Associations of serum cystatin C concentrations with total mortality and mortality of 12 site-specific cancers.

Purpose: Cystatin C (CysC), beyond its biomarker role of renal function, has been implicated in various physical and pathological activities. However, the impact of serum CysC on cancer mortality in a general population remains unknown. We aimed to examine the associations of serum CysC concentrations with total mortality and mortality of 12 site-specific cancers. Methods: We included 241,008 participants of the UK Biobank cohort with CysC measurements who had normal creatinine-based estimated glomerular filtration rates and were free of cancer and renal diseases at baseline (2006-2010). Death information was obtained from the National Health Service death records through 28 February 2021. Multivariable Cox proportional hazards models were used to compute hazard ratios (HR) per one standard deviation increase in log-transformed CysC concentrations and 95% confidence intervals (95% CI) for mortality. Results: Over a median follow-up of 12.1 (interquartile range, 11.3-12.8) years, 5,744 cancer deaths occurred. We observed a positive association between serum CysC concentrations and total cancer mortality (HR = 1.16, 95% CI: 1.12-1.20). Specifically, participants with higher serum CysC concentrations had increased mortality due to lung cancer (HR = 1.12, 95% CI: 1.05-1.20), blood cancer (HR = 1.29, 95% CI: 1.16-1.44), brain cancer (HR = 1.19, 95% CI: 1.04-1.36), esophageal cancer (HR = 1.20, 95% CI: 1.05-1.37), breast cancer (HR = 1.18, 95% CI: 1.03-1.36), and liver cancer (HR = 1.49, 95% CI: 1.31-1.69). Conclusion: Our findings indicate that higher CysC concentrations are associated with increased mortality due to lung, blood, brain, esophageal, breast, and liver cancers. Future studies are necessary to clarify underlying mechanisms.

Additive interaction between birth asphyxia and febrile seizures on autism spectrum disorder: a population-based study.

BACKGROUND: Autism Spectrum Disorder (ASD) is a pervasive neurodevelopmental disorder that can significantly impact an individual's ability to socially integrate and adapt. It's crucial to identify key factors associated with ASD. Recent studies link both birth asphyxia (BA) and febrile seizures (FS) separately to higher ASD prevalence. However, investigations into the interplay of BA and FS and its relationship with ASD are yet to be conducted. The present study mainly focuses on exploring the interactive effect between BA and FS in the context of ASD. METHODS: Utilizing a multi-stage stratified cluster sampling, we initially recruited 84,934 Shanghai children aged 3-12 years old from June 2014 to June 2015, ultimately including 74,251 post-exclusion criteria. A logistic regression model was conducted to estimate the interaction effect after controlling for pertinent covariates. The attributable proportion (AP), the relative excess risk due to interaction (RERI), the synergy index (SI), and multiplicative-scale interaction were computed to determine the interaction effect. RESULTS: Among a total of 74,251 children, 192 (0.26%) were diagnosed with ASD. The adjusted odds ratio for ASD in children with BA alone was 3.82 (95% confidence interval [CI] 2.42-6.02), for FS alone 3.06 (95%CI 1.48-6.31), and for comorbid BA and FS 21.18 (95%CI 9.10-49.30), versus children without BA or FS. The additive interaction between BA and FS showed statistical significance (P < 0.001), whereas the multiplicative interaction was statistically insignificant (P > 0.05). LIMITATIONS: This study can only demonstrate the relationship between the interaction of BA and FS with ASD but cannot prove causation. Animal brain experimentation is necessary to unravel its neural mechanisms. A larger sample size, ongoing monitoring, and detailed FS classification are needed for confirming BA-FS interaction in ASD. CONCLUSION: In this extensive cross-sectional study, both BA and FS were significantly linked to ASD. The coexistence of these factors was associated with an additive increase in ASD prevalence, surpassing the cumulative risk of each individual factor.

Bioactive peptide relieves glucocorticoid-induced osteoporosis by giant macrocyclic encapsulation.

