A Novel Serum Exosomal miRNA Signature in the Early Prediction of Persistent Organ Failure in Patients with Acute Pancreatitis.

OBJECTIVE: Current study aims to investigate whether serum exosomal microRNAs (miRNAs) could be potential biomarkers in predicting APs with POF at early phase. BACKGROUND: Novel biomarkers are sorely needed for early prediction of persistent organ failure (POF) in acute pancreatitis (AP) patients. METHODS: In the discovery stage, exosomal miRNAs were profiled in sera from APs with or without POF (5 vs. 5) using microarrays. POF-associated miRNA signatures then were assessed in training cohort (n=227) and further validated in three independent cohorts (n=516), including one nested case-control cohort. RESULTS: A total of 743 APs were recruited in this large-scale biomarker identification study with a nested case-control study. Data from the discovery cohort demonstrated that 90 exosomal miRNAs were significantly dysregulated in APs with POF compared with controls. One miRNA classifier (Cmi) comprising 3 miRNAs (miR-4265, 1208, 3127-5p) was identified in the training cohort, and was further evaluated in two validation cohorts for their predictive value for POF. AUCs for Cmi ranged from 0.88 to 0.90, which was statistically superior to AUCs of APACHE-II and BISAP, and outperformed BUN and creatinine in POF prediction across all cohorts (P<.05). Higher levels of Cmi indicated increased need for ICU admission, prolonged hospitalization, and elevated mortality rate, thus poor prognosis. In the nested case-control study, Cmi could help identify prediagnostic POF in post-ERCP pancreatitis cases within "golden hours" after ERCP with high efficacy. CONCLUSIONS: Serum exosomal Cmi may be an early predictor for POF in AP, even within "golden hours" after AP onset. TRIAL REGISTRATION: ClinicalTrials.gov (NCT02602808).

I2C: Invertible Continuous Codec for High-Fidelity Variable-Rate Image Compression.

Lossy image compression is a fundamental technology in media transmission and storage. Variable-rate approaches have recently gained much attention to avoid the usage of a set of different models for compressing images at different rates. During the media sharing, multiple re-encodings with different rates would be inevitably executed. However, existing Variational Autoencoder (VAE)-based approaches would be readily corrupted in such circumstances, resulting in the occurrence of strong artifacts and the destruction of image fidelity. Based on the theoretical findings of preserving image fidelity via invertible transformation, we aim to tackle the issue of high-fidelity fine variable-rate image compression and thus propose the Invertible Continuous Codec (I2C). We implement the I2C in a mathematical invertible manner with the core Invertible Activation Transformation (IAT) module. I2C is constructed upon a single-rate Invertible Neural Network (INN) based model and the quality level (QLevel) would be fed into the IAT to generate scaling and bias tensors. Extensive experiments demonstrate that the proposed I2C method outperforms state-of-the-art variable-rate image compression methods by a large margin, especially after multiple continuous re-encodings with different rates, while having the ability to obtain a very fine variable-rate control without any performance compromise.

Low RCAN1.2 mRNA Expression Is Associated with Poor Prognosis of Patients with Esophageal Squamous Cell Carcinoma.

Background: This study aims to explore the role of RCAN1 in esophageal squamous cell carcinoma (ESCC) cells, determine the mRNA level of three RCAN1 isoforms in ESCC tissue, and evaluate the prognostic value of three RCAN1 isoforms. Methods: Colony-forming assay, Wound-healing assay and Transwell assay were used to evaluate the effect of RCAN1 on cell proliferation, migration and invasion. The mRNA expression of three RCAN1 isoforms was detected in paired tumor and normal tissues from 100 ESCC patients by real-time PCR. Kaplan-Meier survival curves and Cox proportional hazards model were used to evaluate the prognostic value of three RCAN1 isoforms. A nomogram was used to predict the probability of 2-year and 5-year overall survival (OS). Results: In vitro, knockdown of RCAN1 could promote ESCC cell proliferation, migration and invasion abilities. Compared to the paired normal tissues, RCAN1 isoform 1 (RCAN1.1, P=0.0027) and RCAN1 isoform 2 (RCAN1.2, P=0.0006) were significantly decreased in tumor tissues. The low expression of RCAN1.2 mRNA was associated with advanced stage (P=0.0176) and lymph node metastasis (LNM, P=0.0219). ESCC patients with low RCAN1.2 mRNA levels had shorter survival time compared to those with high RCAN1.2 levels (P=0.007). Multivariate COX analysis indicated that RCAN1.2 mRNA level was an independent prognostic indicator of OS of patients with ESCC (hazard ratio=0.5266, P=0.03554). The concordance index of nomogram to predict OS was 0.693 based on LNM, RCAN1.2, tumor stage and patients' age. Conclusion: These findings show that RCAN1 gene play a role in preventing proliferation, migration, and invasive activity of ESCC cells. RCAN1.2 mRNA level is a novel prognostic marker in ESCC, targeting RCAN1.2 may provide a potential therapeutic approach in ESCC.

