The IntAct database: efficient access to fine-grained molecular interaction data.

The IntAct molecular interaction database (https://www.ebi.ac.uk/intact) is a curated resource of molecular interactions, derived from the scientific literature and from direct data depositions. As of August 2021, IntAct provides more than one million binary interactions, curated by twelve global partners of the International Molecular Exchange consortium, for which the IntAct database provides a shared curation and dissemination platform. The IMEx curation policy has always emphasised a fine-grained data and curation model, aiming to capture the relevant experimental detail essential for the interpretation of the provided molecular interaction data. Here, we present recent curation focus and progress, as well as a completely redeveloped website which presents IntAct data in a much more user-friendly and detailed way.

The tapetal tissue is essential for the maintenance of redox homeostasis during microgametogenesis in tomato.

The tapetum is a specialized layer of cells within the anther, adjacent to the sporogenous tissue. During its short life, it provides nutrients, molecules and materials to the pollen mother cells and microsporocytes, being essential during callose degradation and pollen wall formation. The interaction between the tapetum and sporogenous cells in Solanum lycopersicum (tomato) plants, despite its importance for breeding purposes, is poorly understood. To investigate this process, gene editing was used to generate loss-of-function mutants that showed the complete and specific absence of tapetal cells. These plants were obtained targeting the previously uncharacterized Solyc03g097530 (SlTPD1) gene, essential for tapetum specification in tomato plants. In the absence of tapetum, sporogenous cells developed and callose deposition was observed. However, sporocytes failed to undergo the process of meiosis and finally degenerated, leading to male sterility. Transcriptomic analysis conducted in mutant anthers lacking tapetum revealed the downregulation of a set of genes related to redox homeostasis. Indeed, mutant anthers showed a reduction in the accumulation of reactive oxygen species (ROS) at early stages and altered activity of ROS-scavenging enzymes. The results obtained highlight the importance of the tapetal tissue in maintaining redox homeostasis during male gametogenesis in tomato plants.

SLPred: a multi-view subcellular localization prediction tool for multi-location human proteins.

SUMMARY: Accurate prediction of the subcellular locations (SLs) of proteins is a critical topic in protein science. In this study, we present SLPred, an ensemble-based multi-view and multi-label protein subcellular localization prediction tool. For a query protein sequence, SLPred provides predictions for nine main SLs using independent machine-learning models trained for each location. We used UniProtKB/Swiss-Prot human protein entries and their curated SL annotations as our source data. We connected all disjoint terms in the UniProt SL hierarchy based on the corresponding term relationships in the cellular component category of Gene Ontology and constructed a training dataset that is both reliable and large scale using the re-organized hierarchy. We tested SLPred on multiple benchmarking datasets including our-in house sets and compared its performance against six state-of-the-art methods. Results indicated that SLPred outperforms other tools in the majority of cases. AVAILABILITY AND IMPLEMENTATION: SLPred is available both as an open-access and user-friendly web-server (https://slpred.kansil.org) and a stand-alone tool (https://github.com/kansil/SLPred). All datasets used in this study are also available at https://slpred.kansil.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Efficient controlled release of cannabinoids loaded in γ-CD-MOFs and DPPC liposomes as novel delivery systems in oral health.

Olivetol (OLV), as a cannabidiol (CBD) analog, was incorporated in γ-cyclodextrin metal-organic frameworks (γ-CD-MOFs) and 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) liposomes as potential analgesic drug delivery systems (DDS) for dental hypersensitivity (DH) treatment. These DDS have been scarcely employed in oral health, being the first time in case of MOFs loaded with cannabinoids. In vitro experiments using bovine teeth were performed to verify if the drug is able to reach the dentin, where it can flow to the pulp tissues and exert its analgesic effect; enamel and dentin regions were analyzed by synchrotron radiation-based FTIR microspectroscopy. Principal component analysis (PCA) was used to process the spectroscopic data as a powerful chemometric tool, and it revealed a similar behavior in both regions. The studied DDS have been characterized by different techniques, and is was demonstrated that DDS is an efficient way to carry the drug through dental tissues without compromising their structure.

Method for Independent Estimation of the False Localization Rate for Phosphoproteomics.