Bioactive peptides play a crucial role in the field of regenerative medicine and tissue engineering. However, their application in vivo and clinic is hindered by their poor stability, short half-life, and low retention rate. Herein, we propose a novel strategy for encapsulating bioactive peptides using giant macrocycles. Platelet-derived growth factor (PDGF) bioactive mimicking peptide Nap-FFGVRKKP (P) was selected as the representative of a bioactive peptide. Quaterphen[4]arene (4) exhibited extensive host-guest complexation with P, and the binding constant was (1.16 ± 0.10) × 107 M-1. In vitro cell experiments confirmed that P + 4 could promote the proliferation of BMSCs by 2.27 times. Even with the addition of the inhibitor dexamethasone (Dex), P + 4 was still able to save 76.94% of the cells in the control group. Compared to the Dex group, the bone mass of the mice with osteoporosis in the P + 4 group was significantly increased. The mean trabecular thickness (Tb.Th) increased by 17.03%, and the trabecular bone volume fraction (BV/TV) values increased by 40.55%. This supramolecular bioactive peptide delivery strategy provides a general approach for delivering bioactive peptides and opens up new opportunities for the development of peptide-based drugs.

Superconductivity and Charge-Density-Wave-Like Transition in Th2Cu4As5.

We report the synthesis, crystal structure, and physical properties of a novel ternary compound, Th2Cu4As5. The material crystallizes in a tetragonal structure with lattice parameters a = 4.0639(3) Å and c = 24.8221(17) Å. Its structure can be described as an alternating stacking of fluorite-type Th2As2 layers with antifluorite-type double-layered Cu4As3 slabs. The measurement of electrical resistivity, magnetic susceptibility, and specific heat reveals that Th2Cu4As5 undergoes bulk superconducting transition at 4.2 K. Additionally, all these physical quantities exhibit anomalies at 48 K, accompanied by a sign change in the Hall coefficient, suggesting a charge-density-wave-like (CDW) phase transition. Drawing from both experimental data and band calculations, we propose that the superconducting and CDW-like phase transitions are, respectively, associated with the Cu4As3 slabs and the As plane in the Th2As2 layers.

Arginine methylation of ALKBH5 by PRMT6 promotes breast tumorigenesis via LDHA-mediated glycolysis.

ALKBH5 is a master regulator of N6-methyladenosine (m6A) modification, which plays a crucial role in many biological processes. Here, we show that ALKBH5 is required for breast tumor growth. Interestingly, PRMT6 directly methylates ALKBH5 at R283, which subsequently promotes breast tumor growth. Furthermore, arginine methylation of ALKBH5 by PRMT6 increases LDHA RNA stability via m6A demethylation, leading to increased aerobic glycolysis. Moreover, PRMT6-mediated ALKBH5 arginine methylation is confirmed in PRMT6-knockout mice. Collectively, these findings identify a PRMT6-ALKBH5-LDHA signaling axis as a novel target for the treatment of breast cancer.

One-Step Hydrothermal Synthesis of NVO Cathodes with Varied Lattice NH4 + Content: Effect on Structural Evolution and Electrochemical Performance.

Aqueous zinc ion batteries have been extensively researched due to their distinctive advantages such as low cost and high safety. Vanadium oxides are important cathode materials, however, poor cycle life caused by vanadium dissolution limits their application. Recent studies show that the lattice NH4 + in vanadium oxides can act as a pillar to enhance structural stability and play a crucial role in improving its cycling stability. Nevertheless, there is still a lack of research on the effect of the lattice NH4 + content on structural evolution and electrochemical performance. Herein, we synthesize vanadium oxides with different contents of lattice NH4 + by a one-step hydrothermal reaction. The vanadium oxides with lattice NH4 + exhibit high initial capacity, as well as good cycling stability and rate performance compared to bare vanadium oxide. Combined with electrochemical analyses, ex-situ structural characterizations, and in-situ X-ray diffraction tests, we reveal that the lattice NH4 + content plays a positive role in vanadium oxides' structural stability and cation diffusion kinetics. This work presents a direction for designing high-performance vanadium cathodes for aqueous zinc ion batteries.

THSD1 Suppresses Autophagy-Mediated Focal Adhesion Turnover by Modulating the FAK-Beclin 1 Pathway.

