Microfluidic fabrication of monodisperse microcapsules with gas cores.

A facile strategy for efficient and continuous fabrication of monodisperse gas-core microcapsules with controllable sizes and excellent ultrasound-induced burst performances is developed based on droplet microfluidics and interfacial polymerization. Monodisperse gas-in-oil-in-water (G/O/W) double emulsion droplets with a gas core and monomer-contained oil layer are fabricated in the upstream of a microfluidic device as templates, and then water-soluble monomers are added into the aqueous continuous phase in the downstream to initiate rapid interfacial polymerization at the O/W interfaces to prepare monodisperse gas-in-oil-in-solid (G/O/S) microcapsules with gas cores. The sizes of both microbubbles and G/O/W droplet templates can be precisely controlled by adjusting the gas supply pressure and the fluid flow rates. Due to the very thin shells of G/O/S microcapsules fabricated via interfacial polymerization, the sizes of the resultant G/O/S microcapsules are almost the same as those of the G/O/W droplet templates, and the microcapsules exhibit excellent deformable properties and ultrasound-induced burst performances. The proposed strategy provides a facile and efficient route for controllably and continuously fabricating monodisperse microcapsules with gas cores, which are highly desired for biomedical applications.

Costunolide Inhibits Chronic Kidney Disease Development by Attenuating IKKß/NF-κB Pathway.

Background: Chronic kidney disease (CKD) is a significant worldwide health concern that leads to high mortality rates. The bioactive substance costunolide (CTD) has demonstrated several pharmacological effects and holds promise as a CKD treatment. This study aims to investigate the impact of CTD on CKD and delve into its mechanisms of action. Methods: Unilateral ureteral obstruction (UUO) methods and renal fibrosis mice models were created. Various concentrations of CTD were injected into UUO mice models to investigate the therapeutic effects of CTD on renal fibrosis of mice. Then, renal morphology, pathological changes, and the expression of genes related to fibrosis, inflammation and ferroptosis were analysed. RNA sequencing was utilized to identify the main biological processes and pathways involved in renal injury. Finally, both overexpression and inhibition of IKKß were studied to examine their respective effects on fibrosis and inflammation in both in vitro and in vivo models. Results: CTD treatment was found to significantly alleviate fibrosis, inflammation and ferroptosis in UUO-induced renal fibrosis mice models. The results of RNA sequencing suggested that the IKKß acted as key regulatory factor in renal injury and the expression of IKKß was increased in vitro and in vivo renal fibrosis model. Functionally, down-regulated IKKß expression inhibits ferroptosis, inflammatory cytokine production and collagen deposition. Conversely, IKKß overexpression exacerbates progressive renal fibrosis. Mechanistically, CTD alleviated renal fibrosis and inflammation by inhibiting the expression of IKKß and attenuating IKKß/NF-κB pathway. Conclusion: This study demonstrates that CTD could mitigate renal fibrosis, ferroptosis and inflammation in CKD by modulating the IKKß/NF-κB pathway, which indicates targeting IKKß has an enormous potential for treating CKD.

Wavelength-tunable high-fidelity entangled photon sources enabled by dual Stark effects.

The construction of a large-scale quantum internet requires quantum repeaters containing multiple entangled photon sources with identical wavelengths. Semiconductor quantum dots can generate entangled photon pairs deterministically with high fidelity. However, realizing wavelength-matched quantum-dot entangled photon sources faces two difficulties: the non-uniformity of emission wavelength and exciton fine-structure splitting induced fidelity reduction. Typically, these two factors are not independently tunable, making it challenging to achieve simultaneous improvement. In this work, we demonstrate wavelength-tunable entangled photon sources based on droplet-etched GaAs quantum dots through the combined use of AC and quantum-confined Stark effects. The emission wavelength can be tuned by ~1 meV while preserving an entanglement fidelity f exceeding 0.955(1) in the entire tuning range. Based on this hybrid tuning scheme, we finally demonstrate multiple wavelength-matched entangled photon sources with f > 0.919(3), paving the way towards robust and scalable on-demand entangled photon sources for quantum internet and integrated quantum optical circuits.

Polystyrene microplastics exacerbated the toxicity of okadaic acid to the small intestine in mice.

