Spectrum mapping technology based creation of a color-contrast reading environment to reach comfort and clarity.

Reading with a bit of yellowish or greenish paper, as compared to white paper, is thought to be more comfortable and friendly, and can help decrease eye fatigue to some degree. In this work, we try to map the light of different colors on a given paper within a region of interest to alter the colors presented by the paper and consequently influence the reading experience. We conducted an ergonomic experiment to study the comfort and clarity under consistent illuminance levels. We adopted 6 color series(red, yellow, green, cyan, blue, and magenta), 5 chroma levels(0, 10, 20, 30, 40), and 4 types of paper with the same hue(yellow) but different lightness(the white, light yellow, yellow, and dark yellow), and conducted pairwise selection experiments within each light color series. Results show that white and low chroma (≈10) color characteristics contribute to comfort, while higher chroma blue(30∼40) color benefits clarity. Referring to white, low chroma greenish and yellowish color characteristics are preferred in terms of comfort and clarity. This work proposes the spectrum mapping technology to endow the paper with new color effects and verifies that although spectrum compositions might differ, people's preferences and comfort perception are consistent with the same object color.

Effect of vitamin D on pregnancy in women with polycystic ovary syndrome: retrospective and prospective studies.

RESEARCH QUESTION: Does vitamin D affect the pregnancy rate of patients with polycystic ovary syndrome (PCOS) receiving ovulation-induction therapy? DESIGN: The retrospective study included 200 patients with PCOS and 200 healthy women. The prospective study included 160 patients with PCOS receiving vitamin D or placebo supplementation. Pregnancy rates were assessed after a maximum of three cycles of ovulation induction. Serum concentrations of 25-hydroxycalciferol [25-(OH)D3], LH, FSH, progesterone, oestradiol, testosterone and fasting insulin; LH/FSH ratio; and body mass index were evaluated. RESULTS: In the retrospective study, patients with PCOS had lower 25-(OH)D3 concentrations than healthy women, pregnant patients with PCOS had higher 25-(OH)D3 concentrations than non-pregnant patients with PCOS (both P = 0.000), and the pregnancy rate was lower in the vitamin-D-deficient group compared with the non-vitamin-D-deficient group (P = 0.022). In the prospective study, compared with placebo supplementation, vitamin D supplementation increased the serum concentration of 25-(OH)D3 (P = 0.000), and reduced the LH/FSH ratio, and concentrations of LH and testosterone significantly (all P ≤ 0.049). After the intervention, it was found that the LH/FSH ratio, and concentrations of LH and testosterone were significantly lower in both groups compared with pre-intervention (P = 0.000). After ovulation induction, the pregnancy rate was higher in patients in the vitamin D supplementation group compared with the placebo supplementation group (P = 0.049). CONCLUSIONS: Vitamin D deficiency is common in patients with PCOS, and vitamin-D-deficient patients with PCOS have lower pregnancy rates after ovulation induction compared with non-vitamin-D-deficient patients with PCOS. Vitamin D supplementation can improve the pregnancy rate and mitigate basic hormone disorders. Therefore, monitoring vitamin D supplementation and checking vitamin D concentrations before and during interventions are essential for patients with PCOS.

A novel model for predicting deep-seated candidiasis due to Candida glabrata among cancer patients: A 6-year study in a cancer center of China.

Followed by Candida albicans, Candida glabrata ranks as the second major species contributing to invasive candidiasis. Given the higher medical burden and lower susceptibility to azoles in C. glabrata infections, identifying these infections is critical. From 2016 to 2021, patients with deep-seated candidiasis due to C. glabrata and non-glabrata Candida met the criteria to be enrolled in the study. Clinical data were randomly divided into training and validation cohorts. A predictive model and nomogram were constructed using R software based on the stepwise algorithm and logistic regression. The performance of the model was assessed by the area under the receiver operating characteristic curve and decision curve analysis (DCA). A total of 197 patients were included in the study, 134 of them infected with non-glabrata Candida and 63 with C. glabrata. The predictive model for C. glabrata infection consisted of gastrointestinal cancer, co-infected with bacteria, diabetes mellitus, and kidney dysfunction. The specificity was 84.1% and the sensitivity was 61.5% in the validation cohort when the cutoff value was set to the same as the training cohort. Based on the model, treatment for patients with a high-risk threshold was better than 'treatment for all' in DCA, while opting low-risk patients out of treatment was also better than 'treatment for none' in opt-out DCA. The predictive model provides a rapid method for judging the probability of infections due to C. glabrata and will be of benefit to clinicians making decisions about therapy strategies.

