Introduction Residual apnoea hypopnoea index (AHI) derived from positive airway pressure (PAP) device downloads is frequently used to assess treatment effectiveness in obstructive sleep apnoea (OSA). However, the accuracy of device reported AHI in real‑world settings remains uncertain. This study evaluated the agreement between PAP device derived AHI and AHI measured using home based cardiorespiratory polygraphy. Methods This secondary analysis of the 3DPiPPIn randomised controlled trial included adults with OSA (AHI ≥15 events/hr) naïve to domiciliary PAP therapy. Participants underwent two‑night home cardiorespiratory polygraphy at three and six months. Residual AHI was obtained from Löwenstein PAP devices via data download. Agreement between methods was assessed using Bland-Altman analysis, intraclass correlation coefficients (ICC), and weighted Kappa. Sensitivity and specificity for detecting inadequate control (AHI ≥7 events/hr) were calculated. The association between average leak and AHI discrepancy was examined using linear regression. Results Ninety‑eight participants were included. PAP derived AHI values consistently underestimated polygraphy derived AHI across all time points. Agreement was poor, with ICCs of 0.02 at three months and 0.16 at six months, and low diagnostic agreement across severity categories at both three months (K0.10, 95%CI: 0 to 0.26) and six months (K0.09, 95%CI: 0 to 0.28). Bland-Altman plots demonstrated systematic underestimation with wide limits of agreement. Sensitivity for detecting inadequate control was very low at both time points (3 months 12% and 6 months 21%), despite high specificity (97% at 3 months and 95% at 6 months). Higher interface leak was significantly associated with greater underestimation of AHI (three months: slope = 0.45, R² = 0.18, p < 0.0001; six months: slope = 0.38, R² = 0.14, p < 0.0001). Conclusion PAP device downloads substantially underestimated residual AHI and demonstrated poor agreement with home cardiorespiratory polygraphy. These findings highlight important limitations of device derived AHI in real‑world practice, particularly in the presence of interface leak. Where treatment adequacy is uncertain, confirmatory assessment with polygraphy remains necessary.
Academic publishing in surgery has undergone profound change during the past several decades. Expansion of medical schools, residency programs, international academic centers, and digital publishing platforms has produced unprecedented growth in manuscript submissions and intensified competition for professional attention. Journals are judged both by readership, as measured by article downloads, and by scientific influence, as reflected in scholarly citation. At The American Surgeon, these changes prompted development of editorial frameworks designed to identify contributions most likely to matter to practicing surgeons and subsequent investigators. Many manuscripts contained observations whose significance was underrecognized by their authors. This observation led to the Hidden Publishable Idea (HPI), a framework for identifying contributions most useful to readers. Once identified, the HPI often revealed methodological limitations that imposed an evidentiary ceiling, preventing definitive conclusions while suggesting new hypotheses for future investigation. Analysis of downloads and citations suggested that readership and scholarly adoption are related but distinct outcomes. This observation led to development of the CitDL matrix, a two-by-two framework based on high and low download and citation performance. The editorial objective was not simply manuscript acceptance, but identification and development of contributions that could move manuscripts toward greater readership, greater scholarly engagement, or both. These concepts represent adaptive responses to the contemporary challenge of helping useful ideas find their audience and contribute to the advancement of surgical practice and science.