Microplastics (MPs) and okadaic acid (OA) are known to coexist in marine organisms, potentially impacting humans through food chain. However, the combined toxicity of OA and MPs remains unknown. In this study, mice were orally administered OA at 200 µg/kg bw and MPs at 2 mg/kg bw. The co-exposure group showed a significant increase in malondialdehyde (MDA) content and significant decreases in superoxide dismutase (SOD) activity and glutathione (GSH) level compared to the control, MPs and OA groups (p < 0.05). Additionally, the co-exposure group exhibited significantly higher levels of IL-1ß and IL-18 compared to other groups (p < 0.05). These results demonstrated that co-exposure to MPs and OA induces oxidative stress and exacerbates inflammation. Histological and cellular ultrastructure analyses suggested that this combined exposure may enhance gut damage and compromise barrier integrity. Consequently, the concentration of OA in the small intestine of the co-exposure group was significantly higher than that in the OA group. Furthermore, MPs were observed in the lamina propria of the gut in the co-exposure group. Transcriptomic analysis revealed that the co-exposure led to increased expression of certain genes related to the NF-κB/NLRP3 pathway compared to the OA and MPs groups. Overall, this combined exposure may disrupt the intestinal barrier, and promote inflammation through the NF-κB/NLRP3 pathway. These findings provide precious information for the understanding of health risks associated with MPs and phycotoxins.

[Construction and Analysis of Machine Learning Based Transportation Carbon Emission Prediction Model].

Addressing the issue of carbon emissions in the transportation sector, this research constructed various predictive models using multiple machine learning algorithms based on panel data from 30 provinces in China from 2005 to 2019. The study aimed to identify the optimal machine learning algorithm and key factors influencing the carbon emissions of transportation, providing potent references for policymakers and decision-makers to reduce carbon emissions and promote the sustainable development of the transportation sector. Initially, drawing from the concept of the fixed effects model, we included the heterogeneity differences among provinces as an important factor. We further employed a combined method of Pearson's correlation coefficient and Spearman's rank correlation coefficient to screen 18 factors influencing transportation carbon emissions. We then made a preliminary selection of seven common machine learning algorithms and used the screened factors as explanatory variables for model training. The three algorithms with the best performance were further optimized and trained. Subsequently, we utilized the K-fold cross-validation method; plotted learning curves to test the performance of each predictive model; and used MSE, MAE, R2, and MAPE as evaluation indicators to determine the best predictive model. SHAP values were chosen to calculate the importance of each explanatory variable in the optimal predictive model. The results indicated that the multicollinearity among the seven factors of provincial differences, total consumption of social goods, urban green space area, freight turnover, number of private cars, transportation industry output, and permanent population was weak, and all passed the significance test. They could be used as explanatory variables in the prediction model of transportation carbon emissions. The prediction results of the Random Forest and XGBoost algorithms were both outstanding, with R2 values above 0.97 and errors below 10 %, showing no signs of overfitting or underfitting. Among them, the XGBoost algorithm performed the best, whereas the KNN algorithm performed poorly. The importance ranking of the explanatory variables was as follows:provincial differences > total consumption of social goods > number of private cars > permanent population > freight turnover > urban green space area > transportation industry output. A comprehensive analysis of relevance and importance showed that provincial differences were an indispensable variable in the prediction of transportation carbon emissions. In conclusion, this study provides a new approach to the governance of carbon emissions in the transportation industry, and the results can serve as a reference for policymakers and decision-makers. In future policy design and decision-making, the distinctive factors of each province should not be overlooked. Measures targeted at specific regions need to be formulated to promote the sustainable development of the transportation industry.

Blood urea nitrogen to albumin ratio is a novel predictor of fatal outcome for patients with severe fever with thrombocytopenia syndrome.

