Pure tone audiogram classification using deep learning techniques.

OBJECTIVE: Pure tone audiometry has played a critical role in audiology as the initial diagnostic tool, offering vital insights for subsequent analyses. This study aims to develop a robust deep learning framework capable of accurately classifying audiograms across various commonly encountered tasks. DESIGN, SETTING, AND PARTICIPANTS: This single-centre retrospective study was conducted in accordance with the STROBE guidelines. A total of 12 518 audiograms were collected from 6259 patients aged between 4 and 96 years, who underwent pure tone audiometry testing between February 2018 and April 2022 at Tongji Hospital, Tongji Medical College, Wuhan, China. Three experienced audiologists independently annotated the audiograms, labelling the hearing loss in degrees, types and configurations of each audiogram. MAIN OUTCOME MEASURES: A deep learning framework was developed and utilised to classify audiograms across three tasks: determining the degrees of hearing loss, identifying the types of hearing loss, and categorising the configurations of audiograms. The classification performance was evaluated using four commonly used metrics: accuracy, precision, recall and F1-score. RESULTS: The deep learning method consistently outperformed alternative methods, including K-Nearest Neighbors, ExtraTrees, Random Forest, XGBoost, LightGBM, CatBoost and FastAI Net, across all three tasks. It achieved the highest accuracy rates, ranging from 96.75% to 99.85%. Precision values fell within the range of 88.93% to 98.41%, while recall values spanned from 89.25% to 98.38%. The F1-score also exhibited strong performance, ranging from 88.99% to 98.39%. CONCLUSIONS: This study demonstrated that a deep learning approach could accurately classify audiograms into their respective categories and could contribute to assisting doctors, particularly those lacking audiology expertise or experience, in better interpreting pure tone audiograms, enhancing diagnostic accuracy in primary care settings, and reducing the misdiagnosis rate of hearing conditions. In scenarios involving large-scale audiological data, the automated classification system could be used as a research tool to efficiently provide a comprehensive overview and statistical analysis. In the era of mobile audiometry, our deep learning framework can also help patients quickly and reliably understand their self-tested audiograms, potentially encouraging timely consultations with audiologists for further evaluation and intervention.

Association Between Neuroinflammation and Parkinson's Disease: A Comprehensive Mendelian Randomization Study.

The objective of the study is to determine the causal relationship and potential mechanisms between Parkinson's disease (PD) and neuroinflammatory and neurotoxic mediators. We conducted two-sample Mendelian randomization (2SMR) study and multivariable Mendelian randomization (MVMR) analysis to investigate the causality between PD and neuroinflammatory and neurotoxic mediators. The mediation analysis with MR was also conducted to determine the potential mediating effect of neuroinflammatory and neurotoxic mediators between asthma and PD. Genetically predicted levels of nine neuroinflammation were associated with changes in PD risk. The associations of PD with CCL24, galectin-3 levels, haptoglobin, and Holo-Transcobalamin-2 remained significant in multivariable analyses. The mediation analysis with MR revealed that asthma affects PD through CCL24 and galectin-3. The results showed neuroinflammation could affect the pathogenesis of PD. In the combined analysis of these nine variables, CCL24, galectin-3 levels, HP, and Holo-Transcobalamin-2 alone were found to be significant. Asthma plays an intermediary role through CCL24 and galectin-3 levels.

Performance of ChatGPT on Chinese Master's Degree Entrance Examination in Clinical Medicine.

BACKGROUND: ChatGPT is a large language model designed to generate responses based on a contextual understanding of user queries and requests. This study utilised the entrance examination for the Master of Clinical Medicine in Traditional Chinese Medicine to assesses the reliability and practicality of ChatGPT within the domain of medical education. METHODS: We selected 330 single and multiple-choice questions from the 2021 and 2022 Chinese Master of Clinical Medicine comprehensive examinations, which did not include any images or tables. To ensure the test's accuracy and authenticity, we preserved the original format of the query and alternative test texts, without any modifications or explanations. RESULTS: Both ChatGPT3.5 and GPT-4 attained average scores surpassing the admission threshold. Noteworthy is that ChatGPT achieved the highest score in the Medical Humanities section, boasting a correct rate of 93.75%. However, it is worth noting that ChatGPT3.5 exhibited the lowest accuracy percentage of 37.5% in the Pathology division, while GPT-4 also displayed a relatively lower correctness percentage of 60.23% in the Biochemistry section. An analysis of sub-questions revealed that ChatGPT demonstrates superior performance in handling single-choice questions but performs poorly in multiple-choice questions. CONCLUSION: ChatGPT exhibits a degree of medical knowledge and the capacity to aid in diagnosing and treating diseases. Nevertheless, enhancements are warranted to address its accuracy and reliability limitations. Imperatively, rigorous evaluation and oversight must accompany its utilization, accompanied by proactive measures to surmount prevailing constraints.

