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Natural killer (NK) cells are enriched in the central nervous system in aging-related atheriosclerotic cerebral small vessel disease (aCSVD), but their roles and underlying mechanism remain to be elucidated. To identify potential cytotoxic molecules released by NK cells in aCSVD lesions, proteomic analysis of cerebrospinal fluid (CSF), plasma, and peripheral NK cells from patients with aCSVD were performed. We found that integrin β2 (ITGB2), cathepsin D (CTSD), and granzyme H (GZMH) were highly expressed in NK cells. ITGB2 interacted with intercellular adhesion molecule 1 in vascular endothelial cells. As assessed by immunofluorescence and scanning electron microscopy of the blood–brain barrier model, transwell membranes covered with primary human brain microvascular endothelial cells and astrocytes, we demonstrated that the CTSD-mediated degradation of collagen in the blood–brain barrier depended on the cytotoxicity of NK cells in aCSVD. With the immunostaining in vitro and in vivo, GZMH disruption of demyelinated nerve fibers was reversed by cotreatment with the inhibitor 3,4-DCIC during white matter hyperintensity (WMH) in aCSVD. Our results indicate that NK cells contribute to CTSD-induced damage to the blood–brain barrier and GZMH-induced disruption of nerve fibers during WMH in aCSVD.

Background: Blood-brain barrier dysfunction in active multiple sclerosis (MS) lesions leads to pathological changes in the cerebrospinal fluid (CSF). This study aimed to investigate the possible association between routine CSF findings, especially CSF chloride, at the time of the first lumbar puncture and the relapse risk and disability progression of relapsing–remitting MS (RRMS). Methods: This retrospective study included 77 patients with RRMS at the MS Center of our institution from January 2012 to December 2020. The Anderson and Gill (AG) model and Spearman correlation analysis were used to explore predictors of relapse and disability during follow-up. Results: In the multivariate AG model, patients with elevated CSF chloride level (hazard ratio [HR], 1.1; 95% confidence interval [CI]: 1.06–1.22; p = 0.001) had a high risk of MS relapse. Using median values of CSF chloride (123.2 mmol/L) as a cut-off, patients with CSF chloride level ≥ 123.2 mmol/L had a 120% increased relapse risk compared with those with CSF chloride level < 123.2 mmol/L (HR = 2.20; 95% CI: 1.19–4.05; p = 0.012). Conclusions: Elevated CSF chloride levels might be a biologically unfavorable predictive factor for disease relapse in RRMS.

Aims: Given the reversibility of methylation, biomarkers with discriminating ability are of great interest for targeted therapeutic sites. Materials & methods: Methylation array data of 461 lung adenocarcinoma (LUAD) patients comprising of 458 tumor and 32 LUAD paracancerous samples were compared using partial least squares discrimination analysis and receiver operating characteristics analysis. Results: A six-DNA methylation signature (corresponding to five genes) was found to significantly discriminate normal and LUAD samples. Kyoto Encyclopedia of Genes and Genomes analysis indicated enrichment of methylation sites in the Wnt pathway in LUAD compared with controls. Conclusion: This six-DNA methylation signature demonstrated potential as a novel biomarker for diagnosis and therapeutic targets. Further, inhibition of Wnt signaling pathway may be an important step in LUAD progression.

Neurofilament light chains (NfL) have been reported as potential markers for neuronal-axonal injury in neuroinflammatory diseases. In the current study, we describe serum NfL levels as a prognostic marker for anti-N-methyl-D-aspartate receptor encephalitis (NMDARE).Serum levels of NfL of 64 patients with anti-NMDARE and 84 healthy controls were measured by Simoa. The anti-NMDAR Encephalitis One-Year Functional Status (NEOS) score, Modified Rankin Scale (mRS) scores, and clinical and cerebrospinal fluid parameters were evaluated in patients with anti-NMDARE. Meanwhile, we performed a receiver-operator characteristic analysis to assess the power of the serum NFL in predicting the 1-year functional status.Serum NfL levels were significantly elevated in patients with anti-NMDARE compared to healthy controls (p 0.001, pOur findings show that serum NfL levels evaluated in anti-NMDAR encephalitis may be a good predictor of the risk of 1-year poor functional status.

