Genome-wide association study of maternal plasma metabolites during pregnancy.

Metabolites are key indicators of health and therapeutic targets, but their genetic underpinnings during pregnancy-a critical period for human reproduction-are largely unexplored. Using genetic data from non-invasive prenatal testing, we performed a genome-wide association study on 84 metabolites, including 37 amino acids, 24 elements, 13 hormones, and 10 vitamins, involving 34,394 pregnant Chinese women, with sample sizes ranging from 6,394 to 13,392 for specific metabolites. We identified 53 metabolite-gene associations, 23 of which are novel. Significant differences in genetic effects between pregnant and non-pregnant women were observed for 16.7%-100% of these associations, indicating gene-environment interactions. Additionally, 50.94% of genetic associations exhibited pleiotropy among metabolites and between six metabolites and eight pregnancy phenotypes. Mendelian randomization revealed potential causal relationships between seven maternal metabolites and 15 human traits and diseases. These findings provide new insights into the genetic basis of maternal plasma metabolites during pregnancy.

Protein complex structure modeling by cross-modal alignment between cryo-EM maps and protein sequences.

Cryo-electron microscopy (cryo-EM) technique is widely used for protein structure determination. Current automatic cryo-EM protein complex modeling methods mostly rely on prior chain separation. However, chain separation without sequence guidance often suffers from errors caused by cross-chain interaction or noise densities, which would accumulate and mislead the subsequent steps. Here, we present EModelX, a fully automated cryo-EM protein complex structure modeling method, which achieves sequence-guiding modeling through cross-modal alignments between cryo-EM maps and protein sequences. EModelX first employs multi-task deep learning to predict Cα atoms, backbone atoms, and amino acid types from cryo-EM maps, which is subsequently used to sample Cα traces with amino acid profiles. The profiles are then aligned with protein sequences to obtain initial structural models, which yielded an average RMSD of 1.17 Å in our test set, approaching atomic-level precision in recovering PDB-deposited structures. After filling unmodeled gaps through sequence-guiding Cα threading, the final models achieved an average TM-score of 0.808, outperforming the state-of-the-art method. The further combination with AlphaFold can improve the average TM-score to 0.911. Analyzes conducted by comparing some EModelX-built models and PDB structures highlight its potential to improve PDB structures. EModelX is accessible at https://bio-web1.nscc-gz.cn/app/EModelX .

Population Pharmacokinetic Modeling of Pyrazinamide Among Chinese Patients With Drug-Sensitive or Multidrug-Resistant Tuberculosis.

BACKGROUND: Pyrazinamide is used to treat drug-susceptible (DS) and multidrug-resistant (MDR) tuberculosis (TB). This study aimed to characterize the factors associated with the pharmacokinetic parameters of pyrazinamide and evaluate the disposition of the current regimen, which could provide suggestions for adequate dosing strategies for therapeutic targets. METHODS: A population pharmacokinetic model of pyrazinamide was developed based on the data from 499 plasma concentrations from 222 Chinese patients diagnosed with DS or MDR TB. Pyrazinamide exposure was best described using a one-compartment model. RESULTS: No significant differences were observed in the pharmacokinetic parameters between DS and MDR TB. The final covariate model showed that total body weight was the only significant covariate for apparent clearance, which increased by 0.45 L/h with a 10 kg increase in body weight. A simulation showed that for typical subjects weighing 40-80 kg, a fixed dosage of 1500 mg daily had an area under the concentration-time curve from 0 to 24 hours (AUC0-24) of 389.9-716.0 mg·h/L and peak serum concentrations of the drug (Cmax) of 32.2-44.8 mg/L. CONCLUSIONS: Fixed pyrazinamide doses of 1500, 1750, and 2000 mg are recommended for patients weighing 40-70, 70-80, and 80-90 kg, respectively, to achieve the exposure targets of AUC0-24 > 363 mg·h/L or Cmax > 35 mg/L to attain efficacy.

Dynamic Correlation Learning and Regularization for Multi-Label Confidence Calibration.

