Tag: statistics

Angew Chem Int Ed Engl. 2023 Mar 28:e202303339. doi: 10.1002/anie.202303339. Online ahead of print.

ABSTRACT

This invited Team Profile was created by the Tiwary group, University of Maryland, College Park (USA) and the Seeliger group, Stony Brook University, New York (USA). They recently published an article on the previously made observation through in-cell screening that the blockbuster cancer drug Gleevec has the same binding affinity, yet different dissociation kinetics against wild-type and N368S-mutated Abl kinase. Through all-atom enhanced molecular dynamics simulations guided by statistical mechanics and information theory, they were able to explain the mechanistic basis of this perplexing observation. Their work has ramifications for how pharmaceutical drugs might experience kinetic resistance due to mutations. “Protein Flexibility and Dissociation Pathway Differentiation Can Explain Onset of Resistance Mutations in Kinases”, M. Shekhar, Z. Smith, M. A. Seeliger, P. Tiwary, Angew. Chem. Int. Ed. 2022, e202200983; Angew. Chem. 2022, e202200983.

PMID:36976457 | DOI:10.1002/anie.202303339

Drug Saf. 2023 Mar 28. doi: 10.1007/s40264-023-01291-7. Online ahead of print.

ABSTRACT

INTRODUCTION AND OBJECTIVE: The risks and benefits of medication use in pregnancy are typically established through post-marketing observational studies. As there is currently no standardised or systematic approach to the post-marketing assessment of medication safety in pregnancy, data generated through pregnancy pharmacovigilance (PregPV) research can be heterogenous and difficult to interpret. The aim of this article is to describe the development of a reference framework of core data elements (CDEs) for collection in primary source PregPV studies that can be used to standardise data collection procedures and, thereby, improve data harmonisation and evidence synthesis capabilities.

METHODS: This CDE reference framework was developed within the Innovative Medicines Initiative (IMI) ConcePTION project by experts in pharmacovigilance, pharmacoepidemiology, medical statistics, risk-benefit communication, clinical teratology, reproductive toxicology, genetics, obstetrics, paediatrics, and child psychology. The framework was produced through a scoping review of data collection systems used by established PregPV datasets, followed by extensive discussion and debate around the value, definition, and derivation of each data item identified from these systems.

RESULTS: The finalised listing of CDEs comprises 98 individual data elements, arranged into 14 tables of related fields. These data elements are openly available on the European Network of Teratology Information Services (ENTIS) website ( http://www.entis-org.eu/cde ).

DISCUSSION: With this set of recommendations, we aim to standardise PregPV primary source data collection processes to improve the speed at which high-quality evidence-based statements can be provided about the safety of medication use in pregnancy.

PMID:36976447 | DOI:10.1007/s40264-023-01291-7

Acta Parasitol. 2023 Mar 28. doi: 10.1007/s11686-023-00670-5. Online ahead of print.

ABSTRACT

PURPOSE: Pastoralists regularly come in contact with ticks as they herd their animals and are exposed to pathogens that cause zoonotic diseases. No study has been conducted in Nigeria to evaluate the knowledge, attitudes, and practices (KAP) of these Pastoralists towards ticks, tick bite, and tick control, and thus this research.

METHODS: A KAP survey of pastoralists (n = 119) was conducted in Plateau State, Nigeria. Data generated were analysed using Statistical Package for Social Sciences (SPSS).

RESULTS: The majority of the pastoralists (99.2%) had knowledge of ticks, with 79% of them being aware that ticks attach and bite humans, whereas only 30.3% believed that ticks transmit diseases to humans. Eighty-four per cent of the pastoralists do not wear protective clothing while herding their animals and 81.5% indicated to having been bitten by ticks, whereas hospital visit after tick bite was low (7.6%). Statistically significant variables were observed when knowledge of the respondents were compared in relation to the ability of ticks to cause diseases (Χ² = 9.980, P = 0.007); hospital visit after a bite (Χ² = 11.453, P = 0.003); and the use of protective clothing for herding (Χ² = 22.596, P = 0). The main tick control measure was hand picking (58.8%).

