The caloric value of food intake structurally adjusts a neuronal mushroom body circuit mediating olfactory learning in Drosophila.

Associative learning enables the adaptive adjustment of behavioral decisions based on acquired, predicted outcomes. The valence of what is learned is influenced not only by the learned stimuli and their temporal relations, but also by prior experiences and internal states. In this study, we used the fruit fly Drosophila melanogaster to demonstrate that neuronal circuits involved in associative olfactory learning undergo restructuring during extended periods of low-caloric food intake. Specifically, we observed a decrease in the connections between specific dopaminergic neurons (DANs) and Kenyon cells at distinct compartments of the mushroom body. This structural synaptic plasticity was contingent upon the presence of allatostatin A receptors in specific DANs and could be mimicked optogenetically by expressing a light-activated adenylate cyclase in exactly these DANs. Importantly, we found that this rearrangement in synaptic connections influenced aversive, punishment-induced olfactory learning but did not impact appetitive, reward-based learning. Whether induced by prolonged low-caloric conditions or optogenetic manipulation of cAMP levels, this synaptic rearrangement resulted in a reduction of aversive associative learning. Consequently, the balance between positive and negative reinforcing signals shifted, diminishing the ability to learn to avoid odor cues signaling negative outcomes. These results exemplify how a neuronal circuit required for learning and memory undergoes structural plasticity dependent on prior experiences of the nutritional value of food.

EGFR/MEK inhibitor therapy induces a distinct inflammatory hair follicle response that includes a collapse of immune privilege.

AIMS: Inhibitors of epidermal growth factor receptor (EGFRi) or mitogen-activated protein kinase (MEKi) induce a folliculitis in 75-90% of patients, whose pathobiology remains insufficiently understood. OBJECTIVES: (1) Characterize changes in the skin immune status and global transcriptional profile of EGFRi-treated patients (2) Probe whether EGFRi affects the hair follicle's (HF) immune privilege (IP) (3) Identify early pro-inflammatory signals induced by EGFRi/MEKi in human scalp HFs ex vivo. METHODS: Scalp biopsies were taken from long-term EGFRi-treated patients exhibiting folliculitis (Chronic-EGFRi, n=9) vs normal scalp skin (n=9) and patients prior to commencing EGFRi therapy and after two weeks of EGFRi therapy (Acute-EGFRi, n=5). Healthy organ-cultured scalp HFs were exposed to EGFRi (Erlotinib) or MEKi (Cobimetinib) (n=5 patients, each). Samples were assessed by quantatitive immunohistomorphometry, RNAseq and in situ hybridization. RESULTS: The Chronic-EGFRi cohort showed CD8+ T cell infiltration of the bulge alongside a partial collapse of the HF's IP, evidenced by upregulated MHC class I, ß2-microglobulin and MHC class II and decreased TGF-ß1 protein expression. Healthy HFs treated with EGFRi/MEKi ex vivo also showed partial HF IP collapse and increased transcription of HLA-A, HLA-DR, ß2-microglobulin transcripts. RNAseq anlysis showed increased transcription of chemokines (CXCL1, CXCL13, CCL18, CCL3, CCL7) and IL-26 in Chronic-EGFRi biopsies, as well as increased interlukin IL-33 and decreased IL-37 expesssion in both Acute-EGFRi biopsies and organ-cultured HFs. CONCLUSION: These data show that EGFRi/MEKi compromise the physiological IP of human scalp HFs and suggest that future clinical management of EGFRi/MEKi-induced folliculitis requires HF IP protection and inhibition of IL-33.

Mode and Characteristics of Arrhythmia Initiation in Idiopathic Ventricular Fibrillation: A THESIS Substudy.

