Circulating microRNA-92a and microRNA-21 as novel minimally invasive biomarkers for primary breast cancer

PURPOSE: MicroRNAs (miRNAs) play an essential role in breast malignant tumor development and progression. The development of clinically validated biomarkers for primary breast cancer (BC) has remained an insurmountable task despite other advances in the field of cancer molecular biology. The objective of this study is to investigate the differential expression of miRNAs and the potential of circulating microRNAs as novel primary breast cancer biomarkers. METHODS: Our analyses were performed on 48 tissue and 100 serum samples of patients with primary BC and a set of 20 control samples of healthy women, respectively. The relative expression of ten candidate miRNAs (miR-106b, miR-125b, miR-17, miR-185, miR-21, miR-558, miR-625, miR-665, miR-92a, and miR-93) from the results of four bioinformatics approaches and literature curation was measured by real-time quantitative reverse transcription PCR (qRT-PCR). RESULTS: The level of miR-92a was significantly lower, while miR-21 was higher, as previous reports, in tissue and serum samples of BC than that of healthy controls (p < 0.001). Logistic regression and receiver operating characteristic curve analyses revealed the significant and independent value (p < 0.001) of the miR-92a and miR-21 expression quantification in serums. Moreover, the comparison with the clinicopathologic data of the BC patients showed that decreased levels of miR-92a and increased levels of miR-21 were associated with tumor size and a positive lymph node status (p < 0.001). CONCLUSIONS: These findings suggest that many miRNAs expressions are altered in BC, whose expression profiling may provide a useful clue for the pathophysiological research. Circulating miR-92a has potential use as novel breast cancer biomarker, which is comparable to miR-21

Global importance of large-diameter trees

Aim: To examine the contribution of large‐diameter trees to biomass, stand structure, and species richness across forest biomes. Location: Global. Time period: Early 21st century. Major taxa studied: Woody plants. Methods: We examined the contribution of large trees to forest density, richness and biomass using a global network of 48 large (from 2 to 60 ha) forest plots representing 5,601,473 stems across 9,298 species and 210 plant families. This contribution was assessed using three metrics: the largest 1% of trees ≥ 1 cm diameter at breast height (DBH), all trees ≥ 60 cm DBH, and those rank‐ordered largest trees that cumulatively comprise 50% of forest biomass. Results: Averaged across these 48 forest plots, the largest 1% of trees ≥ 1 cm DBH comprised 50% of aboveground live biomass, with hectare‐scale standard deviation of 26%. Trees ≥ 60 cm DBH comprised 41% of aboveground live tree biomass. The size of the largest trees correlated with total forest biomass (r2 = .62, p < .001). Large‐diameter trees in high biomass forests represented far fewer species relative to overall forest richness (r2 = .45, p < .001). Forests with more diverse large‐diameter tree communities were comprised of smaller trees (r2 = .33, p < .001). Lower large‐diameter richness was associated with large‐diameter trees being individuals of more common species (r2 = .17, p = .002). The concentration of biomass in the largest 1% of trees declined with increasing absolute latitude (r2 = .46, p < .001), as did forest density (r2 = .31, p < .001). Forest structural complexity increased with increasing absolute latitude (r2 = .26, p < .001). Main conclusions: Because large‐diameter trees constitute roughly half of the mature forest biomass worldwide, their dynamics and sensitivities to environmental change represent potentially large controls on global forest carbon cycling. We recommend managing forests for conservation of existing large‐diameter trees or those that can soon reach large diameters as a simple way to conserve and potentially enhance ecosystem services

Utility and necessity of repeat testing of critical values in the clinical chemistry laboratory.

