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Abstract Background Palliative care evolution focuses on education and medication accessibility. As little as 12% of palliative care needs are met. Assessment of the domains of Palliative care and patients’ and families’ experience are essential in life-limiting conditions. The Lagos University Teaching Hospital (LUTH), have the National Cancer Centre without offering palliative care services. Aim The aim was to examine pattern of admissions and needs assessment for palliative services among patients admitted into LUTH wards. Materials and method Responses were entered into a data sheet inputted into Epi info version 7.2. Descriptive characteristics of the participants were presented as frequencies and percentages for age, sex, pattern of disease, domains of Palliative care, Advance care Plan, Preparation for home care, death and Education about the illness and category of medical conditions (palliative and non-palliative conditions). Together for Short Lives (TfSL) tool was used to categorize respondents’ conditions into Palliative and Non-palliative conditions. Chi-square test was used to determine association between independent variables (pattern of diagnoses, stage of disease, advanced care plan, preparation for home care/ death and education on illness) and dependent variables (category of medical condition). Chi-square test was also used to explore the association between specialty of the managing doctor (independent variable) and Advance care plan (dependent variable). The level of statistical significance was P-value

Abstract Although the gross morphology of the heart is conserved across mammals, subtle interspecific variations exist in the cardiac phenotype, which may reflect evolutionary divergence among closely-related species. Here, we compare the left ventricle (LV) across all extant members of the Hominidae taxon, using 2D echocardiography, to gain insight into the evolution of the human heart. We present compelling evidence that the human LV has diverged away from a more trabeculated phenotype present in all other great apes, towards a ventricular wall with proportionally greater compact myocardium, which was corroborated by post-mortem chimpanzee (Pan troglodytes) hearts. Speckle-tracking echocardiographic analyses identified a negative curvilinear relationship between the degree of trabeculation and LV systolic twist, revealing lower rotational mechanics in the trabeculated non-human great ape LV. This divergent evolution of the human heart may have facilitated the augmentation of cardiac output to support the metabolic and thermoregulatory demands of the human ecological niche.

OBJECTIVE: Major obstacles to data harmonization in neuropsychology include lack of consensus about what constructs and tests are most important and invariant across healthy and clinical populations. This study addressed these challenges using data from the National Neuropsychology Network (NNN). METHOD: Data were obtained from 5,000 NNN participants and Pearson standardization samples. Analyses included variables from four instruments: Wechsler Adult Intelligence Scale, 4th Edition (WAIS-IV); Wechsler Memory Scale, 4th Edition (WMS-IV); California Verbal Learning Test, 3rd Edition (CVLT3); and Delis-Kaplan Executive Function System (D-KEFS). We used confirmatory factor analysis to evaluate models suggested by prior work and examined fit statistics and measurement invariance across samples. We examined relations of factor scores to demographic and clinical characteristics. RESULTS: For each instrument, we identified four first-order and one second-order factor. Optimal models in patients generally paralleled the best-fitting models in the standardization samples, including task-specific factors. Analysis of the NNN data prompted specification of a Recognition-Familiarity factor on the WMS-IV and an Inhibition-Switching factor on the D-KEFS. Analyses showed strong to strict factorial invariance across samples with expected differences in factor means and variances. The Recognition-Familiarity factor correlated with age more strongly in NNN than in the standardization sample. CONCLUSIONS: Factor models derived from healthy groups generally fit well in patients. NNN data helped identify novel Recognition-Familiarity and Inhibition-Switching factors that were also invariant across samples and may be clinically useful. The findings support efforts to identify evidence-based and optimally efficient measurements of neuropsychological constructs that are valid across groups. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Abstract The motor impairments experienced by people with Parkinson's disease (PD) are exacerbated during memory‐guided movements. Despite this, the effect of antiparkinson medication on memory‐guided movements has not been elucidated. We evaluated the effect of antiparkinson medication on motor control during a memory‐guided reaching task with short and long retention delays in participants with PD and compared performance to age‐matched healthy control (HC) participants. Thirty‐two participants with PD completed the motor section of the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS‐UPDRS III) and performed a memory‐guided reaching task with two retention delays (0.5 s and 5 s) while on and off medication. Thirteen HC participants completed the MDS‐UPDRS III and performed the memory‐guided reaching task. In the task, medication increased movement velocity, decreased movement time, and decreased reaction time toward what was seen in the HC. However, movement amplitude and reaching error were unaffected by medication. Shorter retention delays increased movement velocity and amplitude, decreased movement time, and decreased error, but increased reaction times in the participants with PD and HC. Together, these results imply that antiparkinson medication is more effective at altering the neurophysiological mechanisms controlling movement velocity and reaction time compared with other aspects of movement control.

