Self-supervised Learning for Segmentation and Quantification of Dopamine Neurons in Parkinson's Disease

Parkinson's Disease (PD) is the second most common neurodegenerative disease in humans. PD is characterized by the gradual loss of dopaminergic neurons in the Substantia Nigra (SN). Counting the number of dopaminergic neurons in the SN is one of the most important indexes in evaluating drug efficacy in PD animal models. Currently, analyzing and quantifying dopaminergic neurons is conducted manually by experts through analysis of digital pathology images which is laborious, time-consuming, and highly subjective. As such, a reliable and unbiased automated system is demanded for the quantification of dopaminergic neurons in digital pathology images. Recent years have seen a surge in adopting deep learning solutions in medical image processing. However, developing high-performing deep learning models hinges on the availability of large-scale, high-quality annotated data, which can be expensive to acquire, especially in applications like digital pathology image analysis. To this end, we propose an end-to-end deep learning framework based on self-supervised learning for the segmentation and quantification of dopaminergic neurons in PD animal models. To the best of our knowledge, this is the first deep learning model that detects the cell body of dopaminergic neurons, counts the number of dopaminergic neurons, and provides characteristics of individual dopaminergic neurons as a numerical output. Extensive experiments demonstrate the effectiveness of our model in quantifying neurons with high precision, which can provide a faster turnaround for drug efficacy studies, better understanding of dopaminergic neuronal health status, and unbiased results in PD pre-clinical research. As part of our contributions, we also provide the first publicly available dataset of histology digital images along with expert annotations for the segmentation of TH-positive DA neuronal soma

Multiclass Semantic Segmentation to Identify Anatomical Sub-Regions of Brain and Measure Neuronal Health in Parkinson's Disease

Automated segmentation of anatomical sub-regions with high precision has become a necessity to enable the quantification and characterization of cells/ tissues in histology images. Currently, a machine learning model to analyze sub-anatomical regions of the brain to analyze 2D histological images is not available. The scientists rely on manually segmenting anatomical sub-regions of the brain which is extremely time-consuming and prone to labeler-dependent bias. One of the major challenges in accomplishing such a task is the lack of high-quality annotated images that can be used to train a generic artificial intelligence model. In this study, we employed a UNet-based architecture, compared model performance with various combinations of encoders, image sizes, and sample selection techniques. Additionally, to increase the sample set we resorted to data augmentation which provided data diversity and robust learning. In this study, we trained our best fit model on approximately one thousand annotated 2D brain images stained with Nissl/ Haematoxylin and Tyrosine Hydroxylase enzyme (TH, indicator of dopaminergic neuron viability). The dataset comprises of different animal studies enabling the model to be trained on different datasets. The model effectively is able to detect two sub-regions compacta (SNCD) and reticulata (SNr) in all the images. In spite of limited training data, our best model achieves a mean intersection over union (IOU) of 79% and a mean dice coefficient of 87%. In conclusion, the UNet-based model with EffiecientNet as an encoder outperforms all other encoders, resulting in a first of its kind robust model for multiclass segmentation of sub-brain regions in 2D images

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Endothelial Cell Morphological Response to Cyclic Strain -- Effect of Substratum Adhesiveness and Focal Adhesion Proteins

In response to mechanical stimuli, endothelial cells exhibit changes in morphology and cytoskeletal organization. When subjected to cyclic mechanical stretching, time-lapse imaging revealed that endothelial cells underwent significant shape changes with their resultant long axes aligned away from the strain direction. Although this type of response is not the same as motility, it could be governed by motility-related factors such as substratum adhesiveness and focal adhesion proteins. To examine this, human aortic endothelial cells were uniaxially, cyclically stretched on silicone rubber membranes coated with various concentrations of fibronectin, collagen type IV and laminin to produce differing amounts of adhesiveness. For each type of protein there was a parabolic dependence on initial adhesiveness with optimal cell orientation occurring at similar adhesive strengths. This suggests that, like motility, the extent of endothelial cell orientation in response to cyclic stretching is determined, in part, by the cell-substratum adhesiveness

