bluuebunny/crossref_metadata_2025 huggingface dataset:
Metabolic pathways from the gut metatranscriptome are associated with COPD and respiratory function in lung cancer patients
Authors: Dora David , Revisnyei Peter , Mihucz Anna , Kiraly Peter , Szklenarik György , Dulka Edit , Galffy Gabriella , Lohinai Zoltan
Published: November 2024

Introduction

Changes in the human gut microbiome have been linked to various chronic diseases, including chronic obstructive pulmonary disease (COPD). While substantial knowledge is available on the genomic features of fecal communities, little is known about the microbiome’s transcriptional activity. Here, we analyzed the metatranscriptomic (MTR) abundance of MetaCyc pathways, SuperPathways, and protein domain families (PFAM) represented by the gut microbiome in a cohort of non-small cell lung cancer (NSCLC) patients with- or without COPD comorbidity.

Methods

Fecal samples of 40 NSCLC patients with- or without COPD comorbidity were collected at the time of diagnosis. Data was preprocessed using the Metaphlan3/Humann3 pipeline and BioCyc© to identify metabolic SuperPathways. LEfSe analysis was conducted on Pathway- and PFAM abundance data to determine COPD- and non-COPD-related clusters.

Results

Key genera Streptococcus, Escherichia, Gemella, and Lactobacillus were significantly more active transcriptionally compared to their metagenomic presence. LEfSe analysis identified 11 MetaCyc pathways that were significantly overrepresented in patients with- and without COPD comorbidity. According to Spearman’s rank correlation, Smoking PY showed a significant negative correlation with Glycolysis IV, Purine Ribonucleoside Degradation and Glycogen Biosynthesis I, and a significant positive correlation with Superpathway of Ac-CoA Biosynthesis and Glyoxylate cycle, whereas forced expiratory volume in the first second (FEV1) showed a significant negative correlation with Glycolysis IV and a significant positive correlation with Glycogen Biosynthesis I. Furthermore, COPD patients showed a significantly increased MTR abundance in ~60% of SuperPathways, indicating a universally increased MTR activity in this condition. FEV1 showed a significant correlation with SuperPathways Carbohydrate degradation, Glycan biosynthesis, and Glycolysis. Taxonomic analysis suggested a more prominent MTR activity from multiple Streptococcus species, Enterococcus (E.) faecalis, E. faecium and Escherichia (E.) coli than expected from their metagenomic abundance. Multiple protein domain families (PFAMs) were identified as more associated with COPD, E. faecium, E.coli, and Streptococcus salivarius, contributing the most to these PFAMs.

Conclusion

Metatranscriptome analysis identified COPD-related subsets of lung cancer with potential therapeutic relevance.
Ethics and journalistic challenges in the age of artificial intelligence: talking with professionals and experts
Authors: Gutiérrez-Caneda Beatriz , Lindén Carl-Gustav , Vázquez-Herrero Jorge
Published: November 2024

The rapid advancement of artificial intelligence (AI) is transforming the media industry by automating processes, with applications in data analysis, automated writing, format transformation, content personalization, and fact-checking. While AI integration offers new opportunities in journalism, it also raises ethical concerns around data privacy, algorithmic biases, transparency, and potential job displacement. This study employed qualitative interviews with media professionals and researchers to explore their perspectives on the ethical implications of AI integration in newsrooms. Interview data were analyzed to identify common themes and specific challenges related to AI use in journalism. The findings discuss issues such as the tensions between technology and journalism, ethical challenges related to AI, the evolution of professional roles in journalism, media guidelines, and potential future regulations.
Biclustering a dataset using photonic quantum computing
Authors: Borle Ajinkya , Bhave Ameya
Published: November 2024

Biclustering is a problem in machine learning and data mining that seeks to group together rows and columns of a dataset according to certain criteria. In this work, we highlight the natural relation that quantum computing models like boson and Gaussian boson sampling (GBS) have to this problem. We first explore the use of boson sampling to identify biclusters based on matrix permanents. We then propose a heuristic that finds clusters in a dataset using Gaussian boson sampling by (i) converting the dataset into a bipartite graph and then (ii) running GBS to find the densest sub-graph(s) within the larger bipartite graph. Our simulations for the above proposed heuristics show promising results for future exploration in this area.
Early coronary angiography in patients with myocardial infarction without ST elevation after out-of-hospital cardiac arrest: a systematic review and meta-analysis
Authors: Ahmed Warda , Ejaz Arooba , Arshad Muhammad Sameer , Mubeen Manahil , Ahmed Aymen , Siddiqui Asad Ali , Tharwani Zoaib Habib , Deepak F. N. U. , Kumar Prince , Shahid Izza , Memon Muhammad Mustafa
Published: November 2024

Background

Early coronary angiography (CAG) in post-cardiac arrest patients without ST-segment elevation is a topic of debate. This meta-analysis aimed to assess its impact on outcomes.

