Simple, Efficient, and Scalable Structure-Aware Adapter Boosts Protein Language Models.

Fine-tuning pretrained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches. As a widely applied powerful technique in natural language processing, employing parameter-efficient fine-tuning techniques could potentially enhance the performance of PLMs. However, the direct transfer to life science tasks is nontrivial due to the different training strategies and data forms. To address this gap, we introduce SES-Adapter, a simple, efficient, and scalable adapter method for enhancing the representation learning of PLMs. SES-Adapter incorporates PLM embeddings with structural sequence embeddings to create structure-aware representations. We show that the proposed method is compatible with different PLM architectures and across diverse tasks. Extensive evaluations are conducted on 2 types of folding structures with notable quality differences, 9 state-of-the-art baselines, and 9 benchmark data sets across distinct downstream tasks. Results show that compared to vanilla PLMs, SES-Adapter improves downstream task performance by a maximum of 11% and an average of 3%, with significantly accelerated convergence speed by a maximum of 1034% and an average of 362%, the training efficiency is also improved by approximately 2 times. Moreover, positive optimization is observed even with low-quality predicted structures. The source code for SES-Adapter is available at https://github.com/tyang816/SES-Adapter.

Development and validation of an artificial intelligence model for predicting de novo distant bone metastasis in breast cancer: a dual-center study.

OBJECTIVE: Breast cancer has become the most prevalent malignant tumor in women, and the occurrence of distant metastasis signifies a poor prognosis. Utilizing predictive models to forecast distant metastasis in breast cancer presents a novel approach. This study aims to utilize readily available clinical data and advanced machine learning algorithms to establish an accurate clinical prediction model. The overall objective is to provide effective decision support for clinicians. METHODS: Data from 239 patients from two centers were analyzed, focusing on clinical blood biomarkers (tumor markers, liver and kidney function, lipid profile, cardiovascular markers). Spearman correlation and the least absolute shrinkage and selection operator regression were employed for feature dimension reduction. A predictive model was built using LightGBM and validated in training, testing, and external validation cohorts. Feature importance correlation analysis was conducted on the clinical model and the comprehensive model, followed by univariate and multivariate regression analysis of these features. RESULTS: Through internal and external validation, we constructed a LightGBM model to predict de novo bone metastasis in newly diagnosed breast cancer patients. The area under the receiver operating characteristic curve values of this model in the training, internal validation test, and external validation test1 cohorts were 0.945, 0.892, and 0.908, respectively. Our validation results indicate that the model exhibits high sensitivity, specificity, and accuracy, making it the most accurate model for predicting bone metastasis in breast cancer patients. Carcinoembryonic Antigen, creatine kinase, albumin-globulin ratio, Apolipoprotein B, and Cancer Antigen 153 (CA153) play crucial roles in the model's predictions. Lipoprotein a, CA153, gamma-glutamyl transferase, α-Hydroxybutyrate dehydrogenase, alkaline phosphatase, and creatine kinase are positively correlated with breast cancer bone metastasis, while white blood cell ratio and total cholesterol are negatively correlated. CONCLUSION: This study successfully utilized clinical blood biomarkers to construct an artificial intelligence model for predicting distant metastasis in breast cancer, demonstrating high accuracy. This suggests potential clinical utility in predicting and identifying distant metastasis in breast cancer. These findings underscore the potential prospect of developing economically efficient and readily accessible predictive tools in clinical oncology.

PETA: evaluating the impact of protein transfer learning with sub-word tokenization on downstream applications.

