Critical Domains Research Articles

A Consensus Statement on the Administration of Systemic Bevacizumab in Patients with Recurrent Respiratory Papillomatosis.

To provide detailed guidance on the administration of systemic bevacizumab in patients with recurrent respiratory papillomatosis (RRP) based on a detailed review of the scientific literature and a consensus of experts with real-world clinical experience. A bevacizumab consensus working group (N = 10) was composed of adult and pediatric otolaryngologists, adult and pediatric oncologists, and a representative from the RRP Foundation (RRPF), all with experience administering systemic bevacizumab in patients with RRP. After extensive review of the medical literature, a modified Delphi method-based survey series was utilized to establish consensus on the following key areas: clinical and patient characteristics ideal for treatment candidacy, patient perspective in treatment decisions, treatment access, initial dosing, monitoring, guidelines for tapering and discontinuation, and reintensifying therapy. Seventy-nine statements were identified across nine critical domains, and 45 reached consensus [clinical benefits of bevacizumab (3), patient and disease characteristics for treatment consideration (7), contraindications for treatment (3), shared decision-making (incorporating the patient perspective) (5), treatment access (3), initial dosing and administration (8), monitoring (7), tapering and discontinuation (6), and reintensification (3)]. This consensus statement provides the necessary guidance for clinicians to initiate systemic administration of bevacizumab and represents a potential paradigm shift toward nonsurgical treatment options for patients with RRP. 5 Laryngoscope, 2024.

Textile azo dye, Sudan Black B, inducing hepatotoxicity demonstrated in in vivo zebrafish larval model.

Environmental pollution, particularly from textile industry effluents, raises concerns globally. The aim of this study is to investigate the hepatotoxicity of Sudan Black B (SBB), a commonly used textile azo dye, on embryonic zebrafish. SBB exposure led to concentration-dependent mortality, reaching 100% at 0.8mM, accompanied by growth retardation and diverse malformations in zebrafish. Biochemical marker analysis indicated adaptive responses to SBB, including increased SOD, CAT, NO, and LDH, alongside decreased GSH levels. Liver morphology analysis unveiled significant alterations, impacting metabolism and detoxification. Also, glucose level was declined and lipid level elevated in SBB-exposed in vivo zebrafish. Inflammatory gene expressions (TNF-α, IL-10, and INOS) showcased a complex regulatory interplay, suggesting an organismal attempt to counteract pro-inflammatory states during SBB exposure. The increased apoptosis revealed a robust hepatic cellular response due to SBB, aligning with observed liver tissue damage and inflammatory events. This multidimensional study highlights the intricate web of responses due to SBB exposure, which is emphasizing the need for comprehensive understanding and targeted mitigation strategies. The findings bear the implications for both aquatic ecosystems and potentially parallels to human health, underscoring the imperative for sustained research in this critical domain.

Hyperspectral Imaging Predicts Differences in Carbon and Nitrogen Status Among Representative Biocrust Functional Groups of the Colorado Plateau

AbstractBiological soil crusts (biocrusts) are widespread soil photosynthetic communities covering about 12% of Earth's land surface, and play crucial roles in terrestrial carbon (C) and nitrogen (N) cycles, yet scalable quantifications of biocrusts and their biogeochemical contributions are notably lacking. While remote sensing has enormous potential to assess, scale, and contextualize biocrusts and their functions, the applicability of hyperspectral data in predicting C‐ and N‐related biocrust traits remains largely unexplored. We address this issue by evaluating the potential of in situ hyperspectral data to predict C and N across a range of biocrust species and different environmental conditions. We found that in situ hyperspectral reflectance measurements can be used to predict biocrust tissue C/N ratios and N concentrations with relatively high accuracy but to a lesser extent for potential biocrust N2 fixation rates. Critical wavelength domains included the visible region of the spectrum from roughly 490–600 nm, which most effectively captured variations in biocrust tissue C, and the shortwave infrared region from 1,150 to 1,350 nm and 1,550–1,650 nm, which most effectively captured biocrust tissue N and N2 fixation potential. Finally, we provide evidence that multi‐ and hyperspectral missions with targeted band placement, such as the proposed 26‐band Landsat Next, could be effective in predicting biocrust traits. This work provides a critical step in understanding how to apply data from new and upcoming satellite missions to the monitoring of biocrusts.

