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Multidrug-resistant Plasmodium falciparum parasites have emerged in Cambodia and neighboring countries in Southeast Asia, compromising the efficacy of first-line antimalarial combinations. Dihydroartemisinin + piperaquine (PPQ) treatment failure rates have risen to as high as 50% in some areas in this region. For PPQ, resistance is driven primarily by a series of mutant alleles of the P. falciparum chloroquine resistance transporter (PfCRT). PPQ resistance was reported in China three decades earlier, but the molecular driver remained unknown. Herein, we identify a PPQ-resistant pfcrt allele (China C) from Yunnan Province, China, whose genotypic lineage is distinct from the PPQ-resistant pfcrt alleles currently observed in Cambodia. Combining gene editing and competitive growth assays, we report that PfCRT China C confers moderate PPQ resistance while re-sensitizing parasites to chloroquine (CQ) and incurring a fitness cost that manifests as a reduced rate of parasite growth. PPQ transport assays using purified PfCRT isoforms, combined with molecular dynamics simulations, highlight differences in drug transport kinetics and in this transporter's central cavity conformation between China C and the current Southeast Asian PPQ-resistant isoforms. We also report a novel computational model that incorporates empirically determined fitness landscapes at varying drug concentrations, combined with antimalarial susceptibility profiles, mutation rates, and drug pharmacokinetics. Our simulations with PPQ-resistant or -sensitive parasite lines predict that a three-day regimen of PPQ combined with CQ can effectively clear infections and prevent the evolution of PfCRT variants. This work suggests that including CQ in combination therapies could be effective in suppressing the evolution of PfCRT-mediated multidrug resistance in regions where PPQ has lost efficacy.
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Artemisininas/uso terapêutico , Cloroquina/uso terapêutico , Resistência a Múltiplos Medicamentos , Proteínas de Membrana Transportadoras/genética , Piperazinas/uso terapêutico , Plasmodium falciparum/efeitos dos fármacos , Plasmodium falciparum/genética , Proteínas de Protozoários/genética , Quinolinas/uso terapêutico , Alelos , Animais , Antimaláricos/uso terapêutico , Simulação por Computador , Humanos , Malária Falciparum/parasitologiaRESUMO
We propose a new evolutionary approach for discovering causal rules in complex classification problems from batch data. Key aspects include (a) the use of a hypergeometric probability mass function as a principled statistic for assessing fitness that quantifies the probability that the observed association between a given clause and target class is due to chance, taking into account the size of the dataset, the amount of missing data, and the distribution of outcome categories, (b) tandem age-layered evolutionary algorithms for evolving parsimonious archives of conjunctive clauses, and disjunctions of these conjunctions, each of which have probabilistically significant associations with outcome classes, and (c) separate archive bins for clauses of different orders, with dynamically adjusted order-specific thresholds. The method is validated on majority-on and multiplexer benchmark problems exhibiting various combinations of heterogeneity, epistasis, overlap, noise in class associations, missing data, extraneous features, and imbalanced classes. We also validate on a more realistic synthetic genome dataset with heterogeneity, epistasis, extraneous features, and noise. In all synthetic epistatic benchmarks, we consistently recover the true causal rule sets used to generate the data. Finally, we discuss an application to a complex real-world survey dataset designed to inform possible ecohealth interventions for Chagas disease.
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Algoritmos , Evolução Biológica , Doença de Chagas/genética , Doença de Chagas/prevenção & controle , Epistasia Genética , Genoma , Humanos , ProbabilidadeRESUMO
We propose NM landscapes as a new class of tunably rugged benchmark problems. NM landscapes are well defined on alphabets of any arity, including both discrete and real-valued alphabets, include epistasis in a natural and transparent manner, are proven to have known value and location of the global maximum and, with some additional constraints, are proven to also have a known global minimum. Empirical studies are used to illustrate that, when coefficients are selected from a recommended distribution, the ruggedness of NM landscapes is smoothly tunable and correlates with several measures of search difficulty. We discuss why these properties make NM landscapes preferable to both NK landscapes and Walsh polynomials as benchmark landscape models with tunable epistasis.
