AI-Based multimodal Multi-tasks analysis reveals tumor molecular heterogeneity, predicts preoperative lymph node metastasis and prognosis in papillary thyroid carcinoma: A retrospective study.

BACKGROUND: Papillary thyroid carcinoma (PTC) is the predominant form of thyroid cancer globally, especially when lymph node metastasis (LNM) occurs. Molecular heterogeneity, driven by genetic alterations and tumor microenvironment components, contributes to the complexity of PTC. Understanding these complexities is essential for precise risk stratification and therapeutic decisions. METHODS: This study involved a comprehensive analysis of 521 patients with PTC from our hospital and 499 patients from The Cancer Genome Atlas (TCGA). The real-world cohort 1 comprised 256 patients with stage I-III PTC. Tissues from 252 patients were analyzed by DNA-based next-generation sequencing, and tissues from four patients were analyzed by single-cell RNA sequencing (scRNA-seq). Additionally, 586 PTC pathological sections were collected from TCGA, and 275 PTC pathological sections were collected from the real-world cohort 2. A deep learning multimodal model was developed using matched histopathology images, genomic, transcriptomic, and immune cell data to predict LNM and disease-free survival (DFS). RESULTS: This study included a total of 1,011 PTC patients, comprising 256 patients from cohort 1, 275 patients from cohort 2, and 499 patients from TCGA. In cohort 1, we categorized PTC into four molecular subtypes based on BRAF, RAS, RET, and other mutations. BRAF mutations were significantly associated with LNM and impacted DFS. ScRNA-seq identified distinct T cell subtypes and reduced B cell diversity in BRAF-mutated PTC with LNM. The study also explored cancer-associated fibroblasts and macrophages, highlighting their associations with LNM. The deep learning model was trained using 405 pathology slides and RNA sequences from 328 PTC patients and validated with 181 slides and RNA sequences from 140 PTC patients in the TCGA cohort. It achieved high accuracy, with an AUC of 0.86 in the training cohort, 0.84 in the validation cohort, and 0.83 in the real-world cohort 2. High-risk patients in the training cohort had significantly lower DFS rates (P<0.001). Model AUCs were 0.91 at 1 year, 0.93 at 3 years, and 0.87 at 5 years. In the validation cohort, high-risk patients also had lower DFS (P<0.001); the AUCs were 0.89, 0.87, and 0.80 at 1, 3, and 5 years. We utilized the GradCAM algorithm to generate heatmaps from pathology-based deep learning models, which visually highlighted high-risk tumor areas in PTC patients. This enhanced clinicians' understanding of the model's predictions and improved diagnostic accuracy, especially in cases with lymph node metastasis. CONCLUSION: The AI-based analysis uncovered vital insights into PTC molecular heterogeneity, emphasizing BRAF mutations' impact. The integrated deep learning model shows promise in predicting metastasis, offering valuable contributions to improved diagnostic and therapeutic strategies.

Exploring the role of tRNA-derived small RNAs (tsRNAs) in disease: implications for HIF-1 pathway modulation.

The tRNA-derived small RNAs (tsRNAs) can be categorized into two main groups: tRNA-derived fragments (tRFs) and tRNA-derived stress-induced RNAs (tiRNAs). Each group possesses specific molecular sizes, nucleotide compositions, and distinct physiological functions. Notably, hypoxia-inducible factor-1 (HIF-1), a transcriptional activator dependent on oxygen, comprises one HIF-1ß subunit and one HIF-α subunit (HIF-1α/HIF-2α/HIF-3α). The activation of HIF-1 plays a crucial role in gene transcription, influencing key aspects of cancer biology such as angiogenesis, cell survival, glucose metabolism, and invasion. The involvement of HIF-1α activation has been demonstrated in numerous human diseases, particularly cancer, making HIF-1 an attractive target for potential disease treatments. Through a series of experiments, researchers have identified two tiRNAs that interact with the HIF-1 pathway, impacting disease development: 5'tiRNA-His-GTG in colorectal cancer (CRC) and tiRNA-Val in diabetic retinopathy (DR). Specifically, 5'tiRNA-His-GTG promotes CRC development by targeting LATS2, while tiRNA-Val inhibits Sirt1, leading to HIF-1α accumulation and promoting DR development. Clinical data have further indicated that certain tsRNAs' expression levels are associated with the prognosis and pathological features of CRC patients. In CRC tumor tissues, the expression level of 5'tiRNA-His-GTG is significantly higher compared to normal tissues, and it shows a positive correlation with tumor size. Additionally, KEGG analysis has revealed multiple tRFs involved in regulating the HIF-1 pathway, including tRF-Val-AAC-016 in diabetic foot ulcers (DFU) and tRF-1001 in pathological ocular angiogenesis. This comprehensive article reviews the biological functions and mechanisms of tsRNAs related to the HIF-1 pathway in diseases, providing a promising direction for subsequent translational medicine research.

