OBJECTIVES: To investigate the genomic characteristics and prognostic factors of juvenile myelomonocytic leukemia (JMML) with RAS mutations. METHODS: A retrospective analysis was conducted on the clinical data of JMML children with RAS mutations treated at the Hematology Hospital of Chinese Academy of Medical Sciences, from January 2008 to November 2022. RESULTS: A total of 34 children were included, with 17 cases (50%) having isolated NRAS mutations, 9 cases (27%) having isolated KRAS mutations, and 8 cases (24%) having compound mutations. Compared to children with isolated NRAS mutations, those with NRAS compound mutations showed statistically significant differences in age at onset, platelet count, and fetal hemoglobin proportion (P<0.05). Cox proportional hazards regression model analysis revealed that hematopoietic stem cell transplantation (HSCT) and hepatomegaly (≥2 cm below the costal margin) were factors affecting the survival rate of JMML children with RAS mutations (P<0.05); hepatomegaly was a factor affecting survival in the non-HSCT group (P<0.05). CONCLUSIONS Children with NRAS compound mutations have a later onset age compared to those with isolated NRAS mutations. At initial diagnosis, children with NRAS compound mutations have poorer peripheral platelet and fetal hemoglobin levels than those with isolated NRAS mutations. Liver size at initial diagnosis is related to the prognosis of JMML children with RAS mutations. HSCT can improve the prognosis of JMML children with RAS mutations.
Humans ; Leukemia, Myelomonocytic, Juvenile/therapy* ; Mutation ; Male ; Female ; Child, Preschool ; Retrospective Studies ; Child ; Infant ; GTP Phosphohydrolases/genetics* ; Membrane Proteins/genetics* ; Adolescent ; Hematopoietic Stem Cell Transplantation ; Proportional Hazards Models ; Proto-Oncogene Proteins p21(ras)/genetics* ; Prognosis

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

In recent years, polysaccharides have received much attention because of their high safety and good immunological activity. The study of polysaccharide in vivo process is a key scientific problem that needs to be solved for polysaccharide drug development. Some progress has been made in the field of polysaccharide pharmacokinetics and immunomodulation. However, due to the lack of both chromogenic and light-absorbing groups and the complex molecular structure of polysaccharides, the in vivo processes and immunomodulatory mechanisms of polysaccharides have been slow to be investigated. The effective combination of multiple techniques can break the bottleneck of difficult tracing and unknown immunomodulatory mechanism of polysaccharides in vivo, and promote the development and utilization of polysaccharides. In this paper, we systematically summarize the key techniques in the study of polysaccharide in vivo processes and immunomodulatory mechanisms in order to provide technical references and research ideas for the study of polysaccharide in vivo processes and immunomodulatory mechanisms.

This review provides a comprehensive summary of the latest advancements in high-throughput protein structural bioinformatics, a field that has undergone a revolutionary transformation with the advent of deep learning-based protein structure prediction systems like AlphaFold2. These systems have significantly increased the accuracy, speed, and scale of protein structure prediction, resulting in an exponential growth in the number of protein structures available for analysis. Notably, the AlphaFold Protein Structure Database (AFDB) has amassed over 214 million protein structures, surpassing the PDB’s 50-year cumulative data by over 1 000-fold within several months. Big data is driving the comprehensive upgrade of protein structural bioinformatics. This review focuses on three main areas: structure data management, tool development, and structure data mining. In the realm of structure data management, the review spotlights the optimization strategy of AlphaFold-like systems, which significantly reduces the resource requirements for protein folding, enabling more researchers to make custom structure predictions and further enlarging the data scale. The resulting “data explosion” has exerted increased pressure on storage and bandwidth, prompting the development of cutting-edge tools such as Foldcomp, PDC, and ProteStAr for compressing PDB files. Moreover, the review underscores the critical role of public repositories like ModelArchive and PDB-Dev in archiving and sharing third-party AlphaFold models. It also highlights the utilization of independent services like MineProt and 3D-Beacons to create more interactive and accessible data portals. In terms of tool development, the review spotlights recent breakthroughs in structure alignment algorithms, represented by Foldseek, which enable ultra-fast searching of large protein structure databases. It also covers tools for functional annotation of proteins based on their structures, including AlphaFill for ligand annotation, DeepFRI for Gene Ontology (GO) annotation, TT3D for protein-protein interaction (PPI) prediction, among others. It is proposed that 3Di sequences born concurrently with Foldseek can enhance many sequence-based deep learning models developed in the pre-AlphaFold era, enabling them to be applied to structure-based function prediction. The challenges on traditional molecular docking methods in the high-throughput era are mentioned at last, in a gesture to arouse the attention of researchers. Finally, the review explores the burgeoning field of structure data mining. Whole proteome structuring has become feasible in recent years, and scientists are processing large structure datasets from an omics viewpoint, continuously identifying analyzable elements and optimizing methodologies, as well as utilizing newly developed tools to push the boundaries. Notable examples include the identification of new protein families, the development of protein structure clustering, and the integration of AlphaFold with conventional experimental techniques to solve large structures. These advancements are paving the way for a deeper understanding of protein structure and function and have the potential to unlock new discoveries in the life sciences. However, the review also acknowledges the challenges and limitations that persist in the field, including the lack of diversity in high-throughput software for protein structural bioinformatics and the existing bottleneck in rapidly predicting protein complex structures. Overall, structural bioinformatics is expected to play an even more crucial role in the life sciences with the development of high-throughput methodology.

