HBO1 is required for the maintenance of leukaemia stem cells.

Acute myeloid leukaemia (AML) is a heterogeneous disease characterized by transcriptional dysregulation that results in a block in differentiation and increased malignant self-renewal. Various epigenetic therapies aimed at reversing these hallmarks of AML have progressed into clinical trials, but most show only modest efficacy owing to an inability to effectively eradicate leukaemia stem cells (LSCs)1. Here, to specifically identify novel dependencies in LSCs, we screened a bespoke library of small hairpin RNAs that target chromatin regulators in a unique ex vivo mouse model of LSCs. We identify the MYST acetyltransferase HBO1 (also known as KAT7 or MYST2) and several known members of the HBO1 protein complex as critical regulators of LSC maintenance. Using CRISPR domain screening and quantitative mass spectrometry, we identified the histone acetyltransferase domain of HBO1 as being essential in the acetylation of histone H3 at K14. H3 acetylated at K14 (H3K14ac) facilitates the processivity of RNA polymerase II to maintain the high expression of key genes (including Hoxa9 and Hoxa10) that help to sustain the functional properties of LSCs. To leverage this dependency therapeutically, we developed a highly potent small-molecule inhibitor of HBO1 and demonstrate its mode of activity as a competitive analogue of acetyl-CoA. Inhibition of HBO1 phenocopied our genetic data and showed efficacy in a broad range of human cell lines and primary AML cells from patients. These biological, structural and chemical insights into a therapeutic target in AML will enable the clinical translation of these findings.

STGIC: A graph and image convolution-based method for spatial transcriptomic clustering.

Spatial transcriptomic (ST) clustering employs spatial and transcription information to group spots spatially coherent and transcriptionally similar together into the same spatial domain. Graph convolution network (GCN) and graph attention network (GAT), fed with spatial coordinates derived adjacency and transcription profile derived feature matrix are often used to solve the problem. Our proposed method STGIC (spatial transcriptomic clustering with graph and image convolution) is designed for techniques with regular lattices on chips. It utilizes an adaptive graph convolution (AGC) to get high quality pseudo-labels and then resorts to dilated convolution framework (DCF) for virtual image converted from gene expression information and spatial coordinates of spots. The dilation rates and kernel sizes are set appropriately and updating of weight values in the kernels is made to be subject to the spatial distance from the position of corresponding elements to kernel centers so that feature extraction of each spot is better guided by spatial distance to neighbor spots. Self-supervision realized by Kullback-Leibler (KL) divergence, spatial continuity loss and cross entropy calculated among spots with high confidence pseudo-labels make up the training objective of DCF. STGIC attains state-of-the-art (SOTA) clustering performance on the benchmark dataset of 10x Visium human dorsolateral prefrontal cortex (DLPFC). Besides, it's capable of depicting fine structures of other tissues from other species as well as guiding the identification of marker genes. Also, STGIC is expandable to Stereo-seq data with high spatial resolution.

Endoplasmic reticulum stress regulators exhibit different prognostic, therapeutic and immune landscapes in pancreatic adenocarcinoma.

Endoplasmic reticulum stress (ERS) and unfolded protein response are the critical processes of tumour biology. However, the roles of ERS regulatory genes in pancreatic adenocarcinoma (PAAD) remain elusive. A novel ERS-related risk signature was constructed using the Lasso regression analysis. Its prognostic value, immune effect, metabolic influence, mutational feature and therapeutic correlation were comprehensively analysed through multiple bioinformatic approaches. The biofunctions of KDELR3 and YWHAZ in pancreatic cancer (PC) cells were also investigated through colony formation, Transwell assays, flow cytometric detection and a xenograft model. The upstream miRNA regulatory mechanism of KDELR3 was predicted and validated. ERS risk score was identified as an independent prognostic factor and could improve traditional prognostic model. Meanwhile, it was closely associated with metabolic reprogramming and tumour immune. High ERS risk enhanced glycolysis process and nucleotide metabolism, but was unfavourable for anti-tumour immune response. Moreover, ERS risk score could act as a potential biomarker for predicting the efficacy of ICBs. Overexpression of KDELR3 and YWHAZ stimulated the proliferation, migration and invasion of SW1990 and BxPC-3 cells. Silencing KDELR3 suppressed tumour growth in a xenograft model. miR-137 could weaken the malignant potentials of PC cells through inhibiting KDELR3 (5'-AGCAAUAA-3'). ERS risk score greatly contributed to PAAD clinical assessment. KDELR3 and YWHAZ possessed cancer-promoting capacities, showing promise as a novel treatment target.

