Search

As the parent nuclide of 99mTc，99Mo plays an important role for diagnosis in modern nuclear medicine.Fission products from uranium targets irradiated in nuclear reactors，besides 99Mo，a large amount of other radionuclides are produced during this process. Therefore，the highly efficient separation and purification for 99Mo from complex fission products has always been a major challenge. This article summarizes in two directions: new methods for the separation and purification of 99Mo in recent years，and optimization of existing processes. Emerging separation technologies，such as ionic liquid，cloud point extraction，gas phase separation and photo-controlled reaction，due to their high efficiency，environmental protection and easy operation，have shown great potential for application . This article also concludes the optimization method，automatic production，which not only improves the efficiency of separation and purification，but also enhances the safety of operators in the production process. With the continuous innovation of technology，it is expected that the more efficient，environmentally friendly and economical new processes will emerge to promote the sustainable development and upgrading of 99Mo industry in the future.

99Mo is an important medical radionuclide. Solvent extraction is one of the feasible methods for separating fission 99Mo from neutron-irradiated 235U targets in reactor. In this work，the extraction behavior of Mo(VI) in nitric acid system by ionic liquid Cyphos IL 101 was studied. The effects of diluent，initial pH value，extraction time，temperature，ionic liquid concentration and other factors on the extraction efficiency were investigated. The results showed that when toluene was selected as the diluent，high extraction efficiency of Mo(VI) was obtained. At pH 3，the extraction rate of Mo(VI) was over 98.87%. Meanwhile，it indicated that the extraction process was spontaneous，which could reach equilibrium within 5 min. When 1 mol/L Na2CO3 was selected as the stripping agent，96.56% of Mo(VI) could be re-extracted into the aqueous phase in one stripping. Mo(VI) could be separated over from some main fission impurities such as Sr，Cs，Te. This study is expected to pave the way for introduction of ionic liquid Cyphos IL 101 as an extraction agent for the separation process of fission 99Mo from irradiated uranium targets.

Abstract Melatonin (MT) can improve plant resistance and fruit quality. The mechanism by which MT affects soluble sugar and organic acids accumulation in drupe fruits is not clear. In this study, 100 µmol/L MT was sprayed on the leaves of plum trees at the second stage of rapid fruit expansion (90 and 97 d after flowering), and the effects of MT on plum fruit quality and its effects on the soluble sugar-organic acid metabolism were investigated. At 28 d after MT treatment (at maturity), the longitudinal diameter, fruit weight, and vitamin C content of plum fruits were increased by 5.05%, 12.93%, and 56.09%, respectively, compared to the control. MT caused significant increase in the total soluble solids content and decreased the titratable acid content. MT increased the contents of total soluble sugar, sucrose, sorbitol, and citric acid after 21 and 28 days of treatment, while decreasing the contents of fructose, malic acid, quinic acid, and tartaric acid after 28 days of treatment. Additionally, MT increased the activities of sucrose synthase (catabolism direction), sucrose phosphate synthase, glucokinase, fructokinase, sorbitol oxidase, and NADP+-malic enzyme, and decreased the activities of soluble acid converting enzyme, cell wall insoluble converting enzyme, NAD+-sorbitol dehydrogenase, and NAD+-malic dehydrogenase after 21 or 28 days of treatment. Moreover, the differentially expressed genes (DEGs) after 21 and 28 days of treatment were accelerated starch and sucrose metabolism, galactose metabolism, fructose and mannose metabolism, as well as glycolysis, gluconeogenesis, and pentose phosphate metabolism pathways. In conclusion, exogenous MT increases soluble sugar content and decreases organic acid content in plum fruits by regulating various soluble sugar-organic acid metabolic pathways, thereby improving the fruit quality.

Lipidomics, as a metabolomics approach centered on lipids, aims to comprehensively analyze lipids within biological systems. In recent years, mass spectrometry (MS)-based lipidomics analysis techniques have been widely utilized to address qualitative and quantitative challenges of lipids within complex food matrices, leveraging their high sensitivity, high resolution and broad coverage. Firstly, this paper reviewed the workflow of lipidomic analysis, including sample pretreatment, multi-category mass spectrometry data acquisition and analysis. Subsequently, the applications of lipidomics in the researches of food quality were discussed, emphasizing lipidomics contributes to the analysis of food flavor formation mechanisms and the evaluation of food nutritional functions. Finally, the development trend of lipidomics in food quality research were prospected, aiming to provide important references for further exploration and utilization of lipid resources in food.

Abstract The application of medical image segmentation technology enables accurate localization of human tissues, providing doctors with a reliable foundation for diagnosis. While deep learning methods have proven effective in this task, most current approaches rely on a single prediction framework, which overlooks Edge semantic features and results in flawed texture features. Moreover, existing supervised methods face challenges due to limited availability of high‐quality annotations in the field of medical imaging. In this article, a Semi‐supervised Contextual Cognitive Augmentation‐based Cross‐teaching Network is proposed. A Contextual Cognitive Enhancement Module is introduced consisting of two components: data augmentation and information extraction. The data augmentation component provides multi‐level data distribution by incorporating diverse perturbation strategies such as Discrete Cosine Transform and Gaussian noise. The information extraction component employs the Comprehensive Information Extraction module, which consists of Global Perception Information Extraction module and Multi‐channel Information Extraction module to extract perceptual information from images and enhance interaction between image channels, respectively. Additionally, a cross‐teaching strategy is adopted and a hybrid loss function is utilized to encourage knowledge sharing among the networks, leveraging the advantages of dual networks for improved performance. Experimental results demonstrate significant enhancements in multiclass medical image segmentation compared to several state‐of‐the‐art single‐framework networks.

Background Prognostic nutritional index (PNI) has recently been identified as a novel marker of nutritional status. However, existing evidences on the association between PNI and the risk of lower extremity deep venous thrombosis (LEDVT) are limited and conflicting. Objective To determine the association between PNI and the risk of LEDVT, and further evaluate its diagnostic value. Methods Over a 10-year period (2012–2022), a total of 12790 patients who underwent compression ultrasonography examinations were consecutively included, and 1519 (11.9%) LEDVT events occurred. Multivariate logistic regression analysis was used to investigate the association, and receiver operating characteristic (ROC) curve was constructed to evaluate its diagnostic performance. Results After full adjustment, patients in third quartile (odds ratio [OR] = 1.486, 95% confidence interval [CI]: 1.205–1.832), second quartile (OR = 2.436, 95% CI: 1.993–2.978) and first quartile (OR = 3.422, 95% CI: 2.791–4.195) of PNI were at higher risk of LEDVT compared with those in fourth quartile, and the test for trend was significant. Consistently, each unit decrease in PNI was associated with a 6.0 % (95% CI: 1.052–1.069) increased risk of LEDVT. Moreover, adding PNI to a base model improved the area under the curve (AUC) from 0.721 (95% CI: 0.709–0.734) to 0.746 (95% CI: 0.734–0.758). Conclusion PNI is inversely associated with the risk of LEDVT, and provides significant incremental diagnostic value for the identification of LEDVT events. These findings suggest that PNI may be a potential biomarker to help clinicians identify patients at risk of thrombosis and make clinical decisions timely.

