The effect of lymph node ratio on the surgical outcomes in patients with colorectal cancer.

The current study aimed to evaluate the effect of lymph node ratio (LNR) on the short-term and long-term outcomes of colorectal cancer (CRC) patients who underwent radical CRC surgery. We retrospectively collected CRC patients who underwent radical surgery from Jan 2011 to Jan 2020 in a single-center hospital. The patients were divided into the high LNR group and the low group according to the median. The baseline information and the short-term outcomes were compared between the high group and the low group. Univariate and multivariate logistic regression was performed to analyze the independent predictors for overall survival (OS) and disease-free survival (DFS). A 1:1 proportional propensity score matching (PSM) was used to reduce the selection bias between the two groups. Kaplan-Meier method was used to estimate the OS and DFS between the two groups in different T stages. A total of 1434 CRC patients undergoing radical surgery were enrolled in this study, and there were 730 (50.9%) patients in the low LNR group and 704 (49.1%) patients in the high LNR group. After the PSM, there were 618 patients in both groups, the baseline characteristics between the two groups had no significant difference (p > 0.05). After comparing the Surgery-related information and The Short-term outcomes, the high LNR group had a longer hospital stay (after PSM, p < 0.01). In univariate and multivariate logistic regression analyses, age (univariate analysis, p < 0.01; multivariate analysis, p < 0.01), tumor location (univariate analysis, p = 0.020; multivariate analysis, p = 0.024), lymph-vascular space invasion (univariate analysis, p < 0.01; multivariate analysis, p < 0.01), cancer nodules (univariate analysis, p < 0.01; multivariate analysis, p < 0.01), tumor size (univariate analysis, p < 0.01; multivariate analysis, p < 0.01), LNR (univariate analysis, p < 0.01; multivariate analysis, p < 0.01), and overall complications (univariate analysis, p < 0.01; multivariate analysis, p < 0.01) were independent risk factors for OS, and age (univariate analysis, p < 0.01; multivariate analysis, p < 0.01), tumor location (univariate analysis, p = 0.032; multivariate analysis, p = 0.031), T stage (univariate analysis, p < 0.01; multivariate analysis, p = 0.014), lymph-vascular space invasion (univariate analysis, p < 0.01; multivariate analysis, p < 0.01), cancer nodules (univariate analysis, p < 0.01; multivariate analysis, p < 0.01), LNR (univariate analysis, p < 0.01; multivariate analysis, p < 0.01), and overall complications (univariate analysis, p < 0.01; multivariate analysis, p < 0.01) were identified as independent risk factors for DFS. The high LNR group had a worse OS in T3 (p < 0.01) and T4 (p < 0.01) as well as a worse DFS in T3 (p < 0.01) and T4 (p < 0.01). No association was found between LNR and postoperative complications, but the high LNR group had a longer hospital stay. LNR was identified as an independent predictor for OS and DFS. Furthermore, high LNR had a worse OS and DFS under T3 and T4 stages. Therefore, LNR was more prognostically significant for CRC patients under T3 and T4 stages.

Does chronic kidney disease affect the short-term outcomes and prognosis of colorectal cancer surgery? A propensity score matching analysis.

Purpose: The aim of this study was to analyze the effect of chronic kidney disease (CKD) on the short-term outcomes and prognosis of colorectal cancer (CRC) patients who underwent primary surgery. Methods: CRC patients who underwent radical surgery were included from Jan 2011 to Jan 2020 in a single hospital. The short-term outcomes and prognosis were compared between the CKD group and the Non-CKD group using propensity score matching (PSM) analysis. Results: A total of 4056 patients undergoing CRC surgery were included, including 723 patients in the CKD group and 3333 patients in the Non-CKD group. After 1:1 PSM, there were 666 patients in each group, respectively. No significant difference was found in baseline characteristics between the two groups. (p>0.05). After PSM, the CKD group had a longer postoperative hospital stay (P=0.009) and a higher incidence of overall complications (p=0.050). Cox analysis was performed on matched patients to find predictors of overall survival (OS) and disease-free survival (DFS). We found that age (p<0.01, HR=1.045, 95% CI=1.028-1.062), tumor stage (p<0.01, HR=1.931, 95% CI=1.564-2.385) and overall complications (p<0.01, HR=1.858, 95% CI=1.423-2.425) were independent predictors of OS. Age (p<0.01, HR=1.034, 95% CI=1.020-1.049), tumor stage (p<0.01, HR=1.852, 95% CI=1.537-2.231), and overall complications (p<0.01, HR=1.651, 95% CI=1.295-2.10) were independent predictors of DFS. However, CKD was not an independent predictor of OS or DFS (OS: p=0.619, HR=1.070, 95% CI=0.820-1.396; DFS: p=0.472, HR=1.092, 95% CI=0.859-1.389). Conclusion: CKD prolonged postoperative hospital stay; however, CKD might not affect major postoperative complications, OS or DFS of CRC.

