Síndrome semblant a la de Gianotti-Crosti secundària a mollusc contagiós / Síndrome similar al de Gianotti-Crosti secundario a molusco contagioso / Gianotti-Crosti-like syndrome secondary to molluscum contagiosum

Introducció: El mol·lusc contagiós és una malaltia cutàniacomuna en la infància, d’origen viral, que consisteix enl’aparició de pàpules perlades amb umbilicació central. Enalguns casos pot originar reaccions inflamatòries secundàries que cal conèixer per fer un diagnòstic correcte, com ladermatitis per mol·lusc o la síndrome semblant a la deGianotti-Crosti. Cas clínic: Nen de 4 anys amb antecedent d’infecció permolluscum contagiosum que inicia lesions inflamatòries acolzes i genolls, suggestives de reacció inflamatòria secundària, anomenada síndrome semblant a la de Gianot-ti-Crosti. Es va fer un tractament amb corticoides tòpics ila resolució de les lesions va ser completa. Comentari: La síndrome semblant a la de Gianotti-Crostisecundària a molluscum contagiosum és una reacció inflamatòria poc freqüent que cal tenir present per fer un diagnòstic acurat i oferir un tractament adequat. Tendeix a laresolució espontània, tot i que en casos simptomàtics potnecessitar tractament amb corticoides tòpics i antihistamínic oral.(AU)

Introducción: El molusco contagioso es una enfermedad cutáneacomún en la infancia, de origen viral, que consiste en la apariciónde pápulas perladas con umbilicación central. En algunos casospuede originar reacciones inflamatorias secundarias que es necesario conocer para hacer un correcto diagnóstico, como la dermatitis por molusco o el síndrome similar al de Gianotti-Crosti. Caso clínico. Niño de 4 años con antecedente de infección pormolluscum contagiosum que inicia lesiones inflamatorias en codosy rodillas, sugestivas de reacción inflamatoria secundaria, denominada síndrome similar al de Gianotti-Crosti. Se realizó tratamientocon corticoides tópicos con resolución completa de las lesiones. Comentario: El síndrome similar al de Gianotti-Crosti secundario amolluscum contagiosum es una reacción inflamatoria poco frecuente que debemos tener presente para hacer un diagnósticopreciso y ofrecer un tratamiento adecuado. Tiende a la resolución espontánea, aunque en casos sintomáticos puede precisar tratamiento con corticoides tópicos y antihistamínico oral.(AU)

Introduction: Molluscum contagiosum is a common skin disease inchildren. The infection is caused by the molluscum contagiosumvirus. It presents as single or multiple spherical, shiny, pearlywhite papules with a central dimple. In some cases, it can producesecondary inflammatory reactions that it is necessary to recognizesuch as molluscum dermatitis or Gianotti-Crosti-like syndrome. Case report: A four year old patient with a history of molluscumcontagiosum infection developed erythematous lesions in the elbows and knees, suggestive of a secondary inflammatory reaction,Gianotti-Crosti-like syndrome. Treatment with topical corticosteroids was administered with full resolution of the skin lesions. Comments: Gianotti-Crosti-like syndrome secondary to molluscumcontagiosum is a rare inflammatory reaction that pediatriciansneed to be aware of for a proper diagnosis and treatment. Thissyndrome tends to resolve spontaneously, although in symptomaticcases treatment with topical corticosteroids and oral antihistaminemay be required.(AU)

Long-term Evaluation of Residual Viremia in a Clinical Trial of Dolutegravir Plus Lamivudine as Maintenance Treatment for Participants With and Without Prior Lamivudine Resistance.

In this pilot clinical trial, we evaluated rates of residual replication in persons without lamivudine resistance-associated mutations in proviral DNA population sequencing who switched to dolutegravir plus lamivudine. After 144 weeks, there was no signal of changes in residual viremia based on qualitative detection methods, irrespective of past lamivudine resistance. Clinical Trials Registration. NCT03539224.

Incidence and Severity of SARS-CoV-2 Infection in HIV-Infected Individuals During the First Year of the Pandemic.