Functional Ovulatory Menstrual Health Literacy Amongst Adolescent Females in Western Australia

Study objectiveTo assess the knowledge of ovulation and menstruation of adolescent females in Western Australia. MethodsA validated adolescent ovulatory menstrual health literacy questionnaire was used in a cross-sectional study, which included an open-response question inviting participants’ reflections. ResultsParticipants (n=297) were from two single sex and seven co-educational schools of varied socio-educational advantage. Mean chronological age was 15 years and mean gynaecological age was two years. The prevalence of dysmenorrhoea was 69%. Primary sources of information included mothers (91%), friends (61%) and, for post-menarcheal participants (n=274), mobile applications (52%). Most adolescents enjoyed finding out information about ovulatory menstrual health, and understood the information given to them. However, participants’ knowledge of ovulation, menstruation and their occurrence in the cycle were low. Attendance at a single sex or Catholic school or use of mobile applications did not confer a knowledge advantage overall (p<0.05), except for knowing the meaning of cervical mucus. Thematic content analysis of open-ended responses resulted in five themes, including normality, menstrual flow, charting, ovulation and dysmenorrhoea. ConclusionInadequate functional ovulatory menstrual health literacy hampers progression to acquiring complete health literacy. This has negative implications for progressing towards the interactive and critical ovulatory menstrual health literacy domains, which include providing an accurate menstrual history when engaging with healthcare providers.

A Human Feedback Strategy for Photoresponsive Molecules in Drug Delivery: Utilizing GPT-2 and Time-Dependent Density Functional Theory Calculations.

Photoresponsive drug delivery stands as a pivotal frontier in smart drug administration, leveraging the non-invasive, stable, and finely tunable nature of light-triggered methodologies. The generative pre-trained transformer (GPT) has been employed to generate molecular structures. In our study, we harnessed GPT-2 on the QM7b dataset to refine a UV-GPT model with adapters, enabling the generation of molecules responsive to UV light excitation. Utilizing the Coulomb matrix as a molecular descriptor, we predicted the excitation wavelengths of these molecules. Furthermore, we validated the excited state properties through quantum chemical simulations. Based on the results of these calculations, we summarized some tips for chemical structures and integrated them into the alignment of large-scale language models within the reinforcement learning from human feedback (RLHF) framework. The synergy of these findings underscores the successful application of GPT technology in this critical domain.

Assessment of sustainable healthcare management policies and practices in Oman: A comprehensive analysis using an ai-based literature review

Health Vision 2050 Oman is a strategic plan to establish Oman as a regional leader and a research base for world-class health research. The vision emphasises prioritising health research to allocate and plan available resources appropriately. A sustainable healthcare management system is a holistic approach that seeks to balance economic, social, and environmental concerns to ensure the long-term viability and efficacy of healthcare services. There are numerous obstacles to implementing a sustainable healthcare management system in Oman. For example, assessing patient safety culture in primary healthcare settings in Muscat has revealed patient safety concerns throughout the healthcare system. It is critical to address these difficulties to improve the quality and safety of healthcare services, both of which are essential for sustainable healthcare management. The primary goal of this study is to identify the sustainable healthcare areas and policies required for Oman's Healthcare Management System. This research undertakes a comprehensive literature review using artificial intelligence (AI) to analyse Oman's sustainable healthcare management policies and practices. This study uses two AI tools, Scite.ai and SciSpace, to discover areas and policies related to Oman's sustainable healthcare management system. The study highlighted environmental sustainability, public health initiatives, infrastructure development, waste management, and community engagement and education. Furthermore, a variety of interconnected policies are discernible within each domain. This study provides significant data about Oman's sustainable healthcare practices and can guide policymakers and healthcare sector stakeholders. This study contributes significantly to creating sustainable healthcare management strategies and establishing resilient healthcare systems in Oman by focusing on critical domains and policies.

Health systems response to climate change adaptation: a scoping review of global evidence

BackgroundThe health system plays a critical role in safeguarding the well-being of communities in the face of health risks associated with climate change. This review maps evidence on health systems' adaptation to climate risk and barriers to effective adaptation.MethodsThis review followed the recommendations by Arksey and O’Malley for conducting scoping review. Search for records was conducted in PubMed, Central, Web of Science, JSTOR, Google, and Google Scholar. Only peer-reviewed papers published in English language were included in this review. All the 63 included studies were critically appraise d.ResultsWe found that efforts are being made to create resilient health systems by incorporating climate change into health policies. Investments are being made in innovative technologies, climate-resilient health infrastructure, enhancing healthcare delivery, developing the capacity of climate specialists and agencies to provide high-quality evidence for resilient health systems. We also found that several obstacles prevent health system adaptation to climate risk, including poor policy implementation and evaluation. The obstacles are further exacerbated by financial constraints, including poverty, a lack of political commitment, inadequate data, and deficient healthcare systems, especially in developing countries. There is also a lack of integration of climate change into mental health actions and the health and safety of healthcare workers.ConclusionEfforts to develop resilient health systems against climate risks are underway, but persistent obstacles, including inadequate policy implementation, resource limitations, and a lack of integration of climate change into critical health domains, hinder comprehensive adaptation measures, particularly in developing nations.

