Effect of Raltitrexed on ECA109 Cellular Radiosensitivity and Its Mechanism in Esophageal Cancer.

BACKGROUND: To investigate the effect of raltitrexed + X-ray irradiation on esophageal cancer ECA109 cells and analyze the potential action mechanism. METHODS: The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay was used to analyze the inhibitory effect of raltitrexed on cell proliferation. The effect of raltitrexed on radiosensitivity was studied through a clone-forming experiment. The scratch assay and invasion test were performed to understand the cell migration and invasion abilities. The apoptosis rate change was measured using a flow cytometer, and Western Blotting was used to determine the expression of B cell lymphoma-2 (Bcl-2) and Bcl2-associated X protein (Bax) in each group. RESULTS: Raltitrexed significantly inhibited ECA109 proliferation in a time-dose-dependent manner; there were significant differences among different concentrations and times of action. The results of the clone-forming experiment showed a sensitization enhancement ratio of 1.65, and this demonstrated a radiosensitization effect. After the combination of raltitrexed with X-ray, the cell migration distance was shortened, and the number of cells penetrating the membrane was reduced. CONCLUSION: Raltitrexed can inhibit the growth of esophageal cancer ECA109 cells and has a radiosensitization effect.

Modelling the dynamic gas/particle partitioning process of semi-volatile organic compounds emitted from point sources: Quantitative analysis and impact assessment.

The deleterious impact of pollution point sources on the surrounding environment and human has long been a focal point of environmental research. When considering the local atmospheric dispersion of semi-volatile organic compounds (SVOCs) around the emission sites, it is essential to account the dynamic process for the gas/particle (G/P) partitioning, which involves the transition from an initial state to a steady state. In this study, we have developed a model that enables the prediction of the dynamic process for G/P partitioning of SVOCs, particularly considering the influence from emission. It is important to note that the dynamic processes of the concentrations of SVOCs in particle phase (CP) and in gas phase (CG) differ significantly. These differences arise due to the influence of two critical factors: particulate proportion of SVOCs in the emissions (ϕ0) and octanol-air partitioning coefficient (KOA). The validity of our model was assessed by comparing its predictions of the extremum value of the G/P partitioning quotient (KP) with the results obtained from the steady-state model. Remarkably, the characteristic time (tC), used to evaluate the timescale required for SVOCs to reach steady state, demonstrated different variations with KOA for CP and CG. Additionally, the values of tC were quite different for CP and CG, which were markedly influenced by ϕ0. For some SVOCs with high KOA values, it took approximately 35 h to reach steady state. Furthermore, it was found that the time to achieve 95 % of steady state (t95 ≈ 3tC) could reach approximately 105 h. This duration is sufficient for chemicals to disperse from their emission site to the surrounding areas. Therefore, it is crucial to consider the dynamic process of G/P partitioning in local atmospheric transport studies. Moreover, the influence of ϕ0 should be incorporated into future investigations examining the dynamic process of G/P partitioning.

Identification and Evaluation of Blood-Based Biomarkers for Abdominal Aortic Aneurysm.

INTRODUCTION: Blood-based biomarkers for abdominal aortic aneurysm (AAA) have been studied individually; however, we considered a panel of proteins to investigate AAA prognosis and its potential to improve predictive accuracy. MATERIALS AND METHODS: Using a prospectively recruited cohort of patients with/without AAA (n = 452), we conducted a prognostic study to develop a model that accurately predicts AAA outcomes using clinical features and circulating biomarker levels. Serum concentrations of 9 biomarkers were measured at baseline, and the cohort was followed for 2 years. The primary outcome was major adverse aortic event (MAAE; composite of rapid AAA expansion [>0.5 cm/6 months or >1 cm/12 months], AAA intervention, or AAA rupture). Using 10-fold cross-validation, we trained a random forest model to predict 2 year MAAE using (1) clinical characteristics, (2) biomarkers, and (3) clinical characteristics and biomarkers. RESULTS: Two-year MAAE occurred in 114 (25%) patients. Two proteins were significantly elevated in patients with AAA compared with those without AAA (angiopoietin-2 and aggrecan), composing the protein panel. For predicting 2 year MAAE, our random forest model achieved area under the receiver operating characteristic curve (AUROC) 0.74 using clinical features alone, and the addition of the 2-protein panel improved performance to AUROC 0.86. CONCLUSIONS: Using a combination of clinical/biomarker data, we developed a model that accurately predicts 2 year MAAE.

