Technology and Health Care最新文献

Background: Malnutrition is a widespread problem in critically ill patients with neurological disorders.

Objective: The purpose of this study is to investigate the effect of a multidisciplinary collaborative nutritional treatment mode based on a standardized unit for nutritional support on the outcome metrics in patients with neurological disorders who are critically ill.

Methods: We enrolled 84 participants who were hospitalized in the intensive care unit (ICU) of Yancheng No. 1 People's Hospital for neurological disorders between June 2018 and December 2021. The participants were randomly assigned to the control group and the test group. The control group received traditional nutritional support, while the test group was treated with a multidisciplinary collaborative nutritional treatment mode based on a standardized unit for nutritional support. We collected the general information, feeding tolerance (FT), nutritional risk score, and laboratory indicators before intervention, after intervention for one week, and after intervention for 2 weeks, and other data of the participants.

Results: After the intervention, the test group scored significantly lower than the control group in the incidence of gastroparesis and diarrhea, as well as the NUTRIC score, with statistically significant differences (P< 0.001). The prealbumin levels in the test group increased progressively prior to intervention, after intervention for one week, and after intervention for two weeks. Compared to the control group, the test group had higher prealbumin levels prior to intervention, after intervention for one week, and after intervention for two weeks, with statistically significant differences (P< 0.001).

Conclusion: We developed a multidisciplinary collaborative nutritional treatment model based on a standard unit for nutritional support. This model can improve neural function, FT, and pertinent outcome indicators and is generally applicable.

Background: Unicompartmental knee arthroplasty (UKA) is a viable alternative to total knee arthroplasty (TKA) for osteoarthritis patients with single-compartment involvement, with advantages including accelerated recovery, reduced pain, and improved function. Robotic-assisted UKA (rUKA) is a promising development that ensures precise implant positioning and limb alignment. However, concerns about complications remain.

Objective: This study looks at patient satisfaction as a key metric for determining the efficacy of rUKA versus manual UKA (mUKA).

Methods: The search strategy for this study followed PRISMA. Using precise keywords, PubMed, Scopus, Web of Science, and the Cochrane library were searched. English articles were searched until August 2, 2023. Selection criteria included mUKA and rUKA patient satisfaction studies. The NOS scale evaluated study quality. Meta-analysis was done with R and heterogeneity analysis.

Results: This systematic review examined 5 studies with 1060 UKAs (532 robotic-assisted and 528 manual). Variable satisfaction assessment methods were used. Three studies found no difference in patient satisfaction after robotic-assisted UKA, but two found a higher satisfaction. Meta-analysis showed robotic-assisted UKA improved patient satisfaction (OR = 1.72 [1.25-2.37]). Overall, most studies showed low risk of bias, except one with higher bias.

Conclusion: This review suggests that robotic assistance may enhance patient satisfaction in UKA procedures.

Background: Danshen Chuanxiong Injection (DCI) has demonstrated significant clinical efficacy in the treatment of acute pancreatitis (AP); however, the precise molecular mechanisms underlying its therapeutic effects remain incompletely understood.

Objective: In this study, we employed network pharmacology analysis to comprehensively investigate the active components, potential targets, and signaling pathways involved in DCI-mediated treatment of AP.

Methods: We utilized the mouse pancreatic acinar cell line 266-6 to establish an cholecystokinin (CCK)-induced AP cell injury model and evaluated cell viability using the Cell counting kit-8 assay. Western blotting and quantitative PCR were employed to determine the expression levels of key target proteins and genes.

Results: Network pharmacology analysis identified a total of 144 active components and 430 potential targets within DCI. By integrating data from public databases, we identified 762 AP-related genes. Among these, we identified 93 potential targets that may be involved in the therapeutic effects of DCI for AP. These targets were significantly enriched in biological processes such as oxidative stress, regulation of cytokine production, leukocyte migration, and the TNF signaling pathway. Molecular docking studies revealed a high binding affinity between the active components and the key targets AKT1 and NFKBA, indicative of potential interaction. Additionally, CCK-induced acinar cell injury led to upregulation of AKT1, NFKBA, and P53 proteins, as well as TNF, IL6, and MMP9 genes. Conversely, treatment with DCI dose-dependently attenuated CCK-induced acinar cell injury and restored the expression levels of the aforementioned proteins and genes.

Conclusion: Overall, this study provides a comprehensive understanding of the molecular mechanisms underlying the therapeutic effects of DCI in the treatment of AP. Our findings confirm the protective effect of DCI against CCK-induced acinar cell injury and its regulation of key targets.

