Nevin Manimala

Phys Med Biol. 2023 Feb 27. doi: 10.1088/1361-6560/acbf9c. Online ahead of print.

ABSTRACT

Non-ergodicity of neuronal dynamics from rapid ion channel gating through the membrane induces membrane displacement statistics that deviate from Brownian motion. The membrane dynamics from ion channel gating were imaged by phase-sensitive optical coherence microscopy. The distribution of optical displacements of the neuronal membrane showed a Lévy-like distribution and the memory effect of the membrane dynamics by the ionic gating was estimated. The alternation of the correlation time was observed when neurons were exposed to channel-blocking molecules. Non-invasive optophysiology by detecting the anomalous diffusion characteristics of dynamic images is demonstrated.

PMID:36848681 | DOI:10.1088/1361-6560/acbf9c

Phys Med Biol. 2023 Feb 27. doi: 10.1088/1361-6560/acbf9a. Online ahead of print.

ABSTRACT

Range uncertainty is a major concern affecting delivery precision in proton therapy. The Compton camera (CC)-based prompt-gamma (PG) imaging is a promising technique to provide 3D in-vivo range verification. However, conventional back-projected PG images suffer from severe distortions due to the limited view of CC, significantly limiting its clinical utility. Deep learning has demonstrated effectiveness in enhancing medical images from limited-view measurements. But different from other medical images with abundant anatomical structures, PGs emitted from a proton pencil beam take up an extremely low portion of the 3D image space, presenting both the attention and the imbalance challenge for deep learning. To solve these issues, we proposed a two-tier deep learning-based method with a novel weighted axis-projection loss to generate precise 3D PG images for proton range verification. This method consists of two models: first, a localization model is trained to define a region-of-interest (ROI) in the distorted back-projected PG image that contains the proton pencil beam; second, an enhancement model is trained to restore the true PG emissions with ROI attention. In this study, we simulated proton pencil beams delivered at clinical dose rates and levels in a tissue-equivalent phantom using Monte-Carlo (MC). PG detection with a CC was simulated using the MC-Plus-Detector-Effects model. Images were reconstructed using kernel-weighted-back-projection algorithm, and were then enhanced by the proposed method. Results demonstrated that the method effectively restored the 3D shape of PGs with proton pencil beam range clearly visible in all testing cases. Range errors were within 2 pixels (4 mm) in all directions in most cases at a dose level of 10^9 protons/beam. The method is fully automatic and nearly real-time. Overall, the preliminary study demonstrated the feasibility of the proposed method to generate accurate 3D PG images, providing a powerful tool for high-precision in-vivo range verification of proton therapy.

PMID:36848674 | DOI:10.1088/1361-6560/acbf9a

J Nat Prod. 2023 Feb 27. doi: 10.1021/acs.jnatprod.2c00788. Online ahead of print.

ABSTRACT

The Aedes aegypti (Diptera: Culicidae) mosquito is the vector of several arboviruses in tropical and subtropical areas of the globe, and synthetic pesticides remain the most widely used combat strategy. This study describes the investigation of secondary metabolites with larvicidal activity from the Malpighiaceae taxon using a metabolomic and bioactivity-based approach. The workflow initially consisted of a larvicidal screening of 394 extracts from the leaves of 197 Malpighiaceae samples, which were extracted using solvents of different polarity, leading to the selection of Heteropterys umbellata for the identification of active compounds. By employing untargeted mass spectrometry-based metabolomics and multivariate analyses (PCA and PLS-DA), it was possible to determine that the metabolic profiles of different plant organs and collection sites differed significantly. A bioguided approach led to the isolation of isochlorogenic acid A (1) and the nitropropanoyl glucosides karakin (2) and 1,2,3,6-tetrakis-O-[3-nitropropanoyl]-beta-glucopyranose (3). These nitro compounds exhibited larvicidal activity, possibly potentialized by synergistic effects of their isomers in chromatographic fractions. Additionally, targeted quantification of the isolated compounds in different extracts corroborated the untargeted results from the statistical analyses. These results support a metabolomic-guided approach in combination with classical phytochemical techniques to search for natural larvicidal compounds for arboviral vector control.

