West Hertfordshire Teaching Hospitals NHS Trust is using advanced robotic surgery technology to speed up recovery from cancer surgery.
Green steel startup Boston Metal is doubling down on critical metals
The startup Boston Metal has raised a $75 million funding round to produce critical metals, MIT Technology Review can exclusively report.
The company has been known largely for its efforts to clean up steel production, an industry that’s responsible for about 8% of global greenhouse emissions today. With the additional money, the new focus could help it survive at a time when support for industrial decarbonization has been waning in the US.
In addition to steel, Boston Metal has also worked to use its technology with other metals, and a subsidiary (Boston Metal do Brasil) is setting up a commercial facility in Brazil to produce niobium, tantalum, and tin. The funding will help support that facility’s operation as well as future efforts to produce critical metals like vanadium, nickel, and chromium, says CEO Tadeu Carneiro. The funding comes after the company faced cash-flow problems following an industrial accident at the Brazil facility earlier this year.
Boston Metal’s core technology is called molten oxide electrolysis (MOE). It involves running electric current through a reactor filled with ore dissolved in a molten electrolyte. The electricity heats everything up to about 1,600 °C (3,000 °F) and drives chemical reactions that separate the desired metal (or metals) from the ore. The metal gathers at the bottom of the reactor, where it can be siphoned off.
In early 2025, Boston Metal completed the largest run of its pilot industrial cell in Woburn, Massachusetts, producing about a ton of steel.
But the focus is currently on making other metals, which are more valuable and can command a higher price. The company’s Brazilian subsidiary is working to test and start up an industrial-scale plant that takes in a low-grade material and makes a mixture of critical metals. Niobium, for example, is used in some steel alloys, as well as in alloys used to make jet engines and the superconducting magnets of MRI scanners. Tantalum is used in aerospace applications like rocket nozzles and turbine blades, as well as medical devices and electronics.
Construction on the Brazil plant kicked off in 2024 and took about 18 months, but the company ran into some challenges that delayed official startup.
In January there was an issue with the plant’s refractory system, the equipment that insulates the reactor and prevents corrosion. That caused electrolyte to leak. Operators shut down the system and removed the metal, and there weren’t any injuries or environmental issues, Carneiro says.
But the leak did interfere with the timeline for the plant’s opening, which meant the company missed a milestone and lost out on funding that had been committed. It restructured and laid off 71 employees in April.
This new funding will help support the plant moving forward. “Because of this delay, we had a big stress in our cash flow, so the investors came very strong to support us,” Carneiro says. Boston Metal is repairing the facility in Brazil now, and it should be ready to start up in September 2026, he adds.
The funding will also help support other critical metals projects, Carneiro says. The company plans to eventually deploy a US plant to produce chromium, a metal the country imports nearly all its supply of today.
Boston Metal has now raised over $500 million in total. The latest round of funding includes support from existing investors and from the massive Indian steel company Tata Steel Unlimited.
Making a higher-value critical metal now could help Boston Metal prove its technology and pave the way for future steel projects, says Seaver Wang, director of climate and energy at the Breakthrough Institute. “Nobody wants to pay a green premium for steel—hence niobium,” he adds.
Factors associated with psychological distress among family caregivers of preschool children with autism: an analysis
ObjectiveTo identify factors associated with psychological distress among family caregivers of preschool-aged children with Autism Spectrum Disorder (ASD) using LASSO regression and random forest algorithms.MethodsA convenience sampling method was employed to recruit 213 caregivers of preschool-aged children with ASD from three institutions in Urumqi between December 2023 and October 2024. Participants completed a demographic questionnaire and the Symptom Checklist-90. Predictors were screened through LASSO regression, and a random forest risk assessment model was constructed and validated on the test set. A logistic regression model was simultaneously developed for comparative validation.ResultsThe top five factors associated with caregivers’ psychological distress are comorbid conditions in children with ASD, daily care hours, marital status, the severity of the child’s ASD, and employment status. The model outperformed logistic regression on both the training set (AUC = 0.845, sensitivity=0.893, specificity=0.913, accuracy=0.933, F1 score=0.901) and test set (AUC = 0.87, sensitivity=0.733, specificity=0.727, accuracy=0.710, F1 score=0.721). Decision curve analysis demonstrated clinical utility across threshold probability ranges of 0–0.85.ConclusionFactors associated with psychological distress among autism caregivers include comorbidity status, caregiving duration, marital status, disease severity, and employment status. These findings provide evidence-based guidance for early psychological intervention targeting high-risk caregivers.
