Multimodal artificial intelligence-based pathogenomics improves survival prediction in oral squamous cell carcinoma.

In this study, we aimed to develop a novel prognostic algorithm for oral squamous cell carcinoma (OSCC) using a combination of pathogenomics and AI-based techniques. We collected comprehensive clinical, genomic, and pathology data from a cohort of OSCC patients in the TCGA dataset and used machine learning and deep learning algorithms to identify relevant features that are predictive of survival outcomes. Our analyses included 406 OSCC patients. Initial analyses involved gene expression analyses, principal component analyses, gene enrichment analyses, and feature importance analyses. These insights were foundational for subsequent model development. Furthermore, we applied five machine learning/deep learning algorithms (Random Survival Forest, Gradient Boosting Survival Analysis, Cox PH, Fast Survival SVM, and DeepSurv) for survival prediction. Our initial analyses revealed relevant gene expression variations and biological pathways, laying the groundwork for robust feature selection in model building. The results showed that the multimodal model outperformed the unimodal models across all methods, with c-index values of 0.722 for RSF, 0.633 for GBSA, 0.625 for FastSVM, 0.633 for CoxPH, and 0.515 for DeepSurv. When considering only important features, the multimodal model continued to outperform the unimodal models, with c-index values of 0.834 for RSF, 0.747 for GBSA, 0.718 for FastSVM, 0.742 for CoxPH, and 0.635 for DeepSurv. Our results demonstrate the potential of pathogenomics and AI-based techniques in improving the accuracy of prognostic prediction in OSCC, which may ultimately aid in the development of personalized treatment strategies for patients with this devastating disease.

Perioperative point-of-care-testing of plasmacholinesterases identifies older patients at risk for postoperative delirium: an observational prospective cohort study.

BACKGROUND: Postoperative delirium (POD) is a severe perioperative complication that may increase mortality and length-of-stay in older patients. Moreover, POD is a major economic burden to any healthcare system. An altered expression of Acetylcholine- and Butyrylcholinesterases (AChE, BuChE) due to an unbalanced neuroinflammatory response to trauma or an operative stimulus has been reported to play an essential role in the development of POD. We investigated if perioperative measurement of cholinesterases (ChEs) can help identifying patients at risk for the occurrence of POD in both, scheduled and emergency surgery patients. METHODS: This monocentric prospective observational cohort study was performed in a tertiary hospital (departments of orthopaedic surgery and traumatology). One hundred and fifty-one patients aged above 75 years were enrolled for scheduled (n = 76) or trauma-related surgery (n = 75). Exclusion criteria were diagnosed dementia and anticholinergic medication. Plasma samples taken pre- and postoperatively were analysed regarding AChE and BuChE activity. Furthermore, perioperative assessment using different cognitive tests was performed. The type of anaesthesia (general vs. spinal anaesthesia) was analysed. Primary outcome was the incidence of POD assessed by the approved Confusion Assessment Method (CAM) in combination with the expression of AChE and BuChE. RESULTS: Of 151 patients included, 38 (25.2%) suffered from POD; 11 (14%) in scheduled and 27 (36%) in emergency patients. AChE levels showed no difference throughout groups or time course. Trauma patients had lower BuChE levels prior to surgery than scheduled patients (p < 0.001). Decline in BuChE levels correlated positively with the incidence of POD (1669 vs. 1175 U/l; p < 0.001). Emergency patients with BuChE levels below 1556 U/L were at highest risk for POD. There were no differences regarding length of stay between groups or incidence of POD. The type of anaesthesia had no influence regarding the incidence of POD. Only Charlson Comorbidity Index and Mini Nutrition Assessment demonstrated reliable strength in respect of POD. CONCLUSIONS: Perioperative measurement of BuChE activity can be used as a tool to identify patients at risk of POD. As a point-of-care test, quick results may alter the patients' course prior to the development of POD. TRIAL REGISTRATION: https://drks.de/search/de/trial/DRKS00017178 .

Synthetic 3D Spinal Vertebrae Reconstruction from Biplanar X-rays Utilizing Generative Adversarial Networks.