Rapid Sample Pretreatment Facilitating SERS Detection of Trace Weak Organic Acids/Bases in Simple Matrices.

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for highly sensitive qualitative and quantitative analyses of trace targets. However, sensitive SERS detection can only be facilitated with a suitable sample pretreatment in fields related to trace amounts for food safety and clinical diagnosis. Currently, the sample pretreatment for SERS detection is normally borrowed and improved from the ones in the lab, which yields a high recovery but is tedious and time-consuming. Rapid detection of trace targets in a complex environment is still a considerable issue for SERS detection. Herein, we proposed a liquid-liquid extraction method coupled with a back-extraction method for sample pretreatment based on the pH-sensitive reversible phase transition of the weak organic acids and bases, where the lowest detectable concentrations were identical before and after the pretreatment process. The sensitive (µg L-1 level) and rapid (within 5 min) SERS detection of either koumine, a weak base, or celastrol, a weak acid, was demonstrated in different drinking water samples and beverages. Furthermore, target generality was demonstrated for a variety of weak acids and bases (2 < pKa < 12), and the hydrophilicity/hydrophobicity of the target determines the pretreatment efficiency. Therefore, the LLE-BE coupled SERS was developed as an easy, rapid, and low-cost tool for the trace detection of the two types of targets in simple matrices, which paved the way toward trace targets in complex matrices.

Rapid Sample Pretreatment Facilitating SERS Detection of Trace Weak Organic Acids/Bases in Complex Matrices.

Fast and efficient sample pretreatment is the prerequisite for realizing surface-enhanced Raman spectroscopy (SERS) detection of trace targets in complex matrices, which is still a big issue for the practical application of SERS. Recently, we have proposed a highly performed liquid-liquid extraction (LLE)-back extraction (BE) for weak acids/bases extraction in drinking water and beverage samples. However, the performance efficiency decreased drastically on facing matrices like food and biological blood. Based on the total interaction energies among target, interferent, and extractant molecules, solid-phase extraction (SPE) with a higher selectivity was introduced in advance of LLE-BE, which enabled the sensitive (µg L-1 level) and rapid (within 10 min) SERS detection of both koumine (a weak base) and celastrol (a weak acid) in different food and biological samples. Further, the high SERS sensitivity was determined unmanned by Vis-CAD (a machine learning algorithm), instead of the highly demanded expert recognition. The generality of SPE-LLE-BE for various weak acids/bases (2 < pKa < 12), accompanied by the high efficiency, easy operation, and low cost, offers SERS as a powerful on-site and efficient inspection tool in food safety and forensics.

Patch-Based Convolutional Encoder: A Deep Learning Algorithm for Spectral Classification Balancing the Local and Global Information.

Molecular vibrational spectroscopies, including infrared absorption and Raman scattering, provide molecular fingerprint information and are powerful tools for qualitative and quantitative analysis. They benefit from the recent development of deep-learning-based algorithms to improve the spectral, spatial, and temporal resolutions. Although a variety of deep-learning-based algorithms, including those to simultaneously extract the global and local spectral features, have been developed for spectral classification, the classification accuracy is still far from satisfactory when the difference becomes very subtle. Here, we developed a lightweight algorithm named patch-based convolutional encoder (PACE), which effectively improved the accuracy of spectral classification by extracting spectral features while balancing local and global information. The local information was captured well by segmenting the spectrum into patches with an appropriate patch size. The global information was extracted by constructing the correlation between different patches with depthwise separable convolutions. In the five open-source spectral data sets, PACE achieved a state-of-the-art performance. The more difficult the classification, the better the performance of PACE, compared with that of residual neural network (ResNet), vision transformer (ViT), and other commonly used deep learning algorithms. PACE helped improve the accuracy to 92.1% in Raman identification of pathogen-derived extracellular vesicles at different physiological states, which is much better than those of ResNet (85.1%) and ViT (86.0%). In general, the precise recognition and extraction of subtle differences offered by PACE are expected to facilitate vibrational spectroscopy to be a powerful tool toward revealing the relevant chemical reaction mechanisms in surface science or realizing the early diagnosis in life science.

