Automated segmentation of brain metastases with deep learning: a multi-center, randomized crossover, multi-reader evaluation study.

BACKGROUND: Artificial intelligence has been proposed for brain metastasis (BM) segmentation but it has not been fully clinically validated. The aim of this study was to develop and evaluate a system for BM segmentation. METHODS: A deep-learning-based BM segmentation system (BMSS) was developed using contrast-enhanced MR images from 488 patients with 10,338 brain metastases. A randomized crossover, multi-reader study was then conducted to evaluate the performance of the BMSS for BM segmentation using data prospectively collected from 50 patients with 203 metastases at five centers. Five radiology residents and five attending radiologists were randomly assigned to contour the same prospective set in assisted and unassisted modes. Aided and unaided Dice similarity coefficients (DSCs) and contouring times per lesion were compared. RESULTS: The BMSS alone yielded a median DSC of 0.91 (95% confidence interval, 0.90-0.92) in the multi-center set and showed comparable performance between the internal and external sets (p = 0.67). With BMSS assistance, the readers increased the median DSC from 0.87 (0.87-0.88) to 0.92 (0.92-0.92) (p < 0.001) with a median time saving of 42% (40-45%) per lesion. Resident readers showed a greater improvement than attending readers in contouring accuracy (improved median DSC, 0.05 [0.05-0.05] vs. 0.03 [0.03-0.03]; p < 0.001), but a similar time reduction (reduced median time, 44% [40-47%] vs. 40% [37-44%]; p = 0.92) with BMSS assistance. CONCLUSIONS: The BMSS can be optimally applied to improve the efficiency of brain metastasis delineation in clinical practice.

Identifying subtypes and developing prognostic models based on N6-methyladenosine and immune microenvironment related genes in breast cancer.

Breast cancer (BC) is the most prevalent cancer in women globally. The tumor microenvironment (TME), comprising epithelial tumor cells and stromal elements, is vital for breast tumor development. N6-methyladenosine (m6A) modification plays a key role in RNA metabolism, influencing its various aspects such as stability and translation. There is a notable link between m6A methylation and immune cells in the TME, although this relationship is complex and not fully deciphered. In this research, BC expression and clinicopathological data from TCGA were scrutinized to assess expression profiles, mutations, and CNVs of 31 m6A genes and immune microenvironment-related genes, examining their correlations, functions, and prognostic impacts. Lasso and Cox regression identified prognostic genes for constructing a nomogram. Single-cell analyses mapped the distribution and patterns of these genes in BC cell development. We investigated associations between gene-derived risk scores and factors like immune infiltration, TME, checkpoints, TMB, CSC indices, and drug response. As a complement to computational analyses, in vitro experiments were conducted to confirm these expression patterns. We included 31 m6A regulatory genes and discovered a correlation between these genes and the extent of immune cell infiltration. Subsequently, a 7-gene risk score was generated, encompassing HSPA2, TAP1, ULBP2, CXCL1, RBP1, STC2, and FLT3. It was observed that the low-risk group exhibited better overall survival (OS) in BC, with higher immune scores but lower tumor mutational burden (TMB) and cancer stem cell (CSC) indices, as well as lower IC50 values for commonly used drugs. To enhance clinical applicability, age and stage were incorporated into the risk score, and a more comprehensive nomogram was constructed to predict OS. This nomogram was validated and demonstrated good predictive performance, with area under the curve (AUC) values for 1-year, 3-year, and 5-year OS being 0.848, 0.807, and 0.759, respectively. Our findings highlight the profound impact of prognostic-related genes on BC immune response and prognostic outcomes, suggesting that modulation of the m6A-immune pathway could offer new avenues for personalized BC treatment and potentially improve clinical outcomes.

A novel one-stepped synthesized Schiff-base fluorescence probe for specific recognition of zinc ions with highly sensitive and its application in living cells.

