Tag: statistics

Europace. 2023 Dec 3:euad359. doi: 10.1093/europace/euad359. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Autothreshold algorithms enable remote monitoring of patients with conventional pacing, but there is limited information on their performance in left bundle branch pacing (LBBP). Our objective was to analyze the behavior of the autothreshold algorithm in LBBP and compare it with conventional pacing and manual thresholds during initial device programming (acute phase), after 1-7 days (subacute) and 1-3 months later (chronic).

METHODS: A prospective, non-randomized, single-center comparative study was conducted. Consecutive patients with indications for cardiac pacing were enrolled. Implants were performed in the left bundle branch area or the right ventricle endocardium at the discretion of the operator. LBBP was determined according to published criteria. Autothreshold algorithm was activated in both groups whenever allowed by the device.

RESULTS: Seventy-five patients were included, with 50 undergoing LBBP and 25 receiving conventional pacing. Activation of the autothreshold algorithm was more feasible in later phases, showing a favorable trend toward bipolar pacing. Failures in algorithm activation were primarily due to insufficient safety margins (82.8% in LBBP and 90% in conventional pacing). The remainder were attributed to atrial tachyarrhythmias (10.3% and 10%, respectively) and electrical noise (the remaining 6.9% in the LBBP group). In the LBBP group, there were not statistically significant differences between manual and automatic thresholds, and both remained stable during follow-up (mean increase of 0.50V).

CONCLUSIONS: The autothreshold algorithm is feasible in LBBP, with a favorable trend towards bipolar pacing. Automatic thresholds are similar to manual in patients with LBBP, and they remain stable during follow-up.

PMID:38042980 | DOI:10.1093/europace/euad359

Sci Rep. 2023 Dec 2;13(1):21305. doi: 10.1038/s41598-023-48449-y.

ABSTRACT

Methane (CH₄) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH₄. To address this limitation, we developed novel CH₄ prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH₄ production (g CH₄/animal·d, ANIM-B models) and CH₄ yield (g CH₄/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin’s concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH₄ prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH₄ emissions from sheep, providing valuable insights for future research and mitigation strategies.

PMID:38042941 | DOI:10.1038/s41598-023-48449-y

Sci Rep. 2023 Dec 2;13(1):21308. doi: 10.1038/s41598-023-48481-y.

ABSTRACT

Endometrial dating (ED) is the process by which the menstrual cycle day is estimated and is an important tool for the evaluation of uterine status. To date, ED methods remain inaccurate and controversial. We demonstrate how the rise of computerized virtual histology changes the state of affairs and introduce a new ED method. We present the results of a clinical trial where magnified images of ex-vivo endometrial tissue samples were captured at different cycle days, together with measurements of serum hormone levels on the same day. Patient testimonies about their cycle day were also collected. Computerized image analysis, followed by statistical representation of the tissue features, allowed mathematical representation of the cycle day. The samples underwent ED histological assessment, which is currently the ED gold standard. We compared dating results from patient reports, serum hormone levels, and histology to establish their concordance level. We then compared histology-based ED with the new method ED in the secretory phase (i.e. post ovulation). The correlation coefficient between the two resulted in an R = 0.89 with a P-value of P < 10^-4. The new method, Virtual Pathology Endometrial Dating (VPED), has the benefit of being a real time, in-vivo method that can be repeatedly applied without tissue damage, using a dedicated hysteroscope. One practical use of this method may be the determination of accurate real-time embryo transfer timing in IVF treatments.

PMID:38042938 | DOI:10.1038/s41598-023-48481-y

Nat Commun. 2023 Dec 2;14(1):7964. doi: 10.1038/s41467-023-43836-5.