[Downregulation of SETDB1 suppresses epithelial mesenchymal transition and invasion and metastasis in oral cancer via SOX7 methylation].

PURPOSE: To explore the mechanism of SETDB1 inhibiting epithelial mesenchymal transition (EMT),migration and invasion in oral cancer via SOX 7 methylation. METHODS: SETDB1 and SOX7 mRNA and protein expression levels in KB cells of oral cancer and oral mucosal epithelial ATCC cells were determined by qRT-PCR and Western blot (WB). SETDB1 si-RNA was structured, then transfect into KB cells of oral cancer by liposome-mediated method. siRNA-SETDB1 was the experimental group (si-S), siRNA empty vector was the negative control group (si-N), and untransfected KB cells were the blank control group(NC). SETDB1 mRNA and protein expression levels were detected by qRT-PCR and Western blot(WB), to verify the transfection effect. The methylation levels of SOX7 were determined by pyrosequencing. The expression of N-cadherin, Vimentin, ß-catenin, and Slug proteins was detected by WB. Cell viability was measured by MTT assay, migration ability was tested by scratch healing assay, and invasion ability was tested by Transwell chamber assay. Statistical analysis was performed with SPSS 21.0 software package. RESULTS: The results of Rt-qPCR and WB showed that the SETDB1 mRNA and protein expression decreased significantly in si-S group(P＜0.05). Pyrosequencing test results showed that the regulation of SETDB1 could significantly reduce the SOX7 methylation rate and increased the SOX7 protein expression. WB results showed that knockdown of SETDB1 significantly inhibited the expression of EMT-related proteins N-cadherin, Vimentin, ß-catenin and Slug in oral cancer KB cells (P＜0.05). The results of cell functology experiments showed that knockdown of SETDB1 could significantly inhibit survival, migration and invasion of KB cells. CONCLUSIONS: Downregulation of SETDB1 could suppress EMT, migration and invasion of oral cancer cells by regulating SOX7 methylation level, providing new ideas and targets for the diagnosis and treatment of oral cancer.

Turning a CLIP Model into a Scene Text Spotter.

We exploit the potential of the large-scale Contrastive Language-Image Pretraining (CLIP) model to enhance scene text detection and spotting tasks, transforming it into a robust backbone, FastTCM-CR50. This backbone utilizes visual prompt learning and cross-attention in CLIP to extract image and text-based prior knowledge. Using predefined and learnable prompts, FastTCM-CR50 introduces an instance-language matching process to enhance the synergy between image and text embeddings, thereby refining text regions. Our Bimodal Similarity Matching (BSM) module facilitates dynamic language prompt generation, enabling offline computations and improving performance. FastTCM-CR50 offers several advantages: 1) It can enhance existing text detectors and spotters, improving performance by an average of 1.6% and 1.5%, respectively. 2) It outperforms the previous TCM-CR50 backbone, yielding an average improvement of 0.2% and 0.55% in text detection and spotting tasks, along with a 47.1% increase in inference speed. 3) It showcases robust few-shot training capabilities. Utilizing only 10% of the supervised data, FastTCM-CR50 improves performance by an average of 26.5% and 4.7% for text detection and spotting tasks, respectively. 4) It consistently enhances performance on out-of-distribution text detection and spotting datasets, particularly the NightTime-ArT subset from ICDAR2019-ArT and the DOTA dataset for oriented object detection. The code is available at https://github.com/wenwenyu/TCM.

Analysis of the Relationship Between Parkinson's Disease and Diabetic Retinopathy Based on Bioinformatics Methods.

