Browse Articles

Wide local excision margins in melanoma: a systematic review and network meta-analysis

Abstract Wide local excision (WLE) is essential in melanoma treatment to achieve clear margins. However, consensus on optimal excision margins to prevent local recurrence (LR) and increase melanoma-specific survival (MSS) is lacking. The aim of this study was to investigate the effect of surgical margins on LR and 5-year MSS in the treatment of patients with stage II melanoma (pT2b-pT4b, AJCC 8th edition). A literature search was performed in PubMed, EMBASE and Cochrane Library in October 2023. The network meta-analysis (NMA) was performed on LR and MSS, implementing a random effects model. A meta-regression analysis was performed to explore the association between margins and treatment effect. Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines were used. A total of 12 articles with 3988 patients were included. Margins of 1 cm compared to margins of 4 cm resulted in a higher OR for LR (OR 2.61; 95%CI [1.13–6.02]). MSS showed no statistically significant differences between 1 and 2 cm group (OR 1.22; 95%CI [0.86–1.74]). Comparable results for MSS were found comparing 1 cm versus 4 cm (OR 1.36; 95%CI [0.86–2.13]). The meta-regression showed a significant negative association between WLE margins and LR: OR = 0.45 per additional cm (95%CI [0.23–0.90], p  = 0.023). Although 1 cm margins may not yet be established as standard care, the NMA offers an attractive alternative to summarize present findings and complement ongoing RCTs. Based on these results, a smaller margin tends to increase LR, but does not impact the MSS. This emphasizes the importance of the final results in the MelMarT-II study.

A synaptic locus of song learning

An Ultrasonic Electrolysis Strategy for Monomethylamine Synthesis From Nitrate and CO ₂

ABSTRACT Electrocatalytic C─N coupling offers a feasible route for sustainable synthesis of monomethylamine (MMA), yet it still suffers from poor C/N reactant coactivation and low conversion efficiency. In this study, we propose an innovative ultrasonic electrolysis strategy for efficient MMA electrosynthesis from NO 3 − and CO 2 . A bimetallic COF catalyst (CuBi‐ppc) is rationally designed and employed as a model catalyst. Mechanistic investigations reveal that ultrasonic electrolysis induces the unique acoustic cavitation effect which enriches CO 2 /NO 3 − reactants, promotes water dissociation for active H generation and stabilizes key C/N intermediates to facilitate C─N bond formation, consequently creating a favorable microenvironment to promote C─N coupling for selective MMA synthesis. Under the optimized ultrasonic electrolysis conditions, we achieve the high MMA yield rate of 144.89 mmol h −1 g −1 and Faradaic efficiency of 91.86% along with an excellent stability. These results highlight the great potential of ultrasonic electrolysis strategy for powerful and sustainable organonitrogen electrosynthesis.

Multimodal learning for clinically consistent RGP fitting in keratoconus

Abstract Keratoconus is a progressive corneal disorder characterized by highly heterogeneous corneal morphology, which makes rigid gas permeable (RGP) lens fitting strongly dependent on clinician experience and iterative trial processes. This procedure is often time-consuming and may lead to patient discomfort. Existing artificial intelligence approaches typically rely on either corneal imaging or clinical parameters alone, limiting their ability to capture the complex coupling relationships underlying lens fitting. In this study, we propose a unified multimodal deep learning framework, termed CME-Net, to jointly predict key RGP fitting parameters, including base curve (BC), lens diameter (Dia), and fitting strategy. The framework integrates boundary artifact suppression preprocessing with a dual-branch feature extraction network and employs structured multimodal feature fusion to explicitly model interactions between corneal topography and clinical indicators. A unified output head enables collaborative multi-task inference. By integrating complementary information from multiple data sources within a unified framework, the proposed method enables a more comprehensive characterization of corneal morphology and its clinical implications. The proposed method was developed and evaluated using a labeled analytic cohort of 368 keratoconus eyes with complete usable reference labels, derived from a retrospective cohort of 515 eyes collected at the Eye Center of the Second Affiliated Hospital, Zhejiang University School of Medicine. Under the held-out evaluation protocol, CME-Net achieved mean absolute errors of 0.2178 mm for BC and 0.1250 mm for Dia. Compared with the strongest unimodal baseline, the proposed framework reduced the Total MAE by approximately 15%. These results suggest that multimodal integration can enhance the accuracy and consistency of RGP lens fitting prediction. The proposed framework may provide supportive initial fitting guidance for personalized RGP fitting in keratoconus, while clinician oversight remains necessary, particularly for boundary or high-error cases.

