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Firefly algorithm-based optimization of a high-efficiency DC–DC converter for hybrid energy-powered electric vehicles

Engineering of episomal plasmid structure to enhance non-viral Poly(beta-amino ester) nanoparticle gene delivery to liver and brain cancer cells

The objective of this study was to create and evaluate episomal plasmids for use in non-viral polymeric gene delivery to cancer cells. A comparative analysis was conducted utilizing poly(beta-amino ester)s (PBAEs) as the nanocarrier vector and various hepatocellular carcinoma (HCC) and brain cancer cell lines. A total of fourteen reporter plasmids that varied in promoter (CMV, CAG, EF1α), backbone (Z1, pUNO1, Nanoplasmid [NanoP]), length (~2000 to ~7000 base-pairs), and antibiotic selection marker (Kanamycin, Zeocin, Blasticidin, or none) were constructed, characterized, and evaluated for gene delivery performance. GFP and mCherry plasmids were evaluated in six HCC lines: human (Hep3B), murine (Hepa1–6, Hepa1c1c7), and porcine (A92, A272, B239). Luciferase plasmid constructs were evaluated in three brain cancer lines: murine glioma (CT-2A), human astrocytoma (CCF-STTG1), and human meningioma (IOMM-Lee). When comparing promoters within the same backbone (Z1), the GFP nanoparticles (NPs) under control of a CMV promoter demonstrated consistently higher expression than those with a CAG or EF1α promoter. As EF1α promoters are often used for in vivo applications due to their resistance to gene silencing, four GFP plasmids under control of the EF1α promoter were also compared: Z1-EF1α-GFP, pUNO1-GFP, pUNO1-GFP-SV40, and NanoP-GFP. Of these, the NPs delivering NanoP-GFP, a minimal plasmid, resulted in the highest %GFP+ cells in five HCC cell lines, the highest GFP geometric mean fluorescence intensity (gMFI) in all six HCC cell lines, and the highest luciferase signal in two out of three brain cancer cell lines. NanoP was also the only plasmid evaluated without an antibiotic resistance gene, which may be advantageous when selecting a plasmid best able to meet regulatory guidance for clinical translation. Finally, a modest negative correlation between plasmid size and either transfection efficacy or GFP gMFI was observed. Overall, this work helps to inform episomal plasmid design strategy for use with non-viral gene therapies.

Evaluation of the synergistic and antagonistic antibacterial effects of pulsed electromagnetic fields combined with ciprofloxacin and nanochitosan

Abstract This study introduces an innovative electromagnetic nano-approach to combat high-severity bacterial infections without relying solely on high-dose antibiotics. We investigate the synergistic potential of extremely low-frequency pulsed electromagnetic wave (ELF PEMW) exposure (< 20 Hz) as a physical catalyst to enhance the bio-activity of ciprofloxacin-loaded chitosan nanoparticles (Cipro-C-NPs). This method provides a pivotal alternative for managing Escherichia coli , Pseudomonas aeruginosa , and Staphylococcus aureus in an era of escalating multi-drug resistance. Cipro-C-NPs were synthesized with high encapsulation efficiency. Bacteria were subjected to a multi-factorial screening involving three antibacterial agents, two physical fields (pulsed magnetic and electric), and varying frequencies (0.7, 6, and 20 Hz) for durations of 20 and 60 min. As a high-throughput preliminary screen, this work aimed to map the qualitative landscape of bio-electromagnetic interactions. Contrary to the hypothesis of enhanced membrane permeability, ELF PEMW functioned as a biophysical antagonist. The electromagnetic field appeared to trigger membrane hyperpolarization, increasing transmembrane potential and restricting porin-mediated transport of the antibiotic. Simultaneously, the field reduced the zeta-potential of the chitosan nanoparticles, leading to significant colloidal aggregation. These large aggregates were physically excluded from bacterial entry routes, resulting in increased Minimum Inhibitory Concentrations (MICs). Notably, the degree of antagonism was species-specific, suggesting that membrane capacitance and porin density dictate electromagnetic susceptibility. This study reveals a critical bio-electromagnetic trade-off: while physical fields can modulate cellular behavior, poorly tuned parameters can inadvertently fortify bacterial defenses and reduce drug bioavailability. These findings provide a vital “negative roadmap” for future research, highlighting the need for direct electrophysiological mapping of efflux pumps and membrane potentials. This work serves as a foundational step toward precision-targeted, physics-assisted antimicrobial therapies.