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Context: Developing scalable methods for conversation analytics is essential for health care communication science and quality improvement. Purpose: To assess the feasibility of automating the identification of a conversational feature, Connectional Silence, which is associated with important patient outcomes. Methods: Using audio recordings from the Palliative Care Communication Research Initiative cohort study, we develop and test an automated measurement pipeline comprising three machine-learning (ML) tools-a random forest algorithm and a custom convolutional neural network that operate in parallel on audio recordings, and subsequently a natural language processing algorithm that uses brief excerpts of automated speech-to-text transcripts. Results: Our ML pipeline identified Connectional Silence with an overall sensitivity of 84% and specificity of 92%. For Emotional and Invitational subtypes, we observed sensitivities of 68% and 67%, and specificities of 95% and 97%, respectively. Conclusion: These findings support the capacity for coordinated and complementary ML methods to fully automate the identification of Connectional Silence in natural hospital-based clinical conversations.
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Aprendizado de Máquina , Processamento de Linguagem Natural , Humanos , Estudos de Coortes , Algoritmos , ComunicaçãoRESUMO
CONTEXT: Human connection can reduce suffering and facilitate meaningful decision-making amid the often terrifying experience of hospitalization for advanced cancer. Some conversational pauses indicate human connection, but we know little about their prevalence, distribution or association with outcomes. PURPOSE: To describe the epidemiology of Connectional Silence during serious illness conversations in advanced cancer. METHODS: We audio-recorded 226 inpatient palliative care consultations at two academic centers. We identified pauses lasting 2+ seconds and distinguished Connectional Silences from other pauses, sub-categorized as either Invitational (ICS) or Emotional (ECS). We identified treatment decisional status pre-consultation from medical records and post-consultation via clinicians. Patients self-reported quality-of-life before and one day after consultation. RESULTS: Among all 6769 two-second silences, we observed 328 (4.8%) ECS and 240 (3.5%) ICS. ECS prevalence was associated with decisions favoring fewer disease-focused treatments (ORadj: 2.12; 95% CI: 1.12, 4.06). Earlier conversational ECS was associated with improved quality-of-life (p = 0.01). ICS prevalence was associated with clinicians' prognosis expectations. CONCLUSIONS: Connectional Silences during specialist serious illness conversations are associated with decision-making and improved patient quality-of-life. Further work is necessary to evaluate potential causal relationships. PRACTICE IMPLICATIONS: Pauses offer important opportunities to advance the science of human connection in serious illness decision-making.
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Neoplasias , Relações Médico-Paciente , Comunicação , Estado Terminal/epidemiologia , Estado Terminal/terapia , Humanos , Neoplasias/epidemiologia , Neoplasias/terapia , Cuidados Paliativos , Encaminhamento e ConsultaRESUMO
Background: It is unknown whether telemedicine-delivered palliative care (tele-PC) supports emotionally responsive patient-clinician interactions. Objectives: We conducted a mixed-methods formative study at two academic medical centers in rural U.S. states to explore the acceptability, feasibility, and emotional responsiveness of tele-PC. Design: We assessed clinicians' emotional responsiveness through questionnaires, qualitative interviews, and video coding. Results: We completed 11 tele-PC consultations. Mean age was 71 years, 30% did not complete high school, 55% experienced at least moderate financial insecurity, and 2/3 rated their overall health poorly. All patients rated tele-PC as equal to, or better than, in-person PC at providing emotional support. There was a tendency toward higher positive and lower negative emotions following the consultation. Video coding identified 114 instances of patients expressing emotions, and clinicians detected and responded to 98% of these events. Conclusion: Tele-PC appears to support emotionally responsive patient-clinician interactions. A mixed-methods approach to evaluating tele-PC yields useful, complementary insights.
