Circular RNA circEZH2 Promotes Lung Adenocarcinoma Progression by Regulating microRNA-495-3p/Tumor Protein D52 Axis and Activating Nuclear Factor-Kappa B Pathway.

Background: It has been increasingly recognized that circular RNAs (circRNAs) act as a pivotal factor in the onset and progression of human malignancies. Yet, the specific activities and mechanistic roles of these RNAs in the context of lung adenocarcinoma (LUAD) are not fully understood. Methods: Microarray analysis identified a novel LUAD-associated circular RNA, termed hsa_circ_0006357 (also referred to as circEZH2). Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was utilized for the analysis of circEZH2 expression in tissues and cell lines. The characteristics of circEZH2 were verified by RNase R treatment and fluorescence in situ hybridization (FISH) assays. The functions of circEZH2 were detected by Cell Counting Kit-8 (CCK-8), colony formation, wound healing, and Transwell assays. The molecular mechanism of circEZH2 was clarified through bioinformatics analysis as well as RNA pulldown, dual-luciferase reporter, RT-qPCR, and immunoblotting assays. The role of circEZH2 in vivo was investigated using a xenograft model. Results: This investigation revealed that circEZH2 expression was elevated in LUAD cell lines and tumor samples. This elevation was associated with enhanced cell proliferation, migratory capacity, epithelial-mesenchymal transition (EMT), and invasion in vitro. Conversely, silencing of circEZH2 in vivo resulted in a notable decrease in LUAD tumorigenesis, whereas its overexpression led to the opposite effects. Mechanistically, circEZH2 appeared to act as a sponge for miR-495-3p, facilitating the upregulation of tumor protein D52 (TPD52) and triggering the nuclear factor kappa B (NF-κB) signaling pathway, thus contributing to the progression of LUAD. Conclusion: These findings indicate that circEZH2 may function as a competitive endogenous RNA (ceRNA), driving the progression of LUAD by manipulating the miR-495-3p/TPD52 axis and activating the NF-κB pathway.

Research progress of optic nerve imaging during 1991-2023: a bibliometric analysis.

Background: Optic nerve imaging is crucial for diagnosing and understanding optic neuropathies because it provides detailed visualization of the nerve's structure and pathologies through advanced modalities. This study conducted a bibliometric analysis within the field of optic nerve imaging, aiming to pinpoint the latest research trends and focal points in optic nerve imaging. Methods: The core literature on optic nerve imaging published between January 1991 and August 2023 was retrieved from the Web of Science Core Collection. The analysis and visualization of scientific productivity and emerging trends were facilitated through the utilization of Bibliometrix software, CiteSpace, Gephi, VOSviewer, R software, and Python. Results: In total, 15,247 publications on optic nerve imaging were included in the analysis. Notably, the top 3 journals contributing to this field were Investigative Ophthalmology & Visual Science, Ophthalmology, and the British Journal of Ophthalmology. This research on optic nerve imaging extended across 97 countries, with the USA leading in research endeavors. Noteworthy burst term analysis revealed that "Segmentation" and "Machine learning" are gaining attention. Additionally, the Latent Dirichlet Allocation model indicated that image processing has been a hotspot in recent years. Conclusions: This study revealed the research trends, hotspots, and emerging topics in optic nerve imaging through bibliometric analysis and network visualization. At present, the research focus is directed towards employing artificial intelligence for image post-processing. The findings of this study offer valuable insights into future research direction and clinical applications.

Accelerated proton dissociation in an excited state induces superacidic microenvironments around graphene quantum dots.

