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Nevin Manimala Statistics

Virulence and Biofilm Inhibition of 3-Methoxycinnamic Acid against Agrobacterium tumefaciens

J Appl Microbiol. 2022 Aug 11. doi: 10.1111/jam.15774. Online ahead of print.

ABSTRACT

AIMS: In the current study the anti-virulence and anti-biofilm activities of the cinnamic acid derivative, 3-methoxycinnamic acid, was investigated against Agrobacterium tumefaciens.

METHODS AND RESULTS: Based on the disc diffusion test and β-galactosidase activity assay, 3-methoxycinnamic acid was shown to interfere with the quorum sensing (QS) system of A. tumefaciens. Crystal violet staining assay, phenol-sulfuric acid method, Bradford protein assay and confocal laser scanning microscopy (CLSM) revealed that the biofilm formation of A. tumefaciens was inhibited after the treatment of 3-methoxycinnamic acid. Employing high performance liquid chromatography (HPLC) analysis of culture supernatant revealed that the production of 3-oxo-octanoylhomoserine lactone (3-oxo-C8-HSL) decreased concentration-dependently after treatment with 3-methoxycinnamic acid. Swimming and chemotaxis assays also indicated that 3-methoxycinnamic acid had a good effect on reducing the motility and chemotaxis of A. tumefaciens. In addition, the RT-qPCR, molecular docking and simulations further demonstrated that 3-methoxycinnamic acid could competitively inhibit the binding of 3-oxo-C8-HSL to TraR and down-regulate virulence-related genes.

CONCLUSIONS: 3-Methoxycinnamic acid is proved to have good anti-virulence and anti-biofilm activities against A. tumefaciens.

SIGNIFICANCE AND IMPACT OF THE STUDY: This is the first study that investigates the anti-virulence and anti-biofilm activities of 3-methoxycinnamic acid against A. tumefaciens. With its potential QS-related virulence and biofilm inhibitory activities, 3-methoxycinnamic acid is expected to be developed as a potent pesticide or adjuvant for the prevention and treatment of crown gall caused by A. tumefaciens.

PMID:35951737 | DOI:10.1111/jam.15774

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Nevin Manimala Statistics

Nested epistasis enhancer networks for robust genome regulation

Science. 2022 Aug 11:eabk3512. doi: 10.1126/science.abk3512. Online ahead of print.

ABSTRACT

Mammalian genomes possess multiple enhancers spanning an ultralong distance (>megabases) to modulate important genes, yet it is unclear how these enhancers coordinate to achieve this task. Here, we combine multiplexed CRISPRi screening with machine learning to define quantitative enhancer-enhancer interactions. We find that the ultralong distance enhancer network possesses a nested multi-layer architecture that confers functional robustness of gene expression. Experimental characterization reveals that enhancer epistasis is maintained by three-dimensional chromosomal interactions and BRD4 condensation. Machine learning prediction of synergistic enhancers provides an effective strategy to identify non-coding variant pairs associated with pathogenic genes in diseases beyond Genome-Wide Association Studies (GWAS) analysis. Our work unveils nested epistasis enhancer networks, which can better explain enhancer functions within cells and in diseases.

PMID:35951677 | DOI:10.1126/science.abk3512

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Nevin Manimala Statistics

Color Vision and Microperimetry Changes in Nonexudative Age-Related Macular Degeneration After Risuteganib Treatment: Exploratory Endpoints in a Multicenter Phase 2a Double-Masked, Randomized, Sham-Controlled, Crossover Clinical Trial

Ophthalmic Surg Lasers Imaging Retina. 2022 Aug;53(8):430-438. doi: 10.3928/23258160-20220725-02. Epub 2022 Aug 1.

ABSTRACT

BACKGROUND AND OBJECTIVE: To explore the association between best-corrected visual acuity (BCVA) improvement and changes in microperimetry (MP) and color vision in patients with nonexudative age-related macular degeneration following administration of two 1.0-mg intravitreal doses of risuteganib.

PATIENTS AND METHODS: In a phase 2a, prospective, double-masked, sham-controlled study, eyes with nonexudative age-related macular degeneration and Early Treatment Diabetic Retinopathy Study BCVA between 20/40 and 20/200 were randomized to intravitreal risuteganib (1.0 mg) or sham injection. The risuteganib group received a second 1.0-mg dose, and patients in the sham group crossed over to receive 1.0 mg of risuteganib at week 16. Exploratory endpoints included changes in color vision and mesopic MP.

