BraNet: a mobil application for breast image classification based on deep learning algorithms.

Mobile health apps are widely used for breast cancer detection using artificial intelligence algorithms, providing radiologists with second opinions and reducing false diagnoses. This study aims to develop an open-source mobile app named "BraNet" for 2D breast imaging segmentation and classification using deep learning algorithms. During the phase off-line, an SNGAN model was previously trained for synthetic image generation, and subsequently, these images were used to pre-trained SAM and ResNet18 segmentation and classification models. During phase online, the BraNet app was developed using the react native framework, offering a modular deep-learning pipeline for mammography (DM) and ultrasound (US) breast imaging classification. This application operates on a client-server architecture and was implemented in Python for iOS and Android devices. Then, two diagnostic radiologists were given a reading test of 290 total original RoI images to assign the perceived breast tissue type. The reader's agreement was assessed using the kappa coefficient. The BraNet App Mobil exhibited the highest accuracy in benign and malignant US images (94.7%/93.6%) classification compared to DM during training I (80.9%/76.9%) and training II (73.7/72.3%). The information contrasts with radiological experts' accuracy, with DM classification being 29%, concerning US 70% for both readers, because they achieved a higher accuracy in US ROI classification than DM images. The kappa value indicates a fair agreement (0.3) for DM images and moderate agreement (0.4) for US images in both readers. It means that not only the amount of data is essential in training deep learning algorithms. Also, it is vital to consider the variety of abnormalities, especially in the mammography data, where several BI-RADS categories are present (microcalcifications, nodules, mass, asymmetry, and dense breasts) and can affect the API accuracy model.

Tunable optical and semiconducting properties of eco-friendly-prepared reduced graphene oxide.

Wide bandgap oxidized graphenes have garnered particular interest among the materials explored for these applications because of their exceptional semiconducting and optical properties. This study aims to investigate the tunability of the related properties in reduced graphene oxide (rGO) for potential use in energy conversion, storage, and optoelectronic devices. To accomplish this, we scrutinized crucial parameters of the synthesis process such as reduction time and temperature. Our findings demonstrate that controlling these parameters makes it possible to customize the optical bandgap of reduced graphene oxide within a range of roughly 2.2 eV-1.6 eV. Additionally, we observed that reduced graphene oxide has strong and superior absorption in the visible region, which is attributable to the existence of OFGs and defects. Notably, our results indicate that the absorption coefficients of reduced graphene oxide are up to almost three times higher (7426 ml mg-1 m-1) than those observed in dispersions of exfoliated graphene and graphene oxide (GO). To complement our findings, we employed several spectroscopic and morphological characterizations, including scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and electrical measurements. The implications of our results are significant for the development and design of future semiconductors for energy conversion and optoelectronic applications.

Compensatory Base Changes in ITS2 Secondary Structure Alignment, Modelling, and Molecular Phylogeny: An Integrated Approach to Improve Species Delimitation in Tulasnella (Basidiomycota).

BACKGROUND: The delimitation of species of Tulasnella has been extensively studied, mainly at the morphological (sexual and asexual states) and molecular levels-showing ambiguity between them. An integrative species concept that includes characteristics such as molecular, ecology, morphology, and other information is crucial for species delimitation in complex groups such as Tulasnella. OBJECTIVES: The aim of this study is to test evolutionary relationships using a combination of alignment-based and alignment-free distance matrices as an alternative molecular tool to traditional methods, and to consider the secondary structures and CBCs from ITS2 (internal transcribed spacer) sequences for species delimitation in Tulasnella. METHODOLOGY: Three phylogenetic approaches were plotted: (i) alignment-based, (ii) alignment-free, and (iii) a combination of both distance matrices using the DISTATIS and pvclust libraries from an R package. Finally, the secondary structure consensus was modeled by Mfold, and a CBC analysis was obtained to complement the species delimitation using 4Sale. RESULTS AND CONCLUSIONS: The phylogenetic tree results showed delimited monophyletic clades in Tulasnella spp., where all 142 Tulasnella sequences were divided into two main clades A and B and assigned to seven species (T. asymmetrica, T. andina, T. eichleriana ECU6, T. eichleriana ECU4 T. pinicola, T. violea), supported by bootstrap values from 72% to 100%. From the 2D secondary structure alignment, three types of consensus models with helices and loops were obtained. Thus, T. albida belongs to type I; T. eichleriana, T. tomaculum, and T. violea belong to type II; and T. asymmetrica, T. andina, T. pinicola, and T. spp. (GER) belong to type III; each type contains four to six domains, with nine CBCs among these that corroborate different species.

Outcome of Ivermectin in Cancer Treatment: An Experience in Loja-Ecuador.

(1) Background: Cancer is one of the leading causes of death worldwide, and trends in cancer incidence and mortality are increasing over last years in Loja-Ecuador. Cancer treatment is expensive because of social and economic issues which force the patients to look for other alternatives. One such alternative treatment is ivermectin-based antiparasitic, which is commonly used in treating cattle. This paper analyzed ivermectin use as cancer treatment in the rural area of the Loja province and the medical opinion regarding the use of ivermectin in humans. (2) Methods: The study used a mixed methodology using different sampling techniques such as observation, surveys, and interviews. (3) Results: The main findings show that 19% of the participants diagnosed with cancer take medicines based on ivermectin as alternative therapy to the cancer control and treatment without leaving treatment such as chemotherapy, radiotherapy, or immunotherapy, while 81% use it to treat other diseases. (4) Conclusions: Finally, we identify that the interviewed not only use IVM as anticancer treatment, but it is also used as a treatment against other diseases. Although the participants' opinions indicate that they feel improvements in their health after the third dose, the specialist considers that there is no authorization to prescribe these alternative treatments. In addition, they confirmed that currently, there is no scientific knowledge about the application of these treatments in humans and they do not recommend their application. Thus, the anticancer mechanism of ivermectin remains to be further investigated; therefore, we consider that it is important to continue with this research by proposing a new stage to evaluate and determine the pharmacological action of this type of drug through an in vitro study in different cultures of cancer cells.

Radiomics Diagnostic Tool Based on Deep Learning for Colposcopy Image Classification.

BACKGROUND: Colposcopy imaging is widely used to diagnose, treat and follow-up on premalignant and malignant lesions in the vulva, vagina, and cervix. Thus, deep learning algorithms are being used widely in cervical cancer diagnosis tools. In this study, we developed and preliminarily validated a model based on the Unet network plus SVM to classify cervical lesions on colposcopy images. Methodology: Two sets of images were used: the Intel & Mobile ODT Cervical Cancer Screening public dataset, and a private dataset from a public hospital in Ecuador during a routine colposcopy, after the application of acetic acid and lugol. For the latter, the corresponding clinical information was collected, specifically cytology on the PAP smear and the screening of human papillomavirus testing, prior to colposcopy. The lesions of the cervix or regions of interest were segmented and classified by the Unet and the SVM model, respectively. Results: The CAD system was evaluated for the ability to predict the risk of cervical cancer. The lesion segmentation metric results indicate a DICE of 50%, a precision of 65%, and an accuracy of 80%. The classification results' sensitivity, specificity, and accuracy were 70%, 48.8%, and 58%, respectively. Randomly, 20 images were selected and sent to 13 expert colposcopists for a statistical comparison between visual evaluation experts and the CAD tool (p-value of 0.597). Conclusion: The CAD system needs to improve but could be acceptable in an environment where women have limited access to clinicians for the diagnosis, follow-up, and treatment of cervical cancer; better performance is possible through the exploration of other deep learning methods with larger datasets.