Pelvic floor dysfunction (PFD) is a highly prevalent health problem, encompassing urinary incontinence, emptying disorders of the bladder, fecal incontinence, emptying disorders of the bowel, pelvic organ prolapse, sexual dysfunction, and chronic pelvic pain. Mobile health (mHealth) interventions delivered through apps can provide remote health services to improve patient compliance and enhance treatment effectiveness. Although apps for preventing and managing PFD have been developed and used, the features and quality of these apps in China have not been systematically examined. This study aimed to systematically summarize the functions and evaluate the quality of the existing mHealth apps for preventing and managing all kinds of PFD, such as urinary incontinence, fecal incontinence, and chronic pelvic pain. We systematically searched for potential PFD apps on the Apple App Store, Huawei AppGallery, and VIVO App Store. Apps were included if they were free, designed for preventing or managing PFD, in the Chinese language, could be downloaded and run on Android, Harmony, or iOS operating systems (OS), and incorporated elements of preventing and managing PFD. We excluded apps that were intended for use by health care providers and not relevant to PFD. Apps that met the inclusion criteria were downloaded and included for final analysis. The user version of the Mobile App Rating Scale (uMARS) was used to assess the apps' quality and summarize the apps' functionality according to guidelines. Of the 3897 apps screened, 46 apps that met the inclusion criteria were included in the final analysis. All apps were developed by corporations. More than half of the apps had download counts exceeding 10,000, and 24 (52.2%) apps scored 4 or higher in app stores. Furthermore, nearly half of the apps (n=21, 45.7%) had been updated within the past month at the time of retrieval. The overall uMARS scores ranged from 2.29 to 4.50, with a mean uMARS score of 3.46 (SD 0.50), which is considered acceptable quality. Based on uMARS scores, 15.2% (n=7) were rated as poor quality, 65.2% (n=30) as acceptable, and 19.6% (n=9) as good quality. More than half of the apps provided the functions of exercise (n=44, 95.7%), personal information recording (n=31, 67.4%), and health education (n=28, 60.9%). Only 5 apps provided 5 or more functions. The apps for PFD revealed acceptable quality, and the majority provided exercise, personal information recording, and health education functions. However, many apps lacked comprehensive functionalities and did not provide immediate feedback or high-quality educational information. Health care providers should follow international guidelines to create high-quality, evidence-based, multifunctional apps for PFD. Future studies should explore the effects of the apps and real-world user feedback data in clinical settings.
Radiotherapy for lung cancer can lead to significant fatigue and a decrease in physical activity. An app that reminds the patients several times per day to perform a certain number of steps may be helpful in this respect. Such an app has just been developed. We aimed to investigated the functionality and practicability of the app in healthy volunteers before testing it in a prospective clinical trial. Thirty healthy volunteers participated in this prospective study (APPAREL-HV) and evaluated the app by affirming (=satisfaction) or negating ten statements in three sections related to functionality and practicability. If the satisfaction rate was <80%, the app required further optimization. If it was <60%, it appeared not useful. Subgroup analyses were performed for iPhone vs. Android users, German vs. Danish participants, and younger (<50 years) vs. older (≥50 years) patients. Overall satisfaction rates were 86.7% (two statements), 93.3-100% (two statements), and 90.0-100% (six statements) in the sections Download and installation, Navigation, and Content/functions, respectively. Android users were significantly less satisfied with Download and installation (50.0% vs. 95.8%, p=0.018). Otherwise, satisfaction rates were always ≥80%, and subgroup analyses showed no significant differences between the groups compared. According to the results of the APPAREL-HV, the current version of the app appeared not sufficiently useful for Android users and required significant improvement for this subgroup in the section Download and installation.
