In situ microbial aerodynamic microneedles (MM-MNs) represent an autonomous transdermal drug delivery platform that utilizes the gas generated by microbial metabolism (e.g., H₂, NO, and H₂S) to propel drugs into deep tissues, surpassing the penetration limits of traditional microneedles reliant on external stimuli (heat/light/mechanical force). By leveraging controlled microbial metabolism, MM-MNs enable energy-independent, spatiotemporally precise delivery with enhanced targeting and bioavailability. Gas-driven propulsion combines with bioactive gas functions (e.g., NO-induced vasodilation and H₂S-mediated anti-inflammation) to modulate disease microenvironments. The system’s biocompatibility (probiotic strains and Lactobacillus) and scalability (cost-effective patch design) further support its potential for localized therapies (skin diseases and tumors) with minimized systemic exposure. This innovation bridges microbial biotechnology and precision medicine, offering a paradigm shift in transdermal delivery.

Optical coherence tomography angiography (OCTA) is a major advancement in imaging, offering high-resolution microvascular volumetric images crucial for diagnosing and studying dermatological diseases. However, current data analysis and clinical evaluation criteria primarily rely on 2-dimensional (2D) imaging results, resulting in imprecise diagnoses due to the substantial loss of 3D curved structures and microvascular details. To address this issue, we propose a high-fidelity 3D curved processing workflow that integrates an artificial neural network (ANN) with a 3D denoising algorithm based on the curvelet transform and optimal orientation flow (OOF). This innovative workflow enables precise 3D segmentation and accurate quantification of dermal layer microvasculature in atopic dermatitis (AD) in vivo. Furthermore, the use of 3D multiparametric microvasculature quantitative metrics establishes a robust framework for assessing the efficacy of AD treatments in 3D images. Our study results demonstrate that skin structure imaging and the dynamic evolution of 3D microvasculature align with observed pathological changes. Compared to traditional 2D analysis, the maximum variation rate of 3D curved multiparametric information is approximately 10%. Consequently, our research marks a significant advancement in the accurate quantification of microvasculature in AD development and theranostics, paving the way for the clinical application of OCTA in dermatology.

Goldfish (Carassius auratus) are renowned as a premier ornamental fish in the world. Especially, the hood, a distinctive cephalic skin protrusion, is a highly sought-after feature for its endearing aesthetics. Despite a longstanding hypothesis that the hood is a type of tumor, the details of their composition, structure, and the mechanism of its formation have remained enigmatic. In this study, we attempted to demystify the morphogenetic mechanism of hood development by providing a detailed analysis of the hood’s architectural and compositional attributes, complemented by multi-omics changes across its developmental stages. Our results were also validated through dual-luciferase reporter assays and cytological evaluations in vitro and in vivo. We uncovered a 4-layered complex structure (stratum compactum, stratum spongiosum, stratum adventitia, and epithelial cell layer), with the hood’s protrusions mainly resulting from marked collagen accumulation in the stratum spongiosum and epithelial cell proliferation, suggesting that the goldfish hood belongs to a cutaneous fibrosis. Furthermore, we found that the down-regulation of arachidonic acid metabolism triggers an inflammatory response, culminating in the dysregulation of the tumor necrosis factor (TNF) pathway, which in turn enhances collagen deposition and epithelial cell proliferation—central to hood morphogenesis. During post-formation process, the aberrant TNF pathway expression and collagen accumulation inhibit osteoclast differentiation, promoting the irregular proliferation of the skull and the formation of bony protrusions that support hood attachment. Our findings not only shed light on the molecular mechanism underlying cutaneous fibrosis in goldfish but also offer potential parallels to analogous conditions in humans.

Water electrolysis is a key industrial process for producing green hydrogen. To avoid the use of noble metals and fluorinated polymer membranes, liquid water electrolysis is often carried out in alkaline conditions. It is common to distinguish between 3 processes: alkaline electrolysis at high electrolyte concentrations (≥7 M) with porous membranes, alkaline electrolysis at high electrolyte concentrations (≥7 M) with ion-solvating membranes, and alkaline electrolysis at moderate electrolyte concentrations (<2 M) with anion-exchange membranes. Here, we consider the fundamental aspects of water and ion fluxes and of conductivity across the 3 types of membranes. We discuss ionic currents governed by ion–ion interactions and those resulting from a Grotthuss mechanism. Furthermore, in the case of porous membranes made of a polymeric fabric with compressed inorganic fillers such as zirconia, which are negatively charged in the presence of KOH, and of ion-solvating membranes such as polybenzimidazole, which also become negatively charged by deprotonation at high pH, those membranes should be a weak cation exchanger. We here address this dichotomy. All in all, we show that in all 3 cases, the membrane is an anion exchanger where hydroxide ion mobility differs from that of adjacent aqueous solution due to confinement favoring a Grotthuss-type transport and a jump mechanism.

