Time-dependent diffusion MRI-based microstructural mapping for differentiating high-grade serous ovarian cancer from serous borderline ovarian tumor.

PURPOSE: To investigate the value of microstructural characteristics derived from time-dependent diffusion MRI in distinguishing high-grade serous ovarian cancer (HGSOC) from serous borderline ovarian tumor (SBOT) and the associations of immunohistochemical markers with microstructural features. METHODS: Totally 34 HGSOC and 12 SBOT cases who received preoperative pelvic MRI were retrospectively included in this study. Two radiologists delineated the tumors to obtain the regions of interest (ROIs). Time-dependent diffusion MRI signals were fitted by the IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) model, to extract microstructural parameters, including fraction of the intracellular component (fin), cell diameter (d), cellularity and extracellular diffusivity (Dex). Apparent diffusion coefficient (ADC) values were obtained from standard diffusion-weighted imaging (DWI). The parameters of HGSOCs and SBOTs were compared, and the diagnostic performance was evaluated. The associations of microstructural indexes with immunopathological parameters were assessed, including Ki-67, P53, Pax-8, ER and PR. RESULTS: In this study, fin, cellularity, Dex and ADC had good diagnostic performance levels in differentiating HGSOC from SBOT, with AUCs of 0.936, 0.909, 0.902 and 0.914, respectively. There were no significant differences in diagnostic performance among these parameters. Spearman analysis revealed in the HGSOC group, cellularity had a significant positive correlation with P53 expression (P = 0.028, r = 0.389) and Dex had a significant positive correlation with Pax-8 expression (P = 0.018, r = 0.415). ICC showed excellent agreement for all parameters. CONCLUSION: Time-dependent diffusion MRI had value in evaluating the microstructures of HGSOC and SBOT and could discriminate between these tumors.

The Factors for the Occurrence of Pulmonary Infection after Gastrointestinal Surgery and the Construction of a Predictive Model Using sTREM-1 and TIM-4: A Retrospective Study.

AIM: Identifying and intervening with high-risk postoperative pulmonary infections patients pose challenges in clinical practice. This study aims to conduct a comprehensively analysis of the risk factors and predictive factors associated with post-gastrointestinal surgery pulmonary infections and to develop a predictive model that can predict occurrence of pulmonary infection. METHODS: A retrospective analysis was conducted on 96 patients who underwent gastrointestinal surgery at our hospital from May 2021 to October 2023. The occurrence rate of postoperative pulmonary infections was calculated, and patients were categorized into two groups: those with pulmonary infections (the occurrence group) and those without pulmonary infections (the non-occurrence group). Logistic regression analysis was utilized to identify the risk factors for post-gastrointestinal surgery pulmonary infections and to evaluate the predictive value of soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) and T cell immunoglobulin and mucin domain-4 (TIM-4) using nomograms, calibration curves, and Receiver Operating Characteristic (ROC) curves. RESULTS: Out of 96 patients, 20 (20.83%) developed postoperative pulmonary infections. Significant differences were noted between occurrence and non-occurrence groups in terms of smoking (65.00% vs. 34.21%, p = 0.013), surgical duration (70.00% vs. 31.58%, p = 0.002), Preoperative hemoglobin level (35.00% vs. 65.79%, p = 0.013), sTREM-1 levels (23.57 ± 3.16 pg/mL vs. 15.62 ± 2.48 pg/mL, p < 0.001), and TIM-4 levels (61.48 ± 6.35 pg/mL vs. 44.73 ± 5.22 pg/mL, p < 0.001). Logistic regression analysis leads to the development of a risk prediction model for post-gastrointestinal surgery pulmonary infections. The high predictive values of sTREM-1 (Area Under Curve (AUC) = 0.962, 95% confidence interval (CI) 0.917~0.999) and TIM-4 (AUC = 0.970, 95% CI 0.925~1.000) were highlighted by the AUC values, underscoring their clinical importance. CONCLUSIONS: A predictive model utilizing sTREM-1 and TIM-4 for pulmonary infection following gastrointestinal surgery was developed. Additionally, other risk factors such as smoking, surgical duration, and preoperative hemoglobin level were evaluated. This finding can be applied in clinical practice to identify potentially susceptible patients and facilitate early intervention measures.

