Predicting pediatric patient rehabilitation outcomes after spinal deformity surgery with artificial intelligence.

Background: Adolescent idiopathic scoliosis (AIS) is the most common type of scoliosis, affecting 1-4% of adolescents. The Scoliosis Research Society-22R (SRS-22R), a health-related quality-of-life instrument for AIS, has allowed orthopedists to measure subjective patient outcomes before and after corrective surgery beyond objective radiographic measurements. However, research has revealed that there is no significant correlation between the correction rate in major radiographic parameters and improvements in patient-reported outcomes (PROs), making it difficult to incorporate PROs into personalized surgical planning.

Optimized Clinical Feature Analysis for Improved Cardiovascular Disease Risk Screening.

To develop a clinical decision support tool that can predict cardiovascular disease (CVD) risk with high accuracy while requiring minimal clinical feature input, thus reducing the time and effort required by clinicians to manually enter data prior to obtaining patient risk assessment. In this study, we propose a robust feature selection approach that identifies five key features strongly associated with CVD risk, which have been found to be consistent across various models. The machine learning model developed using this optimized feature set achieved state-of-the-art results, with an AUROC of 91.

CD74 is a potential biomarker predicting the response to immune checkpoint blockade.

Background: Immune checkpoint blockade (ICB) has been improving the patient outcome in multiple cancer types. However, not all patients respond to ICB. Biomarkers are needed for selecting appropriate patients to receive ICB.

Clinical decision making under uncertainty: a bootstrapped counterfactual inference approach.

Background: Learning policies for decision-making, such as recommending treatments in clinical settings, is important for enhancing clinical decision-support systems. However, the challenge lies in accurately evaluating and optimizing these policies for maximum efficacy. This paper addresses this gap by focusing on two key aspects of policy learning: evaluation and optimization.

Pan-cancer analysis reveals CCL5/CSF2 as potential predictive biomarkers for immune checkpoint inhibitors.

Background: Currently, there are no optimal biomarkers available for distinguishing patients who will respond to immune checkpoint inhibitors (ICIs) therapies. Consequently, the exploration of novel biomarkers that can predict responsiveness to ICIs is crucial in the field of immunotherapy.

Methods: We estimated the proportions of 22 immune cell components in 10 cancer types (6,128 tumors) using the CIBERSORT algorithm, and further classified patients based on their tumor immune cell proportions in a pan-cancer setting using k-means clustering.

GRASLND regulates melanoma cell progression by targeting the miR-218-5p/STAM2 axis.

Background: Increasing evidence suggests that long noncoding RNAs (lncRNAs) play important regulatory roles in biological processes and are dysregulated in numerous tumors. The lncRNA GRASLND functions as an oncogene in many cancers, but its role in skin cutaneous melanoma (SKCM) requires further investigation.

Methods: SiRNA transfection, wound - healing and transwell assays were performed to evaluate the effect of GRASLND on cellular function.

A facile and on-site sensing strategy for phenolic compounds based on a novel nanozyme with high polyphenol oxidase-like activity.

Phenolic compounds (PCs) are diverse in nature and undergo complex migration and transformations in the environment, making it challenging to use techniques such as chromatography and other traditional methods to determine the concentration of PCs by separation, individual monitoring and subsequent addition. To address this issue, a facile and on-site strategy was developed to measure the concentration of PCs using a novel nanozyme with polyphenol oxidase-like activity. First, the nanozyme was designed by coordinating the asymmetric ligand nicotinic acid with copper to mimic the structure of mononuclear and trinuclear copper clusters of natural laccases.

Developing a novel causal inference algorithm for personalized biomedical causal graph learning using meta machine learning.

Background: Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality. The majority of current machine learning models in clinical decision support systems only predict associations between variables, whereas causal graph learning models causality dynamics through graphs. However, building personalized causal graphs for each individual is challenging due to the limited amount of data available for each patient.

Transcriptomic Insights into Host Metabolism and Immunity Changes after Parasitization by .

Parasitoids commonly manipulate their host's metabolism and immunity to facilitate their offspring survival, but the mechanisms remain poorly understood. Here, we deconstructed the manipulation strategy of a newly discovered parasitoid wasp, , which parasitizes . Using RNA-seq, we analyzed transcriptomes of -parasitized and non-parasitized host larvae.

KAT8/SIRT7-mediated Fascin-K41 acetylation/deacetylation regulates tumor metastasis in esophageal squamous cell carcinoma.

Fascin actin-bundling protein 1 (Fascin) is highly expressed in a variety of cancers, including esophageal squamous cell carcinoma (ESCC), working as an important oncogenic protein and promoting the migration and invasion of cancer cells by bundling F-actin to facilitate the formation of filopodia and invadopodia. However, it is not clear how exactly the function of Fascin is regulated by acetylation in cancer cells. Here, in ESCC cells, the histone acetyltransferase KAT8 catalyzed Fascin lysine 41 (K41) acetylation, to inhibit Fascin-mediated F-actin bundling and the formation of filopodia and invadopodia.

High-resolution 3D imaging in light-field microscopy through Stokes matrices and data fusion.

The trade-off between the lateral and vertical resolution has long posed challenges to the efficient and widespread application of Fourier light-field microscopy, a highly scalable 3D imaging tool. Although existing methods for resolution enhancement can improve the measurement result to a certain extent, they come with limitations in terms of accuracy and applicable specimen types. To address these problems, this paper proposed a resolution enhancement scheme utilizing data fusion of polarization Stokes vectors and light-field information for Fourier light-field microscopy system.

