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Background: Parathyroid glands are difficult to identify during total thyroidectomies and accidental resection can lead to problematic post-operative hypocalcemia. Our main goals were to evaluate the effectiveness of using near-infrared light (NIRL) auto-fluorescence intra-operatively for parathyroid-gland identification; and to measure its impact on postoperative hypocalcemia incidence.Study Design Total thyroidectomies were performed on 170 patients with different thyroid pathologies, block-randomized (1:1) into 2 equal groups. Among controls, traditional overhead white light (WL) was used throughout. In the experimental group, NIRL was used to enhance parathyroid gland recognition before thyroid dissection. The number of parathyroid glands identified was compared after thyroid dissection in controls using WL vs pre-dissection in the experimental using NIRL and with WL vs NIRL before thyroid dissection in the experimental group. Postoperative serum calcium levels and hypocalcemia rates were compared. Results: The mean number of parathyroid glands identified pre-dissection with NIRL was the same identified post-dissection with WL (3.5 vs 3.6). In the experimental group, converting from WL to NIRL increased the number of glands detected from 2.6 to 3.5 (p < 0.001), and revealed at least 1 previously missed gland in 67.1% of patients. Calcium levels ≤7.5 mg/dL were one-tenth as common in the NIRL group (p = 0.005). The adjusted odds of hypocalcemia developing increased by 15% for every 5-g increase in thyroid gland weight (odds ratio 1.15; 95% CI 1.06 to 1.25). All hypocalcemia resolved within 6 months. Conclusions: Using NIRL during thyroidectomy increases intraoperative identification of parathyroid glands, enhances their detection before thyroid dissection, and decreases the incidence of postoperative hypocalcemia. Fil: Dip, Fernando. instituto Argentino de Diagnóstico y Tratamiento; Argentina Fil: Falco, Jorge. Instituto Argentino de Diagnóstico y Tratamiento; Argentina Fil: Verna, Silvina. Instituto Argentino de Diagnóstico y Tratamiento; Argentina Fil: Prunello, Marcos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Cs.bioquimicas y Farmaceuticas. Departamento de Matematica y Estadistica.; Argentina Fil: Loccisano, Matias. Instituto Argentino de Diagnóstico y Tratamiento; Argentina Fil: Quadri, Pablo. Instituto Argentino de Diagnóstico y Tratamiento; Argentina Fil: White, Kevin. Scienceright Research Consultations; Canadá Fil: Rosenthal, Raul. Cleveland Clinic Florida; Estados Unidos

Gem-Pro is a new tool for gene mining and functional profiling of bacteria. It initially identifies homologous genes using BLAST and then applies three filtering steps to select orthologous gene pairs. The first one uses BLAST score values to identify trivial paralogs. The second filter uses the shared identity percentages of found trivial paralogs as internal witnesses of non-orthology to set orthology cutoff values. The third filtering step uses conditional probabilities of orthology and non-orthology to define new cutoffs and generate supportive information of orthology assignations. Additionally, a subsidiary tool, called q-GeM, was also developed to mine traits of interest using logistic regression (LR) or linear discriminant analysis (LDA) classifiers. q-GeM is more efficient in the use of computing resources than Gem-Pro but needs an initial classified set of homologous genes in order to train LR and LDA classifiers. Hence, q-GeM could be used to analyze new set of strains with available genome sequences, without the need to rerun a complete Gem-Pro analysis. Finally, Gem-Pro and q-GeM perform a synteny analysis to evaluate the integrity and genomic arrangement of specific pathways of interest to infer their presence. The tools were applied to more than 2 million homologous pairs encoded by Bacillus strains generating statistical supported predictions of trait contents. The different patterns of encoded traits of interest were successfully used to perform a descriptive bacterial profiling.

Parathyroid gland (PG) identification during thyroid and parathyroid surgery is challenging. Accidental parathyroidectomy increases the rate of postoperative hypocalcaemia. Recently, autofluorescence with near infrared light (NIRL) has been described for PG visualization. The aim of this study is to analyze the increased rate of visualization of PGs with the use of NIRL compared to white light (WL). All patients undergoing thyroid and parathyroid surgery were included in this study. PGs were identified with both NIRL and WL by experienced head and neck surgeons. The number of PGs identified with NIRL and WL were compared. The identification of PGs was correlated to age, sex, and histopathological diagnosis. Seventy-four patients were included in the study. The mean age was 48.4 (SD ±13.5) years old. Mean PG fluorescence intensity (47.60) was significantly higher compared to the thyroid gland (22.32) and background (9.27) (p

