A bibliometric and visualized analysis of preoperative future liver remnant augmentation techniques from 1997 to 2022

BackgroundThe size and function of the future liver remnant (FLR) is an essential consideration for both eligibility for treatment and postoperative prognosis when planning surgical hepatectomy. Over time, a variety of preoperative FLR augmentation techniques have been investigated, from the earliest portal vein embolization (PVE) to the more recent Associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) and liver venous deprivation (LVD) procedures. Despite numerous publications on this topic, no bibliometric analysis has yet been conducted.MethodsWeb of Science Core Collection (WoSCC) database was searched to identify studies related to preoperative FLR augmentation techniques published from 1997 to 2022. The analysis was performed using the CiteSpace [version 6.1.R6 (64-bit)] and VOSviewer [version 1.6.19].ResultsA total of 973 academic studies were published by 4431 authors from 920 institutions in 51 countries/regions. The University of Zurich was the most published institution while Japan was the most productive country. Eduardo de Santibanes had the most published articles, and Masato Nagino was the most frequently co-cited author. The most frequently published journal was HPB, and the most cited journal was Ann Surg, with 8088 citations. The main aspects of preoperative FLR augmentation technique is to enhance surgical technology, expand clinical indications, prevent and treat postoperative complications, ensure long-term survival, and evaluate the growth rate of FLR. Recently, hot keywords in this field include ALPPS, LVD, and Hepatobiliary Scintigraphy.ConclusionThis bibliometric analysis provides a comprehensive overview of preoperative FLR augmentation techniques, offering valuable insights and ideas for scholars in this field

Cancer Mortality in Chinese Chrysotile Asbestos Miners: Exposure-Response Relationships

Objective: This study was conducted to assess the relationship of mortality from lung cancer and other selected causes to asbestos exposure levels. Methods: A cohort of 1539 male workers from a chrysotile mine in China was followed for 26 years. Data on vital status, occupation and smoking were collected from the mine records and individual contacts. Causes and dates of death were further verified from the local death registry. Individual cumulative fibre exposures (f-yr/ml) were estimated based on converted dust measurements and working years at specific workshops. Standardized mortality ratios (SMRs) for lung cancer, gastrointestinal (GI) cancer, all cancers and nonmalignant respiratory diseases (NMRD) stratified by employment years, estimated cumulative fibre exposures, and smoking, were calculated. Poisson models were fitted to determine exposure-response relationships between estimated fibre exposures and cause-specific mortality, adjusting for age and smoking. Results: SMRs for lung cancer increased with employment years at entry to the study, by 3.5-fold in ≥10 years and 5.3-fold in ≥20 years compared with <10 years. A similar trend was seen for NMRD. Smokers had greater mortality from all causes than nonsmokers, but the latter also had slightly increased SMR for lung cancer. No excess lung cancer mortality was observed in cumulative exposures of <20 f-yrs/ml. However, significantly increased mortality was observed in smokers at the levels of ≥20 f-yrs/ml and above, and in nonsmokers at ≥100 f-yrs/ml and above. A similarly clear gradient was also displayed for NMRD. The exposure-response relationships with lung cancer and NMRD persisted in multivariate analysis. Moreover, a clear gradient was shown in GI cancer mortality when age and smoking were adjusted for. Conclusion: There were clear exposure-response relationships in this cohort, which imply a causal link between chrysotile asbestos exposure and lung cancer and nonmalignant respiratory diseases, and possibly to gastrointestinal cancer, at least for smokers

Hypothesis-Driven Source Separation and Dimension Reduction of Neural Time Series Data

Neural dynamics spanning diverse spatial and temporal scales produce electrical fields that can be measured using non-invasive tools like electroencephalography (EEG), which records neural activity from multiple recording sites on the scalp of the head. However, most analytic approaches in EEG research do not fully exploit the rich, high-dimensional data to gain insights into how the brain processes information nor address the volume conduction problem. That is, EEG data contain a mixture of electric fields, produced by hundreds or thousands of neural sources, that propagate simultaneously to all recording sites on the scalp. Although this source mixing problem poses many analytic challenges, it also provides opportunities to develop better algorithms that can provide further theoretical and practical insights. Generalized eigendecomposition (GED) provides a multivariate framework for separating sources and reducing the dimensionality of EEG data while allowing for flexible hypothesis testing. I derived four analytic approaches from this framework and applied them to simulated and empirical data in four studies. Simulations in Study 1 showed that GED-based approaches were effective for separating sources, reducing data dimensionality, increasing signal-to-noise ratio, and testing hypotheses. Study 2 used GED to identify the determinants of cognitive control allocation and test the predictions of a theory of control allocation. Study 3 combined GED with time-frequency approaches to examine cognitive control processes during value-guided choice. Study 4 used GED to isolate sources whose activities reflected trial-varying decision attributes and showed that the timing of these activities was consistent with the predictions of decision models. In summary, my research provides evidence for the value of GED, a hypothesis-driven multivariate source separation and dimension reduction framework that can be used to improve our understanding of brain and cognitive function.Ph.D

