Direct-acting antivirals for RSV treatment, a review.

Respiratory syncytial virus (RSV) causes respiratory disease and complications in infants, the elderly and the immunocompromised. While three vaccines and two prophylactic monoclonal antibodies are now available, only one antiviral, ribavirin, is currently approved for treatment. This review aims to summarize the current state of treatments directly targeting RSV. Two major viral processes are attractive for RSV-specific antiviral drug discovery and development as they play essential roles in the viral cycle: the entry/fusion process carried out by the fusion protein and the replication/transcription process carried out by the polymerase complex constituted of the L, P, N and M2-1 proteins. For each viral target resistance mutations to small molecules of different chemotypes seem to delineate definite binding pockets in the fusion proteins and in the large proteins. Elucidating the mechanism of action of these inhibitors thus helps to understand how the fusion and polymerase complexes execute their functions. While many inhibitors have been studied, few are currently in clinical development for RSV treatment: one is in phase III, three in phase II and two in phase I. Progression was halted for many others because of strategic decisions, low enrollment, safety, but also lack of efficacy. Lessons can be learnt from the halted programs to increase the success rate of the treatments currently in development.

Tongue orthotopic xenografts to study fusion-negative rhabdomyosarcoma invasion and metastasis in live animals.

PAX3/7 fusion-negative rhabdomyosarcoma (FN-RMS) is a childhood mesodermal lineage malignancy with a poor prognosis for metastatic or relapsed cases. Limited understanding of advanced FN-RMS is partially attributed to the absence of sequential invasion and dissemination events and the challenge in studying cell behavior, using, for example, non-invasive intravital microscopy (IVM), in currently used xenograft models. Here, we developed an orthotopic tongue xenograft model of FN-RMS to study cell behavior and the molecular basis of invasion and metastasis using IVM. FN-RMS cells are retained in the tongue and invade locally into muscle mysial spaces and vascular lumen, with evidence of hematogenous dissemination to the lungs and lymphatic dissemination to lymph nodes. Using IVM of tongue xenografts reveals shifts in cellular phenotype, migration to blood and lymphatic vessels, and lymphatic intravasation. Insight from this model into tumor invasion and metastasis at the tissue, cellular, and subcellular level can guide new therapeutic avenues for advanced FN-RMS.

Presence of compensatory curve predicts postoperative curve progression in congenital scoliosis after thoracolumbar hemivertebra resection and short fusion.

PURPOSE: To investigate the impact of preoperative compensatory curve on the postoperative curve progression in congenital scoliosis (CS) patients following thoracolumbar hemivertebra (HV) resection and short fusion. METHODS: This study retrospectively reviewed a consecutive cohort of patients with CS who underwent thoracolumbar HV resection and short fusion with a minimum of 2 years follow-up. According to the preoperative curve pattern, patients were divided into compensatory curve group non-compensatory curve group. Based on the postoperative coronal curve evolution, patients were further divided into the progressed group (Group P, with curve decompensation ≥ 20°) and the non-progressed group (Group NP, characterized by well-compensated curves). RESULTS: A total of 127 patients were included in this study, with 31 patients in the compensatory curve group and 96 patients in the non-compensatory curve group. The incidence of postoperative coronal curve progression was significantly higher in the compensatory curve group than that in non-compensatory curve group (35.5% vs. 13.5%, p = 0.007). In the compensatory curve group, patients who experienced postoperative curve progression showed fewer fusion segments (p = 0.001), greater preoperative UIV translation (p = 0.006), greater preoperative LIV tilt (p = 0.017), and larger postoperative UIV tilt (p < 0.001) compared with patients in group NP. Multiple logistic regression demonstrated that the shorter fusion segments and greater postoperative UIV tilt were two independent risk factors for postoperative curve progression. CONCLUSION: The presence of the compensatory curve was associated with a higher incidence of postoperative curve progression in patients with CS who underwent thoracolumbar HV resection and short fusion. Shorter fusion segments and greater postoperative UIV tilt were found to be the risk factors for postoperative curve progression.

Scaphocapitate arthrodesis with lunate preservation for Kienböck's disease: prospective outcomes study.

