BACKGROUND: Establishing collaborations between cohort studies has been fundamental for progress in health research. However, such collaborations are hampered by heterogeneous data representations across cohorts and legal constraints to data sharing. The first arises from a lack of consensus in standards of data collection and representation across cohort studies and is usually tackled by applying data harmonization processes. The second is increasingly important due to raised awareness for privacy protection and stricter regulations, such as the GDPR. Federated learning has emerged as a privacy-preserving alternative to transferring data between institutions through analyzing data in a decentralized manner.
METHODS: In this study, we set up a federated learning infrastructure for a consortium of nine Dutch cohorts with appropriate data available to the etiology of dementia, including an extract, transform, and load (ETL) pipeline for data harmonization. Additionally, we assessed the challenges of transforming and standardizing cohort data using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) and evaluated our tool in one of the cohorts employing federated algorithms.
RESULTS: We successfully applied our ETL tool and observed a complete coverage of the cohorts\' data by the OMOP CDM. The OMOP CDM facilitated the data representation and standardization, but we identified limitations for cohort-specific data fields and in the scope of the vocabularies available. Specific challenges arise in a multi-cohort federated collaboration due to technical constraints in local environments, data heterogeneity, and lack of direct access to the data.
CONCLUSIONS: In this article, we describe the solutions to these challenges and limitations encountered in our study. Our study shows the potential of federated learning as a privacy-preserving solution for multi-cohort studies that enhance reproducibility and reuse of both data and analyses.

In this paper, the damage initiation/propagation mechanisms and failure modes of open-hole carbon fiber-reinforced thermoplastic composites and thermosetting composites with tension, compression, and bearing loads are investigated, respectively, by experiments and finite element simulations. The experimental evaluations are performed on the specimens using the Combined Loading Compression (CLC) test method, the tensile test method, and the single-shear test method. The differences in macroscopic damage initiation, evolution mode, and damage characteristics between thermoplastic composite materials and thermosetting composite material open-hole structures are obtained and analyzed under compressive load. Based on scanning electron microscope SEM images, a comparative analysis is conducted on the micro-failure modes of fibers, matrices, and fiber/matrix interfaces in the open-hole structures of thermoplastic and thermosetting composites under compressive load. The differences between thermoplastic and thermosetting composites were analyzed from the micro-failure mechanism. Finally, based on continuum damage mechanics (CDM), a damage model is also developed for predicting the initiation and propagation of damage in thermoplastic composites. The model, which can capture fiber breakage and matrix crack, as well as the nonlinear response, is used to conduct virtual compression tests, tensile test, and single-shear test, respectively. Numerical simulation results are compared with the extracted experimental results. The displacement-load curve and failure modes match the experimental result, which indicates that the finite element model has good reliability.

Older adults are at an increased risk of postoperative morbidity. Numerous risk stratification tools exist, but effort and manpower are required.
This study aimed to develop a predictive model of postoperative adverse outcomes in older patients following general surgery with an open-source, patient-level prediction from the Observational Health Data Sciences and Informatics for internal and external validation.
We used the Observational Medical Outcomes Partnership common data model and machine learning algorithms. The primary outcome was a composite of 90-day postoperative all-cause mortality and emergency department visits. Secondary outcomes were postoperative delirium, prolonged postoperative stay (≥75th percentile), and prolonged hospital stay (≥21 days). An 80% versus 20% split of the data from the Seoul National University Bundang Hospital (SNUBH) and Seoul National University Hospital (SNUH) common data model was used for model training and testing versus external validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) with a 95% CI.
Data from 27,197 (SNUBH) and 32,857 (SNUH) patients were analyzed. Compared to the random forest, Adaboost, and decision tree models, the least absolute shrinkage and selection operator logistic regression model showed good internal discriminative accuracy (internal AUC 0.723, 95% CI 0.701-0.744) and transportability (external AUC 0.703, 95% CI 0.692-0.714) for the primary outcome. The model also possessed good internal and external AUCs for postoperative delirium (internal AUC 0.754, 95% CI 0.713-0.794; external AUC 0.750, 95% CI 0.727-0.772), prolonged postoperative stay (internal AUC 0.813, 95% CI 0.800-0.825; external AUC 0.747, 95% CI 0.741-0.753), and prolonged hospital stay (internal AUC 0.770, 95% CI 0.749-0.792; external AUC 0.707, 95% CI 0.696-0.718). Compared with age or the Charlson comorbidity index, the model showed better prediction performance.
The derived model shall assist clinicians and patients in understanding the individualized risks and benefits of surgery.