Multimodal Data Hybrid Fusion and Natural Language Processing for Clinical Prediction Models.

This study aims to propose a novel approach for enhancing clinical prediction models by combining structured and unstructured data with multimodal data fusion. We presented a comprehensive framework that integrated multimodal data sources, including textual clinical notes, structured electronic health records (EHRs), and relevant clinical data from National Electronic Injury Surveillance System (NEISS) datasets. We proposed a novel hybrid fusion method, which incorporated state-of-the-art pre-trained language model, to integrate unstructured clinical text with structured EHR data and other multimodal sources, thereby capturing a more comprehensive representation of patient information. The experimental results demonstrated that the hybrid fusion approach significantly improved the performance of clinical prediction models compared to traditional fusion frameworks and unimodal models that rely solely on structured data or text information alone. The proposed hybrid fusion system with RoBERTa language encoder achieved the best prediction of the Top 1 injury with an accuracy of 75.00% and Top 3 injuries with an accuracy of 93.54%. Our study highlights the potential of integrating natural language processing (NLP) techniques with multimodal data fusion for enhancing clinical prediction models' performances. By leveraging the rich information present in clinical text and combining it with structured EHR data, the proposed approach can improve the accuracy and robustness of predictive models. The approach has the potential to advance clinical decision support systems, enable personalized medicine, and facilitate evidence-based health care practices. Future research can further explore the application of this hybrid fusion approach in real-world clinical settings and investigate its impact on improving patient outcomes.

Mechanoelectronic stimulation of autologous extracellular vesicle biosynthesis implant for gut microbiota modulation.

Pathogenic gut microbiota is responsible for a few debilitating gastrointestinal diseases. While the host immune cells do produce extracellular vesicles to counteract some deleterious effects of the microbiota, the extracellular vesicles are of insufficient doses and at unreliable exposure times. Here we use mechanical stimulation of hydrogel-embedded macrophage in a bioelectronic controller that on demand boost production of up to 20 times of therapeutic extracellular vesicles to ameliorate the microbes' deleterious effects in vivo. Our miniaturized wireless bioelectronic system termed inducible mechanical activation for in-situ and sustainable generating extracellular vesicles (iMASSAGE), leverages on wireless electronics and responsive hydrogel to impose mechanical forces on macrophages to produce extracellular vesicles that rectify gut microbiome dysbiosis and ameliorate colitis. This in vivo controllable extracellular vesicles-produced system holds promise as platform to treat various other diseases.

Hsa_circ_0001278 Facilitates Colorectal Cancer Progression via Sponging miR-338-5p and Regulating AMOTL1 Expression.

BACKGROUND: Colorectal cancer (CRC) ranks as the third most common cancer and is second in terms of mortality worldwide. Circular RNAs are involved in the occurrence and development of malignant tumors by functioning either as oncogenes or tumor suppressors. METHOD: This study investigated the functions of hsa_circ_0001278 in CRC. We analyzed the expression of hsa_circ_0001278 in CRC tissues and adjacent normal tissues. In order to understand the roles of hsa_circ_0001278 in CRC in terms of cellular biological behavior, in vitro experiments were conducted. A mechanistic study was designed to investigate the regulatory effect of hsa_circ_0001278 on CRC. RESULTS: Hsa_circ_0001278 was found to be significantly upregulated in CRC specimens. The functional analysis indicated that hsa_circ_0001278 promotes aggressive phenotypes of CRC cells. Further mechanistic studies revealed that hsa_circ_0001278 sponges miR-338-5p to regulate angiomotin-like 1 (AMOTL1), thereby facilitating CRC progression. CONCLUSION: Our results demonstrate that hsa_circ_0001278 promotes malignant behaviors in CRC cells by sponging miR-338-5p to regulate AMOTL1 expression. This suggests that hsa_circ_0001278 may serve as a novel target for CRC treatment.

Wettability and Frictional Studies of PEEK Composites against Co-Cr Alloys with Surface Textures.

