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Mezcal is an alcoholic beverage produced only in selected regions of Mexico under appellation of origin status from the Word Intellectual Property Organization. While it has been produced in Mexico for many centuries, mezcal's appellation of origin was only granted in 1995. Therefore efforts to produce and market it as a premium product have a relatively short history. This case study examines developments in the production and marketing of this unique product, and the activities of the marketing cooperative El Tecuán in Guerrero State in this process. [ABSTRACT FROM AUTHOR]

To determine whether historical behavior data can predict the occurrence of high-risk behavioral or seizure events in individuals with profound Autism Spectrum Disorder (ASD), thereby facilitating early intervention and improved support. To our knowledge, this is the first work to integrate the prediction of seizures with behavioral data, highlighting the interplay between adverse behaviors and seizure risk., Approach: We analyzed nine years of behavior and seizure data from 353 individuals with profound ASD. Using a deep learning-based algorithm, we predicted the following day's occurrence of seizure and three high-risk behavioral events (aggression, self-injurious behavior (SIB), and elopement). We employed permutationbased statistical tests to assess the significance of our predictive performance., Main Results: Our model achieved accuracies 70.5% for seizures, 78.3% for aggression, 80.2% for SIB, and 85.7% for elopement. All results were significant for more than 85% of the population. These findings suggest that high-risk behaviors can serve as early indicators, not only of subsequent challenging behaviors but also of upcoming seizure events., Significance: By demonstrating, for the first time, that behavioral patterns can predict seizures as well as adverse behaviors, this approach expands the clinical utility of predictive modeling in ASD. Early warning systems derived from these predictions can guide timely interventions, enhance inclusion in educational and community settings, and improve quality of life by helping anticipate and mitigate severe behavioral and medical events.

Background : Bursera microphylla (B) A. Gray, a plant native to northwest Mexico, has long been utilized in traditional medicine for its anti-inflammatory effects. Previous studies have highlighted the bioactivity of B. microphylla fruit extract. Chitosan (Cs), a biopolymer known for its favorable physicochemical properties, has proven effective in encapsulating bioactive compounds. This study aimed to synthesize and characterize Cs-based microparticles containing B. microphylla fruit extract and evaluate their in vitro anti-inflammatory activity. Methods: Cs-based three-dimensional hydrogels were synthesized using physical cross-linking with ammonium hydroxide, incorporating B. microphylla fruit extract. The hydrogels were freeze-dried and mechanically ground into microparticles. The physicochemical properties of the microencapsulates were analyzed through scanning electron microscopy (SEM), optical microscopy (OM), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and moisture absorption tests. Anti-inflammatory activity was assessed by measuring nitric oxide (NO) reduction in LPS-activated RAW 264.7 cells. Antimicrobial activity was evaluated against Staphylococcus aureus . Results: SEM and OM analyses revealed irregular morphologies with rounded protuberances, with particle sizes ranging from 135 to 180 µm. FTIR spectra indicated that no new chemical bonds were formed, preserving the integrity of the original compounds. TGA confirmed that the encapsulated extract was heat-protected. The moisture absorption test indicated the microparticles' hydrophilic nature. In vitro, the microencapsulated extract reduced NO production by 46%, compared to 32% for the non-encapsulated extract. The microencapsulated extract was effective in reducing the microbial load of S. aureus between 15-24%. Conclusions: Cs-based microencapsulates containing B. microphylla fruit extract exhibited no chemical interactions during synthesis and demonstrated significant anti-inflammatory and antimicrobial activity. These results suggest that the Cs-based system is a promising candidate for managing inflammatory conditions.

We introduce the ECG-Image-Database, a large and diverse collection of electrocardiogram (ECG) images generated from ECG time-series data, with real-world scanning, imaging, and physical artifacts. We used ECG-Image-Kit, an open-source Python toolkit, to generate realistic images of 12-lead ECG printouts from raw ECG time-series. The images include realistic distortions such as noise, wrinkles, stains, and perspective shifts, generated both digitally and physically. The toolkit was applied to 977 12-lead ECG records from the PTB-XL database and 1,000 from Emory Healthcare to create high-fidelity synthetic ECG images. These unique images were subjected to both programmatic distortions using ECG-Image-Kit and physical effects like soaking, staining, and mold growth, followed by scanning and photography under various lighting conditions to create real-world artifacts. The resulting dataset includes 35,595 software-labeled ECG images with a wide range of imaging artifacts and distortions. The dataset provides ground truth time-series data alongside the images, offering a reference for developing machine and deep learning models for ECG digitization and classification. The images vary in quality, from clear scans of clean papers to noisy photographs of degraded papers, enabling the development of more generalizable digitization algorithms. ECG-Image-Database addresses a critical need for digitizing paper-based and non-digital ECGs for computerized analysis, providing a foundation for developing robust machine and deep learning models capable of converting ECG images into time-series. The dataset aims to serve as a reference for ECG digitization and computerized annotation efforts. ECG-Image-Database was used in the PhysioNet Challenge 2024 on ECG image digitization and classification.

Objectives: The main objective of this study is to analyze the relationships between academic performance, physical activity, and academic stress in secondary education students, while the secondary objective is to establish differences by gender in the physical activity and academic stress levels of secondary students based on academic performance., Methods: The sample was composed of students from both sexes who attended public institutions. Data collection was conducted applying an ad hoc questionnaire for academic performance, the PAQ-C questionnaire for physical activity, and the QASSE questionnaire for academic stress. Data analysis was performed using descriptive statistics: Spearman's correlation coefficient was used for associations, while comparisons were conducted via the Mann-Whitney U test and Kruskal-Wallis H test., Results: The results show that academic stress is negatively correlated with physical activity and academic performance. Men present significantly higher values in physical activity, while women present higher mean values in general academic stress and the academic overload dimension. Lastly, regarding academic performance, significant differences were observed in the family pressure dimension, with students who perform better academically presenting lower mean values in this dimension of academic stress., Conclusions: In conclusion, the more the general academic stress, the lower the physical activity levels and academic performance. In addition, physical activity appears as a potential coping strategy for academic stress, and its influence on academic performance should be further studied in secondary education.

