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Abstract A novel intumescent fire‐retardant coating formulation has been developed by taking advantage of the inherent stability of large cyclic polyphenols (e.g., tannic acid) to remove the need for a dedicated blowing agent, specifically melamine. Cone calorimetry data indicate a lower fire growth rate (FIGRA) (5.0 ± 1.3 vs. 2.4 ± 0.7 kW m−2 s−1) upon removal of melamine. Total smoke release (TSR) increases following this removal (178 ± 21.4 vs. 305 ± 13.2 m2 m−2), which indicates the role of tannic acid in gas generation. Thermogravimetric analysis resulted in similar residue yield from the examined systems (36 ± 1.3% vs. 34 ± 1.7%) despite removal of a dedicated blowing agent. These results indicate that tannic acids multistep degradation allows it to behave as both a char forming and blowing agent in intumescent systems. The observed reduction of acid loading between the melamine‐tannin system and the melamine‐free composition (15.6% vs. 10.6% wt. ammonium polyphosphate, respectively) is also significant. Tannic acid occurs in agricultural waste products, which is encouraging for the pursuit of sustainable chemistry and addressing potential adverse health effects in humans combined with reducing the overall phosphorous loading. Highlights Melamine was removed from the intumescent without compromising stability. The resulting intumescent char was stronger despite less expansion. Tannic acid is attributed as both the charring and blowing agent. The tannin‐based coating requires a lower acid loading for similar performance.

Microplastics and nanoplastics (MNPs) are becoming an increasingly severe global problem due to their widespread distribution and complex impact on living organisms. Apart from their environmental impact, the effects of MNPs on living organisms have also continued to attract attention. The harmful impact of MNPs has been extensively documented in marine invertebrates and larger marine vertebrates like fish. However, the research on the toxicity of these particles on mammals is still limited, and their possible effects on humans are poorly understood. Considering that MNPs are commonly found in food or food packaging, humans are primarily exposed to them through ingestion. It would be valuable to investigate the potential harmful effects of these particles on gut health. This review focuses on recent research exploring the toxicological impacts of micro- and nanoplastics on the gut, as observed in human cell lines and mammalian models. Available data from various studies indicate that the accumulation of MNPs in mammalian models and human cells may result in adverse consequences, in terms of epithelial toxicity, immune toxicity, and the disruption of the gut microbiota. The paper also discusses the current research limitations and prospects in this field, aiming to provide a scientific basis and reference for further studies on the toxic mechanisms of micro- and nanoplastics.

Abstract Locoregional recurrences represent a frequently unexpected problem in head and neck squamous cell carcinoma (HNSCC). Relapse often (10–30%) occurs in patients with histologically negative resection margins (RMs), probably due to residual tumor cells or hidden pre-cancerous lesions in normal mucosa, both missed by histopathological examination. Therefore, definition of a ‘clean’ or tumor-negative RM is controversial, demanding for novel approaches to be accurately explored. Here, we evaluated next generation sequencing (NGS) and digital PCR (dPCR) as tools to profile TP53 mutational status and circulating microRNA expression aiming at scoring the locoregional risk of recurrence by means of molecular analyses. Serial monitoring of these biomarkers allowed identifying patients at high risk, laying the ground for accurate tracking of disease evolution and potential intensification of post-operative treatments. Additionally, our pipeline demonstrated its applicability into the clinical routine, being cost-effective and feasible in terms of patient sampling, holding promise to accurately (re)-stage RMs in the era of precision medicine.

Environmental policies and commitment is forcing a strong push towards electrification of human activities. Efforts are oriented to progressively dismiss old and outdated Internal Combustion Engines, replacing oil energy sources with electricity vectors. An important switch is involving transportation sector, responsible for 16% of global emissions produced, where the electrification process is converting the private mobility to the use of more sustainable electric vehicles. The drawbacks related to the battery capacity and range is limiting the diffusion among the whole territory, especially where a lack of charging infrastructure is persistent. Therefore, a focus on the adopted driving behaviour must be set to carry benefits in energy saving and potentially extending range. The present paper provides an analysis on different driving behaviour based on real-world data. An experimental campaign is described to acquire different driving styles during test-runs on a real route. An electric vehicle model is also developed in parallel and validated on the real dataset. A test route is identified in the southern region of Puglia in Italy, centered around the city of Lecce, and altitude and speed profiles are acquired and processed into the model. Test-runs were first clustered based on the driver type, then according to the level of auxiliary power consumption. Then, with the help of EV model, a more detailed analysis could be set to retrieve meaningful motivations behind the trends gathered between the two driving styles, especially focusing on the type of road. Results allowed to extrapolate trends between energy consumption and driving styles, showing a considerable impact on the battery usage. A +5% SoC is estimated if an aggressive driving style is adopted, while the impact of auxiliary power is predominant with an eco-driving style, taking into account uneven testing conditions.

