sRNAdeep: a novel tool for bacterial sRNA prediction based on DistilBERT encoding mode and deep learning algorithms.

BACKGROUND: Bacterial small regulatory RNA (sRNA) plays a crucial role in cell metabolism and could be used as a new potential drug target in the treatment of pathogen-induced disease. However, experimental methods for identifying sRNAs still require a large investment of human and material resources. METHODS: In this study, we propose a novel sRNA prediction model called sRNAdeep based on the DistilBERT feature extraction and TextCNN methods. The sRNA and non-sRNA sequences of bacteria were considered as sentences and then fed into a composite model consisting of deep learning models to evaluate classification performance. RESULTS: By filtering sRNAs from BSRD database, we obtained a validation dataset comprised of 2438 positive and 4730 negative samples. The benchmark experiments showed that sRNAdeep displayed better performance in the various indexes compared to previous sRNA prediction tools. By applying our tool to Mycobacterium tuberculosis (MTB) genome, we have identified 21 sRNAs within the intergenic and intron regions. A set of 272 targeted genes regulated by these sRNAs were also captured in MTB. The coding proteins of two genes (lysX and icd1) are implicated in drug response, with significant active sites related to drug resistance mechanisms of MTB. CONCLUSION: In conclusion, our newly developed sRNAdeep can help researchers identify bacterial sRNAs more precisely and can be freely available from https://github.com/pyajagod/sRNAdeep.git .

Classification of Sesame Oil Based on Processing-Originated Differences in the Volatile Organic Compound Profile by a Colorimetric Sensor.

Fragrant edible sesame oil is popular for its unique aroma. The aroma of sesame oil is determined by its volatile organic compound (VOC) profile. Sesame oils produced by different techniques could have different VOC profiles. In addition, blending fragrant sesame oil with refined oil could also alter the VOC profile of the final product. Current practices in aroma analysis, such as sensory evaluation and gas chromatography (GC), still face many restraints. Hence, there is a need for alternatives. We present a novel 14-unit multiplexed paper-based colorimetric sensor for fragrant sesame oil VOC analysis. The sensor was designed to visualize the VOC profile as a color "fingerprint". The sensor was validated with 55 branded sesame oil samples produced by two different techniques, i.e., hot pressing and small milling; the experimental results suggested a processing dependency in color VOC fingerprints. The sensor also demonstrated the potential to detect the change in sesame oil VOC profile due to blending with refined oil, with an estimated limit of detection down to 20% v/v of the refined oil. The colorimetric sensor might be used as a simple, rapid, and cost-effective analytical tool in the production and quality control of fragrant sesame oil.

The EP3-ZNF488 Axis Promotes Self-Renewal of Glioma Stem-like Cells to Induce Resistance to Tumor Treating Fields.

Tumor Treating Fields (TTFields) employ low-intensity, alternating electric fields to exert antitumor activity and have demonstrated efficacy against multiple cancers, including glioblastoma (GBM). Unfortunately, cancer cells inevitably develop resistance to TTFields, highlighting the need to elucidate the underlying mechanisms to develop approaches to induce durable responses. Using a gene network-based machine-learning algorithm, we interrogated TTFields-resistant GBM cells and uncovered a regulatory axis anchored by the prostaglandin E receptor 3 (EP3) and the transcription factor zinc finger 488 (ZNF488). Mechanistically, TTFields induced EP3 upregulation and nuclear envelope localization, where it formed a complex with ZNF488 to induce resistance to TTFields by promoting self-renewal of glioma stem-like cells (GSC). Overexpression of EP3 and/or ZNF488 in TTFields-sensitive GSC conferred resistance and enhanced self-renewal, while expression of non-interacting mutants of these proteins abrogated formation of the nuclear complex and prevented resistance. Inhibition of either partner in this protein complex in resistant GSC, including those freshly isolated from TTFields-resistant GBM tumors, re-sensitized cells to the cytotoxic effects of TTFields, concomitant with reduced self-renewal and in vivo tumorigenicity. Importantly, inhibition of EP3 in TTFields-sensitive GSC preemptively halted the development of resistance. The EP3-ZNF488 axis was significantly upregulated in TTFields-resistant GBM tumors, and co-expression of EP3 and ZNF488 in other cancers correlated with lower survival rates. Collectively, these results indicate that the nuclear EP3-ZNF488 axis is necessary and sufficient to establish TTFields resistance, underscoring the potential to target this axis to prevent or reverse resistance in GBM and possibly other cancers.