Evaluating the necessity of the multiple metrics for assessing explainable AI: A critical examination

This paper investigates the specific properties of Explainable Artificial Intelligence (xAI), particularly when implemented in AI/ML models across high-stakes sectors, in this case cybersecurity. The authors execute a comprehensive systematic review of xAI properties, various evaluation metrics, and existing frameworks to assess their utility and relevance. Subsequently, the experimental sections evaluate selected xAI techniques against these metrics, delivering key insights into their practical utility and effectiveness. The findings highlight that the proliferation of metrics enhances the understanding of xAI systems but simultaneously exposes challenges such as metric duplication, inefficacy, and confusion. These issues underscore the pressing need for standardized evaluation frameworks to streamline their application and strengthen their effectiveness, thereby improving the overall utility of xAI in critical domains.

A comprehensive analysis on software vulnerability detection datasets: trends, challenges, and road ahead

As society’s dependence on information and communication systems (ICTs) grows, so does the necessity of guaranteeing the proper functioning and use of such systems. In this context, it is critical to enhance the security and robustness of the DevSecOps pipeline through timely vulnerability detection. Usually, AI-based models enable desirable features such as automation, performance, and efficacy. However, the quality of such models highly depends on the datasets used during the training stage. The latter encompasses a series of challenges yet to be solved, such as access to extensive labelled datasets with specific properties, such as well-represented and balanced samples. This article explores the current state of practice of software vulnerability datasets and provides a classification of the main challenges and issues. After an extensive analysis, it describes a set of guidelines and desirable features that datasets should guarantee. The latter is applied to create a new dataset, which fulfils these properties, along with a descriptive comparison with the state of the art. Finally, a discussion on how to foster good practices among researchers and practitioners sets the ground for further research and continued improvement within this critical domain.

Exploring the fusion of lattice‐based quantum key distribution for secure Internet of Things communications

AbstractThe integration of lattice‐based cryptography principles with Quantum Key Distribution (QKD) protocols is explored to enhance security in the context of Internet of Things (IoT) ecosystems. With the advent of quantum computing, traditional cryptographic methods are increasingly susceptible to attacks, necessitating the development of quantum‐resistant approaches. By leveraging the inherent resilience of lattice‐based cryptography, a synergistic fusion with QKD is proposed to establish secure and robust communication channels among IoT devices. Through comprehensive Qiskit simulations and theoretical analysis, the feasibility, security guarantees, and performance implications of this novel hybrid approach are thoroughly investigated. The findings not only demonstrate the efficacy of lattice‐based QKD in mitigating quantum threats, but also highlight its potential to fortify IoT communications against emerging security challenges. Moreover, the authors provide valuable insights into the practical implementation considerations and scalability aspects of this fusion approach. This research contributes to advancing the understanding of quantum‐resistant cryptography for IoT applications and paves the way for further exploration and development in this critical domain.

Adverse Events with Hypoglossal Nerve Stimulation in the Treatment of Obstructive Sleep Apnea-A Systematic Review of Clinical Trials and Real-World Data.

