Benefits and risks of clofarabine in adult acute lymphoblastic leukemia investigated in depth by multi-state modeling

BACKGROUND

We recently reported results of the prospective, open-label HOVON-100 trial in 334 adult patients with acute lymphoblastic leukemia (ALL) randomized to first-line treatment with or without clofarabine (CLO). No improvement of event-free survival (EFS) was observed, while a higher proportion of patients receiving CLO obtained minimal residual disease (MRD) negativity.

AIM

In order to investigate the effects of CLO in more depth, two multi-state models were developed to identify why CLO did not show a long-term survival benefit despite more MRD-negativity.

METHODS

The first model evaluated the effect of CLO on going off-protocol (not due to refractory disease/relapse, completion or death) as a proxy of severe treatment-related toxicity, while the second model evaluated the effect of CLO on obtaining MRD negativity. The subsequent impact of these intermediate events on death or relapsed/refractory disease was assessed in both models.

RESULTS

Overall, patients receiving CLO went off-protocol more frequently than control patients (35/168 [21%] vs. 18/166 [11%], p = 0.019; HR 2.00 [1.13-3.52], p = 0.02), especially during maintenance (13/44 [30%] vs. 6/56 [11%]; HR 2.85 [95%CI 1.08-7.50], p = 0.035). Going off-protocol was, however, not associated with more relapse or death. Patients in the CLO arm showed a trend towards an increased rate of MRD-negativity compared with control patients (HR MRD-negativity: 1.35 [0.95-1.91], p = 0.10), which did not translate into a significant survival benefit.

CONCLUSION

We conclude that the intermediate states, i.e., going off-protocol and MRD-negativity, were affected by adding CLO, but these transitions were not associated with subsequent survival estimates, suggesting relatively modest antileukemic activity in ALL.

Aberrant dynamic properties of whole-brain functional connectivity in acute mild traumatic brain injury revealed by hidden Markov models

OBJECTIVES

This study aimed to investigate the temporal dynamics of brain activity and characterize the spatiotemporal specificity of transitions and large-scale networks on short timescales in acute mild traumatic brain injury (mTBI) patients and those with cognitive impairment in detail.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired for 71 acute mTBI patients and 57 age-, sex-, and education-matched healthy controls (HCs). A hidden Markov model (HMM) analysis of rs-fMRI data was conducted to identify brain states that recurred over time and to assess the dynamic patterns of activation states that characterized acute mTBI patients and those with cognitive impairment. The dynamic parameters (fractional occupancy, lifetime, interval time, switching rate, and probability) between groups and their correlation with cognitive performance were analyzed.

RESULTS

Twelve HMM states were identified in this study. Compared with HCs, acute mTBI patients and those with cognitive impairment exhibited distinct changes in dynamics, including fractional occupancy, lifetime, and interval time. Furthermore, the switching rate and probability across HMM states were significantly different between acute mTBI patients and patients with cognitive impairment (all p < 0.05). The temporal reconfiguration of states in acute mTBI patients and those with cognitive impairment was associated with several brain networks (including the high-order cognition network [DMN], subcortical network [SUB], and sensory and motor network [SMN]).

CONCLUSIONS

Hidden Markov models provide additional information on the dynamic activity of brain networks in patients with acute mTBI and those with cognitive impairment. Our results suggest that brain network dynamics determined by the HMM could reinforce the understanding of the neuropathological mechanisms of acute mTBI patients and those with cognitive impairment.

Measures of fragmentation of rest activity patterns: mathematical properties and interpretability based on accelerometer real life data

Accelerometers, devices that measure body movements, have become valuable tools for studying the fragmentation of rest-activity patterns, a core circadian rhythm dimension, using metrics such as inter-daily stability (IS), intradaily variability (IV), transition probability (TP), and self-similarity parameter (named α ). However, their use remains mainly empirical. Therefore, we investigated the mathematical properties and interpretability of rest-activity fragmentation metrics by providing mathematical proofs for the ranges of IS and IV, proposing maximum likelihood and Bayesian estimators for TP, introducing the activity balance index metric, an adaptation of α , and describing distributions of these metrics in real-life setting. Analysis of accelerometer data from 2,859 individuals (age=60-83 years, 21.1% women) from the Whitehall II cohort (UK) shows modest correlations between the metrics, except for ABI and α . Sociodemographic (age, sex, education, employment status) and clinical (body mass index (BMI), and number of morbidities) factors were associated with these metrics, with differences observed according to metrics. For example, a difference of 5 units in BMI was associated with all metrics (differences ranging between -0.261 (95% CI -0.302, -0.220) to 0.228 (0.18, 0.268) for standardised TP rest to activity during the awake period and TP activity to rest during the awake period, respectively). These results reinforce the value of these rest-activity fragmentation metrics in epidemiological and clinical studies to examine their role for health. This paper expands on a set of methods that have previously demonstrated empirical value, improves the theoretical foundation for these methods, and evaluates their empirical worth in a large dataset.

Evaluation of transitions from early hypertension to hypertensive chronic kidney disease, coronary artery disease, stroke and mortality: a Thai real-world data cohort

Objective

Systemic arterial hypertension (HT) is a major modifiable risk factor for cardiovascular disease (CVDs), associated with all-cause death (ACD). Understanding its progression from the early state to late complications should lead to more timely intensification of treatment. This study aimed to construct a real-world cohort profile of HT and to estimate transition probabilities from the uncomplicated state to any of these long-term complications; chronic kidney disease (CKD), coronary artery disease (CAD), stroke, and ACD.

Methods

This real-world cohort study used routine clinical practice data for all adult patients diagnosed with HT in the Ramathibodi Hospital, Thailand from 2010 to 2022. A multi-state model was developed based on the following: state 1-uncomplicated HT, 2-CKD, 3-CAD, 4-stroke, and 5-ACD. Transition probabilities were estimated using Kaplan-Meier method.

Results

A total of 144,149 patients were initially classified as having uncomplicated HT. The transition probabilities (95% CI) from the initial state to CKD, CAD, stroke, and ACD at 10-years were 19.6% (19.3%, 20.0%), 18.2% (17.9%, 18.6%), 7.4% (7.1%, 7.6%), and 1.7% (1.5%, 1.8%), respectively. Once in the intermediate-states of CKD, CAD, and stroke, 10-year transition probabilities to death were 7.5% (6.8%, 8.4%), 9.0% (8.2%, 9.9%), and 10.8% (9.3%, 12.5%).

Conclusions

In this 13-year cohort, CKD was observed as the most common complication, followed by CAD and stroke. Among these, stroke carried the highest risk of ACD, followed by CAD and CKD. These findings provide improved understanding of disease progression to guide appropriate prevention measures. Further investigations of prognostic factors and treatment effectiveness are warranted.

Pseudo-Qutrit Formed by Two Interacting Identical Spins (s = 1/2) in a Variable External Magnetic Field

An analytical solution is obtained for the problem of two interacting, identical but separated spin 1/2 particles in a time-dependent external magnetic field, in a general case. The solution involves isolating the pseudo-qutrit subsystem from a two-qubit system. It is shown that the quantum dynamics of a pseudo-qutrit system with a magnetic dipole-dipole interaction can be described clearly and accurately in an adiabatic representation, using a time-dependent basis set. The transition probabilities between the energy levels for an adiabatically varying magnetic field, which follows the Landau-Majorana-Stuckelberg-Zener (LMSZ) model within a short time interval, are illustrated in the appropriate graphs. It is shown that for close energy levels and entangled states, the transition probabilities are not small and strongly depend on the time. These results provide insight into the degree of entanglement of two spins (qubits) over time. Furthermore, the results are applicable to more complex systems with a time-dependent Hamiltonian.

