Initial Investigations into Physician Acceptance of Medical AI: Examining Trust, Resistance, Perceived Job Insecurity, and Usage Intentions.

This study evaluated physicians' attitudes towards medical AI across three Taiwanese hospitals, focusing on constructs of trust, resistance, job insecurity, and adoption willingness, with a survey based on the Dual-factor Model yielding 282 responses and a 94% response rate. Results showed positive trust in AI, low resistance and job insecurity concerns, and a high willingness to adopt AI, indicating a favorable view of AI as a supportive tool rather than a replacement. Key adoption factors were identified as regulatory standards, accuracy, workflow integration, and result clarity, providing valuable insights for future AI development in medicine.

Artificial intelligence prediction of In-Hospital mortality in patients with dementia: A multi-center study.

BACKGROUND: Prediction of mortality is very important for care planning in hospitalized patients with dementia and artificial intelligence has the potential to serve as a solution; however, this issue remains unclear. Thus, this study was conducted to elucidate this matter. METHODS: We identified 10,573 hospitalized patients aged ≥ 45 years with dementia from three hospitals between 2010 and 2020 for this study. Utilizing 44 feature variables extracted from electronic medical records, an artificial intelligence (AI) model was constructed to predict death during hospitalization. The data was randomly separated into 70 % training set and 30 % testing set. We compared predictive accuracy among six algorithms including logistic regression, random forest, extreme gradient boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), multilayer perceptron (MLP), and support vector machine (SVM). Additionally, another set of data collected in 2021 was used as the validation set to assess the performance of six algorithms. RESULTS: The average age was 79.8 years, with females constituting 54.5 % of the sample. The in-hospital mortality rate was 6.7 %. LightGBM exhibited the highest area under the curve (0.991) for predicting mortality compared to other algorithms (XGBoost: 0.987, random forest: 0.985, logistic regression: 0.918, MLP: 0.898, SVM: 0.897). The accuracy, sensitivity, positive predictive value, and negative predictive value of LightGBM were 0.943, 0.944, 0.943, 0.542, and 0.996, respectively. Among the features in LightGBM, the three most important variables were the Glasgow Coma Scale, respiratory rate, and blood urea nitrogen. In the validation set, the area under the curve of LightGBM reached 0.753. CONCLUSIONS: The AI prediction model demonstrates strong accuracy in predicting in-hospital mortality among patients with dementia, suggesting its potential implementation to enhance future care quality.

Fluorinated polymer zwitterions on gold nanoparticles: patterned catalyst surfaces guide interfacial transport and electrochemical CO2 reduction.

We report the use of fluorinated polymer zwitterions to build hybrid systems for efficient CO2 electroreduction. The unique combination of hydrophilic phosphorylcholine and hydrophobic fluorinated moieties in these polymers creates a fractal structure with mixed branched cylinders on the surface of gold nanoparticles (AuNPs). In the presence of these polymers, the CO faradaic efficiency improves by 50-80% in the range of -0.7 V to -0.9 V. The fractal structures have a domain size of ∼3 nm, showing enhanced mass transfer kinetics of CO2 approaching the catalyst surfaces without limiting ion diffusion. The phase-separated hydrophilic and hydrophobic domains offer separated channeling to water and CO2, as confirmed by attenuated total reflectance surface-enhanced infrared absorption spectroscopy (ATR-SEIRAS) and molecule dynamic (MD) simulations. H2O molecules permeate extensively into the polymer layer that adsorbs on zwitterions, forming continuous chains, while CO2 molecules strongly associate with the fluorinated tails of fluorinated polyzwitterions, with oxygen facing the positively charged amine groups. Overall, this coupling of zwitterion and fluorocarbon in a polymer material creates new opportunities for defining microenvironments of metallic nanocatalysts in hybrid structures.

Machine Learning to Predict the Risk of Malnutrition in Hospitalized Patients with Pneumonia and Analysis of Related Prognostic Factor.

