Application of machine reading comprehension techniques for named entity recognition in materials science.

Materials science is an interdisciplinary field that studies the properties, structures, and behaviors of different materials. A large amount of scientific literature contains rich knowledge in the field of materials science, but manually analyzing these papers to find material-related data is a daunting task. In information processing, named entity recognition (NER) plays a crucial role as it can automatically extract entities in the field of materials science, which have significant value in tasks such as building knowledge graphs. The typically used sequence labeling methods for traditional named entity recognition in material science (MatNER) tasks often fail to fully utilize the semantic information in the dataset and cannot effectively extract nested entities. Herein, we proposed to convert the sequence labeling task into a machine reading comprehension (MRC) task. MRC method effectively can solve the challenge of extracting multiple overlapping entities by transforming it into the form of answering multiple independent questions. Moreover, the MRC framework allows for a more comprehensive understanding of the contextual information and semantic relationships within materials science literature, by integrating prior knowledge from queries. State-of-the-art (SOTA) performance was achieved on the Matscholar, BC4CHEMD, NLMChem, SOFC, and SOFC-Slot datasets, with F1-scores of 89.64%, 94.30%, 85.89%, 85.95%, and 71.73%, respectively in MRC approach. By effectively utilizing semantic information and extracting nested entities, this approach holds great significance for knowledge extraction and data analysis in the field of materials science, and thus accelerating the development of material science.Scientific contributionWe have developed an innovative NER method that enhances the efficiency and accuracy of automatic entity extraction in the field of materials science by transforming the sequence labeling task into a MRC task, this approach provides robust support for constructing knowledge graphs and other data analysis tasks.

The drug loading capacity prediction and cytotoxicity analysis of metal-organic frameworks using stacking algorithms of machine learning.

Metal-organic frameworks (MOFs) have shown excellent performance in the field of drug delivery. Despite the synthesis of a vast array of MOFs exceeding 100,000 varieties, certain formulations have exhibited suboptimal performance characteristics. Therefore, there is a pressing need to enhance their efficacy by identifying MOFs with superior drug loading capacities and minimal cytotoxicity, which can be achieved through machine learning (ML). In this study, a stacking regression model was developed to predict drug loading capacity and cytotoxicity of MOFs using datasets compiled from various literature sources. The model exhibited exceptional predictive capabilities, achieving R2 values of 0.907 for drug loading capacity and 0.856 for cytotoxicity. Furthermore, various model interpretation methods including partial dependence plots, individual conditional expectation, Shapley additive explanation, decision tree, random forest, CatBoost Regressor, and light gradient-boosting machine were employed for feature importance analysis. The results revealed that specific metal atoms such as Zn, Cr, Fe, Zr, and Cu significantly influenced the drug loading capacity and cytotoxicity of MOFs. Through model validation encompassing experimental validation and computational verification, the reliability of the model was thoroughly established. In general, it is a good practice to use ML methods for predicting drug loading capacity and cytotoxicity analysis of MOFs, guiding the development of future property prediction methods for MOFs.

MF-Net: multi-scale feature extraction-integration network for unsupervised deformable registration.

Deformable registration plays a fundamental and crucial role in scenarios such as surgical navigation and image-assisted analysis. While deformable registration methods based on unsupervised learning have shown remarkable success in predicting displacement fields with high accuracy, many existing registration networks are limited by the lack of multi-scale analysis, restricting comprehensive utilization of global and local features in the images. To address this limitation, we propose a novel registration network called multi-scale feature extraction-integration network (MF-Net). First, we propose a multiscale analysis strategy that enables the model to capture global and local semantic information in the image, thus facilitating accurate texture and detail registration. Additionally, we introduce grouped gated inception block (GI-Block) as the basic unit of the feature extractor, enabling the feature extractor to selectively extract quantitative features from images at various resolutions. Comparative experiments demonstrate the superior accuracy of our approach over existing methods.

3D-CAM: a novel context-aware feature extraction framework for neurological disease classification.

