An exploratory study of new predictive biomarkers of castration resistance
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Predictive model of castration resistance in advanced prostate cancer by machine learning using genetic and clinical data: KYUCOG-1401-A study
The predictive power of the treatment efficacy and prognosis in primary androgen deprivation therapy (ADT) for advanced prostate cancer is not satisfactory. The objective of this study was to integrate genetic and clinical data to predict castration resistance in primary ADT for advanced prostate cancer by machine learning (ML).
Neuroinflammatory fluid biomarkers in patients with Alzheimer’s disease: a systematic literature review
Neuroinflammation is associated with both early and late stages of the pathophysiology of Alzheimer’s disease (AD). Fluid biomarkers are gaining significance in clinical practice for diagnosis in presymptomatic stages, monitoring, and disease prognosis. This systematic literature review (SLR) aimed to identify fluid biomarkers for neuroinflammation related to clinical stages across the AD continuum and examined long-term outcomes associated with changes in biomarkers.
Predictive learning as the basis of the testing effect
A prominent learning phenomenon is the testing effect, meaning that testing enhances retention more than studying. Emergent frameworks propose fundamental (Hebbian and predictive) learning principles as its basis. Predictive learning posits that learning occurs based on the contrast (error) between a prediction and the feedback on that prediction (prediction error). Here, we propose that in testing (but not studying) scenarios, participants predict potential answers, and its contrast with the subsequent feedback yields a prediction error, which facilitates testing-based learning. To investigate this, we developed an associative memory network incorporating Hebbian and/or predictive learning, together with an experimental design where human participants studied or tested English-Swahili word pairs followed by recognition. Three behavioral experiments (N = 80, 81, 62) showed robust testing effects when feedback was provided. Model fitting (of 10 different models) suggested that only models incorporating predictive learning can account for the breadth of data associated with the testing effect. Our data and model suggest that predictive learning underlies the testing effect.
Prevalence and transmission risk of colistin and multidrug resistance in long-distance coastal aquaculture
Due to the wide use of antibiotics, intensive aquaculture farms have been recognized as a significant reservoir of antibiotic resistomes. Although the prevalence of colistin resistance genes and multidrug-resistant bacteria (MDRB) has been documented, empirical evidence for the transmission of colistin and multidrug resistance between bacterial communities in aquaculture farms through horizontal gene transfer (HGT) is lacking. Here, we report the prevalence and transmission risk of colistin and multidrug resistance in 27 aquaculture water samples from 9 aquaculture zones from over 5000 km of subtropical coastlines in southern China. The colistin resistance gene mcr−1, mobile genetic element (MGE) intl1 and 13 typical antibiotic resistance genes (ARGs) were prevalent in all the aquaculture water samples. Most types of antibiotic (especially colistin) resistance are transmissible in bacterial communities based on evidence from laboratory conjugation and transformation experiments. Diverse MDRB were detected in most of the aquaculture water samples, and a strain with high-level colistin resistance, named Ralstonia pickettii MCR, was isolated. The risk of horizontal transfer of the colistin resistance of R. pickettii MCR through conjugation and transformation was low, but the colistin resistance could be steadily transmitted to offspring through vertical transfer. The findings have important implications for the future regulation of antibiotic use in aquaculture farms globally to address the growing threat posed by antibiotic resistance to human health.
Emotions and individual differences shape human foraging under threat
A common behavior in natural environments is foraging for rewards. However, this is often in the presence of predators. Therefore, one of the most fundamental decisions for humans, as for other animals, is how to apportion time between reward-motivated pursuit behavior and threat-motivated checking behavior. To understand what affects how people strike this balance, we developed an ecologically inspired task and looked at both within-participant dynamics (moods) and between-participant individual differences (questionnaires about real-life behaviors) in two large internet samples (n = 374 and n = 702) in a cross-sectional design. For the within-participant dynamics, we found that people regulate task-evoked stress homeostatically by changing behavior (increasing foraging and hiding). Individual differences, even in superficially related traits (apathy–anhedonia and anxiety–compulsive checking) reliably mapped onto unique behaviors. Worse task performance, due to maladaptive checking, was linked to gender (women checked excessively) and specific anxiety-related traits: somatic anxiety (reduced self-reported checking due to worry) and compulsivity (self-reported disorganized checking). While anhedonia decreased self-reported task engagement, apathy, strikingly, improved overall task performance by reducing excessive checking. In summary, we provide a multifaceted paradigm for assessment of checking for threat in a naturalistic task that is sensitive to both moods as they change throughout the task and clinical dimensions. Thus, it could serve as an objective measurement tool for future clinical studies interested in threat, vigilance or behavior–emotion interactions in contexts requiring both reward seeking and threat avoidance.
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