RESULTS: Thirty-nine patients (risuteganib, n = 25; sham, n = 14) completed the study. There was a significant (P < .05) correlation between BCVA and the total error score (TES) for both Lanthony and Hue Style. Confusion index was close to the criterion for significance (P = .056) in the risuteganib group. All color vision metrics demonstrated a trend toward improvement in risuteganib responders (BCVA letter gain ≥8 letters) and no change in the nonresponders, with significant differences seen in confusion index between the risuteganib and control group (P = .0493) and between responders and nonresponders (P = .0478). MP showed that risuteganib responders improved in mean sensitivity and change in number of loci ≤11 dB and ≤0 dB, whereas nonresponders worsened.

CONCLUSION: All color vision and MP parameters tested trended toward improvement in risuteganib-treated patients and risuteganib responders. Statistically significant improvement was evident in two metrics: confusion index (in risuteganib-treated patients and responders) and number of loci with decreased sensitivity (in responders). A significant correlation between BCVA and both TES Lanthony and TES Hue Style in risuteganib patients provides concurrent evidence of objective and subjective improvement of retinal function. [Ophthalmic Surg Lasers Imaging Retina 2022;53:430-438.].

PMID:35951718 | DOI:10.3928/23258160-20220725-02

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Nevin Manimala Statistics

Multi-mode movement decisions across widely ranging behavioral processes

PLoS One. 2022 Aug 11;17(8):e0272538. doi: 10.1371/journal.pone.0272538. eCollection 2022.

ABSTRACT

Movement of organisms plays a fundamental role in the evolution and diversity of life. Animals typically move at an irregular pace over time and space, alternating among movement states. Understanding movement decisions and developing mechanistic models of animal distribution dynamics can thus be contingent to adequate discrimination of behavioral phases. Existing methods to disentangle movement states typically require a follow-up analysis to identify state-dependent drivers of animal movement, which overlooks statistical uncertainty that comes with the state delineation process. Here, we developed population-level, multi-state step selection functions (HMM-SSF) that can identify simultaneously the different behavioral bouts and the specific underlying behavior-habitat relationship. Using simulated data and relocation data from mule deer (Odocoileus hemionus), plains bison (Bison bison bison) and plains zebra (Equus quagga), we illustrated the HMM-SSF robustness, versatility, and predictive ability for animals involved in distinct behavioral processes: foraging, migrating and avoiding a nearby predator. Individuals displayed different habitat selection pattern during the encamped and the travelling phase. Some landscape attributes switched from being selected to avoided, depending on the movement phase. We further showed that HMM-SSF can detect multi-modes of movement triggered by predators, with prey switching to the travelling phase when predators are in close vicinity. HMM-SSFs thus can be used to gain a mechanistic understanding of how animals use their environment in relation to the complex interplay between their needs to move, their knowledge of the environment and navigation capacity, their motion capacity and the external factors related to landscape heterogeneity.

PMID:35951664 | DOI:10.1371/journal.pone.0272538

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Nevin Manimala Statistics

Boosting k-means clustering with symbiotic organisms search for automatic clustering problems

PLoS One. 2022 Aug 11;17(8):e0272861. doi: 10.1371/journal.pone.0272861. eCollection 2022.

ABSTRACT

Kmeans clustering algorithm is an iterative unsupervised learning algorithm that tries to partition the given dataset into k pre-defined distinct non-overlapping clusters where each data point belongs to only one group. However, its performance is affected by its sensitivity to the initial cluster centroids with the possibility of convergence into local optimum and specification of cluster number as the input parameter. Recently, the hybridization of metaheuristics algorithms with the K-Means algorithm has been explored to address these problems and effectively improve the algorithm’s performance. Nonetheless, most metaheuristics algorithms require rigorous parameter tunning to achieve an optimum result. This paper proposes a hybrid clustering method that combines the well-known symbiotic organisms search algorithm with K-Means using the SOS as a global search metaheuristic for generating the optimum initial cluster centroids for the K-Means. The SOS algorithm is more of a parameter-free metaheuristic with excellent search quality that only requires initialising a single control parameter. The performance of the proposed algorithm is investigated by comparing it with the classical SOS, classical K-means and other existing hybrids clustering algorithms on eleven (11) UCI Machine Learning Repository datasets and one artificial dataset. The results from the extensive computational experimentation show improved performance of the hybrid SOSK-Means for solving automatic clustering compared to the standard K-Means, symbiotic organisms search clustering methods and other hybrid clustering approaches.

PMID:35951672 | DOI:10.1371/journal.pone.0272861

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Nevin Manimala Statistics

Biological sample donation and informed consent for neurobiobanking: Evidence from a community survey in Ghana and Nigeria

PLoS One. 2022 Aug 11;17(8):e0267705. doi: 10.1371/journal.pone.0267705. eCollection 2022.

ABSTRACT

INTRODUCTION: Genomic research and neurobiobanking are expanding globally. Empirical evidence on the level of awareness and willingness to donate/share biological samples towards the expansion of neurobiobanking in sub-Saharan Africa is lacking.

AIMS: To ascertain the awareness, perspectives and predictors regarding biological sample donation, sharing and informed consent preferences among community members in Ghana and Nigeria.

