Enhancing genome editing in hPSCs through dual inhibition of DNA damage response and repair pathways.

Precise genome editing is crucial for establishing isogenic human disease models and ex vivo stem cell therapy from the patient-derived hPSCs. Unlike Cas9-mediated knock-in, cytosine base editor and prime editor achieve the desirable gene correction without inducing DNA double strand breaks. However, hPSCs possess highly active DNA repair pathways and are particularly susceptible to p53-dependent cell death. These unique characteristics impede the efficiency of gene editing in hPSCs. Here, we demonstrate that dual inhibition of p53-mediated cell death and distinct activation of the DNA damage repair system upon DNA damage by cytosine base editor or prime editor additively enhanced editing efficiency in hPSCs. The BE4stem system comprised of p53DD, a dominant negative p53, and three UNG inhibitor, engineered to specifically diminish base excision repair, improves cytosine base editor efficiency in hPSCs. Addition of dominant negative MLH1 to inhibit mismatch repair activity and p53DD in the conventional prime editor system also significantly enhances prime editor efficiency in hPSCs. Thus, combined inhibition of the distinct cellular cascades engaged in hPSCs upon gene editing could significantly enhance precise genome editing in these cells.

A predictive model to analyze the factors affecting the presence of serious chest injury in the occupants on motor vehicle crashes: Logistic regression approach.

OBJECTIVE: Chest injuries that occur in motor vehicle crashes (MVCs) include rib fractures, pneumothorax, hemothorax, and hemothorax depending on the injury mechanism. Many risk factors are associated with serious chest injuries from MVCs. The Korean In-Depth Accident Study database was analyzed to identify risk factors associated with motor vehicle occupants' serious chest injury. METHODS: Among 3,697 patients who visited the emergency room in regional emergency medical centers after MVCs between 2011 and 2018, we analyzed data from 1,226 patients with chest injuries. Vehicle damage was assessed using the Collision Deformation Classification (CDC) code and images of the damaged vehicle, and trauma scores were used to determine injury severity. Serious chest injury was defined as an Abbreviated Injury Scale (AIS) score for the chest code was more than 3. The patients were divided into two groups: serious chest injury patients with MAIS ≥ 3 and those with non-serious chest injury with MAIS < 3. A predictive model to analyze the factors affecting the presence of serious chest injury in the occupants on MVCs was constructed by a logistic regression analysis. RESULTS: Among the 1,226 patients with chest injuries, 484 (39.5%) had serious chest injuries. Patients in the serious group were older than those in the non-serious group (p=.001). In analyses based on vehicle type, the proportion of light truck occupants was higher in the serious group than in the non-serious group (p=.026). The rate of seatbelt use was lower in the serious group than in the non-serious group (p=.008). The median crush extent (seventh column of the CDC code) was higher in the serious group than in the non-serious group (p<.001). Emergency room data showed that the rates of intensive care unit (ICU) admission and death were higher among patients with serious injuries (p<.001). Similarly, the general ward/ICU admission data showed that the transfer and death rates were higher in patients with serious injuries (p<.001). The median ISS was higher in the serious group than in the non-serious group (p<.001). A predictive model was derived based on sex, age, vehicle type, seating row, belt status, collision type, and crush extent. This predictive model had an explanatory power of 67.2% for serious chest injuries. The model was estimated for external validation using the confusion matrix by applying the predictive model to the 2019 and 2020 data of the same structure as the data at the time of model development in the KIDAS database. CONCLUSIONS: Although this study had a major limitation in that the explanatory power of the predictive model was weak due to the small number of samples and many exclusion conditions, it was meaningful in that it suggested a model that could predict serious chest injuries in motor vehicle occupants (MVOs) based on actual accident investigation data in Korea. Future studies should yield more meaningful results, for example, if the chest compression depth value is derived through the reconstruction of MVCs using accurate collision speed values, and better models can be developed to predict the relationship between these values and the occurrence of serious chest injury.

Risk factors affecting severe thoracic injuries in motor vehicle collisions based on age group and collision directions.

