Heuristics, Community Change Concepts, and Health Policy: Coming to Know.

The purpose of this paper is to discuss heuristics, guided by Parse's (2021a) community model, to understand how health policies emerge from the unique values and beliefs of community constituents. Within this paper, there is a discussion about heuristics, health policy, Parse's humanbecoming paradigm, and policy implications reflected upon with the change concepts of the humanbecoming community model.

Self-reported dental pain in Mexican schoolchildren: a national ecological study.

The objective of this study was to quantify the prevalence of and identify the factors associated with dental pain among elementary- and middle-school students in Mexico. An ecological study was carried out with data from the 2008 National School-based Student-Health Survey. Information on dental pain from schoolchildren (aged 5 to 16 years) was collected from public schools across the 32 states of Mexico. In the original study, a questionnaire was used to explore various factors that affect the oral and dental health status of schoolchildren. The outcome variable was the prevalence rate (for dental pain) reported at state level. Various contextual socioeconomic variables were included, in addition to dental caries. Analyses were performed using Stata software. 52.9% of interviewees were girls; 26.9% of male and female schoolchildren in Mexico experienced gum or dental pain during the period analyzed (95% Confidence Interval = 26.02, 27.77%); according to the Spearman correlation results, self-reported dental pain was unrelated (p > 0.05) to the socioeconomic and sociodemographic variables that make up the Gross Domestic Product (GDP) and the Human Development (HDI), as well as the marginalization and the Gini indices. However, the estimated percentages of self-reported dental pain and caries were positively correlated in the elementary- (r = 0.8958, p < 0.0001), middle-school (r = 0.8958, p < 0.0001) and total populations (r = 0.8542, p < 0.0001). Prevalence of self-reported dental pain was 28%, or about one in three, of the Mexican children and adolescents in the study sample. The state-level sociodemographic and socioeconomic risk indicators were not associated with the prevalence of dental pain. Self-reported caries was positively correlated with self-reported dental pain.

3D Hydrogel Culture System Recapitulates Key Tuberculosis Phenotypes and Demonstrates Pyrazinamide Efficacy.

The mortality caused by tuberculosis (TB) infections is a global concern, and there is a need to improve understanding of the disease. Current in vitro infection models to study the disease have limitations such as short investigation durations and divergent transcriptional signatures. This study aims to overcome these limitations by developing a 3D collagen culture system that mimics the biomechanical and extracellular matrix (ECM) of lung microenvironment (collagen fibers, stiffness comparable to in vivo conditions) as the infection primarily manifests in the lungs. The system incorporates Mycobacterium tuberculosis (Mtb) infected human THP-1 or primary monocytes/macrophages. Dual RNA sequencing reveals higher mammalian gene expression similarity with patient samples than 2D macrophage infections. Similarly, bacterial gene expression more accurately recapitulates in vivo gene expression patterns compared to bacteria in 2D infection models. Key phenotypes observed in humans, such as foamy macrophages and mycobacterial cords, are reproduced in the model. This biomaterial system overcomes challenges associated with traditional platforms by modulating immune cells and closely mimicking in vivo infection conditions, including showing efficacy with clinically relevant concentrations of anti-TB drug pyrazinamide, not seen in any other in vitro infection model, making it reliable and readily adoptable for tuberculosis studies and drug screening.

Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study.

BACKGROUND: This research focused on the development of a motor imagery (MI) based brain-machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages of deep learning, such as automated feature extraction and transfer learning. The experimental protocol to evaluate the BMI was designed as asynchronous, allowing subjects to perform mental tasks at their own will. METHODS: A total of five healthy able-bodied subjects were enrolled in this study to participate in a series of experimental sessions. The brain signals from two of these sessions were used to develop a generic deep learning model through transfer learning. Subsequently, this model was fine-tuned during the remaining sessions and subjected to evaluation. Three distinct deep learning approaches were compared: one that did not undergo fine-tuning, another that fine-tuned all layers of the model, and a third one that fine-tuned only the last three layers. The evaluation phase involved the exclusive closed-loop control of the exoskeleton device by the participants' neural activity using the second deep learning approach for the decoding. RESULTS: The three deep learning approaches were assessed in comparison to an approach based on spatial features that was trained for each subject and experimental session, demonstrating their superior performance. Interestingly, the deep learning approach without fine-tuning achieved comparable performance to the features-based approach, indicating that a generic model trained on data from different individuals and previous sessions can yield similar efficacy. Among the three deep learning approaches compared, fine-tuning all layer weights demonstrated the highest performance. CONCLUSION: This research represents an initial stride toward future calibration-free methods. Despite the efforts to diminish calibration time by leveraging data from other subjects, complete elimination proved unattainable. The study's discoveries hold notable significance for advancing calibration-free approaches, offering the promise of minimizing the need for training trials. Furthermore, the experimental evaluation protocol employed in this study aimed to replicate real-life scenarios, granting participants a higher degree of autonomy in decision-making regarding actions such as walking or stopping gait.

