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Abstract Intrahepatic cholangiocarcinoma (ICC) is the second most common primary hepatic malignancy after hepatocellular carcinoma, with poor prognosis and limited treatment options. The genomic features of ICC in Chinese patients remain largely unknown. In this study, we perform deep whole-exome sequencing of 204 Chinese primary ICCs and characterize genomic alterations and clonal evolution, and reveal their associations with patient outcomes. We identify six mutational signatures, including Signatures A and F, which are highly similar to previously described signatures linked to aristolochic acid and aflatoxin exposures, respectively. We also identify 13 significantly mutated genes in the ICC samples, including SAV1. We find that SAV1 was mutated in 2.9% (20/672) of 672 ICC samples. SAV1 mutation is associated with lower SAV1 protein levels, higher rates of tumor recurrence, and shorter overall patient survival. Biofunctional investigations reveal a tumor-suppressor role of SAV1: its inactivation suppresses Hippo signaling, leading to YAP activation, thereby promoting tumor growth and metastasis. Collectively, our results delineate the genomic landscape of Chinese ICCs and identify SAV1 as a potential driver of ICC.

Abstract Background The efficacy of immune checkpoint blockade therapy in patients with hepatocellular carcinoma (HCC) remains poor. Although serine‐ and arginine‐rich splicing factor (SRSF) family members play crucial roles in tumors, their impact on tumor immunology remains unclear. This study aimed to elucidate the role of SRSF10 in HCC immunotherapy. Methods To identify the key genes associated with immunotherapy resistance, we conducted single‐nuclear RNA sequencing, multiplex immunofluorescence, and The Cancer Genome Atlas and Gene Expression Omnibus database analyses. We investigated the biological functions of SRSF10 in immune evasion using in vitro co‐culture systems, flow cytometry, various tumor‐bearing mouse models, and patient‐derived organotypic tumor spheroids. Results SRSF10 was upregulated in various tumors and associated with poor prognosis. Moreover, SRSF10 positively regulated lactate production, and SRSF10/glycolysis/ histone H3 lysine 18 lactylation (H3K18la) formed a positive feedback loop in tumor cells. Increased lactate levels promoted M2 macrophage polarization, thereby inhibiting CD8+ T cell activity. Mechanistically, SRSF10 interacted with the 3′‐untranslated region of MYB, enhancing MYB RNA stability, and subsequently upregulating key glycolysis‐related enzymes including glucose transporter 1 (GLUT1), hexokinase 1 (HK1), lactate dehydrogenase A (LDHA), resulting in elevated intracellular and extracellular lactate levels. Lactate accumulation induced histone lactylation, which further upregulated SRSF10 expression. Additionally, lactate produced by tumors induced lactylation of the histone H3K18la site upon transport into macrophages, thereby activating transcription and enhancing pro‐tumor macrophage activity. M2 macrophages, in turn, inhibited the enrichment of CD8+ T cells and the proportion of interferon‐γ+CD8+ T cells in the tumor microenvironment (TME), thus creating an immunosuppressive TME. Clinically, SRSF10 could serve as a biomarker for assessing immunotherapy resistance in various solid tumors. Pharmacological targeting of SRSF10 with a selective inhibitor 1C8 enhanced the efficacy of programmed cell death 1 (PD‐1) monoclonal antibodies (mAbs) in both murine and human preclinical models. Conclusions The SRSF10/MYB/glycolysis/lactate axis is critical for triggering immune evasion and anti‐PD‐1 resistance. Inhibiting SRSF10 by 1C8 may overcome anti‐PD‐1 tolerance in HCC.

Abstract The efficacy and safety of venetoclax in newly diagnosed pediatric acute myeloid leukemia (AML) are not well-established as they are in adults. Children newly diagnosed with AML were recommended for induction therapy with venetoclax and chemotherapy or hypomethylating agents (HMAs) as per for the ChiCTR1900027146 trial. Venetoclax was administered at a consistent dose of 200 mg/m2/day for 28 days, with adjustments when used concurrently with azoles. The study measured both the remission rates and the safety assessments of venetoclax. We enrolled 45 newly diagnosed pediatric patients with AML. The complete remission rates were 94.7% in the low/middle-risk group and 80.8% in the high-risk group; MRD-negative rates were 52.6% and 38.5% in the low/middle-risk group and high-risk group, respectively. Venetoclax based combination therapy was well tolerated by the majority of patients. The median duration of venetoclax dosing was 18 days (range 9–28), with hematological toxicity and infection being the most common adverse events. Venetoclax-based induction regimens demonstrated a high response rate and safety profile in newly diagnosed pediatric AML cases. This underscores the significance of venetoclax as a viable treatment option for untreated AML, extending beyond its role as salvage therapy for refractory/relapsed AML.