Computed tomography (CT) offers detailed insights into the internal anatomy of patients, particularly for spinal vertebrae examination. However, CT scans are associated with higher radiation exposure and cost compared to conventional X-ray imaging. In this study, we applied a Generative Adversarial Network (GAN) framework to reconstruct 3D spinal vertebrae structures from synthetic biplanar X-ray images, specifically focusing on anterior and lateral views. The synthetic X-ray images were generated using the DRRGenerator module in 3D Slicer by incorporating segmentations of spinal vertebrae in CT scans for the region of interest. This approach leverages a novel feature fusion technique based on X2CT-GAN to combine information from both views and employs a combination of mean squared error (MSE) loss and adversarial loss to train the generator, resulting in high-quality synthetic 3D spinal vertebrae CTs. A total of n = 440 CT data were processed. We evaluated the performance of our model using multiple metrics, including mean absolute error (MAE) (for each slice of the 3D volume (MAE0) and for the entire 3D volume (MAE)), cosine similarity, peak signal-to-noise ratio (PSNR), 3D peak signal-to-noise ratio (PSNR-3D), and structural similarity index (SSIM). The average PSNR was 28.394 dB, PSNR-3D was 27.432, SSIM was 0.468, cosine similarity was 0.484, MAE0 was 0.034, and MAE was 85.359. The results demonstrated the effectiveness of this approach in reconstructing 3D spinal vertebrae structures from biplanar X-rays, although some limitations in accurately capturing the fine bone structures and maintaining the precise morphology of the vertebrae were present. This technique has the potential to enhance the diagnostic capabilities of low-cost X-ray machines while reducing radiation exposure and cost associated with CT scans, paving the way for future applications in spinal imaging and diagnosis.

Capillary leak and endothelial permeability in critically ill patients: a current overview.

Capillary leak syndrome (CLS) represents a phenotype of increased fluid extravasation, resulting in intravascular hypovolemia, extravascular edema formation and ultimately hypoperfusion. While endothelial permeability is an evolutionary preserved physiological process needed to sustain life, excessive fluid leak-often caused by systemic inflammation-can have detrimental effects on patients' outcomes. This article delves into the current understanding of CLS pathophysiology, diagnosis and potential treatments. Systemic inflammation leading to a compromise of endothelial cell interactions through various signaling cues (e.g., the angiopoietin-Tie2 pathway), and shedding of the glycocalyx collectively contribute to the manifestation of CLS. Capillary permeability subsequently leads to the seepage of protein-rich fluid into the interstitial space. Recent insights into the importance of the sub-glycocalyx space and preserving lymphatic flow are highlighted for an in-depth understanding. While no established diagnostic criteria exist and CLS is frequently diagnosed by clinical characteristics only, we highlight more objective serological and (non)-invasive measurements that hint towards a CLS phenotype. While currently available treatment options are limited, we further review understanding of fluid resuscitation and experimental approaches to target endothelial permeability. Despite the improved understanding of CLS pathophysiology, efforts are needed to develop uniform diagnostic criteria, associate clinical consequences to these criteria, and delineate treatment options.

Performance of artificial intelligence-based algorithms to predict prolonged length of stay after head and neck cancer surgery.

Background: Medical resource management can be improved by assessing the likelihood of prolonged length of stay (LOS) for head and neck cancer surgery patients. The objective of this study was to develop predictive models that could be used to determine whether a patient's LOS after cancer surgery falls within the normal range of the cohort. Methods: We conducted a retrospective analysis of a dataset consisting of 300 consecutive patients who underwent head and neck cancer surgery between 2017 and 2022 at a single university medical center. Prolonged LOS was defined as LOS exceeding the 75th percentile of the cohort. Feature importance analysis was performed to evaluate the most important predictors for prolonged LOS. We then constructed 7 machine learning and deep learning algorithms for the prediction modeling of prolonged LOS. Results: The algorithms reached accuracy values of 75.40 (radial basis function neural network) to 97.92 (Random Trees) for the training set and 64.90 (multilayer perceptron neural network) to 84.14 (Random Trees) for the testing set. The leading parameters predicting prolonged LOS were operation time, ischemia time, the graft used, the ASA score, the intensive care stay, and the pathological stages. The results revealed that patients who had a higher number of harvested lymph nodes (LN) had a lower probability of recurrence but also a greater LOS. However, patients with prolonged LOS were also at greater risk of recurrence, particularly when fewer (LN) were extracted. Further, LOS was more strongly correlated with the overall number of extracted lymph nodes than with the number of positive lymph nodes or the ratio of positive to overall extracted lymph nodes, indicating that particularly unnecessary lymph node extraction might be associated with prolonged LOS. Conclusions: The results emphasize the need for a closer follow-up of patients who experience prolonged LOS. Prospective trials are warranted to validate the present results.