Environmental evaluation of the Reloncaví estuary in southern Chile based on lipophilic shellfish toxins as related to harmful algal blooms.

The Reloncaví estuary in southern Chile is famous for its aquaculture. However, recurring harmful algal blooms have adversely affected mussel production. Therefore, regular monitoring of algal toxins is urgently needed to better understand the contamination status of the estuary. In this study, we quantified 15 types of lipophilic shellfish toxins in Metri Bay in the Reloncaví estuary on a biweekly basis for 4 years. We identified algal species using microscopy and metabarcoding analysis. We also measured water temperature, salinity, chlorophyll-a, and dissolved oxygen to determine the potential relationships of these parameters with algal toxin production. Our results revealed the presence of a trace amount of pectenotoxin and the causal phytoplankton Dinophysis, as well as yessotoxin and the causal phytoplankton Protoceratium. Statistical analysis indicated that fluctuations in water temperature affected the detection of these toxins. Additionally, metabarcoding analysis detected the highly toxic phytoplankton Alexandrium spp. in some samples. Although our results suggest that the level of lipophilic shellfish toxins in Metri Bay during the study period was insignificantly low using our current LC-MS method, the confirmed presence of highly toxic algae in Metri Bay raises concerns, given that favorable environmental conditions could cause blooms.

A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure.

INTRODUCTION: Epilepsy is a disease characterized by an excessive discharge in neurons generally provoked without any external stimulus, known as convulsions. About 2 million people are diagnosed each year in the world. This process is carried out by a neurological doctor using an electroencephalogram (EEG), which is lengthy. METHOD: To optimize these processes and make them more efficient, we have resorted to innovative artificial intelligence methods essential in classifying EEG signals. For this, comparing traditional models, such as machine learning or deep learning, with cutting-edge models, in this case, using Capsule-Net architectures and Transformer Encoder, has a crucial role in finding the most accurate model and helping the doctor to have a faster diagnosis. RESULT: In this paper, a comparison was made between different models for binary and multiclass classification of the epileptic seizure detection database, achieving a binary accuracy of 99.92% with the Capsule-Net model and a multiclass accuracy with the Transformer Encoder model of 87.30%. CONCLUSION: Artificial intelligence is essential in diagnosing pathology. The comparison between models is helpful as it helps to discard those that are not efficient. State-of-the-art models overshadow conventional models, but data processing also plays an essential role in evaluating the higher accuracy of the models.

Exploring EEG-based motor imagery decoding: a dual approach using spatial features and spectro-spatial Deep Learning model IFNet.

Introduction: In recent years, the decoding of motor imagery (MI) from electroencephalography (EEG) signals has become a focus of research for brain-machine interfaces (BMIs) and neurorehabilitation. However, EEG signals present challenges due to their non-stationarity and the substantial presence of noise commonly found in recordings, making it difficult to design highly effective decoding algorithms. These algorithms are vital for controlling devices in neurorehabilitation tasks, as they activate the patient's motor cortex and contribute to their recovery. Methods: This study proposes a novel approach for decoding MI during pedalling tasks using EEG signals. A widespread approach is based on feature extraction using Common Spatial Patterns (CSP) followed by a linear discriminant analysis (LDA) as a classifier. The first approach covered in this work aims to investigate the efficacy of a task-discriminative feature extraction method based on CSP filter and LDA classifier. Additionally, the second alternative hypothesis explores the potential of a spectro-spatial Convolutional Neural Network (CNN) to further enhance the performance of the first approach. The proposed CNN architecture combines a preprocessing pipeline based on filter banks in the frequency domain with a convolutional neural network for spectro-temporal and spectro-spatial feature extraction. Results and discussion: To evaluate the approaches and their advantages and disadvantages, EEG data has been recorded from several able-bodied users while pedalling in a cycle ergometer in order to train motor imagery decoding models. The results show levels of accuracy up to 80% in some cases. The CNN approach shows greater accuracy despite higher instability.

Leading-Following Interpretations: Living Quality and Working, the Musical.

