Melatonin as a master regulator of cell death and inflammation: molecular mechanisms and clinical implications for newborn care

Melatonin, more commonly known as the sleep hormone, is mainly secreted by the pineal gland in dark conditions and regulates the circadian rhythm of the organism. Its intrinsic properties, including high cell permeability, the ability to easily cross both the blood–brain and placenta barriers, and its role as an endogenous reservoir of free radical scavengers (with indirect extra activities), confer it beneficial uses as an adjuvant in the biomedical field. Melatonin can exert its effects by acting through specific cellular receptors on the plasma membrane, similar to other hormones, or through receptor-independent mechanisms that involve complex molecular cross talk with other players. There is increasing evidence regarding the extraordinary beneficial effects of melatonin, also via exogenous administration. Here, we summarize molecular pathways in which melatonin is considered a master regulator, with attention to cell death and inflammation mechanisms from basic, translational and clinical points of view in the context of newborn care

A regional audit system for stillbirth: A way to better understand the phenomenon

Background: Implementation of high-quality national audits for perinatal mortality are needed to improve the registration of all perinatal deaths and the identification of the causes of death. This study aims to evaluate the implementation of a Regional Audit System for Stillbirth in Emilia-Romagna Region, Italy. Methods: For each stillbirth ( 65 22 weeks of gestation, 65 500 g) occurred between January 1, 2014 to December 1, 2016 (n = 332), the same diagnostic workup was performed and a clinical record with data about mother and stillborn was completed. Every case was discussed in a multidisciplinary local audit to assess both the cause of death (ReCoDe classification) and the quality of care. Data were reviewed by the Regional Audit Group. Stillbirth rates, causes of death and the quality of care were established for each case. Results: Total stillbirth rate was 3.09 per 1000 births (332/107,528). Late stillbirth rate was 2.3 per 1000 (251/107,087). Sixteen stillbirths were not registered by the Regional Birth Register. The most prevalent cause of death was placental disorder (33.3%), followed by fetal (17.6%), cord (14.2%) and maternal disorders (7.6%). Unexplained cases were 14%. Compared to local audits, the regional group attributed different causes of death in 17% of cases. At multivariate analysis, infections were associated with early stillbirths (OR 3.38, CI95% 1.62-7.03) and intrapartum cases (OR 6.64, CI95% 2.61-17.02). Placental disorders were related to growth restriction (OR 1.89, CI95% 1.06-3.36) and were more frequent before term (OR 1.86, CI95% 1.11-3.15). Stillbirths judged possibly/probably preventable with a different management (10.9%) occurred more frequently in non-Italian women and were mainly related to maternal disorders (OR 6.64, CI95% 2.61-17.02). Conclusions: Regional Audit System for Stillbirth improves the registration of stillbirth and allows to define the causes of death. Moreover, sub-optimal care was recognized, allowing to identify populations which could benefit from preventive measures

A Noncoding Point Mutation of Zeb1 Causes Multiple Developmental Malformations and Obesity in Twirler Mice

Heterozygous Twirler (Tw) mice develop obesity and circling behavior associated with malformations of the inner ear, whereas homozygous Tw mice have cleft palate and die shortly after birth. Zeb1 is a zinc finger protein that contributes to mesenchymal cell fate by repression of genes whose expression defines epithelial cell identity. This developmental pathway is disrupted in inner ears of Tw/Tw mice. The purpose of our study was to comprehensively characterize the Twirler phenotype and to identify the causative mutation. The Tw/+ inner ear phenotype includes irregularities of the semicircular canals, abnormal utricular otoconia, a shortened cochlear duct, and hearing loss, whereas Tw/Tw ears are severely malformed with barely recognizable anatomy. Tw/+ mice have obesity associated with insulin-resistance and have lymphoid organ hypoplasia. We identified a noncoding nucleotide substitution, c.58+181G>A, in the first intron of the Tw allele of Zeb1 (Zeb1Tw). A knockin mouse model of c.58+181G>A recapitulated the Tw phenotype, whereas a wild-type knockin control did not, confirming the mutation as pathogenic. c.58+181G>A does not affect splicing but disrupts a predicted site for Myb protein binding, which we confirmed in vitro. In comparison, homozygosity for a targeted deletion of exon 1 of mouse Zeb1, Zeb1ΔEx1, is associated with a subtle abnormality of the lateral semicircular canal that is different than those in Tw mice. Expression analyses of E13.5 Twirler and Zeb1ΔEx1 ears confirm that Zeb1ΔEx1 is a null allele, whereas Zeb1Tw RNA is expressed at increased levels in comparison to wild-type Zeb1. We conclude that a noncoding point mutation of Zeb1 acts via a gain-of-function to disrupt regulation of Zeb1Tw expression, epithelial-mesenchymal cell fate or interactions, and structural development of the inner ear in Twirler mice. This is a novel mechanism underlying disorders of hearing or balance