BACKGROUND: We aim to investigate the infection rate, the clinical characteristics and outcomes of COVID-19-disease in a cohort of people living with HIV in Madrid (Spain), during the first year of pandemics. SETTING: Observational single-center study, in which we included all HIV-infected patients (aged ≥ 18 years) with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection as of February 28, 2021, at the Hospital Universitario 12 de Octubre. METHODS: Confirmed disease was defined as any patient with a positive antigen test, reverse transcriptase polymerase chain reaction, or serology for SARS-CoV-2. We compared the characteristics of patients with mild disease (asymptomatic included) with those with moderate or severe disease (requiring admission). RESULTS: Of 2344 HIV-infected patients, 158 (82.9% male; median age, 46.5 years) were diagnosed with SARS-CoV-2 (infection rate, 6.74%; 95% confidence interval, 5.79 to 7.83). Thirty-nine individuals (24.7%) had moderate or severe disease, 43.7% had mild disease, and 31.6% were asymptomatic. Hypertension (23.4%) and obesity (15.8%) were the most prevalent comorbidities; 12.7% had at least 2 comorbidities. One hundred forty-five patients (97.3%) had RNA-HIV viral load of <50 copies per milliliter, and only 3 had CD4 cell count of <200 cells per cubic millimeter before infection. Of those admitted to hospital, 59% required oxygen support and 15.4%, invasive mechanical ventilation. Five patients died. None of the patient taking tenofovir-disoproxil-fumarate required admission. In the multivariate analysis, age remained as the only independent factor for moderate-severe disease (odds ratio, 1.09; 95% confidence interval 1.04 to 1.14; P < 0.001). CONCLUSIONS: People living with HIV are at risk of severe SARS-CoV-2 infection. Age was the only variable with an independent association with moderate-severe disease, after adjusting by comorbidities and other factors.

Detection of archived lamivudine-associated resistance mutations in virologically suppressed, lamivudine-experienced HIV-infected adults by different genotyping techniques (GEN-PRO study).

BACKGROUND: Previously selected lamivudine resistance-associated mutations (RAMs) may remain archived within the proviral HIV-DNA. OBJECTIVES: To evaluate the ability of proviral DNA genotyping to detect lamivudine RAMs in HIV-1 virologically suppressed participants; the correlation between Sanger and next generation sequencing (NGS); and predictive factors for detection of lamivudine RAMs in proviral DNA. METHODS: Cross-sectional study of participants on stable antiretroviral therapy and suppressed for ≥1 year. Analysis of proviral DNA was performed by Sanger sequencing in whole blood and by NGS in PBMCs. RESULTS: We analysed samples from 102 subjects (52 with and 50 without lamivudine RAMs in historical plasma RNA-genotypes). Among participants with previous lamivudine resistance, Sanger sequencing detected RAMs in 26.9%. Detection rates significantly increased using NGS: 47.9%, 64.6%, 75% and 87.5% with the 20%, 10%, 5% and 1% thresholds, respectively. As for participants without historical lamivudine resistance, Sanger detected the RAMs in 1/49 (2%), and NGS (5% threshold) in 8/45 (17.8%). Multivariate models fitted to the whole population revealed that having a history of lamivudine resistance was a risk factor for detection of lamivudine RAMs by NGS. Among participants with historical lamivudine resistance, multivariate analysis showed that a longer time since HIV diagnosis was associated with persistence of archived mutations by NGS at thresholds of >10% [OR 1.10 (95% CI: 1.00-1.24)] and >5% [OR 1.16 (95% CI: 1.02-1.32)]. CONCLUSIONS: Proviral DNA Sanger sequencing does not detect the majority of historical lamivudine RAMs. NGS increases the sensitivity of detection at lower thresholds, although the relevance of these minority populations with lamivudine RAMs needs further evaluation.

Long-term efficacy of dolutegravir plus lamivudine for maintenance of HIV viral suppression in adults with and without historical resistance to lamivudine: Week 96 results of ART-PRO pilot study.