Abstract Introduction Psychotic depression (PD) is characterized by the co-occurrence of emotional dysfunction and psychotic symptoms such as delusions and hallucinations with poor clinical outcomes. TSH may involve in the development of PD. This study aims to explore relationship between TSH and PD. Methods A total of 1718 outpatients diagnosed as FEDN MDD were recruited in this study. The relationship between PD and TSH was evaluated using multivariable binary logistic regression analysis. To assess the presence of non-linear associations, a two-piecewise linear regression model was employed. Furthermore, interaction and stratified analyses were conducted with respect to sex, education, marital status, comorbid anxiety, and suicide attempt. Results Multivariable logistic regression analysis revealed that TSH was positively associated with the risk of PD after adjusting for confounders (OR = 1.26, 95% CI: 1.11 to 1.43; p

ABSTRACT Background and Aims Chronic refractory wound is a disease that seriously impairs the quality of life of patients. Negative pressure wound therapy and platelet‐rich plasma are commonly used to treat various types of wounds. Further research is necessary to explore the efficacy and safety of the combination of negative pressure wound therapy and platelet‐rich plasma in treating chronic refractory wounds. Methods PubMed, Web of Science, EMBASE, Cochrane, CINAHL, CNKI, Sino Med, and Wanfang Med Online up until March 2024 were searched(PROSPERO No. CRD42024507963). Two investigators screened literature according to inclusion and exclusion criteria, evaluated bias and certainty of evidence using RoB 2.0 and GRADE. Stata 12.0 was used to analyze the data. Results A total of 35 randomized controlled trials involving 2495 participants were included. 34 studies were assessed as having some concerns, and 1 study as having high risk in the risk of bias assessment. The results of meta‐analysis showed that effective rate (RR1.23, 95% CI [1.17, 1.30], p

BackgroundAlzheimer’s disease (AD) is a progressive neurodegenerative disorder posing challenges to patients, caregivers, and society. Accessible and accurate information is crucial for effective AD management. ObjectiveThis study aimed to evaluate the accuracy, comprehensibility, clarity, and usefulness of the Generative Pretrained Transformer’s (GPT) answers concerning the management and caregiving of patients with AD. MethodsIn total, 14 questions related to the prevention, treatment, and care of AD were identified and posed to GPT-3.5 and GPT-4 in Chinese and English, respectively, and 4 respondent neurologists were asked to answer them. We generated 8 sets of responses (total 112) and randomly coded them in answer sheets. Next, 5 evaluator neurologists and 5 family members of patients were asked to rate the 112 responses using separate 5-point Likert scales. We evaluated the quality of the responses using a set of 8 questions rated on a 5-point Likert scale. To gauge comprehensibility and participant satisfaction, we included 3 questions dedicated to each aspect within the same set of 8 questions. ResultsAs of April 10, 2023, the 5 evaluator neurologists and 5 family members of patients with AD rated the 112 responses: GPT-3.5: n=28, 25%, responses; GPT-4: n=28, 25%, responses; respondent neurologists: 56 (50%) responses. The top 5 (4.5%) responses rated by evaluator neurologists had 4 (80%) GPT (GPT-3.5+GPT-4) responses and 1 (20%) respondent neurologist’s response. For the top 5 (4.5%) responses rated by patients’ family members, all but the third response were GPT responses. Based on the evaluation by neurologists, the neurologist-generated responses achieved a mean score of 3.9 (SD 0.7), while the GPT-generated responses scored significantly higher (mean 4.4, SD 0.6; P

Abstract Background Patients with cold tumors gain limited benefits from immune checkpoint blockade (ICB) therapy owing to low programmed cell death protein ligand 1 (PD‐L1) expression and minimal immune cell infiltration. Mild photothermal therapy (PTT) using black phosphorus nanosheets (BPNSs) is a promising approach to enhance the efficacy of ICB therapy. However, to ensure that BPNS‐based PTT‐enhanced ICB therapy is clinically adaptable, a noninvasive, bedside‐accessible imaging tool capable of monitoring the status of PD‐L1 is imperative. We demonstrated that positron emission tomography (PET) using [64Cu]HKP2202 precisely delineated PD‐L1 expression in tumors receiving PTT. Methods BPNSs were modified with polyethylene glycol to prepare BPNS@PEG, which were then characterized. MC38 cells and tumor allografts were treated with BPNS@PEG followed by 808 nm near‐infrared light exposure. PET using [64Cu]HKP2202 was performed to monitor PD‐L1 expression in vivo. We also evaluated whether the efficacy of ICB therapy improved after delivering BPNS@PEG‐based PTT. Results BPNS@PEG had a well‐defined lamellar structure with clear edges and an average size of 150 nm. PET and Western blotting assays indicated that PD‐L1 expression was upregulated after BPNS@PEG and NIR‐light treatment. Notably, the antitumor effect of anti PD‐L1 therapy was enhanced in mice treated with BPNS@PEG‐based PTT. Conclusions BPNS@PEG had the capacity to convert cold tumors into hot tumors to facilitate the efficacy of ICB therapy. Importantly, the complementary diagnostic PET radiotracer targeting PD‐L1 allowed real‐time monitoring of PD‐L1 expression in the tumor microenvironment to guide ICB administration, holding great potential to achieve efficient and precise tumor immunotherapy.

Abstract Utilization of digital technologies for cataract screening in primary care is a potential solution for addressing the dilemma between the growing aging population and unequally distributed resources. Here, we propose a digital technology-driven hierarchical screening (DH screening) pattern implemented in China to promote the equity and accessibility of healthcare. It consists of home-based mobile artificial intelligence (AI) screening, community-based AI diagnosis, and referral to hospitals. We utilize decision-analytic Markov models to evaluate the cost-effectiveness and cost-utility of different cataract screening strategies (no screening, telescreening, AI screening and DH screening). A simulated cohort of 100,000 individuals from age 50 is built through a total of 30 1-year Markov cycles. The primary outcomes are incremental cost-effectiveness ratio and incremental cost-utility ratio. The results show that DH screening dominates no screening, telescreening and AI screening in urban and rural China. Annual DH screening emerges as the most economically effective strategy with 341 (338 to 344) and 1326 (1312 to 1340) years of blindness avoided compared with telescreening, and 37 (35 to 39) and 140 (131 to 148) years compared with AI screening in urban and rural settings, respectively. The findings remain robust across all sensitivity analyses conducted. Here, we report that DH screening is cost-effective in urban and rural China, and the annual screening proves to be the most cost-effective option, providing an economic rationale for policymakers promoting public eye health in low- and middle-income countries.

ObjectiveTo investigate the prevalence of chronic disease comorbidity among the elderly population in Jiangsu province to provide evidence for the development of chronic disease management strategies. MethodsA face-to-face survey was conducted among 21 344 residents aged ≥ 65 years recruited using stratified multistage cluster sampling from June to December 2021 in 12 districts/counties of six prefectures in Jiangsu province. A self-designed questionnaire and a brief cognitive impairment rating scale (Cog-12) developed by Chinese researchers were used in the survey. We analyzed the prevalence, prevalent types, and association rules of chronic comorbidity in the province's elderly population. ResultsOf the 20 724 participants with valid responses, 9 759 (47.1%) were identified as having a chronic comorbidity. Of the observed cases of comorbidity, 3 947 (40.4%), 2 731 (28.0%), and 3 081 (31.6%) had two, three, and four or more chronic conditions, respectively. The top three binary chronic disease comorbidity patterns were hypertension combined with cognitive impairment (19.69%), arthritis (16.57%), and cerebrovascular disease (12.20%). In addition, the top three chronic disease comorbidity patterns were hypertension combined with arthritis and cognitive impairment (8.05%), cerebrovascular disease and cognitive impairment (6.48%), and heart disease and cognitive impairment (5.63%). The analysis of comorbidity association rules yielded 325 strong association rules (53 binary comorbidity association rules, 137 ternary comorbidity association rules, and 135 quaternary comorbidity association rules). Among these, heart disease was strongly associated with cerebrovascular disease, renal insufficiency, chronic lung disease, and hypertension. Cerebrovascular disease was strongly associated with renal insufficiency, cognitive impairment, and hypertension. Renal insufficiency was strongly associated with diabetes, arthritis, and hypertension. Chronic pulmonary disease was strongly associated with cognitive impairment and cerebrovascular disease. ConclusionThe prevalence of chronic comorbidity among the elderly in Jiangsu province is high. Hypertension combined with arthritis, cerebrovascular disease, and cognitive dysfunction are the most common comorbidity patterns among the elderly in this region.

