Potential application of the haematology analyser XN-31 prototype for field malaria surveillance in Kenya.

BACKGROUND: Simple and accurate diagnosis is a key component of malaria control programmes. Microscopy is the current gold standard, however it requires extensive training and the results largely rely on the skill of the microscopists. Malaria rapid diagnostic tests (RDT) can be performed with minimal training and offer timely diagnosis, but results are not quantitative. Moreover, some Plasmodium falciparum parasites have evolved and can no longer be detected by existing RDT. Developed by the Sysmex Corporation, the XN-31 prototype (XN-31p) is an automated haematology analyser capable of detecting Plasmodium-infected erythrocytes and providing species differentiation and stage specific parasite counts in venous blood samples without any preparation in approximately one minute. However, factors such as stable electricity supply in a temperature-controlled room, cost of the instrument and its initial set-up, and need for proprietary reagents limit the utility of the XN-31p across rural settings. To overcome some of these limitations, a hub and spoke diagnosis model was designed, in which peripheral health facilities were linked to a central hospital where detection of Plasmodium infections by the XN-31p would take place. To explore the feasibility of this concept, the applicability of capillary blood samples with the XN-31p was evaluated with respect to the effect of sample storage time and temperature on the stability of results. METHODS: Paired capillary and venous blood samples were collected from 169 malaria-suspected outpatients in Homa Bay County Referral Hospital, Kenya. Malaria infections were diagnosed with the XN-31p, microscopy, RDT, and PCR. Capillary blood samples were remeasured on the XN-31p after 24 h of storage at either room (15-25 °C) or chilled temperatures (2-8 °C). RESULTS: Identical results in malaria diagnosis were observed between venous and capillary blood samples processed immediately after collection with the XN-31p. Relative to PCR, the sensitivity and specificity of the XN-31p with capillary blood samples were 0.857 and 1.000, respectively. Short-term storage of capillary blood samples at chilled temperatures had no adverse impact on parasitaemia and complete blood counts (CBC) measured by the XN-31p. CONCLUSION: These results demonstrate the potential of the XN-31p to improve routine malaria diagnosis across remote settings using a hub and spoke model.

Performance of the hematology analyzer XN-31 prototype in the detection of Plasmodium infections in an endemic region of Colombia.

Early and accurate diagnosis is critical in reducing the morbidity and mortality associated with malaria. Microscopy (MI) is the current diagnostic gold standard in the field; however, it requires expert personnel, is time-consuming, and has limited sensitivity. Although rapid diagnostic tests for antigen detection (RDTs) are an alternative to diagnosis, they also have limited sensitivity and produce false positive results in detecting recent past infection. The automated hematology analyzer XN-31 prototype (XN-31p) (Sysmex Corporation, Kobe, Japan) is able to identify plasmodium-infected erythrocytes, count parasitemia and perform complete blood-cell counts within one minute. The performance of the XN-31p in diagnosing malaria was evaluated and compared with real-time polymerase chain reaction (qPCR), MI and RDT in an endemic area of Colombia where Plasmodium falciparum and Plasmodium vivax are present. Acute febrile patients were enrolled from July 2018 to April 2019 in Quibdó, Colombia. Malaria diagnoses were obtained from MI and RDT in the field and later confirmed by qPCR. Venous blood samples in EDTA were processed with an XN-31p in the field. Sensitivity, specificity, positive/negative predictive values, and the likelihood ratios of positive and negative tests were calculated with respect to the results from qPCR, MI and RDT. The intraclass correlation coefficient (ICC) and Bland-Altman plot were used to evaluate the concordance in the parasitemia with respect to MI. A total of 1,754 subjects were enrolled. The mean age was 27.0 years (IQR 14-44); 89.6% were Afro-Colombians, 94.3% lived in urban areas and 0.91% were pregnant. With respect to qPCR, the XN-31p showed a sensitivity of 90% (95% CI 87.24-92.34) and a specificity of 99.83% (95% CI 99.38-99.98) in detecting Plasmodium spp.; both parameters were equivalent to those for MI and RDT. Using MI as the reference, the XN-31p showed a sensitivity of 98.09% (95% CI 96.51-99.08), a specificity of 99.83% (95% CI 99.4-99.98), an ICC of 0.85 (95% CI 0.83-0.87) and an average difference of - 3096 parasites/µL when compared with thick-smear MI and an ICC of 0.98 (95% CI 0.97-0.98) and an average difference of - 0.0013% when compared with thin-smear MI. The XN-31p offers a rapid and accurate alternative method for diagnosing malaria in clinical laboratories in areas where P. falciparum and P. vivax cocirculate.

Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen.

Philadelphia chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs) such as polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis are characterized by abnormal proliferation of mature bone marrow cell lineages. Since various non-hematologic disorders can also cause leukocytosis, thrombocytosis and polycythemia, the detection of abnormal peripheral blood cells is essential for the diagnostic screening of Ph-negative MPNs. We sought to develop an automated diagnostic support system of Ph-negative MPNs. Our strategy was to combine the complete blood cell count and research parameters obtained by an automated hematology analyzer (Sysmex XN-9000) with morphological parameters that were extracted using a convolutional neural network deep learning system equipped with an Extreme Gradient Boosting (XGBoost)-based decision-making algorithm. The developed system showed promising performance in the differentiation of PV, ET, and MF with high accuracy when compared with those of the human diagnoses, namely: > 90% sensitivity and > 90% specificity. The calculated area under the curve of the ROC curves were 0.990, 0.967, and 0.974 for PV, ET, MF, respectively. This study is a step toward establishing a universal automated diagnostic system for all types of hematology disorders.

Fatty acids bound to albumin induce prostaglandin E2 production in human renal proximal tubular epithelial cell line HK-2.

Fatty acids bound to albumin have been reported to be involved in various responses in renal proximal tubular cells following albumin overload, leading to progression of tubulointerstitial damage in the kidneys. In addition, it has been reported that prostaglandin E2 (PGE2) plays an important role in nephrotoxicity. The aim of this study was to examine whether albumin-bound fatty acids induce PGE2 production in human renal proximal tubular epithelial cell line HK-2. Fatty acid-bearing human serum albumin increased PGE2 release in the culture medium in concentration-dependent and time-dependent manners, but fatty acid-depleted albumin had no effect on PGE2 production. Next, we investigated the effect of arachidonic acid, a precursor of eicosanoids, on PGE2 production. Arachidonic acid with fatty acid-free albumin significantly enhanced the release of PGE2 into the medium in a concentration-dependent manner. Furthermore, we examined the effect of arachidonic acid on mRNA expression of hypoxia inducible factor-1α (HIF-1α). Arachidonic acid increased HIF-1α mRNA expression in a concentration-dependent manner. These findings suggest that fatty acids, at least in part arachidonic acid, bound to albumin increase PGE2 production and expression of HIF-1α mRNA and protein, possibly resulting in various cell responses induced by albumin overload.

A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA.

Detection of dysmorphic cells in peripheral blood (PB) smears is essential in diagnostic screening of hematological diseases. Myelodysplastic syndromes (MDS) are hematopoietic neoplasms characterized by dysplastic and ineffective hematopoiesis, which diagnosis is mainly based on morphological findings of PB and bone marrow. We developed an automated diagnostic support system of MDS by combining an automated blood cell image-recognition system using a deep learning system (DLS) powered by convolutional neural networks (CNNs) with a decision-making system using extreme gradient boosting (XGBoost). The DLS of blood cell image-recognition has been trained using datasets consisting of 695,030 blood cell images taken from 3,261 PB smears including hematopoietic malignancies. The DLS simultaneously classified 17 blood cell types and 97 morphological features of such cells with >93.5% sensitivity and >96.0% specificity. The automated MDS diagnostic system successfully differentiated MDS from aplastic anemia (AA) with high accuracy; 96.2% of sensitivity and 100% of specificity (AUC 0.990). This is the first CNN-based automated initial diagnostic system for MDS using PB smears, which is applicable to develop new automated diagnostic systems for various hematological disorders.

Structural basis of the correct subunit assembly, aggregation, and intracellular degradation of nylon hydrolase.

Nylon hydrolase (NylC) is initially expressed as an inactive precursor (36 kDa). The precursor is cleaved autocatalytically at Asn266/Thr267 to generate an active enzyme composed of an α subunit (27 kDa) and a ß subunit (9 kDa). Four αß heterodimers (molecules A-D) form a doughnut-shaped quaternary structure. In this study, the thermostability of the parental NylC was altered by amino acid substitutions located at the A/D interface (D122G/H130Y/D36A/L137A) or the A/B interface (E263Q) and spanned a range of 47 °C. Considering structural, biophysical, and biochemical analyses, we discuss the structural basis of the stability of nylon hydrolase. From the analytical centrifugation data obtained regarding the various mutant enzymes, we conclude that the assembly of the monomeric units is dynamically altered by the mutations. Finally, we propose a model that can predict whether the fate of the nascent polypeptide will be correct subunit assembly, inappropriate protein-protein interactions causing aggregation, or intracellular degradation of the polypeptide.

