Outcome of Mechanical Thrombectomy for Acute Basilar Artery Occlusion in Patients with Intracranial Atherosclerotic Disease.

INTRODUCTION: Intracranial atherosclerotic disease (ICAD) has been identified as a major cause of acute basilar artery occlusion (BAO).This study compared the characteristics and treatment outcomes in acute BAO patients with and without ICAD. METHODS: A prospective cohort study was conducted at 115 People's Hospital, Ho Chi Minh city, Vietnam from August 2021 to June 2023. Patients with acute BAO who underwent endovascular treatment within 24 h from symptom onset were included (thrombectomy alone or bridging with intravenous alteplase). The baseline characteristics and outcomes were analyzed and compared between patients with and without ICAD. Good functional outcome was defined as mRS ≤3 at 90 days. RESULTS: Among the 208 patients enrolled, 112 (53.8%) patients were categorized in the ICAD group, and 96 (46.2%) in the non-ICAD group. Occlusion in the proximal segment of the basilar artery was more common in patients with ICAD (55.4% vs. 21.9%, p < 0.001), whereas the distal segment was the most common location in the non-ICAD group (58.3% vs. 10.7%, p < 0.001). Patients in the ICAD group were more likely to undergo treatment in the late window, with a higher mean onset-to-treatment time compared to the non-ICAD group (11.6 vs. 9.5 h, p = 0.01). In multivariable logistic regression analysis, distal segment BAO was negatively associated with ICAD (aOR 0.13, 95% CI: 0.05-0.32, p < 0.001), while dyslipidemia showed a positive association (aOR 2.44, 95% CI: 1.15-5.17, p = 0.02). There was a higher rate for rescue stenting in the ICAD compared to non-ICAD group (15.2% vs. 0%, p < 0.001). However, no significant differences were found between the two groups in terms of good outcome (45.5% vs. 44.8%, p = 0.91), symptomatic hemorrhage rates (4.5% vs. 8.3%, p = 0.25), and mortality (42% vs. 50%, p = 0.25). CONCLUSION: ICAD was a common etiology in patients with BAO. The location segment of BAO and dyslipidemia were associated with ICAD in patients with BAO. There was no difference in 90-day outcomes between BAO patients with and without ICAD undergoing endovascular therapy.

Flood hazard potential evaluation using decision tree state-of-the-art models.

Floods occur frequently in Romania and throughout the world and are one of the most devastating natural disasters that impact people's lives. Therefore, in order to reduce the potential damages, an accurate identification of surfaces susceptible to flood phenomena is mandatory. In this regard, the quantitative calculation of flood susceptibility has become a very popular practice in the scientific research. With the development of modern computerized methods such as geographic information system and machine learning models, and as a result of the possibility of combining them, the determination of areas susceptible to floods has become increasingly accurate, and the algorithms used are increasingly varied. Some of the most used and highly accurate machine learning algorithms are the decision tree models. Therefore, in the present study focusing on flood susceptibility zonation mapping in the Trotus River basin, the following algorithms were applied: forest by penalizing attribute-weights of evidence (forest-PA-WOE), best first decision tree-WOE, alternating decision tree-WOE, and logistic regression-WOE. The best performant, characterized by a maximum accuracy of 0.981, proved to be forest-PA-WOE, whereas in terms of flood exposure, an area of over 16.22% of the Trotus basin is exposed to high and very high floods susceptibility. The performances applied models in the present work are higher than the models applied in the previous studies in the same study area. Moreover, it should be noted that the accuracy of the models is similar with the accuracies of the decision tree models achieved in the studies focused on other areas across the world. Therefore, we can state that the models applied in the present research can be successfully used in by the researchers in other case studies. The findings of this research may substantially map the flood risk areas and further aid watershed managers in limiting and remediating flood damage in the data-scarce regions. Moreover, the results of this study can be a very useful for the hazard management and planning authorities.

Three-Port Transoral Robotic Thyroidectomy in Papillary Thyroid Carcinoma.

