Ten-year epidemiology and risk factors of cytomegalovirus infection in hematopoietic stem cell transplantation patients in Taiwan.

BACKGROUND: Cytomegalovirus (CMV) can cause infection and critical diseases in hematopoietic stem cell transplantation (HSCT) recipients. This study aimed to explore the cumulative incidence and risk factors for CMV infection and disease among HSCT recipients in Taiwan. METHODS: This retrospective cohort study using the Taiwan Blood and Marrow Transplantation Registry (TBMTR) included HSCT recipients between 2009 and 2018 in Taiwan. The primary outcome was cumulative incidence of CMV infection or disease at day 100 after HSCT. Secondary outcomes included day 180 cumulative incidence of CMV infection or disease, infection sites, risk factors for CMV infection or disease, survival analysis, and overall survival after CMV infection and disease. RESULTS: There were 4394 HSCT recipients included in the study (2044 auto-HSCT and 2350 allo-HSCT). The cumulative incidence of CMV infection and disease was significantly higher in allo-HSCT than in auto-HSCT patients at day 100 (53.7% vs. 6.0%, P < 0.0001 and 6.1% vs. 0.9%, P < 0.0001). Use of ATG (HR 1.819, p < 0.0001), recipient CMV serostatus positive (HR 2.631, p < 0.0001) and acute GVHD grades ≥ II (HR 1.563, p < 0.0001) were risk factors for CMV infection, while matched donor (HR 0.856, p = 0.0180) and myeloablative conditioning (MAC) (HR 0.674, p < 0.0001) were protective factors. CONCLUSION: The study revealed a significant disparity in terms of the incidence, risk factors, and clinical outcomes of CMV infection and disease between auto and allo-HSCT patients. These findings underscore the importance of considering these factors in the management of HSCT recipients to improve outcomes related to CMV infections.

Developing an automatic warning system for anomalous chicken dispersion and movement using deep learning and machine learning.

Chicken is a major source of dietary protein worldwide. The dispersion and movement of chickens constitute vital indicators of their health and status. This is especially evident in Taiwanese native chickens (TNCs), a local variety which is high in physical activity when healthy. Conventionally, the dispersion and movement of chicken flocks are observed in patrols. However, manual patrolling is laborious and time-consuming. Moreover, frequent patrols increase the risk of carrying pathogens into chicken farms. To address these issues, this study proposes an approach to develop an automatic warning system for anomalous dispersion and movement of chicken flocks in commercial chicken farms. Embendded systems were developed to acquire videos of chickens from overhead view in a chicken house, in which approximately 20,000 TNCs were raised for a period of 10 wk. Each video was 5-min in length. The videos were transmitted to a remote cloud server and were converted into images. A You Only Look Once-version 7 tiny (YOLOv7-tiny) object detection model was trained to detect chickens in the images. The dispersion of the chicken flocks in a 5-min long video was calculated using nearest neighbor index (NNI). The movement of the chicken flocks in a 5-min long video was quantified using simple online and real-time tracking algorithm (SORT). The normal ranges (i.e., 95% confidence intervals) of chicken dispersion and movement were established using an autoregressive integrated moving average (ARIMA) model and a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model, respectively. The system allows farmers to check up on the chicken farm only when the dispersion or movement values were not in the normal ranges. Thus, labor time can be saved and the risk of carrying pathogens into chicken farms can be reduced. The trained YOLOv7-tiny model achieved an average precision of 98.2% in chicken detection. SORT achieved a multiple object tracking accuracy of 95.3%. The ARIMA and SARIMAX achieved a mean absolute percentage error 3.71% and 13.39%, respectively, in forecasting dispersion and movement. The proposed approach can serve as a solution for automatic monitoring of anomalous chicken dispersion and movement in chicken farming, alerting farmers of potential health risks and environmental hazards in chicken farms.

Isolation, purification and characterization of hemerythrin from Methylococcus capsulatus (Bath).

Earlier work from our laboratory has indicated that a hemerythrin-like protein was over-produced together with the particulate methane monooxygenase (pMMO) when Methylococcus capsulatus (Bath) was grown under high copper concentrations. A homologue of hemerythrin had not previously been found in any prokaryote. To confirm its identity as a hemerythrin, we have isolated and purified this protein by ion-exchange, gel-filtration and hydrophobic interaction chromatography, and characterized it by mass spectrometry, UV-visible, CD, EPR and resonance Raman spectroscopy. On the basis of biophysical and multiple sequence alignment analysis, the protein isolated from M. capsulatus (Bath) is in accord with hemerythrins previously reported from higher organisms. Determination of the Fe content in conjunction with molecular-weight estimation and mass analysis indicates that the native hemerythrin in M. capsulatus (Bath) is a monomer with molecular mass 14.8 kDa, in contrast to hemerythrins from other eukaryotic organisms, where they typically exist as a tetramer or higher oligomers.