DEAD-box helicase 1 inhibited CD8+ T cell antitumor activity by inducing PD-L1 expression in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) does not respond well to current treatments, even immune checkpoint inhibitors. PD-L1 (programmed cell death ligand 1 or CD274 molecule)-mediated immune escape of tumor cells may be a key factor affecting the efficacy of immune checkpoint inhibitor (ICI) therapy. However, the regulatory mechanisms of PD-L1 expression and immune escape require further exploration. Here, we observed that DDX1 (DEAD-box helicase 1) was overexpressed in HCC tissues and associated with poor prognosis in patients with HCC. Additionally, DDX1 expression correlated negatively with CD8+ T cell frequency. DDX1 overexpression significantly increased interferon gamma (IFN-γ)-mediated PD-L1 expression in HCC cell lines. DDX1 overexpression decreased IFN-γ and granzyme B production in CD8+ T cells and inhibited CD8+ T cell cytotoxic function in vitro and in vivo. In conclusion, DDX1 plays an essential role in developing the immune escape microenvironment, rendering it a potential predictor of ICI therapy efficacy in HCC.

Halogen-Regulated Tc and X-ray Radiation Detection in 2D Hybrid Perovskite Ferroelastic Semiconductor.

Organic-inorganic hybrid perovskites (OIHPs) have received particular attention due to their characteristic structural tunability and flexibility. These features make OIHPs behave with excellent modifications on macroscopic properties, such as ferroicity or semiconductor performances, etc. Herein, we report two 2D hybrid stibium-based halide perovskite (C3H7N)3Sb2X9 (X = Br, 1; Cl, 2) ferroelastic semiconductor possessing dual switching properties of dielectric and second harmonic generation (SHG). Notably, these two hybrids exhibit halogen-regulated ferroelasticity and semiconductor properties. There is a significant difference in Curie temperature (Tc) and X-ray radiation detection sensitivity (S), i.e., the ΔTc and ΔS are 38 K and 87 µC Gyair-1 cm-2, respectively. Meanwhile, crystals 1 and 2 do not show dark current drift in cyclic measurements of different radiation doses with stable switching ratios of 30 and 10, separately. Meanwhile, these results were proven by scientific experimental results and density functional theory (DFT) calculations. Our work presents a facile and practical method to regulate macroproperties on the molecular level, providing a new vision to develop hybrid perovskite ferroic-photoelectric materials.

DNA-based molecular classifiers for the profiling of gene expression signatures.

Although gene expression signatures offer tremendous potential in diseases diagnostic and prognostic, but massive gene expression signatures caused challenges for experimental detection and computational analysis in clinical setting. Here, we introduce a universal DNA-based molecular classifier for profiling gene expression signatures and generating immediate diagnostic outcomes. The molecular classifier begins with feature transformation, a modular and programmable strategy was used to capture relative relationships of low-concentration RNAs and convert them to general coding inputs. Then, competitive inhibition of the DNA catalytic reaction enables strict weight assignment for different inputs according to their importance, followed by summation, annihilation and reporting to accurately implement the mathematical model of the classifier. We validated the entire workflow by utilizing miRNA expression levels for the diagnosis of hepatocellular carcinoma (HCC) in clinical samples with an accuracy 85.7%. The results demonstrate the molecular classifier provides a universal solution to explore the correlation between gene expression patterns and disease diagnostics, monitoring, and prognosis, and supports personalized healthcare in primary care.

Molecular Engineering Regulation Achieving Out-of-Plane Polarization in Rare-Earth Hybrid Double Perovskites for Ferroelectrics and Circularly Polarized Luminescence.

Out-of-plane polarization is a highly desired property of two-dimensional (2D) ferroelectrics for application in vertical sandwich-type photoferroelectric devices, especially in ultrathin ferroelectronic devices. Nevertheless, despite great advances that have been made in recent years, out-of-plane polarization remains unrealized in the 2D hybrid double perovskite ferroelectric family. Here, from our previous work 2D hybrid double perovskite HQERN ((S3HQ)4EuRb(NO3)8, S3HQ=S-3-hydroxylquinuclidinium), we designed a molecular strategy of F-substitution on organic component to successfully obtain FQERN ((S3FQ)4EuRb(NO3)8, S3FQ=S-3-fluoroquinuclidinium) showing circularly polarized luminescence (CPL) response. Remarkably, compared to the monopolar axis ferroelectric HQERN, FQERN not only shows multiferroicity with the coexistence of multipolar axis ferroelectricity and ferroelasticity but also realizes out-of-plane ferroelectric polarization and a dramatic enhancement of Curie temperature of 94 K. This is mainly due to the introduction of F-substituted organic cations, which leads to a change in orientation and a reduction in crystal lattice void occupancy. Our study demonstrates that F-substitution is an efficient strategy to realize and optimize ferroelectric functional characteristics, giving more possibility of 2D ferroelectric materials for applications in micro-nano optoelectronic devices.

