The relationship between Glasgow Prognostic Score and hospital duration in patients with inflammatory bowel diseases.

BACKGROUND AND OBJECTIVES: Both hypoalbuminemia and inflammation were common in patients with inflammatory bowel diseases (IBD), however, the combination of the two parameters on hospital duration re-mained unknown. METHODS AND STUDY DESIGN: This is a retrospective two-centre study performed in two tertiary hospitals in Shanghai, China. Serum levels of C-Reactive Protein (CRP) and albumin (ALB) were measured within 2 days of admission. Glasgow prognostic score (GPS), based on CRP and ALB, was calculated as follows: point "0" as CRP <10 mg/L and ALB ≥35 g/L; point "1" as either CRP ≥10 mg/L or ALB <35 g/L; point "2" as CRP ≥10 mg/L and ALB <35 g/L. Patients with point "0" were classified as low-risk while point "2" as high-risk. Length of hospital stay (LOS) was defined as the interval between admission and discharge. RESULTS: The proportion of low-risk and high-risk was 69.3% and 10.5% respectively among 3,009 patients (65% men). GPS was associated with LOS [ß=6.2 d; 95% CI (confidence interval): 4.0 d, 8.4 d] after adjustment of potential co-variates. Each point of GPS was associated with 2.9 days (95% CI: 1.9 d, 3.9 d; ptrend<0.001) longer in fully adjusted model. The association was stronger in patients with low prealbumin levels, hypocalcaemia, and hypokalaemia relative to their counterparts. CONCLUSIONS: GPS was associated with LOS in IBD patients. Our results highlighted that GPS could serve as a convenient prognostic tool associated with nutritional status and clinical outcome.

Combining blood pressure variability and heart rate variability to analyze the autonomic nervous function of rotenone induced Parkinson's rat model.

BACKGROUND: Parkinson's patients have significant autonomic dysfunction, early detect the disorder is a major challenge. To assess the autonomic function in the rat model of rotenone induced Parkinson's disease (PD), Blood pressure and ECG signal acquisition are very important. NEW METHOD: We used telemetry to record the electrocardiogram and blood pressure signals from awake rats, with linear and nonlinear analysis techniques calculate the heart rate variability (HRV) and blood pressure variability (BPV). we applied nonlinear analysis methods like sample entropy and detrended fluctuation analysis to analyze blood pressure signals. Particularly, this is the first attempt to apply nonlinear analysis to the blood pressure evaluate in rotenone induced PD model rat. RESULTS: HRV in the time and frequency domains indicated sympathetic-parasympathetic imbalance in PD model rats. Linear BPV analysis didn't reflect changes in vascular function and blood pressure regulation in PD model rats. Nonlinear analysis revealed differences in BPV, with lower sample entropy results and increased detrended fluctuation analysis results in the PD group rats. COMPARISON WITH EXISTING METHODS AND CONCLUSIONS: our experiments demonstrate the ability to evaluate autonomic dysfunction in models of Parkinson's disease by combining the analysis of BPV with HRV, consistent with autonomic impairment in PD patients. Nonlinear analysis by blood pressure signal may help in early detection of the PD. It indicates that the fluctuation of blood pressure in the rats in the rotenone model group tends to be regular and predictable, contributes to understand the PD pathophysiological mechanisms and to find strategies for early diagnosis.

Hyperglycemia Inhibits Hepatic SHBG Synthesis Through the NGBR-AMPK-HNF4 Pathway in Rats with Polycystic Ovary Syndrome Induced by Letrozole in Combination with a High-Fat Diet.

SCOPE: Polycystic ovary syndrome (PCOS) is closely related to non-alcoholic fatty liver disease (NAFLD), and sex hormone-binding globulin (SHBG) is a glycoprotein produced by the liver. Hepatic lipogenesis inhibits hepatic SHBG synthesis, which leads to hyperandrogenemia and ovarian dysfunction in PCOS. Therefore, this study aims to characterize the mechanism whereby liver lipogenesis inhibits SHBG synthesis. METHODS AND RESULTS: This study establishes a rat model of PCOS complicated by NAFLD using a high-fat diet in combination with letrozole and performs transcriptomic analysis of the liver. Transcriptomic analysis of the liver shows that the expression of neurite growth inhibitor-B receptor (NgBR), hepatocyte nuclear factor 4α (HNF4α), and SHBG is low. Meantime, HepG2 cells are treated with palmitic acid (PA) to model NAFLD in vitro, which causes decreases in the expression of NgBR, HNF4α, and SHBG. However, the expression of HNF4α and SHBG is restored by treatment with the AMP-activated protein kinase (AMPK) agonist AICAR. CONCLUSIONS: NgBR regulates the expression of HNF4α by activating the AMPK signaling pathway, thereby affecting the synthesis of SHBG in the liver. Further mechanistic studies regarding the effect of liver fat on NGBR expression are warranted.

