Raman Characterizations of Red Blood Cells With β-Thalassemia Using Laser Tweezers Raman Spectroscopy

This study aimed to study the differences in Raman spectra of red blood cells (RBCs) among patients with β-thalassemia and controls using laser tweezers Raman spectroscopy (LTRS) system.A total of 33 patients with β-thalassemia major, 49 with β-thalassemia minor, and 65 controls were studied. Raman spectra of RBCs for each sample were recorded. Principal component analysis (PCA), one-way analysis of variance (ANOVA), and independent-sample t test were performed.The intensities of Raman spectra of β-thalassemia (major and minor) RBCs were lower than those of controls, especially at bands 1546, 1603, and 1619 cm. The intensity ratio of band 1546 cm to band 1448 cm demonstrated that there was a significant difference between the spectra of β-thalassemia major (mostly below 2.15) and those of controls. The spectra of controls could be well distinguished from those of β-thalassemia major using PCA. After normalization, the spectra of two different genotypes with β/β mutations mainly overlapped, while those with β/β mutations had lower intensity at bands 1546, 1603, and 1619 cm.The present study provided Raman characteristics of RBCs in patients with β-thalassemia major and supported the use of LTRS as a method for screening β-thalassemia major. The recognition rate for β-thalassemia minor needs to be further improved

Web3D learning framework for 3D shape retrieval based on hybrid convolutional neural networks

With the rapid development of Web3D technologies, sketch-based model retrieval has become an increasingly important challenge, while the application of Virtual Reality and 3D technologies has made shape retrieval of furniture over a web browser feasible. In this paper, we propose a learning framework for shape retrieval based on two Siamese VGG-16 Convolutional Neural Networks (CNNs), and a CNN-based hybrid learning algorithm to select the best view for a shape. In this algorithm, the AlexNet and VGG-16 CNN architectures are used to perform classification tasks and to extract features, respectively. In addition, a feature fusion method is used to measure the similarity relation of the output features from the two Siamese networks. The proposed framework can provide new alternatives for furniture retrieval in the Web3D environment. The primary innovation is in the employment of deep learning methods to solve the challenge of obtaining the best view of 3D furniture, and to address cross-domain feature learning problems. We conduct an experiment to verify the feasibility of the framework and the results show our approach to be superior in comparison to many mainstream state-of-the-art approaches

Bile dynamics within the biliary tract and microfluidic-based bile component detection: A review

Bilestones are solid masses found in the gallbladder or biliary tract, which block the normal bile flow and eventually result in severe life-threatening complications. Studies have shown that bilestone formation may be related to bile flow dynamics and the concentration level of bile components. The bile flow dynamics in the biliary tract play a critical role in disclosing the mechanism of bile stasis and transportation. The concentration of bile composition is closely associated with processes such as nucleation and crystallization. Recently, microfluidic-based biosensors have been favored for multiple advantages over traditional bench-top detection assays for their less sample consumption, portability, low cost, and high sensitivity for real-time detection. Here, we reviewed the developments in bile dynamics study and microfluidics-based bile component detection methods. These studies may provide valuable insights into the bilestone formation mechanisms and better treatment, alongside our opinions on the future development of in vitro lithotriptic drug screening of bilestones and bile characterization tests

Human papillomavirus and cervical cancer in the microbial world: exploring the vaginal microecology

The vaginal microbiota plays a crucial role in female reproductive health and is considered a biomarker for predicting disease outcomes and personalized testing. However, its relationship with human papillomavirus (HPV) infection and cervical cancer is not yet clear. Therefore, this article provides a review of the association between the vaginal microbiota, HPV infection, and cervical cancer. We discuss the composition of the vaginal microbiota, its dysbiosis, and its relationship with HPV infection, as well as potential mechanisms in the development of cervical cancer. In addition, we assess the feasibility of treatment strategies such as probiotics and vaginal microbiota transplantation to modulate the vaginal microbiota for the prevention and treatment of diseases related to HPV infection and cervical cancer. In the future, extensive replication studies are still needed to gain a deeper understanding of the complex relationship between the vaginal microbiota, HPV infection, and cervical cancer, and to clarify the role of the vaginal microbiota as a potential biomarker for predicting disease outcomes, thus providing a theoretical basis for personalized testing