Recent increases in homophobic and transphobic harassment, hate crimes, anti-lesbian, gay, bisexual, transgender, gender nonconforming, and queer (LGBTQ+) legislation, and discrimination in healthcare toward LGBTQ+ persons require urgent attention.

This study describes seriously ill LGBTQ+ patients’ and partners’ experiences of discriminatory care delivered by healthcare providers.

Qualitative data from a mixed-methods study using an online survey were analyzed using a grounded theory approach. Seriously ill LGBTQ+ persons, their spouses/partners and widows were recruited from a wide range of organizations serving the LGBTQ+ community. Respondents were asked to describe instances where they felt they received poor care from a healthcare provider because they were LGBTQ+.

Six main themes emerged: (1) disrespectful care; (2) inadequate care; (3) abusive care; (4) discriminatory care toward persons who identify as transgender; (5) discriminatory behaviors toward partners; and (6) intersectional discrimination. The findings provide evidence that some LGBTQ+ patients receive poor care at a vulnerable time in their lives. Transgender patients experience unique forms of discrimination that disregard or belittle their identity.

Professional associations, accrediting bodies, and healthcare organizations should set standards for nondiscriminatory, respectful, competent, safe and affirming care for LGBTQ+ patients. Healthcare organizations should implement mechanisms for identifying problems and ensuring nondiscrimination in services and employment; safety for patients and staff; strategies for outreach and marketing to the LGBTQ+ community, and ongoing staff training to ensure high quality care for LGBTQ+ patients, partners, families, and friends. Policy actions are needed to combat discrimination and disparities in healthcare, including passage of the Equality Act by Congress.

Artificial intelligence (AI) has the potential to enhance clinical decision-making, including in infectious diseases. By improving antimicrobial resistance prediction and optimizing antibiotic prescriptions, these technologies may support treatment strategies and address critical gaps in healthcare. This study evaluates the effectiveness of AI in guiding appropriate antibiotic prescriptions for infectious diseases through a systematic literature review.

We conducted a systematic review of studies evaluating AI (machine learning or large language models) used for guidance on prescribing appropriate antibiotics in infectious disease cases. Searches were performed in PubMed, CINAHL, Embase, Scopus, Web of Science, and Google Scholar for articles published up to October 25, 2024. Inclusion criteria focused on studies assessing the performance of AI in clinical practice, with outcomes related to antimicrobial management and decision-making.

Seventeen studies used machine learning as part of clinical decision support systems (CDSS). They improved prediction of antimicrobial resistance and optimized antimicrobial use. Six studies focused on large language models to guide antimicrobial therapy; they had higher prescribing error rates, patient safety risks, and needed precise prompts to ensure accurate responses.

AI, particularly machine learning integrated into CDSS, holds promise in enhancing clinical decision-making and improving antimicrobial management. However, large language models currently lack the reliability required for complex clinical applications. The indispensable role of infectious disease specialists remains critical for ensuring accurate, personalized, and safe treatment strategies. Rigorous validation and regular updates are essential before the successful integration of AI into clinical practice.

Anxiety disorders and treatment-resistant major depressive disorder (TRD) are often comorbid. Studies suggest ketamine has anxiolytic and antidepressant properties.

To investigate if subcutaneous racemic ketamine, delivered twice weekly for 4 weeks, reduces anxiety in people with TRD.

The Ketamine for Adult Depression Study was a multisite 4-week randomised, double-blind, active (midazolam)-controlled trial. The study initially used fixed low dose ketamine (0.5 mg/kg, cohort 1), before protocol revision to flexible, response-guided dosing (0.5–0.9 mg/kg, cohort 2). This secondary analysis assessed anxiety using the Hamilton Anxiety (HAM-A) scale (primary measure) and ‘inner tension’ item 3 of the Montgomery–Åsberg Depression Rating Scale (MADRS), at baseline, 4 weeks (end treatment) and 4 weeks after treatment end. Analyses of change in anxiety between ketamine and midazolam groups included all participants who received at least one treatment (n = 174), with a mixed effects repeated measures model used to assess the primary anxiety measure. The trial was registered at www.anzctr.org.au (ACTRN12616001096448).

