Citations

Abstract Background The number of cancer survivors aged older than 65 years is rising rapidly. Current evidence‐based exercise guidelines lack specific guidance for older cancer survivors as a result of insufficient evidence. An expert panel was convened to develop consensus‐based recommendations for exercise in older cancer survivors. Methods The development of recommendations was guided by the Grading of Recommendations Assessment, Development, and Evaluation Evidence‐to‐Decision framework for good practice statements. The panel drew from the available literature, a Delphi survey of exercise and health professionals, other exercise guidelines, clinical and research expertise, and interest‐holder input provided by a community advisory board of older cancer survivors and caregivers ( n = 11). Recommendations had to be deemed accessible (i.e., no added barriers) to older cancer survivors and feasible to implement. The panel voted on the strength of the recommendation for or against each statement, with consensus set at 85% agreement. Results Consensus was reached on 11 recommendations covering the following areas: medical evaluation/clearance for exercise, pre‐exercise assessment, exercise prescription, exercise tolerance and safety, exercise delivery, and behavioral support. The recommendations aimed to promote engagement in and uptake of appropriately prescribed exercise programming by older cancer survivors, while keeping barriers and risks as low as possible. Conclusions Older cancer survivors can benefit from appropriately prescribed exercise, which should be an essential component of their cancer care. Exercise and health professionals need to consider the unique needs of older cancer survivors to ensure that exercise is safe and effective for this population, while also reducing barriers to reach as many people as possible.

Abstract Background Many risk factors can contribute to the occurrence of Alzheimer`s Disease (AD). However, little is known about the impact of dementia risk factors to the uptake of tau‐PET tracers. Therefore, in this work we aim to investigate the influence of dementia risk factors and comorbidities on 18 F‐Flortaucipir (FTP) and 18 F‐MK6240 (MK) tau‐PET tracers’ uptake. Additionally, we will assess how these factors impact the association of tau‐PET and cognition. Method We accessed 436 individuals across the aging and AD spectrum (251 amyloid negative and 185 amyloid positive) from the HEAD study, with available Aβ‐PET, FTP, MK, and clinical assessments. Linear regression models corrected for age, sex, clinical diagnosis, and study site tested the association of factors with tau‐PET tracers in the medial temporal lobe (MTL). A tau‐PET × risk factor term was added to test the influence of risk factors to the association of tau with cognition. Result In amyloid‐β negative individuals, high BMI were positively associated with the uptake of both FTP and MK, whereas hearing loss were positively associated only with MK in the MTL (Figure 1A). In amyloid‐β positive individuals, high body mass index (BMI), hearing loss and sleep disorders were negatively associated with the uptake of both tau‐PET tracers in the MTL. On the other hand, hypertension showed negative association only with MK uptake (Figure 1B). Using Mini‐Mental State Examination (MMSE) scores as outcome, we observed that amyloid‐β negative individuals with high BMI showed worse cognitive performance as a function of both MK and FTP in the MTL, whereas individuals with vision impairment and hearing loss showed worse cognitive performance as a function of MK only (Figure 2A). Amyloid‐β positive individuals with hypercholesterolemia and hypertension presented worse cognitive performance as a function of both MK and FTP in the MTL (Figure 2B). Conclusion In this preliminary analysis, sleep disorders, hypertension, and high BMI were independently associated with tau‐PET tracer uptake, with the effects varying according to amyloid‐β pathology. These prevalent factors in the elderly also changed the association between tau‐PET and cognition, underscoring the need for further studies to better understand their role in modulating this relationship.

Background Deep brain stimulation (DBS) has been investigated for patients with drug-resistant epilepsy who are not candidates for resective surgery. Because different types of epilepsy involve distinct brain networks, numerous DBS targets have been explored, yet a comprehensive synthesis is lacking. Methods To provide a comprehensive overview of this expanding literature, we conducted a systematic review of studies for DBS in epilepsy, including case series, prospective and retrospective studies. We collected data on surgical targets, individual disease characteristics, outcomes and precise electrode placements. DBS electrode coordinates were gathered into a common template space and related to clinical outcomes. Results We included 124 studies, corresponding to 1210 patients and 20 distinct surgical targets. While the anterior (ANT) and centromedian (CM) nuclei of the thalamus remain the most studied, we also review less commonly used targets that show promise for specific forms of epilepsy and may warrant further investigation. Substantial variability in targeting strategies and electrode placement was observed within each of the target regions. Importantly, significant relationships between stimulation location and outcomes were identified for ANT-DBS and CM-DBS. For ANT-DBS, shorter distance to the mammillothalamic tract junction was associated with greater seizure reduction on both study-level and patient-level analyses (r=−0.55, p<0.001 and r=−0.51, p<0.001, respectively). For CM-DBS, localisation effects may be dependent on the form of epilepsy, with stimulation of the parvocellular CM being associated with better outcomes in generalised epilepsy. Conclusions Our results emphasise the importance of accurate targeting in DBS for epilepsy. Our database and atlas of DBS targets are made publicly available, potentially serving further meta-analytical work. PROSPERO registration number CRD420250649304.

