Diagnosis of human visceral leishmaniasis by PCR using blood samples spotted on filter paper.

Deep vein thrombosis (DVT) is characterized by the formation of blood clots in the deep veins, most commonly in the lower limbs, obstructing blood flow. In about 50% of cases, the clot dislodges and travels to the lungs, causing potentially fatal pulmonary embolism. Diagnosing DVT is challenging, as up to half of cases are asymptomatic. Early and accurate diagnosis is critical, but the current state of the art in ultrasound and plethysmography is insufficient for continuous, operator-independent, point-of-care monitoring. This paper introduces the concept and advances of the ThrombUS+ Horizon Europe project, which aims to innovate toward modular and wearable system for continuous, point-of-care DVT monitoring. The system leverages a multimodal approach, incorporating compression ultrasonography, electrical impedance plethysmography, and light reflection rheography, by re-engineering these clinical technologies for seamless integration into a wearable architecture. Manual compression of an ultrasound transducer is replaced by a sophisticated actuator, enabling precise pressure control via a piezoelectric micropump with feedback signals. The same actuator supports plethysmography modules, providing additional data for DVT assessment. These inputs, combined with physical movement monitoring, are processed by an artificial intelligence-driven central intelligence unit for DVT detection, risk assessment, and prevention. Preliminary laboratory testing of the individual modules highlights the system's potential for autonomous DVT monitoring and detection. The ThrombUS+ uses big datasets for machine learning training collected in the project via 3 large-scale clinical studies. The entire system will be validated in the clinical setting via an early feasibility study and a multicenter clinical trial.