Cetuximab is approved for the treatment of metastatic colorectal cancer (mCRC) with RAS wild-type. Nevertheless, the prognosis remains poor and the effectiveness of cetuximab is limited in KRAS mutant mCRC. Recently, emerging evidence has shown that ferroptosis, a newly discovered form of nonapoptotic cell death, is closely related to KRAS mutant cells. Here, we further investigated whether cetuximab-mediated regulation of p38/Nrf2/HO-1 promotes RSL3-induced ferroptosis and plays a pivotal role in overcoming drug resistance in KRAS mutant colorectal cancer (CRC). In our research, we used two KRAS mutant CRC cell lines, HCT116 and DLD-1, as models of intrinsic resistance to cetuximab. The viability of cells treated with the combination of RSL3 and cetuximab was assessed by the CCK-8 and colony formation assays. The effective of cetuximab to promote RSL3-induced ferroptosis was investigated by evaluating lipid reactive oxygen species accumulation and the expression of the malondialdehyde and the intracellular iron assay. Cetuximab therapy contributed to regulating the p38/Nrf2/HO-1 axis, as determined by western blotting and transfection with small interfering RNAs. Cetuximab promoted RSL3-induced ferroptosis by inhibiting the Nrf2/HO-1 in KRAS mutant CRC cells, and this was further demonstrated in a xenograft nude mouse model. Our work reveals that cetuximab enhances the cytotoxic effect of RSL3 on KRAS mutant CRC cells and that cetuximab enhances RSL3-induced ferroptosis by inhibiting the Nrf2/HO-1 axis through the activation of p38 MAPK.

To review the optimality and safety of different anti‐Amyloid‐β(Aβ) immunotherapies for Alzheimer's disease (AD). Published randomized controlled trials were comprehensively reviewed from electronic databases (Cochrane library, Embase, Pubmed, and Google scholar). Pooled outcomes as mean difference or odds ratio values with 95% confidence interval were reported. The network estimates with confidence and predictive intervals for all pairwise relative effects was evaluated. Optimal intervention was ranked by benefit‐risk ratio based on the surface under the cumulative ranking curve. Eleven eligible RCTs from 9 literatures, including 5141 patients and 5 interventions were included. The quality of evidence was rated low in comparisons. For efficacy, in terms of Mini‐Mental State Examination, aducanumab and solanezumab are significantly effective than placebo. For safety, in terms of Amyloid‐Related Imaging Abnormalities (ARIA), bapineuzumab and aducanumab are significantly worse than placebo. There were no significant differences in outcomes of Alzheimer's disease Assessment Scale‐Cognitive subscale, Disability Assessment for Dementia, Adverse Events, and mortality. Given the clinical therapeutic effects of anti‐Aβ immunotherapies for AD, aducanumab and solanezumab improve the cognitive function, while aducanumab and bapineuzumab may increase the risks of ARIA.

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted ‘gold-standard’ subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.