Health
PUPS – the AI tool that can predict where exactly proteins are in human cells
Dubbed, the Prediction of Unseen Proteins’ Subcellular Localization (or PUPS), the AI tool can account for the effects of protein mutations and cellular stress—key factors in disease progression.

Researchers from MIT, Harvard University, and the Broad Institute have unveiled a groundbreaking artificial intelligence tool that can accurately predict where proteins are located within any human cell, even if both the protein and cell line have never been studied before. The method – Prediction of Unseen Proteins’ Subcellular Localization (or PUPS) – marks a major advancement in biological research and could significantly streamline disease diagnosis and drug discovery.
Protein localization—the precise location of a protein within a cell—is key to understanding its function. Misplaced proteins are known to contribute to diseases like Alzheimer’s, cystic fibrosis, and cancer. However, identifying protein locations manually is expensive and slow, particularly given the vast number of proteins in a single cell.
The new technique leverages a protein language model and a sophisticated computer vision system. It produces a detailed image that highlights where the protein is likely to be located at the single-cell level, offering far more precise insights than many existing models, which average results across all cells of a given type.
“You could do these protein-localization experiments on a computer without having to touch any lab bench, hopefully saving yourself months of effort. While you would still need to verify the prediction, this technique could act like an initial screening of what to test for experimentally,” said Yitong Tseo, a graduate student in MIT’s Computational and Systems Biology program and co-lead author of the study, in a media statement.
Tseo’s co-lead author, Xinyi Zhang, emphasized the model’s ability to generalize: “Most other methods usually require you to have a stain of the protein first, so you’ve already seen it in your training data. Our approach is unique in that it can generalize across proteins and cell lines at the same time,” she said in a media statement.
PUPS was validated through laboratory experiments and shown to outperform baseline AI methods in predicting protein locations with greater accuracy. The tool is also capable of accounting for the effects of protein mutations and cellular stress—key factors in disease progression.
Published in Nature Methods, the research was led by senior authors Fei Chen of Harvard and the Broad Institute, and Caroline Uhler, the Andrew and Erna Viterbi Professor at MIT. Future goals include enabling PUPS to analyze protein interactions and make predictions in live human tissue rather than cultured cells.
Health
How a South African Hospital Team Pioneered the World’s First AI-Powered Cancer Treatment Revolution
Digital Healing: How Bloemfontein Became Ground Zero for the AI Cancer Treatment Revolution

