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Objective: Postoperative cerebrospinal fluid leakage (CSFL) remains a clinically relevant complication following endoscopic endonasal surgery (EES) for pituitary neuroendocrine tumors (PitNETs). This study aimed to identify independent clinical predictors of postoperative CSFL and to facilitate perioperative risk stratification. Methods: We retrospectively analyzed 302 consecutive patients who underwent EES for PitNETs at a single tertiary center. Preoperative anatomical features, tumor characteristics, pathological subtypes, and intraoperative variables were collected. Given the limited number of postoperative CSFL events, we used penalized logistic regression (Firth correction) with a prespecified parsimonious set of predictors to reduce small-sample bias. Results: Penalized multivariable analysis (Firth correction) identified fibrous tumor texture (OR 5.13), strong sellar barrier thickness (> 1 mm; OR 0.09), and intraoperative CSFL grade (Kelly grade 1–2: OR 3.69; Kelly grade 3: OR 34.30) as significant independent predictors. ACTH-secreting pathology demonstrated a trend towards association but did not reach statistical significance (OR 4.76; 95% CI 0.89–25.36; P = 0.068). Conclusions: Postoperative CSFL following EES for PitNETs is associated with a distinct set of clinical and anatomical risk factors. Recognition of these predictors may support individualized reconstruction and perioperative decision-making within a risk-stratified intraoperative decision algorithm; the proposed algorithm is exploratory and requires external validation.

Background The optimal therapeutic approach for cystic prolactinomas remains unclear. This study aimed to evaluate the remission rates of prolactinoma patients after surgical treatment and the risk factors affecting postoperative remission in cystic prolactinoma patients. Methods The clinical data were retrospectively compiled from 141 patients with prolactinomas (including 41 cases of cystic prolactinomas, 21 cases of solid microprolactinomas and 79 cases of solid macroprolactinomas) who underwent transsphenoidal surgery (TSS) between April 2013 and October 2021 at the First Affiliated Hospital of Sun Yat-sen University. Results Early postoperative remission was achieved in 65.83% (n = 27/41) of cystic prolactinomas, 80.95% (n = 17/21) of solid microprolactinomas and 40.51% (n = 32/79) of solid macroprolactinomas. The mean length of follow up in all patients was 43.95 ± 2.33 months (range: 6-105 months). The follow-up remission rates were 58.54%, 71.43% and 44.30% in cystic, solid micro- and solid macroprolactinomas, respectively. For cystic prolactinomas, the early postoperative remission rates in the patients with preoperative dopamine agonists (DA) treatment were significantly higher than those without preoperative DA treatment ( p =  0.033), but the difference in the follow-up remission rates between these two groups was not significant ( p =  0.209). Multivariate stepwise logistic regression analysis indicated that tumor size and preoperative prolactin (PRL) levels < 200 ng/ml were independent predictors for early postoperative remission in cystic prolactinomas. Conclusion For cystic prolactinomas, tumor size and preoperative PRL levels were independent predictors of early postoperative remission. Preoperative DA therapy combined with TSS may be more beneficial to cystic prolactinoma patients.

Reprogramming of metabolic genes and subsequent alterations in metabolic phenotypes occur widely in malignant tumours, including glioblastoma (GBM). FOXM1 is a potent transcription factor that plays an oncogenic role by regulating the expression of many genes. As a SET domain containing protein, SET7 is a protein lysine methyltransferase which monomethylates histone proteins and other proteins. The epigenetic modification of histones regulates gene expressions by epigenetically modifying promoters of DNAs and inter vening in tumor development. Activation of FASN increased de novo fatty acid (FA) synthesis, a hallmark of cancer cells. Here, we report that FOXM1 may directly promote the transcription of SET7 and activate SET7-H3K4me1-FASN axis, which results in the maintenance of de novo FA synthesis.