The objective of the study was to explore the relationship and potential mechanism between Parkinson's disease (PD) and diabetic retinopathy (DR) using bioinformatics methods. We first examined the causal relationship between PD and DR by Mendelian randomization (MR) analysis. The datasets of PD and DR patients from the Gene Expression Omnibus database were used to identify differentially expressed genes (DEGs). Then, we performed the Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and immune infiltration analysis. We also constructed a protein-protein interaction network and receiver operating characteristic (ROC) curve. Finally, an online website was used for drug prediction. The MR analysis demonstrated a causal relationship between DR and PD (odds ratio [OR] = 0.86; 95% confidence interval [CI] 0.79-0.93; p = 3.24E - 04), in which DR acted as a protective factor against PD. There were 81 DEGs identified from the PD and DR datasets, of which 29 genes had protein interaction relationships, and enrichment analysis showed that these genes were mainly related to immune pathways. As indicated by immune cell infiltration analysis, the expression of immune cells between PD and the control group was significantly different. ROC curve results showed five genes had diagnostic value, and several potential chemical compounds were predicted to target the genes. Our findings demonstrate a reduced risk of PD in patients with DR. We also found that PD and DR are closely related in terms of inflammation, which provides clues for further exploring the common mechanisms and interaction of these two diseases.

Charge-Assisted Ionic Hydrogen-Bonded Organic Frameworks: Designable and Stabilized Multifunctional Materials.

Hydrogen-bonded organic frameworks (HOFs) are a class of crystalline framework materials assembled by hydrogen bonds. HOFs have the advantages of high crystallinity, mild reaction conditions, good solution processability, and reproducibility. Coupled with the reversibility and flexibility of hydrogen bonds, HOFs can be assembled into a wide diversity of crystalline structures. Since the bonding energy of hydrogen bonds is lower than that of ligand and covalent bonds, the framework of HOFs is prone to collapse after desolventisation and the stability is not high, which limits the development and application of HOFs. In recent years, numerous stable and functional HOFs have been developed by π-π stacking, highly interpenetrated networks, charge-assisted, ligand-bond-assisted, molecular weaving, and covalent cross-linking. Charge-assisted ionic HOFs introduce electrostatic attraction into HOFs to improve stability while enriching structural diversity and functionality. In this paper, we review the development, the principles of rational design and assembly of charge-assisted ionic HOFs, and introduces the different building block construction modes of charge-assisted ionic HOFs. Highlight the applications of charge-assisted ionic HOFs in gas adsorption and separation, proton conduction, biological applications, etc., and prospects for the diverse design of charge-assisted ionic HOFs structures and multifunctional applications.

Research progress in micro/nanobubbles for ultrasound diagnosis or treatment / 药学学报

In the past few decades, microbubbles were widely used as ultrasound contrast agents in the field of tumor imaging. With the development of research, ultrasound targeted microbubble destruction technology combined with drug-loaded microbubbles can achieve precise drug release and play a therapeutic role. As a micron-scale carrier, microbubbles are difficult to penetrate the endothelial cell space of tumors, and nano-scale drug delivery system—nanobubbles came into being. The structure of the two is similar, but the difference in size highlights the unique advantages of nanobubbles in drug delivery. Based on the classification principle of shell materials, this review summarized micro/nanobubbles used for ultrasound diagnosis or treatment and discussed the possible development directions, providing references for the subsequent development.

Understanding apoptotic induction by Sargentodoxa cuneata-Patrinia villosa herb pair via PI3K/AKT/mTOR signalling in colorectal cancer cells using network pharmacology and cellular studies.

