MRI-based automatic identification and segmentation of extrahepatic cholangiocarcinoma using deep learning network.

BACKGROUND: Accurate identification of extrahepatic cholangiocarcinoma (ECC) from an image is challenging because of the small size and complex background structure. Therefore, considering the limitation of manual delineation, it's necessary to develop automated identification and segmentation methods for ECC. The aim of this study was to develop a deep learning approach for automatic identification and segmentation of ECC using MRI. METHODS: We recruited 137 ECC patients from our hospital as the main dataset (C1) and an additional 40 patients from other hospitals as the external validation set (C2). All patients underwent axial T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI). Manual delineations were performed and served as the ground truth. Next, we used 3D VB-Net to establish single-mode automatic identification and segmentation models based on T1WI (model 1), T2WI (model 2), and DWI (model 3) in the training cohort (80% of C1), and compared them with the combined model (model 4). Subsequently, the generalization capability of the best models was evaluated using the testing set (20% of C1) and the external validation set (C2). Finally, the performance of the developed models was further evaluated. RESULTS: Model 3 showed the best identification performance in the training, testing, and external validation cohorts with success rates of 0.980, 0.786, and 0.725, respectively. Furthermore, model 3 yielded an average Dice similarity coefficient (DSC) of 0.922, 0.495, and 0.466 to segment ECC automatically in the training, testing, and external validation cohorts, respectively. CONCLUSION: The DWI-based model performed better in automatically identifying and segmenting ECC compared to T1WI and T2WI, which may guide clinical decisions and help determine prognosis.

Formononetin Inhibits Microglial Inflammatory Response and Contributes to Spinal Cord Injury Repair by Targeting the EGFR/MAPK Pathway.

Zhenbao Pill contains many Chinese herbal medicinal ingredients and has been proven to have therapeutic effects on the repair of spinal cord injury (SCI). This study attempts to investigate the role of formononetin (FMN), an ingredient of Zhenbao Pill, in regulating neuroinflammation after SCI and the underlying mechanism. Primary microglia isolated from the spinal cord of newborn rats and human microglial clone 3 (HMC3) cells were stimulated with IL-1ß followed by FMN incubation. The cell viability and inflammatory cytokine levels were detected. The target of FMN was predicted and screened using databases. By silencing or overexpression of epidermal growth factor receptor (EGFR), the anti-neuroinflammatory effect of FMN was assessed in vitro. In vivo, FMN was intraperitoneally injected into rats after SCI followed by the neurological function and histopathology examination. The isolated microglia were in high purity, and the different concentrations of FMN incubation had no toxic effects on primary microglia and HMC3 cells. FMN reduced the inflammatory cytokine levels (TNF-α and IL-6) in a concentration-dependent manner. EGFR silencing or FMN incubation decreased p-EGFR and p-p38 levels and down-regulated inflammatory cytokine levels in IL-1ß-stimulated cells or supernatants. Nevertheless, the effects of FMN on microglial inflammation were reversed by EGFR overexpression. In vivo, FMN treatment improved the neuromotor function, repaired tissue injury, and inhibited EGFR/p38MAPK phosphorylation. Formononetin inhibits microglial inflammatory response and contributes to SCI repair via the EGFR/p38MAPK signaling pathway.

Novel 2,6-disubstituted benzofuran-3-one analogues improve cerebral ischemia/reperfusion injury via neuroprotective and antioxidative effects.

There are no highly effective and safe medicines for clinical treatment of ischemic stroke, although the natural product 3-n-butylphthalide (NBP) has been approved in China for mild and moderate ischemic stroke. To discover more potent anti-cerebral ischemic agents and overcome the low stability by phthalide derivatives, benzofuran-3-one was selected as a core moiety and two types of nitric oxide (NO)-donating groups were incorporated into the structure. In this work, a series of 2,6-disubstituted benzofuran-3-one derivatives were designed and synthesised as NBP analogues, and tested as neuroprotective and antioxidative agents. Compounds 5 (without an NO donor) and 16 (with an NO donor) displayed more potent neuroprotective effects than the established clinical drugs Edaravone and NBP. More importantly, 5 and 16 also exhibited good antioxidative activity without cytotoxicity in rat primary neuronal and PC12 cells. Most active compounds showed good blood-brain barrier permeability in a parallel artificial membrane permeability assay. Furthermore, compound 5 reduced the ischemic infarct area significantly in rats subjected to ischemia/reperfusion injury, downregulated ionised calcium-binding adaptor molecule 1 and glial fibrillary acidic protein in inflammatory cells, and upregulated nerve growth factor.

