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.

Predicting Outcomes Following Endovascular Abdominal Aortic Aneurysm Repair Using Machine Learning.

OBJECTIVE: To develop machine learning (ML) models that predict outcomes following endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA). BACKGROUND: EVAR carries non-negligible perioperative risks; however, there are no widely used outcome prediction tools. METHODS: The National Surgical Quality Improvement Program targeted database was used to identify patients who underwent EVAR for infrarenal AAA between 2011 and 2021. Input features included 36 preoperative variables. The primary outcome was 30-day major adverse cardiovascular event (composite of myocardial infarction, stroke, or death). Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 ML models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Model robustness was evaluated with calibration plot and Brier score. Subgroup analysis was performed to assess model performance based on age, sex, race, ethnicity, and prior AAA repair. RESULTS: Overall, 16,282 patients were included. The primary outcome of 30-day major adverse cardiovascular event occurred in 390 (2.4%) patients. Our best-performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.95 (0.94-0.96) compared with logistic regression [0.72 [0.70-0.74)]. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.06. Model performance remained robust on all subgroup analyses. CONCLUSIONS: Our newer ML models accurately predict 30-day outcomes following EVAR using preoperative data and perform better than logistic regression. Our automated algorithms can guide risk mitigation strategies for patients being considered for EVAR.

Using Machine Learning (XGBoost) to Predict Outcomes After Infrainguinal Bypass for Peripheral Artery Disease.

OBJECTIVE: To develop machine learning (ML) algorithms that predict outcomes after infrainguinal bypass. BACKGROUND: Infrainguinal bypass for peripheral artery disease carries significant surgical risks; however, outcome prediction tools remain limited. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent infrainguinal bypass for peripheral artery disease between 2003 and 2023. We identified 97 potential predictor variables from the index hospitalization [68 preoperative (demographic/clinical), 13 intraoperative (procedural), and 16 postoperative (in-hospital course/complications)]. The primary outcome was 1-year major adverse limb event (composite of surgical revision, thrombectomy/thrombolysis, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained 6 ML models using preoperative features. The primary model evaluation metric was the area under the receiver operating characteristic curve (AUROC). The top-performing algorithm was further trained using intraoperative and postoperative features. Model robustness was evaluated using calibration plots and Brier scores. RESULTS: Overall, 59,784 patients underwent infrainguinal bypass, and 15,942 (26.7%) developed 1-year major adverse limb event/death. The best preoperative prediction model was XGBoost, achieving an AUROC (95% CI) of 0.94 (0.93-0.95). In comparison, logistic regression had an AUROC (95% CI) of 0.61 (0.59-0.63). Our XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs (95% CI's) of 0.94 (0.93-0.95) and 0.96 (0.95-0.97), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.08 (preoperative), 0.07 (intraoperative), and 0.05 (postoperative). CONCLUSIONS: ML models can accurately predict outcomes after infrainguinal bypass, outperforming logistic regression.

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.

Using machine learning to predict outcomes following suprainguinal bypass.

