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ABSTRACT Objective Methods Results Conclusion The clivus is trapezoidal in shape with uneven bone structure, the optimal number and position of screws for clival fixation are not clear. Therefore, this study aims to explore the optimization clival screw fixation method for occipitocervical instability using finite element analysis.Seven finite element models were developed to evaluate biomechanical properties of clival screw fixation for treating occipitocervical stability, including (i) one clival screw fixation A1 and A2 models; (ii) two clival screws fixation B1 and B2 models; (iii) three clival screws fixation C1 and C2 models; (iv) four clival screws fixation D1 model. Loads of 1.5 Nm were applied to the model fRoM different directions to induce flexion, extension, lateral bending, and axial rotation movements.The regular triangle C1 type three clival screws fixation exhibited great stability, with RoM of 4.20° in flexion, 5.80° in extension, 0.85° in lateral bending, and 1.60° in axial rotation. The peak stress on the internal fixation devices were relatively low, with maximum screw stress of 194 MPa in flexion, 276 MPa in extension, 180 MPa in lateral bending, and 213 MPa in axial rotation; the maximum plate stress were 126, 554, 426, and 378 MPa, respectively. The areas with higher stress were mainly concentrated at the robust neck section of the plate.The triangular configuration of three clival screws fixation represented the optimized anterior occipitocervical fixation method through the clivus, offering superior biomechanical stability, lower stress on the devices and dispersed stress distribution in the occipitocervical region. [ABSTRACT FROM AUTHOR]

Predictive processing in parietal, temporal, frontal, and sensory cortex allows us to anticipate future meanings to maximize the efficiency of language comprehension, with the temporoparietal junction (TPJ) and inferior frontal gyrus (IFG) thought to be situated towards the top of a predictive hierarchy. Although the regions underpinning this fundamental brain function are well-documented, it remains unclear how they interact to achieve efficient comprehension. To this end we recorded functional magnetic resonance imaging (fMRI) in 22 participants (11 males) while they comprehended sentences presented part-by-part, in which we manipulated the constraint provided by sentential contexts on upcoming semantic information. Using this paradigm, we examined the connectivity patterns of bilateral TPJ and IFG during anticipatory phases (i.e., before the onset of targets) and integration phases (i.e., after the onset of targets). When upcoming semantic content was highly predictable in strong-constraint contexts, both left TPJ and bilateral IFG showed stronger visual coupling, while right TPJ showed stronger connectivity with regions within control, default mode, and visual networks, including IFG, parahippocampal gyrus, posterior cingulate, and fusiform gyrus. These connectivity patterns were weaker when predicted semantic content appeared, in line with predictive coding theory. Conversely, for less predictable content, these connectivity patterns were stronger during the integration phase. Overall, these results suggest that both top-down semantic prediction and bottom-up integration during predictive processing are supported by flexible coupling of frontoparietal regions with control, memory, and sensory systems. Significance Statement Recent work has revealed the neural basis of predictive language comprehension. However, it remains unclear how brain regions change their connectivity in a dynamic fashion to support comprehension in highly predictive and less predictive contexts. Here, we show that stronger frontoparietal connectivity with cognitive control, memory, and sensory areas supports top-down prediction generation in strong-constraint contexts; these connectivity patterns are reduced when the anticipated information appears. This pattern is reversed when upcoming sensory input is unpredictable; connectivity is stronger after word inputs have been presented, allowing semantic integration with preceding low-constraint context. Our findings suggest that both top-down semantic prediction and bottom-up semantic integration in language comprehension rely upon diverse functional coupling of higher-order frontoparietal regions with other brain systems., (Copyright © 2024 He et al.)

The default mode network (DMN) typically deactivates to external tasks, yet supports semantic cognition. It comprises medial temporal (MT), core, and frontotemporal (FT) subsystems, but its functional organization is unclear: the requirement for perceptual coupling versus decoupling, input modality (visual/verbal), type of information (social/spatial), and control demands all potentially affect its recruitment. We examined the effect of these factors on activation and deactivation of DMN subsystems during semantic cognition, across four task-based human functional magnetic resonance imaging (fMRI) datasets, and localized these responses in whole-brain state space defined by gradients of intrinsic connectivity. FT showed activation consistent with a central role across domains, tasks, and modalities, although it was most responsive to abstract, verbal tasks; this subsystem uniquely showed more "tuned" states characterized by increases in both activation and deactivation when semantic retrieval demands were higher. MT also activated to both perceptually coupled (scenes) and decoupled (autobiographical memory) tasks and showed stronger responses to picture associations, consistent with a role in scene construction. Core DMN consistently showed deactivation, especially to externally oriented tasks. These diverse contributions of DMN subsystems to semantic cognition were related to their location on intrinsic connectivity gradients: activation was closer to the sensory-motor cortex than deactivation, particularly for FT and MT, while activation for core DMN was distant from both visual cortex and cognitive control. These results reveal distinctive yet complementary DMN responses: MT and FT support different memory-based representations that are accessed externally and internally, while deactivation in core DMN is associated with demanding, external semantic tasks., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 Shao et al.)

