Objective: To investigate the ability of different concentrations of piceatannol (PIC) solution to biomodify decalcified dentin matrices in primary teeth, thereby providing a theoretical basis for improving the durability of resin-dentin bonding. Methods: This study was approved by the Medical Ethics Committee of the institution. Molecular docking was performed to predict the interaction effects of PIC with type I collagen and matrix metalloproteinases (MMPs) in dentin. A total of 124 clinically extracted intact primary molars were selected due to retention; of these, 76 were prepared into 60 completely decalcified dentin beams and 24 dentin slices. The 60 beams were equally allocated for dry mass loss and swelling ratio testing (30 beams each). For both experiments, the beams were randomly divided into five groups (n = 6): a control group, 5% glutaraldehyde group, and 2.5, 5, and 10 mg/mL PIC solution groups. This was to evaluate the anti-enzymatic hydrolytic properties and mechanical performance of type I collagen. Of the 24 slices, 15 were assigned to the aforementioned five groups (n = 3) for Fourier-transform infrared spectroscopy (FTIR) to explore the cross-linking mechanism. The remaining nine slices were randomly divided into three groups (n = 3): distilled water group A (SEM examination prior to enzymatic hydrolysis), distilled water group B (SEM examination after enzymatic hydrolysis), and a 10 mg/mL PIC solution group (SEM examination after treatment with 10 mg/mL PIC and subsequent enzymatic hydrolysis). This was to evaluate the cross-linking effect on demineralized dentin collagen. The remaining 48 molars were fabricated into bonding specimens and randomly divided into four groups (n = 12): a control group (distilled water group) and 2.5, 5, and 10 mg/mL PIC groups. Immediate and aged shear bond strength (SBS) were measured, and failure modes were observed under a stereomicroscope to investigate the impact of different PIC concentrations on the bonding durability of primary tooth dentin. Results: Molecular docking revealed strong binding activity between PIC and type I collagen/MMPs via hydrogen bonds, electrostatic potential, and hydrophobic forces; the FTIR results confirmed hydrogen bond formation. In the dry mass loss test, mass loss rates in the 2.5, 5, and 10 mg/mL PIC groups were significantly lower than that in the control group (P < 0.05), with the 10 mg/mL PIC group showing the most optimized cross-linking effect. Swelling tests indicated that all three PIC concentrations reduced the swelling ratio of the demineralized matrix, effectively improving its mechanical properties. SEM showed that collagen from the 10 mg/mL PIC group retained its natural three-dimensional network post-enzymolysis, contrasting sharply with the disintegrated collagen in the enzymolyzed distilled water group B but resembling the morphology of the non-enzymolyzed distilled water group A. SBS results demonstrated that the 10 mg/mL PIC group exhibited significantly higher bond strengths than the control group in both the immediate and aged tests (P < 0.05); immediate SBS in the 10 mg/mL PIC group was also significantly greater than that in the 2.5 mg/mL PIC group (P < 0.05), while no other intergroup differences were found (P > 0.05). Failure mode analysis indicated predominantly mixed fractures, with the proportion of interfacial fractures decreasing as PIC concentration increased compared with the control group. Conclusion A 10 mg/mL PIC solution can effectively cross-link decalcified dentin matrices in primary teeth, enhancing the anti-enzymatic hydrolytic capacity and mechanical properties during clinical operation, thereby promoting bond strength and interface stability, which promises to improve the long-term durability of resin-dentin bonding.

Objective:To explore the quantitative indexes of dual energy spectrum CT and related clinical data to establish a predictive model for predicting pneumoconiosis.Methods:In April 2024, the information of 203 pneumoconiosis patients diagnosed by the occupational disease appraisal expert group in the Third People's Hospital of Xinjiang Uygur Autonomous Region (Occupational Disease Hospital of Xinjiang Autonomous Region) from January 2022 to December 2023 was retrospectively analyzed. Another 207 non-pneumoconiosis patients with dust exposure history were selected as control group. The measurement data between the two groups were compared using T test or Wilcoxon in dependent quality test, count date asing chi-square or Fishers test, the energy spectrum related indicators and clinical indicators of the patients were compared between groups, and potential factors for diagnosis of pneumoconiosis were screened through univariate analysis, and independent risk factors were further determined by multivariate logistic regression. Based on the results of regression analysis, the machine learning model was constructed, and the reciver operating characteristic curve (ROC) was drawn to evaluate the efficiency of the model, and the Area under cruve (AUC) value, sensitivity and specificity were calculated.Results:Smoking, lung tissue mass, silicon dioxide (SiO 2) equivalent total mass and SiO 2 equivalent concentration were the risk factors for pneumoconiosis ( P<0.05) . Multivariate logistic regression analysis showed that smoking, lung tissue mass, total lung SiO 2 equivalent total volume and total lung SiO 2 equivalent total mass were independent predicators of the diagnosis of pneumoconiosis ( OR=0.53, 0.99, 1.13, 0.85, P<0.05) . Logistic regression machine learning was used to establish a predictive model, and the training set AUC was 0.74, and the verification set AUC was 0.72, indicating that the model had good accuracy and certain ability to diagnose pneumoconiosis. Conclusion:The machine learning prediction model established by the quantitative analysis index of dual energy spectrum CT and clinical related indexes has a good diagnostic performance for the diagnosis of pneumoconiosis.