AIM: To explore the optimal concentration of endoplasmic reticulum stress immunohistochemical(IHC)staining antibody in mouse retinitis pigmentosa(RP)model, which provides the corresponding index detection method for studying the pathogenesis and intervention measures of RP.METHODS: Clean male C57BL/6J mice were intraperitoneally injected with N-methyl-N-nitrosourea(MNU, 60mg/kg)to prepare RP mouse model. Electroretinogram(ERG)and hematoxylin-eosin(HE)staining were performed on 7d after modeling to verify the successful modeling. The expression of endoplasmic reticulum stress-related proteins(IRE1, ATF6, PERK, GRP78, Caspase-12)was detected by IHC staining.RESULTS: The following proteins, including IRE1, ATF6, PERK, GRP78 and Caspase-12, were positively expressed in retina of RP mouse. The optimal concentrations of the above proteins were as follows: IRE1 antibody concentration was 1:1000, ATF6 antibody concentration was 1:500 and 1:1000(with no difference in positive expression, P>0.05), PERK antibody concentration was 1:1500, GRP78 antibody concentration was 1:200 and Caspase-12 antibody concentration was 1:100, the proteins were well expressed at the above concentrations, and the positive expressions of corresponding proteins were different from those of other antibody concentrations(P<0.05).CONCLUSION: The optimal concentrations for IHC staining in different proteins of mouse RP models were as follows: the concentrations of endoplasmic reticulum stress-related protein antibodies were 1:1000 in IRE1, 1:500 and 1:1000 in ATF6, 1:1500 in PERK, 1:200 in GRP78, and 1:100 in Caspase-12.

AIM: To quantitatively analyze the changes of the peripapillary capillary vessel density(ppVD)and the peripapillary retina nerve fiber layer(pRNFL)thickness in patients with monocular retinal vein occlusion(RVO)by optical coherence tomography angiography(OCTA), and further analyze the correlation between the ppVD and the pRNFL thickness.METHODS: Prospective observational research. A total of 43 patients diagnosed with monocular RVO were enrolled in the Department of Ophthalmology of the First Affiliated Hospital of Anhui University of Science and Technology from January to December 2021, among which 43 RVO eyes were regarded as the affected group and 43 fellow eyes were regarded as the contralateral group. At the same time, 21 healthy volunteers(42 eyes)matching the age and gender with RVO patients were regarded as the control group. The vessel density(VD)of inside optic disc, the whole VD of around disc and the ppVD and pRNFL thickness around the optic disc were measured by OCTA, including peripapillary superior(pS), peripapillary inferior(pI), temporal superior(TS), superior temporal(ST), superior nasal(SN), nasal superior(NS), nasal inferior(NI), inferior nasal(IN), inferior temporal(IT), and temporal inferior(TI). The characteristic changes of ppVD and pRNFL thickness and theirs correlation in the three groups were analyzed.RESULTS: The VD of inside optic disc, the whole VD of around disc and the ppVD in the pS, pI, TI, ST and SN side of the affected group were all significantly decreased compared with the control group(all P<0.05). But only VD of the inside disc in contralateral group was decreased(all P<0.05). Compared with the control group, the pRNFL thickness in the TS side of the affected group was increased, and the ST and IT side pRNFL thickness of the contralateral group were decreased(all P<0.01). The canonical correlation analysis revealed that ppVD and pRNFL thickness were provided with a strong correlation between the two comprehensive variables. There were 2 pairs of canonical correlation variables in affected group and contralateral group, and 3 pairs of canonical correlation variables in control group.CONCLUSION: The VD in the optic disc area of the affected group was decreased in patients with monocular RVO, and the pRNFL thickness in ST and IT side of the contralateral group was thinner. There was a strong positive correlation between ppVD and pRNFL thickness as a whole. The changes of ppVD and pRNFL thickness in the optic disc area were mostly manifested in the superior quadrant of the affected group and the inferior quadrant of the contralateral group.