Metabolomic responses based on transcriptome of the hepatopancreas in Exopalaemon carinicauda under carbonate alkalinity stress.

High carbonate alkalinity is one of the major stress factors for survival of aquatic animals in saline-alkaline water. Exopalaemon carinicauda is a good model for studying the saline-alkaline adaption mechanism in crustacean because of its great adaptive capacity to alkalinity stress. In this study, non-targeted liquid chromatography-mass spectrometry (LC-MS) metabolomics analyses based on high-throughput RNA sequencing (RNA-Seq) were used to study the metabolomic responses of hepatopancreas in E. carinicauda at 12 h and 36 h after acute carbonate alkalinity stress. The results revealed that most of the significantly differential metabolites were related to the lipid metabolism. In particular, the sphingolipid metabolism was observed at 12 h, the glycerophospholipid metabolism was detected at 36 h, and the linoleic acid metabolic pathway was significantly enriched at both 12 h and 36 h. The combined transcriptome and metabolome analysis showed that energy consumption increased at 12 h, resulting in significant enrichment of AMPK signaling pathways, which contributed to maintain energy homeostasis. Subsequently, the hepatopancreas provided sufficient energy supply through cAMP signaling pathway and glycerophosphate metabolism to maintain normal metabolic function at 36 h. These findings might help to understand the molecular mechanisms of the E. carinicauda under carbonate alkalinity stress, thereby promote the research and development of saline-alkaline resistant shrimp.

A Fully Integrated Low-Power Multi-Mode RF Receiver for BDS-3/GPS.

A fully integrated low-power area-efficient receiver using a low-intermediate frequency topology for BDS-3 and GPS L1 bands is presented in this paper. Accurate localization can be achieved without requiring off-chip low-noise amplifiers. The receiver bandwidths for GPS and BDS-3 are 2 MHz and 4 MHz, respectively. Digitally assisted calibration schemes, such as RC calibration, automatic gain control, and DC offset correction are integrated to resist the effects of the process, voltage, and temperature (PVT) variations. The receiver-fabricated in a standard 55 nm CMOS technology-provides a maximum gain of 113.2 dB, a gain control range of 61 dB, and a minimum noise figure of 1.74 dB under a 1.2 V supply. The receiver, with and without the frequency synthesizer that provides the local oscillator frequency, consumes 8.7 mA and 4.8 mA, with areas of 0.73 mm2 and 0.345 mm2, respectively.

The ideal harvest time for seed production in maize (Zea mays L.) varieties of different maturity groups.

BACKGROUND: The correct time for harvesting is a key factor contributing to the production of high-quality maize seeds. We conducted field experiments to harvest seeds at 11 developmental stages for 3 years, to investigate seed vigor traits in three early maturity maize varieties and two late maturity varieties in one location. RESULTS: Significant correlations (r = 0.72 ~ 0.89) were found among six seed-related traits: standard germination (SG), accelerated aging germination (AAG), cold test germination (CTG), hundred-seed weight (HSW), seed moisture content (SMC), and ≥ 10 °C accumulated temperature from pollination to harvest (AT10). Analysis of variance showed that harvest stage, year, and variety had significant effects on all traits, and harvest stage displayed the greatest effect. The responses of SG, AAG, CTG, HSW and SMC to harvest stage fitted quadratic models, and AT10 fitted a linear model. From the quadratic models, an ideal harvest time (IHT, the final date to reach maximum SG, AAG, and CTG) could be calculated for each variety. The three early maturity varieties reached their IHT at 54.94-58.44 days after pollination (DAP); the two later maturity varieties reached IHT several days later (at 59.87-59.90 DAP). The early maturity varieties consistently required less AT10 to reach the IHT than the later maturity varieties. However, all of the varieties reached the IHT at similar SMC levels of about 35%. CONCLUSION: The later maturity varieties reached the IHT at later DAPs when they acquired more AT10 than the early maturity varieties but both reached it at similar SMC levels. © 2022 Society of Chemical Industry.

