Comparison of early postoperative patient-reported outcomes after multiportal robotic-assisted thoracoscopic surgery and uniportal video-assisted thoracoscopic surgery for non-small cell lung cancer.

INTRODUCTION: We aimed to compare early postoperative patient-reported outcomes between multiportal robotic-assisted thoracoscopic surgery (M-RATS) and uniportal video-assisted thoracoscopic surgery (U-VATS) for non-small-cell lung cancer (NSCLC). MATERIALS AND METHODS: Symptom severity and functional status were measured using the Perioperative Symptom Assessment for Lung Surgery at pre-surgery, during postoperative hospitalisation, and within 4 weeks of discharge. A propensity score-matched (PSM) analysis of patients with NSCLC who were treated with M-RATS and U-VATS was performed. The symptom severity and daily functional status presented as proportion of moderate-to-severe scores on a 0-10-point scale, were compared using a generalised estimation equation model. RESULTS: We enrolled 762 patients with NSCLC from a prospective cohort (CN-PRO-Lung 3), including 151 and 611 who underwent M-RATS and U-VATS, respectively, before PSM analysis. After 1:1 PSM, two groups of 148 patients each were created. Pain severity (P = 0.019) and activity limitation (P = 0.001) during hospitalisation were higher in the M-RATS group. However, no significant differences existed post-discharge in pain (P = 0.383), cough (P = 0.677), shortness of breath (P = 0.526), disturbed sleep (P = 0.525), drowsiness (P = 0.304), fatigue (P = 0.153), distress (P = 0.893), walking difficulty (P = 0.242), or activity limitation (P = 0.513). M-RATS caused less intraoperative blood loss (P = 0.013), more stations of dissected lymph nodes (P = 0.001), more numbers of dissected lymph nodes (P = 0.001), and less tube drainage on the first postoperative day (P = 0.003) than U-VATS. CONCLUSION: M-RATS and U-VATS achieved comparable symptom burden and functional impairment after discharge. However, compared to U-VATS, M-RATS was associated with more severe pain and activity limitation in the short postoperative period. TRIAL REGISTRATION NUMBER: ChiCTR2000033016.

PRIORITI: Phase 4 study of triptorelin or active surveillance in high-risk prostate cancer.

AIM: To evaluate the efficacy and safety of triptorelin after radical prostatectomy (RP) in patients with negative lymph nodes. METHODS: PRIORITI (NCT01753297) was a prospective, open-label, randomized, controlled, phase 4 study conducted in China and Russia. Patients with high-risk (Gleason score ≥ 8 and/or pre-RP prostate-specific antigen [PSA] ≥ 20 ng/mL and/or primary tumor stage 3a) prostate adenocarcinoma without evidence of lymph node or distant metastases were randomized to receive triptorelin 11.25 mg at baseline (≤ 8 weeks after RP) and at 3 and 6 months, or active surveillance. The primary endpoint was biochemical relapse-free survival (BRFS), defined as the time from randomization to biochemical relapse (BR; increased PSA > 0.2 ng/mL). Patients were monitored every 3 months for at least 36 months; the study ended when 61 BRs were observed. RESULTS: The intention-to-treat population comprised 226 patients (mean [standard deviation] age, 65.3 [6.4] years), of whom 109 and 117 were randomized to triptorelin or surveillance, respectively. The median BRFS was not reached. The 25th percentile time to BRFS (95% confidence interval) was 39.1 (29.9-not estimated) months with triptorelin and 30.0 (18.6-42.1) months with surveillance (p = 0.16). There was evidence of a lower risk of BR with triptorelin versus surveillance but this was not statistically significant at the 5% level (p = 0.10). Chemical castration was maintained at month 9 in 93.9% of patients who had received triptorelin. Overall, triptorelin was well tolerated and had an acceptable safety profile. CONCLUSION: BRFS was observed to be longer with triptorelin than surveillance, but the difference was not statistically significant.

In Situ Polymerized Zwitterionic Copolymer Ionic Gel Electrolytes with High Performance for Lithium-Ion Batteries.

