Nomogram for predicting in-hospital mortality in trauma patients undergoing resuscitative endovascular balloon occlusion of the aorta: a retrospective multicenter study.

Recently, resuscitative endovascular balloon occlusion of the aorta (REBOA) had been introduced as an innovative procedure for severe hemorrhage in the abdomen or pelvis. We aimed to investigate risk factors associated with mortality after REBOA and construct a model for predicting mortality. This multicenter retrospective study collected data from 251 patients admitted at five regional trauma centers across South Korea from 2015 to 2022. The indications for REBOA included patients experiencing hypovolemic shock due to hemorrhage in the abdomen, pelvis, or lower extremities, and those who were non-responders (systolic blood pressure (SBP) < 90 mmHg) to initial fluid treatment. The primary and secondary outcomes were mortality due to exsanguination and overall mortality, respectively. After feature selection using the least absolute shrinkage and selection operator (LASSO) logistic regression model to minimize overfitting, a multivariate logistic regression (MLR) model and nomogram were constructed. In the MLR model using risk factors selected in the LASSO, five risk factors, including initial heart rate (adjusted odds ratio [aOR], 0.99; 95% confidence interval [CI], 0.98-1.00; p = 0.030), initial Glasgow coma scale (aOR, 0.86; 95% CI 0.80-0.93; p < 0.001), RBC transfusion within 4 h (unit, aOR, 1.12; 95% CI 1.07-1.17; p < 0.001), balloon occlusion type (reference: partial occlusion; total occlusion, aOR, 2.53; 95% CI 1.27-5.02; p = 0.008; partial + total occlusion, aOR, 2.04; 95% CI 0.71-5.86; p = 0.187), and post-REBOA systolic blood pressure (SBP) (aOR, 0.98; 95% CI 0.97-0.99; p < 0.001) were significantly associated with mortality due to exsanguination. The prediction model showed an area under curve, sensitivity, and specificity of 0.855, 73.2%, and 83.6%, respectively. Decision curve analysis showed that the predictive model had increased net benefits across a wide range of threshold probabilities. This study developed a novel intuitive nomogram for predicting mortality in patients undergoing REBOA. Our proposed model exhibited excellent performance and revealed that total occlusion was associated with poor outcomes, with post-REBOA SBP potentially being an effective surrogate measure.

Lyoprotectant Constituents Suited for Lyophilization and Reconstitution of Stem-Cell-Derived Extracellular Vesicles.

Stem-cell-derived extracellular vesicles (EVs) are emerging as an alternative approach to stem cell therapy. Successful lyophilization of EVs could enable convenient storage and distribution of EV medicinal products at room temperature for long periods, thus considerably increasing the accessibility of EV therapeutics to patients. In this study, we aimed to identify an appropriate lyoprotectant composition for the lyophilization and reconstitution of stem-cell-derived EVs. MSC-derived EVs were lyophilized using different lyoprotectants, such as dimethyl sulfoxide, mannitol, trehalose, and sucrose, at varying concentrations. Our results revealed that a mixture of trehalose and sucrose at high concentrations could support the formation of amorphous ice by enriching the amorphous phase of the solution, which successfully inhibited the acceleration of buffer component crystallization during lyophilization. Lyophilized and reconstituted EVs were thoroughly evaluated for concentration and size, morphology, and protein and RNA content. The therapeutic effects of the reconstituted EVs were examined using a tube formation assay with human umbilical vein endothelial cells. After rehydration of the lyophilized EVs, most of their generic characteristics were well-maintained, and their therapeutic capacity recovered to levels similar to those of freshly collected EVs. The concentrations and morphologies of the lyophilized EVs were similar to the initial features of the fresh EV group until day 30 at room temperature, although their therapeutic capacity appeared to decrease after 7 days. Our study suggests an appropriate composition of lyoprotectants, particularly for EV lyophilization, which could encourage the applications of stem-cell-derived EV therapeutics in the health industry.

