Survival Analysis of Cervical Cancer Patients: A Case Study of Bhutan.

OBJECTIVE: The purpose of the study is to identify the risk factors such as age, the stage of patients based on the International Federation of Gynecology and Obstetrics system (FIGO stage) and treatment type, and their effect on the survival of cervical cancer patients receiving treatments at Jigme Dorji Wangchuck National Referral Hospital (JDWNRH) in Bhutan between January 2014 and December 2019. METHODS: In this retrospective study, all 357 women diagnosed with cervical cancer were included. Kaplan-Meier model was applied to estimate survival, and the log-rank test was performed to compare survival distributions between subgroups stratified by each of the risk factors. Baseline demographics, cervical cancer stages, and treatment options were analyzed as factors and predictors of survival by Cox proportional hazards model. RESULTS: The overall estimated 1- to 5-year survival rates are 82.1% (95% CI: 77.8-86.7), 75.6% (70.4-81.1), 65.2% (58.2-73.0), 62.3% (54.7-70.9) and 55.4% (44.9-68.3). The results reveal that age group, FIGO stage, treatment, and frequency of hospital visits are significant factors affecting the survival of cervical cancer patients in Bhutan. Patients aged >45 years increases the risk of dying (HR: 2.1, 95% CI: 1.2-3.9) compared to the young age group (≤45 years). Treatment types other than surgery only are significantly associated with an increased risk of mortality in patients with cervical cancers. The more frequency of hospital visits also reduces the risk of dying (HR: 0.1, 95% CI: 0-0.3). FIGO stage IV is the most significant risk factor for mortality with a hazard ratio of 6 (95% CI: 2.1-17.6). CONCLUSION: The five-year survival rate of cervical cancer patients in this study was low. Late diagnosis of cervical cancer appears to be mainly associated with a higher risk of dying. The results provide valuable information for further research and policymaking in the prevention and management of cervical cancer. 
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A Simple, Realistic Stochastic Model of Gastric Emptying.

Several models of Gastric Emptying (GE) have been employed in the past to represent the rate of delivery of stomach contents to the duodenum and jejunum. These models have all used a deterministic form (algebraic equations or ordinary differential equations), considering GE as a continuous, smooth process in time. However, GE is known to occur as a sequence of spurts, irregular both in size and in timing. Hence, we formulate a simple stochastic process model, able to represent the irregular decrements of gastric contents after a meal. The model is calibrated on existing literature data and provides consistent predictions of the observed variability in the emptying trajectories. This approach may be useful in metabolic modeling, since it describes well and explains the apparently heterogeneous GE experimental results in situations where common gastric mechanics across subjects would be expected.

Modeling seasonal leptospirosis transmission and its association with rainfall and temperature in Thailand using time-series and ARIMAX analyses.

OBJECTIVE: To study the number of leptospirosis cases in relations to the seasonal pattern, and its association with climate factors. METHODS: Time series analysis was used to study the time variations in the number of leptospirosis cases. The Autoregressive Integrated Moving Average (ARIMA) model was used in data curve fitting and predicting the next leptospirosis cases. RESULTS: We found that the amount of rainfall was correlated to leptospirosis cases in both regions of interest, namely the northern and northeastern region of Thailand, while the temperature played a role in the northeastern region only. The use of multivariate ARIMA (ARIMAX) model showed that factoring in rainfall (with an 8 months lag) yields the best model for the northern region while the model, which factors in rainfall (with a 10 months lag) and temperature (with an 8 months lag) was the best for the northeastern region. CONCLUSION: The models are able to show the trend in leptospirosis cases and closely fit the recorded data in both regions. The models can also be used to predict the next seasonal peak quite accurately.

Bariatric surgery and T2DM improvement mechanisms: a mathematical model.

