Early caries confined to the enamel layer represent a critical window for achieving noninvasive intervention in caries management. Caries management has shifted from the traditional “drill-and-fill” model toward a modern paradigm centered on caries risk and lesion management. Based on contemporary concepts, this review systematically summarizes recent advances in early caries management, including caries risk assessment, early diagnosis, treatment strategy selection, and follow-up monitoring, while highlighting the major challenges currently being faced, and further reviewing and discussing the application of artificial intelligence (AI) in early caries management. In terms of risk management, conventional systems including the American Dental Association, Caries Management by Risk Assessment, Cariogram, and the Caries-Risk Assessment Tool remain mainstays in clinical practice. However, AI offers predictive capability through higher-dimensional data processing and the integration of numerous influencing factors, with the potential to improve the accuracy of risk stratification. For diagnosis, visual inspection, tactile examination, and bitewing radiography remain fundamental methods, yet their sensitivity for early caries—particularly proximal lesions—is limited. The application of optical technologies, including quantitative light-induced fluorescence, optical coherence tomography, near-infrared light transillumination, fiber-optic transillumination, and laser-induced fluorescence, enables digital characterization of caries lesions, providing a data foundation for demineralization assessment, lesion activity evaluation, and AI model development. The management of early caries primarily relies on noninvasive and minimally invasive approaches. Remineralization therapy is suitable for superficial lesions, resin infiltration offers the dual advantages of inhibiting lesion progression and improving aesthetics, and microabrasion and bleaching may serve as adjunctive aesthetic treatments. Emerging modalities such as laser, ozone, and photodynamic therapy have also demonstrated potential. Treatment decision-making should comprehensively consider lesion activity, patient caries risk status, demineralization depth, patient compliance, and treatment preferences. However, precise quantification of demineralization depth remains challenging, and standardized decision-making criteria are still lacking. Follow-up management should be individualized based on risk stratification, with attention to lesion changes, patient compliance, and the risk of recurrence. In summary, intelligent and precision-based approaches are expected to define the future of early caries management, and the application of AI in risk prediction, image analysis, and clinical decision support is anticipated to further enhance the efficiency and effectiveness of early caries diagnosis and treatment.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

AIM To study the chemical constituents from Commelina communis L.METHODS The 95％ethanol extract from C.Communis was isolated and purified by activated charcoal,silica gel,Sephadex LH-20,and HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.RESULTS Seventeen compounds were isolated and identified as p-hydroxyl ethyl cinnamate(1),p-hydroxybenzaldehyde(2),vanillin(3),4-hydroxy-2,3-dimethyl-2-nonen-4-olide(4),hemeratrol A(5),chakyunglupulin B(6),chakyunglupulin A(7),2-(2-hydroxyethyl)-3-methylfumaric acid(8),N-cis-feruloyl tyramine(9),N-trans-coumaroyltyramine(10),5,6,7,3',4',5'-hexamethoxyflavone(11),N-trans-sinapoyltyramine(12),dihydro-feruloyltyramine(13),N-trans-feruloyltyramine(14),2-phenylethanol-β-D-glucoside(15),quercetin-3-O-β-D-glucoside(16),and isorhamnetin-3-O-β-D-glucopyranoside(17).CONCLUSION Compounds 4-8,10 and 11 are isolated from Commelina genus for the first time,and 1,9,12-15 are first isolated from this plant.

Following extensive interdisciplinary research and development over several years,mechanical circulatory support devices(MCSD),including ventricular assist device(VAD)and total artificial heart(TAH),are now established as vital treatment options for patients with advanced heart failure.These devices have proven to be crucial in assisting or replacing a failing heart,offering patients a new lease of life and improving their quality of life.Currently,mechanical circulatory support(MCS)has become a well-recognised,long-term treatment option for patients who are unable to undergo heart transplantation due to donor organ shortages or contraindications.Given their continuous availability independent of donor organ limitations,these devices are poised to play an increasingly vital role in the future of medicine.This article aims to summarize the evolution,clinical applications,categorization,and potential complications of MCSD.

