Association of depressive symptoms and sleep disturbances with survival among US adult cancer survivors.

BACKGROUND: Depression and sleep disturbances are associated with increased risks of various diseases and mortality, but their impacts on mortality in cancer survivors remain unclear. The objective of this study was to characterize the independent and joint associations of depressive symptoms and sleep disturbances with mortality outcomes in cancer survivors. METHODS: This population-based prospective cohort study included cancer survivors aged ≥ 20 years (n = 2947; weighted population, 21,003,811) from the National Health and Nutrition Examination Survey (NHANES) 2007-2018 cycles. Depressive symptoms and sleep disturbances were self-reported. Depressive symptoms were assessed using the Patient Health Questionnaire 9 (PHQ-9). Death outcomes were determined by correlation with National Death Index records through December 31, 2019. Primary outcomes included all-cause, cancer-specific, and noncancer mortality. RESULTS: During the median follow-up of 69 months (interquartile range, 37-109 months), 686 deaths occurred: 240 participants died from cancer, 146 from heart disease, and 300 from other causes. Separate analyses revealed that compared with a PHQ-9 score (0-4), a PHQ-9 score (5-9) was associated with a greater risk of all-cause mortality (hazard ratio [HR], 1.28; 95% CI, 1.03-1.59), and a PHQ-9 score (≥ 10) was associated with greater risk of all-cause mortality (HR, 1.37; 95% CI, 1.04-1.80) and noncancer mortality (HR, 1.45; 95% CI, 1.01-2.10). Single sleep disturbances were not associated with mortality risk. In joint analyses, the combination of a PHQ-9 score ≥ 5 and no sleep disturbances, but not sleep disturbances, was associated with increased risks of all-cause mortality, cancer-specific mortality, and noncancer mortality. Specifically, compared with individuals with a PHQ-9 score of 0-4 and no sleep disturbances, HRs for all-cause mortality and noncancer mortality in individuals with a PHQ-9 score of 5-9 and no sleep disturbances were 1.72 (1.21-2.44) and 1.69 (1.10-2.61), respectively, and 2.61 (1.43-4.78) and 2.77 (1.27-6.07), respectively, in individuals with a PHQ-9 score ≥ 10 and no sleep disturbances; HRs for cancer-specific mortality in individuals with a PHQ-9 score ≥ 5 and no sleep disturbances were 1.95 (1.16-3.27). CONCLUSIONS: Depressive symptoms were linked to a high risk of mortality in cancer survivors. The combination of a PHQ-9 score (≥ 5) and an absence of self-perceived sleep disturbances was associated with greater all-cause mortality, cancer-specific mortality, and noncancer mortality risks, particularly in individuals with a PHQ-9 score (≥ 10).

Identification of an HLA-A*11:01-restricted neoepitope of mutant PIK3CA and its specific T cell receptors for cancer immunotherapy targeting hotspot driver mutations.

Hotspot driver mutations presented by human leukocyte antigens might be recognized by anti-tumor T cells. Based on their advantages of tumor-specificity and immunogenicity, neoantigens derived from hotspot mutations, such as PIK3CAH1047L, may serve as emerging targets for cancer immunotherapies. NetMHCpan V4.1 was utilized for predicting neoepitopes of PIK3CA hotspot mutation. Using in vitro stimulation, antigen-specific T cells targeting the HLA-A*11:01-restricted PIK3CA mutation were isolated from healthy donor-derived peripheral blood mononuclear cells. T cell receptors (TCRs) were cloned using single-cell PCR and sequencing. Their functionality was assessed through T cell activation markers, cytokine production and cytotoxic response to cancer cell lines pulsed with peptides or transduced genes of mutant PIK3CA. Immunogenic mutant antigens from PIK3CA and their corresponding CD8+ T cells were identified. These PIK3CA mutation-specific CD8+ T cells were subsequently enriched, and their TCRs were isolated. The TCR clones exhibited mutation-specific and HLA-restricted reactivity, demonstrating varying degrees of functional avidity. Identified TCR genes were transferred into CD8+ Jurkat cells and primary T cells deficient of endogenous TCRs. TCR-expressing cells demonstrated specific recognition and reactivity against the PIK3CAH1047L peptide presented by HLA-A*11:01-expressing K562 cells. Furthermore, mutation-specific TCR-T cells demonstrated an elevation in cytokine production and profound cytotoxic effects against HLA-A*11:01+ malignant cell lines harboring PIK3CAH1047L. Our data demonstrate the immunogenicity of an HLA-A*11:01-restricted PIK3CA hotspot mutation and its targeting therapeutic potential, together with promising candidates of TCR-T cell therapy.

