Association of proinflammatory cytokines and chemotherapy-associated cognitive impairment in breast cancer patients: a multi-centered, prospective, cohort study.

BACKGROUND: Existing evidence suggests that proinflammatory cytokines play an intermediary role in postchemotherapy cognitive impairment. This is one of the largest multicentered, cohort studies conducted in Singapore to evaluate the prevalence and proinflammatory biomarkers associated with cognitive impairment in breast cancer patients. PATIENTS AND METHODS: Chemotherapy-receiving breast cancer patients (stages I-III) were recruited. Proinflammatory plasma cytokines concentrations [interleukin (IL)-1ß, IL-2, IL-4, IL-6, IL-8, IL-10, granulocyte-macrophage colony-stimulating factor, interferon-γ and tumor necrosis factor-α] were evaluated at 3 time points (before chemotherapy, 6 and 12 weeks after chemotherapy initiation). The FACT-Cog (version 3) was utilized to evaluate patients' self-perceived cognitive disturbances and a computerized neuropsychological assessment (Headminder) was administered to evaluate patients' memory, attention, response speed and processing speed. Changes of cognition throughout chemotherapy treatment were compared against the baseline. Linear mixed-effects models were applied to test the relationships of clinical variables and cytokine concentrations on self-perceived cognitive disturbances and each objective cognitive domain. RESULTS: Ninety-nine patients were included (age 50.5 ± 8.4 years; 81.8% Chinese; mean duration of education = 10.8 ± 3.3 years). Higher plasma IL-1ß was associated with poorer response speed performance (estimate: -0.78; 95% confidence interval (CI) -1.34 to -0.03; P = 0.023), and a higher concentration of IL-4 was associated with better response speed performance (P = 0.022). Higher concentrations of IL-1ß and IL-6 were associated with more severe self-perceived cognitive disturbances (P = 0.018 and 0.001, respectively). Patients with higher concentrations of IL-4 also reported less severe cognitive disturbances (P = 0.022). CONCLUSIONS: While elevated concentrations of IL-6 and IL-1ß were observed in patients with poorer response speed performance and perceived cognitive disturbances, IL-4 may be protective against chemotherapy-associated cognitive impairment. This study is important because cytokines would potentially be mechanistic mediators of chemotherapy-associated cognitive changes.

Cognitive changes in multiethnic Asian breast cancer patients: a focus group study.

BACKGROUND: This study aims to gather descriptions from multiethnic Asian breast cancer patients on their experiences and impact of chemotherapy-associated cognitive changes on their daily lives and their coping strategies. PATIENTS AND METHODS: Forty-three chemotherapy-receiving breast cancer patients participated in eight English or Chinese structured focus group discussions, conducted by trained psychosocial oncologists and medical social workers. RESULTS: Participants were unfamiliar and averse to both English and Chinese-translated equivalent of the term 'chemobrain'. Participants viewed this phenomenon holistically as a by-product of the physical (fatigue and aging) and psychosocial (anxiety and mood changes) adverse effects associated with chemotherapy. Most participants encountered memory loss, difficulty in decision making and speech problems after receiving chemotherapy. Married participants expressed frustration as cognitive deterioration limited their conservative roles as homemakers. Despite knowing the potential neurocognitive effects, participants valued the benefits of chemotherapy. Identified coping strategies included playing mahjong for mind stimulation and management of psychosocial factors, such as practicing qi gong, to regulate their moods and to take complementary alternative medicines to reduce the severity of their fatigue. CONCLUSIONS: The phenomenon 'chemobrain' is unfamiliar to most Asian cancer patients yet it has significantly impacted their daily lives. Our results suggested that a culturally relevant approach should be adopted to evaluate and manage cognitive changes in these patients.

P53 and MDM2 co-expression in tobacco and betel chewing-associated oral squamous cell carcinomas.

Oral cancers of tobacco and betel chewers represents a unique in-vivo model to understand the genotoxic effect of tobacco and betel carcinogens on oncogenes and tumor suppressor genes. Coordinated interactions of p53 and MDM2 play an important role in regulation of critical growth control gene following exposure to DNA damaging agents. The purpose of this study is to determine if the tumor suppressor function of p53 is inactivated by mutation or other alternative mechanisms in carcinogen-induced oral squamous cell carcinoma (SCC), and to investigate the clinicopathological significance of p53 and MDM2 expression. The p53 mutation in oral SCC of tobacco and betel chewers (n=40) was detected by polymerase chain reaction - single strand conformation polymorphism (PCR-SSCP) analysis and immunohistochemistry (IHC) was done to investigate p53 and MDM2 proteins overexpression. The incidence of p53 mutation was relatively low (17.5%), but there was a high prevalence of MDM2 overexpression (72.5%). In the total of 40 cases, IHC phenotype showed p53 positive immunostaining with MDM2 positive immunostaining (p53+/MDM2+) 62.5%, p53 negative immunostaining with MDM2 negative immunostaining (p53-/MDM2-) 15%, p53 positive immunostaining with MDM2 negative immunostaining (p53+/MDM2-) 12.5%, and p53 negative immunostaining with MDM2 positive immunostaining (p53-/MDM2+) 10%. A significant correlation was found between MDM2 and p53 overexpression (p=0.0289). Moreover, p53+/MDM2+ phenotype was significantly associated with poorly differentiated tumors (p= 0.0007). These results conclude that other factors than p53 mutation is likely to be the targets of tobacco/betel carcinogens and MDM2 may play an important role in tobacco/betel chewing-related oral SCCs. Overexpression of MDM2 protein may constitute an alternative mechanism for p53 inactivation.

