A Study of Biomedical Relation Extraction Using GPT Models.

Relation Extraction (RE) is a natural language processing (NLP) task for extracting semantic relations between biomedical entities. Recent developments in pre-trained large language models (LLM) motivated NLP researchers to use them for various NLP tasks. We investigated GPT-3.5-turbo and GPT-4 on extracting the relations from three standard datasets, EU-ADR, Gene Associations Database (GAD), and ChemProt. Unlike the existing approaches using datasets with masked entities, we used three versions for each dataset for our experiment: a version with masked entities, a second version with the original entities (unmasked), and a third version with abbreviations replaced with the original terms. We developed the prompts for various versions and used the chat completion model from GPT API. Our approach achieved a F1-score of 0.498 to 0.809 for GPT-3.5-turbo, and a highest F1-score of 0.84 for GPT-4. For certain experiments, the performance of GPT, BioBERT, and PubMedBERT are almost the same.

Anti-neuroinflammatory andrastin-type meroterpenoids from the marine-derived fungus Penicillium chrysogenum HNNU w0032.

A new andrastin-type meroterpenoid penimerodione A (1), and three known analogues (2-4), were isolated from the culture of a marine-derived fungus Penicillium chrysogenum HNNU w0032 by the guidance of MS/MS-based molecular networking. The planar structure of 1 was established by extensive NMR spectroscopic and HRESIMS analyses, and the absolute configuration was elucidated by a single-crystal X-ray diffraction. Compound 1 showed significant inhibitory effect on NO production in LPS-stimulated BV-2 macrophages with an IC50 value of 5.9 ± 0.3 µM. The Western blot result revealed that compound 1 exerted an anti-neuroinflammatory effect via the MAPK signalling pathway.

Strong anisotropic third-harmonic generation in layered violet phosphorus.

We demonstrate that layered violet phosphorus, an emerging 2D semiconductor, undergoes strong anisotropic third-harmonic generation (THG). Polarization dependence of in-plane THG presents a cruciate-flower-shaped curve. Through theoretical modeling of the in-plane THG response, done by considering crystalline symmetry of violet phosphorus, we successfully quantify four non-zero third-order nonlinear optical susceptibility tensor elements. From control experiments, the magnitude of third-order nonlinear optical susceptibility |χ3| is calculated to be about 4.0 × 10-19 m2 V-2, which is comparable to those of conventional 2D layered semiconductors. These results indicate that the layered violet phosphorus can serve as an ideal building block for nonlinear optical applications.

The Research of a Large-Scale Analysis Platform for MNS Blood Group Identification Based on Long-Read Sequencing.

The objective of this study was to devise a novel approach for determining MNS blood group utilizing long-read sequencing (LRS) and to identify intricate genome variations associated with this blood group system. In this study, a total of 60 blood samples were collected from randomly selected Chinese Han blood donors. The amplification of the full-length sequences of GYPA exon 2-7 (11 kb) and GYPB exon 2-6 (7 kb) was conducted on the blood samples obtained from these 60 donors. Subsequently, the sequencing of these amplified sequences was performed using the PacBio platform. The obtained sequencing data were then compared with the reference sequence of the human genome (GRCh38) utilizing the pbmm2 software, resulting in the acquisition of the haploid sequences of GYPA and GYPB. The serological typing prediction was conducted using the International Society of Blood Transfusion (ISBT) database, while the analysis of SNVs sites was performed using deepvariant v1.2.0 software and reference sequence alignment. A total of 60 samples yielded unambiguous high-quality haplotypes, which can serve as a standardized reference sequence for molecular biology typing of MNSs in the Chinese population. In a total of 60 serological samples, the LRS method successfully identified the M, N, S, and s blood group antigens by analyzing specific genetic variations (c.59, c.71, c.72 for GYPA, and c.143 for GYPB), which aligned with the results obtained through conventional serological techniques. 4 Mur samples that had been previously validated through serology and molecular biology were successfully confirmed, and complete haploid sequences were obtained. Notably, one of the Mur samples exhibited a novel breakpoint, GYP (B1-136-B ψ 137-212-A213-229-B230-366), thereby representing a newly identified subtype. Single molecule sequencing, which eliminates the necessity for PCR amplification, effectively encompasses GC and high repeat regions, enhancing accuracy in quantifying mutations with low abundance or frequency. By employing LRS analysis of the core region of GYPA and GYPB, diverse genotypes of MNS can be precisely and reliably identified in a single assay. This approach presents a comprehensive, expeditious, and precise novel method for the categorization and investigation of MNS blood group system.

