UNASSIGNED: Activating mutations in epidermal growth factor receptor (EGFR) have been identified as key predictive biomarkers for the customized treatment with EGFR tyrosine kinase inhibitors (TKIs) in non-small cell lung cancer (NSCLC), aiding in improving patient response rates and survival. However, resistance challenges the efficacy of these treatments, with limited understanding of post-resistance therapeutic strategies. A deep understanding of the biology and resistance mechanisms of EGFR-mutant NSCLC is crucial for developing new treatment approaches. This study, through bibliometric analysis, summarizes the trends in research on resistance to EGFR-TKIs.
UNASSIGNED: Research papers on NSCLC with EGFR inhibitor resistance were collected from the Web of Science Core Collection (WoSCC). The analysis utilized bibliometric tools like CiteSpace, VOSviewer, and other platforms for comprehensive analysis and visualization of the outcomes.
UNASSIGNED: The WoSCC database contains a total of 5866 documents on resistance to EGFR-TKIs treatment, including 4727 articles (93.48%) and 1139 reviews (6.52%), spanning 81 countries and regions, 4792 institutions, with the involvement of 23,594 authors. Since 2016, there has been a significant increase in publications in this field. China has the highest publication output, while the United States has the highest citation count for papers. Harvard University leads in terms of the number of publications. Among the top ten journals with the highest output, Clinical Cancer Research has the highest impact factor at 11.5, with 90% of the journals classified in Q1 or Q2. Rafael Rosell is one of the most influential authors in this field, ranking second in publication volume and fourth in citation count. Research on EGFR-TKIs resistance mainly focuses on genetic testing, resistance mechanisms, and post-resistance treatment strategies.
UNASSIGNED: This study provides researchers with a reliable basis and guidance for finding authoritative references, understanding research trends, and exploring potential directions.

The detection of the amount of aflatoxin M1 (AFM1) in milk is crucial for food safety. Here, we utilize a fiber optic (FO) localized surface plasmon resonance (LSPR) biosensor by constructing gold nanoparticle (AuNP) multimers, in which the nanogaps amplified the LSPR signal by the hot spot effect, and achieved a highly sensitive detection of f AFM1. Through the optimization of parameter conditions for the fabrication of the sensor and detection system, a high performance result from the FO LSPR biosensor was obtained, and the method for AFM1 detection was established, with a wide detection range of 0.05-100 ng/mL and a low limit of detection (LOD) of 0.04 ng/mL, and it has been successfully validated with the actual sample milk. Therefore, it is a good strategy to fabricate highly sensitive FO LSPR sensors for detecting AFM1 by constructing AuNP multimers, and this approach is suitable for developing other biosensors.

Protein-DNA complex interactivity plays a crucial role in biological activities such as gene expression, modification, replication and transcription. Understanding the physiological significance of protein-DNA binding interfacial hot spots, as well as the development of computational biology, depends on the precise identification of these regions. In this paper, a hot spot prediction method called EC-PDH is proposed. First, we extracted features of these hot spots\' solid solvent-accessible surface area (ASA) and secondary structure, and then the mean, variance, energy and autocorrelation function values of the first three intrinsic modal components (IMFs) of these conventional features were extracted as new features via the empirical modal decomposition algorithm (EMD). A total of 218 dimensional features were obtained. For feature selection, we used the maximum correlation minimum redundancy sequence forward selection method (mRMR-SFS) to obtain an optimal 11-dimensional-feature subset. To address the issue of data imbalance, we used the SMOTE-Tomek algorithm to balance positive and negative samples and finally used cat gradient boosting (CatBoost) to construct our hot spot prediction model for protein-DNA binding interfaces. Our method performs well on the test set, with AUC, MCC and F1 score values of 0.847, 0.543 and 0.772, respectively. After a comparative evaluation, EC-PDH outperforms the existing state-of-the-art methods in identifying hot spots.