Genomic and transcriptomic features of androgen receptor signaling inhibitor resistance in metastatic castration-resistant prostate cancer.

BACKGROUNDAndrogen receptor signaling inhibitors (ARSIs) have improved outcomes for patients with metastatic castration-resistant prostate cancer (mCRPC), but their clinical benefit is limited by treatment resistance.METHODSTo investigate the mechanisms of ARSI resistance, we analyzed the whole-genome (n = 45) and transcriptome (n = 31) sequencing data generated from paired metastatic biopsies obtained before initiation of first-line ARSI therapy for mCRPC and after radiographic disease progression. We investigated the effects of genetic and pharmacologic modulation of SSTR1 in 22Rv1 cells, a representative mCRPC cell line.RESULTSWe confirmed the predominant role of tumor genetic alterations converging on augmenting androgen receptor (AR) signaling and the increased transcriptional heterogeneity and lineage plasticity during the emergence of ARSI resistance. We further identified amplifications involving a putative enhancer downstream of the AR and transcriptional downregulation of SSTR1, encoding somatostatin receptor 1, in ARSI-resistant tumors. We found that patients with SSTR1-low mCRPC tumors derived less benefit from subsequent ARSI therapy in a retrospective cohort. We showed that SSTR1 was antiproliferative in 22Rv1 cells and that the FDA-approved drug pasireotide suppressed 22Rv1 cell proliferation.CONCLUSIONOur findings expand the knowledge of ARSI resistance and point out actionable next steps, exemplified by potentially targeting SSTR1, to improve patient outcomes.FUNDINGNational Cancer Institute (NCI), NIH; Prostate Cancer Foundation; Conquer Cancer, American Society of Clinical Oncology Foundation; UCSF Benioff Initiative for Prostate Cancer Research; Netherlands Cancer Institute.

CRISPR-based strategies for sample-to-answer monkeypox detection: current status and emerging opportunities.

The global health threat posed by the Monkeypox virus (Mpox) requires swift, simple, and accurate detection methods for effective management, emphasizing the growing necessity for decentralized point-of-care (POC) diagnostic solutions. The clustered regularly interspaced short palindromic repeats (CRISPR), initially known for its effective nucleic acid detection abilities, presents itself as an attractive diagnostic strategy. CRISPR offers exceptional sensitivity, single-base specificity, and programmability. Here, we reviewed the latest developments in CRISPR-based POC devices and testing strategies for Mpox detection. We explored the crucial role of genetic sequencing in designing crRNA for CRISPR reaction and understanding Mpox transmission and mutations. Additionally, we showed the integration of CRISPR-Cas12 strategy with pre-amplification and amplification-free methods. Our study also focused on the significant role of Cas12 proteins and the effectiveness of Cas12 coupled with recombinase polymerase amplification (RPA) for Mpox detection. We envision the future prospects and challenges, positioning CRISPR-Cas12-based POC devices as a frontrunner in the next generation of molecular biosensing technologies.

Predisposing Factors, Treatment, and Pathophysiology of Retronychia in China: A Retrospective Study of 107 Cases.