ETHNOPHARMACOLOGICAL RELEVANCE: Sargentodoxa cuneata (Sargentodoxa cuneata (Oliv.) Rehder & E.H.Wilson, DXT)-Patrinia villosa(Patrinia villosa (Thunb.) Dufr, BJC) constitutes a commonly employed herb pair in Chinese medicine for colorectal cancer (CRC) treatment. Modern pharmacological investigations have revealed the anticancer activities of both Sargentodoxa cuneata and Patrinia villosa. Nevertheless, comprehensive studies are required to discern the specific antitumor active ingredients and mechanism of action when these two herbs are used in combination. AIM OF THE STUDY: Through the integration of network pharmacology, molecular docking techniques, experimental assays, and bioinformatics analysis, our study aims to forecast the active ingredients, potential targets, and molecular mechanisms underlying the therapeutic efficacy of this herb pair against CRC. MATERIALS AND METHODS: Plant names (1, Sargentodoxa cuneata (Oliv.) Rehder & E.H.Wilson; 2, Patrinia villosa (Thunb.) Dufr.) have been verified through WorldFloraOnline (www.worldFloraonline.org) and MPNs (http://mpns.kew.org). The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) were utilized for screening the active ingredients of the herb pair. The PharmMapper database was employed to predict the target proteins for each active ingredient. CRC-related targets were obtained from the Genecards database, Online Mendelian Inheritance in Man (OMIM) database, Disease Gene Network (DisGeNET) database, and Therapeutic Target Database (TTD). Common targets were identified by intersecting the target proteins of all active ingredients with CRC-related targets. Protein-protein interactions (PPI) for the common target proteins were constructed using the String database and Cytoscape 3.9.1 software. Network topology analysis facilitated the identification of core targets. These core targets were subjected to enrichment analysis of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) using the Metascape database. Molecular docking was performed using Discovery Studio 2019 to investigate the interactions between the active ingredients and core target proteins. The core targets were validated through bioinformatics analysis using GEPIA, HPA, and the cBioPortal database. Finally, a series of experiments were conducted to further validate the results in vitro. RESULT: A total of 15 active ingredients and 255 herb targets were identified, resulting in 66 common targets in conjunction with 6113 disease targets. The PPI analysis highlighted AKT1, EGFR, CASP3, SRC, and ESR1 as core targets. KEGG enrichment analysis indicated significant enrichment in the PI3K-AKT signaling pathway, a pathway associated with cancer. Molecular docking experiments confirmed favorable interactions between dihydroguaiaretic acid and the core target proteins (AKT1, EGFR, CASP3, and ESR1). Bioinformatics analysis revealed differential expression of EGFR and CASP3 in normal and CRC tissues. Cellular experiments further verified that dihydroguaiaretic acid induces apoptosis in colorectal cancer cells through the PI3K-AKT signaling pathway. CONCLUSION: Our network pharmacology study has elucidated that the Sargentodoxa cuneata-Patrinia villosa herb pair exerts the negative regulation of the PI3K/AKT/mTOR signaling pathway, ultimately leading to the induction of apoptosis in colorectal cancer cells. This research has predicted and validated the active ingredients, potential targets, and molecular mechanisms of Sargentodoxa cuneata-Patrinia villosa in the treatment of CRC, providing scientific evidence for the use of traditional Chinese medicine in managing CRC.

Epidemiological analysis reveals a surge in inflammatory bowel disease among children and adolescents: A global, regional, and national perspective from 1990 to 2019 - insights from the China study.

Background: The burden of inflammatory bowel disease (IBD) among children and adolescents is rising globally, with substantial variation in levels and trends of disease in different countries and regions, while data on the burden and trends were sparse in children and adolescents. We aimed to assess the trends and geographical differences in children and adolescents aged zero to 19 in 204 countries and territories over the past 30 years. Methods: Data on IBD among children and adolescents was collected from the Global Burden of Disease (GBD) 2019 database from 1990 to 2019. We used the GBD data and methodologies to describe the change in the burden of IBD among children and adolescents involving prevalence, incidence, disability-adjusted life years (DALYs), and mortality. Results: Globally, the IBD prevalence cases increased between 1990 and 2019. Annual percentage changes (AAPC) = 0.15; 95% confidence interval (CI) = 0.11-0.19, and incidence cases of IBD increased from 20 897.4 (95% CI = 17 008.6-25 520.2 in 1990 to 25 658.6 (95% CI = 21 268.5-31 075.6) in 2019, representing a 22.78% increase, DALYs cases decreased between 1990 and 2019 (AAPC = -3.02; 95% CI = -3.15 to -2.89), and mortality cases of IBD decreased from 2756.5 (95% CI = 1162.6-4484.9) in 1990 to 1208.0 (95% CI = 802.4-1651.4) in 2019, representing a 56.17% decrease. Decomposition analysis showed that IBD prevalence and incidence increased significantly, and a trend exhibited a decrease in underlying age and population-adjusted IBD DALYs and mortality rates. Correlation analysis showed that countries with high health care quality and access (HAQ) had relatively higher IBD age-standardised prevalence rate (ASPR) and age-standardised incidence rate (ASIR), but lower age-standardised DALYs rate (ASDR) and age-standardised mortality rate (ASMR). Conclusions: Global prevalence and incidence rate of IBD among children and adolescents have been increasing from 1990 to 2019, while the DALYs and mortality have been decreasing. Rising prevalence and rising incidence in areas with historically low rates will have crucial health and economic implications.

Promoting wound recovery through stable intestinal flora: Reducing post-operative complications in colorectal cancer surgery patients.

In recent years, the relationship between intestinal flora and post-operative recovery, particularly in colorectal cancer (CRC) surgery patients, it has been hypothesized that intestinal flora stability influences wound healing, reduces complications and improves overall recovery outcomes after surgical interventions. This study examined the relationship between intestinal flora stability and post-operative recovery in patients undergoing CRC surgery. Between May 2020 and 2023, 80 CRC patients from our hospital's Colorectal Surgery Department were enrolled. A random number table was used to divide them into two categories. Both groups were subjected to distinct gastrointestinal preparation protocols. Indicators of clinical therapeutic effect, intestinal flora balance following surgery, post-operative complications and quality of life were evaluated. The observation group, which adhered to a distinct gastrointestinal regimen, demonstrated a statistically significant improvement in post-operative outcomes, with a clinical effectiveness rate of 97.5% compared to the control group's 75%. In addition, the observation group had a lower incidence of intestinal flora imbalance following surgery than the control group. The observation group had lower incidences of intestinal obstruction, infection, anastomotic leakage, incisional tumour implantation and delayed diarrhoea. Using the KPS score and the BMI, post-treatment assessments of the observation group's quality of life revealed significant enhancements in comparison to the control group. Additionally, wound healing rates were superior in the observation group, with a correlation between stable intestinal flora and decreased wound infection rates. The type of post-operative diet influenced the stabilization of the gut flora, with a high-fibre diet producing superior results in both groups. The stability of intestinal flora influences the post-operative rehabilitation of patients undergoing CRC surgery favourably. Appropriate bowel preparation and dietary considerations can reduce post-operative complications, improve wound healing rates and enhance overall quality of life.

Clinical efficacy and safety of tigecycline based on therapeutic drug monitoring for carbapenem-resistant Gram-negative bacterium pneumonia in intensive care units.

BACKGROUND: We investigated the associations between the different doses of tigecycline, its efficacy and safety, and the role of tigecycline therapeutic drug monitoring for patients in the intensive care unit. METHODS: This study was a single-center cohort including patients infected with multidrug-resistant Acinetobacter baumannii (MDR-AB) and multidrug-resistant Klebsiella pneumoniae (MDR-KP) causing pulmonary infections. The steady-state plasma concentration after tigecycline administration was determined by High-Performance Liquid Chromatography (HPLC) in patients admitted to the ICU between October 2020 and December 2021. Multivariate analyses of tigecycline's clinical efficacy and safety were performed to control confounding factors. RESULTS: For this study, we included 45 patients and 45 blood samples to determine steady-state trough concentrations of tigecycline. All patients were divided into the High Dose (HD) and Standard Dose (SD) groups. The median trough concentration of tigecycline was 0.56 µg/mL in the HD group, which was higher than in the SD group (0,21 µg/mL), p = 0.000. There was no significant difference between the two groups of patients in terms of bacterial eradication rate, mortality rate, and clinical efficacy. Multiple regression analysis showed that the ICU days were correlated with mortality OR 1.030(1.005-1.056), p = 0.017. APACHE II was significantly associated with clinical efficacy OR 0.870(0.755-1.002), p = 0.045. The level of fibrinogen decline in the HD group was significantly higher than in the SD group (-3.05 ± 1.67 vs -1.75 ± 1.90), p = 0.038. We identified that age and tigecycline treatment duration influenced fibrinogen decline. CONCLUSIONS: Tigecycline plasma concentrations are significantly increased when using a high dose. However, the plasma concentration of tigecycline is not correlated with clinical efficacy and adverse reactions. Fibrinogen decline appears to be related to the patient's age and days of tigecycline. Large sample data are still needed to confirm the clinical guidance significance of tigecycline TDM.

Focalizing regions of biomarker relevance facilitates biomarker prediction on histopathological images.

Image-based AI has thrived as a potentially revolutionary tool for predicting molecular biomarker statuses, which aids in categorizing patients for appropriate medical treatments. However, many methods using hematoxylin and eosin-stained (H&E) whole-slide images (WSIs) have been found to be inefficient because of the presence of numerous uninformative or irrelevant image patches. In this study, we introduced the region of biomarker relevance (ROB) concept to identify the morphological areas most closely associated with biomarkers for accurate status prediction. We actualized this concept within a framework called saliency ROB search (SRS) to enable efficient and effective predictions. By evaluating various lung adenocarcinoma (LUAD) biomarkers, we showcased the superior performance of SRS compared to current state-of-the-art AI approaches. These findings suggest that AI tools, built on the ROB concept, can achieve enhanced molecular biomarker prediction accuracy from pathological images.

SPTS v2: Single-Point Scene Text Spotting.

End-to-end scene text spotting has made significant progress due to its intrinsic synergy between text detection and recognition. Previous methods commonly regard manual annotations such as horizontal rectangles, rotated rectangles, quadrangles, and polygons as a prerequisite, which are much more expensive than using single-point. Our new framework, SPTS v2, allows us to train high-performing text-spotting models using a single-point annotation. SPTS v2 reserves the advantage of the auto-regressive Transformer with an Instance Assignment Decoder (IAD) through sequentially predicting the center points of all text instances inside the same predicting sequence, while with a Parallel Recognition Decoder (PRD) for text recognition in parallel, which significantly reduces the requirement of the length of the sequence. These two decoders share the same parameters and are interactively connected with a simple but effective information transmission process to pass the gradient and information. Comprehensive experiments on various existing benchmark datasets demonstrate the SPTS v2 can outperform previous state-of-the-art single-point text spotters with fewer parameters while achieving 19× faster inference speed. Within the context of our SPTS v2 framework, our experiments suggest a potential preference for single-point representation in scene text spotting when compared to other representations. Such an attempt provides a significant opportunity for scene text spotting applications beyond the realms of existing paradigms.

SAN: Side Adapter Network for Open-Vocabulary Semantic Segmentation.

This article concentrates on open-vocabulary semantic segmentation, where a well optimized model is able to segment arbitrary categories that appear in an image. To achieve this goal, we present a novel framework termed Side Adapter Network, or SAN for short. Our design principles are three-fold: 1) Recent large-scale vision-language models (e.g. CLIP) show promising open-vocabulary image classification capability; it is training-economized to adapt a pre-trained CLIP model to open-vocabulary semantic segmentation. 2) Our SAN model should be both lightweight and effective in order to reduce the inference cost-to achieve this, we fuse the CLIP model's intermediate features to enhance the representation capability of the SAN model, and drive the CLIP model to focus on the informative areas of an image with the aid of the attention biases predicted by a side adapter network. 3) Our approach should empower mainstream segmentation architectures to have the capability of open-vocabulary segmentation-we present P-SAN and R-SAN, to support widely adopted pixel-wise segmentation and region-wise segmentation, respectively. Experimentally, our approach achieves state-of-the-art performance on 5 commonly used benchmarks while having much less trainable parameters and GFLOPs. For instance, our R-SAN outperforms previous best method OvSeg by +2.3 averaged mIoU across all benchmarks while using only 6% of trainable parameters and less than 1% of GFLOPs. In addition, we also conduct a comprehensive analysis of the open-vocabulary semantic segmentation datasets and verify the feasibility of transferring a well optimzied R-SAN model to video segmentation task.

Variational quantitative phase-field modeling of nonisothermal sintering process.

Phase-field modeling has become a powerful tool in describing the complex pore-structure evolution and the intricate multiphysics in nonisothermal sintering processes. However, the quantitative validity of conventional variational phase-field models involving diffusive processes is a challenge. Artificial interface effects, like the trapping effects, may originate at the interface when the kinetic properties of two opposing phases are different. On the other hand, models with prescribed antitrapping terms do not necessarily guarantee the thermodynamics variational nature of the model. This issue has been solved for liquid-solid interfaces via the development of the variational quantitative solidification phase-field model. However, there is no related work addressing the interfaces in nonisothermal sintering, where the free surfaces between the solid phase and surrounding pore regions exhibit strong asymmetry of mass and thermal properties. Also, additional challenges arise due to the conserved order parameter describing the free surfaces. In this work, we present a variational and quantitative phase-field model for nonisothermal sintering processes. The model is derived via an extended nondiagonal phase-field model. The model evolution equations have naturally cross-coupling terms between the conserved kinetics (i.e., mass and thermal transfer) and the nonconserved one (grain growth). These terms are shown via asymptotic analysis to be instrumental in ensuring the elimination of interface artifacts, while also examined to not modify the thermodynamic equilibrium condition (characterized by a dihedral angle). Moreover, we demonstrate that the trapping effects and the existence of surface diffusion in conservation laws are direction-dependent. An anisotropic interpolation scheme of the kinetic mobilities that differentiates between the normal and tangential directions along the interface is discussed. Numerically, we demonstrate the importance of the cross-couplings and the anisotropic interpolation by presenting thermal-microstructural evolutions.

Long-term SARS-CoV-2 neutralizing antibody level prediction using multimodal deep learning: A prospective cohort study on longitudinal data in Wuhan, China.

The ongoing epidemic of SARS-CoV-2 is taking a substantial financial and health toll on people worldwide. Assessing the level and duration of SARS-CoV-2 neutralizing antibody (Nab) would provide key information for government to make sound healthcare policies. Assessed at 3-, 6-, 12-, and 18-month postdischarge, we described the temporal change of IgG levels in 450 individuals with moderate to critical COVID-19 infection. Moreover, a data imputation framework combined with a novel deep learning model was implemented to predict the long-term Nab and IgG levels in these patients. Demographic characteristics, inspection reports, and CT scans during hospitalization were used in this model. Interpretability of the model was further validated with Shapely Additive exPlanation (SHAP) and Gradient-weighted Class Activation Mapping (GradCAM). IgG levels peaked at 3 months and remained stable in 12 months postdischarge, followed by a significant decline in 18 months postdischarge. However, the Nab levels declined from 6 months postdischarge. By training on the cohort of 450 patients, our long-term antibody prediction (LTAP) model could predict long-term IgG levels with relatively high area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1-score, which far exceeds the performance achievable by commonly used models. Several prognostic factors including FDP levels, the percentages of T cells, B cells and natural killer cells, older age, sex, underlying diseases, and so forth, served as important indicators for IgG prediction. Based on these top 15 prognostic factors identified in IgG prediction, a simplified LTAP model for Nab level prediction was established and achieved an AUC of 0.828, which was 8.9% higher than MLP and 6.6% higher than LSTM. The close correlation between IgG and Nab levels making it possible to predict long-term Nab levels based on the factors selected by our LTAP model. Furthermore, our model identified that coagulation disorders and excessive immune response, which indicate disease severity, are closely related to the production of IgG and Nab. This universal model can be used as routine discharge tests to identify virus-infected individuals at risk for recurrent infection and determine the optimal timing of vaccination for general populations.

Identification of survival-associated biomarkers based on three datasets by bioinformatics analysis in gastric cancer.