Background: Atrial fibrillation (AF) is often asymptomatic and thus under-observed. Given the high risks of stroke and heart failure among patients with AF, early prediction and effective management are crucial. Given the prevalence of obstructive sleep apnea among AF patients, electrocardiogram (ECG) analysis from polysomnography (PSG) offers a unique opportunity for early AF prediction. Our aim is to identify individuals at high risk of AF development from single‑lead ECGs during standard PSG., Methods: We analyzed 18,782 single‑lead ECG recordings from 13,609 subjects undergoing PSG at the Massachusetts General Hospital sleep laboratory. AF presence was identified using ICD-9/10 codes. The dataset included 15,913 recordings without AF history and 2054 recordings from patients diagnosed with AF between one month to fifteen years post-PSG. Data were partitioned into training, validation, and test cohorts ensuring that individual patients remained exclusive to each cohort. The test set was held out during the training process. We employed two different methods for feature extraction to build a final model for AF prediction: Extraction of hand-crafted ECG features and a deep learning method. For extraction of ECG-hand-crafted features, recordings were split into 30-s windows, and those with a signal quality index (SQI) below 0.95 were discarded. From each remaining window, 150 features were extracted from the time, frequency, time-frequency domains, and phase-space reconstructions of the ECG. A compilation of 12 statistical features summarized these window-specific features per recording, resulting in 1800 features (12 × 150). A pre-trained deep neural network from the PhysioNet Challenge 2021 was updated using transfer learning to discriminate recordings with and without AF. The model processed PSG ECGs in 16-s windows to generate AF probabilities, from which 13 statistical features were extracted. Combining 1800 features from feature extraction with 13 from the deep learning model, we performed a feature selection and subsequently trained a shallow neural network to predict future AF and evaluated its performance on the test cohort., Results: On the test set, our model exhibited sensitivity, specificity, and precision of 0.67, 0.81, and 0.3, respectively, for AF prediction. Survival analysis revealed a hazard ratio of 8.36 (p-value: 1.93 × 10 -52 ) for AF outcomes using the log-rank test., Conclusions: Our proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite modest precision, suggesting false positives. This approach could enable low-cost screening and proactive treatment for high-risk patients. Refinements, including additional physiological parameters, may reduce false positives, enhancing clinical utility and accuracy., Competing Interests: Declaration of competing interest None., (Copyright © 2024. Published by Elsevier Inc.)

Poor sleep quality in Autism Spectrum Disorder (ASD) individuals is linked to severe daytime behaviors. This study explores the relationship between a prior night's sleep structure and its predictive power for next-day behavior in ASD individuals. The motion was extracted using a low-cost near-infrared camera in a privacy-preserving way. Over two years, we recorded overnight data from 14 individuals, spanning over 2,000 nights, and tracked challenging daytime behaviors, including aggression, self-injury, and disruption. We developed an ensemble machine learning algorithm to predict next-day behavior in the morning and the afternoon. Our findings indicate that sleep quality is a more reliable predictor of morning behavior than afternoon behavior the next day. The proposed model attained an accuracy of 74% and a F1 score of 0.74 in target-sensitive tasks and 67% accuracy and 0.69 F1 score in target-insensitive tasks. For 7 of the 14, better-than-chance balanced accuracy was obtained (p-value<0.05), with 3 showing significant trends (p-value<0.1). These results suggest off-body, privacy-preserving sleep monitoring as a viable method for predicting next-day adverse behavior in ASD individuals, with the potential for behavioral intervention and enhanced care in social and learning settings.

For the first time, this study shows the nanoarchitectonic process to obtain an acetogenin-enriched nanosystem (AuNPs-Ac) using an aqueous extract from Annona cherimola Mill (ACM) composed of gold nanoparticles embedded in an organic matrix that acts as stabilizing agent and presents anti-inflammatory activity and cytotoxical effect against HepG2 cell line, promoting apoptosis. The synthesis of AuNPs-Ac was confirmed by x-ray diffraction analysis, showing metallic gold as the only phase, and the scanning transmission microscope showed an organic cap covering the AuNPs-Ac. Fourier-transformed infrared suggests that the organic cap comprises a combination of different annonaceous acetogenins, alkaloids, and phenols by the presence of bands corresponding to aromatic rings and hydroxyl groups. High-Performance Liquid Chromatography has demonstrated the presence of annonacin, a potent acetogenin, in the extract of ACM. An in vitro anti-inflammatory activity of the extract of ACM and the AuNPs-Ac was performed using the albumin denaturation method, showing a nonlinear response, which is better than sodium diclofenac salt in a wide range of concentrations that goes from 200 to 400 μ g ml -1 with both samples. The viability assay was studied using trypan blue, treating IMR90 and HepG2 at different concentrations of AuNPs-Ac. The results defined a median lethal dose of 800 μ g ml -1 against HepG2 through apoptosis according to the ratio of caspase-cleaved 9/alpha-tubulin evaluated. It was also demonstrated that the nanosystem presents a higher cytotoxic effect on the HepG2 cell line than in IMR90, suggesting a targeted mechanism. In addition, the nanosystem performs better than using only the extract of ACM in the anti-inflammatory or antiproliferative test, attributed to their higher surface area., (Creative Commons Attribution license.)

