Optically Probing Unconventional Superconductivity in Atomically Thin Bi2Sr2Ca0.92Y0.08Cu2O8+δ.

Atomically thin cuprates exhibiting a superconducting phase transition temperature similar to that of the bulk have recently been realized, although the device fabrication remains a challenge and limits the potential for many novel studies and applications. Here, we use an optical pump-probe approach to noninvasively study the unconventional superconductivity in atomically thin Bi2Sr2Ca0.92Y0.08Cu2O8+δ (Y-Bi2212). Apart from finding an optical response due to the superconducting phase transition that is similar to that of bulk Y-Bi2212, we observe that the sign and amplitude of the pump-probe signal in atomically thin flakes vary significantly in different dielectric environments depending on the nature of the optical excitation. By exploiting the spatial resolution of the optical probe, we uncover the exceptional sensitivity of monolayer Y-Bi2212 to the environment. Our results provide the first optical evidence for the intralayer nature of the superconducting condensate in Bi2212 and highlight the role of double-sided encapsulation in preserving superconductivity in atomically thin cuprates.

Shear Strain-Induced Two-Dimensional Slip Avalanches in Rhombohedral MoS2.

Slip avalanches are ubiquitous phenomena occurring in three-dimensional materials under shear strain, and their study contributes immensely to our understanding of plastic deformation, fragmentation, and earthquakes. So far, little is known about the role of shear strain in two-dimensional (2D) materials. Here we show some evidence of 2D slip avalanches in exfoliated rhombohedral MoS2, triggered by shear strain near the threshold level. Utilizing interfacial polarization in 3R-MoS2, we directly probe the stacking order in multilayer flakes and discover a wide variety of polarization domains with sizes following a power-law distribution. These findings suggest that slip avalanches can occur during the exfoliation of 2D materials, and the stacking orders can be changed via shear strain. Our observation has far-reaching implications for the development of new materials and technologies, where precise control over the atomic structure of these materials is essential for optimizing their properties as well as for our understanding of fundamental physical phenomena.

Identifying predictive biomarkers of apatinib in third-line treatment of advanced colorectal cancer through comprehensive genomic profiling.

Apatinib is a selective inhibitor of vascular endothelial growth factor receptor-2. Despite encouraging anticancer activity in different cancer types, some patients may not benefit from apatinib treatment. Herein, we characterized genomic profiles in colorectal cancer (CRC) patients to explore predictive biomarkers of apatinib at molecular level. We retrospectively recruited 19 CRC patients receiving apatinib as third-line treatment. Tissue samples before apatinib treatment were collected and subjected to genomic profiling using a targeted sequencing panel covering 520 cancer-related genes. After apatinib treatment, the patients achieved an objective response rate of 21% (4/19) and disease control rate of 57.9% (11/19). The median progression-free survival (PFS) and overall survival were 5 and 8.7 months, respectively. Genetic alterations were frequently identified in TP53 (95%), APC (53%), KRAS (53%) and PIK3CA (26%). Higher tumor mutation burden levels were significantly observed in patients harboring alterations in ERBB and RAS signaling pathways. Patients harboring FLT1 amplifications ( n = 3) showed significantly worse PFS than wild-type patients. Our study described molecular profiles in CRC patients receiving apatinib treatment and identified FLT1 amplification as a potential predictive biomarker for poor efficacy of apatinib. Further studies are warranted to validate the use of FLT1 amplification during apatinib treatment.

Association of frailty with clinical outcomes in myelofibrosis: a retrospective cohort study.

There is limited understanding of the impact of frailty on clinical outcomes in patients with myelofibrosis (MF). In this retrospective cohort study on 439 chronic phase MF patients [mean age: 68·7 ± 12 years; median follow-up: 3·4 years (IQR 0·4-8·6)] from 2004 till 2018, we used a 35-variable frailty index (FI) to categorise patient's frailty status as fit (FI < 0·2, reference), prefrail (FI 0·2-0·29) or frail (FI ≥ 0·3). The association of frailty with overall survival (OS) and cumulative JAK inhibitor (JAKi) therapy failure was measured using hazard ratio (HR, 95% CI). In multivariable analysis, prefrail (HR 1·7, 1·1-2·5) and frail patients (HR 2·9, 1·6-5·5), those with higher DIPSS score (HR 2·5, 1·6-3·9) and transfusion dependency (HR 1·9, 1·3-2·9) had shorter OS. In a subset analysis of patients on JAKi treatment (n = 222), frail patients (HR 2·5, 1·1-5·7), patients with higher DIPSS score (HR 1·7, 1·0-3·1) and transfusion dependence (HR 1·7, 1·1-2·7) had higher cumulative incidence of JAKi failure. Age, comorbidities, ECOG performance status, and MPN driver mutations did not impact outcomes. Thus, higher frailty scores are associated with worse OS and increased JAKi failure in MF, and is a superior indicator of fitness in comparison to age, comorbidities, and performance status.