With the aim of promoting the qualities for total hip joint replacement, the wettability and tribological behaviors of PEEK composites pins with two sets of different fillers (PEEK/CF or PEEK/CF/PTFE/graphite) against Co-Cr alloy discs with five categories of surface textures (polished, orthogonal, spiral, r-θ, and orthogonal combined with spiral) were explored. It is revealed that the existence of CF in PEEK matrix increases the hydrophilicity in addition to the strength of PEEK, while the addition of PTFE increases the hydrophobicity of PEEK. The Co-Cr alloy discs with hydrophilic properties can be adjusted as hydrophobic, with the depth of textured grooves exceeding the critical sag height determined by the contact angle and the groove width. It can be concluded that PEEK/CF/PTFE/graphite composite has a lower wear rate than PEEK only reinforced with CF against Co-Cr alloy, both without surface texture and with shallow or deep grooves. The existence of shallow grooves on the disc surface could help the PEEK blends to achieve a steady friction against Co-Cr alloy in addition to collecting the worn debris. PEEK blend pins with 10 vol% CF, 10 vol% PTFE and 10 vol% graphite can achieve a lower friction coefficient of no more than 0.2 against Co-Cr alloy discs with shallow grooves around 3.5 µm in orthogonal or spiral textures.

Value of placental virtual magnetic resonance elastography and intravoxel incoherent motion-based diffusion and perfusion in predicting adverse outcomes of small-for-gestational-age infants.

OBJECTIVE: It is critical to early monitor and manage small-for-gestational age (SGA) infants with truly adverse outcomes not detected by conventional methods. We aimed to explore the value of diffusion-weighted imaging (DWI)-based virtual magnetic resonance elastography (vMRE) and intravoxel incoherent motion (IVIM)-based biexponential and stretched exponential parameters in predicting adverse outcomes of SGA infants. METHODS: Twenty SGA infants with adverse outcomes and forty without adverse outcomes were included in this prospective study. One DWI-based vMRE parameter [the stiffness value (µdiff)], five IVIM-based parameters [true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), diffusion distribution coefficient (DDC), and diffusion heterogeneity index (Alpha)] and apparent diffusion coefficient (ADC) were calculated and compared between groups. The predictive efficiency was compared by the logistic regression analysis and receiver operating characteristic curve analysis. The relationship between the µdiff value with gestational age was also evaluated. RESULTS: The placental µdiff value was remarkably higher, and the f, DDC, and ADC values were considerably lower in the SGA infants with adverse outcomes compared with those without adverse outcomes. The µdiff and f value were predictive risk factors for SGA infants with adverse outcomes. A combined predictive model (µdiff and f) improved the predictive efficacy. Moreover, there was no statistically significant correlation between the placental stiffness value and gestational age. CONCLUSIONS: Functional MRI parameters to quantify placenta elastography and microcirculation in SGA patients. This might be a useful tool to assess placental function and a vital non-invasive supplement for predicting adverse outcomes of SGA infants. CRITICAL RELEVANCE STATEMENT: This prospective study shows DWI-based virtual magnetic resonance elastography and intravoxel incoherent motion-based functional parameters to quantify placenta elastography and microcirculation in small-for-gestational-age patients, which could complement existing non-invasive methods for monitoring and predicting neonatal perinatal adverse outcome. KEY POINTS: • vMRE is an emerging non-invasive imaging technique for evaluating placenta stiffness. • SGA infants with adverse outcome have stiffer placental elasticity and lower microcirculation. • Risk factors combination displayed better efficacy in predicting adverse outcomes of SGA.

CT-based radiomics nomogram analysis for assessing BRCA mutation status in patients with high-grade serous ovarian cancer.

BACKGROUND: Radiomics nomogram analysis is widely preoperatively used to assess gene mutations in various tumors. PURPOSE: To explore the value of computed tomography (CT)-based radiomics nomogram analysis for assessing BRCA gene mutation status of patients with high-grade serous ovarian cancer (HGSOC). MATERIAL AND METHODS: In total, 96 patients with HGSOC were retrospectively screened and randomly divided into primary (n = 68) and validation cohorts (n = 28). The clinical model was constructed based on clinical features and CT morphological features using univariate and multivariate logistic analyses. Maximum-relevance and minimum-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were performed for feature dimensionality reduction and radiomics score was calculated. The nomogram model combining the clinical model and the radiomics score was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curves were generated to assess models' performance. The calibration analysis and decision curve analysis (DCA) were also performed. RESULTS: The clinical model consisted of CA125 level and supradiaphragmatic lymphadenopathy and yielded an area under the curve (AUC) of 0.69 (primary cohort) and 0.81 (validation cohort). The radiomics model was built with seven selected features and showed an AUC of 0.87 (primary cohort) and 0.81 (validation cohort). The nomogram finally showed the highest AUC of 0.89 (primary cohort) and 0.87 (validation cohort). The nomogram presented favorable calibrations in both the primary and validation cohorts. DCA further confirmed the clinical benefits of the constructed nomogram. CONCLUSION: CT-based radiomics nomogram provides a non-invasive method to discriminate BRCA gene mutation status of HGSOC and potentially helps develop precise medical strategies.

