14-3-3σ/Stratifin and p21 limit AKT-related malignant progression in skin carcinogenesis following MDM2-associated p53 loss

To study mechanisms driving/inhibiting skin carcinogenesis, stage-specific expression of 14-3-3σ (Stratifin) was analyzed in skin carcinogenesis driven by activated rasHa/fos expression (HK1.ras/fos) and ablation of PTEN-mediated AKT regulation (K14.creP/Δ5PTENflx/flx). Consistent with 14-3-3σ roles in epidermal differentiation, HK1.ras hyperplasia and papillomas displayed elevated 14-3-3σ expression in supra-basal keratinocytes, paralleled by supra-basal p-MDM2166 activation and sporadic p-AKT473 expression. In bi-genic HK1.fos/Δ5PTENflx/flx hyperplasia, basal-layer 14-3-3σ expression appeared, and alongside p53/p21, was associated with keratinocyte differentiation and keratoacanthoma etiology. Tri-genic HK1.ras/fos-Δ5PTENflx/flx hyperplasia/papillomas initially displayed increased basal-layer 14-3-3σ, suggesting attempts to maintain supra-basal p-MDM2166 and protect basal-layer p53. However, HK1.ras/fos-Δ5PTENflx/flx papillomas exhibited increasing basal-layer p-MDM2166 activation that reduced p53, which coincided with malignant conversion. Despite p53 loss, 14-3-3σ expression persisted in well-differentiated squamous cell carcinomas (wdSCCs) and alongside elevated p21, limited malignant progression via inhibiting p-AKT1473 expression; until 14-3-3σ/p21 loss facilitated progression to aggressive SCC exhibiting uniform p-AKT1473. Analysis of TPA-promoted HK1.ras-Δ5PTENflx/flx mouse skin, demonstrated early loss of 14-3-3σ/p53/p21 in hyperplasia and papillomas, with increased p-MDM2166/p-AKT1473 that resulted in rapid malignant conversion and progression to poorly differentiated SCC. In 2D/3D cultures, membranous 14-3-3σ expression observed in normal HaCaT and SP1ras61 papilloma keratinocytes was unexpectedly detected in malignant T52ras61/v-fos SCC cells cultured in monolayers, but not invasive 3D-cells. Collectively, these data suggest 14-3-3σ/Stratifin exerts suppressive roles in papillomatogenesis via MDM2/p53-dependent mechanisms; while persistent p53-independent expression in early wdSCC may involve p21-mediated AKT1 inhibition to limit malignant progression

Influence of microstructure of carbon fibre reinforced polymer on the metal in contact

Abstract(#br)The influence of the microstructure of carbon fibre reinforced polymers (CFRPs) with epoxy matrix (E-CFRP) and nylon matrix (T-CFRP) on the galvanic behaviour of DP590 steel, 6022-aluminium alloy, 1040-steel and AZ31-magnesium alloy was investigated in the GMW14872 solution. The E-CFRP/metal couples were initially more galvanic corrosion resistant, but their galvanic corrosion gradually became more severe than the T-CFRP/metal couples. The effective micro-defects in the surface polymer layer of the CFRP samples critically determined the galvanic corrosion. A detailed surface layer model was proposed, the electrochemical processes through the surface polymer layers during galvanic corrosion were discussed. A better understanding on the microstructure of CFRP determined by composites manufacture process can be obtained

Facile and effective synthesis of hierarchical TiO2 spheres for efficient dye-sensitized solar cells

National Natural Science Foundation of China [51072170, 21021002]; National Basic Research Program of China [2012CB932900]Three-dimensional (3D) crystalline anatase TiO2 hierarchical spheres were successfully derived from Ti foils via a fast, template-free, low-temperature hydrothermal route followed by a calcination post-treatment. These dandelion-like TiO2 spheres are composed of numerous ultrathin nanoribbons, which were subsequently split into fragile nanoflakes as a result of the decomposition of Ti-complex intermediates to TiO2 and H2O at high temperature. The dye-sensitized solar cells (DSSCs) employing such hierarchically structured TiO2 spheres as the photoanodes exhibited a light-to-electricity conversion efficiency of 8.50%, yielding a 28% enhancement in comparison with that (6.64%) of P25-based DSSCs, which mainly benefited from the enhanced capacity of dye loading in combination with effective light scattering and trapping from hierarchical architecture

Predictive models of resting state networks for assessment of altered functional connectivity in mild cognitive impairment

Due to the difficulties in establishing correspondences between functional regions across individuals and populations, systematic elucidation of functional connectivity alterations in mild cognitive impairment (MCI) in comparison with normal controls (NC) is still a challenging problem. In this paper, we assessed the functional connectivity alterations in MCI via novel, alternative predictive models of resting state networks (RSNs) learned from multimodal resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) data. First, ICA-clustering was used to construct RSNs from R-fMRI data in NC group. Second, since the RSNs in MCI are already altered and can hardly be constructed directly from R-fMRI data, structural landmarks derived from DTI data were employed as the predictive models of RSNs for MCI. Third, given that the landmarks are structurally consistent and correspondent across NC and MCI, functional connectivities in MCI were assessed based on the predicted RSNs and compared with those in NC. Experimental results demonstrated that the predictive models of RSNs based on multimodal R-fMRI and DTI data systematically and comprehensively revealed widespread functional connectivity alterations in MCI in comparison with NC