Background The authors aimed to create a novel model to predict lymphatic metastasis in thymic epithelial tumors. Methods Data of 1018 patients were collected from the Surveillance, Epidemiology, and End Results database from 2004 to 2015. To construct a nomogram, the least absolute shrinkage and selection operator (LASSO) regression model was used to select candidate features of the training cohort from 2004 to 2013. A simple model called the Lymphatic Node Metastasis Risk Scoring System (LNMRS) was constructed to predict lymphatic metastasis. Using patients from 2014 to 2015 as the validation cohort, the predictive performance of the model was determined by receiver operating characteristic (ROC) curves. Results The LASSO regression model showed that age, extension, and histology type were significantly associated with lymph node metastasis, which were used to construct the nomogram. Through analysis of the area under the curve (AUC), the nomogram achieved a AUC value of 0.80 (95 % confidence interval [Cl] 0.75–0.85) in the training cohort and 0.82 (95 % Cl 0.70–0.93) in the validation cohort, and had closed calibration curves. Based on the nomogram, the authors constructed the LNMRS model, which had an AUC of 0.80 (95 % Cl 0.75–0.85) in the training cohort and 0.82 (95% Cl 0.70–0.93) in the validation cohort. The ROC curves indicated that the LNMRS had excellent predictive performance for lymph node metastasis. Conclusion This study established a nomogram for predicting lymph node metastasis. The LNMRS model, constructed to predict lymphatic involvement of patients, was more convenient than the nomogram.

Multiple sclerosis (MS) is rare in China, and the prevalence previously reported may be biased. Currently, few studies that have investigated the prevalence of MS in China based on the latest diagnostic criteria.Through a population-based survey from August 8, 2021 to December 31, 2021, we calculated the prevalence of multiple sclerosis in 18,676,605 residents of Guangzhou, China. MS patients were identified through the health insurance system of the Guangzhou Health Insurance Bureau, and we surveyed 17 large tertiary hospitals using a case-finding approach. All MS patients were diagnosed according to the 2017 McDonald criteria.A total of 143 patients in the resident population of Guangzhou were diagnosed with MS, with a crude prevalence of 0.77 per 100,000 (95% confidence interval (CI): 0.65-0.90), and the prevalence was higher in in females (1.14/100,000) than in males (0.44/100,000). The age-adjusted prevalence was 0.92 per 100,000 (95% CI: 0.77-1.10). The prevalence peaked at the age of 25-29 years (2.86/100,000) for both males and females (1.44/100,000 and 4.42/100,000, respectively).This is the first study to report the prevalence of MS in Guangzhou, China, according to the criteria. Our study shows that the prevalence of MS in Guangzhou is lower than that in other cities in China.

Objective Ascites, an accumulation of peritoneal fluid, is associated with poor prognosis of certain cancers. The potential mechanism that ascites worsens prognosis has not been well understood. Lipids have been reported to correlate with the prognosis of patients with epithelial ovarian cancer (EOC). Therefore, we aimed here to investigate whether lipids mediate the effect of ascites on the recurrence of EOC. Methods We collected the demographic and pathological data of 437 previously untreated patients with EOC to investigate the influence of ascites on recurrence. To identify the mechanism that mediates the potential influence of ascites on recurrence, we used ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS) to determine the plasma lipid profiles of 53 patients with EOC. We used mediation analysis to evaluate if lipids mediated the effects of ascites on the recurrence of EOC. Results Patients with ascites had a poorer prognosis, which was associated with higher levels of carbohydrate antigen-CA125 (CA125) and FIGO stage. We identified six different lipid metabolites that were associated with ascites and recurrence. Mediation analysis revealed that the lipids LysoPC(P-15:0), PC(P-34:4), and PC(38:6) may mediate the effects of ascites on recurrence. Conclusion Our findings suggest that LysoPC(P-15:0), PC(P-34:4), and PC(38:6) mediate the effect of ascites on the prognosis of patients with EOC. We believe therefore that it is reasonable to consider metabolic interventions targeting the metabolism of LysoPC(P-15:0), PC(P-34:4), and PC(38:6) as a palliative treatment for patients with EOC with ascites. Further studies of more patients will be required to validate our findings.

