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Significance: Diagnosis of cancerous and pre-cancerous oral lesions at early stages is critical for the improvement of patient care, to increase survival rates and minimize the invasiveness of tumor resection surgery. Unfortunately, oral precancerous and early-stage cancerous lesions are often difficult to distinguish from oral benign lesions with the existing diagnostic tools used during standard clinical oral examination. In consequence, early diagnosis of oral cancer can be achieved in only about 30% of patients. Therefore, clinical diagnostic technologies for fast, minimally invasive, and accurate oral cancer screening are urgently needed., Aim: This study investigated the use of multispectral autofluorescence imaging endoscopy for the automated and noninvasive discrimination of cancerous and precancerous from benign oral epithelial lesions., Approach: In vivo multispectral autofluorescence endoscopic images of clinically suspicious oral lesions were acquired from 67 patients undergoing tissue biopsy examination. The imaged lesions were classified as precancerous ( n = 4), cancerous ( n = 29), and benign ( n = 34) lesions based on histopathology diagnosis. Multispectral autofluorescence intensity feature maps were generated for each oral lesion and used to train and optimize support vector machine (SVM) models for automated discrimination of cancerous and precancerous from benign oral lesions., Results: After a leave-one-patient-out cross-validation strategy, an optimized SVM model developed with four multispectral autofluorescence features yielded levels of sensitivity and specificity of 85% and 71%, respectively and overall accuracy of 78% in the discrimination of cancerous/precancerous versus benign oral lesions., Conclusion: This study demonstrates the potentials of a computer-assisted detection system based on multispectral autofluorescence imaging endoscopy for the early detection of cancerous and precancerous oral lesions., Competing Interests: Disclosures The authors declare no conflicts of interest.

Background: Tocilizumab (TCZ) has been proposed as potential rescue therapy for severe COVID-19. No previous study has primarily assessed the role of TCZ in preventing severe COVID-19-related multiorgan dysfunction. Hence, this multicenter cohort study aimed to evaluate the effectiveness of TCZ early use versus standard of care in preventing severe COVID-19-related multiorgan dysfunction in COVID-19 critically ill patients during intensive care unit (ICU) stay. Methods: A multicenter, retrospective cohort study includes critically ill adult patients with COVID-19 admitted to the ICUs. Patients were categorized into two groups, the treatment group includes patients who received early TCZ therapy within 24 hours of ICU admission and the control group includes patients who received standard of care. The primary outcome was the multiorgan dysfunction on day three of the ICU admission. The secondary outcomes were 30-day, and in-hospital mortality, ventilator-free days, hospital length of stay (LOS), ICU LOS, and ICU-related complications. Results: After propensity score matching, 300 patients were included in the analysis based on predefined criteria with a ratio of 1:2. Patients who received TCZ had lower multiorgan dysfunction score on day three of ICU admission compared to the control group (beta coefficient: -0.13, 95% CI: -0.26, -0.01, P -value = 0.04). Moreover, respiratory failure requiring MV was statistically significantly lower in patients who received early TCZ compared to the control group (OR 0.52; 95% CI 0.31, 0.91, P -value = 0.02). The 30-day and in-hospital mortality were significantly lower in patients who received TCZ than those who did not (HR 0.56; 95% CI 0.37, 0.85, P -value = 0 .006 and HR 0.54; 95% CI 0.36, 0.82, P -value = 0.003, respectively). Conclusion: In addition to the mortality benefits associated with early TCZ use within 24 hours of ICU admission, the use of TCZ was associated with a significantly lower multiorgan dysfunction score on day three of ICU admission in critically ill patients with COVID-19.

Deep learning approaches for medical image analysis are limited by small data set size due to factors such as patient privacy and difficulties in obtaining expert labelling for each image. In medical imaging system development pipelines, phases for system development and classification algorithms often overlap with data collection, creating small disjoint data sets collected at numerous locations with differing protocols. In this setting, merging data from different data collection centers increases the amount of training data. However, a direct combination of datasets will likely fail due to domain shifts between imaging centers. In contrast to previous approaches that focus on a single data set, we add a domain adaptation module to a neural network and train using multiple data sets. Our approach encourages domain invariance between two multispectral autofluorescence imaging (maFLIM) data sets of in vivo oral lesions collected with an imaging system currently in development. The two data sets have differences in the sub-populations imaged and in the calibration procedures used during data collection. We mitigate these differences using a gradient reversal layer and domain classifier. Our final model trained with two data sets substantially increases performance, including a significant increase in specificity. We also achieve a significant increase in average performance over the best baseline model train with two domains (p = 0.0341). Our approach lays the foundation for faster development of computer-aided diagnostic systems and presents a feasible approach for creating a robust classifier that aligns images from multiple data centers in the presence of domain shifts.