Modern visual recognition models often display overconfidence due to their reliance on complex deep neural networks and one-hot target supervision, resulting in unreliable confidence scores that necessitate calibration. While current confidence calibration techniques primarily address single-label scenarios, there is a lack of focus on more practical and generalizable multi-label contexts. This paper introduces the Multi-Label Confidence Calibration (MLCC) task, aiming to provide well-calibrated confidence scores in multi-label scenarios. Unlike single-label images, multi-label images contain multiple objects, leading to semantic confusion and further unreliability in confidence scores. Existing single-label calibration methods, based on label smoothing, fail to account for category correlations, which are crucial for addressing semantic confusion, thereby yielding sub-optimal performance. To overcome these limitations, we propose the Dynamic Correlation Learning and Regularization (DCLR) algorithm, which leverages multi-grained semantic correlations to better model semantic confusion for adaptive regularization. DCLR learns dynamic instance-level and prototype-level similarities specific to each category, using these to measure semantic correlations across different categories. With this understanding, we construct adaptive label vectors that assign higher values to categories with strong correlations, thereby facilitating more effective regularization. We establish an evaluation benchmark, re-implementing several advanced confidence calibration algorithms and applying them to leading multi-label recognition (MLR) models for fair comparison. Through extensive experiments, we demonstrate the superior performance of DCLR over existing methods in providing reliable confidence scores in multi-label scenarios.

Inhibition of CCL7 improves endothelial dysfunction and vasculopathy in mouse models of diabetes mellitus.

Diabetic vascular disease is a major complication of diabetes mellitus (DM). Chemokine C-C motif ligand 7 (CCL7) attracts macrophages and monocytes, amplifying inflammatory processes in the vasculature. We hypothesized a causal role for CCL7 in diabetic vasculopathy. CCL7 concentrations were higher in the plasma of patients with type 2 DM, as well as in supernatants from their endothelial progenitor cells (EPCs). High-glucose stimulation increased the secretion of CCL7 from human dermal microvascular endothelial cells (HDMECs) through the c-Fos and c-Jun signaling pathways. CCL7 inhibition using knockdown or neutralization antibody treatment reversed the high glucose-induced impaired tube formation and migration abilities of EPCs, human aortic endothelial cells, human coronary artery endothelial cells, and HDMECs. Administration of recombinant human CCL7 protein impaired tube formation and migration abilities by down-regulating the AKT-endothelial nitric oxide synthase and AKT/nuclear factor erythroid 2-related factor 2/heme oxygenase-1/vascular endothelial growth factor/stromal cell-derived factor-1 pathways and by up-regulating ERK/phosphorylated p65/interleukin-1ß/interleukin-6/tumor necrosis factor-α pathways through CC chemokine receptor 3 in endothelial cells. Ccl7 knockout in streptozotocin-treated mice showed improved neovasculogenesis in ischemic limbs and accelerated wound repair, with increased circulating EPCs and capillary density. CCL7 antibody treatment in db/db mice and high-fat diet-induced hyperglycemia mice showed improved neovasculogenesis in ischemic limbs and wound areas, accompanied by up-regulation of angiogenic proteins and down-regulation of inflammatory proteins. Endothelial cell-specific Ccl7-knockout mice showed ameliorated diabetic vasculopathy in streptozotocin-induced DM. This study highlights the potential of CCL7 as a therapeutic target for diabetic vasculopathy.

Continuous Value Assignment: A Doubly Robust Data Augmentation for Off-Policy Learning.

Deep reinforcement learning (RL) has witnessed remarkable success in a wide range of control tasks. To overcome RL's notorious sample inefficiency, prior studies have explored data augmentation techniques leveraging collected transition data. However, these methods face challenges in synthesizing transitions adhering to the authentic environment dynamics, especially when the transition is high-dimensional and includes many redundant/irrelevant features to the task. In this article, we introduce continuous value assignment (CVA), an innovative optimization-level data augmentation approach that directly synthesizes novel training data in the state-action value space, effectively bypassing the need for explicit transition modeling. The key intuition of our method is that the transition plays an intermediate role in calculating the state-action value during optimization, and therefore directly augmenting the state-action value is more causally related to the optimization process. Specifically, our CVA combines parameterized value prediction and nonparametric value interpolation from neighboring states, resulting in doubly robust target values w.r.t. novel states and actions. Extensive experiments demonstrate CVA's substantial improvements in sample efficiency across complex continuous control tasks, surpassing several advanced baselines.

