Diarrhoea outbreak caused by coinfections of Cryptosporidium parvum subtype IIdA20G1 and rotavirus in pre-weaned dairy calves.

Diarrhoea is one of the most important syndromes in neonatal calves. In industrialized nations with intensive animal farming, Cryptosporidium spp. and rotavirus are primary causes of calf diarrhoea, but the role of these and other enteric pathogens is not clear in China. In November and December 2018, a diarrhoea outbreak was identified in over 150 pre-weaned calves on a dairy farm in Heilongjiang Province, northeast China and approximately 60 calves died. To determine the cause of the outbreak, we analyzed 131 faecal samples collected from pre-weaned calves (0-2 months) during (n = 114) and after the outbreak (n = 17). Initially, 10 diarrheic samples during the outbreak and 10 non-diarrheic samples after the outbreak were screened for rotavirus, coronavirus, Escherichia coli K99 and Cryptosporidium parvum by using an enzymatic immunoassay (EIA). In addition, 81 other samples were tested specifically for rotavirus by EIA, and all 131 samples were analyzed for Cryptosporidium spp., Giardia duodenalis and Enterocytozoon bieneusi by PCR. The initial EIA analysis identified C. parvum (8/10) and rotavirus (5/10) as the dominant pathogens in calves during the outbreak, while both pathogens were detected at lower frequency after the outbreak (2/10 and 1/10, respectively). Further PCR analyses indicated that the occurrence of C. parvum infections in calves was significantly higher during the outbreak (75.4%, 86/114) than after the outbreak (11.8%, 2/17; odds ratio [OR] = 23.0), and was significantly associated with the occurrence of watery diarrhoea (OR = 15.7) and high oocyst shedding intensity. All C. parvum isolates were identified as subtype IIdA20G1. Among other pathogens analyzed, the overall prevalence of rotavirus, G. duodenalis and E. bieneusi was 19.8% (20/101), 38.9% (51/131) and 42.0% (55/131) in calves, respectively, without significant differences during and after the outbreak. Among the three pathogens, only the rotavirus infection was associated with diarrhoea in calves. More importantly, coinfections of C. parvum and rotavirus were significantly associated with the occurrence of watery diarrhoea in calves and were seen only during the outbreak. Thus, C. parvum subtype IIdA20G1 and rotavirus appeared to be responsible for this diarrhoea outbreak. Control measures should be implemented to effectively prevent the concurrent transmission of these enteric pathogens in pre-weaned dairy calves in China.

Fine-Tuning Convolutional Neural Networks for COVID-19 Detection from Chest X-ray Images.

As the COVID-19 pandemic continues to ravage the world, the use of chest X-ray (CXR) images as a complementary screening strategy to reverse transcription-polymerase chain reaction (RT-PCR) testing continues to grow owing to its routine clinical application to respiratory diseases. We performed extensive convolutional neural network (CNN) fine-tuning experiments and identified that models pretrained on larger out-of-domain datasets show an improved performance. This suggests that a priori knowledge of models from out-of-field training should also apply to X-ray images. With appropriate hyperparameters selection, we found that higher resolution images carry more clinical information, and the use of mixup in training improved the performance of the model. The experimental showed that our proposed transfer learning present state-of-the-art results. Furthermore, we evaluated the performance of our model with a small amount of downstream training data and found that the model still performed well in COVID-19 identification. We also explored the mechanism of model detection using a gradient-weighted class activation mapping (Grad-CAM) method for CXR imaging to interpret the detection of radiology images. The results helped us understand how the model detects COVID-19, which can be used to discover new visual features and assist radiologists in screening.

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.

The Efficacy of Computerized Cognitive Behavioral Therapy for Depressive and Anxiety Symptoms in Patients With COVID-19: Randomized Controlled Trial.

BACKGROUND: The prevalence of depressive and anxiety symptoms in patients with COVID-19 is higher than usual. Previous studies have shown that there are drug-to-drug interactions between antiretroviral drugs and antidepressants. Therefore, an effective and safe treatment method was needed. Cognitive behavioral therapy (CBT) is the first-line psychological therapy in clinical treatment. Computerized CBT (cCBT) was proven to be an effective alternative to CBT and does not require face-to-face therapy between a therapist and the patient, which suited the COVID-19 pandemic response. OBJECTIVE: This study aims to evaluate the efficacy of the cCBT program we developed in improving depressive and anxiety symptoms among patients with COVID-19. METHODS: We customized a cCBT program focused on improving depressive and anxiety symptoms among patients with COVID-19, and then, we assessed its effectiveness. Screening was based on symptoms of depression or anxiety for patients who scored ≥7 on the Hamilton Depression Rating Scale (HAMD17) or the Hamilton Anxiety Scale (HAMA). A total of 252 patients with COVID-19 at five sites were randomized into two groups: cCBT + treatment as usual (TAU; n=126) and TAU without cCBT (n=126). The cCBT + TAU group received the cCBT intervention program for 1 week. The primary efficacy measures were the HAMD17 and HAMA scores. The secondary outcome measures were the Self-Rating Depression Scale (SDS), Self-Rating Anxiety Scale (SAS), and Athens Insomnia Scale (AIS). Assessments were carried out pre- and postintervention. The patients' symptoms of anxiety and depression in one of the centers were assessed again within 1 month after the postintervention assessment. RESULTS: The cCBT + TAU group displayed a significantly decreased score on the HAMD17, HAMA, SDS, SAS, and AIS after the intervention compared to the TAU group (all P<.001). A mixed-effects repeated measures model revealed significant improvement in symptoms of depression (HAMD17 and SDS scores, both P<.001), anxiety (HAMA and SAS scores, both P<.001), and insomnia (AIS score, P=.002) during the postintervention and follow-up periods in the cCBT + TAU group. Additionally, the improvement of insomnia among females (P=.14) and those with middle school education (P=.48) in the cCBT + TAU group showed no significant differences when compared to the TAU group. CONCLUSIONS: The findings of this study suggest that the cCBT program we developed was an effective nonpharmacological treatment for symptoms of anxiety, depression, and insomnia among patients with COVID-19. Further research is warranted to investigate the long-term effects of cCBT for symptoms of anxiety, depression, and insomnia in patients with COVID-19. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2000030084; http://www.chictr.org.cn/showprojen.aspx?proj=49952.