Learning from multimodal data sets can leverage complementary information and lead to improved performance for prediction tasks. A commonly used strategy to account for feature correlations in high-dimensional data sets is the latent variable approach. Several latent variable methods have been proposed for multimodal data sets; however, these methods either focus on extracting a shared component across all modalities or extracting a shared component and individual components specific to each modality, overlooking correlations within partial subsets of modalities. We propose multimodal fission learning (MMFL), the first supervised latent variable model that adopts a generalizable decomposition into globally joint, partially joint, and individual components from multimodal data sets. A key strength of MMFL is a natural extension to incorporate incomplete multimodal data in either training and test phases by leveraging the learned modality structure. Through simulation studies, we demonstrate that MMFL outperforms a variety of existing multimodal algorithms under both complete modality and incomplete modality settings. We applied MMFL to two real-world case studies: early prediction of Alzheimer’s disease using neuroimaging and genetic data and predicting posttraumatic headache improvement using clinical data collected via questionnaires and brain neuroimaging data. MMFL achieved improved predictive accuracy and enhanced interpretability offering insights for within- and cross-modal relationships of multimodal data sets. History: Rema Padman served as the senior editor for this article. Funding: This research was supported by the National Institutes of Health [Grants 2R42AG053149-02A1, R01AG069453, 30AG072980, R01AG069453, and 1R61NS113315-01], the National Science Foundation [Grant DMS-2053170], and the Department of Defense [Award W81XWH-19-0534]. The grantee organization is the Northern California Institute for Research and Education. In the past, ADNI has also received funding from the National Institute of Biomedical Imaging and Bioengineering, the Canadian Institutes of Health Research, and private sector contributions through the Foundation for the National Institutes of Health including generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. Data Ethics & Reproducibility Note: The code capsule is available at https://github.com/lingchm/MMFL and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2024.0059 ).

Glutathione synthetase (GSS) deficiency is an extremely rare autosomal recessive inborn error of metabolism. This metabolic disorder is caused by mutations in the GSS gene, which encodes GSS—an enzyme that catalyses the ATP-dependent conversion of γ-glutamyl-cysteine and glycine to glutathione. Low glutathione impairs the cellular antioxidant defence mechanism, leaving cells susceptible to oxidative stress and damage.

Abstract Introduction: Maintenance of donor lung pressure is recommended to maintain inflation pressures of 12-15 mm Hg. This is particularly important given graft transportation in aircraft, due to the inevitable pressure changes. Case: We describe a case of a donor lung preservation system which maintained proper airway pressure during sudden cabin pressure loss requiring emergent landing. Discussion: The donor lungs were implanted without complication. Post-transplant course was notable for primary graft dysfunction (PGD) grade 1 at 24 hours, grade 2 at 48 hours, and grade 1 at 72 hours. At 3 months post-transplant, the patient has had no respiratory complications and has been noted to be doing well.

ABSTRACT Artificial intelligence (AI), encompassing machine learning, deep learning and generative AI, is poised to redefine nephrology by enabling earlier detection, more precise risk stratification and workflow-integrated clinical decision support across the spectrum of kidney disease. This state-of-the-art review synthesizes emerging applications of AI in acute kidney injury (AKI), chronic kidney disease (CKD), dialysis and kidney transplantation, with attention to clinical integration, real-world validation, workflow implementation and translational challenges. In AKI, predictive models trained on high-frequency electronic health record data and intensive care unit telemetry have demonstrated strong performance in forecasting critical events, yet translation into routine clinical workflows remains limited. In CKD, machine learning tools support progression risk stratification and phenotype clustering, with the potential to inform individualized surveillance and therapy. AI-enabled dialysis management systems optimize ultrafiltration, anemia control and vascular access surveillance, while generative AI and large language models offer novel capabilities for clinical documentation, triage and patient education. In transplantation, AI applications span organ allocation, dynamic graft monitoring and digital pathology-assisted rejection classification, with validated tools such as the iBox system gaining regulatory recognition. Implementation challenges include data heterogeneity, bias, interpretability, regulatory uncertainty and workflow integration. Looking ahead, multimodal integration of imaging, pathology and multi-omics data may support biologically informed precision nephrology. Reinforcement learning, digital twins and ambient intelligence are emerging as adaptive decision-support paradigms rather than autonomous care systems. Regulatory frameworks are evolving to accommodate adaptive algorithms, underscoring the need for clinician engagement in model development, validation and deployment. As AI matures from pilot to practice, nephrologists who embrace and help shape these tools will lead the transition toward a more personalized, efficient and equitable future for kidney care.

