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.

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: 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: 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.

Auditory hallucinations (AH), typically hearing voices, are a core symptom in schizophrenia. They may result from deficits in dynamic functional connectivity (FC) between cortical regions supporting speech production and language perception that interfere with the ability to recognize self-generated speech as not coming from external sources. We tested this hypothesis by investigating dynamic connectivity between the frontal cortex region related to language production and the temporal cortex region related to auditory processing.
Resting-state fMRI scans were acquired from 18 schizophrenia patients with AH (AH+), 17 schizophrenia patients without AH (AH-) and 22 healthy controls. A multiband sequence with TR = 427 ms was adopted to provide relatively high temporal resolution data for characterizing dynamic FC. Analysis focused on connectivity between speech production and language comprehension areas, eloquent language cortex in the left hemisphere. Two frequency bands of brain oscillatory activity were evaluated (0.01-0.027 Hz, 0.027-0.08 Hz) in which differential alterations that have been previously linked to schizophrenia. Conventional static FC maps of these seeds were also calculated.
Dynamic connectivity analysis indicated that AH+ patients showed not only less temporal variability but transient lower strength in connectivity between speech and auditory areas than healthy controls, while AH- patients not. These findings were restricted to 0.027-0.08 Hz activity. In static connectivity analysis, no significant differences were observed in connectivity between speech production and language comprehension areas in either frequency band.
Reduced temporal variability and connectivity strength between key regions of eloquent language cortex may represent a mechanism for AH in schizophrenia.