Objective To investigate the distribution and antimicrobial resistance profiles of common pathogens isolated from cerebrospinal fluid(CSF)in CHINET program from 2015 to 2021.Methods The bacterial strains isolated from CSF were identified in accordance with clinical microbiology practice standards.Antimicrobial susceptibility test was conducted using Kirby-Bauer method and automated systems per the unified CHINET protocol.Results A total of 14 014 bacterial strains were isolated from CSF samples from 2015 to 2021,including the strains isolated from inpatients(95.3％)and from outpatient and emergency care patients(4.7％).Overall,19.6％of the isolates were from children and 80.4％were from adults.Gram-positive and Gram-negative bacteria accounted for 68.0％and 32.0％,respectively.Coagulase negative Staphylococcus accounted for 73.0％of the total Gram-positive bacterial isolates.The prevalence of MRSA was 38.2％in children and 45.6％in adults.The prevalence of MRCNS was 67.6％in adults and 69.5％in children.A small number of vancomycin-resistant Enterococcus faecium(2.2％)and linezolid-resistant Enterococcus faecalis(3.1％)were isolated from adult patients.The resistance rates of Escherichia coli and Klebsiella pneumoniae to ceftriaxone were 52.2％and 76.4％in children,70.5％and 63.5％in adults.The prevalence of carbapenem-resistant E.coli and K.pneumoniae(CRKP)was 1.3％and 47.7％in children,6.4％and 47.9％in adults.The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)and Pseudomonas aeruginosa(CRPA)was 74.0％and 37.1％in children,81.7％and 39.9％in adults.Conclusions The data derived from antimicrobial resistance surveillance are crucial for clinicians to make evidence-based decisions regarding antibiotic therapy.Attention should be paid to the Gram-negative bacteria,especially CRKP and CRAB in central nervous system(CNS)infections.Ongoing antimicrobial resistance surveillance is helpful for optimizing antibiotic use in CNS infections.

Objective To investigate the antimicrobial resistance of clinical isolates from elderly patients(≥65 years)in major medical institutions across China.Methods Bacterial strains were isolated from elderly patients in 52 hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program during the period from 2015 to 2021.Antimicrobial susceptibility test was carried out by disk diffusion method and automated systems according to the same CHINET protocol.The data were interpreted in accordance with the breakpoints recommended by the Clinical and Laboratory Standards Institute(CLSI)in 2021.Results A total of 514 715 nonduplicate clinical isolates were collected from elderly patients in 52 hospitals from January 1,2015 to December 31,2021.The number of isolates accounted for 34.3％of the total number of clinical isolates from all patients.Overall,21.8％of the 514 715 strains were gram-positive bacteria,and 78.2％were gram-negative bacteria.Majority(90.9％)of the strains were isolated from inpatients.About 42.9％of the strains were isolated from respiratory specimens,and 22.9％were isolated from urine.More than half(60.7％)of the strains were isolated from male patients,and 39.3％isolated from females.About 51.1％of the strains were isolated from patients aged 65-＜75 years.The prevalence of methicillin-resistant strains(MRSA)was 38.8％in 32 190 strains of Staphylococcus aureus.No vancomycin-or linezolid-resistant strains were found.The resistance rate of E.faecalis to most antibiotics was significantly lower than that of Enterococcus faecium,but a few vancomycin-resistant strains(0.2％,1.5％)and linezolid-resistant strains(3.4％,0.3％)were found in E.faecalis and E.faecium.The prevalence of penicillin-susceptible S.pneumoniae(PSSP),penicillin-intermediate S.pneumoniae(PISP),and penicillin-resistant S.pneumoniae(PRSP)was 94.3％,4.0％,and 1.7％in nonmeningitis S.pneumoniae isolates.The resistance rates of Klebsiella spp.(Klebsiella pneumoniae 93.2％)to imipenem and meropenem were 20.9％and 22.3％,respectively.Other Enterobacterales species were highly sensitive to carbapenem antibiotics.Only 1.7％-7.8％of other Enterobacterales strains were resistant to carbapenems.The resistance rates of Acinetobacter spp.(Acinetobacter baumannii 90.6％)to imipenem and meropenem were 68.4％and 70.6％respectively,while 28.5％and 24.3％of P.aeruginosa strains were resistant to imipenem and meropenem,respectively.Conclusions The number of clinical isolates from elderly patients is increasing year by year,especially in the 65-＜75 age group.Respiratory tract isolates were more prevalent in male elderly patients,and urinary tract isolates were more prevalent in female elderly patients.Klebsiella isolates were increasingly resistant to multiple antimicrobial agents,especially carbapenems.Antimicrobial resistance surveillance is helpful for accurate empirical antimicrobial therapy in elderly patients.

