ObjectiveTo investigate the anti‑pancreatic fibrosis mechanism of Klotho‑derived peptide 7 （KL7） by observing its effect on a mouse model of chronic pancreatitis （CP） induced by cerulean， and to provide a basis for clinical medication. MethodsA total of 40 male BALB/c mice were randomly divided into control group， model group， low-dose KL7 group （2 mg/kg）， and high-dose KL7 group （4 mg/kg）， with 10 mice in each group. All mice except those in the control group were given intraperitoneal injection of cerulean （50 μg/kg） 6 times a day at an interval of 1 hour， twice a week for 4 consecutive weeks to establish a model of CP. The mice in the low-dose KL7 group and the high-dose KL7 group were treated with different doses of KL7 once a day for 4 consecutive weeks. In vivo imaging was used to observe the accumulation of KL7 in the pancreas； molecular docking was used to detect the binding of KL7 to transforming growth factor-β type Ⅱ receptor （TβRⅡ）； the mice were measured in terms of body weight and pancreatic weight； HE staining was used to observe the pathological changes of pancreatic tissue； Masson staining was used to observe the degree of pancreatic fibrosis； immunohistochemical staining was used to measure the expression of α-smooth muscle actin （α-SMA） and type Ⅰ collagen （COL1A1）； Western blotting was used to measure the protein expression levels of α-SMA， TβRII， and phosphorylated small mothers against decapentaplegic homolog 2/3 （p-Smad2/3） in pancreatic tissue. A one-way analysis of variance was used for comparison of continuous data between multiple groups， and the least significant difference t-test and the Dunnett’s-T3 test were used for further comparison between two groups. ResultsKL7 was significantly enriched in the pancreatic tissue of CP mice， and there was a strong binding activity between KL7 and TβRⅡ. Compared with the control group， the model group had significant reductions in pancreatic mass and relative pancreatic mass （P<0.000 1）， with disordered structure of pancreatic tissue， an increase in inflammatory cell infiltration， and significant increases in fibrosis degree， the positive areas of α-SMA and COL1A1 （P<0.000 1）， and the protein expression levels of α-SMA， TβRⅡ， and p-Smad2/3 （P<0.05）. Compared with the model group， the high-dose KL7 group had significant increases in pancreatic mass and relative pancreatic mass （P<0.01）， with alleviation of structural damage of pancreatic tissue and inflammatory cell infiltration， a significant reduction in fibrosis degree， and significant reductions in the positive areas of α-SMA and COL1A1 （P<0.001） and the protein expression levels of α-SMA， TβRⅡ， and p-Smad2/3 （P<0.01）. ConclusionKL7 has a significant targeted therapeutic effect on pancreatic fibrosis in CP mice through specific binding of KL7 to TβRⅡ， thereby inhibiting the activation of the TGF-β/Smad signaling pathway.

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 evaluate the impact of donor characteristics on the prognosis of myelodysplastic syndrome (MDS) patients undergoing haplo-identical transplantation (HIDT) .Methods:A retrospective analysis of 203 MDS patients who received HIDT was conducted to evaluate how donor factors influenced transplant outcomes.Results:In MDS patients undergoing haploidentical transplantation, donors over 50 years were associated with higher EBV reactivation (2-year cumulative incidence 42.9% vs 22.0% for <50 years old; P=0.010). Female donors were linked to increased severe chronic GVHD compared with male donors (2-year incidence 11.9% vs 4.0% ; P=0.017). Additionally, 2-year overall survival (OS) was slightly lower with female donors than male donors (56.6% vs 69.7% ), but the difference was not statistically significant ( P=0.073). Donor-recipient blood type did not affect post-transplant OS or cumulative relapse rates. Donor-recipient kinship analysis revealed that child donors, compared to haploidentical sibling or parent donors, had lower rates of grade Ⅱ–Ⅳ acute GVHD (27.2% vs 45.7% vs 53.5%, P=0.007) and 2-year EBV reactivation (13.9% vs 29.3% vs 38.9%, P=0.001). For donors under 20 years, donor gender did not significantly affect 2-year OS ( P=0.913), relapse-free survival ( P=0.716), or 100-day incidence of grade Ⅱ–Ⅳ acute GVHD ( P=0.359) . Conclusion:For MDS patients undergoing HIDT, donors over 50 should be avoided. Male and child donors are preferred, while donor gender does not significantly affect outcomes if the donor is under 20 years old.

