Heparin is an effective treatment for preventing liver failure after hepatectomy.

BACKGROUND: Posthepatectomy liver failure (PHLF) is one of the most important causes of death following liver resection. Heparin, an established anticoagulant, can protect liver function through a number of mechanisms, and thus, prevent liver failure. AIM: To look at the safety and efficacy of heparin in preventing hepatic dysfunction after hepatectomy. METHODS: The data was extracted from Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) v1. 4 pinpointed patients who had undergone hepatectomy for liver cancer, subdividing them into two cohorts: Those who were injected with heparin and those who were not. The statistical evaluations used were unpaired t-tests, Mann-Whitney U tests, chi-square tests, and Fisher's exact tests to assess the effect of heparin administration on PHLF, duration of intensive care unit (ICU) stay, need for mechanical ventilation, use of continuous renal replacement therapy (CRRT), incidence of hypoxemia, development of acute kidney injury, and ICU mortality. Logistic regression was utilized to analyze the factors related to PHLF, with propensity score matching (PSM) aiming to balance the preoperative disparities between the two groups. RESULTS: In this study, 1388 patients who underwent liver cancer hepatectomy were analyzed. PSM yielded 213 matched pairs from the heparin-treated and control groups. Initial univariate analyses indicated that heparin potentially reduces the risk of PHLF in both matched and unmatched samples. Further analysis in the matched cohorts confirmed a significant association, with heparin reducing the risk of PHLF (odds ratio: 0.518; 95% confidence interval: 0.295-0.910; P = 0.022). Additionally, heparin treatment correlated with improved short-term postoperative outcomes such as reduced ICU stay durations, diminished requirements for respiratory support and CRRT, and lower incidences of hypoxemia and ICU mortality. CONCLUSION: Liver failure is an important hazard following hepatic surgery. During ICU care heparin administration has been proved to decrease the occurrence of hepatectomy induced liver failure. This indicates that heparin may provide a hopeful option for controlling PHLF.

Self-reporting intracellular delivery agent for singlet oxygen.

1,4-Dimethylphenazine endoperoxide releases singlet oxygen with a half-life of 89 hours at 37 °C. The thermal cycloreversion reaction is accompanied by a strong increase in the emission intensity with a peak at 490 nm, due to the formation of the phenazine core. The endoperoxide is effective against cancer cells in culture medium and tumor spheroids, with singlet oxygen-mediated cytotoxicity.

Predicting hepatocellular carcinoma: A new non-invasive model based on shear wave elastography.

BACKGROUND: Integrating conventional ultrasound features with 2D shear wave elastography (2D-SWE) can potentially enhance preoperative hepatocellular carcinoma (HCC) predictions. AIM: To develop a 2D-SWE-based predictive model for preoperative identification of HCC. METHODS: A retrospective analysis of 884 patients who underwent liver resection and pathology evaluation from February 2021 to August 2023 was conducted at the Oriental Hepatobiliary Surgery Hospital. The patients were divided into the modeling group (n = 720) and the control group (n = 164). The study included conventional ultrasound, 2D-SWE, and preoperative laboratory tests. Multiple logistic regression was used to identify independent predictive factors for malignant liver lesions, which were then depicted as nomograms. RESULTS: In the modeling group analysis, maximal elasticity (Emax) of tumors and their peripheries, platelet count, cirrhosis, and blood flow were independent risk indicators for malignancies. These factors yielded an area under the curve of 0.77 (95% confidence interval: 0.73-0.81) with 84% sensitivity and 61% specificity. The model demonstrated good calibration in both the construction and validation cohorts, as shown by the calibration graph and Hosmer-Lemeshow test (P = 0.683 and P = 0.658, respectively). Additionally, the mean elasticity (Emean) of the tumor periphery was identified as a risk factor for microvascular invasion (MVI) in malignant liver tumors (P = 0.003). Patients receiving antiviral treatment differed significantly in platelet count (P = 0.002), Emax of tumors (P = 0.033), Emean of tumors (P = 0.042), Emax at tumor periphery (P < 0.001), and Emean at tumor periphery (P = 0.003). CONCLUSION: 2D-SWE's hardness value serves as a valuable marker for enhancing the preoperative diagnosis of malignant liver lesions, correlating significantly with MVI and antiviral treatment efficacy.