Focal adhesions (FAs) play a crucial role in cell spreading and adhesion, and their autophagic degradation is an emerging area of interest. This study investigates the role of Thrombospondin Type 1 Domain-Containing Protein 1 (THSD1) in regulating autophagy and FA stability in brain endothelial cells, shedding light on its potential implications for cerebrovascular diseases. Our research reveals a physical interaction between THSD1 and FAs. Depletion of THSD1 significantly reduces FA numbers, impairing cell spreading and adhesion. The loss of THSD1 also induces autophagy independently of changes in mTOR and AMPK activation, implying that THSD1 primarily governs FA dynamics rather than serving as a global regulator of nutrient and energy status. Mechanistically, THSD1 negatively regulates Beclin 1, a central autophagy regulator, at FAs through interactions with focal adhesion kinase (FAK). THSD1 inactivation diminishes FAK activity and relieves its inhibitory phosphorylation on Beclin 1. This, in turn, promotes the complex formation between Beclin 1 and ATG14, a critical event for the activation of the autophagy cascade. In summary, our findings identify THSD1 as a novel regulator of autophagy that degrades FAs in brain endothelial cells. This underscores the distinctive nature of THSD1-mediated, cargo-directed autophagy and its potential relevance to vascular diseases due to the loss of endothelial FAs. Investigating the underlying mechanisms of THSD1-mediated pathways holds promise for discovering novel therapeutic targets in vascular diseases.

Correlation between double-stranded DNA and acute urticaria.

BACKGROUND: Acute urticaria is a prevalent inflammatory dermatosis characterized by fulminant wheals, often accompanied by severe pruritis. It may also cause nausea, vomiting, and abdominal pain. Numerous studies have substantiated the pivotal involvement of double-stranded DNA (dsDNA) in autoimmunity. However, the role of dsDNA in the pathogenesis of acute urticaria is unclear. METHODS: We measured serum dsDNA levels in patients and controls. The relationship between dsDNA levels and environmental exposures (temperature, ultraviolet [UV] index, and season) was investigated by correlating disease onset dates with archived meteorological data. Finally, we used quantitative PCR to determine the expressions of genes encoding dsDNA receptors, single-stranded RNA (ssRNA) receptors, exosome formation, and type I interferon in the peripheral blood of patients and controls. RESULTS: Serum dsDNA levels were significantly higher in patients with acute urticaria compared with controls (mean values 1.38 and 0.94 ng/ml, respectively, P < 0.001). dsDNA levels were higher in patients exposed to higher environmental temperatures and UV indices and were higher during the summer months. We also found that the expressions of genes encoding dsDNA receptors, ssRNA receptors, absent in melanoma factor 2 (AIM2)-related inflammatory factors, and interferon alpha were up-regulated in patients. CONCLUSIONS: We demonstrated that serum dsDNA levels are elevated in acute urticaria and are influenced by climatic factors such as temperature, ultraviolet index, and season. We also found that elevated dsDNA promotes the expression of AIM2-related factors and type I interferons. This study generates new hypotheses regarding the pathogenesis of acute urticaria and suggests novel therapeutic targets.

Automated Generation and Analysis of Molecular Images Using Generative Artificial Intelligence Models.

The development of scanning probe microscopy (SPM) has enabled unprecedented scientific discoveries through high-resolution imaging. Simulations and theoretical analysis of SPM images are equally important as obtaining experimental images since their comparisons provide fruitful understandings of the structures and physical properties of the investigated systems. So far, SPM image simulations are conventionally based on quantum mechanical theories, which can take several days in tasks of large-scale systems. Here, we have developed a scanning tunneling microscopy (STM) molecular image simulation and analysis framework based on a generative adversarial model, CycleGAN. It allows efficient translations between STM data and molecular models. Our CycleGAN-based framework introduces an approach for high-fidelity STM image simulation, outperforming traditional quantum mechanical methods in efficiency and accuracy. We envision that the integration of generative networks and high-resolution molecular imaging opens avenues in materials discovery relying on SPM technologies.

Unraveling the Mechanisms of On-Surface Photoinduced Reaction with Polarized Light Excitations.