Objective. Cardiovascular diseases are a major cause of mortality globally, and electrocardiograms (ECGs) are crucial for diagnosing them. Traditionally, ECGs are stored in printed formats. However, these printouts, even when scanned, are incompatible with advanced ECG diagnosis software that require time-series data. Digitizing ECG images is vital for training machine learning models in ECG diagnosis, leveraging the extensive global archives collected over decades. Deep learning models for image processing are promising in this regard, although the lack of clinical ECG archives with reference time-series data is challenging. Data augmentation techniques using realistic generative data models provide a solution. Approach. We introduce ECG-Image-Kit , an open-source toolbox for generating synthetic multi-lead ECG images with realistic artifacts from time-series data, aimed at automating the conversion of scanned ECG images to ECG data points. The tool synthesizes ECG images from real time-series data, applying distortions like text artifacts, wrinkles, and creases on a standard ECG paper background. Main results. As a case study, we used ECG-Image-Kit to create a dataset of 21 801 ECG images from the PhysioNet QT database. We developed and trained a combination of a traditional computer vision and deep neural network model on this dataset to convert synthetic images into time-series data for evaluation. We assessed digitization quality by calculating the signal-to-noise ratio and compared clinical parameters like QRS width, RR, and QT intervals recovered from this pipeline, with the ground truth extracted from ECG time-series. The results show that this deep learning pipeline accurately digitizes paper ECGs, maintaining clinical parameters, and highlights a generative approach to digitization. Significance. The toolbox has broad applications, including model development for ECG image digitization and classification. The toolbox currently supports data augmentation for the 2024 PhysioNet Challenge, focusing on digitizing and classifying paper ECG images., (Creative Commons Attribution license.)

Mutations in SETD2 are among the most prevalent drivers of renal cell carcinoma (RCC). We identified a novel single nucleotide polymorphism (SNP) in SETD2, E902Q, within a subset of RCC patients, which manifests as both an inherited or tumor-associated somatic mutation. To determine if the SNP is biologically functional, we used CRISPR-based genome editing to generate the orthologous mutation within the Drosophila melanogaster Set2 gene. In Drosophila, the homologous amino acid substitution, E741Q, reduces H3K36me3 levels comparable to Set2 knockdown, and this loss is rescued by reintroduction of a wild-type Set2 transgene. We similarly uncovered significant defects in spindle morphogenesis, consistent with the established role of SETD2 in methylating α-Tubulin during mitosis to regulate microtubule dynamics and maintain genome stability. These data indicate the Set2 E741Q SNP affects both histone methylation and spindle integrity. Moreover, this work further suggests the SETD2 E902Q SNP may hold clinical relevance., Competing Interests: Conflicts of interest The author(s) declare no conflict of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Regular blood pressure (BP) monitoring in clinical and ambulatory settings plays a crucial role in the prevention, diagnosis, treatment, and management of cardiovascular diseases. Recently, the widespread adoption of ambulatory BP measurement devices has been predominantly driven by the increased prevalence of hypertension and its associated risks and clinical conditions. Recent guidelines advocate for regular BP monitoring as part of regular clinical visits or even at home. This increased utilization of BP measurement technologies has raised significant concerns regarding the accuracy of reported BP values across settings. In this survey, which focuses mainly on cuff-based BP monitoring technologies, we highlight how BP measurements can demonstrate substantial biases and variances due to factors such as measurement and device errors, demographics, and body habitus. With these inherent biases, the development of a new generation of cuff-based BP devices that use artificial intelligence (AI) has significant potential. We present future avenues where AI-assisted technologies can leverage the extensive clinical literature on BP-related studies together with the large collections of BP records available in electronic health records. These resources can be combined with machine learning approaches, including deep learning and Bayesian inference, to remove BP measurement biases and provide individualized BP-related cardiovascular risk indexes.

