Correlation between in vitro starch digestibility and starch structure/physicochemical properties in rice.

Natural resistant starch (RS) in rice provides human health benefits, and its concentration in rice is influenced by the structure and physicochemical properties of starch. The native starch structures and physicochemical properties of three rice varieties, QR, BR58, and BR50, and their relationships to in vitro digestibility were studied. The starch granules in all three varieties were irregular or polyhedral in shape. There were a few oval granules and a few pinhole structures in QR, no oval granules but a higher number of pinholes in BR58, and no oval granules and pinholes in BR50. QR is a low-amylose (13.8 %), low-RS (0.2 %) variety. BR58 is a low-amylose (15.3 %), high-RS (6.5 %) variety. BR50 is a high-amylose (26.7 %), high-RS (8.3 %) variety. All three starches exhibited typical A-type diffraction patterns. Starch molecular weight, chain length distribution, starch branching degree, pasting capabilities, and thermal properties differed considerably between the rice starches. The RS contents of the rice starch varieties were positively correlated with AAC, Mw/Mn, Mz/Mn, peak 3, B, PTime, and Tp and negatively correlated with Mn, peak 2, DB, PV, and BD, according to Pearson's correlation analysis. These findings may be helpful for the breeding and development of high-RS rice varieties.

Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement.

BACKGROUND: Accurate measurement of hemorrhage volume is critical for both the prediction of prognosis and the selection of appropriate clinical treatment after spontaneous intracerebral hemorrhage (ICH). This study aimed to evaluate the performance and accuracy of a deep learning-based automated segmentation algorithm in segmenting spontaneous intracerebral hemorrhage (ICH) volume either with or without intraventricular hemorrhage (IVH) extension. We compared this automated pipeline with two manual segmentation techniques. METHODS: We retrospectively reviewed 105 patients with acute spontaneous ICH. Depending on the presence of IVH extension, patients were divided into two groups: ICH without (n = 56) and with IVH (n = 49). ICH volume of the two groups were segmented and measured using a deep learning-based artificial intelligence (AI) diagnostic system and computed tomography-based planimetry (CTP), and the ABC/2 score were used to measure hemorrhage volume in the ICH without IVH group. Correlations and agreement analyses were used to analyze the differences in volume and length of processing time among the three segmentation approaches. RESULTS: In the ICH without IVH group, the ICH volumes measured using AI and the ABC/2 score were comparable to CTP segmentation. Strong correlations were observed among the three segmentation methods (r = 0.994, 0.976, 0.974; P < 0.001; concordance correlation coefficient [CCC] = 0.993, 0.968, 0.967). But the absolute error of the ICH volume measured by the ABC/2 score was greater than that of the algorithm (P < 0.05). In the ICH with IVH group, there is no significant differences were found between algorithm and CTP(P = 0.614). The correlation and agreement between CTP and AI were strong (r = 0.996, P < 0.001; CCC = 0.996). The AI segmentation took a significantly shorter amount of time than CTP (P < 0.001), but was slightly longer than ABC/2 score technique (P = 0.002). CONCLUSIONS: The deep learning-based AI diagnostic system accurately quantified volumes of acute spontaneous ICH with high fidelity and greater efficiency compared to the CTP measurement and more accurately than the ABC/2 scores. We believe this is a promising tool to help physicians achieve precise ICH quantification in practice.

[Ki67 in the prognosis of prostate cancer].

OBJECTIVE: To investigate the influencing factors and the role of Ki67 in the prognosis of PCa. METHODS: We collected the clinical and follow-up data on 141 cases of PCa pathologically confirmed in the General Hospital of Ningxia Medical University from January 2008 to December 2014. We analyzed the factors possibly influencing the prognosis of PCa, including age, PSA level, Gleason score, clinical stage and Ki67, using the Kaplan-Meier method for univariate analysis, the Cox proportional hazards regression model for multivariate analysis and the multivariate Cox proportional hazards regression model for multiplicative interaction analysis. RESULTS: Kaplan-Meier univariate analysis indicated that the main factors influencing the prognosis of PCa included Ki67 (χ2 = 38.507, P < 0.01), clinical stage (χ2 = 59.486, P < 0.01) and Gleason score (χ2 = 9.062, P < 0.05); Cox multivariate analysis showed Ki67 (HR = 1.88, P < 0.01) and clinical stage (HR = 1.92, P < 0.01) to be the risk factors; and interaction analysis demonstrated that Ki67 was not correlated with either the clinical stage or Gleason score. CONCLUSIONS: Ki67 is a risk factor in the prognosis of PCa.