Electronic equipment and appliances in special wards of hospitals as a source of azole-resistant Aspergillus fumigatus: a multi-centre study from Iran.

BACKGROUND: Azole-resistant Aspergillus fumigatus (ARAf), reported as a global public health concern, has been unexpectedly observed in different countries. AIM: To identify ARAf and detect azole resistance related to the CYP51A mutation in different hospital environmental samples. METHODS: In this multi-centre study from Iran, surfaces of electronic equipment and appliances from different hospitals in Iran were sampled using cotton swabs. All samples were cultured using azole-containing agar plates (ACAPs). Recovered Aspergillus isolates were identified at the species level using partial DNA sequencing of the ß-tubulin gene. The azole susceptibility testing of A. fumigatus isolates was performed using the Clinical and Laboratory Standards Institute M38-A3 guideline. The sequencing of the CYP51A gene was also performed to detect mutations related to resistance. FINDINGS: Out of the 693 collected samples, 89 (12.8%) Aspergillus species were recovered from ACAPs. Aspergillus fumigatus (41.6%) was the most prevalent, followed by A. tubingensis (23.6%) and A. niger (15.6%). Among 37 isolates of A. fumigatus, 19 (51.3%) showed high minimum inhibitory concentration (MIC) values to at least one of the three azoles, voriconazole, itraconazole, and posaconazole. CYP51A polymorphisms were detected in all 19 isolates, of which 52.6% showed the TR34/L98H mutation. Other detected mutations were G432C, G448S, G54E/G138C, F46Y, and Y121F/M220I/D255E. T289F and G432C were the first reported mutations in ARAf. CONCLUSION: There was a considerable level of azole resistance in hospital environmental samples, a serious warning for patients vulnerable to aspergillosis. Our findings have also revealed a different mutation pattern in the CYP51A gene.

Modeling GE advance PET-scanner using FLUKA simulation code.

The general-purpose particle transport code, FLUKA, was used to model the GE Advance Positron Emission Tomography (PET) scanner (GEMS). The required spatial and energy data were extracted using the FLUKA phase-space card, USERDUMP, with its user-routine, mgdraw, consisting of full-events information. Several point and volumetric sources were modeled and the corresponding locations of the sources were identified using three different proposed algorithms which eliminate the use of conventional image-reconstruction packages. Also, having extracted the time information, the time-of-flight approach in PET (TOF-PET) was used to reconstruct the images of several volumetric positron-emitting sources, representing the best results according to the chi-square analysis data. The post-processing calculations, both on CPU and GPU, were undertaken either with MATLAB or Fortran programming.