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Summary: The human transcriptome predominantly consists of noncoding RNAs (ncRNAs), transcripts that do not encode proteins. The noncoding transcriptome governs a multitude of pathophysiological processes, offering a rich source of next-generation biomarkers. Toward achieving a holistic view of disease, the integration of these transcripts with clinical records and additional data from omic technologies (“multiomic” strategies) has motivated the adoption of artificial intelligence (AI) approaches. Given their intricate biological complexity, machine learning (ML) techniques are becoming a key component of ncRNA-based research. This article presents an overview of the potential and challenges associated with employing AI/ML-driven approaches to identify clinically relevant ncRNA biomarkers and to decipher ncRNA-associated pathogenetic mechanisms. Methodological and conceptual constraints are discussed, along with an exploration of ethical considerations inherent to AI applications for healthcare and research. The ultimate goal is to provide a comprehensive examination of the multifaceted landscape of this innovative field and its clinical implications.

Elucidating the pathobiological mechanisms underlying post-acute pulmonary sequelae following SARS-CoV-2 infection is essential for early interventions and patient stratification. Here, we investigated the potential of microRNAs (miRNAs) as theranostic agents for pulmoprotection in critical illness survivors. Multicenter study including 172 ICU survivors. Diffusion impairment was defined as a lung-diffusing capacity for carbon monoxide (DLCO)

Abstract Background The identification of critically ill COVID-19 patients at risk of fatal outcomes remains a challenge. Here, we first validated candidate microRNAs (miRNAs) as biomarkers for clinical decision-making in critically ill patients. Second, we constructed a blood miRNA classifier for the early prediction of adverse outcomes in the ICU. Methods This was a multicenter, observational and retrospective/prospective study including 503 critically ill patients admitted to the ICU from 19 hospitals. qPCR assays were performed in plasma samples collected within the first 48 h upon admission. A 16-miRNA panel was designed based on recently published data from our group. Results Nine miRNAs were validated as biomarkers of all-cause in-ICU mortality in the independent cohort of critically ill patients (FDR

Wireless Sensor Networks (WSNs) are crucial in various fields including Health Care Monitoring, Battlefield Surveillance, and Smart Agriculture. However, WSNs are susceptible to malicious attacks due to the massive quantity of sensors within them. Hence, there is a demand for a trust evaluation framework within WSNs to function as a secure system, to identify and isolate malicious or faulty sensor nodes. This information can be leveraged by neighboring nodes, to prevent collaboration in tasks like data aggregation and forwarding. While numerous trust frameworks have been suggested in the literature to assess trust scores and examine the reliability of sensors through direct and indirect communications, implementing these trust evaluation criteria is challenging due to the intricate nature of the trust evaluation process and the limited availability of datasets. This research conducts a novel comparative analysis of three trust management models: “Lightweight Trust Management based on Bayesian and Entropy (LTMBE)”, “Beta-based Trust and Reputation Evaluation System (BTRES)”, and “Lightweight and Dependable Trust System (LDTS)”. To assess the practicality of these trust management models, we compare and examine their performance in multiple scenarios. Additionally, we assess and compare how well the trust management approaches perform in response to two significant cyber-attacks. Based on the experimental comparative analysis, it can be inferred that the LTMBE model is optimal for WSN applications emphasizing high energy efficiency, while the BTRES model is most suitable for WSN applications prioritizing critical security measures. The conducted empirical comparative analysis can act as a benchmark for upcoming research on trust evaluation frameworks for WSNs.

Background and Purpose: The post‐acute sequelae of SARS‐CoV‐2 infection pose a significant global challenge, with nearly 50% of critical COVID‐19 survivors manifesting persistent lung abnormalities. The lack of understanding about the molecular mechanisms and effective treatments hampers their management. Here, we employed microRNA (miRNA) profiling to decipher the systemic molecular underpinnings of the persistent pulmonary complications. Experimental Approach: We conducted a longitudinal investigation including 119 critical COVID‐19 survivors. A comprehensive pulmonary evaluation was performed in the short‐term (median = 94.0 days after hospital discharge) and long‐term (median = 358 days after hospital discharge). Plasma miRNAs were quantified at the short‐term evaluation using the gold‐standard technique, RT‐qPCR. The analyses combined machine learning feature selection techniques with bioinformatic investigations. Two additional datasets were incorporated for validation. Key Results: In the short‐term, 84% of the survivors exhibited impaired lung diffusion (DLCO < 80% of predicted). One year post‐discharge, 54.4% of this patient subgroup still presented abnormal DLCO. Four feature selection methods identified two specific miRNAs, miR‐9‐5p and miR‐486‐5p, linked to persistent lung dysfunction. The downstream experimentally validated targetome included 1473 genes, with heterogeneous enriched pathways associated with inflammation, angiogenesis and cell senescence. Validation studies using RNA‐sequencing and proteomic datasets emphasized the pivotal roles of cell migration and tissue repair in persistent lung dysfunction. The repositioning potential of the miRNA targets was limited. Conclusion and Implications: Our study reveals early mechanistic pathways contributing to persistent lung dysfunction in critical COVID‐19 survivors, offering a promising approach for the development of targeted disease‐modifying agents. LINKED ARTICLES: This article is part of a themed issue Non‐coding RNA Therapeutics. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v182.2/issuetoc [ABSTRACT FROM AUTHOR]