Predictive Value of Serum Immune-Inflammatory Markers for Adverse Pregnancy Outcomes in Pregnant Women with Thrombophilia: A Retrospective Cohort Study.

Background: Thrombophilia combined with pregnancy poses significant risks for adverse pregnancy outcomes. Unfortunately, there are no indicators at high risk for predicting adverse pregnancy outcomes. This study investigates the predictive efficiency of serum immune-inflammatory markers on adverse pregnancy outcomes. Methods: This retrospective cohort study includes 223 pregnant women diagnosed with thrombophilia who delivered at the Fujian Provincial Hospital South Branch from January 2022 to April 2024. Clinical information and pregnancy outcomes were collected. The systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and lactate dehydrogenase (LDH) were calculated using blood samples. The relationship and predictive accuracy between immune-inflammatory markers and adverse pregnancy outcomes were analyzed. Results: In this study, 50 (22.4%) patients had adverse pregnancy outcomes. Significant differences were observed in neutrophils counts, monocytes counts, LDH, SII, and SIRI levels between the adverse pregnancy outcome groups (APOs) and the control groups (P<0.05). The area under the receiver operating characteristic (ROC) curve analysis revealed that SII (AUC=0.762), SIRI (AUC=0.764), and LDH (AUC=0.732) had high predictive values for adverse pregnancy outcomes. Notably, the combined model had the highest AUC of 0.805. Multivariate logistic regression identified SII had the highest odd ratio (OR) (OR=8.512; 95% CI(3.068-23.614)), followed by LDH (OR=4.905; 95% CI (1.167-11.101)), SIRI (OR=3.549; 95% CI(0.847-8.669)), and neutrophils count (OR=1.726; 95% CI (0.563-2.938)) as independent risk factors for adverse outcomes. Conclusion: Elevated levels of immune-inflammatory markers such as SII, SIRI, and LDH level are strong predictors of adverse pregnancy outcomes in thrombophilia-complicated pregnancies. These markers are significantly associated with maternal-neonatal outcomes. Our findings underscore the importance of monitoring immune-inflammatory markers in pregnant women with thrombophilia to improve maternal and neonatal outcomes.

CXCL5 inhibition improves kidney function by protecting renal tubular epithelial cells in diabetic kidney disease.

Inflammation is one of exacerbating factors of diabetic kidney disease (DKD). Upregulated CXCL5 is found in clinical and experimental diabetes studies. This study aimed to investigate the impact and mechanism of CXCL5 on DKD. DKD patients with different levels of urine albumin-to-creatinine ratio were enrolled. Leprdb/db mice and CXCL5-knockout diabetic mice were used as mouse models for DKD. Human renal tubular epithelial cells were used for in vitro experiments. Circulating CXCL5 were increased in DKD patients compared to the non-DKD subjects. CXCL5 inhibition through CXCL5-neutralizing antibodies or genetic knockout improved kidney function and ameliorated tubular injury and renal fibrosis. In high-glucose-stimulated tubular epithelial cells, administration of CXCL5-neutralizing antibodies or siRNA resulted in reduced phospho-JNK/c-JUN/p65 and the downstream inflammatory, fibrotic, and apoptotic protein expressions. Administration of CXCR2 and JNK inhibitors impeded the CXCL5-induced tubular epithelial cell damages. In conclusion, these findings indicated that anti-CXCL5 strategies may be potential treatments for DKD.

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

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

Checklist and guidance on creating codelists for routinely collected health data research.

Background: Codelists are required to extract meaningful information on characteristics and events from routinely collected health data such as electronic health records. Research using routinely collected health data relies on codelists to define study populations and variables, thus, trustworthy codelists are important. Here, we provide a checklist, in the style of commonly used reporting guidelines, to help researchers adhere to best practice in codelist development and sharing. Methods: Based on a literature search and a workshop with researchers experienced in the use of routinely collected health data, we created a set of recommendations that are 1. broadly applicable to different datasets, research questions, and methods of codelist creation; 2. easy to follow, implement and document by an individual researcher, and 3. fit within a step-by-step process. We then formatted these recommendations into a checklist. Results: We have created a 10-step checklist, comprising 28 items, with accompanying guidance on each step. The checklist advises on which metadata to provide, how to define a clinical concept, how to identify and evaluate existing codelists, how to create new codelists, and how to review, check, finalise, and publish a created codelist. Conclusions: Use of the checklist can reassure researchers that best practice was followed during the development of their codelists, increasing trust in research that relies on these codelists and facilitating wider re-use and adaptation by other researchers.