Laparoscopic Sigmoid Vaginoplasty for the Treatment of Mayer-Rokitansky-Kuster-Hauser Syndrome in a Single Center: 20 years' Experience.

INTRODUCTION AND HYPOTHESIS: We investigate the feasibility, safety, and clinical therapeutic effect of laparoscopic sigmoid vaginoplasty in women with Mayer-Rokitansky-Kuster-Hauser (MRKH) syndrome. METHODS: We performed a retrospective case review cohort study of 56 patients with MRKHs undergoing laparoscopic sigmoid vaginoplasty in Wuhan Union Hospital between 2000 and 2020, and all patients were followed up. RESULTS: The median operating time was 165 min (120-420 min). The median hospital stay was 10 days (rang 7-15 days). A functional neovagina was created 11-15 cm in length and two fingers in breadth in all patients. No introitus stenosis was observed. No intra- or post-operative complications occurred. Two patients were lost to follow-up after 3 months of outpatient visits. Six patients had no intercourse and were required to wear a vaginal mold occasionally. None of the patients had complained of local irritation or dyspareunia. Patients who had post-surgery sexual intercourse were satisfied with their sexual life and the mean total Female Sexual Function Index (FSFI) score was 25.17 ± 0.63. The cosmetic results were excellent. CONCLUSIONS: The laparoscopic sigmoid vaginoplasty can achieve the goal of making a functional neovagina. The main advantage of this surgical technique is that it is minimally invasive and that there are fewer complications post-operation. It is an acceptable procedure for patients with MRKH syndrome.

Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction.

Background: The prognosis of breast cancer is often unfavorable, emphasizing the need for early metastasis risk detection and accurate treatment predictions. This study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival (DFS). Methods: We retrospectively collected pathology imaging, molecular and clinical data from The Cancer Genome Atlas and one independent institution in China. We developed a novel Deep Learning Clinical Medicine Based Pathological Gene Multi-modal (DeepClinMed-PGM) model for DFS prediction, integrating clinicopathological data with molecular insights. The patients included the training cohort (n = 741), internal validation cohort (n = 184), and external testing cohort (n = 95). Result: Integrating multi-modal data into the DeepClinMed-PGM model significantly improved area under the receiver operating characteristic curve (AUC) values. In the training cohort, AUC values for 1-, 3-, and 5-year DFS predictions increased to 0.979, 0.957, and 0.871, while in the external testing cohort, the values reached 0.851, 0.878, and 0.938 for 1-, 2-, and 3-year DFS predictions, respectively. The DeepClinMed-PGM's robust discriminative capabilities were consistently evident across various cohorts, including the training cohort [hazard ratio (HR) 0.027, 95% confidence interval (CI) 0.0016-0.046, P < 0.0001], the internal validation cohort (HR 0.117, 95% CI 0.041-0.334, P < 0.0001), and the external cohort (HR 0.061, 95% CI 0.017-0.218, P < 0.0001). Additionally, the DeepClinMed-PGM model demonstrated C-index values of 0.925, 0.823, and 0.864 within the three cohorts, respectively. Conclusion: This study introduces an approach to breast cancer prognosis, integrating imaging and molecular and clinical data for enhanced predictive accuracy, offering promise for personalized treatment strategies.

Targeting metabolism to influence cellular senescence a promising anti-cancer therapeutic strategy.

