The maternal-fetal neurodevelopmental groundings of preterm birth risk.

Background: Altered neurodevelopment is a major clinical sequela of Preterm Birth (PTB) being currently unexplored in-utero. Aims: To study the link between fetal brain functional (FbF) connectivity and preterm birth, using resting-state functional magnetic resonance imaging (rs-fMRI). Study design: Prospective single-centre cohort study. Subjects: A sample of 31 singleton pregnancies at 28-34 weeks assigned to a low PTB risk (LR) (n = 19) or high PTB risk (HR) (n = 12) group based on a) the Maternal Frailty Inventory (MaFra) for PTB risk; b) a case-specific PTB risk gradient. Methods: Fetal brain rs-fMRI was performed on 1.5T MRI scanner. First, directed causal relations representing fetal brain functional connectivity measurements were estimated using the Greedy Equivalence Search (GES) algorithm. HR vs. LR group differences were then tested with a novel ad-hoc developed Monte Carlo permutation test. Second, a MaFra-only random forest (RF) was compared against a MaFra-Neuro RF, trained by including also the most important fetal brain functional connections. Third, correlation and regression analyses were performed between MaFra-Neuro class probabilities and i) the GA at birth; ii) PTB risk gradient, iii) perinatal clinical conditions and iv) PTB below 37 weeks. Results: First, fewer fetal brain functional connections were evident in the HR group. Second, the MaFra-Neuro RF improved PTB risk prediction. Third, MaFra-Neuro class probabilities showed a significant association with: i) GA at birth; ii) PTB risk gradient, iii) perinatal clinical conditions and iv) PTB below 37 weeks. Conclusion: Fetal brain functional connectivity is a novel promising predictor of PTB, linked to maternal risk profiles, ahead of birth, and clinical markers of neurodevelopmental risk, at birth, thus potentially "connecting" different PTB phenotypes.

Finite sample corrections for average equivalence testing.

Average (bio)equivalence tests are used to assess if a parameter, like the mean difference in treatment response between two conditions for example, lies within a given equivalence interval, hence allowing to conclude that the conditions have "equivalent" means. The two one-sided tests (TOST) procedure, consisting in testing whether the target parameter is respectively significantly greater and lower than some pre-defined lower and upper equivalence limits, is typically used in this context, usually by checking whether the confidence interval for the target parameter lies within these limits. This intuitive and visual procedure is however known to be conservative, especially in the case of highly variable drugs, where it shows a rapid power loss, often reaching zero, hence making it impossible to conclude for equivalence when it is actually true. Here, we propose a finite sample correction of the TOST procedure, the α $$ \alpha $$ -TOST, which consists in a correction of the significance level of the TOST allowing to guarantee a test size (or type-I error rate) of α $$ \alpha $$ . This new procedure essentially corresponds to a finite sample and variability correction of the TOST procedure. We show that this procedure is uniformly more powerful than the TOST, easy to compute, and that its operating characteristics outperform the ones of its competitors. A case study about econazole nitrate deposition in porcine skin is used to illustrate the benefits of the proposed method and its advantages compared to other available procedures.

Generation of potent antibacterial compounds through enzymatic and chemical modifications of the trans-δ-viniferin scaffold.

Stilbene dimers are well-known for their diverse biological activities. In particular, previous studies have demonstrated the high antibacterial potential of a series of trans-δ-viniferin-related compounds against gram-positive bacteria such as Staphylococcus aureus. The trans-δ-viniferin scaffold has multiple chemical functions and can therefore be modified in various ways to generate derivatives. Here we report the synthesis of 40 derivatives obtained by light isomerization, O-methylation, halogenation and dimerization of other stilbene monomers. The antibacterial activities of all generated trans-δ-viniferin derivatives were evaluated against S. aureus and information on their structure-activity relationships (SAR) was obtained using a linear regression model. Our results show how several parameters, such as the O-methylation pattern and the presence of halogen atoms at specific positions, can determine the antibacterial activity. Taken together, these results can serve as a starting point for further SAR investigations.

The action of physiological and synthetic steroids on the calcium channel CatSper in human sperm.

