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Tinnitus diagnosis poses a challenge in otolaryngology owing to an extremely complex pathogenesis, lack of effective objectification methods, and factor-affected diagnosis. There is currently a lack of explainable auxiliary diagnostic tools for tinnitus in clinical practice. This study aims to develop a diagnostic model using an explainable artificial intelligence (AI) method to address the issue of low accuracy in tinnitus diagnosis. In this study, a knowledge graph-based tinnitus diagnostic method was developed by combining clinical medical knowledge with electronic medical records. Electronic medical record data from 1267 patients were integrated with traditional Chinese clinical medical knowledge to construct a tinnitus knowledge graph. Subsequently, weights were introduced, which measured patient similarity in the knowledge graph based on mutual information values. Finally, a collaborative neighbor algorithm was proposed, which scored patient similarity to obtain the recommended diagnosis. We conducted 2 group experiments and 1 case derivation to explore the effectiveness of our models and compared the models with state-of-the-art graph algorithms and other explainable machine learning models. The experimental results indicate that the method achieved 99.4% accuracy, 98.5% sensitivity, 99.6% specificity, 98.7% precision, 98.6% F -score, and 99% area under the receiver operating characteristic curve for the inference of 5 tinnitus subtypes among 253 test patients. Additionally, it demonstrated good interpretability. The topological structure of knowledge graphs provides transparency that can explain the reasons for the similarity between patients. This method provides doctors with a reliable and explainable diagnostic tool that is expected to improve tinnitus diagnosis accuracy.

Deoxynivalenol (DON), a mycotoxin from Fusarium that contaminates cereals, can also induce intestinal injury. However, the mechanisms underlying DON-induced jejunal barrier injury remain unclear. This study demonstrates that shikimic acid (SA) alleviates DON-induced jejunal barrier damage and dysbiosis via antioxidant pathways. Fifty 5-week-aged male KM mice were divided into control (CON), model (MOD, 2.4 mg/kg bw DON), and SA-treated groups (LDG/MDG/HDG: 25/50/100 mg/kg bw SA + DON). After SA treatment, notably MDG, reversed DON-induced weight loss and jejunal hyperemia; ameliorated villus atrophy, crypt deepening and goblet cell loss, increasing villus/crypt ratio; reduced gut permeability markers (D-LA/DAO) and pro-inflammatory cytokines (TNF-α/IL-6/IL-1β); and dose-dependently upregulated tight junction proteins (ZO-1/Occludin/Claudin1). Mechanistically, SA activated the Nrf2/HO-1/NQO1 pathway, elevating antioxidants (GSH/SOD/AOC) while reducing MDA, with MDG showing optimal efficacy. 16S rRNA sequencing revealed MDG counteracted DON-induced dysbiosis by enriching beneficial bacteria (e.g., Bacteroidota at phylum level; Muribaculaceae at family level) and suppressing pathogens (Staphylococcaceae) (LDA score > 4.0). Thus, SA mitigates DON toxicity via Nrf2-mediated barrier restoration, anti-inflammation, and microbiota modulation. This research provides new insights for the further development of Shikimic Acid and the treatment of DON-induced jejunal barrier injury.

The objective of this study was to formulate a compound suspension comprising paeoniflorin and curcumin, assess its quality characteristics, and investigate its protective efficacy against acute liver injury in mice. The prescriptions were screened using a single-factor test, and nine groups of suspensions were prepared using the dispersion method. Fifty KM mice (four weeks old) were selected and randomly divided into five groups: the CON, LD, PF, CUR, and PC groups. The doses of both paeoniflorin and curcumin were 100 mg/kg BW, and different suspensions were given to different groups by gavage for 14 days. All the groups except the CON group were injected intraperitoneally with 20 μg/kg LPS and 700 mg/kg D-GalN on the last day. According to the results, the suspension prepared using the optimal prescriptions was orange-yellow in color, with homogeneous turbidity and good re-dispersibility. The combination treatment could reduce the severity of pathological injuries of liver, improve the ultrastructure of hepatocytes, increase the activities of T-SOD, GSH-Px, and CAT, decrease the levels of IFN-γ, TNF-α, and IL-1, and down-regulate the expression of genes such as TLR4, MyD88, IκBα, and NLRP3. The underlying mechanism might be associated with the enhancement of antioxidant enzyme activities, inhibition of the TLR4/NF-κB/NLRP3 signaling pathway, and suppression of inflammasome assembly and release in hepatic tissues.