Finetuning of GLIDE stable diffusion model for AI-based text-conditional image synthesis of dermoscopic images.

Background: The development of artificial intelligence (AI)-based algorithms and advances in medical domains rely on large datasets. A recent advancement in text-to-image generative AI is GLIDE (Guided Language to Image Diffusion for Generation and Editing). There are a number of representations available in the GLIDE model, but it has not been refined for medical applications. Methods: For text-conditional image synthesis with classifier-free guidance, we have fine-tuned GLIDE using 10,015 dermoscopic images of seven diagnostic entities, including melanoma and melanocytic nevi. Photorealistic synthetic samples of each diagnostic entity were created by the algorithm. Following this, an experienced dermatologist reviewed 140 images (20 of each entity), with 10 samples originating from artificial intelligence and 10 from original images from the dataset. The dermatologist classified the provided images according to the seven diagnostic entities. Additionally, the dermatologist was asked to indicate whether or not a particular image was created by AI. Further, we trained a deep learning model to compare the diagnostic results of dermatologist versus machine for entity classification. Results: The results indicate that the generated images possess varying degrees of quality and realism, with melanocytic nevi and melanoma having higher similarity to real images than other classes. The integration of synthetic images improved the classification performance of the model, resulting in higher accuracy and precision. The AI assessment showed superior classification performance compared to dermatologist. Conclusion: Overall, the results highlight the potential of synthetic images for training and improving AI models in dermatology to overcome data scarcity.

Clinical and radiomics feature-based outcome analysis in lumbar disc herniation surgery.

BACKGROUND: Low back pain is a widely prevalent symptom and the foremost cause of disability on a global scale. Although various degenerative imaging findings observed on magnetic resonance imaging (MRI) have been linked to low back pain and disc herniation, none of them can be considered pathognomonic for this condition, given the high prevalence of abnormal findings in asymptomatic individuals. Nevertheless, there is a lack of knowledge regarding whether radiomics features in MRI images combined with clinical features can be useful for prediction modeling of treatment success. The objective of this study was to explore the potential of radiomics feature analysis combined with clinical features and artificial intelligence-based techniques (machine learning/deep learning) in identifying MRI predictors for the prediction of outcomes after lumbar disc herniation surgery. METHODS: We included n = 172 patients who underwent discectomy due to disc herniation with preoperative T2-weighted MRI examinations. Extracted clinical features included sex, age, alcohol and nicotine consumption, insurance type, hospital length of stay (LOS), complications, operation time, ASA score, preoperative CRP, surgical technique (microsurgical versus full-endoscopic), and information regarding the experience of the performing surgeon (years of experience with the surgical technique and the number of surgeries performed at the time of surgery). The present study employed a semiautomatic region-growing volumetric segmentation algorithm to segment herniated discs. In addition, 3D-radiomics features, which characterize phenotypic differences based on intensity, shape, and texture, were extracted from the computed magnetic resonance imaging (MRI) images. Selected features identified by feature importance analyses were utilized for both machine learning and deep learning models (n = 17 models). RESULTS: The mean accuracy over all models for training and testing in the combined feature set was 93.31 ± 4.96 and 88.17 ± 2.58. The mean accuracy for training and testing in the clinical feature set was 91.28 ± 4.56 and 87.69 ± 3.62. CONCLUSIONS: Our results suggest a minimal but detectable improvement in predictive tasks when radiomics features are included. However, the extent of this advantage should be considered with caution, emphasizing the potential of exploring multimodal data inputs in future predictive modeling.