Protein language models (PLMs) play a dominant role in protein representation learning. Most existing PLMs regard proteins as sequences of 20 natural amino acids. The problem with this representation method is that it simply divides the protein sequence into sequences of individual amino acids, ignoring the fact that certain residues often occur together. Therefore, it is inappropriate to view amino acids as isolated tokens. Instead, the PLMs should recognize the frequently occurring combinations of amino acids as a single token. In this study, we use the byte-pair-encoding algorithm and unigram to construct advanced residue vocabularies for protein sequence tokenization, and we have shown that PLMs pre-trained using these advanced vocabularies exhibit superior performance on downstream tasks when compared to those trained with simple vocabularies. Furthermore, we introduce PETA, a comprehensive benchmark for systematically evaluating PLMs. We find that vocabularies comprising 50 and 200 elements achieve optimal performance. Our code, model weights, and datasets are available at https://github.com/ginnm/ProteinPretraining . SCIENTIFIC CONTRIBUTION: This study introduces advanced protein sequence tokenization analysis, leveraging the byte-pair-encoding algorithm and unigram. By recognizing frequently occurring combinations of amino acids as single tokens, our proposed method enhances the performance of PLMs on downstream tasks. Additionally, we present PETA, a new comprehensive benchmark for the systematic evaluation of PLMs, demonstrating that vocabularies of 50 and 200 elements offer optimal performance.

Identification of DNase I hypersensitive sites in the human genome by multiple sequence descriptors.

DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.

AI models predicting breast cancer distant metastasis using LightGBM with clinical blood markers and ultrasound maximum diameter.

Breast cancer metastasis significantly impacts women's health globally. This study aimed to construct predictive models using clinical blood markers and ultrasound data to predict distant metastasis in breast cancer patients, ensuring clinical applicability, cost-effectiveness, relative non-invasiveness, and accessibility of these models. Analysis was conducted on data from 416 patients across two centers, focusing on clinical blood markers (tumor markers, liver and kidney function indicators, blood lipid markers, cardiovascular biomarkers) and maximum lesion diameter from ultrasound. Feature reduction was performed using Spearman correlation and LASSO regression. Two models were built using LightGBM: a clinical model (using clinical blood markers) and a combined model (incorporating clinical blood markers and ultrasound features), validated in training, internal test, and external validation (test1) cohorts. Feature importance analysis was conducted for both models, followed by univariate and multivariate regression analyses of these features. The AUC values of the clinical model in the training, internal test, and external validation (test1) cohorts were 0.950, 0.795, and 0.883, respectively. The combined model showed AUC values of 0.955, 0.835, and 0.918 in the training, internal test, and external validation (test1) cohorts, respectively. Clinical utility curve analysis indicated the combined model's superior net benefit in identifying breast cancer with distant metastasis across all cohorts. This suggests the combined model's superior discriminatory ability and strong generalization performance. Creatine kinase isoenzyme (CK-MB), CEA, CA153, albumin, creatine kinase, and maximum lesion diameter from ultrasound played significant roles in model prediction. CA153, CK-MB, lipoprotein (a), and maximum lesion diameter from ultrasound positively correlated with breast cancer distant metastasis, while indirect bilirubin and magnesium ions showed negative correlations. This study successfully utilized clinical blood markers and ultrasound data to develop AI models for predicting distant metastasis in breast cancer. The combined model, incorporating clinical blood markers and ultrasound features, exhibited higher accuracy, suggesting its potential clinical utility in predicting and identifying breast cancer distant metastasis. These findings highlight the potential prospects of developing cost-effective and accessible predictive tools in clinical oncology.

To reject, or to accept? A study on radiographers' perspectives on diagnostic radiographs.

INTRODUCTION: The overall reject rate (RR) of our newly set up Radiology department was an average of 14%, higher than the recommended 8% target and 10% threshold set by the American Association of Physicists in Medicine (AAPM). An analysis done to identify potential causes of a high RR suggested that radiographers might have been rejecting images of diagnostic value. A lack of consistency in the definition of a diagnostic value image amongst radiographers may be a possible cause in the higher overall RR. This study aims to investigate potential discrepancies among radiographers in defining a diagnostic radiograph. METHODS: An online survey composed of an image bank with a questionnaire was created, participants grade each image as either accepted or rejected. Fleiss Kappa was used to determine the level of agreement between the radiographers in accepting or rejecting the images in the image bank. RESULTS: Twenty radiographers with varying years of experience participated in this study. There was fair agreement amongst the radiographers' judgements, k=.277 (95% CI, .277 to .278), p < .005. Individual kappa for the "Accept" and "Reject" categories were both 0.277. There is no significant difference in the agreement level across the junior (k=.278), intermediate (k=.371) and senior (k=.275) radiographers. CONCLUSION: The result suggests that there is discrepancy in the radiographers' definition of a diagnostic radiograph and this misalignment of radiographers' perception might be one of the underlying causes of high RR. IMPLICATIONS FOR PRACTICE: This study has provided the researchers with a better insight on the underlying cause of the department high RR. By calibrating the radiographers' definition of a diagnostic radiograph, it will help realign the radiographer's agreement on when a radiograph should be rejected. This will reduce the overall RR and patient's overall dose. A lower RR translates to a more efficient turnaround time in General Radiography services, ensuring quality service is provided without further strain on our limited resources.

