Search Results (5)

Over the last decades, a significant improvement in cancer patient outcomes has occurred due to advances in cancer cell biology, systemic immunity, tumor-immune microenvironment (TIME) and precision cancer therapy. Despite this explosion of knowledge, its usefulness in clinical practice has been limited by the ability to translate multidimensional data into clinical care. Progress in artificial intelligence (AI) opens up a new frontier, with the promise of achieving synergistic and comprehensive integration. The classification of cancer biology and immunobiology into hallmarks of cancer by Hanahan and Weinberg provides a framework for organizing this information. This systematic classification has enabled the understanding of the interplay and cross-talk between its parts. Targeted cancer therapies and immunotherapies have achieved considerable success, yet their combinatorial potential is still being uncovered. Molecular diagnostics has worked hand-in-hand with precision oncology in deploying new therapies in a cancer-informed and patient-specific way. Harnessing the full power of the advances in these three fields with the aid of AI promises a transformation of molecular diagnostics. This review conceptualizes molecular diagnostics in the context of cancer hallmarks using nonsmall cell lung cancer (NSCLC) as a template, highlighting the potential of a new diagnostic science through the integrative power of AI. Full article

Here, we describe the case of an asymptomatic 70-year-old male patient who performed a contrast-enhanced computed tomography examination for prostate cancer staging, and an exceptional finding was reported. Specifically, a probable and never before reported minimal V aortic arch remnant with a thin intima–media band that joins together the anterior and posterior aortic walls. Full article

Thai indigenous pigs (TIPs) are important genetic resources. Crosses with exotic pig breeds and wild boars may cause genetic losses. To date, the physical characteristics of TIPs have been inconsistent. The classification of TIPs by genetic information is needed to pursue an appropriate conservation program. In this study, the genetic diversity, cluster analysis, and phylogenetic relationship of TIPs were investigated using twenty-nine pig microsatellite markers. Blood samples were collected from TIPs from three regions of Thailand: north (NT, n = 118), northeast (NE, n = 61), and south (ST, n = 75). The mean total number of distinct alleles and the effective number of alleles per locus were 11.851 and 5.497, respectively. The mean observed heterozygosity (Ho) and mean expected heterozygosity (He) were 0.562 and 0.837, respectively. The F values of the microsatellite loci were positive under Hardy–Weinberg Equilibrium at p < 0.001, with overall mean values of Fis, Fit, and Fst of 0.247, 0.281, and 0.046, respectively. A total of 5, 5, and 17 private alleles were found at frequencies greater than 0.050 in the NT, NE, and ST pigs, respectively. Three optimal clusters (K = 3) were proposed within the TIP populations. Pigs from the NT and NE regions were mixed in two clusters, while members of the ST region were clearly separated. The phylogenetic tree confirmed that the pigs from NT and NE were each divided into two subgroups, while the pigs from ST were clustered into one group. A microsatellite analysis revealed the high genetic diversity of the TIP populations and confirmed the genetic divergence of the TIPs from the European and Chinese breeds. A genetic admixture of the TIP with the local wild boars was detected. Full article

Stride length and walking distance estimation are becoming a key aspect of many applications. One of the methods of enhancing the accuracy of pedestrian dead reckoning is to accurately estimate the stride length of pedestrians. Existing stride length estimation (SLE) algorithms present good performance in the cases of walking at normal speed and the fixed smartphone mode (handheld). The mode represents a specific state of the carried smartphone. The error of existing SLE algorithms increases in complex scenes with many mode changes. Considering that stride length estimation is very sensitive to smartphone modes, this paper focused on combining smartphone mode recognition and stride length estimation to provide an accurate walking distance estimation. We combined multiple classification models to recognize five smartphone modes (calling, handheld, pocket, armband, swing). In addition to using a combination of time-domain and frequency-domain features of smartphone built-in accelerometers and gyroscopes during the stride interval, we constructed higher-order features based on the acknowledged studies (Kim, Scarlett, and Weinberg) to model stride length using the regression model of machine learning. In the offline phase, we trained the corresponding stride length estimation model for each mode. In the online prediction stage, we called the corresponding stride length estimation model according to the smartphone mode of a pedestrian. To train and evaluate the performance of our SLE, a dataset with smartphone mode, actual stride length, and total walking distance were collected. We conducted extensive and elaborate experiments to verify the performance of the proposed algorithm and compare it with the state-of-the-art SLE algorithms. Experimental results demonstrated that the proposed walking distance estimation method achieved significant accuracy improvement over existing individual approaches when a pedestrian was walking in both indoor and outdoor complex environments with multiple mode changes. Full article

Most giant groupers in the market are derived from inbred stock. Inbreeding can cause trait depression, compromising the animals’ fitness and disease resistance, obligating farmers to apply increased amounts of drugs. In order to solve this problem, a pedigree classification method is needed. Here, microsatellite and mitochondrial DNA were used as genetic markers to analyze the genetic relationships among giant grouper broodstocks. The 776-bp fragment of high polymorphic mitochondrial D-loop sequence was selected for measuring sibling relatedness. In a sample of 118 giant groupers, 42 haplotypes were categorized, with nucleotide diversity (π) of 0.00773 and haplotype diversity (HD) of 0.983. Furthermore, microsatellites were used for investigation of parentage. Six out of 33 microsatellite loci were selected as markers based on having a high number of alleles and compliance with Hardy-Weinberg equilibrium. Microsatellite profiles based on these loci provide high variability with low combined non-exclusion probability, permitting practical use in aquaculture. The method described here could be used to improve grouper broodstock management and lower the chances of inbreeding. This approach is expected to lead to production of higher quality groupers with higher disease resistance, thereby reducing the need for drug application. Full article