FPR1: A critical gatekeeper of the heart and brain.

G protein-coupled receptors (GPCRs) are currently the most widely focused drug targets in the clinic, exerting their biological functions by binding to chemicals and activating a series of intracellular signaling pathways. Formyl-peptide receptor 1 (FPR1) has a typical seven-transmembrane structure of GPCRs and can be stimulated by a large number of endogenous or exogenous ligands with different chemical properties, the first of which was identified as formyl-methionine-leucyl-phenylalanine (fMLF). Through receptor-ligand interactions, FPR1 is involved in inflammatory response, immune cell recruitment, and cellular signaling regulation in key cell types, including neutrophils, neural stem cells (NSCs), and microglia. This review outlines the critical roles of FPR1 in a variety of heart and brain diseases, including myocardial infarction (MI), ischemia/reperfusion (I/R) injury, neurodegenerative diseases, and neurological tumors, with particular emphasis on the milestones of FPR1 agonists and antagonists. Therefore, an in-depth study of FPR1 contributes to the research of innovative biomarkers, therapeutic targets for heart and brain diseases, and clinical applications.

Acutalibacter caecimuris sp. nov., Acutalibacter intestini sp. nov. and Neglectibacter caecimuris sp. nov., three novel species of the family Oscillospiraceae isolated from caecal contents of C57BL/6J mice.

The intestines of mice are colonized by diverse, as-yet-uncultivated bacteria. In this report, we describe the isolation, culture, genotypic and phenotypic characterization, as well as taxonomic classification of three novel anaerobic bacterial strains derived from the caecal contents of C57BL/6J male mice. According to the phenotypic and genotype-based polyphasic taxonomy, we propose three novel species within the family Oscillospiraceae. They are Acutalibacter caecimuris sp. nov. (type strain M00118T=CGMCC 1.18042T=KCTC 25739T), Acutalibacter intestini sp. nov. (type strain M00204T=CGMCC 1.18044T=KCTC 25741T) and Neglectibacter caecimuris sp. nov. (type strain M00184T=CGMCC 1.18043T=KCTC 25740T).

Influence of ambient temperature on the CO2 emitted of light-duty vehicle.

Because of global warming, people have paid more attention to greenhouse gas emitted by vehicles. To quantify the impact of temperature on vehicle CO2 emissions, this study was conducted using the world light vehicle test cycle on two light-duty E10 gasoline vehicles at ambient temperatures of -10, 0, 23, and 40℃, and found that CO2 emission factors of Vehicle 1 in the low-speed phase were 22.07% and 20.22% higher than those of Vehicle 2 at cold start and hot start under -10℃. The reason was vehicle 1 had a larger displacement and more friction pairs than vehicle 2. There was the highest CO2 emission at the low-speed phase due to low average speed, frequent acceleration, and deceleration. The CO2 temperature factor and the ambient temperature had a strong linear correlation (R2 = 0.99). According to CO2 temperature factors and their relationships, CO2 emission factors of other ambient temperatures could be calculated when the CO2 emission factor of 23℃ was obtained, and the method also could be used to obtain the CO2 temperature factors of different vehicles. To separate the effect of load setting and temperature variation on CO2 emission quantitatively, a method was proposed. And results showed that the load setting was dominant for the CO2 emission variation. Compared with 23℃, the CO2 emission for vehicle 1 caused by load setting variation were 62.83 and 47.42 g/km, respectively at -10 and 0℃, while those for vehicle 2 were 45.01 and 35.63 g/km, respectively.

A novel hypoxia-stimulated lncRNA HIF1A-AS3 binds with YBX1 to promote ovarian cancer tumorigenesis by suppressing p21 and AJAP1 transcription.

Hypoxia is characteristic of the ovarian tumor (OC) microenvironment and profoundly affects tumorigenesis and therapeutic response. Long noncoding RNAs (lncRNAs) play various roles in tumor progression; however, the characteristics of lncRNAs in pathological responses of the OC microenvironment are not entirely understood. Through high-throughput sequencing, lncRNA expression in hypoxia (1% O2 ) and normoxia (21% O2 ) SKOV3 cells was explored and analyzed. The 5'- and 3'-rapid amplification of complementary DNA ends was used to detect the full length of the novel HIF1A-AS3 transcript. Real-time quantitative polymerase chain reaction was used to assess HIF1A-AS3 expression in OC cells and tissues. In vitro and in vivo evaluations of the biological functions of hypoxic HIF1A-AS3 were conducted. To clarify the underlying mechanisms of HIF1A-AS3 in hypoxic OC, a dual-luciferase assay, chromatin immunoprecipitation, RNA pull-down, RNA immunoprecipitation, and RNA-sequencing were used. We used high-throughput sequencing to investigate a novel lncRNA, HIF1A-AS3, as a hypoxic candidate significantly elevated in OC cells/tissues. HIF1A-AS3 was predominantly localized in the nucleus and promoted in vitro and in vivo OC growth and tumorigenesis. Hypoxia-inducible factor 1α bound to hypoxia response elements in the HIF1A-AS3 promoter region and stimulated its expression in hypoxia. Under hypoxia, HIF1A-AS3 directly integrated with Y-Box binding protein 1 and inhibited its ability to bind to the promoters of p21 and AJAP1 to repress their transcriptional activity, thereby promoting hypoxic OC progression. Our results revealed the crucial role and mechanism of the novel hypoxic HIF1A-AS3 in the oncogenesis of OC. The novel HIF1A-AS3 could be a crucial biomarker and therapeutic target for future OC treatments.

