An Approach to Identifying Spatial Variability in Observed Infectious Disease Spread in a Prospective Time-Space Series with Applications to COVID-19 and Dengue Incidence.

Most of the growing prospective analytic methods in space-time disease surveillance and intended functions of disease surveillance systems focus on earlier detection of disease outbreaks, disease clusters, or increased incidence. The spread of the virus such as SARS-CoV-2 has not been spatially and temporally uniform in an outbreak. With the identification of an infectious disease outbreak, recognizing and evaluating anomalies (excess and decline) of disease incidence spread at the time of occurrence during the course of an outbreak is a logical next step. We propose and formulate a hypergeometric probability model that investigates anomalies of infectious disease incidence spread at the time of occurrence in the timeline for many geographically described populations (e.g., hospitals, towns, counties) in an ongoing daily monitoring process. It is structured to determine whether the incidence grows or declines more rapidly in a region on the single current day or the most recent few days compared to the occurrence of the incidence during the previous few days relative to elsewhere in the surveillance period. The new method uses a time-varying baseline risk model, accounting for regularly (e.g., daily) updated information on disease incidence at the time of occurrence, and evaluates the probability of the deviation of particular frequencies to be attributed to sampling fluctuations, accounting for the unequal variances of the rates due to different population bases in geographical units. We attempt to present and illustrate a new model to advance the investigation of anomalies of infectious disease incidence spread by analyzing subsamples of spatiotemporal disease surveillance data from Taiwan on dengue and COVID-19 incidence which are mosquito-borne and contagious infectious diseases, respectively. Efficient R programs for computation are available to implement the two approximate formulae of the hypergeometric probability model for large numbers of events.

Continuous aerosol monitoring and comparison of aerosol exposure based on smoke dispersion distance and concentrations during oxygenation therapy.

This study evaluated the aerosol exposure risks while using common noninvasive oxygenation devices. A simulated mannequin was designed to breathe at a minute ventilation of 20 L/min and used the following oxygen-therapy devices: nasal cannula oxygenation (NCO) at 4 and 15 L/min, nonrebreathing mask (NRM) at 15 L/min, simple mask at 6 L/min, combination of NCO at 15 L/min and NRM at 15 L/min, high-flow nasal cannula (HFNC) at 50 L/min, and flush rate NRM. Two-dimension of the dispersion distance and the aerosol concentrations were measured at head, trunk, and foot around the mannequin for over 10 min. HFNC and flush-rate NRM yielded the longest dispersion distance and highest aerosol concentrations over the three sites of the mannequin than the other oxygenation devices and should use with caution. For flow rates of < 15 L/min, oxygenation devices with mask-like effects, such as NRM or NCO with NRM, decreased aerosol dispersion more effectively than NCO alone or a simple mask. In the upright position, the foot area exhibited the highest aerosol concentration regardless of the oxygenation device than the head-trunk areas of the mannequin. Healthcare workers should be alert even at the foot side of the patient while administering oxygenation therapy.

A comparative analysis of aerosol exposure and prevention strategies in bystander, pre-hospital, and inpatient cardiopulmonary resuscitation using simulation manikins.

To evaluate aerosol exposure risk and prevention strategies during bystander, pre-hospital, and inpatient cardiopulmonary resuscitation (CPR). This study compared hands-only CPR, CPR with a surgical or N95 mask, and CPR with a non-rebreather mask at 15 L/min. 30:2 compression-ventilation ratio CPR was tested with face-mask ventilation (FMV), FMV with a high efficiency particulate air (HEPA) filter; supraglottic airway (SGA), SGA with a surgical mask, SGA with a HEPA filter, or SGA with both. Continuous CPR was tested with an endotracheal tube (ET), ET with a surgical mask, a HEPA filter, or both. Aerosol concentration at the head, trunk, and feet of the mannequin were measured to evaluate exposure to CPR personnel. Hands-only CPR with a surgical or N95 face mask coverings and ET tube ventilation CPR with filters showed the lowest aerosol exposure among all study groups, including CPR with NRM oxygenation, FMV, and SGA ventilation. NRM had a mask effect and reduced aerosol exposure at the head, trunk, and feet of the mannequin. FMV with filters during 30:2 CPR reduced aerosol exposure at the head and trunk, but increased at the feet of the mannequin. A tightly-sealed SGA when used with a HEPA filter, reduced aerosol exposure by 21.00-63.14% compared with a loose-fitting one. Hands-only CPR with a proper fit surgical or N95 face mask coverings is as safe as ET tube ventilation CPR with filters, compared with CPR with NRM, FMV, and SGA. FMV or tight-sealed SGA ventilation with filters prolonged the duration to achieve estimated infective dose of SARS-CoV-2 2.4-2.5 times longer than hands-on CPR only. However, a loose-fitting SGA is not protective at all to chest compressor or health workers standing at the foot side of the victim, so should be used with caution even when using with HEPA filters.