Pyroptosis-related long-noncoding RNA signature predicting survival and immunotherapy efficacy in patients with lung squamous cell carcinoma.

Pyroptosis-related long-noncoding RNAs (PRlncRNAs) play an important role in cancer progression. However, their role in lung squamous cell carcinoma (LUSC) is unclear. A risk model was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis based on RNA sequencing data from The Cancer Genome Atlas database. The LUSC cohort was divided into high- and low-risk groups based on the median risk score. For the prognostic value of the model, the Kaplan-Meier analysis, log-rank test, and Cox regression analysis were performed. A nomogram was constructed to predict the prognosis of patients, using a risk score and clinical parameters such as age, sex, clinical stage, and tumor node metastasis classification (TNM) stage. Afterwards, six common algorithms were employed to assess the invasion of immune cells. The Gene Set Enrichment Analysis (GSEA) was conducted to identify differences between patients at high and low risk. Furthermore, the pRRophetic package was employed to forecast the half-maximal inhibitory doses of prevalent chemotherapeutic drugs, while the tumor immune dysfunction and exclusion score was computed to anticipate the response to immunotherapy. The expression levels of the seven PRlncRNAs were examined in both LUSC and normal lung epithelial cell lines using RT-qPCR. Proliferation, migration, and invasion assays were also carried out to investigate the role of MIR193BHG in LUSC cells. Patients in the low-risk group showed prolonged survival in the total cohort or subgroup analysis. The Cox regression analysis showed that the risk model could act as an independent prognostic factor for patients with LUSC. The results of GSEA analysis revealed that the high-risk group showed enrichment of cytokine pathways, Janus tyrosine kinase/signal transducer and activator of the transcription signalling pathway, and Toll-like receptor pathway. Conversely, the low-risk group showed enrichment of several gene repair pathways. Furthermore, the risk score was positively correlated with immune cell infiltration. Moreover, patients in the high-risk category showed reduced responsiveness to conventional chemotherapeutic medications and immunotherapy. The majority of the long noncoding RNAs in the risk model were confirmed to be overexpressed in LUSC cell lines compared to normal lung epithelial cell lines by in vitro tests. Further studies have shown that downregulating the expression of MIR193BHG may inhibit the growth, movement, and infiltration capabilities of LUSC cells, whereas increasing the expression of MIR193BHG could enhance these malignant tendencies. This study found that PRlncRNAs were linked to the prognosis of LUSC patients. The risk model, evaluated across various clinical parameters and treatment modalities, shows potential as a future reference for clinical applications.

Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images.

Intracranial aneurysm is a high-risk disease, with imaging playing a crucial role in their diagnosis and treatment. The rapid advancement of artificial intelligence in imaging technology holds promise for the development of AI-based radiomics predictive models. These models could potentially enable the automatic detection and diagnosis of intracranial aneurysms, assess their status, and predict outcomes, thereby assisting in the creation of personalized treatment plans. In addition, these techniques could improve diagnostic efficiency for physicians and patient prognoses. This article aims to review the progress of artificial intelligence radiomics in the study of intracranial aneurysms, addressing the challenges faced and future prospects, in hopes of introducing new ideas for the precise diagnosis and treatment of intracranial aneurysms.

Development and validation of a nomogram to predict the risk factors of major complications after radical rectal cancer surgery.

