PMBC: a manually curated database for prognostic markers of breast cancer.

Breast cancer is notorious for its high mortality and heterogeneity, resulting in different therapeutic responses. Classical biomarkers have been identified and successfully commercially applied to predict the outcome of breast cancer patients. Accumulating biomarkers, including non-coding RNAs, have been reported as prognostic markers for breast cancer with the development of sequencing techniques. However, there are currently no databases dedicated to the curation and characterization of prognostic markers for breast cancer. Therefore, we constructed a curated database for prognostic markers of breast cancer (PMBC). PMBC consists of 1070 markers covering mRNAs, lncRNAs, miRNAs and circRNAs. These markers are enriched in various cancer- and epithelial-related functions including mitogen-activated protein kinases signaling. We mapped the prognostic markers into the ceRNA network from starBase. The lncRNA NEAT1 competes with 11 RNAs, including lncRNAs and mRNAs. The majority of the ceRNAs in ABAT belong to pseudogenes. The topology analysis of the ceRNA network reveals that known prognostic RNAs have higher closeness than random. Among all the biomarkers, prognostic lncRNAs have a higher degree, while prognostic mRNAs have significantly higher closeness than random RNAs. These results indicate that the lncRNAs play important roles in maintaining the interactions between lncRNAs and their ceRNAs, which might be used as a characteristic to prioritize prognostic lncRNAs based on the ceRNA network. PMBC renders a user-friendly interface and provides detailed information about individual prognostic markers, which will facilitate the precision treatment of breast cancer. PMBC is available at the following URL: http://www.pmbreastcancer.com/.

Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology.

The research focuses on the segmentation and classification of leukocytes, a crucial task in medical image analysis for diagnosing various diseases. The leukocyte dataset comprises four classes of images such as monocytes, lymphocytes, eosinophils, and neutrophils. Leukocyte segmentation is achieved through image processing techniques, including background subtraction, noise removal, and contouring. To get isolated leukocytes, background mask creation, Erythrocytes mask creation, and Leukocytes mask creation are performed on the blood cell images. Isolated leukocytes are then subjected to data augmentation including brightness and contrast adjustment, flipping, and random shearing, to improve the generalizability of the CNN model. A deep Convolutional Neural Network (CNN) model is employed on augmented dataset for effective feature extraction and classification. The deep CNN model consists of four convolutional blocks having eleven convolutional layers, eight batch normalization layers, eight Rectified Linear Unit (ReLU) layers, and four dropout layers to capture increasingly complex patterns. For this research, a publicly available dataset from Kaggle consisting of a total of 12,444 images of four types of leukocytes was used to conduct the experiments. Results showcase the robustness of the proposed framework, achieving impressive performance metrics with an accuracy of 97.98% and precision of 97.97%. These outcomes affirm the efficacy of the devised segmentation and classification approach in accurately identifying and categorizing leukocytes. The combination of advanced CNN architecture and meticulous pre-processing steps establishes a foundation for future developments in the field of medical image analysis.

Procedimiento para la eliminación de documentos administrativos institucionales / Procedure for the elimination of institutional administrative documents

Los presentes Lineamientos tiene como objetivo, estandarizar las actividades a cumplir por el personal responsable de la eliminación de documentos administrativos para volver eficiente el procedimiento y reducir costos de conservación. Están sujetos al cumplimiento del presente procedimiento, todas las dependencias del Minsal y Establecimientos de Salud que generan archivos institucionales de valor primario (administrativo), iniciando con la identificación de la documentación que ya cumplió su plazo de conservación establecido en la TPCD, concluyendo con la destrucción de la documentación

The objective of these Guidelines is to standardize the activities to be carried out by the personnel responsible for the elimination of administrative documents to make the procedure efficient and reduce conservation costs. All Minsal agencies and Health Establishments that generate institutional files of primary (administrative) value are subject to compliance with this procedure, starting with the identification of documentation that has already met its conservation period established in the TPCD, concluding with the destruction of documentation

Learning Without Real Data Annotations to Detect Hepatic Lesions in PET Images.