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Enfermagem de Cuidados Paliativos na Terminalidade da Vida , Telemedicina , Idoso , Emoções , Humanos , Cuidados Paliativos/métodos , Encaminhamento e Consulta , Telemedicina/métodosRESUMO
Conversation has been a primary means for the exchange of information since ancient times. Understanding patterns of information flow in conversations is a critical step in assessing and improving communication quality. In this paper, we describe COnversational DYnamics Model (CODYM) analysis, a novel approach for studying patterns of information flow in conversations. CODYMs are Markov Models that capture sequential dependencies in the lengths of speaker turns. The proposed method is automated and scalable, and preserves the privacy of the conversational participants. The primary function of CODYM analysis is to quantify and visualize patterns of information flow, concisely summarized over sequential turns from one or more conversations. Our approach is general and complements existing methods, providing a new tool for use in the analysis of any type of conversation. As an important first application, we demonstrate the model on transcribed conversations between palliative care clinicians and seriously ill patients. These conversations are dynamic and complex, taking place amidst heavy emotions, and include difficult topics such as end-of-life preferences and patient values. We use CODYMs to identify normative patterns of information flow in serious illness conversations, show how these normative patterns change over the course of the conversations, and show how they differ in conversations where the patient does or doesn't audibly express anger or fear. Potential applications of CODYMs range from assessment and training of effective healthcare communication to comparing conversational dynamics across languages, cultures, and contexts with the prospect of identifying universal similarities and unique "fingerprints" of information flow.
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Estado Terminal/psicologia , Angústia Psicológica , Fala/fisiologia , Ira , Comunicação , Emoções/fisiologia , Medo/psicologia , Humanos , Modelos Teóricos , Cuidados PaliativosRESUMO
CONTEXT: Advancing the science of serious illness communication requires methods for measuring characteristics of conversations in large studies. Understanding which characteristics predict clinically important outcomes can help prioritize attention to scalable measure development. OBJECTIVES: To understand whether audibly recognizable expressions of distressing emotion during palliative care serious illness conversations are associated with ratings of patient experience or six-month enrollment in hospice. METHODS: We audiorecorded initial palliative care consultations involving 231 hospitalized people with advanced cancer at two large academic medical centers. We coded conversations for expressions of fear, anger, and sadness. We examined the distribution of these expressions and their association with pre/post ratings of feeling heard and understood and six-month hospice enrollment after the consultation. RESULTS: Nearly six in 10 conversations included at least one audible expression of distressing emotion (59%; 137 of 231). Among conversations with such an expression, fear was the most prevalent (72%; 98 of 137) followed by sadness (50%; 69 of 137) and anger (45%; 62 of 137). Anger expression was associated with more disease-focused end-of-life treatment preferences, pre/post consultation improvement in feeling heard and understood and lower six-month hospice enrollment. Fear was strongly associated with preconsultation patient ratings of shorter survival expectations. Sadness did not exhibit strong association with patient descriptors or outcomes. CONCLUSION: Fear, anger, and sadness are commonly expressed in hospital-based palliative care consultations with people who have advanced cancer. Anger is an epidemiologically useful predictor of important clinical outcomes.
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Cuidados Paliativos , Tristeza , Ira , Comunicação , Emoções , Medo , HumanosRESUMO
BACKGROUND: Understanding uncertainty in participatory decision-making requires scientific attention to interaction between what actually happens when patients, families and clinicians engage one another in conversation and the multi-level contexts in which these occur. Achieving this understanding will require conceptually grounded and scalable methods for use in large samples of people representing diversity in cultures, speaking and decision-making norms, and clinical situations. DISCUSSION: Here, we focus on serious illness and describe Conversational Stories as a scalable and conceptually grounded framework for characterizing uncertainty expression in these clinical contexts. Using actual conversations from a large direct-observation cohort study, we demonstrate how natural language processing and unsupervised machine learning methods can reveal underlying types of uncertainty stories in serious illness conversations. CONCLUSIONS: Conversational Storytelling offers a meaningful analytic framework for scalable computational methods to study uncertainty in healthcare conversations.
