Nevin Manimala

Eur Stroke J. 2025 Mar 13:23969873251325076. doi: 10.1177/23969873251325076. Online ahead of print.

ABSTRACT

INTRODUCTION: A better understanding of who will develop dementia can inform patient care. Although MRI offers prognostic insights, access is limited globally, whereas CT-imaging is readily available in acute stroke. We explored the prognostic utility of acute CT-imaging for predicting dementia.

PATIENTS AND METHODS: We included stroke or transient ischaemic attack (TIA) survivors from participating stroke centres in Scotland. Acute CT-scans were rated using ordinal scales for neurodegenerative and cerebrovascular changes (old infarcts, white matter lesions (WMLs), medial temporal lobe atrophy (MTA), and global atrophy (GA)) and combined together to a ‘brain-frailty’ score. Dementia status was established at 18-months following stroke or TIA.

RESULTS: Among 195 participants, 33% had dementia after 3 years of follow-up. High brain-frailty score (⩾2/4) correlated with higher risk of dementia (HR (95% CI) 6.02 (1.89-19.21)). As individual predictor, severe MTA was most strongly associated with dementia (adjusted HR (95% CI) 2.09 (1.07-4.08)). Other predictors associated with dementia included older age, higher prestroke morbidity (mRS), WMLs, and GA. Integrated in a prediction model with clinical parameters, prestroke mRS, cardiovascular disease, GA, MTA and Abbreviated-Mental-Test were the strongest predictors of dementia (c-statistic: 0.77).

DISCUSSION AND CONCLUSION: Increased brain-frailty, and its individual components (WMLs, MTA, and GA) are associated with a higher risk of dementia in participants with stroke. Combining clinical and brain-frailty parameters created a moderate dementia prediction model but added little value over clinical parameters in combination with cognitive testing. CT-based brain-frailty may provide better prognostic insights when cognitive testing isn’t feasible and for identifying highest-risk individuals for dementia prevention trials to increase trial efficiency.

PMID:40079226 | DOI:10.1177/23969873251325076

Infect Control Hosp Epidemiol. 2025 Mar 13:1-11. doi: 10.1017/ice.2025.33. Online ahead of print.

ABSTRACT

OBJECTIVE: Evaluate Department of Defense (DoD) antimicrobial stewardship programs (ASPs) by assessing the relationship between key clinical outcome metrics (antibiotic use, incidence of resistant pathogens, and incidence of Clostridioides difficile infections) and CDC Core Element (CE) adherence.

DESIGN: Retrospective, cross-sectional study of DoD hospitals in 2018 and 2021.

METHODS: National Healthcare Safety Network Standardized Antimicrobial Administration Ratios (SAARs) were used to measure antibiotic use and microbiology results to evaluate four types of pathogen incidence. A novel CE scoring approach used scores to quantitatively assess relationships with CE adherence and outcome metrics using correlation and regression models. Assessments were repeated with 2021 data for Priority CE adherence and to conduct adjusted regressions for CEs and Priority CEs controlling for categorical bed size.

RESULTS: Compared to 2022 national data, DoD hospitals in 2021 had a similar proportion of facilities with a SAAR statistically significantly > 1.0. Leadership, Action, and Tracking CEs followed a more normal score distribution, while Reporting and Education were somewhat left-skewed. Unadjusted models often showed a positive relationship with higher CE scores associated with worse outcomes for the SAAR and pathogen incidence. Adjusted models indicated that procedural CEs, particularly Priority Reporting, were associated with better ASP-related outcomes.

CONCLUSIONS: CEs should be more quantitatively assessed. Results provide initial evidence to prioritize procedural CE implementation within the DoD; however, additional investigation for structural CEs is needed. Patient outcome data should be collected as an important indicator of ASP performance.

PMID:40079217 | DOI:10.1017/ice.2025.33

Neurol Neurochir Pol. 2025 Mar 13. doi: 10.5603/pjnns.103718. Online ahead of print.

