The role of small dense LDL-C/large buoyant LDL-C ratio as an independent risk factor in patients with type 2 diabetes mellitus and metabolic syndrome

Dyslipidemia plays a key role in metabolic syndrome (MS), intricately linked to type 2 diabetes mellitus (T2DM). This study aimed to investigate the differences in low-density lipoprotein cholesterol (LDL-C) subfraction levels between T2DM and T2DM with MS, and identify the risk factors associated with the disease. A total of 246 individuals diagnosed with T2DM, including 144 T2DM patients with MS, and 147 healthy subjects were recruited. All participants underwent a comprehensive clinical evaluation. Lipoprotein subfraction analysis was performed using the Lipoprint LDL system. Multivariate logistic regression analysis revealed that several lipid markers, including triglyceride (TG), LDL-C, large buoyant LDL-C (lbLDL-C), small dense LDL-C (sdLDL-C), LDLC2-5, and sdLDL-C/lbLDL-C ratio, were identified as independent risk factors for T2DM. Additionally, TG, sdLDL-C, LDLC-4, LDLC-5, and sdLDL-C/lbLDL-C ratio were found to be independent risk factors for T2DM with MS. Furthermore, the results of the receiver operating characteristic (ROC) curves demonstrated that sdLDL-C, LDLC-4, LDLC-3, and sdLDL-C/lbLDL-C ratio exhibited excellent predictive performance for the risk of T2DM (AUC > 0.9). The sdLDL-C/lbLDL-C ratio emerges as a shared independent risk factor for T2DM and MS complications. Furthermore, sdLDL-C/lbLDL-C ratio, along with LDL-4 and LDL-3, exhibits noteworthy predictive capabilities for T2DM.

Case report: Successful treatment of an anti-D2R and DPPX antibody-associated autoimmune encephalitis patient with high-dose methylprednisolone and intravenous immunoglobulin.

Background: Autoimmune encephalitis is a neurological condition caused by abnormal immune responses, manifesting as cognitive impairments, behavioral abnormalities, and seizures. Its diagnosis depends on the detecting neuronal surface antibodies in serum or cerebrospinal fluid. Despite recent advances in understanding, clinical recognition remains challenging, especially with rare antibodies such as anti-dopamine D2 receptor (D2R) and anti-dipeptidyl-peptidase-like protein 6 (DPPX) antibodies. Delayed diagnosis can lead to severe complications. This case presentation emphasizes the diagnostic intricacies and effective treatment of the anti-D2R and DPPX antibody-associated autoimmune encephalitis. Case description: The patient presented with a 3-day history of fatigue and limb soreness followed by a 3-h episode of confusion and limb convulsions. Upon admission to our facility, the initial diagnosis included status epilepticus, aspiration pneumonia, metabolic acidosis, respiratory alkalosis, and suspected encephalitis. Despite receiving antiepileptic, anti-infection, and antivirus therapy, the patient's condition deteriorated. Both computed tomography (CT) scan and magnetic resonance imaging (MRI) of the brain showed no significant abnormalities. No pathogen was identified in the cerebrospinal fluid (CSF). However, further CSF and serum examination revealed positive results of anti-D2R and anti-DPPX antibodies, confirming a diagnosis of anti-D2R and DPPX antibody-associated autoimmune encephalitis. The patient underwent a comprehensive treatment regimen, including high-dose methylprednisolone pulse therapy combined with intravenous immunoglobulin (IVIG), antiviral and anti-infection treatments, and antiepileptic medications. Significant clinical improvement was observed, and by the 18th day of admission, the patient was stable and coherent. Conclusions: The current patient represents the first reported case of double-positive autoimmune encephalitis for anti-D2R and DPPX antibodies, with epilepsy as a prominent feature. High-dose methylprednisolone pulse therapy combined with IVIG has shown significant safety and efficacy in treating anti-D2R and DPPX antibody-positive autoimmune encephalitis-associated epilepsy.

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).

Deficiency of Trex1 leads to spontaneous development of type 1 diabetes.