Design of AgNPs doped chitosan/sodium lignin sulfonate/polypyrrole films with antibacterial and endotoxin adsorption functions.

There is an urgent need to develop materials to prevent bacterial infection and the deleterious effects of endotoxins. In this study, we introduce a one-step electrodeposition method to prepare films composed of chitosan/Ag/polypyrrole and layer-by-layer self-assembly to introduce lignin sulphonate (LS) to obtain chitosan/Ag/polypyrrole/LS films. Antibacterial effects against both E. coli and S. aureus are shown by bacterial growth profiles and observation of bacteriostatic zones. Meanwhile, the addition of self-assembled LS improved the antibacterial effect of the film. For E. coli, the inhibition zone diameter was 0.93 cm, while for S. aureus, the inhibition zone diameter was 0.72 cm. Rapid and efficient endotoxin adsorption effects were shown whereby the electrostatic interactions between chitosan and endotoxin molecules played a major role. After adsorption for 1 h, in initial concentration of 1 EU/mL endotoxin solution, the adsorption efficiency could reach up to 85 %, while in initial concentration of 5 EU/mL endotoxin solution, the adsorption efficiency could reach up to 87.6 %. The results suggest chitosan/Ag/polypyrrole/LS films for their capability as a new type of antibacterial film with intrinsic endotoxin adsorption activity.

Discriminative Self-Paced Group-Metric Adaptation for Online Visual Identification.

Existing solutions to instance-level visual identification usually aim to learn faithful and discriminative feature extractors from offline training data and directly use them for the unseen online testing data. However, their performance is largely limited due to the severe distribution shifting issue between training and testing samples. Therefore, we propose a novel online group-metric adaptation model to adapt the offline learned identification models for the online data by learning a series of metrics for all sharing-subsets. Each sharing-subset is obtained from the proposed novel frequent sharing-subset mining module and contains a group of testing samples that share strong visual similarity relationships to each other. Furthermore, to handle potentially large-scale testing samples, we introduce self-paced learning (SPL) to gradually include samples into adaptation from easy to difficult which elaborately simulates the learning principle of humans. Unlike existing online visual identification methods, our model simultaneously takes both the sample-specific discriminant and the set-based visual similarity among testing samples into consideration. Our method is generally suitable to any off-the-shelf offline learned visual identification baselines for online performance improvement which can be verified by extensive experiments on several widely-used visual identification benchmarks.

Diagnosis of architectural distortion on digital breast tomosynthesis using radiomics and deep learning.