Backgrounds/Aims Intrahepatic cholangiocarcinoma (ICC) is a highly desmoplastic tumor with poor prognosis even after curative resection. We investigated the associations between the composition of the ICC stroma and immune cell infiltration and aimed to develop a stromal-immune signature to predict prognosis in surgically treated ICC. Methods We recruited 359 ICC patients and performed immunohistochemistry to detect α-smooth muscle actin (α-SMA), CD3, CD4, CD8, Foxp3, CD68, and CD66b. Aniline was used to stain collagen deposition. Survival analyses were performed to detect prognostic values of these markers. Recursive partitioning for a discrete-time survival tree was applied to define a stromal-immune signature with distinct prognostic value. We delineated an integrated stromal-immune signature based on immune cell subpopulations and stromal composition to distinguish subgroups with different recurrence-free survival (RFS) and overall survival (OS) time. Results We defined four major patterns of ICC stroma composition according to the distributions of α-SMA and collagen: dormant (α-SMAlow/collagenhigh), fibrogenic (α-SMAhigh/collagenhigh), inert (α-SMAlow/collagenlow), and fibrolytic (α-SMAhigh/collagenlow). The stroma types were characterized by distinct patterns of infiltration by immune cells. We divided patients into six classes. Class I, characterized by high CD8 expression and dormant stroma, displayed the longest RFS and OS, whereas Class VI, characterized by low CD8 expression and high CD66b expression, displayed the shortest RFS and OS. The integrated stromal-immune signature was consolidated in a validation cohort. Conclusions We developed and validated a stromal-immune signature to predict prognosis in surgically treated ICC. These findings provide new insights into the stromal-immune response to ICC.

With rapid development and maturity of single-cell measurements, single-cell biology and pathology has become an emerging discipline to understand the disease. However, it is important to address concerns raised by clinicians as to how to apply single-cell measurements for clinical practice, translate the signals of single-cell systems biology into determination of clinical phenotype, and predict patient response to therapies. This paper proposes a new system coined as the clinical artificial intelligent single-cell (caiSC) with the dynamic generator of clinical single-cell informatics, artificial intelligent analyzers, molecular multimodal reference boxes, clinical inputs and outputs, and AI-based computerization. This system provides reliable and rapid information for impacting clinical diagnoses, monitoring, and prediction of the disease at the single-cell level. The caiSC represents an important step and milestone to translate the single-cell measurement into clinical application, assist clinicians’ decision-making, and improve the quality of medical services. There is increasing evidence to support the possibility of the caiSC proposal, since the corresponding biotechnologies associated with caiSCs are rapidly developed. Therefore, we call the special attention and efforts from various scientists and clinicians on the caiSCs and believe that the appearance of the caiSCs can shed light on the future of clinical molecular medicine.

Abstract Single-cell technology depicts integrated tumor profiles including both tumor cells and tumor microenvironments, which theoretically enables more robust diagnosis than traditional diagnostic standards based on only pathology. However, the inherent challenges of single-cell RNA sequencing (scRNA-seq) data, such as high dimensionality, low signal-to-noise ratio (SNR), sparse and non-Euclidean nature, pose significant obstacles for traditional diagnostic approaches. The diagnostic value of single-cell technology has been largely unexplored despite the potential advantages. Here, we present a graph neural network-based framework tailored for molecular diagnosis of primary liver tumors using scRNA-seq data. Our approach capitalizes on the biological plausibility inherent in the intercellular communication networks within tumor samples. By integrating pathway activation features within cell clusters and modeling unidirectional inter-cellular communication, we achieve robust discrimination between malignant tumors (including hepatocellular carcinoma, HCC, and intrahepatic cholangiocarcinoma, iCCA) and benign tumors (focal nodular hyperplasia, FNH) by scRNA data of all tissue cells and immunocytes only. The efficacy to distinguish iCCA from HCC was further validated on public datasets. Through extending the application of high-throughput scRNA-seq data into diagnosis approaches focusing on integrated tumor microenvironment profiles rather than a few tumor markers, this framework also sheds light on minimal-invasive diagnostic methods based on migrating/circulating immunocytes.