The availability of comparable epidemiological data on malignant tumours remains limited due to heterogeneous classification systems, divergent reference frameworks, and inconsistent case definitions. Incomplete data collection and an often biased focus on specific subtypes also impair the representativeness of the results. These methodological discrepancies make it difficult to perform a reliable cross-source analysis of tumour frequencies. This study aims to evaluate extensive cancer registry data to enable epidemiological comparisons across locations and to support differential diagnostic decisions by including age, gender, and line differentiation. Data from the Surveillance, Epidemiology, and End Results (SEER) program (2000-2019, n = 12,809,525) and the German Centre for Cancer Registry Data (ZfKD; 2000-2019, n = 9,754,219) were merged. Tumours were classified according to ICD O 3.2. Incomplete and nonspecific datasets were removed and the remaining data visualised. The processed datasets were made available both online and as offline downloads, enabling an aggregated overview based on grouped ICD-O‑3.2 codes as well as a differentiated analysis of morphological phenotypes across 73 anatomical sites. The resulting visualisations provide a uniform epidemiological basis for comparative tumour analyses across a broad spectrum of locations and entities. HINTERGRUND: Die Verfügbarkeit vergleichbarer epidemiologischer Daten zu Malignomen ist durch heterogene Klassifikationssysteme und variierende Referenzrahmen erheblich eingeschränkt. Unvollständige Datenerhebungen sowie ein häufig einseitiger Fokus auf spezifische Subtypen beeinträchtigen zudem die Repräsentativität der Ergebnisse. Diese methodischen Diskrepanzen erschweren eine verlässliche quellenübergreifende Analyse von Tumorhäufigkeiten. Ziel dieser Arbeit ist die Auswertung umfangreicher Krebsregisterdaten, um epidemiologische Vergleiche über Lokalisationen hinweg zu ermöglichen und differentialdiagnostische Entscheidungen durch Einbeziehung von Alter, Geschlecht und Liniendifferenzierung zu untermauern. Daten aus dem Programm Surveillance, Epidemiology, and End Results (SEER; 2000–2019, n = 12.809.525) und dem Deutschen Zentrum für Krebsregisterdaten (ZfKD; 2000–2019, n = 9.754.219) wurden zusammengeführt. Die Klassifizierung der Tumoren erfolgte gemäß ICD-O‑3.2. Unvollständige und unspezifische Datensätze wurden entfernt, und die verbleibenden Daten visualisiert. Die aufbereiteten Datensätze wurden sowohl online als auch offline als Download bereitgestellt und ermöglichen eine aggregierte Übersichtsdarstellung auf Basis gruppierter ICD-O‑3.2‑Codes sowie eine differenzierte Analyse nach morphologischen Phänotypen über 73 anatomische Lokalisationen hinweg. Die resultierenden Darstellungen liefern eine einheitliche epidemiologische Basis für vergleichende Tumoranalysen über ein breites Spektrum von Lokalisationen und Entitäten hinweg.
The EQ Health and Wellbeing (EQ-HWB) is a new, generic instrument designed to evaluate quality-of-life across health, public health, and social care settings. The short form comprises nine items (EQ-HWB-9) and validation across diverse populations and contexts is required. We aimed to investigate the validity of the EQ-HWB-9 in an international sample of adults experiencing poor mental health who downloaded a meditation app. We further examined the impact of four item-level modifications on psychometric performance, including investigating a potential ordering effect for the 'activities' item (hypothesised in prior studies) and three minor changes to response options. The current study was embedded in a larger trial examining engagement with meditation via a free, downloadable app. Participants were randomised to complete the original (2022) and modified (2024) experimental version of the EQ-HWB-9. Psychometric evaluation included analyses of item distribution, known group and convergent validity, and responsiveness to change. There were no differences in demographic characteristics between the EQ-HWB original and modified versions at baseline (n = 865) or follow-up (n = 130). All psychometric tests supported the validity of the EQ-HWB-9 in this population. We found an ordering effect for the activities item, where the activities item showed a greater level of difficulty and a wider distribution over response options when asked before the mobility item, rather than after. There were no observable differences between versions for the other modifications. These findings add to growing literature supporting the EQ-HWB-9 as a suitable instrument for measuring quality-of-life across a range of settings. When the 'activities' item was presented first, as in the modified version, participants appeared to interpret the item more broadly, in line with the developers' intentions. Accordingly, our results support changing the item order of the first two items. Other modifications had little impact on outcomes, suggesting that further qualitative research will be required to inform decisions about their inclusion. Results from this study provide support towards finalisation of the instrument. The development of country-specific value-sets is now critical to support its application in economic evaluation.
