Technology and Health Care最新文献

Background: Elevated levels of tumor necrosis factor-alpha (TNF-α) have been associated with adverse pregnancy outcomes, specifically recurrent pregnancy loss (RPL). These elevated levels may be associated with the presence of autoantibodies. Although TNF-α inhibitors have shown promise in improving pregnancy rates, further research is needed to comprehend their impact and mechanisms in RPL patients.

Objective: This study aims to investigate the association between elevated TNF-α levels and autoantibodies in RPL patients, as well as evaluate the effect of TNF-α inhibition on pregnancy outcomes.

Methods: A total of 249 RPL patients were included in this study. Serum levels of TNF-α, autoantibodies, and complement were measured and monitored. Among these patients, 138 tested positive for TNF-α, while 111 tested negative. The medical records of these patients were retrospectively evaluated. Additionally, 102 patients with elevated TNF-α levels were treated with TNF-α inhibitors, and their pregnancy outcomes were assessed.

Results: TNF-α-positive RPL patients had higher levels of complement C1q, anti-cardiolipin (ACL)-IgA, ACL-IgM ,ACL-IgG, thyroglobulin antibody, and Anti-phosphatidylserine/prothrombin IgM antibody, as well as a higher positive rate of antinuclear antibodies compared to TNF-α-negative patients (23.19% vs. 12.6%, P< 0.05). Conversely, complement C3 were lower in TNF-α-positive patients (t test, P< 0.05). The use of TNF-α inhibitors led to a reduction in the early abortion rate (13.7% vs. 44.4%, P< 0.001) and an improvement in term delivery rate (52.0% vs. 27.8%, P= 0.012). Furthermore, patients who used TNF-α inhibitors before 5 weeks of pregnancy had a lower early abortion rate (7.7% vs. 24.3%, P= 0.033) and a higher term delivery rate (69.2% vs. 48.6%, P= 0.033).

Conclusion: TNF-α plays a role in the occurrence and development of RPL, and its expression is closely associated with autoantibodies and complements. TNF-α inhibitors increase the term delivery rate in TNF-α-positive RPL patients, and their use before 5 weeks of pregnancy may more beneficial.

Background: Osteoarthritis (OA) and osteoporosis (OS) are the most common orthopedic diseases.

Objective: To identify important genes as biomarkers for the pathogenesis of OA and OS.

Methods: Microarray data for OA and OS were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the OA and healthy control groups and between the OS and healthy control groups were identified using the Limma software package. Overlapping hub DEGs were selected using MCC, MNC, DEGREE, and EPC. Weighted gene co-expression network analysis (WGCNA) was used to mine OA- and OS-related modules. Shared hub DEGs were identified, human microRNA disease database was used to screen microRNAs associated with OA and OS, and an miRNA-target gene network was constructed. Finally, the expression of shared hub DEGs was evaluated.

Results: A total of 104 overlapping DEGs were identified in both the OA and OS groups, which were mainly related to inflammatory biological processes, such as the Akt and TNF signaling pathways Forty-six hub DEGs were identified using MCC, MNC, DEGREE, and EPC modules using different algorithms. Seven modules with 392 genes that highly correlated with disease were identified in the WGCNA. Furthermore, 10 shared hub DEGs were identified between the OA and OS groups, including OGN, FAP, COL6A3, THBS4, IGFBP2, LRRC15, DDR2, RND3, EFNB2, and CD48. A network consisting of 8 shared hub DEGs and 55 miRNAs was constructed. Furthermore, CD48 was significantly upregulated in the OA and OS groups, whereas EFNB2, DR2, COL6A3, and RND3 were significantly downregulated in OA and OS. Other hub DEGs were significantly upregulated in OA and downregulated in OS.

Conclusions: The ten genes may be promising biomarkers for modulating the development of both OA and OS.

Background: Autism Spectrum Disorder (ASD) is a condition with social interaction, communication, and behavioral difficulties. Diagnostic methods mostly rely on subjective evaluations and can lack objectivity. In this research Machine learning (ML) and deep learning (DL) techniques are used to enhance ASD classification.