Development and validation of AI models using LR and LightGBM for predicting distant metastasis in breast cancer: a dual-center study.

Objective: This study aims to develop an artificial intelligence model utilizing clinical blood markers, ultrasound data, and breast biopsy pathological information to predict the distant metastasis in breast cancer patients. Methods: Data from two medical centers were utilized, Clinical blood markers, ultrasound data, and breast biopsy pathological information were separately extracted and selected. Feature dimensionality reduction was performed using Spearman correlation and LASSO regression. Predictive models were constructed using LR and LightGBM machine learning algorithms and validated on internal and external validation sets. Feature correlation analysis was conducted for both models. Results: The LR model achieved AUC values of 0.892, 0.816, and 0.817 for the training, internal validation, and external validation cohorts, respectively. The LightGBM model achieved AUC values of 0.971, 0.861, and 0.890 for the same cohorts, respectively. Clinical decision curve analysis showed a superior net benefit of the LightGBM model over the LR model in predicting distant metastasis in breast cancer. Key features identified included creatine kinase isoenzyme (CK-MB) and alpha-hydroxybutyrate dehydrogenase. Conclusion: This study developed an artificial intelligence model using clinical blood markers, ultrasound data, and pathological information to identify distant metastasis in breast cancer patients. The LightGBM model demonstrated superior predictive accuracy and clinical applicability, suggesting it as a promising tool for early diagnosis of distant metastasis in breast cancer.

Radioactive iodine therapy for thyroid cancer coexisting with Hashimoto's thyroiditis: a systematic review and meta-analysis.

OBJECTIVES: To investigate the clinical character of differentiated thyroid cancer (DTC) coexisting with Hashimoto's thyroiditis (HT) and provide state-of-art evidence for personalized radioactive iodine-131 therapy (RAIT) for patients coexisting with HT. METHODS: From January 2000 to January 2023, PubMed, Embase, and Web of Science databases were searched for relevant original articles that published in English on the RAIT efficacy for DTC with HT. RevMan 5.4 and Stata 17.0 were used for data analysis. RESULTS: Eleven studies involving 16 605 DTC patients (3321 with HT) were included. HT was more frequent in female (OR: 2.90, 95% confidence interval [CI]: 1.77-4.76, P < .00001). The size of tumour (MD: -0.20, 95% CI: -0.30 to -0.11), extrathyroidal extension rate (OR: 0.77, 95% CI: 0.67-0.90), and metastasis rate (OR: 0.18, 95% CI: 0.08-0.41) were less in HT, but tumour, node, metastasis (TNM) stage had no significant difference among HT and non-HT group. Disease-free survival (DFS) rate (OR: 1.96, 95% CI: 1.57-2.44, P < .00001), 5-year DFS (OR: 1.73, 95% CI: 1.04-2.89, P = .04), and 10-year DFS (OR: 1.56, 95% CI: 1.17-2.09, P = .003) were higher in HT group. The recurrent (OR: 0.62, 95% CI: 0.45-0.83, P = .002), RAIT dosage (MD = -38.71, 95% CI: -60.86 to -16.56, P = .0006), and treatment (MD: -0.13, 95% CI: -0.22 to -0.03, P = .008) were less in HT group. CONCLUSIONS: DTC coexisting with HT was associated with less invasion. DFS of HT group was higher than non-HT group after RAIT. Low-dose treatment did not impair the efficacy of RAIT in DTC with HT. ADVANCES IN KNOWLEDGE: Hashimoto's thyroiditis is a risk for DTC, but it minimalizes the progression of cancer and enhance the efficacy of RAIT, which should be considered in personalizing RAIT.

