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Combating climate change is a global endeavor, requiring participation from every industry including healthcare. A team comprising of radiologists, radiographers and administrators from the National University Hospital Department of Diagnostic Imaging came together to examine key contributors of carbon emission in a radiology department. Three contributors, namely high electrical consumption from reporting workstations, excessive paper usage and insufficient recycling were identified through data logging and workflow process reviews. Given the high electrical consumption of reporting workstations, automatic shutdown via IT control was implemented after office hours. Educational posters were placed in various locations to remind end-users to shut down their workstations when not in use. Paperless workflow was introduced by implementing electronic documentation through a functionality upgrade of the current Radiology Information System (RIS), procurement of mobile workstations and transitioning staff towards electronic documentation and consent-taking. Lastly, recycling was introduced to bring down the volume of disposed general waste. Clean plastic waste such as expended contrast syringe packages were consolidated for recycling; coupled with end user education, inculcating a culture of reuse, reduce and recycle within the department. End results were measured and compared to baseline readings before implementation. The green solutions implemented brought about a 50% reduction in electrical consumption of reporting workstation, 30% reduction off paper usage and approximately 20 Tonnes in reduction of CO 2 carbon footprint emission. Secondary benefits include monetary savings from reduction in paper procurement and energy cost and improved patient safety from paperless workflow. We must continuously review established workflow and processes to drive sustainability. Apart from using a top-down approach for green practices, the concept of sustainability should be infused into the department culture for positive change to combat climate change. [ABSTRACT FROM AUTHOR]

Background: Childhood intracranial germ cell tumor (GCT) survivors are prone to radiotherapy-related neurotoxicity, which can lead to neurocognitive dysfunctions. Diffusion kurtosis imaging (DKI) is a diffusion MRI technique that is sensitive to brain microstructural changes. This study aimed to investigate the association between DKI metrics versus cognitive and functional outcomes of childhood intracranial GCT survivors. Methods: DKI was performed on childhood intracranial GCT survivors (n = 20) who had received cranial radiotherapy, and age and gender-matched healthy control subjects (n = 14). Neurocognitive assessment was performed using the Hong Kong Wechsler Intelligence Scales, and functional assessment was performed using the Lansky/Karnofsky performance scales (KPS). Survivors and healthy controls were compared using mixed effects model. Multiple regression analyses were performed to determine the effects of microstructural brain changes of the whole brain as well as the association between IQ and Karnofsky scores and the thereof. Results: The mean Intelligence Quotient (IQ) of GCT survivors was 91.7 (95% CI 84.5 – 98.8), which was below the age-specific normative expected mean IQ (P = 0.013). The mean KPS score of GCT survivors was 85.5, which was significantly lower than that of controls (P < 0.001). Cognitive impairments were significantly associated with the presence of microstructural changes in white and grey matter, whereas functional impairments were mostly associated with microstructural changes in white matter. There were significant correlations between IQ versus the mean diffusivity (MD) and mean kurtosis (MK) of specific white matter regions. The IQ scores were negatively correlated with the MD of extensive grey matter regions. Conclusion: Our study identified vulnerable brain regions whose microstructural changes in white and grey matter were significantly associated with impaired cognitive and physical functioning in survivors of pediatric intracranial GCT. [ABSTRACT FROM AUTHOR]

Incorporation of point-of-care ultrasound in the undergraduate medical curriculum is of great importance to ensure early exposure and safe use of the modality. We aimed to assess the students' learning experiences following implementing an ultrasound module in the medical curriculum at the University of Hong Kong. Medical students in semester 6 (n = 221) were enrolled in the module in 2018. It consisted of 1 hour of didactic lecture, followed by 3 hours of hands-on session. The students had the opportunity to enroll into a four-week Special Study Module to further practice their skills. The students had access to an e-learning platform to assist in their learning. Outcome measures include task-based performance, quizzes, feedback, and round-table discussion to assess the learning experiences. The module was highly rated by over 90% of students (response rate of 96%). Students practiced on peer subject on upper abdominal scanning. Post-training assessment showed an increment of 16% in their understanding of the modality. Students were motivated to enroll into the Special Study Module, where they were trained and became proficient with Focused Assessment with Sonography with Trauma. More than 86% of the students found the e-learning platform easy to use and assisted the training session. Round-table discussion suggested more simulated clinical cases to be added and expansion of future modules. Ultrasound module was successfully implemented into the undergraduate medical curriculum at the University of Hong Kong through new pedagogical approaches. This integration was highly rated by the medical students with improved awareness and better understanding of point-of-care ultrasound. [ABSTRACT FROM AUTHOR]

Human metapneumovirus (HMPV) is a newly discovered pathogen thought to be associated with respiratory disease. We report the results of a study of 587 children hospitalized with respiratory infection over a 13-month period. HMPV was detected in the nasopharyngeal aspirates from 32 (5.5%) children by reverse transcription-polymerase chain reaction. HMPV infection was associated with clinical diagnoses of pneumonia (36%), asthma exacerbation (23%), or acute bronchiolitis (10%). When compared to those with respiratory syncytial virus infection, children with HMPV infection were older, and wheezing was more likely to represent asthma exacerbation rather than acute bronchiolitis. HMPV viral activity peaked during the spring-summer period in Hong Kong. Phylogenetically, all HMPV virus strains from Hong Kong belonged to one of the two genetic lineages previously described. HMPV contributed to 441.6 hospital admissions per 100,000 population <6 years of age. [ABSTRACT FROM AUTHOR]

• Developed two simple-to-use nomograms to identify COVID-19-positive patients • Probabilities are provided to allow healthcare leaders to decide suitable cut-offs • Variables are age, white cell count, chest X-ray appearances, and contact history • Model variables are easily available in the general hospital setting To develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation. Patients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer–Lemeshow (H–L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4 and 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). A total of 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. The first prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880−0.941]). The second model developed has the same variables except contact history (AUC = 0.880 [CI = 0.844−0.916]). Both were externally validated on the H–L test (p = 0.781 and 0.155, respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV. Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: We investigated the correlation relationship between ADCs measured by MRI and SUVs measured by PET/CT of lesions on GIST (gastrointestinal stromal tumor) patients to verify if MR is able to replace or serve as an alternative to PET/CT in GIST staging and treatment monitoring. Materials and methods: Between September 2010 and January 2011, five patients with histologically proven metastatic GIST in Queen Mary Hospital, Hong Kong were enrolled into our study. All patients underwent both MRI and PET/CT scans at prognosis. Pearson''s correlations of twenty-nine lesions were conducted between 5 pairs of ADCs and SUVs values. Results: Lesions in the liver, peritoneum or bowel loops were found by PET/CT and no extra-abdominal lesion was identified. All twenty-nine lesions are identifiable by MRI with sensitivity of 100%. Significant inverse correlation were found between ADCmean and SUVmean (P =0.006), ADCmean and SUVmax (P =0.010), ADCmin and SUVmax (P =0.014), ADCmin and SUVmean (P =0.026), rADCmin and rSUVmax (P =0.047). Conclusion: DWI is comparable to PET/CT in visually detecting the GIST lesions’ location. Significant inverse correlations were found between ADCs from DWIBS and SUVs from PET/CT on data of GIST patients. This finding demonstrates that DWI is potentially capable of offering similar information for diagnosis and treatment response evaluating in GIST''s patients as PET/CT does. Furthermore, ADCmin, which is determined by single pixel, is not as reliable as ADCmean, which is weighted average value of the whole lesion volume. [Copyright &y& Elsevier]