Investigating proton transport at the interface in an excited state facilitates the mechanistic investigation and utilization of nanomaterials. However, there is a lack of suitable tools for in-situ and interfacial analysis. Here we addresses this gap by in-situ observing the proton transport of graphene quantum dots (GQDs) in an excited state through reduction of magnetic resonance relaxation time. Experimental results, utilizing 0.1 mT ultra-low-field nuclear magnetic resonance relaxometry compatible with a light source, reveal the light-induced proton dissociation and acidity of GQDs' microenvironment in the excited state (Hammett acidity function: -13.40). Theoretical calculations demonstrate significant acidity enhancement in -OH functionalized GQDs with light induction ( p K a * = -4.62, stronger than that of H2SO4). Simulations highlight the contributions of edge and phenolic -OH groups to proton dissociation. The light-induced superacidic microenvironment of GQDs benefits functionalization and improves the catalytic performances of GQDs. Importantly, this work advances the understanding of interfacial properties of light-induced sp2-sp3 carbon nanostructure and provides a valuable tool for exploring catalyst interfaces in photocatalysis.

Expression profiles of serum transfer RNA-derived fragments and their potential roles in non-small cell lung cancer.

Background: Non-small cell lung cancer (NSCLC) is one of the malignant tumors with the highest morbidity and mortality in the world. Early diagnosis can significantly improve the prognosis of patients. Transfer RNA (tRNA)-derived fragments (tRFs) have been found to have a crucial function in the pathophysiology of cancers. However, the role of tRFs/tRNA halves (tiRNAs) in NSCLC is yet unknown. The present study aimed to investigate unique expression profiles of tRFs/tiRNAs in NSCLC and search novel biomarkers for the diagnosis. Methods: RNA-sequencing was utilized for determining differently expressed tRFs/tiRNAs in serum in NSCLC and healthy controls. Stem-loop quantitative polymerase chain reaction (PCR) was used to confirm the selected tRFs/tiRNAs expressions. Their possible roles in NSCLC were predicted using bioinformatic research. Results: Eleven up-regulated tRFs/tiRNAs and 18 down-regulated tRFs/tiRNAs were determined. Levels of tRF-31-87R8WP9N1EWJ0 and tRF-31-79MP9P9NH57SD were significantly higher in NSCLC serum samples than those of healthy controls; the receiver operating characteristic (ROC) curve suggested that they could serve as new diagnostic biomarkers. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis hinted that tRF-31-87R8WP9N1EWJ0 and tRF-31-79MP9P9NH57SD might influence the development and manifestation of NSCLC. Conclusions: In NSCLC patients' serum, the tRFs/tiRNAs were abnormally regulated and that tRF-31-87R8WP9N1EWJ0 and tRF-31-79MP9P9NH57SD might be the potential biological markers for NSCLC.

Optic nerve compression associated with visual cortex functional alteration in dysthyroid optic neuropathy: A combined orbital and brain imaging study.

AIMS: To investigate the alterations of the optic nerve and visual cortex in dysthyroid optic neuropathy (DON), a subgroup of thyroid eye disease (TED). METHODS: Multiple orbital imaging biomarkers related to optic nerve compression and the amplitude of low-frequency fluctuations (ALFF) of the brain were obtained from 47 patients with DON, 56 TED patients without DON (nDON), and 37 healthy controls (HC). Correlation analyses and diagnostic tests were implemented. RESULTS: Compared with HC, the nDON group showed alterations in orbital imaging biomarkers related to optic nerve compression in posterior segments, as well as ALFF of the right inferior temporal gyrus and left fusiform gyrus. DON differed from nDON group mainly in the modified muscle index of the posterior segment of optic nerve, and ALFF of orbital part of right superior frontal gyrus, right hippocampus, and right superior temporal gyrus. Orbital and brain imaging biomarkers were significantly correlated with each other. Diagnostic models attained an area under a curve of 0.80 for the detection of DON. CONCLUSION: The combined orbital and brain imaging study revealed alterations of the visual pathway in patients with TED and DON as well as provided diagnostic value. The initiation of alterations in the visual cortex in TED may precede the onset of DON.

Understanding the K+/Na+-Selectivity-Enabled Osmotic Power Generation: High Selectivity May Not Be Indispensable.

