OBJECTIVE: To analyze the clinical characteristics, treatment, and prognosis of seven pediatric patients with Acute myeloid leukemia (AML) positive for the t(16;21)(p11;q22) FUS::ERG fusion gene. METHODS: A retrospective analysis was carried out on the clinical data, treatment, and prognosis of seven AML patients with t(16;21)(p11;q22) FUS::ERG fusion gene admitted to Henan Children's Hospital between June 2015 and November 2024. Relevant literature was also reviewed. This study was approved by the Medical Ethics Committee of the Hospital (Ethics No.: 2024-102-001). RESULTS: Among 297 pediatric patients with AML, 7 cases (2.36%) were positive for the t(16;21)(p11;q22) FUS::ERG fusion gene, including 3 males and 4 females, with a median age of 11 years (range: 3 ~ 12 years). According to the FAB classification, these included 1 case of M2, 3 cases of M5, and 3 cases of AML-not otherwise specified (non-M3). All 7 patients were found to harbor the t(16;21)(p11;q22) translocation, with 3 cases showing additional chromosomal abnormalities. Immunophenotyping revealed universal expression of CD13, CD33, CD34, and CD117, with partial expression of CD56, CD4, CD64, CD123, CD15, CD38, CD11b, HLA-DR, cMPO, and CD16. One patient achieved complete remission (CR) after the first course of DAE (cytarabine + daunorubicin + etoposide) induction chemotherapy but relapsed and discontinued the treatment. Six patients received DAH (cytarabine + daunorubicin + homoharringtonine) induction therapy, of whom 2 achieved CR after two courses and underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT), resulting in an overall CR rate of 42.86%. Five children did not receive allo-HSCT and had a median overall survival of 9 months (range: 6 ~ 18 months). Two children who underwent transplantation achieved bone marrow morphological and molecular biological relapse at 6 and 9 months post-transplantation, respectively. After receiving combined chemotherapy and donor lymphocyte infusion, one child failed to achieve remission and died at 22 months post-transplantation, while the other has been followed up to date with positive fusion gene status. Their overall survival was 25 months and 30 months, respectively. CONCLUSION The t(16;21)(p11;q22) FUS::ERG fusion gene is rare in pediatric AML and associated with poor prognosis. Allo-HSCT may mitigate the adverse prognostic impact of the FUS::ERG fusion gene and contribute to prolonged survival.
Humans ; Male ; Child ; Female ; Leukemia, Myeloid, Acute/drug therapy* ; Oncogene Proteins, Fusion/genetics* ; Translocation, Genetic ; Retrospective Studies ; RNA-Binding Protein FUS/genetics* ; Chromosomes, Human, Pair 16/genetics* ; Adolescent ; Child, Preschool ; Chromosomes, Human, Pair 21/genetics* ; Prognosis ; Treatment Outcome

Health economics evidence plays an important role in linking clinical value evidence with health resource allocation decisions in the development of clinical practice guidelines. It can not only effectively balance clinical effectiveness and economic feasibility but also avoid forming "idealized" recommendations that are detached from the affordability of the healthcare system or the burden-bearing capacity of patients. To promote guideline developers to use health economics evidence more standardizedly and fully, this paper conducts an in-depth analysis of the current application status, existing challenges, access channels, and application processes of health economics evidence in current guidelines, and on this basis, puts forward considerations and suggestions for strengthening and standardizing the application of health economics evidence in China's clinical practice guidelines.

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.