CONTEXT: Routine repeat testing of critical values is a long-standing practice in many clinical laboratories; however, its usefulness and necessity remain to be empirically established and no regulatory requirements yet exist for verification of the critical value results obtained by repeat analysis. OBJECTIVE: To determine whether repeat testing of critical values is useful and necessary in a clinical chemistry laboratory. METHODS: A total of 601 chemistry critical values (potassium, n = 255; sodium, n = 132; calcium, n = 108; glucose, n = 106) obtained from 72,259 routine clinical chemistry specimens were repeat tested. The absolute value and the percentage of difference between the two testing runs were calculated for each of the four critical values and then compared with the allowable error limit put forth in the College of American Pathologists (CAP). RESULTS: Among the repeat data for the 601 critical values, a total of 24 showed large differences between the initial result and the repeated result which exceeded the CAP limits for allowable error. The number and rates (%) of large differences for within and outside the analytical measurement range (AMR) were 12 (2.1%) and 12 (41.4%), respectively. For the 572 critical values within the AMR for each test category, the mean absolute difference (mmol/L) and difference(%) between the two testing runs were: potassium, 0.1 mmol/L (2.7%); sodium, 2.1 mmol/L (1.7%); calcium, 0.05 mmol/L (3.0%); glucose, 0.18 mmol/L (2.6%). CONCLUSIONS: When the initial chemistry critical values are within the AMR, repeated testing does not improve accuracy and is therefore unnecessary. When the initial chemistry critical values are outside the AMR, however, the benefit of repeated testing justifies its performance and makes it necessary. Performing repeat clinical testing on a case-by-case, rather than routine, basis can improve patient care by delivering critical values more rapidly while providing savings on reagent costs associated with unnecessary repeat testing

Association of Blood Glucose Variability with Sepsis-Related Disseminated Intravascular Coagulation Morbidity and Mortality

PURPOSE: Sepsis-related disseminated intravascular coagulation (DIC) is closely associated with poor prognosis and high mortality. Higher blood glucose (BG) variability indicates an increased risk of mortality in sepsis; however, its relationship with sepsis-related DIC has not been investigated. This study aimed to determine the association between glucose variability and sepsis-related DIC. PATIENTS AND METHODS: Patients with sepsis admitted to the intensive care unit were enrolled between October 2017 and January 2021. Baseline data and BG records from the first 72 h were collected. We calculated the glucose liability index (GLI), largest amplitude of glucose excursion, BG standard deviation, and coefficient of variation on days 1 and 3. The relationship between GLI and morbidity of sepsis-related DIC was explored using a competing risk model. In subgroup analysis, we divided patients with and without diabetes into three groups according to the BG range. RESULTS: Of the 238 patients enrolled, 28.2% developed DIC during hospitalization (n=67). GLI on day 3 was found to have the closest relationship with DIC incidence as it has the largest area under the ROC curve and the highest associated odds ratio of death per unit change (GLI3-day: AUC=0.891 OR=1.84), also independently increased the occurrence of DIC after adjusting for the competing risk of death (sub-distribution hazard ratios=1.866, p<0.01). In subgroup analysis, patients with diabetes had worse outcomes under hypoglycemia than under hyperglycemia. Patients without diabetes having stable BG had the best outcomes. CONCLUSION: Our study suggested that a higher GLI in patients with sepsis at 72 h was independently associated with an increased risk of sepsis-related DIC, which was not associated with pre-existing diabetes

Meta-Analysis: Diagnostic Accuracy of Anti-Cyclic Citrullinated Peptide Antibody for Juvenile Idiopathic Arthritis

Objective. To estimate the diagnostic accuracy of the anti-CCP test in JIA and to evaluate factors associated with higher accuracy. Methods. Two investigators performed an extensive search of the literature published between January 2000 and January 2014. The included articles were assessed by the Quality Assessment of Diagnostic Accuracy Studies tool. The meta-analysis was performed using a summary ROC (SROC) curve and a bivariate random-effect model to estimate sensitivity and specificity across studies. Results. The bivariate meta-analysis yielded a pooled sensitivity and specificity of 10% (95% confidence interval (CI): 6.0%–15.0%) and 99.0% (95% CI: 98.0%–100.0%). The area under the SROC curve was 0.96. Sensitivity estimates were highly heterogeneous, which was partially explained by the higher sensitivity in the rheumatoid factor-positive polyarthritis (RF+ PA) subtype (48.0%; 95% CI: 31.0%–65.0%) than in the other subtypes (17.0%; 95% CI: 14.0%–20.0%) and the higher sensitivity of the Inova assay (17.0%; 95% CI: 14.0%–20.%%) than the other assays (0.05%; 95% CI: 2.0%–11.0%). Conclusions. Anti-CCP antibody test has a high specificity for the diagnosis of JIA. The sensitivity of this test is low and varies across populations but is higher in RF+ PA than in other JIA subtypes