Design and fabrication of multifunctional holographic optical elements in laser Doppler velocimeter.

Replacing the bulky traditional optical elements in the optical system with a holographic optical element (HOE) is conducive to the functional integration and volume miniaturization. However, when the HOE is used in the infrared system, the mismatch between the recording wavelength and the working wavelength will lead to the reduction of diffraction efficiency and the introduction of aberration, which will seriously affect the performance of the optical system. This paper proposes a design and fabrication method of multifunctional infrared HOEs that can be used in laser Doppler velocimeter (LDV), which can reduce the effect of wavelength mismatch on HOE performance while integrating the functions of the optical system. The restriction relationship and selection method of parameters in typical LDV are summarized; the decrease of diffraction efficiency due to the mismatch between recording and working wavelengths is compensated by designing the angle of signal and reference wave of the HOE; and the aberration caused by wavelength mismatch is compensated by cylindrical lens. The optical experiment shows that the HOE can produce two groups of fringes with opposite gradient, which proves the feasibility of the proposed method. Moreover, this method has a certain degree of universality, and it is expected to design and fabricate HOEs for any working wavelength in the near-infrared band.

Utility of ChatGPT in Clinical Practice.

ChatGPT is receiving increasing attention and has a variety of application scenarios in clinical practice. In clinical decision support, ChatGPT has been used to generate accurate differential diagnosis lists, support clinical decision-making, optimize clinical decision support, and provide insights for cancer screening decisions. In addition, ChatGPT has been used for intelligent question-answering to provide reliable information about diseases and medical queries. In terms of medical documentation, ChatGPT has proven effective in generating patient clinical letters, radiology reports, medical notes, and discharge summaries, improving efficiency and accuracy for health care providers. Future research directions include real-time monitoring and predictive analytics, precision medicine and personalized treatment, the role of ChatGPT in telemedicine and remote health care, and integration with existing health care systems. Overall, ChatGPT is a valuable tool that complements the expertise of health care providers and improves clinical decision-making and patient care. However, ChatGPT is a double-edged sword. We need to carefully consider and study the benefits and potential dangers of ChatGPT. In this viewpoint, we discuss recent advances in ChatGPT research in clinical practice and suggest possible risks and challenges of using ChatGPT in clinical practice. It will help guide and support future artificial intelligence research similar to ChatGPT in health.

Text Dialogue Analysis for Primary Screening of Mild Cognitive Impairment: Development and Validation Study.

BACKGROUND: Artificial intelligence models tailored to diagnose cognitive impairment have shown excellent results. However, it is unclear whether large linguistic models can rival specialized models by text alone. OBJECTIVE: In this study, we explored the performance of ChatGPT for primary screening of mild cognitive impairment (MCI) and standardized the design steps and components of the prompts. METHODS: We gathered a total of 174 participants from the DementiaBank screening and classified 70% of them into the training set and 30% of them into the test set. Only text dialogues were kept. Sentences were cleaned using a macro code, followed by a manual check. The prompt consisted of 5 main parts, including character setting, scoring system setting, indicator setting, output setting, and explanatory information setting. Three dimensions of variables from published studies were included: vocabulary (ie, word frequency and word ratio, phrase frequency and phrase ratio, and lexical complexity), syntax and grammar (ie, syntactic complexity and grammatical components), and semantics (ie, semantic density and semantic coherence). We used R 4.3.0. for the analysis of variables and diagnostic indicators. RESULTS: Three additional indicators related to the severity of MCI were incorporated into the final prompt for the model. These indicators were effective in discriminating between MCI and cognitively normal participants: tip-of-the-tongue phenomenon (P<.001), difficulty with complex ideas (P<.001), and memory issues (P<.001). The final GPT-4 model achieved a sensitivity of 0.8636, a specificity of 0.9487, and an area under the curve of 0.9062 on the training set; on the test set, the sensitivity, specificity, and area under the curve reached 0.7727, 0.8333, and 0.8030, respectively. CONCLUSIONS: ChatGPT was effective in the primary screening of participants with possible MCI. Improved standardization of prompts by clinicians would also improve the performance of the model. It is important to note that ChatGPT is not a substitute for a clinician making a diagnosis.

Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis.

BACKGROUND: Deep learning (DL) prediction models hold great promise in the triage of COVID-19. OBJECTIVE: We aimed to evaluate the diagnostic test accuracy of DL prediction models for assessing and predicting the severity of COVID-19. METHODS: We searched PubMed, Scopus, LitCovid, Embase, Ovid, and the Cochrane Library for studies published from December 1, 2019, to April 30, 2022. Studies that used DL prediction models to assess or predict COVID-19 severity were included, while those without diagnostic test accuracy analysis or severity dichotomies were excluded. QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2), PROBAST (Prediction Model Risk of Bias Assessment Tool), and funnel plots were used to estimate the bias and applicability. RESULTS: A total of 12 retrospective studies involving 2006 patients reported the cross-sectionally assessed value of DL on COVID-19 severity. The pooled sensitivity and area under the curve were 0.92 (95% CI 0.89-0.94; I2=0.00%) and 0.95 (95% CI 0.92-0.96), respectively. A total of 13 retrospective studies involving 3951 patients reported the longitudinal predictive value of DL for disease severity. The pooled sensitivity and area under the curve were 0.76 (95% CI 0.74-0.79; I2=0.00%) and 0.80 (95% CI 0.76-0.83), respectively. CONCLUSIONS: DL prediction models can help clinicians identify potentially severe cases for early triage. However, high-quality research is lacking. TRIAL REGISTRATION: PROSPERO CRD42022329252; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD 42022329252.

Ethical Considerations of Using ChatGPT in Health Care.

ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents potential ethical challenges from legal, humanistic, algorithmic, and informational perspectives. Legal ethics concerns arise from the unclear allocation of responsibility when patient harm occurs and from potential breaches of patient privacy due to data collection. Clear rules and legal boundaries are needed to properly allocate liability and protect users. Humanistic ethics concerns arise from the potential disruption of the physician-patient relationship, humanistic care, and issues of integrity. Overreliance on artificial intelligence (AI) can undermine compassion and erode trust. Transparency and disclosure of AI-generated content are critical to maintaining integrity. Algorithmic ethics raise concerns about algorithmic bias, responsibility, transparency and explainability, as well as validation and evaluation. Information ethics include data bias, validity, and effectiveness. Biased training data can lead to biased output, and overreliance on ChatGPT can reduce patient adherence and encourage self-diagnosis. Ensuring the accuracy, reliability, and validity of ChatGPT-generated content requires rigorous validation and ongoing updates based on clinical practice. To navigate the evolving ethical landscape of AI, AI in health care must adhere to the strictest ethical standards. Through comprehensive ethical guidelines, health care professionals can ensure the responsible use of ChatGPT, promote accurate and reliable information exchange, protect patient privacy, and empower patients to make informed decisions about their health care.

Sequential gene expression analysis of cervical malignant transformation identifies RFC4 as a novel diagnostic and prognostic biomarker.

BACKGROUND: Cervical squamous cell carcinoma (SCC) is known to arise through increasingly higher-grade squamous intraepithelial lesions (SILs) or cervical intraepithelial neoplasias (CINs). This study aimed to describe sequential molecular changes and identify biomarkers in cervical malignant transformation. METHODS: Multidimensional data from five publicly available microarray and TCGA-CESC datasets were analyzed. Immunohistochemistry was carried out on 354 cervical tissues (42 normal, 62 CIN1, 26 CIN2, 47 CIN3, and 177 SCC) to determine the potential diagnostic and prognostic value of identified biomarkers. RESULTS: We demonstrated that normal epithelium and SILs presented higher molecular homogeneity than SCC. Genes in the region (e.g., 3q, 12q13) with copy number alteration or HPV integration were more likely to lose or gain expression. The IL-17 signaling pathway was enriched throughout disease progression with downregulation of IL17C and decreased Th17 cells at late stage. Furthermore, we identified AURKA, TOP2A, RFC4, and CEP55 as potential causative genes gradually upregulated during the normal-SILs-SCC transition. For detecting high-grade SIL (HSIL), TOP2A and RFC4 showed balanced sensitivity (both 88.2%) and specificity (87.1 and 90.1%), with high AUC (0.88 and 0.89). They had equivalent diagnostic performance alone to the combination of p16INK4a and Ki-67. Meanwhile, increased expression of RFC4 significantly and independently predicted favorable outcomes in multi-institutional cohorts of SCC patients. CONCLUSIONS: Our comprehensive study of gene expression profiling has identified dysregulated genes and biological processes during cervical carcinogenesis. RFC4 is proposed as a novel surrogate biomarker for determining HSIL and HSIL+, and an independent prognostic biomarker for SCC.