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Here, we present the second generation of our bicyclic peptide library (NTB), featuring a stereodiversified structure and a simplified construction strategy. We utilized a tandem ring-opening metathesis and ring-closing metathesis reaction (ROM-RCM) to cyclize the linear peptide library in a single step, representing the first reported instance of this reaction being applied to the preparation of macrocyclic peptides. Moreover, the resulting bicyclic peptide can be easily linearized for MS/MS sequencing with a one-step deallylation process. We employed this library to screen against the E363-R378 epitope of MYC and identified several MYC-targeting bicyclic peptides. Subsequent in vitro cell studies demonstrated that one candidate, NT-B2R, effectively suppressed MYC transcription activities and cell proliferation.

Abstract Background Atherogenic index of plasma (AIP) index is an important marker of insulin resistance and a significant risk factor for cardiovascular disease. Abdominal aortic calcification (AAC) is significantly associated with subclinical atherosclerotic disease. However, there are no studies that have examined the relationship between AIP index and AAC, so we investigated the potential association between them in the general population. Methods This was a cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES, 2013–2014). The association of AIP with AAC was estimated by multivariable regression analysis. Results After adjusting for confounders, the odds of extensive AAC doubled per unit increase in the AIP index (OR = 2.00, 95% CI: 1.05, 3.83; P = 0.035). The multivariable OR and 95% CI of the highest AIP index tertile compared with the lowest tertile was significantly different. (OR = 1.73, 95% CI: 1.05, 2.83; P = 0.031). The subgroup analyses indicated that the association was consistent irrespective of age, sex, hypertension, diabetes, smoking status, eGFR and hypercholesteremia. Conclusions The AIP index was independently associated with the presence of extensive AAC in the study population. Further studies are required to confirm this relationship.

Abstract It is a time-consuming and costly process to develop affordable and high-performance organic photovoltaic materials. Computational methods are essential for accelerating the material discovery process by predicting power conversion efficiencies (PCE). In this study, we propose a deep learning-based framework (DeepAcceptor) to design and discover highly efficient small molecule acceptor materials. Specifically, an experimental dataset is constructed by collecting acceptor data from publications. Then, a deep learning-based model is customized to predict PCEs by applying graph representation learning to Bidirectional Encoder Representations from Transformers (BERT), with the atom, bond, and connection information in acceptor molecular structures as the input (abcBERT). The computational dataset derived from density functional theory (DFT) calculations and the experimental dataset from literature are used to pre-train and fine-tune the model, respectively. The abcBERT model outperforms other state-of-the-art models for the PCE prediction with MAE = 1.78 and R 2 = 0.67 on the test set. A molecular generation and screening process is built to find new high-performance acceptors for PM6. Three discovered candidates are further validated by experiment, and the best PCE reaches 14.61%. The released user-friendly interface of DeepAcceptor greatly boosts the accessibility and efficiency of designing and discovering high-performance acceptors. Altogether, the DeepAcceptor framework with abcBERT is promising to predict the PCE and accelerate the discovery of high-performance acceptor materials.

Abstract Background Individuals with metabolic syndrome face elevated cardiovascular and mortality risks, and there is ongoing debate regarding the cardiovascular effects of niacin and its impact on the prognosis of metabolic syndrome. Exposure Levels of dietary niacin intake based on 24-hour dietary recall. Methods Kaplan-Meier survival curves were used to compare survival status among quartiles of dietary niacin intake. Weighted Cox proportional hazards models and restricted cubic splines were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the risk of all-cause and CVD mortality associated with the exposure. Results This cohort study included 8,744 participants, and during a median follow-up period of 106 months, 1,552 (17.7%) deaths were recorded, with 511 attributed to cardiovascular disease. Kaplan-Meier curves comparing quartiles of dietary niacin intake showed significant differences in both all-cause and cardiovascular mortality rates (log-rank p

