Comparative efficacy of antioxidant therapies for sepsis and septic shock in the intensive care unit: A frequentist network meta-analysis.

Background: Antioxidant therapy is gaining traction in managing sepsis and septic shock, owing to its perceived positive impact on patient outcomes. This study sought to compare the efficacy of five antioxidant therapies (melatonin, vitamin C, vitamin E, selenium, and N-acetylcysteine, both individually and in combination with other compounds such as vitamin B1, hydrocortisone, propolis, and glutamine) in treating sepsis or septic shock in the intensive care unit (ICU). Methods: The study involved randomized and multi-arm trials with sepsis or septic shock patients using melatonin, vitamin C, vitamin E, selenium, or N-acetylcysteine. Studies were sourced from PubMed, Embase, Cochrane Library, ClinicalTrials.gov, and WHO - Clinical Trials Registry Platform for the frequentist network meta-analysis on 28-day mortality and Sequential Organ Failure Assessment (SOFA) scores. The risk of bias was assessed using the Physiotherapy Evidence Database scale. Therapies were compared directly and indirectly using R software. Results: The study of 56 trials involving 9,366 patients was included. Bias assessment revealed that 89.3 % of trials achieved excellent or good quality. Based on treatment ranking and pairwise comparisons, melatonin with propolis (SUCRA = 93.29 %) is effective in improving SOFA scores, statistically significant, with no publication bias (p= 0.73). High-dose vitamin C (SUCRA = 83.97 %), vitamin C with vitamin B1 (SUCRA = 78.72 %), and melatonin (SUCRA = 67.03 %) are potential therapies for organ dysfunction. Melatonin (SUCRA = 88.22 %) and high-dose vitamin C (SUCRA = 80.75 %) were the most effective in reducing 28-day mortality rates. However, analysis indicated that the results for 28-day mortality rates were not statistically significant. Also, these results contained publication bias (p= 0.02). Conclusion: The study offers fresh perspectives on antioxidant therapy treatments for sepsis or septic shock in ICU, emphasizing the combination of melatonin and propolis notably reduces SOFA scores for those patients.

EAR-UNet: A deep learning-based approach for segmentation of tympanic membranes from otoscopic images.

This paper presents a method for automatic segmentation of tympanic membranes (TMs) from video-otoscopic images based on deep fully convolutional neural network. Built upon the UNet architecture, the proposed EAR scheme is based on three main paradigms: EfficientNet for the encoder, Attention gate for the skip connection path, and Residual blocks for the decoder. The paper also introduces a new loss function term for the neural networks to perform segmentation tasks. Particularly, we propose to integrate EfficientNet-B4 into the encoder part of the UNet. In addition, the decoder part of the proposed network is constructed based on residual blocks from ResNet architecture. By this way, the proposed approach could take advantages of the EfficientNet and ResNet architectures such as preserving efficient reception field size for the model and avoiding overfitting problem. In addition, in the skip connection path, we employ the attention gate that can handle the varieties in shapes and sizes of interested objects, which are common issues in TM regions. Moreover, for network training, we proposed a new loss function term based on the shape distance between predicted and ground truth masks, and exploited the stochastic weight averaging to avoid being trapped in local minima. We evaluate the proposed approach on a TM dataset which includes 1012 otoscopic images from patients diagnosed with and without otitis media. Experimental results show that the proposed approach achieves high segmentation performance with the average Dice similarity coefficient of 0.929, without any pre- or post-processing steps, that outperforms other state-of-the-art methods.

Predicting ventricular tachyarrhythmia in patients with systolic heart failure based on texture features of the gray zone from contrast-enhanced magnetic resonance imaging.