BACKGROUND: Type 1 diabetes is believed to be an autoimmune condition, characterized by destruction of insulin-producing cells, due to the detrimental inflammation in pancreas. Growing evidences have indicated the important role of type I interferon in the development of type 1 diabetes. METHODS: Trex1-deficient rats were generated by using CRISPR-Cas9. The fasting blood glucose level of rat was measured by a Roche Accuchek blood glucose monitor. The levels of insulin, islet autoantibodies, and interferon-ß were measured using enzyme-linked immunosorbent assay. The inflammatory genes were detected by quantitative PCR and RNA-seq. Hematein-eosin staining was used to detect the pathological changes in pancreas, eye and kidney. The pathological features of kidney were also detected by Masson trichrome and periodic acid-Schiff staining. The distribution of islet cells, immune cells or ssDNA in pancreas was analyzed by immunofluorescent staining. RESULTS: In this study, we established a Trex1-deletion Sprague Dawley rat model, and unexpectedly, we found that the Trex1-/- rats spontaneously develop type 1 diabetes. Similar to human diabetes, the hyperglycemia in rats is accompanied by diabetic complications such as diabetic nephropathy and cataract. Mechanistical investigation revealed the accumulation of ssDNA and the excessive production of proinflammatory cytokines, including IFN-ß, in Trex1 null pancreas. These are likely contributing to the inflammation in pancreas and eventually leading to the decline of pancreatic ß cells. CONCLUSIONS: Our study links the DNA-induced chronic inflammation to the pathogenesis of type 1 diabetes, and also provides an animal model for type 1 diabetes studies.

Clinical utility of metagenomic next-generation sequencing in pathogen detection for lower respiratory tract infections and impact on clinical outcomes in southernmost China.

Background: Today, metagenomic next-generation sequencing (mNGS) has emerged as a diagnostic tool for infections. However, since Hainan has a complicated pathogen spectrum, the diagnostic value and impact on patient outcomes of mNGS in Hainan are to be explored. Methods: From April 2020 to October 2021, 266 suspected lower respiratory tract infections (LRTIs) patients in Hainan were enrolled, and specimens were collected before antibiotic treatment. Bronchoalveolar lavage fluid (BALF) samples were subjected to mNGS and culture to compare the diagnostic performance. Other conventional microbiological tests (CMT) were also performed. Patients' treatments and clinical outcomes were recorded, and the antibiotic resistance genes (ARGs) were detected via mNGS workflow. Results: The positive rate of mNGS outperformed that of culture (87.55% vs. 39.30%, p<0.001) and CMT (87.12% vs. 52.65%, p<0.001). Specifically, mNGS detected more P. aeruginosa (12.03% vs 9.02%, p<0.05), H. influenzae (9.77% vs 2.26%, p<0.001), Aspergillus fumigatus (3.00% vs 0.75%, p<0.05), Candida albicans (26.32% vs 7.52%, p<0.001) and uncommon pathogens. It also demonstrated great diagnostic advantages in Mycobacterium tuberculosis with 80% sensitivity and 97.4% specificity. Over half of the patients (147, 55.26%) had modified empirical treatment according to mNGS results and 89.12% of them responded well. For three deaths with modified treatment, multiple drug resistance was predicted by mNGS and confirmed by antibiotic susceptibility test. Conclusions: The application of mNGS can benefit clinics in pathogen identification and antimicrobial treatment stewardship. Physicians should be alert to some emerging uncommon pathogens, including Chlamydia Psittaci, Nocardia otitidiscaviarum, and rare NTM.

Multigene testing panels reveal pathogenic variants in sporadic breast cancer patients in northern China.