Deciphering phenotype-specific regulatory mechanisms is key to understanding the molecular basis of complex diseases and traits. However, constructing multi-omic regulatory networks (MO-RNs) is challenging, as it requires integrating heterogeneous omics data, incorporating biological context, and detecting regulatory mechanisms that vary across conditions. The R package MORE (Multi-Omics REgulation) addresses these challenges by applying robust statistical models to infer phenotype-specific regulatory networks from multi-omics data. However, the use of MORE typically requires programming expertise, limiting its accessibility to non-specialist users. To democratize access to advanced multi-omics modeling tools, we present MOREshiny, an interactive web application built on Shiny that extends the module of pathway enrichment analysis and automatically guides the choice of statistical methods. MOREshiny enables users to upload multi-omic data, configure their models, and interpret results through a user-friendly interface-without the need for coding skills. MOREshiny also allows users to download MORE results for their later exploration and study. To demonstrate the utility of MOREshiny, we showcase its functionalities on a multi-omic ovarian cancer dataset to understand regulatory differences between patients who did or did not require chemotherapy. MOREshiny is freely available for download as a dockerized R Shiny package at https://github.com/BiostatOmics/MOREshiny.
The Single-Cell Pediatric Cancer Atlas (ScPCA) Portal is a resource for uniformly processed single-cell and single-nucleus RNA sequencing (RNA-seq) data and de-identified metadata from pediatric tumor samples. Originally comprising data from 10 projects funded by Alex's Lemonade Stand Foundation (ALSF), the Portal currently contains summarized gene expression data for over 700 samples across 55 cancer types from ALSF-funded and community-contributed datasets. Downloads include expression data as SingleCellExperiment or AnnData objects containing raw and normalized counts, principal-component analysis (PCA) and uniform manifold approximation and projection (UMAP) coordinates, automated cell-type annotations, and copy-number variation estimates, along with summary reports. Some samples have additional data from bulk RNA-seq, spatial transcriptomics, and/or feature barcoding (e.g., CITE-seq) included in the download. All data on the Portal were uniformly processed using scpca-nf, an efficient and open-source Nextflow workflow that uses alevin-fry to quantify gene expression. Comprehensive documentation, including file descriptions and a getting-started guide, are available online.
Although large language models (LLMs) have undergone substantial development, their applicability to epidemiological research has not been sufficiently examined. This study aims to develop and evaluate an LLM-based framework for hypothesis generation and testing, demonstrating its application in childhood asthma in the National Health and Nutrition Examination Survey (NHANES). Pilot study was conducted to explore factors associated with childhood asthma in the 2001-2020 NHANES cycles. A modular agent system was developed, including Database Query, Statistic, Paper Search, and Paper Download tools, along with two LLM models (Key Generator and Hypothesis Tester). Multivariable logistic regression was used to test for the association between each variable and current asthma, generating a tentative affirmative claim. The Key Generator module produced keywords for literature search, the Paper Search and Paper Download tools queried PubMed and retrieved relevant studies, and the Hypothesis Tester module synthesized evidence and determined the support for claims for each variable. Keywords and conclusions were reviewed by researchers and validated using multiple LLMs (ChatGPT, DeepSeek, and Gemini) to ensure consistency and robustness. 25,839 children with (n = 2928) and without (n = 22,911) current asthma, and 10,359 variables were included in the multivariable analysis, which yielded 100 variables associated with asthma. Of these, 21 were directly related to asthma (supporting published studies), 43 were indirectly related to asthma (based on background knowledge, though not explicitly discussed in the available publications), and 34 were unrelated to asthma. Two variables were excluded due to a lack of discriminative keywords. This study demonstrates the effectiveness of LLM-based models for generating and testing hypotheses about childhood asthma.
Protein structure prediction models released in recent years have presented tectonic changes in the field of structural biology. However, their potential has not yet been harnessed to its fullest due to their demands on hardware and technical expertise required for their usage. In this paper, we present Foldify, which makes prediction models accessible, integrating AlphaFold 3, AlphaFold 2, ColabFold, OmegaFold, and ESMFold into a single user-friendly, easy-to-use graphical interface, and ensures their stable operation within a scalable high-performance computing environment. Foldify accepts protein sequences, submitted through a web-based graphical interface as input, and allows executing multiple prediction models on the same protein sequence. The predicted protein structures can be directly visualized online through Mol* Viewer or can be downloaded from the website. Furthermore, the multiresult comparison mode allows visualization of multiple predicted structures in a single Mol* window, accompanied by qualitative metrics of the models' prediction similarity. The Foldify application is freely available at https://foldify-open.cloud.e-infra.cz/ with no login required.
