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Abstract INTRODUCTION Blood biomarkers could enable cost-effective, early detection of brain tumors. While circulating miRNAs are promising candidates, disease specificity is poorly understood. Here, we use an unbiased approach to identify miRNAs which distinguish brain tumor cases from controls, correlate findings with glioblastoma stem cell (GSC) miRNA expression, and characterize the effects of inhibiting select miRNAs on GSC phenotypes. METHODS Patients undergoing surgery at a single center for newly diagnosed brain tumors (pathology-confirmed glioblastoma n=21, meningioma n=27, or brain metastasis n=9) and age-matched controls undergoing elective surgery for degenerative spinal disease (DSD n=24, e.g. microdiscectomy) were recruited for preoperative research blood collection (2019–2024). 798 miRNAs were quantified per serum sample using nCounter (Nanostring). AutoML (JADBio) was applied to identify miRNAs which optimally classify samples by these diagnoses, and model stability analyses confirmed those that classify samples most consistently. Model performance is reported as area under receiver operating characteristic (AUROC) and mean precision (MP). The same miRNAs were quantified in GSC media. Those which distinguished glioblastoma cases from controls were inhibited in vitro via lipid transfection (qPCR-confirmed) to observe effects on GSC growth (Incucyte), viability (CellTiter-Glo®), and self-renewal (sphere formation). RESULTS miRNA-based classification of brain tumor (n=57) versus DSD controls (n=24) yielded excellent performance (AUROC=0.93 [0.88–0.98], MP=0.95 [0.91–0.98]), as did glioblastoma versus DSD (AUROC=0.94 [0.86–1.00], MP=0.95 [0.91–1.00]) and glioblastoma/metastasis versus DSD (AUROC=0.92 [0.85–0.97], MP=0.95 [0.91–0.98]). Some performance-driving miRNAs (miR-150-5p, miR-1283) were under-expressed in glioblastoma cases while others (miR-873-3p, let-7b-5p) were over-expressed. These miRNAs were also expressed in GSC media. Inhibiting let-7b-5p was noted to reduce GSC growth, viability, and self-renewal, while inhibiting others augmented these phenotypes. CONCLUSIONS A preoperative miRNA signature may accurately distinguish brain tumor patients from age-matched controls. Inhibition of these miRNAs affected GSC phenotypes. Future studies will validate classification models in an independent cohort and investigate the effect of miRNA inhibition on GSC transcript expression (RNA-sequencing).

ABSTRACT The Carina Nebula is an active star-forming region with several open clusters rich in massive OB stars, thus making it an optimal target for studying stellar properties such as rotation for large samples of these early-type stars. We studied a sample of early-type stars probable members of the eight open clusters in the Carina Complex. The observational data consist of high-resolution spectra from the Gaia-ESO public Spectroscopic Survey. Astrometric and photometric data from Gaia EDR3 and radial velocities measured from the observed spectra are used to confirm the cluster members. The projected rotational velocities of 330 early-type stars of Carina are derived from the widths of He i lines at 4388 and 4471 Å. The reported $V \sin {i}$ values are the first estimates for 222 early-type stars. The $V \sin {i}$ distribution for the Carina clusters peaks at $\sim$100–150 km s$^{-1}$, consistent with the distributions for B stars in Galactic clusters. $V \sin {i}$ estimates for stars members of the clusters Trumpler 15, Collinder 228, Collinder 232, and Bochum 11 are presented for the first time in the literature. For a subsample of stars with earlier spectral types from B0 to B3, we find a bimodal distribution, with a third, small peak towards the upper values of $V \sin {i}$. When the full sample is split according to the parent cluster, we find that the oldest cluster in our sample, NGC 3293, presents a higher concentration of rapidly rotating stars. In contrast, Collinder 228 presents a larger number of stars with lower $V \sin {i}$.