Nomogram Predicts Prognostic Factors for Head and Neck Cutaneous Melanoma: A Population-Based Analysis.

BACKGROUND: The head and neck cutaneous melanoma (HNCM) accounts for 20% of newly diagnosed melanoma. Research on prognostic models for their survival yet remains largely unexplored. This study employed a nomogram approach to develop and validate a predictive model for both overall survival (OS) and disease-specific survival (DSS) in patients with HNCM. METHODS: This study analyzed the HNCM patients diagnosed between 2004 and 2014 from Surveillance, Epidemiology, and End Results database. To identify independent prognostic factors for HNCM, we integrated results from univariate Cox regression analysis, random survival forests, and LASSO regression with cross-validation. A nomogram was designed and validated based on the identified characteristics to predict the 3-, 5-, and 8-year OS and DSS of patients with HNCM. RESULTS: Age, Stage, Ulceration, Thickness, Chemotherapy, lymph node metastasis, and Radiation were identified as independent prognostic factors. The nomogram achieved a satisfactory performance with C-indices of 0.824(DSS) and 0.757(OS) in the training cohort and 0.827(DSS) and 0.749(OS) in the validation cohort, respectively. The area under the curves for the OS at 3, 5, and 8 years were 0.789, 0.788, and 0.794 for the training cohort, and 0.778, 0.776, and 0.795 for the validation cohort, respectively. For DSS, the area under the curves at 3, 5, and 8 years were 0.859, 0.842, and 0.828 in the training cohort, and 0.864, 0.844, and 0.834 in the validation cohort, respectively. The calibration curve showed that there was a strong correlation between the observed outcomes and the predicted survival probability. CONCLUSIONS: This study established and validated predictive nomograms for HNCM patients with robust predictive performance.

Online Detection of Hydrogen Fluoride under Corona Discharge in Gas-Insulated Switchgear Based on Photoacoustic Spectroscopy.

Internal discharge and overheating faults in sulfur hexafluoride (SF6) gas-insulated electrical equipment will generate a series of characteristic gas products. Hydrogen fluoride (HF) is one of the main decomposition gases under discharge failure. Because of its extremely corrosive nature, it can react with other materials in gas-insulated switchgear (GIS), resulting in a short existence time, so it needs to be detected online. Resonant gas photoacoustic spectroscopy has the advantage of high sensitivity, fast response, and no sample gas consumption, and can be used for the online detection of flowing gas. In this paper, a simulated GIS corona discharge experimental platform was built, and the HF generated in the discharge was detected online by gas photoacoustic spectroscopy. The absorption peak of HF molecule near 1312.59 nm was selected as the absorption spectral line, and a resonant photoacoustic cell was designed. To improve the detection sensitivity of HF, wavelength modulation and second-harmonic detection technology were used. The online monitoring of HF in the simulated GIS corona discharge fault was successfully realized. The experimental results show that the sensitivity of the designed photoacoustic spectroscopy detection system for HF is 0.445 µV/(µL/L), and the limit of detection (LOD) is 0.611 µL/L.

The hepatocellular carcinoma risk in patients with HBV-related cirrhosis: a competing risk nomogram based on a 4-year retrospective cohort study.