BACKGROUND: In the ART-PRO pilot trial there were no virological failures through 48 weeks of treatment with dolutegravir plus lamivudine in suppressed individuals with and without archived lamivudine resistance-associated mutations (RAMs) detected through next-generation sequencing (NGS) but without evidence of lamivudine RAMs in baseline proviral DNA population sequencing. OBJECTIVES: To present 96 week results from ART-PRO. METHODS: Open-label, single-arm pilot trial. At baseline, all participants switched to dolutegravir plus lamivudine. Participants were excluded if proviral DNA population genotyping detected lamivudine RAMs. To detect resistance minority variants, proviral DNA NGS was retrospectively performed from baseline samples. For this analysis the efficacy endpoint was the proportion of participants with <50 HIV-1 RNA copies/mL at week 96. Safety and tolerability outcomes were incidence of adverse events and treatment discontinuations. RESULTS: Forty-one participants were included, 21 with lamivudine RAMs in historical plasma RNA genotypes. Baseline proviral DNA NGS detected lamivudine RAMs (M184V/I and/or K65R/E/N) above a 5% threshold in 71.4% (15/21) and 15% (3/20) of participants with and without history of lamivudine resistance, respectively. At 96 weeks, 90.2% of participants achieved the efficacy endpoint. Between week 48 and 96 there was one discontinuation due to consent withdrawal and no discontinuations related to adverse events. Two participants had a transient viral rebound, both re-suppressed on dolutegravir plus lamivudine. Through week 96, there were no virological failures. CONCLUSIONS: In this pilot trial, dolutegravir plus lamivudine maintained virological suppression at 96 weeks despite historical lamivudine resistance and persisting archived minority lamivudine RAMs.

Dolutegravir plus lamivudine for maintenance of HIV viral suppression in adults with and without historical resistance to lamivudine: 48-week results of a non-randomized, pilot clinical trial (ART-PRO).

BACKGROUND: We investigated the efficacy of a switch to dolutegravir plus lamivudine in aviremic individuals without evidence of persistent lamivudine resistance-associated mutations in baseline proviral DNA population sequencing. METHODS: Open-label, single-arm, 48-week pilot trial. HIV-1 infected adults, naïve to integrase inhibitors, with CD4+ above 350 cell/µL and fewer than 50 HIV-1 RNA copies per mL the year prior to study entry switched to dolutegravir plus lamivudine. Participants were excluded if baseline proviral DNA population genotyping detected lamivudine resistance-associated mutations. To detect resistance minority variants, proviral DNA next-generation sequencing was retrospectively performed from baseline samples. Primary efficacy endpoint was proportion of participants with fewer than 50 HIV-1 RNA copies per mL at week 48. Safety and tolerability outcomes were incidence of adverse events and treatment discontinuations. ART-PRO is registered with ClinicalTrials.gov, NCT03539224. FINDINGS: 41 participants switched to dolutegravir plus lamivudine, 21 with lamivudine resistance mutations in historical plasma genotypes. Baseline next-generation sequencing detected lamivudine resistance mutations (M184V/I and/or K65R/E/N) over a 5% threshold in 15/21 (71·4%) and 3/20 (15%) of participants with and without history of lamivudine resistance, respectively. At week 48, 92·7% of participants (38/41) had fewer than 50 HIV-1 RNA copies per mL. There were no cases of virologic failure. Three participants with historical lamivudine resistance were prematurely discontinued from the study (2 protocol violations, one adverse event). Ten participants (4 in the group with historical lamivudine resistance) had a transient viral rebound, all resuppressed on dolutegravir plus lamivudine. There were 28 drug-related adverse events, only one leading to discontinuation. INTERPRETATION: In this pilot trial, dolutegravir plus lamivudine was effective in maintaining virologic control despite past historical lamivudine resistance and presence of archived lamivudine resistance-associated mutations detected by next generation sequencing. Further studies are needed to confirm our results. FUNDING: Fondo de Investigaciones Sanitarias, Instituto de Salud Carlos III PI16/00837-PI16/00678.

Validation Prognostic of the Baylor Profound Mental Status Examination.

BACKGROUND: There are no validated prognostic instruments to evaluate severe Alzheimer's disease (AD) patients. OBJECTIVE: To validate the prognostic value of the Baylor Profound Mental Status Examination (BPMSE). METHODS: We selected 200 patients with severe AD. The following prognostic variables were collected: hospitalization, use of the emergency room, death, and prescription of drugs. ROC curve analysis was performed to see the overall behavior of the test when predicting the adverse event. We analyzed the AUC ROC and the best cut point was determined, and by using contingency tables, the risk was calculated. RESULTS: For a BPMSE ≥16 points, there was a risk of 1.8 (95% CI 0.9-3.4) of prescription of psychotropic drugs in 12 months. For memantine in 12 months, for a BPMSE ≥16 points, there was a risk of 2.9 (95% CI 1.1-7.2). Emergency room visits, for a BMPSE ≤15 points, showed a risk of 1.7 (95% CI 1-3.2). The risk of hospitalization at 12 months, for a BPMSE ≤15, was 1.4 (95% CI 0.8-2.6). When comparing medians, patients with a higher BPMSE were prescribed more drugs at 12 months. CONCLUSIONS: BPMSE has a limited predictive value for the variables studied.

