Prediction of post-donation renal function using machine learning techniques and conventional regression models in living kidney donors.

BACKGROUND: Accurate prediction of renal function following kidney donation and careful selection of living donors are essential for living-kidney donation programs. We aimed to develop a prediction model for post-donation renal function following living kidney donation using machine learning. METHODS: This retrospective cohort study was conducted with 823 living kidney donors between 2009 and 2020. The dataset was randomly split into training (80%) and test sets (20%). The main outcome was the post-donation estimated glomerular filtration rate (eGFR) 12 months after nephrectomy. We compared the performance of machine learning techniques, traditional regression models, and models from previous studies. The best-performing model was selected based on the mean absolute error (MAE) and root mean square error (RMSE). RESULTS: The mean age of the participants was 45.2 ± 12.3 years, and 48.4% were males. The mean pre-donation and post-donation eGFRs were 101.3 ± 13.0 and 68.8 ± 12.7 mL/min/1.73 m2, respectively. The XGBoost model with the eGFR, age, serum creatinine, 24-h urine creatinine, 24-h urine sodium, creatinine clearance, cystatin C, cystatin C-based eGFR, computed tomography volume of the remaining kidney/body weight, normalized GFR of the remaining kidney measured through a diethylenetriaminepentaacetic acid scan, and sex, showed the best performance with a mean absolute error of 6.23 and root mean square error of 8.06. An easy-to-use web application titled Kidney Donation with Nephrologic Intelligence (KDNI) was developed. CONCLUSIONS: The prediction model using XGBoost accurately predicted the post-donation eGFR after living kidney donation. This model can be applied in clinical practice using KDNI, the developed web application.

Glucose-mediated mitochondrial reprogramming by cholesterol export at TM4SF5-enriched mitochondria-lysosome contact sites.

BACKGROUND: Transmembrane 4 L six family member 5 (TM4SF5) translocates subcellularly and functions metabolically, although it is unclear how intracellular TM4SF5 translocation is linked to metabolic contexts. It is thus of interests to understand how the traffic dynamics of TM4SF5 to subcellular endosomal membranes are correlated to regulatory roles of metabolisms. METHODS: Here, we explored the metabolic significance of TM4SF5 localization at mitochondria-lysosome contact sites (MLCSs), using in vitro cells and in vivo animal systems, via approaches by immunofluorescence, proximity labelling based proteomics analysis, organelle reconstitution etc. RESULTS: Upon extracellular glucose repletion following depletion, TM4SF5 became enriched at MLCSs via an interaction between mitochondrial FK506-binding protein 8 (FKBP8) and lysosomal TM4SF5. Proximity labeling showed molecular clustering of phospho-dynamic-related protein I (DRP1) and certain mitophagy receptors at TM4SF5-enriched MLCSs, leading to mitochondrial fission and autophagy. TM4SF5 bound NPC intracellular cholesterol transporter 1 (NPC1) and free cholesterol, and mediated export of lysosomal cholesterol to mitochondria, leading to impaired oxidative phosphorylation but intact tricarboxylic acid (TCA) cycle and ß-oxidation. In mouse models, hepatocyte Tm4sf5 promoted mitophagy and cholesterol transport to mitochondria, both with positive relations to liver malignancy. CONCLUSIONS: Our findings suggested that TM4SF5-enriched MLCSs regulate glucose catabolism by facilitating cholesterol export for mitochondrial reprogramming, presumably while hepatocellular carcinogenesis, recapitulating aspects for hepatocellular carcinoma metabolism with mitochondrial reprogramming to support biomolecule synthesis in addition to glycolytic energetics.

Effect of knowledgebase transition of a clinical decision support system on medication order and alert patterns in an emergency department.

A knowledgebase (KB) transition of a clinical decision support (CDS) system occurred at the study site. The transition was made from one commercial database to another, provided by a different vendor. The change was applied to all medications in the institute. The aim of this study was to analyze the effect of KB transition on medication-related orders and alert patterns in an emergency department (ED). Data of patients, medication-related orders and alerts, and physicians in the ED from January 2018 to December 2020 were analyzed in this study. A set of definitions was set to define orders, alerts, and alert overrides. Changes in order and alert patterns before and after the conversion, which took place in May 2019, were assessed. Overall, 101,450 patients visited the ED, and 1325 physicians made 829,474 prescription orders to patients during visit and at discharge. Alert rates (alert count divided by order count) for periods A and B were 12.6% and 14.1%, and override rates (alert override count divided by alert count) were 60.8% and 67.4%, respectively. Of the 296 drugs that were used more than 100 times during each period, 64.5% of the drugs had an increase in alert rate after the transition. Changes in alert rates were tested using chi-squared test and Fisher's exact test. We found that the CDS system knowledgebase transition was associated with a significant change in alert patterns at the medication level in the ED. Careful consideration is advised when such a transition is performed.