CONCLUSIONS: The pastoralists were unaware of the ability of ticks to transmit zoonotic pathogens. Preventive practices were insufficient to reduce tick bites, and thus were constantly exposed to tick-borne diseases. This study hopes to provide important insights for the development of educational awareness programmes for the pastoralists and serve as a guide for the health workers in designing future preventive programmes against tick-borne zoonoses in Nigeria.

PMID:36976439 | DOI:10.1007/s11686-023-00670-5

Neuroinformatics. 2023 Mar 28. doi: 10.1007/s12021-023-09626-6. Online ahead of print.

ABSTRACT

Activation likelihood estimation (ALE) is among the most used algorithms to perform neuroimaging meta-analysis. Since its first implementation, several thresholding procedures had been proposed, all referred to the frequentist framework, returning a rejection criterion for the null hypothesis according to the critical p-value selected. However, this is not informative in terms of probabilities of the validity of the hypotheses. Here, we describe an innovative thresholding procedure based on the concept of minimum Bayes factor (mBF). The use of the Bayesian framework allows to consider different levels of probability, each of these being equally significant. In order to simplify the translation between the common ALE practice and the proposed approach, we analised six task-fMRI/VBM datasets and determined the mBF values equivalent to the currently recommended frequentist thresholds based on Family Wise Error (FWE). Sensitivity and robustness toward spurious findings were also analyzed. Results showed that the cutoff log₁₀(mBF) = 5 is equivalent to the FWE threshold, often referred as voxel-level threshold, while the cutoff log₁₀(mBF) = 2 is equivalent to the cluster-level FWE (c-FWE) threshold. However, only in the latter case voxels spatially far from the blobs of effect in the c-FWE ALE map survived. Therefore, when using the Bayesian thresholding the cutoff log₁₀(mBF) = 5 should be preferred. However, being in the Bayesian framework, lower values are all equally significant, while suggesting weaker level of force for that hypothesis. Hence, results obtained through less conservative thresholds can be legitimately discussed without losing statistical rigor. The proposed technique adds therefore a powerful tool to the human-brain-mapping field.

PMID:36976430 | DOI:10.1007/s12021-023-09626-6

Environ Monit Assess. 2023 Mar 28;195(4):517. doi: 10.1007/s10661-023-11143-7.

ABSTRACT

Considering the importance of limited natural resources, accurately recording and evaluating temperature data is critical. The daily average temperature values obtained for the years 2019-2021 of eight highly correlated meteorological stations, characterized by mountainous and cold climate features in the northeast of Turkey, were analyzed by an artificial neural network (ANN), support vector regression (SVR), and regression tree (RT) methods. Output values produced by different machine learning methods compared with different statistical evaluation criteria and the Taylor diagram. ANN6, ANN12, medium gaussian SVR, and linear SVR were chosen as the most suitable methods, especially due to their success in estimating data at high (> 15 ℃) and low (< 0 ℃) temperatures. All the methodologies and network architectures used produced successful results (NSE-R² > 0.90). Some deviations have been observed in the estimation results due to the decrease in the amount of heat emitted from the ground due to fresh snow, especially in the -1 ~ 5 ℃ range, where snowfall begins, in the mountainous areas characterized by heavy snowfall. In models with low neuron numbers (ANN1,2,3) in ANN architecture, the increase in the number of layers does not affect the results. However, the increase in the number of layers in models with high neuron counts positively affects the accuracy of the estimation.

PMID:36976414 | DOI:10.1007/s10661-023-11143-7

Environ Monit Assess. 2023 Mar 28;195(4):515. doi: 10.1007/s10661-023-11148-2.

