Delirium is a common postoperative neurologic complication among older adults. Despite its prevalence (14%-50%) and likely association with inflammation, the exact mechanisms that underpin postoperative delirium are unclear. This project aimed to characterize systemic and central nervous system (CNS) inflammatory changes following surgery in mice and humans. Matched plasma and cerebrospinal fluid (CSF) samples from the \"Investigating Neuroinflammation Underlying Postoperative Brain Connectivity Changes, Postoperative Cognitive Dysfunction, Delirium in Older Adults\" (INTUIT; NCT03273335) study were compared to murine endpoints. Delirium-like behavior was evaluated in aged mice using the 5-Choice Serial Reaction Time Test (5-CSRTT). Using a well established orthopedic surgical model in the FosTRAP reporter mouse we detected neuronal changes in the prefrontal cortex, an area implicated in attention, but notably not in the hippocampus. In aged mice, plasma interleukin-6 (IL-6), chitinase-3-like protein 1 (YKL-40), and neurofilament light chain (NfL) levels increased after orthopedic surgery, but hippocampal YKL-40 expression was decreased. Given the growing evidence for a YKL-40 role in delirium and other neurodegenerative conditions, we assayed human plasma and CSF samples. Plasma YKL-40 levels were similarly increased after surgery, with a trend toward a greater postoperative plasma YKL-40 increase in patients with delirium. However, YKL-40 levels in CSF decreased following surgery, which paralleled the findings in the mouse brain. Finally, we confirmed changes in the blood-brain barrier (BBB) as early as 9 h after surgery in mice, which warrants more detailed and acute evaluations of BBB integrity following surgery in humans. Together, these results provide a nuanced understanding of neuroimmune interactions underlying postoperative delirium in mice and humans, and highlight translational biomarkers to test potential cellular targets and mechanisms.

Over the past decade, our understanding of human diseases has rapidly grown from the rise of single-cell spatial biology. While conventional tissue imaging has focused on visualizing morphological features, the development of multiplex tissue imaging from fluorescence-based methods to DNA- and mass cytometry-based methods has allowed visualization of over 60 markers on a single tissue section. The advancement of spatial biology with a single-cell resolution has enabled the visualization of cell-cell interactions and the tissue microenvironment, a crucial part to understanding the mechanisms underlying pathogenesis. Alongside the development of extensive marker panels which can distinguish distinct cell phenotypes, multiplex tissue imaging has facilitated the analysis of high dimensional data to identify novel biomarkers and therapeutic targets, while considering the spatial context of the cellular environment. This mini-review provides an overview of the recent advancements in multiplex imaging technologies and examines how these methods have been used in exploring pathogenesis and biomarker discovery in cancer, autoimmune and infectious diseases.

UNASSIGNED: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models.
UNASSIGNED: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks\' test to assess publication bias.
UNASSIGNED: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00.
UNASSIGNED: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

UNASSIGNED: Non-alcoholic fatty liver disease (NAFLD) is widespread chronic disease of the live in humans with the prevalence of 30% of the United States population.1,2 The goal of the study is to validate the performance of quantitative ultrasound algorithms in the assessment of hepatic steatosis in patients with suspected NAFLD.
UNASSIGNED: This prospective study enrolled a total of 31 patients with clinical suspicion of NAFLD to receive liver fat measurements by quantitative ultrasound and reference MRI measurements (proton density fat-fraction, PDFF). The following ultrasound (US) parameters based on both raw ultrasound RF (Radio Frequency) data and 2D B-mode images of the liver were analyzed with subsequent correlation with MRI-PDFF: hepatorenal index, acoustic attenuation coefficient, Nakagami coefficient parameter, shear wave viscosity, shear wave dispersion and shear wave elasticity. Ultrasound parameters were also correlated with the presence of hypertension and diabetes.
UNASSIGNED: The mean (± SD) age and body mass index of the patients were 49.03 (± 12.49) and 30.12 (± 6.15), respectively. Of the aforementioned ultrasound parameters, the hepatorenal index and acoustic attenuation coefficient showed a strong correlation with MRI-PDFF derivations of hepatic steatosis, with r-values of 0.829 and 0.765, respectively. None of the remaining US parameters showed strong correlations with PDFF. Significant differences in Nakagami parameters and acoustic attenuation coefficients were found in those patients with and without hypertension.
UNASSIGNED: Hepatorenal index and acoustic attenuation coefficient correlate well with MRI-PDFF-derived measurements of hepatic steatosis. Quantitative ultrasound is a promising tool for the diagnosis and assessment of patients with NAFLD.

