Nevin Manimala

PLoS One. 2024 Mar 5;19(3):e0299109. doi: 10.1371/journal.pone.0299109. eCollection 2024.

ABSTRACT

Population structure and genetic diversity are the key parameters to study the breeding history of animals. This research aimed to provide a characterization of the population structure and to compare the effective population size (Ne), LD decay, genetic diversity, and genomic inbreeding in Iranian native Caspian (n = 38), Turkmen (n = 24) and Kurdish (n = 29) breeds and some other exotic horses consisting of Arabian (n = 24), Fell pony (n = 21) and Akhal-Teke (n = 20). A variety of statistical population analysis techniques, such as principal component analysis (PCA), discriminant analysis of principal component (DAPC) and model-based method (STRUCTURE) were employed. The results of the population analysis clearly demonstrated a distinct separation of native and exotic horse breeds and clarified the relationships between studied breeds. The effective population size (Ne) for the last six generations was estimated 54, 49, 37, 35, 27 and 26 for the Caspian, Kurdish, Arabian, Turkmen, Akhal-Teke and Fell pony breeds, respectively. The Caspian breed showed the lowest LD with an average r2 value of 0.079, while the highest was observed in Fell pony (0.148). The highest and lowest average observed heterozygosity were found in the Kurdish breeds (0.346) and Fell pony (0.290) breeds, respectively. The lowest genomic inbreeding coefficient based on run of homozygosity (FROH) and excess of homozygosity (FHOM) was in the Caspian and Kurdish breeds, respectively, while based on genomic relationship matrix) FGRM) and correlation between uniting gametes) FUNI) the lowest genomic inbreeding coefficient was found in the Kurdish breed. The estimation of genomic inbreeding rates in the six breeds revealed that FROH yielded lower estimates compared to the other three methods. Additionally, the Iranian breeds displayed lower levels of inbreeding compared to the exotic breeds. Overall, the findings of this study provide valuable insights for the development of effective breeding management strategies aimed at preserving these horse breeds.

PMID:38442089 | DOI:10.1371/journal.pone.0299109

J Stroke Cerebrovasc Dis. 2023 Dec;32(12):107446. doi: 10.1016/j.jstrokecerebrovasdis.2023.107446. Epub 2023 Oct 31.

ABSTRACT

OBJECTIVES: This study aimed to investigate the causal relationship between Atrial Fibrillation (AF) and the risk of Stroke using a Mendelian randomization (MR) approach.

METHODS: A two-sample MR analysis was conducted using publicly available genome-wide association study (GWAS) summary statistics data. In this analysis, genetic variants associated with AF were used as instrumental variables to estimate the causal effect. The inverse-variance weighted (IVW) method, weighted median estimator, and MR-Egger regression were employed for estimation. Additionally, sensitivity analysis was performed using the leave-one-out method.

RESULTS: The analysis included 87 single nucleotide polymorphisms (SNPs) associated with AF. The results from the IVW method indicated a positive association between genetic predisposition to AF and the risk of stroke (OR 1.002, 95 % CI 1.001-1.003, P < 0.001). The weighted median and MR-Egger methods showed consistent results (weighted median: OR 1.001, 95 % CI 1.000-1.002, P = 0.034; MR-Egger: OR 1.001, 95 % CI 1.000-1.003, P = 0.086). Sensitivity analysis demonstrated that no individual SNP significantly influenced the causal inference.

CONCLUSIONS: This study provides evidence of a causal relationship between AF and an elevated risk of stroke. These findings emphasize the significance of managing AF in order to prevent and treat strokes. Additional research is required to better understand the underlying mechanisms of this causal association.

PMID:38442074 | DOI:10.1016/j.jstrokecerebrovasdis.2023.107446

IEEE J Biomed Health Inform. 2024 Mar 5;PP. doi: 10.1109/JBHI.2024.3372999. Online ahead of print.

