Technology and Health Care最新文献

Objective: To detect the changes in the thickness of the Retinal Nerve Fiber Layer (RNFL) in patients with High Myopia (HM) complicated with glaucoma through Optical Coherence Tomography (OCT).

Methods: 80 patients (160 eyes) with HM complicated with glaucoma treated from March 2018 to March 2020 were enrolled as the experimental group, and 60 healthy volunteers (120 eyes) undergoing physical examination in the same period were selected as the control group. OCT measured their RNFL thicknesses.

Results: Compared with that in the control group, the nasal, supratemporal, subnasal, supranasal, and infratemporal RNFL thickness and overall mean RNFL thickness in the experimental group was significantly decreased, while the temporal RNFL thickness was significantly increased in the experimental group (P < 0.05). According to the diopter, patients in the experimental group were assigned into group A (n = 25, 50 eyes, diopter range: ≥ -6.00 D and ≤ -8.00 D), group B (n = 30, 60 eyes, diopter range: > -8.00 D and ≤ -10.00 D) and group C (n = 25, 50 eyes, diopter range: > -10.00 D). The nasal, supratemporal, subnasal, supranasal, and infratemporal RNFL thickness and overall mean RNFL thickness in group A were significantly greater than those in groups B and C (P < 0.05). Spearman correlation analysis revealed that the absolute value of diopter was negatively correlated with the nasal, supratemporal, subnasal, supranasal, and infratemporal RNFL thickness and overall mean RNFL thickness (P < 0.05), and positively correlated with the thickness of temporal RNFL (P < 0.05).

Conclusion: In patients with HM complicated with glaucoma, RNFL is thinner in all quadrants except for temporal RNFL.

Ovarian cancer is a highly prevalent cancer among women; However, it remains difficult to find effective pharmacological solutions to treat this deadly disease. However, early detection can significantly increase life expectancy. To address this issue, a predictive model for early diagnosis of ovarian cancer was developed by applying statistical techniques and machine learning models to clinical data from 349 patients. A hybrid evolutionary deep learning model was proposed by integrating genetic and histopathological imaging modalities within a multimodal fusion framework. Machine learning pipelines have been built using feature selection and dilution approaches to identify the most relevant genes for disease classification. A comparison was performed between the UNeT and transformer models for semantic segmentation, leading to the development of an optimized fuzzy C-means clustering algorithm (FCM-NPOA-PM-UI) for the classification of gynecological abdominopelvic tumors. Performing better than individual classifiers and other machine learning methods, the suggested ensemble model achieved an average accuracy of 98.96%, precision of 97.44%, and F1 score of 98.7%. With average Dice scores of 0.98 and 0.97 for positive tumors and 0.99 and 0.98 for malignant tumors, the Transformer model performed better in segmentation than the UNeT model. Additionally, we observed a 92.8% increase in accuracy when combining five machine learning models with biomarker data: random forest, logistic regression, SVM, decision tree, and CNN. These results demonstrate that the hybrid model significantly improves the accuracy and efficiency of ovarian cancer detection and classification, offering superior performance compared to traditional methods and individual classifiers.

BackgroundMedical waste should be collected, classified, and transported to the treatment plant within 48 h. If it is not disposed of in time, it will cause cross-infection, increasing the risk of disease transmission and environmental pollution. How to reasonably plan transportation routes to ensure that the medical waste can be transported to the treatment plant in time is very important.ObjectiveThere are usually two modes of transportation, the fastest speed and shortest path, how to reasonably plan the transportation scheme so that medical waste can be transported to the treatment plant for disposal in the specified time is the main purpose of this article.MethodsThe multi-agent modeling method is adopted. AnyLogic simulation software is used to model the transportation routes of 118 Grade III hospitals and 2 treatment plants in Beijing under the two transportation modes of fastest speed and shortest path.ResultsBased on the traffic index in Beijing, the speed range of 20 km/h-32 km/h is set up and divided into 4 parts and 24 levels with 0.5 km/h as the unit, and the 24 levels of medical waste transportation data set is formed. The key speed nodes of 21 km/h, 24 km/h and 29.5 km/h are identified.ConclusionsThe medical waste transportation model and transport data set formed in this paper have enriched the theory and data basis of medical waste transportation management. The key speed nodes of transportation model selection have important practical significance for the transportation management decision of medical waste in big cities.

Background: Despite wearable sensors' ability to provide continuous physiologic monitoring, migraine remains challenging to predict due to unpredictability of onset and a variety of triggers. Developing an accurate prediction model requires reducing signal variability by using effective filtering techniques.

Objective: The main objective of this study is to evaluate machine learning models for predicting migraines and analyze the effect of different filtering techniques and classifiers on prediction performance.