By mixing ionic solutions, considerable energy can be harvested from entropy change. Recently, we proposed a concept of potassium-permselectivity enabled osmotic power generation (PoPee-OPG) by mixing equimolar KCl and NaCl solutions via artificial potassium ion channels (APICs, Natl. Sci. Rev. 2023, 10, nwad260). However, a fundamental understanding of the relationship between the K+/Na+ selectivity and optimal performance remains unexplored. Herein, we establish a primitive molecular thermodynamic model to investigate the energy extraction process. We find PoPee-OPG differs from previous charge-selectivity-based techniques, such as the salinity gradient power generation, in two distinct ways. First, the extractable energy density and efficiency positively depend on concentration. More surprisingly, a very high potassium selectivity is not indispensable for satisfactory efficiency and energy density. An optimal K+/Na+ selectivity region of 3 to 10 is found. This somewhat counterintuitive discovery provides a renewed understanding of the emerging PoPee-OPG, and it predicts a broad applicability among existing APICs.

Radiomics in ophthalmology: a systematic review.

BACKGROUND: Radiomics holds great potential in medical image analysis for various ophthalmic diseases. In recent times, there have been numerous endeavors in this area of research. This systematic review aims to provide a comprehensive assessment of the strengths and limitations of radiomics in ophthalmology. METHOD: Conforming to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, we conducted a systematic review with a pre-registered protocol (PROSPERO: CRD42023446317). We explored the PubMed, Embase, and Cochrane databases for original studies on this topic and made a comprehensive descriptive integration. Furthermore, the included studies underwent quality assessment by the radiomics quality score (RQS). RESULTS: A total of 41 articles from an initial search of 227 studies were finally selected for further analysis. These articles included research across five disease categories and covered seven imaging modalities. The radiomics models demonstrated robust performance, with area under the curve (AUC) values mostly falling within 0.7-1.0. The moderate RQS (mean score: 11.17/36) indicated that most studies were retrospectively, single-center analyses without external validation. CONCLUSIONS: Radiomics holds promising utility in the field of ophthalmology, assisting diagnosis, early-stage screening, and prognostication of treatment response. Artificial intelligence algorithms significantly contribute to the construction of radiomics models in ophthalmology. This study highlights the strengths and challenges of radiomics in ophthalmology and suggests potential avenues for future improvement. CLINICAL RELEVANCE STATEMENT: Radiomics represents a valuable approach for generating innovative imaging markers, enhancing efficiency in clinical diagnosis and treatment, and aiding decision-making in clinical contexts of many ophthalmic diseases, thereby improving overall patient prognosis. KEY POINTS: Radiomics has attracted extensive attention in the field of ophthalmology. Articles included five disease categories over seven imaging modalities, consistently yielding AUCs mostly above 0.7. Current research has few prospective and multi-center studies, underlining the necessity for future high-quality studies.

CT-based artificial intelligence prediction model for ocular motility score of thyroid eye disease.

PURPOSE: Thyroid eye disease (TED) is the most common orbital disease in adults. Ocular motility restriction is the primary complaint of patients, while its evaluation is quite difficult. The present study aimed to introduce an artificial intelligence (AI) model based on orbital computed tomography (CT) images for ocular motility score. METHODS: A total of 410 sets of CT images and clinical data were obtained from the hospital. To build a triple classification predictive model for ocular motility score, multiple deep learning models were employed to extract features of images and clinical data. Subgroup analyses based on pertinent clinical features were performed to test the efficacy of models. RESULTS: The ResNet-34 network outperformed Alex-Net and VGG16-Net in prediction of ocular motility score, with the optimal accuracy (ACC) of 0.907, 0.870, and 0.890, respectively. Subgroup analyses indicated no significant difference in ACC between active or inactive phase, functional visual field diplopia or peripheral visual field diplopia (p > 0.05). However, in the gender subgroup, the prediction model performed more accurately in female patients than males (p = 0.02). CONCLUSION: In conclusion, the AI model based on CT images and clinical data successfully realized automatic scoring of ocular motility in TED patients. This approach potentially enhanced the efficiency and accuracy of ocular motility evaluation, thus facilitating clinical application.