Phosphoproteomic methods are commonly employed to identify and quantify phosphorylation sites on proteins. In recent years, various tools have been developed, incorporating scores or statistics related to whether a given phosphosite has been correctly identified or to estimate the global false localization rate (FLR) within a given data set for all sites reported. These scores have generally been calibrated using synthetic datasets, and their statistical reliability on real datasets is largely unknown, potentially leading to studies reporting incorrectly localized phosphosites, due to inadequate statistical control. In this work, we develop the concept of scoring modifications on a decoy amino acid, that is, one that cannot be modified, to allow for independent estimation of global FLR. We test a variety of amino acids, on both synthetic and real data sets, demonstrating that the selection can make a substantial difference to the estimated global FLR. We conclude that while several different amino acids might be appropriate, the most reliable FLR results were achieved using alanine and leucine as decoys. We propose the use of a decoy amino acid to control false reporting in the literature and in public databases that re-distribute the data. Data are available via ProteomeXchange with identifier PXD028840.

Combination of Two Synchrotron Radiation-Based Techniques and Chemometrics to Study an Enhanced Natural Remineralization of Enamel.

The limitations to assess dental enamel remineralization have been overcome by a methodology resulting from the appropriate combination of synchrotron radiation-based techniques on both, infrared microspectroscopy and micro X-ray diffraction, with the help of specific data mining. Since amelogenin plays a key role in modulating the mineralization of tooth enamel, we propose a controlled ion release for fluorapatite structural ions (Ca2+, PO43-, and F-, also including Zn2+) by using weak acid and weak base ion-exchange resins in the presence of amelogenin to remineralize the surface of etched teeth. This combination provides the necessary ions for enamel remineralization and a guide for crystal growth due to the protein. Remineralized tooth samples were analyzed by applying the indicated methodology. The synchrotron data were treated using principal component analysis and multivariate curve resolution to analyze the mineral layer formed in the presence and absence of amelogenin. The remineralizing treatment created a fluorapatite layer free of carbonate impurities and with a similar orientation to that of the natural enamel thanks to amelogenin contribution.

Effectiveness of a group educational intervention - prolact - in primary care to promote exclusive breastfeeding: a cluster randomized clinical trial.

BACKGROUND: The rates of exclusive breastfeeding at 6 months in Spain are far from recommended by the World Health Organization, which is 50% by 2025. Evidence of the effectiveness of group interventions in late postpartum is limited. The objective of this study was to evaluate the effectiveness of the PROLACT group educational intervention for increasing the proportion of mother-child dyads with exclusive breastfeeding at 6 months compared to the usual practice in primary care. METHOD: Multicentre cluster randomized clinical trial. A total of 434 mother-child dyads who breastfed exclusively in the first 4 weeks of the children's life and agreed to participate were included. The main outcome was exclusive breastfeeding at 6 months. Secondary variables were type of breastfeeding, reasons for abandonment, degree of adherence and satisfaction with the intervention. To study the effectiveness, the difference in the proportions of dyads with exclusive breastfeeding at 6 months was calculated, and the relative risk (RR) and number needed to treat (NNT) were calculated with their 95% CIs. To study the factors associated with the maintenance of exclusive breastfeeding at 6 months, a multilevel logistic regression model was fitted. All analyses were performed to intention to treat. RESULTS: The percentage of dyads with exclusive breastfeeding at 6 months was 22.4% in the intervention group and 8.8% in the control group. PROLACT intervention obtained an RR =2.53 (95% CI: 1.54-4.15) and an NNT = 7 (95%CI: 5-14). The factors associated with exclusive breastfeeding at 6 months were the PROLACT intervention, OR = 3.51 (95%CI: 1.55-7.93); age > 39 years, OR = 2.79 (95%CI: 1.02-7.6); previous breastfeeding experience, OR = 2.61 (95%CI: 1.29-5.29); income between 500 and 833.33 €, OR = 3.52 (95%CI 1.47-8.47).); planning to start work before the infant was 6 months old, OR = 0.35 (0.19-0.63) . CONCLUSIONS: The PROLACT intervention in primary care is more effective than the usual practice for maintaining exclusive breastfeeding at 6 months, and can therefore be considered evidence-based practice for implementation in standard practice. TRIAL REGISTRATION: The trial was registered with ClinicalTrials.gov under code number NCT01869920 (03/06/2013).

The power of weak ion-exchange resins assisted by amelogenin for natural remineralization of dental enamel: an in vitro study.