PMID:36848642 | DOI:10.1021/acs.jnatprod.2c00788

JCO Precis Oncol. 2023 Feb;7:e2200606. doi: 10.1200/PO.22.00606.

ABSTRACT

PURPOSE: Adaptive clinical trials use algorithms to predict, during the study, patient outcomes and final study results. These predictions trigger interim decisions, such as early discontinuation of the trial, and can change the course of the study. Poor selection of the Prediction Analyses and Interim Decisions (PAID) plan in an adaptive clinical trial can have negative consequences, including the risk of exposing patients to ineffective or toxic treatments.

METHODS: We present an approach that leverages data sets from completed trials to evaluate and compare candidate PAIDs using interpretable validation metrics. The goal is to determine whether and how to incorporate predictions into major interim decisions in a clinical trial. Candidate PAIDs can differ in several aspects, such as the prediction models used, timing of interim analyses, and potential use of external data sets. To illustrate our approach, we considered a randomized clinical trial in glioblastoma. The study design includes interim futility analyses on the basis of the predictive probability that the final analysis, at the completion of the study, will provide significant evidence of treatment effects. We examined various PAIDs with different levels of complexity to investigate if the use of biomarkers, external data, or novel algorithms improved interim decisions in the glioblastoma clinical trial.

RESULTS: Validation analyses on the basis of completed trials and electronic health records support the selection of algorithms, predictive models, and other aspects of PAIDs for use in adaptive clinical trials. By contrast, PAID evaluations on the basis of arbitrarily defined ad hoc simulation scenarios, which are not tailored to previous clinical data and experience, tend to overvalue complex prediction procedures and produce poor estimates of trial operating characteristics such as power and the number of enrolled patients.

CONCLUSION: Validation analyses on the basis of completed trials and real world data support the selection of predictive models, interim analysis rules, and other aspects of PAIDs in future clinical trials.

PMID:36848613 | DOI:10.1200/PO.22.00606

JCO Precis Oncol. 2023 Feb;7:e2200522. doi: 10.1200/PO.22.00522.

ABSTRACT

PURPOSE: Tumor-infiltrating lymphocytes (TILs) have a significant prognostic value in cancers. However, very few automated, deep learning-based TIL scoring algorithms have been developed for colorectal cancer (CRC).

MATERIALS AND METHODS: We developed an automated, multiscale LinkNet workflow for quantifying TILs at the cellular level in CRC tumors using H&E-stained images from the Lizard data set with annotations of lymphocytes. The predictive performance of the automatic TIL scores (${TIL}_s^{Link}$) for disease progression and overall survival (OS) was evaluated using two international data sets, including 554 patients with CRC from The Cancer Genome Atlas (TCGA) and 1,130 patients with CRC from Molecular and Cellular Oncology (MCO).

RESULTS: The LinkNet model provided outstanding precision (0.9508), recall (0.9185), and overall F1 score (0.9347). Clear continuous TIL-hazard relationships were observed between ${TIL}_s^{Link}$ and the risk of disease progression or death in both TCGA and MCO cohorts. Both univariate and multivariate Cox regression analyses for the TCGA data demonstrated that patients with high TIL abundance had a significant (approximately 75%) reduction in risk for disease progression. In both the MCO and TCGA cohorts, the TIL-high group was significantly associated with improved OS in univariate analysis (30% and 54% reduction in risk, respectively). The favorable effects of high TIL levels were consistently observed in different subgroups (classified according to known risk factors).

CONCLUSION: The proposed deep-learning workflow for automatic TIL quantification on the basis of LinkNet can be a useful tool for CRC. ${TIL}_s^{Link}$ is likely an independent risk factor for disease progression and carries predictive information of disease progression beyond the current clinical risk factors and biomarkers. The prognostic significance of ${TIL}_s^{Link}$ for OS is also evident.