Methods

A search of Medline and Cochrane up to February 2023 was conducted to identify randomized controlled trials and observational studies comparing patients undergoing early CAG vs. delayed/no CAG after experiencing out-of-hospital cardiac arrest. A random-effects model pooled odds ratios (ORs) with 95% confidence intervals (CIs). Meta-regression explored factors modifying effect sizes.

Results

We identified 16 studies (7 RCTs, 9 observational studies) involving 4,737 patients. Early CAG significantly reduced long-term mortality [OR: 0.66 (0.51–0.85)], and increased favorable cerebral performance category (CPC) 1–2 at discharge [OR: 1.49 (1.09–2.03)]. Observational study subgroup showed decreased short-term mortality, long-term mortality, and CPC 1–2 at discharge, unlike RCT subgroup. Meta-regression revealed type 2 diabetes mellitus and follow-up time influencing short-term mortality and CPC 1–2 at discharge, respectively.

Conclusion

Early CAG in post-cardiac arrest patients without ST elevation is associated with long-term clinical benefits, particularly evident in observational studies. Interpretation should be cautious.
Case Report: Spontaneous recanalization of left anterior descending artery occlusive lesion with OCT-guided drug-coated balloon therapy
Authors: Yang Bangguo , Yeh Mengqi , Bai Jie
Published: November 2024

In instances where a patient with acute myocardial infarction (AMI) did not undergo immediate reperfusion therapy during the acute phase, there was a risk of the occlusion progressing to chronic and the chances of spontaneous recanalization decreasing. This case report detailed the experience of a 37-year-old male patient who, 45 days post-AMI, still had a blocked left anterior descending (LAD) artery due to the patient's refusal for intervention. Two years later, a follow-up coronary angiography showed spontaneous recanalization of the LAD artery, with haziness in the middle segment. Optical coherence tomography (OCT) revealed a honeycomb-like structure in the mid-LAD with a minimum area of 0.55 mm2. The lesion was effectively treated with a drug-coated balloon, resulting in an excellent outcome.
Case Report: Restrictive cardiomyopathy due to a rare mutation in troponin I gene (TNNI3) in a patient
Authors: Deng Lili , Luo Liming , Zhang Min , Guo Cheng , Liu Kai
Published: November 2024

Background

Restrictive cardiomyopathy (RCM) is a rare cardiomyopathy often characterized by normal or reduced ventricular chamber volume and bi-atrial enlargement, caused mainly by mutations in the myonodal gene. It has a low incidence, non-specific clinical manifestations, rapid progression, and lack of specific treatment, with heart transplantation usually being the ultimate treatment.

Methods and results

This case reports a case of a 2-year-2-month-old boy located in Yunnan Province, China, who was admitted to the hospital with a 2-month history of orofacial bruising, aggravated by a 1-week history of bilateral eyelid swelling. After admission, electrocardiogram showed bi-atrial enlargement, echocardiography suggested bi-atrial enlargement with right and left ventricular diastolic hypoplasia, and cardiac magnetic resonance showed bi-atrial dilatation and possible localized myocardial fibrosis. A de novo heterozygous mutation (c.574C > T, p.Arg192Cys) in the TNNI3gene was identified by whole exome sequencing and verified by Sanger sequencing. The patient’s family opted for conservative treatment after diagnosis, but the patient died suddenly 2 months after diagnosis.

Conclusion

This study identified a case of RCM due to TNNI3 mutation, emphasizing the importance of cardiac MRI and genetic testing in the clinical diagnosis of RCM and the need for heart transplantation. The study also revealed the possible heterogeneity of TNNI3 mutations across ethnic and geographic backgrounds, suggesting that long-term studies of genetic mutations should be strengthened in the future to promote the development of precision treatment strategies for cardiomyopathy.
Transcatheter intervention of modified Blalock–Taussig shunts in patients with hypoplastic left heart syndrome undergoing stage 1 palliation
Authors: Mini Nathalie , Schneider Martin B. E. , Mikus Marian
Published: November 2024

Background

While several studies have explored the outcomes of transcatheter interventions for modified Blalock–Taussig shunts (MBTSs) in a broad range of congenital heart diseases, none have specifically examined the interventions in patients with hypoplastic left heart syndrome (HLHS) who underwent Norwood palliation (NP).

Methods

This retrospective study was conducted between 2020 and 2024, when 24 urgent interventions were performed on 17 patients at our center. We recorded several key outcomes, including early and late intervention-related complications, the need for reintervention, the interval between the NP and the first intervention, shunt patency following the intervention, associated morbidities, and thrombosis-related sudden events. Additionally, during follow-up, we documented the outcomes for patients who underwent the Glenn procedure and those who were palliated, including late death.