Leveraging natural language processing and geospatial time series model to analyze COVID-19 vaccination sentiment dynamics on Tweets.

Objective: To develop and apply a natural language processing (NLP)-based approach to analyze public sentiments on social media and their geographic pattern in the United States toward coronavirus disease 2019 (COVID-19) vaccination. We also aim to provide insights to facilitate the understanding of the public attitudes and concerns regarding COVID-19 vaccination. Methods: We collected Tweet posts by the residents in the United States after the dissemination of the COVID-19 vaccine. We performed sentiment analysis based on the Bidirectional Encoder Representations from Transformers (BERT) and qualitative content analysis. Time series models were leveraged to describe sentiment trends. Key topics were analyzed longitudinally and geospatially. Results: A total of 3 198 686 Tweets related to COVID-19 vaccination were extracted from January 2021 to February 2022. 2 358 783 Tweets were identified to contain clear opinions, among which 824 755 (35.0%) expressed negative opinions towards vaccination while 1 534 028 (65.0%) demonstrated positive opinions. The accuracy of the BERT model was 79.67%. The key hashtag-based topics include Pfizer, breaking, wearamask, and smartnews. The sentiment towards vaccination across the states showed manifest variability. Key barriers to vaccination include mistrust, hesitancy, safety concern, misinformation, and inequity. Conclusion: We found that opinions toward the COVID-19 vaccination varied across different places and over time. This study demonstrates the potential of an analytical pipeline, which integrates NLP-enabled modeling, time series, and geospatial analyses of social media data. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccination, help address the concerns of vaccine skeptics, and provide support for developing tailored policies and communication strategies to maximize uptake.

Placental perfusion using intravoxel incoherent motion MRI combined with Doppler findings in differentiating between very low birth weight infants and small for gestational age infants.

INTRODUCTION: Abnormalities in placental morphology and function can lead to small for gestational age infants (SGA) and very low birth weight infants (VLBWI). In this study, we explored the value of intravoxel incoherent motion (IVIM) histogram parameters, MRI morphological parameters, and Doppler findings of the placenta in differentiating between VLBWI and SGA. METHODS: Thirty-three pregnant women who were diagnosed with SGA and met the inclusion criteria were enrolled in this retrospective study and divided into two groups: 22 with non-VLBWI and 11 with VLBWI. The IVIM histogram parameters (perfusion fraction (f), true diffusion coefficient (D), and pseudo-diffusion coefficient (D*)), MRI morphological parameters, and Doppler findings were compared between groups. The diagnostic efficiency was compared by receiver operating characteristic (ROC) curve analysis. RESULTS: The Dmean, D90th, D*90th, fmax, and placental area of the VLBWI group were significantly lower than those of the non-VLBWI group (p < 0.05). The values of umbilical artery pulsatility index, resistance index (RI), and peak systolic velocity/end-diastolic velocity were significantly higher in the VLBWI group than in the non-VLBWI group (p < 0.05). D90th, placental area, and umbilical artery RI had the highest areas under the curve (AUCs) of the ROC curves, at 0.787, 0.785, and 0.762, respectively. A combined predictive model (D90th, placental area, and umbilical artery RI) improved the performance in differentiating between VLBWI and SGA compared with the single model (AUC = 0.942). DISCUSSION: IVIM histogram (D90th) and MRI morphological (placental area) parameters and a Doppler finding (umbilical artery RI) may be sensitive indicators for differentiating between VLBWI and SGA.

Preoperative Assessment of MRI-Invisible Early-Stage Endometrial Cancer With MRI-Based Radiomics Analysis.

