Genotype and clinical characteristics of patients with Wolfram syndrome and WFS1-related disorders.

Objective: Wolfram syndrome (WFS) is an autosomal recessive disorder associated with juvenile-onset diabetes mellitus, optic atrophy, diabetes insipidus, and sensorineural hearing loss. We sought to elucidate the relationship between genotypic and phenotypic presentations of Wolfram syndrome which would assist clinicians in classifying the severity and prognosis of Wolfram syndrome more accurately. Approach: Patient data from the Washington University International Registry and Clinical Study for Wolfram Syndrome and patient case reports were analyzed to select for patients with two recessive mutations in the WFS1 gene. Mutations were classified as being either nonsense/frameshift variants or missense/in-frame insertion/deletion variants. Missense/in-frame variants were further classified as transmembrane or non-transmembrane based on whether they affected amino acid residues predicted to be in transmembrane domains of WFS1. Statistical analysis was performed using Wilcoxon rank-sum tests with multiple test adjustment applied via the Bonferonni correction. Results: A greater number of genotype variants correlated with earlier onset and a more severe presentation of Wolfram syndrome. Secondly, non-sense and frameshift variants had more severe phenotypic presentations than missense variants, as evidenced by diabetes mellitus and optic atrophy emerging significantly earlier in patients with two nonsense/frameshift variants compared with zero or one nonsense/frameshift variants. In addition, the number of transmembrane in-frame variants demonstrated a statistically significant dose-effect on age of onset of diabetes mellitus and optic atrophy among patients with either one or two in-frame variants. Summary/Conclusion: The results contribute to our current understanding of the genotype-phenotype relationship of Wolfram syndrome, suggesting that alterations in coding sequences result in significant changes in the presentation and severity of Wolfram. The impact of these findings is significant, as the results will aid clinicians in predicting more accurate prognoses and pave the way for personalized treatments for Wolfram syndrome.

Management and outcomes in secondary diabetes among pediatric patients hospitalized with hemophagocytic lymphohistiocytosis.

OBJECTIVES: Hemophagocytic lymphohistiocytosis (HLH) is a life-threatening disorder marked by massive cytokine release from macrophage and T-cell activation. Hallmarks include fever, splenomegaly, cytopenias, hypertriglyceridemia, hypofibrinogemia, and elevations in ferritin and soluble IL-2 receptor. Given the association of HLH with inflammation and glucocorticoid therapy, the development of hyperglycemia is not unexpected. Descriptions of the prevalence of secondary diabetes in youth diagnosed with HLH are lacking. METHODS: Retrospective review from 2010 through 2019 of hospitalized youth 0-21 years diagnosed with HLH. The primary outcome of interest was the development of secondary diabetes, defined as a serum glucose 200 mg/dL or higher necessitating insulin therapy. RESULTS: Of 28 patients with HLH, 36 % (n=10) developed secondary diabetes. The only risk factor associated with secondary diabetes was an infectious cause of HLH (60 % vs. 27.8 %, p 0.041). Intravenous regular insulin was used in 80 % of patients with a mean duration of 9.5 days (2-24 days). Most (70 %) needed insulin within 5 days of starting steroids. Stays in the ICU were longer (median 20 vs. 3 days, p 0.007) and intubation more likely (90 vs. 45 %, p 0.041) among those with secondary diabetes. Mortality was high (16-30 %) regardless of insulin use (p 0.634). CONCLUSIONS: One-third of hospitalized pediatric patients with HLH developed secondary diabetes requiring insulin therapy. Insulin is typically started within 5 days of initiating steroids, limited to IV infusions, and often is not needed by discharge. Secondary diabetes was associated with longer ICU stays and heightened risk of intubation.

Genotype and Clinical Characteristics of Patients with Wolfram Syndrome and WFS1-related Disorders.

Objective: Wolfram syndrome (WFS) is an autosomal recessive disorder associated with juvenile-onset diabetes mellitus, optic atrophy, diabetes insipidus, and sensorineural hearing loss. We sought to elucidate the relationship between genotypic and phenotypic presentations of Wolfram syndrome which would assist clinicians in classifying the severity and prognosis of Wolfram syndrome more accurately. Approach: Patient data from the Washington University International Registry and Clinical Study for Wolfram Syndrome and patient case reports were analyzed to select for patients with two recessive mutations in the WFS1 gene. Mutations were classified as being either nonsense/frameshift variants or missense/in-frame insertion/deletion variants and statistical analysis was performed using unpaired and paired t-tests and one- and two-way ANOVA with Tukey's or Dunnett's tests. Results: A greater number of genotype variants correlated with earlier onset and a more severe presentation of Wolfram syndrome. Secondly, non-sense and frameshift variants had more severe phenotypic presentations than missense variants, as evidenced by optic atrophy emerging significantly earlier in patients with 2 nonsense/frameshift alleles compared with 0 missense transmembrane variants. In addition, the number of transmembrane in-frame variants demonstrated a statistically significant dose-effect on age of onset of diabetes mellitus and optic atrophy. Summary / Conclusions: The results contribute to our current understanding of the genotype-phenotype relationship of Wolfram syndrome, suggesting that alterations in coding sequences result in significant changes in the presentation and severity of Wolfram. The impact of these findings is significant, as the results will aid clinicians in predicting more accurate prognoses and pave the way for personalized treatments for Wolfram syndrome.

