Synthesis of Quinolinoneylnitrones and Coumarinylnitrones via a Cascade Hydroamination and Aza-MBH-Type Reaction.

Nitrones are quite useful intermediates and have been broadly applied in organic synthesis, drug discovery, and photochemistry research. Many functional nitrones have been successfully prepared using various strategies. In this work, an efficient method for synthesizing novel quinolinoneylnitrone and coumarinylnitrone derivatives was developed. Preliminary mechanistic research suggests that this protocol included a cascade hydroamination and aza-MBH-type reaction.

Machine learning-based nonlinear regression-adjusted real-time quality control modeling: a multi-center study.

OBJECTIVES: Patient-based real-time quality control (PBRTQC), a laboratory tool for monitoring the performance of the testing process, has gained increasing attention in recent years. It has been questioned for its generalizability among analytes, instruments, laboratories, and hospitals in real-world settings. Our purpose was to build a machine learning, nonlinear regression-adjusted, patient-based real-time quality control (mNL-PBRTQC) with wide application. METHODS: Using computer simulation, artificial biases were added to patient population data of 10 measurands. An mNL-PBRTQC was created using eight hospital laboratory databases as a training set and validated by three other hospitals' independent patient datasets. Three different Patient-based models were compared on these datasets, the IFCC PBRTQC model, linear regression-adjusted real-time quality control (L-RARTQC), and the mNL-PBRTQC model. RESULTS: Our study showed that in the three independent test data sets, mNL-PBRTQC outperformed the IFCC PBRTQC and L-RARTQC for all measurands and all biases. Using platelets as an example, it was found that for 20 % bias, both positive and negative, the uncertainty of error detection for mNL-PBRTQC was smallest at the median and maximum values. CONCLUSIONS: mNL-PBRTQC is a robust machine learning framework, allowing accurate error detection, especially for analytes that demonstrate instability and for detecting small biases.

Enhanced patient-based real-time quality control using the graph-based anomaly detection.

OBJECTIVES: Patient-based real-time quality control (PBRTQC) is an alternative tool for laboratories that has gained increasing attention. Despite the progress made by using various algorithms, the problems of data volume imbalance between in-control and out-of-control results, as well as the issue of variation remain challenges. We propose a novel integrated framework using anomaly detection and graph neural network, combining clinical variables and statistical algorithms, to improve the error detection performance of patient-based quality control. METHODS: The testing results of three representative analytes (sodium, potassium, and calcium) and eight independent variables of patients (test date, time, gender, age, department, patient type, and reference interval limits) were collected. Graph-based anomaly detection network was modeled and used to generate control limits. Proportional and random errors were simulated for performance evaluation. Five mainstream PBRTQC statistical algorithms were chosen for comparison. RESULTS: The framework of a patient-based graph anomaly detection network for real-time quality control (PGADQC) was established and proven feasible for error detection. Compared with classic PBRTQC, the PGADQC showed a more balanced performance for both positive and negative biases. For different analytes, the average number of patient samples until error detection (ANPed) of PGADQC decreased variably, and reductions could reach up to approximately 95 % at a small bias of 0.02 taking calcium as an example. CONCLUSIONS: The PGADQC is an effective framework for patient-based quality control, integrating statistical and artificial intelligence algorithms. It improves error detection in a data-driven fashion and provides a new approach for PBRTQC from the data science perspective.

The usefulness of contrast echocardiography in the evaluation of cardiac masses: a multicenter study.