Fluorescent turn-on receptors are extensively employed for the detection of Zn ions contamination in the environment due to its simplicity, convenience and portability. However, developing highly sensitive and cell-imageable fluorescent turn-on probe for the recognition of Zn ions in living organisms remains a significant challenge. Herein, we have successfully synthesized a novel Schiff base probe (H2L) with a significant fluorescence turn-on response (Zn ions) by one-step synthetic method. In this work, H2L exhibited high sensitivity to Zn2+ ions upon interaction with various common metal ions in HEPES buffer solution. Its detection limit is 1.87 × 10-7 M, which is lower than the requirement of Environmental Protection Agency (EPA) and World Health Organization (WHO) guidelines. The fluorescence titration and Job's plot analysis suggested a 1:1 binding ratio between the probe and Zn ion, and the single-crystal structures obtained further confirmed this inference. In addition, the fluorescent sensor demonstrated recyclability, maintaining its fluorescence intensity for up to 6 cycles without significant decrease, which holds promise for future investigations on reversible fluorescent chemosensors. Notably, fluorescence imaging experiments demonstrated that H2L could be successfully used for the detection of Zn2+ in live cells.

Four-dimensional analysis of aortic root motion in normal population using retrospective multiphase computed tomography.

Aims: Aortic root motion is suspected to contribute to proximal aortic dissection. While motion of the aorta in four dimensions can be traced with real-time imaging, displacement and rotation in quantitative terms remain unknown. The hypothesis was to show feasibility of quantification of three-dimensional aortic root motion from dynamic CT imaging. Methods and results: Dynamic CT images of 40 patients for coronary assessment were acquired using a dynamic protocol. Scans were ECG-triggered and segmented in 10 time-stepped phases (0-90%) per cardiac cycle. With identification of the sinotubular junction (STJ), a patient-specific co-ordinate system was created with the z-axis (out-of-plane) parallel to longitudinal direction. The left and right coronary ostia were traced at each time-step to quantify downward motion in reference to the STJ plane, motion within the STJ plane (in-plane), and the degree of rotation. Enrolled individuals had an age of 65 ± 12, and 14 were male (35%). The out-of-plane motion was recorded with the largest displacement of 10.26 ± 2.20 and 8.67 ± 1.69 mm referenced by left and right coronary ostia, respectively. The mean downward movement of aortic root was 9.13 ± 1.86 mm. The largest in-plane motion was recorded at 9.17 ± 2.33 mm and 6.51 ± 1.75 mm referenced by left and right coronary ostia, respectively. The largest STJ in-plane motion was 7.37 ± 1.96 mm, and rotation of the aortic root was 11.8 ± 4.60°. Conclusion: In vivo spatial and temporal displacement of the aortic root can be identified and quantified from multiphase ECG-gated contrast-enhanced CT images. Knowledge of normal 4D motion of the aortic root may help understand its biomechanical impact in patients with aortopathy and pre- and post-surgical or transcatheter aortic valve replacement.

Knockdown of AMIGO2 suppresses proliferation and migration through regulating PPAR-γ in bladder cancer.

PURPOSE: This study aims to reveal the relationship between AMIGO2 and proliferation, migration and tumorigenicity of bladder cancer, and explore the potential molecular mechanisms. METHODS: The expression level of AMIGO2 is measured by qRT-PCR and immunohistochemistry (IHC). Stable AMIGO2 knockdown cell lines T24 and 5637 were established by lentivirus transfection. Cell Counting Kit (CCK-8 assay) was produced to determine cell proliferation, flow cytometry analysis was utilized to detect cell cycle, and wound healing assay was proceeded to test migration ability of bladder cancer cells. Xenograft mouse model was established for investigating the effect of AMIGO2 on tumor formation in vivo. The RNA Sequencing technology was applied to explore the underlying mechanisms. The expression level of PPAR-γ was measured by Western Blot. RESULTS: AMIGO2 was upregulated in bladder cancer cells and tissues. Inhibited expression of AMIGO2 suppresses cell proliferation and migration. Low AMIGO2 expression inhibited tumorigenicity of 5637 in nude mice. According to RNA-Seq and bioinformatics analysis, 917 DEGs were identified. The DEGs were mainly enriched in cell-cell adhesion, peroxisome proliferators-activated receptors (PPARs) signaling pathway and some other pathways. PPAR-γ is highly expressed in bladder cancer cell lines T24 and 5637, but when AMIGO2 is knocked down in T24 and 5637, the expression level of PPAR-γ is also decreased, and overexpression of PPAR-γ could reverse the suppression effect of cell proliferation and migration caused by the inhibition of AMIGO2. CONCLUSION: AMIGO2 is overexpressed in bladder cancer cells and tissues. Knockdown of AMIGO2 suppresses bladder cancer cell proliferation and migration. These processes might be regulated by PPAR-γ signaling pathway.