Lipid-peroxidation-driven ferroptosis represents a vital mode of regulated cell death increasingly recognized for its role in cancer therapy. Lipid metabolism plays a crucial role in determining the vulnerability of cells to ferroptosis, particularly in cancer cells. The biosynthesis and remodeling of polyunsaturated fatty acid–phosphatidylethanolamine in cell membranes rely heavily on the activity of acyl-CoA synthetase family member 4 (ACSL4). However, the regulatory mechanisms governing ACSL4 expression in renal cell carcinoma (RCC) remain unclear. In this study, we screened a library of 765 epigenetic compounds to identify novel regulators of ferroptosis. Notably, we discovered that protein arginine methyltransferase 5 (PRMT5) inhibitors markedly promote ferroptosis in RCC cells. Inhibition or knockdown of PRMT5 in RCC cell lines enhanced lipid peroxidation and reduced cell viability. PRMT5 interacted with and catalyzed the symmetric dimethylation of ACSL4 at the arginine 549 (R549) site, facilitating its degradation via the proteasome. In vivo, the combination of a PRMT5 inhibitor with anti-PD-1 therapy notably increased ferroptosis and reduced tumor growth. Furthermore, elevated PRMT5 levels were associated with unfavorable clinical outcomes in patients with renal cancer. Overall, our findings suggest that PRMT5 regulates ferroptosis in RCC by methylating ACSL4 at the R549 site, and its inhibition enhances the therapeutic efficacy of immunotherapy through the induction of ferroptosis.

Two-dimensional (2D) metals have drawn great attention because of their extraordinary properties, especially in applications that favor van der Waals interaction. The development of advanced characterization tools has facilitated the understanding of formation or growth mechanisms of 2D metals. In this perspective, we discuss 5 common approaches to obtaining 2D metals, including, (top down) van der Waals squeezing and selective extraction, and (bottom up) electron beam-induced growth, self-assembly, and graphene-templated wet chemistry growth. The future opportunities are proposed in the summary section. Furthermore, challenges and problems such as thermodynamic stability and scalability in 2D material growth are proposed for the community to tackle.

Working memory (WM) deficit is a prominent and common cognitive impairment in major psychiatric disorders (MPDs). Altered control of brain state transitions may underlie the neural basis of WM deficit. We investigate whether shared and illness-specific alterations in controllability underlie WM deficits in MPDs. We examined functional magnetic resonance imaging data during an n-back WM task from 105 patients with schizophrenia (SZ), 67 with bipolar disorder (BD), 51 with major depressive disorder (MDD), and 80 healthy controls (HCs). We calculated each brain region’s capacity to steer transitions to connectomic states with less input (average controllability) and to difficult-to-reach states with high input (modal controllability). The effect of altered controllability on clinical and cognitive characteristics and their likely genetic and neurotransmitter basis were investigated. All MPDs demonstrated a common but graded pattern of reduced modal controllability within the frontoparietal network compared to HC, with SZ showing the most pronounced impairment. Relative to BD and MDD, SZ exhibited the broadest profile of reduced average and modal controllability across the cortex, particularly in sensory, default mode, and salience networks. The affected brain regions preferentially expressed genes that determine synaptic biology and chemoarchitecture involving glutamate/γ-aminobutyric acid (GABA) and monoamine [dopamine and 5-hydroxytryptamine (5-HT)] neurotransmitter systems. A graded, transdiagnostic reduction in the influence of the sensory networks and triple network system in implementing state transitions underlies WM deficits in MPDs. This deficit, especially pronounced in SZ, has its likely basis in synaptic biology and in glutamate/GABA and monoamine (dopamine and 5-HT) neurotransmitters.