Two new phenylpropanoids from the whole plant of Ainsliaea bonatii.

Two new phenylpropanoids, ainsbons A and B (1 and 2), along with a known analogue coniferyl diisovalerate (3) were isolated from the whole plant of Ainsliaea bonatii. Their structures were elucidated by analysis of NMR spectroscopic data and HRESIMS, and the absolute configuration of 2 was established by the optical rotation calculations. Compounds 1-3 were evaluated for their effects on LPS-induced nitric oxide production, and 1 and 3 showed inhibitory activities with IC50 values of 43.43 and 7.57 µM, respectively.

Roles and Regulation of Quorum Sensing of Acidophiles in Bioleaching: A Review.

Bioleaching has gained significant attention as a cost-effective and environmentally friendly approach for extracting metals from low-grade ores and industrial byproducts. The application of acidophiles in bioleaching has been extensively studied. Among the various mechanisms leaching microorganisms utilize, quorum sensing (QS) is pivotal in regulating their life activities in response to population density. QS has been confirmed to regulate bioleaching, including cell morphology, community structure, biofilm formation, and cell metabolism. Potential applications of QS have also been proposed, such as increasing mineral leaching rates by adding signaling molecules. This review is helpful for comprehensively understanding the role of QS in bioleaching and promoting the practical application of QS-based strategies in bioleaching process optimization.

Adaptive Prototype Learning for Weakly-supervised Temporal Action Localization.

Weakly-supervised Temporal Action Localization (WTAL) aims to localize action instances with only video-level labels during training, where two primary issues are localization incompleteness and background interference. To relieve these two issues, recent methods adopt an attention mechanism to activate action instances and simultaneously suppress background ones, which have achieved remarkable progress. Nevertheless, we argue that these two issues have not been well resolved yet. On the one hand, the attention mechanism adopts fixed weights for different videos, which are incapable of handling the diversity of different videos, thus deficient in addressing the problem of localization incompleteness. On the other hand, previous methods only focus on learning the foreground attention and the attention weights usually suffer from ambiguity, resulting in difficulty of suppressing background interference. To deal with the above issues, in this paper we propose an Adaptive Prototype Learning (APL) method for WTAL, which includes two key designs: (1) an Adaptive Transformer Network (ATN) to explicitly model background and learn video-adaptive prototypes for each specific video, (2) an OT-based Collaborative (OTC) training strategy to guide the learning of prototypes and remove the ambiguity of the foreground-background separation by introducing an Optimal Transport (OT) algorithm into the collaborative training scheme between RGB and FLOW streams. These two key designs can work together to learn video-adaptive prototypes and solve the above two issues, achieving robust localization. Extensive experimental results on two standard benchmarks (THUMOS14 and ActivityNet) demonstrate that our proposed APL performs favorably against state-of-the-art methods.

Dual role of PpV in Drosophila crystal cell proliferation and survival.

Drosophila melanogaster crystal cells are a specialized type of blood cells for innate immune process upon injury. Under normal conditions, crystal cells rarely proliferate and constitute a small proportion of fly blood cells. Notch signaling has been known to guide the cell fate determination of crystal cells and maintain their survival. Here, we reported that protein phosphatase V (PpV), the unique catalytic subunit of protein phosphatase 6 in Drosophila, is a novel regulator of crystal cell proliferation and integrity. We found that PpV proteins highly accumulated in crystal cells in the larval hematopoietic organ termed the lymph gland. Silencing PpV using RNA interference led to increased crystal cell proliferation in a Notch-independent manner and induced crystal cell rupture dependent on Notch signaling. Moreover, additive PpV prevented the rupture of crystal cells in lymph glands upon a needle injury, suggesting the involvement of PpV in wound healing. Altogether, our results indicated that PpV plays a dual role in lymph glands, preventing crystal cell proliferation to limit the cell number, as well as inhibiting crystal cell rupture to maintain their survival.

Building a stable and robust anti-interference DNA dissipation system by eliminating the accumulation of systemic specified errors.

