Managing Interfacial Defects and Charge-Carriers Dynamics by a Cesium-Doped SnO2 for Air Stable Perovskite Solar Cells.

A high-quality nanostructured tin oxide (SnO2) has garnered massive attention as an electron transport layer (ETL) for efficient perovskite solar cells (PSCs). SnO2 is considered the most effective alternative to titanium oxide (TiO2) as ETL because of its low-temperature processing and promising optical and electrical characteristics. However, some essential modifications are still required to further improve the intrinsic characteristics of SnO2, such as mismatch band alignments, charge extraction, transportation, conductivity, and interfacial recombination losses. Herein, an inorganic-based cesium (Cs) dopant is used to modify the SnO2 ETL and to investigate the impact of Cs-dopant in curing interfacial defects, charge-carrier dynamics, and improving the optoelectronic characteristics of PSCs. The incorporation of Cs contents efficiently improves the perovskite film quality by enhancing the transparency, crystallinity, grain size, and light absorption and reduces the defect states and trap densities, resulting in an improved power conversion efficiency (PCE) of ≈22.1% with Cs:SnO2 ETL, in-contrast to pristine SnO2-based PSCs (20.23%). Moreover, the Cs-modified SnO2-based PSCs exhibit remarkable environmental stability in a relatively higher relative humidity environment (>65%) and without encapsulation. Therefore, this work suggests that Cs-doped SnO2 is a highly favorable electron extraction material for preparing highly efficient and air-stable planar PSCs.

Highly Macroporous Polyimide with Chemical Versatility Prepared from Poly(amic acid) Salt-Stabilized High Internal Phase Emulsion Template.

Macroporous polymers have gained significant attention due to their unique mass transport and size-selective properties. In this study, we focused on Polyimide (PI), a high-performance polymer, as an ideal candidate for macroporous structures. Despite various attempts to create macroporous PI (Macro PI) using emulsion templates, challenges remained, including limited chemical diversity and poor control over pore size and porosity. To address these issues, we systematically investigated the role of poly(amic acid) salt (PAAS) polymers as macrosurfactants and matrices. By designing 12 different PAAS polymers with diverse chemical structures, we achieved stable high internal phase emulsions (HIPEs) with >80 vol % internal volume. The resulting Macro PIs exhibited exceptional porosity (>99 vol %) after thermal imidization. We explored the structure-property relationships of these Macro PIs, emphasizing the importance of controlling pore size distribution. Furthermore, our study demonstrated the utility of these Macro PIs as separators in Li-metal batteries, providing stable charging-discharging cycles. Our findings not only enhance the understanding of emulsion-based macroporous polymers but also pave the way for their applications in advanced energy storage systems and beyond.

Discovery of (4-Pyrazolyl)-2-aminopyrimidines as Potent and Selective Inhibitors of Cyclin-Dependent Kinase 2.

CDK2 is a critical regulator of the cell cycle. For a variety of human cancers, the dysregulation of CDK2/cyclin E1 can lead to tumor growth and proliferation. Historically, early efforts to develop CDK2 inhibitors with clinical applications proved unsuccessful due to challenges in achieving selectivity over off-target CDK isoforms with associated toxicity. In this report, we describe the discovery of (4-pyrazolyl)-2-aminopyrimidines as a potent class of CDK2 inhibitors that display selectivity over CDKs 1, 4, 6, 7, and 9. SAR studies led to the identification of compound 17, a kinase selective and highly potent CDK2 inhibitor (IC50 = 0.29 nM). The evaluation of 17 in CCNE1-amplified mouse models shows the pharmacodynamic inhibition of CDK2, measured by reduced Rb phosphorylation, and antitumor activity.

Large Language Models: A Guide for Radiologists.

Large language models (LLMs) have revolutionized the global landscape of technology beyond natural language processing. Owing to their extensive pre-training on vast datasets, contemporary LLMs can handle tasks ranging from general functionalities to domain-specific areas, such as radiology, without additional fine-tuning. General-purpose chatbots based on LLMs can optimize the efficiency of radiologists in terms of their professional work and research endeavors. Importantly, these LLMs are on a trajectory of rapid evolution, wherein challenges such as "hallucination," high training cost, and efficiency issues are addressed, along with the inclusion of multimodal inputs. In this review, we aim to offer conceptual knowledge and actionable guidance to radiologists interested in utilizing LLMs through a succinct overview of the topic and a summary of radiology-specific aspects, from the beginning to potential future directions.

