High-SNR mid-infrared dual-comb spectroscopy using active phase control cooperating with CWs-dependent phase correction.

Mid-infrared (MIR) dual-comb spectroscopy (DCS) is a highly effective method for molecular metrology of rovibrational transition spectra in a quick accurate manner. However, due to limited comb frequency instability, manipulating coherence between two frequency combs to accomplish high-quality spectral analysis in the MIR region is a huge challenge. Here, we developed a comb-teeth resolved MIR DCS based on active phase control cooperating with a CWs-dependent (CWD) interferogram timing correction. Firstly, four meticulously engineered actuators were individually integrated into two near-infrared (NIR) seed combs to facilitate active coherence maintenance. Subsequently, two PPLN waveguides were adopted to achieve parallel difference frequency generations (DFG), directly achieving a coherent MIR dual-comb spectrometer. To improve coherence and signal-to-noise ratio (SNR), a CWD resampled interferogram timing correction was used to optimize the merit of DCS from 7.5 × 105 to 2.5 × 106. Meanwhile, we carried out the measurement of MIR DCS on the methane hot-band absorption spectra (v3 band), which exhibited a good agreement with HITRAN by a standard deviation on recording residual of 0.76%. These experimental results confirm that this MIR DCS with CWD interferogram timing correction has significant potential to characterize the rovibrational transitions of MIR molecules.

Inequities in human papillomavirus vaccination among children aged 9-14 years old under constrained vaccine supply in China.

BACKGROUND: Inequities in access to human papillomavirus (HPV) vaccine are becoming a growing critical issue globally. Few studies investigate the factors determining HPV vaccine uptake disparities when vaccine supply is constrained, especially in low- and middle-income countries. The aim of this study was to investigate inequities of HPV vaccination and related factors under the constrained vaccine supply in China. METHODS: A cross-sectional survey was conducted in a developed eastern coastal province and a developing western one in China between November and December 2022. Employing multistage stratified cluster random sampling, the study collected data from parents of children aged 9-14. Mixed-effects logistic regression models with school units as random effects were used for analysis. RESULTS: From 4,127 eligible parents (as vaccine decision makers for girls), 1,346 (32.6%) intended to vaccinate their daughters against HPV, of which 836 (62.1%) attempted to schedule a vaccination appointment. Only 16.4% succeeded in booking an appointment. More than half of the intended parents expected the imported 9-valent HPV vaccine. There were significant disparities in HPV vaccine awareness, intention, and vaccination behavior across educational, income, geographic, ethnic, gender, and health literacy levels. Vaccine awareness and intentions were higher among parents with higher socioeconomic status; however, girls from lower socioeconomic families were more likely to receive the HPV vaccine and had a higher domestically produced vaccination rate. Significant disparities exist in vaccination intentions and actual vaccination behaviors, primarily due to large supply constraints of the HPV vaccine. CONCLUSIONS: Sustained health education campaigns are needed to raise awareness of the HPV vaccine, improve health literacy, and decrease over-preference for the 9-valent HPV vaccine. A mother's HPV vaccination behavior was positively associated with increased intention and actual vaccination behavior for her daughter. This study advocates for complementary cervical cancer prevention programs targeting both mothers and daughters.

Single-cell transcriptomes identifies characteristic features of mouse macrophages in liver Mallory-Denk bodies formation.

Mallory-Denk bodies (MDBs) consist of intracellular aggregates of misfolded proteins in ballooned hepatocytes and serve as important markers of progression in certain liver diseases. Resident hepatic macrophage-mediated inflammation influences the development of chronic liver diseases and cancer. Here, the first systematic study of macrophages heterogeneity in mice was conducted to illustrate the pathogenesis of MDB formation using single-nucleus RNA sequencing (snRNA-seq). Furthermore, we provided transcriptional profiles of macrophages obtained from the fractionation of mouse liver tissues following chronic injury. We equally identified seven discrete macrophage subpopulations, each involved in specific cellular activated pathways such as basal metabolism, immune regulation, angiogenesis, and cell cycle regulation. Among these, a specific macrophage cluster (Cluster4), a subpopulation specifically expressing genes that regulate cell division and the cell cycle, was identified. Interestingly, we found that CCR2 was significantly induced in Cluster2, thereby inducing monocytes to migrate to macrophages to promote MDB pathogenesis. Thus, our study is the first to demonstrate the heterogeneity of macrophages associated with liver MDB formation in mice through single-cell resolution. This serves as the basis for further insights into the pathogenesis of liver MDB formation and molecular mechanisms of chronic liver disease progression.