Electroencephalography (EEG) is a widely used tool in neuroscience. To explore the features of the top 100 cited articles related to EEG and aging over the past decade, we conducted a bibliometric analysis using Web of Science Core Collection (WoSCC) data as of January 21, 2024. The selected top 100 cited papers were analyzed using VOSviewer and Excel. We examined the distribution of publication years, authors, institutions, countries/regions, and journals. Hotspots were identified through keyword analysis. The analyzed articles were published between 2014 and 2021, with the majority being published before 2020 (n=91). Citation counts in WoSCC ranged from 24 to 250, with a median of 40 and a mean of 53. A total of 818 authors from 283 institutions in 35 countries/territories contributed to these top papers. The United States of America (USA) (n=37), Germany (n=14), and Canada (n=11) ranked in the top three in terms of total publications or citations. The predominant journals were in the fields of Neuroscience (n=58), Geriatrics & Gerontology (n=22), Clinical Neurology (n=13), and Anesthesiology (n=9), which published most of the high-quality articles. Key themes included EEG, aging, Alzheimer\'s disease, mild cognitive impairment, functional connectivity, and alpha oscillations. Emerging topics included sleep, machine learning, delirium, postoperative cognitive function, virtual reality, monitoring, resting state, coherence, and transcranial direct current stimulation. In conclusion, this study provides a comprehensive overview of the trends in scientific literature on EEG in aging over the past decade. Authors and institutions from North America, Europe, and East Asia led in contributions. Journals focusing on neuroscience, geriatrics, and anesthesiology published the majority of articles. Degenerative neurological diseases and cognitive impairment were prominent topics, suggesting future studies should explore EEG\'s diagnostic utility for these disorders.

Phthalate plasticizers (PAEs) illegally used in food pose a great threat to human health. A new and efficient sensing platform for the sensitive detection of the PAE residues in biological fluids needs to be designed and developed. Here, we report a simple and reliable surface-enhanced Raman spectroscopy (SERS) active platform with extralong hot spots of Au nanobipyramids@Ag nanorods (Au NBPs@Ag NRs) for the rapid and sensitive detection of PAEs in biological fluids. To achieve high activity, Au NBPs@Ag NRs with different shell lengths were fabricated by controlling the synthesis conditions, and the corresponding SERS properties were investigated by using crystal violet (CryV) and butyl benzyl phthalate (BBP). The experimental results showed that a longer shell length correlated to greater Raman activity, which was confirmed by finite-difference time-domain (FDTD) electromagnetic simulation. More importantly, the extralong hot spots of the Au NBPs@Ag NR SERS-active substrate showed excellent homogeneity and reproducibility for the CryV probe molecules (6.21%), and the detection limit was 10-9 M for both BBP and diethylhexyl phthalate (DEHP). Furthermore, through the standard addition method, an extralong hot spots SERS substrate could achieve highly sensitive detection of BBP and DEHP in serum and tears fluids, and the detection limit was as low as 3.52 × 10-8 M and 2.82 × 10-8 M. Therefore, the Au NBPs@Ag NR substrate with an extraordinarily long surface is efficient and versatile, and can potentially be used for high-efficiency sensing analysis in complex biological fluids.

Methodological advancements in road safety research reveal an increasing inclination toward integrating spatial approaches in hot spot identification, spatial pattern analysis, and developing spatially lagged models. Previous studies on hot spot identification and spatial pattern analysis have overlooked crash severities and the spatial autocorrelation of crashes by severity, missing valuable insights into crash patterns and underlying factors. This study investigates the spatial autocorrelation of crash severity by taking two capital cities, Addis Ababa and Berlin, as a case study and compares patterns in low and high-income countries. The study used three-year crash data from each city. It employed the average nearest neighbor distance (ANND) method to determine the significance of spatial clustering of crash data by severity, Global Moran\'s I to examine the statistical significance of spatial autocorrelation, and Local Moran\'s I to identify significant cluster locations with High-High (HH) and Low-Low (LL) crash severity values. The ANND analysis reveals a significant clustering of crashes by severity in both cities, except in Berlin\'s fatal crashes. However, different Global Moran\'s I results were obtained for the two cities, with a strong and statistically significant value for Addis Ababa compared to Berlin. The Local Moran\'s I result indicates that the central business district and residential areas have LL values, while the city\'s outskirts exhibit HH values in Addis Ababa. With some persistent HH value locations, Berlin\'s HH and LL grid clusters are intermingled on the city\'s periphery. Socio-economic factors, road user behavior and roadway factors contribute to the difference in the result. Nevertheless, it is interesting to note the similarity of significant HH value locations on the outskirts of both cities. Finally, the results are consistent with previous studies and indicate the need for further investigation in other locations.

The low cost and high efficiency of microwave-assisted regeneration render it a viable alternative to conventional regeneration methods. To enhance the regeneration performance, we developed a coupled electromagnetic, heat, and mass transfer model to investigate the heat and mass transfer mechanisms of activated carbon during microwave-assisted regeneration. Simulation results demonstrated that the toluene desorption process is governed by temperature distribution. Changing the input power and flow rate can promote the intensity of hot spots and adjust their distribution, respectively, thereby accelerating toluene desorption, inhibiting readsorption, and promoting regeneration efficiency. Ultimately, controlling the input power and flow rate can flexibly adjust toluene emissions to satisfy the processing demands of desorbed toluene. Taken together, this study provides a comprehensive understanding of the heat and mass transfer mechanisms of microwave-assisted regeneration and insights into adsorbent regeneration.

