Impact of Preanesthetic Blood Pressure Deviations on 30-Day Postoperative Mortality in Non-Cardiac Surgery Patients.

BACKGROUND: Blood pressure readings taken before anesthesia often influence the decision to delay or cancel elective surgeries. However, the implications of these specific blood pressure values, especially how they compare to baseline, on postoperative in-hospital 30-day mortality remain underexplored. This research aimed to examine the effect of discrepancies between the baseline blood pressure evaluated in the ward a day before surgery, and the blood pressure observed just before the administration of anesthesia, on the postoperative mortality risks. METHODS: The study encompassed 60,534 adults scheduled for non-cardiac surgeries at a tertiary care center in Seoul, Korea. Baseline blood pressure was calculated as the mean of the blood pressure readings taken within 24 hours prior to surgery. The preanesthetic blood pressure was the blood pressure measured right before the administration of anesthesia. We focused on in-hospital 30-day mortality as the primary outcome. RESULTS: Our research revealed that a lower preanesthetic systolic or mean blood pressure that deviates by 20 mmHg or more from baseline significantly increased the risk of 30-day mortality. This association was particularly pronounced in individuals with a history of hypertension and those aged 65 and above. Higher preanesthetic blood pressure was not significantly associated with an increased risk of 30-day mortality. CONCLUSION: We found that a lower preanesthetic blood pressure compared to baseline significantly increased the 30-day postoperative mortality risk, whereas a higher preanesthetic blood pressure did not. Our study emphasizes the critical importance of accounting for variations in both baseline and preanesthetic blood pressure when assessing surgical risks and outcomes.

CD47;Rag2;IL-2rγ triple knock-out mice pre-conditioning with busulfan could be a novel platform for generating hematopoietic stem cells engrafted humanized mice.

Introduction: Humanized mouse models to recapitulate human biological systems still have limitations, such as the onset of lethal graft-versus-host disease (GvHD), a variable success rate, and the low accessibility of total body irradiation (TBI). Recently, mice modified with the CD47-SIRPA axis have been studied to improve humanized mouse models. However, such trials have been rarely applied in NOD mice. In this study, we created a novel mouse strain, NOD-CD47nullRag2nullIL-2rγnull (RTKO) mice, and applied it to generate humanized mice. Methods: Four-week-old female NOD-Rag2nullIL-2rγnull (RID) and RTKO mice pre-conditioned with TBI or busulfan (BSF) injection were used for generating human CD34+ hematopoietic stem cell (HSC) engrafted humanized mice. Clinical signs were observed twice a week, and body weight was measured once a week. Flow cytometry for human leukocyte antigens was performed at intervals of four weeks or two weeks, and mice were sacrificed at 48 weeks after HSC injection. Results: For a long period from 16 to 40 weeks post transplantation, the percentage of hCD45 was mostly maintained above 25% in all groups, and it was sustained the longest and highest in the RTKO BSF group. Reconstruction of human leukocytes, including hCD3, was also most prominent in the RTKO BSF group. Only two mice died before 40 weeks post transplantation in all groups, and there were no life-threatening GvHD lesions except in the dead mice. The occurrence of GvHD has been identified as mainly due to human T cells infiltrating tissues and their related cytokines. Discussion: Humanized mouse models under all conditions applied in this study are considered suitable models for long-term experiments based on the improvement of human leukocytes reconstruction and the stable animal health. Especially, RTKO mice pretreated with BSF are expected to be a valuable platform not only for generating humanized mice but also for various immune research fields.

Enhancing Prognostication and Treatment Response Evaluation in Primary CNS Lymphoma with 18F-FDG PET/CT.