PMID:36848612 | DOI:10.1200/PO.22.00522

Proc Natl Acad Sci U S A. 2023 Mar 7;120(10):e2208268120. doi: 10.1073/pnas.2208268120. Epub 2023 Feb 27.

ABSTRACT

Wildlife trafficking, whether local or transnational in scope, undermines sustainable development efforts, degrades cultural resources, endangers species, erodes the local and global economy, and facilitates the spread of zoonotic diseases. Wildlife trafficking networks (WTNs) occupy a unique gray space in supply chains-straddling licit and illicit networks, supporting legitimate and criminal workforces, and often demonstrating high resilience in their sourcing flexibility and adaptability. Authorities in different sectors desire, but frequently lack knowledge about how to allocate resources to disrupt illicit wildlife supply networks and prevent negative collateral impacts. Novel conceptualizations and a deeper scientific understanding of WTN structures are needed to help unravel the dynamics of interaction between disruption and resilience while accommodating socioenvironmental context. We use the case of ploughshare tortoise trafficking to help illustrate the potential of key advancements in interdisciplinary thinking. Insights herein suggest a significant need and opportunity for scientists to generate new science-based recommendations for WTN-related data collection and analysis for supply chain visibility, shifts in illicit supply chain dominance, network resilience, or limits of the supplier base.

PMID:36848572 | DOI:10.1073/pnas.2208268120

Proc Natl Acad Sci U S A. 2023 Mar 7;120(10):e2220080120. doi: 10.1073/pnas.2220080120. Epub 2023 Feb 27.

ABSTRACT

Here, we combine international air travel passenger data with a standard epidemiological model of the initial 3 mo of the COVID-19 pandemic (January through March 2020; toward the end of which the entire world locked down). Using the information available during this initial phase of the pandemic, our model accurately describes the main features of the actual global development of the pandemic demonstrated by the high degree of coherence between the model and global data. The validated model allows for an exploration of alternative policy efficacies (reducing air travel and/or introducing different degrees of compulsory immigration quarantine upon arrival to a country) in delaying the global spread of SARS-CoV-2 and thus is suggestive of similar efficacy in anticipating the spread of future global disease outbreaks. We show that a lesson from the recent pandemic is that reducing air travel globally is more effective in reducing the global spread than adopting immigration quarantine. Reducing air travel out of a source country has the most important effect regarding the spreading of the disease to the rest of the world. Based upon our results, we propose a digital twin as a further developed tool to inform future pandemic decision-making to inform measures intended to control the spread of disease agents of potential future pandemics. We discuss the design criteria for such a digital twin model as well as the feasibility of obtaining access to the necessary online data on international air travel.

PMID:36848570 | DOI:10.1073/pnas.2220080120

Horm Mol Biol Clin Investig. 2023 Feb 28. doi: 10.1515/hmbci-2022-0045. Online ahead of print.

ABSTRACT

OBJECTIVES: MicroRNA expression disruptions play an important function in the expansion of gastric cancer. Previous investigation has indicated that miR-372-5p doing as an oncogene in several malignancies. CDX1 and CDX2, as target genes of miR-372-5p, play the role of tumor suppressors and oncogenes in gastric cancer cells, respectively. The current investigation explored the effects of miR-372-5p regulation on CDX2 and CDX1 in AGS cell lines and studied their molecular mechanism.

METHODS: hsa-miR-372-5p miRCURY LNA miRNA Inhibitors and Mimic were transfected into AGS cell line. The cell viability and cell cycle calculation were defined by MTT assay and flow cytometry, respectively. The Expression levels of miR-372-5p, CDX1, CDX2 and transfection efficiency were measured using Real-time PCR. Statistical investigation p values <0.05 were considered to be meaningful.