Gel electrolytes are a promising research direction due to their high safety. However, its poor room temperature conductivity along with complex preparation process hinder its practical application. In this article, a type of zwitterionic gel electrolyte is prepared by in situ polymerization. The introduction of charged but nonmigrating zwitterionic copolymer in the polymer chain is beneficial to the dissociation of the lithium salt, improving the ion transport of the electrolyte on this account. At room temperature, the conductivity of lithium ion reaches 9.1 × 10-4 S cm-1, which contributes to achieve excellent electrochemical performance at high rates. The assembled Li|LiFePO4 cell also shows a capacity retention rate of 90.5% after 150 cycles at 0.5 C at room temperature as well as remarkable cycle stability at 1 C. These offer a novel tactic for the efficient and safe commercial application of lithium-ion batteries.

Enhanced Ferromagnetism and Tunable Magnetic Anisotropy in a van der Waals Ferromagnet.

2D van der Waals (vdW) magnets have recently emerged as a promising material system for spintronic device innovations due to their intriguing phenomena in the reduced dimension and simple integration of magnetic heterostructures without the restriction of lattice matching. However, it is still challenging to realize Curie temperature far above room temperature and controllable magnetic anisotropy for spintronics application in 2D vdW magnetic materials. In this work, the pressure-tuned dome-like ferromagnetic-paramagnetic phase diagram in an iron-based 2D layered ferromagnet Fe3GaTe2 is reported. Continuously tunable magnetic anisotropy from out-of-plane to in-plane direction is achieved via the application of pressure. Such behavior is attributed to the competition between intralayer and interlayer exchange interactions and enhanced DOS near the Fermi level. The study presents the prominent properties of pressure-engineered 2D ferromagnetic materials, which can be used in the next-generation spintronic devices.

The preparation of edible water-soluble films comprising κ-carrageenan/carboxymethyl starch/gum ghatti and their application in instant coffee powder packaging.

This study successfully prepared an edible packaging film that rapidly dissolves in water by utilizing a combination of κ-carrageenan, carboxymethyl starch, and gum ghatti. We investigated the influence of these three materials on the microstructure and physical properties of the film, as well as the impact of the film's dissolution on the stability of beverages. SEM, FTIR, and XRD analyses revealed that the κ-carrageenan, carboxymethyl starch, and gum ghatti primarily interacted through hydrogen bonding, resulting in a more uniform and dense film structure. Surface hydrophilicity and swelling tests indicated an increased presence of hydrophilic groups in the composite film. The inclusion of carboxymethyl starch and gum ghatti significantly improves the film's physical properties, resulting in a notable reduction in water solubility time, an increase in elongation at break from 19.5 % to 26.0 %, a rise in the contact angle from 49.1° to 67.0°, and a decrease in water vapor permeability from 7.5 × 10-10 to 6.2 × 10-10 g/m·s·Pa. Furthermore, coffee packaging bags made from this composite film dissolved entirely in hot water in just 40 s. Dissolving these bags significantly improved the stability of instant coffee, reducing centrifugal sedimentation from 3.8 % to 1.7 %. This study highlights the substantial potential of the κ-carrageenan/carboxymethyl starch/gum ghatti composite film as a packaging material for solid beverages.

A CRISPR/Cas9 screen in embryonic stem cells reveals that Mdm2 regulates totipotency exit.

During early embryonic development, the transition from totipotency to pluripotency is a fundamental and critical process for proper development. However, the regulatory mechanisms governing this transition remain elusive. Here, we conducted a comprehensive genome-wide CRISPR/Cas9 screen to investigate the 2-cell-like cells (2CLCs) phenotype in mouse embryonic stem cells (mESCs). This effort led to the identification of ten regulators that play a pivotal role in determining cell fate during this transition. Notably, our study revealed Mdm2 as a significant negative regulator of 2CLCs, as perturbation of Mdm2 resulted in a higher proportion of 2CLCs. Mdm2 appears to influence cell fate through its impact on cell cycle progression and H3K27me3 epigenetic modifications. In summary, the results of our CRISPR/Cas9 screen have uncovered several genes with distinct functions in regulating totipotency and pluripotency at various levels, offering a valuable resource for potential targets in future molecular studies.

Analysis of risk factors and construction of a prediction model for posttraumatic stress disorder among Chinese college students during the COVID-19 pandemic.

