ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

Blue light inactivation technology, particularly at the 405 nm wavelength, has demonstrated distinct and multifaceted mechanisms of action against both Gram-positive and Gram-negative bacteria, offering a promising alternative to conventional antibiotic therapies. For Gram-positive pathogens such as Bacillus cereus, Listeria monocytogenes, and methicillin-resistant Staphylococcus aureus (MRSA), the bactericidal effects are primarily mediated by endogenous porphyrins (e.g., protoporphyrin III, coproporphyrin III, and uroporphyrin III), which exhibit strong absorption peaks between 400-430 nm. Upon irradiation, these porphyrins are photoexcited to generate cytotoxic reactive oxygen species (ROS), including singlet oxygen, hydroxyl radicals, and superoxide anions, which collectively induce oxidative damage to cellular components. Early studies by Endarko et al. revealed that (405±5) nm blue light at 185 J/cm² effectively inactivated L. monocytogenes without exogenous photosensitizers, supporting the hypothesis of intrinsic photosensitizer involvement. Subsequent work by Masson-Meyers et al. demonstrated that 405 nm light at 121 J/cm² suppressed MRSA growth by activating endogenous porphyrins, leading to ROS accumulation. Kim et al. further elucidated that ROS generated under 405 nm irradiation directly interact with unsaturated fatty acids in bacterial membranes, initiating lipid peroxidation. This process disrupts membrane fluidity, compromises structural integrity, and impairs membrane-bound proteins, ultimately causing cell death. In contrast, Gram-negative bacteria such as Salmonella, Escherichia coli, Helicobacter pylori, Pseudomonas aeruginosa, and Acinetobacter baumannii exhibit more complex inactivation pathways. While endogenous porphyrins remain central to ROS generation, studies reveal additional photodynamic contributors, including flavins (e.g., riboflavin) and bacterial pigments. For instance, H. pylori naturally accumulates protoporphyrin and coproporphyrin mixtures, enabling efficient 405 nm light-mediated inactivation without antibiotic resistance concerns. Kim et al. demonstrated that 405 nm light at 288 J/cm² inactivates Salmonella by inducing genomic DNA oxidation (e.g., 8-hydroxy-deoxyguanosine formation) and disrupting membrane functions, particularly efflux pumps and glucose uptake systems. Huang et al. highlighted the enhanced efficacy of pulsed 405 nm light over continuous irradiation for E. coli, attributing this to increased membrane damage and optimized ROS generation through frequency-dependent photodynamic effects. Environmental factors such as temperature, pH, and osmotic stress further modulate susceptibility, sublethal stress conditions (e.g., high salinity or acidic environments) weaken bacterial membranes, rendering cells more vulnerable to subsequent ROS-mediated damage. The 405 nm blue light inactivates drug-resistant Pseudomonas aeruginosa through endogenous porphyrins, pyocyanin, and pyoverdine, with the inactivation efficacy influenced by bacterial growth phase and culture medium composition. Intriguingly, repeated 405 nm exposure (20 cycles) failed to induce resistance in A. baumannii, with transient tolerance linked to transient overexpression of antioxidant enzymes (e.g., superoxide dismutase) or stress-response genes (e.g., oxyR). For Gram-positive bacteria, porphyrin abundance dictates sensitivity, whereas in Gram-negative species, membrane architecture and accessory pigments modulate outcomes. Critically, ROS-mediated damage is nonspecific, targeting DNA, proteins, and lipids simultaneously, thereby minimizing resistance evolution. The 405 nm blue light technology, as a non-chemical sterilization method, shows promise in medical and food industries. It enhances infection control through photodynamic therapy and disinfection, synergizing with red light for anti-inflammatory treatments (e.g., acne). In food processing, it effectively inactivates pathogens (e.g., E. coli, S. aureus) without altering food quality. Despite efficacy against multidrug-resistant A. baumannii, challenges include device standardization, limited penetration in complex materials, and optimization of photosensitizers/light parameters. Interdisciplinary research is needed to address these limitations and scale applications in healthcare, food safety, and environmental decontamination.

Drug development remains a critical issue in the field of biomedicine. With the rapid advancement of information technologies such as artificial intelligence (AI) and the advent of the big data era, AI-assisted drug development has become a new trend, particularly in predicting drug-target associations. To address the challenge of drug-target prediction, AI-driven models have emerged as powerful tools, offering innovative solutions by effectively extracting features from complex biological data, accurately modeling molecular interactions, and precisely predicting potential drug-target outcomes. Traditional machine learning (ML), network-based, and advanced deep learning architectures such as convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers play a pivotal role. This review systematically compiles and evaluates AI algorithms for drug- and drug combination-target predictions, highlighting their theoretical frameworks, strengths, and limitations. CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions. GCNs provide deep insights into molecular interactions via relational data, whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences. Network-based models offer a systematic perspective by integrating diverse data sources, and traditional ML efficiently handles large datasets to improve overall predictive accuracy. Collectively, these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy. This review summarizes the application of AI in drug development, particularly in drug-target prediction, and offers recommendations on models and algorithms for researchers engaged in biomedical research. It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.