PURPOSE: The study evaluated the efficacy of SC arthrodesis with lunate preservation for treating patients diagnosed with stage IIIB or IIIC Kienböck's disease, who also exhibit neutral ulnar variance. The study further aimed to explore potential variations in outcomes between patients diagnosed with stage IIIB and IIIC Kienböck's disease. METHODS: Thirty-two patients diagnosed with stage IIIB (n = 19) and stage IIIC (n = 13) Kienböck's disease underwent SC arthrodesis with distal radius bone grafting stabilised by Herbert compression screws. All participants underwent pre- and post-operative assessments including VAS score for pain, ROM, grip strength, MMWS, and the Quick DASH score. Additionally, RS angle, LHI ratio, and CHI ratio were assessed. RESULTS: For all patients, the mean operative time was 73 min, follow-up was 45.6 months, time to union was 14 weeks, and time to full return to work was 24 weeks. The rate of union at the arthrodesis site was 91% (29 out of 32 patients) whilst the incidence of postoperative degenerative arthritis was 36% (8 out of 32 patients). Regarding changes in the means of outcomes from pre- to post-operatively, the VAS score decreased from 8.2 to 1.3 and grip strength improved from 36 to 79%. The RS angle was corrected from 59° to 50°. Significant improvements were noted in the mean MMWS from 45 to 75 and QuickDASH score from 78 to 21. However, no significant changes were observed in ROM, LHI, and CHI. There were no significant differences between patients with stage IIIB and stage IIIC in terms of these parameters, except for differences observed in the RS angle, LHI, and CHI preoperatively and in LHI and CHI postoperatively. CONCLUSION: Evidence level: II. Our research demonstrates that SC arthrodesis is a valuable approach for reducing pain, improving grip strength, and enhancing overall function in individuals with advanced Kienböck's disease. Importantly, our results indicate no notable differences in outcomes between patients diagnosed with stage IIIB or IIIC Kienböck's disease.

Enhancing Spatial Resolution in Tandem Mass Spectrometry Ion/Ion Reaction Imaging Experiments through Image Fusion.

We have recently developed a charge inversion ion/ion reaction to selectively derivatize phosphatidylserine lipids via gas-phase Schiff base formation. This tandem mass spectrometry (MS/MS) workflow enables the separation and detection of isobaric lipids in imaging mass spectrometry, but the images acquired using this workflow are limited to relatively poor spatial resolutions due to the current time and limit of detection requirements for these ion/ion reaction imaging mass spectrometry experiments. This trade-off between chemical specificity and spatial resolution can be overcome by using computational image fusion, which combines complementary information from multiple images. Herein, we demonstrate a proof-of-concept workflow that fuses a low spatial resolution (i.e., 125 µm) ion/ion reaction product ion image with higher spatial resolution (i.e., 25 µm) ion images from a full scan experiment performed using the same tissue section, which results in a predicted ion/ion reaction product ion image with a 5-fold improvement in spatial resolution. Linear regression, random forest regression, and two-dimensional convolutional neural network (2-D CNN) predictive models were tested for this workflow. Linear regression and 2D CNN models proved optimal for predicted ion/ion images of PS 40:6 and SHexCer d38:1, respectively.

Fusing 1H NMR and Raman experimental data for the improvement of wine recognition models.

The present study proposes the development of new wine recognition models based on Artificial Intelligence (AI) applied to the mid-level data fusion of 1H NMR and Raman data. In this regard, a supervised machine learning method, namely Support Vector Machines (SVMs), was applied for classifying wine samples with respect to the cultivar, vintage, and geographical origin. Because the association between the two data sources generated an input space with a high dimensionality, a feature selection algorithm was employed to identify the most relevant discriminant markers for each wine classification criterion, before SVM modeling. The proposed data processing strategy allowed the classification of the wine sample set with accuracies up to 100% in both cross-validation and on an independent test set and highlighted the efficiency of 1H NMR and Raman data fusion as opposed to the use of a single-source data for differentiating wine concerning the cultivar and vintage.

Enhancing unmanned ground vehicle performance in SAR operations: integrated gesture-control and deep learning framework for optimised victim detection.