The sperm-specific channel CatSper (cation channel of sperm) controls the intracellular Ca2+ concentration ([Ca2+]i) and plays an essential role in sperm function. It is mainly activated by the steroid progesterone (P4) but is also promiscuously activated by a wide range of synthetic and physiological compounds. These compounds include diverse steroids whose action on the channel is so far still controversial. To investigate the effect of these compounds on CatSper and sperm function, we developed a high-throughput screening (HTS) assay to measure changes in [Ca2+]i in human sperm and screened 1,280 approved and off-patent drugs including 90 steroids from the Prestwick chemical library. More than half of the steroids tested (53%) induced an increase in [Ca2+]i and reduced the P4-induced Ca2+ influx in human sperm in a dose-dependent manner. Ten of the most potent steroids (activating and P4-inhibiting) were selected for a detailed analysis of their action on CatSper and their ability to act on sperm acrosome reaction (AR) and penetration in viscous media. We found that these steroids show an inhibitory effect on P4 but not on prostaglandin E1-induced CatSper activation, suggesting that they compete for the same binding site as P4. Pregnenolone, dydrogesterone, epiandrosterone, nandrolone, and dehydroepiandrosterone acetate (DHEA) were found to activate CatSper at physiologically relevant concentrations within the nanomolar range. Like P4, most tested steroids did not significantly affect the AR while stanozolol and estropipate slightly increased sperm penetration into viscous medium. Furthermore, using a hybrid approach integrating pharmacophore analysis and statistical modelling, we were able to screen in silico for steroids that can activate the channel and define the physicochemical and structural properties required for a steroid to exhibit agonist activity against CatSper. Overall, our results indicate that not only physiological but also synthetic steroids can modulate the activity of CatSper with varying potency and if bound to CatSper prior to P4, could impair the timely CatSper activation necessary for proper fertilization to occur.

Platform combining statistical modeling and patient-derived organoids to facilitate personalized treatment of colorectal carcinoma.

BACKGROUND: We propose a new approach for designing personalized treatment for colorectal cancer (CRC) patients, by combining ex vivo organoid efficacy testing with mathematical modeling of the results. METHODS: The validated phenotypic approach called Therapeutically Guided Multidrug Optimization (TGMO) was used to identify four low-dose synergistic optimized drug combinations (ODC) in 3D human CRC models of cells that are either sensitive or resistant to first-line CRC chemotherapy (FOLFOXIRI). Our findings were obtained using second order linear regression and adaptive lasso. RESULTS: The activity of all ODCs was validated on patient-derived organoids (PDO) from cases with either primary or metastatic CRC. The CRC material was molecularly characterized using whole-exome sequencing and RNAseq. In PDO from patients with liver metastases (stage IV) identified as CMS4/CRIS-A, our ODCs consisting of regorafenib [1 mM], vemurafenib [11 mM], palbociclib [1 mM] and lapatinib [0.5 mM] inhibited cell viability up to 88%, which significantly outperforms FOLFOXIRI administered at clinical doses. Furthermore, we identified patient-specific TGMO-based ODCs that outperform the efficacy of the current chemotherapy standard of care, FOLFOXIRI. CONCLUSIONS: Our approach allows the optimization of patient-tailored synergistic multi-drug combinations within a clinically relevant timeframe.

Influence of Molecular Structure and Physicochemical Properties of Immunosuppressive Drugs on Micelle Formulation Characteristics and Cutaneous Delivery.

The aim of this study was to investigate whether subtle differences in molecular properties affected polymeric micelle characteristics and their ability to deliver poorly water-soluble drugs into the skin. D-α-tocopherol-polyethylene glycol 1000 was used to prepare micelles containing ascomycin-derived immunosuppressants-sirolimus (SIR), pimecrolimus (PIM) and tacrolimus (TAC)-which have similar structures and physicochemical properties and have dermatological applications. Micelle formulations were prepared by thin-film hydration and extensively characterized. Cutaneous delivery and biodistribution were determined and compared. Sub-10 nm micelles were obtained for the three immunosuppressants with incorporation efficiencies >85%. However, differences were observed for drug loading, stability (at the highest concentration), and their in vitro release kinetics. These were attributed to differences in drug aqueous solubility and lipophilicity. Differences between the cutaneous biodistribution profiles and drug deposition in the different skin compartments pointed to the impact of differences in thermodynamic activity. Therefore, despite their structural similarities, SIR, TAC and PIM did not demonstrate the same behaviour either in the micelles or when applied to the skin. These outcomes indicate that polymeric micelles should be optimized even for closely related drug molecules and support the hypothesis that drugs are released from micelles prior to skin penetration.

