Time-varying effect in the competing risks based on restricted mean time lost

Patients with breast cancer tend to die from other diseases, so for studies that focus on breast cancer, a competing risks model is more appropriate. Considering subdistribution hazard ratio, which is used often, limited to model assumptions and clinical interpretation, we aimed to quantify the effects of prognostic factors by an absolute indicator, the difference in restricted mean time lost (RMTL), which is more intuitive. Additionally, prognostic factors may have dynamic effects (time-varying effects) in long-term follow-up. However, existing competing risks regression models only provide a static view of covariate effects, leading to a distorted assessment of the prognostic factor. To address this issue, we proposed a dynamic effect RMTL regression that can explore the between-group cumulative difference in mean life lost over a period of time and obtain the real-time effect by the speed of accumulation, as well as personalized predictions on a time scale. Through Monte Carlo simulation, we validated the dynamic effects estimated by the proposed regression having low bias and a coverage rate of around 95%. Applying this model to an elderly early-stage breast cancer cohort, we found that most factors had different patterns of dynamic effects, revealing meaningful physiological mechanisms underlying diseases. Moreover, from the perspective of prediction, the mean C-index in external validation reached 0.78. Dynamic effect RMTL regression can analyze both dynamic cumulative effects and real-time effects of covariates, providing a more comprehensive prognosis and better prediction when competing risks exist

Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies

AimsTo systematically evaluate the diagnostic value of an artificial intelligence (AI) algorithm model for various types of diabetic retinopathy (DR) in prospective studies over the previous five years, and to explore the factors affecting its diagnostic effectiveness.Materials and methodsA search was conducted in Cochrane Library, Embase, Web of Science, PubMed, and IEEE databases to collect prospective studies on AI models for the diagnosis of DR from January 2017 to December 2022. We used QUADAS-2 to evaluate the risk of bias in the included studies. Meta-analysis was performed using MetaDiSc and STATA 14.0 software to calculate the combined sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of various types of DR. Diagnostic odds ratios, summary receiver operating characteristic (SROC) plots, coupled forest plots, and subgroup analysis were performed according to the DR categories, patient source, region of study, and quality of literature, image, and algorithm.ResultsFinally, 21 studies were included. Meta-analysis showed that the pooled sensitivity, specificity, pooled positive likelihood ratio, pooled negative likelihood ratio, area under the curve, Cochrane Q index, and pooled diagnostic odds ratio of AI model for the diagnosis of DR were 0.880 (0.875-0.884), 0.912 (0.99-0.913), 13.021 (10.738-15.789), 0.083 (0.061-0.112), 0.9798, 0.9388, and 206.80 (124.82-342.63), respectively. The DR categories, patient source, region of study, sample size, quality of literature, image, and algorithm may affect the diagnostic efficiency of AI for DR.ConclusionAI model has a clear diagnostic value for DR, but it is influenced by many factors that deserve further study.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42023389687

The Analysis of Key Factors Related to ADCs Structural Design

Antibody–drug conjugates (ADCs) have developed rapidly in recent decades. However, it is complicated to map out a perfect ADC that requires optimization of multiple parameters including antigens, antibodies, linkers, payloads, and the payload-linker linkage. The therapeutic targets of the ADCs are expected to express only on the surface of the corresponding target tumor cells. On the contrary, many antigens usually express on normal tissues to some extent, which could disturb the specificity of ADCs and limit their clinical application, not to mention the antibody is also difficult to choose. It requires to not only target and have affinity with the corresponding antigen, but it also needs to have a linkage site with the linker to load the payloads. In addition, the linker and payload are indispensable in the efficacy of ADCs. The linker is required to stabilize the ADC in the circulatory system and is brittle to release free payload while the antibody combines with antigen. Also, it is a premise that the dose of ADCs will not kill normal tissues and the released payloads are able to fulfill the killing potency in tumor cells at the same time. In this review, we mainly focus on the latest development of key factors affecting ADCs progress, including the selection of antibodies and antigens, the optimization of payload, the modification of linker, payload-linker linkage, and some other relevant parameters of ADCs

Baveno-VII criteria to predict decompensation and initiate non-selective beta-blocker in compensated advanced chronic liver disease patients

Background/Aims The utility of Baveno-VII criteria of clinically significant portal hypertension (CSPH) to predict decompensation in compensated advanced chronic liver disease (cACLD) patient needs validation. We aim to validate the performance of CSPH criteria to predict the risk of decompensation in an international real-world cohort of cACLD patients. Methods cACLD patients were stratified into three categories (CSPH excluded, grey zone, and CSPH). The risks of decompensation across different CSPH categories were estimated using competing risk regression for clustered data, with death and hepatocellular carcinoma as competing events. The performance of “treating definite CSPH” strategy to prevent decompensation using non-selective beta-blocker (NSBB) was compared against other strategies in decision curve analysis. Results One thousand one hundred fifty-nine cACLD patients (36.8% had CSPH) were included; 7.2% experienced decompensation over a median follow-up of 40 months. Non-invasive assessment of CSPH predicts a 5-fold higher risk of liver decompensation in cACLD patients (subdistribution hazard ratio, 5.5; 95% confidence interval, 4.0–7.4). “Probable CSPH” is suboptimal to predict decompensation risk in cACLD patients. CSPH exclusion criteria reliably exclude cACLD patients at risk of decompensation, regardless of etiology. Among the grey zone, the decompensation risk was negligible among viral-related cACLD, but was substantially higher among the non-viral cACLD group. Decision curve analysis showed that “treating definite CSPH” strategy is superior to “treating all varices” or “treating probable CSPH” strategy to prevent decompensation using NSBB. Conclusions Non-invasive assessment of CSPH may stratify decompensation risk and the need for NSBB in cACLD patients