Results

The median age and weight at the time of intervention were 88 days (range: 15–300 days) and 5 kg (range: 2.6–7.6 kg), respectively. The median interval between the Norwood procedure and the transcatheter intervention was 61 days (7–160 days), with median shunt patency lasting 62 days (1–150 days). Notably, there were no intervention-related complications or deaths. In-stent thrombosis, a late complication, occurred in four patients; two of these had impaired anticoagulation, including extracorporeal membrane oxygenation (ECMO)-related causes, while the other two, who were on aspirin, had multiple stents within the shunt, one of whom experienced sudden death. Six patients required seven reinterventions: four due to shunt obstruction or restenosis and two to delay surgery or provide palliation for patients unfit for surgery, aiding in pulmonary development.

Conclusion

The transcatheter intervention of the MBTSs in patients with HLHS undergoing NP is considered both safe and potentially life-saving in emergent situations. This approach may facilitate improved pulmonary development, postpone the need for subsequent surgeries, and provide medium-term palliative care for critically ill patients. However, managing late complications such as stent thrombosis remains a significant challenge. Our findings indicate that risk factors for shunt thrombosis include using ECMO therapy, underlying coagulation disorders, impaired lymphatic drainage, requiring multiple stents within the shunt, and a prior history of thrombosis before intervention. Dual antiplatelet therapy is recommended to alleviate the risk of thrombotic events in this population.
Efficient out-of-distribution detection via layer-adaptive scoring and early stopping
Authors: Wang Haoliang , Zhao Chen , Chen Feng
Published: November 2024

Introduction

Multi-layer aggregation is key to the success of out-of-distribution (OOD) detection in deep neural networks. Moreover, in real-time systems, the efficiency of OOD detection is equally important as its effectiveness.

Methods

We propose a novel early stopping OOD detection framework for deep neural networks. By attaching multiple OOD detectors to the intermediate layers, this framework can detect OODs early to save computational cost. Additionally, through a layer-adaptive scoring function, it can adaptively select the optimal layer for each OOD based on its complexity, thereby improving OOD detection accuracy.

Results

Extensive experiments demonstrate that our proposed framework is robust against OODs of varying complexity. Adopting the early stopping strategy can increase OOD detection efficiency by up to 99.1% while maintaining superior accuracy.

Discussion

OODs of varying complexity are better detected at different layers. Leveraging the intrinsic characteristics of inputs encoded in the intermediate latent space is important for achieving high OOD detection accuracy. Our proposed framework, incorporating early stopping, significantly enhances OOD detection efficiency without compromising accuracy, making it practical for real-time applications.
Credibility-based knowledge graph embedding for identifying social brand advocates
Authors: Abu-Salih Bilal , Alotaibi Salihah , Al-Okaily Manaf , Aljaafari Mohammed , Almiani Muder
Published: November 2024

Brand advocates, characterized by their enthusiasm for promoting a brand without incentives, play a crucial role in driving positive word-of-mouth (WOM) and influencing potential customers. However, there is a notable lack of intelligent systems capable of accurately identifying online advocates based on their social interactions with brands. Knowledge Graphs (KGs) offer structured and factual representations of human knowledge, providing a potential solution to gain holistic insights into customer preferences and interactions with a brand. This study presents a novel framework that leverages KG construction and embedding techniques to identify brand advocates accurately. By harnessing the power of KGs, our framework enhances the accuracy and efficiency of identifying and understanding brand advocates, providing valuable insights into customer advocacy dynamics in the online realm. Moreover, we address the critical aspect of social credibility, which significantly influences the impact of advocacy efforts. Incorporating social credibility analysis into our framework allows businesses to identify and mitigate spammers, preserving authenticity and customer trust. To achieve this, we incorporate and extend DSpamOnto, a specialized ontology designed to identify social spam, with a focus on the social commerce domain. Additionally, we employ cutting-edge embedding techniques to map the KG into a low-dimensional vector space, enabling effective link prediction, clustering, and visualization. Through a rigorous evaluation process, we demonstrate the effectiveness and performance of our proposed framework, highlighting its potential to empower businesses in cultivating brand advocates and driving meaningful customer engagement strategies.
Artificial intelligence–enabled social media listening to inform early patient-focused drug development: perspectives on approaches and strategies
Authors: Spies Erica , Flynn Jennifer A. , Oliveira Nuno Guitian , Karmalkar Prathamesh , Gurulingappa Harsha
Published: November 2024