BACKGROUND: Cardiac masses can encompass a variety of conditions, such as tumors, thrombi, vegetations, calcific lesions, and other rare diseases. Treatment and management of these types of cardiac masses differ considerably. Thus, accurately distinguishing among thrombi, benign tumors, and malignant tumors in the heart is of great importance. Contrast echocardiography (CE) has emerged as a promising technology. Although published guidelines suggest that CE can enhance image quality and assist in differentiating between benign and malignant lesions, most studies on CE diagnosis of cardiac masses are limited to case reports or retrospective/small-sample-sized prospective cohorts. This study aims to evaluate the diagnostic accuracy of CE in patients with suspected cardiac masses and address the insufficient evidence for differential diagnosis using CE. METHODS: Between April 2018 and July 2022, a prospective multicenter study was conducted, which included 145 consecutive patients suspected to have cardiac masses based on transthoracic echocardiography. All patients underwent CE examinations. The echocardiographic diagnosis relied on qualitative factors such as echogenicity, boundary, morphology of the base, mass perfusion, pericardial effusion, and motility as well as quantitative factors such as the area of the masses and the peak intensity ratio of the masses to adjacent myocardium (A1/A2). RESULTS: The final confirmed diagnoses were as follows: 2 patients had no cardiac mass, 4 patients had pseudomass, 43 patients had thrombus, 66 patients had benign tumors, and 30 patients had malignant tumors. The receiver operating characteristic (ROC) analysis indicated that an optimal A1/A2 cutoff value of 0.499 distinguished a cardiac tumor from a thrombus, with AUC, sensitivity, specificity, PPV, and NPV of 0.977, 97.9%, 90.7%, 95.9%, and 95.1%, respectively. The optimal A1/A2 cutoff value of 1.583 distinguished a cardiac tumor from a thrombus, with AUC, sensitivity, specificity, PPV, and NPV of 0.950, 93.3%, 93.9%, 87.5%, and 96.9%, respectively. CONCLUSIONS: Combined with qualitative and quantitative analyses, CE has the potential to accurately differentiate among different types of cardiac masses.

Bibenzyl-Phenylpropane Hybrids with Immunosuppressive Activities from Dendrobium Nobile Lindl.

Fifteen bibenyls and four fluorenones, including five new bibenzyl-phenylpropane hybrids, were isolated from the aerial part of Dendrobium nobile Lindl. Their structures were determined by spectroscopic methods. Bioassay on the LPS-induced proliferations of mouse splenic B lymphocytes, and Con A-induced T lymphocytes showed that compounds 1, 2, and 14 showed excellent immunosuppressive activities with IC50 values of 1.23, 1.01, and 3.87 µM, respectively, while compounds 3-4, 7, 10, 13, and 15 exhibited moderate immunosuppressive activities with IC50 values ranging from 6.89 to 14.2 µM.

Assessing the comparability of cycle threshold values derived from five external quality assessment rounds for omicron nucleic acid testing.

BACKGROUND: A variety of open-system real-time reverse transcriptase polymerase chain reaction (RT-PCR) assays for several acute respiratory syndrome coronavirus 2 are currently in use. This study aimed to ensure the quality of omicron nucleic acid testing and to assess the comparability of cycle threshold (Ct) values derived from RT-PCR. METHODS: Five external quality assessment (EQA) rounds using the omicron virus-like particles were organized between February 2022 and June 2022. RESULTS: A total of 1401 qualitative EQA reports have been collected. The overall positive percentage agreement was 99.72%, the negative percentage agreement was 99.75%, and the percent agreement was 99.73%. This study observed a significant variance in Ct values derived from different test systems. There was a wide heterogeneity in PCR efficiency among different RT-PCR kits and inter-laboratories. CONCLUSION: There was strong concordance among laboratories performing qualitative omicron nucleic acid testing. Ct values from qualitative RT-PCR tests should not be used for clinical or epidemiological decision-making to avoid the potential for misinterpretation of the results.

Establishment and evaluation of digital PCR methods for HER2 copy number variation in breast cancer.

Accurate measurement of human epidermal growth factor receptor 2 (HER2) copy number variation (CNV) is very important for guiding the tumor target therapy in breast cancer. Digital PCR (dPCR) is a sensitive and an absolute quantitative method, which can be used to detect HER2 CNV. Three HER2 exon-specific digital PCR assays along with three new reference genes assays (homo sapiens ribonuclease P RNA component H1 (RPPH1), glucose-6-phosphate isomerase (GPI), and chromosome 1 open reading frame 43 (C1ORF43), on different chromosomes) were established and validated by using standard reference material, 8 different cell lines and 110 clinical Formalin-fixed and paraffin-embedded (FFPE) samples. DPCR can achieve precise quantification of HER2 CNV by calculating the ratio of HER2/reference gene. The positive and negative coincidence rates were 98% (53/54) and 95% (53/56), respectively, compared with fluorescence in situ hybridization (FISH) diagnostic result 110 of FFPE samples. The common reference gene CEP17 used for FISH diagnostic was not suitable as single reference gene for HER2 CNV measurements by dPCR. The best practice of HER2 CNV determination by dPCR is to conduct the three duplex assays of H1 (HER2 exon 4) with the proposed three new reference genes, with a positive cut-off value of H1/RPPH1 ≥ 2.0 or H1/averaged reference gene ≥ 2.0. The proposed dPCR method in our study can accurately provide absolute copy number of HER2 and reference gene on an alternative chromosome, thus avoiding false negative caused by polysomy of chromosome 17. The improved molecular typing and diagnosis of breast cancer will better guide clinical medication.

