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Alzheimer's disease (AD) is a genetically complex and heterogeneous disorder. To date four genes have been established to either cause early-onset autosomal-dominant AD (APP, PSEN1, and PSEN2(1-4)) or to increase susceptibility for late-onset AD (APOE5). However, the heritability of late-onset AD is as high as 80%, (6) and much of the phenotypic variance remains unexplained to date. We performed a genome-wide association (GWA) analysis using 484,522 single-nucleotide polymorphisms (SNPs) on a large (1,376 samples from 410 families) sample of AD families of self-reported European descent. We identified five SNPs showing either significant or marginally significant genome-wide association with a multivariate phenotype combining affection status and onset age. One of these signals (p = 5.7 x 10(-14)) was elicited by SNP rs4420638 and probably reflects APOE-epsilon4, which maps 11 kb proximal (r2 = 0.78). The other four signals were tested in three additional independent AD family samples composed of nearly 2700 individuals from almost 900 families. Two of these SNPs showed significant association in the replication samples (combined p values 0.007 and 0.00002). The SNP (rs11159647, on chromosome 14q31) with the strongest association signal also showed evidence of association with the same allele in GWA data generated in an independent sample of approximately 1,400 AD cases and controls (p = 0.04). Although the precise identity of the underlying locus(i) remains elusive, our study provides compelling evidence for the existence of at least one previously undescribed AD gene that, like APOE-epsilon4, primarily acts as a modifier of onset age.

The goal of this work was to facilitate the clinical use of Monte Carlo proton dose calculation to support routine treatment planning and delivery. The Monte Carlo code Geant4 was used to simulate the treatment head setup, including a time-dependent simulation of modulator wheels (for broad beam modulation) and magnetic field settings (for beam scanning). Any patient-field-specific setup can be modeled according to the treatment control system of the facility. The code was benchmarked against phantom measurements. Using a simulation of the ionization chamber reading in the treatment head allows the Monte Carlo dose to be specified in absolute units (Gy per ionization chamber reading). Next, the capability of reading CT data information was implemented into the Monte Carlo code to model patient anatomy. To allow time-efficient dose calculation, the standard Geant4 tracking algorithm was modified. Finally, a software link of the Monte Carlo dose engine to the patient database and the commercial planning system was established to allow data exchange, thus completing the implementation of the proton Monte Carlo dose calculation engine ('DoC++'). Monte Carlo re-calculated plans are a valuable tool to revisit decisions in the planning process. Identification of clinically significant differences between Monte Carlo and pencil-beam-based dose calculations may also drive improvements of current pencil-beam methods. As an example, four patients (29 fields in total) with tumors in the head and neck regions were analyzed. Differences between the pencil-beam algorithm and Monte Carlo were identified in particular near the end of range, both due to dose degradation and overall differences in range prediction due to bony anatomy in the beam path. Further, the Monte Carlo reports dose-to-tissue as compared to dose-to-water by the planning system. Our implementation is tailored to a specific Monte Carlo code and the treatment planning system XiO (Computerized Medical Systems Inc.). However, this work describes the general challenges and considerations when implementing proton Monte Carlo dose calculation in a clinical environment. The presented solutions can be easily adopted for other planning systems or other Monte Carlo codes.

Monte Carlo dosimetry calculations are essential methods in radiation therapy. To take full advantage of this tool, the beam delivery system has to be simulated in detail and the initial beam parameters have to be known accurately. The modeling of the beam delivery system itself opens various areas where Monte Carlo calculations prove extremely helpful, such as for design and commissioning of a therapy facility as well as for quality assurance verification. The gantry treatment nozzles at the Northeast Proton Therapy Center (NPTC) at Massachusetts General Hospital (MGH) were modeled in detail using the GEANT4.5.2 Monte Carlo code. For this purpose, various novel solutions for simulating irregular shaped objects in the beam path, like contoured scatterers, patient apertures or patient compensators, were found. The four-dimensional, in time and space, simulation of moving parts, such as the modulator wheel, was implemented. Further, the appropriate physics models and cross sections for proton therapy applications were defined. We present comparisons between measured data and simulations. These show that by modeling the treatment nozzle with millimeter accuracy, it is possible to reproduce measured dose distributions with an accuracy in range and modulation width, in the case of a spread-out Bragg peak (SOBP), of better than 1 mm. The excellent agreement demonstrates that the simulations can even be used to generate beam data for commissioning treatment planning systems. The Monte Carlo nozzle model was used to study mechanical optimization in terms of scattered radiation and secondary radiation in the design of the nozzles. We present simulations on the neutron background. Further, the Monte Carlo calculations supported commissioning efforts in understanding the sensitivity of beam characteristics and how these influence the dose delivered. We present the sensitivity of dose distributions in water with respect to various beam parameters and geometrical misalignments. This allows the definition of tolerances for quality assurance and the design of quality assurance procedures.