Evaluation of the effect of meteorological risk forecasting service intervention on acute onset and medical expenses of patients with COPD in Pudong New Area of Shanghai / 上海预防医学

ObjectiveTo evaluate the intervention effect of meteorological risk forecasting service on acute onset and medical expenses of chronic obstructive pulmonary disease（COPD） patients， and to provide scientific basis for the establishment of health management model for chronic obstructive pulmonary disease（COPD） patients. MethodsStudy subjects were recruited from chronic obstructive pulmonary patients aged ≥40 in Pudong New Area. Propensity score matching method was used to determine the intervention group and the control group. The control group received regular health education and follow-up management， and the intervention group was provided with meteorological and environmental risk forecasting services through WeChat， mobile phone short message service（SMS）and telephone. Finally， a total of2 589 subjects were included in the analysis， including 1 300 in the intervention group and 1 289 in the control group. General demographic data， past medical history and family history of COPD， COPD related knowledge and practice survey， COPD related symptom assessment， acute onset， health service utilization and medical expenses before and after intervention were collected through questionnaire survey. The differences of acute attack， health service utilization and related medical expenses between the two groups before and after intervention were compared to evaluate the intervention effect. ResultsIn terms of acute attacks， after intervention， the incidence of acute attacks in the intervention group was lower than that before intervention（χ2=52.901， P<0.001）， and the incidence of acute attacks in the groups with different intervention methods was lower than that before intervention （P<0.001）. WeChat had the best effect， decreasing the incidence by 14.4%， followed by mobile phone SMS SMS decreasing by 12.3%. In terms of utilization of health services， the outpatient rate due to acute attack was lower in the intervention group after intervention than that before intervention （χ2=7.129， P=0.008）， and the outpatient rate due to acute attack was lower in the subjects who received the forecast service through mobile phone SMS than that before intervention （χ2=4.675， P<0.001）. In terms of medical expenses， there was no significant difference between control group and intervention group with different intervention methods before intervention （P>0.05）. After intervention， the difference between the control group and the intervention group with different intervention methods was statistically significant （H=11.864， P<0.05）. The results of multiple comparisons showed that compared with the control group， the average annual medical expenses of patients receiving mobile phone SMS and telephone forecasting services after intervention were lower than those of the control group， and the difference was statistically significant （P<0.05）. ConclusionMeteorological risk forecasting service can reduce the acute onset of COPD， reduce the rate of consultation and medical expenses due to acute onset， and provide scientific basis for the basic COPD health management model.

Investigating the effect and mechanism of Sophorae Flavescentis Radix and Rhizoma Smilacis Glabrae combination in ameliorating psoriatic lesions / 中国药理学通报

Aim In this study, a mouse model of psoriasis-like lesions induced by 62. 5 mg imiquimod was used to explore the effect and mechanism of Sophorae Flavescentis Radix and Rhizoma Smilacis Glabrae combination for the topical treatment of psoriasis. Methods Firstly, the topical administration of Sophorae Flavescentis Radix and Rhizoma Smilacis Glabrae combination for treating psoriasis in progressive and recurrent stages was evaluated by psoriatic mouse model and HE staining. Secondly, immunohistochemistry was used to study the regulatory effects of Sophorae Flavescentis Radix and Rhizoma Smilacis Glabrae combination on the pivotal pathological mechanism of psoriasis-the positive feedback loop between the abnormal proliferation of keratinocytes and skin immune microenvironment. Finally, metabolomics technology was used to explore whether Sophorae Flavescentis Radix and Rhizoma Smilacis Glabrae combination topically treat psoriasis by regulating inflammation-related metabolism and lipid metabolism pathways. Results The combination of Sophorae Flavescentis Radix and Rhizoma Smilacis Glabrae alleviated psoriasis-like lesions in mice. It effectively relieved the recurrence after the cure of psoriatic lesions in mice, and the efficacy is comparable to that of benweimod. The combination of Sophorae Flavescentis Radix and Rhizoma Smilacis Glabrae inhibited the proliferation of mouse epidermal keratinocytes and reduced the number of T cells in the skin. The potential molecular mechanism was that the combination of Sophorae Flavescentis Radix and Rhizoma Smilacis Glabrae regulated arachidonic acid metabolism, sphin- golipid metabolism, tryptophan metabolism and phenylalanine metabolism. Conclusions The combination of Sophora Flavescens Radix and Rhizoma Smilacis Glabrae can relieve psoriasis-like lesions in mice by inhibiting the proliferation of epidermal keratinocytes and reducing the number of T cells in the skin and regulating metabolism to intervene psoriasis recurrence. This study provides a potential topical drug of psoriasis for relieving psoriasis recurrence.