Fully Conjugated Pseudo‐3D Covalent Organic Frameworks With Accelerated Electron Transport for High‐Performance Lithium‐Ion Batteries

ABSTRACT Organic electrodes are promising candidates for sustainable lithium‐ion batteries due to their environmental friendliness and tunable architectures, yet their practical application is hindered by low electronic conductivity and slow reaction kinetics. Here, we report a fully conjugated pseudo‐three dimensional (3D) covalent organic framework (COF) (TB‐COF‐3D‐S) derived from a two‐dimensional (2D) imine‐linked porphyrin‐diacetylene COF via one‐pot high‐temperature sulfurization, which simultaneously converts imine bonds into planar thiazole units and induces diacetylene crosslinking to form a 3D π‐conjugated network. This architecture markedly enhances both electronic and ionic conductivity while providing abundant redox‐active sites. Consequently, TB‐COF‐3D‐S delivers a high specific capacity of 313 mAh g −1 and an energy density of 670 Wh kg −1 at 0.1 A g −1 , excellent rate performance (96 mAh g −1 at 10 A g −1 ), and stable cycling with 0.0035% capacity decay per cycle over 10,000 cycles. Our study highlights the crucial role of 3D full conjugation in promoting rapid charge transport and efficient redox utilization, offering valuable guidance for designing high‐performance organic cathodes.

Adaptive primal–dual Q-learning for electric vehicle route optimization on real-world charging networks

Abstract Electric Vehicles (EVs) are emerging as sustainable alternatives to internal combustion engine vehicles; however, efficient route planning remains a major challenge due to limited driving range, sparse charging infrastructure, and variable energy consumption patterns. Traditional shortest-path algorithms, such as Dijkstra’s and A*, often fail to account for EV-specific factors, including charging station availability, connector compatibility, and energy constraints. This study presents a comprehensive EV route optimization framework that integrates reinforcement learning (RL) with graph-based methods. A novel Dual Q–Adaptive Weighting model that balances reward and cost through a primal–dual learning mechanism is proposed. The framework learns energy-aware routing strategies from historical navigation experience. The model is compared against standard RL approaches—Q-Learning and Double Q-Learning—as well as enhanced variants of A* and Dijkstra’s algorithms that incorporate charging density and time-penalty considerations. Real-world EV charging infrastructure data from the Alternative Fuels Data Center (AFDC) and Placekey datasets are used to construct a clustered navigation graph via DBSCAN. Experimental results across multiple intercity routes show that the proposed Dual Q–Adaptive model achieves the highest route accuracy of 78.66%, outperforming Double Q-Learning (76.27%), Q-Learning (77.52%), and traditional A* (74.26%) and Dijkstra (60.92%) algorithms. A* and Dijkstra with modifications, use fewer charging stops than traditional algorithms. The Improvised algorithms provide substantial improvements over their baseline counterparts. The results demonstrate that reinforcement learning integrated with graph-theoretic optimization can enable scalable, infrastructure-aware, and efficient EV route planning.

A Self‐Reinforcing LipoTIDE Nanoplatform That Overcomes Lipid‐Buffering Ferroptosis Resistance for Enhanced Cancer Therapy

ABSTRACT Lipid metabolic rewiring is a hallmark of malignancy, allowing tumor cells to sequester fatty acids within lipid droplets (LDs) as a protective reservoir that quenches reactive oxygen species (ROS)–driven lipid peroxidation and thereby evades ferroptosis. Although lipophagy selectively degrades LDs to release free fatty acids (FFAs) and remodel lipid homeostasis, leveraging this process to overcome lipid‐buffering ferroptosis resistance remains largely unexplored. Here, we report LipoTIDE (Lipophagy‐Tuning Induced Death Enhancer), a self‐reinforcing nanoplatform that primes lipophagy‐primed ferroptosis by coupling precise lipophagy activation with catalytic ROS generation to dismantle LDs‐mediated metabolic defenses in tumors. LipoTIDE co‐delivers ultrasmall Pt 3 Co nanoalloys and tamoxifen within a pH‐responsive amphiphilic polymer, enabling tumor‐targeted disassembly and localized therapeutic amplification. Triggered by the tumor acidity, LipoTIDE releases Pt 3 Co nanoalloys for multiple catalytic activities and tamoxifen for initiating lipophagy and decreasing pH value, establishing a self‐reinforcing loop that sustains lipophagy and ferroptosis. Additionally, FFAs from lipophagy, together with the Pt 3 Co nanoalloys, resensitize resistant cancer cells to Pt 3 Co‐catalyzed ROS, thereby amplifying ferroptosis. Consequently, LipoTIDE precisely disrupts lipid homeostasis, triggers robust ferroptotic tumor suppression, and exhibits minimal systemic toxicity. These findings establish lipophagy‐primed ferroptosis as a generalizable and actionable strategy for dismantling lipid‐buffering defenses of tumors.