OBJECTIVE: Suprainguinal bypass for peripheral artery disease (PAD) carries important surgical risks; however, outcome prediction tools remain limited. We developed machine learning (ML) algorithms that predict outcomes following suprainguinal bypass. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent suprainguinal bypass for PAD between 2003 and 2023. We identified 100 potential predictor variables from the index hospitalization (68 preoperative [demographic/clinical], 13 intraoperative [procedural], and 19 postoperative [in-hospital course/complications]). The primary outcomes were major adverse limb events (MALE; composite of untreated loss of patency, thrombectomy/thrombolysis, surgical revision, or major amputation) or death at 1 year following suprainguinal bypass. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained six ML models using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). The best performing algorithm was further trained using intra- and postoperative data. Model robustness was evaluated using calibration plots and Brier scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, rurality, median Area Deprivation Index, symptom status, procedure type, prior intervention for PAD, concurrent interventions, and urgency. RESULTS: Overall, 16,832 patients underwent suprainguinal bypass, and 3136 (18.6%) developed 1-year MALE or death. Patients with 1-year MALE or death were older (mean age, 64.9 vs 63.5 years; P < .001) with more comorbidities, had poorer functional status (65.7% vs 80.9% independent at baseline; P < .001), and were more likely to have chronic limb-threatening ischemia (67.4% vs 47.6%; P < .001) than those without an outcome. Despite being at higher cardiovascular risk, they were less likely to receive acetylsalicylic acid or statins preoperatively and at discharge. Our best performing prediction model at the preoperative stage was XGBoost, achieving an AUROC of 0.92 (95% confidence interval [CI], 0.91-0.93). In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). Our XGBoost model maintained excellent performance at the intra- and postoperative stages, with AUROCs of 0.93 (95% CI, 0.92-0.94) and 0.98 (95% CI, 0.97-0.99), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.12 (preoperative), 0.11 (intraoperative), and 0.10 (postoperative). Of the top 10 predictors, nine were preoperative features including chronic limb-threatening ischemia, previous procedures, comorbidities, and functional status. Model performance remained robust on all subgroup analyses. CONCLUSIONS: We developed ML models that accurately predict outcomes following suprainguinal bypass, performing better than logistic regression. Our algorithms have potential for important utility in guiding perioperative risk mitigation strategies to prevent adverse outcomes following suprainguinal bypass.

High-performance GeSi/Ge multi-quantum well photodetector on a Ge-buffered Si substrate.

This work demonstrates a high-performance photodetector with a 4-cycle Ge0.86Si0.14/Ge multi-quantum well (MQW) structure grown by reduced pressure chemical vapor deposition techniques on a Ge-buffered Si (100) substrate. At -1 V bias, the dark current density of the fabricated PIN mesa devices is as low as 3 mA/cm2, and the optical responsivities are 0.51 and 0.17 A/W at 1310 and 1550 nm, respectively, corresponding to the cutoff wavelength of 1620 nm. At the same time, the device has good high-power performance and continuous repeatable light response. On the other hand, the temperature coefficient of resistance (TCR) of the device is as high as -5.18%/K, surpassing all commercial thermal detectors. These results indicate that the CMOS-compatible and low-cost Ge0.86Si0.14/Ge multilayer structure is promising for short-wave infrared and uncooled infrared imaging.

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.

Discovery of a Potent, selective and orally bioavailable CDK9 degrader for targeting transcription regulation in Triple-Negative breast cancer.

Triple-negative breast cancer (TNBC) is a highly aggressive, heterogeneous and invasive subtype of breast cancer with very limited effective modalities of treatment. Degrading the critical transcription regulator cyclin-dependent kinase 9 (CDK9) by proteolysis targeting chimeras (PROTACs) has shown promising potential for treating TNBC. However, to date, CDK9-targeting PROTACs for oral administration in treatment of cancers have not been reported. We herein present the design, synthesis, and extensive biological evaluation of a series of novel PROTACs as orally bioavailable, potent and selective degraders of CDK9 for targeting transcription regulation in triple-negative breast cancer. The developed compound 29 exhibited a desired potency (DC50 = 3.94 nM) with high efficacy (Dmax = 96 %) on CDK9 degradation, and effectively inhibited the proliferation of TNBC MDA-MB-231 cells. Mechanistic investigations revealed that compound 29 is a bona fide CDK9 degrader and can substantially downregulate the downstream targets c-Myc and MCL-1. Furthermore, compound 29 displayed favorable oral bioavailability in mice, and oral administration of degrader 29 significantly depleted CDK9 protein in TNBC tumor tissues and exhibited tumor growth inhibition in TNBC xenograft mice models. Collectively, our work established that degrader 29 is a highly potent and selective degraders of CDK9 with satisfactory oral bioavailability, which holds promising potential for the treatment of TNBC.

Hearing ability of prairie voles (Microtus ochrogaster).