BACKGROUND: Retronychia, a condition marked by abnormal nail growth, is frequently underdiagnosed and misinterpreted, with a few reported cases in the literature. OBJECTIVE: To investigate the pathophysiology, identify contributing factors, and assess the outcomes of surgical and nonsurgical treatments for retronychia. METHODS AND MATERIALS: A retrospective review of 107 retronychia cases diagnosed at the center between January 2021 and July 2022 was conducted. RESULTS: Predisposing factors were identified in 93.46% of cases, with 88.79% of patients reporting a habit of wearing socks during sleep before symptom onset. Noninvasive treatments targeting these factors led to full recovery in 79.55% of cases, while 13.64% experienced partial improvement, inducing reduced proximal nail fold inflammation. Nail avulsion, which involves removing the entire nail, achieved a relapse-free cure rate of 90.91%. When the innermost nail plate was preserved, the cure rate rose to 96.67%. CONCLUSION: Chronic mechanical forces, frequently overlooked, play a pivotal role in retronychia onset and progression. Conservative management targeting these mechanical factors is recommended for patients with mild cases.

Brusatol improves the efficacy of sorafenib in Huh7 cells via ferroptosis resistance dependent Nrf2 signaling pathway.

BACKGROUND: Hepatocellular carcinoma (HCC) is a common malignancy with a poor prognosis. The recommended treatment of unresectable HCC involves targeted therapy, for example sorafenib, combined with immunotherapy. A recent article reported that sorafenib could induce ferroptosis escape in HCC. Brusatol is a novel Nrf2 inhibitor that takes effects in various diseases. In our study, we aimed to identify whether the addition of Brusatol to sorafenib could reverse ferroptosis escape in Huh7 cells. METHODS: The cultured Huh7 cells treated by sorafenib with or without Brusatol addition were harvested for ferroptotic phenotype experiments and ferroptosis-related markers such as GPX4 and SLC7A11 were detected. In vivo experiments were conducted to discover the effect of Brusatol in combination with sorafenib in liver tumor bearing mice. Mechanism signaling pathways were detected by RNA-sequencing. RESULTS: Brusatol alone could induce Huh7 cell death and sorafenib could moderately mediate Huh7 cell ferroptosis by paradoxically inhibiting GPX4. However, sorafenib simultaneously upregulates Nrf2 signaling in Huh7 cells fighting against ferroptosis to result in sorafenib resistance. The addition of Brusatol could potentiate ferroptosis in Huh7 cells through downregulating Nrf2 and the downstream HO-1 and NQO1, thus enhancing the efficacy of sorafenib, which could be reversed by ferrostatin-1 treatment. CONCLUSION: In conclusion, Brusatol improves the efficacy of sorafenib by inducing ferroptosis via hindering Nrf2 signaling activation in HCC.

PKH Dyes Should Be Avoided in the EVs Biodistribution Study of the Brain: A Call for Caution.

Introduction: Extracellular vesicles (EVs) are nanosized membrane vesicles that are naturally secreted by almost all cells and have gained considerable attention. Many studies have applied EVs to the treatment of brain diseases and validated their effectiveness. Although only a few EVs can penetrate the blood‒brain barrier (BBB) into the brain after administration, it has been proven that EVs and their cargos exert their effects by interacting with brain cells. PKH dyes are commonly used to stain EVs for distribution studies. However, systematic investigations of imaging characteristics of the PKH-labeled EVs distributed in the brain are still scarce. Methods: We stained EVs derived from mesenchymal stem cells with PKH26 or PKH67. PKH26-labeled EVs and PKH67-labeled EVs were administered at the same time into each mouse while PKH26-labeled EVs were given through tail veins and PKH67-labeled EVs were given through intraperitoneal injection. Confocal microscopy was used to explore the distribution difference of two types of EVs given via different routes in the brain. Results: The fluorescence of PKH26 and PKH67 had nearly identical distributions in brain slices after 1 h, 6 h, 12 h and 1 day of EV administration. Under the same confocal parameters, brain slices without EVs administration demonstrated the same result. However, liver slices from mice administered with labeled EVs showed obviously different distributions of two types PKH fluorescence. Discussion: These findings raise questions about the ability of PKH dyes as labels for EVs when explore the EV brain distribution observed via confocal microscopy.