Severe fever with thrombocytopenia syndrome (SFTS) is associated with a high death rate and lacks a targeted therapy plan. The ratio of blood urea nitrogen to albumin, known as BAR, is a valuable method for assessing the outlook of various infectious diseases. The objective of this research was to evaluate the effectiveness of BAR in forecasting the outcome of individuals with SFTS. Four hundred and thirty-seven patients with SFTS from two clinical centers were included in this study according to inclusion and exclusion criteria. Clinical characteristics and test parameters of SFTS patients were analyzed between survival and fatal groups. Least absolute shrinkage and selection operator (LASSO) regression and Cox regression suggested that BAR might serve as a standalone prognostic indicator for patients with SFTS in the initial phase (hazard ratio = 18.669, 95% confidence interval [CI]: 8.558-40.725, p < 0.001). And BAR had a better predictive effectiveness in clinical outcomes in patients with SFTS with an AUC of 0.832 (95% CI: 0.788-0.876, p < 0.001), a cutoff value of 0.19, a sensitivity of 0.812, and a specificity of 0.726 compared to C-reactive protein, procalcitonin, and platelet to lymphocyte ratio via receiver operating characteristic curve. KM (Kaplan Meier) curves demonstrated that high level of BAR was associated with poor survival condition in patients with SFTS. Furthermore, the high level of BAR was associated with long hospital stays and test paraments of kidney, liver, and coagulation function in survival patients. So, BAR could be used as a promising early warning biomarker of adverse outcomes in patients with SFTS.

Multiplex assays reveal anti-EBV antibody profile and its implication in detection and diagnosis of nasopharyngeal carcinoma.

Epstein-Barr virus (EBV) is detected in nearly 100% of nonkeratinizing nasopharyngeal carcinoma (NPC) and EBV-based biomarkers are used for NPC screening in endemic regions. Immunoglobulin A (IgA) against EBV nuclear antigen 1 (EBNA1) and viral capsid antigen (VCA), and recently identified anti-BNLF2b antibodies have been shown to be the most effective screening tool; however, the screening efficacy still needs to be improved. This study developed a multiplex serological assay by testing IgA and immunoglobulin G (IgG) antibodies against representative EBV antigens that are highly transcribed in NPC and/or function crucially in viral reactivation, including BALFs, BNLF2a/b, LF1, LF2, and Zta (BZLF1). Among them, BNLF2b-IgG had the best performance distinguishing NPC patients from controls (area under the curve: 0.951, 95% confidence interval [CI]: 0.913-0.990). Antibodies to lytic antigens BALF2 and VCA were significantly higher in advanced-stage than in early-stage tumors; in contrast, antibodies to latent protein EBNA1 and early lytic antigen BNLF2b were not correlated with tumor progression. Accordingly, a novel strategy combining EBNA1-IgA and BNLF2b-IgG was proposed and validated improving the integrated discrimination by 15.8% (95% CI: 9.8%-21.7%, p < .0001) compared with the two-antibody method. Furthermore, we found EBV antibody profile in patients was more complicated compared with that in healthy carriers, in which stronger correlations between antibodies against different phases of antigens were observed. Overall, our serological assay indicated that aberrant latent infection of EBV in nasopharyngeal epithelial cells was probably a key step in NPC initiation, while more lytic protein expression might be involved in NPC progression.

A knowledge-enhanced interpretable network for early recurrence prediction of hepatocellular carcinoma via multi-phase CT imaging.

BACKGROUND: Predicting early recurrence (ER) of hepatocellular carcinoma (HCC) accurately can guide treatment decisions and further enhance survival. Computed tomography (CT) imaging, analyzed by deep learning (DL) models combining domain knowledge, has been employed for the prediction. However, these DL models utilized late fusion, restricting the interaction between domain knowledge and images during feature extraction, thereby limiting the prediction performance and compromising decision-making interpretability. METHODS: We propose a novel Vision Transformer (ViT)-based DL network, referred to as Dual-Style ViT (DSViT), to augment the interaction between domain knowledge and images and the effective fusion among multi-phase CT images for improving both predictive performance and interpretability. We apply the DSViT to develop pre-/post-operative models for predicting ER. Within DSViT, to balance the utilization between domain knowledge and images within DSViT, we propose an adaptive self-attention mechanism. Moreover, we present an attention-guided supervised learning module for balancing the contributions of multi-phase CT images to prediction and a domain knowledge self-supervision module for enhancing the fusion between domain knowledge and images, thereby further improving predictive performance. Finally, we provide the interpretability of the DSViT decision-making. RESULTS: Experiments on our multi-phase data demonstrate that DSViTs surpass the existing models across multiple performance metrics and provide the decision-making interpretability. Additional validation on a publicly available dataset underscores the generalizability of DSViT. CONCLUSIONS: The proposed DSViT can significantly improve the performance and interpretability of ER prediction, thereby fortifying the trustworthiness of artificial intelligence tool for HCC ER prediction in clinical settings.