In vitro simulated saliva, gastric, and intestinal digestion followed by faecal fermentation reveals a potential modulatory activity of Epimedium on human gut microbiota.

Herba Epimedii, known for its rich array of bioactive ingredients and widespread use in ethnopharmacological practices, still lacks a comprehensive understanding of its gastrointestinal biotransformation. In this study, we qualitatively explored the dynamic changes in Epimedium sagittatum components during in vitro simulated digestions, with a quantitative focus on its five major flavonoids. Notably, significant metabolism of E. sagittatum constituents occurred in the simulated small intestinal fluid and colonic fermentation stages, yielding various low molecular weight metabolites. Flavonoids like kaempferol glycosides were fully metabolized in the simulated intestinal fluid, while hyperoside digestion occurred during simulated colon digestion. Colonic fermentation led to the production of two known bioactive isoflavones, genistein, and daidzein. The content and bioaccessibility of the five major epimedium flavonoids-icariin, epimedin A, epimedin B, epimedin C, and baohuoside I-significantly increased after intestinal digestion. During colon fermentation, these components gradually decreased but remained incompletely metabolized after 72 h. Faecal samples after E. sagittatum fermentation exhibited shift towards dominance by Lactobacillus (Firmicutes), Bifidobacterium (Actinobacteria), Streptococcus (Firmicutes), and Dialister (Firmicutes). These findings enhance our comprehension of diverse stages of Herba Epimedii constituents in the gut, suggesting that the primary constituents become bioaccessible in the colon, where new bioactive compounds may emerge.

A Coal Mine Tunnel Deformation Detection Method Using Point Cloud Data.

In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs.

Development and evaluation of a prediction model for peripheral artery disease-related major adverse limb events using novel biomarker data.

OBJECTIVE: Prognostic tools for individuals with peripheral artery disease (PAD) remain limited. We developed prediction models for 3-year PAD-related major adverse limb events (MALE) using demographic, clinical, and biomarker data previously validated by our group. METHODS: We performed a prognostic study using a prospectively recruited cohort of patients with PAD (n = 569). Demographic/clinical data were recorded including sex, age, comorbidities, previous procedures, and medications. Plasma concentrations of three biomarkers (N-terminal pro-B-type natriuretic peptide [NT-proBNP], fatty acid binding protein 3 [FABP3], and FABP4) were measured at baseline. The cohort was followed for 3 years. MALE was the primary outcome (composite of open/endovascular vascular intervention or major amputation). We trained three machine learning models with 10-fold cross-validation using demographic, clinical, and biomarker data (random forest, decision trees, and Extreme Gradient Boosting [XGBoost]) to predict 3-year MALE in patients. Area under the receiver operating characteristic curve (AUROC) was the primary model evaluation metric. RESULTS: Three-year MALE was observed in 162 patients (29%). XGBoost was the top-performing predictive model for 3-year MALE, achieving the following performance metrics: AUROC = 0.88 (95% confidence interval [CI], 0.84-0.94); sensitivity, 88%; specificity, 84%; positive predictive value, 83%; and negative predictive value, 91% on test set data. On an independent validation cohort of patients with PAD, XGBoost attained an AUROC of 0.87 (95% CI, 0.82-0.90). The 10 most important predictors of 3-year MALE consisted of: (1) FABP3; (2) FABP4; (3) age; (4) NT-proBNP; (5) active smoking; (6) diabetes; (7) hypertension; (8) dyslipidemia; (9) coronary artery disease; and (10) sex. CONCLUSIONS: We built robust machine learning algorithms that accurately predict 3-year MALE in patients with PAD using demographic, clinical, and novel biomarker data. Our algorithms can support risk stratification of patients with PAD for additional vascular evaluation and early aggressive medical management, thereby improving outcomes. Further validation of our models for clinical implementation is warranted.

Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning.