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Comunicação , Atenção à Saúde , Estudos de Coortes , Humanos , IncertezaRESUMO
OBJECTIVE: Serious illness conversations are complex clinical narratives that remain poorly understood. Natural Language Processing (NLP) offers new approaches for identifying hidden patterns within the lexicon of stories that may reveal insights about the taxonomy of serious illness conversations. METHODS: We analyzed verbatim transcripts from 354 consultations involving 231 patients and 45 palliative care clinicians from the Palliative Care Communication Research Initiative. We stratified each conversation into deciles of "narrative time" based on word counts. We used standard NLP analyses to examine the frequency and distribution of words and phrases indicating temporal reference, illness terminology, sentiment and modal verbs (indicating possibility/desirability). RESULTS: Temporal references shifted steadily from talking about the past to talking about the future over deciles of narrative time. Conversations progressed incrementally from "sadder" to "happier" lexicon; reduction in illness terminology accounted substantially for this pattern. We observed the following sequence in peak frequency over narrative time: symptom terms, treatment terms, prognosis terms and modal verbs indicating possibility. CONCLUSIONS: NLP methods can identify narrative arcs in serious illness conversations. PRACTICE IMPLICATIONS: Fully automating NLP methods will allow for efficient, large scale and real time measurement of serious illness conversations for research, education and system re-design.
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Enfermagem de Cuidados Paliativos na Terminalidade da Vida , Processamento de Linguagem Natural , Comunicação , Humanos , Cuidados Paliativos , Encaminhamento e ConsultaRESUMO
We investigate by numerical simulation a threshold model of social contagion on degree-correlated random networks. We show that the class of networks for which global information cascades occur generally expands as degree-degree correlations become increasingly positive. However, under certain conditions, large-scale information cascades can paradoxically occur when degree-degree correlations are sufficiently positive or negative, but not when correlations are relatively small. We also show that the relationship between the degree of the initially infected vertex and its ability to trigger large cascades is strongly affected by degree-degree correlations.
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OBJECTIVE: Automating conversation analysis in the natural clinical setting is essential to scale serious illness communication research to samples that are large enough for traditional epidemiological studies. Our objective is to automate the identification of pauses in conversations because these are important linguistic targets for evaluating dynamics of speaker involvement and turn-taking, listening and human connection, or distraction and disengagement. DESIGN: We used 354 audio recordings of serious illness conversations from the multisite Palliative Care Communication Research Initiative cohort study. SETTING/SUBJECTS: Hospitalized people with advanced cancer seen by the palliative care team. MEASUREMENTS: We developed a Random Forest machine learning (ML) algorithm to detect Conversational Pauses of two seconds or longer. We triple-coded 261 minutes of audio with human coders to establish a gold standard for evaluating ML performance characteristics. RESULTS: ML automatically identified Conversational Pauses with a sensitivity of 90.5 and a specificity of 94.5. CONCLUSIONS: ML is a valid method for automatically identifying Conversational Pauses in the natural acoustic setting of inpatient serious illness conversations.
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Background: Systematic measurement of conversational features in the natural clinical setting is essential to better understand, disseminate, and incentivize high quality serious illness communication. Advances in machine-learning (ML) classification of human speech offer exceptional opportunity to complement human coding (HC) methods for measurement in large scale studies. Objectives: To test the reliability, efficiency, and sensitivity of a tandem ML-HC method for identifying one feature of clinical importance in serious illness conversations: Connectional Silence. Design: This was a cross-sectional analysis of 354 audio-recorded inpatient palliative care consultations from the Palliative Care Communication Research Initiative multisite cohort study. Setting/Subjects: Hospitalized people with advanced cancer. Measurements: We created 1000 brief audio "clips" of randomly selected moments predicted by a screening ML algorithm to be two-second or longer pauses in conversation. Each clip included 10 seconds of speaking before and 5 seconds after each pause. Two HCs independently evaluated each clip for Connectional Silence as operationalized from conceptual taxonomies of silence in serious illness conversations. HCs also evaluated 100 minutes from 10 additional conversations having unique speakers to identify how frequently the ML screening algorithm missed episodes of Connectional Silence. Results:Connectional Silences were rare (5.5%) among all two-second or longer pauses in palliative care conversations. Tandem ML-HC demonstrated strong reliability (kappa 0.62; 95% confidence interval: 0.47-0.76). HC alone required 61% more time than the Tandem ML-HC method. No Connectional Silences were missed by the ML screening algorithm. Conclusions: Tandem ML-HC methods are reliable, efficient, and sensitive for identifying Connectional Silence in serious illness conversations.