Abstract Background The glucose-to-glycated hemoglobin ratio (GAR) represents stress hyperglycemia, which has been closely associated with adverse outcomes in cardio-cerebrovascular diseases. No studies have examined the association between stress hyperglycemia and atrial fibrillation (AF) in critically ill patients. This study aims to explore the relationship between GAR and the prognosis of critically ill patients with AF. Methods A retrospective cohort of patients was selected from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The GAR was calculated based on fasting blood glucose and glycated hemoglobin levels measured after admission. The primary outcome was the 30-day mortality rate, with secondary outcomes being the 90-day and 365-day mortality rates. The GAR was divided into tertiles, and Kaplan–Meier analysis was employed to compare differences in mortality rates between groups. The Cox proportional hazards model and restricted cubic splines (RCS) were utilized to evaluate the relationship between the GAR and mortality. Subsequently, a segmented regression model was constructed to analyze threshold effects in cases where nonlinear relationships were determined. Results In this cohort, the second tertile of the GAR exhibited lower mortality rates at 30 days (10.56% vs 6.33% vs 14.51%), 90 days (17.11% vs 10.09% vs 17.88%), and 365 days (25.30% vs 16.15% vs 22.72%). In the third tertile, the risk of mortality at 30 days increased by 165% (HR = 2.65, 95% CI 1.99–3.54, p

Geometric and floral models are an important part of clothing and have been used for thousands of years. Although the styles of geometric models and flower models have undergone changes over the centuries, they are still one of the important factors of clothing patterns, the important carrier of aesthetics, and the manifestation of people’s spiritual views and cultural needs. The development and application of digital printing technology have freed people from excessive dependence on sewing and embroidery processes. Therefore, while deeply studying the design of clothing patterns, this work sorted and analyzed the geometric flower models of clothing through interactive genetic algorithms, and optimized programming to enrich the models of clothing with geometric textures. The results showed that the deviation value of geometric flower pattern design was constantly decreasing, while the optimal strategy value was constantly increasing. The mean deviation value was 0.82, which was a decrease of 0.21 on the seventh day compared with the first day; the mean value of the optimal strategy value was 0.84, which was an increase of 0.19 on the seventh day compared with the first day. The visual effect and creativity of the clothing flower pattern design under the interactive genetic algorithm are better than the traditional flower pattern design, and the visual effect and creativity under the interactive genetic algorithm are 9% higher than the traditional one.

Abstract This study aimed to explore the link between various forms of obesity, including body mass index (BMI) and waist circumference (WC), and the risk of dyslipidemia among Chinese residents. We selected the study population through a multi-stage random sampling method from permanent residents aged 35 and older in Ganzhou. Obesity was categorized as non-obesity, general obesity, central obesity, or compound obesity according to established diagnostic criteria. We employed a logistic regression model to assess the relationship between different types of obesity and the risk of dyslipidemia. Additionally, we used the restricted cubic spline model to analyze the association between BMI, WC, and the risk of dyslipidemia. The study included 2030 residents aged 35 or older from Ganzhou, China. The prevalence of dyslipidemia was found to be 39.31%, with an age-standardized prevalence of 36.51%. The highest prevalence of dyslipidemia, 58.79%, was observed among those with compound obesity. After adjusting for confounding factors, we found that the risk of dyslipidemia in those with central and compound obesity was respectively 2.00 (95% CI 1.62–2.46) and 2.86 (95% CI 2.03–4.03) times higher than in the non-obese population. Moreover, the analysis using the restricted cubic spline model indicated a nearly linear association between BMI, WC, and the risk of dyslipidemia. The findings emphasize the significant prevalence of both dyslipidemia and obesity among adults aged 35 and above in Ganzhou, China. Notably, individuals with compound obesity are at a substantially increased risk of dyslipidemia. Therefore, it is crucial to prioritize the use of BMI and WC as screening and preventive measures for related health conditions.

Abstract The evolution processes of complex systems carry key information in the systems’ functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating structure prediction, and particularly revealing the key co-evolution features of network structures such as preferential attachment, community structure, local clustering, degree-degree correlation that could not be explained collectively by previous theories. Intriguingly, we discover that for large networks, if the performance of the machine learning model is slightly better than a random guess on the pairwise order of links, reliable restoration of the overall network formation process can be achieved. This suggests that evolution history restoration is generally highly feasible on empirical networks.

