Nevin Manimala

J Clin Oncol. 2022 Jun 22:JCO2102543. doi: 10.1200/JCO.21.02543. Online ahead of print.

ABSTRACT

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the basis of the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.Anaplastic oligodendroglial tumors (AOTs) are chemotherapy-sensitive brain tumors. We report the final very long-term survival results from European Organization for the Research and Treatment of Cancer 26951 and Radiation Therapy Oncology Group 9402 phase III trials initiated in 1990s, which both studied radiotherapy with/without neo/adjuvant procarbazine, lomustine, and vincristine (PCV) for newly diagnosed anaplastic oligodendroglial tumors. The median follow-up duration in both was 18-19 years. For European Organization for the Research and Treatment of Cancer 26951, median, 14-year, and probable 20-year overall survival rates without versus with PCV were 2.6 years, 13.4%, and 10.1% versus 3.5 years, 25.1%, and 16.8% (N = 368 overall; hazard ratio [HR] 0.78; 95% CI, 0.63 to 0.98; P = .033), with 1p19q codeletion 9.3 years, 26.2%, and 13.6% versus 14.2 years, 51.0%, and 37.1% (n = 80; HR 0.60; 95% CI, 0.35 to 1.03; P = .063), respectively. For Radiation Therapy Oncology Group 9402, analogous results were 4.8 years, 16.5%, and 11.2% versus 4.8 years, 29.1%, and 24.6% (N = 289 overall; HR 0.79; 95% CI, 0.61 to 1.03; P = .08), with codeletion 7.3 years, 25.0%, and 14.9% versus 13.2 years, 46.1%, and 37% (n = 125; HR 0.61; 95% CI, 0.40 to 0.94; P = .02), respectively. With that, the studies show similar long-term survival even without tumor recurrence in a significant proportion of patients after first-line treatment with radiotherapy/PCV.

PMID:35731991 | DOI:10.1200/JCO.21.02543

Biometrics. 2022 Jun 22. doi: 10.1111/biom.13708. Online ahead of print.

ABSTRACT

A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high-resolution, high-quality magnetic resonance images (MRI). These methods, however, may not translate to low-resolution images acquired on MRI scanners with lower magnetic field strength. In low-resource settings where low-field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image-based biomarkers, inspired by a project in which children with cerebral malaria were imaged using low-field 0.35 Tesla MRI. We integrate multi-atlas label fusion, which leverages high-resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray-to-white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due to cerebral malaria. This article is protected by copyright. All rights reserved.

PMID:35731973 | DOI:10.1111/biom.13708

ACS Appl Mater Interfaces. 2022 Jun 22. doi: 10.1021/acsami.2c01460. Online ahead of print.

ABSTRACT

Conductive atomic force microscopy (C-AFM) is widely used to determine the electronic conductivity of a sample surface with nanoscale spatial resolution. However, the origin of possible artifacts has not been widely researched, hindering the accurate and reliable interpretation of C-AFM imaging results. Herein, artifact-free C-AFM is used to observe the electron conduction channels in Si-based composite anodes. The origin of a typical C-AFM artifact induced by surface morphology is investigated using a relevant statistical method that enables visualization of the contribution of artifacts in each C-AFM image. The artifact is suppressed by polishing the sample surface using a cooling cross-section polisher, which is confirmed by Pearson correlation analysis. The artifact-free C-AFM image was used to compare the current signals (before and after cycling) from two different composite anodes comprising single-walled carbon nanotubes (SWCNTs) and carbon black as conductive additives. The relationship between the electrical degradation and morphological evolution of the active materials depending on the conductive additive is discussed to explain the improved electrical and electrochemical properties of the electrode containing SWCNTs.

PMID:35731963 | DOI:10.1021/acsami.2c01460

J Perinat Med. 2022 Jun 22. doi: 10.1515/jpm-2022-0227. Online ahead of print.

ABSTRACT

OBJECTIVES: Despite the use of post-mortem investigations, approximately 20% of stillbirths remain unexplained. Cardiac ion channelopathies have been identified as a cause of death in Sudden Infant Death Syndrome (SIDS) and could be associated with unexplained stillbirths. This study aimed to understand if the expression or localisation of cardiac ion channels associated with channelopathies were altered in cases of unexplained stillbirths.

METHODS: A case control study was conducted using formalin-fixed cardiac tissue from 20 cases of unexplained stillbirth and a control group of 20 cases of stillbirths from intrapartum hypoxia. 4 µm tissue sections were stained using haematoxylin and eosin, Masson’s trichrome (MT) and Elastic van Gieson (EVG). Immunohistochemistry (IHC) was performed using antibodies against CACNA1G, KCNJ2, KCNQ1, KCNH2 and KCNE1. The cardiac conduction system in samples stained with MT and EVG could not be identified. Therefore, the levels of immunoperoxidase staining were quantified using QuPath software.