Aim: This study aims to develop a cloud-based digital healthcare system for precision medical hospital information systems (P-HIS). Methods: In 2020, international standardization of P-HIS clinical terms and codes was performed. In 2021, South Korea\'s first tertiary hospital cloud was established and implemented successfully. Results: P-HIS was applied at Korea\'s first tertiary general hospital. Common data model-compatible precision medicine/medical service solutions were developed for medical support. Ultrahigh-quality medical data for precision medicine were acquired and built using big data. Joint global commercialization and dissemination/spreading were achieved using the P-HIS consortium and global common data model-based observational medical outcome partnership network. Conclusion: To provide personalized precision medical services in the future, establishing and using big medical data is essential.

On-chip electrostatic discharge (ESD) protection is required for all integrated circuits (ICs). Conventional on-chip ESD protection relies on in-Si PN junction-based device structures for ESD. However, such in-Si PN-based ESD protection solutions pose significant challenges related to ESD protection design overhead, including parasitic capacitance, leakage current, and noises, as well as large chip area consumption and difficulty in IC layout floor planning. The design overhead effects of ESD protection devices are becoming unacceptable to modern ICs as IC technologies continuously advance, which is an emerging design-for-reliability challenge for advanced ICs. In this paper, we review the concept development of disruptive graphene-based on-chip ESD protection comprising a novel graphene nanoelectromechanical system (gNEMS) ESD switch and graphene ESD interconnects. This review discusses the simulation, design, and measurements of the gNEMS ESD protection structures and graphene ESD protection interconnects. The review aims to inspire non-traditional thinking for future on-chip ESD protection.

Cysteine is considered an essential amino acid in the cultivation of Chinese hamster ovary (CHO) cells. An optimized cysteine supply during fed-batch cultivation supports the protein production capacity of recombinant CHO cell lines. However, we observed that CHO production cell lines seeded at low cell densities in chemically defined media enriched with cysteine greater than 2.5 mm resulted in markedly reduced cell growth during passaging, hampering seed train performance and scale-up. To investigate the underlying mechanism, seeding cell densities and initial cysteine concentrations ranging from low to high cysteine concentrations were varied followed by an analysis of cell culture performance. Additionally, cell cycle analysis, intracellular quantification of reactive oxygen species (ROS) as well as transcriptomic analyses by next-generation sequencing were carried out. Our results demonstrate that CHO cells seeded at low cell densities at high initial cysteine concentrations encountered increased oxidative stress leading to a p21-mediated cell cycle arrest in the G1/S phase. The resulting oxidative stress caused redox imbalance in the endoplasmic reticulum and activation of the unfolded protein response as well as the major antioxidant nuclear factor-like 2 response pathways. Potential signature genes related to oxidative stress and the inhibition of the pentose phosphate pathway were identified in the study. Finally, the study presents that seeding cells at a higher concentration counteract oxidative stress in cysteine-enriched cell culture media.

While the Global North is historically responsible for the majority of greenhouse gas emissions in the atmosphere, Newly Industrializing Countries (NICs) are expected to overtake developed country emissions in the coming years. At the same time, NICs are climbing the ladder of the global economy, increasing their competitiveness on the global stage and catching up with technological competencies of developed economies. Against this background, this paper explores innovation and collaboration in Climate Change Mitigation Technologies (CCMTs) in NICs. The research question is whether the propensity to innovate and diffuse CCMTs is impacted by technological collaboration with two highly developed countries, Germany and The United States. The sample of NICs includes the BRICS (Brazil, Russia, India, China, South Africa) plus Israel, Mexico, and Turkey, in a panel from 1995 to 2015. The empirical results suggest that collaboration with both Germany and the U.S. is highly significant for domestic CCMT innovation in NICs. These findings are important because, stepping beyond the literature on the merits and drawbacks of global climate governance tools such as the Clean Development Mechanism (CDM) and related UNFCCC processes, they show that collaboration for climate and environmental technologies could become a key tool to significantly improve the chances to stay in line with the Paris Climate Agreement. Finally, the policy advice for NICs and developing countries is to, above all else, focus on incubating strong technological innovation systems, including strengthening domestic Intellectual Property Rights (IPR), as well as to enhance technological collaboration with developed countries.