Background: Chemotherapy-induced peripheral neuropathy (CIPN) is a common and debilitating adverse effect of cancer treatment that substantially impairs patients’ quality of life and may lead to dose reduction or treatment discontinuation. Traditional prediction models based on single-modal data have shown limited accuracy in clinical settings. Aim: This study aimed to develop and evaluate a deep learning-based predictive model for CIPN by integrating multimodal data, including clinical, genomic, biosignal, wearable device, and imaging information. Methods: A retrospective and prospective cohort of cancer patients receiving chemotherapy between 2020 and 2025 was analyzed using data collected from multicenter electronic health records (EHRs) and public databases. An intermediate fusion framework was implemented using a Transformer-based architecture, which was compared with LSTM, CNN, and XGBoost models. SHAP (Shapley additive explanations) and Grad-CAM were used to improve model interpretability, while performance was assessed using AUC-ROC (area under the receiver operating characteristic curve), accuracy, sensitivity, specificity, and F1-score. Results: The Transformer-based model achieved the highest performance (AUC = 0.93; accuracy = 88.5%; sensitivity = 85.3%; specificity = 90.1%), outperforming conventional models. SHAP analysis identified chemotherapy dosage, nerve magnetic resonance imaging abnormalities, electrocardiogram changes, CYP2C8 mutations, and diabetes as the most influential predictors. Patients with a high predicted risk of CIPN also demonstrated significantly lower overall survival, indicating a broader systemic impact of CIPN beyond neurological symptoms. Conclusions: This study provides evidence that deep learning models incorporating multimodal data significantly enhance CIPN prediction and have the potential for clinical implementation. The use of explainable artificial intelligence techniques further supports their integration into precision oncology. Future research should focus on multicenter validation, real-time EHR integration, and the development of neuroprotective strategies for high-risk patients.

Aortic dissection (AD) is a cardiovascular disorder with a high mortality rate. Lysine Lactylation (Kla), a novel posttranslational modification, critically regulates inflammation, tumors, and cardiovascular diseases. However, its specific role in AD pathogenesis remains unexplored. Using modification omics, we conducted a macroscopic analysis of the occurrence of extensive lactylation modification in aortic dissection and identified extensive lactylation, particularly in the adenosine triphosphatase activity pathway. Among these proteins, adenosine triphosphate (ATP) synthase F1 subunit α (ATP5F1A), a subunit in the ATP synthase complex, exhibited pronounced lactylation at the K531, catalyzed by sirtuin 3 (Sirt3). Through site-directed mutagenesis (K531R/K531E), we validated the key mechanism of lactylation activation at the K531 site of ATP5F1A and the regulatory enzymes. Functionally, K531 lactylation impairs ATP synthase activity, elevates reactive oxygen species generation, reduces ATP generation, and induces mitochondrial structural abnormalities. These effects ultimately contribute to the phenotypic transformation of human aortic vascular smooth muscle cells and enhanced synthesis and secretion of matrix metalloproteinases. In addition, we assessed the potential therapeutic effect of lactylation inhibition in aortic dissection using a mouse model and a drug based in vivo lactate alteration strategy. In conclusion, targeting the lactate–Sirt3–ATP5F1A axis represents a promising therapeutic strategy for blocking the progression of aortic dissection.

In 1994, P. Shor discovered quantum algorithms that can break both the RSA cryptosystem and the ElGamal cryptosystem. In 2007, a Canadian company D-Wave demonstrated the first quantum computer. These events and quick further developments have brought a crisis to secret communication. In 2022, the National Institute of Standards and Technology (NIST) announced 4 candidates—CRYSTALS-Kyber, CRYSTALS-Dilithium, Falcon, and Sphincs+—for post-quantum cryptography standards. The first 3 are based on lattice theory and the last on Hash functions. In 2024, NIST announced 3 standards: FIPS 203 based on CRYSTALS-Kyber, FIPS 204 based on CRYSTALS-Dilithium, and FIPS 205 based on Sphincs+. The fourth standard based on Falcon is on the way. It is well known that the security of the lattice-based cryptosystems relies on the hardness of the shortest vector problem (SVP), the closest vector problem (CVP), and their generalizations. In fact, the SVP is a ball packing problem and the CVP is a ball covering problem. Furthermore, both SVP and CVP are equivalent to arithmetic problems for positive definite quadratic forms. There are several books and survey papers dealing with the computational complexity of the lattice-based cryptography for classical computers. However, there is no review article to demonstrate the mathematical foundation of the complexity theory. This paper will briefly introduce post-quantum cryptography and demonstrate its mathematical roots in ball packing, ball covering, and positive definite quadratic forms.