Background: Breast cancer, the most prevalent malignancy in women worldwide, presents diverse onset patterns and genetic backgrounds. This study aims to examine the genetic landscape and clinical implications of rare mutations in Chinese breast cancer patients. Methods: Clinical data from 253 patients, including sporadic and familial cases, were analyzed. Comprehensive genomic profiling was performed, categorizing identified rare variants according to the American College of Medical Genetics (ACMG) guidelines. In silico protein modeling was used to analyze potentially pathogenic variants' impact on protein structure and function. Results: We detected 421 rare variants across patients. The most frequently mutated genes were ALK (22.2%), BARD1 (15.6%), and BRCA2 (15.0%). ACMG classification identified 7% of patients harboring Pathogenic/Likely Pathogenic (P/LP) variants, with one case displaying a pathogenic BRCA1 mutation linked to triple-negative breast cancer (TNBC). Also identified were two pathogenic MUTYH variants, previously associated with colon cancer but increasingly implicated in breast cancer. Variants of uncertain significance (VUS) were identified in 112 patients, with PTEN c.C804A showing the highest frequency. The role of these variants in sporadic breast cancer oncogenesis was suggested. In-depth exploration of previously unreported variants led to the identification of three potential pathogenic variants: ATM c.C8573T, MSH3 c.A2723T, and CDKN1C c.C221T. Their predicted impact on protein structure and stability suggests a functional role in cancer development. Conclusion: This study reveals a comprehensive overview of the genetic variants landscape in Chinese breast cancer patients, highlighting the prevalence and potential implications of rare variants. We emphasize the value of comprehensive genomic profiling in breast cancer management and the necessity of continuous research into understanding the functional impacts of these variants.

Metagenomic next-generation sequencing of bronchoalveolar lavage fluid in non-severe and severe pneumonia patients.

Metagenomic next-generation sequencing (mNGS) is widely used as a more promising technology than conventional tests. However, its clinical utility in the context of bronchoalveolar lavage fluid (BALF) samples for discriminating between non-severe and severe pneumonia is not well established. Thus, this study aimed to investigate the diagnostic performance of mNGS on BALF samples from 100 individuals suspected of pneumonia, and compared it with conventional microbiological tests (CMT) of BALF samples and the final clinical diagnosis. Twenty-seven cases of non-severe pneumonia and 73 cases of severe pneumonia patients were finally clinically diagnosed. Among 100 cases, diagnostic performance of mNGS and culture showed a significant difference; 65 cases had the same sample types, of which 25 cases were diagnosed as positive by mNGS only (38.46%) and 1 was diagnosed as positive by culture only (1.54%). Moreover, 24 cases were diagnosed positive in both mNGS and culture (36.92%) and 15 cases tested negative in both mNGS and culture (23.08%). Among 35 cases, 28 out of 35 cases were diagnosed as positive by mNGS, while only 4 out of 35 cases were diagnosed as positive by the indirect immunofluorescence method (IIFT). In addition, the positive rate of mNGS was higher than that of culture in cases regardless of prior antibiotic exposure. Mixed pathogens were found to be significantly more prevalent in severe pneumonia patients than in non-severe pneumonia patients. Importantly, among 38 cases who were diagnosed solely by mNGS, 25 patients experienced an improved outcome after physicians changed the therapy according to the mNGS results. In conclusion, the results showed that mNGS of BALF represents a potentially effective tool for detection of mixed pathogens in severe pneumonia.

Clinical Efficiency of Metagenomic Next-Generation Sequencing in Sputum for Pathogen Detection of Patients with Pneumonia According to Disease Severity and Host Immune Status.

