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Nutritional management of neonates who undergo major surgery for gastrointestinal disorders: a joint position paper of the Italian Society of Neonatology (SIN), the Italian Society of Pediatric Surgery (SICP), and the Italian Society of Pediatric Nutrition (SINUPE)
No full textNeonates undergoing major gastrointestinal surgery for congenital or acquired disorders require complex nutritional management to support their growth and recovery. Prolonged fasting can be detrimental, necessitating timely and appropriate nutritional support. This joint position paper by the Italian Society of Neonatology, the Italian Society of Pediatric Surgery, and the Italian Society of Pediatric Nutrition aims to provide evidence-based suggestions for the nutritional care of these vulnerable infants, addressing the lack of robust randomized controlled trials in this field through expert opinion. A panel of experts in neonatology, paediatric surgery, and paediatric nutrition across Italy reviewed the literature by searching the PubMed database (1990- September 2024) using specific keywords. English-language papers were analysed without restrictions on study design or outcomes. Identified references were cross-checked, and additional relevant literature was included based on expert knowledge. The panel formulated suggestions based on the available evidence and clinical expertise. The position paper provides specific suggestions for various aspects of nutritional management, including the timing and modalities of enteral nutrition (EN), the choice of milk (prioritizing human milk), vitamin and trace element supplementation, and condition-specific guidance for gastrointestinal disorders such as oesophageal atresia, congenital diaphragmatic hernia, chylothorax, intestinal atresia, abdominal wall defects, Hirschsprung disease, necrotizing enterocolitis, and intestinal failure. Early EN (within 48 hours post-surgery) is generally advised and then tailored according to feeding tolerance. The paper also emphasizes the importance of monitoring micronutrient deficiencies and promoting oral feeding skills. This joint position paper offers a comprehensive and multidisciplinary approach to the nutritional management of neonates undergoing major gastrointestinal surgery. Recognizing the limitations of current evidence, these suggestions aim to standardise and optimise nutritional care, based on available data and expert consensus, ultimately improving outcomes for this high-risk population. The paper highlights the need for individualised nutritional strategies, careful monitoring, and further research in this challenging area of neonatal care
From Scalpel to Algorithms in Lung Cancer Management: Precision Requires Wisdom—Beware Not Artificial Intelligence, but Natural Stupidity
No full textGenetic and Iterative Metaheuristics‐Informed Algorithms for Precision Shallow Groundwater Modeling and Drought Inference
No full textCokriging is a widely used geostatistical method for modeling the shallow groundwater table, often incorporating digital elevation models (DEMs) as secondary variables. However, existing approaches rarely include robust validation procedures to reduce local uncertainty or iteratively improve spatial predictions. This study presents a novel cokriging algorithm that integrates metaheuristics and iterative residual correction to enhance the estimation of the shallow water table. The method is based on a novel mathematically demonstrated theory based on overlapping kriging maps. Our framework consists of four steps: (a) a genetic algorithm selects the optimal variogram model based on input data; (b) a DEM-based cokriging routine generates initial estimates; (c) residuals between observed and predicted values are estimated; and (d) residuals are iteratively corrected, and their maps are superimposed to initial estimates until a user-defined convergence threshold is met. Applied to shallow aquifers in Italy, the algorithm minimized squared residuals between observed and simulated piezometric levels, with the stopping condition based on average annual residuals. Validation against historical data and a global groundwater model demonstrated significant improvements in predictive performance. Within three iterations, the correlation coefficient increased from mathematical equation2 = 0.85 to mathematical equation2 = 0.99, an accuracy not achieved with conventional DEM-based cokriging. Importantly, a multiyear analysis of corrected residuals enabled detection of groundwater level declines linked to drought conditions, offering a novel approach to drought detection and impact assessment. By improving spatial accuracy and supporting long-term monitoring, this method helps decision-makers implement informed water conservation strategies, particularly under increasing pressure from climate variability, prolonged droughts, and growing water demand