It is important to explore the ways that the working lives of persons provides meaning in personal, professional, and organizational ways. In this paper, the author utilized the processes of Parse's (2021a) humanbecoming hermeneutic sciencing (discoursing with penetrating engaging, interpreting with quiescent beholding, and understanding with inspiring envisaging) and the leading-following model to further understanding of the meaning of "working" through Stephen Schwarz's Broadway show, Working, the Musical (Browning & Schwartz, 1982/2002). Although not a formal sciencing project, this interpretive reflection provided a way to "see" how work is "lived out" uniquely.

Utilization of dapsone and hemoglobin in the epithelial skin regeneration therapy of cutaneous loxoscelism: A case report and integrative literature review.

BACKGROUND: Loxosceles spp are arthropods found worldwide. Its bite may produce cutaneous loxoscelism (necrotic or edematous) or cutaneous-visceral loxoscelism. Depending on their severity and location, cutaneous forms are managed with local cold application and systemic administration of antihistamines, corticosteroids, antibiotics, polymorphonuclear inhibitors, and analgesics. OBJECTIVE: This study aimed to report a case of cutaneous loxoscelism and to identify the main dermatological manifestations associated with the Loxosceles spp bite. DESIGN AND SETTING: This case report and literature review was conducted in a Mexican university. METHODS: A detailed report on the medical management of a patient with cutaneous loxoscelism treated at the emergency department of a public hospital was published. Scopus, PubMed, Web of Science, and Google Scholar databases were searched to identify articles reporting cutaneous loxoscelism. The following keywords were used during the database search: "loxoscelism" OR "spider bite," OR "loxosceles" OR "loxosceles species" OR "loxosceles venom" OR "loxoscelism case report" AND "cutaneous" OR "dermonecrotic arachnidism." RESULTS: A 62-year-old female patient with cutaneous loxoscelism was treated with systemic dapsone and local heparin spray. Eighteen studies with 22 clinical cases were included in this systematic review. Of the 22 patients, 12 (54.5%) were men. L. rufescens was the predominant spider species. CONCLUSIONS: The administration of dapsone and heparin for the management of cutaneous loxoscelism demonstrated success in this case, with no sequelae observed. In general, the literature review indicated favorable outcomes in patients treated with antimicrobials and corticosteroids, with continuous healing of skin lesions. SYSTEMATIC REVIEW REGISTRATION: PROSPERO ID CRD42023422424 (https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023422424).

Utilization of dapsone and hemoglobin in the epithelial skin regeneration therapy of cutaneous loxoscelism: A case report and integrative literature review

ABSTRACT BACKGROUND: Loxosceles spp are arthropods found worldwide. Its bite may produce cutaneous loxoscelism (necrotic or edematous) or cutaneous-visceral loxoscelism. Depending on their severity and location, cutaneous forms are managed with local cold application and systemic administration of antihistamines, corticosteroids, antibiotics, polymorphonuclear inhibitors, and analgesics. OBJECTIVE: This study aimed to report a case of cutaneous loxoscelism and to identify the main dermatological manifestations associated with the Loxosceles spp bite. DESIGN AND SETTING: This case report and literature review was conducted in a Mexican university. METHODS: A detailed report on the medical management of a patient with cutaneous loxoscelism treated at the emergency department of a public hospital was published. Scopus, PubMed, Web of Science, and Google Scholar databases were searched to identify articles reporting cutaneous loxoscelism. The following keywords were used during the database search: "loxoscelism" OR "spider bite," OR "loxosceles" OR "loxosceles species" OR "loxosceles venom" OR "loxoscelism case report" AND "cutaneous" OR "dermonecrotic arachnidism." RESULTS: A 62-year-old female patient with cutaneous loxoscelism was treated with systemic dapsone and local heparin spray. Eighteen studies with 22 clinical cases were included in this systematic review. Of the 22 patients, 12 (54.5%) were men. L. rufescens was the predominant spider species. CONCLUSIONS: The administration of dapsone and heparin for the management of cutaneous loxoscelism demonstrated success in this case, with no sequelae observed. In general, the literature review indicated favorable outcomes in patients treated with antimicrobials and corticosteroids, with continuous healing of skin lesions. SYSTEMATIC REVIEW REGISTRATION: PROSPERO ID CRD42023422424 (https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023422424).

Policy Crafting: Humanbecoming and the Rainbow PRISM Model.

It is important that nurses guide practice with crafted policies based in nursing knowledge that is distinctive and specific to the values and beliefs of all persons, such as humanbecoming. As policy crafters in nursing, a nursing theory or model must serve as the foundation in service to others. In this article, the author explores crafting policy with the Rainbow PRISM model (Cody, 2003; Ortiz, 2018), in light of Parse's (2021a) humanbecoming paradigm, the humanbecoming community change concepts (Parse, 2003, 2021a), and Parse's (1987, 2021a) principles of inquiry construction.