The Aerosol-Generating Effect Among Noninvasive Positive Pressure Ventilation, High-Flow Nasal Cannula, Nonrebreather Mask, Nasal Cannula, and Ventilator-Assisted Preoxygenation.

STUDY OBJECTIVE: To evaluate aerosol dispersion and exposure risk during oxygenation therapy among health care personnel. METHODS: This study compared the aerosol dispersion effect produced through continuous positive airway pressure (CPAP), bilevel positive airway pressure (BiPAP), BiPAP with face coverings, a high-flow nasal cannula (HFNC) with face coverings, nasal cannula oxygenation (NCO) at 15 L/min with face coverings, nonrebreather mask (NRM), and ventilator-assisted preoxygenation (VAPOX) during oxygenation therapy at a minute ventilation of 10 L/min and 20 L/min. The length and width of aerosol dispersion were recorded, and aerosol concentrations were then detected at a mannequin's head, trunk, and feet. RESULTS: The average length dispersion distance of CPAP was 47.12 cm (SD, 12.56 cm), of BiPAP was 100.13 cm (SD, 6.03 cm), of BiPAP with face coverings was 62.20 cm (SD, 8.46 cm), of HFNC with face coverings was 67.09 cm (SD, 12.74 cm); of NCO with face coverings was 85.55 cm (SD, 7.28 cm); and of NRM was 63.08 cm (SD, 15.33 cm); VAPOX showed no visible dispersion. The aerosol concentrations at the feet under CPAP and at the head under BiPAP were significantly higher than those observed without an oxygen device. Compared with no oxygen device, the aerosol concentration with HFNC was higher at the mannequin's head, trunk, and feet; whereas it was lower with VAPOX and NRM. Moreover, when translated to the number of virus particles required to infect medical personnel (Nf), VAPOX took more time to achieve Nf than other devices. CONCLUSION: Strong flow from the oxygenation devices resulted in increased aerosol concentrations. CPAP at the feet side, BiPAP at the head side, HFNC, and NCO with face coverings significantly increase aerosol exposure and should be used with caution. Aerosol concentrations at all positions were lower with NRM and VAPOX.

Genetic susceptibility to patient-reported xerostomia among long-term oropharyngeal cancer survivors.

Genetic susceptibility for xerostomia, a common sequela of radiotherapy and chemoradiotherapy for head and neck cancer, is unknown. Therefore, to identify genetic variants associated with moderate to severe xerostomia, we conducted a GWAS of 359 long-term oropharyngeal cancer (OPC) survivors using 579,956 autosomal SNPs. Patient-reported cancer treatment-related xerostomia was assessed using the MD Anderson Symptom Inventory. Patient response was dichotomized as moderate to severe or none to mild symptoms. In our study, 39.2% of OPC survivors reported moderate to severe xerostomia. Our GWAS identified eight SNPs suggestively associated with higher risk of moderate to severe xerostomia in six genomic regions (2p13.3, rs6546481, Minor Allele (MA) = A, ANTXR1, P = 4.3 × 10-7; 5p13.2-p13.1, rs16903936, MA = G, EGFLAM, P = 5.1 × 10-6; 4q21.1, rs10518156, MA = G, SHROOM3, P = 7.1 × 10-6; 19q13.42, rs11882068, MA = G, NLRP9, P = 1.7 × 10-5; 12q24.33, rs4760542, MA = G, GLT1D1, P = 1.8 × 10-5; and 3q27.3, rs11714564, MA = G, RTP1, P = 2.9 × 10-5. Seven SNPs were associated with lower risk of moderate to severe xerostomia, of which only one mapped to specific genomic region (15q21.3, rs4776140, MA = G, LOC105370826, a ncRNA class RNA gene, P = 1.5 × 10-5). Although our small exploratory study did not reach genome-wide statistical significance, our study provides, for the first time, preliminary evidence of genetic susceptibility to xerostomia. Further studies are needed to elucidate the role of genetic susceptibility to xerostomia.