Novel nomogram for predicting paradoxical chest wall movement in patients with flail segment of traumatic rib fracture: a retrospective cohort study.

Flail chest is a severe injury to the chest wall and is related to adverse outcomes. A flail chest is classified as the physiologic, paradoxical motion of a chest wall or flail segment of rib fracture (RFX). We hypothesized that patients with paradoxical chest wall movement would present different clinical features from patients with a flail segment. This retrospective observational study included patients with blunt chest trauma who visited our level 1 trauma center between January 2019 and October 2022 and were diagnosed with one or more flail segments by computed tomography. The primary outcome of our study was a clinically diagnosed visible, paradoxical chest wall motion. We used the least absolute shrinkage and selection operator (LASSO) logistic regression model to minimize overfitting. After a feature selection using the LASSO regression model, we constructed a multivariable logistic regression (MLR) model and nomogram. A total of five risk factors were selected in the LASSO model and applied to the multivariable logistic regression model. Of these, four risk factors were statistically significant: the total number of RFX (adjusted OR [aOR], 1.28; 95% confidence interval [CI], 1.09-1.49; p = 0.002), number of segmental RFX including Grade III fractures (aOR, 1.78; 95% CI, 1.14-2.79; p = 0.012), laterally located primary fracture lines (aOR, 4.00; 95% CI, 1.69-9.43; p = 0.002), and anterior-lateral flail segments (aOR, 4.20; 95% CI, 1.60-10.99; p = 0.004). We constructed a nomogram to predict the personalized probability of the flail motion. A novel nomogram was developed in patients with flail segments of traumatic RFX to predict paradoxical chest wall motion. The number of RFX, Grade III segmental RFX, and the location of the RFX were significant risk factors.

Clinical implication of thoracic skeletal muscle volume as a predictor of ventilation-weaning failure in brain-injured patients: A retrospective observational study.

Sarcopenia, a generalized loss of skeletal muscle mass that is primarily evident in the respiratory musculature, is associated with adverse outcomes in critically ill patients. However, the relationship between sarcopenia and ventilation-weaning outcomes has not yet been fully studied in patients with brain injuries. In this study, we examined the effect of reduced respiratory muscle mass on ventilation weaning in patients with brain injury. This observational study retrospectively reviewed the medical records of 73 patients with brain injury between January 2017 and December 2019. Thoracic skeletal muscle volumes were measured from thoracic CT images using the institute's three-dimensional modeling software program of our institute. The thoracic skeletal muscle volumes index (TSMVI) was normalized by dividing muscle volume by the square of patient height. Sarcopenia was defined as a TSMVI of less than the 50th sex-specific percentile. Among 73 patients with brain injury, 12 (16.5%) failed to wean from mechanical ventilation. The patients in the weaning-failure group had significantly higher sequential organ failure assessment scores [7.8 ±â€…2.7 vs 6.1 ±â€…2.2, P = .022] and lower thoracic skeletal muscle volume indexes [652.5 ±â€…252.4 vs 1000.4 ±â€…347.3, P = .002] compared with those in the weaning-success group. In multivariate analysis, sarcopenia was significantly associated with an increased risk of weaning failure (odds ratio 12.72, 95% confidence interval 2.87-70.48, P = .001). Our study showed a significant association between the TSMVI and ventilation weaning outcomes in patients with brain injury.

Nonselective versus Selective Angioembolization for Trauma Patients with Pelvic Injuries Accompanied by Hemorrhage: A Meta-Analysis.

Background and Objectives: Angioembolization has emerged as an effective therapeutic approach for pelvic hemorrhages; however, its exact effect size concerning the level of embolized artery remains uncertain. Therefore, we conducted this systematic review and meta-analysis to investigate the effect size of embolization-related pelvic complications after nonselective angioembolization compared to that after selective angioembolization in patients with pelvic injury accompanying hemorrhage. Materials and Methods: Relevant articles were collected by searching the PubMed, EMBASE, and Cochrane databases until 24 June 2023. Meta-analyses were conducted using odds ratios (ORs) for binary outcomes. Quality assessment was conducted using the risk of bias tool in non-randomized studies of interventions. Results: Five studies examining 357 patients were included in the meta-analysis. Embolization-related pelvic complications did not significantly differ between patients with nonselective and selective angioembolization (OR 1.581, 95% confidence interval [CI] 0.592 to 4.225, I2 = 0%). However, in-hospital mortality was more likely to be higher in the nonselective group (OR 2.232, 95% CI 1.014 to 4.913, I2 = 0%) than in the selective group. In the quality assessment, two studies were found to have a moderate risk of bias, whereas two studies exhibited a serious risk of bias. Conclusions: Despite the favorable outcomes observed with nonselective angioembolization concerning embolization-related pelvic complications, determining the exact effect sizes was limited owing to the significant risk of bias and heterogeneity. Nonetheless, the low incidence of ischemic pelvic complications appears to be a promising result.