Light field measurement of specular surfaces by multi-polarization and hybrid modulated illumination.

Specular highlights present a challenge in light field microscopy imaging fields, leading to loss of target information and incorrect observation results. Existing highlight elimination methods suffer from computational complexity, false information and applicability. To address these issues, an adaptive multi-polarization illumination scheme is proposed to effectively eliminate highlight reflections and ensure uniform illumination without complex optical setup or mechanical rotation.

Improving explainable AI with patch perturbation-based evaluation pipeline: a COVID-19 X-ray image analysis case study.

Recent advances in artificial intelligence (AI) have sparked interest in developing explainable AI (XAI) methods for clinical decision support systems, especially in translational research. Although using XAI methods may enhance trust in black-box models, evaluating their effectiveness has been challenging, primarily due to the absence of human (expert) intervention, additional annotations, and automated strategies. In order to conduct a thorough assessment, we propose a patch perturbation-based approach to automatically evaluate the quality of explanations in medical imaging analysis.

Early and fair COVID-19 outcome risk assessment using robust feature selection.

Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment.

Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence.

With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics molecular data to real-time continuous bio-signals are generated at an unprecedented speed and scale every day. For the first time, these multi-modal biomedical data are able to make precision medicine close to a reality. However, due to data volume and the complexity, making good use of these multi-modal biomedical data requires major effort.

Fine mapping of two recessive powdery mildew resistance genes from Aegilops tauschii accession CIae8.

Two recessive powdery mildew resistance loci pmAeCIae8_2DS and pmAeCIae8_7DS from Aegilops tauschii were mapped and two synthesized hexaploid wheat lines were developed by distant hybridization. Wheat powdery mildew (Pm), one of the worldwide destructive fungal diseases, causes significant yield loss up to 30%. The identification of new Pm resistance genes will enrich the genetic diversity of wheat breeding for Pm resistance.

Risk assessment of dietary factors in global pattern of ischemic heart disease mortality and disability-adjusted life years over 30 years.

Objectives: The aim of this study was to investigate differences in the burden of ischemic heart disease (IHD)-related mortality and disability-adjusted life years (DALYs) caused by dietary factors, as well as the influencing factors with age, period, and cohort effects, in regions with different social-demographic status from 1990 to 2019.

Methods: We extracted data on IHD mortality, DALYs, and age-standardized rates (ASRs) related to dietary risks from 1990 to 2019 as IHD burden measures. Hierarchical age-period-cohort analysis was used to analyze age- and time-related trends and the interaction between different dietary factors on the risk of IHD mortality and DALYs.

Selective sensing of catechol based on a fluorescent nanozyme with catechol oxidase activity.

Nanozymes, an unusual category of nanomaterials possessing enzymatic properties, and have generated considerable interest regarding their application feasibilities on several important fronts. In the present work, an innovative sensing device for catechol was established ground on a fluorescent nanozyme (Cu-BDC-NH) that exhibited catechol oxidase activity. The fluorescent nanozyme combines both functions of catechol recognition and response signal output, and can realize the sensing of catechol without the addition of other chromogenic agents.

Metabolomics Provides New Insights into Host Manipulation Strategies by (Hymenoptera: Braconidae), a Fruit Fly Parasitoid.

(Hymenoptera: Braconidae) is an endoparasitoid wasp that can successfully parasitize a wide range of host species across the genus, including the invasive crop pest . Parasitoids are capable of regulating the host metabolism to produce the nutritional metabolites for the survival of their offspring. Here, we intend to investigate the metabolic changes in hosts after parasitization by , using the non-targeted LC-MS (liquid chromatography-mass spectrometry) metabolomics analysis.

Explainable synthetic image generation to improve risk assessment of rare pediatric heart transplant rejection.

Expert microscopic analysis of cells obtained from frequent heart biopsies is vital for early detection of pediatric heart transplant rejection to prevent heart failure. Detection of this rare condition is prone to low levels of expert agreement due to the difficulty of identifying subtle rejection signs within biopsy samples. The rarity of pediatric heart transplant rejection also means that very few gold-standard images are available for developing machine learning models.

Targeting USP10 induces degradation of oncogenic ANLN in esophageal squamous cell carcinoma.

Anillin (ANLN) is a mitosis-related protein that promotes contractile ring formation and cytokinesis, but its cell cycle-dependent degradation mechanisms in cancer cells remain unclear. Here, we show that high expression of ANLN promotes cytokinesis and proliferation in esophageal squamous cell carcinoma (ESCC) cells and is associated with poor prognosis in ESCC patients. Furthermore, the findings of the study showed that the deubiquitinating enzyme USP10 interacts with ANLN and positively regulates ANLN protein levels.

The odorant receptor co-receptor gene contributes to mating and host-searching behaviors in parasitoid wasps.

Background: Biological control of pest insects by parasitoid wasps is an effective and environmentally friendly strategy compared with the use of synthetic pesticides. Successful courtship and host-search behaviors of parasitoid wasps are important for biological control efficiency and are often mediated by chemical odorant cues. The odorant receptor co-receptor (Orco) gene has an essential role in the perception of odors in insects.