Objective: To construct a Family with sequence similarity 50 member A(FAM50A) gene knockout mouse insulinoma pancreatic β-cell line Beta-TC-6 using CRISPR/Cas9 gene editing technology and to prepare polyclonal antibodies specifically recognizing FAM50A. Methods: Two guide RNAs(sgRNAs) targeting the FAM50A gene were designed,and a recombinant plasmid expressing blue fluorescent protein(BFP) was constructed for gene knockout.The successfully constructed plasmid was transfected into Beta-TC-6 cells,and BFP-positive single cells were isolated for clonal expansion.The expanded monoclonal cell lines were genotyped by Sanger sequencing,and FAM50A protein expression was assessed by Western blot.Purified human recombinant FAM50A protein was used to immunize New Zealand rabbits for the preparation of a polyclonal antibody.The specificity of the prepared antibody was then validated using the successfully established FAM50A knockout cell line. Results: A monoclonal cell line with a successful knockout of the FAM50A gene was identified.Sanger sequencing confirmed base deletions at the target site.Western blot analysis showed a complete absence of FAM50A protein expression in this cell line.The prepared polyclonal antibody successfully recognized endogenous murine FAM50A protein in wild-type Beta-TC-6 cells and in hTERT-RPE1 cells overexpressing human FAM50A-GFP fusion protein,while no signal was detected in the FAM50A knockout cells. Conclusion This study successfully established a FAM50A gene knockout Beta-TC-6 cell model and generated a FAM50A polyclonal antibody,providing powerful tools for future research.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Objective To investigate the relationship between brain white matter lesions (WML) and triglyceride glucose (TyG) index in non-diabetic elderly individuals based on magnetic resonance imaging. Methods A total of 523 non-diabetic elderly individuals aged ≥ 60 years were selected from Jinan, Shandong Province, China from June 2018 to December 2019. According to the quartiles of TyG index, there were 133 participants in the first quartile (Q1) group, 127 in the second quartile (Q2) group, 132 in the third quartile (Q3) group, and 131 in the fourth quartile (Q4) group. All participants underwent brain magnetic resonance imaging to evaluate paraventricular, deep, and total WML volumes, as well as Fazekas scores. Results Compared with Q1, Q2, and Q3 groups, Q4 group showed significant increase in periventricular, deep, and total WML volumes (P < 0.05). The proportion of participants with a Fazekas score ≥ 2 in the periventricular, deep, and total WML was higher in the Q4 group compared with the Q1 and Q2 groups (P < 0.05). The proportion of participants with a Fazekas score ≥ 2 in deep WML was higher in Q4 group than in Q3 group (P < 0.05). TyG index was significantly positively correlated with periventricular, deep, and total WML volumes (r = 0.401, 0.405, and 0.445, P < 0.001). After adjusting for confounding factors, TyG index was still significantly positively correlated with periventricular, deep, and total WML volumes (P < 0.001). Logistic regression analysis showed that compared with Q1 group, the risk of Fazekas score ≥ 2 in periventricular WML was 1.950-fold (95% confidence interval [CI]: 1.154-3.294, P = 0.013) in Q3 group and 3.411-fold (95% CI: 1.984-5.863, P < 0.001) in Q4 group, the risk of Fazekas score ≥ 2 in total WML was 2.529-fold (95%CI: 1.444-4.430, P = 0.001) in Q3 group and 4.486-fold (95%CI: 2.314-8.696, P < 0.001) in Q4 group. The risk of Fazekas score ≥ 2 in deep WML was 2.953-fold (95%CI: 1.708-5.106, P < 0.001) in Q4 group compared with Q1 group. Conclusion Increased TyG index is an independent risk factor for WML in non-diabetic elderly individuals.

Laparoscopic sleeve gastrectomy(LSG)is an effective intervention for obesity and its co-morbidities.The utilization rate of LSG in bariatric surgery continues to increase due to its high safety and sig-nificant efficacy.The utilization rate of LSG in bariatric surgery continues to increase.This procedure not only achieves sustained weight loss but also effectively improves metabolic complications in obese patients.Howev-er,studies have shown that LSG may potentially increase the risk of gastroesophageal reflux disease(GERD).Long-term reflux not only reduces postoperative quality of life but may also become an important risk factor for esophageal cancer.Therefore,preventing GERD after LSG in obese patients holds significant clinical impli-cations.In recent years,anti-reflux procedures have gradually gained prominence in the surgical treatment of GERD,demonstrating favorable outcomes.Nevertheless,the application of anti-reflux surgical techniques in bariatric surgery remains uncommon.This article reviews the current status of combining anti-reflux proce-dures with LSG in obese patients,discussing their feasibility,effectiveness,and potential advantages.

Patients suffering from nerve injury often experience exacerbated pain responses and complain of memory deficits. The dorsal hippocampus (dHPC), a well-defined region responsible for learning and memory, displays maladaptive plasticity upon injury, which is assumed to underlie pain hypersensitivity and cognitive deficits. However, much attention has thus far been paid to intracellular mechanisms of plasticity rather than extracellular alterations that might trigger and facilitate intracellular changes. Emerging evidence has shown that nerve injury alters the microarchitecture of the extracellular matrix (ECM) and decreases ECM rigidity in the dHPC. Despite this, it remains elusive which element of the ECM in the dHPC is affected and how it contributes to neuropathic pain and comorbid cognitive deficits. Laminin, a key element of the ECM, consists of α-, β-, and γ-chains and has been implicated in several pathophysiological processes. Here, we showed that peripheral nerve injury downregulates laminin β1 (LAMB1) in the dHPC. Silencing of hippocampal LAMB1 exacerbates pain sensitivity and induces cognitive dysfunction. Further mechanistic analysis revealed that loss of hippocampal LAMB1 causes dysregulated Src/NR2A signaling cascades via interaction with integrin β1, leading to decreased Ca²⁺ levels in pyramidal neurons, which in turn orchestrates structural and functional plasticity and eventually results in exaggerated pain responses and cognitive deficits. In this study, we shed new light on the functional capability of hippocampal ECM LAMB1 in the modulation of neuropathic pain and comorbid cognitive deficits, and reveal a mechanism that conveys extracellular alterations to intracellular plasticity. Moreover, we identified hippocampal LAMB1/integrin β1 signaling as a potential therapeutic target for the treatment of neuropathic pain and related memory loss.
Animals ; Laminin/genetics* ; Hippocampus/metabolism* ; Neuralgia/metabolism* ; Cognitive Dysfunction/etiology* ; Male ; Peripheral Nerve Injuries/metabolism* ; Extracellular Matrix/metabolism* ; Integrin beta1/metabolism* ; Pyramidal Cells/metabolism* ; Signal Transduction

Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.