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Upon high-frequency data of China Securities Index 300 (CSI 300) exchange-traded fund and index future contracts, we demonstrate a time-series reversal pattern between the last three-hour returns on current day and those on previous day. Further this reversal is also found in China index future market. This predictability has been illustrated to be both statistically and economically significant, and the significance is stronger on more volatile/higher volume days and non-bearish market state. Extensive regression analysis suggests that the time-series reversal is mainly induced by irrational investor overreaction, not by the lack of liquidity provision. Moreover, the economic value of the reversal pattern is evaluated to yield an outstanding trading performance by executing a market timing strategy. First published online 02 April 2025

This study aims to evaluate whether the assessment of a lung ultrasound score (LUS) by lung ultrasonography and of thoracic fluid contents (TFC) by electrical cardiometry may predict RDS severity and the development of bronchopulmonary dysplasia (BPD) in preterm infants with respiratory distress (RDS). Infants ≤ 34 weeks’ gestation admitted with RDS to two neonatal intensive care units were prospectively enrolled in this observational study. A simultaneous evaluation of LUS and TFC was performed during the first 72 h. The predictivity of LUS and TFC towards mechanical ventilation (MV) need after 24 h and BPD development was evaluated using receiver operating characteristic analysis. Sixty-four infants were included. The area under the curve (AUC) for the prediction of MV need was 0.851 (95%CI, 0.776–0.925, p  < 0.001) for LUS and 0.793 (95%CI, 0.724–0.862, p  < 0.001) for TFC, while an AUC of 0.876 (95%CI, 0.807–0.946, p  < 0.001) was obtained for combined LUS and TFC evaluation. LUS and TFC AUC for BPD prediction were 0.769 (95%CI, 0.697–0.842, p  < 0.001) and 0.836 (95%CI, 0.778–0.894, p  < 0.001), respectively, whereas their combined assessment yielded an AUC of 0.867 (95%CI, 0.814–0.919, p  < 0.001). LUS ≥ 11 and TFC ≥ 40 were identified as cut-off values for MV need prediction, whereas LUS ≥ 9 and TFC ≥ 41.4 best predicted BPD development.   Conclusion : A combined evaluation of LUS and TFC by lung ultrasonography and EC during the first 72 h may represent a useful predictive tool towards short- and medium-term pulmonary outcomes in preterm infants with RDS. What is Known: • Lung ultrasonography is largely used in neonatal intensive care and can contribute to RDS diagnosis in preterm infants. •  Little is known on the diagnostic and predictive role of TFC, measured by transthoracic electrical bioimpedance, in neonatal RDS. What is New: • Combining lung ultrasonography and TFC evaluation during the first 72 h can improve the prediction of RDS severity and BPD development in preterm infants with RDS and may aid to establish tailored respiratory approaches to improve these outcomes.

BackgroundThe early detection of patients at risk of post-stroke cognitive impairment (PSCI) may help planning subacute and long-term care. We aimed to determine the predictivity of two screening cognitive tests on the occurrence of mild cognitive impairment or dementia in acute stroke patients.MethodsA cognitive assessment within a few days of ischemic or hemorrhagic stroke was performed in patients consecutively admitted to a stroke unit over 14 months by means of the Clock Drawing Test (CDT) and the Montreal Cognitive Assessment-Basic (MoCA-B).ResultsOut of 191 stroke survivors who were non-demented at baseline, 168 attended at least one follow-up visit. At follow-up (mean duration ± SD 12.8 ± 8.7 months), 28 (18.9%) incident cases of MCI and 27 (18%) cases of dementia were recorded. In comparison with patients who remained cognitively stable at follow-up, these patients were older, less educated, had more comorbidities, a higher score on the National Institutes of Health Stroke Scale (NIHSS) at admission, more severe cerebral atrophy, and lower MoCA-B and CDT scores at baseline. In multi-adjusted (for age, education, comorbidities score, NIHSS at admission and atrophy score) model, a pathological score on baseline CDT (< 6.55) was associated with a higher risk of PSCI at follow-up (HR 2.022; 95% CI 1.025–3.989, p < 0.05) with respect to non-pathological scores. A pathological baseline score on MoCA-B (< 24) did not predict increased risk of cognitive decline at follow-up nor increased predictivity of stand-alone CDT.ConclusionA bedside cognitive screening with the CDT helps identifying patients at higher risk of PSCI.

The idiosyncratic nature of drug-induced liver injury (DILI) represents a current challenge for drug developers, regulators and clinicians. The myriad of agents (including medications, herbals, and dietary supplements) with recognized DILI potential not only strengthens the importance of the post-marketing phase, when urgent withdrawal sometimes occurs for rare unanticipated liver toxicity, but also shows the imperfect predictivity of pre-clinical models and the lack of validated biomarkers beyond traditional, non-specific liver function tests. After briefly reviewing proposed key mechanisms of DILI, we will focus on drug-related risk factors (physiochemical and pharmacokinetic properties) recently proposed as predictors of DILI and use cyclin-dependent kinase 4/6 inhibitors, relatively novel oral anticancer medications approved for breast cancer, as a case study to discuss the feasibility of early detection of DILI signals during drug development: published data from pivotal clinical trials, unpublished post-marketing reports of liver adverse events, and pharmacokinetic properties will be used to provide a comparative evaluation of their liver safety and gain insight into drug-related risk factors likely to explain the observed differences.