Spatially varying effects of measured confounding variables on disease risk.

BACKGROUND: The presence of considerable spatial variability in incidence intensity suggests that risk factors are unevenly distributed in space and influence the geographical disease incidence distribution and pattern. As most human common diseases that challenge investigators are complex traits and as more factors associated with increased risk are discovered, statistical spatial models are needed that investigate geographical variability in the association between disease incidence and confounding variables and evaluate spatially varying effects on disease risk related to known or suspected risk factors. Information on geography that we focus on is geographical disease clusters of peak incidence and paucity of incidence. METHODS: We proposed and illustrated a statistical spatial model that incorporates information on known or hypothesized risk factors, previously detected geographical disease clusters of peak incidence and paucity of incidence, and their interactions as covariates into the framework of interaction regression models. The spatial scan statistic and the generalized map-based pattern recognition procedure that we recently developed were both considered for geographical disease cluster detection. The Freeman-Tukey transformation was applied to improve normality of distribution and approximately stabilize the variance in the model. We exemplified the proposed method by analyzing data on the spatial occurrence of sudden infant death syndrome (SIDS) with confounding variables of race and gender in North Carolina. RESULTS: The analysis revealed the presence of spatial variability in the association between SIDS incidence and race. We differentiated spatial effects of race on SIDS incidence among previously detected geographical disease clusters of peak incidence and incidence paucity and areas outside the geographical disease clusters, determined by the spatial scan statistic and the generalized map-based pattern recognition procedure. Our analysis showed the absence of spatial association between SIDS incidence and gender. CONCLUSION: The application to the SIDS incidence data demonstrates the ability of our proposed model to estimate spatially varying associations between disease incidence and confounding variables and distinguish spatially related risk factors from spatially constant ones, providing valuable inference for targeted environmental and epidemiological surveillance and management, risk stratification, and thorough etiologic studies of disease.

Comparing the effectiveness of negative-pressure barrier devices in providing air clearance to prevent aerosol transmission.

PURPOSE: To investigate the effectiveness of aerosol clearance using an aerosol box, aerosol bag, wall suction, and a high-efficiency particulate air (HEPA) filter evacuator to prevent aerosol transmission. METHODS: The flow field was visualized using three protective device settings (an aerosol box, and an aerosol bag with and without sealed working channels) and four suction settings (no suction, wall suction, and a HEPA filter evacuator at flow rates of 415 liters per minute [LPM] and 530 LPM). All 12 subgroups were compared with a no intervention group. The primary outcome, aerosol concentration, was measured at the head, trunk, and foot of a mannequin. RESULTS: The mean aerosol concentration was reduced at the head (p < 0.001) but increased at the feet (p = 0.005) with an aerosol box compared with no intervention. Non-sealed aerosol bags increased exposure at the head and trunk (both, p < 0.001). Sealed aerosol bags reduced aerosol concentration at the head, trunk, and foot of the mannequin (p < 0.001). A sealed aerosol bag alone, with wall suction, or with a HEPA filter evacuator reduced the aerosol concentration at the head by 7.15%, 36.61%, and 84.70%, respectively (99.9% confidence interval [CI]: -4.51-18.81, 27.48-45.73, and 78.99-90.40); trunk by 70.95%, 73.99%, and 91.59%, respectively (99.9% CI: 59.83-82.07, 52.64-95.33, and 87.51-95.66); and feet by 69.16%, 75.57%, and 92.30%, respectively (99.9% CI: 63.18-75.15, 69.76-81.37, and 88.18-96.42), compared with an aerosol box alone. CONCLUSIONS: As aerosols spread, an airtight container with sealed working channels is effective when combined with suction devices.

Differentiating anomalous disease intensity with confounding variables in space.