An Artificial Intelligence Model for Predicting Trauma Mortality Among Emergency Department Patients in South Korea: Retrospective Cohort Study.

BACKGROUND: Within the trauma system, the emergency department (ED) is the hospital's first contact and is vital for allocating medical resources. However, there is generally limited information about patients that die in the ED. OBJECTIVE: The aim of this study was to develop an artificial intelligence (AI) model to predict trauma mortality and analyze pertinent mortality factors for all patients visiting the ED. METHODS: We used the Korean National Emergency Department Information System (NEDIS) data set (N=6,536,306), incorporating over 400 hospitals between 2016 and 2019. We included the International Classification of Disease 10th Revision (ICD-10) codes and chose the following input features to predict ED patient mortality: age, sex, intentionality, injury, emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and vital signs. We compared three different feature set performances for AI input: all features (n=921), ICD-10 features (n=878), and features excluding ICD-10 codes (n=43). We devised various machine learning models with an ensemble approach via 5-fold cross-validation and compared the performance of each model with that of traditional prediction models. Lastly, we investigated explainable AI feature effects and deployed our final AI model on a public website, providing access to our mortality prediction results among patients visiting the ED. RESULTS: Our proposed AI model with the all-feature set achieved the highest area under the receiver operating characteristic curve (AUROC) of 0.9974 (adaptive boosting [AdaBoost], AdaBoost + light gradient boosting machine [LightGBM]: Ensemble), outperforming other state-of-the-art machine learning and traditional prediction models, including extreme gradient boosting (AUROC=0.9972), LightGBM (AUROC=0.9973), ICD-based injury severity scores (AUC=0.9328 for the inclusive model and AUROC=0.9567 for the exclusive model), and KTAS (AUROC=0.9405). In addition, our proposed AI model outperformed a cutting-edge AI model designed for in-hospital mortality prediction (AUROC=0.7675) for all ED visitors. From the AI model, we also discovered that age and unresponsiveness (coma) were the top two mortality predictors among patients visiting the ED, followed by oxygen saturation, multiple rib fractures (ICD-10 code S224), painful response (stupor, semicoma), and lumbar vertebra fracture (ICD-10 code S320). CONCLUSIONS: Our proposed AI model exhibits remarkable accuracy in predicting ED mortality. Including the necessity for external validation, a large nationwide data set would provide a more accurate model and minimize overfitting. We anticipate that our AI-based risk calculator tool will substantially aid health care providers, particularly regarding triage and early diagnosis for trauma patients.

Novel nomogram for predicting pulmonary complications in patients with blunt chest trauma with rib fractures: a retrospective cohort study.

The direct consequences of chest trauma may cause adverse outcomes. Therefore, the early detection of high-risk patients and appropriate interventions can improve patient outcomes. This study aimed to investigate the risk factor for overall pulmonary complications in patients with blunt traumatic rib fractures. Prospectively recorded data of patients with blunt chest trauma in a level 1 trauma center between January 2019 and October 2022 were retrospectively analyzed. The primary outcomes were one or more pulmonary complications. To minimize the overfitting of the prediction model, we used the least absolute shrinkage and selection operator (LASSO) logistic regression. We input selected features using LASSO regression into the multivariable logistic regression model (MLR). We also constructed a nomogram to calculate approximate individual probability. Altogether, 542 patients were included. The LASSO regression model identified age, injury severity score (ISS), and flail motion of the chest wall as significant risk factors. In the MLR analysis, age (adjusted OR [aOR] 1.06; 95% confidence interval [CI] 1.03-1.08; p < 0.001), ISS (aOR 1.10; 95% CI 1.05-1.16; p < 0.001), and flail motion (aOR 8.82; 95% CI 4.13-18.83; p < 0.001) were significant. An MLR-based nomogram predicted the individual risk, and the area under the receiver operating characteristic curve was 0.826. We suggest a novel nomogram with good performance for predicting adverse pulmonary outcomes. The flail motion of the chest wall may be the most significant risk factor for pulmonary complications.