Multiplex protein imaging technologies enable deep phenotyping and provide rich spatial information about biological samples. Existing methods have shown great success but also harbored trade-offs between various pros and cons, underscoring the persisting necessity to expand the imaging toolkits. Here we present PACIFIC: photoactive immunofluorescence with iterative cleavage, a new modality of multiplex protein imaging methods. PACIFIC achieves iterative multiplexing by implementing photocleavable fluorophores for antibody labeling with one-step spin-column purification. PACIFIC requires no specialized instrument, no DNA encoding, or chemical treatments. We demonstrate that PACIFIC can resolve cellular heterogeneity in both formalin-fixed paraffin-embedded (FFPE) samples and fixed cells. To further highlight how PACIFIC assists discovery, we integrate PACIFIC with live-cell tracking and identify phosphor-p70S6K as a critical driver that governs U87 cell mobility. Considering the cost, flexibility, and compatibility, we foresee that PACIFIC can confer deep phenotyping capabilities to anyone with access to traditional immunofluorescence platforms.

Summary: Background: The accurate evaluation of axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) in breast cancer holds great value. This study aimed to develop an artificial intelligence system utilising multiregional dynamic contrast-enhanced MRI (DCE-MRI) and clinicopathological characteristics to predict axillary pathological complete response (pCR) after NAC in breast cancer. Methods: This study included retrospective and prospective datasets from six medical centres in China between May 2018 and December 2023. A fully automated integrated system based on deep learning (FAIS-DL) was built to perform tumour and ALN segmentation and axillary pCR prediction sequentially. The predictive performance of FAIS-DL was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RNA sequencing analysis were conducted on 45 patients to explore the biological basis of FAIS-DL. Findings: 1145 patients (mean age, 50 years ±10 [SD]) were evaluated. Among these patients, 506 were in the training and validation sets (axillary pCR rate of 40.3%), 127 in the internal test set (axillary pCR rate of 37.8%), 414 in the pooled external test set (axillary pCR rate of 48.8%), and 98 in the prospective test set (axillary pCR rate of 43.9%). For predicting axillary pCR, FAIS-DL achieved AUCs of 0.95, 0.93, and 0.94 in the internal test set, pooled external test set, and prospective test set, respectively, which were also significantly higher than those of the clinical model and deep learning models based on single-regional DCE-MRI (all P

Increasing evidence indicates that the remodeling of immune microenvironment heterogeneity influences pancreatic cancer development, as well as sensitivity to chemotherapy and immunotherapy. However, a gap remains in the exploration of the immunosenescence microenvironment in pancreatic cancer. In this study, we identified two immunosenescence-associated isoforms (IMSP1 and IMSP2), with consequential differences in prognosis and immune cell infiltration. We constructed the MLIRS score, a hazard score system with robust prognostic performance (area under the curve, AUC = 0.91), based on multiple machine learning algorithms (101 cross-validation methods). Patients in the high MLIRS score group had worse prognosis (P

Unilateral auditory deprivation (UAD) results in cross-modal reorganization of the auditory cortex (AC), which can impair auditory and cognitive functions and diminish the recovery effect of cochlear implantation. Moreover, the subcortical areas provide extensive ascending projections to the AC. To date, a thorough systematic study of subcortical auditory neural plasticity has not been undertaken. Therefore, this review aims to summarize the current evidence on the bidirectional remodeling of the central auditory system caused by UAD, particularly the changes in subcortical neural plasticity. Lateral changes occur in the cochlear nucleus, lateral superior olive, medial nucleus of the trapezoid body, inferior colliculus, and AC of individuals with UAD. Moreover, asymmetric neural activity becomes less prominent in the higher auditory nuclei, which may be due to cross-projection regulation of the bilateral pathway. As a result, subcortical auditory neural plasticity caused by UAD may contribute to the outcomes of cochlear implantation in patients with single-sided deafness (SSD), and the development of intervention strategies for patients with SSD is crucial. Considering that previous studies have focused predominantly on the neural plasticity of the AC, we believe that bidirectional remodeling of subcortical areas after UAD is also crucial for investigating the mechanisms of interventions.

