Given their severity and non-healing nature, diabetic chronic wounds are a significant concern to the 30.3 million Americans diagnosed with diabetes mellitus (2015). Peripheral arterial diseases, neuropathy, and infection contribute to the development of these wounds, which lead to an increased incidence of lower extremity amputations. Early recognition, debridement, offloading, and controlling infection are imperative for timely treatment. However, wound characterization and treatment are highly subjective and based largely on the experience of the treating clinician. Many wound dressings have been designed to address particular clinical presentations, but a prescriptive method is lacking for identifying the particular state of chronic, non-healing wounds. The authors suggest that recent developments in wound dressings and biosensing may allow for the quantitative, real-time representation of the wound environment, including exudate levels, pathogen concentrations, and tissue regeneration. Development of such sensing capability could enable more strategic, personalized care at the onset of ulceration and limit the infection leading to amputation. This review presents an overview of the pathophysiology of diabetic chronic wounds, a brief summary of biomaterial wound dressing treatment options, and biosensor development for biomarker sensing in the wound environment.

Since the discovery and subsequent use of penicillin, antibiotics have been used to treat most bacterial infections in the U.S. Over time, the repeated prescription of many antibiotics has given rise to many antibiotic-resistant microbes. A bacterial strain becomes resistant by horizontal gene transfer, where surviving microbes acquire genetic material or DNA fragments from adjacent bacteria that encode for resistance. In order to avoid significant bacterial resistance, novel and target therapeutics are needed. Further advancement of diagnostic technologies could be used to develop novel treatment strategies. The use of biosensors to detect quorum-sensing signaling molecules has the potential to provide timely diagnostic information toward mitigating the multidrug-resistant bacteria epidemic. Resistance and pathogenesis are controlled by quorum-sensing (QS) circuits. QS systems secrete or passively release signaling molecules when the bacterial concentration reaches a certain threshold. Signaling molecules give an early indication of virulence. Detection of these compounds in vitro or in vivo can be used to identify the onset of infection. Whole-cell and cell-free biosensors have been developed to detect quorum-sensing signaling molecules. This review will give an overview of quorum networks in the most common pathogens found in chronic and acute infections. Additionally, the current state of research surrounding the detection of quorum-sensing molecules will be reviewed. Followed by a discussion of future works toward the advancement of technologies to quantify quorum signaling molecules in chronic and acute infections.

Since its inception, Compact Disc (CD)-based centrifugal microfluidic technology has drawn a great deal of interest within research communities due to its potential use in biomedical applications. The technology has been referred to by different names, including compact-disc microfluidics, lab-on-a-disk, lab-on-a-CD and bio-disk. This paper critically reviews the state-of-the-art in CD-based centrifugal microfluidics devices and attempts to identify the challenges that, if solved, would enable their use in the extreme point of care. Sample actuation, manufacturing, reagent storage and implementation, target multiplexing, bio-particle detection, required hardware and system disposal, and sustainability are the topics of focus.

Bloodstream infection with Candida fungal cells remains one of the most life-threatening complications among hospitalized patients around the world. Although most of the cases are still due to Candida albicans, the rising incidence of infections caused by other Candida strains that may not respond to traditional anti-fungal treatments merits the development of a method for species-specific isolation of Candida. To this end, here we present the characterization of the dielectrophoresis (DEP) response of Candida albicans, Candida tropicalis and Candida parapsilosis. We complement such characterization with a study of the Candida cells morphology. The Candida strains exhibited subtle differences in their morphology and dimensions. All the Candida strains exhibited positive DEP in the range 10–500 kHz, although the strength of the DEP response was different for each Candida strain at different frequencies. Only Candida tropicalis showed positive DEP at 750 kHz. The current results show potential for manipulation and enrichment of a specific Candida strain at specific DEP conditions towards aiding in the rapid identification of Candida strains to enable the effective and timely treatment of Candida infections.

The use of machine learning and deep learning has become prominent within various fields of bioprocessing for countless modeling and prediction tasks. Previous reviews have emphasized machine learning applications in various fields of bioprocessing, including biomanufacturing. This comprehensive review highlights many of the different machine learning and multivariate analysis techniques that have been utilized within Chinese hamster ovary cell biomanufacturing, specifically due to their rising significance in the industry. Applications of machine and deep learning within other bioprocessing industries are also briefly discussed.