Objective: To improve the efficiency and standardization of preoperative anemia diagnosis and treatment by establishing a systematic intelligent management platform for preoperative anemia. Methods: A multidisciplinary collaborative model was adopted to develop a preoperative anemia management system that integrates intelligent early warning, standardized treatment pathways, and quality control. The system utilizes natural language processing technology to automatically capture laboratory data and establish evidence-based medical decision support functions. A pre-post study design was employed to compare changes in preoperative anemia screening rates, preoperative anemia intervention rates, reasonable use of iron supplements, and perioperative red blood cell transfusion rates before and after system implementation. Results: After system implementation, the standardization of anemia diagnosis and treatment significantly improved: 1) Screening effectiveness: The anemia screening rate increased to 50.00% (an increase of 27.24%); 2) Intervention effectiveness: The anemia treatment rate rose to 56.30% (an increase of 14.02%); 3) Treatment standardization: The reasonable use rate of iron supplements increased to 55.33% (an increase of 21.02%); the red blood cell transfusion rate decreased to 18.29% (a decrease of 4.07%), and the amount of red blood cell transfusions was reduced by 291 units. Conclusion: This system achieves full-process management of preoperative anemia through information technology, significantly enhancing the standardization of diagnosis and treatment as well as intervention effectiveness, providing an effective solution for perioperative anemia management.

By systematically combing ancient and modern literature， this paper examined Tribuli Fructus and Astragali Complanati Semen（ACS） used in the famous classical formulas from the aspects of name， origin， production area， harvesting and processing， clinical efficacy， so as to provide a basis for the development of famous classical formulas containing such medicinal materials. The results showed that the names of Tribuli Fructus in the past dynasties were mostly derived from its morphology， and there were nicknames such as Baijili， Cijili and Dujili. The name of ACS in the past dynasties were mostly originated from its production areas， and there were nicknames such as Baijili， Shayuan Jili and Tongjili. Because both of them had the name of Baijili， confusion began to appear in the Song dynasty. In ancient and modern times， the main origin of Tribuli Fructus were Tribulus terrestris， and ancient literature recorded the genuine producing areas of Tribuli Fructus was Dali in Shaanxi and Tianshui in Gansu， but today it is mainly cultivated in Anhui and Shandong. The fruit is the medicinal part， harvested in autumn throughout history. There is no description of the quality of Tribuli Fructus in ancient times， and the plump， firm texture， grayish-white color is the best in modern times. Traditional processing methods for Tribuli Fructus included stir-frying and wine processing， while modern commonly used is purified， fried and salt-processed. The ancient records of Tribuli Fructus were spicy， bitter， and warm in nature， with modern research adding that it is slightly toxic. The main effects of ancient and modern times include treating wind disorders， improving vision， promoting muscle growth， and treating vitiligo. The mainstream base of ACS used throughout history is Astragalus complanatus. Ancient texts indicated ACS primarily originated from Shaanxi province. Today， the finest varieties come from Tongguan and Dali in Shaanxi. The medicinal part is the seed， traditionally harvested in autumn. Modern harvesting occurs in late autumn or early winter， followed by sun-drying. Ancient texts valued seeds with a fragrant aroma as superior， while modern standards prioritize plump， uniform and free of impurities. Traditional processing methods for ACS included frying until blackened and wine-frying， while modern practice commonly employs purification methods. In terms of medicinal properties， the ancient and modern records are sweet and warm in nature. Due to originally classified under Tribuli Fructus， its effects were thus regarded as equivalent to those of Tribuli Fructus， serving as the medicine for treating wind disorders， additional functions included tonifying the kidneys and treating vitiligo. The present record of its efficacy is to tonify the kidney and promote Yang， solidify sperm and reduce urine， nourish the liver and brighten the eye， etc. Based on the textual research results， it is suggested that when developing the famous classical formulas of Tribuli Fructus medicinal materials， we should pay attention to the specific reference object of Baijili， T. terrestris and A. complanatus should be identified and selected， and the processing method should be in accordance with the requirements of the formulas.

