Medical Bulletin 16/October/2025

Tuberculosis remains one of the world’s deadliest infectious diseases, with over 10 million people infected each year and 1.25 million deaths globally. While steroids like dexamethasone are already used in some TB cases, especially in TB meningitis, their effects on the immune system have not been fully understood. With the renewed focus on dexamethasone during the COVID-19 pandemic, the research team aimed to explore how the drug impacts the innate immune response, particularly macrophages, which are key to fighting TB in the lungs.

To investigate, scientists treated and infected macrophages, immune cells derived from healthy blood donors and lung fluid with Mycobacterium tuberculosis (Mtb), the bacteria responsible for TB. They then examined how dexamethasone affected the cells. The results revealed that dexamethasone reduces glycolysis in macrophages, decreasing cellular energy use, and dampens the production of inflammatory cytokines such as IL-1β, TNF, IL-6, IL-8, and IL-10. While these cytokines aid immunity, reducing them can prevent tissue damage from excessive inflammation.

Crucially, the study found that dexamethasone-treated, Mtb-infected macrophages survived better and had a lower bacterial burden. The steroid’s effect appeared to be linked to enhanced autophagy and phagosomal acidification, mechanisms that help cells degrade and clear bacteria.

The research suggests that steroids, when used alongside antibiotics, could speed recovery in TB patients and potentially prevent progression from latent to active disease.

Reference: Lorraine Thong et al, Dexamethasone inhibits Mycobacterium tuberculosis-induced glycolysis but preserves antimicrobial function in primary human macrophages, Scientific Reports (2025). DOI: 10.1038/s41598-025-20188-2

AI Predicts Sepsis Risk in Children Within Hours: JAMA Pediatrics Study

A study published in JAMA Pediatrics has revealed that artificial intelligence (AI) models can accurately predict the risk of sepsis in children within 48 hours of arrival at the emergency department. The multi-center research marks the first use of AI to predict pediatric sepsis based on the new Phoenix Sepsis Criteria.

Sepsis, a life-threatening condition where infection leads to organ dysfunction, is a leading cause of childhood mortality worldwide. Because symptoms often escalate rapidly, early identification and treatment are crucial. In this new study, researchers developed and validated AI models that analyze electronic health record (EHR) data collected within the first four hours of a child’s emergency department (ED) visit, before any signs of organ dysfunction are apparent.

The study drew on data from five major health systems participating in the Pediatric Emergency Care Applied Research Network (PECARN), giving researchers access to a large and diverse pediatric population. Importantly, the study excluded children who already showed signs of sepsis upon arrival, focusing solely on predicting future cases. This approach is designed to enable early, targeted therapies that have been proven to save lives.

“The predictive models we developed are a huge step toward precision medicine for sepsis in children,” Dr. Alpern, lead author and Division Head of Emergency Medicine at Lurie Children’s, as well as Professor of Pediatrics at Northwestern University Feinberg School of Medicine. “These models showed robust balance in identifying children in the ED who will later develop sepsis, without overidentifying those who are not at risk.”

Researchers emphasized the importance of avoiding unnecessary aggressive treatment in low-risk children, while enabling faster care for those at true risk. “We evaluated our models to ensure that there were no biases,” said Dr. Alpern. “Future research will need to combine EHR-based AI models with clinician judgment to make even better predictions.”

Reference: Alpern ER, Scott HF, Balamuth F, et al. Derivation and Validation of Predictive Models for Early Pediatric Sepsis. JAMA Pediatr. Published online October 13, 2025. doi:10.1001/jamapediatrics.2025.3892

Eco-Friendly Silicon Ultrasound Patch Outperforms Traditional Devices: Study

In a new development for medical imaging, a team of researchers has created a high-performance, lead-free ultrasound patch using silicon nanostructures, offering a safer and more sustainable alternative to traditional devices. The findings, published in nature communications, highlight a revolutionary silicon-based wearable ultrasound patch that outperforms conventional lead (Pb)-based transducers in both output and image quality, without the environmental or health risks associated with lead.

Wearable ultrasound devices are increasingly used in hospital diagnostics, rehabilitation, and telemedicine. However, most commercial transducers rely on toxic lead-based ceramics to generate and receive sound waves. This raises significant safety and environmental concerns, especially for prolonged or widespread use. To overcome these limitations, the research team engineered a silicon-based disposable ultrasound patch with superior capabilities.

The team used semiconductor processing technology to create an ultrathin, flexible patch by forming silicon into a nanocolumn structure just a few hundred micrometers thick. By eliminating the traditionally required matching and backing layers, they simplified the structure while enhancing performance. This novel design maintains stable function under movement and conforms easily to the skin.

Performance tests showed that the silicon-based patch delivered more than 30% higher output pressure than commercial lead-based transducers, resulting in sharper imaging. It accurately measured blood flow velocity and vessel diameter, even in dynamic areas like the neck, and achieved over 96% accuracy when compared with clinical blood pressure monitors.

Because it uses silicon and standard semiconductor fabrication methods, the device can be mass-produced at a cost approximately 1/20th that of conventional ultrasound units. This makes it not only safer but also significantly more affordable and environmentally sustainable.

Reference: Kang, DH., Cho, S., Kim, H.Y. et al. Silicon nanocolumn-based disposable and flexible ultrasound patches. Nat Commun 16, 6609 (2025). https://doi.org/10.1038/s41467-025-61903-x