Part I of this thesis describes my experimental and theoretical efforts to understand spin injection into semiconductors. I present extensive discussions on the conditions required to achieve significant spin transfer from a ferromagnetic injector into a paramagnetic conductor. Theoretical calculations for ballistic spin-coupled transport are described. To circumvent conductivity mismatch that occurs for diffusive contacts between ferromagnetic metals and semiconductors, I designed and fabricated new spin injection devices that employ the recently discovered dilute semiconductor (Ga,Mn)As as a spin polarizer. Spin-coupled transport signals were observed in these novel devices. In the course of this work, I discovered that bulk (Ga,Mn)As itself manifests what we have termed a “giant” planar Hall effect. This magnetoelectronic phenomenon arises from the strong, intrinsic spin-orbit interaction. This phenomenon offers new prospects for applications in magnetic sensors and storage media, extends the possibility of novel spintronic devices, and enables unprecedented, high resolution measurements of magnetic phenomena. By using the giant planar Hall effect, I have achieved a complete characterization of the magnetic properties of (Ga,Mn)As. This large effect also has enabled the first direct electrical measurements of the propagation of individual domain walls in microdevices. These experiments establish a new approach to the study of ferromagnetodynamics that does not require significant instrumentation. Individual domain walls can be monitored, trapped and manipulated in real time. By such techniques, I have been able to directly investigate the resistance arising from a single magnetic domain wall for the first time. Part II describes my studies of the strain-dependent electrical properties of ballistic GaAs two-dimensional electron gases (2DEGs). I have developed techniques to realize freely-suspended 2DEGs with moderately high mobility. These have been incorporated into novel GaAs nanoelectromechanical systems that yield high sensitivity for NEMS motion detection. They have also led to my discovery of a new, dipolar mechanism for electromechanical actuation. Suspended quantum dots have also been successfully fabricated using my newly developed freely-suspended 2DEG fabrication methods. These demonstrate pronounced charging effects, even at elevated temperature, and offer new prospects for observing electronic interactions with confined phonons.

Recent studies demonstrate that an individual magnetic domain wall (DW) can be trapped and reproducibly positioned within multiterminal (Ga,Mn)As microdevices. The electrical resistance obtained from such measurements is found to be measurably altered by the presence of this single entity. To elucidate these observations we develop a simple model for the electrical potential distribution along a multiterminal device in the presence of a single DW. This is employed to calculate the effect of a single DW upon the longitudinal and transverse resistance. The model provides very good agreement with experimental observations, and serves to highlight important deviations from simple theory. We show that measurements of transverse resistance along the channel permits establishing the position and the shape of the DW contained within it. An experimental scheme is developed that enables unambiguous extraction of the intrinsic DW resistivity. This permits the intrinsic contribution to be differentiated from resistivities originating from the bulk and from magnetic anisotropy - effects that are generally manifested as large backgrounds in the experiments.

Metamaterial absorbers (MMAs) offer a novel and flexible method to realize perfect absorption in specific frequencies, especially in the THz range. Despite the exotic abilities to manipulate light, most previously reported MMAs still suffer from limited bandwidth and tunability. Here we present a thermally switchable terahertz (THz) metasurface that exhibits ultra-broadband absorption and high-transmission characteristics at different ambient temperatures. Our simulations demonstrate that at room temperature the structure is highly transparent. When the ambient temperature reaches 358 K, the proposed design exhibits an ultra-broadband absorption from 0.398 to 1.356 THz with the absorptivity maintaining above 90% and the relative absorption bandwidth reaches up to 109.2%. The structure is demonstrated to be insensitive to the incident angle. Moreover, the bandwidth of such a structure can easily be expanded or reduced by cascading or removing the rings, providing high scalability in practical applications. Such a thermally switchable THz metasurface may have potential applications in various fields, such as optical switching, THz imaging, modulating and filtering.

Brown carbon (BrC) plays an essential impact on radiative forcing due to its ability to absorb sunlight. In this study, the optical properties and molecular characteristics of water-soluble and methanol-soluble organic carbon (OC; MSOC) emitted from the simulated combustion of biomass and coal fuels and vehicle emissions were investigated using ultraviolet–visible (UV–vis) spectroscopy, excitation–emission matrix (EEM) spectroscopy, and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) coupled with electrospray ionization (ESI). The results showed that these smoke aerosol samples from biomass burning (BB) and coal combustion (CC) had a higher mass absorption efficiency at 365 nm (MAE365) than vehicle emission samples. A stronger MAE365 value was also found in MSOC than water-soluble organic carbon (WSOC), indicating low polar compounds would possess a higher light absorption capacity. Parallel factor (PARAFAC) analysis identified six types of fluorophores (P1–6) in WSOC including two humic-like substances (HULIS-1) (P1 and P6), three protein-like substances (PLOM) (P2, P3, and P5), and one undefined substance (P4). HULIS-1 was mainly from aging vehicle exhaust particles; P2 was only abundant in BB aerosols; P3 was ubiquitous in all tested aerosols; P4 was abundant in fossil burning aerosols; and P5 was more intense in fresh vehicle exhaust particles. The MSOC chromophores (six components; C1–6) exhibited consistent characteristics with WSOC, suggesting the method could be used to indicate the origins of chromophores. FT-ICR mass spectra showed that CHO and CHON were the most abundant components of WSOC, but S-containing compounds appeared in a higher abundance in CC aerosols and vehicle emissions than BB aerosols, while considerably fewer S-containing compounds largely with CHO and CHON were detected in MSOC. The unique formulas of different sources in the van Krevelen (VK) diagram presented different molecular distributions. To be specific, BB aerosols with largely CHO and CHON had a medium H ∕ C and low O ∕ C ratio, while CC aerosols and vehicle emissions largely with S-containing compounds had an opposite H ∕ C and O ∕ C ratio. Moreover, the light absorption capacity of WSOC and MSOC was positively associated with the unsaturation degree and molecular weight in the source aerosols. The above results are potentially applicable to further studies on the EEM-based or molecular-characteristic-based source apportionment of chromophores in atmospheric aerosols.