ABSTRACT

A dense network of rain gauges and considerably large variability of the southwest monsoon precipitation across the country make India a suitable test-bed to evaluate any satellite-based precipitation product. In this paper, three real-time infrared-only precipitation products derived from the INSAT-3D satellite namely, INSAT Multispectral Rainfall (IMR), Corrected IMR (IMC) and Hydro-Estimator (HEM) and three rain gauge-adjusted Global Precipitation Measurement (GPM)-based multi-satellite precipitation products namely, Integrated Multi-satellitE Retrievals for GPM (IMERG), Global Satellite Mapping of Precipitation (GSMaP) and an Indian merged satellite-gauge product (INMSG) have been evaluated over India at a daily timescale for the southwest monsoon seasons of 2020 and 2021. An evaluation against rain gauge-based gridded reference dataset shows noticeable reduction of bias in IMC product over IMR, primarily over the orographic regions. However, INSAT-3D infrared-only precipitation retrieval algorithms have limitations in shallow and convective precipitation estimation. Among rain gauge-adjusted multi-satellite products, INMSG is shown to be the best product in the monsoon precipitation estimation over India due to use of rather larger number of rain gauges than IMERG and GSMaP products. All satellite-derived precipitation products, i.e. infrared-only and gauge-adjusted multi-satellite products underestimate heavy monsoon precipitation by 50-70%. The bias decomposition analysis indicates that a simple statistical bias correction would considerably improve the performance of the INSAT-3D precipitation products over the central India, but the same might not work over the west coast due to rather larger contributions of both positive and negative hit bias components. Although rain gauge-adjusted multi-satellite precipitation products show very small or negligible total biases in the monsoon precipitation estimation, positive and negative hit bias components are considerable over the west coast and central India. Furthermore, rain gauge-adjusted multi-satellite precipitation products underestimate very heavy to extremely heavy precipitation with larger magnitudes than the INSAT-3D derived precipitation products over the central India. Among the rain gauge-adjusted multi-satellite precipitation products, INMSG has smaller bias and error than IMERG and GSMaP products for very heavy to extremely heavy monsoon precipitation over the west coast and central India. Preliminary results of this study would be useful for end users in choosing a better precipitation product for real-time and research applications as well as for algorithm developers in further improving these products.

PMID:36976412 | DOI:10.1007/s10661-023-11148-2

J Math Biol. 2023 Mar 28;86(4):62. doi: 10.1007/s00285-023-01889-6.

ABSTRACT

Darwinian evolution is driven by random mutations, genetic recombination (gene shuffling) and selection that favors genotypes with high fitness. For systems where each genotype can be represented as a bitstring of length L, an overview of possible evolutionary trajectories is provided by the L-cube graph with nodes labeled by genotypes and edges directed toward the genotype with higher fitness. Peaks (sinks in the graphs) are important since a population can get stranded at a suboptimal peak. The fitness landscape is defined by the fitness values of all genotypes in the system. Some notion of curvature is necessary for a more complete analysis of the landscapes, including the effect of recombination. The shape approach uses triangulations (shapes) induced by fitness landscapes. The main topic for this work is the interplay between peak patterns and shapes. Because of constraints on the shapes for [Formula: see text] imposed by peaks, there are in total 25 possible combinations of peak patterns and shapes. Similar constraints exist for higher L. Specifically, we show that the constraints induced by the staircase triangulation can be formulated as a condition of universal positive epistasis, an order relation on the fitness effects of arbitrary sets of mutations that respects the inclusion relation between the corresponding genetic backgrounds. We apply the concept to a large protein fitness landscape for an immunoglobulin-binding protein expressed in Streptococcal bacteria.

PMID:36976406 | DOI:10.1007/s00285-023-01889-6

Breast Cancer Res Treat. 2023 Mar 28. doi: 10.1007/s10549-023-06923-1. Online ahead of print.

ABSTRACT

PURPOSE: Metaplastic breast carcinoma (MBC) is a rare subtype of breast cancer, defined as mammary carcinoma with squamous or mesenchymal differentiation, that may include spindle cell, chondroid, osseous, or rhabdomyoid differentiation patterns. The implications of MBC recurrence and survival outcomes remains unclear.

METHODS: Cases were ascertained from a prospectively maintained institutional database of patients treated from 1998 to 2015. Patients with MBC were matched 1:1 to non-MBC cases. Cox proportional-hazards models and Kaplan-Meier estimates were used to evaluate outcome differences between cohorts.

