Carnosol suppresses cardiomyocyte hypertrophy via promoting the activation of AMPK pathway.

Pathological cardiac hypertrophy is associated with adverse cardiovascular events and can gradually lead to heart failure, arrhythmia, and even sudden death. However, the current development of treatment strategies has been unsatisfactory. Therefore, it is of great significance to find new and effective drugs for the treatment of myocardial hypertrophy. We found that carnosol can inhibit myocardial hypertrophy induced by PE stimulation, and the effect is very significant at 5 µM. Moreover, we demonstrated that 50 mg/kg of carnosol protect against cardiac hypertrophy and fibrosis induced by TAC surgery in mice. Mechanically, we proved that the inhibitory effect of carnosol on cardiac hypertrophy depends on its regulation on the phosphorylation activation of AMPK. In conclusion, our study suggested that carnosol may be a novel drug component for the treatment of pathological cardiac hypertrophy.

Improvements to a GLCM-based machine-learning approach for quantifying posterior capsule opacification.

BACKGROUND: Posterior capsular opacification (PCO) is a common complication following cataract surgery that leads to visual disturbances and decreased quality of vision. The aim of our study was to employ a machine-learning methodology to characterize and validate enhancements applied to the grey-level co-occurrence matrix (GLCM) while assessing its validity in comparison to clinical evaluations for evaluating PCO. METHODS: One hundred patients diagnosed with age-related cataracts who were scheduled for phacoemulsification surgery were included in the study. Following mydriasis, anterior segment photographs were captured using a high-resolution photographic system. The GLCM was utilized as the feature extractor, and a supported vector machine as the regressor. Three variations, namely, GLCM, GLCM+C (+axial information), and GLCM+V (+regional voting), were analyzed. The reference value for regression was determined by averaging clinical scores obtained through subjective analysis. The relationships between the predicted PCO outcome scores and the ground truth were assessed using Pearson correlation analysis and a Bland-Altman plot, while agreement between them was assessed through the Bland-Altman plot. RESULTS: Relative to the ground truth, the GLCM, GLCM+C, and GLCM+V methods exhibited correlation coefficients of 0.706, 0.768, and 0.829, respectively. The relationship between the PCO score predicted by the GLCM+V method and the ground truth was statistically significant (p < 0.001). Furthermore, the GLCM+V method demonstrated competitive performance comparable to that of two experienced clinicians (r = 0.825, 0.843) and superior to that of two junior clinicians (r = 0.786, 0.756). Notably, a high level of agreement was observed between predictions and the ground truth, without significant evidence of proportional bias (p > 0.05). CONCLUSIONS: Overall, our findings suggest that a machine-learning approach incorporating the GLCM, specifically the GLCM+V method, holds promise as an objective and reliable tool for assessing PCO progression. Further studies in larger patient cohorts are warranted to validate these findings and explore their potential clinical applications.

Light-Start CRISPR-Cas12a Reaction with Caged crRNA Enables Rapid and Sensitive Nucleic Acid Detection.

The clustered regularly interspaced short palindromic repeats (CRISPR) system is a promising platform for nucleic acid detection. Regulating the CRISPR reaction would be extremely useful to improve the detection efficiency and speed of CRISPR diagnostic applications. Here, we have developed a light-start CRISPR-Cas12a reaction by employing caged CRISPR RNA (crRNA). When combined with recombinase polymerase amplification, a robust photocontrolled one-pot assay is achieved. The photocontrolled one-pot assay is simpler and is 50-fold more sensitive than the conventional assay. This improved detection efficiency also facilitates the development of a faster CRISPR diagnostic method. The detection of clinical samples demonstrated that 10-20 min is sufficient for effective detection, which is much faster than the current gold-standard technique PCR. We expect this advance in CRISPR diagnostics to promote its widespread detection applications in biomedicine, agriculture, and food safety.

[Development of an Independent Heater for Ventilator Tube].

Nowadays, normal humidifier is used to heat and humidify the gas before sending to ventilator tube. A new type of ventilator which offers both breathing tube with heater and humidifier is incorporate. In the light of this, patients already bought ventilator still confront this problem. Therefore, this paper mainly introduces a new manufactural method which is controlling the temperature and humidity of gas sent by breathing machine online by a temperature controller which consist of Silica gel hotline and microcomputer. As a matter of fact, the controller is adaptable in various types of breathing tube and can accurately control the humidity and temperature of gas sent into bodies.

DDE: Deep Dynamic Epidemiological Modeling for Infectious Illness Development Forecasting in Multi-level Geographic Entities.