Novel hypoxia-related gene signature for predicting prognoses that correlate with the tumor immune microenvironment in NSCLC

Background: Intratumoral hypoxia is widely associated with the development of malignancy, treatment resistance, and worse prognoses. The global influence of hypoxia-related genes (HRGs) on prognostic significance, tumor microenvironment characteristics, and therapeutic response is unclear in patients with non-small cell lung cancer (NSCLC).Method: RNA-seq and clinical data for NSCLC patients were derived from The Cancer Genome Atlas (TCGA) database, and a group of HRGs was obtained from the MSigDB. The differentially expressed HRGs were determined using the limma package; prognostic HRGs were identified via univariate Cox regression. Using the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression, an optimized prognostic model consisting of nine HRGs was constructed. The prognostic model’s capacity was evaluated by Kaplan‒Meier survival curve analysis and receiver operating characteristic (ROC) curve analysis in the TCGA (training set) and GEO (validation set) cohorts. Moreover, a potential biological pathway and immune infiltration differences were explained.Results: A prognostic model containing nine HRGs (STC2, ALDOA, MIF, LDHA, EXT1, PGM2, ENO3, INHA, and RORA) was developed. NSCLC patients were separated into two risk categories according to the risk score generated by the hypoxia model. The model-based risk score had better predictive power than the clinicopathological method. Patients in the high-risk category had poor recurrence-free survival in the TCGA (HR: 1.426; 95% CI: 0.997–2.042; p = 0.046) and GEO (HR: 2.4; 95% CI: 1.7–3.2; p < 0.0001) cohorts. The overall survival of the high-risk category was also inferior to that of the low-risk category in the TCGA (HR: 1.8; 95% CI: 1.5–2.2; p < 0.0001) and GEO (HR: 1.8; 95% CI: 1.4–2.3; p < 0.0001) cohorts. Additionally, we discovered a notable distinction in the enrichment of immune-related pathways, immune cell abundance, and immune checkpoint gene expression between the two subcategories.Conclusion: The proposed 9-HRG signature is a promising indicator for predicting NSCLC patient prognosis and may be potentially applicable in checkpoint therapy efficiency prediction

Clinicopathological Significance and Prognostic Value of DNA Methyltransferase 1, 3a, and 3b Expressions in Sporadic Epithelial Ovarian Cancer

Altered DNA methylation of tumor suppressor gene promoters plays a role in human carcinogenesis and DNA methyltransferases (DNMTs) are responsible for it. This study aimed to determine aberrant expression of DNMT1, DNMT3a, and DNMT3b in benign and malignant ovarian tumor tissues for their association with clinicopathological significance and prognostic value. A total of 142 ovarian cancers and 44 benign ovarian tumors were recruited for immunohistochemical analysis of their expression. The data showed that expression of DNMT1, DNMT3a, and DNMT3b was observed in 76 (53.5%), 92 (64.8%) and 79 (55.6%) of 142 cases of ovarian cancer tissues, respectively. Of the serious tumors, DNMT3a protein expression was significantly higher than that in benign tumor samples (P = 0.001); DNMT3b was marginally significant down regulated in ovarian cancers compared to that of the benign tumors (P = 0.054); DNMT1 expression has no statistical difference between ovarian cancers and benign tumor tissues (P = 0.837). Of the mucious tumors, the expression of DNMT3a, DNMT3b, and DNMT1 was not different between malignant and benign tumors. Moreover, DNMT1 expression was associated with DNMT3b expression (P = 0.020, r = 0.195). DNMT1 expression was associated with age of the patients, menopause status, and tumor localization, while DNMT3a expression was associated with histological types and serum CA125 levels and DNMT3b expression was associated with lymph node metastasis. In addition, patients with DNMT1 or DNMT3b expression had a trend of better survival than those with negative expression. Co-expression of DNMT1 and DNMT3b was significantly associated with better overall survival (P = 0.014). The data from this study provided the first evidence for differential expression of DNMTs proteins in ovarian cancer tissues and their associations with clinicopathological and survival data in sporadic ovarian cancer patients

A Study of the Identification, Fragmentation Mode and Metabolic Pathways of Imatinib in Rats Using UHPLC-Q-TOF-MS/MS