Revealing the Denoising Principle of Zero-Shot N2N-Based Algorithm from 1D Spectrum to 2D Image.

Denoising is a necessary step in image analysis to extract weak signals, especially those hardly identified by the naked eye. Unlike the data-driven deep-learning denoising algorithms relying on a clean image as the reference, Noise2Noise (N2N) was able to denoise the noise image, providing sufficiently noise images with the same subject but randomly distributed noise. Further, by introducing data augmentation to create a big data set and regularization to prevent model overfitting, zero-shot N2N-based denoising was proposed in which only a single noisy image was needed. Although various N2N-based denoising algorithms have been developed with high performance, their complicated black box operation prevented the lightweight. Therefore, to reveal the working function of the zero-shot N2N-based algorithm, we proposed a lightweight Peak2Peak algorithm (P2P) and qualitatively and quantitatively analyzed its denoising behavior on the 1D spectrum and 2D image. We found that the high-performance denoising originates from the trade-off balance between the loss function and regularization in the denoising module, where regularization is the switch of denoising. Meanwhile, the signal extraction is mainly from the self-supervised characteristic learning in the data augmentation module. Further, the lightweight P2P improved the denoising speed by at least ten times but with little performance loss, compared with that of the current N2N-based algorithms. In general, the visualization of P2P provides a reference for revealing the working function of zero-shot N2N-based algorithms, which would pave the way for the application of these algorithms toward real-time (in situ, in vivo, and operando) research improving both temporal and spatial resolutions. The P2P is open-source at https://github.com/3331822w/Peak2Peakand will be accessible online access at https://ramancloud.xmu.edu.cn/tutorial.

Deep Learning-Assisted Spectrum-Structure Correlation: State-of-the-Art and Perspectives.

Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.

Signal2signal: Pushing the Spatiotemporal Resolution to the Limit by Single Chemical Hyperspectral Imaging.

There is growing interest in developing a high-performance self-supervised denoising algorithm for real-time chemical hyperspectral imaging. With a good understanding of the working function of the zero-shot Noise2Noise-based denoising algorithm, we developed a self-supervised Signal2Signal (S2S) algorithm for real-time denoising with a single chemical hyperspectral image. Owing to the accurate distinction and capture of the weak signal from the random fluctuating noise, S2S displays excellent denoising performance, even for the hyperspectral image with a spectral signal-to-noise ratio (SNR) as low as 1.12. Under this condition, both the image clarity and the spatial resolution could be significantly improved and present an almost identical pattern with a spectral SNR of 7.87. The feasibility of real-time denoising during imaging was well demonstrated, and S2S was applied to monitor the photoinduced exfoliation of transition metal dichalcogenide, which is hard to accomplish by confocal Raman spectroscopy. In general, the real-time denoising capability of S2S offers an easy way toward in situ/in vivo/operando research with much improved spatial and temporal resolution. S2S is open-source at https://github.com/3331822w/Signal2signal and will be accessible online at https://ramancloud.xmu.edu.cn/tutorial.

MRI-Guided Tumor Therapy Based on Synergy of Ferroptosis, Immunosuppression Reversal and Disulfidptosis.