Collaboration on evidence synthesis in Africa: a network study of growing research capacity.

BACKGROUND: Evidence-based practice in medicine and social policy relies heavily on evidence synthesis. To translate evidence into practical guidelines for low- and middle-income countries, local expertise is essential. The objectives of this study are to assess the change in capacity for conducting evidence synthesis in Africa and to identify key African institutions for regional capacity-building. We take on a network perspective, considering that the position of an institution in the African evidence ecosystem is one constituent of its research capacity. METHODS: We systematically identified 3548 evidence synthesis publications between 2008 and 2019 with at least one author in Africa from the Web of Science Core Collection. These articles involved 3769 institutions. Longitudinal institution-level collaboration network data were constructed based on co-authorship information. We used social network analysis to examine the institutions' connectivity and tendency for intra- and interregional collaboration. We also identified the degree- and betweenness-central African institutions and explored the structure and composition of their local network neighbourhoods. RESULTS: The number of African institutions involved in evidence synthesis has increased substantially over the last decade, from 31 in 2008 to 521 in 2019, and so has the number of evidence synthesis publications with authors in Africa. African institutions in the evidence ecosystem have also become more connected during this period. Although the amount of intercontinental collaboration continues to exceed that of regional collaboration, the tendency for African institutions to collaborate with partners in Africa is increasing. We identified seven institutions-in South Africa, Egypt and Uganda-as central to the collaboration networks between 2008 and 2019, all of whom showed a tendency to collaborate across sectors. CONCLUSION: The development of more regionally based network-building initiatives would help to foster communities of practice and inter-institutional collaboration, strengthening regional research capacity. Moreover, the analysis in this study adds depth beyond a simple bibliometric analysis and illustrates that network analysis could provide a useful tool to evaluate the effectiveness of capacity-building strategies and programmes in the future.

Monoclonal Antibody 2C5-Modified Mixed Dendrimer Micelles for Tumor-Targeted Codelivery of Chemotherapeutics and siRNA.

Targeted delivery of chemotherapeutics to tumors has the potential to reach a high dose at the tumor while minimizing systemic exposure. Incorporation of antibody within a micellar platform represents a drug delivery system for tumor-targeted delivery of antitumor agents. Such modified immunomicelles can result in an increased accumulation of antitumor agents and enhanced cytotoxicity toward cancer cells. Here, mixed dendrimer micelles (MDM) composed of PEG2k-DOPE-conjugated generation 4 polyamidoamine dendrimer G4-PAMAM-PEG2k-DOPE and PEG5k-DOPE were coloaded with doxorubicin and siMDR-1. This formulation was further modified with monoclonal antibodies 2C5 with nucleosome-restricted specificity that effectively recognized cancer cells via the cell-surface-bound nucleosomes. Micelles with attached 2C5 antibodies significantly enhanced cellular association and tumor killing in both monolayer and spheroid tumor models as well as in vivo in experimental animals compared to the nontargeted formulations.

Altered regulation of LncRNA analysis of human alcoholic hepatitis with Mallory-Denk Bodies (MDBs) is revealed by RNA sequencing.