OBJECTIVE: Deep neural networks have been recently applied to lesion identification in fluorodeoxyglucose (FDG) positron emission tomography (PET) images, but they typically rely on a large amount of well-annotated data for model training. This is extremely difficult to achieve for neuroendocrine tumors (NETs), because of low incidence of NETs and expensive lesion annotation in PET images. The objective of this study is to design a novel, adaptable deep learning method, which uses no real lesion annotations but instead low-cost, list mode-simulated data, for hepatic lesion detection in real-world clinical NET PET images. METHODS: We first propose a region-guided generative adversarial network (RG-GAN) for lesion-preserved image-to-image translation. Then, we design a specific data augmentation module for our list-mode simulated data and incorporate this module into the RG-GAN to improve model training. Finally, we combine the RG-GAN, the data augmentation module and a lesion detection neural network into a unified framework for joint-task learning to adaptatively identify lesions in real-world PET data. RESULTS: The proposed method outperforms recent state-of-the-art lesion detection methods in real clinical 68Ga-DOTATATE PET images, and produces very competitive performance with the target model that is trained with real lesion annotations. CONCLUSION: With RG-GAN modeling and specific data augmentation, we can obtain good lesion detection performance without using any real data annotations. SIGNIFICANCE: This study introduces an adaptable deep learning method for hepatic lesion identification in NETs, which can significantly reduce human effort for data annotation and improve model generalizability for lesion detection with PET imaging.

O sigilo médico e os dados sensíveis na telemedicina à luz da Lei Geral de Proteção de Dados / Medical confidentiality and sensitive data in telemedicine in light of the General Data Protection Law / Secret médical et données sensibles en télémédecine au regard de la Loi Générale sur la Protection des Données

A telemedicina, permitida em caráter emergencial durante a covid-19, foi autorizada e regulamentada pela Lei nº 14.510/2022. Reconhecida como serviço imprescindível para a garantia da equidade em saúde, na telemedicina veiculam-se dados considerados sensíveis pela Lei Geral de Proteção de Dados. Este ensaio apresenta uma discussão a respeito de tais dados, os quais detêm relação intrínseca com direitos da personalidade e que devem ser reconhecidos como sigilosos, a fim de garantir o direito à privacidade dos titulares, bem como o respeito ao sigilo médico. Conclui-se que eventual violação dos dados sensíveis pode ensejar sanções administrativas aos agentes de tratamento, mas há divergência doutrinária a respeito do regime de responsabilidade adotado pela Lei Geral de Proteção de Dados, com três possíveis interpretações: responsabilidade objetiva, responsabilidade subjetiva e responsabilidade ativa

Telemedicine, which had been allowed on an emergency basis during covid-19, was authorized and regulat-ed by Law nº 14.510/2022. Recognized as an essential service in guaranteeing equity in health, in telemedi-cine, data considered sensitive by the General Data Protection Law is transmitted. This essay elaborates on a discussion regarding such data, which are intrinsically related to personal rights and must be recognized as confidential in order to ensure the right to privacy of the data subjects, as well as respect for medical confidentiality. It is concluded that any violation of sensitive data may result in administrative sanctions for treatment agents. Still, doctrinal divergence exists regarding the liability regime adopted by the law, with three possible interpretations: strict liability, fault liability, and active liability

La télémédecine, qui avait été permise en urgence pendant le covid-19, a été autorisée et réglementée par la Loi nº 2022-14510. Reconnue comme un service essentiel pour garantir l'équité en santé, en télémédecine, des données considérées comme sensibles par la Loi Générale sur la Protection des Données sont transmises. Cet essai développe une discussion concernant de telles données, qui sont intrinsèquement liées aux droits personnels et doivent être reconnues comme confidentielles afin de garantir le droit à la vie privée des sujets de données, ainsi que le respect de la confidentialité médicale. On en conclut que la violation éventuelle de données sensibles peut entraîner des sanctions administratives pour les agents de traitement. Néanmoins, des divergences doctrinales existent quant au régime de responsabilité adopté par la loi, avec trois interprétations possibles: la responsabilité stricte, la responsabilité pour faute et la responsabilité active

Multistep Automated Data Labelling Procedure (MADLaP) for thyroid nodules on ultrasound: An artificial intelligence approach for automating image annotation.