Objectives: To develop the International Cardiac Arrest Research (I-CARE), a harmonized multicenter clinical and electroencephalography database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest., Design: Multicenter cohort, partly prospective and partly retrospective., Setting: Seven academic or teaching hospitals from the United States and Europe., Patients: Individuals 16 years old or older who were comatose after return of spontaneous circulation following a cardiac arrest who had continuous electroencephalography monitoring were included., Interventions: Not applicable., Measurements and Main Results: Clinical and electroencephalography data were harmonized and stored in a common Waveform Database-compatible format. Automated spike frequency, background continuity, and artifact detection on electroencephalography were calculated with 10-second resolution and summarized hourly. Neurologic outcome was determined at 3-6 months using the best Cerebral Performance Category (CPC) scale. This database includes clinical data and 56,676 hours (3.9 terabytes) of continuous electroencephalography data for 1,020 patients. Most patients died ( n = 603, 59%), 48 (5%) had severe neurologic disability (CPC 3 or 4), and 369 (36%) had good functional recovery (CPC 1-2). There is significant variability in mean electroencephalography recording duration depending on the neurologic outcome (range, 53-102 hr for CPC 1 and CPC 4, respectively). Epileptiform activity averaging 1 Hz or more in frequency for at least 1 hour was seen in 258 patients (25%) (19% for CPC 1-2 and 29% for CPC 3-5). Burst suppression was observed for at least 1 hour in 207 (56%) and 635 (97%) patients with CPC 1-2 and CPC 3-5, respectively., Conclusions: The I-CARE consortium electroencephalography database provides a comprehensive real-world clinical and electroencephalography dataset for neurophysiology research of comatose patients after cardiac arrest. This dataset covers the spectrum of abnormal electroencephalography patterns after cardiac arrest, including epileptiform patterns and those in the ictal-interictal continuum., Competing Interests: Dr. van Putten is the founder of Clinical Science Systems. Dr. Westover is a co-founder of Beacon Biosignals. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2023 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Carbon quantum dots (CQDs) are a new carbon-based nanomaterial that has attracted tremendous attention due to their excellent fluorescent properties, chemical stability, water solubility, and biocompatibility features. Here, fluorescent CQDs synthesized by a green nanoarchitectonic method using Cinchona Pubescens Vahl extract were evaluated as drug nanocarriers for carboplatin (CBP) delivery. The characterization methods showed CQDs with semispherical shapes and sizes around 5 nm, temperature- and pH-dependent functional groups that interact with the CBP molecule adding specificity to the drug-delivery system. Based on the load efficiency results, it seems that the CQDs can carry almost 100 μg of carboplatin for every 1 mg of CQDs. This is possible due to the self-assembly process that takes place through the interaction between the protonation/deprotonation functional groups of CQDs and the hydrolyzed CBP molecule. Through this process, it is created spherical nanoparticles with an average size of 77.44 nm. The CQDs-CBP nanoparticles release the drug through a diffusion-controlled release mechanism where the acidic media is preferred, and the EPR effect also plays a helpful role. Besides, the viability test shows that the CQDs have almost null cytotoxicity suggesting that they could be used as a promising cancer treatment, improving the efficiency of cell internalization and significantly increasing their drug delivery., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs, who may need follow-up diagnostic screening and treatment for abnormal cardiac function. However, experts are needed to interpret the heart sounds, limiting the accessibility of cardiac auscultation in resource-constrained environments. Therefore, the George B. Moody PhysioNet Challenge 2022 invited teams to develop algorithmic approaches for detecting heart murmurs and abnormal cardiac function from phonocardiogram (PCG) recordings of heart sounds. For the Challenge, we sourced 5272 PCG recordings from 1452 primarily pediatric patients in rural Brazil, and we invited teams to implement diagnostic screening algorithms for detecting heart murmurs and abnormal cardiac function from the recordings. We required the participants to submit the complete training and inference code for their algorithms, improving the transparency, reproducibility, and utility of their work. We also devised an evaluation metric that considered the costs of screening, diagnosis, misdiagnosis, and treatment, allowing us to investigate the benefits of algorithmic diagnostic screening and facilitate the development of more clinically relevant algorithms. We received 779 algorithms from 87 teams during the Challenge, resulting in 53 working codebases for detecting heart murmurs and abnormal cardiac function from PCG recordings. These algorithms represent a diversity of approaches from both academia and industry, including methods that use more traditional machine learning techniques with engineered clinical and statistical features as well as methods that rely primarily on deep learning models to discover informative features. The use of heart sound recordings for identifying heart murmurs and abnormal cardiac function allowed us to explore the potential of algorithmic approaches for providing more accessible diagnostic screening in resource-constrained environments. The submission of working, open-source algorithms and the use of novel evaluation metrics supported the reproducibility, generalizability, and clinical relevance of the research from the Challenge., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: AE receives financial support through grant PID2021-122727OB-I00 funded by MCIN/AEI/10.13039/501100011033 and "ERDF A way of making Europe" and by the Basque Government under Grant IT1717-22. FR and MC receive financial support by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020. GC has financial interests in AliveCor, LifeBell AI and Mindchild Medical. GC also holds a board position in LifeBell AI and Mindchild Medical., (Copyright: © 2023 Reyna et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: To develop a harmonized multicenter clinical and electroencephalography (EEG) database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest., Design: Multicenter cohort, partly prospective and partly retrospective., Setting: Seven academic or teaching hospitals from the U.S. and Europe., Patients: Individuals aged 16 or older who were comatose after return of spontaneous circulation following a cardiac arrest who had continuous EEG monitoring were included., Interventions: not applicable., Measurements and Main Results: Clinical and EEG data were harmonized and stored in a common Waveform Database (WFDB)-compatible format. Automated spike frequency, background continuity, and artifact detection on EEG were calculated with 10 second resolution and summarized hourly. Neurological outcome was determined at 3-6 months using the best Cerebral Performance Category (CPC) scale. This database includes clinical and 56,676 hours (3.9 TB) of continuous EEG data for 1,020 patients. Most patients died (N=603, 59%), 48 (5%) had severe neurological disability (CPC 3 or 4), and 369 (36%) had good functional recovery (CPC 1-2). There is significant variability in mean EEG recording duration depending on the neurological outcome (range 53-102h for CPC 1 and CPC 4, respectively). Epileptiform activity averaging 1 Hz or more in frequency for at least one hour was seen in 258 (25%) patients (19% for CPC 1-2 and 29% for CPC 3-5). Burst suppression was observed for at least one hour in 207 (56%) and 635 (97%) patients with CPC 1-2 and CPC 3-5, respectively., Conclusions: The International Cardiac Arrest Research (I-CARE) consortium database provides a comprehensive real-world clinical and EEG dataset for neurophysiology research of comatose patients after cardiac arrest. This dataset covers the spectrum of abnormal EEG patterns after cardiac arrest, including epileptiform patterns and those in the ictal-interictal continuum., Competing Interests: Potential Conflicts of Interest E.A., W.L.Z., M.M.G., M.A., P.K., V.K., J.W.L., L.J.H., S.T.H., A.S., N.G., R.S., M.A.R., G.D.C., and J.H. report no disclosures. M.V.P is the founder of Clinical Science Systems. Clinical Science Systems did not contribute funding nor played any role in the study. M.B.W. is a co-founder of Beacon Biosignals. Beacon Biosignals did not contribute funding nor played any role in the study.