UNASSIGNED: Atrial Epicardial Adipose Tissue (EAT) is presumably involved in the pathogenesis of atrial fibrillation (AF). The transient nature of postoperative AF (POAF) suggests that surgery-induced triggers provoke an unmasking of a pre-existent AF substrate. The aim is to investigate the association between the volume of EAT and the occurrence of POAF. We hypothesise that the likelihood of developing POAF is higher in patients with high compared to low left atrial (LA) EAT volumes.
UNASSIGNED: Quantification of LA EAT based on the Hounsfield Units using custom made software was performed on pre-operative coronary computed tomography angiography scans of patients who underwent cardiac surgery between 2009 and 2019. Patients with mitral valve disease were excluded.
UNASSIGNED: A total of 83 patients were included in this study (CABG = 34, aortic valve = 33, aorta ascendens n = 7, combination n = 9), of which 43 patients developed POAF. The EAT percentage in the LA wall nor indexed EAT volumes differed between patients with POAF and with sinus rhythm (all P > 0.05). In multivariable analysis, age and LA volume index (LAVI) were the only independent predictors for early POAF (OR: 1.076 and 1.056, respectively).
UNASSIGNED: As expected, advanced age and LAVI were independent predictors of POAF. However, the amount of local EAT was not associated with the occurrence of AF after cardiac surgery. This suggests that the role of EAT in POAF is rather limited, or that the association of EAT in the early phase of POAF is obscured by the dominance of surgical-induced triggers.

UNASSIGNED: Accurate prediction of treatment response to neoadjuvant chemotherapy (NACT) in individual patients with locally advanced gastric cancer (LAGC) is essential for personalized medicine. We aimed to develop and validate a deep learning radiomics nomogram (DLRN) based on pretreatment contrast-enhanced computed tomography (CT) images and clinical features to predict the response to NACT in patients with LAGC.
UNASSIGNED: 719 patients with LAGC were retrospectively recruited from four Chinese hospitals between Dec 1st, 2014 and Nov 30th, 2020. The training cohort and internal validation cohort (IVC), comprising 243 and 103 patients, respectively, were randomly selected from center I; the external validation cohort1 (EVC1) comprised 207 patients from center II; and EVC2 comprised 166 patients from another two hospitals. Two imaging signatures, reflecting the phenotypes of the deep learning and handcrafted radiomics features, were constructed from the pretreatment portal venous-phase CT images. A four-step procedure, including reproducibility evaluation, the univariable analysis, the LASSO method, and the multivariable logistic regression analysis, was applied for feature selection and signature building. The integrated DLRN was then developed for the added value of the imaging signatures to independent clinicopathological factors for predicting the response to NACT. The prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness. Kaplan-Meier survival curves based on the DLRN were used to estimate the disease-free survival (DFS) in the follow-up cohort (n = 300).
UNASSIGNED: The DLRN showed satisfactory discrimination of good response to NACT and yielded the areas under the receiver operating curve (AUCs) of 0.829 (95% CI, 0.739-0.920), 0.804 (95% CI, 0.732-0.877), and 0.827 (95% CI, 0.755-0.900) in the internal and two external validation cohorts, respectively, with good calibration in all cohorts (p > 0.05). Furthermore, the DLRN performed significantly better than the clinical model (p < 0.001). Decision curve analysis confirmed that the DLRN was clinically useful. Besides, DLRN was significantly associated with the DFS of patients with LAGC (p < 0.05).
UNASSIGNED: A deep learning-based radiomics nomogram exhibited a promising performance for predicting therapeutic response and clinical outcomes in patients with LAGC, which could provide valuable information for individualized treatment.

Detailed knowledge on tissue-specific metabolic reprogramming in diabetic nephropathy (DN) is vital for more accurate understanding the molecular pathological signature and developing novel therapeutic strategies. In the present study, a spatial-resolved metabolomics approach based on air flow-assisted desorption electrospray ionization (AFADESI) and matrix-assisted laser desorption ionization (MALDI) integrated mass spectrometry imaging (MSI) was proposed to investigate tissue-specific metabolic alterations in the kidneys of high-fat diet-fed and streptozotocin (STZ)-treated DN rats and the therapeutic effect of astragaloside IV, a potential anti-diabetic drug, against DN. As a result, a wide range of functional metabolites including sugars, amino acids, nucleotides and their derivatives, fatty acids, phospholipids, sphingolipids, glycerides, carnitine and its derivatives, vitamins, peptides, and metal ions associated with DN were identified and their unique distribution patterns in the rat kidney were visualized with high chemical specificity and high spatial resolution. These region-specific metabolic disturbances were ameliorated by repeated oral administration of astragaloside IV (100 mg/kg) for 12 weeks. This study provided more comprehensive and detailed information about the tissue-specific metabolic reprogramming and molecular pathological signature in the kidney of diabetic rats. These findings highlighted the promising potential of AFADESI and MALDI integrated MSI based metabolomics approach for application in metabolic kidney diseases.