Loss of Elp1 in cerebellar granule cell progenitors models ataxia phenotype of Familial Dysautonomia.

Familial Dysautonomia (FD) is an autosomal recessive disorder caused by a splice site mutation in the gene ELP1, which disproportionally affects neurons. While classically characterized by deficits in sensory and autonomic neurons, neuronal defects in the central nervous system have also been described. Although ELP1 expression remains high in the normal developing and adult cerebellum, its role in cerebellar development is unknown. To explore the role of Elp1 in the cerebellum, we knocked out Elp1 in cerebellar granule cell progenitors (GCPs) and examined the outcome on animal behavior and cellular composition. We found that GCP-specific conditional knockout of Elp1 (Elp1cKO) resulted in ataxia by 8 weeks of age. Cellular characterization showed that the animals had smaller cerebella with fewer granule cells. This defect was already apparent as early as 7 days after birth, when Elp1cKO animals also had fewer mitotic GCPs and shorter Purkinje dendrites. Through molecular characterization, we found that loss of Elp1 was associated with an increase in apoptotic cell death and cell stress pathways in GCPs. Our study demonstrates the importance of ELP1 in the developing cerebellum, and suggests that loss of Elp1 in the GC lineage may also play a role in the progressive ataxia phenotypes of FD patients.

Corrosion-Induced Cracking Model of Concrete Considering a Transverse Constraint.

The performance of corrosion-induced cracking of reinforced concrete members under transverse constraints was studied. Based on the theory of elastic-plastic mechanics and the hypothesis of uniform corrosion of a steel bar, a three-layer hollow cylinder model was established to predict the critical corrosion of the steel bar at the time of the cracking of the concrete cover. Taking the constraint of stirrups on surrounding concrete into consideration, it can be used to predict the corrosion rate of members with stirrups at the time of the cracking of the concrete cover, which further expands the application range of the corrosion-induced cracking models of concrete. On this basis, the critical corrosion rate of concrete under different stirrup ratios at the time of cracking was measured. The calculated results of the model are in accordance with experimental data. For corner steel bars, when the stirrup spacing is less than 100 mm, the existence of stirrups can effectively delay the occurrence of rust expansion cracks and enhance the durability of the structure. On the basis of this study, the problem of corrosion expansion and cracking of the concrete cover caused by non-uniform corrosion of steel bars along longitudinal and radial directions needs to be further studied in the future.

A Rhodamine-Spiropyran Conjugate Empowering Tunable Mechanochromism in Polymers under Multiple Stimuli.

Mechanochromic functionality realized via the force-responsive mechanophores in polymers has great potential for damage sensing and information storage. Mechanophores with the ability to recognize multiple stimuli for tunable chromic characteristics are highly sought after for versatile sensing ability and color programmability. Nevertheless, the majority of mechanophores are based on single-component chromophores with limited sensitivity, or require additional fabrication technology for multi-modal chromism. Here, we report a novel multifunctional mechanophore capable of vividly detectable and tunable mechanochromism in polymers. This synergistic optical coupling relies on strategically fusing rhodamine and spiropyran (Rh-SP), and tethering polymer chains on both subunits. The mechanochromic behaviors of the Rh-SP-linked polymers under sonication and compression are thoroughly evaluated in response to changes in force and the light-controlled relaxation process. Non-sequential ring-opening of the two subunits under force is identified, endowing high-contrast mechanochromism. Light-induced differential ring-closing reactions of the two subunits, together with the acidichromism of the SP moiety, are employed to engineer elastomers with programmable and wide-spectrum colors. Our work presents an effective strategy for highly appreciable and regulable mechanochromic functionality, and also provides new insights into the rupture mechanisms of π-fused mechanophores, as well as how the stimuli history controls stress accumulation in polymers.