BACKGROUND: Lower extremity endovascular revascularization for peripheral artery disease carries nonnegligible perioperative risks; however, outcome prediction tools remain limited. Using machine learning, we developed automated algorithms that predict 30-day outcomes following lower extremity endovascular revascularization. METHODS AND RESULTS: The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity endovascular revascularization (angioplasty, stent, or atherectomy) for peripheral artery disease between 2011 and 2021. Input features included 38 preoperative demographic/clinical variables. The primary outcome was 30-day postprocedural major adverse limb event (composite of major reintervention, untreated loss of patency, or major amputation) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Overall, 21 886 patients were included, and 30-day major adverse limb event/death occurred in 1964 (9.0%) individuals. The best performing model for predicting 30-day major adverse limb event/death was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92-0.94). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.70-0.74). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.09. The top 3 predictive features in our algorithm were (1) chronic limb-threatening ischemia, (2) tibial intervention, and (3) congestive heart failure. CONCLUSIONS: Our machine learning models accurately predict 30-day outcomes following lower extremity endovascular revascularization using preoperative data with good discrimination and calibration. Prospective validation is warranted to assess for generalizability and external validity.

The role of FPR2-mediated ferroptosis in formyl peptide-induced acute lung injury against endothelial barrier damage and protective effect of the mitochondria-derived peptide MOTS-c.

BACKGROUND: Acute lung injury (ALI) has garnered significant attention in the field of respiratory and critical care due to its high mortality and morbidity, and limited treatment options. The role of the endothelial barrier in the development of ALI is crucial. Several bacterial pathogenic factors, including the bacteria-derived formyl peptide (fMLP), have been implicated in damaging the endothelial barrier and initiating ALI. However, the mechanism by which fMLP causes ALI remains unclear. In this study, we aim to explore the mechanisms of ALI caused by fMLP and evaluate the protective effects of MOTS-c, a mitochondrial-derived peptide. METHODS: We established a rat model of ALI and a human pulmonary microvascular endothelial cell (HPMVEC) model of ALI by treatment with fMLP. In vivo experiments involved lung histopathology assays, assessments of inflammatory and oxidative stress factors, and measurements of ferroptosis-related proteins and barrier proteins to evaluate the severity of fMLP-induced ALI and the type of tissue damage in rats. In vitro experiments included evaluations of fMLP-induced damage on HPMVEC using cell activity assays, assessments of inflammatory and oxidative stress factors, measurements of ferroptosis-related proteins, endothelial barrier function assays, and examination of the key role of FPR2 in fMLP-induced ALI. We also assessed the protective effect of MOTS-c and investigated its mechanism on the fMLP-induced ALI in vivo and in vitro. RESULTS: Results from both in vitro and in vivo experiments demonstrate that fMLP promotes the expression of inflammatory and oxidative stress factors, activates ferroptosis and disrupts the vascular endothelial barrier, ultimately contributing to the development and progression of ALI. Mechanistically, ferroptosis mediated by FPR2 plays a key role in fMLP-induced injury, and the Nrf2 and MAPK pathways are involved in this process. Knockdown of FPR2 and inhibition of ferroptosis can attenuate ALI induced by fMLP. Moreover, MOTS-c could protect the vascular endothelial barrier function by inhibiting ferroptosis and suppressing the expression of inflammatory and oxidative stress factors through Nrf2 and MAPK pathways, thereby alleviating fMLP-induced ALI. CONCLUSION: Overall, fMLP disrupts the vascular endothelial barrier through FPR2-mediated ferroptosis, leading to the development and progression of ALI. MOTS-c demonstrates potential as a protective treatment against ALI by alleviating the damage induced by fMLP.

Examining the impact of Co-residence with a daughter-in-law on older adult health in China: Evidence from a frailty index-based study.

Background: The increasing geriatric population and variation in the disease spectrum among older adults in China contribute to a growing demand for more aged adult care in Chinese society. Relevant studies have shown that living arrangements with various family members have variable impacts on the older adult's health. This study employs the Frailty Index as a unified measurement standard to assess the overall health levels, integrating the specific "in-law relationships" into the research on living arrangements and the health of older adults. Methods: This study used data from the China Longitudinal Aging Social Survey 2016-2018. OLS and Quantile Regression were used to investigate the in-law relationship on older adult health and whether this impact is homogeneous across older individuals with varying infirmity levels. The study used a lag model and propensity score matching to compensate for potential endogeneity concerns. Results: The study found that residing with a daughter-in-law (20.22%) had a significant positive correlation with the frailty index (ß=0.0088, P＜0.001), indicating that the relationship between parents-in-law and daughters-in-law can influence the health of the older adult. This impact is nonlinear and non-homogeneous for older adult people with various levels of frailty, exhibiting an approximately decreasing and then increasing U-shaped distribution, which denotes that older adult people with different health conditions have distinct demands for intergenerational care. In addition, this impact varies among older adult groups of disparate genders, urban and rural areas, and age groups. Conclusion: This study investigates the impact of "in-law relationships" within living arrangements on the health of older adults. It shows that co-residing with a daughter-in-law has adverse effects on the health of older adults. Therefore, the study suggests that when the health and economic conditions of the elderly permit, a "live-near-but-not-with" living arrangement with their children can be considered.