Clinical Evolution After Administering Antivenom in Patients With Loxoscelism.

Spiders are the most numerous arthropods of the arachnid class. More than 45 thousand species of spiders have been identified, and only a few are dangerous to humans. Among them, the "violin spider" or "brown spider" of the genus Loxosceles (family Sicariidae) has a worldwide distribution, and its bite can cause loxoscelism. Initial treatment of a Loxosceles spider bite includes application of local cold, rest, elevation of the extremity if possible, and systemic pharmacotherapy with antihistamines, corticosteroids, antibiotics, polymorphonuclear inhibitors, and analgesics or nonsteroidal anti-inflammatory drugs. During cutaneous or systemic loxoscelism, administration of Loxosceles antivenom (immunoglobulin (Ig)G F(ab')2 fragments) may be indicated to prevent progression to severe systemic phases. In this manuscript, we present three cases of patients with loxoscelism treated with the fabotherapeutic Reclusmyn®, developed and manufactured in Mexico. Two patients had a satisfactory outcome without severe skin or systemic damage. Only one patient with loxoscelism, despite early initiation of antivenom, had extensive skin lesions that healed satisfactorily, leaving only a non-disabling scar. Due to the global presence of this clinical problem, further studies are needed to establish local and general guidelines for the treatment and prevention of loxoscelism. This will allow health professionals to provide more efficient and higher quality medical care and feel supported in their decisions.

Leading-Following and Community Change Concepts: A Humanbecoming Perspective.

Nurses in various settings are called upon to lead new projects and initiatives that further the mission of healthcare organizations or communities. It is important that there is a nursing theoretical framework to serve as a leadership foundation. This foundation grounds the direction of projects and initiatives, so that the path toward success is clear. The purpose of this paper is to explore leading-following within the humanbecoming community change concepts.

Plants of the Rubiaceae Family with Effect on Metabolic Syndrome: Constituents, Pharmacology, and Molecular Targets.

Metabolic syndrome (MetS) predisposes individuals to chronic non-communicable diseases (NCDs) like type 2 diabetes (T2D), non-alcoholic fatty liver disease, atherosclerosis, and cardiovascular disorders caused by systemic inflammation, intestinal dysbiosis, and diminished antioxidant ability, leading to oxidative stress and compromised insulin sensitivity across vital organs. NCDs present a global health challenge characterized by lengthy and costly pharmacological treatments. Complementary and alternative medicine using herbal therapies has gained popularity. Approximately 350,000 plant species are considered medicinal, with 80% of the world's population opting for traditional remedies; however, only 21,000 plants are scientifically confirmed by the WHO. The Rubiaceae family is promissory for preventing and treating MetS and associated NCDs due to its rich content of metabolites renowned for their antioxidative, anti-inflammatory, and metabolic regulatory properties. These compounds influence transcription factors and mitigate chronic low-grade inflammation, liver lipotoxicity, oxidative stress, and insulin resistance, making them a cost-effective non-pharmacological approach for MetS prevention and treatment. This review aims to collect and update data that validate the traditional uses of the Rubiaceae family for treating MetS and associated NCDs from experimental models and human subjects, highlighting the mechanisms through which their extracts and metabolites modulate glucose and lipid metabolism at the molecular, biochemical, and physiological levels.

Classification of Alzheimer's disease stages from magnetic resonance images using deep learning.

Alzheimer's disease (AD) is a progressive type of dementia characterized by loss of memory and other cognitive abilities, including speech. Since AD is a progressive disease, detection in the early stages is essential for the appropriate care of the patient throughout its development, going from asymptomatic to a stage known as mild cognitive impairment (MCI), and then progressing to dementia and severe dementia; is worth mentioning that everyone suffers from cognitive impairment to some degree as we age, but the relevant task here is to identify which people are most likely to develop AD. Along with cognitive tests, evaluation of the brain morphology is the primary tool for AD diagnosis, where atrophy and loss of volume of the frontotemporal lobe are common features in patients who suffer from the disease. Regarding medical imaging techniques, magnetic resonance imaging (MRI) scans are one of the methods used by specialists to assess brain morphology. Recently, with the rise of deep learning (DL) and its successful implementation in medical imaging applications, it is of growing interest in the research community to develop computer-aided diagnosis systems that can help physicians to detect this disease, especially in the early stages where macroscopic changes are not so easily identified. This article presents a DL-based approach to classifying MRI scans in the different stages of AD, using a curated set of images from Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies databases. Our methodology involves image pre-processing using FreeSurfer, spatial data-augmentation operations, such as rotation, flip, and random zoom during training, and state-of-the-art 3D convolutional neural networks such as EfficientNet, DenseNet, and a custom siamese network, as well as the relatively new approach of vision transformer architecture. With this approach, the best detection percentage among all four architectures was around 89% for AD vs. Control, 80% for Late MCI vs. Control, 66% for MCI vs. Control, and 67% for Early MCI vs. Control.