In this study, we address the critical need for enhanced situational awareness and victim detection capabilities in Search and Rescue (SAR) operations amidst disasters. Traditional unmanned ground vehicles (UGVs) often struggle in such chaotic environments due to their limited manoeuvrability and the challenge of distinguishing victims from debris. Recognising these gaps, our research introduces a novel technological framework that integrates advanced gesture-recognition with cutting-edge deep learning for camera-based victim identification, specifically designed to empower UGVs in disaster scenarios. At the core of our methodology is the development and implementation of the Meerkat Optimization Algorithm-Stacked Convolutional Neural Network-Bi-Long Short Term Memory-Gated Recurrent Unit (MOA-SConv-Bi-LSTM-GRU) model, which sets a new benchmark for hand gesture detection with its remarkable performance metrics: accuracy, precision, recall, and F1-score all approximately 0.9866. This model enables intuitive, real-time control of UGVs through hand gestures, allowing for precise navigation in confined and obstacle-ridden spaces, which is vital for effective SAR operations. Furthermore, we leverage the capabilities of the latest YOLOv8 deep learning model, trained on specialised datasets to accurately detect human victims under a wide range of challenging conditions, such as varying occlusions, lighting, and perspectives. Our comprehensive testing in simulated emergency scenarios validates the effectiveness of our integrated approach. The system demonstrated exceptional proficiency in navigating through obstructions and rapidly locating victims, even in environments with visual impairments like smoke, clutter, and poor lighting. Our study not only highlights the critical gaps in current SAR response capabilities but also offers a pioneering solution through a synergistic blend of gesture-based control, deep learning, and purpose-built robotics. The key findings underscore the potential of our integrated technological framework to significantly enhance UGV performance in disaster scenarios, thereby optimising life-saving outcomes when time is of the essence. This research paves the way for future advancements in SAR technology, with the promise of more efficient and reliable rescue operations in the face of disaster.

Analysis on the pathogenesis and treatment progress of NRG1 fusion-positive non-small cell lung cancer.

Lung cancer persistently leads as the primary cause of morbidity and mortality among malignancies. A notable increase in the prevalence of lung adenocarcinoma has become evident in recent years. Although targeted therapies have shown in treating certain subsets of non-small cell lung cancers (NSCLC), a significant proportion of patients still face suboptimal therapeutic outcomes. Neuregulin-1 (NRG1), a critical member of the NRG gene family, initially drew interest due to its distribution within the nascent ventricular endocardium, showcasing an exclusive presence in the endocardium and myocardial microvessels. Recent research has highlighted NRG1's pivotal role in the genesis and progression across a spectrum of tumors, influencing molecular perturbations across various tumor-associated signaling pathways. This review provides a concise overview of NRG1, including its expression patterns, configuration, and fusion partners. Additionally, we explore the unique features and potential therapeutic strategies for NRG1 fusion-positive occurrences within the context of NSCLC.

Joint DNA-RNA-based NGS for diagnosis and treatment of a rare CD47-MET fusion lung adenocarcinoma which was immunoresistant and savoltinib-sensitive: a case report.

Targeted therapy and immunotherapy are both important in the treatment of non-small-cell lung cancer (NSCLC). Accurate diagnose and precise treatment are key in achieving long survival of patients. MET fusion is a rare oncogenic factor, whose optimal detection and treatment are not well established. Here, we report on a 32-year-old female lung adenocarcinoma patient with positive PD-L1 and negative driver gene detected by DNA-based next-generation sequencing (NGS). A radical resection of the primary lesion after chemotherapy combined with PD-1 checkpoint inhibitor administration indicated primary immuno-resistance according to her pathological response and rapid relapse. A rare CD47-MET was detected by RNA-based NGS, which was confirmed by fluorescence in situ hybridization. Multiplex immunofluorescence revealed a PD-L1 related heterogeneous immunosuppressive microenvironment with little distribution of CD4+ T cells and CD8+ T cells. Savolitinib therapy resulted in a progression-free survival (PFS) of >12 months, until a new secondary resistance mutation in MET p.D1228H was detected by re-biopsy and joint DNA-RNA-based NGS after disease progression. In this case, CD47-MET fusion NSCLC was primarily resistant to immunotherapy, sensitive to savolitinib, and developed secondary MET p.D1228H mutation after targeted treatment. DNA-RNA-based NGS is useful in the detection of such molecular events and tracking of secondary mutations in drug resistance. To this end, DNA-RNA-based NGS may be of better value in guiding precise diagnosis and individualized treatment in this patient population.