Background: An increasing number of HIV-negative cryptococcal meningitis (CM) patients have been reported with fatality approaching 30%.At present, HIV-negative CM patients are stratified according to clinical guidelines and clinical experience for individualized treatment, but the effect seems to be not ideal in clinical practice. Therefore, an accurate model that predict the prognosis for HIV-negative CM patients is needed to provide reference for precision treatment. Methods: This retrospective study involved 490 HIV-negative CM patients diagnosed between January 1, 1998, and March 31, 2022, by neurologists from 3 tertiary Chinese centres. Prognosis was evaluated at 10 weeks after the initiation of antifungal therapy. We used least absolute shrinkage and selection operator (LASSO) for feature filtering and developed a machine learning (ML) model to predict the prognosis in HIV-negative CM patients. Fifty-six patients from 2 other hospitals were analysed for external validation. An artificial intelligence (AI)-based detection model was also developed to automate the rapid counting of microscopic cryptococcal counts. Results:The final prediction model for HIV-negative CM patients comprised 8 variables: CSF cryptococcal count, CSF white blood cell (WBC), altered mental status, hearing impairment, CSF chloride levels, CSF opening pressure (OP), aspartate aminotransferase levels at admission and decreased rate of CSF cryptococcal count within 2 weeks after admission. The areas under the curve (AUCs) in the internal and external validation sets were 0.87 (95% CI 0.794-0.944) and 0.86 (95% CI 0.744-0.975), respectively. An AI model was trained to detect and count cryptococci, and the mean average precision (mAP) was 0.993. Additionally, an online and freely available platform for predicting prognosis and detecting and counting cryptococci in HIV-negative CM patients was established. Conclusions:A ML model for predicting prognosis in HIV-negative CM patients was built and validated, and the model might provide a reference for personalized treatment of HIV-negative CM patients. The change in the CSF cryptococcal count in the early phase of HIV-negative CM treatment can reflect the prognosis of the disease. In addition, utilizing AI to detect and count CSF cryptococci in HIV-negative CM patients can eliminate the interference of human factors in detecting cryptococci in CSF samples and reduce the workload of the examiner.

In this work, a fluorescent signal-closing probe of nitrogen-doped carbon quantum dots (NCQDs) was developed for quantitative detection of mercury ions (Hg

In this study, a novel ionic sensor based on nitrogen-doped carbon quantum dots (N-CQDs) was developed for sensitive detection of hypochlorite ions (ClO

Cystatin C has neuroprotective and immunomodulatory effects on the central nervous system. However, the role of cerebrospinal fluid (CSF) cystatin C in anti-N-methyl-d-aspartate receptor encephalitis (anti-NMDARE) remains unknown.In this study, CSF levels of cystatin C were determined in 73 patients with anti-NMDARE; 496 patients with other neurological diseases, comprising 108 with neuromyelitis optica, 77 with multiple sclerosis, 71 with schizophrenia, 68 with cryptococcus meningitis or meningoencephalitis, 43 with tuberculous meningitis or meningoencephalitis, 43 with bacterial meningitis or meningoencephalitis (BM), 35 with Guillain-Barré syndrome, 23 with spinal cord injury (SCI), 14 with amyotrophic lateral sclerosis (ALS), and 14 with idiopathic epilepsy; and 136 control patients with non-inflammatory diseases. The associations of CSF cystatin C with anti-NMDARE and its clinical parameters were evaluated.CSF cystatin C levels were significantly lower in patients with anti-NMDARE than in patients with BM, SCI, and ALS, especially among those with poor functional status (modified Rankin Scale [mRS] ≥4). CSF cystatin C levels were also significantly lower in anti-NMDARE patients with poor functional status (mRS ≥4) than in those with good functional status (mRS4). CSF cystatin C levels were significantly associated with mRS scores and CSF white blood cell counts in anti-NMDARE patients.CSF levels of cystatin C are decreased in anti-NMDARE patients and negatively associated with disease severity.

