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.

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.

Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning.

AIM: Patients with advanced gastrointestinal stromal tumors (GISTs) exhibiting an imatinib plasma trough concentration (IM Cmin) under 1100 ng/ml may show a reduced drug response rate, leading to the suggestion of monitoring for IM Cmin. Consequently, the objective of this research was to create a customized IM Cmin classification model for patients with advanced GISTs from China. METHODS: Initial data and laboratory indicators from patients with advanced GISTs were gathered, and the above information was segmented into a training set, validation set, and testing set in a 6:2:2 ratio. Key variables associated with IM Cmin were identified to construct the classification model using the least absolute shrinkage and selection operator (LASSO) regression and forward stepwise binary logistic regression. Within the training and validation sets, nine ML classification models were constructed via the resampling method and underwent comparison through the Brier scores, the areas under the receiver-operating characteristic curve (AUROC), the decision curve, and the precision-recall (AUPR) curve to determine the most suitable model for this dataset. Two methods of internal validation were used to assess the most suitable model's classification performance: tenfold cross-validation and random split-sample validation (test set), and the value of the test set AUROC was used to evaluate the model's classification performance. RESULTS: Six key variables (gender, daily IM dose, metastatic site, red blood cell count, platelet count, and percentage of neutrophils) were ultimately selected to construct the classification model. In the validation set, it is found by comparison that the Extreme Gradient Boosting (XGBoost) model has the largest AUROC, the lowest Brier score, the largest area under the decision curve, and the largest AUPR value. Furthermore, as evaluated via internal verification, it also performed well in the test set (AUROC = 0.725). CONCLUSION: For patients with advanced GISTs who receive IM, initial data and laboratory indicators could be used to accurately estimate whether the IM Cmin is below 1100 ng/ml. The XGBoost model may stand a chance to assist clinicians in directing the administration of IM.

Effect of artificial intelligence on novice-performed colonoscopy: a multicenter randomized controlled tandem study.

BACKGROUND AND AIMS: The efficacy and safety of colonoscopy performed by artificial intelligence (AI)-assisted novices remain unknown. The aim of this study was to compare the lesion detection capability of novices, AI-assisted novices, and experts. METHODS: This multicenter, randomized, noninferiority tandem study was conducted across 3 hospitals in China from May 1, 2022, to November 11, 2022. Eligible patients were randomized into 1 of 3 groups: the CN group (control novice group, withdrawal performed by a novice independently), the AN group (AI-assisted novice group, withdrawal performed by a novice with AI assistance), or the CE group (control expert group, withdrawal performed by an expert independently). Participants underwent a repeat colonoscopy conducted by an AI-assisted expert to evaluate the lesion miss rate and ensure lesion detection. The primary outcome was the adenoma miss rate (AMR). RESULTS: A total of 685 eligible patients were analyzed: 229 in the CN group, 227 in the AN group, and 229 in the CE group. Both AMR and polyp miss rate were lower in the AN group than in the CN group (18.82% vs 43.69% [P < .001] and 21.23% vs 35.38% [P < .001], respectively). The noninferiority margin was met between the AN and CE groups of both AMR and polyp miss rate (18.82% vs 26.97% [P = .202] and 21.23% vs 24.10% [P < .249]). CONCLUSIONS: AI-assisted colonoscopy lowered the AMR of novices, making them noninferior to experts. The withdrawal technique of new endoscopists can be enhanced by AI-assisted colonoscopy. (Clinical trial registration number: NCT05323279.).

Feasibility of a novel unassisted single-channel transcolonic endoscopic appendectomy for the treatment of appendiceal lesions (with video).