NIR-Driven Self-Healing Phase-Change Solid Slippery Surface with Stability and Promising Antifouling and Anticorrosion Properties.

Slippery liquid-infused porous surfaces (SLIPSs) have great potential to replace traditional antifouling coatings due to their efficient, green, and broad-spectrum antifouling performance. However, the lubricant dissipation problem of SLIPS severely restricts its further development and application, and the robust SLIPS continues to be extremely challenging. Here, a composite phase-change lubricant layer consisting of paraffin, silicone oil, and MXene is designed to readily construct a stable and NIR-responsive self-healing phase-change solid slippery surface (PCSSS). Collective results showed that PCSSS could rapidly achieve phase-change transformation and complete self-healing under NIR irradiation and keep stable after high-speed water flushing, centrifugation, and ultrasonic treatment. The antifouling performance of PCSSS evaluated by protein, bacteria, and algae antiadhesion tests demonstrated the adhesion inhibition rate was as high as 99.99%. Moreover, the EIS and potentiodynamic polarization experiments indicated that PCSSS had stable and exceptional corrosion resistance (|Z|0.01Hz = 3.87 × 108 Ω·cm2) and could effectively inhibit microbiologically influenced corrosion. The 90 day actual marine test reveals that PCSSS has remarkable antifouling performance. Therefore, PCSSS presents a novel, facile, and effective strategy to construct a slippery surface with the prospect of facilitating its application in marine antifouling and corrosion protection.

Immune checkpoint activity exacerbate renal interstitial fibrosis progression by enhancing PD-L1 expression in renal tubular epithelial cells.

Renal interstitial fibrosis (RIF) is often associated with inflammatory cell infiltration and no effective therapy. Programmed death cell-1 (PD-1) and its ligand PD-L1 were playing critical roles in T cell coinhibition and exhaustion, but the role in RIF is unclear. Here the data analyses of serum from 122 IgA nephrology (IgAN) patients showed that high level of soluble PD-1(sPD-1) was an independent risk factor for RIF and renal function progression. PD-L1 was also overexpressed in renal interstitial tissues from both IgAN patients with high level of sPD-1 and the unilateral ureteral obstruction (UUO) mouse. PD-L1 was significantly overexpressed in HK-2 cells with upregulated collagen and α-SMA when stimulated by inflammation or hypoxia in vitro. Additionally, matrix metalloproteinases (MMP-2) could increase the level of sPD-1 in culture supernatant when added in co-culture system of HK-2 and jurkat cells, which implied serum sPD-1 of IgAN might be cleaved by MMP-2 from T cells infiltrated into the tubulointerstitial inflammatory microenvironment. Crucially, injection of PD-L1 fusion protein, the blocker of sPD-1, could ameliorate kidney fibrosis in UUO mice by increasing T cell coinhibition and exhaustion, suggesting the therapeutic potential of PD-L1 fusion targeting for renal fibrosis. Take together, it reveals a novel causal role of sPD-1 in serum and PD-L1 of renal interstitial tissues in the development of renal fibrosis of IgAN, and targeting sPD-1 in serum by PD-L1 fusion protein is a potential therapeutic approach to prevent renal fibrosis of IgAN.

MRI-based radiomics feature combined with tumor markers to predict TN staging of rectal cancer.