BACKGROUND: The investigation of perceived geographical disease clusters serves as a preliminary step that expedites subsequent etiological studies and analysis of epidemicity. With the identification of disease clusters of statistical significance, to determine whether or not the detected disease clusters can be explained by known or suspected risk factors is a logical next step. The models allowing for confounding variables permit the investigators to determine if some risk factors can explain the occurrence of geographical clustering of disease incidence and to investigate other hidden spatially related risk factors if there still exist geographical disease clusters, after adjusting for risk factors. METHODS: We propose to develop statistical methods for differentiating incidence intensity of geographical disease clusters of peak incidence and low incidence in a hierarchical manner, adjusted for confounding variables. The methods prioritize the areas with the highest or lowest incidence anomalies and are designed to recognize hierarchical (in intensity) disease clusters of respectively high-risk areas and low-risk areas within close geographic proximity on a map, with the adjustment for known or suspected risk factors. The data on spatial occurrence of sudden infant death syndrome with a confounding variable of race in North Carolina counties were analyzed, using the proposed methods. RESULTS: The proposed Poisson model appears better than the one based on SMR, particularly at facilitating discrimination between the 13 counties with no cases. Our study showed that the difference in racial distribution of live births explained, to a large extent, the 3 previously identified hierarchical high-intensity clusters, and a small region of 4 mutually adjacent counties with the higher race-adjusted rates, which was hidden previously, emerged in the southwest, indicating that unobserved spatially related risk factors may cause the elevated risk. We also showed that a large geographical cluster with the low race-adjusted rates, which was hidden previously, emerged in the mid-east. CONCLUSION: With the information on hierarchy in adjusted intensity levels, epidemiologists and public health officials can better prioritize the regions with the highest rates for thorough etiologic studies, seeking hidden spatially related risk factors and precisely moving resources to areas with genuine highest abnormalities.

Recognizing spatial and temporal clustering patterns of dengue outbreaks in Taiwan.

BACKGROUND: Dengue fever is the most common arboviral infection in humans, with viral transmissions occurring in more than 100 countries in tropical regions. A global strategy for dengue prevention and control was established more than 10 years ago. However, the factors that drive the transmission of the dengue virus and subsequent viral infection continue unabated. The largest dengue outbreaks in Taiwan since World War II occurred in two recent successive years: 2014 and 2015. METHODS: We performed a systematic analysis to detect and recognize spatial and temporal clustering patterns of dengue incidence in geographical areas of Taiwan, using the map-based pattern recognition procedure and scan test. Our aim was to recognize geographical heterogeneity patterns of varying dengue incidence intensity and detect hierarchical incidence intensity clusters. RESULTS: Using the map-based pattern recognition procedure, we identified and delineated two separate hierarchical dengue incidence intensity clusters that comprise multiple mutually adjacent geographical units with high dengue incidence rates. We also found that that dengue incidence tends to peak simultaneously and homogeneously among the neighboring geographic units with high rates in the same cluster. CONCLUSION: Beyond significance testing, this study is particularly desired by and useful for health authorities who require optimal characteristics of disease incidence patterns on maps and over time. Among the integrated components for effective prevention and control of dengue and dengue hemorrhagic fever are active surveillance and community-based integrated mosquito control, for which this study provides valuable inferences. Effective dengue prevention and control programs in Taiwan are critical, and have the added benefit of controlling the potential emergence of Zika.

Assessing current temporal and space-time anomalies of disease incidence.

Approaches used to early and accurately characterize epidemiologic patterns of disease incidence in a temporal and spatial series are becoming increasingly important. Cluster tests are generally designed for retrospective detection of epidemiologic anomalies in a temporal or space-time series. Timely identification of anomalies of disease or poisoning incidence during ongoing surveillance or an outbreak requires the use of sensitive statistical methods that recognize an incidence pattern at the time of occurrence. This report describes 2 novel analytical methods that focus on detecting anomalies of incidence at the time of occurrence in a temporal and space-time series. The first method describes the paucity of incidence at the time of occurrence in an ongoing surveillance and is designed to evaluate whether a decline in incidence occurs on the single current day or during the most recent few days. The second method provides an overall assessment of current clustering or paucity of incidence in a space-time series, allowing for several space regions. We illustrate the application of these methods using a subsample of a temporal series of data on the largest dengue outbreak in Taiwan in 2015 since World War II and demonstrate that they are useful to efficiently monitor incoming data for current clustering and paucity of incidence in a temporal and space-time series. In light of the recent global emergence and resurgence of Zika, dengue, and chikungunya infection, these approaching for detecting current anomalies of incidence in the ongoing surveillance of disease are particularly desired and needed.

