Revolutionizing Oxygen Delivery: The Adjustable Oxygen Adapter for Uninterrupted Therapy and Enhanced Patient Safety.

This study introduces the "Adjustable Oxygen Adapter," designed to address interruptions in oxygen supply for patients transitioning from oxygen spray bottles, particularly in wards with single-outlet flowmeters. Inspired by the "Three-Way Piston Rotating Adapter," this innovative design, operated by a single switch, ensures uninterrupted oxygen supply during equipment changes. Stability testing, conducted with a flow monitor by a respiratory therapist, confirms that the adapter provides a stable flow rate and oxygen concentration, offering a practical solution for seamless transitions between oxygen devices in clinical settings.

[Using Lean Management to Improve the Nursing Admission Process for COVID-19 Patients in the Intensive Care Unit].

BACKGROUND & PROBLEMS: Safe and efficient operational workflows in nursing care can alleviate workloads, enhance quality of care, and improve job satisfaction. A recent survey indicates that the admission process for patients with COVID-19 in nursing care is excessively lengthy due primarily to the waiting time for physicians to enter the ward, external support, and frequent reorganization of medical materials. Inadequate organization measures have resulted in requisite materials not being centralized, leading to increased travel times, interruptions in nursing records maintenance, unprofessional explanations, and time-consuming consent form signing processes. PURPOSE: In this project, lean management was implemented to reduce the time spent on the nursing admission process for patients with COVID-19. RESOLUTIONS: The nursing admission process and job responsibilities were revised. Furthermore, new policies were implemented, including introducing remote consent form signing, using intelligent digital health education assistants, revising related inventory processes, and planning admission nursing carts to streamline the admission process. RESULTS: The average processing time in the isolation room was reduced by 30.5% from 105 minutes to 73 minutes; the average time spent by nurses on the admission process was reduced by 34.1% from 504 minutes to 332 minutes; and nursing satisfaction levels rose from 55.4% to 82.7%. CONCLUSIONS: In this project, lean management was used to investigate the nursing admission process for patients with COVID-19 and a value stream map was compiled to identify low-value activities within the process. Through the implementation and standardization of project measures, processing time was effectively reduced, manual labor was minimized, and job satisfaction improved.

Dual-Sensitive Gold-Nanocubes Platform with Synergistic Immunotherapy for Inducing Immune Cycle Using NIR-Mediated PTT/NO/IDO.

Currently, the combination therapies based on immunotherapy have been rapidly developed, but the response rate has not always increased as expected. Nano-platform has become a potential strategy which can trigger multi-functions to increase immunotherapeutic efficacy via activating T-cells and photothermal effect. Herein, to avoid the self-degradation and provide pH-sensitive property, S-nitrosoglutathione (GSNO) was loaded in gold nanocubes (AuNCs) with polyacrylic acid (PAA) coating. Subsequently, the layer-by-layer (LbL) assembly of iron oxide nanoparticles (Fe3O4) and betanin can provide the conjugation of 1-methyl-D-tryptophan (1-M-DT) on the nanoparticle to form an NO gas-photothermal-immune nano-platform (GAPFBD) for achieving combinatory therapy of NO gas, photothermal therapy (PTT), and indoleamine 2,3-dioxygenase (IDO) immunotherapy. After irradiation by 808-nm laser, the GSNO was released under a lower pH environment due to the structural transformation of PAA and then transformed into NO production of 64.5 ± 1.6% under PTT. The combination of PTT and NO gas therapy can effectively eliminate cancer cells, resulting in a large amount of tumor-associated antigens (TAAs) compared to the individual treatment in vitro. Additionally, the released 1-M-DT inhibited IDO and combined with TAAs to enhance maturation of dendritic cells (DCs), indicating the excellent synergistic effect of PTT and NO with IDO inhibitors. These results revealed that this dual-sensitive nanoparticle presented a combination strategy of PTT/NO/IDO for the synergistic effect to promote DC maturation.

