Machine learning prediction models for mortality risk in sepsis-associated acute kidney injury: evaluating early versus late CRRT initiation

BackgroundSepsis-associated acute kidney injury (S-AKI) has a significant impact on patient survival, with continuous renal replacement therapy (CRRT) being a crucial intervention. However, the optimal timing for CRRT initiation remains controversial.MethodsUsing the MIMIC-IV database for model development and the eICU database for external validation, we analyzed patients with S-AKI to compare survival rates between early and late CRRT initiation groups. Propensity score matching was performed to address potential selection bias. Subgroup analyses stratified patients by disease severity using SOFA scores (low ≤10, medium 11–15, high >15) and creatinine levels (low ≤3 mg/dL, medium 3–5 mg/dL, high >5 mg/dL). Multiple machine learning models were developed and evaluated to predict patient prognosis, with Shapley Additive exPlanations (SHAP) analysis identifying key prognostic factors.ResultsAfter propensity score matching, late CRRT initiation was associated with improved survival probability, but led to increased hospital and ICU stays. Subgroup analyses showed consistent trends favoring late CRRT across all SOFA categories, with the most pronounced effect in high SOFA scores (>15, p = 0.058). The GBM model demonstrated robust predictive performance (average C-index 0.694 in validation and test sets). SHAP analysis identified maximum lactate levels, age, and minimum SpO2 as the strongest predictors of mortality, while CRRT timing showed relatively lower impact on outcome prediction.ConclusionWhile later initiation of CRRT in S-AKI patients was associated with improved survival, this benefit comes with increased healthcare resource utilization. The clinical parameters, rather than CRRT timing, are the primary determinants of patient outcomes, suggesting the need for a more personalized approach to CRRT initiation based on overall illness severity

Exosomes Derived from Umbilical Cord Mesenchymal Stem Cells Alleviate Mifepristone-Induced Human Endometrial Stromal Cell Injury

The human endometrial stromal cells (hEndoSCs) could maintain endometrial homeostasis and play a critical role in repairing endometrial injury. Mesenchymal stem cells (MSCs) significantly increase the proliferation of damaged hEndoSCs and protect them from apoptosis. Recent studies indicated that exosomes derived from stem cells could be recruited to damaged tissues for regeneration, which exhibit the potential for stem cell therapy as therapeutic vectors. In this study, we isolated human umbilical cord mesenchymal stem cell-derived exosomes (hUCMSC-Exos) and investigated the effects of hUCMSC-Exos on mifepristone-induced hEndoSC injury. Exosome uptake and cell proliferation as well as cell apoptosis of damaged hEndoSCs treated with hUCMSC-Exos were detected. We also assessed the expression of apoptosis-related proteins and the PTEN/AKT signaling pathway. We found hUCMSC-Exos improved the proliferation of damaged hEndoSCs and protected hEndoSCs from the mifepristone-induced apoptosis. hUCMSC-Exos upregulated Bcl-2 level as well as downregulated Cleaved Caspase-3 level and activated the PTEN/AKT signaling pathway to regulate the proliferation and antiapoptosis. These results indicated hUCMSC-Exos protected hEndoSCs from mifepristone-induced apoptosis and played an active role in repairing the damaged hEndoSCs through the PTEN/AKT signaling pathway in vitro. hUCMSC-Exos may hold great promise in the cell-free therapy of endometrial injury

Exosomes Derived from Umbilical Cord Mesenchymal Stem Cells Alleviate Mifepristone-Induced Human Endometrial Stromal Cell Injury

