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Abstract Magnetic resonance imaging (MRI) is a non‐interposition imaging technique that provides rich anatomical and physiological information. Yet it is limited by the long imaging time. Recently, deep neural networks have shown potential to significantly accelerate MRI. However, most of these approaches ignore the correlation between adjacent slices in MRI image sequences. In addition, the existing deep learning‐based methods for MRI are mainly based on convolutional neural networks (CNNs). They fail to capture long‐distance dependencies due to the small receptive field. Inspired by the feature similarity in adjacent slices and impressive performance of Transformer for exploiting the long‐distance dependencies, a novel multislice texture transformer network is presented for undersampled MRI reconstruction (TTMRI). Specifically, the proposed TTMRI is consisted of four modules, namely the texture extraction, correlation calculation, texture transfer and texture synthesis. It takes three adjacent slices as inputs, in which the middle one is the target image to be reconstructed, and the other two are auxiliary images. The multiscale features are extracted by the texture extraction module and their inter‐dependencies are calculated by the correlation calculation module, respectively. Then the relevant features are transferred by the texture transfer module and fused by the texture synthesis module. By considering inter‐slice correlations and leveraging the Transformer architecture, the joint feature learning across target and adjacent slices are encouraged. Moreover, TTMRI can be stacked with multiple layers to recover more texture information at different levels. Extensive experiments demonstrate that the proposed TTMRI outperforms other state‐of‐the‐art methods in both quantitative and qualitative evaluationsions.

Few-shot Named Entity Recognition (NER) is a challenging task that involves identifying new entity types using a limited number of labeled instances for training. Currently, the majority of Few-shot NER methods are based on span, which pay more attention to the boundary information of the spans as candidate entities and the entity-level information. However, these methods often overlook token-level semantic information, which can limit their effectiveness. To address this issue, we propose a novel Joint Span and Token (JST) framework that integrates both the boundary information of an entity and the semantic information of each token that comprises an entity. The JST framework employs span features to extract the boundary features of the entity and token features to extract the semantic features of each token. Additionally, to reduce the negative impact of the Other class, we introduce a method to separate named entities from the Other class in semantic space, which helps to improve the distinction between entities and the Other class. In addition, we used GPT to do data augmentation on the support sentences, generating similar sentences to the original ones. These sentences increase the diversity of the sample and the reliability of our model. Our experimental results on the Few-NERD11 https://ningding97.github.io/fewnerd/. and SNIPS22 https://github.com/AtmaHou/FewShotTagging. datasets demonstrate that our model outperforms existing methods in terms of performance.

Abstract Background Causal inference is a crucial element within medical decision-making. There have been many methods for investigating potential causal relationships between disease and treatment options developed in recent years, which can be categorized into two main types: observational studies and experimental studies. However, due to the nature of experimental studies, financial resources, human resources, and patients' ethical considerations, researchers cannot fully control the exposure of the research participants. Furthermore, most existing observational research designs are limited to determining causal relationships and cannot handle observational data, let alone determine the dosages needed for medical research. Results This paper presents a new experimental strategy called quasi-intervention for quantifying the causal effect between disease and treatment options in observed data by using a causal inference method, which converts the potential effect of different treatment options on disease into computing differences in the conditional probability. We evaluated the accuracy of the quasi-intervention by quantifying the impact of adjusting Chinese patients’ neutrophil-to-lymphocyte ratio (NLR) on their overall survival (OS) (169 lung cancer patients and 79 controls).The results agree with the literature in this study, consisting of nine papers on cohort studies on the NLR and the prognosis of lung cancer patients, proving that our method is correct. Conclusion Taken together, the results imply that quasi-intervention is a promising method for quantifying the causal effect between disease and treatment options without clinical trials, and it could improve confidence about treatment options' efficacy and safety.

