Technology and Health Care最新文献

BackgroundTinnitus, a common auditory disorder, significantly impacts patient quality of life and lacks universally effective treatments. The integration of advanced imaging technology like ultrasound in therapeutic interventions offers new possibilities in healthcare.ObjectiveThis study evaluated the efficacy of ultrasound-guided stellate ganglion block as an innovative approach to managing tinnitus.MethodsEighty patients with tinnitus were randomly assigned to either a control group receiving standard drug therapy or an observation group treated with ultrasound-guided stellate ganglion block in addition to standard therapy. Key metrics, including clinical effectiveness rates, anxiety scores, and tinnitus disability index scores, were assessed pre- and post-treatment.ResultsPost-treatment outcomes revealed that the observation group exhibited significantly improved anxiety scores (38.74 ± 4.05 vs. 50.45 ± 4.86; P < 0.05) and tinnitus disability index scores (37.8 ± 17.56 vs. 50.4 ± 21.26; P < 0.05) compared to the control group. Additionally, the observation group achieved a 100% clinical efficacy rate, outperforming the control group's 84% (P < 0.05).ConclusionUltrasound-guided stellate ganglion block demonstrates superior efficacy in managing tinnitus compared to conventional drug therapy. This study underscores the potential of integrating advanced ultrasound technology into healthcare to optimize treatment outcomes for auditory disorders.

BackgroundMicroRNA-17-5p (miR-17-5p) plays a pivotal role in the tumorigenesis and progression of non-small cell lung cancer (NSCLC) by regulating its target genes. Advances in molecular biology highlight the importance of long non-coding RNAs (lncRNAs) in cancer, yet the mechanistic interactions between miR-17-5p and lncRNAs in NSCLC remain underexplored.ObjectiveThis study investigated the regulatory interplay between miR-17-5p and lncRNA-FGD5-AS1 and evaluated their potential as targets for NSCLC therapy.MethodsA comprehensive set of technologies, including cell transfection, quantitative real-time PCR (qRT-PCR), bioinformatics analysis, and functional assays (proliferation, migration, apoptosis), was employed to examine the role of miR-17-5p and lncRNA-FGD5-AS1 in NSCLC.ResultsElevated lncRNA-FGD5-AS1 expression was observed in NSCLC cell lines A549 and H1299, correlating with poor patient prognosis. Functional assays revealed that miR-17-5p directly downregulates lncRNA-FGD5-AS1, thereby modulating key oncogenic processes. Overexpression of miR-17-5p reduced tumor cell proliferation and migration while inducing apoptosis. Conversely, miR-17-5p inhibition elevated lncRNA-FGD5-AS1 levels and reversed these effects.ConclusionThe findings identify the miR-17-5p/lncRNA-FGD5-AS1 regulatory axis as a novel therapeutic target for NSCLC. By integrating molecular and technological approaches, this study offers insights into precision oncology and highlights the potential for advanced RNA-based interventions.

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.

Background: Alzheimer's disease (AD) is a complex neurodegenerative disorder that complicates our understanding of its origins. Identifying AD-specific biomarkers can reveal its mechanisms and foster the development of innovative diagnostics and therapies, aiming to unlock new ways to combat this pervasive condition.

Methods: We analyzed gene expression data using Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning (random forest, lasso regression, and SVM-REF) to differentiate AD patients from controls and explore gene functions.

Results: We identified 641 differentially expressed genes (DEGs) and 22 co-expressed genes, with functional enrichment analysis revealing their involvement in immune responses. Notably, EGR1 emerged as a potential diagnostic and therapeutic target.

Conclusion: In our study, we applied WGCNA, DEGs and diverse machine learning approaches to uncover potential biomarkers linked to Alzheimer's Disease (AD) and ferroptosis. A particular hub gene emerged as a promising candidate for novel diagnostic and therapeutic markers specifically within the context of ferroptosis in AD. This discovery sheds new light on the pathogenesis of AD, potentially facilitating the development of groundbreaking diagnostic and therapeutic techniques.