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Comunicação , Aprendizado de Máquina , Cuidados Paliativos , Encaminhamento e Consulta , Idoso , Estudos Transversais , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Neoplasias/patologiaRESUMO
The detrimental effects of invasive plant species on ecosystems are well documented. While much research has focused on discovering ecological influences associated with invasiveness, it remains unclear how these influences interact, causing some introduced exotic species to become invasive threats. Here we develop a framework that incorporates the influences of propagule pressure, frequency independent growth rates, feedback relationships, resource competition and spatial scale of interactions. Our results show that these ecological influences interact in complex ways, resulting in expected outcomes ranging from inability to establish, to naturalization, to conditional invasion dependent on quantity and spatial distribution of propagules, to unconditional takeover. We propose a way to predict the likelihood of these four possible outcomes, for a species recently introduced into a given target community. Such information could enable conservation biologists to craft strategies and target remediation efforts more efficiently and effectively in order to help maintain biodiversity in ecological communities.
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Ecossistema , Modelos Biológicos , Plantas , Conservação dos Recursos Naturais , Dinâmica Populacional , Reprodutibilidade dos TestesRESUMO
The goal of this study was to investigate patterns of axonal injury in the first week after mild traumatic brain injury (mTBI). We performed a prospective cohort study of 20 patients presenting to the emergency department with mTBI, using 3.0T diffusion tensor MRI immediately after injury and again at 1 week post-injury. Corresponding data were acquired from 16 controls over a similar time interval. Fractional anisotropy (FA) and other diffusion measures were calculated from 11 a priori selected axon tracts at each time-point, and the change across time in each region was quantified for each subject. Clinical outcomes were determined by standardized neurocognitive assessment. We found that mTBI subjects were significantly more likely to have changes in FA in those 11 regions of interest across the one week time period, compared to control subjects whose FA measurements were stable across time. Longitudinal imaging was more sensitive to these subtle changes in white matter integrity than cross-sectional assessments at either of two time points, alone. Analyzing the sources of variance in our control population, we show that this increased sensitivity is likely due to the smaller within-subject variability obtained by longitudinal analysis with each subject as their own control. This is in contrast to the larger between-subject variability obtained by cross-sectional analysis of each individual subject to normalized data from a control group. We also demonstrated that inclusion of all a priori ROIs in an analytic model as opposed to measuring individual ROIs improves detection of white matter changes by overcoming issues of injury heterogeneity. Finally, we employed genetic programming (a bio-inspired computational method for model estimation) to demonstrate that longitudinal changes in FA have utility in predicting the symptomatology of patients with mTBI. We conclude concussive brain injury caused acute, measurable changes in the FA of white matter tracts consistent with evolving axonal injury and/or edema, which may contribute to post-concussive symptoms.
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Concussão Encefálica/diagnóstico , Imagem de Difusão por Ressonância Magnética , Adolescente , Adulto , Lesões Encefálicas Traumáticas/diagnóstico , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Adulto JovemRESUMO
The increasing availability of genotype-phenotype maps for different combinations of mutations has empowered evolutionary biologists with the tools to interrogate the predictability of adaptive evolution, especially in the context of the evolution of antimicrobial resistance. Large microbial populations are known to generate competing beneficial mutations, but determining how these mutations contribute to the adaptive trajectories that are most likely to be followed remains a challenge. Despite a recognition that there may also be competition between successive alleles on the same trajectory, prior studies have not fully considered how this impacts adaptation rates along, or likelihood of following, individual trajectories. Here, we develop a metric that quantifies the competition between successive alleles along adaptive trajectories and show how this competition largely governs the rate of evolution in simulations on empirical fitness landscapes for proteins involved in drug resistance in two species of malaria (Plasmodium falciparum and P. vivax). Our findings reveal that a trajectory with a larger-than-average initial fitness increase may have smaller fitness increases in later steps, which slows adaptation. In some circumstances, these trajectories may be outcompeted by alleles on faster alternative trajectories that are being explored simultaneously. The ability to predict adaptation rates along accessible trajectories has implications for efforts to manage antimicrobial resistance in real-world settings and for the broader intellectual pursuit of predictive evolution in complex adaptive fitness landscapes for a variety of application domains.