Abstract Achieving selective hydrodeoxygenation of α, β-unsaturated carbonyl groups to alkenes poses a substantial challenge due to the presence of multiple functional groups. In this study, we develop a ZnNC-X catalyst (X represents the calcination temperature) that incorporates both Lewis acidic-basic sites and Zn-Nx sites to address this challenge. Among the catalyst variants, ZnNC-900 catalyst exhibits impressive selectivity for alkenes in the hydrodeoxygenation of α, β-unsaturated carbonyl compounds, achieving up to 94.8% selectivity. Through comprehensive mechanism investigations and catalyst characterization, we identify the Lewis acidic-basic sites as responsible for the selective hydrogenation of C=O bonds, while the Zn-Nx sites facilitate the subsequent selective hydrodeoxygenation step. Furthermore, ZnNC-900 catalyst displays broad applicability across a diverse range of unsaturated carbonyl compounds. These findings not only offer valuable insights into the design of effective catalysts for controlling alkene selectivity but also extend the scope of sustainable transformations in synthetic chemistry.

The fundamental investigation on the chemiluminescence characteristics of NH3-based flames is essential for the development of low-cost, real-time optical diagnostic sensor technologies. In this study, we have experimentally examined the chemiluminescence properties of non-premixed ammonia-methane laminar jet flames under various initial NH3 blending ratios (XNH3 from 0.2 to 1.0 in volume) by conducting the emission spectrum analysis within the 200–800 nm band and capturing the distribution images of key excited radicals. The results revealed that the emission spectra of OH*, CH*, CN*, NH*, and NH2* were clearly identifiable. As anticipated, the chemiluminescence characteristics of NH3-CH4 non-premixed flames were significantly influenced by XNH3; i.e., the overall signal intensity decreased monotonically within the 200–400 nm band but increased within the 400–800 nm band as XNH3 increased. The signal intensity characteristics of OH*, CH*, NH*, and NH2*, indicated by radical images, were consistent with the spectrometer measurements. Particularly, it was found that the intensity ratio of CH*/NH2* was an ideal marker of initial XNH3 in present flames, given their sensitivity with XNH3 and relative ease of measurement with the cost-effective sensors designed for invisible wavelengths. Moreover, in the flame front, CH* was located in the oxidant side, while NH2* was in the fuel side with a broader distribution zone. An increase of XNH3 led to greater flame thickness and shifted the peak position of excited radicals far away from the fuel side.

The soil microbiome plays an important role in wetland ecosystem services and functions. However, the impact of soil hydrological conditions on wetland microorganisms is not well understood. This study investigated the effects of wetted state (WS); wetting–drying state (WDS); and dried state (DS) on the diversity of soil bacteria, fungi, and archaea. The Shannon index of bacterial diversity was not significantly different in various flooding conditions (p > 0.05), however, fungal diversity and archaeal communities were significantly different in different flooding conditions (p < 0.05). Significant differences were found in the beta diversity of bacterial, fungal, and archaeal communities (p < 0.05). Additionally, the composition of bacteria, fungi, and archaea varied. Bacteria were predominantly composed of Proteobacteria and Actinobacteria, fungi mainly consisted of Ascomycota and Mucoromycota, and archaea were primarily represented by Crenarchaeota and Euryarchaeota. Bacteria exhibited correlations with vegetation coverage, fungi with plant diversity, and archaea with aboveground vegetation biomass. The pH influenced bacterial and archaeal communities, while soil bulk density, moisture, soil carbon, soil nitrogen, and plant community diversity impacted fungal communities. This study provides a scientific basis for understanding the effects of different hydrological conditions on microbial communities in the Huihe Nature Reserve; highlighting their relationship with vegetation and soil properties, and offers insights for the ecological protection of the Huihe wetland.

Emitter localization offers significant advantages such as high concealment, long detection range, and low cost, making it indispensable in target positioning. The utilization of low earth orbit satellite formation with AOA (Angle of Arrival) and TDOA (Time Difference of Arrival) measurements is a key technology for achieving emitter localization. To address the issues of requiring numerous cooperative platforms and the poor accuracy of single-epoch solutions with single-parameter closed-form algorithms, this paper proposes a multi-parameter multi-epoch positioning method based on a three-satellite formation. Simulation data are used to analyze the positioning accuracy under various epochs and different TDOA and AOA noise conditions. The experimental results demonstrate that, compared to the traditional single-parameter single-epoch localization method, utilizing a three-satellite formation with combined AOA and TDOA parameters, along with a multi-epoch solution approach, significantly improves localization accuracy to within an order of ten meters. This method enhances robustness and provides a viable strategy for addressing localization challenges caused by underdetermined systems of equations. Additionally, the results verify that an accumulated almanac element duration of 20 s ensures high positioning accuracy while maintaining a low computational cost. The combined multi-parameter multi-epoch method shows substantial advantages in improving both accuracy and robustness, providing valuable insights for future satellite-based emitter localization technologies.

Abstract The current‐generation adenine base editor (ABE) ABE8e, which has evolved from the prokaryotic evolution system, exhibits high efficiency in mediating A‐to‐G conversion and is presumed to be promising for gene therapy. However, its much wider editing window and substantially higher off‐target editing activity restricted its applications in precise base editing for therapeutic use. This study uses a library‐assisted protein evolution approach using eukaryotic cells to generate ABE variants with improved specificity and reduced off‐target editing while maintaining high activity in human cells. The study generated an expanded set of ABEs with efficient editing activities and chose four evolved variants that offered either similar or modestly higher efficiency within a narrower editing window of protospacer position ≈4–7 compared to that of ABE8e in human cells, which would enable minimized bystander editing. Moreover, these variants resulted in reduced off‐target editing events when delivered as plasmid or mRNA into human cells. Finally, these variants can install both disease‐suppressing mutations and disease‐correcting mutations efficiently with minimal undesired bystander editing making them promising approaches for specific therapeutic edits. In summary, the work establishes a mutant‐library‐assisted protein evolution method in eukaryotic cells and generates alternative ABE variants as efficient tools for precise human genome editing.

