Nevin Manimala

Metallomics. 2022 Jul 22:mfac052. doi: 10.1093/mtomcs/mfac052. Online ahead of print.

ABSTRACT

Breast cancer is the leading cause of cancer death and tremendous efforts are undertaken to limit dissemination and to provide effective treatment. Various histopathological parameters are routinely assessed in breast cancer biopsies to provide valuable diagnostic and prognostic information. MMP-11 and CD45 are tumour associated antigens and potentially valuable biomarkers for grading aggressiveness and metastatic probability. This paper presents methods for quantitative and multiplexed imaging of MMP-11 and CD45 in breast cancer tissues and investigates their potential for improved cancer characterisation and patient stratification. An immunohistochemistry (IHC)-assisted LA-ICP-MS method was successfully developed and optimised using lanthanide tagged monoclonal antibodies as proxies to determine spatial distributions and concentrations of the two breast cancer biomarkers. The labelling degree of antibodies was determined via size exclusion-inductively coupled plasma-tandem mass spectrometry (SEC-ICP-MS/MS) employing on-line calibration via post-column isotope dilution analysis. The calibration of spatial distributions of labelled lanthanides in tissues was performed by ablating mould prepared gelatine standards spiked with element standards. Knowledge of labelling degrees enabled the translation of lanthanide concentrations into biomarkers concentrations. k-means clustering was used to select tissue areas for statistical analysis and mean concentrations were compared for sets of metastatic, non-metastatic and healthy samples. MMP-11 was expressed in stroma surrounding tumour areas, while CD45 was predominantly found inside tumour areas of high cell density. There was no significant correlation between CD45 and metastasis (p = 0.70), however, MMP-11 was significantly upregulated (202%) in metastatic samples compared to non-metastatic (p = 0.0077) and healthy tissues (p = 0.0087).

PMID:35867868 | DOI:10.1093/mtomcs/mfac052

ALTEX. 2022 Jul 15. doi: 10.14573/altex.2205081. Online ahead of print.

ABSTRACT

New approach methodologies (NAMs) are in vitro, in chemico, and in silico or computational approaches that can potentially be used to reduce animal testing. For NAMs that require laboratory experiments, it is critical that they provide consistent and reliable results. While guidance has been provided on improving the reproducibility of NAMs that require laboratory experiments, there is not yet an overarching technical framework that details how to add measurement quality features into a protocol. In this manuscript, we discuss such a framework and provide a step-by step process describing how to refine a protocol using basic quality tools. The steps in this framework include 1) conceptual analysis of sources of technical variability in the assay, 2) within laboratory evaluation of assay performance, 3) statistical data analysis, and 4) determination of method transferability (if needed). While each of these steps has discrete components, they are all inter-related and insights from any step can influence the others. Following the steps in this framework can help reveal the advantages and limitations of different choices during the design of an assay such as which in-process control measurements to include and how many replicates to use for each control measurement and for each test substance. Overall, the use of this technical framework can support optimizing NAM reproducibility, thereby supporting meeting research and regulatory needs.

PMID:35867862 | DOI:10.14573/altex.2205081

J Infect Dis. 2022 Jul 22:jiac311. doi: 10.1093/infdis/jiac311. Online ahead of print.

ABSTRACT

BACKGROUND: A novel human parechovirus 3 Australian recombinant (HPeV3-AR) strain emerged in 2013 and coincided with biennial outbreaks of sepsis-like illnesses in infants. We evaluated the molecular evolution of the HPeV-AR strain and its association with severe HPeV infections.

METHODS: HPeV3-positive samples collected from hospitalized infants aged 5-252 days in two Australian states (2013-2020) and from a community-based birth cohort (2010-2014) were sequenced. Coding regions were used to conduct phylogenetic and evolutionary analyses. A recombinant-specific PCR was designed and utilized to screen all clinical and community HPeV3-positive samples.

RESULTS: Complete coding regions of 54 cases were obtained, which showed the HPeV3-AR strain progressively evolving, particularly in the 3′ end of the non-structural genes. The HPeV3-AR strain was not detected in the community birth cohort until the initial outbreak in late 2013. High-throughput screening showed most (>75%) hospitalized HPeV3 cases involved the AR strain in the first three clinical outbreaks, with declining prevalence in the 2019-20 season. The AR strain was not statistically associated with increased clinical severity amongst hospitalised infants.

DISCUSSION: The HPeV3-AR was the dominant strain during the study period. Increased hospital admissions may have been from a temporary fitness advantage and/or increased virulence.

PMID:35867852 | DOI:10.1093/infdis/jiac311

J Chem Inf Model. 2022 Jul 22. doi: 10.1021/acs.jcim.2c00258. Online ahead of print.