METHODS: A questionnaire cross-sectional survey was conducted among randomly selected community members from seven communities in Ghana and Nigeria.

RESULTS: Of the 1015 respondents with mean age 39.3 years (SD 19.5), about a third had heard of blood donation (37.2%, M: 42.4%, F: 32.0%, p = 0.001) and a quarter were aware of blood sample storage for research (24.5%; M: 29.7%, F: 19.4%, p = 0.151). Two out of ten were willing to donate brain after death (18.8%, M: 22.6%, F: 15.0%, p<0.001). Main reasons for unwillingness to donate brain were; to go back to God complete (46.6%) and lack of knowledge related to brain donation (32.7%). Only a third of the participants were aware of informed consent (31.7%; M: 35.9%, F: 27.5%, p<0.001). Predictors of positive attitude towards biobanking and informed consent were being married, tertiary level education, student status, and belonging to select ethnic groups.

CONCLUSION: There is a greater need for research attention in the area of brain banking and informed consent. Improved context-sensitive public education on neurobiobanking and informed consent, in line with the sociocultural diversities, is recommended within the African sub region.

PMID:35951660 | DOI:10.1371/journal.pone.0267705

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Nevin Manimala Statistics

Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer’s disease drug repurposing

PLoS Comput Biol. 2022 Aug 11;18(8):e1010367. doi: 10.1371/journal.pcbi.1010367. Online ahead of print.

ABSTRACT

Predictive modeling of drug-induced gene expressions is a powerful tool for phenotype-based compound screening and drug repurposing. State-of-the-art machine learning methods use a small number of fixed cell lines as a surrogate for predicting actual expressions in a new cell type or tissue, although it is well known that drug responses depend on a cellular context. Thus, the existing approach has limitations when applied to personalized medicine, especially for many understudied diseases whose molecular profiles are dramatically different from those characterized in the training data. Besides the gene expression, dose-dependent cell viability is another important phenotype readout and is more informative than conventional summary statistics (e.g., IC50) for characterizing clinical drug efficacy and toxicity. However, few computational methods can reliably predict the dose-dependent cell viability. To address the challenges mentioned above, we designed a new deep learning model, MultiDCP, to predict cellular context-dependent gene expressions and cell viability on a specific dosage. The novelties of MultiDCP include a knowledge-driven gene expression profile transformer that enables context-specific phenotypic response predictions of novel cells or tissues, integration of multiple diverse labeled and unlabeled omics data, the joint training of the multiple prediction tasks, and a teacher-student training procedure that allows us to utilize unreliable data effectively. Comprehensive benchmark studies suggest that MultiDCP outperforms state-of-the-art methods with unseen cell lines that are dissimilar from the cell lines in the supervised training in terms of gene expressions. The predicted drug-induced gene expressions demonstrate a stronger predictive power than noisy experimental data for downstream tasks. Thus, MultiDCP is a useful tool for transcriptomics-based drug repurposing and compound screening that currently rely on noisy high-throughput experimental data. We applied MultiDCP to repurpose individualized drugs for Alzheimer’s disease in terms of efficacy and toxicity, suggesting that MultiDCP is a potentially powerful tool for personalized drug discovery.

PMID:35951653 | DOI:10.1371/journal.pcbi.1010367

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Nevin Manimala Statistics

Intra- and post-operative risk of retinal breaks during vitrectomy for macular hole and vitreomacular traction

PLoS One. 2022 Aug 11;17(8):e0272333. doi: 10.1371/journal.pone.0272333. eCollection 2022.

ABSTRACT

BACKGROUND/OBJECTIVE: To evaluate the development of intra- and post-operative retinal breaks after pars plana vitrectomy (PPV) for macular hole (MH) and/or vitreomacular traction (VMT).

SUBJECTS/METHODS: Medical records of patients who underwent PPV at Kellogg Eye Center between 1/1/2005-6/30/2018, were evaluated in three groups: group 1, MH/VMT (n = 136); group 2, epiretinal membrane (ERM) without VMT (n = 270); and group 3, diagnostic vitrectomy (DV) or vitreous opacities (n = 35). Statistical analyses were conducted using SAS.

RESULTS: 20.6% of patients with MH/VMT, 8.5% of patients with ERM, and 5.7% of patients with DV or vitreous opacities had either intra-operative or post-operative breaks. Indication of MH/VMT versus ERM was a significant predictor for this outcome (p = .0112). The incidence of retinal breaks was higher in operations using 23-gauge versus 25-gauge PPV (25.0% vs. 7.4%, p < .0001).

CONCLUSIONS: The presence of MH and/or VMT is a significant risk factor for retinal breaks from PPV, as is use of 23-gauge vitrectomy.