BackgroundBurnout among health care professionals is a significant concern, with detrimental effects on health care service quality and patient outcomes. The use of the electronic health record (EHR) system has been identified as a significant contributor to burnout among health care professionals. ObjectiveThis systematic review and meta-analysis aims to assess the prevalence of burnout among health care professionals associated with the use of the EHR system, thereby providing evidence to improve health information systems and develop strategies to measure and mitigate burnout. MethodsWe conducted a comprehensive search of the PubMed, Embase, and Web of Science databases for English-language peer-reviewed articles published between January 1, 2009, and December 31, 2022. Two independent reviewers applied inclusion and exclusion criteria, and study quality was assessed using the Joanna Briggs Institute checklist and the Newcastle-Ottawa Scale. Meta-analyses were performed using R (version 4.1.3; R Foundation for Statistical Computing), with EndNote X7 (Clarivate) for reference management. ResultsThe review included 32 cross-sectional studies and 5 case-control studies with a total of 66,556 participants, mainly physicians and registered nurses. The pooled prevalence of burnout among health care professionals in cross-sectional studies was 40.4% (95% CI 37.5%-43.2%). Case-control studies indicated a higher likelihood of burnout among health care professionals who spent more time on EHR-related tasks outside work (odds ratio 2.43, 95% CI 2.31-2.57). ConclusionsThe findings highlight the association between the increased use of the EHR system and burnout among health care professionals. Potential solutions include optimizing EHR systems, implementing automated dictation or note-taking, employing scribes to reduce documentation burden, and leveraging artificial intelligence to enhance EHR system efficiency and reduce the risk of burnout. Trial RegistrationPROSPERO International Prospective Register of Systematic Reviews CRD42021281173; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021281173

Abstract Objectives To investigate the association between serum per- and polyfluoroalkyl substances (PFAS) and periodontitis, and further explore the possible mediating role of sex hormones in this association. Methods We extracted data from National Health and Nutrition Examination Survey (NHANES) 2009–2014. Univariable and multivariable logistic regression models were performed to investigate the association between serum levels of seven PFASs and periodontitis. Bayesian kernel machine regression (BKMR) was conducted to assess the joint effect of PFASs in mixtures. Mediation analyses were used to explore the potential mediating role of sex hormones. Results Participants with periodontitis had higher concentrations of serum perfluorooctane sulfonate (PFOS) and perfluorononanoic acid (PFNA) than those without periodontitis (both P

Abstract Background Major depressive disorder (MDD) is a common psychiatric disorder worldwide. Psychotic depression has been reported to be frequently under-diagnosed due to poor recognition of psychotic features. Therefore, the purpose of this study was to reveal the rate and risk factors of psychotic symptoms in young, drug-naïve patients with major depressive disorder at the time of their first episode. Methods A total of 917 patients were recruited and divided into psychotic and non-psychotic subgroups based on the Positive and Negative Syndrome Scale (PANSS) positive subscale score. Anxiety symptoms and depressive symptoms were measured by the Hamilton Anxiety Rating Scale (HAMA) and the 17-item Hamilton Depression Rating Scale (HAMD-17), respectively. Several biochemical indicators such as total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting blood glucose (FBG), thyroid stimulating hormone (TSH), free triiodothyronine (FT3), and free thyroxine (FT4) were also measured. Results The rate of psychotic symptoms among young adult MDD patients was 9.1%. There were significant differences in TSH (p

Abstract Background The widespread utilization of chest High-resolution Computed Tomography (HRCT) has prompted detection of pulmonary ground-glass nodules (GGNs) in otherwise asymptomatic individuals. We aimed to establish a simple clinical risk score model for assessing GGNs based on HRCT. Methods We retrospectively analyzed 574 GGNs in 574 patients undergoing HOOK-WIRE puncture and pulmonary nodule surgery from January 2014 to November 2018. Clinical characteristics and imaging features of the GGNs were assessed. We analyzed the differences between malignant and benign nodules using binary logistic regression analysis and constructed a simple risk score model, the VBV Score, for predicting the malignancy status of GGNs. Then, we validated this model via other 1200 GGNs in 1041 patients collected from three independent clinical centers in 2022. Results For the exploratory phase of this study, out of the 574 GGNs, 481 were malignant and 93 were benign. Vacuole sign, air bronchogram, and intra-nodular vessel sign were important indicators of malignancy in GGNs. Then, we derived a VBV Score = vacuole sign + air bronchogram + intra-nodular vessel sign, to predict the malignancy of GGNs, with a sensitivity, specificity, and accuracy of 95.6%, 80.6%, and 93.2%, respectively. We also validated it on other 1200 GGNs, with a sensitivity, specificity, and accuracy of 96.0%, 82.6%, and 95.0%, respectively. Conclusions Vacuole sign, air bronchogram, and intra-nodular vessel sign were important indicators of malignancy in GGNs. VBV Score showed good sensitivity, specificity, and accuracy for differentiating benign and malignant pulmonary GGNs.

Abstract Background The incidence of cervical spine fractures is increasing every day, causing a huge burden on society. This study aimed to develop and verify a nomogram to predict the in-hospital mortality of patients with cervical spine fractures without spinal cord injury. This could help clinicians understand the clinical outcome of such patients at an early stage and make appropriate decisions to improve their prognosis. Methods This study included 394 patients with cervical spine fractures from the Medical Information Mart for Intensive Care III database, and 40 clinical indicators of each patient on the first day of admission to the intensive care unit were collected. The independent risk factors were screened using the Least Absolute Shrinkage and Selection Operator regression analysis method, a multi-factor logistic regression model was established, nomograms were developed, and internal validation was performed. A receiver operating characteristic (ROC) curve was drawn, and the area under the ROC curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination of the model. Moreover, the consistency between the actual probability and predicted probability was reflected using the calibration curve and Hosmer–Lemeshow (HL) test. A decision curve analysis (DCA) was performed, and the nomogram was compared with the scoring system commonly used in clinical practice to evaluate the clinical net benefit. Results The nomogram indicators included the systolic blood pressure, oxygen saturation, respiratory rate, bicarbonate, and simplified acute physiology score (SAPS) II. The results showed that our model had satisfactory predictive ability, with an AUC of 0.907 (95% confidence interval [CI] = 0.853–0.961) and 0.856 (95% CI = 0.746–0.967) in the training set and validation set, respectively. Compared with the SAPS-II system, the NRI values of the training and validation sets of our model were 0.543 (95% CI = 0.147–0.940) and 0.784 (95% CI = 0.282–1.286), respectively. The IDI values of the training and validation sets were 0.064 (95% CI = 0.004–0.123; P = 0.037) and 0.103 (95% CI = 0.002–0.203; P = 0.046), respectively. The calibration plot and HL test results confirmed that our model prediction results showed good agreement with the actual results, where the HL test values of the training and validation sets were P = 0.8 and P = 0.95, respectively. The DCA curve revealed that our model had better clinical net benefit than the SAPS-II system. Conclusion We explored the in-hospital mortality of patients with cervical spine fractures without spinal cord injury and constructed a nomogram to predict their prognosis. This could help doctors assess the patient’s status and implement interventions to improve prognosis accordingly.

Mathematical problem solving is a task that examines the capacity of machine learning systems to perform quantitative and logical reasoning. Existing work employed formulas as intermediate labels in this task to implement a neuro-symbolic approach and achieved remarkable performance. However, we are questioning the limitations of existing methods from two perspectives: the expressive capacity of formulas and the learning capacity of existing models. In this paper, we proposed the Memory-Interactive Learning Engine (MILE), a new framework for the neuro-symbolic solution to mathematical problems. The main contributions of this work include a new formula-representing technique and a new decoding method. In our experiment on the Math23K dataset, MILE outperformed existing methods on not only question-answering accuracy but also robustness and generalization capacity (the software is available at https://github.com/evan-ak/mile).