BACKGROUND: Although transoral thyroidectomy has become popular in thyroid surgery, transoral robotic thyroidectomy (TORT) has only been successfully applied in a very small number of medical centers worldwide.[1,2,3,4,5] In this video, we show a three-port TORT without an axillary incision for papillary thyroid carcinoma. PATIENT AND METHODS: A 35-year-old female with cT1aN0M0 papillary thyroid carcinoma had a strong motivation to proceed with surgery but avoid external neck incisions. Thus, we decided to perform a hemithyroidectomy with isthmusectomy using a transoral robotic approach, employing the da Vinci Xi surgical system. RESULTS: The operation was completed successfully without conversion to open surgery. The working space creation time, docking time, and console time were 30 min, 40 min, and 130 min, respectively. The pathological results were papillary thyroid carcinoma with 6- and 5-mm tumors. The patient was discharged 4 days after surgery without any complications such as bleeding, infection, mental nerve damage, permanent hoarseness, or hypoparathyroidism. The patient was completely satisfied with the cosmetic result. CONCLUSION: Three-port TORT without an axillary incision is a promising approach with optimal cosmetic outcomes. For Vietnam, a developing country, success in the application of TORT using the new da Vinci Xi robotic platform for thyroid cancer is an important milestone in the development of thyroid surgery.

A prospective cohort study on the association between waterpipe tobacco smoking and gastric cancer mortality in Northern Vietnam.

Tobacco consumption, as a worldwide problem, is a risk factor for several types of cancer. In Vietnam, tobacco consumption in the form of waterpipe tobacco smoking is common. This prospective cohort study aimed to study the association between waterpipe tobacco smoking and gastric cancer mortality in Northern Vietnam. A total of 25,619 eligible participants were followed up between 2008 and 2019. Waterpipe tobacco and cigarette smoking data were collected; semi-quantitative food frequency and lifestyle questionnaires were also utilized. Gastric cancer mortality was determined via medical records available at the state health facilities. A Cox proportional hazards model was used to estimate hazard ratios (HR) and 95% confidence intervals (95% CI). During 314,992.8 person-years of follow-up, 55 men and 25 women deaths due to gastric cancer were identified. With never-smokers as the reference, the risk of gastric cancer mortality was significantly increased in participants who were ever-smoking (HR = 2.43, 95% CI = 1.35-4.36). The positive risk was also observed in men but was not significantly increased in women. By types of tobacco use, exclusive waterpipe smokers showed a significantly increased risk of gastric cancer mortality (HR = 3.22, 95% CI = 1.67-6.21) but that was not significantly increased in exclusive cigarette smokers (HR = 1.90, 95% CI = 0.88-4.07). There was a significant positive association between tobacco smoking and gastric cancer death for indicators of longer smoking duration, higher frequency per day, and cumulative frequency of both waterpipe and cigarette smoking. Waterpipe tobacco smoking would significantly increase the risk of gastric cancer mortality in the Vietnamese population. Further studies are required to understand the waterpipe tobacco smoking-driven gastric cancer burden and promote necessary interventions.

Predicting sustainable arsenic mitigation using machine learning techniques.

This study evaluates state-of-the-art machine learning models in predicting the most sustainable arsenic mitigation preference. A Gaussian distribution-based Naïve Bayes (NB) classifier scored the highest Area Under the Curve (AUC) of the Receiver Operating Characteristic curve (0.82), followed by Nu Support Vector Classification (0.80), and K-Neighbors (0.79). Ensemble classifiers scored higher than 70% AUC, with Random Forest being the top performer (0.77), and Decision Tree model ranked fourth with an AUC of 0.77. The multilayer perceptron model also achieved high performance (AUC=0.75). Most linear classifiers underperformed, with the Ridge classifier at the top (AUC=0.73) and perceptron at the bottom (AUC=0.57). A Bernoulli distribution-based Naïve Bayes classifier was the poorest model (AUC=0.50). The Gaussian NB was also the most robust ML model with the slightest variation of Kappa score on training (0.58) and test data (0.64). The results suggest that nonlinear or ensemble classifiers could more accurately understand the complex relationships of socio-environmental data and help develop accurate and robust prediction models of sustainable arsenic mitigation. Furthermore, Gaussian NB is the best option when data is scarce.

Flood risk mapping and analysis using an integrated framework of machine learning models and analytic hierarchy process.

In this study, a new approach of machine learning (ML) models integrated with the analytic hierarchy process (AHP) method was proposed to develop a holistic flood risk assessment map. Flood susceptibility maps were created using ML techniques. AHP was utilized to combine flood vulnerability and exposure criteria. We selected Quang Binh province of Vietnam as a case study and collected available data, including 696 flooding locations of historical flooding events in 2007, 2010, 2016, and 2020; and flood influencing factors of elevation, slope, curvature, flow direction, flow accumulation, distance from river, river density, land cover, geology, and rainfall. These data were used to construct training and testing datasets. The susceptibility models were validated and compared using statistical techniques. An integrated flood risk assessment framework was proposed to incorporate flood hazard (flood susceptibility), flood exposure (distance from river, land use, population density, and rainfall), and flood vulnerability (poverty rate, number of freshwater stations, road density, number of schools, and healthcare facilities). Model validation suggested that deep learning has the best performance of AUC = 0.984 compared with other ensemble models of MultiBoostAB Ensemble (0.958), Random SubSpace Ensemble (0.962), and credal decision tree (AUC = 0.918). The final flood risk map shows 5075 ha (0.63%) in extremely high risk, 47,955 ha (5.95%) in high-risk, 40,460 ha (5.02%) in medium risk, 431,908 ha (53.55%) in low risk areas, and 281,127 ha (34.86%) in very low risk. The present study highlights that the integration of ML models and AHP is a promising framework for mapping flood risks in flood-prone areas.