ABSTRACT

Synthesis protocol exploration is paramount in catalyst discovery, yet keeping pace with rapid literature advances is increasingly time intensive. Automated synthesis protocol analysis is attractive for swiftly identifying opportunities and informing predictive models, however such applications in heterogeneous catalysis remain limited. In this proof-of-concept, we introduce a transformer model for this task, exemplified using single-atom heterogeneous catalysts (SACs), a rapidly expanding catalyst family. Our model adeptly converts SAC protocols into action sequences, and we use this output to facilitate statistical inference of their synthesis trends and applications, potentially expediting literature review and analysis. We demonstrate the model’s adaptability across distinct heterogeneous catalyst families, underscoring its versatility. Finally, our study highlights a critical issue: the lack of standardization in reporting protocols hampers machine-reading capabilities. Embracing digital advances in catalysis demands a shift in data reporting norms, and to this end, we offer guidelines for writing protocols, significantly improving machine-readability. We release our model as an open-source web application, inviting a fresh approach to accelerate heterogeneous catalysis synthesis planning.

PMID:38042926 | DOI:10.1038/s41467-023-43836-5

Sci Rep. 2023 Dec 3;13(1):21309. doi: 10.1038/s41598-023-48614-3.

ABSTRACT

India is the second-highest contributor to the post-2000 global greening. However, with satellite data, here we show that this 18.51% increase in Leaf Area Index (LAI) during 2001-2019 fails to translate into increased carbon uptake due to warming constraints. Our analysis further shows 6.19% decrease in Net Primary Productivity (NPP) during 2001-2019 over the temporally consistent forests in India despite 6.75% increase in LAI. We identify hotspots of statistically significant decreasing trends in NPP over the key forested regions of Northeast India, Peninsular India, and the Western Ghats. Together, these areas contribute to more than 31% of the NPP of India (1274.8 TgC.year^-1). These three regions are also the warming hotspots in India. Granger Causality analysis confirms that temperature causes the changes in net-photosynthesis of vegetation. Decreasing photosynthesis and stable respiration, above a threshold temperature, over these regions, as seen in observations, are the key reasons behind the declining NPP. Our analysis shows that warming has already started affecting carbon uptake in Indian forests and calls for improved climate resilient forest management practices in a warming world.

PMID:38042916 | DOI:10.1038/s41598-023-48614-3

Sci Rep. 2023 Dec 2;13(1):21273. doi: 10.1038/s41598-023-48645-w.

ABSTRACT

The ability to accurately predict long-term kidney transplant survival can assist nephrologists in making therapeutic decisions. However, predicting kidney transplantation (KT) outcomes is challenging due to the complexity of the factors involved. Artificial intelligence (AI) has become an increasingly important tool in the prediction of medical outcomes. Our goal was to utilize both conventional and AI-based methods to predict long-term kidney transplant survival. Our study included 407 KTs divided into two groups (group A: with a graft lifespan greater than 5 years and group B: with poor graft survival). We first performed a traditional statistical analysis and then developed predictive models using machine learning (ML) techniques. Donors in group A were significantly younger. The use of Mycophenolate Mofetil (MMF) was the only immunosuppressive drug that was significantly associated with improved graft survival. The average estimated glomerular filtration rate (eGFR) in the 3rd month post-KT was significantly higher in group A. The number of hospital readmissions during the 1st year post-KT was a predictor of graft survival. In terms of early post-transplant complications, delayed graft function (DGF), acute kidney injury (AKI), and acute rejection (AR) were significantly associated with poor graft survival. Among the 35 AI models developed, the best model had an AUC of 89.7% (Se: 91.9%; Sp: 87.5%). It was based on ten variables selected by an ML algorithm, with the most important being hypertension and a history of red-blood-cell transfusion. The use of AI provided us with a robust model enabling fast and precise prediction of 5-year graft survival using early and easily collectible variables. Our model can be used as a decision-support tool to early detect graft status.

PMID:38042904 | DOI:10.1038/s41598-023-48645-w

Sci Rep. 2023 Dec 3;13(1):21311. doi: 10.1038/s41598-023-48635-y.