Fast quantification of nanorod geometry by DMA-spICP-MS.

A fast, quantitative method for determining the dimensions of nanorods (i.e., length and diameter) is described, based on hyphenation of differential mobility analysis (DMA) with single particle inductively coupled plasma mass spectrometry (spICP-MS). Seven gold nanorod samples with different dimensions (diameters 11.8 nm to 38.2 nm, aspect ratios 1.8 to 6.9) were used to validate the method. We demonstrate that DMA-spICP-MS can (1) achieve quantification of both length and diameter comparable with TEM analysis, (2) make statistically meaningful measurements in minutes at low concentrations (<108 mL-1) and (3) separate nanorods from spheres and quantify the geometry of each population. A robustness analysis of this method was performed to evaluate potential biases in this approach.

Electrospray-Differential Mobility Hyphenated with Single Particle Inductively Coupled Plasma Mass Spectrometry for Characterization of Nanoparticles and Their Aggregates.

The novel hyphenation of electrospray-differential mobility analysis with single particle inductively coupled plasma mass spectrometry (ES-DMA-spICPMS) was demonstrated with the capacity for real-time size, mass, and concentration measurement of nanoparticles (NPs) on a particle-to-particle basis. In this proof-of-concept study, the feasibility of this technique was validated through both concentration and mass calibration using NIST gold NP reference materials. A detection limit of 10(5) NPs mL(-1) was determined under current experimental conditions, which is about 4 orders of magnitude lower in comparison to that of a traditional ES-DMA setup using a condensation particle counter as detector. Furthermore, independent and simultaneous quantification of both size and mass of NPs provides information regarding NP aggregation states. Two demonstrative applications include gold NP mixtures with a broad size range (30-100 nm), and aggregated gold NPs with a primary size of 40 nm. Finally, this technique was shown to be potentially useful for real-world samples with high ionic background due to its ability to remove dissolved ions yielding a cleaner background. Overall, we demonstrate the capacity of this new hyphenated technique for (1) clearly resolving NP populations from a mixture containing a broad size range; (2) accurately measuring a linear relationship, which should inherently exist between mobility size and one-third power of ICPMS mass for spherical NPs; (3) quantifying the early stage propagation of NP aggregation with well-characterized oligomers; and (4) differentiating aggregated NPs and nonaggregated states based on the "apparent density" derived from both DMA size and spICPMS mass.

Absolute quantification method for protein concentration.

A fast and accurate assay to determine the absolute concentration of proteins is described based on direct measurement of droplet entrapped oligomer formation in electrospray. Here we demonstrate the approach using electrospray differential mobility analysis (ES-DMA), which can distinguish monomers and dimers from higher order oligomers. A key feature of the method is that it allows determination of the absolute number concentration of proteins eliminating the need for protein-specific calibration. The method was demonstrated by measuring the concentration of a NIST Standard Reference Material 927e (bovine serum albumin), a high-purity immunoglobulin G 1κ, and a formulated Rituximab. The method may be applied to any electrospray source, regardless of diagnostic tool (e.g., MS or ion-mobility, etc.), provided the electrospray is operated in a droplet-fission mode.

Machine learning enables electrical resistivity modeling of printed lines in aerosol jet 3D printing.

Among various non-contact direct ink writing techniques, aerosol jet printing (AJP) stands out due to its distinct advantages, including a more adaptable working distance (2-5 mm) and higher resolution (~ 10 µm). These characteristics make AJP a promising technology for the precise customization of intricate electrical functional devices. However, complex interactions among the machine, process, and materials result in low controllability over the electrical performance of printed lines. This significantly affects the functionality of printed components, thereby limiting the broad applications of AJP. Therefore, a systematic machine learning approach that integrates experimental design, geometrical features extraction, and non-parametric modeling is proposed to achieve printing quality optimization and electrical resistivity prediction for the printed lines in AJP. Specifically, three classical convolutional neural networks (CNNs) architectures are compared for extracting representative features of printed lines, and an optimal operating window is identified to effectively discriminate better line morphology from inferior printed line patterns within the design space. Subsequently, three representative non-parametric machine learning techniques are employed for resistivity modeling. Following that, the modeling performances of the adopted machine learning methods were systematically compared based on four conventional evaluation metrics. Together, these aspects contribute to optimizing the printed line morphology, while simultaneously identifying the optimal resistivity model for accurate predictions in AJP.

