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Abstract Accurate prediction of drug-target interactions (DTIs) is crucial for advancing drug discovery and repurposing. Computational methods have significantly improved the efficiency of experimental predictions for drug-target interactions in Western medicine. However, accurately predicting the complex relationships between Chinese medicine ingredients and targets remains a formidable challenge due to the vast number and high heterogeneity of these ingredients. In this study, we introduce the CWI-DTI method, which achieves high-accuracy prediction of DTIs using a large dataset of interactive relationships of drug ingredients or candidate targets. Moreover, we present a novel dataset to evaluate the prediction accuracy of both Chinese and Western medicine. Through meticulous collection and preprocessing of data on ingredients and targets, we employ an innovative autoencoder framework to fuse multiple drug (target) topological similarity matrices. Additionally, we employ denoising blocks, sparse blocks, and stacked blocks to extract crucial features from the similarity matrix, reducing noise and enhancing accuracy across diverse datasets. Our results indicate that the CWI-DTI model shows improved performance compared to several existing state-of-the-art methods on the datasets tested in both Western and Chinese medicine databases. The findings of this study hold immense promise for advancing DTI prediction in Chinese and Western medicine, thus fostering more efficient drug discovery and repurposing endeavors. Our model is available at https://github.com/WANG-BIN-LAB/CWIDTI .

Abstract Episodic memory is essential for forming and retaining personal experiences, representing a fundamental aspect of human cognition. Traditional studies of episodic memory have typically used static analysis methods, viewing the brain as an unchanging entity and overlooking its dynamic properties over time. In this study, we utilized dynamic functional connectivity analysis on fMRI data from healthy adults performing an episodic memory task. We quantified integration and recruitment metrics and examined their correlation with memory performance using Pearson correlation. During encoding, integration across the entire brain, especially within the frontoparietal subnetwork, was significantly correlated with memory performance. During retrieval, recruitment becomes significantly associated with memory performance in visual subnetwork, somatomotor subnetwork, and ventral attention subnetwork. At the nodal level, a significant negative correlation was observed between memory scores and integration of the anterior cingulate gyrus, precentral gyrus, and inferior frontal gyrus within the frontoparietal network during encoding task. During retrieval task, a significant negative correlation was found between memory scores and recruitment in the left progranular cortex and right transverse gyral ventral, whereas positive correlations were seen in the right posterior inferior temporal, left middle temporal, right frontal operculum, and left operculum nodes. Moreover, the dynamic reconfiguration of the functional network was predictive of predict memory performance, as demonstrated by a significant correlation between actual and predicted memory scores. These findings advance our understanding network mechanisms underlying memory processes and developing intervention approaches for memory-related disorders as they shed light on critical factors involved in cognitive processes and provide a deeper understanding of the underlying mechanisms driving cognitive function.

Based on the data of outcrop, core, logging, gas testing, and experiments, the natural gas accumulation and aluminous rock mineralization integrated research was adopted to analyze the controlling factors of aluminous rock series effective reservoirs in the Ordos Basin, NW China, as well as the configuration of coal-measure source rocks and aluminous rock series reservoirs. A natural gas accumulation model was constructed to evaluate the gas exploration potential of aluminous rock series under coal seam in the basin. The effective reservoirs of aluminous rock series in the Ordos Basin are composed of honeycomb-shaped bauxites with porous residual pisolitic and detrital structures, with the diasporite content of greater than 80% and dissolved pores as the main storage space. The bauxite reservoirs are formed under a model that planation controls the material supply, karst paleogeomorphology controls diagenesis, and land surface leaching improves reservoir quality. The hot humid climate and sea level changes in the Late Carboniferous–Early Permian dominated the development of a typical coal-aluminum-iron three-stage stratigraphic structure. The natural gas generated by the extensive hydrocarbon generation of coal-measure source rocks was accumulated in aluminous rock series under the coal seam, indicating a model of hydrocarbon accumulation under the source. During the Upper Carboniferous–Lower Permian, the relatively low-lying area on the edge of an ancient land or island in the North China landmass was developed. The gas reservoirs of aluminous rock series, which are clustered at multiple points in lenticular shape, are important new natural gas exploration fields with great potential in the Upper Paleozoic of North China Craton.

In the field of breast cancer treatment, the immunotherapy involving natural killer (NK) cells is increasingly highlighting its distinct potential and significance. Members of the interleukin (IL) family play pivotal regulatory roles in the growth, differentiation, survival, and apoptosis of NK cells, and are central to their anti-tumor activity. These cytokines enhance the ability of NK cells to recognize and eliminate tumor cells by binding to specific receptors and activating downstream signaling pathways. Furthermore, interleukins do not function in isolation; the synergistic or antagonistic interactions between different interleukins can drive NK cells toward various functional pathways, ultimately leading to diverse outcomes for breast cancer patients. This paper reviews the intricate relationship between NK cells and interleukins, particularly within the breast cancer tumor microenvironment. Additionally, we summarize the latest clinical studies and advancements in NK cell therapy for breast cancer, along with the potential applications of interleukin signaling in these therapies. In conclusion, this article underscores the critical role of NK cells and interleukin signaling in breast cancer treatment, providing valuable insights and a significant reference for future research and clinical practice.

Background Breast cancer is a prevalent public health concern affecting numerous women globally and is associated with palmitoylation, a post-translational protein modification. Despite increasing focus on palmitoylation, its specific implications for breast cancer prognosis remain unclear. The work aimed to identify prognostic factors linked to palmitoylation in breast cancer and assess its effectiveness in predicting responses to chemotherapy and immunotherapy. Methods We utilized the “limma” package to analyze the differential expression of palmitoylation-related genes between breast cancer and normal tissues. Hub genes were identified using the “WGCNA” package. Using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we identified a prognostic feature associated with palmitoylation and developed a prognostic nomogram with the “regplot” package. The predictive values of the model for chemotherapy and immunotherapy responses were assessed using immunophenoscore (IPS) and the “pRophetic” package. Results We identified 211 differentially expressed genes related to palmitoylation, among which 44 demonstrated prognostic potential. Subsequently, a predictive model comprising eleven palmitoylation-related genes was developed. Patients were classified into high-risk and low-risk groups based on the median risk score. The findings revealed that individuals in the high-risk group exhibited lower survival rates, while those in the low-risk group showed increased immune cell infiltration and improved responses to chemotherapy and immunotherapy. Moreover, the BC-Palmitoylation Tool website was established. Conclusion This study developed the first machine learning-based predictive model for palmitoylation-related genes and created a corresponding website, providing clinicians with a valuable tool to improve patient outcomes.

Background: Ultrasound therapy is a non-invasive technique used to address a variety of health issues. Objectives: This systematic review and meta-analysis aim to assess the effectiveness of ultrasound therapy in alleviating pain associated with musculoskeletal diseases. Design: This study was conducted following PRISMA guidelines, with relevant articles identified through comprehensive searches in electronic databases. Data sources and methods: We conducted searches across multiple databases, including Scopus, PubMed, MEDLINE, ProQuest, Science Direct, CINAHL, AIM, and ELDIS. Two independent reviewers screened the titles and abstracts of the retrieved articles. We included randomized controlled trials (RCTs) and observational cohort studies published between 2010 and 2023 that evaluated ultrasound therapy for knee and shoulder skeletal disorders. The selected data were analyzed qualitatively and synthesized, with the risk of bias assessed using the RoB2 tool. Results: Initially, 117 articles were reviewed using the search strategy, and 10 trials that met the inclusion criteria were identified. In seven of these studies, the primary musculoskeletal disorder was osteoarthritis, while three studies focused on shoulder pain and impingement. Most studies indicated that ultrasound therapy significantly reduced pain. The meta-analysis showed that ultrasound therapy was significantly more effective than other interventions for knee disorders ( I 2 = 51%, Z = 2.65, p = 0.008). However, for shoulder disorders, both ultrasound and other intervention methods were found to be ineffective ( I 2 = 93%, Z = 0.73, p = 0.46). Conclusion: The current evidence supports the effectiveness of ultrasound therapy in reducing pain and aiding rehabilitation for knee conditions. However, there are mixed results regarding its efficacy for shoulder conditions, highlighting the need for further research in this area.

