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Nevin Manimala Statistics

A Bayesian approach to modeling antimicrobial multidrug resistance

PLoS One. 2021 Dec 29;16(12):e0261528. doi: 10.1371/journal.pone.0261528. eCollection 2021.

ABSTRACT

Multidrug resistance (MDR) has been a significant threat to public health and effective treatment of bacterial infections. Current identification of MDR is primarily based upon the large proportions of isolates resistant to multiple antibiotics simultaneously, and therefore is a belated evaluation. For bacteria with MDR, we expect to see strong correlations in both the quantitative minimum inhibitory concentration (MIC) and the binary susceptibility as classified by the pre-determined breakpoints. Being able to detect correlations from these two perspectives allows us to find multidrug resistant bacteria proactively. In this paper, we provide a Bayesian framework that estimates the resistance level jointly for antibiotics belonging to different classes with a Gaussian mixture model, where the correlation in the latent MIC can be inferred from the Gaussian parameters and the correlation in binary susceptibility can be inferred from the mixing weights. By augmenting the laboratory measurement with the latent MIC variable to account for the censored data, and by adopting the latent class variable to represent the MIC components, our model was shown to be accurate and robust compared with the current assessment of correlations. Applying the model to Salmonella heidelberg samples isolated from human participants in National Antimicrobial Resistance Monitoring System (NARMS) provides us with signs of joint resistance to Amoxicillin-clavulanic acid & Cephalothin and joint resistance to Ampicillin & Cephalothin. Large correlations estimated from our model could serve as a timely tool for early detection of MDR, and hence a signal for clinical intervention.

PMID:34965273 | DOI:10.1371/journal.pone.0261528

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Nevin Manimala Statistics

Evaluating the impacts of non-pharmaceutical interventions on the transmission dynamics of COVID-19 in Canada based on mobile network

PLoS One. 2021 Dec 29;16(12):e0261424. doi: 10.1371/journal.pone.0261424. eCollection 2021.

ABSTRACT

The COVID-19 outbreak has caused two waves and spread to more than 90% of Canada’s provinces since it was first reported more than a year ago. During the COVID-19 epidemic, Canadian provinces have implemented many Non-Pharmaceutical Interventions (NPIs). However, the spread of the COVID-19 epidemic continues due to the complex dynamics of human mobility. We develop a meta-population network model to study the transmission dynamics of COVID-19. The model takes into account the heterogeneity of mitigation strategies in different provinces of Canada, such as the timing of implementing NPIs, the human mobility in retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residences due to work and recreation. To determine which activity is most closely related to the dynamics of COVID-19, we use the cross-correlation analysis to find that the positive correlation is the highest between the mobility data of parks and the weekly number of confirmed COVID-19 from February 15 to December 13, 2020. The average effective reproduction numbers in nine Canadian provinces are all greater than one during the time period, and NPIs have little impact on the dynamics of COVID-19 epidemics in Ontario and Saskatchewan. After November 20, 2020, the average infection probability in Alberta became the highest since the start of the COVID-19 epidemic in Canada. We also observe that human activities around residences do not contribute much to the spread of the COVID-19 epidemic. The simulation results indicate that social distancing and constricting human mobility is effective in mitigating COVID-19 transmission in Canada. Our findings can provide guidance for public health authorities in projecting the effectiveness of future NPIs.

PMID:34965272 | DOI:10.1371/journal.pone.0261424

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Nevin Manimala Statistics

Damage evaluation and precursor of sandstone under the uniaxial compression: Insights from the strain-field heterogeneity

PLoS One. 2021 Dec 29;16(12):e0262054. doi: 10.1371/journal.pone.0262054. eCollection 2021.