In clinical practice and research, the classification and diagnosis of neurological diseases such as Parkinson's Disease (PD) and Multiple System Atrophy (MSA) have long posed a significant challenge. Currently, deep learning, as a cutting-edge technology, has demonstrated immense potential in computer-aided diagnosis of PD and MSA. However, existing methods rely heavily on manually selecting key feature slices and segmenting regions of interest. This not only increases subjectivity and complexity in the classification process but also limits the model's comprehensive analysis of global data features. To address this issue, this paper proposes a novel 3D context-aware modeling framework, named 3D-CAM. It considers 3D contextual information based on an attention mechanism. The framework, utilizing a 2D slicing-based strategy, innovatively integrates a Contextual Information Module and a Location Filtering Module. The Contextual Information Module can be applied to feature maps at any layer, effectively combining features from adjacent slices and utilizing an attention mechanism to focus on crucial features. The Location Filtering Module, on the other hand, is employed in the post-processing phase to filter significant slice segments of classification features. By employing this method in the fully automated classification of PD and MSA, an accuracy of 85.71%, a recall rate of 86.36%, and a precision of 90.48% were achieved. These results not only demonstrates potential for clinical applications, but also provides a novel perspective for medical image diagnosis, thereby offering robust support for accurate diagnosis of neurological diseases.

Long-term nitrogen and phosphorus addition have stronger negative effects on microbial residual carbon in subsoils than topsoils in subtropical forests.

Highly weathered lowland (sub)tropical forests are widely recognized as nitrogen (N)-rich and phosphorus (P)-poor, and the input of N and P affects soil carbon (C) cycling and storage in these ecosystems. Microbial residual C (MRC) plays a crucial role in regulating soil organic C (SOC) stability in forest soils. However, the effects of long-term N and P addition on soil MRC across different soil layers remain unclear. This study conducted a 12-year N and P addition experiment in two typical subtropical plantation forests dominated by Acacia auriculiformis and Eucalyptus urophylla trees, respectively. We measured plant C input (fine root biomass, fine root C, and litter C), microbial community structure, enzyme activity (C/N/P-cycling enzymes), mineral properties, and MRC. Our results showed that continuous P addition reduced MRC in the subsoil (20-40 cm) of both plantations (A. auriculiformis: 28.44% and E. urophylla: 28.29%), whereas no significant changes occurred in the topsoil (0-20 cm). N addition decreased MRC in the subsoil of E. urophylla (25.44%), but had no significant effects on A. auriculiformis. Combined N and P addition reduced MRC (34.63%) in the subsoil of A. auriculiformis but not in that of E. urophylla. The factors regulating MRC varied across soil layers. In the topsoil (0-10 cm), plant C input (the relative contributions to the total variance was 20%, hereafter) and mineral protection (47.2%) were dominant factors. In the soil layer of 10-20 cm, both microbial characteristics (41.3%) and mineral protection (32.3%) had substantial effects, whereas the deeper layer (20-40 cm) was predominantly regulated by microbial characteristics (37.9%) and mineral protection (18.8%). Understanding differential drivers of MRC across soil depth, particularly in deeper soil layers, is crucial for accurately predicting the stability and storage of SOC and its responses to chronic N enrichment and/or increased P limitation in (sub)tropical forests.

Prevalence and contributing factors of malocclusion in Zhuang children aged 7-8 years in southern China.

Introduction: Malocclusion, a common oral health problem in children, is associated with several contributing factors. This study aimed to investigate the prevalence of mixed dentition stage malocclusion and its contributing factors in Chinese Zhuang children aged 7-8 years. Methods: Overall, 2,281 Zhuang children, about 7-8 years old, were randomly selected using a stratified whole-cluster sampling method from schools in counties in Northwestern Guangxi, China. The children were examined on-site for malocclusion and caries by trained dentists, and basic data on the children were collected using questionnaires, including age, sex, parental education, parental accompaniment, and children's knowledge of malocclusion and treatment needs. Data were analyzed using the chi-square test and logistic regression analysis. Results: The total prevalence of malocclusion in Zhuang children aged 7-8 years was 58.5%, with the highest prevalence of anterior crossbite tendency, and the prevalence of anterior crossbite and anterior edge-to-edge occlusion was 15.1% and 7.7%, respectively. This was followed by an anterior increased overjet of 13.3% and an inter-incisor spacing of 10.3%. The lowest prevalence was 2.7% for anterior open bite. Sex, parental accompaniment, parental education, and decayed, missing, and filled teeth of the first primary molar were factors that contributed to malocclusion in Zhuang children. Conclusion: Malocclusion is a common oral problem among Zhuang children. Therefore, more attention must be paid to the intervention and prevention of malocclusion. The impact factors should be controlled as early as possible.

