Category: Nevin Manimala

J Neonatal Perinatal Med. 2023 Dec 1. doi: 10.3233/NPM-230159. Online ahead of print.

ABSTRACT

BACKGROUND: Bowel dilation and bowel wall thickness are common prenatal ultrasound measurements for fetuses with gastroschisis. Data regarding antenatal sonographic bowel findings and postnatal outcomes are conflicting. Our objective was to evaluate the impact of in utero bowel measurements on perinatal outcomes in gastroschisis pregnancies.

METHODS: Retrospective cohort study of 116 pregnancies complicated by gastroschisis between 2011 and 2020. We reviewed ultrasounds documenting fetal bowel measurements. To evaluate the association of these measurements with antepartum and delivery outcomes, we ran logistic and linear models using generalized estimating equations.

RESULTS: Eleven perinatal outcomes reached statistical significance, although with minimal clinical impact given small magnitude of effect. Intra-abdominal bowel dilation was associated with a 0.5 week decrease in delivery gestational age (GA) (95% CI -0.07, -0.03) and a 6.93 g increase in birth weight (95% CI 1.54, 28.73). Intra-abdominal bowel wall thickness was associated with later GA of non-stress test (NST) start of 0.22 weeks (95% CI 0.07, 0.37), increased delivery GA of 0.08 weeks (95% CI 0.02, 0.15), 0.006 decrease in umbilical artery (UA) pH (95% CI -0.009, -0.003), 0.26 increase in UA base deficit (95% CI 0.09, 0.43), and decreased odds of cesarean delivery (OR = 0.83, 95% CI 0.70, 0.99). Extra-abdominal bowel wall thickness was associated with a 0.1 increase in UA base deficit (95% CI 0.02, 0.19) and a 0.05 increase in 5-min APGAR score (95% CI 0.01, 0.09). Stomach cross-section was associated with a 0.01 week decrease in delivery GA (95% CI -0.02, -0.001) and increased odds of receiving betamethasone (OR = 1.02, 95% CI 1.01, 1.04).

CONCLUSIONS: In utero bowel characteristics reached statistical significance for several outcomes, but with minimal meaningful clinical differences in outcomes.

PMID:38043025 | DOI:10.3233/NPM-230159

Cancer Biomark. 2023 Nov 16. doi: 10.3233/CBM-230134. Online ahead of print.

ABSTRACT

BACKGROUND: P-Element-induced wimpy testis (PIWI) proteins, when in combination with PIWI-interacting RNA (piRNA), are engaged in the epigenetic regulation of gene expression in germline cells. Different types of tumour cells have been found to exhibit abnormal expression of piRNA, PIWIL-mRNAs, and proteins. We aimed to determine the mRNA expression profiles of PIWIL1, PIWIL2, PIWIL3, & PIWIL4, in hepatocellular carcinoma patients, and to associate their expression patterns with clinicopathological features.

METHODS: The expression patterns of PIWIL1, PIWIL2, PIWIL3, PIWIL4 mRNA, was assessed via real-time quantitative polymerase chain reaction (RT-QPCR), on tissue and serum samples from HCC patients, their impact for diagnosis was evaluated by ROC curves, prognostic utility was determined, and In Silico analysis was conducted for predicted variant detection, association with HCC microRNAs and Network Analysis.

RESULTS: Expression levels were significantly higher in both HCC tissue and serum samples than in their respective controls (p< 0.001). Additionally, the diagnostic performance was assessed, Risk determination was found to be statistically significant.

CONCLUSION: PIWIL mRNAs are overexpressed in HCC tissue and serum samples, the expression patterns could be valuable molecular markers for HCC, due to their association with age, tumour grade and pattern. To the best of our knowledge, our study is the first to report the expression levels of all PIWIL mRNA and to suggest their remarkable values as diagnostic and prognostic biomarkers, in addition to their correlation to HCC development. Additionally, a therapeutic opportunity might be also suggested through in silico miRNA prediction for HCC and PIWIL genes through DDX4 and miR-124-3p.

PMID:38043006 | DOI:10.3233/CBM-230134

Disabil Rehabil. 2023 Dec 3:1-9. doi: 10.1080/09638288.2023.2290690. Online ahead of print.

ABSTRACT

PURPOSE: This study aims to evaluate the feasibility and effectiveness of a mindfulness-based group intervention (The COndiVIDere program) delivered online to people with MS (PwMS) in the time of COVID-19.

MATERIALS AND METHODS: This is a single-arm longitudinal study with a nested qualitative study. The COndiVIDere program is composed of five weekly sessions (1-h each) plus three booster monthly sessions. Data were collected immediately before the beginning of the program, after the five weekly sessions, and at 3- and 6-month follow-ups.

RESULTS: Fifty PwMS participated in the program. Participants improved in anxiety, stress, loneliness and mindfulness (“non-judgmental inner experience” component). Improvements on most outcomes occurred at post-intervention and reached the statistically significant threshold at 3-month follow-up. Mindfulness improvements keep increasing at each time point. Qualitative data confirmed the COndiVIDere program feasibility and the positive psychological impacts on participants. Mindfulness, compassion and the group setting were considered the most important active elements.

CONCLUSIONS: Study findings support COndiVIDere feasibility and effectiveness with PwMS and its broad applicability in this population.

PMID:38042990 | DOI:10.1080/09638288.2023.2290690

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