Mosaicking is a crucial step in the application of remote sensing images. The amount of remote sensing image data has grown rapidly, along with the expansion of observed areas and increased image resolution. As a result, traditional serial mosaicking techniques are facing significant challenges. In recent times, various studies have utilized high-performance computing to hasten image mosaicking and attain favorable outcomes. Nevertheless, the current research only accelerates mosaicking through external technology, without optimizing from the perspective of algorithm flow, which introduces unnecessary data I/O and slows down the mosaicking. This paper introduces a rapid parallel remote sensing image mosaicking algorithm utilizing read filtering. To begin with, the target images are divided into blocks and stored in a distributed file system. Subsequently, the image blocks are read and filtered based on a designated input format. Finally, the overlapping and non-overlapping areas are read and processed asynchronously, reducing the data I/O and computing overhead, thereby improving the efficiency of parallel computing. The experiments indicate that the mosaicking algorithm introduced in this paper enhances throughput and speedup by an average of 1.38 MB/S and 0.87 relative to the current techniques, respectively, concerning various datasets and cores. This study provides a theoretical foundation and novel ideas for processing remote sensing images on cluster platforms.

The rapid expansion of nuclear technology across various sectors due to global economic growth has led to a substantial rise in the transportation of radioactive materials. The International Atomic Energy Agency (IAEA) estimates that approximately 20 million shipments of radioactive materials occur annually. In this context, ensuring the safety and security of radioactive material transportation is of significant importance. IAEA’s “Security of Radioactive Materials in Transport” (Nuclear Security Series No. 9-G) mandates that an effective transport security system should provide immediate detection of any unauthorized removal of the packages. In the present study, an innovative Adam-optimized BP neural network model is developed for detecting unauthorized movements of radioactive material packages. To analyze the performance of the proposed algorithm, numerous experiments were conducted. The results demonstrate that the proposed method achieves a 99.17% accuracy rate in detecting unauthorized movements of radioactive materials, with a missed alarm rate of 0.72% and a false alarm rate of 0.1%. This method also enables real-time detection of unauthorized removal of radioactive materials and effectively enhances the security of radioactive materials during transport to reduce the risks of theft, loss, diversion, or sabotage.

For loss and theft in the transport of radioactive materials by a single vehicle, this article summarizes the characteristics of “illegal movement” and establishes a security system that senses its inter-relationship and responds though the group network relationship. The security system reminds the vehicle crew through on-site response and linkage response. A failure detection method for on-site response is proposed, that is, the push model is used first, and when the measurement results are suspected, the pull model is used to further confirm the failure. The failure detection for linkage response adopts the push model. According to the different security requirements of the basic and enhanced transportation, the principle of setting the timeout threshold in the failure detection algorithm is proposed. In the enhanced type, the value is smaller, otherwise the value is larger. A specific timeout threshold quantification scheme is proposed. Experiments show that the method proposed in this article is effective.

The loss and theft of radioactive material in transport can be attributed to the illegal movement. In order to distinguish it from the movement caused by the turbulence of the transportation vehicle, this paper proposes the criterion of “illegal movement” as the movement of radioactive materials outside the transportation compartment. Since the interior of the compartment is generally a metal environment, this paper proposes wireless signal strength data as a sensing method. The wireless signal strength data is filtered and converted into distance data. We construct a spatial triangle perpendicular to the top and sides of the compartment based on the distance data. When the radioactive material is inside the compartment, the angle between its corresponding point and the top plane of the compartment is less than 90°. Once it moves out of the compartment, the angle will be greater than 90°. Based on this, a sensing method of “illegal movement” based on spatial triangles is proposed. The simulation research shows that the scheme proposed in this paper is feasible.

This paper proposes to design security measures based on the radioactive material package as the basic unit. The principle of four-layer defense in depth is put forward. Based on the concept of self-security intelligence, combined with out-of-vehicle monitoring, in-vehicle monitoring, and Beidou positioning technology, a security system for transport of radioactive materials was designed. It realized the perception, early warning, delay, and alarm functions and greatly improved the security.

