Search

"Low-lying" posterior communicating artery (PCoA) aneurysms require great attention in surgical clipping due to their distinct anatomical characteristics. In this study, we propose an easy method to immediately recognize "low-lying" PCoA aneurysms in neurosurgical practice. A total of 89 cases with "low-lying" PCoA aneurysms were retrospectively analyzed. All patients underwent preoperative digital subtraction angiography (DSA) examinations and microsurgical clipping. Cases were classified into the "low-lying" and regular groups based on intraoperative findings. The distance- and angle-relevant parameters that reflected the relative location of the aneurysms and tortuosity of the internal carotid artery were measured using 3D-DSA images. The data were sequentially integrated into a mathematical analysis to obtain the prediction model. Finally, we proposed a novel mathematical formula to preoperatively predict the existence of "low-lying" PCoA aneurysms with great accuracy. Neurosurgeons might benefit from this model, which enables them to directly identify "low-lying" PCoA aneurysms and make appropriate surgical decisions accordingly. [ABSTRACT FROM AUTHOR]

Morphological observation and analysis of cerebral microvascular network is an essential way to study cerebral function. Automated labeling of cerebral microvascular in microscopy images is one of the key steps for quantitative analysis of microvascular network in the specimens of brain mantle. It is presented in this work that an automated image processing approach based on curvilinear structure detector is applied to label and analyze the microvascular in the image. Steerable filter is also introduced to address the detecting confusion in branching regions. And then the vascular morphology analysis, such as average microvascular density, is also performed after image processing. Validation has demonstrated that the results from proposed approach are satisfied. The proposed method is finally applied in the study of cerebral microvascular dysfunction induced by γ-ray irradiation. Microsc. Res. Tech., 2011. © 2011 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Neurons that come to populate the six-layered cerebral cortex are born deep within the developing brain in the surface of the embryonic cerebral ventricles. It is very important to detect these neurons for studying histogenesis of the brain and abnormal migration that had been linked to cognitive deficits, mental retardation, and motor disorders. The visualization of labeled cells in brain sections was performed by immunocytochemical examination and its image data were documented to microscopic pictures. Based on the fact, automatic accurate neurons labeling is prerequisite instead of time-consuming manual labeling. In this article, a fully automated image processing approach is proposed to detect all the stained neurons in microscopic images. First of all, dark stained neurons are achieved by thresholding in blue channel of image. And then a modified fuzzy c-means clustering method, called alternative fuzzy c-means is applied to achieve higher classification accuracy in extracting constraint factor. Finally, watershed based on gradient vector flow is employed to the constraint factor image to segment all the neurons, including clustered neurons. The results demonstrate that the proposed method can be a useful tool in neuron image analysis. Microsc. Res. Tech. 2010. © 2009 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Background: Diabetes mellitus (DM) increases the risk of developing tuberculosis (TB), and optimal glycemic control has been shown to reduce the risk of complications and improve the TB treatment outcomes in patients with DM., Objective: This study aims to investigate the role of glycemic control in improving TB treatment outcomes among patients with DM., Methods: MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials databases were searched for randomized controlled trials (RCTs) assessing the impact of oral glycemic control in patients with TB who have DM. Outcomes of interest were radiological findings, treatment success, sputum positivity, and mortality. Evaluations were reported as risk ratios (RRs) with 95% CIs using weighted random-effects models., Results: The analysis included 6919 patients from 7 observational studies. Our meta-analysis showed significant differences between patients with optimal glycemic control and those with poor glycemic control with regard to improved treatment outcomes (RR 1.13, 95% CI 1.02-1.25; P=.02; I²=65%), reduced sputum positivity (RR 0.23, 95% CI 0.09-0.61; P=.003; I²=66%), and fewer cavitary lesions (RR 0.59, 95% CI 0.51-0.68; P<.001; I²=0%) in radiological findings. There was no significant difference between the 2 groups in terms of mortality (RR 0.57, 95% CI 0.22-1.49; P=.25; I²=0%), multilobar involvement (RR 0.57, 95% CI 0.22-1.49; P=.25; I²=0%) on radiologic examination, and upper lobe (RR 0.94, 95% CI 0.76-1.17; P=.58; I²=0%) and lower lobe (RR 1.05, 95% CI 0.48-2.30; P=.91; I²=75%) involvement on radiologic examination., Conclusions: We concluded that optimal glycemic control is crucial for reducing susceptibility, minimizing complications, and improving treatment outcomes in patients with TB with DM. Emphasizing effective health management and health care strategies are essential in achieving this control. Integrating comprehensive care among patients with TB with DM will enhance patient outcomes and alleviate the burden of disease in this population., Trial Registration: PROSPERO CRD42023427362; https://www.crd.york.ac.uk/prospero/display&#95;record.php?RecordID=427362., (©Li Zhao, Feng Gao, Chunlan Zheng, Xuezhi Sun. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 02.04.2024.)