Metabolic disorders are considered the hallmarks of cancer and metabolic reprogramming is emerging as a new strategy for cancer treatment. Exogenous and endogenous stressors can induce cellular senescence; the interactions between cellular senescence and systemic metabolism are dynamic. Cellular senescence disrupts metabolic homeostasis in various tissues, which further promotes senescence, creating a vicious cycle facilitating tumor occurrence, recurrence, and altered outcomes of anticancer treatments. Therefore, the regulation of cellular senescence and related secretory phenotypes is considered a breakthrough in cancer therapy; moreover, proteins involved in the associated pathways are prospective therapeutic targets. Although studies on the association between cellular senescence and tumors have emerged in recent years, further elucidation of this complex correlation is required for comprehensive knowledge. In this paper, we review the research progress on the correlation between cell aging and metabolism, focusing on the strategies of targeting metabolism to modulate cellular senescence and the progress of relevant research in the context of anti-tumor therapy. Finally, we discuss the significance of improving the specificity and safety of anti-senescence drugs, which is a potential challenge in cancer therapy.

RhoJ: an emerging biomarker and target in cancer research and treatment.

RhoJ is a Rho GTPase that belongs to the Cdc42 subfamily and has a molecular weight of approximately 21 kDa. It can activate the p21-activated kinase family either directly or indirectly, influencing the activity of various downstream effectors and playing a role in regulating the cytoskeleton, cell movement, and cell cycle. RhoJ's expression and activity are controlled by multiple upstream factors at different levels, including expression, subcellular localization, and activation. High RhoJ expression is generally associated with a poor prognosis for cancer patients and is mainly due to an increased number of tumor blood vessels and abnormal expression in malignant cells. RhoJ promotes tumor progression through several pathways, particularly in tumor angiogenesis and drug resistance. Clinical data also indicates that high RhoJ expression is closely linked to the pathological features of tumor malignancy. There are various cancer treatment methods that target RhoJ signaling, such as direct binding to inhibit the RhoJ effector pocket, inhibiting RhoJ expression, blocking RhoJ upstream and downstream signals, and indirectly inhibiting RhoJ's effect. RhoJ is an emerging cancer biomarker and a significant target for future cancer clinical research and drug development.

Effects of calcium-to-silicon ratio on the properties of fly ash-based tobermorite and its removal performance of Zn2+ and Mn2.

The effects of calcium-to-silicon ratio on the properties of fly ash (FA)-based tobermorite and its removal performance of Zn2+ and Mn2+ were studied. The calcium-to-silicon ratio had a significant effect on the structural properties of the tobermorite samples. The specific surface area, pore volume, and average pore size of mesoporous tobermorite samples with different calcium-to-silicon ratios (0.8TOB, 1.2TOB, and 1.6TOB) were much larger than those of FA, and those of 1.2TOB were the largest, which were 53.29 m2/g, 0.448 cm3/g, and 30.50 nm, respectively. The removal efficiencies of Zn2+ and Mn2+ by 1.2TOB were 84.19% and 47.67%, respectively, which were much higher than those of 0.8TOB (60.62% and 42.41%), 1.6TOB (46.69% and 24.31%), and FA (4.13% and 6.95%). The adsorption of Zn2+ and Mn2+ by 0.8TOB, 1.2TOB, and 1.6TOB was corresponding to the pseudo-second-order kinetic model and Langmuir isotherm model. Particularly, 1.2 TOB showed the highest maximum adsorption capacities of Zn2+ and Mn2+ calculated from the Langmuir model, which were 129.70 mg/g and 82.09 mg/g, respectively. Moreover, the adsorption mechanisms might be due to the combination with -OH and the interlayer adsorption of the samples. This research provides new insight into the fly ash-based adsorbents towards Zn2+ and Mn2+ in wastewater.

Deciphering the role of apoptosis signature on the immune dynamics and therapeutic prognosis in breast cancer: Implication for immunotherapy.

