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Abstract Objective To evaluate the appropriateness, timeliness and perinatal outcomes of obstetric referrals compared to nonreferrals managed by caesarean section (CS) at a tertiary hospital. Methods Research assistants observed and recorded every delivery prospectively. Neonates were followed to discharge or up to 7 days, and outcomes were recorded. Proportions were used to describe baseline characteristics of the groups, and logistic regression was used to determine factors associated with CS in the referral group. Results A total of 10 353 deliveries occurred during the study period: 7.4% (768) were referrals and 92.6% (9585) were nonreferrals. The proportion of CS among referrals was 41.5% (319), nonreferrals 22.5% (2152) and overall 23.8% (2471). Compared to nonreferrals, referred women arrived at the second stage of labour with complications, ending up with emergency CS (p

Abstract Background The quality of nursing education depends largely on the experience student receive in the clinical environment. The learning environment is complex with factors that may positively or negatively influence students learning. The current study aimed to explore the experiences and perceptions of diploma nursing students toward their clinical learning in Dodoma-Tanzania. Methods A qualitative descriptive study design was employed. The study was conducted in four nursing schools involving 32 nursing students who were purposively selected. Data was collected using focus-group discussions and analyzed using thematic analysis. Results Three main themes emerged during the discussions: experience on personal and technical support for clinical learning, the importance of the clinical environment in clinical learning, and insufficient clinical educational planning. The majority of students had negative experiences including poor clinical supervision, lack of equipment, congestion of students, and inability to meet clinical objectives. Few students had positive experiences related to exposure to a real clinical environment and great support from staff nurses. Conclusion Students had mixed experiences, both positive and negative on their clinical learning. The majority of students had negative experiences. This may have a serious impact on the student completing their education, the services they will offer to patients when employed, and nursing professional development.

Herbs are an important nutritional source for humans since they provide a variety of nutrients. Indigenous people have employed herbs, in particular, as traditional medicines since ancient times. Malaysia has hundreds of plant species; herb detection may be difficult due to the variety of herb species and their shape and color similarities. Furthermore, there is a scarcity of support datasets for detecting these plants. The main objective of this paper is to investigate the performance of convolutional neural network (CNN) on Malaysian medicinal herbs datasets, real data and augmented data. Malaysian medical herbs data were obtained from Taman Herba Pulau Pinang, Malaysia, and ten kinds of native herbs were chosen. Both datasets were evaluated using the CNN model developed throughout the research. Overall, herbs real data obtained an average accuracy of 75%, whereas herbs augmented data achieved an average accuracy of 88%. Based on these findings, herbs augmented data surpassed herbs actual data in terms of accuracy after undergoing the augmentation technique.

Air Susu Ibu (ASI) adalah makanan alami bagi bayi dengan kandungan nutrisi yang sesuai dan mendukung pertumbuhan serta perkembangan bayi secara optimal. Produksi ASI yang baik merupakan salah satu faktor keberhasilan ASI eksklusif. Tujuan penelitian ini adalah untuk Menganalisis efektifitas pemberian Hypnobreastfeeding terhadap produksi ASI pada ibu nifas. Metode penelitian ini menggunakan Quasy eksperimen dengan desain pretest and posttest one group dan tehnik purposive sampling. Pelaksanaan pemberian intervensi ibu nifas pada hari ke 5-7 masa nifas ibu. Populasi dan sampel dalam penelitian ini sejumlah 34 orang.. Data di uji dengan menggunakan uji wilcoxon dan mann whitney Nilai p-value = 0,000 < 0,05 maka ada perbedaan yang signifikan antara produksi ASI hari ke-4 dan hari ke-8. Disimpulkan bahwa terdapat Efektifitas Hypnobreastfeeding Terhadap Produksi ASI Pada Ibu Nifas Di Wilayah Kerja Puskesmas Kendit. Dengan hasil penelitian ini perlu dilakukan penelitian lanjutan tentang kecukupan ASI bagi bayi dengan hypnobreastfeeding.