Triple negative breast cancer (TNBC) cells have a high demand for oxygen and glucose to fuel their growth and spread, shaping the tumor microenvironment (TME) that can lead to a weakened immune system by hypoxia and increased risk of metastasis. To disrupt this vicious circle and improve cancer therapeutic efficacy, a strategy is proposed with the synergy of ferroptosis, immunosuppression reversal and disulfidptosis. An intelligent nanomedicine GOx-IA@HMON@IO is successfully developed to realize this strategy. The Fe release behaviors indicate the glutathione (GSH)-responsive degradation of HMON. The results of titanium sulfate assay, electron spin resonance (ESR) spectra, 5,5'-Dithiobis-(2-nitrobenzoic acid (DTNB) assay and T1-weighted magnetic resonance imaging (MRI) demonstrate the mechanism of the intelligent iron atom (IA)-based cascade reactions for GOx-IA@HMON@IO, generating robust reactive oxygen species (ROS). The results on cells and mice reinforce the synergistic mechanisms of ferroptosis, immunosuppression reversal and disulfidptosis triggered by the GOx-IA@HMON@IO with the following steps: 1) GSH peroxidase 4 (GPX4) depletion by disulfidptosis; 2) IA-based cascade reactions; 3) tumor hypoxia reversal; 4) immunosuppression reversal; 5) GPX4 depletion by immunotherapy. Based on the synergistic mechanisms of ferroptosis, immunosuppression reversal and disulfidptosis, the intelligent nanomedicine GOx-IA@HMON@IO can be used for MRI-guided tumor therapy with excellent biocompatibility and safety.

High-resolution MRI vessel wall enhancement in moyamoya disease: risk factors and clinical outcomes.

OBJECTIVES: Intracranial vessel wall enhancement (VWE) on high-resolution magnetic resonance imaging (HRMRI) is associated with the progression and poor prognosis of moyamoya disease (MMD). This study assessed potential risk factors for VWE in MMD. METHODS: We evaluated MMD patients using HRMRI and traditional angiography examinations. The participants were divided into VWE and non-VWE groups based on HRMRI. Logistic regression was performed to compare the risk factors for VWE in MMD. The incidence of cerebrovascular events of the different subgroups according to risk factors was compared using Kaplan-Meier survival and Cox regression. RESULTS: We included 283 MMD patients, 84 of whom had VWE on HRMRI. The VWE group had higher modified Rankin Scale scores at admission (p = 0.014) and a higher incidence of ischaemia and haemorrhage (p = 0.002) than did the non-VWE group. Risk factors for VWE included the ring finger protein 213 (RNF213) p.R4810K variant (odds ratio [OR] 2.01, 95% confidence interval [CI] 1.08-3.76, p = 0.028), hyperhomocysteinaemia (HHcy) (OR 5.08, 95% CI 2.34-11.05, p < 0.001), and smoking history (OR 3.49, 95% CI 1.08-11.31, p = 0.037). During the follow-up of 63.9 ± 13.2 months (median 65 months), 18 recurrent stroke events occurred. Cox regression showed that VWE and the RNF213 p.R4810K variant were risk factors for stroke. CONCLUSION: The RNF213 p.R4810K variant is strongly associated with VWE and poor prognosis in MMD. HHcy and smoking are independent risk factors for VWE. CLINICAL RELEVANCE STATEMENT: Vessel wall enhancement in moyamoya disease is closely associated with poor prognosis, especially related to the ring finger protein 213 p.R4810K variant, hyperhomocysteinaemia, and smoking, providing crucial risk assessment information for the clinic. KEY POINTS: • The baseline presence of vessel wall enhancement is significantly associated with poor prognosis in moyamoya disease. • The ring finger protein 213 p.R4810K variant is strongly associated with vessel wall enhancement and poor prognosis in moyamoya disease. • Hyperhomocysteinaemia and smoking are independent risk factors for vessel wall enhancement in moyamoya disease.

Resolvin and lipoxin metabolism network regulated by Hyssopus Cuspidatus Boriss extract in asthmatic mice.

Resolvin (Rv) and lipoxin (Lx) play important regulative roles in the development of several inflammation-related diseases. The dysregulation of their metabolic network is believed to be closely related to the occurrence and development of asthma. The Hyssopus Cuspidatus Boriss extract (SXCF) has long been used as a treatment for asthma, while the mechanism of anti-inflammatory and anti-asthma action targeting Rv and Lx has not been thoroughly investigated. In this study, we aimed to investigate the effects of SXCF on Rv, Lx in ovalbumin (OVA)-sensitized asthmatic mice. The changes of Rv, Lx before and after drug administration were analyzed based on high sensitivity chromatography-multiple response monitoring (UHPLC-MRM) analysis and multivariate statistics. The pathology exploration included behavioral changes of mice, IgE in serum, cytokines in BALF, and lung tissue sections stained with H&E. It was found that SXCF significantly modulated the metabolic disturbance of Rv, Lx due to asthma. Its modulation effect was significantly better than that of dexamethasone and rosmarinic acid which is the first-line clinical medicine and the main component of Hyssopus Cuspidatus Boriss, respectively. SXCF is demonstrated to be a potential anti-asthmatic drug with significant disease-modifying effects on OVA-induced asthma. The modulation of Rv and Lx is a possible underlying mechanism of the SXCF effects.