Accurate method for value assignment of carcinoembryonic antigen reference materials.

BACKGROUND: In this study, we explored the commutability of reference materials (RMs) for carcinoembryonic antigen (CEA), selected the appropriate diluent matrix of the first International Reference Preparation (IRP) 73/601 of the World Health Organization (WHO 73/601) for CEA, and improved the comparability of CEA measurement results among different assay systems. METHODS: Forty serum samples were divided into five aliquots. WHO 73/601 was diluted into nine concentrations using five diluents with different components, and the candidate RMs for CEA at five concentrations (C1-C5) were prepared by the Beijing Clinical Laboratory Center (BCCL). The samples were analyzed via five automated CEA immunoassays. RESULTS: Carcinoembryonic antigen candidate RMs were commutable among all immunoassays based on the CLSI approach and among 7 of 10 assay combinations based on the IFCC approach. WHO 73/601 diluted in phosphate-buffered saline (PBS) was commutable among all assays based on the CLSI approach and among 5 of 10 pairwise comparisons based on the IFCC approach with correction of bias at diluted concentrations, except for the lowest concentration, which had the smallest variation among systems. The median percentage biases among assays were decreased after calibration. CONCLUSION: The BCCL candidate RMs (C2-C5) for CEA were commutable among all immunoassays. WHO 73/601 RMs diluted in a PBS buffer matrix were selected as common calibrators for five immunoassays, which reduced bias, thereby effectively improving the harmonization of CEA detection; therefore, they could be used to assign values to CEA candidate RMs developed by BCCL. Our findings promote the harmonization of CEA detection in immunoassays.

Commutability evaluation of candidate reference materials and ERM-DA470k/IFCC for immunoglobulin M using two international approaches.

BACKGROUND: This study aimed to assess the commutability of frozen pooled human serum (PHS), high concentration of Immunoglobulin M (IgM) pure diluted materials (HPDM), commercialized pure materials (CPM), and dilutions of ERM-DA470k/IFCC in IgM detection using the CLSI and IFCC approaches, to support standardization or harmonization of IgM measurement. METHODS: Twenty-four serum samples, relevant reference materials (PHS, HPDM, CPM), and different ERM-DA470k/IFCC dilutions were analyzed in triplicate using six routine methods. The commutability of the relevant reference materials was carried out following CLSI EP30-A and IFCC bias analysis. RESULTS: According to the CLSI approach, low, medium, and high concentrations of PHS, HPDM, and CPM were commutable on 10, 13, 15, 13, and 8 of 15 assay combinations, respectively. Using the IFCC approach, low, medium, and high concentrations of PHS, HPDM, and CPM were commutable on 10, 11, 9, 15, and 10 of 15 assay combinations, respectively. The ERM-DA470k/IFCC dilutions with D-PBS and RPMI-1640 Medium were commutable on 13 of 15 assay combinations according to CLSI and were commutable on all 15 assay combinations using IFCC approach. CONCLUSIONS: High concentration of PHS were commutable on all six detection systems using the CLSI approach. Low and medium concentration of PHS showed unsatisfied commutability. HPDM, not CPM have good commutability, has the potential to become reference materials. ERM-DA470k/IFCC diluted with different medium showed different commutability.

Spatial characteristics of change trends of air pollutants in Chinese urban areas during 2016-2020: The impact of air pollution controls and the COVID-19 pandemic.