Me-LLaMA: Foundation Large Language Models for Medical Applications.

Recent advancements in large language models (LLMs) such as ChatGPT and LLaMA have hinted at their potential to revolutionize medical applications, yet their application in clinical settings often reveals limitations due to a lack of specialized training on medical-specific data. In response to this challenge, this study introduces Me-LLaMA, a novel medical LLM family that includes foundation models - Me-LLaMA 13/70B, along with their chat-enhanced versions - Me-LLaMA 13/70B-chat, developed through continual pre-training and instruction tuning of LLaMA2 using large medical datasets. Our methodology leverages a comprehensive domain-specific data suite, including a large-scale, continual pre-training dataset with 129B tokens, an instruction tuning dataset with 214k samples, and a new medical evaluation benchmark (MIBE) across six critical medical tasks with 12 datasets. Our extensive evaluation using the MIBE shows that Me-LLaMA models achieve overall better performance than existing open-source medical LLMs in zero-shot, few-shot and supervised learning abilities. With task-specific instruction tuning, Me-LLaMA models outperform ChatGPT on 7 out of 8 datasets and GPT-4 on 5 out of 8 datasets. In addition, we investigated the catastrophic forgetting problem, and our results show that Me-LLaMA models outperform other open-source medical LLMs in mitigating this issue. Me-LLaMA is one of the largest open-source medical foundation LLMs that use both biomedical and clinical data. It exhibits superior performance across both general and medical tasks compared to other open-source medical LLMs, rendering it an attractive choice for medical AI applications. We release our models, datasets, and evaluation scripts at: https://github.com/BIDS-Xu-Lab/Me-LLaMA.

Large Language Models for Social Determinants of Health Information Extraction from Clinical Notes - A Generalizable Approach across Institutions.

The consistent and persuasive evidence illustrating the influence of social determinants on health has prompted a growing realization throughout the health care sector that enhancing health and health equity will likely depend, at least to some extent, on addressing detrimental social determinants. However, detailed social determinants of health (SDoH) information is often buried within clinical narrative text in electronic health records (EHRs), necessitating natural language processing (NLP) methods to automatically extract these details. Most current NLP efforts for SDoH extraction have been limited, investigating on limited types of SDoH elements, deriving data from a single institution, focusing on specific patient cohorts or note types, with reduced focus on generalizability. This study aims to address these issues by creating cross-institutional corpora spanning different note types and healthcare systems, and developing and evaluating the generalizability of classification models, including novel large language models (LLMs), for detecting SDoH factors from diverse types of notes from four institutions: Harris County Psychiatric Center, University of Texas Physician Practice, Beth Israel Deaconess Medical Center, and Mayo Clinic. Four corpora of deidentified clinical notes were annotated with 21 SDoH factors at two levels: level 1 with SDoH factor types only and level 2 with SDoH factors along with associated values. Three traditional classification algorithms (XGBoost, TextCNN, Sentence BERT) and an instruction tuned LLM-based approach (LLaMA) were developed to identify multiple SDoH factors. Substantial variation was noted in SDoH documentation practices and label distributions based on patient cohorts, note types, and hospitals. The LLM achieved top performance with micro-averaged F1 scores over 0.9 on level 1 annotated corpora and an F1 over 0.84 on level 2 annotated corpora. While models performed well when trained and tested on individual datasets, cross-dataset generalization highlighted remaining obstacles. To foster collaboration, access to partial annotated corpora and models trained by merging all annotated datasets will be made available on the PhysioNet repository.

Integration of C3H15-mediated transcriptional and post-transcriptional regulation confers plant thermotolerance in Arabidopsis.

