PrimerEvalPy: a tool for in-silico evaluation of primers for targeting the microbiome.

BACKGROUND: The selection of primer pairs in sequencing-based research can greatly influence the results, highlighting the need for a tool capable of analysing their performance in-silico prior to the sequencing process. We therefore propose PrimerEvalPy, a Python-based package designed to test the performance of any primer or primer pair against any sequencing database. The package calculates a coverage metric and returns the amplicon sequences found, along with information such as their average start and end positions. It also allows the analysis of coverage for different taxonomic levels. RESULTS: As a case study, PrimerEvalPy was used to test the most commonly used primers in the literature against two oral 16S rRNA gene databases containing bacteria and archaea. The results showed that the most commonly used primer pairs in the oral cavity did not match those with the highest coverage. The best performing primer pairs were found for the detection of oral bacteria and archaea. CONCLUSIONS: This demonstrates the importance of a coverage analysis tool such as PrimerEvalPy to find the best primer pairs for specific niches. The software is available under the MIT licence at https://gitlab.citius.usc.es/lara.vazquez/PrimerEvalPy .

A systematic overview of dental methods for age assessment in living individuals: from traditional to artificial intelligence-based approaches.

Dental radiographies have been used for many decades for estimating the chronological age, with a view to forensic identification, migration flow control, or assessment of dental development, among others. This study aims to analyse the current application of chronological age estimation methods from dental X-ray images in the last 6 years, involving a search for works in the Scopus and PubMed databases. Exclusion criteria were applied to discard off-topic studies and experiments which are not compliant with a minimum quality standard. The studies were grouped according to the applied methodology, the estimation target, and the age cohort used to evaluate the estimation performance. A set of performance metrics was used to ensure good comparability between the different proposed methodologies. A total of 613 unique studies were retrieved, of which 286 were selected according to the inclusion criteria. Notable tendencies to overestimation and underestimation were observed in some manual approaches for numeric age estimation, being especially notable in the case of Demirjian (overestimation) and Cameriere (underestimation). On the other hand, the automatic approaches based on deep learning techniques are scarcer, with only 17 studies published in this regard, but they showed a more balanced behaviour, with no tendency to overestimation or underestimation. From the analysis of the results, it can be concluded that traditional methods have been evaluated in a wide variety of population samples, ensuring good applicability in different ethnicities. On the other hand, fully automated methods were a turning point in terms of performance, cost, and adaptability to new populations.

A few-shot approach for COVID-19 screening in standard and portable chest X-ray images.

Reliable and effective diagnostic systems are of vital importance for COVID-19, specifically for triage and screening procedures. In this work, a fully automatic diagnostic system based on chest X-ray images (CXR) has been proposed. It relies on the few-shot paradigm, which allows to work with small databases. Furthermore, three components have been added to improve the diagnosis performance: (1) a region proposal network which makes the system focus on the lungs; (2) a novel cost function which adds expert knowledge by giving specific penalties to each misdiagnosis; and (3) an ensembling procedure integrating multiple image comparisons to produce more reliable diagnoses. Moreover, the COVID-SC dataset has been introduced, comprising almost 1100 AnteroPosterior CXR images, namely 439 negative and 653 positive according to the RT-PCR test. Expert radiologists divided the negative images into three categories (normal lungs, COVID-related diseases, and other diseases) and the positive images into four severity levels. This entails the most complete COVID-19 dataset in terms of patient diversity. The proposed system has been compared with state-of-the-art methods in the COVIDGR-1.0 public database, achieving the highest accuracy (81.13% ± 2.76%) and the most robust results. An ablation study proved that each system component contributes to improve the overall performance. The procedure has also been validated on the COVID-SC dataset under different scenarios, with accuracies ranging from 70.81 to 87.40%. In conclusion, our proposal provides a good accuracy appropriate for the early detection of COVID-19.

XAS: Automatic yet eXplainable Age and Sex determination by combining imprecise per-tooth predictions.