Machine learning pre-hospital real-time cardiac arrest outcome prediction (PReCAP) using time-adaptive cohort model based on the Pan-Asian Resuscitation Outcome Study.

To save time during transport, where resuscitation quality can degrade in a moving ambulance, it would be prudent to continue the resuscitation on scene if there is a high likelihood of ROSC occurring at the scene. We developed the pre-hospital real-time cardiac arrest outcome prediction (PReCAP) model to predict ROSC at the scene using prehospital input variables with time-adaptive cohort. The patient survival at discharge from the emergency department (ED), the 30-day survival rate, and the final Cerebral Performance Category (CPC) were secondary prediction outcomes in this study. The Pan-Asian Resuscitation Outcome Study (PAROS) database, which includes out-of-hospital cardiac arrest (OHCA) patients transferred by emergency medical service in Asia between 2009 and 2018, was utilized for this study. From the variables available in the PAROS database, we selected relevant variables to predict OHCA outcomes. Light gradient-boosting machine (LightGBM) was used to build the PReCAP model. Between 2009 and 2018, 157,654 patients in the PAROS database were enrolled in our study. In terms of prediction of ROSC on scene, the PReCAP had an AUROC score between 0.85 and 0.87. The PReCAP had an AUROC score between 0.91 and 0.93 for predicting survived to discharge from ED, and an AUROC score between 0.80 and 0.86 for predicting the 30-day survival. The PReCAP predicted CPC with an AUROC score ranging from 0.84 to 0.91. The feature importance differed with time in the PReCAP model prediction of ROSC on scene. Using the PAROS database, PReCAP predicted ROSC on scene, survival to discharge from ED, 30-day survival, and CPC for each minute with an AUROC score ranging from 0.8 to 0.93. As this model used a multi-national database, it might be applicable for a variety of environments and populations.

TM4SF5-mediated abnormal food-intake behavior and apelin expression facilitate non-alcoholic fatty liver disease features.

Transmembrane 4 L six family member 5 (TM4SF5) engages in non-alcoholic steatohepatitis (NASH), although its mechanistic roles are unclear. Genetically engineered Tm4sf5 mice fed ad libitum normal chow or high-fat diet for either an entire day or a daytime-feeding (DF) pattern were analyzed for metabolic parameters. Compared to wild-type and Tm4sf5-/- knockout mice, hepatocyte-specific TM4SF5-overexpressing Alb-TGTm4sf5-Flag (TG) mice showed abnormal food-intake behavior during the mouse-inactive daytime, increased apelin expression, increased food intake, and higher levels of NASH features. DF or exogenous apelin injection of TG mice caused severe hepatic pathology. TM4SF5-mediated abnormal food intake was correlated with peroxisomal ß-oxidation, mTOR activation, and autophagy inhibition, with triggering NASH phenotypes. Non-alcoholic fatty liver disease (NAFLD) patients' samples revealed a correlation between serum apelin and NAFLD activity score. Altogether, these observations suggest that hepatic TM4SF5 may cause abnormal food-intake behaviors to trigger steatohepatitic features via the regulation of peroxisomal ß-oxidation, mTOR, and autophagy.

Prediction tool for renal adaptation after living kidney donation using interpretable machine learning.

Introduction: Post-donation renal outcomes are a crucial issue for living kidney donors considering young donors' high life expectancy and elderly donors' comorbidities that affect kidney function. We developed a prediction model for renal adaptation after living kidney donation using interpretable machine learning. Methods: The study included 823 living kidney donors who underwent nephrectomy in 2009-2020. AutoScore, a machine learning-based score generator, was used to develop a prediction model. Fair and good renal adaptation were defined as post-donation estimated glomerular filtration rate (eGFR) of ≥ 60 mL/min/1.73 m2 and ≥ 65% of the pre-donation values, respectively. Results: The mean age was 45.2 years; 51.6% were female. The model included pre-donation demographic and laboratory variables, GFR measured by diethylenetriamine pentaacetate scan, and computed tomography kidney volume/body weight of both kidneys and the remaining kidney. The areas under the receiver operating characteristic curve were 0.846 (95% confidence interval, 0.762-0.930) and 0.626 (0.541-0.712), while the areas under the precision-recall curve were 0.965 (0.944-0.978) and 0.709 (0.647-0.788) for fair and good renal adaptation, respectively. An interactive clinical decision support system was developed. Conclusion: The prediction tool for post-donation renal adaptation showed good predictive capability and may help clinical decisions through an easy-to-use web-based application.

