Stability and Performance Study of Fluorescent Organosilica pH Nanosensors.

Long-term stability and function are key challenges for optical nanosensors operating in complex biological environments. While much focus is rightly placed on issues related to specificity, sensitivity, reversibility, and response time, many nanosensors are not capable of transducing accurate results over prolonged time periods. Sensors could fail over time due to the degradation of scaffold material, degradation of signaling dyes and components, or a combination of both. It is critical to investigate how such degradative processes affect sensor output, as the consequences could be severe. Herein, we used fluorescent core-shell organosilica pH nanosensors as a model system, incubating them in a range of common aqueous solutions over time at different temperatures, and then searched for changes in fluorescence signal, particle size, and evidence of silica degradation. We found that these ratiometric nanosensors produced stable optical signals after aging for 30 days at 37 °C in standard saline buffers with and without 10% fetal bovine serum, and without any evidence of material degradation. Next, we evaluated their performance as real-time pH nanosensors in bacterial suspension cultures, observing a close agreement with a pH electrode for control nanosensors, yet observing obvious deviations in signal based on the aging conditions. The results show that while the organosilica scaffold does not degrade appreciably over time, careful selection of dyes and further systematic investigations into the effects of salt and protein levels are required to realize long-term stable nanosensors.

Therapist responsiveness and patient engagement in therapy.

This study tests the hypothesis that therapist responsiveness in the first two sessions of therapy relates to three measures of early patient engagement in treatment. Using videotapes and data from the NIMH Treatment of Depression Collaborative Research Program (TDCRP), an instrument was developed to measure therapist responsiveness in the first two sessions of Cognitive Behavior Therapy and Interpersonal Psychotherapy. A factor measuring positive therapeutic atmosphere, as well as a global item of therapist responsiveness, predicted both the patient's positive perception of the therapeutic relationship after the second session and the patient's remaining in therapy for more than four sessions. A negative therapist behavior factor also predicted early termination. Factors measuring therapist attentiveness and early empathic responding did not predict the engagement variables.

Bionano Interactions of Organosilica Nanoparticles with Myeloid Derived Immune Cells.

Investigating the interactions between nanomaterials and the cells they are likely to encounter in vivo is a critical aspect of designing nanomedicines for imaging and therapeutic applications. Immune cells such as dendritic cells, macrophages, and myeloid derived suppressor cells have a frontline role in the identification and removal of foreign materials from the body, with interactions shown to be heavily dependent on variables such as nanoparticle size, charge, and surface chemistry. Interactions such as cellular association or uptake of nanoparticles can lead to diminished functionality or rapid clearance from the body, making it critical to consider these interactions when designing and synthesizing nanomaterials for biomedical applications ranging from drug delivery to imaging and biosensing. We investigated the interactions between PEGylated organosilica nanoparticles and naturally endocytic immune cells grown from stem cells in murine bone marrow. Specifically, we varied the particle size from 60 nm up to 1000 nm and investigated the effects of size on immune cell association, activation, and maturation with these critical gatekeeper cells. These results will help inform future design parameters for in vitro and in vivo biomedical applications utilizing organosilica nanoparticles.

Assessment of heart-substructures auto-contouring accuracy for application in heart-sparing radiotherapy for lung cancer.

Objectives: We validated an auto-contouring algorithm for heart substructures in lung cancer patients, aiming to establish its accuracy and reliability for radiotherapy (RT) planning. We focus on contouring an amalgamated set of subregions in the base of the heart considered to be a new organ at risk, the cardiac avoidance area (CAA), to enable maximum dose limit implementation in lung RT planning. Methods: The study validates a deep-learning model specifically adapted for auto-contouring the CAA (which includes the right atrium, aortic valve root, and proximal segments of the left and right coronary arteries). Geometric, dosimetric, quantitative, and qualitative validation measures are reported. Comparison with manual contours, including assessment of interobserver variability, and robustness testing over 198 cases are also conducted. Results: Geometric validation shows that auto-contouring performance lies within the expected range of manual observer variability despite being slightly poorer than the average of manual observers (mean surface distance for CAA of 1.6 vs 1.2 mm, dice similarity coefficient of 0.86 vs 0.88). Dosimetric validation demonstrates consistency between plans optimized using auto-contours and manual contours. Robustness testing confirms acceptable contours in all cases, with 80% rated as "Good" and the remaining 20% as "Useful." Conclusions: The auto-contouring algorithm for heart substructures in lung cancer patients demonstrates acceptable and comparable performance to human observers. Advances in knowledge: Accurate and reliable auto-contouring results for the CAA facilitate the implementation of a maximum dose limit to this region in lung RT planning, which has now been introduced in the routine setting at our institution.

