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Background: Lipid metabolism has been implicated in a variety of normal cellular processes and strongly related to the development of multiple diseases, including tumor. Tumor-associated macrophage (TAM) has emerged as a crucial regulator in tumorigenesis and promising target for tumor treatment.Aim of review: A thorough understanding of TAM lipid metabolism and its value in tumorigenesis may provide new ideas for TAM-based anti-tumor therapy.Key scientific concepts of review: TAMs can be divided into two main types, M1-like TAMs and M2-like TAMs, which play anti-tumor and pro-tumor functions in tumor occurrence and development, respectively. Accumulating evidence has shown that lipid metabolic reprogramming, including fatty acid uptake and utilization, cholesterol expulsion, controls the polarization of TAMs and affects the tumorgenesis. These advances in uncovering the intricacies of lipid metabolism and TAMs have yielded new insights on tumor development and treatment. In this review, we aim to provide an update on the current understanding of the lipid metabolic reprogramming made by TAMs to adapt to the harsh tumor microenvironment (TME). In particular, we emphasize that there is complex lipid metabolism connections between TAMs and distinct tumors, which influences TAM to bias from M1 to M2 phenotype in tumor progression, and ultimately promotes tumor occurrence and development. Finally, we discuss the existing issues on therapeutic strategies by reprogramming TAMs based on lipid metabolism regulation (or increasing the ratio of M1/M2-like TAMs) that could be applied in the future to clinical tumor treatment.

Abstract The involvement of B lymphocytes in the pathogenesis of rheumatoid arthritis (RA) is well-established, with their early and aberrant activation being a crucial factor. However, the mechanisms underlying this abnormal activation in RA remain incompletely understood. In this study, we identified a significant reduction in MAPK4 expression in both RA patients and collagen-induced arthritis (CIA) mouse models, which correlates with disrupted B cell activation. Using MAPK4 knockout (KO) mice, we demonstrated that MAPK4 intrinsically promotes the differentiation of marginal zone (MZ) B cells. Loss of MAPK4 in KO mice enhances proximal BCR signaling and activates the PI3K-AKT-mTOR pathway, leading to heightened B cell proliferation. Notably, B cells from MAPK4 KO mice produce significantly higher levels of IL-6, a key pro-inflammatory cytokine in RA. Furthermore, MAPK4 KO mice exhibit impaired T cell-independent humoral immune responses. Mechanistically, MAPK4 inhibits the activation of the PI3K signaling pathway in B cells by activating the IRF4-SHIP1 pathway. Treatment with the MAPK4 agonist Vacquinol-1 enhances MZ B cell differentiation in WT mice and reduces IL-6 secretion in CIA mouse models. In summary, this study reveals the diverse roles of MAPK4 in regulating of B cell functions, with potential implications for developing therapeutic strategies for RA and related autoimmune diseases.

Abstract Background Several approaches are being explored for engineering off-the-shelf chimeric antigen receptor (CAR) T cells. In this study, we engineered chimeric Fcγ receptor (FcγR) T cells and tested their potential as a versatile platform for universal T cell therapy. Methods Chimeric FcγR (CFR) constructs were generated using three distinct forms of FcγR, namely CD16A, CD32A, and CD64. The functionality of CFR T cells was evaluated through degranulation assays, specific target lysis experiments, in vitro cytokine production analysis, and assessment of tumor xenograft destruction specificity in mouse models using different monoclonal antibodies (MoAbs). Results Three types of CFR T cells were engineered, 16s3, 32-8a, 64-8a CFR T cells. In the presence of rituximab (RTX), cytotoxicity of all three types of CFR T cells against CD20+ Raji-wt, K562-CD20+, and primary tumor cells was significantly higher than that of the mock T cells (P

Abstract Diagnosing pneumoconiosis is challenging because the lesions are not easily visible on chest X-rays, and the images often lack clear details. Existing deep detection models utilize Feature Pyramid Networks (FPNs) to identify objects at different scales. However, they struggle with insufficient perception of small targets and gradient inconsistency in medical image detection tasks, hindering the full utilization of multi-scale features. To address these issues, we propose an Optimized Multi-Scale Feature Fusion learning framework, OMSF2, which includes the following components: (1) Data specificity augmentation module is introduced to capture intrinsic data representations and introduce diversity by learning morphological variations and lesion locations. (2) Multi-scale feature learning module is utilized that refines micro-feature localization guided by heatmaps, enabling full extraction of multi-directional features of subtle diffuse targets. (3) Multi-scale feature fusion module is employed that facilitates the fusion of high-level and low-level features to better understand subtle differences between disease stages. Notably, this paper innovatively proposes a method for fine learning of low-resolution micro-features in pneumoconiosis, addressing the issue of maintaining cross-layer gradient consistency under multi-scale feature fusion. We established an enhanced pneumoconiosis X-ray dataset to optimize the lesion detection capability of the OMSF2 model. We also introduced an external dataset to evaluate other chest X-rays with complex lesions. On the AP-50 and R-50 evaluation metrics, OMSF2 improved by 3.25% and 3.31% on the internal dataset, and by 2.28% and 0.24% on the external dataset, respectively. Experimental results show that OMSF2 achieves significantly better performance than state-of-the-art baselines in medical image detection tasks.

BackgroundThe prompt and accurate identification of mild cognitive impairment (MCI) is crucial for preventing its progression into more severe neurodegenerative diseases. However, current diagnostic solutions, such as biomarkers and cognitive screening tests, prove costly, time-consuming, and invasive, hindering patient compliance and the accessibility of these tests. Therefore, exploring a more cost-effective, efficient, and noninvasive method to aid clinicians in detecting MCI is necessary. ObjectiveThis study aims to develop an ensemble learning framework that adaptively integrates multimodal physiological data collected from wearable wristbands and digital cognitive metrics recorded on tablets, thereby improving the accuracy and practicality of MCI detection. MethodsWe recruited 843 participants aged 60 years and older from the geriatrics and neurology departments of our collaborating hospitals, who were randomly divided into a development dataset (674/843 participants) and an internal test dataset (169/843 participants) at a 4:1 ratio. In addition, 226 older adults were recruited from 3 external centers to form an external test dataset. We measured their physiological signals (eg, electrodermal activity and photoplethysmography) and digital cognitive parameters (eg, reaction time and test scores) using the clinically certified Empatica 4 wristband and a tablet cognitive screening tool. The collected data underwent rigorous preprocessing, during which features in the time, frequency, and nonlinear domains were extracted from individual physiological signals. To address the challenges (eg, the curse of dimensionality and increased model complexity) posed by high-dimensional features, we developed a dynamic adaptive feature selection optimization algorithm to identify the most impactful subset of features for classification performance. Finally, the accuracy and efficiency of the classification model were improved by optimizing the combination of base learners. ResultsThe experimental results indicate that the proposed MCI detection framework achieved classification accuracies of 88.4%, 85.5%, and 84.5% on the development, internal test, and external test datasets, respectively. The area under the curve for the binary classification task was 0.945 (95% CI 0.903-0.986), 0.912 (95% CI 0.859-0.965), and 0.904 (95% CI 0.846-0.962) on these datasets. Furthermore, a statistical analysis of feature subsets during the iterative modeling process revealed that the decay time of skin conductance response, the percentage of continuous normal-to-normal intervals exceeding 50 milliseconds, the ratio of low-frequency to high-frequency (LF/HF) components in heart rate variability, and cognitive time features emerged as the most prevalent and effective indicators. Specifically, compared with healthy individuals, patients with MCI exhibited a longer skin conductance response decay time during cognitive testing (P