Free-space laser emission from Nd:YAG elliptical microdisks.

The utilization of deformed microcavities, such as elliptical microdisks, has been widely acknowledged as an effective solution for achieving free-space emission in microcavity lasers. However, the deformations introduced in the microcavity structure tend to decrease the quality factor (Q factor), resulting in weakened output intensity. To address this issue, one potential approach is to employ highly efficient laser gain media that can compensate for the negative impact of the structure on the output intensity. In this study, we employed the exceptional laser crystal material Nd:YAG as the laser gain medium and successfully fabricated an elliptical microdisk laser with a major semiaxis of 15 µm and an eccentricity ratio of 0.15. By utilizing an 808 nm laser for pumping, we were able to achieve free-space laser emission with a slope efficiency of 1.7% and a remarkable maximum output power of 58 µW. This work contributes toward the advancement of the application of deformation microcavity lasers.

Radiating blood flow signal: A new ultrasound feature of thyroid carcinoma.

OBJECTIVE: To summary radiating blood flow signals and evaluate their diagnostic value in differentiating benign and malignant thyroid nodules. MATERIALS AND METHODS: We retrospectively recruited consecutive patients undergoing US at 4 hospitals from 2018 to 2022. In a training dataset, the correlations of US features with malignant thyroid nodules were assessed by multivariate logistic analysis. Multivariate logistic regression models involving the ACR TI-RADS score, radiating blood flow signals and their combination were built and validated internally and externally. The AUC with 95% asymptotic normal confidence interval as well as sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) with 95% exact binomial confidence intervals were calculated. RESULTS: Among 2475 patients (1818 women, age: 42.47 ± 11.57; 657 men, age: 42.16 ± 11.69), there were 3187 nodules (2342 malignant nodules and 845 benign nodules). Radiating blood flow signals were an independent risk factor for diagnosing thyroid carcinoma. In the training set, the AUC of the model using the combination of radiating blood flow signals and the ACR TI-RADS score (0.95 95 % CI: [0.94, 0.97]; P < 0.001) was significantly higher than that of the ACR TI-RADS model (0.91 [0.89, 0.93]). In the two internal validation sets and the external validation set, the AUCs of the combination model were 0.97 [0.96, 0.98], 0.92 [0.88, 0.96], and 0.91 [0.86, 0.95], respectively, and were all significantly higher than that of the ACR TI-RADS score (0.92 [0.90, 0.95], 0.86 [0.81, 0.91], 0.84 [0.79, 0.89]; P < 0.001). CONCLUSION: Radiating blood flow is a new US feature of thyroid carcinomas that can significantly improve the diagnostic performance vs. the ACR TI-RADS score.

HSD17B13 liquid-liquid phase separation promotes leukocyte adhesion in chronic liver inflammation.

The rs72613567:TA polymorphism in 17-beta hydroxysteroid dehydrogenase 13 (HSD17B13) has been found to reduce the progression from steatosis to nonalcoholic steatohepatitis. In this study, we sought to define the pathogenic role of HSD17B13 in triggering liver inflammation. Here we find that HSD17B13 forms liquid-liquid phase separation (LLPS) around lipid droplets in the livers of nonalcoholic steatohepatitis patients. The dimerization of HSD17B13 supports the LLPS formation and promotes its enzymatic function. HSD17B13 LLPS increases the biosynthesis of platelet activating factor (PAF), which in turn promotes fibrinogen synthesis and leukocyte adhesion. Blockade of PAFR or STAT3 pathway inhibited the fibrinogen synthesis and leukocyte adhesion. Importantly, adeno-associated viral-mediated xeno-expression of human HSD17B13 exacerbated western diet/carbon tetrachloride-induced liver inflammation in Hsd17b13-/- mice. In conclusion, our results suggest that HSD17B13 LLPS triggers liver inflammation by promoting PAF-mediated leukocyte adhesion, and targeting HSD17B13 phase transition could be a promising therapeutic approach for treating hepatic inflammation in chronic liver disease.

