Creation of a structured solar cell material dataset and performance prediction using large language models.

Materials scientists usually collect experimental data to summarize experiences and predict improved materials. However, a crucial issue is how to proficiently utilize unstructured data to update existing structured data, particularly in applied disciplines. This study introduces a new natural language processing (NLP) task called structured information inference (SII) to address this problem. We propose an end-to-end approach to summarize and organize the multi-layered device-level information from the literature into structured data. After comparing different methods, we fine-tuned LLaMA with an F1 score of 87.14% to update an existing perovskite solar cell dataset with articles published since its release, allowing its direct use in subsequent data analysis. Using structured information, we developed regression tasks to predict the electrical performance of solar cells. Our results demonstrate comparable performance to traditional machine-learning methods without feature selection and highlight the potential of large language models for scientific knowledge acquisition and material development.

Clinical application of multicolor scanning laser ophthalmology in diagnosis and grading of central retinal artery occlusion.

Purpose: To characterize features of central retinal artery occlusion (CRAO) using multicolor (MC) imaging and to assess the differences in CRAO grading between color fundus photography (CFP) and MC image qualitatively and quantitatively. Methods: We conducted a prospective, cross-sectional study in the Department of Ophthalmology of Renmin Hospital of Wuhan University. In total, 86 acute CRAO patients were included. Spectral-domain optical coherence tomography (SD-OCT), CFP, and MC examinations were taken at baseline. Based on the findings of these three examinations, CRAO was divided into three grades (incomplete, subtotal, and total). Based on OCT grading criteria, we qualitatively compared the ability of grading CRAO by CFP and MC. CRAO patient's visual acuity (VA) was obtained from the initial visit. The retinal thickness was measured by SD-OCT. Superficial capillary plexus (SCP) and deep capillary plexus (DCP) were obtained from optical coherence tomography angiography (OCTA) examinations. Quantitative data were compared across the three acute CRAO subgroups and against three examination findings. Results: MC image had significantly higher power of acute CRAO detection than CFP (P = 0.03). In the same group of CRAO patients, there was no significant difference in VA when comparing OCT with the MC grading system or with the CFP grading system (all P > 0.05). Significant differences in VA were found between the three CRAO subgroups only under MC grading (P = 0.016). In incomplete CRAO patients, significant differences were found in central fovea thickness (CFT) when comparing OCT with the CFP grading system (P = 0.019). In the same group of CRAO patients, there was no significant difference in retinal thickness when comparing OCT with the MC grading system (All P > 0.05). Significance differences in CFT (P < 0.001), innermost retinal layer (IMRL; P < 0.01), middle retinal layer (MRL; P < 0.001), and outer retinal layer (ORL; P = 0.021) were found between the three CRAO subgroups by MC grading. Vessel density of SCP showed a statistically increased as the severity of three CRAO subgroups (P = 0.03), whereas DCP did not have significant differences (P = 0.745). Comparisons were made between the OCT grading method and the MC and CFP grading methods; there is no significant difference in vessel density of SCP and DCP (All P > 0.05). Conclusion: The images obtained by MC are superior to those obtained by CFP in CRAO grading, retinal thickness, and vessel density measurement. MC imaging may be more capable of CRAO grading than OCT. We recommend MC imaging to determine CRAO severity to guide disease treatment and predict visual prognosis.

Peripheral blood transcriptomic analysis identifies potential inflammation and immune signatures for central retinal artery occlusion.

Central retinal artery occlusion (CRAO) is an acute retinal ischaemic disease, but early diagnosis is challenging due to a lack of biomarkers. Blood samples were collected from CRAO patients and cataract patients. Gene expression profiles were distinct between arterial/venous CRAO blood (A-V group) and venous CRAO/control blood (V-C group) samples. Differentially expressed genes (DEGs) were subjected to GO and KEGG enrichment analyses. Hub genes were identified by Cytoscape and used to predict gene interactions via GeneMANIA. Immune cell infiltration was analysed by CIBERSORT. More than 1400 DEGs were identified in the A-V group and 112 DEGs in the V-C group compared to controls. The DEGs in both groups were enriched in the ribosome pathway, and those in the V-C group were also enriched in antigen processing/MHC pathways. Network analysis identified ribosomal proteins (RPS2 and RPS5) as the core genes of the A-V group and MHC genes (HLA-F) as the core genes of the V-C group. Coexpression networks showed ribosomal involvement in both groups, with additional immune responses in the V-C group. Immune cell analysis indicated increased numbers of neutrophils and T cells. Ribosomal and MHC-related genes were identified as potential CRAO biomarkers, providing research directions for prevention, diagnosis, treatment and prognosis.

