Critical genes in human photoaged skin identified using weighted gene co-expression network analysis.

Photoaging is unique to the skin and is accompanied by an increased risk of tumors. To explore the transcriptomic regulatory mechanism of skin photoaging, the epidermis, and dermis of 16 healthy donors (eight exposed and eight non-exposed) were surgically excised and detected using total RNA-Seq. Weighted gene co-expression network analysis (WGCNA) identified the most relevant modules with exposure. The hub genes were identified using correlation, p-value, and enrichment analysis. The critical genes were identified using Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) regression, then enriched using single-gene GSEA. A competitive endogenous RNA (ceRNA) network was constructed and validated using qRT-PCR. Compared with non-exposed sites, 430 mRNAs, 168 lncRNAs, and 136 miRNAs were differentially expressed in the exposed skin. WGCNA identified the module MEthistle and 12 intersecting genes from the 71 genes in this module. The enriched pathways were related to muscle. The critical genes were KLHL41, MYBPC2, and ERAP2. Single-gene GSEA identified the Hippo signaling pathway, basal cell carcinoma, cell adhesion molecules, and other pathways. Six miRNAs and 18 lncRNAs related to the critical genes constituted the ceRNA network and were verified using qPCR. The differential expression of KLHL41, MYBPC2, and ERAP2 at the protein level was verified using immunohistochemistry. KLHL41, MYBPC2, and ERAP2 genes are related to skin photoaging. The prediction model based on the three critical genes can indicate photoaging. These critical genes may have a role in skin photoaging by regulating cell growth, intercellular adhesion, and substance metabolism pathways.

Experimental and Numerical Investigation of Bogie Hunting Instability for Railway Vehicles Based on Multiple Sensors.

Bogie hunting instability is one of the common faults in railway vehicles. It not only affects ride comfort but also threatens operational safety. Due to the lower operating speed of metro vehicles, their bogie hunting stability is often overlooked. However, as wheel tread wear increases, metro vehicles with high conicity wheel-rail contact can also experience bogie hunting instability. In order to enhance the operational safety of metro vehicles, this paper conducts field tests and simulation calculations to study the bogie hunting instability behavior of metro vehicles and proposes corresponding solutions from the perspective of wheel-rail contact relationships. Acceleration and displacement sensors are installed on metro vehicles to collect data, which are processed in real time in 2 s intervals. The lateral acceleration of the frame is analyzed to determine if bogie hunting instability has occurred. Based on calculated safety indicators, it is determined whether deceleration is necessary to ensure the safety of vehicle operation. For metro vehicles in the later stages of wheel wear (after 300,000 km), the stability of their bogies should be monitored in real time. To improve the stability of metro vehicle bogies while ensuring the longevity of wheelsets, metro vehicle wheel treads should be reprofiled regularly, with a recommended reprofiling interval of 350,000 km.

Prediction models of colorectal cancer prognosis incorporating perioperative longitudinal serum tumor markers: a retrospective longitudinal cohort study.

BACKGROUND: Current prognostic prediction models of colorectal cancer (CRC) include only the preoperative measurement of tumor markers, with their available repeated postoperative measurements underutilized. CRC prognostic prediction models were constructed in this study to clarify whether and to what extent the inclusion of perioperative longitudinal measurements of CEA, CA19-9, and CA125 can improve the model performance, and perform a dynamic prediction. METHODS: The training and validating cohort included 1453 and 444 CRC patients who underwent curative resection, with preoperative measurement and two or more measurements within 12 months after surgery, respectively. Prediction models to predict CRC overall survival were constructed with demographic and clinicopathological variables, by incorporating preoperative CEA, CA19-9, and CA125, as well as their perioperative longitudinal measurements. RESULTS: In internal validation, the model with preoperative CEA, CA19-9, and CA125 outperformed the model including CEA only, with the better area under the receiver operating characteristic curves (AUCs: 0.774 vs 0.716), brier scores (BSs: 0.057 vs 0.058), and net reclassification improvement (NRI = 33.5%, 95% CI: 12.3 ~ 54.8%) at 36 months after surgery. Furthermore, the prediction models, by incorporating longitudinal measurements of CEA, CA19-9, and CA125 within 12 months after surgery, had improved prediction accuracy, with higher AUC (0.849) and lower BS (0.049). Compared with preoperative models, the model incorporating longitudinal measurements of the three markers had significant NRI (40.8%, 95% CI: 19.6 to 62.1%) at 36 months after surgery. External validation showed similar results to internal validation. The proposed longitudinal prediction model can provide a personalized dynamic prediction for a new patient, with estimated survival probability updated when a new measurement is collected during 12 months after surgery. CONCLUSIONS: Prediction models including longitudinal measurements of CEA, CA19-9, and CA125 have improved accuracy in predicting the prognosis of CRC patients. We recommend repeated measurements of CEA, CA19-9, and CA125 in the surveillance of CRC prognosis.