Developmental and stem cell biology's bright future.

The next 50 years of developmental biology will illuminate exciting new discoveries but are also poised to provide solutions to important problems society faces. Ten scientists whose work intersects with developmental biology in various capacities tell us about their vision for the future.

Associations between metabolic dysfunction-associated fatty liver disease and atherosclerotic cardiovascular disease.

Non-alcoholic fatty liver disease (NAFLD) is closely related to metabolic syndrome and remains a major global health burden. The increased prevalence of obesity and type 2 diabetes mellitus (T2DM) worldwide has contributed to the rising incidence of NAFLD. It is widely believed that atherosclerotic cardiovascular disease (ASCVD) is associated with NAFLD. In the past decade, the clinical implications of NAFLD have gone beyond liver-related morbidity and mortality, with a majority of patient deaths attributed to malignancy, coronary heart disease (CHD), and other cardiovascular (CVD) complications. To better define fatty liver disease associated with metabolic disorders, experts proposed a new term in 2020 - metabolic dysfunction associated with fatty liver disease (MAFLD). Along with this new designation, updated diagnostic criteria were introduced, resulting in some differentiation between NAFLD and MAFLD patient populations, although there is overlap. The aim of this review is to explore the relationship between MAFLD and ASCVD based on the new definitions and diagnostic criteria, while briefly discussing potential mechanisms underlying cardiovascular disease in patients with MAFLD.

Revolutionizing breast cancer Ki-67 diagnosis: ultrasound radiomics and fully connected neural networks (FCNN) combination method.

PURPOSE: This study aims to assess the diagnostic value of ultrasound habitat sub-region radiomics feature parameters using a fully connected neural networks (FCNN) combination method L2,1-norm in relation to breast cancer Ki-67 status. METHODS: Ultrasound images from 528 cases of female breast cancer at the Affiliated Hospital of Xiangnan University and 232 cases of female breast cancer at the Affiliated Rehabilitation Hospital of Xiangnan University were selected for this study. We utilized deep learning methods to automatically outline the gross tumor volume and perform habitat clustering. Subsequently, habitat sub-regions were extracted to identify radiomics features and underwent feature engineering using the L1,2-norm. A prediction model for the Ki-67 status of breast cancer patients was then developed using a FCNN. The model's performance was evaluated using accuracy, area under the curve (AUC), specificity (Spe), positive predictive value (PPV), negative predictive value (NPV), Recall, and F1. In addition, calibration curves and clinical decision curves were plotted for the test set to visually assess the predictive accuracy and clinical benefit of the models. RESULT: Based on the feature engineering using the L1,2-norm, a total of 9 core features were identified. The predictive model, constructed by the FCNN model based on these 9 features, achieved the following scores: ACC 0.856, AUC 0.915, Spe 0.843, PPV 0.920, NPV 0.747, Recall 0.974, and F1 0.890. Furthermore, calibration curves and clinical decision curves of the validation set demonstrated a high level of confidence in the model's performance and its clinical benefit. CONCLUSION: Habitat clustering of ultrasound images of breast cancer is effectively supported by the combined implementation of the L1,2-norm and FCNN algorithms, allowing for the accurate classification of the Ki-67 status in breast cancer patients.

Diosmetin ameliorates osteoarthritic inflammation in vivo and ECM macromolecules degradation in interleukin-1ß-stimulated murine chondrocytes through the Nrf2/NF-κB pathway.