Plasmalogens and Octanoylcarnitine Serve as Early Warnings for Central Retinal Artery Occlusion.

Central retinal artery occlusion (CRAO) is a kind of ophthalmic emergency which may cause loss of functional visual acuity. However, the limited treatment options emphasize the significance of early disease prevention. Metabolomics has the potential to be a powerful tool for early identification of individuals at risk of CRAO. The aim of the study was to identify potential biomarkers for CRAO through a comprehensive analysis. We employed metabolomics analysis to compare venous blood samples from CRAO patients with cataract patients for the venous difference, as well as arterial and venous blood from CRAO patients for the arteriovenous difference. The analysis of metabolites showed that PC(P-18:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)), PC(P-18:0/20:4(5Z,8Z,11Z,14Z)) and octanoylcarnitine were strongly correlated with CRAO. We also used univariate logistic regression, random forest (RF), and support vector machine (SVM) to screen clinical parameters of patients and found that HDL-C and ApoA1 showed significant predictive efficacy in CRAO patients. We compared the predictive performance of the clinical parameter model with combined model. The prediction efficiency of the combined model was significantly better with area under the receiver operating characteristic curve (AUROC) of 0.815. Decision curve analysis (DCA) also exhibited a notably higher net benefit rate. These results underscored the potency of these three substances as robust predictors of CRAO occurrence.

Automatically Generated Datasets: Present and Potential Self-Cleaning Coating Materials.

The rise of urbanization coupled with pollution has highlighted the importance of outdoor self-cleaning coatings. These revolutionary coatings contribute to the longevity of various surfaces and reduce maintenance costs for a wide range of applications. Despite ongoing research to develop efficient and durable self-cleaning coatings, adopting systematic research methodologies could accelerate these advancements. In this work, we use Natural Language Processing (NLP) strategies to generate open- and traceable-sourced datasets about self-cleaning coating materials from 39,011 multi-disciplinary papers. The data are from function-based and property-based corpora for self-cleaning purposes. These datasets are presented in four different formats for diverse uses or combined uses: material frequency statistics, material dictionary, measurement value datasets for self-cleaning-related properties and optical properties, and sentiment statistics of material stability and durability. This provides a literature-based data resource for the development of self-cleaning coatings and also offers potential pathways for material discovery and prediction by machine learning.

Opinion Mining by Convolutional Neural Networks for Maximizing Discoverability of Nanomaterials.

The scientific literature contains valuable information that can be used for future applications, but manual analysis presents challenges due to its size and disciplinary boundaries. The prevailing solution involves natural language processing (NLP) techniques such as information retrieval. Nonetheless, existing automated systems primarily provide either statistically based shallow information or deep information without traceability, thereby falling short of delivering high-quality and reliable insights. To address this, we propose an innovative approach of leveraging sentiment information embedded within the literature to track the opinions toward materials. In this study, we integrated material knowledge into text representation and constructed opinion data sets to hierarchically train deep learning models, named as Scientific Sentiment Network (SSNet). SSNet can effectively extract knowledge from the energy material literature and accurately categorize expert opinions into challenges and opportunities (94% and 92% accuracy, respectively). By incorporating sentiment features determined by SSNet, we can predict the ranking of emerging thermoelectric materials with a 70% correlation to experimental outcomes. Furthermore, our model achieves a commendable 68% accuracy in predicting suitable nanomaterials for atomic layer deposition (ALD) over time. These promising results offer a practical framework to extract and synthesize knowledge from the scientific literature, thereby accelerating research in the field of nanomaterials.

Improved visual outcomes of central retinal artery occlusion with local intra-arterial fibrinolysis beyond the conventional time window.

Local intra-arterial fibrinolysis (LIF) is a promising therapeutic option for CRAO. However, the narrow time window of 6 h has greatly limited the application of LIF. In this study, we explored the efficacy of LIF beyond the conventional time windows and compared the result with conservative therapy. This prospective study included 179 CRAO patients with baseline visual acuity (VA) ≤ 20/400 treated at Renmin Hospital of Wuhan University. The mean time from vision loss to presentation was 5.5 days. 58 patients received conventional standard therapy (CST) alone.121 patients underwent LIF. Main outcome was VA improvement ≥ 0.3 logMAR. Secondary outcome was a favorable VA outcome of 20/200 or better. Logistic regressions were performed to identify predictors of visual improvement. 43% patients in the LIF group experienced VA improvement versus 19% with CST (P = 0.002). LIF was associated with 4.0-fold higher likelihood of visual improvement compared to CST (P = 0.001). Poor baseline VA (light perception or no light perception) and shortened prothrombin time (PT) were associated with greater chance of visual improvement with LIF. However, LIF showed no significant advantage over CST for favorable VA outcomes. No major complications occurred. LIF beyond the therapeutic time window improved vision in functionally blind CRAO patients and showed better efficacy when compared with CST. PT may be a potential predictor of visual outcome after LIF. Our findings could complement existing time-based treatment guidelines and potentially allow for personalized decisions on the use of LIF beyond time windows.