This article examines the opportunities and benefits of artificial intelligence (AI)–enabled social media listening (SML) in assisting successful patient-focused drug development (PFDD). PFDD aims to incorporate the patient perspective to improve the quality, relevance, safety, and efficiency of drug development and evaluation. Gathering patient perspectives to support PFDD is aided by the participation of patient groups in communicating their treatment experiences, needs, preferences, and priorities through online platforms. SML is a method of gathering feedback directly from patients; however, distilling the quantity of data into actionable insights is challenging. AI–enabled methods, such as natural language processing (NLP), can facilitate data processing from SML studies. Herein, we describe a novel, trainable, AI-enabled, SML workflow that classifies posts made by patients or caregivers and uses NLP to provide data on their experiences. Our approach is an iterative process that balances human expert–led milestones and AI-enabled processes to support data preprocessing, patient and caregiver classification, and NLP methods to produce qualitative data. We explored the applicability of this workflow in 2 studies: 1 in patients with head and neck cancers and another in patients with esophageal cancer. Continuous refinement of AI-enabled algorithms was essential for collecting accurate and valuable results. This approach and workflow contribute to the establishment of well-defined standards of SML studies and advance the methodologic quality and rigor of researchers contributing to, conducting, and evaluating SML studies in a PFDD context.
A framework for processing large-scale health data in medical higher-order correlation mining by quantum computing in smart healthcare
Authors: Mei Peng , Zhang Fuquan
Published: November 2024

This study aims to leverage the advanced capabilities of quantum computing to construct an efficient framework for processing large-scale health data, uncover potential higher-order correlations in medicine, and enhance the accuracy of smart healthcare diagnosis and treatment. A data processing framework is developed using quantum annealing algorithms and quantum circuits. We call it the quantum medical data simulation computational model (Q-MDSC). A unique encoding method based on quantum bits is employed for health data features, such as encoding symptom information from electronic health records into different quantum bits and representing different alleles of genetic data through superposition states of quantum bits. The properties of quantum entanglement are utilized to relate different data types, and quantum parallelism is harnessed to process multiple data combinations simultaneously. Additionally, this quantum computing framework is compared with traditional data mining methods using the same datasets, which include the Cochrane Systematic Review Database (https://www.cochranelibrary.com), the BioASQ Dataset (https://participants-area.bioasq.org), the PubMed Central Dataset (https://www.ncbi.nlm.nih.gov/pmc), and the Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov). The datasets are divided into training and testing sets in a 7:3 ratio during the experiments. Tests are conducted on association mining tasks of varying data scales and complexities, ranging from simple symptom-disease associations to complex gene-symptom-disease higher-order associations. The results indicate that, when processing large-scale data, the quantum computing framework improves overall computational speed by approximately 45% compared to traditional algorithms. Regarding uncovering higher-order correlations, the quantum computing framework enhances accuracy by about 30% relative to traditional algorithms. For early disease prediction, the accuracy achieved with the new framework is approximately 25% higher than that of conventional methods. Furthermore, for personalized treatment plan matching, the matching accuracy of the quantum computing framework surpasses traditional approaches by about 35%. These findings demonstrate the significant potential of the quantum computing-based smart healthcare framework for processing large-scale health data in the context of higher-order correlation mining, paving new pathways for the development of smart healthcare. This study utilizes multiple public datasets to achieve breakthroughs in computational speed, higher-order correlation mining, early disease prediction, and personalized treatment plan matching, thus opening new avenues for advancing smart healthcare.
Collaborative online learning in higher education—quality of digital interaction and associations with individual and group-related factors
Authors: Bach Anabel , Thiel Felicitas
Published: November 2024

Collaborative online learning became a necessity for universities during the COVID-19 pandemic. Even though it is known from research that online collaboration is an effective way of learning, digital interaction can be challenging for learners. Group members have to create a high-quality interaction to ensure the success of the collaborative learning process. Based on a theoretical model of collaborative learning, high-quality interaction can be determined with regard to cognitive group activities (prior knowledge activation, transactivity), meta-cognitive group activities (organization of the work process), and relational group activities (group climate, participation and task-related communication). Our study aims to examine how students manage a self-directed collaborative learning setting, how they perceive the process quality of digital interaction and how the interaction quality is related to self-reported outcomes (learning gain and satisfaction). We use a newly developed questionnaire to assess the quality of digital interaction in terms of the aforementioned dimensions. Furthermore, we focus on associations with the beliefs about web-based learning and the ability of perspective-taking at the individual level as well as the sense of community at the group level. We conducted a quantitative study within online university courses that were implemented asynchronously due to the COVID-19 pandemic. N = 298 undergraduate students in teacher education rated the quality of a digital collaborative learning settings (response rate of 72%). The students worked on collaborative tasks autonomously without any guidance from the teacher. We find differences between (meta-)cognitive and relational factors of interaction quality, and differences in the strength of the associations with outcomes and individual and group-related factors. Our study provides insights into students´ collaborative online learning and examines the relationships between different dimensions of group interaction quality and the input and outcome variables. Limitations and areas for further research are discussed.
Learning English as a second language with artificial intelligence for prospective teachers: a systematic review
Authors: Peña-Acuña Beatriz , Corga Fernandes Durão Rosana
Published: November 2024

Introduction

Artificial intelligence is revolutionizing English language teaching with personalized assessments and advanced tools, though more studies are needed on its effectiveness and equitable accessibility.

