Technology and Health Care最新文献

Background: It is crucial to understand weight trends in patients undergoing total hip arthroplasty (THA).

Objective: To evaluate preoperative and postoperative weight trends for patients undergoing primary THA and factors associated with clinically significant weight change.

Methods: A prospective cohort who underwent primary unilateral THA (n= 3,011) at a tertiary healthcare system (January 2016 to December 2019) were included in the study. The primary outcomes were clinically significant weight change (> 5% change in body mass index [BMI]) during the one-year preoperative and one-year postoperative periods.

Results: Preoperatively, 66.6% maintained a stable weight, 16.0% gained and 17.4% lost weight, respectively. Postoperatively, 64.0% maintained a stable weight, while 22.6% gained and 13.4% lost weight, respectively. Female sex, Black race, obesity, higher Charlson Comorbidity Index (CCI) scores, and older age were associated with preoperative weight loss. Female sex, obesity, higher CCI scores, and Medicare insurance were associated with postoperative weight loss. Preoperative weight loss was associated with postoperative weight gain (OR = 3.37 [CI: 2.67 to 4.25]; p< 0.001), and preoperative weight gain was associated with postoperative weight loss (OR = 1.74 [CI: 1.30 to 2.3]; p< 0.001).

Conclusion: Most patients maintained a stable BMI one-year before and one-year after THA. Several factors are associated with weight loss before and after THA. Preoperative weight changes were associated with a reciprocal rebound in BMI post-operatively.

Background: Cervical malignancy is considered among the most perilous cancers affecting women in numerous East African and South Asian nations, both in terms of its prevalence and fatality rates.

Objective: This research aims to propose an efficient automated system for the segmentation of cancerous regions in cervical images.

Methods: The proposed techniques encompass preprocessing, feature extraction with an optimized feature set, classification, and segmentation. The original cervical image undergoes smoothing using the Gaussian Filter technique, followed by the extraction of Local Binary Pattern (LBP) and Grey Level Co-occurrence Matrix (GLCM) features from the enhanced cervical images. LBP features capture pixel relationships within a mask window, while GLCM features quantify energy metrics across all pixels in the images. These features serve to distinguish normal cervical images from abnormal ones. The extracted features are optimized using Genetic Algorithm (GA) as an optimization method, and the optimized sets of features are classified using the Co-Active Adaptive Neuro-Fuzzy Inference System (CANFIS) classification method. Subsequently, a morphological segmentation technique is employed to categorize irregular cervical images, identifying and segmenting malignant regions within them.

Results: The proposed approach achieved a sensitivity of 99.09%, specificity of 99.39%, and accuracy of 99.36%.

Conclusion: The proposed approach demonstrated superior performance compared to state-of-the-art techniques, and the results have been validated by expert radiologists.

Background: Histopathological evaluation is currently the gold standard for grading gliomas; however, this technique is invasive.

Objective: This study aimed to develop and validate a diagnostic prediction model for glioma by employing multiple machine learning algorithms to identify risk factors associated with high-grade glioma, facilitating the prediction of glioma grading.

Methods: Data from 1114 eligible glioma patients were obtained from The Cancer Genome Atlas (TCGA) database, which was divided into a training set (n= 781) and a test set (n= 333). Fifty machine learning algorithms were employed, and the optimal algorithm was selected to construct a prediction model. The performance of the machine learning prediction model was compared to the clinical prediction model in terms of discrimination, calibration, and clinical validity to assess the performance of the prediction model.

Results: The area under the curve (AUC) values of the machine learning prediction models (training set: 0.870 vs. 0.740, test set: 0.863 vs. 0.718) were significantly improved from the clinical prediction models. Furthermore, significant improvement in discrimination was observed for the Integrated Discrimination Improvement (IDI) (training set: 0.230, test set: 0.270) and Net Reclassification Index (NRI) (training set: 0.170, test set: 0.170) from the clinical prognostic model. Both models showed a high goodness of fit and an increased net benefit.

Conclusion: A strong prediction accuracy model can be developed using machine learning algorithms to screen for high-grade glioma risk predictors, which can serve as a non-invasive prediction tool for preoperative diagnostic grading of glioma.

Background: Early identification of sepsis has been shown to significantly improve patient prognosis.

Objective: Therefore, the aim of this meta-analysis is to systematically evaluate the diagnostic efficacy of machine-learning algorithms for sepsis prediction.