Residents' willingness to engage in carbon generalized system of preferences - A personality insight study based on the extended theory of planned behavior.

In the context of China's dual carbon target, reducing personal carbon emissions has been identified as a crucial strategy to achieve the target. The 2022 Digital China Development Report emphasizes the significance of implementing the Carbon Generalized System of Preferences (CGSP) in driving individual carbon reduction efforts in China. However, the psychological factors influencing public participation in CGSP are still unknown. Based on the Extended Theory of Planned Behavior (TPB), this study explored the psychological factors of different personality trait groups' participation in the CGSP and categorized 712 respondents into Compatible, Positive, Responsible, and Susceptible based on the MINI-IPIP scale and the K-means method. The results show that the strength of willingness to engage (WTE) in CGSP was ranked as: Compatible > Positive > Responsible > Susceptible and the WTE of compatible groups is more influenced by attitude, while Perceived Behavioral Control (PBC) plays a more crucial role in other groups. Personal Norms (PN) and Policy Awareness (PA) were significant for all specific personality groups except the Susceptible group. Surprisingly subjective norms had little to do with WTE. We believe that policymakers should consider the impact of PBC on WTE when formulating policies and raise the expectation of residents in terms of the value they can obtain from participating in CGSP. Additionally, promotional activities related to PN and PA in connection with CGSP should be conducted. These efforts may help individuals better engage in CGSP.

Comparison of drug-eluting bead with conventional transcatheter arterial chemoembolization for hepatocellular carcinoma with portal vein tumor thrombus: A randomized clinical trial.

BACKGROUND: Drug-eluting bead transarterial chemoembolization (DEB-TACE) has shown efficacy for treating hepatocellular carcinoma (HCC) with portal vein tumor thrombus (PVTT). However, whether DEB-TACE is superior to conventional TACE (cTACE) remains unclear. OBJECTIVE: This randomized controlled trial aimed to compare the efficacy and safety of DEB-TACE versus cTACE in treating HCC with PVTT. METHODS: The study was conducted in a tertiary care center in Southeast China. HCC patients with PVTT were randomized at a 1:1 ratio to the DEB-TACE or cTACE groups. The primary endpoint was progression-free survival (PFS), and the secondary endpoints were overall survival (OS) and incidence of adverse events (AEs). An independent review committee assessed the radiologic response according to the modified Response Evaluation Criteria in Solid Tumors (mRECIST). AEs were assessed by the Common Terminology Criteria for Adverse Events (CTCAE) version 4.0. Systemic therapies were not limited. RESULTS: Between September 2018, and July 2020, 163 patients were randomized to undergo DEB-TACE (n=82) or cTACE (n=81). Nine patients were excluded, and 154 patients were included in the final analysis; the median age was 55 years (range, 24-78 y), and 140 (90.9%) were male. The median PFS in the DEB-TACE group was 6.0 months (95% CI, 5.0 to 10.0) versus 4.0 months (95% CI, 3.0 to 5.0) in the cTACE group (hazard ratio, 0.63; 95% CI, 0.42 to 0.95; P=0.027). The DEB-TACE group showed a higher response rate (51[66.2%] vs. 36 [46.8%]; P=0.0015) and a longer median OS (12.0 months [95% CI, 9.0 to 16.0] vs. 8.0 months [95% CI, 7.0 to 11.0], P=0.039) than the cTACE group. Multivariate analysis showed that the treatment group, ALBI score, distant metastasis and additional TKIs were the four independent prognostic factors correlated with PFS. In addition, the treatment group, PVTT group and combined with surgery were independently correlated with OS. AEs were similar in the two groups, and postembolization syndrome was the most frequent AEs. CONCLUSION: DEB-TACE is superior to cTACE in treating HCC patients with PVTT due to the improved PFS and OS with an acceptable safety profile and may become a promising treatment strategy for HCC patients with PVTT. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR1800018035.

