Long Noncoding RNA and Predictive Model To Improve Diagnosis of Clinically Diagnosed Pulmonary Tuberculosis.

Clinically diagnosed pulmonary tuberculosis (PTB) patients lack microbiological evidence of Mycobacterium tuberculosis, and misdiagnosis or delayed diagnosis often occurs as a consequence. We investigated the potential of long noncoding RNAs (lncRNAs) and corresponding predictive models to diagnose these patients. We enrolled 1,764 subjects, including clinically diagnosed PTB patients, microbiologically confirmed PTB cases, non-TB disease controls, and healthy controls, in three cohorts (screening, selection, and validation). Candidate lncRNAs differentially expressed in blood samples of the PTB and healthy control groups were identified by microarray and reverse transcription-quantitative PCR (qRT-PCR) in the screening cohort. Logistic regression models were developed using lncRNAs and/or electronic health records (EHRs) from clinically diagnosed PTB patients and non-TB disease controls in the selection cohort. These models were evaluated by area under the concentration-time curve (AUC) and decision curve analyses, and the optimal model was presented as a Web-based nomogram, which was evaluated in the validation cohort. Three differentially expressed lncRNAs (ENST00000497872, n333737, and n335265) were identified. The optimal model (i.e., nomogram) incorporated these three lncRNAs and six EHRs (age, hemoglobin, weight loss, low-grade fever, calcification detected by computed tomography [CT calcification], and interferon gamma release assay for tuberculosis [TB-IGRA]). The nomogram showed an AUC of 0.89, a sensitivity of 0.86, and a specificity of 0.82 in differentiating clinically diagnosed PTB cases from non-TB disease controls of the validation cohort, which demonstrated better discrimination and clinical net benefit than the EHR model. The nomogram also had a discriminative power (AUC, 0.90; sensitivity, 0.85; specificity, 0.81) in identifying microbiologically confirmed PTB patients. lncRNAs and the user-friendly nomogram could facilitate the early identification of PTB cases among suspected patients with negative M. tuberculosis microbiological evidence.

[Identification and functional analysis of a testis-specific E3 ubiquitin ligase gene Rnf148 in mouse].

OBJECTIVE: To investigate the temporal and spatial features of mouse Rnf148 gene expression and the function of RING finger domain of Rnf148 protein. METHODS: The whole RNA was extracted from different tissues of adult mice, embryo in four developmental stages, and testes of postnatal mice respectively. RT-PCR and Northern blotting analysis were used to investigate the expression of Rnf148 gene in the above tissues. The in vitro expression vector for GST-Rnf148 fused protein was constructed, which encompassing the entire RING domain of Rnf148 protein. GST-Rnf148 fused protein was expressed in Escherichia coli. BL21(DE3) cells and purified with glutathione-sepharose 4B. In vitro ubiquitination assay was performed to analyze whether GST-Rnf148 fused protein possess the function of E3 ubiquitin ligase. RESULTS: The Mice Rnf148 mRNA expression was only observed in testis, and Northern blotting confirmed that there was only one 1.2 kb mRNA band present in mice testis. Rnf148 mRNA started to appear in the testis of day 21 mice, and then increased dramatically and reached to the highest level in day 25, and continued to express thereafter. GST-Rnf148 fused protein was induced and purified, in vitro ubiquitination reaction showed that the recombinant protein has E3 ubiquitin ligase activity. CONCLUSION: Rnf148 gene is specifically expressed in mice testis.

Sister haplotypes and recombination disequilibrium: a new approach to identify associations of haplotypes with complex diseases.

Haplotype-based association analysis has several advantages over single-SNP association analysis. However, to date all haplotype-disease associations have not excluded recombination interference among multiple loci and hence some results might be confounded by recombination interference. Association of sister haplotypes with a complex disease, based on recombination disequilibrium (RD) was presented. Sister haplotypes can be determined by translating notation of DNA base haplotypes to notation of genetic genotypes. Sister haplotypes provide haplotype pairs available for haplotype-disease association analysis. After performing RD tests in control and case cohorts, a two-by-two contingency table can be constructed using sister haplotype pair and case-control pair. With this standard two-by-two table, one can perform classical Chi-square test to find statistical haplotype-disease association. Applying this method to a haplotype dataset of Alzheimer disease (AD), association of sister haplotypes containing ApoE3/4 with risk for AD was identified under no RD. Haplotypes within gene IL-13 were not associated with risk for breast cancer in the case of no RD and no association of haplotypes in gene IL-17A with risk for coronary artery disease were detected without RD. The previously reported associations of haplotypes within these genes with risk for these diseases might be due to strong RD and/or inappropriate haplotype pairs.