Purpose: Severe pneumonia causes the highest mortality rate in immunocompromised patients. This study aimed to investigate the pathogen diagnostic efficacy of metagenomic next-generation sequencing (mNGS) using sputum sample in patients with pneumonia according to patients' disease severity and immune conditions. Patients and Methods: A total of 180 patients suffering from pneumonia were recruited, and sputum samples were collected in duplicate for pathogen detection by both conventional microbiological tests (CMT) and mNGS. Then, the performance of pathogen identification was examined between two methods, according to disease severity and patients' immune status. Results: In comparison to CMT, mNGS had higher positivity rates in all patients with pneumonia (85.0% vs 62.2%, P=9.445e-07). The most commonly detected microorganism in sputum of pneumonia patients was Acinetobacter baumannii (42/180, 23.3%) in bacterum level, Candida albicans in fungus level (44/180, 24.4%), and Human herpesvirus 1 (39/180, 27.5%) in virus level. However, for mNGS results, Candida albicans in 34.9% of positive patients, and Human herpesvirus 1 in 7.7% of positive cases were confirmed as pathogens causing pneumonia. Acinetobacter baumannii detected by mNGS in 75% of positive patients was diagnosed as pathogen of pneumonia. The microorganism profile of sputum mNGS differed according to disease severity and immune status of patients. Pneumocystis jirovecii was more likely to infect immunocompromised patients (P=0.002). Pseudomonas aeruginosa (14.8% vs 0.0%, P=0.008) and Human herpesvirus 1 (26.1% vs 5.3%, P=0.004) had higher infection rate in patients with severe pneumonia compared with non-severe cases. mNGS had overwhelming advantages over CMT in detecting a lot of microorganisms including Streptococcus pneumoniae, Enterococcus faecium, Pneumocystis jirovecii, and majority of viruses. Conclusion: mNGS is a complementary tool of CMT for detecting suspected pathogens for patients with lower respiratory infections. The interpretation of opportunistic pathogens identified by mNGS is challenging, and needs comprehensive consideration of sequencing data and clinical factors.

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.

Individualized folic acid supplementation based on MTHFR and MTRR gene polymorphisms reduces the risk of gestational diabetes mellitus in a Chinese population.

OBJECTIVE: Folic acid (FA) may contribute to the development of gestational diabetes mellitus (GDM), but available studies are inconsistent. We studied the genotype distribution and allele frequencies of methylenetetrahydrofolate reductase (MTHFR) C677T, A1298C, and methionine synthase reductase (MTRR) A66G polymorphisms in pregnant Chinese women and compared the effects of individualized and traditional FA supplementation on GDM. METHODS: In this retrospective study, genotype distribution and allele frequencies in 968 pregnant women were tested. FA metabolism was tested by dividing patients into four groups, each of which was supplemented with different doses of FA at different times. Pregnancy complications were followed up and compared to 1940 pregnant women traditionally supplemented with FA in the same hospital as a control group. RESULTS: The allele frequencies were 63.3% (C) and 36.7% (T) for MTHFR C677T, 79.3% (A) and 20.7% (C) for MTHFR A1298C and 75.0% (A) and 25.0% (G) for MTRR A66G. The incidence of GDM after FA supplementation was significantly lower in the case group compared to the control group, especially in high-risk pregnancies. CONCLUSION: Using genetic polymorphisms to elucidate FA metabolism in pregnant women and providing appropriate FA supplementation can be effective in reducing GDM, especially in high-risk groups.

Corrigendum: Microbiological diagnostic performance of metagenomic next-generation sequencing compared with conventional culture for patients with community-acquired pneumonia.

[This corrects the article DOI: 10.3389/fcimb.2023.1136588.].

Depletion of butyrate-producing microbes of the Firmicutes predicts nonresponse to FMT therapy in patients with recurrent Clostridium difficile infection.

Approximately 10% of individuals diagnosed with Clostridium difficile infection (CDI) show the resistance to fecal microbiota transplantation (FMT), with the underlying mechanisms remaining elusive. Deciphering the intricate microbiome profile within this particular subset of FMT-refractory patients via clinical FMT investigations assumes paramount importance, as it holds the key to designing targeted therapeutic interventions tailored for CDI, particularly recurrent CDI (rCDI). A cohort of twenty-three patients afflicted with rCDI, exhibiting congruent clinical baselines, was meticulously selected for FMT. Rigorous screening of thousands of healthy individuals identified ten FMT donors who met stringent health standards, while a total of 171 stool samples were collected to serve as healthy controls. To assess the influence of microbiome dynamics on FMT efficacy, fecal samples were collected from four donors over a continuous period of twenty-five weeks. After FMT treatment, seven individuals exhibited an inadequate response to FMT. These non-remission patients displayed a significant reduction in α-diversity indexes. Meanwhile, prior to FMT, the abundance of key butyrate-producing Firmicutes bacteria, including Christensenellaceae_R_7_group, Ruminococcaceae_unclassified, Coprococcus_2, Fusicatenibacter, Oscillospira, and Roseburia, were depleted in non-remission patients. Moreover, Burkholderiales_unclassified, Coprococcus_2, and Oscillospira failed to colonize non-remission patients both pre- and post-treatment. Conversely, patients with a favorable FMT response exhibited a higher relative abundance of Veillonella prior to treatment, whereas its depletion was commonly observed in non-remission individuals. Genera interactions in lower effectiveness FMT donors were more similar to those in non-remission patients, and Burkholderiales_unclassified, Coprococcus_2, and Oscillospira were frequently depleted in these lower effectiveness donors. Older patients were not conducive to the colonization of Veillonella, consistent with their poor prognosis after FMT. FMT non-remission rCDI patients exhibited distinct characteristics that hindered the colonization of beneficial butyrate-producing Firmicutes microbes. These findings hold promise in advancing the precision of FMT therapy for rCDI patients.

