Finding a few tumor cells hidden among billions of blood cells is one of the biggest challenges in liquid biopsy. Circulating tumor cells (CTCs), which break away from primary tumors and enter the bloodstream, can provide important clues about cancer progression and treatment response. However, their extremely low abundance—often only one to ten cells among a billion blood cells—makes them difficult to isolate and accurately identify.

In a new study published in Biomedical Analysis, researchers from Taiyuan University of Technology and Shanxi Bethune Hospital developed a new strategy that combines a microfluidic chip with artificial intelligence to address this challenge. The platform first separates tumor cells from surrounding blood cells according to their physical differences, then uses deep learning to identify the enriched cells from ordinary microscope images. This approach provides a new route toward simpler, less label-dependent CTC analysis.

Despite cancer rates similar to Western Europe, patients in the Balkans are far more likely to die from the disease. Experts point to delayed detection, weak primary care, and limited access to advanced diagnostics as the principal culprits.

Across the Balkan Peninsula, health systems face a stark contradiction: while the incidence of solid tumours broadly mirrors that of Western Europe, mortality remains stubbornly elevated. Lung, colorectal, and prostate cancers are the leading killers among men, while breast, lung, colorectal, and cervical cancers take the heaviest toll on women.

New treatments – including experimental cancer vaccines from Moderna show promise in late-stage trials, but their cost puts them out of reach for most patients in the region. According to Dr Marchela Koleva, founder and Head of the Department of Medical Oncology at St. Sophia Hospital the core problem is not a lack of therapies but a chronic failure to diagnose early enough.

Satellites that navigate by talking to each other slowly lose their sense of direction, because nothing in a purely Earth-orbiting network provides a fixed reference in space.

With the continuous advancement of spaceborne synthetic aperture radar (SAR) technology, L-band SAR has come to play an increasingly important role in Earth observation; however, its imaging, interferometric, and polarimetric performance are susceptible to ionospheric disturbances. In equatorial nighttime data, amplitude streak artifacts induced by equatorial plasma bubbles frequently occur and have traditionally been regarded as interference factors in SAR imaging. Nevertheless, these artifacts essentially carry critical information on ionospheric irregularities and can be exploited to retrieve physical parameters such as scintillation intensity and turbulence spectral indices. Existing methods either rely on comparisons between disturbed and undisturbed images or are limited to preliminary one-dimensional parameter estimation along the track, making it difficult to achieve two-dimensional fine-scale retrieval of scintillation structures, and lacking systematic validation of their spatial correlation with equatorial ionospheric anomalies. Therefore, how to extract the two-dimensional spatial distribution of ionospheric scintillation parameters from amplitude streak artifacts and reveal their intrinsic relationship with regions of enhanced electron density has become an important scientific question in the field of ionospheric remote sensing.

Researchers report that intestinal neutral ceramidase promotes MASH by reshaping gut microbes and increasing 2-hydroxyhippuric acid, a metabolite that suppresses epithelial AhR signalling. In mouse models, deleting the enzyme in intestinal epithelial cells restored fucosylation, strengthened the gut barrier, reduced endotoxin translocation, and attenuated liver inflammation and fibrosis. The findings identify a microbiota–2-HHA–AhR–fucosylation axis that could guide barrier-restoring, microbiome-based therapies for MASH and related systemic inflammatory complications in future clinical translation and drug development programs.

With the advancement of deep-space missions such as crewed lunar exploration and Mars exploration, artificial intelligence (AI) technologies are being increasingly embedded into aerospace engineering management processes, and managers' AI competency has become a critical factor affecting mission delivery performance and safety compliance. However, traditional project management competency models have been primarily constructed around process control and redundancy design within deterministic systems, failing to explicitly address governance challenges introduced by AI integration—including black-box opacity, accountability allocation, traceable review of AI outputs, and accountability mechanisms for assisted decision-making. Although existing NASA and ESA frameworks provide fundamental support for aerospace project delivery, AI-related competencies in aerospace management exhibit inherent interdependencies. Managers are required to continuously balance delivery schedules and cost pressures against mission assurance obligations such as verification and validation, configuration control, and compliance, making it impossible to simply represent AI competency as a linear list of attributes. Therefore, how to construct an AI competency assessment framework that captures governance priorities and multi-attribute interdependencies has become a critical issue urgently to be addressed in aerospace engineering management in the AI era.