EARLY PREDICTION OF UNEXPECTED LATENT SHOCK IN THE EMERGENCY DEPARTMENT USING VITAL SIGNS.

ABSTRACT: Objective/Introduction : Sequential vital-sign information and trends in vital signs are useful for predicting changes in patient state. This study aims to predict latent shock by observing sequential changes in patient vital signs. Methods : The dataset for this retrospective study contained a total of 93,194 emergency department (ED) visits from January 1, 2016, and December 31, 2020, and Medical Information Mart for Intensive Care (MIMIC)-IV-ED data. We further divided the data into training and validation datasets by random sampling without replacement at a 7:3 ratio. We carried out external validation with MIMIC-IV-ED. Our prediction model included logistic regression (LR), random forest (RF) classifier, a multilayer perceptron (MLP), and a recurrent neural network (RNN). To analyze the model performance, we used area under the receiver operating characteristic curve (AUROC). Results : Data of 89,250 visits of patients who met prespecified criteria were used to develop a latent-shock prediction model. Data of 142,250 patient visits from MIMIC-IV-ED satisfying the same inclusion criteria were used for external validation of the prediction model. The AUROC values of prediction for latent shock were 0.822, 0.841, 0.852, and 0.830 with RNN, MLP, RF, and LR methods, respectively, at 3 h before latent shock. This is higher than the shock index or adjusted shock index. Conclusion : We developed a latent shock prediction model based on 24 h of vital-sign sequence that changed with time and predicted the results by individual.

Impact of the 24-hour time target policy for emergency departments in South Korea: a mixed method study in a single medical center.

BACKGROUND: In South Korea, after the spread of the Middle East Respiratory Syndrome epidemic was aggravated by long stays in crowded emergency departments (EDs), a 24-hour target policy for EDs was introduced to prevent crowding and reduce patients' length of stay (LOS). The policy requires at least 95% of all patients to be admitted, discharged or transferred from an ED within 24 hours of arrival. This study analyzes the effects of the 24-hour target policy on ED LOS and compliance rates and describes the consequences of the policy. METHODS: A mixed-methods approach was applied to a retrospective observational study of ED visits combined with a survey of medical professionals. The primary measure was ED LOS, and the secondary measure was policy compliance rate which refers to the proportion of patient visits with a LOS shorter than 24 hours. Patient flow, quality of care, patient safety, staff workload, and staff satisfaction were also investigated through surveys. Mann-Whitney U and χ2 tests were used to compare variables before and after the introduction of the policy. RESULTS: The median ED LOS increased from 3.9 hours (interquartile range [IQR] = 2.1-7.6) to 4.5 hours (IQR = 2.5-8.5) after the policy was introduced. This was likely influenced by the average monthly number of patients, which greatly increased from 4819 (SD = 340) to 5870 (SD = 462) during the same period. The proportion of patients with ED LOS greater than 24 hours remained below5% only after 6 months of policy implementation, but the number of patients whose disposition was decided at 23 hours increased by 4.84 times. Survey results suggested that patient flow and quality of care improved slightly, while the workload of medical staff worsened. CONCLUSIONS: After implementing the 24-hour target policy, the proportion of patients whose ED LOS exceeded 24 hours decreased, even though the median ED LOS increased. However, the unintended consequences of the policy were observed such as increased medical professional workload and abrupt expulsion of patients before 24 hours.

Appropriateness of Alerts and Physicians' Responses With a Medication-Related Clinical Decision Support System: Retrospective Observational Study.