Dynamics of career intentions in a medical student cohort: a four-year longitudinal study.

BACKGROUND: Medical students' career intentions often change between matriculation and graduation, yet little is known about the precise timing and dynamics of individual students' career decisions. This study expands on previous research by exploring the stability of individual students' career intentions over four years and by analyzing associations between unstable career intentions and students' characteristics. METHODS: Medical students from two classes were recruited into a cohort during their first academic year and completed a yearly survey over a four-year period (end of pre-clinical curriculum to graduation). Measures included career intention (specialty and practice type), personality, coping strategies, empathy, and motives for becoming a physician. The authors developed a score ranging from 0 to 10 quantifying instability of career intentions (0 = stable; 10 = unstable). The distribution of the score was analyzed descriptively, and the association between the score and other variables was quantified using a stepwise beta regression model. RESULTS: The sample included 262 students (61% females). The mean score was 3.07 with a median of 3. 18% of students (N = 46) did not change their specialty intention over the four years, whereas 10% (N = 26) changed every year. No further subgroups were identified between these extremes. An intention to work in private practice in year 3 and the motive care for patients were significantly associated with more stable career intentions. CONCLUSION: Most students are situated on a continuum between the two extremes of being firmly committed and undecided. Extrinsic factors may be more important drivers of these fluctuations than personal characteristics and should be explored in future research. This study's findings also provide avenues for supporting students in their career decision-making.

The Generalized Method of Wavelet Moments with eXogenous inputs: a fast approach for the analysis of GNSS position time series.

The global navigation satellite system (GNSS) daily position time series are often described as the sum of stochastic processes and geophysical signals which allow to study global and local geodynamical effects such as plate tectonics, earthquakes, or ground water variations. In this work, we propose to extend the Generalized Method of Wavelet Moments (GMWM) to estimate the parameters of linear models with correlated residuals. This statistical inferential framework is applied to GNSS daily position time-series data to jointly estimate functional (geophysical) as well as stochastic noise models. Our method is called GMWMX, with X standing for eXogenous variables: it is semi-parametric, computationally efficient and scalable. Unlike standard methods such as the widely used maximum likelihood estimator (MLE), our methodology offers statistical guarantees, such as consistency and asymptotic normality, without relying on strong parametric assumptions. At the Gaussian model, our results (theoretical and obtained in simulations) show that the estimated parameters are similar to the ones obtained with the MLE. The computational performances of our approach have important practical implications. Indeed, the estimation of the parameters of large networks of thousands of GNSS stations (some of them being recorded over several decades) quickly becomes computationally prohibitive. Compared to standard likelihood-based methods, the GMWMX has a considerably reduced algorithmic complexity of order O { log ( n ) n } for a time series of length n. Thus, the GMWMX appears to provide a reduction in processing time of a factor of 10-1000 compared to likelihood-based methods depending on the considered stochastic model, the length of the time series and the amount of missing data. As a consequence, the proposed method allows the estimation of large-scale problems within minutes on a standard computer. We validate the performances of our method via Monte Carlo simulations by generating GNSS daily position time series with missing observations and we consider composite stochastic noise models including processes presenting long-range dependence such as power law or Matérn processes. The advantages of our method are also illustrated using real time series from GNSS stations located in the Eastern part of the USA.

Dexamethasone exposure in normal-weight and obese hospitalized COVID-19 patients: An observational exploratory trial.