A polysaccharide PRCP from Rosa cymosa Tratt fruit: Structural characteristics and immunomodulatory effects via MAPK pathway modulation in vitro.

The Rosa cymosa Tratt, an herbal plant from the Rosaceae family, has historically been valued in China for its medicinal and edible properties. In this study, a novel polysaccharide from R. cymosa fruit, termed PRCP (purified R. cymosa polysaccharide), was isolated using water extraction, decolorization, deproteinization, and ion-exchange chromatography. The structural characteristics of PRCP were investigated using monosaccharide composition analysis, methylation, GPC, FTIR, CD, and NMR spectroscopy. The immunomodulatory effect and potential mechanism of PRCP were evaluated in vitro using a macrophage cell model. Results indicated that PRCP (37.28 kDa) is a highly branched polysaccharide (72.61 %) primarily composed of arabinogalactan, rhamnogalacturonan, and galactoglucan domains with 13 types of glycosidic linkage fragments. Furthermore, PRCP appears to modulate immunomodulatory effects by influencing the phosphorylation of P38 and JNK proteins in the MAPK pathway. Collectively, these findings highlight the potential of PRCP as a promising natural functional food ingredient for immunostimulation.

The OsZIP2 transporter is involved in root-to-shoot translocation and intervascular transfer of cadmium in rice.

Cadmium (Cd) is a toxic metal that poses serious threats to human health. Rice is a major source of dietary Cd but how rice plants transport Cd to the grain is not fully understood. Here, we characterize the function of the ZIP (ZRT, IRT-like protein) family protein, OsZIP2, in the root-to-shoot translocation of Cd and intervascular transfer of Cd in nodes. OsZIP2 is localized at the plasma membrane and exhibited Cd2+ transport activity when heterologously expressed in yeast. OsZIP2 is strongly expressed in xylem parenchyma cells in roots and in enlarged vascular bundles in nodes. Knockout of OsZIP2 significantly enhanced root-to-shoot translocation of Cd and alleviated the inhibition of root elongation by excess Cd stress; whereas overexpression of OsZIP2 decreased Cd translocation to shoots and resulted in Cd sensitivity. Knockout of OsZIP2 increased Cd allocation to the flag leaf but decreased Cd allocation to the panicle and grain. We further reveal that the variation of OsZIP2 expression level contributes to grain Cd concentration among rice germplasms. Our results demonstrate that OsZIP2 functions in root-to-shoot translocation of Cd in roots and intervascular transfer of Cd in nodes, which can be used for breeding low Cd rice varieties.

Gut microbiota-derived acetate attenuates lung injury induced by influenza infection via protecting airway tight junctions.