TÁC ĐỘNG CỦA NGƯỜI CÓ UY TÍN TRONG CỘNG ĐỒNG ĐẾN TIẾP CẬN ĐẤT ĐAI VÀ THỰC HIỆN QUYỀN SỬ DỤNG ĐẤT CỦA ĐỒNG BÀO DÂN TỘC THIỂU SỐ H’RÊ TẠI HUYỆN AN LÃO, TỈNH BÌNH ĐỊNH

The study was conducted to set the basis for the Binh Dinh Provincial People's Committee and the Ethnic Minority Committee to issue policies and support programs to further promote the role of reputable people in ethnic minority communities. The research model and hypothesis were tested by using the Smart PLS 3 software with 118 samples. The results show that the H're ethnic minority people's access to Land and exercise of Land use rights are strongly influenced by the role of reputable people in the communities, evidenced from the level of significance of 1%. At the same time, the β coefficient of the path to assess the relationship from the role of reputable people in the community to accessing Land and exercising Land-use rights is up to 0.795. An estimated path coefficient close to +1 indicates strong positive relationships.Nghiên cứu được thực hiện để làm cơ sở cho UBND tỉnh Bình Định và Ủy Ban dân tộc ban hành các chính sách và các chương trình hỗ trợ nhằm phát huy hơn nữa vai trò người có uy tín trong cộng đồng đồng bào dân tộc thiểu số. Mô hình và giả thuyết nghiên cứu được kiểm định bằng phần mềm Smart PLS 3 với 118 mẫu khảo sát. Kết quả cho thấy việc tiếp cận đất đai và thực hiện quyền sử dụng đất của đồng bào dân tộc thiểu số H’rê chịu tác động rất mạnh từ vai trò của người có uy tín trong cộng đồng, thể hiện thông qua mức ý nghĩa mô hình nghiên cứu đạt được là 1%. Đồng thời, hệ số β đường dẫn để đánh giá mối quan hệ từ vai trò của người có uy tín trong cộng đồng đến tiếp cận đất đai và thực hiện quyền sử dụng đất lên đến 0,795. Hệ số đường dẫn ước lượng gần bằng +1, biểu thị các mối quan hệ cùng chiều dương mạnh mẽ

EVALUATION AND PREDICTION OF LAND USE, AND LAND COVER CHANGES USING REMOTE SENSING AND CA-ANN MODEL IN HUONG HOA DISTRICT, QUANG TRI PROVINCE

Evaluation of land use and land cover change (LULC) is necessary for densely vegetated areas like Huong Hoa district, Quang Trị province. It is a basis for sustainable development strategies. Therefore, the study aims to evaluate the LULC change in the 10-year period of 2013–2023 by using Landsat 8 satellite image data with the Maximum Likelihood Classification method and to predict future LULC changes. The LULC maps for 2013, 2018, and 2023 are accurate, with Kappa coefficients from 0.82 to 0.85. In the period of 2013–2023, the dense vegetation area tended to decrease by 1.4%. The decrease was mainly due to the transition to sparse vegetation cover. Bare land increased by 0.5%, and the built-up area decreased by 0.6%. Meanwhile, the water body changed slightly. The prediction of LULC change with the CA-ANN model in the QGIS MOLUSCE plugin is based on the history of LULC change and two spatial variables: DEM and distance to the road. The accuracy of the CA-ANN model is satisfactory, with an overall accuracy of 86% and a Kappa coefficient of 0.76. In the simulated LULC of 2033, dense vegetation is predicted to keep a higher decrease by 2% in the area in comparison with the LULC of 2023. Sparse vegetation steadily increased by 1.3% over the subsequent 10 years. Similarly, the built-up area, water body, and bare land extended slightly by 0.5, 0.1, and 0.1%, respectively. The CA-ANN model in the QGIS MOLUSCE plugin is suitable for the simulated LULC changes for the studied area