Research Progress on the Role of Akkermansia Muciniphila in Prevention and Treatment of Diabetes Mellitus / 中国医学科学院学报

The correlation between intestinal flora and diseases has become a hot research topic in recent years.Since the incidence of diabetes is closely related to chronic low-grade inflammation and intestinal flora disorders,the intervention of intestinal flora imbalance has become a research focus in the prevention and treatment of diabetes mellitus.Akkermansia muciniphila(A.muciniphila) stands out among the intestinal flora as it can alleviate the diabetes-related symptoms by regulating glucagon-like peptide 1 (GLP-1) level,improving intestinal barrier function,and inhibiting chronic inflammation,which is a potential target for the prevention and treatment of diabetes.The reduction in the abundance of A.muciniphila is a marker for the early diagnosis of diabetes.The available studies have demonstrated that the administration with A.muciniphila alone can significantly attenuate inflammation and other related symptoms of diabetic patients.Moreover,A.muciniphila has good safety and can be tolerated by human body.Therefore,A.muciniphila has the potential to serve as a new species of probiotics for the treatment of diabetes.The clinical measures for treating diabetes,such as metformin,Chinese herbal medicines,and functional diet,have been confirmed to be associated with the increased abundance of A.muciniphila.Among them,Chinese herbal medicines can treat diabetes via multiple targets and pathways in a systemic manner.Studies have reported that A.muciniphila is a potential target of Chinese herbal medicines intervening in diabetes.After the administration of Chinese herbal medicines,the improvement of diabetes-related indicators was positively correlated with the abundance of A.muciniphila.The above evidence provides a new idea for the research on the interaction between Chinese herbal medicines and intestinal flora in the treatment of diabetes.Therefore,this paper reviewed the role of A.muciniphila in diabetes and the correlation between the abundance of A.muciniphila and the administration of Chinese herbal medicines,aiming to provide new measures for the prevention and treatment of diabetes.

Model for Predicting In-Hospital Mortality of Physical Trauma Patients Using Artificial Intelligence Techniques: Nationwide Population-Based Study in Korea.

BACKGROUND: Physical trauma-related mortality places a heavy burden on society. Estimating the mortality risk in physical trauma patients is crucial to enhance treatment efficiency and reduce this burden. The most popular and accurate model is the Injury Severity Score (ISS), which is based on the Abbreviated Injury Scale (AIS), an anatomical injury severity scoring system. However, the AIS requires specialists to code the injury scale by reviewing a patient's medical record; therefore, applying the model to every hospital is impossible. OBJECTIVE: We aimed to develop an artificial intelligence (AI) model to predict in-hospital mortality in physical trauma patients using the International Classification of Disease 10th Revision (ICD-10), triage scale, procedure codes, and other clinical features. METHODS: We used the Korean National Emergency Department Information System (NEDIS) data set (N=778,111) compiled from over 400 hospitals between 2016 and 2019. To predict in-hospital mortality, we used the following as input features: ICD-10, patient age, gender, intentionality, injury mechanism, and emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and procedure codes. We proposed the ensemble of deep neural networks (EDNN) via 5-fold cross-validation and compared them with other state-of-the-art machine learning models, including traditional prediction models. We further investigated the effect of the features. RESULTS: Our proposed EDNN with all features provided the highest area under the receiver operating characteristic (AUROC) curve of 0.9507, outperforming other state-of-the-art models, including the following traditional prediction models: Adaptive Boosting (AdaBoost; AUROC of 0.9433), Extreme Gradient Boosting (XGBoost; AUROC of 0.9331), ICD-based ISS (AUROC of 0.8699 for an inclusive model and AUROC of 0.8224 for an exclusive model), and KTAS (AUROC of 0.1841). In addition, using all features yielded a higher AUROC than any other partial features, namely, EDNN with the features of ICD-10 only (AUROC of 0.8964) and EDNN with the features excluding ICD-10 (AUROC of 0.9383). CONCLUSIONS: Our proposed EDNN with all features outperforms other state-of-the-art models, including the traditional diagnostic code-based prediction model and triage scale.

