Fast and accurate 3-D spine MRI segmentation using FastCleverSeg.

Accurate and efficient segmenting of vertebral bodies, muscles, and discs is crucial for analyzing various spinal diseases. However, traditional methods are either laborious and time-consuming (manual segmentation) or require extensive training data (fully automatic segmentation). FastCleverSeg, our proposed semi-automatic segmentation approach, addresses those limitations by significantly reducing user interaction while maintaining high accuracy. First, we reduce user interaction by requiring the manual annotation of only two or three slices. Next, we automatically Estimate the Annotation on Intermediary Slices (EANIS) using traditional computer vision/graphics concepts. Finally, our proposed method leverages improved voxel weight balancing to achieve fast and precise volumetric segmentation in the segmentation process. Experimental evaluations on our assembled diverse MRI databases comprising 179 patients (60 male, 119 female), demonstrate a remarkable 25 ms (30 ms standard deviation) processing time and a significant reduction in user interaction compared to existing approaches. Importantly, FastCleverSeg maintains or surpasses the segmentation quality of competing methods, achieving a Dice score of 94%. This invaluable tool empowers physicians to efficiently generate reliable ground truths, expediting the segmentation process and paving the way for future integration with deep learning approaches. In turn, this opens exciting possibilities for future fully automated spine segmentation.

Time series causal relationships discovery through feature importance and ensemble models.

Inferring causal relationships from observational data is a key challenge in understanding the interpretability of Machine Learning models. Given the ever-increasing amount of observational data available in many areas, Machine Learning algorithms used for forecasting have become more complex, leading to a less understandable path of how a decision is made by the model. To address this issue, we propose leveraging ensemble models, e.g., Random Forest, to assess which input features the trained model prioritizes when making a forecast and, in this way, establish causal relationships between the variables. The advantage of these algorithms lies in their ability to provide feature importance, which allows us to build the causal network. We present our methodology to estimate causality in time series from oil field production. As it is difficult to extract causal relations from a real field, we also included a synthetic oil production dataset and a weather dataset, which is also synthetic, to provide the ground truth. We aim to perform causal discovery, i.e., establish the existing connections between the variables in each dataset. Through an iterative process of improving the forecasting of a target's value, we evaluate whether the forecasting improves by adding information from a new potential driver; if so, we state that the driver causally affects the target. On the oil field-related datasets, our causal analysis results agree with the interwell connections already confirmed by tracer information; whenever the tracer data are available, we used it as our ground truth. This consistency between both estimated and confirmed connections provides us the confidence about the effectiveness of our proposed methodology. To our knowledge, this is the first time causal analysis using solely production data is employed to discover interwell connections in an oil field dataset.

Segmenting Cellular Retinal Images by Optimizing Super-pixels, Multi-level Modularity, and Cell Boundary Representation.

We introduce an interactive method for retina layer segmentation in gray-level and RGB images based on super-pixels, multi-level optimization of modularity, and boundary erosion. Our method produces highly accurate segmentation results and can segment very large images. We have evaluated our method with two datasets of 2D confocal microscopy (CM) images of a mammalian retina.We have obtained average Jaccard index values of 0.948 and 0.942 respectively, confirming the high-quality segmentation performance of our method relative to a known ground truth segmentation. Average processing time was two seconds.

Relación del malestar emocional y el consumo de alcohol en adolescentes / Relationship of emotional distress and alcohol consumption in adolescents / Relação do sofrimento emocional e o consumo de álcool em adolescentes

Objetivo: determinar la relación y el efecto del malestar emocional en el consumo de alcohol de los adolescentes. Método: estudio cuantitativo transversal, descriptivo y predictivo, llevado a cabo en 200 adolescentes de ambos sexos de una institución pública de educación secundaria de Ciudad del Carmen Campeche, México; durante el mes enero de 2018. Se utilizó una cedula de datos sociodemográficos y antecedentes del consumo de alcohol y el Cuestionario de Factores de Riesgo y de Protección para el Consumo de Drogas. Los datos se procesaron en el programa SPSS a través de la estadística descriptiva e inferencial. Resultados: 19.5% de los adolescentes manifestaron haber ingerido alguna bebida alcohol en algún momento de su vida y el 14% en el último año. 64% presenta malestar emocional, de los cuales destaca la tristeza, alegría, aburrimiento y pensamientos repetitivos. Se observó una relación positiva (rs=.473, p=.003) y efecto (p=.001, f=10.787, gl=1) del malestar emocional con el consumo de alcohol. Conclusión: se pudo identificar que el malestar emocional está asociado a la ingesta de alcohol, esto como una forma de mitigar ese conjunto de emociones y sentimientos negativas y/o positivas característicos de esta etapa.

