Effects of Balanced Versus Saline-based Solutions on Acute Kidney Injury in Off-pump Coronary Artery Bypass Surgery: A Randomized Controlled Trial.

OBJECTIVES: To determine whether balanced solutions can reduce the incidence of acute kidney injury after off-pump coronary artery bypass surgery compared with saline. DESIGN: Randomized controlled trial. SETTING: Single tertiary care center. PARTICIPANTS: Patients who underwent off-pump coronary artery bypass surgery between June 2014 and July 2020. INTERVENTIONS: Balanced solution-based chloride-restrictive intravenous fluid strategy. MEASUREMENTS AND MAIN RESULTS: The primary outcome was acute kidney injury within 7 postoperative days, as defined by the 2012 Kidney Disease: Improving Global Outcomes Clinical Practice Guideline. The incidence of acute kidney injury was 4.4% (8/180) in the balanced group and 7.3% (13/178) in the saline group. The difference was not statistically significant (risk difference, -2.86%; 95% confidence interval [CI], -7.72% to 2.01%; risk ratio, 0.61, 95% CI, 0.26 to 1.43; p = 0.35). Compared with the balanced group, the saline group had higher levels of intraoperative serum chloride and lower base excess, which resulted in a lower pH. CONCLUSIONS: In patients undergoing off-pump bypass surgery with a normal estimated glomerular filtration rate, the intraoperative balanced solution-based chloride-restrictive intravenous fluid administration strategy did not decrease the rate of postoperative acute kidney injury compared with the saline-based chloride-liberal intravenous fluid administration strategy.

Solar-Driven Sustainability: III-V Semiconductor for Green Energy Production Technologies.

Long-term societal prosperity depends on addressing the world's energy and environmental problems, and photocatalysis has emerged as a viable remedy. Improving the efficiency of photocatalytic processes is fundamentally achieved by optimizing the effective utilization of solar energy and enhancing the efficient separation of photogenerated charges. It has been demonstrated that the fabrication of III-V semiconductor-based photocatalysts is effective in increasing solar light absorption, long-term stability, large-scale production and promoting charge transfer. This focused review explores on the current developments in III-V semiconductor materials for solar-powered photocatalytic systems. The review explores on various subjects, including the advancement of III-V semiconductors, photocatalytic mechanisms, and their uses in H2 conversion, CO2 reduction, environmental remediation, and photocatalytic oxidation and reduction reactions. In order to design heterostructures, the review delves into basic concepts including solar light absorption and effective charge separation. It also highlights significant advancements in green energy systems for water splitting, emphasizing the significance of establishing eco-friendly systems for CO2 reduction and hydrogen production. The main purpose is to produce hydrogen through sustainable and ecologically friendly energy conversion. The review intends to foster the development of greener and more sustainable energy source by encouraging researchers and developers to focus on practical applications and advancements in solar-powered photocatalysis.

Privacy-Preserving Prediction of Postoperative Mortality in Multi-Institutional Data: Development and Usability Study.

BACKGROUND: To circumvent regulatory barriers that limit medical data exchange due to personal information security concerns, we use homomorphic encryption (HE) technology, enabling computation on encrypted data and enhancing privacy. OBJECTIVE: This study explores whether using HE to integrate encrypted multi-institutional data enhances predictive power in research, focusing on the integration feasibility across institutions and determining the optimal size of hospital data sets for improved prediction models. METHODS: We used data from 341,007 individuals aged 18 years and older who underwent noncardiac surgeries across 3 medical institutions. The study focused on predicting in-hospital mortality within 30 days postoperatively, using secure logistic regression based on HE as the prediction model. We compared the predictive performance of this model using plaintext data from a single institution against a model using encrypted data from multiple institutions. RESULTS: The predictive model using encrypted data from all 3 institutions exhibited the best performance based on area under the receiver operating characteristic curve (0.941); the model combining Asan Medical Center (AMC) and Seoul National University Hospital (SNUH) data exhibited the best predictive performance based on area under the precision-recall curve (0.132). Both Ewha Womans University Medical Center and SNUH demonstrated improvement in predictive power for their own institutions upon their respective data's addition to the AMC data. CONCLUSIONS: Prediction models using multi-institutional data sets processed with HE outperformed those using single-institution data sets, especially when our model adaptation approach was applied, which was further validated on a smaller host hospital with a limited data set.

