Unveiling divergent treatment prognoses in IDHwt-GBM subtypes through multiomics clustering: a swift dual MRI-mRNA model for precise subtype prediction.

BACKGROUND: IDH1-wildtype glioblastoma multiforme (IDHwt-GBM) is a highly heterogeneous and aggressive brain tumour characterised by a dismal prognosis and significant challenges in accurately predicting patient outcomes. To address these issues and personalise treatment approaches, we aimed to develop and validate robust multiomics molecular subtypes of IDHwt-GBM. Through this, we sought to uncover the distinct molecular signatures underlying these subtypes, paving the way for improved diagnosis and targeted therapy for this challenging disease. METHODS: To identify stable molecular subtypes among 184 IDHwt-GBM patients from TCGA, we used the consensus clustering method to consolidate the results from ten advanced multiomics clustering approaches based on mRNA, lncRNA, and mutation data. We developed subtype prediction models using the PAM and machine learning algorithms based on mRNA and MRI data for enhanced clinical utility. These models were validated in five independent datasets, and an online interactive system was created. We conducted a comprehensive assessment of the clinical impact, drug treatment response, and molecular associations of the IDHwt-GBM subtypes. RESULTS: In the TCGA cohort, two molecular subtypes, class 1 and class 2, were identified through multiomics clustering of IDHwt-GBM patients. There was a significant difference in survival between Class 1 and Class 2 patients, with a hazard ratio (HR) of 1.68 [1.15-2.47]. This difference was validated in other datasets (CGGA: HR = 1.75[1.04, 2.94]; CPTAC: HR = 1.79[1.09-2.91]; GALSS: HR = 1.66[1.09-2.54]; UCSF: HR = 1.33[1.00-1.77]; UPENN HR = 1.29[1.04-1.58]). Additionally, class 2 was more sensitive to treatment with radiotherapy combined with temozolomide, and this sensitivity was validated in the GLASS cohort. Correspondingly, class 2 and class 1 exhibited significant differences in mutation patterns, enriched pathways, programmed cell death (PCD), and the tumour immune microenvironment. Class 2 had more mutation signatures associated with defective DNA mismatch repair (P = 0.0021). Enriched pathways of differentially expressed genes in class 1 and class 2 (P-adjust < 0.05) were mainly related to ferroptosis, the PD-1 checkpoint pathway, the JAK-STAT signalling pathway, and other programmed cell death and immune-related pathways. The different cell death modes and immune microenvironments were validated across multiple datasets. Finally, our developed survival prediction model, which integrates molecular subtypes, age, and sex, demonstrated clinical benefits based on the decision curve in the test set. We deployed the molecular subtyping prediction model and survival prediction model online, allowing interactive use and facilitating user convenience. CONCLUSIONS: Molecular subtypes were identified and verified through multiomics clustering in IDHwt-GBM patients. These subtypes are linked to specific mutation patterns, the immune microenvironment, prognoses, and treatment responses.

Coupling machine learning and theoretical models to compare key properties of biochar in adsorption kinetics rate and maximum adsorption capacity for emerging contaminants.

Insights into key properties of biochar with a fast adsorption rate and high adsorption capacity are urgent to design biochar as an adsorbent in pollution emergency treatment. Machine learning (ML) incorporating classical theoretical adsorption models was applied to build prediction models for adsorption kinetics rate (i.e., K) and maximum adsorption capacity (i.e., Qm) of emerging contaminants (ECs) on biochar. Results demonstrated that the prediction performance of adaptive boosting algorithm significantly improved after data preprocessing (i.e., log-transformation) in the small unbalanced datasets with R2 of 0.865 and 0.874 for K and Qm, respectively. The surface chemistry, primarily led by ash content of biochar significantly influenced the K, while surface porous structure of biochar showed a dominant role in predicting Qm. An interactive platform was deployed for relevant scientists to predict K and Qm of new biochar for ECs. The research provided practical references for future engineered biochar design for ECs removal.

A method for studying escape behavior to terrestrial threats in rodents.