ASL MRI Denoising via Multi Channel Collaborative Low-Rank Regularization.

Arterial spin labeling (ASL) perfusion MRI is the only non-invasive imaging technique for quantifying regional cerebral blood flow (CBF), which is a fundamental physiological variable. ASL MRI has a relatively low signal-to-noise-ratio (SNR). In this study, we proposed a novel ASL denoising method by simultaneously exploiting the inter- and intra-receive channel data correlations. MRI including ASL MRI data have been routinely acquired with multi-channel coils but current denoising methods are designed for denoising the coil-combined data. Indeed, the concurrently acquired multi-channel images differ only by coil sensitivity weighting and random noise, resulting in a strong low-rank structure of the stacked multi-channel data matrix. In our method, this matrix was formed by stacking the vectorized slices from different channels. Matrix rank was then approximately measured through the logarithm-determinant of the covariance matrix. Notably, our filtering technique is applied directly to complex data, avoiding the need to separate magnitude and phase or divide real and imaginary data, thereby ensuring minimal information loss. The degree of low-rank regularization is controlled based on the estimated noise level, striking a balance between noise removal and texture preservation. A noteworthy advantage of our framework is its freedom from parameter tuning, distinguishing it from most existing methods. Experimental results on real-world imaging data demonstrate the effectiveness of our proposed approach in significantly improving ASL perfusion quality. By effectively mitigating noise while preserving important textural information, our method showcases its potential for enhancing the utility and accuracy of ASL perfusion MRI, paving the way for improved neuroimaging studies and clinical diagnoses.

Fiber optic-based integrated system for in vivo multiscale pharmacokinetic monitoring.

This paper presents the development of a fiber-optic-based fluorescence detection system for multi-scale monitoring of drug distribution in living animals. The integrated system utilized dual laser sources at the wavelengths of 488 nm and 650 nm and three photomultiplier channels for multi-color fluorescence detection. The emission spectra of fluorescent substances were tracked using the time-resolved fluorescence spectroscopy module to continuously monitor their blood kinetics. The fiber bundle, consisting of 30,000 optic filaments, was designed for wide-field mesoscopic imaging of the drug's interactions within organs. The inclusion of a gradient refractive index (GRIN) lens within the setup enabled fluorescence confocal laser scanning microscopy to visualize the drug distribution at the cellular level. The system performance was verified by imaging hepatic and renal tissues in mice using cadmium telluride quantum dots (CdTe QDs) and R3. By acquiring multi-level images and real-time data, our integrated system underscores its potential as a potent tool for drug assessment, specifically within the realms of pharmacokinetic and pharmacodynamic investigations.

Rapidly damping hydrogels engineered through molecular friction.

Hydrogels capable of swift mechanical energy dissipation hold promise for a range of applications including impact protection, shock absorption, and enhanced damage resistance. Traditional energy absorption in such materials typically relies on viscoelastic mechanisms, involving sacrificial bond breakage, yet often suffers from prolonged recovery times. Here, we introduce a hydrogel designed for friction-based damping. This hydrogel features an internal structure that facilitates the motion of a chain walker within its network, effectively dissipating mechanical stress. The hydrogel network architecture allows for rapid restoration of its damping capacity, often within seconds, ensuring swift material recovery post-deformation. We further demonstrate that this hydrogel can significantly shield encapsulated cells from mechanical trauma under repetitive compression, owing to its proficient energy damping and rapid rebound characteristics. Therefore, this hydrogel has potential for dynamic load applications like artificial muscles and synthetic cartilage, expanding the use of hydrogel dampers in biomechanics and related areas.

Identifying Driving Factors of Atmospheric N2O5 with Machine Learning.