Objective: The study aimed to build and validate a competitive risk nomogram to predict the cumulative incidence of hepatocellular carcinoma (HCC) for patients with hepatitis B virus (HBV)-related cirrhosis. Methods: A total of 1401 HBV-related cirrhosis patients were retrospectively enrolled from January 1, 2011 to December 31, 2014. Application of 20 times imputation dealt with missing data using multiple imputation by chained equations (MICE). The patients were randomly divided into a training set (n = 1017) and a validation set (n = 384) at a ratio of 3:1. A prediction study was carried out using a competing risk model, where the event of interest was HCC and the competing events were death and liver transplantation, and subdistribution hazard ratios (sHRs) with 95% CIs were reported. The multivariate competing risk model was constructed and validated. Results: There was a negligible difference between the original database and the 20 imputed datasets. At the end of follow-up, the median follow-up time was 69.9 months (interquartile range: 43.8-86.6). There were 31.5% (442/1401) of the patients who developed HCC, with a 5-year cumulative incidence of 22.9 (95%CI, 20.8%-25.2%). The univariate and multivariate competing risk regression and construction of the nomogram were performed in 20 imputed training datasets. Age, sex, antiviral therapy history, hepatitis B e antigen, alcohol drinking history, and alpha-fetoprotein levels were included in the nomogram. The area under receiver operating characteristic curve values at 12, 24, 36, 60, and 96 months were 0.68, 0.69, 0.70, 0.68, and 0.80, and the Brier scores were 0.30, 0.25, 0.23, 0.21, and 0.20 in the validation set. According to the cumulative incidence function, the nomogram effectively screened out high-risk HCC patients from low-risk patients in the presence of competing events (Fine-Gray test p < 0.001). Conclusion: The competitive risk nomogram was allowed to be used for predicting HCC risk in individual patients with liver cirrhosis, taking into account both the association between risk factors and HCC and the modifying effect of competition events on this association.

Treatment Strategy for Splenic Artery Aneurysms and Novel Classification Based on Imaging.

OBJECTIVE: The incidence of splenic artery aneurysms (SAAs) has increased with advancements in imaging techniques, necessitating a comprehensive classification to guide treatment strategies. This study aims to propose a novel classification system for SAAs based on aneurysm characteristics and to review treatment outcomes at our center. METHODS: This retrospective study included 113 SAAs patients admitted at Peking Union Medical College Hospital from January 2019 to December 2023, assessed using computed tomography angiography (CTA) or digital subtraction angiography (DSA). A new classification system was devised based on the aneurysms' location, morphology, integrity, and parent artery anatomy. Treatment strategies were determined based on these characteristics, with interventions ranging from endovascular therapy to laparoscopic and open surgery. Patients were followed up post-intervention to assess mortality, complications, reinterventions, and aneurysm-related outcomes. RESULTS: The study cohort of 113 patients with 127 SAAs had a predominance of female patients (63.7%) and a mean age of 52.7 years. The SAAs were classified into five types, with Type I being the most common. The intervention techniques varied across types, with sac embolization, covered stent implantation, and artery embolization being the most frequently employed. The overall technical success rate was 94.7%, with perioperative complication and reintervention rates of 25% and 0.9%, respectively, and no deaths within 30 days post-intervention. The median follow-up duration was 21 months, with overall complications rate of 3.5% and no aneurysm-related complications or deaths. CONCLUSIONS: The proposed classification system effectively guides the selection of treatment strategies for SAAs, incorporating key anatomical and morphological features. This system facilitated high technical success and low complication rates, underscoring the importance of tailored techniques in managing SAAs. Further research is necessary to validate this classification system and optimize treatment algorithms.

The Effect of Low HBV-DNA Viral Load on Recurrence in Hepatocellular Carcinoma Patients Who Underwent Primary Locoregional Treatment and the Development of a Nomogram Prediction Model.