Association between oxidative balance score and kidney stones: data from the national health and nutrition examination survey (NHANES).

PURPOSE: Some studies have found that the pathological formation of kidney stones is closely related to injury and inflammatory response. Behaviors such as dietary composition, physical activity, obesity and smoking can all affect the body's oxidative stress levels. In order to evaluate the effects of various diets and lifestyles on the body's oxidative and antioxidant systems, an oxidative balance score was developed. To investigate whether the OBS is associated with the development of kidney stones. METHODS: Data were taken from the National Health and Nutrition Examination Survey (NHANES) from 2007-2018, followed by retrospective observational studies. The association between kidney stones and OBS was analyzed using survey-weighted logistic regression by adjusting for demographics, laboratory tests, and medical comorbidity covariates. The oxidative balance score is calculated by screening 16 nutrients and 4 lifestyle factors, including 5 prooxidants and 15 antioxidants, based on prior information about the relationship between oxidation levels in the body and nutrients or lifestyle factors. RESULTS: A total of 26,786 adult participants were included in the study, of which 2,578, or 9.62%, had a history of nephrolithiasis. Weighted logistic regression analysis found an association between OBS and kidney stones. In the fully tuned model, i.e., model 3, the highest quartile array of OBS was associated with the lowest quartile array of OBS (OR = 0.73 (0.57, 0.92)) with the risk of kidney stone (p = 0.01), and was statistically significant and remained relatively stable in each model. At the same time, the trend test in the model is also statistically significant. With the increase of OBS, the OR value of kidney stones generally tends to decrease. CONCLUSIONS: There is an inverse correlation between OBS and kidney stone disease. At the same time, higher OBS suggests that antioxidant exposure is greater than pro-oxidative exposure in diet and lifestyle, and is associated with a lower risk of kidney stones.

The dispersibility of biphasic stabilized oil-in-water emulsions improved by the interaction between curdlan and soy protein isolate.

Curdlan, a natural polysaccharide, exhibits emulsion-stabilizing and viscosity-modifying properties. However, when employed solely in the aqueous phase, curdlan's adhesive nature impedes droplet dispersion, resulting in a gel-like structure with limited applicability. This investigation formulated a biphasic stabilized oil-in-water emulsion by supplementing the oil phase with beeswax and the aqueous phase with curdlan and soy protein isolate (SPI). The addition of SPI transformed the structural characteristics from a gel-like to a mayonnaise-like structure. Maximal electrostatic repulsion was observed at an internal phase volume fraction of 30%, effectively precluding droplet aggregation owing to the absolute zeta potentials surpassing 40 mV. The emulsions displayed shear-thinning rheological behavior, with a higher storage modulus than the loss modulus, indicative of favorable elastic properties. Molecular docking revealed the predominant role of polar amino acids in facilitating hydrogen bond formation. This study provides a template for developing emulsions with biphasic stability and desirable dispersibility.

Identification of a Prognostic Model Based on NK Cell-Related Genes in Multiple Myeloma Using Single-Cell and Transcriptomic Data Analysis.

Background: Multiple myeloma (MM), an incurable plasma cell malignancy. The significance of the relationship between natural killer (NK) cell-related genes and clinical factors in MM remains unclear. Methods: Initially, we extracted NK cell-related genes from peripheral blood mononuclear cells (PBMC) of healthy donors and MM samples by employing single-cell transcriptome data analysis in TISCH2. Subsequently, we screened NK cell-related genes with prognostic significance through univariate Cox regression analysis and protein-protein interaction (PPI) network analysis. Following the initial analyses, we developed potential subtypes and prognostic models for MM using consensus clustering and lasso regression analysis. Additionally, we conducted a correlation analysis to explore the relationship between clinical features and risk scores. Finally, we constructed a weighted gene co-expression network analysis (WGCNA) and identified differentially expressed genes (DEGs) within the MM cohort. Results: We discovered that 153 NK cell-related genes were significantly associated with the prognosisof MM patients (P <0.05). Patients in NK cluster A exhibited poorer survival outcomes compared to those in cluster B. Furthermore, our NK cell-related genes risk model revealed that patients with a high risk score had significantly worse prognoses (P <0.05). Patients with a high risk score were more likely to exhibit adverse clinical markers. Additionally, the nomogram based on NK cell-related genes demonstrated strong prognostic performance. The enrichment analysis indicated that immune-related pathways were significantly correlated with both the NK subtypes and the NK cell-related genes risk model. Ultimately, through the combined use of WGCNA and DEGs analysis, and by employing Venn diagrams, we determined that ITM2C is an independent prognostic marker for MM patients. Conclusion: In this study, we developed a novel model based on NK cell-related genes to stratify the prognosis of MM patients. Notably, higher expression levels of ITM2C were associated with more favorable survival outcomes in these patients.