Flood susceptibility evaluation through deep learning optimizer ensembles and GIS techniques.

It is difficult to predict and model with an accurate model the floods, that are one of the most destructive risks across the earth's surface. The main objective of this research is to show the prediction power of three ensemble algorithms with respect to flood susceptibility estimation. These algorithms are: Iterative Classifier Optimizer - Alternating Decision Tree - Frequency Ratio (ICO-ADT-FR), Iterative Classifier Optimizer - Deep Learning Neural Network - Frequency Ratio (ICO-DLNN-FR) and Iterative Classifier Optimizer - Multilayer Perceptron - Frequency Ratio (ICO-MLP-FR). The first stage of the manuscript consisted of the collection and processing of the geodatabase needed in the present study. The geodatabase comprises a number of 14 flood predictors and 132 known flood locations. The Correlation-based Feature Selection (CFS) method was used in order to assess the prediction capacity of the 14 predictors in terms of flood susceptibility estimation. The training and validation of the three ensemble models constitute the next stage of the scientific workflow. Several statistical metrics and ROC curve method were involved in the evaluation of the model's performance and accuracy. According to ROC curves all the models achieved high performances since their AUC had values above 0.89. ICO-DLNN-FR proved to be the most accurate model (AUC = 0.959). The outcomes of the study can be used to guide future flood risk management and sustainable land-use planning in the designated area.

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors.

There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network-Frequency Ratio (DLNN-FR), Deep Learning Neural Network-Weights of Evidence (DLNN-WOE), Alternating Decision Trees-Frequency Ratio (ADT-FR) and Alternating Decision Trees-Weights of Evidence (ADT-WOE). The model's performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPIDLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.

A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns.

In this study, a novel hybrid surrogate machine learning model based on a feedforward neural network (FNN) and one step secant algorithm (OSS) was developed to predict the load-bearing capacity of concrete-filled steel tube columns (CFST), whereas the OSS was used to optimize the weights and bias of the FNN for developing a hybrid model (FNN-OSS). For achieving this goal, an experimental database containing 422 instances was firstly gathered from the literature and used to develop the FNN-OSS algorithm. The input variables in the database contained the geometrical characteristics of CFST columns, and the mechanical properties of two CFST constituent materials, i.e., steel and concrete. Thereafter, the selection of the appropriate parameters of FNN-OSS was performed and evaluated by common statistical measurements, for instance, the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). In the next step, the prediction capability of the best FNN-OSS structure was evaluated in both global and local analyses, showing an excellent agreement between actual and predicted values of the load-bearing capacity. Finally, an in-depth investigation of the performance and limitations of FNN-OSS was conducted from a structural engineering point of view. The results confirmed the effectiveness of the FNN-OSS as a robust algorithm for the prediction of the CFST load-bearing capacity.

Left ventricular assist device recovery: does duration of mechanical support matter?

Heart failure is a widespread condition in the United States that is predicted to significantly increase in prevalence in the next decade. Many heart failure patients are given a left ventricular assist device (LVAD) while they wait for a heart transplant, while those that are not able to undergo a heart transplant may be given an LVAD permanently. However, past studies have observed a small subset of heart failure patients that recovered cardiac function of their native heart after being placed on an LVAD. As a result, some patients have been able to have their LVAD explanted and no longer needed a heart transplant. In this review, we analyzed the data of 15 studies that observed recovery of cardiac function in LVAD patients in order to investigate the effects that duration of LVAD support has on patient outcomes. From our review, we identified that there may be negative consequences of prolonged duration of mechanical support such as myocardial atrophy and abnormal calcium cycling as well as circumstances that may allow for a longer duration of LVAD support such as in patients using a continuous-flow LVAD, non-ischemic cardiomyopathy patients, and the specific pharmacological therapy.

Effectiveness of Community Intervention Program on Knowledge and Practice of HPV Prevention Among Married Females in Vietnam Rural Areas.