This study explored machine learning's potential in predicting the nutritional status and outcomes for pneumonia patients. It focused on 4,368 patients in a Taiwan medical center from Jan 2016 to Feb 2022, excluding ICU cases. The average age was 77.6 years, with 10.2% well-nourished, 76.3% at-risk, and 13.5% malnourished. Machine learning models, particularly LightGBM and XGBoost, showed high accuracy in predicting hospital stays, mortality rates, and readmissions. These findings emphasize the role of data-driven methods in enhancing patient care and managing conditions like pneumonia more effectively.

Enhancing Healthcare Efficiency: Integrating ChatGPT in Nursing Documentation.

Our study at Chi Mei Medical Center introduced "A+ Nurse," a ChatGPT-based LLM tool, into the nursing documentation process to enhance efficiency and accuracy. The tool offers optimized recording and critical reminders, reducing documentation time from 15 to 5 minutes per patient while maintaining record quality. Nurses appreciated the tool's intuitive design and its effectiveness in improving documentation. This successful integration of AI-generated content in healthcare illustrates the potential of AI to streamline processes and improve patient care, setting a precedent for future AI-driven healthcare innovations.

A homeostatic gut-to-brain insulin antagonist restrains neuronally stimulated fat loss.

In C. elegans mechanisms by which peripheral organs relay internal state information to the nervous system remain unknown, although strong evidence suggests that such signals do exist. Here we report the discovery of a peptide of the ancestral insulin superfamily called INS-7 that functions as an enteroendocrine peptide and is secreted from specialized cells of the intestine. INS-7 secretion is stimulated by food withdrawal, increases during fasting and acts as a bona fide gut-to-brain peptide that attenuates the release of a neuropeptide that drives fat loss in the periphery. Thus, INS-7 functions as a homeostatic signal from the intestine that gates the neuronal drive to stimulate fat loss during food shortage. Mechanistically, INS-7 functions as an antagonist at the canonical DAF-2 receptor and functions via FOXO and AMPK signaling in ASI neurons. Phylogenetic analysis suggests that INS-7 bears greater resemblance to members of the broad insulin/relaxin superfamily than to conventional mammalian insulin and IGF peptides. The discovery of an endogenous insulin antagonist secreted by specialized intestinal cells with enteroendocrine functions suggests unexpected and important properties of the intestine and its role in directing neuronal functions.

Longitudinal Risk Analysis of Second Primary Cancer after Curative Treatment in Patients with Rectal Cancer.

Predicting and improving the response of rectal cancer to second primary cancers (SPCs) remains an active and challenging field of clinical research. Identifying predictive risk factors for SPCs will help guide more personalized treatment strategies. In this study, we propose that experience data be used as evidence to support patient-oriented decision-making. The proposed model consists of two main components: a pipeline for extraction and classification and a clinical risk assessment. The study includes 4402 patient datasets, including 395 SPC patients, collected from three cancer registry databases at three medical centers; based on literature reviews and discussion with clinical experts, 10 predictive variables were considered risk factors for SPCs. The proposed extraction and classification pipelines that classified patients according to importance were age at diagnosis, chemotherapy, smoking behavior, combined stage group, and sex, as has been proven in previous studies. The C5 method had the highest predicted AUC (84.88%). In addition, the proposed model was associated with a classification pipeline that showed an acceptable testing accuracy of 80.85%, a recall of 79.97%, a specificity of 88.12%, a precision of 85.79%, and an F1 score of 79.88%. Our results indicate that chemotherapy is the most important prognostic risk factor for SPCs in rectal cancer survivors. Furthermore, our decision tree for clinical risk assessment illuminates the possibility of assessing the effectiveness of a combination of these risk factors. This proposed model may provide an essential evaluation and longitudinal change for personalized treatment of rectal cancer survivors in the future.

Implementation of a machine learning model in acute coronary syndrome and stroke risk assessment for patients with lower urinary tract symptoms.

