Previous research has observed that the speed of alpha band oscillations (8–12 Hz range) recorded during resting electroencephalography is slowed in chronic pain patients. While this slowing may reflect pathological changes that occur during the chronification of pain, an alternative explanation is that healthy individuals with slower alpha oscillations are more sensitive to prolonged pain, and by extension, more susceptible to developing chronic pain. To test this hypothesis, we examined the relationship between the pain-free, resting alpha oscillation speed of healthy individuals and their sensitivity to two models of prolonged pain, Phasic Heat Pain and Capsaicin Heat Pain, at two visits separated by 8 weeks on average (n = 61 Visit 1, n = 46 Visit 2). We observed that the speed of an individual’s pain-free alpha oscillations was negatively correlated with sensitivity to both models and that this relationship was reliable across short (minutes) and long (weeks) timescales. Furthermore, the speed of pain-free alpha oscillations can successfully identify the most pain sensitive individuals, which we validated on data from a separate, independent study. These results suggest that alpha oscillation speed is a reliable biomarker of prolonged pain sensitivity with potential for prospectively identifying pain sensitivity in the clinic.

Ovarian cancer is a gynecological malignancy with the highest recurrence and mortality rates. Although niraparib can effectively affect its progression, the challenge of drug resistance remains. Herein, niraparib-resistant ovarian cancer cell lines were constructed to identify the abnormally activated enhancers and associated target genes via RNA in situ conformation sequencing. Notably, the target gene RAD23A was markedly upregulated in niraparib-resistant cells, and inhibiting RAD23A restored their sensitivity. Additionally, abnormal activation of glycolysis in niraparib-resistant cells induced lactate accumulation, which promoted the lactylation of histone H4K12 lysine residues. Correlation analysis showed that key glycolysis enzymes such as pyruvate kinase M and lactate dehydrogenase A were significantly positively correlated with RAD23A expression in ovarian cancer. Additionally, H4K12la activated the super-enhancer (SE) of niraparib and RAD23A expression via MYC transcription factor, thereby enhancing the DNA damage repair ability and promoting the drug resistance of ovarian cancer cells. Overall, the findings of this study indicate that lactic acid accumulation leads to lactylation of histone H4K12la, thereby upregulating SE-mediated abnormal RAD23A expression and promoting niraparib resistance in ovarian cancer cells, suggesting RAD23A as a potential therapeutic target for niraparib-resistant ovarian cancer.

Patients with schizophrenia have patterns of brain deficits including reduced cortical thickness, subcortical gray matter volumes, and cerebral white matter integrity. We proposed the regional vulnerability index (RVI) to translate the results of Enhancing Neuro Imaging Genetics Meta‐Analysis studies to the individual level. We calculated RVIs for cortical, subcortical, and white matter measurements and a multimodality RVI. We evaluated RVI as a measure sensitive to schizophrenia‐specific neuroanatomical deficits and symptoms and studied the timeline of deficit formations in: early (≤5 years since diagnosis, N = 45, age = 28.8 ± 8.5); intermediate (6–20 years, N = 30, age 43.3 ± 8.6); and chronic (21+ years, N = 44, age = 52.5 ± 5.2) patients and healthy controls (N = 76, age = 38.6 ± 12.4). All RVIs were significantly elevated in patients compared to controls, with the multimodal RVI showing the largest effect size, followed by cortical, white matter and subcortical RVIs (d = 1.57, 1.23, 1.09, and 0.61, all p < 10⁻⁶). Multimodal RVI was significantly correlated with multiple cognitive variables including measures of visual learning, working memory and the total score of the MATRICS consensus cognitive battery, and with negative symptoms. The multimodality and white matter RVIs were significantly elevated in the intermediate and chronic versus early diagnosis group, consistent with ongoing progression. Cortical RVI was stable in the three disease‐duration groups, suggesting neurodevelopmental origins of cortical deficits. In summary, neuroanatomical deficits in schizophrenia affect the entire brain; the heterochronicity of their appearance indicates both the neurodevelopmental and progressive nature of this illness. These deficit patterns may be useful for early diagnosis and as quantitative targets for more effective treatment strategies aiming to alter these neuroanatomical deficit patterns.

Machine learning models for continuous outcomes often yield systematically biased predictions, particularly for values that largely deviate from the mean. Specifically, predictions for large-valued outcomes tend to be negatively biased (underestimating actual values), while those for small-valued outcomes are positively biased (overestimating actual values). We refer to this linear central tendency warped bias as the “systematic bias of machine learning regression”. In this paper, we first demonstrate that this systematic prediction bias persists across various machine learning regression models, and then delve into its theoretical underpinnings. To address this issue, we propose a general constrained optimization approach designed to correct this bias and develop computationally efficient implementation algorithms. Simulation results indicate that our correction method effectively eliminates the bias from the predicted outcomes. We apply the proposed approach to the prediction of brain age using neuroimaging data. In comparison to competing machine learning regression models, our method effectively addresses the longstanding issue of “systematic bias of machine learning regression” in neuroimaging-based brain age calculation, yielding unbiased predictions of brain age.