Methods: Systematic searches were conducted in PubMed, Embase and Cochrane databases, covering literature up to December 2023. The keywords included machine learning, sepsis and prediction. After screening, data were extracted and analysed from studies meeting the inclusion criteria. Key evaluation metrics included sensitivity, specificity and the area under the curve (AUC) for diagnostic accuracy.

Results: The meta-analysis included a total of 21 studies with a data sample size of 4,158,941. Overall, the pooled sensitivity was 0.82 (95% confidence interval [CI] = 0.70-0.90; P< 0.001; I2= 99.7%), the specificity was 0.91 (95% CI = 0.86-0.94; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.91-0.96). The subgroup analysis revealed that in the emergency department setting (6 studies), the pooled sensitivity was 0.79 (95% CI = 0.68-0.87; P< 0.001; I2= 99.6%), the specificity was 0.94 (95% CI 0.90-0.97; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.92-0.96). In the Intensive Care Unit setting (11 studies), the sensitivity was 0.91 (95% CI = 0.75-0.97; P< 0.001; I2= 98.3%), the specificity was 0.85 (95% CI = 0.75-0.92; P< 0.001; I2= 99.9%), and the AUC was 0.93 (95% CI = 0.91-0.95). Due to the limited number of studies in the in-hospital and mixed settings (n< 3), no pooled analysis was performed.

Conclusion: Machine-learning algorithms have demonstrated excellent diagnostic accuracy in predicting the occurrence of sepsis, showing potential for clinical application.

Background: As a traditional digestive medicine, stir-fried Raphani Semen (SRS) has been used to treat food retention for thousands of years in China. Modern research has shown that SRS has a good therapeutic effect on functional dyspepsia (FD). However, the active components and mechanism of SRS in the treatment of FD are still unclear.

Objective: The purpose of this study is to elucidate the material basis and mechanism of SRS for treating FD based on UPLC-Q-Exactive Orbitrap MS/MS combined with network pharmacology and molecular docking.

Methods: The compounds of SRS water decoction were identified by UPLC-Q-Exactive Orbitrap MS/MS and the potential targets of these compounds were predicted by Swiss Target Prediction. FD-associated targets were collected from disease databases. The overlapped targets of SRS and FD were imported into STRING to construct Protein-Protein Interaction (PPI) network. Then, the Metascape was used to analyze Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway after introducing overlapped targets. Finally, the active components and core targets were obtained by analyzing the "component-target-pathway" network, and the affinity between them was verified by molecular docking.

Results: 53 components were identified, and 405 targets and 1487 FD-related targets were collected. GO and KEGG analysis of 174 overlapped targets showed that SRS had important effects on hormone levels, serotonin synapses, calcium signaling pathway and cAMP signaling pathway. 7 active components and 15 core targets were screened after analyzing the composite network. Molecular docking results showed that multiple active components had high affinity with most core targets.

Conclusion: SRS can treat FD through a variety of pathways, which provides a direction for the modern application of SRS in FD treatment.

Background: Wireless Body Area Networks (WBANs) are integral to modern healthcare systems, providing continuous health monitoring and real-time data transmission. The sensitivity of medical data being transmitted makes security a significant concern in WBANs.

Objective: This study explores the application of the Elliptic Curve Cryptography (ECC)-based Diffie-Hellman Key Exchange (ECDH) algorithm to enhance security within WBANs.

Method: The study investigates the suitability of ECC for this context and evaluates the performance and security implications of implementing ECDH in WBANs.

Results: The findings reveal that ECDH provides a robust and computationally efficient solution for secure key exchange in WBANs, addressing inherent vulnerabilities.

Conclusion: The adoption of ECC-based ECDH is poised to bolster data confidentiality and integrity in WBANs, promoting trust and widespread use of these networks in healthcare applications. This research contributes to the growing body of knowledge regarding security measures in WBANs and opens new avenues for the secure transmission of sensitive medical information.

Background: With the widespread use of low-dose spiral computed tomography (LDCT) and increasing awareness of personal health, the detection rate of pulmonary nodules is steadily rising.

Objective: To evaluate the success rate and safety of two different models of Hook-Wire needle localization procedures for pulmonary small nodule biopsy.

Methods: Ninety-four cases with a total of 97 pulmonary small nodules undergoing needle localization biopsy were retrospectively analyzed. The cases were divided into two groups: Group A, using breast localization needle steel wire (Bard Healthcare Science Co., Ltd.); Group B, using disposable pulmonary nodule puncture needle (SensCure Biotechnology Co., Ltd.). All patients underwent video-assisted thoracoscopic surgery (VATS) for nodule removal on the same day after localization and biopsy. The puncture localization operation time, success rate, complications such as pulmonary hemorrhage, pneumothorax, hemoptysis, and postoperative comfort were observed and compared.