Background/Objectives: Hypoglossal nerve stimulation (HNS) emerged as an alternative treatment for patients with obstructive sleep apnea (OSA) a decade ago. Long-term clinical trials and real-world data show that HNS treatment provides significant and sustained improvements in both OSA disease control and quality-of-life measures over time. Given the nature of HNS treatment, with the requirement of using an implantable neurostimulation system, patient safety is a critical domain in the assessment of this technology. The objective of this review was to evaluate adverse events (AEs) and complications with HNS therapy in a systematic review of published evidence. Methods: Medline, Cochrane, and Web of Science were systematically searched to identify randomized controlled and real-world observational studies reporting relevant outcomes with HNS therapy for treatment of OSA that included procedure-, device-, and treatment-related AEs. Results: Out of 418 articles screened, 27 were reviewed for eligibility, and 17 studies, the majority found to have low-to-moderate risk of bias, with data on 1962 patients were included for further analysis. Across included studies, reporting of AEs was heterogeneous with regard to the classifications used and the extent of reporting. Over an average follow-up duration of 17.5 ± 16.9 months, the pooled mortality rate was 0.01% (95% CI = 0.0 to 0.2%), with all reported deaths being unrelated to HNS treatment. The HNS system survival probability over the follow-up time of 60 months was 0.9834 (95% CI = 0.9768 to 0.9882), with infections and request for removal by patients being the most common indications. The pooled surgical revision rate was 0.08% (95% CI 0.0 to 0.2%). Most reported treatment-related side effects were transient stimulation-related discomfort (0.08%, 95% CI = 0.0 to 0.2%) and tongue abrasions (0.07%, 95% CI = 0.0 to 0.2%). Based on the systematic review, a standardized set of endpoints was defined, aiming to harmonize safety data relevant to HNS therapy. Conclusions: In this systematic review, HNS therapy for treatment of OSA is associated with a positive patient safety profile. AEs occur mainly at device implantation and during the treatment acclimatization period. Due to a lack of available evidence, partially implantable HNS systems are underrepresented in this review, which limits the generalizability of the results. Significant heterogeneity was found for adverse event reporting. A framework for reporting HNS outcomes that includes AEs and side effects is proposed to facilitate comparability of the reported data.

Prevalent fast evolution of genes involved in heterochromatin functions.

Heterochromatin is a gene-poor and repeat-rich genomic compartment universally found in eukaryotes. Despite its low transcriptional activity, heterochromatin plays important roles in maintaining genome stability, organizing chromosomes, and suppressing transposable elements (TEs). Given the importance of these functions, it is expected that the genes involved in heterochromatin regulation would be highly conserved. Yet, a handful of these genes were found to evolve rapidly. To investigate whether these previous findings are anecdotal or general to genes modulating heterochromatin, we compile an exhaustive list of 106 candidate genes involved in heterochromatin functions and investigate their evolution over short and long evolutionary time scales in Drosophila. Our analyses find that these genes exhibit significantly more frequent evolutionary changes, both in the forms of amino acid substitutions and gene copy number change, when compared to genes involved in Polycomb-based repressive chromatin. While positive selection drives amino acid changes within both structured domains with diverse functions and intrinsically disordered regions (IDRs), purifying selection may have maintained the proportions of IDRs of these proteins. Together with the observed negative associations between evolutionary rates of these genes and genomic TE abundance, we propose an evolutionary model where the fast evolution of genes involved in heterochromatin functions is an inevitable outcome of the unique functional roles of heterochromatin, while the rapid evolution of TEs may be an effect rather than cause. Our study provides an important global view of the evolution of genes involved in this critical cellular domain and provides insights into the factors driving the distinctive evolution of heterochromatin.

Linking homocysteine and ferroptosis in cardiovascular disease: insights and implications.

Homocysteine (Hcy) is a metabolic intermediate product derived from methionine. Hyperhomocysteinemia is a condition associated with various diseases. Hcy is recognized as a risk factor for cardiovascular disease (CVD). Ferroptosis, a novel form of cell death, is primarily characterized by substantial iron accumulation and lipid peroxidation. Recent research indicates a close association between ferroptosis and the pathophysiological processes of tumors, neurological diseases, CVD, and other ailments. However, limited research has been conducted on the impact of Hcy on ferroptosis. Therefore, this paper aimed to investigate the potential roles and mechanisms of homocysteine and ferroptosis in the context of cardiovascular disease. By conducting comprehensive literature research and analysis, we aimed to summarize recent advancements in understanding the effects of homocysteine on ferroptosis in cardiovascular diseases. This research contributes to a profound understanding of this critical domain.

An implementation manual for an interprofessional enhanced recovery after surgery protocol in cardiac surgery following international established frameworks.

