SLAS Technology最新文献

This study presents a scientometric analysis of the intersection between rehabilitation science and artificial intelligence (AI) technologies, using data from the Web of Science (WOS) database from 2002 to 2022. The analysis employed a comprehensive search query with key AI-related terms, focusing on a wide range of publications in rehabilitation science. Utilizing the Citespace tool, the study visualizes and quantifies the relationships between key terms, identifies research trends, and assesses the impact of AI technologies in rehabilitation science. Findings reveal a significant increase in AI-related research in this field, particularly from 2017 onwards, peaking in 2021. The United States has been a leading contributor, followed by countries like England, Australia, Germany, and Canada. Major institutional contributions come from Harvard University and the Pennsylvania Commonwealth System of Higher Education, among others. A keyword co-occurrence network constructed through Citespace identifies nine distinct hot topics and various research frontiers, highlighting evolving focus areas within the field. Burst analysis of keywords indicates a shift from performance and injury-related research to an increasing emphasis on AI and deep learning in recent years. The study also predicts the potential impact of papers, spotlighting works by Kunze KN and others as significantly influencing future research directions. Additionally, it examines the evolution of knowledge bases in AI-related rehabilitation science research, revealing a multidisciplinary core that includes neurology, rehabilitation, and ophthalmology, extending to complementary fields such as medicine and social sciences. This scientometric analysis provides a comprehensive overview of AI's application in rehabilitation science, offering insights into its evolution, impact, and emerging trends over the past two decades. The findings suggest strategic directions for future research, policy-making, and interdisciplinary collaboration in rehabilitation science and AI.

This research emphasises the value of physical training for table tennis players, particularly as ball speed and spin rate decline and emphasises how important intensity quality is to the game. Chinese table tennis players' dual identities place greater demands on the general growth of their learning and training as a crucial component of talent development preparation. Athletes' general quality, competitive level, and ability to avoid sports injuries are all improved by scientific and focused physical training. In order to achieve the functions of intelligent camera, multi-angle broadcasting, and 3D scene reproduction, this study combines the physical training model of artificial intelligence. This gives the audience a more engaging and in-depth viewing experience. More feature extraction of the match footage is made possible by deep learning and convolutional neural networks when combined with large-scale video data, greatly enhancing the match information for viewers. The experimental findings demonstrate that the accuracy of table tennis human technical movement recognition reaches 98.88 % based on the enhanced AM-Softmax classification algorithm.

This study aims to evaluate the changes in helper T lymphocyte (Th)1/Th2 factor levels in peripheral blood of patients with severe multiple injuries and their prognostic value for nosocomial infection using bioinformatic analysis. The experimental group consisted of 180 patients with numerous injuries admitted to our hospital between January 2021 and June 2023, with 80 healthy volunteers serving as controls. Th1 cytokines (interleukin-2 and interferon-γ) and Th2 cytokines (IL-4 and IL-10) were evaluated 48 hours after admission using enzyme-linked immunosorbent assays. The experimental group was separated into two groups: those with systemic inflammatory response syndrome (SIRS) and those without SIRS, for cytokine analysis and SIRS incidence. Furthermore, the study examined Th1 and Th2 cytokine levels in trauma patients in various body locations within the experimental group. A receiver operating characteristic (ROC) curve analysis was performed to determine the predictive value of Th1/Th2 cytokines for SIRS incidence. The experimental group had lower IL-2 and IFN-γ levels compared to the control group, but greater levels of IL-4 and IL-10. There were no significant variations in Th1 and Th2 cytokine levels across the experimental groups. Patients with SIRS had lower levels of IL-2 and IFN-γ but greater levels of IL-4 and IL-10 compared to those without SIRS. Combined cytokine levels have a better predictive value for SIRS than individual cytokines alone. In conclusion, individuals with severe multiple injuries had a change from Th1 to Th2 cytokine profiles, which was most evident in those with SIRS. The combined cytokine levels had a substantial predictive value for SIRS incidence in this patient cohort.

Most of the biopharmaceuticals that are currently on the market are expressed using the Chinese Hamster Ovary (CHO) cell lines. However, the production yield of these biopharmaceuticals is affected due to CHO cellular heterogeneity and challenges in adaptability during the bioreactor scale-up stage. In this communication, we report the protocol for the miniaturized directed evolution process for CHO cells. The results of the directed evolution process would guide adapting the CHO cell line before bioreactor scale-up. With our approach, we have established the protocol that can be used to streamline superior CHO cell lines for biopharmaceutical production which would be the first of its kind in Africa. Our directed evolution protocol includes a method for a low-cost multiplex directed evolution process that can be used on CHO cells using 20 stressors in 8 concentrations and provides stable trial results for the scale-up process. Using our process, we can provide a simple consumable kit that manufacturers can use for the CHO cell phenotype stability test before the scale-up process. With our approach, we would further develop a platform that can streamline superior CHO cell lines for biopharmaceutical production. This approach would be the first of its kind in South Africa/ Africa.