Automated Detection and Measurement of Dural Sack Cross-Sectional Area in Lumbar Spine MRI Using Deep Learning.

Lumbar spine magnetic resonance imaging (MRI) is a critical diagnostic tool for the assessment of various spinal pathologies, including degenerative disc disease, spinal stenosis, and spondylolisthesis. The accurate identification and quantification of the dural sack cross-sectional area are essential for the evaluation of these conditions. Current manual measurement methods are time-consuming and prone to inter-observer variability. Our study developed and validated deep learning models, specifically U-Net, Attention U-Net, and MultiResUNet, for the automated detection and measurement of the dural sack area in lumbar spine MRI, using a dataset of 515 patients with symptomatic back pain and externally validating the results based on 50 patient scans. The U-Net model achieved an accuracy of 0.9990 and 0.9987 on the initial and external validation datasets, respectively. The Attention U-Net model reported an accuracy of 0.9992 and 0.9989, while the MultiResUNet model displayed a remarkable accuracy of 0.9996 and 0.9995, respectively. All models showed promising precision, recall, and F1-score metrics, along with reduced mean absolute errors compared to the ground truth manual method. In conclusion, our study demonstrates the potential of these deep learning models for the automated detection and measurement of the dural sack cross-sectional area in lumbar spine MRI. The proposed models achieve high-performance metrics in both the initial and external validation datasets, indicating their potential utility as valuable clinical tools for the evaluation of lumbar spine pathologies. Future studies with larger sample sizes and multicenter data are warranted to validate the generalizability of the model further and to explore the potential integration of this approach into routine clinical practice.

Angiopoietin-2 is associated with capillary leak and predicts complications after cardiac surgery.

BACKGROUND: Patients undergoing cardiac surgery are prone to numerous complications. Increased vascular permeability may be associated with morbidity and mortality due to hemodynamic instability, fluid overload, and edema formation. We hypothesized that markers of endothelial injury and inflammation are associated with capillary leak, ultimately increasing the risk of postoperative complications. METHODS: In this prospective, observational, multidisciplinary cohort study at our tertiary academic medical center, we recruited 405 cardiac surgery patients. Patients were assessed daily using body impedance electrical analysis, ultrasound, sublingual intravital microscopy, and analysis of serum biomarkers. Multivariable models, as well as machine learning, were used to study the association of angiopoietin-2 with extracellular water as well as common complications after cardiac surgery. RESULTS: The majority of patients underwent coronary artery bypass grafting, valvular, or aortic surgeries. Across the groups, extracellular water increased postoperatively (20 ± 6 preoperatively to 29 ± 7L on postoperative day 2; P < 0.001). Concomitantly, the levels of the biomarker angiopoietin-2 rose, showing a strong correlation based on the time points of measurements (r = 0.959, P = 0.041). Inflammatory (IL-6, IL-8, CRP) and endothelial biomarkers (VE-Cadherin, syndecan-1, ICAM-1) suggestive of capillary leak were increased. After controlling for common risk factors of edema formation, we found that an increase of 1 ng/mL in angiopoietin-2 was associated with a 0.24L increase in extracellular water (P < 0.001). Angiopoietin-2 showed increased odds for the development of acute kidney injury (OR 1.095 [95% CI 1.032, 1.169]; P = 0.004) and was furthermore associated with delayed extubation, longer time in the ICU, and a higher chance of prolonged dependence on vasoactive medication. Machine learning predicted postoperative complications when capillary leak was added to standard risk factors. CONCLUSIONS: Capillary leak and subsequent edema formation are relevant problems after cardiac surgery. Levels of angiopoietin-2 in combination with extracellular water show promising potential to predict postoperative complications after cardiac surgery. TRIAL REGISTRATION NUMBER: German Clinical Trials Registry (DRKS No. 00017057), Date of registration 05/04/2019, www.drks.de.

Direct assessment of microcirculation in shock: a randomized-controlled multicenter study.