Thyroid immune-related adverse events (irAEs) are associated with programmed cell death protein 1 (PD-1) blockade efficacy in non-small cell lung cancer (NSCLC). However, their independence from PD-L1 expression and quantitative impact on predicting PD-1 blockade efficacy remain unexplored. This multicenter, retrospective, longitudinal study from Korea included 71 metastatic NSCLC patients who underwent PD-L1 expression and thyroid function testing during PD-1 blockade. Disease progression by the Response Evaluation Criteria for Solid Tumors was the main outcome. Three-stage analyses were performed: (1) multivariate Cox regression models adjusted for PD-L1 expression according to thyroid irAEs; (2) subgroup analyses; (3) regrouping and comparing predictivity of current and alternative staging. Patients with thyroid irAE + exhibited a longer progression-free survival [7/20 vs. 34/51, adjusted HR 0.19 (0.07–0.47); P < 0.001] than those with thyroid irAE-, independent of PD-L1 expression; the results remained across most subgroups without interaction. The three groups showed different adjusted HR for disease progression (Group 1: PD L1 + and thyroid irAE + ; Group 2: PD-L1 + or thyroid irAE + : 5.08 [1.48–17.34]; Group 3: PD-L1− and thyroid irAE− : 30.49 [6.60–140.78]). Alternative staging (Group 1 in stage IVB → stage IVA; Group 3 in stage IVA → stage IVB) improved the prognostic value (PVE: 21.7% vs. 6.44%; C-index: 0.706 vs. 0.617) compared with the 8th Tumor–Node–Metastasis staging. Our study suggests thyroid irAEs and PD-L1 expression are independent biomarkers that improve predicting PD-1 blockade efficacy in NSCLC. Thyroid irAEs would be helpful to identify NSCLC patients who benefit from PD-1 blockade in early course of treatment.

AIM: Phylogenetic trees provide a framework for understanding the evolution of features (properties, characters or traits) of species, where closely related species share many common or similar features. This property of phylogenetic trees has practical use in applications such as bio‐prospecting, where an optimal strategy exploits phylogenetic information to target closely related species to search for shared features of interest. The implicit corollary of this is that distantly related species share few features in common. This property of phylogenetic trees is thought to be useful for conservation evaluation in choosing sets of species that maximize the present utilitarian benefits of extant feature diversity (such as biologically active compounds or source systems for genetic engineering) as well as maximizing the range of evolutionary trajectories into the future. LOCATION: Global. METHODS: Here, we investigate the relationship between phylogenetic trees and biological features through both simulation and meta‐analysis of 223 publicly available feature matrices. RESULTS: We demonstrate that phylogenetic tree distance, both in real and simulated datasets, is correlated with feature similarity only for a short relative distance along the tree, such that there is no relationship for the majority of the length of most phylogenetic trees. In other words, close relatives share more features than distant relatives but beyond a certain threshold increasingly more distant relatives are not more divergent in phenotype. MAIN CONCLUSIONS: Measures of phylogenetic diversity based upon maximizing phylogenetic distance may not maximize feature diversity.

Two-dimensional (2D) tumor model has always poorly predicted drug response of animal model due to the lack of recapitulation of tumor microenvironment. Establishing a biomimetic, controllable, and cost-effective three-dimensional (3D) model and large-scale validation of its in vivo predictivity has shown promise in bridging the gap between the 2D tumor model and animal model. Here, we established a matrigel-based 3D micro-tumor model on an array chip for large-scale anticancer drug evaluation. Compared with the 2D tumor model, the 3D tumor model on the chip showed spheroid morphology, slower proliferation kinetics, and comparable reproducibility. Next, the results of the chemotherapeutic evaluation from 18 drugs against 27 cancer cell lines showed 17.6% of drug resistance on the 3D tumor model. Moreover, the evaluation results of targeted drugs showed expected sensitivity and higher specificity on the 3D tumor model compared with the 2D model. Finally, the evaluation results on the 3D tumor model were more consistent with the in vivo cell-derived xenograft model, and excluded 95% false-positive results from the 2D model. Overall, the matrigel-based 3D micro-tumor model on the array chip provides a promising tool to accelerate anticancer drug discovery.

We propose approaching prediction from a framework grounded in the theoretical correct prediction rate of a variable set as a parameter of interest. This framework allows us to define a measure of predictivity that enables assessing variable sets for, preferably high, predictivity. We first define the prediction rate for a variable set and consider, and ultimately reject, the naive estimator, a statistic based on the observed sample data, due to its inflated bias for moderate sample size and its sensitivity to noisy useless variables. We demonstrate that the I-score of the PR method of VS yields a relatively unbiased estimate of a parameter that is not sensitive to noisy variables and is a lower bound to the parameter of interest. Thus, the PR method using the I-score provides an effective approach to selecting highly predictive variables. We offer simulations and an application of the I-score on real data to demonstrate the statistic’s predictive performance on sample data. We conjecture that using the partition retention and I-score can aid in finding variable sets with promising prediction rates; however, further research in the avenue of sample-based measures of predictivity is much desired.

Interpretability is becoming increasingly important for predictive model analysis. Unfortunately, as remarked by many authors, there is still no consensus regarding this notion. The goal of this paper is to propose the definition of a score that allows for quickly comparing interpretable algorithms. This definition consists of three terms, each one being quantitatively measured with a simple formula: predictivity, stability and simplicity. While predictivity has been extensively studied to measure the accuracy of predictive algorithms, stability is based on the Dice-Sorensen index for comparing two rule sets generated by an algorithm using two independent samples. The simplicity is based on the sum of the lengths of the rules derived from the predictive model. The proposed score is a weighted sum of the three terms mentioned above. We use this score to compare the interpretability of a set of rule-based algorithms and tree-based algorithms for the regression case and for the classification case.