Loss of LRP1 Promotes Hepatocellular Carcinoma Progression via UFL1-Mediated Activation of NF-κB Signaling.

Low-density lipoprotein receptor-related protein-1 (LRP1) is thought to be correlated with hepatocellular carcinoma (HCC) invasion and metastasis. However, the precise mechanism through which LRP1 contributes to HCC progression remains unclear. Here, lower LRP1 levels are associated with malignant progression, and poor prognosis in patients with HCC is shown. LRP1 knockdown enhances the tumorigenicity of HCC cells in vitro and in vivo, whereas overexpression of either LRP1 or its ß-chain has the opposite effect. Mechanistically, LRP1 knockdown promotes the binding of ubiquitin-like modifier 1 ligating enzyme 1 (UFL1) to OGA and accelerates ubiquitin-mediated OGA degradation, leading to increased O-GlcNAcylation of nuclear factor-kappa B (NF-κB) and subsequent inhibition of pro-apoptotic gene expression. Conversely, exogenously expressed truncated ß-chain (ß∆) stabilizes OGA by disrupting the association between UFL1 and OGA, consequently abolishing the anti-apoptotic effects of O-GlcNAcylated NF-κB. The findings identify LRP1, particularly its ß-chain, as a novel upstream control factor that facilitates the stabilization of the OGA protein, thereby suppressing NF-κB signaling and attenuating HCC progression, thus suggesting a novel therapeutic strategy for HCC.

Small Molecule Compound DHPA Screened by Computer-Aided Drug Design and Molecular Dynamics Simulation Inhibits Neuroblastoma Cell Proliferation by Targeting TrkB.

Neuroblastoma (NB) is a rare and malignant pediatric solid tumor. Due to its heterogeneity, it poses significant challenges for treatment, resulting in a high mortality rate. This study aimed to identify new therapeutic drugs by modeling the TrkB receptor from PDB 4AT5 and conducting virtual screening of compounds from the YaTCM database (containing 47,696 compounds derived from 6220 Traditional Chinese Medicines). The screening utilized the E-pharmacophore approach to select compounds with potential binding affinity to TrkB. The binding abilities of these compounds were tested through molecular dynamics simulations, stretch dynamics simulations, and US simulations. Among the top 11 optimized hit compounds, DHPA and 3″-demethylhexahydrocurcumin are prominent. Further simulations reveal that they form stable receptor-ligand binary complexes with TrkB. In subsequent in vitro cell experiments, 3″-demethylhexahydrocurcumin is eliminated due to its high IC50 for killing NB cells. Low concentrations of DHPA can significantly kill NB cells. Additionally, DHPA can inhibit the expression of TrkB, the activation of TrkB's downstream signaling pathways, and affect the thermal stability of TrkB protein and its response to streptase protease degradation. DHPA may be a potential TrkB inhibitor.

In-depth analysis of OTC A208T case induced by OTC gene mutation and research on the prediction and simulation of the impact on protein function.