BACKGROUND: The role of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) in the prognostication and response evaluation of primary central nervous system lymphoma (PCNSL) remains inadequately defined. METHODS: We conducted a retrospective analysis of 268 consecutive newly diagnosed patients with PCNSL between 2006 and 2020. Of these patients, 105 and 110 patients were included to evaluate the prognostic value of baseline and post-treatment 18F-FDG-PET/CT scans, respectively. Tumor uptake was considered positive when it exceeded that of the contralateral brain upon visual assessment. Quantitative analysis of baseline 18F-FDG-PET/CT included measurement of the maximal standardized uptake value (SUVmax), total metabolic tumor volume (TMTV), and total lesion glycolysis (TLG). RESULTS: The median age of the 268 patients was 62 years (range: 17-85), with 55% being male. The median progression-free survival (PFS) was 24.5 months (95% confidence interval [CI], 19.9-29.1), and the median overall survival (OS) was 34.5 months (95% CI, 22.9-46.1). The average SUVmax was 15.3 ± 5.7 and the mean TMTV and TLG were 12.6 ± 13.9 cm3 and 135.0 ± 152.7 g, respectively. Patients with a baseline TMTV ≥17.0 cm3 had significantly shorter OS (12.5 vs. 74.0 months, p=0.011). Post-treatment metabolic response by 18F-FDG-PET/CT significantly predicted PFS (median: 10.5 vs. 46.0 months, p=0.001) and OS (median: 21.0 vs. 62.0 months, p=0.002), whereas anatomic response by contrast-enhanced MRI showed no statistically significant differences in PFS (p=0.130) or OS (p=0.540). CONCLUSION: Baseline TMTV and post-treatment metabolic response, as assessed by 18F-FDG-PET/CT, are significant prognostic factors in patients with PCNSL.

Predicting Delayed Postoperative Length of Stay Following Robotic Kidney Transplantation: Development and Simulation of Perioperative Risk Factors.

Background and Objective: Early discharge following robot-assisted kidney transplantation (RAKT) is a cost-effective strategy for reducing healthcare expenses while maintaining favorable short- and long-term prognoses. This study aims to identify predictors of postoperative delayed discharge in RAKT patients and develop a predictive model to enhance clinical outcomes. Materials and Methods: This retrospective study included 146 patients aged 18 years and older who underwent RAKT at a single tertiary medical center from August 2020 to January 2024. Data were collected on demographics, comorbidities, social and medical histories, preoperative labs, surgical information, intraoperative data, and postoperative outcomes. The primary outcome was delayed postoperative discharge (length of hospital stay > 7 days). Risk factors for delayed discharge were identified through univariate and multivariate regression analyses, leading to the development of a risk scoring system, the effectiveness of which was evaluated through receiver operating characteristic curve analysis. Results: 110 patients (74.8%) were discharged within 7 days post-transplant, while 36 (24.7%) remained hospitalized for 8 days or longer. Univariate and multivariate logistic regression analyses identified ABO incompatibility, BUN levels, anesthesia time, and vasodilator use as risk factors for delayed discharge. The RAKT score, incorporating these factors, demonstrated a predictive performance with a C-statistic of 0.789. Conclusions: This study identified risk factors for delayed discharge after RAKT and developed a promising risk scoring system for real-world clinical application, potentially improving postoperative outcome stratification in RAKT recipients.

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.

Unveiling the distinctive mechanical and thermal properties of γ-GeSe.

γ-GeSe is a newly identified polymorph among group-IV monochalcogenides, characterized by a distinctive interatomic bonding configuration. Despite its promising applications in electrical and thermal domains, the experimental verification of its mechanical and thermal properties remains unreported. Here, we experimentally characterize the in-plane Young's modulus (E) and thermal conductivity ([Formula: see text]) of γ-GeSe. The mechanical vibrational modes of freestanding γ-GeSe flakes are measured using optical interferometry. Nano-indentation via atomic force microscopy is also conducted to induce mechanical deformation and to extract the E. Comparison with finite-element simulations reveals that the E is 97.3[Formula: see text]7.5 GPa as determined by optical interferometry and 109.4[Formula: see text]13.5 GPa as established through the nano-indentation method. Additionally, optothermal Raman spectroscopy reveals that γ-GeSe has a lattice thermal conductivity of 2.3 [Formula: see text] 0.4 Wm-1K-1 and a total thermal conductivity of 7.5 [Formula: see text] 0.4 Wm-1K-1 in the in-plane direction at room temperature. The notably high [Formula: see text] ratio in γ-GeSe, compared to other layered materials, underscores its distinctive structural and dynamic characteristics.

Time to initiation of extracorporeal membrane oxygenation in conventional cardiopulmonary resuscitation affects the patient survival prognosis.