BACKGROUND: Consensus exists that several bariatric surgery procedures produce a rapid improvement of glucose homeostasis in obese diabetic patients, improvement apparently uncorrelated with the degree of eventual weight loss after surgery. Several hypotheses have been suggested to account for these results: among these, the anti-incretin, the ghrelin and the lower-intestinal dumping hypotheses have been discussed in the literature. Since no clear-cut experimental results are so far available to confirm or disprove any of these hypotheses, in the present work a mathematical model of the glucose-insulin-incretin system has been built, capable of expressing these three postulated mechanisms. The model has been populated with critically evaluated parameter values from the literature, and simulations under the three scenarios have been compared. RESULTS: The modeling results seem to indicate that the suppression of ghrelin release is unlikely to determine major changes in short-term glucose control. The possible existence of an anti-incretin hormone would be supported if an experimental increase of GIP concentrations were evident post-surgery. Given that, on the contrary, collected evidence suggests that GIP concentrations decrease post-surgery, the lower-intestinal dumping hypothesis would seem to describe the mechanism most likely to produce the observed normalization of Type 2 Diabetes Mellitus (T2DM) after bariatric surgery. CONCLUSIONS: The proposed model can help discriminate among competing hypotheses in a context where definitive data are not available and mechanisms are still not clear.

Oxidative stress increases angiotensin receptor type I responsiveness by increasing receptor degree of aggregation using image correlation spectroscopy.

Oxidative stress and hyper-functioning of angiotensin II receptor type I (AT(1)R) are commonly observed in hypertensive patients but the relationship between oxidative stress and AT(1)R function is still unclear. We investigated the effects of H(2)O(2) treatment on AT(1)R-mediated intracellular calcium [Ca(2+)](i) signaling and its cell surface distribution pattern in HEK cells stably expressing EGFP-tagged rat AT(1)R using image correlation spectroscopy (ICS). Following H(2)O(2) treatment (50-800µM), [Ca(2+)](i) was significantly increased upon angiotensin II stimulation. Similarly ICS revealed a significant increase in degree of AT(1)R aggregation in H(2)O(2) treated group during Ang II activation but no difference in cluster density compared with untreated control cells or those with N-acetyl cysteine pretreatment. Thus, oxidative stress-induced AT(1)R hyper-responsiveness can be attributed by an increase in cell surface receptor aggregation state, possibly stemming in part from oxidant-related increase receptor-receptor interactions.

Optimal nutritional intake for fetal growth.

The regular nutritional intake of an expectant mother clearly affects the weight development of the fetus. Assuming the growth of the fetus follows a deterministic growth law, like a logistic equation, albeit dependent on the nutritional intake, the ideal solution is usually determined by the birth-weight being pre-assigned, for example, as a percentage of the mother's average weight. This problem can then be specified as an optimal control problem with the daily intake as the control, which appears in a Michaelis-Menten relationship, for which there are well-developed procedures to follow. The best solution is determined by requiring minimum total intake under which the preassigned birth weight is reached. The algorithm has been generalized to the case where the fetal weight depends in a detailed way on the cumulative intake, suitably discounted according to the history. The optimality system is derived and then solved numerically using an iterative method for the specific values of parameter. The procedure is generic and can be adapted to any growth law and any parameterisation obtained by the detailed physiology.

Stochastic cellular automata model and Monte Carlo simulations of CD4+ T cell dynamics with a proposed alternative leukapheresis treatment for HIV/AIDS.

Acquired Immunodeficiency Syndrome (AIDS) is responsible for millions of deaths worldwide. To date, many drug treatment regimens have been applied to AIDS patients but none has resulted in a successful cure. This is mainly due to the fact that free HIV particles are frequently in mutation, and infected CD4(+) T cells normally reside in the lymphoid tissue where they cannot (so far) be eradicated. We present a stochastic cellular automaton (CA) model to computationally study what could be an alternative treatment, namely Leukapheresis (LCAP), to remove HIV infected leukocytes in the lymphoid tissue. We base our investigations on Monte Carlo computer simulations. Our major objective is to investigate how the number of infected CD4(+) T cells changes in response to LCAP during the short-time (weeks) and long-time (years) scales of HIV/AIDS progression in an infected individual. To achieve our goal, we analyze the time evolution of the CD4(+) T cell population in the lymphoid tissue (i.e., the lymph node) for HIV dynamics in treatment situations with various starting times and frequencies and under a no treatment condition. Our findings suggest that the effectiveness of the treatment depends mainly on the treatment starting time and the frequency of the LCAP. Other factors (e.g., the removal proportion, the treatment duration, and the state of removed cells) that likely influence disease progression are subjects for further investigation.

Mesoscale modeling technique for studying the dynamics oscillation of Min protein: pattern formation analysis with lattice Boltzmann method.