OBJECTIVE: This study aimed to investigate the effect of age and collision direction on the severity of thoracic injuries based on a real-world crash database. METHODS: This was a retrospective, observational study. We used the Korean In-Depth Accident Study (KIDAS) database, which was collected from crash injury patients who visited emergency medical centers between January 2011 and February 2022 in Korea. Among the 4520 patients enrolled in the database, we selected 1908 adult patients with abbreviated injury scale (AIS) scores between 0 and 6 in the thoracic region. We classified patients with an AIS score of 3 or higher into the severe injury group. RESULTS: The incidence rate of severe thoracic injuries due to motor vehicle accidents was 16.4%. Between the severe and non-severe thoracic injury groups, there were significant differences in sex, age, collision direction, crash object, seatbelt use, and delta-V parameters. Among the age groups, over 55 years occupants had a higher risk in the thoracic regions than those under 54 years occupants. The risk of severe thoracic injury was highest in near-side collisions in all collision directions. Far-side and rear-end collisions showed a lower risk than frontal collisions. Occupants with unfastened seatbelts were at greater risk. CONCLUSIONS: The risk of severe thoracic injury is high in near-side collisions among elderly occupants. However, the risk of injury for elderly occupants increases in a super-aging society. To reduce thoracic injury, safety features made for elderly occupants in near-side collisions are required.

Machine learning-based injury severity prediction of level 1 trauma center enrolled patients associated with car-to-car crashes in Korea.

Injury prediction models enables to improve trauma outcomes for motor vehicle occupants in accurate decision-making and early transport to appropriate trauma centers. This study aims to investigate the injury severity prediction (ISP) capability in machine-learning analytics based on five-different regional Level 1 trauma center enrolled patients in Korea. We study car crash-related injury data of 1417 patients enrolled in the Korea In-Depth Accident Study database from January 2011 to April 2021. Severe injury classification was defined using an Injury Severity Score of 15 or greater. A planar crash was considered by excluding rollovers to compromise an accurate prediction. Furthermore, dissimilarities of the collision partner component based on vehicle segmentation were assumed for crash incompatibility. To handle class-imbalanced clinical datasets, we used four data-sampling techniques (i.e., class-weighting, resampling, synthetic minority oversampling, and adaptive synthetic sampling). Machine-learning analytics based on logistic regression, extreme gradient boosting (XGBoost), and a multilayer perceptron model were used for the evaluations. Each model was executed using five-fold cross-validation to solve overfitting consistent with the hyperparameters tuned to improve model performance. The area under the receiver operating characteristic curve of 0.896. Additionally, the present ISP model showed an under-triage rate of 6.1%. The Delta-V, age, and Principal ~ were significant predictors. The results demonstrated that the data-balanced XGBoost model achieved a reliable performance on injury severity classification of emergency department patients. This finding considers ISP model selection, which affected prediction performance based on overall predictor variables.

Preventive Effectiveness of Thoracic Side Airbags in Side-Impact Crashes Based on Korea In-Depth Accident Study (KIDAS) Database.

Studies on the effectiveness of thoracic side airbags (tSABs) in preventing thoracic injuries is limited and conflicting. This retrospective observational study aims to evaluate the effectiveness of tSABs in side-impact crashes based on data for motor vehicle occupants (MVOs) who visited an emergency department in Korea. The data were obtained from the Korean In-Depth Accident Study (KIDAS) database for patients treated at Wonju Severance Christian Hospital between January 2011 and April 2020. Of the 3899 patients with road traffic injuries, data for 490 patients were used. The overall frequency of tSAB deployment in side-impact crashes was found to be 8.1%. In the multivariate analysis, elderly age, near-side impact, colliding with fixed objects, non-oblique force, and higher crush extent were found to be factors associated with higher thoracic injuries (Abbreviated Injury Scale ≥ 2). MVOs in crashes with tSAB deployment were at an increased risk of injury compared with MVOs in crashes with no deployment, but no statistical difference was observed [adjusted odds ratios (AORs): 1.65 (0.73-3.73)]. Further, the incidence of lung injury and rib fractures increased with tSAB activation (p < 0.05). These results demonstrate the limited capability of tSABs in preventing thoracic injuries in motor vehicle crashes.

AC-motif: a DNA motif containing adenine and cytosine repeat plays a role in gene regulation.

I-motif or C4 is a four-stranded DNA structure with a protonated cytosine:cytosine base pair (C+:C) found in cytosine-rich sequences. We have found that oligodeoxynucleotides containing adenine and cytosine repeats form a stable secondary structure at a physiological pH with magnesium ion, which is similar to i-motif structure, and have named this structure 'adenine:cytosine-motif (AC-motif)'. AC-motif contains C+:C base pairs intercalated with putative A+:C base pairs between protonated adenine and cytosine. By investigation of the AC-motif present in the CDKL3 promoter (AC-motifCDKL3), one of AC-motifs found in the genome, we confirmed that AC-motifCDKL3 has a key role in regulating CDKL3 gene expression in response to magnesium. This is further supported by confirming that genome-edited mutant cell lines, lacking the AC-motif formation, lost this regulation effect. Our results verify that adenine-cytosine repeats commonly present in the genome can form a stable non-canonical secondary structure with a non-Watson-Crick base pair and have regulatory roles in cells, which expand non-canonical DNA repertoires.