(1) Background: HBV-DNA is an essential clinical indicator of primary hepatocellular carcinoma (HCC) prognosis. Our study aimed to investigate the prognostic implication of a low load of HBV-DNA in HCC patients who underwent local treatment. Additionally, we developed and validated a nomogram to predict the recurrence of patients with low (20-100 IU/mL) viral loads (L-VL). (2) Methods: A total of 475 HBV-HCC patients were enrolled, including 403 L-VL patients and 72 patients with very low (<20 IU/mL) viral loads (VL-VL). L-VL HCC patients were randomly divided into a training set (N = 282) and a validation set (N = 121) at a ratio of 7:3. Utilizing the Lasso-Cox regression analysis, we identified independent risk factors for constructing a nomogram. (3) Results: L-VL patients had significantly shorter RFS than VL-VL patients (38.2 m vs. 23.4 m, p = 0.024). The content of the nomogram included gender, BCLC stage, Glob, and MLR. The C-index (0.682 vs. 0.609); 1-, 3-, and 5-year AUCs (0.729, 0.784, and 0.783, vs. 0.631, 0.634, the 0.665); calibration curves; and decision curve analysis (DCA) curves of the training and validation cohorts proved the excellent predictive performance of the nomogram. There was a statistically significant difference in RFS between the low-, immediate-, and high-risk groups both in the training and validation cohorts (p < 0.001); (4) Conclusions: Patients with L-VL had a worse prognosis. The nomogram developed and validated in this study has the advantage of predicting patients with L-VL.

FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation.

Adapting a medical image segmentation model to a new domain is important for improving its cross-domain transferability, and due to the expensive annotation process, Unsupervised Domain Adaptation (UDA) is appealing where only unlabeled images are needed for the adaptation. Existing UDA methods are mainly based on image or feature alignment with adversarial training for regularization, and they are limited by insufficient supervision in the target domain. In this paper, we propose an enhanced Filtered Pseudo Label (FPL+)-based UDA method for 3D medical image segmentation. It first uses cross-domain data augmentation to translate labeled images in the source domain to a dual-domain training set consisting of a pseudo source-domain set and a pseudo target-domain set. To leverage the dual-domain augmented images to train a pseudo label generator, domain-specific batch normalization layers are used to deal with the domain shift while learn the domain-invariant structure features, generating high-quality pseudo labels for target-domain images. We then combine labeled source-domain images and target-domain images with pseudo labels to train a final segmentor, where image-level weighting based on uncertainty estimation and pixel-level weighting based on dual-domain consensus are proposed to mitigate the adverse effect of noisy pseudo labels. Experiments on three public multi-modal datasets for Vestibular Schwannoma, brain tumor and whole heart segmentation show that our method surpassed ten state-of-the-art UDA methods, and it even achieved better results than fully supervised learning in the target domain in some cases.

Present situation of minimally invasive surgical treatment for early gastric cancer.

Minimally invasive surgery is a kind of surgical operation, which is performed by using professional surgical instruments and equipment to inactivate, resect, repair or reconstruct the pathological changes, deformities and wounds in human body through micro-trauma or micro-approach, in order to achieve the goal of treatment, its surgical effect is equivalent to the traditional open surgery, while avoiding the morbidity of conventional surgical wounds. In addition, it also has the advantages of less trauma, less blood loss during operation, less psychological burden and quick recovery on patients, and these minimally invasive techniques provide unique value for the examination and treatment of gastric cancer patients. Surgical minimally invasive surgical techniques have developed rapidly and offer numerous options for the treatment of early gastric cancer (EGC): endoscopic mucosal resection (EMR), underwater EMR (UEMR), endoscopic submucosal dissection (ESD), endoscopic full-thickness resection (EFTR), endoscopic submucosal excavation (ESE), submucosal tunnel endoscopic resection), laparoscopic and endoscopic cooperative surgery (LECS); Among them, EMR, EFTR and LECS technologies have a wide range of applications and different modifications have been derived from their respective surgical operations, such as band-assisted EMR (BA-EMR), conventional EMR (CEMR), over-the-scope clip-assisted EFTR, no-touch EFTR, the inverted LECS, closed LECS, and so on. These new and improved minimally invasive surgeries are more precise, specific and effective in treating different types of EGC.

