Transforming medical equipment management in digital public health: a decision-making model for medical equipment replacement

IntroductionIn the rapidly evolving field of digital public health, effective management of medical equipment is critical to maintaining high standards of healthcare service levels and operational efficiency. However, current decisions to replace large medical equipment are often based on subjective judgments rather than objective analyses and lack a standardized approach. This study proposes a multi-criteria decision-making model that aims to simplify and enhance the medical equipment replacement process.MethodsThe researchers developed a multi-criteria decision-making model specifically for the replacement of medical equipment. The model establishes a system of indicators for prioritizing and evaluating the replacement of large medical equipment, utilizing game theory to assign appropriate weights, which uniquely combines the weights of the COWA and PCA method. In addition, which uses the GRA method in combination with the TOPSIS method for a more comprehensive decision-making model.ResultsThe study validates the model by using the MRI equipment of a tertiary hospital as an example. The results of the study show that the model is effective in prioritizing the most optimal updates to the equipment. Significantly, the model shown a higher level of differentiation compared to the GRA and TOPSIS methods alone.DiscussionThe present study shows that the multi-criteria decision-making model presented provides a powerful and accurate tool for optimizing decisions related to the replacement of large medical equipment. By solving the key challenges in this area as well as giving a solid basis for decision making, the model makes significant progress toward the field of management of medical equipment

Morphological diversity of single neurons in molecularly defined cell types.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits

Cellular anatomy of the mouse primary motor cortex.

An essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input-output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture

A multimodal cell census and atlas of the mammalian primary motor cortex

ABSTRACT We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties

Injectable Nanomedicine–Hydrogel for NIR Light Photothermal–Chemo Combination Therapy of Tumor

Traditional hydrogels have drawbacks such as surgical implantation, large wound surfaces, and uncontrollable drug release during tumor treatment. In this paper, targeted nanomedicine has been combined with injectable hydrogel for photothermal–chemotherapy combination therapy. First, targeted nanomedicine (ICG—MTX) was fabricated by combining near-infrared (NIR) photothermal reagents (ICG) and chemotherapy drugs (MTX). The ICG—MTX was then mixed with the hydrogel precursor and radical initiator to obtain an injectable hydrogel precursor solution. Under the irradiation of NIR light, the precursor solution could release alkyl radicals, which promote the transition of the precursor solution from a liquid to a colloidal state. As a result, the nanomedicine could effectively remain at the site of the tumor and continue to be released from the hydrogel. Due to the targeted nature of MTX, the released ICG—MTX could target tumor cells and improve the accuracy of photothermal–chemo combination therapy. The results indicated that the injectable nanomedicine–hydrogel system has a favorable therapeutic effect on tumors

Short sandwich tubes subjected to internal explosive loading

The response of sandwich tubes under internal explosive loading was investigated experimentally, numerically and analytically in this paper. Experiments were conducted first to capture the fundamental deformation and failure patterns and they served as a basis of validation for both the FE and analytical models. Further detailed deformation and blast loading history were revealed by the FE model. An explicit analytical solution for the deformation of sandwich tubes under blast loading has been worked out and used to obtain the optimum sandwich configurations, which would outperform their corresponding monolithic tubes

Bioagriculture Outlier Elimination Based on 3D View of X-Y Variance and Leverage Measurement

Aiming at effective outlier elimination in the biological near-infrared spectral and achieving high accuracy predictive modeling, this paper proposes a novel outlier elimination method based on X-Y variance and leverage analysis. Firstly, the characters of near-infrared spectral are summarized; then residual sample X-variance, leverage, and residual sample Y-variance are concatenated as a divergence measurement. We further compared the proposed method with X-Y variance, Mahalanobis distance, and Hotelling T2 statistical analysis; the experiment results demonstrate that the proposed methods have competitive outlier elimination and better performance in time complexity and accuracy. The proposed method can also be adopted for other outlier elimination tasks

Assessing Numerical Simulation Methods for Reinforcement–Soil/Block Interactions in Geosynthetic-Reinforced Soil Structures

The purpose of this study is to assess effects of two different simulation methods (i.e., interfaces with a single spring-slider system and interfaces with double spring-slider systems) for interactions between reinforcement and the surrounding medium on the performances of geosynthetic-reinforced soil (GRS) structures when conducting numerical analyses. The fundamental difference between these two methods is the number of the spring-slider systems used to connect the nodes of structural elements simulating the geosynthetic reinforcement and the points of solid grids simulating the surrounding medium. Numerical simulation results of pull-out tests show that both methods reasonably predicted the pullout failure mode of the reinforcement embedded in the surrounding medium. However, the method using the interfaces with a single spring-slider system could not correctly predict the interface shear failure mode between the geosynthetics and surrounding medium. Further research shows that these two methods resulted in different predictions of the performance of GRS piers as compared with results of a laboratory load test. Numerical analyses show that a combination of interfaces with double spring-slider systems for reinforcement between facing blocks and interfaces with a single spring-slider system for reinforcement in soil resulted in the best performance prediction of the GRS structures as compared with the test results. This study also proposes and verifies an equivalent method for determining/converting the interface stiffness and strength parameters for these two methods

Modeling Long-range Dependencies and Epipolar Geometry for Multi-view Stereo

This article proposes a network, referred to as Multi-View Stereo TRansformer (MVSTR) for depth estimation from multi-view images. By modeling long-range dependencies and epipolar geometry, the proposed MVSTR is capable of extracting dense features with global context and 3D consistency, which are crucial for reliable matching in multi-view stereo (MVS). Specifically, to tackle the problem of the limited receptive field of existing CNN-based MVS methods, a global-context Transformer module is designed to establish intra-view long-range dependencies so that global contextual features of each view are obtained. In addition, to further enable features of each view to be 3D consistent, a 3D-consistency Transformer module with an epipolar feature sampler is built, where epipolar geometry is modeled to effectively facilitate cross-view interaction. Experimental results show that the proposed MVSTR achieves the best overall performance on the DTU dataset and demonstrates strong generalization on the Tanks & Temples benchmark dataset