Prediction of early improvement of major depressive disorder to antidepressant medication in adolescents with radiomics analysis after ComBat harmonization based on multiscale structural MRI.

BACKGROUND: Due to individual differences and lack of objective biomarkers, only 30-40% patients with major depressive disorder (MDD) achieve remission after initial antidepressant medication (ADM). We aimed to employ radiomics analysis after ComBat harmonization to predict early improvement to ADM in adolescents with MDD by using brain multiscale structural MRI (sMRI) and identify the radiomics features with high prediction power for selection of selective serotonin reuptake inhibitors (SSRIs) and serotonin norepinephrine reuptake inhibitors (SNRIs). METHODS: 121 MDD patients were recruited for brain sMRI, including three-dimensional T1 weighted imaging (3D-T1WI)and diffusion tensor imaging (DTI). After receiving SSRIs or SNRIs for 2 weeks, the subjects were divided into ADM improvers (SSRIs improvers and SNRIs improvers) and non-improvers according to reduction rate of the Hamilton Depression Rating Scale, 17 item (HAM-D17) score. Then, sMRI data were preprocessed, and conventional imaging indicators and radiomics features of gray matter (GM) based on surface-based morphology (SBM) and voxel-based morphology (VBM) and diffusion properties of white matter (WM) were extracted and harmonized with ComBat harmonization. Two-level reduction strategy with analysis of variance (ANOVA) and recursive feature elimination (RFE) was utilized sequentially to decrease high-dimensional features. Support vector machine with radial basis function kernel (RBF-SVM) was used to integrate multiscale sMRI features to construct models for early improvement prediction. Area under the curve (AUC), accuracy, sensitivity, and specificity based on the leave-one-out cross-validation (LOO-CV) and receiver operating characteristic (ROC) curve analysis were calculated to evaluate the model performance. Permutation tests were used for assessing the generalization rate. RESULTS: After 2-week ADM, 121 patients were divided into 67 ADM improvers (31 SSRIs improvers and 36 SNRIs improvers) and 54 ADM non-improvers. After two-level dimensionality reduction, 8 conventional indicators (2 VBM-based features and 6 diffusion features) and 49 radiomics features (16 VBM-based features and 33 diffusion features) were selected. The overall accuracy of RBF-SVM models based on conventional indicators and radiomics features was 74.80% and 88.19%. The radiomics model achieved the AUC, sensitivity, specificity, and accuracy of 0.889, 91.2%, 80.1% and 85.1%, 0.954, 89.2%, 87.4% and 88.5%, 0.942, 91.9%, 82.5% and 86.8% for predicting ADM improvers, SSRIs improvers and SNRIs improvers, respectively. P value of permutation tests were less than 0.001. The radiomics features predicting ADM improver were mainly located in the hippocampus, medial orbitofrontal gyrus, anterior cingulate gyrus, cerebellum (lobule vii-b), body of corpus callosum, etc. The radiomics features predicting SSRIs improver were primarily distributed in hippocampus, amygdala, inferior temporal gyrus, thalamus, cerebellum (lobule vi), fornix, cerebellar peduncle, etc. The radiomics features predicting SNRIs improver were primarily located in the medial orbitofrontal cortex, anterior cingulate gyrus, ventral striatum, corpus callosum, etc. CONCLUSIONS: These findings suggest the radiomics analysis based on brain multiscale sMRI after ComBat harmonization could effectively predict the early improvement of ADM in adolescent MDD patients with a high accuracy, which was superior to the model based on the conventional indicators. The radiomics features with high prediction power may help for the individual selection of SSRIs and SNRIs.

Significance of tumor deposits combined with lymph node metastasis in stage III colorectal cancer patients: a retrospective multi-center cohort study from China.