Many authors have studied the effects of thyroid hormones and their diasteroisomers on mitochondrial activity. This topic is extensively debated because some authors have questioned the direct action of thyroid hormones on mitochondrial oxidative phosphorylation. […]

Abstract This study aimed at predicting the filtration surgery (FS) outcome using a machine learning (ML) approach. 102 glaucomatous patients undergoing FS were enrolled and underwent ocular surface clinical tests (OSCTs), determination of surgical site-related biometric parameters (SSPs) and conjunctival vascularization. Break-up-time, Schirmer test I, corneal fluorescein staining, Meibomian gland expressibility; conjunctival hyperemia, upper bulbar conjunctiva area of exposure, limbus to superior eyelid distance; and conjunctival epithelial and stromal (CET, CST) thickness and reflectivity (ECR, SCR) at AS-OCT were considered. Successful FS required a 30% baseline intraocular pressure reduction, with values ≤ 18 mmHg with or without medications. The classification tree (CT) was the ML algorithm used to analyze data. At the twelfth month, FS was successful in 60.8% of cases, whereas failed in 39.2%. At the variable importance ranking, CST and SCR were the predictors with the greater relative importance to the CART tree construction, followed by age. CET and ECR showed less relative importance, whereas OSCTs and SSPs were not important features. Within the CT, CST turned out the most important variable for discriminating success from failure, followed by SCR and age, with cut-off values of 75 µm, 169 on gray scale, and 62 years, respectively. The ROC curve for the classifier showed an AUC of 0.784 (0.692–0.860). In this ML approach, CT analysis found that conjunctival stroma thickness and reflectivity, along with age, can predict the FS outcome with good accuracy. A pre-operative thick and hyper-reflective stroma, and a younger age increase the risk of FS failure.

Malnutrition poses a critical challenge in inflammatory bowel disease, with the potential to detrimentally impact medical treatment, surgical outcomes, and general well-being. Parenteral nutrition is crucial in certain clinical scenarios, such as with patients suffering from short bowel syndrome, intestinal insufficiency, high-yielding gastrointestinal fistula, or complete small bowel obstruction, to effectively manage malnutrition. Nevertheless, research over the years has attempted to define the potential effects of parenteral nutrition on the intestinal barrier and the composition of the gut microbiota. In this narrative review, we have gathered and analyzed findings from both preclinical and clinical studies on this topic. Based on existing evidence, there is a clear correlation between short- and long-term parenteral nutrition and negative effects on the intestinal system. These include mucosal atrophic damage and immunological and neuroendocrine dysregulation, as well as alterations in gut barrier permeability and microbiota composition. However, the mechanistic role of these changes in inflammatory bowel disease remains unclear. Therefore, further research is necessary to effectively address the numerous gaps and unanswered questions pertaining to these issues.

El objetivo de este artículo es ofrecer una perspectiva analítica para comprender y situar en su dimensión específica los diferentes debates que atraviesan la conversación pública sobre las Inteligencias Artificiales generativas y los modelos de lenguaje grandes (LLM, por sus siglas en inglés). En primer lugar, identificamos cinco rasgos de las IA: no son una tecnología, sino que se trata de metatecnologías; constituyen no un dispositivo técnico, sino un mundoambiente; pueden ser tecnologías de alto riesgo y requieren de un tratamiento acorde en su ciclo de vida; las IA generativas y en particular los LLM no son sólo Inteligencia Artificial, sino también Sociedad Artificial; la perspectiva de la ética de la IA no es suficiente para abordarlas y es preciso promover un enfoque desde la ética organizacional de la IA y desde el pensamiento sistémico. En segundo lugar, recortamos las distintas escalas en las que actualmente se desarrollan las IA: la escala micro, la escala meso (la más propia para situar las políticas públicas) y la escala macro. En tercer lugar, presentamos dos enfoques transversales para el abordaje de las IA: el jurídico, orientado a la responsabilidad, y el sistémico, orientado a la protección y a la seguridad.