In this study, graduate students and postdoctoral scholars participated in a hybrid, multi-institutional workshop series about course design. Trainees developed college courses based on their research expertise, posting works-in-progress to a shared, online drive for peer review and collaboration. Learners also met weekly with local facilitators at their institution. The program led to similar learning outcomes as when the program was previously run in a face-to-face only format at one institution. However, the multi-institutional design led to additional benefits, especially for leaders at each institution, who described a rich learning community in their collaborative work.

BackgroundThis study provides a global overview of the management of patients with acute cholecystitis during the initial phase of the COVID-19 pandemic.MethodsCHOLECOVID is an international, multicentre, observational comparative study of patients admitted to hospital with acute cholecystitis during the COVID-19 pandemic. Data on management were collected for a 2-month study interval coincident with the WHO declaration of the SARS-CoV-2 pandemic and compared with an equivalent pre-pandemic time interval. Mediation analysis examined the influence of SARS-COV-2 infection on 30-day mortality.ResultsThis study collected data on 9783 patients with acute cholecystitis admitted to 247 hospitals across the world. The pandemic was associated with reduced availability of surgical workforce and operating facilities globally, a significant shift to worse severity of disease, and increased use of conservative management. There was a reduction (both absolute and proportionate) in the number of patients undergoing cholecystectomy from 3095 patients (56.2 per cent) pre-pandemic to 1998 patients (46.2 per cent) during the pandemic but there was no difference in 30-day all-cause mortality after cholecystectomy comparing the pre-pandemic interval with the pandemic (13 patients (0.4 per cent) pre-pandemic to 13 patients (0.6 per cent) pandemic; P = 0.355). In mediation analysis, an admission with acute cholecystitis during the pandemic was associated with a non-significant increased risk of death (OR 1.29, 95 per cent c.i. 0.93 to 1.79, P = 0.121).ConclusionCHOLECOVID provides a unique overview of the treatment of patients with cholecystitis across the globe during the first months of the SARS-CoV-2 pandemic. The study highlights the need for system resilience in retention of elective surgical activity. Cholecystectomy was associated with a low risk of mortality and deferral of treatment results in an increase in avoidable morbidity that represents the non-COVID cost of this pandemic.

ObjectiveThe National Neuropsychology Network (NNN) is a multicenter clinical research initiative funded by the National Institute of Mental Health (NIMH; R01 MH118514) to facilitate neuropsychology's transition to contemporary psychometric assessment methods with resultant improvement in test validation and assessment efficiency.MethodThe NNN includes four clinical research sites (Emory University; Medical College of Wisconsin; University of California, Los Angeles (UCLA); University of Florida) and Pearson Clinical Assessment. Pearson Q-interactive (Q-i) is used for data capture for Pearson published tests; web-based data capture tools programmed by UCLA, which serves as the Coordinating Center, are employed for remaining measures.ResultsNNN is acquiring item-level data from 500-10,000 patients across 47 widely used Neuropsychology (NP) tests and sharing these data via the NIMH Data Archive. Modern psychometric methods (e.g., item response theory) will specify the constructs measured by different tests and determine their positive/negative predictive power regarding diagnostic outcomes and relationships to other clinical, historical, and demographic factors. The Structured History Protocol for NP (SHiP-NP) helps standardize acquisition of relevant history and self-report data.ConclusionsNNN is a proof-of-principle collaboration: by addressing logistical challenges, NNN aims to engage other clinics to create a national and ultimately an international network. The mature NNN will provide mechanisms for data aggregation enabling shared analysis and collaborative research. NNN promises ultimately to enable robust diagnostic inferences about neuropsychological test patterns and to promote the validation of novel adaptive assessment strategies that will be more efficient, more precise, and more sensitive to clinical contexts and individual/cultural differences.

To support the global restart of elective surgery, data from an international prospective cohort study of 8492 patients (69 countries) was analysed using artificial intelligence (machine learning techniques) to develop a predictive score for mortality in surgical patients with SARS-CoV-2. We found that patient rather than operation factors were the best predictors and used these to create the COVIDsurg Mortality Score (https://covidsurgrisk.app). Our data demonstrates that it is safe to restart a wide range of surgical services for selected patients.

Antibiotic Resistance Genes (ARGs) are contaminants of emerging concern with marked potential to impact public and environmental health. This review focusses on factors that influence the presence, abundance, and dissemination of ARGs within Wastewater Treatment Plants (WWTPs) and associated effluents. Antibiotic-Resistant Bacteria (ARB) and ARGs have been detected in the influent and the effluent of WWTPs worldwide. Different levels of wastewater treatment (primary, secondary, and tertiary) show different degrees of removal efficiency of ARGs, with further differences being observed when ARGs are captured as intracellular or extracellular forms. Furthermore, routinely used molecular methodologies such as quantitative polymerase chain reaction or whole genome sequencing may also vary in resistome identification and in quantifying ARG removal efficiencies from WWTP effluents. Additionally, we provide an overview of the One Health risk assessment framework, as well as future strategies on how WWTPs can be assessed for environmental and public health impact.