Growth in children with chronic kidney disease and associated risk factors for short stature.

INTRODUCTION: Growth failure in chronic kidney disease is related to high morbidity and mortality. Growth retardation in this disease is multifactorial. Knowing the modifiable factors and establishing strategies to improve care for affected children is paramount. OBJECTIVES: To describe growth patterns in children with chronic kidney disease and the risk factors associated with short stature. METHODS: We retrospectively analyzed anthropometric and epidemiological data, birth weight, prematurity, and bicarbonate, hemoglobin, calcium, phosphate, alkaline phosphatase, and parathormone levels of children with stages 3-5 CKD not on dialysis, followed for at least one year. RESULTS: We included 43 children, the majority of which were boys (65%). The mean height/length /age z-score of the children at the beginning and follow-up was -1.89 ± 1.84 and -2.4 ± 1.67, respectively (p = 0.011). Fifty-one percent of the children had short stature, and these children were younger than those with adequate stature (p = 0.027). PTH levels at the beginning of the follow-up correlated with height/length/age z-score. A sub-analysis with children under five (n = 17) showed that 10 (58.8%) of them failed to thrive and had a lower weight/age z-score (0.031) and lower BMI/age z-score (p = 0.047). CONCLUSION: Children, particularly younger ones, with chronic kidney disease who were not on dialysis had a high prevalence of short stature. PTH levels were correlated with height z-score, and growth failure was associated with worse nutritional status. Therefore, it is essential to monitor the growth of these children, control hyperparathyroidism, and provide nutritional support.

Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report.

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: (1) MRI-to-CT and (2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥0.87/0.90) and gamma pass rates for photon (≥98.1%/99.0%) and proton (≥97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning.

Accurate segmentation of head and neck radiotherapy CT scans with 3D CNNs: consistency is key.

Objective.Automatic segmentation of organs-at-risk in radiotherapy planning computed tomography (CT) scans using convolutional neural networks (CNNs) is an active research area. Very large datasets are usually required to train such CNN models. In radiotherapy, large, high-quality datasets are scarce and combining data from several sources can reduce the consistency of training segmentations. It is therefore important to understand the impact of training data quality on the performance of auto-segmentation models for radiotherapy.Approach.In this study, we took an existing 3D CNN architecture for head and neck CT auto-segmentation and compare the performance of models trained with a small, well-curated dataset (n= 34) and then a far larger dataset (n= 185) containing less consistent training segmentations. We performed 5-fold cross-validations in each dataset and tested segmentation performance using the 95th percentile Hausdorff distance and mean distance-to-agreement metrics. Finally, we validated the generalisability of our models with an external cohort of patient data (n= 12) with five expert annotators.Main results.The models trained with a large dataset were greatly outperformed by models (of identical architecture) trained with a smaller, but higher consistency set of training samples. Our models trained with a small dataset produce segmentations of similar accuracy as expert human observers and generalised well to new data, performing within inter-observer variation.Significance.We empirically demonstrate the importance of highly consistent training samples when training a 3D auto-segmentation model for use in radiotherapy. Crucially, it is the consistency of the training segmentations which had a greater impact on model performance rather than the size of the dataset used.

Optimising a 3D convolutional neural network for head and neck computed tomography segmentation with limited training data.