Purpose: To implement two Artificial Intelligence (AI) methods, radiomics and deep learning, to build diagnostic models for patients presenting with architectural distortion on Digital Breast Tomosynthesis (DBT) images. Materials and Methods: A total of 298 patients were identified from a retrospective review, and all of them had confirmed pathological diagnoses, 175 malignant and 123 benign. The BI-RADS scores of DBT were obtained from the radiology reports, classified into 2, 3, 4A, 4B, 4C, and 5. The architectural distortion areas on craniocaudal (CC) and mediolateral oblique (MLO) views were manually outlined as the region of interest (ROI) for the radiomics analysis. Features were extracted using PyRadiomics, and then the support vector machine (SVM) was applied to select important features and build the classification model. Deep learning was performed using the ResNet50 algorithm, with the binary output of malignancy and benignity. The Gradient-weighted Class Activation Mapping (Grad-CAM) method was utilized to localize the suspicious areas. The predicted malignancy probability was used to construct the ROC curves, compared by the DeLong test. The binary diagnosis was made using the threshold of ≥ 0.5 as malignant. Results: The majority of malignant lesions had BI-RADS scores of 4B, 4C, and 5 (148/175 = 84.6%). In the benign group, a substantial number of patients also had high BI-RADS ≥ 4B (56/123 = 45.5%), and the majority had BI-RADS ≥ 4A (102/123 = 82.9%). The radiomics model built using the combined CC+MLO features yielded an area under curve (AUC) of 0.82, the sensitivity of 0.78, specificity of 0.68, and accuracy of 0.74. If only features from CC were used, the AUC was 0.77, and if only features from MLO were used, the AUC was 0.72. The deep-learning model yielded an AUC of 0.61, significantly lower than all radiomics models (p<0.01), which was presumably due to the use of the entire image as input. The Grad-CAM could localize the architectural distortion areas. Conclusion: The radiomics model can achieve a satisfactory diagnostic accuracy, and the high specificity in the benign group can be used to avoid unnecessary biopsies. Deep learning can be used to localize the architectural distortion areas, which may provide an automatic method for ROI delineation to facilitate the development of a fully-automatic computer-aided diagnosis system using combined AI strategies.

Comparison of dynamic changes in the peripheral CD8+ T cells function and differentiation in ESCC patients treated with radiotherapy combined with anti-PD-1 antibody or concurrent chemoradiotherapy.

Objective: The systematic immune status of cancer patients undergoing immunotherapy is little known. We prospectively identified the function and differentiation traits of peripheral CD8+ T cells based on our phase 1b clinical trial (NCT03222440) of radiotherapy combined with camrelizumab in patients with locally advanced esophageal squamous cell carcinoma (ESCC) and compared it with concurrent chemoradiotherapy (CCRT). Methods: 19 and 18 patients were included in the cohort of radiotherapy plus camrelizumab and cohort of CCRT treatment. By using flow cytometry, we evaluated the expression levels of PD-1, Eomes, T-bet and IFN-γ (function), CD38 and HLA-DR (activation), and differentiation subsets classified according to the expression levels of CD45RA and CD62L in peripheral CD8+ T cells before and during treatment. Results: Effective binding of anti-PD-1 antibody camrelizumab with PD-1 on CD8+ T cells was detected during treatment. Both two treatments elevated the expression levels of activation molecules CD38 and HLA-DR on CD8+ T cells. PD-1+CD8+ T cells had more activation features than PD-1-CD8+ T cells in two groups and the treatments did not alter these differences. The two treatments activated both PD-1+ and PD-1- CD8+ T cells. PD-1+CD8+ T cells had less Naïve and TEMRA but more Tcm and Tem than PD-1-CD8+ T cells in two groups and both two treatments changed the ratio of memory T cells in PD-1+ and PD-1- cells. RT plus camrelizumab treatment reduced Naïve T cells and TEMRA subsets both in PD-1+ and PD-1- CD8+ T cells while elevated Tcm subset in PD-1+CD8+ T cells and Tem subset in PD-1-CD8+ T cells. CCRT elevated Tcm subset and reduced TEMRA subset in PD-1-CD8+ T cells while did not change any subset in PD-1+CD8+ T cells. Furthermore, patients undergoing radiotherapy plus immunotherapy were found to obtain better prognosis than those receiving CCRT. Conclusions: This study identified the dynamic changes of systematic immune status of patients undergoing treatment. The two treatments had similar activation effects on peripheral CD8+ T cells with different PD-1 properties but had different effects on their differentiation status. These results provided potential clues to the reasons underlying the difference in prognosis of the two treatments.

Treponema pallidum FlaA2 inducing the release of pro-inflammatory cytokines is mediated via TLR2 in keratinocytes.