BACKGROUND: Transcolonic endoscopic appendectomy (TEA) is rapidly evolving and has been reported as a minimally invasive alternative to appendectomy. We aimed to characterize the feasibility and safety of a novel unassisted single-channel TEA. METHOD: We retrospectively investigated 23 patients with appendicitis or appendiceal lesions who underwent TEA from February 2016 to December 2022. We collected clinicopathological characteristics, procedure­related parameters, and follow­up data and analyzed the impact of previous abdominal surgery and traction technique. RESULTS: The mean age was 56.0 years. Of the 23 patients with appendiceal lesions, fourteen patients underwent TEA and nine underwent traction-assisted TEA (T-TEA). Eight patients (34.8%) had previous abdominal surgery. The En bloc resection rate was 95.7%. The mean procedure duration was 91.1 ± 45.5 min, and the mean wound closure time was 29.4 ± 18.6 min. The wounds after endoscopic appendectomy were closed with clips (21.7%) or a combination of clip closure and endoloop reinforcement (78.3%), and the median number of clips was 7 (range, 3-15). Three patients (13.0%) experienced major adverse events, including two delayed perforations (laparoscopic surgery) and one infection (salvage endoscopic suture). During a median follow-up of 23 months, no residual or recurrent lesions were observed, and no recurrence of abdominal pain occurred. There were no significant differences between TEA and T-TEA groups and between patients with and without abdominal surgery groups in each factor. CONCLUSION: Unassisted single-channel TEA for patients with appendiceal lesions has favorable short- and long-term outcomes. TEA can safely and effectively treat appendiceal disease in appropriately selected cases.

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.

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.

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.

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.

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.

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.

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.

Exploring the causal association between rheumatoid arthritis and the risk of cervical cancer: a two-sample Mendelian randomization study.

OBJECTIVE: Whether rheumatoid arthritis patients have an increased risk of cervical cancer remains controversial, and further research is needed on this clinical question. This study aims to investigate the association between rheumatoid arthritis and the susceptibility to cervical cancer by employing Mendelian randomization methodology, utilizing the extensive dataset from human genome-wide association data analysis. METHODS: The publicly accessible MR base database was utilized to obtain the complete genome, relevant research findings, and summarized data pertaining to rheumatoid arthritis and cervical cancer. Genetic tool variables, specifically single-nucleotide polymorphisms closely linked to rheumatoid arthritis, were chosen for analysis. Four methods, namely inverse variance weighted analysis, weighted median analysis, weighted mode, and MR-Egger regression, were employed. Statistical analysis was conducted to explore the potential association between rheumatoid arthritis and susceptibility to cervical cancer. RESULTS: The results of the inverse variance weighted analysis (OR = 1.096, 95% CI: 1.018-1.180, P = 0.015) indicate a significant causal relationship between rheumatoid arthritis and an increased risk of cervical cancer. Furthermore, the absence of horizontal pleiotropic effects (MR-Egger intercept = 0.00025, P = 0.574) and heterogeneity (QEgger = 2.239, I2Egger = 0.225, PEgger = 0.268, QIVW = 2.734, I2IVW = 0.220, PIVW = 0.999) suggests that the observed association is not influenced by confounding factors. Sensitivity analysis and other statistical methods also support the conclusion that genetic pleiotropy does not introduce bias to the findings. CONCLUSION: There is a causal relationship between rheumatoid arthritis and the occurrence of cervical cancer. People with rheumatoid arthritis is one of the high-risk groups for early screening of cervical cancer. The IL-18 may play a significant role in elevating the risk of cervical cancer among rheumatoid arthritis 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. CONCLUSIONS: 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.

Fast detection of moisture content and freshness for loquats using optical fiber spectroscopy.