Background and purpose: Convolutional neural networks (CNNs) are increasingly used to automate segmentation for radiotherapy planning, where accurate segmentation of organs-at-risk (OARs) is crucial. Training CNNs often requires large amounts of data. However, large, high quality datasets are scarce. The aim of this study was to develop a CNN capable of accurate head and neck (HN) 3D auto-segmentation of planning CT scans using a small training dataset (34 CTs). Materials and Method: Elements of our custom CNN architecture were varied to optimise segmentation performance. We tested and evaluated the impact of: using multiple contrast channels for the CT scan input at specific soft tissue and bony anatomy windows, resize vs. transpose convolutions, and loss functions based on overlap metrics and cross-entropy in different combinations. Model segmentation performance was compared with the inter-observer deviation of two doctors' gold standard segmentations using the 95th percentile Hausdorff distance and mean distance-to-agreement (mDTA). The best performing configuration was further validated on a popular public dataset to compare with state-of-the-art (SOTA) auto-segmentation methods. Results: Our best performing CNN configuration was competitive with current SOTA methods when evaluated on the public dataset with mDTA of ( 0.81 ± 0.31 ) mm for the brainstem, ( 0.20 ± 0.08 ) mm for the mandible, ( 0.77 ± 0.14 ) mm for the left parotid and ( 0.81 ± 0.28 ) mm for the right parotid. Conclusions: Through careful tuning and customisation we trained a 3D CNN with a small dataset to produce segmentations of HN OARs with an accuracy that is comparable with inter-clinician deviations. Our proposed model performed competitively with current SOTA methods.

Necrotizing Clostridium subterminale infection with an odontogenic origin.

Clostridium subterminale is an anaerobic spore-forming bacterium rarely isolated in human infections. This case study presents a necrotizing C. subterminale infection stemming from a dental abscess that progressed into sepsis, a small pericardial effusion, moderate bilateral pulmonary effusions, and multiple organ dysfunction syndrome. The management of the infection, along with other relevant cases is discussed.

Transfer learning for data-efficient abdominal muscle segmentation with convolutional neural networks.

BACKGROUND: Skeletal muscle segmentation is an important procedure for assessing sarcopenia, an emerging imaging biomarker of patient frailty. Data annotation remains the bottleneck for training deep learning auto-segmentation models. PURPOSE: There is a need to define methodologies for applying models to different domains (e.g., anatomical regions or imaging modalities) without dramatically increasing data annotation. METHODS: To address this problem, we empirically evaluate the generalizability of various source tasks for transfer learning: natural image classification, natural image segmentation, unsupervised image reconstruction, and self-supervised jigsaw solving. Axial CT slices at L3 were extracted from PET-CT scans for 204 oesophago-gastric cancer patients and the skeletal muscle manually delineated by an expert. Features were transferred and segmentation models trained on subsets ( n = 5 , 10 , 25 , 50 , 75 , 100 , 125 $n=5,10,25,50,75,100,125$ ) of the manually annotated training set. Four-fold cross-validation was performed to evaluate model generalizability. Human-level performance was established by performing an inter-observer study consisting of ten trained radiographers. RESULTS: We find that accurate segmentation models can be trained on a fraction of the data required by current approaches. The Dice similarity coefficient and root mean square distance-to-agreement were calculated for each prediction and used to assess model performance. Models pre-trained on a segmentation task and fine-tuned on 10 images produce delineations that are comparable to those from trained observers and extract reliable measures of muscle health. CONCLUSIONS: Appropriate transfer learning can generate convolutional neural networks for abdominal muscle segmentation that achieve human-level performance while decreasing the required data by an order of magnitude, compared to previous methods ( n = 160 → 10 $n=160 \rightarrow 10$ ). This work enables the development of future models for assessing skeletal muscle at other anatomical sites where large annotated data sets are scarce and clinical needs are yet to be addressed.

Column Agglutination Assay Using Polystyrene Microbeads for Rapid Detection of Antibodies against SARS-CoV-2.