BACKGROUND: Previous research showed that gray zone detected by late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) imaging could help identify high-risk patients. In this study, we investigated whether LGE-CMR gray zone heterogeneity measured by image texture features could predict cardiovascular events in patients with heart failure (HF). METHOD: This is a retrospective cohort study. Patients with systolic HF undergoing CMR imaging were enrolled. Cine and LGE images were analyzed to derive left ventricular (LV) function and scar characteristics. Entropy and uniformity of gray zones were derived by texture analysis. RESULTS: A total of 82 systolic HF patients were enrolled. After a median 1021 (25%-75% quartiles, 205-2066) days of follow-up, the entropy (0.60 ± 0.260 vs. 0.87 ± 0.28, p = 0.013) was significantly increased while the uniformity (0.68 ± 0.14 vs. 0.53±0.15, p = 0.016) was significantly decreased in patients with ventricular tachycardia or ventricular fibrillation (VT/VF). The percentage of core scar (21.9 ± 10.6 vs. 30.6 ± 10.4, p = 0.029) was higher in cardiac mortality group than survival group while the uniformity (0.55 ± 0.17 vs. 0.67 ± 0.14, p = 0.018) was lower in cardiac mortality group than survival group. A multivariate Cox regression model showed that higher percentage of gray zone area (HR = 8.805, 1.620-47.84, p = 0.045), higher entropy (>0.85) (HR = 1.391, 1.092-1.772, p = 0.024) and lower uniformity (≦0.54) (HR = 0.535, 0.340-0.842, p = 0.022) were associated with VT/VF attacks. Also, higher percentage of gray zone area (HR = 5.716, 1.379-23.68, p = 0.017), core scar zone (HR = 1.939, 1.056-3.561, p = 0.025), entropy (>0.85) (HR = 1.434, 1.076-1.911, p = 0.008) and lower uniformity (≦0.54) (HR = 0.513, 0.296-0.888, p = 0.009) were associated with cardiac mortality during follow-up. CONCLUSIONS: Gray zone heterogeneity by texture analysis method could provide additional prognostic value to traditional LGE-CMR substrate analysis method.

Probing age-related changes in cardio-respiratory dynamics by multimodal coupling assessment.

Quantifying respiratory sinus arrhythmia (RSA) can provide an index of parasympathetic function. Fourier spectral analysis, the most widely used approach, estimates the power of the heart rate variability in the frequency band of breathing. However, it neglects the time-varying characteristics of the transitions as well as the nonlinear properties of the cardio-respiratory coupling. Here, we propose a novel approach based on Hilbert-Huang transform, called the multimodal coupling analysis (MMCA) method, to assess cardio-respiratory dynamics by examining the instantaneous nonlinear phase interactions between two interconnected signals (i.e., heart rate and respiration) and compare with the counterparts derived from the wavelet-based method. We used an online database. The corresponding RSA components of the 90-min ECG and respiratory signals of 20 young and 20 elderly healthy subjects were extracted and quantified. A cycle-based analysis and a synchro-squeezed wavelet transform were also introduced to assess the amplitude or phase changes of each respiratory cycle. Our results demonstrated that the diminished mean and standard deviation of the derived dynamical RSA activities can better discriminate between elderly and young subjects. Moreover, the degree of nonlinearity of the cycle-by-cycle RSA waveform derived from the differences between the instantaneous frequency and the mean frequency of each respiratory cycle was significantly decreased in the elderly subjects by the MMCA method. The MMCA method in combination with the cycle-based analysis can potentially be a useful tool to depict the aging changes of the parasympathetic function as well as the waveform nonlinearity of RSA compared to the Fourier-based high-frequency power and the wavelet-based method.

Empirical Mode Decomposition and Monogenic Signal-Based Approach for Quantification of Myocardial Infarction From MR Images.

Quantification of myocardial infarction on late Gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) images into heterogeneous infarct periphery (or gray zone) and infarct core plays an important role in cardiac diagnosis, especially in identifying patients at high risk of cardiovascular mortality. However, quantification task is challenging due to noise corrupted in cardiac MR images, the contrast variation, and limited resolution of images. In this study, we propose a novel approach for automatic myocardial infarction quantification, termed DEMPOT, which consists of three key parts: Decomposition of image into intrinsic modes, monogenic phase performing on combined dominant modes, and multilevel Otsu thresholding on the phase. In particular, inspired by the Hilbert-Huang transform, we perform the multidimensional ensemble empirical mode decomposition and 2-D generalization of the Hilbert transform known as the Riesz transform on the MR image to obtain the monogenic phase that is robust to noise and contrast variation. Then, a two-stage algorithm using multilevel Otsu thresholding is accomplished on the monogenic phase to automatically quantify the myocardium into healthy, gray zone, and infarct core regions. Experiments on LGE-CMR images with myocardial infarction from 82 patients show the superior performance of the proposed approach in terms of reproducibility, robustness, and effectiveness.

Development of an Automatic Diagnostic Algorithm for Pediatric Otitis Media.