Abstract Background Remimazolam, as a novel anesthetic, has recently been shown to improve hemodynamic stability during anesthesia induction and maintenance; however, it has not been reported in the hypertensive population. This study aimed to compare the effects of remimazolam and propofol on hemodynamic stability in hypertensive patients undergoing breast cancer surgery. Methods We enrolled 120 hypertensive patients undergoing breast cancer surgery in this prospective study and randomly allocated them to remimazolam (n = 60) or propofol (n = 60) groups. Anesthesia regimens were consistent between groups, except for the administration of remimazolam and propofol. Our primary outcome was the incidence of post-induction hypotension, which was either an absolute mean arterial pressure (MAP) 30% relative drop in MAP compared to baseline within 20 min of induction or from induction to the start of surgery. Secondary outcomes included minimum MAP and MAP at different time points during anesthesia, the application of vasoactive drugs, adverse events, and the patient’s self-reported Quality of Recovery-40 scale for the day after surgery. Results The incidence of post-induction hypotension was lower and the minimum MAP during induction was higher in the remimazolam group than those in the propofol group. There were no significant differences between the two groups in the remaining outcomes. Conclusion Remimazolam is safe and effective in hypertensive patients undergoing breast cancer surgery. Induction with remimazolam in hypertensive patients may result in more stable hemodynamics than propofol. Trial registration This study was registered at the Chinese Clinical Trials Registry ( http://www.chictr.org.cn ) on 03/12/2020, with registration number ChiCTR2000040579.

Abstract This study sought to identify molecular subtypes of breast cancer (BC) and develop a breast cancer stem cells (BCSCs)-related gene risk score for predicting prognosis and assessing the potential for immunotherapy. Unsupervised clustering based on prognostic BCSC genes was used to determine BC molecular subtypes. Core genes of BC subtypes identified by non-negative matrix factorization algorithm (NMF) were screened using weighted gene co-expression network analysis (WGCNA). A risk model based on prognostic BCSC genes was constructed using machine learning as well as LASSO regression and multivariate Cox regression. The tumor microenvironment and immune infiltration were analyzed using ESTIMATE and CIBERSORT, respectively. A CD79A+CD24-PANCK+-BCSC subpopulation was identified and its spatial relationship with microenvironmental immune response state was evaluated by multiplexed quantitative immunofluorescence (QIF) and TissueFAXS Cytometry. We identified two distinct molecular subtypes, with Cluster 1 displaying better prognosis and enhanced immune response. The constructed risk model involving ten BCSC genes could effectively stratify patients into subgroups with different survival, immune cell abundance, and response to immunotherapy. In subsequent QIF validation involving 267 patients, we demonstrated the existence of CD79A+CD24-PANCK+-BCSC in BC tissues and revealed that this BCSC subtype located close to exhausted CD8+FOXP3+ T cells. Furthermore, both the densities of CD79A+CD24-PANCK+-BCSCs and CD8+FOXP3+T cells were positively correlated with poor survival. These findings highlight the importance of BCSCs in prognosis and reshaping the immune microenvironment, which may provide an option to improve outcomes for patients.

Breast cancer is the most common cause of female morbidity and death worldwide. Compared with other cancers, early detection of breast cancer is more helpful to improve the prognosis of patients. In order to achieve early diagnosis and treatment, clinical treatment requires rapid and accurate diagnosis. Therefore, the development of an automatic detection system for breast cancer suitable for patient imaging is of great significance for assisting clinical treatment. Accurate classification of pathological images plays a key role in computer-aided medical diagnosis and prognosis. However, in the automatic recognition and classification methods of breast cancer pathological images, the scale information, the loss of image information caused by insufficient feature fusion, and the enormous structure of the model may lead to inaccurate or inefficient classification. To minimize the impact, we proposed a lightweight PCSAM-ResCBAM model based on two-stage convolutional neural network. The model included a Parallel Convolution Scale Attention Module network (PCSAM-Net) and a Residual Convolutional Block Attention Module network (ResCBAM-Net). The first-level convolutional network was built through a 4-layer PCSAM module to achieve prediction and classification of patches extracted from images. To optimize the network's ability to represent global features of images, we proposed a tiled feature fusion method to fuse patch features from the same image, and proposed a residual convolutional attention module. Based on the above, the second-level convolutional network was constructed to achieve predictive classification of images. We evaluated the performance of our proposed model on the ICIAR2018 dataset and the BreakHis dataset, respectively. Furthermore, through model ablation studies, we found that scale attention and dilated convolution play an important role in improving model performance. Our proposed model outperforms the existing state-of-the-art models on 200 × and 400 × magnification datasets with a maximum accuracy of 98.74 %.

We performed a Bayesian network meta-analysis to indirectly compare the relative efficacy and safety of the latest JAK inhibitors for moderate-to-severe alopecia areata (AA). 13 trials totaling 3,613 patients were included. Two low-dose groups of oral formulations (ritlecitinib 10mg and ivarmacitinib 2mg) and two topical formulations (delgocitinib ointment and ruxolitinib cream) appeared to be relatively ineffective against moderate-to-severe AA. Ranking analysis suggested that brepocitinib 30mg has the best relative effect in reducing the SALT score (sucra = 0.9831), and demonstrated comparable efficacy to deuruxolitinib 12mg (sucra = 0.9245), followed by deuruxolitinib 8mg (sucra = 0.7736). Regarding the SALT50 response, brepocitinib 30mg ranked highest (sucra = 0.9567), followed by ritlecitinib 50mg (sucra = 0.8689) and deuruxolitinib 12mg (sucra = 0.7690). For achieving the SALT75 response, deuruxolitinib 12mg had the highest probability (sucra = 0.9761), followed by deuruxolitinib 8mg (sucra = 0.8678) and brepocitinib 30mg (sucra = 0.8448). Deuruxolitinib 12mg might be the most effective therapy for patients with severe AA (sucra = 0.9395), followed by ritlecitinib 50mg (sucra = 0.8753) and deuruxolitinib 8mg (sucra = 0.8070). Deuruxolitinib 12mg/8mg demonstrated notable efficacy for moderate-to-severe AA, and is expected to be a new treatment option for AA. It was worth noting that deuruxolitinib exhibit a greater likelihood of causing adverse events in comparison to other JAK inhibitors. Ritlecitinib 50mg seemed to exhibit fewer adverse effects in the high-dose groups of oral JAK inhibitors and might be an optimal choice to balance safety and efficacy. The majority of JAK inhibitors exhibited acceptable short-term safety profiles. To enhance the applicability and accuracy of our research, further head-to-head trials with longer follow-up periods are needed.Systematic Review Registration: identifier [CRD42022368012].