Prevalence and risk factors for dementia and mild cognitive impairment among older people in Southeast China: a community-based study.

BACKGROUND: With the aging population, the number of individuals with dementia in China is increasing rapidly. This community-based study aimed to investigate the prevalence and risk factors for dementia and mild cognitive impairment (MCI) among older adults in China. METHODS: In this study, 20,070 individuals aged ≥ 65 were recruited between January 1, 2022, and February 1, 2023, from ten communities in Xiamen City, China. We collected data on age, sex, level of education, and medical history, as well as global cognition and functional status. The prevalence of dementia and MCI was examined, and the risk factors for different groups were assessed. RESULTS: The overall prevalence of dementia and MCI was approximately 5.4% (95% confidence interval [CI], 5.1-5.7) and 7.7% (95% CI, 7.4-8.1), respectively. The results also indicated that dementia and MCI share similar risk factors, including older age, female sex, hypertension, and diabetes mellitus. Compared with individuals with no formal education, those with > 6 years of education had an odds ratio for MCI of 1.83 (95% CI, 1.49-2.25). We also found that only 5.5% of the positive participants chose to be referred to the hospital for further diagnosis and treatment during follow-up visits. CONCLUSIONS: This study estimated the prevalence and risk factors for dementia and MCI among individuals aged ≥ 65 years in Southeast China. These findings are crucial for preventing and managing dementia and MCI in China.

Rapid detection of multidrug resistance in tuberculosis using nanopore-based targeted next-generation sequencing: a multicenter, double-blind study.

Background: Resistance to anti-tuberculous drugs is a major challenge in the treatment of tuberculosis (TB). We aimed to evaluate the clinical availability of nanopore-based targeted next-generation sequencing (NanoTNGS) for the diagnosis of drug-resistant tuberculosis (DR-TB). Methods: This study enrolled 253 patients with suspected DR-TB from six hospitals. The diagnostic efficacy of NanoTNGS for detecting Mycobacterium tuberculosis and its susceptibility or resistance to first- and second-line anti-tuberculosis drugs was assessed by comparing conventional phenotypic drug susceptibility testing (pDST) and Xpert MTB/RIF assays. NanoTNGS can be performed within 12 hours from DNA extraction to the result delivery. Results: NanoTNGS showed a remarkable concordance rate of 99.44% (179/180) with the culture assay for identifying the Mycobacterium tuberculosis complex. The sensitivity of NanoTNGS for detecting drug resistance was 93.53% for rifampicin, 89.72% for isoniazid, 85.45% for ethambutol, 74.00% for streptomycin, and 88.89% for fluoroquinolones. Specificities ranged from 83.33% to 100% for all drugs tested. Sensitivity for rifampicin-resistant tuberculosis using NanoTNGS increased by 9.73% compared to Xpert MTB/RIF. The most common mutations were S531L (codon in E. coli) in the rpoB gene, S315T in the katG gene, and M306V in the embB gene, conferring resistance to rifampicin, isoniazid, and ethambutol, respectively. In addition, mutations in the pncA gene, potentially contributing to pyrazinamide resistance, were detected in 32 patients. Other prevalent variants, including D94G in the gyrA gene and K43R in the rpsL gene, conferred resistance to fluoroquinolones and streptomycin, respectively. Furthermore, the rv0678 R94Q mutation was detected in one sample, indicating potential resistance to bedaquiline. Conclusion: NanoTNGS rapidly and accurately identifies resistance or susceptibility to anti-TB drugs, outperforming traditional methods. Clinical implementation of the technique can recognize DR-TB in time and provide guidance for choosing appropriate antituberculosis agents.

Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma.

BACKGROUND: Surgery combined with radiotherapy substantially escalates the likelihood of encountering complications in early-stage cervical squamous cell carcinoma(ESCSCC). We aimed to investigate the feasibility of Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in ESCSCC and minimize the occurrence of adverse events associated with the treatment. METHODS: A dataset comprising MR images was obtained from 289 patients who underwent radical hysterectomy and pelvic lymph node dissection between January 2019 and April 2022. The dataset was randomly divided into two cohorts in a 4:1 ratio.The postoperative radiotherapy options were evaluated according to the Peter/Sedlis standard. We extracted clinical features, as well as intratumoral and peritumoral radiomic features, using the least absolute shrinkage and selection operator (LASSO) regression. We constructed the Clinical Signature (Clinic_Sig), Radiomics Signature (Rad_Sig) and the Deep Transformer Learning Signature (DTL_Sig). Additionally, we fused the Rad_Sig with the DTL_Sig to create the Deep Learning Radiomic Signature (DLR_Sig). We evaluated the prediction performance of the models using the Area Under the Curve (AUC), calibration curve, and Decision Curve Analysis (DCA). RESULTS: The DLR_Sig showed a high level of accuracy and predictive capability, as demonstrated by the area under the curve (AUC) of 0.98(95% CI: 0.97-0.99) for the training cohort and 0.79(95% CI: 0.67-0.90) for the test cohort. In addition, the Hosmer-Lemeshow test, which provided p-values of 0.87 for the training cohort and 0.15 for the test cohort, respectively, indicated a good fit. DeLong test showed that the predictive effectiveness of DLR_Sig was significantly better than that of the Clinic_Sig(P < 0.05 both the training and test cohorts). The calibration plot of DLR_Sig indicated excellent consistency between the actual and predicted probabilities, while the DCA curve demonstrating greater clinical utility for predicting the pathological features for adjuvant radiotherapy. CONCLUSION: DLR_Sig based on intratumoral and peritumoral MRI images has the potential to preoperatively predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma (ESCSCC).

Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence.

The prediction of patient disease risk via computed tomography (CT) images and artificial intelligence techniques shows great potential. However, training a robust artificial intelligence model typically requires large-scale data support. In practice, the collection of medical data faces obstacles related to privacy protection. Therefore, the present study aims to establish a robust federated learning model to overcome the data island problem and identify high-risk patients with postoperative gastric cancer recurrence in a multicentre, cross-institution setting, thereby enabling robust treatment with significant value. In the present study, we collect data from four independent medical institutions for experimentation. The robust federated learning model algorithm yields area under the receiver operating characteristic curve (AUC) values of 0.710, 0.798, 0.809, and 0.869 across four data centres. Additionally, the effectiveness of the algorithm is evaluated, and both adaptive and common features are identified through analysis.

Effect of mixture microstructure/compatibility on the properties of type-A gelatin-dextran edible films.

The influence of phase separation behavior on bio-based film properties has attracted more and more attention. This work investigated the effects of microstructure and compatibility of the type-A gelatin (GE)-dextran (DE) mixtures on GE-DE edible film properties. Three kinds of GE-DE edible films with different textures were prepared via modulating the microstructure and compatibility of film-forming mixtures using the method of gelation-drying, e.g., homogeneous films, microphase separated films with relatively homogeneous texture, and microphase separated films with uneven texture. The optical, mechanical, water barrier, and thermal properties of films were characterized. Results showed that microstructure and compatibility significantly affected the film properties. In general, films with DE-in-GE microstructure exhibited the best film properties, followed by films with water-in-water-in-water/bicontinuous microstructure, and then films with GE-in-DE microstructure. And homogeneous films showed the best film properties, followed by films with relatively homogeneous texture, and then films with uneven texture. The weight loss results suggested the potential of GE-DE edible films for application in cherry tomato preservation. This work provided interesting information for the design of film with fabricated microstructure and properties.

Visual Color Change During Spin-coating Guiding the Quality of Cu2 ZnSn(S,Se)4 Films.