Purpose: The aim of this study was to establish a validated nomogram to predict risk factors for major post-operative complications in patients with rectal cancer (RC) by analyzing the factors contributing to major post-operative complications in RC patients. Methods: We retrospectively collected baseline and surgical information on patients who underwent RC surgery between December 2012 and December 2022 at a single-center teaching hospital. The entire cohort was randomly divided into two subsets (60% of the data for development, 40% for validation). Independent risk factors for major post-operative complications were identified using multivariate logistic regression analyses, and predictive models were developed. Area under the curve (AUC) was calculated using receiver operating characteristic curve (ROC) to assess predictive probability, calibration curves were plotted to compare the predicted probability of the nomogram with the actual probability, and the clinical efficacy of the nomogram was assessed using decision curve analysis (DCA). Results: Our study included 3151 patients who underwent radical surgery for RC, including 1892 in the development set and 1259 in the validation set. Forty (2.1%) patients in the development set and 26 (2.1%) patients in the validation set experienced major post-operative complications. Through multivariate logistic regression analysis, age (p<0.01, OR=1.044, 95% CI=1.016-1.074), pre-operative albumin (p<0.01, OR=0.913, 95% CI=0.866-0.964), and open surgery (p<0.01, OR=2.461, 95% CI=1.284-4.761) were identified as independent risk factors for major post-operative complications in RC, and a nomogram prediction model was established. The AUC of the ROC plot for the development set was 0.7161 (95% Cl=0.6397-0.7924), and the AUC of the ROC plot for the validation set was 0.7191 (95% CI=0.6182-0.8199). The predicted probabilities in the calibration curves were highly consistent with the actual probabilities, which indicated that the prediction model had good predictive ability. The DCA also confirmed the good clinical performance of the nomogram. Conclusion: In this study, a validated nomogram containing three predictors was created to identify risk factors for major complications after radical RC surgery. Due to its accuracy and convenience, it could contribute to personalized management of patients in the perioperative period.

The role of early cerebral edema and hematoma assessment in aneurysmal subarachnoid hemorrhage (a-SAH) in predicting early brain injury (EBI) and cognitive impairment: a case controlled study.

BACKGROUND: Early assessment and management of cerebral edema and hematoma following aneurysmal subarachnoid hemorrhage (a-SAH) can significantly impact clinical cognitive outcomes. However, current clinical practices lack predictive models to identify early structural brain abnormalities affecting cognition. To address this gap, the authors propose the development of a predictive model termed the a-SAH Early Brain Edema/Hematoma Compression Neural (Structural Brain) Networks Score System (SEBE-HCNNSS). METHODS: In this study, 202 consecutive patients with spontaneous a-SAH underwent initial computed tomography (CT) or MRI scans within 24 h of ictus with follow-up 2 months after discharge. Using logistic regression analysis (univariate and multivariate), the authors evaluated the association of clinically relevant factors and various traditional scale ratings with cognitive impairment (CI). Risk factors with the highest area under the curve (AUC) values were included in the multivariate analysis and least absolute shrinkage and selection operator (LASSO) analysis or Cox regression analysis. RESULTS: A total of 177 patients were enrolled in the study, and 43 patients were classified with a high SEBE-HCNNSS grade (3-5). After a mean follow-up of 2 months, 121 individuals (68.36%) with a-SAH and three control subjects developed incident CI. The CT interobserver reliability of the SEBE-HCNNSS scale was high, with a Kappa value of 1. Furthermore, ROC analysis identified the SEBE-HCNNSS scale (OR 3.322, 95% CI: 2.312-7.237, P =0.00025) as an independent predictor of edema, CI, and unfavorable prognosis. These results were also replicated in a validation cohort. CONCLUSION: Overall, the SEBE-HCNNSS scale represents a simple assessment tool with promising predictive value for CI and clinical outcomes post-a-SAH. Our findings indicate its practical utility as a prognostic instrument for risk evaluation after a-SAH, potentially facilitating early intervention and treatment.

Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms.