A Physical Measure for Characterizing Crossover from Integrable to Chaotic Quantum Systems

In this paper, a quantity that describes a response of a system's eigenstates to a very small perturbation of physical relevance is studied as a measure for characterizing crossover from integrable to chaotic quantum systems. It is computed from the distribution of very small, rescaled components of perturbed eigenfunctions on the unperturbed basis. Physically, it gives a relative measure to prohibition of level transitions induced by the perturbation. Making use of this measure, numerical simulations in the so-called Lipkin-Meshkov-Glick model show in a clear way that the whole integrability-chaos transition region is divided into three subregions: a nearly integrable regime, a nearly chaotic regime, and a crossover regime.

Behavioral stability of alcohol consumption and socio-demographic correlates of change among a nationally representative cohort of US adults

AIMS

To estimate the probability of transitioning between different categories of alcohol use (drinking states) among a nationally representative cohort of United States (US) adults and to identify the effects of socio-demographic characteristics on those transitions.

DESIGN, SETTING AND PARTICIPANTS

Secondary analysis of data from the National Epidemiologic Survey of Alcohol and Related Conditions (NESARC), a prospective cohort study conducted in 2001-02 and 2004-05; a US nation-wide, population-based study. Participants included 34 165 adults (mean age = 45.1 years, standard deviation = 17.3; 52% women).

MEASUREMENTS

Alcohol use was self-reported and categorized based on the grams consumed per day: (1) non-drinker (no drinks in past 12 months), (2) category I (women = ≤ 20; men = ≤ 40), (3) category II (women = 21-40; men = 41-60) and (4) category III (women = ≥ 41; men = ≥ 61). Multi-state Markov models estimated the probability of transitioning between drinking states, conditioned on age, sex, race/ethnicity and educational attainment. Analyses were repeated with alcohol use categorized based on the frequency of heavy episodic drinking.

FINDINGS

The highest transition probabilities were observed for staying in the same state; after 1 year, the probability of remaining in the same state was 90.1% [95% confidence interval (CI) = 89.7%, 90.5%] for non-drinkers, 90.2% (95% CI = 89.9%, 90.5%) for category I, 31.8% (95% CI = 29.7, 33.9%) category II and 52.2% (95% CI = 46.0, 58.5%) for category III. Women, older adults, and non-Hispanic Other adults were less likely to transition between drinking states, including transitions to lower use. Adults with lower educational attainment were more likely to transition between drinking states; however, they were also less likely to transition out of the 'weekly HED' category. Black adults were more likely to transition into or stay in higher use categories, whereas Hispanic/Latinx adults were largely similar to White adults.

CONCLUSIONS

In this study of alcohol transition probabilities, some demographic subgroups appeared more likely to transition into or persist in higher alcohol consumption states.

Modelling method of inter-building movement for campus-scale occupancy simulation: A case study

As an important factor in the investigation of building energy consumption, occupant behavior (OB) has been widely studied on the building level. However so far, studies of OB modelling on the district scale remain limited. Indeed, district-scale OB modelling has been facing the challenges from the scarcity of district-scale data, modelling methods, as well as simulation application. This study initiates the extrapolation of occupancy modelling methodology from building level to district scale through proposing modelling methods of inter-building movements. The proposed modelling methods utilize multiple distribution fittings and Bayesian network to upscale the event description methods from inter-zone movement events at the building level to inter-building movement events at the district level. This study provides a framework on the application of the proposed modelling methods for a university campus in the suburbs of Shanghai, taking advantages of data sensing, monitoring and survey techniques. With the collected campus-scale occupancy data, this paper defines five patterns of inter-building movement. One pattern represents the dominated inter-building movement events for one kind of students in their daily campus life. Based on the quantitative descriptions for various inter-building movement events, this study performs the stochastic simulation for the campus district, using Markov chain models. The simulation results are then validated with the campus-scale occupancy measurement data. Furthermore, the impact of inter-building movement modelling methods on building energy demand is evaluated for the library building, taking the deterministic occupancy schedules suggested by current building design standard as a baseline.

Transition patterns of weight status: A cohort study of Chinese school-age children

Background

Childhood overweight and obesity are increasing public concerns. However, little is known about the transition patterns of childhood weight status, especially in developing countries. In this study, we aimed to evaluate patterns of change in weight status and the risk factors among Chinese school-age children.

Methods

This retrospective cohort study included 2,334 children aged 6 years with complete 5-year (2012-2017) physical examination data in Minhang District, Shanghai. A time-homogeneous three-state Markov model was fit to the longitudinal data with dynamic outcomes (normal weight, overweight, and obesity).

Results

According to the Markov model, 42.3% of school-age children who were initially overweight transitioned to another weight status within 1 year, with 24.8% (95% confidence interval [CI]: 23.1, 27.0) transitioning to normal weight and 17.5% (95% CI: 15.9, 19.3) becoming obese. In contrast, children who were initially normal weight (92.9% [95% CI: 92.3, 93.5]) or obese (83.1% [95% CI: 81.1, 84.8]) tended to maintain their initial weight status. Male sex, semi-urban area, absence of late adiposity rebound, lower annual height increments, higher annual weight increments, and higher initial body mass index were significantly associated with a higher risk of developing or maintaining overweight and obesity (p < 0.05).

Conclusions

The weight status of Chinese school-age children is more likely to change among those who are initially overweight than in those who are initially obese. Interventions to promote healthy weight status may be more effective if key groups are targeted, such as overweight and pre-school-age children.

Discrete-space continuous-time models of marine mammal exposure to Navy sonar

Assessing the patterns of wildlife attendance to specific areas is relevant across many fundamental and applied ecological studies, particularly when animals are at risk of being exposed to stressors within or outside the boundaries of those areas. Marine mammals are increasingly being exposed to human activities that may cause behavioral and physiological changes, including military exercises using active sonars. Assessment of the population-level consequences of anthropogenic disturbance requires robust and efficient tools to quantify the levels of aggregate exposure for individuals in a population over biologically relevant time frames. We propose a discrete-space, continuous-time approach to estimate individual transition rates across the boundaries of an area of interest, informed by telemetry data collected with uncertainty. The approach allows inferring the effect of stressors on transition rates, the progressive return to baseline movement patterns, and any difference among individuals. We apply the modeling framework to telemetry data from Blainville's beaked whale (Mesoplodon densirostris) tagged in the Bahamas at the Atlantic Undersea Test and Evaluation Center (AUTEC), an area used by the U.S. Navy for fleet readiness training. We show that transition rates changed as a result of exposure to sonar exercises in the area, reflecting an avoidance response. Our approach supports the assessment of the aggregate exposure of individuals to sonar and the resulting population-level consequences. The approach has potential applications across many applied and fundamental problems where telemetry data are used to characterize animal occurrence within specific areas.

Feasibility of Using Boltzmann Plots to Evaluate the Stark Broadening Parameters of Cu(I) Lines

A linear Boltzmann plot was constructed using Cu(I) lines of well-known atomic parameters. Aligning other spectral lines to the plot was adopted as a viable way to estimate the most probable values of Stark broadening parameters of Cu(I) lines at 330.79, 359.91, and 360.2 nm. Plasma was generated by focusing neodymium-doped yttrium aluminum garnet (Nd:YAG) laser radiation at wavelength 532 nm on a pure copper target in open air. Plasma emission was recorded at delay times of 3, 4, 5, 7, and 10 μs. The in situ optically thin H_α line was used to determine the plasma reference electron density over the entire experiment. Following this method, the missing values of the Stark broadening parameters of the three Cu(I) lines turn out to be about 0.15 ± 0.05 Å (for 330.79 nm transition) and 0.17 ± 0.05 Å (for 359.91 360.20 nm transition) at reference electron density of (1 ± 0.09) × 10¹⁷cm^-3 and temperature of 10 800 ± 630 K. The apparent variation in plasma parameters at different delay times was found to scale with electron density and temperature as ∼n_e.T_e^0.166.