ABSTRACT Maintaining a healthy status is crucial for the successful captive breeding of critically endangered Rhinopithecus brelichi, it is conducive to ex situ conservation of this species and rejuvenation of its population. However, changes in the feeding environment and food can affect the composition and function of the gut microbiota in R. brelichi, ultimately impacting its health and adaptation. Herein, 16S rRNA gene sequencing was employed to determine the gut microbiota composition and functional variations between wild and captive R. brelichi populations. The results showed that the captive group had higher alpha diversity than the wild group, and significant differences were observed in their beta diversity. Captive and wild R. brelichi showed similar microbiota at the phylum level, which mainly comprised Firmicutes, Bacteroidota, and Spirochaetota, but captivity reduced the Firmicutes/Bacteroides ratio. Differential abundance analysis revealed that the relative abundance of microbiota related to cellulose degradation, such as Prevotellaceae_UCG_001, Christensenellaceae_R_7_group, Ruminococcus, and Fibrobacter, differed significantly between the two groups. Furthermore, the potential pathogens Acinetobacter and Treponema were significantly abundant in wild and captive groups, respectively. Functional predictions demonstrated that the most significant functional pathways at the second level between captive and wild monkeys were carbohydrate, amino acid, and lipid metabolisms. The captive monkeys exhibited higher digestive capacity and endocrine regulation as well as a higher risk of infectious diseases than wild monkeys. These findings can serve as a valuable theoretical basis for promoting the healthy breeding of R. brelichi and as a guide for future evaluation of the health of wild and captive monkeys.

The widespread presence of polycyclic aromatic hydrocarbons (PAHs) and toxic heavy metals in soils is having harmful effects on food crops and the environment. However, the defense mechanisms and capacity of plants to counteract these substances have not been comprehensively explored, necessitating a systematic categorization of their inhibitory effects. Accordingly, an experimental investigation was conducted to examine the growth and physiological response of maize (Zea mays L.) to different concentrations and combinations of pyrene, copper (Cu), and cadmium (Cd), with an indicator developed to assess the joint stress. The results showed that 57-day culture with contaminations significantly inhibited the plant biomass via causing root cell necrosis, inducing lipid peroxidation, and damaging photosynthesis. Cd (50-100 mg/kg) induced stronger inhibition than Cu (800-1000 mg/kg) under both single and joint stress, and their co-existence further aggravated the adverse effects and generated synergetic inhibition. Although the presence of pyrene at a low concentration (5-50 mg/kg) can somewhat diminish the metal stress, the elevated pollutant concentrations (400-750 mg/kg pyrene, 50-100 mg/kg Cd, and 800-1000 mg/kg Cu) switched the antagonistic effect to additive inhibition on maize growth. A satisfactory tolerance of a low-level pyrene and/or metal stress was determined, associated with a relative stability of chlorophyll-a (Chl-a) content and antioxidant enzymes activity. Nevertheless, the photosynthesis and antioxidant system were significantly damaged with increasing contaminant concentrations, resulting in chlorosis and biomass reduction. These findings could provide valuable knowledge for ensuring crop yield and food quality as well as implementing soil phytoremediation.

Men who have sex with men and people living with HIV are disproportionately affected in the 2022 multi-country monkeypox epidemic. The smallpox vaccine can induce cross-reactive antibodies against the monkeypox virus (MPXV) and reduce the risk of infection. Data on antibodies against MPXV induced by historic smallpox vaccination in people with HIV are scarce. In this observational study, plasma samples were collected from people living with and without HIV in Shenzhen, China. We measured antibodies binding to two representative proteins of vaccinia virus (VACV; A27L and A33R) and homologous proteins of MPXV (A29L and A35R) using an enzyme-linked immunosorbent assay. We compared the levels of these antibodies between people living with and without HIV. Stratified analyses were performed based on the year of birth of 1981 when the smallpox vaccination was stopped in China. Plasma samples from 677 people living with HIV and 746 people without HIV were tested. A consistent pattern was identified among the four antibodies, regardless of HIV status. VACV antigen-reactive and MPXV antigen-reactive antibodies induced by historic smallpox vaccination were detectable in the people born before 1981, and antibody levels reached a nadir during or after 1981. The levels of smallpox vaccine-induced antibodies were comparable between people living with HIV and those without HIV. Our findings suggest that the antibody levels against MPXV decreased in both people living with and without HIV due to the cessation of smallpox vaccination.

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is an effective measure for the treatment of hematological disease. With the progress and wide use of allo-HSCT, post-transplant central nervous system complications (CNSC) have gotten more and more attention because of its poor prognosis and high mortality. Since there is no standard treatment for patients with immune-mediated CNSC currently, new treatments are needed to be developed urgently. Here, we attempted a novel therapy regimen of intrathecal tocilizumab injection in two pediatric patients with immune-mediated CNSC manifested as cytokine release syndrome (CRS) after haploidentical hematopoietic stem cell transplantation (halpo-HSCT). In the two patients, persistent seizure symptoms could not be resolved 7 h after intravenous tocilizumab, while the symptoms were controlled rapidly only 2 hours or 1 hour after the first intrathecal injection of tocilizumab. Moreover, the level of interleukin 6 in the cerebrospinal fluid returned to normal after the fifth intrathecal injection. Even more appealing, no acute or chronic adverse reactions were observed during injection and subsequent follow-up. In conclusion, intrathecal tocilizumab seems to be more rapid and effective than intravenous administration for immune-mediated CNSC manifested as CRS in haplo-HSCT recipients. We recommend this treatment modality for further investigation.

The gut microbiota offers numerous benefits to the human body, including the promotion of nutrient absorption, participation in metabolic processes, and enhancement of immune function. Recent studies have introduced the concept of the gut-organ axis, which encompasses interactions such as the gut-brain axis, gut-liver axis, and gut-lung axis. This concept underscores the complex interplay between gut microbiota and various organs and tissues, including the brain, heart, lungs, liver, kidneys, muscles, and bones. Growing evidence indicates that gut microbiota can influence the onset and progression of multi-organ system diseases through their effects on the gut-organ axis. Traditional Chinese medicine has demonstrated significant efficacy in regulating the gastrointestinal system, leveraging its unique advantages. Considerable advancements have been made in understanding the role of gut microbiota and the gut-organ axis within the mechanisms of action of traditional Chinese medicine. This review aims to elucidate the roles of gut microbiota and the gut-organ axis in human health, explore the potential connections between traditional Chinese medicine and gut microbiota, and examine the therapeutic effects of traditional Chinese medicine on the microbiota-gut-organ axis. Furthermore, the review addresses the limitations and challenges present in current research while proposing potential directions for future investigations in this area.

Inflammation is the response of the human body to injury, infection, or other abnormal states, which is involved in the development of many diseases. As a member of the Krüppel-like transcription factors (KLFs) family, KLF4 plays a crucial regulatory role in physiological and pathological processes due to its unique dual domain of transcriptional activation and inhibition. A growing body of evidence has demonstrated that KLF4 plays a pivotal role in the pathogenesis of various inflammatory disorders, including inflammatory bowel disease, osteoarthritis, renal inflammation, pneumonia, neuroinflammation, and so on. Consequently, KLF4 has emerged as a promising new therapeutic target for inflammatory diseases. This review systematically generalizes the molecular regulatory network, specific functions, and mechanisms of KLF4 to elucidate its complex roles in inflammatory diseases. An in-depth study on the biological function of KLF4 is anticipated to offer a novel research perspective and potential intervention strategies for inflammatory diseases.