Lifestyle, psychological and demographic predictors of anxiety: insights from a large-scale survey and machine learning analysis
ObjectiveAnxiety is influenced by a combination of lifestyle, psychological, and demographic factors. This study aimed to evaluate these associations and explore the potential of machine learning in predicting anxiety severity.MethodsAnxiety levels were evaluated using a large survey-based dataset of 11, 000 adults alongside demographic, physiological, and psychological measures. Descriptive statistics and inferential analyses were conducted in IBM SPSS to identify associations between key variables. Several machine learning regression algorithms, including linear, regularized, and ensemble models, were implemented in Python to predict anxiety levels. Model performance was evaluated using standard error metrics.ResultsOur findings revealed significant associations of anxiety with stress and sleep duration, while demographic attributes such as family history of anxiety and occupation also influenced outcomes. Ensemble machine learning algorithms achieved superior performance compared to single and linear-model approaches. Feature importance analysis identified stress, sleep, and caffeine intake as top predictors of anxiety.ConclusionsThe integration of statistical approaches with machine learning applications highlights the multifactorial nature of anxiety and demonstrates the potential of predictive modeling in mental health care. Future research should emphasize longitudinal designs and the incorporation of biological and digital markers to enhance clinical applicability and prediction.
Seasonal and gender-specific patterns in prescriptions for hypnotic and sedative medications in primary care
IntroductionPopulation-level prevalence of sleep disorders can be assessed using prescription data for hypnotic and sedative medications. Such prescribing patterns exhibit seasonality that may be linked to variations in daylight exposure. The aim of this study was to analyze temporal trends in prescriptions for drugs with sedative-hypnotic properties.MethodsPrescription data for hypnotics and sedatives were analyzed retrospectively and stratified by month, year, and patient gender. Seasonal patterns, associations with day length, and the effects of transitions between daylight saving time and standard time were examined. Changes in prescription numbers during the COVID-19 pandemic were also assessed. Relative differences in prescription counts were evaluated using incidence rate ratios (IRR).ResultsPrescription numbers were lowest in summer (May–August) and highest in winter and early spring. Increasing day length was significantly associated with reduced prescription rates. A decline in prescriptions occurred earlier and was more pronounced in men (February–September; IRR 0.88–0.95), whereas in women the changes were weaker and mainly limited to summer months (June–August; IRR 0.94–0.97), with a slight increase observed in February. During the COVID-19 pandemic, prescription numbers decreased significantly. Transitions between standard and daylight saving time exerted measurable short-term effects on sleep-related health at the population level.ConclusionsBased on data from a single primary care center in Poland, prescribing patterns for hypnotic and sedative medications demonstrate clear seasonality and significant gender differences. Longer daylight exposure and transitions to daylight saving time are associated with lower prescription rates. The COVID-19 pandemic substantially disrupted previous trends in sleep medication prescribing, which may be related to reduced access to healthcare services and changes in healthcare delivery. In addition, transitions between standard and daylight saving time were associated with statistically significant short-term changes in prescription rates.
Exploring the life stories of young adult men in prison with a history of dual harm
People who engage in both self-harm and violence (‘dual harm’) in prison cause widespread disruption to prison services. Whilst the behavioural profile of such individuals is gaining attention, there is very little research which explores their life histories and how these contextualise their dual harm. This study qualitatively explored how five young men in a medium secure prison in England with a history of dual harm (in the community, prison, or both) made sense of their life experiences and engagement in dual harm behaviours. Participants were interviewed using an in-depth life story interview protocol. A narrative analysis identified three themes: ‘Beginning: Making sense of a traumatic childhood’, ‘Middle: Exploring challenges during late adolescence’ and ‘End: Who I am now, and who I must be’. These themes, grounded in life experiences and associated meaning, offer valuable insights into the underpinnings of dual harm behaviours and direction for interventions addressing dual harm in prisons. The paper concludes with a discussion of key implications and directions for future research and practice in relation to the support and management of people who dual harm in custody.
HPV vaccines, 20 years on
Nature Medicine, Published online: 20 May 2026; doi:10.1038/s41591-026-04374-x
Women are now at the very center of the global cancer control agenda, but there are major challenges ahead.