During the latest pandemic, the RECOVERY study showed the benefits of dexamethasone (DEX) use in COVID-19 patients. Obesity has been proven to be an independent risk factor for severe forms of infection, but little information is available in the literature regarding DEX dose adjustment according to body weight. We conducted a prospective, observational, exploratory study at Geneva University Hospitals to assess the impact of weight on DEX pharmacokinetics (PK) in normal-weight versus obese COVID-19 hospitalized patients. Two groups of patients were enrolled: normal-weight and obese (body mass index [BMI] 18.5-25 and >30 kg/m2 , respectively). All patients received the standard of care therapy of 6 mg DEX orally. Blood samples were collected, and DEX concentrations were measured. The mean DEX AUC0-8 and Cmax were lower in the obese compared to the normal-weight group (572.02 ± 258.96 vs. 926.92 ± 552.12 ng h/ml and 138.67 ± 68.03 vs. 203.44 ± 126.30 ng/ml, respectively). A decrease in DEX AUC0-8 of 4% per additional BMI unit was observed, defining a significant relationship between weight and DEX AUC0-8 (p = 0.004, 95% CI 2-7%). In women, irrespective of the BMI, DEX AUC0-8 increased by 214% in comparison to men (p < 0.001, 95% CI 154-298%). Similarly, the mean Cmax increased by 205% in women (p < 0.001, 95% CI 141-297%). Conversely, no significant difference between the obese and normal-weight groups was observed for exploratory treatment outcomes, such as the length of hospitalization. BMI, weight, and gender significantly affected DEX AUC. We conclude that dose adjustment would be needed if the aim is to achieve the same exposures in normal-weight and obese patients.

Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks.

Non-coding micro RNAs (miRNAs) dysregulation seems to play an important role in the pathways involved in breast cancer occurrence and progression. In different studies, opposite functions may be assigned to the same miRNA, either promoting the disease or protecting from it. Our research tackles the following issues: (i) why aren't there any concordant findings in many research studies regarding the role of miRNAs in the progression of breast cancer? (ii) could a miRNA have either an activating effect or an inhibiting one in cancer progression according to the other miRNAs with which it interacts? For this purpose, we analyse the AHUS dataset made available on the ArrayExpress platform by Haakensen et al. The breast tissue specimens were collected over 7 years between 2003 and 2009. miRNA-expression profiling was obtained for 55 invasive carcinomas and 70 normal breast tissue samples. Our statistical analysis is based on a recently developed model and feature selection technique which, instead of selecting a single model (i.e. a unique combination of miRNAs), delivers a set of models with equivalent predictive capabilities that allows to interpret and visualize the interaction of these features. As a result, we discover a set of 112 indistinguishable models (in a predictive sense) each with 4 or 5 miRNAs. Within this set, by comparing the model coefficients, we are able to identify three classes of miRNA: (i) oncogenic miRNAs; (ii) protective miRNAs; (iii) undefined miRNAs which can play both an oncogenic and a protective role according to the network with which they interact. These results shed new light on the biological action of miRNAs in breast cancer and may contribute to explain why, in some cases, different studies attribute opposite functions to the same miRNA.

Students' intentions to practice primary care are associated with their motives to become doctors: a longitudinal study.

BACKGROUND: Medical schools can contribute to the insufficient primary care physician workforce by influencing students' career preferences. Primary care career choice evolves between matriculation and graduation and is influenced by several individual and contextual factors. This study explored the longitudinal dynamics of primary care career intentions and the association of students' motives for becoming doctors with these intentions in a cohort of undergraduate medical students followed over a four-year period. METHODS: The sample consisted of medical students from two classes recruited into a cohort study during their first academic year, and who completed a yearly survey over a four-year period from their third (end of pre-clinical curriculum) to their sixth (before graduation) academic year. Main outcome measures were students' motives for becoming doctors (ten motives rated on a 6-point scale) and career intentions (categorized into primary care, non-primary care, and undecided). Population-level flows of career intentions were investigated descriptively. Changes in the rating of motives over time were analyzed using Wilcoxon tests. Two generalized linear mixed models were used to estimate which motives were associated with primary care career intentions. RESULTS: The sample included 217 students (60% females). Career intentions mainly evolved during clinical training, with smaller changes at the end of pre-clinical training. The proportion of students intending to practice primary care increased over time from 12.8% (year 3) to 24% (year 6). Caring for patients was the most highly rated motive for becoming a doctor. The importance of the motives cure diseases, saving lives, and vocation decreased over time. Primary care career intentions were positively associated with the motives altruism and private practice, and negatively associated with the motives prestige, academic interest and cure diseases. CONCLUSION: Our study indicates that career intentions are not fixed and change mainly during clinical training, supporting the influence of clinical experiences on career-related choices. The impact of students' motives on primary care career choice suggests strategies to increase the attractivity of this career, such as reinforcing students' altruistic values and increasing the academic recognition of primary care.