INTRODUCTION: Osteoarthritis (OA) is a prevailing degenerative disease in elderly population and can lead to severe joint dysfunction. Studies have revealed various pharmacological activities of diosmetin, including the anti-OA efficacy. The present study further investigated its effect on interleukin (IL)-1ß-induced OA in chondrocytes. MATERIAL AND METHODS: Primary chondrocytes were isolated from young mice, stimulated with IL-1ß (10 ng/mL), and pretreated with diosmetin (10 and 20 µM) to conduct the in vitro assays. CCK-8 assay assessed the cytotoxicity of diosmetin whereas the levels of inflammatory factors (PGE2, nitrite, TNF-α, and IL-6) in homogenized cells were evaluated by ELISA. The levels of inflammatory cytokines, content of extracellular matrix (ECM), and signaling-related proteins (Nrf2, HO-1, and NF-κB p65) were assessed by western blotting. Expression of collagen II, p65, and Nrf2 in the chondrocytes was confirmed by immunofluorescence staining. The chondrocytes treated with IL-1ß and diosmetin were transfected with Nrf2 knockdown plasmid (si-Nrf2) to investigate the role of Nrf2. In vivo OA mouse model was induced by surgically destabilizing the medial meniscus (DMM). Safranin O staining was conducted to assess the OA severity in the knee-joint tissue. RESULTS: Diosmetin suppressed the expression of iNOS, COX-2, PGE2, nitrite, TNF-α, IL-6, MMP-13, and ADAMTS-5 induced by IL-1ß in chondrocytes. The expression of p-p65, p-IκBα, and nuclear p65 was decreased whereas that of Nrf2 and HO-1 increased by diosmetin treatment in IL-1ß-treated chondrocytes. Nrf2 knockdown by siRNA reversed the inhibitory effect of diosmetin on IL-1ß-induced degradation of ECM proteins and inflammatory factors in cultured chondrocytes. In the DMM-induced model of OA, diosmetin alleviated cartilage degeneration and decreased the Osteoarthritis Research Society International score. C: ONCLUSIONS: Diosmetin ameliorates expression of inflammation biomarkers and ECM macromolecules degradation in cultured murine chondrocytes via inactivation of NF-κB signaling by activating Nrf2/HO-1 signaling pathway.

Multi-risk factors joint prediction model for risk prediction of retinopathy of prematurity.

Purpose: Retinopathy of prematurity (ROP) is a retinal vascular proliferative disease common in low birth weight and premature infants and is one of the main causes of blindness in children.In the context of predictive, preventive and personalized medicine (PPPM/3PM), early screening, identification and treatment of ROP will directly contribute to improve patients' long-term visual prognosis and reduce the risk of blindness. Thus, our objective is to establish an artificial intelligence (AI) algorithm combined with clinical demographics to create a risk model for ROP including treatment-requiring retinopathy of prematurity (TR-ROP) infants. Methods: A total of 22,569 infants who underwent routine ROP screening in Shenzhen Eye Hospital from March 2003 to September 2023 were collected, including 3335 infants with ROP and 1234 infants with TR-ROP among ROP infants. Two machine learning methods of logistic regression and decision tree and a deep learning method of multi-layer perceptron were trained by using the relevant combination of risk factors such as birth weight (BW), gestational age (GA), gender, whether multiple births (MB) and mode of delivery (MD) to achieve the risk prediction of ROP and TR-ROP. We used five evaluation metrics to evaluate the performance of the risk prediction model. The area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCPR) were the main measurement metrics. Results: In the risk prediction for ROP, the BW + GA demonstrated the optimal performance (mean ± SD, AUCPR: 0.4849 ± 0.0175, AUC: 0.8124 ± 0.0033). In the risk prediction of TR-ROP, reasonable performance can be achieved by using GA + BW + Gender + MD + MB (AUCPR: 0.2713 ± 0.0214, AUC: 0.8328 ± 0.0088). Conclusions: Combining risk factors with AI in screening programs for ROP could achieve risk prediction of ROP and TR-ROP, detect TR-ROP earlier and reduce the number of ROP examinations and unnecessary physiological stress in low-risk infants. Therefore, combining ROP-related biometric information with AI is a cost-effective strategy for predictive diagnostic, targeted prevention, and personalization of medical services in early screening and treatment of ROP.

Precision dose prediction for breast cancer patients undergoing IMRT: The Swin-UMamba-Channel Model.