Abstract Objective This study aims to assess the prognostic and diagnostic value of inflammatory indexes related to gestational diabetes mellitus (GDM) from the second trimester to the third trimester of pregnancy. Materials and methods In this study, we randomly selected 65 pregnant women diagnosed with GDM at our hospital from December 2022 to June 2023 to form the GDM group (n = 65). Additionally, 65 pregnant women at the same gestational weeks without GDM were selected as the Normal group (n = 65). We collected gestational information and serum samples at 24 and 36 weeks of gestation from the participants. The levels of NLRP3, IL-1Ra, and TBP-2 were determined using enzyme-linked immunosorbent assay (ELISA) to explore their changes during pregnancy. Further, this study analyzed the changes in the levels of NLRP3, IL-1Ra, and TBP-2 at 24 and 36 weeks of gestation in GDM patients and their correlation with gestational diabetes mellitus. Results The study showed that pre-pregnancy body mass index (BMI), neonatal weight, gestational hypertension, and macrosomia are significantly associated with the occurrence of GDM (P

Abstract Purpose This study aimed to explore the prognostic factors and survival patterns based on the histological type for the perimenopausal endometrial carcinoma (PIPEC) patients treated with hysterectomy. Methods The PIPEC patients were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Methods of random survival forest (RSF) and Cox regression were used to identify the possible prognostic factors of PIPEC patients. Then overall survival (OS) and cancer-specific survival (CSS) of PIPEC data were analyzed by histological types with regional lymph nodes status and SEER-stage to investigate the survival patterns of the PIPEC patients. Results A total of 14,178 PIPEC patients were included in the study. We found tumor size, grade, histology, SEER-stage, AJCC-stage, AJCC-T stage, metastasis to distant organs and regional lymph nodes status had a significant survival outcome for PIPEC both for OS and CSS (all p

Abstract Background Bed rest during pregnancy can lead to reduced physical activity, impairing lower limb venous blood flow and increasing the risk of deep vein thrombosis (DVT) and muscle atrophy. We investigated the clinical efficacy of foam rolling intervention (FRI) in enhancing lower limb venous blood flow, mitigating the risk of DVT and muscle atrophy in pregnant women on bed rest. Methods This single-blind, randomised controlled trial enrolled 86 pregnant women with long-term bed rest for foetal protection (≥ 7 days), gestational age ≥ 20 weeks, and maternal age 0.05), however, the peak blood velocities of the popliteal veins were significantly higher in the control group (P = 0.031). On the seventh day post-intervention, the experimental group had significantly higher mean and peak blood velocities in femoral and popliteal veins, significantly (P

Abstract T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy that commonly affects children and adolescents with a poor prognosis. The terminal unfolded protein response (UPR) is an emerging anti-cancer approach, although its role in pediatric T-ALL remains unclear. In our pediatric T-ALL cohort from different centers, a lower QRICH1 expression was found associated with a worse prognosis of pediatric T-ALL. Overexpression of QRICH1 significantly inhibited cell proliferation and stimulated apoptosis of T-ALL both in vitro and in vivo. Upregulation of QRICH1 significantly downregulated 78 KDa glucose-regulated protein (GRP78) and upregulated CHOP, thus activating the terminal UPR. Co-overexpression of GRP78 in T-ALL cells overexpressing QRICH1 partially reverted the inhibited proliferation and stimulated apoptosis. QRICH1 bound to the residues Asp212 and Glu155 of the nucleotide-binding domain (NBD) of GRP78, thereby inhibiting its ATP hydrolysis activity. In addition, QRICH1 was associated with endoplasmic reticulum (ER) stress in T-ALL, and overexpression of QRICH1 reversed drug resistance. Overall, low QRICH1 expression is an independent risk factor for a poor prognosis of pediatric T-ALL. By inhibiting GRP78, QRICH1 suppresses pediatric T-ALL.

