Hastings Center

Deep brain stimulation (DBS) has increasingly been pursued as a treatment for obesity over the past couple of decades. This has occurred despite limited scientific justification or a serious ethical analysis of the concerns it raises. These interventions have relied on an “obese brain” model, which treats fatness as a neurological defect that can and should be corrected—reflecting and reinforcing stigmatizing, racialized assumptions about self-control, deviance, and bodily normalcy. To show this, we situate DBS for obesity within the broader and ethically troubling history of psychosurgery. We critique the scientific basis of the obese brain model and argue that its moral justification ultimately collapses. In this narrative framing, we argue that DBS for obesity is not a therapeutic innovation; instead it is a continuation of medicine’s role in pathologizing nonconformity.

Introduction: The use of electronic cigarettes as a seemingly safer alternative to combustible tobacco has risen sharply in recent years. While primarily linked to worsening respiratory conditions, emerging data suggest broader systemic effects. Recent evidence shows that exposure impairs key neutrophil functions—chemotaxis, phagocytosis, and extracellular trap formation—heightening vulnerability to infections and their severity. Despite these concerns, regulatory oversight remains limited, underscoring the urgent need for comprehensive investigation into its uncertain impact. Methods: We conducted a retrospective analysis using data from the 2020–2022 National Inpatient Sample (NIS) database. Patients with sepsis and concurrent e-cigarette use were identified via validated ICD-10 codes. Multivariate logistic regression adjusted for confounders including sex, age, race, cigarette smoking, obesity, and comorbidity burden (Charlson and Elixhauser Indices). Statistical analyses were performed using STATA 18. Results: A total of 1,435,602 patients were admitted with a primary diagnosis of sepsis, of whom 14.6% (n = 209,203) had concurrent e-cigarette use. The mean age was 64 years in both groups. Females comprised 43.8% of the e-cigarette user group. Patients with concurrent e-cigarette use had significantly worse in-hospital outcomes, including higher mortality (OR: 3.68; 95% CI: 3.58–3.79; p < 0.001), increased use of high-flow nasal cannula (OR: 7.15; 95% CI: 6.68–7.66; p < 0.001), non-invasive mechanical ventilation (OR: 2.73; 95% CI: 2.63–2.84; p < 0.001), invasive mechanical ventilation (OR: 2.74; 95% CI: 2.66–2.83; p < 0.001), and extracorporeal membrane oxygenation (OR: 4.05; 95% CI: 3.91–4.20; p < 0.001). They also experienced longer hospital stays (mean: 10.93 vs. 7.44 days; 95% CI: 3.44–3.53; p < 0.001) and higher hospitalization costs (mean: $156,007 vs. $97,312; p < 0.001). Conclusions: E-cigarette use is associated with more severe outcomes in patients with sepsis, including increased mortality, greater need for respiratory support, prolonged hospital stays, and higher healthcare costs. It should be recognized as a potential risk factor in the clinical management of sepsis, underscoring the need for further research into its mechanisms, adverse effects and targeted prevention strategies.

Background: The equine roundworm Parascaris spp. commonly infects foals, and high burdens can cause fatal small intestinal impactions. Progressive anthelmintic resistance challenges infection control, posing a risk for an increase of Parascaris spp. r

A bstract Background Sleep is essential for overall health and well-being, but assessing sleep architecture and quality is often costly and time-consuming, relying primarily on polysomnography (PSG). Wearable and nearable devices offer potential alternatives, but they regularly lack rigorous validation, especially in real-world settings. Objectives This study evaluates the accuracy and reliability of Withings Sleep Analyzer (WSA), a contactless sleep monitoring device, compared to PSG in a home setting using a large and diverse cohort of healthy individuals. Methods A total of 117 participants (69 women; 39.9 ± 11.4 years, mean ± std) underwent home-based polysomnography (PSG) and simultaneous WSA recording. Data analysis focused on evaluating sleep-wake distinction and sleep stage identification using standard classification metrics. Results WSA demonstrates high sensitivity (93%) for sleep detection and moderate sensitivity (73%) for wakefulness, achieving an overall accuracy of 87% for sleep-wake distinction. The device showed consistent performance across various demographic subgroups, including different age, BMI, mattress and sleep arrangements (with or without bed partner) categories. Challenges were noted in accurately classifying specific sleep stages, particularly in distinguishing between light and deep sleep, with a mean accuracy of 63% and a Cohen’s Kappa of 0.49. The WSA tended to overestimate total sleep time (+20 min) and light sleep (+1h21 min) while underestimating REM (−15 min) and deep sleep (−46 min) durations. Disagreements between expert reviewers, particularly between light and deep sleep stages, mirrored in part the WSA’s misclassifications. Participants reported significantly altered perceived sleep quality during the night with the PSG, suggesting potential discomfort during sleep. Conclusions WSA offers a promising approach to sleep monitoring in natural home environments. Being contactless and placed under the mattress, the WSA’s allows for long-term monitoring of sleep measures. It shows competitive performance in sleep-wake and sleep stage identification compared to other consumer devices. Progress in wearable and nearable devices is necessary to enhance their accuracy to better support the monitoring of populations with strongly impaired sleep, although limited by an imperfect gold standard. This work also emphasizes the importance of using large, diverse, and challenging datasets, as well as the need for a standardized methodology for accurate sleep stage classification.