A Two-sample Nonparametric Test for Circular Data- its Exact Distribution and Performance.

A nonparametric test labelled 'Rao Spacing-frequencies test' is explored and developed for testing whether two circular samples come from the same population. Its exact distribution and performance relative to comparable tests such as the Wheeler-Watson test and the Dixon test in small samples, are discussed. Although this test statistic is shown to be asymptotically normal, as one would expect, this large sample distribution does not provide satisfactory approximations for small to moderate samples. Exact critical values for small samples are obtained and tables provided here, using combinatorial techniques, and asymptotic critical regions are assessed against these. For moderate sample sizes in-between i.e. when the samples are too large making combinatorial techniques computationally prohibitive but yet asymptotic regions do not provide a good approximation, we provide a simple Monte Carlo procedure that gives very accurate critical values. As is well-known, the large number of usual rank-based tests are not applicable in the context of circular data since the values of such ranks depend on the arbitrary choice of origin and the sense of rotation used (clockwise or anti-clockwise). Tests that are invariant under the group of rotations, depend on the data through the so-called 'spacing frequencies', the frequencies of one sample that fall in between the spacings (or gaps) made by the other. The Wheeler-Watson, Dixon, and the proposed Rao tests are of this form and are explicitly useful for circular data, but they also have the added advantage of being valid and useful for comparing any two samples on the real line. Our study and simulations establish the 'Rao spacing-frequencies test' as a desirable, and indeed preferable test in a wide variety of contexts for comparing two circular samples, and as a viable competitor even for data on the real line. Computational help for implementing any of these tests, is made available online "TwoCircles" R package and is part of this paper.

Polymeric micelle formulations for the cutaneous delivery of sirolimus: A new approach for the treatment of facial angiofibromas in tuberous sclerosis complex.

Facial angiofibromas are benign tumors characteristic of tuberous sclerosis complex. The disease involves the mTOR pathway and the cutaneous manifestation responds to topical treatment with sirolimus (SIR). However, there are no approved topical SIR products and extemporaneous formulations have been sub-optimal. The aims of this study were (i) to develop aqueous formulations of SIR loaded in polymeric micelles prepared using D-α-tocopherol polyethylene glycol 1000 succinate (TPGS) and (ii) to use the cutaneous biodistribution method, in conjunction with a new statistical approach, to investigate the feasibility of SIR delivery to the viable epidermis. Optimized micelle solutions and hydrogels (0.2%) were developed and stable at 4 °C for at least 6 and 3 months, respectively. Cutaneous delivery experiments (infinite and finite dose) using porcine skin demonstrated that both formulations increased SIR cutaneous bioavailability as compared to the control (ointment 0.2%). Moreover, studies with the micellar hydrogel 0.2% demonstrated SIR deposition in the viable epidermis with no transdermal permeation. These encouraging results confirmed that polymeric micelles enabled development of aqueous SIR formulations capable of targeted epidermal delivery. Furthermore, the cutaneous biodistribution provided a detailed insight into drug bioavailability in the different skin compartments that could complement/explain clinical observations of formulation efficacy.

Granger-causal testing for irregularly sampled time series with application to nitrogen signalling in Arabidopsis.