Genetic manipulation of Patescibacteria provides mechanistic insights into microbial dark matter and the epibiotic lifestyle.

Patescibacteria, also known as the candidate phyla radiation (CPR), are a diverse group of bacteria that constitute a disproportionately large fraction of microbial dark matter. Its few cultivated members, belonging mostly to Saccharibacteria, grow as epibionts on host Actinobacteria. Due to a lack of suitable tools, the genetic basis of this lifestyle and other unique features of Patescibacteira remain unexplored. Here, we show that Saccharibacteria exhibit natural competence, and we exploit this property for their genetic manipulation. Imaging of fluorescent protein-labeled Saccharibacteria provides high spatiotemporal resolution of phenomena accompanying epibiotic growth, and a transposon-insertion sequencing (Tn-seq) genome-wide screen reveals the contribution of enigmatic Saccharibacterial genes to growth on their hosts. Finally, we leverage metagenomic data to provide cutting-edge protein structure-based bioinformatic resources that support the strain Southlakia epibionticum and its corresponding host, Actinomyces israelii, as a model system for unlocking the molecular underpinnings of the epibiotic lifestyle.

Mobilizing nurses to address the opioid misuse epidemic.

BACKGROUND: The opioid epidemic is a major health challenge in the United States. PURPOSE: Members from the American Academy of Nursing joined to write a consensus paper about nurses' role in the opioid epidemic. METHODS: The panel reviewed the history of the opioid epidemic and policies to care for patients with opioid use disorder (OUD) and how registered nurses (RNs) and advanced practice nurses (APRNs) could increase care for people with OUD. DISCUSSION: Recommendations are presented to advance policies that empower RNs and APRNs to abate the opioid epidemic. CONCLUSION: Recommendations include (a) advance legislation that supports RNs and APRNs full scope of practice and expands professional role in pain management and addiction prevention; (b) evaluate effective policies that promote RN and APRN care; support federal elimination of X-waiver with state law alignment; (c) sustain the use of nurses in telemedicine; (d) support nursing research on nurse involvement in all aspects of OUD.

MtrA modulates Mycobacterium tuberculosis cell division in host microenvironments to mediate intrinsic resistance and drug tolerance.

The success of Mycobacterium tuberculosis (Mtb) is largely attributed to its ability to physiologically adapt and withstand diverse localized stresses within host microenvironments. Here, we present a data-driven model (EGRIN 2.0) that captures the dynamic interplay of environmental cues and genome-encoded regulatory programs in Mtb. Analysis of EGRIN 2.0 shows how modulation of the MtrAB two-component signaling system tunes Mtb growth in response to related host microenvironmental cues. Disruption of MtrAB by tunable CRISPR interference confirms that the signaling system regulates multiple peptidoglycan hydrolases, among other targets, that are important for cell division. Further, MtrA decreases the effectiveness of antibiotics by mechanisms of both intrinsic resistance and drug tolerance. Together, the model-enabled dissection of complex MtrA regulation highlights its importance as a drug target and illustrates how EGRIN 2.0 facilitates discovery and mechanistic characterization of Mtb adaptation to specific host microenvironments within the host.

Design and Evaluation of a Potential Non-Invasive Neurostimulation Strategy for Treating Persistent Anosmia in Post-COVID-19 Patients.

A new pandemic was declared at the end of 2019 because of coronavirus disease 2019 (COVID-19). One of the effects of COVID-19 infection is anosmia (i.e., a loss of smell). Unfortunately, this olfactory dysfunction is persistent in around 5% of the world's population, and there is not an effective treatment for it yet. The aim of this paper is to describe a potential non-invasive neurostimulation strategy for treating persistent anosmia in post-COVID-19 patients. In order to design the neurostimulation strategy, 25 subjects who experienced anosmia due to COVID-19 infection underwent an olfactory assessment while their electroencephalographic (EEG) signals were recorded. These signals were used to investigate the activation of brain regions during the olfactory process and identify which regions would be suitable for neurostimulation. Afterwards, 15 subjects participated in the evaluation of the neurostimulation strategy, which was based on applying transcranial direct current stimulation (tDCS) in selected brain regions related to olfactory function. The results showed that subjects with lower scores in the olfactory assessment obtained greater improvement than the other subjects. Thus, tDCS could be a promising option for people who have not fully regained their sense of smell following COVID-19 infection.