Bacterial infections are a major threat to human health, exacerbated by increasing antibiotic resistance. These infections can result in tremendous morbidity and mortality, emphasizing the need to identify and treat pathogenic bacteria quickly and effectively. Recent developments in detection methods have focused on electrochemical, optical, and mass-based biosensors. Advances in these systems include implementing multifunctional materials, microfluidic sampling, and portable data-processing to improve sensitivity, specificity, and ease of operation. Concurrently, advances in antibacterial treatment have largely focused on targeted and responsive delivery for both antibiotics and antibiotic alternatives. Antibiotic alternatives described here include repurposed drugs, antimicrobial peptides and polymers, nucleic acids, small molecules, living systems, and bacteriophages. Finally, closed-loop therapies are combining advances in the fields of both detection and treatment. This review provides a comprehensive summary of the current trends in detection and treatment systems for bacterial infections.

This study shows that endothelial dysfunction occurs early in the pathophysiology of diabetes and is a link between cardiovascular risk factors and DPN.

Diabetic foot ulcers (DFUs) are a major clinical problem exacerbated by prolonged bacterial infection. Macrophages, the primary innate immune cells, are multifunctional cells that regulate diverse processes throughout multiple phases of wound healing. To better understand the influence of microbial species on macrophage behavior, we cultured primary human monocyte‐derived macrophages from four donors for 24 hours in media conditioned by bacteria and fungi (Pseudomonas aeruginosa, Corynebacterium amycolatum, Corynebacterium striatum, Staphylococcus aureus, Staphylococcus simulans, and Candida albicans) isolated from the DFUs of six patients. The effects of these microbe‐derived signals on macrophage behavior were assessed by measuring the gene expression of a panel of 25 genes related to macrophage phenotype, angiogenesis, bacterial recognition, and cell survival, as well as secretion of two inflammatory cytokines using NanoString multiplex analysis. Principal component analysis showed that macrophage gene expression and protein secretion were affected by both microbial species as well as human donor. S. simulans and C. albicans caused up‐regulation of genes associated with a proinflammatory (M1) phenotype, and P. aeruginosa caused an increase in the secretion of the proinflammatory cytokine and M1 marker tumor necrosis factor‐alpha (TNFα). Together, these results suggest that macrophages respond to secreted factors from microbes by up‐regulating inflammatory markers, and that the effects are strongly dependent on the monocyte donor. Ultimately, increased understanding of macrophage–microbe interactions will lead to the development of more targeted therapies for DFU healing.

Background: Impaired healing after rotator cuff repair is a major concern, with retear rates as high as 94%. A method to predict whether patients are likely to experience poor surgical outcomes would change clinical practice. While various patient factors, such as age and tear size, have been linked to poor functional outcomes, it is currently very challenging to predict outcomes before surgery. Purpose: To evaluate gene expression differences in tissue collected during surgery between patients who ultimately went on to have good outcomes and those who experienced a retear, in an effort to determine if surgical outcomes can be predicted. Study Design: Case-control study; Level of evidence, 3. Methods: Rotator cuff tissue was collected at the time of surgery from 140 patients. Patients were tracked for a minimum of 6 months to identify those with good or poor outcomes, using clinical functional scores and follow-up magnetic resonance imaging to confirm failure to heal or retear. Gene expression differences between 8 patients with poor outcomes and 28 patients with good outcomes were assessed using a multiplex gene expression analysis via NanoString and a custom-curated panel of 145 genes related to various stages of rotator cuff healing. Results: Although significant differences in the expression of individual genes were not observed, gene set enrichment analysis highlighted major differences in gene sets. Patients who had poor healing outcomes showed greater expression of gene sets related to extracellular matrix production ( P < .0001) and cellular biosynthetic pathways ( P < .001), while patients who had good healing outcomes showed greater expression of genes associated with the proinflammatory (M1) macrophage phenotype ( P < .05). Conclusion: These results suggest that a more proinflammatory, fibrotic environment before repair may play a role in poor healing outcome. With validation in a larger cohort, these results may ultimately lead to diagnostic methods to preoperatively predict those at risk for poor surgical outcomes.