Wearable sensors on the face are invisible to the eye

Functional-level analysis of post-stroke social participation: A nationwide cohort study using the ICF framework

Background Social participation is a key goal in stroke rehabilitation, but its influencing factors vary by functional status. Few large-scale studies have categorized patients by functional level and analyzed associated factors using the International Classification of Functioning, Disability and Health (ICF) framework. Objective To identify determinants of social participation 12 months after stroke, stratified by functional level. Design and participants A cross-sectional analysis was conducted on 2,695 stroke survivors from a nationwide cohort in Korea. Participants were stratified into five groups based on Fugl-Meyer Assessment scores: Severe, Marked, Moderate, Slight, and Unimpaired. Methods Social participation was measured using the Reintegration to Normal Living Index. Independent variables were categorized into four ICF domains: personal factors, body functions and structures, activities and participation, and environmental factors. Multivariate regression analyses were performed by functional group. Results Predictors varied significantly across functional levels. In the Severe group (FMA < 50), environmental factors (home accessibility and caregiver-related variables such as age and relationship) were the primary determinants. Conversely, in higher-functioning groups (FMA ≥ 85), social participation was more strongly influenced by body functions and structures (cognitive, language, and psychosocial functions) and activities and participation factors (vocational status). Across all groups, activities and participation (functional independence and health-related quality of life) remained consistent positive predictors, while environmental barriers (caregiver burden) were universal constraints. Conclusion Determinants of social participation differ by functional status: individuals with lower functional levels are more significantly affected by environmental factors, whereas those with higher function face greater barriers related to body functions and structures. These findings suggest that stroke rehabilitation must provide functional-level-specific interventions, moving beyond physical recovery to address these distinct multidimensional barriers.

Study on underwater explosion bubble dynamics in a finite open domain

Acquired genetic and cell-state changes in IDH-mutant glioma progression

Abstract Gliomas with mutant isocitrate dehydrogenase (IDH) are malignant brain tumours that typically arise in early to mid-adulthood and nearly always recur following treatment 1,2 . However, the genetic and cellular-state changes that drive IDH-mutant glioma progression under treatment remain incompletely understood. Here we integrated single-nucleus transcriptomic profiles, chromatin accessibility profiles and bulk DNA and RNA sequencing from 75 temporally separated gliomas across 35 patients comprising both the oligodendroglioma and astrocytoma IDH-mutant glioma tumour types. We show that malignant cell states transcriptionally resemble stages of normal glial–neuronal lineage development or a reactive mesenchymal-like state, mirroring states previously described in IDH wild-type glioblastoma 3,4 . Malignant cell states displayed distinct chromatin accessibility profiles that were comparable between both IDH-mutant glioma types. The abundance of less differentiated malignant cells increased with grade and with genetic alterations such as PDGFRA amplification. Longitudinal analysis highlighted two major malignant cell-state transition patterns. First, reduced lineage differentiation and increased proliferative malignant cells at recurrence were enriched in gliomas that acquired recurrence-associated genetic events. These included treatment-associated hypermutation, increased copy number changes and cell cycle alterations. Second, increased mesenchymal-like-state abundance occurred independently of acquired genetic alterations and instead coincided with elevated macrophage expression. Overall, our findings provide an integrative model that traces the cell intrinsic and extrinsic factors that shape cellular states during IDH-mutant glioma disease progression.

Chinese small and medium-sized city image communication on Douyin: Algorithmic heuristics and cultural resilience in short-video ecologies

This study examines how Chinese small and medium-sized cities gain visibility and sustain place-based meaning within Douyin’s platformized short-video ecology. Focusing on Shaoguan, Guangdong Province, it analyzes 30 high-interaction Douyin videos published between May 2023 and May 2025 and applies crisp-set Qualitative Comparative Analysis (csQCA) to identify configurations associated with high dissemination performance. The findings suggest that city-related hashtags function as a necessary platform-indexing condition in the sampled archive, while emotional resonance and narrative structure strengthen specific high-impact configurations. Two communicative logics are particularly salient: non-local creators can generate rapid visibility through immersive, fragmented, and affective presentations, whereas locally embedded storytelling can reinforce cultural resilience and place identity. The study contributes to digital urban communication by integrating the Heuristic-Systematic Model with debates on platformization, algorithmic visibility, and digital place branding.

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

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.

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.

Changes in facial recognition analysis after orthognathic surgery

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

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

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

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

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.