BackgroundPerianal infection is a common problem for patients with acute leukemia. However, neutropenia and bleeding tendency are relatively contraindicated to surgical intervention. The epidemiology, microbiology, clinical manifestations and outcomes of perianal infection in leukemic patients are also rarely discussed.MethodThe medical records of 1102 adult patients with acute leukemia at a tertiary medical center in Taiwan between 2001 and 2010 were retrospectively reviewed and analyzed.ResultThe prevalence of perianal infection was 6.7% (74 of 1102) in adult patients with acute leukemia. Twenty-three (31%) of the 74 patients had recurrent episodes of perianal infections. Patients with acute myeloid leukemia had higher recurrent rates than acute lymphoblastic leukemia patients (p = 0.028). More than half (n = 61, 53%) of the perianal infections were caused by gram-negative bacilli, followed by gram-positive cocci (n = 36, 31%), anaerobes (n = 18, 15%) and Candida (n = 1, 1%) from pus culture. Eighteen patients experienced bacteremia (n = 24) or candidemia (n = 1). Overall 41 (68%) of 60 patients had polymicrobial infection. Escherichia coli (25%) was the most common micro-organism isolated, followed by Enterococcus species (22%), Klebsiella pneumoniae (13%), and Bacteroides species (11%). Twenty-five (34%) of 74 patients received surgical intervention. Acute leukemia patients with surgically managed anal fistulas tended to have fewer recurrences (p = 0.067). Four (5%) patients died within 30 days after diagnosis of perianal infection. Univariate analysis of 30-day survival revealed the elderly (≧ 65 years) (p = 0.015) and patients with shock (p<0.001) had worse outcome. Multivariate analysis showed septic shock to be the independent predictive factor of 30-day crude mortality of perianal infections (p = 0.016).ConclusionPerianal infections were common and had high recurrence rate in adult patients with acute leukemia. Empirical broad-spectrum antibiotics with anaerobic coverage should be considered. Shock independently predicted 30-day crude mortality. Surgical intervention for perianal infection remains challenging in patients with acute leukemia.
Gene duplication and sequence divergence are driving forces toward establishing protein families. To examine how sequence changes affect carbohydrate specificity, the two closely related proto-type chicken galectins CG-14 and CG-16 were selected as models. Binding properties were analyzed using a highly sensitive solid-phase assay. We tested 56 free saccharides and 34 well-defined glycoproteins. The two galectins share preference for the II (Galb1-4GlcNAc) versus I (Galb1-3GlcNAc) version of b-galactosides. A pronounced difference is found owing to the reactivity of CG-14 with histo-blood group ABH active oligosaccharides and A/B active glycoproteins. These experimental results prompted to determine activitystructure correlations by modeling. Computational analysis included consideration of the flexibility of binding partners and the presence of water molecules. It provided a comparative description of complete carbohydrate recognition domains, which had so far not been characterized in animal galectins. The structural models assigned II, I selectivity to a region downstream of the central Trp moiety. Docking revealed that the tetrasaccharides can be accommodated in their free-state low-energy conformations. CG-14's preference for A versus B epitopes could be attributed to a contact between His124 and the Nacetyl group of GalNAc. Regarding intergalectin comparison, the Ala53/Cys51 exchange affects the interaction potential of His54/His52. Close inspection of simulated dynamic interplay revealed reorientation of His124 at the site of the His124/Glu123 substitution, with potential impact on ligand dissociation. In summary, this study identifies activity differences and provides information on their relation to structural divergence, epitomizing the value of this combined approach beyond galectins.
Agaricus bisporus agglutinin (ABA) isolated from edible mushroom has a potent anti-proliferative effect on malignant colon cells with considerable therapeutic potential as an anti-neoplastic agent. Since previous studies on the structural requirement for binding were limited to molecular or submolecular levels of Galbeta1-3GalNAc (T; Thomsen-Friedenreich disaccharide glycotope; where Gal represents D-galactopyranose and GalNAc represents 2-acetamido-2-deoxy-D-galactopyranose) and its derivatives, the binding properties of ABA were further investigated using our collection of glycans by enzyme-linked lectinosorbent assay and lectin-glycan inhibition assay. The results indicate that polyvalent Galbeta1-related glycotopes, GalNAcalpha1-Ser/Thr (Tn), and their cryptoforms, are the most potent factor for ABA binding. They were up to 5.5x10(5) and 4.7x10(6) times more active than monomeric T and GalNAc respectively. The affinity of ABA for ligands can be ranked as: multivalent T (alpha) (Galbeta1-3GalNAcalpha1-), Tn and I / II (Galbeta1-3GlcNac/Galbeta1-4GlcNAc, where GlcNAc represents 2-acetamido-2-deoxy-D-glucopyranose)>>>>monomeric T (alpha) and Tn > I >>GalNAc>>> II, L (Galbeta1-4Glc, where Glc represents D-glucopyranose) and Gal (inactive). These specific binding features of ABA establish the importance of affinity enhancement by high-density polyvalent (versus multiantennary I / II) glycotopes and facilitate our understanding of the lectin receptor recognition events relevant to its biological activities.
BackgroundMulticolor flow cytometry (MFC) analysis is widely used to identify minimal residual disease (MRD) after treatment for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). However, current manual interpretation suffers from drawbacks of time consuming and interpreter idiosyncrasy. Artificial intelligence (AI), with the expertise in assisting repetitive or complex analysis, represents a potential solution for these drawbacks.MethodsFrom 2009 to 2016, 5333 MFC data from 1742 AML or MDS patients were collected. The 287 MFC data at post-induction were selected as the outcome set for clinical outcome validation. The rest were 4:1 randomized into the training set (n = 4039) and the validation set (n = 1007). AI algorithm learned a multi-dimensional MFC phenotype from the training set and input it to support vector machine (SVM) classifier after Gaussian mixture model (GMM) modeling, and the performance was evaluated in The validation set.FindingsPromising accuracies (84·6% to 92·4%) and AUCs (0·921–0·950) were achieved by the developed algorithms. Interestingly, the algorithm from even one testing tube achieved similar performance. The clinical significance was validated in the outcome set, and normal MFC interpreted by the AI predicted better progression-free survival (10·9 vs 4·9 months, p < 0·0001) and overall survival (13·6 vs 6·5 months, p < 0·0001) for AML.InterpretationThrough large-scaled clinical validation, we showed that AI algorithms can produce efficient and clinically-relevant MFC analysis. This approach also possesses a great advantage of the ability to integrate other clinical tests.FundThis work was supported by the Ministry of Science and Technology (107-2634-F-007-006 and 103–2314-B-002-185-MY2) of Taiwan.
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