Primary Hyperparathyroidism Patients with Positive Preoperative Sestamibi Scan and Negative Ultrasound Are More Likely to Have Posteriorly Located Upper Gland Adenomas (PLUGs)

BackgroundStandard preoperative imaging for primary hyperparathyroidism usually includes sestamibi scanning (MIBI) and ultrasound (US). In a subset of patients with a positive MIBI and a negative US, we hypothesize that the parathyroid adenomas are more likely to be located posteriorly in the neck, where anatomically they are more difficult to detect by US.MethodsWe retrospectively reviewed the records of 661 patients treated for primary hyperparathyroidism between 2004 and 2009 at a tertiary referral center. We included patients who for their first operation had a MIBI that localized a single lesion in the neck and an US that found no parathyroid adenoma. We excluded patients with persistent or recurrent hyperparathyroidism, and patients with MIBIs that were negative, that had more than one positive focus, or that had foci outside of the neck. Sixty-six cases were included in the final analysis.ResultsA total of 54 patients (83%) had a single adenoma, 4 (6%) had double adenomas, and 7 (11%) had hyperplasia. Thirty-three patients (51%) had a single upper gland adenoma; 19 of these (58%) were posteriorly located upper gland adenomas (PLUGs). PLUGs occurred more often on the right side than on the left (P = 0.048, Fisher's test). PLUGs were also larger than other single adenomas (mean 1.85 vs. 1.48 cm, P = 0.021, t-test). Seventy-six percent of patients successfully underwent a unilateral or focused exploration. Six patients (9%) had persistent disease, which is double our group's overall average (4-5%).ConclusionsPrimary hyperparathyroid patients with preoperative positive MIBI and negative US are more likely to have PLUGs

Acute otitis externa: Consensus definition, diagnostic criteria and core outcome set development.

OBJECTIVE: Evidence for the management of acute otitis externa (AOE) is limited, with unclear diagnostic criteria and variably reported outcome measures that may not reflect key stakeholder priorities. We aimed to develop 1) a definition, 2) diagnostic criteria and 3) a core outcome set (COS) for AOE. STUDY DESIGN: COS development according to Core Outcome Measures in Effectiveness Trials (COMET) methodology and parallel consensus selection of diagnostic criteria/definition. SETTING: Stakeholders from the United Kingdom. SUBJECTS AND METHODS: Comprehensive literature review identified candidate items for the COS, definition and diagnostic criteria. Nine individuals with past AOE generated further patient-centred candidate items. Candidate items were rated for importance by patient and professional (ENT doctors, general practitioners, microbiologists, nurses, audiologists) stakeholders in a three-round online Delphi exercise. Consensus items were grouped to form the COS, diagnostic criteria, and definition. RESULTS: Candidate COS items from patients (n = 28) and literature (n = 25) were deduplicated and amalgamated to a final candidate list (n = 46). Patients emphasised quality-of-life and the impact on daily activities/work. Via the Delphi process, stakeholders agreed on 31 candidate items. The final COS covered six outcomes: pain; disease severity; impact on quality-of-life and daily activities; patient satisfaction; treatment-related outcome; and microbiology. 14 candidate diagnostic criteria were identified, 8 reaching inclusion consensus. The final definition for AOE was 'diffuse inflammation of the ear canal skin of less than 6 weeks duration'. CONCLUSION: The development and adoption of a consensus definition, diagnostic criteria and a COS will help to standardise future research in AOE, facilitating meta-analysis. Consulting former patients throughout development highlighted deficiencies in the outcomes adopted previously, in particular concerning the impact of AOE on daily life

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Understanding family dynamics in a cross-cultural sample: a multi-national study

The Family Systems Circumplex Model posits that balanced levels of cohesion and adaptability are associated with positive familial outcomes, whereas extremely high or low levels of these factors are associated with deleterious outcomes. Despite the popularity and utility of this model in Western cultures, there is a dearth of empirical data supporting its use in more culturally diverse contexts. The current, preregistered study assessed the Family Circumplex Model, cultural factors, and emerging adult outcomes across seven countries (i.e., China, Iran, Nigeria, Switzerland, Turkey, the United Kingdom, and the United States). Participants were N = 3,593 emerging adults, mostly self-identifying as women (71.3%). Collaborators were participants in Psi Chi’s Network for International Collaborative Exchange (NICE) and administered measures related to family dynamics and cultural orientation to participants in a random order. Results indicated that the Family Circumplex Model did not fit cross-culturally. As such, a new model was adapted, the Expanded Circumplex Model, which demonstrated invariance across samples and between women and men. The Expanded Circumplex Model retained 6 constructs with differences regarding the separation of disengagement into 2 variables and the combining of adaptive flexibility and cohesion. The current study suggests that the cultural context in which family dynamics occur should be taken into consideration when conceptualizing family dynamics theory and measurement. Future work should seek to replicate and further apply the Expanded Circumplex Model to familial outcomes

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century