Plant diversity and productivity, two crucial properties that sustain ecosystem structures, functions, and services, are intrinsically linked to numerous ecological fields, making productivity–richness relationships (PRR) a central ecological concern. Despite extensive research from the Darwinian era to the 21st century, the various shapes of PRR and their underlying theories have sparked ongoing debates. While several processes, theories, and integrative models have been proposed to explain PRR, a comprehensive understanding of the types of PRR, the effects of these processes on plant productivity and richness, and the relationships between PRR shapes remains elusive. This paper proposes a new integrative framework that focuses on these aspects, aiming to elucidate the diverse shapes of PRR and their interconnections. We review recent integrative methods that explain the roles of processes and the varying shapes in PRR to support this new framework. The paper traces the distinct phases in PRR research, including the discovery of PRR shapes, tests of influencing processes, and integrative research. We discuss the application of the Structural Equation Model (SEM), Statistical Dynamical Model (SDM), and Differential Dynamical Model (DDM) in integrative research. This integrative framework can guide theoretical and applied ecologists in identifying, deriving, explaining, and predicting the interconnected but distinct shapes of PRR. The humped, asymptotic, positive, negative, and irregular shapes of PRR are interconnected, with one shape potentially transforming into another. The balance between the positive and negative effects of different processes determines the different shapes of PRR, ultimately leading to a globally positive effect of plant diversity on plant productivity and other ecosystem functions.

Brain network analysis provides essential insights into the diagnosis of brain disease. Integrating multiple neuroimaging modalities has been demonstrated to be more effective than using a single modality for brain network analysis. However, a majority of existing brain network analysis methods based on multiple modalities often overlook both complementary information and unique characteristics from various modalities. To tackle this issue, we propose the Beta-Informativeness-Diffusion Multilayer Graph Embedding (BID-MGE) method. The proposed method seamlessly integrates structural connectivity (SC) and functional connectivity (FC) to learn more comprehensive information for diagnosing neuropsychiatric disorders. Specifically, a novel beta distribution mapping function (beta mapping) is utilized to increase vital information and weaken insignificant connections. The refined information helps the diffusion process concentrate on crucial brain regions to capture more discriminative features. To maximize the preservation of the unique characteristics of each modality, we design an optimal scale multilayer brain network, the inter-layer connections of which depend on node informativeness. Then, a multilayer informativeness diffusion is proposed to capture complementary information and unique characteristics from various modalities and generate node representations by incorporating the features of each node with those of their connected nodes. Finally, the node representations are reconfigured using principal component analysis (PCA), and cosine distances are calculated with reference to multiple templates for statistical analysis and classification. We implement the proposed method for brain network analysis of neuropsychiatric disorders. The results indicate that our method effectively identifies crucial brain regions associated with diseases, providing valuable insights into the pathology of the disease, and surpasses other advanced methods in classification performance.

Pancreatic adenocarcinoma (PAAD) is an aggressive, heterogeneous malignancy. We studied the potential of ferroptosis-related tumor vaccines for PAAD treatment. Ferroptosis-related genes, gene expression profiles, and clinical information were extracted from the FerrDB, UCSC Xena, and International Cancer Genome Consortium databases. Differential expression levels and prognostic indices were calculated, genetic alterations and correlations with immune-infiltrating cells were explored, and consensus clustering analysis was performed to identify ferroptosis subtypes and gene modules. Immune enrichment scores were calculated using gene set enrichment analysis, and gene modules were screened using weighted gene co-expression network analysis. The ferroptosis subtype distribution was visualized using graph learning-based dimensionality reduction analysis of the Monocle package with a Gaussian distribution. We identified four ferroptosis-related tumor antigens, AGPS, KDM5A, NRAS, and OSBPL9, which were associated with pancreatic cancer prognosis and antigen-presenting cell infiltration. We determined three minor ferroptosis subtypes, with different clinical prognosis and tumor immune status. Of the subtypes, FS3 may be more suitable for mRNA therapy. We constructed a PAAD ferroptosis landscape to identify the ferroptosis status of patients and predict their prognosis. Finally, we found that the eigengene of the green module was an independent prognostic factor, with a significantly better prognosis in the high-score group than in the low-score group. In conclusion, we identified four ferroptosis-related genes as targets for mRNA vaccines and three ferroptosis subtypes, providing a theoretical basis for the anti-PAAD mRNA vaccine and defining suitable patients for vaccination.

Background: Malignant pericardial effusion (MPE) is a common complication of advanced breast cancer (BRCA) and plays an important role in BRCA. This study is aims to construct a prognostic model based on MPE-related genes for predicting the prognosis of breast cancer. Methods: The BRCA samples are analyzed based on the expression of MPE-related genes by using an unsupervised cluster analysis method. This study processes the data by least absolute shrinkage and selection operator and multivariate Cox analysis, and uses machine learning algorithms to construct BRCA prognostic model and develop web tool. Results: BRCA patients are classified into three clusters and a BRCA prognostic model is constructed containing 9 MPE-related genes. There are significant differences in signature pathways, immune infiltration, immunotherapy response and drug sensitivity testing between the high and low-risk groups. Of note, a web-based tool (http://wys.helyly.top/cox.html) is developed to predict overall survival as well as drug-therapy response of BRCA patients quickly and conveniently, which can provide a basis for clinicians to formulate individualized treatment plans. Conclusion: The MPE-related prognostic model developed in this study can be used as an effective tool for predicting the prognosis of BRCA and provides new insights for the diagnosis and treatment of BRCA patients.

Abstract Background Based on previous studies which failed to analyze important confounding variables, the association between preoperative anemia and outcomes of patients who underwent colorectal cancer (CRC) surgery has not been clearly demonstrated. This study aimed to investigate the relationship between preoperative anemia and short-term outcomes in patients with CRC. Methods Data from a retrospective collective database of patients who underwent CRC surgery at our hospital between September 1, 2019 and September 30, 2021 were retrieved and analyzed, and the short-term postoperative outcomes of anemic (hemoglobin

Abstract Objective This study evaluated the association between serum albumin levels and preoperative deep vein thrombosis (DVT) in geriatric hip fractures. Methods Older adult patients with hip fractures were screened between January 2015 and September 2019. The demographic and clinical characteristics of the patients were collected. Multivariate binary logistic regression and generalized additive model were used to identify the linear and nonlinear association between albumin levels and preoperative DVT. Analyses were performed using EmpowerStats and the R software. Results A total of 1819 patients were included in this study. The average age was 79.37 ± 6.88 years. There were 550 males and 1269 females. The preoperative albumin was 38.19 ± 4.07 g/L. There were 580 (31.89%) preoperative DVTs. Multivariate binary logistic regression showed that albumin level was associated with preoperative DVT (odds ratio [OR] = 0.94, 95% confidence interval [CI]: 0.91–0.97, P = 0.0002) after adjusting for confounding factors. The fully adjusted model showed a DVT risk decrease of 6% when albumin concentration increased by one g/L after controlling for confounding factors. In addition, the trend test and propensity score matching also showed a stable linear correlation between albumin level and preoperative DVT. Conclusion Serum albumin is associated with preoperative DVT in geriatric patients with hip fractures, and it could be considered a predictor for the risk of DVT. Registration ID ChiCTR2200057323.