Enhanced Recovery After Surgery (ERAS) protocols represent a paradigm shift in perioperative care, aim to optimize patient outcomes and accelerate recovery. This manual presents findings from implementing the INCREASE study, a bicentric prospective randomized controlled trial focusing on ERAS in minimally invasive heart valve surgery. Utilizing the Consolidated Framework for Implementation Research (CFIR) and the Template for Intervention Description and Replication (TIDieR), the study examined contextual factors, intervention components, and implementation strategies. Key findings from the CFIR analysis revealed critical domains influencing implementation success. These included innovation characteristics, external and internal settings, and individual dynamics. The study showcased ERAS's adaptability to diverse healthcare systems, emphasizing its potential for successful integration across varying contexts. Furthermore, the importance of interprofessional collaboration emerged as a foundation of practical implementation, fostering teamwork, communication, and patient-centered care. Utilizing the TIDieR framework, this manual comprehensively describes ERAS intervention components, detailing preoperative counseling, intraoperative management, and postoperative care strategies. The manual underscored the importance of tailored, patient-centered approaches, highlighting the role of an academic ERAS nurse, early mobilization, and psychosomatic interventions in promoting optimal recovery outcomes. In conclusion, the INCREASE study provided valuable insights for creating an implementation manual for ERAS in cardiac surgery, emphasizing adaptability, collaboration, and ongoing evaluation as key drivers of successful implementation. These findings have broad implications for improving patient care outcomes and advancing perioperative practices in cardiac surgery settings.

Defining the Rules of Engagement: Legal and Ethical Standards in Cyber Conflict

In an increasingly digitized world, cyber conflicts are emerging as a critical domain of modern warfare and international relations. This paper examines the legal and ethical standards that govern cyber conflict, aiming to define clear rules of engagement. Through a detailed analysis of current international laws, national legislation, and ethical theories relevant to cyber operations, this research identifies gaps and challenges in the existing frameworks. Case studies of notable cyber incidents illustrate the practical implications of these legal and ethical standards. The study proposes a set of refined rules of engagement designed to address these deficiencies, ensuring ma ore coherent and consistent application of legal and ethical principles in cyber conflict. The findings suggest that while international consensus and cooperation are crucial, there is also a need for dynamic and adaptable rules that can keep pace with rapid technological advancements. This paper contributes to the growing discourse on cyber conflict by providing a comprehensive understanding of the legal and ethical dimensions and offering actionable recommendations for policymakers, legal experts, and cybersecurity practitioners.

Nd3+-doped double-clad fluoro-sulfo-phosphate fiber for a compact kilohertz-linewidth single-frequency laser

The utilization of single-frequency fiber lasers spans critical domains including gravitational wave detection, coherent optical communication, and atomic physics. Glass fibers doped with rare earth ions serve as pivotal gain media for them, and the quest for advanced fiber matrices is paramount. Herein, a newly developed Nd3+-doped AlF3-Na2SO4-KPO3-Zn(PO3)2 glass is reported. The glass showcases exceptional attributes including high anti-crystallization stability (&gt;150∘C), elastic modulus (75 GPa), chemical durability (8.1×10−7g⋅cm−2⋅min−1), Nd3+ concentration (&gt;1020ions/cm3), broad effective linewidth (45 nm), and extended lifetime (465 µs), surpassing those of conventional silica and phosphate glasses. Moreover, a custom-designed double-clad Nd3+-doped fiber with a net gain coefficient of 3.2 dB/cm at 1064 nm is fabricated, and single-frequency laser output with a narrow linewidth of 11 kHz has been obtained utilizing a 1.4-cm-long fiber, indicating the potential of this fiber as a promising gain medium for ultra-narrow-linewidth single-frequency fiber lasers.

Proteostasis Landscapes of Selective versus Poorly Responsive CFTR Variants Reveals Structural Vulnerabilities to Correction.

Cystic Fibrosis (CF) is a lethal genetic disorder caused by variants in CF transmembrane conductance regulator (CFTR). Many disease variants are treatable with corrector compounds, which enhance the folding and trafficking of CFTR. However, correctors fail to elicit a response for every CFTR variant. Approximately 3% of persons with CF harbor poorly responsive CFTR variants. Here, we reveal that a group of poorly responsive variants overlap with selectively responsive variants in a critical domain interface (nucleotide-binding domain 1/intracellular loop 4 - NBD1/ICL4). Affinity purification mass spectrometry proteomics was used to profile the protein homeostasis (proteostasis) changes of CFTR variants during corrector treatment to assess modulated interactions with protein folding and maturation pathways. Responsive variant interactions converged on similar proteostasis pathways during correction. In contrast, poorly responsive variants subtly diverged, revealing a partial restoration of protein quality control surveillance and a capacity to correct some mutations. Computational structural modeling showed that corrector VX-445 failed to confer enough NBD1 stability to poorly responsive variants. NBD1 secondary stabilizing mutations rescued poorly responsive variants, revealing structural vulnerabilities in NBD1 required for treating poor responders. Our study provides a framework for discerning the underlying protein quality control and structural defects of CFTR variants not reached with existing drugs. These insights can help expand therapeutics to all susceptible CFTR variants to enhance personalized medicine efforts.