BACKGROUND: There is limited information on the mode of arrhythmia initiation in idiopathic ventricular fibrillation (IVF). A non-pause-dependent mechanism has been suggested to be the rule. OBJECTIVES: The aim of this study was to assess the mode and characteristics of initiation of polymorphic ventricular tachycardia (PVT) in patients with short or long-coupled PVT/IVF included in THESIS (THerapy Efficacy in Short or long-coupled idiopathic ventricular fibrillation: an International Survey), a multicenter study involving 287 IVF patients treated with drugs or radiofrequency ablation. METHODS: We reviewed the initiation of 410 episodes of ≥1 PVT triplet in 180 patients (58.3% females; age 39.6 ± 13.6 years) with IVF. The incidence of pause-dependency arrhythmia initiation (prolongation by >20 ms of the preceding cycle length) was assessed. RESULTS: Most arrhythmias (n = 295; 72%) occurred during baseline supraventricular rhythm without ambient premature ventricular complexes (PVCs), whereas 106 (25.9%) occurred during baseline rhythm including PVCs. Nine (2.2%) arrhythmias occurred during atrial/ventricular pacing and were excluded from further analysis. Mode of PVT initiation was pause-dependent in 45 (15.6%) and 64 (60.4%) of instances in the first and second settings, respectively, for a total of 109 of 401 (27.2%). More than one type of pause-dependent and/or non-pause-dependent initiation (mean: 2.6) occurred in 94.4% of patients with ≥4 events. Coupling intervals of initiating PVCs were <350 ms, 350-500 ms, and >500 ms in 76.6%, 20.72%, and 2.7% of arrhythmia initiations, respectively. CONCLUSIONS: Pause-dependent initiation occurred in more than a quarter of arrhythmic episodes in IVF patients. PVCs having long (between 350 and 500 ms) and very long (>500 ms) coupling intervals were observed at the initiation of nearly a quarter of PVT episodes.

MedYOLO: A Medical Image Object Detection Framework.

Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to make voxel-accurate segmentations of the region of interest. However, the labels required to train these CNNs are time-consuming to generate and require attention from subject matter experts to ensure quality. For tasks where voxel-level precision is not required, object detection models offer a viable alternative that can reduce annotation effort. Despite this potential application, there are few options for general-purpose object detection frameworks available for 3-D medical imaging. We report on MedYOLO, a 3-D object detection framework using the one-shot detection method of the YOLO family of models and designed for use with medical imaging. We tested this model on four different datasets: BRaTS, LIDC, an abdominal organ Computed tomography (CT) dataset, and an ECG-gated heart CT dataset. We found our models achieve high performance on a diverse range of structures even without hyperparameter tuning, reaching mean average precision (mAP) at intersection over union (IoU) 0.5 of 0.861 on BRaTS, 0.715 on the abdominal CT dataset, and 0.995 on the heart CT dataset. However, the models struggle with some structures, failing to converge on LIDC resulting in a mAP@0.5 of 0.0.

Evaluation of the trade-off between water use efficiency and nutrient use efficiency in two semiarid coniferous tree species growing on an organic amended metalliferous mine tailing substrate.

We evaluated the ecophysiological responses of two semiarid coniferous tree species, Pinus halepensis and Tetraclinis articulata, growing on a nutrient-poor metalliferous mine tailings substrate to organic amendments (biochar and/or organic municipal waste). The trees were grown in mesocosms under irrigated conditions for 20 months. Then, a comprehensive characterization of soil and plant parameters (including stable isotopes) was carried out. Treatments containing municipal waste showed better soil fertility indicators (approximately 2-fold higher organic carbon and total nitrogen concentrations) and higher plant biomass (up to 5-fold higher) than unamended and only biochar treatments. Trees in most of the treatments exhibited leaf N/P ratios <14 indicating severe N limitation of plant growth. Metal uptake was below phytotoxic levels across all the treatments. Leaf δ13C values correlated positively with δ18O across treatments for both species indicating increasing water use efficiency with tighter stomatal regulation of water flux, and with T. articulata exhibiting tighter stomatal control (higher δ18O values) than P. halepensis. Trees in treatments containing only biochar did not differ in ecophysiological performance from those in the unamended treatments. In contrast, leaf stable isotopes revealed sharply increased of time-integrated photosynthetic activity (favoured by higher leaf N concentrations) combined with lower time-integrated stomatal conductance in the treatments containing municipal waste, indicating greatly enhanced water use efficiency in better nourished plants. Trade-offs between water use efficiency and nutrient (N and P) use efficiency were evident across treatments, with higher leaf nutrient concentrations associated with higher water use efficiency, at the cost of a lower nutrient use efficiency. These trade-offs were not impaired by the high metal concentrations of the tailings substrate, indicating that ecophysiological adjustments in response to changes in plant nutrient status promoted by the addition of organic amendments are critical for the adaptability of native tree species employed in the phytostabilisation of mine tailings.