BACKGROUND: The emergence of DNA nanotechnology has enabled the systematic design of diverse bionic dissipative behaviors under the precise control of nucleic acid nanodevices. Nevertheless, when compared to the dissipation observed in robust living systems, it is highly desirable to enhance the anti-interference for artificial DNA dissipation to withstand perturbations and facilitate repairs within the complex biological environments. RESULTS: In this study, we introduce strategically designed "trash cans" to facilitate kinetic control over interferences, transforming the stochastic binding of individual components within a homogeneous solution into a competitive binding process. This approach effectively eliminates incorrect binding and the accumulation of systemic interferences while ensuring a consistent pattern of energy fluctuation from response to silence. Remarkably, even in the presence of numerous interferences differing by only one base, we successfully achieve complete system reset through multiple cycles, effectively restoring the energy level to a minimum. SIGNIFICANCE: The system was able to operate stably without any adverse effect under conditions of irregular interference, high-abundance interference, and even multiplex interferences including DNA and RNA crosstalk. This work not only provides an effective paradigm for constructing robust DNA dissipation systems but also greatly broadens the potential of DNA dissipation for applications in high-precision molecular recognition and complex biological reaction networks.

Latent profiles of narrative competence and professional identity among nursing students: A cross-sectional analytic study based on the Ring theory of personhood.

AIM: To identify latent profiles of narrative competence in nursing students and examine the association between the potential competence profiles and professional identity from a person-centred perspective. BACKGROUND: According to the Ring theory of personhood, nursing students can develop their professional identities from individual, relational and social aspects through interaction with patients, as well as listening to, understanding and responding to patients' disease narratives. However, few studies have examined the relationship between narrative competence and professional identity through the quantitative method. DESIGN: A cross-sectional analytic study. METHODS: A total of 472 nursing students responded to the survey between March and May 2023. The Professional Identity Questionnaire for Nurse Students and the Narrative Competence Scale were given to participants. Latent profile analysis was conducted to identify narrative competence profiles. The Bolck-Croon-Hagenaars method was used to analyse whether these latent profiles for narrative competence affected nursing students' general, individual, interpersonal and social professional identities. RESULTS: Latent profiles were identified as "low narrative competence" (12.1 %), "relatively low narrative competence" (39.9 %), "moderate narrative competence" (40.1 %) and "high narrative competence" (7.9 %). The profiles only show level differences rather than combinations of competence areas. These profiles had varying effects on the nursing students' general professional identities, as well as their individual, relational and social professional identities. CONCLUSION: This study highlights the significance of providing tailored guidance and support to nursing students, taking into account their unique narrative competency profile, to promote the formation of professional identity from individual, relational and social aspects. Nursing educators should effectively distinguish nursing students with inadequate narrative competence and value patients' disease narratives to promote narrative competence and professional identity.

Modified Unit-Mediated Strand Displacement Reactions for Direct Detection of Single Nucleotide Variants in Active Double-Stranded DNA.

Accurate identification of single nucleotide variants (SNVs) in key driver genes holds a significant value for disease diagnosis and treatment. Fluorescent probes exhibit tremendous potential in specific, high-resolution, and rapid detection of SNVs. However, additional steps are required in most post-PCR assays to convert double-stranded DNA (dsDNA) products into single-stranded DNA (ssDNA), enabling them to possess hybridization activity to trigger subsequent reactions. This process not only prolongs the complexity of the experiment but also introduces the risk of losing target information. In this study, we proposed two strategies for enriching active double-stranded DNA, involving PCR based on obstructive groups and cleavable units. Building upon this, we explored the impact of modified units on the strand displacement reaction (SDR) and assessed their discriminatory efficacy for mutations. The results showed that detection of low variant allele frequencies (VAF) as low as 0.1% can be achieved. The proposed strategy allowed orthogonal identification of 45 clinical colorectal cancer tissue samples with 100% specificity, and the results were generally consistent with sequencing results. Compared to existing methods for enriching active targets, our approach offers a more diverse set of enrichment strategies, characterized by the advantage of being simple and fast and preserving original information to the maximum extent. The objective of this study is to offer an effective solution for the swift and facile acquisition of active double-stranded DNA. We anticipate that our work will facilitate the practical applications of SDR based on dsDNA.

Mapping annual 10-m soybean cropland with spatiotemporal sample migration.

China, as the world's biggest soybean importer and fourth-largest producer, needs accurate mapping of its planting areas for global food supply stability. The challenge lies in gathering and collating ground survey data for different crops. We proposed a spatiotemporal migration method leveraging vegetation indices' temporal characteristics. This method uses a feature space of six integrals from the crops' phenological curves and a concavity-convexity index to distinguish soybean and non-soybean samples in cropland. Using a limited number of actual samples and our method, we extracted features from optical time-series images throughout the soybean growing season. The cloud and rain-affected data were supplemented with SAR data. We then used the random forest algorithm for classification. Consequently, we developed the 10-meter resolution ChinaSoybean10 maps for the ten primary soybean-producing provinces from 2019 to 2022. The map showed an overall accuracy of about 93%, aligning significantly with the statistical yearbook data, confirming its reliability. This research aids soybean growth monitoring, yield estimation, strategy development, resource management, and food scarcity mitigation, and promotes sustainable agriculture.