Correction to: Metabolic pathway of 6-aminohexanoate in the nylon oligomer-degrading bacterium Arthrobacter sp. KI72: identification of the enzymes responsible for the conversion of 6-aminohexanoate to adipate.

The original publication of this paper contains mistakes for Tables 1 and 2 legends as well as the sublabels in Figs. 2, 4, 5, 6, and 7.

Metabolic pathway of 6-aminohexanoate in the nylon oligomer-degrading bacterium Arthrobacter sp. KI72: identification of the enzymes responsible for the conversion of 6-aminohexanoate to adipate.

Arthrobacter sp. strain KI72 grows on a 6-aminohexanoate oligomer, which is a by-product of nylon-6 manufacturing, as a sole source of carbon and nitrogen. We cloned the two genes, nylD 1 and nylE 1 , responsible for 6-aminohexanoate metabolism on the basis of the draft genomic DNA sequence of strain KI72. We amplified the DNA fragments that encode these genes by polymerase chain reaction using a synthetic primer DNA homologous to the 4-aminobutyrate metabolic enzymes. We inserted the amplified DNA fragments into the expression vector pColdI in Escherichia coli, purified the His-tagged enzymes to homogeneity, and performed biochemical studies. We confirmed that 6-aminohexanoate aminotransferase (NylD1) catalyzes the reaction of 6-aminohexanoate to adipate semialdehyde using α-ketoglutarate, pyruvate, and glyoxylate as amino acceptors, generating glutamate, alanine, and glycine, respectively. The reaction requires pyridoxal phosphate (PLP) as a cofactor. For further metabolism, adipate semialdehyde dehydrogenase (NylE1) catalyzes the oxidative reaction of adipate semialdehyde to adipate using NADP+ as a cofactor. Phylogenic analysis revealed that NylD1 should be placed in a branch of the PLP-dependent aminotransferase sub III, while NylE1 should be in a branch of the aldehyde dehydrogenase superfamily. In addition, we established a NylD1/NylE1 coupled system to quantify the aminotransferase activity and to enable the conversion of 6-aminohexaoate to adipate via adipate semialdehyde with a yield of > 90%. In the present study, we demonstrate that 6-aminohexanoate produced from polymeric nylon-6 and nylon oligomers (i.e., a mixture of 6-aminohexaoate oligomers) by nylon hydrolase (NylC) and 6-aminohexanoate dimer hydrolase (NylB) reactions are sequentially converted to adipate by metabolic engineering technology.

Draft Genome Sequence of the Nylon Oligomer-Degrading Bacterium Arthrobacter sp. Strain KI72.

We report here the 4.6-Mb genome sequence of a nylon oligomer-degrading bacterium, Arthrobacter sp. strain KI72. The draft genome sequence of strain KI72 consists of 4,568,574 bp, with a G+C content of 63.47%, 4,372 coding sequences (CDSs), 54 tRNAs, and six rRNAs.

Mutations affecting the internal equilibrium of the reaction catalyzed by 6-aminohexanoate-dimer hydrolase.

UNLABELLED: The enzyme 6-aminohexanoate-dimer hydrolase catalyzes amide synthesis. The yield of this reverse reaction in 90% t-butyl alcohol was found to vary drastically when enzyme mutants with substitutions of several amino acids located at the entrance of the catalytic cleft were used. Movement of the loop region and the flip-flop of Tyr170 generate a local hydrophobic environment at the catalytic center of the enzyme. Here, we propose that the shift of the internal equilibrium between the enzyme-substrate complex and enzyme-product complex by the 'water-excluding effect' alters the rate of the forward and reverse reactions. Moreover, we suggest that the local hydrophobic environment potentially provides a reaction center suitable for efficient amide synthesis. DATABASE: PDB code 3VWL: Hyb-24DNY-S(187) PDB code 3VWM: Hyb-24DNY-A(187) PDB code 3VWN: Hyb-24DNY-G(187) PDB code 3A65: Hyb-24DN-A(112) /Ahx complex PDB code 3A66: Hyb-24DNY-A(112) /Ahx complex PDB code 3VWP: Hyb-24DNY-S(187) A(112) /Ahx complex PDB code 3VWQ: Hyb-24DNY-A(187) A(112) /Ahx complex PDB code 3VWR: Hyb-24DNY-G(187) A(112) /Ahx complex.