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The comprehensive adoption of Electronic Medical Records (EMRs) offers numerous benefits but also introduces risks of privacy leakage, particularly for patients with Sexually Transmitted Infections (STI) who need protection from social secondary harm. Despite advancements in privacy protection research, the effectiveness of these strategies in real-world data remains debatable. The objective is to develop effective information extraction and privacy protection strategies to safeguard STI patients in the Chinese healthcare environment and prevent unnecessary privacy leakage during the data-sharing process of EMRs. The research was conducted at a national healthcare data center, where a committee of experts designed rule-based protocols utilizing natural language processing techniques to extract STI information. Extraction Protocol of Sexually Transmitted Infections Information (EPSTII), designed specifically for the Chinese EMRs system, enables accurate and complete identification and extraction of STI-related information, ensuring high protection performance. The protocol was refined multiple times based on the calculated precision and recall. Final protocol was applied to 5,000 randomly selected EMRs to calculate the success rate of privacy protection. A total of 3,233,174 patients were selected based on the inclusion criteria and a 50% entry ratio. Of these, 148,856 patients with sensitive STI information were identified from disease history. The identification frequency varied, with the diagnosis sub-dataset being the highest at 4.8%. Both the precision and recall rates have reached over 95%, demonstrating the effectiveness of our method. The success rate of privacy protection was 98.25%, ensuring the utmost privacy protection for patients with STI. Finding an effective method to protect privacy information in EMRs is meaningful. We demonstrated the feasibility of applying the EPSTII method to EMRs. Our protocol offers more comprehensive results compared to traditional methods of including STI information.

The frozen section (FS) diagnoses of pathology experts are used in China to determine whether sentinel lymph nodes of breast cancer have metastasis during operation. Direct implementation of a deep neural network (DNN) in clinical practice may be hindered by misdiagnosis of the algorithm, which affects a patient's treatment decision. In this study, we first obtained the prediction result of the commonly used patch-DNN, then we present a relative risk classification and regression tree (RRCART) to identify the misdiagnosed whole-slide images (WSIs) and recommend them to be reviewed by pathologists. Applying this framework to 2362 WSIs of breast cancer lymph node metastasis, test on frozen section results in the mean area under the curve (AUC) reached 0.9851. However, the mean misdiagnosis rate (0.0248), was significantly higher than the pathologists’ misdiagnosis rate ( p  < 0.01). The RRCART distinguished more than 80% of the WSIs as a high-accuracy group with an average accuracy reached to 0.995, but the difference with the pathologists’ performance was not significant ( p  > 0.01). However, the other low-accuracy group included most of the misdiagnoses of DNN models. Our research shows that the misdiagnosis from deep learning model can be further enriched by our method, and that the low-accuracy WSIs must be selected for pathologists to review and the high-accuracy ones may be ready for pathologists to give diagnostic reports.

Polyurethane phase change materials (PUPCMs) have been extensively applied in smart textiles and wearable electronic devices because of their excellent energy storage capacity. To realize the flexibility of PUPCMs for certain deformation and compact contact with objects, suitable support structures have been chosen to prepare polyurethane phase change composites (PUFPCCs) with energy storage capacity and device-level flexibility. In this work, PUPCM was prepared by the prepolymer method with polyethylene glycol (PEG) as the soft segment, 4,4-dicyclohexylmethane diisocyanate (HMDI) and 1,2-hexanediol as the hard segment. And polyurethane-based adhesives (PUA) were chosen to provide a support structure for PUFPCCs by physically blending and casting with prepared PUPCM. PUFPCCs showed good flexibility attributed to the film-forming performance of polyurethane-based adhesive in the composites. The chemical structure, crystallization properties, phase transformation properties and thermal stability of the prepared PUPCM and PUFPCCs were investigated via Fourier transform infrared spectroscopy (FT-IR), 1 H NMR spectroscopy, X-ray diffraction (XRD), polarizing optical microscope (POM), differential scanning calorimetry (DSC) and thermogravimetric (TG) analysis respectively. The phase change temperature of PUFPCCs ranged from 36 to 40 ℃. The maximum enthalpy value of PUFPCCs was up to 40 J/g for daily application. Moreover, the thermal stability of PUPCM was improved attribute to the support structure of PUA in PUFPCCs. Therefore, the prepared PUFPCCs have great potential for application in flexible wearable devices due to their excellent flexibility, suitable phase transition temperature close to human body temperature, high enthalpy value and improved thermal stability.

