In this study, near-infrared (NIR) technology was utilized to develop a low-cost real-time near infrared (NIR) guiding device for cannulation. A portable device that can be used by medical practitioners and also by students for their skills development training in performing cannulation. Methods. First, is the development of a reflectance type optical vein finder using three (3) light emitting diode (LED) lights with 960 nm wavelength, complementary metal-oxide-semiconductor-infrared (CMOS-IR) sensor camera with 1920 × 1080 UXGA (1080P), IR filter set for the given wavelength, and an open-source image processing software. Second, is the actual in-vitro human testing in two sites: the arm and dorsal hand of 242 subjects. The following parameters were included, such as gender, age, mass index (BMI), and skin tone. In order to maximize the assessment process towards the device, the researchers included the arm circumference. This augmented subcutaneous vein imaging study using the develop vein finder device compared the difference in the captured vein images through visual and digital imaging approaches. The human testing was performed in accordance with the ethical standards of the Trinity University of Asia—Institutional Ethics Review Committee (TUA—IERC). Results. The NIR imaging system of the developed vein finder in this study showed its capability as an efficient guiding device through real-time vein pattern recognition, for both sites. Improved captured vein images were observed, having 100% visibility of vein patterns on the dorsal hand site. Fourteen (5.79%) out of 242 subjects reported non-visible peripheral subcutaneous veins in the arm sites. Conclusions. The developed vein finder device with the NIR technology and reflected light principle with low-energy consumption was efficient for real-time peripheral subcutaneous vein imaging without the application of a tourniquet. This might be utilized as a guiding device in locating the vein for the purpose of cannulation, at a very low cost as compared to the commercially available vein finders. Moreover, it may be used as an instructional device for student training in performing cannulation.

Ce/Cr codoped TiO₂ nanoparticles were synthesized using sol–gel and Pechini methods with heat treatment at 400 °C for 4 h. A conventional sol–gel process produced well-crystallized anatase, while Pechini synthesis yielded less-ordered mixed-phase anatase + rutile; this suggests that the latter method enhances Ce solubility and increases chemical homogeneity but destabilizes the TiO₂ lattice. Greater structural disruption from the decomposition of the Pechini precursor formed more open agglomerated morphologies, while the lower levels of structural disruption from pyrolysis of the dried sol–gel precursor resulted in denser agglomerates of lower surface areas. Codoping and associated destabilization of the lattice reduced the binding energies in both powders. Cr⁴⁺ formation in sol–gel powders and Cr⁶⁺ formation in Pechini powders suggest that these valence changes derive from synergistic electron exchange from intervalence and/or multivalence charge transfer. Since Ce is too large to allow either substitutional or interstitial solid solubility, the concept of integrated solubility is introduced, in which the Ti site and an adjacent interstice are occupied by the large Ce ion. The photocatalytic performance data show that codoping was detrimental owing to the effects of reduced crystallinity from lattice destabilization and surface area. Two regimes of mechanistic behavior are seen, which are attributed to the unsaturated solid solutions at lower codopant levels and supersaturated solid solutions at higher levels. The present work demonstrates that the Pechini method offers a processing technique that is superior to sol–gel because the former facilitates solid solubility and consequent chemical homogeneity.

Cancer is one of the common diseases. Quantitative biomarkers extracted from standard-of-care computed tomography (CT) scan can create a robust clinical decision tool for the diagnosis of hepatocellular carcinoma (HCC). According to the current clinical methods, the situation usually accounts for high expenditure of time and resources. To improve the current clinical diagnosis and therapeutic procedure, this paper proposes a deep learning-based approach, called Successive Encoder-Decoder (SED), to assist in the automatic interpretation of liver lesion/tumor segmentation through CT images. The SED framework consists of two different encoder-decoder networks connected in series. The first network aims to remove unwanted voxels and organs and to extract liver locations from CT images. The second network uses the results of the first network to further segment the lesions. For practical purpose, the predicted lesions on individual CTs were extracted and reconstructed on 3D images. The experiments conducted on 4300 CT images and LiTS dataset demonstrate that the liver segmentation and the tumor prediction achieved 0.92 and 0.75 in Dice score, respectively, by as-proposed SED method.