A novel hybridization capture method for direct whole genome sequencing of clinical specimens to inform Legionnaires' disease investigations.

The unprecedented precision and resolution of whole genome sequencing (WGS) can provide definitive identification of infectious agents for epidemiological outbreak tracking. WGS approaches, however, are frequently impeded by low pathogen DNA recovery from available primary specimens or unculturable samples. A cost-effective hybrid capture assay for Legionella pneumophila WGS analysis directly on primary specimens was developed. DNA from a diverse range of sputum and autopsy specimens PCR-positive for L. pneumophila serogroup 1 (LPSG1) was enriched with this method, and WGS was performed. All tested specimens were determined to be enriched for Legionella reads (up to 209,000-fold), significantly improving the discriminatory power to compare relatedness when no clinical isolate was available. We found the WGS data from some enriched specimens to differ by less than five single-nucleotide polymorphisms (SNPs) when compared to the WGS data of a matched culture isolate. This testing and analysis retrospectively provided previously unconfirmed links to environmental sources for clinical specimens of sputum and autopsy lung tissue. The latter provided the additional information needed to identify the source of these culture-negative cases associated with the South Bronx 2015 Legionnaires' disease (LD) investigation in New York City. This new method provides a proof of concept for future direct clinical specimen hybrid capture enrichment combined with WGS and bioinformatic analysis during outbreak investigations.IMPORTANCELegionnaires' disease (LD) is a severe and potentially fatal type of pneumonia primarily caused by inhalation of Legionella-contaminated aerosols from man-made water or cooling systems. LD remains extremely underdiagnosed as it is an uncommon form of pneumonia and relies on clinicians including it in the differential and requesting specialized testing. Additionally, it is challenging to obtain clinical lower respiratory specimens from cases with LD, and when available, culture requires specialized media and growth conditions, which are not available in all microbiology laboratories. In the current study, a method for Legionella pneumophila using hybrid capture by RNA baiting was developed, which allowed us to generate sufficient genome resolution from L. pneumophila serogroup 1 PCR-positive clinical specimens. This new approach offers an additional tool for surveillance of future LD outbreaks where isolation of Legionella is not possible and may help solve previously unanswered questions from past LD investigations.

Self-Trained Convolutional Neural Network (CNN) for Tuberculosis Diagnosis in Medical Imaging.

Background Tuberculosis (TB) is a serious infectious disease that primarily affects the lungs. Despite advancements in the medical industry, TB remains a significant global health challenge. Early and accurate detection of TB is crucial for effective treatment and reducing transmission. This article presents a deep learning approach using convolutional neural networks (CNNs) to improve TB detection in chest X-ray images. Methods For the dataset, we collected 7000 images from Kaggle.com, of which 3500 exhibit tuberculosis evidence and the remaining 3500 are normal. Preprocessing techniques such as wavelet transformation, contrast-limited adaptive histogram equalisation (CLAHE), and gamma correction were applied to enhance the image quality. Random flipping, random rotation, random resizing, and random rescaling were among the techniques employed to increase dataset variability and model robustness. Convolutional, max-pooling, flatten, and dense layers comprised the CNN model architecture. For binary classification, sigmoid activation was utilised in the output layer and rectified linear unit (ReLU) activation in the input and hidden layers. Results The CNN model achieved an accuracy of ~96.57% in detecting TB from chest X-ray images, demonstrating the effectiveness of deep learning, particularly CNNs, in this application. Self-trained CNNs have optimised the results as compared to the transfer learning of various pre-trained models. Conclusion This study shows how well deep learning-in particular, CNNs-performs in the identification of tuberculosis. Subsequent efforts have to give precedence to optimising the model by obtaining more extensive datasets from the local hospitals and localities, which are vulnerable to TB, and stress the possibility of augmenting diagnostic knowledge in medical imaging via machine learning methodologies.

Observational study on the clinical profile and treatment outcome on long-term follow-up of COVID-19 associated mucormycosis.

MATERIALS AND METHODS: Patients diagnosed with COVID-19 associated mucormycosis were followed up for 6 months to study the clinical profile, readmissions, long-term treatment outcome and the mortality rate. RESULTS: Among 37 patients with COVID-19 associated mucormycosis, the mortality rate was 33.3 %, 42.9% and 100 % among patients with mild, moderate and severe COVID-19 infection. One month after discharge, among the 20 patients who survived, 10 (50 %) patients had worsening symptoms and required readmission. Nine patients required readmission for amphotericin and 1 patient was admitted for surgical intervention. On follow-up at 1 month, 30 % (6/20) patients became asymptomatic. However, at 3 months, 45 % (9/20) of the patients were asymptomatic. At 6 months of follow-up, 80 % (16/20) were asymptomatic. At 6 months, one each had residual abnormalities like visual loss in one eye, visual field deficit, change in voice and residual weakness of the limbs along with cranial nerve paresis. CONCLUSION: The follow-up study revealed that a significant number of patients required readmission within the first month, but most of the patients became asymptomatic by 6 months. The readmission rate was higher in patients who received a shorter duration of amphotericin.