This study aims to develop an innovative dental product to remineralize dental enamel by a proper combination of ion-exchange resins as controlled release of mineral ions that form dental enamel, in the presence of amelogenin to guide the appropriate crystal growth. The novel product proposed consists of a combination of ion-exchange resins (weak acid and weak base) individually loaded with the remineralizing ions: Ca2+, PO43- and F-, also including Zn2+ in a minor amount as antibacterial, together with the protein amelogenin. Such cocktail provides onsite controlled release of the ions necessary for enamel remineralization due to the weak character of the resins and at the same time, a guiding tool for related crystal growth by the indicated protein. Amelogenin protein is involved in the structural development of natural enamel and takes a key role in controlling the crystal growth morphology and alignment at the enamel surface. Bovine teeth were treated by applying the resins and protein together with artificial saliva. Treated teeth were evaluated with nanoindentation, scanning electron microscopy and energy-dispersive X-ray spectroscopy. The innovative material induces the dental remineralization creating a fluorapatite layer with a hardness equivalent to sound enamel, with the appropriate alignment of corresponding nanocrystals, being the fluorapatite more acid resistant than the original mineral. Our results suggest that the new product shows potential for promoting long-term remineralization leading to the inhibition of caries and protection of dental structures.

Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.

The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.

UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase.

MOTIVATION: The number of protein records in the UniProt Knowledgebase (UniProtKB: https://www.uniprot.org) continues to grow rapidly as a result of genome sequencing and the prediction of protein-coding genes. Providing functional annotation for these proteins presents a significant and continuing challenge. RESULTS: In response to this challenge, UniProt has developed a method of annotation, known as UniRule, based on expertly curated rules, which integrates related systems (RuleBase, HAMAP, PIRSR, PIRNR) developed by the members of the UniProt consortium. UniRule uses protein family signatures from InterPro, combined with taxonomic and other constraints, to select sets of reviewed proteins which have common functional properties supported by experimental evidence. This annotation is propagated to unreviewed records in UniProtKB that meet the same selection criteria, most of which do not have (and are never likely to have) experimentally verified functional annotation. Release 2020_01 of UniProtKB contains 6496 UniRule rules which provide annotation for 53 million proteins, accounting for 30% of the 178 million records in UniProtKB. UniRule provides scalable enrichment of annotation in UniProtKB. AVAILABILITY AND IMPLEMENTATION: UniRule rules are integrated into UniProtKB and can be viewed at https://www.uniprot.org/unirule/. UniRule rules and the code required to run the rules, are publicly available for researchers who wish to annotate their own sequences. The implementation used to run the rules is known as UniFIRE and is available at https://gitlab.ebi.ac.uk/uniprot-public/unifire.

Dermoscopy of Juvenile Xanthogranuloma.

BACKGROUND: Dermoscopy is useful for the evaluation of juvenile xanthogranuloma (JXG). The classical "setting sun" pattern is characteristic of JXG, but its sensibility appears to be limited. An extensive description of other dermoscopic findings is not available in the literature. OBJECTIVES: The aim of this study was to valuate and describe the clinical and dermoscopic characteristics of a series of JXG cases. METHODS: This is a retrospective descriptive study, including cases with histopathologic diagnosis of JXG, and the availability of clinical and dermoscopic images, assessed for the presence of dermoscopic features based on the available literature. RESULTS: A total of 17 lesions were analyzed. 70.6% showed global symmetry, 35.3% presented the typical "setting sun" pattern. All lesions showed yellow-orange and/or pink-red structureless color. Other dermoscopic features were yellow globules (35.3%), shiny white streaks (23.5%), brown globules (17.6%), pale-brown network (11.8%), negative network (11.8%), erosion/ulceration (11.8%), rosettes (5.9%), and hemorrhage (5.9%). Scales were seen in 64.7% of patients. Vascular structures were observed in all the lesions, mostly in an irregular distribution (76.5%). The observed vessel types were dotted (52.9%), linear (52.9%), branching-arboriform (29.4%), comma-like (23.5%), hairpin-like (17.6%), globular (17.6%), coiled (11.8%), and milky-red globules (5.9%). CONCLUSIONS: Symmetry, yellow/orange-pink/red color, yellow globules, shiny white streaks, and irregularly distributed different types of vascular structures are the main dermoscopic features of JXG. This is the largest dermoscopic registry of JXG published to date.

Baseline haemoglobin and thromboelastometry are predictive of red blood cell requirements and one-year mortality in liver transplantation.