Development and Evaluation of a Machine Learning Prediction Model for Flap Failure in Microvascular Breast Reconstruction.

BACKGROUND: Despite high success rates, flap failure remains an inherent risk in microvascular breast reconstruction. Identifying patients who are at high risk for flap failure would enable us to recommend alternative reconstructive techniques. However, as flap failure is a rare event, identification of risk factors is statistically challenging. Machine learning is a form of artificial intelligence that automates analytical model building. It has been proposed that machine learning can build superior prediction models when the outcome of interest is rare. METHODS: In this study we evaluate machine learning resampling and decision-tree classification models for the prediction of flap failure in a large retrospective cohort of microvascular breast reconstructions. RESULTS: A total of 1012 patients were included in the study. Twelve patients (1.1%) experienced flap failure. The ROSE informed oversampling technique and decision-tree classification resulted in a strong prediction model (AUC 0.95) with high sensitivity and specificity. In the testing cohort, the model maintained acceptable specificity and predictive power (AUC 0.67), but sensitivity was reduced. The model identified four high-risk patient groups. Obesity, comorbidities and smoking were found to contribute to flap loss. The flap failure rate in high-risk patients was 7.8% compared with 0.44% in the low-risk cohort (p = 0.001). CONCLUSIONS: This machine-learning risk prediction model suggests that flap failure may not be a random event. The algorithm indicates that flap failure is multifactorial and identifies a number of potential contributing factors that warrant further investigation.

Implementation of a Novel Electronic Patient-Directed Smoking Cessation Platform for Cancer Patients: Interrupted Time Series Analysis.

BACKGROUND: Continued smoking in cancer patients undergoing treatment results in significantly higher rates of treatment toxicities and persistent effects, increased risk of recurrence and second malignancy, and increased all-cause mortality. Despite this, routine tobacco use screening and the provision of smoking cessation treatment has yet to be implemented widely in the cancer setting. OBJECTIVE: The objective of this study was to implement and evaluate the adoption and impact of an innovative Smoking Cessation e-referral System (CEASE) to promote referrals to smoking cessation programs in cancer patients. METHODS: A patient-directed electronic smoking cessation platform (CEASE) was developed to promote smoking screening and referral and implemented at 1 of Canada's largest cancer centers. The implementation and evaluation were guided by the Ottawa Model of Research Use. An interrupted time series design was used to examine the impact of CEASE on screening rates, referrals offered, and referrals accepted compared with a previous paper-based screening program. A subsample of smokers or recent quitters was also assessed and compared pre- and postimplementation to examine the effect of CEASE on subsequent contact with smoking cessation programs and quit attempts. RESULTS: A total of 17,842 new patients attended clinics over the 20-month study period. The CEASE platform was successfully implemented across all disease sites. Screening rates increased from 44.28% (2366/5343) using the paper-based approach to 65.72% (3538/5383) using CEASE (P<.01), and referrals offered to smokers who indicated interest in quitting increased from 18.6% (58/311) to 98.8% (421/426; P<.01). Accepted referrals decreased from 41% (24/58) to 20.4% (86/421), though the overall proportion of referrals generated from total current/recent tobacco users willing to quit increased from 5.8% (24/414) to 20.2% (86/426) due to the increase in referrals offered. At 1-month postscreening, there was no significant difference in the proportion that was currently using tobacco and had not changed use in the past 4 weeks (pre: 28.9% [24/83] and post: 28.8% [83/288]). However, contact with the referral program increased from 0% to 78% in the postCEASE cohort (P<.001). CONCLUSIONS: CEASE is an innovative tool to improve smoking screening and can be implemented in both a time- and cost-effective manner which promotes sustainability. CEASE was successfully implemented across all clinics and resulted in improvements in overall screening and referral rates and engagement with referral services.

Spatiotemporal epidemic characteristics and risk factor analysis of malaria in Yunnan Province, China.