Mallory-Denk Bodies (MDBs) are prevalent in a variety of liver diseases including alcoholic hepatitis (AH) and are formed in mice livers by feeding DDC. Long noncoding RNAs (lncRNAs) are considered as emerging new gene regulators, which participates in many functional activities through diverse mechanisms. We previously reported the mechanisms involved in the formation of liver MDBs in mouse model and in AH livers where MDBs had formed. To investigate the regulation of mRNAs expression and the probable role of lncRNAs in AH livers with MDBs, RNA-Seq analyses was further conducted to determine the mRNA and lncRNA expression profiles of the AH livers compared with the normal livers. It showed that different lncRNAs have different information contribution degrees by principal component analysis, and the integrated analysis of lncRNA-mRNA co-expression networks were linked to endocytosis, cell cycle, p53 signaling pathways in the human. Based on the co-expression networks, we identify 36 mRNAs that could be as potential biomarkers of alcoholic liver disease (ALD) and hepatocellular carcinoma (HCC). To our knowledge, this is the first report on the regulatory network of lncRNAs associated with liver MDB formation in human, and these results might offer new insights into the molecular mechanisms of liver MDB formation and the progression of AH to HCC.

Folate targeted lipid chitosan hybrid nanoparticles for enhanced anti-tumor efficacy.

Folic acid is often used for active targeting of tumor cells to enhance therapeutic outcomes. Here, folic acid was conjugated with chitosan and folate-conjugated chitosan-lipid hybrid nanoparticles were prepared by ionic gelation method using anionic lipid. These nanoparticles were in size range of 200 to 400 nm with spherical shape. In vitro drug release data suggested a sustained release of cisplatin. The therapeutic efficacy of the folate-conjugated hybrid nanoparticles was evaluated in SK-OV-3, A2780 and MCF-7 cancer cell lines. A significant increase in cytotoxicity was observed with folate targeted LPHNPs compared to non-targeted LPHNPs. Significantly enhanced cellular uptake and cell cycle arrest resulting from folate-targeted nanoparticles were confirmed using fluorescence microscopy and flow cytometry. The therapeutic efficacy and tumor penetration were further evaluated in 3D spheroid tumor models. These studies suggest that folate-conjugated lipid-chitosan nanoparticles could enhance therapeutic activity and may represent a promising platform for active targeting of tumor cells.

Polymeric Co-Delivery Systems in Cancer Treatment: An Overview on Component Drugs' Dosage Ratio Effect.

Multiple factors are involved in the development of cancers and their effects on survival rate. Many are related to chemo-resistance of tumor cells. Thus, treatment with a single therapeutic agent is often inadequate for successful cancer therapy. Ideally, combination therapy inhibits tumor growth through multiple pathways by enhancing the performance of each individual therapy, often resulting in a synergistic effect. Polymeric nanoparticles prepared from block co-polymers have been a popular platform for co-delivery of combinations of drugs associated with the multiple functional compartments within such nanoparticles. Various polymeric nanoparticles have been applied to achieve enhanced therapeutic efficacy in cancer therapy. However, reported drug ratios used in such systems often vary widely. Thus, the same combination of drugs may result in very different therapeutic outcomes. In this review, we investigated polymeric co-delivery systems used in cancer treatment and the drug combinations used in these systems for synergistic anti-cancer effect. Development of polymeric co-delivery systems for a maximized therapeutic effect requires a deeper understanding of the optimal ratio among therapeutic agents and the natural heterogenicity of tumors.

Dendrimers as Nanocarriers for Nucleic Acid and Drug Delivery in Cancer Therapy.

Dendrimers are highly branched polymers with easily modifiable surfaces. This makes them promising structures for functionalization and also for conjugation with drugs and DNA/RNA. Their architecture, which can be controlled by different synthesis processes, allows the control of characteristics such as shape, size, charge, and solubility. Dendrimers have the ability to increase the solubility and bioavailability of hydrophobic drugs. The drugs can be entrapped in the intramolecular cavity of the dendrimers or conjugated to their functional groups at their surface. Nucleic acids usually form complexes with the positively charged surface of most cationic dendrimers and this approach has been extensively employed. The presence of functional groups in the dendrimer's exterior also permits the addition of other moieties that can actively target certain diseases and improve delivery, for instance, with folate and antibodies, now widely used as tumor targeting strategies. Dendrimers have been investigated extensively in the medical field, and cancer treatment is one of the greatest areas where they have been most used. This review will consider the main types of dendrimer currently being explored and how they can be utilized as drug and gene carriers and functionalized to improve the delivery of cancer therapy.