BACKGROUND: Radiomics-based analyses have demonstrated impact on studies of endometrial cancer (EC). However, there have been no radiomics studies investigating preoperative assessment of MRI-invisible EC to date. PURPOSE: To develop and validate radiomics models based on sagittal T2-weighted images (T2WI) and T1-weighted contrast-enhanced images (T1CE) for the preoperative assessment of MRI-invisible early-stage EC and myometrial invasion (MI). STUDY TYPE: Retrospective. POPULATION: One hundred fifty-eight consecutive patients (mean age 50.7 years) with MRI-invisible endometrial lesions were enrolled from June 2016 to March 2022 and randomly divided into the training (n = 110) and validation cohort (n = 48) using a ratio of 7:3. FIELD STRENGTH/SEQUENCE: 3-T, T2WI, and T1CE sequences, turbo spin echo. ASSESSMENT: Two radiologists performed image segmentation and extracted features. Endometrial lesions were histopathologically classified as benign, dysplasia, and EC with or without MI. In the training cohort, 28 and 20 radiomics features were selected to build Model 1 and Model 2, respectively, generating rad-score 1 (RS1) and rad-score 2 (RS2) for evaluating MRI-invisible EC and MI. STATISTICAL TESTS: The least absolute shrinkage and selection operator logistic regression method was used to select radiomics features. Mann-Whitney U tests and Chi-square test were used to analyze continuous and categorical variables. Receiver operating characteristic curve (ROC) and decision curve analysis were used for performance evaluation. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated. A P-value <0.05 was considered statistically significant. RESULTS: Model 1 had good performance for preoperative detecting of MRI-invisible early-stage EC in the training and validation cohorts (AUC: 0.873 and 0.918). In addition, Model 2 had good performance in assessment of MI of MRI-invisible endometrial lesions in the training and validation cohorts (AUC: 0.854 and 0.834). DATA CONCLUSION: MRI-based radiomics models may provide good performance for detecting MRI-invisible EC and MI. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Application of enhanced computed tomography-based radiomics nomogram analysis to differentiate metastatic ovarian tumors from epithelial ovarian tumors.

OBJECTIVE: To investigate the value of nomogram analysis based on conventional features and radiomics features of computed tomography (CT) venous phase to differentiate metastatic ovarian tumors (MOTs) from epithelial ovarian tumors (EOTs). METHODS: A dataset involving 286 patients pathologically confirmed with EOTs (training cohort: 133 cases, validation cohort: 68 cases) and MOTs (training cohort: 54 cases, validation cohort: 31 cases) is assembled in this study. Radiomics features are extracted from the venous phase of CT images. Logistic regression is employed to build models based on conventional features (model 1), radiomics features (model 2), and the combination of model 1 and model 2 (model 3). Diagnostic performance is assessed and compared. Additionally, a nomogram is plotted for model 3, and decision curve analysis is applied for clinical use. RESULTS: Age, abdominal metastasis, para-aortic lymph node metastasis, location, and septation are chosen to build Model 1. Ten optimal radiomics features are ultimately selected and radiomics score (rad-score) is calculated to build Model 2. Nomogram score is calculated to build model 3 that shows optimal diagnostic performance in both the training (AUC = 0.952) and validation cohorts (AUC = 0.720), followed by model 1 (AUC = 0.872 for training cohort and AUC = 0.709 for validation cohort) and model 2 (AUC = 0.833 for training cohort and AUC = 0.620 for validation cohort). Additionally, Model 3 achieves accuracy, sensitivity, and specificity of 0.893, 0.880, and 0.926 in the training cohort and 0.737, 0.853, and 0.613 in the validation cohort. CONCLUSION: Model 3 demonstrates the best diagnostic performance for preoperative differentiation of MOTs from EOTs. Thus, nomogram analysis based on Model 3 may be used as a biomarker to differentiate MOTs from EOTs.

Simulation of a Miniature Linear Ion Trap with Half-Round Rod Electrodes.