Exploring UNet-based models for prostate lesion segmentation from multi-sequence MRI (T2W, ADC, DWI)
No full textAccurate delineation of lesions in prostate MRI is crucial for the diagnosis of prostate cancer. Manual segmentation is time-consuming, requires advanced medical expertise, and is subject to inter-operator variability. Automatic lesion segmentation therefore represents a valuable tool to support clinicians by reducing workload, minimizing observer bias, and enabling more consistent image analysis. In this work, we investigated the performance of four deep learning architectures for prostate lesion segmentation: nnU-Net, DenseUNet, SegResUNet, and U-Net. Unlike many existing studies that rely on publicly available data, we constructed a dedicated dataset to better capture real-world variability and challenges. The dataset, comprising T2-weighted (T2W), apparent diffusion coefficient (ADC), and diffusion-weighted imaging (DWI) sequences, was carefully annotated by medical experts to ensure high-quality labels. Training was performed using the full combination of these modalities. Two cohorts were considered based on lesion severity, as defined by PI-RADS (Prostate Imaging–Reporting and Data System) scores: one with only PI-RADS 4–5 lesions (151 patients), and another including PI-RADS 3 cases, totaling 209 patients. Evaluation was conducted both on a patient-by-patient basis and in a consolidated all-patient setting. In the patient-level analysis, nnU-Net achieved the highest Dice similarity coefficient (DSC) of 0.60 when trained on PI-RADS 4–5 lesions, while in the all-patient analysis, DenseUNet attained a DSC of 0.57 on the same dataset. These results are within the range reported in recent prostate lesion segmentation studies, and in some cases are comparable to or exceed those obtained with substantially larger datasets
Post-tuberculosis lung disease: a guide for clinicians
No full textPost-tuberculosis lung disease (PTLD) is an increasingly recognized condition that significantly affects survivors’ quality of life, creating disability and incrementing the risk of mortality. PTLD includes a spectrum of structural and functional lung impairments such as obstructive, restrictive, and mixed patterns, bronchiectasis, and pulmonary fibrosis that persist beyond microbiological cure. Global prevalence data highlight a heavy burden of PTLD, especially in high-incidence regions, driven by late diagnosis and suboptimal treatment. Functional and radiological evaluation remains critical for timely diagnosis, with spirometry and imaging revealing lasting abnormalities in a large proportion of TB survivors. Multidisciplinary care is essential and includes bronchodilator therapy, infections/complications management and prevention, pulmonary rehabilitation, and, in selected cases, surgical intervention. Despite increasing recognition, standardized diagnostic and therapeutic pathways for PTLD are still lacking, and data on optimal follow-up, rehabilitation strategies, and preventive measures remain limited. Prospective studies, better stratification tools, and patient education initiatives are urgently needed to reduce PTLD morbidity and mortality. This narrative review synthesizes current evidence on PTLD epidemiology, clinical evaluation and management while offering practical suggestions for clinicians taking care of people with TB and addressing research needs
General anesthesia versus deep sedation in patients undergoing Subcutaneous Implantable Defibrillator implantation: a propensity-matched analysis from the i-SUSI project
No full textInnovative statistical method for longitudinal and hierarchical data modeling: the GMEXGBoost method
No full textIntroduction and objectives: Over recent decades, the exponential growth of data, especially in healthcare, has necessitated advanced analytical methods. Conventional machine learning algorithms often assume independence among data points, limiting their effectiveness with longitudinal and hierarchical data. This study introduces a novel algorithm called GMEXGBoost, a methodological extension of generalized mixed-effects models that leverages the boosting framework of XGBoost for estimating fixed effects while simultaneously accounting for random effects. The innovation lies in GMEXGBoost's ability to explicitly incorporate data correlations while retaining the predictive power of boosted trees.