PURPOSE: The study aimed to explore the value of tumor deposits in stage III colorectal cancer (CRC) and verify whether patients with more tumor deposit numbers have higher risk of recurrence. METHODS: The retrospective cohort analysis was performed at two cancer centers of China. Stage III CRC patients who underwent radical resection at the center between April 2008 and February 2019 were identified. The Univariate/Multivariate Cox regression, Kaplan-Meier analysis, and PSM were recurrence-free survival (RFS) used. RESULTS: Total 1080 stage III CRC patients (634 [58.7%] men; median [IQR] age, 60 [50-68] years) who underwent radical surgical resection were identified for inclusion in this study. Patients with tumor deposits had a 12.8% lower 3-year RFS (n = 236 [69.9%]) than the patients without tumor deposits (n = 844 [82.7%]) (P ≤ 0.0001). The 3-year RFS of patients with stage N2 (n = 335 [61.2%]) was 18.6% lower (P ≤ 0.0001) than the original cohort of patients with stage N1 (n = 745 [79.8%]), but it was similar to the RFS of patients with 4 or more tumor deposits plus lymph node metastases (n = 58 [61.4%]) (P = 0.91). The RFS for patients with 4 or more tumor deposits plus number of lymph node metastases (n = 58 [61.4%]) was 15.8% lower than the cohort of patients with 1-3 tumor deposits + number of lymph node metastases (n = 687 [77.2%]) (P = 0.001). Multivariate analysis confirmed that patients with 4 or more tumor deposits + the number of lymph node metastases (hazard ratio [HR], 1.88; 95% CI, 1.24-2.87) were independently associated with a shorter RFS. CONCLUSION: The number of tumor deposits is an indicator of poor postoperative prognosis. It is necessary to incorporate the number of tumor deposits combined with the number of lymph node metastases to stratify postoperative stratification of stage III CRC, which may provide a new theoretical basis for adjuvant therapy for patients with N1 stage CRC after surgery.

Gray and white matter structural examination for diagnosis of major depressive disorder and subthreshold depression in adolescents and young adults: a preliminary radiomics analysis.

BACKGROUND: Radiomics is an emerging image analysis framework that provides more details than conventional methods. In present study, we aimed to identify structural radiomics features of gray matter (GM) and white matter (WM), and to develop and validate the classification model for major depressive disorder (MDD) and subthreshold depression (StD) diagnosis using radiomics analysis. METHODS: A consecutive cohort of 142 adolescents and young adults, including 43 cases with MDD, 49 cases with StD and 50 healthy controls (HC), were recruited and underwent the three-dimensional T1 weighted imaging (3D-T1WI) and diffusion tensor imaging (DTI). We extracted radiomics features representing the shape and diffusion properties of GM and WM from all participants. Then, an all-relevant feature selection process embedded in a 10-fold cross-validation framework was used to identify features with significant power for discrimination. Random forest classifiers (RFC) were established and evaluated successively using identified features. RESULTS: The results showed that a total of 3030 features were extracted after preprocessing, including 2262 shape-related features from each T1-weighted image representing GM morphometry and 768 features from each DTI representing the diffusion properties of WM. 25 features were selected ultimately, including ten features for MDD versus HC, eight features for StD versus HC, and seven features for MDD versus StD. The accuracies and area under curve (AUC) the RFC achieved were 86.75%, 0.93 for distinguishing MDD from HC with significant radiomics features located in the left medial orbitofrontal cortex, right superior and middle temporal regions, right anterior cingulate, left cuneus and hippocampus, 70.51%, 0.69 for discriminating StD from HC within left cuneus, medial orbitofrontal cortex, cerebellar vermis, hippocampus, anterior cingulate and amygdala, right superior and middle temporal regions, and 59.15%, 0.66 for differentiating MDD from StD within left medial orbitofrontal cortex, middle temporal and cuneus, right superior frontal, superior temporal regions and hippocampus, anterior cingulate, respectively. CONCLUSION: These findings provide preliminary evidence that radiomics features of brain structure are valid for discriminating MDD and StD subjects from healthy controls. The MRI-based radiomics approach, with further improvement and validation, might be a potential facilitating method to clinical diagnosis of MDD or StD.

Multiparametric MRI and Whole Slide Image-Based Pretreatment Prediction of Pathological Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer: A Multicenter Radiopathomic Study.

