The emergence of resistance during multidrug chemotherapy impedes the treatment of many human diseases, including malaria, TB, HIV, and cancer. Although certain combination therapies have long been known to be more effective in curing patients than single drugs, the impact of such treatments on the evolution of drug resistance is unclear. In particular, very little is known about how the evolution of resistance is affected by the nature of the interactions-synergy or antagonism-between drugs. Here we directly measure the effect of various inhibitory and subinhibitory drug combinations on the rate of adaptation. We develop an automated assay for monitoring the parallel evolution of hundreds of Escherchia coli populations in a two-dimensional grid of drug gradients over many generations. We find a correlation between synergy and the rate of adaptation, whereby evolution in more synergistic drug combinations, typically preferred in clinical settings, is faster than evolution in antagonistic combinations. We also find that resistance to some synergistic combinations evolves faster than resistance to individual drugs. The accelerated evolution may be due to a larger selective advantage for resistance mutations in synergistic treatments. We describe a simple geometric model in which mutations conferring resistance to one drug of a synergistic pair prevent not only the inhibitory effect of that drug but also its enhancing effect on the other drug. Future study of the profound impact that synergy and other drug-pair properties can have on the rate of adaptation may suggest new treatment strategies for combating the spread of antibiotic resistance.adaptation ͉ antagonism ͉ synergy ͉ antibiotics ͉ antibiotic resistance

Background: Cladribine tablets 3.5 mg/kg cumulative over 2 years (CT3.5) had significant clinical/imaging effects in patients with clinically isolated syndrome (CIS; ORACLE-MS) or relapsing-remitting MS (RRMS; CLARITY and CLARITY Extension). This analysis compared the effect of cladribine tablets on the dynamics of immune cell reduction and reconstitution in ORACLE-MS, CLARITY, and CLARITY Extension during the first year of treatment (i.e. the first course of CT1.75) in patients randomized to CT3.5. Methods: Lymphocyte subtypes were analyzed using multiparameter flow cytometry. Changes in cell counts and relative proportions of lymphocytes were evaluated at weeks 5, 13, 24, and 48. Results: Across studies, consistent and comparable selective kinetics of immune cell populations occurred following the first treatment year with CT. A rapid reduction in CD16+/CD56+ cells (week 5 nadir), a more marked reduction in CD19+ B cells (week 13 nadir), and a less-pronounced effect on CD4+ (week 13 nadir) and CD8+ T cells (week 24 nadir) was shown. There was little effect on neutrophils or monocytes. Lymphocyte recovery began after treatment with CT3.5. Regarding relative proportions of naïve and memory T-cell subtypes in ORACLE-MS, the proportion of naïve-like naturally occurring T-regulatory cells (nTregs) decreased, and the proportion of memory-like nTregs increased, relative to total CD4+ T cells. Conclusions: CT3.5 has comparable effects on the immune systems of patients with CIS or RRMS. The pronounced reduction and recovery dynamics of CD19+ B cells and relative changes in the proportion of some immune cell subtypes may underlie the clinical effects of CT3.5.

Objectives/Hypothesis: Speech perception scores using cochlear implants have ranged widely in all published series. The underlying determinants of success in word recognition are incompletely defined. Although it has been assumed that residual spiral ganglion cell population in the deaf ear may play a critical role, published data from temporal bone specimens from patients have not supported this hypothesis. The depth of insertion of a multichannel cochlear implant has also been suggested as a clinical variable that may be correlated with word recognition. In the current study these correlations were evaluated in 15 human subjects. Study Design: Retrospective review of temporal bone histopathology-.Methods: Temporal bones were fixed and prepared for histological study by standard techniques. Specimens were then serially sectioned and reconstructed by two-dimensional methods. The spiral ganglion cells were counted, and the depth of insertion of the cochlear implant as measured from the round window was determined. Correlation analyses were then performed between the NU6 word scores and spiral ganglion cell counts and the depth of insertion. Results: The segmental and total spiral ganglion cell counts were not significantly correlated (P > .50) with NU6 word scores for the 15 subjects. Statistically significant correlations were not achieved by separate analysis of implant types. Similarly, no significant correlation between the depth of insertion of the electrode array and postoperative NU6 word score was identified for the group. Conclusion: Although it is unlikely that the number of residual spiral ganglion cell counts is irrelevant to the determination of word recognition following cochlear implantation, there are, clearly, other clinical variables not yet identified that play an important role in determining success with cochlear implantation.

Sudden sensorineural hearing loss (SSNHL) can cause significant morbidity. Treatment with steroids can improve outcome. Delay in initiation of treatment reduces the chance to regain hearing. For this reason SSNHL is considered an emergency. Diagnosis is based on history, physical examination and a standard audiogram, the latter requiring specialized equipment and personnel. Standard audiogram may not be available at the time and place of patient presentation. A smartphone or tablet computer-based hearing test may aid in the decision to prescribe steroids in this setting. In this study the uHear™ hearing test application was utilized. The output of this ear-level air conduction hearing test is reported in hearing grades for 6 frequencies ranging from 250 to 6000 Hz. A total of 32 patients with unilateral SSNHL proven by a standard audiogram were tested. The results of standard and iPod hearing tests were compared. Based on the accepted criterion of SSNHL (at least 30 dB loss - or 2 hearing grades - in 3 consecutive frequencies) the test had a sensitivity of 0.76 and specificity of 0.91. Using a less stringent criterion of a loss of 2 hearing grades over at least 2 frequencies the sensitivity was 0.96 and specificity 0.86. The correlation coefficient for the comparison of the average hearing grade across the 6 measured frequencies of the study and standard audiogram was 0.83. uHear more accurately reflected hearing thresholds at mid and high tones. Similarly to previously published data, low frequency thresholds could be artificially elevated. In conclusion, uHear can be useful in the initial evaluation of patients with single-sided SSNHL by providing important information guiding the decision to initiate treatment before a standard audiogram is available.