Human papillomavirus (HPV) is identified as the leading cause of cervical cancer which is the second most common cancer of females in the world. This study aimed to evaluate the effects of a community-based intervention on knowledge and practice of HPV prevention among married females aged 15 to 49 in rural areas, Vietnam. This study used a quasi-experimental design with serial cross-sectional surveys at one intervention commune (Chi Linh, Hai Duong) and one control commune at other province (Thanh Thuy, Phu Tho). Number of participants in these surveys were respectively 317 and 320 in Chi Linh and 334 and 335 in Thanh Thuy at pre- and postintervention period. The time of intervention was 15 months from April 2015 to June 2016. The study used behavior models to build up a logical framework for identifying related factors of knowledge and practice among females and developing intervention strategies. A difference-in-differences analysis approach was used to evaluate the effects of this intervention program. The study identified that the intervention had a significant change of knowledge of HPV prevention among married females after the intervention (odds ratio = 3.16, 95% confidence interval: 1.3-7.66) after adjusting for other confounders but no any significant change of practice of HPV prevention (eg, condom use, numbers of sexual partner, HPV vaccination, and screening test for cervical cancer). This might be caused by a short intervention program that did not lead to changes of practice but only change of knowledge.

SPION-Decorated Nanofibers by RAFT-Mediated Free Radical Emulsion Polymerization-Induced Self Assembly.

RAFT-mediated free-radical emulsion polymerization is successfully used to synthesize polystyrene nanofibers using triblock amphiphilic macro-RAFT copolymers as stabilizers. The polymerization is under RAFT control, producing various morphologies from spherical particles, nanofibers, nanoplatelets, and polymer vesicles. Optimum conditions are established for the synthesis of predominantly negatively charged polymer nanofibers. Superparamagnetic iron oxide nanoparticles (SPION)-decorated nanofibers are formed by simple mixing of the SPIONs with the fibers at an appropriate pH. The composite material has been found to be superparamagnetic and could be aligned under a magnetic field.

Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data.

Gas multisensor devices offer an effective approach to monitor air pollution, which has become a pandemic in many cities, especially because of transport emissions. To be reliable, properly trained models need to be developed that combine output from sensors with weather data; however, many factors can affect the accuracy of the models. The main objective of this study was to explore the impact of several input variables in training different air quality indexes using fuzzy logic combined with two metaheuristic optimizations: simulated annealing (SA) and particle swarm optimization (PSO). In this work, the concentrations of NO2 and CO were predicted using five resistivities from multisensor devices and three weather variables (temperature, relative humidity, and absolute humidity). In order to validate the results, several measures were calculated, including the correlation coefficient and the mean absolute error. Overall, PSO was found to perform the best. Finally, input resistivities of NO2 and nonmetanic hydrocarbons (NMHC) were found to be the most sensitive to predict concentrations of NO2 and CO.

A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran).

In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).

Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling.

Landslides are a major geological hazard worldwide. Landslide susceptibility assessments are useful to mitigate human casualties, loss of property, and damage to natural resources, ecosystems, and infrastructures. This study aims to evaluate landslide susceptibility using a novel hybrid intelligence approach with the rotation forest-based credal decision tree (RF-CDT) classifier. First, 152 landslide locations and 15 landslide conditioning factors were collected from the study area. Then, these conditioning factors were assigned values using an entropy method and subsequently optimized using correlation attribute evaluation (CAE). Finally, the performance of the proposed hybrid model was validated using the receiver operating characteristic (ROC) curve and compared with two well-known ensemble models, bagging (bag-CDT) and MultiBoostAB (MB-CDT). Results show that the proposed RF-CDT model had better performance than the single CDT model and hybrid bag-CDT and MB-CDT models. The findings in the present study overall confirm that a combination of the meta model with a decision tree classifier could enhance the prediction power of the single landslide model. The resulting susceptibility maps could be effective for enforcement of land management regulations to reduce landslide hazards in the study area and other similar areas in the world.

Aqueous Polymeric Hollow Particles as an Opacifier by Emulsion Polymerization Using Macro-RAFT Amphiphiles.

A robust polymerization technique that enables the surfactant-free aqueous synthesis of a high solid content latex containing polymeric hollow particles is presented. Uniquely designed amphiphilic macro-reversible addition fragmentation chain transfer (RAFT) copolymers were used as sole stabilizers for monomer emulsification as well as for free-radical emulsion polymerization. The polymerization was found to be under RAFT control, generating various morphologies from spherical particles, wormlike structures to polymer vesicles. The final particles were dominantly polymeric vesicles which had a substantially uniform and continuous polymer layer around a single aqueous filled void. They produced hollow particles once dried and were successfully used as opacifiers to impart opacity into polymer paint films. This method is simple, can be performed in a controllable and reproducible manner, and may be performed using diverse procedures.

Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping.

The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.

Biodistribution and Clearance of Stable Superparamagnetic Maghemite Iron Oxide Nanoparticles in Mice Following Intraperitoneal Administration.

Nanomedicine is an emerging field with great potential in disease theranostics. We generated sterically stabilized superparamagnetic iron oxide nanoparticles (s-SPIONs) with average core diameters of 10 and 25 nm and determined the in vivo biodistribution and clearance profiles. Healthy nude mice underwent an intraperitoneal injection of these s-SPIONs at a dose of 90 mg Fe/kg body weight. Tissue iron biodistribution was monitored by atomic absorption spectroscopy and Prussian blue staining. Histopathological examination was performed to assess tissue toxicity. The 10 nm s-SPIONs resulted in higher tissue-iron levels, whereas the 25 nm s-SPIONs peaked earlier and cleared faster. Increased iron levels were detected in all organs and body fluids tested except for the brain, with notable increases in the liver, spleen, and the omentum. The tissue-iron returned to control or near control levels within 7 days post-injection, except in the omentum, which had the largest and most variable accumulation of s-SPIONs. No obvious tissue changes were noted although an influx of macrophages was observed in several tissues suggesting their involvement in s-SPION sequestration and clearance. These results demonstrate that the s-SPIONs do not degrade or aggregate in vivo and intraperitoneal administration is well tolerated, with a broad and transient biodistribution. In an ovarian tumor model, s-SPIONs were shown to accumulate in the tumors, highlighting their potential use as a chemotherapy delivery agent.

Preparation of Inert Polystyrene Latex Particles as MicroRNA Delivery Vectors by Surfactant-Free RAFT Emulsion Polymerization.

We present the preparation of 11 nm polyacrylamide-stabilized polystyrene latex particles for conjugation to a microRNA model by surfactant-free RAFT emulsion polymerization. Our synthetic strategy involved the preparation of amphiphilic polyacrylamide-block-polystyrene copolymers, which were able to self-assemble into polymeric micelles and "grow" into polystyrene latex particles. The surface of these sterically stabilized particles was postmodified with a disulfide-bearing linker for the attachment of the microRNA model, which can be released from the latex particles under reducing conditions. These nanoparticles offer the advantage of ease of preparation via a scaleable process, and the versatility of their synthesis makes them adaptable to a range of applications.

Characteristics of lymph node metastasis and short-term outcome of esophageal squamous-cell carcinoma undergoing minimally invasive esophagectomy: a prospective cross-sectional study (with video).

Introduction: Surgery for esophageal squamous-cell carcinoma (ESCC) presents many potential challenges owing to malignant lymph node metastasis, complex procedures and severe postoperative complications. The appropriate lymphadenectomy for ESCC remains controversial. This study aims to evaluate the characteristics of lymph node metastasis and postoperative complications in patients with ESCC undergoing minimally invasive esophagectomy and extended two-field lymph node dissection. Patients and methods: This prospective, single-center, cross-sectional study was conducted from October 2022 to May 2024. All patients with ESCC who underwent minimally invasive esophagectomy and extended two-field lymph node dissection were selected for this study. Postoperative lymph nodes were divided into upper thoracic, middle thoracic, lower thoracic and abdominal lymph node groups. Results: Seventy-four patients with ESCC, including 49 patients who underwent upfront surgery and 25 patients who received preoperative chemoradiotherapy, were selected. The rate of lymph node metastasis in all patients was 39.2%, with 13.6% of patients having upper thoracic metastasis. The factors affecting the rate of lymph node metastasis included preoperative chemoradiotherapy, tumor stage, poor differentiation, lymphovascular/perineural invasion, and tumor size greater than 2 cm, all of which were significantly different (P<0.05). Common postoperative complications included pneumonia (25.7%), recurrent laryngeal nerve (RLN) palsy (10.8%) and anastomotic leak (4.1%). There were no cases required conversion to open surgery, nor any deaths within 90 days postoperatively. Conclusion: Lymph node metastasis in esophageal squamous-cell carcinoma has a high incidence, occurs in the early stages, and is widely distributed in all regions of the mediastinum and abdomen. Minimally invasive esophagectomy and extended two-field lymph node dissection are feasible and safe, with low complication rates.