On-surface reaction has been shown as a powerful strategy to achieve atomically precise nanostructures. Numerous reactions have been realized on surfaces with thermal annealing as the primary excitation. In contrast, far fewer reactions have been triggered by light on surfaces despite its advantages due to the nonthermal process. This is possibly ascribed to our limited understanding on the excitation mechanisms of on-surface photoinduced reactions. In this work, we have studied the photoinduced debrominated coupling by using a linearly polarized light. We successfully achieved the reaction with no annealing process and obtained oligomers as the primary reaction products, which is in contrast with the formation of polymers with traditional thermal treatments. By exploring the dependence of reaction yield on the angle of incidence, we demonstrate an experimental method that can provide fundamental insights. The comparison with the theoretical approximation suggests indirect hot carrier excitation as the leading excitation mechanism. Our results not only provide fundamental insight into the surface photochemical reactions but also set the basis for harnessing light to construct unconventional nanomaterials.

Dickkopf-related protein 1 as a biomarker of local immune status and worse prognosis of Oral squamous cell carcinoma.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is an infiltrative malignancy characterized by a significantly elevated recurrence rate. Dickkopf-related protein 1 (DKK1), which plays an oncogene role in many cancers, acts as an inhibitor of the Wingless protein (Wnt) signaling pathway. Currently, there is a lack of consensus regarding the role of DKK1 in OSCC or its clinical significance. OBJECTIVE: To examine the role and effect of DKK1 in OSCC. METHODS: The identification of differentially expressed genes (DEGs) in OSCC was conducted by utilizing databases such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A comprehensive analysis of gene expression profile interactions (GEPIA) and Kaplan-Meier curve were conducted to investigate the associations among DEGs, patient survival and prognosis in individuals with OSCC. The biological function of DKK1 in OSCC was investigated by using molecular biology approaches. RESULTS: The expression of DKK1 was found to be upregulated in OSCC tissues at various stages. High levels of DKK1 expression exhibited a positive correlation with the overall survival (OS) and progression-free survival (PFS) rates among OSCC patients. DKK1 knockdown suppressed the proliferation and induced apoptotic response in OSCC cells. Moreover, DKK1 exerted a positive regulatory effect on HMGA2 expression, thereby modulating cell growth and apoptosis in OSCC. The expression of DKK1 was found to be positively correlated with the infiltration of immune cells in patients with OSCC. Additionally, higher levels of CD4+ T cells were associated with improved 5-year survival rates. CONCLUSION: DKK1 is a prognostic biomarker for patients with OSCC.

Rational Sentence Interpretation in Mandarin Chinese.

Previous work has shown that English native speakers interpret sentences as predicted by a noisy-channel model: They integrate both the real-world plausibility of the meaning-the prior-and the likelihood that the intended sentence may be corrupted into the perceived sentence. In this study, we test the noisy-channel model in Mandarin Chinese, a language taxonomically different from English. We present native Mandarin speakers sentences in a written modality (Experiment 1) and an auditory modality (Experiment 2) in three pairs of syntactic alternations. The critical materials are literally implausible but require differing numbers and types of edits in order to form more plausible sentences. Each sentence is followed by a comprehension question that allows us to infer whether the speakers interpreted the item literally, or made an inference toward a more likely meaning. Similar to previous research on related English constructions, Mandarin participants made the most inferences for implausible materials that could be inferred as plausible by deleting a single morpheme or inserting a single morpheme. Participants were less likely to infer a plausible meaning for materials that could be inferred as plausible by making an exchange across a preposition. And participants were least likely to infer a plausible meaning for materials that could be inferred as plausible by making an exchange across a main verb. Moreover, we found more inferences in written materials than spoken materials, possibly a result of a lack of word boundaries in written Chinese. Overall, the fact that the results were so similar to those found in related constructions in English suggests that the noisy-channel proposal is robust.

COMPARISONS BETWEEN DIGITAL-GUIDED AND NONDIGITAL PROTOCOL IN IMPLANT PLANNING, PLACEMENT, AND RESTORATIONS: A SYSTEMATIC REVIEW AND META-ANALYSIS OF RANDOMIZED CONTROLLED TRIALS.