ABSTRACT

The stress-induced microcrack evolution in rock specimens causes a series of physical changes and heterogeneous deformations. Some of these attributes (such as sound, electricity, heat, etc.) have been effectively used to identify the damage state and precursory information of the rock specimens. However, the strain-field heterogeneity has not been investigated previously. In this study, the relationship of the strain-field heterogeneity and damage evolution of three sandstone specimens under the uniaxial compressive load was analyzed statistically. The acoustic emission (AE) and two-dimensional digital image correlation were employed for real-time evaluation of the AE parameters and strain-field heterogeneity. The results showed that the strain-field heterogeneity was closely related to the rock damage that amplified with the applied stress, and exhibited two features; numerical difference and spatial concentration. Subsequently, these two features were characterized by the two proposed heterogeneous quantitative indicators (i.e., the degree and space heterogeneities). Further, their four transition processes were in agreement with the damage stages confirmed by AE parameters: a relatively constant trend; growth with a relatively constant rate; drastic increase trend; and increase with a high rate to maximum value. Moreover, a time sequence chain for damage precursor was built, where the heterogeneous quantitative indicators and AE parameters differed in sensitivity to microcrack development and can be used as a damage warning at the varying magnitude of the external load.

PMID:34965268 | DOI:10.1371/journal.pone.0262054

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Nevin Manimala Statistics

Predictors of colorectal cancer survival using cox regression and random survival forests models based on gene expression data

PLoS One. 2021 Dec 29;16(12):e0261625. doi: 10.1371/journal.pone.0261625. eCollection 2021.

ABSTRACT

Understanding and identifying the markers and clinical information that are associated with colorectal cancer (CRC) patient survival is needed for early detection and diagnosis. In this work, we aimed to build a simple model using Cox proportional hazards (PH) and random survival forest (RSF) and find a robust signature for predicting CRC overall survival. We used stepwise regression to develop Cox PH model to analyse 54 common differentially expressed genes from three mutations. RSF is applied using log-rank and log-rank-score based on 5000 survival trees, and therefore, variables important obtained to find the genes that are most influential for CRC survival. We compared the predictive performance of the Cox PH model and RSF for early CRC detection and diagnosis. The results indicate that SLC9A8, IER5, ARSJ, ANKRD27, and PIPOX genes were significantly associated with the CRC overall survival. In addition, age, sex, and stages are also affecting the CRC overall survival. The RSF model using log-rank is better than log-rank-score, while log-rank-score needed more trees to stabilize. Overall, the imputation of missing values enhanced the model’s predictive performance. In addition, Cox PH predictive performance was better than RSF.

PMID:34965262 | DOI:10.1371/journal.pone.0261625

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Nevin Manimala Statistics

A Predictive Visual Analytics System for Studying Neurodegenerative Disease based on DTI Fiber Tracts

IEEE Trans Vis Comput Graph. 2021 Dec 29;PP. doi: 10.1109/TVCG.2021.3137174. Online ahead of print.

ABSTRACT

Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain. We introduce a predictive visual analytics system for studying patient groups based on their labeled DTI fiber tract data and corresponding statistics. The systems machine-learning-augmented interface guides the user through an organized and holistic analysis space, including the statistical feature space, the physical space, and the space of patients over different groups. We use a custom machine learning pipeline to help narrow down this large analysis space and then explore it pragmatically through a range of linked visualizations. We conduct several case studies using DTI and T1-weighted images from the research database of Parkinsons Progression Markers Initiative.

PMID:34965212 | DOI:10.1109/TVCG.2021.3137174

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Nevin Manimala Statistics

Application of a constrained mixture design for lipase production by Penicillium roqueforti ATCC 10110 under solid-state fermentation and using agro-industrial wastes as substrate

Prep Biochem Biotechnol. 2021 Dec 29:1-9. doi: 10.1080/10826068.2021.2004547. Online ahead of print.