HYPOTHESIS: The artificial intelligence and image processing technology can develop automatic diagnostic algorithm for pediatric otitis media (OM) with accuracy comparable to that from well-trained otologists. BACKGROUND: OM is a public health issue that occurs commonly in pediatric population. Caring for OM may incur significant indirect cost that stems mainly from loss of school or working days seeking for medical consultation. It makes great sense for the homecare of OM. In this study, we aim to develop an automatic diagnostic algorithm for pediatric OM. METHODS: A total of 1,230 otoscopic images were collected. Among them, 214 images diagnosed of acute otitis media (AOM) and otitis media with effusion (OME) are used as the database for image classification in this study. For the OM image classification system, the image database is randomly partitioned into the test and train subsets. Of each image in the train and test sets, the desired eardrum image region is first segmented, then multiple image features such as color, and shape are extracted. The multitask joint sparse representation-based classification to combine different features of the OM image is used for classification. RESULTS: The multitask joint sparse representation algorithm was applied for the classification of the AOM and OME images. The approach is able to differentiate the OME from AOM images and achieves the classification accuracy as high as 91.41%. CONCLUSION: Our results demonstrated that this automatic diagnosis algorithm has acceptable accuracy to diagnose pediatric OM. The cost-effective algorithm can assist parents for early detection and continuous monitoring at home to decrease consequence of the disease.

Endocardial Remodeling in Heart Failure Patients with Impaired and Preserved Left Ventricular Systolic Function--A Magnetic Resonance Image Study.

Left ventricular (LV) trabeculation has been studied in certain forms of cardiomyopathy. However, the changes of LV endocardial trabeculation during the remodeling process leading to heart failure (HF) are unclear. Seventy-four patients with systolic heart failure (SHF), 65 with heart failure with preserved ejection fraction (HFpEF) and 61 without HF were prospectively enrolled. All subjects received magnetic resonance imaging (MRI) study including cine, T1 and late gadolinium enhancement (LGE) images. Trabecular-papillary muscle (TPM) mass, fractal dimension (FD) and extracellular volume (ECV) were derived. The results showed that TPM mass index was higher in patients with SHF than that in patients with HFpEF and non-HF. The TPM mass-LV mass ratio (TPMm/LVM) was higher in SHF group than that in HFpEF and non-HF. FD was not different among groups. The presence of LGE was inversely associated with TPM mass index and TPMm/LVM while the ECV were positively associated with TPMm/LVM. The FD was positively associated with LV chamber size. In conclusion, TPM increases in patients with SHF and are probably related to myocardial cell hypertrophy and fibrotic repair during remodeling. The FD increases with the dilatation of LV chamber but remain unchanged with the deterioration of LV function.

Acoustic-integrated dynamic MR imaging for a patient with obstructive sleep apnea.

Obstructive sleep apnea syndrome (OSAS) is caused by multi-level upper airway obstruction. Anatomic changes at the sites of obstruction may modify the physical or acoustic properties of snores. The surgical success of OSA depends upon precise localization of obstructed levels. We present a case of OSAS who received simultaneous dynamic MRI and snore acoustic recordings. The synchronized image and acoustic information successfully characterize the sites of temporal obstruction during sleep-disordered breathing events.

Synchronized imaging and acoustic analysis of the upper airway in patients with sleep-disordered breathing.

Progressive narrowing of the upper airway increases airflow resistance and can produce snoring sounds and apnea/hypopnea events associated with sleep-disordered breathing due to airway collapse. Recent studies have shown that acoustic properties during snoring can be altered with anatomic changes at the site of obstruction. To evaluate the instantaneous association between acoustic features of snoring and the anatomic sites of obstruction, a novel method was developed and applied in nine patients to extract the snoring sounds during sleep while performing dynamic magnetic resonance imaging (MRI). The degree of airway narrowing during the snoring events was then quantified by the collapse index (ratio of airway diameter preceding and during the events) and correlated with the synchronized acoustic features. A total of 201 snoring events (102 pure retropalatal and 99 combined retropalatal and retroglossal events) were recorded, and the collapse index as well as the soft tissue vibration time were significantly different between pure retropalatal (collapse index, 2 ± 11%; vibration time, 0.2 ± 0.3 s) and combined (retropalatal and retroglossal) snores (collapse index, 13 ± 7% [P ≤ 0.0001]; vibration time, 1.2 ± 0.7 s [P ≤ 0.0001]). The synchronized dynamic MRI and acoustic recordings successfully characterized the sites of obstruction and established the dynamic relationship between the anatomic site of obstruction and snoring acoustics.