Solution method provides a low-cost and environmentally friendly route for the fabrication of Cu2 ZnSn(S,Se)4 (CZTSSe) thin-film solar cells. However, uncontrollable quality of the CZTSSe absorber layer will severely limit the device's performance. In this study, it is find that the thickness and the quality of the formed precursor is not stable because of the variation of the viscosity of the precursor solution. Combined by different characterization methods, the results disclose that such change is strongly related to the reflected color of the first coating layer during precursor growth. Further studies disclose that only by maintaining the appropriate reflected color can a well-crystallized CZTSSe film be prepared, thereby obtaining good solar cell efficiency. This semi-empirical pattern is confirmed by thin-film interference theory. Under the guidance of this method, CZTSSe absorbers with high quality are obtained easily, and the highly efficient CZTSSe solar cell can be fabricated easily. This study provides a feasible and effective strategy to obtain the optimal structure and composition of CZTSSe film toward the production of highly efficient kesterite solar cells, which can also be widely applied to the preparation of other films by solution-based method.

ß-Cyclodextrin based Pickering emulsions for α-tocopherol delivery: Antioxidation stability and bioaccessibility.

ß-Cyclodextrin (ß-CD) Pickering emulsion and cinnamaldehyde/ß-cyclodextrin (CIN/ß-CD) Pickering emulsion were prepared and the influences of oxidation and digestion were investigated. CIN/ß-CD composite was better dispersed at the oil-water interface than ß-CD. Hydrophobic group of CIN anchored in the oil phase and Hydrophilic hydroxyl group of ß-CD extended into the aqueous phase, which allowed CIN/ß-CD composite to be oriented at the oil-water interface and formed a more stable oil-water interface layer. ß-CD Pickering emulsion was more susceptible to oxidative deterioration than CIN/ß-CD Pickering emulsion, its malondialdehyde (MDA) value was as high as 509.41 ± 9.37 nmol/L. Digestion experiment indicated that CIN/ß-CD Pickering emulsion was released inner oil phase in the small intestine and free fatty acid (FFA) release rate was 44.32 ± 1.08%. Pharmacokinetic parameters manifested that α-tocopherol peak concentration (Cmax) was 64.32 ± 6.45 mg/L and the peak time (Tmax) appeared at 5 h after administration of CIN/ß-CD Pickering emulsion.

Radiomics Prediction of Muscle Invasion in Bladder Cancer Using Semi-Automatic Lesion Segmentation of MRI Compared with Manual Segmentation.

Conventional radiomics analysis requires the manual segmentation of lesions, which is time-consuming and subjective. This study aimed to assess the feasibility of predicting muscle invasion in bladder cancer (BCa) with radiomics using a semi-automatic lesion segmentation method on T2-weighted images. Cases of non-muscle-invasive BCa (NMIBC) and muscle-invasive BCa (MIBC) were pathologically identified in a training cohort and in internal and external validation cohorts. For bladder tumor segmentation, a deep learning-based semi-automatic model was constructed, while manual segmentation was performed by a radiologist. Semi-automatic and manual segmentation results were respectively used in radiomics analyses to distinguish NMIBC from MIBC. An equivalence test was used to compare the models' performance. The mean Dice similarity coefficients of the semi-automatic segmentation method were 0.836 and 0.801 in the internal and external validation cohorts, respectively. The area under the receiver operating characteristic curve (AUC) were 1.00 (0.991) and 0.892 (0.894) for the semi-automated model (manual) on the internal and external validation cohort, respectively (both p < 0.05). The average total processing time for semi-automatic segmentation was significantly shorter than that for manual segmentation (35 s vs. 92 s, p < 0.001). The BCa radiomics model based on semi-automatic segmentation method had a similar diagnostic performance as that of manual segmentation, while being less time-consuming and requiring fewer manual interventions.

Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma.

BACKGROUND: The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status. PURPOSE: This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients. STUDY TYPE: Retrospective. POPULATION: 398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40). FIELD STRENGTH/SEQUENCE: 3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP). ASSESSMENT: Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022. STATISTICAL TESTS: Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant. RESULTS: The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months). DATA CONCLUSION: MR deep learning model based on VETC and MVI provides a potential tool for survival assessment. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Short-Chain Fatty-Acid-Producing Micro-Organisms Regulate the Pancreatic FFA2-Akt/PI3K Signaling Pathway in a Diabetic Rat Model Affected by Pumpkin Oligosaccharides.