PURPOSE: Shock is a life-threatening condition characterized by substantial alterations in the microcirculation. This study tests the hypothesis that considering sublingual microcirculatory perfusion variables in the therapeutic management reduces 30-day mortality in patients admitted to the intensive care unit (ICU) with shock. METHODS: This randomized, prospective clinical multicenter trial-recruited patients with an arterial lactate value above two mmol/L, requiring vasopressors despite adequate fluid resuscitation, regardless of the cause of shock. All patients received sequential sublingual measurements using a sidestream-dark field (SDF) video microscope at admission to the intensive care unit (± 4 h) and 24 (± 4) hours later that was performed blindly to the treatment team. Patients were randomized to usual routine or to integrating sublingual microcirculatory perfusion variables in the therapy plan. The primary endpoint was 30-day mortality, secondary endpoints were length of stay on the ICU and the hospital, and 6-months mortality. RESULTS: Overall, we included 141 patients with cardiogenic (n = 77), post cardiac surgery (n = 27), or septic shock (n = 22). 69 patients were randomized to the intervention and 72 to routine care. No serious adverse events (SAEs) occurred. In the interventional group, significantly more patients received an adjustment (increase or decrease) in vasoactive drugs or fluids (66.7% vs. 41.8%, p = 0.009) within the next hour. Microcirculatory values 24 h after admission and 30-day mortality did not differ [crude: 32 (47.1%) patients versus 25 (34.7%), relative risk (RR) 1.39 (0.91-1.97); Cox-regression: hazard ratio (HR) 1.54 (95% confidence interval (CI) 0.90-2.66, p = 0.118)]. CONCLUSION: Integrating sublingual microcirculatory perfusion variables in the therapy plan resulted in treatment changes that do not improve survival at all.

Determination of selective antegrade perfusion flow rate in aortic arch surgery to restore baseline cerebral near-infrared spectroscopy values: a single-centre observational study.

OBJECTIVES: Neuroprotection during aortic arch surgery involves selective antegrade cerebral perfusion. The parameters of cerebral perfusion, e.g. flow rate, are inconsistent across centres and are subject of debate. The aim of this study was to determine the cerebral perfusion flow rate during hypothermic circulatory arrest required to meet preoperative awake baseline regional cerebral oxygen saturation (rSO2). METHODS: Patients scheduled for aortic arch surgery with hypothermic circulatory arrest were enrolled in this prospective observational study. After initiation of hypothermic circulatory arrest, bilateral selective antegrade cerebral perfusion was established and cerebral flow rate was continuously increased. The primary end point was the difference of cerebral saturation from baseline during cerebral perfusion flow rates of 6, 8 and 10 ml/kg/min. RESULTS: A total of 40 patients were included. During antegrade cerebral perfusion, rSO2 was significantly lower than the baseline at 6 ml/kg/min [-7.3, 95% confidence interval (CI): -1.7, -12.9; P = 0.0015]. In contrast, flow rates of 8 and 10 ml/kg/min resulted in rSO2 that did not significantly differ from the baseline (-2; 95% CI: -4.3, 8.3; P > 0.99 and 1.8; 95% CI: -8.5%, 4.8%; P > 0.99). Cerebral saturation was significantly more likely to meet baseline values during selective antegrade cerebral perfusion with 8 ml/kg/min than at 6 ml/kg/min (44.1%; 95% CI: 27.4%, 60.8% vs 11.8%; 95% CI: 0.9%, 22.6%; P = 0.0001). CONCLUSIONS: At 8 ml/kg/min cerebral flow rate during selective antegrade cerebral perfusion, regional cerebral oximetry baseline values are significantly more likely to be achieved than at 6 ml/kg/min. Further increasing the cerebral flow rate to 10 ml/kg/min does not significantly improve rSO2.

Real-Time Multiplanar Reconstruction Imaging Using 3-Dimensional Transesophageal Echocardiography in Structural Heart Interventions.