This dataset presents 352 nuclear genes assembled from whole genome skimming data of 43 Rhododendron samples. The data were generated from 14 Rhododendron dauricum collected from seven distinct geographical populations in Northeast China, together with sequence data from 29 additional Rhododendron samples downloaded from the NCBI database. Using the universal set of 353 angiosperm nuclear genes as a reference, all genes were assembled with the HybPiper v2.1.1 pipeline. The dataset contains raw assembly sequences in FASTA format for each gene. Sequence alignment, trimming, and phylogenetic analysis were performed to construct phylogenetic trees. The resulting phylogenies based on concatenated 352-gene dataset and the screened 17-gene sub-dataset clearly distinguished R. dauricum from other Rhododendron species. Moreover, both datasets resolved individuals from the same population into distinct clades, enabling geographical origin traceability for the protected species R. dauricum. This dataset provides high-resolution molecular markers for research on Rhododendron phylogenomics, population genetics, conservation, and molecular identification.
Recurrent spontaneous abortion (RSA) is a major pregnancy complication with largely unknown causes. Carbohydrate metabolism (CM) disorders can affect the progression of endothelial cell senescence (ES), thereby leading to placental dysfunction. However, research on the interplay between CM and ES in RSA is limited. This study aims to identify key genes at the intersection of CM- and ES-related pathways in RSA via various bioinformatics methods. Datasets GSE165004 (training) and GSE26787 (validation) were downloaded from the GEO database. ES- and CM-related genes were obtained from previous literature. Key genes were obtained through WGCNA, two machine learning algorithms, gene expression analysis in the training and validation datasets, and RT-qPCR analysis. A comprehensive evaluation was conducted to assess the diagnostic potential of key genes. Two genes (BDH1, PIK3C2G) were recognized as key genes in RSA, which were upregulated in RSA. Immune infiltration analysis revealed that the key genes were negatively correlated with three distinct immune cell types. Both genes are linked to the cytokine-cytokine receptor interaction pathway. They were predicted to interact with two transcription factors (FOXL1 and YY1) and several chemicals. This study identifies BDH1 and PIK3C2G as candidate genes associated with CM and ES-related pathways in RSA.
This work studies trustworthy use of large language models for remote sensing satellite downlink scheduling. Rather than accepting a generated optimization model at face value, we organize the workflow into three guarded steps: candidate generation, benchmark-based validation, and fallback exact solving. The core technical component is a global time-slicing validator that converts visibility windows into atomic intervals; so, mutual exclusion at the ground-station side, mutual exclusion at the satellite side, and per-satellite download caps can be checked in a physically faithful manner. Results on a prototype instance indicate that LLM-based modeling can be integrated into a dependable scheduling pipeline when external verification and recovery are built into the loop.
High-throughput genomic analyses of germline and cancer genomes facilitate the identification of causal and actionable genetic variants. The recent advances in next-generation sequencing technology generated large-scale genomic and/or multi-omics dataset. Due to huge volume of data, scientists are facing challenges in visualizing, and interpreting the data. Currently available tools to visualize genetic variants from VCF files are not very user-friendly as most of them require knowledge of command line tools or scripts to install and run those software. Therefore, graphical user interface based tools or software are needed to summarize and visualize the VCF data. We have developed a Shiny App, interactive tool using the R programming language that utilizes existing R packages like "vcfR" and "maftools" to visualize and generate quality control metrics for genetic data. Our tool is powered by Shiny, making it easier to summarize and visualize genomic data using a GUI. XVCF has been developed for the summarization and visualization of genomic variation data. The tool offers an easy and friendly interface, allowing users to perform data loading, summarization, and visualization interactively. XVCF extract useful information such as read depth, mapping quality, genotype, quality control summary, and allele frequency from unannotated data. In the second module of XVCF, the cancer genomic data is analyzed using "maftools" to produce oncoplot, lollipop plot, gene summary, etc. XVCF is available for free download from https://github.com/rashidma/XVCF. Being a shiny R package, XVCF can be installed across different operating systems and utilize different computer hardware configurations. Visualizing genomic data has always been challenging. Existing tools/software seem to be difficult to use due to lack of technical computer programming knowledge. We offer XVCF to visualize and/or summarize genomic data at a greater ease due to its graphical user interface and powerful cross-platform R shiny framework.