Mitigating Multipath Bias Using a Dual-Polarization Antenna: Theoretical Performance, Algorithm Design, and Simulation

It is well known that multipath effect remains a dominant error source that affects the positioning accuracy of Global Navigation Satellite System (GNSS) receivers. Significant efforts have been made by researchers and receiver manufacturers to mitigate multipath error in the past decades. Recently, a multipath mitigation technique using dual-polarization antennas has become a research hotspot for it provides another degree of freedom to distinguish the line-of-sight (LOS) signal from the LOS and multipath composite signal without extensively increasing the complexity of the receiver. Numbers of multipath mitigation techniques using dual-polarization antennas have been proposed and all of them report performance improvement over the single-polarization methods. However, due to the unpredictability of multipath, multipath mitigation techniques based on dual-polarization are not always effective while few studies discuss the condition under which the multipath mitigation using a dual-polarization antenna can outperform that using a single-polarization antenna, which is a fundamental question for dual-polarization multipath mitigation (DPMM) and the design of multipath mitigation algorithms. In this paper we analyze the characteristics of the signal received by a dual-polarization antenna and use the maximum likelihood estimation (MLE) to assess the theoretical performance of DPMM in different received signal cases. Based on the assessment we answer this fundamental question and find the dual-polarization antenna’s capability in mitigating short delay multipath—the most challenging one among all types of multipath for the majority of the multipath mitigation techniques. Considering these effective conditions, we propose a dual-polarization sequential iterative maximum likelihood estimation (DP-SIMLE) algorithm for DPMM. The simulation results verify our theory and show superior performance of the proposed DP-SIMLE algorithm over the traditional one using only an RHCP antenna

Distinguishing Astragalus mongholicus and Its Planting Soil Samples from Different Regions by ICP-AES

“Daodi herb” enjoys a good reputation for its quality and clinical effects. As one of the most popular daodi herbs, Astragalus membranaceus (Fisch.) Bge var. mongholicus (Bge.) Hsiao (A. membranaceus) is popularly used for its anti-oxidant, anti-inflammatory and immune-enhancing properties. In this study, we used inductively coupled plasma atomic emission spectrometry (ICP-AES) technique to investigate the inorganic elements contents in A. mongholicu and its soil samples from daodi area (Shanxi) and non-daodi areas (Inner Mongolia and Gansu). A total of 21 inorganic elements (Pb, Cd, As, Hg, Cu, P, K, Zn, Mn, Ca, Mg, Fe, Se, B, Al, Na, Cr, Ni, Ba, Ti and Sr) were simultaneously determined. Principal component analysis (PCA) was performed to differentiate A. mongholicu and soil samples from the three main producing areas. It was found that the inorganic element characteristics as well as the uptake and accumulation behavior of the three kinds of samples were significantly different. The high contents of Fe, B, Al, Na, Cr and Ni could be used as a standard in the elements fingerprint to identify daodi and non-daodi A. Mongholicus. As the main effective compounds were closely related to the pharmacodynamics activities, the inter-relationships between selected elements and components could reflect that the quality of A. Mongholicus from Shanxi were superior to others to a certain degree. This finding highlighted the usefulness of ICP-AES elemental analysis and evidenced that the inorganic element profile can be employed to evaluate the genuineness of A. mongholicus

Water Quality and Microbial Community Changes in an Urban River after Micro-Nano Bubble Technology in Situ Treatment

Currently, black-odor river has received great attention in China. In this study, the micro-nano bubble technology (MBT) was used to mitigate the water pollution rapidly and continuously by increasing the concentration of dissolved oxygen (DO) in water. During treatment, the concentration of DO increased from 0.60 mg/L to over 5.00 mg/L, and the oxidation reduction potential (ORP) also changed from a negative value to over 100.00 mV after only five days aeration. High throughput pyrosequencing technology was employed to identify the microbial community structure. At genus level, the dominant bacteria were anaerobic and nutrient-loving microbes (e.g., Arcobacter sp., Azonexus sp., and Citrobacter sp.) before, and the relative abundances of aerobic and functional microbes (e.g., Perlucidibaca sp., Pseudarcicella sp., Rhodoluna sp., and Sediminibacterium sp.) were increased after treatment. Meanwhile, the water quality was significantly improved with about 50% removal ratios of chemical oxygen demand (CODCr) and ammonia nitrogen (NH4+-N). Canonical correspondence analysis (CCA) results showed that microbial community structure shaped by COD, DO, NH4+-N, and TP, CCA1 and CCA2 explained 41.94% and 24.56% of total variances, respectively. Overall, the MBT could improve the water quality of urban black-odor river by raising the DO and activate the aerobic microbes