In this study, a waterborne polyurethane (WPU) is synthesized by using polytetramethylene ether glycol (PTMEG) to form the soft segment, 1,4-butanediol (BDO) as the chain extender, n-methyldiethanolamine (MDEA) as a hydrophilic chain extender, and isophorone diisocyanate (IPDI) to form the hard segment. Furthermore, the modified cationic WPU emulsion and its films are created through a reaction between the WPU and a linear polyether-blocked amino silicone (LEPS), which is an organosilicon compound that imparts flexibility. The properties of the structure and formed WPU films are then characterized by using Fourier transform infrared spectrometry, a thermogravimetric analysis, atomic force microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy, as well as by measuring the water contact angle, testing the water absorption, etc. It is found that, with an increase in the LEPS content in the WPU, the particle size of the modified WPU emulsion is increased, the WPU films are more flexible, and the resistance of the modified WPU films to heat and water are increased, while the crystallinity is reduced. The polysiloxane chain segment, which is added to the LEPS-modified WPU emulsion, is significantly enriched on the surface of the modified WPU films, while there are no adverse effects of the LEPS-modified WPU emulsion on the adhesive force between the WPU and substrate. When the LEPS content of the WPU is 14.0 wt%, the modified WPU emulsion and film provide the best performance.

Cancer informatics has significantly progressed in the big data era. We summarize the application of informatics approaches to the cancer domain from both the informatics perspective (e.g., data management and data science) and the clinical perspective (e.g., cancer screening, risk assessment, diagnosis, treatment, and prognosis). We discuss various informatics methods and tools that are widely applied in cancer research and practices, such as cancer databases, data standards, terminologies, high‐throughput omics data mining, machine‐learning algorithms, artificial intelligence imaging, and intelligent radiation. We also address the informatics challenges within the cancer field that pursue better treatment decisions and patient outcomes, and focus on how informatics can provide opportunities for cancer research and practices. Finally, we conclude that the interdisciplinary nature of cancer informatics and collaborations are major drivers for future research and applications in clinical practices. It is hoped that this review is instrumental for cancer researchers and clinicians with its informatics‐specific insights. This review summarized the progress of cancer informatics in the big data era. Various informatics methods and tools that are widely applied in cancer research and practices were discussed. This review further addressed the informatics challenges and opportunities for the comprehensive cancer field. It is expected that this review is instrumental for cancer researchers and clinicians with an informatics‐specific insight.

Background Artificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability. Methods We present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, Trained on Akoya-scanned images only, the model utilizes color augmentation and image appearance migration to address scanner variations. We evaluate it on Whole Slide Images (WSI) from another five scanners and conduct validations with pathologists to assess AI efficacy and PAI. Results Our model achieves an average F1 score of 0.80 on annotations and 0.71 Quadratic Weighted Kappa on WSIs for Akoya-scanned images. Applying our generalization solution increases the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners. The model accelerates Gleason scoring time by 43% while maintaining accuracy. Additionally, PAI improve annotation efficiency by 2.5 times and led to further improvements in model performance. Conclusions This pipeline represents a notable advancement in AI-assisted Gleason grading for improved consistency, accuracy, and efficiency. Unlike previous methods limited by scanner specificity, our model achieves outstanding performance across diverse scanners. This improvement paves the way for its seamless integration into clinical workflows. Plain language summary Gleason grading is a well-accepted diagnostic standard to assess the severity of prostate cancer in patients’ tissue samples, based on how abnormal the cells in their prostate tumor look under a microscope. This process can be complex and time-consuming. We explore how artificial intelligence (AI) can help pathologists perform Gleason grading more efficiently and consistently. We build an AI-based system which automatically checks image quality, standardizes the appearance of images from different equipment, learns from pathologists’ feedback, and constantly improves model performance. Testing shows that our approach achieves consistent results across different equipment and improves efficiency of the grading process. With further testing and implementation in the clinic, our approach could potentially improve prostate cancer diagnosis and management. Huo, Ong et al. present a comprehensive workflow to try to overcome key limitations in prior approaches for artificial intelligence (AI)-assisted prostate cancer Gleason grading. Their approach incorporates automated quality control, efficient annotation and visualization, and pathologist-AI interaction.