The miniaturization of ion trap mass analyzers is an important direction in the development of mass spectrometers. In this work, we proposed two models of miniaturized HreLIT with a field radius of about 2 mm based on the existing research on conventional HreLIT and other ion traps, one with ions ejection slits on one pair of electrodes only (2-slit model) and the other with the same slits on all electrodes (4-slit model). The relationship of mass resolution with r/rx and the "stretch" distance of electrodes in the ejection direction is investigated by theoretical simulations. Trends of electric fields inside the ion traps were discussed as well. The comparable maximum resolution is observed at r/rx = 2/1.4 in both models, but stretching simulations revealed that the peak resolution of the 2-slit model was higher than that of the other model by about 8%. The highest value of 517 was obtained when stretching 1.1 mm. Furthermore, the resolution of ions with m/z = 119 could exceed 1000 when the scan rate was reduced to 800 Th/s. The mass spectrometry capability of miniature HreLIT has been confirmed theoretically, and it laid the foundation for the subsequent fabrication with MEMS technology.

Value of non-Gaussian diffusion imaging with a fractional order calculus model combined with conventional MRI for differentiating histological types of cervical cancer.

OBJECTIVE: This study aimed to evaluate the value of a fractional order calculus (FROC) model combined with conventional magnetic resonance imaging (MRI) for differentiating cervical adenocarcinoma (CAC) from squamous cell carcinoma (SCC). METHODS: Diffusion-weighted imaging (DWI) with 9 b values (0-2000s/mm2) was carried out in 57 cervical cancer patients. Diffusion coefficient (D), fractional order parameter (ß), and microstructural quantity (µ) together with apparent diffusion coefficient (ADC) were calculated and compared between the CAC and SCC groups. Conventional MRI features included T2WI signal intensity (SI), unenhanced-T1WI SI, enhanced-T1WI SI, and ∆T1WI SI, which were also compared between the two groups. Receiver operating characteristic (ROC) analysis was employed to assess the performance of FROC parameters, ADC, and conventional MRI features in differentiating CAC from SCC. RESULTS: ß was significantly lower in the CAC group than in the SCC group (0.682 ± 0.054 vs. 0.723 ± 0.084, P = 0.035), while D and µ were not significantly different between the two groups (D, P = 0.171; µ, P = 0.127). There was no significant difference in the ADC value between the two groups (P = 0.053). In conventional MRI features, enhanced-T1WI SI was significantly higher in the SCC group than in the CAC group (985.78 ± 130.83 vs. 853.92 ± 149.65, P = 0.002). The area under the curve (AUC) of ß, ADC, and enhanced-T1WI SI was 0.700, 0.683, and 0.799, respectively. The combination of ß, ADC, and enhanced-T1WI SI revealed optimal diagnostic performance in differentiating CAC from SCC (AUC = 0.930), followed by ß + enhanced-T1WI SI (AUC = 0.869), ADC+ enhanced-T1WI SI (AUC = 0.817), and ß + ADC (AUC = 0.761). CONCLUSION: The FROC model can serve as a noninvasive and quantitative imaging technique for differentiating CAC from SCC. ß combined with ADC and enhanced-T1WI SI had the highest diagnostic efficiency.

LINC00922 promotes deterioration of gastric cancer.

Several studies have demonstrated the association of lncRNAs with a variety of cancers. Here, we explored the role of LINC00922 in gastric cancer (GC) using bioinformatics approaches and in vitro experiments. We examined the expression of LINC00922 and the prognosis of GC patients based on data from The Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA). LINC00922-related genes were identified by the Multi Experiment Matrix (MEM) database and The Atlas of Noncoding RNAs in Cancer (TANRIC), followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction analysis. The significance of LINC00922 in cell proliferation, apoptosis, invasion and migration was assessed by MTT assay, flow cytometry, Transwell assay and wound-healing assay. The expression of LINC00922 was increased in GC tissues compared with adjacent non-tumor tissues, and increased LINC00922 expression was correlated with poor overall survival and disease-free survival. In addition, 336 overlapping genes were identified by the MEM database and TANRIC and found to be involved in GC-related biological processes, such as cell adhesion and migration, as well as TGF-ß signaling. In the protein-protein interaction network, hub genes, such as FSTL3 and LAMC1, were identified. LINC00922 overexpression significantly promoted cell proliferation and invasion in vitro, whereas LINC00922 knockdown exerted opposite effects. In summary, our findings indicate that LINC00922 is overexpressed in GC tissues, suggesting that it might play a role in the development and progression of GC, and thus, it might serve as a prognostic indicator of GC.