Climate change-related biodiversity fluctuations and composition changes in an old-growth subtropical forest: A 26-yr study.

The detection and attribution of biodiversity change is of great scientific interest and central to policy effects aimed at meeting biodiversity targets. Yet, how such a diverse climate scenarios influence forest biodiversity and composition dynamics remains unclear, particularly in high diversity systems of subtropical forests. Here we used data collected from the permanent sample plot spanning 26 years in an old-growth subtropical forest. Combining various climatic events (extreme drought, subsequent drought, warming, and windstorm), we analyzed long-term dynamics in multiple metrics: richness, turnover, density, abundance, reordering and stability. We did not observe consistent and directional trends in species richness under various climatic scenarios. Still, drought and windstorm events either reduced species gains or increased species loss, ultimately increased species turnover. Tree density increased significantly over time as a result of rapid increase in smaller individuals due to mortality in larger trees. Climate events caused rapid changes in dominant populations due to a handful of species undergoing strong increases or declines in abundance over time simultaneously. Species abundance composition underwent significant changes, particularly in the presence of drought and windstorm events. High variance ratio and species synchrony weaken community stability under various climate stress. Our study demonstrates that all processes underlying forest community composition changes often occur simultaneously and are equally affected by climate events, necessitating a holistic approach to quantifying community changes. By recognizing the interconnected nature of these processes, future research should accelerate comprehensive understanding and predicting of how forest vegetation responds to global climate change.

Socioeconomic Inequalities in Oral Health among Adults in Guangxi, China.

PURPOSE: To examine the relationship between socioeconomic inequalities and oral health among adults in the Guangxi province of China. MATERIALS AND METHODS: The present work was designed as a cross-sectional study, and comprises a secondary analysis of the Fourth National Oral Health Survey from 2015-2016. A multistage cluster sampling method was adopted for this survey, conducted in three urban and three rural districts Guangxi province. Dental examinations were conducted to determine oral health indicators: decayed teeth (DT), clinical attachment loss (CAL) and missing teeth (MT). The outcome measures were DT, CAL and MT. A structured questionnaire was used to collect data on demographic characteristics and socioeconomic status (SES). Multiple logistic regression models were used to analyse the relationship between SES and oral health by adjusting covariates. RESULTS: The sample consisted of 651 participants aged 35-74 years. Logisitic analysis showed a statistically significant association between SES and oral health indicators. In the fully adjusted model, participants with primary education were more likely to suffer more DT (OR = 2.67, 95% CI: 1.17-6.10), teeth with CAL ≥ 4 mm (OR = 2.15, 95% CI: 1.25-3.67) and MT (OR = 3.04, 95% CI: 1.65-5.60) compared to the higher education group. Participants with secondary education exhibited a higher likelihood of experiencing increased MT compared to those in the higher education group in the fully adjusted model (OR = 3.21, 95% CI: 1.78-5.76). Household income was associated with DT and MT in the unadjusted model only. CONCLUSIONS: There was strong relationship between SES and oral health of adults. The survey suggested a relationship between low educational attainment and oral health.

Socioeconomic Status and Tooth Loss Impact on Oral Health-Related Quality of Life in Chinese Elderly.

OBJECTIVE: We studied the association between the socioeconomic status (SES), tooth loss, and oral health-related quality of life (OHRQoL) in an adult cohort in western China. As socioeconomic inequalities in oral health are often neglected in oral health promotion. we aimed to verify the impact of SES on tooth loss and OHRQoL. METHODS: In all, 348 participants aged 60 years and older were selected for this study. Relationships amongst SES, tooth loss, and OHRQoL were identified by using a structural equation model (SEM). RESULTS: In the final sample, 312 people were included, and the response rate was 89.7%. The bias-corrected 95% confidence intervals of the total, direct, and indirect effects were (-0.267 to 0.475), (-0.489 to 0.185), and (0.088 to 0.450), respectively. The comparative fit index of SEM was 0.943. The model showed that their SES directly affected tooth loss in the elderly population. This indirectly affects their oral health-related quality of life. The numbers of natural teeth and occlusal units (with standardised path coefficients of 0.79 and 0.74, respectively) were found to be the most significant factors relating to tooth loss. CONCLUSION: SES affected the oral health-related quality of life in elderly people through tooth loss in a Chinese study population. Our data suggest that improvements in the social and economic environments are a primary measure that should be implmented to prevent tooth loss and improve the OHRQoL.