ABSTRACT

INTRODUCTION: This study aimed to identify predictive factors for long-term incomplete nidus obliteration following stereotactic radiosurgery (SRS) for brain arteriovenous malformations (AVMs).

MATERIAL AND METHODS: A systematic search across the PubMed, Web of Science, and Scopus databases identified observational studies reporting such factors. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. The study protocol was registered through PROSPERO. Each eligible study’s quality was assessed using the modified Newcastle-Ottawa Scale. Odds ratios (ORs) were calculated for dichotomous parameters.

RESULTS: Two high-quality prospective cohort and three high-quality retrospective cohort studies were included, covering patients with complete (n = 638) and incomplete (n = 297) nidus obliteration. The mean age of the patients was 25.54 ± 12.81 years and the mean follow-up time was 95.98 ± 27.64 months. Predictors for incomplete obliteration of nidus included: AVM classified as Spetzler-Martin (SM) grade ≥ IV (odds ratio (OR) 10.57, 95% confidence interval (CI) 2.00-55.96, p = 0.006), the presence of multiple (> 1) feeding arteries (OR 6.47, 95% CI 2.20-19.10, p = 0.0007), nidus volume > 10 mL (OR 5.08, 95% CI 1.68-15.33, p = 0.004), and the occurrence of intranidal aneurysm (OR 3.33, 95% CI 1.10-10.08, p = 0.03). No statistically significant difference in proportions of patients with incomplete nidus obliteration was found between paediatric (≤ 18 years) and adult (> 18) patient cohorts (p = 0.95).

CONCLUSIONS: The following factors were found to be predictive for long-term incomplete nidus obliteration post-SRS for brain AVMs: SM grade equal to or higher than IV; the presence of multiple feeding arteries; AVM nidus volume exceeding 10 mL; and the occurrence of intranidal aneurysm. These findings will be beneficial in refining patient selection for radiosurgical treatment.

PMID:40079216 | DOI:10.5603/pjnns.103718

J Chem Theory Comput. 2025 Mar 13. doi: 10.1021/acs.jctc.4c01774. Online ahead of print.

ABSTRACT

Protein evolution has shaped enzymes that maintain stability and function across diverse thermal environments. While sequence variation, thermal stability and conformational dynamics are known to influence an enzyme’s thermal adaptation, how these factors collectively govern stability and function across diverse temperatures remains unresolved. Cytosolic malate dehydrogenase (cMDH), a citric acid cycle enzyme, is an ideal model for studying these mechanisms due to its temperature-sensitive flexibility and broad presence in species from diverse thermal environments. In this study, we employ techniques inspired by deep learning and statistical mechanics to uncover how sequence variation and conformational dynamics shape patterns of cMDH’s thermal adaptation. By integrating coevolutionary models with variational autoencoders (VAE), we generate a latent generative landscape (LGL) of the cMDH sequence space, enabling us to explore mutational pathways and predict fitness using direct coupling analysis (DCA). Structure predictions via AlphaFold and molecular dynamics simulations further illuminate how variations in hydrophobic interactions and conformational flexibility contribute to the thermal stability of warm- and cold-adapted cMDH orthologs. Notably, we identify the ratio of hydrophobic contacts between two regions as a predictive order parameter for thermal stability features, providing a quantitative metric for understanding cMDH dynamics across temperatures. The integrative computational framework employed in this study provides mechanistic insights into protein adaptation at both sequence and structural levels, offering unique perspectives on the evolution of thermal stability and creating avenues for the rational design of proteins with optimized thermal properties.

PMID:40079215 | DOI:10.1021/acs.jctc.4c01774

Infect Control Hosp Epidemiol. 2025 Mar 13:1-6. doi: 10.1017/ice.2025.9. Online ahead of print.

ABSTRACT

BACKGROUND: Concerns about penicillin-cephalosporin cross-reactivity have historically led to conservative prescribing and avoidance of cephalosporins in patients with penicillin allergy labels, potentially causing suboptimal outcomes. Recent evidence suggests a lower risk of cross-reactivity, prompting a reassessment of alert systems.