Abstract Background Calcific aortic valve stenosis (CAVS) is one of the most challenging heart diseases in clinical with rapidly increasing prevalence. However, study of the mechanism and treatment of CAVS is hampered by the lack of suitable, robust and efficient models that develop hemodynamically significant stenosis and typical calcium deposition. Here, we aim to establish a mouse model to mimic the development and features of CAVS. Methods The model was established via aortic valve wire injury (AVWI) combined with vitamin D subcutaneous injected in wild type C57/BL6 mice. Serial transthoracic echocardiography was applied to evaluate aortic jet peak velocity and mean gradient. Histopathological specimens were collected and examined in respect of valve thickening, calcium deposition, collagen accumulation, osteogenic differentiation and inflammation. Results Serial transthoracic echocardiography revealed that aortic jet peak velocity and mean gradient increased from 7 days post model establishment in a time dependent manner and tended to be stable at 28 days. Compared with the sham group, simple AVWI or the vitamin D group, the hybrid model group showed typical pathological features of CAVS, including hemodynamic alterations, increased aortic valve thickening, calcium deposition, collagen accumulation at 28 days. In addition, osteogenic differentiation, fibrosis and inflammation, which play critical roles in the development of CAVS, were observed in the hybrid model. Conclusions We established a novel mouse model of CAVS that could be induced efficiently, robustly and economically, and without genetic intervention. It provides a fast track to explore the underlying mechanisms of CAVS and to identify more effective pharmacological targets.

Abstract The study of drug–target interaction plays an important role in the process of drug development. The subject of DTI forecasting has advanced significantly in the last several years, yielding numerous significant research findings and methodologies. Heterogeneous data sources provide richer information and comprehensive perspectives for drug–target interaction prediction, so many existing methods rely on heterogeneous networks, and graph embedding technology becomes an important technology to extract information from heterogeneous networks. These approaches, however, are less concerned with potential noisy information in heterogeneous networks and more focused on the extent of information extraction in those networks. Based on this, a potential DTI predictive network model called FBRWPC is proposed in this paper. It uses a fine-grained similarity selection program to first integrate similarity on similar networks and then a bidirectional random walk graph embedding learning method with restart to obtain an updated drug target interaction matrix. Through the use of similarity selection and fine-grained selection similarity integration, the framework can effectively filter out the noise present in heterogeneous networks and enhance the model's prediction performance. The experimental findings demonstrate that, even after being split up into four distinct types of data sets, FBRWPC can still retain great prediction performance, a sign of the model's resilience and good generalization.

Abstract Background Environmental factors greatly impact infectious disease-related mortality, yet there's a lack of comprehensive global studies on the contemporary burden and trends. This study aims to evaluate the global burden and trends of infectious disease mortality caused by air pollution, unsafe water, poor sanitation, and non-optimal temperature across Socio-Demographic Index (SDI) regions from 1990 to 2019. Methods This observational study utilized data from the Global Burden of Diseases Study to examine mortality rates from infectious diseases attributed to environmental risk factors between 1990 and 2019, including air pollution, unsafe water, sanitation, handwashing facilities (UWSH), and non-optimal temperatures. Age-standardized mortality rates (ASMRs) and estimated annual percentage change (EAPC) were utilized to present infectious disease mortality, and its trajectory influenced by environmental risk factors over the years. Nonlinear regression was conducted to explore the association between the SDI and ASMRs across regions from 1990 to 2019. Results In 2019, global infectious disease deaths linked to air pollution, UWSH, and non-optimal temperature reached a startling 2,556,992. Disease mortality varied widely across SDI regions, with the highest number of deaths due to air pollution and UWSH in Low SDI regions, and deaths from non-optimal temperature primarily in High SDI regions. Age disparities emerged, with children under five and the elderly most affected. However, an increasing mortality trend was observed among seniors (65–69, 75–79, and over 80) in High SDI regions due to enteric infections linked to UWSH. Globally, a consistent decrease in ASMR was seen from 1990 to 2019 for all diseases connected to these factors, except for respiratory infections linked to non-optimal temperature. Conclusions Our study underscores the significant impact of air pollution, UWSH, and non-optimal temperatures on global infectious disease mortality, particularly among vulnerable groups such as children and the elderly. It's important to tackle these challenges with targeted interventions aiming to enhance environmental quality, improve water and sanitation systems, and control extreme temperatures. In addition, international cooperation is essential for bridging regional disparities and driving global public health initiatives forward, thereby helping achieve Sustainable Development Goals more effectively.