Inhibitors of histone acetyltransferases KAT6A/B induce senescence and arrest tumour growth.

Acetylation of histones by lysine acetyltransferases (KATs) is essential for chromatin organization and function1. Among the genes coding for the MYST family of KATs (KAT5-KAT8) are the oncogenes KAT6A (also known as MOZ) and KAT6B (also known as MORF and QKF)2,3. KAT6A has essential roles in normal haematopoietic stem cells4-6 and is the target of recurrent chromosomal translocations, causing acute myeloid leukaemia7,8. Similarly, chromosomal translocations in KAT6B have been identified in diverse cancers8. KAT6A suppresses cellular senescence through the regulation of suppressors of the CDKN2A locus9,10, a function that requires its KAT activity10. Loss of one allele of KAT6A extends the median survival of mice with MYC-induced lymphoma from 105 to 413 days11. These findings suggest that inhibition of KAT6A and KAT6B may provide a therapeutic benefit in cancer. Here we present highly potent, selective inhibitors of KAT6A and KAT6B, denoted WM-8014 and WM-1119. Biochemical and structural studies demonstrate that these compounds are reversible competitors of acetyl coenzyme A and inhibit MYST-catalysed histone acetylation. WM-8014 and WM-1119 induce cell cycle exit and cellular senescence without causing DNA damage. Senescence is INK4A/ARF-dependent and is accompanied by changes in gene expression that are typical of loss of KAT6A function. WM-8014 potentiates oncogene-induced senescence in vitro and in a zebrafish model of hepatocellular carcinoma. WM-1119, which has increased bioavailability, arrests the progression of lymphoma in mice. We anticipate that this class of inhibitors will help to accelerate the development of therapeutics that target gene transcription regulated by histone acetylation.

Pyruvate kinase M2 sustains cardiac mitochondrial integrity in septic cardiomyopathy by regulating PHB2-dependent mitochondrial biogenesis.

Previous studies have highlighted the protective effects of pyruvate kinase M2 (PKM2) overexpression in septic cardiomyopathy. In our study, we utilized cardiomyocyte-specific PKM2 knockout mice to further investigate the role of PKM2 in attenuating LPS-induced myocardial dysfunction, focusing on mitochondrial biogenesis and prohibitin 2 (PHB2). Our findings confirmed that the deletion of PKM2 in cardiomyocytes significantly exacerbated LPS-induced myocardial dysfunction, as evidenced by impaired contractile function and relaxation. Additionally, the deletion of PKM2 intensified LPS-induced myocardial inflammation. At the molecular level, LPS triggered mitochondrial dysfunction, characterized by reduced ATP production, compromised mitochondrial respiratory complex I/III activities, and increased ROS production. Intriguingly, the absence of PKM2 further worsened LPS-induced mitochondrial damage. Our molecular investigations revealed that LPS disrupted mitochondrial biogenesis in cardiomyocytes, a disruption that was exacerbated by the absence of PKM2. Given that PHB2 is known as a downstream effector of PKM2, we employed PHB2 adenovirus to restore PHB2 levels. The overexpression of PHB2 normalized mitochondrial biogenesis, restored mitochondrial integrity, and promoted mitochondrial function. Overall, our results underscore the critical role of PKM2 in regulating the progression of septic cardiomyopathy. PKM2 deficiency impeded mitochondrial biogenesis, leading to compromised mitochondrial integrity, increased myocardial inflammation, and impaired cardiac function. The overexpression of PHB2 mitigated the deleterious effects of PKM2 deletion. This discovery offers a novel insight into the molecular mechanisms underlying septic cardiomyopathy and suggests potential therapeutic targets for intervention.