Background: Thrombotic events are common in critically ill patients with COVID-19 and have been linked with COVID-19- induced hyperinflammatory state. In addition to anticoagulant effects, heparin and its derivatives have various anti-inflammatory and immunomodulatory properties that may affect patient outcomes. This study compared the effectiveness and safety of prophylactic standard-doses of enoxaparin and unfractionated heparin (UFH) in critically ill patients with COVID-19.  METHODS: A multicenter, retrospective cohort study included critically ill adult patients with COVID-19 admitted to the ICU between March 2020 and July 2021. Patients were categorized into two groups based on the type of pharmacological VTE thromboprophylaxis given in fixed doses (Enoxaparin 40 mg SQ every 24 hours versus UFH 5000 Units SQ every 8 hours) throughout their ICU stay. The primary endpoint was all cases of thrombosis. Other endpoints were considered secondary. Propensity score (PS) matching was used to match patients (1:1 ratio) between the two groups based on the predefined criteria. Multivariable logistic, Cox proportional hazards, and negative binomial regression analysis were used as appropriate.  RESULTS: A total of 306 patients were eligible based on the eligibility criteria; 130 patients were included after PS matching (1:1 ratio). Patients who received UFH compared to enoxaparin had higher all thrombosis events at crude analysis (18.3% vs. 4.6%; p-value = 0.02 as well in logistic regression analysis (OR: 4.10 (1.05, 15.93); p-value = 0.04). Although there were no significant differences in all bleeding cases and major bleeding between the two groups (OR: 0.40 (0.07, 2.29); p-value = 0.31 and OR: 1.10 (0.14, 8.56); p-value = 0.93, respectively); however, blood transfusion requirement was higher in the UFH group but did not reach statistical significance (OR: 2.98 (0.85, 10.39); p-value = 0.09). The 30-day and in-hospital mortality were similar between the two groups at Cox hazards regression analysis. In contrast, hospital LOS was longer in the UFH group; however, it did not reach the statistically significant difference (beta coefficient: 0.22; 95% CI: -0.03, 0.48; p-value = 0.09)., Conclusion: Prophylactic enoxaparin use in critically ill patients with COVID-19 may significantly reduce all thrombosis cases with similar bleeding risk compared to UFH., (© 2022. The Author(s).)

Background: Aspirin is widely used as a cardioprotective agent due to its antiplatelet and anti-inflammatory properties. The literature has assessed and evaluated its role in hospitalized COVID-19 patients. However, no data are available regarding its role in COVID-19 critically ill patients. This study aimed to evaluate the use of low-dose aspirin (81-100 mg) and its impact on outcomes in critically ill patients with COVID-19., Method: A multicenter, retrospective cohort study of all critically ill adult patients with confirmed COVID-19 admitted to intensive care units (ICUs) between March 1, 2020, and March 31, 2021. Eligible patients were classified into two groups based on aspirin use during ICU stay. The primary outcome was in-hospital mortality, and other outcomes were considered secondary. Propensity score matching was used (1:1 ratio) based on the selected criteria., Results: A total of 1033 patients were eligible, and 352 patients were included after propensity score matching. The in-hospital mortality (HR 0.73 [0.56, 0.97], p = 0.03) was lower in patients who received aspirin during stay. Conversely, patients who received aspirin had a higher odds of major bleeding than those in the control group (OR 2.92 [0.91, 9.36], p = 0.07); however, this was not statistically significant. Additionally, subgroup analysis showed a possible mortality benefit for patients who used aspirin therapy prior to hospitalization and continued during ICU stay (HR 0.72 [0.52, 1.01], p = 0.05), but not with the new initiation of aspirin (HR 1.22 [0.68, 2.20], p = 0.50)., Conclusion: Continuation of aspirin therapy during ICU stay in critically ill patients with COVID-19 who were receiving it prior to ICU admission may have a mortality benefit; nevertheless, it may be associated with an increased risk of significant bleeding. Appropriate evaluation for safety versus benefits of utilizing aspirin therapy during ICU stay in COVID19 critically ill patients is highly recommended.

