Long-lived charge carriers in Mn-doped CdS quantum dots for photoelectrochemical cytosensing.

Photoelectrochemical (PEC) biosensing with semiconductor quantum dots (QDs) has received great attention because it integrates the advantages of both photo-excitation and electrochemical detection. During the photon-to-electricity conversion in PEC processes, electron-hole (charge) separation competes with electron-hole recombination, and the net effect essentially determines the performance of PEC biosensors. Herein, we propose a new approach for slowing down electron-hole recombination to increase charge separation efficiency for PEC biosensor development. Through doping with Mn(2+) , a pair of d bands ((4) T1 and (6) A1 ) is inserted between the conduction and valence bands of CdS QDs, which alters the electron-hole separation and recombination dynamics, allowing the generation of long-lived charge carriers with ms-scale lifetime that decay about 10(4) -10(5) -fold more slowly than in the case of undoped QDs. Photocurrent tests indicated that Mn(2+) doping resulted in an approximately 80 % increase in photocurrent generation compared with undoped CdS QDs. For application, the Mn-doped CdS QDs were coated on the surface of a glassy carbon electrode and functionalized with a cell surface carbohydrate-specific ligand (3-aminophenylboronic acid). In this way, a sensitive cytosensor for K562 leukemia cells was constructed. Moreover, the sugar-specific binding property of 3-aminophenylboronic acid allowed the electrode to serve as a switch for the capture and release of cells. This has been further explored with a view to developing a reusable PEC cytosensing platform.

Comparison of the upper and lower airway microbiome in early postoperative lung transplant recipients.

The upper and lower respiratory tract may share microbiome because they are directly continuous, and the nasal microbiome contributes partially to the composition of the lung microbiome. But little is known about the upper and lower airway microbiome of early postoperative lung transplant recipients (LTRs). Using 16S rRNA gene sequencing, we compared paired nasal swab (NS) and bronchoalveolar lavage fluid (BALF) microbiome from 17 early postoperative LTRs. The microbiome between the two compartments were significantly different in Shannon diversity and beta diversity. Four and eight core NS-associated and BALF-associated microbiome were identified, respectively. NS samples harbored more Corynebacterium, Acinetobacter, and Pseudomonas, while BALF contained more Ralstonia, Stenotrophomonas, Enterococcus, and Pedobacter. The within-subject dissimilarity was higher than the between-subject dissimilarity, indicating a greater impact of sampling sites than sampling individuals on microbial difference. There were both difference and homogeneity between NS and BALF microbiome in early postoperative LTRs. High levels of pathogens were detected in both samples, suggesting that both of them can reflect the diseases characteristics of transplanted lung. The differences between upper and lower airway microbiome mainly come from sampling sites instead of sampling individuals. IMPORTANCE: Lung transplantation is the only therapeutic option for patients with end-stage lung disease, but its outcome is much worse than other solid organ transplants. Little is known about the NS and BALF microbiome of early postoperative LTRs. Here, we compared paired samples of the nasal and lung microbiome from 17 early postoperative LTRs and showed both difference and homogeneity between the two samples. Most of the "core" microbiome in both NS and BALF samples were recognized respiratory pathogens, suggesting that both samples can reflect the diseases characteristics of transplanted lung. We also found that the differences between upper and lower airway microbiome in early postoperative LTRs mainly come from sampling sites instead of sampling individuals.

The Airway Microbiota Signatures of Infection and Rejection in Lung Transplant Recipients.

Infection and rejection are the two most common complications after lung transplantation (LT) and are associated with increased morbidity and mortality. We aimed to examine the association between the airway microbiota and infection and rejection in lung transplant recipients (LTRs). Here, we collected 181 sputum samples (event-free, n = 47; infection, n = 103; rejection, n = 31) from 59 LTRs, and performed 16S rRNA gene sequencing to analyze the airway microbiota. A significantly different airway microbiota was observed among event-free, infection and rejection recipients, including microbial diversity and community composition. Nineteen differential taxa were identified by linear discriminant analysis (LDA) effect size (LEfSe), with 6 bacterial genera, Actinomyces, Rothia, Abiotrophia, Neisseria, Prevotella, and Leptotrichia enriched in LTRs with rejection. Random forest analyses indicated that the combination of the 6 genera and procalcitonin (PCT) and T-lymphocyte levels showed area under the curve (AUC) values of 0.898, 0.919 and 0.895 to differentiate between event-free and infection recipients, event-free and rejection recipients, and infection and rejection recipients, respectively. In conclusion, our study compared the airway microbiota between LTRs with infection and acute rejection. The airway microbiota, especially combined with PCT and T-lymphocyte levels, showed satisfactory predictive efficiency in discriminating among clinically stable recipients and those with infection and acute rejection, suggesting that the airway microbiota can be a potential indicator to differentiate between infection and acute rejection after LT. IMPORTANCE Survival after LT is limited compared with other solid organ transplantations mainly due to infection- and rejection-related complications. Differentiating infection from rejection is one of the most important challenges to face after LT. Recently, the airway microbiota has been reported to be associated with either infection or rejection of LTRs. However, fewer studies have investigated the relationship between airway microbiota together with infection and rejection of LTRs. Here, we conducted an airway microbial study of LTRs and analyzed the airway microbiota together with infection, acute rejection, and clinically stable recipients. We found different airway microbiota between infection and acute rejection and identify several genera associated with each outcome and constructed a model that incorporates airway microbiota and clinical parameters to predict outcome. This study highlighted that the airway microbiota was a potential indicator to differentiate between infection and acute rejection after LT.