Microbiological diagnostic performance of metagenomic next-generation sequencing compared with conventional culture for patients with community-acquired pneumonia.

Background: Community-acquired pneumonia (CAP) is an extraordinarily heterogeneous illness, both in the range of responsible pathogens and the host response. Metagenomic next-generation sequencing (mNGS) is a promising technology for pathogen detection. However, the clinical application of mNGS for pathogen detection remains challenging. Methods: A total of 205 patients with CAP admitted to the intensive care unit were recruited, and broncho alveolar lavage fluids (BALFs) from 83 patients, sputum samples from 33 cases, and blood from 89 cases were collected for pathogen detection by mNGS. At the same time, multiple samples of each patient were tested by culture. The diagnostic performance was compared between mNGS and culture for pathogen detection. Results: The positive rate of pathogen detection by mNGS in BALF and sputum samples was 89.2% and 97.0%, which was significantly higher (P < 0.001) than that (67.4%) of blood samples. The positive rate of mNGS was significantly higher than that of culture (81.0% vs. 56.1%, P = 1.052e-07). A group of pathogens including Mycobacterium abscessus, Chlamydia psittaci, Pneumocystis jirovecii, Orientia tsutsugamushi, and all viruses were only detected by mNGS. Based on mNGS results, Escherichia coli was the most common pathogen (15/61, 24.59%) of non-severe patients with CAP, and Mycobacterium tuberculosis was the most common pathogen (21/144, 14.58%) leading to severe pneumonia. Pneumocystis jirovecii was the most common pathogen (26.09%) in severe CAP patients with an immunocompromised status, which was all detected by mNGS only. Conclusion: mNGS has higher overall sensitivity for pathogen detection than culture, BALF, and sputum mNGS are more sensitive than blood mNGS. mNGS is a necessary supplement of conventional microbiological tests for the pathogen detection of pulmonary infection.

Development of Machine Learning Tools for Predicting Coronary Artery Disease in the Chinese Population.

Purpose: Coronary artery disease (CAD) is one of the major cardiovascular diseases and the leading cause of death globally. Blood lipid profile is associated with CAD early risk. Therefore, we aim to establish machine learning models utilizing blood lipid profile to predict CAD risk. Methods: In this study, 193 non-CAD controls and 2001 newly-diagnosed CAD patients (1647 CAD patients who received lipid-lowering therapy and 354 who did not) were recruited. Clinical data and the result of routine blood lipids tests were collected. Moreover, low-density lipoprotein cholesterol (LDL-C) subfractions (LDLC-1 to LDLC-7) were classified and quantified using the Lipoprint system. Six predictive models (k-nearest neighbor classifier (KNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost)) were established and evaluated by the confusion matrix, area under the receiver operating characteristic (ROC) curve (AUC), recall (sensitivity), accuracy, precision, and F1 score. The selected features were analyzed and ranked. Results: While predicting the CAD development risk of the CAD patients without lipid-lowering therapy in the test set, all models obtained AUC values above 0.94, and the accuracy, precision, recall, and F1 score were above 0.84, 0.85, 0.92, and 0.88, respectively. While predicting the CAD development risk of all CAD patients in the test set, all models obtained AUC values above 0.91, and the accuracy, precision, recall, and F1 score were above 0.87, 0.94, 0.87, and 0.92, respectively. Importantly, small dense LDL-C (sdLDL-C) and LDLC-4 play pivotal roles in predicting CAD risk. Conclusions: In the present study, machine learning tools combining both clinical data and blood lipid profile showed excellent overall predictive power. It suggests that machine learning tools are suitable for predicting the risk of CAD development in the near future.

