Evaluation of Paromomycin Treatment for Cryptosporidium serpentis Infection in Eastern Indigo Snakes (Drymarchon couperi)

Thirty-four eastern indigo snakes ( Drymarchon couperi ) naturally infected with Cryptosporidium serpentis were randomly divided into two groups. The first group received 360 mg/kg paromomycin twice weekly in a food item for six weeks, while the second group received the food item with no treatment. Cloacal swabs were collected every two months for six months to measure C. serpentis shedding by quantitative polymerase chain reaction testing (qPCR). Snakes that were qPCR negative after six 6 months were immunosuppressed with a single dose of 4 mg/kg dexamethasone sodium-phosphate SC. These snakes were then screened by qPCR for an additional 6 months as described above. Snakes that were qPCR negative after one year of serial sampling were then re-evaluated for C. serpentis via gastric biopsy for histological and qPCR analyses. The paromomycin-treated group were significantly (p=0.008) more likely to test qPCR negative (8/17; 47%, 95% Confidence Interval [CI]: 23.2-70.7) than the control snakes (1/17; 5.8%, 95% CI: 0.01-16.9) prior to immunosuppression. However, there was no significant difference (p=0.5) in C. serpentis status following immunosuppression as only 2/17 (11.7%, 95% CI: 0.01-26.9) paromomycin-treated snakes were qPCR negative six months after immunosuppression compared to 1/17 (5.8%, 95% CI: 95% CI: 0.01-16.9) control snakes. These findings suggest that 360 mg/kg paromomycin twice weekly for six weeks in a food item is ineffective in eliminating C. serpentis in naturally infected D. couperi .

Deep learning for COVID-19 detection based on CT images

COVID-19 has tremendously impacted patients and medical systems globally. Computed tomography images can effectively complement the reverse transcription-polymerase chain reaction testing. This study adopted a convolutional neural network for COVID-19 testing. We examined the performance of different pre-trained models on CT testing and identified that larger, out-of-field datasets boost the testing power of the models. This suggests that a priori knowledge of the models from out-of-field training is also applicable to CT images. The proposed transfer learning approach proves to be more successful than the current approaches described in literature. We believe that our approach has achieved the state-of-the-art performance in identification thus far. Based on experiments with randomly sampled training datasets, the results reveal a satisfactory performance by our model. We investigated the relevant visual characteristics of the CT images used by the model; these may assist clinical doctors in manual screening.

Aspergillus Polymerase Chain Reaction—An Update on Technical Recommendations, Clinical Applications, and Justification for Inclusion in the Second Revision of the EORTC/MSGERC Definitions of Invasive Fungal Disease

Aspergillus polymerase chain reaction testing of blood and respiratory samples has recently been included in the second revision of the EORTC/MSGERC definitions for classifying invasive fungal disease. This is a result of considerable efforts to standardize methodology, the availability of commercial assays and external quality control programs, and additional clinical validation. This supporting article provides both clinical and technical justifications for its inclusion while also summarizing recent advances and likely future developments in the molecular diagnosis of invasive aspergillosis.

COVID-19 Preprocedural Testing: What About the False Positives?

In the COVID-19 era, preprocedural patients are almost uniformly screened for symptoms, asked to quarantine preoperatively, and then undergo a test of uncertain validity with very low pretest probability. A small percentage of these tests return positive. As a result, surgical procedures are delayed and patients are required to quarantine. Are these asymptomatic patients truly positive for COVID-19? What are the impacts of these test results on the patient and the health care system? In the following commentary, we review how the uncertain validity of reverse transcription polymerase chain reaction testing combined with a low-prevalence population predisposes for false-positive results. As a mitigation strategy, we ask that readers refocus on the fundamental principal of diagnostic testing: pretest probability.