Machine learning (ML) for diagnosis of thyroid nodules on ultrasound is an active area of research. However, ML tools require large, well-labeled datasets, the curation of which is time-consuming and labor-intensive. The purpose of our study was to develop and test a deep-learning-based tool to facilitate and automate the data annotation process for thyroid nodules; we named our tool Multistep Automated Data Labelling Procedure (MADLaP). MADLaP was designed to take multiple inputs including pathology reports, ultrasound images, and radiology reports. Using multiple step-wise 'modules' including rule-based natural language processing, deep-learning-based imaging segmentation, and optical character recognition, MADLaP automatically identified images of a specific thyroid nodule and correctly assigned a pathology label. The model was developed using a training set of 378 patients across our health system and tested on a separate set of 93 patients. Ground truths for both sets were selected by an experienced radiologist. Performance metrics including yield (how many labeled images the model produced) and accuracy (percentage correct) were measured using the test set. MADLaP achieved a yield of 63 % and an accuracy of 83 %. The yield progressively increased as the input data moved through each module, while accuracy peaked part way through. Error analysis showed that inputs from certain examination sites had lower accuracy (40 %) than the other sites (90 %, 100 %). MADLaP successfully created curated datasets of labeled ultrasound images of thyroid nodules. While accurate, the relatively suboptimal yield of MADLaP exposed some challenges when trying to automatically label radiology images from heterogeneous sources. The complex task of image curation and annotation could be automated, allowing for enrichment of larger datasets for use in machine learning development.

Cross-platform dataset of multiplex fluorescent cellular object image annotations.

Defining cellular and subcellular structures in images, referred to as cell segmentation, is an outstanding obstacle to scalable single-cell analysis of multiplex imaging data. While advances in machine learning-based segmentation have led to potentially robust solutions, such algorithms typically rely on large amounts of example annotations, known as training data. Datasets consisting of annotations which are thoroughly assessed for quality are rarely released to the public. As a result, there is a lack of widely available, annotated data suitable for benchmarking and algorithm development. To address this unmet need, we release 105,774 primarily oncological cellular annotations concentrating on tumor and immune cells using over 40 antibody markers spanning three fluorescent imaging platforms, over a dozen tissue types and across various cellular morphologies. We use readily available annotation techniques to provide a modifiable community data set with the goal of advancing cellular segmentation for the greater imaging community.

Clinicians' perception of oral potentially malignant disorders: a pitfall for image annotation in supervised learning.

OBJECTIVE: The present study aims to quantify clinicians' perceptions of oral potentially malignant disorders (OPMDs) when evaluating, classifying, and manually annotating clinical images, as well as to understand the source of inter-observer variability when assessing these lesions. The hypothesis was that different interpretations could affect the quality of the annotations used to train a Supervised Learning model. STUDY DESIGN: Forty-six clinical images from 37 patients were reviewed, classified, and manually annotated at the pixel level by 3 labelers. We compared the inter-examiner assessment based on clinical criteria through the κ statistics (Fleiss's kappa). The segmentations were also compared using the mean pixel-wise intersection over union (IoU). RESULTS: The inter-observer agreement for homogeneous/non-homogeneous criteria was substantial (κ = 63, 95% CI: 0.47-0.80). For the subclassification of non-homogeneous lesions, the inter-observer agreement was moderate (κ = 43, 95% CI: 0.34-0.53) (P < .001). The mean IoU of 0.53 (±0.22) was considered low. CONCLUSION: The subjective clinical assessment (based on human visual observation, variable criteria that have suffered adjustments over the years, different educational backgrounds, and personal experience) may explain the source of inter-observer discordance for the classification and annotation of OPMD. Therefore, there is a strong probability of transferring the subjectivity of human analysis to artificial intelligence models. The use of large data sets and segmentation based on the union of all labelers' annotations holds the potential to overcome this limitation.

Effective and efficient active learning for deep learning-based tissue image analysis.

MOTIVATION: Deep learning attained excellent results in digital pathology recently. A challenge with its use is that high quality, representative training datasets are required to build robust models. Data annotation in the domain is labor intensive and demands substantial time commitment from expert pathologists. Active learning (AL) is a strategy to minimize annotation. The goal is to select samples from the pool of unlabeled data for annotation that improves model accuracy. However, AL is a very compute demanding approach. The benefits for model learning may vary according to the strategy used, and it may be hard for a domain specialist to fine tune the solution without an integrated interface. RESULTS: We developed a framework that includes a friendly user interface along with run-time optimizations to reduce annotation and execution time in AL in digital pathology. Our solution implements several AL strategies along with our diversity-aware data acquisition (DADA) acquisition function, which enforces data diversity to improve the prediction performance of a model. In this work, we employed a model simplification strategy [Network Auto-Reduction (NAR)] that significantly improves AL execution time when coupled with DADA. NAR produces less compute demanding models, which replace the target models during the AL process to reduce processing demands. An evaluation with a tumor-infiltrating lymphocytes classification application shows that: (i) DADA attains superior performance compared to state-of-the-art AL strategies for different convolutional neural networks (CNNs), (ii) NAR improves the AL execution time by up to 4.3×, and (iii) target models trained with patches/data selected by the NAR reduced versions achieve similar or superior classification quality to using target CNNs for data selection. AVAILABILITY AND IMPLEMENTATION: Source code: https://github.com/alsmeirelles/DADA.