Development of a machine learning approach for prediction of red blood cell transfusion in patients undergoing Cesarean section at a single institution.

Despite recent advances in surgical techniques and perinatal management in obstetrics for reducing intraoperative bleeding, blood transfusion may occur during a cesarean section (CS). This study aims to identify machine learning models with an optimal diagnostic performance for intraoperative transfusion prediction in parturients undergoing a CS. Additionally, to address model performance degradation due to data imbalance, this study further investigated the variation in predictive model performance depending on the ratio of event to non-event data (1:1, 1:2, 1:3, and 1:4 model datasets and raw data).The area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) were evaluated to compare the predictive accuracy of different machine learning algorithms, including XGBoost, K-nearest neighbor, decision tree, support vector machine, multilayer perceptron, logistic regression, random forest, and deep neural network. We compared the predictive performance of eight prediction algorithms that were applied to five types of datasets. The intraoperative transfusion in maternal CS was 7.2% (1020/14,254). XGBoost showed the highest AUROC (0.8257) and AUPRC (0.4825) among the models. The most significant predictors for transfusion in maternal CS as per machine learning models were placenta previa totalis, haemoglobin, placenta previa partialis, and platelets. In all eight prediction algorithms, the change in predictive performance based on the AUROC and AUPRC according to the resampling ratio was insignificant. The XGBoost algorithm exhibited optimal performance for predicting intraoperative transfusion. Data balancing techniques employed to alter the event data composition ratio of the training data failed to improve the performance of the prediction model.

Internal and External Validation of Machine Learning Models for Predicting Acute Kidney Injury Following Non-Cardiac Surgery Using Open Datasets.

This study developed and validated a machine learning model to accurately predict acute kidney injury (AKI) after non-cardiac surgery, aiming to improve patient outcomes by assessing its clinical feasibility and generalizability. We conducted a retrospective cohort study using data from 76,032 adults who underwent non-cardiac surgery at a single tertiary medical center between March 2019 and February 2021, and used data from 5512 patients from the VitalDB open dataset for external model validation. The predictive variables for model training consisted of demographic, preoperative laboratory, and intraoperative data, including calculated statistical values such as the minimum, maximum, and mean intraoperative blood pressure. When predicting postoperative AKI, our gradient boosting machine model incorporating all the variables achieved the best results, with AUROC values of 0.868 and 0.757 for the internal and external validations using the VitalDB dataset, respectively. The model using intraoperative data performed best in internal validation, while the model with preoperative data excelled in external validation. In this study, we developed a predictive model for postoperative AKI in adult patients undergoing non-cardiac surgery using preoperative and intraoperative data, and external validation demonstrated the efficacy of open datasets for generalization in medical artificial modeling research.

Numerical investigation of the effect of air layer on drag reduction in channel flow over a superhydrophobic surface.

This study investigates the effects of an air layer on drag reduction and turbulence dynamics in channel flow over a superhydrophobic surface (SHS). Employing the OpenFOAM platform, direct numerical simulation was conducted to investigate turbulent channel flow with an air layer over an SHS. The simulations, which take into account the interaction between water and air, analyze various parameters such as velocity distribution, drag reduction (DR), Reynolds stress, turbulent kinetic energy (TKE), and coherent structures near the water-air interface. The presence of an air layer significantly alters the velocity distribution, leading to higher velocities at the interface compared to simulations without the air layer. Notably, the thickness of the air layer emerges as an important factor, with larger thicknesses resulting in increased velocities and drag reduction. This study underscores the substantial impact of the air layer on TKE near the superhydrophobic surface, emphasizing its role in understanding and optimizing drag reduction. Furthermore, the nonlinear relationship between slip velocity, Q contours, and coherent structures near the SHS are investigated.

Clinical outcomes according to the timing of the first tracheostomy tube change.