Method

A systematic literature review (SLR) was conducted using articles from Web of Science, Scopus, and ERIC to establish theoretical foundations.

Results

AI in learning English as a second language offers personalized tools such as chatbots, pronunciation improvement apps, and platforms that adapt content according to student progress.

Discussion

The study highlights how AI is revolutionizing English learning by personalizing lessons, improving pronunciation, and promoting cultural understanding.
18F-AlF-NOTA-octreotide PET/CT and 3D printing technology for precision diagnosis and treatment of phosphaturic mesenchymal tumors in patients with tumor-induced osteomalacia: two case reports
Authors: Zhao Gang , Guan Lijuan , Zhang Yongqiang , Shi Xingzhen , Luo Wenming , Yang Maiqing , Wang Qi , Liu Zhen , Liu Yongqiang , Ding Xiaolei , Zhao Jie
Published: November 2024

Objective

This study aims to report the application of 18F-AlF-NOTA-Octreotide PET/CT and 3D printing technology in the diagnosis and treatment of phosphaturic mesenchymal tumors (PMT) in patients with tumor-induced osteomalacia (TIO).

Case presentation

A 68-year-old male patient (Case 1) was admitted to the Weifang People’s Hospital in August 2022 with complaints of “persistent pain in the bilateral flank and lumbosacral region”. 18F-AlF-NOTA-Octreotide PET/CT showed high octreotide expression in the left femoral region. A 48-year-old male patient (Case 2) was admitted to the Weifang People’s Hospital in November 2022, complaining of “pain in the lumbar region and ribs”. 18F-AlF-NOTA-Octreotide PET/CT showed high octreotide expression in the pancreatic uncinate process and the left acetabulum. They were diagnosed with hypophosphatemic osteomalacia, with a strong consideration of an underlying neuroendocrine tumor. Preoperative design of 3D virtual surgery, CAD/CAM, and 3D printing technology were used to customize the digital surgical guide plates, and the surgery was carried out. They were both finally confirmed as phosphateuric mesenchymal tumors (PMT) based on postoperative pathology and immunohistochemistry results. Both patients experienced substantial relief from their clinical manifestations after surgery.

Conclusion

18F-AlF-NOTA-Octreotide PET/CT may be a precise diagnostic method for TIO, while 3D printing technology may serve as an effective and dependable adjunct for the treatment of PMT in patients with TIO.
Interplay between lipid profile and anthropometric measures as indicators of cardiometabolic risk in women with polycystic ovary syndrome
Authors: Jabczyk Marzena , Nowak Justyna , Jagielski Paweł , Hudzik Bartosz , Borszcz Jakub , Zubelewicz-Szkodzińska Barbara
Published: November 2024

Objectives

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder that often coexists with cardiometabolic risk factors. Women with PCOS have a two-fold increased risk of developing type 2 diabetes and substantially elevated risk for cardiovascular disease (CVD) events later in life. PCOS patients may require more comprehensive metabolic screening to identify populations at higher risk of developing CVD and dyslipidemia. It is recommended to evaluate lipid profile, glucose tolerance and of women with PCOS every 2-3 years. Simple, short, and easy methods for the assessment of CVD risk in women with PCOS may be useful tools for implementing CVD prevention strategies by doctors or nutritionists. The aim of this study was to investigate the usefulness of anthropometric indices in the assessment of cardiometabolic risk based on lipid profile in patients with PCOS.

Material and methods

The study involved 49 of Caucasian women aged 18-39 who were diagnosed with PCOS based on the Rotterdam criteria and divided into two groups with normal lipid profile (N=14) and dyslipidemia (N=35). Biochemical parameters were tested in the morning while fasting. Anthropometric parameters such as Body Mass Index (BMI), Body Adiposity Index (BAI), Waist-to-Hip Ratio (WHR), and Waist-to-Height Ratio (WHtR) were calculated, while the Percent of Body Fat was measured using a body analyzer.

Results

The study demonstrated that women with dyslipidemia were older than the control group, 33 years (27-37) vs 24 years (21-26), p<0.01. Neither BMI nor BAI (%) correlated with total cholesterol (p=0.63 and p=0.27). Other lipid parameters, such as serum HDL cholesterol (R=-0.68, p<0.01; R=-0.58, p<0.01), LDL cholesterol (R=0.34, p=0.02; R=0.37, p=0.01), non-HDL cholesterol (R=0.40, p<0.01; R=0.42, p<0.01), and triglycerides (R=0.56, p<0.01; R=0.51, p<0.01) correlated with BMI and BAI (%). ROC analysis demonstrated a high predictive value for age in identifying dyslipidemia. ROC analysis demonstrated poor predictive value for BMI, BAI, WHR, WHtR in identifying dyslipidemia.