Changes in facial recognition analysis after orthognathic surgery

Synergistic Effect of Chirality and Symmetry Reduction Enables Metal‐Free Perovskite Single Crystal With Stable Self‐Driven X‐ray Detection

ABSTRACT Exploring chiral components to induce a significant bulk photovoltaic effect (BPVE) for pursuing controllable dark current and device stability represents an effective avenue in metal‐free perovskite (MFP) X‐ray detection. However, the key challenges of increasing total polarization degree in the MFPs with complex hydrogen‐bond networks retard the construction of stable self‐driven X‐ray detection. Herein, we designed a novel R/S‐2MPAZE‐NH 4 I 3 ·H 2 O SCs with right‐ or left‐handed screw conformations through incorporating chiral cations of R/S‐2MPAZE 2+ for stable self‐driven MFPs‐based detectors by amplifying the BPVE and exploring the functional mechanism of chiral A‐site design. Theoretical simulations and experiments reveal that the R‐2MPAZE 2+ with a large dipole moment and bulk stereo‐configurations can induce an obvious structural reconfiguration to achieve a significant BPVE with an intrinsic radiation photovoltage (0.49 ± 0.09 V). Strikingly, chiral cations can optimize tolerance factor, increase ionic‐migration energy, and modulate the band nature for realizing the strengthened carrier transport capacity and stability. The designed self‐driven detectors delivered a considerable sensitivity of 65 µC Gy air −1  cm −2 , a low detection limit of (67.3 nGy air  s −1 ) and good detection stability. This research will enrich the material selection and component design of stable self‐driven X‐ray detectors, moving toward an environmentally friendly direction.

Influence of GO, MXene, and MoS₂ on sodium alginate–polyacrylic acid hydrogels for forward osmosis groundwater treatment

AI is set to completely transform cybersecurity — here’s how researchers must prepare

Mapping Proton‐Coupled Electron Transfer With Real Space Coordinates

ABSTRACT Both electron transfer (ET) and proton transfer (PT) are common steps in energy conversion across chemistry and biology. Coupling of these two transfer events reduces the energy demand relative to either individual process, but introduces a fundamentally new process—proton‐coupled electron transfer (PCET)—with its own demands for a suitable theoretical description. Conceptualization of PCET usually involves a square scheme representing ET, PT, and PCET steps, each generating states with different energies. While intuitive, these square schemes do not offer structural insights such as the identities and contributions of nuclear motions to PCET. Herein, we present a computational approach that maps these square schemes onto real space coordinates (Å), from which ground and excited‐state potential energy surfaces can be generated. This mapping involves the identification of PT and ET coordinates and reconstruction of the potential energy surfaces in orthogonalized coordinates. We find qualitative differences in key features of the surfaces for two distinct processes within PCET, namely concerted proton–electron transfer and hydrogen atom transfer, which may help to distinguish different PCET scenarios.

Full‑speed sensorless control of HEAFSPMM using nonlinear adaptive flux observer, improved MRAS, and ADRC speed controller

Flash Production of High‐Purity V ^3.5+ Electrolyte via Electron Beam‐Induced Radiolysis Under Ambient Conditions

ABSTRACT The quest for high‐performance vanadium redox flow batteries has long been hindered by the time‐consuming and impurity‐prone synthesis of mixed‐valence vanadium electrolytes (V 3.5+ ). Distinct from the existing synthetic approaches, we develop an effective, catalyst‐free strategy to produce V 3.5+ electrolytes only in minutes by using electron‐beam irradiation. We show water radiolysis can generate a burst of hydrated electrons and reductive radicals that rapidly convert V 4+ to V 3+ under ambient conditions, thus achieving a remarkably high‐purity V 3+ generation rate of 0.65 mol·L −1 ·h −1 , exceeding the reported values from existing methods. In addition, the resulting electrolyte unlocks outstanding battery performance, delivering a coulombic efficiency of 97.92%, an energy efficiency of 85.05% at 100 mA cm −2 , and a slow capacity degradation of 0.201% per cycle over 120 cycles. Owing to its simplicity, high‐throughput, and scalability radiation‐driven approach provides a promising pathway toward efficient, sustainable, and cost‐effective energy storage, making it a competitive and transformative technique for advanced electrolyte fabrication.

Game-theoretic security optimization in UAV networks through AI-enabled digital twin intelligence

A Comprehensive ¹⁹ F NMR Framework for Fragment‐Based Drug Discovery: The Validated Screening Library OpenFL600 and Efficient Affinity Ranking by CSAR

