Spectroscopic insights into peri-implant mucositis and peri-implantitis: unveiling peri-implant crevicular fluid profiles using surface enhanced Raman scattering.

The precise identification and differentiation of peri-implant diseases, without the need for intrusive procedures, is crucial for the successful clinical treatment and overall durability of dental implants. This work introduces a novel approach that combines surface-enhanced Raman scattering (SERS) spectroscopy with advanced chemometrics to analyse peri-implant crevicular fluid (PICF) samples. The primary purpose is to offer an unbiased evaluation of implant health. A detailed investigation was performed on PICF samples obtained from a cohort of patients exhibiting different levels of peri-implant health, including those with healthy implants, implants impacted by peri-implantitis, and implants with peri-implant mucositis. The obtained SERS spectra were analysed using canonical-powered partial least squares (CPPLS) to identify unique chemical characteristics associated with each inflammatory state. Significantly, our research findings unveil the presence of a common inflammatory SERS spectral pattern in cases of peri-implantitis and peri-implant mucositis. Furthermore, the SERS-based scores obtained from CPPLS were combined with established clinical scores and subjected to a linear discriminant analysis (LDA) classifier. Repeated double cross-validation was used to validate the method's capacity to discriminate different implant conditions. The integrated approach showcased high sensitivity and specificity and an overall balanced accuracy of 92%, demonstrating its potential to serve as a non-invasive diagnostic tool for real-time implant monitoring and early detection of inflammatory conditions.

HLA class II genes in precision-based care of childhood diseases: what we can learn from celiac disease.

Celiac disease (CeD) is a chronic immuno-mediated enteropathy caused by dietary gluten with marked autoimmunity traits. The human leukocyte antigen (HLA) class II heterodimers represent the main predisposing factor, although environmental agents, as viral infection, gut microbiota, and dietary regimen, also contribute to CeD risk. These molecules are involved in autoimmunity as they present self-antigens to autoreactive T cells that have escaped the thymic negative selection. In CeD, the HLA class II risk alleles, DQA1*05-DQB1*02 and DQA1*03-DQB1*03, encode for DQ2.5 and DQ8 heterodimers, and, furthermore, disease susceptibility was found strictly dependent on the dose of these genes. This finding questioned how the expression of HLA-DQ risk genes, and of relative surface protein on antigen-presenting cells, might be relevant for the magnitude of anti-gluten inflammatory response in CeD patients, and impact the natural history of disease, its pathomechanisms, and compliance to dietary treatment. In this scenario, new personalized medical approaches will be desirable to support an early, accurate, and non-invasive diagnosis, and to define genotype-guided preventive and therapeutic strategies for CeD. To reach this goal, a stratification of genetic risk, disease outcome, and therapy compliance based on HLA genotypes, DQ2.5/DQ8 expression measurement and magnitude of T cell response to gluten is mandatory. IMPACT: This article revises the current knowledge on how different HLA haplotypes, carrying the DQ2.5/DQ8 risk alleles, impact the onset of CeD. This review discusses how the expression of susceptibility HLA-DQ genes can determine the risk assessment, outcome, and prevention of CeD. The recent insights on the environmental factors contributing to CeD in childhood are reviewed. This review discusses the use of HLA risk gene expression as a tool to support medical precision approaches for an early and non-invasive diagnosis of CeD, and to define genotype-guided preventive and therapeutic strategies.

Label-free analysis of gingival crevicular fluid (GCF) by surface enhanced Raman scattering (SERS).

Gingival crevicular fluid (GCF) is an interesting biofluid reflecting the physiological and pathological states of a single dental element. Due to this unique feature, in recent years, metabolomic analysis of GCF has gained attention as a biometric tool for the diagnosis and therapy of periodontal disease. Traditional methods are, however, too slow, cumbersome and expensive for a health-care routine. Surface enhanced Raman scattering (SERS) can offer rapid and label-free detailed molecular fingerprints that can be used for biofluid analysis. Here we report the first SERS characterization of GCF using an easy and quick sample preparation. The dominant features in the SERS spectrum of GCF are ascribed to very few metabolites, in particular to uric acid, hypoxanthine, glutathione and ergothioneine. Additionally, we succeeded in differentiating between the SERS signal of GCF collected from healthy volunteers and the one collected from patients with periodontal disease.