Introduction: The epithelial immunomodulation and regeneration are intrinsic critical events against inflammatory bowel disease (IBD). MiR-7 is well documented as a promising regulator in the development of various diseases including inflammatory diseases. Objectives: This study aimed to assess the effect of miR-7 in intestinal epithelial cells (IECs) in IBD. Methods: MiR-7def mice were given dextran sulfate sodium (DSS) to induce enteritis model. The infiltration of inflammatory cells was measured by FCM and immunofluorescence assay. 5′deletion assay and EMSA assays were performed to study the regulatory mechanism of miR-7 expression in IECs. The inflammatory signals and the targets of miR-7 were analyzed by RNA-seq and FISH assay. IECs were isolated from miR-7def, miR-7oe and WT mice to identify the immunomodulation and regeneration capacity. IEC-specific miR-7 silencing expression vector was designed and administered by the tail vein into murine DSS-induced enteritis model to evaluate the pathological lesions of IBD. Results: We found miR-7 deficiency improved the pathological lesions of DSS-induced murine enteritis model, accompanied by elevated proliferation and enhanced transduction of NF-κB/AKT/ERK signals in colonic IECs, as well as decreased local infiltration of inflammatory cells. MiR-7 was dominantly upregulated in colonic IECs in colitis. Moreover, the transcription of pre-miR-7a-1, orchestrated by transcription factor C/EBPα, was a main resource of mature miR-7 in IECs. As for the mechanism, EGFR, a miR-7 target gene, was downregulated in colonic IECs in colitis model and Crohn's disease patients. Furthermore, miR-7 also controlled the proliferation and inflammatory-cytokine secretion of IECs in response to inflammatory-signals through EGFR/NF-κB/AKT/ERK pathway. Finally, IEC-specific miR-7 silencing promoted the proliferation and transduction of NF-κB pathway in IECs and alleviated the pathological damage of colitis. Conclusion: Our results present the unknown role of miR-7/EGFR axis in IEC immunomodulation and regeneration in IBD and might provide clues for the application of miRNA-based therapeutic strategies in colonic diseases.

Abstract Gastrointestinal cancer, one of the most common cancers, continues to be a major cause of mortality and morbidity globally. Accumulating evidence has shown that alterations in mitochondrial energy metabolism are involved in developing various clinical diseases. NADH dehydrogenase 1 alpha subcomplex 4 (NDUFA4), encoded by the NDUFA4 gene located on human chromosome 7p21.3, is a component of mitochondrial respiratory chain complex IV and integral to mitochondrial energy metabolism. Recent researchers have disclosed that NDUFA4 is implicated in the pathogenesis of various diseases, including gastrointestinal cancer. Aberrant expression of NDUFA4 leads to the alteration in mitochondrial energy metabolism, thereby regulating the growth and metastasis of cancer cells, indicating that it might be a new promising target for cancer intervention. This article comprehensively reviews the structure, regulatory mechanism, and biological function of NDUFA4. Of note, the expression and roles of NDUFA4 in gastrointestinal cancer including colorectal cancer, liver cancer, gastric cancer, and so on were discussed. Finally, the existing problems of NDUFA4-based intervention on gastrointestinal cancer are discussed to provide help to strengthen the understanding of the carcinogenesis of gastrointestinal cancer, as well as the development of new strategies for clinical intervention.

Mitochondrial metabolism plays a pivotal role in regulating the synthesis of secondary metabolites, which are crucial for the survival and adaptation of organisms. These metabolites are synthesized during specific growth stages or in response to environmental stress, reflecting the organism’s ability to adapt to changing conditions. Mitochondria, while primarily known for their role in energy production, directly regulate secondary metabolite biosynthesis by providing essential precursor molecules, energy, and reducing equivalents necessary for metabolic reactions. Furthermore, they indirectly influence secondary metabolism through intricate signaling pathways, including reactive oxygen species (ROS), metabolites, and redox signaling, which modulate various metabolic processes. This review explores recent advances in understanding the molecular mechanisms governing mitochondrial metabolism and their regulatory roles in secondary metabolite biosynthesis, which highlights the involvement of transcription factors, small RNAs, and post-translational mitochondrial modifications in shaping these processes. By integrating current insights, it aims to inspire future research into mitochondrial regulatory mechanisms in Arabidopsis thaliana, Solanum tuberosum, Nicotiana tabacum, and others that may enhance their secondary metabolite production. A deeper understanding of the roles of mitochondria in secondary metabolism could contribute to the development of new approaches in biotechnology applications.

Cancer is a serious global public health issue, and a great deal of research has been made to treat cancer. Of these, discovery of promising compounds that effectively fight cancer always has been the main point of interest in pharmaceutical research. Carnosic acid (CA) is a phenolic diterpenoid compound widely present in Lamiaceae plants such as Rosemary (Rosmarinus officinalis L.). In recent years, there has been increasing evidence that CA has significant anti-cancer activity, such as leukaemia, colorectal cancer, breast cancer, lung cancer, liver cancer, pancreatic cancer, stomach cancer, lymphoma, prostate cancer, oral cancer, etc. The potential mechanisms involved by CA, including inhibiting cell proliferation, inhibiting metastasis, inducing cell apoptosis, stimulating autophagy, regulating the immune system, reducing inflammation, regulating the gut microbiota, and enhancing the effects of other anti-cancer drugs. This article reviews the biosynthesis, pharmacokinetics and metabolism, safety and toxicity, as well as the molecular mechanisms and signaling pathways of the anticancer activity of CA. This will contribute to the development of CA or CA-containing functional foods for the prevention and treatment of cancer, providing important advances in the advancement of cancer treatment strategies.

IntroductionPathologists rely on whole slide images (WSIs) to diagnose cancer by identifying tumor cells and subtypes. Deep learning models, particularly weakly supervised ones, classify WSIs using image tiles but may overlook false positives and negatives due to the heterogeneous nature of tumors. Both cancerous and healthy cells can proliferate in patterns that extend beyond individual tiles, leading to errors at the tile level that result in inaccurate tumor-level classifications.MethodsTo address this limitation, we introduce NATMIL (Neighborhood Attention Transformer Multiple Instance Learning), which utilizes the Neighborhood Attention Transformer to incorporate contextual dependencies among WSI tiles. NATMIL enhances multiple instance learning by integrating a broader tissue context into the model. Our approach enhances the accuracy of tumor classification by considering the broader tissue context, thus reducing errors associated with isolated tile analysis.ResultsWe conducted a quantitative analysis to evaluate NATMIL’s performance against other weakly supervised algorithms. When applied to subtyping non-small cell lung cancer (NSCLC) and lymph node (LN) tumors, NATMIL demonstrated superior accuracy. Specifically, NATMIL achieved accuracy values of 89.6% on the Camelyon dataset and 88.1% on the TCGA-LUSC dataset, outperforming existing methods. These results underscore NATMIL’s potential as a robust tool for improving the precision of cancer diagnosis using WSIs.DiscussionOur findings demonstrate that NATMIL significantly improves tumor classification accuracy by reducing errors associated with isolated tile analysis. The integration of contextual dependencies enhances the precision of cancer diagnosis using WSIs, highlighting NATMILs´ potential as a robust tool in pathology.