BACKGROUND: Malaria remains an important public health concern in China and is particularly serious in Yunnan, a China's provincial region of high malaria burden with an incidence of 1.79/105 in 2012. This study aims to examine the epidemiologic profile and spatiotemporal aspects of epidemics of malaria, and to examine risk factors which may influence malaria epidemics in Yunnan Province. METHODS: The data of malaria cases in 2012 in 125 counties of Yunnan Province was used in this research. The epidemical characteristics of cases were revealed, and time and space clusters of malaria were detected by applying scan statistics method. In addition, we applied the geographically weighted regression (GWR) model in identifying underlying risk factors. RESULTS: There was a total of 821 cases of malaria, and male patients accounted for 83.9% (689) of the total cases. The incidence in the group aged 20-30 years was the highest, at 3.00/105. The majority (84.1%) of malaria cases occurred in farmers and migrant workers, according to occupation statistics. On a space-time basis, epidemics of malaria of varying severity occurred in the summer and autumn months, and the high risk regions were mainly distributed in the southwest counties. Annual average temperature, annual cumulative rainfall, rice yield per square kilometer and proportion of rural employees mainly showed a positive association with the malaria incidence rate, according to the GWR model. CONCLUSIONS: Malaria continues to be one of serious public health issues in Yunnan Province, especially in border counties in southwestern Yunnan. Temperature, precipitation, rice cultivation and proportion of rural employees were positively associated with malaria incidence. Individuals, and disease prevention and control departments, should implement more stringent preventative strategies in locations with hot and humid environmental conditions to control malaria.

Spatial-temporal pattern and risk factor analysis of bacillary dysentery in the Beijing-Tianjin-Tangshan urban region of China.

BACKGROUND: Bacillary dysentery remains a major public health concern in China. The Beijing-Tianjin-Tangshan urban region is one of the most heavily infected areas in the country. This study aimed to analyze epidemiological features of bacillary dysentery, detect spatial-temporal clusters of the disease, and analyze risk factors that may affect bacillary dysentery incidence in the region. METHODS: Bacillary dysentery case data from January 2011 to December 2011 in Beijing-Tianjin-Tangshan were used in this study. The epidemiological features of cases were characterized, then scan statistics were performed to detect spatial temporal clusters of bacillary dysentery. A spatial panel model was used to identify potential risk factors. RESULTS: There were a total of 28,765 cases of bacillary dysentery in 2011. The results of the analysis indicated that compared with other age groups, the highest incidence (473.75/105) occurred in individuals <5 years of age. The incidence in males (530.57/105) was higher compared with females (409.06/105). On a temporal basis, incidence increased rapidly starting in April. Peak incidence occurred in August (571.10/105). Analysis of the spatial distribution model revealed that factors such as population density, temperature, precipitation, and sunshine hours were positively associated with incidence rate. Per capita gross domestic product was negatively associated with disease incidence. CONCLUSIONS: Meteorological and socio-economic factors have affected the transmission of bacillary dysentery in the urban Beijing-Tianjin-Tangshan region of China. The success of bacillary dysentery prevention and control department strategies would benefit from giving more consideration to climate variations and local socio-economic conditions.

Unraveling the metastatic niche in breast cancer bone metastasis through single-cell RNA sequencing.

PURPOSE: Breast cancer (BRCA) is characterized by a unique metastatic pattern, often presenting with bone metastasis (BoM), posing significant clinical challenges. Through the study of the immune microenvironment in BRCA BoM offer perspectives for therapeutic interventions targeting this specific metastatic manifestation of BRCA. METHODS: This study employs single-cell RNA sequencing and TCGA data analysis to comprehensively compare primary tumors (PT), lymph node metastasis (LN), and BoM. RESULTS AND CONCLUSIONS: Our investigation identifies a metastatic niche in BoM marked by an increased abundance of cancer-associated fibroblasts (CAFs) and reduced immune cell presence. A distinct subtype (State 1) of BRCA BoM cells associated with adverse prognosis is identified. State 1, displaying heightened stemness traits, may represent an initiation phase for BoM in BRCA. Complex cell communications involving tumor, stromal, and immune cells are revealed. Interactions of FN1, SPP1, and MDK correlate with elevated immune cells in BoM. CD46, MDK, and PTN interactions drive myofibroblast activation and proliferation, contributing to tissue remodeling. Additionally, MDK, PTN, and FN1 interactions influence FAP+ CAF activation, impacting cell adhesion and migration in BoM. These insights deepen our understanding of the metastatic niche in breast cancer BoM.

Response of fine particulate matter and ozone concentrations to meteorology and anthropogenic precursors over the "2+26" cities of northern China.