Hepatocellular carcinoma (HCC) is a significant global health challenge, requiring innovative methods to improve patient survival. Due to different disease backgrounds, different HCC management guidelines have been developed, especially in the field of surgical treatment, with the aim of reducing the risk of incidence and enhancing the therapeutic effect. Focusing on the progress and challenges in the development of surgical management strategies for HCC in recent years, this article systematically elaborates on the research and clinical application of precision surgical treatment, including improvement of the surgical evaluation system, breakthroughs in surgical techniques, and updates in perioperative treatment concepts. In addition, clinical research on surgical treatment for HCC has received unprecedented attention. The conclusions of innovative clinical trials in surgery will provide important guidance for the development of practice guidelines and the selection of appropriate treatment strategies for HCC patients.

Abstract Background Tumor heterogeneity presents a formidable challenge in understanding the mechanisms driving tumor progression and metastasis. The heterogeneity of hepatocellular carcinoma (HCC) in cellular level is not clear. Methods Integration analysis of single-cell RNA sequencing data and spatial transcriptomics data was performed. Multiple methods were applied to investigate the subtype of HCC tumor cells. The functional characteristics, translation factors, clinical implications and microenvironment associations of different subtypes of tumor cells were analyzed. The interaction of subtype and fibroblasts were analyzed. Results We established a heterogeneity landscape of HCC malignant cells by integrated 52 single-cell RNA sequencing data and 5 spatial transcriptomics data. We identified three subtypes in tumor cells, including ARG1+ metabolism subtype (Metab-subtype), TOP2A+ proliferation phenotype (Prol-phenotype), and S100A6+ pro-metastatic subtype (EMT-subtype). Enrichment analysis found that the three subtypes harbored different features, that is metabolism, proliferating, and epithelial-mesenchymal transition. Trajectory analysis revealed that both Metab-subtype and EMT-subtype originated from the Prol-phenotype. Translation factor analysis found that EMT-subtype showed exclusive activation of SMAD3 and TGF-β signaling pathway. HCC dominated by EMT-subtype cells harbored an unfavorable prognosis and a deserted microenvironment. We uncovered a positive loop between tumor cells and fibroblasts mediated by SPP1-CD44 and CCN2/TGF-β-TGFBR1 interaction pairs. Inhibiting CCN2 disrupted the loop, mitigated the transformation to EMT-subtype, and suppressed metastasis. Conclusion By establishing a heterogeneity landscape of malignant cells, we identified a three-subtype classification in HCC. Among them, S100A6+ tumor cells play a crucial role in metastasis. Targeting the feedback loop between tumor cells and fibroblasts is a promising anti-metastatic strategy.

Abstract Background The advances in deep learning-based pathological image analysis have invoked tremendous insights into cancer prognostication. Still, lack of interpretability remains a significant barrier to clinical application. Methods We established an integrative prognostic neural network for intrahepatic cholangiocarcinoma (iCCA), towards a comprehensive evaluation of both architectural and fine-grained information from whole-slide images. Then, leveraging on multi-modal data, we conducted extensive interrogative approaches to the models, to extract and visualize the morphological features that most correlated with clinical outcome and underlying molecular alterations. Results The models were developed and optimized on 373 iCCA patients from our center and demonstrated consistent accuracy and robustness on both internal (n = 213) and external (n = 168) cohorts. The occlusion sensitivity map revealed that the distribution of tertiary lymphoid structures, the geometric traits of the invasive margin, the relative composition of tumor parenchyma and stroma, the extent of necrosis, the presence of the disseminated foci, and the tumor-adjacent micro-vessels were the determining architectural features that impacted on prognosis. Quantifiable morphological vector extracted by CellProfiler demonstrated that tumor nuclei from high-risk patients exhibited significant larger size, more distorted shape, with less prominent nuclear envelope and textural contrast. The multi-omics data (n = 187) further revealed key molecular alterations left morphological imprints that could be attended by the network, including glycolysis, hypoxia, apical junction, mTORC1 signaling, and immune infiltration. Conclusions We proposed an interpretable deep-learning framework to gain insights into the biological behavior of iCCA. Most of the significant morphological prognosticators perceived by the network are comprehensible to human minds. Graphical Abstract