Purpose: The first tracheostomy tube replacement is a critical procedure that can cause various complications, but there are few studies on the optimal timing of tracheostomy tube replacement in adult patients. This study aimed to evaluate the appropriate timing to replace the first tracheostomy tube to improve outcomes in adult patients. Materials and methods: This study was a retrospective cohort study that included 3957 patients aged ≥18 years who underwent the first tracheostomy tube change from January 2010 to February 2021. The primary outcome was all-cause mortality after the first tracheostomy tube change. Results: The all-cause mortality was statistically significantly lower in group changing the first tracheostomy tube between 7 and 9 days than in other groups (42.1%, P = 0.001). After adjustments in the multivariable analyses, early first tracheostomy tube change within 6 days was independently associated with increased all-cause mortality. The hospital stay, ICU stay, and post-procedural pulmonary complications seemed to increase as the replacement time was delayed. Conclusions: The timing of the first tracheostomy tube change between 7 and 9 days after tracheostomy was associated with improved clinical outcomes, including all-cause mortality. Further prospective investigations are needed to determine whether the optimal timing of the first tracheostomy tube change can reduce mortality.

PIBF1 regulates trophoblast syncytialization and promotes cardiovascular development.

Proper placental development in early pregnancy ensures a positive outcome later on. The developmental relationship between the placenta and embryonic organs, such as the heart, is crucial for a normal pregnancy. However, the mechanism through which the placenta influences the development of embryonic organs remains unclear. Trophoblasts fuse to form multinucleated syncytiotrophoblasts (SynT), which primarily make up the placental materno-fetal interface. We discovered that endogenous progesterone immunomodulatory binding factor 1 (PIBF1) is vital for trophoblast differentiation and fusion into SynT in humans and mice. PIBF1 facilitates communication between SynT and adjacent vascular cells, promoting vascular network development in the primary placenta. This process affected the early development of the embryonic cardiovascular system in mice. Moreover, in vitro experiments showed that PIBF1 promotes the development of cardiovascular characteristics in heart organoids. Our findings show how SynTs organize the barrier and imply their possible roles in supporting embryogenesis, including cardiovascular development. SynT-derived factors and SynT within the placenta may play critical roles in ensuring proper organogenesis of other organs in the embryo.

Real Time Observation of Halide Segregation in Mixed Halide Perovskite Solar Cells.

In this work, a novel real-time current-voltage (J-V) absorbance spectroscopy (RTJAS) setup is introduced for directly observing halide segregation in mixed halide perovskite solar cells under broadband light illumination, simulating solar exposure. The setup incorporates a broadband light source calibrated to one sun irradiation and a CMOS camera for simultaneous capture of all diffracted wavelengths. J-V measurements are performed concurrently with absorbance spectra collection, enabling in situ analysis of light-induced degradation due to halide segregation, including bandgap shifts and cell performance data. Comparison of photoluminescence measurements with RTJAS data reveals differing rates of bandgap decrease, underscoring the advantages of real-time measurement techniques. The work highlights the importance of accounting for experimental conditions, such as humidity and voltage injection, which can accelerate halide segregation, ultimately emphasizing the need for careful consideration of experimental conditions to accurately characterize perovskite solar cell behavior under realistic conditions.

Preoperative low muscle mass and early postoperative outcomes in children undergoing living donor liver transplantation: A retrospective study.

Low skeletal muscle mass may develop in children with end-stage liver disease, affecting postoperative outcomes. We retrospectively investigated whether preoperative low muscle mass was associated with early postoperative outcomes in pediatric patients undergoing living donor liver transplantation (LDLT). Electronic medical records of children (age below 12 y) who underwent LDLT between February 1, 2007, and January 31, 2018, were reviewed. The cross-sectional areas of psoas, quadratus lumborum, and erector spinae muscles at the level of fourth-fifth lumbar intervertebral disks were measured using abdominal CT images, divided by the square of the height and were added to obtain the total skeletal muscle index (TSMI). The patients were divided into two groups according to the median TSMI in the second quintile (1859.1 mm 2 /m 2 ). Complications in the early postoperative period (within 30 d after surgery) classified as Clavien-Dindo grade 3 or higher were considered major complications. Logistic regression analyses were performed to determine the association between preoperative low muscle mass and early postoperative outcomes. In the study population of 123 patients (median age, 14 mo; range, 8-38 mo) who underwent LDLT, 29% and 71% were classified in the low (mean TSMI, 1642.5 ± 187.0 mm 2 /m 2 ) and high (mean TSMI 2188.1 ± 273.5 mm 2 /m 2 ) muscle mass groups, respectively. The rates of major complications, mechanical ventilation >96 hours, intensive care unit stay >14 days, hospital stay >30 days, and in-hospital mortality were not significantly different between the 2 groups. Additionally, adverse outcomes according to pediatric end-stage liver disease scores and sex were not significantly different between the 2 groups. In conclusion, preoperative low muscle mass defined by TSMI was not associated with early postoperative outcomes in pediatric patients undergoing LDLT.