BACKGROUND: Gut microbiota (GM) have been implicated as important regulators of gastrointestinal symptom which is commonly occurred along with respiratory influenza A virus (IAV) infection, suggesting the involvement of the gut-to-lung axis in a host's response to IAV. IAV primarily destroys airway epithelium tight junctions (TJs) and consequently causes acute respiratory disease syndrome. It is known that GM and their metabolism produce an anti-influenza effect, but their role in IAV-induced airway epithelial integrity remains unknown. METHODS: A mouse model of IAV infection was established. GM were analyzed using 16S rRNA gene sequencing, and short-chain fatty acids (SCFAs) levels were measured. GM depletion and fecal microbiota transplantation (FMT) were conducted to validate the role of GM in IAV infection. A pair-feeding experiment was conducted to reveal whether IAV-induced GM dysbiosis is attributed to impaired food intake. Furthermore, human bronchial epithelial (HBE) cells were cocultured with IAV in the presence or absence of acetate. TJs function was analyzed by paracellular permeability and transepithelial electronic resistance (TEER). The mechanism of how acetate affects TJs integrity was evaluated in HBE cells transfected with G protein-coupled receptor 43 (GPR43) short hairpin RNA (shRNA). RESULTS: IAV-infected mice exhibited lower relative abundance of acetate-producing bacteria (Bacteroides, Bifidobacterium, and Akkermansia) and decreased acetate levels in gut and serum. These changes were partly caused by a decrease in food consumption (due to anorexia). GM depletion exacerbated and FMT restored IAV-induced lung inflammatory injury. IAV infection suppressed expressions of TJs (occludin, ZO-1) leading to disrupted airway epithelial barrier function as evidenced by decreased TEER and increased permeability. Acetate pretreatment activated GPR43, partially restored IAV-induced airway epithelial barrier function, and reduced inflammatory cytokines levels (TNF-α, IL-6, and IL-1ß). Such protective effects of acetate were absent in HBE cells transfected with GPR43 shRNA. Acetate and GPR43 improved TJs in an AMP-activated protein kinase (AMPK)-dependent manner. CONCLUSION: Collectively, our results demonstrated that GM protected airway TJs by modulating GPR43-AMPK signaling in IAV-induced lung injury. Therefore, improving GM dysbiosis may be a potential therapeutic target for patients with IAV infection.

Immune-Mediated Necrotizing Myopathy Associated With Anti-signal Recognition Particle Antibody Complicated With Acute Respiratory Distress Syndrome: A Report of Two Cases.

Immune-mediated necrotizing myopathy (IMNM) represents a rare category of inflammatory myopathies characterized by more severe and rapid progression of symmetrical proximal muscle weakness. It is also marked by notably elevated serum muscle enzyme levels and distinct histological features, setting it apart from other types of myositis. Moreover, acute chronic lung respiratory dysfunction is a major comorbidity of great concern. We herein present two cases of IMNM associated with anti-signal recognition particle antibodies complicated by acute respiratory distress syndrome.

Balance between FeIV-NiIV synergy and Lattice Oxygen Contribution for Accelerating Water Oxidation.

Hydrogen obtained from electrochemical water splitting is the most promising clean energy carrier, which is hindered by the sluggish kinetics of the oxygen evolution reaction (OER). Thus, the development of an efficient OER electrocatalyst using nonprecious 3d transition elements is desirable. Multielement synergistic effect and lattice oxygen oxidation are two well-known mechanisms to enhance the OER activity of catalysts. The latter is generally related to the high valence state of 3d transition elements leading to structural destabilization under the OER condition. We have found that Al doping in nanosheet Ni-Fe hydroxide exhibits 2-fold advantage: (1) a strong enhanced OER activity from 277 mV to 238 mV at 10 mA cm-2 as the Ni valence state increases from Ni3.58+ to Ni3.79+ observed from in situ X-ray absorption spectra. (2) Operational stability is strengthened, while weakness is expected since the increased NiIV content with 3d8L2 (L denotes O 2p hole) would lead to structural instability. This contradiction is attributed to a reduced lattice oxygen contribution to the OER upon Al doping, as verified through in situ Raman spectroscopy, while the enhanced OER activity is interpreted as an enormous gain in exchange energy of FeIV-NiIV, facilitated by their intersite hopping. This study reveals a mechanism of Fe-Ni synergy effect to enhance OER activity and simultaneously to strengthen operational stability by suppressing the contribution of lattice oxygen.

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine.

Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.

Value of CT-Based Deep Learning Model in Differentiating Benign and Malignant Solid Pulmonary Nodules ≤ 8 mm.