NR0B1 suppresses ferroptosis through upregulation of NRF2/c-JUN-CBS signaling pathway in lung cancer cells.

Ferroptosis has demonstrated significant potential in treating radiochemotherapy-resistant cancers, but its efficacy can be affected by recently discovered ferroptosis suppressors. In this study, we discovered that NR0B1 protects against erastin- or RSL3-induced ferroptosis in lung cancer cells. Transcriptomic analysis revealed that NR0B1 significantly interfered with the expression of 12 ferroptosis-related genes, and the expression level of NR0B1 positively correlated with that of c-JUN, NRF2, and CBS. We further revealed that NR0B1 suppression of ferroptosis depended on the activities of c-JUN, NRF2, and CBS. NR0B1 directly promoted the expression of NRF2 and c-JUN and indirectly upregulated CBS expression through enhancing NRF2 and/or c-JUN transcription. Moreover, we showed that NR0B1 depletion restrained xenograft tumor growth and facilitated RSL3-induced ferroptosis in the tumors. In conclusion, our findings uncover that NR0B1 suppresses ferroptosis by activating the c-JUN/NRF2-CBS signaling pathway in lung cancer cells, providing new evidence for the involvement of NR0B1 in drug resistance during cancer therapy.

Use of Wearable Technology and Deep Learning to Improve the Diagnosis of Brugada Syndrome.

BACKGROUND: The diagnosis of Brugada syndrome by 12-lead electrocardiography (ECG) is challenging because the diagnostic type 1 pattern is often transient. OBJECTIVES: This study sought to improve Brugada syndrome diagnosis by using deep learning (DL) to continuously monitor for Brugada type 1 in 24-hour ambulatory 12-lead ECGs (Holters). METHODS: A convolutional neural network was trained to classify Brugada type 1. The training cohort consisted of 10-second standard/high precordial leads from 12-lead ECGs (n = 1,190) and 12-lead Holters (n = 380) of patients with definite and suspected Brugada syndrome. The performance of the trained model was evaluated in 2 testing cohorts of 10-second standard/high precordial leads from 12-lead ECGs (n = 474) and 12-lead Holters (n = 716). RESULTS: DL achieved a receiver-operating characteristic area under the curve of 0.976 (95% CI: 0.973-0.979) in classifying Brugada type 1 from 12-lead ECGs and 0.975 (95% CI: 0.966-0.983) from 12-lead Holters. Compared with cardiologist reclassification of Brugada type 1, DL had similar performance and produced robust results in experiments evaluating scalability and explainability. When DL was applied to consecutive 10-second, clean ECGs from 24-hour 12-lead Holters, spontaneous Brugada type 1 was detected in 48% of patients with procainamide-induced Brugada syndrome and in 33% with suspected Brugada syndrome. DL detected no Brugada type 1 in healthy control patients. CONCLUSIONS: This novel DL model achieved cardiologist-level accuracy in classifying Brugada type 1. Applying DL to 24-hour 12-lead Holters significantly improved the detection of Brugada type 1 in patients with procainamide-induced and suspected Brugada syndrome. DL analysis of 12-lead Holters may provide a robust, automated screening tool before procainamide challenge to aid in the diagnosis of Brugada syndrome.

Genetic architecture of tuberculosis susceptibility: A comprehensive research synopsis, meta-analyses, and epidemiological evidence.