Trace amounts of palladium catalysed the Suzuki-Miyaura reaction of deactivated and hindered aryl chlorides.

Electron-rich and hindered aryl chlorides are the most challenging substrates in Suzuki-Miyaura cross-coupling (SMC) reactions. Herein, we report a highly efficient catalytic system for the SMC reaction using trace amounts of commercially available catalysts [Pd(PPh3)4/(t-Bu)PCy2; Pd loading as low as 9.5 × 10-5 mol%]. This catalytic system can efficiently couple deactivated and sterically hindered aryl chlorides with various substituted phenylboronic acids, even in one-pot multiple coupling reactions (yield of products up to 92%). The impact of solvents on SMC reactions and the mechanisms of by-product formation in aryl boronic acid couplings are analyzed using density functional theory (DFT). Utilizing trace amounts of commercially available catalysts avoids complex synthesis, reduces costs, and minimizes metal residues.

Enhancing prognostic prediction of invasive candidiasis among cancer patients with a serum C5a-based scoring model.

PURPOSE: Invasive candidiasis poses a life-threatening risk, and early prognosis assessment is vital for timely interventions to reduce mortality. Serum C5a levels have recently been linked to prognosis, but confirmation in cancer patients is pending. METHODS: We detected the concentrations of serum C5a in hospitalized cancer patients with invasive candidiasis from 2020 to 2023, and retrospectively analyzed the clinical data. RESULTS: 372 cases were included in this study, with a 90-day mortality rate of 21.8%. Candida albicans (48.7%) remained the predominant pathogen, followed by Candida glabrata (25.5%), Candida tropicalis (12.4%), and Candida parapsilosis (8.3%). Gastrointestinal cancer was the most diagnosed pathology type (37.6%). Serum C5a demonstrated a noteworthy correlation with 90-day mortality, and employing a cutoff value of 36.7 ng/ml revealed significantly higher 90-day mortality in low-C5a patients (41.2%) compared to high-C5a patients (6.3%) (p < 0.001). We also identified no source control, no surgery, metastasis, or chronic renal failure independently correlated with the 90-day mortality. Based on this, a prognostic model combining C5a and clinical parameters was constructed, which performed better than models built solely on C5a or clinical parameters. Furthermore, we weighted scores to each parameter in the model and presented diagnostic sensitivity and specificity corresponding to different score points calculated by the model. CONCLUSION: We constructed a prognostic scoring model including serum C5a and clinical parameters, which would contribute to precise prognosis assessment and benefit the outcome among cancer patients.

Accurately predicting the risk of unfavorable outcomes after endovascular coil therapy in patients with aneurysmal subarachnoid hemorrhage: an interpretable machine learning model.

BACKGROUND: Despite endovascular coiling as a valid modality in treatment of aneurysmal subarachnoid hemorrhage (aSAH), there is a risk of poor prognosis. However, the clinical utility of previously proposed early prediction tools remains limited. We aimed to develop a clinically generalizable machine learning (ML) models for accurately predicting unfavorable outcomes in aSAH patients after endovascular coiling. METHODS: Functional outcomes at 6 months after endovascular coiling were assessed via the modified Rankin Scale (mRS) and unfavorable outcomes were defined as mRS 3-6. Five ML algorithms (logistic regression, random forest, support vector machine, deep neural network, and extreme gradient boosting) were used for model development. The area under precision-recall curve (AUPRC) and receiver operating characteristic curve (AUROC) was used as main indices of model evaluation. SHapley Additive exPlanations (SHAP) method was applied to interpret the best-performing ML model. RESULTS: A total of 371 patients were eventually included into this study, and 85.4% of them had favorable outcomes. Among the five models, the DNN model had a better performance with AUPRC of 0.645 (AUROC of 0.905). Postoperative GCS score, size of aneurysm, and age were the top three powerful predictors. The further analysis of five random cases presented the good interpretability of the DNN model. CONCLUSION: Interpretable clinical prediction models based on different ML algorithms have been successfully constructed and validated, which would serve as reliable tools in optimizing the treatment decision-making of aSAH. Our DNN model had better performance to predict the unfavorable outcomes at 6 months in aSAH patients compared with Yan's nomogram model.