Chronological age and biological sex estimation are two key tasks in a variety of procedures, including human identification and migration control. Issues such as these have led to the development of both semiautomatic and automatic prediction models, but the former are expensive in terms of time and human resources, while the latter lack the interpretability required to be applicable in real-life scenarios. This paper therefore proposes a new, fully automatic methodology for the estimation of age and sex. This first applies a tooth detection by means of a modified CNN with the objective of extracting the oriented bounding boxes of each tooth. Then, it feeds the image features inside the tooth boxes into a second CNN module designed to produce per-tooth age and sex probability distributions. The method then adopts an uncertainty-aware policy to aggregate these estimated distributions. Our approach yielded a lower mean absolute error than any other previously described, at 0.97 years. The accuracy of the sex classification was 91.82%, confirming the suitability of the teeth for this purpose. The proposed model also allows analyses of age and sex estimations on every tooth, enabling experts to identify the most relevant for each task or population cohort or to detect potential developmental problems. In conclusion, the performance of the method in both age and sex predictions is excellent and has a high degree of interpretability, making it suitable for use in a wide range of application scenarios.

Automated description of the mandible shape by deep learning.

PURPOSE: The shape of the mandible has been analyzed in a variety of fields, whether to diagnose conditions like osteoporosis or osteomyelitis, in forensics, to estimate biological information such as age, gender, and race or in orthognathic surgery. Although the methods employed produce encouraging results, most rely on the dry bone analyses or complex imaging techniques that, ultimately, hamper sample collection and, as a consequence, the development of large-scale studies. Thus, we proposed an objective, repeatable, and fully automatic approach to provide a quantitative description of the mandible in orthopantomographies (OPGs). METHODS: We proposed the use of a deep convolutional neural network (CNN) to localize a set of landmarks of the mandible contour automatically from OPGs. Furthermore, we detailed four different descriptors for the mandible shape to be used for a variety of purposes. This includes a set of linear distances and angles calculated from eight anatomical landmarks of the mandible, the centroid size, the shape variations from the mean shape, and a group of shape parameters extracted with a point distribution model. RESULTS: The fully automatic digitization of the mandible contour was very accurate, with a mean point to the curve error of 0.21 mm and a standard deviation comparable to that of a trained expert. The combination of the CNN and the four shape descriptors was validated in the well-known problems of forensic sex and age estimation, obtaining 87.8% of accuracy and a mean absolute error of 1.57 years, respectively. CONCLUSION: The methodology proposed, including the shape model, can be valuable in any field that requires a quantitative description of the mandible shape and a visual representation of its changes such as clinical practice, surgery management, dental research, or legal medicine.

In-Silico Detection of Oral Prokaryotic Species With Highly Similar 16S rRNA Sequence Segments Using Different Primer Pairs.

Although clustering by operational taxonomic units (OTUs) is widely used in the oral microbial literature, no research has specifically evaluated the extent of the limitations of this sequence clustering-based method in the oral microbiome. Consequently, our objectives were to: 1) evaluate in-silico the coverage of a set of previously selected primer pairs to detect oral species having 16S rRNA sequence segments with ≥97% similarity; 2) describe oral species with highly similar sequence segments and determine whether they belong to distinct genera or other higher taxonomic ranks. Thirty-nine primer pairs were employed to obtain the in-silico amplicons from the complete genomes of 186 bacterial and 135 archaeal species. Each fasta file for the same primer pair was inserted as subject and query in BLASTN for obtaining the similarity percentage between amplicons belonging to different oral species. Amplicons with 100% alignment coverage of the query sequences and with an amplicon similarity value ≥97% (ASI97) were selected. For each primer, the species coverage with no ASI97 (SC-NASI97) was calculated. Based on the SC-NASI97 parameter, the best primer pairs were OP_F053-KP_R020 for bacteria (region V1-V3; primer pair position for Escherichia coli J01859.1: 9-356); KP_F018-KP_R002 for archaea (V4; undefined-532); and OP_F114-KP_R031 for both (V3-V5; 340-801). Around 80% of the oral-bacteria and oral-archaea species analyzed had an ASI97 with at least one other species. These very similar species play different roles in the oral microbiota and belong to bacterial genera such as Campylobacter, Rothia, Streptococcus and Tannerella, and archaeal genera such as Halovivax, Methanosarcina and Methanosalsum. Moreover, ~20% and ~30% of these two-by-two similarity relationships were established between species from different bacterial and archaeal genera, respectively. Even taxa from distinct families, orders, and classes could be grouped in the same possible OTU. Consequently, regardless of the primer pair used, sequence clustering with a 97% similarity provides an inaccurate description of oral-bacterial and oral-archaeal species, which can greatly affect microbial diversity parameters. As a result, OTU clustering conditions the credibility of associations between some oral species and certain health and disease conditions. This significantly limits the comparability of the microbial diversity findings reported in oral microbiome literature.