Objective: The objective of this study is to develop a model to predicts the postoperative Hunt-Hess grade in patients with intracranial aneurysms by integrating radiomics and deep learning technologies, using preoperative CTA imaging data. Thereby assisting clinical decision-making and improving the assessment and prognosis of postoperative neurological function. Methods: This retrospective study encompassed 101 patients who underwent aneurysm embolization surgery. 851 radiomic features were extracted from CTA images. 512 deep learning features are extracted from last layer of ResNet50 deep convolutional neural network model. The feature screening process pipeline encompassed intraclass correlation coefficient analysis, principal component analysis, U test, spearman correlation analysis, minimum redundancy maximum relevance algorithm and Lasso regression, to identify features most correlated with postoperative Hunt-Hess grading. In the model construction phase, three distinct models were constructed: radiomics feature-based model (RSM), deep learning feature-based model (DLM), and deep learning-radiomics feature fusion model (DLRSCM). The study also calculated the radiomics score and combined it with clinical data to construct a Nomogram for predictive modeling. DLM, RSM and DLRSCM model was constructed by 9 base algorithms and 1 ensemble learning algorithm - Stacking ensemble model. Model performance was evaluated based on the area under the Receiver Operating Characteristic (ROC) curve (AUC), Matthews Correlation Coefficient (MCC), calibration curves, and decision curves analysis. Results: 5 significant radiomic feature and 4 significant deep learning features were obtained through the feature selection process. These features were utilized for model construction. Bootstrap resampling method was used for internal validation of the models. In terms of model evaluation, the DLM model, the stacking ensemble algorithm results achieved an AUC of 0.959 and MCC of 0.815. In the RSM model, the stacking ensemble model AUC was 0.935 and MCC was 0.793. The stacking ensemble model in DLRSCM outperformed others, with an AUC of 0.968 and MCC of 0.820. Results indicated that the ANN performed optimally among all base models, while the stacked ensemble learning model exhibited the highest predictive performance. Conclusion: This study demonstrates that the combination of radiomics and deep learning is an effective approach to predict the postoperative Hunt-Hess grade in patients with intracranial aneurysms. This holds significant value in the early identification of postoperative neurological complications and in enhancing clinical decision-making.

MiR-92a-2-5p suppresses esophageal squamous cell carcinoma cell proliferation and invasion by targeting PRDX2.

MicroRNAs (miRNAs) can function as negative regulators of gene expression by binding to the 3'-untranslated region (3'-UTR) of target genes. The aberrant expression of miRNAs in neoplasm is extensively associated with tumorigenesis and cancer progression, including esophageal squamous cell carcinoma (ESCC). Our previous investigation has identified the oncogenic roles of Peroxiredoxin2 (PRDX2) in ESCC progression; however, its upstream regulatory mechanism remains to be elucidated. By merging the prediction results from miRWalk2.0 and miRNA differential expression analysis results based on The Cancer Genome Atlas Esophageal Carcinoma (TCGA-ESCA) database, eight miRNA candidates were predicted to be the potential regulatory miRNAs of PRDX2, followed by further identification of miR-92a-2-5p as the putative miRNA of PRDX2. Subsequent functional studies demonstrated that miR-92a-2-5p can suppress ESCC cell proliferation and migration, as well as tumor growth in subcutaneous tumor xenograft models, which might be mediated by the suppression of AKT/mTOR and Wnt3a/ß-catenin signaling pathways upon miR-92a-2-5p mimic transfection condition. These data revealed the tumor suppressive functions of miR-92a-2-5p in ESCC by targeting PRDX2.

Performance of [18F]FDG PET/CT versus FAPI PET/CT for lung cancer assessment: a systematic review and meta-analysis.

