Harvard University Press

Developing gene therapies involving gene editing is a rapidly evolving field with large potential implications for improving health for both rare and common diseases. Ensuring that these technologies are developed safely, efficiently, and fairly is essential. To better understand the ethical considerations and regulatory requirements and challenges with gene therapies involving gene editing that may advance precision health. Through a multistakeholder workshop and subsequent engagements, multiple ethical and regulatory barriers to developing and implementing gene therapies involving gene editing were identified. Eight major themes emerged that warrant careful consideration, including (1) objectives (treatment, risk reduction, and enhancement) for the intervention; (2) competing interests of safety, equity, and desire for research efficiencies; (3) unique aspects of gene editing related to rare and ultrarare genetic conditions; (4) considerations in the pediatric population; (5) regulatory requirements and ethics oversight; (6) challenges with long-term follow-up and data sharing; and (7) communication. To promote the safe, efficient development of gene therapies involving gene editing that will reach their full potential, all stakeholders will have to undertake an unprecedented degree of collaboration. However, this will be essential to ensure that these interventions are effective, ethically sound, and patient-centered.

Vaccinia-related kinase 1 (VRK1) is a promising therapeutic target in gliomas and glioblastomas where VRK2 is silenced by promoter methylation, rendering VRK1 essential for accurate nuclear envelope reassembly following mitosis. Small-molecule ATP-site drug discovery for VRK1 has been hindered by the absence of robust and reproducible biochemical assays. Through virtual screening, we identified previously unreported VRK1-binding scaffolds and validated them in biochemical kinase assays, yielding an 82 nM inhibitor with high selectivity for VRK1 over VRK2. During characterization of this compound, we found that a commonly used commercial time-resolved fluorescence resonance energy transfer VRK1 activity assay is dependent on purification tag-mediated VRK1 dimerization. Leveraging the new inhibitor, we developed fluorogenic tool compounds that increase in fluorescence intensity upon binding to the active site of VRK1 and do not require artificial dimerization of VRK1. The top probe exhibits a Kd of 180 nM and is useful for ligand displacement assays using both fluorescence enhancement and time-resolved fluorescence resonance energy transfer readouts. Together, these results introduce new chemical scaffolds for targeting VRK1, define an assay artifact that has complicated VRK1 inhibitor discovery, and deliver fluorogenic tool compounds for high-throughput screening of ATP-site VRK1 inhibitors, enabling future drug discovery efforts against this emerging cancer vulnerability.

People constantly invest effort to seek information in everyday life, even if the information is of no instrumental value for the decision at hand. However, the mechanism underlying people’s willingness to exert effort in exchange for information remains unclear. Previous work has suggested that desirability and uncertainty are two key factors influencing information-seeking, but few studies have tested if these factors are important when exerting physical effort in exchange for information. Additionally, it remains unknown whether different kinds of uncertainty, i.e., risk and ambiguity, have differential impacts. In the current study, we developed a novel information-seeking task to quantify the value of non-instrumental information using incentive-compatible physical effort to examine how effort changes as a function of desirability and uncertainty. Across two studies (N = 123), we found that people’s willingness to exert effort is positively associated with outcome expected value under both risk and ambiguity. Additionally, people exert more effort when outcome distribution uncertainty increases in risky situations, but are insensitive to ambiguity, except when facing extreme ambiguity. Our results demonstrate an unexpected dissociation. Humans will engage in effort-based information-seeking, even for non-instrumental information, when facing risk. In contrast, they show a much lower willingness to expend effort to resolve non-instrumental ambiguity.

Chronic hand eczema (CHE) is associated with substantial functional impairment and reduced quality of life. Delgocitinib, a pan-Janus kinase (JAK) inhibitor, has emerged as a promising topical treatment for CHE. A systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to assess the efficacy and safety of topical delgocitinib 20-30 mg/g compared with vehicle in patients with CHE. PubMed, Embase, Cochrane Library, ClinicalTrials.gov, EU CTR, and WHO ICTRP were searched up to 15 February 2026. The primary outcome was treatment success at week 16, defined as clear or almost clear skin with a ≥ 2-point improvement on the Investigator's or Physician's Global Assessment (IGA/PGA). Secondary outcomes included treatment success at weeks 4 and 8, changes in Hand Eczema Symptom Diary (HESD) itch and pain scores, and safety outcomes. Relative risks (RRs) and mean differences (MDs) with 95% confidence intervals (CIs) were calculated using random-effects models. Three publications reporting four RCTs involving 1154 patients were included, with 752 receiving delgocitinib and 402 receiving vehicle. At week 16, treatment success was significantly higher with delgocitinib (RR 3.17; 95% CI 1.78-5.65; p < 0.01), corresponding to an absolute risk difference of 16.9% and a number needed to treat (NNT) of 6 (95% CI 3-16). Similar results at weeks 4 and 8. Delgocitinib also led to significantly greater improvements in HESD itch and pain scores. No significant differences were found in overall adverse events (AEs), treatment-related AEs, or serious AEs. Discontinuation due to AEs was lower in the delgocitinib group. Delgocitinib 20-30 mg/g demonstrated greater efficacy than vehicle and a favourable safety profile, supporting its use as a topical treatment for CHE.