Abstract Background The purpose of this project was to improve perinatal survival by introducing Moyo Fetal Heart Rate (FHR) Monitor coupled with neonatal resuscitation simulation training. Methods The implementation was done at three district hospitals. We assessed health care workers’ (HCW’s) skills and perinatal death trends during implementation. Baseline data were collected from the hospitals before implementation. Newborn resuscitation (NR) skills were assessed before and after simulation training. Assessment of perinatal outcomes was done over 2 years of implementation. We used descriptive analysis; a t-test (paired and independent two-sample) and a one-way Anova test to report the findings. Results A total of 107 HCW’s were trained on FHR monitoring using Moyo and NR knowledge and skills using NeoNatalie simulators. The knowledge increased post-training by 13.6% (p

The Transformer-based approaches to solving natural language processing (NLP) tasks such as BERT and GPT are gaining popularity due to their ability to achieve high performance. These approaches benefit from using enormous data sizes to create pre-trained models and the ability to understand the context of words in a sentence. Their use in the information retrieval domain is thought to increase effectiveness and efficiency. This paper demonstrates a BERT-based method (CASBERT) implementation to build a search tool over data annotated compositely using ontologies. The data was a collection of biosimulation models written using the CellML standard in the Physiome Model Repository (PMR). A biosimulation model structurally consists of basic entities of constants and variables that construct higher-level entities such as components, reactions, and the model. Finding these entities specific to their level is beneficial for various purposes regarding variable reuse, experiment setup, and model audit. Initially, we created embeddings representing compositely-annotated entities for constant and variable search (lowest level entity). Then, these low-level entity embeddings were vertically and efficiently combined to create higher-level entity embeddings to search components, models, images, and simulation setups. Our approach was general, so it can be used to create search tools with other data semantically annotated with ontologies - biosimulation models encoded in the SBML format, for example. Our tool is named Biosimulation Model Search Engine (BMSE).

Maximising FAIRness of biosimulation models requires a comprehensive description of model entities such as reactions, variables, and components. The COmputational Modeling in BIology NEtwork (COMBINE) community encourages the use of Resource Description Framework with composite annotations that semantically involve ontologies to ensure completeness and accuracy. These annotations facilitate scientists to find models or detailed information to inform further reuse, such as model composition, reproduction, and curation. SPARQL has been recommended as a key standard to access semantic annotation with RDF, which helps get entities precisely. However, SPARQL is unsuitable for most repository users who explore biosimulation models freely without adequate knowledge of ontologies, RDF structure, and SPARQL syntax. We propose here a text-based information retrieval approach, CASBERT, that is easy to use and can present candidates of relevant entities from models across a repository’s contents. CASBERT adapts Bidirectional Encoder Representations from Transformers (BERT), where each composite annotation about an entity is converted into an entity embedding for subsequent storage in a list of entity embeddings. For entity lookup, a query is transformed to a query embedding and compared to the entity embeddings, and then the entities are displayed in order based on their similarity. The list structure makes it possible to implement CASBERT as an efficient search engine product, with inexpensive addition, modification, and insertion of entity embedding. To demonstrate and test CASBERT, we created a dataset for testing from the Physiome Model Repository and a static export of the BioModels database consisting of query-entities pairs. Measured using Mean Average Precision and Mean Reciprocal Rank, we found that our approach can perform better than the traditional bag-of-words method.

Documentation of fetal to neonatal heart rate (HR) transition is limited. The aim of the current study was to describe HR changes from one hour before to one hour after normal vaginal deliveries. We conducted a prospective observational cohort study in Tanzania from 1 October 2020 to 30 August 2021, including normal vaginal deliveries with normal neonatal outcomes. HR was continuously recorded from one hour before to one hour after delivery, using the Moyo fetal HR meter, NeoBeat newborn HR meter, and the Liveborn Application for data storage. The median, 25th, and 75th HR percentiles were constructed. Overall, 305 deliveries were included. Median (interquartile range; IQR) gestational age was 39 (38–40) weeks and birthweight was 3200 (3000–3500) grams. HR decreased slightly during the last 60 min before delivery from 136 (123,145) to 132 (112,143) beats/minute. After delivery, HR increased within one minute to 168 (143,183) beats/min, before decreasing to around 136 (127,149) beats/min at 60 min after delivery. The drop in HR in the last hour of delivery reflects strong contractions and pushing. The rapid increase in initial neonatal HR reflects an effort to establish spontaneous breathing.

