Laboratory Methodology Important in the Diagnosis and Prognosis of Antiphospholipid Syndrome

Antiphospholipid syndrome (APS) is an autoimmune disease, characterized by thrombosis and pregnancy complications with persistently elevated levels of antiphospholipid antibodies (aPL). Recently, a unique mathematical calculation has been presented to assess the risk of thrombosis in patients with APS called antiphospholipid score or global antiphospholipid syndrome score (GAPSS). This new approach in the diagnosis of APS leads to the assessment of the risk of thrombosis considering the results of different aPL (lupus anticoagulants (LA), anticardiolipin antibodies (aCL), antibodies against β2GPI (anti-β2GPI), and phosphatidylserine-dependent antiprothrombin antibodies (aPS/PT) (isotypes IgG and IgM). This chapter provides an overview of the algorithm strategy for APS diagnosis with the aims of characterizing in detail the laboratory methodology of criteria aPL (LA, aCL, and anti-β2GPI) and noncriteria aPL, such as IgA aCL and IgA anti-β2GPI, anti-domain I β2GPI, and antiprothrombin antibodies. In order to improve APS diagnosis, several new approaches in aPL detection have recently been suggested, such as multiline immunodot assay, detection of aPL by flow cytometry using beads with particular surface properties, and the newly developed automated BioPlex system technology for parallel detection of aCL and anti-β2GPI antibodies of IgG, IgA, and IgM isotypes. A completely different and promising approach in future research lies in the potential of microRNAs as biomarkers for risk of thrombosis and/or obstetric complication

Power control during remote laser welding using a convolutional neural network

The increase in complex workpieces with changing geometries demands advanced control algorithms in order to achieve stable welding regimes. Usually, many experiments are required to identify and confirm the correct welding parameters. We present a method for controlling laser power in a remote laser welding system with a convolutional neural network (CNN) via a PID controller, based on optical triangulation feedback. AISI 304 metal sheets with a cumulative thickness of 1.5 mm were used. A total accuracy of 94% was achieved for CNN models on the test datasets. The rise time of the controller to achieve full penetration was less than 1.0 s from the start of welding. The Gradient-weighted Class Activation Mapping (Grad-CAM) method was used to further understand the decision making of the model. It was determined that the CNN focuses mainly on the area of the interaction zone and can act accordingly if this interaction zone changes in size. Based on additional testing, we proposed improvements to increase overall controller performance and response time by implementing a feed-forward approach at the beginning of welding

Estimating quantitative physiological and morphological tissue parameters of murine tumor models using hyperspectral imaging and optical profilometry

Understanding tumors and their micro-environment are essential for successfuland accurate disease diagnosis. Tissuephysiology and morphology are altered intumors compared to healthy tissues, andthere is a need to monitor tumors and their surrounding tissues, includingblood vessels, non-invasively. This preliminary study utilizes a multimodaloptical imaging system combining hyperspectral imaging (HSI) and three-dimensional (3D) optical profilometry (OP) to capture hyperspectral imagesand surface shapes of subcutaneously grown murine tumor models. Hyper-spectral images are corrected with 3D OP data and analyzed using the inverse-adding doubling (IAD) method to extract tissue properties such as melaninvolume fraction and oxygenation. Blood vessels are segmented using theB-COSFIRE algorithm from oxygenation maps. From 3D OP data, tumor vol-umes are calculated and compared to manual measurements using a verniercaliper. Results show that tumors can be distinguished from healthy tissuebased on most extracted tissue parameters (p<0:05). Furthermore, blood oxy-genation is 50% higher within the blood vessels than in the surrounding tissue,and tumor volumes calculated using 3D OP agree within 26% with manualmeasurements using a vernier caliper. Results suggest that combining HSI andOP could provide relevant quantitative information about tumors and improvethe disease diagnosis

Hyperspectral imaging of 4T1 mammary carcinomas grown in dorsal skinfold window chambers: spectral renormalization and fluorescence modeling

Significance: Hyperspectral imaging (HSI) of murine tumor models grown in dorsal skinfold window chambers (DSWCs) offers invaluable insight into the tumor microenvironment. However, light loss in a glass coverslip is often overlooked, and particular tissue characteristics are improperly modeled, leading to errors in tissue properties extracted from hyperspectral images. Aim: We highlight the significance of spectral renormalization in HSI of DSWC models and demonstrate the benefit of incorporating enhanced green fluorescent protein (EGFP) excitation and emission in the skin tissue model for tumors expressing genes to produce EGFP. Approach: We employed an HSI system for intravital imaging of mice with 4T1 mammary carcinoma in a DSWC over 14 days. We performed spectral renormalization of hyperspectral images based on the measured reflectance spectra of glass coverslips and utilized an inverse adding–doubling (IAD) algorithm with a two-layer murine skin model, to extract tissue parameters, such as total hemoglobin concentration and tissue oxygenation (StO$_2$). The model was upgraded to consider EGFP fuorescence excitation and emission. Moreover, we conducted additional experiments involving tissue phantoms, human forearm skin imaging, and numerical simulations. Results: Hyperspectral image renormalization and the addition of EGFP fluorescence in the murine skin model reduced the mean absolute percentage errors (MAPEs) of fitted and measured spectra by up to 10% in tissue phantoms, 0.55% to 1.5% in the human forearm experiment and numerical simulations, and up to 0.7% in 4T1 tumors. Similarly, the MAPEs for tissue parameters extracted by IAD were reduced by up to 3% in human forearms and numerical simulations. For some parameters, statistically significant differences (p < 0.05) were observed in 4T1 tumors. Ultimately, we have shown that fluorescence emission could be helpful for 4T1 tumor segmentation. Conclusions: The results contribute to improving intravital monitoring of DWSC models using HSI and pave the way for more accurate and precise quantitative imaging

Estimating quantitative physiological and morphological tissue parameters of murine tumor models using hyperspectral imaging and optical profilometry

Understanding tumors and their microenvironment are essential for successful and accurate disease diagnosis. Tissue physiology and morphology are altered in tumors compared to healthy tissues, and there is a need to monitor tumors and their surrounding tissues, including blood vessels, non-invasively. This preliminary study utilizes a multimodal optical imaging system combining hyperspectral imaging (HSI) and threedimensional (3D) optical profilometry (OP) to capture hyperspectral images and surface shapes of subcutaneously grown murine tumor models. Hyperspectral images are corrected with 3D OP data and analyzed using the inverseadding doubling (IAD) method to extract tissue properties such as melanin volume fraction and oxygenation. Blood vessels are segmented using the B-COSFIRE algorithm from oxygenation maps. From 3D OP data, tumor volumes are calculated and compared to manual measurements using a vernier caliper. Results show that tumors can be distinguished from healthy tissue based on most extracted tissue parameters (p< 0.05). Furthermore, blood oxygenation is 50% higher within the blood vessels than in the surrounding tissue, and tumor volumes calculated using 3D OP agree within 26% with manual measurements using a vernier caliper. Results suggest that combining HSI and OP could provide relevant quantitative information about tumors and improve the disease diagnosis