To date, many studies have been conducted to investigate associations between variants and tuberculosis risk; however, the results have been inconclusive. Here, we systematically provide a summary of the understanding of the genetic architecture of tuberculosis susceptibility. We searched PubMed, Embase and Web of Science to identify genetic association studies of tuberculosis published through October 31, 2021. We conducted meta-analyses for the genetic association with tuberculosis risk. We graded levels of cumulative epidemiological evidence of significant associations with risk of tuberculosis and false-positive report probability tests. We performed functional annotations for these variants using data from the Encyclopedia of DNA Elements (ENCODE) Project and other databases. We identified 703 eligible articles comprising 298,074 cases and 879,593 controls through screening a total of 24,398 citations. Meta-analyses were conducted for 614 genetic variants in 469 genes or loci. We found 39 variants that were nominally significantly associated with tuberculosis risk. Cumulative epidemiological evidence for a significant association was graded strong for 9 variants in or near 9 genes. Among them, 5 variants were associated with tuberculosis risk in at least three main ethnicity (African, Asian and White) which together explained approximately 9.59% of the familial relative risk of tuberculosis. Data from ENCODE and other databases suggested that 8 of these 9 genetic variants with strong evidence might fall within putative functional regions. Our study summarizes the current literature on the genetic architecture of tuberculosis susceptibility and provides useful data for designing future studies to investigate the genetic association with tuberculosis risk.

[Relationships between stress perception and stress biomarkers in family caregivers].

BACKGROUND: Family caregivers (FCGs) of persons with dementia (PWDs) face chronic stress. However, their stress has often been assessed by their distress in the absence of physiological indicators. Studies to date have rarely documented the relationships between distress and various stress biomarkers. PURPOSE: The aim of this study was to explore the relationships between distress and stress biomarkers in FCGs. METHODS: This was a secondary data analysis study that used data collected by two projects funded by the National Science Council. Samples included 113 dyads of PWDs and their FCGs willing to donate blood samples. Original study data sites comprised two teaching hospitals (memory clinics and psychiatric outpatients), two regional hospitals (neurology clinics), and two dementia daycare centers for community-dwelling PWDs in northern Taiwan. FCG distress was assessed using the Chinese Neuropsychological Inventory-Caregiver Distress Scale (CNPI-CD); Stress biomarkers included interleukin (IL)-1ß, IL-6, IL-10, cortisol, and C-reactive protein (CRP). RESULTS: Stress biomarker levels did not correlate with overall FCG distress related to PWD neuropsychological problems. However, IL-1ß, IL-6, and IL-10 levels did correlate with specific FCG distress toward specific PWD neuropsychological symptoms. CONCLUSIONS: This study found certain stress biomarkers (IL-1ß, IL-6, IL-10) associated with specific PWDs' neuropsychological symptoms (p < .05). Further longitudinal research is needed to clarify causal relationships between subjective distress and objective stress biomarkers to evaluate FCG stress levels more comprehensively.

Improving domain adaptation in de-identification of electronic health records through self-training.

OBJECTIVE: De-identification is a fundamental task in electronic health records to remove protected health information entities. Deep learning models have proven to be promising tools to automate de-identification processes. However, when the target domain (where the model is applied) is different from the source domain (where the model is trained), the model often suffers a significant performance drop, commonly referred to as domain adaptation issue. In de-identification, domain adaptation issues can make the model vulnerable for deployment. In this work, we aim to close the domain gap by leveraging unlabeled data from the target domain. MATERIALS AND METHODS: We introduce a self-training framework to address the domain adaptation issue by leveraging unlabeled data from the target domain. We validate the effectiveness on 4 standard de-identification datasets. In each experiment, we use a pair of datasets: labeled data from the source domain and unlabeled data from the target domain. We compare the proposed self-training framework with supervised learning that directly deploys the model trained on the source domain. RESULTS: In summary, our proposed framework improves the F1-score by 5.38 (on average) when compared with direct deployment. For example, using i2b2-2014 as the training dataset and i2b2-2006 as the test, the proposed framework increases the F1-score from 76.61 to 85.41 (+8.8). The method also increases the F1-score by 10.86 for mimic-radiology and mimic-discharge. CONCLUSION: Our work demonstrates an effective self-training framework to boost the domain adaptation performance for the de-identification task for electronic health records.

Red Zn2SiO4:Eu3+ and Mg2TiO4:Mn4+ nanophosphors for on-site rapid optical detections: Synthesis and characterization.