PurposeThis study aimed to develop and validate a radiogenomics nomogram for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) on the basis of MRI and microRNAs (miRNAs).Materials and methodsThis cohort study included 168 patients (training cohort: n = 116; validation cohort: n = 52) with pathologically confirmed HCC, who underwent preoperative MRI and plasma miRNA examination. Univariate and multivariate logistic regressions were used to identify independent risk factors associated with MVI. These risk factors were used to produce a nomogram. The performance of the nomogram was evaluated by receiver operating characteristic curve (ROC) analysis, sensitivity, specificity, accuracy, and F1-score. Decision curve analysis was performed to determine whether the nomogram was clinically useful.ResultsThe independent risk factors for MVI were maximum tumor length, rad-score, and miRNA-21 (all P < 0.001). The sensitivity, specificity, accuracy, and F1-score of the nomogram in the validation cohort were 0.970, 0.722, 0.884, and 0.916, respectively. The AUC of the nomogram was 0.900 (95% CI: 0.808–0.992) in the validation cohort, higher than that of any other single factor model (maximum tumor length, rad-score, and miRNA-21).ConclusionThe radiogenomics nomogram shows satisfactory predictive performance in predicting MVI in HCC and provides a feasible and practical reference for tumor treatment decisions.

Abstract Background Previous studies have found that frailty and sarcopenia are commonly diagnosed in inflammatory bowel disease (IBD) patients, indicating an association between these conditions. Nonetheless, the cause‒effect connection between IBD, frailty, and sarcopenia remains unclear. Methods We sourced the genetic variants for the exposures and outcomes from publicly accessible, extensive genome-wide association studies (GWAS). Specifically, we obtained IBD data from the International IBD Genetics Consortium, frailty index (FI) data from the United Kingdom Biobank and Swedish TwinGene, and sarcopenia data from a recent GWAS meta-analysis. Five methods, including inverse variance weighted (IVW), simple mode, MR-Egger, weighted mode, and the weighted median, were used to proceed with MR estimates. We also performed heterogeneity and horizontal pleiotropy tests. Results Our results indicated a positive causal relationship between ulcerative colitis (UC) (IVW: β = 0.014, 95% CI, 0.006 to 0.021, p = 0.001) and Crohn's disease (CD) (IVW: β = 0.012; 95% CI, 0.006 to 0.018, p = 2e−04) with the FI. However, we uncovered no proof of a cause-and-effect relationship between UC (IVW: β = 0.001, 95% CI, −0.015 to 0.017, p = 0.344) or CD (IVW: β = 0.003, 95% CI, −0.009 to 0.015, p = 0.214) and sarcopenia. Additionally, in the inverse order, we also discovered no cause-and-effect connection between FI or sarcopenia on UC or CD in this study. Conclusion The MR analysis showed a positive causal association between IBD and FI, indicating that IBD patients may exhibit aging-related characteristics. Therefore, frailty assessments should be conducted as early as possible in IBD patients.

Abstract Background Pyroptosis plays a pivotal role in the tumor immune microenvironment (TME) dynamics, particularly in non‐small cell lung cancer (NSCLC). The aim of our study was to explore its effects on tumor progression, TME patterns, and the efficacy of therapeutic interventions in NSCLC. Methods Our investigation encompassed a thorough analysis of pyroptosis‐related genes (PRGs), integrating immunohistochemistry (IHC) data, TME characteristics, stemness indices, and anticancer drug sensitivities. We aimed to analyze mRNA expression profiles across various cancers, constructing benchmark datasets to assess the clinical significance of PRGs in NSCLC. This included evaluating their association with clinical responses and efficacy. Notably, both our and HPA IHC data demonstrated significantly elevated GSDMD‐N protein levels in lung squamous cell carcinoma (LUSC) tissues. Results The expression of PRGs differed significantly between tumor and normal tissues across various cancers, as validated by IHC data, and was correlated with prognosis (p