BackgroundEarly detection of cognitive impairment or dementia is essential to reduce the incidence of severe neurodegenerative diseases. However, currently available diagnostic tools for detecting mild cognitive impairment (MCI) or dementia are time-consuming, expensive, or not widely accessible. Hence, exploring more effective methods to assist clinicians in detecting MCI is necessary. ObjectiveIn this study, we aimed to explore the feasibility and efficiency of assessing MCI through movement kinetics under tablet-based “drawing and dragging” tasks. MethodsWe iteratively designed “drawing and dragging” tasks by conducting symposiums, programming, and interviews with stakeholders (neurologists, nurses, engineers, patients with MCI, healthy older adults, and caregivers). Subsequently, stroke patterns and movement kinetics were evaluated in healthy control and MCI groups by comparing 5 categories of features related to hand motor function (ie, time, stroke, frequency, score, and sequence). Finally, user experience with the overall cognitive screening system was investigated using structured questionnaires and unstructured interviews, and their suggestions were recorded. ResultsThe “drawing and dragging” tasks can detect MCI effectively, with an average accuracy of 85% (SD 2%). Using statistical comparison of movement kinetics, we discovered that the time- and score-based features are the most effective among all the features. Specifically, compared with the healthy control group, the MCI group showed a significant increase in the time they took for the hand to switch from one stroke to the next, with longer drawing times, slow dragging, and lower scores. In addition, patients with MCI had poorer decision-making strategies and visual perception of drawing sequence features, as evidenced by adding auxiliary information and losing more local details in the drawing. Feedback from user experience indicates that our system is user-friendly and facilitates screening for deficits in self-perception. ConclusionsThe tablet-based MCI detection system quantitatively assesses hand motor function in older adults and further elucidates the cognitive and behavioral decline phenomenon in patients with MCI. This innovative approach serves to identify and measure digital biomarkers associated with MCI or Alzheimer dementia, enabling the monitoring of changes in patients’ executive function and visual perceptual abilities as the disease advances.

An accurate ultra-short-term time series prediction of a power load is an important guarantee for power dispatching and the safe operation of power systems. Problems of the current ultra-short-term time series prediction algorithms include low prediction accuracy, difficulty capturing the local mutation features, poor stability, and others. From the perspective of series decomposition, a multi-scale sequence decomposition model (TFDNet) based on power spectral density and the Morlet wavelet transform is proposed that combines the multidimensional correlation feature fusion strategy in the time and frequency domains. By introducing the time-frequency energy selection module, the "prior knowledge" guidance module, and the sequence denoising decomposition module, the model not only effectively delineates the global trend and local seasonal features, completes the in-depth information mining of the smooth trend and fluctuating seasonal features, but more importantly, realizes the accurate capture of the local mutation seasonal features. Finally, on the premise of improving the forecasting accuracy, single-point load forecasting and quantile probabilistic load forecasting for ultra-short-term load forecasting are realized. Through the experiments conducted on three public datasets and one private dataset, the TFDNet model reduces the mean square error (MSE) and mean absolute error (MAE) by 19.80 and 11.20% on average, respectively, as compared with the benchmark method. These results indicate the potential applications of the TFDNet model.

Abstract Novel methods are required to enhance lung cancer detection, which has overtaken other cancer-related causes of death as the major cause of cancer-related mortality. Radiologists have long-standing methods for locating lung nodules in patients with lung cancer, such as computed tomography (CT) scans. Radiologists must manually review a significant amount of CT scan pictures, which makes the process time-consuming and prone to human error. Computer-aided diagnosis (CAD) systems have been created to help radiologists with their evaluations in order to overcome these difficulties. These systems make use of cutting-edge deep learning architectures. These CAD systems are designed to improve lung nodule diagnosis efficiency and accuracy. In this study, a bespoke convolutional neural network (CNN) with a dual attention mechanism was created, which was especially crafted to concentrate on the most important elements in images of lung nodules. The CNN model extracts informative features from the images, while the attention module incorporates both channel attention and spatial attention mechanisms to selectively highlight significant features. After the attention module, global average pooling is applied to summarize the spatial information. To evaluate the performance of the proposed model, extensive experiments were conducted using benchmark dataset of lung nodules. The results of these experiments demonstrated that our model surpasses recent models and achieves state-of-the-art accuracy in lung nodule detection and classification tasks.

Any large city mayor or public administrator has a sense that the world with which his/her city must interface is turbulent and changing exponentially faster. Advancing communication, transportation and other technologies have made the people of the world increasingly interconnected. Immediate crises such as financial collapse, pandemics, climate change and military tensions bring the negative side of this global phenomenon to the fore. Nevertheless, even in their absence, other incipient, but persistent, and portentous issues are increasingly demanding of public action. Simultaneously, pressure to engage all citizens in public policy debates and decision-making is growing and becoming central to trust in government and the survival of democracies.The collision of the two forces, turbulence v. demand for increasing public debate and involvement, gives further salience to the notion that science-based technologies are charging ahead while the development of social technologies, such as democratic institutions, are lagging.This paper looks at several approaches to citizen engagement: co-production, asset-oriented public management, strategic planning, and participatory modeling. It then addresses various forms of e-governance and investigates how new technologies might be used to expand and improve citizen engagement while allowing the government to deal with the more turbulent environment. Some criticisms and cautions are listed. Finally, the paper investigates how well higher education in public administration is responding to the collision through the offering of specialized courses and programs. The methodology was to catalog the course offerings of the top ranked PA programs. The conclusion is that these programs are not adequately responding to the issues presented in this paper.

Vehicle-to-Vehicle (V2V) energy trading has emerged as a promising scheme to relieve the load imposed on the grid without intermediaries. The blockchain has always been regarded as the most potential solution for addressing the security and privacy issues of the Internet of Electric Vehicles (IoEV). The consensus mechanism is the core of the blockchain, it determines the security, efficiency, and scalability of the system. However, consensus mechanisms currently employed in V2V energy trading are traditional algorithms, which are unsuitable for the IoEV due to their high computational power and communication overhead. Therefore, we are motivated to propose a Block Alliance Consensus (BAC) mechanism based on Hashgraph. BAC can maintain the high throughput of the Hashgraph, and it solves the problem that the Hashgraph cannot support the dynamic addition and deletion of nodes if it is directly applied to V2V energy trading. We utilize the sharding technique, and each shard generates a consortium blockchain in accordance with the characteristic of the IoEV. The centralized component is retained to ensure macroeconomic control by state and local governments. We design a cryptography-based leader election combined with a reputation incentive mechanism to fairly elect centralized leader committee and motivate honest electric vehicles (EVs). We implement the BAC consensus and V2V energy trading blockchain (ETB) on the Hyperledger Fabric. The performance and practicality of BAC and the V2V ETB are verified through experiments.

Abstract The emergence of image-based systems to improve diagnostic pathology precision, involving the intent to label sets or bags of instances, greatly hinges on Multiple Instance Learning for Whole Slide Images(WSIs). Contemporary works have shown excellent performance for a neural network in MIL settings. Here, we examine a graph-based model to facilitate end-to-end learning and sample suitable patches using a tile-based approach. We propose MIL-GNN to employ a graph-based Variational Auto-encoder with a Gaussian mixture model to discover relations between sample patches for the purposes to aggregate patch details into an individual vector representation. Using the classical MIL dataset MUSK and distinguishing two lung cancer sub-types, lung cancer called adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), we exhibit the efficacy of our technique. We achieved a 97.42% accuracy on the MUSK dataset and a 94.3% AUC on the classification of lung cancer sub-types utilizing features.