OBJECTIVE: The global population is aging and the burden of lower urinary tract symptoms (LUTS) is expected to increase. According to the National Health Insurance Research Database, our previous studies have showed LUTS may predispose patients to cardiovascular disease. However, it is difficult to provide a personalized risk assessment in the context of "having acute coronary syndrome (ACS) and stroke." This study aimed to develop an artificial intelligence (AI)-based prediction model for patients with LUTS. MATERIAL AND METHODS: We retrospectively reviewed the electronic medical records of 1799 patients with LUTS at Chi Mei Medical Center between January 1, 2001 and December, 31, 2018. Features with >10 cases and high correlations with outcomes were imported into six machine learning algorithms. The study outcomes included ACS and stroke. Model performances was evaluated using the area under the receiver operating characteristic curve (AUC). The model with the highest AUC was used to implement the clinical risk prediction application. RESULTS: Age, systemic blood pressure, diastolic blood pressure, creatinine, glycated hemoglobin, hypertension, diabetes mellitus and hyperlipidemia were the most relevant features that affect the outcomes. Based on the AUC, our optimal model was built using multilayer perception (AUC = 0.803) to predict ACS and stroke events within 3 years. CONCLUSION: We successfully built an AI-based prediction system that can be used as a prediction model to achieve time-saving, precise, personalized risk evaluation; it can also be used to offer warning, enhance patient adherence, early intervention and better health care outcomes.

Risk Factors and Nomogram Model for Hepatocellular Carcinoma Development in Chronic Hepatitis B Patients with Low-Level Viremia.

Background and aim: Patients with chronic hepatitis B patients (CHB) with low-level viremia (LLV) are not necessarily at low risk of developing hepatocellular carcinoma (HCC). The question of whether CHB patients with LLV require immediate antiviral agent (AVT) or long-term AVT remains controversial. The study aims to investigate the risk of HCC development and the risk factors in CHB patients with LLV and construct a nomogram model predicting the risk of HCC. Methods: We conducted a retrospective cohort study that enrolled 16,895 CHB patients from January 2008 to December 2020. The patients were divided into three groups for comparison: the LLV group, maintained virological response (MVR) group and HBV-DNA>2000 group. The cumulative incidence of progression to HCC was assessed. Cox regression analysis was performed to determine the final risk factors, and a nomogram model was constructed. The 10-fold Cross-Validation method was utilized for internal validation. Results: A total of 408 new cases of HCC occurred during the average follow-up period of 5.78 years. The 3, 5, and 10-year cumulative HCC risks in the LLV group were 3.56%, 4.96%, and 9.51%, respectively. There was a significant difference in the cumulative risk of HCC between the HBV-DNA level > 2000 IU/mL and LLV groups (p = 0.049). Independent risk factors for HCC development in LLV group included male gender, age, presence of cirrhosis, and platelets count. The Area Under the Curve (AUC) values for the 3-year and 5-year prediction from our HCC risk prediction model were 0.75 and 0.76, respectively. Conclusion: Patients with LLV and MVR are still at risk for developing HCC. The nomogram established for CHB patient with LLV, incorporating identified significant risk factors, serves as an effective tool for predicting HCC-free outcomes. This nomogram model provides valuable information for determining appropriate surveillance strategies and prescribing AVT.

Transplanting mitochondria from gastric epithelial cells reduces the malignancy of gastric cancer.

Mitochondria are essential for energy supplementation and metabolic homeostasis of cancer cells. Using mitochondria transplantation to reduce the malignancy of gastric cancer (GC) cells is herein proposed. In our study normal human gastric mucous epithelium cell line (GES-1) showed a lower mitochondrial membrane potential (MMP) compared to immortalized human vascular endothelial cell line (EAhy 926) and human gastric adenocarcinoma cell line (AGS). The transplantation of GES-1 mitochondria to AGS were confirmed both by confocal microscopy and flow cytometry. After transplanting GES-1 mitochondria, the AGS showed a reduced cell migration, and invasion without affecting cell viability and apoptosis. Investigating the expression of proteins involved in epithelial-mesenchymal-transition (EMT), transplanted GES-1 mitochondria reduced the expression of mesenchymal markers α-SMA, MMP-9, snail, vimentin and N-cadherin, whereas the epithelial markers E-cadherin and clauding-1 were not changed. The proteins implicated in the cell cycle such as cyclin B1 and D1 were decreased. In mice, inoculation with AGS carrying the transplanted GES-1 mitochondria resulted in smaller sized tumors. Further investigating the mitochondrial balance, the transplanted GES-1 mitochondria were more stably preserved compared to endogenous AGS mitochondria. The MMP, ATP production and mitochondrial mass decreased in GES-1 mitochondria and the mitophagic proteins LC3 II and PINK1 were up-regulated. In conclusion the decreased malignancy of AGS was a result of exogenous GES-1 mitochondria transplantation. This suggests for a therapy with low efficiency mitochondria transplantation in the treatment of cancer cells.