Phenotype and Clinical Outcomes in Desmin-Related Arrhythmogenic Cardiomyopathy.

BACKGROUND: Desmin (DES) pathogenic variants cause a small proportion of arrhythmogenic cardiomyopathy (ACM). Outcomes data on DES-related ACM are scarce. OBJECTIVES: This study sought to provide information on the clinical phenotype and outcomes of patients with ACM caused by pathogenic variants of the DES gene in a multicenter cohort. METHODS: We collected phenotypic and outcomes data from 16 families with DES-related ACM from 10 European centers. We assessed in vitro DES aggregates. Major cardiac events were compared to historical controls with lamin A/C truncating variant (LMNA-tv) and filament C truncating variant (FLNC-tv) ACM. RESULTS: Of 82 patients (54% males, median age: 36 years), 11 experienced sudden cardiac death (SCD) (n = 7) or heart failure death (HFd)/heart transplantation (HTx) (n = 4) before clinical evaluation. Among 68 survivors, 59 (86%) presented signs of cardiomyopathy, with left ventricular (LV) dominant (50%) or biventricular (34%) disease. Mean LV ejection fraction was 51% ± 13%; 36 of 53 had late gadolinium enhancement (ring-like pattern in 49%). During a median of 6.73 years (Q1-Q3: 3.55-9.52 years), the composite endpoint (sustained ventricular tachycardia, aborted SCD, implantable cardioverter-defibrillator therapy, SCD, HFd, and HTx) was achieved in 15 additional patients with HFd/HTx (n = 5) and SCD/aborted SCD/implantable cardioverter-defibrillator therapy/sustained ventricular tachycardia (n = 10). Male sex (P = 0.004), nonsustained ventricular tachycardia (P = 0.017) and LV ejection fraction ≤50% (P = 0.012) were associated with the composite endpoint. Males with DES variants had similar outcomes to historical FLNC-tv and LMNA-tv controls. However, females showed better outcomes than those with LMNA-tv. In vitro experiments showed the characteristic finding of DES aggregates in 7 of 12 variants. CONCLUSIONS: DES ACM is associated with poor outcomes which can be predicted with potentially successful treatments, underscoring the importance of familial evaluation and genetic studies to identify at risk individuals.

Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms.

Aims: Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record. Methods and results: We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively. Conclusion: The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.

Artificial intelligence-based classification of echocardiographic views.

Aims: Augmenting echocardiography with artificial intelligence would allow for automated assessment of routine parameters and identification of disease patterns not easily recognized otherwise. View classification is an essential first step before deep learning can be applied to the echocardiogram. Methods and results: We trained two- and three-dimensional convolutional neural networks (CNNs) using transthoracic echocardiographic (TTE) studies obtained from 909 patients to classify nine view categories (10 269 videos). Transthoracic echocardiographic studies from 229 patients were used in internal validation (2582 videos). Convolutional neural networks were tested on 100 patients with comprehensive TTE studies (where the two examples chosen by CNNs as most likely to represent a view were evaluated) and 408 patients with five view categories obtained via point-of-care ultrasound (POCUS). The overall accuracy of the two-dimensional CNN was 96.8%, and the averaged area under the curve (AUC) was 0.997 on the comprehensive TTE testing set; these numbers were 98.4% and 0.998, respectively, on the POCUS set. For the three-dimensional CNN, the accuracy and AUC were 96.3% and 0.998 for full TTE studies and 95.0% and 0.996 on POCUS videos, respectively. The positive predictive value, which defined correctly identified predicted views, was higher with two-dimensional rather than three-dimensional networks, exceeding 93% in apical, short-axis aortic valve, and parasternal long-axis left ventricle views. Conclusion: An automated view classifier utilizing CNNs was able to classify cardiac views obtained using TTE and POCUS with high accuracy. The view classifier will facilitate the application of deep learning to echocardiography.