Rapid serology platforms are essential in disease pandemics for a variety of applications, including epidemiological surveillance, contact tracing, vaccination monitoring, and primary diagnosis in resource-limited areas. Laboratory-based enzyme-linked immunosorbent assay (ELISA) platforms are inherently multistep processes that require trained personnel and are of relatively limited throughput. As an alternative, agglutination-based systems have been developed; however, they rely on donor red blood cells and are not yet available for high-throughput screening. Column agglutination tests are a mainstay of pretransfusion blood typing and can be performed at a range of scales, ranging from manual through to fully automated testing. Here, we describe a column agglutination test using colored microbeads coated with recombinant SARS-CoV-2 spike protein that agglutinates when incubated with serum samples collected from patients recently infected with SARS-CoV-2. After confirming specific agglutination, we optimized centrifugal force and time to distinguish samples from uninfected vs SARS-CoV-2-infected individuals and then showed concordant results against ELISA for 22 clinical samples, and also a set of serial bleeds from one donor at days 6-10 postinfection. Our study demonstrates the use of a simple, scalable, and rapid diagnostic platform that can be tailored to detect antibodies raised against SARS-CoV-2 and can be easily integrated with established laboratory frameworks worldwide.

Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge.

Automatic segmentation of abdominal organs in CT scans plays an important role in clinical practice. However, most existing benchmarks and datasets only focus on segmentation accuracy, while the model efficiency and its accuracy on the testing cases from different medical centers have not been evaluated. To comprehensively benchmark abdominal organ segmentation methods, we organized the first Fast and Low GPU memory Abdominal oRgan sEgmentation (FLARE) challenge, where the segmentation methods were encouraged to achieve high accuracy on the testing cases from different medical centers, fast inference speed, and low GPU memory consumption, simultaneously. The winning method surpassed the existing state-of-the-art method, achieving a 19× faster inference speed and reducing the GPU memory consumption by 60% with comparable accuracy. We provide a summary of the top methods, make their code and Docker containers publicly available, and give practical suggestions on building accurate and efficient abdominal organ segmentation models. The FLARE challenge remains open for future submissions through a live platform for benchmarking further methodology developments at https://flare.grand-challenge.org/.

The Development of Nanoparticles for the Detection and Imaging of Ovarian Cancers.

Ovarian cancer remains as one of the most lethal gynecological cancers to date, with major challenges associated with screening, diagnosis and treatment of the disease and an urgent need for new technologies that can meet these challenges. Nanomaterials provide new opportunities in diagnosis and therapeutic management of many different types of cancers. In this review, we highlight recent promising developments of nanoparticles designed specifically for the detection or imaging of ovarian cancer that have reached the preclinical stage of development. This includes contrast agents, molecular imaging agents and intraoperative aids that have been designed for integration into standard imaging procedures. While numerous nanoparticle systems have been developed for ovarian cancer detection and imaging, specific design criteria governing nanomaterial targeting, biodistribution and clearance from the peritoneal cavity remain key challenges that need to be overcome before these promising tools can accomplish significant breakthroughs into the clinical setting.

Rapid Gel Card Agglutination Assays for Serological Analysis Following SARS-CoV-2 Infection in Humans.

High-throughput and rapid serology assays to detect the antibody response specific to severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) in human blood samples are urgently required to improve our understanding of the effects of COVID-19 across the world. Short-term applications include rapid case identification and contact tracing to limit viral spread, while population screening to determine the extent of viral infection across communities is a longer-term need. Assays developed to address these needs should match the ASSURED criteria. We have identified agglutination tests based on the commonly employed blood typing methods as a viable option. These blood typing tests are employed in hospitals worldwide, are high-throughput, fast (10-30 min), and automated in most cases. Herein, we describe the application of agglutination assays to SARS-CoV-2 serology testing by combining column agglutination testing with peptide-antibody bioconjugates, which facilitate red cell cross-linking only in the presence of plasma containing antibodies against SARS-CoV-2. This simple, rapid, and easily scalable approach has immediate application in SARS-CoV-2 serological testing and is a useful platform for assay development beyond the COVID-19 pandemic.

Opto-fluidic ring resonator lasers based on highly efficient resonant energy transfer.