Enhanced Cytotoxicity of Folic Acid-Targeted Liposomes Co-Loaded with C6 Ceramide and Doxorubicin: In Vitro Evaluation on HeLa, A2780-ADR, and H69-AR Cells.

Current research in cancer therapy is beginning to shift toward the use of combinational drug treatment regimens. However, the efficient delivery of drug combinations is governed by a number of complex factors in the clinical setting. Therefore, the ability to synchronize the pharmacokinetics of the individual therapeutic agents present in combination not only to allow for simultaneous tumor accumulation but also to allow for a synergistic relationship at the intracellular level could prove to be advantageous. In this work, we report the development of a novel folic acid-targeted liposomal formulation simultaneously co-loaded with C6 ceramide and doxorubicin [FA-(C6+Dox)-LP]. In vitro cytotoxicity assays showed that the FA-(C6+Dox)-LP was able to significantly reduce the IC50 of Dox when compared to that after the treatment with the doxorubicin-loaded liposomes (Dox-LP) as well as the untargeted drug co-loaded (C6+Dox)-LP on HeLa, A2780-ADR, and H69-AR cells. The analysis of the cell cycle distribution showed that while the C6 liposomes (C6-LP) did not cause cell cycle arrest, all the Dox-containing liposomes mediated cell cycle arrest in HeLa cells in the G2 phase at Dox concentrations of 0.3 and 1 µM and in the S phase at the higher concentrations. It was also found that this arrest in the S phase precedes the progression of the cells to apoptosis. The targeted FA-(C6+Dox)-LP were able to significantly enhance the induction of apoptotic events in HeLa cell monolayers as compared to the other treatment groups. Next, using time-lapse phase holographic imaging microscopy, it was found that upon treatment with the FA-(C6+Dox)-LP, the HeLa cells underwent rapid progression to apoptosis after 21 h as evidenced by a drastic drop in the average area of the cells after loss of cell membrane integrity. Finally, upon evaluation in a HeLa spheroid cell model, treatment with the FA-(C6+Dox)-LP showed significantly higher levels of cell death compared to those with C6-LP and Dox-LP. Overall, this study clearly shows that the co-delivery of C6 ceramide and Dox using a liposomal platform significantly correlates with an antiproliferative effect due to cell cycle regulation and subsequent induction of apoptosis and thus warrants its further evaluation in preclinical animal models.

Micelle-like nanoparticles as carriers for DNA and siRNA.

Gene therapy represents a potential efficient approach of disease prevention and therapy. However, due to their poor in vivo stability, gene molecules need to be associated with delivery systems to overcome extracellular and intracellular barriers and allow access to the site of action. Cationic polymeric nanoparticles are popular carriers for small interfering RNA (siRNA) and DNA-based therapeutics for which efficient and safe delivery are important factors that need to be optimized. Micelle-like nanoparticles (MNP) (half micelles, half polymeric nanoparticles) can overcome some of the disadvantages of such cationic carriers by unifying in one single carrier the best of both delivery systems. In this review, we will discuss how the unique properties of MNP including self-assembly, condensation and protection of nucleic acids, improved cell association and gene transfection, and low toxicity may contribute to the successful application of siRNA- and DNA-based therapeutics into the clinic. Recent developments of MNP involving the addition of stimulus-sensitive functions to respond specifically to pathological or externally applied "triggers" (e.g., temperature, pH or enzymatic catalysis, light, or magnetic fields) will be discussed. Finally, we will overview the use of MNP as two-in-one carriers for the simultaneous delivery of different agents (small molecules, imaging agents) and nucleic acid combinations.

A Review on Commercial Oligonucleotide Drug Products.