Abstract Non-alcoholic fatty liver disease (NAFLD) has been shown to influence breast cancer progression, but the underlying mechanisms remain unclear. In this study, we investigated the impact of NAFLD on breast cancer tumor growth and cell viability through the potential mediator, hepatic fibroblast growth factor 21 (FGF21). Both peritumoral and systemic administration of FGF21 promoted breast cancer tumor growth, while FGF21 knockout attenuated the tumor-promoting effects of the high-fat diet. Mechanistically, exogenous FGF21 treatment enhanced the anti-apoptotic ability of breast cancer cells through STAT3 and Akt/FoXO1 signaling pathways, and mitigated doxorubicin-induced cell death. Furthermore, we observed overexpression of FGF21 in tumor tissues from breast cancer patients, which was associated with poor prognosis. These findings suggest a novel role for FGF21 as an upregulated mediator in the context of NAFLD, promoting breast cancer development and highlighting its potential as a therapeutic target for cancer treatment.

Abstract Radial spokes (RS) transmit mechanochemical signals between the central pair (CP) and axonemal dynein arms to coordinate ciliary motility. Atomic-resolution structures of metazoan RS and structures of axonemal complexes in ependymal cilia, whose rhythmic beating drives the circulation of cerebrospinal fluid, however, remain obscure. Here, we present near-atomic resolution cryo-EM structures of mouse RS head-neck complex in both monomer and dimer forms and reveal the intrinsic flexibility of the dimer. We also map the genetic mutations related to primary ciliary dyskinesia and asthenospermia on the head-neck complex. Moreover, we present the cryo-ET and sub-tomogram averaging map of mouse ependymal cilia and build the models for RS1-3, IDAs, and N-DRC. Contrary to the conserved RS structure, our cryo-ET map reveals the lack of IDA-b/c/e and the absence of Tektin filaments within the A-tubule of doublet microtubules in ependymal cilia compared with mammalian respiratory cilia and sperm flagella, further exemplifying the structural diversity of mammalian motile cilia. Our findings shed light on the stepwise mammalian RS assembly mechanism, the coordinated rigid and elastic RS-CP interaction modes beneficial for the regulation of asymmetric ciliary beating, and also facilitate understanding on the etiology of ciliary dyskinesia-related ciliopathies and on the ependymal cilia in the development of hydrocephalus.

The dynamic changes in grassland types are crucial for conserving grassland biodiversity, conducting comprehensive health assessments, and gaining insights into ecosystem evolution. However, accurately mapping grassland types remains an ongoing challenge, especially over large areas. In response, we developed a hierarchical classification framework using random forest to tackle this task. This framework was structured into two tiers: land cover (LC) classification and grassland-type mapping, with each tier using specific features tailored to its respective objectives. In this framework, LC samples were automatically generated using existing LC products combined with vegetation indices and phenological features. We fused spectral information, phenological features, and habitat factors such as topographic indices, soil, and climate from multisource earth observation (EO) data to enhance grassland-type mapping performance. This framework successfully generated distribution maps of grassland types on the Qinghai Plateau for the years 1990–2020. Our findings revealed the following: (1) Using the two-tier classification framework and fusing multisource features, an ideal distribution map of grassland types was obtained, with a macro-average F1 score (F1) of 91% and an overall accuracy (OA) of 96%; (2) Compared to a one-shot classification framework, the two-tier classification framework achieved higher accuracy, with F1 and OA increasing by 11% and 7%, respectively; (3) Climate, topographical, soil, and phenological features assisted in distinguishing grassland types with similar spectral characteristics, especially for zonal grasslands. Adding these features increased F1 by 21%, 10%, 7%, and 3%, respectively, while OA increased by 9%, 5%, 5%, and 2%, respectively. Among these features, relative humidity, total precipitation from May to September, geographic coordinates, and elevation had the greatest effect on grassland type differentiation. June and July were the optimal phenological periods for mapping; (4) Over the 30-year period, the grasslands on the Qinghai Plateau showed an expanding trend, with extensive areas of alpine steppes transforming into alpine meadows. The results of this study provide the first elucidation of grassland-type changes across the Qinghai Plateau from 1990 to 2020. Moreover, they underscore the potential of hierarchical classification frameworks and the integration of multisource EO data in mapping grassland-type distribution.

