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Thirty-year % increase of adults with hypertension in the European/ Americas and South-East Asia/ Western Pacific (WHO region). Create using the data from: World Health Organization. Global report on hypertension: the race against a silent killer. Geneva, Switzerland: 2023. Thirty-year % increase of adults with hypertension in the European/ Americas and South-East Asia/ Western Pacific (WHO region). Create using the data from: World Health Organization. Global report on hypertension: the race against a silent killer. Geneva, Switzerland: 2023.

Correspondence to Dr Mark John Johnson, Department of Neonatal Medicine, University Hospital Southampton NHS Foundation Trust, Southampton, SO16 5YA, UK; m.johnson@soton.ac.uk Since its first neonatal application in an infant with small bowel atresia in 1968,1 parenteral nutrition (PN) has become a key tool in the management of preterm infants, and has likely contributed to the improvements in survival seen over the past few decades. Studies in adults have already established that early PN may be detrimental, and more recently the Pediatric Early vs Late Parenteral Nutrition in Intensive Care Unit (PEPaNIC) Study has shown that withholding PN for 7 days improves outcomes in critically ill children on paediatric intensive care units (PICUs).4 The proposed mechanisms for this include an effect of early amino acid administration to reduce autophagy, an innate immune process which regulates cell damage and in turn energy utilisation. While a subgroup analysis of the PEPaNIC Study focusing on a more neonatal population (term infants aged <28 days) found similar benefits to those seen in the PEPaNIC Study population as a whole, these patients are still somewhat different to the typical neonatal unit population on PN.5 It therefore seems reasonable to explore this concept in the neonatal population, as Webbe et al and Uthaya et al have done here. [...]a targeted or more cautious incremental approach is perhaps warranted when investigating this further.

Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical data sets will make precision medicine a reality. Impact Biotechnological advances have made multiomic evaluations feasible and integration of multiomics data may provide a holistic view of disease pathophysiology. Artificial Intelligence and machine learning tools are being increasingly used in healthcare for diagnosis, prognostication, and outcome predictions. Leveraging artificial intelligence and machine learning tools for integration of multiomics and clinical data will pave the way for precision medicine in perinatology.

It is common practice in perinatal epidemiology to calculate gestational-age-specific or birth-weight-specific associations between an exposure and a perinatal outcome. Gestational age or birth weight, for example, might lie on a pathway from the exposure to the outcome. This practice of conditioning on a potential intermediate has come under critique for various reasons. First, if one is interested in assessing the overall effect of an exposure on an outcome, it is not necessary to stratify, and indeed, it is important not to stratify, on an intermediate. Second, if one does condition on an intermediate, to try to obtain what might conceived of as a \"direct effect\" of the exposure on the outcome, then various biases and paradoxical results can arise. It is now well documented theoretically and empirically that, when there is an unmeasured common cause of the intermediate and the outcome, associations adjusted for the intermediate are subject to bias. In this paper, we propose 3 approaches to facilitate valid inference when effects conditional on an intermediate are in view. These 3 approaches correspond to (i) conditioning on the predicted risk of the intermediate, (ii) conditioning on the intermediate itself in conjunction with sensitivity analysis, and (iii) conditioning on the subgroup of individuals for whom the intermediate would occur irrespective of the exposure received. The second and third approaches both require sensitivity analysis, and they result in a range of estimates. Each of the 3 approaches can be used to resolve the \"birth-weight paradox\" that exposures such as maternal smoking seem to have a protective effect among low-birth-weight infants. The various methodologic approaches described in this paper are applicable to a number of similar settings in perinatal epidemiology.

Precision medicine stems from a new approach to the prevention, diagnosis and treatment of patients, due to the shift in focus away from pathology and towards the uniqueness of the individual, personalising the diagnostic–therapeutic pathway. This paradigm shift has been made possible by the emergence of new high-throughput technologies capable of generating large amounts of data on multiple levels of a biological system, identifying pathology-related genes, transcripts, proteins and metabolites. Metabolomics plays a primary role in this context, providing, through non-invasive sampling, a very close image of the phenotype of the organism being studied by detecting metabolites, end products downstream of gene transcription, present in cells, tissues, organs and biological fluids. The enormous amount of data that these modern technologies make available, together with the need to elucidate the complex interplay of the various biological levels by combining data from distinct omics, has led to the need to employ advanced informatics techniques, among which artificial intelligence has recently emerged. These innovations are of great interest in the field of perinatology, representing an attempt to optimise the diagnostic timeline for the most critical newborns. In addition, they may contribute to the improvement of prevention strategies available to date. All these contributions prove to be crucial at very vulnerable life stages, allowing crucial intervention opportunities. In this review, we have analysed studies that have integrated metabolomics with at least one other omics in the perinatal field, attempting to highlight the usefulness of multiomics integration and the different methods employed.