ApoB, non-HDL-C, and LDL-C Are More Prominent in Retinal Artery Occlusion Compared to Retinal Vein Occlusion.

PURPOSE: To evaluate and compare the blood lipid profile in retinal artery occlusion (RAO) and retinal vein occlusion (RVO). METHODS: We included 82 RAO patients and 95 RVO patients in this retrospective case-control study. Controls were matched to RAO or RVO patients at a 1:1 ratio, respectively. Associated lipid variates were analyzed in multivariable logistic regression models. RESULTS: LDL-C (OR = 1.69), non-HDL-C (OR = 1.87), and ApoB (OR = 11.72) individually significantly increased the risk of RAO. ApoA1 was associated with RVO (OR = 0.02), and with 75.8% sensitivity and 67.4% specificity. TG (OR = 1.61), LDL-C (OR = 1.69), non-HDL-C (OR = 1.91), and ApoB (OR = 12.12) each significantly increased the risk of RAO when compared with RVO. CONCLUSIONS: ApoB, non-HDL-C, and LDL-C may be potential biomarkers in RAO patients. Low ApoA1 is an independent risk factor for the development of RVO.

Development and Validation of a Four Adenosine-to-Inosine RNA Editing Site-Relevant Prognostic Signature for Assessing Survival in Breast Cancer Patients.

Background: Adenosine-to-inosine RNA editing (ATIRE) is increasingly being used to characterize cancer. However, no studies have been conducted to identify an ATIRE signature for predicting cancer survival. Methods: Breast cancer (BRCA) samples with ATIRE profiles from The Cancer Genome Atlas were divided into training (n = 452) and internal validation cohorts (n = 311), and 197 additional BRCA patients were recruited as an external validation cohort. The ATIRE signature for BRCA overall survival (OS) and disease-free survival (DFS) were identified using forest algorithm analysis and experimentally verified by direct sequencing. An ATIRE-based risk score (AIRS) was established with these selected ATIRE sites. Significantly prognostic factors were incorporated to generate a nomogram that was evaluated using Harrell's C-index and calibration plot for all cohorts. Results: Seven ATIRE sites were revealed to be associated with both BRCA OS and DFS, of which four sites were experimentally confirmed. Patients with high AIRS displayed a higher risk of death than those with low AIRS in the training (hazard ratio (HR) = 3.142, 95%CI = 1.932-5.111), internal validation (HR = 2.097, 95%CI = 1.123-3.914), and external validation cohorts (HR = 2.680, 95%CI = 1.000-7.194). A similar hazard effect of high AIRS on DFS was also observed. The nomogram yielded Harrell's C-indexes of 0.816 (95%CI = 0.784-0.847), 0.742 (95%CI = 0.684-0.799), and 0.869 (95%CI = 0.835-0.902) for predicting OS and 0.767 (95%CI = 0.708-0.826), 0.684 (95%CI = 0.605-0.763), and 0.635 (95%CI = 0.566-0.705) for predicting DFS in the three cohorts. Conclusion: AIRS nomogram could help to predict OS and DFS of patients with BRCA.

Tübingen hip flexion splint for the treatment of developmental dysplasia of the hip in children younger than six months age: a meta-analysis.