Epidemiological survey of cystic echinococcosis in southwest China: From the Qinghai-Tibet plateau to the area of Yunnan.

BACKGROUND: Echinococcosis is prevalent in 9 provinces in Western and Northern China. An epidemiological survey of echinococcosis in 2012 and 2016 showed cases of echinococcosis in Yunnan Province. AIM: To understand the spatial distribution and epidemiological characteristics of echinococcosis in Yunnan for the prevention and control of echinococcosis and to reduce the risk of infection in Yunnan Province. METHODS: Based on the China Information System for Disease Control and Prevention (CISDCP), echinococcosis cases reported from 36 hospitals and 34 Centers for Disease Control were investigated and epidemiologically analyzed from 2021 to 2022. The exclusion criteria included suspected cases, same case only counted once and cases not from Yunnan. A total of 705 cases were investigated, of which 397 cases were suitable for statistical analysis. In these 397 cases, epidemiological investigation was tracked in 187 cases. All data were inputted using double entry in the Excel database, with error correction by double-entry comparison. The data on echinococcosis cases in Yunnan Province were analyzed by ArcGIS 10.1 software to generate a density map of echinococcosis distribution. All statistical analyses were conducted using SPSS 17.0, including the chi-square test, linear regression test and logistic univariate and multivariate regression analyses. RESULTS: A total of 397 cases were found in 89 counties in Yunnan Province. The number of cases in the top three prefectures were Dali (38.1%), Diqing (10.1%), and Kunming (8.3%), and the top five counties were Jianchuan (9.1%), Shangri La (8.3%), Eryuan (7. 6%), Heqing (6.9%), and Dali Districts (5.0%). There were significant differences between the different areas. The case reporting rate by CISDCP (33.8%) was low; the first case was reported by CISDCP in 2002, and the highest number of cases was 50 (2017). Confirmed and clinical cases accounted for 62.5% and 37.5%, respectively. However, 90.9% of the cases of hydatid disease were reported by the hospital system, and only 9.1% of the cases of hydatid disease were found in the community through active screening. The difference between the two methods of case detection was statistically significant. Most of the cases of echinococcosis were found in farmers/herdsmen (75.1%) and students (9.1%). In addition, Han (43.6%) and Bai (26.2%) had a higher incidence of infection than other nationalities, and the liver (87.7%) and lung (6.8%) were the most common sites of cyst formation. Among the analyzed cases, 187 were epidemiologically analyzed and the clinical symptoms were not obvious in the early stage in 47.1% of cases. The results of logistic regression analysis showed that the age group, education level, presence of dogs in the family (either previously or currently), and handwashing (occasionally or not) were factors related to echinococcosis infection. 55.6% of cases were in endemic areas, and 44.4% of cases were in non-endemic areas. Among 83 cases in non-endemic areas, only 4 cases had been to endemic areas and had a history of living, working, travelling, or hunting in echinococcosis epidemic areas. CONCLUSION: Cases of echinococcosis were reported throughout the entire Yunnan province, with the majority distributed in Western Yunnan, suggesting that echinococcosis control should be strengthened in this area. We suggest that an epidemiological investigation should be carried out in the future, based on the clues from newly discovered cases in hospitals or from the CISDCP. The newly discovered cases in the hospital provided clues to comprehensively determine the location of cases and where epidemic spot investigation should be conducted.

Ultra-Confined Phonon Polaritons and Strongly Coupled Microcavity Exciton Polaritons in Monolayer MoSi2 N4 and WSi2 N4.