Objective: Murmurs are abnormal heart sounds, identified by experts through cardiac auscultation. The murmur grade, a quantitative measure of the murmur intensity, is strongly correlated with the patient's clinical condition. This work aims to estimate each patient's murmur grade (i.e., absent, soft, loud) from multiple auscultation location phonocardiograms (PCGs) of a large population of pediatric patients from a low-resource rural area., Methods: The Mel spectrogram representation of each PCG recording is given to an ensemble of 15 convolutional residual neural networks with channel-wise attention mechanisms to classify each PCG recording. The final murmur grade for each patient is derived based on the proposed decision rule and considering all estimated labels for available recordings. The proposed method is cross-validated on a dataset consisting of 3456 PCG recordings from 1007 patients using a stratified ten-fold cross-validation. Additionally, the method was tested on a hidden test set comprised of 1538 PCG recordings from 442 patients., Results: The overall cross-validation performances for patient-level murmur gradings are 86.3% and 81.6% in terms of the unweighted average of sensitivities and F1-scores, respectively. The sensitivities (and F1-scores) for absent, soft, and loud murmurs are 90.7% (93.6%), 75.8% (66.8%), and 92.3% (84.2%), respectively. On the test set, the algorithm achieves an unweighted average of sensitivities of 80.4% and an F1-score of 75.8%., Conclusions: This study provides a potential approach for algorithmic pre-screening in low-resource settings with relatively high expert screening costs., Significance: The proposed method represents a significant step beyond detection of murmurs, providing characterization of intensity, which may provide an enhanced classification of clinical outcomes.

Background: It has been hypothesized that low access to healthy and nutritious food increases health disparities. Low-accessibility areas, called food deserts, are particularly commonplace in lower-income neighborhoods. The metrics for measuring the food environment's health, called food desert indices, are primarily based on decadal census data, limiting their frequency and geographical resolution to that of the census. We aimed to create a food desert index with finer geographic resolution than census data and better responsiveness to environmental changes., Materials and Methods: We augmented decadal census data with real-time data from platforms such as Yelp and Google Maps and crowd-sourced answers to questionnaires by the Amazon Mechanical Turks to create a real-time, context-aware, and geographically refined food desert index. Finally, we used this refined index in a concept application that suggests alternative routes with similar ETAs between a source and destination in the Atlanta metropolitan area as an intervention to expose a traveler to better food environments., Results: We made 139,000 pull requests to Yelp, analyzing 15,000 unique food retailers in the metro Atlanta area. In addition, we performed 248,000 walking and driving route analyses on these retailers using Google Maps' API. As a result, we discovered that the metro Atlanta food environment creates a strong bias towards eating out rather than preparing a meal at home when access to vehicles is limited. Contrary to the food desert index that we started with, which changed values only at neighborhood boundaries, the food desert index that we built on top of it captured the changing exposure of a subject as they walked or drove through the city. This model was also sensitive to the changes in the environment that occurred after the census data was collected., Conclusions: Research on the environmental components of health disparities is flourishing. New machine learning models have the potential to augment various information sources and create fine-tuned models of the environment. This opens the way to better understanding the environment and its effects on health and suggesting better interventions., (© 2023. The Author(s).)

Objective: In Ecuador, studies on clinical daily practice problems focused on parenteral nutrition in neonates are scarce. Therefore, this research aimed to identify negative results associated with medications (NRAM) in neonates with parenteral nutrition (PN) in a third-level hospital in Ecuador., Material and Methods: An observational, prospective, descriptive study was designed in the neonatology area of a tertiary-level public hospital, where, for over four months, the medical records, PN prescriptions, and pharmacy-managed databases of 78 patients were analyzed. Drug-related problems (DRPs) as possible causes of NRAM were classified through administrative, physicochemical, and clinical validation., Results: DRPs classified as follows were found: 78.81% by physicochemical, 17.62% by clinical, and 3.57% by administrative validation. The NRAM were 72% quantitatively uncertain, 16% needed, and 11% quantitatively ineffective., Conclusion: The NRAM associated with DRPs were statistically related to prematurity condition, APGAR score, PN time, and the number of medications administered, which suggests the need to create a nutritional therapy committee at the health facility.

In this work, the influence of the Sargassum natans I alga extract on the morphological characteristics of synthesized ZnO nanostructures, with potential biological and environmental applications, was evaluated. For this purpose, different ZnO geometries were synthesized by the co-precipitation method, using Sargassum natans I alga extract as stabilizing agent. Four extract volumes (5, 10, 20, and 50 mL) were evaluated to obtain the different nanostructures. Moreover, a sample by chemical synthesis, without the addition of extract, was prepared. The characterization of the ZnO samples was carried out by UV-Vis spectroscopy, FT-IR spectroscopy, X-ray diffraction, and scanning electron microscopy. The results showed that the Sargassum alga extract has a fundamental role in the stabilization process of the ZnO nanoparticles. In addition, it was shown that the increase in the Sargassum alga extract leads to preferential growth and arrangement, obtaining well-defined shaped particles. ZnO nanostructures demonstrated significant anti-inflammatory response by the in vitro egg albumin protein denaturation for biological purposes. Additionally, quantitative antibacterial analysis (AA) showed that the ZnO nanostructures synthesized with 10 and 20 mL of extract demonstrated high AA against Gram (+) S. aureus and moderate AA behavior against Gram (-) P. aeruginosa , depending on the ZnO arrangement induced by the Sargassum natans I alga extract and the nanoparticles' concentration (ca. 3200 µg/mL). Additionally, ZnO samples were evaluated as photocatalytic materials through the degradation of organic dyes. Complete degradation of both methyl violet and malachite green were achieved using the ZnO sample synthesized with 50 mL of extract. In all cases, the well-defined morphology of ZnO induced by the Sargassum natans I alga extract played a key role in the combined biological/environmental performance.

