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As the biocompatible materials, hydrogels have been widely used in three- dimensional (3D) bioprinting/organ printing to load cell for tissue engineering. It is important to precisely control hydrogels deposition during printing the mimic organ structures. However, the printability of hydrogels about printing parameters is seldom addressed. In this paper, we systemically investigated the printability of hydrogels from printing lines (one dimensional, 1D structures) to printing lattices/films (two dimensional, 2D structures) and printing 3D structures with a special attention to the accurate printing. After a series of experiments, we discovered the relationships between the important factors such as air pressure, feedrate, or even printing distance and the printing quality of the expected structures. Dumbbell shape was observed in the lattice structures printing due to the hydrogel diffuses at the intersection. Collapses and fusion of adjacent layer would result in the error accumulation at Z direction which was an important fact that could cause printing failure. Finally, we successfully demonstrated a 3D printing hydrogel scaffold through harmonize with all the parameters. The cell viability after printing was compared with the casting and the results showed that our bioprinting method almost had no extra damage to the cells.

This paper examines the nonlinear effects of Digital Inclusive Finance (DIF) on urban–rural integration (URI) using a provincial panel for mainland China (31 provinces, 2011–2023). We construct a multidimensional URI index and decompose DIF into coverage breadth (D1), usage depth (D2) and digitalization level (D3). Estimation proceeds with two-way fixed-effects models and Hansen-style panel threshold regressions with bootstrap inference; robustness checks include placebo tests and instrumental-variable specifications. The evidence shows that DIF’s impact on URI is regime-dependent: marginal returns are limited at low development levels but increase sharply once DIF and complementary institutional conditions cross empirically identified thresholds. Disaggregation reveals that usage depth (D2) consistently promotes integration, whereas the benefits of coverage (D1) and digitalization (D3) materialize mainly in digitally mature regimes. Traditional finance exhibits declining marginal contribution beyond its effective range, underlining the catalytic role of digital systems. We document heterogeneity across regions and show that negative baseline coefficients on openness and education reflect spatial concentration rather than intrinsic harms. The findings reconcile mixed results in prior work and imply that policy should be threshold-aware: prioritize foundational access where coverage is low, while in advanced contexts emphasize usage, platform interoperability, and regulatory safeguards to manage platform concentration and distributional risks.

Mathematical models can produce desired dynamics and statistical properties with the insertion of suitable nonlinear terms, while energy characteristics are crucial for practical application because any hardware realizations of nonlinear systems are relative to energy flow. The involvement of memristive terms relative to memristors enables multistability and initial-dependent property in memristive systems. In this study, two kinds of memristors are used to couple a capacitor or an inductor, along with a nonlinear resistor, to build different neural circuits. The corresponding circuit equations are derived to develop two different types of memristive oscillators, which are further converted into two kinds of memristive maps after linear transformation. The Hamilton energy function for memristive oscillators is obtained by applying the Helmholz theorem or by mapping from the field energy of the memristive circuits. The Hamilton energy functions for both memristive maps are obtained by replacing the gains and discrete variables for the memristive oscillator with the corresponding parameters and variables. The two memristive maps have rich dynamic behaviors including coherence resonance under noisy excitation, and an adaptive growth law for parameters is presented to express the self-adaptive property of the memristive maps. A digital signal process (DSP) platform is used to verify these results. Our scheme will provide a theoretical basis and experimental guidance for oscillator-to-map transformation and discrete map-energy calculation.

With an estimated one million new cases reported annually, gastric cancer (GC) ranks as the fifth most diagnosed malignancy worldwide. The early detection of GC remains a major challenge, and the prognosis worsens either when patients develop resistance to chemotherapy or radiotherapy or when the cancer metastasizes. The precise pathogenesis underlying GC is not well understood, which further complicates its treatment. Circular RNAs (circRNAs), a recently discovered class of noncoding RNAs that originate from parental genes through “back-splicing”, have been shown to play a key role in various biological processes in both eukaryotes and prokaryotes. CircRNAs have been linked to cardiovascular diseases, diabetes, hypertension, Alzheimer's disease, and the occurrence and progression of tumors. Prior studies have established that circRNAs play a crucial role in GC, impacting tumorigenesis, diagnosis, progression, and therapy resistance. This review aims to summarize how circRNAs contribute to GC tumorigenesis and progression, examine their roles in the development of drug resistance, discuss their potential as biotechnological drugs, and summarize their response to therapeutic drugs and microorganism in GC.

Alterations in the vascular smooth muscle cells (VSMC) phenotype play a critical role in the pathogenesis of several cardiovascular diseases, including hypertension, atherosclerosis, and restenosis after angioplasty. MicroRNAs (miRNAs) are a class of endogenous noncoding RNAs (approximately 19–25 nucleotides in length) that function as regulators in various physiological and pathophysiological events. Recent studies have suggested that aberrant miRNAs’ expression might underlie VSMC phenotypic transformation, appearing to regulate the phenotypic transformations of VSMCs by targeting specific genes that either participate in the maintenance of the contractile phenotype or contribute to the transformation to alternate phenotypes, and affecting atherosclerosis, hypertension, and coronary artery disease by altering VSMC proliferation, migration, differentiation, inflammation, calcification, oxidative stress, and apoptosis, suggesting an important regulatory role in vascular remodeling for maintaining vascular homeostasis. This review outlines recent progress in the discovery of miRNAs and elucidation of their mechanisms of action and functions in VSMC phenotypic regulation. Importantly, as the literature supports roles for miRNAs in modulating vascular remodeling and for maintaining vascular homeostasis, this area of research will likely provide new insights into clinical diagnosis and prognosis and ultimately facilitate the identification of novel therapeutic targets.