When a person receives many types of health care, such as a doctor registering a diagnosis or prescribing a drug, information is collected in their computer system. This information is often organised in a structured way, so that each piece of information can be assigned a "code". For example, if a person was diagnosed with type 1 diabetes, this could be recorded with the code E10 from the International classification of diseases, which contains codes on all possible diseases. For type 2 diabetes the code would be E11. To use this information for research, researchers need to define which people they want to study by making a list of all the relevant codes (a "codelist"). For example, to study people with type 1 and 2 diabetes they would need to include E10 and E11 in their codelist. The international classification of diseases coding system includes over 70,000 codes, and other medical dictionaries can include hundreds of thousands of codes. These lists can therefore be long and complex to create. While they are very important in ensuring that research using this data is correct, no step-by-step guidelines exist to help researchers create codelists. To tackle this, we created a checklist and guidance document which researchers can now use to make sure they don't miss important steps and checks while creating their codelists, and to help them share their codelists so they can be re-used by other researchers. We collected recommendations that other authors have made before us, and developed detailed guidance together with experts in using these types of data for research.

Shikonin, a natural naphthoquinone phytochemical, exerts anti-leukemia effects in human CBF-AML cell lines and zebrafish xenograft models.

Core binding factor acute myeloid leukemia (CBF-AML) stands out as the most common type of adult AML, characterized by specific chromosomal rearrangements involving CBF genes, particularly t(8;21). Shikonin (SHK), a naphthoquinone phytochemical widely employed as a food colorant and traditional Chinese herbal medicine, exhibits antioxidant, anti-inflammatory, and anti-cancer activities. In this study, we aim to investigate the antileukemic effects of SHK and its underlying mechanisms in human CBF-AML cells and zebrafish xenograft models. Our study revealed that SHK reduced the viability of CBF-AML cells. SHK induced cell cycle arrest, promoted cell apoptosis, and induced differentiation in Kasumi-1 cells. Additionally, SHK downregulated the gene expression of AML1-ETO and c-KIT in Kasumi-1 cells. In animal studies, SHK showed no toxic effects in zebrafish and markedly inhibited the growth of leukemia cells in zebrafish xenografts. Transcriptomic analysis showed that differentially expressed genes (DEGs) altered by SHK are linked to key biological processes like DNA repair, replication, cell cycle regulation, apoptosis, and division. Furthermore, KEGG pathways associated with cell growth, such as the cell cycle and p53 signaling pathway, were significantly enriched by DEGs. Analysis of AML-associated genes in response to SHK treatment using DisGeNET and the STRING database indicated that SHK downregulates the expression of cell division regulators regarding AML progression. Finally, we found that SHK combined with cytarabine synergistically reduced the viability of Kasumi-1 cells. In conclusion, our findings provide novel insights into the mechanisms of SHK in suppressing leukemia cell growth, suggesting its potential as a chemotherapeutic agent for human CBF-AML.

Molecular classification of geriatric breast cancer displays distinct senescent subgroups of prognostic significance.

Breast cancer in the elderly presents distinct biological characteristics and clinical treatment responses compared with cancer in younger patients. Comprehensive Geriatric Assessment is recommended for evaluating treatment efficacy in elderly cancer patients based on physiological classification. However, research on molecular classification in older cancer patients remains insufficient. In this study, we identified two subgroups with distinct senescent clusters among geriatric breast cancer patients through multi-omics analysis. Using various machine learning algorithms, we developed a comprehensive scoring model called "Sene_Signature," which more accurately distinguished elderly breast cancer patients compared with existing methods and better predicted their prognosis. The Sene_Signature was correlated with tumor immune cell infiltration, as supported by single-cell transcriptomics, RNA sequencing, and pathological data. Furthermore, we observed increased drug responsiveness in patients with a high Sene_Signature to treatments targeting the epidermal growth factor receptor and cell-cycle pathways. We also established a user-friendly web platform to assist investigators in assessing Sene_Signature scores and predicting treatment responses for elderly breast cancer patients. In conclusion, we developed a novel model for evaluating prognosis and therapeutic responses, providing a potential molecular classification that assists in the pre-treatment assessment of geriatric breast cancer.

