Researchers at the University of Turku, Finland, have succeeded in producing sensors from single-wall carbon nanotubes that could enable major advances in health care, such as continuous health monitoring. Single-wall carbon nanotubes are nanomaterial consisting of a single atomic layer of graphene.

A long-standing challenge in developing the material has been that the nanotube manufacturing process produces a mix of conductive and semi-conductive nanotubes which differ in their chirality, i.e., in the way the graphene sheet is rolled to form the cylindrical structure of the nanotube. The electrical and chemical properties of nanotubes are largely dependent on their chirality.

Han Li, Collegium Researcher in materials engineering at the University of Turku, has developed methods to separate nanotubes with different chirality. In the current study, published in Physical Chemistry Chemical Physics, the researchers succeeded in distinguishing between two carbon nanotubes with very similar chirality and identifying their typical electrochemical properties.

Harmful microorganisms such as bacteria represent one of the largest threats to human health. Efficient sterilization methods are thus a necessity.

In the journal Angewandte Chemie, a research team has now introduced a novel, sustainable, electrocatalytic sterilization method based on electrodes covered with copper oxide nanowires. These generate very strong local electric fields, thereby producing highly alkaline microenvironments that efficiently kill bacteria.

Conventional disinfection methods, such as chlorination, treatment with ozone, hydrogen peroxide oxidation, and irradiation with ultraviolet light have disadvantages, including harmful by-products and high energy consumption.

Heman Bekele has just been named Time’s 2024 Kid of the Year.

S 15, is already spending part of every weekday working in a lab at the Johns Hopkins Bloomberg School of Public Health in Baltimore, hoping to bring his dream to fruition. ‘.

Last year NPR interviewed Heman Bekele about his invention of a soap to fight skin cancer. He was motivated by his childhood in Ethiopia: He saw people working in the sun and thought of health risks.

Skin_diseases are a major global health concern, encompassing a wide range of conditions with varying severity. Prompt and precise diagnosis is critical for effective treatment. However, traditional methods often rely on dermatologists, creating disparities in access to care. This study creates and assesses a highly accurate Convolutional Neural Network (CNN) model that can use digital photos of skin lesions to diagnose a variety of skin conditions, and looks into how well various CNN architectures and pre-trained models may increase the precision and effectiveness of diagnosing skin conditions.

Abstract

Skin conditions are a worldwide health issue that requires prompt and accurate diagnosis in order to be effectively treated. This study presents a Convolutional Neural Network (CNN)-based automated skin disease diagnostic method. The work uses preprocessing methods like scaling, normalization, and augmentation to improve model robustness using the DermNet dataset, which consists of 19,500 pictures from 23 disease categories. TensorFlow and Keras were used to create a unique CNN architecture, which produced an impressive accuracy of 94.65%. Metrics like precision, recall, and F1-score were used to validate the model’s performance, showing that it outperformed more conventional machine learning techniques like SVM and KNN. The system incorporates patient-reported symptoms in addition to diagnosis to provide a comprehensive approach to health support, allowing for remote accessibility and tailored therapy suggestions. This work recognizes issues like dataset variability and processing needs while showcasing the revolutionary potential of AI in dermatology. In order to improve model interpretability and clinical integration, future possibilities include dataset extension, real-world validation, and the use of explainable AI.

Skin Disease Diagnosis, Dermatological Image Analysis, Medical Image Classification, Convolutional Neural Networks (CNNs), Healthcare Accessibility, Deep Learning Applications, DermNet Dataset

The Automated Intimate Partner Violence Risk Support System (AIRS) utilizes clinical history and radiologic data to pinpoint patients seen in the emergency room who may be at a risk for intimate partner violence (IPV). Developed over the past five years, AIRS has been rolled out to the Brigham and Women’s Hospital’s Emergency Rooms in Boston as well as surrounding primary care sites. Currently, the tool has been validated at the University of California-San Francisco Medical Center and is being evaluated by the Alameda Health System for its role in clinical workflow.

“Data labeling quality is a huge concern—not just with intimate partner violence care, but in machine learning for healthcare and machine learning, broadly speaking,” says cofounder Irene Chen. “Our hope is that with training, clinicians can be taught how to spot intimate partner violence—we are hoping to find a set of cleaner labels.”

Mirror life, a concept involving synthetic organisms with reversed molecular structures, carries significant risks despite its potential for medical advancements.

Experts warn that mirror bacteria could escape natural biological controls, potentially evolving to exploit resources in ways that disrupt ecosystems and pose unforeseen dangers to the environment and public health.

Mirror Life

Tires and degrading garbage shed tiny pieces of plastic into the air, creating a form of air pollution that UC San Francisco researchers suspect may be causing respiratory and other illnesses.

A review of some 3,000 studies implicates these particles in a variety of serious health problems. These include male and female infertility, colon cancer and poor lung function. The particles also may contribute to chronic pulmonary inflammation, which can increase the risk of lung cancer.

“These microplastics are basically particulate matter air pollution, and we know this type of air pollution is harmful,” said Tracey J. Woodruff, Ph.D., MPH, a professor of obstetrics, gynecology and reproductive sciences at UCSF.

Over the past two years, the U.S. Centers for Disease Control and Prevention (CDC) has issued Travel Health Advisories focused on measles outbreaks.

These advisories highlight where there is an active health risk when people visit the highlighted countries.

On February 21, 2025, the CDC reissued a Level 1, Practice Usual Precautions, alert for 57 countries. This CDC list does not integrate the Region of the Americas, with numerous countries reporting 537 measles outbreaks this year.

A research team has identified different subtypes of white matter (WM) astrocytes, including a unique type with the ability to multiply and potentially aid in brain repair. Using single-cell RNA sequencing and spatial transcriptomics, the scientists mapped astrocyte diversity across different brain regions and species, providing the first detailed molecular profile of WM astrocytes.

The team was led by Dr. Judith Fischer-Sternjak from Helmholtz Munich and Ludwig-Maximilians-Universität (LMU) München, alongside Prof. Magdalena Götz from Helmholtz Munich, LMU and the Munich Cluster for Systems Neurology (SyNergy). The research is published in the journal Nature Neuroscience.

Unveiling white matter astrocyte diversity Astrocytes, known for their crucial role in supporting neurons and maintaining brain health, have been predominantly studied in gray matter (GM), which is involved in information processing. However, white matter astrocytes, which support long-range neural connections, remain poorly understood. This study fills a major knowledge gap by showing that WM astrocytes are not a uniform population but consist of distinct subtypes with specialized roles.

Scientists still don’t know how microplastics impact human health, but they do have ideas for stopping their spread.