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Clinical Microbiology Reviews Dec 2023The characterization of wild-type minimum inhibitory concentration (MIC) and zone diameter distributions with the setting of epidemiological cut-off values (ECOFFs or... (Review)
Review
The characterization of wild-type minimum inhibitory concentration (MIC) and zone diameter distributions with the setting of epidemiological cut-off values (ECOFFs or ECVs) provides a reference for the otherwise relative MIC values in the international system for antimicrobial susceptibility testing. Distributions of MIC values for a species and an agent follow a log-normal distribution, which in the absence of resistance mechanisms is monomodal and designated wild type (WT). The upper end of the WT distribution, the ECOFF, can be identified with statistical methods. In the presence of phenotypically detectable resistance, the distribution has at least one more mode (the non-WT), but despite this, the WT is most often identifiable using the same methods. The ECOFF provides the most sensitive measure of resistance development in a species against an agent. The WT and non-WT modes are independent of the organism´s response to treatment, but when the European Committee on Antimicrobial Susceptibility Testing (EUCAST) determines the clinical breakpoints, the committee avoids breakpoints that split WT distributions of target species. This is to avoid the poorer reproducibility of susceptibility categorization when breakpoints split major populations but also because the EUCAST has failed to identify different clinical outcomes for isolates with different MIC values inside the wild-type distribution. In laboratory practice, the ECOFF is used to screen for and exclude resistance and allows the comparison of resistance between systems with different breakpoints from different breakpoint organizations, breakpoints evolving over time, and different breakpoints between human and animal medicine. The EUCAST actively encourages colleagues to question MIC distributions as presented on the website (https://www.eucast.org/mic_and_zone_distributions_and_ecoffs) and to contribute MIC and inhibition zone diameter data.
Topics: Animals; Humans; Reproducibility of Results; Microbial Sensitivity Tests; Anti-Infective Agents; Anti-Bacterial Agents
PubMed: 38038445
DOI: 10.1128/cmr.00100-22 -
Cerebral Cortex (New York, N.Y. : 1991) Aug 2023Numbers of neurons and their spatial variation are fundamental organizational features of the brain. Despite the large corpus of cytoarchitectonic data available in the...
Numbers of neurons and their spatial variation are fundamental organizational features of the brain. Despite the large corpus of cytoarchitectonic data available in the literature, the statistical distributions of neuron densities within and across brain areas remain largely uncharacterized. Here, we show that neuron densities are compatible with a lognormal distribution across cortical areas in several mammalian species, and find that this also holds true within cortical areas. A minimal model of noisy cell division, in combination with distributed proliferation times, can account for the coexistence of lognormal distributions within and across cortical areas. Our findings uncover a new organizational principle of cortical cytoarchitecture: the ubiquitous lognormal distribution of neuron densities, which adds to a long list of lognormal variables in the brain.
Topics: Animals; Neurons; Brain; Mammals; Cerebral Cortex; Statistical Distributions
PubMed: 37409647
DOI: 10.1093/cercor/bhad160 -
Physical Review. E Dec 2023Kappa-distributed velocities in plasmas are common in a wide variety of settings, from low-density to high-density plasmas. To date, they have been found mainly in space...
Kappa-distributed velocities in plasmas are common in a wide variety of settings, from low-density to high-density plasmas. To date, they have been found mainly in space plasmas, but are recently being considered also in the modeling of laboratory plasmas. Despite being routinely employed, the origin of the kappa distribution remains, to this day, unclear. For instance, deviations from the Maxwell-Boltzmann distribution are sometimes regarded as a signature of the nonadditivity of the thermodynamic entropy, although there are alternative frameworks such as superstatistics where such an assumption is not needed. In this work we recover the kappa distribution for particle velocities from the formalism of nonequilibrium steady-states, assuming only a single requirement on the dependence between the kinetic energy of a test particle and that of its immediate environment. Our results go beyond the standard derivation based on superstatistics, as we do not require any assumption about the existence of temperature or its statistical distribution, instead obtaining them from the requirement on kinetic energies. All of this suggests that this family of distributions may be more common than usually assumed, widening its domain of application in particular to the description of plasmas from fusion experiments. Furthermore, we show that a description of kappa-distributed plasma is simpler in terms of features of the superstatistical inverse temperature distribution rather than the traditional parameters κ and the thermal velocity v_{th}.
PubMed: 38243483
DOI: 10.1103/PhysRevE.108.065207 -
PloS One 2023The typical hospital Length of Stay (LOS) distribution is known to be right-skewed, to vary considerably across Diagnosis Related Groups (DRGs), and to contain markedly...
BACKGROUND
The typical hospital Length of Stay (LOS) distribution is known to be right-skewed, to vary considerably across Diagnosis Related Groups (DRGs), and to contain markedly high values, in significant proportions. These very long stays are often considered outliers, and thin-tailed statistical distributions are assumed. However, resource consumption and planning occur at the level of medical specialty departments covering multiple DRGs, and when considered at this decision-making scale, extreme LOS values represent a significant component of the distribution of LOS (the right tail) that determines many of its statistical properties.