Analyzing the influencing factors of fine particulate matter and ozone formation and identifying the coupling relationship between the two are the basis for implementing the synergistic pollutants control. However, the current research on the synergistic relationship between the two still needs to be further explored. Using the Geodetector model, we analyzed the effects of meteorology and emissions on fine particulate matter and ozone concentrations over the "2 + 26" cities at multiple timescales, and also explored the coupling relationship between the two pollutants. Fine particulate matter concentrations showed overall decreasing trends on inter-season and inter-annual scale from 2015 to 2021, whereas ozone concentrations showed overall increasing trends. While ozone concentrations displayed an inverted U-shaped distribution from month to month, fine particulate matter concentrations displayed a U-shaped fluctuation. On inter-annual scale, climatic factors, with planet boundary layer height as the main determinant, have higher effects for both pollutants than human precursors. In summer and autumn, sunshine duration had the most influence on fine particulate matter, while planet boundary layer height was the greatest factor in winter. Fine particulate matter is the leading impacting factor on ozone concentrations in summer, and there were positive associations between them on both annual and seasonal scale. The impact of nitrogen oxides and volatile organic compounds for both pollutants concentrations varied significantly between seasons. The two pollutants concentration were enhanced by the interactions between the various components. On inter-annual scale, interactions between the planet boundary layer height and other factors dominated the concentrations of the two pollutants, whereas in summer, interactions between fine particulate matter and other factors dominated the concentrations of ozone. The study has implications for the treatment of atmospheric pollution in China and other nations and can serve as an important reference for the creation of integrated atmospheric pollution regulation policies over the "2 + 26" cities.

Non-volatile electrical polarization switching via domain wall release in 3R-MoS2 bilayer.

Understanding the nature of sliding ferroelectricity is of fundamental importance for the discovery and application of two-dimensional ferroelectric materials. In this work, we investigate the phenomenon of switchable polarization in a bilayer MoS2 with natural rhombohedral stacking, where the spontaneous polarization is coupled with excitonic effects through asymmetric interlayer coupling. Using optical spectroscopy and imaging techniques, we observe how a released domain wall switches the polarization of a large single domain. Our results highlight the importance of domain walls in the polarization switching of non-twisted rhombohedral transition metal dichalcogenides and open new opportunities for the non-volatile control of their optical response.

Application of microbially induced calcium carbonate precipitation (MICP) process in concrete self-healing and environmental restoration to facilitate carbon neutrality: a critical review.

Soil contamination, land desertification and concrete cracking can have significant adverse impacts on sustainable human economic and societal development. Cost-effective and environmentally friendly approaches are recommended to resolve these issues. Microbially induced carbonate precipitation (MICP) is an innovative, attractive and cost-effective in situ biotechnology with high potential for remediation of polluted or desertified soils/lands and cracked concrete and has attracted widespread attention in recent years. Accordingly, the principles of MICP technology and its applications in the remediation of heavy metal-contaminated and desertified soils and self-healing of concrete were reviewed in this study. The production of carbonate mineral precipitates during the MICP process can effectively reduce the mobility of heavy metals in soils, improve the cohesion of dispersed sands and realize self-healing of cracks in concrete. Moreover, CO2 can be fixed during MICP, which can facilitate carbon neutrality and contribute to global warming mitigation. Overall, MICP technology exhibits great promise in environmental restoration and construction engineering applications, despite some challenges remaining in its large-scale implementation, such as the substantial impacts of fluctuating environmental factors on microbial activity and MICP efficacy. Several methods, such as the use of natural materials or wastes as nutrient and calcium sources and isolation of bacterial strains with strong resistance to harsh environmental conditions, are employed to improve the remediation performance of MICP. However, more studies on the efficiency enhancement, mechanism exploration and field-scale applications of MICP are needed.

Single-cell analysis reveals cellular reprogramming in advanced colon cancer following FOLFOX-bevacizumab treatment.

Introduction: The combination of FOLFOX and bevacizumab (FOLFOX-Bev) is a promising treatment for advanced colorectal cancer (CRC). However, the response of the tumor microenvironment to FOLFOX-Bev is still largely unexplored. Methods: We conducted single-cell transcriptomic analysis of CRC samples derived from a patient before and after treatment to gain insights into the cellular changes associated with FOLFOX-Bev treatment. Results: We found that cancer cells with high proliferative, metastatic, and pro-angiogenic properties respond better to FOLFOX-Bev treatment. Moreover, FOLFOX-Bev enhances CD8+ T cell cytotoxicity, thereby boosting the anti-tumor immune response. Conversely, FOLFOX-Bev impairs the functionality of tumor-associated macrophages, plasma cells, and cancer-associated fibroblasts, leading to a decrease in VEGFB-mediated angiogenesis. Furthermore, FOLFOX-Bev treatment reset intercellular communication, which could potentially affect the function of non-cancer cells. Discussion: Our findings provide valuable insights into the molecular mechanisms underlying the response of advanced CRC to FOLFOX-Bev treatment and highlight potential targets for improving the efficacy of this treatment strategy.