RESULTS: 111 patients with MBC were matched 1:1 with non-MBC patients from an initial set of 2400 patients. Median follow-up time was 8 years. Most patients with MBC received chemotherapy (88%) and radiotherapy (71%). On univariate competing risk regression, MBC was not associated with locoregional recurrence (HR = 1.08; p = 0.8), distant recurrence (HR = 1.65; p = 0.092); disease-free survival (HR = 1.52; p = 0.065), or overall survival (HR = 1.56; p = 0.1). Absolute differences were noted in 8-year disease-free survival (49.6% MBC vs 66.4% non-MBC) and overall survival (61.3% MBC vs 74.4% non-MBC), though neither of these reached statistical significance (p = 0.07 and 0.11, respectively).

CONCLUSION: Appropriately-treated MBC may exhibit recurrence and survival outcomes that are difficult to distinguish from those of non-MBC. While prior studies suggest that MBC has a worse natural history than non-MBC triple-negative breast cancer, prudent use of chemotherapy and radiotherapy may narrow these differences, although studies with more power will be required to inform clinical management. Longer follow-up among larger populations may further elucidate the clinical and therapeutic implications of MBC.

PMID:36976395 | DOI:10.1007/s10549-023-06923-1

World J Microbiol Biotechnol. 2023 Mar 28;39(5):137. doi: 10.1007/s11274-023-03589-1.

ABSTRACT

Electrical instruments are an integral part of human life resulting in a vast electronic waste generation (74.7 Mt by 2030), threatening human life and the environment due to its hazardous nature. Therefore, proper e-waste management is a necessity. Currently, bio-metallurgy is a sustainable process and an emerging research field. Simultaneous leaching of metals using two groups of indigenous heterotrophs and autotrophs was an exciting work done in this study. Bioleaching experiments using pre-adapted cultures were investigated at three e-waste densities: 5, 10, and 15 g/L. Statistical analysis was done using two-way ANOVA. Copper (93%), zinc (21.5%), and nickel (10.5%) had the highest recovery efficiencies. There was a significant difference between copper, nickel, tin, and zinc concentrations and the bacterial group (P < 0.05); Iron-oxidizing bacteria showed the most weight decrease and recovered 46-47% of total metals, mainly copper and nickel, while sulfur oxidizers were more capable of zinc leaching. The heterotrophs solubilized tin preferably and substantially decreased e-waste weight. Using heterotrophs alongside autotrophs is proposed to promote metal recovery.

PMID:36976392 | DOI:10.1007/s11274-023-03589-1

J Int Med Res. 2023 Mar;51(3):3000605231161204. doi: 10.1177/03000605231161204.

ABSTRACT

OBJECTIVE: To develop a nomogram that discriminates lung cancer from benign lung nodules through metabolic profiling.

METHODS: This was a retrospective cohort study that recruited 848 participants who were randomized into training and validation sets at a 7:3 ratio. Clinical characteristics and metabolic profiles were retrieved. Variables in the training set with statistically significant differences were selected for further least absolute shrinkage and selection operator (LASSO) regression. The nomogram was built from 13 variables identified by stepwise regression analysis. Receiver operating characteristic, calibration curve, and decision curve analyses were conducted to evaluate the performance of the nomogram by internal validation.

RESULTS: Thirteen variables were selected through LASSO regression to build the nomogram: age, sex, ornithine, tyrosine, glutamine, valine, serine, asparagine, arginine, methylmalonylcarnitine, tetradecenoylcarnitine, 3-hydroxyisovaleryl carnitine/2-methyl-3-hydroxybutyrylcarnitine, and hydroxybutyrylcarnitine. The nomogram had good discrimination for the training set, with an area under the curve of 0.836 (95% confidence interval: 0.830-0.890). Moreover, the calibration curve with 1000 bootstrap resamples showed that the predicted value coincided well with the actual value. Decision curve analysis described a net benefit superior to baseline within the threshold probability range of 15% to 93%.

CONCLUSIONS: The nomogram constructed from metabolic profiling accurately predicted risk of lung cancer.

PMID:36974888 | DOI:10.1177/03000605231161204