Detection of the moisture content (MC) and freshness for loquats is crucial for achieving optimal taste and economic efficiency. Traditional methods for evaluating the MC and freshness of loquats have disadvantages such as destructive sampling and time-consuming. To investigate the feasibility of rapid and non-destructive detection of the MC and freshness for loquats, optical fiber spectroscopy in the range of 200-1000 nm was used in this study. The full spectra were pre-processed using standard normal variate method, and then, the effective wavelengths were selected using competitive adaptive weighting sampling (CARS) and random frog algorithms. Based on the selected effective wavelengths, prediction models for MC were developed using partial least squares regression (PLSR), multiple linear regression, extreme learning machine, and back-propagation neural network. Furthermore, freshness level discrimination models were established using simplified k nearest neighbor, support vector machine (SVM), and partial least squares discriminant analysis. Regarding the prediction models, the CARS-PLSR model performed relatively better than the other models for predicting the MC, with R 2 P and RPD values of 0.84 and 2.51, respectively. Additionally, the CARS-SVM model obtained superior discrimination performance, with 100% accuracy for both calibration and prediction sets. The results demonstrated that optical fiber spectroscopy technology is an effective tool to fast detect the MC and freshness for loquats.

Establishing a 3D culture system for early organogenesis of monkey embryos ex vivo and single-cell transcriptome analysis of cultured embryos.

Creating in vitro culture platforms for monkey embryos is crucial for understanding the initial 4 weeks of early primate embryogenesis. Here, we present a protocol to culture cynomolgus monkey embryos in vitro for 25 days post-fertilization and to delineate the key developmental events of gastrulation and early organogenesis. We describe steps for culturing with a 3D system, immunofluorescence analysis, single-cell RNA sequencing, and bioinformatic analysis. For complete details on the use and execution of this protocol, please refer to Gong et al. (2023).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.

Significance of N6-methyladenosine RNA methylation regulators in diagnosis and subtype classification of primary Sjögren's syndrome.

The importance of N6-methyladenine (m6A) in mRNA metabolism, physiology, pathology and other life processes is well recognized. However, the exact role of m6A regulators in primary Sjögren's syndrome (PSS) remains unclear. In this study, we used bioinformatics and machine learning random forest approach to screen eight key m6A regulators from the Gene Expression Omnibus GSE7451, GSE40611 and GSE84844 datasets. An accurate nomogram model for predicting PSS risk was established based on these regulators. And using consensus clustering, patients diagnosed with PSS were classified into two different m6A patterns. We found that patients in group B had higher m6A scores compared to those in group A: furthermore, both groups were closely related to immunity and possibly to other diseases. These results emphasise the important role of m6A regulators in the pathogenesis of PSS. Our study of m6A patterns may inform future immunotherapy strategies for PSS.

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.

LUCMT: Learnable under-sampling and reconstructed network with cross multi-head attention transformer for accelerating MR image reconstruction.

BACKGROUND AND OBJECTIVE: As a widely used technique for Magnetic Resonance Image (MRI) acceleration, compressed sensing MRI involves two main issues: designing an effective sampling strategy and reconstructing the image from significantly under-sampled K-space data. In this paper, an innovative approach is proposed to address these two challenges simultaneously. METHODS: A novel MRI reconstruction method, termed as LUCMT, is implemented by integrating a learnable under-sampling strategy with a reconstruction network based on the Cross Multi-head Attention Transformer. In contrast to conventional static sampling methods, the proposed adaptive sampling scheme is processed optimally by learning the optimal sampling technique, which involves binarizing the sampling pattern by a sigmoid function and computing gradients by backpropagation. And the reconstruction network is designed by using CS-MRI depth unfolding network that incorporates a Cross Multi-head Attention (CMA) module with inertial and gradient descent terms. RESULTS: T1 brain MR images from the FastMRI dataset are used to validate the performance of the proposed method. A series of experiments are conducted to validate the superior performance of our proposed network in terms of quantitative metrics and visual quality. Compared with other state-of-the-art reconstruction methods, LUCMT achieves better reconstruction performances with more accurate details. Specifically, LUCMT achieves PSNR and SSIM results of 41.87/0.9749, 46.64/0.9868, 50.41/0.9924, and 53.51/0.9955 at sampling rates of 10 %, 20 %, 30 %, and 40 %, respectively. CONCLUSIONS: The proposed LUCMT method can provide a promising way for generating optimal under-sampling mask and accelerating MRI reconstruction accurately.