Grassland degradation could affect the composition, structure, and ecological function of plant communities and threaten the stability of their ecosystems. It is essential to accurately evaluate grassland degradation and elucidate its impacts on the vegetation–soil relationship. In this study, remote sensing data based on vegetation coverage were used to assess the degradation status of Hulunbuir grassland, and five different grassland degradation degrees were classified. Vegetation community composition, diversity, biomass, soil nutrient status, and their relationships in different degraded grasslands were investigated using field survey data. The results showed that grassland degradation significantly affected the species composition of the vegetation community. As degradation intensified, species richness declined, with the proportion of Gramineae and Legume species decreasing and Asteraceae species increasing. Additionally, the proportion of annual species initially increased and then decreased. Degradation also markedly reduced aboveground, belowground, and litter biomass within the communities. Soil moisture, electrical conductivity, organic carbon, total carbon, total potassium, and hydrolyzable nitrogen contents in non-degraded areas were higher than those in severely degraded areas. Conversely, soil total phosphorus content and bulk density gradually increased with degradation. Nitrate nitrogen and ammonium nitrogen levels in severely degraded soils were significantly higher than those in non-degraded soils. Plant diversity in the study area was significantly positively correlated with aboveground biomass and belowground biomass, and it positively correlated with soil nutrient total carbon and available carbon but negatively correlated with soil bulk density. Results of the partial least squares path model showed that grassland degradation had significant negative effects on plant diversity, soil nutrients, and biomass. Soil nutrients were the main factors affecting ecosystem productivity. The direct effect of plant diversity on biomass was not significant, suggesting that soil nutrients may play a more important role than plant diversity in determining biomass during grassland degradation. The results illustrated the relationships among soil nutrients, plant diversity, and biomass in degraded grasslands and emphasized the importance of an integrated approach in the effective management and restoration of degraded grasslands.

BiFeO3 (BFO) films with ferroelectricity are the most promising candidates regarding the next generation of storage devices and sensors. The comprehensive understanding of ferroelectric switchable properties is challenging and critical to robust domain wall nanoelectronics. Herein, the domain dynamic was explored in detail under external bias conditions using scanning probe microscopy, which is meaningful for the understanding of domain dynamics and the foundation of ferroelectric devices. The results show that domain reversal occurred under external electric fields with sufficient energy excitation, combined with the existence of a charged domain wall. These findings extend the domain dynamic and current paths in ferroelectric films and shed light on the potential applications for ferroelectric devices.

Precisely estimating the grassland biomass carbon storage is vital for evaluating grassland carbon sequestration potential and the monitoring and management of grassland resources. With the increasing intensity of climate change (CC) and human activities (HA), it is necessary to explore spatiotemporal variations in biomass carbon storage and its response to CC and HA. In this study, we focused on the Hulunbuir Grassland, utilizing sample plots data, MODIS data, environmental factors (terrain, soil, and climate), location factor, and texture characteristics to assess the performance of four machine learning algorithms: random forest, support vector machine, gradient boosting decision tree, and extreme gradient boosting in estimating grassland aboveground biomass (AGB). Based on the optimal model combined with root-shoot ratio data, grassland distribution data, and carbon content coefficients, the spatiotemporal characteristics and driving factors of biomass carbon storage from 2001–2022 were analyzed. The results showed that (1) the random forest achieved the highest prediction accuracy for grassland AGB, making it appropriate for AGB estimation in the Hulunbuir Grassland. (2) The spectral indices were the key variables of the grassland AGB, especially the enhanced vegetation index and difference vegetation index. (3) The 22-year average total biomass (TB) of the study area was 1037.10 gC/m2, of which the 22-year average AGB was 48.73 gC/m2 and 22-year average belowground biomass was 988.37 gC/m2, showing a spatial distribution feature of gradual increase from west to east. (4) From 2001–2022, TB carbon storage showed an insignificant growth trend (p > 0.05). The 22-year average carbon storage of TB was 72.34 ± 18.07 gC. (5) Climate factors were the main driving factors for the spatial pattern of grassland TB carbon density, while the combined effects of CC and HA were the main contributors to the interannual increase in grassland TB carbon density.

Abstract Background The incidence of nonhip femoral fractures is gradually increasing, but few studies have explored the risk factors for in-hospital death in patients with nonhip femoral fractures in the ICU or developed mortality prediction models. Therefore, we chose to study this specific patient group, hoping to help clinicians improve the prognosis of patients. Methods This is a retrospective study based on the data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Least absolute shrinkage and selection operator (LASSO) regression was used to screen risk factors. The receiver operating characteristic (ROC) curve was drawn, and the areas under the curve (AUC), net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination of the model. The consistency between the actual probability and the predicted probability was assessed by the calibration curve and Hosmer–Lemeshow goodness of fit test (HL test). Decision curve analysis (DCA) was performed, and the nomogram was compared with the scoring system commonly used in clinical practice to evaluate the clinical net benefit. Results The LASSO regression analysis showed that heart rate, temperature, red blood cell distribution width, blood urea nitrogen, Glasgow Coma Scale (GCS), Simplified Acute Physiology Score II (SAPSII), Charlson comorbidity index and cerebrovascular disease were independent risk factors for in-hospital death in patients with nonhip femoral fractures. The AUC, IDI and NRI of our model in the training set and validation set were better than those of the GCS and SAPSII scoring systems. The calibration curve and HL test results showed that our model prediction results were in good agreement with the actual results (P = 0.833 for the HL test of the training set and P = 0.767 for the HL test of the validation set). DCA showed that our model had a better clinical net benefit than the GCS and SAPSII scoring systems. Conclusion In this study, the independent risk factors for in-hospital death in patients with nonhip femoral fractures were determined, and a prediction model was constructed. The results of this study may help to improve the clinical prognosis of patients with nonhip femoral fractures.

In the two-step analysis of Pebble-Bed type High-Temperature Gas-Cooled Reactor (PB-HTGR), the lattice physics calculation for the generation of homogenized cross-sections is based on the fuel pebble. However, the randomly-dispersed fuel particles in the fuel pebble introduce double heterogeneity and randomness. Compared to the deterministic method, the Monte Carlo method which is flexible in geometry modeling provides a high-fidelity treatment. Therefore, the Monte Carlo code NECP-MCX is extended in this study to perform the lattice physics calculation of the PB-HTGR. Firstly, the capability for the simulation of randomly-dispersed media, using the explicit modeling approach, is developed in NECP-MCX. Secondly, the capability for the generation of the homogenized cross-section is also developed in NECP-MCX. Finally, simplified PB-HTGR problems are calculated by a two-step neutronics analysis tool based on Monte Carlo homogenization. For the pebble beds mixed by fuel pebble and graphite pebble, the bias is less than 100 pcm when compared to the high-fidelity model, and the bias is increased to 269 pcm for pebble bed mixed by depleted fuel pebble. Numerical results show that the Monte Carlo lattice physics calculation for the two-step analysis of PB-HTGR is feasible.

Abstract Purpose Cholesterol metabolism is a risk factor for cardiovascular disease, and recent studies have shown that cholesterol metabolism poses a residual risk of cardiovascular disease even when conventional lipid risk factors are in the optimal range. The association between remnant cholesterol (RC) and cardiovascular disease has been demonstrated; however, its association with hypertension, type 2 diabetes mellitus (T2DM), and the concomitance of the two diseases requires further study. This study aimed to evaluate the association of RC with hypertension, T2DM, and both in a large sample of the U.S. population, and to further explore the potential mechanisms involved. Methods This cross-sectional study used data from the 2005—2018 cycles of the National Health and Nutrition Examination Survey (N = 17,749). Univariable and multivariable logistic regression analyses were performed to explore the relationships of RC with hypertension, T2DM, and both comorbidities. A restricted cubic spline regression model was used to reveal the dose effect. Mediation analyses were performed to explore the potential mediating roles of inflammation-related indicators in these associations. Results Of the 17,749 participants included (mean [SD] age: 41.57 [0.23] years; women: 8983 (50.6%), men: 8766 (49.4%)), the prevalence of hypertension, T2DM, and their co-occurrence was 32.6%, 16.1%, and 11.0%, respectively. Higher RC concentrations were associated with an increased risk of hypertension, T2DM, and their co-occurrence (adjusted odds ratios for per unit increase in RC were 1.068, 2.259, and 2.362, and 95% confidence intervals were 1.063–1.073, 1.797–2.838, and 1.834–3.041, respectively), with a linear dose–response relationship. Even when conventional lipids were present at normal levels, positive associations were observed. Inflammation-related indicators (leukocytes, lymphocytes, monocytes, and neutrophils) partially mediated these associations. Among these, leukocytes had the greatest mediating effect (10.8%, 14.5%, and 14.0%, respectively). Conclusion The results of this study provide evidence that RC is associated with the risk of hypertension, T2DM, and their co-occurrence, possibly mediated by an inflammatory response.

