Outcomes Associated with the Utilization of Antidepressants and Psychotherapy Amongst Cancer Patients with Depression

Objective: To determine patterns of use, prescription medicine costs, office-based visit costs and quality of life (QOL) across classes of antidepressants/psychotherapy in elderly cancer patients. Methods: Medical Expenditure Panel Survey data files from 2005-2015 for cancer patients with depression aged 18 years or older were used for the study. Frequencies of patients under specific classes of antidepressants/psychotherapy were identified. Costs and QOL scores were compared using Analysis of Variance (ANOVA). Generalized linear models, linear/multinomial logistic regression were used for analyses adjusted for demographics, overall health status, number and type of comorbidities. Results: The study sample consisted of 17,671 cancer patients with depression. 32.08% patients had an antidepressant prescribed whereas 15.30% reported psychotherapy. SSRI (62.44%) was the most frequently prescribed class. The prescription and office-based visits costs were adjusted for demographics, overall health status, number and type of comorbidities. These adjusted prescription costs were the highest for SNRI (Mean = $112.92), adjusted office-based (psychotherapy) visit costs were the highest for those receiving psychotherapy only without any antidepressant (Mean =$166.39/visit). QOL scores were higher amongst patients who had combinations of antidepressants prescribed, specifically SSRI with either a TCA or SNRI as compared to those who were prescribed an individual class or those who did not receive any treatment at all. Conclusion: Antidepressants were prescribed more often than psychotherapy amongst cancer patients with depression in the United States. The prescription costs and associated QOL scores were higher amongst those with antidepressants prescribed as compared to those receiving psychotherapy with or without an antidepressant for cancer patients with depression in the United States

Development and Validation of Predictive Risk and Multiple Criteria Decision Analysis Models to Evaluate Cardiovascular Outcomes Among Cancer Patients

Objective: The objectives of our study were to characterize the study population with cancer and cardiovascular diseases (CVD) both as compared to those without and to build a predictive model using machine learning (ML) algorithms that can predict the risk of CVD in cancer patients. In addition, our objective was also to evaluate characteristics associated with cardiotoxic adverse events of breast cancer therapies and develop a multiple criteria decision analysis (MCDA) model to conduct benefit-risk assessment of breast cancer therapy regimens. Methods: We used Medical Expenditure Panel Survey (MEPS) and FDA Adverse Events Reporting System (FAERS) 2005-2015 files along with literature evidence for our study. We used MEPS database to train our predictive models using ML algorithms such as random forest (RF), gradient boosting and deep learning and compared these to standard regression models. Separate predictive models were built for chronic and acute CVD. We characterized the population with both cancer and CVD and those with cancer therapy associated cardiotoxic adverse events using multinomial logistic models . FAERS and literature evidence were also used to build the MCDA model to rank the breast cancer therapy regimens given the benefits and the risks involved in the treatment alternatives. Results: Our study sample consisted of 44,217 cancer patients identified using MEPS 2005-2015 files out of which 12,339 (28.7%) patients were also diagnosed with CVD. Age, marital status, education and employment status were the sociodemographic characteristics that differed significantly across cancer patients with and without CVD. We observed that most of the ML models for chronic (RF c-statistic: 0.9872, gradient boosting c-statistic: 0.7608, deep learning c-statistic: 0.7662) and acute CVD (RF c-statistic: 0.9738, gradient boosting c-statistic: 0.7853, deep learning c-statistic: 0.8267) were more accurate than the standard regression models for chronic (standard regression model c-statistic: 0.7641, GLM net model c-statistic: 0.7349) and acute (standard regression model c-statistic: 0.7534, GLM net model c-statistic: 0.7853) CVD. We then used the most accurate RF model to build a web-based application that could predict CVD risk. We then identified 35,630,544 breast cancer patients using FAERS dataset. Our findings suggest that breast cancer patients receiving targeted therapies were more likely to be diagnosed with CVD as compared to those who were receiving conventional therapies (OR = 1.213, 95% CI = 1.180, 1.247). On conducting MCDA, we found that the breast cancer therapy regimen 3 with trastuzumab, cyclophosphamide/ carboplatin and a taxane (paclitaxel/ docetaxel) was the most preferred therapy alternative given the benefits and the risks associated with each of the alternatives. Conclusion: Our study thus evaluated the use of newer analytical techniques such as ML algorithms and MCDA to evaluate certain outcomes. Our study suggests that ML algorithms were more accurate in predicting CVD risk in cancer patients. In addition, our MCDA model suggested that the breast cancer therapy regimen with trastuzumab, cyclophosphamide/ carboplatin and a taxane was the most preferred alternative considering the survival and adverse events benefits and risks