Understanding and addressing the dynamics of infectious diseases, such as coronavirus disease 2019, are essential for effectively managing the current situation and developing intervention strategies. Epidemiologists commonly use mathematical models, known as epidemiological equations (EE), to simulate disease spread. However, accurately estimating the parameters of these models can be challenging due to factors like variations in social distancing policies and intervention strategies. In this study, we propose a novel method called deep dynamic epidemiological modeling (DDE) to address these challenges. The DDE method combines the strengths of EE with the capabilities of deep neural networks to improve the accuracy of fitting real-world data. In DDE, we apply neural ordinary differential equations to solve variant-specific equations, ensuring a more precise fit for disease progression in different geographic regions. In the experiment, we tested the performance of the DDE method and other state-of-the-art methods using real-world data from five diverse geographic entities: the USA, Colombia, South Africa, Wuhan in China, and Piedmont in Italy. Compared to the state-of-the-art method, DDE significantly improved accuracy, with an average fitting Pearson coefficient exceeding 0.97 across the five geographic entities. In summary, the DDE method enhances the accuracy of parameter fitting in epidemiological models and provides a foundation for constructing simpler models adaptable to different geographic areas.

DSMT-Net: Dual Self-Supervised Multi-Operator Transformation for Multi-Source Endoscopic Ultrasound Diagnosis.

Pancreatic cancer has the worst prognosis of all cancers. The clinical application of endoscopic ultrasound (EUS) for the assessment of pancreatic cancer risk and of deep learning for the classification of EUS images have been hindered by inter-grader variability and labeling capability. One of the key reasons for these difficulties is that EUS images are obtained from multiple sources with varying resolutions, effective regions, and interference signals, making the distribution of the data highly variable and negatively impacting the performance of deep learning models. Additionally, manual labeling of images is time-consuming and requires significant effort, leading to the desire to effectively utilize a large amount of unlabeled data for network training. To address these challenges, this study proposes the Dual Self-supervised Multi-Operator Transformation Network (DSMT-Net) for multi-source EUS diagnosis. The DSMT-Net includes a multi-operator transformation approach to standardize the extraction of regions of interest in EUS images and eliminate irrelevant pixels. Furthermore, a transformer-based dual self-supervised network is designed to integrate unlabeled EUS images for pre-training the representation model, which can be transferred to supervised tasks such as classification, detection, and segmentation. A large-scale EUS-based pancreas image dataset (LEPset) has been collected, including 3,500 pathologically proven labeled EUS images (from pancreatic and non-pancreatic cancers) and 8,000 unlabeled EUS images for model development. The self-supervised method has also been applied to breast cancer diagnosis and was compared to state-of-the-art deep learning models on both datasets. The results demonstrate that the DSMT-Net significantly improves the accuracy of pancreatic and breast cancer diagnosis.

SSM-Net: Semi-supervised multi-task network for joint lesion segmentation and classification from pancreatic EUS images.

Pancreatic cancer does not show specific symptoms, which makes the diagnosis of early stages difficult with established image-based screening methods and therefore has the worst prognosis among all cancers. Although endoscopic ultrasonography (EUS) has a key role in diagnostic algorithms for pancreatic diseases, B-mode imaging of the pancreas can be affected by confounders such as chronic pancreatitis, which can make both pancreatic lesion segmentation and classification laborious and highly specialized. To address these challenges, this work proposes a semi-supervised multi-task network (SSM-Net) to leverage unlabeled and labeled EUS images for joint pancreatic lesion classification and segmentation. Specifically, we first devise a saliency-aware representation learning module (SRLM) on a large number of unlabeled images to train a feature extraction encoder network for labeled images by computing a contrastive loss with a semantic saliency map, which is obtained by our spectral residual module (SRM). Moreover, for labeled EUS images, we devise channel attention blocks (CABs) to refine the features extracted from the pre-trained encoder on unlabeled images for segmenting lesions, and then devise a merged global attention module (MGAM) and a feature similarity loss (FSL) for obtaining a lesion classification result. We collect a large-scale EUS-based pancreas image dataset (LS-EUSPI) consisting of 9,555 pathologically proven labeled EUS images (499 patients from four categories) and 15,500 unlabeled EUS images. Experimental results on the LS-EUSPI dataset and a public thyroid gland lesion dataset show that our SSM-Net clearly outperforms state-of-the-art methods.

A deep learning system for predicting time to progression of diabetic retinopathy.

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

Integrated image-based deep learning and language models for primary diabetes care.

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening.

TMM-Nets: Transferred Multi- to Mono-Modal Generation for Lupus Retinopathy Diagnosis.