Objective To determine the relationship between basal metabolic rate (BMR) and multiple sclerosis (MS) susceptibility, we used genome-wide association study (GWAS) summary statistics data from the International Multiple Sclerosis Genetics Consortium on a total of 115,803 participants of European descent, including 47,429 MS patients and 68,374 controls. Methods We selected 378 independent genetic variants strongly associated with BMR in a GWAS involving 454,874 participants as instrumental variables to examine a potential causal relationship between BMR and MS. Results A genetically predicted higher BMR was associated with a greater risk of MS (odds ratio [OR]: 1.283 per one standard deviation increase in BMR, 95% confidence interval [CI]: 1.108–1.486, P = 0.001). Moreover, we used the lasso method to eliminate heterogeneity (Q statistic = 384.58, P = 0.370). There was no pleiotropy in our study and no bias was found in the sensitivity analysis using the leave-one-out test. Conclusions We provide novel evidence that a higher BMR is an independent causal risk factor in the development of MS. Further work is warranted to elucidate the potential mechanisms.

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is an autoimmune disease of the central nervous system. Gasdermin D (GSDMD) is associated with autoimmune disorders and neuroinflammatory disorders, but its role in anti-NMDAR encephalitis is unclear. In this study, we measured serum levels of GSDMD in 42 patients with anti-NMDAR encephalitis and 25 healthy controls. Of the 42 patients, 17 had follow-up evaluation of GSDMD levels and modified Rankin scale (mRS) scores at 3 months. Association of GSDMD with anti-NMDAR encephalitis and its clinical parameters were evaluated. Serum GSDMD levels were significantly higher in patients with anti-NMDAR encephalitis than in healthy controls (p = 0.002, padjusted = 0.009), especially in males (p = 0.001, padjusted = 0.022). This was also evident in patients with severe impairment (mRS >3 vs mRS ≤3; p

This paper combines 3D printing technology (3DPT) with microwave components to obtain better performance than traditional fabrication. Here presents a single-layer and a multi-layer coaxial wideband Wilkinson power divider working at 1 GHz with a third-octave bandwidth. The size of the single-layer structure is $75 \times 88\times 15\ \text{mm}^{3}$ as a reference. Then, the multi-layer one is introduced to achieve miniaturized characteristic under folding topology, whose size is $45\times 76\times 23\ \text{mm}^{3}$ . The isolations between the outputs are better than 20 dB and the insertion loss of each port is around 0.2 dB over the operating band. The performance hardly deteriorates with almost half size reduction via 3DPT.

Background Glioma accounts for a large proportion of cancer, and an effective treatment for this disease is still lacking because of the absence of specific driver molecules. Current challenges in the treatment of glioma are the accurate and timely diagnosis of brain glioma and targeted treatment plans. To investigate the diagnostic biomarkers and prospective role of miRNAs in the tumorigenesis and progression of glioma, we analyzed the expression of miRNAs and key genes in glioma based on The Cancer Genome Atlas database. Methods Of the 701 cases that were downloaded, five were normal and 696 were glioma. Then, 1626 differentially expressed genes were identified, and 173 aberrantly expressed miRNAs were calculated by edgeR. GO and KEGG pathway enrichment analyses were performed using Cytoscape software. A coexpression network was built by weighted correlation network analysis (WGCNA). A cell scratch test and transwell, cell apoptosis and cell cycle assays were performed to validate the function of hsa-let-7b-5p. Results Based on crosstalk genes in the KEGG, PPI network, and WGCNA analyses, PLK1, CCNA2, cyclin B2 (CCNB2), and AURKA were screened as candidate diagnostic marker genes. The survival analysis revealed that high mRNA expression of PLK1, CCNA2, and AURKA was significantly associated with poor overall survival. Furthermore, hsa-let-7b-5p was identified as a core miRNA in the regulation of candidate genes involved in glioma development. We confirmed that hsa-let-7b-5p could inhibit the migration, invasion, and cell cycle of glioma cells. Conclusions This study provides four potential biomarkers for the diagnosis of glioma, offers a potential explanation of its pathogenesis, and proposes hsa-let-7b-5p as a therapeutic target. Electronic supplementary material The online version of this article (10.1186/s12967-019-1882-7) contains supplementary material, which is available to authorized users.