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Near-infrared fluorescence tomography using molecularly targeted lifetime-sensitive, fluorescent contrast agents have applications for early-stage cancer diagnostics. Yet, although the measurement of fluorescent lifetime imaging microscopy (FLIM) is extensively used in microscopy and spectroscopy applications, demonstration of fluorescence lifetime tomography for medical imaging is limited to two-dimensional studies. Herein, the feasibility of three-dimensional fluorescence-lifetime tomography on clinically relevant phantom volumes is established, using (i) a gainmodulated intensified charge coupled device (CCD) and modulated laser diode imaging system, (ii) two fluorescent contrast agents, e.g., Indocyanine green and 3-3'-Diethylthiatricarbocyanine iodide differing in their fluorescence lifetime by 0.62 ns, and (iii) a two stage approximate extended Kalman filter reconstruction algorithm. Fluorescence measurements of phase and amplitude were acquired on the phantom surface under different target to background fluorescence absorption (70:1, 100:1) and fluorescence lifetime (1:1, 2.1:1) contrasts at target depths of 1.4-2 cm. The Bayesian tomography algorithm was employed to obtain three-dimensional images of lifetime and absorption owing to the fluorophores.
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Processamento de Imagem Assistida por Computador/métodos , Microscopia de Fluorescência/métodos , Tomografia Óptica/métodos , Algoritmos , Teorema de Bayes , Benzotiazóis , Mama/patologia , Carbocianinas , Corantes Fluorescentes/farmacologia , Humanos , Interpretação de Imagem Assistida por Computador , Imageamento Tridimensional , Verde de Indocianina/farmacologia , Lasers , Imagens de Fantasmas , Tiazóis/farmacologia , Fatores de TempoRESUMO
Being able to design genetic regulatory networks (GRNs) to achieve a desired cellular function is one of the main goals of synthetic biology. However, determining minimal GRNs that produce desired time-series behaviors is non-trivial. In this paper, we propose a 'top-down' approach to evolving small GRNs and then use these to recursively boot-strap the identification of larger, more complex, modular GRNs. We start with relatively dense GRNs and then use differential evolution (DE) to evolve interaction coefficients. When the target dynamical behavior is found embedded in a dense GRN, we narrow the focus of the search and begin aggressively pruning out excess interactions at the end of each generation. We first show that the method can quickly rediscover known small GRNs for a toggle switch and an oscillatory circuit. Next we include these GRNs as non-evolvable subnetworks in the subsequent evolution of more complex, modular GRNs. Successful solutions found in canonical DE where we truncated small interactions to zero, with or without an interaction penalty term, invariably contained many excess interactions. In contrast, by incorporating aggressive pruning and the penalty term, the DE was able to find minimal or nearly minimal GRNs in all test problems.
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A novel image-intensified charge-coupled device (ICCD) imaging system has been developed to perform 3D fluorescence tomographic imaging in the frequency-domain using near-infrared contrast agents. The imager is unique since it (i) employs a large tissue-mimicking phantom, which is shaped and sized to resemble a female breast and part of the extended chest-wall region, and (ii) enables rapid data acquisition in the frequency-domain by using a gain-modulated ICCD camera. Diffusion model predictions are compared to experimental measurements using two different referencing schemes under two different experimental conditions of perfect and imperfect uptake of fluorescent agent into a target. From these experimental measurements, three-dimensional images of fluorescent absorption were reconstructed using a computationally efficient variant of the approximate extended Kalman filter algorithm. The current work represents the first time that 3D fluorescence-enhanced optical tomographic reconstructions have been achieved from experimental measurements of the time-dependent light propagation on a clinically relevant breast-shaped tissue phantom using a gain-modulated ICCD camera.