Electric Load Data Compression and Classification Based on Deep Stacked Auto-Encoders

With the development of advanced metering infrastructure (AMI), electrical data are collected frequently by smart meters. Consequently, the load data volume and length increase dramatically, which aggravates the data storage and transmission burdens in smart grids. On the other hand, for event detection or market-based demand response applications, load service entities (LSEs) want smart meter readings to be classified in specific and meaningful types. Considering these challenges, a stacked auto-encoder (SAE)-based load data mining approach is proposed. First, an innovative framework for smart meter data flow is established. On the user side, the SAEs are utilized to compress load data in a distributed way. Then, centralized classification is adopted at remote data center by softmax classifier. Through the layer-wise feature extracting of SAE, the sparse and lengthy raw data are expressed in compact forms and then classified based on features. A global fine-tuning strategy based on a well-defined labeled subset is embedded to improve the extracted features and the classification accuracy. Case studies in China and Ireland demonstrate that the proposed method is more capable to achieve the minimum of error and satisfactory compression ratios (CR) than benchmark compressors. It also significantly improves the classification accuracy on both appliance and house level datasets

Association of Blood Glucose Variability with Sepsis-Related Disseminated Intravascular Coagulation Morbidity and Mortality

Purpose: Sepsis-related disseminated intravascular coagulation (DIC) is closely associated with poor prognosis and high mortality. Higher blood glucose (BG) variability indicates an increased risk of mortality in sepsis; however, its relationship with sepsis-related DIC has not been investigated. This study aimed to determine the association between glucose variability and sepsis-related DIC. Patients and Methods: Patients with sepsis admitted to the intensive care unit were enrolled between October 2017 and January 2021. Baseline data and BG records from the first 72 h were collected. We calculated the glucose liability index (GLI), largest amplitude of glucose excursion, BG standard deviation, and coefficient of variation on days 1 and 3. The relationship between GLI and morbidity of sepsis-related DIC was explored using a competing risk model. In subgroup analysis, we divided patients with and without diabetes into three groups according to the BG range. Results: Of the 238 patients enrolled, 28.2% developed DIC during hospitalization (n=67). GLI on day 3 was found to have the closest relationship with DIC incidence as it has the largest area under the ROC curve and the highest associated odds ratio of death per unit change (GLI3-day: AUC=0.891 OR=1.84), also independently increased the occurrence of DIC after adjusting for the competing risk of death (sub-distribution hazard ratios=1.866, p<0.01). In subgroup analysis, patients with diabetes had worse outcomes under hypoglycemia than under hyperglycemia. Patients without diabetes having stable BG had the best outcomes. Conclusion: Our study suggested that a higher GLI in patients with sepsis at 72 h was independently associated with an increased risk of sepsis-related DIC, which was not associated with pre-existing diabetes

Critical Values, Allowable Error Limits, and AMRs.

*<p>Extrapolated from CAP participant surveys.</p>#<p>According to the reagent manufacturers’ instructions (Beckman Coulter Inc.).</p

Features of the Large Difference.

*<p>Extrapolated from CAP participant surveys.</p

Synthesis of 3‑Amino-3-hydroxymethyloxindoles and 3‑Hydroxy-3-hydroxymethyloxindoles by Rh₂(OAc)₄‑Catalyzed Three-Component Reactions of 3‑Diazooxindoles with Formaldehyde and Anilines or Water

Efficient Rh­(II)-catalyzed three-component reactions of 3-diazooxindoles and formaldehyde with either anilines or water were developed to give a series of substituted 3-amino-3-hydroxymethyloxindoles or 3-hydroxy-3-hydroxymethyloxindoles in good to excellent yields. In this atom- and step-economic transformation, Rh₂(OAc)₄-catalyzed decomposition of 3-diazooxindoles with anilines or water forms the corresponding active ammonium or oxonium ylides. Electrophilic trapping of the resulting ammonium ylides or oxonium ylides by formaldehyde in the form of formalin efficiently produces the title compounds in one step