The Comparison of Metagenomic Next-Generation Sequencing with Conventional Microbiological Tests for Identification of Pathogens and Antibiotic Resistance Genes in Infectious Diseases.

Background: Metagenomic next-generation sequencing (mNGS) has been widely studied, due to its ability of detecting all the microbial genetic information unbiasedly in a sample at one time and not relying on traditional culture. However, the application of mNGS in the diagnosis of clinical pathogens remains challenging. Methods: From December 2019 to March 2021, 134 specimens including Broncho alveolar lavage fluid (BAFL), blood, sputum, cerebrospinal fluid (CSF), bile, pleural fluid, pus, were continuously collected in The First Hospital of Qinhuangdao, and their retrospective diagnoses were classified into infectious disease (128, 95.5%) and noninfectious disease (6, 4.5%). The pathogen-detection performance of mNGS was compared with conventional microbiological tests (CMT) and culture method. In addition, the antibiotic resistance genes (ARGs) and evolutionary relationship of common drug-resistant A. baumannii were also analyzed. Results: Compared with CMT and culture methods, mNGS showed higher sensitivity in pathogen detection (74.2% vs 57.8%; P < 0.001 and 66.3% vs 31.7%; P < 0.001, respectively). Importantly, for cases that mNGS-positive only, 18 (35%) cases result in diagnosis modification, and 7 (23%) cases confirmed the clinical diagnosis. In 17 cases that A. baumannii were both detected in mNGS and culture, ade genes were the most frequently detected ARGs (from 13 cases), followed by sul2 and APH(3")-Ib (both from 12 cases). High consistency was observed among these ARGs and the related phenotype (100% for ade genes, 91.6% for sul2 and APH(3")-Ib). A. baumannii strains were classified into three groups, and most were well-clustered. It suggested those strains may be the epidemic strains. Conclusion: In our study, mNGS had a higher sensitivity than CMT and culture method. And the result of ARGs frequency and cluster analysis of A. baumannii was of great significance to the anti-infective therapy.

Effects of N6-Methyladenosine Regulators on LAG3 and Immune Infiltrates in Lung Adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer, which is one of the most commonly diagnosed tumors and the leading causes of death from cancer around the world. Since RNA methylation is a posttranscriptional modification and affects so much biological progress, it is urged to explore the role of N6-methyladenosine (m6A) methylation in LUAD. Methods: We explored the expression of 24 m6A methylation genes, as well as their correlations with LAG3 in 561 LUAD samples from TCGA. Consensus clustering was applied to m6A methylation genes, and two LUAD subgroups were identified. The expression of m6A genes was analyzed by the Wilcoxon test. KEGG and GO enrichment analyses were performed to indicate the pathway affected by differentially expressed genes in the two groups. A prognostic model based on LASSO regression using an eleven-m6A gene signature was constructed according to the expression of these genes. Receiver operating characteristic (ROC) curve was used to confirm the accuracy of the model in the TCGA cohort, as well as in the test cohort from the Gene Expression Omnibus (GEO) database. Results: Compared to cluster 1, cluster 2 showed poorer overall survival (OS) and higher LAG3 expression. In addition, KEGG and GO enrichment analyses indicated that differentially expressed genes are enriched in the immune response. We also observed that the expression of LAG3 is positively correlated with IGF2BP2, CBLL1, and HNRNPA2B1 and negatively correlated with YTHDF2, YTHDF3, and FTO. For patients in the TCGA cohort, the AUC score is 0.7, and the AUC score for the GSE50081 cohort is 0.675. Patients with lower risk scores exhibited better overall survival and lower expression of LAG3 than patients with higher risk scores. Conclusions: In brief, our results indicated the important role of m6 methylation in affecting the tumor immune microenvironment and the survival of patients with LUAD. The m6A methylation gene signatures might serve as promising therapeutic targets and help the immunotherapy of LUAD in the future.