Two Cases of Wolfram Syndrome Who Were Initially Diagnosed With Type 1 Diabetes.

Objective: Early diagnosis of syndromic monogenic diabetes allows for proper management and can lead to improved quality of life in the long term. This report aimed to describe 2 genetically confirmed cases of Wolfram syndrome, a rare endoplasmic reticulum disorder characterized by insulin-dependent diabetes mellitus, optic nerve atrophy, and progressive neurodegeneration. Case Report: A 16-year-old Caucasian male patient and a 25-year-old Caucasian female patient with a history of diabetes mellitus and optic nerve atrophy presented at our medical center. Both patients were initially diagnosed with type 1 diabetes but negative for islet autoantibodies. Their body mass indexes were under 25 at the diagnosis. Their history and presentation were highly suspicious for Wolfram syndrome. Discussion: The genetic tests revealed a known Wolfram syndrome 1 (WFS1) pathogenic variant (homozygous) in the 16-year-old male patient and 2 known WFS1 pathogenic variants (compound heterozygous) in the 25-year-old female patient with diabetes mellitus and optic nerve atrophy, confirming the diagnosis of Wolfram syndrome. The first patient had a moderate form, and the second patient had a milder form of Wolfram syndrome. Conclusion: Providers should consider monogenic diabetes genetic testing, including WFS1 gene, for patients with early-onset diabetes who are negative for islet autoantibodies and lean. Two patients described in this article could have been diagnosed with Wolfram syndrome before they developed optic nerve atrophy. Genetic testing is a valuable tool for the early detection of Wolfram syndrome, which leads to proper management and improved quality of life in patients with this rare medical condition.

Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears.

Computer-assisted algorithms have become a mainstay of biomedical applications to improve accuracy and reproducibility of repetitive tasks like manual segmentation and annotation. We propose a novel pipeline for red blood cell detection and counting in thin blood smear microscopy images, named RBCNet, using a dual deep learning architecture. RBCNet consists of a U-Net first stage for cell-cluster or superpixel segmentation, followed by a second refinement stage Faster R-CNN for detecting small cell objects within the connected component clusters. RBCNet uses cell clustering instead of region proposals, which is robust to cell fragmentation, is highly scalable for detecting small objects or fine scale morphological structures in very large images, can be trained using non-overlapping tiles, and during inference is adaptive to the scale of cell-clusters with a low memory footprint. We tested our method on an archived collection of human malaria smears with nearly 200,000 labeled cells across 965 images from 193 patients, acquired in Bangladesh, with each patient contributing five images. Cell detection accuracy using RBCNet was higher than 97 %. The novel dual cascade RBCNet architecture provides more accurate cell detections because the foreground cell-cluster masks from U-Net adaptively guide the detection stage, resulting in a notably higher true positive and lower false alarm rates, compared to traditional and other deep learning methods. The RBCNet pipeline implements a crucial step towards automated malaria diagnosis.

Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy.

Despite the remarkable progress that has been made to reduce global malaria mortality by 29% in the past 5 years, malaria is still a serious global health problem. Inadequate diagnostics is one of the major obstacles in fighting the disease. An automated system for malaria diagnosis can help to make malaria screening faster and more reliable. We present an automated system to detect and segment red blood cells (RBCs) and identify infected cells in Wright-Giemsa stained thin blood smears. Specifically, using image analysis and machine learning techniques, we process digital images of thin blood smears to determine the parasitemia in each smear. We use a cell extraction method to segment RBCs, in particular overlapping cells. We show that a combination of RGB color and texture features outperforms other features. We evaluate our method on microscopic blood smear images from human and mouse and show that it outperforms other techniques. For human cells, we measure an absolute error of 1.18% between the true and the automatic parasite counts. For mouse cells, our automatic counts correlate well with expert and flow cytometry counts. This makes our system the first one to work for both human and mouse.