Development and validation of a survival prediction model for 113,239 patients with colon cancer: a retrospective cohort study.

Background: Colon cancer (CC) is the third most commonly diagnosed malignant tumor and remains the second leading cause of cancer-related deaths worldwide. However, the risk assessment of poor prognosis of CC is limited in previous studies. This study aimed to develop a predictive nomogram for the survival of CC patients. Methods: In this retrospective cohort study, 113,239 CC patients from the Surveillance, Epidemiology, and End Results (SEER) database were randomly divided into training (n=56,619) and testing (n=56,620) sets with a ratio of 1:1. Demographic, clinical data and survival status of patients were extracted. The outcomes were 3- and 5-year survival of CC. Univariate and multivariate Cox regression analyses were used to screen the predictors to develop the predictive nomogram. Internal validation and stratified analyses were further assessed the nomogram. The C-index and area under the curve (AUC) were calculated to estimate the model's predictive capacity, and calibration curves were adopted to estimate the model fit. Results: Totally 38,522 (34.02%) patients died during the 5-year follow-up. The nomogram incorporated variables associated with the prognosis of CC patients, including age, gender, marital status, insurance status, tumor grade, stage (T/N/M), surgery, and number of nodes examined, with a C-index of 0.775 in the training set and 0.774 in the testing set. The AUCs of the nomogram for the 3- and 5-year survival prediction in the training set were 0.817 and 0.808, with the sensitivity of 0.688 and 0.716, and the specificity of 0.785 and 0.740, respectively. Similar results were found in the testing set. The C-index of the predictive nomogram for male, female, White, Black, and other races was 0.769, 0.779, 0.773, 0.770, and 0.770, respectively. The calibration curves for the nomogram in the above five cohorts showed a good agreement between actual and predicted values. Conclusions: The nomogram may exhibit a certain predictive performance based on the SEER database, which may provide individual survival predictions for CC patients.

PM2.5 Concentrations Variability in North China Explored with a Multi-Scale Spatial Random Effect Model.

Compiling fine-resolution geospatial PM2.5 concentrations data is essential for precisely assessing the health risks of PM2.5 pollution exposure as well as for evaluating environmental policy effectiveness. In most previous studies, global and local spatial heterogeneity of PM2.5 is captured by the inclusion of multi-scale covariate effects, while the modelling of genuine scale-dependent variabilities pertaining to the spatial random process of PM2.5 has not yet been much studied. Consequently, this work proposed a multi-scale spatial random effect model (MSSREM), based a recently developed fixed-rank Kriging method, to capture both the scale-dependent variabilities and the spatial dependence effect simultaneously. Furthermore, a small-scale Monte Carlo simulation experiment was conducted to assess the performance of MSSREM against classic geospatial Kriging models. The key results indicated that when the multiple-scale property of local spatial variabilities were exhibited, the MSSREM had greater ability to recover local- or fine-scale variations hidden in a real spatial process. The methodology was applied to the PM2.5 concentrations modelling in North China, a region with the worst air quality in the country. The MSSREM provided high prediction accuracy, 0.917 R-squared, and 3.777 root mean square error (RMSE). In addition, the spatial correlations in PM2.5 concentrations were properly captured by the model as indicated by a statistically insignificant Moran's I statistic (a value of 0.136 with p-value > 0.2). Overall, this study offers another spatial statistical model for investigating and predicting PM2.5 concentration, which would be beneficial for precise health risk assessment of PM2.5 pollution exposure.

Interplay of Environmental Regulation and Local Protectionism in China.