Metacognitive Therapy is an indispensable guide both for experienced and newly trained therapists who sometimes feel stuck or who are looking for a clear roadmap for conceptualizing and treating complex and comorbid problems. Drawing on the transdiagnostic approach pioneered by Wells in 2009, Göran Parment uses his extensive clinical experience to guide the reader through the core concepts in MCT, illustrating the theoretical model with clinical examples and references to the latest evidence-based research. The book points out the significant transdiagnostic possibilities across disorders and shows how MCT can be applied to a wide array of common clinical disorders and comorbidities. The case formulation and the therapeutic procedures and applications are illustrated with clinical vignettes and therapist-client dialogues. The book also discusses common doubts and questions about MCT's application as well as MCT's potential as a paradigm-shifting form of psychotherapy.

Abstract Background Multidrug-resistant (MDR) Pseudomonas aeruginosa has been documented as a significant threat associated with adverse patient outcomes. We aimed to describe the rapid increase in the prevalence of an MDR P.aeruginosa phenotype at the national level, as detected through the Greek Electronic System for the Surveillance of Antimicrobial Resistance (WHONET-Greece). Methods Routine susceptibility data of 22,236 P.aeruginosa isolates of hospitalized patients in forty-five hospitals, isolated over the past four years (2020-2023) were analyzed using ‘Resistance profiles’ analysis of WHONET software(ver.2023), considering five antimicrobial groups/agents. Results We confirmed the rapid increase in the prevalence of a carbapenem-resistant P.aeruginosa (CRPA) phenotype resistant to all five antimicrobial groups/agents tested (carbapenems, piperacillin-tazobactam, cephalosporins, fluoroquinolones and aminoglycosides). The prevalence increased by 63.8% in 2023 (from 15.2% to 24.9%, 1.64-fold increase), whereas in the last 24 months of the study period a 2.13-fold increase was observed (from 11.7% to 24.9%). In parallel, the prevalence of all other CRPA profiles decreased in 2023 from 22.5% to 17%. We also examined the prevalence of this MDR phenotype in the different NUTS-1 regions of the country, which revealed that the rapid increase is mainly observed in Attika (EL3, 19/45 hospitals) and Central Greece (EL6, 8/45 hospitals) where prevalence reached 25.4% and 26.2% in the second half of 2023 respectively. Northern Greece (EL5, 15/45 hospitals) showed a steady increase over the four years (12% to 19.3%), while in the Aegean Islands/Crete region (EL4, 3/45 hospitals) the prevalence levels were stable. Conclusions There is an emerging multi-drug CRPA phenotype in Greece. Only with systematic, nationwide surveillance could we identify and verify such threats. The findings of this study should be used for targeted interventions and further analysis through molecular techniques. Key messages • The early detection of such emerging multi-drug CRPA phenotype in Greece allows for for targeted interventions and further analysis through molecular techniques. • Systematic surveillance of antimicrobial resistance based on routine susceptibility data is a great way to identify and verify emerging public health threats.

Abstract Background JAK inhibitors have demonstrated efficacy in ulcerative colitis (UC), Crohn’s disease (CD), and inflammatory arthritis. However, limited data exist on their effectiveness in treating extraintestinal manifestations (EIM) in patients with inflammatory bowel disease (IBD) in real-world scenarios. This multicenter, retrospective study aims to assess the efficacy of JAK inhibitors (JAKI), specifically tofacitinib or upadacitinib, in IBD patients with EIM. Methods This was a retrospective multicenter cohort study. Inclusion criteria included IBD patients with active EIM documented before JAKI treatment initiation, with a follow-up up to 52 weeks. EIM Clinical response was defined as either improvement or remission of EIM complaints, as subjectively described by the patient or taken from the treating physician’s assessment. Results Eighty-six patients from eight centers were included, with 64% having UC (n=55). Women constituted 60% of the patients (n=51). Tofacitinib and upadacitinib were administered to 63% (n=54/86) and 36% (n=32/86) of patients, respectively. Eleven patients (13%) were biologically naïve, all treated with tofacitinib. Arthralgia was the predominant complaint, affecting 55% (n=30/54) of tofacitinib-treated and 44% (n=14/32) of upadacitinib-treated patients. Patient characteristics, including EIM distribution, are detailed in Table 1. At 3 months, 86% (n=72/83) achieved EIM clinical response, with tofacitinib responders at 94% (n=49/52) compared to 74% (n=23/31) for upadacitinib (p=0.01). Seven percent (n=6) discontinued treatment due to lack of response. Peripheral arthralgia improved or achieved remission in 86% (n=32/38) of patients, while 68% (n=13/19) with axial arthropathy experienced clinical response. At 6 months, EIM clinical response was achieved by 76% (n=44/75), with tofacitinib at 86% (n=44/51) and upadacitinib at 54% (n=13/24) (p=0.004). Twelve percent(n=10) discontinued treatment due to loss of response. Peripheral arthralgia responded in 86% (n=31/36), and axial arthropathy responded in 42% (n=8/19). At 12 months, 65% (n=44/68) of patients improved or had remission, with tofacitinib responders at 73% (n=35/48) and upadacitinib responders at 45% (n=9/20) (p<0.05). Eighty-eight percent (n=23/26) with peripheral arthralgia and 50% (n=5/10) with axial arthropathy achieved clinical response. Conclusion This study demonstrates the effectiveness of JAK inhibitors in treating EIM, with a high percentage of responders. Tofacitinib appears more effective than upadacitinib, possibly influenced by a higher proportion of biologically experienced patients and differences in IBD subtype distribution between the two groups, with upadacitinib mainly used in CD patients and tofacitinib in UC patients.