The SQLX system: generating explanations for clinical rules encoded in SQL.

We have developed a system to provide case-specific explanations for SQL query results. The explanation facility, called SQLX, is intended to improve user acceptance of clinical rules that are encoded in the SQL query language and are triggered by updates to a relational database. Our design goal is to combine the efficiency and familiarity of the SQL query model with a more intuitive and case-specific display of query results. SQLX defines an augmented SQL syntax that allows the incorporation of explanation text directly into query specifications. A query analyzer determines which retrieved data logically contribute to rule firing, and combines the data with explanation text to generate a case-specific explanation. Although features of the SQL query model limit the use of this method as a general-purpose rule-explanation facility, the current design accommodates a wide range of SQL query formulations and can provide efficient processing and intuitive explanations of many clinical rules.

Reuse of knowledge represented in the Arden syntax.

Knowledge Data Systems is building a medical expert system for monitoring clinical events. This system uses the Arden syntax as a knowledge representation. Having encoded may different types of rules in the Arden syntax, we have noticed a number of shortcomings of the syntax. Many of these shortcomings originate from Arden's procedural orientation, from its failure to separate factual medical knowledge from knowledge of how the medical facts should be applied to a particular clinical situation. The absence of this separation leads to redundancy of knowledge and to difficulties in knowledge reuse. We suggest that standards for representing medical logic preserve this separation to engender knowledge reuse. We propose a general framework for representing medical logic which supports both knowledge sharing and reuse.

Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. I. The probabilistic model and inference algorithms.

In Part I of this two-part series, we report the design of a probabilistic reformulation of the Quick Medical Reference (QMR) diagnostic decision-support tool. We describe a two-level multiply connected belief-network representation of the QMR knowledge base of internal medicine. In the belief-network representation of the QMR knowledge base, we use probabilities derived from the QMR disease profiles, from QMR imports of findings, and from National Center for Health Statistics hospital-discharge statistics. We use a stochastic simulation algorithm for inference on the belief network. This algorithm computes estimates of the posterior marginal probabilities of diseases given a set of findings. In Part II of the series, we compare the performance of QMR to that of our probabilistic system on cases abstracted from continuing medical education materials from Scientific American Medicine. In addition, we analyze empirically several components of the probabilistic model and simulation algorithm.

Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. II. Evaluation of diagnostic performance.

We have developed a probabilistic reformulation of the Quick Medical Reference (QMR) system. In Part I of this two-part series, we described a two-level, multiply connected belief-network representation of the QMR knowledge base and a simulation algorithm to perform probabilistic inference on the reformulated knowledge base. In Part II of this series, we report on an evaluation of the probabilistic QMR, in which we compare the performance of QMR to that of our probabilistic system on cases abstracted from continuing medical education materials from Scientific American Medicine. In addition, we analyze empirically several components of the probabilistic model and simulation algorithm.

An empirical analysis of likelihood-weighting simulation on a large, multiply connected medical belief network.

We are developing a probabilistic reformulation of the Quick Medical Reference (QMR) system. Our current probabilistic model of the QMR knowledge base of internal medicine consists of a two-level, multiply connected, belief network. Because of the size and connectivity of this belief network, most exact algorithms for calculating the posterior marginal probabilities of diseases are not applicable. In this paper, we analyze the convergence properties of an approximation algorithm, called likelihood-weighting simulation, on the QMR-DT belief network. Specifically, on two difficult diagnostic cases, we examine the effects of Markov blanket scoring, importance sampling, and self-importance sampling, demonstrating that the Markov blanket scoring and self-importance sampling significantly improve the convergence of the simulation on our model.

Validating the knowledge base of a therapy planning system.

Validation of expert system knowledge bases has proved to be difficult. This paper presents a description of a system called ScriptGen that generates test data for validating the knowledge base of the ONCOCIN cancer therapy planning system. Because of the size and complexity of the ONCOCIN knowledge base, we require tools for automated validation. ScriptGen, which applies techniques developed in testing both traditional software and expert systems, uses a parallel model of the ONCOCIN knowledge base and its own inference engine to generate test cases. We derived the limits of the system from a study that seeded errors into an existing knowledge base.