UTRN as a potential biomarker in breast cancer: a comprehensive bioinformatics and in vitro study.

Utrophin (UTRN), known as a tumor suppressor, potentially regulates tumor development and the immune microenvironment. However, its impact on breast cancer's development and treatment remains unstudied. We conducted a thorough examination of UTRN using both bioinformatic and in vitro experiments in this study. We discovered UTRN expression decreased in breast cancer compared to standard samples. High UTRN expression correlated with better prognosis. Drug sensitivity tests and RT-qPCR assays revealed UTRN's pivotal role in tamoxifen resistance. Furthermore, the Kruskal-Wallis rank test indicated UTRN's potential as a valuable diagnostic biomarker for breast cancer and its utility in detecting T stage of breast cancer. Additionally, our results demonstrated UTRN's close association with immune cells, inhibitors, stimulators, receptors, and chemokines in breast cancer (BRCA). This research provides a novel perspective on UTRN's role in breast cancer's prognostic and therapeutic value. Low UTRN expression may contribute to tamoxifen resistance and a poor prognosis. Specifically, UTRN can improve clinical decision-making and raise the diagnosis accuracy of breast cancer.

CD276-dependent efferocytosis by tumor-associated macrophages promotes immune evasion in bladder cancer.

Interplay between innate and adaptive immune cells is important for the antitumor immune response. However, the tumor microenvironment may turn immune suppressive, and tumor associated macrophages are playing a role in this transition. Here, we show that CD276, expressed on tumor-associated macrophages (TAM), play a role in diminishing the immune response against tumors. Using a model of tumors induced by N-butyl-N-(4-hydroxybutyl) nitrosamine in BLCA male mice we show that genetic ablation of CD276 in TAMs blocks efferocytosis and enhances the expression of the major histocompatibility complex class II (MHCII) of TAMs. This in turn increases CD4 + and cytotoxic CD8 + T cell infiltration of the tumor. Combined single cell RNA sequencing and functional experiments reveal that CD276 activates the lysosomal signaling pathway and the transcription factor JUN to regulate the expression of AXL and MerTK, resulting in enhanced efferocytosis in TAMs. Proving the principle, we show that simultaneous blockade of CD276 and PD-1 restrain tumor growth better than any of the components as a single intervention. Taken together, our study supports a role for CD276 in efferocytosis by TAMs, which is potentially targetable for combination immune therapy.

METTL3 recruiting M2-type immunosuppressed macrophages by targeting m6A-SNAIL-CXCL2 axis to promote colorectal cancer pulmonary metastasis.

BACKGROUND: The regulatory role of N6-methyladenosine (m6A) modification in the onset and progression of cancer has garnered increasing attention in recent years. However, the specific role of m6A modification in pulmonary metastasis of colorectal cancer remains unclear. METHODS: This study identified differential m6A gene expression between primary colorectal cancer and its pulmonary metastases using transcriptome sequencing and immunohistochemistry. We investigated the biological function of METTL3 gene both in vitro and in vivo using assays such as CCK-8, colony formation, wound healing, EDU, transwell, and apoptosis, along with a BALB/c nude mouse model. The regulatory mechanisms of METTL3 in colorectal cancer pulmonary metastasis were studied using methods like methylated RNA immunoprecipitation quantitative reverse transcription PCR, RNA stability analysis, luciferase reporter gene assay, Enzyme-Linked Immunosorbent Assay, and quantitative reverse transcription PCR. RESULTS: The study revealed high expression of METTL3 and YTHDF1 in the tumors of patients with pulmonary metastasis of colorectal cancer. METTL3 promotes epithelial-mesenchymal transition in colorectal cancer by m6A modification of SNAIL mRNA, where SNAIL enhances the secretion of CXCL2 through the NF-κB pathway. Additionally, colorectal cancer cells expressing METTL3 recruit M2-type macrophages by secreting CXCL2. CONCLUSION: METTL3 facilitates pulmonary metastasis of colorectal cancer by targeting the m6A-Snail-CXCL2 axis to recruit M2-type immunosuppressive macrophages. This finding offers new research directions and potential therapeutic targets for colorectal cancer treatment.