ABSTRACT

Performance degradation due to distribution discrepancy is a longstanding challenge in intelligent imaging, particularly for chest X-rays (CXRs). Recent studies have demonstrated that CNNs are biased toward styles (e.g., uninformative textures) rather than content (e.g., shape), in stark contrast to the human vision system. Radiologists tend to learn visual cues from CXRs and thus perform well across multiple domains. Motivated by this, we employ the novel on-the-fly style randomization modules at both image (SRM-IL) and feature (SRM-FL) levels to create rich style perturbed features while keeping the content intact for robust cross-domain performance. Previous methods simulate unseen domains by constructing new styles via interpolation or swapping styles from existing data, limiting them to available source domains during training. However, SRM-IL samples the style statistics from the possible value range of a CXR image instead of the training data to achieve more diversified augmentations. Moreover, we utilize pixel-wise learnable parameters in the SRM-FL compared to pre-defined channel-wise mean and standard deviations as style embeddings for capturing more representative style features. Additionally, we leverage consistency regularizations on global semantic features and predictive distributions from with and without style-perturbed versions of the same CXR to tweak the model’s sensitivity toward content markers for accurate predictions. Our proposed method, trained on CheXpert and MIMIC-CXR datasets, achieves 77.32&±0.35, 88.38&±0.19, 82.63&±0.13 AUCs(%) on the unseen domain test datasets, i.e., BRAX, VinDr-CXR, and NIH chest X-ray14, respectively, compared to 75.56&±0.80, 87.57&±0.46, 82.07&±0.19 from state-of-the-art models on five-fold cross-validation with statistically significant results in thoracic disease classification.

PMID:38442052 | DOI:10.1109/JBHI.2024.3372999

IEEE J Biomed Health Inform. 2024 Mar 5;PP. doi: 10.1109/JBHI.2024.3372968. Online ahead of print.

ABSTRACT

OBJECTIVE: To develop a cuffless method for estimating blood pressure (BP) from fingertip strain plethysmography (SPG) recordings.

METHODS: A custom-built micro-electromechanical systems (MEMS) strain sensor is employed to record heartbeat-induced vibrations at the fingertip. An XGboost regressor is then trained to relate SPG recordings to beat-to-beat systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP) values. For this purpose, each SPG segment in this setup is represented by a feature vector consisting of cardiac time interval, amplitude features, statistical properties, and demographic information of the subjects. In addition, a novel concept, coined geometric features, are introduced and incorporated into the feature space to further encode the dynamics in SPG recordings. The performance of the regressor is assessed on 32 healthy subjects through 5-fold cross-validation (5-CV) and leave-subject-out cross validation (LSOCV).

RESULTS: Mean absolute errors (MAEs) of 3.88 mmHg and 5.45 mmHg were achieved for DBP and SBP estimations, respectively, in the 5-CV setting. LSOCV yielded MAEs of 8.16 mmHg for DBP and 16.81 mmHg for SBP. Through feature importance analysis, 3 geometric and 26 integral-related features introduced in this work were identified as primary contributors to BP estimation. The method exhibited robustness against variations in blood pressure level (normal to critical) and body mass index (underweight to obese), with MAE ranges of [1.28, 4.28] mmHg and [2.64, 7.52] mmHg, respectively.

CONCLUSION: The findings suggest high potential for SPG-based BP estimation at the fingertip.

SIGNIFICANCE: This study presents a fundamental step towards the augmentation of optical sensors that are susceptible to dark skin tones.

PMID:38442050 | DOI:10.1109/JBHI.2024.3372968

JMIR Form Res. 2024 Mar 5;8:e49411. doi: 10.2196/49411.

ABSTRACT

BACKGROUND: Research gaps refer to unanswered questions in the existing body of knowledge, either due to a lack of studies or inconclusive results. Research gaps are essential starting points and motivation in scientific research. Traditional methods for identifying research gaps, such as literature reviews and expert opinions, can be time consuming, labor intensive, and prone to bias. They may also fall short when dealing with rapidly evolving or time-sensitive subjects. Thus, innovative scalable approaches are needed to identify research gaps, systematically assess the literature, and prioritize areas for further study in the topic of interest.

OBJECTIVE: In this paper, we propose a machine learning-based approach for identifying research gaps through the analysis of scientific literature. We used the COVID-19 pandemic as a case study.