The hearing abilities of mammals are impacted by factors such as social cues, habitat, and physical characteristics. Despite being used commonly to study social behaviors, hearing of the monogamous prairie vole (Microtus ochrogaster) has never been characterized. In this study, anatomical features are measured and auditory brainstem responses (ABRs) are used to measure auditory capabilities of prairie voles, characterizing monaural and binaural hearing and hearing range. Sexually naive male and female voles were measured to characterize differences due to sex. It was found that prairie voles show a hearing range with greatest sensitivity between 8 and 32 kHz, binaural hearing across interaural time difference ranges appropriate for their head sizes. No differences are shown between the sexes in binaural hearing or hearing range (except at 1 kHz), however, female voles have increased amplitude of peripheral ABR waves I and II and longer latency of waves III and IV compared to males. The results confirm that prairie voles have a broad hearing range, binaural hearing consistent with rodents of similar size, and differences in amplitudes and thresholds of monaural physiological measures between the sexes. These data further highlight the necessity to understand sex-specific differences in neural processing that may underly variability in responses between sexes.

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.

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.

Lessons Learned from Offering Video Visits as an Alternative to Phone Visits: A Case Study from a Safety-Net Obstetrics Clinics.

Before the Covid-19 pandemic, human-centered design work in San Francisco found video visits promising for Medicaid-insured pregnant individuals. They were deemed likely better than phone at addressing concerns about remoteness. We describe our experience with introducing video visits within a safety net clinic that had rapidly adopted phone visits as the standard telemedicine option early in the pandemic. By utilizing Kotter's change framework, providing an equity-focused vision, and supporting the implementation with a skilled, on-the-ground project manager, temporary uptake of offering video visits was achieved. However, competing priorities, staffing structure, and institutional culture were barriers to creating sustained change once grant funding ended, even after improvement of digital infrastructure. Efforts to increase video visit uptake in systems where telephone visits are the norm-as is in many safety net systems-may have limited success without leadership-driven prioritization and culture change at all levels.

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.

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).

Inflammatory Biomarkers to Predict Major Adverse Cardiovascular Events in Patients with Carotid Artery Stenosis.

Background and Objectives: Inflammatory proteins and their prognostic value in patients with carotid artery stenosis (CAS) have not been adequately studied. Herein, we identified CAS-specific biomarkers from a large pool of inflammatory proteins and assessed the ability of these biomarkers to predict adverse events in individuals with CAS. Materials and Methods: Samples of blood were prospectively obtained from 336 individuals (290 with CAS and 46 without CAS). Plasma concentrations of 29 inflammatory proteins were determined at recruitment, and the patients were followed for 24 months. The outcome of interest was a major adverse cardiovascular event (MACE; composite of stroke, myocardial infarction, or death). The differences in plasma protein concentrations between patients with vs. without a 2-year MACE were determined using the independent t-test or Mann-Whitney U test to identify CAS-specific prognostic biomarkers. Kaplan-Meier and Cox proportional hazards analyses with adjustment for baseline demographic and clinical characteristics were performed to assess the prognostic value of differentially expressed inflammatory proteins in predicting a 2-year MACE in patients with CAS. Results: The mean age of the cohort was 68.8 (SD 10.2) years and 39% were female. The plasma concentrations of two inflammatory proteins were significantly higher in individuals with a 2-year MACE relative to those without a 2-year MACE: IL-6 (5.07 (SD 4.66) vs. 3.36 (SD 4.04) pg/mL, p = 0.03) and CD163 (233.825 (SD 230.306) vs. 159.673 (SD 175.669) pg/mL, p = 0.033). Over a follow-up period of 2 years, individuals with elevated levels of IL-6 were more likely to develop MACE (HR 1.269 (95% CI 1.122-1.639), p = 0.042). Similarly, over a 2-year period, patients with high levels of CD163 were more likely to develop MACE (HR 1.413 (95% CI 1.022-1.954), p = 0.036). Conclusions: The plasma levels of inflammatory proteins IL-6 and CD163 are independently associated with adverse outcomes in individuals with CAS. These CAS-specific prognostic biomarkers may assist in the risk stratification of patients at an elevated risk of a MACE and subsequently guide further vascular evaluation, specialist referrals, and aggressive medical/surgical management, thereby improving outcomes for patients with CAS.