Implementing Explainable Machine Learning Models for Practical Prediction of Early Neonatal Hypoglycemia.

Hypoglycemia is a common metabolic disorder that occurs in the neonatal period. Early identification of neonates at risk of developing hypoglycemia can optimize therapeutic strategies in neonatal care. This study aims to develop a machine learning model and implement a predictive application to assist clinicians in accurately predicting the risk of neonatal hypoglycemia within four hours after birth. Our retrospective study analyzed data from neonates born ≥35 weeks gestational age and admitted to the well-baby nursery between 1 January 2011 and 31 August 2021. We collected electronic medical records of 2687 neonates from a tertiary medical center in Southern Taiwan. Using 12 clinically relevant features, we evaluated nine machine learning approaches to build the predictive models. We selected the models with the highest area under the receiver operating characteristic curve (AUC) for integration into our hospital information system (HIS). The top three AUC values for the early neonatal hypoglycemia prediction models were 0.739 for Stacking, 0.732 for Random Forest and 0.732 for Voting. Random Forest is considered the best model because it has a relatively high AUC and shows no significant overfitting (accuracy of 0.658, sensitivity of 0.682, specificity of 0.649, F1 score of 0.517 and precision of 0.417). The best model was incorporated in the web-based application integrated into the hospital information system. Shapley Additive Explanation (SHAP) values indicated mode of delivery, gestational age, multiparity, respiratory distress, and birth weight < 2500 gm as the top five predictors of neonatal hypoglycemia. The implementation of our machine learning model provides an effective tool that assists clinicians in accurately identifying at-risk neonates for early neonatal hypoglycemia, thereby allowing timely interventions and treatments.

The Stamping Method Utilizing a Double-Trough Die in Microforming to Enhance Formability.

Currently, the field of microgear manufacturing faces various processing challenges, particularly in terms of size reduction; these challenges increase the complexity and costs of manufacturing. In this study, a technique for microgear manufacturing is aimed at reducing subsequent processing steps and enhancing material utilization. This technique involves the use of trough dies with extrusion-cutting processing, which enables workpieces to undergo forming in a negative clearance state, thus reducing subsequent processing time for micro products. We conducted finite element simulations using microgear dies, measuring stress, velocity, and flow during the forming process of four types of dies-flat, internal-trough, external-trough, and double-trough dies. The results indicated that the buffering effect of the troughs reduced the rate of increase in the material's internal stress. In the cavity, the material experiences a significant increase in hydrostatic pressure, leading to the formation of a "hydrostatic pressure wall". This pressure barrier imposes substantial constraints on the flow of the material during dynamic processes, making it difficult for the material to move into the remaining areas. This effectively enhances the blockage of material flow, demonstrating the critical role of hydrostatic pressure in controlling material distribution and movement. In addition, combining the characteristics of both into a double-trough die enhances the overall stability of forming velocity, reduces forming load and energy consumption, and maximizes material utilization. Results further revealed that microgears manufactured using double-trough dies exhibited defect-free surfaces, with a dimensional error of less than 5 µm and tolerances ranging from IT5 to IT6. Overall, this study offers new insights into the traditional field of microgear manufacturing, highlighting potential solutions for the challenges encountered in current microstamping processes.

Feasibility and Preliminary Efficacy of α-Calcium Sulfate Hemihydrate in Socket Preservation: Protocol for a Pilot Randomized Controlled Trial.

