Search

Abstract Background About 50% of cutaneous melanoma (CM) harbors the activating BRAFV600 mutation which exerts most of the oncogenic effects through the MAPK signaling pathway. In the last years, a number of MAPK modulators have been identified, including Spry1. In this context, we have recently demonstrated that knockout of Spry1 (Spry1KO) in BRAFV600-mutant CM led to cell cycle arrest and apoptosis, repressed cell proliferation in vitro, and reduced tumor growth in vivo. Despite these findings, however, the precise molecular mechanism linking Spry1 to BRAFV600-mutant CM remains to be elucidated. Materials and methods Immunoprecipitation coupled to mass spectrometry was employed to gain insight into Spry1 interactome. Spry1 gene was knocked-out using the CRISPR strategy in the BRAF-mutant cell lines. Transmission electron microscopy was used to assess the relationship between Spry1 expression and mitochondrial morphology. By using in vitro and in vivo models, the effects of Spry1KO were investigated through RNA-sequencing, quantitative real-time PCR, Western blot, and immunofluorescence analyses. The Seahorse XF24 assay allowed real-time measurement of cellular metabolism in our model. Angiogenic potential was assessed through in vitro tube formation assays and in vivo CD31 staining. Results Spry1 was mainly located in mitochondria in BRAFV600-mutant CM cells where it interacted with key molecules involved in mitochondrial homeostasis. Spry1 loss resulted in mitochondrial shape alterations and dysfunction, which associated with increased reactive oxygen species production. In agreement, we found that nuclear hypoxia-inducible factor-1 alpha (HIF1α) protein levels were reduced in Spry1KO clones both in vitro and in vivo along with the expression of its glycolysis related genes. Accordingly, Ingenuity Pathway Analysis identified “HIF1α Signaling” as the most significant molecular and cellular function affected by Spry1 silencing, whereas the glycolytic function was significantly impaired in Spry1 depleted BRAFV600-mutant CM cells. In addition, our results indicated that the expression of the vascular endothelial growth factor A was down-regulated following Spry1KO, possibly as a result of mitochondrial dysfunction. Consistently, we observed a substantial impairment of angiogenesis, as assessed by the tube formation assay in vitro and the immunofluorescence staining of CD31 in vivo. Conclusions Altogether, these findings identify Spry1 as a potential regulator of mitochondrial homeostasis, and uncover a previously unrecognized role for Spry1 in regulating nuclear HIF1α expression and angiogenesis in BRAFV600-mutant CM. Significance Spry1KO profoundly impacts on mitochondria homeostasis, while concomitantly impairing HIF1α-dependent glycolysis and reducing angiogenesis in BRAF-mutant CM cells, thus providing a potential therapeutic target to improve BRAFV600-mutant CM treatment.

Abstract Background Low back pain is the leading cause of disability worldwide with a significant socioeconomic burden; artificial intelligence (AI) has proved to have a great potential in supporting clinical decisions at each stage of the healthcare process. In this article, we have systematically reviewed the available literature on the applications of AI-based Decision Support Systems (DSS) in the clinical prevention and management of Low Back Pain (LBP) due to lumbar degenerative spine disorders. Methods A systematic review of Pubmed and Scopus databases was performed according to the PRISMA statement. Studies reporting the application of DSS to support the prevention and/or management of LBP due to lumbar degenerative diseases were included. The QUADAS-2 tool was utilized to assess the risk of bias in the included studies. The area under the curve (AUC) and accuracy were assessed for each study. Results Twenty five articles met the inclusion criteria. Several different machine learning and deep learning algorithms were employed, and their predictive ability on clinical, demographic, psychosocial, and imaging data was assessed. The included studies mainly encompassed three tasks: clinical score definition, clinical assessment, and eligibility prediction and reached AUC scores of 0.93, 0.99 and 0.95, respectively. Conclusions AI-based DSS applications showed a high degree of accuracy in performing a wide set of different tasks. These findings lay the foundation for further research to improve the current understanding and encourage wider adoption of AI in clinical decision-making.

