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Skin injuries can have unexpected surfaces, leading to uneven wound surfaces and inadequate dressing contact with these irregular surfaces. This can decrease the dressing's haemostatic action and increase the healing period. This study recommends the use of sticky and flexible cryogel coverings to promote faster haemostasis and efficiently handle uneven skin wounds. Alginate cryogels have a fast haemostatic effect and shape flexibility due to their macroporous structure. The material demonstrates potent antibacterial characteristics and enhances skin adherence by adding grafted chitosan with gallic acid. In irregular defect wound models, cryogels can cling closely to uneven damage surfaces due to their amorphous nature. Furthermore, their macroporous structure allows for quick haemostasis by quickly absorbing blood and wound exudate. After giving the dressing a thorough rinse, its adhesive strength reduces and it is simple to remove without causing any damage to the wound. Cryogel demonstrated faster haemostasis than gauze in a wound model on a rat tail, indicating that it has considerable potential for use as a wound dressing in the biomedical area. [ABSTRACT FROM AUTHOR]

High altitude pulmonary edema (HAPE) is a life-threatening form of non-cardiogenic pulmonary edema. In recent years, association studies have become the main method for identifying HAPE genetic loci. A genome-wide association study (GWAS) of HAPE risk-associated loci was performed in Chinese male Han individuals (164 HAPE cases and 189 healthy controls) by the Precision Medicine Diversity Array Chip with 2,771,835 loci (Applied Biosystems Axiom™). Eight overlapping candidate loci in CCNG2, RP11-445O3.2, NUPL1 and WWOX were finally selected. In silico functional analyses displayed the PPI network, functional enrichment and signal pathways related to CCNG2, NUPL1, WWOX and NRXN1. This study provides data supplements for HAPE susceptibility gene loci and new insights into HAPE susceptibility. [ABSTRACT FROM AUTHOR]

In this paper, we study the global existence and boundedness of a Neumann initial-boundary value problem for the following three-species predator-prey model with power-like inter-specific interaction mechanism and prey-taxis:$ \begin{align*} \begin{cases} u_t = d_1\Delta u+u(1-u)-b_1u^{1+\alpha}v^{1-\alpha}, \; &x\in{\Omega}, t>0, \\ v_t = d_2\Delta v-\nabla\cdot(\xi v\nabla u)+u^{1+\alpha}v^{1-\alpha}-b_2v^{1+\beta}w^{1-\beta}-\theta_1v, \; &x\in{\Omega}, t>0, \\ w_t = \Delta w-\nabla\cdot(\chi w\nabla v)+v^{1+\beta}w^{1-\beta}-\theta_2w, \; &x\in{\Omega}, t>0, \ \end{cases} \end{align*} $in a bounded smooth domain $ \Omega\subset \mathbb{R}^2 $. The inter-specific interaction of the forms $ b_1u^{1+\alpha}v^{1-\alpha} $ and $ b_2v^{1+\beta}w^{1-\beta} $ with $ \alpha\in (0, 1) $, $ \beta\in(0, 1) $ is a natural extension of the classical predator-prey types $ b_1uv $ and $ b_2vw $. By delicate coupling energy estimates, we first establish the global existence of classical solutions in two dimensional spaces for appropriate initial data. [ABSTRACT FROM AUTHOR]

In make-to-order manufacturing workshops, accurate prediction value of production progress (PP) is a significant reference index for dynamic optimisation of production process and on-time delivery of production orders. The implementation of big data and Industrial Internet of Things (IIoT) in manufacturing workshops makes it possible to obtain large amounts of production data which can affect PP. However, the particularities of massive historical order data are not fully excavated and the amount of target order data is insufficient to support the training of high-precision prediction model, which will result in bad training approximation and generalisation. To overcome these shortcomings, a PP prediction approach consisting of two models with transfer learning (TL) is proposed. TL can avoid the training of PP prediction model from scratch every time. Consequently, computational efficiency can be greatly improved. A convolutional neural network (CNN) model with TL is devised to excavate the comprehensive features from historical and current orders. Additionally, a long short-term memory network (LSTM) model with TL is constructed to fit the nonlinear relation of the features provided by CNN-TL model for PP prediction. In order to validate the performance of the proposed PP prediction approach, comparative experiments of eight algorithms are conducted in an IIoT-enabled manufacturing workshop. [ABSTRACT FROM AUTHOR]