Heat stress is an environmental factor that significantly threatens crop production worldwide. Nevertheless, the molecular mechanisms governing plant responses to heat stress are not fully understood. Plant zinc finger CCCH proteins have roles in stress responses as well as growth and development through protein-RNA, protein-DNA, and protein-protein interactions. Here, we reveal an integrated multi-level regulation of plant thermotolerance that is mediated by the CCCH protein C3H15 in Arabidopsis. Heat stress rapidly suppressed C3H15 transcription, which attenuated C3H15-inhibited expression of its target gene HEAT SHOCK TRANSCRIPTION FACTOR A2 (HSFA2), a central regulator of heat stress response (HSR), thereby activating HEAT SHOCK COGNATE 70 (HSC70.3) expression. The RING-type E3 ligase MED25-BINDING RING-H2 PROTEIN 2 (MBR2) was identified as an interacting partner of C3H15. The mbr2 mutant was susceptible to heat stress compared to wild-type plants, whereas plants overexpressing MBR2 showed increased heat tolerance. MBR2-dependent ubiquitination mediated the degradation of phosphorylated C3H15 protein in the cytoplasm, which was enhanced by heat stress. Consistently, heat sensitivities of C3H15 overexpression lines increased in MBR2 loss-of-function and decreased in MBR2 overexpression backgrounds. Heat stress-induced accumulation of HSC70.3 promoted MBR2-mediated degradation of C3H15 protein, implying that an auto-regulatory loop involving C3H15, HSFA2, and HSC70.3 regulates HSR. Heat stress also led to the accumulation of C3H15 in stress granules (SGs), a kind of cytoplasmic RNA granule. This study advances our understanding of the mechanisms plants use to respond to heat stress, which will facilitate technologies to improve thermotolerance in crops.

Ligand-Accelerated Pd-Catalyzed C(sp2)-H and C(sp3)-H Arylation under the Catalytic System.

It is universally acknowledged that ligands can improve the reaction activity to simplify the reaction operating conditions and enrich the applicability of the reaction. Therefore, we developed N-octylglycine ligand-accelerated Pd-catalyzed ortho-arylation of benzoic acids under mild conditions with just 6 h; moreover, this N-octylglycine ligand was successfully implemented to carboxyl-directed Pd-catalyzed ß-C(sp3)-H arylation and ortho-arylation of phenylacetic acids under mild conditions.

Efficacy of intestinal microorganisms on immunotherapy of non-small cell lung cancer.

While the 5-year survival rate of patients with advanced non-small cell lung cancer (NSCLC) has seen some improvement, the majority of NSCLC patients fail to respond to immunotherapy with immune checkpoint inhibitors (ICIs). It is critical to identify effective biomarkers that can enhance the efficacy of immunotherapy. The clinical data in the current study were collected from NSCLC patients treated with ICIs, and two groups were classified according to treatment effect: good group with consistent efficacy, poor group with only progressiveness. Differences in intestinal microbiota between the two groups were analyzed using 16s rRNA sequencing. Beta diversity analysis indicated differences between the two groups that were available for differentiation. Comparison of the number of common or unique operational taxonomic units (OTUs) among different groups suggested that there were 53 unique OTUs in the good group and 51 unique OTUs in the poor group. At the phylum level, there was a difference between the two groups for several bacterial groups with the highest abundance values, among which Firmicutes, Actinobacteria and Fusobacteria were more abundant in the good group. Members of the genera Bifidobacterium and Lactobacillus were abundant in the good group, while the abundance of Bacteroides was low. Biomarkers in the poor group included Bacteroides, Bacteroidetes, Bacteroidia, Bacteroidales, Bacteroidaceae and Veillonellaceae. The intestinal microbiota composition affected the immunotherapy process for NSCLC, which might offer more rational instructions for the clinical application of ICIs in NSCLC patients.

FPNC Net: A hydrogenation catalyst image recognition algorithm based on deep learning.

The identification research of hydrogenation catalyst information has always been one of the most important businesses in the chemical industry. In order to aid researchers in efficiently screening high-performance catalyst carriers and tackle the pressing challenge at hand, it is imperative to find a solution for the intelligent recognition of hydrogenation catalyst images. To address the issue of low recognition accuracy caused by adhesion and stacking of hydrogenation catalysts, An image recognition algorithm of hydrogenation catalyst based on FPNC Net was proposed in this paper. In the present study, Resnet50 backbone network was used to extract the features, and spatially-separable convolution kernel was used to extract the multi-scale features of catalyst fringe. In addition, to effectively segment the adhesive regions of stripes, FPN (Feature Pyramid Network) is added to the backbone network for deep and shallow feature fusion. Introducing an attention module to adaptively adjust weights can effectively highlight the target features of the catalyst. The experimental results showed that the FPNC Net model achieved an accuracy of 94.2% and an AP value improvement of 19.37% compared to the original CenterNet model. The improved model demonstrates a significant enhancement in detection accuracy, indicating a high capability for detecting hydrogenation catalyst targets.