Abstract Background Metabolic dysfunction-associated steatotic liver disease (MASLD) encompasses a range of histological findings from the generally benign simple steatosis to steatohepatitis (MASH) which can progress to fibrosis and cirrhosis. Several factors, including the microbiome, may contribute to disease progression. Results Here, we demonstrate links between the presence and abundance of specific bacteria in the adipose and liver tissues, inflammatory genes, immune cell responses, and disease severity. Overall, in MASLD patients, we observed a generalized obesity-induced translocation of gut bacteria to hepatic and adipose tissues. We identified microbial patterns unique to more severely diseased tissues. Specifically, Enterococcus, Granulicatella, and Morganellaceae abundance is positively correlated with immune cell counts and inflammatory gene expression levels, and both genera are significantly enriched in MASH patients. Brevibacterium is enriched in adipose tissues of patients with liver fibrosis. Conclusion Together, these results provide further insight into the microbial factors that may be driving disease severity. Video Abstract

Introduction: Medical evacuation has historically been rooted in military contexts. It involves the systematic transfer of sick or injured individuals from the battlefield to designated medical facilities. Medical evacuation has found application in civilian settings: the Ebola outbreak and the COVID-19 pandemic. This paper examines the medical evacuation that occurred during the COVID-19 pandemic in Lombardy, Italy (Operation “MEDEVAC”), where overloaded hospitals required the transfer of patients to other regions and even internationally. Methods: MEDEVAC was implemented by the Regional Emergency and Urgency Agency (AREU). Data from MEDEVAC were analysed using the AREU register, detailing patient characteristics, transfer logistics, and outcomes. Results: From March to April 2020, 121 intensive care patients were transferred via MEDEVAC, primarily by air. 65 % of patients were moved to other Italian regions. The Federal Republic of Germany received 35 % of patients, all transported by air. Outcome and mortality rates among transferred patients were comparable to those within regional intensive care units. One life-threatening event was reported during transport. The return of patient to Lombardy showed a 100 % survival rate. Conclusion: This study emphasises the importance of standardised protocols, improved information exchange systems, and enhanced training for medical personnel involved in medical evacuation. MEDEVAC was found to be feasible and able to cope with demands that were excessive in relation to available resources. This study proposes the development of a shared protocol for air transport of patients and a model for international cooperation among emergency response agencies to optimise future medical evacuation activities in civil context.

Summary: A Delphi consensus-seeking procedure was conducted to validate a list of ICD-9-CM codes that could help identify hospital admissions in which antimicrobials are more likely to be prescribed. The panel agreed to include 2967 codes out of 16229 (18.28%). Such codes could support AMS strategies by large-scale monitoring of drug consumption.

Abstract In processes taking place at energies much higher than the weak scale, electroweak corrections can be taken into account by using electroweak evolution equations, that are analogous to the DGLAP equations in QCD. We show that weak isospin conservation in these equations imposes to modify the expressions of the splitting functions commonly used in the literature. These modifications have a profound impact on the parton distribution functions.

The COVID-19 pandemic has impacted society in different areas. In education, several reports show the deleterious effects of the disease on the physical and mental health of students, family members, and teachers around the world. Also, in Brazil, affect studies indicate the prevalence of anxiety, stress, and depression among students. The present research, of a qualitative nature, explores what it means, under the lens of affect and from the student's perspective, to experience remote education during the COVID-19 pandemic. An online questionnaire of 41 closed- and open-ended questions was given to 363 students from a public school in southeastern Brazil. This article analyzes the affective fields that emerged from the discursive textual analysis of the students' responses (n = 100). Four affective fields were categorized: friends, classes, home, and teachers; intersecting emotions, attitudes, values, beliefs, and motivation. In general, students expressed more negative than positive affect but a positive disposition toward face-to-face classes. Boys focused their affect more on classes, while girls on teachers. The affective fields allow us to consider the friends-home-teachers tripod as fundamental to overcoming the phenomenon of affective fatigue that has been identified.

Summary: Mother’s milk contains diverse bacterial communities, although their impact on microbial colonization in very-low-birth-weight (VLBW,