Prediction and curation of missing biomedical identifier mappings with Biomappings.

MOTIVATION: Biomedical identifier resources (such as ontologies, taxonomies, and controlled vocabularies) commonly overlap in scope and contain equivalent entries under different identifiers. Maintaining mappings between these entries is crucial for interoperability and the integration of data and knowledge. However, there are substantial gaps in available mappings motivating their semi-automated curation. RESULTS: Biomappings implements a curation workflow for missing mappings which combines automated prediction with human-in-the-loop curation. It supports multiple prediction approaches and provides a web-based user interface for reviewing predicted mappings for correctness, combined with automated consistency checking. Predicted and curated mappings are made available in public, version-controlled resource files on GitHub. Biomappings currently makes available 9274 curated mappings and 40 691 predicted ones, providing previously missing mappings between widely used identifier resources covering small molecules, cell lines, diseases, and other concepts. We demonstrate the value of Biomappings on case studies involving predicting and curating missing mappings among cancer cell lines as well as small molecules tested in clinical trials. We also present how previously missing mappings curated using Biomappings were contributed back to multiple widely used community ontologies. AVAILABILITY AND IMPLEMENTATION: The data and code are available under the CC0 and MIT licenses at https://github.com/biopragmatics/biomappings.

A Self Supervised StyleGAN for Image Annotation and Classification With Extremely Limited Labels.

The recent success of learning-based algorithms can be greatly attributed to the immense amount of annotated data used for training. Yet, many datasets lack annotations due to the high costs associated with labeling, resulting in degraded performances of deep learning methods. Self-supervised learning is frequently adopted to mitigate the reliance on massive labeled datasets since it exploits unlabeled data to learn relevant feature representations. In this work, we propose SS-StyleGAN, a self-supervised approach for image annotation and classification suitable for extremely small annotated datasets. This novel framework adds self-supervision to the StyleGAN architecture by integrating an encoder that learns the embedding to the StyleGAN latent space, which is well-known for its disentangled properties. The learned latent space enables the smart selection of representatives from the data to be labeled for improved classification performance. We show that the proposed method attains strong classification results using small labeled datasets of sizes 50 and even 10. We demonstrate the superiority of our approach for the tasks of COVID-19 and liver tumor pathology identification.

Antecedentes de la formación archivística en Costa Rica / Antecedents of the archivistic formation in Costa Rica

Resumen El artículo tiene como objetivo centrar su atención en el análisis, para darlo a conocer a la comunidad archivística costarricense, de los principales antecedentes que dieron origen a la formación archivística universitaria en Costa Rica; es decir, se investigó sobre la antesala de lo que 1978 dio origen al Diplomado en Archivo Administrativo en la entonces Escuela de Historia y Geografía de Facultad de Ciencias Sociales de Universidad de Costa Rica. Por lo tanto, el trabajo corresponde a una investigación histórica y exploratoria; en el primer caso porque se analiza e interpreta un proceso en perspectiva histórica y el segundo porque es un tema poco estudiado por la historiografía archivística costarricense. La metodología para la realización del trabajo fue el análisis documental, a partir de fuentes de información bibliográficas y documentos de archivos, que quedan reflejados en apartado de referencias bibliográficas. Así las cosas, el artículo presenta un recorrido por el origen de la Archivística en Costa Rica y los diferentes acontecimientos que antecedieron a la creación del Diplomado en Archivo Administrativo. Se concluye, entre otras cosas, que el Proyecto Piloto de la Unesco sentó las bases para el desarrollo archivístico costarricense, la modernización de los archivos y el origen de la formación archivística reglada en el país.

