Objective:To explore the safety and efficacy of intraosseous regional administration (IORA) of vancomycin for preventing infection in primary total knee arthroplasty (TKA).Methods:A total of 124 patients with knee osteoarthritis undergoing TKA between February 2024 and May 2024 at nine hospitals were enrolled. Preoperative infection prophylaxis involved either IORA (0.5 g vancomycin administered via intraosseous regional infusion before incision) or intravenous infusion (1 g vancomycin via peripheral vein). The IORA group included 15 males and 47 females with a median age of 66.5 years (range, 60.0-70.0 years), while the intravenous group included 14 males and 48 females with a median age of 66.0 years (range, 61.8-70.3 years) years. Intraoperative samples were collected including fat and synovium tissues after incision, before prosthesis placement, and after tourniquet release; distal femoral cancellous bone during femoral osteotomy; proximal tibial cancellous bone during tibial osteotomy; proximal intercondylar cancellous bone before prosthesis placement; and peripheral blood from non-infused arms at surgery initiation and after tourniquet release. Vancomycin concentrations were measured using liquid chromatography-tandem mass spectrometry. Vital sign changes were recorded from admission to 5~10 minutes post-IORA (IORA group) or post-incision (intravenous group). Follow-ups were conducted on postoperative day 1 and 3, and at 1 and 3 months, to document complications including IORA-related adverse events, periprosthetic joint infections, surgical site infections, red man syndrome, acute kidney injury, deep vein thrombosis and so on.Results:Vancomycin concentrations in bone, fat, and synovial tissue samples were significantly higher in the IORA group than in the intravenous group ( P<0.05), while vancomycin concentrations in blood samples were significantly lower in the IORA group than in the intravenous group ( P<0.05). Only 7.3%(41/558) of tissue samples in the IORA group had vancomycin concentrations below 2.0 μg/g (the minimum inhibitory concentration of vancomycin against coagulase-negative staphylococcus), compared to 59.3%(331/558) in the intravenous group (χ 2=11.285, P<0.001). In the intravenous group, 16.9%(21/124) of blood samples had vancomycin concentrations exceeding 15.0 mg/L (the threshold associated with a significantly increased risk of nephrotoxicity), while all concentrations in the IORA group were below this threshold, the difference was statistically significant (χ 2=22.943, P<0.001). There were no statistically significant difference ( P>0.05) in vital signs changes before and after vancomycin administration between the two groups. Two patients in the intravenous group experienced incision exudate, while no other related complications occurred in either group. Conclusions:Compared to the traditional intravenous infusion of 1 g vancomycin, intraosseous injection of a low dose (0.5 g) of vancomycin achieves higher local tissue concentrations in the knee joint with a lower incidence of adverse reactions and is safe for infection prophylaxis. Despite guidelines not recommending the routine use of vancomycin for preventing infection after primary TKA, intraosseous injection of 0.5 g vancomycin may be considered intraoperatively for primary TKA in the following scenarios: patients in medical institutions with a high prevalence of methicillin-resistant staphylococcus aureus (MRSA) infections, patients with potential preoperative MRSA colonization, or patients with cephalosporin allergy.

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.

OBJECTIVES: We propose a multi-feature fusion model based on manually extracted features and deep learning features from endoscopic images for grading rebleeding risk of peptic ulcers. METHODS: Based on the endoscopic appearance of peptic ulcers, color features were extracted to distinguish active bleeding (Forrest I) from non-bleeding ulcers (Forrest II and III). The edge and texture features were used to describe the morphology and appearance of the ulcers in different grades. By integrating deep features extracted from a deep learning network with manually extracted visual features, a multi-feature representation of endoscopic images was created to predict the risk of rebleeding of peptic ulcers. RESULTS: In a dataset consisting of 3573 images from 708 patients with Forrest classification, the proposed multi-feature fusion model achieved an accuracy of 74.94% in the 6-level rebleeding risk classification task, outperforming the experienced physicians who had a classification accuracy of 59.9% (P<0.05). The F1 scores of the model for identifying Forrest Ib, IIa, and III ulcers were 90.16%, 75.44%, and 77.13%, respectively, demonstrating particularly good performance of the model for Forrest Ib ulcers. Compared with the first model for peptic ulcer rebleeding classification, the proposed model had improved F1 scores by 5.8%. In the simplified 3-level risk (high-risk, low-risk, and non-endoscopic treatment) classification task, the model achieved F1 scores of 93.74%, 81.30%, and 73.59%, respectively. CONCLUSIONS The proposed multi-feature fusion model integrating deep features from CNNs with manually extracted visual features effectively improves the accuracy of rebleeding risk classification for peptic ulcers, thus providing an efficient diagnostic tool for clinical assessment of rebleeding risks of peptic ulcers.
Humans ; Deep Learning ; Peptic Ulcer ; Risk Assessment ; Peptic Ulcer Hemorrhage ; Recurrence

