Molecular Detection and Characterization of Newcastle Disease Virus from Chickens in Mid-Rift Valley and Central Part of Ethiopia.

Background: Newcastle disease (ND) is a highly infectious poultry disease that causes major economic losses worldwide. The disease is caused by Newcastle Disease Virus (NDV) and early detection and identification of the viral strain is essential. Having knowledge of the NDV strain genotype that circulates in some regions would help in designing an effective vaccine to control the disease. In this regard, there is little information on NDV strain in chickens in mid Rift Valley and the central part of Ethiopia. Therefore, the purpose of this study was to detect and characterize NDV strain genotype from chickens in mid-Rift Valley and the central part of Ethiopia and test whether this NDV strain genotype matches the vaccine strain currently used in the study area. Methods: A total of 98 samples: 78 (tracheal and cloacal) swabs from chicken pools of five and 20 tissue samples were collected. To detect NDV strain, conserved region of the virus Matrix (M) gene was amplified by qRT-PCR. To characterize NDV strain genotypes, M-gene positive samples were specifically re-amplified by conventional PCR targeting the Fusion (F) gene region and sequenced by Sanger method. Results: 13.26% of tested samples were positive for NDV strain in the study area with statistically significant difference (P<0.05) among the study sites. Further characterization of the F genes from NDV strain isolates by phylogenetic analysis indicated that one field isolate clustered with genotype VII whereas three of the isolates clustered to genotype I, II, and III. The isolate of the current NDV strain vaccine in use in the study area clustered with genotype II. Conclusion: The current study indicates the existence of different NDV strain genotype from that of the vaccine strain currently used. Even though large-scale characterization of several isolates is required at national level, the current study laid baseline information for the existence of variations between field NDV strain genotype and vaccine strain currently used against ND in the country.

A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules.

BACKGROUND: Large lung nodules (≥15 mm) have the highest risk of malignancy, and may exhibit important differences in phenotypic or clinical characteristics to their smaller counterparts. Existing risk models do not stratify large nodules well. We aimed to develop and validate an integrated segmentation and classification pipeline, incorporating deep-learning and traditional radiomics, to classify large lung nodules according to cancer risk. METHODS: 502 patients from five U.K. centres were recruited to the large-nodule arm of the retrospective LIBRA study between July 2020 and April 2022. 838 CT scans were used for model development, split into training and test sets (70% and 30% respectively). An nnUNet model was trained to automate lung nodule segmentation. A radiomics signature was developed to classify nodules according to malignancy risk. Performance of the radiomics model, termed the large-nodule radiomics predictive vector (LN-RPV), was compared to three radiologists and the Brock and Herder scores. FINDINGS: 499 patients had technically evaluable scans (mean age 69 ± 11, 257 men, 242 women). In the test set of 252 scans, the nnUNet achieved a DICE score of 0.86, and the LN-RPV achieved an AUC of 0.83 (95% CI 0.77-0.88) for malignancy classification. Performance was higher than the median radiologist (AUC 0.75 [95% CI 0.70-0.81], DeLong p = 0.03). LN-RPV was robust to auto-segmentation (ICC 0.94). For baseline solid nodules in the test set (117 patients), LN-RPV had an AUC of 0.87 (95% CI 0.80-0.93) compared to 0.67 (95% CI 0.55-0.76, DeLong p = 0.002) for the Brock score and 0.83 (95% CI 0.75-0.90, DeLong p = 0.4) for the Herder score. In the international external test set (n = 151), LN-RPV maintained an AUC of 0.75 (95% CI 0.63-0.85). 18 out of 22 (82%) malignant nodules in the Herder 10-70% category in the test set were identified as high risk by the decision-support tool, and may have been referred for earlier intervention. INTERPRETATION: The model accurately segments and classifies large lung nodules, and may improve upon existing clinical models. FUNDING: This project represents independent research funded by: 1) Royal Marsden Partners Cancer Alliance, 2) the Royal Marsden Cancer Charity, 3) the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London, 4) the National Institute for Health Research (NIHR) Biomedical Research Centre at Imperial College London, 5) Cancer Research UK (C309/A31316).

Awareness of vitamin D deficiency among at-risk patients.

BACKGROUND: Vitamin D deficiency is a significant problem for a growing proportion of the UK population. Individuals with dark or covered skin are at particularly high risk due to ethno-cultural, environmental and genetic factors. We assessed the level of awareness of vitamin D deficiency among at-risk patients in order to identify groups most in need of education. FINDINGS: A cross-sectional survey using a piloted questionnaire was conducted among consecutive at-risk patients without a diagnosis of Vitamin D deficiency arriving at a large inner city general practice in the North West of England over a five day period. The survey was completed by 221 patients. The mean age was 35 years. 28% of them (n = 61) had never heard about vitamin D. Older patients (p = 0.003) were less likely to have heard about vitamin D. 54% of participants were unaware of the commonest symptoms of vitamin D deficiency. 34% did not expose their skin other than their face in the last one year, and 11% did not include vitamin D rich foods in their diet. CONCLUSION: The majority of at-risk patients are aware of vitamin D; nevertheless, there is a significant lack of knowledge among older people, who have higher morbidity. A programme of targeted education of the at-risk population is recommended.