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Abstract The effectiveness of indirect Intravascular laser irradiation of blood (ILIB) is not fully understood. In this study, we provided a novel experiment that employs metabolomics to investigate the effects of ILIB in women. Twenty-eight volunteers underwent indirect ILIB and had their plasma collected before and after this procedure. The ILIB was applied at the radial artery for 30 min, using low-power photobiomodulation (660 nm), and a power output of 0.1 W. Plasma samples were extracted and analyzed using liquid chromatography—high-resolution mass spectrometry in an untargeted approach. Partial Least Squares Discriminant Analysis revealed 151 molecules with the Variable In Projection score of ≥ 1. From these, 26 were identified. After checking for molecules related to dietary intake, fasting, medication, or part of the human exposome, 15 were affected by ILIB. The abundances of Estradiol 17b-glucuronide 3-sulfate, CAR 14:3, PI 22:6/PGJ2, and CAR 12:1 significantly increased by ILIB, while AcylGlcADG 62:9, Tyrosyl-Glutamine, and CDP-DG 22:3/PGF1 had the contrary effect. ILIB was shown to modulate molecules from different chemical classes, although its impact on plasma metaboloma was minimal. Further research is warranted to fully elucidate the implications of these findings across various metabolic pathways, thus advancing the science surrounding ILIB.

Abstract Metformin is an antihyperglycemic used to treat type 2 diabetes mellitus (T2DM). Patients with T2DM are at increased risk of cardiovascular disease. We explored the association between metformin use and cardiovascular magnetic resonance (CMR) derived stress myocardial blood flow (MBF), myocardial perfusion reserve (MPR) and major adverse cardiovascular events (MACE; all cause death, MI, stroke, heart failure hospitalisation and coronary revascularisation) in patients with T2DM. Multi-centre study of patients with T2DM, and healthy controls, underwent quantitative myocardial perfusion CMR using an artificial intelligence supported process. Multivariable regression analysis, and cox proportional hazard models of propensity score weighted patients quantified associations between metformin use, MBF, MPR, all cause death and MACE. Analysis included 572 patients with T2DM (68% prescribed metformin) with median follow-up 851 days (IQR 935 − 765). Metformin use was associated with an increase of MPR of 0.12 [0.08–0.40], p = 0.004. There were 82 MACE events (14.3%) including 25 (4.4%) deaths of which 16 were in those not prescribed metformin (8.7%), compared to 9 in patients prescribed metformin (2.3%): adjusted hazard ratio 0.24 (95% CI 0.08–0.70, p = 0.009). MACE events were similar between groups. This multicentre, inverse probability weighting propensity score analysis study showed that in patients with T2DM, metformin use is associated with higher MPR and improved all cause survival.

The pandemic has abruptly compelled education systems to change and adapt to a new situation. Across different communities and contexts, the sudden and unplanned transition to distance learning induced by the pandemic has generated many challenges, but has simultaneously offered several opportunities. One of its most widely appreciated effects is the increased involvement of families in the school system. This study was conducted in a refugee community in Lebanon, addressing the needs of out-of-school Syrian children through a distance learning programme for early childhood education and basic literacy and numeracy. Through a phenomenological design, this study investigates the parents' or caregivers' role and the challenges they faced in facilitating the process of distance learning during the pandemic period. A total of 68 parents and caregivers participated in 07 focus group discussions. According to the findings, distance learning can be significantly hampered by the lack of a stable internet connection and a minimum level of digital devices. At the same time, the effectiveness of the process can be greatly affected by the caregivers' logistic and pedagogical readiness to support their children while they were enrolled in a distance learning programme. External support from schools or civil society organisations can improve the caregivers' readiness and positively affect the children's learning outcomes.