BACKGROUND: Cardiopulmonary resuscitation (CPR) is the cornerstone intervention for cardiac arrest, with extracorporeal CPR (ECPR) demonstrating enhanced survival and neurologic outcomes in in-hospital cardiac arrest. This study explores the time interval between CPR initiation and the onset of extracorporeal membrane oxygenation (ECMO) in ECPR recipients, investigating its impact on survival outcomes. METHODS: This retrospective analysis included 1950 adults who received CPR at a single medical center between March 2019 and April 2023. Data from 198 adult patients who had ECMO inserted during CPR were analyzed. The interval from CPR initiation to ECMO initiation was quantified and categorized as ≤20, 20-40, and >40 min. Cox regression analysis assessed associations between CPR-to-ECMO time and short- and long-term mortalities. RESULTS: Among the 198 patients who underwent ECPR, 116 (58.6%) experienced 30-day mortality. Initiation of ECMO within 20 min occurred in 46 (23.2%), whereas 74 (37.4%) had ECMO initiated after 40 min. Cox regression revealed a significant association between time from CPR to ECMO initiation and 30-day mortality (adjusted hazard ratio [HR]: 2.20 in >40 min, HR: 2.63 in 20-40 min, p = 0.006) and 6-month mortality (HR: 1.81, in >40 min, HR: 1.99 in 20-40 min, p = 0.021). CONCLUSIONS: This study revealed that, in ECPR recipients, a shorter duration between CPR initiation and ECMO flow commencement is associated with improved short- and long-term patient prognoses. These findings emphasize the critical role of timely ECMO application in optimizing outcomes for patients undergoing ECPR.

Comparison of the Effects of Multiple Frailty and Nutritional Indexes on Postoperative Outcomes in Critically Ill Patients Undergoing Lung Transplantation.

Background and Objective: Lung transplantation is the only life-extending therapy for end-stage pulmonary disease patients, but its risks necessitate an understanding of outcome predictors, with the frailty index and nutritional status being key assessment tools. This study aims to evaluate the relationship between preoperative frailty and nutritional indexes and the postoperative mortality rate in patients receiving lung transplants, and to determine which measure is a more potent predictor of outcomes. Materials and Methods: This study reviewed 185 adults who received lung transplants at a single medical center between January 2013 and May 2023. We primarily focused on postoperative 7-year overall survival. Other outcomes measured were short-term mortalities, acute rejection, kidney complications, infections, and re-transplantation. We compared the predictive abilities of preoperative nutritional and frailty indicators for survival using receiver operating characteristic curve analysis and identified factors affecting survival through regression analyses. Results: There were no significant differences in preoperative nutritional indicators between survivors and non-survivors. However, preoperative frailty indicators did differ significantly between these groups. Multivariate analysis revealed that the American Society of Anesthesiologists Class V, clinical frailty scale, and Charlson Comorbidity Index (CCI) were key predictors of 7-year overall survival. Of these, the CCI had the strongest predictive ability with an area under the curve of 0.755, followed by the modified frailty index at 0.731. Conclusions: Our study indicates that for critically ill patients undergoing lung transplantation, frailty indexes derived from preoperative patient history and functional autonomy are more effective in forecasting postoperative outcomes, including survival, than indexes related to preoperative nutritional status.

MUC16 as a serum-based prognostic indicator of prometastatic gastric cancer.

Metastatic gastric cancer (GC) presents significant clinical challenges due to its poor prognosis and limited treatment options. To address this, we conducted a targeted protein biomarker discovery study to identify markers predictive of metastasis in advanced GC (AGC). Serum samples from 176 AGC patients (T stage 3 or higher) were analyzed using the Olink Proteomics Target panels. Patients were retrospectively categorized into nonmetastatic, metastatic, and recurrence groups, and differential protein expression was assessed. Machine learning and gene set enrichment analysis (GSEA) methods were applied to discover biomarkers and predict prognosis. Four proteins (MUC16, CAIX, 5'-NT, and CD8A) were significantly elevated in metastatic GC patients compared to the control group. Additionally, GSEA indicated that the response to interleukin-4 and hypoxia-related pathways were enriched in metastatic patients. Random forest classification and decision-tree modeling showed that MUC16 could be a predictive marker for metastasis in GC patients. Additionally, ELISA validation confirmed elevated MUC16 levels in metastatic patients. Notably, high MUC16 levels were independently associated with metastatic progression in T3 or higher GC. These findings suggest the potential of MUC16 as a clinically relevant biomarker for identifying GC patients at high risk of metastasis.