Construction of machine learning tools to predict threatened miscarriage in the first trimester based on AEA, progesterone and ß-hCG in China: a multicentre, observational, case-control study.

BACKGROUND: Endocannabinoid anandamide (AEA), progesterone (P4) and ß-human chorionic gonadotrophin (ß-hCG) are associated with the threatened miscarriage in the early stage. However, no study has investigated whether combing these three hormones could predict threatened miscarriage. Thus, we aim to establish machine learning models utilizing these three hormones to predict threatened miscarriage risk. METHODS: This is a multicentre, observational, case-control study involving 215 pregnant women. We recruited 119 normal pregnant women and 96 threatened miscarriage pregnant women including 58 women with ongoing pregnancy and 38 women with inevitable miscarriage. P4 and ß-hCG levels were detected by chemiluminescence immunoassay assay. The level of AEA was tested by ultra-high-performance liquid chromatography-tandem mass spectrometry. Six predictive machine learning models were established and evaluated by the confusion matrix, area under the receiver operating characteristic (ROC) curve (AUC), accuracy and precision. RESULTS: The median concentration of AEA was significantly lower in the healthy pregnant women group than that in the threatened miscarriage group, while the median concentration of P4 was significantly higher in the normal pregnancy group than that in the threatened miscarriage group. Only the median level of P4 was significantly lower in the inevitable miscarriage group than that in the ongoing pregnancy group. Moreover, AEA is strongly positively correlated with threatened miscarriage, while P4 is negatively correlated with both threatened miscarriage and inevitable miscarriage. Interestingly, AEA and P4 are negatively correlated with each other. Among six models, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) models obtained the AUC values of 0.75, 0.70 and 0.70, respectively; and their accuracy and precision were all above 0.60. Among these three models, the LR model showed the highest accuracy (0.65) and precision (0.70) to predict threatened miscarriage. CONCLUSIONS: The LR model showed the highest overall predictive power, thus machine learning combined with the level of AEA, P4 and ß-hCG might be a new approach to predict the threatened miscarriage risk in the near feature.

Circulating Tumor-Derived Endothelial Cells: An Effective Biomarker for Breast Cancer Screening and Prognosis Prediction.

Background: Circulating tumor-derived endothelial cell (CTEC) is a new potential tumor biomarker to be associated with cancer development and treatment efficacy. However, few evidences are available for breast cancer. Methods: Eighty-nine breast cancer patients were recruited, and preoperative and postoperative blood samples were collected. Besides, 20 noncancer persons were enrolled as controls. An improved subtraction enrichment and immunostaining-fluorescence in situ hybridization (SE-iFISH) method was adopted to codetect CD31+ aneuploid CTEC and CD31- aneuploid circulating tumor cell (CTC). Then, the clinical significance of CTCs and CTECs on breast cancer screening and prognosis prediction was evaluated and compared. Results: The positive rate of CTCs and CTECs in newly diagnosed breast cancer patients was 68.75% and 71.88%. Among detected aneuploid circulating rare cells, CTEC accounts for a greater proportion than CTC in breast cancer patients. CTEC-positive rate and level were significantly higher in breast cancer patients with lymph node metastasis (LNM) than those without LNM (P=0.043), while there was no significant difference in CTC. CTEC (area under the curve, AUC = 0.859) had better performance than CTC (AUC = 0.795) to distinguish breast cancer patients from controls by receiver operator characteristic curve analysis. Preoperative CTEC count ≥ 2 was a significant risk factor for reducing PFS of breast cancer patients. Conclusions: CTECs may function as a reliable supplementary biomarker in breast cancer screening and prognosis prediction.