One of the earliest indicators of autism spectrum disorder (ASD) is delay in language and social communication. Despite consensus on the benefits of earlier diagnosis and intervention, our understanding of the language growth of children with ASD during the first years of life remains limited. Therefore, this study compared communication growth patterns of infants and toddlers with ASD to growth benchmarks of a standardized language assessment. We conducted a retrospective analysis of growth on the Early Communication Indicator (ECI) of 23 infants and toddlers who received an ASD diagnosis in the future. At 42 months of age, children with ASD had significantly lower rates of gestures, single words, and multiple words, but significantly higher rates of nonword vocalizations. Children with ASD had significantly slower growth of single and multiple words, but their rate of vocalization growth was significantly greater than benchmark. Although more research is needed with larger samples, because the ECI was designed for practitioners to monitor children's response to intervention over time, these findings show promise for the ECI's use as a progress monitoring measure for young children with ASD. Limitations and the need for future research are discussed.

Functional roles of SIGLEC15 in hepatocellular carcinoma (HCC) were not clear, which was recently found to be an immune inhibitor with similar structure of inhibitory B7 family members. SIGLEC15 expression in HCC was explored in public databases and further examined by PCR analysis. SIGLEC15 and PD-L1 expression patterns were examined in HCC samples through immunohistochemistry. SIGLEC15 expression was knocked-down or over-expressed in HCC cell lines, and CCK8 tests were used to examine cell proliferative ability in vitro. Influences of SIGLEC15 expression on tumor growth were examined in immune deficient and immunocompetent mice respectively. Co-culture system of HCC cell lines and Jurkat cells, flow cytometry analysis of tumor infiltrated immune cells and further sequencing analyses were performed to investigate how SIGLEC15 could affect T cells in vitro and in vivo. We found SIGLEC15 was increased in HCC tumor tissues and was negatively correlated with PD-L1 in HCC samples. In vitro and in vivo models demonstrated inhibition of SIGLEC15 did not directly influence tumor proliferation. However, SIGLEC15 could promoted HCC immune evasion in immune competent mouse models. Knock-out of Siglec15 could inhibit tumor growth and reinvigorate CD8+ T cell cytotoxicity. Anti-SIGLEC15 treatment could effectively inhibit tumor growth in mouse models with or without mononuclear phagocyte deletion. Bulk and single-cell RNA sequencing data of treated mouse tumors demonstrated SIGLEC15 could interfere CD8+ T cell viability and induce cell apoptosis. In all, SIGLEC15 was negatively correlated with PD-L1 in HCC and mainly promote HCC immune evasion through inhibition of CD8+ T cell viability and cytotoxicity.

Nonalcoholic fatty liver disease (NAFLD) is characterized by over 5% hepatic fat accumulation without secondary causes. The prevalence of NAFLD has escalated in recent years due to shifts in dietary patterns and socioeconomic status, making it the most prevalent chronic liver disease and a significant public health concern globally. Serum uric acid (SUA) serves as the end product of purine metabolism in the body and is intricately linked to metabolic syndrome. Elevated SUA levels have been identified as an independent risk factor for the incidence and progression of NAFLD. This paper reviews the relationship between SUA and NAFLD, the underlying mechanisms of SUA involved in NAFLD, and the potential benefits of SUA-lowering therapy in treating NAFLD. The aim is to raise awareness of SUA management in patients with NAFLD, and to encourage further investigation into pharmacological interventions in this area.

Abstract Lymph node metastasis (LNM) facilitates distant tumor colonization and leads to the high mortality in patients with intrahepatic cholangiocarcinoma (ICC). However, it remains elusive how ICC cells subvert immune surveillance within the primary tumor immune microenvironment (TIME) and subsequently metastasize to lymph nodes (LNs). In this study, scRNA-seq and bulk RNA-seq analyses identified decreased infiltration of dendritic cells (DCs) into primary tumor sites of ICC with LNM, which was further validated via dual-color immunofluorescence staining of 219 surgically resected ICC samples. Tumor-infiltrating DCs correlated with increased CD8+ T cell infiltration and better prognoses in ICC patients. Mechanistically, β-catenin-mediated CXCL12 suppression accounted for the impaired DC recruitment in ICC with LNM. Two mouse ICC cell lines MuCCA1 and mIC-23 cells were established from AKT/NICD or AKT/YAP-induced murine ICCs respectively and were utilized to construct the footpad tumor LNM model. We found that expansion and activation of conventional DCs (cDCs) by combined Flt3L and poly(I:C) (FL-pIC) therapy markedly suppressed the metastasis of mIC-23 cells to popliteal LNs. Moreover, β-catenin inhibition restored the defective DC infiltration into primary tumor sites and reduced the incidence of LNM in ICC. Collectively, our findings identify tumor cell intrinsic β-catenin activation as a key mechanism for subverting DC-mediated anti-tumor immunity in ICC with LNM. FL-pIC therapy or β-catenin inhibitor could merit exploration as a potential regimen for mitigating ICC cell metastasis to LNs and achieving effective tumor immune control.