Fine-Granularity Functional Interaction Signatures for Characterization of Brain Conditions

In the human brain, functional activity occurs at multiple spatial scales. Current studies on functional brain networks and their alterations in brain diseases via resting-state functional magnetic resonance imaging (rs-fMRI) are generally either at local scale (regionally confined analysis and inter-regional functional connectivity analysis) or at global scale (graph theoretic analysis). In contrast, inferring functional interaction at fine-granularity sub-network scale has not been adequately explored yet. Here our hypothesis is that functional interaction measured at fine-granularity subnetwork scale can provide new insight into the neural mechanisms of neurological and psychological conditions, thus offering complementary information for healthy and diseased population classification. In this paper, we derived fine-granularity functional interaction (FGFI) signatures in subjects with Mild Cognitive Impairment (MCI) and Schizophrenia by diffusion tensor imaging (DTI) and rsfMRI, and used patient-control classification experiments to evaluate the distinctiveness of the derived FGFI features. Our experimental results have shown that the FGFI features alone can achieve comparable classification performance compared with the commonly used inter-regional connectivity features. However, the classification performance can be substantially improved when FGFI features and inter-regional connectivity features are integrated, suggesting the complementary information achieved from the FGFI signatures

Inferring Group-Wise Consistent Multimodal Brain Networks via Multi-View Spectral Clustering

Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI) and functional MRI (fMRI) data have received extensive interest recently. However, the regularity of these structural and functional brain networks across multiple neuroimaging modalities and also across different individuals is largely unknown. This paper presents a novel approach to inferring group-wise consistent brain sub-networks from multimodal DTI/resting-state fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed upon our recently developed and validated large-scale cortical landmarks - DICCCOL (Dense Individualized and Common Connectivity-based Cortical Landmarks). We applied the algorithms on DTI data of 100 healthy young females and 50 healthy young males, obtained consistent multimodal brain networks within and across multiple groups, and further examined the functional roles of these networks. Our experimental results demonstrated that the derived brain networks have substantially improved inter-modality and inter-subject consistency

Anatomy-Guided Dense Individualized and Common Connectivity-Based Cortical Landmarks (A-DICCCOL)

Establishment of structural and functional correspondences of human brain that can be quantitatively encoded and reproduced across different subjects and populations is one of the key issues in brain mapping. As an attempt to address this challenge, our recently developed Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) system reported 358 connectional landmarks, each of which possesses consistent DTI-derived white matter fiber connection pattern that is reproducible in over 240 healthy brains. However, the DICCCOL system can be substantially improved by integrating anatomical and morphological information during landmark initialization and optimization procedures. In this paper, we present a novel anatomy-guided landmark discovery framework that defines and optimizes landmarks via integrating rich anatomical, morphological, and fiber connectional information for landmark initialization, group-wise optimization and prediction, which are formulated and solved as an energy minimization problem. The framework finally determined 555 consistent connectional landmarks. Validation studies demonstrated that the 555 landmarks are reproducible, predictable, and exhibited reasonably accurate anatomical, connectional, and functional correspondences across individuals and populations and thus are named anatomy-guided DICCCOL or A-DICCCOL. This A-DICCCOL system represents common cortical architectures with anatomical, connectional, and functional correspondences across different subjects and would potentially provide opportunities for various applications in brain science

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition

ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data

Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels. However, complex and diverse radiology reports with cross-source heterogeneity pose a huge generalizability challenge to the current methods under massive data volume, mainly because the style and normativity of radiology reports are obviously distinctive among institutions, body regions inspected and radiologists. Recently, the advent of large language models (LLM) offers great potential for recognizing signs of health conditions. To resolve the above problem, we collaborate with the Second Xiangya Hospital in China and propose ChatRadio-Valuer based on the LLM, a tailored model for automatic radiology report generation that learns generalizable representations and provides a basis pattern for model adaptation in sophisticated analysts' cases. Specifically, ChatRadio-Valuer is trained based on the radiology reports from a single institution by means of supervised fine-tuning, and then adapted to disease diagnosis tasks for human multi-system evaluation (i.e., chest, abdomen, muscle-skeleton, head, and maxillofacial $\&$ neck) from six different institutions in clinical-level events. The clinical dataset utilized in this study encompasses a remarkable total of \textbf{332,673} observations. From the comprehensive results on engineering indicators, clinical efficacy and deployment cost metrics, it can be shown that ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al., in terms of the diseases diagnosis from radiology reports. ChatRadio-Valuer provides an effective avenue to boost model generalization performance and alleviate the annotation workload of experts to enable the promotion of clinical AI applications in radiology reports