BACKGROUND: The aim of this work is to combine radiological and pathological information of tumor to develop a signature for pretreatment prediction of discrepancies of pathological response at several centers and restage patients with locally advanced rectal cancer (LARC) for individualized treatment planning. PATIENTS AND METHODS: A total of 981 consecutive patients with evaluation of response according to tumor regression grade (TRG) who received nCRT were retrospectively recruited from four hospitals (primary cohort and external validation cohort 1-3); both pretreatment multiparametric MRI (mp-MRI) and whole slide image (WSI) of biopsy specimens were available for each patient. Quantitative image features were extracted from mp-MRI and WSI and used to construct a radiopathomics signature (RPS) powered by an artificial-intelligence model. Models based on mp-MRI or WSI alone were also constructed for comparison. RESULTS: The RPS showed overall accuracy of 79.66-87.66% in validation cohorts. The areas under the curve of RPS at specific response grades were 0.98 (TRG0), 0.93 (≤ TRG1), and 0.84 (≤ TRG2). RPS at each grade of pathological response revealed significant improvement compared with both signatures constructed without combining multiscale tumor information (P < 0.01). Moreover, RPS showed relevance to distinct probabilities of overall survival and disease-free survival in patients with LARC who underwent nCRT (P < 0.05). CONCLUSIONS: The results of this study suggest that radiopathomics, combining both radiological information of the whole tumor and pathological information of local lesions from biopsy, could potentially predict discrepancies of pathological response prior to nCRT for better treatment planning.

Value of the Cinematic Rendering From Volumetric Computed Tomography Data in Evaluating the Relationship Between Deep Soft Tissue Sarcomas of the Extremities and Adjacent Major Vessels: A Preliminary Study.

OBJECTIVE: The aim of the study was to assess the value of cinematic rendering (CR) from volumetric computed tomography data in evaluating the relationship between deep soft tissue sarcomas (STSs) of the extremities and the adjacent major vessels. METHODS: Preoperative contrast-enhanced axial imaging (CEAI) in the arterial phase with three-dimensional volume rendering (VR) and CR of contrast-enhanced computed tomography were used to assess adjacent vascular invasion in 43 cases of deep STSs of the extremities. The imaging assessments were compared with surgical findings and interpreted as negative (no vascular invasion) or positive (vascular invasion was present). Intrareader and interreader agreement were assessed using Cohen κ statistics. The diagnostic performance of CEAI, VR, and CR was evaluated by receiver operating curve analysis and compared using the DeLong test. RESULTS: Thirty-four and nine cases were classified as negative and positive, respectively, in surgery. Intrareader agreement values for the CEAI, VR, and CR assessments were all excellent (0.984, 0.934, and 0.914, respectively), whereas the interreader agreement for CEAI assessments was greater than that for VR and CR (0.969 vs 0.804 and 0.761). Cinematic rendering showed lower accuracy (0.698), sensitivity (0.778), specificity (0.676), positive predictive values (0.389), and negative predictive values (0.920) for vascular invasion diagnosis than CEAI or VR; the accuracy, sensitivity, specificity, positive predictive values, and negative predictive values increased to 0.767, 0.889, 0.735, 0.471, and 0.962 for both CEAI and VR. The results were not statistically significant (all P > 0.05). CONCLUSIONS: Cinematic rendering has the potential to be used to evaluate vascular invasion in cases of deep STSs of the extremities, but it should be used alongside the traditional methods such as CEAI.

Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: A pilot study.

OBJECTIVE: The standard treatment for patients with locally advanced gastric cancer has relied on perioperative radio-chemotherapy or chemotherapy and surgery. The aim of this study was to investigate the wealth of radiomics for pre-treatment computed tomography (CT) in the prediction of the pathological response of locally advanced gastric cancer with preoperative chemotherapy. METHODS: Thirty consecutive patients with CT-staged II/III gastric cancer receiving neoadjuvant chemotherapy were enrolled in this study between December 2014 and March 2017. All patients underwent upper abdominal CT during the unenhanced, late arterial phase (AP) and portal venous phase (PP) before the administration of neoadjuvant chemotherapy. In total, 19,985 radiomics features were extracted in the AP and PP for each patient. Four methods were adopted during feature selection and eight methods were used in the process of building the classifier model. Thirty-two combinations of feature selection and classification methods were examined. Receiver operating characteristic (ROC) curves were used to evaluate the capability of each combination of feature selection and classification method to predict a non-good response (non-GR) based on tumor regression grade (TRG). RESULTS: The mean area under the curve (AUC) ranged from 0.194 to 0.621 in the AP, and from 0.455 to 0.722 in the PP, according to different combinations of feature selection and the classification methods. There was only one cross-combination machine-learning method indicating a relatively higher AUC (>0.600) in the AP, while 12 cross-combination machine-learning methods presented relatively higher AUCs (all >0.600) in the PP. The feature selection method adopted by a filter based on linear discriminant analysis + classifier of random forest achieved a significantly prognostic performance in the PP (AUC, 0.722±0.108; accuracy, 0.793; sensitivity, 0.636; specificity, 0.889; Z=2.039; P=0.041). CONCLUSIONS: It is possible to predict non-GR after neoadjuvant chemotherapy in locally advanced gastric cancers based on the radiomics of CT.

