Activity

Low oxygen and mechanical loading may play roles in regulating the fibrocartilaginous phenotype of the human inner meniscus, but their combination in engineered tissues remains unstudied. Here, we investigated how continuous low oxygen (“hypoxia”) combined with dynamic compression would affect the fibrocartilaginous “inner meniscus-like” matrix-forming phenotype of human meniscus fibrochondrocytes (MFCs) in a porous type I collagen scaffold. find more Freshly-seeded MFC scaffolds were cultured for 4 weeks in either 3 or 20% O2 or pre-cultured for 2 weeks in 3% O2 and then dynamically compressed for 2 weeks (10% strain, 1 Hz, 1 h/day, 5 days/week), all with or without TGF-β3 supplementation. TGF-β3 supplementation was found necessary to induce matrix formation by MFCs in the collagen scaffold regardless of oxygen tension and application of the dynamic compression loading regime. Neither hypoxia under static culture nor hypoxia combined with dynamic compression had significant effects on expression of specific protein and mRNA markers for the fibrocartilaginous matrix-forming phenotype. Mechanical properties significantly increased over the two-week loading period but were not different between static and dynamic-loaded tissues after the loading period. These findings indicate that 3% O2 applied immediately after scaffold seeding and dynamic compression to 10% strain do not affect the fibrocartilaginous matrix-forming phenotype of human MFCs in this type I collagen scaffold. It is possible that a delayed hypoxia treatment and an optimized pre-culture period and loading regime combination would have led to different outcomes.Diarrheal diseases lead to an estimated 1.3 million deaths each year, with the majority of those deaths occurring in patients over five years of age. As the severity of diarrheal disease can vary widely, accurately assessing dehydration status remains the most critical step in acute diarrhea management. The objective of this study is to empirically derive clinical diagnostic models for assessing dehydration severity in patients over five years with acute diarrhea in low resource settings. We enrolled a random sample of patients over five years with acute diarrhea presenting to the icddr,b Dhaka Hospital. Two blinded nurses independently assessed patients for symptoms/signs of dehydration on arrival. Afterward, consecutive weights were obtained to determine the percent weight change with rehydration, our criterion standard for dehydration severity. Full and simplified ordinal logistic regression models were derived to predict the outcome of none (9%) dehydration. The reliability and accuracy of each model were assessed. Bootstrapping was used to correct for over-optimism and compare each model’s performance to the current World Health Organization (WHO) algorithm. 2,172 patients were enrolled, of which 2,139 (98.5%) had complete data for analysis. The Inter-Class Correlation Coefficient (reliability) was 0.90 (95% CI = 0.87, 0.91) for the full model and 0.82 (95% CI = 0.77, 0.86) for the simplified model. The area under the Receiver-Operator Characteristic curve (accuracy) for severe dehydration was 0.79 (95% CI 0.76-0.82) for the full model and 0.73 (95% CI 0.70, 0.76) for the simplified model. The accuracy for both the full and simplified models were significantly better than the WHO algorithm (p less then 0.001). This is the first study to empirically derive clinical diagnostic models for dehydration severity in patients over five years. Once prospectively validated, the models may improve management of patients with acute diarrhea in low resource settings.Polycystic ovary syndrome (PCOS) is the major endocrine related disorder in young age women. Physical appearance, menstrual irregularity as well as infertility are considered as a sole cause of mental distress affecting health-related quality of life (HRQOL). This prospective case-control study was conducted among 100 PCOS and 200 healthy control cases attending tertiary care set up of AIIMS, Patna during year 2017 and 2018. Pre-validated questionnaires like Short Form Health survey-36 were used for evaluating impact of PCOS in women. Multivariate analysis was applied for statistical analysis. In PCOS cases, socioeconomic status was comparable in comparison to healthy control. But, PCOS cases showed significantly decreased HRQOL. The higher age of menarche, irregular/delayed menstrual history, absence of child, were significantly altered in PCOS cases than control. Number of child, frequency of pregnancy, and miscarriage were also observed higher in PCOS cases. Furthermore, in various category of age, BMI, educational status and marital status, significant differences were observed in the different domain of SF-36 between PCOS and healthy control. Altogether, increased BMI, menstrual irregularities, educational status and marital status play a major role in altering HRQOL in PCOS cases and psychological care must be given during patient care.

The atomizers of electronic cigarettes (ECs) contain metals that transfer to the aerosol upon heating and may present health hazards. This study analyzed 4th-generation EC pod atomizer design features and characterized their elemental/metal composition.

Eleven EC pods from six brands/manufacturers were purchased at local shops and online. Pods were dissected and imaged using a Canon EOS Rebel SL2 camera. Elemental analysis and mapping of atomizer components was done using a scanning electron microscope coupled with an energy dispersive x-ray spectrometer.

EC pods varied in size and design. The internal atomizer components were similar across brands except for variations occurring mainly in the wicks and filaments of some products. The filaments were either Elinvar (nickel, iron, and chromium) (36.4%), nichrome (36.4%), iron-chromium (18.2%), or nickel (9%). Thick wires present in 55% of the atomizers were mainly nickel and were joined to filaments by brazing. Wire-connector joints were Elinvar. Metal ails and adversely affect health and accumulate in the environment.With advances in neuroimaging and genetics, imaging genetics is a naturally emerging field that combines genetic and neuroimaging data with behavioral or cognitive outcomes to examine genetic influence on altered brain functions associated with behavioral or cognitive variation. We propose a statistical approach, termed imaging genetics generalized structured component analysis (IG-GSCA), which allows researchers to investigate such gene-brain-behavior/cognitive associations, taking into account well-documented biological characteristics (e.g., genetic pathways, gene-environment interactions, etc.) and methodological complexities (e.g., multicollinearity) in imaging genetic studies. We begin by describing the conceptual and technical underpinnings of IG-GSCA. We then apply the approach for investigating how nine depression-related genes and their interactions with an environmental variable (experience of potentially traumatic events) influence the thickness variations of 53 brain regions, which in turn affect depression severity in a sample of Korean participants.