Targeting Pokemon is a novel strategy to suppress cancer aggressiveness of non-small cell lung cancer: Identification of Pokemon as ideal target for developing anti-NSCLC drugs.

Although it is widely reported that Pokemon acts as an oncogene in the pathogenesis of multiple cancers, but its role and detailed molecular mechanisms in regulating non-small cell lung cancer (NSCLC) progression have not been fully delineated. Here, by performing Real-Time qPCR analysis, we verified that Pokemon was high-expressed in NSCLC tissues and cells, compared to the corresponding normal lung tissues and epithelial cells. Then, the small interfering RNA (siRNA) for Pokemon was transfected into the NSCLC cells to verify its biological functions, and our results suggested that silencing of Pokemon suppressed the malignant phenotypes, including cell viability, mitosis, colony formation, epithelial-mesenchymal transition (EMT), mobility and cancer stem cell (CSC) properties in NSCLC cells. Mechanistically, we confirmed that knockdown of Pokemon decreased the expression levels of phosphorylated Akt (p-Akt), phosphorylated GSK-3ß (p-GSK-3ß) and Snail to inactivate the oncogenic Akt/GSK-3ß/Snail signal pathway, and deletion of Snail also had similar effects to hamper the development of NSCLC. Next, our rescuing experiments validated that Pokemon ablation-induced suppressing effects on NSCLC cell malignancy were all abrogated by overexpressing Snail. Finally, the in vivo experiments confirmed that silencing of Pokemon downregulated Snail to hamper tumorigenesis of NSCLC cells in xenograft tumor-bearing mice models. Taken together, we firstly uncovered the underlying mechanisms by which the Pokemon/Akt/GSK-3ß/Snail signal pathway contributed to the development of NSCLC, and this signal pathway could be potentially used as therapeutic targets for the development of personalized anti-NSCLC drugs.

A visualized MAC nomogram online predicts the risk of three-month mortality in Chinese elderly aneurysmal subarachnoid hemorrhage patients undergoing endovascular coiling.

OBJECTIVE: Aneurysmal subarachnoid hemorrhage (aSAH) is an aggressive disease with higher mortality rate in the elderly population. Unfortunately, the previous models for predicting clinical prognosis are still not accurate enough. Therefore, we aimed to construct and validate a visualized nomogram model to predict online the 3-month mortality in elderly aSAH patients undergoing endovascular coiling. METHOD: We conducted a retrospective analysis of 209 elderly aSAH patients at People's Hospital of Hunan Province, China. A nomogram was developed based on multivariate logistic regression and forward stepwise regression analysis, then validated using the bootstrap validation method (n = 1000). In addition, the performance of the nomogram was evaluated by various indicators to prove its clinical value. RESULT: Morbid pupillary reflex, age, and using a breathing machine were independent predictors of 3-month mortality. The AUC of the nomogram was 0.901 (95% CI: 0.853-0.950), and the Hosmer-Lemeshow goodness-of-fit test showed good calibration of the nomogram (p = 0.4328). Besides, the bootstrap validation method internally validated the nomogram with an area under the curve of the receiver operator characteristic (AUROC) of 0.896 (95% CI: 0.846-0.945). Decision curve analysis (DCA) and clinical impact curve (CIC) indicated the nomogram's excellent clinical utility and applicability. CONCLUSION: An easily applied visualized nomogram model named MAC (morbid pupillary reflex-age-breathing machine) based on three accessible factors has been successfully developed. The MAC nomogram is an accurate and complementary tool to support individualized decision-making and emphasizes that patients with higher risk of mortality may require closer monitoring. Furthermore, a web-based online version of the risk calculator would greatly contribute to the spread of the model in this field.

Oridonin Induces Apoptosis in Esophageal Squamous Cell Carcinoma by Inhibiting Cytoskeletal Protein LASP1 and PDLIM1.