BACKGROUND: Alert fatigue is unavoidable when many irrelevant alerts are generated in response to a small number of useful alerts. It is necessary to increase the effectiveness of the clinical decision support system (CDSS) by understanding physicians' responses. OBJECTIVE: This study aimed to understand the CDSS and physicians' behavior by evaluating the clinical appropriateness of alerts and the corresponding physicians' responses in a medication-related passive alert system. METHODS: Data on medication-related orders, alerts, and patients' electronic medical records were analyzed. The analyzed data were generated between August 2019 and June 2020 while the patient was in the emergency department. We evaluated the appropriateness of alerts and physicians' responses for a subset of 382 alert cases and classified them. RESULTS: Of the 382 alert cases, only 7.3% (n=28) of the alerts were clinically appropriate. Regarding the appropriateness of the physicians' responses about the alerts, 92.4% (n=353) were deemed appropriate. In the classification of alerts, only 3.4% (n=13) of alerts were successfully triggered, and 2.1% (n=8) were inappropriate in both alert clinical relevance and physician's response. In this study, the override rate was 92.9% (n=355). CONCLUSIONS: We evaluated the appropriateness of alerts and physicians' responses through a detailed medical record review of the medication-related passive alert system. An excessive number of unnecessary alerts are generated, because the algorithm operates as a rule base without reflecting the individual condition of the patient. It is important to maximize the value of the CDSS by comprehending physicians' responses.

Systemic TM4SF5 overexpression in ApcMin/+ mice promotes hepatic portal hypertension associated with fibrosis.

Mutation of the gene for adenomatous polyposis coli (APC), as seen in ApcMin/+ mice, leads to intestinal adenomas and carcinomas via stabilization of ß-catenin. Transmembrane 4 L six family member 5 (TM4SF5) is involved in the development of non-alcoholic fatty liver disease, fibrosis, and cancer. However, the functional linkage between TM4SF5 and APC or ß-catenin has not been investigated for pathological outcomes. After interbreeding ApcMin/+ with TM4SF5-overexpressing transgenic (TgTM4SF5) mice, we explored pathological outcomes in the intestines and livers of the offspring. The intestines of 26-week-old dual-transgenic mice (ApcMin/+:TgTM4SF5) had intramucosal adenocarcinomas beyond the single-crypt adenomas in ApcMin/+ mice. Additional TM4SF5 overexpression increased the stabilization of ß-catenin via reduced glycogen synthase kinase 3ß (GSK3ß) phosphorylation on Ser9. Additionally, the livers of the dualtransgenic mice showed distinct sinusoidal dilatation and features of hepatic portal hypertension associated with fibrosis, more than did the relatively normal livers in ApcMin/+ mice. Interestingly, TM4SF5 overexpression in the liver was positively linked to increased GSK3ß phosphorylation (opposite to that seen in the colon), ß-catenin level, and extracellular matrix (ECM) protein expression, indicating fibrotic phenotypes. Consistent with these results, 78-week-old TgTM4SF5 mice similarly had sinusoidal dilatation, immune cell infiltration, and fibrosis. Altogether, systemic overexpression of TM4SF5 aggravates pathological abnormalities in both the colon and the liver. [BMB Reports 2022; 55(12): 609-614].

Liver-originated small extracellular vesicles with TM4SF5 target brown adipose tissue for homeostatic glucose clearance.

Transmembrane 4 L six family member 5 (TM4SF5) is involved in chronic liver disease, although its role in glucose homeostasis remains unknown. TM4SF5 deficiency caused age-dependent glucose (in)tolerance with no link to insulin sensitivity. Further, hepatic TM4SF5 binding to GLUT1 promoted glucose uptake and glycolysis. Excessive glucose repletion caused hepatocytes to secrete small extracellular vesicles (sEVs) loaded with TM4SF5 (hep-sEVTm4sf5 ), suggesting a role for sEVTm4sf5 in glucose metabolism and homeostasis. Hep-sEVTm4sf5 were smaller than sEVControl and recruit proteins for efficient organ tropism. Liver-derived sEVs, via a liver-closed vein circuit (LCVC) using hepatic TM4SF5-overexpressing (Alb-Tm4sf5 TG) mice (liv-sEVTm4sf5 ), improved glucose tolerance in Tm4sf5-/- KO mice and targeted brown adipose tissues (BATs), possibly allowing the clearance of blood glucose as heat independent of UCP1. Taken together, hep-sEVTm4sf5 might clear high extracellular glucose levels more efficiently by targeting BAT compared with hep-sEVControl , suggesting an insulin-like role for sEV™4SF5 in affecting age-related metabolic status and thus body weight (BW).