Abstract Epithelial plasticity has been suggested in lungs of mice following genetic depletion of stem cells but is of unknown physiological relevance. Viral infection and chronic lung disease share similar pathological features of stem cell loss in alveoli, basal cell (BC) hyperplasia in small airways, and innate immune activation, that contribute to epithelial remodeling and loss of lung function. We show that a subset of distal airway secretory cells, intralobar serous (IS) cells, are activated to assume BC fates following influenza virus infection. Injury-induced hyperplastic BC (hBC) differ from pre-existing BC by high expression of IL-22Ra1 and undergo IL-22-dependent expansion for colonization of injured alveoli. Resolution of virus-elicited inflammation results in BC to IS re-differentiation in repopulated alveoli, and increased local expression of protective antimicrobial factors, but fails to restore normal alveolar epithelium responsible for gas exchange.

Abstract HMG (high mobility group) proteins are a diverse family of nonhistone chromosomal proteins that interact with DNA and a wide range of transcriptional regulators to regulate the structural architecture of DNA. HMGXB4 (also known as HMG2L1) is an HMG protein family member that contains a single HMG box domain. Our previous studies have demonstrated that HMGXB4 suppresses smooth muscle differentiation and exacerbates endotoxemia by promoting a systemic inflammatory response in mice. However, the expression of Hmgxb4 in vivo has not fully examined. Herein, we generated a mouse model that harbors a gene trap in the form of a lacZ gene insertion into the Hmgxb4 gene. This mouse enables the visualization of endogenous HMGXB4 expression in different tissues via staining for the β‐galactosidase activity of LacZ which is under the control of the endogenous Hmgxb4 gene promoter. We found that HMGXB4 is widely expressed in mouse tissues and is a nuclear protein. Furthermore, the Hmgxb4 gene trap mice exhibit normal cardiac function and blood pressure. Measurement of β‐galactosidase activity in the Hmgxb4 gene trap mice demonstrated that the arterial injury significantly induces Hmgxb4 expression. In summary, the Hmgxb4 gene trap reporter mouse described here provides a valuable tool to examine the expression level of endogenous Hmgxb4 in both physiological and pathological settings in vivo.

A distinct rise of global interest in the role of cultural and creative industries in post-industrial urban development calls for the transformation of our industrial heritage into creative clusters. Despite many factories being removed in deindustrialization, some enterprises still preserve and reuse the former industrial lands. However, the inconclusive judgments on the performances of creative clusters call for an in-depth examination of the underlying mechanism of transforming dilapidated urban industrial spaces. In addition, few empirical studies have revealed the commonalities and differences of market entities employing both the top-down and bottom-up approaches during the redevelopment.In this article, a multilevel transformation, and the consequent spatial performance in three postindustrial lands in Beijing have been examined based on big data analysis, field observation and in-depth interviews. We reached three conclusions. First, in addition to the angle of participatory planning, the deep exploration of specific market entities as an intermediate joint among different parties in protecting and reusing the postindustrial lands uncovers a distinct perspective of urban revitalization. Second, the finding of the enterprises' role in mitigating the conflicts between bottom-up and top-down approaches further testifies to a less-dichotomous perspective in urban regeneration studies. Last, though the examination of the multilevel transformation mechanism reflecting the difference between state and nonstate enterprises in property-led management, a generic paradigm behind the trend of relying on enterprises in revitalizing the large-scale postindustrial lands is revealed.

Liver disease is a common and serious threat to human health. The progression of liver diseases is influenced by many physiologic processes, including oxidative stress, inflammation, bile acid metabolism, and autophagy. Various factors lead to the dysfunction of these processes and basing on the different pathogeny, pathology, clinical manifestation, and pathogenesis, liver diseases are grouped into different categories. Specifically, Sirtuin1 (SIRT1), a member of the sirtuin protein family, has been extensively studied in the context of liver injury in recent years and are confirmed the significant role in liver disease. SIRT1 has been found to play a critical role in regulating key processes in liver injury. Further, SIRT1 seems to cause divers outcomes in different types of liver diseases. Recent studies have showed some therapeutic strategies involving modulating SIRT1, which may bring a novel therapeutic target. To elucidate the mechanisms underlying the role of sirtuin1 in liver injury and its potentiality as a therapeutic target, this review outlines the key signaling pathways associated with sirtuin1 and liver injury, and discusses recent advances in therapeutic strategies targeting sirtuin1 in liver diseases.

Abstract Aim Evaluate the fully online flipped classroom's effects during the pandemic. Design A comparative descriptive study with historical control design. Methods In an internal medicine nursing course, the fully online flipped classroom instruction was used with 53 undergraduates in 2020. Their final examinations were compared with the 50 students taught with traditional offline methods in 2019. Online interactions and students' experiences were described. Pass rates in both classes were over 90% (Χ2 = 0.276, p = 0.60), but the median score in 2019 was higher than in 2020 (Z = −2.491, p = 0.01). There were 996 online interactions and 734 valid interactions in total. All 49 students believed the online flipped classroom schedule was reasonable and all but three said it was helpful. However, 19 students (39%) felt traditional teaching is more effective. Conclusions The fully online flipped classroom method was fairly effective during the pandemic. This model also did increase class participation and sufficient faculty–student interactions in remote education. However, fewer students earned outstanding scores, with possible reasons including the online flipped classroom, lack of clinical practice, stress from COVID‐19 and the shortened exam time. Overall, the method is worth recommending under public health emergencies like COVID‐19, and future research exploring potential concerns about scores is necessary.

Abstract Several bispecific antibodies (bsAbs) targeting BCMA, GPRC5D, and FcRH5 are in clinical trials for heavily pretreated multiple myeloma (MM) patients. Teclistamab was approved for relapsed/refractory MM therapy in 2022, while elranatamab, linvoseltamab, F182112, talquetamab, and cevostamab are currently undergoing clinical trials. This study summarizes several latest reports on bsAbs for the treatment of MM from the ASCO 2023 Annual Meeting.

Abstract Multiple bispecific antibodies (bsAbs) have been approved for cancer immunotherapy. Several CD20 × CD3 bsAbs have demonstrated significant anti-B-cell non-Hodgkin lymphoma (NHL) activity by engaging T cells to target CD20+ NHL cells in clinical trials. Mosunetuzumab, epcoritamab and glofitamab have been approved recently for B-cell NHL therapy. In this study, we summarized several latest reports on CD20 × CD3 bsAbs for the therapy of B-cell NHL from the ASCO 2023 annual meeting (ASCO2023).

Abstract Recently, lung cancer prediction based on imaging genomics has attracted great attention. However, such studies often have many challenges, such as small sample size, high-dimensional information redundancy, and the inefficiency of multimodal fusion. Therefore, in this paper, a deep convolution cascade attention fusion network (DCCAFN) based on imaging genomics is proposed for the prediction of lung cancer patients’ survival. The network consists of three modules: an image feature extraction module (IFEM), a gene feature extraction module (GFEM), and an attention fusion network (AFN). In the IFEM, a pretrained residual network based on transfer learning is used to extract deep image features to fully capture the computed tomography (CT) image information conducive to prognosis prediction. In the GFEM, the F-test is first used for gene screening to eliminate redundant information, and then, a cascade network with the convolution cascade module (CCM) that contains a convolution operation, a pooling operation, and an ensemble forest classifier is designed to better extract the gene features. In the AFN, a bimodal attention fusion mechanism is proposed to fuse deep image features and gene features to improve the performance of predicting lung cancer survival. The experimental results show that the DCCAFN model achieves good performance, and its accuracy and AUC are 0.831 and 0.816, respectively. It indicates that the model is an effective multimodal data fusion method for predicting the survival prognosis of lung cancer, which can greatly help physicians stratify patients' risks, and achieve personalized treatment for improving the quality of patients' lives.

