Estimating regional CO2 and NOx emissions from road transport using real-world data-based emission factors in Korea.

The average-speed emission model (Speed-based model), a widely used and simple method of calculating road vehicle emissions, offers easy accessibility by expressing emissions as a function of average speed. However, there are limitations in expressing emissions generated through complex mechanisms simply as a function of speed. Real-world driving tests using a portable emission measurement system can incorporate the impact of vehicle driving load on emissions. In this study, we analyzed real-world emissions data from 94 light-duty vehicles and developed time-based emission factors depending on vehicle speed and vehicle-specific power (VSP). We also propose a speed-VSP based model to estimate regional CO2 and NOx emissions by combining time-based emission factors and vehicle operating times. The speed-based model and Speed-VSP based model exhibit a 44% difference in NOx emissions and a 29% difference in CO2 emission. In a comparison of the two models against RDE test results, the speed-VSP based model achieved high accuracy in predicting NOx and CO2 emissions with a lower root mean square error (RMSE). Specifically, for NOx emissions predictions, the speed-VSP based model achieved an RMSE of 122-270 mg/km, while the speed-based model showed a much higher RMSE of 435-476 mg/km. For CO2 emissions predictions, the speed-VSP based model achieved an RMSE of 34-56 mg/km, while the speed-based model showed a much higher RMSE of 36-72 mg/km. The results of this study present an opportunity to reassess and improve conventional method of measuring and evaluating emissions from road transport.

Usefulness of the BIG Score in Predicting Massive Transfusion and In-Hospital Death in Adult Trauma Patients.

The base deficit (B), international normalized ratio (I), and Glasgow coma scale (GCS) (BIG) score is useful in predicting mortality in pediatric trauma patients; however, studies on the use of BIG score in adult patients with trauma are sparse. In addition, studies on the correlation between the BIG score and massive transfusion (MT) have not yet been conducted. This study aimed to evaluate the predictive value of BIG score for mortality and the need for MT in adult trauma patients. This retrospective study used data collected between 2016 and 2020 at our hospital's trauma center and registry. The predictive value of BIG score was compared with that of the Injury Severity Score (ISS) and Revised Trauma Score (RTS). Logistic regression analysis was carried out to assess whether BIG score was an independent risk factor. Receiver operating characteristic (ROC) curve analysis was performed, and predictive values were evaluated by measuring the area under the ROC curve (AUROC). In total, 5,605 patients were included in this study. In logistic regression analysis, BIG score was independently associated with in-hospital mortality (odds ratio (OR): 1.1859; 95% confidence interval (CI): 1.1636-1.2086) and MT (OR: 1.0802; 95% CI: 1.0609-1.0999). The AUROCs of BIG score for in-hospital mortality and MT were 0.852 (0.842-0.861) and 0.848 (0.838-0.857), respectively. Contrastingly, the AUROCs of ISS and RTS for in-hospital mortality were 0.795 (0.784-0.805) and 0.859 (0.850-0.868), respectively. Moreover, AUROCs of ISS and RTS for MT were 0.812 (0.802-0.822) and 0.838 (0.828-0.848), respectively. The predictive value of BIG score for mortality and MT was significantly higher than that of the ISS. The BIG score also showed a better AUROC for predicting in-hospital mortality compared with RTS. In conclusion, the BIG score is a useful indicator for predicting mortality and the need for MT in adult trauma patients.

Effect of driving characteristics and ambient temperature on the particle emissions during engine restart of spark ignition hybrid electric vehicle.