This study investigated pharmacokinetics, tissue distribution and excretion of ACT001 in Sprague-Dawley rats. Stability study and metabolism study of ACT001 are conducted. The absolute bioavailability of ACT001 is 50.82%. ACT001 has no accumulation effect and displayed wide tissue distribution. ACT001 can be rapidly distributed to tissues after oral administration and can diffuse through the blood-brain barrier. The total cumulative excretion of ACT001 in feces, urine and bile were found to be 0.05, 3.42 and 0.012%, respectively. UPLC/ESI-QTOF-MS coupled with MetaboLynx XS software was utilized to detect the metabolites of ACT001 in vitro. Five metabolites (M1, M2, M3, M4 and M5) were detected. M2 wasn't discovered in human liver microsome samples and bile samples. M1 and M2 weren't discovered in rat plasma and human plasma. M3, M4 and M5 weren't discovered in bile samples. M5 is an active metabolite named micheliolide (MCL). There is no significant difference in half-life, type of identified metabolites and the amount of each metabolites between using rat plasma and human plasma. Owing to the species differences of hepatomicrosome enzymes, significant differences were shown in half-life, type of identified metabolites and the amount of each metabolites between using rat liver microsome and human liver microsome.

Axonal transport plays a significant role in the establishment of neuronal polarity, axon growth, and synapse formation during neuronal development. The axon of a naturally growing neuron is a highly complex and multifurcated structure with a large number of bends and branches. Nowadays, the study of dynamic axonal transport in morphologically complex neurons is greatly limited by the technological barrier. Here, a sparse gene transfection strategy was developed to locate fluorescent mCherry in the lysosome of primary neurons, thus enabling us to track the lysosome-based axonal transport with a single-particle resolution. Thereby, several axonal transport models were observed, including forward or backward transport model, stop-and-go model, repeated back-and-forth transport model, and cross-branch transport model. Then, the accurate single-particle velocity quantification by TrackMate revealed a highly heterogeneous and discontinuous transportation process of lysosome-based axonal transport in freely orientated axons. And, multiple physical factors, such as the axonal structure and the size of particles, were disclosed to affect the velocity of particle transporting in freely orientated axons. The combined single-particle fluorescence tracking and TrackMate assay can be served as a facile tool for evaluating axonal transport in neuronal development and axonal transport-related diseases.

Background Postoperative systemic inflammatory response syndrome (SIRS) is common in surgical ICU patients especially in elderly patients, and the geriatric population with SIRS is more susceptible to sepsis, MODS, and death. There have been no reliable and accurate methods to predict SIRS in the elderly. Therefore, we aimed to develop and validate a novel effective model for predicting postoperative SIRS in elderly patients. Methods Data of 16141 patients aged ≥ 65 years who underwent surgery in two centers of Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2020 were retrieved and analyzed. We developed a predictive model for postoperative SIRS based on a retrospectively cohort study of 5904 patients spanning from January 2015 to December 2019. The discriminative performance of this model was determined by area under the receiver operating characteristics curve (AUC). We also used the model to validate outcomes in 1105 patients who underwent surgery at the same institutions from January 2020 to September 2020, and to perform a stratified analysis. Results A total of 5904 patients were enrolled in the training cohort and 1105 patients comprised the validation cohort, in which incidence rates of postoperative SIRS were 24.6% and 20.2%, respectively. Patients with SIRS had more postoperative complications and a worse in-hospital survival rate than patients without SIRS. Six feature variables including preoperative fever, preoperative serum albumin level, ASA classification, total intraoperative infusion volume, surgical duration, and postoperative intensive care unit admission were identified as valuable predictors to construct the nomogram, with high AUCs in both the training and validation cohorts (0.800 [0.787, 0.813] and 0.823 [0.791, 0.855], respectively). Conclusions We constructed an effective nomogram using 6 routinely obtained variables to assist clinicians to predict postoperative SIRS in elderly patients.