BACKGROUND: To explore the risk factors of post-traumatic stress disorder (PTSD) among Chinese college students during the COVID-19 pandemic and the construction and validation of risk prediction models. METHODS: A total of 10,705 university students were selected for the study. The questionnaire included the Generalized Anxiety Disorder 7 (GAD-7), Patient Health Questionnaire 9 (PHQ-9), PTSD Checklist for DSM-5 (PCL-5), and self-designed questionnaire. These assessments were conducted to facilitate the survey, construct the predictive model and validate the model's validity. RESULTS: Sex, left-behind experience, poverty status, anxiety score, and depression score were identified as independent risk factors influencing psychological trauma among Chinese college students during the COVID-19 pandemic, while COVID-19 infection emerged as a protective factor against psychological trauma. A column chart was constructed to visualize the six independent risk factors derived from logistic regression analysis. The Hosmer-Lemeshow test results (χ2 = 13.021, P = 0.111) indicated that the risk prediction model fitted well. The receiver operating characteristic (ROC) curve showed an area under the curve (AUC) of 0.864 in the model group and 0.855 in the validation group. The calibration curves of the model closely resembled the ideal curve. Decision curve analysis (DCA) revealed that the model provided net benefit and demonstrated good clinical utility. LIMITATIONS: The validation of the model is currently restricted to internal assessments. However, further confirmation through larger sample sizes, multicenter investigations, and prospective studies is necessary. CONCLUSIONS: The model effectively predicted PTSD risk among Chinese college students during the COVID-19 pandemic, indicating strong clinical applicability.

Diagnosis and Treatment of Presumed Naltrexone-XR-precipitated Opioid Withdrawal in a Patient Chronically Treated With Buprenorphine-XR: A Case Report.

ABSTRACT: Naltrexone, buprenorphine, and methadone are Food and Drug Administration-approved medications for the treatment of opioid use disorder in the United States. Naltrexone, an opioid antagonist, can precipitate opioid withdrawal if administered too quickly after the use of full or partial opioid agonists for those with either dependence or use disorder. We describe a case of severe precipitated opioid withdrawal syndrome after reported buprenorphine extended-release (XR) administration, despite the patient having been stable on buprenorphine-XR for several years, with no missed doses or recent opioid use. Naltrexone levels were sent and helped to diagnose suspected inadvertent naltrexone-XR administration in this patient, which was likely the etiology of his precipitated opioid withdrawal syndrome. We suggest the use of high-dose buprenorphine, as well as adjunctive medications including benzodiazepines, as a treatment strategy for naltrexone-XR precipitated withdrawal in the setting of chronic buprenorphine-XR treatment.

Electroencephalographic insights into the pathophysiological mechanisms of emergence delirium in children and corresponding clinical treatment strategies.

Emergence delirium is a common postoperative complication in patients undergoing general anesthesia, especially in children. In severe cases, it can cause unnecessary self-harm, affect postoperative recovery, lead to parental dissatisfaction, and increase medical costs. With the widespread use of inhalation anesthetic drugs (such as sevoflurane and desflurane), the incidence of emergence delirium in children is gradually increasing; however, its pathogenesis in children is complex and unclear. Several studies have shown that age, pain, and anesthetic drugs are strongly associated with the occurrence of emergence delirium. Alterations in central neurophysiology are essential intermediate processes in the development of emergence delirium. Compared to adults, the pediatric nervous system is not fully developed; therefore, the pediatric electroencephalogram may vary slightly by age. Moreover, pain and anesthetic drugs can cause changes in the excitability of the central nervous system, resulting in electroencephalographic changes. In this paper, we review the pathogenesis of and prevention strategies for emergence delirium in children from the perspective of brain electrophysiology-especially for commonly used pharmacological treatments-to provide the basis for understanding the development of emergence delirium as well as its prevention and treatment, and to suggest future research direction.

Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information.

The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous methods and produces the best results in a large-scale protein dynamics dataset; this model can accurately infer the dynamic information of a protein in only a few seconds. By learning effectively from experimental protein structure data and cryo-electron microscopy (cryo-EM) data integration, our approach is able to accurately identify the interactive bidirectional constraints and supervision between cryo-EM maps and PDB models in maximizing the dynamic prediction efficacy. Rigorous 5-fold cross-validation on the dataset demonstrates that RMSF-net achieves test correlation coefficients of 0.746 ± 0.127 at the voxel level and 0.765 ± 0.109 at the residue level, showcasing its ability to deliver dynamic predictions closely approximating molecular dynamics simulations. Additionally, it offers real-time dynamic inference with minimal storage overhead on the order of megabytes. RMSF-net is a freely accessible tool and is anticipated to play an essential role in the study of protein dynamics.