PURPOSE: This article aims to compare the diagnostic performance of 18-fluorodeoxyglucose ([18F]FDG) PET/CT and fibroblast activating protein inhibitor (FAPI) PET/CT in the assessment of primary tumors, lymph nodes, and distant metastases in lung cancer patients. METHODS: A systematic search was conducted on the Cochrane Library, Embase, and PubMed/MEDLINE databases from inception until November 1, 2022. Included studies assessed the use of FAPI PET/CT and [18F]FDG PET/CT in patients with lung cancer. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to evaluate the risk of bias. A random variable model was used to analyze the diagnostic tests of the two imaging modalities. RESULTS: The sensitivity of FAPI PET/CT in detecting primary lung cancer lesions was 0.98 (95% CI: 0.88-1.00), while the sensitivity of [18F]FDG PET/CT was 0.99 (95% CI: 0.74-1.00). For the detection of metastatic lesions (lymph node metastases and distant metastases), FAPI PET/CT had a sensitivity of 0.99 (95% CI: 0.90-1.00), while the sensitivity of [18F]FDG PET/CT was 0.77 (95% CI: 0.66-0.85). However, the specificity of the two imaging modalities could not be assessed due to the lack of sufficient information on pertinent true negatives. CONCLUSION: In the diagnosis of metastatic lung cancer lesions, FAPI PET/CT demonstrated a higher sensitivity compared to [18F]FDG PET/CT. Therefore, FAPI PET/CT may be considered an alternative imaging modality for the assessment of primary lung cancer tumors, lymph node metastases, and distant metastases. CLINICAL RELEVANCE STATEMENT: FAPI may be an alternative to [18F]FDG in the assessment of primary lung cancer tumors, lymph node metastases, and distant metastases, which plays a very important role in treatment. KEY POINTS: • This article is to compare the performance of [18F]FDG PET/CT with FAPI PET/CT in the assessment of primary tumors, lymph nodes, and distant metastases in lung cancer. • However, FAPI PET/CT has a higher sensitivity for the diagnostic assessment of metastatic lung cancer lesions.

Diagnostic value of new biliary biopsy cannulae for malignant bile duct strictures via endoscopic retrograde cholangiopancreatography pathway.

BACKGROUND: Endoscopic retrograde cholangiopancreatography (ERCP) plays a major role in the diagnosis of malignant biliary strictures. ERCP fluoroscopy-guided biliary biopsy is more sensitive than brushing, but it is more difficult to perform and less successful. Therefore, a new technique of biliary biopsy using a new biliary biopsy cannula via the ERCP route was developed in our center with the aim of improving the diagnosis rate of malignant biliary strictures. METHODS: This is a retrospective study that included 42 patients who underwent ERCP-guided biliary brushing and biliary biopsy for biliary strictures using a new biliary biopsy cannula in our department from January 2019 to May 2022. The final diagnosis was determined after brushing, biliary biopsy under the new biliary biopsy cannula or adequate follow-up. Diagnostic rates were calculated and analyzed for relevant factors. RESULTS: The satisfactory rates of pathological specimens of 42 patients who underwent bile duct biopsy with bile duct brush and new bile duct biopsy cannula were 57.14% and 95.24% respectively. Cholangiocarcinoma was diagnosed in 45.23% and 83.30% of the samples by biliary brush examination and biliary biopsy using the new biliary biopsy cannula, respectively (p < 0.001). CONCLUSIONS: The ERCP route using a new biliary biopsy cannula for biliary biopsy technique can improve pathology positivity and benefit ratio. It provides a new approach in the diagnosis of malignant stenosis in the bile duct.

Generalized Quantum Measurements on a Higher-Dimensional System via Quantum Walks.

Quantum measurements play a fundamental role in quantum mechanics. Especially, generalized quantum measurements provide a powerful and versatile tool to extract information from quantum systems. However, how to realize them on an arbitrary higher-dimensional quantum system remains a challenging task. Here we propose a simple recipe for the implementation of a general positive-operator valued measurement (POVM) on a higher-dimensional quantum system via a one-dimensional discrete-time quantum walk with a two-dimensional coin. Furthermore, using single photons and linear optics, we realize experimentally a symmetric, informationally complete (SIC) POVM on a three-dimensional system with high fidelity. As an application, we realize a qutrit state tomography with SIC-POVM and confirm that the infidelity scaling achieved by the qutrit SIC-POVM is as good as that based on mutually unbiased bases. Our study paves the way to explore physics and information in higher-dimensional quantum systems and finds applications in various quantum information processing tasks that rely on generalized quantum measurements.

Leveraging a disulfidptosis-related signature to predict the prognosis and immunotherapy effectiveness of cutaneous melanoma based on machine learning.