Air pollution is a threat to public health in China, and several actions and plans have been implemented by Chinese authorities in recent years to mitigate it. This study examined the spatial distribution of changes in urban air pollutants (UAP) in 336 Chinese cities from 2016 to 2020 and their responses to air pollution controls and the COVID-19 pandemic. Based on the harmonic model, decreases in fine particles (PM2.5), inhalable particles (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) levels were found in 90.7%, 91.9%, 75.2%, 94.3%, and 88.7% of cities, respectively, while an increase in ozone (O3) was found in 87.2% of cities. Notable spatial heterogeneity was observed in the air pollution trends. The greatest improvement in air quality occurred mainly in areas with poor air quality, such as Hebei province and its surrounding cities. However, some areas (i.e., Yunnan and Hainan provinces) with good air quality showed a worsening trend. During the 13th Five-Year Plan period (2016-2020), the remarkable effects of PM2.5 and SO2 pollution control plans were confirmed. Additionally, economic growth in 74.2% of the Chinese provinces decoupled from air quality after implementing pollution control measures. In 2020, several Chinese cities were locked down to reduce the spread of COVID-19. Except for SO2, the national air pollution in 2020 improved to a greater extent than that in 2016-2019; In particularly, the contribution of simulated COVID-19 pandemic to NO2 reduction was 66.7%. Overall, air pollution control actions improved urban PM2.5, PM10, SO2, and CO, whereas NO2 was reduced primarily because of the COVID-19 pandemic.

Molecularly imprinted voltammetric sensor sensibilized by nitrogen-vacancy graphitized carbon nitride and Ag-MWCNTs towards the detection of acetaminophen.

The overdose of acetaminophen (AP) can cause serious acute liver injury even the irreversible liver necrosis. The quantitative detection of AP is of great significance not only for clinical applications but also for the quantity control of its pharmaceutical formulations. In this paper, a sensitive molecularly imprinted voltammetric sensor towards AP was constructed based on synergistic enhancement of nitrogen-vacancy graphitized carbon nitride (NV-g-C3 N4 ) and carboxylated MWCNTs loaded with silver nanoparticles (Ag-MWCNTs). The powder X-Ray diffraction spectrum, field emission scanning and transmission electron microscopes, cyclic voltammetry (CV), and electrochemical impedance spectrum were used to characterize the composites. The results show that NV-g-C3 N4 and Ag-MWCNTs closely embedded each other, forming loose porous hybrid structure by hydrogen bond. The prepared sensor molecular imprinting polymer (MIP)/C3 N4 /Ag-CNTs/GCE shows a strong synergistic enhancement of electroanalytical response by CV and differential pulse voltammetry (DPV) tests when compared with NV-g-C3 N4 /GCE, Ag-CNTs/GCE, and MIP/GCE. Through the optimization of the ratio of monomer and template, electropolymerization cycle, elution cycle, incubation time, and pH, linear ranges of 0.007-5 and 5-100 µM were found with the limit of detection of 2.33 nM by DPV. Moreover, its selectivity towards AP was satisfied when compared with detection towards ascorbic acid, dopamine, and glucose. The recovery range of 96.3%-100.5% was obtained in the spiked human serum and urine samples with the SD below 3.0%. In addition, the prepared sensor shows great detecting robustness with good anti-interference, reproducibility, and stability.

A highly accurate delta check method using deep learning for detection of sample mix-up in the clinical laboratory.

OBJECTIVES: Delta check (DC) is widely used for detecting sample mix-up. Owing to the inadequate error detection and high false-positive rate, the implementation of DC in real-world settings is labor-intensive and rarely capable of absolute detection of sample mix-ups. The aim of the study was to develop a highly accurate DC method based on designed deep learning to detect sample mix-up. METHODS: A total of 22 routine hematology test items were adopted for the study. The hematology test results, collected from two hospital laboratories, were independently divided into training, validation, and test sets. By selecting six mainstream algorithms, the Deep Belief Network (DBN) was able to learn error-free and artificially (intentionally) mixed sample results. The model's analytical performance was evaluated using training and test sets. The model's clinical validity was evaluated by comparing it with three well-recognized statistical methods. RESULTS: When the accuracy of our model in the training set reached 0.931 at the 22nd epoch, the corresponding accuracy in the validation set was equal to 0.922. The loss values for the training and validation sets showed a similar (change) trend over time. The accuracy in the test set was 0.931 and the area under the receiver operating characteristic curve was 0.977. DBN demonstrated better performance than the three comparator statistical methods. The accuracy of DBN and revised weighted delta check (RwCDI) was 0.931 and 0.909, respectively. DBN performed significantly better than RCV and EDC. Of all test items, the absolute difference of DC yielded higher accuracy than the relative difference for all methods. CONCLUSIONS: The findings indicate that input of a group of hematology test items provides more comprehensive information for the accurate detection of sample mix-up by machine learning (ML) when compared with a single test item input method. The DC method based on DBN demonstrated highly effective sample mix-up identification performance in real-world clinical settings.