TM4SF5-Mediated Regulation of Hepatocyte Transporters during Metabolic Liver Diseases.

Nonalcoholic fatty liver disease (NAFLD) is found in up to 30% of the world's population and can lead to hepatocellular carcinoma (HCC), which has a poor 5-year relative survival rate of less than 40%. Clinical therapeutic strategies are not very successful. The co-occurrence of metabolic disorders and inflammatory environments during the development of steatohepatitis thus needs to be more specifically diagnosed and treated to prevent fatal HCC development. To improve diagnostic and therapeutic strategies, the identification of molecules and/or pathways responsible for the initiation and progression of chronic liver disease has been explored in many studies, but further study is still required. Transmembrane 4 L six family member 5 (TM4SF5) has been observed to play roles in the regulation of metabolic functions and activities in hepatocytes using in vitro cell and in vivo animal models without or with TM4SF5 expression in addition to clinical liver tissue samples. TM4SF5 is present on the membranes of different organelles or vesicles and cooperates with transporters for fatty acids, amino acids, and monocarbohydrates, thus regulating nutrient uptake into hepatocytes and metabolism and leading to phenotypes of chronic liver diseases. In addition, TM4SF5 can remodel the immune environment by interacting with immune cells during TM4SF5-mediated chronic liver diseases. Because TM4SF5 may act as an NAFLD biomarker, this review summarizes crosstalk between TM4SF5 and nutrient transporters in hepatocytes, which is related to chronic liver diseases.

Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury.

The study aims to measure the effectiveness of an AI-based traumatic intracranial hemorrhage prediction model in the decisions of emergency physicians regarding ordering head computed tomography (CT) scans. We developed a deep-learning model for predicting traumatic intracranial hemorrhages (DEEPTICH) using a national trauma registry with 1.8 million cases. For simulation, 24 cases were selected from previous emergency department cases. For each case, physicians made decisions on ordering a head CT twice: initially without the DEEPTICH assistance, and subsequently with the DEEPTICH assistance. Of the 528 responses from 22 participants, 201 initial decisions were different from the DEEPTICH recommendations. Of these 201 initial decisions, 94 were changed after DEEPTICH assistance (46.8%). For the cases in which CT was initially not ordered, 71.4% of the decisions were changed (p < 0.001), and for the cases in which CT was initially ordered, 37.2% (p < 0.001) of the decisions were changed after DEEPTICH assistance. When using DEEPTICH, 46 (11.6%) unnecessary CTs were avoided (p < 0.001) and 10 (11.4%) traumatic intracranial hemorrhages (ICHs) that would have been otherwise missed were found (p = 0.039). We found that emergency physicians were likely to accept AI based on how they perceived its safety.

Effect of fever or respiratory symptoms on leaving without being seen during the COVID-19 pandemic in South Korea.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) has notably altered the emergency department isolation protocol, imposing stricter requirements on probable infectious disease patients that enter the department. This has caused adverse effects, such as an increased rate of leave without being seen (LWBS). This study describes the effect of fever/respiratory symptoms as the main cause of isolation regarding LWBS after the COVID-19 pandemic. METHODS: We retrospectively analyzed emergency department visits before (March to July 2019) and after (March to July 2020) the COVID-19 pandemic. Patients were grouped based on existing fever or respiratory symptoms, with the LWBS rate as the primary outcome. Logistic regression analysis was used to identify the risk factors of LWBS. Logistic regression was performed using interaction terminology (fever/respiratory symptom patient [FRP] × post-COVID-19) to determine the interaction between patients with FRPs and the COVID-19 pandemic period. RESULTS: A total of 60,290 patients were included (34,492 in the pre-COVID-19, and 25,298 in the post-COVID-19 group). The proportion of FRPs decreased significantly after the pandemic (P < 0.001), while the LWBS rate in FRPs significantly increased from 2.8% to 19.2% (P < 0.001). Both FRPs (odds ratio, 1.76; 95% confidence interval, 1.59-1.84 (P < 0.001) and the COVID-19 period (odds ratio, 2.29; 95% confidence interval, 2.15-2.44; P < 0.001) were significantly associated with increased LWBS. Additionally, there was a significant interaction between the incidence of LWBS in FRPs and the COVID-19 pandemic period (P < 0.001). CONCLUSION: The LWBS rate has increased in FRPs after the COVID-19 pandemic; additionally, the effect observed was disproportionate compared with that of nonfever/respiratory symptom patients.