Diversified Chemical Structures and Bioactivities of the Chemical Constituents Found in the Brown Algae Family Sargassaceae.

Sargassaceae, the most abundant family in Fucales, was recently formed through the merging of the two former families Sargassaceae and Cystoseiraceae. It is widely distributed in the world's oceans, notably in tropical coastal regions, with the exception of the coasts of Antarctica and South America. Numerous bioactivities have been discovered through investigations of the chemical diversity of the Sargassaceae family. The secondary metabolites with unique structures found in this family have been classified as terpenoids, phlorotannins, and steroids, among others. These compounds have exhibited potent pharmacological activities. This review describes the new discovered compounds from Sargassaceae species and their associated bioactivities, citing 136 references covering from March 1975 to August 2023.

Seeing Is Not Necessarily Believing: Is the Surface-Enhanced Raman Spectroscopy Signal Really from the Target?

Reagent purity is crucial to experimental research, considering that the ignorance of ultratrace impurities may induce wrong conclusions in either revealing the reaction nature or qualifying the target. Specifically, in the field of surface science, the strong interaction between the impurity and the surface will bring a non-negligible negative effect. Surface-enhanced Raman spectroscopy (SERS) is a highly surface-sensitive technique, providing fingerprint identification and near-single molecule sensitivity. In the SERS analysis of trace chloromethyl diethyl phosphate (DECMP), we figured out that the SERS performance of DECMP is significantly distorted by the trace impurities from DECMP. With the aid of gas chromatography-based techniques, one strongly interfering impurity (2,2-dichloro-N,N-dimethylacetamide), the byproduct during the synthesis of DECMP, was confirmed. Furthermore, the nonignorable interference of impurities on the SERS measurement of NaBr, NaI, or sulfadiazine was also observed. The generality ignited us to refresh and consolidate the guideline for the reliable SERS qualitative analysis, by which the potential misleading brought by ultratrace impurities, especially those strongly adsorbed on Au or Ag surfaces, could be well excluded.

1D Gradient-Weighted Class Activation Mapping, Visualizing Decision Process of Convolutional Neural Network-Based Models in Spectroscopy Analysis.

Being characterized by the self-adaption and high accuracy, the deep learning-based models have been widely applied in the 1D spectroscopy-related field. However, the "black-box" operation and "end-to-end" working style of the deep learning normally bring the low interpretability, where a reliable visualization is highly demanded. Although there are some well-developed visualization methods, such as Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM), for the 2D image data, they cannot correctly reflect the weights of the model when being applied to the 1D spectral data, where the importance of position information is not considered. Here, aiming at the visualization of Convolutional Neural Network-based models toward the qualitative and quantitative analysis of 1D spectroscopy, we developed a novel visualization algorithm (1D Grad-CAM) to more accurately display the decision-making process of the CNN-based models. Different from the classical Grad-CAM, with the removal of the gradient averaging (GAP) and the ReLU operations, a significantly improved correlation between the gradient and the spectral location and a more comprehensive spectral feature capture were realized for 1D Grad-CAM. Furthermore, the introduction of difference (purity or linearity) and feature contribute in the CNN output in 1D Grad-CAM achieved a reliable evaluation of the qualitative accuracy and quantitative precision of CNN-based models. Facing the qualitative and adulteration quantitative analysis of vegetable oils by the combination of Raman spectroscopy and ResNet, the visualization by 1D Grad-CAM well reflected the origin of the high accuracy and precision brought by ResNet. In general, 1D Grad-CAM provides a clear vision about the judgment criterion of CNN and paves the way for CNN to a broad application in the field of 1D spectroscopy.

The new insights into autophagy in thyroid cancer progression.