HiTE: a fast and accurate dynamic boundary adjustment approach for full-length transposable element detection and annotation.

Recent advancements in genome assembly have greatly improved the prospects for comprehensive annotation of Transposable Elements (TEs). However, existing methods for TE annotation using genome assemblies suffer from limited accuracy and robustness, requiring extensive manual editing. In addition, the currently available gold-standard TE databases are not comprehensive, even for extensively studied species, highlighting the critical need for an automated TE detection method to supplement existing repositories. In this study, we introduce HiTE, a fast and accurate dynamic boundary adjustment approach designed to detect full-length TEs. The experimental results demonstrate that HiTE outperforms RepeatModeler2, the state-of-the-art tool, across various species. Furthermore, HiTE has identified numerous novel transposons with well-defined structures containing protein-coding domains, some of which are directly inserted within crucial genes, leading to direct alterations in gene expression. A Nextflow version of HiTE is also available, with enhanced parallelism, reproducibility, and portability.

Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4.

Large language models (LLMs) are seen to have tremendous potential in advancing medical diagnosis recently, particularly in dermatological diagnosis, which is a very important task as skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases. Here we present SkinGPT-4, which is an interactive dermatology diagnostic system based on multimodal large language models. We have aligned a pre-trained vision transformer with an LLM named Llama-2-13b-chat by collecting an extensive collection of skin disease images (comprising 52,929 publicly available and proprietary images) along with clinical concepts and doctors' notes, and designing a two-step training strategy. We have quantitatively evaluated SkinGPT-4 on 150 real-life cases with board-certified dermatologists. With SkinGPT-4, users could upload their own skin photos for diagnosis, and the system could autonomously evaluate the images, identify the characteristics and categories of the skin conditions, perform in-depth analysis, and provide interactive treatment recommendations.

Multi-omic characterization of allele-specific regulatory variation in hybrid pigs.

Hybrid mapping is a powerful approach to efficiently identify and characterize genes regulated through mechanisms in cis. In this study, using reciprocal crosses of the phenotypically divergent Duroc and Lulai pig breeds, we perform a comprehensive multi-omic characterization of regulatory variation across the brain, liver, muscle, and placenta through four developmental stages. We produce one of the largest multi-omic datasets in pigs to date, including 16 whole genome sequenced individuals, as well as 48 whole genome bisulfite sequencing, 168 ATAC-Seq and 168 RNA-Seq samples. We develop a read count-based method to reliably assess allele-specific methylation, chromatin accessibility, and RNA expression. We show that tissue specificity was much stronger than developmental stage specificity in all of DNA methylation, chromatin accessibility, and gene expression. We identify 573 genes showing allele specific expression, including those influenced by parent-of-origin as well as allele genotype effects. We integrate methylation, chromatin accessibility, and gene expression data to show that allele specific expression can be explained in great part by allele specific methylation and/or chromatin accessibility. This study provides a comprehensive characterization of regulatory variation across multiple tissues and developmental stages in pigs.

Acupuncture modulation of the ACE/Ang II/AT1R and ACE2/Ang(1-7)/MasR pathways in the rostral ventrolateral medulla reduces sympathetic output and prevents cardiac injury caused by SHR hypertension.

Acupuncture can reduce blood pressure, heart rate (HR), and ameliorate cardiac damage by modulating the excitability of the sympathetic nervous system, but the exact mechanism of this effect remains unclear. This study investigated the potential mechanisms of acupuncture in the treatment of cardiac damage in hypertension. Spontaneously hypertensive rats (SHR) were used as the hypertension model with Wistar-Kyoto rats as the control. Manual acupuncture, electroacupuncture, and metoprolol were used as interventions. Systolic and diastolic blood pressure (SBP, DBP) plus HR were monitored with cardiac structure determined using Masson staining. Angiotensin II (Ang II) and norepinephrine in myocardium were detected with ELISA as was Ang(1-7) and gamma aminobutyric acid (GABA) in the rostral ventrolateral medulla (RVLM). Expression of mRNA for collagen type I (Col-I), Col-III, actin α1 (ACTA1), and thrombospondin 4 (THBS4) in myocardium was detected using real-time PCR. Expression of angiotensin converting enzyme (ACE), Ang II, angiotensin II type 1 receptor (AT1R), ACE2, and Mas receptor (MasR) proteins in RVLM was monitored using western blot. After manual acupuncture and electroacupuncture treatment, SHRs showed decreased SBP, DBP and HR, reduced myocardial damage. There was decreased expression of the ACE/Ang II/AT1R axis, and increased expression of the ACE2/Ang(1-7)/MasR axis within the RVLM. GABA levels were increased within the RVLM and norepinephrine levels were decreased in myocardial tissue. Metoprolol was more effective than either manual acupuncture or electroacupuncture. Acupuncture directed against hypertensive cardiac damage may be associated with regulation of ACE/Ang II/AT1R and the ACE2/Ang(1-7)/MasR pathway within the RLVM to reduce cardiac sympathetic excitability.

HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses.

While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identification is critical for developing vaccines and cancer immunotherapies. Current prediction methods are limited, primarily due to a lack of high-quality training epitope datasets and algorithmic constraints. To predict the exogenous HLA class II-restricted peptides across most of the human population, we utilized the mass spectrometry data to profile >223 000 eluted ligands over HLA-DR, -DQ, and -DP alleles. Here, by integrating these data with peptide processing and gene expression, we introduce HLAIImaster, an attention-based deep learning framework with adaptive domain knowledge for predicting neoepitope immunogenicity. Leveraging diverse biological characteristics and our enhanced deep learning framework, HLAIImaster is significantly improved against existing tools in terms of positive predictive value across various neoantigen studies. Robust domain knowledge learning accurately identifies neoepitope immunogenicity, bridging the gap between neoantigen biology and the clinical setting and paving the way for future neoantigen-based therapies to provide greater clinical benefit. In summary, we present a comprehensive exploitation of the immunogenic neoepitope repertoire of cancers, facilitating the effective development of "just-in-time" personalized vaccines.

M2 Macrophages Guide Periosteal Stromal Cell Recruitment and Initiate Bone Injury Regeneration.

The periosteum plays a critical role in bone repair and is significantly influenced by the surrounding immune microenvironment. In this study, we employed 10× single-cell RNA sequencing to create a detailed cellular atlas of the swine cranial periosteum, highlighting the cellular dynamics and interactions essential for cranial bone injury repair. We noted that such injuries lead to an increase in M2 macrophages, which are key in modulating the periosteum's immune response and driving the bone regeneration process. These macrophages actively recruit periosteal stromal cells (PSCs) by secreting Neuregulin 1 (NRG1), a crucial factor in initiating bone regeneration. This recruitment process emphasizes the critical role of PSCs in effective bone repair, positioning them as primary targets for therapeutic interventions. Our results indicate that enhancing the interaction between M2 macrophages and PSCs could significantly improve the outcomes of treatments aimed at cranial bone repair and regeneration.

Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for Radiology Reports.

In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging. Current metrics, such as Conventional Natural Language Generation (NLG) and Clinical Efficacy (CE), often fall short in capturing the semantic intricacies of clinical contexts or overemphasize clinical details, undermining report clarity. To overcome these issues, our proposed method synergizes the expertise of professional radiologists with Large Language Models (LLMs), like GPT-3.5 and GPT-4. Utilizing In-Context Instruction Learning (ICIL) and Chain of Thought (CoT) reasoning, our approach aligns LLM evaluations with radiologist standards, enabling detailed comparisons between human and AI-generated reports. This is further enhanced by a Regression model that aggregates sentence evaluation scores. Experimental results show that our "Detailed GPT-4 (5-shot)" model achieves a 0.48 score, outperforming the METEOR metric by 0.19, while our "Regressed GPT-4" model shows even greater alignment with expert evaluations, exceeding the best existing metric by a 0.35 margin. Moreover, the robustness of our explanations has been validated through a thorough iterative strategy. We plan to publicly release annotations from radiology experts, setting a new standard for accuracy in future assessments. This underscores the potential of our approach in enhancing the quality assessment of AI-driven medical reports.

Connection between protein-tyrosine kinase inhibition and coping with oxidative stress in Bacillus subtilis.