BACKGROUND: Gastric cancer (GC) is one of the most common malignant tumors with poor prognosis in terms of advanced stage. However, the survival-associated biomarkers for GC remains unclear. AIM: To investigate the potential biomarkers of the prognosis of patients with GC, so as to provide new methods and strategies for the treatment of GC. METHODS: RNA sequencing data from The Cancer Genome Atlas (TCGA) database of STAD tumors, and microarray data from Gene Expression Omnibus (GEO) database (GSE19826, GSE79973 and GSE29998) were obtained. The differentially expressed genes (DEGs) between GC patients and health people were picked out using R software (x64 4.1.3). The intersections were underwent between the above obtained co-expression of differential genes (co-DEGs) and the DEGs of GC from Gene Expression Profiling Interactive Analysis database, and Gene Ontology (GO) analysis, Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis, Gene Set Enrichment Analysis (GSEA), Protein-protein Interaction (PPI) analysis and Kaplan-Meier Plotter survival analysis were performed on these DEGs. Using Immunohistochemistry (IHC) database of Human Protein Atlas (HPA), we verified the candidate Hub genes. RESULTS: With DEGs analysis, there were 334 co-DEGs, including 133 up-regulated genes and 201 down-regulated genes. GO enrichment analysis showed that the co-DEGs were involved in biological process, cell composition and molecular function pathways. KEGG enrichment analysis suggested the co-DEGs pathways were mainly enriched in ECM-receptor interaction, protein digestion and absorption pathways, etc. GSEA pathway analysis showed that co-DEGs mainly concentrated in cell cycle progression, mitotic cell cycle and cell cycle pathways, etc. PPI analysis showed 84 nodes and 654 edges for the co-DEGs. The survival analysis illustrated 11 Hub genes with notable significance for prognosis of patients were screened. Furtherly, using IHC database of HPA, we confirmed the above candidate Hub genes, and 10 Hub genes that associated with prognosis of GC were identified, namely BGN, CEP55, COL1A2, COL4A1, FZD2, MAOA, PDGFRB, SPARC, TIMP1 and VCAN. CONCLUSION: The 10 Hub genes may be the potential biomarkers for predicting the prognosis of GC, which can provide new strategies and methods for the diagnosis and treatment of GC.

Content-Adaptive Auto-Occlusion Network for Occluded Person Re-Identification.

The occluded person re-identification (ReID) aims to match person images captured in severely occluded environments. Current occluded ReID works mostly rely on auxiliary models or employ a part-to-part matching strategy. However, these methods may be sub-optimal since the auxiliary models are constrained by occlusion scenes and the matching strategy will deteriorate when both query and gallery set contain occlusion. Some methods attempt to solve this problem by applying image occlusion augmentation (OA) and have shown great superiority in their effectiveness and lightness. But there are two defects that existed in the previous OA-based method: 1) The occlusion policy is fixed throughout the entire training and cannot be dynamically adjusted based on the current training status of the ReID network. 2) The position and area of the applied OA are completely random, without reference to the image content to choose the most suitable policy. To address these challenges, we propose a novel Content-Adaptive Auto-Occlusion Network (CAAO), that is able to dynamically select the proper occlusion region of an image based on its content and the current training status. Specifically, CAAO consists of two parts: the ReID network and the Auto-Occlusion Controller (AOC) module. AOC automatically generates the optimal OA policy based on the feature map extracted from the ReID network and applies occlusion on the images for ReID network training. An on-policy reinforcement learning based alternating training paradigm is proposed to iteratively update the ReID network and AOC module. Comprehensive experiments on occluded and holistic person ReID benchmarks demonstrate the superiority of CAAO.

CycMuNet+: Cycle-Projected Mutual Learning for Spatial-Temporal Video Super-Resolution.

Spatial-Temporal Video Super-Resolution (ST-VSR) aims to generate high-quality videos with higher resolution (HR) and higher frame rate (HFR). Quite intuitively, pioneering two-stage based methods complete ST-VSR by directly combining two sub-tasks: Spatial Video Super-Resolution (S-VSR) and Temporal Video Super-Resolution (T-VSR) but ignore the reciprocal relations among them. 1) T-VSR to S-VSR: temporal correlations help accurate spatial detail representation; 2) S-VSR to T-VSR: abundant spatial information contributes to the refinement of temporal prediction. To this end, we propose a one-stage based Cycle-projected Mutual learning network (CycMuNet) for ST-VSR, which makes full use of spatial-temporal correlations via the mutual learning between S-VSR and T-VSR. Specifically, we propose to exploit the mutual information among them via iterative up- and down projections, where spatial and temporal features are fully fused and distilled, helping high-quality video reconstruction. In addition, we also show interesting extensions for efficient network design (CycMuNet+), such as parameter sharing and dense connection on projection units and feedback mechanism in CycMuNet. Besides extensive experiments on benchmark datasets, we also compare our proposed CycMuNet (+) with S-VSR and T-VSR tasks, demonstrating that our method significantly outperforms the state-of-the-art methods.