In this study, we analyzed particle emission characteristics in the engine restart (ER) phase of a hybrid electric vehicle (HEV) based on driving characteristics and ambient temperature. The ambient temperature was set at intervals of 10 °C from - 10 °C to 20 °C. ES-582.1, PPS-M, EEPS, and temperature sensors were installed to acquire hybrid control unit (HCU), particle number (PN), PN size distribution, and exhaust temperature data. The on board test route was conducted in the South Korean real driving emissions (RDE) certification route, consisting of urban, rural, and motorway phases. The test HEV was controlled by dividing the engine operation during driving into ER and normal phases. Within 5 s immediately after ER, it emitted PN equivalent to 90% of the total test emissions. The count of ER was higher in urban phases compared to rural and motorway phases. As the ambient temperature decreased, PN emissions increased regardless of the driving mode, but the ER PN percent decreased. Immediately after ER, PN emissions increased rapidly, peaked at around 2-3 s, and then decreased thereafter. The average engine-off time before ER was the longest in the urban phase, and the average ER exhaust temperature was the highest in the motorway phase. The size fraction of large particles increased as the ambient temperature decreased.

Etomidate versus propofol for sedation in gastrointestinal endoscopy: A systematic review and meta-analysis of outcomes.

BACKGROUND: Propofol is increasingly being used for sedation in gastrointestinal endoscopy; however, owing to its side effects, an alternative drug is needed. We aimed to compare the safety, satisfaction, and efficacy outcomes of etomidate versus propofol in patients undergoing gastrointestinal endoscopy, including advanced endoscopic procedures. METHODS: We systematically searched Embase, PubMed, Cochrane Central Register of Controlled Trials, CINAHL (via EBSCO), China National Knowledge Infrastructure, and Web of Science (1946-April 2020) databases for randomized controlled trials of gastrointestinal endoscopy (upper gastrointestinal endoscopy, colonoscopy, and advanced endoscopy) using etomidate or propofol as sedatives. We pooled odds ratios (ORs) for the safety profile and patient and anesthesiologist satisfaction using mixed-effects conditional logistic models and standardized mean differences for efficiency outcomes using random-effects models. RESULTS: Twenty-four studies involving 3875 patients were included. Compared with propofol, etomidate resulted in significantly reduced apnea (OR: 0.22; 95% confidence interval [CI]: 0.13-0.37; P < .001), hypoxemia (OR: 0.43; 95% CI: 0.35-0.54; P < .001), hypotension (OR: 0.20; 95% CI: 0.11-0.36; P < .001), and bradycardia (OR: 0.52; 95% CI: 0.30-0.91; P = .02) but led to increased myoclonus (OR: 8.54; 95% CI: 5.20-14.01; P < .001) and lowered anesthesiologist satisfaction (OR: 0.60; 95% CI: 0.39-0.91; P = .02). CONCLUSION: Etomidate may be a good alternative to propofol for gastrointestinal endoscopy, especially advanced endoscopy. Etomidate appears to be safe as an inducer for hemodynamically unstable patients or older adult patients undergoing gastrointestinal endoscopy.

Development of vehicle emission rates based on vehicle-specific power and velocity.

The US Environmental Protection Agency developed the MOtor Vehicle Emission Simulator (MOVES) operating modes, which defines modal emission rates according to vehicle speed and vehicle-specific power using binning method. However, as MOVES was based on emissions data for vehicle fleets in the US, it is used primarily to estimate US emissions. To adopt this approach in other regions, here, we take into account regional conditions, such as vehicle fleet composition, emissions regulations, and driving environments. Real-world emissions test data for 17 light-duty gasoline and diesel vehicles mainly sold in Korea were used to develop CO2, NOx, and CO emission rates. Typically, the vehicle experiment and data acquisition are costly and time consuming, the amount of data needed to develop robust emission rates were considered. In addition, we studied how a re-binning of vehicle-specific power and velocity could lead to better emission rates estimates from on-road vehicles. To compare the estimates by different binning methods and real-world emissions, root mean square error (RMSE) and R-squared (R2) values were adopted. The comparison result shows that the re-binning method-based emission predictions were more accurate than MOVES prediction results under the real-world condition. The R2 of CO2 and NOx predictions were increased from 075 to 0.78 and from 0.17 to 0.2, respectively. The CO prediction accuracy was slightly increased. These findings provide the re-binning method is advantageous for developing modal-based emission rates using real-world emissions test data.