Uncertainty quantification of computational fluid dynamics-based predictions for fractional flow reserve and wall shear stress of the idealized stenotic coronary.

Introduction: In clinical practice, hemodynamics-based functional indices, such as fractional flow reserve (FFR) and wall shear stress (WSS), are useful in assessing the severity and risk of rupture of atherosclerotic lesions. Computational fluid dynamics (CFD) is widely used to predict these indices noninvasively. Method: In this study, uncertainty quantification and sensitivity analysis are performed for the computational prediction of WSS and FFR directly from 3D-0D coupled CFD simulations on idealized stenotic coronary models. Five geometric parameters (proximal, mid, and distal lengths of stenosis; reference lumen diameter; and stenosis severity) and two physiological parameters (mean aortic pressure and microcirculation resistance) are considered as uncertain input variables. Results: When employing the true values of stenosis severity and mean aortic pressure, a discernible reduction of 25% and 9.5% in the uncertainty of the computed proximal WSS, respectively. In addition, degree of stenosis, reference lumen diameter, and coronary resistance contributed the uncertainty of computed FFR, accounting for 41.2%, 31.9%, and 24.6%, respectively. Conclusion: This study demonstrated that accurate measurement of the degree of stenosis and mean aortic pressure is crucial for improving the computational prediction of WSS. In contrast, the reference lumen diameter, degree of stenosis, and coronary resistance are the most impactful parameters for FFR.

HDAC5-mediated exosomal Maspin and miR-151a-3p as biomarkers for enhancing radiation treatment sensitivity in hepatocellular carcinoma.

BACKGROUND: Tumor-derived exosomes are critical elements of the cell-cell communication response to various stimuli. This study aims to reveal that the histone deacetylase 5 (HDAC5) and p53 interaction upon radiation in hepatocellular carcinoma intricately regulates the secretion and composition of exosomes. METHODS: We observed that HDAC5 and p53 expression were significantly increased by 2 Gy and 4 Gy radiation exposure in HCC. Normal- and radiation-derived exosomes released by HepG2 were purified to investigate the exosomal components. RESULTS: We found that in the radiation-derived exosome, exosomal Maspin was notably increased. Maspin is known as an anti-angiogenic gene. The expression of Maspin was regulated at the cellular level by HDAC5, and it was elaborately regulated and released in the exosome. Radiation-derived exosome treatment caused significant inhibition of angiogenesis in HUVECs and mouse aortic tissues. Meanwhile, we confirmed that miR-151a-3p was significantly reduced in the radiation-derived exosome through exosomal miRNA sequencing, and three HCC-specific exosomal miRNAs were also decreased. In particular, miR-151a-3p induced an anti-apoptotic response by inhibiting p53, and it was shown to induce EMT and promote tumor growth by regulating p53-related tumor progression genes. In the HCC xenograft model, radiation-induced exosome injection significantly reduced angiogenesis and tumor size. CONCLUSIONS: Our present findings demonstrated HDAC5 is a vital gene of the p53-mediated release of exosomes resulting in tumor suppression through anti-cancer exosomal components in response to radiation. Finally, we highlight the important role of exosomal Maspin and mi-151a-3p as a biomarker in enhancing radiation treatment sensitivity. Therapeutic potential of HDAC5 through p53-mediated exosome modulation in radiation treatment of hepatocellular carcinoma.

Utilizing patient-specific 3D printed kidney surgical guide with realistic phantom for partial nephrectomy.

