sites (oxygen vacancy and Co3+) to boost oxygen activation and toluene chemisorption, thereby accelerating the transformation of reaction intermediates through Langmuir-Hinshelwood (L-H) and Mars-van Krevelen (MvK) mechanisms. Moreover, the growth mechanism reveals that 2D Co3O4/Fe3O4 composite interfaces are generated in situ in molten salt, inducing the growth of 2D Co3O4 onto the surface lattice of 2D Fe3O4. This study provides new insights into enhancing oxygen activation and opens an unprecedented avenue in preparing efficient monolithic catalysts for VOC oxidation.

Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases.

OBJECTIVE: Existing approaches to fairness evaluation often overlook systematic differences in the social determinants of health, like demographics and socioeconomics, among comparison groups, potentially leading to inaccurate or even contradictory conclusions. This study aims to evaluate racial disparities in predicting mortality among patients with chronic diseases using a fairness detection method that considers systematic differences. METHODS: We created five datasets from Mass General Brigham's electronic health records (EHR), each focusing on a different chronic condition: congestive heart failure (CHF), chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), chronic liver disease (CLD), and dementia. For each dataset, we developed separate machine learning models to predict 1-year mortality and examined racial disparities by comparing prediction performances between Black and White individuals. We compared racial fairness evaluation between the overall Black and White individuals versus their counterparts who were Black and matched White individuals identified by propensity score matching, where the systematic differences were mitigated. RESULTS: We identified significant differences between Black and White individuals in age, gender, marital status, education level, smoking status, health insurance type, body mass index, and Charlson comorbidity index (p-value < 0.001). When examining matched Black and White subpopulations identified through propensity score matching, significant differences between particular covariates existed. We observed weaker significance levels in the CHF cohort for insurance type (p = 0.043), in the CKD cohort for insurance type (p = 0.005) and education level (p = 0.016), and in the dementia cohort for body mass index (p = 0.041); with no significant differences for other covariates. When examining mortality prediction models across the five study cohorts, we conducted a comparison of fairness evaluations before and after mitigating systematic differences. We revealed significant differences in the CHF cohort with p-values of 0.021 and 0.001 in terms of F1 measure and Sensitivity for the AdaBoost model, and p-values of 0.014 and 0.003 in terms of F1 measure and Sensitivity for the MLP model, respectively. DISCUSSION AND CONCLUSION: This study contributes to research on fairness assessment by focusing on the examination of systematic disparities and underscores the potential for revealing racial bias in machine learning models used in clinical settings.

Identification and bioinformatics analysis of lncRNAs in serum of patients with ankylosing spondylitis.

OBJECTIVES: The aim of this study was to explore the long non-coding RNA (lncRNA) expression profiles in serum of patients with ankylosing spondylitis (AS). The role of these lncRNAs in this complex autoimmune situation needs to be evaluated. METHODS: We used high-throughput whole-transcriptome sequencing to generate sequencing data from three patients with AS and three normal controls (NC). Then, we performed bioinformatics analyses to identify the functional and biological processes associated with differentially expressed lncRNAs (DElncRNAs). We confirmed the validity of our RNA-seq data by assessing the expression of eight lncRNAs via quantitative reverse transcription polymerase chain reaction (qRT-PCR) in 20 AS and 20 NC samples. We measured the correlation between the expression levels of lncRNAs and patient clinical index values using the Spearman correlation test. RESULTS: We identified 72 significantly upregulated and 73 significantly downregulated lncRNAs in AS patients compared to NC. qRT-PCR was performed to validate the expression of selected DElncRNAs; the results demonstrated that the expression levels of MALAT1:24, NBR2:9, lnc-DLK1-35:13, lnc-LARP1-1:1, lnc-AIPL1-1:7, and lnc-SLC12A7-1:16 were consistent with the sequencing analysis results. Enrichment analysis showed that DElncRNAs mainly participated in the immune and inflammatory responses pathways, such as regulation of protein ubiquitination, major histocompatibility complex class I-mediated antigen processing and presentation, MAPkinase activation, and interleukin-17 signaling pathways. In addition, a competing endogenous RNA network was constructed to determine the interaction among the lncRNAs, microRNAs, and mRNAs based on the confirmed lncRNAs (MALAT1:24 and NBR2:9). We further found the expression of MALAT1:24 and NBR2:9 to be positively correlated with disease severity. CONCLUSION: Taken together, our study presents a comprehensive overview of lncRNAs in the serum of AS patients, thereby contributing novel perspectives on the underlying pathogenic mechanisms of this condition. In addition, our study predicted MALAT1 has the potential to be deeply involved in the pathogenesis of AS.

Retrospective analysis of predictive factors for AVF dysfunction in patients undergoing MHD.

To construct an early clinical prediction model for AVF dysfunction in patients undergoing Maintenance Hemodialysis (MHD) and perform internal and external verifications. We retrospectively examined clinical data from 150 patients diagnosed with MHD at Hefei Third People's Hospital from January 2014 to June 2023. Depending on arteriovenous fistula (AVF) functionality, patients were categorized into dysfunctional (n = 62) and functional (n = 88) cohorts. Using the least absolute shrinkage and selection operator(LASSO) regression model, variables potentially influencing AVF functionality were filtered using selected variables that underwent multifactorial logistic regression analysis. The Nomogram model was constructed using the R software, and the Area Under Curve(AUC) value was calculated. The model's accuracy was appraised through the calibration curve and Hosmer-Lemeshow test, with the model undergoing internal validation using the bootstrap method. There were 11 factors exhibiting differences between the group of patients with AVF dysfunction and the group with normal AVF function, including age, sex, course of renal failure, diabetes, hyperlipidemia, Platelet count (PLT), Calcium (Ca), Phosphorus, D-dimer (D-D), Fibrinogen (Fib), and Anastomotic width. These identified factors are included as candidate predictive variables in the LASSO regression analysis. LASSO regression identified age, sex, diabetes, hyperlipidemia, anastomotic diameter, blood phosphorus, and serum D-D levels as 7 predictive factors. Unconditional binary logistic regression analysis revealed that advanced age (OR = 4.358, 95% CI: 1.454-13.062), diabetes (OR = 4.158, 95% CI: 1.243-13.907), hyperlipidemia (OR = 3.651, 95% CI: 1.066-12.499), D-D (OR = 1.311, 95% CI: 1.063-1.616), and hyperphosphatemia (OR = 4.986, 95% CI: 2.513-9.892) emerged as independent risk factors for AVF dysfunction in MHD patients. The AUC of the predictive model was 0.934 (95% CI: 0.897-0.971). The Hosmer-Lemeshow test showed high consistency between the model's predictive results and actual clinical observations (χ2 = 1.553, P = .092). Internal validation revealed an AUC of 0.911 (95% CI: 0.866-0.956), with the Calibration calibration curve nearing the ideal curve. Advanced age, coexisting diabetes, hyperlipidemia, blood D-D levels, and hyperphosphatemia are independent risk factors for AVF dysfunction in patients undergoing MHD.

Development of the Chinese coaches' autonomy-supportive-laissez-faire coaching style scale.

Introduction: The autonomy-supportive coaching style is recognized for its positive impact on athletes' well-being and performance. However, the transition of excessive autonomy into a laissez-faire coaching style has not been thoroughly examined within the context of coach evaluation scales. Existing scales focus predominantly on the positive dimensions of autonomy support, and do not possess the capabilities to measure outcomes which may be viewed as negative or other outcomes. This study aims to integrate the autonomy-supportive and laissez-faire coaching styles within the same measurement framework. Methods: Our study developed a comprehensive scale to assess both the autonomy-supportive and laissez-faire coaching styles, drawing on items from the Sport Climate Questionnaire for autonomy support and adapting items from leadership research for laissez-faire coaching. We conducted two studies: the first with 148 athletes to refine the laissez-faire items and the second with 460 athletes to validate the full scale, utilizing exploratory factor analysis, confirmatory factor analysis, and correlation analysis. We also measured internal consistency and split-half reliability. Results: The finalized scale includes a 6-item autonomy-supportive subscale and a 5-item laissez-faire subscale. Validation processes confirmed the scale's construct and criterion validity, alongside its reliability. Discussion: The Chinese Coaches' Autonomy-Supportive-Laissez-Faire Coaching Style Scale effectively captures both the beneficial and potentially detrimental aspects of coaching styles, addressing a critical gap in the literature and providing a reliable tool for evaluating coaching approaches.