<![CDATA[Learn how to spot bipolar mixed features, avoid antidepressant pitfalls, and choose evidence-based antipsychotics.]]>
Aspects of Quality of Life in Interstitial Lung Disease: Pilot Observational Cross-Sectional Study in a Single Center
Background: Quality of life (QOL) is an important aspect of every chronic disease, including interstitial lung disease (ILD). QOL is perceived as a significant patient-centered outcome. Objective: This study aims to identify factors correlating with different aspects of QOL in patients with various ILDs. Methods: We recruited 57 participants hospitalized in a tertiary care clinical center to this pilot observational cross-sectional study. These included 22 patients with idiopathic interstitial pneumonia (IIP), 19 patients with connective tissue disease–associated ILD (CTD-ILD), and 16 patients with interstitial pneumonia with autoimmune features (IPAF). The Saint George’s Respiratory Questionnaire (SGRQ) and World Health Organization Quality of Life Questionnaire (WHOQOL-BREF) were used to assess QOL, and the Hospital Anxiety and Depression Scale – Modified Version (HADS-M) and Patient Health Questionnaire – 9 (PHQ-9) were used to evaluate depression severity. Functional parameters including forced vital capacity (FVC), forced expiratory volume in 1 second (FEV), transfer lung capacity for carbon monoxide (TLCO), and 6-minute walk distance (6MWD) were assessed. Assessment of QOL was a secondary outcome measure in a multicenter prospective study aimed at determining the characteristics of Polish patients with interstitial pneumonia with autoimmune features. Results: In each study group, positive correlations existed between the WHOQOL-BREF physical domain score and FEV % predicted value (=.001) and TLCO % predicted value (=.03). Regardless of diagnosis, higher depression, anxiety, and aggression scores (ie, worse mental health) correlated negatively with multiple domains of QOL measured using the WHOQOL-BREF. Predictors of QOL aspects varied in each study group. In the IPAF group, the TLCO % predicted value was a predictor of QOL expressed as the SGRQ total score (=.005). In the CTD-ILD group, short 6MWD (<.001) and high HADS-M aggression score (=.01) correlated with low QOL (expressed as a high SGRQ total score). In the IIP group, 6MWD (=.002) and PHQ-9 scores (<.001) were predictors for SGRQ symptoms score. Gender-based differences were revealed: In all study groups, men had higher scores in the psychological, social, and environmental domains of the WHOQOL-BREF, indicating better QOL, without a statistically significant difference in the physical domain scores between genders. Diagnosis-based differences in the psychological aspects of QOL were also revealed: The QOL psychological domain scores were significantly lower in the CTD-ILD and IPAF groups than in the IIP group, indicating worse QOL (=.01). Conclusions: QOL is a multifaceted issue with various factors impacting its assessment. 6MWD, TLCO predicted value, and worse functional ability might specifically impact QoL in ILD. Mental health is an important aspect of QOL in the ILD population, as patients with a chronic, potentially life-limiting disease may be more prone to developing depression or anxiety. Assessment of QOL should be taken into account in clinical decision-making and research on chronic diseases, as this patient-related outcome may impact therapeutic decisions and patient compliance. Trial Registration: ClinicalTrials.gov NCT03870828; https://clinicaltrials.gov/study/NCT03870828
Multigesture Electromyographic Control Complexity in Upper Limb Prostheses Actuated via Single Sensor Input Contraction Magnitude: Qualitative Study for Evaluating Performance and Cognitive Load
<strong>Background:</strong> Lack of functionality is one factor that contributes to prosthetic rejection rates. Electromyographic upper limb prostheses are controlled through muscle contractions in the user’s residual limb. The incorporation of multigesture controls into a novel, in-house developed upper limb prosthesis requires users to differentiate between the strengths of muscle contractions to trigger programmed gestures. Little research exists on the limitations of expanding device capabilities. This expansion may lead to a decline in accuracy and perceived usability or an increase in training time and cognitive workload. <strong>Objective:</strong> This study aimed to determine the feasibility of implementing multiple gestures when learning electromyographic controls during a single training session. <strong>Methods:</strong> Participants with full upper extremity control were fitted with a Flex Controller, a surface electromyography device that measures muscle contraction. Contractions were visualized as peaks and calibrated through an adjustable scale on a tablet. A training app was developed in-house to test novice users on an electromyography control system. Users interacted with 1, 3, or 5 zones on the screen. Each horizontal zone represented a threshold required to trigger a distinct gesture on the prosthesis. The cohorts were labeled A1 (n=9), A2 (n=10), A3 (n=9), and B1 (n=26). Every participant completed 3 trials per arm, and each trial consisted of 15 randomized cues. Each cue was represented by a green color change, with 1 point earned after a successful peak. Collected outcomes included performance, the System Usability Scale, and the National Aeronautics and Space Administration Task Load Index. <strong>Results:</strong> Scores decreased significantly as zones increased (Kruskal-Wallis H<sub>3</sub>=24.9, <i>P</i><.001). The mean scores were 15.0 (SD 0.0) for 1 zone, 9.1 (SD 1.1) for 3 zones, and 5.5 (SD 1.1) for 5 zones. Perceived usability, measured by System Usability Scale, showed modest omnibus difference across cohorts (Kruskal-Wallis H<sub>3</sub>=5.22, <i>P</i>=.16); however, a pairwise comparison showed the 5-gesture cohort rated usability lower than the progressive cohort (2-tailed Welch t<sub>11</sub>=–2.19, <i>P</i>=.05). The 5-gesture cohort rated the system lowest (mean 63.3, SD 16.2). Cognitive workload, assessed through the National Aeronautics and Space Administration Task Load Index, increased with the number of gestures. The performance subscale showed a significant omnibus difference across cohorts (Kruskal-Wallis H<sub>3</sub>=21.4, <i>P</i><.001). Mean performance subscale scores were 84.4 (SD 14.7) for the single-gesture condition, 30.6 (SD 21.7) for the 5-gesture condition, and 44.2 (SD 21.2) for the progressive cohort, reflecting increasing perceived difficulty with more gestures. The sample size for quantitative analysis was 54. <strong>Conclusions:</strong> These findings support the implementation of progressive training for 3 gestures. Usability perceptions were the highest among the more complicated progressive cohort, which is likely related to perceived improvement. Progressively learning 3 gestures enables a balance between device capability, user intention, perceived usability, and cognitive workload.