In this article, we propose a new vision of educational development that reimagines how graduate instructors are socialized and professionalized in academic settings. We describe a transgressive learning community that empowers graduate instructors with tools to reveal, mitigate, and disrupt oppressive structures in higher education. Our learning community is founded on critical race and feminist conceptualizations of pedagogical inquiry in its design, implementation, and assessment to serve underprivileged, underserved, and historically underrepresented graduate students. We argue that the intersections of marginalized and graduate student identities create distinct experiences of discrimination, marginalization, tokenism, isolation, and impostor syndrome due to a lack of sustained teaching mentorship within the academy. The transgressive learning community model that we propose in this article functions to create spaces of transgressive and transformational pedagogical engagement for graduate students who exist at the intersections of these identities.

Abstract Seven knowledge domains were originally defined for the learning health system (LHS) scientist. To assess proficiency in each of these domains, we developed and published an assessment tool for use by emerging LHS scientists and training programs. (LHS, October 2022). In mid‐2022, the AHRQ adopted an eighth LHS knowledge domain, Equity and Justice. The addition of this eighth domain emphasizes the importance and centrality of equity in the LHS and improvement science. To extend our prior LHS competency assessment, we developed a proficiency assessment for the new equity and justice domain. Content experts and trainees iteratively defined, reviewed, and edited the assessment criteria. The items were developed by trainees and experts at one LHS training center with experience conducting research focused on healthcare inequities among marginalized populations. The proficiency assessment criteria for the Equity domain apply the same four levels of mastery: “no exposure,” “foundational awareness,” “emerging,” and “proficient” as were used for original competencies. LHS training programs can use these proficiency criteria to monitor skills among emerging scientists across the eight domains, with particular attention to equity and justice.

Advancing biomedical research in low and middle income countries (LMICs) to expand the capacity for LMICs to integrate biomedical research into their health care systems and education has been the focus of many programs in global health over the past two decades. Central to the success of these programs is effective research mentoring, characterized by academic, career and psychosocial support through culturally appropriate practices. Research mentoring is a learned skill, developed through training, mutual discussions, practice and feedback. The majority of extant training programs are designed and delivered by US partners, so the next stage in building capacity is to train facilitators within the LMIC partner institutions to contextualize and advance mentoring specifically within their cultural and institutional norms by co-developing, delivering and evaluating semi-annual research mentoring training. To this end, we describe the development, delivery and outcome evaluation of a 5-week course in the art and skill of facilitation. Care was taken to explicitly distinguish between concepts of “teaching” and “facilitation,” since “teaching” is closely connected to a transmission or banking model of education, which is characterized by “top-down,” hierarchical relationship. The course discussed power and positionality, themes that resonate with partners in Nigeria and Tanzania. These themes provided unique entry into deeper conversations core to advancing mentoring practice away from the traditional dyadic power structure that remains from colonization. Evaluation findings indicate significant advances in awareness of differences between teaching and facilitating, increased confidence in facilitation skills, especially in the area of structured planning and organization, as well as improved communication and interpersonal skills. All respondents felt that students in Nigeria and Tanzania would respond well to the facilitation approach conveyed during the course and they found value in participating in the course as a cohort.

Objective.The National Neuropsychology Network (NNN) is a multicenter clinical research initiative funded by the National Institute of Mental Health (NIMH; R01 MH118514) to facilitate neuropsychology's transition to contemporary psychometric assessment methods with resultant improvement in test validation and assessment efficiency.MethodThe NNN includes four clinical research sites (Emory University; Medical College of Wisconsin; University of California, Los Angeles (UCLA); University of Florida) and Pearson Clinical Assessment. Pearson Q-interactive (Q-i) is used for data capture for Pearson published tests; web-based data capture tools programmed by UCLA, which serves as the Coordinating Center, are employed for remaining measures.ResultsNNN is acquiring item-level data from 500-10,000 patients across 47 widely used Neuropsychology (NP) tests and sharing these data via the NIMH Data Archive. Modern psychometric methods (e.g., item response theory) will specify the constructs measured by different tests and determine their positive/negative predictive power regarding diagnostic outcomes and relationships to other clinical, historical, and demographic factors. The Structured History Protocol for NP (SHiP-NP) helps standardize acquisition of relevant history and self-report data.ConclusionsNNN is a proof-of-principle collaboration: by addressing logistical challenges, NNN aims to engage other clinics to create a national and ultimately an international network. The mature NNN will provide mechanisms for data aggregation enabling shared analysis and collaborative research. NNN promises ultimately to enable robust diagnostic inferences about neuropsychological test patterns and to promote the validation of novel adaptive assessment strategies that will be more efficient, more precise, and more sensitive to clinical contexts and individual/cultural differences.

Chang et al. classify people with Temporal Lobe Epilepsy (TLE), Alzheimer’s disease and healthy controls using a convolutional neural network algorithm applied to magnetic resonance imaging (MRI) scans. People with TLE can be distinguished, including those without easily identifiable TLE-associated MRI features.