The human endometrial stromal cells (hEndoSCs) could maintain endometrial homeostasis and play a critical role in repairing endometrial injury. Mesenchymal stem cells (MSCs) significantly increase the proliferation of damaged hEndoSCs and protect them from apoptosis. Recent studies indicated that exosomes derived from stem cells could be recruited to damaged tissues for regeneration, which exhibit the potential for stem cell therapy as therapeutic vectors. In this study, we isolated human umbilical cord mesenchymal stem cell-derived exosomes (hUCMSC-Exos) and investigated the effects of hUCMSC-Exos on mifepristone-induced hEndoSC injury. Exosome uptake and cell proliferation as well as cell apoptosis of damaged hEndoSCs treated with hUCMSC-Exos were detected. We also assessed the expression of apoptosis-related proteins and the PTEN/AKT signaling pathway. We found hUCMSC-Exos improved the proliferation of damaged hEndoSCs and protected hEndoSCs from the mifepristone-induced apoptosis. hUCMSC-Exos upregulated Bcl-2 level as well as downregulated Cleaved Caspase-3 level and activated the PTEN/AKT signaling pathway to regulate the proliferation and antiapoptosis. These results indicated hUCMSC-Exos protected hEndoSCs from mifepristone-induced apoptosis and played an active role in repairing the damaged hEndoSCs through the PTEN/AKT signaling pathway in vitro. hUCMSC-Exos may hold great promise in the cell-free therapy of endometrial injury.</jats:p

Dataset for Accurate Inverse Design of Fabry&ndash;P&eacute;rot-Cavity-Based Color Filters far beyond sRGB via a Bidirectional Artificial Neural Network

Data to support article &quot;Accurate inverse design of Fabry&ndash;Perot-cavity-based color filters far beyond sRGB via a bidirectional artificial neural network&quot;. Peng Dai, Yasi Wang, Yueqiang Hu, C. H. de Groot, Otto Muskens, Huigao Duan, and Ruomeng Huang. PHOTONICS Research. 10.1364/PRJ.415141</span

Accurate inverse design of Fabry–Perot-cavity-based color filters far beyond sRGB via a bidirectional artificial neural network

Structural color based on Fabry–Perot (F-P) cavity enables a wide color gamut with high resolution at submicroscopic scale by varying its geometrical parameters. The ability to design such parameters that can accurately display the desired color is therefore crucial to the manufacturing of F-P cavities for practical applications. This work reports the first inverse design of F-P cavity structure using deep learning through a bidirectional artificial neural network. It enables the production of a significantly wider coverage of color space that is over 215% of sRGB with extremely high accuracy, represented by an average Δ E 2000 value below 1.2. The superior performance of this structural color-based neural network is directly ascribed to the definition of loss function in the uniform CIE 1976-Lab color space. Over 100,000 times improvement in the design efficiency has been demonstrated by comparing the neural network to the metaheuristic optimization technique using an evolutionary algorithm when designing the famous painting of “Haystacks, end of Summer” by Claude Monet. Our results demonstrate that, with the correct selection of loss function, deep learning can be very powerful to achieve extremely accurate design of nanostructured color filters with very high efficiency.</jats:p

Altered morphological connectivity mediated white matter hyperintensity-related cognitive impairment

White matter hyperintensities (WMH) are widely observed in older adults and are closely associated with cognitive impairment. However, the underlying neuroimaging mechanisms of WMH-related cognitive dysfunction remain unknown. This study recruited 61 WMH individuals with mild cognitive impairment (WMH-MCI, n = 61), 48 WMH individuals with normal cognition (WMH-NC, n = 48) and 57 healthy control (HC, n = 57) in the final analyses. We constructed morphological networks by applying the Kullback-Leibler divergence to estimate interregional similarity in the distributions of regional gray matter volume. Based on morphological networks, graph theory was applied to explore topological properties, and their relationship to WMH-related cognitive impairment was assessed. There were no differences in small-worldness, global efficiency and local efficiency. The nodal local efficiency, degree centrality and betweenness centrality were altered mainly in the limbic network (LN) and default mode network (DMN). The rich-club analysis revealed that WMH-MCI subjects showed lower average strength of the feeder and local connections than HC (feeder connections: P = 0.034; local connections: P = 0.042). Altered morphological connectivity mediated the relationship between WMH and cognition, including language (total indirect effect: −0.010; 95 % CI: −0.024, −0.002) and executive (total indirect effect: −0.010; 95 % CI: −0.028, −0.002) function. The altered topological organization of morphological networks was mainly located in the DMN and LN and was associated with WMH-related cognitive impairment. The rich-club connection was relatively preserved, while the feeder and local connections declined. The results suggest that single-subject morphological networks may capture neurological dysfunction due to WMH and could be applied to the early imaging diagnostic protocol for WMH-related cognitive impairment