Early detection is critical for improving the survival rate and quality of life of oral cancer patients; unfortunately, dysplastic and early-stage cancerous oral lesions are often difficult to distinguish from oral benign lesions during standard clinical oral examination. Therefore, there is a critical need for novel clinical technologies that would enable reliable oral cancer screening. The autofluorescence properties of the oral epithelial tissue provide quantitative information about morphological, biochemical, and metabolic tissue and cellular alterations accompanying carcinogenesis. This study aimed to identify novel biochemical and metabolic autofluorescence biomarkers of oral dysplasia and cancer that could be clinically imaged using novel multispectral autofluorescence lifetime imaging (maFLIM) endoscopy technologies. In vivo maFLIM clinical endoscopic images of benign, precancerous, and cancerous lesions from 67 patients were acquired using a novel maFLIM endoscope. Widefield maFLIM feature maps were generated, and statistical analyses were applied to identify maFLIM features providing contrast between dysplastic/cancerous vs. benign oral lesions. A total of 14 spectral and time-resolved maFLIM features were found to provide contrast between dysplastic/cancerous vs. benign oral lesions, representing novel biochemical and metabolic autofluorescence biomarkers of oral epithelial dysplasia and cancer. To the best of our knowledge, this is the first demonstration of clinical widefield maFLIM endoscopic imaging of novel biochemical and metabolic autofluorescence biomarkers of oral dysplasia and cancer, supporting the potential of maFLIM endoscopy for early detection of oral cancer., Competing Interests: The authors declare no conflicts of interest., (© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.)

Background: Severe coronavirus disease 2019 (COVID-19) can boost the systematic inflammatory response in critically ill patients, causing a systemic hyperinflammatory state leading to multiple complications. In COVID-19 patients, the use of inhaled corticosteroids (ICS) is surrounded by controversy regarding their impacts on viral infections. This study aims to evaluate the safety and efficacy of ICS in critically ill patients with COVID-19 and its clinical outcomes. Method: A multicenter, noninterventional, cohort study for critically ill patients with COVID-19 who received ICS. All patients aged ≥ 18 years old with confirmed COVID-19 and admitted to intensive care units (ICUs) between March 1, 2020 and March 31, 2021 were screened. Eligible patients were classified into two groups based on the use of ICS ± long-acting beta-agonists (LABA) during ICU stay. Propensity score (PS)-matched was used based on patient's Acute Physiology and Chronic Health Evaluation II (APACHE II) score, Sequential Organ Failure Assessment (SOFA) score, systemic corticosteroids use, and acute kidney injury (AKI) within 24 h of ICU admission. We considered a P -value of < 0.05 statistically significant. Results: A total of 954 patients were eligible; 130 patients were included after PS matching (1:1 ratio). The 30-day mortality (hazard ratio [HR] [95% confidence interval [CI]]: 0.53 [0.31, 0.93], P -value = 0.03) was statistically significant lower in patients who received ICS. Conversely, the in-hospital mortality, ventilator-free days (VFDs), ICU length of stay (LOS), and hospital LOS were not statistically significant between the two groups. Conclusion: The use of ICS ± LABA in COVID-19 patients may have survival benefits at 30 days. However, it was not associated with in-hospital mortality benefits nor VFDs.

In contrast to previous studies that focused on classical machine learning algorithms and hand-crafted features, we present an end-to-end neural network classification method able to accommodate lesion heterogeneity for improved oral cancer diagnosis using multispectral autofluorescence lifetime imaging (maFLIM) endoscopy. Our method uses an autoencoder framework jointly trained with a classifier designed to handle overfitting problems with reduced databases, which is often the case in healthcare applications. The autoencoder guides the feature extraction process through the reconstruction loss and enables the potential use of unsupervised data for domain adaptation and improved generalization. The classifier ensures the features extracted are task-specific, providing discriminative information for the classification task. The data-driven feature extraction method automatically generates task-specific features directly from fluorescence decays, eliminating the need for iterative signal reconstruction. We validate our proposed neural network method against support vector machine (SVM) baselines, with our method showing a 6.5%-8.3% increase in sensitivity. Our results show that neural networks that implement data-driven feature extraction provide superior results and enable the capacity needed to target specific issues, such as inter-patient variability and the heterogeneity of oral lesions.Clinical relevance- We improve standard classification algorithms for in vivo diagnosis of oral cancer lesions from maFLIm for clinical use in cancer screening, reducing unnecessary biopsies and facilitating early detection of oral cancer.