BACKGROUND: Disulfidptosis is a recently discovered programmed cell death pathway. However, the exact molecular mechanism of disulfidptosis in cutaneous melanoma remains unclear. METHODS: In this study, clustering analysis was performed using data from public databases to construct a prognostic model, which was subsequently externally validated. The biological functions of the model genes were then investigated through various experimental techniques, including qRT-PCR, Western blotting, CCK-8 assay, wound healing assay, and Transwell assay. RESULTS: We constructed a signature using cutaneous melanoma (CM) data, which accurately predicts the overall survival (OS) of patients. The predictive value of this signature for prognosis and immune therapy response was validated using multiple external datasets. High-risk CM subgroups may exhibit decreased survival rates, alterations in the tumor microenvironment (TME), and increased tumor mutation burden. We initially verified the expression levels of five optimum disulfidptosis-related genes (ODRGs) in normal tissues and CM. The expression levels of these genes were further confirmed in HaCaT cells and three melanoma cell lines using qPCR and protein blotting analysis. HLA-DQA1 emerged as the gene with the highest regression coefficient in our risk model, highlighting its role in CM. Mechanistically, HLA-DQA1 demonstrated the ability to suppress CM cell growth, proliferation, and migration. CONCLUSION: In this study, a novel signature related to disulfidptosis was constructed, which accurately predicts the survival rate and treatment sensitivity of CM patients. Additionally, HLA-DQA1 is expected to be a feasible therapeutic target for effective clinical treatment of CM.

Dual-waveguide stacked graphene light modulator based on an MZI structure.

In order to solve the defects of the high driving voltage and a large volume of the existing electro-optical modulators, a double-waveguide stacked graphene optical modulator based on a Mach-Zehnder Interferometer structure is designed in this paper. First, the modulator size of traditional planar structure is effectively reduced by stacking two modulators vertically. Secondly, by changing the relative position of the electrode and the waveguide, the coupling effect of the electrode and the waveguide is enhanced, and the driving voltage is reduced. Finally, the performance of the designed electro-optic modulator is verified by the finite element method. The half-wave voltage of 0.55 V · cm and the modulation bandwidth of 58.8 GHz are realized on the basis of the length of 1.14 mm. The insertion loss is 1.15 dB, and the return loss is -44.8d B.

Long-term potentiation-based screening identifies neuronal PYGM as a synaptic plasticity regulator participating in Alzheimer's disease.

Synaptic dysfunction is an important pathological hallmark and cause of Alzheimer's disease (AD). High-frequency stimulation (HFS)-induced long-term potentiation (LTP) has been widely used to study synaptic plasticity, with impaired LTP found to be associated with AD. However, the exact molecular mechanism underlying synaptic plasticity has yet to be completely elucidated. Whether genes regulating synaptic plasticity are altered in AD and contribute to disease onset also remains unclear. Herein, we induced LTP in the hippocampal CA1 region of wild-type (WT) and AD model mice by administering HFS to the CA3 region and then studied transcriptome changes in the CA1 region. We identified 89 genes that may participate in normal synaptic plasticity by screening HFS-induced differentially expressed genes (DEGs) in mice with normal LTP, and 43 genes that may contribute to synaptic dysfunction in AD by comparing HFS-induced DEGs in mice with normal LTP and AD mice with impaired LTP. We further refined the 43 genes down to 14 by screening for genes with altered expression in pathological-stage AD mice without HFS induction. Among them, we found that the expression of Pygm, which catabolizes glycogen, was also decreased in AD patients. We further demonstrated that down-regulation of PYGM in neurons impaired synaptic plasticity and cognition in WT mice, while its overexpression attenuated synaptic dysfunction and cognitive deficits in AD mice. Moreover, we showed that PYGM directly regulated energy generation in neurons. Our study not only indicates that PYGM-mediated energy production in neurons plays an important role in synaptic function, but also provides a novel LTP-based strategy to systematically identify genes regulating synaptic plasticity under physiological and pathological conditions.

Neoadjuvant therapy with anlotinib in a locally advanced and pulmonary metastasis PTC patient harboring TERT promoter and BRAFV600E mutations: a case report.

