Biomedical subjects

Anuradha Roy

Publications and source records attributed to Anuradha Roy.

6 recordsLinked to original sources

PubMed2006

Age-related effect on the concentration of collagen crosslinks in human osteonal and interstitial bone tissue.

Collagen crosslinks are important to the quality of bone and may be contributors to the age-related increase in bone fracture. This study was performed to investigate whether age and gender effects on collagen crosslinks are similar in osteonal and interstitial bone tissues. Forty human cadaveric femurs were collected and divided into two age groups: middle-aged (42-63 years of age) and elderly (69-90 years of age) with ten males and ten females in each group (n = 10). Micro-cores of bone tissue from both secondary osteons and interstitial regions in the medial quadrant of the diaphysis were extracted using a custom-modified, computer-controlled milling machine. The bone specimens were then analyzed using high performance liquid chromatography to determine the effects of age and gender on the concentration of mature, enzymatic crosslinks (hydroxylysyl-pyridinoline-HP and lysyl-pyridinoline-LP) and a non-enzymatic crosslink (pentosidine-PE) at these two microstructural sites. The results indicate that age has a significant effect on the concentration of LP and PE, while gender has a significant effect on HP and LP. In addition, the concentration of the crosslinks in the secondary osteons is significantly different from that in the interstitial bone regions. These results suggest that the amount of non-enzymatic crosslinking may increase while that of mature enzymatic crosslinking may decrease with age. Such changes could potentially reduce the inherent quality of the bone tissue in the elderly skeleton.

Adult↗

PubMed2006

A new classification rule for incomplete doubly multivariate data using mixed effects model with performance comparisons on the imputed data.

A mixed effects model, enhanced by a Kronecker product structure for the residual variance-covariance matrix, is used in conjunction with a discriminant analysis technique, to devise a new statistical classification method on incomplete doubly multivariate data. The proposed method is efficient in small scale clinical trials that use relatively few patients. The new classification method is also applied to multiply imputed data sets. The misclassification error rates (MERs) are compared in order to investigate the effectiveness of the new classification rule on an incomplete data set. The classification method is applied to a real data set. The error rates on the incomplete data set are found to be much less than the median error rate on the multiply imputed data sets. Non-parametric methods, such as kernel method and k-nearest neighbourhood method, are also applied to multiply imputed data sets. Results illustrating the advantages of the new classification method over classic non-parametric classification methods are presented.

Bone Density↗

PubMed2006

Estimating correlation coefficient between two variables with repeated observations using mixed effects model.

We estimate the correlation coefficient between two variables with repeated observations on each variable, using linear mixed effects (LME) model. The solution to this problem has been studied by many authors. Bland and Altman (1995) considered the problem in many ad hoc methods. Lam, Webb and O'Donnell (1999) solved the problem by considering different correlation structures on the repeated measures. They assumed that the repeated measures are linked over time but their method needs specialized software. However, they never addressed the question of how to choose the correlation structure on the repeated measures for a particular data set. Hamlett et al. (2003) generalized this model and used Proc Mixed of SAS to solve the problem. Unfortunately, their method also cannot implement the correlation structure on the repeated measures that is present in the data. We also assume that the repeated measures are linked over time and generalize all the previous models, and can account for the correlation structure on the repeated measures that is present in the data. We study how the correlation coefficient between the variables gets affected by incorrect assumption of the correlation structure on the repeated measures itself by using Proc Mixed of SAS, and describe how to select the correlation structure on the repeated measures. We also extend the model by including random intercept and random slope over time for each subject. Our model will also be useful when some of the repeated measures are missing at random.

Algorithms↗

PubMed2006

The influence of water removal on the strength and toughness of cortical bone.