Immune checkpoint inhibitors and anti-vascular endothelial growth factor antibody/tyrosine kinase inhibitors with or without transarterial chemoembolization as first-line treatment for advanced hepatocellular carcinoma (CHANCE2201): a target trial emulation study.

Background: The role of transarterial chemoembolization (TACE) in the treatment of advanced hepatocellular carcinoma (HCC) is unconfirmed. This study aimed to assess the efficacy and safety of immune checkpoint inhibitors (ICIs) plus anti-vascular endothelial growth factor (anti-VEGF) antibody/tyrosine kinase inhibitors (TKIs) with or without TACE as first-line treatment for advanced HCC. Methods: This nationwide, multicenter, retrospective cohort study included advanced HCC patients receiving either TACE with ICIs plus anti-VEGF antibody/TKIs (TACE-ICI-VEGF) or only ICIs plus anti-VEGF antibody/TKIs (ICI-VEGF) from January 2018 to December 2022. The study design followed the target trial emulation framework with stabilized inverse probability of treatment weighting (sIPTW) to minimize biases. The primary outcome was overall survival (OS). Secondary outcomes included progression-free survival (PFS), objective response rate (ORR), and safety. The study is registered with ClinicalTrials.gov, NCT05332821. Findings: Among 1244 patients included in the analysis, 802 (64.5%) patients received TACE-ICI-VEGF treatment, and 442 (35.5%) patients received ICI-VEGF treatment. The median follow-up time was 21.1 months and 20.6 months, respectively. Post-application of sIPTW, baseline characteristics were well-balanced between the two groups. TACE-ICI-VEGF group exhibited a significantly improved median OS (22.6 months [95% CI: 21.2-23.9] vs 15.9 months [14.9-17.8]; P < 0.0001; adjusted hazard ratio [aHR] 0.63 [95% CI: 0.53-0.75]). Median PFS was also longer in TACE-ICI-VEGF group (9.9 months [9.1-10.6] vs 7.4 months [6.7-8.5]; P < 0.0001; aHR 0.74 [0.65-0.85]) per Response Evaluation Criteria in Solid Tumours (RECIST) version 1.1. A higher ORR was observed in TACE-ICI-VEGF group, by either RECIST v1.1 or modified RECIST (41.2% vs 22.9%, P < 0.0001; 47.3% vs 29.7%, P < 0.0001). Grade ≥3 adverse events occurred in 178 patients (22.2%) in TACE-ICI-VEGF group and 80 patients (18.1%) in ICI-VEGF group. Interpretation: This multicenter study supports the use of TACE combined with ICIs and anti-VEGF antibody/TKIs as first-line treatment for advanced HCC, demonstrating an acceptable safety profile. Funding: National Natural Science Foundation of China, National Key Research and Development Program of China, Jiangsu Provincial Medical Innovation Center, Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, and Nanjing Life Health Science and Technology Project.

Dihydroartemisinin-driven TOM70 inhibition leads to mitochondrial destabilization to induce pyroptosis against lung cancer.

Enhancement of malignant cell immunogenicity to relieve immunosuppression of lung cancer microenvironment is essential in lung cancer treatment. In previous study, we have demonstrated that dihydroartemisinin (DHA), a kind of phytopharmaceutical, is effective in inhibiting lung cancer cells and boosting their immunogenicity, while the initial target of DHA's intracellular action is poorly understood. The present in-depth analysis aims to reveal the influence of DHA on the highly expressed TOM70 in the mitochondrial membrane of lung cancer. The affinity of DHA and TOM70 was analyzed by microscale thermophoresis (MST), pronase stability, and thermal stability. The functions and underlying mechanism were investigated using western blots, qRT-PCR, flow cytometry, and rescue experiments. TOM70 inhibition resulted in mtDNA damage and translocation to the cytoplasm from mitochondria due to the disruption of mitochondrial homeostasis. Further ex and in vivo findings also showed that the cGAS/STING/NLRP3 signaling pathway was activated by mtDNA and thereby malignant cells underwent pyroptosis, leading to enhanced immunogenicity of lung cancer cells in the presence of DHA. Nevertheless, DHA-induced mtDNA translocation and cGAS/STING/NLRP3 mobilization were synchronously attenuated when TOM70 was replenished. Finally, DHA was demonstrated to possess potent anti-lung cancer efficacy in vitro and in vivo. Taken together, these data confirm that TOM70 is an important target for DHA to disturb mitochondria homeostasis, which further activates STING and arouses pyroptosis to strengthen immunogenicity against lung cancer thereupon. The present study provides vital clues for phytomedicine-mediated anti-tumor therapy.