METHODS: We conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. Our approach is based on the BERTopic topic modeling technique, which leverages transformers and class-based term frequency-inverse document frequency to create dense clusters allowing for easily interpretable topics. Our BERTopic-based approach involves 3 stages: embedding documents, clustering documents (dimension reduction and clustering), and representing topics (generating candidates and maximizing candidate relevance).

RESULTS: After applying the study selection criteria, we included 33,206 abstracts in the analysis of this study. The final list of research gaps identified 21 different areas, which were grouped into 6 principal topics. These topics were: “virus of COVID-19,” “risk factors of COVID-19,” “prevention of COVID-19,” “treatment of COVID-19,” “health care delivery during COVID-19,” “and impact of COVID-19.” The most prominent topic, observed in over half of the analyzed studies, was “the impact of COVID-19.”

CONCLUSIONS: The proposed machine learning-based approach has the potential to identify research gaps in scientific literature. This study is not intended to replace individual literature research within a selected topic. Instead, it can serve as a guide to formulate precise literature search queries in specific areas associated with research questions that previous publications have earmarked for future exploration. Future research should leverage an up-to-date list of studies that are retrieved from the most common databases in the target area. When feasible, full texts or, at minimum, discussion sections should be analyzed rather than limiting their analysis to abstracts. Furthermore, future studies could evaluate more efficient modeling algorithms, especially those combining topic modeling with statistical uncertainty quantification, such as conformal prediction.

PMID:38441952 | DOI:10.2196/49411

JMIR Form Res. 2024 Mar 5;8:e51141. doi: 10.2196/51141.

ABSTRACT

BACKGROUND: Restorative dentistry plays a crucial role in dental practice, necessitating professionals to stay abreast with the latest advancements in the field. The advancement of technology has made web-based learning a widely used method of education delivery in dentistry, providing learners with extensive information and flexibility.

OBJECTIVE: This study aims to evaluate how effective an online educational course in restorative dentistry is for dental graduates in Syria.

METHODS: This study used a pre-experimental study design, with pretest and posttest assessments to measure changes in participants’ knowledge and skills. A total of 21 dental graduates completed the online course in restorative dentistry, which was hosted on Moodle, using the learning management system of the Syrian Virtual University. Participants were provided with a suggested learning sequence and had the flexibility to navigate the course on their own and at their own pace. The course was developed based on the principles of web course design and web-based course development using the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) general instructional design model. The pretest and posttest assessments consisted of 50 multiple-choice questions with a single correct answer, aligning with the course content. Furthermore, participants were asked to complete a course acceptance survey upon finishing the course.

RESULTS: The results showed a significant improvement in the participants’ knowledge of restorative dentistry, supported by a statistically significant P value of less than .05. The effect size of the difference between the pre and posttest indicated that the effect size, as indicated by ω², demonstrated a significant 62.1% difference between the pre and posttest, indicating a high and statistically significant effect. Furthermore, the value derived from the Haridy obtained work ratio formula indicated that the educational program was effective, with an effectiveness amount of 3.36%. Additionally, 93% (n=19) of respondents expressed confidence in having gained the expected benefits from the educational course upon its completion.

CONCLUSIONS: The findings indicated a notable enhancement in the participants’ understanding of restorative dentistry. The participants’ high satisfaction rate and positive feedback from the course acceptance survey further emphasize the favorable reception of the web-based learning approach. This study highlights the potential of web-based learning in dental education, opening the door for future research in this area. The findings of this study carry important implications for the design and implementation of web-based educational programs in dentistry, suggesting that such programs can serve as an effective tool for continuous professional development in the field.

PMID:38441921 | DOI:10.2196/51141

Phytochem Anal. 2024 Mar 4. doi: 10.1002/pca.3345. Online ahead of print.

ABSTRACT

INTRODUCTION: Plants of the Rosa genus are renowned for their pronounced and pleasant aroma and colors.

OBJECTIVE: The aim of this work was to develop a novel liquid chromatographic triple quadrupole time-of-flight tandem mass spectrometric (LC-QTOF-MS/MS) method for the investigation of the bioactive fingerprint of petals of different genotypes belonging to Rosa damascena and Rosa centifolia species.