Higher Blood-brain barrier permeability in patients with major depressive disorder identified by DCE-MRI imaging.

BACKGROUND: Studies from animal models and clinical trials of blood and cerebrospinal fluid have proposed that blood-brain barrier (BBB) dysfunction in depression (MDD). But there are no In vivo proves focused on BBB dysfunction in MDD patients. The present study aimed to identify whether there was abnormal BBB permeability, as well as the association with clinical status in MDD patients using dynamic contrast-enhanced magnetic resonance (DCE-MRI) imaging. METHODS: Patients with MDD and healthy adults were recruited and underwent DCE-MRI and structural MRI scans. The mean volume transfer constant (Ktrans) values were calculated for a quantitative assessment of BBB leakage. For each subject, the mean Ktrans values were calculated for the whole gray matter, white matter, and 90 brain regions of the anatomical automatic labeling template (AAL). The differences in Ktrans values between patients and controls and between treated and untreated patients were compared. RESULTS: 23 MDD patients (12 males and 11 females, mean age 28.09 years) and 18 healthy controls (HC, 8 males and 10 females, mean age 30.67 years) were recruited in the study. We found that the Ktrans values in the olfactory, caudate, and thalamus were higher in MDD patients compared to healthy controls (p<0.05). The Ktrans values in the orbital lobe, anterior cingulate gyrus, putamen, and thalamus in treated patients were lower than the patients never treated. There were positive correlations between HAMD total score with Ktrans values in whole brain WM, hippocampus and thalamus. The total HAMA score was positively correlated with the Ktrans of hippocampus. CONCLUSION: These findings supported a link between blood-brain barrier leakage and depression and symptom severity. The results also suggested a role for non-invasive DCE-MRI in detecting blood-brain barrier dysfunction in depression patients.

A nomogram for predicting liver metastasis in patients with gastric gastrointestinal stromal tumor.

BACKGROUND: Liver metastasis (LIM) is an important factor in the diagnosis, treatment, follow-up, and prognosis of patients with gastric gastrointestinal stromal tumor (GIST). There is no simple tool to assess the risk of LIM in patients with gastric GIST. Our aim was to develop and validate a nomogram to identify patients with gastric GIST at high risk of LIM. METHODS: Patient data diagnosed as having gastric GIST between 2010 and 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training cohort and internal validation cohort in a 7:3 ratio. For external validation, retrospective data collection was performed on patients diagnosed as having gastric GIST at Yunnan Cancer Center (YNCC) between January 2015 and May 2023. Univariate and multivariate logistic regression analyses were used to identify independent risk factors associated with LIM in patients with gastric GIST. An individualized LIM nomogram specific for gastric GIST was formulated based on the multivariate logistic model; its discriminative performance, calibration, and clinical utility were evaluated. RESULTS: In the SEER database, a cohort of 2341 patients with gastric GIST was analyzed, of which 173 cases (7.39%) were found to have LIM; 239 patients with gastric GIST from the YNCC database were included, of which 25 (10.46%) had LIM. Multivariate analysis showed tumor size, tumor site, and sex were independent risk factors for LIM (P < .05). The nomogram based on the basic clinical characteristics of tumor size, tumor site, sex, and age demonstrated significant discrimination, with an area under the curve of 0.753 (95% CI, 0.692-0.814) and 0.836 (95% CI, 0.743-0.930) in the internal and external validation cohort, respectively. The Hosmer-Lemeshow test showed that the nomogram was well calibrated, whereas the decision curve analysis and the clinical impact plot demonstrated its clinical utility. CONCLUSION: Tumor size, tumor subsite, and sex were significantly correlated with the risk of LIM in gastric GIST. The nomogram for patients with GIST can effectively predict the individualized risk of LIM and contribute to the planning and decision making related to metastasis management in clinical practice.

Machine Learning-Based Prediction of Pathological Responses and Prognosis After Neoadjuvant Chemotherapy for Non-Small-Cell Lung Cancer: A Retrospective Study.