RESULTS: The nuclear-cytoplasmic ratio of sections stained with haematoxylin and eosin was higher for the hypoxia group (hypoxia median 0.13 vs. 0.04 unexplained, p < 0.001). CACNA1G (unexplained median 0.26 vs. hypoxia 0.30, p=0.009) and KCNJ2 (unexplained median 0.35 vs. hypoxia 0.41, p=0.001) had lower staining intensity in the unexplained stillbirth group. There were no statistically significant differences in the staining intensity of KCNQ1, KCNH2 and KCNE1.

CONCLUSIONS: Two ion channels associated with channelopathies demonstrated lower levels of expression in cases of unexplained stillbirth. Further genetic studies using human tissue should be performed to understand the association between channelopathies and otherwise unexplained stillbirths.

PMID:35731905 | DOI:10.1515/jpm-2022-0227

PLoS Biol. 2022 Jun 22;20(6):e3001685. doi: 10.1371/journal.pbio.3001685. Online ahead of print.

ABSTRACT

Historically, emerging and reemerging infectious diseases have caused large, deadly, and expensive multinational outbreaks. Often outbreak investigations aim to identify who infected whom by reconstructing the outbreak transmission tree, which visualizes transmission between individuals as a network with nodes representing individuals and branches representing transmission from person to person. We compiled a database, called OutbreakTrees, of 382 published, standardized transmission trees consisting of 16 directly transmitted diseases ranging in size from 2 to 286 cases. For each tree and disease, we calculated several key statistics, such as tree size, average number of secondary infections, the dispersion parameter, and the proportion of cases considered superspreaders, and examined how these statistics varied over the course of each outbreak and under different assumptions about the completeness of outbreak investigations. We demonstrated the potential utility of the database through 2 short analyses addressing questions about superspreader epidemiology for a variety of diseases, including Coronavirus Disease 2019 (COVID-19). First, we found that our transmission trees were consistent with theory predicting that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events. Additionally, we investigated patterns in how superspreaders are infected. Across trees with more than 1 superspreader, we found preliminary support for the theory that superspreaders generate other superspreaders. In sum, our findings put the role of superspreading in COVID-19 transmission in perspective with that of other diseases and suggest an approach to further research regarding the generation of superspreaders. These data have been made openly available to encourage reuse and further scientific inquiry.

PMID:35731837 | DOI:10.1371/journal.pbio.3001685

PLoS One. 2022 Jun 22;17(6):e0270102. doi: 10.1371/journal.pone.0270102. eCollection 2022.

ABSTRACT

BACKGROUND: Polycystic ovary syndrome (PCOS) is a common female reproductive endocrine problem worldwide. The prevalence of mental disorder is increasing among PCOS patients due to various physical, psychological, and social issues. Here we aimed to evaluate the mental health and associated factors among women suffering from PCOS in Bangladesh.

METHODS: We performed an online cross-sectional survey among 409 participants with PCOS using Google Forms. We used structured questionnaires to collect socio-demographic information and lifestyle-related factors. Also, we applied patient health questionnaire (PHQ-9), generalized anxiety disorder (GAD-7) scale, and UCLA loneliness (UCLA-3) scale for psychometric assessment of the participants. Finally, we applied several statistical tools and performed data interpretations to evaluate the prevalence of mental health disorders and associated factors among patients with PCOS in Bangladesh.

RESULTS: Prevalence of loneliness, generalized anxiety disorder and depressive illness among the women with PCOS were 71%, 88%, and 60%, respectively. Among the mental illness, mild, moderate, and severe cases were 39%, 18%, and 14% for loneliness; 39%, 23% and 26% for generalized anxiety disorder; and 35%, 18%, and 7% for depressive disorder. According to the present findings, obesity, financial condition, physical exercise, mealtime, food habit, daily water consumption, birth control method, and long-term oral contraceptive pills contribute to developing mental health disorders among females with PCOS in Bangladesh.

CONCLUSION: According to present study results, high proportion of women suffering from PCOS experience several mental disorders in Bangladesh. Although several socio-demographic and lifestyle-related factors were found to be associated with the poor mental health of women with PCOS; however, PCOS itself is a condition that favors poor physical and psychological health. Therefore, we recommend proper treatment, public awareness, and a healthy lifestyle to promote the good mental health of women suffering from PCOS.

PMID:35731829 | DOI:10.1371/journal.pone.0270102

PLoS One. 2022 Jun 22;17(6):e0269790. doi: 10.1371/journal.pone.0269790. eCollection 2022.

ABSTRACT

Tourism and outdoor leisure is an important economic sector for many countries, and has a substantial reliance on natural capital. Natural capital may be the primary purpose for tourism, or it may be a secondary factor, where the choice of location for a leisure activity is influenced by natural capital. Typically, when valuing tourism and outdoor leisure, all expenditure associated with the activity is assigned to the ecosystem it occurs in. However, this value illustrates the dependency on natural capital, rather than the contribution of natural capital. In natural capital accounting, a major challenge is to separately identify the contribution of natural capital from that of other forms of capital. In this study we develop a transparent and repeatable method that is able to attribute the contribution of natural capital (here defined as ecosystems) to the output of multiple tourism and outdoor leisure activities. Using national statistics from Great Britain, we calculate the natural capital contribution to tourism spend by activity at a national and regional scale, and for a case study map and value the contributing ecosystems. We estimated that, out of a total £36 billion spent on tourism and leisure activities in 2017, £22.5 billion was attributable to natural capital. This equates to 0.9% of the UK GDP. The Gross Value Added component of this attributable was £10.5 billion, equivalent to 0.4% of the UK GDP. Regions with the highest natural capital contribution in Great Britain were Scotland and Wales, with the lowest being Greater London and the West Midlands in England. For the case study, the ecosystems with the greatest contribution to terrestrial activities were marine and enclosed farmland. These methods can be applied worldwide for anywhere with aggregate economic statistics on expenditure associated with tourism and outdoor leisure, with the aid of open source GIS datasets.