BACKGROUND: Syphilis, caused by Treponema pallidum (T. pallidum), is a multi-organ, multiple systems, multi-stage sexually transmitted diseases with various clinical manifestations, among of which pathological lesions of skin and mucosa are the typical clinical manifestations of syphilis. However, the immunopathogenesis of this process is poorly understood. T. pallidum flagellin FlaA2, as a part of the important organelle responsible for the causative agent's motility, may contributes to the host skin inflammatory response. OBJECTIVES: To determine the mechanisms of T. pallidum FlaA2 stimulating the expression of pro-inflammatory cytokines in human keratinocytes. METHODS: Recombinant FlaA2 protein was performed to stimulate human keratinocytes. The mRNA transcription levels and protein expression levels of IL-6 and IL-8 were detected by qRT-PCR and ELISA, respectively. Western blot was used to detect the total protein and phosphorylation levels of ERK, p38, JNK and NF-κB, respectively. The intracellular location of NF-κB p65 was detected by immunofluorescence staining. RESULTS: Recombinant FlaA2 could considerably induced the expression of pro-inflammation cytokines IL-6 and IL-8 in HaCaT cells, and FlaA2-induced IL-6 and IL-8 secretion could be decreased by inhibiting TLR2 using pZERO-hTLR2. Further investigation showed that FlaA2 could activate the phosphorylation of ERK, p38 and IκBα and FlaA2-stimulated secretion of IL-6, IL-8 were attenuated by ERK, p38 and NF-κB inhibitors in HaCaT cells. Moreover, FlaA2 activates the ERK, p38 and NF-κB pathways through TLR2 signaling pathway in HaCaT cells. CONCLUSIONS: From the findings above, these results confirm that T. pallidum FlaA2 activates ERK, p38 and NF-κB signaling pathway through TLR2 pathway to induce the production of IL-6 and IL-8, which could contribute to enhance the understanding of the skin inflammatory response induced by the pathogen in syphilis patients.

Aptamers: A prospective tool for infectious diseases diagnosis.

It is well known that people's health is seriously threatened by various pathogens (such as Mycobacterium tuberculosis, Treponema pallidum, Novel coronavirus, HIV, Mucor, etc.), which leads to heavy socioeconomic burdens. Therefore, early and accurate pathogen diagnosis is essential for timely and effective therapies. Up to now, diagnosing human contagious diseases at molecule and nano levels is remarkably difficult owing to insufficient valid probes when it comes to determining the biological markers of pathogens. Aptamers are a set of high-specificity and high-sensitivity plastic oligonucleotides screened in vitro via the selective expansion of ligands by exponential enrichment (SELEX). With the advent of aptamer-based technologies, their merits have aroused mounting academic interest. In recent years, as new detection and treatment tools, nucleic acid aptamers have been extensively utilized in the field of biomedicine, such as pathogen detection, new drug development, clinical diagnosis, nanotechnology, etc. However, the traditional SELEX method is cumbersome and has a long screening cycle, and it takes several months to screen out aptamers with high specificity. With the persistent development of SELEX-based aptamer screening technologies, the application scenarios of aptamers have become more and more extensive. The present research briefly reviews the research progress of nucleic acid aptamers in the field of biomedicine, especially in the diagnosis of contagious diseases.

A Promising Tool in Serological Diagnosis: Current Research Progress of Antigenic Epitopes in Infectious Diseases.

Infectious diseases, caused by various pathogens in the clinic, threaten the safety of human life, are harmful to physical and mental health, and also increase economic burdens on society. Infections are a complex mechanism of interaction between pathogenic microorganisms and their host. Identification of the causative agent of the infection is vital for the diagnosis and treatment of diseases. Etiological laboratory diagnostic tests are therefore essential to identify pathogens. However, due to its rapidity and automation, the serological diagnostic test is among the methods of great significance for the diagnosis of infections with the basis of detecting antigens or antibodies in body fluids clinically. Epitopes, as a special chemical group that determines the specificity of antigens and the basic unit of inducing immune responses, play an important role in the study of immune responses. Identifying the epitopes of a pathogen may contribute to the development of a vaccine to prevent disease, the diagnosis of the corresponding disease, and the determination of different stages of the disease. Moreover, both the preparation of neutralizing antibodies based on useful epitopes and the assembly of several associated epitopes can be used in the treatment of disease. Epitopes can be divided into B cell epitopes and T cell epitopes; B cell epitopes stimulate the body to produce antibodies and are therefore commonly used as targets for the design of serological diagnostic experiments. Meanwhile, epitopes can fall into two possible categories: linear and conformational. This article reviews the role of B cell epitopes in the clinical diagnosis of infectious diseases.