Delivering cargo molecules across the plasma membrane is critical for biomedical research, and the need to develop molecularly well-defined tags that enable cargo transportation is ever-increasing. We report here a hydrophilic endocytosis-promoting peptide (EPP6) rich in hydroxyl groups with no positive charge. EPP6 can transport a wide array of small-molecule cargos into a diverse panel of animal cells. Mechanistic studies revealed that it entered the cells through a caveolin- and dynamin-dependent endocytosis pathway, mediated by the surface receptor fibrinogen C domain-containing protein 1. After endocytosis, EPP6 trafficked through early and late endosomes within 30 min. Over time, EPP6 partitioned among cytosol, lysosomes, and some long-lived compartments. It also demonstrated prominent transcytosis abilities in both in vitro and in vivo models. Our study proves that positive charge is not an indispensable feature for hydrophilic cell-penetrating peptides and provides a new category of molecularly well-defined delivery tags for biomedical applications.

With the widespread application of energy storage stations, BMS has become an important subsystem in modern power systems, leading to an increasing demand for improving the accuracy of SOC prediction in lithium-ion battery energy storage systems. Currently, common methods for predicting battery SOC include the Ampere-hour integration method, open circuit voltage method, and model-based prediction techniques. However, these methods often have limitations such as single-variable research, complex model construction, and inability to capture real-time changes in SOC. In this paper, a novel prediction method based on the KF-SA-Transformer model is proposed by combining model-based prediction techniques with data-driven methods. By using temperature, voltage, and current as inputs, the limitations of single-variable studies in the Ampere-hour integration method and open circuit voltage method are overcome. The Transformer model can overcome the complex modeling process in model-based prediction techniques by implementing a non-linear mapping between inputs and SOC. The presence of the Kalman filter can eliminate noise and improve data accuracy. Additionally, a sparse autoencoder mechanism is integrated to optimize the position encoding embedding of input vectors, further improving the prediction process. To verify the effectiveness of the algorithm in predicting battery SOC, an open-source lithium-ion battery dataset was used as a case study in this paper. The results show that the proposed KF-SA-Transformer model has superiority in improving the accuracy and reliability of battery SOC prediction, playing an important role in the stability of the grid and efficient energy allocation.

Epilepsy is a common chronic brain disorder. Detecting epilepsy by observing electroencephalography (EEG) is the main method neurologists use, but this method is time-consuming. EEG signals are non-stationary, nonlinear, and often highly noisy, so it remains challenging to recognize epileptic EEG signals more accurately and automatically. This paper proposes a novel classification system of epileptic EEG signals for single-channel EEG based on the attention network that integrates time-frequency and nonlinear dynamic features. The proposed system has three novel modules. The first module constructs the Hilbert spectrum (HS) with high time-frequency resolution into a two-channel parallel convolutional network. The time-frequency features are fully extracted by complementing the high-dimensional features of the two branches. The second module constructs a grayscale recurrence plot (GRP) that contains more nonlinear dynamic features than traditional RP, fed into the residual-connected convolution module for effective learning of nonlinear dynamic features. The third module is the feature fusion module based on a self-attention mechanism to assign optimal weights to different types of features and further enhance the information extraction capability of the system. Therefore, the system is named HG-SANet. The results of several classification tasks on the Bonn EEG database and the Bern-Barcelona EEG database show that the HG-SANet can effectively capture the contribution degree of the extracted features from different domains, significantly enhance the expression ability of the model, and improve the accuracy of the recognition of epileptic EEG signals. The HG-SANet can improve the diagnosis and treatment efficiency of epilepsy and has broad application prospects in the fields of brain disease diagnosis.

Abstract Understanding the impact of long‐term opioid exposure on the embryonic brain is critical due to the surging number of pregnant mothers with opioid dependency. However, this has been limited by human brain inaccessibility and cross‐species differences in animal models. Here, a human midbrain model is established that uses hiPSC‐derived midbrain organoids to assess cell‐type‐specific responses to acute and chronic fentanyl treatment and fentanyl withdrawal. Single‐cell mRNA sequencing of 25,510 cells from organoids in different treatment groups reveals that chronic fentanyl treatment arrests neuronal subtype specification during early midbrain development and alters synaptic activity and neuron projection. In contrast, acute fentanyl treatment increases dopamine release but does not significantly alter gene expression related to cell lineage development. These results provide the first examination of the effects of opioid exposure on human midbrain development at the single‐cell level.