Analysis of the Impact of the Coronavirus Disease Epidemic on the Emergency Medical System in South Korea Using the Korean Triage and Acuity Scale.

PURPOSE: Severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19), has spread worldwide. Global health systems, including emergency medical systems, are suffering from a lack of medical resources. Using a method for classifying patients visiting the emergency department (ED), we aimed to investigate trends in emergency medical system usage during the COVID-19 epidemic in Korea. MATERIALS AND METHODS: This retrospective observational study included patients who visited emergency medical institutions registered with the National Emergency Department Information System database from January 1, 2017 to May 31, 2020. The primary outcome was identification of changes in the distribution of patients visiting the ED according to the type of emergency medical institution. The secondary outcome was a detailed comparison of Korean Triage and Acuity Scale (KTAS) levels and patient distributions before and during the infectious disaster crisis period. RESULTS: Severe patients visited regional emergency centers (RECs) and local emergency centers (LECs) more frequently during the COVID-19 period, and disposition status warranting admission to the intensive care unit or resulting in death was more common in RECs and LECs during the COVID-19 period [RECs, before COVID-19: 300686 (6.3%), during COVID-19: 33548 (8.0%) (p<0.001); LECs, before COVID-19: 373593 (3.7%), during COVID-19: 38873 (4.5%) (p<0.001)]. CONCLUSION: During the COVID-19 period, severe patients were shifted to advanced emergency medical institutions, and the KTAS better reflected severe patients. Patient distribution according to the stage of emergency medical institution improved, and validation of the KTAS triage increased more in RECs.

In vivo genome editing in single mammalian brain neurons through CRISPR-Cas9 and cytosine base editors.

Gene manipulation is a useful approach for understanding functions of genes and is important for investigating basic mechanisms of brain function on the level of single neurons and circuits. Despite the development and the wide range of applications of CRISPR-Cas9 and base editors (BEs), their implementation for an analysis of individual neurons in vivo remained limited. In fact, conventional gene manipulations are generally achieved only on the population level. Here, we combined either CRISPR-Cas9 or BEs with the targeted single-cell electroporation technique as a proof-of-concept test for gene manipulation in single neurons in vivo. Our assay consisted of CRISPR-Cas9- or BEs-induced gene knockout in single Purkinje cells in the cerebellum. Our results demonstrate the feasibility of both gene editing and base editing in single cells in the intact brain, providing a tool through which molecular perturbations of individual neurons can be used for analysis of circuits and, ultimately, behaviors.

A Predictive Model to Analyze the Factors Affecting the Presence of Traumatic Brain Injury in the Elderly Occupants of Motor Vehicle Crashes Based on Korean In-Depth Accident Study (KIDAS) Database.

Traumatic brain injury (TBI), also known as intracranial injury, occurs when an external force injures the brain. This study aimed to analyze the factors affecting the presence of TBI in the elderly occupants of motor vehicle crashes. We defined elderly occupants as those more than 55 years old. Damage to the vehicle was presented using the Collision Deformation Classification (CDC) code by evaluation of photos of the damaged vehicle, and a trauma score was used for evaluation of the severity of the patient's injury. A logistic regression model was used to identify factors affecting TBI in elderly occupants and a predictive model was constructed. We performed this study retrospectively and gathered all the data under the Korean In-Depth Accident Study (KIDAS) investigation system. Among 3697 patients who visited the emergency room in the regional emergency medical center due to motor vehicle crashes from 2011 to 2018, we analyzed the data of 822 elderly occupants, which were divided into two groups: the TBI patients (N = 357) and the non-TBI patients (N = 465). According to multiple logistic regression analysis, the probabilities of TBI in the elderly caused by rear-end (OR = 1.833) and multiple collisions (OR = 1.897) were higher than in frontal collision. Furthermore, the probability of TBI in the elderly was 1.677 times higher in those with unfastened seatbelts compared to those with fastened seatbelts (OR = 1.677). This study was meaningful in that it incorporated several indicators that affected the occurrence of the TBI in the elderly occupants. In addition, it was performed to determine the probability of TBI according to sex, vehicle type, seating position, seatbelt status, collision type, and crush extent using logistic regression analysis. In order to derive more precise predictive models, it would be needed to analyze more factors for vehicle damage, environment, and occupant injury in future studies.