Environmental regulation (ER) and local protectionism (LP) are important policy tools for Chinese local governments to improve the environment and promote growth, respectively, but we know little about their interplay in dealing with pollution-intensive industries and enterprises. Using spatial correlation analysis and spatial panel simultaneous equations models, we investigated the spatial characteristics and interactions of the ER and LP in China's 285 prefectural cities. We found that the high-ER-intensity areas were spreading from the eastern to the central and western regions, and the patterns of LP transited from high in the north and low in the south to high in the west and low in the east. There was a negative correlation spatially between ER and LP. LP could inhibit the increase in ER intensity, while the continuously increasing ER intensity could restrict LP through the competitive behavior from the "race to the bottom" to the "race to the top" among local governments. The effect of ER restricting LP was significant from 2008 to 2013 and prominent in the east, which was dominated by "race to the top" competition, while LP had a greater inhibitory effect on ER in the central and western regions, which preferred to obtain tax revenues from pollution-intensive industries. The results imply that removing the roots of local protectionism, improving the environmental governance system, and formulating differentiated regional environmental regulatory measures will help local governments balance economic growth and environmental protection.

Ultrafast response of spontaneous photovoltaic effect in 3R-MoS2-based heterostructures.

Rhombohedrally stacked MoS2 has been shown to exhibit spontaneous polarization down to the bilayer limit and can sustain a strong depolarization field when sandwiched between graphene. Such a field gives rise to a spontaneous photovoltaic effect without needing any p-n junction. In this work, we show that the photovoltaic effect has an external quantum efficiency of 10% for devices with only two atomic layers of MoS2 at low temperatures, and identify a picosecond-fast photocurrent response, which translates to an intrinsic device bandwidth at ∼100-GHz level. To this end, we have developed a nondegenerate pump-probe photocurrent spectroscopy technique to deconvolute the thermal and charge-transfer processes, thus successfully revealing the multicomponent nature of the photocurrent dynamics. The fast component approaches the limit of the charge-transfer speed at the graphene-MoS2 interface. The remarkable efficiency and ultrafast photoresponse in the graphene-3R-MoS2 devices support the use of ferroelectric van der Waals materials for future high-performance optoelectronic applications.

Effects of anthropogenic precursor emissions and meteorological conditions on PM2.5 concentrations over the "2+26" cities of northern China.

Elucidating the characteristics and influencing mechanisms of PM2.5 concentrations is the premise and key to the precise prevention and control of air pollution. However, the temporal and spatial heterogeneity of PM2.5 concentrations and its driving mechanism are complex and need to be further analyzed. We analyzed the temporal and spatial variations of PM2.5 concentrations in the "2 + 26" cities from 2015 to 2021, and quantified the influence of meteorological factors and anthropogenic emissions and their interactions on PM2.5 concentrations based on geographic detector model. We find the inter-annual and inter-season PM2.5 concentrations show downward trend from 2015 to 2021, and the inter-month PM2.5 concentrations present a U-shaped distribution. The PM2.5 concentrations in the "2 + 26" cities manifest a spatial distribution pattern of high in the south and low in the north, and high in the middle and low in the surroundings. Meteorological conditions have stronger effects on PM2.5 concentrations than anthropogenic emissions, and planetary boundary layer height and temperature are the two main driving factors at the annual scale. On the seasonal scale, sunshine duration is the dominant factor of PM2.5 concentrations in summer and autumn, and planetary boundary layer height is the dominant factor of PM2.5 concentrations in winter. The effect of anthropogenic emissions on PM2.5 concentration is higher in winter and spring than in summer and autumn, and ammonia and ozone have stronger effects on PM2.5 concentrations than other anthropogenic emissions. Interactions between the factors significantly enhance the PM2.5 concentrations. The interactions between planetary boundary layer height and other impacting factors play dominant roles on PM2.5 concentrations at annual scale and in winter. Our results not only provide crucial information for further developing air quality policies of the "2 + 26" cities, but also bear out several important implications for clean air policies in China and other regions of the world.

Assessment of durable chemoimmunotherapy response via circulating tumor DNA in advanced esophageal squamous cell carcinoma.

Immune checkpoint inhibitor (ICI)-based therapies have shown promising advances for the first-line treatment of advanced or metastatic esophageal cancer (EC). However, few studies concerning the identification of patients who achieve durable response from ICIs have been previously reported. In the present study, pre- and on-treatment plasma circulating tumor DNA (ctDNA) were analyzed in 10 patients with advanced esophageal squamous cell cancer (ESCC) receiving first-line chemoimmunotherapy. Patients with decreased molecular tumor burden index (mTBI) >7% experienced longer progression-free survival (PFS) and durable clinical benefit (DCB, PFS ≥ 6 months). In addition, five patients showed stable disease at first scan, all three patients with decreased mTBI > 7% achieved DCB, while two cases with decreased mTBI ≤ 7% experienced non-DCB. Our results demonstrate that ctDNA monitor might help identify which ESCC patients respond to chemoimmunotherapy.