Using ovarian cancer datasets from public databases, identify copper death-related genes in ovarian cancer tissues and construct a clinical prognostic risk scoring model for ovarian cancer patients based on these genes. We downloaded the OC data of TCGA, the GSE26193, GSE63885 dataset from GEO and retrieved 10 cuproptosis related genes (CRGs) and analyzed their chromosomal localization, expression correlation, and mutation patterns based on the datasets. Using these genes, we clustered the OC samples to identify different molecular subtypes of copper induced death. We analyzed the differential genes and functional enrichment between different subtypes and obtained feature genes with predictive ability for prognosis through survival regression analyses. Based on these feature genes, we constructed a risk scoring model and incorporated the clinical characteristics ofpatients to jointly predict their survival rate. In ovarian cancer samples, 10 copper death-related genes can stably divide the samples into two molecular subtypes, and there are significant differences in clinical and immune characteristics and drug sensitivity between them. After further screening, seven prognostic genes (RARRES1、CXCL10、PI3、CXCL11、THEMIS2、GBP2、RPL39L) were obtained, and the risk model based on them combined with age predicted that the AUC of patients' 1-, 3-, and 5-year survival rates were all greater than 0.7, showing good clinical application prospects. The mechanism of cuproptosis and its key genes might become therapeutic targets for ovarian cancer. The subtypes of cuproptosis provide a theoretical basis for personalized clinical treatment. The predictive model constructed by key prognostic genes has promising clinical application effects. 利用公共数据库中的卵巢癌数据集，确定卵巢癌组织中的铜死亡相关基因，并基于这些基因构建卵巢癌患者临床预后风险评分模型。 从 TCGA OC数据库和GEO数据库中的GSE26193、GSE63885数据集，筛选出10个铜死亡相关基因，并基于数据集分析其染色体定位、表达相关性和突变模式。基于这些基因，我们对卵巢癌样本进行聚类，以识别铜诱导死亡的不同分子亚型。分析不同亚型之间的差异表达基因和功能富集，并通过生存回归分析获得了具有预后预测能力的特征基因。基于这些特征基因，我们构建了一个风险评分模型，并结合患者的临床特征，共同预测其生存率。 在卵巢癌样本中，10个铜死亡相关基因可将样本稳定区分为两种分子亚型，二者临床及免疫特征、药物敏感性等方面差异明显。进一步筛选获得7个预后基因（RARRES1、CXCL10、PI3、CXCL11、THEMIS2、GBP2、RPL39L），基于其建立的风险模型结合年龄后对患者1、3、5年生存率预测的曲线下面积均大于0.7，显示良好临床应用前景。 铜死亡的机制及其关键基因可能成为卵巢癌的治疗靶点。该分型为临床个体化治疗提供了理论依据。由关键预后基因构建的预测模型具有良好的临床应用效果。
The use of virtual crossmatch for HLA antigen compatibility assessment before transplantation has become common practice in transplantation medicine. The accuracy of virtual crossmatch relies on accurate and complete donor HLA antigen typing and up-to-date patient HLA antigen antibody characterization. Here, we report a case in which anti-HLA-DP antibodies were detected in the patient, and the donor HLA-DPB1*29:01 was not included in the bead panel of Luminex-based single antigen bead assay (LSA). The deceased donor HLA antigen typing results were downloaded from the United Network for Organ Sharing. Serum samples were tested for HLA antibodies using LSA. Epitope analysis was performed manually based on alignment of HLA-DP using the Sequence Alignment Tool from the IPD-IMGT/HLA database (https://www.ebi.ac.uk/ipd/imgt/hla/). The LSA showed that anti-HLA-DP3, DP6, DP9, DP11, DP14, DP15, DP17, and DP20 were positive. HLA antigen typing with real-time polymerase chain reaction showed that the donor carried HLA-DPB1*29:01. Epitope analysis showed that the anti-HLA-DPB1*29:01 donor-specific antibody was present in this patient. The LSA can miss antibodies against HLA antigens not represented by the beads, leading to false-negative results for donor-specific antibodies. Failure to consider the possibility of unrepresented HLA proteins may potentially lead to incorrect clinical decision. Epitope analysis may help predict reactivity to HLA antigens not present on LSA beads.