OBJECTIVE: To assess the impact of limited penicillin cross-reactivity alerts on outpatient cephalosporin use and the incidence of adverse reactions in a healthcare setting.

METHODS: This retrospective cohort study compared cephalosporin prescribing and adverse reactions in patients labeled as penicillin-allergic before and after limiting penicillin cross-reactivity alerts in the electronic medical record at a large academic medical center.

RESULTS: Among 17,174 patients (8,131 pre- and 9,043 post-implementation), there was a statistically significant increase in outpatient cephalosporin prescribing by 8% (P < .001). The use of alternative antibiotic classes decreased. There was no statistically significant increase in adverse events pre- and post-implementation (0.036%-0.058%, P = .547), and no severe events were attributable to cross-reactivity. The alert modification reduced alerts by 92% (P < .001).

CONCLUSION: The reduction of penicillin-cephalosporin cross-reactivity alerts was associated with increased cephalosporin use, without a significant increase in adverse reactions. This demonstrates that the practice is safe and decreases alert burden.

PMID:40079207 | DOI:10.1017/ice.2025.9

Birth Defects Res. 2025 Mar;117(3):e2463. doi: 10.1002/bdr2.2463.

ABSTRACT

BACKGROUND: Congenital heart defects (CHDs) are the most prevalent birth defects globally and the second leading cause of death in Mexican children under five. This study examines how industrial activity and social vulnerabilities independently and jointly influence CHD incidence across 2446 Mexican municipalities from 2008 to 2019.

METHODS: Using negative binomial regression models, we evaluated associations between polluting industries, healthcare access, and CHD incidence. We analyzed these factors independently, jointly, and through interaction terms to assess potential effect modification by healthcare access. Incidence rate ratios (IRRs) and 95% confidence intervals were estimated across healthcare access strata.

RESULTS: Municipalities without healthcare facilities were more likely to host polluting industries, highlighting structural inequities. The presence of polluting industries was associated with increased CHD incidence, even after adjusting for healthcare access. For instance, municipalities with poor healthcare access and two or more polluting industries exhibited a 42% higher CHD incidence (IRR = 1.42, 95% CI: 1.25-1.60) compared to a 26% increase in municipalities with better healthcare access (IRR = 1.26, 95% CI: 1.02-1.57).

CONCLUSIONS: These results show how environmental pollutant exposure and social vulnerabilities interact synergistically, disproportionately impacting socially vulnerable populations. Targeted policy interventions addressing both environmental pollution and healthcare inequities are crucial. Further research is also needed to clarify the mechanisms linking pollution to CHDs and to guide public health strategies aimed at reducing these disparities in Mexico.

PMID:40079194 | DOI:10.1002/bdr2.2463

BJOG. 2025 Mar 13. doi: 10.1111/1471-0528.18119. Online ahead of print.

ABSTRACT

BACKGROUND: Many studies have developed or validated prediction models to estimate the risk of stress urinary incontinence (SUI) in pregnant and postpartum women, but the quality of the model development and model applicability remains unknown.

OBJECTIVES: To systematically review and critically evaluate currently available prediction models for SUI in pregnant and postpartum women.

SEARCH STRATEGY: Cochrane Library, EBSCO, PubMed, Web of Science, EMBASE, Chinese CNKI, Wanfang and VIP databases were searched from inception until February 2024.

SELECTION CRITERIA: Prospective cohort or retrospective studies were considered eligible if they developed or validated prediction models for SUI in pregnant or postpartum women.

DATA COLLECTION AND ANALYSIS: Two reviewers independently screened the literature, extracted data and evaluated the quality of the included studies using PROBAST.

MAIN RESULTS: A total of 15 models were included. Eleven models were internally validated, including cross-validation and bootstrap and four models were externally validated. The most commonly used predictors were age, body mass index (BMI) and mode of delivery. The area under the curve or C-statistics reported by the modelling and validation groups ranged from 0.602 to 0.888. Only one study had a low risk of bias and 14 studies had a high risk of bias.