The aim of this study is to evaluate the predictive ability of MRI-based radiomics combined with tumor markers for TN staging in patients with rectal cancer and to develop a prediction model for TN staging. A total of 190 patients with rectal adenocarcinoma who underwent total mesorectal excision at the First Affiliated Hospital of the Air Force Medical University between January 2016 and December 2020 were included in the study. An additional 54 patients from a prospective validation cohort were included between August 2022 and August 2023. Preoperative tumor markers and MRI imaging data were collected from all enrolled patients. The 190 patients were divided into a training cohort (n = 133) and a validation cohort (n = 57). Radiomics features were extracted by outlining the region of interest (ROI) on T2WI sequence images. Feature selection and radiomics score (Rad-score) construction were performed using least absolute shrinkage and selection operator regression analysis (LASSO). The postoperative pathology TNM stage was used to differentiate locally advanced rectal cancer (T3/4 or N1/2) from locally early rectal cancer (T1/2, N0). Logistic regression was used to construct separate prediction models for T stage and N stage. The models' predictive performance was evaluated using DCA curves and calibration curves. The T staging model showed that Rad-score, based on 8 radiomics features, was an independent predictor of T staging. When combined with CEA, tumor diameter, mesoretal fascia (MRF), and extramural venous invasion (EMVI), it effectively differentiated between T1/2 and T3/4 stage rectal cancers in the training cohort (AUC 0.87 [95% CI: 0.81-0.93]). The N-staging model found that Rad-score, based on 10 radiomics features, was an independent predictor of N-staging. When combined with CA19.9, degree of differentiation, and EMVI, it effectively differentiated between N0 and N1/2 stage rectal cancers. The training cohort had an AUC of 0.84 (95% CI: 0.77-0.91). The calibration curves demonstrated good precision between the predicted and actual results. The DCA curves indicated that both sets of predictive models could provide net clinical benefits for diagnosis. MRI-based radiomics features are independent predictors of T staging and N staging. When combined with tumor markers, they have good predictive efficacy for TN staging of rectal cancer.

Exploring the optimal indicator of short-term peridiagnosis weight dynamics to predict cancer survival: A multicentre cohort study.

BACKGROUND: Body weight and its changes have been associated with cancer outcomes. However, the associations of short-term peridiagnosis weight dynamics in standardized, clinically operational time frames with cancer survival remain largely unknown. This study aimed to screen for and evaluate the optimal indicator of short-term peridiagnosis weight dynamics to predict overall survival (OS) in patients with cancer. METHODS: This multicentre cohort study prospectively collected data from 7460 patients pathologically diagnosed with cancer between 2013 and 2019. Body weight data were recorded 1 month before, at the time of and 1 month following diagnosis. By permuting different types (point value in kg, point height-adjusted value in kg/m2, absolute change in kg or relative change in percentage) and time frames (prediagnosis, postdiagnosis or peridiagnosis), we generated 12 different weight-related indicators and compared their prognostic performance using Harrell's C-index, integrated discrimination improvement, continuous net reclassification improvement and time-dependent C-index. We analysed associations of peridiagnosis relative weight change (RWC) with OS using restricted cubic spine (RCS), Kaplan-Meier analysis and multivariable-adjusted Cox regression models. RESULTS: The study enrolled 5012 males and 2448 females, with a median age of 59 years. During a median follow-up of 37 months, 1026 deaths occurred. Peridiagnosis (1 month before diagnosis to 1 month following diagnosis) RWC showed higher prognostic performance (Harrell's C-index = 0.601, 95% confidence interval [CI] = [0.583, 0.619]) than other types of indicators including body mass index (BMI), absolute weight change, absolute BMI change, prediagnosis RWC and postdiagnosis RWC in the study population (all P < 0.05). Time-dependent C-index analysis also indicated that peridiagnosis RWC was optimal for predicting OS. The multivariable-adjusted RCS analysis revealed an N-shaped non-linear association between peridiagnosis RWC and OS (PRWC < 0.001, Pnon-linear < 0.001). Univariate survival analysis showed that the peridiagnosis RWC groups could represent distinct mortality risk stratifications (P < 0.001). Multivariable survival analysis showed that, compared with the maintenance group (weight change < 5%), the significant (gain >10%, hazard ratio [HR] = 0.530, 95% CI = [0.413, 0.680]) and moderate (gain 5-10%, HR = 0.588, 95% CI = [0.422, 0.819]) weight gain groups were both associated with improved OS. In contrast, the moderate (loss 5-10%, HR = 1.219, 95% CI = [1.029, 1.443]) and significant (loss >10%, HR = 1.280, 95% CI = [1.095, 1.497]) weight loss groups were both associated with poorer OS. CONCLUSIONS: The prognostic performance of peridiagnosis RWC is superior to other weight-related indicators in patients with cancer. The findings underscore the importance of expanding the surveillance of body weight from at diagnosis to both past and future, and conducting it within clinically operational time frames, in order to identify and intervene with patients who are at risk of weight change-related premature deaths.