Unusual Raman Enhancement Effect of Ultrathin Copper Sulfide.

In surface-enhanced Raman spectroscopy (SERS), 2D materials are explored as substrates owing to their chemical stability and reproducibility. However, they exhibit lower enhancement factors (EFs) compared to noble metal-based SERS substrates. This study demonstrates the application of ultrathin covellite copper sulfide (CuS) as a cost-effective SERS substrate with a high EF value of 7.2 × 104 . The CuS substrate is readily synthesized by sulfurizing a Cu thin film at room temperature, exhibiting a Raman signal enhancement comparable to that of an Au noble metal substrate of similar thickness. Furthermore, computational simulations using the density functional theory are employed and time-resolved photoluminescence measurements are performed to investigate the enhancement mechanisms. The results indicate that polar covalent bonds (Cu─S) and strong interlayer interactions in the ultrathin CuS substrate increase the probability of charge transfer between the analyte molecules and the CuS surface, thereby producing enhanced SERS signals. The CuS SERS substrate demonstrates the selective detection of various dye molecules, including rhodamine 6G, methylene blue, and safranine O. Furthermore, the simplicity of CuS synthesis facilitates large-scale production of SERS substrates with high spatial uniformity, exhibiting a signal variation of less than 5% on a 4-inch wafer.

Evaluation of crowdsourced mortality prediction models as a framework for assessing artificial intelligence in medicine.

OBJECTIVE: Applications of machine learning in healthcare are of high interest and have the potential to improve patient care. Yet, the real-world accuracy of these models in clinical practice and on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate methods that predict healthcare outcomes. We focused on the prediction of all-cause mortality as the community challenge question. MATERIALS AND METHODS: Using a Model-to-Data framework, 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries, generated 25 accurate models all trained on a dataset of over 1.1 million patients and evaluated on patients prospectively collected over a 1-year observation of a large health system. RESULTS: The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI, 0.942-0.951) and an area under the precision-recall curve of 0.487 (95% CI, 0.458-0.499) on a prospectively collected patient cohort. DISCUSSION: Post hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data. CONCLUSION: This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI.

Biomedical relation extraction with knowledge base-refined weak supervision.

Biomedical relation extraction (BioRE) is the task of automatically extracting and classifying relations between two biomedical entities in biomedical literature. Recent advances in BioRE research have largely been powered by supervised learning and large language models (LLMs). However, training of LLMs for BioRE with supervised learning requires human-annotated data, and the annotation process often accompanies challenging and expensive work. As a result, the quantity and coverage of annotated data are limiting factors for BioRE systems. In this paper, we present our system for the BioCreative VII challenge-DrugProt track, a BioRE system that leverages a language model structure and weak supervision. Our system is trained on weakly labelled data and then fine-tuned using human-labelled data. To create the weakly labelled dataset, we combined two approaches. First, we trained a model on the original dataset to predict labels on external literature, which will become a model-labelled dataset. Then, we refined the model-labelled dataset using an external knowledge base. Based on our experiment, our approach using refined weak supervision showed significant performance gain over the model trained using standard human-labelled datasets. Our final model showed outstanding performance at the BioCreative VII challenge, achieving 3rd place (this paper focuses on our participating system in the BioCreative VII challenge). Database URL: http://wonjin.info/biore-yoon-et-al-2022.

Green and Facile Synthesis of Hybrid Composites with Ultralow Dielectric Properties from Water-Soluble Polyimide and Dual-Porous Silica Nanoparticles.

Here, we proposed an eco-friendly synthetic method for synthesizing hybrid composites with ultralow dielectric properties at high frequencies up to 28 GHz for true 5G communication from aqueous aromatic polyimide (PI) polymers and dual-porous silica nanoparticles (DPS). The "one-step" water-based emulsion template method was used to synthesize the macroporous silica nanoparticles (MPS). A substantially negative ζ potential was produced along the surface of MPS by the poly(vinylpyrrolidone)-based chemical functionalization, enabling excellent aqueous dispersion stability. The water-soluble poly(amic acid) (PAA), as a precursor to PI, was also "one-step" polymerized in an aqueous solution. The MPS were dispersed in a water-soluble PAA matrix to create the hybrid composite films using an entirely water-based approach. The compatibility between the PAA matrix and MPS was elucidated by investigating relatively diverse end-terminated PAAs (with either amine or carboxyl group). It was also discovered that, during a thermally activated imidization reaction, the MPS are in situ converted into the DPS with macro- and microporous structures (with a surface area of 1522.4 m2/g). The thermal, dielectric, mechanical, and morphological characteristics of each composite film were examined, while the amount of DPS in the PI matrix varied from 1 to 20 wt %. With the addition of 5 wt % DPS as an optimum condition, it showed ultralow dielectric properties, with the Dk and Df being 1.615 and 0.003 at a frequency of 28 GHz, respectively, and compatible mechanical properties, with the tensile strength and elastic modulus being 78.2 MPa and 0.32 GPa, respectively. These results can comprehensively satisfy various physical properties required as a substrate material for 5G communication devices.

