Using trained dogs and organic semi-conducting sensors to identify asymptomatic and mild SARS-CoV-2 infections: an observational study

BACKGROUND: A rapid, accurate, non-invasive diagnostic screen is needed to identify people with SARS-CoV-2 infection. We investigated whether organic semi-conducting (OSC) sensors and trained dogs could distinguish between people infected with asymptomatic or mild symptoms, and uninfected individuals, and the impact of screening at ports-of-entry. METHODS: Odour samples were collected from adults, and SARS-CoV-2 infection status confirmed using RT-PCR. OSC sensors captured the volatile organic compound (VOC) profile of odour samples. Trained dogs were tested in a double-blind trial to determine their ability to detect differences in VOCs between infected and uninfected individuals, with sensitivity and specificity as the primary outcome. Mathematical modelling was used to investigate the impact of bio-detection dogs for screening. RESULTS: About, 3921 adults were enrolled in the study and odour samples collected from 1097 SARS-CoV-2 infected and 2031 uninfected individuals. OSC sensors were able to distinguish between SARS-CoV-2 infected individuals and uninfected, with sensitivity from 98% (95% CI 95–100) to 100% and specificity from 99% (95% CI 97–100) to 100%. Six dogs were able to distinguish between samples with sensitivity ranging from 82% (95% CI 76–87) to 94% (95% CI 89–98) and specificity ranging from 76% (95% CI 70–82) to 92% (95% CI 88–96). Mathematical modelling suggests that dog screening plus a confirmatory PCR test could detect up to 89% of SARS-CoV-2 infections, averting up to 2.2 times as much transmission compared to isolation of symptomatic individuals only. CONCLUSIONS: People infected with SARS-CoV-2, with asymptomatic or mild symptoms, have a distinct odour that can be identified by sensors and trained dogs with a high degree of accuracy. Odour-based diagnostics using sensors and/or dogs may prove a rapid and effective tool for screening large numbers of people. Trial Registration NCT04509713 (clinicaltrials.gov)

Contiguous Deletion of the X-Linked Adrenoleukodystrophy Gene (ABCD1) and DXS1357E: A Novel Neonatal Phenotype Similar to Peroxisomal Biogenesis Disorders

X-linked adrenoleukodystrophy (X-ALD) results from mutations in ABCD1. ABCD1 resides on Xq28 and encodes an integral peroxisomal membrane protein (ALD protein [ALDP]) that is of unknown function and that belongs to the ATP-binding cassette–transporter superfamily. Individuals with ABCD1 mutations accumulate very-long-chain fatty acids (VLCFA) (carbon length >22). Childhood cerebral X-ALD is the most devastating form of the disease. These children have the earliest onset (age 7.2 ± 1.7 years) among the clinical phenotypes for ABCD1 mutations, but onset does not occur at <3 years of age. Individuals with either peroxisomal biogenesis disorders (PBD) or single-enzyme deficiencies (SED) in the peroxisomal β-oxidation pathway—disorders such as acyl CoA oxidase deficiency and bifunctional protein deficiency—also accumulate VLCFA, but they present during the neonatal period. Until now, it has been possible to distinguish unequivocally between individuals with these autosomal recessively inherited syndromes and individuals with ABCD1 mutations, on the basis of the clinical presentation and measurement of other biochemical markers. We have identified three newborn boys who had clinical symptoms and initial biochemical results consistent with PBD or SED. In further study, however, we showed that they lacked ALDP, and we identified deletions that extended into the promoter region of ABCD1 and the neighboring gene, DXS1357E. Mutations in DXS1357E and the ABCD1 promoter region have not been described previously. We propose that the term “contiguous ABCD1 DXS1357E deletion syndrome” (CADDS) be used to identify this new contiguous-gene syndrome. The three patients with CADDS who are described here have important implications for genetic counseling, because individuals with CADDS may previously have been misdiagnosed as having an autosomal recessive PBD or SE

Cornelia de Lange syndrome in diverse populations

Cornelia de Lange syndrome (CdLS) is a dominant multisystemic malformation syndrome due to mutations in five genes—NIPBL, SMC1A, HDAC8, SMC3, and RAD21. The characteristic facial dysmorphisms include microcephaly, arched eyebrows, synophrys, short nose with depressed bridge and anteverted nares, long philtrum, thin lips, micrognathia, and hypertrichosis. Most affected individuals have intellectual disability, growth deficiency, and upper limb anomalies. This study looked at individuals from diverse populations with both clinical and molecularly confirmed diagnoses of CdLS by facial analysis technology. Clinical data and images from 246 individuals with CdLS were obtained from 15 countries. This cohort included 49% female patients and ages ranged from infancy to 37 years. Individuals were grouped into ancestry categories of African descent, Asian, Latin American, Middle Eastern, and Caucasian. Across these populations, 14 features showed a statistically significant difference. The most common facial features found in all ancestry groups included synophrys, short nose with anteverted nares, and a long philtrum with thin vermillion of the upper lip. Using facial analysis technology we compared 246 individuals with CdLS to 246 gender/age matched controls and found that sensitivity was equal or greater than 95% for all groups. Specificity was equal or greater than 91%. In conclusion, we present consistent clinical findings from global populations with CdLS while demonstrating how facial analysis technology can be a tool to support accurate diagnoses in the clinical setting. This work, along with prior studies in this arena, will assist in earlier detection, recognition, and treatment of CdLS worldwide.Supplementary Table 1 Participants with photographs in Figures 2-5 from 10 countries. Supplementary Table 2. Geometric and texture feature comparison of Global (combined African descent, Asian, Latin American, Caucasian) CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 3. Geometric and texture feature comparison of African descent CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 4. Geometric and texture feature comparison of Asian CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 5. Geometric and texture feature comparison of Latin American CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 6. Geometric and texture feature comparison of Caucasian CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Figure 1. Global: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 2. African: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 3. Asian: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 4. Latin American: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 5. Caucasian: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selectedPK and MM are supported by the Division of Intramural Research at the National Human Genome Research, NIH. Partial funding of this project was from a philanthropic gift from the Government of Abu Dhabi to the Children's National Health System. VS is supported by the Chulalongkorn Academic Advancement Into Its 2nd Century Project and the Thailand Research Fund. We would also like to acknowledge other clinicians who supported this work—MZ, JP, and GC. We would like to acknowledge that IDK, LD, MK, and SR are supported by the CdLS Center Endowed Funds at The Children's Hospital of Philadelphia and PO1 HD052860 from the NICHD. ES is supported by a fellowship from PKS Italia and PKSKids USA. LD was also supported by a postdoctoral training grant (T32 GM008638) from the NIGMS.http://wileyonlinelibrary.com/journal/ajmga2020-02-01hj2019Genetic