Results: In Group A, the average localization operation time for 97 nodules was 15.47 ± 5.31 minutes, with a success rate of 94.34%. The complication rate was 71.69% (12 cases of pneumothorax, 35 cases of pulmonary hemorrhage, 2 cases of hemoptysis), and 40 cases of post-localization discomfort were reported. In Group B, the average localization operation time was 25.32 ± 7.83 minutes, with a 100% success rate. The complication rate was 29.55% (3 cases of pneumothorax, 15 cases of pulmonary hemorrhage, 0 cases of hemoptysis), and 3 cases reported postoperative discomfort. According to the data analysis in this study, Group B had a lower incidence of puncture-related complications than Group A, along with a higher success rate and significantly greater postoperative comfort.

Conclusions: The disposable pulmonary nodule puncture needle is safer and more effective in pulmonary small nodule localization biopsy, exhibiting increased comfort compared to the breast localization needle. Additionally, the incidence of complications is significantly lower.

Background: Flight accidents caused by spatial disorientation (SD) greatly affect flight safety.

Objective: Few studies have been devoted to the evaluation of SD.

Methods: 10 pilots and 10 non-pilots were recruited for the experimental induction of SD. Videos for giving optical flow stimuli were played at two different flow speeds to induce SD. Subjective judgment and center of foot pressure (CoP) data were collected from the tests. The data were combined to determine the occurrence of SD and analyze the SD types.

Results: The number of self-reported SD events was slightly smaller in the pilots than in the non-pilots. The average upper bound of the confidence interval for the standard deviation of CoP was 0.32 ± 0.09 cm and 0.38 ± 0.12 cm in the pilots and non-pilots, respectively. This indicator was significantly lower in the pilots than in the non-pilots (P= 0.03). The success rate of the experimental induction of unrecognized SD was 26.7% and 45.0% in the pilots and non-pilots, respectively.

Conclusion: The method offered a new to analyze unrecognized SD. We could determine the occurrence unrecognized SD. This is an essential means of reducing flight accidents caused by unrecognized SD.

Background: Heart failure poses a significant challenge in the global health domain, and accurate prediction of mortality is crucial for devising effective treatment plans. In this study, we employed a Seq2Seq model from deep learning, integrating 12 patient features. By finely modeling continuous medical records, we successfully enhanced the accuracy of mortality prediction.

Objective: The objective of this research was to leverage the Seq2Seq model in conjunction with patient features for precise mortality prediction in heart failure cases, surpassing the performance of traditional machine learning methods.

Methods: The study utilized a Seq2Seq model in deep learning, incorporating 12 patient features, to intricately model continuous medical records. The experimental design aimed to compare the performance of Seq2Seq with traditional machine learning methods in predicting mortality rates.

Results: The experimental results demonstrated that the Seq2Seq model outperformed conventional machine learning methods in terms of predictive accuracy. Feature importance analysis provided critical patient risk factors, offering robust support for formulating personalized treatment plans.

Conclusions: This research sheds light on the significant applications of deep learning, specifically the Seq2Seq model, in enhancing the precision of mortality prediction in heart failure cases. The findings present a valuable direction for the application of deep learning in the medical field and provide crucial insights for future research and clinical practices.

Background: Physical activity (PA) holds profound implications for the holistic development of college students. However, students with chronic diseases or physical disabilities experience significantly limited PA during adaptive sports.

Objective: This study aims to investigate the relationship between physical activity and Functional Movement Screening (FMS) among university students who participate in the adaptive physical course.

Methods: 36 university students (from the adaptive physical course) completed the International Physical Activity Questionnaire-Long Form (IPAQ-L). Body measurements and FMS were assessed. Correlation analysis and t-tests were used to determine relationships and differences between various indicators. A two-way analysis of variance was used to investigate potential variations in FMS scores based on gender and weight status.

Results: The results show that gender, PA, and BMI significantly influence FMS scores in students participating in adaptive physical courses. FMS score is significantly negatively correlated with BMI and significantly positively correlated with PA. The FMS score for males, as well as the scores for Trunk Stability Push-Up and Rotary Stability, are significantly higher than those for females.

Conclusion: University students in adaptive physical courses can benefit from increased PA and FMS scores. Improving functional movement and enhancing physical activity are crucial for promoting overall health in this population.