Mechanism of Mulberry Leaves and Black Sesame in Alleviating Slow Transit Constipation Revealed by Multi-Omics Analysis.

Traditional Chinese medicine (TCM) possesses the potential of providing good curative effects with no side effects for the effective management of slow transit constipation (STC), an intestinal disease characterized by colonic dyskinesia. Mulberry leaves (Morus alba L.) and black sesame (Sesamum indicum L.), referred to as SH, are processed and conditioned as per standardized protocols. SH has applications as food and medicine. Accordingly, we investigated the therapeutic potential of SH in alleviating STC. The analysis of SH composition identified a total of 504 compounds. The intervention with SH significantly improved intestinal motility, reduced the time for the first black stool, increased antioxidant activity, and enhanced water content, thereby effectively alleviating colon damage caused by STC. Transcriptome analysis revealed the SH in the treatment of STC related to SOD1, MUC2, and AQP1. The analysis of 16S rRNA gene sequences indicated notable differences in the abundance of 10 bacteria between the SH and model. Metabolomic analysis further revealed that SH supplementation increased the levels of nine metabolites associated with STC. Integrative analysis revealed that SH modulated amino acid metabolism, balanced intestinal flora, and targeted key genes (i.e., SOD1, MUC2, AQP1) to exert its effects. SH also inhibited the AQP1 expression and promoted SOD1 and MUC2 expression.

Protein Engineering with Lightweight Graph Denoising Neural Networks.

Protein engineering faces challenges in finding optimal mutants from a massive pool of candidate mutants. In this study, we introduce a deep-learning-based data-efficient fitness prediction tool to steer protein engineering. Our methodology establishes a lightweight graph neural network scheme for protein structures, which efficiently analyzes the microenvironment of amino acids in wild-type proteins and reconstructs the distribution of the amino acid sequences that are more likely to pass natural selection. This distribution serves as a general guidance for scoring proteins toward arbitrary properties on any order of mutations. Our proposed solution undergoes extensive wet-lab experimental validation spanning diverse physicochemical properties of various proteins, including fluorescence intensity, antigen-antibody affinity, thermostability, and DNA cleavage activity. More than 40% of ProtLGN-designed single-site mutants outperform their wild-type counterparts across all studied proteins and targeted properties. More importantly, our model can bypass the negative epistatic effect to combine single mutation sites and form deep mutants with up to seven mutation sites in a single round, whose physicochemical properties are significantly improved. This observation provides compelling evidence of the structure-based model's potential to guide deep mutations in protein engineering. Overall, our approach emerges as a versatile tool for protein engineering, benefiting both the computational and bioengineering communities.

Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables.

OBJECTIVE: This retrospective observational study aims to develop and validate artificial intelligence (AI) pathomics models based on pathological Hematoxylin-Eosin (HE) slides and pathological immunohistochemistry (Ki67) slides for predicting the pathological staging of colorectal cancer. The goal is to enable AI-assisted accurate pathological staging, supporting healthcare professionals in making efficient and precise staging assessments. METHODS: This study included a total of 267 colorectal cancer patients (training cohort: n = 213; testing cohort: n = 54). Logistic regression algorithms were used to construct the models. The HE image features were used to build the HE model, the Ki67 image features were used for the Ki67 model, and the combined model included features from both the HE and Ki67 images, as well as tumor markers (CEA, CA724, CA125, and CA242). The predictive results of the HE model, Ki67 model, and tumor markers were visualized through a nomogram. The models were evaluated using ROC curve analysis, and their clinical value was estimated using decision curve analysis (DCA). RESULTS: A total of 260 deep learning features were extracted from HE or Ki67 images. The AUC for the HE model and Ki67 model in the training cohort was 0.885 and 0.890, and in the testing cohort, it was 0.703 and 0.767, respectively. The combined model and nomogram in the training cohort had AUC values of 0.907 and 0.926, and in the testing cohort, they had AUC values of 0.814 and 0.817. In clinical DCA, the net benefit of the Ki67 model was superior to the HE model. The combined model and nomogram showed significantly higher net benefits compared to the individual HE model or Ki67 model. CONCLUSION: The combined model and nomogram, which integrate pathomics multi-modal data and clinical-pathological variables, demonstrated superior performance in distinguishing between Stage I-II and Stage III colorectal cancer. This provides valuable support for clinical decision-making and may improve treatment strategies and patient prognosis. Furthermore, the use of immunohistochemistry (Ki67) slides for pathomics modeling outperformed HE slide, offering new insights for future pathomics research.