The aim of this study was to investigate the risk factors for brucellar spondylitis.Electronic medical record data for patients with brucellosis at Yidu Central Hospital in Weifang City were retrospectively collected from January 2018 to April 2024,including general data,clinical characteristics,and laboratory examinations.The patients were divided into a spinal in-volvement group and a no spinal involvement group.The risk factors for brucellar spondylitis were determined through multi-factorial logistic regression model analysis.Of the 124 patients with brucellosis,59 had brucellar spondylitis,and 65 had bru-cellosis alone.There were more patients with age ≥55 years(x2=17.71),time from onset to diagnosis ≥30 days(x2=26.17),and low back pain(x2=52.71)in the spinal involvement group than in the group without spinal involvement,and the difference was statistically significant(all P＜0.001);there were more patients with headaches in the group without spinal in-volvement than in the group with spinal involvement,and the difference was statistically significant(x2=8.34,P＜0.05).and there were more patients in the spinal involvement group with neutrophil percent(NEU％)(t=2.94),platelet count(PLT)(t=122.00),blood sedimentation rate(ESR)(Z=-6.74),C-reactive protein(CRP)(Z=-5.74),and interleukin-6(IL-6)(Z=-2.08)were higher in the spine-involved group than in the group without spine-involvement,and the differences were all statistically significant(all P＜0.05);Lactate dehydrogenase was significantly lower in the spine-involved group(LDH)than the group without spinal involvement(t=-2.04,P＜0.042).A multifactorial logistic regression analysis indicated that a du-ration of out-of-hospital symptoms ≥30 days(OR=6.265,95％CI 1.181-33.241),symptoms of low back pain(OR=14.885,95％CI 3.144-70.472),elevated PLT(OR=1.013,95％CI 1.004-1.023),and elevated ESR(OR=1.053,95％CI 1.008-1.100)were risk factors for brucellar spondylitis(all P＜0.05).The optimal cut-off values for ROC analysis were PLT＞278.5 ×109/L(sensitivity 89.2％,specificity 59.3％)and ESR＞16.5 mm/h(sensitivity 69.2％,specificity of 86.4％);using both PLT and ESR for diagnosis yielded an AUROC of 0.891(95％CI 0.831-0.950),a sensitivity of 86.2％,and a specificity of 84.7％.When patients with brucellosis present with symptoms of low back pain,a time from onset to diagnosis of ≥30 days,and markedly elevated ESR and PLT,lumbar magnetic resonance examination is recommended to rule out brucellar spondylitis,to enable early diagnosis and timely treatment,improve patient prognosis,shorten illness duration,and improve patient quality of life.

Aim To investigate the anti-acute lung injury(ALI)effect of Sterculiae Lychnophorae Semen(SLS)and its mechanism.Methods The main ac-tive components of SLS and their core targets and path-ways of action against ALI were obtained by network pharmacology methods.Subsequently,molecular doc-king technology and in vitro cellular experiments were applied for validation.Results A total of 19 core tar-gets were obtained,including HSP90AA1,CASP3,TNF,MAPK8 and MAPK14.The mechanisms may in-volve signaling pathways such as cancer,PI3K/Akt and MAPK.Molecular docking confirmed that the key targets of SLS formed a better binding activity with the relevant active ingredients.The in vitro results showed that SLS was able to protect the PM2.5-contaminated BEAS-2B cells,inhibit their NO,IL-1β and TNF-αlevels,and reduce the expression of p-p38 MAPK and p-JNK proteins.Conclusions The study successfully predicts the active ingredients,targets and signaling pathways of SLS against ALI,and in vitro experiments demonstrate that SLS might protect BEAS-2B cells from PM2.5 stimulus-induced inflammation and apoptosis by inhibiting the over-activation of p38 MAPK and JNK signaling pathways.

Blood from a case of group B epidemic cerebrospinal meningitis identified in February 2024 in Ganzhou City,Jiangxi Province,and throat swabs from close contacts were collected for isolation and culture.The isolates were subjected to serogrouping,drug sensitivity testing,and whole genome sequencing and analysis,to provide a basis for epidemiological inves-tigation and clinical drug use.One strain of Neisseria meningitidis was isolated from the blood of the case and denoted group B.The MLST type was ST-7962,with no clonal group attribution.The phylogenetic tree showed that it was genetically close to the 1977 Shanghai carrier isolate(id-52231).Drug sensitivity results indicated that the strain was sensitive to 8 drugs:azithro-mycin,cefotaxime,minocycline,ceftriaxone,chloramphenicol,meropenem,rifampicin,and benzylpenicillin;resistant to cot-rimoxazole,levofloxacin,and ciprofloxacin;and showed an intermediate response to penicillin.This report describes the first case of ST-7962 group B meningoencephalitis found in Jiangxi Province.Monitoring of Neisseria meningitidis carriage,drug re-sistance,and molecular characteristics of strains in the healthy population in this region should be strengthened,to provide la-boratory support for the clinical use of medications,traceability,and control of the pathogen underlying meningoencephalitis infection.

Objective:To revise the quality standards of pharmaceutical excipients egg yolk lecithin and egg yolk lecithin(for injection)in Chinese Pharmacopoeia of 2025.Methods:Study on the quality of egg yolk lecithin by research on the crucial quality attributes and comparing domestic and abroad quality standards in pharmacopoeias.Results:Identification,water,residual solvents,and assay have been revised and free fatty acids has been deleted in the quality standard for egg yolk lecithin.Identification,water,residual solvents,related substances,bacterial endotoxins,and assay have been revised and free fatty acids has been deleted in the quality standard for egg yolk lecithin(for injection).Conclusion:The revised quality standards for egg yolk lecithin and egg yolk lecithin(for injection)have been included in the Chinese Pharmacopoeia of 2025,which provide data support and basis for qual-ity control and scientific supervision of egg yolk lecithin.