Electroencephalography is the most commonly used diagnostic tool for epilepsy. However, interpreting electroencephalograms (EEGs) requires expertise that is not widely available. Advances in digital technology and wearables have enabled large-scale EEG recording, generating vast amounts of data that cannot be managed through traditional visual interpretation by experts. Artificial intelligence (AI) has the potential to augment human expertise and reduce workloads. The application of artificial neural networks in analysing clinical EEG recordings has led to major breakthroughs, bringing AI-based EEG interpretation closer to clinical implementation. In this Review, we summarise the most important research and development results in this field from a clinical perspective. We provide an overview of AI applications in spike and seizure detection; analysis of data from wearable electroencephalographs, patients who are critically ill, and epilepsy surgery; and the automated interpretation of clinical EEGs.
Myeloid malignancies carrying somatic DNMT3A mutations (DNMT3Amut) are refractory to standard therapy. DNMT3Amut leukemia cells accumulate toxic DNA double-strand breaks (DSBs) and stalled replication forks, rendering them dependent on DNA damage response (DDR). We report here that DNA polymerase theta (Pol9), a key element in DSB repair by end-joining (Pol9-mediated end-joining [TMEJ]) and in fork restarting, promotes survival and proliferation of DNMT3Amut leukemia cells. Pol9 is overexpressed in DNMT3Amut leukemia cells due to abrogation of PARP1 PARylation-dependent UBE2O E3 ligase-mediated ubiquitination and proteasomal degradation of Pol9. In addition, PARP1-mediated recruitment of the SMARCAD1-MSH2/ MSH3 repressive complex to DSBs is diminished in DNMT3Amut leukemia cells, which facilitates association of Pol9 with DNA damage. Pol9 inhibitors enhance the anti-leukemic effects of standard drugs such as FLT3 kinase inhibitor quizartinib, cytarabine +/- doxorubicin, and etoposidein vitro and in mice with DNMT3Amut leukemia. Altogether, Pol9 is an attractive target in DNMT3Amut hematological malignancies.
Stigma remains a critical social determinant of health for individuals with serious mental illnesses (SMIs), influencing access to resources, social inclusion, and overall well-being. This study uses a multi-method approach to examine how individual and interpersonal stigma operate within social networks, shaping experiences of exclusion, disclosure, safety, and community participation. Using semi-structured qualitative interviews and egocentric social network interviews, we employed case study and thematic analysis to examine sources and types of stigma among 30 participants with SMIs receiving community-based services in a large Western U.S. city. Findings highlight three primary stigma types—anticipated, experienced, and internalized—emerging within social networks, with family members, friends, service providers, and acquaintances identified as sources of stigma. The interplay between stigma types within social networks contributed to downstream psychological and behavioral consequences, including self-imposed isolation, disclosure dilemmas, and heightened safety concerns. These findings underscore the need for multi-level interventions that address stigma at both the individual and interpersonal levels, including challenging negative self-perceptions, strengthening social support networks, and fostering inclusive environments. By examining stigma in social networks, this study contributes to a growing body of qualitative research on stigma, mental health, and community participation, offering critical insights for policy, practice, and future research.
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PURPOSE:Incidentally found brain metastases often lead to emergency room referrals even in asymptomatic patients-a pathway of care that could be unnecessary. We sought to compare time and financial toxicity, and treatment outcomes between a multidisciplinary outpatient (MP) and acute care pathways (AP). METHODS:Patients referred for de novo asymptomatic brain metastases at an NCI-designated Cancer Center with a Multidisciplinary Brain Metastasis Program were identified via retrospective review. Scans, encounters, time to local interventions, and survival data were collected and compared. RESULTS:Seventy-eight patients were identified, 47 referred to MP and 31 AP. Both groups had similar disease-specific prognostic scores and received similar treatments. Patients managed via AP had larger dominant lesions (2.9 cm vs. 2.0 cm, p < 0.002) and shorter time to therapy (13.7 days vs. 9.2 days; p = 0.047). AP patients also had more medical-encounter (7.1 vs. 2.5; p < 0.001) and admitted days (5.9 vs. 1.1; p < 0.001), with increased median gross charge amount ($185,961 vs. $126,831; p = 0.003) despite similar 6-month survival (83% MP vs. 81% AP, p > 0.999) and local tumor control (95% MP vs. 96% AP, p = 0.849). CONCLUSION:Patients with asymptomatic brain metastases managed through an outpatient pathway attained similar disease outcomes with lower time and financial toxicity compared to patients managed through inpatient pathways. Characteristics of such patients that qualify them for outpatient pathway should be confirmed prospectively.
