Objectives: This systematic review aimed to assess the properties and feasibility of existing risk pre diction models for post-intensive care syndrome outcomes in adult survivors of critical illness. Methods: As of November 1, 2023, Cochrane Library, PubMed, Embase, CINAHL, Web of Science, PsycInfo, China National Knowledge Infrastructure (CNKI), SinoMed, Wanfang database, and China Science and Technology Journal Database (VIP) were searched. Following the literature screening process, we extracted data encompassing participant sources, post-intensive care syndrome (PICS) outcomes, sample sizes, missing data, predictive factors, model development methodologies, and metrics for model per formance and evaluation. We conducted a review and classification of the PICS domains and predictive factors identified in each study. The Prediction Model Risk of Bias Assessment Tool was employed to assess the quality and applicability of the studies. Results: This systematic review included a total of 16 studies, comprising two cognitive impairment studies, four psychological impairment studies, eight physiological impairment studies, and two studies on all three domains. The discriminative ability of prediction models measured by area under the receiver operating characteristic curve was 0.68e0.90. The predictive performance of most models was excellent, but most models were biased and overfitted. All predictive factors tend to encompass age, pre ICU functional impairment, in-ICU experiences, and early-onset new symptoms. Conclusions: This review identified 16 prediction models and the predictive factors for PICS. Nonetheless, due to the numerous methodological and reporting shortcomings identified in the studies under review, clinicians should exercise caution when interpreting the predictions made by these models. To avert the development of PICS, it is imperative for clinicians to closely monitor prognostic factors, including the in ICU experience and early-onset new symptoms.

The rapid advancement of single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) has opened unprecedented windows into cellular diversity, yet existing methods for multiplexing samples struggle with scalability and accuracy. Traditional techniques relying on antibodies or lipid-based barcodes often fail to uniformly label cells across different types or species, particularly in complex clinical samples. These limitations—cell-type bias, cross-contamination risks, and loss of rare cell populations—hinder large-scale studies and clinical translation. To overcome these challenges, a team led by Professor Yiwei Li at Huazhong University of Science and Technology (HUST) has pioneered Toti-N-Seq, a groundbreaking technology that harnesses the universal presence of N-glycans on cell and nuclear surfaces. Published as a cover story in Research (2025, DOI: 10.34133/research.0678), this innovation redefines how researchers approach high-throughput cellular profiling.

A new study reveals that water-extractable organic matter (WEOM) from air-dried soils can effectively estimate microbial biomass, offering a safer alternative to traditional methods. This approach reduces the use of toxic solvents, simplifies sampling, and shows a strong correlation between WEOM and microbial biomass carbon. The findings open new doors for accessible and scalable soil microbiology research. This innovative method provides a safer, more efficient alternative to traditional chloroform fumigation extraction, enhancing soil microbiology research.

The Center for Space Exploration Research at the University of California, Davis, has partnered with Proteus Space to launch a US government-sponsored satellite into space with a custom AI-enabled payload. The UC Davis-designed payload is a dynamic digital twin that models the current condition and predicts the future state of the spacecraft’s power system, running in real time onboard the spacecraft instead of in ground-based mission control.