Natural product-based drug combinations (NPDCs) present distinctive advantages in treating complex diseases. While high-throughput screening (HTS) and conventional computational methods have partially accelerated synergistic drug combination discovery, their applications remain constrained by experimental data fragmentation, high costs, and extensive combinatorial space. Recent developments in artificial intelligence (AI), encompassing traditional machine learning and deep learning algorithms, have been extensively applied in NPDC identification. Through the integration of multi-source heterogeneous data and autonomous feature extraction, prediction accuracy has markedly improved, offering a robust technical approach for novel NPDC discovery. This review comprehensively examines recent advances in AI-driven NPDC prediction, presents relevant data resources and algorithmic frameworks, and evaluates current limitations and future prospects. AI methodologies are anticipated to substantially expedite NPDC discovery and inform experimental validation.
Artificial Intelligence ; Biological Products/chemistry* ; Humans ; Drug Combinations ; Drug Discovery/methods* ; Machine Learning ; Algorithms

Peri-implant keratinized mucosa (PIKM) augmentation refers to surgical procedures aimed at increasing the width of PIKM. Consensus reports emphasize the necessity of maintaining a minimum width of PIKM to ensure long-term peri-implant health. Currently, several surgical techniques have been validated for their effectiveness in increasing PIKM. However, the selection and application of PIKM augmentation methods may present challenges for dental practitioners due to heterogeneity in surgical techniques, variations in clinical scenarios, and anatomical differences. Therefore, clear guidelines and considerations for PIKM augmentation are needed. This expert consensus focuses on the commonly employed surgical techniques for PIKM augmentation and the factors influencing their selection at second-stage surgery. It aims to establish a standardized framework for assessing, planning, and executing PIKM augmentation procedures, with the goal of offering evidence-based guidance to enhance the predictability and success of PIKM augmentation.
Humans ; Consensus ; Dental Implants ; Mouth Mucosa/surgery* ; Keratins

Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.

Increasing evidence showed that histone deacetylase 6 (HDAC6) dysfunction is directly associated with the onset and progression of various diseases, especially cancers, making the development of HDAC6-targeted anti-tumor agents a research hotspot. In this study, artificial intelligence (AI) technology and molecular simulation strategies were fully integrated to construct an efficient and precise drug screening pipeline, which combined Voting strategy based on compound-protein interaction (CPI) prediction models, cascade molecular docking, and molecular dynamic (MD) simulations. The biological potential of the screened compounds was further evaluated through enzymatic and cellular activity assays. Among the identified compounds, Cmpd.18 exhibited more potent HDAC6 enzyme inhibitory activity (IC₅₀ = 5.41 nM) than that of tubastatin A (TubA) (IC₅₀ = 15.11 nM), along with a favorable subtype selectivity profile (selectivity index ≈ 117.23 for HDAC1), which was further verified by the Western blot analysis. Additionally, Cmpd.18 induced G2/M phase arrest and promoted apoptosis in HCT-116 cells, exerting desirable antiproliferative activity (IC₅₀ = 2.59 μM). Furthermore, based on long-term MD simulation trajectory, the key residues facilitating Cmpd.18's binding were identified by decomposition free energy analysis, thereby elucidating its binding mechanism. Moreover, the representative conformation analysis also indicated that Cmpd.18 could stably bind to the active pocket in an effective conformation, thus demonstrating the potential for in-depth research of the 2-(2-phenoxyethyl)pyridazin-3(2H)-one scaffold.

The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Generative Artificial Intelligence

Doxorubicin(DOX)is a commonly administered chemotherapy drug for treating hematological malignancies and solid tumors;however,its clinical application is limited by significant cardiotoxicity.Cynaroside(Cyn)is a flavonoid glycoside distributed in honeysuckle,with confirmed potential biological functions in regulating inflammation,pyroptosis,and oxidative stress.Herein,the effects of Cyn were evaluated in a DOX-induced cardiotoxicity(DIC)mouse model,which was established by intraperitoneal injections of DOX(5 mg/kg)once a week for three weeks.The mice in the treatment group received dexrazoxane,MCC950,and Cyn every two days.Blood biochemistry,histopathology,immunohistochemistry,reverse transcription-quantitative polymerase chain reaction(RT-qPCR),and western blotting were conducted to investigate the cardioprotective effects and potential mechanisms of Cyn treatment.The results demonstrated the significant benefits of Cyn treatment in mitigating DIC;it could effectively alleviate oxidative stress to a certain extent,maintain the equilibrium of cell apoptosis,and enhance the cardiac function of mice.These effects were realized via regulating the transcription levels of pyroptosis-related genes,such as nucleotide-binding oligomerization domain-like receptor protein 3(NLRP3),caspase-1,and gasdermin D(GSDMD).Mechanistically,for DOX-induced myocardial injury,Cyn could significantly modulate the expression of pivotal genes,including adenosine monophosphate-activated protein kinase(AMPK),peroxisome proliferator-activated receptor γ coactivator-1α(PGC-1α),sirtuin 3(SIRT3),and nuclear factor erythroid 2-related factor 2(Nrf2).We attribute it to the mediation of AMPK/SIRT3/Nrf2 pathway,which plays a central role in preventing DOX-induced cardiomyocyte injury.In conclusion,the present study confirms the therapeutic potential of Cyn in DIC by regulating the AMPK/SIRT3/Nrf2 pathway.