Mootha et al. 1 propose a statistical method (Gene Set Enrichment Analysis; GSEA) to discern changes in expression levels of sets of genes selected a priori in transcriptional profiling experiments. Although consideration of groups of genes is an interesting strategy, the proposed test statistic may not necessarily determine "…if the members of a given gene set are enriched among the most differentially expressed genes between two classes" 1. Situations will probably arise when using GSEA in which genes with the highest values of the difference metric will be ignored solely due to the size of the selected gene sets, unrelated to any biological context of the genes comprising the set. By way of illustration, consider the following hypothetical example. Assume that a given data set consists of three potentially interesting sets of genes S1, S2 and S3, of respective sizes n, 5n and 4n genes, where n is any integer. Assume also that all of the genes in S1 are ranked higher (i.e., they have greater differences in expression) than the genes in S2, which in turn are ranked higher than the genes in S3. The GSEA procedure yields enrichment scores (ES) 1 of 3n, 4n and 0 for S1, S2 and S3, respectively. The maximum ES 1 is 4n and is attributed to S2. S2 will therefore be singled out as the candidate for further investigation over S1, even though S1 comprises the highest ranked genes. This does not seem reasonable, because S2 has been chosen only by virtue of containing a larger number of genes. In other words, GSEA can be at odds with the picture suggested by the gene ranking. A second observation, using the same illustrative example as above, gives another counterintuitive result. In the absence of a defined third gene set (S3), the ES for S2 = 0 and the ES for S1 remains positive. Therefore, S1, and not S2, is chosen by GSEA, a result opposite to that of the previous scenario. An unusual situation has arisen in which a choice or preference between sets of high ranking is affected simply by the presence or absence of a lower ranking set.

We use the Sampson and Guttorp approach to model the non!stationary correlation function r"x\ x?# of a Gaussian spatial process through a bijective space deformation\ f\ so that in the deformed space the spatial correlation function can be considered isotropic\ namely r"x\ x?# r"= f "x#−f "x?#=#\ where r belongs to a known parametric family[ Given the locations in the deformed space of a number of geographic sites at which data are available\ we smoothly extrapolate the deformation to the whole region of interest[ Using a Bayesian framework\ we estimate jointly these locations\ as well as the parameters of the correlation function and the variance parameters[ The advantage of our Bayesian approach is that it allows us to obtain measures of uncertainty of all these parameters[ As the parameter space is of a very high dimension\ we implement an MCMC method for obtaining samples from the posterior distributions of interest[ We demonstrate our method through a simulation study\ and show an application to a real data set[ Copyright Þ 1990 John Wiley + Sons\ Ltd[ KEY WORDS] thin!plate splines^Markov Chain Monte Carlo^Gaussian spatial processes 0[ INTRODUCTIONThe estimation of heterogeneous spatial covariance is an important problem in environmetrics and geostatistics[ A general\ semi!parametric approach to this\ based on repeated observations from a network of monitoring stations\ was introduced by Sampson and Guttorp "0881# and developed further in a sequence of papers "Guttorp and Sampson\ 0883^Meiring et al[\ 0886\ 0887^Perrin and Meiring\ 0888#[ One of the advantages with the SampsonÐGuttorp approach is that it enables estimation of the covariance between spatial locations where no observations have been made\ without relying on an assumption of homogeneity or stationarity of the underlying spaceÐtime random _eld[ A weakness of the current methodology is its reliance on somewhat adCorrespondence to] D[ Damian\

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Fligh-throughput biomolecular proliling techniques such as transcriptomics, proteomics and metabolomics are increasingly being used in in vivo studies to recognize and characterize effects of xenobiotics on organs and systems, Of particular interest are biornalkers of treatment-related effects which are detectible in easily accessible biological fluids such as bloocl. A funclamentai challenge in such biomarker stuciies is selecting among the plethora of biomolecular changes induced by a compound and revealed by molecular profìling, to identify biomarkers which are exclusively or predominantly clLre to specific processes. In this work we present a cross-cornpaftrnent con'elation network approach, involvirig no a priori supervision or design, to integrate proteomic, metabolomic ancl transcriptomic data for selecting circulating biomarkers. The case stucly we present is the identification of bioniarkers of drug-induced hepatic toxicity effects in a rodent moclel. Biornolecular' profiling of both blood plasma and liver tissüe froni Wistar l-Iannover rats admirristerecl a toxic compound yielded many hundreds of statistically signihcant molecular changes, We exploited dmgincluced correlations between blood plasma analytes and liver tissue molecules across study animals in order to nominate selected plasnia molecules as biomarkers of drug-incluced hepatic alterations of lipid metabolisrn and urea cycle processes.