Photoacoustic (PA) technology has been used extensively on vessel imaging due to its capability of identifying molecular specificities and achieving high optical-diffraction-limited lateral resolution down to the cellular level. Vessel images carry essential medical information that provides guidelines for a professional diagnosis. Modern image processing techniques provide a decent contribution to vessel segmentation. However, these methods suffer from under or over-segmentation. Thus, we demonstrate both the results of adopting a fully convolutional network and U-net, and propose a hybrid network consisting of both applied on PA vessel images. Comparison results indicate that the hybrid network can significantly increase the segmentation accuracy and robustness.

Pathology interpretations of tissue rely on the gold standard of histology imaging, potentially hampering timely access to critical information for diagnosis and management of neoplasms because of tedious sample preparations. Slide-free capture of cell nuclei in unprocessed specimens without staining is preferable; however, inevitable irregular surfaces in fresh tissues results in limitations. An ultraviolet metasurface with the ability to generate an ultraviolet optical focus maintaining < 1.1-µm in lateral resolution and ∼290 µm in depth of field (DOF) is proposed for fast, high resolution, label-free photoacoustic histological imaging of unprocessed tissues with uneven surfaces. Microanatomical characteristics of the cell nuclei can be observed, as demonstrated by the mouse brain samples that were cut by hand and a ∼3 × 3-mm² field of view was imaged in ∼27 min. Therefore, ultraviolet metasurface-assisted photoacoustic microscopy is anticipated to benefit intraoperative pathological assessments and basic scientific research by alleviating laborious tissue preparations.

Photoacoustic microscopy (PAM) enables the measurement of properties associated with optical absorption within tissues and complements sophisticated technologies employing optical microscopy. An inadequate frequency response as determined by a piezoelectric ultrasonic transducer results, however, in poor depth resolution and inaccurate measurements of the coefficients of optical absorption. We developed a PAM system configured as an attenuated total reflectance sensor with a ten-layer graphene film sandwiched between a prism and water (the coupling medium) for photoacoustic (PA) wave detection. Transients of the PA pressure cause perturbations in the refractive index of the water thereby changing the polarization-dependent absorption of the graphene film. The signal in PA detection involves recording the difference in the temporal-varying reflectance intensity between the two orthogonally polarized probe beams. The graphene-based sensor has an estimated noise-equivalent-pressure sensitivity of ∼550 Pa over an approximately linear pressure response from 11.0 kPa to 55.0 kPa. Moreover, it enables a much broader PA bandwidth detection of up to ∼150 MHz, primarily dominated by a highly localized evanescent field. From the strong optical absorption of inherent hemoglobin, in vivo label-free PAM imaging provided a three-dimensional viewing of the microvasculature of a mouse ear. These results suggest great potential for graphene-based PAM in biomedical investigations, such as microcirculation studies.

Since the discovery of cell-free foetal DNA in the plasma of pregnant women, many non-invasive prenatal testing assays have been developed. In the area of skeletal dysplasia diagnosis, some PCR-based non-invasive prenatal testing assays have been developed to facilitate the ultrasound diagnosis of skeletal dysplasias that are caused by de novo mutations. However, skeletal dysplasias are a group of heterogeneous genetic diseases, the PCR-based method is hard to detect multiple gene or loci simultaneously, and the diagnosis rate is highly dependent on the accuracy of the ultrasound diagnosis. In this study, we investigated the feasibility of using targeted capture sequencing to detect foetal de novo pathogenic mutations responsible for skeletal dysplasia. Three families whose foetuses were affected by skeletal dysplasia and two control families whose foetuses were affected by other single gene diseases were included in this study. Sixteen genes related to some common lethal skeletal dysplasias were selected for analysis, and probes were designed to capture the coding regions of these genes. Targeted capture sequencing was performed on the maternal plasma DNA, the maternal genomic DNA, and the paternal genomic DNA. The de novo pathogenic variants in the plasma DNA data were identified using a bioinformatical process developed for low frequency mutation detection and a strict variant interpretation strategy. The causal variants could be specifically identified in the plasma, and the results were identical to those obtained by sequencing amniotic fluid samples. Furthermore, a mean of 97% foetal specific alleles, which are alleles that are not shared by maternal genomic DNA and amniotic fluid DNA, were identified successfully in plasma samples. Our study shows that capture sequencing of maternal plasma DNA can be used to non-invasive detection of de novo pathogenic variants. This method has the potential to be used to facilitate the prenatal diagnosis of skeletal dysplasia.