Effect of primary and secondary Fasciola gigantica infection on specific IgG responses, hepatic enzyme levels and weight gain in buffaloes.

Buffaloes, as highly susceptible definitive hosts of Fasciola gigantica, suffer from a high infection rate of fasciolosis, which causes enormous economic losses. Repeat infection is responsible for this high rate; thus, elucidating the protective immunity mechanism in repeat infection is decisive in fasciolosis prevention. Herein, a secondary experimental infection model was established to preliminarily reveal the protective immunity that occurs in repeat infection. In brief, animals were assigned to three groups: group A (uninfected control), group B (primary infection) and group C (secondary infection). Buffaloes were autopsied 20 weeks post-infection for measurements of the recovered flukes and hepatic examination. In addition, the detection of specific antibody (IgG) responses to F. gigantica excretory-secretory product (FgESP) throughout the whole period and weight gain throughout the first 4 months as a percentage (%) of the starting weight were also determined. The serum hepatic enzyme gamma glutathione transferase (GGT) levels were monitored to assess hepatic damage throughout the study period. Infection establishment was compared between group B and group C. Similar specific IgG patterns were observed between group B and group C, and hepatic damage was more severe in group C than group B. Significant differences in weight gain as a percentage of the start weight were observed between group A and group B at the 3rd and 4th months postprimary infection, while significant differences were not observed between group A and group C or group B and group C. Our results suggest that challenge infection cannot induce resistance against F. gigantica in buffaloes, which is consistent with the protective immunity against Fasciola hepatica reinfection observed in sheep and goats.

Identification of Kidney Transplant Recipients with Coronavirus Disease 2019.

Coronavirus disease 2019 (COVID-19) is a novel and lethal infectious disease, posing a threat to global health security. The number of cases has increased rapidly, but no data concerning kidney transplant (KTx) recipients infected with COVID-19 are available. To present the epidemiological, clinical, and therapeutic characteristics of KTx recipients infected with COVID-19, we report on a case series of five patients who were confirmed as having COVID-19 through nucleic acid testing (NAT) from January 1, 2020 to February 28, 2020. The most common symptoms on admission to hospital were fever (five patients, 100%), cough (five patients, 100%), myalgia or fatigue (three patients, 60%), and sputum production (three patients, 60%); serum creatinine or urea nitrogen levels were slightly higher than those before symptom onset. Four patients received a reduced dose of maintenance immunosuppressive therapy during hospitalization. As of March 4, 2020 NAT was negative for COVID-19 in three patients twice in succession, and their computed tomography scans showed improved images. Although greater patient numbers and long-term follow-up data are needed, our series demonstrates that mild COVID-19 infection in KTx recipients can be managed using symptomatic support therapy combined with adjusted maintenance immunosuppressive therapy.

Oil source recognition technology using concentration-synchronous-matrix-fluorescence spectroscopy combined with 2D wavelet packet and probabilistic neural network.

Developing an accurate, rapid and economic oil source recognition method is essential for water recourses protection. Concentration-synchronous-matrix-fluorescence (CSMF) spectroscopy combined with 2D wavelet packet and probabilistic neural network (PNN) was proposed for source recognition of crude oil and petroleum products samples in this study. 2D wavelet packet was used to extract wavelet packet coefficients as the feature vectors from CSMF contour image and four algorithms, Back-propagation (BP) neural network, Radial based function neural network (RBFNN), Support vector Machine (SVM) and probabilistic neural network (PNN) were carried out for pattern recognition. With the introduction of interference factors such as weathering and sea water adulteration to the three samples from Bohai bay territory of China, the comparison about accuracy and recognition time of the four methods was discussed and the results showed that PNN network maintain the highest recognition accuracy and speed. These findings may offer potential application for oil spill recognition for unconventional oil.