Abstract The article has the objective to center your attention in the analysis, to be known by the Costa Rican archivistic community, of the main antecedents that gave birth to the university archivistic formation in Costa Rica; meaning that an investigation was developed in the anteroom of 1978 with origin of the diploma in administrative archive in the old School of Geography and History of the Faculty of Social Science of the Costa Rican University. The work belongs to an historical and exploratory investigation, cause analyses one process in an historical perspective, and the second one, because it has been little studied by the Costa Rican archivistic historiography. The methodology applied for the work was the documentary analysis based on bibliographical information, and documents from archives that are reflected in the bibliographical references. The article shows a travel around the origins of the archive studies in Costa Rica, and the different events before the creation of the degree in Administrative Archive. To conclude this, the pilot project of the UNESCO, has created the bases for the archivistic development in Costa Rica, the modernization of the archives and the origins of the current archive formation rules in the country.

Merging data curation and machine learning to improve nanomedicines.

Nanomedicine design is often a trial-and-error process, and the optimization of formulations and in vivo properties requires tremendous benchwork. To expedite the nanomedicine research progress, data science is steadily gaining importance in the field of nanomedicine. Recently, efforts have explored the potential to predict nanomaterials synthesis and biological behaviors via advanced data analytics. Machine learning algorithms process large datasets to understand and predict various material properties in nanomedicine synthesis, pharmacologic parameters, and efficacy. "Big data" approaches may enable even larger advances, especially if researchers capitalize on data curation methods. However, the concomitant use of data curation processes needed to facilitate the acquisition and standardization of large, heterogeneous data sets, to support advanced data analytics methods such as machine learning has yet to be leveraged. Currently, data curation and data analytics areas of nanotechnology-focused data science, or 'nanoinformatics', have been proceeding largely independently. This review highlights the current efforts in both areas and the potential opportunities for coordination to advance the capabilities of data analytics in nanomedicine.

Raspberry Pi-Based Data Archival System for Electroencephalogram Signals From the SedLine Root Device.

BACKGROUND: The retrospective analysis of electroencephalogram (EEG) signals acquired from patients under general anesthesia is crucial in understanding the patient's unconscious brain's state. However, the creation of such database is often tedious and cumbersome and involves human labor. Hence, we developed a Raspberry Pi-based system for archiving EEG signals recorded from patients under anesthesia in operating rooms (ORs) with minimal human involvement. METHODS: Using this system, we archived patient EEG signals from over 500 unique surgeries at the Emory University Orthopaedics and Spine Hospital, Atlanta, for about 18 months. For this, we developed a software package that runs on a Raspberry Pi and archives patient EEG signals from a SedLine Root EEG Monitor (Masimo) to a secure Health Insurance Portability and Accountability Act (HIPAA) compliant cloud storage. The OR number corresponding to each surgery was archived along with the EEG signal to facilitate retrospective EEG analysis. We retrospectively processed the archived EEG signals and performed signal quality checks. We also proposed a formula to compute the proportion of true EEG signal and calculated the corresponding statistics. Further, we curated and interleaved patient medical record information with the corresponding EEG signals. RESULTS: We retrospectively processed the EEG signals to demonstrate a statistically significant negative correlation between the relative alpha power (8-12 Hz) of the EEG signal captured under anesthesia and the patient's age. CONCLUSIONS: Our system is a standalone EEG archiver developed using low cost and readily available hardware. We demonstrated that one could create a large-scale EEG database with minimal human involvement. Moreover, we showed that the captured EEG signal is of good quality for retrospective analysis and combined the EEG signal with the patient medical records. This project's software has been released under an open-source license to enable others to use and contribute.

The reactome pathway knowledgebase 2022.

The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied ('dark') proteins from analyzed datasets in the context of Reactome's manually curated pathways.

Molecular traits and functional analysis of Rapid Alkalinization Factors (RALFs) in four Gossypium species.

Rapid Alkalinization Factors (RALFs) are plant-secreted, cysteine-rich polypeptides which are known to play essential roles in plant developmental processes and in several defense mechanisms. So far, RALF polypeptides have not been investigated in the Gossypium genus. In this study, 42, 38, 104 and 120 RALFs were identified from diploid G. arboreum and G. raimondi and tetraploid G. hirsutum and G. barbadense, respectively. These were further divided into four groups. Protein characteristics, sequence alignment, gene structure, conserved motifs, chromosomal location and cis-element identification were comprehensively analyzed. Whole genome duplication (WGD) /segmental duplication may be the reason why the number of RALF genes doubled in tetraploid Gossypium species. Expression patterns analysis showed that GhRALFs had different transcript accumulation patterns in the tested tissues and were differentially expressed in response to various abiotic stresses. Furthermore, GhRALF41-3 over-expressing (OE) plants showed reduction in root length and developed later with short stems and small rosettes than that of the wild type. The GhRALF14-8 and GhRALF27-8 OE plants, especially the latter, showed increase in seed abortion. Both transgenic Arabidopsis and VIGS cotton demonstrate that three GhRALFs are negative regulators in response to salt stress. Our systematic analyses provided insights into the characterization of RALF genes in Gossypium, which forms genetic basis for further exploration in their potential applications in cotton production.