ABSTRACT

Selecting the most appropriate compounds to synthesize and test is a vital aspect of drug discovery. Methods like clustering and diversity present weaknesses in selecting the optimal sets for information gain. Active learning techniques often rely on an initial model and computationally expensive semi-supervised batch selection. Herein, we describe a new subset-based selection method, Coverage Score, that combines Bayesian statistics and information entropy to balance representation and diversity to select a maximally informative subset. Coverage Score can be influenced by prior selections and desirable properties. In this paper, subsets selected through Coverage Score are compared against subsets selected through model-independent and model-dependent techniques for several datasets. In drug-like chemical space, Coverage Score consistently selects subsets that lead to more accurate predictions compared to other selection methods. Subsets selected through Coverage Score produced Random Forest models that have a root-mean-square-error up to 12.8% lower than subsets selected at random and can retain up to 99% of the structural dissimilarity of a diversity selection.

PMID:35867814 | DOI:10.1021/acs.jcim.2c00258

Cytopathology. 2022 Jul 22. doi: 10.1111/cyt.13170. Online ahead of print.

ABSTRACT

OBJECTIVES: To determine the prevalence of cellular debris (CD) on benign cervicovaginal liquid-based cytology (LBC) smears and which factors predict the presence and larger amount of CD.

METHODS: Cervicovaginal smears evaluated as negative for intraepithelial lesion or malignancy (NILM) between January 1st and March 31st, 2020, were retrospectively reviewed to record the presence and amount of CD. All smears were prepared with the SurePath platform. Patients’ age and past medical and surgical histories were also retrieved. Multivariate regression analyses were performed to find positive predictors of a larger amount of CD.

RESULTS: 349 NILM smears were included in this study. The cohort consisted of 222 cervical smears (CS) and 127 vaginal smears (VS), which were taken from patients who had undergone hysterectomy. Overall, CD was observed in 111 (31.8%) cases. The positive predictors of CD were increasing age, specimen type (VS compared to CS), history of chemotherapy or radiation therapy (CRT), and more than mild background inflmmation. Among VS group, CD was present in 64 cases (50.4%) regardless of the time between the specimen collection and hysterectomy. Positive predictors in the VS group were age and more than mild inflammation. Contrary, in the CS group, the prevalence of CD was 21.2%, and age was the only positive predictor. Histories of CRT, conization and inflammation were not statistically significant positive predictors for CD among CS.

CONCLUSIONS: CD can be seen in as much as 50% of NILM smears taken after hysterectomy, regardless of the time since hysterectomy. Increasing age is a positive predictor of the presence and a larger quantity of CD. These findings are helpful when evaluating smears with moderate to abundant debris in the background with questionable cellular atypia.

PMID:35867812 | DOI:10.1111/cyt.13170

Proc Natl Acad Sci U S A. 2022 Jul 12;119(28):e2204761119. doi: 10.1073/pnas.2204761119. Epub 2022 Jul 7.

ABSTRACT

It is established that changes in sea level influence melt production at midocean ridges, but whether changes in melt production influence the pattern of bathymetry flanking midocean ridges has been debated on both theoretical and empirical grounds. To explore the dynamics that may give rise to a sea-level influence on bathymetry, we simulate abyssal hills using a faulting model with periodic variations in melt supply. For 100-ky melt-supply cycles, model results show that faults initiate during periods of amagmatic spreading at half-rates >2.3 cm/y and for 41-ky melt-supply cycles at half-rates >3.8 cm/y. Analysis of bathymetry across 17 midocean ridge regions shows characteristic wavelengths that closely align with the predictions from the faulting model. At intermediate-spreading ridges (half-rates >2.3 cm/y and [Formula: see text]3.8 cm/y) abyssal hill spacing increases with spreading rate at 0.99 km/(cm/y) or 99 ky (n [Formula: see text] 12; 95% CI, 87 to 110 ky), and at fast-spreading ridges (half-rates >3.8 cm/y) spacing increases at 38 ky (n [Formula: see text] 5; 95% CI, 29 to 47 ky). Including previously published analyses of abyssal-hill spacing gives a more precise alignment with the primary periods of Pleistocene sea-level variability. Furthermore, analysis of bathymetry from fast-spreading ridges shows a highly statistically significant spectral peak (P < 0.01) at the 1/(41-ky) period of Earth’s variations in axial tilt. Faulting models and observations both support a linkage between glacially induced sea-level change and the fabric of the sea floor over the late Pleistocene.

PMID:35867751 | DOI:10.1073/pnas.2204761119

Proc Natl Acad Sci U S A. 2022 Jul 12;119(28):e2204074119. doi: 10.1073/pnas.2204074119. Epub 2022 Jul 7.