Abstract Miniaturization of optical spectrometers is important to enable spectroscopic analysis to play a role in in situ, or even in vitro and in vivo characterization systems. However, scaled-down spectrometers generally exhibit a strong trade-off between spectral resolution and operating bandwidth, and are often engineered to identify signature spectral peaks only for specific applications. In this paper, we propose and demonstrate a novel global sampling strategy with distributed filters for generating ultra-broadband pseudo-random spectral responses. The geometry of all-pass ring filters is tailored to ensure small self- and cross-correlation for effective information acquisition across the whole spectrum, which dramatically reduces the requirement on sampling channels. We employ the power of reconfigurable photonics in spectrum shaping by embedding the engineered distributed filters. Using a moderate mesh of MZIs, we create 256 diverse spectral responses on a single chip and demonstrate a resolution of 20 pm for single spectral lines and 30 pm for dual spectral lines over a broad bandwidth of 115 nm, to the best of our knowledge achieving a new record of bandwidth-to-resolution ratio. Rigorous simulations reveal that this design will readily be able to achieve single-picometer-scale resolution. We further show that the reconfigurable photonics provides an extra degree of programmability, enabling user-defined features on resolution, computation complexity, and relative error. The use of SiN integration platform enables the spectrometer to exhibit excellent thermal stability of ±2.0 °C, effectively tackling the challenge of temperature variations at picometer-scale resolutions.

Abstract Background Pregnancy begins with a fertilized ovum that normally attaches to the uterine endometrium. However, an ectopic pregnancy can occur when a fertilized egg implants and grows outside the uterine cavity. Tubal ectopic pregnancy is the most common type (over 95%), with ovarian, abdominal, cervical, broad ligament, and uterine cornual pregnancy being less common. As more cases of ectopic pregnancy are diagnosed and treated in the early stages, the survival rate and fertility retention significantly improve. However, complications of abdominal pregnancy can sometimes be life-threatening and have severe consequences. Case presentation We present a case of intraperitoneal ectopic pregnancy with fetal survival. Ultrasound and magnetic resonance imaging showed a right cornual pregnancy with a secondary abdominal pregnancy. In September 2021, we performed an emergency laparotomy, along with additional procedures such as transurethral ureteroscopy, double J-stent placement, abdominal fetal removal, placentectomy, repair of the right uterine horn, and pelvic adhesiolysis, in the 29th week of pregnancy. During laparotomy, we diagnosed abdominal pregnancy secondary to a rudimentary uterine horn. The mother and her baby were discharged eight days and 41 days, respectively, after surgery. Conclusions Abdominal pregnancy is a rare condition. The variable nature of ectopic pregnancy can cause delays in timely diagnosis, resulting in increased morbidity and mortality, especially in areas with inadequate medical and social services. A high index of suspicion, coupled with appropriate imaging studies, can help facilitate its diagnosis in any suspected case.

Immunotherapy has revolutionized the cancer treatment landscape by opening up novel avenues for intervention. As the use of immune checkpoint inhibitors (ICIs) has exponentially increased, so have immune-related adverse events (irAEs). The mechanism of irAEs may involve the direct damage caused by monoclonal antibodies and a sequence of immune responses triggered by T cell activation. Common side effects include dermatologic toxicity, endocrine toxicity, gastrointestinal toxicity, and hepatic toxicity. While relatively rare, neurotoxicity, cardiotoxicity, and pulmonary toxicity can be fatal. These toxicities pose a clinical dilemma regarding treatment discontinuation since they can result in severe complications and necessitate frequent hospitalization. Vigilant monitoring of irAEs is vital in clinical practice, and the principal therapeutic strategy entails the administration of oral or intravenous glucocorticoids (GSCs). It may be necessary to temporarily or permanently discontinue the use of ICIs in severe cases. Given that irAEs can impact multiple organs and require diverse treatment approaches, the involvement of a multidisciplinary team of experts is imperative. This review aims to comprehensively examine the pathogenesis, clinical manifestations, incidence, and treatment options for various irAEs.

In face-to-face household surveys, field interviewers are sometimes asked to make notes of characteristics of the dwelling unit on the sampled address as well as its surroundings before making contact with a household member living at the sample address. Field interviewer observations of this kind are used to improve efficiency of field data collection and to be used as nonresponse adjustment. However, field interviewer observations can be expensive and the quality of observations needs to be improved. Recently, survey organizations start to utilize Google Street View to conduct virtual observations of the dwelling unit and the neighborhood. This paper reports a feasibility study that evaluates the feasibility of using virtual observations, assesses its agreement with field interviewer observation results, and examine whether virtual observations correlate with survey response status and survey estimates. We found moderate to high agreements between virtual and interviewer observation results. We also found that some observation results are significantly related to response status and survey estimates. However, virtual observations using GSV have coverage issues, which could limit their potential use.

In patients with esophageal squamous cell carcinoma (ESCC), the incidence and mortality rate of ESCC in our country are also higher than those in the rest of the world. Despite advances in the treatment department method, patient survival rates have not obviously improved, which often leads to treatment obstruction and cancer repeat. ESCC has special cells called cancer stem-like cells (CSLCs) with self-renewal and differentiation ability, which reflect the development process and prognosis of cancer. In this review, we evaluated CSLCs, which are identified from the expression of cell surface markers in ESCC. By inciting EMTs to participate in tumor migration and invasion, stem cells promote tumor redifferentiation. Some factors can inhibit the migration and invasion of ESCC via the EMT-related pathway. We here summarize the research progress on the surface markers of CSLCs, EMT pathway, and the microenvironment in the process of tumor growth. Thus, these data may be more valuable for clinical applications.

Young adults are generally hard to survey, presenting researchers with numerous difficulties. They are hard to locate and contact due to high mobility. They are hard to persuade and exhibit high levels of resistance to survey participation. As a result, they pose a greater challenge for longitudinal surveys. This paper explores the role of mode of data collection in young adults’ decisions to stay in a longitudinal panel. We draw on data from the National Young Adult Health Study (NYAHS). NYAHS is a longitudinal study (three annual waves and 2 brief between-wave follow-up surveys) of adults aged 18-34 initially recruited in 2019 through RDD sampling of cell phone numbers nationwide. Once screened in, one-third of young adults were randomly assigned to continue all rounds of interviews by telephone (“single telephone mode” condition). One-third were assigned to alternate between telephone and web for the later interviews (“mixed mode” condition). The last third was asked to complete subsequent interviews online (“single web mode” condition). We examined the impact of mode switching on young adults’ likelihood of participating in later surveys and on nonresponse bias in key survey outcomes. We found that switching young adults from telephone to web had an immediate negative effect on their likelihood of participating in that web survey, but it did not have a continued negative effect. Switching them from web to telephone increased response rates and reduced nonresponse bias. The findings have important practical implications on how to survey young adults.

Elevated CA-125 levels, polyserous effusions (such as pleural effusion, ascites, etc.) in young women with systemic lupus erythematosus (SLE) may signal pseudo-pseudo Meigs’ syndrome (PPMS), after excluding other causes. We describe a 32-year-old SLE patient with recurrent bilateral pleural effusions and unexplained hypercalcemia for 10 months. Extensive evaluations revealed no infections or tumors. Cytokine analysis showed elevated interleukin (IL) levels, especially IL-6 in pleural effusion. Treatment with immunosuppressive therapy resulted in reduced cancer antigen (CA) 125 levels and decreased effusion volume, demonstrating a positive response to intervention in this case of PPMS.