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: In view of the lack of attention and predictability in postpartum breastfeeding in primiparas, health education on breastfeeding during pregnancy should be carried out to publicize the benefits of breastfeeding.

Objective: To investigate how well the primiparas during pregnancy are known of breastfeeding knowledge, and to provide a basis for developing health education measures for them.

Methods: With the adoption of the objective sampling method and the principle of saturation, 10 primiparas in the obstetrics outpatient department of the Hunan Provincial People's Hospital were selected as the study participants. Semi-structured in-depth interviews combined with the observation method were used for data collection. The interview data were analyzed and the theme was refined by Colaizzi's seven-step method.

Results: The results of the four themes of the cognition of breastfeeding knowledge among primiparas were as follows: Lack of knowledge and curiosity about breastfeeding in some women, lack of access to correct breastfeeding knowledge, insufficient support from family members for postpartum breastfeeding, and lack of approach to solve problems during breastfeeding among primiparas.

Conclusion: Due to the current problems of the cognition of breastfeeding knowledge in primiparas, it was imperative to build a health education model suitable for primiparas to improve this knowledge.

Background: Obstructive sleep apnea (OSA) is a common sleep disordered breathing disorder, which can cause serious damage to multiple human systems. Although polysomnography (PSG) is the current gold standard for diagnosis, it is complex and expensive. Therefore, it is of great significance to find a simple, economical and rapid primary screening and diagnosis method to replace PSG for the diagnosis of OSA.

Objective: The purpose of this study is to propose a new method for the diagnosis and classification of OSA, which is used to automatically detect the duration of sleep apnea hypopnea events (AHE), so as to estimate the ratio(S) of the total duration of all-night AHE to the total sleep time only based on the sound signal of sleep respiration, and to identify OSA.

Methods: We performed PSG tests on participants and extracted relevant sleep breathing sound signal data. This study is carried out in two stages. In the first stage, the relevant PSG report data of eligible subjects were recorded, the total duration of AHE in each subject's data was extracted, and the S value was calculated to evaluate the severity of OSA. In the second stage, only the sleep breath sound signal data of the same batch of subjects were used for automatic detection, and the S value in the sleep breath sound signal was extracted, and the S value was compared with the PSG diagnosis results to calculate the accuracy of the experimental method.

Results: Among 225 subjects. Using PSG as the reference standard, the S value extracted from the PSG diagnostic data report can accurately diagnose OSA(accuracy rate 99.56%) and distinguish its severity (accuracy rate 95.11%). The accuracy of the S value detected in the sleep breathing sound signal in the diagnosis of severe OSA reached 100%.

Conclusion: The results show that the experimental parameter S value is feasible in OSA diagnosis and classification. OSA can be identified and evaluated only by sleep breathing sounds. This method helps to simplify the diagnostic grading of traditional OSA and lays a foundation for the subsequent development of simple diagnostic grading equipment.

Background: Standing is a basic human function that healthy adults take for granted, yet it is a complex perceptual-motor process that requires sensation of position and motion from the sensory systems.

Objective: We assessed agreement between center of pressure data from a laboratory force-platform and head position data from an HTC Vive head-mounted display (HMD) for the evaluation of standing postural control. We investigated the impact of different statistical choices when assessing the relationship between two measurements. Specifically: 1) How does correlation and agreement statistics relate before and after logarithmic transformation? 2) Is there systemic or proportional bias between the force-platform and HMD measurements?

Methods: We tested 37 adults (26 controls, 11 with unilateral vestibular hypofunction) standing on foam, observing a static or dynamic visual scene projected from the HMD. We quantified anterior-posterior and medio-lateral sway via Directional Path, Root Mean Square Velocity, Variance, and Power Spectral Density (PSD) from a force-platform and the HMD.

Results: Intra-class correlations (ICCs) were moderate-to-good for the non-transformed data and good-to-excellent after logarithmic transformation for all outcomes except for PSD above 1 Hz. Correlations were higher than ICCs. Bland-Altman plots indicated proportional bias but not after logarithmic transformation.

Conclusions: Both devices correlated linearly, and measure people's postural responses but cannot be used interchangeably, mostly because they appear to diverge with larger sway as evident on Bland-Altman plots of non-transformed data. Agreement between devices was excellent for low frequency movement but poor for high frequency small corrective movements.

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.