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Background: The immune system must carefully balance fighting pathogens with minimization of inflammation and avoidance of autoimmune responses. Over the past ten years, researchers have extensively studied the mechanisms regulating this delicate balance. Comprehending these mechanisms is essential for developing treatments for inflammatory conditions. Aim: This review aims to synthesize knowledge of immunoregulatory processes published from 2014–2024 and to highlight discoveries that provide fresh perspectives on this complex balance. Methods: The keywords "molecular mechanisms", "immune regulation", "immune signaling pathways", and "immune homeostasis" were used to search PubMed for articles published between 2014 and 2024, with a preference for articles published in the past three years. Results: Recent research has pinpointed the impact of factors such as cytokine signaling, T-cell regulation, epigenetic regulation, and immunometabolism on immune function. Discussion: New research highlights the intricate interactions between the immune system and other molecular elements. A key area of interest is the impact of non-coding RNAs and metabolic pathways on the regulation of immune responses. Conclusions: Exploring the mechanisms by which the immune system is regulated will provide new avenues for developing treatments to address autoimmune and inflammatory conditions. [ABSTRACT FROM AUTHOR]

Developing affordable, rapid, and accurate biosensors is essential for SARS-CoV-2 surveillance and early detection. We created a bio-inspired peptide, using the SAGAPEP AI platform, for COVID-19 salivary diagnostics via a portable electrochemical device coupled to Machine Learning algorithms. SAGAPEP enabled molecular docking simulations against the SARS-CoV-2 Spike protein's RBD, leading to the synthesis of Bio-Inspired Artificial Intelligence Peptide 1 (BIAI1). Molecular docking was used to confirm interactions between BIAI1 and SARS-CoV-2, and BIAI1 was functionalized on rhodamine-modified electrodes. Cyclic voltammetry (CV) using a [Fe(CN)6]3−/4 solution detected virus levels in saliva samples with and without SARS-CoV-2. Support vector machine (SVM)-based machine learning analyzed electrochemical data, enhancing sensitivity and specificity. Molecular docking revealed stable hydrogen bonds and electrostatic interactions with RBD, showing an average affinity of −250 kcal/mol. Our biosensor achieved 100% sensitivity, 80% specificity, and 90% accuracy for 1.8 × 10⁴ focus-forming units in infected saliva. Validation with COVID-19-positive and -negative samples using a neural network showed 90% sensitivity, specificity, and accuracy. This BIAI1-based electrochemical biosensor, integrated with machine learning, demonstrates a promising non-invasive, portable solution for COVID-19 screening and detection in saliva. [ABSTRACT FROM AUTHOR]

Marine plankton include organisms driving multiple ecosystem services (ESs). In this study, we identified ESs provided by planktonic consortia worldwide from the analysis of scientific literature. We also mapped the identified ESs onto forty-nine plankton trophic networks derived from presence–absence data obtained from two coastal surveys in three areas along the coast of the Campania region in the Tyrrhenian Sea (NW Mediterranean). The systematic review evidenced that ESs associated with goods provision and ecosystem regulation were the most studied categories, while cultural ESs were the least considered. The mapping of ESs across the Campania coast revealed significant spatiotemporal variations in plankton-based ESs, which depend on seasonal variability and local environmental conditions. Among the ESs, those connected with the regulation and maintenance of marine ecosystems dominated both temporally and spatially, highlighting the crucial role of plankton in ecosystem stability and resilience. Moreover, although the direct provision of goods and materials was less represented, food provision to higher trophic levels was widespread within each coastal sector, highlighting the crucial role of plankton biodiversity in directly sustaining the whole marine ecosystem and related economic activities such as fisheries and aquacultures. [ABSTRACT FROM AUTHOR]

This study investigates how CEO characteristics, board composition, and firm size influence the technical efficiency (TE) of energy firms. We aim to understand how these factors contribute to production inefficiencies, which may help explain fluctuations in oil prices. Using stochastic frontier analysis (SFA), we analyze data from 100 American energy firms over the period from 2006 to 2019. Our results show that inefficiencies in production are primarily driven by specific CEO traits, the size and structure of the board, and the overall size of the firm. Based on the findings of this study, we recommend focusing on the selection of executive managers with specific qualifications, particularly those with extensive experience in managing oil and gas companies. Leadership positions should prioritize seasoned managers with accumulated expertise in this sector, and preference should be given to candidates with advanced educational backgrounds. Encouraging CEOs to acquire equity stakes in the company can significantly boost the technical efficiency of oil and gas firms. Additionally, offering competitive salaries and performance-based bonuses may further enhance managerial effectiveness and drive technical improvements. In addition, expanding the size of boards of directors in oil and gas companies is also anticipated to positively influence their technical efficiency. Finally, pursuing mergers and acquisitions to grow the scale of oil and gas companies represents a strategic approach to improving operational efficiency while contributing to the stability of global energy prices. [ABSTRACT FROM AUTHOR]