Objective: determine the relationship and effect of emotional distress on adolescents' alcohol consumption. Method: quantitative, cross-sectional, descriptive and predictive study was conducted in 200 middle school adolescents of both sexes in Ciudad del Carmen, Campeche, Mexico; during the month of January 2018. A sociodemographic data and alcohol consumption history and the Questionnaire on Risk Factors and Protection for Drug Use was used. Results: 19.5% of the adolescents claimed to have ingested an alcoholic beverage at some time in their life and 14% in the last year. 64% present emotional discomfort, of which sadness, happiness, boredom and repetitive thoughts stand out. A positive relationship was observed (rs=.473, p=.003) and effect (p=.001, f=10.787, gl=1) of emotional distress with alcohol consumption. Conclusion: it was possible to identify that the emotional distress is associated with alcohol consumption, this as a way to mitigate that set of negative and/or positive emotions and feelings characteristics of this stage.

Objetivo: determinar a relação e o efeito do desconforto emocional no consumo de álcool dos adolescentes. Método: estudo quantitativo de corte transversal, descritivo e preditivo, junto a 200 adolescentes de ambos sexos de uma instituição pública de ensino fundamental e médio da Ciudad del Carmen, Campeche, México, durante o mês de janeiro de 2018. Foi utilizado um questionário com dados sociodemográficos e uma história de álcool e o Questionário de Fatores de Risco e de Proteção para o Consumo de Drogas. Resultados: 19,5% dos adolescentes manifestaram ter ingerido alguma bebida alcoólica em algum momento da sua vida e o 14% no último ano. 64% apresenta desconforto emocional, dos quais destaca a tristeza, alegria, tédio e pensamentos repetitivos. Observou-se uma relação positiva (rs=.473, p=.003) e efeito (p=.001, f=10,787, gl=1) do desconforto emocional com o consumo de álcool. Conclusão: foi possível identificar que o desconforto emocional está associado ao consumo de álcool, isso como uma forma de mitigar esse conjunto de emoções e sentimentos negativos e/ou positivos caraterísticos dessa etapa.

Methadone Recycling Sustains Drug Reservoir in Tissue.

We hypothesize that there is a tissue store of methadone content in humans that is not directly accessible, but is quantifiable. Further, we hypothesize the mechanism by which methadone content is sustained in tissue stores involves methadone uptake, storage, and release from tissue depots in the body (recycling). Accordingly, we hypothesize that such tissue stores, in part, determine plasma methadone levels. We studied a random sample of six opioid-naïve healthy subjects. We performed a clinical trial simulation in silico using pharmacokinetic modeling. We found a large tissue store of methadone content whose size was much larger than methadone's size in plasma in response to a single oral dose of methadone 10 mg. The tissue store measured 13-17 mg. This finding could only be explained by the contemporaneous storage of methadone in tissue with dose recycling. We found that methadone recycles 2-5 times through an inaccessible extravascular compartment (IAC), from an accessible plasma-containing compartment (AC), before exiting irreversibly. We estimate the rate of accumulation (or storage) of methadone in tissue was 0.029-7.29 mg/h. We predict 39 ± 13% to 83 ± 6% of methadone's tissue stores "spillover" into the circulation. Our results indicate that there exists a large quantifiable tissue store of methadone in humans. Our results support the notion that methadone in humans undergoes tissue uptake, storage, release into the circulation, reuptake from the circulation, and re-release into the circulation, and that spillover of methadone from tissue stores, in part, maintain plasma methadone levels in humans.

In silico ordinary differential equation/partial differential equation hemodialysis model estimates methadone removal during dialysis.