Esophageal squamous cell carcinoma is a severe malignancy for its high mortality and poor prognosis. Mainstay chemotherapies cause serious side effects for their ways of inducing cell death. Oridonin is the main bioactive constituent from natural plants that has anticancer ability and weak side effects. The proteomics method is efficient to understand the anticancer mechanism. However, proteins identified by proteomics aimed at understanding oridonin's anticancer mechanism is seldom overlapped by different groups. This study used proteomics based on two-dimensional electrophoresis sodium dodecyl sulfate-polyacrylamide gel electrophoresis (2-DE SDS-PAGE) integrated with mass spectrometry and Gene Set Enrichment Analysis (GSEA) to understand the anticancer mechanism of oridonin on esophageal squamous cell carcinoma (ESCC). The results showed that oridonin induced ESCC cell death via apoptosis by decreasing the protein expression of LASP1 and PDLIM1.

Interpretable machine learning model to predict rupture of small intracranial aneurysms and facilitate clinical decision.

Estimating whether to treat the rupture risk of small intracranial aneurysms (IAs) with size ≤ 7 mm in diameter is difficult but crucial. We aimed to construct and externally validate a convenient machine learning (ML) model for assessing the rupture risk of small IAs. One thousand four patients with small IAs recruited from two hospitals were included in our retrospective research. The patients at hospital 1 were stratified into training (70%) and internal validation set (30%) randomly, and the patients at hospital 2 were used for external validation. We selected predictive features using the least absolute shrinkage and selection operator (LASSO) method and constructed five ML models applying diverse algorithms including random forest classifier (RFC), categorical boosting (CatBoost), support vector machine (SVM) with linear kernel, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost). The Shapley Additive Explanations (SHAP) analysis provided interpretation for the best ML model. The training, internal, and external validation cohorts included 658, 282, and 64 IAs, respectively. The best performance was presented by SVM as AUC of 0.817 in the internal [95% confidence interval (CI), 0.769-0.866] and 0.893 in the external (95% CI, 0.808-0.979) validation cohorts, which overperformed compared with the PHASES score significantly (all P < 0.001). SHAP analysis showed maximum size, location, and irregular shape were the top three important features to predict rupture. Our SVM model based on readily accessible features presented satisfying ability of discrimination in predicting the rupture IAs with small size. Morphological parameters made important contributions to prediction result.

Development and assessment of machine learning models for predicting recurrence risk after endovascular treatment in patients with intracranial aneurysms.

Intracranial aneurysms (IAs) remain a major public health concern and endovascular treatment (EVT) has become a major tool for managing IAs. However, the recurrence rate of IAs after EVT is relatively high, which may lead to the risk for aneurysm re-rupture and re-bleed. Thus, we aimed to develop and assess prediction models based on machine learning (ML) algorithms to predict recurrence risk among patients with IAs after EVT in 6 months. Patient population included patients with IAs after EVT between January 2016 and August 2019 in Hunan Provincial People's Hospital, and an adaptive synthetic (ADASYN) sampling approach was applied for the entire imbalanced dataset. We developed five ML models and assessed the models. In addition, we used SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. A total of 425 IAs were enrolled into this study, and 66 (15.5%) of which recurred in 6 months. Among the five ML models, gradient boosting decision tree (GBDT) model performed best. The area under curve (AUC) of the GBDT model on the testing set was 0.842 (sensitivity: 81.2%; specificity: 70.4%). Our study firstly demonstrated that ML-based models can serve as a reliable tool for predicting recurrence risk in patients with IAs after EVT in 6 months, and the GBDT model showed the optimal prediction performance.

Detecting pore size distribution of activated carbon by low-field nuclear magnetic resonance.

In this study, the transverse relaxation time (T2 ) of activated carbon (AC) in different relative environment humidity was detected firstly by low-field nuclear magnetic resonance (LFNMR). The pore size (diameter) of AC distributions was calculated by the relationship between T2 and surface relaxation rate (ρ), where ρ was obtained by the detection of nine porous materials with known pore size. The results showed that the pore size distributions of AC calculated by ρ < 0.19 nm/ms were in good agreement with that obtained by nitrogen adsorption method and proved that LFNMR as a new detection method was feasible for characterizing AC pore size distribution.