Clinical impact of remote heart failure management using the multiparameter ICD HeartLogic alert.

INTRODUCTION AND OBJECTIVES: The multiparametric implantable cardioverter-defibrillator HeartLogic index has proven to be a sensitive and timely predictor of impending heart failure (HF) decompensation. We evaluated the impact of a standardized follow-up protocol implemented by nursing staff and based on remote management of alerts. METHODS: The algorithm was activated in HF patients at 19 Spanish centers. Transmitted data were analyzed remotely, and patients were contacted by telephone if alerts were issued. Clinical actions were implemented remotely or through outpatient visits. The primary endpoint consisted of HF hospitalizations or death. Secondary endpoints were HF outpatient visits. We compared the 12-month periods before and after the adoption of the protocol. RESULTS: We analyzed 392 patients (aged 69±10 years, 76% male, 50% ischemic cardiomyopathy) with implantable cardioverter-defibrillators (20%) or cardiac resynchronization therapy defibrillators (80%). The primary endpoint occurred 151 times in 86 (22%) patients during the 12 months before the adoption of the protocol, and 69 times in 45 (11%) patients (P<.001) during the 12 months after its adoption. The mean number of hospitalizations per patient was 0.39±0.89 pre- and 0.18±0.57 postadoption (P<.001). There were 185 outpatient visits for HF in 96 (24%) patients before adoption and 64 in 48 (12%) patients after adoption (P<.001). The mean number of visits per patient was 0.47±1.11 pre- and 0.16±0.51 postadoption (P<.001). CONCLUSIONS: A standardized follow-up protocol based on remote management of HeartLogic alerts enabled effective remote management of HF patients. After its adoption, we observed a significant reduction in HF hospitalizations and outpatient visits.

Valorization applications of pineapple and papaya byproducts in food industry.

The recent increase in the harvesting and industrial processing of tropical fruits such as pineapple and papaya is leading to unavoidable amounts of byproducts rich in valuable compounds. Given the significance of the chemical composition of these byproducts, new research avenues are opening up to exploit them in the food industry. In this sense, the revalorization of pineapple and papaya byproducts is an emerging trend that is encouraging the full harnessing of these tropical fruits, offering the opportunity for developing innovative value-added products. Therefore, the main aim of this review is to provide an overview of the state of the art of the current valorization applications of pineapple and papaya byproducts in the field of food industry. For that proposal, comprehensive research of valorization applications developed in the last years has been conducted using scientific databases, databases, digital libraries, and scientific search engines. The latest valorization applications of pineapple and papaya byproducts in the food industry have been systematically revised and gathered with the objective of synthesizing and critically analyzing existing scientific literature in order to contribute to the advancement of knowledge in the field of tropical byproduct revalorization providing a solid foundation for further research and highlighting scientific gaps and new challenges that should be addressed in the future.

Proteomic Insights into Seminal Plasma and Spermatozoa Proteins of Small-Spotted Catsharks, Scyliorhinus canicula: Implications for Reproductive Conservation in Aquariums.