Traceable machine learning real-time quality control based on patient data.

OBJECTIVES: Patient-based real-time quality control (PBRTQC) has gained attention as an alternative/integrative tool for internal quality control (iQC). However, it is still doubted for its performance and its application in real clinical settings. We aim to generate a newly and easy-to-access patient-based real-time QC by machine learning (ML) traceable to standard reference data with assigned values by National Institute of Metrology of China (NIM), and to compare it with PBRTQC for clinical validity evaluation. METHODS: For five representative biochemistry analytes, 1,195 000 patient testing results each were collected. After data processing, independent training and test sets were divided. Machine learning internal quality control (MLiQC) was set up by Random Forest in ML and was validated by way of both metrology algorithm traceability and 4 PBRTQC methods recommended by IFCC analytical working group. RESULTS: MLiQC were established. As an example of albumin (ALB) at the critical bias, the uncertainty of MLiQC was 0.14%, which was evaluated by standard reference data produced by NIM. Compared with four optimal PBRTQC methods at critical bias, the average of the number of patient samples from a bias introduced until detected (ANPed) of MLiQC averagely decreased from 600 to 20. The median and 95 quantiles of NPeds (MNPed and 95NPed) of MLiQC were superior to all optimal PBRTQCs above 90% for all test items. CONCLUSIONS: MLiQC is highly superior to PBRTQC and well-suited in real settings. The validation of the model from two aspects of algorithm traceability and clinical effectiveness confirms its satisfactory performance.

Glutathionylation Decreases Methyltransferase Activity of PRMT5 and Inhibits Cell Proliferation.

Glutathionylation is an important posttranslational modification that protects proteins from further oxidative damage as well as influencing protein structure and activity. In the present study, we demonstrate that the cysteine-42 residue in protein arginine N-methyltransferase 5 (PRMT5) is glutathionylated in aged mice or in cells that have been exposed to oxidative stress. Deglutathionylation of this protein is catalyzed by glutaredoxin-1 (Grx1). Using mutagenesis and subsequent biochemical analyses, we show that glutathionylation decreased the binding affinity of PRMT5 with methylosome protein-50 (MEP50) and reduced the methyltransferase activity of PRMT5. Furthermore, overexpression of PRMT5-C42A mutant caused a significant increase in histone methylation in HEK293T and A549 cells and promoted cell growth, whereas overexpression of the PRMT5-C42D mutant, a mimic of glutathionylated PRMT5, inhibited cell proliferation. Taken together, our results demonstrate a new mechanism of regulation of PRMT5 methyltransferases activity and suggest that PRMT5 glutathionylation is partly responsible for reactive oxygen species-mediated cell growth inhibition.

Mori fructus aqueous extracts attenuate carbon tetrachloride-induced renal injury via the Nrf2 pathway and intestinal flora.

Mori fructus aqueous extracts (MFAEs) have been used as a traditional Chinese medicine for thousands of years with the function of strengthening the liver and tonifying the kidney. However, its inner mechanism to alleviative renal injury is unclear. To investigate the attenuation of MFAEs on nephrotoxicity and uncover its potential molecular mechanism, we established a nephrotoxicity model induced by carbon tetrachloride (CCl4). The mice were randomly divided into control group, CCl4 model group (10% CCl4), CCl4 + low and high MFAEs groups (10% CCl4 + 100 mg/kg and 200 mg/kg MFAEs). We found that MFAEs decreased the kidney index of mice, restored the pathological changes of renal structure induced by CCl4, reduced cystatin C, neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule 1 (Kim-1) blood urea nitrogen and creatinine contents in serum, promoted the nuclear transportation of Nrf2 (nuclear factor erythroid derived 2 like 2), elevated the expression of HO-1 (heme oxygenase 1), GPX4 (glutathione peroxidase 4), SLC7A11 (solute carrier family 7 member 11), ZO-1 (zonula occludens-1) and Occludin, suppressed the expression of Keap1 (kelch-like ECH-associated protein 1), HMGB1 (High Mobility Group Protein 1), ACSL4 (acyl-CoA synthetase long chain family member 4) and TXNIP (thioredoxin interacting protein), upregulated the flora of Akkermansia, Anaerotruncus, Clostridium_sensu_stricto, Ihubacter, Alcaligenes, Dysosmobacter, and downregulated the flora of Clostridium_XlVa, Helicobacter, Paramuribaculum. Overlapped with Disbiome database, Clostridium_XlVa, Akkermansia and Anaerotruncus may be the potential genera treated with renal injury. It indicated that MFAEs could ameliorate kidney injury caused by CCl4 via Nrf2 signaling.