BACKGROUND: There is a need to have a model to study methadone's losses during hemodialysis to provide informed methadone dose recommendations for the practitioner. AIM: To build a one-dimensional (1-D), hollow-fiber geometry, ordinary differential equation (ODE) and partial differential equation (PDE) countercurrent hemodialyzer model (ODE/PDE model). METHODOLOGY: We conducted a cross-sectional study in silico that evaluated eleven hemodialysis patients. Patients received a ceiling dose of methadone hydrochloride 30 mg/day. Outcome measures included: the total amount of methadone removed during dialysis; methadone's overall intradialytic mass transfer rate coefficient, km ; and, methadone's removal rate, j ME. Each metric was measured at dialysate flow rates of 250 mL/min and 800 mL/min. RESULTS: The ODE/PDE model revealed a significant increase in the change of methadone's mass transfer with increased dialysate flow rate, %Δkm =18.56, P=0.02, N=11. The total amount of methadone mass transferred across the dialyzer membrane with high dialysate flow rate significantly increased (0.042±0.016 versus 0.052±0.019 mg/kg, P=0.02, N=11). This was accompanied by a small significant increase in methadone's mass transfer rate (0.113±0.002 versus 0.014±0.002 mg/kg/h, P=0.02, N=11). The ODE/PDE model accurately predicted methadone's removal during dialysis. The absolute value of the prediction errors for methadone's extraction and throughput were less than 2%. CONCLUSION: ODE/PDE modeling of methadone's hemodialysis is a new approach to study methadone's removal, in particular, and opioid removal, in general, in patients with end-stage renal disease on hemodialysis. ODE/PDE modeling accurately quantified the fundamental phenomena of methadone's mass transfer during hemodialysis. This methodology may lead to development of optimally designed intradialytic opioid treatment protocols, and allow dynamic monitoring of outflow plasma opioid concentrations for model predictive control during dialysis in humans.

CYP2D6 phenotype-specific codeine population pharmacokinetics.

Codeine's metabolic fate in the body is complex, and detailed quantitative knowledge of it, and that of its metabolites is lacking among prescribers. We aimed to develop a codeine pharmacokinetic pathway model for codeine and its metabolites that incorporates the effects of genetic polymorphisms. We studied the phenotype-specific time courses of plasma codeine, codeine-6-glucoronide, morphine, morphine-3-glucoronide, and morphine-6-glucoronide. A codeine pharmacokinetic pathway model accurately fit the time courses of plasma codeine and its metabolites. We used this model to build a population pharmacokinetic codeine pathway model. The population model indicated that about 10% of a codeine dose was converted to morphine in poor-metabolizer phenotype subjects. The model also showed that about 40% of a codeine dose was converted to morphine in EM subjects, and about 51% was converted to morphine in ultrarapid-metabolizers. The population model further indicated that only about 4% of MO formed from codeine was converted to morphine-6-glucoronide in poor-metabolizer phenotype subjects. The model also showed that about 39% of the MO formed from codeine was converted to morphine-6-glucoronide in extensive-metabolizer phenotypes, and about 58% was converted in ultrarapid-metabolizers. We conclude, a population pharmacokinetic codeine pathway model can be useful because beyond helping to achieve a quantitative understanding the codeine and MO pathways, the model can be used for simulation to answer questions about codeine's pharmacogenetic-based disposition in the body. Our study suggests that pharmacogenetics for personalized dosing might be most effectively advanced by studying the interplay between pharmacogenetics, population pharmacokinetics, and clinical pharmacokinetics.

Individualized Hydrocodone Therapy Based on Phenotype, Pharmacogenetics, and Pharmacokinetic Dosing.

OBJECTIVES: (1) To quantify hydrocodone (HC) and hydromorphone (HM) metabolite pharmacokinetics with pharmacogenetics in CYP2D6 ultra-rapid metabolizer (UM), extensive metabolizer (EM), and poor metabolizer (PM) metabolizer phenotypes. (2) To develop an HC phenotype-specific dosing strategy for HC that accounts for HM production using clinical pharmacokinetics integrated with pharmacogenetics for patient safety. SETTING: In silico clinical trial simulation. PARTICIPANTS: Healthy white men and women without comorbidities or history of opioid, or any other drug or nutraceutical use, age 26.3±5.7 years (mean±SD; range, 19 to 36 y) and weight 71.9±16.8 kg (range, 50 to 108 kg). MAIN OUTCOME MEASURES: CYP2D6 phenotype-specific HC clinical pharmacokinetic parameter estimates and phenotype-specific percentages of HM formed from HC. RESULTS: PMs had lower indices of HC disposition compared with UMs and EMs. Clearance was reduced by nearly 60% and the t1/2 was increased by about 68% compared with EMs. The canonical order for HC clearance was UM>EM>PM. HC elimination mainly by the liver, represented by ke, was reduced about 70% in PM. However, HC's apparent Vd was not significantly different among UMs, EMs, and PM. The canonical order of predicted plasma HM concentrations was UM>EM>PM. For each of the CYP2D6 phenotypes, the mean predicted HM levels were within HM's therapeutic range, which indicates HC has significant phenotype-dependent pro-drug effects. CONCLUSIONS: Our results demonstrate that pharmacogenetics afford clinicians an opportunity to individualize HC dosing, while adding enhanced opportunity to account for its conversion to HM in the body.