Status and influencing factors of knowledge, attitude and self-reported practice regarding hospice care among nurses in Hainan, China: A cross-sectional study.

AIM: This study is to investigate the current status of knowledge, attitude and self-reported practice in hospice care among nurses in Hainan, China, and then to analyse its influencing factors and mediating effects. This provides a basis for formulating scientific and standardized hospice care training programmes for nurses. METHODS: This cross-sectional study investigated knowledge, attitude and self-reported practice in hospice care among 1819 nurses in Hainan, China. Convenience sampling was used to select participants from 45 hospitals and nursing homes in 14 cities and counties from October to December 2021. A scale of knowledge, attitude and self-reported practice of healthcare providers in hospice care (Chinese version) was administered to collect data during the study period. Statistical analyses, including t-tests, one-way ANOVA, post-hoc analysis and multiple linear regression, assessed the status of knowledge, attitude and self-reported practice of hospice care in nurses and identified influencing factors. The PROCESS macro program model 4.0 was employed to explore the mediating effect of attitude on knowledge and self-reported practice in hospice care. RESULTS: Nurses in Hainan displayed low knowledge (mean = 7.68, SD = 3.53), moderate attitudes (mean = 88.13, SD = 12.10) and self-reported practice (mean = 51.81, SD = 9.82) in hospice care. Current employment and willingness to engage in hospice care were significant factors influencing knowledge, attitude and self-reported practice in hospice care. Attitude partially mediated the relationship between knowledge and self-reported practice. PATIENT OR PUBLIC CONTRIBUTION: This study focuses on nurses' knowledge, attitude and self-reported practice in hospice care and does not directly involve patients or the public. However, the findings enhance hospice care provided to patients and the broader community by improving nurses' knowledge and skills. This study informs evidence-based training programmes and interventions, benefiting those in need of hospice care services.

Research advances on microplastics contamination in terrestrial geoenvironment: A review.

The contamination of microplastics in terrestrial geoenvironment (CMTG) is widespread and severe and has, received considerable attention. However, studies on CMTG are in their initial stages. The literature on CMTG published in the past decade was analyzed through bibliometric analysis, such as the annual publications, countries with the highest contributions, prolific authors, and author keywords. The sources, compositions, migrations and environmental impacts of CMTG are summarized, and possible future directions are proposed. This study analyzed the annual publications, countries with the highest contributions, prolific authors, and author keywords related to microplastics. The results demonstrated that 15,306 articles were published between 2014 and 2023. China is the leading country in terms of the total number of publications. The main sources of CMTG include landfills, agricultural non-point sources, sewage treatment systems and transportation systems. The composition of the CMTG exhibits significantly temporal and spatial variability from different sources. The migration paths of the CMTG were within the soil, groundwater seepage and wind transportation of suspended particles. Microplastics increase soil cohesion, decrease porosity, reduce pore scale, decrease air circulation, and increase water retention capacity, and the exudation of highly water-soluble additives in microplastics can cause secondary contamination of geological entities. Microplastics have an adverse effect on plant growth, animal digestion, microbial activity, energy and lipid metabolism, oxidative stress, and respiratory diseases in humans. It is recommended to develop more efficient and convenient quantitative testing methods for microplastics, formulate globally harmonized testing and evaluation standards, include microplastic testing in testing programs for contaminated soils, and develop efficient methods for the remediation of microplastic contaminated geological bodies.

Rehabilitation training enhanced the therapeutic effect of calycosin on neurological function recovery of rats following spinal cord injury.

