Objective To investigate and analyze the difficulties of nosocomial infection management in different-level medical institutions in Shanghai, and to provide scientific basis for improving the level of nosocomial infection management. Methods A questionnaire was designed to include 10 difficulties in nosocomial infection management such as professional title promotion, salary, and personnel allocation. In October 2023, the Shanghai Nosocomial Infection Quality Control Center, in collaboration with the Shanghai Hospital Association, conducted a questionnaire survey among the heads of nosocomial infection management departments in medical institutions in Shanghai. The scores of difficulties were analyzed by stratification according to hospital level, allocation and changes of full-time personnel. Results A total of 548 questionnaires were distributed, and 530 valid questionnaires were retrieved, with a recovery rate of 96.72%. There were 55 public tertiary, 93 public secondary, 169 public primary and 213 social medical institutions. The rates of full-time personnel allocation meeting standards were 76.36% (42/55), 72.04% (67/93), 31.95% (54/169), and 21.60% (46/213), respectively. There was a statistically significant difference in the rates of full-time personnel allocation meeting standards among different levels of medical institutions (χ2=105.149, P<0.001). There was no statistical difference in the total scores of nosocomial infection management difficulties among different-level medical institutions (F=1.657, P=0.176). There were statistically significant differences in the scores of difficulties in professional title promotion, cumbersome daily norms and requirements, insufficient allocation of full-time personnel, and high personnel turnover (P<0.05). Conclusions The main difficulties in nosocomial management of medical institutions at all levels in Shanghai include the difficulty in career promotion, cumbersome daily norms and requirements, insufficient allocation of full-time personnel and lack of experience. In the future, medical institutions should strengthen the allocation of full-time personnel and enhance their capabilities, provide smooth promotion channels, to promote the high-quality development of nosocomial infection management ultimately.
ObjectiveTo summarize research progress of the effect of knee flexion position on postoperative blood loss and knee range of motion (ROM) after total knee arthroplasty (TKA).MethodsThe relevant literature at home and abroad was reviewed and summarized from mechanism, research status, progress, and clinical outcome. The differences of clinical results caused by different positions, flexion angles, and keeping time were compared.ResultsKeeping knee flexion after TKA can reduce postoperative blood loss through the angle change of blood vessels and increase knee early ROM by improving flexion muscle strength. When the flexion angle of the knee is large and the flexion position is keeping for a long time, the postoperative blood loss and the knee ROM can be significantly improved. However, the amount of blood loss and ROM are not further improved in the patients with keeping knee flexion for more than 24 hours compared with less than 24 hours.ConclusionKeeping knee flexion after TKA is a simple and effective method to reduce postoperative blood loss and improve knee ROM. However, the optimal knee flexion angle and time are needed to be further explored.
Real-time free breathing cardiac cine imaging is a reproducible method with shorter acquisition time and without breath-hold for cardiac magnetic resonance imaging. However, the detection of end-diastole and end-systole frames of real-time free breathing cardiac cine imaging for left ventricle function analysis is commonly completed by visual identification, which is time-consuming and laborious. In order to save processing time, we propose a method for semi-automatic identification of end-diastole and end-systole frames. The method fits respiratory motion signal and acquires the expiration phase, end-diastole and end-systole frames by cross correlation coefficient. The procedure successfully worked on ten healthy volunteers and validated by the analysis of left ventricle function compared to the standard breath-hold steady-state free precession cardiac cine imaging without any significant statistical differences. The results demonstrated that the present method could correctly detect end-diastole and end-systole frames. In the future, this technique may be used for rapid left ventricle function analysis in clinic.