Cell Cycle Commitment and the Origins of Cell Cycle Variability

Exit of cells from quiescence following mitogenic stimulation is highly asynchronous, and there is a great deal of heterogeneity in the response. Even in a single, clonal population, some cells re-enter the cell cycle after a sub-optimal mitogenic signal while other, seemingly identical cells, do not, though they remain capable of responding to a higher level of stimulus. This review will consider the origins of this variability and heterogeneity, both in cells re-entering the cycle from quiescence and in the context of commitment decisions in continuously cycling populations. Particular attention will be paid to the role of two interacting molecular networks, namely the RB-E2F and APC/C^CDH1 "switches." These networks have the property of bistability and it seems likely that they are responsible for dynamic behavior previously described kinetically by Transition Probability models of the cell cycle. The relationship between these switches and the so-called Restriction Point of the cell cycle will also be considered.

States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model

Background: Postpartum depression (PPD) has been recognized as a severe public health problem worldwide due to its high incidence and the detrimental consequences not only for the mother but for the infant and the family. However, the pattern of natural transition trajectories of PPD has rarely been explored. Methods: In this research, a quantitative longitudinal study was conducted to explore the PPD progression process, providing information on the transition probability, hazard ratio, and the mean sojourn time in the three postnatal mental states, namely normal state, mild PPD, and severe PPD. The multi-state Markov model was built based on 912 depression status assessments in 304 Chinese primiparous women over multiple time points of six weeks postpartum, three months postpartum, and six months postpartum. Results: Among the 608 PPD status transitions from one visit to the next visit, 6.2% (38/608) showed deterioration of mental status from the level at the previous visit; while 40.0% (243/608) showed improvement at the next visit. A subject in normal state who does transition then has a probability of 49.8% of worsening to mild PPD, and 50.2% to severe PPD. A subject with mild PPD who does transition has a 20.0% chance of worsening to severe PPD. A subject with severe PPD is more likely to improve to mild PPD than developing to the normal state. On average, the sojourn time in the normal state, mild PPD, and severe PPD was 64.12, 6.29, and 9.37 weeks, respectively. Women in normal state had 6.0%, 8.5%, 8.7%, and 8.8% chances of progress to severe PPD within three months, nine months, one year, and three years, respectively. Increased all kinds of supports were associated with decreased risk of deterioration from normal state to severe PPD (hazard ratio, HR: 0.42-0.65); and increased informational supports, evaluation of support, and maternal age were associated with alleviation from severe PPD to normal state (HR: 1.46-2.27). Conclusions: The PPD state transition probabilities caused more attention and awareness about the regular PPD screening for postnatal women and the timely intervention for women with mild or severe PPD. The preventive actions on PPD should be conducted at the early stages, and three yearly; at least one yearly screening is strongly recommended. Emotional support, material support, informational support, and evaluation of support had significant positive associations with the prevention of PPD progression transitions. The derived transition probabilities and sojourn time can serve as an importance reference for health professionals to make proactive plans and target interventions for PPD.

Interspecific variation in mortality and growth and changes in their relationship with size class in an old-growth temperate forest

Understanding trade-offs between demographic parameters is crucial when investigating community assembly rules in high-diversity forests. To this end, we estimated mortality and growth parameters, and correlations among them, across entire size classes for 17 tree species (Betula, Carpinus, Fagus, Quercus, Castanea, Acer, Cerasus, Swida, Kalopanax, and Styrax) using a dataset over 18 years obtained from an old-growth forest in Japan.Size classes were represented by 12 categories determined by age, height, and diameter at breast height (DBH) from new seedlings to stems of DBH >85 cm. We derived the annual mortality and growth for each species and class using estimates of transition probabilities between classes. Trade-offs or synergies in growth and survival among species per size class were analyzed with and without the inclusion of phylogenetic relationships.Annual mortality showed U-shaped patterns across size classes for species that could potentially reach a DBH ≥55 cm: 0.2-0.98 for seedlings, 0.002-0.01 at DBH 35-45 cm, and ca. 0.01 at DBH ≥55 cm. Other species demonstrated monotonically decreasing mortality toward specific maximum size classes. When phylogenetic information was included in analyses, the correlations between survival and growth changed across size classes were significant for some classes: As an overall tendency, synergy was observed in growth and survival for seedling to sapling classes, trade-offs for juvenile to DBH 15-25 cm classes, and synergy again for larger classes. When phylogenetic information was not included, a significant trade-off was observed only at DBH 5-15 cm. Synthesis. Trade-offs at intermediate classes imply differentiation in demographic characteristics related to life history strategies. However, evolutionarily obtained demographic characteristics are not substantial drivers of niche differentiation in the study area. The polylemma of mortality, growth, and other parameters such as the onset of reproduction may also be important factors driving species-specific demographic traits.

Research on Disability Grading Based on ICF Functional Framework: Empirical Evidence From Zhejiang Province, China

Through assignment method, the total score of disability in multiple dimensions is obtained, and it is divided into five functional states—severe disability, partial disability, moderate disability, mild disability, and health—according to the score, and the probability of death is constructed. Using the Chinese Longitudinal Healthy Longevity Survey (CLHLS) database tracking survey data, by constructing a multistate transition probability matrix, the empirical calculation of the multistate disability transfer probability, with the help of the sixth national census data, we estimated maintenance time of each state, life expectancy, etc. The results show that the 3 year transfer probability of the initial healthy elderly is the highest, and the mortality rate is also the lowest. It can be found that the disability state transition probability measurement based on the data is more accurate than the model estimation; the disability scale and life expectancy estimated based on the multistate transition probability matrix are more reliable.

Comparison of liver disease state progression in patients with eradication of versus persistent infection with hepatitis C virus: Markov chain analysis

To investigate the long-term prognosis of liver disease in patients with hepatitis C virus (HCV) eradication after antiviral therapy versus those with persistent HCV infection. Four hundred and eighty patients (5259 person-years [PYs]) who received interferon-based therapy and achieved sustained virologic response and 848 patients (3853 PYs) with persistent HCV infection were included. In the analysis of 1-year liver disease state transition probability matrices using Markov chain models, progression to cirrhosis from the chronic hepatitis state was observed (0.00%-0.63%) in patients with HCV eradication. Among patients with chronic hepatitis or cirrhosis and HCV eradication, hepatocellular carcinoma (HCC) development was observed in males aged ≥ 50 years (0.97%-1.96%) and females aged ≥ 60 years (0.26%-5.00%). Additionally, in patients with cirrhosis and HCV eradication, improvement to chronic hepatitis was also observed (4.94%-10.64%). Conversely, in patients with chronic hepatitis and persistent HCV infection, progression to cirrhosis was observed in males aged ≥ 30 years and female aged ≥ 40 years (0.44%-1.99%). In males aged ≥ 40 years and female aged ≥ 50 years with cirrhosis, the transition probability for HCC was relatively high (4.17%-14.02%). Under the assumption of either chronic hepatitis or cirrhosis at age 40 or 60 years as the starting condition for simulation over the next 30 or 40 years, respectively, the probability of HCC was higher in patients with persistent HCV infection than those with HCV eradication. In conclusion, HCV eradication can reduce the risk of developing cirrhosis or HCC in patients with chronic HCV infection.