BACKGROUND: Radiation therapy is one of the crucial treatment modalities for cancer. An excellent radiation therapy plan relies heavily on an outstanding dose distribution map, which is traditionally generated through repeated trials and adjustments by experienced physicists. However, this process is both time-consuming and labor-intensive, and it comes with a degree of subjectivity. Now, with the powerful capabilities of deep learning, we are able to predict dose distribution maps more accurately, effectively overcoming these challenges. METHODS: In this study, we propose a novel Swin-UMamba-Channel prediction model specifically designed for predicting the dose distribution of patients with left breast cancer undergoing radiotherapy after total mastectomy. This model integrates anatomical position information of organs and ray angle information, significantly enhancing prediction accuracy. Through iterative training of the generator (Swin-UMamba) and discriminator, the model can generate images that closely match the actual dose, assisting physicists in quickly creating DVH curves and shortening the treatment planning cycle. Our model exhibits excellent performance in terms of prediction accuracy, computational efficiency, and practicality, and its effectiveness has been further verified through comparative experiments with similar networks. RESULTS: The results of the study indicate that our model can accurately predict the clinical dose of breast cancer patients undergoing intensity-modulated radiation therapy (IMRT). The predicted dose range is from 0 to 50 Gy, and compared with actual data, it shows a high accuracy with an average Dice similarity coefficient of 0.86. Specifically, the average dose change rate for the planning target volume ranges from 0.28 % to 1.515 %, while the average dose change rates for the right and left lungs are 2.113 % and 0.508 %, respectively. Notably, due to their small sizes, the heart and spinal cord exhibit relatively higher average dose change rates, reaching 3.208 % and 1.490 %, respectively. In comparison with similar dose studies, our model demonstrates superior performance. Additionally, our model possesses fewer parameters, lower computational complexity, and shorter processing time, further enhancing its practicality and efficiency. These findings provide strong evidence for the accuracy and reliability of our model in predicting doses, offering significant technical support for IMRT in breast cancer patients. CONCLUSION: This study presents a novel Swin-UMamba-Channel dose prediction model, and its results demonstrate its precise prediction of clinical doses for the target area of left breast cancer patients undergoing total mastectomy and IMRT. These remarkable achievements provide valuable reference data for subsequent plan optimization and quality control, paving a new path for the application of deep learning in the field of radiation therapy.

A fundus image dataset for intelligent retinopathy of prematurity system.

Image-based artificial intelligence (AI) systems stand as the major modality for evaluating ophthalmic conditions. However, most of the currently available AI systems are designed for experimental research using single-central datasets. Most of them fell short of application in real-world clinical settings. In this study, we collected a dataset of 1,099 fundus images in both normal and pathologic eyes from 483 premature infants for intelligent retinopathy of prematurity (ROP) system development and validation. Dataset diversity was visualized with a spatial scatter plot. Image classification was conducted by three annotators. To the best of our knowledge, this is one of the largest fundus datasets on ROP, and we believe it is conducive to the real-world application of AI systems.

Analysis of the situations and influencing factors of public anxiety in China: based on Baidu index data.

Background: Anxiety disorders have emerged as one of the most prevalent mental health problems and health concerns. However, previous research has paid limited attention to measuring public anxiety from a broader perspective. Furthermore, while we know many factors that influence anxiety disorders, we still have an incomplete understanding of how these factors affect public anxiety. We aimed to quantify public anxiety from the perspective of Internet searches, and to analyze its spatiotemporal changing characteristics and influencing factors. Methods: This study collected Baidu Index from 2014 to 2022 in 31 provinces in mainland China to measure the degree of public anxiety based on the Baidu Index from 2014 to 2022. The spatial autocorrelation analysis method was used to study the changing trends and spatial distribution characteristics of public anxiety. The influencing factors of public anxiety were studied using spatial statistical modeling methods. Results: Empirical analysis shows that the level of public anxiety in my country has continued to rise in recent years, with significant spatial clustering characteristics, especially in the eastern and central-southern regions. In addition, we constructed ordinary least squares (OLS) and geographically weighted regression (GWR) spatial statistical models to examine the relationship between social, economic, and environmental factors and public anxiety levels. We found that the GWR model that considers spatial correlation and dependence is significantly better than the OLS model in terms of fitting accuracy. Factors such as the number of college graduates, Internet traffic, and urbanization rate are significantly positively correlated with the level of public anxiety. Conclusion: Our research results draw attention to public anxiety among policymakers, highlighting the necessity for a more extensive examination of anxiety issues, especially among university graduates, by the public and relevant authorities.