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.</p

Building a lung and ovarian cancer data warehouse

Objectives: Despite the collection of vast amounts of data by the healthcare sector, effective decision-making in medical practice is still challenging. Data warehousing technology can be applied for the collection and management of clinical data from various sources to provide meaningful insights for physicians and administrators. Cancer data are extremely compli-cated and massive; hence, a clinical data warehouse system can provide insights into prevention, diagnosis and treatment processes through the use of online analytical processing tools for the analysis of multi-dimensional data at different granu-larity levels. Methods: In this study, a clinical data warehouse was developed for lung cancer data, which were kindly pro-vided by the United States National Cancer Institute. Lung and ovarian cancer data were imported in specific formats and cleaned to remove errors and redundancies. SQL server integration services (SSIS) were used for the extract-transform-load (ETL) process. Results: The design of the clinical data warehouse responds efficiently to all types of queries by adopting the fact constellation schema model. Various online analytical processing queries can be expressed using the proposed approach. Conclusions: This model succeeded in responding to complex queries, and the analysis of data is facilitated by using online analytical processing cubes and viewing multilevel data details. © 2020 The Korean Society of Medical Informatics

A semantic trajectory data warehouse for improving nursing productivity

A Trajectory Data Warehouse is a central repository of large amount of data focusing on moving objects, which have been collected and integrated from multiple sources with spatial and temporal dimensions as the main metrics of analysis. By adding semantic-related contextual information, it is converted to a Semantic Trajectory Data Warehouse. It transforms raw trajectories to valuable information that can be utilized for decision-making purposes in ubiquitous applications. Human recourses management is a domain that may benefit significantly from semantic trajectory data warehouses. In particular, employees working shifts can be considered as trajectories. In this work, standard data warehousing tools are used to store data about nursing personnel shifts as trajectories of moving persons. The conceptual and logical modelling of the semantic trajectory data warehouse is developed. The objective is the observation, management and scheduling of nurses’ shifts data by the computation of OLAP operations over them. A prototype implementation has also been realized to illustrate the functionality of the proposed model. The produced results prove the efficiency in improving nursing productivity. © 2020, Springer Nature Switzerland AG

Maintaining Dimension's history in data warehouses effectively

A data warehouse is considered a key aspect of success for any decision support system. Research on temporal databases have produced important results in this field, and data warehouses, which store historical data, can clearly benefit from such studies. A slowly changing dimension is a dimension in which any of its attributes in a data warehouse can change infrequently over time. Although different solutions have been proposed, each has its own particular disadvantages. The authors propose the Object-Relational Temporal Data Warehouse (O-RTDW) model for the slowly changing dimensions in this research work. Using this approach, it is possible to keep track of the whole history of an object in a data warehouse efficiently. The proposed model has been implemented on a real data set and tested successfully. Several limitations implied in other solutions, such as redundancy, surrogate keys, incomplete historical data, and creation of additional tables are not present in our solution. Copyright © 2019, IGI Global

Integrating star and snowflake schemas in data warehouses

A fundamental issue encountered by the research community of data warehouses (DWs) is the modeling of data. In this paper, a new design is proposed, named the starnest schema, for the logical modeling of DWs. Using nested methodology, data semantics can be explicitly represented. Part of the design involves providing a translation mechanism from the star/snowflake schemas to a nested representation. The novel schema proposed in this paper is accomplished by converting the fact-dimension schema to a fact-nested dimension schema. The transformation of the denormalized dimension tables to nested dimension tables increases the efficiency of query execution by reducing the number of tuples accessed for query retrieval since dimensional attributes can be used directly in the Group-by clause. In order to facilitate the implementation of the proposed approach, specific algorithms are built based on the starnest schema

Data pipelines for educational data mining in distance education

New challenges in education demand effective solutions. Although Learning Analytics (LA), Educational Data Mining (EDM) and the use of Big Data are often presented as a panacea, there is a lot of ground to be covered in order for the EDM to answer the real questions of educators. An important step toward this goal is to implement holistic solutions that allow educational stakeholders to engage in the core of the EDM processes. The effectiveness of such an attempt relies on (a) having access to data arranged in an organized and meaningful way and (b) setting a sequence of processes that are flexible and reusable. Therefore, a data pipeline that imports data from a specially developed data warehouse is designed and created. Additionally, it is tested in real-life data, and results are discussed. © 2023 Informa UK Limited, trading as Taylor & Francis Group