Existing emotion recognition research is typically limited to static laboratory settings and has not fully handle the changes in emotional states in dynamic scenarios. To address this problem, this paper proposes a method for dynamic continuous emotion recognition based on electroencephalography (EEG) and eye movement signals. Firstly, an experimental paradigm was designed to cover six dynamic emotion transition scenarios including happy to calm, calm to happy, sad to calm, calm to sad, nervous to calm, and calm to nervous. EEG and eye movement data were collected simultaneously from 20 subjects to fill the gap in current multimodal dynamic continuous emotion datasets. In the valence-arousal two-dimensional space, emotion ratings for stimulus videos were performed every five seconds on a scale of 1 to 9, and dynamic continuous emotion labels were normalized. Subsequently, frequency band features were extracted from the preprocessed EEG and eye movement data. A cascade feature fusion approach was used to effectively combine EEG and eye movement features, generating an information-rich multimodal feature vector. This feature vector was input into four regression models including support vector regression with radial basis function kernel, decision tree, random forest, and K-nearest neighbors, to develop the dynamic continuous emotion recognition model. The results showed that the proposed method achieved the lowest mean square error for valence and arousal across the six dynamic continuous emotions. This approach can accurately recognize various emotion transitions in dynamic situations, offering higher accuracy and robustness compared to using either EEG or eye movement signals alone, making it well-suited for practical applications.
ObjectiveTo assess the feasibility of intravoxel incoherent motion diffusion-weighted imaging (IVIM) in evaluating microvessel density (MVD) and microvascular invasion (MVI) of hepatocellular carcinoma (HCC).MethodsRat models were established to be scanned by IVIM. HCC lesions corresponding to IVIM image were examined pathologically to get data of MVD and MVI. Spearman correlation analysis was used to compare the apparent diffusion coefficient (ADC), D, D*, and f with MVD, independent samples t test was used to compare ADC, D, D*, and f between MVI (+) and MVI (–) groups.ResultsFifty HCC lesions were included finally. ADC and D values both showed a negative correlation with MVD (r=–0.406, P=0.003; r=–0.468, P=0.001), D* and f showed no statistical correlation with MVD (P=0.172, 0.074, respectively). The differences in ADC and all the IVIM parameters (D, D*, and f) between MVI (+) and MVI (–) HCCs were not statistically significant (P=0.393, 0.395, 0.221, 0.550).ConclusionADC and D can be used to evaluate MVD of HCC, but ADC and IVIM parameters were limited in evaluating MVI.
ObjectiveTo explore and clarify the relationship between epileptic seizure and inducing factors. Avoid inducing factors and reduce epileptic seizure, so as to improve the quality of life in patients with epilepsy.MethodsClinical data of 604 patients diagnosed with epilepsy in Xijing Hospital of Air Force Military Medical University from January 2018 to January 2019 were collected. The clinical data of patients with epilepsy were followed up 6 months.ResultsAmong the 604 patients, 318 (52.6%) were seizure-free in the last 6 months, 286 (47.4%) had seizures. 169 (59.1%) had seizures with at least one inducing factor. Common inducing factors: 123 cases of sleep disorder (72.8%), 114 cases of emotion changes (67.5%), 87 cases of irregular medication (51.5%), 97 cases of diet related (57.4%), 33 cases of menstruation and pregnancy (19.5%), etc. Using the χ2 test, seizures with age, gender differences had no statistical significance (P > 0.05), but seizure type was statistically different between inducing factors. In generalized seizures, tonic-clonic seizures associated with sleep deprivation (χ2= 0.189), absence seizures and anger (χ2= 0.237), pressure (χ2= 0.203), irregular life (χ2= 0.214). In the focal seizures, focal motor seizures was correlated with coffee consumption (χ2=0.145), focal sensory seizures with cold (χ2=0.235), electronic equipment use (χ2 =0.153), satiety (χ2 =0.257). Complex partial seizures was correlated with anger (χ2 =0.229), stress (χ2 =0.187), and cold (χ2 =0.198). The secondarily generalized seizures was correlated with drug missing (χ2 =0.231), sleep deprivation (χ2 =0.158), stress (χ2 =0.161), cold (χ2 =0.263), satiety (χ2 =0.182). Among the inducing factors, sleep deprivation was correlated with anger (χ2 =0.167), fatigue (χ2 =0.283), and stress (χ2 =0.230).ConclusionsEpileptic seizure were usually induced by a variety of factors. Generalized seizures were associated with sleep disorders, emotional changes, stress, irregular life, etc. While focal seizures were associated with stress, emotional changes, sleep disorders, cold, satiety, etc. An analysis of the triggers found that sleep deprivation was associated with anger, fatigue, and stress. Therefore, to clarify the inducing factors of epileptic seizure, avoid the inducing factors as much as possible, reduce the harm caused by seizures, and improve the quality of life of patients.