Multistate Models for Examining the Progression of Intermittently Measured Patient-Reported Symptoms Among Patients With Cancer: The Importance of Accounting for Interval Censoring

CONTEXT

Patients with cancer in Ontario, Canada, receive symptom monitoring in a standardized fashion using the Edmonton Symptom Assessment System (ESAS). These measurements can be used to understand symptom progression during the cancer trajectory.

OBJECTIVES

This study demonstrates the implementation of multistate models for examining symptom progression, while appropriately accounting for intermittent observation. We also compare the estimates when the panel nature of the data is ignored.

METHODS

This was a population-based retrospective cohort study using linked administrative health-care databases. The cohort consisted of patients who were newly diagnosed with a primary cancer and had at least one ESAS assessment completed between 2007 and 2015 in Ontario, Canada. A 5-state model was developed to examine the progression of symptom severity, where estimation was conducted with and without accommodating for the panel nature of the symptom data.

RESULTS

The study cohort consisted of 212,615 patients diagnosed with cancer, collectively having 1,006,360 ESAS assessments within the first year after diagnosis. The median (interquartile range) of the number of ESAS assessments per patient was 3 (1-6), and the average gap time between consecutive assessments was approximately three months. The estimated mean sojourn time in each state was consistently and significantly greater when ignoring interval censoring than when accounting for it. This held true for all states and symptoms.

CONCLUSION

Our work demonstrates the use of multistate models and the importance of accommodating for intermittent observation when examining symptom progression using ESAS among patients with cancer. This work serves as a methodological guide for applied researchers interested in modeling disease progression under the presence of intermittent observation.

Assessing via Simulation the Operating Characteristics of the WHO Scale for COVID-19 Endpoints

Many clinical trials of treatments for patients hospitalised for COVID-19 use an ordinal scale recommended by the World Heath Organisation. The scale represents intensity of medical intervention, with higher scores for interventions more burdensome for the patient, and highest score for death. There is uncertainty about use of this ordinal scale in testing hypotheses. With the objective of assessing the power and Type I error of potential endpoints and analyses based on the ordinal scale, trajectories of the score over 28 days were simulated for scenarios based closely on results of two trials recently published. The simulation used transition probabilities for the ordinal scale over time. No one endpoint was optimal across scenarios, but a ranked measure of trajectory fared moderately well in all scenarios. Type I error was controlled at close to the nominal level for all endpoints. Because not tied to a particular population with regard to baseline severity, the use of transition probabilities allows plausible assessment of endpoints in populations with configurations of baseline score for which data is not yet published, provided some data on the relevant transition probabilities are available. The results could support experts in the choice of endpoint based on the ordinal scale.

Long-term prognosis of liver disease in patients with eradicated chronic hepatitis C virus: An analysis using a Markov chain model

AIM

The long-term prognosis of patients with chronic hepatitis C virus (HCV) infection who have received antiviral therapy and who demonstrate HCV eradication remains incompletely characterized. In this study, we investigated the long-term prognosis of liver disease in patients with eradication of HCV.

METHODS

A total of 552 patients with chronic HCV infection (6815 person-years) who were treated with interferon-based therapy and who achieved sustained virologic response were included. Yearly transition probabilities for each liver state (chronic hepatitis, cirrhosis, and hepatocellular carcinoma [HCC]) were calculated using a Markov chain model.

RESULTS

In the analysis of 1-year liver disease state transition probabilities, progression to cirrhosis occurred in 0.5-2.1% of male patients with chronic hepatitis across all age groups. In male patients with cirrhosis, HCC developed in 0.6-1.9% of patients over the age of 50 years. In female patients with chronic hepatitis, progression to cirrhosis occurred in 0.4-2.1% of patients across all age groups. In addition, in female patients with cirrhosis, HCC developed in those aged 60-69 (0.4%) and 70-79 (0.4%) years. Under the assumption of either a chronic hepatitis or cirrhosis state at age 40 or 60 years as the starting condition for simulation over the next 40 or 20 years, respectively, the probability of HCC gradually increased with age and was higher in male patients.

CONCLUSIONS

The development or progression of cirrhosis and the development of HCC are risks in HCV patients despite HCV eradication, not only in those with cirrhosis but also in those with chronic hepatitis.

Nonparametric analysis of nonhomogeneous multistate processes with clustered observations

Frequently, clinical trials and observational studies involve complex event history data with multiple events. When the observations are independent, the analysis of such studies can be based on standard methods for multistate models. However, the independence assumption is often violated, such as in multicenter studies, which makes standard methods improper. This work addresses the issue of nonparametric estimation and two‐sample testing for the population‐averaged transition and state occupation probabilities under general multistate models with cluster‐correlated, right‐censored, and/or left‐truncated observations. The proposed methods do not impose assumptions regarding the within‐cluster dependence, allow for informative cluster size, and are applicable to both Markov and non‐Markov processes. Using empirical process theory, the estimators are shown to be uniformly consistent and to converge weakly to tight Gaussian processes. Closed‐form variance estimators are derived, rigorous methodology for the calculation of simultaneous confidence bands is proposed, and the asymptotic properties of the nonparametric tests are established. Furthermore, I provide theoretical arguments for the validity of the nonparametric cluster bootstrap, which can be readily implemented in practice regardless of how complex the underlying multistate model is. Simulation studies show that the performance of the proposed methods is good, and that methods that ignore the within‐cluster dependence can lead to invalid inferences. Finally, the methods are illustrated using data from a multicenter randomized controlled trial.

Analyzing wearable device data using marked point processes

This paper introduces two sets of measures as exploratory tools to study physical activity patterns: active-to-sedentary/sedentary-to-active rate function (ASRF/SARF) and active/sedentary rate function (ARF/SRF). These two sets of measures are complementary to each other and can be effectively used together to understand physical activity patterns. The specific features are illustrated by an analysis of wearable device data from National Health and Nutrition Examination Survey (NHANES). A two-level semiparametric regression model for ARF and the associated activity magnitude is developed under a unified framework using the marked point process formulation. The inactive and active states measured by accelerometers are treated as a 0-1 point process, and the activity magnitude measured at each active state is defined as a marked variable. The commonly encountered missing data problem due to device nonwear is referred to as "window censoring," which is handled by a proper estimation approach that adopts techniques from recurrent event data. Large sample properties of the estimator and comparison between two regression models as measurement frequency increases are studied. Simulation and NHANES data analysis results are presented. The statistical inference and analysis results suggest that ASRF/SARF and ARF/SRF provide useful analytical tools to practitioners for future research on wearable device data.

Seasonal influenza vaccination among people with diabetes: influence of patients' characteristics and healthcare use on behavioral changes

Annual seasonal influenza vaccination (SIV) is recommended for people with diabetes, but vaccine coverage remains low. We estimated the probabilities of stopping or starting SIV, their correlates, and the expected time spent in the vaccinated state over 10 seasons for different patient profiles. We set up a retrospective cohort study of patients with diabetes in 2006 (n = 16,026), identified in a representative sample of beneficiaries of the French National Health Insurance Fund. We followed them up over 10 seasons (2005/06–2015/16). We used a Markov model to estimate transition probabilities and a proportional hazards model to study covariates. Between two consecutive seasons, the probabilities of starting (0.17) or stopping (0.09) SIV were lower than those of remaining vaccinated (0.91) or unvaccinated (0.83). Men, older patients, those with type 1 diabetes, treated diabetes or more comorbidities, frequent contacts with doctors, and with any hospital stay for diabetes or influenza during the last year were more likely to start and/or less likely to stop SIV. The mean expected number of seasons with SIV uptake over 10 seasons (range: 2.6–7.9) was lowest for women <65 years with untreated diabetes and highest for men ≥65 years with type 1 diabetes. Contacts with doctors and some clinical events may play a key role in SIV adoption. Healthcare workers have a crucial role in reducing missed opportunities for SIV. The existence of empirical patient profiles with different patterns of SIV uptake should encourage their use of tailored educational approaches about SIV to address patients’ vaccine hesitancy.