IntroductionColorectal cancer (CRC) is the third most incident and the second most lethal malignant tumor. Despite the recognized association between obesity and CRC, further clarification is necessary regarding the lipids that are overexpressed during the development of CRC. In this scenario, the combination of metabolomics and a three-dimensional (3D) co-culture model involving CRC tumor cells and lipids can enhance the knowledge of energy metabolism modifications at the cross-talk between colorectal cancer and adipocytes. This study aimed to screen potential metabolites in the three dimensional (3D) co-culture of CRC and adipocytes by investigating the metabolome composition of this co-culture released into the extracellular space, which is known as the secretome.MethodsPre-adipocyte cells (3T3-L1), human colon carcinoma (HT-29), and the 3D co-culture (3T3-L1 + HT-29) were cultured for the secretome obtention. Then, ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) was employed to analyze the metabolomics of each secretome.ResultsOverall, 3.731 molecules were detected independent of the cell culture. When comparing the three cultures, 105 molecules presented a statistically significant difference in abundance between groups. Among these molecules, 16 were identified, with a particular emphasis on six lipids (PG 20:0, octadecenal, 3-Hydroxytetracosanoyl-CoA, 9,10-dihydroxy-octadecenoic acid, palmitoleic acid, and PA 18:4) and one amino acid derivative (acetylglutamic acid), which presented significant scores during the partial least-squares discriminant analysis (PLS-DA).DiscussionAlthough it is too early to determine the possible impact of such molecules in a CRC microenvironment, these results open new avenues for further studies on the energy metabolism at the cross-talk of colorectal cancer adipocytes.

Background: The introduction of a noninvasive diagnostic algorithm in 2016 led to increased awareness and recognition of cardiac amyloidosis (CA). Objectives: The purpose of this study was to analyze the impact of the introduction of the noninvasive diagnostic algorithm on diagnosis and prognosis in a multicenter Italian CA cohort. Methods: This was a retrospective analysis of 887 CA patients from 5 Italian Cardiomyopathies Referral Centers: 311 light-chain CA, 87 variant transthyretin (TTR)-related CA, 489 wild-type TTR-related CA. Clinical characteristics and outcomes (all-cause mortality and heart failure [HF] hospitalizations) were compared overall and for each CA subtype between patients diagnosed before versus after 2016. Outcomes were further compared by propensity score weighted Kaplan-Meier analysis and Cox regression analysis. Results: CA diagnoses increased after 2016, in particular for wild-type TTR-related CA. Patients diagnosed after versus before 2016 were older, had less frequently a history of HF prior to diagnosis, and NYHA functional class III-IV at diagnosis. Over a median follow-up of 18 months, 172 (86%) patients diagnosed before 2016 died or had an HF hospitalization, versus 300 (44%) diagnosed after 2016. Propensity score weighted Kaplan-Meier analysis showed worse outcomes (P

Placed as a hinge between analysis and decisions, the diagnosis involves interpretations on whose correctness may depend the restorations’ outcome and the building’s future life. In the diagnosis’ context, an important role is played by the knowledge of the restoration interventions’ history and investigations in order to orient new diagnostic investigations and to compare data acquired at different times with the aim to integrating them and discovering constructive and structural variations occurred. This integrated methodology aspires to multiple objectives including the design of a new investigation plan made reliable and effective through ‘archival diagnostics’. In this sense, archival research stands as a means of adding information on the conservation history and degradation, representing a useful diagnostic tool. Hence the intention to begin a reasoning on the added value that the anamnesis and diagnosis based on archive data can provide from a cognitive and design point of view.

IntroductionObesity aligned with quadriceps muscle weakness contributes to the high incidence of knee osteoarthritis (KOA), which is prevalent in women. Although molecular signatures of KOA have been suggested, the association between biopsychosocial responses and the plasma metabolomic profile in overweight/ obese women with KOA remains in its early stages of investigation. This study aims to associate the plasma metabolome with biopsychosocial parameters of overweight/obese women diagnosed with KOA.MethodsTwenty-eight overweight/obese women (Control-n = 14; KOA-n = 14) underwent two visits to the laboratory. Functional tests and questionnaires assessing biopsychosocial parameters were administered during the first visit. After 48 h, the participants returned to the laboratory for blood collection. Specific to the KOA condition, the Numerical Pain Rating Scale (NPRS), Tampa Scale for Kinesiophobia (TSK), and Knee injury and Osteoarthritis Outcome Score (KOOS) were appliedResultsThirteen molecules were different between groups, and four correlated with KOA’s biopsychosocial parameters. DG 22:4-2OH and gamma-Glutamylvaline were inversely associated with KOSS leisure and TSK score, respectively. LysoPE 18:0 and LysoPE 20:5 were positively associated with KOSS symptoms and TSK score, respectively.DiscussionWhile the correlations of LysoPE 18:0 and gamma-Glutamylvaline are supported by existing literature, this is not the case for DG 22:4-2OH and LysoPE 20:5. Further studies are recommended to better elucidate these correlations before dismissing their potential involvement in the biopsychosocial factors of the disease.