In this paper, we study the following density-dependent SIRS epidemic model with power-like infection mechanism$ \begin{align*} \left\{ \begin{aligned} &u_t = \Delta(d_1(v)u)-\beta(x) u^qv^p+\gamma w, \quad &&x\in\Omega, t>0, \\ &v_t = d_2\Delta v+\beta(x) u^qv^p-(\delta+\alpha)v, \quad &&x\in\Omega, t>0, \\ &w_t = \Delta(d_3(v)w)+\delta v-\gamma w, \quad &&x\in\Omega, t>0, \end{aligned} \right. \end{align*} $in a bounded smooth domain $ \Omega\subset \mathbb{R}^n (n\geq2) $. Here $ p $, $ q $, $ \gamma $, $ \delta $, $ \alpha>0 $ are constants. We prove that the problem possesses a global classical solution in $ n $ dimension $ (n\geq2) $ when $ q<\frac{2}{n} $, $ p\leq1 $. Moreover, we show that the above solution will converge to the steady state $ (u^*, 0, 0) $, where $ u^* = \vert\Omega\vert^{-1}\left(\int_{\Omega}(u_0+v_0+w_0)-\alpha\int_{0}^{\infty}\int_{\Omega} v\right) $. [ABSTRACT FROM AUTHOR]

Simple, sensitive, and accurate molecular diagnostics are critical for preventing rapid spread of infection and initiating early treatment of diseases. However, current molecular detection methods typically rely on extensive nucleic acid sample preparation and expensive instrumentation. Here, a simple, fully integrated, lab‐in‐a‐magnetofluidic tube (LIAMT) platform is presented for "sample‐to‐result" molecular detection of virus. By leveraging magnetofluidic transport of micro/nano magnetic beads, the LIAMT device integrates viral lysis, nucleic acid extraction, isothermal amplification, and CRISPR detection within a single engineered microcentrifuge tube. To enable point‐of‐care molecular diagnostics, a palm‐sized processor is developed for magnetofluidic separation, nucleic acid amplification, and visual fluorescence detection. The LIAMT platform is applied to detect SARS‐CoV‐2 and HIV viruses, achieving a detection sensitivity of 73.4 and 63.9 copies µL−1, respectively. Its clinical utility is further demonstrated by detecting SARS‐CoV‐2 and HIV in clinical samples. This simple, affordable, and portable LIAMT platform holds promise for rapid and sensitive molecular diagnostics of infectious diseases at the point‐of‐care. [ABSTRACT FROM AUTHOR]

The CRISPR‐Cas12a system has emerged as a powerful tool for next‐generation nucleic acid‐based molecular diagnostics. However, it has long been believed to be effective only on DNA targets. Here, we investigate the intrinsic RNA‐enabled trans‐cleavage activity of AsCas12a and LbCas12a and discover that they can be directly activated by full‐size RNA targets, although LbCas12a exhibits weaker trans‐cleavage activity than AsCas12a on both single‐stranded DNA and RNA substrates. Remarkably, we find that the RNA‐activated Cas12a possesses higher specificity in recognizing mutated target sequences compared to DNA activation. Based on these findings, we develop the "Universal Nuclease for Identification of Virus Empowered by RNA‐Sensing" (UNIVERSE) assay for nucleic acid testing. We incorporate a T7 transcription step into this assay, thereby eliminating the requirement for a protospacer adjacent motif (PAM) sequence in the target. Additionally, we successfully detect multiple PAM‐less targets in HIV clinical samples that are undetectable by the conventional Cas12a assay based on double‐stranded DNA activation, demonstrating unrestricted target selection with the UNIVERSE assay. We further validate the clinical utility of the UNIVERSE assay by testing both HIV RNA and HPV 16 DNA in clinical samples. We envision that the intrinsic RNA targeting capability may bring a paradigm shift in Cas12a‐based nucleic acid detection and further enhance the understanding of CRISPR‐Cas biochemistry. [ABSTRACT FROM AUTHOR]