Abstract Controversial evidence exists on the relationship between body mass index (BMI) and suicide attempts (SA) in patients with major depressive disorder (MDD). This cross-sectional study aimed to explore the association between BMI and SA in first-episode drug-naïve (FEDN) MDD patients in China. The study was conducted from 2016 to 2018 in Taiyuan, China. Univariate and multivariate logistic regression analyzed the BMI–SA association, with subgroup analysis for gender. Threshold effects were examined using two-piecewise regression. In males, BMI was significantly associated with SA (OR = 0.84, 95% CI 0.74–0.94, P = 0.003) after full adjustment, but not in females (OR = 0.97, 95% CI 0.89–1.06, P = 0.541). The interaction with gender was significant (P for interaction 0.05). Results showed an L-shaped nonlinear BMI–SA relationship in FEDN MDD patients but differing BMI inflection points between genders, thus contributing to effective prevention programs for suicide.

Abstract This study aimed to explore sex differences in the relationship between thyroid function indicators and suicide attempts in first-episode and drug-naïve young major depressive disorder (MDD) patients with comorbid anxiety (MDA). A total of 917 MDD patients (aged 18–35 years) were recruited. The Hamilton depression rating scale (HAMD-17), Hamilton anxiety rating scale (HAMA), positive and negative syndrome scale (PANSS) positive subscale and clinical global impression of severity scale (CGI-S) were used. 467 patients were classified as MDA. The prevalence of suicide attempts was 31.3% in MDA patients, which was significantly higher than that (7.3%) in MDD patients without anxiety. Compared with MDA patients without suicide attempts, MDA patients with suicide attempts were older, had a later age of onset, higher HAMD-17, HAMA, and PANSS positive symptom subscale scores, as well as higher TSH, TgAb and TPOAb levels. For male patients, TSH and TPOAb levels were independently associated with suicide attempts. For female patients, HAMA, PANSS positive symptom scores, CGI-S score and TPOAb levels were independently associated with suicide attempts. Our results suggest that the indicators of thyroid function which can predict suicide attempts in male and female MDA patients have sex differences.

Abstract Objective Previous studies have shown that transcranial direct current stimulation(tDCS) led to an improvement of cognitive function in patients with schizophrenia, but rare study has explored the effect of tDCS on long-term hospitalized chronic schizophrenia with tardive dyskinesia (TD). The present research explored if cognitive function in patients with long-term hospitalized chronic schizophrenia with TD could be improved through tDCS. Methods This study is a randomized, double-blind, sham-controlled clinical trial. Of the 52 patients, 14 dropped out, and 38 completed the experiment. Thirty-eight patients on stable treatment regimens were randomly assigned to receive active tDCS(n = 21) or sham stimulation(n = 17) on weekdays of the first, third, and fifth weeks of treatment. Patients performed the Pattern Recognition Memory (PRM) and the Intra/Extradimensional Set Shift (IED) from the Cambridge Neuropsychological Test Automated Battery (CANTAB) at baseline and the end of week 3, week 5. Clinical symptoms were also measured at the baseline and the fifth week using the Scale for the Assessment of Negative Symptoms (SANS) and the Positive and Negative Syndrome Scale (PANSS). Side effects of tDCS were assessed with an experimenter-administered open-ended questionnaire during the whole experiment. Results There were no significant differences in PRM and IED performance metrics, SANS total score and PANSS total score between active and sham tDCS groups at the end of week 5 (p > 0.05). Furthermore, there was a significant difference in the adverse effects of the tingling sensation between the two groups (p 0.05). Conclusion According to these findings, no evidence supports using anodal stimulation over the left dorsolateral prefrontal cortex to improve cognitive function in patients with long-term hospitalized chronic schizophrenia with TD.

Abstract Background Recent studies have reported that psychotic symptoms are common in patients with major depressive disorder (MDD). However, few studies have reported the relationship between thyroid function, lipid metabolism and clinical profiles in female MDD patients. Thus, this study aimed to investigate the prevalence of psychotic depression (PD) and its risk factors in first-episode and drug naive (FEDN) depression among the female population in China. Methods This was a cross-sectional study involving a representative probability sample of 1,130 FEDN female outpatients with MDD (aged 18 years or older) in China. We collected information relating to socio-demographic characteristics, clinical data and blood samples. The Hamilton Depression Rating Scale 17-item version (HAMD-17), Hamilton Anxiety Rating Scale 14-item version (HAMA-14), and Positive and Negative Syndrome Scale (PANSS) were used to evaluate depressive, anxiety, and psychotic symptoms. Results The prevalence of psychotic symptoms in female MDD patients was 10.97%. The findings revealed significant differences between MDD female patients with psychotic symptoms and non-PD female patients in the following areas: higher HAMD scores, higher HAMA scores, more severe anxiety and an increased risk of suicide attempts. Further logistic regression analysis showed that psychotic symptoms were associated with higher thyroid-stimulating hormone (TSH) levels and an odds ratio of 1.168. Conclusions Our findings supported the hypothesis that higher TSH levels were correlated with psychotic symptoms in female MDD patients. Therefore, serum TSH levels may be a potential biomarker of PD in female MDD patients. In addition, we found that PD was closely associated with suicide attempts and lipid levels, but did not reach statistical significance.

Abstract Background Genome-wide association studies (GWASs) have identified many single-nucleotide polymorphisms (SNPs) associated with complex phenotypes in the European (EUR) population; however, the extent to which EUR-associated SNPs can be generalized to other populations such as East Asian (EAS) is not clear. Results By leveraging summary statistics of 31 phenotypes in the EUR and EAS populations, we first evaluated the difference in heritability between the two populations and calculated the trans-ethnic genetic correlation. We observed the heritability estimates of some phenotypes varied substantially across populations and 53.3% of trans-ethnic genetic correlations were significantly smaller than one. Next, we examined whether EUR-associated SNPs of these phenotypes could be identified in EAS using the trans-ethnic false discovery rate method while accounting for winner's curse for SNP effect in EUR and difference of sample sizes in EAS. We found on average 54.5% of EUR-associated SNPs were also significant in EAS. Furthermore, we discovered non-significant SNPs had higher effect heterogeneity, and significant SNPs showed more consistent linkage disequilibrium and allele frequency patterns between the two populations. We also demonstrated non-significant SNPs were more likely to undergo natural selection. Conclusions Our study revealed the extent to which EUR-associated SNPs could be significant in the EAS population and offered deep insights into the similarity and diversity of genetic architectures underlying phenotypes in distinct ancestral groups.

In recent years, it has been gradually recognized that efficient scheduling of automated guided vehicles (AGVs) can help companies find the balance between energy consumption and workstation satisfaction. Therefore, the energy consumption of AGVs for the manufacturing environment and the AGV energy efficient scheduling problem with customer satisfaction (AGVEESC) in a flexible manufacturing system are investigated. A new multi-objective non-linear programming model is developed to minimize energy consumption while maximizing workstation satisfaction by optimizing the pick-up and delivery processes of the AGV for material handling. Through the introduction of auxiliary variables, the model is linearized. Then, an interactive fuzzy programming approach is developed to obtain a compromise solution by constructing a membership function for two conflicting objectives. The experimental results show that a good level of energy consumption and workstation satisfaction can be achieved through the proposed model and algorithm.