Dinitrogen pentoxide (N2O5) plays an essential role in tropospheric chemistry, serving as a nocturnal reservoir of reactive nitrogen and significantly promoting nitrate formations. However, identifying key environmental drivers of N2O5 formation remains challenging using traditional statistical methods, impeding effective emission control measures to mitigate NOx-induced air pollution. Here, we adopted machine learning assisted by steady-state analysis to elucidate the driving factors of N2O5 before and during the 2022 Winter Olympics (WO) in Beijing. Higher N2O5 concentrations were observed during the WO period compared to the Pre-Winter-Olympics (Pre-WO) period. The machine learning model accurately reproduced ambient N2O5 concentrations and showed that ozone (O3), nitrogen dioxide (NO2), and relative humidity (RH) were the most important driving factors of N2O5. Compared to the Pre-WO period, the variation in trace gases (i.e., NO2 and O3) along with the reduced N2O5 uptake coefficient was the main reason for higher N2O5 levels during the WO period. By predicting N2O5 under various control scenarios of NOx and calculating the nitrate formation potential from N2O5 uptake, we found that the progressive reduction of nitrogen oxides initially increases the nitrate formation potential before further decreasing it. The threshold of NOx was approximately 13 ppbv, below which NOx reduction effectively reduced the level of night-time nitrate formations. These results demonstrate the capacity of machine learning to provide insights into understanding atmospheric nitrogen chemistry and highlight the necessity of more stringent emission control of NOx to mitigate haze pollution.

Global brain connectivity: Test-retest stability and association with biological and neurocognitive variables.

BACKGROUND: Global brain connectivity (GBC) enables measuring brain regions' functional connectivity strength at rest by computing the average correlation between each brain voxel's time-series and that of all other voxels. NEW METHOD: We used resting-state fMRI (rs-fMRI) data of young adult participants from the Human Connectome Project (HCP) dataset to explore the test-retest stability of GBC, the brain regions with higher or lower GBC, as well as the associations of this measure with age, sex, and fluid intelligence. GBC was computed by considering separately the positive and negative correlation coefficients (positive GBC and negative GBC). RESULTS: Test-retest stability was higher for positive compared to negative GBC. Areas with higher GBC were located in the default mode network, insula, and visual areas, while regions with lower GBC were in subcortical regions, temporal cortex, and cerebellum. Higher age was related to global reduction of positive GBC. Males displayed higher positive GBC in the whole brain. Fluid intelligence was associated to increased positive GBC in fronto-parietal, occipital and temporal regions. COMPARISON WITH EXISTING METHOD: Compared to previous works, this study adopted a larger sample size and tested GBC stability using data from different rs-fMRI sessions. Moreover, these associations were examined by testing positive and negative GBC separately. CONCLUSIONS: Lower stability for negative compared to positive GBC suggests that negative correlations may reflect less stable couplings between brain regions. Our findings indicate a greater importance of positive compared to negative GBC for the associations of functional connectivity strength with biological and neurocognitive variables.

Biodegradable poly(lactic acid) blocked polyurethane/carbon nanotubes coated cotton fabric prepared by ultrasonic-assisted inkjet printing for high performance strain sensors.

In order to fulfill the demands for degradability, a broad working range, and heightened sensitivity in flexible sensors, biodegradable polyurethane (BTPU) was synthesized and combined with CNTs to produce BTPU/CNTs coated cotton fabric using an ultrasonic-assisted inkjet printing process. The synthesized BTPU displayed a capacity for degradation in a phosphate buffered saline solution, resulting in a weight loss of 25 % after 12 weeks of degradation. The BTPU/CNTs coated cotton fabric sensor achieved an extensive strain sensing range of 0-137.5 %, characterized by high linearity and a notable sensitivity (gauge factor (GF) of 126.8). Notably, it demonstrated a low strain detection limit (1 %), rapid response (within 280 ms), and robust durability, enabling precise monitoring of both large and subtle human body movements such as finger, wrist, neck, and knee bending, as well as swallowing. Moreover, the BTPU/CNTs coated cotton fabric exhibited favorable biocompatibility with human epidermis, enabling potential applications as wearable skin-contact sensors. This work provides insight into the development of degradable and high sensing performance sensors suitable for applications in electronic skins and health monitoring devices.