Systematic review of the economic evaluation model of assisted reproductive technology.

BACKGROUND: With the increasing demand for fertility services, it is urgent to select the most cost-effective assisted reproductive technology (ART) treatment plan and include it in medical insurance. Economic evaluation reports are an important reference for medical insurance negotiation. The aim of this study is to systematically evaluate the economic evaluation research of ART, analyze the existing shortcomings, and provide a reference for the economic evaluation of ART. METHODS: PubMed, EMbase, Web of Science, Cochrane Library and ScienceDirect databases were searched for relevant articles on the economic evaluation of ART. These articles were screened, and their quality was evaluated based on the Comprehensive Health Economics Evaluation Report Standard (CHEERS 2022), and the data on the basic characteristics, model characteristics and other aspects of the included studies were summarized. RESULTS: One hundred and two related articles were obtained in the preliminary search, but based on the inclusion criteria, 12 studies were used for the analysis, of which nine used the decision tree model. The model parameters were mainly derived from published literature and included retrospective clinical data of patients. Only two studies included direct non-medical and indirect costs in the cost measurement. Live birth rate was used as an outcome indicator in half of the studies. CONCLUSION: Suggesting the setting of the threshold range in the field of fertility should be actively discussed, and the monetary value of each live birth is assumed to be in a certain range when the WTP threshold for fertility is uncertain. The range of the parameter sources should be expanded. Direct non-medical and indirect costs should be included in the calculation of costs, and the analysis should be carried out from the perspective of the whole society. In the evaluation of clinical effect, the effectiveness and safety indexes should be selected for a comprehensive evaluation, thereby making the evaluation more comprehensive and reliable. At least subgroup analysis based on age stratification should be considered in the relevant economic evaluation.

A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma.

OBJECTIVES: At present, there are many limitations in the evaluation of lymph node metastasis of lung adenocarcinoma. Currently, there is a demand for a safe and accurate method to predict lymph node metastasis of lung cancer. In this study, radiomics was used to accurately predict the lymph node status of lung adenocarcinoma patients based on contrast-enhanced CT. METHODS: A total of 503 cases that fulfilled the analysis requirements were gathered from two distinct hospitals. Among these, 287 patients exhibited lymph node metastasis (LNM +) while 216 patients were confirmed to be without lymph node metastasis (LNM-). Using both traditional and deep learning methods, 22,318 features were extracted from the segmented images of each patient's enhanced CT. Then, the spearman test and the least absolute shrinkage and selection operator were used to effectively reduce the dimension of the feature data, enabling us to focus on the most pertinent features and enhance the overall analysis. Finally, the classification model of lung adenocarcinoma lymph node metastasis was constructed by machine learning algorithm. The Accuracy, AUC, Specificity, Precision, Recall and F1 were used to evaluate the efficiency of the model. RESULTS: By incorporating a comprehensively selected set of features, the extreme gradient boosting method (XGBoost) effectively distinguished the status of lymph nodes in patients with lung adenocarcinoma. The Accuracy, AUC, Specificity, Precision, Recall and F1 of the prediction model performance on the external test set were 0.765, 0.845, 0.705, 0.784, 0.811 and 0.797, respectively. Moreover, the decision curve analysis, calibration curve and confusion matrix of the model on the external test set all indicated the stability and accuracy of the model. CONCLUSIONS: Leveraging enhanced CT images, our study introduces a noninvasive classification prediction model based on the extreme gradient boosting method. This approach exhibits remarkable precision in identifying the lymph node status of lung adenocarcinoma patients, offering a safe and accurate alternative to invasive procedures. By providing clinicians with a reliable tool for diagnosing and assessing disease progression, our method holds the potential to significantly improve patient outcomes and enhance the overall quality of clinical practice.

Protocol for generation of and high-throughput drug testing with patient-derived colorectal cancer organoids.

Drug sensitivity testing of patient-derived tumor organoids (PDTOs) is a promising tool for personalizing cancer treatment. Here, we present a protocol for generation of and high-throughput drug testing with PDTOs. We describe detailed steps for PDTO establishment from colorectal cancer tissues, preparation of PDTOs for high-throughput drug testing, and quantification of drug testing results using image analysis. This protocol provides a standardized workflow for PDTO testing of standard-of-care therapies, along with exploring the activity of new agents, for translational research. For complete details on the use and execution of this protocol, please refer to Tan et al.1.

