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Introduction: Psoriasis is a multisystem, chronic, inflammatory dermatological disease. In routine clinical practice, the management of psoriasis varies significantly. The current study aimed to develop a set of practice guidelines relevant to dermatology practice in Singapore. Method: The Psoriasis Therapeutic Guidelines Workgroup, comprising members of the Dermatological Society of Singapore with a subspecialisation in psoriasis, was convened to develop the guidelines. Clinical questions on selected topics were generated and refined by the workgroup. A literature search using PubMed was performed on their assigned topics from June 2013 to December 2023. The articles were included and graded based on the level of evidence. Results: The guidelines address topics ranging from clinical assessment to practical considerations in the management of mild, moderate and severe psoriasis, including delivery of care, referrals to specialists and adherence to treatment. The recommended therapies include phototherapy, methotrexate, acitretin, cyclosporine; apremilast; topical corticosteroids, calcipotriol, topical calcineurin inhibitors; and biologics (i.e. adalimumab, infliximab, secukinumab, ixekizumab, ustekinumab, etanercept) either in combination or as monotherapy. Common therapeutic concerns relating to biologic use were addressed. Recommendations on generalised pustular psoriasis, palmoplantar pustular psoriasis and psoriatic arthritis were also made. Patients on systemic therapy would receive appropriate vaccine counselling. Therapeutic implications in special populations, such as pregnant/lactating women, children, the elderly, those undergoing surgery and those suffering from specific infections and cancer were addressed. Conclusion: These guidelines were developed for dermatologists, family physicians, rheumatologists and other specialists to support their selection of appropriate management options.

Abstract Metabolic brain biomarkers have been incorporated in various diagnostic guidelines of neurodegenerative diseases, recently. To improve their diagnostic accuracy a biologically and clinically homogeneous sample is needed for their identification. Alzheimer’s disease-related pattern (ADRP) has been identified previously in cohorts of clinically diagnosed patients with dementia due to Alzheimer’s disease (AD), meaning that its diagnostic accuracy might have been reduced due to common clinical misdiagnosis. In our study, we aimed to identify ADRP in a cohort of AD patients with CSF confirmed diagnosis, validate it in large out-of-sample cohorts and explore its relationship with patients’ clinical status. For identification we analyzed 2-[18F]FDG PET brain scans of 20 AD patients and 20 normal controls (NCs). For validation, 2-[18F]FDG PET scans from 261 individuals with AD, behavioral variant of frontotemporal dementia, mild cognitive impairment and NC were analyzed. We identified an ADRP that is characterized by relatively reduced metabolic activity in temporoparietal cortices, posterior cingulate and precuneus which co-varied with relatively increased metabolic activity in the cerebellum. ADRP expression significantly differentiated AD from NC (AUC = 0.95) and other dementia types (AUC = 0.76–0.85) and its expression correlated with clinical measures of global cognition and neuropsychological indices in all cohorts.

Abstract Differential diagnosis of parkinsonism early upon symptom onset is often challenging for clinicians and stressful for patients. Several neuroimaging methods have been previously evaluated; however specific routines remain to be established. The aim of this study was to systematically assess the diagnostic accuracy of a previously developed 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) based automated algorithm in the diagnosis of parkinsonian syndromes, including unpublished data from a prospective cohort. A series of 35 patients prospectively recruited in a movement disorder clinic in Stockholm were assessed, followed by systematic literature review and meta-analysis. In our cohort, automated image-based classification method showed excellent sensitivity and specificity for Parkinson Disease (PD) vs. atypical parkinsonian syndromes (APS), in line with the results of the meta-analysis (pooled sensitivity and specificity 0.84; 95% CI 0.79–0.88 and 0.96; 95% CI 0.91 –0.98, respectively). In conclusion, FDG-PET automated analysis has an excellent potential to distinguish between PD and APS early in the disease course and may be a valuable tool in clinical routine as well as in research applications.