Abstract Background Parotid gland carcinoma (PGC) is a rare malignant tumor. The purpose of this study was to investigate the role of immune-inflammatory-nutrition indicators and age-adjusted Charlson comorbidity index score (ACCI) of PGC and develop the nomogram model for predicting prognosis. Method All patients diagnosed with PGC in two tertiary hospitals, treated with surgical resection, from March 2012 to June 2018 were obtained. Potential prognostic factors were identified by univariate and multivariate Cox regression analyses. The nomogram models were established based on these identified independent prognostic factors. The performance of the developed prognostic model was estimated by related indexes and plots. Result The study population consisted of 344 patients with PGC who underwent surgical resection, 285 patients without smoking (82.8%), and 225 patients (65.4%) with mucoepidermoid carcinoma, with a median age of 50.0 years. American Joint Committee on Cancer (AJCC) stage (p

Abstract To increase the production of biomass and astaxanthin from Haematococcus pluvialis to meet the high market demand for astaxanthin, this study recruited two typical and negligible phytohormones (namely resveratrol and catechol) for the stepwise treatments of H. pluvialis. It was found that the hybrid and sequential treatments of resveratrol (200 μmol) and catechol (100 μmol) had achieved the maximum astaxanthin content at 33.96 mg/L and 42.99 mg/L, respectively. Compared with the hybrid treatment, the physiological data of H. pluvialis using the sequential strategy revealed that the enhanced photosynthetic performance via the Calvin cycle by RuBisCO improved the biomass accumulation during the macrozooid stage; meanwhile, the excessive ROS production had occurred to enhance astaxanthin production with the help of NADPH overproduction during the hematocyst stage. Overall, this study provides improved knowledge of the impacts of phytohormones in improving biomass and astaxanthin of H. pluvialis, which shed valuable insights for advancing microalgae-based biorefinery.

Abstract A comprehensive evaluation of collagen-based historical artifacts is crucial for the preservation and inheritance of cultural relics, necessitating interdisciplinary approaches that integrate scientific knowledge with practical expertise to develop effective conservation strategies. The present work was focused on a piece of historical leather armor housed in Turpan Museum, Xinjiang, China. SEM–EDS, XRD, ATR-FTIR, and NMR were carried out on this historical leather armor from macro and micro perspectives, mainly including morphology observation, composition analysis, structural assessment, which could provide both quantitative and qualitative insights into the deterioration of the historical leather armor. Additionally, the non-destructive sampling methods and the third-generation sequencing technology (TGS) were employed to identify a total of 13 bacterial species and 8 fungal species, and their metabolic pathways were predicted, providing guidance for preventive conservation and restoration strategies. By the present work, necessary fundamental knowledge might be provided for the effective preservation and inheritance of collagen-based historical artifacts. Graphical Abstract

Abstract Background This study reported height prediction and longitudinal growth changes in Chinese pediatric patients with acute myeloid leukemia (AML) during and after treatment and their associations with outcomes. Methods Changes in 88 children with AML in percentages according to the growth percentile curve for Chinese boys/girls aged 2–18/0–2 years for body mass index (BMI), height, and weight from the time of diagnosis to 2 years off therapy were evaluated. The outcomes of AML were compared among patients with different BMI levels. Results The proportion of underweight children (weight 75th percentile) after 2 years of drug withdrawal. Unhealthy BMI at the initial diagnosis and during intensive chemotherapy leads to poorer outcomes. For height, all patients were in the range of genetic height predicted based on their parents’ height at final follow-up. Conclusions Physicians should pay more attention to the changes in height and weight of children with AML at these crucial treatment stages and intervene in time.