Abstract Background Autistic girls are underdiagnosed compared to autistic boys, even when they experience similar clinical impact. Research suggests that girls present with distinct symptom profiles across a variety of domains, such as language, which may contribute to their underdiagnosis. In this study, we examine sex differences in the temporal dynamics of natural conversations between naïve adult confederates and school-aged children with or without autism, with the goal of improving our understanding of conversational behavior in autistic girls and ultimately improving identification. Methods Forty-five school-aged children with autism (29 boys and 16 girls) and 47 non-autistic/neurotypical (NT) children (23 boys and 24 girls) engaged in a 5-min “get-to-know-you” conversation with a young adult confederate that was unaware of children’s diagnostic status. Groups were matched on IQ estimates. Recordings were time-aligned and orthographically transcribed by trained annotators. Several speech and pause measures were calculated. Groups were compared using analysis of covariance models, controlling for age. Results Autistic girls used significantly more words than autistic boys, and produced longer speech segments than all other groups. Autistic boys spoke more slowly than NT children, whereas autistic girls did not differ from NT children in total word counts or speaking rate. Autistic boys interrupted confederates’ speech less often and produced longer between-turn pauses (i.e., responded more slowly when it was their turn) compared to other children. Within-turn pause duration did not differ by group. Limitations Our sample included verbally fluent children and adolescents aged 6–15 years, so our study results may not replicate in samples of younger children, adults, and individuals who are not verbally fluent. The results of this relatively small study, while compelling, should be interpreted with caution and replicated in a larger sample. Conclusion This study investigated the temporal dynamics of everyday conversations and demonstrated that autistic girls and boys have distinct natural language profiles. Specifying differences in verbal communication lays the groundwork for the development of sensitive screening and diagnostic tools to more accurately identify autistic girls, and could inform future personalized interventions that improve short- and long-term social communication outcomes for all autistic children.

Abstract Autism was formally recognized by the medical community in the first half of the twentieth century. Almost 100 years later, a small but growing literature has reported sex differences in the behavioral expression of autism. Recent research has also begun to explore the internal experiences of individuals with autism, including social and emotional insight. The current study examines sex differences in language-based markers of social and emotional insight in girls and boys with autism and non-autistic peers during semi-structured clinical interviews. Sixty-four participants aged 5 to 17 years were individually matched on chronological age and full-scale IQ to form four groups: autistic girls, autistic boys, non-autistic girls, and non-autistic boys. Transcribed interviews were scored using four scales that index aspects of social and emotional insight. Results revealed the main effects of diagnosis, such that youth with autism exhibited lower insight than non-autistic youth on scales indexing social cognition and object relations, emotional investment, and social causality. With regards to sex differences, across diagnoses, girls were rated higher than boys on the social cognition and object relations, emotional investment, and social causality scales. Examined within each diagnosis separately, clear sex differences emerged: both autistic and non-autistic girls demonstrated better social cognition and understanding of social causality than boys in their respective diagnostic groups. No within-diagnosis sex differences were found on the emotional insight scales, however. These results suggest that relatively enhanced social cognition and understanding of social causality in girls may be a population-level sex difference that is preserved in autism, despite the core social challenges that characterize this condition. The current findings reveal critical new information about insight into social and emotional thinking and relationships in autistic girls versus boys that have important implications for improving identification and designing effective interventions.

Inflammatory bowel diseases (IBDs) are intricate systemic conditions that can extend beyond the gastrointestinal tract through both direct and indirect mechanisms. Sarcopenia, characterized by a reduction in muscle mass and strength, often emerges as a consequence of the clinical course of IBDs. Indeed, sarcopenia exhibits a high prevalence in Crohn’s disease (52%) and ulcerative colitis (37%). While computed tomography and magnetic resonance imaging remain gold-standard methods for assessing muscle mass, ultrasound is gaining traction as a reliable, cost-effective, and widely available diagnostic method. Muscle strength serves as a key indicator of muscle function, with grip strength test emerging nowadays as the most reliable assessment method. In IBDs, sarcopenia may arise from factors such as inflammation, malnutrition, and gut dysbiosis, leading to the formulation of the ‘gut–muscle axis’ hypothesis. This condition determines an increased need for surgery with poorer post-surgical outcomes and a reduced response to biological treatments. Sarcopenia and its consequences lead to reduced quality of life (QoL), in addition to the already impaired QoL. Of emerging concern is sarcopenic obesity in IBDs, a challenging condition whose pathogenesis and management are still poorly understood. Resistance exercise and nutritional interventions, particularly those aimed at augmenting protein intake, have demonstrated efficacy in addressing sarcopenia in IBDs. Furthermore, anti-TNF biological therapies showed interesting outcomes in managing this condition. This review seeks to furnish a comprehensive overview of sarcopenia in IBDs, elucidating diagnostic methodologies, pathophysiological mechanisms, and clinical implications and management. Attention will also be paid to sarcopenic obesity, exploring the pathophysiology and possible treatment modalities of this condition.

