The optimal connection model for blood vessels segmentation and the MEA-Net

Vascular diseases have long been regarded as a significant health concern. Accurately detecting the location, shape, and afflicted regions of blood vessels from a diverse range of medical images has proven to be a major challenge. Obtaining blood vessels that retain their correct topological structures is currently a crucial research issue. Numerous efforts have sought to reinforce neural networks' learning of vascular geometric features, including measures to ensure the correct topological structure of the segmentation result's vessel centerline. Typically, these methods extract topological features from the network's segmentation result and then apply regular constraints to reinforce the accuracy of critical components and the overall topological structure. However, as blood vessels are three-dimensional structures, it is essential to achieve complete local vessel segmentation, which necessitates enhancing the segmentation of vessel boundaries. Furthermore, current methods are limited to handling 2D blood vessel fragmentation cases. Our proposed boundary attention module directly extracts boundary voxels from the network's segmentation result. Additionally, we have established an optimal connection model based on minimal surfaces to determine the connection order between blood vessels. Our method achieves state-of-the-art performance in 3D multi-class vascular segmentation tasks, as evidenced by the high values of Dice Similarity Coefficient (DSC) and Normalized Surface Dice (NSD) metrics. Furthermore, our approach improves the Betti error, LR error, and BR error indicators of vessel richness and structural integrity by more than 10% compared to other methods, and effectively addresses vessel fragmentation and yields blood vessels with a more precise topological structure.Comment: 19 page

Advances in the application of CRISPR-Cas technology in rapid detection of pathogen nucleic acid

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas) are widely used as gene editing tools in biology, microbiology, and other fields. CRISPR is composed of highly conserved repetitive sequences and spacer sequences in tandem. The spacer sequence has homology with foreign nucleic acids such as viruses and plasmids; Cas effector proteins have endonucleases, and become a hotspot in the field of molecular diagnosis because they recognize and cut specific DNA or RNA sequences. Researchers have developed many diagnostic platforms with high sensitivity, high specificity, and low cost by using Cas proteins (Cas9, Cas12, Cas13, Cas14, etc.) in combination with signal amplification and transformation technologies (fluorescence method, lateral flow technology, etc.), providing a new way for rapid detection of pathogen nucleic acid. This paper introduces the biological mechanism and classification of CRISPR-Cas technology, summarizes the existing rapid detection technology for pathogen nucleic acid based on the trans cleavage activity of Cas, describes its characteristics, functions, and application scenarios, and prospects the future application of this technology

Clinical Syndromes and Genetic Screening Strategies of Pheochromocytoma and Paraganglioma

Pheochromocytomas (PCCs) are rare neuroendocrine tumors that originate from chromaffin cells of the adrenal medulla, and paragangliomas (PGLs) are extra-adrenal pheochromocytomas. These can be mainly found in clinical syndromes including multiple endocrine neoplasia (MEN), von Hippel–Lindau (VHL) syndrome, neurofibromatosis-1 (NF-1) and familial paraganglioma (FPGL). PCCs and PGLs are thought to have the highest degree of heritability among human tumors, and it has been estimated that 60% of the patients have genetic abnormalities. This review provides an overview of the clinical syndrome and the genetic screening strategies of PCCs and PGLs. Comprehensive screening principles and strategies, along with specific screening based on clinical symptoms, biochemical tests and immunohistochemistry, are discussed

Up-Converting Nanoparticle-Based Immunochromatographic Strip for Multi-Residue Detection of Three Organophosphorus Pesticides in Food

Organophosphorus (OP) pesticides are widely used to control pests because of their high activity. This study described a rapid and sensitive lateral flow immunochromatographic (LFIC) assay based on up-converting nanoparticles (UCNPs) for multi-residue detection of three OP pesticides. The developed assay integrated novel fluorescent material UCNPs labeled with a broad-specific monoclonal antibody. Based on the competitive platform by immobilized antigen in the test zone, the optimized UCNPs-LFIC assay enabled sensitive detection for parathion, parathion-methyl, and fenitrothion with IC50 of 3.44, 3.98, and 12.49 ng/mL (R2 ≥ 0.9776) within 40 min. The detectable ability ranged from 0.98 to 250 ng/mL. There was no cross-reactivity with fenthion, phoxim, isocarbophos, chlorpyrifos, or triazophos, even at a high concentration of 500 ng/mL. Matrix interference from various agricultural products was also studied in food sample detection. In the spiked test, recoveries of the three OP pesticides ranged from 67 to 120% and relative standard deviations were below 19.54%. These results indicated that the proposed strip assay can be an alternative screening tool for rapid detection of the three OP pesticides in food samples

Antitumor Effect of Malaria Parasite Infection in a Murine Lewis Lung Cancer Model through Induction of Innate and Adaptive Immunity

BACKGROUND: Lung cancer is the most common malignancy in humans and its high fatality means that no effective treatment is available. Developing new therapeutic strategies for lung cancer is urgently needed. Malaria has been reported to stimulate host immune responses, which are believed to be efficacious for combating some clinical cancers. This study is aimed to provide evidence that malaria parasite infection is therapeutic for lung cancer. METHODOLOGY/PRINCIPAL FINDINGS: Antitumor effect of malaria infection was examined in both subcutaneously and intravenously implanted murine Lewis lung cancer (LLC) model. The results showed that malaria infection inhibited LLC growth and metastasis and prolonged the survival of tumor-bearing mice. Histological analysis of tumors from mice infected with malaria revealed that angiogenesis was inhibited, which correlated with increased terminal deoxynucleotidyl transferase-mediated (TUNEL) staining and decreased Ki-67 expression in tumors. Through natural killer (NK) cell cytotoxicity activity, cytokine assays, enzyme-linked immunospot assay, lymphocyte proliferation, and flow cytometry, we demonstrated that malaria infection provided anti-tumor effects by inducing both a potent anti-tumor innate immune response, including the secretion of IFN-γ and TNF-α and the activation of NK cells as well as adaptive anti-tumor immunity with increasing tumor-specific T-cell proliferation and cytolytic activity of CD8(+) T cells. Notably, tumor-bearing mice infected with the parasite developed long-lasting and effective tumor-specific immunity. Consequently, we found that malaria parasite infection could enhance the immune response of lung cancer DNA vaccine pcDNA3.1-hMUC1 and the combination produced a synergistic antitumor effect. CONCLUSIONS/SIGNIFICANCE: Malaria infection significantly suppresses LLC growth via induction of innate and adaptive antitumor responses in a mouse model. These data suggest that the malaria parasite may provide a novel strategy or therapeutic vaccine vector for anti-lung cancer immune-based therapy

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead