Icariin improves oxidative stress injury during ischemic stroke via inhibiting mPTP opening.

BACKGROUND: Ischemic stroke presents a significant threat to human health due to its high disability rate and mortality. Currently, the clinical treatment drug, rt-PA, has a narrow therapeutic window and carries a high risk of bleeding. There is an urgent need to find new effective therapeutic drugs for ischemic stroke. Icariin (ICA), a key ingredient in the traditional Chinese medicine Epimedium, undergoes metabolism in vivo to produce Icaritin (ICT). While ICA has been reported to inhibit neuronal apoptosis after cerebral ischemia-reperfusion (I/R), yet its underlying mechanism remains unclear. METHODS: PC-12 cells were treated with 200 µM H2O2 for 8 h to establish a vitro model of oxidative damage. After administration of ICT, cell viability was detected by Thiazolyl blue tetrazolium Bromide (MTT) assay, reactive oxygen species (ROS) and apoptosis level, mPTP status and mitochondrial membrane potential (MMP) were detected by flow cytometry and immunofluorescence. Apoptosis and mitochondrial permeability transition pore (mPTP) related proteins were assessed by Western blotting. Middle cerebral artery occlusion (MCAO) model was used to establish I/R injury in vivo. After the treatment of ICA, the neurological function was scored by ZeaLonga socres; the infarct volume was observed by 2,3,5-Triphenyltetrazolium chloride (TTC) staining; HE and Nissl staining were used to detect the pathological state of the ischemic cortex; the expression changes of mPTP and apoptosis related proteins were detected by Western blotting. RESULTS: In vitro: ICT effectively improved H2O2-induced oxidative injury through decreasing the ROS level, inhibiting mPTP opening and apoptosis. In addition, the protective effects of ICT were not enhanced when it was co-treated with mPTP inhibitor Cyclosporin A (CsA), but reversed when combined with mPTP activator Lonidamine (LND). In vivo: Rats after MCAO shown cortical infarct volume of 32-40%, severe neurological impairment, while mPTP opening and apoptosis were obviously increased. Those damage caused was improved by the administration of ICA and CsA. CONCLUSIONS: ICA improves cerebral ischemia-reperfusion injury by inhibiting mPTP opening, making it a potential candidate drug for the treatment of ischemic stroke.

Description of a pathogenic strain of Aeromonas dhakensis isolated from Ancherythroculter nigrocauda in an inland region of China.

Aeromonas dhakensis is reported as an emerging pathogenic species within the genus Aeromonas and is widely distributed in tropical coastal areas. This study provided a detailed description and characterization of a strain of A. dhakensis (202108B1) isolated from diseased Ancherythroculter nigrocauda in an inland region of China. Biochemical tests identified the isolate at the genus level, and the further molecular analysis of concatenated housekeeping gene sequences revealed that the strain belonged to the species A. dhakensis. The isolated A. dhakensis strain was resistant to five antibiotics, namely, penicillin, ampicillin, clindamycin, cephalexin and imipenem, while it was susceptible or showed intermediate resistance to most of the other fifteen tested antibiotics. The isolated strain of A. dhakensis caused acute haemorrhagic septicaemia and tissue damage in artificially infected A. nigrocauda, with a median lethal dose of 7.76×104 CFU/fish. The genome size of strain 202108B1 was 5043286 bp, including one chromosome and four plasmids. This is the first detailed report of the occurrence of infection caused by an A. dhakensis strain causing infection in an aquaculture system in inland China, providing important epidemiological data on this potential pathogenic species.

Uncertain prediction of deformable image registration on lung CT using multi-category features and supervised learning.

The assessment of deformable registration uncertainty is an important task for the safety and reliability of registration methods in clinical applications. However, it is typically done by a manual and time-consuming procedure. We propose a novel automatic method to predict registration uncertainty based on multi-category features and supervised learning. Three types of features, including deformation field statistical features, deformation field physiologically realistic features, and image similarity features, are introduced and calculated to train the random forest regressor for local registration uncertain prediction. Deformation field statistical features represent the numerical stability of registration optimization, which are correlated to the uncertainty of deformation fields; deformation field physiologically realistic features represent the biomechanical properties of organ motions, which mathematically reflect the physiological reality of deformation; image similarity features reflect the similarity between the warped image and fixed image. The multi-category features comprehensively reflect the registration uncertainty. The strategy of spatial adaptive random perturbations is also introduced to accurately simulate spatial distribution of registration uncertainty, which makes deformation field statistical features more discriminative to the uncertainty of deformation fields. Experiments were conducted on three publicly available thoracic CT image datasets. Seventeen randomly selected image pairs are used to train the random forest model, and 9 image pairs are used to evaluate the prediction model. The quantitative experiments on lung CT images show that the proposed method outperforms the baseline method for uncertain prediction of classical iterative optimization-based registration and deep learning-based registration with different registration qualities. The proposed method achieves good performance for registration uncertain prediction, which has great potential in improving the accuracy of registration uncertain prediction.

Ultrathin, ultralight dual-scale fibrous networks with high-infrared transmittance for high-performance, comfortable and sustainable PM0.3 filter.

Highly permeable particulate matter (PM) can carry various bacteria, viruses and toxics and pose a serious threat to public health. Nevertheless, current respirators typically sacrifice their thickness and base weight for high-performance filtration, which inevitably causes wearing discomfort and significant consumption of raw materials. Here, we show a facile yet massive splitting eletrospinning strategy to prepare an ultrathin, ultralight and radiative cooling dual-scale fiber membrane with about 80% infrared transmittance for high-protective, comfortable and sustainable air filter. By tailoring antibacterial surfactant-triggered splitting of charged jets, the dual-scale fibrous filter consisting of continuous nanofibers (44 ± 12 nm) and submicron-fibers (159 ± 32 nm) is formed. It presents ultralow thickness (1.49 µm) and base weight (0.57 g m-2) but superior protective performances (about 99.95% PM0.3 removal, durable antibacterial ability) and wearing comfort of low air resistance, high heat dissipation and moisture permeability. Moreover, the ultralight filter can save over 97% polymers than commercial N95 respirator, enabling itself to be sustainable and economical. This work paves the way for designing advanced and sustainable protective materials.

Therapeutic response to four artemisinin-based combination therapies in Angola, 2021.

Monitoring antimalarial efficacy is important to detect the emergence of parasite drug resistance. Angola conducts in vivo therapeutic efficacy studies (TESs) every 2 years in its fixed sentinel sites in Benguela, Lunda Sul, and Zaire provinces. Children with uncomplicated Plasmodium falciparum malaria were treated with artemether-lumefantrine (AL), artesunate-amodiaquine (ASAQ), dihydroartemisinin-piperaquine (DP), or artesunate-pyronaridine (ASPY) and followed for 28 (AL and ASAQ) or 42 days (DP and ASPY) to assess clinical and parasitological response to treatment. Two drugs were sequentially assessed in each site in February-July 2021. The primary indicator was the Kaplan-Meier estimate of the PCR-corrected efficacy at the end of the follow-up period. A total of 622 patients were enrolled in the study and 590 (95%) participants reached a study endpoint. By day 3, ≥98% of participants were slide-negative in all study sites and arms. After PCR correction, day 28 AL efficacy was 88.0% (95% CI: 82%-95%) in Zaire and 94.7% (95% CI: 90%-99%) in Lunda Sul. For ASAQ, day 28 efficacy was 92.0% (95% CI: 87%-98%) in Zaire and 100% in Lunda Sul. Corrected day 42 efficacy was 99.6% (95% CI: 99%-100%) for ASPY and 98.3% (95% CI: 96%-100%) for DP in Benguela. High day 3 clearance rates suggest no clinical evidence of artemisinin resistance. This was the fourth of five rounds of TES in Angola showing a corrected AL efficacy <90% in a site. For Zaire, AL has had an efficacy <90% in 2013, 2015, and 2021. ASAQ, DP, and ASPY are appropriate choices as artemisinin-based combination therapies in Angola.

USP29 activation mediated by FUBP1 promotes AURKB stability and oncogenic functions in gastric cancer.

BACKGROUND: Gastric cancer is a highly prevalent cancer type and the underlying molecular mechanisms are not fully understood. Ubiquitin-specific peptidase (USP) 29 has been suggested to regulate cell fate in several types of cancer, but its potential role in gastric carcinogenesis remains unclear. METHODS: The expression of USP29 in normal and gastric cancer tissues was analyzed by bioinformatics analysis, immunohistochemistry and immunoblot. Gene overexpression, CRISPR-Cas9 technology, RNAi, and Usp29 knockout mice were used to investigate the roles of USP29 in cell culture, xenograft, and benzo[a]pyrene (BaP)-induced gastric carcinogenesis models. We then delineated the underlying mechanisms using mass spectrometry, co-immunoprecipitation (Co-IP), immunoblot, ubiquitination assay, chromatin immunoprecipitation (ChIP), quantitative real-time PCR (qRT-PCR), and luciferase assays. RESULTS: In this study, we found that USP29 expression was significantly upregulated in gastric cancers and associated with poor patient survival. Ectopic expression of USP29 promoted, while depletion suppressed the tumor growth in vitro and in vivo mouse model. Mechanistically, transcription factor far upstream element binding protein 1 (FUBP1) directly activates USP29 gene transcription, which then interacts with and stabilizes aurora kinase B (AURKB) by suppressing K48-linked polyubiquitination, constituting a FUBP1-USP29-AURKB regulatory axis that medicates the oncogenic role of USP29. Importantly, systemic knockout of Usp29 in mice not only significantly decreased the BaP-induced carcinogenesis but also suppressed the Aurkb level in forestomach tissues. CONCLUSIONS: These findings uncovered a novel FUBP1-USP29-AURKB regulatory axis that may play important roles in gastric carcinogenesis and tumor progression, and suggested that USP29 may become a promising drug target for cancer therapy.

IRF4 induces M1 macrophage polarization and aggravates ulcerative colitis progression by the Bcl6-dependent STAT3 pathway.

Ulcerative colitis (UC) is an idiopathic chronic intestinal inflammation. An increasing body of evidence shows that macrophages play an important role in the pathogenesis of UC. Interferon regulatory factor 4 (IRF4) is crucial for the development of autoimmune diseases via regulating immune cells. This research was designed to explore the function of IRF4 in UC and its association with macrophage polarization. The in vitro model of UC was established by stimulating colonic epithelial cells with tumor necrosis factor α (TNF-α). A mouse model of UC was constructed by injecting C57BL/6 mice with dextran sulfate sodium salt. Flow cytometry was used to assess percentage of CD11b+ CD86+ and CD11b+ CD206+ cells in bone marrow macrophages. Occult blood tests were used to detect hematochezia. Hematoxylin and eosin staining assay was used to assess colon pathological changes. Enzyme-linked immunosorbent assay (ELISA) was used to detect concentrations of inflammatory cytokines. The interaction of IRF4 and B-cell lymphoma 6 (Bcl6) was confirmed using GST pull-down and coimmunoprecipitation assays. Our findings revealed that IRF4 promoted cell apoptosis and stimulated M1 macrophage polarization in vitro. Furthermore, IRF4 aggravated symptoms of the mouse model of UC and aggravated M1 macrophage polarization in vivo. IRF4 negatively regulated Bcl6 expression. Downregulation of Bcl6 promoted apoptosis and M1 macrophage polarization in the presence of IRF4 in vitro and in vivo. Moreover, Bcl6 positively mediated the Janus kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) signaling pathway. In conclusion, IRF4 aggravated UC progression through promoting M1 macrophage polarization via Bcl6/JAK2/STAT3 pathway. These findings suggested that IRF4 might be a good target to competitively inhibit or to treat with UC.

N6-methyladenosine methylation analysis of long noncoding RNAs and mRNAs in 5-FU-resistant colon cancer cells.

N6 methyladenosine (m6A), methylation at the sixth N atom of adenosine, is the most common and abundant modification in mammalian mRNAs and non-coding RNAs. Increasing evidence shows that the alteration of m6A modification level could regulate tumour proliferation, metastasis, self-renewal, and immune infiltration by regulating the related expression of tumour genes. However, the role of m6A modification in colorectal cancer (CRC) drug resistance is unclear. Here, MeRIP-seq and RNA-seq techniques were utilized to obtain mRNA, lncRNA expression, and their methylation profiles in 5-Fluorouracil (5-FU)-resistant colon cancer HCT-15 cells and control cells. In addition, we performed detailed bioinformatics analysis as well as in vitro experiments of lncRNA to explore the function of lncRNA with differential m6A in CRC progression and drug resistance. In this study, we obtained the m6A methylomic landscape of CRC cells and resistance group cells by MeRIP-seq and RNA-seq. We identified 3698 differential m6A peaks, of which 2224 were hypermethylated, and 1474 were hypomethylated. Among the lncRNAs, 60 were hypermethylated, and 38 were hypomethylated. GO and KEGG analysis annotations showed significant enrichment of endocytosis and MAPK signalling pathways. Moreover, knockdown of lncRNA ADIRF-AS1 and AL139035.1 promoted CRC proliferation and invasive metastasis in vitro. lncRNA- mRNA network showed that ADIRF-AS1 and AL139035.1 May play a key role in regulating drug resistance formation. We provide the first m6A methylation profile in 5-FU resistance CRC cells and analyse the functions of differential m6A-modified mRNAs and lncRNAs. Our results indicated that differential m6A RNAs were significantly associated with MAPK signalling and endocytosis after induction of 5-FU resistance. Knockdown of LncRNA ADIRF-AS1 and AL139035.1 promotes CRC progression and might be critical in regulating drug resistance formation.

We outline the first m6A methylation profile of mRNA and lncRNA in CRC cells involved in 5-FU resistance.This study sought to identify the critical genes that produced 5-FU resistance by analysing the functions of differentially m6A-modified mRNAs and lncRNAs.

Narrow-band loss - a novel loss function focused on target boundary.

Loss functions widely employed in medical image segmentation, e.g., Dice or Generalized Dice, treat each pixel of segmentation target(s) equally. These region-based loss functions are concerned with the overall segmentation accuracy. However, in clinical applications, the focus of attention is often the boundary area of the target organ(s). Existing region-based loss functions lack attention to boundary areas. We designed narrow-band loss, which computes the integration of the predicted probability within the narrow-band around the target boundary. From the aspect of how it's derived, Narrow-band loss belongs to the region-based loss function. The difference from normal region-based loss is that Narrow-band loss calculates based on the degree of coincidence of the region surrounding the organ boundary. The advantage is that narrow-band loss can guide networks to focus on the target's boundary and neighborhood. We also generalize narrow-band loss to multi-target segmentation. We tested narrow-band loss on two datasets of different parts of the human body: the brain dataset with 416 cases, each case with 35 labels, and the abdominal dataset with 50 cases, each case with 12 labels. Narrow-band loss has improved greatly in hd95 metric and dice metric compared with baseline, which is dice loss only.

An artificial intelligence-assisted diagnosis modeling software (AIMS) platform based on medical images and machine learning: a development and validation study.

Background: Supervised machine learning methods [both radiomics and convolutional neural network (CNN)-based deep learning] are usually employed to develop artificial intelligence models with medical images for computer-assisted diagnosis and prognosis of diseases. A classical machine learning-based modeling workflow involves a series of interconnected components and various algorithms, but this makes it challenging, tedious, and labor intensive for radiologists and researchers to build customized models for specific clinical applications if they lack expertise in machine learning methods. Methods: We developed a user-friendly artificial intelligence-assisted diagnosis modeling software (AIMS) platform, which supplies standardized machine learning-based modeling workflows for computer-assisted diagnosis and prognosis systems with medical images. In contrast to other existing software platforms, AIMS contains both radiomics and CNN-based deep learning workflows, making it an all-in-one software platform for machine learning-based medical image analysis. The modular design of AIMS allows users to build machine learning models easily, test models comprehensively, and fairly compare the performance of different models in a specific application. The graphical user interface (GUI) enables users to process large numbers of medical images without programming or script addition. Furthermore, AIMS also provides a flexible image processing toolkit (e.g., semiautomatic segmentation, registration, morphological operations) to rapidly create lesion labels for multiphase analysis, multiregion analysis of an individual tumor (e.g., tumor mass and peritumor), and multimodality analysis. Results: The functionality and efficiency of AIMS were demonstrated in 3 independent experiments in radiation oncology, where multiphase, multiregion, and multimodality analyses were performed, respectively. For clear cell renal cell carcinoma (ccRCC) Fuhrman grading with multiphase analysis (sample size =187), the area under the curve (AUC) value of the AIMS was 0.776; for ccRCC Fuhrman grading with multiregion analysis (sample size =177), the AUC value of the AIMS was 0.848; for prostate cancer Gleason grading with multimodality analysis (sample size =206), the AUC value of the AIMS was 0.980. Conclusions: AIMS provides a user-friendly infrastructure for radiologists and researchers, lowering the barrier to building customized machine learning-based computer-assisted diagnosis models for medical image analysis.

IMH-Net: a convolutional neural network for end-to-end EEG motor imagery classification.

As the main component of Brain-computer interface (BCI) technology, the classification algorithm based on EEG has developed rapidly. The previous algorithms were often based on subject-dependent settings, resulting in BCI needing to be calibrated for new users. In this work, we propose IMH-Net, an end-to-end subject-independent model. The model first uses Inception blocks extracts the frequency domain features of the data, then further compresses the feature vectors to extract the spatial domain features, and finally learns the global information and classification through Multi-Head Attention mechanism. On the OpenBMI dataset, IMH-Net obtained 73.90 ± 13.10% accuracy and 73.09 ± 14.99% F1-score in subject-independent manner, which improved the accuracy by 1.96% compared with the comparison model. On the BCI competition IV dataset 2a, this model also achieved the highest accuracy and F1-score in subject-dependent manner. The IMH-Net model we proposed can improve the accuracy of subject-independent Motor Imagery (MI), and the robustness of the algorithm is high, which has strong practical value in the field of BCI.

Saponins of Panax japonicus ameliorates cardiac aging phenotype in aging rats by enhancing basal autophagy through AMPK/mTOR/ULK1 pathway.

Heart disease is a significant health concern for elderly individuals, with heart aging being the primary cause. Recent studies have shown that autophagy can play a protective role in preventing cardiac aging. Our previous research confirmed that Chikusetsu saponin IVa, a fundamental component of Saponins of Panax japonics (SPJ), can enhance basic autophagy levels in cardiomyocyte of isoproterenol induced cardiac fibrosis mice. However, it remains unclear whether SPJ possesses a protective effect on cardiac dysfunction during the natural aging process. Rats were randomly divided into four groups: adult control group (6 months old), aging group (24 months old), aging group treated with 10 mg/kg SPJ, and aging group treated with 30 mg/kg SPJ. The heart function, blood pressure, and heart mass index (HMI) were measured. Hematoxylin and eosin staining (H&E) and Wheat Germ Agglutinin (WGA) staining were used to observe the changes in morphology, while Masson staining was used to examine collagen deposition in the rat hearts and CD45 immunohistochemistry was conducted to examine the macrophage infiltration in heart tissues. TUNEL kit was used to detect apoptosis level of cardiomyocyte, and western blot was used to evaluate autophagy-related proteins as well as AMPK/mTOR/ULK1 pathway-related markers. SPJ treatment improved the cardiac function, reduced HMI, attenuated myocardial fiber disorder, inhibited inflammatory cell infiltration, and decreased collagen deposition and cardiomyocyte apoptosis in aging rats. Additionally, SPJ treatment decreased the expression of aging-related proteins and restored the expression of autophagy-related markers. SPJ activated autophagy through the activation of AMPK, which in turn increased the phosphorylation of ULK1(Ser555), while inhibited the phosphorylation of mTOR and ULK1(Ser757). Our study demonstrates that SPJ improves the cardiac function of aging rats by enhancing basal autophagy through the AMPK/mTOR/ULK1 pathway. These results offer a theoretical foundation and empirical evidence to support the clinical advancement of SPJ in enhancing age-related cardiac dysfunction.

Saponins from Panax japonicus improve neuronal mitochondrial injury of aging rats.

CONTEXT: Panax japonicus is the dried rhizome of Panax japonicus C.A. Mey. (Araliaceae). Saponins from Panax japonicus (SPJ) exhibit anti-oxidative and anti-aging effects. OBJECTIVE: We evaluated the neuroprotective effects of SPJ on aging rats. MATERIALS AND METHODS: Sprague-Dawley rats (18-months-old) were randomly divided into aging and SPJ groups (n = 8). Five-month-old rats were taken as the adult control (n = 8). The rats were fed a normal chow diet or the SPJ-containing diet (10 or 30 mg/kg) for 4 months. An in vitro model was established by d-galactose (d-Gal) in the SH-SY5Y cell line and pretreated with SPJ (25 and 50 µg/mL). The neuroprotection of SPJ was evaluated via Nissl staining, flow cytometry, transmission electron microscopy and Western blotting in vivo and in vitro. RESULTS: SPJ improved the neuronal degeneration and mitochondrial morphology that are associated with aging. Meanwhile, SPJ up-regulated the protein levels of mitofusin 2 (Mfn2) and optic atrophy 1 (Opa1) and down-regulated the protein level of dynamin-like protein 1 (Drp1) in the hippocampus of aging rats (p < 0.05 or p < 0.01 vs. 22 M). The in vitro studies also demonstrated that SPJ attenuated d-Gal-induced cell senescence concomitant with the improvement in mitochondrial function; SPJ, also up-regulated the Mfn2 and Opa1 protein levels, whereas the Drp1 protein level (p < 0.05 or p < 0.01 vs. d-Gal group) was down-regulated. DISCUSSION AND CONCLUSIONS: Further research on the elderly population will contribute to the development and utilization of SPJ for the treatment of neurodegenerative disorders.

macJNet: weakly-supervised multimodal image deformable registration using joint learning framework and multi-sampling cascaded MIND.

Deformable multimodal image registration plays a key role in medical image analysis. It remains a challenge to find accurate dense correspondences between multimodal images due to the significant intensity distortion and the large deformation. macJNet is proposed to align the multimodal medical images, which is a weakly-supervised multimodal image deformable registration method using a joint learning framework and multi-sampling cascaded modality independent neighborhood descriptor (macMIND). The joint learning framework consists of a multimodal image registration network and two segmentation networks. The proposed macMIND is a modality-independent image structure descriptor to provide dense correspondence for registration, which incorporates multi-orientation and multi-scale sampling patterns to build self-similarity context. It greatly enhances the representation ability of cross-modal features in the registration network. The semi-supervised segmentation networks generate anatomical labels to provide semantics correspondence for registration, and the registration network helps to improve the performance of multimodal image segmentation by providing the consistency of anatomical labels. 3D CT-MR liver image dataset with 118 samples is built for evaluation, and comprehensive experiments have been conducted to demonstrate that macJNet achieves superior performance over state-of-the-art multi-modality medical image registration methods.

Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma.

Objective: This study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC). Methods: A total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC). Results: The multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039). Conclusion: The multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations.

Maximum potential of vegetation carbon sink in Chinese forests.

Forest vegetation is essential in sequestering carbon dioxide (CO2) from the atmosphere and mediating global warming. The carbon (C) sink potential of forest vegetation in different provinces is vital for policymakers to develop C-neutral technical routes and regional priorities in China; however, the mechanism remains unclear. In this study, we compiled the public data on forest vegetation biomass or storage along forest succession series between 2003 and 2022 and obtained the spatial variation of the maximum C storage(BCmax) of forest vegetation using classic logistic equation and nonlinear fitting. Furthermore, the C sink potential (∆Cpot) of the Chinese forest vegetation was calculated based on the differences between the BCmax and intensive field-investigated data in the 2010s. The results showed that the BCmax in the Chinese forest vegetation was approximately 19.03 Pg. The BCmax in southwest and northeast China were higher than those in other regions. The ∆Cpot was estimated as 8.83 Pg. Moreover, 1 km × 1 km spatial raster data for ∆Cpot were produced using the spatial raster calculation. Similarly, the per capita ∆Cpot of regions with low economic development (southwest, central, and southern Chinese provinces) were five to ten times higher than those of regions with a higher economic level. The ∆Cpot correlated negatively with gross domestic product (GDP)across all Chinese provinces. Our findings provide new insights into the ∆Cpot of the Chinese forest vegetation under natural restoration and emphasize that some differences in financial and political support among different provinces facilitate achieving a large ∆Cpot for C neutrality.

GCNfold: A novel lightweight model with valid extractors for RNA secondary structure prediction.

RNA secondary structure is essential for predicting the tertiary structure and understanding RNA function. Recent research tends to stack numerous modules to design large deep-learning models. This can increase the accuracy to more than 70%, as well as significant training costs and prediction efficiency. We proposed a model with three feature extractors called GCNfold. Structure Extractor utilizes a three-layer Graph Convolutional Network (GCN) to mine the structural information of RNA, such as stems, hairpin, and internal loops. Structure and Sequence Fusion embeds structural information into sequences with Transformer Encoders. Long-distance Dependency Extractor captures long-range pairwise relationships by UNet. The experiments indicate that GCNfold has a small number of parameters, a fast inference speed, and a high accuracy among all models with over 80% accuracy. Additionally, GCNfold-Small takes only 90ms to infer an RNA secondary structure and can achieve close to 90% accuracy on average. The GCNfold code is available on Github https://github.com/EnbinYang/GCNfold.

New insights into nanosystems for non-small-cell lung cancer: diagnosis and treatment.

Lung cancer is caused by a malignant tumor that shows the fastest growth in both incidence and mortality and is also the greatest threat to human health and life. At present, both in terms of incidence and mortality, lung cancer is the first in male malignant tumors, and the second in female malignant tumors. In the past two decades, research and development of antitumor drugs worldwide have been booming, and a large number of innovative drugs have entered clinical trials and practice. In the era of precision medicine, the concept and strategy of cancer from diagnosis to treatment are experiencing unprecedented changes. The ability of tumor diagnosis and treatment has rapidly improved, the discovery rate and cure rate of early tumors have greatly improved, and the overall survival of patients has benefited significantly, with a tendency to transform to a chronic disease with tumor. The emergence of nanotechnology brings new horizons for tumor diagnosis and treatment. Nanomaterials with good biocompatibility have played an important role in tumor imaging, diagnosis, drug delivery, controlled drug release, etc. This article mainly reviews the advancements in lipid-based nanosystems, polymer-based nanosystems, and inorganic nanosystems in the diagnosis and treatment of non-small-cell lung cancer (NSCLC).

Unsupervised registration for liver CT-MR images based on the multiscale integrated spatial-weight module and dual similarity guidance.

PURPOSE: Multimodal registration is a key task in medical image analysis. Due to the large differences of multimodal images in intensity scale and texture pattern, it is a great challenge to design distinctive similarity metrics to guide deep learning-based multimodal image registration. Besides, since the limitation of the small receptive field, existing deep learning-based methods are mainly suitable for small deformation, but helpless for large deformation. To address the above issues, we present an unsupervised multimodal image registration method based on the multiscale integrated spatial-weight module and dual similarity guidance. METHODS: In this method, a U-shape network with our multiscale integrated spatial-weight module is embedded into a multi-resolution image registration architecture to achieve end-to-end large deformation registration, where the spatial-weight module can effectively highlight the regions with large deformation and aggregate discriminative features, and the multi-resolution architecture further helps to solve the optimization problem of the network in a coarse-to-fine pattern. Furthermore, we introduce a special loss function based on dual similarity, which represents both global gray-scale similarity and local feature similarity, to optimize the unsupervised multimodal registration network. RESULTS: We verified the effectiveness of the proposed method on liver CT-MR images. Experimental results indicate that the proposed method achieves the optimal DSC value and TRE value of 92.70 ± 1.75(%) and 6.52 ± 2.94(mm), compared with other state-of-the-art registration algorithms. CONCLUSION: The proposed method can accurately estimate the large deformation field by aggregating multiscale features, and achieve higher registration accuracy and fast registration speed. Comparative experiments also demonstrate the effectiveness and generalization ability of the algorithm.

Irreducible anteromedial radial head dislocation without fracture caused by transposed biceps tendon in an adult: A case report and literature review.

Irreducible anteromedial radial head dislocation (IARHD) caused by transposed biceps tendon is rare. Delayed diagnosis and surgical failure often occur. A 46-year-old fisherman presented with 10 days history of painful swelling and restricted movement of his right elbow due to strangulation injury by a fishing boat cable. On examination, the images of right elbow reveals in a "semi-extended and pronated" elastic fixation position. Radiography and three-dimensional reconstruction CT reveals an isolated anteromedial radial head dislocation with extreme protonation of the radius and the bicipital tuberosity towards the posterior aspect of the elbow joint, and MRI shows biceps tendon wrapping around the radial neck, similar to umbilical cord wrapping seen in newborns. The Henry approach was applied for the first time to reduce the biceps tendon. The patient achieved a good functional recovery at 26 months, which represents the first reported case of IARHD without fracture caused by biceps tendon in an adult. In treatment of IARHD, attention should be paid to the phenomenon of biceps tendon transposition. Careful clinical examination, comprehensive imaging modalities, and appropriate surgical approach are the keys to successful management.