Background: Ornithine transcarbamylase deficiency (OTCD), a rare hereditary disease caused by gene mutation of ornithine transcarbamylase (OTC), is the most prevalent type among urea cycle disorders. OTCD typically leads to mitochondrial enzyme dysfunction, preventing the synthesis of citrulline from carbamoyl phosphate and ornithine, and is characterized by a remarkable increase in blood ammonia. Specific symptoms may include neurological abnormalities, growth retardation, and other manifestations. Methods: We presented a case of a child diagnosed with OTCD (OMIM: 311250). By using whole-genome sequencing (WGS) for the pedigree and in-depth whole-exome sequencing (WES), we aimed to identify the disease-causing genes. Gene mutation prediction tools were employed to verify the pathogenicity, and the molecular dynamics simulation method was utilized to assess the impact of this mutation on the activity and structural stability of the OTC protein. Results: Whole-exome sequencing detected an OTC variant [NM_000531: c.622 (exon6) G > A, p.A208T]. Through comprehensive analysis with various gene mutation prediction tools and in line with the ACMG guidelines, this mutation site was firmly established as a pathogenic site. Moreover, the molecular dynamics simulation results clearly demonstrated that this mutation would significantly compromise the stability of the OTC protein structure. Conclusion: This study deepens our understanding of the clinical manifestations and characteristics of OTCD, especially the OTC A208T gene mutation site. Given the lack of specific clinical manifestations in OTCD patients, early and accurate diagnosis is crucial for effective treatment and prognosis improvement. To our knowledge, this is the first case of this mutation site reported in China.

Self-supervised generative models for crystal structures.

Inspired by advancements in natural language processing, we utilize self-supervised learning and an equivariant graph neural network to develop a unified platform for training generative models capable of generating inorganic crystal structures, as well as efficiently adapting to downstream tasks in material property prediction. To mitigate the challenge of evaluating the reliability of generated structures during training, we employ a generative adversarial network (GAN) with its discriminator being a cost-effective reliability evaluator, significantly enhancing model performance. We demonstrate the utility of our model in optimizing crystal structures under predefined conditions. Without external properties acquired experimentally or numerically, our model further displays its capability to help understand inorganic crystal formation by grouping chemically similar elements. This paper extends an invitation to further explore the scientific understanding of material structures through generative models, offering a fresh perspective on the scope and efficacy of machine learning in material science.

Unveiling MRI markers for Parkinson's Disease: GABAergic dysfunction and cortical changes.

OBJECTIVE: The study aimed to investigate changes in basal levels of the inhibitory γ-aminobutyric acid (GABA) neurotransmitter in the sensorimotor cortex (SMC) and cortical gyrification in patients with Parkinson's disease (PD), which could further identify potential imaging biomarkers for PD, particularly in patients with early-onset Parkinson's disease (EOPD). METHOD: Fifty patients with PD (EOPD: 10, late-onset Parkinson's disease [LOPD]: 40) and fifty-two age- and gender-matched healthy controls (HC) underwent GABA-edited 1H MRS of the SMC and high-resolution 3D T1-weighted brain imaging. GABA levels and local gyrification index (LGI) were calculated to assess GABAergic and cortical gyrification deficits in PD. RESULT: The Pearson correlation coefficients revealed significant negative associations between eight indicators, including GABA/Cr level and local gyrification index (LGI) of specific cortical regions (precentral, postcentral, entorhinal, superiortemporal, posteriorcingulate, cuneus, and transversetemporal cortex), and the likelihood of Parkinson's disease (r < -0.4, p < 0.001). Additionally, GABA levels were significantly lower in the SMC region of both EOPD and LOPD patients compared to healthy controls (mean ± SD [u.i.]: EOPD=0.081 ± 0.022 vs. Young-HC=0.112 ± 0.021, p = 0.003; LOPD=0.054 ± 0.024 vs. Old-HC=0.099 ± 0.021, p < 0.001). The logistic regression model was established by using multivariate analysis, identifying two statistically significant indicators: GABA/Cr and LGI of the transversetemporal. The combined model exhibited the highest AUC values in both younger and older populations. CONCLUSION: GABAergic dysfunction may play an important role in the pathogenesis of PD patients. Changes in neurotransmitter and morphological may serve as potential markers for the preclinical diagnosis and progression of PD, including EOPD.

Filamentous Actin in the Nucleus in Triple-Negative Breast Cancer Stem Cells: A Key to Drug-Induced Nucleolar Stress and Stemness Inhibition?

