Category: Nevin Manimala

Nat Hum Behav. 2023 Jan;7(1):15-26. doi: 10.1038/s41562-022-01497-2. Epub 2023 Jan 26.

ABSTRACT

Flexibility in the design, analysis and interpretation of scientific studies creates a multiplicity of possible research outcomes. Scientists are granted considerable latitude to selectively use and report the hypotheses, variables and analyses that create the most positive, coherent and attractive story while suppressing those that are negative or inconvenient. This creates a risk of bias that can lead to scientists fooling themselves and fooling others. Preregistration involves declaring a research plan (for example, hypotheses, design and statistical analyses) in a public registry before the research outcomes are known. Preregistration (1) reduces the risk of bias by encouraging outcome-independent decision-making and (2) increases transparency, enabling others to assess the risk of bias and calibrate their confidence in research outcomes. In this Perspective, we briefly review the historical evolution of preregistration in medicine, psychology and other domains, clarify its pragmatic functions, discuss relevant meta-research, and provide recommendations for scientists and journal editors.

PMID:36707644 | DOI:10.1038/s41562-022-01497-2

Eur J Hum Genet. 2023 Jan 27. doi: 10.1038/s41431-022-01257-w. Online ahead of print.

ABSTRACT

Evidence from literature, including the BRIDGES study, indicates that germline protein truncating variants (PTVs) in FANCM confer moderately increased risk of ER-negative and triple-negative breast cancer (TNBC), especially for women with a family history of the disease. Association between FANCM missense variants (MVs) and breast cancer risk has been postulated. In this study, we further used the BRIDGES study to test 689 FANCM MVs for association with breast cancer risk, overall and in ER-negative and TNBC subtypes, in 39,885 cases (7566 selected for family history) and 35,271 controls of European ancestry. Sixteen common MVs were tested individually; the remaining rare 673 MVs were tested by burden analyses considering their position and pathogenicity score. We also conducted a meta-analysis of our results and those from published studies. We did not find evidence for association for any of the 16 variants individually tested. The rare MVs were significantly associated with increased risk of ER-negative breast cancer by burden analysis comparing familial cases to controls (OR = 1.48; 95% CI 1.07-2.04; P = 0.017). Higher ORs were found for the subgroup of MVs located in functional domains or predicted to be pathogenic. The meta-analysis indicated that FANCM MVs overall are associated with breast cancer risk (OR = 1.22; 95% CI 1.08-1.38; P = 0.002). Our results support the definition from previous analyses of FANCM as a moderate-risk breast cancer gene and provide evidence that FANCM MVs could be low/moderate risk factors for ER-negative and TNBC subtypes. Further genetic and functional analyses are necessary to clarify better the increased risks due to FANCM MVs.

PMID:36707629 | DOI:10.1038/s41431-022-01257-w

Sci Rep. 2023 Jan 27;13(1):1548. doi: 10.1038/s41598-023-28809-4.

NO ABSTRACT

PMID:36707616 | DOI:10.1038/s41598-023-28809-4

NPJ Parkinsons Dis. 2023 Jan 27;9(1):11. doi: 10.1038/s41531-023-00462-8.

ABSTRACT

There is growing evidence that patients with type 2 diabetes mellitus (DM) have an increased risk of developing Parkinson’s disease (PD) and share similar dysregulated pathways. We aimed to determine whether the risk of PD increases as diabetes progresses among patients with type 2 DM. Using a nationally representative database from the Korean National Health Insurance System, 2,362,072 individuals (≥40 years of age) with type 2 DM who underwent regular health checkups during 2009-2012 were followed up until the end of 2018. The diabetes severity score parameters included the number of oral hypoglycemic agents, diabetes duration, insulin use, or presence of chronic kidney disease, diabetic retinopathy, or cardiovascular disease. Each of these characteristics was scored as one unit of diabetes severity and their sum was defined as a diabetes severity score from 0-6. We identified 17,046 incident PD cases during the follow-up. Each component of the diabetes severity score showed a similar intensity for the risk of PD. Compared with subjects with no parameters, HR values (95% confidence intervals) of PD were 1.09 (1.04-1.15) in subjects with one diabetes severity score parameter, 1.28 (1.22-1.35) in subjects with two parameters, 1.55 (1.46-1.65) in subjects with three parameters, 1.96 (1.82-2.11) in subjects with four parameters, 2.08 (1.83-2.36) in subjects with five parameters, and 2.78 (2.05-3.79) in subjects with six parameters. Diabetes severity was associated with an increased risk of developing PD. Severe diabetes may be a risk factor for the development of PD.