Flat Inferior Vena Cava on Computed Tomography for Predicting Shock and Mortality in Trauma: A Meta-Analysis.

Hypovolemia may be underestimated due to compensatory mechanisms. In this systematic review and meta-analysis, we investigated the diagnostic accuracy of a flat inferior vena cava (IVC) on computed tomography (CT) for predicting the development of shock and mortality in trauma patients. Relevant studies were obtained by searching PubMed, EMBASE, and Cochrane databases (articles up to 16 September 2022). The number of 2-by-2 contingency tables for the index test were collected. We adopted the Bayesian bivariate random-effects meta-analysis model. Twelve studies comprising a total of 1706 patients were included. The flat IVC on CT showed 0.46 pooled sensitivity (95% credible interval [CrI] 0.32-0.63), 0.87 pooled specificity (95% CrI 0.78-0.94), and 0.78 pooled AUC (95% CrI 0.58-0.93) for the development of shock. The flat IVC for mortality showed 0.48 pooled sensitivity (95% CrI 0.21-0.94), 0.70 pooled specificity (95% CrI 0.47-0.88), and 0.60 pooled AUC (95% CrI 0.26-0.89). Regarding the development of shock, flat IVC provided acceptable accuracy with high specificity. Regarding in-hospital mortality, the flat IVC showed poor accuracy. However, these results should be interpreted with caution due to the high risk of bias and substantial heterogeneity in some included studies.

Time from Injury to Initial Operation May Be the Sole Risk Factor for Postoperative Leakage in AAST-OIS 2 and 3 Traumatic Duodenal Injury: A Retrospective Cohort Study.

Background and Objectives: Traumatic duodenal injury is a rare disease with limited evidence. We aimed to evaluate the risk factors for postoperative leakage and outcomes of pyloric exclusion after duodenal grade 2 and 3 injury. Materials and Methods: We reviewed a prospectively collected trauma database for the period January 2004-December 2020. Patients with grade 2 and 3 traumatic duodenal injury were included. To identify the risk factors for postoperative leakage, we used a stepwise multivariable logistic regression model and a least absolute shrinkage and selection operator (LASSO) logistic model. We constructed a receiver operator characteristic (ROC) curve to predict risk factors for postoperative leakage. Results: During the 17-year period, 179,887 trauma patients were admitted to a regional trauma center in Korea. Of these patients, 74 (0.04%) had duodenal injuries. A total of 49 consecutive patients had grade 2 and 3 traumatic duodenal injuries and underwent laparotomy. The incidence of postoperative leakage was 32.6% (16/49). Overall mortality was 18.4% (9/49). A stepwise multivariable logistic regression and LASSO logistic regression model showed that time from injury to initial operation was the sole statistically significant risk factor. The ROC curve at the optimal threshold of 15.77 h showed the following: area under ROC curve, 0.782; sensitivity, 68.8%; specificity, 87.9%; positive predictive value, 73.3%; and negative predictive value, 85.3%. There was no significant difference in outcomes between primary repair alone and pyloric exclusion. Conclusions: Time from injury to initial operation may be the sole significant risk factor for postoperative duodenal leakage. Pyloric exclusion may not be able to prevent postoperative leakage.

Effect of Different Anesthesia and Puncture Methods of Percutaneous Kyphoplasty on More Than 90-Year-Old Osteoporotic Vertebral Fracture: Advantages of the ERAS Concept.