Amitriptyline protects afferent synapses in the cochlea against excitotoxic trauma in vitro.

Afferent synapses between inner hair cells (IHCs) and the type I spiral ganglion neurons (SGNs) in the cochlea provide over 95% of sensory signals for auditory perception in the brain. However, these afferent synapses are particularly vulnerable to damage, for example from excitotoxicity, and exposure to noise in the environment which often leads to noise-induced cochlear synaptopathy (NICS). In this study, we simulated excitotoxic trauma by incubating kainic acid, a non-desensitizing agonist for AMPA type glutamate receptors on cultured cochleae. The possible protective effects of amitriptyline against NICS were examined. We found that, in IHCs, amitriptyline reversed the decrease of Ca2+ current and exocytosis caused by excitotoxic trauma. In SGNs, amitriptyline promoted the recovery of neurite loss caused by excitotoxic trauma. Furthermore, we found that the protective effects of amitriptyline are likely mediated by suppressing apoptosis factors that were upregulated during excitotoxic trauma. In conclusion, our results suggest that amitriptyline could protect afferent synapses in the cochlea from NICS, making it a potential drug candidate for hearing protection.

Danshensu reduces neuronal excitability by enhancing potassium currents in bushy cells in the mouse cochlear nucleus.

Danshensu, also known as salvianic acid A, is a primary active compound extracted from a traditional Chinese herb Danshen (Salvia miltiorrhiza). While its antioxidative and neuroprotective effects are well-documented, the underlying mechanisms are poorly understood. In this study, we sought out to investigate if and how Danshensu modulates neuronal excitability and voltage-gated ionic currents in the central nervous system. We prepared brain slices of the mouse brainstem and performed patch-clamp recording in bushy cells in the anteroventral cochlear nucleus, with or without Danshensu incubation for 1 h. QX-314 was used internally to block Na+ current, while tetraethylammonium and 4-aminopyridine were used to isolate different subtypes of K+ current. We found that Danshensu of 100 µm decreased the input resistance of bushy cells by approximately 60% and shifted the voltage threshold of spiking positively by approximately 7 mV, resulting in significantly reduced excitability. Furthermore, we found this reduced excitability by Danshensu was caused by enhanced voltage-gated K+ currents in these neurons, including both low voltage-activated IK,A, by approximately 100%, and high voltage-activated IK,dr, by approximately 30%. Lastly, we found that the effect of Danshensu on K+ currents was dose-dependent in that no enhancement was found for Danshensu of 50 µm and Danshensu of 200 µm failed to cause significantly more enhancement on K+ currents when compared to that of 100 µm. We found that Danshensu reduced neuronal excitability in the central nervous system by enhancing voltage-gated K+ currents, providing mechanistic support for its neuroprotective effect widely seen in vivo.

Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models.

Objectives: Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. However, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to handle various biases in AI models developed using EHR data. Materials and Methods: We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, analyzing articles from PubMed, Web of Science, and IEEE published between January 01, 2010 and December 17, 2023. The review identified key biases, outlined strategies for detecting and mitigating bias throughout the AI model development, and analyzed metrics for bias assessment. Results: Of the 450 articles retrieved, 20 met our criteria, revealing 6 major bias types: algorithmic, confounding, implicit, measurement, selection, and temporal. The AI models were primarily developed for predictive tasks, yet none have been deployed in real-world healthcare settings. Five studies concentrated on the detection of implicit and algorithmic biases employing fairness metrics like statistical parity, equal opportunity, and predictive equity. Fifteen studies proposed strategies for mitigating biases, especially targeting implicit and selection biases. These strategies, evaluated through both performance and fairness metrics, predominantly involved data collection and preprocessing techniques like resampling and reweighting. Discussion: This review highlights evolving strategies to mitigate bias in EHR-based AI models, emphasizing the urgent need for both standardized and detailed reporting of the methodologies and systematic real-world testing and evaluation. Such measures are essential for gauging models' practical impact and fostering ethical AI that ensures fairness and equity in healthcare.

Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models.