ABSTRACT

Massive scientific productivity accompanied the COVID-19 pandemic. We evaluated the citation impact of COVID-19 publications relative to all scientific work published in 2020 to 2021 and assessed the impact on scientist citation profiles. Using Scopus data until August 1, 2021, COVID-19 items accounted for 4% of papers published, 20% of citations received to papers published in 2020 to 2021, and >30% of citations received in 36 of the 174 disciplines of science (up to 79.3% in general and internal medicine). Across science, 98 of the 100 most-cited papers published in 2020 to 2021 were related to COVID-19; 110 scientists received ≥10,000 citations for COVID-19 work, but none received ≥10,000 citations for non-COVID-19 work published in 2020 to 2021. For many scientists, citations to their COVID-19 work already accounted for more than half of their total career citation count. Overall, these data show a strong covidization of research citations across science, with major impact on shaping the citation elite.

PMID:35867747 | DOI:10.1073/pnas.2204074119

Proc Natl Acad Sci U S A. 2022 Jul 12;119(28):e2201250119. doi: 10.1073/pnas.2201250119. Epub 2022 Jul 6.

ABSTRACT

Phase separation has emerged as an essential concept for the spatial organization inside biological cells. However, despite the clear relevance to virtually all physiological functions, we understand surprisingly little about what phases form in a system of many interacting components, like in cells. Here we introduce a numerical method based on physical relaxation dynamics to study the coexisting phases in such systems. We use our approach to optimize interactions between components, similar to how evolution might have optimized the interactions of proteins. These evolved interactions robustly lead to a defined number of phases, despite substantial uncertainties in the initial composition, while random or designed interactions perform much worse. Moreover, the optimized interactions are robust to perturbations, and they allow fast adaption to new target phase counts. We thus show that genetically encoded interactions of proteins provide versatile control of phase behavior. The phases forming in our system are also a concrete example of a robust emergent property that does not rely on fine-tuning the parameters of individual constituents.

PMID:35867744 | DOI:10.1073/pnas.2201250119

PLoS One. 2022 Jul 22;17(7):e0271539. doi: 10.1371/journal.pone.0271539. eCollection 2022.

ABSTRACT

Considering the vast biological diversity and high mortality rate in high-grade ovarian cancers, identification of novel biomarkers, enabling precise diagnosis and effective, less aggravating treatment, is of paramount importance. Based on scientific literature data, we selected 80 cancer-related genes and evaluated their mRNA expression in 70 high-grade serous ovarian cancer (HGSOC) samples by Real-Time qPCR. The results were validated in an independent Northern American cohort of 85 HGSOC patients with publicly available NGS RNA-seq data. Detailed statistical analyses of our cohort with multivariate Cox and logistic regression models considering clinico-pathological data and different TP53 mutation statuses, revealed an altered expression of 49 genes to affect the prognosis and/or treatment response. Next, these genes were investigated in the validation cohort, to confirm the clinical significance of their expression alterations, and to identify genetic variants with an expected high or moderate impact on their products. The expression changes of five genes, PROM1, CXCL8, RUNX1, NAV1, TP73, were found to predict prognosis or response to treatment in both cohorts, depending on the TP53 mutation status. In addition, we revealed novel and confirmed known SNPs in these genes, and showed that SNPs in the PROM1 gene correlated with its elevated expression.

PMID:35867729 | DOI:10.1371/journal.pone.0271539

PLoS One. 2022 Jul 22;17(7):e0271677. doi: 10.1371/journal.pone.0271677. eCollection 2022.

ABSTRACT

Soil disinfestation with steam was evaluated as an alternative to fumigation. Following soil disinfestation, plant health has traditionally been measured using plant size and yield. Plant health can be measured in a timely manner more efficiently, more easily and non-destructively using image analysis. We hypothesized that plant health could be quantified and treatments can be differentiated using an RGB (Red, Green, Blue) image analysis program, particularly by observing the greenness of plant leaves. However, plant size or the proportion of green area could be unreliable due to plant loss and camera’s position and angle. To this end, we decided to evaluate plant health by analyzing the RGB codes associated with the green color only, which detects the chlorophyll reflectance and nutrient status, noting that the degree of greenness within the green-leaf-area was not affected by the plant size. We identified five RGB codes that are commonly observed in the plant leaves and ordered them from dark green to light green. Among the five RGB codes, the relative percentage covered by the darkest green to the lightest green was significantly different between the steam and chloropicrin treatments and the control, and it was not significantly different between the steam and chloropicrin treatments. Furthermore, the result was correlated with the total yield, and the trend observed in the first year was replicated in the second year of this experiment. In this study, we demonstrate that the RGB image analysis can be used as an early marker of the treatment effect on the plant health and productivity.

PMID:35867725 | DOI:10.1371/journal.pone.0271677