Background Polycystic ovary syndrome (PCOS) is the most common metabolic disorder among women of reproductive age. Many factors are involved in the development of PCOS, among which genetic predisposition is probably the main contributor that is also influenced by lifestyle and environmental factors. This study aims to determine the prevalence of PCOS in different continents based on Rotterdam, AES and NIH diagnostic criteria. Methods We conducted a systematic review and meta-analysis to evaluate the prevalence of polycystic ovary syndrome in women according to (Preferred Reporting Items for Systematic Review and Meta-Analysis) PRISMA guidelines. PubMed, Scopus, Science Direct, Web of Science and Google Scholar databases were comprehensively searched until February 2021 for relevant articles. Heterogeneity between the studies was assessed using the I 2 index. Begg and Mazumdar’s test was used to evaluate publication bias. Results A total of 35 studies with 12,365,646 subjects were retrieved. The mean age ranged from 10–45 years. Global prevalence of PCOS was 9.2% (95% CI: 6.8–12.5%) based on meta-analysis, our results showed that the global prevalence of PCOS was 5.5% (95% CI: 3.9–7.7%) based on NIH criteria, 11.5 (95% CI: 6.6–19.4) based on Rotterdam criteria, and 7.1% (95% CI: 2.3–20.2%) based on AES criteria. According to self-report subgroup analysis, the prevalence of PCOS was found to be 11% (95% CI: 5.2–21.8%). Conclusion Based on the results of the present study, the prevalence of PCOS in the world was 9.2% (95% CI: 6.8–12.5%). According to the results of the present study and the high prevalence of PCOS, especially in the Africa continent, it is necessary for health systems to implement measures to timely prevent and treat this syndrome.

Background Polycystic ovarian syndrome (PCOS) is the most prevalent metabolic disorder in reproductive-age women. It is indeed a multifactorial condition evidenced by ovarian dysfunction, hyperandrogenaemia, infertility, hormonal imbalance and chronic anovulation. Experimental evidence infers that PCOS women are prone to cardiovascular problems and insulin resistance. Purpose To furnish the details about the association of inflammatory markers in PCOS. Design An extensive literature search on PubMed, science direct and google scholar has been performed for articles about PCOS and inflammation in PCOS. A comprehensive analysis using original articles, reviews, systemic and meta-analysis was conducted for better understanding the relationship between inflammatory cytokines and PCOS. Results The inflammatory markers perform a substantial part in managing the functions of the ovary. Any disturbances in their levels can lead to ovarian dysfunction. Inflammatory markers are associated with PCOS pathogenesis. The interplay between inflammatory cytokines in the PCOS ovary strongly implies that inflammation is one of the most potent risk factors of PCOS. Conclusion Inflammatory markers have a significant role in regulating the ovary. This manuscript highlights the significance of metabolic and inflammatory markers with PCOS. Since PCOS is always considered as a metabolic disorder, researchers can also consider focusing on the relationship between the inflammatory markers in PCOS to establish a new treatment or management of the disease and to improve women's health.

BackgroundAs the available information about breast cancer is growing every day, the decision-making process for the therapy is getting more complex. ChatGPT as a transformer-based language model possesses the ability to write scientific articles and pass medical exams. But is it able to support the multidisciplinary tumor board (MDT) in the planning of the therapy of patients with breast cancer?Material and MethodsWe performed a pilot study on 10 consecutive cases of breast cancer patients discussed in MDT at our department in January 2023. Included were patients with a primary diagnosis of early breast cancer. The recommendation of MDT was compared with the recommendation of the ChatGPT for particular patients and the clinical score of the agreement was calculated.ResultsResults showed that ChatGPT provided mostly general answers regarding chemotherapy, breast surgery, radiation therapy, chemotherapy, and antibody therapy. It was able to identify risk factors for hereditary breast cancer and point out the elderly patient indicated for chemotherapy to evaluate the cost/benefit effect. ChatGPT wrongly identified the patient with Her2 1 + and 2 + (FISH negative) as in need of therapy with an antibody and called endocrine therapy “hormonal treatment”.ConclusionsSupport of artificial intelligence by finding individualized and personalized therapy for our patients in the time of rapidly expanding amount of information is looking for the ways in the clinical routine. ChatGPT has the potential to find its spot in clinical medicine, but the current version is not able to provide specific recommendations for the therapy of patients with primary breast cancer.