Rare diseases, which are severely underrepresented in basic and clinical research, can particularly benefit from machine learning techniques. However, current learning-based approaches usually focus on either mono-modal image data or matched multi-modal data, whereas the diagnosis of rare diseases necessitates the aggregation of unstructured and unmatched multi-modal image data due to their rare and diverse nature. In this study, we therefore propose diagnosis-guided multi-to-mono modal generation networks (TMM-Nets) along with training and testing procedures. TMM-Nets can transfer data from multiple sources to a single modality for diagnostic data structurization. To demonstrate their potential in the context of rare diseases, TMM-Nets were deployed to diagnose the lupus retinopathy (LR-SLE), leveraging unmatched regular and ultra-wide-field fundus images for transfer learning. The TMM-Nets encoded the transfer learning from diabetic retinopathy to LR-SLE based on the similarity of the fundus lesions. In addition, a lesion-aware multi-scale attention mechanism was developed for clinical alerts, enabling TMM-Nets not only to inform patient care, but also to provide insights consistent with those of clinicians. An adversarial strategy was also developed to refine multi- to mono-modal image generation based on diagnostic results and the data distribution to enhance the data augmentation performance. Compared to the baseline model, the TMM-Nets showed 35.19% and 33.56% F1 score improvements on the test and external validation sets, respectively. In addition, the TMM-Nets can be used to develop diagnostic models for other rare diseases.

Salidroside suppresses proliferation and migration in prostate cancer via the PI3K/AKT pathway.

BACKGROUND: Prostate cancer (PCa) is one of the most common malignancies in men. PCa is difficult to detect in its early stages, and most patients are diagnosed in the middle to late stages. At present, drug therapy for advanced PCa is still insufficient. Some patients develop drug resistance in the later stage of therapy, which leads to tumor recurrence, metastasis and even treatment failure. Therefore, it is crucial to find new and effective drugs to treat prostate cancer. OBJECTIVE: The aim of this study was to investigate the anti-cancer effect of salidroside, an active ingredient in a traditional Chinese herbal medicine, on PCa. METHODS: Two human PCa cell lines, PC3 and DU145, were cultured and treated with salidroside. Cell viability and proliferation ability were analyzed through CCK-8 and colony assays, and cell migration ability was detected by Transwell and Scratch assays. RT-PCR and WB were used to detected the expression levels of moleculars related to cell proliferation, apoptosis, migration, and AKT signaling pathway. Forthmore, we performed rescue experiments with agonist to verify the affected signaling pathway. RESULTS: Salidroside inhibited the proliferation, colony formation, and migration of PCa cells. Meanwhile, apoptosis of PCa cells was enhanced. Moreover, salidroside inhibited PI3K/AKT pathway in PCa cells. The treatment of AKT agonist 740Y-P abrogated the inhibitory effect of salidroside on the PI3K/AKT signaling pathway. CONCLUSIONS: Our study demonstrated that in PCa cells, salidroside inhibites proliferation and migration and promots apoptosis via inhibiting PI3K/AKT pathway.

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.

Social changes through the lens of language: A big data study of Chinese modal verbs.

Leech's corpus-based comparison of English modal verbs from 1961 to 1992 showed the steep decline of all modal verbs together, which he ascribed to continuing changes towards a more equal and less authority-driven society. This study inspired many diachronic and synchronic studies, mostly on English modal verbs and largely assuming the correlation between the use of modal verbs and power relations. Yet, there are continuing debates on sampling design and the choices of corpora. In addition, this hypothesis has not been attested in any other language with comparable corpus size or examined with longitudinal studies. This study tracks the use of Chinese modal verbs from 1901 to 2009, covering the historical events of the New Culture Movement, the establishment of the PRC, the implementation of simplified characters and the completion and finalization of simplification of the Chinese writing system. We found that the usage of modal verbs did rise and fall during the last century, and for more complex reasons. We also demonstrated that our longitudinal end-to-end approach produces convincing analysis on English modal verbs that reconciles conflicting results in the literature adopting Leech's point-to-point approach.

Fusion of Whole Night Features and Desaturation Segments Combined with Feature Extraction for Event-Level Screening of Sleep-Disordered Breathing.

Purpose: Misdiagnosis and missed diagnosis of sleep-disordered breathing (SDB) is common because polysomnography (PSG) is time-consuming, expensive, and uncomfortable. The use of recording methods based on the oxygen saturation (SpO2) signals detected by wearable devices is impractical and inaccurate for extracting signal features and detecting apnoeic events. We propose a method to automatically detect the apnoea-based SpO2 signal segments and compute the apnoea-hypopnea index (AHI) for SDB screening and grading. Patients and Methods: First, apnoea-related desaturation segments in raw SpO2 signals were detected; global features were extracted from whole night signals. Then, the SpO2 signal segments and global features were fed into a bi-directional long short-term memory convolutional neural network model to identify apnoea-related and non-apnoea-related events. The apnoea-related segments were used to assess the AHI. Results: The model was trained on 500 individuals and tested on 8131 individuals from two public hospitals and one private centre. In the testing data, the classification accuracy for apnoea-related segments was 84.3%. Individuals with SDB (AHI 15) were identified with a mean accuracy of 88.95%. Conclusion: Using automatic SDB detection based on SpO2 signals can accurately screen for SDB.