The contagious COVID-19 has recently emerged and evolved into a world-threatening pandemic outbreak. After pursuing rigorous prophylactic measures two years ago, most activities globally reopened despite the emergence of lethal genetic strains. In this context, assessing and mapping activity characteristics-based hot spot regions facilitating infectious transmission is essential. Hence, our research question is: How can the potential hotspots of COVID-19 risk be defined intra-cities based on the spatial planning of commercial activity in particular? In our research, Zayed and October cities, Egypt, characterized by various commercial activities, were selected as testbeds. First, we analyzed each activity\'s spatial and morphological characteristics and potential infection risk based on the Centre for Disease Control and Prevention (CDCP) criteria and the Kriging Interpolation method. Then, using Google Mobility, previous reports, and semi-structured interviews, points of interest and population flow were defined and combined with the last step as interrelated horizontal layers for determining hotspots. A validation study compared the generated activity risk map, spatial COVID-19 cases, and land use distribution using logistic regression (LR) and Pearson coefficients (rxy). Through visual analytics, our findings indicate the central areas of both cities, including incompatible and concentrated commercial activities, have high-risk peaks (LR = 0.903, rxy = 0.78) despite the medium urban density of districts, indicating that urban density alone is insufficient for public health risk reduction. Health perspective-based spatial configuration of activities is advised as a risk assessment tool along with urban density for appropriate decision-making in shaping pandemic-resilient cities.

Biogeochemical hot spots play a crucial role in the cycling and transport of redox-sensitive elements (RSEs) in the hyporheic zone (HZ). However, the transformation mechanisms of RSEs and patterns of RSEs hot spots in the HZ remain poorly understood. In this study, hydrochemistry and multi-isotope (N/C/S/O) datasets were collected to investigate the transformation mechanisms of RSEs, and explore the distribution characteristics of RSEs transformation hot spots. The results showed that spatial variability in key drivers was evident, while temporal change in RSEs concentration was not significant, except for dissolved organic carbon. Bacterial sulfate reduction (BSR) was the primary biogeochemical process for sulfate and occurred throughout the area. Ammonium enrichment was mainly caused by the mineralization of nitrogenous organic matter and anthropogenic inputs, with adsorption serving as the primary attenuation mechanism. Carbon dynamics were influenced by various biogeochemical processes, with dissolved organic carbon mainly derived from C3 plants and dissolved inorganic carbon from weathering of carbonate rocks and decomposition of organic matter. The peak contribution of dissolved organic carbon decomposition to the DIC pool was 46.44 %. The concentration thresholds for the ammonium enrichment and BSR hot spots were identified as 1.5 mg/L and 8.84 mg/L, respectively. The distribution pattern of RSEs hot spots was closely related to the hydrogeological conditions. Our findings reveal the complex evolution mechanisms and hot spots distribution characteristics of RSEs in the HZ, providing a basis for the safe utilization and protection of groundwater resources.

In silico approaches were employed to examine the characteristics of interactions between human mitochondrial thioredoxin 2 (HsTrx2) and its 38 previously identified mitochondrial protein ligands. All interactions appeared driven mainly by electrostatic forces. The statistically significant residues of HsTrx2 for interactions were characterized as \"contact hot spots\". Since these were identical/adjacent to putative thermodynamic hot spots, an energy network approach identified their neighbors to highlight possible contact interfaces. Three distinct areas for binding emerged: (i) one around the active site for covalent interactions, (ii) another antipodal to the active site for strong non-covalent interactions, and (iii) a third area involved in both kinds of interactions. The contact interfaces of HsTrx2 were projected as respective interfaces for Escherichia coli Trx1 (EcoTrx1), 2, and HsTrx1. Comparison of the interfaces and contact hot spots of HsTrx2 to the contact residues of EcoTx1 and HsTrx1 from existing crystal complexes with protein ligands supported the hypothesis, except for a part of the cleft/groove adjacent to Trp30 preceding the active site. The outcomes of this study raise the possibility for the rational design of selective inhibitors for the interactions of HsTrx2 with specific protein ligands without affecting the entirety of the functions of the Trx system.