Purpose::Internal iliac artery ligation (IIAL) has been used as a damage control procedure to treat hemodynamically unstable pelvic fracture for many years. However, there is ongoing debate regarding the effectiveness and safety of this hemostatic method. Therefore, we performed a systematic literature review to assess the efficacy and safety of IIAL for pelvic fracture hemostasis.Methods::Three major databases, PubMed, Embase, and Google Scholar, were searched to screen eligible original studies published in English journals. Two reviewers independently read the titles, abstracts, and full texts of all literature. Articles were included if they reported the use and effects of IIAL.Results::A total of 171 articles were initially identified, with 22 fully meeting the inclusion criteria. Among the analyzed cases, up to 66.7% of patients had associated abdominal and pelvic organ injuries, with the urethra being the most frequently injured organ, followed by the bowel. The outcomes of IIAL for achieving hemostasis in pelvic fractures were found to be satisfactory, with an effective rate of 80%. Hemorrhagic shock was the leading cause of death, followed by craniocerebral injury. Notably, no reports of ischemic complications involving the pelvic organs due to IIAL were found.Conclusion::IIAL has a good effect in treating hemodynamically unstable pelvic fracture without the risk of pelvic organ ischemia. This procedure should be considered a priority for hemodynamically unstable pelvic fracture patients with abdominal organ injuries.

Objective To investigate the prediction of diffusion kurtosis imaging(DKI)and intra voxel incoherent motion(IVIM)imaging global histogram parameters for the efficacy of neoadjuvant chemotherapy(NAC)in patients diagnosed with breast invasive ductal carcinoma.Methods A total of 69 patients with breast invasive ductal carcinoma confirmed by penetration pathology were retrospectively selected.Prior to undergoing NAC,all patients underwent sequential scans including MR conventional(T1WI,T2WI),dynamic contrast enhancement(DCE),DKI,and IVIM.After surgery,the patients were divided into significant group(40 patients)and non-significant group(29 patients)based on the efficacy of NAC,which was evaluated using the Miller-Payne(MP)grading method criteria.The differences in global histogram parameters of DKI[mean diffusivity(MD),mean kurtosis(MK)]and IVIM(D value,f value,D*value)between the significant group and the non-significant group were compared by the two-independent sample t-test and Mann-Whitney U test.In addition,the receiver operating characteristic(ROC)curve was plotted,and Spearman rank correlation analysis was used to evaluate the diagnostic efficacy and correlation of DKI and IVIM global histogram parameters in predicting NAC efficacy.The DeLong test was used to compare whether there was statistical significance in area under the curve(AUC)differences among histogram parameters.Results The DKI MD value(90th percentile,mean,median,maximum,range,root mean square),IVIM D value(mean,range,root mean square,median)and D*value(entropy,90th percentile)of patients with breast invasive ductal carcinoma in the significant group with efficacy of NAC were higher than those in the non-significant group,while the DKI MK value(mean and median)were lower than those in the non-significant group,and all the differences mentioned above were statistically significant(P＜0.05).The AUC,sensitivity and specificity of DKI combined with IVIM global histogram parameters were the highest,which were 0.816,72.41％and 90.00％,respectively.MD value,D value and D*value were negatively correlated with the efficacy of NAC in breast invasive ductal carcinoma.MK value was positively correlated with NAC efficacy significance.Conclusion DKI combined with IVIM global histogram parameters can effectively predict the efficacy of NAC in patients with breast invasive ductal carcinoma and provide effective value for clinical NAC preoperative efficacy evaluation.