Mixed-Weight Neural Bagging for Detecting m6A Modifications in SARS-CoV-2 RNA Sequencing.

OBJECTIVE: The m6A modification is the most common ribonucleic acid (RNA) modification, playing a role in prompting the virus's gene mutation and protein structure changes in the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Nanopore single-molecule direct RNA sequencing (DRS) provides data support for RNA modification detection, which can preserve the potential m6A signature compared to second-generation sequencing. However, due to insufficient DRS data, there is a lack of methods to find m6A RNA modifications in DRS. Our purpose is to identify m6A modifications in DRS precisely. METHODS: We present a methodology for identifying m6A modifications that incorporated mapping and extracted features from DRS data. To detect m6A modifications, we introduce an ensemble method called mixed-weight neural bagging (MWNB), trained with 5-base RNA synthetic DRS containing modified and unmodified m6A. RESULTS: Our MWNB model achieved the highest classification accuracy of 97.85% and AUC of 0.9968. Additionally, we applied the MWNB model to the COVID-19 dataset; the experiment results reveal a strong association with biomedical experiments. CONCLUSION: Our strategy enables the prediction of m6A modifications using DRS data and completes the identification of m6A modifications on the SARS-CoV-2. SIGNIFICANCE: The Corona Virus Disease 2019 (COVID-19) outbreak has significantly influence, caused by the SARS-CoV-2. An RNA modification called m6A is connected with viral infections. The appearance of m6A modifications related to several essential proteins affects proteins' structure and function. Therefore, finding the location and number of m6A RNA modifications is crucial for subsequent analysis of the protein expression profile.

DeepDRiD: Diabetic Retinopathy-Grading and Image Quality Estimation Challenge.

We described a challenge named "Diabetic Retinopathy (DR)-Grading and Image Quality Estimation Challenge" in conjunction with ISBI 2020 to hold three sub-challenges and develop deep learning models for DR image assessment and grading. The scientific community responded positively to the challenge, with 34 submissions from 574 registrations. In the challenge, we provided the DeepDRiD dataset containing 2,000 regular DR images (500 patients) and 256 ultra-widefield images (128 patients), both having DR quality and grading annotations. We discussed details of the top 3 algorithms in each sub-challenges. The weighted kappa for DR grading ranged from 0.93 to 0.82, and the accuracy for image quality evaluation ranged from 0.70 to 0.65. The results showed that image quality assessment can be used as a further target for exploration. We also have released the DeepDRiD dataset on GitHub to help develop automatic systems and improve human judgment in DR screening and diagnosis.

NHBS-Net: A Feature Fusion Attention Network for Ultrasound Neonatal Hip Bone Segmentation.

Ultrasound is a widely used technology for diagnosing developmental dysplasia of the hip (DDH) because it does not use radiation. Due to its low cost and convenience, 2-D ultrasound is still the most common examination in DDH diagnosis. In clinical usage, the complexity of both ultrasound image standardization and measurement leads to a high error rate for sonographers. The automatic segmentation results of key structures in the hip joint can be used to develop a standard plane detection method that helps sonographers decrease the error rate. However, current automatic segmentation methods still face challenges in robustness and accuracy. Thus, we propose a neonatal hip bone segmentation network (NHBS-Net) for the first time for the segmentation of seven key structures. We design three improvements, an enhanced dual attention module, a two-class feature fusion module, and a coordinate convolution output head, to help segment different structures. Compared with current state-of-the-art networks, NHBS-Net gains outstanding performance accuracy and generalizability, as shown in the experiments. Additionally, image standardization is a common need in ultrasonography. The ability of segmentation-based standard plane detection is tested on a 50-image standard dataset. The experiments show that our method can help healthcare workers decrease their error rate from 6%-10% to 2%. In addition, the segmentation performance in another ultrasound dataset (fetal heart) demonstrates the ability of our network.

A deep learning system for detecting diabetic retinopathy across the disease spectrum.

Retinal screening contributes to early detection of diabetic retinopathy and timely treatment. To facilitate the screening process, we develop a deep learning system, named DeepDR, that can detect early-to-late stages of diabetic retinopathy. DeepDR is trained for real-time image quality assessment, lesion detection and grading using 466,247 fundus images from 121,342 patients with diabetes. Evaluation is performed on a local dataset with 200,136 fundus images from 52,004 patients and three external datasets with a total of 209,322 images. The area under the receiver operating characteristic curves for detecting microaneurysms, cotton-wool spots, hard exudates and hemorrhages are 0.901, 0.941, 0.954 and 0.967, respectively. The grading of diabetic retinopathy as mild, moderate, severe and proliferative achieves area under the curves of 0.943, 0.955, 0.960 and 0.972, respectively. In external validations, the area under the curves for grading range from 0.916 to 0.970, which further supports the system is efficient for diabetic retinopathy grading.