Citations

SUMMARY Artificial intelligence (AI)-based methods are increasingly critical in peptide drug discovery, but their applications are limited in a narrow scope such as antimicrobial peptides. For indications where peptide therapy inherently excels, such as metabolism, endocrinology, and tissue regeneration, AI-based pipeline for therapeutic peptide discovery is long awaited but still unmet. Here, we propose a deep learning-based pipeline, Deepeptide, capable of identifying therapeutic oligopeptides for various metabolism-related indications. Leveraging the intrinsical relationship of ‘disease indication — biological processes — molecular functions’, Deepeptide discovers oligopeptides with indication-ameliorating-related molecular functions as lead candidates for indication of interest. Deepeptide was applied in five representative indications of metabolism, endocrinology, and tissue regeneration: angiogenesis, lipid metabolism, osteogenesis, glucose metabolism, and anti-angiogenesis. Overall, 62% of the identified oligopeptide candidates demonstrated significant bioactivity in vitro, with most of them showing comparable potency to the first-line drugs. Notably, the heptapeptide AP7 exhibited angiogenic potency comparable to VEGF in excisional wound splinting mouse model by promoting cell migration rather than proliferation, and hexapeptide TP6 showed significant dual-efficacy against hyperlipidemia and obesity in high-fat diet mice by inhibiting lipid synthesis and regulating gut microbiota. These findings highlight the potential and generalizability of Deepeptide in therapeutic oligopeptide discovery for metabolic diseases.

Purpose: This viewpoint addresses the complexity of the nomenclature of progressive speech-language disorders, which often exists at the intersection of descriptive symptoms, clinical syndromes, and underlying neuropathology. Using primary progressive apraxia of speech (PPAOS) as an example, we explore how overlapping diagnostic frameworks employed by speech-language pathologists, neurologists, and other clinicians and researchers can lead to different diagnostic labels that may evolve throughout disease progression. These discrepancies and changes can complicate patient understanding, clinical care, and affect eligibility for clinical trials. We highlight the importance of diagnostic specificity, flexibility, and multidisciplinary collaboration in improving patient counseling, prognostication, treatment planning, and clinical research alignment, particularly in the context of the growing number of clinical trials and specialized, multidisciplinary clinics. Conclusions: This viewpoint explores how symptom-based, syndrome-based, and pathology-based diagnoses can work together to support patient care and research. Accurate and specific diagnosis of speech-language disorders has important implications for patient understanding of their condition, identity, prognostic accuracy, access to care, clinical trial enrollment, and treatment planning and efficacy. Misdiagnosis or vague labeling can result in inappropriate interventions, delayed support, and skewed research findings. We argue that multidisciplinary collaboration and flexibility in recognizing evolving symptom-based and syndrome-based labels over the disease course—and as the fields learn more—is essential to empower patients with accurate, actionable information. Embracing both the specificity and the complexity of progressive speech-language disorders can both enhance communication and facilitate research.

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.