Background: Despite the well-known efficacy of anti-COVID-19 vaccines in preventing morbidity and mortality, several vaccinated individuals are diagnosed with SARS-CoV-2 breakthrough infection, which might require hospitalisation. This multicentre, observational, and retrospective study aimed to investigate the clinical characteristics and outcomes of vaccinated vs. non-vaccinated patients, both hospitalised with SARS-CoV-2 infection in 3 major hospitals in Northern Italy. Methods: Data collection was retrospective, and paper and electronic medical records of adult patients with a diagnosed SARS-CoV-2 infection were pseudo-anonymised and analysed. Vaccinated and non-vaccinated individuals were manually paired, using a predetermined matching criterion (similar age, gender, and date of hospitalisation). Demographic, clinical, treatment, and outcome data were compared between groups differing by vaccination status using Pearson’s Chi-square and Mann-Whitney tests. Moreover, multiple logistic regression analyses were performed to assess the impact of vaccination status on ICU admission or intra-hospital mortality. Results: Data from 360 patients were collected. Vaccinated patients presented with a higher prevalence of relevant comorbidities, like kidney replacement therapy or haematological malignancy, despite a milder clinical presentation at the first evaluation. Non-vaccinated patients required intensive care more often than their vaccinated counterparts (8.8% vs. 1.7%, p = 0.002). Contrariwise, no difference in intra-hospital mortality was observed between the two groups (19% vs. 20%, p = 0.853). These results were confirmed by multivariable logistic regressions, which showed that vaccination was significantly associated with decreased risk of ICU admission (aOR=0.172, 95%CI: 0.039–0.542, p = 0.007), but not of intra-hospital mortality (aOR=0.996, 95%CI: 0.582–1.703, p = 0.987). Conclusions: This study provides real-world data on vaccinated patients hospitalised with COVID-19 in Northern Italy. Our results suggest that COVID-19 vaccination has a protective role in individuals with higher risk profiles, especially regarding the need for ICU admission. These findings contribute to our understanding of SARS-CoV-2 infection outcomes among vaccinated individuals and emphasise the importance of vaccination in preventing severe disease, particularly in those countries with lower first-booster uptake rates.

Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer biology, and immunology, with object detection, image feature extraction, classification, and segmentation applications. Advancements in deep learning (DL) research have been a critical factor in advancing computer techniques for biomedical image analysis and data mining. A significant improvement in the accuracy of cell detection and segmentation algorithms has been achieved as a result of the emergence of open-source software and innovative deep neural network architectures. Automated cell segmentation now enables the extraction of quantifiable cellular and spatial features from microscope images of cells and tissues, providing critical insights into cellular organization in various diseases. This review aims to examine the latest AI and DL techniques for cell analysis and data mining in microscopy images, aid the biologists who have less background knowledge in AI and machine learning (ML), and incorporate the ML models into microscopy focus images.

During pregnancy, primary Toxoplasma gondii infection can cause congenital toxoplasmosis (CT). We described the newborns’ outcomes from a multicentre cohort of mothers with seroconversion (SC) at different gestational ages. This retrospective observational study (from 2007 to 2018) was conducted in two Italian referral hospitals: Fondazione IRCCS Policlinico San Matteo in Pavia and Spedali Civili in Brescia. In total, 247 pregnant women were enrolled: seroconversions were enrolled: seroconversions documented as having occurred in the two months preceding pregnancy in 12 cases (4.9%; 95% CI 2.2–7.5%), and during pregnancy in 235 cases (95.1%; 95% CI 92.5–97.8%). SC is defined as the appearance of specific anti-Toxoplasma antibodies (IgM/IgG) during pregnancy in a previously seronegative woman. A total of 56 (22.5%; 95% CI 17.3–27.7%) newborns were lost to follow-up; thus, the outcome of 193 (77.5%; 95% CI 72.3–82.7%) newborns was analyzed. The overall transmission rate of T. gondii infection was 23.8% (95% CI 17.8–29.8%), 0% (95% CI 0.0–11.9%) among the 1st trimester SCs, 12.5% (95% CI 5.6–19.4%) among the 2nd trimester SCs, 53.8% (95% CI 41.7–66.0%) among the 3rd trimester ones. No CT were found in the group of periconceptional infection. Among the infected newborns, clinically manifest cases were 12 (26.1%; 95% CI 13.4–38.8%), including 1 case (2.2%; 95% CI 2.0–6.4%) of stillbirth and 11 symptomatic neonates (23.9%; 95% CI 11.6–36.2%). A total of 83 amniocentesis were performed (33.6%; 95% CI 27.7–39.5%), no complication was recorded and no false positive or false negative results were registered. The results are in line with the fetal risks reported in literature for T. gondii infection during pregnancy, even if at a lower percentage probably due to a prompt treatment.

On-surface chemistry holds the potential for ultimate miniaturization of functional devices. Porphyrins are promising building-blocks in exploring advanced nanoarchitecture concepts. More stable molecular materials of practical interest with improved charge transfer properties can be achieved by covalently interconnecting molecular units. On-surface synthesis allows to construct extended covalent nanostructures at interfaces not conventionally available. Here, we address the synthesis and properties of covalent molecular network composed of interconnected constituents derived from halogenated nickel tetraphenylporphyrin on Au(111). We report that the π-extended two-dimensional material exhibits dispersive electronic features. Concomitantly, the functional Ni cores retain the same single-active site character of their single-molecule counterparts. This opens new pathways when exploiting the high robustness of transition metal cores provided by bottom-up constructed covalent nanomeshes.