Triacetyl Resveratrol Inhibits PEDV by Inducing the Early Apoptosis In Vitro.

PEDV represents an ancient Coronavirus still causing huge economic losses to the porcine breeding industry. Resveratrol has excellent antiviral effects. Triacetyl resveratrol (TCRV), a novel natural derivative of resveratrol, has been recently discovered, and its pharmacological effects need to be explored further. This paper aims to explore the relationship between PEDV and TCRV, which offers a novel strategy in the research of antivirals. In our study, Vero cells and IPEC-J2 cells were used as an in vitro model. First, we proved that TCRV had an obvious anti-PEDV effect and a strong inhibitory effect at different time points. Then, we explored the mechanism of inhibition of PEDV infection by TCRV. Our results showed that TCRV could induce the early apoptosis of PEDV-infected cells, in contrast to PEDV-induced apoptosis. Moreover, we observed that TCRV could promote the expression and activation of apoptosis-related proteins and release mitochondrial cytochrome C into cytoplasm. Based on these results, we hypothesized that TCRV induced the early apoptosis of PEDV-infected cells and inhibited PEDV infection by activating the mitochondria-related caspase pathway. Furthermore, we used the inhibitors Z-DEVD-FMK and Pifithrin-α (PFT-α) to support our hypothesis. In conclusion, the TCRV-activated caspase pathway triggered early apoptosis of PEDV-infected cells, thereby inhibiting PEDV infections.

Quantification of ecosystem services supply-demand and the impact of demographic change on cultural services in Shenzhen, China.

Previous studies on the supply and demand of ecosystem services (ES) mainly focused on inter-annual changes, and no studies have explored the impact of demographic change on the ES supply and demand on fine-grained time scales. Thus, taking Shenzhen as an example, the status of ES supply and demand, as well as diurnal population changes and their impacts on cultural services were analyzed at different time periods using mobile phone signaling data, ecological supply-demand ratio (ESDR), Geo-Informatic Tupu, InVEST model and buffer zone. The results showed that the population declines successively on workdays, weekends and holidays, and that the daytime population is greater than the nighttime. Water yield services can basically meet the demand in terms of quantity and spatial distribution, however, carbon sequestration and cultural services showed the opposite results. The main type of ESDR changes in cultural services are the mutual conversion of deficit and balance, and these are concentrated in areas with high forest coverage and small populations, but frequent population changes. In addition, when the fixed population is too large, the use of time-varying population data will conceal the impact of demographic changes on ES supply and demand, and other data are needed for auxiliary analysis. Overall, this study provides a new research perspective for the ES supply and demand and can provide a theoretical basis for refined sustainable urban management.

Trueness assessment of routine electrolytes measuring systems using the candidate reference method by ion chromatography.

Electrolytes for sodium, potassium, magnesium, and calcium are important serum ions that are frequently assayed in clinical laboratories. In this study, we assessed the trueness of routine analytical systems for four cations using an inexpensive candidate reference method aimed to promote the standardization of serum electrolyte detection. An ion chromatography (IC) method with Cesium as an internal standard was developed and evaluated. The residual clinical serum samples at Chaoyang Hospital were collected and prepared into three human serum pools of electrolytes, which were used for the trueness evaluation of five routine analytical systems. Furthermore, the agreement between routine methods and the IC method was verified using 40 individual human samples. The recovery rates of sodium, potassium, magnesium and calcium were 99.69%, 100.34%, 100.43% and 99.89%, respectively. The intra-batch standard deviation and intra-laboratory precision of NIST SRM 956c were all less than 1% for the four ions. The certified values were within the validation range, and the deviation between the results and the certified values were less than 0.5%. The three serum pools were homogeneous and stable. All routine systems aligned with the IC method for four cations and achieved the analytical quality specifications for potassium and magnesium at 3 different concentrations. The developed IC method is simple, practical, accurate, and precise, which can be used as a candidate reference method for serum electrolytes measurement. Five routine analytical systems for electrolytes measurement had the acceptable bias for potassium and magnesium and their results showed good concordance.

