Classification and counting of cells in brightfield microscopy images: an application of convolutional neural networks.

Microscopy is integral to medical research, facilitating the exploration of various biological questions, notably cell quantification. However, this process's time-consuming and error-prone nature, attributed to human intervention or automated methods usually applied to fluorescent images, presents challenges. In response, machine learning algorithms have been integrated into microscopy, automating tasks and constructing predictive models from vast datasets. These models adeptly learn representations for object detection, image segmentation, and target classification. An advantageous strategy involves utilizing unstained images, preserving cell integrity and enabling morphology-based classification-something hindered when fluorescent markers are used. The aim is to introduce a model proficient in classifying distinct cell lineages in digital contrast microscopy images. Additionally, the goal is to create a predictive model identifying lineage and determining optimal quantification of cell numbers. Employing a CNN machine learning algorithm, a classification model predicting cellular lineage achieved a remarkable accuracy of 93%, with ROC curve results nearing 1.0, showcasing robust performance. However, some lineages, namely SH-SY5Y (78%), HUH7_mayv (85%), and A549 (88%), exhibited slightly lower accuracies. These outcomes not only underscore the model's quality but also emphasize CNNs' potential in addressing the inherent complexities of microscopic images.

Cell quantification in digital contrast microscopy images with convolutional neural networks algorithm.

High Content Screening (HCS) combines high throughput techniques with the ability to generate cellular images of biological systems. The objective of this work is to evaluate the performance of predictive models using CNN to identify the number of cells present in digital contrast microscopy images obtained by HCS. One way to evaluate the algorithm was through the Mean Squared Error metric. The MSE was 4,335.99 in the A549 cell line, 25,295.23 in the Huh7 and 36,897.03 in the 3T3. After obtaining these values, different parameters of the models were changed to verify how they behave. By reducing the number of images, the MSE increased considerably, with the A549 cell line changing to 49,973.52, Huh7 to 79,473.88 and 3T3 to 52,977.05. Correlation analyzes were performed for the different models. In lineage A549, the best model showed a positive correlation with R = 0.953. In Huh7, the best correlation of the model was R = 0.821, it was also a positive correlation. In 3T3, the models showed no correlation, with the best model having R = 0.100. The models performed well in quantifying the number of cells, and the number and quality of the images interfered with this predictive ability.

Effect of fragmentation on the natural genetic diversity of Theobroma speciosum Willd. ex Spreng. populations.

Forest fragmentation reduces the effective size of natural populations, isolates individuals in the landscape, and, consequently, changes species' mating systems by increasing the degree of relatedness between individuals and inbreeding. Investigating the impact of habitat degradation on forest fragments helps to assess the genetic and ecological consequences of these changes, and allows the development of effective and sustainable conservation strategies to manage the genetic resources of species living in degraded landscapes. The aim of the present study was to assess the genetic diversity of fragmented Theobroma speciosum populations using microsatellite markers. Three urban forest fragments were selected in the municipality of Alta Floresta, Mato Grosso State, Brazil, namely C/E park, J park, and Zoo Botanical park. Seventy-five individuals (25 in each fragment) were sampled by collecting their leaves for genomic DNA extraction. Polymerase chain reaction amplifications were performed using nine polymorphic simple sequence repeat primers, which amplified 84 alleles. The mean expected heterozygosity was 0.970, and it was always higher than the observed heterozygosity. Analysis of molecular variance revealed that most variability occurred within populations (64%) rather than between them (36%). The Structure software and an unweighted pair group method with arithmetic mean dendrogram revealed three distinct groups, showing that individuals were allocated to their correct populations. Genotype number 3 from C/E park, number 45 from J park, and number 51 from Zoo Botanical park could be used as stock plants in breeding programs, because they were the most dissimilar within the populations studied. The high genetic diversity levels detected in all three populations studied emphasize the importance of protecting this species in its natural habitat.

Recovery of Arapaima sp. populations by community-based management in floodplains of the Purus River, Amazon.