PMID:36707543 | DOI:10.1038/s41531-023-00462-8

Commun Med (Lond). 2023 Jan 27;3(1):11. doi: 10.1038/s43856-023-00244-6.

ABSTRACT

BACKGROUND: The oxytocin receptor gene (OXTR) is regulated, in part, by DNA methylation. This mechanism has implications for uterine contractility during labor and for prevention or treatment of postpartum hemorrhage, an important contributor to global maternal morbidity and mortality.

METHODS: We measured and compared the level of OXTR DNA methylation between matched blood and uterine myometrium to evaluate blood as an indicator of uterine methylation status using targeted pyrosequencing and sites from the Illumina EPIC Array. Next, we tested for OXTR DNA methylation differences in blood between individuals who experienced a postpartum hemorrhage arising from uterine atony and matched controls following vaginal birth. Bivariate statistical tests, generalized linear modeling and Poisson regression were used in the analyses.

RESULTS: Here we show a significant positive correlation between blood and uterine DNA methylation levels at several OXTR loci. Females with higher OXTR DNA methylation in blood had required significantly more exogenous oxytocin during parturition. With higher DNA methylation, those who had oxytocin administered during labor had significantly greater relative risk for postpartum hemorrhage (IRR 2.95, 95% CI 1.53-5.71).

CONCLUSIONS: We provide evidence that epigenetic variability in OXTR is associated with the amount of oxytocin administered during parturition and moderates subsequent postpartum hemorrhage. Methylation can be measured using a peripheral tissue, suggesting potential use in identifying individuals susceptible to postpartum hemorrhage. Future studies are needed to quantify myometrial gene expression in connection with OXTR methylation.

PMID:36707542 | DOI:10.1038/s43856-023-00244-6

Sci Rep. 2023 Jan 27;13(1):1498. doi: 10.1038/s41598-023-28794-8.

ABSTRACT

Bambara groundnut has significant role to play in terms of food security, even though researchers in agriculture have paid very little attention to the crop in the past. This study aimed to investigate the high-yielding accessions in three environments. A total of 34 phenological, vegetative and yield traits were measured and analyzed statistically with R software. There were significant differences in all the traits except for plant height, initial plant stand, panicle length per stem, and petiole length. Across the three environments, TVSU-455 gave the highest values for the total number of pods (42.67), final plant stands (7.67), fresh seed weights (45.83), number of seeds per plant (46.62), hundred seed weight with a value (124.56), dry seed weight (27.14), fresh pod weight (92.65), harvest index of 0.57, yield per plot (45.83) and unshelled yield per plot (550.26). TVSU-455 was the only accession in cluster I of the dendrogram based on its superiority over other accessions. The clustering analysis produced a dendrogram categorizing the 15 accessions into 4 groups based on the vegetative, phenological, and yield traits. There were significant differences among the correlations of the 34 traits. The first two principle components explained 56.16% of the total variation with each dimension accounting for 39.85% and 16. 31% variation, respectively. TVSU-455 can be recommended for stability analysis.

PMID:36707537 | DOI:10.1038/s41598-023-28794-8

Sci Rep. 2023 Jan 27;13(1):1530. doi: 10.1038/s41598-023-28479-2.

ABSTRACT

Tumour cells exhibit altered metabolic pathways that lead to radiation resistance and disease progression. Raman spectroscopy (RS) is a label-free optical modality that can monitor post-irradiation biomolecular signatures in tumour cells and tissues. Convolutional Neural Networks (CNN) perform automated feature extraction directly from data, with classification accuracy exceeding that of traditional machine learning, in cases where data is abundant and feature extraction is challenging. We are interested in developing a CNN-based predictive model to characterize clinical tumour response to radiation therapy based on their degree of radiosensitivity or radioresistance. In this work, a CNN architecture is built for identifying post-irradiation spectral changes in Raman spectra of tumour tissue. The model was trained to classify irradiated versus non-irradiated tissue using Raman spectra of breast tumour xenografts. The CNN effectively classified the tissue spectra, with accuracies exceeding 92.1% for data collected 3 days post-irradiation, and 85.0% at day 1 post-irradiation. Furthermore, the CNN was evaluated using a leave-one-out- (mouse, section or Raman map) validation approach to investigate its generalization to new test subjects. The CNN retained good predictive accuracy (average accuracies 83.7%, 91.4%, and 92.7%, respectively) when little to no information for a specific subject was given during training. Finally, the classification performance of the CNN was compared to that of a previously developed model based on group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF) classification. We found that CNN yielded higher classification accuracy, sensitivity, and specificity in mice assessed 3 days post-irradiation, as compared with the GBR-NMF-RF approach. Overall, the CNN can detect biochemical spectral changes in tumour tissue at an early time point following irradiation, without the need for previous manual feature extraction. This study lays the foundation for developing a predictive framework for patient radiation response monitoring.