Measures of young children's social development are needed in the Multi-Tier System of Supports (MTSS) approach to early childhood. In 2004, we reported initial development of an observational measure of infants' and toddlers' social skills designed for early educators, the Early Social Indicator (ESI). Here, we report preliminary findings on the ESI's feasibility, sustainability, and sensitivity to growth in social engagement based on a large, multiyear sample of children in one early childhood program that agreed to pilot the measure. Results indicated that ESI use by program staff was sustained over a 5-year period. Program staff were reliable coding a range of children's positive and negative nonverbal and verbal social skills. However, staff were not reliable when coding the target of a child's social response when it was not the Adult play partner (i.e., the Peer, or Nondirected target). Results documented sensitivity to growth over time, dynamic patterns of change within and across key skills consistent with the typical course of social development, and moderation by children's home language and Individual Family Service Plan (IFSP) status. Implications are discussed.

Abstract With rapid development and mature of single‐cell measurements, single‐cell biology and pathology become an emerging discipline to understand the disease. However, it is important to address concerns raised by clinicians as to how to apply single‐cell measurements for clinical practice, translate the signals of single‐cell systems biology into determination of clinical phenotype, and predict patient response to therapies. The present Perspective proposes a new system coined as the clinical artificial intelligent single‐cell (caiSC) with the dynamic generator of clinical single‐cell informatics, artificial intelligent analyzers, molecular multimodal reference boxes, clinical inputs and outs, and AI‐based computerization. This system provides reliable and rapid information for impacting clinical diagnoses, monitoring, and prediction of the disease at the single‐cell level. The caiSC represents an important step and milestone to translate the single‐cell measurement into clinical application, assist clinicians’ decision‐making, and improve the quality of medical services. There is increasing evidence to support the possibility of the caiSC proposal, since the corresponding biotechnologies associated with caiSCs are rapidly developed. Therefore, we call the special attention and efforts from various scientists and clinicians on the caiSCs and believe that the appearance of the caiSCs can shed light on the future of clinical molecular medicine.

Background: Lung adenocarcinoma (LUAD) is a common type of malignant tumor with therapeutic challenges. Cancer-associated fibroblasts (CAFs) promote LUAD growth and metastasis, regulate the tumor immune response, and influence tumor treatment responses and drug resistance. However, the molecular mechanisms through which CAFs control LUAD progression are largely unknown. In this study, we aimed to determine the correlations between CAF-related genes and overall survival (OS) in patients with LUAD. Methods: We acquired the gene expression data and clinical information of 522 patients with LUAD patients from The Cancer Genome Atlas (TCGA) and 442 patients with LUAD from the Gene Expression Omnibus (GEO) databases. CAF infiltration levels were assessed using the Microenvironment Cell Population (MCP) counter, the Estimating the Proportions of Immune and Cancer cells (EPIC) algorithm, and Tumor Immune Dysfunction and Exclusion (TIDE) scores. A CAF-related gene network was constructed using the Weighted gene co-expression network analysis (WGCNA). Based on the CAF-related genes, univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analyses were performed to identify prognostic genes. Gene expression levels within the prognostic model were validated using the Cancer Cell Line Encyclopedia (CCLE) databases and Western blotting. Results: Our results demonstrated that high CAF scores were associated with lower survival rates in patients with LUAD. Gene modules that were highly correlated with high CAF scores were closely associated with tissue characteristics and extracellular matrix structures in LUAD. In addition, correlations between CAF scores and responses to immunotherapy and chemotherapy were observed. Finally, we found that SNAI2 expression was higher in lung cancer tissues than in normal tissues. Conclusion: Deepening our understanding of the influence of CAFs on tumor progression and treatment response at the molecular level can aid the development of more effective therapeutic strategies. This study provides important insights into the functional mechanisms of action of CAFs in LUAD and highlights their clinical implications.

The complex damping model is only applied in frequency-domain. Based on the constitutive relation of viscoelastic materials, a vibration equation is equivalent to the constitutive relation of complex damping theory and the frequency response function satisfying the causality constraint. Compared with the existing equivalent complex damping model, the proposed equivalent damping model can consider more natural frequencies. The calculation formulas of Gauss precise integral method and improvement constant average acceleration method are derived. By the equivalent complex damping, the seismic time-history response of the typical example could carry on the numerical calculation. Compared with the Gauss precise integral calculation results of complex damping vibration equation, some conclusions can be analyzed. The proposed two methods could avoid divergent phenomenon in the time-domain numerical solution of complex damping vibration equation. The time-domain integral method of equivalent complex damping theory is stable and convergent. The Gauss precise integral method has strong equivalence and big computational complexity. The improvement constant average acceleration method has weak equivalence and small computational complexity.