Objective To investigate the efficacy and safety of CT-guided radioactive 125I seed implantation(ISI)for blocking celiac plexus in the treatment of refractory abdominal cancerous pain.Methods The clinical data of 60 patients with refractory cancerous pain caused by abdominal malignancies(visual analogue scale score ≥4 points)were retrospectively analyzed.Patients of group A(n=29)received anhydrous ethanol injection treatment and patients of group B(n=29)received ISI treatment.The primary endpoints were pain score(visual analogue scale,VAS),daily morphine consumption(DMC),and local tumor control(LTC)rate.Secondary endpoints included the success rate of the procedure and postoperative complications.Results The technical success rate of surgery was 100％in both groups,and no serious complications occurred after surgery.The VAS pain scores in both groups showed a continuous decline from 1 to 8 weeks after surgery,but the difference was not statistically significant.However,the VAS scores in group A was significantly increased at T8 to T12 weeks(P＜0.01).In contrast,the pain relief time in group B remained stable until T16 to T24 weeks when an increase in pain improvement was observed.At T8 week,the LTC rate in group B was significantly better than that in group A,and the difference was statistically significant(P＜0.01).Conclusion Celiac plexus block with ISI can effectively alleviate the refractory cancerous pain caused by abdominal tumors.Compared with anhydrous ethanol injection,ISI has a longer pain relief time(up to T16-T24 weeks)and can effectively control the growth of local tumor,which is worthy of clinical promotion and application.

Objective To investigate the population composition,seasonal dynamics,and infestation levels of cockroaches in Lanzhou,China,and to provide information for the scientific development of cockroach control strategies.Methods Monitoring was conducted at three locations using the sticky trap method.Habitats included farm product markets,catering establishments,hotels,hospitals,and residential areas.Results From 2016 to 2023,the average cockroach density was 0.77 insects per board,with an average infestation rate of 10.84%.Blattella germanica was the dominant species.Seasonal density of cockroaches showed an approximately unimodal distribution,peaking in September.The highest average density and infestation rates were observed in farm product markets.Conclusions Cockroach density and infestation levels in Lanzhou remained relatively low.A comprehensive prevention and control strategy focusing on environmental management in key areas should be implemented according to the seasonal fluctuations.

Objective:To explore the quantitative indexes of dual energy spectrum CT and related clinical data to establish a predictive model for predicting pneumoconiosis.Methods:In April 2024, the information of 203 pneumoconiosis patients diagnosed by the occupational disease appraisal expert group in the Third People's Hospital of Xinjiang Uygur Autonomous Region (Occupational Disease Hospital of Xinjiang Autonomous Region) from January 2022 to December 2023 was retrospectively analyzed. Another 207 non-pneumoconiosis patients with dust exposure history were selected as control group. The measurement data between the two groups were compared using T test or Wilcoxon in dependent quality test, count date asing chi-square or Fishers test, the energy spectrum related indicators and clinical indicators of the patients were compared between groups, and potential factors for diagnosis of pneumoconiosis were screened through univariate analysis, and independent risk factors were further determined by multivariate logistic regression. Based on the results of regression analysis, the machine learning model was constructed, and the reciver operating characteristic curve (ROC) was drawn to evaluate the efficiency of the model, and the Area under cruve (AUC) value, sensitivity and specificity were calculated.Results:Smoking, lung tissue mass, silicon dioxide (SiO 2) equivalent total mass and SiO 2 equivalent concentration were the risk factors for pneumoconiosis ( P<0.05) . Multivariate logistic regression analysis showed that smoking, lung tissue mass, total lung SiO 2 equivalent total volume and total lung SiO 2 equivalent total mass were independent predicators of the diagnosis of pneumoconiosis ( OR=0.53, 0.99, 1.13, 0.85, P<0.05) . Logistic regression machine learning was used to establish a predictive model, and the training set AUC was 0.74, and the verification set AUC was 0.72, indicating that the model had good accuracy and certain ability to diagnose pneumoconiosis. Conclusion:The machine learning prediction model established by the quantitative analysis index of dual energy spectrum CT and clinical related indexes has a good diagnostic performance for the diagnosis of pneumoconiosis.

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

″Boundarics in biomedicine″(or″Biomedical boundarics″)is an emerging frontier interdisciplinary subject that focuses on addressing key scientific issues related to the formation,identification,and evolution of biological boundaries within living organisms.In this field,the study of tumor boundaries is of particular importance.Imaging tumor boundaries not only helps to reveal the molecular mechanisms of tumor boundary evolution and interaction with the microenvironment,tumor invasion and metastasis,but is also crucial for clinical tumor diagnosis,treatment decision-making,efficacy monitoring and prognosis evaluation.Molecular probes,as functional substances that enhance imaging signals,play a crucial role in tumor boundary recognition.In this article,the basic concepts and research significance of boundarics in biomedicine and tumor boundarics in biomedicine were summarized firstly.Then a comprehensive review of the research progress in tumor boundary imaging molecular probes was provided,covering areas such as magnetic imaging,optical imaging,acoustic imaging,nuclear imaging,and multimodal imaging.The strategies to regulate the sensitivity,specificity,and safety of molecular probes through chemical structure modifications,conjugation with targeting ligands,and tumor microenvironment-responsive designs were emphasized.Finally,the research trends of molecular probes for tumor boundary imaging were analyzed,and the challenges faced in this field and the future research directions were discussed.