Construction and validation of a machine learning-based nomogram to predict the prognosis of HBV associated hepatocellular carcinoma patients with high levels of hepatitis B surface antigen in primary local treatment: a multicenter study.

Background: Hepatitis B surface antigen (HBsAg) clearance is associated with improved long-term outcomes and reduced risk of complications. The aim of our study was to identify the effects of levels of HBsAg in HCC patients undergoing TACE and sequential ablation. In addition, we created a nomogram to predict the prognosis of HCC patients with high levels of HBsAg (≥1000U/L) after local treatment. Method: This study retrospectively evaluated 1008 HBV-HCC patients who underwent TACE combined with ablation at Beijing Youan Hospital and Beijing Ditan Hospital from January 2014 to December 2021, including 334 patients with low HBsAg levels and 674 patients with high HBsAg levels. The high HBsAg group was divided into the training cohort (N=385), internal validation cohort (N=168), and external validation cohort (N=121). The clinical and pathological features of patients were collected, and independent risk factors were identified using Lasso-Cox regression analysis for developing a nomogram. The performance of the nomogram was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves in the training and validation cohorts. Patients were classified into high-risk and low-risk groups based on the risk scores of the nomogram. Result: After PSM, mRFS was 28.4 months (22.1-34.7 months) and 21.9 months (18.5-25.4 months) in the low HBsAg level and high HBsAg level groups (P<0.001). The content of the nomogram includes age, BCLC stage, tumor size, globulin, GGT, and bile acids. The C-index (0.682, 0.666, and 0.740) and 1-, 3-, and 5-year AUCs of the training, internal validation, and external validation cohorts proved good discrimination of the nomogram. Calibration curves and DCA curves suggested accuracy and net clinical benefit rates. The nomogram enabled to classification of patients with high HBsAg levels into low-risk and high-risk groups according to the risk of recurrence. There was a statistically significant difference in RFS between the two groups in the training, internal validation, and external validation cohorts (P<0.001). Conclusion: High levels of HBsAg were associated with tumor progression. The nomogram developed and validated in the study had good predictive ability for patients with high HBsAg levels.

Integrating bulk and single-cell sequencing data to construct a Scissor+ dendritic cells prognostic model for predicting prognosis and immune responses in ESCC.

Esophageal squamous cell carcinoma (ESCC) is characterized by molecular heterogeneity with various immune cell infiltration patterns, which have been associated with therapeutic sensitivity and resistance. In particular, dendritic cells (DCs) are recently discovered to be associated with prognosis and survival in cancer. However, how DCs differ among ESCC patients has not been fully comprehended. Recently, the advance of single-cell RNA sequencing (scRNA-seq) enables us to profile the cell types, states, and lineages in the heterogeneous ESCC tissues. Here, we dissect the ESCC tumor microenvironment at high resolution by integrating 192,078 single cells from 60 patients, including 4379 DCs. We then used Scissor, a method that identifies cell subpopulations from single-cell data that are associated bulk samples with genomic and clinical information, to stratify DCs into Scissorhi and Scissorlow subtypes. We applied the Scissorhi gene signature to stratify ESCC scRNAseq patient, and we found that PD-L1, TIGIT, PVR and IL6 ligand-receptor-mediated cell interactions existed mainly in Scissorhi patients. Finally, based on the Scissor results, we successfully developed a validated prognostic risk model for ESCC and further validated the reliability of the risk prediction model by recruiting 40 ESCC clinical patients. This information highlights the importance of these genes in assessing patient prognosis and may help in the development of targeted or personalized therapies for ESCC.

Single cell analysis unveils B cell-dominated immune subtypes in HNSCC for enhanced prognostic and therapeutic stratification.