In this study, green chemistry was used as a tool to obtain gold nanoparticles using Amphipterygium adstringens extracts as a synthesis medium. Green ethanolic and aqueous extracts were obtained using ultrasound and shock wave-assisted extraction. Gold nanoparticles with sizes ranging between 100 and 150 nm were obtained with ultrasound aqueous extract. Interestingly, homogeneous quasi-spherical gold nanoparticles with sizes between 50 and 100 nm were achieved with shock wave aqueous-ethanolic extracts. Furthermore, 10 nm gold nanoparticles were obtained by the traditional methanolic macerate extraction method. The physicochemical characteristics, morphology, size, stability, and Z potential of the nanoparticles were determined using microscopic and spectroscopic techniques. The viability assay in leukemia cells (Jurkat) was performed using two different sets of gold nanoparticles, with final IC 50 values of 87 µM and 94.7 µM, reaching a maximum cell viability decrease of 80% The results do not indicate a significant difference between the cytotoxic effects produced by the gold nanoparticles synthesized in this study and vincristine on normal lymphoblasts (CRL-1991).

The present work shows the synthesis of ZnO nanoparticles through a green method, using sargassum extracts, which provide the reducing and stabilizing compounds. The conditions of the medium in which the reaction was carried out was evaluated, that is, magnetic stirring, ultrasound assisted, and resting condition. UV-Vis, FTIR spectroscopy, and X-ray diffraction results confirmed the synthesis of ZnO with nanometric crystal size. The scanning electron microscopy analysis showed that the morphology and size of the particles depends on the synthesis condition used. It obtained particles between 20 and 200 nm in the sample without agitation, while the samples with stirring and ultrasound were 80 nm and 100 nm, respectively. ZnO nanoparticles showed antibacterial activity against Gram-positive S. aureus and Gram-negative P. aeruginosa . A quantitative analysis was performed by varying the concentration of ZnO nanoparticles. In all cases, the antibacterial activity against Gram-positives was greater than against Gram-negatives. Ultrasound-assisted ZnO nanoparticles showed the highest activity, around 99% and 80% for S. aureus and P. aeruginosa , respectively. Similar results were obtained in the study of the anti-inflammatory activity of ZnO nanoparticles; the ultrasound-assisted sample exhibited the highest percentage (93%), even above that shown by diclofenac, which was used as a reference. Therefore, the ZnO nanoparticles synthesized with sargassum extracts have properties that can be used safely and efficiently in the field of biomedicine.

Interoperability is defined as the ability of a system or device to communicate between different technologies and software applications. This allows the exchange and use of data in an efficient, precise, and robust way. The present article gives researchers and healthcare information systems developers a qualitative and quantitative synthesis of the state of knowledge related to data formats and data standards proposed for mHealth devices interoperability in healthcare information systems that retrieve and store ECG data. We carry out a scoping review to answer to following questions: (1) What digital data formats or data standards have been proposed for the interoperability of electrocardiograph data between traditional healthcare information systems and mobile healthcare information systems? (2) What are the advantages and disadvantages of these data formats or data standards? The scoping review was conducted in four databases in accordance with the JBI methodology for scoping reviews, and in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). A total of 4018 studies were identified of which 30 studies met the inclusion criteria. Based on our findings, we identify four standards and nine formats for capturing and storing streaming ECG data in mobile health applications. The standards used were HL7, SCP-ECG, x73-PHD, and PDF/A. Formats include CSV, PDF-ECG, and seven XML-based formats. These are ECG-XML, HL7-XML, mPCG-XML, mECGML, JSON, SaECG, and CDA R2.

Despite the recent explosion of machine learning applied to medical data, very few studies have examined algorithmic bias in any meaningful manner, comparing across algorithms, databases, and assessment metrics. In this study, we compared the biases in sex, age, and race of 56 algorithms on over 130,000 electrocardiograms (ECGs) using several metrics and propose a machine learning model design to reduce bias. Participants of the 2021 PhysioNet Challenge designed and implemented working, open-source algorithms to identify clinical diagnosis from 2- lead ECG recordings. We grouped the data from the training, validation, and test datasets by sex (male vs female), age (binned by decade), and race (Asian, Black, White, and Other) whenever possible. We computed recording-wise accuracy, area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), F-measure, and the Challenge Score for each of the 56 algorithms. The Mann-Whitney U and the Kruskal-Wallis tests assessed the performance differences of algorithms across these demographic groups. Group trends revealed similar values for the AUROC, AUPRC, and F-measure for both male and female groups across the training, validation, and test sets. However, recording-wise accuracies were 20% higher (p < 0.01) and the Challenge Score 12% lower (p = 0.02) for female subjects on the test set. AUPRC, F-measure, and the Challenge Score increased with age, while recording-wise accuracy and AUROC decreased with age. The results were similar for the training and test sets, but only recording-wise accuracy (12% decrease per decade, p < 0.01), Challenge Score (1% increase per decade, p < 0.01), and AUROC (1% decrease per decade, p < 0.01) were statistically different on the test set. We observed similar AUROC, AUPRC, Challenge Score, and F-measure values across the different race categories. But, recording-wise accuracies were significantly lower for Black subjects and higher for Asian subjects on the training (31% difference, p < 0.01) and test (39% difference, p < 0.01) sets. A top performing model was then retrained using an additional constraint which simultaneously minimized differences in performance across sex, race and age. This resulted in a modest reduction in performance, with a significant reduction in bias. This work provides a demonstration that biases manifest as a function of model architecture, population, cost function and optimization metric, all of which should be closely examined in any model., Competing Interests: Declaration of Competing Interest None., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Objective. The standard twelve-lead electrocardiogram (ECG) is a widely used tool for monitoring cardiac function and diagnosing cardiac disorders. The development of smaller, lower-cost, and easier-to-use ECG devices may improve access to cardiac care in lower-resource environments, but the diagnostic potential of these devices is unclear. This work explores these issues through a public competition: the 2021 PhysioNet Challenge. In addition, we explore the potential for performance boosting through a meta-learning approach. Approach. We sourced 131,149 twelve-lead ECG recordings from ten international sources. We posted 88,253 annotated recordings as public training data and withheld the remaining recordings as hidden validation and test data. We challenged teams to submit containerized, open-source algorithms for diagnosing cardiac abnormalities using various ECG lead combinations, including the code for training their algorithms. We designed and scored the algorithms using an evaluation metric that captures the risks of different misdiagnoses for 30 conditions. After the Challenge, we implemented a semi-consensus voting model on all working algorithms. Main results. A total of 68 teams submitted 1,056 algorithms during the Challenge, providing a variety of automated approaches from both academia and industry. The performance differences across the different lead combinations were smaller than the performance differences across the different test databases, showing that generalizability posed a larger challenge to the algorithms than the choice of ECG leads. A voting model improved performance by 3.5%. Significance. The use of different ECG lead combinations allowed us to assess the diagnostic potential of reduced-lead ECG recordings, and the use of different data sources allowed us to assess the generalizability of the algorithms to diverse institutions and populations. The submission of working, open-source code for both training and testing and the use of a novel evaluation metric improved the reproducibility, generalizability, and applicability of the research conducted during the Challenge., (© 2022 Institute of Physics and Engineering in Medicine.)

