Search

Alzheimer’s disease is a progressive neurodegenerative disorder, the early detection of which is crucial for timely intervention and enrollment in clinical trials. However, the preclinical diagnosis of Alzheimer’s encounters difficulties with gold-standard methods. The current definitive diagnosis of Alzheimer’s still relies on expensive instrumentation and post-mortem histological examinations. Here, we explore label-free Raman spectroscopy with machine learning as an alternative to preclinical Alzheimer’s diagnosis. A special feature of this study is the inclusion of patient samples from different cohorts, sampled and measured in different years. To develop reliable classification models, partial least squares discriminant analysis in combination with variable selection methods identified discriminative molecules, including nucleic acids, amino acids, proteins, and carbohydrates such as taurine/hypotaurine and guanine, when applied to Raman spectra taken from dried samples of cerebrospinal fluid. The robustness of the model is remarkable, as the discriminative molecules could be identified in different cohorts and years. A unified model notably classifies preclinical Alzheimer’s, which is particularly surprising because of Raman spectroscopy’s high sensitivity regarding different measurement conditions. The presented results demonstrate the capability of Raman spectroscopy to detect preclinical Alzheimer’s disease for the first time and offer invaluable opportunities for future clinical applications and diagnostic methods.

Background: Down syndrome (DS) is the most common genetically determined intellectual disability. In recent decades, it has experienced an exponential increase in life expectancy, leading to a rise in age-related diseases, including Alzheimer’s disease (AD). Specific health plans for the comprehensive care of the DS community are an unmet need, which is crucial for the early and accurate diagnosis of main medical comorbidities. We present the protocol of a newly created clinical and research cohort and its feasibility in real life. Methods: The Down Syndrome—Basque Alzheimer Initiative (DS-BAI) is a population-based, inclusive, multidisciplinary initiative for the clinical-assistance and clinical-biological research approach to aging in DS led by the CITA-Alzheimer Foundation (Donostia, Basque Country). It aims to achieve the following: (1) provide comprehensive care for adults with DS, (2) optimize access to rigorous and quality training for socio-family and healthcare references, and (3) create a valuable multimodal clinical-biological research platform. Results: During the first year, 114 adults with DS joined the initiative, with 36% of them showing symptoms indicative of AD. Furthermore, adherence to training programs for healthcare professionals and families has been high, and the willingness to collaborate in basic and translational research has been encouraging. Conclusion: Specific health plans for DS and conducting clinical and translational research on the challenges of aging, including AD, are necessary and feasible.

Hydraulic turbines have become indispensable for harnessing renewable energy sources, particularly in-pipe hydraulic turbine technology, which leverages excess energy within pipeline systems like drinking water distribution pipes to produce electrical power. Among these turbines, the propeller-type axial turbine with circular blades stands out for its efficiency. However, there is a notable lack of literature on fluid dynamics and structural behavior under various operational conditions. This study introduces a comprehensive methodology to numerically investigate the hydraulic and structural responses of turbines designed for in-pipe installation. The methodology encompasses the design of circular blades, followed by parametric studies on fluid dynamics and structural analysis. The circular blade's performance was evaluated across different materials, incorporating static, modal, and harmonic response analyses. Results showed that the circular blade achieved a peak hydraulic efficiency of 75.5% at a flow rate of 10 l/s, generating 1.86 m of head pressure drop and 138 W of mechanical power. Structurally, it demonstrated a safety factor exceeding 1 across the entire hydraulic range without encountering resonance or fatigue issues. This research and its methodology significantly contribute to advancing the understanding of designing and assessing the fluid dynamic behavior and structural integrity of circular blades in axial propeller-type turbines for in-pipe installations, serving as a valuable resource for future studies in similar domains. [ABSTRACT FROM AUTHOR]