Head and neck squamous cell carcinoma (HNSCC) is characterized by high recurrence or distant metastases rate and the prognosis is challenging. There is mounting evidence that tumor-infiltrating B cells (TIL-Bs) have a crucial, synergistic role in tumor control. However, little is known about the role TIL-Bs play in immune microenvironment and the way TIL-Bs affect the outcome of immune checkpoint blockade. Using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database, the study identified distinct gene expression patterns in TIL-Bs. HNSCC samples were categorized into TIL-Bs inhibition and TIL-Bs activation groups using unsupervised clustering. This classification was further validated with TCGA HNSCC data, correlating with patient prognosis, immune cell infiltration, and response to immunotherapy. We found that the B cells activation group exhibited a better prognosis, higher immune cell infiltration, and distinct immune checkpoint levels, including elevated PD-L1. A prognostic model was also developed and validated, highlighting four genes as potential biomarkers for predicting survival outcomes in HNSCC patients. Overall, this study provides a foundational approach for B cells-based tumor classification in HNSCC, offering insights into targeted treatment and immunotherapy strategies.

Long-term survival of a patient with epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC) and untreated multiple brain metastases treated with zorifertinib: A case report.

Brain metastases (BM) are common in patients with epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC) and confer poor prognoses. Zorifertinib (AZD3759), an EGFR-tyrosine kinase inhibitor (TKI) with high blood-brain barrier penetration, has previously demonstrated promising systemic and intracranial antitumor activity in phase 1-3 studies. This is the first report of a patient with EGFR-mutant (exon 21 L858R) NSCLC and symptomatic untreated multiple BM who achieved a long overall survival (OS) of more than 65 months after sequential treatment with zorifertinib and a third-generation EGFR-TKI. This new treatment paradigm offers a new treatment option and deserves further clinical exploration to prolong OS of patients with EGFR-mutant NSCLC and untreated multiple BM.

Early-life exposure to PM2.5 leads to ASD-like phenotype in male offspring rats through activation of PI3K-AKT signaling pathway.

Previous studies have shown that early-life exposure to fine particulate matter (PM2.5) is associated with an increasing risk of autism spectrum disorder (ASD), however, the specific sensitive period of ASD is unknown. Here, a model of dynamic whole-body concentrated PM2.5 exposure in pre- and early-postnatal male offspring rats (MORs) was established. And we found that early postnatal PM2.5 exposed rats showed more typical ASD behavioral characteristics than maternal pregnancy exposure rats, including poor social interaction, novelty avoidance and anxiety disorder. And more severe oxidative stress and inflammatory responses were observed in early postnatal PM2.5 exposed rats. Moreover, the expression level of phosphatase and tensin homolog deleted on chromosome ten (PTEN) was down-regulated and the ratios of p-PI3K/PI3K and p-AKT/AKT were up-regulated in early postnatal PM2.5 exposed rats. This study suggests that early postnatal exposure to PM2.5 is more susceptible to ASD-like phenotype in offspring than maternal pregnancy exposure and the activation of PI3K-AKT signaling pathway may represent underlying mechanisms.

A bidirectional interpretable compound-protein interaction prediction framework based on cross attention.