Partial nephrectomy has been demonstrated to preserve renal function compared with radical nephrectomy. Computed tomography (CT) is used to reveal localized renal cell carcinoma (RCC). However, marking RCC directly and quantitatively on a patient's kidney during an operation is difficult. We fabricated and evaluated a 3D-printed kidney surgical guide (3DP-KSG) with a realistic kidney phantom. The kidney phantoms including parenchyma and three different RCC locations and 3DP-KSG were designed and fabricated based on a patient's CT image. 3DP-KSG was used to insert 16-gauge intravenous catheters into the kidney phantoms, which was scanned by CT. The catheter insertion points and angle were evaluated. The measurement errors of insertion points were 1.597 ± 0.741 mm, and cosine similarity of trajectories was 0.990 ± 0.010. The measurement errors for X-axis, Y-axis, and Z-axis in the insertion point were 0.611 ± 0.855 mm, 0.028 ± 1.001 mm, and - 0.510 ± 0.923 mm. The 3DP-KSG targeted the RCC accurately, quantitatively, and immediately on the surface of the kidney, and no significant difference was shown between the operators. Partial nephrectomy will accurately remove the RCC using 3DP-KSG in the operating room.

Utilizing ChatGPT in clinical research related to anesthesiology: a comprehensive review of opportunities and limitations.

Chat generative pre-trained transformer (ChatGPT) is a chatbot developed by OpenAI that answers questions in a human-like manner. ChatGPT is a GPT language model that understands and responds to natural language created using a transformer, which is a new artificial neural network algorithm first introduced by Google in 2017. ChatGPT can be used to identify research topics and proofread English writing and R scripts to improve work efficiency and optimize time. Attempts to actively utilize generative artificial intelligence (AI) are expected to continue in clinical settings. However, ChatGPT still has many limitations for widespread use in clinical research, owing to AI hallucination symptoms and its training data constraints. Researchers recommend avoiding scientific writing using ChatGPT in many traditional journals because of the current lack of originality guidelines and plagiarism of content generated by ChatGPT. Further regulations and discussions on these topics are expected in the future.

Prognostic significance of 25-hydroxyvitamin D levels in patients with peripheral T-cell lymphoma.

Vitamin D insufficiency has been linked to unfavourable outcomes in diverse malignancies. However, the prognostic significance of vitamin D levels in peripheral T-cell lymphoma (PTCL) remains unclear. In this study, we thus aimed to assess the prognostic relevance of 25-hydroxyvitamin D [25(OH)D] levels in patients newly diagnosed with PTCL. The analysis included 144 patients with PTCL treated from March 2015 to May 2020. The median 25(OH)D level was 12.2 (1.7-48.8) ng/mL, and 59 (41%) patients had vitamin D deficiency. Patients with vitamin D deficiency demonstrated significantly worse event-free survival (EFS) and overall survival (OS). In the multivariate analysis, vitamin D was independently associated with OS, with a hazard ratio of 1.66 (95% confidence interval, 1.05-2.63, p = 0.030). These findings suggest that vitamin D deficiency significantly correlates with poor survival outcomes in patients with PTCL.

The result of prospective evaluation of 3-dimensional printing-aided extensive thoracoabdominal aorta repair.

Objectives: Paraplegia is a distressing complication after open thoracoabdominal aortic aneurysm (TAAA) repair, and revascularization of T8-L2-level segmental arteries is considered pivotal to prevent paraplegia. We employed 3-dimensional (3D) printing to efficiently revascularize segmental/visceral arteries and prospectively evaluated its safety and efficacy. Methods: From January 1, 2020, to June 30, 2022, we prospectively enrolled patients of extent I, II, or III TAAA repair. Guidance models were 3D-printed based on preoperative computed tomography, and multibranched aortic grafts were manually constructed upon this model before surgery. The composite outcome of operative mortality, permanent stroke, and permanent spinal cord deficit (SCD) was compared with the historical control group (n = 77, in 2015-2020), subjected to similar TAAA repair without 3D printing. Results: A total of 38 patients (58.6 ± 13.2 years) underwent open TAAA repair with the aid of 3D printing. Extent I, II, and III repairs were performed in 14 (36.8%), 17 (44.7%), and 7 (18.4%), respectively. Concomitant arch repair and bi-iliac reconstruction were performed in 7 (18.4%) and 6 patients (15.8%), respectively. Mean pump time was 107.7 ± 55.5 minutes. Operative mortality, permanent stroke, and permanent SCD each occurred in 1 patient (2.6%), and the incidence of the composite outcome was 7.9% (3/38). In the control group, mean pump time was 166.0 ± 83.9 minutes, significantly longer than the 3D-printing group (P < .001), and operative mortality, permanent stroke, permanent SCD, and the composite outcome occurred in 7 (9.1%), 9 (11.7%), 8 (10.4%), and 19 (24.7%), respectively. Conclusions: Open repairs of extensive TAAA with 3D printing showed favorable safety and efficacy, which need further validation by larger studies.