OBJECTIVE
To build actionable statistical models of LOS for resource planning at the level of healthcare units.
METHODS
Through a study of 46, 364 electronic health records over four medical specialty departments (Pediatrics, Obstetrics/Gynecology, Surgery, and Rehabilitation Medicine) in the largest hospital in Thailand (Siriraj Hospital in Bangkok), we show that the distribution of LOS exhibits a tail behavior that is consistent with a subexponential distribution. We analyze some empirical properties of such a distribution that are of relevance to cost and resource planning, notably the concentration of resource consumption among a minority of admissions/patients, an increasing residual LOS, where the longer a patient has been admitted, the longer they would be expected to remain admitted, and a slow convergence of the Law of Large Numbers, making empirical estimates of moments (e.g. mean, variance) unreliable.
RESULTS
We propose a novel Beta-Geometric model that shows a good fit with observed data and reproduces these empirical properties of LOS. Finally, we use our findings to make practical recommendations regarding the pricing and management of LOS.
Topics: Humans; Child; Length of Stay; Thailand; Hospitals; Hospitalization; Medicine
PubMed: 37440494
DOI: 10.1371/journal.pone.0288239 -
Insects Jan 2024The Italian fauna includes about 170 species/subspecies of dung beetles, being one of the richest in Europe. We used data on dung beetle distribution in the Italian...
The Italian fauna includes about 170 species/subspecies of dung beetles, being one of the richest in Europe. We used data on dung beetle distribution in the Italian regions to investigate some macroecological patterns. Specifically, we tested if species richness decreased southward (peninsula effect) or northward (latitudinal gradient). We also considered the effects of area (i.e., the species-area relationship), topographic complexity, and climate in explaining dung beetle richness. Finally, we used multivariate techniques to identify biotic relationships between regions. We found no support for the peninsula effect, whereas scarabaeines followed a latitudinal gradient, thus supporting a possible role of southern areas as Pleistocene refuges for this group of mainly thermophilic beetles. By contrast, aphodiines were more associated with cold and humid climates and do not show a distinct latitudinal pattern. In general, species richness was influenced by area, with the Sardinian fauna being however strongly impoverished because of its isolation. Faunal patterns for mainland regions reflect the influence of current ecological settings and historical factors (Pleistocene glaciations) in determining species distributions.
PubMed: 38249045
DOI: 10.3390/insects15010039 -
PloS One 2023Topic models are widely used to discover the latent representation of a set of documents. The two canonical models are latent Dirichlet allocation, and Gaussian latent...
Topic models are widely used to discover the latent representation of a set of documents. The two canonical models are latent Dirichlet allocation, and Gaussian latent Dirichlet allocation, where the former uses multinomial distributions over words, and the latter uses multivariate Gaussian distributions over pre-trained word embedding vectors as the latent topic representations, respectively. Compared with latent Dirichlet allocation, Gaussian latent Dirichlet allocation is limited in the sense that it does not capture the polysemy of a word such as "bank." In this paper, we show that Gaussian latent Dirichlet allocation could recover the ability to capture polysemy by introducing a hierarchical structure in the set of topics that the model can use to represent a given document. Our Gaussian hierarchical latent Dirichlet allocation significantly improves polysemy detection compared with Gaussian-based models and provides more parsimonious topic representations compared with hierarchical latent Dirichlet allocation. Our extensive quantitative experiments show that our model also achieves better topic coherence and held-out document predictive accuracy over a wide range of corpus and word embedding vectors which significantly improves the capture of polysemy compared with GLDA and CGTM. Our model learns the underlying topic distribution and hierarchical structure among topics simultaneously, which can be further used to understand the correlation among topics. Moreover, the added flexibility of our model does not necessarily increase the time complexity compared with GLDA and CGTM, which makes our model a good competitor to GLDA.
Topics: Normal Distribution; Learning
PubMed: 37436968
DOI: 10.1371/journal.pone.0288274 -
Heliyon Nov 2023A two-parameter unit distribution and its regression model plus its extension to 0 and 1 inflation is introduced and studied. The distribution is called the unit upper...
A two-parameter unit distribution and its regression model plus its extension to 0 and 1 inflation is introduced and studied. The distribution is called the unit upper truncated Weibull (UUTW) distribution, while the inflated variant is called the inflated unit upper truncated Weibull (ZOIUUTW) distribution. The UUTW distribution has an increasing and a J-shaped hazard rate function. The parameters of the proposed models are estimated by the method of maximum likelihood estimation. For the UUTW distribution, two practical examples involving household expenditure and maximum flood level data are used to show its flexibility and the proposed distribution demonstrates better fit tendencies than some of the competing unit distributions. Application of the proposed regression model demonstrates adequate capability in describing the real data set with better modeling proficiency than the existing competing models. Then, for the ZOIUUTW distribution, the CD34+ data involving cancer patients are analyzed to show the flexibility of the model in characterizing inflation at both endpoints of the unit interval.