Expression of the bHLH transcription protein Atoh7 is a crucial factor conferring competence to retinal progenitor cells for the development of retinal ganglion cells. Several studies have emerged establishing ATOH7 as a retinal disease gene. Remarkably, such studies uncovered ATOH7 variants associated with global eye defects including optic nerve hypoplasia, microphthalmia, retinal vascular disorders, and glaucoma. The complex genetic networks and cellular decisions arising downstream of atoh7 expression, and how their dysregulation cause development of such disease traits remains unknown. To begin to understand such Atoh7-dependent events in vivo, we performed transcriptome analysis of wild-type and atoh7 mutant (lakritz) zebrafish embryos at the onset of retinal ganglion cell differentiation. We investigated in silico interplays of atoh7 and other disease-related genes and pathways. By network reconstruction analysis of differentially expressed genes, we identified gene clusters enriched in retinal development, cell cycle, chromatin remodeling, stress response, and Wnt pathways. By weighted gene coexpression network, we identified coexpression modules affected by the mutation and enriched in retina development genes tightly connected to atoh7. We established the groundwork whereby Atoh7-linked cellular and molecular processes can be investigated in the dynamic multi-tissue environment of the developing normal and diseased vertebrate eye.

Purpose: To evaluate the ability of preoperative MRI-based measurements to predict the pathological T (pT) stage and cervical lymph node metastasis (CLNM) via machine learning (ML)-driven models trained in oral tongue squamous cell carcinoma (OTSCC). Materials and methods: 108 patients with a new diagnosis of OTSCC were enrolled. The preoperative MRI study included post-contrast high-resolution T1-weighted images acquired in all patients. MRI-based depth of invasion (DOI) and tumor dimension—together with shape-based and intensity-based features—were extracted from the lesion volume segmentation. The entire dataset was randomly divided into a training set and a validation set, and the performances of different types of ML algorithms were evaluated and compared. Results: MRI-based DOI and tumor dimension together with several shape-based and intensity-based signatures significantly discriminated the pT stage and LN status. The overall accuracy of the model for predicting the pT stage was 0.86 (95%CI, 0.78–0.92) and 0.81 (0.64–0.91) in the training and validation sets, respectively. There was no improvement in the model performance upon including shape-based and intensity-based features. The model for predicting CLNM based on DOI and tumor dimensions had a fair accuracy of 0.68 (0.57–0.78) and 0.69 (0.51–0.84) in the training and validation sets, respectively. The shape-based and intensity-based signatures have shown potential for improving the model sensitivity, with a comparable accuracy. Conclusion: MRI-based models driven by ML algorithms could stratify patients with OTSCC according to the pT stages. They had a moderate ability to predict cervical lymph node metastasis.

Rangelands cover ~23 million hectares and support a $3.4 billion annual cattle industry in California. Large variations in forage production from year to year and across the landscape make grazing management difficult. We here developed optimized methods to map high-resolution forage production using multispectral remote sensing imagery. We conducted monthly flights using a Small Unmanned Aerial System (sUAS) in 2017 and 2018 over a 10-ha deferred grazing rangeland. Daily maps of NDVI at 30-cm resolution were first derived by fusing monthly 30-cm sUAS imagery and more frequent 3-m PlanetScope satellite observations. We estimated aboveground net primary production as a product of absorbed photosynthetically active radiation (APAR) derived from NDVI and light use efficiency (LUE), optimized as a function of topography and climate stressors. The estimated forage production agreed well with field measurements having a R2 of 0.80 and RMSE of 542 kg/ha. Cumulative NDVI and APAR were less correlated with measured biomass (R2 = 0.68). Daily forage production maps captured similar seasonal and spatial patterns compared to field-based biomass measurements. Our study demonstrated the utility of aerial and satellite remote sensing technology in supporting adaptive rangeland management, especially during an era of climatic extremes, by providing spatially explicit and near-real-time forage production estimates.