Reinvestigating the performance of artificial intelligence classification algorithms on COVID-19 X-Ray and CT images.

There are concerns that artificial intelligence (AI) algorithms may create underdiagnosis bias by mislabeling patient individuals with certain attributes (e.g., female and young) as healthy. Addressing this bias is crucial given the urgent need for AI diagnostics facing rapidly spreading infectious diseases like COVID-19. We find the prevalent AI diagnostic models show an underdiagnosis rate among specific patient populations, and the underdiagnosis rate is higher in some intersectional specific patient populations (for example, females aged 20-40 years). Additionally, we find training AI models on heterogeneous datasets (positive and negative samples from different datasets) may lead to poor model generalization. The model's classification performance varies significantly across test sets, with the accuracy of the better performance being over 40% higher than that of the poor performance. In conclusion, we developed an AI bias analysis pipeline to help researchers recognize and address biases that impact medical equality and ethics.

Injectable temperature-sensitive hydrogel facilitating endoscopic submucosal dissection.

Purpose: Early gastrointestinal tumors can be removed by endoscopic procedures. Endoscopic mucosal dissection (ESD) requires submucosal fluid injection to provide mucosal elevation and prevent intraoperative perforation. However, the clinically applied normal saline mucosal elevation height is low for a short time, which often requires multiple intraoperative injections that increase the inconvenience and procedure time. In addition, recently researched submucosal injection materials (SIM) suffer from complex preparation, poor economy, and poor biocompatibility. Therefore, there is an urgent need for a new type of SIM that can provide long, safe and effective mucosal elevation in support of the endoscopic procedures. Methods: The FS hydrogel is based on polyethylene-polypropylene glycol (F-127) mixed with sodium alginate (SA). The different physicochemical properties of FS hydrogels were characterized through various experiments. Afterward, various biosafety assessments were carried out. Finally, the performance of FS hydrogels was evaluated by in vitro submucosal injection and in vivo swine ESD. Results: The experimental results show that the FS hydrogel is liquid at room temperature, making it easy to inject, and when injected under the mucosa, it undergoes temperature-induced cross-linking, transforming from a liquid to a solid state to provide long-lasting mucosal augmentation. At the same time, the FS hydrogel exhibits controllable gelation, stability, and biocompatibility. The results of in vitro submucosal injections and in vivo ESD procedures showed that FS achieves high mucosal augmentation and provides good submucosal cushioning in the long term. Conclusion: In summary, the F-127/SA hydrogel is simple to synthesize, cost-effective, safe, easy to store, and able to assist ESD well from the perspective of practical clinical problems, indicating that the FS hydrogel can be an ideal potent submucosal injection substitution.

Harnessing artificial intelligence for prostate cancer management.

Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology. This review highlights the success of AI in detecting and grading PCa, predicting patient outcomes, and identifying molecular subtypes. We propose that AI-based methods could collaborate with pathologists to reduce workload and assist clinicians in formulating treatment recommendations. We also introduce the general process and challenges in developing AI pathology models for PCa. Importantly, we summarize publicly available datasets and open-source codes to facilitate the utilization of existing data and the comparison of the performance of different models to improve future studies.

Xiaoqing: A Q&A model for glaucoma based on LLMs.

Glaucoma is one of the leading cause of blindness worldwide. Individuals affected by glaucoma, including patients and their family members, frequently encounter a deficit in dependable support beyond the confines of clinical environments. Seeking advice via the internet can be a difficult task due to the vast amount of disorganized and unstructured material available on these sites, nevertheless. This research explores how Large Language Models (LLMs) can be leveraged to better serve medical research and benefit glaucoma patients. We introduce Xiaoqing, a Natural Language Processing (NLP) model specifically tailored for the glaucoma field, detailing its development and deployment. To evaluate its effectiveness, we conducted two forms of experiments: comparative and experiential. In the comparative analysis, we presented 22 glaucoma-related questions in simplified Chinese to three medical NLP models (Xiaoqing LLMs, HuaTuo, Ivy GPT) and two general models (ChatGPT-3.5 and ChatGPT-4), covering a range of topics from basic glaucoma knowledge to treatment, surgery, research, management standards, and patient lifestyle. Responses were assessed for informativeness and readability. The experiential experiment involved glaucoma patients and non-patients interacting with Xiaoqing, collecting and analyzing their questions and feedback on the same criteria. The findings demonstrated that Xiaoqing notably outperformed the other models in terms of informativeness and readability, suggesting that Xiaoqing is a significant advancement in the management and treatment of glaucoma in China. We also provide a Web-based version of Xiaoqing, allowing readers to directly experience its functionality. The Web-based Xiaoqing is available at https://qa.glaucoma-assistant.com//qa.