The identification of compound-protein interactions (CPIs) plays a vital role in drug discovery. However, the huge cost and labor-intensive nature in vitro and vivo experiments make it urgent for researchers to develop novel CPI prediction methods. Despite emerging deep learning methods have achieved promising performance in CPI prediction, they also face ongoing challenges: (i) providing bidirectional interpretability from both the chemical and biological perspective for the prediction results; (ii) comprehensively evaluating model generalization performance; (iii) demonstrating the practical applicability of these models. To overcome the challenges posed by current deep learning methods, we propose a cross multi-head attention oriented bidirectional interpretable CPI prediction model (CmhAttCPI). First, CmhAttCPI takes molecular graphs and protein sequences as inputs, utilizing the GCW module to learn atom features and the CNN module to learn residue features, respectively. Second, the model applies cross multi-head attention module to compute attention weights for atoms and residues. Finally, CmhAttCPI employs a fully connected neural network to predict scores for CPIs. We evaluated the performance of CmhAttCPI on balanced datasets and imbalanced datasets. The results consistently show that CmhAttCPI outperforms multiple state-of-the-art methods. We constructed three scenarios based on compound and protein clustering and comprehensively evaluated the model generalization ability within these scenarios. The results demonstrate that the generalization ability of CmhAttCPI surpasses that of other models. Besides, the visualizations of attention weights reveal that CmhAttCPI provides chemical and biological interpretation for CPI prediction. Moreover, case studies confirm the practical applicability of CmhAttCPI in discovering anticancer candidates.

Anlotinib plus docetaxel vs. docetaxel alone for advanced non-small-cell lung cancer patients who failed first-line treatment: A multicenter, randomized phase II trial.

OBJECTIVES: Given the modest efficacy of docetaxel in advanced non-small cell lung cancer (NSCLC), this study assesses the therapeutic potential and safety profile of anlotinib in combination with docetaxel compared to docetaxel monotherapy as a second-line therapy for patients with advanced NSCLC. MATERIALS AND METHODS: In this phase II study, patients with advanced NSCLC experiencing failure with first-line platinum-based regimens were randomized in a 1:1 ratio to receive either anlotinib plus docetaxel or docetaxel alone. Primary endpoint was progression-free survival (PFS), with overall survival (OS), objective response rate (ORR), disease control rate (DCR), and safety as secondary endpoints. RESULTS: A total of 83 patients were randomized. The combination of anlotinib and docetaxel significantly extended median PFS to 4.4 months compared to 1.6 months for docetaxel alone (hazard ratio [HR] = 0.38, 95 % confidence interval [CI]: 0.23-0.63, P = 0.0002), and also demonstrated superior ORR (32.5 % vs. 9.3 %, P = 0.0089) and DCR (87.5 % vs. 53.5 %, P = 0.0007). Median OS was observed at 12.0 months in the combination group vs. 10.9 months in the monotherapy group (HR = 0.82, 95 % CI: 0.47-1.43, P = 0.4803). For patients previously treated with immunotherapy, the median PFS was notably longer at 7.8 vs. 1.7 months (HR = 0.22, 95 % CI: 0.09-0.51, P = 0.0290). The incidence of grade ≥ 3 treatment-related adverse events, predominantly leukopenia (15.0 % vs. 7.0 %) and neutropenia (10.0 % vs. 5.0 %), was manageable across both groups. CONCLUSION: Anlotinib plus docetaxel offers a viable therapeutic alternative for patients with advanced NSCLC who failed first-line platinum-based treatments.

Structural insights into the unique pH-responsive characteristics of the anti-TIGIT therapeutic antibody Ociperlimab.

TIGIT is mainly expressed on T cells and is an inhibitory checkpoint receptor that binds to its ligand PVR in the tumor microenvironment. Anti-TIGIT monoclonal antibodies (mAbs) such as Ociperlimab and Tiragolumab block the TIGIT-PVR interaction and are in clinical development. However, the molecular blockade mechanism of these mAbs remains elusive. Here, we report the crystal structures of TIGIT in complex with Ociperlimab_Fab and Tiragolumab_Fab revealing that both mAbs bind TIGIT with a large steric clash with PVR. Furthermore, several critical epitopic residues are identified. Interestingly, the binding affinity of Ociperlimab toward TIGIT increases approximately 17-fold when lowering the pH from 7.4 to 6.0. Our structure shows a strong electrostatic interaction between ASP103HCDR3 and HIS76TIGIT explaining the pH-responsive mechanism of Ociperlimab. In contrast, Tiragolumab does not show an acidic pH-dependent binding enhancement. Our results provide valuable information that could help to improve the efficacy of therapeutic antibodies for cancer treatment.