Semi-Supervised Medical Image Segmentation Guided by Bi-Directional Constrained Dual-Task Consistency.

BACKGROUND: Medical image processing tasks represented by multi-object segmentation are of great significance for surgical planning, robot-assisted surgery, and surgical safety. However, the exceptionally low contrast among tissues and limited available annotated data makes developing an automatic segmentation algorithm for pelvic CT challenging. METHODS: A bi-direction constrained dual-task consistency model named PICT is proposed to improve segmentation quality by leveraging free unlabeled data. First, to learn more unmarked data features, it encourages the model prediction of the interpolated image to be consistent with the interpolation of the model prediction at the pixel, model, and data levels. Moreover, to constrain the error prediction of interpolation interference, PICT designs an auxiliary pseudo-supervision task that focuses on the underlying information of non-interpolation data. Finally, an effective loss algorithm for both consistency tasks is designed to ensure the complementary manner and produce more reliable predictions. RESULTS: Quantitative experiments show that the proposed PICT achieves 87.18%, 96.42%, and 79.41% mean DSC score on ACDC, CTPelvic1k, and the individual Multi-tissue Pelvis dataset with gains of around 0.8%, 0.5%, and 1% compared to the state-of-the-art semi-supervised method. Compared to the baseline supervised method, the PICT brings over 3-9% improvements. CONCLUSIONS: The developed PICT model can effectively leverage unlabeled data to improve segmentation quality of low contrast medical images. The segmentation result could improve the precision of surgical path planning and provide input for robot-assisted surgery.

Autonomous path planning for robot-assisted pelvic fracture closed reduction with collision avoidance.

BACKGROUND: Robot-assisted pelvic fracture closed reduction (RPFCR) positively contributes to patient treatment. However, the current path planning suffers from incomplete obstacle avoidance and long paths. METHOD: A collision detection method is proposed for applications in the pelvic environment to improve the safety of RPFCR surgery. Meanwhile, a defined orientation planning strategy (OPS) and linear sampling search (LSS) are coupled into the A* algorithm to optimise the reduction path. Subsequently, pelvic in vitro experimental platform is built to verify the augmented A*algorithm's feasibility. RESULTS: The augmented A* algorithm planned the shortest path for the same fracture model, and the paths planned by the A* algorithm and experience-based increased by 56.12% and 89.02%, respectively. CONCLUSIONS: The augmented A* algorithm effectively improves surgical safety and shortens the path length, which can be adopted as an effective model for developing RPFCR path planning.

TMEM16A, a Homoharringtonine Receptor, as a Potential Endogenic Target for Lung Cancer Treatment.

Lung cancer has the highest rate of incidence and mortality among all cancers. Most chemotherapeutic drugs used to treat lung cancer cause serious side effects and are susceptible to drug resistance. Therefore, exploring novel therapeutic targets for lung cancer is important. In this study, we evaluated the potential of TMEM16A as a drug target for lung cancer. Homoharringtonine (HHT) was identified as a novel natural product inhibitor of TMEM16A. Patch-clamp experiments showed that HHT inhibited TMEM16A activity in a concentration-dependent manner. HHT significantly inhibited the proliferation and migration of lung cancer cells with high TMEM16A expression but did not affect the growth of normal lung cells in the absence of TMEM16A expression. In vivo experiments showed that HHT inhibited the growth of lung tumors in mice and did not reduce their body weight. Finally, the molecular mechanism through which HHT inhibits lung cancer was explored by western blotting. The findings showed that HHT has the potential to regulate TMEM16A activity both in vitro and in vivo and could be a new lead compound for the development of anti-lung-cancer drugs.

Extensive serum biomarker analysis in patients with non-small-cell lung carcinoma.

Lung cancer is a common malignant disease, nearly 2.09 million new patients occurred last year. Approximately 85% of the patients are classified as non-small-cell lung cancer (NSCLC). It is therefore important to identify new diagnostic and prognostic biomarkers for the early detection of this disease. The presented study identifies biomarkers in the serum of NSCLC patients. The expression of 274 cytokines was measured by a novel antibody array methodology and ELISA was applied to validate the array results. The levels of MIP-1 α, IL-8, MIP-1 ß, Resistin, GDF-15, HGF, CA125, FLRG, VCAM-1, DKK-3, sTNF-R1, CTACK, Acrp30, CXCL-16 and LYVE-1 were significantly higher in serum from NSCLC patients, while the level of TIMP-2 and IGFBP-6 were lower. More importantly, the validation supported the result of the antibody array. The result of the antibody array indicates that these cytokines might be novel auxiliary biomarkers in the diagnosis and prognosis of NSCLC.

Functionally distinct and selectively phosphorylated GPCR subpopulations co-exist in a single cell.

G protein-coupled receptors (GPCRs) transduce pleiotropic intracellular signals in a broad range of physiological responses and disease states. Activated GPCRs can undergo agonist-induced phosphorylation by G protein receptor kinases (GRKs) and second messenger-dependent protein kinases such as protein kinase A (PKA). Here, we characterize spatially segregated subpopulations of ß2-adrenergic receptor (ß2AR) undergoing selective phosphorylation by GRKs or PKA in a single cell. GRKs primarily label monomeric ß2ARs that undergo endocytosis, whereas PKA modifies dimeric ß2ARs that remain at the cell surface. In hippocampal neurons, PKA-phosphorylated ß2ARs are enriched in dendrites, whereas GRK-phosphorylated ß2ARs accumulate in soma, being excluded from dendrites in a neuron maturation-dependent manner. Moreover, we show that PKA-phosphorylated ß2ARs are necessary to augment the activity of L-type calcium channel. Collectively, these findings provide evidence that functionally distinct subpopulations of this prototypical GPCR exist in a single cell.

Modification of cut-off values for HE4, CA125 and the ROMA algorithm for early-stage epithelial ovarian cancer detection: Results from 1021 cases in South China.

OBJECTIVES: The purpose of this study was to evaluate the performance of human epididymis protein 4 (HE4) and the Risk of Ovarian Malignancy Algorithm (ROMA) in early stage epithelial ovarian cancer (EOC) detection in patients in southern China. Additionally, this study proposes a possible ideal cut-off value for each marker to its own population in South China. DESIGN AND METHODS: Serum HE4 and CA125 were measured in 756 patients with pelvic masses (275 malignancies, 53 borderline tumors and 428 benign diseases), and their ROMA values were calculated. Areas under the receiver operator characteristic (ROC) curves (AUC) were assessed for HE4, CA125, ROMA and combinations of these biomarkers. RESULTS: Both HE4 and ROMA performed better diagnostically than CA125 alone for early stage EOC, with AUCs ranging from 0.714 for HE4, 0.699 for ROMA, and 0.463 for CA125 in premenopausal subjects, and 0.902 for ROMA, 0.880 for HE4, and 0.256 for CA125 in postmenopausal subjects. CONCLUSIONS: HE4 and ROMA alone were found to be better than CA125 for detecting borderline tumors and early-stage EOC. The optimal cut-off values (HE4: 70pmol/l for all; CA125: 60U/ml for pre- and 35U/ml for postmenopausal women) could notably improve diagnostic performance in EOC detection in patients in southern China.