A sustainable carbon aerogel from waste paper with exceptional performance for antibiotics removal from water.

In this work, a sustainable 3D carbon aerogel (AO-WPC) is prepared from waste paper (WP), and used for efficient antibiotics removal from water. The AO-WPC aerogel shows good mechanical property and can recover after 100th of 30 % compression strain. The specific surface area of AO-WPC aerogel is up to 654.58 m2/g. More importantly, this aerogel reveals proper pore size distribution, including micro sized macropores between carbon fibers and intrinsic nano scale mesopores (11.86 nm), which is conducive to remove antibiotics from water. Taking tetracycline (Tc) as an example, the maximum adsorption capacity and adsorption rate of AO-WPC for Tc are as high as 384.6 mg/g and 0.510 g/(mg‧min), respectively, which exhibits significant advantages over most of the recent absorbents, and the adsorption toward Tc reveals good resistance to various environmental factors, including pH, various ions, and dissolved organic matter (DOM). Moreover, good thermal stability enables the AO-WPC aerogel to be regenerated through simple burning, and the adsorption capacity of Tc only decreases by 10.4 % after 10 cycles. Mechanism research shows that hydrogen bonding and π-π electron-donor-acceptor (EDA) interaction play the important role in the adsorption. The excellent mechanical property and adsorption performance imply good practical prospect of the AO-WPC aerogel.

SEETrials: Leveraging Large Language Models for Safety and Efficacy Extraction in Oncology Clinical Trials.

Background: Initial insights into oncology clinical trial outcomes are often gleaned manually from conference abstracts. We aimed to develop an automated system to extract safety and efficacy information from study abstracts with high precision and fine granularity, transforming them into computable data for timely clinical decision-making. Methods: We collected clinical trial abstracts from key conferences and PubMed (2012-2023). The SEETrials system was developed with four modules: preprocessing, prompt modeling, knowledge ingestion and postprocessing. We evaluated the system's performance qualitatively and quantitatively and assessed its generalizability across different cancer types- multiple myeloma (MM), breast, lung, lymphoma, and leukemia. Furthermore, the efficacy and safety of innovative therapies, including CAR-T, bispecific antibodies, and antibody-drug conjugates (ADC), in MM were analyzed across a large scale of clinical trial studies. Results: SEETrials achieved high precision (0.958), recall (sensitivity) (0.944), and F1 score (0.951) across 70 data elements present in the MM trial studies Generalizability tests on four additional cancers yielded precision, recall, and F1 scores within the 0.966-0.986 range. Variation in the distribution of safety and efficacy-related entities was observed across diverse therapies, with certain adverse events more common in specific treatments. Comparative performance analysis using overall response rate (ORR) and complete response (CR) highlighted differences among therapies: CAR-T (ORR: 88%, 95% CI: 84-92%; CR: 95%, 95% CI: 53-66%), bispecific antibodies (ORR: 64%, 95% CI: 55-73%; CR: 27%, 95% CI: 16-37%), and ADC (ORR: 51%, 95% CI: 37-65%; CR: 26%, 95% CI: 1-51%). Notable study heterogeneity was identified (>75% I 2 heterogeneity index scores) across several outcome entities analyzed within therapy subgroups. Conclusion: SEETrials demonstrated highly accurate data extraction and versatility across different therapeutics and various cancer domains. Its automated processing of large datasets facilitates nuanced data comparisons, promoting the swift and effective dissemination of clinical insights.

Novel Panel of Long Noncoding RNAs as Diagnostic Biomarkers for Amnestic Mild Cognitive Impairment in Peripheral Blood.