Climate change reshapes plant trait spectrum to explain biomass dynamics in an old-growth subtropical forest.

Climate change leads to novel species interactions and continues to reshuffle ecological communities, which significantly declines carbon accumulation rates in mature forests. Still, little is known about the potential influence of multiple global change factors on long-term biomass dynamics and functional trait combinations. We used temporal demographic records spanning 26 years and extensive databases of functional traits to assess how old-growth subtropical forest biomass dynamics respond to various climatic change scenarios (extreme drought, subsequent drought, warming, elevated CO2 concentrations, and windstorm). We found that the initial severe drought, subsequent drought and windstorm events increased biomass loss due to tree mortality, which exceeded the biomass gain produced by survivors and recruits, ultimately resulting in more negative net biomass balances. These drought and windstorm events caused massive biomass loss due to tree mortality that tended towards acquisition species with high hydraulic efficiency, whereas biomass growth from survivors and recruits tended to consist of acquisition species with high hydraulic safety. Compensatory growth in this natural forest provided good explanation for the increase in biomass growth after drought and windstorm events. Notably, these dominant-species transitions reduced carbon storage and residence time, forming a positive carbon-climate feedback loop. Our findings suggest that climate changes could alter functional strategies and cause shifts in new dominant species, which could greatly reduce ecological functions and carbon gains of old-growth subtropical forests.

Dose reduction in dynamic synaptic vesicle glycoprotein 2A PET imaging using artificial neural networks.

Objective. Reducing dose in positron emission tomography (PET) imaging increases noise in reconstructed dynamic frames, which inevitably results in higher noise and possible bias in subsequently estimated images of kinetic parameters than those estimated in the standard dose case. We report the development of a spatiotemporal denoising technique for reduced-count dynamic frames through integrating a cascade artificial neural network (ANN) with the highly constrained back-projection (HYPR) scheme to improve low-dose parametric imaging.Approach. We implemented and assessed the proposed method using imaging data acquired with11C-UCB-J, a PET radioligand bound to synaptic vesicle glycoprotein 2A (SV2A) in the human brain. The patch-based ANN was trained with a reduced-count frame and its full-count correspondence of a subject and was used in cascade to process dynamic frames of other subjects to further take advantage of its denoising capability. The HYPR strategy was then applied to the spatial ANN processed image frames to make use of the temporal information from the entire dynamic scan.Main results. In all the testing subjects including healthy volunteers and Parkinson's disease patients, the proposed method reduced more noise while introducing minimal bias in dynamic frames and the resulting parametric images, as compared with conventional denoising methods.Significance. Achieving 80% noise reduction with a bias of -2% in dynamic frames, which translates into 75% and 70% of noise reduction in the tracer uptake (bias, -2%) and distribution volume (bias, -5%) images, the proposed ANN+HYPR technique demonstrates the denoising capability equivalent to a 11-fold dose increase for dynamic SV2A PET imaging with11C-UCB-J.

IL-15 armoring enhances the antitumor efficacy of claudin 18.2-targeting CAR-T cells in syngeneic mouse tumor models.