TFEB/LAMP2 contributes to PM0.2-induced autophagy-lysosome dysfunction and alpha-synuclein dysregulation in astrocytes.

Atmospheric particulate matter (PM) exacerbates the risk factor for Alzheimer's and Parkinson's diseases (PD) by promoting the alpha-synuclein (α-syn) pathology in the brain. However, the molecular mechanisms of astrocytes involvement in α-syn pathology underlying the process remain unclear. This study investigated PM with particle size <200 nm (PM0.2) exposure-induced α-syn pathology in ICR mice and primary astrocytes, then assessed the effects of mammalian target of rapamycin inhibitor (PP242) in vitro studies. We observed the α-syn pathology in the brains of exposed mice. Meanwhile, PM0.2-exposed mice also exhibited the activation of glial cell and the inhibition of autophagy. In vitro study, PM0.2 (3, 10 and 30 µg/mL) induced inflammatory response and the disorders of α-syn degradation in primary astrocytes, and lysosomal-associated membrane protein 2 (LAMP2)-mediated autophagy underlies α-syn pathology. The abnormal function of autophagy-lysosome was specifically manifested as the expression of microtubule-associated protein light chain 3 (LC3II), cathepsin B (CTSB) and lysosomal abundance increased first and then decreased, which might both be a compensatory mechanism to toxic α-syn accumulation induced by PM0.2. Moreover, with the transcription factor EB (TFEB) subcellular localization and the increase in LC3II, LAMP2, CTSB, and cathepsin D proteins were identified, leading to the restoration of the degradation of α-syn after the intervention of PP242. Our results identified that PM0.2 exposure could promote the α-syn pathological dysregulation in astrocytes, providing mechanistic insights into how PM0.2 increases the risk of developing PD and highlighting TFEB/LAMP2 as a promising therapeutic target for antagonizing PM0.2 toxicity.

KEYNOTE-033: Randomized phase 3 study of pembrolizumab vs docetaxel in previously treated, PD-L1-positive, advanced NSCLC.

KEYNOTE-033 (NCT02864394) was a multicountry, open-label, phase 3 study that compared pembrolizumab vs docetaxel in previously treated, programmed death-ligand 1 (PD-L1)-positive, advanced non-small cell lung cancer (NSCLC), with most patients enrolled in mainland China. Eligible patients were randomized (1:1) to pembrolizumab 2 mg/kg or docetaxel 75 mg/m2 every 3 weeks. Primary endpoints were overall survival (OS) and progression-free survival and were evaluated sequentially using stratified log-rank tests, first in patients with PD-L1 tumor proportion score (TPS) ≥50% and then in patients with PD-L1 TPS ≥1% (significance threshold: P < .025, one-sided). A total of 425 patients were randomized to pembrolizumab (N = 213) or docetaxel (N = 212) between 8 September 2016 and 17 October 2018. In patients with a PD-L1 TPS ≥50% (n = 227), median OS was 12.3 months with pembrolizumab and 10.9 months with docetaxel; the hazard ratio (HR) was 0.83 (95% confidence interval [CI]: 0.61-1.14; P = .1276). Because the significance threshold was not met, sequential testing of OS and PFS was ceased. In patients with a PD-L1 TPS ≥1%, the HR for OS for pembrolizumab vs docetaxel was 0.75 (95% CI: 0.60-0.95). In patients from mainland China (n = 311) with a PD-L1 TPS ≥1%, HR for OS was 0.68 (95% CI: 0.51-0.89). Incidence of grade 3 to 5 treatment-related AEs was 11.3% with pembrolizumab vs 47.5% with docetaxel. In summary, pembrolizumab improved OS vs docetaxel in previously treated, PD-L1-positive NSCLC without unexpected safety signals; although the statistical significance threshold was not reached, the numerical improvement is consistent with that previously observed for pembrolizumab in previously treated, advanced NSCLC.

Phase 1 dose-escalation study to evaluate the safety, tolerability, pharmacokinetics, and anti-tumor activity of FCN-159 in adults with neurofibromatosis type 1-related unresectable plexiform neurofibromas.