Semantic annotation is a crucial step to assure reusability and reproducibility of biosimulation models in biology and physiology. For this purpose, the COmputational Modeling in BIology NEtwork (COMBINE) community recommends the use of the Resource Description Framework (RDF). This grounding in RDF provides the flexibility to enable searching for entities within models (e.g., variables, equations, or entire models) by utilizing the RDF query language SPARQL. However, the rigidity and complexity of the SPARQL syntax and the nature of the tree-like structure of semantic annotations, are challenging for users. Therefore, we propose NLIMED, an interface that converts natural language queries into SPARQL. We use this interface to query and discover model entities from repositories of biosimulation models. NLIMED works with the Physiome Model Repository (PMR) and the BioModels database and potentially other repositories annotated using RDF. Natural language queries are first “chunked” into phrases and annotated against ontology classes and predicates utilizing different natural language processing tools. Then, the ontology classes and predicates are composed as SPARQL and finally ranked using our SPARQL Composer and our indexing system. We demonstrate that NLIMED's approach for chunking and annotating queries is more effective than the NCBO Annotator for identifying relevant ontology classes in natural language queries.Comparison of NLIMED's behavior against historical query records in the PMR shows that it can adapt appropriately to queries associated with well-annotated models.

Stemming has an important role in text processing. Stemming of each language is different and strongly affected by the type of text language. Besides that, each language has different rules in the use of words with an affix. A large number of the words used in the Indonesian language are formed by combining root words with affixes and other combining forms. One of the problems in Indonesian stemming is having different types of affixes, and also having some prefixes that changes according to the first letters of the root words. Implementing Idris stemmer for Indonesian text is of interest because Indonesia and Malaysia have the same language root. However, the results do not always produce the actual word, because the Idris algorithm first removes the prefix according to Rule 2. This elimination directly affected the Idris stemmer result when implemented to Indonesian text. In this study, we focus on a modified Idris stemmer (from Malay) to IN-Indris with Indonesia context. In order to test the proposed modification to the original algorithm, Indonesian online novels excerpts are used to measure the performance of IN-Idris.test was conducted to compare the proposed algorithm with other stemmers. From the experiment result, IN-Idris had an accuracy of approximately 82.81%. There was an increased accuracy up to 5.25% when compared to Idris accuracy. Moreover, the proposed stemmer is also running faster than Idris with a gap of speed of around 0.25 seconds. ABSTRAK: Stemming mempunyai peranan penting dalam pemprosesan teks. Stem setiap bahasa adalah berbeza dan sangat dipengaruhi oleh jenis bahasa teks. Selain itu, setiap bahasa mempunyai peraturan yang berbeza dalam penggunaan kata dengan awalan. Sebilangan besar kata-kata yang digunakan dalam bahasa Indonesia dibentuk dengan menggabungkan kata akar dengan afiks dan bentuk gabungan lain. Salah satu masalah dalam bahasa Indonesia adalah mempunyai pelbagai jenis awalan, dan juga mempunyai beberapa awalan yang berubah sesuai dengan huruf pertama kata dasar. Menerapkan stemder Idris untuk teks Indonesia adalah minat kerana Indonesia dan Malaysia mempunyai akar bahasa yang sama. Namun, hasilnya tidak selalu menghasilkan kata yang sebenarnya, kerana algoritma Idris pertama kali menghapus awalan menurut Peraturan 2. Penghapusan ini secara langsung mempengaruhi hasil batang Idris ketika diterapkan ke teks Indonesia. Dalam kajian ini, kami memfokuskan pada stemmer Idris yang diubahsuai (dari bahasa Melayu) ke IN-Indris dengan konteks Indonesia. Untuk menguji cadangan pengubahsuaian pada algoritma asli, petikan novel dalam talian Indonesia digunakan untuk mengukur prestasi IN-Idris. Ujian dilakukan untuk membandingkan algoritma yang dicadangkan dengan stemmer lain. Dari hasil eksperimen, IN-Idris mempunyai ketepatan sekitar 82,81%, ada peningkatan ketepatan hingga 5,25% dibandingkan dengan ketepatan Idris. Selain itu, stemmer yang dicadangkan juga berjalan lebih cepat daripada Idris dengan jurang kelajuan sekitar 0.25 saat.