Methods: A feature set based on ANOVA analysis of four key physiological signals was used. After the pre-processing, filtering methods, including median, Butterworth, and Savitzky-Golay filter, were applied. Five classification models, Extreme Gradient Boosting, Histogram-Based Gradient Boosting, Random Forest, Support Vector Machine, and K-Nearest Neighbors, were evaluated.

Results: The highest predictive performance was achieved using the Savitzky-Golay filter. The Random Forest model demonstrated the best accuracy (0.858) and precision (0.815), and an F1-score of 0.677, indicating the potential of investigated signals for migraine prediction. Furthermore, the Histogram-Based Gradient Boosting model achieved the highest recall using the Savitzky-Golay filter (0.719), demonstrating its effectiveness in identifying true positive cases of migraines.

Conclusion: The results indicate significant potential for healthcare applications for early migraine prediction and treatment using wearable technology. The study highlights the importance of selecting appropriate features and filtering methods to improve the accuracy and reliability of the predictions.

BackgroundOrthodontic clear aligners are a technologically advanced treatment modality that improves aesthetics and comfort while impacting patients' oral health-related quality of life (OHRQoL).ObjectiveThis study assessed and compared the OHRQoL of adult orthodontic patients receiving clear aligners and fixed appliances (metal and ceramic brackets) during orthodontic treatments.MethodologyOne hundred and five orthodontic patients were recruited and classified according to the treatment received. Group 1 clear aligners, group 2- fixed appliances/metal brackets, and Group 3-fixed appliances/ceramic brackets. The patients were surveyed using an Arabic version of the Oral Health Impact Profile 14 (OHIP- 14) questionnaire before (T0), 1-week (T1), and 3-months (T2) after the start of orthodontic treatment. Data was analysed using SPSS software at a significance level set at ≤0.05.ResultsThe mean OHIP scores showed no significant difference between the 3 groups at T0 (p = 0.09) and T2 (p = 0.41) time intervals. On the contrary, the mean OHIP scores at T1 significantly differed between 3 groups (p = 0.03). The mean OHIP scores within the groups at different time intervals was significantly different. Multiple comparison within the groups showed significant reduction in the mean OHIP scores from T0 to T1 and T2 period and further from T1 to T2 period, and the mean differences were statistically significant (p < 0.001).ConclusionAdult patients treated with clear aligners had significantly higher OHRQoL than those who underwent conventional fixed bracket-based treatment after 7 days of treatment but OHRQoL was similar after three months of treatment.

BackgroundRare disease clinical trials face challenges due to limited sample sizes and ethical imperatives to minimize futile treatments. Bayesian sequential design dynamically optimizes decisions under uncertainty, offering efficiency gains over traditional fixed-sample approaches.MethodsPropose a framework integrating sequential Bayes factor and adaptive stopping rules for trials with binary endpoint. Bayesian posterior probabilities define early termination thresholds (superiority/futility), while Bayes Factor Design Analysis validates trial feasibility. Sequential Bayes factor updates iteratively guide interim decisions based on evidence strength.ResultsThe approach enables earlier trial termination (for superiority or futility), reducing sample size, time, and costs. Patients avoid unnecessary exposure to futility treatments, while results remain interpretable even if thresholds are unmet.ConclusionThe primary goal is to confirm treatment efficacy earlier, enabling trials to be stopped promptly for either superiority or futility treatments. This strategy reduces sample size, time, and financial costs, and prevents patient exposure to futile treatments. Moreover, the study aims to promote the adoption of Bayesian sequential decision-making, thereby accelerating rare disease clinical trial approvals and drug marketing.

BackgroundWith the use of sevoflurane, the incidence of emergence agitation (EA) has also increased.ObjectiveWe aimed to investigate whether S-ketamine can prevent EA after sevoflurane anesthesia in children.MethodsChildren undergoing otolaryngology surgery were assigned to one of four groups randomly. Drugs were given five minutes before the operation was accomplished. The incidence of EA was measured by the Pediatric Anesthesia Emergence Delirium Scale (PAED) scores. Face, Legs, Activity, Cry, and Consolability scale (FLACC) scores and the rate of adverse events were evaluated.ResultsThe incidence of EA was significantly lower in children given 2 mg/kg propofol, 0.25 mg/kg S-ketamine and 0.5 mg/kg S-ketamine compared with that in children given normal saline. At 3 h and 6 h after operation, the FLACC scores in children given 0.25 mg/kg S-ketamine and 0.5 mg/kg S-ketamine were significantly lower than those in children given 2 mg/kg propofol and saline (p < 0.001). No statistical differences were found in adverse reactions among children in the four groups.ConclusionIntravenous injection of propofol 2 mg/kg, S-ketamine 0.25 mg/kg and S-ketamine 0.5 mg/kg before end of the operation can all reduce the incidence of occurrence of emergence agitation in children undergoing tonsillectomy with or without adenoidectomy after sevoflurane anesthesia. Compared with children given propofol 2 mg/kg and S-ketamine 0.5 mg/kg, children given S-ketamine 0.25 mg/kg has the advantage of not prolonging the awakening time.