The 2D semiconductors are an ideal platform for exploration of bosonic fluids composed of coupled photons and collective excitations of atoms or excitons, primarily due to large excitonic binding energies and strong light-matter interaction. Based on first-principles calculations, it is demonstrated that the phonon polaritons formed by two infrared-active phonon modes in monolayer MoSi2 N4 and WSi2 N4 possess ultra-high confinement factors of around ≈105 and 103 , surpassing those of conventional polaritonic thin-film materials by two orders of magnitude. It is observed that the first bright exciton possesses a substantial binding energies of 750 and 740 meV in these two monolayers, with the radiative recombination lifetimes as long as 25 and 188 ns, and the Rabi splitting of the formed cavity-exciton polaritons reaching 373 and 321 meV, respectively. The effective masses of the cavity exciton polaritons are approximately 10-5 me , providing the potential for high-temperature quantum condensation. The ultra-confined and ultra-low-loss phonon polaritons, as well as strongly-coupled cavity exciton polaritons with ultra-small polaritonic effective masses in these two monolayers, offering the flexible control of light at the nanoscale, probably leading to practical applications in nanophotonics, meta-optics, and quantum materials.

An Introductory Guide to Artificial Intelligence in Interventional Radiology: Part 1 Foundational Knowledge.

Artificial intelligence (AI) is rapidly evolving and has transformative potential for interventional radiology (IR) clinical practice. However, formal training in AI may be limited for many clinicians and therefore presents a challenge for initial implementation and trust in AI. An understanding of the foundational concepts in AI may help familiarize the interventional radiologist with the field of AI, thus facilitating understanding and participation in the development and deployment of AI. A pragmatic classification system of AI based on the complexity of the model may guide clinicians in the assessment of AI. Finally, the current state of AI in IR and the patterns of implementation are explored (pre-procedural, intra-procedural, and post-procedural).

Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD.

Importance: Endovascular intervention for peripheral artery disease (PAD) carries nonnegligible perioperative risks; however, outcome prediction tools are limited. Objective: To develop machine learning (ML) algorithms that can predict outcomes following endovascular intervention for PAD. Design, Setting, and Participants: This prognostic study included patients who underwent endovascular intervention for PAD between January 1, 2004, and July 5, 2023, with 1 year of follow-up. Data were obtained from the Vascular Quality Initiative (VQI), a multicenter registry containing data from vascular surgeons and interventionalists at more than 1000 academic and community hospitals. From an initial cohort of 262 242 patients, 26 565 were excluded due to treatment for acute limb ischemia (n = 14 642) or aneurysmal disease (n = 3456), unreported symptom status (n = 4401) or procedure type (n = 2319), or concurrent bypass (n = 1747). Data were split into training (70%) and test (30%) sets. Exposures: A total of 112 predictive features (75 preoperative [demographic and clinical], 24 intraoperative [procedural], and 13 postoperative [in-hospital course and complications]) from the index hospitalization were identified. Main Outcomes and Measures: Using 10-fold cross-validation, 6 ML models were trained using preoperative features to predict 1-year major adverse limb event (MALE; composite of thrombectomy or thrombolysis, surgical reintervention, or major amputation) or death. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). After selecting the best performing algorithm, additional models were built using intraoperative and postoperative data. Results: Overall, 235 677 patients who underwent endovascular intervention for PAD were included (mean [SD] age, 68.4 [11.1] years; 94 979 [40.3%] female) and 71 683 (30.4%) developed 1-year MALE or death. The best preoperative prediction model was extreme gradient boosting (XGBoost), achieving the following performance metrics: AUROC, 0.94 (95% CI, 0.93-0.95); accuracy, 0.86 (95% CI, 0.85-0.87); sensitivity, 0.87; specificity, 0.85; positive predictive value, 0.85; and negative predictive value, 0.87. In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). The XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively. Conclusions and Relevance: In this prognostic study, ML models were developed that accurately predicted outcomes following endovascular intervention for PAD, which performed better than logistic regression. These algorithms have potential for important utility in guiding perioperative risk-mitigation strategies to prevent adverse outcomes following endovascular intervention for PAD.

Silicon eccentric shell nanoparticles fabricated by template-assisted deposition for Mie magnetic resonances enhanced light confinement.

We report a structure of silicon eccentric shell particles array, fabricated by the SiO2particles monolayer array assisted deposition of amorphous Si, for high-efficiency light confinement. The SiO2particles monolayer array is tailored to regulate its interparticle distance, followed by silicon film deposition to obtain silicon eccentric shell arrays with positive and negative off-center distancee. We studied the Mie resonances of silicon solid sphere, concentric shell, eccentric shell and observed that the eccentric shell with positive off-centeresupports superior light confinement because of the enhanced Mie magnetic resonances. Spectroscopic measurements and finite difference time domain simulations were conducted to examine the optical performance of the eccentric shell particles array. Results show that the Mie magnetic resonance wavelength can be easily regulated by the size of the inner void of the silicon shell to realize tunable enhanced light confinement. It was found silicon shell withD= 460/520 nm offered high enhanced light absorption efficiency at wavelength ofλ= 830 nm, almost beyond the bandgap of the amorphous silicon.