Exploring the relationship between heavy metals and diabetic retinopathy: a machine learning modeling approach.

Diabetic retinopathy (DR) is one of the leading causes of adult blindness in the United States. Although studies applying traditional statistical methods have revealed that heavy metals may be essential environmental risk factors for diabetic retinopathy, there is a lack of analyses based on machine learning (ML) methods to adequately explain the complex relationship between heavy metals and DR and the interactions between variables. Based on characteristic variables of participants with and without DR and heavy metal exposure data obtained from the NHANES database (2003-2010), a ML model was developed for effective prediction of DR. The best predictive model for DR was selected from 11 models by receiver operating characteristic curve (ROC) analysis. Further permutation feature importance (PFI) analysis, partial dependence plots (PDP) analysis, and SHapley Additive exPlanations (SHAP) analysis were used to assess the model capability and key influencing factors. A total of 1042 eligible individuals were randomly assigned to two groups for training and testing set of the prediction model. ROC analysis showed that the k-nearest neighbour (KNN) model had the highest prediction performance, achieving close to 100% accuracy in the testing set. Urinary Sb level was identified as the critical heavy metal affecting the predicted risk of DR, with a contribution weight of 1.730632 ± 1.791722, which was much higher than that of other heavy metals and baseline variables. The results of the PDP analysis and the SHAP analysis also indicated that antimony (Sb) had a more significant effect on DR. The interaction between age and Sb was more significant compared to other variables and metal pairs. We found that Sb could serve as a potential predictor of DR and that Sb may influence the development of DR by mediating cellular and systemic senescence. The study revealed that monitoring urinary Sb levels can be useful for early non-invasive screening and intervention in DR development, and also highlighted the important role of constructed ML models in explaining the effects of heavy metal exposure on DR.

MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer.

BACKGROUND AND PURPOSE: Adjuvant treatments are valuable to decrease the recurrence rate and improve survival for early-stage cervical cancer patients (ESCC), Therefore, recurrence risk evaluation is critical for the choice of postoperative treatment. A magnetic resonance imaging (MRI) based radiomics nomogram integrating postoperative adjuvant treatments was constructed and validated externally to improve the recurrence risk prediction for ESCC. MATERIAL AND METHODS: 212 ESCC patients underwent surgery and adjuvant treatments from three centers were enrolled and divided into the training, internal validation, and external validation cohorts. Their clinical data, pretreatment T2-weighted images (T2WI) were retrieved and analyzed. Radiomics models were constructed using machine learning methods with features extracted and screen from sagittal and axial T2WI. A nomogram for recurrence prediction was build and evaluated using multivariable logistic regression analysis integrating radiomic signature and adjuvant treatments. RESULTS: A total of 8 radiomic features were screened out of 1020 extracted features. The extreme gradient boosting (XGboost) model based on MRI radiomic features performed best in recurrence prediction with an area under curve (AUC) of 0.833, 0.822 in the internal and external validation cohorts, respectively. The nomogram integrating radiomic signature and clinical factors achieved an AUC of 0.806, 0.718 in the internal and external validation cohorts, respectively, for recurrence risk prediction for ESCC. CONCLUSION: In this study, the nomogram integrating T2WI radiomic signature and clinical factors is valuable to predict the recurrence risk, thereby allowing timely planning for effective treatments for ESCC with high risk of recurrence.

Understanding the Mechanochemistry of Mechano-Radicals in Self-Growth Materials by Single-Molecule Force Spectroscopy.