Therapeutic effects of TM4SF5-targeting chimeric and humanized monoclonal antibodies in hepatocellular and colon cancer models.

The transmembrane 4 L six family member 5 (TM4SF5) is aberrantly expressed in hepatocellular and colorectal cancers, and has been implicated in tumor progression, suggesting that it could serve as a novel therapeutic target. Previously, we screened a murine antibody phage-display library to generate a novel monoclonal antibody, Ab27, that is specific to the extracellular loop 2 of TM4SF5. In this study, we evaluated the effects of chimeric Ab27 using cancer cells expressing endogenous TM4SF5 or stably overexpressing TM4SF5 in vivo and in vitro. Monotherapy with Ab27 significantly decreased tumor growth in liver and colon cancer xenograft models, including a sorafenib-resistant model, and decreased the phosphorylation of focal adhesion kinase (FAK), p27Kip1, and signal transducer and activator of transcription 3 (STAT3). No general Ab27 toxicity was observed in vivo. Combination treatment with Ab27 and sorafenib or doxorubicin exerted higher antitumor activity than monotherapy. In addition, we humanized the Ab27 sequence by the complementarity-determining region (CDR) grafting method. The humanized antibody Ab27-hz9 had reduced immunogenicity but exhibited target recognition and antitumor activity comparable with those of Ab27. Both Ab27 and Ab27-hz9 efficiently targeted tumor cells expressing TM4SF5 in vivo. These observations strongly support the further development of Ab27-hz9 as a novel therapeutic agent against liver and colorectal cancers.

Polyacrylamide Functionalized Graphene Oxide/Alginate Beads for Removing Ciprofloxacin Antibiotics.

Ciprofloxacin (CPX), a widely used antibiotic, was removed by synthesizing graphene oxide/calcium alginate-polyacrylamide (GO/Ca-Alg2-PAM) beads, a three-dimensional double-network complex. The synthesis of GO/Ca-Alg2-PAM beads was performed by crosslinking and cation exchange mechanisms with graphene oxide (GO), sodium alginate (Na-Alg), and polyacrylamide (PAM). The properties of GO/Ca-Alg2-PAM beads were confirmed using field emission scanning electron microscopy, Fourier transform infrared spectroscopy, and a thermogravimetric analysis. Furthermore, isothermal adsorption experiments were performed and fitted using three isothermal adsorption models (Langmuir, Freundlich, and Temkin). The adsorption isotherm experimental data fit well with the Langmuir isotherm model with a qm value of 6.846 mg/g. In addition, the spontaneous reaction of the CPX adsorption using GO/Ca-Alg2-PAM was confirmed by temperature-dependent experiments.

Crosstalk between TM4SF5 and GLUT8 regulates fructose metabolism in hepatic steatosis.

OBJECTIVE: Transmembrane 4 L six family member 5 (TM4SF5) is likely involved in non-alcoholic steatohepatitis, although its roles and cross-talks with glucose/fructose transporters in phenotypes derived from high-carbohydrate diets remain unexplored. Here, we investigated the modulation of hepatic fructose metabolism by TM4SF5. METHODS: Wild-type or Tm4sf5-/- knockout mice were evaluated via different diets, including normal chow, high-sucrose diet, or high-fat diet without or with fructose in drinking water (30% w/v). Using liver tissues and blood samples from the mice or hepatocytes, the roles of TM4SF5 in fructose-mediated de novo lipogenesis (DNL) and steatosis via a crosstalk with glucose transporter 8 (GLUT8) were assessed. RESULTS: Tm4sf5 suppression or knockout in both in vitro and in vivo models reduced fructose uptake, DNL, and steatosis. Extracellular fructose treatment of hepatocytes resulted in an inverse relationship between fructose-uptake activity and TM4SF5-mediated translocalization of GLUT8 through dynamic binding at the cell surface. Following fructose treatment, TM4SF5 binding to GLUT8 transiently decreased with translocation to the plasma membrane (PM), where GLUT8 separated and became active for fructose uptake and DNL. CONCLUSIONS: Overall, hepatic TM4SF5 modulated GLUT8 localization and activity through transient binding, leading to steatosis-related fructose uptake and lipogenesis. Thus, TM4SF5 and/or GLUT8 may be promising treatment targets against liver steatosis resulting from excessive fructose consumption.