Successful true cavity pathfinding with balloon assisted CTO with bifurcation lesions: Two case reports.

BACKGROUND: Coronary artery disease is a prevalent global cardiovascular ailment, with percutaneous coronary intervention (PCI) standing out as a crucial method for relieving symptoms and enhancing the quality of life in patients with coronary heart disease. However, the presence of concurrent chronic total occlusion (CTO) and bifurcation lesions within coronary arteries elevates the complexity and treatment risks, especially when the entry point of the CTO is ambiguous. OBJECTIVE: This study aims to present an innovative approach for treating CTO complicated with bifurcation lesions, focusing on true cavity pathfinding assisted by a balloon. METHODS: Two cases of CTO patients with concomitant bifurcation lesions are described. One case involves CTO of the left anterior descending artery) combined with anterior non-angle trigeminal lesions, while the other entails CTO of the posterior left artery combined with posterior angle trigeminal lesions. True lumen identification using a balloon and subsequent opening of the CTO blood vessel were performed in both cases. RESULTS: In both cases, the true lumen was successfully located with the assistance of a balloon, leading to the successful opening of the CTO blood vessel. This approach not only simplified the procedure but also reduced procedural difficulty and associated risks of complications compared to traditional guide wire operations. CONCLUSION: The application of true cavity pathfinding assisted by a balloon offers a novel and effective strategy for managing CTO complicated with bifurcation lesions. The method simplifies the procedure, decreases procedural difficulty, and lowers the risk of complications associated with guide wire operations. However, further studies and long-term follow-up data are warranted to validate the reliability and long-term efficacy of this innovative approach.

Predicting outcomes following lower extremity open revascularization using machine learning.

Lower extremity open revascularization is a treatment option for peripheral artery disease that carries significant peri-operative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following lower extremity open revascularization. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity open revascularization for chronic atherosclerotic disease between 2011 and 2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day major adverse limb event (MALE; composite of untreated loss of patency, major reintervention, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using tenfold cross-validation, we trained 6 ML models. Overall, 24,309 patients were included. The primary outcome of 30-day MALE or death occurred in 2349 (9.3%) patients. Our best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.93 (0.92-0.94). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.08. Our ML algorithm has potential for important utility in guiding risk mitigation strategies for patients being considered for lower extremity open revascularization to improve outcomes.

A machine learning algorithm for peripheral artery disease prognosis using biomarker data.

Peripheral artery disease (PAD) biomarkers have been studied in isolation; however, an algorithm that considers a protein panel to inform PAD prognosis may improve predictive accuracy. Biomarker-based prediction models were developed and evaluated using a model development (n = 270) and prospective validation cohort (n = 277). Plasma concentrations of 37 proteins were measured at baseline and the patients were followed for 2 years. The primary outcome was 2-year major adverse limb event (MALE; composite of vascular intervention or major amputation). Of the 37 proteins tested, 6 were differentially expressed in patients with vs. without PAD (ADAMTS13, ICAM-1, ANGPTL3, Alpha 1-microglobulin, GDF15, and endostatin). Using 10-fold cross-validation, we developed a random forest machine learning model that accurately predicts 2-year MALE in a prospective validation cohort of PAD patients using a 6-protein panel (AUROC 0.84). This algorithm can support PAD risk stratification, informing clinical decisions on further vascular evaluation and management.

Reclamation history and land use types across multiple spatial scales shape anuran communities in the coastal land reclamation region.