Background: Percutaneous kyphoplasty (PKP) is an effective minimally invasive technique for the treatment of osteoporotic vertebral fracture (OVF) in recent years. This study focuses on the analysis of PKP surgery and anesthesia in osteoporotic vertebral facture patients over 90 years old with the concept of "enhanced recovery after surgery." Methods: This study reviewed 239 patients who were diagnosed with OVF retrospectively between October 2015 and June 2019. According to the method of anesthesia, these patients were divided into Group A (n = 125) and Group B (n = 114). According to the pedicle puncture approach, these patients were divided into Group C (n = 102) and Group D (n = 137). The anterior vertebral height (AVH) and local kyphosis angle (LKA) were used to evaluate the degree of vertebral damage and restoration. The visual analogue scale (VAS) and the Oswestry Disability Index (ODI) scores were used for assessing functional outcomes. Some parameters were used to assess the perioperative conditions such as operation time, amount of bone cement perfusion, intraoperative fluoroscopy times, anesthesia recovery time, time out of the bed, hospital stay, hospitalization cost, and complications. Results: The visual analogue scale (VAS), Oswestry Disability Index (ODI), anterior vertebral height (AVH), and local kyphosis angle (LKA) 1 day, 1 year after surgery, and at the last follow-up all showed significant improvement (P < 0.05) in comparison with those before surgery both in Groups A and B and Groups C and D. The ODI 1 day after surgery was significantly better in Group B than Group A (P < 0.05). Compared with Group B, Group A required longer time of anesthesia, operation time, anesthesia recovery time, time to get out of bed, and length of hospital stay and more hospitalization costs (P < 0.05). Group D required longer operation time, longer time to get out of bed, more bone cement volume, fluoroscopy time, and more operation hospitalization costs compared with Group C (P < 0.05). Conclusion: We recommend unilateral puncture under local anesthesia for OVF in the patients aged over 90 from the perspective of rapid recovery.

Efficacy and Safety of Laparoscopy for Mild and Moderate Pediatric Abdominal Trauma: A Systematic Review and Meta-Analysis.

In this systematic review and meta-analysis, we aimed to investigate the efficacy and safety of laparoscopy for pediatric patients with abdominal trauma. Relevant articles were obtained by searching the MEDLINE PubMed, EMBASE, and Cochrane databases until 7 December 2021. Meta-analyses were performed using odds ratio (OR) for binary outcomes, standardized mean differences (SMDs) for continuous outcome measures, and overall proportion for single proportional outcomes. Nine studies examining 12,492 patients were included in our meta-analysis. Our meta-analysis showed younger age (SMD -0.47, 95% confidence interval (CI) -0.52 to -0.42), lower injury severity score (SMD -0.62, 95% CI -0.67 to -0.57), shorter hospital stay (SMD -0.55, 95% CI -0.60 to -0.50), less complications (OR 0.375, 95% CI 0.309 to 0.455), and lower mortality rate (OR 0.055, 95% CI 0.0.28 to 0.109) in the laparoscopy group compared to the laparotomy group. The majority of patients were able to avoid laparotomy (0.816, 95% CI 0.800 to 0.833). There were no missed injuries during the laparoscopic procedures in seven eligible studies. Laparoscopy for stable pediatric patients showed favorable outcomes in terms of morbidity and mortality. There were no missed injuries, and laparotomy could be avoided for the majority of patients.

Gender Bias in Artificial Intelligence: Severity Prediction at an Early Stage of COVID-19.

Artificial intelligence (AI) technologies have been applied in various medical domains to predict patient outcomes with high accuracy. As AI becomes more widely adopted, the problem of model bias is increasingly apparent. In this study, we investigate the model bias that can occur when training a model using datasets for only one particular gender and aim to present new insights into the bias issue. For the investigation, we considered an AI model that predicts severity at an early stage based on the medical records of coronavirus disease (COVID-19) patients. For 5,601 confirmed COVID-19 patients, we used 37 medical records, namely, basic patient information, physical index, initial examination findings, clinical findings, comorbidity diseases, and general blood test results at an early stage. To investigate the gender-based AI model bias, we trained and evaluated two separate models-one that was trained using only the male group, and the other using only the female group. When the model trained by the male-group data was applied to the female testing data, the overall accuracy decreased-sensitivity from 0.93 to 0.86, specificity from 0.92 to 0.86, accuracy from 0.92 to 0.86, balanced accuracy from 0.93 to 0.86, and area under the curve (AUC) from 0.97 to 0.94. Similarly, when the model trained by the female-group data was applied to the male testing data, once again, the overall accuracy decreased-sensitivity from 0.97 to 0.90, specificity from 0.96 to 0.91, accuracy from 0.96 to 0.91, balanced accuracy from 0.96 to 0.90, and AUC from 0.97 to 0.95. Furthermore, when we evaluated each gender-dependent model with the test data from the same gender used for training, the resultant accuracy was also lower than that from the unbiased model.

Artificial intelligence to predict in-hospital mortality using novel anatomical injury score.