In order to clarify the pathways closely linked to denervated muscle contracture, this work uses IoMT-enabled healthcare stratergies to examine changes in gene expression patterns inside atrophic muscles following brachial plexus damage. The gene expression Omnibus (GEO) database searching was used to locate the dataset GSE137606, which is connected to brachial plexus injuries. Strict criteria (|logFC|≥2 & adj.p < 0.05) were used to extract differentially expressed genes (DEGs). To identify dysregulated activities and pathways in denervated muscles, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were used. Hub genes were found using Cytoscape software's algorithms, which took into account parameters like as proximity, degree, and MNC. Their expression, enriched pathways, and correlations were then examined. The results showed that 316 DEGs were predominantly concentrated in muscle-related processes such as tissue formation and contraction pathways. Of these, 297 DEGs were highly expressed in denervated muscles, whereas 19 DEGs were weakly expressed. GSEA showed improvements in the contraction of striated and skeletal muscles. In addition, it was shown that in denervated muscles, Myod1, Myog, Myh7, Myl2, Tnnt2, and Tnni1 were elevated hub genes with enriched pathways such adrenergic signaling and tight junction. These results point to possible therapeutic targets for denervated muscular contracture, including Myod1, Myog, Myh7, Myl2, Tnnt2, and Tnni1. This highlights treatment options for this ailment which enhances the mental state of patient.

The purpose of this work is to investigate the function of SNHG1, a long non-coding RNA implicated in disease progression, apoptosis, and proliferation, in order to solve the problem of hypoxic-ischemic encephalopathy (HIE) in newborn care. We investigated the impact of overexpressing SNHG1 on hypoxia-induced apoptosis and studied its expression in BV2 microglial cells under hypoxic circumstances. As a result of modifying YY1 expression, SNHG1′s overexpression prevents apoptosis, as our data demonstrate that it is considerably downregulated under hypoxia. We demonstrate that SNHG1 might potentially reduce microglial ischemia-reperfusion damage by using sophisticated nanoengineering drug delivery technologies to target it. This provides encouraging information for the therapy of ischemic epilepsy.

Objective

This study aims to diagnose Rotator Cuff Tears (RCT) and classify the severity of RCT in patients with Osteoporosis (OP) through the analysis of shoulder joint anteroposterior (AP) X-ray-based localized proximal humeral bone mineral density (BMD) measurements and clinical information based on machine learning (ML) models.

Methods

A retrospective cohort of 89 patients was analyzed, including 63 with both OP and RCT (OPRCT) and 26 with OP only. The study analyzed a series of shoulder radiographs from April 2021 to April 2023. Grayscale values were measured after plotting ROIs based on AP X-rays of shoulder joint. Five kinds of ML models were developed and compared based on their performance in predicting the occurrence and severity of RCT from ROIs' greyscale values and clinical information (age, gender, advantage side, lumbar BMD, and acromion morphology (AM)). Further analysis using SHAP values illustrated the significant impact of selected features on model predictions.

Results

R1-6 had a positive correlation with BMD respectively. The nine variables, including greyscale R1-6, age, BMD, and AM, were used in the prediction models. The RF model was determined to be superior in effectively diagnosing RCT in OP patients, with high AUC scores of 0.998, 0.889, and 0.95 in the training, validation, and testing sets, respectively. SHAP values revealed that the most influential factors on the diagnostic outcomes were the grayscale values of all cancellous bones in ROIs. A column-line graph prediction model based on nine variables was constructed, and DCA curves indicated that RCT prediction in OP patients was favored based on this model. Furthermore, the RF model was also the most superior in predicting the types of RCT within the OPRCT group, with an accuracy of 86.364% and 73.684% in the training and test sets, respectively. SHAP values indicated that the most significant factor affecting the predictive outcomes was the AM, followed by the grayscale values of the greater tubercle, among others.

Conclusions

ML models, particularly the RF algorithm, show significant promise in diagnosing RCT occurrence and severity in OP patients using conventional shoulder X-rays based on the nine variables. This method presents a cost-effective, accessible, and non-invasive diagnostic strategy that has the potential to substantially enhance the early detection and management of RCT in OP patient population.