Land reclamation is a widely adopted method for managing land shortage and promoting coastal economic development globally. However, its impacts on biodiversity vary based on distinct reclamation histories and land use management strategies in different regions. This study aims to examine the effects of reclamation history and land use types at different spatial scales on anuran communities in coastal reclaimed land, which are an important taxon in the coastal ecosystem. We used visual and acoustic encounter methods to survey anurans in 2016 and 2017 across 20 1-km radius coastal land reclamation landscapes with different reclamation histories (10, 20, and 60 y after reclamation) in Nanhui Dongtan of Shanghai, an important coastal land reclamation region along the Yangtze River Estuary. Landscape variables (farmlands, woodlands, and impermeable surface covers, and the landscape Shannon diversity index) at four different spatial scales (250 m, 500 m, 750 m and 1000 m) and water salinity in each landscape were measured. Our findings reveal differences in anuran communities between study sites with 10, 20, and 60 years of reclamation history. Abundances of the ornamented pygmy frog (Microhyla fissipes) and Beijing gold-striped pond frog (Pelophylax plancyi) in landscapes with a 10-year reclamation history were significantly lower compared to those with histories of 20 and 60 years. Zhoushan toad (Bufo gargarizans) abundance was significantly negatively related to farmland cover at the 1000 m scale and impermeable surface cover at the 250 m scale; Hong Kong rice-paddy frog (Fejervarya multistriata) abundance was significantly positively related to farmland cover at the 1000 m scale; ornamented pygmy frog abundance was positively related to farmland cover at the 1000 m scale; and Beijing gold-striped pond frog abundance was significantly positively and negatively related to the landscape Shannon diversity index at the 1000 m scale and to water salinity, respectively. Amphibians quickly migrated and colonized coastal reclaimed land from older natural lands. However, two anuran species with specific habitat requirements tended to avoid areas with shorter reclamation histories. The single-species models revealed different responses to various land uses at the various scales, which indicated that land use management was important to amphibian conservation in coastal reclamation regions.

Monitoring SF6 Gas Leakage Based on a Customized Binocular System.

Sulfur hexafluoride (SF6) gas is extensively utilized as an insulating and arc-quenching medium in the circuit breakers and isolating switches of electrical equipment. It effectively isolates the circuits from the atmosphere and promptly extinguishes arcs. Therefore, the issue of SF6 gas leakage poses a significant threat to the related application fields, and the detection of SF6 gas leakage becomes extremely important. Infrared imaging detection offers advantages including non-contact, high precision, and visualization. However, most existing infrared detection systems are equipped with only one filter to detect SF6 gas. The images captured contain background noise and system noise, making these systems vulnerable to interference from such noises. To address these issues, we propose a method for monitoring SF6 gas leakage based on a customized binocular imaging (CBI) system. The CBI system has two filters, greatly reducing the interference of system noise and background noise. The first filter features the absorption resonant peak of SF6 gas. The second filter is used to record background noise and system noise. One aspect to note is that, in order to avoid the interference of other gases, the central wavelength of this second filter should keep away from the absorption resonant peaks of those gases. Accordingly, the central wavelengths of our customized filters were determined as 10,630 nm and 8370 nm, respectively. Then, two cameras of the same type were separately assembled with a customized filter, and the CBI prototype was accomplished. Finally, we utilized the difference method using two infrared images captured by the CBI system, to monitor the SF6 gas leakage. The results demonstrate that our developed system achieves a high accuracy of over 99.8% in detecting SF6 gas. Furthermore, the CBI system supports a plug-and-play customization to detect various gases for different scenarios.

Strong Intervalley Scattering-Induced Renormalization of Electronic and Thermal Transport Properties and Selection Rule Analysis in 2D Tellurium.

The electron-phonon interaction (EPI) and phonon-phonon interactions are ubiquitous in promising two-dimensional (2D) semiconductors, determining both electronic and thermal transport properties. In this work, based on ab initio calculations, the effects of intervalley scattering on EPI and higher-order four-phonon interactions of α-Te and ß-Te are investigated. Through the proposed selection rules for scattering channels and calculations of full electron-phonon scattering rates, we demonstrate that multiple nearly degenerate local valleys/peaks produce more scattering channels, resulting in stronger intervalley scattering over intravalley scattering. The lattice thermal conductivities of α-Te and ß-Te are decreased by as much as 10.9% and 30.8% by considering EPI under the carrier concentration of 2 × 1013 cm-2 (n-type) at 300 K compared to those limited by three-phonon scattering, respectively. However, when further considering four-phonon scattering, EPI reduces the lattice thermal conductivities by 2.6% and 19.4% for α-Te and ß-Te, respectively. Furthermore, it is revealed that the four-phonon interaction is more dominant in phonon transport for α-Te than that for ß-Te due to the presence of an acoustic-optical phonon gap in α-Te. Finally, we demonstrate strong intervalley scattering induces significant renormalization effects from EPI on all the constituent parameters of thermoelectric performance. Our results show the contributions of intervalley scattering to the electronic properties as well as thermal transport properties in band-convergent thermoelectric materials are essential and highlight the potential of monolayer tellurium as a promising candidate for advanced thermoelectric applications.