ABSTRACT

Wildlife monitoring in tropical rainforests poses additional challenges due to species often being elusive, cryptic, faintly colored, and preferring concealable, or difficult to access habitats. Unmanned aerial vehicles (UAVs) prove promising for wildlife surveys in different ecosystems in tropical forests and can be crucial in conserving inaccessible biodiverse areas and their associated species. Traditional surveys that involve infiltrating animal habitats could adversely affect the habits and behavior of elusive and cryptic species in response to human presence. Moreover, collecting data through traditional surveys to simultaneously estimate the abundance and demographic rates of communities of species is often prohibitively time-intensive and expensive. This study assesses the scope of drones to non-invasively access the Bukit Tigapuluh Landscape (BTL) in Riau-Jambi, Indonesia, and detect individual elephants of interest. A rotary-wing quadcopter with a vision-based sensor was tested to estimate the elephant population size and age structure. We developed hierarchical modeling and deep learning CNN to estimate elephant abundance and age structure. Drones successfully observed 96 distinct individuals at 8 locations out of 11 sampling areas. We obtained an estimate of the elephant population of 151 individuals (95% CI [124, 179]) within the study area and predicted more adult animals than subadults and juvenile individuals in the population. Our calculations may serve as a vital spark for innovation for future UAV survey designs in large areas with complex topographies while reducing operational effort.

PMID:38042901 | DOI:10.1038/s41598-023-48635-y

Sci Rep. 2023 Dec 2;13(1):21288. doi: 10.1038/s41598-023-48390-0.

ABSTRACT

Data on population health are vital to evidence-based decision making but are rarely adequately localized or updated in continuous time. They also suffer from low ascertainment rates, particularly in rural areas where barriers to healthcare can cause infrequent touch points with the health system. Here, we demonstrate a novel statistical method to estimate the incidence of endemic diseases at the community level from passive surveillance data collected at primary health centers. The zero-corrected, gravity-model (ZERO-G) estimator explicitly models sampling intensity as a function of health facility characteristics and statistically accounts for extremely low rates of ascertainment. The result is a standardized, real-time estimate of disease incidence at a spatial resolution nearly ten times finer than typically reported by facility-based passive surveillance systems. We assessed the robustness of this method by applying it to a case study of field-collected malaria incidence rates from a rural health district in southeastern Madagascar. The ZERO-G estimator decreased geographic and financial bias in the dataset by over 90% and doubled the agreement rate between spatial patterns in malaria incidence and incidence estimates derived from prevalence surveys. The ZERO-G estimator is a promising method for adjusting passive surveillance data of common, endemic diseases, increasing the availability of continuously updated, high quality surveillance datasets at the community scale.

PMID:38042891 | DOI:10.1038/s41598-023-48390-0

Sci Rep. 2023 Dec 2;13(1):21270. doi: 10.1038/s41598-023-48536-0.