Random on-board pixel sampling (ROPS) X-ray Camera

Recent advances in compressed sensing theory and algorithms offer new possibilities for high-speed X-ray camera design. In many CMOS cameras, each pixel has an independent on-board circuit that includes an amplifier, noise rejection, signal shaper, an analog-to-digital converter (ADC), and optional in-pixel storage. When X-ray images are sparse, i.e., when one of the following cases is true: (a.) The number of pixels with true X-ray hits is much smaller than the total number of pixels; (b.) The X-ray information is redundant; or (c.) Some prior knowledge about the X-ray images exists, sparse sampling may be allowed. Here we first illustrate the feasibility of random on-board pixel sampling (ROPS) using an existing set of X-ray images, followed by a discussion about signal to noise as a function of pixel size. Next, we describe a possible circuit architecture to achieve random pixel access and in-pixel storage. The combination of a multilayer architecture, sparse on-chip sampling, and computational image techniques, is expected to facilitate the development and applications of high-speed X-ray camera technology.Comment: 9 pages, 6 figures, Presented in 19th iWoRI

Simultaneous X-ray diffraction and phase-contrast imaging for investigating material deformation mechanisms during high-rate loading

Using a high-speed camera and an intensified charge-coupled device (ICCD), a simultaneous X-ray imaging and diffraction technique has been developed for studying dynamic material behaviors during high-rate tensile loading. A Kolsky tension bar has been used to pull samples at 1000 s(−1) and 5000 s(−1) strain-rates for super-elastic equiatomic NiTi and 1100-O series aluminium, respectively. By altering the ICCD gating time, temporal resolutions of 100 ps and 3.37 µs have been achieved in capturing the diffraction patterns of interest, thus equating to single-pulse and 22-pulse X-ray exposure. Furthermore, the sample through-thickness deformation process has been simultaneously imaged via phase-contrast imaging. It is also shown that adequate signal-to-noise ratios are achieved for the detected white-beam diffraction patterns, thereby allowing sufficient information to perform quantitative data analysis diffraction via in-house software (WBXRD_GUI). Of current interest is the ability to evaluate crystal d-spacing, texture evolution and material phase transitions, all of which will be established from experiments performed at the aforementioned elevated strain-rates

Dense cataract and microphthalmia (dcm) in BALB/c mice is caused by mutations in the GJA8 locus

A spontaneous mutation in BALB/c mice that causes congenital dense cataract and microphthalmia (dcm) was reported previously. This abnormality was found to be inheritable and the mode of inheritance indicated that this phenotype is due to mutation of an autosomal recessive gene. We performed genetic screen to identify the underlying mutations through linkage analysis with the dcm progenies of F1 intercross. We identified the region of mutation on chromosome 3 and further mapping and sequence analysis identified the mutation in the GJA8 gene that encodes for connexin 50. The mutation represents a single nucleotide change at position 64 (G to C) that results in a change in the amino acid glycine to arginine at position 22 (G22R) and is identical to the mutation previously characterized as lop10. However, the phenotype of these mice differ from that of lop10 mice and since it is one of the very few genetic models with recessive pattern of inheritance, we propose that dcm mice can serve as a useful model for studying the dynamics and interaction of the gap junction formation in mouse eye development

What we ate then and what we eat now : a grandmother’s tale

Malnutrition levels in India remain a major public health challenge. According to the fourth round of the District Level Household Survey (DLHS-4, 2012-13), almost 30 percent of all children under the age of 5 are underweight in most states. This is a serious cause for concern for several reasons: low weight-for-age has been associated with a range of disadvantages, including a higher risk of dying due to several disease conditions among young children; changes in the autonomic nervous system; higher risk of hypertension and insulinresistance in adulthood; adverse impacts on brain development, cognitive ability and school achievement; and behavioural problems such as anxiety and hostility later in life

Pseudo-unitary symmetry and the Gaussian pseudo-unitary ensemble of random matrices