To improve reactivity and achieve a higher material efficiency, catalysts are often used in the form of clusters with nanometer dimensions, down to single atoms. Since the corresponding properties are highly structure‐dependent, a suitable support is thus required to ensure cluster stability during operating conditions. Herein, an efficient method to stabilize cobalt nanoclusters on graphene grown on nickel substrates, exploiting the anchoring effect of nickel atoms incorporated in the carbon network is presented. The anchored nanoclusters are studied by in situ variable temperature scanning tunneling microscopy at different temperatures and upon gas exposure. Cluster stability upon annealing up to 200 °C and upon CO exposure at least up to 1 × 10−6 mbar CO partial pressure is demonstrated. Moreover, the dimensions of the cobalt nanoclusters remain surprisingly small (

A large proportion of the prison population uses psychoactive substances, which constitutes a major public health problem. We conducted a qualitative survey on drug use practices in prison and the care of addiction. French legislation stipulates that health care for prisoners must be equivalent to that of the general population, including harm reduction, which is the policy implemented in France since HIV epidemic in the late 1980s. Our article explores how this policy is accepted and applied within the prison system. Are the delivery of addiction care and the implementation of harm reduction measures rooted in an ethic of care?

Cranberry-derived proanthocyanidin (PAC) is processed by the gut microbiota to produce 3-(4-hydroxyphenyl)-propionic acid (HPPA), among other metabolites. These data are in support of the article entitled, “Cranberry proanthocyanidin and its microbial metabolite 3,4-dihydroxyphenylacetic acid, but not 3-(4-hydroxyphenyl)-propionic acid, partially reverse pro-inflammatory microRNA responses in human intestinal epithelial cells,” published in Molecular Nutrition and Food Research [1]. Here we describe data generated by nCounterⓇ Human v3 miRNA Expression Panel of RNA obtained from Caco-2BBe1 cells exposed to two different concentrations of cranberry extract rich in PAC (50 µg/ml or 100 µg/ml) or 3-(4-hydroxyphenyl)-propionic acid (5 µg/ml or 10 µg/ml) for 24 h, then stimulated with 1 ng/ml of IL-1ß or not (mock) for three hours. The raw data are publicly available at the NCBI GEO database GSE237078. This work also includes descriptive methodological procedures, treatment-responsive microRNA (miRNA) expression profiles in Caco-2BBe1 cells, and in silico mRNA gene target and pathway enrichment analyses of significantly differentially expressed miRNAs (q < 0.001). Cranberry and its components have recognized health benefits, particularly in relation to combatting inflammation and pathogenic bacterial adhesion. These data will be valuable as a reference to study the response of intestinal cells to other polyphenol-rich food sources, analyze gut microbial responses to cranberry and its metabolites in different cell lines and mammalian hosts to elucidate individualized effects, and to delineate the role of the gut microbiota in facilitating the benefits of cranberry. Moreover, these data will aid in expanding our knowledge on the mechanisms underlying the benefits of cranberry and its components.

In the era of digitalization, the biomedical sector has been affected by the spread of artificial intelligence. In recent years, the possibility of using deep and machine learning methods for clinical diagnostic and therapeutic interventions has been emerging as an essential resource for biomedical imaging. Digital pathology represents innovation in a clinical world that looks for faster and better-performing diagnostic methods, without losing the accuracy of current human-guided analyses. Indeed, artificial intelligence has played a key role in a wide variety of applications that require the analysis of a massive amount of data, including segmentation processes in medical imaging. In this context, artificial intelligence enables the improvement of image segmentation methods, moving towards the development of fully automated systems of analysis able to support pathologists in decision-making procedures. The aim of this review is to aid biologists and clinicians in discovering the most common segmentation open-source tools, including ImageJ (v. 1.54), CellProfiler (v. 4.2.5), Ilastik (v. 1.3.3) and QuPath (v. 0.4.3), along with their customized implementations. Additionally, the tools’ role in the histological imaging field is explored further, suggesting potential application workflows. In conclusion, this review encompasses an examination of the most commonly segmented tissues and their analysis through open-source deep and machine learning tools.