Case Report and Review of Literature: Camrelizumab Combined with Fuzuloparib and Apatinib for Platinum-Resistant Recurrent Ovarian Cancer.

Background: The mortality rate of ovarian cancer (OC) ranks first among female genital tract malignant tumors, which seriously threatens women's life and health. Because of its insidious onset and poor prognosis, it has become a thorny problem in the clinic, especially for patients with platinum-resistant recurrent ovarian cancer (PROC). In recent years, the medical treatment of OC has made gratifying results, bringing hope to the patients. Case Description: A 54-year-old OC patient who has failed previous neoadjuvant chemotherapy, cytoreductive surgery, and postoperative chemotherapy was diagnosed with PROC. Then she received combination treatment of fuzuloparib (100mg PO BID), apatinib (250mg PO QD), and camrelizumab (200mg IV Q3W) for every 3-week cycle in a Phase II study for PROC patients. In the phase II study, her condition stabilized, responded well to treatment with a sharp decrease by 91.14% of target lesions and disappearances of non-target lesions, and continued to receive regular treatment with progression-free survival exceeding 15 months and no serious adverse events. Conclusion: The present case proves PROC patients might have a sustained response to triplet combination with camrelizumab, combined with fuzuloparib and apatinib.

Dynamic change of COVID-19 lung infection evaluated using co-registration of serial chest CT images.

Purpose: To evaluate the volumetric change of COVID-19 lesions in the lung of patients receiving serial CT imaging for monitoring the evolution of the disease and the response to treatment. Materials and methods: A total of 48 patients, 28 males and 20 females, who were confirmed to have COVID-19 infection and received chest CT examination, were identified. The age range was 21-93 years old, with a mean of 54 ± 18 years. Of them, 33 patients received the first follow-up (F/U) scan, 29 patients received the second F/U scan, and 11 patients received the third F/U scan. The lesion region of interest (ROI) was manually outlined. A two-step registration method, first using the Affine alignment, followed by the non-rigid Demons algorithm, was developed to match the lung areas on the baseline and F/U images. The baseline lesion ROI was mapped to the F/U images using the obtained geometric transformation matrix, and the radiologist outlined the lesion ROI on F/U CT again. Results: The median (interquartile range) lesion volume (cm3) was 30.9 (83.1) at baseline CT exam, 18.3 (43.9) at first F/U, 7.6 (18.9) at second F/U, and 0.6 (19.1) at third F/U, which showed a significant trend of decrease with time. The two-step registration could significantly decrease the mean squared error (MSE) between baseline and F/U images with p < 0.001. The method could match the lung areas and the large vessels inside the lung. When using the mapped baseline ROIs as references, the second-look ROI drawing showed a significantly increased volume, p < 0.05, presumably due to the consideration of all the infected areas at baseline. Conclusion: The results suggest that the registration method can be applied to assist in the evaluation of longitudinal changes of COVID-19 lesions on chest CT.

Electrochemical sensing of Staphylococcus aureus based on conductive anti-fouling interface.

A system for the rapid and ultra-sensitive detection of Staphylococcus aureus (S. aureus), a prevalent foodborne pathogen is introduced. Limitations of typical electrochemical sensing, often subjected to interference from non-specific protein adsorption are addressed. A dual-aptamer-based sandwich immunobiosensor is shown for its benefits regarding specificity and anti-fouling capacity, endowed by a sulfonated polyaniline layer combined with signal amplification via highly conductive gold nanoparticles. EIS spectra (Nyquist plots) were recorded at pH 7.4 PBS containing 5 mM Fe(CN)63-/Fe(CN)64-, in order to verify the possibility of the electrochemical sensing for detection of S. aureus. Results demonstrated that the constructed immunobiosensor presents an extended detection range (1 × 101 to 1 × 105 CFU/mL) and detection limit as low as 2 CFU/mL. The resistance values of the immunobiosensor developed  maintain at a stable value during 2 weeks.  Besides, the specificity of the system is highlighted by testing raw milk, and the results of which demonstrate the excellent prospects of the system for monitoring foodborne pathogens.

Linear and Deep Order-Preserving Wasserstein Discriminant Analysis.