Impact of Value-Driven Healthcare Strategies for Biosimilar Adoption: The Singapore Story.

BACKGROUND: Healthcare sustainability is a global challenge. Various value-driven healthcare strategies have been implemented by Singapore's national health technology assessment (HTA) agency, the Agency for Care Effectiveness (ACE). Considering the high and growing expenditure on biologics, strategies have been implemented to drive the use of biosimilars. As Singapore has reached the 5-year mark since the subsidy listing of the first monoclonal antibody biosimilar infliximab, this review aimed to evaluate the impact of these strategies on the changes in adoption rates, utilisation, spending and cost savings for biosimilars in the public healthcare sector. METHODS: A retrospective cross-sectional study was conducted using aggregated drug utilisation data from all public healthcare institutions. Five monoclonal antibodies with biosimilars, namely infliximab, adalimumab, trastuzumab, rituximab and bevacizumab, were included in this study. The outcomes evaluated were the monthly trends for utilisation volume, proportion attributed to biosimilar use, and drug spending up to December 2022. The simulated cost savings associated with biosimilar adoption were also reported. RESULTS: After subsidy implementation, an upward trend in biosimilar use and proportion attributed to biosimilar adoption was observed, while spending reduced substantially. The adoption rate of most biosimilars reached more than 95% within 1 year of listing. Drugs with more than one approved biosimilar brand at the time of subsidy listing reported substantial price reductions of over 80%. Overall, spending for the five monoclonal antibodies have significantly reduced after biosimilar subsidy listing, with an estimated cumulative cost savings of $136 million over 5 years. CONCLUSION: Value-driven healthcare strategies implemented in Singapore's public healthcare institutions have contributed to high adoption rates of biosimilars and have improved affordable access through lower treatment costs. This in turn has led to significant cost savings to the healthcare system.

An update of the molecular mechanisms underlying anthracycline induced cardiotoxicity.

Anthracycline drugs mainly include doxorubicin, epirubicin, pirarubicin, and aclamycin, which are widely used to treat a variety of malignant tumors, such as breast cancer, gastrointestinal tumors, lymphoma, etc. With the accumulation of anthracycline drugs in the body, they can induce serious heart damage, limiting their clinical application. The mechanism by which anthracycline drugs cause cardiotoxicity is not yet clear. This review provides an overview of the different types of cardiac damage induced by anthracycline-class drugs and delves into the molecular mechanisms behind these injuries. Cardiac damage primarily involves alterations in myocardial cell function and pathological cell death, encompassing mitochondrial dysfunction, topoisomerase inhibition, disruptions in iron ion metabolism, myofibril degradation, and oxidative stress. Mechanisms of uptake and transport in anthracycline-induced cardiotoxicity are emphasized, as well as the role and breakthroughs of iPSC in cardiotoxicity studies. Selected novel cardioprotective therapies and mechanisms are updated. Mechanisms and protective strategies associated with anthracycline cardiotoxicity in animal experiments are examined, and the definition of drug damage in humans and animal models is discussed. Understanding these molecular mechanisms is of paramount importance in mitigating anthracycline-induced cardiac toxicity and guiding the development of safer approaches in cancer treatment.

Construction of a Liver Cancer Prognostic Model Based on Interferon-Gamma-Related Genes for Revealing the Immune Landscape.

Inferferon-gamma (LFN-γ) exerts anti-tumor effects, but there is currently no reliable and comprehensive study on prognostic function of IFN-γ-related genes in liver cancer. In this study, IFN-γ-related differentially expressed genes (DEGs) in liver cancer were identified through GO/KEGG databases and open-access literature. Based on these genes, individuals with liver cancer were clustered. A prognostic model was built based on the intersection genes between differential genes in clusters and in liver cancer. Then, model predictive performance was analyzed and validated in GEO dataset. Regression analysis was fulfilled on the model, and a nomogram was utilized to evaluate model ability as an independent prognostic factor and its clinical application value. An immune-related analysis was conducted on both the H- and L-groups, with an additional investigation into link of model genes to drug sensitivity. Significant differential expression of IFN-γ-related genes was observed between the liver cancer and control groups. Subsequently, individuals with liver cancer were classified into two subtypes based on these genes, which displayed a notable difference in survival between the two subtypes. A 10-gene liver cancer prognostic model was constructed, with good prognostic performance and was an independent prognosticator for patient analysis. L-group patients possessed higher immune infiltration levels, immune checkpoint expression levels, and immunophenoscore, as well as lower TIDE scores. Drugs that had high correlations with the feature genes included SPANXB1: PF-04217903, SGX-523, MMP1: PF-04217903, DUSP13: Imatinib, TFF1: KHK-Indazole, and Fulvestrant. We built a 10-gene liver cancer prognostic model. It was found that L-group patients were more suitable for immunotherapy. This study provided valuable information on the prognosis of liver cancer.