The complexity of structural heart interventions has led to a demand for sophisticated periprocedural imaging guidance. Although traditional 2-dimensional (2D) transesophageal techniques are used widely, new-generation 3D ultrasound probes enable high temporal and spatial resolution. Multiplanar reconstruction of acquired 3D datasets has gained considerable momentum for precise imaging and to increase the validity of measurements. Previously, this technique was used after the acquisition of suitable 3D datasets. Recent advances in technology have enabled the use of live mode for multiplanar reconstruction across different ultrasound vendor platforms. The use of live multiplanar reconstruction can enhance the precision in real-time imaging, enable simultaneous visualization of structures of interest in multiple planes, reduce the need for probe manipulation, and thereby contribute to the success of the procedures. In this narrative review, the authors describe the rationale and utility for 3D transesophageal live multiplanar reconstruction, and outline its use for the structural heart interventions of mitral and tricuspid valve edge-to-edge repair, left atrial appendage occlusion, and the Lampoon procedure. A 3D transesophageal echocardiogram with live-multiplanar reconstruction has the potential to advance guidance of these complex interventions.

Argon preconditioning protects neuronal cells with a Toll-like receptor-mediated effect.

The noble gas argon has the potential to protect neuronal cells from cell death. So far, this effect has been studied in treatment after acute damage. Preconditioning using argon has not yet been investigated. In this study, human neuroblastoma SH-SY5Y cells were treated with different concentrations of argon (25%, 50%, and 74%; 21% O2, 5% CO2, balance nitrogen) at different time intervals before inflicting damage with rotenone (20 µM, 4 hours). Apoptosis was determined by flow cytometry after annexin V and propidium iodide staining. Surface expressions of Toll-like receptors 2 and 4 were also examined. Cells were also processed for analysis by western blot and qPCR to determine the expression of apoptotic and inflammatory proteins, such as extracellular-signal regulated kinase (ERK1/2), nuclear transcription factor-κB (NF-κB), protein kinase B (Akt), caspase-3, Bax, Bcl-2, interleukin-8, and heat shock proteins. Immunohistochemical staining was performed for TLR2 and 4 and interleukin-8. Cells were also pretreated with OxPAPC, an antagonist of TLR2 and 4 to elucidate the molecular mechanism. Results showed that argon preconditioning before rotenone application caused a dose-dependent but not a time-dependent reduction in the number of apoptotic cells. Preconditioning with 74% argon for 2 hours was used for further experiments showing the most promising results. Argon decreased the surface expression of TLR2 and 4, whereas OxPAPC treatment partially abolished the protective effect of argon. Argon increased phosphorylation of ERK1/2 but decreased NF-κB and Akt. Preconditioning inhibited mitochondrial apoptosis and the heat shock response. Argon also suppressed the expression of the pro-inflammatory cytokine interleukin-8. Immunohistochemistry confirmed the alteration of TLRs and interleukin-8. OxPAPC reversed the argon effect on ERK1/2, Bax, Bcl-2, caspase-3, and interleukin-8 expression, but not on NF-κB and the heat shock proteins. Taken together, argon preconditioning protects against apoptosis of neuronal cells and mediates its action via Toll-like receptors. Argon may represent a promising therapeutic alternative in various clinical settings, such as the treatment of stroke.

COVID-19 increases the risk for the onset of atrial fibrillation in hospitalized patients.

COVID-19 is associated with significant extrapulmonary symptoms. Myocardial involvement has been described for infections with SARS-CoV-2 which may lead to an increase in morbidity and mortality. The objective of our study was to investigate the association of COVID-19 and atrial fibrillation (AF) or atrial flutter (AFl) in hospitalized patients. This retrospective study used electronic medical records to detect patients with COVID-19 and their comorbidities within the Mass General Brigham hospital system. All patients ≥ 18 years who were hospitalized and received a PCR test for SARS-CoV-2 were screened for inclusion as well as patients from a pre-pandemic cohort. We matched on common risk factors for AF and then used multivariable logistic regression to estimate the odds for AF or AFl. Of 78,725 patients eligible for analysis, 11,004 COVID-19 negative patients were matched to 3,090 COVID-19 positive patients and 5005 pre-pandemic patients were matched to 2283 COVID-19 positive patients. After adjusting for demographics and comorbidities, COVID-19 positive patients had 1.19 times the odds (95% CI 1.00, 1.41) of developing AF compared to COVID-19 negative patients and 1.57 times the odds (95% CI 1.23, 2.00) of developing AF compared to pre-pandemic patients. Our study demonstrated an increased risk for AF, directing the attention for improved screening and treatment regimens for the sequelae of COVID-19. While COVID-19 continues to affect many people around the world, AF may be a significant cause for morbidity and mortality. Adequate detection and treatment of AF is essential to reduce the burden of disease.