Delirium is a common cause and complication of hospitalization in the elderly and is associated with higher risk of future dementia and progression of existing dementia, of which 70% is Alzheimer's disease (AD). AD and delirium, which are known to be aggravated by one another, represent significant societal challenges, especially in light of the absence of effective treatments. The intricate biological mechanisms have led to numerous clinical trial setbacks and likely contribute to the limited efficacy of existing therapeutics. Artificial intelligence (AI) presents a promising avenue for overcoming these hurdles by deploying algorithms to uncover hidden patterns across diverse data types. This review explores the pivotal role of AI in revolutionizing drug discovery for AD and delirium from target identification to the development of small molecule and protein-based therapies. Recent advances in deep learning, particularly in accurate protein structure prediction, are facilitating novel approaches to drug design and expediting the discovery pipeline for biological and small molecule therapeutics. This review concludes with an appraisal of current achievements and limitations, and touches on prospects for the use of AI in advancing drug discovery in AD and delirium, emphasizing its transformative potential in addressing these two and possibly other neurodegenerative conditions.

OBJECTIVE: To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis (TB) in the Guangdong Province between 2010 and 2019. METHOD: Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering. Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive (ST-CAR) model. RESULTS: Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000 in 2019. Spatial hotspots were found in northeastern Guangdong, particularly in Heyuan, Shanwei, and Shantou, while Shenzhen, Dongguan, and Foshan had the lowest rates in the Pearl River Delta. The ST-CAR model showed that the TB risk was lower with higher per capita Gross Domestic Product (GDP) [Relative Risk ( RR), 0.91; 95% Confidence Interval ( CI): 0.86-0.98], more the ratio of licensed physicians and physician ( RR, 0.94; 95% CI: 0.90-0.98), and higher per capita public expenditure ( RR, 0.94; 95% CI: 0.90-0.97), with a marginal effect of population density ( RR, 0.86; 95% CI: 0.86-1.00). CONCLUSION The incidence of TB in Guangdong varies spatially and temporally. Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection. Strategies focusing on equitable health resource distribution and economic development are the key to TB control.
Humans ; China/epidemiology* ; Incidence ; Bayes Theorem ; Spatio-Temporal Analysis ; Tuberculosis/epidemiology* ; Socioeconomic Factors

BACKGROUND: Retinal ganglion cell (RGC) death caused by acute ocular hypertension is an important characteristic of acute glaucoma. Receptor-interacting protein kinase 3 (RIPK3) that mediates necroptosis is a potential therapeutic target for RGC death. However, the current understanding of the targeting agents and mechanisms of RIPK3 in the treatment of glaucoma remains limited. Notably, artificial intelligence (AI) technologies have significantly advanced drug discovery. This study aimed to discover RIPK3 inhibitor with AI assistance. METHODS: An acute ocular hypertension model was used to simulate pathological ocular hypertension in vivo . We employed a series of AI methods, including large language and graph neural network models, to identify the target compounds of RIPK3. Subsequently, these target candidates were validated using molecular simulations (molecular docking, absorption, distribution, metabolism, excretion, and toxicity [ADMET] prediction, and molecular dynamics simulations) and biological experiments (Western blotting and fluorescence staining) in vitro and in vivo . RESULTS: AI-driven drug screening techniques have the potential to greatly accelerate drug development. A compound called HG9-91-01, identified using AI methods, exerted neuroprotective effects in acute glaucoma. Our research indicates that all five candidates recommended by AI were able to protect the morphological integrity of RGC cells when exposed to hypoxia and glucose deficiency, and HG9-91-01 showed a higher cell survival rate compared to the other candidates. Furthermore, HG9-91-01 was found to protect the retinal structure and reduce the loss of retinal layers in an acute glaucoma model. It was also observed that the neuroprotective effects of HG9-91-01 were highly correlated with the inhibition of PANoptosis (apoptosis, pyroptosis, and necroptosis). Finally, we found that HG9-91-01 can regulate key proteins related to PANoptosis, indicating that this compound exerts neuroprotective effects in the retina by inhibiting the expression of proteins related to apoptosis, pyroptosis, and necroptosis. CONCLUSION AI-enabled drug discovery revealed that HG9-91-01 could serve as a potential treatment for acute glaucoma.
Animals ; Glaucoma/metabolism* ; Neuroprotective Agents/pharmacology* ; Mice ; Receptor-Interacting Protein Serine-Threonine Kinases/metabolism* ; Artificial Intelligence ; Retinal Ganglion Cells/metabolism* ; Disease Models, Animal ; Molecular Docking Simulation ; Mice, Inbred C57BL ; Male