Platinum-based chemotherapy is one of the predominant strategies for treating ovarian cancer (OC), however, platinum resistance greatly influences the therapeutic effect. Circular RNAs (circRNAs) have been found to participate in the pathogenesis of platinum resistance. Our aim was to explore the involvement of circ_0078607 in OC cell cisplatin (DDP) resistance and its potential mechanisms. Circ_0078607, miR-196b-5p, and growth arrest-specific 7 (GAS7) levels were assessed by qPCR. Circ_0078607 stability was assessed by ribonuclease R digestion and actinomycin D treatment. Cell viability of various conic of DDP treatment was measured by CCK-8. The cell proliferation was determined by CCK-8 and colony formation assay. Western blotting was performed for determining GAS7, ABCB1, CyclinD1 and Bcl-2 protein levels. The direct binding between miR-196b-5p and circ_0078607 or GAS7 was validated by dual-luciferase reporter and RIP assay. DDP resistance in vivo was evaluated in nude mice. Immunohistochemistry staining for detecting Ki67 expression in xenograft tumours. Circ_0078607 and GAS7 was down-regulated, but miR-196b-5p was up-regulated in OC samples and DDP-resistant cells. Overexpression of circ_0078607 inhibited DDP resistance, cell growth and induced apoptosis in DDP-resistant OC cells. Mechanistically, circ_0078607 sequestered miR-196b-5p to up-regulate GAS7. MiR-196b-5p mimics reversed circ_0078607 or GAS7 overexpression-mediated enhanced sensitivity. Finally, circ_0078607 improved the sensitivity of DDP in vivo. Circ_0078607 attenuates DDP resistance via miR-196b-5p/GAS7 axis, which highlights the therapeutic potential of circ_0078607 to counter DDP resistance in OC.

Abstract For practitioners, it is very crucial to realize accurate and automatic vision-based quality identification of Longjing tea. Due to the high similarity between classes, the classification accuracy of traditional image processing combined with machine learning algorithm is not satisfactory. High-performance deep learning methods require large amounts of annotated data, but collecting and labeling massive amounts of data is very time consuming and monotonous. To gain as much useful knowledge as possible from related tasks, an instance-based deep transfer learning method for the quality identification of Longjing tea is proposed. The method mainly consists of two steps: (i) The MobileNet V2 model is trained using the hybrid training dataset containing all labeled samples from source and target domains. The trained MobileNet V2 model is used as a feature extractor, and (ii) the extracted features are input into the proposed multiclass TrAdaBoost algorithm for training and identification. Longjing tea images from three geographical origins, West Lake, Qiantang, and Yuezhou, are collected, and the tea from each geographical origin contains four grades. The Longjing tea from West Lake is regarded as the source domain, which contains more labeled samples. The Longjing tea from the other two geographical origins contains only limited labeled samples, which are regarded as the target domain. Comparative experimental results show that the method with the best performance is the MobileNet V2 feature extractor trained with a hybrid training dataset combined with multiclass TrAdaBoost with linear support vector machine (SVM). The overall Longjing tea quality identification accuracy is 93.6% and 91.5% on the two target domain datasets, respectively. The proposed method can achieve accurate quality identification of Longjing tea with limited samples. It can provide some heuristics for designing image-based tea quality identification systems.

Abstract Background Although the morphological changes of sella turcica have been drawing increasing attention, the acquirement of linear parameters of sella turcica relies on manual measurement. Manual measurement is laborious, time-consuming, and may introduce subjective bias. This paper aims to develop and evaluate a deep learning-based model for automatic segmentation and measurement of sella turcica in cephalometric radiographs. Methods 1129 images were used to develop a deep learning-based segmentation network for automatic sella turcica segmentation. Besides, 50 images were used to test the generalization ability of the model. The performance of the segmented network was evaluated by the dice coefficient. Images in the test datasets were segmented by the trained segmentation network, and the segmentation results were saved in binary images. Then the extremum points and corner points were detected by calling the function in the OpenCV library to obtain the coordinates of the four landmarks of the sella turcica. Finally, the length, diameter, and depth of the sella turcica can be obtained by calculating the distance between the two points and the distance from the point to the straight line. Meanwhile, images were measured manually using Digimizer. Intraclass correlation coefficients (ICCs) and Bland–Altman plots were used to analyze the consistency between automatic and manual measurements to evaluate the reliability of the proposed methodology. Results The dice coefficient of the segmentation network is 92.84%. For the measurement of sella turcica, there is excellent agreement between the automatic measurement and the manual measurement. In Test1, the ICCs of length, diameter and depth are 0.954, 0.953, and 0.912, respectively. In Test2, ICCs of length, diameter and depth are 0.906, 0.921, and 0.915, respectively. In addition, Bland–Altman plots showed the excellent reliability of the automated measurement method, with the majority measurements differences falling within ± 1.96 SDs intervals around the mean difference and no bias was apparent. Conclusions Our experimental results indicated that the proposed methodology could complete the automatic segmentation of the sella turcica efficiently, and reliably predict the length, diameter, and depth of the sella turcica. Moreover, the proposed method has generalization ability according to its excellent performance on Test2.

Hydrochar as a carbon-based fertiliser is hypothesised to permanently improve soils by modifying soil carbon quality through the regulation of soil organic carbon dynamics, aggregation properties and microbial diversity. However, the interactions between soil organic carbon (SOC) molecular structure, soil aggregates and soil microbial communities as a result of hydrochar application have not been fully elucidated. In this study, the use of hydrochar derived from duck farm biomass waste for a maize cultivation experiment verified that hydrochar had a promoting effect on maize growth, effectively increasing the nutrient supply to the soil. The application of hydrochar increased the soil organic carbon content by 78 to 253 per cent, which was dominated by CHON-type lignin, carbohydrates and condensed aromatic structural compounds. Meanwhile, hydrochar had a significant effect on both soil aromatic structures and oxygenated functional groups, forming more soil macroaggregates. In addition, hydrochar had a positive effect on soil bacterial abundance. This study suggests that the key mechanism by which hydrochar regulates soil carbon dynamics is mainly through the stabilising effect of hydrochar on macroaggregates while increasing the abundance of carbon-related microscopic bacteria. These results will help to elucidate the potential effects of aqueous carbon on the biogeochemical cycling of carbon in soils.

Abstract The past decade has witnessed ongoing progress in immune therapy to ameliorate human health. As an emerging technique, chimeric antigen receptor (CAR) T-cell therapy has the advantages of specific killing of cancer cells, a high remission rate of cancer-induced symptoms, rapid tumor eradication, and long-lasting tumor immunity, opening a new window for tumor treatment. However, challenges remain in CAR T-cell therapy for solid tumors due to target diversity, tumor heterogeneity, and the complex microenvironment. In this review, we have outlined the development of the CAR T-cell technique, summarized the current advances in tumor-associated antigens (TAAs), and highlighted the importance of tumor-specific antigens (TSAs) or neoantigens for solid tumors. We also addressed the challenge of the TAA binding domain in CARs to overcome off-tumor toxicity. Moreover, we illustrated the dominant tumor microenvironment (TME)-induced challenges and new strategies based on TME-associated antigens (TMAs) for solid tumor CAR T-cell therapy.

Abstract Image-based fruit classification offers many useful applications in industrial production and daily life, such as self-checkout in the supermarket, automatic fruit sorting and dietary guidance. However, fruit classification task will have different data distributions due to different application scenarios. One feasible solution to solve this problem is to use domain adaptation that adapts knowledge from the original training data (source domain) to the new testing data (target domain). In this paper, we propose a novel deep learning-based unsupervised domain adaptation method for cross-domain fruit classification. A hybrid attention module is proposed and added to MobileNet V3 to construct the HAM-MobileNet that can suppress the impact of complex backgrounds and extract more discriminative features. A hybrid loss function combining subdomain alignment and implicit distribution metrics is used to reduce domain discrepancy during model training and improve model classification performance. Two fruit classification datasets covering several domains are established to simulate common industrial and daily life application scenarios. We validate the proposed method on our constructed grape classification dataset and general fruit classification dataset. The experimental results show that the proposed method achieves an average accuracy of 95.0% and 93.2% on the two datasets, respectively. The classification model after domain adaptation can well overcome the domain discrepancy brought by different fruit classification scenarios. Meanwhile, the proposed datasets and method can serve as a benchmark for future cross-domain fruit classification research.