BACKGROUND: Neoadjuvant chemotherapy has variable efficacy in patients with non-small-cell lung cancer (NSCLC), yet reliable noninvasive predictive markers are lacking. This study aimed to develop a radiomics model predicting pathological complete response and postneoadjuvant chemotherapy survival in NSCLC. MATERIALS AND METHODS: Retrospective data collection involved 130 patients with NSCLC who underwent neoadjuvant chemotherapy and surgery. Patients were randomly divided into training and independent testing sets. Nine radiomics features from prechemotherapy computed tomography (CT) images were extracted from intratumoral and peritumoral regions. An auto-encoder model was constructed, and its performance was evaluated. X-tile software classified patients into high and low-risk groups based on their predicted probabilities. survival of patients in different risk groups and the role of postoperative adjuvant chemotherapy were examined. RESULTS: The model demonstrated area under the receiver operating characteristic (ROC) curve of 0.874 (training set) and 0.876 (testing set). The larger the area under curve (AUC), the better the model performance. Calibration curve and decision curve analysis indicated excellent model calibration (Hosmer-Lemeshow test, P = .763, the higher the P-value, the better the model fit) and potential clinical applicability. Survival analysis revealed significant differences in overall survival (P = .011) and disease-free survival (P = .017) between different risk groups. Adjuvant chemotherapy significantly improved survival in the low-risk group (P = .041) but not high-risk group (P = 0.56). CONCLUSION: This study represents the first successful prediction of pathological complete response achievement after neoadjuvant chemotherapy for NSCLC, as well as the patients' survival, utilizing intratumoral and peritumoral radiomics features.

Optimization Co-Culture of Monascus purpureus and Saccharomyces cerevisiae on Selenium-Enriched Lentinus edodes for Increased Monacolin K Production.

Selenium-enriched Lentinus edodes (SL) is a kind of edible fungi rich in organic selenium and nutrients. Monascus purpureus with high monacolin K (MK) production and Saccharomyces cerevisiae were selected as the fermentation strains. A single-factor experiment and response surface methodology were conducted to optimize the production conditions for MK with higher contents from selenium-enriched Lentinus edodes fermentation (SLF). Furthermore, we investigated the nutritional components, antioxidant capacities, and volatile organic compounds (VOCs) of SLF. The MK content in the fermentation was 2.42 mg/g under optimal fermentation conditions. The organic selenium content of SLF was 7.22 mg/kg, accounting for 98% of the total selenium content. Moreover, the contents of total sugars, proteins, amino acids, reducing sugars, crude fiber, fat, and ash in SLF were increased by 9%, 23%, 23%, 94%, 38%, 44%, and 25%, respectively. The antioxidant test results demonstrated that 1.0 mg/mL of SLF exhibited scavenging capacities of 40%, 70%, and 79% for DPPH, ABTS, and hydroxyl radicals, respectively. Using gas chromatography-ion mobility spectrometry technology, 34 unique VOCs were identified in SLF, with esters, alcohols, and ketones being the main components of its aroma. This study showed that fungal fermentation provides a theoretical reference for enhancing the nutritional value of SL.

Cognitive changes are associated with increased blood-brain barrier leakage in non-brain metastases lung cancer patients.

To explore the relationship between cognitive function and blood-brain barrier leakage in non-brain metastasis lung cancer and healthy controls. 75 lung cancers without brain metastasis and 29 healthy controls matched with age, sex, and education were evaluated by cognitive assessment, and the Patlak pharmacokinetic model was used to calculate the average leakage in each brain region according to the automated anatomical labeling atlas. After that, the relationships between cognitive and blood-brain barrier leakage were evaluated. Compared with healthy controls, the leakage of bilateral temporal gyrus and whole brain gyrus were higher in patients with lung cancers (P < 0.05), mainly in patients with advanced lung cancer (P < 0.05), but not in patients with early lung cancer (P > 0.05). The cognitive impairment of advanced lung cancers was mainly reflected in the damage of visuospatial/executive, and delayed recall. The left temporal gyrus with increased blood-brain barrier leakage showed negative correlations with delayed recall (r = -0.201, P = 0.042). An increase in blood-brain barrier leakage was found in non-brain metastases advanced lung cancers that corresponded to decreased delayed recall. With progression in lung cancer staging, blood-brain barrier shows higher leakage and may lead to brain metastases and lower cognitive development.

Preoperative splenic area as a prognostic biomarker of early-stage non-small cell lung cancer.