The field of computer vision has progressed rapidly over the past ten years, with noticeable improvements in techniques to detect, locate, and classify objects. Concurrent with these advances, improved accessibility through machine learning software libraries has sparked investigations of applications across multiple domains. In the areas of fisheries research and management, efforts have centered on the localization of fish and classification by species, as such tools can estimate the health, size, and movement of fish populations. To aid in the interpretation of computer vision for fisheries research management tasks, a survey of the recent literature was conducted. In contrast to prior reviews, this survey focuses on employed evaluation metrics and datasets as well as the challenges associated with applying machine learning to a fisheries research and management context. Misalignment between applications and commonly used evaluation metrics and datasets mischaracterizes the efficacy of emerging computer vision techniques for fisheries research and management tasks. Aqueous, turbid, and variable lighted deployment settings further complicate the use of computer vision and generalizability of the reported results. Informed by these inherent challenges, culling surveillance data, exploratory data collection in remote settings, and selective passage and traps are presented as opportunities for future research. [ABSTRACT FROM AUTHOR]

Myocardial infarction (MI) is a severe hypoxic event, resulting in the loss of up to one billion cardiomyocytes (CMs). Due to the limited intrinsic regenerative capacity of the heart, cell-based regenerative therapies, which feature the implantation of stem cell-derived cardiomyocytes (SC-CMs) into the infarcted myocardium, are being developed with the goal of restoring lost muscle mass, re-engineering cardiac contractility, and preventing the progression of MI into heart failure (HF). However, such cell-based therapies are challenged by their susceptibility to oxidative stress in the ischemic environment of the infarcted heart. To maximize the therapeutic benefits of cell-based approaches, a better understanding of the heart environment at the cellular, tissue, and organ level throughout MI is imperative. This review provides a comprehensive summary of the cardiac pathophysiology occurring during and after MI, as well as how these changes define the cardiac environment to which cell-based cardiac regenerative therapies are delivered. This understanding is then leveraged to frame how cell culture treatments may be employed to enhance SC-CMs' hypoxia resistance. In this way, we synthesize both the complex experience of SC-CMs upon implantation and the engineering techniques that can be utilized to develop robust SC-CMs for the clinical translation of cell-based cardiac therapies. [ABSTRACT FROM AUTHOR]

With the advent of a variety of vaccines against viral infections, there are multiple viruses that can be prevented via vaccination. However, breakthrough infections or uncovered strains can still cause vaccine-preventable viral infections (VPVIs). Therefore, timely diagnosis, treatment, and surveillance of these viruses is critical to patient care and public health. Point-of-care (POC) viral diagnostics tools have brought significant improvements in the detection and management of VPVIs. These cutting-edge technologies enable prompt and accurate results, enhancing patient care by facilitating timely treatment decisions. This review delves into the advancements in POC testing, including antigen/antibody detection and molecular assays, while focusing on their impact on the diagnosis, treatment, and surveillance of VPVIs such as mpox, viral hepatitis, influenza, flaviviruses (dengue, Zika, and yellow fever virus), and COVID-19. The role of POC tests in monitoring viral infection is crucial for tracking disease progression and managing outbreaks. Furthermore, the application of POC diagnostics has shown to be vital for public health strategies. In this review, we also highlight emerging POC technologies such as CRISPR-based diagnostics and smartphone-integrated POC devices, which have proven particularly beneficial in resource-limited settings. We underscore the importance of continued research to optimize these diagnostic tools for wider global use for mpox, viral hepatitis, influenza, dengue, and COVID-19, while also addressing current challenges related to their sensitivity, specificity, availability, efficiency, and more. [ABSTRACT FROM AUTHOR]

