Paracrine effects of oocyte secreted factors and stem cell factor on porcine granulosa and theca cells in vitro

Oocyte control of granulosa and theca cell function may be mediated by several growth factors via a local feedback loop(s) between these cell types. This study examined both the role of oocyte-secreted factors on granulosa and thecal cells, cultured independently and in co-culture, and the effect of stem cell factor (SCF); a granulosa cell derived peptide that appears to have multiple roles in follicle development. Granulosa and theca cells were isolated from 2–6 mm healthy follicles of mature porcine ovaries and cultured under serum-free conditions, supplemented with: 100 ng/ml LR3 IGF-1, 10 ng/ml insulin, 100 ng/ml testosterone, 0–10 ng/ml SCF, 1 ng/ml FSH (granulosa), 0.01 ng/ml LH (theca) or 1 ng/ml FSH and 0.01 ng/ml LH (co-culture) and with/without oocyte conditioned medium (OCM) or 5 oocytes. Cells were cultured in 96 well plates for 144 h, after which viable cell numbers were determined. Medium was replaced every 48 h and spent medium analysed for steroids. Oocyte secreted factors were shown to stimulate both granulosa cell proliferation (P < 0.001) and oestradiol production (P < 0.001) by granulosa cells throughout culture. In contrast, oocyte secreted factors suppressed granulosa cell progesterone production after both 48 and 144 hours (P < 0.001). Thecal cell numbers were increased by oocyte secreted factors (P = 0.02), together with a suppression in progesterone and androstenedione synthesis after 48 hours (P < 0.001) and after 144 hours (P = 0.02), respectively. Oocyte secreted factors also increased viable cell numbers (P < 0.001) in co-cultures together with suppression of progesterone (P < 0.001) and oestradiol (P < 0.001). In granulosa cell only cultures, SCF increased progesterone production in a dose dependent manner (P < 0.001), whereas progesterone synthesis by theca cells was reduced in a dose dependent manner (P = 0.002). Co-cultured cells demonstrated an increase in progesterone production with increasing SCF dose (P < 0.001) and an increase in oestradiol synthesis at the highest dose of SCF (100 ng/ml). In summary, these findings demonstrate the presence of a co-ordinated paracrine interaction between somatic cells and germ cells, whereby oocyte derived signals interact locally to mediate granulosa and theca cell function. SCF has a role in modulating this local interaction. In conclusion, the oocyte is an effective modulator of granulosa-theca interactions, one role being the inhibition of luteinization

The Significance of the CC-Numerical Range and the Local CC-Numerical Range in Quantum Control and Quantum Information

This paper shows how C-numerical-range related new strucures may arise from practical problems in quantum control--and vice versa, how an understanding of these structures helps to tackle hot topics in quantum information. We start out with an overview on the role of C-numerical ranges in current research problems in quantum theory: the quantum mechanical task of maximising the projection of a point on the unitary orbit of an initial state onto a target state C relates to the C-numerical radius of A via maximising the trace function |\tr \{C^\dagger UAU^\dagger\}|. In quantum control of n qubits one may be interested (i) in having U\in SU(2^n) for the entire dynamics, or (ii) in restricting the dynamics to {\em local} operations on each qubit, i.e. to the n-fold tensor product SU(2)\otimes SU(2)\otimes >...\otimes SU(2). Interestingly, the latter then leads to a novel entity, the {\em local} C-numerical range W_{\rm loc}(C,A), whose intricate geometry is neither star-shaped nor simply connected in contrast to the conventional C-numerical range. This is shown in the accompanying paper (math-ph/0702005). We present novel applications of the C-numerical range in quantum control assisted by gradient flows on the local unitary group: (1) they serve as powerful tools for deciding whether a quantum interaction can be inverted in time (in a sense generalising Hahn's famous spin echo); (2) they allow for optimising witnesses of quantum entanglement. We conclude by relating the relative C-numerical range to problems of constrained quantum optimisation, for which we also give Lagrange-type gradient flow algorithms.Comment: update relating to math-ph/070200