Multistate Models to Predict Development of Late Complications of Type 2 Diabetes in an Open Cohort Study

BACKGROUND

Increase in the prevalence of type 2 diabetic mellitus (T2DM) as a complex disease, its complications, and spread has become a dominant global health threat in recent decades.

OBJECTIVE

The aim of the current study was to investigate the impact of risk factors and transition probability on the development and progression of the late complications of T2DM.

METHODS

This study was an open cohort one which was conducted at Isfahan Endocrine and Metabolism Research Center (IEMRC). The data were collected from 1993 to 2018. The sample size consisted of 2519 adults diagnosed with type 2 diabetes. We applied the homogeneous multistate models including no complication, retinopathy alone, coronary artery disease (CAD), microalbuminuria, retinopathy and CAD, and the final absorbing mortality states.

RESULTS

Based on our results, time-varying hypertension strongly intensified the hazard of transition to mortality in CAD, no complication, CAD and retinopathy, and retinopathy patients by 4.99, 4.09, 3.42, and 2.65 times, respectively. Hypertension seemed to be a potential factor for the transition of microalbuminuria to no complication in diabetic patients. One-unit increase in LDL increased the hazard ratio of transition from CAD, and retinopathy and CAD to mortality by 1.8% and 2.4%, respectively. Moreover, one level increase in time-varying HbA1c increased the hazard ratio of transition to retinopathy and mortality among no complication diabetic patients by 30% and 67%, respectively. One level increase in time-varying HbA1c also intensified the hazard ratio of transition from retinopathy to mortality by 45%. The same level of increase in time-varying HbA1c also intensified the hazard ratio of transition from CAD alone to CAD and retinopathy, and microalbuminuria to retinopathy by 26% and 50%, respectively.

CONCLUSION

In addition to glycemic control, our study indicates that controlling hypertension and hyperlipidemia is more effective in reducing mortality and the diabetic macro- and microvascular complications.

The Structure and Parameterization of the Breast Cancer Transition Model Among Chinese Women

OBJECTIVES

Markov model simulation based on the natural history of disease is commonly employed for the comparative research of health interventions. The present study aims to simulate the natural progression of breast cancer and parameterize the initial and transition probabilities of multiple states of breast cancer development among Chinese women.

METHODS

The age-specific incidence, mortality, and clinical stage distribution of breast cancer; and relapse rate of each clinical stage were collected from China's cancer registry yearbooks and clinical epidemiological studies to simulate the process from full health to breast cancer to death among Chinese women aged 30 to 80 through a Markov cohort study. The validity analysis was conducted to evaluate the accuracy of the model estimation.

RESULTS

A Markov transition model with 7 states (no breast cancer, clinical stages 0-IV breast cancer, and death) was constructed for Chinese women. The age-specific incidence, mortality, and clinical stage distribution of breast cancer estimated by the initial and transition probabilities among different Markov states were highly consistent with the registered data and observed studies.

CONCLUSION

A breast cancer transition model for Chinese women has been established with validity. It could be a point of reference for further economic evaluations and breast cancer screening policy formulation.

Natural history of liver-related disease in patients with chronic hepatitis C virus infection: An analysis using a Markov chain model

BACKGROUND

Long-term prognosis of patients with chronic hepatitis C infection (HCV) remains incompletely characterized. We investigated the long-term prognosis of liver disease in patients with chronic HCV infection who have not received antiviral therapy.

METHODS

A total of 2304 patients with chronic HCV who were not received interferon-based therapy were included.

RESULTS

In the assessment of 1-year disease state of liver transition probabilities, progression to chronic hepatitis occurred in 12% to 14% of patients across all age groups in male asymptomatic carriers. In male patients with chronic hepatitis, progression to cirrhosis was observed mostly in the 60 to 69 (7.6%) and ≥70 age groups (9.6%). In addition, in male patients with cirrhosis, HCC development occurred in approximately 5% of patients over the age of 40. In female asymptomatic carriers, progression to chronic hepatitis was observed in 6% to 14% of patients across all age groups. In female patients with chronic hepatitis, progression to cirrhosis was observed mostly in the 60 to 69 (8.7%) and ≥70 (7.4%) age groups. In addition, in female patients with cirrhosis, HCC development occurred in 0.9% to 3.3% of patients over the age of 50. Under assumptions of either chronic hepatitis or asymptomatic carrier state at age 40 as the starting condition for simulation over the following 40 years, the probability of HCC gradually increased with age and was higher in male patients.

CONCLUSIONS

There is a risk of cirrhosis or HCC development in HCV patients with not only chronic hepatitis but the asymptomatic carrier state as well.

Quantitative Studies for Cell-Division Cycle Control

The cell-division cycle (CDC) is driven by cyclin-dependent kinases (CDKs). Mathematical models based on molecular networks, as revealed by molecular and genetic studies, have reproduced the oscillatory behavior of CDK activity. Thus, one basic system for representing the CDC is a biochemical oscillator (CDK oscillator). However, genetically clonal cells divide with marked variability in their total duration of a single CDC round, exhibiting non-Gaussian statistical distributions. Therefore, the CDK oscillator model does not account for the statistical nature of cell-cycle control. Herein, we review quantitative studies of the statistical properties of the CDC. Over the past 70 years, studies have shown that the CDC is driven by a cluster of molecular oscillators. The CDK oscillator is coupled to transcriptional and mitochondrial metabolic oscillators, which cause deterministic chaotic dynamics for the CDC. Recent studies in animal embryos have raised the possibility that the dynamics of molecular oscillators underlying CDC control are affected by allometric volume scaling among the cellular compartments. Considering these studies, we discuss the idea that a cluster of molecular oscillators embedded in different cellular compartments coordinates cellular physiology and geometry for successful cell divisions.

Greater eosinophil counts at first COPD hospitalization are associated with more readmissions and fewer deaths

Purpose

The impacts of high blood eosinophil count (HBEC) at admission for COPD exacerbation on posthospitalization outcomes are still unclear. Previous studies have focused on its associations with first readmission rates; yet, its impacts on longitudinal outcomes such as subsequent readmissions still have to be explored. The main objective of this study is to investigate outcomes associated with HBEC following a first hospitalization for COPD exacerbation.

Patients and methods

This is an observational cohort study design. We retrospectively analyzed data of patients with a first hospitalization within 5 years for COPD exacerbation between April 2006 and March 2013. Patients were stratified into the HBEC group if the blood eosinophil count at admission was ≥200 cells/µL and/or ≥2% of the total white blood cells. With information on exact dates of subsequent hospitalizations and death, we modeled readmissions and death as states in a multi-state Markov model and estimated transition probabilities to the next states. Sensitivity analyses were performed by varying thresholds for the definition of HBEC (≥300 cells/µL and/or ≥3%).

Results

A total of 479 patients were included, of which 173 had HBEC. The transition probabilities for a first readmission was 74% (95% CI, 66%–83%) for patients with HBEC vs 70% (95% CI, 63%–77%) for patients with normal blood eosinophil count (NBEC). The transition probabilities for a second readmission were 91% (95% CI, 84%–100%) for HBEC patients in contrast with 83% (95% CI, 74%–92%) for NBEC patients. Meanwhile, transition probability for death was lower in patients with HBEC. The differences enlarged in sensitivity analyses with higher cutoff.