BACKGROUND: Calycosin (CA), a flavonoids component, has demonstrated potential neuroprotection effects by inhibiting oxidative stress in spinal cord injury (SCI) models. This study aims to investigate the impact of combined rehabilitation training (RT) and calycosin therapy on neurological function following SCI, primarily by assessing changes in motor function recovery, neuronal survival, neuronal oxidative stress levels, and neural proliferation, in order to provide novel insights for the treatment of SCI. MATERIALS AND METHODS: The SCI model was constructed by compressing the spinal cord using vascular clamps. Calycosin was injected intraperitoneally into the SCI model rats, and a group of 5 rats underwent RT. The motor function of rats after SCI was evaluated using the Basso Beattle Bresnaha (BBB) score and the inclined plate test. Histopathological changes were evaluated by NeuN immunohistochemistry, HE and Nissl staining. Apoptosis was detected by TUNEL staining. The antioxidant effect of combined treatment was assessed by measuring changes in oxidative stress markers after SCI. Western blot analysis was conducted to examine changes in Hsp90-Akt/ASK1-p38 pathway-related proteins. Finally, cell proliferation was detected by BrdU and Ki67 assays. RESULTS: RT significantly improved the BBB score and angle of incline promoted by calycosin, resulting in enhanced motor function recovery in rats with SCI. Combining rehabilitation training with calycosin has a positive effect on morphological recovery. Similarly, combined RT enhanced the Nissl and NeuN staining signals of spinal cord neurons increased by calycosin, thereby increasing the number of neurons. TUNEL staining results indicated that calycosin treatment reduced the apoptosis signal in SCI, and the addition of RT further reduced the apoptosis. Moreover, RT combined with calycosin reduced oxidative stress by increasing SOD and GSH levels, while decreasing MDA, NO, ROS, and LDH expressions compared to the calycosin alone. RT slightly enhanced the effect of calycosin in activating Hsp90 and Akt and inhibiting the activation of ASK1 and p38, leading to enhanced inhibition of oxidative stress by calycosin. Additionally, the proliferation indexes (Ki67 and BrdU) assays showed that calycosin treatment alone increased both, whereas the combination treatment further promoted cell proliferation. CONCLUSION: Our research findings demonstrate that rehabilitation training enhances the ability of calycosin to reduce oxidative stress, resulting in a decrease in neuronal apoptosis and an increase in proliferation, ultimately promoting neuronal survival.

Multi-omics Data Integration Analysis Identified Therapeutic Targets and Potential Reuse Drugs for Osteoporosis.

AIMS: To facilitate drug discovery and development for the treatment of osteoporosis. BACKGROUND: With global aging, osteoporosis has become a common problem threatening the health of the elderly. It is of important clinical value to explore new targets for drug intervention and develop promising drugs for the treatment of osteoporosis. OBJECTIVE: To understand the major molecules that mediate the communication between the cell populations of bone marrow-derived mesenchymal stem cells (BM-MSCs) in osteoporosis and osteoarthritis patients and identify potential reusable drugs for the treatment of osteoporosis. METHODS: Single-cell RNA sequencing (scRNA-seq) data of BM-MSCs in GSE147287 dataset were classified using the Seurat package. CellChat was devoted to analyzing the ligand-receptor pairs (LR pairs) contributing to the communication between BM-MSCs subsets. The LR pairs that were differentially expressed between osteoporosis samples and control samples and significantly correlated with immune score were screened in the GSE35959 dataset, and the differentially expressed gene in both GSE35959 and GSE13850 data sets were identified as targets from a single ligand or receptor. The therapeutic drugs for osteoporosis were screened by network proximity method, and the top-ranked drugs were selected for molecular docking and molecular dynamics simulation with the target targets. RESULTS: Twelve subsets of BM-MSCs were identified, of which CD45-BM-MSCS_4, CD45-BM- MSCS_5, and CD45+ BM-MSCs_5 subsets showed significantly different distributions between osteoporosis samples and osteoarthritis samples. Six LR pairs were identified in the bidirectional communication between these three BM-MSCs subsets and other BM-MSCs subsets. Among them, MIF-CD74 and ITGB2-ICAM2 were significantly correlated with the immune score. CD74 was identified as the target, and a total of 48 drugs targeting CD47 protein were identified. Among them, DB01940 had the lowest free energy binding score with CD74 protein and the binding state was very stable. CONCLUSION: This study provided a new network-based framework for drug reuse and identified initial insights into therapeutic agents targeting CD74 in osteoporosis, which may be meaningful for promoting the development of osteoporosis treatment.

Simulation analysis of visual perception model based on pulse coupled neural network.