A 71-year-old woman with recurrent papillary thyroid carcinoma (PTC) was referred to our hospital. A computed tomography scan revealed extensive recurrence in the neck, invading sternocleidomastoid muscle, internal jugular vein, sternal end of the clavicle, strap muscle and skin; and lateral compartment and subclavian lymph nodes were also involved. Multiple pulmonary micrometastases also noticed. The tumor was considered unresectable; however, the patient was unwilling to accept highly invasive surgery. Therefore, we initiated neoadjuvant therapy with anlotinib, 12mg p.o. daily with a 2-week on/1-week off regimen. The tumor shrunk to resectable state after 4 cycles of treatment, and after 3 weeks of withdrawal, successful surgical resection without gross tumor residual was performed. Pathology confirmed as classic PTC harboring coexistent TERT promoter and BRAFV600E mutations by NGS. After anlotinib therapy, apoptosis induction was observed, and proliferation increased, which was due to three weeks of anlotinib withdraw. Structual recurrence was recorded at 6 months after operation due to no further treatment was taken. Our finding suggests that anlotinib could represent as a good treatment option for patients with locally advanced (with or without distant metastasis) PTC; Anlotinib treatment resulted in sufficient reduction of the tumor mass to enable total thyroidectomy and radioactive iodine treatment, providing long-term control of the disease.

Crude polysaccharide from Danggui Buxue decoction enhanced the anti-tumor effect of gemcitabine by remodeling tumor-associated macrophages.

Tumor-associated macrophages (TAMs) with an M2-phenotype mediate gemcitabine resistance to cancer by influencing the metabolic enzymes of gemcitabine and releasing competitive deoxycytidine (dC). Our previous studies showed that Danggui Buxue Decoction (DBD), a traditional Chinese medicinal recipe, enhances the anti-tumor activity of gemcitabine in vivo and alleviates gemcitabine-induced myelosuppression. However, the material basis and exact mechanism underlying its enhanced effects remain unclear. In this study, a bioactive polysaccharide consisting of arabinose, mannose, ribose, and glucose was isolated from DBD. In vivo results demonstrated that DBD crude polysaccharide (DBDP) ameliorated gemcitabine-induced immune system disorders. Moreover, DBDP improved the sensitivity of Lewis lung carcinoma-bearing mice to gemcitabine by reshaping the tumor-promoting M2-like macrophages into tumor-inhibiting M1-phenotypes. Furthermore, in vitro results further revealed that DBDP blocked the protective effects of TAMs and M2-macrophages against gemcitabine by inhibiting the excessive secretion of dC and decreasing the high expression of cytidine deaminase. In conclusion, our results demonstrated that DBDP, as the pharmacodynamic material basis of DBD, enhanced the anti-tumor activity of gemcitabine against lung cancer in vitro and in vivo, which was associated with remodeling of the M2-phenotype.

Meta-analysis reveals Helicobacter pylori mutual exclusivity and reproducible gastric microbiome alterations during gastric carcinoma progression.

Accumulating evidence shows that the gastric bacterial community may contribute to the development of gastric cancer (GC). However, the reported alterations of gastric microbiota were not always consistent among the literature. To assess reproducible signals in gastric microbiota during the progression of GC across studies, we performed a meta-analysis of nine publicly available 16S datasets with standard tools of the state-of-the-art. Despite study-specific batch effect, significant changes in the composition of the gastric microbiome were found during the progression of gastric carcinogenesis, especially when the Helicobacter pylori (HP) reads were removed from analyses to mitigate its compositional effect as they accounted for extremely large proportions of sequencing depths in many gastric samples. Differential microbes, including Fusobacterium, Leptotrichia, and several lactic acid bacteria such as Bifidobacterium, Lactobacillus, and Streptococcus anginosus, which were frequently and significantly enriched in GC patients compared with gastritis across studies, had good discriminatory capacity to distinguish GC samples from gastritis. Oral microbes were significantly enriched in GC compared to precancerous stages. Intriguingly, we observed mutual exclusivity of different HP species across studies. In addition, the comparison between gastric fluid and mucosal microbiome suggested their convergent dysbiosis during gastric disease progression. Taken together, our systematic analysis identified novel and consistent microbial patterns in gastric carcinogenesis.

Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity.