Beyond opinion classification: Extracting facts, opinions and experiences from health forums.

INTRODUCTION: Surveys indicate that patients, particularly those suffering from chronic conditions, strongly benefit from the information found in social networks and online forums. One challenge in accessing online health information is to differentiate between factual and more subjective information. In this work, we evaluate the feasibility of exploiting lexical, syntactic, semantic, network-based and emotional properties of texts to automatically classify patient-generated contents into three types: "experiences", "facts" and "opinions", using machine learning algorithms. In this context, our goal is to develop automatic methods that will make online health information more easily accessible and useful for patients, professionals and researchers. MATERIAL AND METHODS: We work with a set of 3000 posts to online health forums in breast cancer, morbus crohn and different allergies. Each sentence in a post is manually labeled as "experience", "fact" or "opinion". Using this data, we train a support vector machine algorithm to perform classification. The results are evaluated in a 10-fold cross validation procedure. RESULTS: Overall, we find that it is possible to predict the type of information contained in a forum post with a very high accuracy (over 80 percent) using simple text representations such as word embeddings and bags of words. We also analyze more complex features such as those based on the network properties, the polarity of words and the verbal tense of the sentences and show that, when combined with the previous ones, they can boost the results.

Feature engineering for sentiment analysis in e-health forums.

INTRODUCTION: Exploiting information in health-related social media services is of great interest for patients, researchers and medical companies. The challenge is, however, to provide easy, quick and relevant access to the vast amount of information that is available. One step towards facilitating information access to online health data is opinion mining. Even though the classification of patient opinions into positive and negative has been previously tackled, most works make use of machine learning methods and bags of words. Our first contribution is an extensive evaluation of different features, including lexical, syntactic, semantic, network-based, sentiment-based and word embeddings features to represent patient-authored texts for polarity classification. The second contribution of this work is the study of polar facts (i.e. objective information with polar connotations). Traditionally, the presence of polar facts has been neglected and research in polarity classification has been bounded to opinionated texts. We demonstrate the existence and importance of polar facts for the polarity classification of health information. MATERIAL AND METHODS: We annotate a set of more than 3500 posts to online health forums of breast cancer, crohn and different allergies, respectively. Each sentence in a post is manually labeled as "experience", "fact" or "opinion", and as "positive", "negative" and "neutral". Using this data, we train different machine learning algorithms and compare traditional bags of words representations with word embeddings in combination with lexical, syntactic, semantic, network-based and emotional properties of texts to automatically classify patient-authored contents into positive, negative and neutral. Beside, we experiment with a combination of textual and semantic representations by generating concept embeddings using the UMLS Metathesaurus. RESULTS: We reach two main results: first, we find that it is possible to predict polarity of patient-authored contents with a very high accuracy (≈ 70 percent) using word embeddings, and that this considerably outperforms more traditional representations like bags of words; and second, when dealing with medical information, negative and positive facts (i.e. objective information) are nearly as frequent as negative and positive opinions and experiences (i.e. subjective information), and their importance for polarity classification is crucial.

Feature engineering for MEDLINE citation categorization with MeSH.