ObjectiveTo evaluate the diagnostic accuracy of fusion imaging contrast-enhanced ultrasound (FI-CEUS) of magnetic resonance imaging (MRI) LI-RADS-indeterminate (LR-3/4) and conventional ultrasound undetected focal liver lesions (FLLs) in patients at risk for hepatocellular carcinoma (HCC).MethodsBetween February 2020 and July 2021, 71 FLLs in 63 patients were registered for diagnostic performance evaluation respectively for ultrasound-guided thermal ablation evaluation in this retrospective study. Diagnostic performance regarding FLLs was compared between FI-CEUS and contrast-enhanced MRI (CE-MRI).ResultsFor diagnostic performance evaluation, among 71 lesions in 63 patients, the diagnostic efficacy of FI-CEUS with LI-RADS was significantly higher than that of CE-MRI (P < 0.05) in both overall and hierarchical comparison (except for the group with lesion diameter ≥2 cm). For malignant lesions, the proportion of arterial phase hyperenhancement (APHE) and washout on FI-CEUS was higher than that on CE-MRI (P < 0.05).ConclusionFI-CEUS has a high value in the precise qualitative diagnosis of small FLLs (

P2-type layered oxide cathodes suffer from poor capacity retention and detrimental phase transitions at high voltage due to the presence of Na vacancies. During deep desodiation, irreversible lattice oxygen release increases capacity but also accelerates lattice collapse. In this work, to address the capacity limitations of the P2-type layered oxide cathode and leverage its structural stability, we develop a high-voltage P2-type layered oxide cathode, Na0.84La0.01Li0.12Ni0.22Mn0.66O2 (P2-NLLNMO). The La-doped material exhibits less volume variation (1.8%) and more stable crystal structure over a wide voltage window of 2.0–4.5 V. P2-NLLNMO yields a high capacity of 136.6 mAh g–1 and excellent cycling stability (89.2% retention after 200 cycles at 0.5 C) with a cutoff voltage 4.5 V. In addition, we reveal that La–O orbital hybridization is important for the enhanced performance when La atoms are doped in Na layers of P2-NLLNMO. This discovery provides valuable insights and guidance for the design of P2-type cathodes in high-energy-density sodium-ion batteries.

Summary: Data quality issues have been acknowledged as one of the greatest obstacles in medical artificial intelligence research. Here, we present DeepFundus, which employs deep learning techniques to perform multidimensional classification of fundus image quality and provide real-time guidance for on-site image acquisition. We describe steps for data preparation, model training, model inference, model evaluation, and the visualization of results using heatmaps. This protocol can be implemented in Python using either the suggested dataset or a customized dataset.For complete details on the use and execution of this protocol, please refer to Liu et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

The tensile debonding process of a polyurethane (PU)-asphalt concrete (AC) adhesion system under different temperature fields was simulated in this paper using LAMMPS molecular simulation software, and the interfacial energy, radial distribution function, mean square displacement, number density curve, and force-displacement curve of the system were calculated. When the temperature was below 280 K, adsorption occurred between PU and asphalt, asphalt and silica, and PU and silica, but the interfacial energy between PU and silica and PU and asphalt was larger than that between asphalt and silica. A large number of PU molecules exist in the range of 5Å from the silica and the MSD curve of PU has a low value as a function. During the tensile process, the silica crystals and asphalt in PU-AC systems travel with the PU, and the final fracture location emerges inside the asphalt molecule rather than at the PU/AC contact. When the temperature rises over 280 degrees Celsius, only asphalt and silica remain after the stretching action, and adsorption between asphalt and PU occurs, but adsorption between PU and silica vanishes. Distance from the silica is 5Å range only a small amount of PU molecules, stretching model, PU molecules gradually detached from the asphalt and silica, the final damage section close to the PU/AC interface. The mechanical characteristics of PU/AC bonded specimens at different temperatures were evaluated in this work to validate the correctness of molecular simulation, and the test findings were in good agreement with the conclusions of molecular simulation. The results of this paper provide molecular-scale insights into the debonding mechanism of PU/AC interfaces.

Background: To explore the long-term relationship between maternal smoking during pregnancy and early childhood growth in the UK Biobank cohort. Methods: To estimate the effect of maternal smoking during pregnancy on offspring height and body size at ten years old, we performed binary logistic analyses and reported odds ratios (OR) as well as 95% confidence intervals (95%CIs). We also implemented the cross-contextual comparison study to examine whether such influence could be repeatedly observed among three different ethnicities in the UK Biobank cohort (n = 22,140 for White, n = 7094 for South Asian, and n = 5000 for Black). In particular, we conducted the sibling cohort study in White sibling cohort (n = 9953 for height and n = 7239 for body size) to control for unmeasured familial confounders. Results: We discovered that children whose mothers smoked during pregnancy had greater risk of being shorter or plumper at age ten in the full UK Biobank White cohort, with 15.3% (95% CIs: 13.0%∼17.7%) higher risk for height and 32.4% (95%CIs: 29.5%∼35.4%) larger risk for body size. Similar associations were identified in the South Asian and Black ethnicities. These associations were robust and remained significant in the White sibling cohort (12.6% [95%CIs: 5.0%∼20.3%] for height and 36.1% [95%CIs: 26.3%∼45.9%] for body size) after controlling for family factors. Conclusion: This study robustly confirms that maternal smoking during pregnancy can promote height deficit and obesity for offspring at ten years old. Our findings strongly encourage mothers to quit smoking during pregnancy for improving growth and development of offspring.

Abstract Organoids are three‐dimensional cell aggregates with near‐physiologic cell behaviors and can undergo long‐term expansion in vitro. They are amenable to high‐throughput drug screening processes, which renders them a viable preclinical model for drug development. The procedure of organoid‐based high‐throughput screening has been extensively employed to discover small‐molecule drugs, encompassing the steps of generating organoids, examining efficient drugs in organoid cultures, and data assessment. Compared to small molecules, peptides are more straightforward to synthesize, can be modified chemically, and demonstrate high target specificity and low cytotoxicity. Therefore, they have emerged as promising carriers to deliver drugs to disease‐associated targets and could be efficient therapeutic drugs for various diseases. To date, organoids have been used to evaluate the efficacy of certain peptide agents; however, no organoid‐based high‐throughput screening of peptide drugs has been reported. Given the advantages of peptide drugs, there is an urgent need to establish organoid‐based peptide high‐throughput screening platforms. In this review, we discuss the typical approach of screening small‐molecular drugs with the use of organoid cultures, as well as provide an overview of the studies that have incorporated organoids in peptide research. Drawing on the knowledge from small molecular screens, we explore the difficulties and potential avenues for creating new platforms to identify peptide agents using organoid models.

Here, Liao and colleagues apply adenine base editor ABE8e and its PAM-less variant ABE8e-SpRY to β-thalassemia patient hematopoietic stem cells in the form of ribonucleoprotein complexes, resulting in efficient long-term editing and β-thalassemia alleviation.