BACKGROUND: Understanding human genetic influences on the gut microbiota helps elucidate the mechanisms by which genetics may influence health outcomes. Typical microbiome genome-wide association studies (GWAS) marginally assess the association between individual genetic variants and individual microbial taxa. We propose a novel approach, the covariate-adjusted kernel RV (KRV) framework, to map genetic variants associated with microbiome beta-diversity, which focuses on overall shifts in the microbiota. The KRV framework evaluates the association between genetics and microbes by comparing similarity in genetic profiles, based on groups of variants at the gene level, to similarity in microbiome profiles, based on the overall microbiome composition, across all pairs of individuals. By reducing the multiple-testing burden and capturing intrinsic structure within the genetic and microbiome data, the KRV framework has the potential of improving statistical power in microbiome GWAS. RESULTS: We apply the covariate-adjusted KRV to the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) in a two-stage (first gene-level, then variant-level) genome-wide association analysis for gut microbiome beta-diversity. We have identified an immunity-related gene, IL23R, reported in a previous microbiome genetic association study and discovered 3 other novel genes, 2 of which are involved in immune functions or autoimmune disorders. In addition, simulation studies show that the covariate-adjusted KRV has a greater power than other microbiome GWAS methods that rely on univariate microbiome phenotypes across a range of scenarios. CONCLUSIONS: Our findings highlight the value of the covariate-adjusted KRV as a powerful microbiome GWAS approach and support an important role of immunity-related genes in shaping the gut microbiome composition. Video Abstract.

The sequence kernel association test for multicategorical outcomes.

Disease heterogeneity is ubiquitous in biomedical and clinical studies. In genetic studies, researchers are increasingly interested in understanding the distinct genetic underpinning of subtypes of diseases. However, existing set-based analysis methods for genome-wide association studies are either inadequate or inefficient to handle such multicategorical outcomes. In this paper, we proposed a novel set-based association analysis method, sequence kernel association test (SKAT)-MC, the sequence kernel association test for multicategorical outcomes (nominal or ordinal), which jointly evaluates the relationship between a set of variants (common and rare) and disease subtypes. Through comprehensive simulation studies, we showed that SKAT-MC effectively preserves the nominal type I error rate while substantially increases the statistical power compared to existing methods under various scenarios. We applied SKAT-MC to the Polish breast cancer study (PBCS), and identified gene FGFR2 was significantly associated with estrogen receptor (ER)+ and ER- breast cancer subtypes. We also investigated educational attainment using UK Biobank data ( N = 127 , 127 $N=127,127$ ) with SKAT-MC, and identified 21 significant genes in the genome. Consequently, SKAT-MC is a powerful and efficient analysis tool for genetic association studies with multicategorical outcomes. A freely distributed R package SKAT-MC can be accessed at https://github.com/Zhiwen-Owen-Jiang/SKATMC.

Treatment response to isotretinoin correlates with specific shifts in Cutibacterium acnes strain composition within the follicular microbiome.

There are no drugs as effective as isotretinoin for acne. Deciphering the changes in the microbiome induced by isotretinoin in the pilosebaceous follicle of successfully treated patients can pave the way to identify novel therapeutic alternatives. We determined how the follicular microbiome changes with isotretinoin and identified which alterations correlate with a successful treatment response. Whole genome sequencing was done on casts from facial follicles of acne patients sampled before, during and after isotretinoin treatment. Alterations in the microbiome were assessed and correlated with treatment response at 20 weeks as defined as a 2-grade improvement in global assessment score. We investigated the α-diversity, ß-diversity, relative abundance of individual taxa, Cutibacterium acnes strain composition and bacterial metabolic profiles with a computational approach. We found that increased ß-diversity of the microbiome coincides with a successful treatment response to isotretinoin at 20 weeks. Isotretinoin selectively altered C. acnes strain diversity in SLST A and D clusters, with increased diversity in D1 strains correlating with a successful clinical response. Isotretinoin significantly decreased the prevalence of KEGG Ontology (KO) terms associated with four distinct metabolic pathways inferring that follicular microbes may have limited capacity for growth or survival following treatment. Importantly, these alterations in microbial composition or metabolic profiles were not observed in patients that failed to achieve a successful response at 20 weeks. Alternative approaches to recapitulate this shift in the balance of C. acnes strains and microbiome metabolic function within the follicle may be beneficial in the future treatment of acne.