Conclusion

Greater blood eosinophil cell counts during a first hospitalization for COPD predict increased susceptibility to up to two readmissions. These patients may however have a lower risk of death.

Neural Correlate of Transition Violation and Deviance Detection in the Songbird Auditory Forebrain

Deviants are stimuli that violate one's prediction about the incoming stimuli. Studying deviance detection helps us understand how nervous system learns temporal patterns between stimuli and forms prediction about the future. Detecting deviant stimuli is also critical for animals' survival in the natural environment filled with complex sounds and patterns. Using natural songbird vocalizations as stimuli, we recorded multi-unit and single-unit activity from the zebra finch auditory forebrain while presenting rare repeated stimuli after regular alternating stimuli (alternating oddball experiment) or rare deviant among multiple different common stimuli (context oddball experiment). The alternating oddball experiment showed that neurons were sensitive to rare repetitions in regular alternations. In the absence of expectation, repetition suppresses neural responses to the 2nd stimulus in the repetition. When repetition violates expectation, neural responses to the 2nd stimulus in the repetition were stronger than expected. The context oddball experiment showed that a stimulus elicits stronger neural responses when it is presented infrequently as a deviant among multiple common stimuli. As the acoustic differences between deviant and common stimuli increase, the response enhancement also increases. These results together showed that neural encoding of a stimulus depends not only on the acoustic features of the stimulus but also on the preceding stimuli and the transition patterns between them. These results also imply that the classical oddball effect may result from a combination of repetition suppression and deviance enhancement. Classification analyses showed that the difficulties in decoding the stimulus responsible for the neural responses differed for deviants in different experimental conditions. These findings suggest that learning transition patterns and detecting deviants in natural sequences may depend on a hierarchy of neural mechanisms, which may be involved in more complex forms of auditory processing that depend on the transition patterns between stimuli, such as speech processing.

Natural course of persistent hepatitis B virus infection in hepatitis B e antigen-positive and hepatitis B e antigen-negative cohorts in Japan based on the Markov model

This population-based study examined the natural course of hepatitis B e antigen (HBeAg)-positive or HBeAg-negative persistent hepatitis B virus (HBV) infection, adjusted by age and liver disease states using a Markov model. Using 12 417 person-years data (n = 862), annual transition probabilities were estimated, and age-adjusted cumulative incidence and natural history of persistent HBV infection were simulated in both sexes of groups 1 (HBeAg-negative status with HBV DNA level <4.0 log IU/mL at entry) and 2 (persistent HBeAg-positive status throughout the study). In group 1, 15.26% of 30-years old men with chronic hepatitis (CH) were expected to remain in the same state at age 65 years, 28.32% subsided into an hepatitis B surface antigen (HBsAg)-negative state, and 13.20% developed hepatocellular carcinoma (HCC). The expectations for 40-years old men in group 1 were 21.43%, 19.86%, and 15.04%, respectively. The expectations for 30 years women in group 1 were 30.57%, 21.15%, and 4.08%, respectively. These results suggest that HBeAg positivity caused a higher risk of HCC onset in persistent HBV infection after adjustments for age, sex, and liver disease state. HCC was likely to develop, but unlikely to subside into HBsAg clearance, remaining in a CH state with aging, regardless of HBeAg state. Furthermore, both HCC development and HBsAg clearance occurred more frequently in men than in women, irrespective of HBeAg status.

Dynamic transitions between marijuana use and cigarette smoking among US adolescents and emerging adults

BACKGROUND

Marijuana and tobacco are considered two closely related substances. It is of great significance to understand the mutual impact of marijuana and cigarette use when more states in the US have legalized marijuana use.

OBJECTIVE

This study aims to investigate the transitions between marijuana and cigarette use among adolescents and emerging adults.

METHOD

Guided by the probabilistic discrete events systems (PDES) theory, a five-stage model with 21 transition paths was constructed to quantify dynamic transitions between marijuana and cigarette use. The five stages were NU: Never-user, MU: Current marijuana user, CU: Current cigarette user, MCU: Current marijuana-cigarette user, and FU: Former-user. The proposed five-stage PDES model was tested using the 2013 National Survey on Drug Use and Health data (N = 26,665, 50.45% male). Transition probabilities were estimated using the Moore-Penrose generalized inverse matrix method.

RESULT

Among the adolescents, 51.14% of the CUs transited to use marijuana (MCU/MU), higher than the proportion of those who first used marijuana and then transferred to cigarettes (MCU/CU) (41.66%). The quitting rates for MUs, CUs and MCUs were 29.38%, 25.93% and 27.76%, respectively. Of the total FUs, 31.90% transited to MUs, 17.06% to CUs, and 17.39% to MCUs. Among the young adults, more people progressed from MUs to CUs. Transition probabilities by single year of age were also estimated.

CONCLUSION

This is the first study to quantify marijuana-cigarette transitions. Study findings indicate more cigarette-to-marijuana transitions for adolescents and more marijuana-to-cigarette transitions for emerging adults. Future intervention programs should consider this age-related difference in marijuana-cigarette use transitions.

A case-cohort approach for multi-state models in hospital epidemiology

Analysing the determinants and consequences of hospital-acquired infections involves the evaluation of large cohorts. Infected patients in the cohort are often rare for specific pathogens, because most of the patients admitted to the hospital are discharged or die without such an infection. Death and discharge are competing events to acquiring an infection, because these individuals are no longer at risk of getting a hospital-acquired infection. Therefore, the data is best analysed with an extended survival model - the extended illness-death model. A common problem in cohort studies is the costly collection of covariate values. In order to provide efficient use of data from infected as well as uninfected patients, we propose a tailored case-cohort approach for the extended illness-death model. The basic idea of the case-cohort design is to only use a random sample of the full cohort, referred to as subcohort, and all cases, namely the infected patients. Thus, covariate values are only obtained for a small part of the full cohort. The method is based on existing and established methods and is used to perform regression analysis in adapted Cox proportional hazards models. We propose estimation of all cause-specific cumulative hazards and transition probabilities in an extended illness-death model based on case-cohort sampling. As an example, we apply the methodology to infection with a specific pathogen using a large cohort from Spanish hospital data. The obtained results of the case-cohort design are compared with the results in the full cohort to investigate the performance of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

Non-parametric estimation of transition probabilities in non-Markov multi-state models: The landmark Aalen-Johansen estimator

The topic non-parametric estimation of transition probabilities in non-Markov multi-state models has seen a remarkable surge of activity recently. Two recent papers have used the idea of subsampling in this context. The first paper, by de Uña Álvarez and Meira-Machado, uses a procedure based on (differences between) Kaplan-Meier estimators derived from a subset of the data consisting of all subjects observed to be in the given state at the given time. The second, by Titman, derived estimators of transition probabilities that are consistent in general non-Markov multi-state models. Here, we show that the same idea of subsampling, used in both these papers, combined with the Aalen-Johansen estimate of the state occupation probabilities derived from that subset, can also be used to obtain a relatively simple and intuitive procedure which we term landmark Aalen-Johansen. We show that the landmark Aalen-Johansen estimator yields a consistent estimator of the transition probabilities in general non-Markov multi-state models under the same conditions as needed for consistency of the Aalen-Johansen estimator of the state occupation probabilities. Simulation studies show that the landmark Aalen-Johansen estimator has good small sample properties and is slightly more efficient than the other estimators.

Statistical learning of temporal community structure in the hippocampus

The hippocampus is involved in the learning and representation of temporal statistics, but little is understood about the kinds of statistics it can uncover. Prior studies have tested various forms of structure that can be learned by tracking the strength of transition probabilities between adjacent items in a sequence. We test whether the hippocampus can learn higher-order structure using sequences that have no variance in transition probability and instead exhibit temporal community structure. We find that the hippocampus is indeed sensitive to this form of structure, as revealed by its representations, activity dynamics, and connectivity with other regions. These findings suggest that the hippocampus is a sophisticated learner of environmental regularities, able to uncover higher-order structure that requires sensitivity to overlapping associations.