Chronic obstructive pulmonary disease (COPD) is one of the most common chronic diseases in the elderly population and is characterized by persistent respiratory symptoms and airflow obstruction. During COPD progression, a variety of pulmonary and extrapulmonary complications develop, with sarcopenia being one of the most common extrapulmonary complications. Factors that contribute to the pathogenesis of coexisting COPD and sarcopenia include systemic inflammation, hypoxia, hypercapnia, oxidative stress, protein metabolic imbalance, and myocyte mitochondrial dysfunction. These factors, individually or in concert, affect muscle function, resulting in decreased muscle mass and strength. The occurrence of sarcopenia severely affects the quality of life of patients with COPD, resulting in increased readmission rates, longer hospital admission, and higher mortality. In recent years, studies have found that oral supplementation with protein, micronutrients, fat, or a combination of nutritional supplements can improve the muscle strength and physical performance of these patients; some studies have also elucidated the possible underlying mechanisms. This review aimed to elucidate the role of nutrition among patients with coexisting COPD and sarcopenia.

The prolonged course of the COVID-19 pandemic necessitates sustained surveillance of emerging variants. This study aimed to develop a multiplex real-time polymerase chain reaction (rt-PCR) suitable for the real-time tracking of Omicron subvariants in clinical and wastewater samples. Plasmids containing variant-specific mutations were used to develop a MeltArray assay. After a comprehensive evaluation of both analytical and clinical performance, the established assay was used to detect Omicron variants in clinical and wastewater samples, and the results were compared with those of next-generation sequencing (NGS) and droplet digital PCR (ddPCR). The MeltArray assay identified 14 variant-specific mutations, enabling the detection of five Omicron sublineages (BA.2*, BA.5.2*, BA.2.75*, BQ.1*, and XBB.1*) and eight subvariants (BF.7, BN.1, BR.2, BQ.1.1, XBB.1.5, XBB.1.16, XBB.1.9, and BA.4.6). The limit of detection (LOD) of the assay was 50 copies/reaction, and no cross-reactivity was observed with 15 other respiratory viruses. Using NGS as the reference method, the clinical evaluation of 232 swab samples exhibited a clinical sensitivity of > 95.12% (95% CI 89.77–97.75%) and a specificity of > 95.21% (95% CI, 91.15–97.46%). When used to evaluate the Omicron outbreak from late 2022 to early 2023, the MeltArray assay performed on 1408 samples revealed that the epidemic was driven by BA.5.2* (883, 62.71%) and BF.7 (525, 37.29%). Additionally, the MeltArray assay demonstrated potential for estimating variant abundance in wastewater samples. The MeltArray assay is a rapid and scalable method for identifying SARS-CoV-2 variants. Integrating this approach with NGS and ddPCR will improve variant surveillance capabilities and ensure preparedness for future variants.

With the promotion of China’s ”peak carbon and carbon neutrality” target, PV power generation is gradually entering the era of price parity, and cost will no longer be the main obstacle to its scale development, but grid consumption will become the main factor to restrict its further development. In order to further promote the consumption of PV and realize the low-carbon transformation of microgrid, this paper establishes a game pricing model led by Photovoltaic Cluster (PVC) based on the response of power demand, carries out the research on the optimal scheduling of PV in microgrid by establishing the game relationship among grid, PVC and users. Firstly, a new IEEE 33-node arithmetic simulation model is built, and secondly, three PVCs are divided among the nodes, and the feasibility of the model is verified by solving the optimal tariff strategy through genetic algorithm. The final results show that the optimal scheduling method is effective in meeting the demand response of users in PVCs while reducing the cost of electricity consumption and promoting the local consumption of PV; in addition, after increasing the proportion of PV access, PV will become the main supplier of electricity and the operation mode of PVCs will be free from dependence on the grid.

BackgroundThe goal of this study was to develop and validate a radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) preoperatively differentiating luminal and non-luminal molecular subtypes in patients with invasive breast cancer.MethodsOne hundred and thirty-five invasive breast cancer patients with luminal (n = 78) and non-luminal (n = 57) molecular subtypes were divided into training set (n = 95) and testing set (n = 40) in a 7:3 ratio. Demographics and MRI radiological features were used to construct clinical risk factors. Radiomics signature was constructed by extracting radiomics features from the second phase of DCE-MRI images and radiomics score (rad-score) was calculated. Finally, the prediction performance was evaluated in terms of calibration, discrimination, and clinical usefulness.ResultsMultivariate logistic regression analysis showed that no clinical risk factors were independent predictors of luminal and non-luminal molecular subtypes in invasive breast cancer patients. Meanwhile, the radiomics signature showed good discrimination in the training set (AUC, 0.86; 95% CI, 0.78–0.93) and the testing set (AUC, 0.80; 95% CI, 0.65–0.95).ConclusionThe DCE-MRI radiomics signature is a promising tool to discrimination luminal and non-luminal molecular subtypes in invasive breast cancer patients preoperatively and noninvasively.

Hepatic alveolar echinococcosis (HAE) is caused by the metacestode of Echinococcus multilocularis, which shows characteristics of malignant tumors with high mortality. However, traditional diagnostic imaging methods are still not sufficient for the recognition of HAE microlesions in the early stages. Near-infrared-II (900–1700 nm, NIR-II) fluorescence microscopic imaging (NIR-II-FMI) has shown great potential for biomedical detection. A novel type of negative target imaging method based on NIR-II-FMI with the assistance of indocyanine green (ICG) was explored. Then, NIR-II-FMI was applied to the early detection of HAE for the first time. The negative targeting NIR-II fluorescence imaging of HAE-infected mice at different stages with the assistance of ICG under 808 nm of laser irradiation was obtained. Especially, HAE microlesions at the early stage were detected clearly. Moreover, clear intrahepatic angiography was achieved under the same NIR-II-FMI system.

Non-small cell lung cancer (NSCLC) is the most common lung cancer diagnosis, among which epidermal growth factor receptor (EGFR), Kirsten rat sarcoma (KRAS), and anaplastic lymphoma kinase (ALK) mutations are the common genetic drivers. Their relative tyrosine kinase inhibitors (TKIs) have shown a better response for oncogene-driven NSCLC than chemotherapy. However, the development of resistance is inevitable following the treatments, which need a new strategy urgently. Although immunotherapy, a hot topic for cancer therapy, has shown an excellent response for other cancers, few responses for oncogene-driven NSCLC have been presented from the existing evidence, including clinical studies. Recently, the tumor microenvironment (TME) is increasingly thought to be a key parameter for the efficacy of cancer treatment such as targeted therapy or immunotherapy, while evidence has also shown that the TME could be affected by multi-factors, such as TKIs. Here, we discuss changes in the TME in NSCLC after TKI treatments, especially for EGFR-TKIs, to offer information for a new therapy of oncogene-driven NSCLC.