RNA modifications constitute a crucial class of post-transcriptional chemical alterations that profoundly influence RNA stability and translational efficiency, thereby shaping cellular protein expression profiles. These diverse chemical marks are ubiquitously involved in key biological processes, including cell proliferation, differentiation, apoptosis, and metastatic potential, and they exert precise regulatory control over these functions. A major advance in the field is the recognition that RNA modifications do not act in isolation. Instead, they participate in complex, dynamic interactions—through synergistic enhancement, antagonism, competitive binding, and functional crosstalk—forming what is now termed the “RNA modification interactome” or “RNA modification interaction network.” The formation and functional operation of this interactome rely on a multilayered regulatory framework orchestrated by RNA-modifying enzymes—commonly referred to as “writers,” “erasers,” and “readers.” These enzymes exhibit hierarchical organization within signaling cascades, often functioning in upstream-downstream sequences and converging at critical regulatory nodes. Their integration is further mediated through shared regulatory elements or the assembly into multi-enzyme complexes. This intricate enzymatic network directly governs and shapes the interdependent relationships among various RNA modifications. This review systematically elucidates the molecular mechanisms underlying both direct and indirect interactions between RNA modifications. Building upon this foundation, we introduce novel quantitative assessment frameworks and predictive disease models designed to leverage these interaction patterns. Importantly, studies across multiple disease contexts have identified core downstream signaling axes driven by specific constellations of interacting RNA modifications. These findings not only deepen our understanding of how RNA modification crosstalk contributes to disease initiation and progression, but also highlight its translational potential. This potential is exemplified by the discovery of diagnostic biomarkers based on interaction signatures and the development of therapeutic strategies targeting pathogenic modification networks. Together, these insights provide a conceptual framework for understanding the dynamic and multidimensional regulatory roles of RNA modifications in cellular systems. In conclusion, the emerging concept of RNA modification crosstalk reveals the extraordinary complexity of post-transcriptional regulation and opens new research avenues. It offers critical insights into the central question of how RNA-modifying enzymes achieve substrate specificity—determining which nucleotides within specific RNA transcripts are selectively modified during defined developmental or pathological stages. Decoding these specificity determinants, shaped in large part by the modification interactome, is essential for fully understanding the biological and pathological significance of the epitranscriptome.

Approximately 20% to 30% of the global workforce is engaged in shift work. As a significant cause of circadian disruption, shift work is closely associated with an increased risk for periodontitis. Nevertheless, how shift work-related circadian disruption functions in periodontitis remains unknown. Herein, we employed a simulated shift work model constructed by controlling the environmental light-dark cycles and revealed that shift work-related circadian disruption exacerbated the progression of experimental periodontitis. RNA sequencing and in vitro experiments indicated that downregulation of the core circadian protein brain and muscle ARNT-like protein 1 (BMAL1) and activation of the Gasdermin D (GSDMD)-mediated pyroptosis were involved in the pathogenesis of that. Mechanically, BMAL1 regulated GSDMD-mediated pyroptosis by suppressing NOD-like receptor protein 3 (NLRP3) inflammasome signaling through modulating nuclear receptor subfamily 1 group D member 1 (NR1D1), and inhibiting Gsdmd transcription via directly binding to the E-box elements in its promoter. GSDMD-mediated pyroptosis accelerated periodontitis progression, whereas downregulated BMAL1 under circadian disruption further aggravated periodontal destruction by increasing GSDMD activity. And restoring the level of BMAL1 by circadian recovery and SR8278 injection alleviated simulated shift work-exacerbated periodontitis via lessening GSDMD-mediated pyroptosis. These findings provide new evidence and potential interventional targets for circadian disruption-accelerated periodontitis.
Pyroptosis/physiology* ; ARNTL Transcription Factors/metabolism* ; Animals ; Periodontitis/etiology* ; Mice ; Phosphate-Binding Proteins/metabolism* ; Shift Work Schedule/adverse effects* ; Intracellular Signaling Peptides and Proteins/metabolism* ; Mice, Inbred C57BL ; Male ; Disease Models, Animal ; Gasdermins