Abstract As an emerging cancer treatment strategy, reactive oxygen species-based tumor catalytic therapies face enormous challenges due to hypoxia and the overexpression of glutathione (GSH) in the tumor microenvironment. Herein, a self-assembled copper-based nanoplatform, TCCHA, was designed for enzyme-like catalysis-enhanced chemodynamic/photodynamic/antiangiogenic tritherapy against hepatocellular carcinoma. TCCHA was fabricated from Cu2+, 3,3′-dithiobis (propionohydrazide), and photosensitizer chlorine e6 via a facile one-pot self-assembly strategy, after which an aldehyde hyaluronic acid was coated, followed by loading of the antivascular drug AL3818. The obtained TCCHA nanoparticles exhibited pH/GSH dual-responsive drug release behaviors and multienzymatic activities, including Fenton, glutathione peroxidase-, and catalase-like activities. TCCHA, a redox homeostasis disruptor, promotes ⋅OH generation and GSH depletion, thus increasing the efficacy of chemodynamic therapy. TCCHA, which has catalase-like activity, can also reinforce the efficacy of photodynamic therapy by amplifying O2 production. In vivo, TCCHA efficiently inhibited tumor angiogenesis and suppressed tumor growth without apparent systemic toxicity. Overall, this study presents a facile strategy for the preparation of multienzyme-like nanoparticles, and TCCHA nanoparticles display great potential for enzyme catalysis-enhanced chemodynamic/photodynamic/antiangiogenic triple therapy against cancer. Graphical Abstract

Background: Zika virus (ZIKV) infection during pregnancy presents a significant health risk in women of reproductive age and their offspring due to severe neurological complications. It is meaningful to assess its global burden and temporal trends. Methods: This study extracted annual incidence cases and rates of ZIKV among women of reproductive age (15–49 years) between 2011 and 2021 from Global Burden of Diseases (GBD) 2021, including global level, 21 GBD regions, 5 socio-demographic index (SDI) regions, 7 age groups, and 204 countries and territories. Relative percent change in cases and estimated annual percentage change (EAPC) of incidence rates were used to quantify the temporal trends. Results: The incidence rate of ZIKV infection exhibited a pronounced peak in 2016 at 174.27 per 100,000 population, with an EAPC of 158.30 % from 2011 to 2016 and −51.86 % from 2016 to 2021 at 3.06 per 100,000 population. And only 5 out of the 21 GBD regions reported ZIKV infection in 2021, predominantly concentrated in Latin America and Caribbean. The outbreaks were primarily concentrated in low-middle and middle SDI regions. In 2021, at the global level, the incidence rates of ZIKV infection among women of reproductive age were similar across different age groups, ranging from 2.41 to 3.39 per 100,000 population. The proportion of ZIKV infection cases was slightly higher in women aged 25–29 and 30–34 years compared to other age groups in 2021, whereas a higher proportion of cases were observed in younger age groups in 2011 and 2016. Conclusions: Women of reproductive age in Latin America and Caribbean continue to face the threat of ZIKV. Regions with lower SDI had a disproportionately severe burden. Future public health strategies should focus on high-risk areas and populations of reproductive age, enhancing surveillance, prevention, and education efforts to further mitigate the public health threat posed by ZIKV.