Simple Summary: While considering previously progressed metastatic castration-resistant prostate cancer (mCRPC) patients presenting deleterious BRCA1/2 mutations, poly(adenosine diphosphate–ribose) polymerase (PARP) inhibitors provide survival benefits over the current standard of care (SOC). PARP inhibitors are, however, associated with additional costs and their use is constrained by access to genetic testing, potentially placing an economic strain on the healthcare system. This paper aims to assess the cost-effectiveness of PARP inhibitors as a therapeutic class and not only as a single agent, considering approved and soon-to-be approved therapies. This flexible approach considers multiple treatment options comparing the SOC and PARP inhibitors to represent more accurately the clinical management of mCRPC and the potential impact of PARP inhibitors on the treatment landscape. Results from the Canadian healthcare system perspective suggest that PARP inhibitors are not cost-effective. This research impacts policymakers, guiding future reimbursement negotiations and future health economic research in the field. Background/Objectives: Through phase III clinical trials, PARP inhibitors have demonstrated outcome improvements in mCRPC patients with alterations in BRCA1/2 genes who have progressed on a second-generation androgen receptor pathway inhibitor (ARPI). While improving outcomes, PARP inhibitors contribute to the ever-growing economic burden of PCa. The objective of this project is to evaluate the cost-effectiveness of PARP inhibitors (olaparib, rucaparib, or talazoparib) versus the SOC (docetaxel or androgen receptor pathway inhibitors (ARPI)) for previously progressed mCRPC patients with BRCA1/2 mutations from the Canadian healthcare system perspective. Methods: Partitioned survival models were created to represent mCRPC disease after progression until death. Survival inputs for BRCA1/2-mutated patients were extracted from the PROfound, TRITON3, and TALAPRO-1 clinical trials, while Canadian-specific costs are presented in 2023 dollars. Upon progression, patients were treated with chemotherapy. The considered time horizon was 5 years and outcomes were discounted at 1.5% per year. Results: PARP inhibitors provide an additional survival of 0.19 quality-adjusted life years (QALY) when compared to the current standard of care, with additional costs of CAD 101,679 resulting in an incremental cost-utility ratio (ICUR) of CAD 565,383/QALY. The results were most sensitive to PARP inhibitors' acquisition costs and health-state utilities. PARP inhibitors required price reductions of up to 83% to meet the CAD 50,000/QALY willingness-to-pay threshold (WTP). Conclusions: While providing survival benefits to previously progressed mCRPC patients presenting deleterious BRCA1/2 gene mutations, PARP inhibitors are not cost-effective and require major price reductions to reach local WTP thresholds. [ABSTRACT FROM AUTHOR]

Brain–computer interfaces (BCI) are an effective tool for recognizing motor imagery and have been widely applied in the motor control and assistive operation domains. However, traditional intention-recognition methods face several challenges, such as prolonged training times and limited cross-subject adaptability, which restrict their practical application. This paper proposes an innovative method that combines a lightweight convolutional neural network (CNN) with domain adaptation. A lightweight feature extraction module is designed to extract key features from both the source and target domains, effectively reducing the model's parameters and improving the real-time performance and computational efficiency. To address differences in sample distributions, a domain adaptation strategy is introduced to optimize the feature alignment. Furthermore, domain adversarial training is employed to promote the learning of domain-invariant features, significantly enhancing the model's cross-subject generalization ability. The proposed method was evaluated on an fNIRS motor imagery dataset, achieving an average accuracy of 87.76% in a three-class classification task. Additionally, lightweight experiments were conducted from two perspectives: model structure optimization and data feature selection. The results demonstrated the potential advantages of this method for practical applications in motor imagery recognition systems. [ABSTRACT FROM AUTHOR]

Decision support systems based on machine learning (ML) techniques are already empowering neuro-oncologists. These systems provide comprehensive diagnostics, offer a deeper understanding of diseases, predict outcomes, and assist in customizing treatment plans to individual patient needs. Collectively, these elements represent artificial intelligence (AI) in neuro-oncology. This paper reviews recent studies which apply machine learning algorithms to optical spectroscopy data from central nervous system (CNS) tumors, both ex vivo and in vivo. We first cover general issues such as the physical basis of the optical-spectral methods used in neuro-oncology, and the basic algorithms used in spectral signal preprocessing, feature extraction, data clustering, and supervised classification methods. Then, we review in more detail the methodology and results of applying ML techniques to fluorescence, elastic and inelastic scattering, and IR spectroscopy. [ABSTRACT FROM AUTHOR]