Conclusions

Analysis of simple and rapid parameters used to assess body fat, such as BMI, BAI, WHR, and WHtR, has shown that they are poor predictors of dyslipidemia in women with PCOS. In young women with PCOS, age appears to be a more reliable predictor of dyslipidemia.
Exploring the pathogenesis, biomarkers, and potential drugs for type 2 diabetes mellitus and acute pancreatitis through a comprehensive bioinformatic analysis
Authors: Zhong Lei , Yang Xi , Shang Yuxuan , Yang Yao , Li Junchen , Liu Shuo , Zhang Yunshu , Liu Jifeng , Jiang Xingchi
Published: November 2024

Background

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease that accounts for > 90% of all diabetes cases. Acute pancreatitis (AP) can be triggered by various factors and is a potentially life-threatening condition. Although T2DM has been shown to have a close relationship with AP, the common mechanisms underlying the two conditions remain unclear.

Methods

We identified common differentially expressed genes (DEGs) in T2DM and AP and used functional enrichment analysis and Mendelian randomization to understand the underlying mechanisms. Subsequently, we used several machine learning algorithms to identify candidate biomarkers and construct a diagnostic nomogram for T2DM and AP. The diagnostic performance of the model was evaluated using ROC, calibration, and DCA curves. Furthermore, we investigated the potential roles of core genes in T2DM and AP using GSEA, xCell, and single-cell atlas and by constructing a ceRNA network. Finally, we identified potential small-molecule compounds with therapeutic effects on T2DM and AP using the CMap database and molecular docking.

Results

A total of 26 DEGs, with 14 upregulated and 12 downregulated genes, were common between T2DM and AP. According to functional and DisGeNET enrichment analysis, these DEGs were mainly enriched in immune effector processes, blood vessel development, dyslipidemia, and hyperlipidemia. Mendelian randomization analyses further suggested that lipids may be a potential link between AP and T2DM. Machine learning algorithms revealed ARHGEF9 and SLPI as common genes associated with the two diseases. ROC, calibration, and DCA curves showed that the two-gene model had good diagnostic efficacy. Additionally, the two genes were found to be closely associated with immune cell infiltration. Finally, imatinib was identified as a potential compound for the treatment of T2DM and AP.

Conclusion

This study suggests that abnormal lipid metabolism is a potential crosstalk mechanism between T2DM and AP. In addition, we established a two-gene model for the clinical diagnosis of T2DM and AP and identified imatinib as a potential therapeutic agent for both diseases.
Transcriptome combined with Mendelian randomization to screen key genes associated with mitochondrial and programmed cell death causally associated with diabetic retinopathy
Authors: Jiang Shule , Han Xuemei
Published: November 2024

Background

Mitochondrial dysfunction in the retina can induce apoptosis of retinal capillary cells, leading to diabetic retinopathy (DR). This study aimed to explore key genes related to programmed cell death (PCD) and mitochondria in DR via bioinformatic analysis.

Methods

A differential analysis was performed to identify differentially expressed genes (DEGs) between DR and control samples using the GSE94019 dataset from the Gene Expression Omnibus (GEO) database. Pearson correlation analysis was then utilized to select genes linked to mitochondrial function and PCD (M-PCD). Candidate genes were identified by overlapping DR-DEGs and M-PCD genes, followed by functional annotation. Mendelian randomization (MR) analysis was employed to identify genes with causal relationships to DR. Key genes were identified through protein-protein interaction (PPI) analysis using six algorithms (DEgree, DMNC, EPC, MCC, Genes are BottleNeck, and MNC) within Cytoscape software. The expression patterns of these genes were validated using GSE94019 and GSE60436 datasets, as well as RT-qPCR. Enrichment analysis provided insights into the function and pathways of these key genes in DR. Differential immune cell profiles were determined via immune infiltration analysis, followed by exploring the relationships between immune cells, cytokines, and the identified genes. Correlations between key genes and apoptosis genes were also examined. In vivo experiments using RT-PCR, immunohistochemistry (IHC), and western blot analysis confirmed that MYC and SLC7A11 expression was significantly elevated in DR rat retinal tissues.

Results

From 658 candidate genes, 12 showed significant causal associations with DR. MYC and SLC7A11 were particularly notable, showing upregulated expression in DR samples and involvement in apoptosis and diabetes-related pathways. These genes were significantly associated with apoptotic genes and correlated positively with altered immune cell types and cytokines, suggesting a link between immune response and DR pathogenesis. In vivo findings confirmed that MYC and SLC7A11 expression was elevated in DR rat retinal tissues.