Financial Fraud Detection and Prediction in Listed Companies Using SMOTE and Machine Learning Algorithms.

This paper proposes a new method that can identify and predict financial fraud among listed companies based on machine learning. We collected 18,060 transactions and 363 indicators of finance, including 362 financial variables and a class variable. Then, we eliminated 9 indicators which were not related to financial fraud and processed the missing values. After that, we extracted 13 indicators from 353 indicators which have a big impact on financial fraud based on multiple feature selection models and the frequency of occurrence of features in all algorithms. Then, we established five single classification models and three ensemble models for the prediction of financial fraud records of listed companies, including LR, RF, XGBOOST, SVM, and DT and ensemble models with a voting classifier. Finally, we chose the optimal single model from five machine learning algorithms and the best ensemble model among all hybrid models. In choosing the model parameter, optimal parameters were selected by using the grid search method and comparing several evaluation metrics of models. The results determined the accuracy of the optimal single model to be in a range from 97% to 99%, and that of the ensemble models as higher than 99%. This shows that the optimal ensemble model performs well and can efficiently predict and detect fraudulent activity of companies. Thus, a hybrid model which combines a logistic regression model with an XGBOOST model is the best among all models. In the future, it will not only be able to predict fraudulent behavior in company management but also reduce the burden of doing so.

Bevacizumab for radiation necrosis following radiotherapy of brain metastatic disease: a systematic review & meta-analysis.

BACKGROUND: Radiotherapy is the mainstay of brain metastasis (BM) management. Radiation necrosis (RN) is a serious complication of radiotherapy. Bevacizumab (BV), an anti-vascular endothelial growth factor monoclonal antibody, has been increasingly used for RN treatment. We systematically reviewed the medical literature for studies reporting the efficacy and safety of bevacizumab for treatment of RN in BM patients. MATERIALS AND METHODS: PubMed, Medline, EMBASE, and Cochrane library were searched with various search keywords such as "bevacizumab" OR "anti-VEGF monoclonal antibody" AND "radiation necrosis" OR "radiation-induced brain necrosis" OR "RN" OR "RBN" AND "Brain metastases" OR "BM" until 1st Aug 2020. Studies reporting the efficacy and safety of BV treatment for BM patients with RN were retrieved. Study selection and data extraction were carried out by independent investigators. Open Meta Analyst software was used as a random effects model for meta-analysis to obtain mean reduction rates. RESULTS: Two prospective, seven retrospective, and three case report studies involving 89 patients with RN treated with BV were included in this systematic review and meta-analysis. In total, 83 (93%) patients had a recorded radiographic response to BV therapy, and six (6.7%) had experienced progressive disease. Seven studies (n = 73) reported mean volume reductions on gadolinium-enhanced T1 (mean: 47.03%, +/- 24.4) and T2-weighted fluid-attenuated inversion recovery (FLAIR) MRI images (mean: 61.9%, +/- 23.3). Pooling together the T1 and T2 MRI reduction rates by random effects model revealed a mean of 48.58 (95% CI: 38.32-58.85) for T1 reduction rate and 62.017 (95% CI: 52.235-71.799) for T2W imaging studies. Eighty-five patients presented with neurological symptoms. After BV treatment, nine (10%) had stable symptoms, 39 (48%) had improved, and 34 (40%) patients had complete resolution of their symptoms. Individual patient data was available for 54 patients. Dexamethasone discontinuation or reduction in dosage was observed in 30 (97%) of 31 patients who had recorded dosage before and after BV treatment. Side effects were mild. CONCLUSIONS: Bevacizumab presents a promising treatment strategy for patients with RN and brain metastatic disease. Radiographic response and clinical improvement was observed without any serious adverse events. Further class I evidence would be required to establish a bevacizumab recommendation in this group of patients.

Design, synthesis and anticancer evaluation of new 4-anilinoquinoline-3-carbonitrile derivatives as dual EGFR/HER2 inhibitors and apoptosis inducers.

