Phantom study of a self-shielded X-ray bone age assessment instrument against scattered radiation in children.

Hand-wrist radiography is the most common and accurate method for evaluating children's bone age. To reduce the scattered radiation of radiosensitive organs in bone age assessment, we designed a small X-ray instrument with radioprotection function by adding metal enclosure for X-ray shielding. We used a phantom operator to compare the scattered radiation doses received by sensitive organs under three different protection scenarios (proposed instrument, radiation personal protective equipment, no protection). The proposed instrument showed greater reduction in the mean dose of a single exposure compared with radiation personal protective equipment especially on the left side which was proximal to the X-ray machine (≥80.0% in eye and thyroid, ≥99.9% in breast and gonad). The proposed instrument provides a new pathway towards more convenient and efficient radioprotection.

Design and implementation of an automatic nursing assessment system based on CDSS technology.

BACKGROUND: Various quantitative and quality assessment tools are currently used in nursing to evaluate a patient's physiological, psychological, and socioeconomic status. The results play important roles in evaluating the efficiency of healthcare, improving the treatment plans, and lowing relevant clinical risks. However, the manual process of the assessment imposes a substantial burden and can lead to errors in digitalization. To fill these gaps, we proposed an automatic nursing assessment system based on clinical decision support system (CDSS). The framework underlying the CDSS included experts, evaluation criteria, and voting roles for selecting electronic assessment sheets over paper ones. METHODS: We developed the framework based on an expert voting flow to choose electronic assessment sheets. The CDSS was constructed based on a nursing process workflow model. A multilayer architecture with independent modules was used. The performance of the proposed system was evaluated by comparing the adverse events' incidence and the average time for regular daily assessment before and after the implementation. RESULTS: After implementation of the system, the adverse nursing events' incidence decreased significantly from 0.43 % to 0.37 % in the first year and further to 0.27 % in the second year (p-value: 0.04). Meanwhile, the median time for regular daily assessments further decreased from 63 s to 51 s. CONCLUSIONS: The automatic assessment system helps to reduce nurses' workload and the incidence of adverse nursing events.

Chemical fingerprint analysis and quality assessment of Tibetan medicine Triphala from different origins by high-performance liquid chromatography.

INTRODUCTION: Although the Tibetan medicine Triphala (THL) is widely used in many countries, insufficient progress has been made in quality control. OBJECTIVES: The present study aimed to propose a methodology for quality control of THL based on HPLC fingerprinting combined with an orthogonal array design. METHODS: Seven identified peaks were used as indicators to examine the effects of temperature, extraction time, and solid-liquid ratio on the dissolution of active ingredients in THL. Fingerprint analysis was performed on 20 batches of THL from four geographical areas (China, Laos, Thailand, and Vietnam). For further chemometric assessment, analysis techniques including similarity analysis, hierarchical clustering analysis, principal component analysis, and orthogonal partial least squares discrimination analysis (OPLS-DA) were used to classify the 20 batches of samples. RESULTS: Fingerprints were established and 19 common peaks were identified. The similarity of 20 batches of THL was more than 0.9 and the batches were divided into two clusters. Four differential components of THL were identified based on OPLS-DA, including chebulinic acid, chebulagic acid, and corilagin. The optimal extraction conditions were an extraction time of 30 min, a temperature of 90°C, and a solid-liquid ratio of 30 mL/g. CONCLUSION: HPLC fingerprinting combined with an orthogonal array design could be used for comprehensive evaluation and quality assessment of THL, providing a theoretical basis for further development and utilization of THL.

Efficacy assessment of superficial temporal artery-middle cerebral artery bypass surgery in treating moyamoya disease from a hemodynamic perspective: a pilot study using computational modeling and perfusion imaging.