Oligonucleotide drug products commercially approved in the US and the EU are reviewed. A total of 20 products that includes 1 aptamer, 12 antisense oligonucleotides (ASOs), 6 small interfering ribonucleic acids (siRNAs), and 1 mixture of single-stranded and double-stranded polydeoxyribonucleotides have been identified. A typical oligonucleotide formulation is composed of an oligonucleotide with buffering agent(s), pH adjusting agents, and a tonicity adjusting agent. All the products are presented as 2.1 - 200 mg/mL solutions at pH between 6 and 8.7. Majority of the products are approved for intravenous (IV) and subcutaneous (SC) routes, with two for intravitreal (IVT), two for intrathecal (IT), and one for intramuscular (IM) routes. The primary packaging includes vials and prefilled syringes (PFS). Products approved for IV and IT administration routes and requiring >1.5 mL dose volumes are supplied in vials, while those approved for SC, IM, and IVT and requiring ≤1.5 mL dose volume are supplied in PFS. Based on the compiled dataset, we propose a generalized starting point for an oligonucleotide formulation during early phase development for IV, SC, and IT administration routes. Overall, we believe this harmonized evaluation and understanding of various oligonucleotide drug product attributes will help derive platform generalizations and allows for accelerated early phase development for first-in-human studies.

Multi-Channel Optimization Generative Model for Stable Ultra-Sparse-View CT Reconstruction.

Score-based generative model (SGM) has risen to prominence in sparse-view CT reconstruction due to its impressive generation capability. The consistency of data is crucial in guiding the reconstruction process in SGM-based reconstruction methods. However, the existing data consistency policy exhibits certain limitations. Firstly, it employs partial data from the reconstructed image of the iteration process for image updates, which leads to secondary artifacts with compromising image quality. Moreover, the updates to the SGM and data consistency are considered as distinct stages, disregarding their interdependent relationship. Additionally, the reference image used to compute gradients in the reconstruction process is derived from the intermediate result rather than ground truth. Motivated by the fact that a typical SGM yields distinct outcomes with different random noise inputs, we propose a Multi-channel Optimization Generative Model (MOGM) for stable ultra-sparse-view CT reconstruction by integrating a novel data consistency term into the stochastic differential equation model. Notably, the unique aspect of this data consistency component is its exclusive reliance on original data for effectively confining generation outcomes. Furthermore, we pioneer an inference strategy that traces back from the current iteration result to ground truth, enhancing reconstruction stability through foundational theoretical support. We also establish a multi-channel optimization reconstruction framework, where conventional iterative techniques are employed to seek the reconstruction solution. Quantitative and qualitative assessments on 23 views datasets from numerical simulation, clinical cardiac and sheep's lung underscore the superiority of MOGM over alternative methods. Reconstructing from just 10 and 7 views, our method consistently demonstrates exceptional performance.

Self-supervised tomographic image noise suppression via residual image prior network.

Computed tomography (CT) denoising is a challenging task in medical imaging that has garnered considerable attention. Supervised networks require a lot of noisy-clean image pairs, which are always unavailable in clinical settings. Existing self-supervised algorithms for suppressing noise with paired noisy images have limitations, such as ignoring the residual between similar image pairs during training and insufficiently learning the spectrum information of images. In this study, we propose a Residual Image Prior Network (RIP-Net) to sufficiently model the residual between the paired similar noisy images. Our approach offers new insights into the field by addressing the limitations of existing methods. We first establish a mathematical theorem clarifying the non-equivalence between similar-image-based self-supervised learning and supervised learning. It helps us better understand the strengths and limitations of self-supervised learning. Secondly, we introduce a novel regularization term to model a low-frequency residual image prior. This can improve the accuracy and robustness of our model. Finally, we design a well-structured denoising network capable of exploring spectrum information while simultaneously sensing context messages. The network has dual paths for modeling high and low-frequency compositions in the raw noisy image. Additionally, context perception modules capture local and global interactions to produce high-quality images. The comprehensive experiments on preclinical photon-counting CT, clinical brain CT, and low-dose CT datasets, demonstrate that our RIP-Net is superior to other unsupervised denoising methods.