An engineered ultrahigh affinity bi-paratopic uPAR targeting agent confers enhanced tumor targeting.

Urokinase-type plasminogen activator receptor (uPAR) is overexpressed on tumor cells in multiple types of cancer and contributes to disease progression and metastasis. In this work, we engineered a novel bi-paratopic uPAR targeting agent by fusing the binding domains of two native uPAR ligands: uPA and vitronectin, with a flexible peptide linker. The linker length was optimized to facilitate simultaneous engagement of both domains to their adjacent epitopes on uPAR, resulting in a high affinity and avid binding interaction. Furthermore, the individual domains were affinity-matured using yeast surface display and directed evolution, resulting in a bi-paratopic protein with affinity in the picomolar to femtomolar range. This engineered uPAR targeting agent demonstrated significantly enhanced tumor localization in mouse tumor models compared to the native uPAR ligand and warrants further investigation as a diagnostic and therapeutic agent for cancer.

Analyzing Large Language Models' Responses to Common Lumbar Spine Fusion Surgery Questions: A Comparison Between ChatGPT and Bard.

OBJECTIVE: In the digital age, patients turn to online sources for lumbar spine fusion information, necessitating a careful study of large language models (LLMs) like chat generative pre-trained transformer (ChatGPT) for patient education. METHODS: Our study aims to assess the response quality of Open AI (artificial intelligence)'s ChatGPT 3.5 and Google's Bard to patient questions on lumbar spine fusion surgery. We identified 10 critical questions from 158 frequently asked ones via Google search, which were then presented to both chatbots. Five blinded spine surgeons rated the responses on a 4-point scale from 'unsatisfactory' to 'excellent.' The clarity and professionalism of the answers were also evaluated using a 5-point Likert scale. RESULTS: In our evaluation of 10 questions across ChatGPT 3.5 and Bard, 97% of responses were rated as excellent or satisfactory. Specifically, ChatGPT had 62% excellent and 32% minimally clarifying responses, with only 6% needing moderate or substantial clarification. Bard's responses were 66% excellent and 24% minimally clarifying, with 10% requiring more clarification. No significant difference was found in the overall rating distribution between the 2 models. Both struggled with 3 specific questions regarding surgical risks, success rates, and selection of surgical approaches (Q3, Q4, and Q5). Interrater reliability was low for both models (ChatGPT: k = 0.041, p = 0.622; Bard: k = -0.040, p = 0.601). While both scored well on understanding and empathy, Bard received marginally lower ratings in empathy and professionalism. CONCLUSION: ChatGPT3.5 and Bard effectively answered lumbar spine fusion FAQs, but further training and research are needed to solidify LLMs' role in medical education and healthcare communication.

Predictors of Persistent Postoperative Numbness Following Lumbar Fusion in Patients Older Than 75 Years: A Minimum 2-Year Follow-up.

OBJECTIVE: To evaluate the preoperative and perioperative predictors of persistent leg numbness following lumbar fusion in patients aged ≥ 75 years. METHODS: This single-center retrospective study examined 304 patients aged ≥ 75 years who underwent lumbar fusion for lumbar degenerative disease (102 men, 202 women; mean age, 79.2 [75-90] years). The visual analogue scale (VAS) score for leg numbness was examined preoperatively and at 2 years postoperatively. The persistent leg numbness group included patients with a 2-year postoperative VAS score for leg numbness ≥ 5 points. The demographic data were also reviewed. A multivariate stepwise logistic regression analysis was performed for variables with univariate analysis values of p < 0.2 on univariate analysis. RESULTS: In total, 71 patients (23.4%) experienced persistent postoperative leg numbness. Multivariate logistic regression analysis revealed that a history of lumbar decompression, longer symptom duration, and a preoperative VAS score for leg numbness ≥ 5 points were associated with greater postoperative persistent leg numbness following lumbar fusion. In contrast, other factors, such as sex, body mass index, vertebral fracture, diabetes mellitus, depression, symptom duration, dural injury, operative time, and estimated blood loss, were not. CONCLUSION: A history of preoperative lumbar decompression, longer symptom duration, and greater preoperative VAS scores for leg numbness were preoperative predictors of persistent postoperative leg numbness following lumbar fusion in older patients. Although lumbar fusion is expected to improve leg numbness, surgeons should consider the surgical history, duration, and preoperative numbness intensity and explain the potential postoperative persistent leg numbness in advance.