Childhood neurodevelopment after prescription of maintenance methadone for opioid dependency in pregnancy:a systematic review and meta-analysis

Aim: To systematically review and meta‐analyse studies of neurodevelopmental outcome of children born to mothers prescribed methadone in pregnancy. Method: MEDLINE, Embase, and PsycINFO were searched for studies published from 1975 to 2017 reporting neurodevelopmental outcomes in children with prenatal methadone exposure. Results: Forty‐one studies were identified (2283 participants). Eight studies were amenable to meta‐analysis: at 2 years the Mental Development Index weighted mean difference of children with prenatal methadone exposure compared with unexposed infants was −4.3 (95% confidence interval [CI] −7.24 to −1.63), and the Psychomotor Development Index weighted mean difference was −5.42 (95% CI −10.55 to −0.28). Seven studies reported behavioural scores and six found scores to be lower among methadone‐exposed children. Twelve studies reported visual outcomes: nystagmus and strabismus were common; five studies reported visual evoked potentials of which four described abnormalities. Factors that limited the quality of some studies, and introduced risk of bias, included absence of blinding, small sample size, high attrition, uncertainty about polydrug exposure, and lack of comparison group validity. Interpretation: Children born to mothers prescribed methadone in pregnancy are at risk of neurodevelopmental problems but risk of bias limits inference about harm. Research into management of opioid use disorder in pregnancy should include evaluation of childhood neurodevelopmental outcome

Manipulating infrared photons using plasmons in transparent graphene superlattices

Superlattices are artificial periodic nanostructures which can control the flow of electrons. Their operation typically relies on the periodic modulation of the electric potential in the direction of electron wave propagation. Here we demonstrate transparent graphene superlattices which can manipulate infrared photons utilizing the collective oscillations of carriers, i.e., plasmons of the ensemble of multiple graphene layers. The superlattice is formed by depositing alternating wafer-scale graphene sheets and thin insulating layers, followed by patterning them all together into 3-dimensional photonic-crystal-like structures. We demonstrate experimentally that the collective oscillation of Dirac fermions in such graphene superlattices is unambiguously nonclassical: compared to doping single layer graphene, distributing carriers into multiple graphene layers strongly enhances the plasmonic resonance frequency and magnitude, which is fundamentally different from that in a conventional semiconductor superlattice. This property allows us to construct widely tunable far-infrared notch filters with 8.2 dB rejection ratio and terahertz linear polarizers with 9.5 dB extinction ratio, using a superlattice with merely five graphene atomic layers. Moreover, an unpatterned superlattice shields up to 97.5% of the electromagnetic radiations below 1.2 terahertz. This demonstration also opens an avenue for the realization of other transparent mid- and far-infrared photonic devices such as detectors, modulators, and 3-dimensional meta-material systems.Comment: under revie

Search algorithms as a framework for the optimization of drug combinations

Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms, originally developed for digital communication, modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs with only one third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.Comment: 36 pages, 10 figures, revised versio

Histone deacetylases as new therapy targets for platinum-resistant epithelial ovarian cancer

Introduction: In developed countries, ovarian cancer is the fourth most common cancer in women. Due to the nonspecific symptomatology associated with the disease many patients with ovarian cancer are diagnosed late, which leads to significantly poorer prognosis. Apart from surgery and radiotherapy, a substantial number of ovarian cancer patients will undergo chemotherapy and platinum based agents are the mainstream first-line therapy for this disease. Despite the initial efficacy of these therapies, many women relapse; therefore, strategies for second-line therapies are required. Regulation of DNA transcription is crucial for tumour progression, metastasis and chemoresistance which offers potential for novel drug targets. Methods: We have reviewed the existing literature on the role of histone deacetylases, nuclear enzymes regulating gene transcription. Results and conclusion: Analysis of available data suggests that a signifant proportion of drug resistance stems from abberant gene expression, therefore HDAC inhibitors are amongst the most promising therapeutic targets for cancer treatment. Together with genetic testing, they may have a potential to serve as base for patient-adapted therapies