BACKGROUND: Tooth extraction procedures often lead to bone resorption, which can have adverse effects on the dimensions of the alveolar ridge. Research has shown that socket preservation techniques using bone graft substitutes can effectively minimize early bone loss in such cases. α-calcium sulfate hemihydrate (α-CSH) has garnered significant attention as a potential bone graft material due to its favorable properties, including osteoconductivity, angiogenic potential, and biocompatibility. Considering these facts, we developed a preliminary protocol for applying α-CSH in addressing alveolar bone loss following tooth extraction. OBJECTIVE: This research's general objective is to evaluate the feasibility and initial effectiveness of α-CSH as bone-inducing graft material for socket preservation after tooth extraction. METHODS: This preliminary clinical trial will involve 30 fresh extraction sockets from individuals aged 18-35 years. The participants will be divided into 2 groups: one group will receive α-CSH graft material after tooth extraction for socket preservation, while the other group will not receive any graft material. Throughout the study, the participants will be closely monitored for safety measures, which will include clinical examinations, radiographic imaging, and blood tests. Radiographic imaging will be used extensively to assist the progress of bone formation. RESULTS: The study commenced enrollment in August 2022 and is scheduled to conclude post assessments and analyses by the end of 2023. The results of the study are anticipated to be accessible in late 2024. CONCLUSIONS: This clinical study represents the initial investigation in humans to assess the feasibility and efficacy of α-CSH in alveolar bone regeneration. We hypothesize that the inclusion of α-CSH can greatly expedite the process of bone formation within fresh sockets, resulting in a swift restoration of bone height without the disadvantages associated with harvesting autogenous bone graft. TRIAL REGISTRATION: Indonesia Registry Center INA-D02FAHP; https://tinyurl.com/2jnf6n3s. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49922.

The expression of immune co-stimulators as a prognostic predictor of head and neck squamous cell carcinomas and oral squamous cell carcinomas.

Background/purpose: T cells require second immune checkpoint molecules for activation and immune memory after antigen presentation. We found that inducible co-stimulator (ICOS) has been a favorable prognostic factor amongst B7 immune checkpoint co-stimulators (ICSs) families in head and neck squamous cell carcinoma (HNSCC) and oral SCC (OSCC). Materials and methods: This study analyzed the expression of non-B7 tumor necrosis factor (TNF) superfamily ICSs in the Cancer Genome Atlas (TCGA) HNSCC cohort, our OSCC cohort, and TCGA pan-cancer datasets. The correlation in expression, prognosis, and immune status was assessed. Results: The higher expression of CD27, CD30, CD40L, death domain 3 (DR3), and OX40, presumably on the T cell surface, defined better overall survival of HNSCC patients. Besides, CD27, CD30, CD40L, and OX40 were highly correlated with ICOS expression in tumors. CD27, CD40L, and DR3 expression are higher in HPV+ HNSCC tumors than in HPV- tumors. The combined expression level of CD27/OX40 or CD27/CD40L/OX40 enables the potent survival prediction of small, less nodal involvement, early stage, and HPV + tumor subsets. Tumors expressing high CD27, CD30, CD40L, ICOS, and OX40 exhibited enhanced immune cell infiltration. The high correlation in the expression of these ICSs was also noted in the vast majority of tumor types in TCGA datasets. Conclusion: The findings of this study not only confirm the potential of the concordant stimulation of CD27, CD30, CD40L, ICOS, and OX40 as a crucial strategy in cancer immunotherapy but also inspire further exploration into the field, highlighting the promising future of cancer treatment.

Exploring the Charge-Transport and Optical Characteristics of Organic Doublet Radicals: A Theoretical and Experimental Study with Photovoltaic Applications.

Herein, we present a series of stable radicals containing a trityl carbon-centered radical moiety exhibiting interesting properties. The radicals demonstrate the most blue-shifted anti-Kasha doublet emission reported so far with high color purity (full width at half-maximum of 46 nm) and relatively high photoluminescence quantum yields of deoxygenated toluene solutions reaching 31%. The stable radicals demonstrate equilibrated bipolar charge transport with charge mobility values reaching 10-4 cm2/V·s at high electric fields. The experimental results in combination with the results of TD-DFT calculations confirm that the blue emission of radicals violates the Kasha rule and originates from higher excited states, whereas the bipolar charge transport properties are found to stem from the particularity of radicals to involve the same molecular orbital(s) in electron and hole transport. The radicals act as the efficient materials for interlayers, passivating interfacial defects and enhancing charge extraction in PSCs. Consequently, this leads to outstanding performance of PSC, with power conversion efficiency surpassing 21%, accompanied by a remarkable increase in open-circuit voltage and exceptional stability.