The HLA-DRB1 risk alleles for multiple sclerosis are differentially expressed in blood cells of patients from Southern Italy.

HLA gene expression has an important role in the autoimmune disease predisposition. We investigated the mRNA expression profile of the risk alleles HLA-DRB1*15 and HLA-DRB1*13 in a cohort of subjects both multiple sclerosis (MS) patients and healthy controls. Moreover, we explored the expression of the allele HLA-DRB1*11 that is very frequent in our cohort from southern Italy. We found that the expression of MS-associated alleles in heterozygous MS patients was always higher than the nonassociated alleles. The differential risk allele expression occurred also in nonaffected subjects, though with a lower increment compared to MS patients.

Characterisation, in vitro digestibility and expected glycemic index of commercial starches as uncooked ingredients.

In this study native starches as ingredients (corn, rice, wheat, tapioca and potato) were characterized for microstructure, physicochemical, functional and thermal properties, in vitro digestibility and glycemic index. There was a significant variation in the granule shape and size distribution of the starches. Particle size monomodal (corn, tapioca, potato) and bimodal (rice, wheat) distribution was observed amongst the starches. The potato starch showed the biggest size granules while the rice showed the smallest. The examined properties and nutritional characteristics of starches were significantly different. Thermal properties were studied using Differential Scanning Calorimeter (DSC). DSC results showed that the transition temperatures (58.8-78.7 °C) and enthalpies of gelatinization (2.3-8.2 J/g) of the starches appeared to be greatly influenced by microstructure and chemical composition (e.g. resistant starch). Nutritional properties such as slowly digestible starch and expected glycemic index values followed the order: rice > wheat > tapioca > corn > potato. In particular, the highest resistant starch was recorded for potato starch.

Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder.

Background: Major Depressive Disorder (MDD) is a prevalent mental health condition characterized by persistent low mood, cognitive and physical symptoms, anhedonia (loss of interest in activities), and suicidal ideation. The World Health Organization (WHO) predicts depression will become the leading cause of disability by 2030. While biological markers remain essential for understanding MDD's pathophysiology, recent advancements in social signal processing and environmental monitoring hold promise. Wearable technologies, including smartwatches and air purifiers with environmental sensors, can generate valuable digital biomarkers for depression assessment in real-world settings. Integrating these with existing physical, psychopathological, and other indices (autoimmune, inflammatory, neuroradiological) has the potential to improve MDD recurrence prevention strategies. Methods: This prospective, randomized, interventional, and non-pharmacological integrated study aims to evaluate digital and environmental biomarkers in adolescents and young adults diagnosed with MDD who are currently taking medication. The study implements a sensor-integrated platform built around an open-source "Pothos" air purifier system. This platform is designed for scalability and integration with third-party devices. It accomplishes this through software interfaces, a dedicated app, sensor signal pre-processing, and an embedded deep learning AI system. The study will enroll two experimental groups (10 adolescents and 30 young adults each). Within each group, participants will be randomly allocated to Group A or Group B. Only Group B will receive the technological equipment (Pothos system and smartwatch) for collecting digital biomarkers. Blood and saliva samples will be collected at baseline (T0) and endpoint (T1) to assess inflammatory markers and cortisol levels. Results: Following initial age-based stratification, the sample will undergo detailed classification at the 6-month follow-up based on remission status. Digital and environmental biomarker data will be analyzed to explore intricate relationships between these markers, depression symptoms, disease progression, and early signs of illness. Conclusion: This study seeks to validate an AI tool for enhancing early MDD clinical management, implement an AI solution for continuous data processing, and establish an AI infrastructure for managing healthcare Big Data. Integrating innovative psychophysical assessment tools into clinical practice holds significant promise for improving diagnostic accuracy and developing more specific digital devices for comprehensive mental health evaluation.

An advanced Artificial Intelligence platform for a personalised treatment of Eating Disorders.