The University of the Free State (UFS), South Africa, and Universitas Academic Hospital have achieved a global healthcare milestone by becoming the first clinical site worldwide to successfully integrate artificial intelligence into cancer treatment planning, marking a transformative advancement in oncology care, according to a statement issued by UFS.
AI implementation
The Departments of Medical Physics and Oncology at UFS, in partnership with Universitas Academic Hospital, have implemented the Radiation Planning Assistant (RPA), a sophisticated web-based AI platform developed by MD Anderson Cancer Center in Houston, Texas. This pioneering initiative has already treated nearly 50 patients, positioning the Bloemfontein-based teams as global leaders in the clinical application of AI in radiotherapy.
Under the leadership of Dr. William Shaw, Senior Lecturer and Deputy Manager in the Department of Medical Physics, the institution has built a robust academic partnership with Professor Laurence Court and his team at MD Anderson Cancer Center—a collaboration that is now yielding remarkable real-world results.
“The introduction and clinical integration of the RPA at the UFS and Universitas Hospital represents a major advancement for oncology services—both regionally and nationally,” Dr. Shaw explained. “It signifies the transition from research collaboration to real-world application, where artificial intelligence is being used to improve access to safe, high-quality cancer care.”
Revolutionizing treatment planning
The RPA technology addresses one of the most time-consuming aspects of cancer care: creating patient-specific radiation treatment plans. The cloud-based platform automates critical components of the treatment planning process, enabling consistent production of high-quality radiotherapy plans while reducing demands on specialized clinical staff.
Dr. Shaw described the streamlined process: “The process begins with the acquisition of a planning CT scan, which serves as the sole imaging input to the RPA. Once the CT dataset has been captured, it is uploaded to the RPA platform via a secure web interface.”
The system uses advanced machine learning algorithms to automatically identify and delineate both tumour volumes and critical normal tissues. Following the completion of the contouring process, the platform automatically generates a comprehensive radiotherapy treatment plan.
Expanding treatment applications
Initially implemented for cervix cancer treatment—representing the largest proportion of radiotherapy patients at the institution—the RPA has since expanded to encompass breast cancer, head and neck cancers, and primary brain tumors. With ongoing institutional support, the system shows significant promise for broader application across nearly all major tumor types treated with external beam radiotherapy.
Professor Vasu Reddy, Deputy Vice-Chancellor for Research and Internationalisation at UFS, praised the achievement: “We extend our congratulations to our colleagues for their exemplary collaborative achievements. Your pioneering work represents the transformative power of multidisciplinary research in advancing medical science and improving patient outcomes.”
Immediate patient benefits
The technology delivers immediate, meaningful improvements for cancer patients by enabling faster access to well-constructed, evidence-based treatment plans reviewed and refined by experts. This translates to more timely care, fewer unplanned treatment interruptions, and improved protection of normal tissues, resulting in fewer side effects and better overall outcomes.
“Our aim is to use artificial intelligence not as a shortcut, but as a tool to standardize, scale, and improve cancer care in places where the need is greatest,” Dr. Shaw emphasized. “The RPA enhances the quality, consistency, and timeliness of cancer treatment in radiotherapy settings—particularly in environments where clinical capacity is limited.”
International expansion
The success in Bloemfontein serves as a model for broader health system innovation, providing a foundation for the safe, phased rollout of similar systems in other provinces. Professor Court has already extended access to the RPA to other radiotherapy centers in South Africa, with expansion to additional countries planned for the near future.
The Department of Oncology, led by Professor Alicia Sherriff, has joined the initiative as an active clinical partner, establishing a multi-disciplinary collaboration that lays the foundation for further research and innovation at the intersection of medical physics, oncology, and data science.
Advanced treatment techniques
Beyond external beam radiotherapy, the UFS and Universitas teams are advancing the use of interstitial brachytherapy for cervix cancer. While not the first globally to implement this specialized technique, the Bloemfontein team ranks among the earliest adopters on the African continent, helping expand access to this advanced modality where it’s most needed.
Future vision
This work received support from the Nuclear Technologies in Medicine and the Biosciences Initiative (NTeMBI), a national technology platform developed and managed by the South African Nuclear Energy Corporation (Necsa) and funded by the Technology Innovation Agency (TIA).
Dr. Shaw’s team has played a central role in developing safe, reliable clinical processes to integrate AI tools like the RPA into daily practice, ensuring that automation enhances rather than replaces professional expertise.
Professor Reddy outlined the broader vision, “The future we are heading towards is one where human innovation and digital technologies work together to elevate the standard of care, rather than replace humanity in medicine. It is encouraging to see how our colleagues are internationalizing our footprint, together with machine precision to enhance detection, personalize treatment and, perhaps importantly, empowering clinicians with data-driven insights for patient care.”
This innovation represents a significant step forward for cancer care in South Africa and demonstrates how international partnerships can bring cutting-edge technologies to healthcare frontlines, making them work effectively in real clinics for real patients. As cancer incidence rises across low- and middle-income countries, the leadership shown by the UFS and Universitas teams offers a compelling model for how academic medical centers can respond with agility, scientific rigor, and global solidarity.
Edited by Chris Jose
Health
Researchers Develop Low-Cost Sensor for Real-Time Detection of Toxic Sulfur Dioxide Gas
Sulfur dioxide, a toxic air pollutant primarily released from vehicle exhaust and industrial processes, is notorious for triggering respiratory irritation, asthma attacks, and long-term lung damage.