Aberrant DNA methylation disrupts normal gene expression in cancer and broadly contributes to oncogenesis. We previously developed MethylMix, a model-based algorithmic approach to identify epigenetically regulated driver genes. MethylMix identifies genes where methylation likely executes a functional role by using transcriptomic data to select only methylation events that can be linked to changes in gene expression. However, given that proteins more closely link genotype to phenotype recent high-throughput proteomic data provides an opportunity to more accurately identify functionally relevant abnormal methylation events. Here we present a MethylMix analysis that refines nominations for epigenetic driver genes by leveraging quantitative high-throughput proteomic data to select only genes where DNA methylation is predictive of protein abundance. Applying our algorithm across three cancer cohorts we find that using protein abundance data narrows candidate nominations, where the effect of DNA methylation is often buffered at the protein level. Next, we find that MethylMix genes predictive of protein abundance are enriched for biological processes involved in cancer including functions involved in epithelial and mesenchymal transition. Moreover, our results are also enriched for tumor markers which are predictive of clinical features like tumor stage and we find clustering using MethylMix genes predictive of protein abundance captures cancer subtypes., Author summary To elucidate the molecular basis of cancer we examine the variation and dynamics characterizing the flow of information from epigenome to the transcriptome and proteome. Conducting the first genome wide analysis of epigenome-proteome associations, we present a MethylMix analysis that leverages protein abundance data taking advantage of recent high-throughput proteomic data generated using mass-spectrometry technology to elucidate the role of DNA methylation in cancer. By integrating across molecular data types, we confirm the benefit of using protein abundance data to provide additional insights into pathways and processes involved in oncogenesis and how they manifest as clinical phenotypes. Applying our method across three large cancer cohorts including breast cancer, ovarian cancer and colorectal cancer, MethylMix identifies key genes and describes molecular features and subtypes in these cancers.

Chilling and heat requirements for breaking dormancy and flowering were studied in 63 nectarine and118 peach genotypes (Prunus persica (L.) Batsch) for seven years. We compared two methods for estima-tion of requirements and four models for calculation of chill accumulation: Chilling Hours (CH), ChillingUnits (CU), Positive Chilling Units (PCU) and Dynamic, while heat accumulations were estimated in grow-ing degree hours (GDH). Additionally, correlations between chilling requirements, heat requirements andflowering date were established. Our results suggest that the Luedeling model is better than the Alonsomodel to estimate chilling and heat requirements in our field conditions. Moreover, it is recommend-able to calculate the chill accumulation in accordance with the Chilling Hours or Positive Utah models.Additionally, since chilling requirements were positively correlated with flowering dates, they seem tobe important to regulate blooming in nectarine and peach genotypes. Fil: Maulión, Evangelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (i); Argentina Fil: Valentini, Gabriel Hugo. Instituto Nacional de Tecnologia Agropecuaria. Centro Reg.buenos Aires Norte. Estacion Exptal.agrop.san Pedro; Argentina Fil: Kovalevski, Leandro. Universidad Nacional de Rosario. Facultad de Cs.economicas y Estadistica. Escuela de Estadistica; Argentina Fil: Prunello, Marcos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas; Argentina Fil: Monti, Laura Lucía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (i); Argentina Fil: Daorden, María Elena. Instituto Nacional de Tecnologia Agropecuaria. Centro Reg.buenos Aires Norte. Estacion Exptal.agrop.san Pedro; Argentina Fil: Quaglino, Marta. Universidad Nacional de Rosario. Facultad de Cs.economicas y Estadistica. Escuela de Estadistica; Argentina Fil: Cervigni, Gerardo Domingo Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (i); Argentina

Apply and assess a Bayesian approach to projecting cancer mortality rates by fitting age-period-cohort (APC) models.The Bayesian estimation method was applied to bladder cancer mortality data in Argentina. A second-order autoregressive model was adopted for a priori specification of APC model coefficients. The estimates obtained were compared with all available information and excluding age groups with low mortality, to assess behavior of the approach in light of scattered data. Mortality was projected for two successive periods following the ones observed.Robustness of the method was verified, which avoids excluding age groups with null or low mortality. Observed rates all fall within the credibility bands and confirm the model's goodness of fit. An overall downward trend in bladder cancer mortality was observed. Estimates and projections of these rates are more precise in age groups that have greater incidence of mortality.The Bayesian formulation used herein makes it possible to reduce random variation between adjacent estimates by specifying that the effects of each scale depend on the immediately preceding ones. It was demonstrated that the approach has the capacity to handle low frequencies and obtain reliable mortality estimates, as well as precise projections, without the need for making additional assumptions, as happens in classical APC model fitting.

OBJETIVO: Aplicar y valorar el enfoque bayesiano para realizar proyecciones de tasas de mortalidad por cáncer a través del ajuste de modelos edad-período-cohorte (EPC). MÉTODOS: El método de estimación bayesiano se aplica a datos de mortalidad por cáncer de vejiga en Argentina. Se adopta un esquema autorregresivo de segundo orden para la especificación a priori de los coeficientes del modelo EPC. Se comparan las estimaciones obtenidas con toda la información disponible y excluyendo los grupos de edad con tasas de mortalidad bajas, a fin de valorar el comportamiento del enfoque ante datos esparcidos. Se proyectan las tasas de mortalidad a dos períodos sucesivos a los observados. RESULTADOS: Se comprueba la robustez del método, lo cual evita excluir los grupos de edad con tasas de mortalidad nulas o bajas. Las tasas observadas caen todas dentro de las bandas de credibilidad y confirman la bondad del ajuste del modelo. Se observa una tendencia general decreciente de las tasas de mortalidad por cáncer de vejiga. Las estimaciones y proyecciones de estas tasas resultan más precisas en los grupos etarios que presentan mayor incidencia de mortalidad. CONCLUSIONES: La formulación bayesiana utilizada permite reducir la variación aleatoria entre estimaciones adyacentes al especificar que los efectos de cada escala dependan de los inmediatos anteriores. Se demuestra la capacidad del enfoque para manejar frecuencias bajas y obtener estimaciones confiables de las tasas de mortalidad, como así también proyecciones precisas sin necesidad de realizar supuestos adicionales como sucede en el ajuste clásico de un modelo EPC.