Effect of intake manifold geometry on cylinder-to-cylinder variation and tumble enhancement in gasoline direct injection engine.

In this study, the effect of intake manifold geometry on cylinder-to-cylinder variation was investigated considering the volumetric efficiency, early tumble development, turbulent kinetic energy, and spark plug gap velocity using computational fluid dynamic program, CONVERGE v2.4. The simulation model was validated based on the PIV experiment in the cylinder and Mie-scattering experiment of intake manifold, and its results agreed well with the experiment results. The curved intake manifold and straight manifold were compared. As a result, it was advantageous for cylinder-to-cylinder variation in the straight intake manifold compared to the curved intake manifold in perspective of volumetric efficiency which were a maximum deviation of 1.7% in curved manifold and 0.6% in straight manifold. And the straight manifold had an effect of the strengthening the in-cylinder flow, so that the turbulent kinetic energy near TDC was increased to maximum 11% than curved manifold. And considering the effect of manifold curve radius on in-cylinder flow intensity in straight manifold, with increasing engine speed, the in-cylinder flow intensified during compression with decreasing the intake manifold radius due to the short distance between manifold inlet and port. Especially at 2000 rpm, the tumble ratio increased 55% at intake manifold radius of 10 cm than of 7 cm at bTDC 280 deg. Therefore, for the purpose of enhancing the in-cylinder flow near spark plug timing, shortened distance between intake manifold inlet and port and increasing the manifold radius is required.

The Addition of ROTEM Parameter Did Not Significantly Improve the Massive Transfusion Prediction in Severe Trauma Patients.

Background: Rotational thrombelastometry (ROTEM) has been used to evaluate the coagulation state, predict transfusion, and optimize hemostatic management in trauma patients. However, there were limited studies on whether the prediction value could be improved by adding the ROTEM parameter to the prediction model for in-hospital mortality and massive transfusion (MT) in trauma patients. Objective: This study assessed whether ROTEM data could improve the MT prediction model. Method: This was a single-center, retrospective study. Patients who presented to the trauma center and underwent ROTEM between 2016 and 2020 were included. The primary and secondary outcomes were massive transfusions and in-hospital mortality, respectively. We constructed two models using multivariate logistic regression with backward conditional stepwise elimination (Model 1: without the ROTEM parameter and Model 2: with the ROTEM parameter). The area under the receiver operating characteristic curve (AUROC) was calculated to assess the predictive ability of the models. Result: In total, 969 patients were included; 196 (20.2%) received MT. The in-hospital mortality rate was 14.1%. For MT, the AUROC was 0.854 (95% confidence interval [CI], 0.825-0.883) and 0.860 (95% CI, 0.832-0.888) for Model 1 and 2, respectively. For in-hospital mortality, the AUROC was 0.886 (95% CI, 0.857-0.915) and 0.889 (95% CI, 0.861-0.918) for models 1 and 2, respectively. The AUROC values for models 1 and 2 were not statistically different for either MT or in-hospital mortality. Conclusion: We found that the addition of the ROTEM parameter did not significantly improve the predictive power of MT and in-hospital mortality in trauma patients.

Application of the New Preoperative Frailty Risk Score in Elderly Patients Undergoing Emergency Surgery.