Remazolam affects the phenotype and function of astrocytes to improve traumatic brain injury by regulating the Cx43.

PURPOSE: To explore the mechanism by which Remazolam affects the phenotype and function of astrocytes to improve traumatic brain injury (TBI). METHODS: The oxygen -glucose deprivation/recovery (OGD/R) cell model was constructed to simulate the pathological state of astrocytes in a TBI environment. The viability of astrocytes was measured by CCK-8, and the cytoskeleton changes were observed by Phalloidin- TRITC staining. The expressions of differentiation markers, Cx43 and phosphorylated Cx43 (P-Cx43) of A1/A2 astrocytes were detected by Western blot, and the complement C3 and S100A10 of A1/A2 astrocytes were detected by ELISA. The TBI rat model was established. The water content of brain tissue was measured by dry-wet specific gravity method, the pathological morphology of brain tissue in cortical injury area was observed by HE staining method, ROS was detected by fluorescence quantitative method, Cx43 expression was detected by immunohistochemistry method, and the differentiation markers of A1/A2 astrocytes were detected by immunofluorescence. RESULTS: In the TBI environment, astrocytes showed decreased cell viability, blurred skeleton, and increased expression of Cx43. In TBI rats, the water content of brain tissue increased, the brain tissue in the cortex injury area was seriously damaged, ROS and Cx43 expression were significantly increased, and mainly distributed in A2 astrocytes. Remazolam can reverse the above results after the intervention. CONCLUSION: Remazolam affects the phenotype and function of astrocytes to improve TBI via regulating Cx43, and plays a role in protecting the neurological function of TBI rats.

Selected legacy and emerging organic contaminants in sediments of China's Yangtze - the world's third longest river: Response to anthropogenic activities.

To explore contaminant concerns as a result of anthropogenic disturbance of the river system, this study provided the first extensive investigation of the contamination profiles, possible driving factors, and ecological risks of 40 target compounds including pharmaceuticals and personal care products (PPCPs), neonicotinoid pesticides (NNIs), polybrominated diphenyl ethers (PBDEs), and polychlorinated biphenyls (PCBs) in sediments of the whole Yangtze River (the world's third longest river). Among these target compounds, PPCPs were the dominant contaminants with a total concentration (∑15PPCPs) of 2.13-14.99 ng/g, followed by ∑7PCBs (

Analysis of the potential application of a residential composite energy storage system based on a double-layer optimization model.

Along with the further integration of demand management and renewable energy technology, making optimal use of energy storage devices and coordinating operation with other devices are key. The present study takes into account the current situation of power storage equipment. Based on one year of measured data, four cases are designed for a composite energy storage system (ESS). In this paper, a two-tiered optimization model is proposed and is used to optimizing the capacity of power storage devices and the yearly production of the system. Furthermore, this paper performs a comparative analysis of the performance of the four cases from the energy, environmental and economic perspectives. It is concluded that this kind of energy storage equipment can enhance the economics and environment of residential energy systems. The thermal energy storage system (TESS) has the shortest payback period (7.84 years), and the CO2 emissions are the lowest. Coupled with future price volatility and the carbon tax, the electrothermal hybrid energy storage system (HESS) has good development potential. However, the current investment cost is very high, and it will not be possible to recover this cost in 10 years. Finally, it is recommended that the cost of equipment be reduced in combination with subsidies and incentives for further promotion. The research results not only fill a gap in the study area, but also provide some suggestions for further development of industry and research on user-side energy storage.