Abstract A method for determination of trace mercury in water was established. The trace mercury in water was adsorbed quantitatively by activated carbon, and then determined by electrical pyrolysis atomic absorption spectrometry. In comparison with the detection methods of total mercury in water at present, the method avoids the steps of digestion, reduces the mercury pollution and the loss of the mercury, and is simple in operation. The effects of particle size of activated carbon, acid treatment method, acid medium and enrichment time on the enrichment efficiency were investigated. The effect of the pyrolysis temperature and the interfering ions on the determination results was investigated. Three standard addition procedures including activated carbon blank addition, solution blank addition and environmental water samples addition were studied. Regression correlation coefficients of three standard curves drawn by the three methods reached 0 . 9999 . The slope of the three standard curves had no difference by statistical test, indicating that the determination of mercury in environmental water samples under the experiment conditions was not interfered by the coexistent elements, which showed that the activated carbon blank addition method could be directly used for preparing standard curve of the method. The water samples containing 5 ng/L and 50 ng/L mercury were determined by the method, and the relative standard deviation were 7. 2% and 4. 2% (n=11), respectively, with a detection limit of 1. 2 ng/L. The recovery experiment was carried out after adding 10 ng/L mercury to the surface water and tap water samples, and the recoveries were between 92. 0% and 103. 0%. Analysis results were compared with ICP-MS as control and the deviation of the two methods were between 2 . 9% and 3 . 4%, indicating that the method was accurate and reliable, and had good precision.

Objective To compare and analyze the occurrence of delayed cerebral ischemia(DCI)after coiling and clipping of intracranial aneurysms, and explore the risk factors of DCI. Methods A total of 236 patients with aneurysms diagnosed by CT angiography (CTA) or digital subtraction angiography (DSA) in Department of Neurosurgery, Tianjin Medical University General Hospital were enrolled in this study from March 2011 to May 2014. Patients were divided into clipping group(n=135) and coiling group(n=101). The clinical characteristics were compared between two groups, including gender, age, medical history, GCS score, Hunt-Hess grade, Fisher grade, WFNS grade, aneurysm location, prognosis and incidence of DCI. Risk factors for DCI were investigated by Logistic regression analysis. Results DCI was occurred in 36 patients (26.7%) underwent clipping operation while in 11 patients (10.9%) underwent coiling operation. The incidence was significantly higher in clipping group compared with that of coiling group (P <0.01). The patients were followed up for 6 months. The poor prognosis rates were 17.0%and 25.7%in clipping group and coiling group, respectively (P>0.01). The overall mortality was 11.0%, the former had a lower mortality rate (5.9% vs. 17.8%, P <0.01). According to Logistic regression analysis, Fisher Grade 3-4, postoperative pulmonary infection and surgical procedure were independent risk factors for DCI (P<0.01). Conclusion DCI is one of the most significant factors for high fatality and morbidity of postoperative aneurysm patients. There is a low occurrence of DCI after coiling compared with that of clipping. If we pay more attention to risk factors associated with the DCI, it will improve the prognosis of postoperative aneurysm patients greatly.

Objective: To compare the diagnosis and treatment experience of brain abscesses and improve prognosis. Methods: The data of 302 patients of brain abscess at Department of Neurosurgery in Tianjin Medical University General Hospital from 1980 to 2014 was analyzed retrospectively. There were 215 male and 87 female patients aged from 11 to 82 years with mean age of (30±8) years. The patients was divided into 1980-2001 group and 2002-2014 group according to different diagnosis and the treatment methods. The therapy methods include operation and conservative treatment. There were 196 cases received operation, including 95 cases of excision, 89 cases of ventriculopuncture, 12 cases of excision after ventriculopuncture, 106 cases received drug conservative therapy. Two groups of information including clinical manifestation, abscess location, therapeutic effect and prognosis were compared by χ² test. Results: Compared to 1980-2001 group, adjacent infection incidence declined(χ²=8.000, P=0.005). The ratio of single abscess declined and multiple abscess increased(χ²=11.060, P=0.001), the infection proportion of frontal lobe and temporal lobe decreased(χ²=9.080, P=0.003; χ²=15.440, P=0.000). The ratio of headache and vomit and papilledema declined significantly(χ²=23.290, P=0.000; χ²=21.020, P=0.000; χ²=2.290, P=0.001). Total mortality of 302 patients were 23 cases and 5 cases of 1980-2001 group and 2002-2014 group (10.4% vs. 6.3%, χ²=1.180, P=0.277). However, there were statistical difference in postoperative mortality between both groups (14.4% vs. 4.0%, χ² =3.880, P=0.049). Conclusion With the application of antibiotics and the development of neurosurgical techniques, the prognosis of brain abscess has been improved.

Multiple injuries caused by trauma have high rates of disability and mortality and are difficult to treat, which have a negative impact on the patients, their families and the society. At present, the medical model of trauma treatment is still inadequate, and the treatment of trauma patients faces great challenges. Artificial intelligence (AI) is an intelligent technology based on machine learning, reinforcement learning and deep learning algorithm, and it has been applied to the treatment of patients with trauma. Its efficient and accurate computer vision, planning and decision-making, and big data statistical analysis not only improve the safety and efficiency in the treatment of trauma, but also reduce the workload of clinicians, which makes up for the deficiency of the traditional model of trauma care. After screening the recent studies of AI in trauma care, the authors review its application in emergency triage, diagnosis, treatment and prevention of war trauma, in order to introduce the latest research progress of AI in trauma care and provide references for future developments.

Artificial Intelligence (AI) is an interdisciplinary subject developed on the basis of computer technology, cybernetics, mathematics, philosophy and brain science. The purpose of AI is to study new ways to extend the intelligence of human brain in various fields. In recent years, the rapid development of AI technology has brought innovation to medical science and health care. During the pandemic of coronavirus disease 2019 (COVID-19) AI has been widely used in epidemiological investigation and outbreak prediction, clinical diagnosis and treatment, hospital management, research and development of new drugs and vaccines. The application of AI has reduced the clinical workload and the consumption of medical resources, greatly assisted the battle against COVID-19.This article introduces the progresses on the applications of AI technology to provide information for its further application in the fighting against COVID-19.

OBJECTIVES: To explore the risk factors for femoral neck fracture in elderly population. METHODS: A total of 124 elderly patients (≥60 years old) in hospital for trauma were enrolled, including 71 patients (57%) with femoral neck fracture and 53 non-femoral neck fracture patients (43%). All patients' age, gender, body mass index (BMI), bone mineral density (BMD), thigh length and average circumference were collected. Single factor analysis and multivariate logistic regression analysis were performed to explore whether the above factors were risk factors for femoral neck fracture. RESULTS: Single factor analysis showed that the age, gender, BMI, BMD, thigh length, and average thigh circumference between the 2 groups were statistically different (all CONCLUSIONS Older age, female, lower BMI index (low body weight), lower BMD (osteoporosis), longer thigh length, and lower average circumference are risk factors for femoral neck fracture in the elderly population.
Absorptiometry, Photon ; Aged ; Body Mass Index ; Bone Density ; Female ; Femoral Neck Fractures/etiology* ; Humans ; Middle Aged ; Osteoporosis ; Risk Factors