Abstract Aiming at the problems of long time, high cost, invasive sampling damage, and easy emergence of drug resistance in lung cancer gene detection, a reliable and non-invasive prognostic method is proposed. Under the guidance of weakly supervised learning, deep metric learning and graph clustering methods are used to learn higher-level abstract features in CT imaging features. The unlabeled data is dynamically updated through the k-nearest label update strategy, and the unlabeled data is transformed into weak label data and continue to update the process of strong label data to optimize the clustering results and establish a classification model for predicting new subtypes of lung cancer imaging. Five imaging subtypes are confirmed on the lung cancer dataset containing CT, clinical and genetic information downloaded from the TCIA lung cancer database. The successful establishment of the new model has a significant accuracy rate for subtype classification (ACC = 0.9793), and the use of CT sequence images, gene expression, DNA methylation and gene mutation data from the cooperative hospital in Shanxi Province proves the biomedical value of this method. The proposed method also can comprehensively evaluate intratumoral heterogeneity based on the correlation between the final lung CT imaging features and specific molecular subtypes.

The generation of Traditional Chinese Medicine (TCM) prescription is one of the most challenging tasks in the research of intelligent TCM. Current researches usually use transfer learning methods to apply the relevant technology of text generation to this task simply and roughly. Either they need to train a model with large number of standardized data set, or they ignore the domain knowledge and expertise of TCM. In order to solve these problems, we propose a hybrid neural network architecture for TCM prescription generation—PreGenerator. It includes a novel hierarchical retrieval mechanism, which can automatically extract prescription and herbal templates to facilitate accurate clinical prescription generation. Firstly, PreGenerator uses the Symptom-Prescription Retrieval (SHR) module to retrieve the most relevant prescriptions for a given patient’s symptoms. In order to follow the rule of compatibility of herbs, the Herb-Herb Retrieval (HHR) module is introduced to retrieve the next most relevant herb according to the conditioned generated herbs. Finally, the prescription decoder (PreD) fuses the symptom features, the retrieved prescription and herbal template features to generate the most relevant and effective Chinese medicine prescription. The validity of the model is verified by automatic evaluation and manual evaluation on the real medical case dataset. In addition, our model can recommend herbs that do not appear on the prescription label but are useful for relieving symptoms, which shows that our model can learn some interactions between herbs and symptoms. This research also lays a foundation for the future research on intelligent query and prescription generation of traditional Chinese medicine.

Context Zuojin Pill (ZJP) has been used to treat gastrointestinal problems in China for hundreds of years.Objective To discover more potential active ingredients and evaluate the gastroprotective mechanisms of ZJP.Materials and methods An approach involving UPLC-Q-Orbitrap HRMS and serum pharmacochemistry was established to screen the multiple chemical constituents of ZJP. Male Sprague-Dawley (SD) rats were divided into six groups: normal control, ulcer control, omeprazole (30 mg/kg), and three ZJP groups (1.0, 2.0 and 4.0 g/kg). After oral treatment with ZJP or omeprazole for 7 days, all groups except the normal control group were orally administered 5 mL/kg ethanol to induce gastric ulceration. Histopathological assessment of gastric tissue was performed by haematoxylin and eosin staining. Antioxidant parameters and inflammatory mediators were determined using ELISA Kit and immunohistochemical analysis.Results Ninety components were identified in ZJP. Among them, 23 prototypes were found in rat serum after oral administration of ZJP. The ulcer inhibition was over 90.0% for all the ZJP groups. Compared with the ulcer control rats, ZJP (4.0 g/kg) enhanced the antioxidant capacity of gastric tissue: superoxide dismutase (1.33-fold), catalase (2.61-fold), glutathione (2.14-fold), and reduced the malondialdehyde level (0.48-fold). Simultaneously, the ZJP meaningfully lowered the content of tumour necrosis factor-α (0.76-fold), interleukin-6 (0.66-fold), myeloperoxidase (0.21-fold), and nuclear factor kappa B (p65) (0.62-fold).Discussion and conclusions This study showed ZJP could mitigate ethanol-induced rat gastric ulcers, which might benefit from the synergistic actions of multiple ingredients. The findings could support the quality control and clinical trials of ZJP.

The constant presence of infectious diseases poses an everlasting threat to the entire world. In recent years, there has been an increased attention toward the application of traditional Chinese medicine (TCM) in the treatment of emerging infectious diseases, as it has played a significant role. The aim of this article is to provide a concise overview of the roles and mechanisms of TCM in treating infectious diseases. TCM possesses the ability to modulate relevant factors, impede signaling pathways, and inhibit microbial growth, thereby exhibiting potent antiviral, antibacterial, and anti-inflammatory effects that demonstrate remarkable efficacy against viral and bacterial infections. This article concludes that the comprehensive regulatory features of Chinese herbal medicines, with their various components, targets, and pathways, result in synergistic effects. The significance of Chinese herbal medicines in the context of infectious diseases should not be underestimated; however, it is crucial to also acknowledge their underutilization. This paper presents constructive suggestions regarding the challenges and opportunities faced by Chinese medicines. Particularly, it emphasizes the effectiveness and characteristics of Chinese medicines in the treatment of infectious diseases, specifying how these medicines’ active substances can be utilized to target infectious diseases. This perspective is advantageous in facilitating researchers’ pharmacological studies on Chinese medicines, focusing on the specific points of action. The mechanism of action of Chinese herbal medicines in the treatment of infectious diseases is comprehensively elucidated in this paper, providing compelling evidence for the superior treatment of infectious diseases through Chinese medicine. This information is favorable for advancing the development of TCM and its potential applications in the field of infectious diseases.

In the wake of the development of metagenomic, metabolomic, and metatranscriptomic approaches, the intricate interactions between the host and various microbes are now being progressively understood. Numerous studies have demonstrated evident changes in gut microbiota during the process of a variety of diseases, such as diabetes, obesity, aging, and cancers. Notably, gut microbiota is viewed as a potential source of novel therapeutics. Currently, Next-generation probiotics (NGPs) are gaining popularity as therapeutic agents that alter the gut microbiota and affect cancer development. Akkermansia muciniphila (A. muciniphila), a representative commensal bacterium, has received substantial attention over the past decade as a promising NGP. The components and metabolites of A. muciniphila can directly or indirectly affect tumorigenesis, in particular through its effects on antitumor immunosurveillance, including the stimulation of pattern recognition receptors (PRRs), which also leads to better outcomes in a variety of situations, including the prevention and curation of cancers. In this article, we systematically summarize the role of A. muciniphila in tumorigenesis (involving gastrointestinal and non-gastrointestinal cancers) and in tumor therapy. In particular, we carefully discuss some critical scientific issues that need to be solved for the future using A. muciniphila as a representative beneficial bacterium in tumor treatment, which might provide bright clues and assistance for the application of drugs targeting A. muciniphila in clinical oncotherapy.

Emotion is one of the most important higher cognitive functions of the human brain and plays an important role in transaction processing and decisions. In traditional emotion recognition studies, the frequency band features in EEG signals have been shown to have a high correlation with emotion production. However, traditional emotion recognition methods cannot satisfactorily solve the problem of individual differences in subjects and data heterogeneity in EEG, and subject-independent emotion recognition based on EEG signals has attracted extensive attention from researchers. In this paper, we propose a subject-independent emotion recognition model based on adaptive extraction of layer structure based on frequency bands (BFE-Net), which is adaptive in extracting EEG map features through the multi-graphic layer construction module to obtain a frequency band-based multi-graphic layer emotion representation. To evaluate the performance of the model in subject-independent emotion recognition studies, extensive experiments are conducted on two public datasets including SEED and SEED-IV. The experimental results show that in most experimental settings, our model has a more advanced performance than the existing studies of the same type. In addition, the visualization of brain connectivity patterns reveals that some of the findings are consistent with previous neuroscientific validations, further validating the model in subject-independent emotion recognition studies.

