Chronic Polyhydramnios: A Medical Entity Which Could Be a Model of Muscle Development in Decreased Mechanical Loading Condition

Polyhydramnios is a condition related to an excessive accumulation of amniotic fluid in the third trimester of pregnancy and it can be acute and chronic depending on the duration. Published data suggest that during muscle development, in the stage of late histochemical differentiation decreased mechanical loading cause decreased expression of myosin heavy chain (MHC) type 1 leading to slow-to-fast transition. In the case of chronic polyhydramnios, histochemical muscle differentiation could be affected as a consequence of permanent decreased physical loading. Most affected would be muscles which are the most active i.e., spine extensor muscles and muscles of legs. Long-lasting decreased mechanical loading on muscle should cause decreased expression of MHC type 1 leading to slow-to-fast transition, decreased number of muscle fiber type I especially in extensor muscles of spine and legs. Additionally, because MHC type 1 is present in all skeletal muscles it could lead to various degrees of hypotrophy depending on constituting a percentage of MHC type 1 in affected muscles. These changes in the case of preexisting muscle disorders have the potential to deteriorate the muscle condition additionally. Given these facts, idiopathic chronic polyhydramnios is a rare opportunity to study the influence of reduced physical loading on muscle development in the human fetus. Also, it could be a medical entity to examine the influence of micro- and hypogravity conditions on the development of the fetal muscular system during the last trimester of gestation.

Enhancing unsupervised medical entity linking with multi-instance learning

Background A lot of medical mentions can be extracted from a huge amount of medical texts. In order to make use of these medical mentions, a prerequisite step is to link those medical mentions to a medical domain knowledge base (KB). This linkage of mention to a well-defined, unambiguous KB is a necessary part of the downstream application such as disease diagnosis and prescription of drugs. Such demand becomes more urgent in colloquial and informal situations like online medical consultation, where the medical language is more casual and vaguer. In this article, we propose an unsupervised method to link the Chinese medical symptom mentions to the ICD10 classification in a colloquial background. Methods We propose an unsupervised entity linking model using multi-instance learning (MIL). Our approach builds on a basic unsupervised entity linking method (named BEL), which is an embedding similarity-based EL model in this paper, and uses MIL training paradigm to boost the performance of BEL. First, we construct a dataset from an unlabeled large-scale Chinese medical consultation corpus with the help of BEL. Subsequently, we use a variety of encoders to obtain the representations of mention-context and the ICD10 entities. Then the representations are fed into a ranking network to score candidate entities. Results We evaluate the proposed model on the test dataset annotated by professional doctors. The evaluation results show that our method achieves 60.34% accuracy, exceeding the fundamental BEL by 1.72%. Conclusions We propose an unsupervised entity linking method to the entity linking in the medical domain, using MIL training manner. We annotate a test set for evaluation. The experimental results show that our model behaves better than the fundamental model BEL, and provides an insight for future research.

Patients’ assessment of medical services quality – a determinant of changes in medical entity management on the example of Poland

PurposeThe article’s primary goal is to identify areas requiring improvement in the activities of healthcare entities, suggest directions for future changes, and indicate the strengths and weaknesses of the clinic’s operation based on patients’ opinions. Subjectively expressed opinions of patients are treated as acceptance of the current state of affairs or the need to introduce changes in a given area.Design/methodology/approachThe empirical research was based on information obtained from questionnaire surveys on patients’ opinions about services provided by medical entities. The hypothesis was verified by research conducted in 23 (out of 50 possible) the most dynamically developing non-public healthcare institutions in one of the regions of Poland. The conducted research was based on a proprietary survey using questions on qualitative and quantitative scales.FindingsThe results of empirical research allowed us to identify areas requiring improvement and to propose future directions of changes in the surveyed units. The suggested changes should significantly improve efficiency in the organisation and management of a health facility, focused on medical effectiveness and patients’ health effectiveness.Originality/valueFrom a broader perspective, research results may become a starting point for further considerations on changes in the organisation and management of healthcare facilities. Using the study’s conclusions in practice may positively affect the improvement of the functioning of healthcare facilities, their better reputation and contribute to increasing competitiveness in the medical services market.

Anti-Inflammatory Azaphilones from the Edible Alga-Derived Fungus Penicillium sclerotiorum

To discover the new medical entity from edible marine algae, our continuously natural product investigation focused on endophytes from marine macroalgae Grateloupia sp. Two new azaphilones, 8a-epi-hypocrellone A (1), 8a-epi-eupenicilazaphilone C (2), together with five known azaphilones, hypocrellone A (3), eupenicilazaphilone C (4), ((1E,3E)-3,5-dimethylhepta-1,3-dien-1-yl)-2,4-dihydroxy-3-methylbenzaldehyde (5), sclerotiorin (6), and isochromophilone IV (7) were isolated from the alga-derived fungus Penicillium sclerotiorum. The structures of isolated azaphilones (1–7) were elucidated by spectrometric identification, especially HRESIMS, CD, and NMR data analyses. Concerning bioactivity, cytotoxic, anti-inflammatory, and anti-fibrosis activities of those isolates were evaluated. As a result, compound 1 showed selective toxicity toward neuroblastoma cell line SH-SY5Y among seven cancer and one fibroblast cell lines. 20 μM of compounds 1, 3, and 7 inhibited the TNF-α-induced NFκB phosphorylation but did not change the NFκB activity. Compounds 2 and 6 respectively promoted and inhibited SMAD-mediated transcriptional activities stimulated by TGF-β.