Although flavonoid compounds exhibit various pharmacological activities, their clinical applications are restricted by low oral bioavailability owing to their poor solubility. Nanocrystals (NCs) represent an excellent strategy for enhancing the oral bioavailability of flavonoids. Hydroxyethyl starch (HES), a biomaterial compound used as a plasma expander, could be an ideal stabilizer material for preparing flavonoid NCs. HES was used to stabilize flavonoid nanocrystals (NCs), using luteolin (LUT) as a model drug. After full characterization, the freeze-drying and storage stability, solubility, intestinal absorption, pharmacokinetics, and in vivo anti-hyperuricemic effect of the optimized HES-stabilized LUT NCs (LUT-HES NCs) were investigated. Uniformed LUT-HES NCs were prepared with mean particle size of 191.1±16.8 nm, zeta potential of about -23 mV, drug encapsulation efficiency of 98.52 ± 1.01%, and drug loading of 49.26 ± 0.50%. The freeze-dried LUT-HES NCs powder showed good re-dispersibility and storage stability for 9 months. Notably, compared with the coarse drug, LUT-HES NCs exhibited improved saturation solubility (7.49 times), increased drug dissolution rate, enhanced Caco-2 cellular uptake (2.78 times) and oral bioavailability (Fr=355.7%). Pharmacodynamic studies showed that LUT-HES NCs remarkably lowered serum uric acid levels by 69.93% and ameliorated renal damage in hyperuricemic mice. HES is a potential stabilizer for poorly soluble flavonoid NCs and provides a promising strategy for the clinical application of these compounds. LUT-HES NCs may be an alternative or complementary strategy for hyperuricemia treatment.

Luteolin is a natural flavonoid, which exists in many plants, including onions, broccoli, carrots, peppers, celery, olive oil, and mint. Luteolin is a dietary flavonoid with potent uric acid‐lowering and antioxidant bioactivities. To date, the mechanism by which luteolin alleviates hyperuricemia nephropathy (HN) still needs to be better defined. This study aims to evaluate the therapeutic efficacy of luteolin in a preclinical mouse model and in vitro. Luteolin was administered in the HN mice induced by the combination of potassium oxonate and hypoxanthine to evaluate the potential renoprotective effects in vivo. The NRK‐52E cells were stimulated with adenosine for in vitro evaluation. Hematoxylin and eosin staining, biochemical analysis, immunoblotting, immunofluorescence, and immunohistochemistry were performed for the histopathologic and mechanistic investigations. The results suggest that luteolin attenuated tubular dilation and epithelial atrophy in the renal tissue of HN mice. Further, luteolin improved biochemical indicators concerning renal functions and oxidative stress in vivo. Mechanistically, luteolin reduced the renal expressions of KIM‐1 and caspase‐3. Luteolin activated renal SIRT1/6 cascade and its downstream Nrf2‐mediated antioxidant pathway. Furthermore, luteolin elevated the renal expressions of ATP‐binding cassette subfamily G isoform 2 protein (ABCG2) and organic anion/cation transporters. In addition, livers of luteolin‐treated HN mice exhibited robust inhibition of xanthine oxidase. Together, our study shows that luteolin alleviates renal injury in the HN mice by activating urate excretion and Nrf2/HO‐1/NQO1 antioxidant pathways and inhibiting liver xanthine oxidase activity. Thus, luteolin may be a potential agent for the treatment of HN. This study provides mechanistic insights into how luteolin suppresses renal injury caused by hyperuricemic nephropathy (HN). Luteolin intervention significantly improved renal injury in HN mice. Through in vivo and in vitro experiments, we found that luteolin can significantly activate uric acid excretion and Nrf2/HO1/NQO1 antioxidant pathway and inhibit the activity of xanthine oxidase in the liver.

Cancer cells preferentially metabolize glucose through aerobic glycolysis, a phenomenon known as the Warburg effect. Emerging evidence has shown that long non‐coding RNAs (lncRNAs) act as key regulators of multiple cancers. However, it remains largely unexplored whether and how lncRNA regulates glucose metabolism in cancer cells. In this study, we show that lncRNA UCA1 promotes glycolysis in bladder cancer cells, and that UCA1‐induced hexokinase 2 (HK2) functions as an important mediator in this process. We further show that UCA1 activates mTOR to regulate HK2 through both activation of STAT3 and repression of microRNA143. Taken together, these findings provide the first evidence that UCA1 plays a positive role in cancer cell glucose metabolism through the cascade of mTOR–STAT3/microRNA143–HK2, and reveal a novel link between lncRNA and the altered glucose metabolism in cancer cells. UCA1 plays a positive role in cancer cell glucose metabolism. UCA1 exerts its role in glycolysis through the cascade of mTOR‐STAT3/miR143‐HK2.