METHODOLOGY: Central composite design (CCD) of response surface methodology (RSM) was used for the optimization of the LC-QTOF-MS/MS method. The method was validated and target, suspect, and non-target screening workflows were applied. Statistical analysis and chemometric tools were utilized to explore the metabolic fingerprint of the Rosa species.

RESULTS: RSM revealed that the optimal extraction parameters involved mixing 11 mg of sample with 1 mL of MeOH:H₂ O (70:30, v/v). Target analysis confirmed the presence of 11 analytes, all of which demonstrated low limits of quantification (LOQs; as low as 0.048 ng mg^-1 ) and sufficient recoveries (RE: 85%-107%). In total, 28 compounds were tentatively identified through suspect analysis. Non-target analysis enabled the generation of robust OPLS-DA and HCA models that classified the samples according to their species with 100% accuracy.

CONCLUSIONS: A novel LC-QTOF-MS/MS method was developed and applied in the analysis of 47 R. centifolia and R. damascena flowers belonging to different genotypes.

PMID:38439140 | DOI:10.1002/pca.3345

Pharm Stat. 2024 Mar 4. doi: 10.1002/pst.2376. Online ahead of print.

ABSTRACT

Recent years have seen an increasing interest in incorporating external control data for designing and evaluating randomized clinical trials (RCT). This may decrease costs and shorten inclusion times by reducing sample sizes. For small populations, with limited recruitment, this can be especially important. Bayesian dynamic borrowing (BDB) has been a popular choice as it claims to protect against potential prior data conflict. Digital twins (DT) has recently been proposed as another method to utilize historical data. DT, also known as PROCOVA™, is based on constructing a prognostic score from historical control data, typically using machine learning. This score is included in a pre-specified ANCOVA as the primary analysis of the RCT. The promise of this idea is power increase while guaranteeing strong type 1 error control. In this paper, we apply analytic derivations and simulations to analyze and discuss examples of these two approaches. We conclude that BDB and DT, although similar in scope, have fundamental differences which need be considered in the specific application. The inflation of the type 1 error is a serious issue for BDB, while more evidence is needed of a tangible value of DT for real RCTs.

PMID:38439136 | DOI:10.1002/pst.2376

J Sleep Res. 2024 Mar 4:e14183. doi: 10.1111/jsr.14183. Online ahead of print.

ABSTRACT

We assessed the relation between air pollution, weather, and adherence to positive airway pressure (PAP) therapy in a retrospective community-based repeated-measures study of adults with obstructive sleep apnea who purchased PAP devices from a registered provider between 2013 and 2017 (Ottawa, Ontario, Canada) and had at least one day of data. Daily PAP-derived data, air pollution, and weather databases were linked using postal code. The exposures were mean nocturnal (8:00 p.m. to 8:00 a.m.) (i) residential concentrations of nitrogen dioxide (NO₂ ), fine particulate matter <=2.5 μm (PM_2.5 ), ozone (O₃ ), and Air Quality Health Index (AQHI), and (ii) temperature, relative humidity, and barometric pressure. Covariates in the main model were demographics, season, exposure year, and PAP therapy mode. We analysed 8148 adults (median age of 54 years and 61% men) and 2,071,588 days of data. Based on daily data, the median (interquartile range) daily PAP usage was 416 (323-487) min. Using mixed-effect regression analyses to incorporate daily data and clustering by individuals, we found a statistically significant decrease in adherence for increased levels of NO₂ , PM_2.5 , and AQHI. The largest effect was for NO₂ : a decrease in daily PAP use while comparing the highest versus lowest quartiles (Qs) was 3.4 (95% confidence interval [CI] 2.8-3.9) min. Decreased PAP adherence was also associated with increased temperature (Q4 versus Q1: 2.6 [95% CI: 1.5-3.7] min) and decreased barometric pressure (Q1 versus Q4: 2.0 [95% CI 1.5-2.5] min). We observed modest but statistically significant acute effects of air pollution and weather on daily PAP adherence.

PMID:38439127 | DOI:10.1111/jsr.14183