Atroposelective Synthesis of Axial Biaryls by Dynamic Kinetic Resolution Using Engineered Imine Reductases.

Axially chiral biaryl compounds are ubiquitous scaffolds in natural products, bioactive molecules, chiral ligands and catalysts, but biocatalytic methods for their asymmetric synthesis are limited. Herein, we report a highly efficient biocatalytic route for the atroposelective synthesis of biaryls by dynamic kinetic resolution (DKR). This DKR approach features a transient six-membered aza-acetal-bridge-promoted racemization followed by an imine reductase (IRED)-catalyzed stereoselective reduction to construct the axial chirality under ambient conditions. Directed evolution of an IRED from Streptomyces sp. GF3546 provided a variant (S-IRED-Ss-M11) capable of catalyzing the DKR process to access a variety of biaryl aminoalcohols in high yields and excellent enantioselectivities (up to 98 % yield and >99 : 1 enantiomeric ratio). Molecular dynamics simulation studies on the S-IRED-Ss-M11 variant revealed the origin of its improved activity and atroposelectivity. By exploiting the substrate promiscuity of IREDs and the power of directed evolution, our work further extends the biocatalysts' toolbox to construct challenging axially chiral molecules.

Integration of metagenomics and metaproteomics in the intestinal lavage fluids benefits construction of discriminative model and discovery of biomarkers for HBV liver diseases.

Intestinal lavage fluid (IVF) containing the mucosa-associated microbiota instead of fecal samples was used to study the gut microbiota using different omics approaches. Focusing on the 63 IVF samples collected from healthy and hepatitis B virus-liver disease (HBV-LD), a question is prompted whether omics features could be extracted to distinguish these samples. The IVF-related microbiota derived from the omics data was classified into two enterotype sets, whereas the genomics-based enterotypes were poorly overlapped with the proteomics-based one in either distribution of microbiota or of IVFs. There is lack of molecular features in these enterotypes to specifically recognize healthy or HBV-LD. Running machine learning against the omics data sought the appropriate models to discriminate the healthy and HBV-LD IVFs based on selected genes or proteins. Although a single omics dataset is basically workable in such discrimination, integration of the two datasets enhances discrimination efficiency. The protein features with higher frequencies in the models are further compared between healthy and HBV-LD based on their abundance, bringing about three potential protein biomarkers. This study highlights that integration of metaomics data is beneficial for a molecular discriminator of healthy and HBV-LD, and reveals the IVF samples are valuable for microbiome in a small cohort.

Healthcare utilisation in people with long COVID: an OpenSAFELY cohort study.