RESULTS: miR-372-5p particularly was upregulated in control cells and also after transfection by mimic. While its expression was reduced by the inhibitor. Upregulation of miR-372-5p remarkably increased cell growth and led to accumulation in the G2/M phase, although the inhibitor decreased cell growth and accumulation in the S phase. Accordingly, upregulation of miR-372-5p increased CDX2 and decreased CDX1 expression. By inhibition of miR-372-5p, expression of CDX2 was decreased and expression of CDX1 was increased.

CONCLUSIONS: Up and down-regulation of miR-372-5P has a potential effect on the expression levels of its target genes, CDX1 and CDX22. Accordingly, the downregulation of miR-372-5p may be assumed as a possible therapeutic target in treating gastric cancer.

PMID:36848481 | DOI:10.1515/hmbci-2022-0045

PLoS One. 2023 Feb 27;18(2):e0282282. doi: 10.1371/journal.pone.0282282. eCollection 2023.

ABSTRACT

INTRODUCTION: In the earlier phase of the pandemic situation, the Government of Bangladesh (GoB) badly suffered to adhere their people to preventive measures probably due to less knowledge and attitude toward Covid-19. To tackle the second wave of coronavirus, the GoB has again enforced an array of preventive measures, but still encountering the same problem after a year of the pandemic situation. As an attempt to find out the reasons behind this, our study aimed to assess the present knowledge and fear level regarding Covid-19, and attitude and practice of students toward Covid-19 preventive measures (CPM).

METHODS: A cross-sectional study was designed and conducted from 15th to 25th April 2021. A total of 382 participants met all the inclusion criteria and were considered for performing all the statistical analyses (Descriptive statistics, Mann-Whitney U test, Kruskal-Wallis H test, Multiple logistic regression, Spearman rank-order correlation).

RESULTS: All the participants were students aged 16 to 30 years. 84.8%, and 22.3% of participants had respectively more accurate knowledge, and moderate to high fear level regarding Covid-19. And, 66%, and 55% of participants had more positive attitude, and more frequent practice toward CPM, respectively. Knowledge, attitude, practice, and fear were interrelated directly or indirectly. It was found knowledgeable participants were more likely to have more positive attitude (AOR = 2.34, 95% CI = 1.23-4.47, P < 0.01) and very little fear (AOR = 2.17, 95% CI = 1.10-4.26, P < 0.05). More positive attitude was found as a good predictor of more frequent practice (AOR = 4.00, 95% CI = 2.44-6.56, P < 0.001), and very less fear had negative impact on both attitude (AOR = 0.44, 95% CI = 0.23-0.84, P < 0.01) and practice (AOR = 0.47, 95% CI = 0.26-0.84, P < 0.01).

CONCLUSIONS: The findings reflect that students had appreciable knowledge and very little fear, but disappointedly had average attitude and practice toward Covid-19 prevention. In addition, students lacked confidence that Bangladesh would win the battle against Covid-19. Thus, based on our study findings we recommend that policymakers should be more focused to scale up students’ confidence and attitude toward CPM by developing and implementing well-conceived plan of actions besides insisting them to practice CPM.

PMID:36848394 | DOI:10.1371/journal.pone.0282282

PLoS One. 2023 Feb 27;18(2):e0281670. doi: 10.1371/journal.pone.0281670. eCollection 2023.

ABSTRACT

The macro policy of the stock market is an important market information. The implementation goal of the macro policy of the stock market is mainly to improve the effectiveness of the stock market. However, whether this effectiveness has achieved the goal is worth verifying through empirical data. The exertion of this information utility is closely related to the effectiveness of the stock market. Use the run test method in statistics to collect and sort out the daily data of stock price index in recent 30 years, the linkage between 75 macro policy events and 35 trading days of market efficiencies before and after the macro event are tested since 1992 to 2022. The results show that 50.66% of the macro policies are positively linked to the effectiveness of the stock market, while 49.34% of the macro policies have reduced the effectiveness of the market operation. This shows that the effectiveness of China’s stock market is not high, and the nonlinear characteristics are obvious, so the policy formulation of the stock market needs further improvement.

PMID:36848368 | DOI:10.1371/journal.pone.0281670