RATIONALE AND OBJECTIVES: We examined the effectiveness of computed tomography (CT)-based deep learning (DL) models in differentiating benign and malignant solid pulmonary nodules (SPNs) ≤ 8 mm. MATERIALS AND METHODS: The study patients (n = 719) were divided into internal training, internal validation, and external validation cohorts; all had small SPNs and had undergone preoperative chest CTs and surgical resection. We developed five DL models incorporating features of the nodule and five different peri-nodular regions with the Multiscale Dual Attention Network (MDANet) to differentiate benign and malignant SPNs. We selected the best-performing model, which was then compared to four conventional algorithms (VGG19, ResNet50, ResNeXt50, and DenseNet121). Furthermore, another five DL models were constructed using MDANet to distinguish benign tumors from inflammatory nodules and the one performed best was selected out. RESULTS: Model 4, which incorporated the nodule and 15 mm peri-nodular region, best differentiated benign and malignant SPNs. The model had an area under the curve (AUC), accuracy, recall, precision, and F1-score of 0.730, 0.724, 0.711, 0.705, and 0.707 in the external validation cohort. Model 4 also performed better than the other four conventional algorithms. Model 8, which incorporated the nodule and 10 mm peri-nodular region, was the best model for distinguishing benign tumors from inflammatory nodules. The model had an AUC, accuracy, recall, precision, and F1-score of 0.871, 0.938, 0.863, 0.904, and 0.882 in the external validation cohort. CONCLUSION: The study concludes that CT-based DL models built with MDANet can accurately discriminate among small benign and malignant SPNs, benign tumors and inflammatory nodules.

A fundamental study on postmortem submersion interval estimation by metabolomics analyzing of gastrocnemius muscle from submersed rat models in freshwater.

In forensic practice, determining the postmortem submersion interval (PMSI) and cause-of-death of cadavers in aquatic ecosystems has always been challenging task. Traditional approaches are not yet able to address these issues effectively and adequately. Our previous study proposed novel models to predict the PMSI and cause-of-death based on metabolites of blood from rats immersed in freshwater. However, with the advance of putrefaction, it is hardly to obtain blood samples beyond 3 days postmortem. To further assess the feasibility of PMSI estimation and drowning diagnosis in the later postmortem phase, gastrocnemius, the more degradation-resistant tissue, was collected from drowned rats and postmortem submersion model in freshwater immediately after death, and at 1 day, 3 days, 5 days, 7 days, and 10 days postmortem respectively. Then the samples were analyzed with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to investigate the dynamic changes of the metabolites. A total of 924 metabolites were identified. Similar chronological changes of gastrocnemius metabolites were observed in the drowning and postmortem submersion groups. The difference in metabolic profiles between drowning and postmortem submersion groups was only evident in the initial 1 day postmortem, which was faded as the PMSI extension. Nineteen metabolites representing temporally-dynamic patterns were selected as biomarkers for PMSI estimation. A regression model was built based on these biomarkers with random forest algorithm, which yielded a mean absolute error (± SE) of 5.856 (± 1.296) h on validation samples from an independent experiment. These findings added to our knowledge of chronological changes in muscle metabolites from submerged vertebrate remains during decomposition, which provided a new perspective for PMSI estimation.

Repeatability, interocular correlation and agreement of optic nerve head vessel density in healthy eyes: a swept-source optical coherence tomographic angiography study.

AIM: To assess the repeatability, interocular correlation, and agreement of quantitative swept-source optical coherence tomography angiography (OCTA) optic nerve head (ONH) parameters in healthy subjects. METHODS: Thirty-three healthy subjects were enrolled. The ONH of both eyes were imaged four times by a swept-source-OCTA using a 3 mm ×3 mm scanning protocol. Images of the radial peripapillary capillary were analyzed by a customized Matlab program, and the vessel density, fractal dimension, and vessel diameter index were measured. The repeatability of the four scans was determined by the intraclass correlation coefficient (ICC). The most well-centered optic disc from the four repeated scans was then selected for the interocular correlation and agreement analysis using the Pearson correlation coefficient, ICC and Bland-Altman plots. RESULTS: All swept-source-OCTA ONH parameters exhibited certain repeatability, with ICC>0.760 and coefficient of variation (CoV)≤7.301%. The obvious interocular correlation was observed for papillary vessel density (ICC=0.857), vessel diameter index (ICC=0.857) and fractal dimension (ICC=0.906), while circumpapillary vessel density exhibited moderate interocular correlation (ICC=0.687). Bland-Altman plots revealed an agreement range of -5.26% to 6.21% for circumpapillary vessel density. CONCLUSION: OCTA ONH parameters demonstrate good repeatability in healthy subjects. The interocular correlations of papillary vessel density, fractal dimension and vessel diameter index are high, but the correlation for circumpapillary vessel density is moderate.