Background: In breast cancer oncogenesis, the precise role of cell apoptosis holds untapped potential for prognostic and therapeutic insights. Thus, it is important to develop a model predicated for breast cancer patients' prognosis and immunotherapy response based on apoptosis-related signature. Methods: Our approach involved leveraging a training dataset from The Cancer Genome Atlas (TCGA) to construct an apoptosis-related gene prognostic model. The model's validity was then tested across several cohorts, including METABRIC, Sun Yat-sen Memorial Hospital Sun Yat-sen University (SYSMH), and IMvigor210, to ensure its applicability and robustness across different patient demographics and treatment scenarios. Furthermore, we utilized Quantitative Polymerase Chain Reaction (qPCR) analysis to explore the expression patterns of these model genes in breast cancer cell lines compared to immortalized mammary epithelial cell lines, aiming to confirm their differential expression and underline their significance in the context of breast cancer. Results: Through the development and validation of our prognostic model based on seven apoptosis-related genes, we have demonstrated its substantial predictive power for the survival outcomes of breast cancer patients. The model effectively stratified patients into high and low-risk categories, with high-risk patients showing significantly poorer overall survival in the training cohort and across all validation cohorts. Importantly, qPCR analysis confirmed that the genes constituting our model indeed exhibit differential expression in breast cancer cell lines when contrasted with immortalized mammary epithelial cell lines. Conclusion: Our study establishes a groundbreaking prognostic model using apoptosis-related genes to enhance the precision of breast cancer prognosis and treatment, particularly in predicting immunotherapy response.

Catalytic Asymmetric Access to Structurally Diverse N-Alkoxy Amines via a Kinetic Resolution Strategy.

Chiral N-alkoxy amines are increasingly vital substrates in bioscience. However, asymmetric synthetic strategies for these compounds remain scarce. Catalytic kinetic resolution represents an attractive approach to prepare structurally diverse enantiopure N-alkoxy amines, which has remained elusive due to the notably reduced nucleophilicity of the nitrogen atom together with the low bond dissociation energies of labile NO-C and N-O bonds. We here report a general kinetic resolution of N-alkoxy amines through chemo- and enantioselective oxygenation. The mild and green titanium-catalyzed approach features broad substrate scope (55 examples), noteworthy functional group compatibility, high catalyst turnover number (up to 5200), excellent selectivity factor (s > 150), and scalability.

p27Kip1 and cytoplasmic pSer10p27 are promising biomarkers for predicting prognosis and chemotherapy response in ovarian cancer.

PURPOSE: The biological function of p27Kip1 largely depends on its subcellular localization and phosphorylation status. Different subcellular localizations and phosphorylation statuses of p27Kip1 may represent distinct clinical values, which are unclear in ovarian cancer. This study aimed to elucidate different subcellular localizations of p27Kip1 and pSer10p27 in predicting prognosis and chemotherapy response in ovarian cancer. METHODS: Meta-analyses were executed to evaluate the association of p27Kip1 and phosphorylated p27Kip1 with the prognosis of ovarian cancer patients. The expression levels and patterns of p27Kip1 and pSer10p27 were evaluated by immunohistochemistry. The correlations between different p27Kip1 states, clinicopathological features, and prognosis were analyzed. p27Kip1 and pSer10p27 expression levels in cisplatin-sensitive and cisplatin-resistant ovarian cancer cell lines were detected using WB. KEGG analysis and WB were performed to evaluate the pathways in which p27Kip1 was involved. RESULTS: Meta-analyses showed that p27Kip1 was associated with significantly better overall survival (OS) in ovarian cancer (HR=2.14; 95% CI [1.71-2.68]) and pSer10p27 was associated with significantly poor OS in mixed solid tumors (HR=2.56; 95% CI [1.76-3.73]). In our cohort of ovarian cancer patients, low total p27Kip1 remained independent risk factors of OS (HR=2.097; 95% CI [1.121-3.922], P=0.021) and PFS (HR=2.483; 95% CI [1.364-4.518], P=0.003), while low cytoplasmic pSer10p27 had independent protective effects in terms of OS (HR=0.472; 95% CI [0.248-0.898], P=0.022) and PFS (HR=0.488; 95% CI [0.261-0.910], P=0.024). Patients with low total p27Kip1/pSer10p27 and low nuclear p27Kip1 had worse chemotherapy responses, while patients with low cytoplasmic pSer10p27 expression had better chemotherapy responses. The protein levels of p27Kip1 and pSer10p27 were significantly reduced in the cisplatin-resistant cell lines SKOV3-cDDP and A2780-cDDP, and the level of p27Kip1/pSer10p27 was subjective to Akt activation. CONCLUSIONS: The present study demonstrates that p27Kip1 and cytoplasmic pSer10p27 are promising biomarkers for predicting prognosis and chemotherapy response in ovarian cancer.

System-level time computation and representation in the suprachiasmatic nucleus revealed by large-scale calcium imaging and machine learning.