Background: Long noncoding RNAs (lncRNAs) regulate the pathogenesis of Alzheimer's disease (AD). Objective: To identify lncRNAs in the peripheral blood as potential diagnostic biomarkers for amnestic mild cognitive impairment. Methods: In the discovery group, a microarray was used to screen for significant differences in lncRNA expression between patients with mild cognitive impairment (MCI) caused by AD and normal controls (NCs) (n = 10; MCI, 5; NC, 5). Furthermore, two analytic groups were assessed (analytic group 1: n = 10; amnestic MCI (aMCI), 5; NC, 5; analytic group 2: n = 30; AD, 10; aMCI, 10; NC, 10) and finalized in the validation group (n = 150; AD, 50; aMCI, 50; NC, 50). In the analytic and validation groups, real-time quantitative reverse-transcription polymerase chain reaction was used to identify differentially expressed lncRNAs between the aMCI and NC groups. Results: We identified 67 upregulated and 220 downregulated lncRNAs among the expression profiles. The panel with lncRNAs T324988, NR_024049, ENST00000567919, and ENST00000549762 displayed the highest discrimination ability between patients with aMCI and NCs. The area under the receiver operating characteristic curve of this combined model was 0.941, with a sensitivity of 92.00% and specificity of 84.00%. Conclusions: This study reports on a panel of four lncRNAs as promising biomarkers to diagnose aMCIs.

Molecular orbital breaking in photo-mediated organosilicon Schiff base ferroelectric crystals.

Ferroelectric materials, whose electrical polarization can be switched under external stimuli, have been widely used in sensors, data storage, and energy conversion. Molecular orbital breaking can result in switchable structural and physical bistability in ferroelectric materials as traditional spatial symmetry breaking does. Differently, molecular orbital breaking interprets the phase transition mechanism from the perspective of electronics and sheds new light on manipulating the physical properties of ferroelectrics. Here, we synthesize a pair of organosilicon Schiff base ferroelectric crystals, (R)- and (S)-N-(3,5-di-tert-butylbenzylidene)-1-((triphenylsilyl)oxy)ethanamine, which show optically controlled phase transition accompanying the molecular orbital breaking. The molecular orbital breaking is manifested as the breaking and reformation of covalent bonds during the phase transition process, that is, the conversion between C = N and C-O in the enol form and C-N and C = O in the keto form. This process brings about photo-mediated bistability with multiple physical channels such as dielectric, second-harmonic generation, and ferroelectric polarization. This work further explores this newly developed mechanism of ferroelectric phase transition and highlights the significance of photo-mediated ferroelectric materials for photo-controlled smart devices and bio-sensors.

IGF2BP2 regulates the inflammation of fibroblast-like synoviocytes via GSTM5 in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune disease with an unknown etiology. RA cannot be fully cured and requires lengthy treatment, imposing a significant burden on both individuals and society. Due to the lack of specific drugs available for treating RA, exploring a key new therapeutic target for RA is currently an important task. Activated fibroblast-like synoviocytes (FLSs) play a crucial role in the progression of RA, which release interleukin (IL)-1ß, IL-6 and tumor necrosis factor (TNF)-α resulting in abnormal inflammatory reaction in the synovium. A previous study has highlighted the correlation of m6A reader insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) with inflammation-related diseases in human. However, the role of IGF2BP2 in the inflammatory reaction of FLSs during RA progression has not been assessed. In this study, IGF2BP2 expression was decreased in the synovial tissues of RA patients and collagen-induced arthritis (CIA) rats. Intra-articular injection of an adeno-associated virus (AAV) vector overexpressing IGF2BP2 relieved paw swelling, synovial hyperplasia and cartilage destruction in CIA rats. IGF2BP2 overexpression also inhibited lipopolysaccharide (LPS)-mediated RA fibroblast-like synoviocytes (RA-FLSs) migration and invasion accompanied by a decreased level of inflammatory factors in vitro. Conversely, IGF2BP2 suppression promoted RA-FLSs migration and invasion with an elevated level of inflammatory factors in vitro. The sequencing result showed that glutathione S-transferase Mu 5 (GSTM5), a key antioxidant gene, was the target mRNA of IGF2BP2. Further experiments demonstrated that IGF2BP2 strengthened the stability of GSTM5 mRNA, leading to weakened inflammatory reaction and reduced expression of matrix metalloproteinase 9 and 13 (MMP9, MMP13). Therefore, IGF2BP2-GSTM5 axis may represent a potential therapeutic target for RA treatment.

CDC73 serves as a tumour-promoting factor in oesophageal cancer.