Platelet-lymphocyte ratio as a predictor of lymph node metastasis in small bowel cancer.

The purpose of this research was to investigate the potential predictive value of preoperative systemic inflammatory indexes in identifying lymph node metastasis among patients diagnosed with small bowel cancer. A retrospective analysis of clinical data was conducted on small bowel cancer patients who underwent surgical treatment at the gastrointestinal surgery department of our hospital between January 2010 and June 2021. Patients were divided into groups based on the presence or absence of lymph node metastasis as confirmed by postoperative pathological results. The study compared the differences in preoperative inflammatory indexes and clinical data between the two groups using single factor analysis and multifactorial Logistic regression analysis. Furthermore, a nomogram model for predicting lymph node metastasis in colorectal cancer was constructed using R software and internally validated. The study sample consisted of 140 small bowel cancer patients,postoperative pathology confirmed lymph node metastasis in 72 cases. Univariate analysis results indicated associations between preoperative inflammatory indexes and clinical data with lymph node metastasis in small bowel cancer. Multifactorial logistic regression analysis revealed that gender, PLR, number of lymph node dissection, and lymphovascular invasion independently influenced lymph node metastasis in small bowel cancer patients. The developed nomogram model demonstrated a C-index of 0.855 (95% CI 0.792-0.917), with a calibrated prediction curve closely resembling the ideal curve. An elevated PLR is an independent risk factor for LNM in patients with small bowel cancer.

Severe and continuous immunoparesis during induction or maintenance therapy in nontransplant patients with multiple myeloma is a sign of poor prognosis.

OBJECTIVE: Multiple myeloma (MM) varies in clinical behavior, response to treatment and prognosis due to the heterogeneity of the disease. Data on the association between the immunoparesis status during treatment and prognosis in nontransplant MM patients are limited. METHODS: In a retrospective analysis of 142 patients with MM, we examined the relationship between immunoparesis status and prognosis during treatment. All patients received novel agent-based therapy and did not undergo autologous stem cell transplantation. One, two, or three uninvolved immunoglobulins (Igs) below the lowest thresholds of normalcy were used to identify immunoparesis. RESULTS: Patients with a greater degree of immunoparesis during treatment had shorter progression-free survival (PFS) and overall survival (OS). A total of 46.5% of the patients had severe and continuous immunoparesis (at least two uninvolved Igs suppressed continuously during treatment), representing a worse prognosis than those with complete or partial normalization of Igs during treatment. Among patients who achieved at least complete remission, PFS was poor in patients with severe and continuous immunoparesis. Furthermore, severe and continuous immunoparesis during treatment was a poor prognostic factor for PFS and OS according to multivariate analyses. CONCLUSION: The degree of immunoparesis during treatment is a follow-up indicator for survival in nontransplant myeloma patients, and severe and continuous immunoparesis in nontransplant myeloma patients might be a sign of poor prognosis.

Integrated neutrophil-to-lymphocyte ratio and handgrip strength better predict survival in patients with cancer cachexia.