Ansamitocin P3-Loaded Gold-NanoCage Conjugated with Immune Checkpoint Inhibitor to Enhance Photo-Chemo-Thermal Maturation of Dendritic Cells for Hepatocellular Carcinoma.

Immunotherapy is a newly developed method for cancer treatment, but still generates limited response in partial patients for hepatocellular carcinoma (HCC) because the immunity cycle is limited by the tumor microenvironment (TME). Herein, we introduce multifunctional gold nanocages (AuNCs)-based nanocarriers with Ansamitocin P3 (AP3) loaded and anti-PDL1 binding (AP3-AuNCs-anti-PDL1) which can combine photothermal therapy, chemotherapeutic agent-triggered DCs maturation, and checkpoint immunotherapy in one platform. The AP3-AuNCs-anti-PDL1 using Avidin-biotin to bind anti-PDL1 on the surface of AP3-AuNCs showed specifically cellular targeting compared to AuNCs, which can increase the immune responses. The AP3-AuNCs+NIR-10 min exhibited the highly activated DCs maturation with two-fold higher than control+NIR, which can be attributed to the significant release of AP3. The results illustrated the synergistic effect of tumor-associated antigens (TAAs) and controlled AP3 release under near infrared (NIR) in triggering effective DCs maturation. Among them, AP3 release played the more important role than the TAAs under PTT in promoting T-cell activation. These results illustrate the promising potential of AuNCs-based nanocarriers combined with AP3 and the checkpoint inhibitors to strengthen the positive loop of immunity cycle.

Advances in Magnetic Nanoparticle-Mediated Cancer Immune-Theranostics.

Cancer immunotherapy is a cutting-edge strategy that eliminates cancer cells by amplifying the host's immune system. However, the low response rate and risks of inducing systemic toxicity have raised uncertainty in the treatment. Magnetic nanoparticles (MNPs) as a versatile theranostic tool can be used to target delivery of multiple immunotherapeutics and monitor cell/tissue responses. These capabilities enable the real-time characterization of the factors that contribute to immunoactivity so that future treatments can be optimized. The magnetic properties of MNPs further allow the implementation of magnetic navigation and magnetic hyperthermia for boosting the efficacy of immunotherapy. The multimodal approach opens an avenue to induce robust immune responses, minimize safety issues, and monitor immune activities simultaneously. Thus, the object of this review is to provide an overview of the burgeoning fields and to highlight novel technologies for next-generation immunotherapy. The review further correlates the properties of MNPs with the latest treatment strategies to explore the crosstalk between magnetic nanomaterials and the immune system. This comprehensive review of MNP-derived immunotherapy covers the obstacles and opportunities for future development and clinical translation.

Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms.

BACKGROUND: The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention strategies for diabetic retinopathy in the most high-risk population. The purpose of this study is to build a prediction model for the DR in type 2 diabetes mellitus using data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions. RESULTS: Experimental results demonstrated that prediction performance by support vector machines performed better than the other machine learning algorithms and achieved 79.5% and 0.839 in accuracy and area under the receiver operating characteristic curve using percentage split (i.e., data set divided into 80% as trainning and 20% as test), respectively. Evaluated by three-way data split scheme (i.e., data set divided into 60% as training, 20% as validation, and 20% as independent test), our method obtained slightly lower performance compared to percentage split, which suggested that three-way data split is a better way to evaluate the real performance and prevent overestimation. Moreover, we incorporated approaches proposed in previous studies to evaluate our data set and our prediction performance outperformed the other previous studies in most evaluation measures. This lends support to our assumption that appropriate machine learning algorithms combined with discriminative clinical features can effectively detect diabetic retinopathy. CONCLUSIONS: Our method identifies use of insulin and duration of diabetes as novel interpretable features to assist with clinical decisions in identifying the high-risk populations for diabetic retinopathy. If duration of DM increases by 1 year, the odds ratio to have DMR is increased by 9.3%. The odds ratio to have DR is increased by 3.561 times for patients who use insulin compared to patients who do not use insulin. Our results can be used to facilitate development of clinical decision support systems for clinical practice in the future.