BACKGROUND: Escape is one of the most essential behaviors for an animal's survival because it could be a matter of life and death. Much of our current understanding of the neural mechanisms underlying escape is derived from the looming paradigm, which mimics a diving aerial predator. Yet, the idea of the looming paradigm does not account for all types of threats like lions hunting antelopes or cats stalking mice. Escape responses to such terrestrial threats may require different strategies and neural mechanisms. NEW METHODS: Here, we developed a real-time interactive platform to study escape behavior to terrestrial threats in mice. A closed-loop controlled robot was magnetically pulled to mimic a terrestrial threat that chases a mouse. By using strong magnets and high-precision servo motors, the robot is capable of moving precisely with a high spatial-temporal resolution. Different algorithms can be used to achieve single approach or persistent approach. RESULTS: Animal experiments showed that mice exhibited consistent escape behavior when exposed to an approaching robotic predator. When presented with a persistently approaching predator, the mice were able to rapidly adapt their behavior, as evidenced by a decrease in startle responses and changes in movement patterns. COMPARISON WITH EXISTING METHODS: In comparison to existing methods for studying escape behavior, such as the looming paradigm, this approach is more suitable for investigating animal behavior in response to sustained threats. CONCLUSION: In conclusion, we have developed a flexible platform to study escape behavior to terrestrial threats in mice.

IAS-FET: An intelligent assistant system and an online platform for enhancing successful rate of in-vitro fertilization embryo transfer technology based on clinical features.

BACKGROUND: Among all of the assisted reproductive technology (ART) methods, in vitro fertilization-embryo transfer (IVF-ET) holds a prominent position as a key solution for overcoming infertility. However, its success rate hovers at a modest 30% to 70%. Adding to the challenge is the absence of effective models and clinical tools capable of predicting the outcome of IVF-ET before embryo formation. Our study is dedicated to filling this critical gap by aiming to predict IVF-ET outcomes and ultimately enhance the success rate of this transformative procedure. METHODS: In this retrospective study, infertile patients who received artificial assisted pregnancy treatment at Gansu Provincial Maternity and Child-care Hospital in China were enrolled from 2016 to 2020. Individual's clinical information were studied by cascade XGBoost method to build an intelligent assisted system for predicting the outcome of IVF-ET, called IAS-FET. The cascade XGBoost model was trained using clinical information from 2292 couples and externally tested using clinical information from 573 couples. In addition, several schemes which will be of help for patients to adjust their physical condition to improve their success rate on ART were suggested by IAS-FET. RESULTS: The outcome of IVF-ET can be predicted by the built IAS-FET method with the area under curve (AUC) value of 0.8759 on the external test set. Besides, this IAS-FET method can provide several schemes to improve the successful rate of IVF-ET outcomes. The built tool for IAS-FET is addressed as a free platform online at http://www.cppdd.cn/ART for the convenient usage of users. CONCLUSIONS: It suggested the significant influence of personal clinical features for the success of ART. The proposed system IAS-FET based on the top 27 factors could be a promising tool to predict the outcome of ART and propose a plan for the patient's physical adjustment. With the help of IAS-FET, patients can take informed steps towards increasing their chances of a successful outcome on their journey to parenthood.

The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach.

BACKGROUND: Machine learning and artificial intelligence have shown promising results in many areas and are driven by the increasing amount of available data. However, these data are often distributed across different institutions and cannot be easily shared owing to strict privacy regulations. Federated learning (FL) allows the training of distributed machine learning models without sharing sensitive data. In addition, the implementation is time-consuming and requires advanced programming skills and complex technical infrastructures. OBJECTIVE: Various tools and frameworks have been developed to simplify the development of FL algorithms and provide the necessary technical infrastructure. Although there are many high-quality frameworks, most focus only on a single application case or method. To our knowledge, there are no generic frameworks, meaning that the existing solutions are restricted to a particular type of algorithm or application field. Furthermore, most of these frameworks provide an application programming interface that needs programming knowledge. There is no collection of ready-to-use FL algorithms that are extendable and allow users (eg, researchers) without programming knowledge to apply FL. A central FL platform for both FL algorithm developers and users does not exist. This study aimed to address this gap and make FL available to everyone by developing FeatureCloud, an all-in-one platform for FL in biomedicine and beyond. METHODS: The FeatureCloud platform consists of 3 main components: a global frontend, a global backend, and a local controller. Our platform uses a Docker to separate the local acting components of the platform from the sensitive data systems. We evaluated our platform using 4 different algorithms on 5 data sets for both accuracy and runtime. RESULTS: FeatureCloud removes the complexity of distributed systems for developers and end users by providing a comprehensive platform for executing multi-institutional FL analyses and implementing FL algorithms. Through its integrated artificial intelligence store, federated algorithms can easily be published and reused by the community. To secure sensitive raw data, FeatureCloud supports privacy-enhancing technologies to secure the shared local models and assures high standards in data privacy to comply with the strict General Data Protection Regulation. Our evaluation shows that applications developed in FeatureCloud can produce highly similar results compared with centralized approaches and scale well for an increasing number of participating sites. CONCLUSIONS: FeatureCloud provides a ready-to-use platform that integrates the development and execution of FL algorithms while reducing the complexity to a minimum and removing the hurdles of federated infrastructure. Thus, we believe that it has the potential to greatly increase the accessibility of privacy-preserving and distributed data analyses in biomedicine and beyond.