Claudin 18.2 (CLDN18.2)-targeting chimeric antigen receptor (CAR)-modified T cells are one of the few cell therapies currently producing an impressive therapeutic effect in treating solid tumors; however, their long-term therapeutic efficacy is not satisfactory with a short duration of response. Transgenic expression of interleukin (IL)-15 has been reported to promote T-cell expansion, survival, and function and enhance the antitumor activity of engineered T cells in vitro and in vivo. Therefore, this study aimed to explore whether IL-15 modification would increase the antitumor activity of CLDN18.2-targeting CAR-modified T (CAR-T) cells in immunocompetent murine tumor models. CLDN18.2-specific CAR-T cells with (H9 CAR-IL15) or without transgenic IL-15 expression (H9 CAR) were generated by retroviral transduction of mouse splenic T cells. In vitro, compared with H9 CAR T cells, H9 CAR-IL15 T cells exhibited better expansion and viability in the absence of antigen stimulation, with a less differentiated and T-cell exhausted phenotype; although IL-15 modification did not affect the production of effector cytokines and cytotoxic activity in the short-term killing assay, it moderately improved the in vitro recursive killing activity of CAR-T cells against CLDN18.2-expressing tumor cells. In vivo, H9 CAR T cells showed no antitumor activity against CLDN18.2-expressing pancreatic tumors in immunocompetent mice without lymphodepleting pretreatment; however, H9 CAR-IL15 T cells produced significant tumor-suppressive effects. Furthermore, H9 CAR-IL15 T cells exhibited greater in vivo expansion and tumor infiltration when combined with lymphodepleting preconditioning, resulting in superior antitumor activity in two murine tumor models and a survival advantage in one tumor model. We further demonstrated that recurrent tumors following H9 CAR-IL15 T-cell therapy downregulated CLDN18.2 expression, suggesting immune escape through the selection of antigen-negative cells under persistent CAR-T-cell immune pressure. In conclusion, our findings provide preclinical evidence supporting the clinical evaluation of IL-15-expressing CLDN18.2 CAR-T cells in patients with CLDN18.2-positive tumors.

Comparison of the seasonal and successional variation of asymbiotic and symbiotic nitrogen fixation along a glacial retreat chronosequence.

Climate change is resulting in accelerated retreat of glaciers worldwide and much nitrogen-poor debris is left after glacier retreats. Asymbiotic dinitrogen (N2) fixation (ANF) can be considered a 'hidden' source of nitrogen (N) for non-nodulating plants in N limited environments; however, seasonal variation and its relative importance in ecosystem N budgets, especially when compared with nodulating symbiotic N2-fixation (SNF), is not well-understood. In this study, seasonal and successional variations in nodulating SNF and non-nodulating ANF rates (nitrogenase activity) were compared along a glacial retreat chronosequence on the eastern edge of the Tibetan Plateau. Key factors regulating the N2-fixation rates as well as the contribution of ANF and SNF to ecosystem N budget were also examined. Significantly greater nitrogenase activity was observed in nodulating species (0.4-17,820.8 nmol C2H4 g-1 d-1) compared to non-nodulating species (0.0-9.9 nmol C2H4 g-1 d-1) and both peaked in June or July. Seasonal variation in acetylene reduction activity (ARA) rate in plant nodules (nodulating species) and roots (non-nodulating species) was correlated with soil temperature and moisture while ARA in non-nodulating leaves and twigs was correlated with air temperature and humidity. Stand age was not found to be a significant determinant of ARA rates in nodulating or non-nodulating plants. ANF and SNF contributed 0.3-51.5 % and 10.1-77.8 %, respectively, of total ecosystem N input in the successional chronosequence. In this instance, ANF exhibited an increasing trend with successional age while SNF increased only at stages younger than 29 yr and then decreased as succession proceeded. These findings help improve our understanding of ANF activity in non-nodulating plants and N budgets in post glacial primary succession.

Unexpected high retention of 15 N-labeled nitrogen in a tropical legume forest under long-term nitrogen enrichment.

The responses of forests to nitrogen (N) deposition largely depend on the fates of deposited N within the ecosystem. Nitrogen-fixing legume trees widely occur in terrestrial forests, but the fates of deposited N in legume-dominated forests remain unclear, which limit a global evaluation of N deposition impacts and feedbacks on carbon sequestration. Here, we performed the first ecosystem-scale 15 N labeling experiment in a typical legume-dominated forest as well as in a nearby non-legume forest to determine the fates of N deposition between two different forest types and to explore their underlying mechanisms. The 15 N was sprayed bimonthly for 1 year to the forest floor in control and N addition (50 kg N ha-1  year-1 for 10 years) plots in both forests. We unexpectedly found a strong capacity of the legume forest to retain deposited N, with 75 ± 5% labeled N recovered in plants and soils, which was higher than that in the non-legume forest (56 ± 4%). The higher 15 N recovery in legume forest was mainly driven by uptake by the legume trees, in which 15 N recovery was approximately 15% more than that in the nearby non-legume trees. This indicates higher N-demand by the legume than non-legume trees. Mineral soil was the major sink for deposited N, with 39 ± 4% and 34 ± 3% labeled N retained in the legume and non-legume forests, respectively. Moreover, N addition did not significantly change the 15 N recovery patterns of both forests. Overall, these findings indicate that legume-dominated forests act as a strong sink for deposited N regardless of high soil N availability under long-term atmospheric N deposition, which suggest a necessity to incorporate legume-dominated forests into N-cycling models of Earth systems to improve the understanding and prediction of terrestrial N budgets and the global N deposition effects.