Identification of DNase I hypersensitive sites in the human genome by multiple sequence descriptors.

DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.

Perioperative complications and cost of posterior decompression with fusion in thoracic spine for ossification of the posterior longitudinal ligament and ossification of the ligamentum flavum -a comparative study using a national inpatient database.

BACKGROUND: Although posterior decompression with fusion (PDF) are effective for treating thoracic myelopathy, surgical treatment has a high risk of various complications. There is currently no information available on the perioperative complications in thoracic ossification of the longitudinal ligament (T-OPLL) and thoracic ossification of the ligamentum flavum (T-OLF). We evaluate the perioperative complication rate and cost between T-OPLL and T-OLF for patients underwent PDF. METHODS: Patients undergoing PDF for T-OPLL and T-OLF from 2012 to 2018 were detected in Japanese nationwide inpatient database. One-to-one propensity score matching between T-OPLL and T-OLF was performed based on patient characteristics and preoperative comorbidities. We examined systemic and local complication rate, reoperation rate, length of hospital stays, costs, discharge destination, and mortality after matching. RESULTS: In a total of 2,660 patients, 828 pairs of T-OPLL and T-OLF patients were included after matching. The incidence of systemic complications did not differ significantly between the T-OPLL and OLF groups. However, local complications were more frequently occurred in T-OPLL than in T-OLF groups (11.4% vs. 7.7% P = 0.012). Transfusion rates was also significantly higher in the T-OPLL group (14.1% vs. 9.4%, P = 0.003). T-OPLL group had longer hospital stay (42.2 days vs. 36.2 days, P = 0.004) and higher medical costs (USD 32,805 vs. USD 25,134, P < 0.001). In both T-OPLL and T-OLF, the occurrence of perioperative complications led to longer hospital stay and higher medical costs. While fewer patients in T-OPLL were discharged home (51.6% vs. 65.1%, P < 0.001), patients were transferred to other hospitals more frequently (47.5% vs. 33.5%, P = 0.001). CONCLUSION: This research identified the perioperative complications of T-OPLL and T-OLF in PDF using a large national database, which revealed that the incidence of local complications was higher in the T-OPLL patients. Perioperative complications resulted in longer hospital stays and higher medical costs.

PL-Net: progressive learning network for medical image segmentation.

In recent years, deep convolutional neural network-based segmentation methods have achieved state-of-the-art performance for many medical analysis tasks. However, most of these approaches rely on optimizing the U-Net structure or adding new functional modules, which overlooks the complementation and fusion of coarse-grained and fine-grained semantic information. To address these issues, we propose a 2D medical image segmentation framework called Progressive Learning Network (PL-Net), which comprises Internal Progressive Learning (IPL) and External Progressive Learning (EPL). PL-Net offers the following advantages: 1) IPL divides feature extraction into two steps, allowing for the mixing of different size receptive fields and capturing semantic information from coarse to fine granularity without introducing additional parameters; 2) EPL divides the training process into two stages to optimize parameters and facilitate the fusion of coarse-grained information in the first stage and fine-grained information in the second stage. We conducted comprehensive evaluations of our proposed method on five medical image segmentation datasets, and the experimental results demonstrate that PL-Net achieves competitive segmentation performance. It is worth noting that PL-Net does not introduce any additional learnable parameters compared to other U-Net variants.

YOLOv8's advancements in tuberculosis identification from chest images.

Tuberculosis (TB) is a chronic and pathogenic disease that leads to life-threatening situations like death. Many people have been affected by TB owing to inaccuracy, late diagnosis, and deficiency of treatment. The early detection of TB is important to protect people from the severity of the disease and its threatening consequences. Traditionally, different manual methods have been used for TB prediction, such as chest X-rays and CT scans. Nevertheless, these approaches are identified as time-consuming and ineffective for achieving optimal results. To resolve this problem, several researchers have focused on TB prediction. Conversely, it results in a lack of accuracy, overfitting of data, and speed. For improving TB prediction, the proposed research employs the Selection Focal Fusion (SFF) block in the You Look Only Once v8 (YOLOv8, Ultralytics software company, Los Angeles, United States) object detection model with attention mechanism through the Kaggle TBX-11k dataset. The YOLOv8 is used for its ability to detect multiple objects in a single pass. However, it struggles with small objects and finds it impossible to perform fine-grained classifications. To evade this problem, the proposed research incorporates the SFF technique to improve detection performance and decrease small object missed detection rates. Correspondingly, the efficacy of the projected mechanism is calculated utilizing various performance metrics such as recall, precision, F1Score, and mean Average Precision (mAP) to estimate the performance of the proposed framework. Furthermore, the comparison of existing models reveals the efficiency of the proposed research. The present research is envisioned to contribute to the medical world and assist radiologists in identifying tuberculosis using the YOLOv8 model to obtain an optimal outcome.

