To examine the use of acetone- or ethanol-fixed frozen tissue sections as the "gold standard" for immunohistochemical analysis, we evaluated frozen sections with various conditions of fixation and antigen retrieval (AR). Fresh human tissues were frozen in OCT. An adjacent tissue block was fixed in 10% neutral buffered formalin (NBF) and paraffin embedded (FFPE). Frozen sections were fixed by 6 protocols: acetone, ethanol, NBF (2 durations), and NBF + calcium chloride (2 durations). AR was used for all NBF-fixed sections. More than half of the antibodies (16/26 [62%]) showed immunohistochemical results indistinguishable between acetone- and NBF-fixed sections; 8 (31%) showed better immunohistochemical signals following NBF and AR; 2 gave better immunohistochemical results for acetone-fixed sections. Most cytoplasmic proteins (10/13) showed comparable immunohistochemical signals between acetone- and NBF-fixed sections. For nuclear proteins, NBF-fixed sections gave better immunohistochemical signals than did acetone-fixed sections. In most cases, NBF yielded stronger signals with less background and better morphology. The data do not support the use of acetone-fixed frozen tissue sections as the gold standard for immunohistochemical analysis. In evaluating new antibodies, a combination of acetone- and NBF-fixed frozen sections should be used, although in practice, FFPE tissue sections may serve as the standard for most antigens for immunohistochemical analysis.

Real-world stressors are complex and multimodal, involving physical, psychological, and social dimensions. However, the brain networks that mediate stress responses to these stimuli need to be further studied. We used c-Fos mapping in mice to characterize brain circuits activated by exposure to a single episode of multimodal stress (MMS), and compared these to circuits activated by electric foot shocks (EFS). We focused on characterizing c-Fos activity in stress-relevant brain regions including the paraventricular nucleus (PVN) of the hypothalamus and the bed nucleus of the stria terminalis (BNST). We also assessed stress-induced activation of CRH-positive neurons in each of these structures. MMS and EFS activated an overlapping network of brain regions with a similar time course. c-Fos expression within the PVN and the BNST peaked 30–60 min after exposure to both MMS and EFS, and returned to baseline levels within 24 h. Quantification of c-Fos expression within BNST subregions revealed that while c-Fos expression peaked in all subregions 30–60 min after MMS and EFS exposure, the neuronal density of c-Fos expression was significantly higher in the dorsomedial and ventral BNST relative to the dorsolateral BNST. Our preliminary assessment indicated that a great majority of MMS or EFS-activated neurons in the PVN were CRH-positive (>87%); in contrast, about 6–35% of activated neurons in the BNST were CRH-positive. Our findings indicate that both MMS and EFS are effective at activating stress-relevant brain areas and support the use of MMS as an effective approach for studying multidimensional stress in animal models. The results also reveal that the PVN and BNST are part of a common neural circuit substrate involved in neural processing related to stress.

No abstract

Malaria is an infectious disease caused by Plasmodium parasites, transmitted through mosquito bites. Symptoms include fever, headache, and vomiting, and in severe cases, seizures and coma. The World Health Organization reports that there were 228 million cases and 405,000 deaths in 2018, with Africa representing 93% of total cases and 94% of total deaths. Rapid diagnosis and subsequent treatment are the most effective means to mitigate the progression into serious symptoms. However, many fatal cases have been attributed to poor access to healthcare resources for malaria screenings. In these low-resource settings, the use of light microscopy on a thin blood smear with Giemsa stain is used to examine the severity of infection, requiring tedious and manual counting by a trained technician. To address the malaria endemic in Africa and its coexisting socioeconomic constraints, we propose an automated, mobile phone-based screening process that takes advantage of already existing resources. Through the use of convolutional neural networks (CNNs), we utilize a SSD multibox object detection architecture that rapidly processes thin blood smears acquired via light microscopy to isolate images of individual red blood cells with 90.4% average precision. Then we implement a FSRCNN model that upscales 32 × 32 low-resolution images to 128 × 128 high-resolution images with a PSNR of 30.2, compared to a baseline PSNR of 24.2 through traditional bicubic interpolation. Lastly, we utilize a modified VGG16 CNN that classifies red blood cells as either infected or uninfected with an accuracy of 96.5% in a balanced class dataset. These sequential models create a streamlined screening platform, giving the healthcare provider the number of malaria-infected red blood cells in a given sample. Our deep learning platform is efficient enough to operate exclusively on low-tier smartphone hardware, eliminating the need for high-speed internet connection.

Objective We reduced the CT-reconstructed field of view (FOV), increasing pixel density across airway structures and reducing partial volume effects, to determine whether this would improve accuracy of airway wall thickness quantification. Methods We performed CT imaging on a lung phantom and 29 subjects. Images were reconstructed at 30, 15, and 10 cm FOV using a medium-smooth kernel. Cross-sectional airway dimensions were compared at each FOV with repeated-measures analysis of variance. Results Phantom measurements were more accurate when FOV decreased from 30 to 15 cm (p<0.05). Decreasing FOV further to 10 cm did not significantly improve accuracy. Human airway measurements similarly decreased by decreasing FOV (p<0.001). Percent changes in all measurements when reducing FOV from 30 to 15 cm were less than 3%. Conclusions Airway measurements at 30 cm FOV are near the limits of CT resolution using a medium-smooth kernel. Reducing reconstructed FOV would minimally increase sensitivity to detect differences in airway dimensions.