To tackle the misdiagnosis and missed diagnosis problem in the process of manual diagnosis and screening due to the variable size of thyroid nodule and fuzzy edge of nodule in ultrasonic image, in this paper, a mutil-scale attention UNet (MSA-UNet) method for ultrasonic segmentation of thyroid nodule was proposed. The algorithm first extracts the characteristic information of thyroid lesions by using cavity convolution with different void rates, and then fuses the characteristic information of different scales to solve the influence of different thyroid nodule sizes on ultrasonic image segmentation. Considering the influence of location relation information learning and deep semantic feature screening on the segmentation model, channel attention mechanism is used to make the network model more focused on more useful feature information, to improve the segmentation accuracy of thyroid nodes and achieve fine segmentation of lesion region. Experimental results show that the recall rate in thyroid ultrasound image data set is 87%, the segmentation accuracy is 86.1%, and the Dice value is 84.6%, higher than those of existing deep learning method. This work can provide a new research idea for the detection and diagnosis of thyroid nodules.

This paper presents a novel Adaptive Mutation Quantum-inspired Squirrel Search Algorithm (AM-QSSA). Firstly, based on the population mutation, a location-update of quantum state correlative and attractor method is proposed. By introducing a random process to modify the sliding factor of the local attractor, the inevitable lack of diversity in the population renewal method is solved. The premature convergence problem of adaptive mutation rate improvement algorithm based on squirrel position update mode is introduced. Meanwhile, the paper decomposes the location update process of the SSA, and improves it with quantum-behavior. Furthermore, it proposes a novel quantum-inspired squirrels search algorithm. This method finds the complementary effect between quantum behavior and squirrel search algorithm, and solves the problem of premature convergence probability of SSA. In addition, it improves population diversity, and achieves the balance between global and local search. The efficiency of the proposed AM-QSSA is evaluated using exploitation analysis, exploration analysis, success rate analysis, convergence rate analysis on classical benchmark functions as well as Congress on Evolutionary Computation (CEC) 2017 test functions. For further study, AM-QSSA optimizes an image registration problem for an extensive study to check its applicability. The results reveal that AM-QSSA is more efficient and stable than SSA. And it is comparable to the most advanced optimization algorithms.

In the application of long-distance transmission pipeline, the explicit mathematical model method may not completely obtain all accurate values since the parameters along the line cannot be measured point by point. A practical method based on CNN-LSTM neural network was proposed. This method uses CNN network to find spatial features and uses LSTM to explore temporal features. The real data collected from operating pipeline are used to train and verify the deep learning model, and then use the model to predict the flow rate, with an error range from 0.3% to 0.7% of main line's flow rate. By continuously comparing the actual with the predicted values in real time, a pipeline leak can be found. In addition, an improved location algorithm based on the curve distance between relevant pressure points was proposed. The field test on actual pipeline shows that the proposed new algorithm has reliable performance and does not generate false alarms during pressure fluctuation such as the operation of pipeline equipment.

Imaging genomics based on artificial intelligence technology has shown great potential in personalized treatment and precision medicine, providing important theoretical basis and technical support for the intelligent diagnosis and prognosis prediction of lung cancer. In order to facilitate the more effective exploration and wider application of diagnosis and prognosis of lung cancer, the relevant research progress is described in detail, expecting that such review will be beneficial to the researchers in this field. First, it introduces the role and application of artificial intelligence in image genomics, and the main research direction of artificial intelligence technology in lung cancer imaging genomics, which are reviewed from lung cancer gene phenotype identification, image gene bidirectional correlation analysis, and prognosis prediction. In addition, the advantages and disadvantages of radiomics and deep learning algorithm are evaluated in each direction, as well as the facing problems and challenges. Second, it summarizes the main challenges facing intelligent imaging genomics at this stage and presents future research directions. Although imaging genomics based on artificial intelligence has made some achievements in lung cancer diagnosis, survival recurrence prediction, efficacy response evaluation, and understanding tumor biological mechanism, it also needs to combining the actual clinical needs, establish a unified, standard, perfect computer-assisted clinical program further explore interpretability, reproducibility, and universal verification for the application of artificial intelligence in lung cancer imaging genomics, so as to provide guarantee for the intelligent diagnosis treatment and evaluation of lung cancer.

In recent years, inducing pluripotent stem cells to differentiate into functional primordial germ cells (PGCs) in vitro has become an important method of obtaining a large number of PGCs. However, the instability and low induction efficiency of the in vitro PGC induction system restrict the application of PGCs in transgenic animal production, germplasm resource conservation and other fields. In this study, we successfully established a two-step induction model of chicken PGCs in vitro, which significantly improved the formation efficiency of PGC-like cells (PGCLCs). To further improve the PGC formation efficiency in vitro, 5025 differentially expressed genes (DEGs) were obtained between embryonic stem cells (ESCs) and PGCs through RNA-seq. GO and KEGG enrichment analysis revealed that signaling pathways such as BMP4, Wnt and Notch were significantly activated during PGC formation, similar to other species. In addition, we noted that cAMP was activated during PGC formation, while MAPK was suppressed. Based on the results of our analysis, we found that the PGC formation efficiency was significantly improved after activating Wnt and inhibiting MAPK, and was lower than after activating cAMP. To sum up, in this study, we successfully established a two-step induction model of chicken PGCs in vitro with high PGC formation efficiency, which lays a theoretical foundation for further demonstrating the regulatory mechanism of PGCs and realizing their specific applications.

In recent years, with the continuous development of artificial intelligence and machine vision technology, computer-assisted technology has increasingly affected the medical field, providing important support means for the development and promotion of informatization, digitization, and standardization of traditional Chinese medicine. In order to facilitate a further and effective exploration of the application of image processing technology in the field of traditional Chinese medicine, the relevant research progress is systematically reviewed. First of all, starting from the development and application of image processing technology, the main research and application directions of image processing technology in the field of image processing in Chinese medicine are clarified, and they are based on the three modules: traditional Chinese medicine inspection, identification of Chinese medicinal materials, and digital meridian three-dimensional reconstruction and visualization. The various research sub-categories in the modules are summarized, and the advantages and disadvantages of traditional and deep learning algorithms in the field of TCM image processing are shown, as well as the problems and challenges they face. Second, the main problems facing by the field of TCM image intelligent processing at this stage is summarized, and an outlook on the future research direction is made. Although TCM intelligent image processing and analysis based on computer image processing technology have achieved a series of meaningful research results, it is still necessary to sort out a unified, standard, and well-labeled TCM image dataset according to the actual clinical needs. Combining artificial intelligence with classic digital image processing algorithms and non-invasive and efficient multi-modal TCM four-diagnosis data, it truly realizes the informatization and intelligentization of TCM diagnosis and treatment technology.