Qualification and Verification of Serological Biomarker Candidates for Lung Adenocarcinoma by Targeted Mass Spectrometry.

Lung cancer is the leading cause of cancer mortality worldwide. Although many biomarkers have been identified for lung cancer, their low specificity and sensitivity present an urgent need for the identification of more candidate biomarkers. In this study, we conducted MRM-based targeted analysis to evaluate the potential utility of a list of candidate proteins for lung cancer diagnosis. A total of 1249 transitions of 420 peptides representing 102 candidate proteins from our previous study and the literature were first screened by MRM analysis in pooled plasma samples, resulting in 78 proteins remaining in the list. Relative quantification of these 78 proteins was further performed in 60 individual plasma samples from lung adenocarcinoma patients in stages I-III and matched healthy control subjects. Ultimately, nine proteins were found to be able to distinguish patients from controls. Further combinations of five, three, and two candidate marker proteins improved the sensitivity to discriminate patients from controls and resulted in a merged AUC value of nearly 1.00 in stages I-III patients versus controls. Our results highlighted several possible markers for lung adenocarcinoma, and the proposed protein panels require further validation in a larger cohort to evaluate their potential use in clinical applications or development of therapeutics.

Alpha-actinin 4 is associated with cancer cell motility and is a potential biomarker in non-small cell lung cancer.

BACKGROUND: Differential expression and secretion of alpha-actinin 4 (ACTN4) in the lung cancer cell lines CL1-0 and CL1-5 have been reported in previous proteomic studies. The aim of this study is to investigate the functional properties of the ACTN4 protein in non-small-cell lung cancer (NSCLC) cells and evaluate its clinical importance. METHODS: We used RNA interference to knock down and overexpress ACTN4 protein to evaluate the effects of this intervention on cancer cell invasion and migration, as well as on microscopic cellular morphology. Furthermore, we examined by immunohistochemistry the expression of ACTN4 protein in tissue samples at different stages of lung cancer and compared the protein levels of ACTN4 in blood plasma samples from patients with histologically confirmed lung cancer and healthy controls. RESULTS: CL1-5 cell motility was significantly suppressed by the knockdown of ACTN4 protein. The morphology of CL1-5 cells changed from a predominantly mesenchymal-like shape into a globular shape in response to ACTN4 protein knockdown. A quantitative immunohistochemical assessment of lung cancer tissues revealed that ACTN4 protein level was considerably higher in cancerous tissues than in the adjacent normal ones, and the area under the receiver operating characteristic curve was 0.736 (p < 0.001). According to an enzyme-linked immunosorbent assay, the plasma levels of ACTN4 protein were significantly different between cancer patients and healthy controls, and the areas under the receiver operating characteristic curves were 0.828 and 0.909, respectively, for two independent cohorts (p < 0.001). CONCLUSIONS: We demonstrate that the knockdown of ACTN4 protein inhibited cell invasion and migration. These results suggest that ACTN4 is associated with lung cancer cell motility. Thus, the level of ACTN4 in cancerous tissue and plasma is related to the presence of lung cancer.

Perceptions of skill development of participants in three national career development programs for women faculty in academic medicine.

PURPOSE: The Association of American Medical Colleges (AAMC) and Drexel University College of Medicine have designed and implemented national career development programs (CDPs) to help women faculty acquire and strengthen skills needed for success in academic medicine. The authors hypothesized that skills women acquired in CDPs would vary by career stage and program attended. METHOD: In 2011, the authors surveyed a national cohort of 2,779 women listed in the AAMC Faculty Roster who also attended one of three CDPs (Early- and Mid-Career Women in Medicine Seminars, and/or Executive Leadership in Academic Medicine) between 1988 and 2010 to examine their characteristics and CDP experiences. Participants indicated from a list of 16 skills whether each skill was newly acquired, improved, or not improved as a result of their program participation. RESULTS: Of 2,537 eligible CDP women, 942 clicked on the link in an invitation e-mail, and 879 (93%) completed the survey. Respondents were representative of women faculty in academic medicine. Participants rated the CDPs highly. Almost all reported gaining and/or improving skills from the CDP. Four skills predominated across all three programs: interpersonal skills, leadership, negotiation, and networking. The skills that attendees endorsed differed by respondents' career stages, more so than by program attended. CONCLUSIONS: Women participants perceived varying skills gained or improved from their attendance at the CDPs. Determining ways in which CDPs can support women's advancement in academic medicine requires a deeper understanding of what participants seek from CDPs and how they use program content to advance their careers.