Supervised dimensionality reduction for sequence data learns a transformation that maps the observations in sequences onto a low-dimensional subspace by maximizing the separability of sequences in different classes. It is typically more challenging than conventional dimensionality reduction for static data, because measuring the separability of sequences involves non-linear procedures to manipulate the temporal structures. In this paper, we propose a linear method, called order-preserving Wasserstein discriminant analysis (OWDA), and its deep extension, namely DeepOWDA, to learn linear and non-linear discriminative subspace for sequence data, respectively. We construct novel separability measures between sequence classes based on the order-preserving Wasserstein (OPW) distance to capture the essential differences among their temporal structures. Specifically, for each class, we extract the OPW barycenter and construct the intra-class scatter as the dispersion of the training sequences around the barycenter. The inter-class distance is measured as the OPW distance between the corresponding barycenters. We learn the linear and non-linear transformations by maximizing the inter-class distance and minimizing the intra-class scatter. In this way, the proposed OWDA and DeepOWDA are able to concentrate on the distinctive differences among classes by lifting the geometric relations with temporal constraints. Experiments on four 3D action recognition datasets show the effectiveness of OWDA and DeepOWDA.

Relationship between small dense low-density lipoprotein cholesterol with carotid plaque in Chinese individuals with abnormal carotid artery intima-media thickness.

AIM: To investigate the relationship of small dense low-density lipoprotein cholesterol (sdLDL-C) to carotid artery intima-media thickness (CA-IMT) and carotid plaque (CAP) in Chinese general population, and to evaluate whether sdLDL-C could be an independent risk factor for individuals with subclinical atherosclerosis. METHODS: A total of 729 subjects were randomly collected from consecutive individuals from April 2019 to April 2020 for an annual health checkup. CA-IMT > 1.0 mm was defined as abnormal IMT. Plaque stability was measured by ultrasound examination based on the property of the echo. And sdLDL-C levels were detected by LipoPrint system. Multivariate logistic regression analysis was performed to identify factors associated with CA-IMT and carotid plaque. RESULTS: The abnormal IMT group had significantly higher sdLDL-C levels than control group (p < 0.0001). And sdLDL-C levels were significantly positively correlated with IMT value (r = 0.1396, p = 0.0021) and presence of carotid plaque (r = 0.14, p = 0.002) in the subjects with abnormal IMT. In addition, subjects with higher levels of sdLDL-C (r = 0.11, p = 0.035) tended to have unstable CAP. After adjustment for age, gender and blood glucose, sdLDL-C level was an independent risk factor of the presence of CAP (OR = 1.59, 95% CI: 1.02-1.83, p = 0.034) in subjects with abnormal IMT. CONCLUSION: SdLDL-C is an independent risk factor of the occurrence of CAP in the Chinese subjects with abnormal IMT. Our findings provide supporting evidence that sdLDL-C might be an alternative way to predict CVD in early stage.

Electrochemical sensing technology for liquid biopsy of circulating tumor cells-a review.

In recent years, a lot of new detection techniques for circulating tumor cells (CTCs) have been developed. Among them, electrochemical sensing technology has gradually developed because of its advantages of good selectivity, high sensitivity, low cost and rapid detection. Especially in the latest decade, the field of electrochemical biosensing has witnessed great progress, thanks to the merging of biosensing research area with nanotechnology, immunotechnology, nucleic acid technology, and microfluidic technology. In this review, the recent progress for the detection of CTCs according to the principle of detection was summarized and how they can contribute to the enhanced performance of such biosensors was explained. The latest electrode construction strategies such as rolling circle amplification reaction, DNA walker and microfluidic technology and their advantages were also introduced emphatically. Moreover, the main reasonswhy the existing biosensors have not been widely used clinically and the next research points were clearly put forward.

Effect of Psychological-Behavioral Intervention on the Depression and Anxiety of COVID-19 Patients.