The suprachiasmatic nucleus (SCN) is the mammalian central circadian pacemaker with heterogeneous neurons acting in concert while each neuron harbors a self-sustained molecular clockwork. Nevertheless, how system-level SCN signals encode time of the day remains enigmatic. Here we show that population-level Ca2+ signals predict hourly time, via a group decision-making mechanism coupled with a spatially modular time feature representation in the SCN. Specifically, we developed a high-speed dual-view two-photon microscope for volumetric Ca2+ imaging of up to 9000 GABAergic neurons in adult SCN slices, and leveraged machine learning methods to capture emergent properties from multiscale Ca2+ signals as a whole. We achieved hourly time prediction by polling random cohorts of SCN neurons, reaching 99.0% accuracy at a cohort size of 900. Further, we revealed that functional neuron subtypes identified by contrastive learning tend to aggregate separately in the SCN space, giving rise to bilaterally symmetrical ripple-like modular patterns. Individual modules represent distinctive time features, such that a module-specifically learned time predictor can also accurately decode hourly time from random polling of the same module. These findings open a new paradigm in deciphering the design principle of the biological clock at the system level.

TIE1 promotes cervical cancer progression via Basigin-matrix metalloproteinase axis.

Background: Tyrosine kinase with immunoglobulin and EGF-like domains 1 (TIE1) is known as an orphan receptor prominently expressed in endothelial cells and participates in angiogenesis by regulating TIE2 activity. Our previous study demonstrated elevated TIE1 expression in cervical cancer cells. However, the role of TIE1 in cervical cancer progression, metastasis and treatment remains elusive. Methods: Immunohistochemistry staining for TIE1 and Basigin was performed in 135 human cervical cancer tissues. Overexpressing vectors and siRNAs were used to manipulate gene expression in tumor cells. Colony formation, wound healing, and transwell assays were used to assess cervical cancer cell proliferation and migration in vitro. Subcutaneous xenograft tumor and lung metastasis mouse models were established to examine tumor growth and metastasis. Co-Immunoprecipitation and Mass Spectrometry were applied to explore the proteins binding to TIE1. Immunoprecipitation and immunofluorescence staining were used to verify the interaction between TIE1 and Basigin. Cycloheximide chase assay and MG132 treatment were conducted to analyze protein stability. Results: High TIE1 expression was associated with poor survival in cervical cancer patients. TIE1 overexpression promoted the proliferation, migration and invasion of cervical cancer cells in vitro, as well as tumor growth and metastasis in vivo. In addition, Basigin, a transmembrane glycoprotein, was identified as a TIE1 binding protein, suggesting a pivotal role in matrix metalloproteinase regulation, angiogenesis, cell adhesion, and immune responses. Knockdown of Basigin or treatment with the Basigin inhibitor AC-73 reversed the tumor-promoting effect of TIE1 in vitro and in vivo. Furthermore, we found that TIE1 was able to interact with and stabilize the Basigin protein and stimulate the Basigin-matrix metalloproteinase axis. Conclusion: TIE1 expression in cervical cells exerts a tumor-promoting effect, which is at least in part dependent on its interaction with Basigin. These findings have revealed a TIE2-independent mechanism of TIE1, which may provide a new biomarker for cervical cancer progression, and a potential therapeutic target for the treatment of cervical cancer patients.

The Female-Biased General Odorant Binding Protein 2 of Semiothisa cinerearia Displays Binding Affinity for Biologically Active Host Plant Volatiles.

Herbivorous insects rely on volatile chemical cues from host plants to locate food sources and oviposition sites. General odorant binding proteins (GOBPs) are believed to be involved in the detection of host plant volatiles. In the present study, one GOBP gene, ScinGOBP2, was cloned from the antennae of adult Semiothisa cinerearia. Reverse-transcription PCR and real-time quantitative PCR analysis revealed that the expression of ScinGOBP2 was strongly biased towards the female antennae. Fluorescence-based competitive binding assays revealed that 8 of the 27 host plant volatiles, including geranyl acetone, decanal, cis-3-hexenyl n-valerate, cis-3-hexenyl butyrate, 1-nonene, dipentene, α-pinene and ß-pinene, bound to ScinGOBP2 (KD = 2.21-14.94 µM). The electrical activities of all eight ScinGOBP2 ligands were confirmed using electroantennography. Furthermore, oviposition preference experiments showed that eight host volatiles, such as decanal, cis-3-hexenyl n-valerate, cis-3-hexenyl butyrate, and α-pinene, had an attractive effect on female S. cinerearia, whereas geranyl acetone, 1-nonene, ß-pinene, and dipentene inhibited oviposition in females. Consequently, it can be postulated that ScinGOBP2 may be implicated in the perception of host plant volatiles and that ScinGOBP2 ligands represent significant semiochemicals mediating the interactions between plants and S. cinerearia. This insight could facilitate the development of a chemical ecology-based approach for the management of S. cinerearia.