OBJECTIVES: The present study aimed to systematically review the current randomized clinical trials (RCTs) with respect to computer-aided design/computer-aided manufactured (CAD/CAM) techniques in the process of implant planning, placement, and rehabilitation. MATERIALS AND METHODS: Four independent reviewers conducted an electronic and manual literature search using several databases, including the National Library of Medicine (MEDLINE-PubMed), Cochrane Central Register of Controlled Trials (CENTRAL), and EMBASE. Articles were included if they were RCTs involving the interventions regarding the computer-guided impression, placement, and manufacturing process. The outcomes of interest include clinical and patient-reported outcomes and time efficiency. A meta-analysis was conducted to evaluate the time efficiency, pain severity, accuracy of implant placement, and postsurgery marginal bone level. RESULTS: A total of 39 and 25 articles were included in the qualitative and quantitative analysis, respectively. The results of the meta-analysis showed that significantly less time was spent performing the digital impression procedure than the conventional impression (P = .002). In addition, the average adjustment time of the final prosthesis was significantly less than the nondigital fabricated prosthesis (P = .0005). Computer-guided groups reported significantly lower painkiller consumption compared to control groups (P = .03). CONCLUSIONS: Digital impressions and CAD/CAM procedures are time-saving and provide stable and predictable outcomes. Moreover, computer-guided surgery can effectuate an accurate implant placement and less postsurgery discomfort.

Network Pharmacology-Based and Molecular Docking-Based Analysis of Cornus Officinalis for the Treatment of Spinal Cord Injury.

PURPOSE: Based on network pharmacology and molecular docking, this study aimed to screen out the active ingredients existing in Cornus officinalis for the treatment of spinal cord injury (SCI) and explore their potential mechanisms. METHODS: We collected the active ingredients of Cornus officinalis and its corresponding target proteins. The target proteins corresponding to Cornus officinalis active ingredients were obtained by the Uniport. The SCI genes were obtained through the GeneCards. The active ingredient-acting target network and the interaction between action targets and a target protein interaction network were built by the String and the CytoScape 3.7.2. The core targets were analyzed by the Metascape. The active components and core targets were verified by the AutoDock. RESULTS: We collected eighteen active ingredients, including tetrahydroalstonine. 390 targets, 50 targets related to SCI were obtained. The Key targrts were AKT1, MAPK1, TNF. Four major signaling pathways are involved, including MAPK pathway. The active components of Cornus officinalis have good affinity with the core targets of SCI. CONCLUSION: Our study summarized the active ingredients of Cornus officinalis and the mechanism of action in the treatment of SCI, providing implications for the development of the active ingredients of Cornus officinalis in the treatment of SCI.

Order-Disorder Transition of Two-Dimensional Molecular Networks through a Stoichiometric Design.

Materials with disordered structures may exhibit interesting properties. Metal-organic frameworks (MOFs) are a class of hybrid materials composed of metal nodes and coordinating organic linkers. Recently, there has been growing interest in MOFs with structural disorder and the investigations of amorphous structures on surfaces. Herein, we demonstrate a bottom-up method to construct disordered molecular networks on metal surfaces by selecting two organic molecule linkers with the same symmetry but different sizes for preparing two-component samples with different stoichiometric ratios. The amorphous networks are directly imaged by scanning tunneling microscopy under ultrahigh vacuum with a submolecular resolution, allowing us to quantify its degree of disorder and other structural properties. Furthermore, we resort to molecular dynamics simulations to understand the formation of the amorphous metal-organic networks. The results may advance our understanding of the mechanism of formation of monolayer molecular networks with structural disorders, facilitating the design and exploration of amorphous MOF materials with intriguing properties.

Transition from natal downs to juvenile feathers: conserved regulatory switches in Neoaves.

The transition from natal downs for heat conservation to juvenile feathers for simple flight is a remarkable environmental adaptation process in avian evolution. However, the underlying epigenetic mechanism for this primary feather transition is mostly unknown. Here we conducted time-ordered gene co-expression network construction, epigenetic analysis, and functional perturbations in developing feather follicles to elucidate four downy-juvenile feather transition events. We discovered that LEF1 works as a key hub of Wnt signaling to build rachis and converts radial downy to bilateral symmetry. Extracellular matrix reorganization leads to peripheral pulp formation, which mediates epithelial -mesenchymal interactions for branching morphogenesis. ACTA2 compartments dermal papilla stem cells for feather cycling. Novel usage of scale keratins strengthens feather sheath with SOX14 as the epigenetic regulator. We found this primary feather transition largely conserved in chicken (precocious) and zebra finch (altricial) and discussed the possibility that this evolutionary adaptation process started in feathered dinosaurs.