Transition probabilities for four states of alcohol use in adolescence and young adulthood: what factors matter when?

BACKGROUND AND AIMS

Risky single-occasion drinking (RSOD) is a health threat, particularly at younger ages. The study aimed to quantify transition probabilities (TPs) between abstinence, use of alcohol, RSOD and frequent RSOD, and to understand how TPs are associated with key demographic factors.

DESIGN

Cohort study (baseline, two follow-ups). A Markov model was fitted to estimate annual TPs and hazard ratios (HRs) for age, sex and socio-economic status (SES).

SETTING

Adolescent and young adult general population of Munich (Germany) and surrounding areas.

PARTICIPANTS

A total of 3021 people aged 14-25 years at baseline in 1995 followed-up in 1998/1999 (n = 2548) and 2003-2005 (n = 2210).

MEASUREMENTS

Alcohol use, RSOD status, age, sex and SES (subjective financial situation) were assessed in a standardized interview.

FINDINGS

The highest TPs (> 65%) were found for staying in the same drinking state. Higher age [hazard ratio (HR) for 1-year increase = 0.87, 95% confidence interval (CI) = 0.84-0.91], being female (HR = 0.30, 95% CI = 0.21-0.42), and a high SES (HR = 0.64, 95% CI = 0.43-0.97) were associated with a lower hazard to progress from use to RSOD. While age was associated predominantly with transitions between abstinence and alcohol use, sex was more relevant for transitions associated with RSOD and frequent RSOD.

CONCLUSIONS

German adolescents and young adults tend to be stable in the drinking states of abstinence, use of alcohol, risky single-occasion drinking and frequent risky single-occasion drinking. Females are less likely to transition to riskier states and more likely to transition back from frequent risky single-occasion drinking, higher age is associated with lower hazard of transitioning and participants of higher socio-economic status are less likely to transition from 'use of alcohol' to 'risky single-occasion drinking'.

The effects of exercise on dynamic sleep morphology in healthy controls and patients with chronic fatigue syndrome

Effects of exercise on dynamic aspects of sleep have not been studied. We hypothesized exercise altered dynamic sleep morphology differently for healthy controls relative to chronic fatigue syndrome (CFS) patients. Sixteen controls (38 ± 9 years) and 17 CFS patients (41 ± 8 years) underwent polysomnography on baseline nights and nights after maximal exercise testing. We calculated transition probabilities and rates (as a measure of relative and temporal transition frequency, respectively) between sleep stages and cumulative duration distributions (as a measure of continuity) of each sleep stage and sleep as a whole. After exercise, controls showed a significantly greater probability of transition from N1 to N2 and a lower rate of transition from N1 to wake than at baseline; CFS showed a significantly greater probability of transition from N2 to N3 and a lower rate of transition from N2 to N1. These findings suggest improved quality of sleep after exercise. After exercise, controls had improved sleep continuity, whereas CFS had less continuous N1 and more continuous rapid eye movement (REM) sleep. However, CFS had a significantly greater probability and rate of transition from REM to wake than controls. Probability of transition from REM to wake correlated significantly with increases in subjective fatigue, pain, and sleepiness overnight in CFS – suggesting these transitions may relate to patient complaints of unrefreshing sleep. Thus, exercise promoted transitions to deeper sleep stages and inhibited transitions to lighter sleep stages for controls and CFS, but CFS also reported increased fatigue and continued to have REM sleep disruption. This dissociation suggests possible mechanistic pathways for the underlying pathology of CFS.

Estimation of sensitivity depending on sojourn time and time spent in preclinical state

The probability model for periodic screening was extended to provide statistical inference for sensitivity depending on sojourn time, in which the sensitivity was modeled as a function of time spent in the preclinical state and the sojourn time. The likelihood function with the proposed sensitivity model was then evaluated with simulated data to check its reliability in terms of the mean estimation and the standard error. Simulation results showed that the maximum likelihood estimates of the proposed model have little bias and small standard errors. The extended probability model was further applied to the Johns Hopkins Lung Project data using both maximum likelihood estimation and Bayesian Markov chain Monte Carlo.

Sleep-stage dynamics in patients with chronic fatigue syndrome with or without fibromyalgia

STUDY OBJECTIVES

Chronic fatigue syndrome (CFS) and fibromyalgia (FM) are medically unexplained conditions that often have overlapping symptoms, including sleep-related complaints. However, differences between the 2 conditions have been reported, and we hypothesized that dynamic aspects of sleep would be different in the 2 groups of patients.

PARTICIPANTS

Subjects were 26 healthy control subjects, 14 patients with CFS but without FM (CFS alone), and 12 patients with CFS and FM (CFS+FM)-all women.

MEASUREMENTS AND RESULTS

We studied transition probabilities and rates between sleep stages (waking, rapid eye movement [REM] sleep, stage 1 [S1], stage 2 [S2], and slow-wave sleep [SWS]) and duration distributions of each sleep stage. We found that the probability of transition from REM sleep to waking was significantly greater in subjects with CFS alone than in control subjects, which may be the specific sleep problem for people with CFS alone. Probabilities of (a) transitions from waking, REM sleep, and S1 to S2 and (b) those from SWS to waking and S1 were significantly greater in subjects with CFS+FM than in control subjects; in addition, rates of these transitions were also significantly increased in subjects with CFS+FM. Result (a) might indicate increased sleep pressure in subjects with CFS+FM whereas result (b) may be the specific sleep problem of subjects with CFS+FM. We also found that shorter durations of S2 sleep are specific to patients with CFS+FM, not to CFS alone.

CONCLUSIONS

These results suggest that CFS and FM may be different illnesses associated with different problems of sleep regulation.

NREM sleep stage transitions control ultradian REM sleep rhythm

STUDY OBJECTIVES

The cyclic sequence of NREM and REM sleep, the so-called ultradian rhythm, is a highly characteristic feature of sleep. However, the mechanisms responsible for the ultradian REM sleep rhythm, particularly in humans, have not to date been fully elucidated. We hypothesize that a stage transition mechanism is involved in the determination of the ultradian REM sleep rhythm.

PARTICIPANTS

Ten healthy young male volunteers (AGE: 22 ± 4 years, range 19-31 years) spent 3 nights in a sleep laboratory. The first was the adaptation night, and the second was the baseline night. On the third night, the subjects received risperidone (1 mg tablet), a central serotonergic and dopaminergic antagonist, 30 min before the polysomnography recording.

MEASUREMENTS AND RESULTS

We measured and investigated transition probabilities between waking, REM, and NREM sleep stages (N1, N2, and N3) within the REM-onset intervals, defined as the intervals between the onset of one REM period and the beginning of the next, altered by risperidone. We also calculated the transition intensity (i.e., instantaneous transition rate) and examined the temporal pattern of transitions within the altered REM-onset intervals. We found that when the REM-onset interval was prolonged by risperidone, the probability of transitions from N2 to N3 was significantly increased within the same prolonged interval, with a significant delay and/or recurrences of the peak intensity of transitions from N2 to N3.

CONCLUSIONS

These results suggest that the mechanism governing NREM sleep stage transitions (from light to deep sleep) plays an important role in determining ultradian REM sleep rhythms.