In cohort 1 (n = 68) the reduction in HAM-A score was not statistically significant: −1.4 (95% CI [−8.6, 3.2], P = 0.37), whereas a significant reduction was seen for cohort 2 (n = 106) of −4.0 (95% CI [−10.6, −1.9], P = 0.0058), favouring ketamine over midazolam. These effects were mediated by total MADRS and were not maintained at 4 weeks after treatment end. MADRS item 3 was also significantly reduced in cohort 2 (P = 0.026) but not cohort 1 (P = 0.96).

Ketamine reduces anxiety in people with TRD when administered subcutaneously in adequate doses.

The Centers for Disease Control and Prevention (CDC)-funded Cancer Prevention and Control Research Network (CPCRN) has been a leader in cancer-related dissemination & implementation (D&I) science. Given increased demand for D&I research, the CPCRN Scholars Program launched in 2021 to expand the number of practitioners, researchers, and trainees proficient in cancer D&I science methods.

The evaluation was informed by a logic model and data collected through electronic surveys. Through an application process (baseline survey), we assessed scholars’ competencies in D&I science domains/subdomains, collected demographic data, and asked scholars to share proposed project ideas. We distributed an exit survey one month after program completion to assess scholars’ experience and engagement with the program and changes in D&I competencies. A follow-up survey was administered to alumni nine months post-program to measure their continued network engagement, accomplishments, and skills.

Three cohorts completed the program, consisting of 20, 17, and 25 scholars in Years 1-3, respectively. There was a significant increase in the total D&I competency scores for all three cohorts for 4 overarching domains and 43 subdomains (MPre = 1.38 MPost = 1.89). Differences were greatest for the domain of Practice-Based Considerations (0.50 mean difference) and Theory & Analysis (0.47 mean difference). Alumni surveys revealed that scholars appreciated access to D&I-focused webinars, toolkits, and training resources. 80% remain engaged with CPCRN workgroups and investigators.

Program evaluation with scholars and alumni helped with ongoing quality assurance, introspection, and iterative program adaptation to meet scholars’ needs. This approach is recommended for large-scale capacity-building training programs.

Candida auris is an emerging fungal pathogen increasingly recognized as a cause of healthcare-associated infections including outbreaks.

We performed a mixed-methods study to characterize the emergence of C. auris in the state of Maryland from 2019 to 2022, with a focus on socioeconomic vulnerability and infection prevention opportunities. We describe all case-patients of C. auris among Maryland residents from June 2019 to December 2021 detected by Maryland Department of Health. We compared neighborhood socioeconomic characteristics of skilled nursing facilities (SNFs) with and without C. auris transmission outbreaks using both the social vulnerability index (SVI) and the area deprivation index (ADI). The SVI and the ADI were obtained at the state level, with an SVI ≥ 75th percentile or an ADI ≥ 80th percentile considered severely disadvantaged. We summarized infection control assessments at SNFs with outbreaks using a qualitative analysis.

A total of 140 individuals tested positive for C. auris in the study period in Maryland; 46 (33%) had a positive clinical culture. Sixty (43%) were associated with a SNF, 37 (26%) were ventilated, and 87 (62%) had a documented wound. Separate facility-level neighborhood analysis showed SNFs with likely C. auris transmission were disproportionately located in neighborhoods in the top quartile of deprivation by the SVI, characterized by low socioeconomic status and high proportion of racial/ethnic minorities. Multiple infection control deficiencies were noted at these SNFs.

Neighborhood socioeconomic vulnerability may contribute to the emergence and transmission of C. auris in a community.

To determine whether poorer performance on the Boston Naming Test (BNT) in individuals with transactive response DNA-binding protein 43 pathology (TDP-43+) is due to greater loss of word knowledge compared to retrieval-based deficits.

Retrospective clinical-pathologic study of 282 participants with Alzheimer’s disease neuropathologic changes (ADNC) and known TDP-43 status. We evaluated item-level performance on the 60-item BNT for first and last available assessment. We fit cross-sectional negative binomial count models that assessed total number of incorrect items, number correct of responses with phonemic cue (reflecting retrieval difficulties), and number of “I don’t know” (IDK) responses (suggestive of loss of word knowledge) at both assessments. Models included TDP-43 status and adjusted for sex, age, education, years from test to death, and ADNC severity. Models that evaluated the last assessment adjusted for number of prior BNT exposures.