Abstract As a powerful and attractive method for detecting gene expression, qRT-PCR has been broadly used in aquaculture research. Understanding the biology of taimen (Hucho taimen) has drawn increasing interest because of its ecological and economic value. Stable reference genes are required for the reliable quantification of gene expression, but such genes have not yet been optimized for taimen. In this study, the stability levels of 10 commonly used candidate reference genes were evaluated using geNorm, NormFinder, BestKeeper, and RefFinder. The expression levels of the 10 genes were detected using 240 samples from 48 experimental groups consisting of 40 individuals treated under four heat-stress conditions (18, 20, 22, and 24 °C) for 24 h and 26 °C for 4, 24, 48, and 72 h. Six tissues (blood, heart, brain, gill, skin, and liver) were collected from each individual. Ribosomal protein S29 (RPS29) and ribosomal protein L19 (RPL19) were the most stable genes among all of the samples, whereas 28S ribosomal RNA (28S rRNA), attachment region binding protein (ARBP), and 18S ribosomal RNA (18S rRNA) were the least stable. These results were verified by an expression analysis of taimen heat-stress genes (heat shock protein 60, hsp60, and heat shock protein 70, hsp70). In conclusion, RPS29 and RPL19 are the optimal reference genes for qRT-PCR analyses of taimen, irrespective of the tissue and experimental conditions. These results allow the reliable study of gene expression in taimen.

ObjectivesThis study aimed to establish and validate a prognostic model based on magnetic resonance imaging and clinical features to predict the survival time of patients with glioblastoma multiforme (GBM).MethodsIn this study, a convolutional denoising autoencoder (DAE) network combined with the loss function of the Cox proportional hazard regression model was used to extract features for survival prediction. In addition, the Kaplan–Meier curve, the Schoenfeld residual analysis, the time-dependent receiver operating characteristic curve, the nomogram, and the calibration curve were performed to assess the survival prediction ability.ResultsThe concordance index (C-index) of the survival prediction model, which combines the DAE and the Cox proportional hazard regression model, reached 0.78 in the training set, 0.75 in the validation set, and 0.74 in the test set. Patients were divided into high- and low-risk groups based on the median prognostic index (PI). Kaplan–Meier curve was used for survival analysis (p = < 2e-16 in the training set, p = 3e-04 in the validation set, and p = 0.007 in the test set), which showed that the survival probability of different groups was significantly different, and the PI of the network played an influential role in the prediction of survival probability. In the residual verification of the PI, the fitting curve of the scatter plot was roughly parallel to the x-axis, and the p-value of the test was 0.11, proving that the PI and survival time were independent of each other and the survival prediction ability of the PI was less affected than survival time. The areas under the curve of the training set were 0.843, 0.871, 0.903, and 0.941; those of the validation set were 0.687, 0.895, 1.000, and 0.967; and those of the test set were 0.757, 0.852, 0.683, and 0.898.ConclusionThe survival prediction model, which combines the DAE and the Cox proportional hazard regression model, can effectively predict the prognosis of patients with GBM.

BackgroundIntra-abdominal candidiasis (IAC) is the predominant type of invasive candidiasis with high mortality in critically ill patients. This study aimed to investigate whether the polymerase chain reaction (PCR) assay for detecting Candida DNA in peritoneal fluids (PF) is useful in diagnosing and management of IAC in high-risk patients in intensive care unit (ICU).MethodsA prospective single-center cohort study of surgical patients at high risk for IAC was conducted in the ICU. PF was collected from the abdominal drainage tubes (within 24 h) or by percutaneous puncture. Direct PF smear microscopy, PF culture, blood culture, and serum (1–3)-β-D-glucan were performed in all patients. For Candida PCR assay, the ITS1/ITS4 primers that targeted the ITS1-5.8 s-ITS2 regions were used for PCR, and sequencing analysis was used to identify the pathogen at the species level. IAC was defined according to the 2013 European consensus criteria.ResultsAmong 83 patients at high risk for IAC, the IAC criteria were present in 17 (20.5%). The sensitivity and specificity of the Candida PCR assay were 64.7 and 89.4%, respectively, and the area under the receiver operating characteristic curve was 0.77 (95% CI: 0.63–0.91). In this cohort, the positive predictive value and negative predictive value were 90.8% (95% CI: 80.3–96.2%) and 61.1% (95% CI: 36.1–81.7%), respectively. Diagnostic consistency was moderate (kappa 0.529, p

Abstract Oxygymnocypris stewartii is a cold‐water cyprinid indigenous to China, where it inhabits the Yarlung Zangbo river and its tributaries at high elevations on the Qinghai–Tibet plateau. Owing to its restricted distribution and a gradual reduction in population size, this fish is listed as near threatened on the IUCN Red List, and it is classified as endangered at the national level. To investigate the species’ genetic diversity, this study used 26 pairs of microsatellite markers to analyse the genetic structure of six geographic populations of O. stewartii in the Yarlung Zangbo river basin (YZRB). The results showed moderate genetic diversity in O. stewartii, with an average effective number of alleles of 2.4052, expected heterozygosity of 0.4759, and polymorphism information content of 0.4210. Genetic structure analysis detected significant differentiation among the six populations (p

ABSTRACT Rapid identification and continuous surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are critical for guiding the response to the COVID-19 pandemic. Whole-genome sequencing (WGS) is a preferred tool for this aim, but many laboratories suffer from a lack of resources to support population-level sequencing. Here, we describe two PCR strategies targeting spike protein mutations to identify the Alpha, Delta, and Omicron variants. Signature mutations were selected using a dedicated bioinformatic program. The selected mutations in Alpha and Delta variants were detected using multicolor melting curve analysis (MMCA). Thirty-two mutations of the Omicron variant were targeted using the MeltArray approach in one reaction, which was able to detect the Omicron subvariants BA.1, BA.2, BA.3, and BA.4/5. The limits of detection varied from five to 50 copies of RNA templates/reactions. No cross-reactivity was observed with nine other respiratory viruses, including other coronaviruses. We validated the MMCA and MeltArray assays using 309 SARS-CoV-2 positive samples collected at different time points. These assays exhibited 98.3% to 100% sensitivity and 100% specificity compared with WGS. Multiplexed real-time PCR strategies represent an alternative tool capable of identifying current SARS-CoV-2 VOCs, adaptable for emerging variants and accessible for laboratories using existing equipment and personnel. IMPORTANCE Rapid detection and mutation surveillance of SARS-CoV-2 VOCs is crucial for COVID-19 control, management, and prevention. We developed two rapid molecular assays based on the real-time PCR platform to identify important variants of concern, including the Omicron variant with a large number of mutations. Signature mutations were selected by an R program. Then, MMCA assays were established for Alpha and Delta variants, and a MeltArray assay targeting 32 mutations was developed for Omicron variant. These multiplexed PCR assays could be performed in a 96-well real-time PCR instrument within 2.5 h, offering a high-throughput choice for dynamic monitoring of SARS-CoV-2 VOCs in a standard microbiology laboratory.

Photothermal therapy (PTT) has attracted extensive attention in cancer treatment due to its non-invasiveness, high efficiency, and repeatability in recent years. Photothermal agents (PTAs) are the key factor for PTT. Recently, although an increasing number of PTAs have been developed, there is still a great demand for optimized photothermal nanoparticles (NPs) with low toxicity, bio-safety and stability. Herein, new indocyanine green (IR820) with near-infrared (NIR:700–1,700 nm) fluorescence emission was selected as a photothermal agent (PTA). To enhance the PTT property, IR820 was encapsulated with another kind of PTA, polydopamine (PDA) under alkaline conditions. Furthermore, to improve the biocompatibility of the NPs, methoxy polyethylene glycol amine (mPEG-NH2) was modified via a Michael addition to form a novel kind of IR820@PDA@PEG NPs. After detailed characterization and analysis, the obtained IR820@PDA@PEG NPs showed a spherical shape with an average diameter of ∼159.6 nm. Meanwhile, the formed IR820@PDA@PEG NPs exhibited better photostability and lower cytotoxicity than free IR820 molecules. The photothermal performance of IR820@PDA@PEG NPs was further analyzed in vitro, and the temperature of IR820@PDA@PEG NPs (100 μg/ml) reached 54.8°C under 793 nm laser irradiation. Afterwards, the cellular uptake of IR820@PDA@PEG NPs was evaluated via confocal laser scanning fluorescence microscopic imaging. Then, PTT experiments on HeLa cells demonstrated that IR820@PDA@PEG NPs can hyperthermal ablate cancer cells (∼49.1%) under 793 nm laser irradiation. Therefore, IR820@PDA@PEG NPs would be a promising PTA for the treatment of cervical cancer HeLa cells.

