Background : A multicenter study conducted in Southeast Asia to derive reference intervals (RIs) for 72 commonly measured analytes (general chemistry, inflammatory markers, hormones, etc.) featured centralized measurement to clearly detect regionality in test results. The results of 31 standardized analytes are reported, with the remaining analytes presented in the next report. Method : The study included 63 clinical laboratories from South Korea, China, Vietnam, Malaysia, Indonesia, and seven areas in Japan. A total of 3541 healthy individuals aged 20 -65 years (Japan 2082, others 1459) were recruited mostly from hospital workers using a well-defined common protocol. All serum specimens were transported to Tokyo at − 80 ° C and collectively measured using reagents from four manufacturers. Three-level nested ANOVA was used to quantitate variation (SD) of test results due to region, sex, and age. A ratio of SD for a given factor over residual SD (representing net between-individual variations) (SDR) exceeding 0.3 was considered significant. Traceability of RIs was ensured by recalibration using value-assigned reference materials. RIs were derived parametrically. Results : SDRs for sex and age were significant for 19 and 16 analytes, respectively. Regional difference was significant for 11 analytes, including high density lipoprotein (HDL)-cholesterol and inflammatory markers. However, when the data were limited to those from Japan, regionality was not observed in any of the analytes. Accordingly, RIs were derived with or without partition by sex and region. Conclusions : RIs applicable to a wide area in Asia were established for the majority of analytes with traceability to reference measuring systems, whereas regional partitioning was required for RIs of the other analytes.

Hematological reference samples for external quality assessment (EQA) in hematology are essential for many laboratories in Vietnam, however, the prices are high along with short storing time. This study attempted to establish an optimal formula for the manufacture of hematological reference samples for hematological EQA programs. Human red blood cells (RBCs) were mixed with goose RBCs (pseudo-leucocytes) and goat RBCs (pseudo-platelets) as alternatives for white blood cells (WBCs) and platelets (PLTs), respectively, in different formulas of storage media. The optimal formula was obtained using response surface methodology–central composite design (RSM-CCD). Three main factors affecting the concentrations of RBCs were determined by Plackett–Burmann matrix. The optimal concentrations determined by RSM-CCD were 46% serum, 0.44 g/L neomycin sulfate, and 2.5% glycerol. The model predicted that the maximum number of human RBCs was 4.1 × 1012 cells/L, pseudo-leucocytes was 6.4 × 109 cells/L, and pseudo-platelets was 188 × 109 cells/L. The practical concentrations of RBCs, pseudo-leucocytes, and pseudo-platelets were 4.21 × 1012 cells/L, 6.4 × 109 cells/L, and 187 × 109 cells/L, respectively, which showed 98.93% similarity with theoretical data. This result could be a premise for further study to improve manufacturing of hematological reference samples for hematology EQA programs.

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The research into and production of hematological reference samples used to implement an external quality assessment (EQA) to check the quality of hematology tests are necessary for hematology laboratories in Vietnam. In this research, the study team determined the assessment values of blood cell count (human RBCs, pseudo-leucocytes, and pseudo-platelets) by the impedance method used in hematology EQA programs. The hematological reference samples were controlled at three concentration levels: low, normal, and high. Determination of the assigned value (mean ± 2SD) was performed for the following hematology analyzer series by impedance method: ABX Micros 60, Celldyn 1700, and Mindray BC 2000. Each device was sent to 10 different laboratories for evaluation. Research results for assigned values of each model (ABX Micros 60, Celldyn 1700, and Mindray BC 2000) were determined at the three concentrations. For the ABX Micros 60 and Celldyn 1700 series, 80% of laboratories had analytical results within assigned values. For the Mindray BC 2000 series, 100% of laboratories had analytical results within assigned values. The measurement results for the number of human RBCs, pseudo-leucocytes, and pseudo-platelets on each analyzer were similar between the 10 laboratories; the results of the three hematology analyzer series using the impedance method were different and the difference was statistically significant (p < 0.05). Thus, hematological reference samples for measuring the number of blood cells meeting the standards so that they can evaluate the results of laboratories using the impedance method: ABX, Celldyn 1700, Mindray BC 2000.

The campaign “No action today, no cure tomorrow” against antimicrobial resistance proposed by the World Health Organization (WHO) has not only propeled people to take action to prevent antimicrobial resistance, but has also encouraged researchers to develop new antimicrobial agents. 4(3H)-quinazolinone and its derivatives belong to a group of compounds with many potential applications; this study was conducted to find new derivatives of heterocyclic 4(3H)-quinazolinone with biological effects, contributing to research on antibacterial and antifungal compounds. Using the closed-loop method between anthranilic acid and acetic anhydride, followed by reaction with aniline derivatives, a substituted product of position 3 of 4(3H)-quinazolinone was obtained, along with bromizing to replace the hydrogen of the methyl group in position 2 with dibromo. Heterocyclic derivatives such as imidazole, triazole, and thiazole were replaced from this dibromo product to obtain 19 derivatives. The structures of these derivatives were checked by modern methods such as IR, 1H-NMR, and MS. The results indicated that all of the structures were as expected, so the process of creating new derivatives from 4(3H)-quinazolinone was achieved in this study. Fourteen of the derivatives, namely 3d, 3e, 3f, 3g, 3h, 3i, 3j, 3k, 3m, 3o, 3p, 3q, 3r, and 3s, had antibacterial or antifungal effects. Among these, there were five potential derivatives: Antifungal activity was observed on A. niger by 3j and 3f (MIC: 32 μg/mL) and 3s (MIC: 64 μg/mL), and on C. albicans by 3f (MIC: 8 μg/mL); antibacterial activity was observed on S. aureus by 3p (MIC: 16 μg/mL) and 3f and 3r (MIC: 32 μg/mL), on MRSA by 3f and 3r (MIC: 32 μg/mL), and on E. coli by 3f (MIC: 32 μg/mL).