Organ-specific microcirculatory mass transport of oxycodone in humans: clinical implications for therapeutic use.

OBJECTIVES: To begin to address the problem of heterogeneity of distribution of oxycodone (OC) in humans, we developed an organ-specific microcirculatory capillary-tissue exchange 2-compartment model for studying regional OC mass transport. MATERIALS AND METHODS: The model was developed in silico. It quantifies OC's organ-specific mass transport rates, clearances and recycling, and it considers the effects of blood flow on OC's convective and diffusive transport. RESULTS: What is new is the finding that OC undergoes local recycling at the level of organ-specific capillary-tissue exchange units in humans. Results indicate recycled OC occurs in sufficient amounts to function as a reusable source of circulating OC; which has important implications for OC dosing. Results show the brain, which is central to OC effects only receives about 8% of OC delivered to all organs via the microcirculation. This suggests that differential regulation of receptor binding, trafficking, internalization, or desensitization in the brain likely plays a dominant role in OC's central analgesic effects. DISCUSSION: Organ-specific OC mass transport kinetics provide new information for OC dosing in pain management. The model promotes patient safety in opioid prescribing because it allows predictions to be made about the relative contribution that OC recycling makes to circulating OC levels. The model indicates that pharmacologic modulation of the microcirculation may give way to site-specific delivery of opioids in the future. Our study demonstrates that translation of bench in silico research data into clinical practice, although still challenging, is feasible and can assist in OC dose regimen design for patient safety.

Oxycodone recycling: a novel hypothesis of opioid tolerance development in humans.

We hypothesize that oxycodone (OC) recycling promotes sustained synaptic OC content, which prolongs OC's exposure to local µ-opioid receptors (µORs). In that way, OC recycling gives rise to OC tolerance in humans. To pilot test our hypothesis, we developed a whole-body OC mass transport tolerance recovery model. The model derived quantifiable measure of tolerance is TΩ. TΩ estimates OC's tolerance recovery in days; It is defined as the rate of recovery of OC's pharmacologic response after OC is stopped. We studied a random sample of five opioid intolerant healthy male subjects with no history of opioid or illicit drug use, or comorbidities in silico. Subjects were age 24.5 ± 2.3 yr (all values mean ± SD), weight 93 ± 20 kg, and CYP2D6 EM phenotype. Each subject was studied under two experimental conditions: (1) administration of a single oral dose of OC 12 ± 7 mg; and, after complete washout of OC from the intravascular pool, (2) administration of repetitive oral OC doses every 4h for 5 half-lives (t1/2 = 4.5h)-after which time steady-state was assumed. Repetitive OC dose TΩ fell 61% compared to single OC dose TΩ (5.2 ± 1.1 vs. 3.5 ± 0.7 days, p = 0.001). The fall in TΩ was associated with a significant 3-fold increase in extravascular OC content, which was accompanied by 2-fold increase in OC spillover from the extravascular pool, into the intravascular pool. Thus, the model predicted that a single dose of orally administered OC could give rise to tolerance. This is consistent with the widely held view of acute opioid tolerance. In addition, the dynamic changes accompanying repetitive OC dosing suggested that local unbound OC gave rise to both higher extravascular OC content and increased OC spillover. This reflects that OC stimulated endocytosis of µORs was accompanied by a reduction in the availability OC responsive neuroeffector cell surface µOR binding sites. We conclude that our hypothesis extends current concepts of opioid tolerance development to include OC recycling. OC recycling is a novel hypothesis of OC tolerance development in humans.

