Visible-Light-Activated Molecular Machines Kill Fungi by Necrosis Following Mitochondrial Dysfunction and Calcium Overload.

Invasive fungal infections are a growing public health threat. As fungi become increasingly resistant to existing drugs, new antifungals are urgently needed. Here, it is reported that 405-nm-visible-light-activated synthetic molecular machines (MMs) eliminate planktonic and biofilm fungal populations more effectively than conventional antifungals without resistance development. Mechanism-of-action studies show that MMs bind to fungal mitochondrial phospholipids. Upon visible light activation, rapid unidirectional drilling of MMs at ≈3 million cycles per second (MHz) results in mitochondrial dysfunction, calcium overload, and ultimately necrosis. Besides their direct antifungal effect, MMs synergize with conventional antifungals by impairing the activity of energy-dependent efflux pumps. Finally, MMs potentiate standard antifungals both in vivo and in an ex vivo porcine model of onychomycosis, reducing the fungal burden associated with infection.

Light-based technologies for management of COVID-19 pandemic crisis.

The global dissemination of the novel coronavirus disease (COVID-19) has accelerated the need for the implementation of effective antimicrobial strategies to target the causative agent SARS-CoV-2. Light-based technologies have a demonstrable broad range of activity over standard chemotherapeutic antimicrobials and conventional disinfectants, negligible emergence of resistance, and the capability to modulate the host immune response. This perspective article identifies the benefits, challenges, and pitfalls of repurposing light-based strategies to combat the emergence of COVID-19 pandemic.

Early network properties of the COVID-19 pandemic - The Chinese scenario.

OBJECTIVES: To control epidemics, sites more affected by mortality should be identified. METHODS: Defining epidemic nodes as areas that included both most fatalities per time unit and connections, such as highways, geo-temporal Chinese data on the COVID-19 epidemic were investigated with linear, logarithmic, power, growth, exponential, and logistic regression models. A z-test compared the slopes observed. RESULTS: Twenty provinces suspected to act as epidemic nodes were empirically investigated. Five provinces displayed synchronicity, long-distance connections, directionality and assortativity - network properties that helped discriminate epidemic nodes. The rank I node included most fatalities and was activated first. Fewer deaths were reported, later, by rank II and III nodes, while the data from rank I-III nodes exhibited slopes, the data from the remaining provinces did not. The power curve was the best fitting model for all slopes. Because all pairs (rank I vs. rank II, rank I vs. rank III, and rank II vs. rank III) of epidemic nodes differed statistically, rank I-III epidemic nodes were geo-temporally and statistically distinguishable. CONCLUSIONS: The geo-temporal progression of epidemics seems to be highly structured. Epidemic network properties can distinguish regions that differ in mortality. This real-time geo-referenced analysis can inform both decision-makers and clinicians.