Study on Health Evaluation of an Ecological Irrigation District in Helan County, China

The construction of ecological irrigation districts is of great significance to protect the Yellow River ecology and achieve sustainable development of the local ecological economy. Taking the ecological irrigation district of Helan County as the study area, a health evaluation index system of the irrigation district was established, including three primary indexes of ecological environment, modernization level, and agricultural production and benefit, and 20 secondary indexes. Then, the Topsis method, entropy weight evaluation method, fuzzy pattern recognition model, and variable fuzzy model were used to evaluate the health of the Helan ecological irrigation district. In order to avoid the one-sidedness of the evaluation results of a single evaluation method, a combined evaluation method named deviation maximization combined evaluation method was used to combine each single evaluation result. The evaluation results by the combined evaluation method showed the following: (1) The ecological health of Helan irrigation district had a trend of becoming better from 2007 to 2016. (2) The grey correlation analysis showed that the soil salt content, groundwater depth, canal lining rate, ratio of efficient water-saving irrigation area, information level of the irrigation district, water productivity, agricultural unilateral aquatic output value, irrigation water consumption per mu, and coefficient of effective utilization of farmland irrigation water were closely related to the evaluation results. (3) In order to effectively improve the ecological health of Helan irrigation districts, it is necessary to reduce soil salt content and groundwater salinity, increase canal linings, promote water-saving irrigation measures, and agricultural information construction, etc.

Can artificial intelligence replace ultrasound as a complementary tool to mammogram for the diagnosis of the breast cancer?

Objectives: to study the impact of artificial intelligence (AI) on the performance of mammogram with regard to the classification of the detected breast lesions in correlation to ultrasound aided mammograms. Methods: Ethics committee approval was obtained in this prospective analysis. The study included 2000 mammograms. The mammograms were interpreted by the radiologists and breast ultrasound was performed for all cases. The Breast Imaging Reporting and Data System (BI-RADS) score was applied regarding the combined evaluation of the mammogram and the ultrasound modalities. Each breast side-was individually assessed with the aid of AI scanning in the form of targeted heat-map and then, a probability of malignancy (abnormality scoring percentage) was obtained. Operative and the histopathology data were the standard of reference. Results: Normal assigned cases (BI-RADS 1) with no lesions were excluded from the statistical evaluation. The study included 538 benign and 642 malignant breast lesions (n = 1180, 59%). BI-RADS categories for the breast lesions with regard to the combined evaluation of the digital mammogram and ultrasound were assigned BI-RADS 2 (Benign) in 385 lesions with AI median value of the abnormality scoring percentage of 10, (n = 385/1180, 32.6%), and BI-RADS 5 (malignant) in 471, that had showed median percentage AI value of 88 (n = 471/1180, 39.9%). AI abnormality scoring of 59% yielded a sensitivity of 96.8% and specificity of 90.1% in the discrimination of the breast lesions detected on the included mammograms. Conclusions: AI could be considered as an optional primary reliable complementary tool to the digital mammogram for the evaluation of the breast lesions. The color hue and the abnormality scoring percentage presented a credible method for the detection and discrimination of breast cancer of near accuracy to the breast ultrasound. So consequently, AI- mammogram combination could be used as a one setting method to discriminate between cases that require further imaging or biopsy from those that need only time interval follows up. Advances in knowledge: Recently, the indulgence of AI in the work up of breast cancer was concerned. AI noted as a screening strategy for the detection of breast cancer. In the current work, the performance of AI was studied with regard to the diagnosis not just the detection of breast cancer in the mammographic-detected breast lesions. The evaluation was concerned with AI as a possible complementary reading tool to mammogram and included the qualitative assessment of the color hue and the quantitative integration of the abnormality scoring percentage.

Evaluation of Visual Disability with Digital Systems

Background: Vision loss is a major health issue and people with visual impairments are at higher risk for visually related complaints, as poor postural control, accompanying fear of moving and higher risk for falls compared to people with normal vision. Consequently, it is important to develop and improve treatment and prevention programs aimed at reducing these problems. In this endeavour, high quality screening instruments are a prerequisite for successful research and clinical practice. Scope: The aim of this study is to verify the validity level of a new system for assessing the degree of visual disability that provides a numerical score, resulting from a combined assessment of both the Visual Field (VF) and Visual Acuity (VA) loss, obtained using a digital technology visor. Methods: To verify if the assessments of the visual impairment coefficient carried out through the two different systems produce homogeneous results, a comparison between results obtained on a group of sixty-five subjects who were visually impaired or had fragile vision was performed. Results: A great level of inhomogeneities in the evaluation of visual disability coefficient, obtained through the digital system, which provides for the combined evaluation of the visual field and visual acuity, and the conventional one, which involves the separate evaluation of the two parameters, was found. Conclusions: These results confirm therefore that the discrepancies found in the assessment of the disability level obtained through two different systems is not due to the different tools used, but to the different evaluation systems. The evaluation system carried out using the new digital visor, which provides for the combined evaluation of the VF and VA, constitutes a method to ensure a more homogeneous and reliable visual disability assessment compared to those carried out with traditional systems.

Combined evaluation of coronary artery disease and high-sensitivity cardiac troponin T for prediction of adverse events in patients with hypertrophic cardiomyopathy

Background This study was performed to investigate the clinical significance of combined evaluation of both coronary artery disease (CAD) and high-sensitivity cardiac troponin T (hs-cTnT) for prediction of major adverse cardiovascular events (MACEs) in patients with hypertrophic cardiomyopathy (HCM). Methods We performed clinical evaluations, including coronary artery imaging and hs-cTnT measurement, in 162 patients with HCM. Results The patients were followed up for a median period of 3.7 years (interquartile range 2.4–5.6 years; total of 632.3 person-years [PYs]), during which time MACEs occurred in 24 (14.8%) patients. The incidence of MACEs was 6.4 and 2.7 per 100 PYs for patients with CAD and normal coronary arteries, respectively; similarly, the incidence was 5.8 and 2.1 per 100 PYs in patients with an elevated hs-cTnT concentration (> 14.0 ng/L) and a normal hs-cTnT concentration, respectively. The multivariate analysis suggested that CAD and an elevated hs-cTnT concentration tended to be positively associated with MACEs. When the groups were allocated according to these two markers, the patients were divided into four groups, which further improved the predictive values. The incidence of MACEs was 10.4 per 100 PYs in the CAD and elevated hs-cTnT group, which was much higher than the incidence in all other groups (range, 2.0–3.5 per 100 PYs). With the normal coronary arteries and normal hs-cTnT group serving as a reference, the adjusted hazard ratio was 5.0 (95% confidence interval 1.0–23.8; P = 0.046) for the CAD and elevated hs-cTnT group. In addition, the subgroup analysis showed similar findings among the patients without severe CAD. Conclusions In patients with HCM, combined evaluation of both CAD and hs-cTnT might facilitate more reliable prediction of MACEs than evaluation of a single marker. These may serve as clinically useful markers to guide risk management.