ABSTRACT

The Multidimensional Assessment of Interoceptive Awareness (MAIA) was translated into many languages and frequently used in the last decade to assess self-reported interoceptive awareness. However, many studies demonstrated weaknesses regarding unstable factor structure and poor reliability of some scales. The 24-item Brief MAIA-2 questionnaire was developed, with only three items demonstrating the highest factor loadings in each of the eight scales of the MAIA-2. The cross-sectional online study used the 37-item MAIA-2 questionnaire in a non-clinical sample of 323 people aged between 16 and 75 (M = 26.17, SD = 9.12), including 177 women (54.80%). The sample comprised 156 athletes (48.30%) and 167 non-athletes (51.70%). The Confirmatory Factor Analysis showed adequate fit indices for a multidimensional model of the Brief MAIA-2, with the original eight scales: Noticing (awareness of subtle bodily sensations, such as the heartbeat, digestive sensations, or the breath), Not Distracting (ability to maintain attention to bodily sensations without being easily distracted by external stimuli), Not Worrying (tendency to not be overly concerned or anxious about bodily sensations or changes in the body), Attention Regulation (ability to regulate attention to bodily sensations and to shift attention between internal and external stimuli), Emotional Awareness (awareness and understanding of how emotions are associated with bodily sensations), Self Regulation (ability to regulate emotional responses and manage distress through an awareness of bodily sensations), Body Listening (tendency to listen to the body for insight and understanding), and Trusting (trust in bodily sensations as a source of information about one’s feelings and needs). The hierarchical bi-factor (S·I – 1) model showed even better-fit indices. Therefore, the general factor of interoception was considered in further statistical tests. Confirmatory composite analysis showed high reliability and discriminant and convergent validity for most Brief MAIA-2 scales, except Noticing. Measurement invariance was confirmed across genders (Women, Men) and sports participation (Athletes, Non-athletes). However, group differences were also found for mean scores in particular scales of the Brief MAIA-2. Men scored significantly lower than women in Not Distracting but higher in Not Worrying, Attention Regulation, Self Regulation, Trusting, and the total score of interoceptive awareness. Gender discrepancies may be influenced by linguistic socialization, which tends to categorize shifts in internal states as either physiological or emotional. Athletes scored significantly lower than Non-athletes on the Not Distracting scale, but they showed higher scores in Noticing, Attention Regulation, Emotion Awareness, Self-Regulation, Body Listening, Trusting, and the global score, suggesting that physical training can improve most areas of interoception. Therefore, physical exercises and mindfulness training may be recommended to improve interoception, especially in women and people suffering from somatic and mental problems. The Brief MAIA-2 is a reliable and valid tool to measure multidimensional interoceptive sensibility in a non-clinical population. To improve well-being and athletic performance, Brief MAIA-2 can be used to assess the body’s current perception of interoception and to detect its weak areas requiring improvement. However, the study has some limitations, such as a cross-sectional online self-report survey in a conventional non-clinical sample from Poland. Future cross-cultural studies should include representative samples for non-clinical and clinical populations from different countries and geographic regions to compare the Brief MAIA-2 with more objective psychophysiological methods of measuring interoception to reduce the limitations of these studies.

PMID:38042880 | DOI:10.1038/s41598-023-48536-0

Arthritis Res Ther. 2023 Dec 2;25(1):234. doi: 10.1186/s13075-023-03204-6.

ABSTRACT

BACKGROUND: Currently, it is not possible to predict whether patients with hyperuricemia (HUA) will develop gout and how this progression may be affected by urate-lowering treatment (ULT). Our study aimed to evaluate differences in plasma lipidome between patients with asymptomatic HUA detected ≤ 40 years (HUA ≤ 40) and > 40 years, gout patients with disease onset ≤ 40 years (Gout ≤ 40) and > 40 years, and normouricemic healthy controls (HC).

METHODS: Plasma samples were collected from 94 asymptomatic HUA (77% HUA ≤ 40) subjects, 196 gout patients (59% Gout ≤ 40), and 53 HC. A comprehensive targeted lipidomic analysis was performed to semi-quantify 608 lipids in plasma. Univariate and multivariate statistics and advanced visualizations were applied.

RESULTS: Both HUA and gout patients showed alterations in lipid profiles with the most significant upregulation of phosphatidylethanolamines and downregulation of lysophosphatidylcholine plasmalogens/plasmanyls. More profound changes were observed in HUA ≤ 40 and Gout ≤ 40 without ULT. Multivariate statistics differentiated HUA ≤ 40 and Gout ≤ 40 groups from HC with an overall accuracy of > 95%.

CONCLUSION: Alterations in the lipidome of HUA and Gout patients show a significant impact on lipid metabolism. The most significant glycerophospholipid dysregulation was found in HUA ≤ 40 and Gout ≤ 40 patients, together with a correction of this imbalance with ULT.

PMID:38042879 | DOI:10.1186/s13075-023-03204-6