Objective: This study focuses on improving ASD and TD classification accuracy with a minimal number of EEG channels. ML and DL models are used with EEG data, including Mu Rhythm from the Sensory Motor Cortex (SMC) for classification.

Methods: Non-linear features in time and frequency domains are extracted and ML models are applied for classification. The EEG 1D data is transformed into images using Independent Component Analysis-Second Order Blind Identification (ICA-SOBI), Spectrogram, and Continuous Wavelet Transform (CWT).

Results: Stacking Classifier employed with non-linear features yields precision, recall, F1-score, and accuracy rates of 78%, 79%, 78%, and 78% respectively. Including entropy and fuzzy entropy features further improves accuracy to 81.4%. In addition, DL models, employing SOBI, CWT, and spectrogram plots, achieve precision, recall, F1-score, and accuracy of 75%, 75%, 74%, and 75% respectively. The hybrid model, which combined deep learning features from spectrogram and CWT with machine learning, exhibits prominent improvement, attained precision, recall, F1-score, and accuracy of 94%, 94%, 94%, and 94% respectively. Incorporating entropy and fuzzy entropy features further improved the accuracy to 96.9%.

Conclusions: This study underscores the potential of ML and DL techniques in improving the classification of ASD and TD individuals, particularly when utilizing a minimal set of EEG channels.

Background: Current treatments do not support direct exposure of fracture fragments, resulting in the inability to directly observe the articular surface during surgery for accurate reduction and firm fixation.

Objective: The aim of the study was to explore the treatment effect of digital virtual reduction combined with individualized guide plate of lateral tibial condyle osteotomy on tibial plateau fracture involving the lateral posterior condyle collapse.

Methods: 41 patients with tibial plateau fracture involving the lateral posterior condyle collapse were recruited in the trial. All patients underwent Computed Tomography (CT) scanning before operation. After operation, fracture reduction was evaluated using Rasmussen score and function of knee joint was assessed using hospital for special surgery (HSS) score.

Results: 41 patients were followed-up 6-26 months (mean, 15.2 months). Fracture reduction was good after operation, with an average of 13.3 weeks of fracture healing without serious complications. The excellent and good rate was 97.6%. The joint movement degree was -5∘∼0∘∼135∘ with an average of 125.5∘.

Conclusions: Digital virtual reduction combined with individualized guide plate of lateral tibial condyle osteotomy was effectively for treating tibial plateau fracture involving the lateral posterior condyle collapse.

Background: Heart disease is a severe health issue that results in high fatality rates worldwide. Identifying cardiovascular diseases such as coronary artery disease (CAD) and heart attacks through repetitive clinical data analysis is a significant task. Detecting heart disease in its early stages can save lives. The most lethal cardiovascular condition is CAD, which develops over time due to plaque buildup in coronary arteries, causing incomplete blood flow obstruction. Machine Learning (ML) is progressively used in the medical sector to detect CAD disease.

Objective: The primary aim of this work is to deliver a state-of-the-art approach to enhancing CAD prediction accuracy by using a DL algorithm in a classification context.

Methods: A unique ML technique is proposed in this study to predict CAD disease accurately using a deep learning algorithm in a classification context. An ensemble voting classifier classification model is developed based on various methods such as Naïve Bayes (NB), Logistic Regression (LR), Decision Tree (DT), XGBoost, Random Forest (RF), Convolutional Neural Network (CNN), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Bidirectional LSTM and Long Short-Term Memory (LSTM). The performance of the ensemble models and a novel model are compared in this study. The Alizadeh Sani dataset, which consists of a random sample of 216 cases with CAD, is used in this study. Synthetic Minority Over Sampling Technique (SMOTE) is used to address the issue of imbalanced datasets, and the Chi-square test is used for feature selection optimization. Performance is assessed using various assessment methodologies, such as confusion matrix, accuracy, recall, precision, f1-score, and auc-roc.

Results: When a novel algorithm achieves the highest accuracy relative to other algorithms, it demonstrates its effectiveness in several ways, including superior performance, robustness, generalization capability, efficiency, innovative approaches, and benchmarking against baselines. These characteristics collectively contribute to establishing the novel algorithm as a promising solution for addressing the target problem in machine learning and related fields.