dNTPpoolDB: a manually curated database of experimentally determined dNTP pools and pool changes in biological samples.

Stimulated by the growing interest in the role of dNTP pools in physiological and malignant processes, we established dNTPpoolDB, the database that offers access to quantitative data on dNTP pools from a wide range of species, experimental and developmental conditions (https://dntppool.org/). The database includes measured absolute or relative cellular levels of the four canonical building blocks of DNA and of exotic dNTPs, as well. In addition to the measured quantity, dNTPpoolDB contains ample information on sample source, dNTP quantitation methods and experimental conditions including any treatments and genetic manipulations. Functions such as the advanced search offering multiple choices from custom-built controlled vocabularies in 15 categories in parallel, the pairwise comparison of any chosen pools, and control-treatment correlations provide users with the possibility to quickly recognize and graphically analyse changes in the dNTP pools in function of a chosen parameter. Unbalanced dNTP pools, as well as the balanced accumulation or depletion of all four dNTPs result in genomic instability. Accordingly, key roles of dNTP pool homeostasis have been demonstrated in cancer progression, development, ageing and viral infections among others. dNTPpoolDB is designated to promote research in these fields and fills a longstanding gap in genome metabolism research.

ProCanBio: A Database of Manually Curated Biomarkers for Prostate Cancer.

Prostate cancer (PCa) is the second lethal malignancy in men worldwide. In the past, numerous research groups investigated the omics profiles of patients and scrutinized biomarkers for the diagnosis and prognosis of PCa. However, information related to the biomarkers is widely scattered across numerous resources in complex textual format, which poses hindrance to understand the tumorigenesis of this malignancy and scrutinization of robust signature. To create a comprehensive resource, we collected all the relevant literature on PCa biomarkers from the PubMed. We scrutinize the extensive information about each biomarker from a total of 412 unique research articles. Each entry of the database incorporates PubMed ID, biomarker name, biomarker type, biomolecule, source, subjects, validation status, and performance measures such as sensitivity, specificity, and hazard ratio (HR). In this study, we present ProCanBio, a manually curated database that maintains detailed data on 2053 entries of potential PCa biomarkers obtained from 412 publications in user-friendly tabular format. Among them are 766 protein-based, 507 RNA-based, 157 genomic mutations, 260 miRNA-based, and 122 metabolites-based biomarkers. To explore the information in the resource, a web-based interactive platform was developed with searching and browsing facilities. To the best of the authors' knowledge, there is no resource that can consolidate the information contained in all the published literature. Besides this, ProCanBio is freely available and is compatible with most web browsers and devices. Eventually, we anticipate this resource will be highly useful for the research community involved in the area of prostate malignancy.

A highly annotated database of genes associated with platinum resistance in cancer.

Platinum-based chemotherapy, including cisplatin, carboplatin, and oxaliplatin, is prescribed to 10-20% of all cancer patients. Unfortunately, platinum resistance develops in a significant number of patients and is a determinant of clinical outcome. Extensive research has been conducted to understand and overcome platinum resistance, and mechanisms of resistance can be categorized into several broad biological processes, including (1) regulation of drug entry, exit, accumulation, sequestration, and detoxification, (2) enhanced repair and tolerance of platinum-induced DNA damage, (3) alterations in cell survival pathways, (4) alterations in pleiotropic processes and pathways, and (5) changes in the tumor microenvironment. As a resource to the cancer research community, we provide a comprehensive overview accompanied by a manually curated database of the >900 genes/proteins that have been associated with platinum resistance over the last 30 years of literature. The database is annotated with possible pathways through which the curated genes are related to platinum resistance, types of evidence, and hyperlinks to literature sources. The searchable, downloadable database is available online at http://ptrc-ddr.cptac-data-view.org .