BACKGROUND: Long COVID potentially increases healthcare utilisation and costs. However, its impact on the NHS remains to be determined. METHODS: This study aims to assess the healthcare utilisation of individuals with long COVID. With the approval of NHS England, we conducted a matched cohort study using primary and secondary care data via OpenSAFELY, a platform for analysing anonymous electronic health records. The long COVID exposure group, defined by diagnostic codes, was matched with five comparators without long COVID between Nov 2020 and Jan 2023. We compared their total healthcare utilisation from GP consultations, prescriptions, hospital admissions, A&E visits, and outpatient appointments. Healthcare utilisation and costs were evaluated using a two-part model adjusting for covariates. Using a difference-in-difference model, we also compared healthcare utilisation after long COVID with pre-pandemic records. RESULTS: We identified 52,988 individuals with a long COVID diagnosis, matched to 264,867 comparators without a diagnosis. In the 12 months post-diagnosis, there was strong evidence that those with long COVID were more likely to use healthcare resources (OR: 8.29, 95% CI: 7.74-8.87), and have 49% more healthcare utilisation (RR: 1.49, 95% CI: 1.48-1.51). Our model estimated that the long COVID group had 30 healthcare visits per year (predicted mean: 29.23, 95% CI: 28.58-29.92), compared to 16 in the comparator group (predicted mean visits: 16.04, 95% CI: 15.73-16.36). Individuals with long COVID were more likely to have non-zero healthcare expenditures (OR = 7.66, 95% CI = 7.20-8.15), with costs being 44% higher than the comparator group (cost ratio = 1.44, 95% CI: 1.39-1.50). The long COVID group costs approximately £2500 per person per year (predicted mean cost: £2562.50, 95% CI: £2335.60-£2819.22), and the comparator group costs £1500 (predicted mean cost: £1527.43, 95% CI: £1404.33-1664.45). Historically, individuals with long COVID utilised healthcare resources more frequently, but their average healthcare utilisation increased more after being diagnosed with long COVID, compared to the comparator group. CONCLUSIONS: Long COVID increases healthcare utilisation and costs. Public health policies should allocate more resources towards preventing, treating, and supporting individuals with long COVID.

Cryopreservation-enhanced differentiation capacity of embryogenic cultures of Castanea mollissima.

A cryopreservation protocol has been developed for embryogenic cultures (ECs) of Castanea mollissima, an important economic species of the Castanea genus in China. We achieved 100 % regrowth when ECs were treated with Plant Vitrification Solution 2 (PVS2) for 30, 60 and 90 min on ice. Optimal PVS2 treatment for cryopreservation was determined to be 30 min on ice based on the highest biomass regrowth after thawing. Fluorescein diacetate (FDA) staining could rapidly and reliably determine post-thaw cell viability and its use facilitated the optimization of the cryopreservation protocols. Although the proliferation rate of the re-established ECs remained largely unchanged compared to non-cryopreserved ECs, the capacity of the re-established ECs to differentiate (on two media) into somatic embryos nearly doubled to approximately 2200-2300 globular somatic embryos per 1 g of re-established ECs. Based on cell cluster size analysis, this enhanced growth is primarily attributed to the presence of significantly greater cell clusters with a diameter of 100-200 µm, which have the highest level of differentiation ability. In order to understand the increased embryogenic potential following cryopreservation, we analyzed the expression of key genes related to somatic embryogenesis. Genes CmWUS and CmABP1 were downregulated while CmLEC1, CmAGL15, CmGRF2, and CmFUS3 were upregulated in re-established ECs when compared to non-cryopreserved ECs.

Uncertainty-Aware Active Domain Adaptive Salient Object Detection.

Due to the advancement of deep learning, the performance of salient object detection (SOD) has been significantly improved. However, deep learning-based techniques require a sizable amount of pixel-wise annotations. To relieve the burden of data annotation, a variety of deep weakly-supervised and unsupervised SOD methods have been proposed, yet the performance gap between them and fully supervised methods remains significant. In this paper, we propose a novel, cost-efficient salient object detection framework, which can adapt models from synthetic data to real-world data with the help of a limited number of actively selected annotations. Specifically, we first construct a synthetic SOD dataset by copying and pasting foreground objects into pure background images. With the masks of foreground objects taken as the ground-truth saliency maps, this dataset can be used for training the SOD model initially. However, due to the large domain gap between synthetic images and real-world images, the performance of the initially trained model on the real-world images is deficient. To transfer the model from the synthetic dataset to the real-world datasets, we further design an uncertainty-aware active domain adaptive algorithm to generate labels for the real-world target images. The prediction variances against data augmentations are utilized to calculate the superpixel-level uncertainty values. For those superpixels with relatively low uncertainty, we directly generate pseudo labels according to the network predictions. Meanwhile, we select a few superpixels with high uncertainty scores and assign labels to them manually. This labeling strategy is capable of generating high-quality labels without incurring too much annotation cost. Experimental results on six benchmark SOD datasets demonstrate that our method outperforms the existing state-of-the-art weakly-supervised and unsupervised SOD methods and is even comparable to the fully supervised ones. Code will be released at: https://github.com/czh-3/UADA.