CONCLUSIONS: Fourteen models for predicting SUI in pregnant and postpartum women had a high risk of bias according to the PROBAST. Future research should focus on improving the methodological quality of the existing prediction models and developing new models.

PMID:40079163 | DOI:10.1111/1471-0528.18119

Arterioscler Thromb Vasc Biol. 2025 Mar 13. doi: 10.1161/ATVBAHA.124.320259. Online ahead of print.

ABSTRACT

There is a recent dramatic increase in research on thoracic aortic diseases that includes aneurysms, dissections, and rupture. Experimental studies predominantly use mice in which aortopathy is induced by chemical interventions, genetic manipulations, or both. Many parameters should be deliberated in experimental design in concert with multiple considerations when providing dimensional data and characterization of aortic tissues. The purpose of this review is to provide recommendations on guidance in (1) the selection of a mouse model and experimental conditions for the study, (2) parameters for standardizing detection and measurements of aortic diseases, (3) meaningful interpretation of characteristics of diseased aortic tissue, and (4) reporting standards that include rigor and transparency.

PMID:40079138 | DOI:10.1161/ATVBAHA.124.320259

J Biopharm Stat. 2025 Mar 13:1-22. doi: 10.1080/10543406.2024.2444242. Online ahead of print.

ABSTRACT

The restricted mean survival time has been widely used in the field of medical research because of its clear physical and simple clinical interpretation. In this paper, we propose an efficient estimation that incorporates the auxiliary restricted mean survival information into the estimation of the proportional hazard (PH) model. Compared to conventional models that do not incorporate available auxiliary information, the proposed method improves efficiency in estimating regression parameters by utilizing the double empirical likelihood method. We prove that the estimator asymptotically follows a multivariate normal distribution with a covariance matrix that can be consistently estimated. To address scenarios where the PH assumption is violated, we also extended the method to the stratified Cox model. In addition, simulation studies show that the proposed estimators are more efficient than those derived from the conventional partial likelihood approach. A type 2 diabetes dataset is then used to evaluate the risk of antidiabetic drugs and demonstrate the proposed method.

PMID:40079137 | DOI:10.1080/10543406.2024.2444242

Psychol Med. 2025 Mar 13;55:e85. doi: 10.1017/S0033291725000455.

ABSTRACT

BACKGROUND: Population-level preventive interventions are urgently needed and may be effective for psychosis due to social determinants. We tested three syndemic models along pathways from childhood adversity (CA) to psychotic spectrum disorder (PSD) and their implications for prevention.

METHODS: Cross-sectional data from 7461 British men surveyed in 5 population subgroups. We tested interactions on both additive and multiplicative scales for a syndemic of violence/criminality (VC), sexual behavior (SH), and substance misuse (SM) according to the presence of CA and adult traumatic life events; mediation analysis of path models; and partial least squares path modeling, with PSD as outcome.

RESULTS: Multiplicative synergistic interactions were found between VC, SH, and SM among men, who experienced CA and traumatic adult life events. However, when disaggregated, only SM mediated the pathway from CA to PSD. Path modeling showed traumatic life events acted on PSD through the syndemic and had no direct effect on PSD. Higher syndemic scores and living in areas of deprivation characterized men with PSD and CA.

CONCLUSIONS: Our findings support a broad division of PSD into cases due to (i) biological/inherent causes, and (ii) social determinants, the latter including a syndemic pathway determined by CA. Preventive strategies should focus primarily on preventing adverse effects of CA on developmental pathways which result in PSD. Single component prevention strategies may prevent triggering effects of SM on PSD during adolescence/early adulthood among vulnerable individuals due to CA. Future research should determine applicability and transferability of interventions based on these findings to different populations, specifically those experiencing syndemics.

PMID:40079091 | DOI:10.1017/S0033291725000455