In this study, The metabolites, metabolic pathways, and metabolic fragmentation mode of a tyrosine kinase inhibitor- (TKI-) imatinib in rats were investigated. The samples for analysis were pretreated via solid-phase extraction, and the metabolism of imatinib in rats was studied using ultra-high-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF-MS/MS). Eighteen imatinib metabolites were identified in rat plasma, 21 in bile, 18 in urine, and 12 in feces. Twenty-seven of the above compounds were confirmed as metabolites of imatinib and 9 of them were newly discovered for the first time. Oxidation, hydroxylation, dealkylation, and catalytic dehydrogenation are the main metabolic pathways in phase I. For phase II, the main metabolic pathways were N-acetylation, methylation, cysteine, and glucuronidation binding. The fragment ions of imatinib and its metabolites were confirmed to be produced by the cleavage of the C-N bond at the amide bond. The newly discovered metabolite of imatinib was identified by UHPLC-Q-TOF-MS/MS. The metabolic pathway of imatinib and its fragmentation pattern were summarized. These results could be helpful to study the safety of imatinib for clinical use

Environmental Influence on the Spatiotemporal Variability of Spawning Grounds in the Western Guangdong Waters, South China Sea

Spawning grounds occupy an important position in the supplementary population of fishery resources, especially in Western Guangdong waters (WGWs) in the northern South China Sea (SCS), where fishery resources are being depleted. This study investigated the environmental effects on the spatiotemporal variability of spawning grounds in WGWs, on the basis of generalized additive models (GAMs) and central spawning-ground gravity (CoSGG) by using satellite and in situ observations. Results showed that 57.2% of the total variation in fish-egg density in WGWs was explained. On the basis of stepwise GAMs, the most important factor was sea surface salinity (SSS), with a contribution of 32.1%, followed by sea surface temperature (SST), water depth, month, and chlorophyll a concentration (Chl-a), with contributions of 10.7%, 8.8%, 2.6%, and 2.6%, respectively. Offshore distance had slight influence on the model, explaining approximately 0.4% of the variation in fish-egg density. In summary, fish eggs in WGWs were mainly distributed in the area with SSS of 32.0–34.0 Practical Salinity Unit (PSU), SST of 24–27 °C, and depth of 0–18 m. CoSGG shifted eastwards by 0.38° N and northwards by 0.26° E from April to June. The distribution of spawning grounds in the WGW was affected by the Western Guangdong coastal current (WGCC), cyclonic circulation, the SCS warm current (SCSWC), and changes in the habitat environment (such as SST). Fish in WGWs tend to spawn in areas with a high seabed slope and steep terrain (near the Qiongzhou Strait)

Spatial–Temporal Distribution of Fish Larvae in the Pearl River Estuary Based on Habitat Suitability Index Model

The spawning grounds are important areas for the survival and reproduction of aquatic organisms and play an important role in the replenishment of fishery resources. The density of fish larvae in the Pearl River Estuary (PRE) was analyzed to establish Habitat Suitability Index (HSI) based on marine environmental factors. Survey data and satellite remote sensing data, including sea surface temperature, sea surface salinity and chlorophyll a concentration, from 2014 to 2017 during April–September were analyzed. Results showed that the accuracy of the HSI model based on the larval density and environmental factors was more than 60%, and the distribution trend of HSI was consistent with the distribution trend of larval density. The HSI models constructed based on Arithmetic Mean Model (AMM), Geometric Mean Model (GMM) and Minimum Model (MINM) methods can better predict the spatial–temporal distribution of larvae in the PRE. Among them, the accuracy of the HSI model constructed by the AMM and GMM methods was the highest in April (71%) and September (93%); the accuracy of the HSI model constructed by the MINM method was the highest in June (70%), July (84%) and August (64%). In general, the areas with high HSI values are mainly distributed in the offshore waters of the PRE. The spatial–temporal distribution of larvae in the PRE was influenced by monsoon, Pearl River runoff, Guangdong coastal currents and the invasion of high-salinity seawater from the outer sea

Environmental Effects on the Spatiotemporal Variability of Fish Larvae in the Western Guangdong Waters, China

Spawning grounds occupy an important position in the survival and reproduction of aquatic life, which plays an important role in the replenishment of fishery resources, especially in the China coasts where fishery resources are depleting. This study investigated environmental effects on the spatiotemporal variability of fish larvae in the western Guangdong waters (WGWs), on the basis of generalized additive models (GAMs) and center of gravity (CoG). Satellite data including sea surface salinity (SSS), sea surface temperature (SST), and in situ observations for fish larvae from April to June in 2014–2015 were used. Results showed that 40.3% of the total variation in fish larvae density was explained. SST, SSS, and depth showed positive effects in 23–24 °C and 27–30 °C, 24–32 PSU, and 0–60 m, and showed negative effects in 24–27 °C, 32–34.2 PSU, 60–80 m. Based on the stepwise GAMs, the most important factor was month, with a contribution of 10.6%, followed by longitude, offshore distance, depth, and latitude, with contributions of 7.0%, 7.0%, 6.3%, 4.2%, 3.9%, and 1.3%, respectively. Fish larvae CoG shifted northward by 0.6° N and eastwards by 0.13° E from April to June. The distribution of fish larvae in the WGWs was affected by complex submarine topography in the Qiongzhou Strait, coastal upwelling in the WGWs, and runoff from the Pearl River