This study investigated the feasibility of using fluorescence hyperspectral imaging technology to diagnose of early‐stage gastric cancer. Fluorescence spectral images of 76 patients who were pathologically diagnosed as non‐atrophic gastritis, premalignant lesions and gastric cancer were collected. Fluorescence spectra at 100‐pixel points were randomly extracted after binarization. Diagnostic models of non‐atrophic gastritis, premalignant lesions and gastric cancer were constructed through partial‐least‐square discriminant analysis (PLS‐DA) and support vector machine (SVM) algorithms. The prediction effects of PLS‐DA and SVM models were compared. Results showed that the average spectra of normal, precancerous and gastric cancer tissues significantly differed at 496, 546, 640 and 670 nm, and regular changes in fluorescence intensity at 546 nm were in the following order: normal > precancerous lesions > gastric cancer. Additionally, the effect of the diagnostic model established by SVM is significantly better than PLS‐DA which accuracy, specificity and sensitivity are above 94%. Experimental results revealed that the fast diagnostic model of early gastric cancer by combining fluorescence hyperspectral imaging technology and improved SVM was effective and feasible, thereby providing an accurate and rapid method for diagnosing early‐stage gastric cancer.
We construct a microscopic hyperspectral imaging system to distinguish between normal and cancerous gastric cells. We study common transmission-spectra features that only emerge when the samples are dyed with hematoxylin and eosin (H&E) stain. Subsequently, we classify the obtained visible-range transmission spectra of the samples into three zones. Distinct features are observed in the spectral responses between the normal and cancerous cell nuclei in each zone, which depend on the pH level of the cell nucleus. Cancerous gastric cells are precisely identified according to these features. The average cancer-cell identification accuracy obtained with a backpropagation algorithm program trained with these features is 95%.
Osteoporosis is a skeletal system disease characterized by low bone mass and altered bone microarchitecture, with an increased risk of fractures. Classical theories hold that osteoporosis is essentially a bone remodeling disorder caused by estrogen deficiency/aging (primary osteoporosis) or secondary to diseases/drugs (secondary osteoporosis). However, with the in-depth understanding of the intricate nexus between both bone and the immune system in recent decades, the novel field of “Immunoporosis” was proposed by Srivastava et al. (2018, 2022), which delineated and characterized the growing importance of immune cells in osteoporosis. This review aimed to summarize the response of the immune system (immune cells and inflammatory factors) in different types of osteoporosis. In postmenopausal osteoporosis, estrogen deficiency-mediated alteration of immune cells stimulates the activation of osteoclasts in varying degrees. In senile osteoporosis, aging contributes to continuous activation of the immune system at a low level which breaks immune balance, ultimately resulting in bone loss. Further in diabetic osteoporosis, insulin deficiency or resistance-induced hyperglycemia could lead to abnormal regulation of the immune cells, with excessive production of proinflammatory factors, resulting in osteoporosis. Thus, we reviewed the pathophysiology of osteoporosis from a novel insight-immunoporosis, which is expected to provide a specific therapeutic target for different types of osteoporosis.
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