Iterative Residual Optimization Network for Limited-Angle Tomographic Reconstruction.

Limited-angle tomographic reconstruction is one of the typical ill-posed inverse problems, leading to edge divergence with degraded image quality. Recently, deep learning has been introduced into image reconstruction and achieved great results. However, existing deep reconstruction methods have not fully explored data consistency, resulting in poor performance. In addition, deep reconstruction methods are still mathematically inexplicable and unstable. In this work, we propose an iterative residual optimization network (IRON) for limited-angle tomographic reconstruction. First, a new optimization objective function is established to overcome false negative and positive artifacts induced by limited-angle measurements. We integrate neural network priors as a regularizer to explore deep features within residual data. Furthermore, the block-coordinate descent is employed to achieve a novel iterative framework. Second, a convolution assisted transformer is carefully elaborated to capture both local and long-range pixel interactions simultaneously. Regarding the visual transformer, the multi-head attention is further redesigned to reduce computational costs and protect reconstructed image features. Third, based on the relative error convergence property of the convolution assisted transformer, a mathematical convergence analysis is also provided for our IRON. Both numerically simulated and clinically collected real cardiac datasets are employed to validate the effectiveness and advantages of the proposed IRON. The results show that IRON outperforms other state-of-the-art methods.

Effects of antibiotics on the anti-tumor efficacy of immune checkpoint inhibitor therapy.

PURPOSE: Immunotherapy using immune checkpoint inhibitors (ICIs) has shown several benefits over traditional therapies. However, the eligible population remains small. Antibiotic (ATB) use might reduce immunotherapy efficacy by disrupting the gut microbiota. However, in China, ATB effect on ICI therapy efficacy remains unelucidated. We aimed to assess the effects of ATBs on the anti-tumor efficacy of ICIs to provide a reference for clinical use. METHODS: We included 134 patients with advanced tumors undergoing ICI therapy at Shanghai Jiading District Central Hospital from January 1, 2021, to October 1, 2023. They were divided into Non-ATB and ATB groups based on ATB use within 30 days before and after ICI administration. Moreover, we compared progression-free (PFS) and overall (OS) survival between the groups. RESULTS: Median PFS and OS were lower in the ATB than in the Non-ATB group (PFS: 4.0 vs. 5.5 months; OS: 5.4 vs. 6.5 months). Univariate analysis revealed that ATB use significantly affected PFS (hazard ratio [HR] = 2.318, 95% confidence interval [CI] = 1.281-4.194, P = 0.005) and OS (HR = 2.115, 95% CI = 1.161-3.850, P = 0.014). Moreover, multivariate analysis revealed poor PFS (HR = 2.573, 95% CI = 1.373-4.826, P = 0.003) and OS (HR = 2.452, 95% CI = 1.298-4.632, P = 0.006) in patients who received ATBs during ICI therapy. CONCLUSIONS: ATB use is negatively correlated with ICI therapy efficacy, leading to reduced PFS and OS in patients undergoing such treatment. Owing to the significant impact of ATBs on the human gut microbiome, regulation of the gut microbiome may emerge as a novel therapeutic target that can enhance the clinical activity of ICIs.

Current state of nanomedicine drug products: An industry perspective.

Nanomedicine drug products have reached an unprecedented high in terms of global commercial acceptance and media exposure with the approvals of the mRNA COVID-19 vaccines in 2021. In this paper, we examine the current state of the art for nanomedicine technologies as applied for pharmaceutical products and compare those trends with results from a recent IQ Consortium industry survey on nanomedicine drug products. We find that 1) industry companies continue to push the envelope in terms of new technologies for characterizing their specific drug products, 2) new analytical technologies continue to be utilized by industry to characterize the increasingly complex nanomedicine drug products and 3) alignment and communication are key between industry and regulatory authorities to better understand the regulatory filings that are being submitted. There are many CMC challenges that a company must overcome to successfully file a nanomedicine drug product. In 2022, the FDA Guidance on Drug Products containing Nanomaterials was published, and it provides a roadmap for submission of a nanomedicine drug product. We propose that our paper serves as a complimentary guide providing knowledge on specific CMC issues such as quality attributes, physicochemical characterization methods, excipients, and stability.