Lanthanum-modified tobermorite synthesized from fly ash for efficient phosphate removal.

Phosphate removal from water by lanthanum-modified tobermorite synthesized from fly ash (LTFA) with different lanthanum concentrations was studied. LTFA samples were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, and Brunauer‒Emmett‒Teller specific surface area analysis. The results showed that the LTFA samples were mainly composed of mesoporous tobermorite-11 Å, and LTFA1 with a lanthanum concentration of 0.15 M had a high specific surface area (83.82 m2/g) and pore volume (0.6778 cm3/g). The phosphate adsorption capacities of LTFA samples were highest at pH 3 and gradually decreased with increasing pH. The phosphate adsorption kinetics data on LTFA samples were most accurately described by the Elovich model. The adsorption isotherms were in the strongest agreement with the Temkin model, and LTFA1 showed the highest phosphate adsorption capacity (282.51 mg P/g), which was higher than that of most other lanthanum-modified adsorbents. LTFA1 presented highly selective adsorption of phosphate with other coexisting ions (HCO3-, Cl-, SO42-, and NO3-). In addition, phosphate was adsorbed onto LTFA samples by forming inner-sphere phosphate complexes and amorphous lanthanum phosphate. This study provides technical support for development of efficient fly ash-based phosphate adsorbents.

Interface-Targeting Carrier-Catalytic Integrated Design Contributing to Lithium Dihalide-Rich SEI toward High Interface Stability for Long-Life Solid-State Lithium-Metal Batteries.

The generation of solid electrolyte interphase (SEI) largely determines the comprehensive performance of all-solid-state batteries. Herein, a novel "carrier-catalytic" integrated design is strategically exploited to in situ construct a stable LiF-LiBr rich SEI by improving the electron transfer kinetics to accelerate the bond-breaking dynamics. Specifically, the high electron transport capacity of Br-TPOM skeleton increases the polarity of C-Br, thus promoting the generation of LiBr. Then, the enhancement of electron transfer kinetics further promotes the fracture of C-F from TFSI- to form LiF. Finally, the stable and homogeneous artificial-SEI with enriched lithium dihalide is constructed through the in situ co-growth mechanism of LiF and LiBr, which facilitatse the Li-ion transport kinetics and regulates the lithium deposition behavior. Impressively, the PEO-Br-TPOM paired with LiFePO4 delivers ultra-long cycling stability over 1000 cycles with 81 % capacity retention at 1 C while the pouch cells possess 88 % superior capacity retention after 550 cycles with initial discharge capacity of 145 mAh g-1at 0.2 C in the absence of external pressure. Even under stringent conditions, the practical pouch cells possess the practical capacity with stable electric quantities plateau in 30 cycles demonstrates its application potential in energy storage field.

Unraveling the role of M1 macrophage and CXCL9 in predicting immune checkpoint inhibitor efficacy through multicohort analysis and single-cell RNA sequencing.

The exact function of M1 macrophages and CXCL9 in forecasting the effectiveness of immune checkpoint inhibitors (ICIs) is still not thoroughly investigated. We investigated the potential of M1 macrophage and C-X-C Motif Chemokine Ligand 9 (CXCL9) as predictive markers for ICI efficacy, employing a comprehensive approach integrating multicohort analysis and single-cell RNA sequencing. A significant correlation between high M1 macrophage and improved overall survival (OS) and objective response rate (ORR) was found. M1 macrophage expression was most pronounced in the immune-inflamed phenotype, aligning with increased expression of immune checkpoints. Furthermore, CXCL9 was identified as a key marker gene that positively correlated with M1 macrophage and response to ICIs, while also exhibiting associations with immune-related pathways and immune cell infiltration. Additionally, through exploring RNA epigenetic modifications, we identified Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3G (APOBEC3G) as linked to ICI response, with high expression correlating with improved OS and immune-related pathways. Moreover, a novel model based on M1 macrophage, CXCL9, and APOBEC3G-related genes was developed using multi-level attention graph neural network, which showed promising predictive ability for ORR. This study illuminates the pivotal contributions of M1 macrophages and CXCL9 in shaping an immune-active microenvironment, correlating with enhanced ICI efficacy. The combination of M1 macrophage, CXCL9, and APOBEC3G provides a novel model for predicting clinical outcomes of ICI therapy, facilitating personalized immunotherapy.