To investigate a prognostic risk model for thyroid cancer based on immune genes and its link to immune cell infiltration in tumors. This was a retrospective study of 117 thyroid cancer patients treated at our hospital from May 2021 to December 2023. Patients were categorized into 82 with a good prognosis and 35 with a poor prognosis. A COX regression model was validated using ROC curves and goodness-of-fit tests. Flow cytometry was used to detect the proportion of each immune cell subset in tumor tissues. The correlation was analyzed using Spearson correlation test. The TCGA thyroid cancer RNA-seq data (n = 512) were downloaded for external validation. LNM, capsule invasion, and immune genes were independent factors for poor prognosis in thyroid cancer (p < 0.05). A prognosis prediction model was developed: [1/1 + exp. (4.854 + 1.085 × LNM + 1.510 × capsule invasion + 1.318 × CDK1 + 1.940 × B3GNT7 + 0.860 × S100A9 + 0.956 × MMP12)], with an AUC of 0.925 and a chi-square value of 10.846 (p = 0.178 > 0.05). Poor-prognosis patients showed reduced B, CD4 + T, and CD8 + T lymphocyte infiltration, but increased neutrophil and macrophage infiltration (p < 0.05). CDK1, B3GNT7, S100A9, and MMP12 mRNA levels were negatively correlated with B lymphocyte infiltration in thyroid cancer. CD4 + T lymphocytes and CD8 + T lymphocytes were positively correlated with the infiltration abundance of neutrophils and macrophages (p < 0.05). Spearman correlation analysis of the TCGA database showed that S100A9 was positively correlated with infiltration of B cells, CD4 + T cells, macrophages, and NK cells, but negatively correlated with infiltration of CD8 + T cells and endothelial cells. MMP12 was positively correlated with infiltration of B cells, CD4 + T cells, macrophages, and NK cells, but negatively correlated with infiltration of endothelial cells (all p < 0.05). The prognostic prediction model based on the influencing factors had high predictive value. The immune-related genes were correlated with the abundance of immune cell infiltration; the above correlation has been partially validated in the TCGA database.
While studies suggested that Huangqi Guizhi Wuwu Decoction (HGWD) can mitigate doxorubicin-induced cardiotoxicity (DIC), the specific mechanism of action remains unclear. GSE106297, GSE157282, and GSE206803 were downloaded to screen for differentially expressed genes (DEGs), followed by gene set enrichment analysis and immune infiltration analysis. DIC-related genes were obtained by the intersection of weighted gene co-expression network analysis and DEGs. The active ingredients and target genes of HGWD were obtained from the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform database, and HGWD-DIC common targets were identified by intersecting them with DIC-related genes. A drug-active ingredient-target network was constructed to select the core components of HGWD. Mitophagy-related genes were obtained from GeneCards, PHARMGKB, and OMIM databases, and intersecting them with common targets yielded the core genes, which were then subjected to enrichment analyses. A protein-protein interaction network was constructed to identify key genes, further assessing their diagnostic value. The effect of HGWD on the expression of key genes was further validated using prepared medicated serum. The interactions between the core components and key genes were validated through molecular docking and molecular dynamics simulation. A total of 2344 DEGs were identified, with gene set enrichment analysis results primarily enriched in categories such as apoptosis, p53 signaling pathway, cell cycle, PLK1 pathway, mitochondrial translation, and metabolism of RNA. Immune infiltration analysis suggested that the immune response may also be involved in the pathogenesis of DIC. We identified 2969 key modular genes by weighted gene co-expression network analysis, and intersecting these with DEGs yielded 1569 DIC-related genes. Network pharmacology analysis revealed 74 active ingredients and 692 target genes of HGWD, resulting in 64 common targets when intersected with DIC-related genes. The core components of HGWD were identified as quercetin and kaempferol. By intersecting the obtained mitophagy-related genes with common targets, 13 core genes were identified, with enrichment analyses indicating significant associations with cellular response to mitophagy and autophagy. Further analysis showed that 5 key genes: AKT1, TP53, BCL2L1, FASN, and HRAS, all demonstrated good diagnostic value, and their DOX-induced expression alterations were reversed by HGWD. Molecular docking and molecular dynamics simulation showed a strong binding affinity between the core components and key genes. HGWD may alleviate DIC by regulating mitophagy.