Cancer patients undergoing radiation treatment are exposed to high doses to the target (tumour), intermediate doses to adjacent tissues and low doses from scattered radiation to all parts of the body. In the case of proton therapy, secondary neutrons generated in the accelerator head and inside the patient reach many areas in the patient body. Due to the improved efficacy of management of cancer patients, the number of long term survivors post-radiation treatment is increasing substantially. This results in concern about the risk of radiation-induced cancer appearing at late post-treatment times. This paper presents a case study to determine the effective dose from secondary neutrons in patients undergoing proton treatment. A whole-body patient model, VIP-Man, was employed as the patient model. The geometry dataset generated from studies made on VIP-Man was implemented into the GEANT4 Monte Carlo code. Two proton treatment plans for tumours in the lung and paranasal sinus were simulated. The organ doses and ICRP-60 radiation and tissue weighting factors were used to calculate the effective dose. Results show whole body effective doses for the two proton plans of 0.162 Sv and 0.0266 Sv, respectively, to which the major contributor is due to neutrons from the proton treatment nozzle. There is a substantial difference among organs depending on the treatment site.

Objective To examine the role of targeted indirect calorimetry in detecting the adequacy of energy intake and the risk of cumulative energy imbalance in a subgroup of critically ill children suspected to have alterations in resting energy expenditure. We examined the accuracy of standard equations used for estimating resting energy expenditure in relation to measured resting energy expenditure in relation to measured resting energy expenditure and cumulative energy balance over 1 week in this cohort. Design A prospective cohort study. Setting Pediatric intensive care unit in a tertiary academic center. Interventions A subgroup of critically ill children in the pediatric intensive care unit was selected using a set of criteria for targeted indirect calorimetry. Measurements Measured resting energy expenditure from indirect calorimetry and estimated resting energy expenditure from standard equations were obtained. The metabolic state of each patient was assigned as hypermetabolic (measured resting energy expenditure/estimated resting energy expenditure >110%), hypometabolic (measured resting energy expenditure/estimated resting energy expenditure <90%), or normal (measured resting energy expenditure/estimated resting energy expenditure = 90– 110%). Clinical variables associated with metabolic state and factors influencing the adequacy of energy intake were examined. Main Results Children identified by criteria for targeted indirect calorimetry, had a median length of stay of 44 days, a high incidence (72%) of metabolic instability and alterations in resting energy expenditure with a predominance of hypometabolism in those admitted to the medical service. Physicians failed to accurately predict the true metabolic state in a majority (62%) of patients. Standard equations overestimated the energy expenditure and a high incidence of overfeeding (83%) with cumulative energy excess of up to 8000 kcal/week was observed, especially in children <1 yr of age. We did not find a correlation between energy balance and respiratory quotient (RQ) in our study. Conclusions We detected a high incidence of overfeeding in a subgroup of critically ill children using targeted indirect calorimetry The predominance of hypometabolism, failure of physicians to correctly predict metabolic state, use of stress factors, and inaccuracy of standard equations all contributed to overfeeding in this cohort. Critically ill children, especially those with a longer stay in the PICU, are at a risk of unintended overfeeding with cumulative energy excess.

Background Oral pathology is a commonly reported extraintestinal manifestation of Crohn’s disease (CD). The host–microbe interaction has been implicated in the pathogenesis of inflammatory bowel disease (IBD) in genetically susceptible hosts, yet limited information exists about oral microbes in IBD. We hypothesize that the microbiology of the oral cavity may differ in patients with IBD. Our laboratory has developed a 16S rRNA-based technique known as the Human Oral Microbe Identification Microarray (HOMIM) to study the oral microbiome of children and young adults with IBD. Methods Tongue and buccal mucosal brushings from healthy controls, CD, and ulcerative colitis (UC) patients were analyzed using HOMIM. Shannon Diversity Index (SDI) and Principal Component Analysis (PCA) were employed to compare population and phylum-level changes among our study groups. Results In all, 114 unique subjects from the Children’s Hospital Boston were enrolled. Tongue samples from patients with CD showed a significant decrease in overall microbial diversity as compared with the same location in healthy controls (P = 0.015) with significant changes seen in Fusobacteria (P < 0.0002) and Firmicutes (P = 0.022). Tongue samples from patients with UC did not show a significant change in overall microbial diversity as compared with healthy controls (P = 0.418). Conclusions As detected by HOMIM, we found a significant decrease in overall diversity in the oral microbiome of pediatric CD. Considering the proposed microbe–host interaction in IBD, the ease of visualization and direct oral mucosal sampling of the oral cavity, further study of the oral microbiome in IBD is of potential diagnostic and prognostic value.

The GEANT4 Monte Carlo code provides many powerful functions for conducting particle transport simulations with great reliability and flexibility. However, as a general purpose Monte Carlo code, not all the functions were specifically designed and fully optimized for applications in radiation therapy. One of the primary issues is the computational efficiency, which is especially critical when patient CT data have to be imported into the simulation model. In this paper we summarize the relevant aspects of the GEANT4 tracking and geometry algorithms and introduce our work on using the code to conduct dose calculations based on CT data. The emphasis is focused on modifications of the GEANT4 source code to meet the requirements for fast dose calculations. The major features include a quick voxel search algorithm, fast volume optimization, and the dynamic assignment of material density. These features are ready to be used for tracking the primary types of particles employed in radiation therapy such as photons, electrons, and heavy charged particles. Recalculation of a proton therapy treatment plan generated by a commercial treatment planning program for a paranasal sinus case is presented as an example.