Dictionary Learning Constrained Direct Parametric Estimation in Dynamic Myocardial Perfusion PET.

In myocardial perfusion imaging with dynamic positron emission tomography (PET), direct parametric reconstruction from the projection data allows accurate modeling of the Poisson noise in the projection domain to provide more reliable estimate of the parametric images. In this study, we propose to incorporate a superior denoiser to efficiently suppress the unfavorable noise propagation during the direct reconstruction. The dictionary learning (DL) based sparse representation serves as a regularization term to constrain the intermediate K1 estimation. We rewrite the DL regularizer into a voxel-separable form to facilitate the decoupling of a DL penalized curve fitting from the reconstruction of dynamic frames. The nonlinear fitting is then solved by a damped Newton method with uniform initialization. Using simulated and patient 82Rb dynamic PET data, we study the performance of the proposed DL direct algorithm and quantitatively compare it with the indirect method with or without post-filtering, the direct reconstruction without regularization, and the quadratic penalty regularized direct algorithm. The DL regularized direct reconstruction achieves improved noise versus bias performance in the reconstructed K1 images as well as superior recovery of a reduced myocardial blood flow defect. The dictionary learned from a 3D self-created hollow sphere image yields comparable results to those using the dictionary learned from the corresponding magnetic resonance image. The uniform initializations converge to K1 estimations similar to the result from initializing with the indirect reconstruction. To summarize, we demonstrate the potential of the proposed DL constrained direct parametric reconstruction in improving quantitative dynamic PET imaging.

Association between socioeconomic status and dental caries among Chinese preschool children: a cross-sectional national study.

OBJECTIVES: Socioeconomic inequalities in oral health are often neglected in oral health promotion. This cross-sectional study assessed the association between dental caries and socioeconomic status (SES) among preschool children in China. DESIGN: Cross-sectional study. SETTING: Data from the Fourth National Oral Health Survey of China (2015), comprising of 40 360 children aged 3-5 years was used. METHODS: Dental caries indicators including prevalence of dental caries, dental pain experience and number of decayed, missing and filling teeth (dmft). SES indicators included parental education and household income. The associations between SES and dental caries were analysed by using negative binomial regression or Poisson regression models according to data distribution. Relative and absolute inequalities in dental caries were quantified by using the Relative Index of Inequality (RII) and Slope Index of Inequality (SII), respectively. RESULTS: There were significant associations between SES and prevalence of dental caries and dmft (p<0.001). Children from lower educated (RII 1.36, 95% CI 1.3 to 1.43; SII 0.97, 95% CI 0.81 to 1.13) and lower household income (RII 1.17, 95% CI 1.11 to 1.24; SII 0.55, 95% CI 0.35 to 0.75) families had higher dmft than those from well-educated and most affluent families. Relative and absolute inequalities in dental caries were larger in urban areas by household income, and in rural areas by parental education. CONCLUSIONS: Association between dental caries and SES was demonstrated and socioeconomic inequalities in dental caries existed among Chinese preschool children.

Coherent multidimensional photoelectron spectroscopy of ultrafast quasiparticle dressing by light.