43% were TDP-43+. The TDP-43+ group had worse performance on BNT total score at first (p = .01) and last assessments (p = .01). At first assessment, TDP-43+ individuals had an estimated 29% (CI: 7%–56%) higher mean number of incorrect items after adjusting for covariates, and a 51% (CI: 15%–98%) higher number of IDK responses compared to TDP-43−. At last assessment, compared to TDP-43−, the TDP-43+ group on average missed 31% (CI: 6%–62%; p = .01) more items and had 33% more IDK responses (CI: 1% fewer to 78% more; p = .06).

An important component of poorer performance on the BNT in participants who are TDP-43+ is having loss of word knowledge versus retrieval difficulties.

The Stricker Learning Span (SLS) is a computer-adaptive word list memory test specifically designed for remote assessment and self-administration on a web-based multi-device platform (Mayo Test Drive). Given recent evidence suggesting the prominence of learning impairment in preclinical Alzheimer’s disease (AD), the SLS places greater emphasis on learning than delayed memory compared to traditional word list memory tests (see Stricker et al., Neuropsychology in press for review and test details). The primary study aim was to establish criterion validity of the SLS by comparing the ability of the remotely-administered SLS and inperson administered Rey Auditory Verbal Learning Test (AVLT) to differentiate biomarkerdefined groups in cognitively unimpaired (CU) individuals on the Alzheimer’s continuum.

Mayo Clinic Study of Aging CU participants (N=319; mean age=71, SD=11; mean education=16, SD=2; 47% female) completed a brief remote cognitive assessment (∼0.5 months from in-person visit). Brain amyloid and brain tau PET scans were available within 3 years. Overlapping groups were formed for 1) those on the Alzheimer’s disease (AD) continuum (A+, n=110) or not (A-, n=209), and for 2) those with biological AD (A+T+, n=43) vs no evidence of AD pathology (A-T-, n=181). Primary neuropsychological outcome variables were sum of trials for both the SLS and AVLT. Secondary outcome variables examined comparability of learning (1-5 total) and delay performances. Linear model ANOVAs were used to investigate biomarker subgroup differences and Hedge’s G effect sizes were derived, with and without adjusting for demographic variables (age, education, sex).

Both SLS and AVLT performances were worse in the biomarker positive relative to biomarker negative groups (unadjusted p’s<.05). Because biomarker positive groups were significantly older than biomarker negative groups, group differences were attenuated after adjusting for demographic variables, but SLS remained significant for A+ vs A- and for A+T+ vs A-T- comparisons (adjusted p’s<.05) and AVLT approached significance (p’s .05-.10). The effect sizes for the SLS were slightly better (qualitatively, no statistical comparison) for separating biomarker-defined CU groups in comparison to AVLT. For A+ vs A- and A+T+ vs A-T- comparisons, unadjusted effect sizes for SLS were -0.53 and -0.81 and for AVLT were -0.47 and -0.61, respectively; adjusted effect sizes for SLS were -0.25 and -0.42 and for AVLT were -0.19 and -0.26, respectively. In secondary analyses, learning and delay variables were similar in terms of ability to separate biomarker groups. For example, unadjusted effect sizes for SLS learning (-.80) was similar to SLS delay (.76), and AVLT learning (-.58) was similar to AVLT 30-minute delay (-.55) for the A+T+ vs AT- comparison.

Remotely administered SLS performed similarly to the in-person-administered AVLT in its ability to separate biomarker-defined groups in CU individuals, providing evidence of criterion validity. The SLS showed significantly worse performance in A+ and A+T+ groups (relative to A- and A-T-groups) in this CU sample after demographic adjustment, suggesting potential sensitivity to detecting transitional cognitive decline in preclinical AD. Measures emphasizing learning should be given equal consideration as measures of delayed memory in AD-focused studies, particularly in the preclinical phase.