Proper forecasting of remaining useful life (RUL) of lithium-ion batteries is critical for safety, operational stability, and cost-effectiveness in modern electric vehicles. Because battery degradation arises from complex electrochemical and thermal processes, data-driven approaches have become a practical alternative to physics-based modeling. This study develops and critically compares supervised machine learning models, including Random Forest, Gradient Boosting, Linear Regression, Ridge Regression, K-Nearest Neighbors, Deep Neural Networks (DNN), and Convolutional Neural Networks (CNN), for cycle-level RUL estimation using a publicly available lithium-ion battery dataset. The dataset comprises engineered voltage-time features, capacity, and charge–discharge indicators, which are preprocessed using Min–Max normalization to support stable training and fair model comparison. Among the evaluated methods, the Random Forest regressor achieved the best overall performance, reaching an R2 of 0.9998 with lower MAE and RMSE than both traditional regressors and deep learning architectures. The results indicate that ensemble tree-based models are especially effective at capturing nonlinear degradation behavior when trained on engineered tabular features rather than raw time-series signals. While the DNN and CNN achieved high R2 values, their error metrics were comparatively larger, suggesting that deep architectures may benefit from higher-resolution time-series inputs or alternative sequence modeling formulations. Finally, the top-performing model was integrated into an end-to-end prediction system using FastAPI, SQLite, and a responsive web dashboard to demonstrate real-world applicability. The deployment allows estimating RUL in real time and at lowlatency and provides stakeholders with an accessible tool for battery health monitoring. Overall, the study shows that ensemble learning techniques can deliver strong performance for battery condition monitoring and provides a deployable architecture to practical RUL prediction in electric-vehicle battery management systems.

Abstract Regulatory review is often seen as a barrier to innovation, increasing costs and delaying new medicines. Yet approval may also expand markets by certifying quality and reducing uncertainty. We test this by studying FDA approval for follow-on indications—uses that physicians could already prescribe “offlabel” — and find approval raises use in newly approved diseases by 25 percent within a year, with larger increases in smaller off-label markets. Subsequent approvals in the same disease yield smaller gains. Our results suggest regulatory approval of medicines expands market size by increasing demand, rather than easing insurer-imposed restrictions, revealing limits to marketbased learning.

Without emission reductions, climate change may increase ozone and PM2.5 air pollution in the United States; however, we do not know how this will affect air quality alerts that prompt people to stay indoors. Here, we use an integrated modeling framework to find distributions of daily Air Quality Index (AQI) during the smog season at the start, middle, and end-of-century. Considering natural variability, climate change may cause air quality alerts to double (increase by a factor of 2 ± 0.2) by 2100. Days when both ozone and PM2.5 exceed alert thresholds quadruple (4.3 ± 1.2). More than 100,000,000 (±45,000,000) people experience mean air pollution deemed "Unhealthy for Sensitive Groups", a growth of 7 (±3) times compared to 2000. If people follow alerts by staying inside, they reduce exposure to outdoor-generated pollutants. Their health benefits are similar whether the alert is caused by ozone or PM2.5. Senior (age 65+) populations receive much higher benefits per day by adapting (e.g., 95CI for PM2.5: $4.60 to $147) as young adults (age 18-35; 95CI: $0.15 to $4.22)─more than 45 times higher on average. This disproportionate impact requires targeted messaging and guidance, especially as climate-related risks rise.

ABSTRACT We live in an interdependent world riven by ideology, factions, and rivalries, and we face enormous challenges such as climate disruption, an aging population, and coping with the consequences of technologies such as gene editing and artificial intelligence. This paper considers global mega-challenges: huge problems that are unbounded by national borders; pose profound risk to humanity, often with unequal impact; and are complex, deeply rooted, multi-faceted, and inter-connected. After presenting seven illustrative mega-challenges, the paper introduces a “Ten-C” spectrum of relations between countries. Taking account of the philosophy of the renowned global leader in medicine Dr. Taro Takemi, the paper suggests positive ways that global collaboration in science, education, and health can lead to a more successful and resilient world.