Medical equipment effectiveness evaluation model based on cone-constrained DEA and attention-based bi-LSTM.

This study constructs a composite indicator system covering the core dimensions of medical equipment input and output. Based on this system, an innovative cone-constrained data envelopment analysis (DEA) model is designed. The model integrates the advantages of the analytic hierarchy process (AHP) with an improved criterion importance through intercriteria correlation (CRITIC) method to determine subjective and objective weights and employs game theory to obtain the final combined weights, which are further incorporated as constraints to form the cone-constrained DEA model. Finally, a bidirectional long short-term memory (Bi-LSTM) model with an attention mechanism is introduced for integration, aiming to provide a novel and practical model for evaluating the effectiveness of medical equipment. The proposed model has essential reference value for optimizing medical equipment management decision-making and investment strategies.

Mean value of pulse pressure: The key feature in ambulatory arterial stiffness index estimation using regression models.

OBJECTIVE: Ambulatory arterial stiffness index (AASI) is an index which indicates arterial stiffness. This work aims to explore the mathematical relationship between AASI and mean value of PP (PP‾), and reveal the importance of PP‾ during AASI estimating. Meanwhile, a well-performing AASI estimation model is presented. METHODS: To evaluate AASI, electrocardiograph (ECG) signal, photoplethysmogram (PPG) signal and arterial blood pressure (ABP) are used as the source of AASI estimation. Features are extracted from the above three signals. Meanwhile, fitting curve analysis and regression models are implemented to describe the relationship between AASI and PP‾. RESULTS: Among three fitting curves on AASI and PP‾, cubic polynomial curve performs best. The introduction of feature PP‾ in AASI estimation reduced LR's MAE from 0.0556 to 0.0372, SVMR's MAE from 0.0413 to 0.0343 and RFR's MAE from 0.0386 to 0.0256. All three estimation models obtain considerable improvement, especially on the previous worst-performing linear regression. SIGNIFICANCE: This work presents the mathematical association between AASI and PP‾. AASI estimation using regression models can be significantly improved by involving PP‾ as its key feature, which is not only meaningful for exploring the connection between vascular elasticity function and pulse pressure, but also hold importance for the diagnosis of cardiovascular arteriosclerosis and atherosclerosis at the early stage.

Cytosolic and mitochondrial ribosomal proteins mediate the locust phase transition via divergence of translational profiles.

The phase transition from solitary to gregarious locusts is crucial in outbreaks of locust plague, which threaten agricultural yield and food security. Research on the regulatory mechanisms of phase transition in locusts has focused primarily on the transcriptional or posttranslational level. However, the translational regulation of phase transition is unexplored. Here, we show a phase-dependent pattern at the translation level, which exhibits different polysome profiles between gregarious and solitary locusts. The gregarious locusts exhibit significant increases in 60S and polyribosomes, while solitary locusts possess higher peaks of the monoribosome and a specific "halfmer." The polysome profiles, a molecular phenotype, respond to changes in population density. In gregarious locusts, ten genes involved in the cytosolic ribosome pathway exhibited increased translational efficiency (TE). In solitary locusts, five genes from the mitochondrial ribosome pathway displayed increased TE. The high expression of large ribosomal protein 7 at the translational level promotes accumulation of the free 60S ribosomal subunit in gregarious locusts, while solitary locusts employ mitochondrial small ribosomal protein 18c to induce the assembly of mitochondrial ribosomes, causing divergence of the translational profiles and behavioral transition. This study reveals the translational regulatory mechanism of locust phase transition, in which the locusts employ divergent ribosome pathways to cope with changes in population density.

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

Introduction: In 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. Methods: The 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. Results: The 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. Discussion: The 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.

A novel non-invasive identification of genome editing mutants from insect exuviae.

With the wide application of genome editing in insects, a simple and efficient identification method is urgently needed to meet the increasing demand for mutation detection. Here, taking migratory locusts as a model system, we developed a non-invasive method to accurately identify genome-edited mutants by using DNA from insect exuviae. We compared the quantity and quality of genomic DNA from exuviae in five instar hoppers and found that the 1st instar exuviae had the highest DNA yield and content, while the 3rd instar exuviae had the best quality. Consensus genotypes were identified from genomic DNA of hoppers at different developmental stages in the same individuals. Moreover, we demonstrated that the amplification products from DNA extracted from locust exuviae are the consensus sequences with those from the hemolymph and foreleg pre-tarsus. Therefore, non-invasive samples provide the same genotyping results as minimally invasive and invasive samples of the same individuals. Furthermore, this identification method that uses genomic DNA from exuviae can be used for early screening of positive genome-edited individuals in each generation for adult crossing. In our study, the non-invasive identification method was not only simpler and provided results earlier than existing methods, but also had a better reproducibility and accuracy. This non-invasive identification approach using genomic DNA from exuviae can be adapted to meet the growing demand for genetic analysis and will find wide application in insect genome editing research.