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.

Background: Poorly regulated and insufficiently maintained medical devices (MDs) carry high risk on safety and performance parameters impacting the clinical effectiveness and efficiency of patient diagnosis and treatment. As infant incubators are used as a form of fundamental healthcare support for the most sensitive population, prematurely born infants, special care mus be taken to ensure their proper functioning. This is done through a standardized process of post-market surveillance.

Objective: To address the issue of faulty infant incubators being undetected and used between yearly post-market surveillance, an automated system based on machine learning was developed for prediction of infant incubator performance status.

Methods: In total, 1997 samples were collected during the inspection process of infant incubator inspections performed by an ISO 17020 accredited laboratory at various healthcare institutions in Bosnia and Herzegovina. Various machine learning algorithms were considered, including Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR) for the development of the automated system.

Results: The aforementioned algorithms were selected because of their ability to handle large datasets and their potential for achieving high prediction accuracy. The 0.93 AUC of Naïve Bayes indicates that it is overall stronger in predictive capabilities than decision tree and random forest which displayed superior accuracy in comparison to Naïve Bayes.

Conclusion: The results of this study demonstrate that machine learning algorithms can be effectively used to predict infant incubator performance status on the basis of measurements taken during post-market surveillance. Adoption of these automated systems based on artificial intelligence will help in overcoming challenges of ensuring quality of infant incubators that are already being used in healthcare institutions.

BackgroundT-cell exhaustion (TEX) in the tumor microenvironment causes immunotherapy resistance and poor prognosis.ObjectiveWe used bioinformatics to identify crucial TEX genes associated with the molecular classification and risk stratification of lung adenocarcinoma (LUAD).MethodsBulk RNA sequencing data of patients with LUAD were acquired from open sources. LUAD samples exhibited abnormal TEX gene expression, compared with normal samples. TEX gene-based prognostic signature was established and validated in both TCGA and GSE50081 datasets. Immune correlation and risk group-related functional analyses were also performed.ResultsEight optimized TEX genes were identified using the LASSO algorithm: ERG, BTK, IKZF3, DCC, EML4, MET, LATS2, and LOX. Several crucial Kyoto encyclopedia of genes and genomes (KEGG) pathways were identified, such as T-cell receptor signaling, toll-like receptor signaling, leukocytes trans-endothelial migration, Fcγ R-mediated phagocytosis, and GnRH signaling. Eight TEX gene-based risk score models were established and validated. Patients with high-risk scores had worse prognosis (P < 0.001). A nomogram model comprising three independent clinical factors showed good predictive efficacy for survival rate in patients with LUAD. Correlation analysis revealed that the TEX signature significantly correlated with immune cell infiltration, tumor purity, stromal cells, estimate, and immunophenotype score.ConclusionTEX-derived risk score is a promising and effective prognostic factor that is closely correlated with the immune microenvironment and estimated score. TEX signature may be a useful clinical diagnostic tool for evaluating pre-immune efficacy in patients with LUAD.

BackgroundLung cancer (LC) remains the most common malignancy in China with high mortality. The early stages of LC tend to manifest no apparent symptoms or only mild coughing, to which patients attach no importance.Objective: To gain an in-depth understanding of the perioperative real experiences and care needs of patients undergoing lung cancer ablation, to make a reliable basis for the construction of care programs for perioperative complications of lung cancer ablation, and to further improve the quality of perioperative clinical care interventions for such patients.MethodsSixteen lung cancer patients undergoing lung cancer ablation who were hospitalized in the Department of Oncology of our hospital from April 2023 to October 2023 as well as six healthcare professionals of Shanghai Pulmonary Hospital were recruited by purposive sampling. Semi-structured interviews were conducted with the enrolled individuals by using a phenomenological approach, and the Colaizzi's 7-step analysis method was used to analyze, summarize and refine the themes of the interview data.ResultsA total of seven study themes were identified emotional reactions of ablation patients, financial stress, needs for information on treatment outcomes and health education during recovery, reassurance of living and care needs during hospitalization, concerns about the treatment of complications, correct and detailed preoperative assessment, and doctor-nurse collaboration for good management of patient complications.ConclusionIn the clinical nursing of patients with lung cancer, we should construct standardized perioperative ablation nursing process as soon as possible, create diversified health guidance materials, strengthen the continuity of nursing after discharge, improve patients' understanding of the disease, pay attention to their emotional changes, and provide more maintenance support, so as to reduce complications and improve the quality of life of patients with lung cancer ablation.