Dual targeting of EGFR/HER2 receptor is an attractive strategy for cancer therapy. Four series of 4-anilinoquinoline-3-carbonitrile derivatives were designed and prepared by introducing various functional groups, including a polar hydrophilic group (carboxylic acid), a heterocyclic substituent possessing polarity to some extent, and an unpolar hydrophobic phenyl portion, at the C-6 position of the quinoline skeleton. All of the prepared derivatives were screened for their inhibitory activities against EGFR /HER2 receptors and their antiproliferative activities against the SK-BR-3 and A431 cell lines. Compounds 6a, 6 g and 6d exhibited significant activities against the target cell lines. In particular, the antiproliferative activity of 6d (IC50 = 1.930 µM) against SK-BR-3 was over 2-fold higher than that of neratinib (IC50 = 3.966 µM), and comparable to that of Lapatinib (IC50 = 2.737 µM). On the other hand, 6d (IC50 = 1.893 µM) was more active than the reference drug Neratinib (IC50 = 2.151 µM), and showed comparable potency to Lapatinib (IC50 = 1.285 µM) against A431. Cell cycle analysis and apoptosis assays indicated that 6d arrests the cell cycle in the S phase, and it is a potent apoptotic inducer. Moreover, molecular docking exhibited the binding modes of compound 6d in EGFR and HER2 binding sites, respectively. Compound 6d can be considered as a candidate for further investigation as a more potent anticancer agent.

[Progress in the Treatment of Tachyarrhythmia by Pulsed Electric Field Ablation Catheter Ablation].

Pulsed electric field(PEF) provides high-energy instantaneous pulse and release energy to myocardial cell membrane, resulting in irreversible electroporation and causes myocardial cell contents leakage, destruction of intracellular homeostasis, cell death, and slight inflammatory response. PEF as non-thermal energy promotes the design and application of arrhythmia ablation catheter to enter a new stage. There are currently limited clinical studies that have proved the safety and effectieness of Farawave PEF catheter, PVAC GOLD PEF catheter, Lattice-tip Sphere-9 PEF and radiofrequency (RF) catheter used for atrial fibrillation ablation, but still need further discussion. The research of atrial fibrillation ablation with PEF is under study in China. In this paper, the design and application of PEF ablation for tachyarrhythmia are reviewed.

Can Prognostic Nutritional Index be a Prediction Factor in Esophageal Cancer?: A Meta-Analysis.

Objective: Prognostic nutritional index (PNI) can be used for survival predication in patients with esophageal cancer (EC). However, the prognostic value of PNI in EC is inconclusive in accordance to the literature. This meta-analysis aimed to evaluate the prediction value of PNI in EC.Methods: Studies focus on the association of PNI and EC were retrieved from the electronic databases. Standard meta-analysis methods were used for data evaluation.Results: Our search yield 12 studies, involving 3118 patients with EC for data analysis. The pooled data suggested that low PNI was correlated with worse overall survival (hazard ratio (HR) = 1.29, 95% confidence interval (CI):1.11-1.50, P = 0.001) and cancer-specific survival (HR = 2.18, 95% CI: 1.68-2.83, P < 0.0001). Moreover, lower PNI was associated with unfavorable prognostic factors (the presence of lymph node metastasis, deeper tumor invasion and advanced TNM stages).Conclusion: The lower PNI was correlated with unfavorable prognostic factor and poor prognosis in patients with EC.

Characteristics of Circular RNA Expression Profiles of Porcine Granulosa Cells in Healthy and Atretic Antral Follicles.

Circular RNAs (circRNAs) are thought to play essential roles in multiple biological processes, including apoptosis, an important process in antral follicle atresia. We aimed to investigate the potential involvement of circRNAs in granulosa cell apoptosis and thus antral follicle atresia. CircRNA expression profiles were generated from porcine granulosa cells isolated from healthy antral (HA) and atretic antral (AA) follicles. Over 9632 circRNAs were identified, of which 62 circRNAs were differentially expressed (DE-circRNAs). Back-splicing, RNase R resistance, and stability of DE-circRNAs were validated, and miRNA binding sites and related target genes were predicted. Two exonic circRNAs with low false discovery rate (FDR) high fold change, miRNA binding sites, and relevant biological functions-circ_CBFA2T2 and circ_KIF16B-were selected for further characterization. qRT-PCR and linear regression analysis confirmed expression and correlation of the targeted genes-the antioxidant gene GCLC (potential target of circ_CBFA2T2) and the apoptotic gene TP53 (potential target of circ_KIF16B). Increased mRNA content of TP53 in granulosa cells of AA follicles was further confirmed by strong immunostaining of both p53 and its downstream target pleckstrin homology like domain family a member 3 (PHLDA3) in AA follicles compared to negligible staining in granulosa cells of HA follicles. Therefore, we concluded that aberrantly expressed circRNAs presumably play a potential role in antral follicular atresia.