Mayo Test Drive (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web-based multi-device (smartphone, tablet, personal computer) platform optimized for remote self-administered cognitive assessment that includes a computer-adaptive word list memory test (Stricker Learning Span; SLS; Stricker et al., 2022; Stricker et al., in press) and a measure of processing speed (Symbols Test: Wilks et al., 2021). Study aims were to determine criterion validity of MTD by comparing the ability of the MTD raw composite and in-person administered cognitive measures to differentiate biomarkerdefined groups in cognitively unimpaired (CU) individuals on the Alzheimer’s continuum.

Mayo Clinic Study of Aging CU participants (N=319; mean age=71, SD=11, range=37-94; mean education=16, SD=2, range=6-20; 47% female) completed a brief remote cognitive assessment (∼0.5 months from in-person visit). Brain amyloid and brain tau PET scans were available within 3 years. Overlapping groups were formed for 1) those on the Alzheimer’s disease (AD) continuum (A+, n=110) or not (A-, n=209), and for 2) those with biological AD (A+T+, n=43) or with no evidence of AD pathology (A-T-, n=181). Primary outcome variables were MTD raw composite (SLS sum of trials + an accuracy-weighted Symbols response time measure), Global-z (average of 9 in-person neuropsychological measures) and an in-person screening measure (Kokmen Short Test of Mental Status, STMS; which is like the MMSE). Linear model ANOVAs were used to investigate biomarker subgroup differences and Hedge’s G effect sizes were derived, with and without adjusting for demographic variables (age, education, sex).

Remotely administered MTD raw composite showed comparable to slightly larger effect sizes compared to Global-z. Unadjusted effect sizes for MTD raw composite for differentiating A+ vs. A- and A+T+ vs. A-T- groups, respectively, were -0.57 and -0.84 and effect sizes for Global-z were -0.54 and -0.73 (all p’s<.05). Because biomarker positive groups were significantly older than biomarker negative groups, group differences were attenuated after adjusting for demographic variables, but MTD raw composite remained significant for A+T+ vs A-T- (adjusted effect size -0.35, p=.007); Global-z did not reach significance for A+T+ vs A-T- (adjusted effect size -0.19, p=.08). Neither composite reached significance for adjusted analyses for the A+ vs A- comparison (MTD raw composite adjusted effect size= -.22, p=.06; Global-z adjusted effect size= -.08, p=.47). Results were the same for an alternative MTD composite using traditional z-score averaging methods, but the raw score method is preferred for comparability to other screening measures. The STMS screening measure did not differentiate biomarker groups in any analyses (unadjusted and adjusted p’s>.05; d’s -0.23 to 0.05).

Remotely administered MTD raw composite shows at least similar ability to separate biomarker-defined groups in CU individuals as a Global-z for person-administered measures within a neuropsychological battery, providing evidence of criterion validity. Both the MTD raw composite and Global-z showed greater ability to separate biomarker positive from negative CU groups compared to a typical screening measure (STMS) that was unable to differentiate these groups. MTD may be useful as a screening measure to aid early detection of Alzheimer’s pathological changes.

To summarize presentations and discussions from the 2022 trans-agency workshop titled “Overlapping science in radiation and sulfur mustard (SM) exposures of skin and lung: Consideration of models, mechanisms, organ systems, and medical countermeasures.”

Summary on topics includes: (1) an overview of the radiation and chemical countermeasure development programs and missions; (2) regulatory and industry perspectives for drugs and devices; 3) pathophysiology of skin and lung following radiation or SM exposure; 4) mechanisms of action/targets, biomarkers of injury; and 5) animal models that simulate anticipated clinical responses.

There are striking similarities between injuries caused by radiation and SM exposures. Primary outcomes from both types of exposure include acute injuries, while late complications comprise chronic inflammation, oxidative stress, and vascular dysfunction, which can culminate in fibrosis in both skin and lung organ systems. This workshop brought together academic and industrial researchers, medical practitioners, US Government program officials, and regulators to discuss lung-, and skin- specific animal models and biomarkers, novel pathways of injury and recovery, and paths to licensure for products to address radiation or SM injuries.

Regular communications between the radiological and chemical injury research communities can enhance the state-of-the-science, provide a unique perspective on novel therapeutic strategies, and improve overall US Government emergency preparedness.