OBJECTIVES: Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. However, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to handle various biases in AI models developed using EHR data. MATERIALS AND METHODS: We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, analyzing articles from PubMed, Web of Science, and IEEE published between January 01, 2010 and December 17, 2023. The review identified key biases, outlined strategies for detecting and mitigating bias throughout the AI model development, and analyzed metrics for bias assessment. RESULTS: Of the 450 articles retrieved, 20 met our criteria, revealing 6 major bias types: algorithmic, confounding, implicit, measurement, selection, and temporal. The AI models were primarily developed for predictive tasks, yet none have been deployed in real-world healthcare settings. Five studies concentrated on the detection of implicit and algorithmic biases employing fairness metrics like statistical parity, equal opportunity, and predictive equity. Fifteen studies proposed strategies for mitigating biases, especially targeting implicit and selection biases. These strategies, evaluated through both performance and fairness metrics, predominantly involved data collection and preprocessing techniques like resampling and reweighting. DISCUSSION: This review highlights evolving strategies to mitigate bias in EHR-based AI models, emphasizing the urgent need for both standardized and detailed reporting of the methodologies and systematic real-world testing and evaluation. Such measures are essential for gauging models' practical impact and fostering ethical AI that ensures fairness and equity in healthcare.

Adsorption of humic acid from different organic solid waste compost to phenanthrene, is fluorescence excitation or quenching?

Humic acid (HA) from different organic solid waste (OSW) compost has been shown good adsorption properties for phenanthrene. However, the raw material of HA can affect its structure, resulting in differences in adsorption capacity. Therefore, this study focused on the adsorption characteristics of phenanthrene by HA from different OSW compost. In this work, chicken manure (CM), rice straw (RS) and lawn waste (LW) were selected as sources of composted HA. The adsorption mechanism of HA from different OSW compost were revealed through analytical techniques including three-dimensional fluorescence spectroscopy (EEM), two-dimensional correlation spectroscopy (2DCOS), and Fourier-transform infrared spectroscopy (FTIR). The results suggested that HA from LW compost had a better adsorption affinity for phenanthrene because of its more complex fluorescent component, where C1 as a simple component determined the adsorption process specifically. Furthermore, after HA from LW compost adsorbed phenanthrene, the increase in aromatic -COOH and -NH was the main reason for fluorescence quenching. These results indicated that HA from LW compost had better adsorption effect for phenanthrene. The results of this study were expected to provide a selection scheme for the control of phenanthrene pollution and environmental remediation.

Frailty Is More Predictive of Mortality than Age in Patients With Hip Fractures.

OBJECTIVES: To investigate the association between the Comprehensive Geriatric Assessment-based Frailty Index and adverse outcomes in older adult patients undergoing hip fracture surgery. DESIGN: Retrospective cohort study. SETTING: Academic Level 1 Trauma Center. PATIENTS: All patients aged 65 or older who underwent surgical repair of a hip fracture between May 2018 and August 2020 were identified through institutional database review. OUTCOME MEASURES AND COMPARISONS: Data including demographics, FI, injury presentation, and hospital course were collected. Patients were grouped by FI as nonfrail (FI < 0.21), frail (0.21 ≤ FI < 0.45), and severely frail (FI > 0.45). Adverse outcomes of these groups were compared using Kaplan Meier survival analysis. Risk factors for 1-year rehospitalization and 2-year mortality were evaluated using Cox hazard regression. RESULTS: Three hundred sixteen patients were included, with 62 nonfrail, 185 frail, and 69 severely frail patients. The total population was on average 83.8 years old, predominantly white (88.0%), and majority female (69.9%) with an average FI of 0.33 (SD: 0.14). The nonfrail cohort was on average 78.8 years old, 93.6% white, and 80.7% female; the frail cohort was on average 84.5 years old, 92.4% white, and 71.9% female; and the severely frail cohort was on average 86.4 years old, 71.0% white, and 55.1% female. Rate of 1-year readmission increased with frailty level, with a rate of 38% in nonfrail patients, 55.6% in frail patients, and 74.2% in severely frail patients (P = 0.001). The same pattern was seen in 2-year mortality rates, with a rate of 2.8% in nonfrail patients, 36.7% in frail patients, and 77.5% in severely frail patients (P < 0.0001). Being classified as frail or severely frail exhibited greater association with mortality within 2 years than age, with hazard ratio of 17.81 for frail patients and 56.81 for severely frail patients compared with 1.19 per 5 years of age. CONCLUSIONS: Increased frailty as measured by the Frailty Index is significantly associated with increased 2-year mortality and 1-year hospital readmission rates after hip fracture surgery. Degree of frailty predicts mortality more strongly than age alone. Assessing frailty with the Frailty Index can identify higher-risk surgical candidates, facilitate clinical decision making, and guide discussions about goals of care with family members, surgeons, and geriatricians. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Assessing Risk Factors for Cognitive Decline Using Electronic Health Record Data: A Scoping Review.