In research applications, mental health problems such as alcohol-related problems and depression are commonly assessed and evaluated using scale scores or latent trait scores derived from factor analysis or item response theory models. This tutorial paper demonstrates the use of cognitive diagnosis models (CDMs) as an alternative approach to characterizing mental health problems of young adults when item-level data are available. Existing measurement approaches focus on estimating the general severity of a given mental health problem at the scale level as a unidimensional construct without accounting for other symptoms of related mental health problems. The prevailing approaches may ignore clinically meaningful presentations of related symptoms at the item level. The current study illustrates CDMs using item-level data from college students (40 items from 719 respondents; 34.6% men, 83.9% White, and 16.3% first-year students). Specifically, we evaluated the constellation of four postulated domains (i.e., alcohol-related problems, anxiety, hostility, and depression) as a set of attribute profiles using CDMs. After accounting for the impact of each attribute (i.e., postulated domain) on the estimates of attribute profiles, the results demonstrated that when items or attributes have limited information, CDMs can utilize item-level information in the associated attributes to generate potentially meaningful estimates and profiles, compared to analyzing each attribute independently. We introduce a novel visual inspection aid, the lens plot, for quantifying this gain. CDMs may be a useful analytical tool to capture respondents\' risk and resilience for prevention research.

UNASSIGNED: To evaluate the real-world incidence of endophthalmitis after intravitreal anti-vascular endothelial growth factor (VEGF) injection through the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).
UNASSIGNED: Patients with endophthalmitis developing within 6 weeks after intravitreal anti-VEGF injections were identified in three large OMOP CDM databases.
UNASSIGNED: We identified 23,490 patients who received 128,123 intravitreal anti-VEGF injections. The incidence rates of endophthalmitis were 15.75 per 10,000 patients and 2.97 per 10,000 injections. The incidence rates of endophthalmitis for bevacizumab, ranibizumab, and aflibercept (per 10,000 injections) were 3.65, 1.39, and 0.76, respectively. The annual incidences have been below 5.00 per 10,000 injections since 2011 although the number of intravitreal anti-VEGF injections have been increasing. Bevacizumab presented a higher incidence rate for endophthalmitis than ranibizumab and aflibercept (incidence rate ratio = 3.17; P value = 0.021).
UNASSIGNED: The incidence of endophthalmitis after intravitreal anti-VEGF injections have been stabilized since 2011 despite the explosive increase of anti-VEGF injections. The off-label use of bevacizumab raised more endophthalmitis significantly. The OMOP CDM, which includes off-label uses, laboratory data, and scalable standardized database, could provide a novel strategy to reveal real-world evidence especially in ophthalmic fields.

Cell-derived matrices (CDM) are the decellularised extracellular matrices (ECM) of tissues obtained by the laboratory culture process. CDM is developed to mimic, to a certain extent, the properties of the needed natural tissue and thus to obviate the use of animals. The composition of CDM can be tailored for intended applications by carefully optimising the cell sources, culturing conditions and decellularising methods. This unique advantage has inspired the increasing use of CDM for biomedical research, ranging from stem cell niches to disease modelling and regenerative medicine. However, while much effort is spent on extracting different types of CDM and exploring their utilisation, little is spent on the scale-up aspect of CDM production. The ability to scale up CDM production is essential, as the materials are due for clinical trials and regulatory approval, and in fact, this ability to scale up should be an important factor from the early stages. In this review, we first introduce the current CDM production and characterisation methods. We then describe the existing scale-up technologies for cell culture and highlight the key considerations in scaling-up CDM manufacturing. Finally, we discuss the considerations and challenges faced while converting a laboratory protocol into a full industrial process. Scaling-up CDM manufacturing is a challenging task since it may be hindered by technologies that are not yet available. The early identification of these gaps will not only quicken CDM based product development but also help drive the advancement in scale-up cell culture and ECM extraction.