Immune checkpoint inhibitors (ICIs) and their combination with other therapies such as chemotherapy, fail in most cancer patients. We previously identified the PDZ-LIM domain-containing protein 2 (PDLIM2) as a bona fide tumor suppressor that is repressed in lung cancer to drive cancer and its chemo and immunotherapy resistance, suggesting a new target for lung cancer therapy improvement. In this study, human clinical samples and data were used to investigate PDLIM2 genetic and epigenetic changes in lung cancer. Using an endogenous mouse lung cancer model faithfully recapitulating refractory human lung cancer and a clinically feasible nano-delivery system, we investigated the therapeutic efficacy, action mechanism, and safety of systemically administrated PDLIM2 expression plasmids encapsulated in nanoparticles (nanoPDLIM2) and its combination with PD-1 antibody and chemotherapeutic drugs. Our analysis indicate that PDLIM2 repression in human lung cancer involves both genetic deletion and epigenetic alteration. NanoPDLIM2 showed low toxicity, high tumor specificity, antitumor activity, and greatly improved the efficacy of anti-PD-1 and chemotherapeutic drugs, with complete tumor remission in most mice and substantial tumor reduction in the remaining mice by their triple combination. Mechanistically, nanoPDLIM2 increased major histocompatibility complex class I (MHC-I) expression, suppressed multi-drug resistance 1 (MDR1) induction and survival genes and other tumor-related genes expression in tumor cells, and enhanced lymphocyte tumor infiltration, turning the cold tumors hot and sensitive to ICIs and rendering them vulnerable to chemotherapeutic drugs and activated tumor-infiltrating lymphocytes (TILs) including those unleashed by ICIs. These studies established a clinically applicable PDLIM2-based combination therapy with great efficacy for lung cancer and possibly other cold cancers.

BackgroundPatients receiving chemotherapy require ongoing symptom monitoring and management to optimize their outcomes. In recent years, digital remote monitoring interventions have emerged to provide enhanced cancer care delivery experiences to patients and clinicians. However, patient and clinician experiential evaluations of these technologies are rare. Therefore, we explored user experiences and perceptions of one such intervention—Advanced Symptom Management System (ASyMS)—after its scaled deployment in the context of the Electronic Symptom Management System Remote Technology (eSMART) trial. The eSMART trial was a large, multicenter randomized controlled trial to evaluate the efficacy of ASyMS in 12 clinical sites in 5 European countries.ObjectiveIn this qualitative study, both patients’ and clinicians’ experiences of using ASyMS for up to 6 cycles of chemotherapy were explored to understand the impact of ASyMS on patients’ experiences, clinical practice, and supportive care delivery.MethodsFor this analysis, individual, semistructured, one-to-one interviews with 29 patients with breast, colorectal, and hematological cancers and 18 clinicians from Austria, Greece, Ireland, Norway, and the United Kingdom were conducted. Interviews focused on patients’ and clinicians’ experiences of using ASyMS, care organization and changes in practice following the introduction of ASyMS, perceived changes in care associated with the use of ASyMS, and its potential for future integration into routine chemotherapy care pathways.ResultsThematic analysis identified several themes that describe patients’ and clinicians’ experiences using ASyMS. One central orienting theme—ASyMS as a facilitator of change—was supported by 5 key themes associated with human and technology monitoring: reassurance, enhanced communications and relationships, knowing what is “normal” and what is to be expected, enhancing cancer care experiences, and informing future cancer care.ConclusionsThis study is the first to evaluate both patients’ and clinicians’ experiences of using a digital health intervention to remotely monitor chemotherapy symptoms across 5 countries. Experiences with ASyMS were positive from both patients’ and clinicians’ perspectives, although some improvements to support the wider-scale rollout and sustained implementation were identified. Overall, this study demonstrates that real-time remote monitoring systems can help patients feel more reassured during their chemotherapy treatments and can help clinicians provide the right care, at the right time, and in the right place.Trial RegistrationClinicalTrials.gov NCT02356081; https://clinicaltrials.gov/study/NCT02356081International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2016-015016

This paper argues that the headline-grabbing nature of existential risk (X-Risk) diverts attention away from immediate artificial intelligence (AI) threats, including fairly disseminating AI risks and benefits and justly transitioning towards AI-centred societies. Section I introduces a working definition of X-Risk, considers its likelihood and explores possible subtexts. It highlights conflicts of interest that arise when tech luminaries lead ethics debates in the public square. Section II flags AI ethics concerns brushed aside by focusing on X-Risk, including AI existential benefits (X-Benefits), non-AI X-Risk and AI harms occurring now. Taking the entire landscape of X-Risk into account requires considering how big risks compare, combine and rank relative to one another. As we transition towards more AI-centred societies, which we, the authors, would like to be fair, we urge embedding fairness in the transition process, especially with respect to groups historically disadvantaged and marginalised. Section III concludes by proposing a wide-angle lens that takes X-Risk seriously alongside other urgent ethics concerns.

ObjectivesArtificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation.Materials and MethodsWe analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle that consolidates these themes into a more comprehensive lifecycle. We then considered the potential benefits and harms of AI through this lifecycle to identify ethical questions that can arise at each step and to identify where conflicts and errors could arise in ethical analysis. We illustrated the approach in 3 case studies that highlight how different ethical dilemmas arise at different points in the lifecycle.Results, Discussion, and ConclusionThrough case studies, we show how a systematic lifecycle-informed approach to the ethical analysis of AI enables mapping of the effects of AI onto different steps to guide deliberations on benefits and harms. The lifecycle-informed approach has broad applicability to different stakeholders and can facilitate communication on ethical issues for patients, healthcare professionals, research participants, and other stakeholders.