Actin, primarily a cytoplasmic cytoskeleton protein, is transported in and out of the nucleus with the help of actin-binding proteins (ABPs). Actin exists in two forms, i.e., monomeric globular (G-actin) and polymerized filamentous (F-actin). While G-actin promotes gene transcription by associating with RNA polymerases, F-actin can inhibit this effect in the nucleus. Unexpectedly, we found that lovastatin, an FDA-approved lipid-lowering drug, induces actin redistribution and its translocation into the nucleus in triple-negative breast cancer (TNBC) cancer stem cells. Lovastatin treatment also decreased levels of rRNAs and stemness markers, which are transcription products of RNA Pol I and Pol II, respectively. Bioinformatics analysis showed that actin genes were positively correlated with ABP genes involved in the translocation/polymerization and transcriptional regulation of nuclear actin in breast cancer. Similar correlations were found between actin genes and RNA Pol I genes and stemness-related genes. We propose a model to explain the roles of lovastatin in inducing nucleolar stress and inhibiting stemness in TNBC cancer stem cells. In our model, lovastatin induces translocation/accumulation of F-actin in the nucleus/nucleolus, which, in turn, induces nucleolar stress and stemness inhibition by suppressing the synthesis of rRNAs and decreasing the expression of stemness-related genes. Our model has opened up a new field of research on the roles of nuclear actin in cancer biology, offering potential therapeutic targets for the treatment of TNBC.

Identification and verification of four candidate biomarkers for early diagnosis of osteoarthritis by machine learning.

Background: Osteoarthritis (OA) is a common chronic joint disease. This study aimed to investigate possible OA diagnostic biomarkers and to verify their significance in clinical samples. Methods: We exploited three datasets from the Gene Expression Omnibus (GEO) database, serving as the training set. We first determined differentially expressed genes and screened candidate diagnostic biomarkers by applying three machine learning algorithms (Random Forest, Least Absolute Shrinkage and Selection Operator logistic regression, Support Vector Machine-Recursive Feature Elimination). Another GEO dataset was used as the validation set. The test set consisted of RNA-sequenced peripheral blood samples collected from patients and healthy donors. Blood samples and chondrocytes were collected for quantitative real-time PCR to confirm expression levels. Receiver operating characteristic curves were generated for individual and combined biomarkers. Results: In total, 251 DEGs were screened, where B3GALNT1, SCRG1 and ZNF423 were screened by all three algorithms. The area under the curve (AUC) of various biomarkers in our test set did not reach as high as that in public datasets. GRB10 exhibited highest AUC of 0.947 in the training set but 0.691 in our test set, while the favorable combined model comprising B3GALNT1, GRB10, KLF9 and SCRG1 demonstrated an AUC of 0.986 in the training set, 1.000 in the validation set and 0.836 in our test set. Conclusion: We identified a combined model for early diagnosis of OA that includes B3GALNT1, GRB10, KLF9 and SCRG1. This finding offers new avenues for further exploration of mechanisms underlying OA.

Impact of Combined Pollution of Ciprofloxacin and Copper on the Diversity of Archaeal Communities and Antibiotic-Resistance Genes.

This study aimed to explore the response of archaeal communities and antibiotic-resistance genes (ARGs) to ciprofloxacin (CIP, 0.05-40 mg/L) and copper (Cu, 3 mg/L) combined pollution during stress- and post-effect periods in an activated sludge system. With the increase in the CIP concentration, the diversity of archaea decreased, but the richness increased under the stress of 10 mg/L CIP. Under stress and post effects, the change in unknown archaeal community structure was more significant than that of the known archaea. The relative abundance of unknown archaea was significantly reduced with the increase in CIP concentration. Meanwhile, there were certain archaea that belonged to abundant and rare taxa with different resistance and recovery characteristics. Among them, Methanosaeta (49.15-83.66%), Methanoculleus (0.11-0.45%), and Nitrososphaera (0.03-0.36%) were the typical resistant archaea to combined pollution. And the resistance of the abundant taxa to combined pollution was significantly higher than that of the rare taxa. Symbiotic and competitive relationships were observed between the known and the unknown archaea. The interactions of abundant known taxa were mainly symbiotic relationships. While the rare unknown taxa were mainly competitive relationships in the post-effect period. Rare archaea showed an important ecological niche under the stress-effect. Some archaea displayed positive correlation with ARGs and played important roles as potential hosts of ARGs during stress- and post-periods. Methanospirillum, Methanosphaerula, Nitrososphaera and some rare unknown archaea also significantly co-occurred with a large number of ARGs. Overall, this study points out the importance of interactions among known and unknown archaeal communities and ARGs in a wastewater treatment system under the stress of antibiotics and heavy metal combined pollution.