This study reports the synthesis and characterization of the red nanophosphors Zn2SiO4:Eu3+ (ZSO:Eu3+) and Mg2TiO4:Mn4+ (MTO:Mn4+). The use of phosphors as a fluorescence label for lateral flow immunochromatographic assay (LFIA) has also been described. The optimal photoluminescence (PL) for ZSO:Eu3+ was obtained when it was synthesized with 7 mol% of Eu3+ and annealed at 1100 °C for 1 h. Long fluorescence lifetime (1.01 ms), high activation energy E a (0.28 eV), and low PL degeneration (10% at 110 °C) are the characteristics of ZSO:Eu3+. MTO:Mn4+ also exhibited high PL intensity along with a high E a of 0.32 eV. The emission wavelengths of phosphors are biocompatible with the optical bio-window of tissues. When human immunoglobulin G (human IgG) at a constant concentration of 100 µg/mL was used for detection, the PL ratios of the test line to the control line were 2.15 and 2.28 for the ZSO:Eu3+- and MTO:Mn4+-labeled LFIA, respectively. Thus, the ZSO:Eu3+ and MTO:Mn4+ nanophosphors are capable of human IgG recognition and are the promising candidates as fluorescent labels for on-site rapid optical biodetection. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00339-021-04733-0.

Deep Learning Classification of Unipolar Electrograms in Human Atrial Fibrillation: Application in Focal Source Mapping.

Focal sources are potential targets for atrial fibrillation (AF) catheter ablation, but they can be time-consuming and challenging to identify when unipolar electrograms (EGM) are numerous and complex. Our aim was to apply deep learning (DL) to raw unipolar EGMs in order to automate putative focal sources detection. We included 78 patients from the Focal Source and Trigger (FaST) randomized controlled trial that evaluated the efficacy of adjunctive FaST ablation compared to pulmonary vein isolation alone in reducing AF recurrence. FaST sites were identified based on manual classification of sustained periodic unipolar QS EGMs over 5-s. All periodic unipolar EGMs were divided into training (n = 10,004) and testing cohorts (n = 3,180). DL was developed using residual convolutional neural network to discriminate between FaST and non-FaST. A gradient-based method was applied to interpret the DL model. DL classified FaST with a receiver operator characteristic area under curve of 0.904 ± 0.010 (cross-validation) and 0.923 ± 0.003 (testing). At a prespecified sensitivity of 90%, the specificity and accuracy were 81.9 and 82.5%, respectively, in detecting FaST. DL had similar performance (sensitivity 78%, specificity 89%) to that of FaST re-classification by cardiologists (sensitivity 78%, specificity 79%). The gradient-based interpretation demonstrated accurate tracking of unipolar QS complexes by select DL convolutional layers. In conclusion, our novel DL model trained on raw unipolar EGMs allowed automated and accurate classification of FaST sites. Performance was similar to FaST re-classification by cardiologists. Future application of DL to classify FaST may improve the efficiency of real-time focal source detection for targeted AF ablation therapy.

Integrating exosomal microRNAs and electronic health data improved tuberculosis diagnosis.

Background Tuberculosis (TB) is difficult to diagnose under complex clinical conditions as electronic health records (EHRs) are often inadequate in making an affirmative diagnosis. As exosomal miRNAs emerged as promising biomarkers, we investigated the potential of using exosomal miRNAs and EHRs in TB diagnosis. METHODS: A total of 370 individuals, including pulmonary tuberculosis (PTB), tuberculous meningitis (TBM), non-TB disease controls and healthy state controls, were enrolled. Exosomal miRNAs were profiled in the exploratory cohort using microarray and miRNA candidates were selected in the selection cohort using qRT-PCR. EHRs and follow-up information of the patients were collected accordingly. miRNAs and EHRs were used to develop diagnostic models for PTB and TBM in the selection cohort with the Support Vector Machine (SVM) algorithm. These models were further evaluated in an independent testing cohort. FINDINGS: Six exosomal miRNAs (miR-20a, miR-20b, miR-26a, miR-106a, miR-191, miR-486) were differentially expressed in the TB patients. Three SVM models, "EHR+miRNA", "miRNA only" and "EHR only" were compared, and "EHR + miRNA" model achieved the highest diagnostic efficacy, with an AUC up to 0.97 (95% CI 0.80-0.99) in TBM and 0.97 (0.87-0.99) in PTB, respectively. However, "EHR only" model only showed an AUC of 0.67 (0.46-0.83) in TBM. After 2-month anti-tuberculosis therapy, overexpressed miRNAs presented a decreased expression trend (p= 4.80 × 10-5). INTERPRETATION: Our results showed that the combination of exosomal miRNAs and EHRs could potentially improve clinical diagnosis of TBM and PTB. FUND: Funds for the Central Universities, the National Natural Science Foundation of China.