Background: The data and information contained within electronic health records (EHR) provide a rich, diverse, longitudinal view of real-world patient histories, offering valuable opportunities to study antecedent risk factors for cognitive decline. However, the extent to which such records' data have been utilized to elucidate the risk factors of cognitive decline remains unclear. Methods: A scoping review was conducted following the PRISMA guideline, examining articles published between January 2010 and April 2023, from PubMed, Web of Science, and CINAHL. Inclusion criteria focused on studies using EHR to investigate risk factors for cognitive decline. Each article was screened by at least two reviewers. Data elements were manually extracted based on a predefined schema. The studied risk factors were classified into categories, and a research gap was identified. Results: From 1,593 articles identified, 80 were selected. The majority (87.5%) were retrospective cohort studies, with 66.3% using datasets of over 10,000 patients, predominantly from the US or UK. Analysis showed that 48.8% of studies addressed medical conditions, 31.3% focused on medical interventions, and 17.5% on lifestyle, socioeconomic status, and environmental factors. Most studies on medical conditions were linked to an increased risk of cognitive decline, whereas medical interventions addressing these conditions often reduced the risk. Conclusions: EHR data significantly enhanced our understanding of medical conditions, interventions, lifestyle, socioeconomic status, and environmental factors related to the risk of cognitive decline.

Sustainable all-weather CO2 utilization by mimicking natural photosynthesis in a single material.

Solar-driven CO2 conversion into hydrocarbon fuels is a sustainable approach to synchronously alleviating the energy crisis and achieving net CO2 emissions. However, the dependence of the conversion process on solar illumination hinders its practical application due to the intermittent availability of sunlight at night and on cloudy or rainy days. Here, we report a model material of Pt-loaded hexagonal tungsten trioxide (Pt/h-WO3) for decoupling light and dark reaction processes, demonstrating the sustainable CO2 conversion under dark conditions for the first time. In such a material system, hydrogen atoms can be produced by photocatalytic water splitting under solar illumination, stored together with electrons in the h-WO3 through the transition of W6+ to W5+ and spontaneously released to trigger catalytic CO2 reduction under dark conditions. Furthermore, we demonstrate using natural light that CH4 production can persist at night and on rainy days, proving the accomplishment of all-weather CO2 conversion via a sustainable way.

Caesalpinflavans D-F and caesalpinnone B, hybrid flavan-chalcones and normonoterpene-chalcone heterodimer from Caesalpinia digyna.

Three undescribed hybrid flavan-chalcones, caesalpinflavans D-F, and an unreported normonoterpene-chalcone heterodimer, caesalpinnone B, along with three known biflavonoids were isolated from the twigs and leaves of Caesalpinia digyna. Their structures were elucidated based on extensive spectroscopic analysis and quantum chemical calculations. Caesalpinflavan F was identified as a bis-(hybrid flavan-chalcone), its natural occurrence was supported by HPLC-IT-TOF-MS analysis. The condensation of caesalpinflavan B with acetone was possibly a key step in the biosynthesis of caesalpinflavan F. Caesalpinnone B represents an unprecedented meroterpenoid featuring a cyclobutane central framework, which was derived from chalcone and normonoterpenoid via a key [2 + 2] cyclization reaction. Biological evaluation revealed that compounds caesalpinflavan D, oxytrodiflavanone A, and caesalpinnone B exhibited moderate cytotoxicity against HL-60, SMMC-7721, SW480, A-549 and/or MDA-MB-231 cell lines with IC50 values ranging from 8.051 ± 0.673 to 24.26 ± 0.61 µM. This study provided evidence for further research and possible utilization of C. digyna in the future.