Introduction: Eating Disorders (EDs) affect individuals globally and are associated with significant physical and mental health challenges. However, access to adequate treatment is often hindered by societal stigma, limited awareness, and resource constraints. Methods: The project aims to utilize the power of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve EDs diagnosis and treatment. The Master Data Plan (MDP) will collect and analyze data from diverse sources, utilize AI algorithms for risk factor identificat io n, treatment planning, and relapse prediction, and provide a patient-facing chatbot for information and support. This platform will integrate patient data, support healthcare professionals, and empower patients, thereby enhancing care accessibility, personalizing treatment plans, and optimizing care pathways. Robust data governance measures will ensure ethical and secure data management. Results: Anticipated outcomes include enhanced care accessibility and efficiency, personalized treatment plans leading to improved patient outcomes, reduced waiting lists, heightened patient engagement, and increased awareness of EDs with improved resource allocation. Discussion: This project signifies a pivotal shift towards data-driven, patient-centered ED care in Italy. By integrat ing AI and promoting collaboration, it seeks to redefine mental healthcare standards and foster better well- being among individuals with EDs.

In vitro differentiation of macrophages from peripheral blood cells of celiac patients.

Macrophages have both a protective and pathological role in many autoimmune and inflammatory diseases. Macrophages phenotype is regulated by the environment that affects their polarization toward a pro- or anti-inflammatory phenotype. We describe a protocol for in vitro differentiation of macrophages from blood peripheral monocytes, that may be adopted to study different pathologies. Here, we are interested to study the phenotype of macrophages differentiated from patients affected by acute celiac disease (CD) or subjects following a gluten free diet (GFD), after in vitro gliadin challenge. We assess the pro-inflammatory phenotype of these macrophages by cytokines quantization on the cell supernatant. Moreover, our proposed protocol allows the preparation of total RNA to analyze the expression profile of many genes.

A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms.

Introduction: Depression is the leading cause of worldwide disability, until now only 3% of patients with major depressive disorder (MDD) experiences full recovery or remission. Different studies have tried to better understand MDD pathophysiology and its resistant forms (TRD), focusing on the identification of candidate biomarkers that would be able to reflect the patients' state and the effects of therapy. Development of digital technologies can generate useful digital biomarkers in a real-world setting. This review aims to focus on the use of digital technologies measuring symptom severity and predicting treatment outcomes for individuals with mood disorders. Methods: Two databases (PubMed and APA PsycINFO) were searched to retrieve papers published from January 1, 2013, to July 30, 2023, on the use of digital devices in persons with MDD. All papers had to meet specific inclusion criteria, which resulted in the inclusion of 12 articles. Results: Research on digital biomarkers confronts four core aspects: (I) predicting diagnostic status, (II) assessing symptom severity and progression, (III) identifying treatment response and (IV) monitoring real-word and ecological validity. Different wearable technologies have been applied to collect physiological, activity/sleep, or subjective data to explore their relationships with depression. Discussion: Depression's stable rates and high relapse risk necessitate innovative approaches. Wearable devices hold promise for continuous monitoring and data collection in real world setting. Conclusion: More studies are needed to translate these digital biomarkers into actionable interventions to improve depression diagnosis, monitoring and management. Future challenges will be the applications of wearable devices routinely in personalized medicine.

Pseudo-Bartter syndrome in infant with cystic fibrosis screen positive, inconclusive diagnosis: A case report.

The introduction of newborn screening for cystic fibrosis (CF) increased diagnosis of cystic fibrosis screen positive inconclusive diagnosis (CFSPID). We described the case of a 12-month-old boy with CFSPID who, during summer, presented Pseudo-Bartter syndrome with no diagnostic criteria for CF.

Relationship between handedness and persistent emotional distress in adults experiencing an earthquake.