The aim of the study is to develop artificial intelligence (AI) algorithm based on a deep learning model to predict mortality using abbreviate injury score (AIS). The performance of the conventional anatomic injury severity score (ISS) system in predicting in-hospital mortality is still limited. AIS data of 42,933 patients registered in the Korean trauma data bank from four Korean regional trauma centers were enrolled. After excluding patients who were younger than 19 years old and those who died within six hours from arrival, we included 37,762 patients, of which 36,493 (96.6%) survived and 1269 (3.4%) deceased. To enhance the AI model performance, we reduced the AIS codes to 46 input values by organizing them according to the organ location (Region-46). The total AIS and six categories of the anatomic region in the ISS system (Region-6) were used to compare the input features. The AI models were compared with the conventional ISS and new ISS (NISS) systems. We evaluated the performance pertaining to the 12 combinations of the features and models. The highest accuracy (85.05%) corresponded to Region-46 with DNN, followed by that of Region-6 with DNN (83.62%), AIS with DNN (81.27%), ISS-16 (80.50%), NISS-16 (79.18%), NISS-25 (77.09%), and ISS-25 (70.82%). The highest AUROC (0.9084) corresponded to Region-46 with DNN, followed by that of Region-6 with DNN (0.9013), AIS with DNN (0.8819), ISS (0.8709), and NISS (0.8681). The proposed deep learning scheme with feature combination exhibited high accuracy metrics such as the balanced accuracy and AUROC than the conventional ISS and NISS systems. We expect that our trial would be a cornerstone of more complex combination model.

Reduced infectivity but increased immune escape of the new SARS-CoV-2 variant of concern Omicron

A new detected SARS-CoV-2 variant Omicron (B.1.1.529) had reported from more than 80 countries. In the past few weeks, a new wave of infection driven by Omicron is in progress. Omicron Spike (S) protein pseudotyped virus was used to determine the effect of S mutations on its capacity of infectivity and immune evasion. Our results showed the lower entry efficiency and less cleavage ability of Omicron than D614G variant. Pseudotype-based neutralizing assay was performed to analyze neutralizing antibodies elicited by previously infection or the RBD-based protein subunit vaccine ZF2001 against the Omicron variant. Sera sampled at around one month after symptom onset from 12 convalescents who were previously infected by SARS-CoV-2 original strain shows a more than 20-fold decrease of neutralizing activity against Omicron variant, when compared to D614G variant. Among 12 individuals vaccinated by RBD subunit vaccine, 58.3% (7/12) sera sampled at 15-60 days after 3rd-dose vaccination did not neutralize Omicron. Geometric mean titers (GMTs, 50% inhibitory dose [ID50]) of these sera against Omicron were 9.4-fold lower than against D614G. These results suggested a higher risk of Omicron breakthrough infections and reduced efficiency of the protective immunity elicited by existing vaccines. There are important implications about the modification and optimization of the current epidemic prevention and control including vaccine strategies and therapeutic antibodies against Omicron variant.

Efficacy and Safety of Vacuum-Assisted Excision for Benign Breast Mass Lesion: A Meta-Analysis.

Background and Objective: Breast mass lesions are common; however, determining the malignant potential of the lesion can be ambiguous. Recently, to evaluate breast mass lesions, vacuum-assisted excision (VAE) biopsy has been widely used for both diagnostic and therapeutic purposes. This study aimed to investigate the therapeutic role of VAE. Materials and Methods: Relevant articles were obtained by searching PubMed and EMBASE on 3 September 2021. Meta-analyses were performed using odds ratios and proportions. To assess heterogeneity, we conducted a subgroup analysis and meta-regression tests. Results: Finally, 26 studies comprising 18,170 patients were included. All of these were observational studies. The meta-analysis showed that the complete resection rate of VAE was 0.930. In the meta-regression test, there was no significant difference. The meta-analysis showed a recurrence rate of 0.039 in the VAE group. The meta-regression test showed no statistical significance. Postoperative hematoma, pain, and ecchymosis after VAE were 0.092, 0.082, and 0.075, respectively. Conclusion: VAE for benign breast lesions showed favorable outcomes with respect to complete resection and complications. This meta-analysis suggested that VAE for low-risk benign breast lesions is a reasonable option for both diagnostic and therapeutic purposes.