Personalized prediction of intradialytic hypotension in clinical practice: Development and evaluation of a novel AI dashboard incorporating risk factors from previous and current dialysis sessions.

BACKGROUND: Intradialytic hypotension (IDH) is one of the most common and critical complications of hemodialysis. Despite many proven factors associated with IDH, accurately predicting it before it occurs for individual patients during dialysis sessions remains a challenge. PURPOSE: To establish artificial intelligence (AI) predictive models for IDH, which consider risk factors from previous and ongoing dialysis to optimize model performance. We then implement a novel digital dashboard with the best model for continuous monitoring of patients' status undergoing hemodialysis. The AI dashboard can display the real-time probability of IDH for each patient in the hemodialysis center providing an objective reference for care members for monitoring IDH and treating it in advance. METHODS: Eight machine learning (ML) algorithms, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Light Gradient Boosting Machine (LightGBM), Multilayer Perception (MLP), eXtreme Gradient Boosting (XGBoost), and NaiveBayes, were used to establish the predictive model of IDH to determine if the patient will acquire IDH within 60 min. In addition to real-time features, we incorporated several features sourced from previous dialysis sessions to improve the model's performance. The electronic medical records of patients who had undergone hemodialysis at Chi Mei Medical Center between September 1, 2020 and December 31, 2020 were included in this research. Impact evaluation of AI assistance was conducted by IDH rate. RESULTS: The results showed that the XGBoost model had the best performance (accuracy: 0.858, sensitivity: 0.858, specificity: 0.858, area under the curve: 0.936) and was chosen for AI dashboard implementation. The care members were delighted with the dashboard providing real-time scientific probabilities for IDH risk and historic predictive records in a graphic style. Other valuable functions were appended in the dashboard as well. Impact evaluation indicated a significant decrease in IDH rate after the application of AI assistance. CONCLUSION: This AI dashboard provides high-quality results in IDH risk prediction during hemodialysis. High-risk patients for IDH will be recognized 60 min earlier, promoting individualized preventive interventions as part of the treatment plan. Our approachis believed to promise an excellent way for IDH management.

Silk fibroin increases the elasticity of alginate-gelatin hydrogels and regulates cardiac cell contractile function in cardiac bioinks.

Silk fibroin (SF) is a natural protein extracted fromBombyx morisilkworm thread. From its common use in the textile industry, it emerged as a biomaterial with promising biochemical and mechanical properties for applications in the field of tissue engineering and regenerative medicine. In this study, we evaluate for the first time the effects of SF on cardiac bioink formulations containing cardiac spheroids (CSs). First, we evaluate if the SF addition plays a role in the structural and elastic properties of hydrogels containing alginate (Alg) and gelatin (Gel). Then, we test the printability and durability of bioprinted SF-containing hydrogels. Finally, we evaluate whether the addition of SF controls cell viability and function of CSs in Alg-Gel hydrogels. Our findings show that the addition of 1% (w/v) SF to Alg-Gel hydrogels makes them more elastic without affecting cell viability. However, fractional shortening (FS%) of CSs in SF-Alg-Gel hydrogels increases without affecting their contraction frequency, suggesting an improvement in contractile function in the 3D cultures. Altogether, our findings support a promising pathway to bioengineer bioinks containing SF for cardiac applications, with the ability to control mechanical and cellular features in cardiac bioinks.

Possible Strategies to Reduce the Tumorigenic Risk of Reprogrammed Normal and Cancer Cells.