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 investigate the factors affecting the outcome of high-risk human papillomavirus ( HR- HPV) infection in patients with normal cervix examined by colposcopy in Hefei area and the relationship between persistent HR-HPV infection and cervical cytology． Methods : Data of colposcopy patients were collected from 478 HR-HPV infected patients with normal cervix through colposcopy．Their age，number of sexual partners，contracep- tive methods and other relevant basic information were recorded．Vaginal interferon use，HR-HPV infection at year 1 and year 2，and cervical liquid-based cytology test ( LCT) results were tracked，univariate and multivariate ana- lyses were performed based on basic information，and ROC curves were plotted． Results : The HR-HPV clearance rate at 1 year was 59. 41% ，and the clearance rate at 2 years was 66. 75%．The other 12 types of infection ( 31， 33，35，39，45，51，52，56，58，59，66，68) were more common than the 16 and 18 types．Univariate and mult- ivariate analyses showed that age＞50 years，number of sexual partners ≥2，and history of cervical conectomy in- creased the risk of persistent HR-HPV infection ( χge = 21. 676，P ＜0. 001; χumber of sexual partners = 8. 262，P =0. 004; χistory of cervical conectomy = 11. 267，P = 0. 001 ) ． The risk of HR-HPV infection was significantly lower when condom or vaginal interferon was used ( χondom use = 10. 885，P = 0. 001; χnterferon use = 4. 099，P = 0. 043) ．The area under the ROC curve (AUC) of combined diagnosis of HR-HPV persistent infection was higher than that of single diagnosis，and the AUC of combined diagnosis was 0. 737．Persistent HR-HPV infection was an independent risk factor for abnormal LCT，and the AUC predicted by the model was 0. 755．No cancer was found in patients with persistent HR-HPV infection for 2 years，and the proportion of abnormal LCT was higher than that in patients with negative HR-HPV．The difference was statistically significant ( χ2 = 39. 64，P＜0. 001) ． Conclusion The combined ROC model constructed for patients＞50 years old，with multiple sexual partners，history of cervical surgery， no vaginal interferon use，and no condom use has certain value in predicting persistent HR-HPV infection，and per- sistent HR-HPV infection has predictive value in predicting LCT abnormalities．

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.

Traditional Chinese medicine (TCM) is characterized by complex, multicomponent herbal formulations that challenge the conventional“one drug, one target” paradigm. Network pharmacology, through the construction of multilayered drug-target-disease networks, provides a systematic framework for unraveling TCM’s multitarget and multipathway mechanisms. Recent advancements in artificial intelligence, particularly large language models (LLMs), further enhance data integration, target identification, and clinical decision-making. This review synthesizes current progress in the application of network pharmacology and LLMs in TCM, highlighting their potential to deepen mechanistic insights and optimize drug discovery. By bridging traditional medical wisdom with modern computational tools, this integrative approach aims to advance the scientific validation of TCM and foster innovative healthcare solutions.

Objective To explore the potential role and underlying mechanism of the functionally uncharacterized gene Gm49394 on regulating β-cell apoptosis under diabetic conditions.Methods The expression and translational activity of Gm49394 in pancreatic β-cell lines and non-β-cell lines were validated using RNA fluorescence in situ hybridization(RNA-FISH),quantitative real-time PCR(qPCR),Western blotting,and immunofluorescence(IF)assay.The β-cell lines(NIT-1/Min6)with Gm49394 overexpression or knockdown were constructed.The proliferation,apoptosis,mitochondrial function,as well as oxidative stress and endoplasmic reticulum stress markers in these β-cell lines under physiological homeostasis or pathological stress conditions,such as high glucose(30 mmol/L),inflammation(10 ng/mL IFN-γ alone or combined with 10 ng/mL IL-6),and hydrogen peroxide(100 μmol/L H2O2)were detected by flow cytometry and Western blotting.Results RNA-FISH and qPCR indicated that Gm49394 was specifically expressed in pancreatic β-cell lines and up-regulated under high glucose or inflammatory stimulation.IF assay and Western blotting showed that Gm49394 had protein-coding activity.Flow cytometry and Western blotting identified that Gm49394 overexpression did not affect β-cell proliferation,but promoted β-cell apoptosis and increased reactive oxygen species(ROS)and mitochondrial superoxide(MitoSOX)levels in β cells under physiological homeostasis or pathological stress conditions(P<0.05).Under physiological conditions,Gm49394 knockdown failed to induce significant alterations on β-cell apoptosis,ROS,or MitoSOX levels.Under pathological stress conditions,Gm49394 knockdown significantly suppressed β-cell proliferation,apoptosis,as well as oxidative and endoplasmic reticulum stress(P<0.05).Conclusion Gm49394 may promote β-cell apoptosis via oxidative stress and endoplasmic reticulum stress.