Cornelia de Lange syndrome in diverse populations.

Cornelia de Lange syndrome (CdLS) is a dominant multisystemic malformation syndrome due to mutations in five genes-NIPBL, SMC1A, HDAC8, SMC3, and RAD21. The characteristic facial dysmorphisms include microcephaly, arched eyebrows, synophrys, short nose with depressed bridge and anteverted nares, long philtrum, thin lips, micrognathia, and hypertrichosis. Most affected individuals have intellectual disability, growth deficiency, and upper limb anomalies. This study looked at individuals from diverse populations with both clinical and molecularly confirmed diagnoses of CdLS by facial analysis technology. Clinical data and images from 246 individuals with CdLS were obtained from 15 countries. This cohort included 49% female patients and ages ranged from infancy to 37 years. Individuals were grouped into ancestry categories of African descent, Asian, Latin American, Middle Eastern, and Caucasian. Across these populations, 14 features showed a statistically significant difference. The most common facial features found in all ancestry groups included synophrys, short nose with anteverted nares, and a long philtrum with thin vermillion of the upper lip. Using facial analysis technology we compared 246 individuals with CdLS to 246 gender/age matched controls and found that sensitivity was equal or greater than 95% for all groups. Specificity was equal or greater than 91%. In conclusion, we present consistent clinical findings from global populations with CdLS while demonstrating how facial analysis technology can be a tool to support accurate diagnoses in the clinical setting. This work, along with prior studies in this arena, will assist in earlier detection, recognition, and treatment of CdLS worldwide

Cornelia de Lange Syndrome in Diverse Populations.

Cornelia de Lange syndrome (CdLS) is a dominant multisystemic malformation syndrome due to mutations in five genes—NIPBL, SMC1A, HDAC8, SMC3, and RAD21. The characteristic facial dysmorphisms include microcephaly, arched eyebrows, synophrys, short nose with depressed bridge and anteverted nares, long philtrum, thin lips, micrognathia, and hypertrichosis. Most affected individuals have intellectual disability, growth deficiency, and upper limb anomalies. This study looked at individuals from diverse populations with both clinical and molecularly confirmed diagnoses of CdLS by facial analysis technology. Clinical data and images from 246 individuals with CdLS were obtained from 15 countries. This cohort included 49% female patients and ages ranged from infancy to 37 years. Individuals were grouped into ancestry categories of African descent, Asian, Latin American, Middle Eastern, and Caucasian. Across these populations, 14 features showed a statistically significant difference. The most common facial features found in all ancestry groups included synophrys, short nose with anteverted nares, and a long philtrum with thin vermillion of the upper lip. Using facial analysis technology we compared 246 individuals with CdLS to 246 gender/age matched controls and found that sensitivity was equal or greater than 95% for all groups. Specificity was equal or greater than 91%. In conclusion, we present consistent clinical findings from global populations with CdLS while demonstrating how facial analysis technology can be a tool to support accurate diagnoses in the clinical setting. This work, along with prior studies in this arena, will assist in earlier detection, recognition, and treatment of CdLS worldwide.Supplementary Table 1 Participants with photographs in Figures 2-5 from 10 countries. Supplementary Table 2. Geometric and texture feature comparison of Global (combined African descent, Asian, Latin American, Caucasian) CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 3. Geometric and texture feature comparison of African descent CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 4. Geometric and texture feature comparison of Asian CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 5. Geometric and texture feature comparison of Latin American CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 6. Geometric and texture feature comparison of Caucasian CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Figure 1. Global: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 2. African: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 3. Asian: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 4. Latin American: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 5. Caucasian: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selectedPK and MM are supported by the Division of Intramural Research at the National Human Genome Research, NIH. Partial funding of this project was from a philanthropic gift from the Government of Abu Dhabi to the Children's National Health System. VS is supported by the Chulalongkorn Academic Advancement Into Its 2nd Century Project and the Thailand Research Fund. We would also like to acknowledge other clinicians who supported this work—MZ, JP, and GC. We would like to acknowledge that IDK, LD, MK, and SR are supported by the CdLS Center Endowed Funds at The Children's Hospital of Philadelphia and PO1 HD052860 from the NICHD. ES is supported by a fellowship from PKS Italia and PKSKids USA. LD was also supported by a postdoctoral training grant (T32 GM008638) from the NIGMS.http://wileyonlinelibrary.com/journal/ajmga2020-02-01hj2019Genetic