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Photodynamic therapy (PDT) shows promise in tumor treatment, particularly when combined with nanotechnology. This study examines the impact of deep learning, particularly the Cellpose algorithm, on the comprehension of cancer cell responses to PDT. The Cellpose algorithm enables robust morphological analysis of cancer cells, while logistic growth modelling predicts cellular behavior post-PDT. Rigorous model validation ensures the accuracy of the findings. Cellpose demonstrates significant morphological changes after PDT, affecting cellular proliferation and survival. The reliability of the findings is confirmed by model validation. This deep learning tool enhances our understanding of cancer cell dynamics after PDT. Advanced analytical techniques, such as morphological analysis and growth modeling, provide insights into the effects of PDT on hepatocellular carcinoma (HCC) cells, which could potentially improve cancer treatment efficacy. In summary, the research examines the role of deep learning in optimizing PDT parameters to personalize oncology treatment and improve efficacy. [ABSTRACT FROM AUTHOR]

Background: Background and objective Follow up by computed tomography (CT) and growth evaluation are routine methods for the differential diagnosis of indeterminate pulmonary nodules in clinical practice. Pulmonary nodules with diverse biological behaviors may show different growth patterns and velocities. The aim of this study is to identify the volume growth curve of both benign and malignant pulmonary nodules. This work also intends to determine these nodules' growth patterns and provide evidence for the establishment of a follow-up strategy., Methods: The CT data of 111 pulmonary nodules (54 solid, 57 subsolid) were retrospectively evaluated using 3D volumetric software. All of these nodules have been followed up at least twice. Of these nodules, 35 were confirmed as lung cancers, whereas 5 were confirmed as benign by pathology or histology. Moreover, 71 nodules showed no growth in more than 2 years. Stable nodules were defined as low-risk nodules, as confirmed by reevaluation from experts. On the basis of their densities and diameters, the nodules were classified into four types: benign/low-risk solid nodules, malignant solid nodules (diameter ≤1 cm and >1 cm), benign/low-risk subsolid nodules, and malignant subsolid nodules (diameter ≤1 cm and >1 cm). The follow-up interval time (d) were plotted on the x-axis, and the nodules' volume (mm3) and logarithmic volume were plotted on the y-axis. Two radiologists subjectively determined the type of growth curve. Chi-square test was performed to compare the growth curves of benign/low-risk and malignant nodules., Results: Of 18 solid cancers, 12 cases (66%) were found with steep ascendant growth curves. Those of 3 cases (16.7%) were flat ascendant, 2 cases (11.1%) slowly ascendant, and 1 (5.56%) case flat. Of 17 subsolid cancers, 8 cases (47.1%) manifested steep ascendant growth curves. Those of 4 cases (23.5%) were slowly ascendant, 3 (17.6%) flat, and 2 (11.8%) descendant-ascendant. Of 36 benign/low-risk solid nodules, 5 cases (13.9%) manifested descendant growth curves, 17 cases (47.2%) flat, 8 cases (21.6%) slowly ascendant, and 6 cases (16.7%) undulate. Of 40 benign/low-risk subsolid nodules, 4 cases (10%) manifested descendant growth curves, 21 cases (52.5%) flat, 9 cases (22.5%) slowly ascendant, and 6 cases (15%) undulate. The distribution of growth curve types significantly differed between benign/low-risk and malignant nodules (χ2=42.4, P<0.01)., Conclusions: The growth curves of lung cancers are heterogeneous. A steep ascendant curve is the main type for lung cancer, with the exception of flat, slowly ascendant, or even descendant curve. A slowly ascendant curve cannot exclude the diagnosis of lung cancer, especially for subsolid nodules.
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Background: The management of pulmonary nodules relies on cancer risk assessment, in which the only widely accepted criterion is diameter. The development of volumetric computed tomography (CT) and three-dimensional (3D) software enhances the clarity in displaying the nodules' characteristics. This study evaluated the values of the nodules' volume and 3D morphological characteristics (edge, shape and location) in cancer risk assessment., Methods: The CT data of 200 pulmonary nodules were retrospectively evaluated using 3D volumetric software. The malignancy or benignity of all the nodules was confirmed by pathology, histology or follow up (>2 years). Logistic regression analysis was performed to calculate the odds ratios (ORs) of the 3D margin (smooth, lobulated or spiculated/irregular), shape (spherical or non-spherical), location (purely intraparenchymal, juxtavascular or pleural-attached), and nodule volume in cancer risk assessment for total and sub-centimeter nodules. The receiver operating characteristic (ROC) curve was employed to determine the optimal threshold for the nodule volume., Results: Out of 200 pulmonary nodules, 78 were malignant, whereas 122 were benign. The Logistic regression analysis showed that the volume (OR=3.3; P<0.001) and the 3D margin (OR=13.4, 9.8; both P=0.001) were independent predictive factors of malignancy, whereas the location and 3D shape exhibited no total predictive value (P>0.05). ROC analysis showed that the optimal threshold for malignancy was 666 mm³. For sub-centimeter nodules, the 3D margin was the only valuable predictive factor of malignancy (OR=60.5, 75.0; P=0.003, 0.007)., Conclusions: The volume and 3D margin are important factors considered to assess the cancer risk of pulmonary nodules. Volumes larger than 666 mm³ can be determined as high risk for pulmonary nodules; by contrast, nodules with lobulated, spiculated, or irregular margin present a high malignancy probability.

Oligosaccharides play crucial roles in many biorecognition processes by the so-called "cluster glycosidic effect". We here report a facile synthesis of lactose-CdSeS/ZnS quantum dot conjugate (Lac-QDs) by use of 1-thiol-beta-D-lactose via ligand exchange, which exhibits significantly high affinity and specificity to leukocytes in contrast to the monovalent lactose. Structural analyses indicate that there are about 132 lactosyl molecules assembled on single QDs and the hydrodynamic diameter is small, close to 8.2 nm. Further, Lac-QDs display good fluorescence and physicochemical stability in physiological conditions, as well as extremely low cytotoxicity. These properties facilitate the use of Lac-QDs in fluorescent labeling of live leukocytes.