Background: Breast cancer poses severe threats to human health as radioresistance becomes increasingly prevalent. The mechanisms of radioresistance are hard to expound completely. This study aims to explore proteomic changes of radioresistance, which will help elucidate the potential mechanisms responsible for breast cancer radioresistance and explore potential therapeutic targets., Methods: A radioresistant breast cancer cell line was established by repeated irradiation. Liquid Chromatograph Mass Spectrometer (LC-MS) was used to quantify protein expression. Proteomic changes associated with radioresistance were evaluated by proteomic analysis. Further, cell radioresistance and several identified proteins were verified in in vitro experiments., Results: In the study, more than 3000 proteins were detected, 243 of which were identified as up-regulated proteins and another 633 as down-regulated proteins. Gene Ontology (GO) enrichment analysis indicated that these proteins were mainly expressed in the lysosome and ribosome, associated with coenzyme binding and the structural constituent of the ribosome, involved in mitotic cytokinesis and ribonucleoprotein complex biogenesis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that many biological processes were extensively altered, particularly spliceosome and thermogenesis. It is worth noting that the functions and pathways related to ribosomes were significantly enriched, therefore ribosomal proteins ( RPL6 and RPS13 ) were identified through western blot and highly expressed in relatively radiosensitive cells. Additionally, several identified proteins, including S100A4 , RanBP9 , and ISG15 , were also verified to be differentially expressed in different radiosensitive cells., Conclusions: Our results provide a framework for further studies into the mechanisms of radioresistance and serve as a basis to construct a predictive model of radioresistance in breast cancer. Ribosome may participate in the radioresistance of breast cancer, which provides new insights into the proteomic characteristics of the mechanisms of radioresistance., Competing Interests: The authors declare no conflict of interest., (© 2023 The Author(s). Published by IMR Press.)

Neutrophils are the most important component of the innate immune system. Mechanistic understanding of the mechanism underlying neutrophil differentiation remains elusive. Using genome-wide RNA-seq, we identified genes whose expression is dramatically up-regulated during neutrophil differentiation. Among them is nucleotide-binding leucine-rich repeat and pyrindomain-containing receptor 12 (NLRP12), which plays a role in immune inflammatory responses. Genetic ablation of NLRP12 suppresses NF-κB inducing kinase (NIK) stabilization, RelB nuclear translocation and neutrophil differentiation in vitro. At a mechanistic level, NLRP12 inhibits the activity of mitogen-activated protein kinases (MAPK)/extracellular signal-regulated kinases (ERK1/2), relieves ERK1/2 suppression of NIK protein levels. Thus, NLRP12 enhances noncanonical NF-κB signaling through inhibition of ERK1/2 signaling, thereby promoting neutrophil differentiation., Competing Interests: Competing Interests: The authors have declared that no competing interest exists.

Aim: To investigate the stress distribution on artificial atlantoaxial-odontoid joint (AAOJ) components during flexion, extension, lateral bending and rotation of AAOJ model constructed with the finite element (FE) method., Material and Methods: Human cadaver specimens of normal AAOJ were CT scanned with 1 mm -thickness and transferred into Mimics software to reconstruct the three-dimensional models of AAOJ. These data were imported into Freeform software to place a AAOJ into a atlantoaxial model. With Ansys software, a geometric model of AAOJ was built. Perpendicular downward pressure of 40 N was applied to simulate gravity of a skull, then 1.53 N• m torque was exerted separately to simulate the range of motion of the model., Results: An FE model of atlantoaxial joint after AAOJ replacement was constructed with a total of 103 053 units and 26 324 nodes. In flexion, extension, right lateral bending and right rotation, the AAOJ displacement was 1.109 mm, 3.31 mm, 0.528 mm, and 9.678 mm, respectively, and the range of motion was 1.6°, 5.1°, 4.6° and 22°., Conclusion: During all ROM, stress distribution of atlas-axis changed after AAOJ replacement indicating that AAOJ can offload stress. The stress distribution in the AAOJ can be successfully analyzed with the FE method.