Designing biomimetic two-dimensional channels for uranium separation from seawater.

Efficient separation of uranium from seawater stands as a pivotal challenge. This study unveils an approach focusing on the ingenious design of biomimetic two-dimensional (2D) membranes tailored explicitly for this purpose. Leveraging the unique interplay of DNA strands housing U aptamers, pH-responsive i-motifs, and poly A(10) segments ingeniously embedded within graphene oxide membranes, a distinctive biomimetic 2D channel is engineered. The strategic integration of these bio-inspired elements enables dynamic adjustment of interlayer spacing, augmenting both the permeability of the membrane and the selectivity of the aptamer for uranyl ions. During the separation process, the encounter between uranyl ions and the enhanced aptamer within the interlayers initiates a crucial interaction, triggering a specific concentration polarization mechanism. This mechanism stands as the cornerstone for achieving a highly selective separation of uranyl ions from the vast and complex matrix of seawater. The membrane exhibits excellent performance in real seawater, with a rejection rate of uranyl ions of ≈100% and sustained selectivity of uranyl ions over ten cycles. Importantly, the selectivity of uranium and vanadium can reach 14.66. The significance of this research lies not only in the effective separation of uranyl ions but also in showcasing the broader applicability of 2D membrane design in chemical engineering.

Study on the electrodeposition of uranium in chloride molten salt.

This study focuses on the recovery of UO2 from oxide spent fuel using electrodeposition. U3O8 was used as the initial material and dissolved in NaCl-2CsCl using NH4Cl at high temperatures by means of chlorination reaction. The electrolysis process was conducted using a three-electrode system to investigate the effects of cathode material and diameter, electrolysis temperature, electrolysis time, electrolysis voltage, and uranium concentration in the molten salt on the electrolysis reaction. By optimizing the electrolysis conditions, pure UO2 with a recovery efficiency of 97% was obtained, and the products were characterized using XRD, SEM-EDS, ICP-AES and XPS. It was found that within the scope of this experiment, increasing the cathode diameter, extending the electrolysis time, and increasing the reduction voltage appropriately all led to an improvement in the recovery efficiency of the electrolysis reaction, while other conditions had minimal effect on the reaction. Furthermore, doping of the electrolyte system was performed by adding La, Ce and Nd elements, while the removal of La showed good purification effects, with a maximum decontamination factor of 119. Furthermore, the system showed good purification effects for Nd, with a decontamination factor of 57.

Identification of an ANCA-Associated Vasculitis Cohort Using Deep Learning and Electronic Health Records.

Background: ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electronic health records (EHR) can more accurately identify AAV cases. Methods: We examined the Mass General Brigham (MGB) repository of clinical documentation from 12/1/1979 to 5/11/2021, using expert-curated keywords and ICD codes to identify a large cohort of potential AAV cases. Three labeled datasets (I, II, III) were created, each containing note sections. We trained and evaluated a range of machine learning and deep learning algorithms for note-level classification, using metrics like positive predictive value (PPV), sensitivity, F-score, area under the receiver operating characteristic curve (AUROC), and area under the precision and recall curve (AUPRC). The deep learning model was further evaluated for its ability to classify AAV cases at the patient-level, compared with rule-based algorithms in 2,000 randomly chosen samples. Results: Datasets I, II, and III comprised 6,000, 3,008, and 7,500 note sections, respectively. Deep learning achieved the highest AUROC in all three datasets, with scores of 0.983, 0.991, and 0.991. The deep learning approach also had among the highest PPVs across the three datasets (0.941, 0.954, and 0.800, respectively). In a test cohort of 2,000 cases, the deep learning model achieved a PPV of 0.262 and an estimated sensitivity of 0.975. Compared to the best rule-based algorithm, the deep learning model identified six additional AAV cases, representing 13% of the total. Conclusion: The deep learning model effectively classifies clinical note sections for AAV diagnosis. Its application to EHR notes can potentially uncover additional cases missed by traditional rule-based methods.

MiR-134-3p targets HMOX1 to inhibit ferroptosis in granulosa cells of sheep follicles.