Spectrum learning for super-resolution tomographic reconstruction.

Objective. Computed Tomography (CT) has been widely used in industrial high-resolution non-destructive testing. However, it is difficult to obtain high-resolution images for large-scale objects due to their physical limitations. The objective is to develop an improved super-resolution technique that preserves small structures and details while efficiently capturing high-frequency information.Approach. The study proposes a new deep learning based method called spectrum learning (SPEAR) network for CT images super-resolution. This approach leverages both global information in the image domain and high-frequency information in the frequency domain. The SPEAR network is designed to reconstruct high-resolution images from low-resolution inputs by considering not only the main body of the images but also the small structures and other details. The symmetric property of the spectrum is exploited to reduce weight parameters in the frequency domain. Additionally, a spectrum loss is introduced to enforce the preservation of both high-frequency components and global information.Main results. The network is trained using pairs of low-resolution and high-resolution CT images, and it is fine-tuned using additional low-dose and normal-dose CT image pairs. The experimental results demonstrate that the proposed SPEAR network outperforms state-of-the-art networks in terms of image reconstruction quality. The approach successfully preserves high-frequency information and small structures, leading to better results compared to existing methods. The network's ability to generate high-resolution images from low-resolution inputs, even in cases of low-dose CT images, showcases its effectiveness in maintaining image quality.Significance. The proposed SPEAR network's ability to simultaneously capture global information and high-frequency details addresses the limitations of existing methods, resulting in more accurate and informative image reconstructions. This advancement can have substantial implications for various industries and medical diagnoses relying on accurate imaging.

Intracellular heat shock protein 70: a possible therapeutic target for preventing postoperative atrial fibrillation.

Postoperative atrial fibrillation is a frequent complication after cardiac surgery, associated with an increased risk of mortality and morbidity; thus, additional treatment and increasing costs of postoperative care are required. Inflammation and oxidative stress caused by ischemia-reperfusion during cardiac surgery may play an important role in the pathogenesis of postoperative atrial fibrillation. Stress-inducible heat shock proteins act as molecular chaperones that maintain cell homeostasis against stress in these events. Heat shock protein 70, the 70-kDa family of heat shock proteins, has been shown to closely associate with the incidence of postoperative atrial fibrillation in patients undergoing cardiac surgery. Extracellular heat shock protein 70 may be pro-inflammatory in the myocardial innate immune response caused by ischemia-reperfusion. In contrast, intracellular heat shock protein 70 exerts primarily anti-inflammatory and anti-apoptotic effects by preventing response to inflammatory cytokines, and inhibiting the nuclear factor Kappa B signaling pathway and different stages of mitochondrial-dependent pathways. Furthermore, the intracellular molecule can inhibit the induction and the maintenance of atrial fibrillation by attenuating Ca2+ overload in injuried myocardial cells. It is the intracellular heat shock protein 70, but not the extracellular molecule that holds as a therapeutic strategy for preventing postoperative atrial fibrillation.

Multi-domain integrative Swin transformer network for sparse-view tomographic reconstruction.

Decreasing projection views to a lower X-ray radiation dose usually leads to severe streak artifacts. To improve image quality from sparse-view data, a multi-domain integrative Swin transformer network (MIST-net) was developed and is reported in this article. First, MIST-net incorporated lavish domain features from data, residual data, image, and residual image using flexible network architectures, where a residual data and residual image sub-network was considered as a data consistency module to eliminate interpolation and reconstruction errors. Second, a trainable edge enhancement filter was incorporated to detect and protect image edges. Third, a high-quality reconstruction Swin transformer (i.e., Recformer) was designed to capture image global features. The experimental results on numerical and real cardiac clinical datasets with 48 views demonstrated that our proposed MIST-net provided better image quality with more small features and sharp edges than other competitors.