Prediction and visualization map for physicochemical indices of kiwifruits by hyperspectral imaging.

Soluble solid content (SSC), firmness, and color (L*, a*, and b*) are important physicochemical indices for assessing the quality and maturity of kiwifruits. Therefore, this research aimed to realize the nondestructive detection and visualization map for the physicochemical indices of kiwifruits at different maturity stages by hyperspectral imaging coupled with the chemometrics. To further improve the detection accuracy and working efficiency of the models, competitive adaptive reweighted sampling (CARS) and successive projection algorithm were employed to choose feature wavelengths for predicting the physicochemical indices of kiwifruits. Multiple linear regression (MLR) was designed to develop simplified detection models based on feature wavelengths for determining the physicochemical indices of kiwifruits. The results showed that 32, 18, 26, 29, and 32 feature wavelengths were extracted from 256 full wavelengths to predict the SSC, firmness, L*, a*, and b*, respectively, with the CARS algorithm. Not only was the working efficiency of the CARS-MLR model improved, but the prediction accuracy of the CARS-MLR model for determining the physicochemical indices was also at its relative best. The residual predictive deviations of the CARS-MLR model for determining the SSC, firmness, L*, a*, and b* were 3.09, 2.90, 2.32, 2.74, and 2.91, respectively, which were all above 2.3. Compared with the model based on the full spectra, the CARS-MLR model could be used to predict the physicochemical indices of kiwifruits. Finally, the visualization map for the physicochemical indices of kiwifruits at different maturity stages was generated by calculating the spectral response of each pixel on the kiwifruit samples with the CARS-MLR model. This made the detection for the physicochemical indices of kiwifruits more intuitive. This study demonstrates that hyperspectral imaging coupled with the chemometrics is promising for the nondestructive detection and visualization map for the physicochemical indices of kiwifruits, and also provides a novel theoretical basis for the nondestructive detection of kiwifruit quality.

BCL-XL inhibitors enhance the apoptotic efficacy of BRAF inhibitors in BRAFV600E colorectal cancer.

Metastatic BRAFV600E colorectal cancer (CRC) carries an extremely poor prognosis and is in urgent need of effective new treatments. While the BRAFV600E inhibitor encorafenib in combination with the EGFR inhibitor cetuximab (Enc+Cet) was recently approved for this indication, overall survival is only increased by 3.6 months and objective responses are observed in only 20% of patients. We have found that a limitation of Enc+Cet treatment is the failure to efficiently induce apoptosis in BRAFV600E CRCs, despite inducing expression of the pro-apoptotic protein BIM and repressing expression of the pro-survival protein MCL-1. Here, we show that BRAFV600E CRCs express high basal levels of the pro-survival proteins MCL-1 and BCL-XL, and that combining encorafenib with a BCL-XL inhibitor significantly enhances apoptosis in BRAFV600E CRC cell lines. This effect was partially dependent on the induction of BIM, as BIM deletion markedly attenuated BRAF plus BCL-XL inhibitor-induced apoptosis. As thrombocytopenia is an established on-target toxicity of BCL-XL inhibition, we also examined the effect of combining encorafenib with the BCL-XL -targeting PROTAC DT2216, and the novel BCL-2/BCL-XL inhibitor dendrimer conjugate AZD0466. Combining encorafenib with DT2216 significantly increased apoptosis induction in vitro, while combining encorafenib with AZD0466 was well tolerated in mice and further reduced growth of BRAFV600E CRC xenografts compared to either agent alone. Collectively, these findings demonstrate that combined BRAF and BCL-XL inhibition significantly enhances apoptosis in pre-clinical models of BRAFV600E CRC and is a combination regimen worthy of clinical investigation to improve outcomes for these patients.