Protocol for a scoping review on technology use and sexual and gender minority youth and mental health.

INTRODUCTION: Research indicates that sexual and gender minority youth [SGMY] may engage more with information communication technologies [ICTs] more than their non-SGMY counterparts Craig SL et al. 2020. While scholarship generally explores youth's use of ICTs, there are gaps in scholarship that connect SGMY, their ICT engagement and influences to mental health. This scoping review will synthesize the literature that connects these core concepts in order to better understand the influence ITCs have on the mental health of SGMY and to develop a more fulsome understanding of this emerging area of literature. METHODS AND ANALYSIS: Following the scoping review framework of Arksey and O'Malley, the search will be conducted in the PsycINFO [Ovid interface, 1980-], MEDLINE [Ovid interface, 1948-], CINAHL [EBSCO interface, 1937-], Sociological Abstracts [ProQuest interface, 1952-], Social Services Abstracts [ProQuest interface, 1979-], and Scopus. Descriptive summaries and thematic analysis will summarize the articles that meet the inclusion criteria using an extraction table. ETHICS AND DISSEMINATION: The review outlined in this paper provides an overview of information that exists on the technology use of SGMY, ICTs and the interconnection with mental health. Results will be disseminated through peer reviewed journals and national and international conferences. As information collected for this paper as is retrieved from publicly available sources, ethics approval is not required.

Evaluating the role of a hospital pharmacist in a cardiac day ward.

OBJECTIVES: To evaluate the role of clinical pharmacists in cardiac day wards. METHODS: A service evaluation was conducted during 24 February 2020-27 March 2020 to assess the role of clinical pharmacists for all patients admitted to an Australian tertiary hospital cardiac day ward. KEY FINDINGS: Overall, 297 patients were included. Medication review occurred for 80% (237/297) and a best possible medication history was obtained for 65% (193/297) of patients. Acceptance of interventions for medication-related problems was 93% (84/90). When compared with medication plans outlined in standard catheterisation laboratory documentation without pharmacist input, a pharmacist medication review resulted in increased documentation of medication plans in the patient's medical record at the time of discharge (20% (1/5) versus 95% (142/150), P < 0.001). CONCLUSION: Pharmacists can optimise the medication management of patients in cardiac day wards by performing medication review, and facilitating implementation and communication of medication changes at hospital discharge to patients and primary healthcare providers.

The diagnostic power of CD117, CD13, CD56, CD64, and MPO in rapid screening acute promyelocytic leukemia.

OBJECTIVE: The same immuno-phenotype between HLA-DR-negative acute myeloid leukemia (AML) and acute promyelocytic leukemia (APL) causes APL rapid screening to become difficult. This study aimed to identify the associated antigens for APL and the best model in clinical uses. RESULTS: A total of 36 APL (PML-RARA+) and 29 HLA-DR-negative non-APL patients enrolled in this study. When a cut-off point of 20% events was applied to define positive or negative status, APL and non-APL patients share a similar immuno-phenotype of CD117, CD34, CD11b, CD13, CD33, and MPO (P > 0.05). However, expression intensity of CD117 (P = 0.002), CD13 (P < 0.001), CD35 (P < 0.001), CD64 (P < 0.001), and MPO (P < 0.001) in APL are significantly higher while CD56 (P = 0.049) is lower than in non-APL subjects. The Bayesian Model Averaging (BMA) analysis identified CD117 (≥ 49% events), CD13 (≥ 88% events), CD56 (≤ 25% events), CD64 (≥ 42% events), and MPO (≥ 97% events) antigens as an optimal model for APL diagnosis. A combination of these factors resulted in an area under curve (AUC) value of 0.98 together with 91.7% sensitivity and 93.1% specificity, which is better than individual markers (AUC were 0.76, 0.84, 0.65, 0.82, and 0.85, respectively) (P = 0.001).