Intracranial pressure (ICP) monitoring is necessary for managing patients with traumatic brain injury (TBI). Although gold-standard methods include intraventricular or intraparenchymal transducers, these systems cannot be used in patients with coagulopathies or in those who are at high risk of catheter-related infections, nor can they be used in resource-constrained settings. Therefore, a non-invasive modality that is more widely available, cost effective, and safe would have tremendous impact. Among such non-invasive choices, transcranial Doppler (TCD) provides indirect ICP estimates through waveform analysis of cerebral hemodynamic changes. The objective of this scoping review is to describe the existing evidence for the use of TCD-derived methods in estimating ICP in adult TBI patients as compared with gold-standard invasive methods. This review was conducted in accordance with the Joanna Briggs Institute methodology for scoping reviews, with a main search of PubMed and Embase. The search was limited to studies conducted in adult TBI patients published in any language between 2012 and 2022. Twenty-two studies were included for analysis, with most being prospective studies conducted in high-income countries. TCD-derived non-invasive ICP (nICP) methods are either mathematical or non-mathematical, with the former having slightly better correlation with invasive methods, especially when using time-trending ICP dynamics over one-time estimated values. Nevertheless, mathematical methods are associated with greater cost and complexity in their application. Formula-based methods showed promise in excluding elevated ICP, exhibiting a high negative predictive value. Therefore, TCD-derived methods could be useful in assessing ICP changes instead of absolute ICP values for high-risk patients, especially in low-resource settings. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a progressive neurodegenerative disorder, the early detection of which is crucial for timely intervention and enrollment in clinical trials. However, the preclinical diagnosis of Alzheimer's encounters difficulties with gold-standard methods. The current definitive diagnosis of Alzheimer's still relies on expensive instrumentation and post-mortem histological examinations. Here, we explore label-free Raman spectroscopy with machine learning as an alternative to preclinical Alzheimer's diagnosis. A special feature of this study is the inclusion of patient samples from different cohorts, sampled and measured in different years. To develop reliable classification models, partial least squares discriminant analysis in combination with variable selection methods identified discriminative molecules, including nucleic acids, amino acids, proteins, and carbohydrates such as taurine/hypotaurine and guanine, when applied to Raman spectra taken from dried samples of cerebrospinal fluid. The robustness of the model is remarkable, as the discriminative molecules could be identified in different cohorts and years. A unified model notably classifies preclinical Alzheimer's, which is particularly surprising because of Raman spectroscopy's high sensitivity regarding different measurement conditions. The presented results demonstrate the capability of Raman spectroscopy to detect preclinical Alzheimer's disease for the first time and offer invaluable opportunities for future clinical applications and diagnostic methods. [ABSTRACT FROM AUTHOR]

Cutaneous squamous cell carcinoma (cSCC) is the second most common form of skin cancer. The incidence of metastasis for cSCC is estimated to be around 1.2–5%. Ribosomal protein S6 (p-S6) and the p21 protein (p21) are two proteins that play central roles in other cancers. These proteins may be equally important in cSCC, and together, these could constitute a good candidate for metastasis risk assessment of these patients. We investigate the relationship of p-S6 and p21 expression with the impact on the prognosis of head and neck cSCC (cSCCHN). p-S6 and p21 expression was analyzed by immunohistochemistry on paraffin-embedded tissue samples from 116 patients with cSCCHN and associations sought with clinical characteristics. Kaplan–Meier estimators and Cox proportional hazard regression models were also used. The expression of p-S6 was significantly inversely associated with tumor thickness, tumor size, desmoplastic growth, pathological stage, perineural invasion and tumor buds. p21 expression was significantly inversely correlated with >6 mm tumor thickness, desmoplastic growth, and perineural invasion. p-S6-negative expression significantly predicted an increased risk of nodal metastasis (HR = 2.63, 95% CI 1.51–4.54; p < 0.001). p21 expression was not found to be a significant risk factor for nodal metastasis. These findings demonstrate that p-S6-negative expression is an independent predictor of nodal metastasis. The immunohistochemical expression of p-S6 might aid in better risk stratification and management of patients with cSCCHN. [ABSTRACT FROM AUTHOR]

Copyright of Archivos de Cardiología de México is the property of Publicidad Permanyer SLU and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Down syndrome (DS) is the most common genetically determined intellectual disability. In recent decades, it has experienced an exponential increase in life expectancy, leading to a rise in age-related diseases, including Alzheimer's disease (AD). Specific health plans for the comprehensive care of the DS community are an unmet need, which is crucial for the early and accurate diagnosis of main medical comorbidities. We present the protocol of a newly created clinical and research cohort and its feasibility in real life. Methods: The Down Syndrome—Basque Alzheimer Initiative (DS-BAI) is a population-based, inclusive, multidisciplinary initiative for the clinical-assistance and clinical-biological research approach to aging in DS led by the CITA-Alzheimer Foundation (Donostia, Basque Country). It aims to achieve the following: (1) provide comprehensive care for adults with DS, (2) optimize access to rigorous and quality training for socio-family and healthcare references, and (3) create a valuable multimodal clinical-biological research platform. Results: During the first year, 114 adults with DS joined the initiative, with 36% of them showing symptoms indicative of AD. Furthermore, adherence to training programs for healthcare professionals and families has been high, and the willingness to collaborate in basic and translational research has been encouraging. Conclusion: Specific health plans for DS and conducting clinical and translational research on the challenges of aging, including AD, are necessary and feasible. [ABSTRACT FROM AUTHOR]