Since its addition to additive manufacturing (AM), polyamide 12 has dominated the selective laser sintering (SLS) market, thanks to its stable thermal property and high mechanical quality. However, substantial un-sintered residue powders lead to economic losses and are burdensome to the environment. Though several works have reported the aging mechanism and reusability of the polyamide 12 residues in SLS, the quantitative degradation and decomposition changes of differently reused polyamide 12 are not available. This work experiments successive reuse of polyamide 12 residues and quantitatively monitors the thermal decomposition, molecular evolution, and compositional changes of the material in SLS AM. To understand the characteristics of such changes, we reused the same bucket of polyamide 12 powders up to 8 times, collected powder samples, and printed 3- and 32-layer part samples. Our tests revealed that the basic flowability energies per reuse are reduced by 9.90, 15.59, and 12.74 mJ in the 2-, 5-, and 8-time reused powders, respectively. These values are essential to control the flowability of polyamide 12. Laser and heat lower the material onset decomposition temperatures, making the material more labile at a decreased temperature after reuse. On the other hand, the nitrogen atmosphere delays the onset of thermal decomposition to a higher temperature. The 1 H NMR spectra reveal the degradation of polyamide 12 with reuse: in polyamide 12 parts 3D-printed using 8-time reused powders, the relative area of the peak on C-H bonds adjacent to nitrogen has a 50% reduction compared to parts using new powders. The carbon deposit and degradation raise the atomic percentages of C and O by 72.49% and 7.13%, respectively, from new powders to the part printed using 8-time reused powders. This study further reveals the surface carbon deposit of polyamide 12 during successive reuse of SLS and explains the effect of laser in inducing polymer decomposition apart from high temperatures.

This paper mainly investigates the stabilization problem of Takagi-Sugeno (T-S) fuzzy asynchronous Boolean control networks (ABCNs) under aperiodic sample-data state-feedback control. First, the system has been converted into a discrete time-delay system by using the theory of the semi-tensor product for matrices. After that, algebraic forms of the augmented ABCNs are obtained. Second, the stabilization of T-S fuzzy ABCNs with fixed time delay is researched. Based on that, noise is further considered in the time delay of ABCNs and the fixed time delay is altered to the unfixed case. Then, sufficient and necessary conditions for proposed stabilizations are derived by using different approaches. Ultimately, examples are provided to demonstrate the effectiveness and superiority of achieved results.

In this paper, some properties of the fractional-order four-dimensional (4D) hyperchaotic memristive system are analyzed by the phase diagram, Lyapunov exponent spectrum and bifurcation diagram according to the Adomian decomposition method. Based on the chaotic system, a color image encryption scheme is proposed through combining the DNA sequence operation. The algorithm simulation results and security feature analysis show that the encryption scheme has good encryption effect and high safety performance, which provides an experimental basis and theoretical guidance for the safe transmission of image information.

The function of interleukin-22 (IL-22) in intestinal barrier homeostasis remains controversial. Here, we map the transcriptional landscape regulated by IL-22 in human colonic epithelial organoids and evaluate the biological, functional and clinical significance of the IL-22 mediated pathways in ulcerative colitis (UC). We show that IL-22 regulated pro-inflammatory pathways are involved in microbial recognition, cancer and immune cell chemotaxis; most prominently those involving CXCR2 + neutrophils. IL-22-mediated transcriptional regulation of CXC-family neutrophil-active chemokine expression is highly conserved across species, is dependent on STAT3 signaling, and is functionally and pathologically important in the recruitment of CXCR2 + neutrophils into colonic tissue. In UC patients, the magnitude of enrichment of the IL-22 regulated transcripts in colonic biopsies correlates with colonic neutrophil infiltration and is enriched in non-responders to ustekinumab therapy. Our data provide further insights into the biology of IL-22 in human disease and highlight its function in the regulation of pathogenic immune pathways, including neutrophil chemotaxis. The transcriptional networks regulated by IL-22 are functionally and clinically important in UC, impacting patient trajectories and responsiveness to biological intervention. Mechanisms of non-response to ustekinumab, a biologic targeting IL-23, are currently unclear. Here, the authors show that the transcriptional program regulated by IL-22, an IL-23 responsive cytokine, is enriched in patients with ulcerative colitis unresponsive to ustekinumab and associated with higher colon neutrophil recruitment and activation of upstream IL-22 regulators.

Echocardiography is the first-line diagnostic technique for heart diseases. Although artificial intelligence techniques have made great improvements in the analysis of echocardiography, the major limitations remain to be the built neural networks are normally adapted to a few diseases and specific equipment. Here, we present an end-to-end deep learning framework named AIEchoDx that differentiates four common cardiovascular diseases (Atrial Septal Defect, Dilated Cardiomyopathy, Hypertrophic Cardiomyopathy, prior Myocardial Infarction) from normal subjects with performance comparable to that of consensus of three senior cardiologists in AUCs (99.50% vs 99.26%, 98.75% vs 92.75%, 99.57% vs 97.21%, 98.52% vs 84.20%, and 98.70% vs 89.41%), respectively. Meanwhile, AIEchoDx accurately recognizes critical lesion regions of interest along with each disease by visualizing the decision-making process. Furthermore, our analysis indicates that heterogeneous diseases, like dilated cardiomyopathy, could be classified into two phenogroups with distinct clinical characteristics. Finally, AIEchoDx performs efficiently as an anomaly detection tool when applying handheld device-produced videos. Together, AIEchoDx provides a potential diagnostic assistant tool in either cart-based echocardiography equipment or handheld echocardiography device for primary and point-of-care medical personnel with high diagnostic performance, and the application of lesion region identification and heterogeneous disease phenogrouping, which may broaden the application of artificial intelligence in echocardiography.