MRI radiomics combined with clinicopathologic features to predict disease-free survival in patients with early-stage cervical cancer.

OBJECTIVE: To establish a comprehensive model including MRI radiomics and clinicopathological features to predict post-operative disease-free survival (DFS) in early-stage (pre-operative FIGO Stage IB-IIA) cervical cancer. METHODS: A total of 183 patients with early-stage cervical cancer admitted to our Jiangsu Province Hospital underwent radical hysterectomy were enrolled in this retrospective study from January 2013 to June 2018 and their clinicopathology and MRI information were collected. They were then divided into training cohort (n = 129) and internal validation cohort (n = 54). The radiomic features were extracted from the pre-operative T1 contrast-enhanced (T1CE) and T2 weighted image of each patient. Least absolute shrinkage and selection operator regression and multivariate Cox proportional hazard model were used for feature selection, and the rad-score (RS) of each patient were evaluated individually. The clinicopathology model, T1CE_RS model, T1CE + T2_RS model, and clinicopathology combined with T1CE_RS model were established and compared. Patients were divided into high- and low-risk groups according to the optimum cut-off values of four models. RESULTS: T1CE_RS model showed better performance on DFS prediction of early-stage cervical cancer than clinicopathological model (C-index: 0.724 vs 0.659). T1CE+T2_RS model did not improve predictive performance (C-index: 0.671). The combination of T1CE_RS and clinicopathology features showed more accurate predictive ability (C-index=0.773). CONCLUSION: The combination of T1CE_RS and clinicopathology features showed more accurate predictive performance for DFS of patients with early-stage (pre-operative IB-IIA) cervical cancer which can aid in the design of individualised treatment strategies and regular follow-up. ADVANCES IN KNOWLEDGE: A radiomics signature composed of T1CE radiomic features combined with clinicopathology features allowed differentiating patients at high or low risk of recurrence.

Processing and Profile Control of Microhole Array for PDMS Mask with Femtosecond Laser.

Polydimethylsiloxane (PDMS) is hailed as one of the foundational materials that have been applied to different products in various fields because of its chemical resistance, low cost, excellent flexibility, and high molding capability. With the aim to achieve surface texture with high efficiency by means of electrochemical micromachining with PDMS mask, a femtosecond laser is utilized to process a precision array of micro-through-holes on PDMS films as the molds. The ablation process of PDMS with a femtosecond laser was investigated via numerical simulation verified with experiments indicating a laser energy density of 4.865 mJ/mm2 as the ablation threshold of PDMS with the melting temperature of 930 K. The spiral scanning path with optimized radial offset was developed to ablate materials from the PDMS film to form the laminated profiles, and a tapered through hole was then formed with multilayer scanning. The profile dimension and accuracy were examined as control targets in terms of laser pulse energy and scanning speed, showing that a 12 µJ femtosecond laser pulse energy and 1000 mm/s scanning speed could bring about a nearly circular laminating profile with expected smaller exit diameter than the entry diameter. All the cross-section diameters of the microcone decreased with the increase of laser scanning speed, while the taper increased gradually and then saturated around a laser scanning speed of 800 mm/s due to the energy absorption resulting in smaller ablation in diameter and depth.

Added value of radiomics analysis in MRI invisible early-stage cervical cancers.