BackgroundWith the increasing need for early prediction and intervention of Pregnancy-Induced Hypertension (PIH), researchers have begun to explore the use of multiserum biomarker models to improve the accuracy and reliability of predictions. It is estimated that between 5% and 8% of pregnant women worldwide experience pregnancy-induced hypertension, which is one of the leading causes of maternal death and adverse neonatal outcomes. Given the potential negative impact of pregnancy-induced hypertension on maternal and infant health, early identification of high-risk individuals and appropriate preventive measures are particularly important.ObjectiveTo assess the feasibility of using a multivariate serum biomarker model in early pregnancy to predict gestational hypertension.MethodsRetrospective analysis was conducted on the clinical data of 125 pregnant women admitted to our hospital from January 2021 to December 2022. The occurrence of gestational hypertension was recorded and multiple serum biomarkers were collected and compared between the exposure and non-exposure groups. Logistic regression analysis was performed to identify influencing factors for gestational hypertension. Correlations between each factor and gestational hypertension were analyzed, and a line chart model was constructed. The discriminative ability of the model was evaluated using the C-index, and internal validation was conducted using ten-fold cross-validation and bootstrap validation.ResultsOut of 125 pregnant women, 35 (28.00%) developed gestational hypertension. β-HCG and Hcy were identified as independent risk factors, while PAPP-A, AFP, and uE3 were identified as independent protective factors. There was a positive correlation between Hcy, β-HCG, and gestational hypertension, and a negative correlation between PAPP-A, AFP, uE3, and gestational hypertension. The predictive line chart model had a C-index of 0.885 and an average AUC value of 0.853 after internal validation.Conclusionβ-HCG and Hcy are risk factors, while PAPP-A, AFP, and uE3 are protective factors for gestational hypertension. A line chart model based on these factors can help identify pregnant women at risk of developing gestational hypertension in early pregnancy.

BackgroundThe primary recognition of diabetic retinopathy (DR) is a pivotal requirement to prevent blindness and vision impairment. This deadly condition is identified by highly qualified professionals by examining colored retinal images.ObjectiveThe physical diagnostics for this condition was time-consuming and prone to fault. The development of computer-vision based intelligent systems has develop a main research area to effectually diagnosis the pathologies from an image.MethodsIn this research, a novel Deep learning based Dual Features Integrated classification (DD-FIC) framework is designed to detect the DR from a color retinal image. Initially, the fundus images are denoised by Wavelet integrated Retinex (WIR) algorithm to remove the noise artifacts which provide high contrast image. This DD-FIC model contains two phases of feature extraction module to evaluation of several retinal areas. Initially, global features of the fundus image are retrieved by the assist of attention fused efficient model, whereas the attention module dynamically highlights the important features. Afterwards, the segmented retinal vessels data is converted into features for learning the local features.ResultsFinally, the collective of features is processed into the Random Forest based feature selection model for the optimal prediction with five different classes using multi-class support vector machine (MCSVM). The efficacy of the proposed DD-FIC framework is estimated by Kaggle dataset with the detection accuracy of 98.6%.Conclusions: The proposed framework rises the accuracy of 1.54%, 3.65%, 13.79% and 6.28% for Multi-channel CNN, CNN, VGG NiN and Shallow CNN respectively.