Multispectral autofluorescence lifetime imaging (maFLIM) can be used to clinically image a plurality of metabolic and biochemical autofluorescence biomarkers of oral epithelial dysplasia and cancer. This study tested the hypothesis that maFLIM-derived autofluorescence biomarkers can be used in machine-learning (ML) models to discriminate dysplastic and cancerous from healthy oral tissue. Clinical widefield maFLIM endoscopy imaging of cancerous and dysplastic oral lesions was performed at two clinical centers. Endoscopic maFLIM images from 34 patients acquired at one of the clinical centers were used to optimize ML models for automated discrimination of dysplastic and cancerous from healthy oral tissue. A computer-aided detection system was developed and applied to a set of endoscopic maFLIM images from 23 patients acquired at the other clinical center, and its performance was quantified in terms of the area under the receiver operating characteristic curve (ROC-AUC). Discrimination of dysplastic and cancerous from healthy oral tissue was achieved with an ROC-AUC of 0.81. This study demonstrates the capabilities of widefield maFLIM endoscopy to clinically image autofluorescence biomarkers that can be used in ML models to discriminate dysplastic and cancerous from healthy oral tissue. Widefield maFLIM endoscopy thus holds potential for automated in situ detection of oral dysplasia and cancer.

Background: Superinfection is a new isolate pathogen after 48 h of antibiotic treatment or within one week of treatment discontinuation. In many studies carbapenem and piperacillin-tazobactam were associated with high risk of superinfection., Aim: To evaluate the rate of superinfections during carbapenem and piperacillin/tazobactam treatment. Also, to identify risk factors for superinfections., Methods: A Retrospective observational study was conducted in King Abdulaziz Medical City. Approval from the institutional Review Board was obtained. The study included all adult patient treated with carbapenem or piperacillin/tazobactam for more than 72 h. Univariate and multivariate analysis was conducted to compare piperacillin/tazobactam versus carbapenems and to identify the associated risk factor to develop superinfection., Finding: 507 patients were included in this study. The mean age of the patients was 61 years ± 19.33. Of these, 278 received carbapenems and 229 received piperacillin/tazobactam. In univariate analysis superinfections were significantly higher with carbapenems compared with piperacillin-tazobactam (28.77% versus 20.96%; P value = 0.044). After adjustment of cofounders in multivariate analysis, presence of tracheostomy, endotracheal ventilation, foley catheter and duration of antibiotic were associated with higher risk to developed superinfection adjusted odd ratio (aOR) 3.23 (95% CI,1.39-7.52) P < 0.01, aOR 2.556 (95% CI,1.30-5.02) P < 0.01, aOR 2.20 (95% CI,1.35-3.61) P < 0.001, aOR 1.051(95% CI,1.02-1.08) P < 0.001 respectively, but not carbapenems use aOR 1.052 (95% CI,0.657-1.685)., Conclusions: The use of carbapenems were not associated with higher risk to developed superinfection. The most important risk factors associated with superinfection were presence of tracheostomy, endotracheal mechanical ventilation, Foley catheter and the duration of antibiotics., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Introduction: Incomplete head and neck cancer resection occurs in up to 85% of cases, leading to increased odds of local recurrence and regional metastases; thus, image-guided surgical tools for accurate, in situ and fast detection of positive margins during head and neck cancer resection surgery are urgently needed. Oral epithelial dysplasia and cancer development is accompanied by morphological, biochemical, and metabolic tissue and cellular alterations that can modulate the autofluorescence properties of the oral epithelial tissue., Objective: This study aimed to test the hypothesis that autofluorescence biomarkers of oral precancer and cancer can be clinically imaged and quantified by means of multispectral fluorescence lifetime imaging (FLIM) endoscopy., Methods: Multispectral autofluorescence lifetime images of precancerous and cancerous lesions from 39 patients were imaged in vivo using a novel multispectral FLIM endoscope and processed to generate widefield maps of biochemical and metabolic autofluorescence biomarkers of oral precancer and cancer., Results: Statistical analyses applied to the quantified multispectral FLIM endoscopy based autofluorescence biomarkers indicated their potential to provide contrast between precancerous/cancerous vs. healthy oral epithelial tissue., Conclusion: To the best of our knowledge, this study represents the first demonstration of label-free biochemical and metabolic clinical imaging of precancerous and cancerous oral lesions by means of widefield multispectral autofluorescence lifetime endoscopy. Future studies will focus on demonstrating the capabilities of endogenous multispectral FLIM endoscopy as an image-guided surgical tool for positive margin detection during head and neck cancer resection surgery., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)