Synergistic Contribution of Oligo(ethylene glycol) and Fluorine Substitution of Conjugated Polymer Photocatalysts toward Solar Driven Sacrificial Hydrogen Evolution.

To separately explore the importance of hydrophilicity and backbone planarity of polymer photocatalyst, a series of benzothiadiazole-based donor-acceptor alternating copolymers incorporating alkoxy, linear oligo(ethylene glycol) (OEG) side chain, and backbone fluorine substituents is presented. The OEG side chains in the polymer backbone increase the surface energy of the polymer nanoparticles, thereby improving the interaction with water and facilitating electron transfer to water. Moreover, the OEG-attached copolymers exhibit enhanced intermolecular packing compared to polymers with alkoxy side chains, which is possibly attributed to the self-assembly properties of the side chains. Fluorine substituents on the polymer backbone produce highly ordered lamellar stacks with distinct π-π stacking features; subsequently, the long-lived polarons toward hydrogen evolution are observed by transient absorption spectroscopy. In addition, a new nanoparticle synthesis strategy using a methanol/water mixed solvent is first adopted, thereby avoiding the screening effect of surfactants between the nanoparticles and water. Finally, hydrogen evolution rate of 26 000 µmol g-1  h-1 is obtained for the copolymer incorporated with both OEG side chains and fluorine substituents under visible-light irradiation (λ > 420 nm). This study demonstrates how the glycol side chain strategy can be further optimized for polymer photocatalysts by controlling the backbone planarity.

Changes in retro-odontoid mass after upper cervical spine surgery.

A non-neoplastic mass posterior to the dens is termed a retro-odontoid mass (R-OM). This retrospective study evaluated radiographic and clinical outcomes and R-OM changes after upper cervical spine surgery. This study included 69 patients who underwent upper cervical spine surgery, including atlantoaxial fusion, occipitocervical fusion, or decompression. All patients underwent preoperative magnetic resonance imaging (MRI). Six-month follow-up MRI examinations were performed in 30 patients who had preoperative R-OMs. Radiographic outcomes of the anterior and posterior atlantodental intervals were measured using X-rays and computed tomography. The R-OM and space available for the cord (SAC) were measured using MRI. Clinical outcomes were evaluated using neck and arm pain visual analog scales, the Japanese Orthopedic Association score, the neck disability index, and the patient-reported subjective improvement rate. The anterior atlantodental interval decreased, while the posterior atlantodental interval and SAC increased postoperatively. Among the clinical outcomes, the neck and arm pain and the neck disability index decreased postoperatively, while the Japanese Orthopedic Association score increased. All clinical and radiographic outcomes improved postoperatively. The R-OM either decreased in size or disappeared after fusion surgery in all cases, except in one patient who underwent decompression surgery. In conclusion, stabilization through fusion surgery is essential for treating R-OM.

Discovery of 5,7-Dihydro-6H-pyrrolo[2,3-d]pyrimidin-6-ones as Highly Selective CDK2 Inhibitors.

A series of exceptionally selective CDK2 inhibitors are described. Starting from an HTS hit, we successfully scaffold hopped to a 5,7-dihydro-6H-pyrrolo[2,3-d]pyrimidin-6-one core structure, which imparted a promising initial selectivity within the CDK family. Extensive further SAR identified additional factors that drove selectivity to above 200× for CDKs 1/4/6/7/9. General kinome selectivity was also greatly improved. Finally, use of in vivo metabolite identification allowed us to pinpoint sulfonamide dealkylation as the primary metabolite, which was ameliorated through the deuterium effect.

Direct Internal Fixation for Unstable Atlas Fractures.