IntroductionMicrostate analysis enables the characterization of quasi-stable scalp potential fields on a sub-second timescale, preserving the temporal dynamics of EEG and spatial information of scalp potential distributions. Owing to its capacity to provide comprehensive pathological insights, it has been widely applied in the investigation of schizophrenia (SCZ). Nevertheless, previous research has primarily concentrated on differences in individual microstate temporal characteristics, neglecting potential distinctions in microstate semantic sequences and not fully considering the issue of the universality of microstate templates between SCZ patients and healthy individuals.MethodsThis study introduced a microstate semantic modeling analysis method aimed at schizophrenia recognition. Firstly, microstate templates corresponding to both SCZ patients and healthy individuals were extracted from resting-state EEG data. The introduction of a dual-template strategy makes a difference in the quality of microstate sequences. Quality features of microstate sequences were then extracted from four dimensions: Correlation, Explanation, Residual, and Dispersion. Subsequently, the concept of microstate semantic features was proposed, decomposing the microstate sequence into continuous sub-sequences. Specific semantic sub-sequences were identified by comparing the time parameters of sub-sequences.ResultsThe SCZ recognition test was performed on the public dataset for both the quality features and semantic features of microstate sequences, yielding an impressive accuracy of 97.2%. Furthermore, cross-subject experimental validation was conducted, demonstrating that the method proposed in this paper achieves a recognition rate of 96.4% between different subjects.DiscussionThis research offers valuable insights for the clinical diagnosis of schizophrenia. In the future, further studies will seek to augment the sample size to enhance the effectiveness and reliability of this method.

AIM: To investigate the changes in retinal nerve fiber layer(RNFL)and macular retinal thickness(MRT)in children with refractive abnormalities and amblyopia, and their predictive value of outcome.METHODS: A total of 168 children with myopic refractive abnormalities and monocular amblyopia admitted to our hospital from January 2020 to October 2022 were selected as the observation group, with 118 cases of mild to moderate amblyopia and 50 cases of severe amblyopia, and 168 children with normal vision were included as the control group in a 1:1 ratio during the same period. The changes of RNFL and MRT in two groups of children were statistically counted, and the correlation between the severity of refractive abnormalities and RNFL and MRT in children with amblyopia was analyzed. Additionally, the observation group was divided into effective subgroup and ineffective subgroup based on the therapeutic effect. The general information, as well as RNFL and MRT of the effective subgroup and the ineffective subgroups before and after treatment were compared. Logistic was used to analyze the factors influencing efficacy, and ROC curves was plotted to analyze the predictive value of RNFL and MRT alone or in combination for efficacy.RESULTS: RNFL and MRT of cases of severe amblyopia were higher than those of the mild to moderate amblyopia and the control groups(all P

Objective: Laryngeal cancer (LC) is one of the most common squamous cell carcinomas of the head and neck in clinical practice, and its incidence has been increasing in recent years, but the prognosis of the patients is not favorable. Hence, it is critical to re-understand and deeply study the causes and mechanisms of LC and explore new effective treatment methods and strategies. In this study, we analyzed the effect of Dihydroartemisinin (DHA) on the pathological progression of LC through the periostin (POSTN)/Yes-associated protein (YAP)/interleukin (IL)-6 pathway, which can provide new clinical references and guidelines. Methods: POSTN, YAP, and IL-6 levels in 18 pairs of fresh LC tissues and adjacent counterparts in our hospital were detected. Additionally, LC TU686 cell line was purchased for DHA treatment of various concentrations to detect changes in cell biological behavior. Finally, we built a tumor-bearing mouse model with C57BL/6 mice and intragastrically administrated DHA to the animals to observe the growth of living tumors and to measure POSTN, YAP, and IL-6 expression in tumor tissues. Results: As indicated by PCR, Western blotting, and immunohistochemistry, POSTN, YAP, and IL-6 presented higher expression in LC tissues than in adjacent counterparts. In cell experiments, the cloning rate of LC cells decreased and the apoptosis rate increased after DHA intervention, with 160 μmol/L DHA contributing to the most significant effect on LC activity inhibition. Furthermore, DHA-intervened cells exhibited markedly reduced POSTN, YAP, and IL-6 levels. Finally, the tumorigenesis experiment in nude mice showed inhibited tumor growth after DHA administration. And consistently, the expressions of POSTN, YAP, and IL-6 in living tumors decreased. Conclusions: DHA can inhibit POSTN/YAP/IL-6 transduction, accelerate LC cell apoptosis, and alleviate the malignant progression of LC.