Conclusion: Implementing the novel model in this study significantly improved performance, achieving a prediction accuracy rate of 92% in the detection of CAD. These findings are competitive and on par with the top outcomes among other methods.

Background: Thanks to modern methods of assisted reproduction (ART), parenthood has become an attainable goal for couples in which the male partner has experienced spinal cord injury (SCI).

Objective: The aim of our study was to determine the success of the treatment of infertile patients with SCI with intracytoplasmic sperm injection (ICSI) of cryopreserved sperm obtained by the testicular sperm aspiration (TESA) procedure.

Methods: In this retrospective study 156 infertile couples were included, in which the male partner is primarily infertile due to azoospermia. Infertile couples were divided into two groups. The first group (n= 82) includes men with SCI, and the second (n= 74) men with obstructive azoospermia (OA) as the cause of infertility. All infertile men were examined and processed in the diagnostic procedure, and based on the urological findings, surgical extraction of sperm from the testicles was indicated. Exclusion criteria were the age of women over 40 and men over 45.

Results: We found that the quality of sperm was worse in the group with SCI, compared to the group with OA, but without statistical significance. Zenica and Johnsen score (p= 0.001; p= 0.000) showed worse semen characteristics in the group with SCI.     There were no significant differences in the average number of embryos (p= 0.698), pregnancy rates per cycle (p= 0.979) and pregnancy rates per embrio transfer (ET), clinical pregnancy rates per ET (p= 0.987) and delivery rates per ET (p= 0.804) in couples with SCI, compared to couples with OA.

Conclusion: Based on the results of this research, the TESA and ICSI procedures can be recommended as a successful method in the treatment of male infertility caused by azoospermia due to SCI.

Background: Renal failure is one of the most common chronic complications of diabetes. Simultaneous pancreas-kidney transplantation (SPK) is considered the preferred treatment for individuals with diabetes and chronic renal failure. This procedure has demonstrated efficacy in enhancing the quality of life for patients and minimizing the complications associated with diabetes.

Objective: In this study, we analyzed the incidence and safety of complications in different thrombosis prevention techniques post simultaneous pancreas-kidney transplantation (SPK).

Methods: Patients who underwent SPK between January 2019 and December 2022 were selectively categorized into two groups: the heparin group and the non-heparin group depending on the utilization of low molecular weight heparin. The occurrence of complications and clinical outcomes were subsequently calculated in each group.

Results: In this study, we included a total of 58 recipients who underwent SPK, with 36 in the heparin group and 22 in the non-heparin group. Among the 58 participants, there were 3 cases of pancreatic thrombosis complications, with 2 cases (5.6%) in the heparin group and 1 case (4.6%) in the non-heparin group, and the differences were not statistically significant (P> 0.05). Regarding gastrointestinal bleeding, there were 17 cases out of the total 58 patients, with 14 cases (38.9%) in the heparin group and 3 cases (13.6%) in the non-heparin group, and the difference was statistically significant (P< 0.05).

Conclusion: After surgery, the use of low molecular weight heparin anticoagulation may increase the likelihood of experiencing gastrointestinal bleeding. Prior to the surgery, a comprehensive evaluation of the coagulation status and medical history of the patient should be performed, enabling stratification of risks involved. Based on this assessment, either low-molecular-weight heparin or aspirin should be selected as a preventive measure against thrombosis.

Background: Healthcare is crucial to patient care because it provides vital services for maintaining and restoring health. As healthcare technology evolves, cutting-edge tools facilitate faster diagnosis and more effective patient treatment. In the present age of pandemics, the Internet of Things (IoT) offers a potential solution to the problem of patient safety monitoring by creating a massive quantity of data about the patient through the linked devices around them and then analyzing it to estimate the patient's current status. Utilizing the IoT-based meta-heuristic algorithm allows patients to be remotely monitored, resulting in timely diagnosis and improved care. Meta-heuristic algorithms are successful, resilient, and effective in solving real-world enhancement, clustering, predicting, and grouping. Healthcare organizations need an efficient method for dealing with big data since the prevalence of such data makes it challenging to analyze for diagnosis. The current techniques used in medical diagnostics have limitations due to imbalanced data and the overfitting issue.