CTHNet: a network for wheat ear counting with local-global features fusion based on hybrid architecture.

Accurate wheat ear counting is one of the key indicators for wheat phenotyping. Convolutional neural network (CNN) algorithms for counting wheat have evolved into sophisticated tools, however because of the limitations of sensory fields, CNN is unable to simulate global context information, which has an impact on counting performance. In this study, we present a hybrid attention network (CTHNet) for wheat ear counting from RGB images that combines local features and global context information. On the one hand, to extract multi-scale local features, a convolutional neural network is built using the Cross Stage Partial framework. On the other hand, to acquire better global context information, tokenized image patches from convolutional neural network feature maps are encoded as input sequences using Pyramid Pooling Transformer. Then, the feature fusion module merges the local features with the global context information to significantly enhance the feature representation. The Global Wheat Head Detection Dataset and Wheat Ear Detection Dataset are used to assess the proposed model. There were 3.40 and 5.21 average absolute errors, respectively. The performance of the proposed model was significantly better than previous studies.

CIC-DUX4 Rearranged Sarcoma Presenting in the Skin: Case Report.

Introduction: Capicua transcriptional repressor (CIC)-DUX4 rearranged sarcoma is a subtype of CIC-rearranged sarcomas composed of undifferentiated Wilms' tumor 1 (WT1)+, CD99+ round cells with recurrent CIC gene rearrangement. The diagnosis of CIC-rearranged sarcoma remains challenging, and the prognosis of CIC-rearranged sarcomas is poor. Case Presentation: In this report, we described a case of CIC-DUX4 rearranged sarcoma presenting in the skin, expressing WT1 and CD99 in a dot-like pattern. In addition, the assessment of genomic alterations using genome panel testing was useful to confirm the accurate diagnosis. Conclusion: Our present case suggests that widespread use of genomic panel testing in the future may lead to early treatment and improve the prognosis of CIC-rearranged sarcomas.

The Molecular and Biological Function of MEF2D in Leukemia.

Myocyte enhancer factor 2 (MEF2) is a key transcription factor (TF) in skeletal, cardiac, and neural tissue development and includes four isoforms: MEF2A, MEF2B, MEF2C, and MEF2D. These isoforms significantly affect embryonic development, nervous system regulation, muscle cell differentiation, B- and T-cell development, thymocyte selection, and effects on tumorigenesis and leukemia. This chapter describes the multifaceted roles of MEF2 family proteins, covering embryonic development, nervous system regulation, and muscle cell differentiation. It further elucidates the contribution of MEF2 to various blood and immune cell functions. Specifically, in B-cell precursor acute lymphoblastic leukemia (BCP-ALL), MEF2D is aberrantly expressed and forms a fusion protein with BCL9, CSF1R, DAZAP1, HNRNPUL1, and SS18. These fusion proteins are closely related to the pathogenesis of leukemia. In addition, it specifically introduces the regulatory effect of MEF2D fusion protein on the proliferation and growth of B-cell acute lymphoblastic leukemia (B-ALL) cells. Finally, we detail the positive feedback loop between MEF2D and IRF8 that significantly promotes the progression of acute myeloid leukemia (AML) and the importance of the ZMYND8-BRD4 interaction in regulating the IRF8 and MYC transcriptional programs. The MEF2D-CEBPE axis is highlighted as a key transcriptional mechanism controlling the block of leukemic cell self-renewal and differentiation in AML. This chapter starts with the structure and function of MEF2 family proteins, specifically summarizing and analyzing the role of MEF2D in B-ALL and AML, mediating the complex molecular mechanisms of transcriptional regulation and exploring their implications for human health and disease.