Adolescent depression is increasingly recognized as a serious mental health disorder with distinct clinical and molecular features. Using single-nucleus RNA sequencing, we identified cell-specific transcriptomic changes in the nucleus accumbens (NAc), particularly in astrocytes, of adolescent macaques exhibiting depressive-like behaviors. The level of diacylglycerol kinase beta was significantly reduced in neurons and glial cells of depressed macaques, while FKBP5 levels increased in glial cells. Disruption of GABAergic synapses and disruption of D-glutamine and D-glutamate metabolism were linked to depressive phenotypes in medium spiny neurons (MSNs) and subtypes of astrocytes. Communication pathways between astrocytes and D1/D2-MSNs were also disrupted, involving factors like bone morphogenetic protein-6 and Erb-B2 receptor tyrosine kinase-4. Bulk transcriptomic and proteomic analyses corroborated these findings, and FKBP5 upregulation was confirmed by qRT-PCR, western blotting, and immunofluorescence in the NAc of rats and macaques with chronic unpredictable mild stress. Our results highlight the specific roles of different cell types in adolescent depression in the NAc, offering potential targets for new antidepressant therapies.
Animals ; Nucleus Accumbens/metabolism* ; Male ; Transcriptome ; Depression/genetics* ; Astrocytes/metabolism* ; Neurons/metabolism* ; Rats

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.

Objective To explore the efficacy of hypofractionated radiotherapy at different time intervals after surgery for keloid,and to analyze the factors affecting the efficacy.Methods A total of 76 patients who underwent 20 Gy/5 postoperative radiotherapy regimen in the Fifth Affiliated Hospital of Zhengzhou University from January 2021 to June 2023 were selected as study subjects,and a total of 100 keloids were divided into effective group(n=79)and recurrence group(n=21).Regular follow-up and record of the patients after radiotherapy treatment effect and adverse effects,and multivariate Logistic was used to analyze factors of recurrence in keloid patients.Results Multivariate Logistic regression analysis found that postoperative radiotherapy time and scar incision length were related to recurrence after treatment,radiotherapy within 7h of surgery was an independent risk factor for recurrence after treatment(OR＞1,P=0.022),and scar incision≤5cm was an independent protective factor for recurrence after treatment(OR＜1,P=0.028).Conclusion Surgical excision combined with hypofractionated radiotherapy is one of the effective measures to prevent and treat keloid recurrence,though keloids on the trunk may need more effective treatment options.The recurrence rate of radiotherapy initiated 7-48h after surgery is relatively the lowest,and it is worthy of clinical promotion and application.

Objective To explore the mechanism of malt alcohol extract improving depression-like behavior induced by CUMS in rats by regulating gut microbiota.Methods The depression model of rats was established using an 8-weeks CUMS procedure,and the administration group was given low(59.6 mg·kg-1)and high(178.8 mg·kg-1)doses of malt alcohol extract,respectively.The depression-like behavior of rats was evaluated by classic behavioral test.The composition of intestinal microbiota of rats was analyzed by 16S rRNA sequencing.The morphological changes of colon were observed by hematoxylin and eosin(HE),the expression of ZO-1 and Occludin in colon was detected by immunofluorescence(IF),and the expression of IL-10,IL-1βand 5-HT were detected by ELISA.Results The low dose of malt alcohol extract attenuated the depressive behavior and restored the expression of 5-HT in the brain of CUMS rats.16S rRNA sequencing results showed that the diversity and relative abundance of gut microbiota changed after treatment with the low dose of malt alcohol extract.ELISA results showed that the low dose of malt alcohol extract significantly reversed the CUMS-induced reduction of IL-10 and elevation of IL-1 β.HE results showed that the low dose of malt alcohol extract significantly ameliorated CUMS-induced structural damage in colon.IF results showed increased protain expression of intestinal epithelial barrier tight junction proteins ZO-1 and Occludin by the low dose of malt alcohol extract.Conclusion The low dose of malt alcohol extract can ameliorate CUMS-induced depressive-like behavior in rats by modulating intestinal flora,restoring 5-HT expression in the brain,inhibiting inflammation,and repairing the intestinal barrier.

Abstract Adolescent health literacy constitutes a fundamental, economical and effective strategy for addressing their health issues and fostering healthy behaviors, while assessing health literacy plays a pivotal role in evaluating adolescents health literacy. The study systematically reviews existing adolescent health literacy assessment tools at both domestically and internationally, and analyzes them through three dimensions:structural components, applicability and scientific validity. It further examines emerging trends in the development of such tools, aiming to offer theoretical underpinnings and practical recommendations for their refinement, thereby more effectively addressing the evolving health needs of adolescents.