PURPOSE: To investigate the radiologic and clinical outcomes of direct internal fixation for unstable atlas fractures. MATERIALS AND METHODS: This retrospective study included 12 patients with unstable atlas fractures surgically treated using C1 lateral mass screws, rods, and transverse connector constructs. Nine lateral mass fractures with transverse atlantal ligament (TAL) avulsion injury and three 4-part fractures with TAL injury (two avulsion injuries, one TAL substance tear) were treated. Radiologic outcomes included the anterior atlantodental interval (AADI) in flexion and extension cervical spine lateral radiographs at 6 months and 1 year after treatment. CT was also performed to visualize bony healing of the atlas at 6 months and 1 year. Visual Analog Scale (VAS) scores for neck pain, Neck Disability Index (NDI) values, and cervical range of motion (flexion, extension, and rotation) were recorded at 6 months after surgery. RESULTS: The mean postoperative extension and flexion AADIs were 3.79±1.56 (mean±SD) and 3.13±1.01 mm, respectively. Then mean AADI was 3.42±1.34 and 3.33±1.24 mm at 6 months and 1 year after surgery, respectively. At 1 year after surgery, 11 patients showed bony healing of the atlas on CT images. Only one patient underwent revision surgery 8 months after primary surgery due to nonunion and instability findings. The mean VAS score for neck pain was 0.92±0.99, and the mean NDI value was 8.08±5.70. CONCLUSION: C1 motion-preserving direct internal fixation technique results in good reduction and stabilization of unstable atlas fractures. This technique allows for the preservation of craniocervical and atlantoaxial motion.

Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival.

Most electronic medical records, such as free-text radiological reports, are unstructured; however, the methodological approaches to analyzing these accumulating unstructured records are limited. This article proposes a deep-transfer-learning-based natural language processing model that analyzes serial magnetic resonance imaging reports of rectal cancer patients and predicts their overall survival. To evaluate the model, a retrospective cohort study of 4,338 rectal cancer patients was conducted. The experimental results revealed that the proposed model utilizing pre-trained clinical linguistic knowledge could predict the overall survival of patients without any structured information and was superior to the carcinoembryonic antigen in predicting survival. The deep-transfer-learning model using free-text radiological reports can predict the survival of patients with rectal cancer, thereby increasing the utility of unstructured medical big data.

USP28 deletion and small-molecule inhibition destabilizes c-MYC and elicits regression of squamous cell lung carcinoma.

Lung squamous cell carcinoma (LSCC) is a considerable global health burden, with an incidence of over 600,000 cases per year. Treatment options are limited, and patient's 5-year survival rate is less than 5%. The ubiquitin-specific protease 28 (USP28) has been implicated in tumourigenesis through its stabilization of the oncoproteins c-MYC, c-JUN, and Δp63. Here, we show that genetic inactivation of Usp28-induced regression of established murine LSCC lung tumours. We developed a small molecule that inhibits USP28 activity in the low nanomole range. While displaying cross-reactivity against the closest homologue USP25, this inhibitor showed a high degree of selectivity over other deubiquitinases. USP28 inhibitor treatment resulted in a dramatic decrease in c-MYC, c-JUN, and Δp63 proteins levels and consequently induced substantial regression of autochthonous murine LSCC tumours and human LSCC xenografts, thereby phenocopying the effect observed by genetic deletion. Thus, USP28 may represent a promising therapeutic target for the treatment of squamous cell lung carcinoma.

A Useful MRI Classification for Symptomatic Discoid Lateral Meniscus.

PURPOSE: The purpose of this study is to classify the discoid lateral meniscus (DLM) according to the signal and shape in magnetic resonance imaging (MRI), and to provide information not only in diagnosis but also in treatment. MATERIALS AND METHODS: We reviewed 162 cases who diagnosed with DLM by MRI and underwent arthroscopic procedures from April 2010 to March 2018. Three observers reviewed MRI findings of all cases and predicted arthroscopic tear using three MRI criteria (criterion 1,2 and 3). Among three criteria, the criterion that most accurately predicts arthroscopic tear was selected. Using this criterion, the cases of predicted tear were named group 1. In addition, group 1 was divided into three subgroups (group 1a, 1b and 1c) by deformation or displacement on MRI and arthroscopic type of tear and procedures were analyzed according to these subgroups. RESULTS: The intra-meniscal signal change itself (criterion 3) on MRI showed the highest agreement with the arthroscopic tear. No meniscal deformation and displacement on MRI (group 1a) showed no specific type of tear and more cases of meniscal saucerization. The meniscal deformation on MRI (group 1b) showed more simple horizontal tears and more cases of meniscal saucerization. The meniscal displacement on MRI (group 1c) showed more peripheral tears and more cases of meniscal repair and subtotal meniscectomy. Comparing arthroscopic type of tear and type of arthroscopic procedure between three subgroups, there were significant differences in three groups (P < .05). CONCLUSIONS: Intra-meniscal signal change itself on MRI is the most accurate finding to predict arthroscopic tear in symptomatic DLM. In addition, subgroup analysis by deformation or displacement on MRI is helpful to predict the type of arthroscopic tear and procedures.