The role of human cell division cycle 73 (CDC73) in human cancers has sparked controversy; however, its significance in oesophageal cancer remains elusive. This study aimed to elucidate CDC73 expression and its biological implications in human oesophageal cancer. Our findings unveiled a notable upregulation of CDC73 in both oesophageal cancer cell lines and tissues. Importantly, elevated CDC73 levels in patients with oesophageal cancer correlated with an unfavourable prognosis. Functional investigations revealed that CDC73 knockdown effectively curtailed the proliferation and growth of oesophageal cancer cells both in vitro and in vivo. Mechanistically, RRP15 emerged as a potential downstream target of CDC73 through a screening process involving identification of the top co-expressed genes, subsequent knockdown experiments, and observation of significant inhibition of cell proliferation, with RRP15 showing the most pronounced effect. This finding was further supported by the positive correlation observed between CDC73 and RRP15 in ESCA samples analysed using the ENCORI Pan-Cancer Analysis Platform. Notably, depletion of RRP15 in CDC73-overexpressing cells led to a shift from augmented to diminished tumour growth. Collectively, our findings underscore the pivotal role of CDC73 in oesophageal cancer through the modulation of RRP15 expression, suggesting CDC73 as a potential therapeutic target for treating oesophageal cancer.

Guest-guided anchoring patterns of cyclodextrin supramolecular microcrystals on droplet surfaces.

The growth of cyclodextrin inclusion complexes (ICs) on oil/water interfaces represents a beautiful example of spontaneous pattern formation in nature. How the supramolecules evolve remains a challenge because surface confinement can frustrate microcrystal growth and give rise to unusual phase transitions. Here we investigate the self-assembly of ICs on droplet surfaces using microfluidics, which allows directly visualizing packing, wetting and ordering of the microcrystals anchored on the surface. The oil guests of distinct molecular structures can direct the assembly of the ICs and largely affect anchoring dynamics of the ICs microcrystals, leading to a range of behaviors including orientating, slipping, buckling, jamming, or merging. We discuss the behaviors observed in terms of the flexibility of the building blocks, which offers a new degree of freedom through which to tailor their properties and gives rise to a striking feature of anchoring patterns that have no counterpart in normal colloidal crystals.

A Principal Component Regression-Based Electrophysiological Study of Patients with Severe Infections.

Objective: Severe infections can lead to neuromyopathy in critically ill patients, resulting in limb weakness and difficulty in weaning from a ventilator. This study aims to assess the electrophysiological test results in patients with severe infection and their correlation with severity scores (APACHE II and SOFA). Methods: Thirty-one patients with severe infection in the EICU were prospectively studied. Factor analysis and principal component regression were applied to develop linear models of electrophysiological diagnostic outcomes with APACHE II and SOFA scores for the entire patient cohort, the younger group (age<55) cohort, and the older group (age>55) cohort of patients with severe infections, respectively. Results: Among patients with a severe infection in the EICU, the proportion of patients without critical neuromyopathy with more than 50% F-wave presence in the median, ulnar, and tibial nerves (64.9%, 56.8%, 48.6%, respectively) was significantly higher than in the group with critical neuromyopathy (52.1%, 35.4%, 29.2%, respectively.), and the proportion of patients with critical neuromyopathy who did not elicit the three types of F wave was significantly higher in the cohort of patients with critical neuromyopathy (40.5%, 32.4%, 35.1%, respectively.) were significantly higher than in the cohort of patients without critical illness (18.8%, 12.5%, 20.8%, respectively). In addition, on average, patients with critical neuromyopathy had a much lower CMAP for the median nerve (wrist, elbow) (2.4, 1.88, respectively) (4.3, 3.9, respectively in undiagnosed cohort), ulnar nerve (wrist, elbow) (2.4, 1.88, respectively) (5.65, 5.4, respectively in undiagnosed cohort), and tibial nerve(ankle, popliteal fossa) (2.7, 1.57, respectively)(6.55, 5.3, respectively in undiagnosed cohort) nerves than patients without critical neuromyopathy, and showed more non-elicitation, which was not seen in the cohort of patients without critical neuromyopathy. The CMAP returned to normal in the cohort of patients without critical neuromyopathy. Therefore, with respect to our selected electrophysiological parameters, the two patient groups showed significant differences in terms of the specific values and statistical analysis (Table 1). Through factor analysis and principal component regression, we found that CMAP and F-wave were highly correlated with APACHE II and SOFA scores, and the correlation between the electrophysiological wave spectrum and the two scores was further quantified by principal component regression. Conclusion: Electrophysiological spectroscopy can serve as an early warning for the development of neuromuscular disease in EICU patients. Abnormal electrophysiological diagnosis prior to actual neuromuscular abnormalities and its subsequent return to normal can help identify high-risk patients and implement early interventions.