Phase change materials with high energy storage density and stable phase change temperature are ideal choices for personal thermal therapy and heat management. However, leakage and poor flexibility have long been bottlenecks in their application. Excellent latent heat performance and flexibility are crucial, especially in the thermal management of flexible wearable devices. In this study, a simple strategy was adopted to prepare a flexible polyurethane-based phase change material using a prepolymer method with polyethylene glycol (PEG) as the phase change material. Attributed to the cross-linked network structure in the polymer, the prepared phase change material exhibits excellent thermal stability and shape stability without leakage. Additionally, it shows relatively high latent heat and good flexibility. Based on these significant comprehensive properties, a performance-improved thermal wrist wrap was designed to achieve thermal therapy and efficient human heat management. This work provides insights into the rational design of flexible and shape-stable high latent heat phase change materials, demonstrating tremendous potential applications in wearable thermal management. Graphical abstract

In the preparation of microencapsulated phase change materials (MicroPCMs) with a three-composition shell through interfacial polymerization, the particle size, phase change behaviors, core contents, encapsulation efficiency morphology, thermal stability and chemical structure were investigated. The compactness of the MicroPCMs was analyzed through high-temperature drying and weighing. The effect of the core/shell ratio and stirring rate of the system was studied. The results indicated that the microcapsules thus-obtained possessed a spherical shape and high thermal stability and the surfaces were intact and compact. Furthermore, in the emulsification stage, the stirring speed had a significant influence on the microcapsules’ particle size, and smaller particles could be obtained under the higher stirring speed, and the distributions were more uniform in these cases. When the core/shell ratio was lower than 4, both the core content and the encapsulation efficiency was high. Additionally, when the core/shell ratio was higher than 4, the encapsulation efficiency was decreased significantly. The three-composition shell greatly increased the compactness of microcapsules, and when the core/shell ratio was adjusted to 3, the mass loss of the MicroPCMs was lower than 6% after drying at 120 °C for 1 h. After the microencapsulation, double exothermic peaks appeared on the crystallization curve of the MicroPCMs, the crystallization mechanism was changed from the heterogeneous nucleation to the homogeneous nucleation and the super cooling degree was enhanced.

Genome-wide association studies (GWAS) of human complex traits or diseases often implicate genetic loci that span hundreds or thousands of genetic variants, many of which have similar statistical significance. While statistical fine-mapping in individuals of European ancestry has made important discoveries, cross-population fine-mapping has the potential to improve power and resolution by capitalizing on the genomic diversity across ancestries. Here we present SuSiEx, an accurate and computationally efficient method for cross-population fine-mapping. SuSiEx integrates data from an arbitrary number of ancestries, explicitly models population-specific allele frequencies and linkage disequilibrium patterns, accounts for multiple causal variants in a genomic region and can be applied to GWAS summary statistics. We comprehensively assessed the performance of SuSiEx using simulations. We further showed that SuSiEx improves the fine-mapping of a range of quantitative traits available in both the UK Biobank and Taiwan Biobank, and improves the fine-mapping of schizophrenia-associated loci by integrating GWAS across East Asian and European ancestries. The cross-population Sum of Single Effects (SuSiEx) model is a robust and computationally efficient method for conducting multi-ancestry fine-mapping of genome-wide association signals, producing smaller credible sets and capturing population-specific causal variants.