ABSTRACT

Solid state fermentation (SSF) simulates the natural conditions fungal growth, where the amount of water in the reaction medium must be restricted, thus limiting the use of liquid substrate. An analytical strategy to deal with this limitation is the design of blending with constraints. Thus, the objective of the work was to optimize two constrained waste mixtures for the production of lipase by Penicillium roqueforti ATCC 10110 under SSF, using different substrates that combine solid and liquid waste. For this, the best fermentation time was determined through a fermentative profile, afterwards a restricted-mix design with lower and upper limits of the components of mixture I (cocoa residue, solid palm oil residue and liquid palm oil residue) and II (cocoa residue, mango residue and palm oil residue liquid palm) was applied. By means of Pareto and contour graphs, the maximum production points of lipase in mixtures I (6.67 ± 0.34 U g-1) and II (6.87 ± 0.35 U g-1) were obtained. The restricted mixture design proved to be a promising tool in the production of lipase by P. roqueforti ATCC 10110 under SSF since the use of restrictions is useful when intending to combine solid and liquid residues in fermentation processes.

PMID:34965202 | DOI:10.1080/10826068.2021.2004547

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Nevin Manimala Statistics

Impact of intensive lifestyle intervention on gut microbiota composition in type 2 diabetes: a post-hoc analysis of a randomized clinical trial

Gut Microbes. 2022 Jan-Dec;14(1):2005407. doi: 10.1080/19490976.2021.2005407.

ABSTRACT

Type 2 diabetes (T2D) management is based on combined pharmacological and lifestyle intervention approaches. While their clinical benefits are well studied, less is known about their effects on the gut microbiota. We aimed to investigate if an intensive lifestyle intervention combined with conventional standard care leads to a different gut microbiota composition compared to standard care alone treatment in individuals with T2D, and if gut microbiota is associated with the clinical benefits of the treatments. Ninety-eight individuals with T2D were randomized to either an intensive lifestyle intervention combined with standard care group (N = 64), or standard care alone group (N = 34) for 12 months. All individuals received standardized, blinded, target-driven medical therapy, and individual counseling. The lifestyle intervention group moreover received intensified physical training and dietary plans. Clinical characteristics and fecal samples were collected at baseline, 3-, 6-, 9-, and 12-month follow-up. The gut microbiota was profiled with 16S rRNA gene amplicon sequencing. There were no statistical differences in the change of gut microbiota composition between treatments after 12 months, except minor and transient differences at month 3. The shift in gut microbiota alpha diversity at all time windows did not correlate with the change in clinical characteristics, and the gut microbiota did not mediate the treatment effect on clinical characteristics. The clinical benefits of intensive lifestyle and/or pharmacological interventions in T2D are unlikely to be explained by, or causally related to, changes in the gut microbiota composition.

PMID:34965188 | DOI:10.1080/19490976.2021.2005407

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Nevin Manimala Statistics

The Management of Penetrating Neck Injury With Retained Knife: 15-Year Experience From a Major Trauma Center in South Africa

Am Surg. 2021 Dec 29:31348211065127. doi: 10.1177/00031348211065127. Online ahead of print.

ABSTRACT

BACKGROUND: This study reviews our cumulative experience with the management of patients presenting with a retained knife following a penetrating neck injury (PNI).

METHODS: A retrospective cohort study was conducted at a major trauma center in South Africa over a 15-year period from July 2006 to December 2020. All patients who presented with a retained knife in the neck following a stab wound (SW) were included.

RESULTS: Twenty-two cases were included: 20 males (91%), mean age: 29 years. 77% (17/22) were retained knives and 23% (5/22) were retained blades. Eighteen (82%) were in the anterior neck, and the remaining 4 cases were in the posterior neck. Plain radiography was performed in 95% (21/22) of cases, and computed tomography (CT) was performed in 91% (20/22). Ninety-five percent (21/22) had the knife or blade extracted in the operating room (OR). Formal neck exploration (FNE) was undertaken in 45% (10/22) of cases, and the remaining 55% (12/22) underwent simple extraction (SE) only. Formal neck exploration was more commonly performed for anterior neck retained knives than the posterior neck, although not statistically significant [56% (10/18) vs 0% (0/18), P = .096]. There were no significant differences in the need for intensive care admission, length of hospital stay, morbidities, or mortalities between anterior and posterior neck retained knives.