Injury analysis of patients according to impact patterns involved in pedestrian traffic crashes.

OBJECTIVES: In cases of car-to-person pedestrian traffic crashes (PTCs), the principal issue is determining at what point the car collided with the pedestrian. Accordingly, the objective of the present study was to use the medical records of patients injured in PTCs to investigate the characteristics of crash types and the areas and injury severity and to determine whether there are differences in injuries due to the angle, motion, and position at the point of impact. METHODS: The present study examined 231 PTC patients admitted to the emergency room (ER) between January and December 2014. Electronic medical records from the hospital were used to divide the patient data according to Abbreviated Injury Scale (AIS) codes for injured areas based on sex, age, time of the crash, outcomes after ER treatment, and major symptoms. Among 231 patients, police reports on 67 crash cases, involving 70 people, were obtained with the help of local police departments, and these reports were used to reconstruct details of the actual crash. For statistical analysis, a chi-square test and a one-way analysis of variance calculation were used to compare the Injury Severity Score (ISS) based on groups and stages, with a statistical significance level set to P < .05. RESULTS: With respect to patients who were admitted for PTC, 52.4% were females and 47.6% were males. The frequency of crashes was high in middle-aged and elderly groups, as well as for youths between 10 and 19 years old. With respect to outcomes after ER treatment, discharge to home after symptom improvement was the most common outcome (24.6%). Admissions to the intensive care unit (25.1%) and to the general ward (23.8%) were also high. In terms of major symptoms, the most common injuries were to the head, resulting from a rotatory motion post impact (35.9%), and injuries to the legs, resulting from the impact of a direct collision with an object (25.1%). CONCLUSIONS: This study demonstrated that injuries to the chest and abdomen were the most severe in the fender vault group and head and neck injuries were the most severe in the roof vault group. In particular, the Injury Severity Score was highest in the roof vault group.

A predictive model to analyze factors affecting the presence of mild whiplash-associated disorders in minor motor vehicle crashes based on the Korean In-Depth Accidents Study (KIDAS) Database.

OBJECTIVES: We aimed to analyze factors affecting the severity of mild whiplash-associated disorders (WADs) and to develop a predictive model to evaluate the presence of mild WAD in minor motor vehicle crashes (MVCs). METHODS: We used the Korean In-Depth Accident Study (KIDAS) database, which collects data from 4 regional emergency centers, to obtain data from 2011 to 2017. The Collision Deformation Classification code was obtained as vehicle's damage information, and Abbreviated Injury Scale (AIS), Maximum Abbreviated Injury Scale (MAIS), and Injury Severity Score (ISS) were used as occupant's injury information. The degree of WAD was determined using the Quebec Task Force (QTF) classification, comprised of 5 stages (QTF 0-4), depending on the occupant's pain and the physician's findings. QTF 1 was defined as mild WAD, and we used QTF 0 to define those who were uninjured. For KIDAS data between 2011 and 2016, a logistic regression model was used to identify factors affecting the occurrence of mild WAD and a predictive model was constructed. Internal validity was estimated using random bootstrapping, and external validity was evaluated by applying 2017 KIDAS data. Of the 2,629 occupants in the KIDAS database from 2011 to 2016, after applying several exclusion conditions, 459 occupants were used to develop the predictive model. The external validity of the derived predictive model was assessed using the 13 MVC occupants from the 2017 KIDAS database meeting our inclusion criteria. Among the 137 MVC occupants from the 2017 KIDAS database for analysis of the external validity of the derived predictive model, the predictive model was verified for 13 MVC occupants. RESULTS: Logistic regression analysis was used to derive a predictive model based on sex, age, body mass index, type of vehicle, belt status, seating row, crush type, and crush extent. This predictive model had an explanatory power of 65.5% to determine an actual QTF of 0 and 1 (c-statistics: 0.655). As a result of the external validity analysis of the predictive model using data from the 2017 KIDAS database (N = 13), sensitivity, specificity, and accuracy were 0.500, 0.857, and 0.692, respectively. CONCLUSIONS: Using the predictive model, the results of the external validity analysis showed low sensitivity but high specificity. This predictive model provided meaningful results, with a high success rate for determining no injury to an occupant. Given our study results, future research is needed to create a more accurate predictive model that includes relevant technical and sociological factors.