Conclusion

Key genes (MYC and SLC7A11) associated with mitochondrial function and PCD in DR were identified, offering insights into DR’s pathological mechanisms and potential targets for diagnostic and therapeutic strategies.
Factors related to type 2 diabetic retinopathy and their clinical application value
Authors: Lian Xue-Nan , Zhu Ming-Ming
Published: November 2024

Objective

To compare the differences in clinical-related factors between patients with type 2 diabetes (T2DM) and those without diabetic retinopathy (DR) and to explore the risk factors or protective factors affecting DR in T2DM patients.

Methods

We performed a retrospective analysis of 380 patients with type 2 diabetes admitted to Handan Central Hospital from June 2023 to May 2024. Clinical data collected included baseline characteristics, hematological tests, metabolic indicators, and information on diabetic complications and comorbidities.

Results

Our findings identified intervention, neck vascular disease, bilateral lower limb venous thrombosis, high creatinine, high glomerular filtration rate, high chloride, high fasting C-peptide, and high lactate dehydrogenase as risk factors for DR. In contrast, High 2-hour postprandial C-peptide is a protective factor for diabetic retinopathy. A logistic regression model was constructed using stepwise regression to predict DR occurrence, achieving an accuracy of 0.80 and an AUC of 0.83.
Flexible low carbon optimal dispatch of distribution networks considering the demand response of heat storage industrial loads
Authors: Wang Wendi , Huang Hao , Zhang Xinsheng , Tan Jie , Sun Shaobin
Published: November 2024

To cope with the uncertainty brought by the large-scale integration of renewable energy under the goal of carbon neutrality, it is necessary to tap and utilize flexible and adjustable resources from both the source and the load side at the same time. Hence, a flexible low-carbon optimal scheduling method for distribution networks is proposed in this paper, which takes into account the participation of heat storage industrial loads in demand response. Firstly, the model of the gas turbine equipped with a flexible carbon capture device is established, and the non-convex constraint introduced by the adjustable flue gas diversion ratio is convexified. Then the model of the fused magnesium load, a representative of heat storage industrial loads, is established for its participation in demand response. The segment linearization and convexification methods are performed on the conditional productivity constraints of the fused magnesium load. On this basis, a mixed-integer linear programming model for flexible and low-carbon optimal dispatch of the distribution network is developed by using the stochastic optimization theory and solved by commercial solvers. The proposed method is verified to be able to ensure the economic operation of the distribution network while reducing carbon emissions and promoting renewable energy consumption.
Simulation and attribution analysis of agricultural nitrogen non-point source pollution in Huang-Huai-Hai Plain based on the InVEST model
Authors: Yu Ying , Chen Weiqiang , Ji Guangxing , Guo Yulong , Chen Yinan , Sheng Siyu , Deng Yaru , Weng Qianwen
Published: November 2024

The study of agricultural non-point source pollution (ANNSP) patterns and driving mechanisms is crucial for regional ecological management. This paper evaluates the sources of ANNSP in the Huang-Huai-Hai Plain for the years 2000, 2010, and 2020 using the source list method. Additionally, nitrogen source data for the InVEST model were supplemented and adjusted, and the nitrogen output load and proportions derived in farmland from both methods were compared. The temporal and spatial distribution of nitrogen emission intensity was analyzed based on InVEST model outputs. Key source areas were identified through hotspot analysis, and spatial driving factors were examined using geodetector. Key findings reveal: 1) While the source list method estimated a higher nitrogen output load, both methods concurred on the proportion of agricultural nitrogen. The main sources of nitrogen input were overuse of fertilizer and livestock breeding. 2) Over this period, nitrogen emission intensity declined, exhibiting a distinct spatial pattern of “northwest low, southeast high.” 3) The extent of critical source areas underwent a dynamic shift, initially contracting before expanding. 4) Rainfall emerged as the primary driver of spatial variability in agricultural TN emission intensity. These insights offer critical reference points for achieving efficient ANNSP management in the Huang-Huai-Hai Plain.
Evergreen or seasonal? Quantitative research on the color of urban scenic forests based on stress—attention electroencephalogram feedback
Authors: Wu Linjia , Zhang Yixuan , Mao Meiqin , Li Chunyu , Zhang Qingmei , Zhao Wei , Sui Xin , Li Jingting , Ma Junbin , Li Yanlin , Dong Qidi
Published: November 2024

