Identification of protein changes in human spermatozoa throughout the cryopreservation process.

Cryoinjury is a consequence of cryopreservation and may have a negative impact on sperm quality regarding motility, morphology, and viability. This study was designed to identify potential proteomic changes in human sperm cells throughout the cryopreservation process. Comparisons made within this study included the detection of the sperm proteomic changes induced by incubation of the sperm cells with a protein-free cryoprotectant (with and without CryoSperm), and the proteomic changes induced by freezing, thawing, and subsequent after-thawing incubation at two different temperatures (0 °C vs. 23 °C). Tandem Mass Tag (TMT) peptide labeling coupled with LC-MS/MS was used for protein quantification. LC-MS/MS resulted in the identification of 769 quantifiable proteins. The abundance of 105 proteins was altered upon CryoSperm incubation. Freezing and thawing also induced substantial protein changes. However, fewer changes were observed when semen was thawed and then maintained after-thawing at approximately 0 °C than when it was maintained after-thawing at 23 °C, with 60 and 99 differential proteins detected, respectively, as compared to unfrozen semen incubated in CryoSperm. Collectively, these differences indicate that substantial changes occur in the sperm proteome at every stage of the cryopreservation process which may ultimately impair the sperm fertilizing capability. This is the first study to compare protein levels in fresh and cryopreserved semen using the TMT technology coupled to LC-MS/MS.

Risk prediction of major complications in individuals with diabetes: the Atherosclerosis Risk in Communities Study.

AIMS: To develop a prediction equation for 10-year risk of a combined endpoint (incident coronary heart disease, stroke, heart failure, chronic kidney disease, lower extremity hospitalizations) in people with diabetes, using demographic and clinical information, and a panel of traditional and non-traditional biomarkers. METHODS: We included in the study 654 participants in the Atherosclerosis Risk in Communities (ARIC) study, a prospective cohort study, with diagnosed diabetes (visit 2; 1990-1992). Models included self-reported variables (Model 1), clinical measurements (Model 2), and glycated haemoglobin (Model 3). Model 4 tested the addition of 12 blood-based biomarkers. We compared models using prediction and discrimination statistics. RESULTS: Successive stages of model development improved risk prediction. The C-statistics (95% confidence intervals) of models 1, 2, and 3 were 0.667 (0.64, 0.70), 0.683 (0.65, 0.71), and 0.694 (0.66, 0.72), respectively (p < 0.05 for differences). The addition of three traditional and non-traditional biomarkers [ß-2 microglobulin, creatinine-based estimated glomerular filtration rate (eGFR), and cystatin C-based eGFR] to Model 3 significantly improved discrimination (C-statistic = 0.716; p = 0.003) and accuracy of 10-year risk prediction for major complications in people with diabetes (midpoint percentiles of lowest and highest deciles of predicted risk changed from 18-68% to 12-87%). CONCLUSIONS: These biomarkers, particularly those of kidney filtration, may help distinguish between people at low versus high risk of long-term major complications.

Seasonality of tuberculosis in New York City, 1990-2007.

SETTING: Several non-US-based studies have found seasonal fluctuations in the incidence of tuberculosis (TB). OBJECTIVE: The current study examined patterns of TB seasonality for New York City verified TB cases from January 1990 to December 2007. DESIGN: Autocorrelation functions and Fourier analysis were used to detect a cyclical pattern in monthly incidence rates. Analysis of variance was used to compare seasonal mean case proportions. RESULTS: A cyclical pattern was detected every 12 months. Of the 34,004 TB cases included, 21.9% were in the fall (September-November), 24.7% in winter (December-February), 27.3% in spring (March-May), and 26.1% in the summer (June-August). The proportion of cases was lowest in fall (P < 0.0001) and highest in the spring (P < 0.0002). CONCLUSION: Possible explanations for seasonal variations in TB incidence include lower vitamin D levels in winter, leading to immune suppression and subsequent reactivation of latent TB; indoor winter crowding, increasing the likelihood of TB transmission; and providers attributing TB symptoms to other respiratory illnesses in winter, resulting in a delay in TB diagnosis until spring. Understanding TB seasonality may help TB programs better plan and allocate resources for TB control activities.