We investigated the serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP) levels in a cohort of Chinese patients with neuromyelitis optica spectrum disorders (NMOSD) and multiple sclerosis (MS) in relation to clinical disease course and treatment. sNfL and sGFAP levels were determined by ultrasensitive single molecule array (Simoa) assay in patients with NMOSD (n = 102) and MS (n = 98) and healthy controls (HCs; n = 84). Notably, 13 patients with NMOSD and 27 patients with MS were enrolled in the 1-year follow-up cohort. Levels were compared with data such as clinical course, disease duration, Expanded Disability Status Scale (EDSS) score, and lesions on MRI. Higher levels of sNfL and sGFAP were found in subjects with NMOSD and MS than in HCs (sNfL, median 12.11, 17.5 vs. 8.88 pg/ml, p

Background: Chronic hepatitis B patients with severe acute exacerbation is at progression stage of acute-on-chronic liver failure (ACLF). We aimed to develop and validate an accurate risk model to early identify the patients at high-risk of liver failure and predict patient’s survival. Methods: We selected the best variable combination using recursive feature elimination algorithm to develop and validate a multivariate logistic regression risk model as well as online application on cloud server from the training cohort with a total of 342 patients and two external cohorts with a sample size of 96 and 65 patients, respectively. Findings: An excellent prediction model called PATA model including four predictors, prothrombin time, age, total bilirubin and alanine aminotransferase could achieve AUROC of 0.959 (95% CI 0.941-0.977) in the development set and AUROC of 0.932 (95% CI 0.876-0.987) and 0.905 (95% CI 0.826-0.984) in two external validation cohorts, respectively. The C-index of the model was 0.720 (95% CI 0.675-0.765) in prognostic stratification of 90-days mortality for training set patients, while the end-stage liver disease score model had a C-index of 0.549 (95% CI 0.506-0.592). Interpretation: The highly-predictive risk model and easy-to-use online application can accurately predict the risk of ACLF and patient survival, which is expected to become a widely accepted prediction model and guide therapeutic options. Funding This work was supported by a grant for National Key RD National Natural Science Foundation of China; Major Science and Technology Projects in 13th Five-Year and Natural Science Foundation of Guangdong. Declaration of Interest: The authors declare that they have no competing interests. Ethical Approval: This study was approved by the Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University ([2015]2-206 No.1). This study was conducted according to the Declaration of Helsinki. All adult participants provided written informed consent.

Matrix metalloproteinases (MMPs) are enzymes that are integral in extracellular matrix (ECM) remodeling. In age or disease, ECM may become dysregulated and contribute to fibrosis, which impairs cardiac electrical conduction. Two alleles regulate matrix metalloproteinase-3 (MMP-3) activity: one with five adenosine bases (5A, associated with higher MMP-3 activity and decreased fibrosis) and another with six adenosine bases (6A, associated with lower MMP-3 activity and increased fibrosis). Here, we determined whether ECG-derived QTc and related parameters are associated with the MMP-3 5A/6A genotype in a cross-section of the Australian rural population. A retrospective cross-sectional population was obtained from the Charles Sturt University Diabetes Screening Research Initiative. Genotype and resting 12-lead ECG parameters of 295 participants were analyzed. Amongst these participants, 85 individuals carried the 5A/5A genotype, 141 individuals carried the 5A/6A genotype, and 65 individuals carried the 6A/6A genotype. Compared to 5A/5A genotype carriers, 5A/6A genotype carriers had a significantly longer QTc duration by 9.50 ms (95% CI: 3.48–15.52, p = 0.002), whilst 6A/6A genotype carriers had an even longer QTc duration by 12.19 ms (95% CI: 5.04–19.34, p = 0.001). We found an association between MMP-3 5A/6A polymorphisms and QTc, independent of adjustments for age, gender, alcohol consumption, smoking status, body mass index and blood pressure.