BackgroundTo evaluate the changes in the global burden of foreign body aspiration (FBA) among children under 5 years old at regional, age, sex, and socio-demographic index (SDI) levels between 1990 and 2019.MethodsData on FBA was derived from the Global Burden of Disease (GBD) Study 2019 database on pulmonary aspiration and foreign body in airway. The means and 95% uncertainty intervals (UIs) were calculated for incidence, and disability-adjusted life-years (DALYs). The temporal trends were represented by estimated annual percentage change (EAPC) using Joinpoint regression.ResultsGlobally, FBA caused 109.6 (95% UI: 69.5, 175.7) per 100,000 incidence and 317.9 (95% UI: 270.7, 372.4) per 100,000 DALYs under 5 years old in 2019. Many European countries (such as Italy, Netherlands, Iceland, etc.) showed a high incidence rate, but did not cause a large disease burden (DALYs all less than 200 per 100,000). Compared to 1990, although a decrease in both incidence and DALYs occurred in 2019, the Joinpoint regression showed an increasing trend in incidence rate from 2014 to 2019 [APC: both (2.10), female (2.25), male (1.98), P

Lidar observations of metal layers play a significant role in research on the chemistry and dynamics of the mesosphere and lower thermosphere. This work reports on Fe lidar observations conducted in Beijing and Mohe. Utilizing the same laser emission system, a 1064 nm seed laser was injected into an Nd: YAG laser to generate a single longitudinal-mode pulse 532 nm laser, which pumped a dye laser to produce a 572 nm laser. The 572 nm laser and the remaining 1064 nm fundamental frequency laser passed through a sum–frequency module to generate a 372 nm laser to detect the Fe layer. According to a total of 52.6 h of observations for 10 nights in Beijing, the Fe layer has an average column density of 1.24 × 1010 cm−2, an RMS width of 4.4 km and a centroid altitude of 89.4 km. In Mohe, observed for 16 nights and a total of 91.5 h, the Fe layer has an average column density of 1.08 × 1010 cm−2, an RMS width of 4.6 km and a centroid altitude of 89.5 km. The probability of the occurrence of sporadic Fe layers was 42.4% in Beijing and 29.4% in Mohe. Compared to simultaneously observed Na layers, the occurrence probabilities of sporadic Fe layers were higher than those of sporadic Na layers in both stations. Based on the two cases observed in Beijing, it is conjectured that the formation mechanism of sporadic metal layers above approximately 100 km has a more significant influence on sporadic Fe layers than on sporadic Na layers. The lower thermospheric Fe layers with densities significantly larger than those of the main layer were observed during two nights in Mohe. This work contributes to the refinement of the global distribution of Fe layers and provides abundant observational data for the modeling and study of the metal layers.

The distribution of rooftop PV is spatial heterogeneity with the location of buildings. With the assistance of geographic information systems, this study proposed an integrated research framework for the geo-spatial potential assessment of PV systems on the roof of urban buildings as well as its feasibility analysis. Furthermore, a geo-spatialization of electricity self-sufficiency rate is also introduced to explore the contribution of rooftop PV on urban energy security. The proposed approach was verified by a case study in the central area of Wuhan, China with a 100 m spatial resolution. The results show that the maximum annual PV power generation potential in the central area of Wuhan is 10,757 GWh, which could satisfy 31.83% of local energy consumption. A 100% deployment of rooftop PV would also lead to a great carbon emission reduction, i.e. 8.62 million tons per year, with an annual total cost of 8.07 bn USD. According to the distribution characteristics of the PV self-sufficiency rate on the map, priority is given to investing in the peripheral areas of the city with the higher return. It is estimated that when the PV conversion rate increases by 5%, the peak self-sufficiency rate to the area will increase by 20% in Wuhan. Moreover, if the conversion rate of PV system reaches 47.13%, a fully utilized rooftop in Wuhan for PV deployment could meet the local electricity demand, which implies a 100% electricity self-sufficiency. This study is helpful to understand the spatial heterogeneity of rooftop PV in urban areas, and could serve as the guidelines for local governments to develop renewable energy in a sustainable way.

Abstract Background In recent years, the rates of suicide among young people have been increasing, and major depressive disorder (MDD) is regarded to be its main cause. Many factors such as thyroid dysfunction and metabolic abnormalities are thought to mediate this process, but the conclusions are inconsistent. This study investigated the rate of suicide attempts and associated risk factors among young, first-episode and drug-naïve Chinese Han patients with MDD. Methods A total of 917 patients with MDD (aged 18 ~ 35 years) were recruited. Demographic and clinical data were collected and thyroid function, fasting blood glucose and lipid profiles were measured. The Hamilton Depression Rating Scale-17 items (HAMD-17), Hamilton Anxiety Rating Scale (HAMA), positive symptom subscale of Positive and Negative Syndrome Scale (PANSS) and clinical global impression of severity scale (CGI-S) were adopted to assess depression, anxiety, psychotic symptoms and disease severity respectively. Results The rate of suicide attempts was 19.5% in young MDD patients. There were significant differences in age (p = 0.003), education level (p = 0.001), age of onset (p = 0.004) and disease duration (p = 0.001) between patients with and without suicide attempts. Compared with patients without suicide attempts, patients with suicide attempts had significantly higher scores on the HAMD-17, HAMA, PANSS positive symptom subscale and CGI-S (all p

Abstract Background Detecting trans-ethnic common associated genetic loci can offer important insights into shared genetic components underlying complex diseases/traits across diverse continental populations. However, effective statistical methods for such a goal are currently lacking. Methods By leveraging summary statistics available from global-scale genome-wide association studies, we herein proposed a novel genetic overlap detection method called CONTO (COmposite Null hypothesis test for Trans-ethnic genetic Overlap) from the perspective of high-dimensional composite null hypothesis testing. Unlike previous studies which generally analyzed individual genetic variants, CONTO is a gene-centric method which focuses on a set of genetic variants located within a gene simultaneously and assesses their joint significance with the trait of interest. By borrowing the similar principle of joint significance test (JST), CONTO takes the maximum P value of multiple associations as the significance measurement. Results Compared to JST which is often overly conservative, CONTO is improved in two aspects, including the construction of three-component mixture null distribution and the adjustment of trans-ethnic genetic correlation. Consequently, CONTO corrects the conservativeness of JST with well-calibrated P values and is much more powerful validated by extensive simulation studies. We applied CONTO to discover common associated genes for 31 complex diseases/traits between the East Asian and European populations, and identified many shared trait-associated genes that had otherwise been missed by JST. We further revealed that population-common genes were generally more evolutionarily conserved than population-specific or null ones. Conclusion Overall, CONTO represents a powerful method for detecting common associated genes across diverse ancestral groups; our results provide important implications on the transferability of GWAS discoveries in one population to others.

ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents potential ethical challenges from legal, humanistic, algorithmic, and informational perspectives. Legal ethics concerns arise from the unclear allocation of responsibility when patient harm occurs and from potential breaches of patient privacy due to data collection. Clear rules and legal boundaries are needed to properly allocate liability and protect users. Humanistic ethics concerns arise from the potential disruption of the physician-patient relationship, humanistic care, and issues of integrity. Overreliance on artificial intelligence (AI) can undermine compassion and erode trust. Transparency and disclosure of AI-generated content are critical to maintaining integrity. Algorithmic ethics raise concerns about algorithmic bias, responsibility, transparency and explainability, as well as validation and evaluation. Information ethics include data bias, validity, and effectiveness. Biased training data can lead to biased output, and overreliance on ChatGPT can reduce patient adherence and encourage self-diagnosis. Ensuring the accuracy, reliability, and validity of ChatGPT-generated content requires rigorous validation and ongoing updates based on clinical practice. To navigate the evolving ethical landscape of AI, AI in health care must adhere to the strictest ethical standards. Through comprehensive ethical guidelines, health care professionals can ensure the responsible use of ChatGPT, promote accurate and reliable information exchange, protect patient privacy, and empower patients to make informed decisions about their health care.