Depending on the applied strength, electromagnetic fields in electronic materials can induce dipole transitions between eigenstates or distort the Coulomb potentials that define them. Between the two regimes, they can also modify the electronic properties in more subtle ways when electron motion becomes governed by time and space-periodic potentials. The optical field introduces new virtual bands through Floquet engineering that under resonant conditions interacts strongly with the preexisting bands. Under such conditions the virtual bands can become real, and real ones become virtual as the optical fields and electronic band dispersions entangle the electronic response. We reveal optical dressing of electronic bands in a metal by exciting four-photon photoemission from the Cu(111) surface involving a three-photon resonant transition from the Shockley surface band to the first image potential band. Attosecond resolved interferometric scanning between identical pump-probe pulses and its Fourier analysis reveal how the optical field modifies the electronic properties of a solid through combined action of dipole excitation and field dressing.

SPH3643: A novel cyclin-dependent kinase 4/6 inhibitor with good anticancer efficacy and strong blood-brain barrier permeability.

The cyclin-dependent kinase (CDK)4/6-cyclin D1-Rb-p16/ink4a pathway is responsible for regulating cell progression past the G1 restriction point during the cell cycle. The development of a majority of human tumors is associated with dysregulation of this pathway, resulting in increased cancer cell proliferation. Both CDK4 and CDK6, well-validated cancer drug targets, function primarily as catalytic enzymes that mediate the phosphorylation of retinoblastoma protein (Rb). Here, we determined that SPH3643 is a novel potent antiproliferative agent that inhibits CDK4/6 kinase activity. In biochemical assays, SPH3643 showed more potent inhibition of both CDK4 and CDK6 than did 2 published CDK4/6 inhibitors, LY2835219 and palbociclib, and had better selectivity than LY2835219. Further in vitro study revealed that SPH3643 blocked Cdk/Rb signaling by inhibiting the phosphorylation of RbSer780 and arrested the MCF-7 cancer cells at G0 /G1 phase, resulting in marked inhibition of the proliferation of Rb-positive cancer cell lines. In vivo SPH3643 treatment in mice bearing xenograft tumor models of breast cancer, colon cancer, acute myelocytic leukemia, and glioblastoma resulted in significant decreases in tumor growth. SPH3643 was able to particularly strongly inhibit glioblastoma (U87-MG) cell growth in the brains of orthotopic carcinoma xenograft mice due to its high degree of intracerebral penetration and significant persistence in this setting. Together these results revealed that SPH3643 is a potent, orally active small-molecule inhibitor of CDK4/6 with robust anticancer efficacy and a high degree of blood-brain barrier permeability.

Nonlinear Plasmonic Photoelectron Response of Ag(111).

Photons can excite collective and single-particle excitations in metals; the collective plasmonic excitations are of keen interest in physics, chemistry, optics, and nanotechnology because they enhance coupling of electromagnetic energy and can drive nonlinear processes in electronic materials, particularly where their dielectric function ϵ(ω) approaches zero. We investigate the nonlinear angle-resolved two-photon photoemission (2PP) spectroscopy of the Ag(111) surface through the ϵ(ω) near-zero region. In addition to the Einsteinian single-particle photoemission, the 2PP spectra report unequivocal signatures of nonlocal dielectric, plasmonically enhanced, excitation processes.

Follicular hyperandrogenism and insulin resistance in polycystic ovary syndrome patients with normal circulating testosterone levels.

Polycystic ovary syndrome (PCOS) is a common reproductive disease with high heterogeneity. The role of excess androgen in PCOS etiology remains disputed, since around 20%-50% of PCOS women do not display hyperandrogenemia. The microenvironment of the ovary critically influences follicular development. In the present study, we assessed the role of androgen in PCOS by investigating whether excessive follicular fluid androgen was present in PCOS patients with normal serum androgen levels and influenced by follicular fluid insulin resistance (IR). Follicular fluid samples of 105 women with PCOS and 105 controls were collected. Levels of steroid hormones, glucose and insulin in the follicular fluid were examined and compared with data from serum biochemistry tests. We found that 64.9% (63/97) of PCOS patients with normal serum androgen levels displayed abnormally high follicular fluid androgen level. The follicular fluid androgen level was positively correlated with follicular fluid IR within a certain range and follicular fluid estrogen-to-testosterone (E2/T) ratio was significantly reduced in these patients. These results indicated that there existed a subgroup of PCOS patients who displayed excessive follicular fluid androgen and IR despite their normal circulating testosterone (T) levels. Our study highlights the importance of ovary hyperandrogenism and IR in the etiology of PCOS.