Continuous Nursing Intervention Based on Network Interactive in Early Diabetic Kidney Disease Patients.

Objective: To analyze the effect of continuous nursing intervention based on network interactive platform on improving blood glucose level and self-efficacy in patients with early diabetic kidney disease. Methods: The control group received basic routine nursing, and the study group received continuous nursing based on the network interactive platform. The blood glucose level, renal function, inflammatory factors, self-efficacy, self-management behavior, nursing efficacy and adverse reactions were compared. Results: There was no significant difference in baseline data between the two groups. After the application of continuous nursing based on network interactive platform, the blood glucose level and renal function of patients were significantly decreased, and those of the study group were lower than those of the control group. The inflammatory factors in the study group were significantly lower than those in the control group. Conclusion: In the nursing of patients with early-onset diabetic kidney disease, the application of continuous nursing based on network interactive platform can effectively reduce the level of inflammatory factors, improve the level of blood glucose and renal function, improve the self-efficacy and self-management behavior of patients, and reduce the occurrence of adverse reactions. It is worthy of promotion and application in nursing.

nHA-loaded gelatin/alginate hydrogel with combined physical and bioactive features for maxillofacial bone repair.

Critical-sized maxillofacial bone defects have been a tough clinical challenge considering their requirements for functional and structural repair. In this study, an injectable in-situ forming double cross-linked hydrogel was prepared from gelatin (Gel), 20 mg/mL alginate dialdehyde (ADA), 4.5 mg/mL Ca2+ and borax. Improved properties of composite hydrogel might well fit and cover irregular geometric shape of facial bone defects, support facial structures and conduct masticatory force. We innovatively constructed a bioactive poly-porous structure by decoration with nano-sized hydroxyapatite (nHA). The highly ordered, homogeneous and size-confined porous surface served as an interactive osteogenic platform for communication and interplay between macrophages and bone marrow derived stem cells (BMSCs). Effective macrophage-BMSC crosstalk well explained the remarkable efficiency of nHA-loaded gelatin/alginate hydrogel (nHA@Gel/ADA) in the repair of critical-size skull bone defect. Collectively, the composite hydrogel constructed here might serve as a promising alternative in repair process of complex maxillofacial bone defects.

Patients' self-triage for unscheduled urgent care: a preliminary study on the accuracy and factors affecting the performance of a Belgian self-triage platform.

BACKGROUND: Management of unscheduled urgent care is a complex concern for many healthcare providers. Facing the challenge of appropriately dispatching unscheduled care, primary and emergency physicians have collaboratively implemented innovative strategies such as telephone triage. Currently, new original solutions tend to emerge with the development of new technologies. We created an interactive patient self-triage platform, ODISSEE, and aimed to explore its accuracy and potential factors affecting its performance using clinical case scenarios. METHODS: The ODISSEE platform was developed based on previously validated triage protocols for out-of-hours primary care. ODISSEE is composed of 18 icons leading to algorithmic questions that finally provide an advised orientation (emergency or primary care services). To investigate ODISSEE performance, we used 100 clinical case scenarios, each associated with a preestablished orientation determined by a group of experts. Fifteen volunteers were asked to self-triage with 50 randomly selected scenarios using ODISSEE on a digital tablet. Their triage results were compared with the experts' references. RESULTS: The 15 participants performed a total of 750 self-triages, which matched the experts references regarding the level of care in 85.6% of the cases. The orientation was incorrect in 14.4%, with an undertriage rate of 1.9% and an overtriage rate of 12.5%. The tool's specificity and sensitivity to advise participants on the appropriate level of care were 69% (95% CI: 64-74) and 97% (95% CI: 95-98) respectively. When combined with advice on the level of urgency, the tool only found the correct orientation in 68.4% with 9.2% of undertriages and 22.4% of overtriages. Some participant characteristics and the types of medical conditions demonstrated a significant association with the tool performance. CONCLUSION: Self-triage apps, such as the ODISSEE platform, could represent an innovative method to allow patients to self-triage to the most appropriate level of care. This study based on clinical vignettes highlights some positive arguments regarding ODISSEE safety, but further research is needed to assess the generalizability of such tools to the population without equity issues.