Employing the currently discussed notion of pseudo-Hermiticity, we define a pseudo-unitary group. Further, we develop a random matrix theory which is invariant under such a group and call this ensemble of pseudo-Hermitian random matrices as the pseudo-unitary ensemble. We obtain exact results for the nearest-neighbour level spacing distribution for (2 X 2) PT-symmetric Hamiltonian matrices which has a novel form, s log (1/s) near zero spacing. This shows a level repulsion in marked distinction with an algebraic form in the Wigner surmise. We believe that this paves way for a description of varied phenomena in two-dimensional statistical mechanics, quantum chromodynamics, and so on.Comment: 9 pages, 2 figures, LaTeX, submitted to the Physical Review Letters on August 20, 200

Quantum chaos, random matrix theory, and statistical mechanics in two dimensions - a unified approach

We present a theory where the statistical mechanics for dilute ideal gases can be derived from random matrix approach. We show the connection of this approach with Srednicki approach which connects Berry conjecture with statistical mechanics. We further establish a link between Berry conjecture and random matrix theory, thus providing a unified edifice for quantum chaos, random matrix theory, and statistical mechanics. In the course of arguing for these connections, we observe sum rules associated with the outstanding counting problem in the theory of braid groups. We are able to show that the presented approach leads to the second law of thermodynamics.Comment: 23 pages, TeX typ

Supersymmetric Many-particle Quantum Systems with Inverse-square Interactions

The development in the study of supersymmetric many-particle quantum systems with inverse-square interactions is reviewed. The main emphasis is on quantum systems with dynamical OSp(2|2) supersymmetry. Several results related to exactly solved supersymmetric rational Calogero model, including shape invariance, equivalence to a system of free superoscillators and non-uniqueness in the construction of the Hamiltonian, are presented in some detail. This review also includes a formulation of pseudo-hermitian supersymmetric quantum systems with a special emphasis on rational Calogero model. There are quite a few number of many-particle quantum systems with inverse-square interactions which are not exactly solved for a complete set of states in spite of the construction of infinitely many exact eigen functions and eigenvalues. The Calogero-Marchioro model with dynamical SU(1,1|2) supersymmetry and a quantum system related to short-range Dyson model belong to this class and certain aspects of these models are reviewed. Several other related and important developments are briefly summarized.Comment: LateX, 65 pages, Added Acknowledgment, Discussions and References, Version to appear in Jouranl of Physics A: Mathematical and Theoretical (Commissioned Topical Review Article

An open-label, 1-year extension study of the long-term safety and efficacy of once-daily OROS® hydromorphone in patients with chronic cancer pain

<p>Abstract</p> <p>Background</p> <p>Opioid analgesics have proven efficacy in the short-term management of chronic cancer pain, but data on their long-term use is more limited. OROS^®hydromorphone is a controlled-release formulation of oral hydromorphone that may be particularly well suited to long-term management of chronic cancer pain because it provides stable plasma concentrations and consistent analgesia with convenient once-daily dosing. The objective of this study (DO-118X) was to characterise the pain control achieved with long-term repeated dosing of OROS^®hydromorphone in patients with chronic cancer pain.</p> <p>Methods</p> <p>In this multicentre, phase III, open-label, single treatment, 1-year extension study, OROS^®hydromorphone was administered to 68 patients with moderate-to-severe chronic cancer pain, who had successfully completed a short-term equivalence study, and whose pain was controlled with a stable dose of medication (≥ 8 mg OROS^®hydromorphone or equivalent controlled-release morphine). Patients were started on the dose of OROS^®hydromorphone equivalent to the opioid dose on which they achieved dose-stable pain control in the equivalence study; dose adjustments were made as necessary and breakthrough pain medication was permitted. Efficacy was assessed with the Brief Pain Inventory (BPI) and patient and investigator global evaluations of treatment effectiveness. No formal statistical analysis was done.</p> <p>Results</p> <p>The mean (standard deviation) duration of exposure to study medication was 139 (129.9) days and the mean (standard deviation) average daily consumption of OROS^®hydromorphone was 43.7 (28.14) mg/day. All scores were maintained at a mild to moderate severity throughout the study; however, BPI scores for pain at its worst, pain at its least, pain on average, pain right now, and pain relief were slightly worsened at end point compared with baseline. Mean BPI pain interference with daily activities and patient and investigator global evaluation scores also remained generally stable. Treatment effectiveness was rated as fair to good throughout the study. The most frequently reported adverse events were nausea (n = 24, 35.3%), constipation (n = 22, 32.4%), and vomiting (n = 15, 22.1%).</p> <p>Conclusion</p> <p>The results of this extension study suggest that long-term repeated dosing with once-daily OROS^®hydromorphone can be beneficial in the continuing management of persistent, moderate-to-severe cancer pain.</p