GLOSS TO THE JUDGEMENT OF SUPREME ADMINISTRATIVE COURT OF DECEMBER 17, 2019. – ENTRUSTING THE DUTIES OF HEAD OF THE MEDICAL ENTITY

The primary procedure for filling the post of manager of a non-entrepreneurial medical entity (as well as for other posts referred to in Article 49(1) of the Law on Medicinal Activities) is to select a candidate by means of a competition. However, there may be cases where it is necessary to fill the post of manager, but there is no possibility of a competition procedure. In such situations, it should be possible to entrust duties in this post, but only for the time necessary to conduct the competition. In so doing, it cannot be considered that the delegation of duties constitutes one of the means of filling the post of manager of a medical entity, which is only a temporary solution enabling the medical entity to function until that post has been filled by means of a competition. In view of the legal personality of an independent public health establishment and the principle of legality in the operation of local government units in the exercise of its powers, it is appropriate to limit the powers of that body as a medical entity only to the situations expressly referred to in the provisions of the Law. However, the provisions of the Law on local government employees do not apply either to the manager of the medical entity or to its other employees.

A giant cutaneous horn of oral commissure: a case report

Cutaneous horn is a conical, circumscribed, dense hyperkeratotic protrusion from skin with epithelial cornification. It is also known by the Latin name ‘Cornu cutaneum’. This rare medical entity resembles animal horn but histological disparity is present between both. They are more commonly present in sun exposed sites or areas that are prone for actinic radiation, burns and hence frequently seen in forearm and upper part of face. Only few cases have been reported with cutaneous horns in unusual sites. Cutaneous horns occurring in oral cavity or perioral regions are extremely rare. The significance of knowing about this dead keratinous cutaneous horn is that it may occur as a part of or in association with a wide range of underlying pathologies, either malignant, premalignant or benign. Majority are due to benign pathologies. We report an unusual presentation of cutaneous horn in left oral commissure of a 45-year-old gentleman which is an extremely rare perioral location for such an ailment.

Medical Named Entity Recognition from Un-labelled Medical Records based on Pre-trained Language Models and Domain Dictionary

Medical named entity recognition (NER) is an area in which medical named entities are recognized from medical texts, such as diseases, drugs, surgery reports, anatomical parts, examination documents, and so on. Conventional medical NER methods do not make full use of un-labelled medical texts embedded in medical documents. To address this issue, we propose a medical NER approach based on pre-trained language models and a domain dictionary. First, we construct a medical entity dictionary by extracting medical entities from labelled medical texts and collecting medical entities from other resources, such as the Yidu-N4K dataset. Second, we employ this dictionary to train domain-specific pre-trained language models using un-labelled medical texts. Third, we employ a pseudo labelling mechanism in un-labelled medical texts to automatically annotate texts and create pseudo labels. Fourth, the BiLSTM-CRF sequence tagging model is used to fine-tune the pre-trained language models. Our experiments on the un-labelled medical texts, which are extracted from Chinese electronic medical records, show that the proposed NER approach enables the strict and relaxed F1 scores to be 88.7% and 95.3%, respectively.

Extraction of Traditional Chinese Medicine Entity: Design of a Novel Span-Level Named Entity Recognition Method With Distant Supervision

Background Traditional Chinese medicine (TCM) clinical records contain the symptoms of patients, diagnoses, and subsequent treatment of doctors. These records are important resources for research and analysis of TCM diagnosis knowledge. However, most of TCM clinical records are unstructured text. Therefore, a method to automatically extract medical entities from TCM clinical records is indispensable. Objective Training a medical entity extracting model needs a large number of annotated corpus. The cost of annotated corpus is very high and there is a lack of gold-standard data sets for supervised learning methods. Therefore, we utilized distantly supervised named entity recognition (NER) to respond to the challenge. Methods We propose a span-level distantly supervised NER approach to extract TCM medical entity. It utilizes the pretrained language model and a simple multilayer neural network as classifier to detect and classify entity. We also designed a negative sampling strategy for the span-level model. The strategy randomly selects negative samples in every epoch and filters the possible false-negative samples periodically. It reduces the bad influence from the false-negative samples. Results We compare our methods with other baseline methods to illustrate the effectiveness of our method on a gold-standard data set. The F1 score of our method is 77.34 and it remarkably outperforms the other baselines. Conclusions We developed a distantly supervised NER approach to extract medical entity from TCM clinical records. We estimated our approach on a TCM clinical record data set. Our experimental results indicate that the proposed approach achieves a better performance than other baselines.