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.

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.

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.

Multiplex analysis for the identification of plasma protein biomarkers for predicting lung cancer immunotherapy response.

Background: Programmed death-ligand (PD-L1) expression serves as a predictive biomarker for immune checkpoint inhibitor (ICI) sensitivity in non-small cell lung cancer (NSCLC). Nevertheless, the development of biomarkers that reliably predict ICI response remains an ongoing endeavor due to imperfections in existing methodologies. Objectives: ICIs have led to a new paradigm in the treatment of NSCLC. The current companion PD-L1 diagnostics are insufficient in predicting ICI response. Therefore, we sought whether the Olink platform could be applied to predict response to ICIs in NSCLC. Design: We collected blood samples from patients with NSCLC before ICI treatment and retrospectively analyzed proteomes based on their response to ICI. Methods: Overall, 76 NSCLC patients' samples were analyzed. Proteomic plasma analysis was performed using the Olink platform. Intraplate reproducibility, validation, and statistical analyses using elastic net regression and generalized linear models with clinical parameters were evaluated. Results: Intraplate coefficient of variation (CV) assays ranged from 3% to 6%, and the interplate CV was 14%. In addition, the Pearson correlation coefficient of the Olink Normalized Protein eXpression data was validated. No statistical differences were observed in the analyses of progressive disease and response to ICIs. Furthermore, no single proteome showed prognostic value in terms of progression-free survival. Conclusion: In this study, the proximity extension assay-based approach of the Olink panel could not predict the patient's response to ICIs. Our proteomic analysis failed to achieve predictive value in both response or progression to ICIs and progression-free survival (PFS).

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.

Machine Learning-Assisted Identification of Single-Layer Graphene via Color Variation Analysis.

Techniques such as using an optical microscope and Raman spectroscopy are common methods for detecting single-layer graphene. Instead of relying on these laborious and expensive methods, we suggest a novel approach inspired by skilled human researchers who can detect single-layer graphene by simply observing color differences between graphene flakes and the background substrate in optical microscope images. This approach implemented the human cognitive process by emulating it through our data extraction process and machine learning algorithm. We obtained approximately 300,000 pixel-level color difference data from 140 graphene flakes from 45 optical microscope images. We utilized the average and standard deviation of the color difference data for each flake for machine learning. As a result, we achieved F1-Scores of over 0.90 and 0.92 in identifying 60 and 50 flakes from green and pink substrate images, respectively. Our machine learning-assisted computing system offers a cost-effective and universal solution for detecting the number of graphene layers in diverse experimental environments, saving both time and resources. We anticipate that this approach can be extended to classify the properties of other 2D materials.

Nomogram Using Prostate Health Index for Predicting Prostate Cancer in the Gray Zone: Prospective, Multicenter Study.

PURPOSE: To create a nomogram that can predict the probability of prostate cancer using prostate health index (PHI) and clinical parameters of patients. And the optimal cut-off value of PHI for prostate cancer was also assessed. MATERIALS AND METHODS: A prospective, multi-center study was conducted. PHI was evaluated prior to biopsy in patients requiring prostate biopsy due to high prostate-specific antigen (PSA). Among screened 1,010 patients, 626 patients with clinically suspected prostate cancer with aged 40 to 85 years, and with PSA levels ranging from 2.5 to 10 ng/mL were analyzed. RESULTS: Among 626 patients, 38.82% (243/626) and 22.52% (141/626) were diagnosed with prostate cancer and clinically significant prostate cancer, respectively. In the PSA 2.5 to 4 ng/mL group, the areas under the curve (AUCs) of the nomograms for overall prostate cancer and clinically significant prostate cancer were 0.796 (0.727-0.866; p<0.001), and 0.697 (0.598-0.795; p=0.001), respectively. In the PSA 4 to 10 ng/mL group, the AUCs of nomograms for overall prostate cancer and clinically significant prostate cancer were 0.812 (0.783-0.842; p<0.001), and 0.839 (0.810-0.869; p<0.001), respectively. CONCLUSIONS: Even though external validations are necessary, a nomogram using PHI might improve the prediction of prostate cancer, reducing the need for prostate biopsies.

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.