PMID:35731823 | DOI:10.1371/journal.pone.0269790

PLoS Comput Biol. 2022 Jun 22;18(6):e1010182. doi: 10.1371/journal.pcbi.1010182. Online ahead of print.

ABSTRACT

Internal models capture the regularities of the environment and are central to understanding how humans adapt to environmental statistics. In general, the correct internal model is unknown to observers, instead they rely on an approximate model that is continually adapted throughout learning. However, experimenters assume an ideal observer model, which captures stimulus structure but ignores the diverging hypotheses that humans form during learning. We combine non-parametric Bayesian methods and probabilistic programming to infer rich and dynamic individualised internal models from response times. We demonstrate that the approach is capable of characterizing the discrepancy between the internal model maintained by individuals and the ideal observer model and to track the evolution of the contribution of the ideal observer model to the internal model throughout training. In particular, in an implicit visuomotor sequence learning task the identified discrepancy revealed an inductive bias that was consistent across individuals but varied in strength and persistence.

PMID:35731822 | DOI:10.1371/journal.pcbi.1010182

PLoS Comput Biol. 2022 Jun 22;18(6):e1009414. doi: 10.1371/journal.pcbi.1009414. Online ahead of print.

ABSTRACT

Gene expression is controlled by pathways of regulatory factors often involving the activity of protein kinases on transcription factor proteins. Despite this well established mechanism, the number of well described pathways that include the regulatory role of protein kinases on transcription factors is surprisingly scarce in eukaryotes. To address this, PhosTF was developed to infer functional regulatory interactions and pathways in both simulated and real biological networks, based on linear cyclic causal models with latent variables. GeneNetWeaverPhos, an extension of GeneNetWeaver, was developed to allow the simulation of perturbations in known networks that included the activity of protein kinases and phosphatases on gene regulation. Over 2000 genome-wide gene expression profiles, where the loss or gain of regulatory genes could be observed to perturb gene regulation, were then used to infer the existence of regulatory interactions, and their mode of regulation in the budding yeast Saccharomyces cerevisiae. Despite the additional complexity, our inference performed comparably to the best methods that inferred transcription factor regulation assessed in the DREAM4 challenge on similar simulated networks. Inference on integrated genome-scale data sets for yeast identified ∼ 8800 protein kinase/phosphatase-transcription factor interactions and ∼ 6500 interactions among protein kinases and/or phosphatases. Both types of regulatory predictions captured statistically significant numbers of known interactions of their type. Surprisingly, kinases and phosphatases regulated transcription factors by a negative mode or regulation (deactivation) in over 70% of the predictions.

PMID:35731801 | DOI:10.1371/journal.pcbi.1009414

PLoS One. 2022 Jun 22;17(6):e0270254. doi: 10.1371/journal.pone.0270254. eCollection 2022.

ABSTRACT

This study aims to investigate the supply chain challenges of public sector agriculture development projects in Bangladesh using the modified Delphi, Best Worst Method (BWM), and Interpretive Structural Modelling (ISM) methods. Based on these three widely acclaimed statistical techniques, the study identified, ranked, and identified interrelationships among the challenges. The study is unique not only in terms of research findings, but also in terms of methodology, as it is the first to use the three MCDM (Multicriteria Decision Making) tools to examine supply chain issues in public sector agriculture development projects in a developing country context. A literature review and two modified Delphi rounds with 15 industry experts’ opinions were applied to identify and validate a list of 11 key supply chain challenges. To determine the priority of the challenges, a panel of eight industry experts was consulted, and their responses were analysed using the BWM. Then, another group of 10 experts was consulted using ISM to investigate the contextual relationships among the challenges, resulting in a five-layered Interpretive Structural Model (ISM) and MICMAC (cross-impact matrix multiplication applied to classification) analysis of the challenges. According to relative importance (global weights), “improper procurement planning (0.213), “delay in project initiation (0.177), “demand forecasting error (0.146)”, “lack of contract monitoring mechanism (0.097)”, and “lack of competent staff (0.095)” are the top five ranked key challenges that have a significant impact on the project supply chain. Regarding contextual relationships, the ISM model and ISM-MICMAC analysis identified the “political influence” challenge as the most influential, and also independent of the other challenges. The findings are critical for project managers in managing challenges because understanding both relative importance and contextual relationships are required to address the challenges holistically. Additionally, these findings will benefit policymakers, academics, and future researchers.

PMID:35731792 | DOI:10.1371/journal.pone.0270254