Comprehensive sequencing of patient tumors reveals genomic mutations across tumor types that enable tumorigenesis and progression. A subset of oncogenic driver mutations results in neomorphic activity where the mutant protein mediates functions not engaged by the parental molecule. Here, we identify prevalent variant-enabled neomorph-protein-protein interactions (neoPPI) with a quantitative high-throughput differential screening (qHT-dS) platform. The coupling of highly sensitive BRET biosensors with miniaturized coexpression in an ultra-HTS format allows large-scale monitoring of the interactions of wild-type and mutant variant counterparts with a library of cancer-associated proteins in live cells. The screening of 17,792 interactions with 2,172,864 data points revealed a landscape of gain of interactions encompassing both oncogenic and tumor suppressor mutations. For example, the recurrent BRAF V600E lesion mediates KEAP1 neoPPI, rewiring a BRAF V600E /KEAP1 signaling axis and creating collateral vulnerability to NQO1 substrates, offering a combination therapeutic strategy. Thus, cancer genomic alterations can create neo-interactions, informing variant-directed therapeutic approaches for precision medicine., Competing Interests: Declaration of interests H.F. is scientific founder of PiVista Therapeutics., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being used as inputs in the human-computer interface that controls interaction through hand gestures. Although there is a gap between academic publications on the control of an upper-limb prosthesis developed in laboratories and its service in the natural environment, there are attempts to achieve easier control using multiple muscle signals. This work contributes to this, using a database and biomechanical simulation software, both open access, to seek simplicity in the classifiers, anticipating their implementation in microcontrollers and their execution in real time. Fifteen predefined finger movements of the hand were identified using classic classifiers such as Bayes, linear and quadratic discriminant analysis. The idealized movements of the database were modeled with Opensim for visualization. Combinations of two preprocessing methods-the forward sequential selection method and the feature normalization method-were evaluated to increase the efficiency of these classifiers. The statistical methods of cross-validation, analysis of variance (ANOVA) and Duncan were used to validate the results. Furthermore, the classifier with the best recognition result was redesigned into a new feature space using the sparse matrix algorithm to improve it, and to determine which features can be eliminated without degrading the classification. The classifiers yielded promising results-the quadratic discriminant being the best, achieving an average recognition rate for each individual considered of 96.16%, and with 78.36% for the total sample group of the eight subjects, in an independent test dataset. The study ends with the visual analysis under Opensim of the classified movements, in which the usefulness of this simulation tool is appreciated by revealing the muscular participation, which can be useful during the design of a multifunctional prosthesis.

Lab-on-a-Chip (LoC) devices are described as versatile, fast, accurate, and low-cost platforms for the handling, detection, characterization, and analysis of a wide range of suspended particles in water-based environments. However, for gas-based applications, particularly in atmospheric aerosols science, LoC platforms are rarely developed. This review summarizes emerging LoC devices for the classification, measurement, and identification of airborne particles, especially those known as Particulate Matter (PM), which are linked to increased morbidity and mortality levels from cardiovascular and respiratory diseases. For these devices, their operating principles and performance parameters are introduced and compared while highlighting their advantages and disadvantages. Discussing the current applications will allow us to identify challenges and determine future directions for developing more robust LoC devices to monitor and analyze airborne PM.

Sargassum species-based extracts were used to carry out the synthesis of homogeneous gold nanoparticles. Various techniques were used to determine the characteristics and composition of the nanoparticles. The UV-Vis results showed that the 50% water/ethanol extract had the most reducing agents and stabilizers. Therefore, this type of extract was used to synthesize nanoparticles and for their subsequent characterization. Crystallinity and crystal size were evaluated using X-ray diffraction. Size and morphology were analyzed using scanning electron microscopy, showing that the gold nanoparticles were mostly spherical, with a size range of 15-30 nm. The catalytic activity of the gold nanoparticles was evaluated through the degradation of organic dyes: methylene blue, methyl orange, and methyl red. The degradation rates were different, depending on the nature of each dye, the simplest to degrade was methylene blue and methyl red was the most difficult to degrade. The results indicated that the use of Sargassum spp. for the synthesis of gold nanoparticles has potential in the remediation of water that is contaminated with organic dyes. Moreover, given the recent serious environmental and economic problems caused by the overpopulation of Sargassum spp. in the Mexican Caribbean, the findings hold promise for their practical and sustainable use in the synthesis of nanomaterials.