We presented an application of the Lattice Boltzmann method (LBM) to study the dynamics of Min proteins oscillations in Escherichia coli. The oscillations involve MinC, MinD and MinE proteins, which are required for proper placement of the division septum in the middle of a bacterial cell. Here, the LBM is applied to a set of the deterministic reaction diffusion equations which describes the dynamics of the Min proteins. This determines the midcell division plane at the cellular level. We specifically use the LBM to study the dynamic pole-to-pole oscillations of the Min proteins in two dimensions. We observed that Min proteins' pattern formation depends on the cell's shape. The LBM numerical results are in good agreement with previous findings, using other methods and agree qualitatively well with experimental results. Our results indicate that the LBM can be an alternative computational tool for simulating the dynamics of these Min protein systems and possibly for the study of complex biological systems which are described by reaction-diffusion equations. Moreover, these findings suggest that LBM could also be useful for the investigation of possible evolutionary connection between the cell's shape and cell division of E. coli. The results show that the oscillatory pattern of Min protein is the most consistent with experimental results when the dimension of the cell is 1 x 2. This suggests that as the cell's shape is close to being a square, the oscillatory pattern no longer places the cell division of E. coli at the proper location. These findings may have a significant implication on why, by natural selection, E. coli is maintained in a rod shape or bacillus form.

Mathematical modeling and application of genetic algorithm to parameter estimation in signal transduction: trafficking and promiscuous coupling of G-protein coupled receptors.

G-protein-coupled receptors (GPCRs) constitute a large and diverse family of proteins whose primary function is to transduce extracellular stimuli into intracellular signals. These receptors play a critical role in signal transduction, and are among the most important pharmacological drug targets. Upon binding of extracellular ligands, these receptor molecules couple to one or several subtypes of G-protein which reside at the intracellular side of the plasma membrane to trigger intracellular signaling events. The question of how GPCRs select and activate a single or multiple G-protein subtype(s) has been the topic of intense investigations. Evidence is also accumulating; however, that certain GPCRs can be internalized via lipid rafts and caveolae. In many cases, the mechanisms responsible for this still remain to be elucidated. In this work, we extend the mathematical model proposed by Chen et al. [Modelling of signalling via G-protein coupled receptors: pathway-dependent agonist potency and efficacy, Bull. Math. Biol. 65 (5) (2003) 933-958] to take into account internalization, recycling, degradation and synthesis of the receptors. In constructing the model, we assume that the receptors can exist in multiple conformational states allowing for a multiple effecter pathways. As data on kinetic reaction rates in the signalling processes measured in reliable in vivo and in vitro experiments is currently limited to a small number of known values. In this paper, we also apply a genetic algorithm (GA) to estimate the parameter values in our model.

The stochastic model of non-equilibrium mutagen-induced alterations of DNA: implication to genetic instability in cancer.

Genetic alterations such as point mutations, chromosomal rearrangements, modification of DNA methylation and chromosome aberrations accumulate during the lifetime of an organism. They can be caused by intrinsic errors in the DNA replication and repair as well as by external factors such as exposure to mutagenic substances or radiation. The main purpose of the present work is to begin an exploration of the stochastic nature of non-equilibrium DNA alteration caused by events such as tautomeric shifts. This is done by modeling the genetic DNA code chain as a sequence of DNA-bit values ('1' for normal bases and '-1' for abnormal bases). We observe the number of DNA-bit changes resulting from the random point mutation process which, in the model, is being induced by a stochastic Brownian mutagen (BM) as it diffuses through the DNA-bit systems. Using both an analytical and Monte Carlo (MC) simulation techniques, we observe the local and global number of DNA-bit changes. It is found that in 1D, the local DNA-bit density behaves like 1/t, the global total number of the switched (abnormal) DNA-bit increases as t. The probability distribution P(b, 0, t) of b(0, t) is log-normal. It is also found that when the number of mutagens is increased, the number of the total abnormal DNA-bits does not grow linearly with the number of mutagens. All analytic results are in good agreement with the simulation results.

Spatial turing-type pattern formation in a model of signal transduction involving membrane-based receptors coupled by g proteins.