The reprogramming of somatic cells to pluripotent stem cells has immense potential for use in regenerating or redeveloping tissues for transplantation, and the future application of this method is one of the most important research topics in regenerative medicine. These cells are generated from normal cells, adult stem cells, or neoplastic cancer cells. They express embryonic stem cell markers, such as OCT4, SOX2, and NANOG, and can differentiate into all tissue types in adults, both in vitro and in vivo. However, tumorigenicity, immunogenicity, and heterogeneity of cell populations may hamper the use of this method in medical therapeutics. The risk of cancer formation is dependent on mutations of these stemness genes during the transformation of pluripotent stem cells to cancer cells and on the alteration of the microenvironments of stem cell niches at genetic and epigenetic levels. Recent reports have shown that the generation of induced pluripotent stem cells (iPSCs) derived from human fibroblasts could be induced using chemicals, which is a safe, easy, and clinical-grade manufacturing strategy for modifying the cell fate of human cells required for regeneration therapies. This strategy is one of the future routes for the clinical application of reprogramming therapy. Therefore, this review highlights the recent progress in research focused on decreasing the tumorigenic risk of iPSCs or iPSC-derived organoids and increasing the safety of iPSC cell preparation and their application for therapeutic benefits.

An Innovative Design to Enhance Osteoinductive Efficacy and Biomechanical Behavior of a Titanium Dental Implant.

The present study investigated the in vivo bone-forming efficacy of an innovative titanium (Ti) dental implant combined with a collagen sponge containing recombinant human bone morphogenetic protein-2 (BMP-2) in a pig model. Two different concentrations of BMP-2 (20 and 40 µg/mL) were incorporated into collagen sponges and placed at the bottom of Ti dental implants. The investigated implants were inserted into the edentulous ridge at the canine-premolar regions of Lanyu small-ear pigs, which were then euthanized at weeks 1, 2, 4, 8, and 12 post-implantation. Specimens containing the implants and surrounding bone tissue were collected for histological evaluation of their bone-to-implant contact (BIC) ratios and calculation of maximum torques using removal torque measurement. Analytical results showed that the control and BMP-2-loaded implants presented good implant stability and bone healing for all testing durations. After 1 week of healing, the BMP-2-loaded implants with a concentration of 20 µg/mL exhibited the highest BIC ratios, ranging from 58% to 76%, among all groups (p = 0.034). Additionally, they also possessed the highest removal torque values (50.1 ± 1.3 N-cm) throughout the 8-week healing period. The BMP-2-loaded implants not only displayed excellent in vivo biocompatibility but also presented superior osteoinductive performance. Therefore, these findings demonstrate that BMP-2 delivered through a collagen sponge can potentially enhance the early-stage osseointegration of Ti dental implants.

Optimizing myocardial cell protection with xanthine derivative KMUP-3 potentiates autophagy through the PI3K/Akt/eNOS axis.

BACKGROUND: Autophagy can have either beneficial or detrimental effects on various heart diseases. Pharmacological interventions improve cardiac function, which is correlated with enhanced autophagy. To assess whether a xanthine derivative (KMUP-3) treatment coincides with enhanced autophagy while also providing cardio-protection, we investigated the hypothesis that KMUP-3 treatment activation of autophagy through PI3K/Akt/eNOS signalling offered cardioprotective properties. METHODS: The pro-autophagic effect of KMUP-3 was performed in a neonatal rat model targeting cardiac fibroblasts and cardiomyocytes, and by assessing the impact of KMUP-3 treatment on cardiotoxicity, we used antimycin A-induced cardiomyocytes. RESULTS: As determined by transmission electron microscopy observation, KMUP-3 enhanced autophagosome formation in cardiac fibroblasts. Furthermore, KMUP-3 significantly increased the expressions of autophagy-related proteins, LC3 and Beclin-1, both in a time- and dose-dependent manner; moreover, the pro-autophagy and nitric oxide enhancement effects of KMUP-3 were abolished by inhibitors targeting eNOS and PI3K in cardiac fibroblasts and cardiomyocytes. Notably, KMUP-3 ameliorated cytotoxic effects induced by antimycin A, demonstrating its protective autophagic response. CONCLUSION: These findings enable the core pathway of PI3K/Akt/eNOS axis in KMUP-3-enhanced autophagy activation and suggest its principal role in safeguarding against cardiotoxicity.