Perilaldehyde activates AMP-activated protein kinase to suppress the growth of gastric cancer via induction of autophagy.

BACKGROUND AND AIM: Perillaldehyde (PAH), one of the major oil components in Perilla frutescens, is very critical to health maintenance, for a wide range of human chronic diseases, including cancers. AMP-activated protein kinase (AMPK) has been implicated in the activation of autophagy in distinct tissues. This study was designed to explore whether PAH prevents gastric cancer growth and to investigate the molecular mechanism. METHODS AND RESULTS: In cultured mouse gastric cancer cell line MFCs and human gastric cancer cell lines GC9811-P, PAH activated AMPK by increasing the Thr172 phosphorylation and activity in a time-/concentration-dependent manner. Furthermore, incubation of MFCs with PAH also increased autophagy as determined by monodansylcadaverine (MDC) staining, which was reversed by AMPK inhibitor compound C. PAH further decreased MFCs cell survival, which was abolished by compound C or autophagy inhibitor 3-Methyladenine (3-MA). In vivo studies indicated that 4-week administration of PAH (100 mg/kg/day) suppressed the growth of gastric cancer and increased the levels of autophagy-related proteins, including beclin-1, LC3-II, cathepsin, caspase-3, p53, and cathepsin in tumors isolated from the xenograft model of gastric cancer in mice. Moreover, these anticancer effects produced by PAH were abolished by coadministration of compound C or 3-MA in vivo. CONCLUSIONS: PAH increases AMPK phosphorylation and activity to induce gastric cancer cell autophagy to inhibit the growth of gastric cancer. In perspective, therapy of PAH should be applied to treat patients with gastric cancer.

Comparative analysis of robotic gastrectomy and laparoscopic gastrectomy for gastric cancer in terms of their long-term oncological outcomes: a meta-analysis of 3410 gastric cancer patients.

BACKGROUND: Data regarding the long-term oncological outcomes of robotic gastrectomy (RG) are limited despite the increased commonality of this method as an alternative for gastric cancer treatment. Here, we conducted a meta-analysis to evaluate the long-term oncological outcomes of RG in comparison to that of laparoscopic gastrectomy (LG). METHODS: The PubMed, ISI Web of Science, EMBASE, and Cochrane Library databases were comprehensively searched for studies that compared RG and LG in terms of their long-term survival outcomes. The hazard ratios (HRs) of overall survival (OS), disease-free survival (DFS), and relapse-free survival (RFS) were obtained, while the odds ratio (OR) was recorded for the recurrence rate. A sensitivity analysis was performed. Egger's test and Begg's test were applied to evaluate publication bias. RESULTS: Eight studies were identified and involved 3410 gastric cancer patients (RG, 1009; LG, 2401). The two groups had no significant differences in OS (HR, 0.98; 95% CI, 0.80-1.20; P = 0.81), DFS (HR, 1.36; 95% CI, 0.33-5.59; P = 0.67), RFS (HR, 0.92; 95% CI, 0.72-1.19; P = 0.53), or recurrence rate (OR, 0.92; 95% CI, 0.71-1.19; P = 0.53). Moreover, the two techniques were comparable in length of hospital stay (LOS), postoperative complication rate, 30-day mortality rate, and rate of conversion to open surgery. CONCLUSIONS: The long-term oncological outcomes, expressed as OS, DFS, RFS, and recurrence rate, were similar between RG and LG. However, more randomized controlled trials with rigorous study designs and patient cohorts are needed to evaluate the oncologic outcomes of RG in patients with gastric cancer.