Background: Acute respiratory failure occurs frequently in hospitalized patients and often begins outside the ICU, associated with increased length of stay, cost, and mortality. Delays in decompensation recognition are associated with worse outcomes., Objectives: The objective of this study is to predict acute respiratory failure requiring any advanced respiratory support (including noninvasive ventilation). With the advent of the coronavirus disease pandemic, concern regarding acute respiratory failure has increased., Derivation Cohort: All admission encounters from January 2014 to June 2017 from three hospitals in the Emory Healthcare network (82,699)., Validation Cohort: External validation cohort: all admission encounters from January 2014 to June 2017 from a fourth hospital in the Emory Healthcare network (40,143). Temporal validation cohort: all admission encounters from February to April 2020 from four hospitals in the Emory Healthcare network coronavirus disease tested (2,564) and coronavirus disease positive (389)., Prediction Model: All admission encounters had vital signs, laboratory, and demographic data extracted. Exclusion criteria included invasive mechanical ventilation started within the operating room or advanced respiratory support within the first 8 hours of admission. Encounters were discretized into hour intervals from 8 hours after admission to discharge or advanced respiratory support initiation and binary labeled for advanced respiratory support. Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment, our eXtreme Gradient Boosting-based algorithm, was compared against Modified Early Warning Score., Results: Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment had significantly better discrimination than Modified Early Warning Score (area under the receiver operating characteristic curve 0.85 vs 0.57 [test], 0.84 vs 0.61 [external validation]). Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment maintained a positive predictive value (0.31-0.21) similar to that of Modified Early Warning Score greater than 4 (0.29-0.25) while identifying 6.62 (validation) to 9.58 (test) times more true positives. Furthermore, Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment performed more effectively in temporal validation (area under the receiver operating characteristic curve 0.86 [coronavirus disease tested], 0.93 [coronavirus disease positive]), while achieving identifying 4.25-4.51× more true positives., Conclusions: Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment is more effective than Modified Early Warning Score in predicting respiratory failure requiring advanced respiratory support at external validation and in coronavirus disease 2019 patients. Silent prospective validation necessary before local deployment., Competing Interests: Dr. Wong is supported by the National Institute of General Medical Sciences (NIGMS) 2T32GM095442 and the Clinical and Translational Science Award pilot informatics grant by National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) under UL1TR002378. He holds equity and management roles in Ataia Medical. Dr. Kamaleswaran is supported by the Michael J. Fox Foundation (Grant No. 17267). Dr. Reyna is supported by NIH U54EB027690 and HHS0100201900015C. Dr. Josef is supported by the NIGMS 2T32GM095442. Dr. Holder is supported by the NIGMS under award number K23GM137182 for Advancing Translational Sciences of the NIH under Award Number UL1TR002378. Dr. Nemati is supported by the NIH (No. K01ES025445) and the Gordon and Betty Moore Foundation (No. GBMF9052). Dr. Buchman is supported by the Society of Critical Care Medicine and the Biomedical Advanced Research and Development Authority. He is an Editor in Chief for Critical Care Medicine and has recused himself from editorial influence on this article. Dr. Blum is supported by the NCATS of the NIH under Award Number UL1TR002378. He is a consultant for Clew Medical. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

A classic problem in computational biology is the identification of altered subnetworks : subnetworks of an interaction network that contain genes/proteins that are differentially expressed, highly mutated, or otherwise aberrant compared with other genes/proteins. Numerous methods have been developed to solve this problem under various assumptions, but the statistical properties of these methods are often unknown. For example, some widely used methods are reported to output very large subnetworks that are difficult to interpret biologically. In this work, we formulate the identification of altered subnetworks as the problem of estimating the parameters of a class of probability distributions that we call the Altered Subset Distribution (ASD). We derive a connection between a popular method, jActiveModules, and the maximum likelihood estimator (MLE) of the ASD. We show that the MLE is statistically biased , explaining the large subnetworks output by jActiveModules. Based on these insights, we introduce NetMix, an algorithm that uses Gaussian mixture models to obtain less biased estimates of the parameters of the ASD. We demonstrate that NetMix outperforms existing methods in identifying altered subnetworks on both simulated and real data, including the identification of differentially expressed genes from both microarray and RNA-seq experiments and the identification of cancer driver genes in somatic mutation data.

Objective: Vast 12-lead ECGs repositories provide opportunities to develop new machine learning approaches for creating accurate and automatic diagnostic systems for cardiac abnormalities. However, most 12-lead ECG classification studies are trained, tested, or developed in single, small, or relatively homogeneous datasets. In addition, most algorithms focus on identifying small numbers of cardiac arrhythmias that do not represent the complexity and difficulty of ECG interpretation. This work addresses these issues by providing a standard, multi-institutional database and a novel scoring metric through a public competition: the PhysioNet/Computing in Cardiology Challenge 2020., Approach: A total of 66 361 12-lead ECG recordings were sourced from six hospital systems from four countries across three continents; 43 101 recordings were posted publicly with a focus on 27 diagnoses. For the first time in a public competition, we required teams to publish open-source code for both training and testing their algorithms, ensuring full scientific reproducibility., Main Results: A total of 217 teams submitted 1395 algorithms during the Challenge, representing a diversity of approaches for identifying cardiac abnormalities from both academia and industry. As with previous Challenges, high-performing algorithms exhibited significant drops ([Formula: see text]10%) in performance on the hidden test data., Significance: Data from diverse institutions allowed us to assess algorithmic generalizability. A novel evaluation metric considered different misclassification errors for different cardiac abnormalities, capturing the outcomes and risks of different diagnoses. Requiring both trained models and code for training models improved the generalizability of submissions, setting a new bar in reproducibility for public data science competitions.