In this paper, a model of signaling pathways involving G proteins is investigated. The model incorporates reaction-diffusion mechanisms in which various reactants participate inside and on the extra-cellular surface membrane. The messenger molecules may diffuse over the surface of the cell membrane and signal transduction across the cell membrane is mediated by membrane receptor bound proteins which connect the genetically controlled biochemical intra-cellular reactions to the production of the second messenger, leading to desired functional responses. Dynamic and steady-state properties of the model are then investigated through weakly nonlinear stability analysis. Turing-type patterns are shown to form robustly under different delineating conditions on the system parameters. The theoretical predictions are then discussed in the context of some recently reported experimental evidence.

A delay-differential equation model of the feedback-controlled hypothalamus-pituitary-adrenal axis in humans.

The present work develops and analyses a model system of delay-differential equations which describes the core dynamics of the stress-responsive hypothalamus-pituitary-adrenal axis. This neuroendocrine ensemble exhibits prominent pulsatile secretory patterns governed by nonlinear and time-delayed feedforward and feedback signal interchanges. Formulation and subsequent bifurcation analysis of the model provide a qualitative and mathematical frame work for a better understanding of the delayed responsive mechanisms as well as the dynamic variations in different pathological situations.

Application of log-linear models to cancer patients: a case study of data from the National Cancer Institute.

Cancer is a noninfectious disease which is on the increase throughout the world and has become a serious problem for public health in many countries, including Thailand. In Thailand, cancer has risen significantly to become a leading cause of death and most patients are admitted to the National Cancer Institute. The objective of this study is to identify the associated factors between personal, cancer/clinical variables of cancer patients using log-linear models. Tests of independence are used (chi-square and Cramer's V-value tests) to find out the relationships between any two variables. In addition two- and three-dimensional log-linear models are used to obtain estimated parameters and expected frequencies for these models. Amongst the models fitted, the best are chosen based on the analysis of deviance. The results of this study show that most paired variables of personal, cancer/clinical variables are significantly related at p-value <0.05. For both male and female patients, the variable site of the cancer is highly related to marital status, diagnostic evidence and treatment, which provide the highest Cramer's V value. Moreover, the site of cancer also affects the method of diagnostic evidence and treatment. Since the site of cancer in each sex is different, prevention for various sites of cancer should be considered for each specific sex. In addition, for male and female patients, treatment is related to the site of cancer. Consequently, physicians may consider these factors before selecting the appropriate method of treatment.

Modeling of bone formation and resorption mediated by parathyroid hormone: response to estrogen/PTH therapy.

Bone, a major reservoir of body calcium, is under the hormonal control of the parathyroid hormone (PTH). Several aspects of its growth, turnover, and mechanism, occur in the absence of gonadal hormones. Sex steroids such as estrogen, nonetheless, play an important role in bone physiology, and are extremely essential to maintain bone balance in adults. In order to provide a basis for understanding the underlying mechanisms of bone remodeling as it is mediated by PTH, we propose here a mathematical model of the process. The nonlinear system model is then utilized to study the temporal effect of PTH as well as the action of estrogen replacement therapy on bone turnover. Analysis of the model is done on the assumption, supported by reported clinical evidence, that the process is characterized by highly diversified dynamics, which warrants the use of singular perturbation arguments. The model is shown to exhibit limit cycle behavior, which can develop into chaotic dynamics for certain ranges of the system's parametric values. Effects of estrogen and PTH administrations are then investigated by extending on the core model. Analysis of the model seems to indicate that the paradoxical observation that intermittent PTH administration causes net bone deposition while continuous administration causes net bone loss, and certain other reported phenomena may be attributed to the highly diversified dynamics which characterizes this nonlinear remodeling process.

Modeling of AIDS incidence and the response of transmission rates to increase prevention effort: a case study of the Thai province of Nakhon Pathom.

AIDS is a serious public health problem. Our projections of the likely incidence of AIDS are of vital importance to the assessment of future healthcare needs. This paper considers an epidemic model of the population dynamics of AIDS, which has been adjusted to take into account the changes in the transmission rate in response to changes in risk behaviors and increased AIDS awareness due to public health policy, AIDS campaigns, and other means of disease prevention. The model, adjusted for reporting delays and for the variable incubation period of the disease, has been applied to AIDS incidence data gathered in Nakhon Pathom, Thailand. Using the least-squares criterion, we solved for the appropriate values of the parameters which gave the best fit of the model to the observation data. The model was found to be capable of generating short-term projections, and offers an explanation for the decline in the number of cases that is evident in more recent data.