Анализ 19,9 млн публикаций базы данных PubMed/MEDLINE методами искусственного интеллекта: подходы к обобщению накопленных данных и феномен “fake news”

Introduction. The English-language databases PubMed/MEDLINE and Embase are valuable information resources for finding original publications in basic and clinical medicine. Currently, there are no artificial intelligence systems to evaluate the quality of these publications.Aim. Development and testing of a system for sentiment analysis (i.e. analysis of emotional modality) of biomedical publications.Materials and methods. The technique of analysis of the “Big data” of biomedical publications was formulated on the basis of the topological theory of sentiment analysis. Algorithms have been developed that allow for the classification of texts from 16 sentiment classes with 90% accuracy (manipulative speech, research without positive results, propaganda, falsification of results, negative personal attitude, aggressive text, negative emotional background, etc.). Based on the algorithms, a scale for assessing the sentiment quality of research (β-score) is proposed.Results. Abstracts of 19.9 million publications registered in PubMed/MEDLINE over the past 50 years (1970–2019) were analyzed. It was shown that publications with low sentiment quality (the value of the β-score of the text is less than zero, which corresponds to the prevalence of manipulative and negative sentiments in the text) comprise only 18.5% (3.68 out of 19.9 million). The greatest values of the β-score were characterized by publications on sports medicine, systems biology, nutrition, on the use of applied mathematics and data mining in medicine. The rubrication of the entire array of publications by 27,840 headings (MESH-system of PubMed/MEDLINE) indicated an increase in the β-score by years (i.e., the positive dynamics of sentiment quality of the texts of publications) for 27,090 of the studied headings. The most intense positive dynamics was found for research in genetics, physiology, pharmacology, and gerontology. 249 headings with sharply negative dynamics of sentiment quality and with a pronounced increase in the manipulative sentiments characteristic of the tabloid press were highlighted. Separate assessments of international experts are presented that confirm the patterns identified.Conclusion. The proposed artificial intelligence system allows a researcher to make an effective assessment of the sentiment quality of biomedical research papers, filtering out potentially inappropriate publications disguised as “evidence-based”.  Введение. Англоязычные базы данных PubMed/MEDLINE и Embase являются ценными информационными ресурсами для нахождения оригинальных публикаций по фундаментальной и клинической медицине. В настоящее время не существует систем искусственного интеллекта, позволяющих оценивать качество этих публикаций.Цель. Разработка и апробация системы для проведения сентимент-анализа (то есть анализа эмоциональной модальности) публикаций по биомедицине.Материалы и методы. Сформулирована методика анализа «больших данных» биомедицинских публикаций, основанная на топологической теории сентимент-анализа медицинских текстов. Разработаны алгоритмы, позволяющие с 90%-й точностью классифицировать тексты по 16 классам сентиментов (манипулятивные обороты речи, исследования без положительных результатов, пропаганда, подделка результатов, негативное личное отношение, агрессивность текста, негативный эмоциональный фон и др.). На основе алгоритмов предложена балльная шкала оценки сентимент-качества исследований (β-балл).Результаты. Проведен анализ текстов абстрактов 19,9 млн публикаций, зарегистрированных в PubMed/MEDLINE за последние 50 лет (1970–2019). Показано, что публикации с низким сентимент-качеством (значение β-балла текста меньше нуля, что соответствует преобладанию манипулятивных и негативных сентиментов в тексте) составляют всего 18,5% (3,68 из 19,9 млн).  Наибольшими значениями β-балла характеризовались публикации по спортивной медицине,  системной биологии, нутрициологии, по использованию методов прикладной математики и интеллектуального анализа данных в медицине. Рубрикация всего массива публикаций по 27840 рубрикам (MESH-система PubMed/MEDLINE) указала на повышение β-балла по годам (то есть на положительную динамику сентимент-качества текстов публикаций) для 27090 исследованных рубрик. Наиболее интенсивная положительная динамика найдена для исследований по генетике, физиологии, фармакологии и геронтологии. Выделены 249 рубрик с резко отрицательной  динамикой сентимент-качества и с выраженным нарастанием манипулятивных сентиментов,  характерных для «желтой» англоязычной прессы. Приведены отдельные оценки международных экспертов, которые подтверждают выявленные закономерности. Заключение. Разработанная система искусственного интеллекта позволяет проводить  эффективную оценку сентимент-качества биомедицинских исследований, отфильтровывая  потенциально неадекватные публикации, публикуемые под маской «доказательных». 

CURRENT VIEWS ON THE TREATMENT OF ACROMEGALY WITH SOMATOSTATIN ANALOGUES

Acromegaly is a disease with multiple organ failure. Furthermore, acromegaly is frequently accompanied by psychological disorders, with a significant decrease in the quality of life. Neurosurgical treatment (transsphenoidal adenomectomy) is recommended as first-line treatment for most patients with acromegaly. According to the recent guidelines, patients after surgery who failed to achieve biochemical control should receive pharmacotherapy. [1, 2, 6] If radical removal of somatotropinoma is unlikely (for an invasive tumor that spread outside the sella, 20 mm or more in size) and there are no strict indications for surgery, many global experts recommend somatostatin analogues as the first-line treatment, which are the absolute leaders among drugs for the treatment of acromegaly