In the ex situ conservation of chondrichthyan species, successful reproduction in aquaria is essential. However, these species often exhibit reduced reproductive success under human care. A key aspect is that conventional sperm analyses do not provide insights into the functional competence of sperm. However, proteomics analysis enables a better understanding of male physiology, gaining relevance as a powerful tool for discovering protein biomarkers related to fertility. The present work aims to build the first proteome database for shark semen and to investigate the proteomic profiles of seminal plasma and spermatozoa from small-spotted catsharks (Scyliorhinus canicula) related to the underlying adaptations to both natural and aquarium environments, thereby identifying the reproductive impact in aquarium specimens. A total of 305 seminal plasma and 535 spermatozoa proteins were identified. Among these, 89 proteins (29.2% of the seminal plasma set) were common to both spermatozoa and seminal plasma. In the seminal plasma, only adenosylhomocysteinase protein showed differential abundance (DAP) between wild and aquarium animals. With respect to the spermatozoa proteins, a total of 107 DAPs were found between groups. Gene Ontology enrichment analysis highlighted the primary functional roles of these DAPs involved in oxidoreductase activity. Additionally, KEGG analysis indicated that these DAPs were primarily associated with metabolic pathways and carbon metabolism. In conclusion, we have successfully generated an initial proteome database for S. canicula seminal plasma and spermatozoa. Furthermore, we have identified protein variations, predominantly within spermatozoa, between aquarium and wild populations of S. canicula. These findings provide a foundation for future biomarker discovery in shark reproduction studies. However, additional research is required to determine whether these protein variations correlate with reproductive declines in captive sharks.

The prognostic value of artificial intelligence to predict cardiac amyloidosis in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement.

Aims: Cardiac amyloidosis (CA) is common in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). Cardiac amyloidosis has poor outcomes, and its assessment in all TAVR patients is costly and challenging. Electrocardiogram (ECG) artificial intelligence (AI) algorithms that screen for CA may be useful to identify at-risk patients. Methods and results: In this retrospective analysis of our institutional National Cardiovascular Disease Registry (NCDR)-TAVR database, patients undergoing TAVR between January 2012 and December 2018 were included. Pre-TAVR CA probability was analysed by an ECG AI predictive model, with >50% risk defined as high probability for CA. Univariable and propensity score covariate adjustment analyses using Cox regression were performed to compare clinical outcomes between patients with high CA probability vs. those with low probability at 1-year follow-up after TAVR. Of 1426 patients who underwent TAVR (mean age 81.0 ± 8.5 years, 57.6% male), 349 (24.4%) had high CA probability on pre-procedure ECG. Only 17 (1.2%) had a clinical diagnosis of CA. After multivariable adjustment, high probability of CA by ECG AI algorithm was significantly associated with increased all-cause mortality [hazard ratio (HR) 1.40, 95% confidence interval (CI) 1.01-1.96, P = 0.046] and higher rates of major adverse cardiovascular events (transient ischaemic attack (TIA)/stroke, myocardial infarction, and heart failure hospitalizations] (HR 1.36, 95% CI 1.01-1.82, P = 0.041), driven primarily by heart failure hospitalizations (HR 1.58, 95% CI 1.13-2.20, P = 0.008) at 1-year follow-up. There were no significant differences in TIA/stroke or myocardial infarction. Conclusion: Artificial intelligence applied to pre-TAVR ECGs identifies a subgroup at higher risk of clinical events. These targeted patients may benefit from further diagnostic evaluation for CA.

Machine learning for prediction of ventricular arrhythmia episodes from intracardiac electrograms of automatic implantable cardioverter-defibrillators.