Pulse-coupled neural networks perform well in many fields such as information retrieval, depth estimation and object detection. Based on pulse coupled neural network (PCNN) theory, this paper constructs a visual perception model framework and builds a real image reproduction platform. The model firstly analyzes the structure and generalization ability of neural network multi-class classifier, uses the minimax criterion of feature space as the splitting criterion of visual perception decision node, which solves the generalization problem of neural network learning algorithm. In the simulation process, the initial threshold is optimized by the two-dimensional maximum inter-class variance method, and in order to improve the real-time performance of the algorithm, the fast recurrence formula of neural network is derived and given. The PCNN image segmentation method based on genetic algorithm is analyzed. The genetic algorithm improves the loop termination condition and the adaptive setting of model parameters of PCNN image segmentation algorithm, but the PCNN image segmentation algorithm still has the problem of complexity. In order to solve this problem, this paper proposed an IGA-PCNN image segmentation method combining the improved algorithm and PCNN model. Firstly, it used the improved immune genetic algorithm to adaptively obtain the optimal threshold, and then replaced the dynamic threshold in PCNN model with the optimal threshold, and finally used the pulse coupling characteristics of PCNN model to complete the image segmentation. From the coupling characteristics of PCNN, junction close space of image and gray level characteristics, it determined the local gray mean square error of image connection strength coefficient. The feature extraction and object segmentation properties of PCNN come from the spike frequency of neurons, and the number of neurons in PCNN is equal to the number of pixels in the input image. In addition, the spatial and gray value differences of pixels should be considered comprehensively to determine their connection matrix. Digital experiments show that the multi-scale multi-task pulse coupled neural network model can shorten the total training time by 17 h, improve the comprehensive accuracy of the task test data set by 1.04%, and shorten the detection time of each image by 4.8 s compared with the series network model of multiple single tasks. Compared with the traditional PCNN algorithm, it has the advantages of fast visual perception and clear target contour segmentation, and effectively improves the anti-interference performance of the model.

N6-methyladenosine (m6A) writer KIAA1429 accelerates gastric cancer oxaliplatin chemoresistance by targeting FOXM1.

PURPOSE: Chemical modification plays a critical role in regulating human cancer progression, especially N6-methyladenosine (m6A). However, m6A writer KIAA1429-mediated m6A modification in gastric cancer (GC) tumorigenesis remains largely unknown. METHODS: The levels of mRNA and protein were detected using RT-qPCR and western blot. The half maximal inhibitory concentration (IC50) of oxaliplatin (OXA) resistance is detected using CCK-8 assay. The binding within moleculars was identified using RIP-PCR. RESULTS: Results found that KIAA1429 was upregulated in GC tissue samples and its high expression acted as a prognostic factor of poor survival in patients with GC. Functional assays indicated that KIAA1429 promoted the proliferation of GC cells, besides, KIAA1429 accelerated the half maximal inhibitory concentration (IC50) of oxaliplatin (OXA) resistance. Mechanistically, online prediction found that there was possible m6A modification site on FOXM1 mRNA. KIAA1429 could target the m6A modification site on FOXM1. Notably, KIAA1429 facilitated the GC OXA resistance in GC cells by promoting FOXM1 mRNA stability. CONCLUSIONS: Taken together, our study reveals the functions and mechanism for KIAA1429 and exposes KIAA1429 as a key player in GC chemoresistance.

Tracking the Dynamic Neural Connectivity via Conjugate Gradient Optimization.

Neural connectivity describes how neuron populations coordinate and create cognitive and behavioral functions. Neural connectivity performs dynamics where its population spiking responses to stimuli or intention change over time. Brain-machine interface (BMI) provides a framework for studying dynamical neural connectivity. In BMI, point process is a powerful technique in analyzing the single neuronal tuning. And generalized linear mode (GLM) as an encoding model can incorporate the tuning in kinematics and the neural connectivity. Quantification and tracking of dynamic neural connectivity can contribute to the elucidation of the generation of brain functions in a computational way. However, most of the previous work focused on single neuronal adaptation to kinematics. When a neuron is significantly modulated by some other neurons in some tasks, the shape of the log likelihood function for single neuronal observations can be narrowed in some dimensions. And the existing gradient-based methods are not able to reach the optimum in a fast and adaptive searching way. In this work, to maximize the likelihood of observations and obtain the dynamic neural connectivity tuning parameters, we proposed a conjugate gradient-based encoding model (CGE). We illustrate CGE for likelihood function using the real experimental data under manual control and brain control. The results show that the proposed CGE has better performance in tracking the dynamic neural connectivity tuning parameters and modeling neural encoding.Clinical Relevance- Not directly related.