BACKGROUND: Predicting preoperative frailty risk in emergency surgery is difficult with limited information because preoperative evaluation is not commonly performed properly. A recent study attempted to predict preoperative frailty risk using only diagnostic and surgical codes that can be extracted from the electronic medical records system. OBJECTIVE: This study aimed to validate whether the prediction model of preoperative frailty risk presented in the previous study is well applied to other medical hospitals' data. METHODS: This is a retrospective cohort study including 1,557 patients (≥75 years old) who were admitted to a single institution for emergency operations between January 1, 2010, and December 31, 2019, for study analysis. The Charlson comorbidity index, Hospital Frailty Risk Score, and the recently developed Operation Frailty Risk Score (OFRS) were calculated using the patient's diagnostic and operation codes. The predictive performances of these calculated risk scores and the American Society of Anesthesiologists-Physical Status classification for postoperative 90-day mortality were compared by using the receiver operating characteristic curve analysis. FINDINGS: The predictive performance of the OFRS, Charlson comorbidity index, American Society of Anesthesiologists-Physical Status, and Hospital Frailty Risk Score for postoperative 90-day mortality was 0.81, 0.630, 0.699, and 0.549 on a c-statistics basis, respectively. CONCLUSIONS: The OFRS using diagnostic and operation codes may show the best predictive performance for 90-day mortality compared to other risk scores, and it can be the clinically applicable model to evaluate the preoperative frailty risk in elderly patients undergoing emergency surgery.

Effects of pre-mixed gas composition on combustion characteristics of micro pilot dual fuel (MPDF) in a heavy-duty dual fuel engine.

The effects of premixed gas compositions on the combustion characteristics in micro pilot duel fuel (MPDF) conditions were investigated. Propane, hydrogen, and carbon dioxide gases were added to methane gas, and engine experiments were conducted under various premixed gas compositions. A single-cylinder heavy-duty engine with a combustion chamber volume of 1100 mm3 and compression ratio of 17.0 was used. A 55 kW DC dynamometer was used to operate the single-cylinder dual-fuel engine at a constant engine speed. At high propane mixture ratios, knocking combustion occurred, accompanied by intense engine vibrations, owing to the low octane number of propane. Knocking combustion led to an increase in the combustion variation and ringing intensity (which represents the knocking combustion intensity). In contrast, at high ratios of hydrogen, which has a high octane number, knocking combustion was suppressed, and the speed of combustion was lower than that in the case of high propane mixture ratios. The optimum conditions corresponded to a ringing intensity of 3-5 MW/m2. The addition of even a small amount of propane gas enhanced the engine performances in misfiring conditions. In contrast, a considerable amount of hydrogen gas was required to prevent abnormal combustion because of the low density of hydrogen gas. The presence of carbon dioxide effectively stabilized MPDF combustion by suppressing knocking combustion.

Effects of lubricant-fuel mixing on particle emissions in a single cylinder direct injection spark ignition engine.

Gasoline direct injection (GDI) engines emit less carbon dioxide (CO2) than port fuel injection (PFI) engines when fossil fuel conditions are the same. However, GDI engines emit more ultrafine particulate matter, which can have negative health effects, leading to particulate emission regulations. To satisfy these regulations, various studies have been done to reduce particulate matter, and several studies focused on lubricants. This study focuses on the influence of lubricant on the formation of particulate matter and its effect on particulate emissions in GDI engines. An instrumented, combustion and optical singe-cylinder GDI engine fueled by four different lubricant-gasoline blends was used with various injection conditions. Combustion experiments were used to determine combustion characteristics, and gaseous emissions indicated that the lubricant did not influence mixture homogeneity but had an impact on unburned fuels. Optical experiments showed that the lubricant did not influence spray but did influence wall film formation during the injection period, which is a major factor affecting particulate matter generation. Particulate emissions indicated that lubricant included in the wall film significantly affected PN emissions depending on injection conditions. Additionally, the wall film influenced by the lubricant affected the overall particle size and its distribution.

SARS-CoV-2 Variant Screening Using a Virus-Receptor-Based Electrical Biosensor.