BackgroundCerebral infarction is often associated with underlying cerebral vascular stenosis, such as carotid artery stenosis or cerebral artery stenosis due to arteriosclerosis. Existing imaging techniques, including carotid ultrasound, computed tomography angiography (CTA), and magnetic resonance angiography (MRA), are useful for diagnosis, but have limitations such as radiation exposure, contrast medium use side effects, and high cost. Therefore, the need for a simple, noninvasive, and cost-effective screening tool is emerging.ObjectiveIn this study, we propose a novel cerebral infarction screening technique using PPG signals measured from both index fingers for 120 s.MethodsPPG is a noninvasive optical technology that measures pulse waves that appear according to changes in blood volume. The collected waveforms were divided into windows and then normalized. Maximum Positive Amplitude (MPA) and Maximum Negative Amplitude (MNA) were extracted from each section, and the normal group and cerebral infarction patients were classified through linear discriminant analysis.ResultsAs a result of analyzing a total of 100 subjects (50 patients with cerebral infarction and 50 normal controls), the recognition rate based on MNA was 84%, MPA was 81%, and when the two indices were combined, it was 80%. Sensitivity was 80% for MNA and 72% for MPA, and specificity was 88% and 90%, respectively, suggesting that amplitude-based PPG indices can effectively reflect the presence or absence of cerebrovascular lesions.ConclusionThis study suggests the possibility of simply identifying patients with cerebral infarction by analyzing PPG signals of both fingers. The proposed technique can be used as a screening tool to complement existing imaging techniques, and is expected to contribute to reducing the burden of stroke through early diagnosis and preventive intervention in the future.

BackgroundFunctional dyspepsia (FD) is a prevalent gastrointestinal disorder associated with impaired motility, mucosal barrier disruption, and dysbiosis of intestinal microbiota. While pharmacological treatments exist, non-invasive therapeutic approaches leveraging traditional medicine are gaining attention for their holistic benefits and minimal side effects.ObjectiveThis study aimed to evaluate the therapeutic impact of abdominal massage, a non-pharmacological intervention rooted in Traditional Chinese Medicine (TCM), on gastrointestinal motility, duodenal barrier integrity, and gut microbiota in a rat model of FD.MethodsEighteen male rats were assigned to control, model, and massage groups. The FD model was established via multifactorial stress. The massage group underwent daily abdominal massage for 7 days. Food intake, body weight, gastric emptying, and intestinal propulsion were assessed. Duodenal tissue morphology, expression of tight junction proteins (occludin and claudin-1), and intestinal microbiota profiles (via 16S rRNA sequencing) were analyzed using histological, immunofluorescence, and high-throughput sequencing technologies.ResultsAbdominal massage significantly improved gastric emptying and intestinal propulsion in FD rats. Histological examination revealed restoration of duodenal villi architecture, and immunofluorescence showed increased expression of occludin and claudin-1. Microbiota analysis suggested a shift toward a more balanced community, with trends indicating increased abundance of beneficial taxa such as Enterococcaceae and Lachnospiraceae.ConclusionAbdominal massage effectively enhances gastrointestinal motility, suggests potential to promote intestinal microbiota homeostasis, and repairs mucosal barrier dysfunction in FD rats. These findings suggest the potential of massage therapy as a safe, non-invasive, and technologically integrable modality in the management of gastrointestinal disorders.

BackgroundAcupoint thread embedding treatment (ATET) is a traditional therapeutic approach used in stroke rehabilitation.ObjectiveTo explore the application of ATET in combination with modern medical technology, examining its effects on neurological function and key serum biomarkers in stroke patients.MethodsA total of 108 stroke patients were randomly assigned to ATE treatment group (n = 52) and Control Group (n = 56). Various parameters including baseline characteristics, levels of inflammatory markers, macrophage efferocytosis-related factors, the National Institutes of Health Stroke Scale (NIHSS) scores, therapeutic efficacy, and adverse events were assessed and compared between the two groups.ResultsA significant improvement in NIHSS scores was observed in the treatment group compared to the control group. Additionally, serum levels of SIRT1, HIF-1α, and macrophage efferocytosis-related factors were significantly altered, suggesting that ATET may influence biological pathways involved in stroke recovery.ConclusionThe application of ATET, supported by advanced diagnostic technologies, shows promising effects in stroke rehabilitation. This study highlights the potential for integrating traditional therapies with modern medicine to improve clinical outcomes in stroke patients.