Synthesis and characterisation of ultrasound imageable heat-sensitive liposomes for HIFU therapy.

BACKGROUND/OBJECTIVE: Novel approaches allowing efficient, readily translatable image-guided drug delivery (IGDD) against solid tumours is needed. The objectives of this study were to: 1) develop echogenic low temperature sensitive liposomes (E-LTSLs) loaded with an ultrasound (US) contrast agent (perfluoropentane, PFP), 2) determine the in vitro and in vivo stability of contrast agent encapsulation, 3) co-encapsulate and characterise doxorubicin (Dox) E-LTSL, and cellular uptake and cytotoxicity in combination with high intensity focused ultrasound (HIFU). METHOD: E-LTSLs were loaded passively with PFP and actively with Dox. PFP encapsulation in E-LTSL was determined by transmission electron microscopy (TEM), and US imageability was determined in tissue-mimicking phantoms and mouse tumour model. Dox release from E-LTSL in physiological buffer was quantified by fluorescence spectroscopy. Cellular uptake and cytotoxicity of E-LTSL in the presence of HIFU-induced mild hyperthermia (∼40-42 °C) was determined in a 3D tumour spheroid model. RESULTS: TEM and US confirmed that the PFP emulsion was contained within LTSLs. Phantom and animal studies showed that the E-LTSLs were echogenic. Temperature versus size increase and Dox release kinetics of E-LTSLs demonstrated no difference compared to LTSL alone. Dox release was <5% within 1 h at baseline (25 °C) and body (37 °C) temperatures, and was >99% under hyperthermia. E-LTSL plus HIFU achieved significantly greater Dox uptake in spheroids and cytotoxicity compared to body temperature. CONCLUSION: A stable US-imageable liposome co-loaded with Dox and PFP for in vivo IGDD was developed. Data suggest that HIFU can induce cellular uptake and toxicity with E-LTSLs.

Increased cardiac PFK-2 protects against high-fat diet-induced cardiomyopathy and mediates beneficial systemic metabolic effects.

A healthy heart adapts to changes in nutrient availability and energy demands. In metabolic diseases like type 2 diabetes (T2D), increased reliance on fatty acids for energy production contributes to mitochondrial dysfunction and cardiomyopathy. A principal regulator of cardiac metabolism is 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase (PFK-2), which is a central driver of glycolysis. We hypothesized that increasing PFK-2 activity could mitigate cardiac dysfunction induced by high-fat diet (HFD). Wild type (WT) and cardiac-specific transgenic mice expressing PFK-2 (GlycoHi) were fed a low fat or HFD for 16 weeks to induce metabolic dysfunction. Metabolic phenotypes were determined by measuring mitochondrial bioenergetics and performing targeted quantitative proteomic and metabolomic analysis. Increasing cardiac PFK-2 had beneficial effects on cardiac and mitochondrial function. Unexpectedly, GlycoHi mice also exhibited sex-dependent systemic protection from HFD, including increased glucose homeostasis. These findings support improving glycolysis via PFK-2 activity can mitigate mitochondrial and functional changes that occur with metabolic syndrome.

Tracking of Systemic Lupus Erythematosus (SLE) Longitudinally Using Biosensor and Patient-Reported Data: A Report on the Fully Decentralized Mobile Study to Measure and Predict Lupus Disease Activity Using Digital Signals-The OASIS Study.

(1) Objective: Systemic lupus erythematosus (SLE) is a complex disease involving immune dysregulation, episodic flares, and poor quality of life (QOL). For a decentralized digital study of SLE patients, machine learning was used to assess patient-reported outcomes (PROs), QOL, and biometric data for predicting possible disease flares. (2) Methods: Participants were recruited from the LupusCorner online community. Adults self-reporting an SLE diagnosis were consented and given a mobile application to record patient profile (PP), PRO, and QOL metrics, and enlisted participants received smartwatches for digital biometric monitoring. The resulting data were profiled using feature selection and classification algorithms. (3) Results: 550 participants completed digital surveys, 144 (26%) agreed to wear smartwatches, and medical records (MRs) were obtained for 68. Mining of PP, PRO, QOL, and biometric data yielded a 26-feature model for classifying participants according to MR-identified disease flare risk. ROC curves significantly distinguished true from false positives (ten-fold cross-validation: p < 0.00023; five-fold: p < 0.00022). A 25-feature Bayesian model enabled time-variant prediction of participant-reported possible flares (P(true) > 0.85, p < 0.001; P(nonflare) > 0.83, p < 0.0001). (4) Conclusions: Regular profiling of patient well-being and biometric activity may support proactive screening for circumstances warranting clinical assessment.