BACKGROUND: Research in biomedical text categorization has mostly used the bag-of-words representation. Other more sophisticated representations of text based on syntactic, semantic and argumentative properties have been less studied. In this paper, we evaluate the impact of different text representations of biomedical texts as features for reproducing the MeSH annotations of some of the most frequent MeSH headings. In addition to unigrams and bigrams, these features include noun phrases, citation meta-data, citation structure, and semantic annotation of the citations. RESULTS: Traditional features like unigrams and bigrams exhibit strong performance compared to other feature sets. Little or no improvement is obtained when using meta-data or citation structure. Noun phrases are too sparse and thus have lower performance compared to more traditional features. Conceptual annotation of the texts by MetaMap shows similar performance compared to unigrams, but adding concepts from the UMLS taxonomy does not improve the performance of using only mapped concepts. The combination of all the features performs largely better than any individual feature set considered. In addition, this combination improves the performance of a state-of-the-art MeSH indexer. Concerning the machine learning algorithms, we find that those that are more resilient to class imbalance largely obtain better performance. CONCLUSIONS: We conclude that even though traditional features such as unigrams and bigrams have strong performance compared to other features, it is possible to combine them to effectively improve the performance of the bag-of-words representation. We have also found that the combination of the learning algorithm and feature sets has an influence in the overall performance of the system. Moreover, using learning algorithms resilient to class imbalance largely improves performance. However, when using a large set of features, consideration needs to be taken with algorithms due to the risk of over-fitting. Specific combinations of learning algorithms and features for individual MeSH headings could further increase the performance of an indexing system.

Comparing different knowledge sources for the automatic summarization of biomedical literature.

OBJECTIVE: Automatic summarization of biomedical literature usually relies on domain knowledge from external sources to build rich semantic representations of the documents to be summarized. In this paper, we investigate the impact of the knowledge source used on the quality of the summaries that are generated. MATERIALS AND METHODS: We present a method for representing a set of documents relevant to a given biological entity or topic as a semantic graph of domain concepts and relations. Different graphs are created by using different combinations of ontologies and vocabularies within the UMLS (including GO, SNOMED-CT, HUGO and all available vocabularies in the UMLS) to retrieve domain concepts, and different types of relationships (co-occurrence and semantic relations from the UMLS Metathesaurus and Semantic Network) are used to link the concepts in the graph. The different graphs are next used as input to a summarization system that produces summaries composed of the most relevant sentences from the original documents. RESULTS AND CONCLUSIONS: Our experiments demonstrate that the choice of the knowledge source used to model the text has a significant impact on the quality of the automatic summaries. In particular, we find that, when summarizing gene-related literature, using GO, SNOMED-CT and HUGO to extract domain concepts results in significantly better summaries than using all available vocabularies in the UMLS. This finding suggests that successful biomedical summarization requires the selection of the appropriate knowledge source, whose coverage, specificity and relations must be in accordance to the type of the documents to summarize.

MeSH indexing based on automatically generated summaries.

BACKGROUND: MEDLINE citations are manually indexed at the U.S. National Library of Medicine (NLM) using as reference the Medical Subject Headings (MeSH) controlled vocabulary. For this task, the human indexers read the full text of the article. Due to the growth of MEDLINE, the NLM Indexing Initiative explores indexing methodologies that can support the task of the indexers. Medical Text Indexer (MTI) is a tool developed by the NLM Indexing Initiative to provide MeSH indexing recommendations to indexers. Currently, the input to MTI is MEDLINE citations, title and abstract only. Previous work has shown that using full text as input to MTI increases recall, but decreases precision sharply. We propose using summaries generated automatically from the full text for the input to MTI to use in the task of suggesting MeSH headings to indexers. Summaries distill the most salient information from the full text, which might increase the coverage of automatic indexing approaches based on MEDLINE. We hypothesize that if the results were good enough, manual indexers could possibly use automatic summaries instead of the full texts, along with the recommendations of MTI, to speed up the process while maintaining high quality of indexing results. RESULTS: We have generated summaries of different lengths using two different summarizers, and evaluated the MTI indexing on the summaries using different algorithms: MTI, individual MTI components, and machine learning. The results are compared to those of full text articles and MEDLINE citations. Our results show that automatically generated summaries achieve similar recall but higher precision compared to full text articles. Compared to MEDLINE citations, summaries achieve higher recall but lower precision. CONCLUSIONS: Our results show that automatic summaries produce better indexing than full text articles. Summaries produce similar recall to full text but much better precision, which seems to indicate that automatic summaries can efficiently capture the most important contents within the original articles. The combination of MEDLINE citations and automatically generated summaries could improve the recommendations suggested by MTI. On the other hand, indexing performance might be dependent on the MeSH heading being indexed. Summarization techniques could thus be considered as a feature selection algorithm that might have to be tuned individually for each MeSH heading.

Evaluating the use of different positional strategies for sentence selection in biomedical literature summarization.