OBJECTIVES: Systemic inflammation and skeletal muscle strength play crucial roles in the development and progression of cancer cachexia. In this study we aimed to evaluate the combined prognostic value of neutrophil-to-lymphocyte ratio (NLR) and handgrip strength (HGS) for survival in patients with cancer cachexia. METHODS: This multicenter cohort study involved 1826 patients with cancer cachexia. The NLR-HGS (NH) index was defined as the ratio of neutrophil-to-lymphocyte ratio to handgrip strength. Harrell's C index and receiver operating characteristic (ROC) curve analysis were used to assess the prognosis of NH. Kaplan-Meier analysis and Cox regression models were used to evaluate the association of NH with all-cause mortality. RESULTS: Based on the optimal stratification, 380 women (NH > 0.14) and 249 men (NH > 0.19) were classified as having high NH. NH has shown greater predictive value compared to other indicators in predicting the survival of patients with cancer cachexia according to the 1-, 3-, and 5-y ROC analysis and Harrell's C index calculation. Multivariate survival analysis showed that higher NH was independently associated with an increased risk of death (hazard ratio = 1.654, 95% confidence interval = 1.389-1.969). CONCLUSION: This study demonstrates that the NH index, in combination with NLR and HGS, is an effective predictor of the prognosis of patients with cancer cachexia. It can offer effective prognosis stratification and guidance for their treatment.

Development and validation of serological dynamic risk score to predict outcome in gastric cancer with adjuvant chemotherapy: a multicentre, longitudinal, cohort study.

Background: Baseline serological biomarkers have the potential to predict the benefits of adjuvant chemotherapy in patients with gastric cancer. However, the fluctuating nature of postoperative recurrence risk makes precise treatment challenging. We aimed to develop a risk score in real-time predicting outcomes for postoperative GC patients using blood chemistry tests. Materials and methods: This was a retrospective, multicentre, longitudinal cohort study from three cancer centres in China, with a total of 2737 GC patients in the pTNM stage Ib to III. Among them, 1651 patients with at least two serological records were assigned to the training cohort. Model validation was carried out using separate testing data with area under curve (AUC). The least absolute shrinkage and selection operator (LASSO) and random forest-recursive feature elimination (RF-RFE) algorithm were used to select the parameters. Results: The Cox regression model derived six risk factors to construct a composite score (low-risk: 0-2 score; high risk: 3-6 score), including CEA, CA125, CA199, haemoglobin, albumin, and neutrophil to lymphocyte ratio. The risk score accurately predicted mortality in 1000-time bootstrap (AUROCs:0.658; 95% CI: 0.645, 0.670), with the highest AUROC (0.767; 95% CI: 0.743, 0.791) after 1 year since the gastrectomy. In validation dataset, the risk score had an AUROC of 0.586 (95% CI 0.544, 0.628). Furthermore, patients with high risk at 1 month derived significant clinical benefits from adjuvant chemotherapy (P for interaction <0.0001). Compared with the low-low-low risk group, the low-low-high risk group of the long-term state chain (risk state at baseline, 6 months, 1 year) had the worse OS (HR, 6.91; 95%CI: 4.27, 11.19) and DFS (HR, 7.27; 95%CI: 4.55, 11.63). Conclusion: The dynamic risk score is an accurate and user-friendly serological risk assessment tool for predicting outcomes and assisting clinical decisions after gastrectomy.

The inflammation score predicts the prognosis of gastric cancer patients undergoing Da Vinci robot surgery.