Herein, we applied the Illumina MiSeq pyrosequencing platform to amplify the V3-V4 hypervariable regions of the 16 S rRNA gene of the gut microbiota (GM) and a gas chromatograph-mass spectrometer to detect the metabolites after supplementation with pumpkin oligosaccharides (POSs) to determine the metabolic markers and mechanisms in rats with type 2 diabetes (T2D). The POSs alleviated glucolipid metabolism by decreasing the serum low-density lipoprotein (LDL), total cholesterol (TC), and glucose levels. These responses were supported by a shift in the gut microbiota, especially in the butyric-acid-producing communities. Meanwhile, elevated total short-chain fatty acid (SCFA), isovaleric acid, and butyric acid levels were observed after supplementation with POSs. Additionally, this work demonstrated that supplementation with POSs could reduce TNF-α and IL-6 secretion via the FFA2-Akt/PI3K pathway in the pancreas. These results suggested that POSs alleviated T2D by changing the SCFA-producing gut microbiota and SCFA receptor pathways.

Performance and mechanism of co-culture of Monascus purpureus, Lacticaseibacillus casei, and Saccharomyces cerevisiae to enhance lovastatin production and lipid-lowering effects.

To facilitate lipid-lowering effects, a lovastatin-producing microbial co-culture system (LPMCS) was constituted with a novel strain Monascus purpureus R5 in combination with Lacticaseibacillus casei S5 and Saccharomyces cerevisiae J7, which increased lovastatin production by 54.21% compared with the single strain R5. Response Surface Methodology (RSM) optimization indicated lovastatin yield peaked at 7.43 mg/g with a fermentation time of 13.88 d, water content of 50.5%, and inoculum ratio of 10.27%. Meanwhile, lovastatin in LPMCS co-fermentation extracts (LFE) was qualitatively and quantitatively analyzed by Thin-Layer Chromatography (TLC) and High-Performance Liquid Chromatography (HPLC). Cellular experiments demonstrated that LFE exhibited no obvious cytotoxicity to L-02 cells and exhibited excellent biosafety. Most notably, high-dose LFE (100 mg/L) exhibited the highest reduction of lipid accumulation, total cholesterol, and triglycerides simultaneously in oleic acid-induced L-02 cells, which decreased by 71.59%, 38.64%, and 58.85% than untreated cells, respectively. Overall, LPMCS provides a potential approach to upgrade the lipid-lowering activity of Monascus-fermented products with higher health-beneficial effects.

Inhibition of autophagy can promote the apoptosis of bladder cancer cells induced by SC66 through the endoplasmic reticulum stress pathway.

Bladder cancer is among the ten most prevalent cancer types worldwide, and its prognosis has not improved significantly in the past three decades because of cognitive limitations in the molecular mechanisms that drive the malignant progression of bladder cancer. Therefore, there is an urgent need to identify new therapeutic drugs or molecular targets to improve the prognosis of patients with bladder cancer. SC66, a novel allosteric inhibitor of AKT, has recently been reported to exert potent anticancer effects on various cancer cells. However, the mechanisms underlying its anticancer effects in bladder cancer remain largely unknown. Consequently, this study aimed to conduct a series of molecular and cellular biology experiments to verify the anticancer effect and potential mechanism of action of SC66 in bladder cancer in vitro. A xenograft tumor model was established to confirm its anticancer role in vivo. Our results showed that SC66 inhibited cell proliferation, triggered mitochondria-mediated apoptosis, and initiated autophagy in bladder cancer cells dose-dependently. In addition, our results suggested that SC66-caused apoptosis and autophagy were endoplasmic reticulum stress-dependent. Interestingly, the activation of autophagy can partially protect bladder cancer cells from apoptosis under endoplasmic reticulum stress induced by SC66 treatment. This study shows that SC66 exerts its anticancer impact on bladder cancer by inhibiting cell proliferation and inducing apoptosis. It also reveals that inhibiting autophagy can increase the cytotoxic effects of SC66 in bladder cancer. Overall, this is the first study on the anticancer effect of SC66 mediated by the endoplasmic reticulum stress pathway and the first report on the AKT-independent anticancer mechanism of SC66 in bladder cancer. Conclusively, exploring the relationship between apoptosis, autophagy, and endoplasmic reticulum stress induced by SC66 indicates that SC66 is a promising novel agent for patients with bladder cancer.