Automated evaluation of retinal hyperreflective foci changes in diabetic macular edema patients before and after intravitreal injection.

Purpose: Fast and automated reconstruction of retinal hyperreflective foci (HRF) is of great importance for many eye-related disease understanding. In this paper, we introduced a new automated framework, driven by recent advances in deep learning to automatically extract 12 three-dimensional parameters from the segmented hyperreflective foci in optical coherence tomography (OCT). Methods: Unlike traditional convolutional neural networks, which struggle with long-range feature correlations, we introduce a spatial and channel attention module within the bottleneck layer, integrated into the nnU-Net architecture. Spatial Attention Block aggregates features across spatial locations to capture related features, while Channel Attention Block heightens channel feature contrasts. The proposed model was trained and tested on 162 retinal OCT volumes of patients with diabetic macular edema (DME), yielding robust segmentation outcomes. We further investigate HRF's potential as a biomarker of DME. Results: Results unveil notable discrepancies in the amount and volume of HRF subtypes. In the whole retinal layer (WR), the mean distance from HRF to the retinal pigmented epithelium was significantly reduced after treatment. In WR, the improvement in central macular thickness resulting from intravitreal injection treatment was positively correlated with the mean distance from HRF subtypes to the fovea. Conclusion: Our study demonstrates the applicability of OCT for automated quantification of retinal HRF in DME patients, offering an objective, quantitative approach for clinical and research applications.

Digital workflow of labial guides for alveolar ridge reduction during implant-supported full-arch rehabilitation.

This article presents a technique for fabricating two-piece labial guides using resin and cobalt-chromium for guided alveoloplasty, implant placement, and immediate full-arch implant-supported fixed complete denture placement. This technique reduces tissue damage and overcomes the negative effect of the anatomical position of the palatal neuropore and mobility of the tissue flap on the positioning and stability of the guide. The use of labial retention metal guides with improved mechanical properties of cobalt-chromium and fixation plugs allows the unilateral placement of the guide. Thus, minimally invasive implant surgery can be performed owing to the absence of large palatal flap elevation and obstruction. Such a design provides better stability of the guides and clear visual access during surgery.

Predicting Molecular Self-Assembly on Metal Surfaces Using Graph Neural Networks Based on Experimental Data Sets.

The application of supramolecular chemistry on solid surfaces has received extensive attention in the past few decades. To date, combining experiments with quantum mechanical or molecular dynamic methods represents the key strategy to explore the molecular self-assembled structures, which is, however, often laborious. Recently, machine learning (ML) has become one of the most exciting tools in material research, allowing for both efficiency and accuracy in predicting molecular properties. In this work, we constructed a graph neural network to predict the self-assembly of functional polycyclic aromatic hydrocarbons (PAHs) on metal surfaces. Using scanning tunneling microscopy (STM), we characterized the self-assembled nanostructures of a homologous series of PAH molecules on different metal surfaces to construct an experimental data set for model training. Compared with traditional ML algorithms, our model exhibits better predictive performance. Finally, the generalization of the model is further verified by comparing the ML predictions and experimental results of different functionalized molecule. Our results demonstrate training experimental data sets to produce a predictive ML model of molecular self-assembly with generalization performance, which allows for the predictive design of nanostructures with functional molecules.

Applying a Deep-Learning-Based Keypoint Detection in Analyzing Surface Nanostructures.

Scanning tunneling microscopy (STM) imaging has been routinely applied in studying surface nanostructures owing to its capability of acquiring high-resolution molecule-level images of surface nanostructures. However, the image analysis still heavily relies on manual analysis, which is often laborious and lacks uniform criteria. Recently, machine learning has emerged as a powerful tool in material science research for the automatic analysis and processing of image data. In this paper, we propose a method for analyzing molecular STM images using computer vision techniques. We develop a lightweight deep learning framework based on the YOLO algorithm by labeling molecules with its keypoints. Our framework achieves high efficiency while maintaining accuracy, enabling the recognitions of molecules and further statistical analysis. In addition, the usefulness of this model is exemplified by exploring the length of polyphenylene chains fabricated from on-surface synthesis. We foresee that computer vision methods will be frequently used in analyzing image data in the field of surface chemistry.