BACKGROUND: Superficial temporal artery-middle cerebral artery (STA-MCA) bypass is a common surgery in treating moyamoya disease (MMD) with occluded MCA. Computational fluid dynamics (CFD) simulation might provide a simple, non-invasive, and low-cost tool to evaluate the efficacy of STA-MCA surgery. AIM: We aim to quantitatively investigate the treatment efficacy of STA-MCA surgery in improving the blood flow of MMD patients using CFD simulation. METHODS: This retrospective study included 11 MMD patients with occlusion around proximal MCA who underwent STA-MCA bypass surgery. CFD simulation was performed using patient-specific blood pressure and postoperative artery geometry. The volumetric flow rates of STA and the bypass, average flow velocity in the proximal segment of transcranial bypass, transcranial pressure drop, and transcranial flow resistance were measured and compared with a postoperative increment of cerebral blood flow (CBF) in MCA territories derived from perfusion imaging. Per-branch pressure drop from model inlet to bypass branch outlet was calculated. RESULTS: The volumetric flow rates of STA and the bypass were 80.84 ± 14.54 mL/min and 46.03 ± 4.21 mL/min. Average flow velocity in proximal bypass, transcranial pressure drop, and transcranial flow resistance were 0.19 ± 0.07 m/s, 3.72 ± 3.10 mmHg, and 6.54 ± 5.65 10-8 Pa s m-3. Postoperative mean increment of CBF in MCA territories was 16.03 ± 11.72 mL·100 g-1·min-1. Per-branch pressure drop was 10.96 ± 5.59 mmHg and 7.26 ± 4.25 mmHg in branches with and without stenosis. CONCLUSIONS: CFD simulation results are consistent with CBF observation in verifying the efficacy of STA-MCA bypass, where postoperative stenosis may influence the hemodynamics.

Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning.

BACKGROUND: The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis results, may achieve early, accurate, and fine-grained assessment of COVID-19 severity, which is an urgent clinical need. OBJECTIVE: To evaluate if machine learning algorithms using CT-based clinico-radiological features could achieve the accurate fine-grained assessment of COVID-19 severity. METHODS: The clinico-radiological features were collected from 78 COVID-19 patients with different severities. A neural network was developed to automatically measure the lesion volume from CT images. The severity was clinically diagnosed using two-type (severe and non-severe) and fine-grained four-type (mild, regular, severe, critical) classifications, respectively. To investigate the key features of COVID-19 severity, statistical analyses were performed between patients' clinico-radiological features and severity. Four machine learning algorithms (decision tree, random forest, SVM, and XGBoost) were trained and applied in the assessment of COVID-19 severity using clinico-radiological features. RESULTS: The CT imaging features (CTscore and lesion volume) were significantly related with COVID-19 severity (p < 0.05 in statistical analysis for both in two-type and fine-grained four-type classifications). The CT imaging features significantly improved the accuracy of machine learning algorithms in assessing COVID-19 severity in the fine-grained four-type classification. With CT analysis results added, the four-type classification achieved comparable performance to the two-type one. CONCLUSIONS: CT-based clinico-radiological features can provide an important reference for the accurate fine-grained assessment of illness severity using machine learning to achieve the early triage of COVID-19 patients.

Assessment of continuous neutrophil CD64 index measurement for diagnosing sepsis and predicting outcome in a Chinese pediatric intensive care unit: a prospective study.

BACKGROUND: The high affinity immunoglobulin-Fc fragment receptor I CD64 on neutrophils is widely assumed to be a useful biomarker in the early identification of sepsis, and it improves outcomes. We aimed to determine its ability to diagnose sepsis and predict its prognosis with continuous measurements. METHODS: A total of 335 patients admitted to a Chinese PICU were prospectively stratified into two groups according to the presence of sepsis (defined by clinical criteria for sepsis) between 2018 and 2019. Serum concentrations of the nCD64 index, C-reactive protein (CRP), and procalcitonin (PCT) were measured. Sensitivity, specificity and receiver operating characteristic (ROC) curves were calculated to evaluate the diagnostic value for sepsis. A multiple logistic regression model was used to estimate the prognostic value of continuous nCD64 index measurement for in-hospital death. RESULTS: The baseline nCD64 index and levels of PCT and CRP were significantly higher in septic children than in nonseptic children (P<0.05). The nCD64 index presented a higher sensitivity (0.90), specificity (0.78) and area under the ROC curve [0.91 (0.90, 0.93)] than CRP and PCT in discriminating septic children with an optimal cutoff value of 5.78. The nCD64 index decreased with the progression of sepsis, and the baseline nCD64 index was strongly associated with in-hospital death (OR: 2.18, 95% CI: 1.02-4.74). Moreover, the more rapidly the nCD64 index declined, the lower the in-hospital death rate was (OR: 0.89, 95% CI: 0.63-1.35) after adjusting for the baseline nCD64 index and other confounders. CONCLUSIONS: The nCD64 index was not only effective for the early diagnosis of childhood sepsis but also positively associated with the prognosis of sepsis. Moreover, the nCD64 decline was inversely associated with the in-hospital death rate.