This article describes a new sentence corpus developed for use with children 5 years of age and older that is based on the Basic English Lexicon (BEL) sentence corpus and is referred to as the BabyBEL. Sentences were constructed using words found in the expressive vocabulary of native English-speaking kindergarten and first-grade children and organized into 20 lists of 20 sentences each. A female talker recorded all sentences, and speech-shaped masking noise was created based on these recordings. First, psychometric properties of each list as a function of signal-to-noise ratio (SNR) in a cohort of adults were evaluated. Second, speech recognition in noise by list at -2 dB SNR in a cohort of 5- to 7-year-olds was evaluated. Participants had normal hearing and were native speakers of English. Results indicate some differences between lists. The speech recognition threshold associated with 75% correct in adult data varied by no more than ±0.5 dB for 17 of 20 lists. Percent correct scores for children varied by no more than 3 percentage points for 12 of 20 lists. The pattern of performance across lists was similar for adults and children. The vocabulary and sentence structure of the BabyBEL sentences are appropriate for use with children as young as 5 years of age. Recordings of the BabyBEL corpus are freely available for download. When designing future experiments, it is important to consider performance patterns both across lists and between groups of child and adult listeners. https://doi.org/10.23641/asha.32715975.
PRRSV-2 prevalent strains mainly include C-PRRSV, HP-PRRSV, and NADC30-like, with the latter being the current dominant lineage. Due to the high recombination and genetic variation of PRRSV, existing RT-qPCR assays face an increasing risk of false negatives. Therefore, based on the current prevalent strain sequences, it is of great significance to update and establish a one-step multiplex RT-qPCR method that can simultaneously detect C-PRRSV, HP-PRRSV and NADC30-like. By downloading the latest prevalent strain full genome sequences from NCBI and isolating them in our laboratory, the conserved and type-specific target regions of the three strains were screened in the high-variable region of the nsp2 gene. TaqMan MGB probes and primers were designed. After optimizing the reaction conditions, the standard curve, amplification efficiency, sensitivity, specificity, repeatability and clinical application effect of this method were evaluated. The established standard curve showed a good linear relationship within the range of 1 × 108 to 1 × 103 copies/μL, with a correlation coefficient R2 of 0.998 for all. The amplification efficiency ranged from 97.58 to 103.53%. The minimum detection limits for C-PRRSV, HP-PRRSV and NADC30-like were 10.128 copies, 8.998 copies and 8.458 copies, respectively. This method showed no cross-reaction with common porcine pathogens such as PRRSV-1, PCV2 and CSFV, and the intra-batch and inter-batch coefficient of variation was less than 2%. The positive rate of PRRSV in 588 samples was 15.5% (91/588), which was higher than 13.94% (82/588) of the reported methods. The consistency Kappa of the two methods was 0.87. This study successfully established a one-step multiplex RT-qPCR method based on current prevalent strain sequences, which offers high sensitivity, strong specificity, and good repeatability, and can be used for rapid differential diagnosis of the three PRRSV subtypes in clinical samples, thereby supporting precise diagnosis, epidemiological monitoring, and prevention and control of PRRSV in China.