SARS-CoV-2 variants are of particular interest because they can potentially increase the transmissibility and virulence of COVID-19 or reduce the effectiveness of available vaccines. However, screening SARS-CoV-2 variants is a challenge because biosensors target viral components that can mutate. One promising strategy is to screen variants via angiotensin-converting enzyme 2 (ACE2), a virus receptor shared by all known SARS-CoV-2 variants. Here we designed a highly sensitive and portable COVID-19 screening biosensor based on the virus receptor. We chose a dual-gate field-effect transistor to overcome the low sensitivity of virus-receptor-based biosensors. To optimize the biosensor, we introduced a synthetic virus that mimics the important features of SARS-CoV-2 (size, bilayer structure, and composition). The developed biosensor successfully detected SARS-CoV-2 in 20 min and showed sensitivity comparable to that of molecular diagnostic tests (∼165 copies/mL). Our results indicate that a virus-receptor-based biosensor can be an effective strategy for screening infectious diseases to prevent pandemics.

Development of a cold-start emission model for diesel vehicles using an artificial neural network trained with real-world driving data.

During the cold start and warm-up phase, modern vehicles emit considerable amounts of pollutants due to the incomplete combustion and deteriorated performance of aftertreatment devices. In terms of emission modeling, there have been many attempts to estimate cold start emission such as cold-hot conversion factor, regression model, and physis-based model. However, as the emission characteristic become complicated due to the adoption of aftertreatment devices and various emission control strategies for the strengthened emission regulations, the conventional cold start emission models do not always show satisfactory performances. In this study, artificial neural networks were used to predict the cold start emissions of carbon dioxide, nitrogen oxides, carbon monoxide, and total hydrocarbon of diesel passenger vehicles. We used real-world driving data to train neural networks as an emission prediction tool. Through machine leaning, numerous trainable variables of neural networks were properly adjusted to predict cold start emissions. For input variables of the ANN model, the velocity, vehicle specific power, engine speed, engine torque, and engine coolant temperature were used. The proposed ANN models accurately predicted sharp increases in carbon monoxide, hydrocarbon, and nitrogen oxides during the cold start phase. In addition to the quantitative estimations, the correlations between the operating variables and exhaust gas emissions were visually described in the form of emission maps. The emission map graphically showed the emission levels according to the vehicle and engine operating parameters.

Prediction of pathogen positive-culture results in acute poisoning patients with suspected aspiration

Purpose@#This study sought to compare the characteristics of patients with pathogen-positive and negative cultures, and to investigate factors predicting pathogen-positive culture results in patients of acute poisoning with suspected aspiration. @*Methods@#Consecutive patients with acute poisoning admitted to an intensive care unit between January 2016 and December 2018 were retrospectively studied. Respiratory specimens were collected from the enrolled patients at the time of the suspected aspiration. We compared the characteristics of patients with pathogen-positive and negative culture results and analyzed the causative pathogens. @*Results@#Among the 526 patients, 325 showed no clinical features that could be attributed to aspiration, and 201 patients had clinical features suggestive of aspiration. Of these, 113 patients had pathogen-positive culture, 61 were negative, and the specimens of 27 patients contained poor-quality sputum. In univariate analysis, patients with a positive culture showed a longer time to culture from ingestion (p=0.01), faster heart rate (p=0.01), and higher partial pressure of arterial oxygen to the fraction of inspired oxygen (PaO2/FiO2) (p=0.02) than patients with negative culture. Multivariate analysis demonstrated that PaO2/FiO2 (adjusted odd ratio, 1.005; 95% confidence interval [CI], 1.002-1.008; p=0.005) was a significant risk factor for pathogen-positive culture. The area under the receiver operating characteristic curve of PaO2/FiO2 was 0.591 (95% CI, 0.510-0.669, p=0.05). Gram-negative pathogens (GNPs) were predominant and at least one GNP was observed in 84 (73.3%) patients among those with pathogen positive culture. @*Conclusion@#We failed to find any clinical factors associated with positive culture results. Antibiotics that cover GNPs could be considered when deciding the initial antibiotic regimen at the time of suspected aspiration.

Prediction of instantaneous real-world emissions from diesel light-duty vehicles based on an integrated artificial neural network and vehicle dynamics model.