DISCUSSION: Uncontrolled extraction of a retained knife in the neck outside of the operating room may be dangerous. Retained knives in the anterior neck commonly required formal neck exploration but not for posterior neck retained knives.

PMID:34965158 | DOI:10.1177/00031348211065127

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Nevin Manimala Statistics

Factors Predicting Increased Length of Stay in Abdominal Wall Reconstruction

Am Surg. 2021 Dec 29:31348211047503. doi: 10.1177/00031348211047503. Online ahead of print.

ABSTRACT

BACKGROUND: Enhanced recovery after surgery (ERAS) programs have become increasingly popular in general surgery, yet no guidelines exist for an abdominal wall reconstruction (AWR)-specific program. We aimed to evaluate predictors of increased length of stay (LOS) in the AWR population to aid in creating an AWR-specific ERAS protocol.

METHODS: A prospective, single institution hernia center database was queried for all patients undergoing open AWR (1999-2019). Standard statistical methods and linear and logistic regression were used to evaluate for predictors of increased LOS. Groups were compared based on LOS below or above the median LOS of 6 days (IQR = 4-8).

RESULTS: Inclusion criteria were met by 2,505 patients. On average, the high LOS group was older, with higher rates of CAD, COPD, diabetes, obesity, and pre-operative narcotic use (all P < .05). Longer LOS patients had more complex hernias with larger defects, higher rates of mesh infection/fistula, and more often required a component separation (all P < .05). Multivariate analysis identified age (β0.04,SE0.02), BMI (β0.06,SE0.03), hernia defect size (β0.003,SE0.001), active mesh infection or mesh fistula (β1.8,SE0.72), operative time (β0.02,SE0.002), and ASA score >4 (β3.6,SE1.7) as independently associated factors for increased LOS (all P < .05). Logistic regression showed that an increased length of stay trended toward an increased risk of hernia recurrence (P = .06).

CONCLUSIONS: Multiple patient and hernia characteristics are shown to significantly affect LOS, which, in turn, increases the odds of AWR failure. Weight loss, peri-operative geriatric optimization, prehabilitation of comorbidities, and operating room efficiency can enhance recovery and shorten LOS following AWR.

PMID:34965157 | DOI:10.1177/00031348211047503

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Nevin Manimala Statistics

Examination of Student-Teacher Interpersonal Relationships Circumplex Model in the Greek Educational Context

Psychol Rep. 2021 Dec 29:332941211061078. doi: 10.1177/00332941211061078. Online ahead of print.

ABSTRACT

Student-teacher interpersonal relationships contribute significantly to the academic trajectory and achievement of children and adolescents. The Questionnaire on Teacher Interaction (QTI) is one of the most widely applied measures for assessing students’ perceptions about the teachers’ interpersonal behaviour. QTI comprises eight subscales that are assumed to follow a circumplex model. Prior studies on QTI’s psychometric properties are inconclusive and report mixed findings. The purpose of this study was to examine the applicability of QTI in the Greek cultural context, by testing its circumplex structure and levels of reliability. QTI was administered to 1669 secondary education students, from 85 different classrooms. A cross-validation approach and a variety of statistical techniques were employed. Subscales’ internal consistency and their ability to discriminate among classes were satisfactory. Exploratory statistical techniques provided initial support of the circular pattern. Application of a specifically designed package for testing the circumplex structure of an instrument, showed that a model in which the eight QTI subscales are placed on the circumference of a circle with equal distances form the centre was tenable. However, the assumption of equal distances was not confirmed. Deviation from the theoretical position of the subscales was mainly due to students’ difficulty to discriminate teachers’ proximity behaviour, a finding reported in various studies and across different cultural contexts. Suggestions for improving the psychometric properties of the QTI are discussed.

PMID:34965156 | DOI:10.1177/00332941211061078