DenTiUS Plaque, a Web-Based Application for the Quantification of Bacterial Plaque: Development and Usability Study.

BACKGROUND: In the dentistry field, the analysis of dental plaque is vital because it is the main etiological factor in the 2 most prevalent oral diseases: caries and periodontitis. In most of the papers published in the dental literature, the quantification of dental plaque is carried out using traditional, non-automated, and time-consuming indices. Therefore, the development of an automated plaque quantification tool would be of great value to clinicians and researchers. OBJECTIVE: This study aimed to develop a web-based tool called DenTiUS and various clinical indices to evaluate dental plaque levels using image analysis techniques. METHODS: The tool was executed as a web-based application to facilitate its use by researchers. Expert users are free to define experiments, including images from either a single patient (to observe an individual plaque growth pattern) or several patients (to perform a group characterization) at a particular moment or over time. A novel approach for detecting visible plaque has been developed as well as a new concept known as nonvisible plaque. This new term implies the classification of the remaining dental area into 3 subregions according to the risk of accumulating plaque in the near future. New metrics have also been created to describe visible and nonvisible plaque levels. RESULTS: The system generates results tables of the quantitative analysis with absolute averages obtained in each image (indices about visible plaque) and relative measurements (indices about visible and nonvisible plaque) relating to the reference moment. The clinical indices that can be calculated are the following: plaque index of an area per intensity (API index, a value between 0 and 100), area growth index (growth rate of plaque per unit of time in hours; percentage area/hour), and area time index (the time in days needed to achieve a plaque area of 100% concerning the initial area at the same moment). Images and graphics can be obtained for a moment from a patient in addition to a full report presenting all the processing data. Dentistry experts evaluated the DenTiUS Plaque software through a usability test, with the best-scoring questions those related to the workflow efficiency, value of the online help, attractiveness of the user interface, and overall satisfaction. CONCLUSIONS: The DenTiUS Plaque software allows automatic, reliable, and repeatable quantification of dental plaque levels, providing information about area, intensity, and growth pattern. Dentistry experts recognized that this software is suitable for quantification of dental plaque levels. Consequently, its application in the analysis of plaque evolution patterns associated with different oral conditions, as well as to evaluate the effectiveness of various oral hygiene measures, can represent an improvement in the clinical setting and the methodological quality of research studies.

Deep Neural Networks for Chronological Age Estimation From OPG Images.

Chronological age estimation is crucial labour in many clinical procedures, where the teeth have proven to be one of the best estimators. Although some methods to estimate the age from tooth measurements in orthopantomogram (OPG) images have been developed, they rely on time-consuming manual processes whose results are affected by the observer subjectivity. Furthermore, all those approaches have been tested only on OPG image sets of good radiological quality without any conditioning dental characteristic. In this work, two fully automatic methods to estimate the chronological age of a subject from the OPG image are proposed. The first (DANet) consists of a sequential Convolutional Neural Network (CNN) path to predict the age, while the second (DASNet) adds a second CNN path to predict the sex and uses sex-specific features with the aim of improving the age prediction performance. Both methods were tested on a set of 2289 OPG images of subjects from 4.5 to 89.2 years old, where both bad radiological quality images and images showing conditioning dental characteristics were not discarded. The results showed that the DASNet outperforms the DANet in every aspect, reducing the median Error (E) and the median Absolute Error (AE) by about 4 months in the entire database. When evaluating the DASNet in the reduced datasets, the AE values decrease as the real age of the subjects decreases, until reaching a median of about 8 months in the subjects younger than 15. The DASNet method was also compared to the state-of-the-art manual age estimation methods, showing significantly less over- or under-estimation problems. Consequently, we conclude that the DASNet can be used to automatically predict the chronological age of a subject accurately, especially in young subjects with developing dentitions.

Improving zebrafish embryo xenotransplantation conditions by increasing incubation temperature and establishing a proliferation index with ZFtool.