BACKGROUND: The correlation between the preoperative splenic area measured on CT scans and the overall survival (OS) of early-stage non-small cell lung cancer (NSCLC) patients remains unclear. METHODS: A retrospective discovery cohort and validation cohort consisting of consecutive NSCLC patients who underwent resection and preoperative CT scans were created. The patients were divided into two groups based on the measurement of their preoperative splenic area: normal and abnormal. The Cox proportional hazard model was used to analyse the correlation between splenic area and OS. RESULTS: The discovery and validation cohorts included 2532 patients (1374 (54.27%) males; median (IQR) age 59 (52-66) years) and 608 patients (403 (66.28%) males; age 69 (62-76) years), respectively. Patients with a normal splenic area had a 6% higher 5-year OS (n = 727 (80%)) than patients with an abnormal splenic area (n = 1805 (74%)) (p = 0.007) in the discovery cohort. A similar result was obtained in the validation cohort. In the univariable analysis, the OS hazard ratios (HRs) for the patients with abnormal splenic areas were 1.32 (95% confidence interval (CI): 1.08, 1.61) in the discovery cohort and 1.59 (95% CI: 1.01, 2.50) in the validation cohort. Multivariable analysis demonstrated that abnormal splenic area was independent of shorter OS in the discovery (HR: 1.32, 95% CI: 1.08, 1.63) and validation cohorts (HR: 1.84, 95% CI: 1.12, 3.02). CONCLUSION: Preoperative CT measurements of the splenic area serve as a prognostic indicator for early-stage NSCLC patients, offering a novel metric with potential implications for personalized therapeutic strategies in top-tier oncology research.

Molecular Dynamics Simulation Study on the Interactions of Mixed Cationic/Anionic Collectors on Muscovite (001) Surface in Aqueous Solution.

In this study, the adsorption mechanisms of dodecylamine hydrochloride(DDAHC), sodium dodecyl sulfate (SDS), sodium dodecyl benzene sulfonate(SDBS), and their mixed anionic/cationic collectors at ten different molar ratios on a muscovite (Mcv) surface in neutral aqueous solution were assessed by molecular dynamics simulations (MDS). According to the snapshot, interaction energy, radial distribution function (RDF), and density profile between the Mcv surface and collector molecules, the individual DDAHC collector was an effective collector for the flotation of Mcv. The molar ratio of anionic/cationic collectors was determined to be an essential factor in the flotation recovery of Mcv. The DDAHC collector was involved in the adsorption of the mixed anionic/cationic collectors on the Mcv (001) surface, whereas SDS and SDBS collectors were co-adsorbed with DDAHC. The mixed cationic/anionic collector showed the best adsorption on the Mcv surface in a molar ratio of 2. Additionally, SDBS, which has one more benzene ring than SDS, was more likely to form spherical micelles with DDAHC, thus resulting in better adsorption on the Mcv surface. The results of micro-flotation experiments indicated that the DDAHC collector could improve the flotation recovery of Mcv in neutral aqueous solution, which was in agreement with MDS-derived findings. In conclusion, DDAHC alone is the optimum collector for Mcv flotation under the neutral aqueous conditions, while the mixture of DDAHC and SDBS collectors (molar ratio = 2:1) exhibits the similar flotation performance.

Blood-brain barrier and brain structural changes in lung cancer patients with non-brain metastases.

Purpose: To explore the relationship between blood-brain barrier (BBB) leakage and brain structure in non-brain metastasis lung cancer (LC) by magnetic resonance imaging (MRI) as well as to indicate the possibility of brain metastasis (BM) occurrence. Patients and methods: MRI were performed in 75 LC patients and 29 counterpart healthy peoples (HCs). We used the Patlak pharmacokinetic model to calculate the average leakage in each brain region according to the automated anatomical labeling (AAL) atlas. The thickness of the cortex and the volumes of subcortical structures were calculated using the FreeSurfer base on Destrieux atlas. We compared the thickness of the cerebral cortex, the volumes of subcortical structures, and the leakage rates of BBB, and evaluated the relationships between these parameters. Results: Compared with HCs, the leakage rates of seven brain regions were higher in patients with advanced LC (aLC). In contrast to patients with early LC (eLC), the cortical thickness of two regions was decreased in aLCs. The volumes of twelve regions were also reduced in aLCs. Brain regions with increased BBB penetration showed negative correlations with thinner cortices and reduced subcortical structure volumes (P<0.05, R=-0.2 to -0.50). BBB penetration was positively correlated with tumor size and with levels of the tumor marker CYFRA21-1 (P<0.05, R=0.2-0.70). Conclusion: We found an increase in BBB permeability in non-BM aLCs that corresponded to a thinner cortical thickness and smaller subcortical structure volumes. With progression in LC staging, BBB shows higher permeability and may be more likely to develop into BM.