PURPOSE: To investigate the success rate of the Tübingen hip flexion splint (THFS) for the treatment of developmental dysplasia of the hip (DDH), of different severity as per the Graf classification, among infants younger than six months of age. The type and incidence rate of complications associated with THFS treatment were also evaluated. METHODS: The following databases were searched using keywords and limited for age less than six months: PubMed, Embase, Web of Science, Cochrane Library, and SinoMed, between inception and July 2020. Articles were screened and extracted by two researchers, and the quality of the included literature was evaluated (methodological index for non-randomized studies criteria). R studio 1.3 was used for statistical analysis. The review process was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. RESULTS: After screening, eight articles were included in the analysis, contributing 1211 hips (875 patients). The overall success rate of THFS treatment is 91% (95% confidence interval (CI) 0.82 to 0.95). The success rate by Graf type is as follows: type-II, 98% (95% CI 0.94 to 1.00); type-III, 96% (95% CI 0.88 to 1.00); and type-IV, 32% (95% CI 0.18 to 0.47). Complications (24/1211, 2%) included transient femoral nerve palsy (n = 1); avascular necrosis of the femoral head (n = 9) and residual acetabular dysplasia (n = 14). CONCLUSION: THFS treatment is successful for Graf type-II and -III, but low for type-IV, with a low rate of complication. THFS may be an effective treatment option for DDH among infants less than six months of age. However, those with Graf type-IV require close monitoring. LEVEL OF EVIDENCE: III.

LXRß is involved in the control of platelet production from megakaryocytes.

Liver X receptor ß (LXRß), a nuclear receptor involved in important cellular processes such as cholesterol, glucose and fatty acid metabolism, was suggested to be involved in platelet aggregation but its detailed roles are not clear. In the present study, we evaluated the contribution of LXRß to platelet functions and production. In the systemic collagen-epinephrine thrombosis mouse model, LXRß-deficient mice showed increased area of blood clots compared with control wide-type littermates. The aggregation of LXRß-deficient platelets in response to ADP was stronger than that of control mice platelets. More importantly, the number of platelets in blood of LXRß-deficient mice was significantly higher than that of wild-type mice, especially for female mice. Knockdown of LXRß expression in human megakaryoblastic Dami cells also enhanced cell polyploidization, formation of proplatelets and production of platelet-like particles. Increase in expression levels of proteins related to oxidative phosphorylation such as NADH:ubiquinone oxidoreductase core subunit V1 (Ndufv1) was observed in LXRß-knockdown Dami cells. The levels of Ndufv1 in LXRß-deficient mice platelets were also higher than that of wild-type mice. Taken together, our findings suggested LXRß might participate in control of platelet production from megakaryocytes by regulating mitochondrial metabolism.

Abnormal patterns recognition in bivariate autocorrelated process using optimized random forest and multi-feature extraction.

Traditional multivariate control charts are unable to determine the specific abnormal variables as detecting process abnormality. To solve this problem, a new model based on optimized random forest (RF) and multi-feature extraction has been proposed. First, four patterns of process state according to different combinations of abnormal variables are defined. Next, four statistical features and seven shape features are extracted to construct a feature vector, which is used as input of RF in the advanced model. Finally, the particle swarm optimization (PSO) is introduced to optimize the two key parameters of RF. The recognition accuracies of the proposed model are studied through simulation experiments. The experiment results show that the accuracy of this model rises from 91.25% to 98.33% through extracting multi-feature and PSO optimization. The superiority of the proposed model is verified, as evidence by comparing with other algorithms. Thus, we confirm that the proposed model is promising for being applied in real-time process control.

Gastrodin protects H9c2 cardiomyocytes against oxidative injury by ameliorating imbalanced mitochondrial dynamics and mitochondrial dysfunction.

Gastrodin (GAS) is the main bioactive component of Tianma, a traditional Chinese medicine widely used to treat neurological disorders as well as cardio- and cerebrovascular diseases. In the present study, the protective effects of GAS on H9c2 cells against ischemia-reperfusion (IR)-like injury were found to be related to decreasing of oxidative stress. Furthermore, GAS could protect H9c2 cells against oxidative injury induced by H2O2. Pretreatment of GAS at 20, 50, and 100 µM for 4 h significantly ameliorated the decrease in cell viability and increase in apoptosis of H9c2 cells treated with 400 µM H2O2 for 3 h. Furthermore, we showed that H2O2 treatment induced fragmentation of mitochondria and significant reduction in networks, footprint, and tubular length of mitochondria; H2O2 treatment strongly inhibited mitochondrial respiration; H2O2 treatment induced a decrease in the expression of mitochondrial fusion factors Mfn2 and Opa1, and increase in the expression of mitochondrial fission factor Fis1. All these alterations in H2O2-treated H9c2 cells could be ameliorated by GAS pretreatment. Moreover, we revealed that GAS pretreatment enhanced the nuclear translocation of Nrf2 under H2O2 treatment. Knockdown of Nrf2 expression abolished the protective effects of GAS on H2O2-treated H9c2 cells. Our results suggest that GAS may protect H9c2 cardiomycytes against oxidative injury via increasing the nuclear translocation of Nrf2, regulating mitochondrial dynamics, and maintaining the structure and functions of mitochondria.