The COVID-19 epidemic has caused increasing public panic and mental health stress. In this study, we explore the prevalence and factors linked to anxiety and depression in hospitalized patients with COVID-19. A total of 144 patients diagnosed with COVID-19 underwent depression and anxiety assessment by using the Hospital Anxiety and Depression Scale (HADS). Social support level was also evaluated by the Perceived Social Support Scale (PSSS) at admission. Results showed that gender, age, oxygen saturation, and social support were associated with anxiety for COVID-19 patients. In addition, age, family infection with SARS-CoV-2, and social support were the risk factors associated with depression. Moreover, we designed a psychological-behavioral intervention (PBI) program that included psychological support and breathing exercises, and explored its effects on patients with COVID-19. Of the 144 participants, 26 patients with both anxiety and depression symptoms (cutoff score of ≥8 on HADS-A and HADS-D) were randomly assigned to the intervention group and the control group at a 1:1 ratio. After 10-day treatment, the HADS scores of depression and anxiety were significantly reduced in the intervention group, and PSSS scores were also significantly improved. However, no significant differences in HADS and PSSS scores between pre- and post-treatment were found in the control group. Our findings indicate that mental concern and appropriate intervention are essential parts of clinical care for COVID-19 patients.

Efficacy and safety of tocilizumab in COVID-19 patients.

In this retrospective study we assessed the efficacy and safety of tocilizumab in patients with critical or severe coronavirus disease 2019 (COVID-19). We enrolled 181 patients admitted to Huoshenshan Hospital (Wuhan, China) with confirmed COVID-19 between January 2020 and February 2020. Ninety-two patients were treated with tocilizumab, and 89 patients were treated conventionally. We analyzed the clinical manifestations, changes in CT scan images, and laboratory tests before and after tocilizumab treatment, and compared these results with the conventionally treated group. A significant reduction in the level of C-reactive protein was observed 1 week after tocilizumab administration. In some cases this meant the end of the IL-6-related cytokine storm. In addition, tocilizumab relieved fever, cough, and shortness of breath with no reported adverse drug reactions. These findings suggest tocilizumab improves clinical outcomes and is effective for treatment of patients with critical or severe COVID-19. However, future clinical trials are needed to better understand the impact of tocilizumab interference with IL-6 and provide a therapeutic strategy for treatment of COVID-19.

Alteration of plasma metabolites associated with chemoradiosensitivity in esophageal squamous cell carcinoma via untargeted metabolomics approach.

BACKGROUND: To investigate the differences in plasma metabolomic characteristics between pathological complete response (pCR) and non-pCR patients and identify biomarker candidates for predicting the response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC). METHODS: A total of 46 ESCC patients were included in this study. Gas chromatography time-of- flight mass spectrometry (GC-TOF/MS) technology was applied to detect the plasma samples collected before nCRT via untargeted metabolomics analysis. RESULTS: Five differentially expressed metabolites (out of 109) was found in plasma between pCR and non-pCR groups. Compared with non-pCR group, isocitric acid (p = 0.0129), linoleic acid (p = 0.0137), citric acid (p = 0.0473) were upregulated, while L-histidine (p = 0.0155), 3'4 dihydroxyhydrocinnamic acid (p = 0.0339) were downregulated in the pCR plasma samples. Pathway analyses unveiled that citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolic pathway were associated with ESCC chemoradiosensitivity. CONCLUSION: The present study provided supporting evidence that GC-TOF/MS based metabolomics approach allowed identification of metabolite differences between pCR and non-pCR patients in plasma levels, and the systemic metabolic status of patients may reflect the response of ESCC patient to neoadjuvant chemoradiotherapy.

Learning Visual Instance Retrieval from Failure: Efficient Online Local Metric Adaptation from Negative Samples.

Existing visual instance retrieval (VIR) approaches attempt to learn a faithful global matching metric or discriminative feature embedding offline to cover enormous visual appearance variations, so as to directly use it online on various unseen probes for retrieval. However, their requirement for a huge set of positive training pairs is very demanding in practice and the performance is largely constrained for the unseen testing samples due to the severe data shifting issue. In contrast, this paper advocates a different paradigm: part of the learning can be performed online but with nominal costs, so as to achieve online metric adaptation for different query probes. By exploiting easily-available negative samples, we propose a novel solution to achieve the optimal local metric adaptation effectively and efficiently. The insight of our method is the local hard negative samples can actually provide tight constraints to fine tune the metric locally. Our local metric adaptation method is generally applicable to be used on top of any offline-learned baselines. In addition, this paper gives in-depth theoretical analyses of the proposed method to guarantee the reduction of the classification error both asymptotically and practically. Extensive experiments on various VIR tasks have confirmed our effectiveness and superiority.