Objective: This study introduces the particle swarm optimization and convolutional neural network to be used as a meta-heuristic optimization method for extensive data analysis in the IoT to monitor patients' health conditions.

Method: Particle Swarm Optimization is used to optimize the data used in the study. Information for a diabetes diagnosis model that includes cardiac risk forecasting is collected. Particle Swarm Optimization and Convolutional Neural Networks (PSO-CNN) results effectively make illness predictions. Support Vector Machine has been used to predict the possibility of a heart attack based on the classification of the collected data into projected abnormal and normal ranges for diabetes.

Results: The results of the simulations reveal that the PSO-CNN model used to predict diabetic disease increased in accuracy by 92.6%, precision by 92.5%, recall by 93.2%, F1-score by 94.2%, and quantization error by 4.1%.

Conclusion: The suggested approach could be applied to identify cancer cells.

Background: The objective performance evaluation of an athlete is essential to allow detailed research into elite sports. The automatic identification and classification of football teaching and training exercises overcome the shortcomings of manual analytical approaches. Video monitoring is vital in detecting human conduct acts and preventing or reducing inappropriate actions in time. The video's digital material is classified by relevance depending on those individual actions.

Objective: The research goal is to systematically use the data from an inertial measurement unit (IMU) and data from computer vision analysis for the deep Learning of football teaching motion recognition (DL-FTMR). There has been a search for many libraries. The studies included have examined and analyzed training through profound model construction learning methods. Investigations show the ability to distinguish the efficiency of qualified and less qualified officers for sport-specific video-based decision-making assessments.

Methods: Video-based research is an effective way of assessing decision-making due to the potential to present changing in-game decision-making scenarios more environmentally friendly than static picture printing. The data showed that the filtering accuracy of responses is improved without losing response time. This observation indicates that practicing with a video monitoring system offers a play view close to that seen in a game scenario. It can be an essential way to improve the perception of selection precision. This study discusses publicly accessible training datasets for Human Activity Recognition (HAR) and presents a dataset that combines various components. The study also used the UT-Interaction dataset to identify complex events.

Results: Thus, the experimental results of DL-FTMR give a performance ratio of 94.5%, behavior processing ratio of 92.4%, athletes energy level ratio of 92.5%, interaction ratio of 91.8%, prediction ratio of 92.5%, sensitivity ratio of 93.7%, and the precision ratio of 94.86% compared to the optimized convolutional neural network (OCNN), Gaussian Mixture Model (GMM), you only look once (YOLO), Human Activity Recognition- state-of-the-art methodologies (HAR-SAM).

Conclusion: This finding proves that exercising a video monitoring system that provides a play view similar to that seen in a game scenario can be a valuable technique to increase selection accuracy perception.

Background: Multi-channel acquisition systems of brain neural signals can provide a powerful tool with a wide range of information for the clinical application of brain computer interfaces. High-throughput implantable systems are limited by size and power consumption, posing challenges to system design.

Objective: To acquire more comprehensive neural signals and wirelessly transmit high-throughput brain neural signals, a FPGA-based acquisition system for multi-channel brain nerve signals has been developed. And the Bluetooth transmission with low-power technology are utilized.

Methods: To wirelessly transmit large amount of data with limited Bluetooth bandwidth and improve the accuracy of neural signal decoding, an improved sharing run length encoding (SRLE) is proposed to compress the spike data of brain neural signal to improve the transmission efficiency of the system. The functional prototype has been developed, which consists of multi-channel data acquisition chips, FPGA main control module with the improved SRLE, a wireless data transmitter, a wireless data receiver and an upper computer. And the developed functional prototype was tested for spike detection of brain neural signal by animal experiments.

Results: From the animal experiments, it shows that the system can successfully collect and transmit brain nerve signals. And the improved SRLE algorithm has an excellent compression effect with the average compression rate of 5.94%, compared to the double run-length encoding, the FDR encoding, and the traditional run-length encoding.

Conclusion: The developed system, incorporating the improved SRLE algorithm, is capable of wirelessly capturing spike signals with 1024 channels, thereby realizing the implantable systems of High-throughput brain neural signals.