ObjectiveTo systematically review the model and mechanism, efficacy and its influencing factors of the promotion and application of appropriate health technology in China. MethodsSuch database as VIP, CNKI, CBM and WanFang Data were electronically searched to collect studies on the promotion and application of appropriate health technology in China published from the establishment dates to January 2013. According to the inclusion and exclusion criteria, two reviewers independently screened literature, extracted and cross-checked data, and assessed quality. Qualitative synthesis method was used to systematically summarize the included studies. ResultsA total of 73 studies were finally included. Targeted populations included primary medical staff and personnel who provided or developed appropriate health technologies, medical institutions and their managers, as well as patients who received service or treatment of appropriate health technology. The promotion of appropriate health technologies mainly contained five fields: Chinese medicine, biomedicine, family planning services, maternal and child health, and community health services. The government-leading model had been widely applied currently, besides, there were other four models: government-guidance, government-driven, market-leading and a third sector-leading models. Factors related to project funding, organization and management, technology holders, the supply and demand parties in technical services and technology itself might affect the application of appropriate health technology. Among all evaluation studies, the number of needs assessment was less, the contents of process evaluation were one-sided and the effect evaluation was mainly cross-sectional studies. ConclusionThis study shows that sustainable development of appropriate health technology promotion model need to fully consider market demand and people's needs, on the basis of using government-leading or introducing a third sector-leading model. Corresponding measures should be taken according to the influencing factors of promotion and application of appropriate health technology. It is necessary to conduct high quality evaluation studies in future and comprehensively evaluate the efficacy of the promotion and application of appropriate health technology.
Photoplethysmography (PPG) is a non-invasive technique to measure heart rate at a lower cost, and it has been recently widely used in smart wearable devices. However, as PPG is easily affected by noises under high-intensity movement, the measured heart rate in sports has low precision. To tackle the problem, this paper proposed a heart rate extraction algorithm based on self-adaptive heart rate separation model. The algorithm firstly preprocessed acceleration and PPG signals, from which cadence and heart rate history were extracted respectively. A self-adaptive model was made based on the connection between the extracted information and current heart rate, and to output possible domain of the heart rate accordingly. The algorithm proposed in this article removed the interference from strong noises by narrowing the domain of real heart rate. From experimental results on the PPG dataset used in 2015 IEEE Signal Processing Cup, the average absolute error on 12 training sets was 1.12 beat per minute (bpm) (Pearson correlation coefficient: 0.996; consistency error: −0.184 bpm). The average absolute error on 10 testing sets was 3.19 bpm (Pearson correlation coefficient: 0.990; consistency error: 1.327 bpm). From experimental results, the algorithm proposed in this paper can effectively extract heart rate information under noises and has the potential to be put in usage in smart wearable devices.
Emotion recognition refers to the process of determining and identifying an individual's current emotional state by analyzing various signals such as voice, facial expressions, and physiological indicators etc. Using electroencephalogram (EEG) signals and virtual reality (VR) technology for emotion recognition research helps to better understand human emotional changes, enabling applications in areas such as psychological therapy, education, and training to enhance people’s quality of life. However, there is a lack of comprehensive review literature summarizing the combined researches of EEG signals and VR environments for emotion recognition. Therefore, this paper summarizes and synthesizes relevant research from the past five years. Firstly, it introduces the relevant theories of VR and EEG signal emotion recognition. Secondly, it focuses on the analysis of emotion induction, feature extraction, and classification methods in emotion recognition using EEG signals within VR environments. The article concludes by summarizing the research’s application directions and providing an outlook on future development trends, aiming to serve as a reference for researchers in related fields.