Pretreatment Contrast-Enhanced Computed Tomography Radiomics for Prediction of Pathological Regression Following Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer: A Preliminary Multicenter Study.

BACKGROUND: Sensitivity to neoadjuvant chemotherapy in locally advanced gastric cancer patients varies; however, an effective predictive marker is currently lacking. We aimed to propose and validate a practical treatment efficacy prediction method based on contrast-enhanced computed tomography (CECT) radiomics. METHOD: Data of l24 locally advanced gastric carcinoma patients who underwent neoadjuvant chemotherapy were acquired retrospectively between December 2012 and August 2020 from three different cancer centers. In total, 1216 radiomics features were initially extracted from each lesion's pretreatment portal venous phase computed tomography image. Subsequently, a radiomics predictive model was constructed using machine learning software. Clinicopathological data and radiological parameters of the enrolled patients were collected and analyzed retrospectively. Univariate and multivariate logistic regression analyses were performed to screen for independent predictive indices. Finally, we developed an integrated model combining clinicopathological predictive parameters and radiomics features. RESULT: In the training set, 10 (14.9%) patients achieved a good response (GR) after preoperative neoadjuvant chemotherapy (n = 77), whereas in the testing set, seven (17.5%) patients achieved a GR (n = 47). The radiomics predictive model showed competitive prediction efficacy in both the training and independent external validation sets. The areas under the curve (AUC) values were 0.827 (95% confidence interval [CI]: 0.609-1.000) and 0.854 (95% CI: 0.610-1.000), respectively. Similarly, when only the single hospital data were included as an independent external validation set (testing set 2), AUC values of the models were 0.827 (95% CI: 0.650-0.952) and 0.889 (95% CI: 0.663-1.000) in the training set and testing set 2, respectively. CONCLUSION: Our study is the first to discover that CECT radiomics could provide powerful and consistent predictions of therapeutic sensitivity to neoadjuvant chemotherapy among gastric cancer patients across different hospitals.

Association Between Serum Carcinoembryonic Antigen Levels at Different Perioperative Time Points and Colorectal Cancer Outcomes.

BACKGROUND: Whether elevated postoperative serum carcinoembryonic antigen (CEA) levels are prognostic in patients with stage II colorectal cancer (CRC) remains controversial. PATIENTS AND METHODS: Primary and sensitivity analysis populations were obtained from a retrospective, multicenter longitudinal cohort including consecutive patients without neoadjuvant treatment undergoing curative resection for stage I-III CRC. Serum CEA levels before (CEApre-m1) and within 1 (CEApost-m1), 2-3 (CEApost-m2-3), and 4-6 months (CEApost-m4-6) after surgery were obtained, and their associations with recurrence-free survival (RFS) and overall survival (OS) were assessed using Cox regression. Sensitivity and subgroup analyses were performed. RESULTS: Primary and sensitivity analysis populations included 710 [415 men; age, 54.8 (11.6) years] and 1556 patients [941 men; age, 56.2 (11.8) years], respectively. Recurrence hazard ratios (HRs) in the elevated CEApre-m1, CEApost-m1, CEApost-m2-3, and CEApost-m4-6 groups were 1.30 (95% CI: 0.91-1.85), 1.53 (95% CI: 0.89-2.62), 1.88 (95% CI: 1.08-3.28), and 1.15 (95% CI: 0.91-1.85), respectively. The HRs of the elevated CEApre-m1, CEApost-m1, CEApost-m2-3, and CEApost-m4-6 groups for OS were 1.09 (95% CI: 0.60-1.97), 2.78 (95% CI: 1.34-5.79), 2.81 (95% CI: 1.25-6.30), and 3.30 (95% CI: 1.67-.536), respectively. Adjusted multivariate analyses showed that both in the primary and sensitivity analysis populations, elevated CEApost-m2-3, rather than CEApre-m1, CEApost-m1, and CEApost-m4-6, was an independent risk factor for recurrence, but not for OS. The RFS in the elevated and normal CEApost-m2-3 groups differed significantly among patients with stage II disease [n = 266; HR, 2.89; 95% CI, 1.02-8.24 (primary analysis); n = 612; HR, 2.69; 95% CI, 1.34-5.38 (sensitivity analysis)]. CONCLUSIONS: Elevated postoperative CEA levels are prognostic in patients with stage II CRC, with 2-3 months after surgery being the optimal timing for CEA measurement.