This paper presents a road vehicle emission model that integrates an artificial neural network (ANN) model with a vehicle dynamics model to predict the instantaneous carbon dioxide (CO2), nitrogen oxides (NOx) and total hydrocarbon (THC) emissions of diesel light-duty vehicles. Real-world measurement data were used to train a multi-layer feed-forward ANN model. The optimal combination of the various experimental variables was selected as the ANN input through a parametric study considering both practicality and accuracy. For CO2 prediction, two variables (engine speed and engine torque) are enough to develop an accurate ANN model. In order to achieve satisfactory accuracy for CO and NOx prediction, more variables were used for ANN training. The trained ANN model was used to predict road vehicle emissions by integrating the vehicle dynamics model, which was used as a supplementary tool to produce ANN input data. The integrated model is practical because it requires relatively simple data for input such as vehicle specifications, velocity, and road gradient. In the accuracy validation, the proposed model showed satisfactory prediction accuracy for road vehicle emissions.

Effects of methane ratio on MPDF (micro-pilot dual-fuel) combustion characteristic in a heavy-duty single cylinder engine.

In this study, the characteristics of micro-pilot dual-fuel combustion with respect to the fuel mixture ratio in a single cylinder dual-fuel engine have been investigated. In order to analyze the characteristics of micro-pilot dual-fuel combustion, a metal engine and an optical single cylinder dual-fuel engine were used. The fuel mixture ratio was varied for experimental purposes; the diesel was directly injected into combustion chamber and the methane gas was supplied via intake port. The present study reports that increasing the methane mixture ratio from 0 to 97.67% changes the diesel combustion to pre-mixed combustion. As a result, the peak cylinder pressure was increased from 184 to 198 bar, and the rate of heat release was greatly advanced. In the MPDF condition, the nitrogen oxides emissions were reduced by about 90%p, and the fuel conversion efficiency increased about 5%p because of the low combustion temperature of pre-mixed combustion. However, for the same reason, the hydrocarbon emissions were increased about 95%p. The fastest combustion speed was found form the results of methane mixture ratio between 40 and 80%. In the condition of diesel combustion and micro-pilot dual-fuel combustion, the combustion periods of middle and initial were increased, respectively, resulting in the low combustion speed. The standard deviation of peak cylinder pressure, which represents the combustion variation, was correlated with initial combustion period. While the condition of methane gas mixture ratio between 40 and 80% shows the lowest combustion variation, the highest combustion variation was occurred by MPDF condition. Through the optical engine experiment, it can be found that the cycle to cycle combustion variation is ascribed to the turbulent flow and the variation of ignition position. The combustion images show that the unpredictable characteristics of the ignition position and slow flame propagation speed caused the combustion variation in micro-pilot dual-fuel combustion.

Low-Temperature Molten Salts Synthesis: CsPbBr3 Nanocrystals with High Photoluminescence Emission Buried in Mesoporous SiO2.

Using mesoporous SiO2 to encapsulate CsPbBr3 nanocrystals is one of the best strategies to exploit such materials in devices. However, the CsPbBr3/SiO2 composites produced so far do not exhibit strong photoluminescence emission and, simultaneously, high stability against heat and water. We demonstrate a molten-salts-based approach delivering CsPbBr3/mesoporous-SiO2 composites with high PLQY (89 ± 10%) and high stability against heat, water, and aqua regia. The molten salts enable the formation of perovskite nanocrystals and other inorganic salts (KNO3-NaNO3-KBr) inside silica and the sealing of SiO2 pores at temperatures as low as 350 °C, representing an important technological advancement (analogous sealing was observed only above 700 °C in previous reports). Our CsPbBr3/mesoporous-SiO2 composites are attractive for different applications: as a proof-of-concept, we prepared a white-light emitting diode exhibiting a correlated color temperature of 7692K. Our composites are also stable after immersion in saline water at high temperatures (a typical underground environment of oil wells), therefore holding promise as oil tracers.