MOTIVATION: Identification of system-wide causal relationships can contribute to our understanding of long-distance, intercellular signalling in biological organisms. Dynamic transcriptome analysis holds great potential to uncover coordinated biological processes between organs. However, many existing dynamic transcriptome studies are characterized by sparse and often unevenly spaced time points that make the identification of causal relationships across organs analytically challenging. Application of existing statistical models, designed for regular time series with abundant time points, to sparse data may fail to reveal biologically significant, causal relationships. With increasing research interest in biological time series data, there is a need for new statistical methods that are able to determine causality within and between time series data sets. Here, a statistical framework was developed to identify (Granger) causal gene-gene relationships of unevenly spaced, multivariate time series data from two different tissues of Arabidopsis thaliana in response to a nitrogen signal. RESULTS: This work delivers a statistical approach for modelling irregularly sampled bivariate signals which embeds functions from the domain of engineering that allow to adapt the model's dependence structure to the specific sampling time. Using maximum-likelihood to estimate the parameters of this model for each bivariate time series, it is then possible to use bootstrap procedures for small samples (or asymptotics for large samples) in order to test for Granger-Causality. When applied to the A.thaliana data, the proposed approach produced 3078 significant interactions, in which 2012 interactions have root causal genes and 1066 interactions have shoot causal genes. Many of the predicted causal and target genes are known players in local and long-distance nitrogen signalling, including genes encoding transcription factors, hormones and signalling peptides. Of the 1007 total causal genes (either organ), 384 are either known or predicted mobile transcripts, suggesting that the identified causal genes may be directly involved in long-distance nitrogen signalling through intercellular interactions. The model predictions and subsequent network analysis identified nitrogen-responsive genes that can be further tested for their specific roles in long-distance nitrogen signalling. AVAILABILITY AND IMPLEMENTATION: The method was developed with the R statistical software and is made available through the R package 'irg' hosted on the GitHub repository https://github.com/SMAC-Group/irg where also a running example vignette can be found (https://smac-group.github.io/irg/articles/vignette.html). A few signals from the original data set are made available in the package as an example to apply the method and the complete A.thaliana data can be found at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97500. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Targeting hallmarks of cancer with a food-system-based approach.

Although extensive resources are dedicated to the development and study of cancer drugs, the cancer burden is expected to rise by about 70% over the next 2 decade. This highlights a critical need to develop effective, evidence-based strategies for countering the global rise in cancer incidence. Except in high-risk populations, cancer drugs are not generally suitable for use in cancer prevention owing to potential side effects and substantial monetary costs (Sporn, 2011). There is overwhelming epidemiological and experimental evidence that the dietary bioactive compounds found in whole plant-based foods have significant anticancer and chemopreventative properties. These bioactive compounds often exert pleiotropic effects and act synergistically to simultaneously target multiple pathways of cancer. Common bioactive compounds in fruits and vegetables include carotenoids, glucosinolates, and polyphenols. These compounds have been shown to target multiple hallmarks of cancer in vitro and in vivo and potentially to address the diversity and heterogeneity of certain cancers. Although many studies have been conducted over the past 30 y, the scientific community has still not reached a consensus on exactly how the benefit of bioactive compounds in fruits and vegetables can be best harnessed to help reduce the risk for cancer. Different stages of the food processing system, from "farm-to-fork," can affect the retention of bioactive compounds and thus the chemopreventative properties of whole foods, and there are opportunities to improve handling of foods throughout the stages in order to best retain their chemopreventative properties. Potential target stages include, but are not limited to, pre- and postharvest management, storage, processing, and consumer practices. Therefore, there is a need for a comprehensive food-system-based approach that not only taking into account the effects of the food system on anticancer activity of whole foods, but also exploring solutions for consumers, policymakers, processors, and producers. Improved knowledge about this area of the food system can help us adjust farm-to-fork operations in order to consistently and predictably deliver desired bioactive compounds, thus better utilizing them as invaluable chemopreventative tools in the fight to reduce the growing burden of cancer worldwide.

Is nonmetastatic cutaneous melanoma predictable through genomic biomarkers?

