Under-strain Synergistic Oscillatory Twisting Manipulates the Bifunction in Rubrene Crystal.

Photophysical properties of condensed systems generally originate from collective contributions of all components in their stochastically fluctuated structures and are strongly influenced under strain of chromophores. To precisely identify how the stochastically fluctuated monomers synergistically manipulate the properties, we propose a statistic strategy over sufficient ab initio molecular dynamics (AIMD) samplings and for the first time uncover that synergistic oscillatory twisting (SOT) of neighboring under-strain monomers manipulates the bifunction of rubrene crystal.  The under-strain trunk SOT can regulate both singlet fission (SF) and triplet-triplet annihilation (TTA), enabling their coexistence and dominance switching by dynamically modulating the matching of excitation energies.

Bioorthogonal Chemistry-Guided Inhalable Nanoprodrug to Circumvent Cisplatin Resistance in Orthotopic Nonsmall Cell Lung Cancer.

Pulmonary delivery of anticancer therapeutics has shown encouraging performance in treating nonsmall cell lung cancer (NSCLC), which is characterized by high aggressiveness and poor prognosis. Cisplatin, a key member of the family of DNA alkylating agents, is extensively employed during NSCLC therapy. However, the development of chemoresistance and the occurrence of side effects severely impede the long-term application of cisplatin-based chemotherapies.

Fair prediction of 2-year stroke risk in patients with atrial fibrillation.

Objective: This study aims to develop machine learning models that provide both accurate and equitable predictions of 2-year stroke risk for patients with atrial fibrillation across diverse racial groups.

Materials And Methods: Our study utilized structured electronic health records (EHR) data from the All of Us Research Program. Machine learning models (LightGBM) were utilized to capture the relations between stroke risks and the predictors used by the widely recognized CHADS2 and CHA2DS2-VASc scores.

Black Phosphorus Quantum Dot Loaded Bioinspired Nanoplatform Synergized with aPD-L1 for Multimode Cancer Immunotherapy.

Efforts to prolong the blood circulation time and bypass immune clearance play vital roles in improving the therapeutic efficacy of nanoparticles (NPs). Herein, a multifunctional nanoplatform (BPP@RTL) that precisely targets tumor cells is fabricated by encapsulating ultrasmall phototherapeutic agent black phosphorus quantum dot (BPQD), chemotherapeutic drug paclitaxel (PTX), and immunomodulator PolyMetformin (PM) in hybrid membrane-camouflaged liposomes. Specifically, the hybrid cell membrane coating derived from the fusion of cancer cell membrane and red blood cell membrane displays excellent tumor targeting efficiency and long blood circulation property due to the innate features of both membranes.

Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models.

Objective: The timely stratification of trauma injury severity can enhance the quality of trauma care but it requires intense manual annotation from certified trauma coders. The objective of this study is to develop machine learning models for the stratification of trauma injury severity across various body regions using clinical text and structured electronic health records (EHRs) data.

Materials And Methods: Our study utilized clinical documents and structured EHR variables linked with the trauma registry data to create 2 machine learning models with different approaches to representing text.

Blood-Brain Barrier-Penetrating and Lesion-Targeting Nanoplatforms Inspired by the Pathophysiological Features for Synergistic Ischemic Stroke Therapy.

Ischemic stroke is a dreadful vascular disorder that poses enormous threats to the public health. Due to its complicated pathophysiological features, current treatment options after ischemic stroke attack remains unsatisfactory. Insufficient drug delivery to ischemic lesions impeded by the blood-brain barrier (BBB) largely limits the therapeutic efficacy of most anti-stroke agents.

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals.

Reducing Perovskite/C Interface Losses via Sequential Interface Engineering for Efficient Perovskite/Silicon Tandem Solar Cell.

Wide-bandgap (WBG) perovskite solar cells hold tremendous potential for realizing efficient tandem solar cells. However, nonradiative recombination and carrier transport losses occurring at the perovskite/electron-selective contact (e.g.

A framework for future national pediatric pandemic respiratory disease severity triage: The HHS pediatric COVID-19 data challenge.

Introduction: With persistent incidence, incomplete vaccination rates, confounding respiratory illnesses, and few therapeutic interventions available, COVID-19 continues to be a burden on the pediatric population. During a surge, it is difficult for hospitals to direct limited healthcare resources effectively. While the overwhelming majority of pediatric infections are mild, there have been life-threatening exceptions that illuminated the need to proactively identify pediatric patients at risk of severe COVID-19 and other respiratory infectious diseases.

A hybrid system to understand the relations between assessments and plans in progress notes.

Objective: The paper presents a novel solution to the 2022 National NLP Clinical Challenges (n2c2) Track 3, which aims to predict the relations between assessment and plan subsections in progress notes.

Methods: Our approach goes beyond standard transformer models and incorporates external information such as medical ontology and order information to comprehend the semantics of progress notes. We fine-tuned transformers to understand the textual data and incorporated medical ontology concepts and their relationships to enhance the model's accuracy.

Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research.

Globally, every year about 11% of infants are born preterm, defined as a birth prior to 37 weeks of gestation, with significant and lingering health consequences. Multiple studies have related the vaginal microbiome to preterm birth. We present a crowdsourcing approach to predict: (a) preterm or (b) early preterm birth from 9 publicly available vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from raw sequences via an open-source tool, MaLiAmPi.

A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization.

Importance: Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic.

Objectives: To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups.

More Generalizable Models For Sepsis Detection Under Covariate Shift.

Sepsis is a major cause of mortality in the intensive care units (ICUs). Early intervention of sepsis can improve clinical outcomes for sepsis patients. Machine learning models have been developed for clinical recognition of sepsis.

[Effects of three-dimensional culture on cell phenotype and hematopoietic cytokine expression of umbilical cord mesenchymal stem cells].

This study was aimed to investigate the change of cell phenotype and the expression of hematopoiesis associated cytokines in umbilical cord mesenchymal stem cells (UC-MSC) in three-dimensional (3-D) system. MSC were isolated from umbilical cord, and then cultured in 2-D and 3-D system respectively. The phenotype of MSC was detected by flow cytometry; the angiogenic capability of MSC cultured in 2-D and 3-D syitem was assessed using in vitro capillary formation assay.

A novel functional TagSNP Rs7560488 in the DNMT3A1 promoter is associated with susceptibility to gastric cancer by modulating promoter activity.

DNA-methyltransferase (DNMT)-3A which contains DNMT3A1 and DNMT3A2 isoforms have been suggested to play a crucial role in carcinogenesis and showed aberrant expression in most cancers. Accumulated evidences also indicated that single nucleotide polymorphisms (SNP) in DNMT genes were associated with susceptibility to different tumors. We hypothesized that genetic variants in DNMT3A1 promoter region are associated with gastric cancer risk.