BACKGROUND: Despite effectiveness of the implantable cardioverter-defibrillator (ICD) in saving patients with life-threatening ventricular arrhythmias (VAs), the temporal occurrence of VA after ICD implantation is unpredictable. OBJECTIVE: The study aimed to apply machine learning (ML) to intracardiac electrograms (IEGMs) recorded by ICDs as a unique biomarker for predicting impending VAs. METHODS: The study included 13,516 patients who received Biotronik ICDs and enrolled in the CERTITUDE registry between January 1, 2010, and December 31, 2020. Database extraction included IEGMs from standard quarterly transmissions and VA event episodes. The processed IEGM data were pulled from device transmissions stored in a centralized Home Monitoring Service Center and reformatted into an analyzable format. Long-range (baseline or first scheduled remote recording), mid-range (scheduled remote recording every 90 days), or short-range predictions (IEGM within 5 seconds before the VA onset) were used to determine whether ML-processed IEGMs predicted impending VA events. Convolutional neural network classifiers using ResNet architecture were employed. RESULTS: Of 13,516 patients (male, 72%; age, 67.5 ± 11.9 years), 301,647 IEGM recordings were collected; 27,845 episodes of sustained ventricular tachycardia or ventricular fibrillation were observed in 4467 patients (33.0%). Neural networks based on convolutional neural networks using ResNet-like architectures on far-field IEGMs yielded an area under the curve of 0.83 with a 95% confidence interval of 0.79-0.87 in the short term, whereas the long-range and mid-range analyses had minimal predictive value for VA events. CONCLUSION: In this study, applying ML to ICD-acquired IEGMs predicted impending ventricular tachycardia or ventricular fibrillation events seconds before they occurred, whereas midterm to long-term predictions were not successful. This could have important implications for future device therapies.

Mortality, Functional Status, and Quality of Life after 5 Years of Patients Admitted to Critical Care for Spontaneous Intracerebral Hemorrhage.

BACKGROUND: The objective of this study was to assess long-term outcome in patients with spontaneous intracerebral hemorrhage admitted to the intensive care unit. METHODS: Mortality and Glasgow Outcome Scale, Barthel Index, and 5-level EQ-5D version (EQ-5D-5L) scores were analyzed in a multicenter cohort study of three Spanish hospitals (336 patients). Mortality was also analyzed in the Medical Information Mart for Intensive Care III (MIMIC-III) database. RESULTS: The median (25th percentile-75th percentile) age was 62 (50-70) years, the median Glasgow Coma Score was 7 (4-11) points, and the median Acute Physiology and Chronic Health disease Classification System II (APACHE-II) score was 21 (15-26) points. Hospital mortality was 54.17%, mortality at 90 days was 56%, mortality at 1 year was 59.2%, and mortality at 5 years was 66.4%. In the Glasgow Outcome Scale, a normal or disabled self-sufficient situation was recorded in 21.5% of patients at 6 months, in 25.5% of patients after 1 year, and in 22.1% of patients after 5 years of follow-up (4.5% missing). The Barthel Index score of survivors improved over time: 50 (25-80) points at 6 months, 70 (35-95) points at 1 year, and 90 (40-100) points at 5 years (p < 0.001). Quality of life evaluated with the EQ-5D-5L at 1 year and 5 years indicated that greater than 50% of patients had no problems or slight problems in all items (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression). In the MIMIC-III study (N = 1354), hospital mortality was 31.83% and was 40.5% at 90 days and 56.2% after 5 years. CONCLUSIONS: In patients admitted to the intensive care unit with a diagnosis of nontraumatic intracerebral hemorrhage, hospital mortality up to 90 days after admission is very high. Between 90 days and 5 years after admission, mortality is not high. A large percentage of survivors presented a significant deficit in quality of life and functional status, although with progressive improvement over time. Five years after the hemorrhagic stroke, a survival of 30% was observed, with a good functional status seen in 20% of patients who had been admitted to the hospital.

Metabolite Profiling of Colvillea racemosa via UPLC-ESI-QTOF-MS Analysis in Correlation to the In Vitro Antioxidant and Cytotoxic Potential against A549 Non-Small Cell Lung Cancer Cell Line.