Colorectal cancer (CRC) remains an incurable disease. Previous metabolomic studies show that metabolic signatures in plasma distinguish CRC patients from healthy controls. Chronic enteritis (CE) represents a risk factor for CRC, with a 20 fold greater incidence than in healthy individuals. However, no studies have performed metabolomic profiling to investigate CRC biomarkers in CE. Our aims were to identify metabolomic signatures in CRC and CE and to search for blood-derived metabolite biomarkers distinguishing CRC from CE, especially early-stage biomarkers. In this case–control study, 612 subjects were prospectively recruited between May 2015 and May 2016, and including 539 CRC patients (stage I, 102 cases; stage II, 259 cases; stage III, 178 cases) and 73 CE patients. Untargeted metabolomics was performed to identify CRC-related metabolic signatures in CE. Five pathways were significantly enriched based on 153 differential metabolites between CRC and CE. 16 biomarkers were identified for diagnosis of CRC from CE and for guiding CRC staging. The AUC value for CRC diagnosis in the external validation set was 0.85. Good diagnostic performances were also achieved for early-stage CRC (stage I and stage II), with an AUC value of 0.84. The biomarker panel could also stage CRC patients, with an AUC of 0.72 distinguishing stage I from stage II CRC and AUC of 0.74 distinguishing stage II from stage III CRC. The identified metabolic biomarkers exhibit promising properties for CRC monitoring in CE patients and are superior to commonly used clinical biomarkers (CEA and CA19-9).

Background: Chronic patients with flare of hepatitis B virus (HBV) may progress to acute-on-chronic liver failure (ACLF), which is characterized by high mortality. Uniform models to predict risk of ACLF are lacking. We aimed to present an accurate risk prediction model that incorporates clinical manifestations and laboratory results in chronic HBV-infected patients with flare of hepatitis. Methods: We selected the best variable combination using recursive feature elimination (RFE) from a perspective cohort including 360 patients with hepatitis flare at the Third Affiliated Hospital of Sun Yat-sen University to develop a risk prediction model and nomogram scoring system, then validated the model using two external independent sets consisting of 96 and 65 individuals, and compared its performance with End-Stage Liver Disease (MELD) score model. Findings: An excellent prediction model called the PAT model using the five final identified predictors including prothrombin time (PT), total bilirubin (TBil), ascites, pneumonia and hemoglobin (HGB) was constructed. The model could achieve an Area under the Receiver Operating Characteristic curve (AUROC) of 0.964 (95% confidence interval [CI]: 0.949-0.980) in the development set and performed well in external validation cohorts (validation cohort 1, AUROC = 0.909, 95% CI 0.840-0.977; validation cohort 2, AUROC = 0.898, 95% CI 0.813-0.983, both of which were significantly higher than the MELD score model (P

Background: Breakthroughs have been made in cancer immunotherapy using antibodies against immune checkpoints. But successful immune checkpoint blockade (ICB) responses only occur in a fraction of people, and we have no idea which patients will be the lucky one before treatment. Picking out the effective population accurately can improve the cure rate significantly. We set out to design a novel immunophenotyping approaches for kidney renal clear cell carcinoma (KIRC) based on the tumor immune microenvironment(TIME), which target to distinguish immunotherapy responders. Methods: Five hundred and thirty KIRC samples from The Cancer Genome Atlas (TCGA) were analyzed as the training cohort. Non-negative matrix factorization (NMF) was used to extract immune-related features from complex tumor bulks. We associated the expression patterns with a series of immune-related gene signatures and clinicopathological features. GSE73731 and GSE40435 validation datasets were analyzed to further confirm our findings. Findings: We found that almost 40% of KIRC patients in the training cohort (209/530) were designated as the Immune Class with a high enrichment score for inflammatory response, cytolytic activity, and CD8 T cells (all P