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OBJECTIVE: To explore the effect of collaborative nursing based on Roy Adaptive Mode (RAM) on postoperative functional reconstruction, soft tissue pain and quality of life in patients with femoral intertrochanteric fracture. METHODS: A retrospective matched control method was used in this study. A total of 96 patients with femoral intertrochanteric fracture admitted to our hospital from July 2018 to September 2021 were selected. According to different nursing methods, the patients were divided into a collaborative group and a routine group, with 48 cases in each group. Patients in both groups were treated with intramedullary nail surgery. The routine group was given routine perioperative nursing intervention, and the collaborative group was given collaborative nursing intervention on this basis. The hip function recovery and quality of life before and after the intervention were compared between the two groups. The preoperative and postoperative pain degree, and the perioperative complications of the two groups were recorded. Logistic multivariate regression analysis was used to analyze the risk factors affecting the recovery of hip joint function in patients with femoral intertrochanteric fracture after operation, thereby constructing a risk prediction model. ROC curve was used to analyze the clinical value of influencing factors in predicting postoperative hip function recovery in patients with femoral intertrochanteric fracture. RESULTS: Harris score each dimension after intervention in the collaborative group was obviously higher than that of before intervention and the conventional group (P < 0.05). After intervention, the excellent and good rate of hip joint function the collaborative group was 83.33%, which was significantly higher than 60.42% in the routine group (P < 0.05). Postoperative VAS scores each time point in the collaborative group was obviously lower than that in the routine group (P < 0.05). After intervention, the scores of physiological function, physiological role, body pain and general health in the collaborative group were significantly higher than those in the routine group (P < 0.05). The incidence of complications in the collaborative group was 6.25%, which was significantly lower than 22.92% in the routine group (P < 0.05). There were statistically significant differences in age, preoperative ASA grade, internal fixation method, osteoporosis grade and perioperative nursing methods between the excellent hip recovery group and the poor hip recovery group (P < 0.05). Logistic multivariate regression analysis showed that age, preoperative ASA grade, internal fixation method and osteoporosis grade were the risk factors affecting the recovery of hip joint function after operation, and perioperative nursing method was the protective factor (P < 0.05). Among the influencing factors, the internal fixation method and the grade of osteoporosis had certain clinical value in predicting the recovery of hip joint function in patients with femoral intertrochanteric fracture after operation. CONCLUSION: The RAM model-based collaborative nursing method may effectively restore the hip joint function of patients with femoral intertrochanteric fracture after operation, and may reduce the perioperative pain degree of patients, improve the quality of life of patients and reduce the incidence of complications, which can be popularized and applied in clinical practice. In addition, there are many factors influencing the recovery of hip joint function in patients with femoral intertrochanteric fracture after operation, and targeted measures should be taken according to the influencing factors to improve the effect of intramedullary nail treatment.
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Fijación Intramedular de Fracturas , Fracturas de Cadera , Dolor Postoperatorio , Calidad de Vida , Recuperación de la Función , Humanos , Femenino , Masculino , Fracturas de Cadera/cirugía , Estudios Retrospectivos , Anciano , Dolor Postoperatorio/etiología , Dolor Postoperatorio/diagnóstico , Dolor Postoperatorio/prevención & control , Persona de Mediana Edad , Fijación Intramedular de Fracturas/métodos , Fijación Intramedular de Fracturas/efectos adversos , Anciano de 80 o más Años , Resultado del Tratamiento , Articulación de la Cadera/cirugía , Articulación de la Cadera/fisiopatologíaRESUMEN
Silicon is regarded as the most promising candidate due to its ultrahigh theoretical energy density (4200 mAh g-1). However, the large volume expansion of silicon nanoparticles would result in the destruction of electrodes and a shortened cycle lifetime. Here, inspired by the natural structure of bamboo, the silicon anode with vascular bundle-like structure is proposed to improve the electrochemical performance for the first time. The dense channel wall in the silicon anode can accommodate the volume change of silicon nanoparticles and the transport of ions and electrons is also enhanced. The obtained silicon anodes display excellent mechanical properties (50% compression resilience and the average peel force of 4.34 N) and good wettability. What more, the silicon anodes exhibit high initial coulombic efficiency (94.5%), excellent cycle stability (2100 mAh g-1 after 300 cycles) which stands out among the silicon anodes. Specially, the silicon anode with impressive areal capacity of 36.36 mAh cm-2 and initial coulombic efficiency of 84% is also achieved. This work offers a novel and efficient strategy for the preparation of the flexible electrodes with outstanding performance.
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Silicon is expected to become the ideal anode material for the next generation of high energy density lithium battery because of its high theoretical capacity (4200 mAh g-1 ). However, for silicon electrodes, the initial coulombic efficiency (ICE) is low and the volume of the electrode changes by over 300% after lithiation. The capacity of the silicon electrode decreases rapidly during cycling, hindering the practical application. In this work, a slidable and highly ionic conductive flexible polymer binder with a specific single-ion structure (abbreviated as SSIP) is presented in which polyrotaxane acts as a dynamic crosslinker. The ionic conducting network is expected to reduce the overall resistance, improve ICE and stabilize the electrode interface. Furthermore, the introduction of slidable polyrotaxane increases the reversible dynamics of the binder and improves the long-term cycling stability and rate performance. The silicon anode based on SSIP provides a discharge capacity of ≈1650 mAh g-1 after 400 cycles at 0.5C with a high ICE of upto 92.0%. Additionally, the electrode still exhibits a high ICE of 87.5% with an ultra-high Si loading of 3.84 mg cm-2 and maintains a satisfying areal capacity of 5.9 mAh cm-2 after 50 cycles, exhibiting the potential application of SSIP in silicon-based anodes.
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In order to explore the efficacy of using artificial intelligence (AI) algorithm-based ultrasound images to diagnose iliac vein compression syndrome (IVCS) and assist clinicians in the diagnosis of diseases, the characteristics of vein imaging in patients with IVCS were summarized. After ultrasound image acquisition, the image data were preprocessed to construct a deep learning model to realize the position detection of venous compression and the recognition of benign and malignant lesions. In addition, a dataset was built for model evaluation. The data came from patients with thrombotic chronic venous disease (CVD) and deep vein thrombosis (DVT) in hospital. The image feature group of IVCS extracted by cavity convolution was the artificial intelligence algorithm imaging group, and the ultrasound images were directly taken as the control group without processing. Digital subtraction angiography (DSA) was performed to check the patient's veins one week in advance. Then, the patients were rolled into the AI algorithm imaging group and control group, and the correlation between May-Thurner syndrome (MTS) and AI algorithm imaging was analyzed based on DSA and ultrasound results. Satisfaction of intestinal venous stenosis (or occlusion) or formation of collateral circulation was used as a diagnostic index for MTS. Ultrasound showed that the AI algorithm imaging group had a higher percentage of good treatment effects than that of the control group. The call-up rate of the DMRF-convolutional neural network (CNN), precision, and accuracy were all superior to those of the control group. In addition, the degree of venous swelling of patients in the artificial intelligence algorithm imaging group was weak, the degree of pain relief was high after treatment, and the difference between the artificial intelligence algorithm imaging group and control group was statistically considerable (p < 0.005). Through grouped experiments, it was found that the construction of the AI imaging model was effective for the detection and recognition of lower extremity vein lesions in ultrasound images. To sum up, the ultrasound image evaluation and analysis using AI algorithm during MTS treatment was accurate and efficient, which laid a good foundation for future research, diagnosis, and treatment.