In this study, flower and leaf extracts of Colvillea racemosa were considered a source of bioactive compounds. In this context, the objective of the study focused on investigating the anticancer potential as well as the phytochemical composition of both extracts. The extracts were analyzed by UPLC-ESI-QTOF-MS, and the bioactivity was tested using in vitro antioxidant assays (FRAP, DPPH, and ABTS) in addition to cytotoxic assays on non-small cell lung cancer cell line (A549). Our results clearly indicated the potent radical scavenging capacity of both extracts. Importantly, the flower extract exhibited a greater antioxidant capacity than the leaf extract. In terms of cytotoxic activity, leaf and flower extracts significantly inhibited cell viability with IC50 values of 17.0 and 17.2 µg/mL, respectively. The phytochemical characterization enabled the putative annotation of 42 metabolites, such as saccharides, phenolic acids, flavonoids, amino acids, and fatty acids. Among them, the flavonoid C-glycosides stand out due to their high relative abundance and previous reports on their anticancer bioactivity. For a better understanding of the bioactive mechanisms, four flavonoids (vitexin, kaempferol-3-O-rutinoside, luteolin, and isoorientin) were selected for molecular docking on hallmark protein targets in lung cancer as represented by γ-PI3K, EGFR, and CDK2 through in-silico studies. In these models, kaempferol-3-O-rutinoside and vitexin had the highest binding scores on γ-PI3K and CDK2, followed by isoorientin, so they could be highly responsible for the bioactive properties of C. racemosa extracts.

Effect of Active Workstations on Neurocognitive Performance and Typing Skills: A Randomized Clinical Trial.

BACKGROUND: Extended sedentary behavior is a risk factor for chronic disease and mortality, even among those who exercise regularly. Given the time constraints of incorporating physical activity into daily schedules, and the high likelihood of sitting during office work, this environment may serve as a potentially feasible setting for interventions to reduce sedentary behavior. METHODS AND RESULTS: A randomized cross-over clinical trial was conducted at an employee wellness center. Four office settings were evaluated on 4 consecutive days: stationary or sitting station on day 1 (referent), and 3 subsequent active workstations (standing, walking, or stepper) in randomized order. Neurocognitive function (Selective Attention, Grammatical Reasoning, Odd One Out, Object Reasoning, Visuospatial Intelligence, Limited-Hold Memory, Paired Associates Learning, and Digit Span) and fine motor skills (typing speed and accuracy) were tested using validated tools. Average scores were compared among stations using linear regression with generalized estimating equations to adjust standard errors. Bonferroni method adjusted for multiple comparisons. Healthy subjects were enrolled (n=44), 28 (64%) women, mean±SD age 35±11 years, weight 75.5±17.1 kg, height 168.5±10.0 cm, and body mass index 26.5±5.2 kg/m2. When comparing active stations to sitting, neurocognitive test either improved or remained unchanged, while typing speed decreased without affecting typing errors. Overall results improved after day 1, suggesting habituation. We observed no major differences across active stations, except decrease in average typing speed 42.5 versus 39.7 words per minute with standing versus stepping (P=0.003). CONCLUSIONS: Active workstations improved cognitive performance, suggesting that these workstations can help decrease sedentary time without work performance impairment. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT06240286.

Electrocardiography-based Artificial Intelligence Algorithms Aid in Prediction of Long-term Mortality After Kidney Transplantation.