AIM: Post-traumatic emotional distress follows exposure to trauma and may be affected by atypical cerebral lateralisation. We aimed to explore the relationship between handedness and emotional dysfunction in people exposed to a nat-ural disaster. METHODS: About 22 months after an earthquake, 326 exposed adults completed the Edinburgh Handedness Inventory, the Impact of Events Scale-Revised, and the Insomnia Severity Index. RESULTS: Mixed-handed people, compared to right-handed, had a 3.3 fold increase in odds to have emotional distress. Consistent left-handers scored higher than consistent right- and mixed-handers on the ISI scale. CONCLUSIONS: Findings support that lateral preference is associated with emotion-al distress in people exposed to trauma.

The less you judge, the better you sleep: The benefits of mindfulness and forgiveness for insomnia and sleep problems.

Mindfulness and forgiveness can contribute to decreased rumination, which in turn has been found to play an important role in sleep problems, such as insomnia and poor sleep quality. The authors explored rumination as a potential explanatory variable underlying the association of mindfulness with insomnia and sleep quality, with a model encompassing different mindfulness facets and controlling for interpersonal dispositional forgiveness. One hundred adults (74% females, Mage = 27.62, SD = 8.11) completed measures of Mindfulness, Forgivingness, Rumination, Sleep Quality, and Insomnia. Nonjudging was the main mindfulness facet contributing to better sleep quality. Rumination completely mediated the associations of nonjudgmental attitude with insomnia and sleep quality, and partially mediated the associations of dispositional forgiveness with the outcomes. These findings suggest that a mindful nonjudgmental attitude toward the self and a forgiving interpersonal disposition may reduce rumination by helping individuals let go of thoughts, mental images, and emotions, thus promoting better sleep.

Effect of Gliadin Stimulation on HLA-DQ2.5 Gene Expression in Macrophages from Adult Celiac Disease Patients.

Macrophages play an important role in the pathogenesis of celiac disease (CD) because they are involved in both inflammatory reaction and antigen presentation. We analyzed the expression of CD-associated HLA-DQ2.5 risk alleles on macrophages isolated by two cohorts of adult patients, from the U.S. and Italy, at different stages of disease and with different genotypes. After isolating and differentiating macrophages from PBMC, we assessed the HLA genotype and quantified the HLA-DQ2.5 mRNAs by qPCR, before and after gliadin stimulation. The results confirmed the differences in expression between DQA1*05:01 and DQB1*02:01 predisposing alleles and the non-CD associated alleles, as previously shown on other types of APCs. The gliadin challenge confirmed the differentiation of macrophages toward a proinflammatory phenotype, but above all, it triggered an increase of DQA1*05:01 mRNA, as well as a decrease of the DQB1*02:01 transcript. Furthermore, we observed a decrease in the DRB1 genes expression and a downregulation of the CIITA transactivator. In conclusion, our findings provide new evidences on the non-coordinated regulation of celiac disease DQ2.5 risk genes and support the hypothesis that gliadin could interfere in the three-dimensional arrangement of chromatin at the HLA locus.

Differential expression of predisposing HLA-DQ2.5 alleles in DR5/DR7 celiac disease patients affects the pathological immune response to gluten.

The DR5-DQ7/DR7-DQ2 genotype is very frequent among patients affected by celiac disease (CD), in Europe. This genotype, associated to high risk of CD, carries the HLA-DQA1*05 and HLA-DQB1*02 predisposing alleles, in trans configuration. The alleles encode the DQ2.5 heterodimer responsible of gluten peptide presentation on the surface of antigen-presenting cells (APCs), and consequent pathogenic CD4+ T cell activation. We demonstrated that DR5/DR7 APCs induce an anti-gluten CD4+ T cell response, of comparable intensity to that observed with APCs carrying DR1/DR3 genotype, which risk alleles are in cis configuration. In addition, we showed that DR5/DR7 APCs from celiac patients stimulated an effector CD4+ T cell response higher with respect to that induced by DR5/DR7 APCs from healthy subjects. To explain these findings, we assessed the DQ2.5 RNA and protein quantity. We showed that the expression of DQA1*05 and DQB1*02 risk alleles is much higher than the expression of non-CD-associated alleles, in agreement with the previous results obtained with DR1/DR3 genotype. The differential expression of transcripts influences the quantity of DQα1*05 and DQß1*02 chains and, as consequence, the cell surface density of DQ2.5 heterodimers. Moreover, both RNA and proteins, are more abundant in APCs from celiac patients than controls. Finally, to unravel the mechanism regulating the expression of predisposing DQA1*05 and DQB1*02 alleles, we quantified the new synthetized RNA and found that the differential expression is explained by their transcription rate. Our results confirmed that the strength of antigen-specific CD4+ T cell response is mainly determined by the amount of gluten in the diet and provided a new possible approach for a personalized diagnosis and for risk stratification.