Ethanol (EtOH) is a common trigger for gastric mucosal diseases, and mitigating oxidative stress is essential for attenuating gastric mucosal damage. Capsaicin (CAP) has been identified as a potential agent to counteract oxidative damage in the gastric mucosa; however, its precise mechanism remains unclear. This study demonstrates that CAP alleviates EtOH-induced gastric mucosal injuries through two primary pathways: by suppressing the chemokine receptor 4 (CCR4)/Src/p47phox axis, thereby reducing oxidative stress, and by inhibiting the phosphorylation and nuclear translocation of nuclear factor-κB p65 (NF-κB) p65, resulting in diminished inflammatory responses. These findings elucidate the mechanistic pathways of CAP and provide a theoretical foundation for its potential therapeutic application in the treatment of gastric mucosal injuries.
Ethanol/toxicity* ; Animals ; Gastric Mucosa/metabolism* ; Signal Transduction/drug effects* ; Oxidative Stress/drug effects* ; Capsaicin/pharmacology* ; Male ; NADPH Oxidases/genetics* ; Mice ; Humans ; src-Family Kinases/genetics*

Objective: To evaluate the application value of a digital technology-based multiparameter vital signs monitoring system in perioperative comprehensive full-cycle surveillance. Methods: A comprehensive multidimensional vital signs monitoring system was developed through the integration of medical-grade wireless wearable devices, incorporating patch-type ambulatory electrocardiographic monitor, continuous glucose monitoring sensor, pulse oximeter, wireless digital thermometer, smart wristband, and bioelectrical impedance analyzer. This system facilitates continuous real-time acquisition of multiple physiological parameters including electrocardiogram, blood glucose, oxygen saturation, body temperature, physical activity, and body composition indices. The acquired data were systematically integrated and analyzed through a four-level digital architecture consisting of nurse mobile interfaces, bedside patient terminals, centralized ward monitoring displays, and hospital management information systems. One patient with gastric cancer complicated by diabetes mellitus was selected for full-cycle digital monitoring from preoperative evaluation to hospital discharge. The technical performance of the monitoring system was assessed in terms of data acquisition continuity and timeliness of abnormal event alerts. Results: The monitoring system effectively identified early postoperative abnormalities, such as decreased oxygen saturation and blood glucose fluctuations, providing timely guidance for clinical intervention. The built-in algorithm enabled visualization of perioperative stress levels through heart rate variability indices and continuous glucose monitoring data. The patient demonstrated good compliance with early postoperative mobilization, and the satisfaction score for monitoring management was 4 points based on the Likert 5-point scale. Conclusions: The multiparameter vital signs monitoring system enhanced the precision of perioperative management through continuous and dynamic physiological status assessment. Its modular design aligns with the principles of enhanced recovery after surgery, offering a novel technological solution for intelligent perioperative management.
Humans ; Stomach Neoplasms/physiopathology* ; Vital Signs ; Monitoring, Physiologic/instrumentation* ; Diabetes Mellitus ; Wearable Electronic Devices ; Perioperative Period

Coronavirus-related diseases pose a significant challenge to the global health system. Given the diversity of coronaviruses and the unpredictable nature of disease outbreaks, the traditional "one bug, one drug" paradigm struggles to address the growing number of emerging crises. Therefore, there is an urgent need for therapeutic agents with broad-spectrum anti-coronavirus activity. Here, we provide evidence that ATV006, an anti-SARS-CoV-2 nucleoside analog targeting RNA-dependent RNA polymerase (RdRp), has broad antiviral activity against human and animal coronaviruses. Using mouse hepatitis virus (MHV) and human coronavirus NL63 (HCoV-NL63) as a model, we show that ATV006 has potent prophylactic and therapeutic activity against murine coronavirus infection in vivo. Remarkably, ATV006 successfully inhibits viral replication in mice even when administered 96 h after infection. Due to its oral bioavailability and potency against multiple coronaviruses, ATV006 has the potential to become a useful antiviral agent against SARS-CoV-2 and other circulating and emerging coronaviruses in humans and animals.

Photodynamic therapy(PDT)is a precise targeted therapy that selectively treats certain benign diseases and malignant tumors by combining therapeutic light sources,photosensitizers,and oxygen molecules.The wavelength range of the light source,as a key factor in inducing PDT,has a decisive impact on the triggering and therapeutic effect of the treatment.However,there is a lack of relevant reviews on the selection of light sources for photodynamic therapy.This article reviews the PDT-related applications of commonly used light sources with different wavelength ranges of excitation,such as visible light,near-infrared,and X-ray,including the excitation characteristics of this band of light,as well as the multi-therapy combination and multi-range breakthroughs of PDT cancer treatment under the excitation of this band of light.The aim is to provide feasible directions for the development of photodynamic therapy bands and subsequent applications.

Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,of-fering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accu-racy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summa-rizes the application of AI in drug development,particularly in drug-target prediction,and offers rec-ommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.