Mapping the landscape: Bibliometric trends in gender diversity research (2013-2023)

This study delves into the realm of gender diversity research, by scrutinizing published scientific studies from 2013 to 2023. Using the Scopus repository, an analysis is conducted on bibliometric characteristics employing tools such as Vosviewer, R-studio, and SPAR-4. The results indicate a notable 211% surge in gender diversity publications over the span of a decade, predominantly in scholarly periodicals (72.64%). Notably, the United States and the United Kingdom dominate this field, accounting for nearly half of all related research. Eminent scholars such as Galbreath (USA) and Kulik (UK) are recognized for their scholarly contributions, while the journal "Academic Medicine" has emerged as a frontrunner in terms of paper dissemination. In contrast to the abundance of bibliometric analyses in the natural and life sciences, the social sciences, particularly gender diversity studies, have not received commensurate attention. This investigation addresses this gap by providing valuable insights into trends in academic publishing, prominent authors and publications, and the geographical distribution of contributions within this critical domain of social science. Through its comprehensive examination, this study enables future research endeavours by shedding light on significant contributions and areas warranting further exploration.

Serum dysregulation of serine and glycine metabolism as predictive biomarker for cognitive decline in frail elderly subjects

Frailty is a common age-related clinical syndrome characterized by a decline in the function of multiple organ systems, increased vulnerability to stressors, and a huge socio-economic burden. Despite recent research efforts, the physiopathological mechanisms underlying frailty remain elusive and biomarkers able to predate its occurrence in the early stages are still lacking. Beyond its physical component, cognitive decline represents a critical domain of frailty associated with higher risk of adverse health outcomes. We measured by High-Performance Liquid Chromatography (HPLC) a pool of serum amino acids including L-glutamate, L-aspartate, glycine, and D-serine, as well as their precursors L-glutamine, L-asparagine, and L-serine in a cohort of elderly subjects encompassing the entire continuum from fitness to frailty. These amino acids are known to orchestrate excitatory and inhibitory neurotransmission, and in turn, to play a key role as intermediates of energy homeostasis and in liver, kidney, muscle, and immune system metabolism. To comprehensively assess frailty, we employed both the Edmonton Frail Scale (EFS), as a practical tool to capture the multidimensionality of frailty, and the frailty phenotype, as a measure of physical function. We found that D-serine and D-/Total serine ratio were independent predictors of EFS but not of physical frailty. Furthermore, higher levels of glycine, glycine/L-serine and D-/Total serine were associated with worse cognition and depressive symptoms in the frail group. These findings suggest that changes in peripheral glycine and serine enantiomers homeostasis may represent a novel biochemical correlate of frailty.

Explainability analysis: An in-depth comparison between Fuzzy Cognitive Maps and LAMDA

Currently, it has become very relevant that machine learning techniques can provide an explanation of the results they generate, which is even more relevant in certain domains, called critical, such as health and energy, among others. For this reason, several explainability methods have been generated in the literature. Some of them are Local Interpretable Model-Agnostic Explanations (LIME) and Feature Importance, which have been used in a wide range of problems. The objective of this work is to analyze the explainability capacity of the Learning Algorithm for Multivariate Data Analysis (LAMDA) and Fuzzy Cognitive Maps (FCM), which have been used with great interest due to their interpretability, management of uncertainty during the inference process, simplicity in its use, among other things. For the development of this work, data from two critical domains have been considered, health (COVID-19 and Dengue datasets) and energy (energy price dataset), for which prediction/classification models have been developed using LAMDA and FCM techniques. Afterward, two explainability techniques were used to analyze the explainability provided by each method, one based on the LIME method and the other on the feature importance method, the latter adapted to our work by being based on the permutation of values. Finally, the work proposes two new explainability methods, one based on causal inference and the other on the degrees of membership of the variables to the classes. The latter, in particular, allows doing an explainability analysis by class. The new explainability methods reproduce the results of well-known explainability methods in the literature such as LIME and feature importance, with less execution cost, and also, with an explainability analysis by class. This work opens the doors to new work on class explainability. Furthermore, we see that machine learning approaches based on causal or fuzzy relationships are quite self-explanatory, but specific explainability methods such as those we propose in the work allow us to study particular aspects, such as highly important variables, which general explainability methods do not allow us to do, such as LIME and feature importance.