Recent research on mechano-radicals has provided valuable insights into self-growth and adaptive responsive materials. Typically, mechanophores must remain inert in the absence of force but respond quickly to external tension before other linkages within the polymer network. Azo compounds exhibit promising combinations of mechanical stability and force-triggered reactivity, making them widely used as mechano-radicals in force-responsive materials. However, the activation conditions and behavior of azo compounds have yet to be quantitatively explored. In this study, we investigated the mechanical strength of three azo compounds using single-molecule force spectroscopy. Our results revealed that these compounds exhibit rupture forces ranging from ~500 to 1000 pN, at a loading rate of 3×104 pN s-1. Importantly, these mechanophores demonstrate distinct kinetic properties. Their unique mechanical attributes enable azo bond scission and free radical generation before causing major polymer backbone damage of entire material during polymer network deformation. This fundamental understanding of mechanophores holds significant promise for the development of self-growth materials and their related applications.

Preparation and optimization of dummy molecularly imprinted polymer-based solid-phase extraction system for selective enrichment of p-toluene sulfonate esters genotoxic impurities.

Sulfonate esters, one class of genotoxic impurities (GTIs), have gained significant attention in recent years due to their potential to cause genetic mutations and cancer. In the current study, we employed the dummy template molecular imprinting technology with a dummy template molecule replacing the target molecule to establish a pretreatment method for samples containing p-toluene sulfonate esters. Through computer simulation and ultraviolet-visible spectroscopy analysis, the optimal functional monomer acrylamide and polymerization solvent chloroform were selected. Subsequently, a dummy template molecularly imprinted polymer (DMIP) was prepared by the precipitation polymerization method, and the polymer was characterized in morphology, particle size, and composition. The results of the adsorption and enrichment study demonstrated that the DMIP has high adsorption capability (Q = 7.88 mg/g) and favorable imprinting effects (IF = 1.37); Further, it could simultaneously adsorb three p-toluene sulfonate esters. The optimal adsorption conditions were obtained by conditional optimization of solid-phase extraction (SPE). A pH 7 solution was selected as the loading condition, the methanol/1 % phosphoric acid solution (20:80, v/v) was selected as the washing solution, and acetonitrile containing 10 % acetic acid in 6 mL was selected as the elution solvent. Finally, we determined methyl p-toluene sulfonate alkyl esters, ethyl p-toluene sulfonate alkyl esters, and isopropyl p-toluene sulfonate alkyl esters in tosufloxacin toluene sulfonate and capecitabine at the 10 ppm level (relative to 1 mg/mL active pharmaceutical ingredient (API) samples) by using DMIP-based SPE coupled with HPLC. This approach facilitated the selective enrichment of p-toluene sulfonate esters GTIs from complex API samples.

Immunomodulatory activity of heat-killed Lacticaseibacillus paracaseiMCC1849 based on the activation of plasmacytoid dendritic cells in the peripheral blood of healthy adults.

Probiotics are widely used in food for their health benefits to the host. Inactivated probiotics also reportedly improve the intestinal environment and immune regulation. Our previous studies showed that heat-killed Lacticaseibacillus paracasei MCC1849 (hk-MCC1849) effectively induced IL-12 production in mouse spleen cells and significantly reduced cold symptoms in clinical trial subjects. To further elucidate the mechanism of host immune regulation by hk-MCC1849, human peripheral blood mononuclear cells (PBMCs) were cocultured with hk-MCC1849. The Toll-like receptor 9 ligands CpG-ODN 2216 and hk-MCC1849 and the heat-killed Lacticaseibacillus rhamnosus ATCC53103 were used as positive and negative controls, respectively. The results showed that, compared with the control, hk-MCC1849 significantly increased the expression of the plasmacytoid dendritic cell (pDC) marker CD86 (p < .0001) and the pDC marker HLA-DR (p < .001) in PBMCs. The expression levels of the IL-12p40, IFNα, IFNα1, IFNγ, and ISG15 genes were significantly increased after coculture with hk-MCC1849 (p < .05, p < .05, p < .05, p < .05, and p < .05, respectively, vs. control). Furthermore, to confirm whether hk-MCC1849 directly interacted with pDCs, DCs were enriched with PBMCs following 24 h of coculture with hk-MCC1849. Phagocytosis of fluorescently labeled hk-MCC1849 by pDCs was observed, and there were significant increases in CD86 (p < .05) and HLA-DR (p < .0001) expression in pDCs. These results suggest that hk-MCC1849 exerts a potential immunomodulatory effect on the host through the activation of peripheral pDCs.