Coronary microcirculation dysfunction (CMD) is one of the main causes of cardiovascular disease. Traditional treatment methods lack specificity, making it difficult to fully consider the differences in patient conditions and achieve effective treatment and intervention. The complexity and diversity of CMD require more standardized diagnosis and treatment plans to clarify the best treatment strategy and long-term outcomes. The existing treatment measures mainly focus on symptom management, including medication treatment, lifestyle intervention, and psychological therapy. However, the efficacy of these methods is not consistent for all patients, and the long-term efficacy is not yet clear. GSEA is a bioinformatics method used to interpret gene expression data, particularly for identifying the enrichment of predefined gene sets in gene expression data. In order to achieve personalized treatment and improve the quality and effectiveness of interventions, this article combined GSEA (Gene Set Enrichment Analysis) technology to conduct in-depth research on potential drug targets and their interaction networks in coronary microcirculation dysfunctions. This article first utilized the Coremine medical database, GeneCards, and DrugBank public databases to collect gene data. Then, filtering methods were used to preprocess the data, and GSEA was used to analyze the preprocessed gene expression data to identify and calculate pathways and enrichment scores related to CMD. Finally, protein sequence features were extracted through the calculation of autocorrelation features. To verify the effectiveness of GSEA, this article conducted experimental analysis from four aspects: precision, receiver operating characteristic (ROC) curve, correlation, and potential drug targets, and compared them with Gene Regulatory Networks (GRN) and Random Forest (RF) methods. The results showed that compared to the GRN and RF methods, the average precision of GSEA improved by 0.11. The conclusion indicated that GSEA helped identify and explore potential drug targets and their interaction networks, providing new ideas for personalized quality of CMD.

Lymphoma is a malignant tumor originating from the lymphopoietic system, which can affect all tissues and organs of the body. Lymphoma is highly heterogeneous and the therapeutic effect varies greatly. Different pathological types and stages of lymphoma differ greatly in terms of treatment intensity and prognosis. Early diagnosis of lymphoma is very important to improve the prognosis of patients. Therefore, this work explored the diagnostic value of independent and combined detection of computed tomography (CT), ultrasound, and positron emission tomography - computed tomography (PET-CT) for lymphoma. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) in the PET-CT and combination groups were greatly higher than those in the CT and ultrasound groups, showing obvious differences (P < 0.05). The area under curve (AUC) values in the CT group, ultrasound group, PET-CT group, and combination group were 0.632 (P = 0.032), 0.614 (P = 0.025), 0.793 (P = 0.002), and 0.859 (P = 0.001), respectively, exhibiting observable differences (P < 0.05). the sensitivity and specificity of PET/CT for lymphoma were higher than those of CT and ultrasound, which can clearly show the early mild results of lymphatic lymphoma. Therefore, the combined diagnosis of lymphatic lymphoma with PET/CT was of high clinical value.

This study probed the importance of computed tomography perfusion (CTP) on assessing collateral circulation and prognosis in patients with acute anterior circulation large vessel occlusion (AAC-LVO) after endovascular therapy (EVT). Retrospective analysis was performed on the case data of 124 AAC-LVO patients who achieved EVT in the First People's Hospital of Lianyungang. All patients received computed tomography (CT) examination. Based on the multi-phase computed tomography angiography (mCTA) score, patients were separated into poor collateral circulation group and good collateral circulation group. Based on modified Rankin scale (mRS) score, patients were separated into good prognosis group and poor prognosis group. The receiver operating characteristic (ROC) curve was used to measure the efficacy of CTP parameters in predicting good collateral circulation or good prognosis. Correlation between CTP parameters with mCTA collateral and 90-day mRS circulation score was analyzed using the Spearman correlation analysis. The age and admission national Institutes of Health stroke scale (NIHSS) scores of the good collateral circulation group were lower than the poor collateral circulation group, and low perfusion area volume with Tmax > 6 s (V_Tmax>6 s), infarct core area volume (V_{CBF<30 %})and hypoperfusion intensity ratio (HIR) were also lower. The mCTA collateral cycle score was negatively related to V_{Tmax>6 s}, V_{CBF<30 %} and HIR. The area under the curve (AUC) values of V_{Tmax>6 s} and V_{CBF<30 %} and HIR for predicting good collateral circulation were 0.763, 0.884 and 0.842, respectively, which suggested that perfusion parameters V_{Tmax>6 s}, V_{CBF<30 %} and HIR could effectively indicate the status of patients’ collateral circulation. Relative to the poor prognosis group, patients in the good prognosis group possessed lower admission NIHSS score, younger age, smaller final infarct volume, lower HIR, V_{CBF<30 %}, V_Tmax>6 s, Alberta Stroke Program Early CT(ASPECT) score, and higher mCTA score. Spearman correlation analysis unveiled that ASPECT score, mCTA score and 90-day mRS were negatively correlated. The final infarct volume, perfusion parameters HIR and V_{CBF<30 %} were positively correlated with 90-day mRS. ROC analysis showed that all variates had good prognostic value for acute anterior circulation great vessel occlusion patients, while V_{CBF<30 %} and HIR had high diagnostic value for prognosis. To sum up, CTP can provide a comprehensive imaging assessment of the collateral circulation of patients with AAC-LVO and has a higher predictive value for the prognosis assessment of patients with EVT in terms of V_{CBF<30 %}, HIR score and mCTA collateral circulation score.