Abstract Aquagenic palmoplantar keratoderma is a rare dermatological condition characterized by mild palmar thickening and burning pain upon exposure to water. A 31-year-old female with aquagenic keratoderma of the palms associated with palmoplantar hyperhidrosis presenting with wrinkling and hyperkeratosis within minutes of hand contact with water, resolving quickly after drying. While cystic fibrosis is a known association, most cases remain idiopathic. Treatment options such as 20% aluminum hydroxide, aluminum chloride (15%–20%), urea, salicylic acid, and botulinum toxin can be challenging, with the patient being evaluated after treatment with 16% aluminum chloride.

Abstract Background The incidence of early onset breast cancers (BCs) has increased, paralleling rising trends in delayed childbearing. We hypothesize that a distinct postpartum BC (PPBC) subtype, identifiable by time since last birth (TSLB) and biomarker expression, contributes to this trend. Methods We applied GeoMx Digital Spatial Profiling (DSP) to measure associations between TSLB and 71 proteins in 640 BCs from women aged ≤40 years included in the Young Women’s Breast Cancer Study. We analyzed data using univariable linear regression and multivariable sliced inverse regression to account for higher order interactions among biomarkers. Results In keratin-rich segments, progesterone receptor (PR) (P = 1.00 × 10−4) and PTEN (P = 1.00 × 10−3) were associated with longer TSLB; multivariable analyses revealed positive associations for GZMB (P = 4.00 × 10−4), SMA (P = 1.00 × 10−4), and NF-1 (P = 1.00 × 10−3). In keratin-poor segments, univariable significant positive associations were found for PR (P = 2.00 × 10−4) and PTEN (P = 1.00 × 10−3), whereas CD20 (P = 3.00 × 10−4) and CTLA4 (P = 4.00 × 10−5) were negatively associated; multivariable significant associations were found for fibronectin (P = 3.00 × 10−5) and pan-Akt (P = 1.00 × 10−3). Associations persisted after adjustment for multiple comparisons and BC molecular subtypes. Associations including for PR, PTEN, and CD20 were strongest among women with the shortest TSLB. OPAL multiplex immunofluorescence assays for PR, PTEN, CD20, SMA, and CTLA4 replicated several DSP findings, particularly when stratified by subtype and with compartment matching. In TCGA, RNA species linked to proteins associated with TSLB correlated strongly with a T-cell exhaustion signature previously linked to poor prognosis among premenopausal women. Conclusion These data support PPBC as a biologically coherent phenotype defined by TSLB and biomarker profile, with potential implications for prevention and therapy.

Abstract Background Critical illness is a risk factor for carnitine deficiency. Carnitine deficiency may result in serious medical complications and poor clinical outcomes. This study aimed to identify the prevalence and potential predictors of carnitine deficiency. Methods This was a descriptive cross‐sectional study conducted in a convenience sample of 144 critically ill adults admitted to a surgical or cardiovascular intensive care unit at the University of Utah Hospital, with serum carnitine levels measured from January 1, 2022, to December 31, 2024. Binary and multivariable logistic regression models were constructed to explore potential predictors of carnitine deficiency, which was defined as a free serum carnitine level <36 µmol/L. Results The mean age of patients was 56.5 years; 41.7% of the sample had carnitine deficiency. For each unit decrease in body mass index, the odds of developing carnitine deficiency increased by 5% compared with maintaining normal free carnitine status (odds ratio [OR], 0.95; 95% confidence interval [CI], 0.9–0.99). Those prescribed continuous renal replacement therapy were 2.43 times more likely to develop carnitine deficiency than those not on continuous renal replacement therapy (OR, 2.43; 95% CI, 1.12–5.25). Conclusion A high proportion of our study population had carnitine deficiency. Continuous renal replacement therapy and a lower body mass index were significant predictors in the multivariable model. Female sex and a history of chronic kidney disease may warrant further exploration based on significance in the univariable analysis.