UNASSIGNED: Grade of brain tumor is thought to be the most significant and crucial component in treatment management. Recent development in medical imaging techniques have led to the introduce non-invasive methods for brain tumor grading such as different magnetic resonance imaging (MRI) protocols. Combination of different MRI protocols with fusion algorithms for tumor grading is used to increase diagnostic improvement. This paper investigated the efficiency of the Laplacian Re-decomposition (LRD) fusion algorithms for glioma grading.
UNASSIGNED: In this study, 69 patients were examined with MRI. The T1 post enhancement (T1Gd) and diffusion-weighted images (DWI) were obtained. To evaluated LRD performance for glioma grading, we compared the parameters of the receiver operating characteristic (ROC) curves.
UNASSIGNED: We found that the average Relative Signal Contrast (RSC) for high-grade gliomas is greater than RSCs for low-grade gliomas in T1Gd images and all fused images. No significant difference in RSCs of DWI images was observed between low-grade and high-grade gliomas. However, a significant RSCs difference was detected between grade III and IV in the T1Gd, b50, and all fussed images.
UNASSIGNED: This research suggests that T1Gd images are an appropriate imaging protocol for separating low-grade and high-grade gliomas. According to the findings of this study, we may use the LRD fusion algorithm to increase the diagnostic value of T1Gd and DWI picture for grades III and IV glioma distinction. In conclusion, this article has emphasized the significance of the LRD fusion algorithm as a tool for differentiating grade III and IV gliomas.

UNASSIGNED: To determine if radiomics analysis based on preoperative computed tomography (CT) can predict early postoperative recurrence of giant cell tumor of bone (GCTB) in the spine.
UNASSIGNED: In a retrospective review, 62 patients with pathologically confirmed spinal GCTB from March 2008 to February 2018, with a minimum follow-up of 24 months, were identified. The mean follow-up was 73.7 months (range, 28.7-152.1 months). The clinical information including age, gender, lesion location, multi-vertebral involvement, and surgical methods, were obtained. CT images acquired before the operation were retrieved for radiomics analysis. For each case, the tumor regions of interest (ROI) was manually outlined, and a total of 107 radiomics features were extracted. The features were selected via the sequential selection process by using the support vector machine (SVM), then used to construct classification models with Gaussian kernels. The differentiation between recurrence and non-recurrence groups was evaluated by ROC analysis, using 10-fold cross-validation.
UNASSIGNED: Of the 62 patients, 17 had recurrence with a recurrence rate of 27.4%. None of the clinical information was significantly different between the two groups. Patients receiving curettage had a higher recurrence rate (6/16 = 37.5%) compared to patients receiving TES (6/26 = 23.1%) or intralesional spondylectomy (5/20 = 25%). The final radiomics model was built using 10 selected features, which achieved an accuracy of 89% with AUC of 0.78.
UNASSIGNED: The radiomics model developed based on pre-operative CT can achieve a high accuracy to predict the recurrence of spinal GCTB. Patients who have a high risk of early recurrence should be treated more aggressively to minimize recurrence.

Nodular regenerative hyperplasia (NRH) of the liver may lead to noncirrhotic portal hypertension with subsequent development of portosystemic shunts. While extrahepatic and macrovascular shunts are readily visualized with imaging or endoscopy, there is no standard technique to detect intrahepatic microvascular portosystemic shunting and quantitatively assess shunt burden. We present a case of a 53-year-old female with suspected NRH and hepatopulmonary syndrome with inconclusive liver biopsies and absent portosystemic shunts per abdominal imaging. A percutaneous transportal infusion of Technetium-99m labeled macroaggregated albumin (99mTc-MAA) successfully identified intrahepatic microvascular portosystemic shunting and quantified a lung shunt fraction of more than 30%. NRH was subsequently confirmed with a surgical wedge biopsy and the patient was successfuly treated with a liver transplant. Transportal 99mTc-MAA could be used to both identify and quantify otherwise occult microvascular portosystemic shunts in patients with clinical sequelae of portal hypertension.