An effective vaccine is needed to end the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Here, we assess the preliminary safety, tolerability and immunogenicity data from an ongoing single-center (in Jiangsu province, China), parallel-group, double-blind phase 1 trial of the vaccine candidate BNT162b1 in 144 healthy SARS-CoV-2-naive Chinese participants. These participants are randomized 1:1:1 to receive prime and boost vaccinations of 10 µg or 30 µg BNT162b1 or placebo, given 21 d apart, with equal allocation of younger (aged 18–55 years) and older adults (aged 65–85 years) to each treatment group (ChiCTR2000034825). BNT162b1 encodes the SARS-CoV-2 spike glycoprotein receptor-binding domain (RBD) and is one of several messenger RNA-based vaccine candidates under clinical investigation. Local reactions and systemic events were generally dose dependent, transient and mild to moderate. Fever was the only grade 3 adverse event. BNT162b1 induced robust interferon-γ T cell responses to a peptide pool including the RBD in both younger and older Chinese adults, and geometric mean neutralizing titers reached 2.1-fold (for younger participants) and 1.3-fold (for the older participants) that of a panel of COVID-19 convalescent human sera obtained at least 14 d after positive SARS-CoV-2 polymerase chain reaction test. In summary, BNT162b1 has an acceptable safety profile and produces high levels of humoral and T cell responses in an Asian population. Phase 1 trial results of the messenger RNA vaccine candidate BNT162b1, which encodes the receptor-binding domain of the SARS-CoV-2 spike protein, show safety and elicitation of antibody and T cell responses in both younger and older Chinese adults.

Haze in China has been increasing in frequency of occurrence as well as the area of the affected region. Here, we report on a new mechanism of haze formation, in which coexistence with NOx can reduce the environmental capacity for SO 2 , leading to rapid conversion of SO 2 to sulfate because NO 2 and SO 2 have a synergistic effect when they react on the surface of mineral dust. Monitoring data from five severe haze episodes in January of 2013 in the Beijing-Tianjin-Hebei regions agreed very well with the laboratory simulation. The combined air pollution of motor vehicle exhaust and coal-fired flue gases greatly reduced the atmospheric environmental capacity for SO 2 and the formation of sulfate was found to be a main reason for the growth of fine particles, which led to the occurrence of haze. These results indicate that the impact of motor vehicle exhaust on the atmospheric environment might be underestimated.

This study proposes to construct a model using random forest method, an efficient machine learning‐based method, to predict the spatial structure and temporal evolution of the sea surface temperature (SST) cooling induced by northwest Pacific tropical cyclones (TCs), a process of the so‐called wind pump. The predictors in use include 12 predictors related to TC characteristics and pre‐storm ocean conditions. The model is shown to skillfully predict the spatiotemporal evolutions of the cold wake generated by TCs of different intensity groups, and capture the cross‐case variance in the observed SST response. Another model is further built based on the same method to assess the relative importance of the 12 predictors in determining the magnitude of the maximum cooling. Computations of feature scores of those predictors show that TC intensity, translation speed and size, and pre‐storm mixed layer depth and SST dominate, depending on the area where the cooling is considered. Plain Language Summary While many studies have been devoted to understanding the processes and mechanisms underlying the sea surface temperature (SST) cooling induced by tropical cyclones (TCs), few studies have attempted to predict the spatial and temporal evolution of the sea surface temperature (SST) cooling triggered by TCs. In this study, we proposed to achieve this goal by building a model using an efficient and robust machine learning‐based method. The constructed model uses 12 predictors associated with TC characteristics (e.g., intensity, and translation speed) and pre‐storm ocean states (e.g., mixed layer depth). The model performs well in producing the TC‐induced spatial structure and temporal evolution of the cold wake and can capture most of the variance in the observed SST response. We quantified the relative importance of the 12 predictors, and found that TC intensity, translation speed and size, and pre‐storm mixed layer depth and SST dominate in deciding the magnitude of the SST response. The results and proposed method have important implications for predicting the response of ocean primary production to the TC wind pump effects. Key Points A machine learning‐based model is built to predict the spatiotemporal evolution of the tropical cyclone‐induced sea surface temperature response The model well predicts the spatial structure and temporal evolution of the observed response and captures the observed cross‐case variance Feature scores are computed to assess the relative importance of the predictors in determining the magnitude of the SST response