Background: Malignant tumours, particularly non-small cell lung cancer (NSCLC), pose a significant threat to human health due to their prevalence and lethality. Treatment methods for NSCLC vary greatly among individuals, making it crucial to identify predictive markers. Moreover, during tumour initiation and progression, tumour cells can release signaling molecules to induce polarization of macrophages towards a more tumour friendly M2 phenotype, which can promote tumour growth, metastasis, and drug resistance. Methods: We employed a comprehensive approach, combining bulk RNA-seq and single-cell sequencing analysis. Results: In our study, we used bulk RNA-seq and single-cell sequencing methods to analyze differential cells in NSCLC and adjacent tissues, searching for relevant marker genes that can predict prognosis and drug efficacy. We scrutinized biological phenomena such as macrophage-related gene methylation, copy number variation, and alternative splicing. Additionally, we utilized a co-culture technique of immune and tumour cells to explore the role of these genes in macrophage polarization. Our findings revealed distinct differences in macrophages between cancerous and adjacent tissues. We identified ANP32A, CCL20, ERAP2, MYD88, TMEM126B, TUBB6, and ZNF655 as macrophage-related genes that correlate with NSCLC patient prognosis and immunotherapy efficacy. Notably, ERAP2, TUBB6, CCL20, and TMEM126B can induce macrophage M0 to M2 polarization, promoting tumour proliferation. Conclusion: These findings significantly contribute to our understanding of the NSCLC tumour immune microenvironment. They pave the way for further research into the potential of these genes as targets for regulating tumour occurrence and development.

Summary: Background: The mRNA vaccine has demonstrated significant effectiveness in protecting against SARS-CoV-2 during the pandemic, including against severe forms of the disease caused by emerging variants. In this study, we examined safety, immunogenicity, and relative efficacy of a heterologous booster of the lipopolyplex (LPP)-based mRNA vaccine (SW-BIC-213) versus a homologous booster of an inactivated vaccine (BBIBP) in Laos. Methods: In this phase 3 clinical trial, which was randomized, parallel controlled and double-blinded, healthy adults aged 18 years and above were recruited from the Southern Savannakhet Provincial Hospital and Champhone District Hospital. The primary outcomes were safety and immunogenicity, with efficacy as an exploratory endpoint. Participants who were fully immunized with a two-dose inactivated vaccine for more than 6 months were assigned equally to either the SW-BIC-213 group (25 μg) or BBIBP group. The primary safety endpoint was to describe the safety profile of all participants in each group up to 6 months post-booster immunization. The primary immunogenic outcome was to demonstrate the superiority of the neutralizing antibody response, in terms of geometric mean titers (GMTs) of SW-BIC-213, compared with BBIBP 28 days after the booster dose. The exploratory efficacy endpoint aimed to assess the relative efficacy of SW-BIC-213 compared to BBIBP against virologically confirmed symptomatic COVID-19 over a 6-month period. The trial was registered with ClinicalTrials.gov (NCT05580159). Findings: Between October 10, 2022, and January 13, 2023, 1200 participants were assigned to SW-BIC-213 group and 1203 participants in the BBIBP group. All adverse reactions observed during the study were tolerable, transient, and resolved spontaneously. Solicited local reactions were the main adverse reactions in both the SW-BIC-213 group (43.8%) and BBIBP group (14.8%) (p