In recent decades, the incidence of thyroid cancer keeps growing at a shocking rate, which has aroused increasing concerns worldwide. Autophagy is a fundamental and ubiquitous biological event conserved in mammals including humans. Basically, autophagy is a catabolic process that cellular components including small molecules and damaged organelles are degraded for recycle to meet the energy needs, especially under the extreme conditions. The dysregulated autophagy has indicated to be involved in thyroid cancer progression. The enhancement of autophagy can lead to autophagic cell death during the degradation while the produced energies can be utilized by the rest of the cancerous tissue, thus this influence could be bidirectional, which plays either a tumor-suppressive or oncogenic role. Accordingly, autophagy can be suppressed by therapeutic agents and is thus regarded as a drug target for thyroid cancer treatments. In the present review, a brief description of autophagy and roles of autophagy in tumor context are given. We have addressed summary of the mechanisms and functions of autophagy in thyroid cancer. Some potential autophagy-targeted treatments are also summarized. The aim of the review is linking autophagy to thyroid cancer, so as to develop novel approaches to better control cancer progression.

Development of a novel tool: a nomogram for predicting in-hospital mortality of patients in intensive care unit after percutaneous coronary intervention.

BACKGROUNDS: Increased risk of in-hospital mortality is critical to guide medical decisions and it played a central role in intensive care unit (ICU) with high risk of in-hospital mortality after primary percutaneous coronary intervention (PCI). At present，most predicting tools for in-hospital mortality after PCI were based on the results of coronary angiography, echocardiography, and laboratory results which are difficult to obtain at admission. The difficulty of using these tools limit their clinical application. This study aimed to develop a clinical prognostic nomogram to predict the in-hospital mortality of patients in ICU after PCI. METHODS: We extracted data from a public database named the Medical Information Mart for Intensive Care (MIMIC III). Adult patients with coronary artery stent insertion were included. They were divided into two groups according to the primary outcome (death in hospital or survive). All patients were randomly divided into training set and validation set randomly at a ratio of 6:4. Least absolute shrinkage and selection operator (LASSO) regression was performed in the training set to select optimal variables to predict the in-hospital mortality of patients in ICU after PCI. The multivariate logistical analysis was performed to develop a nomogram. Finally, the predictive efficiency of the nomogram was assessed by area under the receiver operating characteristic curve (AUROC)，integrated discrimination improvement (IDI), and net reclassification improvement (NRI), and clinical net benefit was assessed by Decision curve analysis (DCA). RESULTS: A total of 2160 patients were recruited in this study. By using LASSO, 17 variables were finally included. We used multivariate logistic regression to construct a prediction model which was presented in the form of a nomogram. The calibration plot of the nomogram revealed good fit in the training set and validation set. Compared with the sequential organ failure assessment (SOFA) and scale for the assessment of positive symptoms II (SAPS II) scores, the nomogram exhibited better AUROC of 0.907 (95% confidence interval [CI] was 0.880-0.933, p <  0.001) and 0.901 (95% CI was 0.865-0.936, P <  0.001) in the training set and validation set, respectively. In addition, DCA of the nomogram showed that it could achieve good net benefit in the clinic. CONCLUSIONS: A new nomogram was constructed, and it presented excellent performance in predicting in-hospital mortality of patients in ICU after PCI.

EEG-Based Driver Fatigue Monitoring within a Human-Ship-Environment System: Implications for Ship Braking Safety.

To address the uncontrollable risks associated with the overreliance on ship operators' driving in current ship safety braking methods, this study aims to reduce the impact of operator fatigue on navigation safety. Firstly, this study established a human-ship-environment monitoring system with functional and technical architecture, emphasizing the investigation of a ship braking model that integrates brain fatigue monitoring using electroencephalography (EEG) to reduce braking safety risks during navigation. Subsequently, the Stroop task experiment was employed to induce fatigue responses in drivers. By utilizing principal component analysis (PCA) to reduce dimensionality across multiple channels of the data acquisition device, this study extracted centroid frequency (CF) and power spectral entropy (PSE) features from channels 7 and 10. Additionally, a correlation analysis was conducted between these features and the Fatigue Severity Scale (FSS), a five-point scale for assessing fatigue severity in the subjects. This study established a model for scoring driver fatigue levels by selecting the three features with the highest correlation and utilizing ridge regression. The human-ship-environment monitoring system and fatigue prediction model proposed in this study, combined with the ship braking model, achieve a safer and more controllable ship braking process. By real-time monitoring and prediction of driver fatigue, appropriate measures can be taken in a timely manner to ensure navigation safety and driver health.