Estimating co-occurring behavioral trajectories within a neighborhood context: a case study of multivariate transition models for clustered data

Comorbidity is well-documented in psychiatric and risk behavior epidemiology. The authors present a novel application of clustered multivariate transition models to study comorbidity within a clustered context. The authors used data from the Project on Human Development in Chicago Neighborhoods (1995-2002) to assess trajectories in substance use, problems with police, and antisocial behavior among 1,517 participants in 80 neighborhoods followed from ages 12-15 years through ages 18-21 years. The authors used pairwise odds ratios to quantify behavior comorbidity at the individual and neighborhood levels. Risk behaviors co-occurred within individuals at specific points in time: antisocial behavior and substance use were 3.37 times more likely to co-occur within an individual at wave 1, as compared with the co-occurrence of any 2 behaviors from different individuals, while substance use and police problems were 2.94 times more likely to co-occur than substance use and antisocial behavior at wave 2. The authors also evaluated sequential comorbidity. Antisocial behavior was sequentially comorbid with substance use and police problems: 31% of youths who had reported antisocial behavior at baseline reported police problems or drug use at wave 2. These models can prove instrumental in answering the persistent questions about possible sequential relations among problem behaviors.

Simulation Procedure in Periodic Cancer Screening Trials

A general simulation procedure is described to validate model fitting algorithms for complex likelihood functions that are utilized in periodic cancer screening trials. Although screening programs have existed for a few decades, there are still many unsolved problems, such as how age or hormone affects the screening sensitivity, the sojourn time in the preclinical state, and the transition probability from disease-free state to the preclinical state. Simulations are needed to check reliability or validity of the likelihood function combined with the associated effect functions. One bottleneck in the simulation procedure is the very time consuming calculations of the maximum likelihood estimates (MLE) from generated data. A practical procedure is presented, along with results for when both sensitivity and transition probability into the preclinical state are age-dependent. The procedure is also suitable for other applications.

A computational model for quantitative analysis of cell cycle arrest and its contribution to overall growth inhibition by anticancer agents

Most anticancer agents induce cell cycle arrest (cytostatic effect) and cell death (cytotoxic effect), resulting in the inhibition of population growth of cancer cells. When asynchronous cells are to be examined, the currently used flow cytometric method can not provide checkpoint-specific and quantitative information on the drug-induced cell cycle arrest. Hence, despite its significance, no good method to analyze in detail the mechanism of cell cycle arrest and its contribution to overall growth inhibition induced by an anticancer agent has yet been established. We describe in this study the development of a discrete time (Markov model)-based computational model for cell cycle progression / arrest with transition probability (TP(i)) as a model parameter. TP(i) was calculated using model equations that include easily measurable parameters such as the fraction of cells in each cell cycle phase and population doubling time. The TP(i) was then used to analyze checkpoint-specific and quantitative changes in cell cycle progression. We also used TP(i) in a Monte-Carlo simulation to predict growth inhibition caused by cell cycle arrest only. Human SCLC cells (SBC-3) exposed to UCN-01 were used to validate the model. The model-predicted growth curves agreed with the observed data for SBC-3 cells not treated or treated at a cytostatic concentration (0.2 mM) of UCN-01, indicating validity of the present model. The changes in TP(i) indicated that UCN-01 reduced the G(1)-to-S transition rate and increased the S-to-G(2) / M and G(2) / M-to-G(1) transition rates of SBC-3 cells in a concentration- and time-dependent manner. When the model-predicted growth curves were compared with the observed data for cells treated at a cytotoxic concentration (2 mM), they suggested that 22% out of 65% and 32% out of 73% of the growth inhibition could be attributed to the cell cycle arrest effect after 48 h and 72 h exposure, respectively. In conclusion, we report here the establishment of a novel method of analysis that can provide checkpoint-specific and quantitative information about cell cycle arrest induced by an anticancer agent and that can be used to assess the contribution of cell cycle arrest effect to the overall growth inhibition.

An ordered sequence of events is required before BALB/c-3T3 cells become committed to DNA synthesis

An ordered sequence of events must be completed before cells become committed to synthesize DNA. A platelet-derived growth factor (PDGF), present in heated (100 degrees ) extracts of human platelets, induces density-inhibited BALB/c-3T3 cells to become competent to proliferate. Platelet-poor plasma induces these competent cells to leave the competence point, progress through G(0)/G(1), and enter the S phase. Treatment of G(0)-arrested, incompetent cells with plasma, before the addition of PDGF, did not shorten the latent period for DNA synthesis or increase the rate of entry into the S phase. Growth arrest points in the plasma-dependent progression sequence were detected in G(0)/G(1). PDGF-treated competent cells were exposed to an optimal concentration of plasma (5%) for various lengths of time and were then transferred to medium lacking plasma; the subsequent readdition of plasma stimulated the cells to enter the S phase. The lag period until DNA synthesis, in such experiments, was dictated by the length of the initial exposure to plasma. PDGF-treated competent cells that were incubated with plasma for 5 hr during the initial exposure did not leave the competence point; they began DNA synthesis 12 hr after the readdition of plasma. However, a population of cells treated with plasma for 10 hr became arrested at a point 6 hr before DNA synthesis, whereas a population treated with plasma for 12-15 hr became arrested at a point immediately before DNA synthesis. Cells remained arrested at this latter point for as long as 24 hr, and these arrested cells were not committed to DNA synthesis. The addition of plasma induced immediate entry into the S phase with an apparent first-order rate of entry being determined by the plasma concentration. This plasma-dependent commitment (transition) to DNA synthesis was blocked by cycloheximide but not by hydroxyurea. Removal of the hydroxyurea allowed cells to enter the S phase synchronously in the absence of plasma.

Ion-Neutralization Spectroscopy

The ion-neutralization spectroscopy (INS) is discussed in comparison with other spectroscopies of solids. It is shown that INS probes the local density of states of the solid at or just outside the solid surface. It is believed that this accounts for the clear-cut differences between INS results and those of other spectroscopies. Because of its unique specificity to the surface region INS is particularly useful in studying the surface electronic structures of atomically clean surfaces and of surfaces having ordered arrays of known atoms adsorbed upon them. In the latter case INS determines a portion of the molecular orbital spectrum of surface molecules formed from the adsorbed foreign atom and surface atoms of the bulk crystal. Such spectra provide information on local bonding symmetry and structure and electrical charging within the surface molecule which is as yet unavailable by any other method. INS is the first attempt to base a spectroscopy of electronic states on a two-electron process. More recent work on experimental and mathematical problems which such a spectroscopy entails are also briefly mentioned in this paper.

Excitonic Effects in X-Ray Transitions in Metals

In the study of soft x-ray transitions in solids, there has always been some hope that the results provide a direct measure of the density of states. This assumes that (a) matrix element variations over the band and (b) final state interactions are small. Both of these assumptions are now known to be incorrect. To illustrate the possible strength of these effects, two approximate calculations are presented: the one electron oscillator strength of a simple bcc metal as a function of energy; and the strength of the Nozieres - DeDominicis singularity at threshold, with phase shifts estimated from an assumed Yukawa interaction between conduction electrons and core hole.

Oscillator Strengths for Ultraviolet Lines of Fe I

Relative intensities and oscillator strengths for 2000 ultraviolet lines of Fe I from several investigations between 2080 and 4150 Å have been critically discussed and adjusted to a uniform absolute scale. New values for many lines not measured heretofore are reported.

Arc Measurement of Some Argon Transition Probabilities

Transition probabilities and line shape parameters for three argon lines have been measured in a wall-stablilized argon arc containing a trace of hydrogen. The H _β line shape measurement and the assumption of local thermodynamic equilibrium provided the determination of the argon level populations. From these three lines ten other argon line transition probabilities have been obtained by relative measurements in a pure argon arc. The results are compared with those of other investigators and sources of error are discussed.