Extractive electrospray ionization mass spectrometry for analytical evaluation and synthetic preparation of pharmaceutical chemicals.

Extraction electrospray ionization mass spectrometry (EESI-MS), due to the unique configuration of its ionization module, enables the effective ionization of trace molecules of interest in samples containing complex matrices with high sensitivity, high selectivity and high responding speed without requiring sample pretreatment, and allows high-energy molecular species to undergo specially designed reactions for advanced functionalization. The typical effects of operating conditions on the analytical performance of extraction electrospray ionization mass spectrometry for various pharmaceutical compounds, pharmaceutical preparations and herbal materials were systematically reviewed. The application prospect of extraction electrospray ionization in molecular functionalization for advanced drug discovery is also briefly introduced.

Polyetheretherketone (PEEK) as a Potential Material for the Repair of Maxillofacial Defect Compared with E-poly(tetrafluoroethylene) (e-PTFE) and Silicone.

Silicone and e-poly(tetrafluoroethylene) (e-PTFE) are the most commonly used artificial materials for repairing maxillofacial bone defects caused by facial trauma and tumors. However, their use is limited by poor histocompatibility, unsatisfactory support, and high infection rates. Polyetheretherketone (PEEK) has excellent mechanical strength and biocompatibility, but its application to the repair of maxillofacial bone defects lacks a theoretical basis. The microstructure and mechanical properties of e-PTFE, silicone, and PEEK were evaluated by electron microscopy, BOSE machine, and Fourier transformed infrared spectroscopy. Mouse fibroblast L929 cells were incubated on the surface of the three materials to assess cytotoxicity and adhesion. The three materials were implanted onto the left femoral surface of 90 male mice, and samples of the implants and surrounding soft tissues were evaluated histologically at 1, 2, 4, 8, and 12 weeks post-surgery. PEEK had a much higher Young's modulus than either e-PTFE or silicone (p < 0.05 each), and maintained high stiffness without degradation long after implantation. Both PEEK and e-PTFE facilitated L929 cell adhesion, with PEEK having lower cytotoxicity than e-PTFE and silicone (p < 0.05 each). All three materials similarly hindered the motor function of mice 12 weeks after implantation (p > 0.05 each). Connective tissue ingrowth was observed in PEEK and e-PTFE, whereas a fibrotic peri-prosthetic capsule was observed on the surface of silicone. The postoperative infection rate was significantly lower for both PEEK and silicone than for e-PTFE (p < 0.05 each). PEEK shows excellent biocompatibility and mechanical stability, suggesting that it can be effective as a novel implant to repair maxillofacial bone defects.

Genomic instability drives tumorigenesis and metastasis and its implications for cancer therapy.

Genetic instability can be caused by external factors and may also be associated with intracellular damage. At the same time, there is a large body of research investigating the mechanisms by which genetic instability occurs and demonstrating the relationship between genomic stability and tumors. Nowadays, tumorigenesis development is one of the hottest research areas. It is a vital factor affecting tumor treatment. Mechanisms of genomic stability and tumorigenesis development are relatively complex. Researchers have been working on these aspects of research. To explore the research progress of genomic stability and tumorigenesis, development, and treatment, the authors searched PubMed with the keywords "genome instability" "chromosome instability" "DNA damage" "tumor spread" and "cancer treatment". This extracts the information relevant to this study. Results: This review introduces genomic stability, drivers of tumor development, tumor cell characteristics, tumor metastasis, and tumor treatment. Among them, immunotherapy is more important in tumor treatment, which can effectively inhibit tumor metastasis and kill tumor cells. Breakthroughs in tumorigenesis development studies and discoveries in tumor metastasis will provide new therapeutic techniques. New tumor treatment methods can effectively prevent tumor metastasis and improve the cure rate of tumors.