BACKGROUND: Zebrafish (Danio rerio) is a model organism that has emerged as a tool for cancer research, cancer being the second most common cause of death after cardiovascular disease for humans in the developed world. Zebrafish is a useful model for xenotransplantation of human cancer cells and toxicity studies of different chemotherapeutic compounds in vivo. Compared to the murine model, the zebrafish model is faster, can be screened using high-throughput methods and has a lower maintenance cost, making it possible and affordable to create personalized therapies. While several methods for cell proliferation determination based on image acquisition and quantification have been developed, some drawbacks still remain. In the xenotransplantation technique, quantification of cellular proliferation in vivo is critical to standardize the process for future preclinical applications of the model. METHODS: This study improved the conditions of the xenotransplantation technique - quantification of cellular proliferation in vivo was performed through image processing with our ZFtool software and optimization of temperature in order to standardize the process for a future preclinical applications. ZFtool was developed to establish a base threshold that eliminates embryo auto-fluorescence and measures the area of marked cells (GFP) and the intensity of those cells to define a 'proliferation index'. RESULTS: The analysis of tumor cell proliferation at different temperatures (34 °C and 36 °C) in comparison to in vitro cell proliferation provides of a better proliferation rate, achieved as expected at 36°, a maintenance temperature not demonstrated up to now. The mortality of the embryos remained between 5% and 15%. 5- Fluorouracil was tested for 2 days, dissolved in the incubation medium, in order to quantify the reduction of the tumor mass injected. In almost all of the embryos incubated at 36 °C and incubated with 5-Fluorouracil, there was a significant tumor cell reduction compared with the control group. This was not the case at 34 °C. CONCLUSIONS: Our results demonstrate that the proliferation of the injected cells is better at 36 °C and that this temperature is the most suitable for testing chemotherapeutic drugs like the 5-Fluorouracil.

In Situ Antibacterial Activity of Essential Oils with and without Alcohol on Oral Biofilm: A Randomized Clinical Trial.

Currently, there is little evidence on the in situ antibacterial activity of essential oils (EO) without alcohol. This study aimed to evaluate in situ the substantivity and antiplaque effect on the plaque-like biofilm (PL-biofilm) of two solutions, a traditional formulation that contains EO with alcohol (T-EO) and an alcohol-free formulation of EO (Af-EO). Eighteen healthy adults performed a single mouthwash of: T-EO, Af-EO, and sterile water (WATER) after wearing an individualized disk-holding splint for 2 days. The bacterial viability (BV) and thickness of the PL-biofilm were quantified at baseline, 30 s, and 1, 3, 5, and 7 h post-rinsing (Test 1). Subsequently, each volunteer wore the splint for 4 days, applying two daily mouthwashes of: T-EO, Af-EO, and WATER. The BV, thickness, and covering grade (CG) of the PL-biofilm were quantified (Test 2). Samples were analyzed by confocal laser scanning microscopy after staining with the LIVE/DEAD® BacLight™ solution. To conduct the computations of the BV automatically, a Matlab toolbox called Dentius Biofilm was developed. In test 1, both EO antiseptics had a similar antibacterial effect, reducing BV after a single rinse compared to the WATER, and keeping it below baseline levels up to 7 h post-rinse (P < 0.001). The mean thickness of the PL-biofilm after rinsing was not affected by any of the EO formulations and ranged from 18.58 to 20.19 µm. After 4 days, the T-EO and Af-EO solutions were significantly more effective than the WATER, reducing the BV, thickness, and CG of the PL-biofilm (P < 0.001). Although, both EO antiseptics presented a similar bactericidal activity, the Af-EO rinses led to more significant reductions in the thickness and CG of the PL-biofilm than the T-EO rinses (thickness = 7.90 vs. 9.92 µm, P = 0.012; CG = 33.36 vs. 46.61%, P = 0.001). In conclusion, both essential oils antiseptics had very high immediate antibacterial activity and substantivity in situ on the 2-day PL-biofilm after a single mouthwash. In the 4-day PL-biofilm, both essential oils formulations demonstrated a very good antiplaque effect in situ, although the alcohol-free formula performed better at reducing the biofilm thickness and covering grade.