Polygenic risk-stratified screening for nasopharyngeal carcinoma in high-risk endemic areas of China: a cost-effectiveness study.

Background: Nasopharyngeal carcinoma (NPC) has an extremely high incidence rate in Southern China, resulting in a severe disease burden for the local population. Current EBV serologic screening is limited by false positives, and there is opportunity to integrate polygenic risk scores for personalized screening which may enhance cost-effectiveness and resource utilization. Methods: A Markov model was developed based on epidemiological and genetic data specific to endemic areas of China, and further compared polygenic risk-stratified screening [subjects with a 10-year absolute risk (AR) greater than a threshold risk underwent EBV serological screening] to age-based screening (EBV serological screening for all subjects). For each initial screening age (30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, and 65-69 years), a modeled cohort of 100,000 participants was screened until age 69, and then followed until age 79. Results: Among subjects aged 30 to 54 years, polygenic risk-stratified screening strategies were more cost-effective than age-based screening strategies, and almost comprised the cost-effectiveness efficiency frontier. For men, screening strategies with a 1-year frequency and a 10-year absolute risk (AR) threshold of 0.7% or higher were cost-effective, with an incremental cost-effectiveness ratio (ICER) below the willingness to pay (¥203,810, twice the local per capita GDP). Specifically, the strategies with a 10-year AR threshold of 0.7% or 0.8% are the most cost-effective strategies, with an ICER ranging from ¥159,752 to ¥201,738 compared to lower-cost non-dominated strategies on the cost-effectiveness frontiers. The optimal strategies have a higher probability (29.4-35.8%) of being cost-effective compared to other strategies on the frontier. Additionally, they reduce the need for nasopharyngoscopies by 5.1-27.7% compared to optimal age-based strategies. Likewise, for women aged 30-54 years, the optimal strategy with a 0.3% threshold showed similar results. Among subjects aged 55 to 69 years, age-based screening strategies were more cost-effective for men, while no screening may be preferred for women. Conclusion: Our economic evaluation found that the polygenic risk-stratified screening could improve the cost-effectiveness among individuals aged 30-54, providing valuable guidance for NPC prevention and control policies in endemic areas of China.

Tf2O-Mediated P(O)-N Bond Formation of Either P(O)-OH or P(O)-H Reagents with Multitype Amines.

We have developed a Tf2O-mediated approach for the direct amination of either P(O)-OH or P(O)-H reagents with a variety of aliphatic or aromatic amines. Without the requirement of precious metals and toxic reagents, this protocol provides an alternative route to various phosphinamides and phosphoramides. The reaction proceeds under simple and mild conditions and can be effectively scaled up with similar efficiency.

Analysis of risk factors associated with pre-myopia among primary school students in the Mianyang Science City.

Objectives To find out the prevalence rate of pre-myopia among primary school students in the Mianyang Science City Area, analyze its related risk factors, and thus provide a reference for local authorities to formulate policies on the prevention and control of myopia for primary school students. Methods From September to October 2021, Cluster sampling was adopted by our research group to obtain the vision levels of primary school students employing a diopter test in the Science City Area. In addition, questionnaires were distributed to help us find the risk factors associated with pre-myopia. Through the statistical analysis, we identify the main risk factors for pre-myopia and propose appropriate interventions. Results The prevalence rate of pre-myopia among primary school students in the Science City Area was 45.27% (1020/2253), of which 43.82% were boys and 46.92% were girls, with no statistically significant difference in the prevalence rate of myopia between boys and girls (2 =2.171, P=0.141). The results of the linear trend test showed that the prevalence rate of pre-myopia tends to decrease with increasing age (Z=296.521, P=0.000). Logistic regression analysis demonstrated that the main risk factors for pre-myopia were having at least one parent with myopia, spending less than 2 hours a day outdoors, using the eyes continuously for more than 1 hour, looking at electronic screens for more than 2 hours, and having an improper reading and writing posture. Conclusion The Science City Area has a high prevalence rate of pre-myopia among primary school students. It is proposed that students, schools, families, and local authorities work together to increase the time spent outdoors, reduce digital screens and develop scientific use of eye habits.