Abstract We formulate a continuous-time price discovery model and investigate how the standard price discovery measures vary with respect to the sampling interval. We find that the component share (CS) measure is invariant to the sampling interval, and hence, discrete-sampled prices suffice to identify the continuous-time CS. In contrast, information share (IS) estimates are not comparable across different sampling intervals because the contemporaneous correlation between markets increases in magnitude as the sampling interval grows. We show how to back out the continuous-time IS from discrete-sampled prices under certain assumptions on the contemporaneous correlation. We assess our continuous-time model by comparing the estimates of the (continuous-time) CS and IS at different sampling intervals for 30 stocks in the United States. We find that both price discovery measures are typically stable across the different sampling intervals, suggesting that our continuous-time price discovery model fits the data very well.

This paper proposes a valid bootstrap‐based distributional approximation for M‐estimators exhibiting a Chernoff (1964)‐type limiting distribution. For estimators of this kind, the standard nonparametric bootstrap is inconsistent. The method proposed herein is based on the nonparametric bootstrap, but restores consistency by altering the shape of the criterion function defining the estimator whose distribution we seek to approximate. This modification leads to a generic and easy‐to‐implement resampling method for inference that is conceptually distinct from other available distributional approximations. We illustrate the applicability of our results with four examples in econometrics and machine learning.

Feature (or variable) selection is the process of identifying the minimal set of features withthe highest predictive performance on the target variable of interest. Numerous feature selectionalgorithms have been developed over the years, but only few have been implementedin R as a package. The R package MXM is such an example, which not only offers a varietyof feature selection algorithms, but has unique features that make it advantageous overits competitors: a) it contains feature selection algorithms that can treat numerous types oftarget variables, including continuous, percentages, time to event (survival), binary, nominal,ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models toplug into the feature selection algorithms; c) it includes an algorithm for detecting multiplesolutions (many sets of equivalent features); and d) it includes memory efficient algorithmsfor high volume data, data that cannot be loaded into R. In this paper we qualitatively compareMXM with other relevant packages and discuss its advantages and disadvantages. Wealso provide a demonstration of its algorithms using real high-dimensional data from variousapplications.

Based on high-frequency data for more than fifty commodities, currencies, equity indices, and fixed-income instruments spanning more than two decades, we document strong similarities in realized volatility patterns within and across asset classes. Exploiting these similarities through panel-based estimation of new realized volatility models results in superior out-of-sample risk forecasts, compared to forecasts from existing models and conventional procedures that do not incorporate the similarities in volatilities. We develop a utility-based framework for evaluating risk models that shows significant economic gains from our new risk model. Lastly, we evaluate the effects of transaction costs and trading speed in implementing different risk models.Received March 7, 2016; editorial decision February 3, 2018 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.