After more than 20 years of multidisciplinary integration of medical science and technology,as well as research and practice in innovative diagnosis and treatment,digital medicine 4.0 has made a profound and important impact on the development of traditional surgery. To combine traditional surgery with digital medicine 4.0 technology is the direction of surgery development in the future.New technologies represented by digital intelligent navigation surgery have been deeply explored and widely applied in the diagnosis and treatment of many surgical diseases. With the innovative development and application of artificial intelligence,Big Data and mixed reality technology,the surgery will develop in ways similar to aerospace automatic and intelligent navigation,leading to the advent of digital medicine 5.0.
Artificial Intelligence ; Humans ; Medicine ; Surgery, Computer-Assisted ; Technology

Objective: To investigate the application effect of augmented reality and mixed reality navigation technology in three-dimensional(3D) laparoscopic narrow right hepatectomy(LRH). Methods: A retrospective analysis was performed on the clinical data of 5 patients with hepatic malignancy admitted to the First Department of Hepatobiliary Surgery,Zhujiang Hospital,Southern Medical University from September 2020 to June 2021,all of whom were males,aged from 42 to 74 years.Preoperative evaluation was performed using the self-developed 3D abdominal medical image visualization system; if all the 5 patients were to receive right hemihepatectomy,the remnant liver volume would be insufficient,so LRH were planned.During the operation,the independently developed 3D laparoscopic augmented reality and mixed reality surgical navigation system was used to perform real-time multi-modal image fusion and interaction between the preoperative 3D model and 3D laparoscopic scene.Meanwhile,intraoperative ultrasound assisted indocyanine green fluorescence was used to determine the surgical path.In this way,the LRH under the guidance of augmented reality and mixed reality navigation was completed.The predicted liver resection volume was evaluated before surgery,actual resected liver volume,surgical indicators and postoperative complications were analyzed. Results: All the 5 patients completed LRH under the guidance of augmented reality and mixed reality navigation technology,with no conversion to laparotomy.The median operative time was 300 minutes(range:270 to 360 minutes),no intraoperative blood transfusion was performed,and the median postoperative hospital stay was 8 days(range:7 to 9 days).There were no perioperative deaths,or postoperative complications such as liver failure,bleeding,or biliary fistula. Conclusion: For patients who need to undergo LRH,the use of augmented and mixed reality navigation technology can safely and effectively guide the implementation of surgery,retain more functional liver volume,improve surgical safety,and reduce postoperative complications.
Adult ; Aged ; Augmented Reality ; Hepatectomy/methods* ; Humans ; Imaging, Three-Dimensional ; Laparoscopy/methods* ; Liver Neoplasms/surgery* ; Male ; Middle Aged ; Retrospective Studies ; Technology

OBJECTIVE: To evaluate whether the efficacy of Getong Tongluo Capsule (, GTC, consisted of total flavone of Radix Puerariae) on improving patients' quality of life and lowering blood pressure are superior to the extract of Ginkgo biloba (EGB) for patients with convalescent-phase ischemic stroke and primary hypertension. METHODS: This randomized, positive-drug- and placebo-controlled, double-blind trial was conducted from September 2015 to October 2017. Totally 477 eligible patients from 18 hospitals in China were randomly assigned in a 2:1:1 ratio to the following interventions, twice a day for 12 weeks: (1) GTC 250 mg plus EGB-matching placebo 40 mg (237 cases, GTC group), (2) EGB 40 mg plus GTC-matching placebo 250 mg (120 cases, EGB group) or (3) GTC-matching placebo 250 mg plus EGB-matching placebo 40 mg (120 cases, placebo group). Moreover, all patients were orally administered aspirin enteric-coated tablets 100 mg, once a day for 12 weeks. The primary outcome was the Barthel Index (BI). The secondary outcomes included the control rate of blood pressure and National Institutes of Health Stroke Scale (NIHSS) scores. The incidence and severity of adverse events (AEs) were calculated and assessed. RESULTS: The BI relative independence rates, the clinical recovery rates of NIHSS, and the total effective rates of NIHSS in the GTC and EGB groups were significantly higher than the placebo group at 12 weeks after treatment (P<0.05), and no statistical significance was found between the GTC and EGB groups (P>0.05). The control rate of blood pressure in the GTC group was significantly higher than the EGB and placebo groups at 12, 18 and 24 weeks after treatment (P<0.01). There were no statistically significant differences in the incidences of AEs, adverse drug reactions, or serious AEs among the 3 groups (P>0.05). CONCLUSION GTC exhibited significant efficacy in improving patients' quality of life as well as neurological function and controlling hypertension. (Registration No. ChiCTR1800016667).

Next-generation sequencing has allowed identification of millions of somatic mutations in human cancer cells. A key challenge in interpreting cancer genomes is to distinguish drivers of cancer development among available genetic mutations. To address this issue, we present the first web-based application, consensus cancer driver gene caller (C), to identify the consensus driver genes using six different complementary strategies, i.e., frequency-based, machine learning-based, functional bias-based, clustering-based, statistics model-based, and network-based strategies. This application allows users to specify customized operations when calling driver genes, and provides solid statistical evaluations and interpretable visualizations on the integration results. C is implemented in Python and is freely available for public use at http://drivergene.rwebox.com/c3.