Abstract Background The therapeutic potential of immune checkpoint blockade (ICB) extends across various cancers; however, its effectiveness in treating hepatocellular carcinoma (HCC) is frequently curtailed by both inherent and developed resistance. Objective This research explored the effectiveness of integrating anlotinib (a broad‐spectrum tyrosine kinase inhibitor) with programmed death‐1 (PD‐1) blockade and offers mechanistic insights into more effective strategies for treating HCC. Methods Using patient‐derived organotypic tissue spheroids and orthotopic HCC mouse models, we assessed the effectiveness of anlotinib combined with PD‐1 blockade. The impact on the tumour immune microenvironment and underlying mechanisms were assessed using time‐of‐flight mass cytometry, RNA sequencing, and proteomics across cell lines, mouse models, and HCC patient samples. Results The combination of anlotinib with an anti‐PD‐1 antibody enhanced the immune response against HCC in preclinical models. Anlotinib remarkably suppressed the expression of transferrin receptor (TFRC) via the VEGFR2/AKT/HIF‐1α signaling axis. CD8+ T‐cell infiltration into the tumour microenvironment correlated with low expression of TFRC. Anlotinib additionally increased the levels of the chemokine CXCL14, crucial for attracting CD8+ T cells. CXCL14 emerged as a downstream effector of TFRC, exhibiting elevated expression following the silencing of TFRC. Importantly, low TFRC expression was also associated with a better prognosis, enhanced sensitivity to combination therapy, and a favourable response to anti‐PD‐1 therapy in patients with HCC. Conclusions Our findings highlight anlotinib's potential to augment the efficacy of anti‐PD‐1 immunotherapy in HCC by targeting TFRC and enhancing CXCL14‐mediated CD8+ T‐cell infiltration. This study contributes to developing novel therapeutic strategies for HCC, emphasizing the role of precision medicine in oncology. Highlights Synergistic effects of anlotinib and anti‐PD‐1 immunotherapy demonstrated in HCC preclinical models. Anlotinib inhibits TFRC expression via the VEGFR2/AKT/HIF‐1α pathway. CXCL14 upregulation via TFRC suppression boosts CD8+ T‐cell recruitment. TFRC emerges as a potential biomarker for evaluating prognosis and predicting response to anti‐PD‐1‐based therapies in advanced HCC patients.

Targeted mass spectrometry (MS) holds promise for precise protein and protein‐representative peptide identification and quantification, enhancing disease diagnosis. However, its clinical application is hindered by complex data analysis and expert review requirements. It is hypothesized that machine learning (ML) models can automate data analysis to accelerate the clinical application of MS. The approach involves an ML‐driven pipeline that extracts statistical and morphological features from an MS target region and feeds these features into ML algorithms to generate and assess predictive models. The findings demonstrate ML prediction models exhibit superior performance when trained on extracted features versus raw spectra intensity data and that random forest models exhibit robust classification performance in both internal and external validation datasets. These models remain effective across varying training dataset sizes and positive sample rates and are enhanced by a nested active learning approach. This approach can thus revolutionize clinical MS applications.

BackgroundIndividuals with subjective cognitive decline (SCD) are at risk of developing Alzheimer's Disease (AD). Traditional seed-based analysis has shown biased functional connectivity (FC) in SCD individuals. To investigate unbiased altered FC by the brain-wide association study (BWAS) and to determine its association with cognition in SCD individuals.MethodsMeasure of association (MA) analysis was applied to detect significant voxels with FC changes. Based on these changes, we identified regions of interest (ROIs) and conducted ROI-wise FC analyses. Correlation analyses were then performed between these FC circuits and cognition.ResultsMA analysis identified 10 ROIs with significantly altered voxels. ROI-wise FC analyses revealed 14 strengthened FC, predominantly parietal-occipital link alterations. The FC between the right superior occipital gyrus and the right postcentral gyrus correlated positively with executive function, while the FC between the right middle occipital gyrus and the left angular gyrus correlated positively with episodic memory in SCD individuals.ConclusionSCD involves multifocal impairments, of which regions of default mode network (DMN) and occipital lobe should be specially focused. Cross-hemispheric alterations indicate an internal interactive impairment pattern in SCD. The reduced FC between the right superior occipital gyrus and the right postcentral gyrus, and between the right middle occipital gyrus and the left angular gyrus, which correlate with specific cognitive functions, could serve as potential biomarkers for SCD diagnosis.