We demonstrate an opto-fluidic ring resonator dye laser using highly efficient energy transfer. The active lasing material consists of a donor and acceptor mixture and flows in a fused silica capillary whose circular cross section forms a ring resonator and supports the whispering gallery modes (WGMs) of high Q-factors (>107). The excited states are created in the donor and transferred to the acceptor through the fluorescence resonant energy transfer (FRET), whose emission is coupled into the WGM. Due to the high energy transfer efficiency and high Q-factors, the acceptor exhibits a lasing threshold as low as 0.3 muJ/mm2. We further analyze the energy transfer mechanisms and find that non-radiative Förster transfer is the dominant effect to support the acceptor lasing. FRET lasers using cascade energy transfer and using quantum dots (QDs) as the donor are also presented. Our study will not only lead to development of novel microfluidic lasers with low lasing thresholds and excitation/emission flexibility, but also open an avenue for future laser intra-cavity bio/chemical sensing.

Airway nitric oxide release is reduced after PBS inhalation in asthma.

Exhaled nitric oxide (NO) is elevated in asthma, but the underlying mechanisms remain poorly understood. Recent results in subjects with asthma have reported a decrease in exhaled breath pH and ammonia, as well as altered expression and activity of glutaminase in both alveolar and airway epithelial cells. This suggests that pH-dependent nitrite conversion to NO may be a source of exhaled NO in the asthmatic airway epithelium. However, the anatomic location (i.e., airway or alveolar region) of this pH-dependent NO release has not been investigated and could impact potential therapeutic strategies. We quantified airway (proximal) and alveolar (peripheral) contributions to exhaled NO at baseline and then after PBS inhalation in stable (mild-intermittent to severe) asthmatic subjects (20-44 yr old; n = 9) and healthy controls (22-41 yr old; n = 6). The mean (SD) maximum airway wall flux (pl/s) and alveolar concentration (ppb) at baseline in asthma subjects and healthy controls was 2,530 (2,572) and 5.42 (7.31) and 1,703 (1,567) and 1.88 (1.29), respectively. Compared with baseline, there is a significant decrease in the airway wall flux of NO in asthma as early as 15 min and continuing for up to 60 min (maximum -28% at 45 min) after PBS inhalation without alteration of alveolar concentration. Healthy control subjects did not display any changes in exhaled NO. We conclude that elevated airway NO at baseline in asthma is reduced by inhaled PBS. Thus airway NO may be, in part, due to nitrite conversion to NO and is consistent with airway pH dysregulation in asthma.

SNOR and wheeze: the asthma enzyme?

Conventionally, asthma is defined as involving both airway inflammation and airway smooth muscle hyper-responsiveness. However, Que and coworkers have recently uncoupled these concepts, showing that mice lacking an S-nitrosothiol reductase have allergen-induced airway inflammation but do not have airway hyper-responsiveness. These data are consistent with recent clinical evidence that: (i) S-nitrosothiol signaling is abnormal in human asthma, (ii) nitric oxide in exhaled air might be only a biomarker for the metabolism of more physiologically relevant nitrogen oxides and (iii) the biochemical response to airway inflammation is central to asthma pathophysiology.

Prolonged single-agent versus combination chemotherapy in indolent follicular lymphomas: a study of the cancer and leukemia group B.

PURPOSE: The array of options for the initial management of follicular small cleaved lymphoma (FSCL) and follicular mixed lymphoma (FML) ranges from little or no therapy to the use of intensive combinations of drugs. The Cancer and Leukemia Group B (CALGB) compared two contrasting approaches: a single agent, and combination chemotherapy capable of curing diffuse aggressive lymphomas. PATIENTS AND METHODS: A total of 228 patients with stage III or IV FSCL or FML were randomized to cyclophosphamide or the combination of cyclophosphamide, doxorubicin, vincristine, prednisone, and bleomycin (CHOP-B). Treatment was continued in responders for 2 years beyond maximal response. The primary end point was survival in the most common subtype, FSCL. RESULTS: Ninety-one percent of all patients responded; complete responses were seen in 66% of those treated with cyclophosphamide and in 60% treated with CHOP-B (P =.36). At 10 years with either cyclophosphamide or CHOP-B, respectively, overall time to failure (25% failure free v 33%; P =.107) and survival (44% alive v 46%; P =.79) were similar by treatment. Outcomes in FSCL also were similar. In 46 patients with FML, at 10 years the combination was associated with better failure-free (9% v 48%; P =.005) and overall (25% v 61%; P =.024) survival. Acute toxic effects were more common with combination chemotherapy. Second malignancies, which might be attributed to treatment, were seen with both approaches. CONCLUSION: There is no advantage to the initial use of the relatively intensive combination, CHOP-B, for patients with FSCL compared with the less toxic single agent, cyclophosphamide. However, in an unplanned subgroup analysis, patients with FML who received the combination experienced improved disease control and survival.