BACKGROUND: The position of a sentence in a document has been traditionally considered an indicator of the relevance of the sentence, and therefore it is frequently used by automatic summarization systems as an attribute for sentence selection. Sentences close to the beginning of the document are supposed to deal with the main topic and thus are selected for the summary. This criterion has shown to be very effective when summarizing some types of documents, such as news items. However, this property is not likely to be found in other types of documents, such as scientific articles, where other positional criteria may be preferred. The purpose of the present work is to study the utility of different positional strategies for biomedical literature summarization. RESULTS: We have evaluated three different positional strategies: (1) awarding the sentences at the beginning of the document, (2) preferring those at the beginning and end of the document, and (3) weighting the sentences according to the section in which they appear. To this end, we have implemented two summarizers, one based on semantic graphs and the other based on concept frequencies, and evaluated the summaries they produce when combined with each of the positional strategies above using ROUGE metrics. Our results indicate that it is possible to improve the quality of the summaries by weighting the sentences according to the section in which they appear (≈17% improvement in ROUGE-2 for the graph-based summarizer and ≈20% for the frequency-based summarizer), and that the sections containing the more salient information are the Methods and Material and the Discussion and Results ones. CONCLUSIONS: It has been found that the use of traditional positional criteria that award sentences at the beginning and/or the end of the document are not helpful when summarizing scientific literature. In contrast, a more appropriate strategy is that which weights sentences according to the section in which they appear.

Studying the correlation between different word sense disambiguation methods and summarization effectiveness in biomedical texts.

BACKGROUND: Word sense disambiguation (WSD) attempts to solve lexical ambiguities by identifying the correct meaning of a word based on its context. WSD has been demonstrated to be an important step in knowledge-based approaches to automatic summarization. However, the correlation between the accuracy of the WSD methods and the summarization performance has never been studied. RESULTS: We present three existing knowledge-based WSD approaches and a graph-based summarizer. Both the WSD approaches and the summarizer employ the Unified Medical Language System (UMLS) Metathesaurus as the knowledge source. We first evaluate WSD directly, by comparing the prediction of the WSD methods to two reference sets: the NLM WSD dataset and the MSH WSD collection. We next apply the different WSD methods as part of the summarizer, to map documents onto concepts in the UMLS Metathesaurus, and evaluate the summaries that are generated. The results obtained by the different methods in both evaluations are studied and compared. CONCLUSIONS: It has been found that the use of WSD techniques has a positive impact on the results of our graph-based summarizer, and that, when both the WSD and summarization tasks are assessed over large and homogeneous evaluation collections, there exists a correlation between the overall results of the WSD and summarization tasks. Furthermore, the best WSD algorithm in the first task tends to be also the best one in the second. However, we also found that the improvement achieved by the summarizer is not directly correlated with the WSD performance. The most likely reason is that the errors in disambiguation are not equally important but depend on the relative salience of the different concepts in the document to be summarized.

A semantic graph-based approach to biomedical summarisation.

OBJECTIVE: Access to the vast body of research literature that is available in biomedicine and related fields may be improved by automatic summarisation. This paper presents a method for summarising biomedical scientific literature that takes into consideration the characteristics of the domain and the type of documents. METHODS: To address the problem of identifying salient sentences in biomedical texts, concepts and relations derived from the Unified Medical Language System (UMLS) are arranged to construct a semantic graph that represents the document. A degree-based clustering algorithm is then used to identify different themes or topics within the text. Different heuristics for sentence selection, intended to generate different types of summaries, are tested. A real document case is drawn up to illustrate how the method works. RESULTS: A large-scale evaluation is performed using the recall-oriented understudy for gisting-evaluation (ROUGE) metrics. The results are compared with those achieved by three well-known summarisers (two research prototypes and a commercial application) and two baselines. Our method significantly outperforms all summarisers and baselines. The best of our heuristics achieves an improvement in performance of almost 7.7 percentage units in the ROUGE-1 score over the LexRank summariser (0.7862 versus 0.7302). A qualitative analysis of the summaries also shows that our method succeeds in identifying sentences that cover the main topic of the document and also considers other secondary or "satellite" information that might be relevant to the user. CONCLUSION: The method proposed is proved to be an efficient approach to biomedical literature summarisation, which confirms that the use of concepts rather than terms can be very useful in automatic summarisation, especially when dealing with highly specialised domains.