BACKGROUND: To determine the predictive capacity of baseline haemoglobin and maxim clot firmness (MCF) EXTEM thromboelastometry for intraoperative red blood cell (RBC) requirements and its influence on mortality. METHODS: 591 adult liver transplant (LT) recipients from ten Spanish centres were reviewed. The main outcomes were the percentage of patients who received RBC and massive transfusion (≥ 6 RBC units), RBC units transfused, and mortality. RESULTS: 76 % received a donor after brain death graft and 24 % a controlled donor after circulatory death graft. Median (interquartile ranges) RBC transfusion was 2 (0-4) units, and 63 % of patients were transfused. Comparing transfused and non-transfused patients, mean (standard deviation) for baseline haemoglobin was 10.4 (2.1) vs. 13.0 (1.9) g/dl (p = 0.001), EXTEM MCF was 51(11) vs. 55(9) mm (p = 0.001). Haemoglobin and EXTEM MCF were inversely associated with the need of transfusion odds ratio (OR) of 0.558 (95 % CI 0.497-0.627, p < 0.001) and OR 0.966 (95 % CI0.945-0.987, p = 0.002), respectively. Pre-operative baseline haemoglobin ≤ 10 g/dL predicted RBC transfusion, sensitivity of 93 % and specificity of 47 %. Massive transfusion (MT) was received by 19 % of patients. Haemoglobin ≤10 g/dL predicted MT with sensitivity 73 % and specificity of 52 %. One-year patient and graft survival were significantly lower in patients who required MT (78 % and 76 %, respectively) vs. those who did not (94 % and 93 %, respectively). DISCUSSION: whereas EXTEM MCF is less dreterminant predicting RBC requirements, efforts are required to improve preoperative haemoglobin up to 10 g/dl in patients awaiting LT.

Advances and Applications in the Quest for Orthologs.

Gene families evolve by the processes of speciation (creating orthologs), gene duplication (paralogs), and horizontal gene transfer (xenologs), in addition to sequence divergence and gene loss. Orthologs in particular play an essential role in comparative genomics and phylogenomic analyses. With the continued sequencing of organisms across the tree of life, the data are available to reconstruct the unique evolutionary histories of tens of thousands of gene families. Accurate reconstruction of these histories, however, is a challenging computational problem, and the focus of the Quest for Orthologs Consortium. We review the recent advances and outstanding challenges in this field, as revealed at a symposium and meeting held at the University of Southern California in 2017. Key advances have been made both at the level of orthology algorithm development and with respect to coordination across the community of algorithm developers and orthology end-users. Applications spanned a broad range, including gene function prediction, phylostratigraphy, genome evolution, and phylogenomics. The meetings highlighted the increasing use of meta-analyses integrating results from multiple different algorithms, and discussed ongoing challenges in orthology inference as well as the next steps toward improvement and integration of orthology resources.

Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data.

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

Breaking the rules: tooth whitening by means of a reducing agent.

OBJECTIVES: It is widely accepted that current tooth whitening treatment effect is based on the oxidizing action of peroxides, even if the mechanism of action remains still unclear. Treatments are claimed to be safe, but several secondary effects have been described, since long application times and high concentrations are needed. A faster whitening ingredient which permits the use of lower concentrations and shorter application times could potentially overcome this problem. In this work, a different approach based on a reducing agent, sodium metabisulfite (MBS), is explored. MATERIALS AND METHODS: The reaction between tannic acid (TA) with carbamide peroxide (CP), MBS, and potassium persulfate (PS), as an oxidizing agent, was monitored for 48 hours by measuring its absorbance, comparing their different whitening effects. The reduction process between TA and MBS was confirmed by cyclic voltammetry. An in vitro test was used to observe if MBS whitens also stained teeth. RESULTS: It is shown that MBS bleaching effect is faster and higher than CP's effect over time. PS produced a darkening effect after the 3rd hour because of the strong absorbance of the oxidation metabolite. Cyclic voltammetry showed a progressive increase in the intensity of the TA anodic peak when MBS was present, demonstrating that a reduction is taking place. In vitro, MBS showed a faster whitening performance than CP, using lower concentrations. CONCLUSIONS: Using a TA solution as a staining model, it was possible to show that MBS has a visible bleaching effect through a reduction reaction, faster than CP, both in solution and in vitro. Low concentrations of MBS are effective in whitening. CLINICAL SIGNIFICANCE: This work shows MBS as a promising candidate to develop novel whitening treatments, which is acting by reducing mechanism instead of oxidation.

Tooth whitening: From the established treatments to novel approaches to prevent side effects.

OBJECTIVE: The aim is to review the most important aspects about tooth whitening treatments, their side effects, and the new emerging approaches to overcome them. OVERVIEW: This review is focused on origin of tooth stains, the whitening systems and their chemistry, their side effects, and the new approaches. The search of bibliography of the period 1965-2018 has been analyzed. CONCLUSIONS: Tooth whitening has become one of the most requested dental treatments by the public. Tooth stains are classified according to their origin into two categories: intrinsic and extrinsic. The whitening systems are generally organized into two classes: in-office and at-home products. Most of the whitening systems use hydrogen peroxide as the active oxidative agent to degrade the organic compounds that cause stains. The concentration ranges depending on the treatment, and it may be applied directly or produced in a chemical reaction from carbamide peroxide that is more stable. Besides its popularity, tooth whitening still has some side effects being tooth hypersensitivity the most common. In order to decrease these side effects, new treatments are constantly in renewal processes. CLINICAL SIGNIFICANCE: Despite all the data and new strategies known about tooth whitening, there are many aspects that are not totally fully understood and methodologies that are not completely effective. Therefore, the development of effective, efficient, and long-lasting whitening treatments is still necessary.

ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature.

BACKGROUND: The automated prediction of the enzymatic functions of uncharacterized proteins is a crucial topic in bioinformatics. Although several methods and tools have been proposed to classify enzymes, most of these studies are limited to specific functional classes and levels of the Enzyme Commission (EC) number hierarchy. Besides, most of the previous methods incorporated only a single input feature type, which limits the applicability to the wide functional space. Here, we proposed a novel enzymatic function prediction tool, ECPred, based on ensemble of machine learning classifiers. RESULTS: In ECPred, each EC number constituted an individual class and therefore, had an independent learning model. Enzyme vs. non-enzyme classification is incorporated into ECPred along with a hierarchical prediction approach exploiting the tree structure of the EC nomenclature. ECPred provides predictions for 858 EC numbers in total including 6 main classes, 55 subclass classes, 163 sub-subclass classes and 634 substrate classes. The proposed method is tested and compared with the state-of-the-art enzyme function prediction tools by using independent temporal hold-out and no-Pfam datasets constructed during this study. CONCLUSIONS: ECPred is presented both as a stand-alone and a web based tool to provide probabilistic enzymatic function predictions (at all five levels of EC) for uncharacterized protein sequences. Also, the datasets of this study will be a valuable resource for future benchmarking studies. ECPred is available for download, together with all of the datasets used in this study, at: https://github.com/cansyl/ECPred . ECPred webserver can be accessed through http://cansyl.metu.edu.tr/ECPred.html .

Large-scale automated function prediction of protein sequences and an experimental case study validation on PTEN transcript variants.

Recent advances in computing power and machine learning empower functional annotation of protein sequences and their transcript variations. Here, we present an automated prediction system UniGOPred, for GO annotations and a database of GO term predictions for proteomes of several organisms in UniProt Knowledgebase (UniProtKB). UniGOPred provides function predictions for 514 molecular function (MF), 2909 biological process (BP), and 438 cellular component (CC) GO terms for each protein sequence. UniGOPred covers nearly the whole functionality spectrum in Gene Ontology system and it can predict both generic and specific GO terms. UniGOPred was run on CAFA2 challenge target protein sequences and it is categorized within the top 10 best performing methods for the molecular function category. In addition, the performance of UniGOPred is higher compared to the baseline BLAST classifier in all categories of GO. UniGOPred predictions are compared with UniProtKB/TrEMBL database annotations as well. Furthermore, the proposed tool's ability to predict negatively associated GO terms that defines the functions that a protein does not possess, is discussed. UniGOPred annotations were also validated by case studies on PTEN protein variants experimentally and on CHD8 protein variants with literature. UniGOPred protein functional annotation system is available as an open access tool at http://cansyl.metu.edu.tr/UniGOPred.html.

Oocyte developmental competence is independent of ovarian reserve in women younger than 35 years.

RESEARCH QUESTION: Is higher antral follicle count (AFC) in healthy young women associated with higher oocyte developmental competence? DESIGN: Retrospective study of 1985 first oocyte donation cycles, corresponding to 3210 fresh embryo transfers in oocyte recipients, conducted between January 2010 and May 2014. RESULTS: Donors with higher AFC who underwent ovarian stimulation, produced both more cumulus-oocyte complexes (COC) (B coefficient = 0.47, 95% CI 0.41 to 0.53;P ≤ 0.001) and MII oocytes (B coefficient = 0.36, 95% CI 0.32 to 0.41;P ≤ 0.001) at linear regression analysis. Donors with low AFC had a higher risk of cancellation (OR 4.79, 95% CI 2.99 to 7.69;P < 0.001) or obtaining fewer than four metaphase II (MII) at ovum retrieval (OR 3.87, 95% CI 2.38 to 6.27;P < 0.001). No association was found between AFC and biochemical (OR 1.13, 95% CI 0.93 to 1.36), clinical (OR 1.05, 95% CI 0.87 to 1.28), ongoing pregnancy (OR 1.00, 95%, CI 0.82 to 1.21) or live birth rates (OR 0.97, 95% CI 0.8 to 1.19), when at least four MII were obtained at ovum retrieval. CONCLUSIONS: AFC does not relate to the developmental competence of oocytes in women younger than 35 years.