Whole-genome detection of disease-associated deletions or excess homozygosity in a case-control study of rheumatoid arthritis.

Unlike genome-wide association studies, few comprehensive studies of copy number variation's contribution to complex human disease susceptibility have been performed. Copy number variations are abundant in humans and represent one of the least well-studied classes of genetic variants; in addition, known rheumatoid arthritis susceptibility loci explain only a portion of familial clustering. Therefore, we performed a genome-wide study of association between deletion or excess homozygosity and rheumatoid arthritis using high-density 550 K SNP genotype data from a genome-wide association study. We used a genome-wide statistical method that we recently developed to test each contiguous SNP locus between 868 cases and 1194 controls to detect excess homozygosity or deletion variants that influence susceptibility. Our method is designed to detect statistically significant evidence of deletions or homozygosity at individual SNPs for SNP-by-SNP analyses and to combine the information among neighboring SNPs for cluster analyses. In addition to successfully detecting the known deletion variants on major histocompatibility complex, we identified 4.3 and 28 kb clusters on chromosomes 10p and 13q, respectively, which were significant at a Bonferroni-type-corrected 0.05 nominal significant level. Independently, we performed analyses using PennCNV, an algorithm for identifying and cataloging copy numbers for individuals based on a hidden Markov model, and identified cases and controls that had chromosomal segments with copy number <2. Using Fisher's exact test for comparing the numbers of cases and controls with copy number <2 per SNP, we identified 26 significant SNPs (protective; more controls than cases) aggregating on chromosome 14 with P-values <10(-8).

Systems of career influences: a conceptual model for evaluating the professional development of women in academic medicine.

BACKGROUND: Surprisingly little research is available to explain the well-documented organizational and societal influences on persistent inequities in advancement of women faculty. METHODS: The Systems of Career Influences Model is a framework for exploring factors influencing women's progression to advanced academic rank, executive positions, and informal leadership roles in academic medicine. The model situates faculty as agents within a complex adaptive system consisting of a trajectory of career advancement with opportunities for formal professional development programming; a dynamic system of influences of organizational policies, practices, and culture; and a dynamic system of individual choices and decisions. These systems of influence may promote or inhibit career advancement. Within this system, women weigh competing influences to make career advancement decisions, and leaders of academic health centers prioritize limited resources to support the school's mission. RESULTS AND CONCLUSIONS: The Systems of Career Influences Model proved useful to identify key research questions. We used the model to probe how research in academic career development might be applied to content and methods of formal professional development programs. We generated a series of questions and hypotheses about how professional development programs might influence professional development of health science faculty members. Using the model as a guide, we developed a study using a quantitative and qualitative design. These analyses should provide insight into what works in recruiting and supporting productive men and women faculty in academic medical centers.

Joint effects of germ-line TP53 mutation, MDM2 SNP309, and gender on cancer risk in family studies of Li-Fraumeni syndrome.

Li-Fraumeni syndrome (LFS) is a rare familial cancer syndrome characterized by early cancer onset, diverse tumor types, and multiple primary tumors. Germ-line TP53 mutations have been identified in most LFS families. A high-frequency single-nucleotide polymorphism, SNP309 (rs2279744), in MDM2 was recently confirmed to be a modifier of cancer risk in several case-series studies: substantially earlier cancer onset was observed in SNP309 G-allele carriers than in wild-type individuals by 7-16 years. However, cancer risk analyses that jointly account for measured hereditary TP53 mutations and MDM2 SNP309 have not been systematically investigated in familial cases. Here, we determined the combined effects of measured TP53 mutations, MDM2 SNP309, and gender and their interactions simultaneously in LFS families. We used the method that is designed for extended pedigrees and structured for age-specific risk models based on Cox proportional hazards regression. We analyzed the cancer incidence in 19 extended pedigrees with germ-line TP53 mutations ascertained through the clinical LFS phenotype. The dataset consisted of 463 individuals with 129 TP53 mutation carriers. Our analyses showed that the TP53 germ-line mutation and its interaction with gender were strongly associated with familial cancer incidence and that the association between MDM2 SNP309 and increased cancer risk was modest. In contrast with several case-series studies, the interaction between MDM2 SNP309 and TP53 mutation was not statistically significant in our LFS family cohort. Our results showed that SNP309 G-alleles were associated with accelerated tumor formation in both carriers and non-carriers of germ-line TP53 mutations.