A transit compartment model unmasks OxyContin's reflective pharmacokinetics from urine measurements in humans.

The absorption pattern of orally administered OxyContin (OXC) reflected in urine indicates that its appearance into systemic circulation undergoes transit absorption delays. The authors developed an OXC transit-delay compartment model that identified a new source of oxycodone hydrochloride (OC): the rate of appearance of OC due to OXC tablet dissolution in transit through the gastrointestinal (GI) tract (R(a)(GI)), which is due to disintegration of OXC's AcroContin delivery system. R(a)(GI) is independent of the biphasic dissolution and release of OC from the delivery system. The authors conclude that an OXC transit-delay compartment model can be of value in the interpretation of OXC pharmacokinetics.

Personalized oxycodone dosing: using pharmacogenetic testing and clinical pharmacokinetics to reduce toxicity risk and increase effectiveness.

OBJECTIVE: To develop a framework for integrating pharmacogenetics with clinical pharmacokinetics for personalized oxycodone dosing based on a patient's CYP2D6 phenotype. DESIGN: Randomized, crossover, double-blind, placebo-controlled. Subjects were genotyped as CYP2D6 ultra-rapid metabolizer, extensive metabolizer, or poor metabolizer phenotypes. Five subjects from each phenotype were randomly selected for inclusion in our study. SETTING: Studies were performed in silico. SUBJECTS: The subjects were male, age 26 years, height 181.2 cm, and weight 76.3 kg. They were healthy without comorbidities, and their medical examinations were normal. METHODS: The trajectories of phenotype-specific plasma oxycodone concentration-time profiles were analyzed using weighted nonlinear least-squares regression with WinSAAM software. A global two-stage population-based model data analysis procedure was used to analyze the studies. Clinical pharmacokinetics were calculated using the R package cpk, eliminating the need to perform hand-calculations. RESULTS: Our study shows how clinicians can reduce risk and increase effectiveness for oxycodone dosing by (1) determining the patient's likely metabolic response through testing a patient's CYP2D6 phenotype, and (2) calculating clinical pharmacokinetics specific to the patient's CYP2D6 phenotype to design a personalized oxycodone dosing regimen. CONCLUSIONS: Personalized oxycodone dosing is a new tool for a clinician treating chronic pain patients requiring oxycodone. By expressing a patient's CYP2D6 phenotype pharmacokinetically, a clinician (at least theoretically) can improve the safety and efficacy of oxycodone and decrease the risk for iatrogenically induced overdose or death. Pharmacokinomics provides a general framework for the integration of pharmacogenetics with clinical pharmacokinetics into clinical practice for gene-based prescribing.

The CYP2D6 gene determines oxycodone's phenotype-specific addictive potential: implications for addiction prevention and treatment.

We propose a hypothesis for predicting addictive potential of oral drugs, in general, and oxycodone's addictive potential, in particular. We hypothesize that a patient's CYP2D6 phenotype determines oxycodone's addictive potential, in part, via genotype-specific regulation of its clearance; although, other possible modulators of oxycodone's addiction potential exist. For example, brain CYPs related to phenotype could be involved. To pilot test our hypothesis, we used a mathematical model which postulates that oxycodone's addictive potential is given by: LAP=E/(ka/ke), where LAP represents addictive potential, E represents euphoric potency, ka is the absorption rate constant of drug from the gastrointestinal tract, and ke is the systemic elimination rate constant of drug by all processes responsible for its removal from plasma. Using CYP2D6 phenotype-specific oxycodone pharmacokinetic parameter values derived from published data, our hypothesis predicted that the canonical order of oxycodone's addictive potential was UM>EM>IM>PM, with corresponding LAP values of 0.24, 0.21, 0.17, and 0.15 respectively. Our hypothesis about oxycodone's addictive potential may provide a unifying approach useful for both personalized medicine dosing and predicting addictive potential of oral drugs in humans, since it is based on both oxycodone's pharmacogenetics and pharmacokinetics.

A new model for using quantitative urine testing as a diagnostic tool for oxycodone treatment and compliance.