Predicting mechanism of action of novel compounds using compound structure and transcriptomic signature coembedding.

MOTIVATION: Identifying mechanism of actions (MoA) of novel compounds is crucial in drug discovery. Careful understanding of MoA can avoid potential side effects of drug candidates. Efforts have been made to identify MoA using the transcriptomic signatures induced by compounds. However, these approaches fail to reveal MoAs in the absence of actual compound signatures. RESULTS: We present MoAble, which predicts MoAs without requiring compound signatures. We train a deep learning-based coembedding model to map compound signatures and compound structure into the same embedding space. The model generates low-dimensional compound signature representation from the compound structures. To predict MoAs, pathway enrichment analysis is performed based on the connectivity between embedding vectors of compounds and those of genetic perturbation. Results show that MoAble is comparable to the methods that use actual compound signatures. We demonstrate that MoAble can be used to reveal MoAs of novel compounds without measuring compound signatures with the same prediction accuracy as that with measuring them. AVAILABILITY AND IMPLEMENTATION: MoAble is available at https://github.com/dmis-lab/moable. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Crowdsourced mapping of unexplored target space of kinase inhibitors.

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.

Transient fixation of L4 vertebra preserves lumbar motion and function in Lenke Type 5C and 6C scoliosis.

This study investigated the efficacy of a novel surgical method that relies on the transient fixation of L4 in Lenke Type 5C and 6C adolescent idiopathic scoliosis. Thirty-six transient surgically treated L4 fixation patients were retrospectively evaluated. The first surgery involved mechanical correction of scoliosis; the lowest instrumented vertebra (LIV) was L4. After an average of 1.3 years (range, 0.3-3.4), the second surgery to remove transient L4 pedicle screws was performed. Radiographic parameters and SRS-22 scores were measured. Cobb's angle, coronal balance, LIV tilting angle, and LIV coronal disc angle clearly improved after the first surgery (p < 0.01). After the second surgery, the corrected Cobb angle (p = 0.446) and coronal balance were maintained (p = 0.271). Although L3/S1 lumbar lordosis decreased after the first surgery (p < 0.01), after removal of transient L4 pedicle screws, it recovered slightly (p = 0.03). Similarly, the preoperative L3/4 lateral disc mobility eventually recovered after transient L4 screw removal (p < 0.01). The function domain of the SRS-22 showed better scores after removal of transient L4 screws (p = 0.04). L4 transient fixation surgery is beneficial for Lenke Type 5C and 6C scolioses that do not fully satisfy LIV (L3) criteria. It preserves L3/4 disc motion, increases functional outcomes, and maintains spinal correction and coronal balance.

The deubiquitylase USP9X controls ribosomal stalling.

When a ribosome stalls during translation, it runs the risk of collision with a trailing ribosome. Such an encounter leads to the formation of a stable di-ribosome complex, which needs to be resolved by a dedicated machinery. The initial stalling and the subsequent resolution of di-ribosomal complexes requires activity of Makorin and ZNF598 ubiquitin E3 ligases, respectively, through ubiquitylation of the eS10 and uS10 subunits of the ribosome. We have developed a specific small-molecule inhibitor of the deubiquitylase USP9X. Proteomics analysis, following inhibitor treatment of HCT116 cells, confirms previous reports linking USP9X with centrosome-associated protein stability but also reveals a loss of Makorin 2 and ZNF598. We show that USP9X interacts with both these ubiquitin E3 ligases, regulating their abundance through the control of protein stability. In the absence of USP9X or following chemical inhibition of its catalytic activity, levels of Makorins and ZNF598 are diminished, and the ribosomal quality control pathway is impaired.