Progress and outlook of Sn-Pb mixed perovskite solar cells.

Organic-inorganic hybrid perovskites have revolutionized solar cell research owing to their excellent material properties. Most previous research has been done on Pb-based perovskites. Recently, efforts to discover a Pb-free or Pb-less perovskite material with an ideal bandgap ranging 1.1-1.3 eV have led researchers to investigate Sn-Pb mixed perovskites. Sn-Pb mixed perovskites have a bandgap of ~ 1.25 eV, which is suitable for high-efficiency single-junction and perovskite/perovskite tandem solar cells. Moreover, the Pb content of Sn-Pb mixed perovskites is 50-60% lower than that of Pb-based perovskites, partially mitigating the Pb toxicity issue. However, incorporating Sn2+ into the crystal structure also causes various drawbacks, such as inhomogeneous thin film morphologies, easy oxidation of Sn2+, and more vulnerable surface properties. Researchers have made substantial progress in addressing these challenges through improvements in compositional design, structural optimization, precursor design, and surface treatments. In this review, we provide a comprehensive overview of the progress in Sn-Pb mixed perovskite solar cells. Furthermore, we analyze the key variables and trends as well as provide an outlook for future directions in the research on Sn-Pb mixed perovskites.

An ensemble machine learning approach to predict postoperative mortality in older patients undergoing emergency surgery.

BACKGROUND: Prediction of preoperative frailty risk in the emergency setting is a challenging issue because preoperative evaluation cannot be done sufficiently. In a previous study, the preoperative frailty risk prediction model used only diagnostic and operation codes for emergency surgery and found poor predictive performance. This study developed a preoperative frailty prediction model using machine learning techniques that can be used in various clinical settings with improved predictive performance. METHODS: This is a national cohort study including 22,448 patients who were older than 75 years and visited the hospital for emergency surgery from the cohort of older patients among the retrieved sample from the Korean National Health Insurance Service. The diagnostic and operation codes were one-hot encoded and entered into the predictive model using the extreme gradient boosting (XGBoost) as a machine learning technique. The predictive performance of the model for postoperative 90-day mortality was compared with those of previous frailty evaluation tools such as Operation Frailty Risk Score (OFRS) and Hospital Frailty Risk Score (HFRS) using the receiver operating characteristic curve analysis. RESULTS: The predictive performance of the XGBoost, OFRS, and HFRS for postoperative 90-day mortality was 0.840, 0.607, and 0.588 on a c-statistics basis, respectively. CONCLUSIONS: Using machine learning techniques, XGBoost to predict postoperative 90-day mortality, using diagnostic and operation codes, the prediction performance was improved significantly over the previous risk assessment models such as OFRS and HFRS.

Improvement of semantic segmentation through transfer learning of multi-class regions with convolutional neural networks on supine and prone breast MRI images.

Semantic segmentation of breast and surrounding tissues in supine and prone breast magnetic resonance imaging (MRI) is required for various kinds of computer-assisted diagnoses for surgical applications. Variability of breast shape in supine and prone poses along with various MRI artifacts makes it difficult to determine robust breast and surrounding tissue segmentation. Therefore, we evaluated semantic segmentation with transfer learning of convolutional neural networks to create robust breast segmentation in supine breast MRI without considering supine or prone positions. Total 29 patients with T1-weighted contrast-enhanced images were collected at Asan Medical Center and two types of breast MRI were performed in the prone position and the supine position. The four classes, including lungs and heart, muscles and bones, parenchyma with cancer, and skin and fat, were manually drawn by an expert. Semantic segmentation on breast MRI scans with supine, prone, transferred from prone to supine, and pooled supine and prone MRI were trained and compared using 2D U-Net, 3D U-Net, 2D nnU-Net and 3D nnU-Net. The best performance was 2D models with transfer learning. Our results showed excellent performance and could be used for clinical purposes such as breast registration and computer-aided diagnosis.