PMID:35951646 | DOI:10.1371/journal.pone.0272333

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Nevin Manimala Statistics

Risk stratification and prognostic value of prothrombin time and activated partial thromboplastin time among COVID-19 patients

PLoS One. 2022 Aug 11;17(8):e0272216. doi: 10.1371/journal.pone.0272216. eCollection 2022.

ABSTRACT

BACKGROUND: COVID-19 is a viral disease caused by a new strain of corona virus. Currently, prognosis and risk stratification of COVID-19 patients is done by the disease’s clinical presentation. Therefore, identifying laboratory biomarkers for disease prognosis and risk stratification of COVID-19 patients is critical for prompt treatment. Therefore, the main objective of this study was to assess the risk stratification and prognostic value of basic coagulation parameters and factors associated with disease severity among COVID-19 patients at the Tibebe Ghion Specialized Hospital, COVID-19 treatment center, Northwest Ethiopia.

METHODS: A follow-up study was conducted among conveniently recruited COVID-19 patients attended from March to June 2021. Socio-demographic and clinical data were collected using a structured questionnaire and checklist, respectively. Prothrombin time (PT) and activated partial thromboplastin time (APTT) were analyzed by the HUMACLOT DUE PLUS® machine. Descriptive statistics were used to summarize the socio-demographic and clinical characteristics of study participants. Kruskal Wallis tests were used to compare the difference between parametric and non-parametric continuous variables, respectively. The area under the receiver operating characteristic curve (AUC) was used to evaluate the value of PT and APTT in the risk stratification and disease prognosis of COVID-19 patients. Ordinal logistic regression was used to identify the factors associated with disease severity and prognosis. A P-value < 0.05 was defined as statistically significant for all results.

RESULT: Baseline PT at a cut-off value ≥ 16.25 seconds differentiated severe COVID-19 patients from mild and moderate patients (AUC: 0.89, 95% CI: 0.83-0.95). PT also differentiated mild COVID-19 patients from moderate and severe patients at a cut-off value ≤ 15.35 seconds (AUC: 0.90, 95% CI: 0.84-0.96). Moreover, alcohol drinkers were a 3.52 times more likely chance of having severe disease than non-drinkers (95% CI: 1.41-8.81). A one-year increment in age also increased the odds of disease severity by 6% (95% CI: 3-9%). An increment of ≥ 0.65 seconds from the baseline PT predicted poor prognosis (AUC: 0.93, 0.87-0.99).

CONCLUSIONS AND RECOMMENDATIONS: Prolonged baseline PT was observed in severe COVID-19 patients. Prolonged baseline PT was also predicted to worsen prognosis. An increase from the baseline PT was associated with worsen prognosis. Therefore, PT can be used as a risk stratification and prognostic marker in COVID-19 patients.

PMID:35951632 | DOI:10.1371/journal.pone.0272216

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Nevin Manimala Statistics

Antimicrobial susceptibility profiles of Mycoplasma hyorhinis strains isolated from five European countries between 2019 and 2021

PLoS One. 2022 Aug 11;17(8):e0272903. doi: 10.1371/journal.pone.0272903. eCollection 2022.

ABSTRACT

Mycoplasma hyorhinis is an emerging swine pathogen bacterium causing polyserositis and polyarthritis in weaners and finishers. The pathogen is distributed world-wide, generating significant economic losses. No commercially available vaccine is available in Europe. Therefore, besides improving the housing conditions for prevention, antimicrobial therapy of the diseased animals is the only option to control the infection. Our aim was to determine the minimal inhibitory concentrations (MIC) of ten antimicrobials potentially used against M. hyorhinis infection. The antibiotic susceptibility of 76 M. hyorhinis isolates from Belgium, Germany, Hungary, Italy and Poland collected between 2019 and 2021 was determined by broth micro-dilution method and mismatch amplification mutation assay (MAMA). Low concentrations of tiamulin (MIC90 0.312 μg/ml), doxycycline (MIC90 0.078 μg/ml), oxytetracycline (MIC90 0.25 μg/ml), florfenicol (MIC90 2 μg/ml) and moderate concentrations of enrofloxacin (MIC90 1.25 μg/ml) inhibited the growth of the isolates. For the tested macrolides and lincomycin, a bimodal MIC pattern was observed (MIC90 >64 μg/ml for lincomycin, tulathromycin, tylosin and tilmicosin and 5 μg/ml for tylvalosin). The results of the MAMA assay were in line with the conventional method with three exceptions. Based on our statistical analyses, significant differences in MIC values of tiamulin and doxycycline were observed between certain countries. Our results show various levels of antimicrobial susceptibility among M. hyorhinis isolates to the tested antibiotics. The data underline the importance of susceptibility monitoring on pan-European level and provides essential information for proper antibiotic choice in therapy.

PMID:35951622 | DOI:10.1371/journal.pone.0272903