We conducted a prospective, randomized, cross-sectional study to develop and validate a new model to predict oxycodone in urine that can be used to help evaluate whether patients are complying with their oxycodone dosing regimens. We studied 20 patients: eight black women, two white women, six black men, and four white men; ages 48 ± 10 years (mean ± SD); weight 97 ± 32 kg. Pain levels before treatment averaged 9.5 ± 0.9 out of 10. We prescribed oral oxycodone for each patient, tailoring the dosing regimen using clinical pharmacokinetics and measured the oxycodone concentration in each patient's urine 10 to 14 days after starting the dosing regimen. For each patient, we predicted oxycodone in their urine using our model, checked the actual concentration, and compared predicted with actual concentrations. For 18 of 20 patients (90%), actual results fell within ±10% of our model's prediction. One patient was 35% below the prediction; the other was 51% above. Our model accurately predicts oxycodone in urine (±10% for 90% of the patients). The model appears clinically useful for evaluating the results of a quantitative urine test, since it objectively discriminates between (1) a "normal" patient complying with their oxycodone dosing regimen, and (2) a patient who may require genetic testing to distinguish between unusual metabolism or abuse.

Computational opioid prescribing: a novel application of clinical pharmacokinetics.

We implemented a pharmacokinetics-based mathematical modeling technique using algebra to assist prescribers with point-of-care opioid dosing. We call this technique computational opioid prescribing (COP). Because population pharmacokinetic parameter values are needed to estimate drug dosing regimen designs for individual patients using COP, and those values are not readily available to prescribers because they exist scattered in the vast pharmacology literature, we estimated the population pharmacokinetic parameter values for 12 commonly prescribed opioids from various sources using the bootstrap resampling technique. Our results show that opioid dosing regimen design, evaluation, and modification is feasible using COP. We conclude that COP is a new technique for the quantitative assessment of opioid dosing regimen design evaluation and adjustment, which may help prescribers to manage acute and chronic pain at the point-of-care. Potential benefits include opioid dose optimization and minimization of adverse opioid drug events, leading to potential improvement in patient treatment outcomes and safety.

Ordinary differential equation PK/PD models using the SAS macro NLINMIX.

We describe some theory and recent enhancements for the SAS macro NLINMIX (Wolfinger, R. D. (1993). Laplace's approximation for nonlinear mixed effects models. Biometrika 80:791-795) that enable model calculation to take place within the interaction matrix language SAS/IML (SAS Institute Inc. (1999a). SAS/IML User's Guide Version 7. Cary, NC: SAS Institute Inc.). They provide greater flexibility and scope for the specification and analysis of complex nonlinear mixed models. For example, using data from a frequently sampled intravenous glucose test, we fit a two-compartment kinetics model that has no closed-form representation. It is derived as the solution of a system of ordinary differential equations and specified as such in SAS/IML. Additional details and example NLINMIX code are available in Appendix A.

Cardiac-specific norepinephrine mass transport and its relationship to left ventricular size and systolic performance.

Objectives of this study were to develop a technique for quantifying cardiac-specific norepinephrine (NE) mass transport and determine whether cardiac NE kinetic modeling parameters were related to physiological variables of left ventricular (LV) size and systolic performance in nine patients with chronic mitral regurgitation. Biplane contrast cineventriculograms were used to determine LV size and ejection fraction (EF), micromanometer LV pressures and radionuclide LV volumes from a range of loading conditions to calculate LV end-systolic elastance, and [(3)H]NE infusions with LV and coronary sinus sampling for [(3)H]NE and endogenous NE during and after termination of infusions to model NE mass transport. Total NE release rate into cardiac interstitial fluid (M(IF)(R)) averaged 859 +/- 214 and NE released de novo into cardiac interstitial fluid (M(IF)(u,r,en)) averaged 546 +/- 174 pmol/min. Both M(IF)(R) and M(IF)(u,r,en)correlated directly with LV end-systolic volume (r = 0.84, P = 0.005; r = 0.86, P = 0.003); inversely with LV EFs (r = -0.75, P = 0.02; r = -0.81, P = 0.008); and inversely with LV end-systolic elastance values, optimally fit by a nonlinear function (r = 0.89, P = 0.04; r = 0.96, P = 0.01). We conclude that total and newly released NE into interstitial fluid of the heart, determined by regional mass transport kinetic model, are specific measures of regional cardiac-specific sympathetic nervous system activity and are strongly related to measures of LV size and systolic performance. These data support the concept that this new model of organ-specific NE kinetics has physiological relevance.