In bacteria, attenuation of protein-tyrosine phosphorylation occurs during oxidative stress. The main described mechanism behind this effect is the H2O2-triggered conversion of bacterial phospho-tyrosines to protein-bound 3,4-dihydroxyphenylalanine. This disrupts the bacterial tyrosine phosphorylation-based signaling network, which alters the bacterial polysaccharide biosynthesis. Herein, we report an alternative mechanism, in which oxidative stress leads to a direct inhibition of bacterial protein-tyrosine kinases (BY-kinases). We show that DefA, a minor peptide deformylase, inhibits the activity of BY-kinase PtkA when Bacillus subtilis is exposed to oxidative stress. High levels of PtkA activity are known to destabilize B. subtilis pellicle formation, which leads to higher sensitivity to oxidative stress. Interaction with DefA inhibits both PtkA autophosphorylation and phosphorylation of its substrate Ugd, which is involved in exopolysaccharide formation. Inactivation of defA drastically reduces the capacity of B. subtilis to cope with oxidative stress, but it does not affect the major oxidative stress regulons PerR, OhrR, and Spx, indicating that PtkA inhibition is the main pathway for DefA involvement in this stress response. Structural analysis identified DefA residues Asn95, Tyr150, and Glu152 as essential for interaction with PtkA. Inhibition of PtkA depends also on the presence of a C-terminal α-helix of DefA, which resembles PtkA-interacting motifs from known PtkA activators, TkmA, SalA, and MinD. Loss of either the key interacting residues or the inhibitory helix of DefA abolishes inhibition of PtkA in vitro and impairs postoxidative stress recovery in vivo, confirming the involvement of these structural features in the proposed mechanism.

HELM-GPT: de novo macrocyclic peptide design using generative pre-trained transformer.

MOTIVATION: Macrocyclic peptides hold great promise as therapeutics targeting intracellular proteins. This stems from their remarkable ability to bind flat protein surfaces with high affinity and specificity while potentially traversing the cell membrane. Research has already explored their use in developing inhibitors for intracellular proteins, such as KRAS, a well-known driver in various cancers. However, computational approaches for de novo macrocyclic peptide design remain largely unexplored. RESULTS: Here, we introduce HELM-GPT, a novel method that combines the strength of the hierarchical editing language for macromolecules (HELM) representation and generative pre-trained transformer (GPT) for de novo macrocyclic peptide design. Through reinforcement learning (RL), our experiments demonstrate that HELM-GPT has the ability to generate valid macrocyclic peptides and optimize their properties. Furthermore, we introduce a contrastive preference loss during the RL process, further enhanced the optimization performance. Finally, to co-optimize peptide permeability and KRAS binding affinity, we propose a step-by-step optimization strategy, demonstrating its effectiveness in generating molecules fulfilling both criteria. In conclusion, the HELM-GPT method can be used to identify novel macrocyclic peptides to target intracellular proteins. AVAILABILITY AND IMPLEMENTATION: The code and data of HELM-GPT are freely available on GitHub (https://github.com/charlesxu90/helm-gpt).

Water-Based Continuous Fabrication of Highly Elastic Electromagnetic Fibers.

Elastic electromagnetic fibers are promising building blocks for next-generation flexible electronics. However, fabrication of elastic fibers is still difficult and usually requires organic solvents or high temperatures, restricting their widespread applications. Furthermore, the continuous production of electromagnetic fibers has not been realized previously. In this study, we propose an ionic chelation strategy to continuously produce electromagnetic fibers with a magnetic liquid metal (MLM) as the core and elastic polyurethane as the sheath in water at room temperature. Sodium alginate (SA) has been introduced to rapidly chelate with calcium ions (Ca2+) in a coagulation bath to support the continuous spinning of waterborne polyurethane (WPU) as a sheath. Meanwhile, WPU-encapsulated MLM microparticles efficiently suppress the fluid instability of MLM for continuous extrusion as the core. The resultant fiber exhibits excellent mechanical performances (tensile strength and toughness up to 32 MPa and 124 MJ/m3, respectively), high conductive stability in large deformations (high conductivity of 7.6 × 104 S/m at 580% strain), and magnetoactive properties. The applications of this electromagnetic fiber have been demonstrated by conductance-stable wires, sensors, actuation, and electromagnetic interference shielding. This work offers a water-based molecular principle for efficient and green fabrication of multifunctional fibers and will inspire a series of applications.