In the present study a unique dataset on population abundance in various community-based management (CBM) and non-CBM areas is analysed to address the question of whether CBM can recover overexploited populations of Arapaima sp. in river-floodplain ecosystems. All non-CBM areas possessed depleted Arapaima sp. populations with a mean density of 0·01 individuals ha(-1) . Arapaima sp. population densities in all CBM areas changed over time from depleted to overexploited or well managed status, with a mean rate of increase of 77% year(-1) . Rates of Arapaima sp. population recovery in CBM areas differed, probably reflecting differences in ecosystem productivity and compliance with management regulations. These results indicate that CBM schemes can be effective tools for the recovery and conservation of fish populations with non-migratory life cycles in tropical river-floodplain ecosystems.

Single point mutations in the helicase domain of the NS3 protein enhance dengue virus replicative capacity in human monocyte-derived dendritic cells and circumvent the type I interferon response.

Dengue is the most prevalent arboviral disease worldwide. The outcome of the infection is determined by the interplay of viral and host factors. In the present study, we evaluated the cellular response of human monocyte-derived DCs (mdDCs) infected with recombinant dengue virus type 1 (DV1) strains carrying a single point mutation in the NS3hel protein (L435S or L480S). Both mutated viruses infect and replicate more efficiently and produce more viral progeny in infected mdDCs compared with the parental, non-mutated virus (vBACDV1). Additionally, global gene expression analysis using cDNA microarrays revealed that the mutated DVs induce the up-regulation of the interferon (IFN) signalling and pattern recognition receptor (PRR) canonical pathways in mdDCs. Pronounced production of type I IFN were detected specifically in mdDCs infected with DV1-NS3hel-mutated virus compared with mdDCs infected with the parental virus. In addition, we showed that the type I IFN produced by mdDCs is able to reduce DV1 infection rates, suggesting that cytokine function is effective but not sufficient to mediate viral clearance of DV1-NS3hel-mutated strains. Our results demonstrate that single point mutations in subdomain 2 have important implications for adenosine triphosphatase (ATPase) activity of DV1-NS3hel. Although a direct functional connection between the increased ATPase activity and viral replication still requires further studies, these mutations speed up viral RNA replication and are sufficient to enhance viral replicative capacity in human primary cell infection and circumvent type I IFN activity. This information may have particular relevance for attenuated vaccine protocols designed for DV.

Effect of compression load and temperature on thermomechanical tests for gutta-percha and Resilon®.

AIM: To analyse a method used to evaluate the thermomechanical properties of gutta-percha and Resilon(®) at different temperatures and compression loads. METHODOLOGY: Two hundred and seventy specimens measuring 10 mm in diameter and 1.5 mm in height were made from the following materials: conventional gutta-percha (GCO), thermoplastic gutta-percha (GTP) and Resilon(®) cones (RE). After 24 h, the specimens were placed in water at 50 °C, 60 °C or 70 °C for 60 s. After that, specimens were placed between two glass slabs, and loads weighing 1.0, 3.0 or 5.0 kg were applied. Images of the specimens were digitized before and after the test and analysed using imaging software to determine their initial and final areas. The thermomechanical property of each material was determined by the difference between the initial and final areas of the specimens. Data were subjected to anova and SNK tests at 5% significance. To verify a possible correlation between the results of the materials, linear regression coefficients (r) were calculated. RESULTS: Data showed higher flow area values for RE under all compression loads at 70 °C and under the 5.0 kg load at 60 °C (P < 0.05). Regarding gutta-percha, GTP showed higher flow under loads weighing 3.0 and 5.0 kg, at 60 and 70 °C (P < 0.05). GCO presented higher flow at 70 °C with a load of 5.0 kg. Regression analyses showed a poor linear correlation amongst the results of the materials under the different experimental conditions. CONCLUSION: Gutta-percha and Resilon(®) cones require different compression loads and temperatures for evaluation of their thermomechanical properties. For all materials, the greatest flow occurred at 70 °C under a load of 5.0 kg; therefore, these parameters may be adopted when evaluating endodontic filling materials.