Although the effects of dehydration on the mechanical behavior of cortical bone are known, the underlying mechanisms for such effects are not clear. We hypothesize that the interactions of water with the collagen and mineral phases each have a unique influence on mechanical behavior. To study this, strength, toughness, and stiffness were measured with three-point bend specimens made from the mid-diaphysis of human cadaveric femurs and divided into six test groups: control (hydrated), drying in a vacuum oven at room temperature (21 degrees C) for 30 min and at 21, 50, 70, or 110 degrees C for 4 h. The experimental data indicated that water loss significantly increased with each increase in drying condition. Bone strength increased with a 5% loss of water by weight, which was caused by drying at 21 degrees C for 4 h. With water loss exceeding 9%, caused by higher drying temperatures (> or =70 degrees C), strength actually decreased. Drying at 21 degrees C (irrespective of time in vacuum) significantly decreased bone toughness through a loss of plasticity. However, drying at 70 degrees C and above caused toughness to decrease through decreases in strength and fracture strain. Stiffness linearly increased with an increase in water loss. From an energy perspective, the water-mineral interaction is removed at higher temperatures than the water-collagen interaction. Therefore, we speculate that loss of water in the collagen phase decreases the toughness of bone, whereas loss of water associated with the mineral phase decreases both bone strength and toughness.

Adult↗

PubMed2004

Tree-based model for breast cancer prognostication.

PURPOSE: To define prognostic groups for recurrence-free survival in breast cancer, assess relative effects of prognostic factors, and examine the influence of treatment variations on recurrence-free survival in patients with similar prognostic-factor profiles. PATIENTS AND METHODS: We analyzed 1,055 patients diagnosed with stage I-III breast cancer between 1990 and 1996. Variables studied included socioeconomic factors, tumor characteristics, concurrent medical conditions, and treatment. The primary end point was recurrence-free survival (RFS). Multivariable analyses were performed using recursive partitioning and Cox proportional hazards regression. RESULTS: The most significant difference in prognosis was between patients with fewer than four and those with at least four positive nodes (P <.0001). Four distinct prognostic groups (5-year RFS, 97%, 78%, 58%, and 27%) were developed, defined by the number of positive nodes, tumor size, progesterone receptor (PR) status, differentiation, race, and marital status. Patients with fewer than four positive nodes and tumor < or = 2 cm, PR positive, and well or moderately differentiated had the best prognosis. RFS in this group was unaffected by type of adjuvant therapy (P =.38). Patients with at least four positive nodes and PR-negative tumors had the worst prognosis, and those treated with tamoxifen plus chemotherapy had the best outcome in this group (P =.0001). Among patients in the two intermediate-risk groups, those treated with tamoxifen or a combination of tamoxifen and chemotherapy had the best outcome. CONCLUSION: Lymph node status, PR status, tumor size, differentiation, race, and marital status are valuable for prognostication in breast cancer. The prognostic groups derived can provide guidance for clinical trial design, patient management, and future treatment policy.

Adult↗

PubMed2004

A retrospective evaluation of digital wound imaging to predict response to hyperbaric oxygen treatment.

As new wound care treatments become available, correct initial treatment selection and dynamic modification of regimens, based on wound response to treatment, must be applied to improve outcomes and reduce cost. One alternative is wound morphometry using digital wound images to evaluate wound response to treatment in realtime. To determine whether wound area measurements taken during the first 3 weeks of hyperbaric oxygen treatment predict eventual treatment response and how demographic and disease factors impact hyperbaric oxygen treatment response, a retrospective study using digital wound images, demographic data, and available clinical laboratory values was conducted. Participants included 29 wound care patients with nonhealing wounds of the lower extremities receiving treatment at a hyperbaric wound care facility. Conventional wound care (ie, debridement, dressing changes, and topical agents) plus hyperbaric oxygen treatment (100% oxygen breathing at 2.4 atmospheres absolute for 90 minutes) given once every weekday for up to 20 weeks was provided. Graphical analysis of normalized wound area over time revealed two groups: minimal responders (n=13) and robust responders (n=16). Minimal response was characterized by delayed onset of wound area reduction and virtual cessation of reduction by week 3. Robust response was continuous, sustained, and resulted in average wound area reduction of 80% by end of treatment, compared to 47% in minimally responsive patients. Age, blood glucose, and serum creatinine significantly affected the wound healing response to hyperbaric oxygen treatment (P<0.05). Digital images obtained during the first 3 weeks of treatment predicted if a patient is minimally responsive to hyperbaric oxygen treatment with 100% accuracy. Area measurements obtained in this manner can be used to identify patients minimally responsive to hyperbaric oxygen treatment, enabling rapid assessment of treatment response to make timely changes in therapy in order to optimize treatment outcomes.

Aged↗