BACKGROUND: Predicting long-term mortality postkidney transplantation (KT) using baseline clinical data presents significant challenges. This study aims to evaluate the predictive power of artificial intelligence (AI)-enabled analysis of preoperative electrocardiograms (ECGs) in forecasting long-term mortality following KT. METHODS: We analyzed preoperative ECGs from KT recipients at three Mayo Clinic sites (Minnesota, Florida, and Arizona) between January 1, 2006, and July 30, 2021. The study involved 6 validated AI algorithms, each trained to predict future development of atrial fibrillation, aortic stenosis, low ejection fraction, hypertrophic cardiomyopathy, amyloid heart disease, and biological age. These algorithms' outputs based on a single preoperative ECG were correlated with patient mortality data. RESULTS: Among 6504 KT recipients included in the study, 1764 (27.1%) died within a median follow-up of 5.7 y (interquartile range: 3.00-9.29 y). All AI-ECG algorithms were independently associated with long-term all-cause mortality (P < 0.001). Notably, few patients had a clinical cardiac diagnosis at the time of transplant, indicating that AI-ECG scores were predictive even in asymptomatic patients. When adjusted for multiple clinical factors such as recipient age, diabetes, and pretransplant dialysis, AI algorithms for atrial fibrillation and aortic stenosis remained independently associated with long-term mortality. These algorithms also improved the C-statistic for predicting overall (C = 0.74) and cardiac-related deaths (C = 0.751). CONCLUSIONS: The findings suggest that AI-enabled preoperative ECG analysis can be a valuable tool in predicting long-term mortality following KT and could aid in identifying patients who may benefit from enhanced cardiac monitoring because of increased risk.

Assessing Biological Age: The Potential of ECG Evaluation Using Artificial Intelligence: JACC Family Series.

Biological age may be a more valuable predictor of morbidity and mortality than a person's chronological age. Mathematical models have been used for decades to predict biological age, but recent developments in artificial intelligence (AI) have led to new capabilities in age estimation. Using deep learning methods to train AI models on hundreds of thousands of electrocardiograms (ECGs) to predict age results in a good, but imperfect, age prediction. The error predicting age using ECG, or the difference between AI-ECG-derived age and chronological age (delta age), may be a surrogate measurement of biological age, as the delta age relates to survival, even after adjusting for chronological age and other covariates associated with total and cardiovascular mortality. The relative affordability, noninvasiveness, and ubiquity of ECGs, combined with ease of access and potential to be integrated with smartphone or wearable technology, presents a potential paradigm shift in assessment of biological age.

Penetrance of Dilated Cardiomyopathy in Genotype-Positive Relatives.

BACKGROUND: Disease penetrance in genotype-positive (G+) relatives of families with dilated cardiomyopathy (DCM) and the characteristics associated with DCM onset in these individuals are unknown. OBJECTIVES: This study sought to determine the penetrance of new DCM diagnosis in G+ relatives and to identify factors associated with DCM development. METHODS: The authors evaluated 779 G+ patients (age 35.8 ± 17.3 years; 459 [59%] females; 367 [47%] with variants in TTN) without DCM followed at 25 Spanish centers. RESULTS: After a median follow-up of 37.1 months (Q1-Q3: 16.3-63.8 months), 85 individuals (10.9%) developed DCM (incidence rate of 2.9 per 100 person-years; 95% CI: 2.3-3.5 per 100 person-years). DCM penetrance and age at DCM onset was different according to underlying gene group (log-rank P = 0.015 and P <0.01, respectively). In a multivariable model excluding CMR parameters, independent predictors of DCM development were: older age (HR per 1-year increase: 1.02; 95% CI: 1.0-1.04), an abnormal electrocardiogram (HR: 2.13; 95% CI: 1.38-3.29); presence of variants in motor sarcomeric genes (HR: 1.92; 95% CI: 1.05-3.50); lower left ventricular ejection fraction (HR per 1% increase: 0.86; 95% CI: 0.82-0.90) and larger left ventricular end-diastolic diameter (HR per 1-mm increase: 1.10; 95% CI: 1.06-1.13). Multivariable analysis in individuals with cardiac magnetic resonance and late gadolinium enhancement assessment (n = 360, 45%) identified late gadolinium enhancement as an additional independent predictor of DCM development (HR: 2.52; 95% CI: 1.43-4.45). CONCLUSIONS: Following a first negative screening, approximately 11% of G+ relatives developed DCM during a median follow-up of 3 years. Older age, an abnormal electrocardiogram, lower left ventricular ejection fraction, increased left ventricular end-diastolic diameter, motor sarcomeric genetic variants, and late gadolinium enhancement are associated with a higher risk of developing DCM.