Differences in actionable genomic alterations between brain metastases and non­brain metastases in patients with non­small cell lung cancer.

Brain metastases (BM) have been closely associated with increased morbidity and poor survival outcomes in patients with non­small cell lung cancer (NSCLC). Excluding risk factors in histological subtypes, genomic alterations, including epidermal growth factor receptor mutations and anaplastic lymphoma kinase (ALK) rearrangements have been also regarded as greater risk factors for BM in the aspect of molecular subtypes. In the present study, 69 tumor tissues and 51 peripheral blood samples from patients with NSCLC were analyzed using a hybridization capture­based next­generation sequencing (NGS) panel, including 95 known cancer genes. Among the 90 patients with stage IV NSCLC, 26 cases suffered from BM and 64 cases did not. In total, 174 somatic mutations in 35 mutated genes were identified, and 12 of these genes were concurrently present in the BM group and the non­BM group. Importantly, five mutated genes including ALK, cytidine deaminase (CDA), SMAD family member 4 (SMAD4), superoxide dismutase 2 (SOD2) and Von Hippel­Lindau tumor suppressor (VHL) genes were uniquely detected in the BM group, and they were enriched in the Hippo signaling pathway, pyrimidine metabolism and pantothenate and co­enzyme A (CoA) biosynthesis, as demonstrated using Kyoto Encyclopedia of Genes and Genomes enrichment analysis. RNA polymerase II transcription regulator complex and promyelocytic leukemia nuclear body were the top functional categories according to the Gene Ontology enrichment analysis in the BM group and non­BM group, respectively. Furthermore, 43.33% (13/30) of mutated genes were detected by both tumor tissue deoxyribonucleic acid (DNA) and plasma­derived circulating tumor DNA (ctDNA) in the non­BM group, while this percentage was only limited to 29.41% (5/17) in the BM group. To summarize, significant differences in somatic mutations, somatic interactions, key signaling pathways, functional biological information, and clinical actionability for the therapy of targeted agents were founded between the BM group and the non­BM group, and ctDNA analysis may by applied as a more credible alternative for genomic profiling in patients with advanced NSCLC without BM, due to its higher consistency for genomic profiling between ctDNA analysis and tissue DNA analysis.

The Influence of Heavy Metals on Gastric Tumorigenesis.

Objectives: This study aimed to observe the relationship among heavy metals concentration, microsatellite instability (MSI), and human epidermal growth factor receptor type 2 (HER2) gene amplification in gastric cancer (GC) patients. Methods: The concentrations of 18 heavy metals in the plasma of GC patients and healthy controls were measured by inductive coupled plasma emission spectrometry (ICP-MS). MSI detection was conducted by detecting 5 microsatellite repeat markers by PCR analysis. HER2 gene amplification was detected by fluorescence in situ hybridization (FISH). The relationship among heavy metal elements, tumor biomarkers, HER2 amplification, and MSI status was analyzed by Pearson correlation analysis. Results: A total of 105 GC patients and 62 healthy controls were recruited in this study. The concentration of arsenic (As), chromium (Cr), cuprum (Cu), mercury (Hg), manganese (Mn), lead (Pb), stibium (Sb), selenium (Se), stannum (Sn), strontium (Sr), thallium (Tl), vanadium (V), and zinc (Zn) were significantly different between GC patients and controls. Among 105 GC patients, including 87 microsatellite-stable (MSS) samples and 18 MSI samples, the concentration of Ga is significantly higher in the MSI group than that in the MSS group. Meanwhile, in 97 GC patients having detected HER2 gene amplification, 69 of 97 had negative HER2 gene amplification and the rest 28 GC patients had positive HER2 gene amplification. The concentration of Hg, Sn, and Tl is noticeably higher in the HER2 positive group than in the HER2 negative group. Only Sb was positively correlated with MSI, but none of these heavy metals was correlated with HER2 gene amplification. Conclusions: The results indicated that Sb has significant positive correlation with the MSI status, which suggests that Sb may cause MSI in GC. However, further research studies are required to elucidate the mechanisms in the near feature.