Alzheimer’s disease (AD) is a degenerative brain disease, and the condition is difficult to assess. In the past, numerous brain dynamics models have made remarkable contributions to neuroscience and the brain from the microcosmic to the macroscopic scale. Recently, large-scale brain dynamics models have been developed based on dual-driven multimodal neuroimaging data and neurodynamics theory. These models bridge the gap between anatomical structure and functional dynamics and have played an important role in assisting the understanding of the brain mechanism. Large-scale brain dynamics have been widely used to explain how macroscale neuroimaging biomarkers emerge from potential neuronal population level disturbances associated with AD. In this review, we describe this emerging approach to studying AD that utilizes a biophysically large-scale brain dynamics model. In particular, we focus on the application of the model to AD and discuss important directions for the future development and analysis of AD models. This will facilitate the development of virtual brain models in the field of AD diagnosis and treatment and add new opportunities for advancing clinical neuroscience.

Abstract Aims/Introduction Emerging evidence shows that epigenetic modifications occurring during fetal development in response to intrauterine exposures could be one of the mechanisms involved in the early determinants of adult metabolic disorders. This study aimed to investigate whether the placental maternally expressed gene 3 (MEG3) deoxyribonucleic acid (DNA) methylation profile is associated with maternal gestational diabetes mellitus status and newborn birthweight. Materials and Methods Samples for measurement were collected from 23 women with gestational diabetes mellitus and 23 healthy controls. MEG3 gene expression and DNA methylation levels were assessed using quantitative real‐time polymerase chain reaction and MethylTargetTM, respectively. Pearson correlation analyses were used to examine associations between placental DNA methylation levels and clinical variables of interest. The associated results were adjusted by multivariate linear regression for maternal age, body mass index, height, gestational age and newborn sex as confounders. Results We found that the DNA methylation levels in the MEG3 differentially methylated region were significantly different between the gestational diabetes mellitus and control groups on the maternal side of the placenta (40.64 ± 2.15 vs 38.33 ± 2.92; P = 0.004). Furthermore, the mean MEG3 DNA methylation levels were correlated positively with maternal fasting glucose concentrations (R = 0.603, P

Background:Aedes albopictus is an indigenous primary vector of dengue and Zika viruses in China. Wolbachia is a gram-negative and common intracellular bacteria, which is maternally inherited endosymbionts and could expand their propagation in host populations by means of various manipulations. Compared with research on the dispersion of Ae. albopictus at the macrospatial level (mainly at the country or continent level), little is known about its variation and Wolbachia infection at the microspatial level, which is essential for its management. Meanwhile, no local cases of dengue fever have been recorded in the history of Nanjing, which implies that few adulticides have been applied in the city. Thus, the present study examines how the Ae. albopictus population varies and the Wolbachia infection status of each population among microspatial regions of Nanjing City.Methods: The genetic structure of 17 Aedes albopictus populations collected from urban, urban fringe, and rural regions of Nanjing City was investigated based on 9 microsatellite loci and the mitochondrial coxI gene. The Wolbachia infection status of each population was also assessed with Wolbachia A- and Wolbachia B-specific primers.Results: Nine out of 58 tested pairs of microsatellite markers were highly polymorphic, with a mean PIC value of 0.560, and these markers were therefore chosen for microsatellite genotyping analysis. The Na value of each Ae. albopictus population was very high, and the urban area populations (7.353 ± 4.975) showed a lower mean value than the urban fringe region populations (7.866 ± 5.010). A total of 19 coxI haplotypes were observed among 329 Ae. albopictus individuals via haplotype genotyping, with the highest diversity observed among the urban fringe Ae. albopictus populations (Hd = 0.456) and the lowest among the urban populations (Hd = 0.277). Each Ae. albopictus population showed significant departure from HWE, and significant population expansion was observed in only three populations from the urban (ZSL), urban fringe (HAJY), and rural areas (HSZY) (p < 0.05). Combined with DAPC analysis, all the Ae. albopictus populations were adequately allocated to two clades with significant genetic differences according to population structure analysis, and the best K value was equal to two. AMOVA results showed that most (96.18%) of the genetic variation detected in Ae. albopictus occurred within individuals (FIT = 0.22238, p < 0.0001), while no significant positive correlation was observed via isolation by distance (IBD) analysis (R2 = 0.03262, p = 0.584). The TCS network of all haplotypes showed that haplotype 1 (H1) and haplotype 4 (H4) were the most frequent haplotypes among all populations, and the haplotype frequency significantly increased from urban regions (36.84%) to rural regions (68.42%). Frequent migration was observed among Ae. albopictus populations from rural to urban regions via the urban fringe region, with four direct migration routes between rural and urban regions. Furthermore, Wolbachia genotyping results showed that most of the individuals of each population were coinfected with Wolbachia A and Wolbachia B. The independent infection rate of Wolbachia A was slightly higher than that of Wolbachia B, and no significant differences were observed among different regions.Conclusion: In the microspatial environment of Nanjing City, the urban fringe region is an important region for the dispersion of Ae. albopictus populations between rural and urban areas, and Wolbachia A and Wolbachia B coinfection is the most common Wolbachia infection status in all Ae. albopictus populations among different regions.

We aimed to investigate prognostic factors of in-stent restenosis (ISR) within 3 years in older acute coronary syndrome (ACS) patients after drug-eluting stent (DES) implantation and establish a clinical model for predicting ISR. We retrospectively collected 215 older ACS patients who followed coronary angiography (CAG) after DES implantation, divided into ISR group and non in-stent restenosis (non-ISR) group according to the results of reviewed CAG. Logistic regression analysis was performed to screen independent predictors related to ISR and build the clinical predictive model, which clinical application was assessed by decision curve analysis (DCA) and clinical impact curve (CIC). Kaplan-Meier survival curves for ISR by independent predictors. In multivariate logistic regression analysis showed that the red cell distribution width (RDW) was higher in ISR group compared with non-ISR (odds ratio (OR) = 1.54, 95% confidence interval (CI): 1.14–2.08, p

Abstract Background Mastoscopic surgery is proven to have lower incidence of postoperative complications and better postoperative recovery than traditional breast cancer surgery. This study aimed to examine the feasibility of mastoscopic modified radical mastectomy (MRM) with skin nipple-areola preservation under air cavity-free suspension hook and stage I silicone prosthesis implantation (SMALND) compared with routine MRM. Methods This was a retrospective study of patients who underwent MRM for breast cancer at the Shengjing Hospital Affiliated to China Medical University between January 1, 2019, and June 30, 2019. Surgical outcomes, complications, satisfaction, and quality of life (Functional Assessment of Cancer Therapy-Breast [FACT-B] [Chinese version]) were compared between the two groups. Results A total of 87 patients were enrolled, with 30 underwent SMALND and 57 underwent routine MRM. The intraoperative blood loss in the SMALND group was lower than in the control group (165.3±44.1 vs. 201.4±52.7 ml, P=0.001), the operation time was longer (220.5±23.9 vs. 155.6±9.2 min, P