Neutrophil-to-lymphocyte ratio (NLR), calculated from peripheral blood immune-inflammatory cell counts, is considered a predictor of survival in various cancers. Nevertheless, there is a lack of research into the predictive value of NLR specifically in gastric cancer patients following surgery using the Da Vinci robot. Investigate the objectives of this research, confirm the positive predictive value of NLR in the prognosis of gastric cancer patients undergoing Da Vinci robotic-assisted surgery by comparing its prognostic ability with other inflammation markers and tumor biomarkers. In this retrospective analysis, information from 128 individuals diagnosed with gastric cancer and treated with da Vinci robot-assisted surgery was examined. The study examined various markers in the peripheral blood, including neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), lymphocyte/monocyte ratio (LMR), systemic immune-inflammatory index (SII) prognostic nutrition index (PNI), cancer antigen 125 (CA125), carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 72-4 (CA72-4), carcinoembryonic antigen (CEA) and alpha-fetoprotein (AFP).To ascertain the prognostic ability and optimal cutoff values of each parameter, operating characteristic curves and the area under the curve were utilized in the analysis. For evaluation of independent prognostic factors, we utilized Kaplan-Meier curves and multifactorial Cox analysis. The variables from the multifactorial Cox analysis were used to construct a nomogram. NLR, LMR, CEA, AFP, primary location, largest tumor size and TNM stage were all found to be significant predictive elements for overall survival (OS). Multivariate Cox identified NLR (P = 0.005), LMR (P = 0.03) and AFP (P = 0.007) as the only separate predictive variables among hematological indicators. The nomogram built using NLR demonstrates excellent predictive performance at 1 year (AUC = 0.778), 3 years (AUC = 0.773), and 5 years (AUC = 0.781). Cross-validation demonstrates that this model has favorable predictive performance and discriminative ability. NLR is an uncomplicated yet potent marker for forecasting the survival result of individuals with gastric cancer following da Vinci robotic surgery, and it possesses considerable predictive significance. The nomogram based on NLR provides patients with a visual and accurate prognosis prediction.

A comparison of keratoconus progression following collagen cross-linkage using standard or personalised keratometry thresholds.

OBJECTIVE: To define how estimates of keratoconus progression following collagen cross-linking (CXL) vary according to the parameter selected to measure corneal shape. MATERIALS AND METHODS: We estimated progression following CXL in 1677 eyes. We compared standard definitions of keratoconus progression based on published thresholds for Kmax, front K2, or back K2, or progression of any two of these three parameters, with the option of an increased threshold for Kmax values ≥ 55D. As corneal thickness reduces unpredictably after CXL, it was excluded from the principal analysis. We then repeated the analysis using novel adaptive estimates of progression for Kmax, front K2, or back K2, developed separately using 6463 paired readings from keratoconus eyes, with a variation of the Bland-Altman method to determine the 95% regression-based limits of agreement (LoA). We created Kaplan-Meier survival plots for both standard and adaptive thresholds. The primary outcome was progression five years after a baseline visit 9-15 months following CXL. RESULTS: Progression rates were 8% with a standard (≥ 1.5D) threshold for K2 or 6% with the static multi-parameter definition. With a ≥ 1D threshold for Kmax, the progression was significantly higher at 29%. With adaptive Kmax or K2, the progression rates were similar (20%) but less than with the adaptive multi-parameter method (22%). CONCLUSIONS: Estimates of keratoconus progression following CXL vary widely according to the reference criteria. Using adaptive thresholds (LoA) to define the repeatability of keratometry gives estimates for progression that are markedly higher than with the standard multi-parameter method.

Arabidopsis protein S-acyl transferases positively mediate BR signaling through S-acylation of BSK1.

Protein S-acyl transferases (PATs) catalyze S-acylation, a reversible post-translational modification critical for membrane association, trafficking, and stability of substrate proteins. Many plant proteins are potentially S-acylated but few have corresponding PATs identified. By using genomic editing, confocal imaging, pharmacological, genetic, and biochemical assays, we demonstrate that three Arabidopsis class C PATs positively regulate BR signaling through S-acylation of BRASSINOSTEROID-SIGNALING KINASE1 (BSK1). PAT19, PAT20, and PAT22 associate with the plasma membrane (PM) and the trans-Golgi network/early endosome (TGN/EE). Functional loss of all three genes results in a plethora of defects, indicative of reduced BR signaling and rescued by enhanced BR signaling. PAT19, PAT20, and PAT22 interact with BSK1 and are critical for the S-acylation of BSK1, and for BR signaling. The PM abundance of BSK1 was reduced by functional loss of PAT19, PAT20, and PAT22 whereas abolished by its S-acylation-deficient point mutations, suggesting a key role of S-acylation in its PM targeting. Finally, an active BR analog induces vacuolar trafficking and degradation of PAT19, PAT20, or PAT22, suggesting that the S-acylation of BSK1 by the three PATs serves as a negative feedback module in BR signaling.