Abstract Acinetobacter baumannii is a Gram-negative bacterium considered an emerging multi-drug-resistant pathogen. Furthermore, this bacterium can survive in extreme environmental conditions, which makes it a frequent cause of nosocomial infection outbreaks. Gene expression analyses by Reverse Transcription Quantitative real-time PCR (RT-qPCR) depend on a reference gene, also called an endogenous gene, which is used to normalize the generated data and thus ensure an accurate analysis with minimal errors. Currently, gene expression analyses in A. baumannii are compromised, as there are no reports in the literature describing the identification of validated reference genes for use in RT-qPCR analyses. For this reason, we selected twelve candidate reference genes of A. baumannii and assessed their expression profile under different experimental and culture conditions. The expression stability of the candidate genes was evaluated by using statistical algorithms such as BestKeeper, geNorm, NormFinder, Delta CT, and RefFinder, in order to identify the most suitable candidate reference genes for RT-qPCR analyses. The statistical analyses indicated rpoB, rpoD, and fabD genes as the most adequate to ensure accurate normalization of RT-qPCR data in A. baumannii. The accuracy of the proposed reference genes was validated by using them to normalize the expression of the ompA gene, encoding the outer membrane protein A, in A. baumannii sensible and resistant to the antibiotic polymyxin. The present work provides suitable reference genes for precise RT-qPCR data normalization on future gene expression studies with A. baumannii.

Background: Central nervous system (CNS) tumors are the second most frequent type of neoplasm in childhood and adolescence, after leukemia. Despite the incorporation of molecular classification and improvement of protocols combining chemotherapy, surgery, and radiotherapy, CNS tumors are still the most lethal neoplasm in this age group. Mass spectrometry imaging (MSI) is a powerful tool to map the distribution of molecular species in tissue sections. Among MSI techniques, desorption electrospray ionization (DESI-MSI) has been demonstrated to enable reliable agreement with the pathological evaluation of different adult cancer types, along with an acceptable time scale for intraoperative use. Methods: In the present work, we aimed to investigate the chemical profile obtained by DESI-MSI as an intraoperative surgical management tool by profiling 162 pediatric brain biopsies and reporting the results according to the histopathology and molecular profile of the tumors. Results: The 2D chemical images obtained by DESI-MSI allowed us to distinguish tumor-transformed tissue from non-tumor tissue with an accuracy of 96.8% in the training set and 94.3% in the validation set after statistical modeling of our data using Lasso. In addition, high-grade and low-grade tumors also displayed a distinct chemical profile when analyzed by DESI-MSI. We also provided evidence that the chemical profile of brain tumors obtained by DESI-MSI correlates with methylation-based molecular classes and specific immunophenotypes found in brain biopsies. Conclusions: The results presented herein support the incorporation of DESI-MSI analysis as an intraoperative assistive tool in prospective clinical trials for pediatric brain tumors management in the near future.

With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite–gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

Background Transthyretin cardiac amyloidosis (ATTR‐CA) is a progressive and ultimately fatal cardiomyopathy. Biomarkers reflecting multiorgan dysfunction are of increasing importance in patients with heart failure; however, their significance in ATTR‐CA remains largely unknown. The aims of this study were to characterize the multifaceted nature of ATTR‐CA using blood biomarkers and assess the association between blood biomarkers and prognosis. Methods and Results This is a retrospective cohort study of 2566 consecutive patients diagnosed with ATTR‐CA between 2007 and 2023. Anemia (39%), high urea (52%), hyperbilirubinemia (18%), increased alkaline phosphatase (16%), increased CRP (C‐reactive protein; 27%), and increased troponin (98.2%) were common findings in the overall population, whereas hyponatremia (6%) and hypoalbuminemia (2%) were less common. These abnormalities were most common in patients with p.(V142I) hereditary ATTR‐CA, and became more prevalent as the severity of cardiac disease increased. Multivariable Cox regression analysis demonstrated that anemia (hazard ratio [HR], 1.19 [95% CI, 1.04–1.37]; P=0.01), high urea (HR, 1.23 [95% CI, 1.04–1.45]; P=0.01), hyperbilirubinemia (HR, 1.32 [95% CI, 1.13–1.57; P=0.001), increased alkaline phosphatase (HR, 1.20 [95% CI, 1.01–1.42; P=0.04), hyponatremia (HR, 1.65 [95% CI, 1.28–2.11]; P56 ng/L (HR, 1.72 [95% CI, 1.46–2.03]; P