ABSTRACT: Spermatogonia Stem Cells (SSCs) are the basis of spermatogenesis. In the poultry industry, asthenospermia and azoospermia in roosters seriously reduce economic benefits. In this study, we explored SSCs formation mechanisms in detail. TDRD1, which is a downstream target gene of TCF7L2 and is modified by histone methylation, was screened through multiomics analysis. Functionally, RT-qPCR, flow cytometry, immunohistochemistry, and indirect immunofluorescence results showed that H3K4me2 regulated TDRD1 to promote SSCs formation both in vivo and in vitro. Furthermore, ChIP-qPCR and dual luciferase assays showed that H3K4me2 was enriched in the −800 to 0 bp region of the TDRD1 promoter and positively regulated TDRD1 transcription to promote SSCs formation. Interestingly, in mechanistic terms, dual luciferase assays showed that TDRD1 transcription levels were significantly decreased after co-transfection with dCas9-LSD1-P1/P2/P3 and OETCF7L2, while TDRD1 transcript levels were not significantly altered after transfecting dCas9-LSD1-P4 and OETCF7L2. These results suggested that H3K4me2 enrichment in P1, P2, and P3 of the TDRD1 promoter promotes TDRD1 transcription by reducing enrichment of TCF7L2. This study explored the specific regulatory mechanisms involving the Wnt signaling pathway, H3K4me2, and TDRD1, enriched the regulatory network regulating the formation of SSCs, and laid a theoretical foundation for the specific application of SSCs.

IntroductionAdvances in mobile computing platforms and the rapid development of wearable devices have made possible the continuous monitoring of patients with mild cognitive impairment (MCI) and their daily activities. Such rich data can reveal more subtle changes in patients’ behavioral and physiological characteristics, providing new ways to detect MCI anytime, anywhere. Therefore, we aimed to investigate the feasibility and validity of digital cognitive tests and physiological sensors applied to MCI assessment.MethodsWe collected photoplethysmography (PPG), electrodermal activity (EDA) and electroencephalogram (EEG) signals from 120 participants (61 MCI patients, 59 healthy controls) during rest and cognitive testing. The features extracted from these physiological signals involved the time domain, frequency domain, time-frequency domain and statistics. Time and score features during the cognitive test are automatically recorded by the system. In addition, selected features of all modalities were classified by tenfold cross-validation using five different classifiers.ResultsThe experimental results showed that the weighted soft voting strategy combining five classifiers achieved the highest classification accuracy (88.9%), precision (89.9%), recall (88.2%), and F1 score (89.0%). Compared to healthy controls, the MCI group typically took longer to recall, draw, and drag. Moreover, during cognitive testing, MCI patients showed lower heart rate variability, higher electrodermal activity values, and stronger brain activity in the alpha and beta bands.DiscussionIt was found that patients’ classification performance improved when combining features from multiple modalities compared to using only tablet parameters or physiological features, indicating that our scheme could reveal MCI-related discriminative information. Furthermore, the best classification results on the digital span test across all tasks suggest that MCI patients may have deficits in attention and short-term memory that came to the fore earlier. Finally, integrating tablet cognitive tests and wearable sensors would provide a new direction for creating an easy-to-use and at-home self-check MCI screening tool.

In order to solve the problems such as the complicated process of manual diagnosis and retrieval, high misdiagnosis rate, large amount of data, sparse Hash codes, a 3D ResNet network based on multi-label semantic supervision was proposed to quantify the medical signs of pulmonary nodules and construct a multi-label data set. 3D lung nodules were constructed by trilinear interpolation method, and loss functions were designed by similarity measurement for 3D feature learning. Then Hash codes were constructed. An alternate minimization optimization method was proposed to solve the problem that the traditional method cannot be used because of the discrete Hash code, and the closely expressed Hash code was learned. Finally, a multi-level lung cancer image retrieval algorithm was proposed. The experimental results show that the average accuracy is improved by 18.5% by using the 3D feature proposed in this paper, and the average retrieval accuracy reaches 94.83% on the expanded public data set and the cooperative hospital data set.

Autophagy plays an important role in the pluripotency and differentiation of stem cells. Transcriptome data showed that the autophagy genes MAP1LC3A and MAP1LC3B were significantly upregulated in primordial germ cells (PGCs). The Kyoto Encyclopedia of Genes and Genome (KEGG) results showed that the lysosome signaling pathway, which is related to autophagy, was significantly enriched in PGCs. Quantitative RT-PCR, western blotting, and transmission electron microscopy (TEM) results showed that autophagy was expressed in both embryonic stem cells (ESCs) and PGCs but was significantly activated in PGCs. To explore the role of autophagy in the differentiation of chicken ESCs into PGCs, autophagy was activated and inhibited using rapamycin and bafilomycin A1, respectively. Results of qRT-PCR, flow cytometry, and indirect immunofluorescence showed that the efficiency of PGC formation significantly decreased after autophagy inhibition. Our results showed, for the first time, that autophagy plays an indispensable role in the formation of chicken PGCs, which lays the foundation for studying the mechanism of autophagy in chicken PGCs and in bird gene editing and the rescue of endangered birds.

Coordination polymers (CPs) are a diverse class of multi-dimensional compounds that show promise as photocatalysts for degrading dyes in polluted water. Herein, a new 1D Cd(II)-based coordination polymer with the formula [Cd(bpyp)(nba)2] (1) (bpyp = 2,5-bis(pyrid-4-yl)pyridine and Hnba = 4-nitrobenzoic acid) is synthesized and characterized. In 1, the two carboxyl groups of two different nba− ligands show μ2-η1:η1 and μ1-η1:η1 coordination modes to connect the CdII centers and sit on either side of the chain along the b direction. The produced CP 1 was utilized as the photocatalyst in the process of the photodegradation of methyl blue (MB), methyl orange (MO), rhodamine B (RhB), and methyl violet (MV) dyes when exposed to UV light. The photocatalytic degradation activities of CP 1 were analyzed, and the results suggest that it exhibits an extraordinary efficiency in the degradation of MB, MV, MO, and RhB. RhB has a 95.52% efficiency of degradation, whereas MV has a 58.92% efficiency, MO has 35.44%, and MB has 29.24%. The photodecomposition of dyes is catalyzed mostly by •O2− and •OH−, as shown by research involving the trapping of radicals.

In response to China's new “people-oriented urbanization” strategy, investigating the current integration situation of rural-to-urban migrants (RUMs) in cities is essential. However, existing studies have not focused on their subjective willingness and adaptive capacity. Moreover, the mechanism of RUMs' urban integration has not been fully explored.The conceptual framework of vulnerability, which originated from the discipline of natural hazards, is perfectly constructive to address these drawbacks. It consists of three main components, including exposure, sensitivity, and adaptive capacity. Vulnerability index has been constructed and calculated by combining the analytic hierarchy process (AHP) and Delphi method. The spatial pattern of vulnerability level is also analyzed with geographic information system (GIS). Furthermore, the underlying mechanism of vulnerability and its associated factors are investigated with the principle component regression.Three major conclusions are reached. First, the vulnerability level of the RUMs to social exclusion presents a heterogeneous pattern between inland and coastal cities in Shandong Province. Second, while the exposure and sensitivity level of each city correlate spatially with the vulnerability level, RUMs’ adaptive capacity further moderates it. Lastly, the level of urban development is revealed to be a key factor influencing vulnerability. This study offers meaningful references to promote inclusive urbanization and sustainable urban development.

Providing equal geographical access to hospitals, either in the public or private healthcare sector, is vital and will benefit public health in general. Against the background of the partial privatization of the healthcare sector, the impact of private hospitals on equal healthcare access has been a highly neglected issue. We have applied an assessment methodology to study this situation by comparing the status quo scenario with one without private hospitals, based on accessibility analysis and spatial equality measurements. The case study of Beijing, China revealed a double-sided impact. With the presence of private hospitals, the Gini coefficient of spatial accessibility in urban districts was reduced from 0.03391 to 0.03211, while it increased from 0.1734 to 0.1914 in suburban districts. Thus, private hospitals improved spatial equality in urban districts in Beijing but jeopardized it in suburban districts. These research findings should enlighten policymakers to promote healthcare equality but would also need to be repeated in some other big cities.