Buffering airway acid decreases exhaled nitric oxide in asthma.

BACKGROUND: The human airway is believed to be acidified in asthma. In an acidic environment nitrite is converted to nitric oxide (NO). OBJECTIVE: We hypothesized that buffering airway lining fluid acid would decrease the fraction of exhaled NO (F(ENO)). METHODS: We treated 28 adult nonsmoking subjects (9 healthy control subjects, 11 subjects with mild intermittent asthma, and 8 subjects with persistent asthma) with 3 mL of 10 mmol/L phosphate buffered saline (PBS) through a nebulizer and then serially measured F(ENO) levels. Six subjects also received PBS mouthwash alone. RESULTS: F(ENO) levels decreased after buffer inhalation. The maximal decrease occurred between 15 and 30 minutes after treatment; F(ENO) levels returned to pretreatment levels by 60 minutes. The decrease was greatest in subjects with persistent asthma (-7.1 +/- 1.0 ppb); this was more than in those with either mild asthma (-2.9 +/- 0.3 ppb) or healthy control subjects (-1.7 +/- 0.3 ppb, P < .001). Levels did not decrease in subjects who used PBS mouthwash. CONCLUSION: Neutralizing airway acid decreases F(ENO) levels. The magnitude of this change is greatest in persistent asthma. These data suggest that airway pH is a determinant of F(ENO) levels downstream from NO synthase activation. CLINICAL IMPLICATIONS: Airway biochemistry modulates F(ENO) levels. For example, nitrite is converted to NO in the airway, particularly the inflamed airway, by means of acid-based chemistry. Thus airway pH should be considered in interpreting clinical F(ENO) values. In fact, PBS challenge testing integrates airway pH and F(ENO) analysis, potentially improving the utility of F(ENO) as a noninvasive test for the type and severity of asthmatic airway inflammation.

S-nitrosylating agents: a novel class of compounds that increase cystic fibrosis transmembrane conductance regulator expression and maturation in epithelial cells.

The endogenous bronchodilator, S-nitrosoglutathione (GSNO), increases expression, maturation, and function of both the wild-type and the DeltaF508 mutant of the cystic fibrosis transmembrane conductance regulatory protein (CFTR). Though transcriptional mechanisms of action have been identified, GSNO seems also to have post-transcriptional effects on CFTR maturation. Here, we report that 1) GSNO is only one of a class of S-nitrosylating agents that, at low micromolar concentrations, increase DeltaF508 and wild-type CFTR expression and maturation; 2) NO itself (at these concentrations) and 8-bromocyclic GMP are minimally active on CFTR; 3) a novel agent, S-nitrosoglutathione diethyl ester, bypasses the need for GSNO bioactivation by gamma-glutamyl transpeptidase to increase CFTR maturation; 4) surprisingly, expression-but not S-nitrosylation-of cysteine string proteins (Csp) 1 and 2 is increased by GSNO; 5) the effect of GSNO to increase full maturation of wild-type CFTR is inhibited by Csp silencing (si)RNA; 6) proteins relevant to CFTR trafficking are SNO-modified, and SNO proteins traffic through the endoplasmic reticulum (ER) and Golgi after GSNO exposure; and 7) GSNO alters the interactions of DeltaF508 CFTR with Csp and Hsc70 in the ER and Golgi. These data suggest that GSNO is one of a class of S-nitrosylating agents that act independently of the classic NO radical/cyclic GMP pathway to increase CFTR expression and maturation. They also suggest that the effect of GSNO is dependent on Csp and on intracellular SNO trafficking. We speculate that these data will be of relevance to the development of NO donor-based therapies for CF.