In view of the global demographic shift, a scientific symposium was organised by the European Society for Clinical Nutrition and Metabolism (ESPEN) to address nutrition-related challenges of the older population and provide an overview of the current state of knowledge. Eighteen nutrition-related issues of the ageing global society were presented by international experts during the symposium and summarised in this report. Anorexia of ageing, dysphagia, malnutrition, frailty, sarcopenia, sarcopenic obesity, and the metabolic syndrome were highlighted as major nutrition-related geriatric syndromes. Great progress has been made in recent years through standardised definitions of some but not all syndromes. Regarding malnutrition, the GLIM approach has shown to be suitable also in older adults, justifying its continuous implementation. For anorexia of ageing, a consensus definition is still required. Intervention approaches should be integrated and person-centered with the aim of optimizing intrinsic capacity and maintaining functional capacity. Landmark studies like EFFORT and FINGER have impressively documented the potential of individualised and multifactorial interventions for functional and health benefits. Combining nutritional intervention with physical training seems particularly important whereas restrictive diets and drug treatment should generally be used with caution because of undesirable risks. Obesity management in older adults should take into account the risk of promoting sarcopenia. In the future, even more individualised approaches like precision nutrition may enable better nutritional care. Meanwhile all stakeholders should focus on a better implementation of currently available strategies and work closely together to improve nutritional care for older adults.

Background: Critically ill neonates are at greater risk for adverse drug reactions (ADRs). The differentiation of ADRs from reactions associated with organ dysfunction/immaturity or genetic variability is difficult. Methods: In this prospective cohort study, each ADR was assessed using newborn-specific severity and probability scales by the clinical pharmacist. Subsequently, a machine learning-based risk score was designed to predict ADR presence in neonates. Results: In 98/412 (23.8%) of (56.3%; male) neonates included, 187 ADRs (0.42 ADR/patient) were determined related to 49 different drugs (37.12%). Drugs identified as high risk were enoxaparin, dexmedetomidine, vinblastine, dornase alfa, etoposide/carboplatin and prednisolone. The independent variables included in the risk score to predict ADR presence, according to the random forest importance criterion, were: systemic hormones (2 points), cardiovascular drugs (3 points), diseases of the circulatory system (1 point), nervous system drugs (1 point), and parenteral nutrition treatment (1 point), (cut-off value: 3 points). This risk score correctly classified 91.1% of the observations in the test set (c-index: 0.914). Conclusions: Using the high-performing risk score specific to neonates, it is expected that high-risk neonatal ADRs can be determined and prevented before they occur. Moreover, the awareness of clinicians of these drugs can be improved with this web-tool, and mitigation strategies (change of drug, dose, treatment duration, etc.) can be considered, based on a benefit-harm relationship for suspected drugs with a newborn-centered approach.

Objectives: Coronavirus (COVID-19) cases and deaths related to the virus have been reported all over the world. Pharmacists play an important role in conveying accurate information about COVID-19 to the community. The aim of this study was to evaluate the knowledge and attitudes among hospital pharmacists about COVID-19. Materials and Methods: A questionnaire was distributed to pharmacists participating in the Th National Hospital and Institution Pharmacists Congress. The questions included in this questionnaire were created using the Turkish COVID-19 Scientific Committee guideline (COVID-19 Guideline). Results: Analysis of 237 questionnaires (72.6% completed by women) showed that the media (television, newspaper), internet (nonscientific resources), internet (scientific resources), and social media were the most popular sources of information (60.3%, 53.6%, 53.2%, and 41.4%, respectively). The participants' age and the source of information that they used had an important influence on their knowledge of and attitudes towards COVID-19 infection. The majority of the participants (72.6%) stated that they were not wearing any kind of mask. Transmission of the disease by airborne route was well known by the participants (91.1%). as well as the main symptoms such as fever (92.4%), cough (84.4%), and dyspnea (60.3%). The participants were aware of the risk groups for COVID-19 infection such as advanced age (84.8%) and having comorbidities (80.2%). Washing hands with soap (92.0%), using hand disinfectants (80.6%), and avoiding contact with sick people (81.9%) were popular answers for protection from the disease, but wearing an N95 mask was also mentioned by 59.1% of the participants. Prevention of the disease by rinsing the nose with saline solution was believed in by 43.9% of the participants. Conclusion: Classical media and social media affect the attitudes of both the public and health professionals. Using media tools for accurate information is one of the basic conditions for preventing and controlling the spread of the disease.

Hospitalized newborns have an increased risk of malnutrition and, especially preterm infants, often experience malnutrition-related extrauterine growth restriction (EUGR). The aim of this study was to predict the discharge weight and the presence of weight gain at discharge with machine learning (ML) algorithms. The demographic and clinical parameters were used to develop the models using fivefold cross-validation in the software-R with a neonatal nutritional screening tool (NNST). A total of 512 NICU patients were prospectively included in the study. Length of hospital stay (LOS), parenteral nutrition treatment (PN), postnatal age (PNA), surgery, and sodium were the most important variables in predicting the presence of weight gain at discharge with a random forest classification (AUROC:0.847). The AUROC of NNST-Plus, which was improved by adding LOS, PN, PNA, surgery, and sodium to NNST, increased by 16.5%. In addition, weight at admission, LOS, gestation-adjusted age at admission (> 40 weeks), sex, gestational age, birth weight, PNA, SGA, complications of labor and delivery, multiple birth, serum creatinine, and PN treatment were the most important variables in predicting discharge weight with an elastic net regression (R² = 0.748). This is the first study on the early prediction of EUGR with promising clinical performance based on ML algorithms. It is estimated that the incidence of EUGR can be improved with the implementation of this ML-based web tool (http://www.softmed.hacettepe.edu.tr/NEO-DEER/) in clinical practice.