Cutaneous melanoma is a highly aggressive skin cancer whose treatment and prognosis are critically affected by the presence of metastasis. In this study, we address the following issue: which gene transcripts and what kind of interactions between them can allow to predict nonmetastatic from metastatic melanomas with a high level of accuracy? We carry out a meta-analysis on the first gene expression set of the Leeds melanoma cohort, as made available online on 11 May 2016 through the ArrayExpress platform with MicroArray Gene Expression number 4725. According to the authors, primary melanoma mRNA expression was measured in 204 tumours using an illumina DASL HT12 4 whole-genome array. The tumour transcripts were selected through a recently proposed predictive-based regression algorithm for gene-network selection. A set of 64 equivalent models, each including only two gene transcripts, were each sufficient to accurately classify primary tumours into metastatic and nonmetastatic melanomas. The sensitivity and specificity of the genomic-based models were, respectively, 4% (95% confidence interval: 0.11-21.95%) and 99% (95% confidence interval: 96.96-99.99%). The very high specificity coupled with a significantly large positive likelihood ratio leads to a conclusive increase in the likelihood of disease when these biomarkers are present in the primary tumour. In conjunction with other highly sensitive methods, this approach can aspire to be part of the future standard diagnosis methods for the screening of metastatic cutaneous melanoma. The small dimension of the selected transcripts models enables easy handling of large-scale genomic testing procedures. Moreover, some of the selected transcripts have an understandable link with what is known about cutaneous melanoma oncogenesis, opening a window on the molecular pathways underlying the metastatic process of this disease.

A Predictive Based Regression Algorithm for Gene Network Selection.

Gene selection has become a common task in most gene expression studies. The objective of such research is often to identify the smallest possible set of genes that can still achieve good predictive performance. To do so, many of the recently proposed classification methods require some form of dimension-reduction of the problem which finally provide a single model as an output and, in most cases, rely on the likelihood function in order to achieve variable selection. We propose a new prediction-based objective function that can be tailored to the requirements of practitioners and can be used to assess and interpret a given problem. Based on cross-validation techniques and the idea of importance sampling, our proposal scans low-dimensional models under the assumption of sparsity and, for each of them, estimates their objective function to assess their predictive power in order to select. Two applications on cancer data sets and a simulation study show that the proposal compares favorably with competing alternatives such as, for example, Elastic Net and Support Vector Machine. Indeed, the proposed method not only selects smaller models for better, or at least comparable, classification errors but also provides a set of selected models instead of a single one, allowing to construct a network of possible models for a target prediction accuracy level.

Use of a Publicly Available Database to Determine the Impact of Diabetes on Length of Hospital Stay for Elective Orthopedic Procedures in California.

In California, 1 in 3 hospital beds are occupied by adults with diabetes. The aim of this study was to examine whether diabetes impacts length of stay (LOS) following common elective orthopedic procedures compared to nondiabetic individuals, and also the performance of hospitals across California for these procedures. Using the Public Use California Patient Discharge Data Files for 2010-2012, the authors examined LOS for elective discharges for hip, spine, or knee surgery (n = 318,861) from the total population of all discharges (n = 11,476,073) for 309 hospitals across California. In all, 16% of discharges had a codiagnosis of diabetes. Unadjusted average LOS was 3.11 days without and 3.40 days with diabetes (mean difference 0.29 [95% confidence interval (0.27, 0.31) days, P < 0.01]). After adjusting for covariates, diabetes no longer resulted in a significant difference in LOS. However, the presence of common comorbidities did significantly impact LOS. Average LOS for patients with diabetes also varied widely by hospital, ranging between -50% and +100% of the mean LOS for all hospitals. Diabetes does not prolong LOS after orthopedic procedures unless comorbidities are present. Nevertheless, across California there is significant variation in LOS between individual hospitals, which may inform the decision-making process for prospective patients and payers.

Wavelet-Variance-Based Estimation for Composite Stochastic Processes.

This article presents a new estimation method for the parameters of a time series model. We consider here composite Gaussian processes that are the sum of independent Gaussian processes which, in turn, explain an important aspect of the time series, as is the case in engineering and natural sciences. The proposed estimation method offers an alternative to classical estimation based on the likelihood, that is straightforward to implement and often the only feasible estimation method with complex models. The estimator furnishes results as the optimization of a criterion based on a standardized distance between the sample wavelet variances (WV) estimates and the model-based WV. Indeed, the WV provides a decomposition of the variance process through different scales, so that they contain the information about different features of the stochastic model. We derive the asymptotic properties of the proposed estimator for inference and perform a simulation study to compare our estimator to the MLE and the LSE with different models. We also set sufficient conditions on composite models for our estimator to be consistent, that are easy to verify. We use the new estimator to estimate the stochastic error's parameters of the sum of three first order Gauss-Markov processes by means of a sample of over 800,000 issued from gyroscopes that compose inertial navigation systems. Supplementary materials for this article are available online.