Dual-constrained physics-enhanced untrained neural network for lensless imaging.

An untrained neural network (UNN) paves a new way to realize lensless imaging from single-frame intensity data. Based on the physics engine, such methods utilize the smoothness property of a convolutional kernel and provide an iterative self-supervised learning framework to release the needs of an end-to-end training scheme with a large dataset. However, the intrinsic overfitting problem of UNN is a challenging issue for stable and robust reconstruction. To address it, we model the phase retrieval problem into a dual-constrained untrained network, in which a phase-amplitude alternating optimization framework is designed to split the intensity-to-phase problem into two tasks: phase and amplitude optimization. In the process of phase optimization, we combine a deep image prior with a total variation prior to retrain the loss function for the phase update. In the process of amplitude optimization, a total variation denoising-based Wirtinger gradient descent method is constructed to form an amplitude constraint. Alternative iterations of the two tasks result in high-performance wavefield reconstruction. Experimental results demonstrate the superiority of our method.

Synergistic role of activated CD4+ memory T cells and CXCL13 in augmenting cancer immunotherapy efficacy.

The development of immune checkpoint inhibitors (ICIs) has significantly advanced cancer treatment. However, their efficacy is not consistent across all patients, underscoring the need for personalized approaches. In this study, we examined the relationship between activated CD4+ memory T cell expression and ICI responsiveness. A notable correlation was observed between increased activated CD4+ memory T cell expression and better patient survival in various cohorts. Additionally, the chemokine CXCL13 was identified as a potential prognostic biomarker, with higher expression levels associated with improved outcomes. Further analysis highlighted CXCL13's role in influencing the Tumor Microenvironment, emphasizing its relevance in tumor immunity. Using these findings, we developed a deep learning model by the Multi-Layer Aggregation Graph Neural Network method. This model exhibited promise in predicting ICI treatment efficacy, suggesting its potential application in clinical practice.

Failure Behavior and Fracture Evolution Mechanism of Coal with Fissures around Holes.

The fracture of coal is the main channel of gas flow and an important factor affecting the stability and efficiency of gas drainage boreholes. The coal structure, the development of hole cracks, and the degree of deformation are different. It affects the strength and mechanical deformation characteristics of coal to a great extent. In order to investigate the law of fracture evolution around the borehole of fractured coal, uniaxial and triaxial compression tests of raw coal samples have been carried out. The stress field evolution characteristics of fractured coal under compression were analyzed by Particle Flow Code (PFC2D). The strength, deformation, and fracture evolution behavior of fractured coal around boreholes under different confining pressures were studied. The results show that the compressive strength and fracture morphology evolution characteristics of coal around the hole are obviously related to the confining pressure and fracture occurrence of raw coal. The borehole structure itself has an important influence on the distribution location of the shear failure zone of the fracture around the hole, and its influence degree increases with the decrease of borehole confining pressure. During the deformation of coal with cracks around the hole, the initiation, propagation, and union behavior of cracks are related to the crack angle ß. The cracks with ß 0 and 180° are most easily closed during compression and the cracks with ß 90° have little effect on the crack propagation zone. When the crack angle ß is 45°, it is most easy to sprout and expand at the end; when the coal is compressed to the ultimate strength, the increase rate of the tensile crack increases, and the polymerization and combination behavior of the crack is more obvious. The evolution cloud map of the stress field can better reflect the evolution characteristics of fracture development, expansion, and fracture in the process of coal loading. Studying the failure behavior and fracture evolution mechanism of the coal around the hole can better predict and control the gas migration and extraction effect, which is of great significance to prevent the occurrence of gas accidents.