Biomedical subjects

Han Li

Publications and source records attributed to Han Li.

4 recordsLinked to original sources

PubMed2026

A sequence motif enables widespread use of noncanonical redox cofactors in natural enzymes.

Noncanonical redox cofactors (NRCs) are low-cost alternatives to the natural redox cofactors nicotinamide adenine dinucleotide (NAD+) and nicotinamide adenine dinucleotide phosphate (NADP+) for biomanufacturing, offering exquisite electron-delivery control, yet their adoption is limited by the scarcity of compatible enzymes. Screening the aldehyde dehydrogenase (ALDH) family, we identified a conserved RH/QxxR motif that enables widespread NRC activity among natural enzymes. Bos taurus ALDH3a1 exhibits unprecedented turnover with nicotinamide mononucleotide (NMN+), with kcat values exceeding NAD+ and surpassing most engineered NRC-active enzymes by 10-105-fold. Structural analyses reveal that this motif reinforces cofactor positioning and preorganizes the active site independently of the NAD+ adenosine monophosphate moiety. This motif supports activity across simple-synthetic NRCs such as 1-(2-carbamoylmethyl)nicotinamide and, when introduced into diverse ALDH scaffolds, enhances NMN+ activity up to 60-fold. These findings elucidate nature's solution to engineering NRC-active enzymes and offer a blueprint to mine latent evolutionary plasticity in natural enzymes that serve as superior engineering starting points.

Journal Article↗

PubMed2026

ProMeta: a meta-learning framework for robust disease diagnosis and prediction from plasma proteomics.

MOTIVATION: The plasma proteome offers a dynamic window of human health, capturing the real-time intersections between genetics and physiology. However, the application of deep learning to proteomics is currently hindered by a reliance on large-scale labeled datasets, rendering standard models ineffective for rare or novel diseases where patient samples are inherently scarce. RESULTS: Here, we present ProMeta, a meta-learning framework designed to enable robust disease modeling under extreme data restrictions. By integrating knowledge-guided pathway encoding with bi-level meta-optimization, ProMeta projects unstructured proteomic profiles into biologically interpretable functional tokens. This architecture allows the model to learn a global initialization containing transferable biological priors from biobank-scale data, facilitating rapid adaptation to novel tasks. Through comprehensive benchmark experiments, ProMeta consistently outperformed transfer learning and traditional machine learning baselines in both disease diagnosis and prediction tasks. In the most challenging 4-shot scenarios (utilizing only 2 cases and 2 controls), the model achieved robust generalization with an average AUROC of ∼0.69, representing a 24.6% relative improvement over the best-performing baseline methods. Mechanistic investigation revealed that ProMeta disentangles cases from controls in the latent space prior to task-specific adaptation, confirming the acquisition of universal biological rules rather than rote memorization. Furthermore, gradient-based interpretation identified disease-specific protein biomarkers and functional pathways consistent with known pathophysiology. Collectively, ProMeta overcomes the data-scarcity bottleneck in precision medicine, providing a scalable, interpretable framework for characterizing the full spectrum of human diseases, particularly for rare conditions lacking extensive clinical cohorts. AVAILABILITY AND IMPLEMENTATION: The source code of ProMeta is available at GitHub (https://github.com/lihan97/ProMeta).

Proteomics↗

PubMed2026

Journey Mapping of the Patient Experience from Diagnosis to End of Life in Lung Cancer: A Qualitative Meta-Synthesis.

OBJECTIVES: This study aimed to systematically synthesize the lived experiences and journey narratives of lung cancer patients across disease stages, and identify key tasks and pain points during the disease course through patient journey mapping, providing evidence for comprehensive disease management throughout the patient journey. METHODS: Ten databases, including PubMed, Embase, Web of Science, Scopus, PsycINFO, CINAHL, Cochrane Library, CNKI, Wanfang, and SinoMed, were systematically searched, with a search period from database inception to August 15, 2025. The JBI Critical Appraisal Tool for qualitative studies was used to evaluate the quality of studies, and the results were integrated using a meta-aggregative approach. RESULTS: Thirteen studies were included. Based on the patient journey mapping, the lung cancer patient journey comprises four potential stages: evaluation and diagnosis, initial treatment, maintenance therapy, and end-of-life. A total of 30 themes emerged within three dimensions: tasks, emotions, and pain points. Each dimension of each stage consists of 2-3 themes. CONCLUSION: The journey of lung cancer patients is protracted and complex, characterized by stage-specific needs and challenges. Future management strategies should be tailored to these distinct phases, providing precision supportive care to optimize treatment outcomes and enhance patients' quality of life. IMPLICATIONS FOR NURSING PRACTICE: This Patient Journey Map integrates routine clinical pathways with patients' lived experiences across each stage, revealing stage-specific challenges and providing targets for tailored nursing interventions. The framework promotes multidisciplinary, digitally enabled supportive care and indicates the importance of including patients' social circles to enhance patient-centered outcomes.

Humans↗

PubMed2025

A Sequence Motif Enables Widespread Use of Non-Canonical Redox Cofactors in Natural Enzymes.

Non-canonical redox cofactors (NRCs) are promising alternatives to nicotinamide adenine dinucleotide (phosphate) (NAD(P)+) for biomanufacturing due to low cost and exquisite electron delivery control, yet their adoption is limited by the scarcity of compatible enzymes. Here, we screened the aldehyde dehydrogenase (ALDH) protein family and identified a conserved RH/QxxR sequence motif that enables widespread NRC activity among natural enzymes. Bos taurus ALDH3a1 and Pseudanabaena biceps ALDH exhibit unprecedented turnover with nicotinamide mononucleotide (NMN+), with kcat values matching or exceeding that of NAD+ and surpassing most engineered NRC-active enzymes by 10 to 105-fold, based on the relative NRC to native activity. Structural and dynamic analyses reveal this motif reinforces cofactor positioning and pre-organizes the active site without dependence on the adenosine monophosphate moiety of NAD+. When introduced into diverse ALDH scaffolds, the RH/QxxR motif enhances NMN+ activity up to 60-fold. In addition to NMN+, this motif also supports activity across multiple non-nucleotide, simple synthetic NRCs such as 1-(2-carbamoylmethyl)nicotinamide (AmNA+). These findings elucidate Nature's solution to the engineering challenge of obtaining NRC-active enzymes and offers a blueprint to mine latent evolutionary plasticity in natural enzymes that serve as superior engineering starting points.

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