Brain-wide functional connectome analysis of 40,000 individuals reveals brain networks that show aging effects in older adults.

The functional connectome changes with aging. We systematically evaluated aging related alterations in the functional connectome using a whole-brain connectome network analysis in 39,675 participants in UK Biobank project. We used adaptive dense network discovery tools to identify networks directly associated with aging from resting-state fMRI data. We replicated our findings in 499 participants from the Lifespan Human Connectome Project in Aging study. The results consistently revealed two motor-related subnetworks (both permutation test p-values <0.001) that showed a decline in resting-state functional connectivity (rsFC) with increasing age. The first network primarily comprises sensorimotor and dorsal/ventral attention regions from precentral gyrus, postcentral gyrus, superior temporal gyrus, and insular gyrus, while the second network is exclusively composed of basal ganglia regions, namely the caudate, putamen, and globus pallidus. Path analysis indicates that white matter fractional anisotropy mediates 19.6% (p<0.001, 95% CI [7.6% 36.0%]) and 11.5% (p<0.001, 95% CI [6.3% 17.0%]) of the age-related decrease in both networks, respectively. The total volume of white matter hyperintensity mediates 32.1% (p<0.001, 95% CI [16.8% 53.0%]) of the aging-related effect on rsFC in the first subnetwork.

KBTBD4 Cancer Hotspot Mutations Drive Neomorphic Degradation of HDAC1/2 Corepressor Complexes.

Cancer mutations can create neomorphic protein-protein interactions to drive aberrant function 1 . As a substrate receptor of the CULLIN3-RBX1 E3 ubiquitin ligase complex, KBTBD4 is recurrently mutated in medulloblastoma (MB) 2 , the most common embryonal brain tumor in children, and pineoblastoma 3 . These mutations impart gain-of-function to KBTBD4 to induce aberrant degradation of the transcriptional corepressor CoREST 4 . However, their mechanism of action remains unresolved. Here, we elucidate the mechanistic basis by which KBTBD4 mutations promote CoREST degradation through engaging HDAC1/2, the direct neomorphic target of the substrate receptor. Using deep mutational scanning, we systematically map the mutational landscape of the KBTBD4 cancer hotspot, revealing distinct preferences by which insertions and substitutions can promote gain-of-function and the critical residues involved in the hotspot interaction. Cryo-electron microscopy (cryo-EM) analysis of two distinct KBTBD4 cancer mutants bound to LSD1-HDAC1-CoREST reveals that a KBTBD4 homodimer asymmetrically engages HDAC1 with two KELCH-repeat propeller domains. The interface between HDAC1 and one of the KBTBD4 propellers is stabilized by the MB mutations, which directly insert a bulky side chain into the active site pocket of HDAC1. Our structural and mutational analyses inform how this hotspot E3-neo-substrate interface can be chemically modulated. First, our results unveil a converging shape complementarity-based mechanism between gain-of-function E3 mutations and a molecular glue degrader, UM171. Second, we demonstrate that HDAC1/2 inhibitors can block the mutant KBTBD4-HDAC1 interface, the aberrant degradation of CoREST, and the growth of KBTBD4-mutant MB models. Altogether, our work reveals the structural and mechanistic basis of cancer mutation-driven neomorphic protein-protein interactions and pharmacological strategies to modulate their action for therapeutic applications.