Sodium Butyrate Ameliorates Streptozotocin-Induced Type 1 Diabetes in Mice by Inhibiting the HMGB1 Expression.

Type 1 diabetes (T1D) is an autoimmune disease characterized by the immune cell-mediated progressive destruction of pancreatic ß-cells. High-mobility group box 1 protein (HMGB1) has been recognized as a potential immune mediator to enhance the development of T1D. So we speculated that HMGB1 inhibitors could have anti-diabetic effect. Sodium butyrate is a short fatty acid derivative possessing anti-inflammatory activity by inhibiting HMGB1. In the current study, we evaluated the effects of sodium butyrate in streptozotocin (STZ)-induced T1D mice model. Diabetes was induced by multiple low-dose injections of STZ (40 mg/kg/day for 5 consecutive days), and then sodium butyrate (500 mg/kg/day) was administered by intraperitoneal injection for 7 consecutive days after STZ treatment. Blood glucose, incidence of diabetes, body weight, pancreatic histopathology, the amounts of CD4+T cell subsets, IL-1ß level in serum and pancreatic expressions levels of HMGB1, and NF-κB p65 protein were analyzed. The results showed that sodium butyrate treatment decreased blood glucose and serum IL-1ß, improved the islet morphology and decreased inflammatory cell infiltration, restored the unbalanced Th1/Th2 ratio, and down-regulated Th17 to normal level. In addition, sodium butyrate treatment can inhibit the pancreatic HMGB1 and NF-κB p65 protein expression. Therefore, we proposed that sodium butyrate should ameliorate STZ-induced T1D by down-regulating NF-κB mediated inflammatory signal pathway through inhibiting HMGB1.

Prevalence of ß-lactamase and 16S rRNA methylase genes among clinical Escherichia coli isolates carrying plasmid-mediated quinolone resistance genes from animals.

Plasmid-mediated quinolone-resistance (PMQR) genes were determined by polymerase chain reactions (PCRs) in 250 Escherichia coli isolates from food-producing animals in Guangdong, China, in 2009-2010. Then, the prevalence of plasmid-mediated ß-lactamase and 16S rRNA methylase genes was determined by PCRs among the PMQR-positive isolates. One hundred fifty-seven (62.8%) isolates were found to harbor at least one PMQR gene, and qnrS (84) and oqxAB (97) were the most two prevalent PMQR genes. ß-lactamase (ESBL and/or AmpC type) genes were detected in 106 of the 157 PMQR-positive strains. The bla(TEM-1) (78) was the most prevalent ß-lactamase gene in the 157 PMQR-positive isolates, followed by bla(CMY-2) (28), bla(CTX-M) (25), bla(SHV-1) (3), and bla(DHA-1) (3). Twenty-nine were detected to produce more than one type of ß-lactamase. The rmtB was the most prevalent 16S rRNA methylase gene detected (11.5%, 18/157), and armA was detected in only two (1.27%, 2/157) isolates, with one isolate coharboring rmtB and armA. Sixteen isolates were found to coharbor the three types of resistance genes detected in this study. Only 1 transconjugant JGDA2 harboring oqxAB, aac(6')-Ib-cr, bla(DHA-1), and rmtB was obtained from the 16 isolates harboring the three types of resistance genes, by conjugation experiment. The results of Southern blot hybridization revealed that oqxAB, bla(DHA-1), and rmtB were colocated on the same plasmid of ∼54 kb in the JGDA2. To our knowledge, this is the first description of the coexistence of the oqxAB, rmtB, and bla(DHA-1) resistance genes on the same plasmid in one E. coli strain.