BACKGROUND: The intricate interplay of gene expression within ovarian granulosa cells (GCs) is not fully understood. This study aimed to investigate the miRNA regulatory mechanisms of ferroptosis during the process of follicle development in lamb GCs. METHODS: Employing transcriptome sequencing, we compared differentially expressed mRNAs (DE-mRNAs) and miRNAs (DE-miRNAs) in GCs from lambs treated with follicle-stimulating hormone (FL) to untreated controls (CL). We further screened differentially expressed ferroptosis-related genes and identified potential miRNA regulatory factors. The expression patterns of HMOX1 and miRNAs in GCs were validated using qRT‒PCR and Western blotting. Additionally, we investigated the regulatory effect of oar-miR-134-3p on HMOX1 and its function in ferroptosis through cell transfection and erastin treatment. RESULTS: We identified a total of 4,184 DE-mRNAs and 304 DE-miRNAs. The DE-mRNAs were mainly enriched in ferroptosis, insulin resistance, and the cell cycle. Specifically, we focused on the differential expression of ferroptosis-related genes. Notably, the ferroptosis-related genes HMOX1 and SLC3A2, modulated by DE-miRNAs, were markedly suppressed in FLs. Experimental validation revealed that HMOX1 was significantly downregulated in FL and large follicles, while oar-miR-134-3p was significantly upregulated compared to that in the CLs. HMOX1 expression was regulated by the targeting effect of oar-miR-134-3p. Functional assays further revealed that modulation of oar-miR-134-3p influenced HMOX1 expression and altered cellular responses to ferroptosis induction by erastin. CONCLUSION: This study suggested that oar-miR-134-3p and HMOX1 may be one of the pathways regulating ferroptosis in GCs. This finding provides new clues to understanding the development and regulatory process of follicles.

Exploration of the Healthy Donor Effect Among 0.6 Million Blood Donors in China: Longitudinal Study.

BACKGROUND: The World Health Organization emphasizes the importance of completely voluntary blood donation to maintain safe and sustainable blood supplies. However, the benefits of blood donation for donors, such as reducing the risk of disease, remain a topic of debate due to the existence of the healthy donor effect (HDE). This effect arises because of inherent health differences between blood donors and the general population, and it is also considered a methodological issue. OBJECTIVE: This study aims to generate a more detailed health profile of blood donors from a donor cohort study to mitigate and quantify the HDE and properly interpret the association between blood donation and disease outcomes among blood donors. METHODS: A retrospective cohort study was conducted between January 2012 and December 2018 among donors before their first donation. One-to-one propensity score matching was conducted through a random selection of individuals without any history of blood donation, as reported from their electronic health records. We conducted a Poisson regression between blood donors and non-blood donors before the first donation to estimate the adjusted incidence rate ratio (AIRR) of selected blood donation-related diseases, as defined by 13 categories of International Classification of Diseases, Tenth Revision (ICD-10) codes. RESULTS: Of the 0.6 million blood donors, 15,115 had an inpatient record before their first donation, whereas 17,356 non-blood donors had an inpatient record. For the comparison between blood donors and the matched non-blood donors, the HDE (the disease incidence rate ratio between non-blood donors and blood donors) was an AIRR of 1.152 (95% CI 1.127-1.178; P<.001). Among disease categories not recommended for blood donation in China, the strongest HDE was observed in the ICD-10 D50-D89 codes, which pertain to diseases of the blood and blood-forming organs as well as certain disorders involving the immune mechanism (AIRR 3.225, 95% CI 2.402-4.330; P<.001). After age stratification, we found that people who had their first blood donation between 46-55 years old had the strongest HDE (AIRR 1.816, 95% CI 1.707-1.932; P<.001). Both male and female donors had significant HDE (AIRR 1.082, 95% CI 1.05-1.116; P=.003; and AIRR 1.236, 95% CI 1.196-1.277; P<.001, respectively) compared with matched non-blood donors. CONCLUSIONS: : Our research findings suggest that the HDE is present among blood donors, particularly among female donors and those who first donated blood between the ages of 46 and 55 years. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2200055983; https://www.chictr.org.cn/showproj.html?proj=51760.