Inhalative as well as Intravenous Administration of H2S Provides Neuroprotection after Ischemia and Reperfusion Injury in the Rats' Retina.

BACKGROUND: Neuronal ischemia-reperfusion injury (IRI), such as it can occur in glaucoma or strokes, is associated with neuronal cell death and irreversible loss of function of the affected tissue. Hydrogen sulfide (H2S) is considered a potentially neuroprotective substance, but the most effective route of application and the underlying mechanism remain to be determined. METHODS: Ischemia-reperfusion injury was induced in rats by a temporary increase in intraocular pressure (1 h). H2S was then applied by inhalation (80 ppm at 0, 1.5, and 3 h after reperfusion) or by intravenous administration of the slow-releasing H2S donor GYY 4137. After 24 h, the retinas were harvested for Western blotting, qPCR, and immunohistochemical staining. Retinal ganglion cell survival was evaluated 7 days after ischemia. RESULTS: Both inhalative and intravenously delivered H2S reduced retinal ganglion cell death with a better result from inhalative application. H2S inhalation for 1.5 h, as well as GYY 4137 treatment, increased p38 phosphorylation. Both forms of application enhanced the extracellular signal-regulated kinase 1/2 (ERK1/2) phosphorylation, and inhalation showed a significant increase at all three time points. H2S treatment also reduced apoptotic and inflammatory markers, such as caspase-3, intracellular adhesion molecule 1 (ICAM-1), vascular endothelial growth factor (VEGF), and inducible nitric oxide synthase (iNOS). The protective effect of H2S was partly abolished by the ERK1/2 inhibitor PD98059. Inhalative H2S also reduced the heat shock response including heme oxygenase (HO-1) and heat shock protein 70 (HSP-70) and the expression of radical scavengers such as superoxide dismutases (SOD1, SOD2) and catalase. CONCLUSION: Hydrogen sulfide acts, at least in part, via the mitogen-activated protein kinase (MAPK) ERK1/2 to reduce apoptosis and inflammation. Both inhalative H2S and intravenous GYY 4137 administrations can improve neuronal cell survival.

The association of the Activities of Daily Living and the outcome of old intensive care patients suffering from COVID-19.

PURPOSE: Critically ill old intensive care unit (ICU) patients suffering from Sars-CoV-2 disease (COVID-19) are at increased risk for adverse outcomes. This post hoc analysis investigates the association of the Activities of Daily Living (ADL) with the outcome in this vulnerable patient group. METHODS: The COVIP study is a prospective international observational study that recruited ICU patients ≥ 70 years admitted with COVID-19 (NCT04321265). Several parameters including ADL (ADL; 0 = disability, 6 = no disability), Clinical Frailty Scale (CFS), SOFA score, intensive care treatment, ICU- and 3-month survival were recorded. A mixed-effects Weibull proportional hazard regression analyses for 3-month mortality adjusted for multiple confounders. RESULTS: This pre-specified analysis included 2359 patients with a documented ADL and CFS. Most patients evidenced independence in their daily living before hospital admission (80% with ADL = 6). Patients with no frailty and no disability showed the lowest, patients with frailty (CFS ≥ 5) and disability (ADL < 6) the highest 3-month mortality (52 vs. 78%, p < 0.001). ADL was independently associated with 3-month mortality (ADL as a continuous variable: aHR 0.88 (95% CI 0.82-0.94, p < 0.001). Being "disable" resulted in a significant increased risk for 3-month mortality (aHR 1.53 (95% CI 1.19-1.97, p 0.001) even after adjustment for multiple confounders. CONCLUSION: Baseline Activities of Daily Living (ADL) on admission provides additional information for outcome prediction, although most critically ill old intensive care patients suffering from COVID-19 had no restriction in their ADL prior to ICU admission. Combining frailty and disability identifies a subgroup with particularly high mortality. TRIAL REGISTRATION NUMBER: NCT04321265.