Exploring the Molecular Mechanism of Schisandrin C for the Treatment of Atherosclerosis via the PI3K/AKT/mTOR Autophagy Pathway.

Atherosclerosis (AS) is a common cardiovascular disease that poses a major threat to health. Schisandra chinensis is a medicinal and edible plant that is commonly used to treat cardiovascular diseases. In this paper, HPLC was used to detect and analyze 5 different components in Schisandra chinensis. Network pharmacological predictions highlight the PI3K/AKT/mTOR pathway as an important pharmacological pathway. The effective ingredient Schisandrin C was screened by the molecular docking technique. ox-LDL-induced HUVECs were used to construct the atherosclerosis model for further experimental verification. The results showed that Schisandrin C interfered with the PI3K/AKT/mTOR autophagy pathway. This study lays a foundation for the further application of Schisandrin C in the prevention and treatment of atherosclerosis in the future.

Batch production prediction for the mechanical cutting industry based on process capability.

In the mechanical cutting industry, trial production is used for predicting and evaluating the quality of product processes before batch production, and it can be expressed through the qualification rate. However, it cannot objectively and comprehensively evaluate the quality of product processes. This study optimizes the analysis of outliers and stability in mathematical statistics to better apply it in the mechanical cutting industry; then, it combines them with process capability analysis. Simultaneously, considering the non-normal distribution of process parameters, a batch production-prediction model is proposed. The reliability of batch production-prediction model is verified by the diameter, roundness and roughness of structural common samples. Meanwhile, for other mechanical parts in the mechanical cutting industry, the model proposed in this paper can be used to quickly and accurately predict and evaluate batch production.

Superassembled Mesoporous Carbon-Fe2O3 Heterochannels for Photothermal-Enhanced Hyaluronidase Sensing.

Developing an activity detection platform for hyaluronidase (HAase) is crucial for diagnosing and treating cancer. However, traditional detection of HAase is based on changes in the flow rate caused by viscosity or requires complex modifications and processing, which limits the detection accuracy and sensitivity. Herein, hyaluronic acid (HA)-modified mesoporous-based heterochannels (mesoporous carbon-doped γ-Fe2O3 nanoparticles/anodized aluminum oxide, MC-γ-Fe2O3/AAO) featuring ordered 3D transport frameworks and a photothermal property were developed for high performance HAase detection. The HA molecules on the surface of the mesoporous layer provide abundant active sites for HAase detection. An improved ionic current was realized after enzymatic hydrolysis reactions between HA and HAase due to enhanced surface charges and more hydrophilicity, leading to highly sensitive and accurate HAase detection. Notably, the detection performance can be further upgraded with the assistance of the photothermal property of γ-Fe2O3. An amplified detection current signal was achieved owing to a synergistic effect between ion currents and photoresponsive currents. A wide linear detection range from 1 to 50 U/mL and a low detection limit of 0.348 U/mL were obtained, achieving a 2% improvement under illumination. Importantly, the heterochannels have also been successfully applied for HAase detection in fetal bovine serum samples, manifesting considerable application prospects. This work provides a new strategy in constructing photoresponsive nanochannels with a photothermal property for a highly efficient biosensing platform.