ABSTRACT Small‐molecule drug discovery relies on the identification of initial chemical starting points, or screening hits, and their subsequent optimization for potency and selectivity. Fragment‐based drug discovery (FBDD), particularly through NMR screening, is a powerful method for hit identification due to its high sensitivity. Here, we present the design and validation of a non‐proprietary NMR screening library (OpenFL600) capable of probing diverse binding pockets, as demonstrated for RNA, G protein‐coupled receptors (GPCRs), kinases, protein–protein interactions, and proteases. Our screening campaigns yielded specific hits for 11 respective targets and therefore valuable structure–activity relationship (SAR). The absence of promiscuous binders confirms the high quality of the OpenFL600 library, a result of both careful fragment selection and rigorous molecular quality control. Subsequent affinity ranking of identified hits—typically a laborious task—is considerably streamlined by fast chemical shift anisotropy (CSA) Affinity Ranking (CSAR). CSAR is enabled by providing CSA values for each fragment. This creates a highly efficient workflow, from initial hit screening to the prioritization of leads. In summary, we provide a comprehensive framework for NMR‐based screening across diverse target classes, assessing their druggability, and enabling efficient affinity ranking to facilitate hit‐to‐lead progression.

A pilot single-cell RNA sequencing study of endothelial activation signatures in large benign prostatic hyperplasia

A Hydrogen Bond‐Mediated Förster Resonance Energy Transfer (FRET) Glue Strategy for Long‐Lived Room‐Temperature Phosphorescence From Silver Clusters

ABSTRACT Luminescent coinage metal clusters (CMCs) hold great promise for bioimaging and optical applications, but long‐lived room‐temperature phosphorescence (RTP) with high quantum yield remains challenging. Herein, a Förster resonance energy transfer (FRET) glue strategy using Ag + to bridge donor–acceptor (D/A) pairs is adopted to synthesize a series of clusters in CH 3 CN ( Ag 4 ), CH 3 OH [ Ag 4 (CH 3 OH) 2 ], C 2 H 5 OH [ Ag 4 (C 2 H 5 OH) ], and i ‐C 3 H 7 OH [ Ag 2 ( i ‐C 3 H 7 OH) 2 ] realizing near‐unity FRET efficiency. Among them, Ag 4 (CH 3 OH) 2 delivers superior thermally activated delayed phosphorescence with a high photoluminescence quantum yield of 96% and an ultralong RTP lifetime of around 100 ms, which are 5.6 times and 400 times higher than those of Ag 4 , respectively. Mechanistic studies reveal that isolated Ag + as single‐point linkers circumvent the overly strong spin–orbit coupling of larger Ag cores while retaining an appropriate heavy‐atom effect to facilitate effective phosphorescence emission. In addition, hydrogen bonds from coordinating solvents can adjust the spatial position of ligand, optimizing D/A arrangement, which enhances FRET efficiency and suppresses nonradiative deactivation, ultimately achieving high‐efficiency luminescence of silver clusters. Leveraging the solvent‐mediated regulation of cluster structure and performance, this system enables reversible single‐crystal‐to‐single‐crystal transformation and subsequent time‐solvent‐temperature‐gated multilevel information encryption. This work provides a general design principle for CMCs with high‐performance RTP.

Atractylenolide Ⅲ protects granulosa cells from oxidative injury by regulating the NOX2/NRF2 axis in polycystic ovary syndrome

Cervical cancer screening coverage and associated factors among women aged 25–59 in Eswatini: Insights from the 2021 Population-Based HIV Impact Assessment (PHIA)

Background Cervical cancer is the most common cancer and the leading cause of cancer mortality among women in Eswatini, which reported the highest cervical cancer incidence and mortality rates globally in 2022. However, recent nationally representative published data on cervical cancer screening coverage in Eswatini remain limited. This study aimed to estimate cervical cancer screening coverage and identify factors associated with screening uptake among women aged 25–59 years in Eswatini. Method Data were drawn from the 2021 Eswatini Population-based HIV Impact Assessment (PHIA), a nationally representative cross-sectional household survey. Survey-weighted bivariable and multivariable logistic regression analyses were conducted to identify factors associated with screening uptake. Results are reported as adjusted odds ratios (AORs) with 95% confidence intervals (CIs). Result Among 3,192 women aged 25–59 years, 48.2% reported ever having undergone cervical cancer screening. Higher odds of screening were observed among women living with HIV (AOR 2.26, 95% CI 1.93–2.65), those aged 36–45 years (AOR 1.35, 95% CI 1.12–1.62), and those in the fourth wealth quintile (AOR 1.40, 95% CI 1.05–1.88). Lower odds of screening were observed among women who were not married (AOR 0.65, 95% CI 0.54–0.78), with no formal education (AOR 0.56, 95% CI 0.39–0.80), no recent healthcare visit (AOR 0.67, 95% CI 0.55–0.83), no family planning use (AOR 0.82, 95% CI 0.69–0.97), and one sexual partners (AOR 0.78, 95% CI 0.65–0.94). Conclusion Despite notable progress, cervical cancer screening coverage in Eswatini remains below World Health Organization targets. Enhancing community awareness, addressing stigma and structural barriers, and integrating cervical cancer screening into routine and reproductive healthcare services may accelerate progress toward achieving the WHO 2030 targets.