Single-atom metal catalysts have sparked tremendous attention, but direct transformation of cheap and easily obtainable bulk metal oxide into single atoms is still a great challenge. Here we report a facile and versatile gas-transport strategy to synthesize isolated single-atom copper sites (Cu ISAS/NC) catalyst at gram levels. Commercial copper (I) oxide powder is sublimated as mobile vapor at nearly melting temperature (1500 K) and subsequently can be trapped and reduced by the defect-rich nitrogen-doped carbon (NC), forming the isolated copper sites catalyst. Strikingly, this thermally stable Cu ISAS/NC, which is obtained above 1270 K, delivers excellent oxygen reduction performance possessing a recorded half-wave potential of 0.92 V vs RHE among other Cu-based electrocatalysts. By varying metal oxide precursors, we demonstrate the universal synthesis of different metal single atoms anchored on NC materials (M ISAS/NC, where M refers to Mo and Sn). This strategy is readily scalable and the as-prepared sintering-resistant M ISAS/NC catalysts hold great potential in high-temperature applications. Single-atom catalysts attract lots of attention, but direct transformation of bulk metal oxide into single atoms remains challenging. Here the authors report a gas-transport route to transform monolithic copper (I) oxide into copper single-atoms catalyst with a high activity for oxygen reduction.

Cognitive Radio (CR) is widely acknowledged as a pivotal technology for mitigating the scarcity of spectrum resources, with Transform Domain Communication Systems (TDCSs) regarded as one of the primary candidate technologies for CR. However, conventional Wideband Spectrum Sensing (WBSS) techniques utilized in TDCS exhibit limitations and are insufficient for adapting to the current complex electromagnetic environment. This paper tackles the time–frequency reconstruction challenge in WBSS by proposing a fast time–frequency reconstruction (FTFR) algorithm. The proposed algorithm acquires sub-Nyquist samples through the introduction of a Multi-Coset Sampling structure and reconstructs the autocorrelation of signals across various windows through a series of low-complexity operations. It captures the dynamic variations of signals by integrating spectra from adjacent time windows. In comparison to existing time–frequency reconstruction algorithms in WBSS, the proposed algorithm demonstrates reduced computational complexity. Simulation experiments indicate that the FTFR algorithm can effectively reconstruct the time–frequency characteristics of signals and significantly restore the primary temporal and frequency distributions, even in low Signal-to-Noise Ratio (SNR) environments.

Atherosclerosis, a chronic inflammatory condition primarily affecting large and medium arteries, is the main cause of cardiovascular diseases. Macrophages are key mediators of inflammatory responses. They are involved in all stages of atherosclerosis development and progression, from plaque formation to transition into vulnerable plaques, and are considered important therapeutic targets. Increasing evidence suggests that the modulation of macrophage polarization can effectively control the progression of atherosclerosis. Herein, we explore the role of macrophage polarization in the progression of atherosclerosis and summarize emerging therapies for the regulation of macrophage polarization. Thus, the aim is to inspire new avenues of research in disease mechanisms and clinical prevention and treatment of atherosclerosis.

Spectrum sensing is a key technology and prerequisite for Transform Domain Communication Systems (TDCS). The traditional approach typically involves selecting a working sub-band and maintaining it without further changes, with spectrum sensing being conducted periodically. However, this approach presents two main issues: on the one hand, if the selected working band has few idle channels, TDCS devices are unable to flexibly switch sub-bands, leading to reduced performance; on the other hand, periodic sensing consumes time and energy, limiting TDCS’s transmission efficiency. In contrast to previous studies that unrealistically modeled the problem as a Markov Decision Process (MDP), this study accounts for the fact that TDCS devices cannot fully observe the entire spectrum state and must rely on historical observations, along with the current state of sub-bands, to make informed decisions. We innovatively model this as a Partially Observable Markov Decision Process (POMDP). Moreover, we consider both the number of skipped time slots and the selection of idle sub-bands, establishing distinct termination conditions for each action. By assigning different weights to balance sensing overhead and spectrum utilization while reducing conflicts, the algorithm’s adaptability and performance are improved. To address the Q-value overestimation problem inherent in traditional Deep Recurrent Q-Network (DRQN) due to the use of a single network, we propose a DDRQN-BandShift strategy that combines Double Deep Q-Network (DDQN) and DRQN. Simulation results show that the proposed scheme significantly improves TDCS transmission efficiency while effectively reducing sensing costs.