Abstract In the past century, scholars from both domestic and international communities have delved into the study of numerical weather prediction models to promptly understand meteorological factors and mitigate the impacts of extreme weather events on humanity. Effective and precise prediction models enable the forecasting of meteorological conditions in the upcoming days, empowering individuals to implement proactive measures to minimize the adverse effects of extreme weather (Liang et al., 2021). The WRF (Weather Research and Forecasting) modeling system is commonly used for forecasting meteorological elements. However, uncertainties terribly hamper the correctness of the forecasting results. To this end, the present study was conducted to build a secondary model on the basis of the WRF forecast model. The WRF‐BPNN model was proposed for verification after constructing the network, the temperature vertical profile and the mixing ratio vertical profile were predicted, and the results on the validation set were tested. The results showed that the WRF‐BPNN model could effectively predict the temperature profile and mixing ratio profile, presenting better performance than the traditional WRF model.

Persistent infection with high-risk human papillomavirus (HR-HPV) is the main cause of cervical cancer. These chronic infections are characterized by high expression of the HPV E6 and E7 oncogenes and the absence of the L1 and L2 capsid proteins. The regulation of HPV gene expression plays a crucial role in both the viral life cycle and rare oncogenic events. Alternative splicing of HPV mRNA is a key mechanism in post-transcriptional regulation. Through alternative splicing, HPV mRNA is diversified into various splice isoforms with distinct coding potentials, encoding multiple proteins and influencing the expression of HPV genes. The spliced mRNAs derived from a donor splicing site within the E6 ORF and one of the different acceptor sites located in the early mRNA contain E6 truncated mRNAs, named E6*. E6* is one of the extensively studied splicing isoforms. However, the role of E6* proteins in cancer progression remains controversial. Here, we reviewed and compared the alternative splicing events occurring in the genomes of HR-HPV and LR-HPV. Recently, new HPV alternative splicing regulatory proteins have been continuously discovered, and we have updated the regulation of HPV alternative splicing. In addition, we summarized the functions of known splice isoforms from three aspects: anti-tumorigenic, tumorigenic, and other cancer-related functions, including not only E6*, but also E6^E7, E8^E2, and so on. Comprehending their contributions to cancer development enhances insights into the carcinogenic mechanisms of HPV and explores the potential utility of alternative splicing in the diagnosis and treatment of cervical cancer.

Dimensional data directly reflects the growth rate of individual fish, an important economic trait of interest to fish researchers. Efficiently obtaining large-scale fish dimension data would be valuable for both selective breeding and production. To address this, our study proposes a custom dimension measurement method for fish using the YOLOv5-keypoint framework with multi-attention mechanisms. We optimized the YOLOv5 framework, incorporated the SimAM attention mechanism to achieve more accurate and faster fish detection, and added customizable landmarks to the network structure, enabling flexible configuration of the number and location of feature points in the training dataset. This method is applicable to various aquacultural species and other objects. We tested the effectiveness of the method using the economically important grass carp (Ctenopharyngodon idella). The proposed method outperforms pure YOLOv5, Faster R-CNN, and SSD in terms of precision and recall rates, achieving an impressive average precision of 0.9781. Notably, field trials confirmed the method's exceptional measurement accuracy, exceeding 97% compatibility with manual measurements, while demonstrating a real-time speed of 38 frames per second on the NVIDIA RTX A4000. This enables efficient and accurate large-scale surface dimension measurements of economic fish. To facilitate massive measurements in agricultural research, we have implemented this method as an online platform, called Mode-recognition Ruler (MrRuler, http://bioinfo.ihb.ac.cn/mrruler). The platform identifies objects in a single image at an average speed of 0.486 ​± ​0.005 ​s, based on a dataset of 10,000 images. MrRuler includes two preset carp models and allows users to upload training datasets for custom models of their targets of interest.