Correlation between the Perfusion Index and Intraoperative Hypothermia: A Prospective Observational Pilot Study.

Background and Objectives: We examined the association between the baseline perfusion index (PI) and changes in intraoperative body temperature during general anesthesia. The PI reflects the peripheral perfusion state. The PI may be associated with changes in body temperature during general anesthesia because the degree of redistribution of body heat from the central to the peripheral compartment varies depending on the peripheral perfusion state. Materials and Methods: Thirty-eight patients who underwent brain surgery were enrolled in this study. The baseline PI and body temperature of the patients were measured on entering the operating room. Body temperature was recorded every 15 min after induction of anesthesia using an esophageal temperature probe. Univariate and multivariate logistic regression analyses were performed to identify the risk factors for intraoperative hypothermia. Results: Eighteen patients (47 %) developed hypothermia intraoperatively. The baseline PI was significantly lower among patients in the hypothermia group (1.8 ± 0.7) than among those in the normothermia group (3.0 ± 1.2) (P < 0.001). The baseline PI and body temperature were independently associated with intraoperative hypothermia (PI: odds ratio [OR], 0.270; 95% confidence interval [CI], 0.105-0.697; P = 0.007, baseline body temperature: OR, 0.061; 95% CI, 0.005-0.743; P = 0.028). Conclusions: This study showed that low baseline PI was the factor most related to the development of intraoperative hypothermia. Future studies should consider the PI as a predictor of intraoperative hypothermia.

Noninvasive Precision Screening of Prostate Cancer by Urinary Multimarker Sensor and Artificial Intelligence Analysis.

Screening for prostate cancer relies on the serum prostate-specific antigen test, which provides a high rate of false positives (80%). This results in a large number of unnecessary biopsies and subsequent overtreatment. Considering the frequency of the test, there is a critical unmet need of precision screening for prostate cancer. Here, we introduced a urinary multimarker biosensor with a capacity to learn to achieve this goal. The correlation of clinical state with the sensing signals from urinary multimarkers was analyzed by two common machine learning algorithms. As the number of biomarkers was increased, both algorithms provided a monotonic increase in screening performance. Under the best combination of biomarkers, the machine learning algorithms screened prostate cancer patients with more than 99% accuracy using 76 urine specimens. Urinary multimarker biosensor leveraged by machine learning analysis can be an important strategy of precision screening for cancers using a drop of bodily fluid.

Long-term Prognosis of Localized Lymphoid Hyperplasia of the Rectum.

Background/Aims: Although localized lymphoid hyperplasia (LLH) of the rectum is occasionally observed, its clinical implications are unclear. This study aimed to investigate the clinical course and significance of LLH of the rectum. Methods: We identified 65 patients diagnosed with LLH of the rectum using a histopathologic examination and who received follow-up endoscopies between January 2009 and June 2015. Patients with a history of inflammatory bowel disease, lymphoma, familial adenomatous polyposis, or uncontrolled malignancy and patients who underwent scar biopsy after endoscopic resection or surgery were excluded. Endoscopic findings and clinical courses were analyzed. Results: During the median follow-up of 31 months (interquartile range, 19 to 40 months), 81.5% (53/65) of LLHs of the rectum were resolved. Clinically significant diseases, including ulcerative colitis (UC, n=5) and mucosa-associated lymphoid tissue (MALT) lymphoma (n=1), were diagnosed in 9.2% of patients (6/65). The other six patients showed no significant changes in the lesion (n=3) or a waxing and waning appearance (n=3). According to endoscopic findings, all of the 47 polypoid types showed resolution or waxing and waning patterns. Five of the 11 nodular types (45.5%) developed into UC. One of the seven submucosal tumor (SMT)-like types (14.3%) developed into MALT lymphoma. Conclusions: LLH of the rectum with persistent symptoms or the endoscopic appearance of the nodular or SMT-like type may lead to clinically significant disease. Risk stratification according to endoscopic findings and careful surveillance are required for these lesions.