In a significant breakthrough for environmental monitoring and public health, scientists from the Centre for Nano and Soft Matter Sciences (CeNS), Bengaluru, India, have developed an affordable and highly sensitive sensor capable of detecting sulfur dioxide (SO₂) gas at extremely low concentrations.
Sulfur dioxide, a toxic air pollutant primarily released from vehicle exhaust and industrial processes, is notorious for triggering respiratory irritation, asthma attacks, and long-term lung damage. Monitoring its presence in real time is essential, but existing technologies are often expensive, power-hungry, or ineffective at detecting the gas at trace levels.
To address this gap, the CeNS team, under the leadership of Dr. S. Angappane, has engineered a novel sensor by combining two metal oxides — nickel oxide (NiO) and neodymium nickelate (NdNiO₃). NiO serves as the receptor that captures SO₂ molecules, while NdNiO₃ acts as a transducer that converts the chemical interaction into an electrical signal. This innovative design enables the sensor to detect SO₂ at concentrations as low as 320 parts per billion (ppb), outperforming many commercial alternatives.
Speaking about the development, Dr. Angappane said in a media statement, “This sensor system not only advances the sensitivity benchmark but also brings real-time gas monitoring within reach for a wider range of users. It demonstrates how smart materials can provide practical solutions for real-world environmental challenges.”

The CeNS team has also built a portable prototype incorporating the sensor. It features a user-friendly threshold-triggered alert system with color-coded indicators: green for safe levels, yellow for warning, and red for danger. This visual approach ensures that even non-specialist users can understand and respond to pollution risks instantly. Its compact size and lightweight design make it ideal for deployment in industrial zones, urban neighborhoods, and enclosed environments requiring continuous air quality surveillance.
The sensor system was conceptualized and designed by Mr. Vishnu G Nath, with key contributions from Dr. Shalini Tomar, Mr. Nikhil N. Rao, Dr. Muhammed Safeer Naduvil Kovilakath, Dr. Neena S. John, Dr. Satadeep Bhattacharjee, and Prof. Seung-Cheol Lee. The research findings were recently published in the journal Small.
With this innovation, CeNS reinforces the role of advanced materials science in developing cost-effective technologies that protect both public health and the environment.
Health
Researchers Unveil 50-Cent DNA Sensors That Could Revolutionize Disease Diagnosis
The innovation lies in a low-cost electrochemical sensor stabilized with a polymer coating, which allows the device to be stored for months at high temperatures and used far from traditional lab settings

In a breakthrough that could make life-saving diagnostics accessible to millions, MIT researchers have developed a disposable, DNA-coated sensor capable of detecting diseases like cancer, HIV, and influenza — all for just 50 cents. The innovation lies in a low-cost electrochemical sensor stabilized with a polymer coating, which allows the device to be stored for months at high temperatures and used far from traditional lab settings.
At the heart of this sensor is a CRISPR-based enzyme system. When the sensor detects a target disease gene, the enzyme — acting like a molecular lawnmower — begins to shred DNA on the electrode, disrupting the electric signal and indicating a positive result.
“Our focus is on diagnostics that many people have limited access to, and our goal is to create a point-of-use sensor,” said Ariel Furst, MIT chemical engineering professor and senior author of the study, in a media statement. “People wouldn’t even need to be in a clinic to use it. You could do it at home.”
Previously, such sensors faced a major hurdle: the DNA coating degraded rapidly, requiring immediate use and refrigerated storage. Furst’s team overcame this by using polyvinyl alcohol (PVA) — a cheap and widely available polymer — to form a protective film over the DNA, significantly extending shelf life.
The sensors were tested to successfully detect PCA3, a prostate cancer biomarker found in urine, even after two months of storage at 150°F. The technology builds on Furst’s earlier work that enabled detection of HIV and HPV genetic material using similar CRISPR-based methods.
“This is the same core technology used in glucose meters, but adapted with programmable DNA,” said lead author Xingcheng Zhou, an MIT graduate student. “It’s inexpensive, portable, and extremely versatile.”
The team now aims to expand testing for other infectious and emerging diseases. They’ve been accepted into MIT’s delta v venture accelerator, signaling commercial interest and real-world application potential. The ability to ship sensors without refrigeration could be transformative for low-resource and remote settings.
“Our limitation before was that we had to make the sensors on site,” added Furst. “Now that we can protect them, we can ship them. That allows us to access a lot more rugged or non-ideal environments for testing.”
With further development, these pocket-sized DNA sensors could redefine early disease detection — from rural clinics to living rooms.
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