BACKGROUND CHANGES OF THE DIGESTIVE SYSTEM MUCOSA IN PATIENTS WITH ACROMEGALY

Background: Gastrointestinal tract lesions due to the growth hormone impact on mucosa of both the stomach and colon contribute to the complications of acromegaly. In this case, there is a deficiency of the objective assessment data concerning the gastrointestinal tract condition in the acromegaly patients. Aim: To reveal the character of changes in the mucosa of both stomach and colon in the acromegaly patients. Materials and methods: The study is based on the results of analysis of 107 in-patients (32 men and 75 women) with suspected acromegaly who were observed during the period of 2006 to 2012 in the MONIKI Department of Therapeutic Endocrinology. Diagnosis of acromegaly was confirmed. Distribution of patients by gender showed that women suffer from acromegaly in the majority of cases. The ratio men/women equal to 1:2.2 correlates with the literature data. All acromegaly patients underwent the fibroesophago-gastroduodenoscopy, morphological investigation of the stomach mucosa, cytological analysis in search for Helicobacter pylori as well as fibrocolonoscopy plus biopsy in case of the pathological neoplasm identification. Results: Comparative study of changes in the gastrointestinal tract mucosa demonstrated that endoscopic investigation more often showed pathological changes in the upper areas of the digestion tract. It may be explained by the Helicobacter pylori contamination or a side effect of somatostatin analogues in the given group of patients which impairs the stomach epithelium vital activity and enables development of neoplasms in the acromegaly patients. Analysis of the morphofunctional alterations of the stomach-and-colon mucosa in the acromegaly patients revealed concomitant diseases in these patients which were as follows: foveolar hyperplasia of the tegumental-foveolar epithelium, hyperplastic polyps, and tubular adenomas which are an indication for obligatory morphologic investigation of the stomach-and-colon mucosa in this group of patients. Our study proved a high degree of the stomach mucosa contamination with Helicobacter pylori in the acromegaly patients: in 81% of our patients. Conclusion: Our results showed that endoscopy with obligatory targeted biopsy and a search for Helicobacter pylori are extremely necessary for this group of patients. The results obtained are indicative of the necessity of annual gastroenterological examination of patients suffering from acromegaly

MINERAL METABOLISM AND BONE MINERAL DENSITY IN PATIENTS WITH CENTRAL HYPOGONADISM AS INDICATORS OF PREMATURE AGING

Aim of this study was to estimate the markers of mineral turnover and BMD in young women with the central hypogonadism, to compare them with healthy young women and healthy postmenopausal women of middle/advanced age. Materials and methods. One hundred seventy women were included in the study: 73 patients with the central hypogonadism (isolated hypogonadism n=35, hypopituitarism n=38), age of 25 [21.2; 30.5] y.o., amenorrhea duration 5 [2.3; 10.1] years; 47 healthy women with regular menstrual cycles, age 24 [23.1; 28.0] y.o., and 50 healthy women with natural menopause, age 56 [53; 58] y.o., menopause duration 6.0 [2.1; 10.0] years. Groups did not differ by age. Results. In patients with central female hypogonadism concentrations of calcium and alkaline phosphatase were significantly higher than in healthy women of similar age, however did not differ from the parameters in postmenopausal women. T-scores <-2.5 SD in lumbar spine were noted in 55% and 28% patients with central hypogonadism and in menopause respectively (р<0.001), and in a hip - 27% and 7%, respectively (r=0.002). The factors promoting lower values of BMD in young women with central hypogonadism were the amenorrhea duration, low level of total testosterone, primary amenorrhea. Distinctions of mineral turnover and BMD in isolated hypogonadism and a hypopituitarism were not revealed. Conclusions. Central hypogonadism in women at young age is a higher prognostic factor of low BMD than natural menopause, and might be considered as a marker of premature aging

Analysis of 19.9 million publications from the PubMed/MEDLINE database using artificial intelligence methods: approaches to the generalizations of accumulated data and the phenomenon of “fake news

Introduction. The English-language databases PubMed/MEDLINE and Embase are valuable information resources for finding original publications in basic and clinical medicine. Currently, there are no artificial intelligence systems to evaluate the quality of these publications.Aim. Development and testing of a system for sentiment analysis (i.e. analysis of emotional modality) of biomedical publications.Materials and methods. The technique of analysis of the “Big data” of biomedical publications was formulated on the basis of the topological theory of sentiment analysis. Algorithms have been developed that allow for the classification of texts from 16 sentiment classes with 90% accuracy (manipulative speech, research without positive results, propaganda, falsification of results, negative personal attitude, aggressive text, negative emotional background, etc.). Based on the algorithms, a scale for assessing the sentiment quality of research (β-score) is proposed.Results. Abstracts of 19.9 million publications registered in PubMed/MEDLINE over the past 50 years (1970–2019) were analyzed. It was shown that publications with low sentiment quality (the value of the β-score of the text is less than zero, which corresponds to the prevalence of manipulative and negative sentiments in the text) comprise only 18.5% (3.68 out of 19.9 million). The greatest values of the β-score were characterized by publications on sports medicine, systems biology, nutrition, on the use of applied mathematics and data mining in medicine. The rubrication of the entire array of publications by 27,840 headings (MESH-system of PubMed/MEDLINE) indicated an increase in the β-score by years (i.e., the positive dynamics of sentiment quality of the texts of publications) for 27,090 of the studied headings. The most intense positive dynamics was found for research in genetics, physiology, pharmacology, and gerontology. 249 headings with sharply negative dynamics of sentiment quality and with a pronounced increase in the manipulative sentiments characteristic of the tabloid press were highlighted. Separate assessments of international experts are presented that confirm the patterns identified.Conclusion. The proposed artificial intelligence system allows a researcher to make an effective assessment of the sentiment quality of biomedical research papers, filtering out potentially inappropriate publications disguised as “evidence-based”