BACKGROUND: Surgery is a common treatment strategy for patients with neurofibromatosis type 1 (NF1)-related plexiform neurofibroma (PN) and has limited efficacy. FCN-159 is a novel anti-tumorigenic drug via selective inhibition of MEK1/2. This study assesses the safety and efficacy of FCN-159 in patients with NF1-related PN. METHODS: This is a multicenter, open-label, single-arm, phase I dose-escalation study. Patients with NF1-related PN that was non-resectable or unsuitable for surgery were enrolled; they received FCN-159 monotherapy daily in 28-day cycles. RESULTS: Nineteen adults were enrolled in the study, 3 in 4 mg, 4 in 6 mg, 8 in 8 mg, and 4 in 12 mg. Among patients included in dose-limiting toxicity (DLT) analysis, DLTs (grade 3 folliculitis) were reported in 1 of 8 patients (16.7%) receiving 8 mg and 3 of 3 (100%) patients receiving 12 mg. The maximum tolerated dose was determined to be 8 mg. FCN-159-related treatment-emergent adverse events (TEAEs) were observed in 19 patients (100%); most of which were grade 1 or 2. Nine (47.4%) patients reported grade 3 study-drug-related TEAEs across all dose levels, including four experiencing paronychia and five experiencing folliculitis. Of the 16 patients analyzed, all (100%) had reduced tumor size and six (37.5%) achieved partial responses; the largest reduction in tumor size was 84.2%. The pharmacokinetic profile was approximately linear between 4 and 12 mg, and the half-life supported once daily dosing. CONCLUSIONS: FCN-159 was well tolerated up to 8 mg daily with manageable adverse events and showed promising anti-tumorigenic activity in patients with NF1-related PN, warranting further investigation in this indication. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04954001. Registered 08 July 2021.

Ultra-Long Cycle of Prussian Blue Analogs Achieved by Equilibrium Electrolyte for Aqueous Sodium-Ion Batteries.

Aqueous sodium-ion batteries have promising prospects in large-scale electrical energy storage, which lack of suitable cathode with high specific capacity and long cycle lifespan, unfortunately. Manganese-based Prussian blue analogs (PBAs) (KMnHCF/NaMnHCF) are ideal candidates for low-cost and high theoretical specific capacity merits. But the rapid decline hinders their application, due to side reactions caused by water imbalance. Here, an equilibrium strategy, which can balance the interstitial water supplement and water attack, is proposed. As proof of the concept, xCS (x: proportion, CS: co-solvent, such as polyethylene glycol and trimethyl phosphate) equilibrium electrolytes are introduced to solve the rapid decline. Assisting with the electrolyte, KMnHCF realizes excellent performance (10 000 cycles), which is beyond most cathode materials of sodium-ion batteries. The full batteries composed of KMnHCF cathode and NaTi2 (PO4 )3 anode also display outstanding performance (7000 cycles) and promising application prospects at low-temperature and engineering scenes. And then, the equilibrium electrolyte concept is verified by NaMn0.8 Fe0.2 HCF and NaMnHCF, proving its universality for low-cost and long-life manganese based PBAs.

Generation of an ultrashort pulse train through ultrafast parity-time symmetry switching.

We propose a scheme for the direct generation of an ultrashort pulse train as well as the further compression of pulsed lasers based on the nonlinearity inherent to parity-time (PT) symmetric optical systems. Implementation of optical parametric amplification in a directional coupler of χ(2) waveguides enables ultrafast gain switching through pump-controlled breaking of PT symmetry. We theoretically demonstrate that pumping such a PT symmetric optical system with a periodically amplitude-modulated laser enables periodic gain switching, which can directly convert a continuous-wave signal laser into a train of ultrashort pulses. We further demonstrate that by engineering the PT symmetry threshold, an apodized gain switching that enables the production of ultrashort pulses without side lobes. This work suggests a new approach for exploring the non-linearity inherent to various PT symmetric optical structures to extend optical manipulation capabilities.