PubMed: 38058617
DOI: 10.1016/j.heliyon.2023.e22260 -
Artificial Intelligence in Medicine Sep 2023Difference of Gaussians (DoG) convolutional filters are one of the earliest image processing methods employed for detecting microcalcifications on mammogram images...
Difference of Gaussians (DoG) convolutional filters are one of the earliest image processing methods employed for detecting microcalcifications on mammogram images before machine and deep learning methods became widespread. DoG is a blob enhancement filter that consists in subtracting one Gaussian-smoothed version of an image from another less Gaussian-smoothed version of the same image. Smoothing with a Gaussian kernel suppresses high-frequency spatial information, thus DoG can be regarded as a band-pass filter. However, due to their small size and overimposed breast tissue, microcalcifications vary greatly in contrast-to-noise ratio and sharpness. This makes it difficult to find a single DoG configuration that enhances all microcalcifications. In this work, we propose a convolutional network, named DoG-MCNet, where the first layer automatically learns a bank of DoG filters parameterized by their associated standard deviations. We experimentally show that when employed for microcalcification detection, our DoG layer acts as a learnable bank of band-pass preprocessing filters and improves detection performance by 4.86% AUFROC over baseline MCNet and 1.53% AUFROC over state-of-the-art multicontext ensemble of CNNs.
Topics: Humans; Algorithms; Calcinosis; Mammography; Normal Distribution
PubMed: 37673567
DOI: 10.1016/j.artmed.2023.102629 -
Journal of Biological Rhythms Feb 2024This study examines population-level daily patterns of time-stamped emergency medical service (EMS) dispatches to establish their situational predictability. Using...
This study examines population-level daily patterns of time-stamped emergency medical service (EMS) dispatches to establish their situational predictability. Using visualization, sinusoidal regression, and statistical tests to compare empirical cumulative distributions, we analyzed 311,848,450 emergency medical call records from the US National Emergency Medical Services Information System (NEMSIS) for years 2010 through 2022. The analysis revealed a robust daily pattern in the hourly distribution of distress calls across 33 major categories of medical emergency dispatch types. Sinusoidal regression coefficients for all types were statistically significant, mostly at the < 0.0001 level. The coefficient of determination ranged from 0.84 and 0.99 for all models, with most falling in the 0.94 to 0.99 range. The common sinusoidal pattern, peaking in mid-afternoon, demonstrates that all major categories of medical emergency dispatch types appear to be influenced by an underlying daily rhythm that is aligned with daylight hours and common sleep/wake cycles. A comparison of results with previous landmark studies revealed new and contrasting EMS patterns for several long-established peak occurrence hours-specifically for chest pain, heart problems, stroke, convulsions and seizures, and sudden cardiac arrest/death. Upon closer examination, we also found that heart attacks, diagnosed by paramedics in the field via 12-lead cardiac monitoring, followed the identified common daily pattern of a mid-afternoon peak, departing from prior generally accepted morning tendencies. Extended analysis revealed that the normative pattern prevailed across the NEMSIS data when reorganized to consider monthly, seasonal, daylight-savings versus civil time, and pre-/post-COVID-19 periods. The predictable daily EMS patterns provide impetus for more research that links daily variation with causal risk and protective factors. Our methods are straightforward and presented with detail to provide accessible and replicable implementation for researchers and practitioners.
Topics: Circadian Rhythm; Emergency Medical Services; Retrospective Studies
PubMed: 37786272
DOI: 10.1177/07487304231193876 -
Clinical Pharmacology and Therapeutics Apr 2024Virtual patient simulation is increasingly performed to support model-based optimization of clinical trial designs or individualized dosing strategies. Quantitative...
Virtual patient simulation is increasingly performed to support model-based optimization of clinical trial designs or individualized dosing strategies. Quantitative pharmacological models typically incorporate individual-level patient characteristics, or covariates, which enable the generation of virtual patient cohorts. The individual-level patient characteristics, or covariates, used as input for such simulations should accurately reflect the values seen in real patient populations. Current methods often make unrealistic assumptions about the correlation between patient's covariates or require direct access to actual data sets with individual-level patient data, which may often be limited by data sharing limitations. We propose and evaluate the use of copulas to address current shortcomings in simulation of patient-associated covariates for virtual patient simulations for model-based dose and trial optimization in clinical pharmacology. Copulas are multivariate distribution functions that can capture joint distributions, including the correlation, of covariate sets. We compare the performance of copulas to alternative simulation strategies, and we demonstrate their utility in several case studies. Our work demonstrates that copulas can reproduce realistic patient characteristics, both in terms of individual covariates and the dependence structure between different covariates, outperforming alternative methods, in particular when aiming to reproduce high-dimensional covariate sets. In conclusion, copulas represent a versatile and generalizable approach for virtual patient simulation which preserve relationships between covariates, and offer an open science strategy to facilitate re-use of patient data sets.
Topics: Humans; Patient Simulation; Computer Simulation; Models, Statistical
PubMed: 37946529
DOI: 10.1002/cpt.3099