IntroductionBronchial aspirates (BAS) obtained during invasive mechanical ventilation (IMV) constitutes a useful tool for molecular phenotyping and decision making.AimTo identify the proteomic determinants associated with disease pathogenesis, all-cause mortality and respiratory sequelae in BAS samples from critically ill patients with SARS-CoV-2-induced ARDSMethodsMulticenter study including 74 critically ill patients with COVID-19 and non-COVID-19 ARDS. BAS were obtained by bronchoaspiration after IMV initiation. Three hundred sixty-four proteins were quantified using proximity extension assay (PEA) technology. Random forest models were used to assess predictor importance.ResultsAfter adjusting for confounding factors, CST5, NADK, SRPK2 and TGF-α were differentially detected in COVID-19 and non-COVID-19 patients. In random forest models for COVID-19, CST5, DPP7, NADK, KYAT1 and TYMP showed the highest variable importance. In COVID-19 patients, reduced levels of ENTPD2 and PTN were observed in nonsurvivors of ICU stay, even after adjustment. AGR2, NQO2, IL-1α, OSM and TRAIL showed the strongest associations with in-ICU mortality and were used to construct a protein-based prediction model. Kaplan-Meier curves revealed a clear separation in mortality risk between subgroups of PTN, ENTPD2 and the prediction model. Cox regression models supported these findings. In survivors, the levels of FCRL1, NTF4 and THOP1 in BAS samples obtained during the ICU stay correlated with lung function (i.e., DLCO levels) 3 months after hospital discharge. Similarly, Flt3L and THOP1 levels were correlated with radiological features (i.e., TSS). These proteins are expressed in immune and nonimmune lung cells. Poor host response to viral infectivity and an inappropriate reparative mechanism seem to be linked with the pathogenesis of the disease and fatal outcomes, respectively.ConclusionBAS proteomics identified novel factors associated with the pathology of SARS-CoV-2-induced ARDS and its adverse outcomes. BAS-based protein testing emerges as a novel tool for risk assessment in the ICU.

Industry 4.0 is reshaping the way all the economic sectors are working. The complete adoption and understanding by the companies, large or small, of the digital transformation opportunities and benefits, requires the existence of effective instruments. The Digital Innovation Hubs (DIHs) are an important instrument, strongly promoted by the European Commission, to help companies to take advantage of digital opportunities. Aiming to boost and create synergies between several DIHs and research centers located in the northern region of Portugal and Spain, and considering the disruptive ICT technologies, the establishment of an inter-regional DIH that results from the cooperation between these entities assumes a crucial importance to transform this region into an innovative pole of reference. This paper aims to describe the strategy for the management and improvement of the quality of the innovation being developed by this inter-regional DIH under the scope of the DISRUPTIVE project. Besides considering the individual strategies of the associated members, it includes cooperative aspects, regarding the sharing of knowledge, technology, and skills, aiming to improve the quality of innovation, as well as the adoption of digitization by the companies in this region.

The term moonlighting proteins refers to those proteins that present alternative functions performed by a single polypeptide chain acquired throughout evolution (called canonical and moonlighting, respectively). Over 78% of moonlighting proteins are involved in human diseases, 48% are targeted by current drugs, and over 25% of them are involved in the virulence of pathogenic microorganisms. These facts encouraged us to study the link between the functions of moonlighting proteins and disease. We found a large number of moonlighting functions activated by pathological conditions that are highly involved in disease development and progression. The factors that activate some moonlighting functions take place only in pathological conditions, such as specific cellular translocations or changes in protein structure. Some moonlighting functions are involved in disease promotion while others are involved in curbing it. The disease-impairing moonlighting functions attempt to restore the homeostasis, or to reduce the damage linked to the imbalance caused by the disease. The disease-promoting moonlighting functions primarily involve the immune system, mesenchyme cross-talk, or excessive tissue proliferation. We often find moonlighting functions linked to the canonical function in a pathological context. Moonlighting functions are especially coordinated in inflammation and cancer. Wound healing and epithelial to mesenchymal transition are very representative. They involve multiple moonlighting proteins with a different role in each phase of the process, contributing to the current-phase phenotype or promoting a phase switch, mitigating the damage or intensifying the remodeling. All of this implies a new level of complexity in the study of pathology genesis, progression, and treatment. The specific protein function involved in a patient’s progress or that is affected by a drug must be elucidated for the correct treatment of diseases.

ABSTRACT Objectives: Severe pelvic deficiency presents a difficult problem in hip arthroplasty. Specifically, the goals are to restore the pelvic bone stock, place the acetabular component in the correct anatomical position, and optimize joint stability. Currently, many surgical techniques have been developed for prosthetic revision surgery for acetabular complex defects, but no consensus has been reached on the best treatment. The objective of this study was to review mid-term cases of severe bone defect (Paprosky type III) treated with a bone allograft and ring Bursch-Schneider anti-protrusion cage (BSAC). Methods: A retrospective consecutive series review of the first 23 complex acetabular reconstructions performed between 2006 and 2011 was conducted. The series included the learning curve of the procedure and a minimum 5-year follow-up. Conclusion: Our study confirmed the efficacy of using a frozen morselized allograft combined with a metal ring-type BSAC for acetabular reconstruction. The anatomical location of the center of rotation of the hip must be recovered for long-term success. In massive loosening cases, the anatomical center of rotation can only be restored by bone density reconstruction using a graft protected by a ring to improve the centering of the head. Level of Evidence IV, Case Series.