Abstract Ionic conductive hydrogels (ICHs) have recently gained prominence in biosensing, indicating their potential to redefine future biomedical applications. However, the integration of these hydrogels into sensor technologies and their long‐term efficacy in practical applications pose substantial challenges, including a synergy of features, such as mechanical adaptability, conductive sensitivity, self‐adhesion, self‐regeneration, and microbial resistance. To address these challenges, this study introduces a novel hydrogel system using an imidazolium salt with a ureido backbone (UL) as the primary monomer. Fabricated via a straightforward one‐pot copolymerization process that includes betaine sulfonate methacrylate (SBMA) and acrylamide (AM), the hydrogel demonstrates multifunctional properties. The innovation of this hydrogel is attributed to its robust mechanical attributes, outstanding strain responsiveness, effective water retention, and advanced self‐regenerative and healing capabilities, which collectively lead to its superior performance in various applications. Moreover, this hydrogel exhibited broad‐spectrum antibacterial activity. Its potential for biomechanical monitoring, especially in tandem with contact and noncontact electrocardiogram (ECG) devices, represents a noteworthy advancement in precise real‐time cardiac monitoring in clinical environments. In addition, the conductive properties of the hydrogel make it an ideal substrate for electrophoretic patches aimed at treating infected wounds and consequently enhancing the healing process.

Objectives To estimate the interaction between economic status (ES) and healthy lifestyle in long COVID among Chinese older people infected with SARS-CoV-2.Design A cross-sectional study based on the Peking University Health Cohort in Anning, Yunnan.Setting All primary health institutions in Anning, Yunnan Province, China, from April to May 2023.Participants A total of 4804 people aged 60 and older infected with SARS-CoV-2 were included in this study.Primary and secondary outcome measures Long COVID was measured by participants’ self-reported symptoms using structured questionnaires. ES was measured by last-month personal income, and participants’ ES was defined as low if their income was below the per capita monthly income of local residents. Lifestyle score was equal to the number of healthy behaviours (including smoking, drinking, weight, exercise and diet) and grouped using the median score as the cut-off point. Univariate and multivariate logistic models were employed to estimate the association of ES with long COVID. Interaction between ES and lifestyle in long COVID was assessed by multiplicative interaction term.Results We enrolled a total of 4804 participants infected with SARS-CoV-2, of whom 57.3% (2754 of 4804) had at least one long COVID symptom. Fatigue (1546, 56.1%), cough (1263, 45.9%) and muscle pain (880, 32.0%) were the top three common symptoms. Patients with low ES had a 48% (adjusted OR: 1.48; 95% CI 1.22, 1.82) increased risk of long COVID. A significant interaction was observed between ES and lifestyle (p value for interaction

Bone defect repair and tumor recurrence are the main challenges in the postoperative treatment of bone tumors. The incorporation of zoledronate (ZOL) into scaffolds presents a promising approach, attributed to its osteogenic and anti-tumor properties. However, there are still some unfavorable factors that make it difficult to eradicate tumor cells at the surgical site, including drug rapid release, the insufficient anti-tumor efficacy of ZOL and the multidrug resistance of chemotherapy. Herein, a novel nano drug delivery platform mesoporous silicon-coated graphene oxide (GO/MSN) and ZOL loaded nanoparticle (GO/MSN-ZOL) were developed. Then PLLA/ GO/MSN-ZOL scaffold that integrates photothermal therapy (PTT) and chemotherapy was fabricated using poly (L-lactic acid) as raw materials by selective laser sintering (SLS) technology. The GO not only imparted scaffold with photothermal properties for localized tumor cell ablation but also significantly enhanced its anti-tumor efficacy through synergistic effects in combination with chemotherapy. The mesoporous structure and large specific surface area of MSN contribute to the sustained release of ZOL. Additionally, GO could promote osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs), which in combine with ZOL’s osteoclast inhibition, enhances the bone repair capacity. This study offers a straightforward and promising strategy for treating tumor-related bone defects.

Epilepsy is a chronic neurological disease. Here we describe the generation of induced pluripotent stem cells (iPSCs) from a patient diagnosed as epilepsy caused by ATP1A2 gene mutation. Induced pluripotent stem cells (iPSCs) were developed using non-integrating episomal vectors containing OCT4, SOX2, KLF4, BCL-XL and C-MYC. The established iPSC line (SDCHi007-A) displayed pluripotent cell morphology, high expression levels of pluripotency markers, differentiation potential in vitro, normal karyotype, and remaining the original ATP1A2 gene mutation.