PMID:36707535 | DOI:10.1038/s41598-023-28479-2

Schizophrenia (Heidelb). 2023 Jan 27;9(1):6. doi: 10.1038/s41537-023-00332-5.

ABSTRACT

Smartphone technology provides us with a more convenient and less intrusive method of detecting changes in behavior and symptoms that typically precede schizophrenia relapse. To take advantage of the aforementioned, this study examines the feasibility of predicting schizophrenia relapse by identifying statistically significant anomalies in patient data gathered through mindLAMP, an open-source smartphone app. Participants, recruited in Boston, MA in the United States, and Bangalore and Bhopal in India, were invited to use mindLAMP for up to a year. The passive data (geolocation, accelerometer, and screen state), active data (surveys), and data quality metrics collected by the app were then retroactively fed into a relapse prediction model that utilizes anomaly detection. Overall, anomalies were 2.12 times more frequent in the month preceding a relapse and 2.78 times more frequent in the month preceding and following a relapse compared to intervals without relapses. The anomaly detection model incorporating passive data proved a better predictor of relapse than a naive model utilizing only survey data. These results demonstrate that relapse prediction models utilizing patient data gathered by a smartphone app can warn the clinician and patient of a potential schizophrenia relapse.

PMID:36707524 | DOI:10.1038/s41537-023-00332-5

Nat Commun. 2023 Jan 27;14(1):451. doi: 10.1038/s41467-023-36013-1.

ABSTRACT

The genetic regulation of post-prandial glucose levels is poorly understood. Here, we characterise the genetic architecture of blood glucose variably measured within 0 and 24 h of fasting in 368,000 European ancestry participants of the UK Biobank. We found a near-linear increase in the heritability of non-fasting glucose levels over time, which plateaus to its fasting state value after 5 h post meal (h² = 11%; standard error: 1%). The genetic correlation between different fasting times is > 0.77, suggesting that the genetic control of glucose is largely constant across fasting durations. Accounting for heritability differences between fasting times leads to a ~16% improvement in the discovery of genetic variants associated with glucose. Newly detected variants improve the prediction of fasting glucose and type 2 diabetes in independent samples. Finally, we meta-analysed summary statistics from genome-wide association studies of random and fasting glucose (N = 518,615) and identified 156 independent SNPs explaining 3% of fasting glucose variance. Altogether, our study demonstrates the utility of random glucose measures to improve the discovery of genetic variants associated with glucose homeostasis, even in fasting conditions.

PMID:36707517 | DOI:10.1038/s41467-023-36013-1

Environ Geochem Health. 2023 Jan 27. doi: 10.1007/s10653-023-01490-2. Online ahead of print.

ABSTRACT

This article proposes a mathematical model to characterize phytoremediation processes in soils contaminated with heavy metals. In particular, the proposed model constructs characteristic curves for the concentrations of several metals (As, Cd, Cu, Fe, Pb, Sb, and Zn) in soils and plants based on the experimental data retrieved from several bibliographical sources comprising 305 vegetal species. The proposed model is an extension of previous models of characteristic curves in phytoremediation processes developed by Lam et al. for root measurements using the bioconcentration factor. However, the proposed model extends this approach to consider roots, as well as aerial parts and shoots of the plant, while at the same time providing a less complex mathematical formula compared to the original. The final model shows an adjusted R2 of 0.712, and all its parameters are considered statistically significant. The model may be used to assess samples from a given plant species to identify its potential as an accumulator in the context of soil phytoremediation processes. Furthermore, a simplified version of the model was constructed using an approximation to provide an easy-to-compute alternative that is valid for concentrations below 37,000 mg/kg. This simplified model shows results similar to the original model for concentrations below this threshold and it uses an adjusted factor defined as [Formula: see text] that must be compared with a threshold depending on the metal, type of measurement, and target (e.g., accumulator or hyperaccumulator). The full model construction shows that 90 out of the 305 species assessed have a potential behavior as accumulators and 10 of them as hyperaccumulators. Finally, out of the 1405 experimental measurements, 1177 were shown to be accumulators or hyperaccumulators. In particular, 85% of the results coincide with the reported values, thus validating the proposed model.

PMID:36707498 | DOI:10.1007/s10653-023-01490-2