OBJECTIVES: To determine the diagnostic ability of cervical mucosa radiomics signature of sagittal T2WI and T1 contrast-enhanced (CE) imaging in detecting early-stage cervical cancers with negative MRI. METHODS: Preoperative images of postoperative pathology confirmed early-stage cervical cancer patients and normal cervix patients admitted to our hospital between January 2013 and December 2020 were retrospectively reviewed. Patients with cancer signals on T2WI, T1CE and DWI were deleted. Regions of interests (ROIs) were delineated on cervical mucosa (from cervical canal to cervical dome) with 5 mm width on sagittal T2WI and T1CE. The maximum-relevance and minimumredundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods were used for the calculation of radiomics signature scores. Diagnostic performance was assessed and compared between radiomics prediction models (model 1: T1CE; model 2: T2WI; model 3: model one combined with model 2). Differential diagnostic ability of radiomics signature in detecting lymphatic vascular space invasion (LVSI) was further explored. RESULTS: Diagnostic performance of model three was higher than model 1 and model 2 both in primary (model 3 0.874, model 1 0.857, model 2 0.816) and validation (model 3 0.853, model 1 0.847, model 2 0.634) cohorts. Model 3 showed statistical diagnostic difference compared with model 2 (primary p = 0.008, validation p = 0.000). However, the diagnostic improvement ability of model 3 showed no statistical difference compared with model 1 (primary p = 0.351, validation p = 0.739). Diagnostic efficiency of model 3 in detecting LVSI was not apparent (AUC 0.64). CONCLUSIONS: Radiomics analysis of cervical mucosa combining T1CE and T2WI is promising for predicting MRI invisible early-stage cervical cancers, however further ability in detecting LVSI was not apparent. ADVANCES IN KNOWLEDGE: Conventional MRI was originally defined as meaningless in very early-stage cervical cancers. However, whether MRI radiomics analysis of cervical mucosa can detecting tiny changes of invisible early stage cervical cancers has not been researched yet.

Material Removal Capability and Profile Quality Assessment on Silicon Carbide Micropillar Fabrication with a Femtosecond Laser.

Silicon carbide (SiC) has a variety of applications because of its favorable chemical stability and outstanding physical characteristics, such as high hardness and high rigidity. In this study, a femtosecond laser with a spiral scanning radial offset of 5 µm and a spot radius of 6 µm is utilized to process micropillars on a SiC plate. The influence of pulsed laser beam energies and laser translation velocities on the micropillar profiles, dimensions, surface roughness Ra, and material removal capability (MRC) of micropillars was investigated. The processing results indicate that the micropillar has the best perpendicularity, with a micropillar bottom angle of 75.59° under a pulsed beam energy of 50 µJ in the range of 10-70 µJ, with a pulsed repetition rate of 600 kHz and a translation velocity of 0.1 m/s. As the laser translation velocity increases between 0.2 m/s and 1.0 m/s under a fixed pulsed beam energy of 50 µJ and a constant pulsed repetition rate of 600 kHz, the micropillar height decreases from 119.88 µm to 81.79 µm, with the MRC value increasing from 1.998 µm3/µJ to 6.816 µm3/µJ, while the micropillar bottom angle increases from 68.87° to 75.59°, and the Ra value diminishes from 0.836 µm to 0.341 µm. It is suggested that a combination of a higher pulsed laser beam energy with a faster laser translation speed is recommended to achieve micropillars with the same height, as well as an improved processing efficiency and surface finish.

Detection of rapidly accumulating stress-induced SUMO in prostate cancer cells by a fluorescent SUMO biosensor.

SUMO conjugates and SUMO chains form when SUMO, a small ubiquitin-like modifier protein, is covalently linked to other cellular proteins or itself. During unperturbed growth, cells maintain balanced levels of SUMO conjugates. In contrast, eukaryotic cells that are exposed to proteotoxic and genotoxic insults mount a cytoprotective SUMO stress response (SSR). One hallmark of the SSR is a rapid and massive increase of SUMO conjugates in response to oxidative, thermal, and osmotic stress. Here, we use a recombinant fluorescent SUMO biosensor, KmUTAG-fl, to investigate differences in the SSR in a normal human prostate epithelial cell line immortalized with SV40 (PNT2) and two human prostate cancer cell lines that differ in aggressiveness and response to androgen (LNCaP and PC3). In cells that grow unperturbed, SUMO is enriched in the nuclei of all three cell lines. However, upon 30 min of exposure to ultraviolet radiation or oxidative stress, we detected significant cytosolic enrichment of SUMO as measured by KmUTAG-fl staining. This rapid enrichment in cytosolic SUMO levels was on average fivefold higher in the LNCaP and PC3 prostate cancer cell lines compared to normal immortalized PNT2 cells. Additionally, this enhanced enrichment of cytosolic SUMO was reversible as cells recovered from stress exposure. Our study validates the use of the fluorescent KmUTAG-fl SUMO biosensor to detect differences of SUMO levels and localization between normal and cancer cells and provides new evidence that cancer cells may exhibit an enhanced SSR.