Methods: The GMEXGBoost model was evaluated through extensive simulations and a real-world cohort study, benchmarking against GLMM, GLMMTree, GMERF, and XGBoost. Also, its performance was assessed using predictive mean absolute deviation (PMAD), predictive misclassification rate (PMCR), sensitivity, specificity, accuracy, and AUC. Simulation analyses were conducted using multiple synthetic datasets, each comprising training and testing groups with varying effect structures, including random intercepts and slopes. All computations were performed in RStudio(version 2023.06.0).
Results: Our results indicate that while XGBoost achieved the lowest average errors across most scenarios, GMEXGBoost consistently demonstrated superior stability and accuracy when random-effect variance was large or correlations were strong. Also, in real data, GMEXGBoost outperformed other models in terms of the performance metrics.
Conclusion: The GMEXGBoost algorithm, by combining the estimates of the GLMM and XGBoost models, leverages the capabilities of both and delivers improved performance in complex problems. Although it is not universally superior, but demonstrates clear advantages in the analysis of hierarchical and longitudinal datasets with strong correlations. These properties make it a valuable tool for decision-making in healthcare and other domains that involve complex, structured data
α,β-Unsaturated (Bis)Enones as Valuable Precursors in Innovative Methodologies for the Preparation of Cyclic Molecules by Intramolecular Single-Electron Transfer
No full textThe synthesis of monocyclic and bicyclic compounds plays a fundamental role in organic chemistry, and the need for novel synthetic methodologies is still under investigation. In particular, α,β-unsaturated (bis)enones have emerged as valuable precursors for the formation of cyclic (both mono and bicyclic) structures through single-electron transfer (SET) processes. Single-electron transfer (SET) is a redox process where one electron moves from a donor species to an acceptor, generating radical ions or neutral radicals that drive unique reaction pathways. Thanks to the advent of radical chemistry, it was possible to discover an entirely new reactivity of α,β-unsaturated (bis)enones, which, after a SET event, undergo the formation of cyclic molecules, both in intra and inter-molecular reactions, under several possible pathways, including formal [2+2] cycloaddition reaction (22CA) and 5-exo-trig cyclization, for ring closure. Today, the generation of radical species can be broadly classified into three main approaches: photochemical and photocatalytic, metal-driven and electrochemical processes. In this review, we summarize the progress achieved to date in the synthesis of cyclic molecules from α,β-unsaturated (bis)enones via single-electron transfer events under these three main classes of processes. Whenever possible, the reaction pathway and fate of the radical species generated through SET is discussed
Association Between Fetal Congenital Heart Disease and Assisted Reproductive Technologies in the First Trimester of Pregnancy: A Retrospective Study
No full textObjective: To determine whether there is a higher rate of major fetal congenital heart diseases (CHDs) at first-trimester scan in pregnancies conceived by assisted reproductive technology (ART). Methods: A retrospective study was conducted from 2014 to 2022. It included 20,009 singleton pregnancies undergoing ultrasound between 11 and 15 weeks for first-trimester aneuploidy screening or referral for suspected fetal abnormality. Fetal heart assessment was performed through sequential analysis. In cases of CHDs, extracardiac malformations, or other risk factors for major aneuploidies, fetal karyotype evaluation was conducted. CHDs were categorized as major or minor. Results: A CHD was diagnosed in 133 (0.7%) of 18,532 natural pregnancies and 14 (0.9%) of 1477 ART pregnancies. The prevalence of major CHDs in natural pregnancies was 0.5%, with no significant difference compared to ART pregnancies (0.7%; p = 0.47). Overall, 48 CHD cases (43.2%) were associated with extracardiac abnormalities, with no differences between natural and ART pregnancies (p = 0.38). The frequency of abnormal karyotype and isolated CHDs (normal karyotype and no extracardiac abnormalities) also did not differ. Conclusion: The rate of major CHDs detectable at the end of the first trimester does not differ between ART and natural pregnancies
