Search

Background: To investigate the prognostic impact of the controlling nutritional status (CONUT) score in non-small-cell lung cancer (NSCLC) patients receiving first-line chemotherapy. Methods: We retrospectively reviewed 278 consecutive patients undergoing chemotherapy for stage III-IV NSCLC between May 2012 and July 2020. CONUT score was calculated by incorporating serum albumin, total cholesterol, and total lymphocyte count. The patients were divided into two groups: CONUT ≥ 3 and CONUT < 3, according to receiver operating characteristic (ROC) analysis. The associations of CONUT with clinicopathological factors and survival were evaluated. Results: A high CONUT score was significantly associated with older age(P = 0.003), worse ECOG-PS(P = 0.018), advanced clinical stage(P = 0.006), higher systematic inflammation index (SII) (P < 0.001)and lower prognostic nutritional index (PNI) (P < 0.001).The high CONUT group had a significantly shorter progression-free survival(PFS) and overall survival(OS) than the low CONUT group. In the univariate analysis, higher SII, higher CONUT, advanced clinical stage and lower PNI were associated with worse PFS (Pall < 0.05). Worse ECOG-PS, higher SII, higher CONUT, advanced clinical stage and lower PNI were associated with worse OS (Pall < 0.05). In multivariate analysis, CONUT(HR, 2.487; 95%CI 1.818 ~ 3.403; P < 0.001) was independently associated with PFS, while PNI(HR, 0.676; 95%CI 0.494 ~ 0.927; P = 0.015) and CONUT(HR, 2.186; 95%CI 1.591 ~ 3.002; P < 0.001)were independently associated with OS. In ROC analysis, CONUT had a higher area under the ROC curve (AUC) for the prediction of 24-month PFS and OS than the SII or PNI. When the time-dependent AUC curve was used to predict PFS and OS, CONUT tended to maintain its predictive accuracy for long-term prognosis at a significantly higher level for an extended period after chemotherapy when compared with the other markers tested. The CONUT score showed better accuracy of predicting OS (C-index: 0.711) and PFS(C-index: 0.753). Conclusion: CONUT score is an independent prognostic indicator of poor outcomes for patients with stage III-IV NSCLC and is superior to the SII and PNI in terms of prognostic ability. [ABSTRACT FROM AUTHOR]

Study question: To construct prediction models based on the Bayesian network (BN) learning method for the probability of fertilization failure (including low fertilization rate [LRF] and total fertilization failure [TFF]) in assisted reproductive technology (ART) treatment. Summary answer: A BN model was developed to predict TFF/LFR. The model showed relatively high calibration in external validation, which could facilitate the identification of risk factors for fertilization disorders and improve the efficiency of in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatment. What is known already: The prediction of TFF/LFR is very complex. Although some studies attempted to construct prediction models for TFF/LRF, most of the reported models were based on limited variables and traditional regression-based models, which are unsuitable for analyzing real-world clinical data. Therefore, none of the reported models have been widely used in routine clinical practice. To date, BN modeling analysis is a prominent and increasingly popular machine learning method that is powerful in dealing with dynamic and complex real-world data. Study design, size, duration: A retrospective study was performed with 106,640 fresh embryo IVF/ICSI cycles from 2009 to 2019 in one of China's largest reproductive health centers. Participants/materials, setting, methods: A total of 106, 640 cycles were included in this study, including 97,102 controls, 4,339 LFR cases, and 5,199 TFF cases. Twenty-four predictors were initially included, including 13 female-related variables, five male-related variables, and six variables related to IVF/ICSI treatment. BN modeling analysis with tenfold cross-validation was performed to construct the predictive model for TFF/LFR. The receiver operating characteristic (ROC) curves and the corresponding area under the curves (AUCs) were used to evaluate the performance of the BN model. Main results and the role of chance: All twenty-four predictors were first organized into seven hierarchical layers in a theoretical BN model, according to prior knowledge from previous literature and clinical practice. A machine-learning BN model was generated based on real-world clinical data, containing a total of eighteen predictors, of which the infertility type, ART method, and number of retrieved oocytes directly influence the probabilities of LFR/TFF. The prediction accuracy of the BN model was 91.7%. The AUC of the TFF versus control groups was 0.779 (95% CI: 0.766-0.791), with a sensitivity of 71.2% and specificity of 70.1%; the AUC of of TFF versus LFR groups was 0.807 (95% CI: 0.790-0.824), with a sensitivity of 49.0% and specificity of 99.0%. Limitations, reason for caution: First, our study was based on clinical data from a single center, and the results of this study should be further verified by external data. In addition, some critical data (e.g., the detailed IVF laboratory parameters of the sperm and oocytes used for insemination) were not available in this study, which should be given full consideration when further improving the performance of the BN model. Wider implications of the findings: Based on extensive clinical real-world data, we developed a BN model to predict the probabilities of fertilization failures in ART, which provides new clues for clinical decision-making support for clinicians in formulating personalized treatment plans and further improving ART treatment outcomes. Study funding/competing interest(s): Dr. Y. Wang was supported by grants from the Beijing Municipal Science & Technology Commission (Z191100006619086). We declare that there are no conflicts of interest. Trial registration number: N/A. [ABSTRACT FROM AUTHOR]