HLA-DQA1 and HLA-DQB1 Alleles, Conferring Susceptibility to Celiac Disease and Type 1 Diabetes, are More Expressed Than Non-Predisposing Alleles and are Coordinately Regulated.

HLA DQA1*05 and DQB1*02 alleles encoding the DQ2.5 molecule and HLA DQA1*03 and DQB1*03 alleles encoding DQ8 molecules are strongly associated with celiac disease (CD) and type 1 diabetes (T1D), two common autoimmune diseases (AD). We previously demonstrated that DQ2.5 genes showed a higher expression with respect to non-CD associated alleles in heterozygous DQ2.5 positive (HLA DR1/DR3) antigen presenting cells (APC) of CD patients. This differential expression affected the level of the encoded DQ2.5 molecules on the APC surface and established the strength of gluten-specific CD4+ T cells response. Here, we expanded the expression analysis of risk alleles in patients affected by T1D or by T1D and CD comorbidity. In agreement with previous findings, we found that DQ2.5 and DQ8 risk alleles are more expressed than non-associated alleles also in T1D patients and favor the self-antigen presentation. To investigate the mechanism causing the high expression of risk alleles, we focused on HLA DQA1*05 and DQB1*02 alleles and, by ectopic expression of a single mRNA, we modified the quantitative equilibrium among the two transcripts. After transfection of DR7/DR14 B-LCL with HLA-DQA1*05 cDNA, we observed an overexpression of the endogenous DQB1*02 allele. The DQ2.5 heterodimer synthesized was functional and able to present gluten antigens to cognate CD4+ T cells. Our results indicated that the high expression of alpha and beta transcripts, encoding for the DQ2.5 heterodimeric molecules, was strictly coordinated by a mechanism acting at a transcriptional level. These findings suggested that, in addition to the predisposing HLA-DQ genotype, also the expression of risk alleles contributed to the establishment of autoimmunity.

Tristetraprolin/ZFP36 Regulates the Turnover of Autoimmune-Associated HLA-DQ mRNAs.

HLA class II genes encode highly polymorphic heterodimeric proteins functioning to present antigens to T cells and stimulate a specific immune response. Many HLA genes are strongly associated with autoimmune diseases as they stimulate self-antigen specific CD4+ T cells driving pathogenic responses against host tissues or organs. High expression of HLA class II risk genes is associated with autoimmune diseases, influencing the strength of the CD4+ T-mediated autoimmune response. The expression of HLA class II genes is regulated at both transcriptional and post-transcriptional levels. Protein components of the RNP complex binding the 3'UTR and affecting mRNA processing have previously been identified. Following on from this, the regulation of HLA-DQ2.5 risk genes, the main susceptibility genetic factor for celiac disease (CD), was investigated. The DQ2.5 molecule, encoded by HLA-DQA1*05 and HLA-DQB1*02 alleles, presents the antigenic gluten peptides to CD4+ T lymphocytes, activating the autoimmune response. The zinc-finger protein Tristetraprolin (TTP) or ZFP36 was identified to be a component of the RNP complex and has been described as a factor modulating mRNA stability. The 3'UTR of CD-associated HLA-DQA1*05 and HLA-DQB1*02 mRNAs do not contain canonical TTP binding consensus sequences, therefore an in silico approach focusing on mRNA secondary structure accessibility and stability was undertaken. Key structural differences specific to the CD-associated mRNAs were uncovered, allowing them to strongly interact with TTP through their 3'UTR, conferring a rapid turnover, in contrast to lower affinity binding to HLA non-CD associated mRNA.