Evaluation of Uniformity and Stability of Frozen Mixed Serum Potassium Candidate Reference Materials.

BACKGROUND: We tested the uniformity and stability of candidate reference materials (RMs) for serum potassium and aimed to design RMs with better quality that can meet all clinical test requirements and effectively solve the quantity traceability transfer problem. METHODS: Three levels of frozen mixed serum potassium candidate RMs were prepared and packed in freezing tubes. RMs were determined in triplicate in 10 vials randomly selected from each level. A one-way analysis of variance was used to evaluate the uniformity using a ratio of the mean squares among groups to mean squares within groups F < F0.05 as the criteria. Stability was studied by synchronization; the short-term stability of the serum potassium in the transport conditions was observed for 30, 15, and 7 days at refrigeration (2 - 8°C), room temperature (18 - 25°C), and 37°C, respectively. By linear regression analysis of variance, the straight line was used as an empirical model. The criterion for judging is |b1| < t0.95, n - 2·sb1. RESULTS: Based on the statistical analysis using SPSS 17, the F values for the homogeneity tests of each level of the frozen mixed serum potassium RMs were 0.247, 0.117, and 0.162. These values were less than F0.05 (9, 20) = 2.39. When the short-term stability of the serum potassium was observed for 30, 12, and 4 days at 2 - 8°C, room temperature, and 37°C, respectively, |b1| < t0.95, n - 2·sb1, and instability was not observed. CONCLUSIONS: The three levels of the frozen mixed serum potassium RMs have good uniformity. At 2 - 8°C, room temperature, and 37°C, the stability can be ensured for at least 30, 12, and 4 days, respectively. The serum potassium candidate RMs are sufficiently stable under these transportation conditions.

Establishment of Multiple Myeloma Diagnostic Model Based on Logistic Regression in Clinical Laboratory.

BACKGROUND: Due to the insidious onset of multiple myeloma (MM), missed diagnosis and misdiagnosis have a serious impact on the health of MM patients. Simple, rapid, and valid laboratory screening is critical for MM clinical diagnosis. METHODS: We used routine laboratory tests to establish a simple, inexpensive, and non-invasive diagnostic model for MM based on logistic regression. In the retrospective analysis, a total of 273 newly diagnosed MM inpatients and 288 non-MM participants, from January 2016 to December 2018 in Beijing Chaoyang hospital, Capital Medical University, were divided into training set and validation set. Age, gender, and the related routine laboratory tests for MM, including albumin (ALB), globulin (GLB), lactate dehydrogenase (LDH), creatinine (Cr), calcium (Ca2+), hemoglobin (Hb) and platelet (PLT), were analyzed by multivariate logistic regression to develop a diagnostic model. RESULTS: A diagnostic model was calculated using the formula MM index=-((-18×gender-3×ALB-Hb)/10), based on the logistic regression. The MM index [22 (20 - 25)] of MM patients was significantly lower than that of non-MM [30 (29 - 31)] in the training set (p < 0.001). It showed an excellent diagnostic performance in diagnosing MM through a receiver operating characteristic (ROC) curve, and its corresponding sensitivity, specificity, and area under the curve (AUC) were 95.6%, 96.7%, and 0.982 (0.968, 0.997), respectively. At a diagnostic risk threshold of 28, the model identified MM with a sensitivity of 95.6% and a specificity of 98.1% by using independent validation data. There was a significant positive correlation (r = 0.845, p < 0.001) between the DS grading and the MM index among all the participants. CONCLUSIONS: The established diagnostic model of MM index can successfully identify newly diagnosed MM from healthy controls. The diagnostic model of MM index may also act as a predictor of the severity of MM without therapy.