Background: Imbalances in alkali elements (AEs) and alkaline earth elements (AEEs) cause reproductive disorders. However, it remains unclear whether AEs/AEEs in follicular fluid have a relationship with the serious reproductive disorder known as diminished ovarian reserve (DOR). Methods: A nested case‒control study was carried out in China. Follicular fluid samples from 154 DOR patients and 154 controls were collected and assessed for nine AEs/AEE levels. Both the mixed and single effects of the elements on DOR were estimated with a Bayesian kernel machine (BKMR) and logistic regressions. Results: The DOR group had higher median concentrations of Li, Na, and K in follicular fluid (all P values < 0.05). The logistic regression showed that compared with their lowest tertile, the high tertiles of K [OR:2.45 (1.67–4.43)], Li [OR: 1.89 (1.06–3.42)], and Cs [OR: 1.97 (1.10–3.54)] were significantly associated with the odds of DOR. The BKMR model reported that the DOR likelihood increased linearly across the 25th through 75th percentiles of the nine-AE/AEE mixture, while the AE group contributed more to the overall effect. Conclusion: This study revealed an association in which the likelihood of DOR increased with higher overall concentrations of AE/AEEs in follicular fluid. Among the nine detected elements, K, Li, and Cs exhibited significant individual associations with DOR. We provide new clues for the environmental factors on female fertility decline. Trial registration: Retrospectively registered. [ABSTRACT FROM AUTHOR]

Sustained retina drug delivery and rational drug combination are considered essential for enhancing the efficacy of therapy for wet age-related macular degeneration (wAMD) due to the conservative structure of the posterior ocular segment and the multi-factorial pathological mechanism. Designing a drug co-delivery system that can simultaneously achieve deep penetration and long-lasting retention in the vitreous is highly desired, yet remains a huge challenge. In this study, we fabricated Bor/RB-M@TRG as an intravitreal-injectable hydrogel depot for deep penetration into the posterior ocular segment and long-lasting distribution in the retinal pigment epithelium (RPE) layer. The Bor/RB-M@TRG consisted of borneol-decorated rhein and baicalein-coloaded microemulsions (Bor/RB-M, the therapy entity) and a temperature-responsive hydrogel matrix (the intravitreal depot). Bor/RB-M exhibited the strongest in vitro anti-angiogenic effects among all the groups studied, which is potentially associated with improved cellular uptake, as well as the synergism of rhein and baicalein, acting via anti-angiogenic and anti-oxidative stress pathways, respectively. Importantly, a single intravitreal (IVT) injection with Bor/RB-M@TRG displayed significant inhibition against the CNV of wAMD model mice, compared to all other groups. Particularly, coumarin-6-labeled Bor/RB-M@TRG (Bor/C6-M@TRG) could not only deeply penetrate into the retina but also stably accumulate in the RPE layer for at least 14 days. Our design integrates the advantages of borneol-decorated microemulsions and hydrogel depots, offering a promising new approach for clinically-translatable retinal drug delivery and synergistic anti-wAMD treatment. [ABSTRACT FROM AUTHOR]