Effect of the whole seamless connection of nursing from WeChat interactive platform on stigma and quality of life in patients with urinary system cancer.

Objective: To evaluate the effects of intervention by "whole seamless connection of nursing from WeChat interactive platform" on stigma and quality of life of the patients with urinary system cancer. Methods: Overall, 80 patients with urinary cancer were randomly divided (40 cases per group) into control and observation groups. Routine nursing was provided to the control group, whereas positive psychological intervention was provided to the intervention group through a "whole seamless connection of nursing from the WeChat interactive platform" in addition to routine nursing. The Chinese version of social impact and cancer patients' quality of life scales were used to evaluate the effects before and after the intervention. Results: After the intervention, the total score for stigma was significantly lower (p < 0.01), while that of quality of life was higher (p < 0.05) in the observation group relative to the control group. Conclusions: The whole seamless connection of nursing from the WeChat interactive platform could reduce stigma and improve the quality of life of patients with urinary cancer.

Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and Brazil.

São Paulo is the most populous state in Brazil, home to around 22% of the country's population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country's fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model's coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given.

CemrgApp: An interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research.

Personalised medicine is based on the principle that each body is unique and will respond to therapies differently. In cardiology, characterising patient specific cardiovascular properties would help in personalising care. One promising approach for characterising these properties relies on performing computational analysis of multimodal imaging data. An interactive cardiac imaging environment, which can seamlessly render, manipulate, derive calculations, and otherwise prototype research activities, is therefore sought-after. We developed the Cardiac Electro-Mechanics Research Group Application (CemrgApp) as a platform with custom image processing and computer vision toolkits for applying statistical, machine learning and simulation approaches to study physiology, pathology, diagnosis and treatment of the cardiovascular system. CemrgApp provides an integrated environment, where cardiac data visualisation and workflow prototyping are presented through a common graphical user interface.

Application of virtual reality technology in teaching course of radiotherapy technology / 中华放射肿瘤学杂志

Objective To discuss the necessity and feasibility of application of virtual reality (VR) technology in the teaching course of radiotherapy technology based on the contradictions between the theory and practice of current teaching mode.Methods After in-depth analysis of the characteristics of the existing disciplinary teaching mode,VR technology was introduced to design software,glasses,operating handles and establish a special interactive platform. The teaching courses could be delivered via mobile phone side AR, helmet and AR, touch screen and PC virtual simulation with VR virtual simulation, etc. Six processes of radiotherapy were tested through online courses and offline groups to analyze the feasibility of this technology applied in the training of radiotherapy professionals. Results After the design of software and hardware and the analysis of test results,the online teaching could be utilized to write interactive programs,build virtual experience scenes, create course resource database and construct practical training courses and teaching system. The offline practice test could be applied to the simulation learning of feedback of the whole process, which possessed feasibility and development value. It could be applied to the theory and practice teaching of radiation therapy technology,making the teaching more convenient,vivid and intuitive. Conclusions VR technique combined with radiotherapy technology training can be delivered through online and offline teaching courses of theory and practice by using the plane and virtual simulation technology, which is convenient, quick and highly efficient and deserves widespread application.

Construction of information support platform for infectious disease epidemic in countries and regions along the One Belt and One Road / 中华医学图书情报杂志

An information service platform was constructed for infectious disease epidemic by standard collecting, organizing and systematizing the information of related infectious diseases, which could provide information support service for data store, knowledge share, online education and decision-making. The platform is consisted of special news module, self-developed database module, encyclopedic knowledge module and background management module, and can thus meet the needs of related persons in countries and regions along the One Belt and One Road.

Opening the interactive platform of network course to improve medical undergraduates’ study interest / 中华医学教育探索杂志

Objective This article elaborates how to improve the medical undergraduates’ study interest from the angle of network course platform. Methods Based on the analysis of characteristic and application of network course platform,it discusses the problems and solutions existing in network course platform. Results The meaning of utilizing interactive platform in Medical College is explained. Conclusion Utilizing interactive platform rationally can stimulate the medical undergraduate’s study interest and improve the teaching result.