Accuracy evaluation of RBC velocity measurement in nail-fold capillaries.

Cutaneous red blood cell velocity in vivo can be measured by using capillaroscopy with image processing techniques. However, unlike simulated blood flow images, there is no standard to determine the accuracy of the techniques for computing blood flow velocities. In this paper, we quantitatively evaluated the accuracy of previously proposed optical flow method for measuring red blood cell velocity in nail-fold capillaries. Blood flow images of subjects under normal and occlusion-release conditions were examined by a capillaroscope. To obtain velocity values, the images were further analyzed by using optical flow, cross-correlation and visual inspection methods, respectively. Visual inspection method was taken as the golden standard to determine the accuracy of blood flow velocity measurement using optical flow and cross-correlation techniques. Results showed that optical flow estimation provided superior accuracy to cross-correlation when assessing real blood flow velocity in nail-fold capillaries. Optical flow estimation is able to measure red blood cell velocity with a high accuracy of 91% and 86% when the observed velocity is less than 0.5mm/s under normal and occlusion-release conditions, respectively. In addition, optical flow method showed good agreement with visual inspection in determining blood flow velocity in both normal and occlusion-release conditions when the high-velocity zone is excluded.

Hemodynamic analysis of capillary in finger nail-fold using computational fluid dynamics and image estimation.

Red blood cell (RBC) dynamics in capillaries is a useful diagnostic tool for many diseases. Previous study showed that optical flow estimation (OFE) is capable of accurately calculating RBC velocities using image registration technique. The computational fluid dynamics (CFD) method is explored in this study to calculate the RBC velocity in capillaries of finger nail-fold for six cases. The two-dimensional capillary images were reconstructed to three-dimensional, assuming circular cross sections. The no-slip boundary conditions were applied on the vessel walls. The initial velocity of the RBC going into each capillary was calculated by OFE. The velocities of multiple points along each capillary calculated by CFD, V(CFD), were compared with OFE calculations, V(OFE). The calculated RBC velocity was in the range of 56-685µm/s. The average difference (V(CFD) - V(OFE)) with one standard deviation is -2.66±18.61µm/s for all the 48 calculation points, and 0.03±0.12µm/s for all except one points (47 points), indicating that CFD can provide a reasonable accuracy in RBC velocity calculation in finger nail-fold capillaries.

False-Negative-Rate Based Approach for Selecting Top Single-Nucleotide Polymorphisms in the First Stage of a Two-Stage Genome-Wide Association Study.

Genome-wide association (GWA) studies, where hundreds of thousands of single-nucleotide polymorphisms (SNPs) are tested simultaneously, are becoming popular for identifying disease loci for common diseases. Most commonly, a GWA study involves two stages: the first stage includes testing the association between all SNPs and the disease and the second stage includes replication of SNPs selected from the first stage to validate associations in an independent sample. The first stage is considered to be more fundamental since the second stage is contingent on the results of the first stage. Selection of SNPs from stage one for genotyping in stage two is typically based on an arbitrary threshold or controlling type I errors. These strategies can be inefficient and have potential to exclude genotyping of disease-associated SNPs in stage two. We propose an approach for selecting top SNPs that uses a strategy based on the false-negative rate (FNR). Using the FNR approach, we proposed the number of SNPs that should be selected based on the observed p-values and a pre-specified multi-testing power in the first stage. We applied our method to simulated data and a GWA study of glioma (a rare form of brain tumor) data. Results from simulation and the glioma GWA indicate that the proposed approach provides an FNR-based way to select SNPs using pre-specified power.