Urban scenic forests provide urban residents with various physical and mental health and wellbeing services. However, in the research on the color quantification and health services of scenic forests, it is still unclear how their color quantification characteristics feedback on the stress - attention of the adolescent group. In this study, visual color elements (green, red and yellow) of three landscape forests were used to generate 48 images of four groups of urban landscape forests according to color combinations and proportions. Virtual images were used to assess the stress indicators and attention indicators of participants before and after viewing. The results showed that the four groups of experimental groups showed varying degrees of α wave reduction and β/α ratio increase after viewing, G1 group showed an extremely significant increase in β wave after image stimulation, G1 and G3 group significantly decreased θ/β power, G4 group and G5 control group had no significant change. Among the 16 channels of absolute α wave and absolute β wave in the brain, the F3 and F4 channels corresponding to the frontal lobe of the G3 group showed the most prominent consistency of β wave in the frontal cortex during highly concentrated mental activities. Our study shows that positive EEG (Electroencephalogram) feedback of arousal and attention recovery can be obtained by using monochromatic or two-color changing color features in urban landscape forest. This study can provide references and methods for urban forest planning, design and visual evaluation.
Case report: A case of holocarboxylase synthetase deficiency with respiratory tract as the initial symptom
Authors: Zou Haiying , Yang Li , Zhang Renlong , Qin Yao
Published: November 2024

Introduction

Holocarboxylase synthetase deficiency (HLCSD) is a rare autosomal recessive genetic disorder caused by mutations in the holocarboxylase synthetase (HLCS) gene, which affects multiple systems. Common clinical manifestations include metabolic acidosis, rash, feeding difficulties, and growth retardation, with predominant involvement of the nervous system, skin, and hair. However, respiratory symptoms as the initial manifestation are relatively rare.

Case Presentation

We report the case of a 1 year and 4-month-old Chinese male patient who presented with a 2-day history of cough, followed by half a day of wheezing and shortness of breath. Despite supportive treatment with antibiotics upon admission, the infant continued to experience rapid and deep breathing accompanied by groaning, and obvious wheezing. Blood gas analysis revealed metabolic acidosis that was difficult to correct. Blood tandem mass spectrometry showed elevations in C50H, C3, C4OH, and urine organic acid analysis revealed elevations in lactate, 3-hydroxybutyric acid, 3-hydroxyisovaleric acid, acetoacetic acid, 3-methylcrotonylglycine, and methylcitric acid. Genetic testing revealed two variants in the HLCS gene in the infant: NM_001352514: exon6: c.1088T>A: p.V363D variant and exon11: c.2434C>T: p.R812* heterozygous variant, resulting in HLCSD. Ultimately, the diagnosis of HLCSD was established, and oral biotin treatment achieved good clinical efficacy.

Conclusion

This article summarizes the clinical data of a case of HLCSD in an infant, primarily presenting with respiratory symptoms. It provides a comprehensive summary of the etiology, diagnosis, and treatment, offering insights for the diagnosis of rare diseases by clinical physicians.
Comparative study of the three-dimensional genomes of granulosa cells in germinal vesicle and metaphase II follicles
Authors: Mao Rurong , Cai Zhongkun , Wang Tao , Li Yan , Tian Shilin , Li Diyan , Li Penghao
Published: November 2024

Introduction

Follicle development is a critical process in the female reproductive system, with significant implications for fertility and reproductive health. Germinal vesicle (GV) oocytes are primary oocytes that are arrested in the dictyate stage, also known as the diplotene stage of meiotic prophase I. Metaphase II (MII) is the stage at which the oocyte is typically retrieved for assisted reproductive technologies such as in vitro fertilization (IVF). The granulosa cells play a pivotal role in follicle development processes. 3D chromatin organization is a fundamental aspect of cellular biology that has significant implications for gene regulation and cellular function.

Methods

In this study, we investigated 3D chromatin organization in granulosacells from GV and MII follicles, which is essential for understanding the regulatory mechanisms governing oocyte development.

Results

The results revealed distinct compartmentalization patterns,including stable genomic regions and transitions during oocyte maturation. Notably, there was a significant shift in functional gene activation, particularly in processes related to hormone metabolic pathways. Furthermore, alterations in topologically associating domains (TADs) were observed, with differential expression observed in genes that are involved in crucial biological processes. The analysis also identified a subset of genes with altered promoter-enhancer interactions (PEIs), reflecting a regulatory shift in gene expression related to reproductive processes.

Discussion

These findings provide valuable insights into 3D genome organization in granulosa cells with implications for reproductive health and the development of assisted reproductive technologies. Understanding spatial genome organization at different stages of follicular development may help realize novel strategies for enhancing success rates in assisted reproductive technologies.
Populations of Latvia and Lithuania in the context of some Indo-European and non-Indo-European speaking populations of Europe and India: insights from genetic structure analysis
Authors: Daniūtė Gintė , Pranckėnienė Laura , Pakerys Jurgis , Kloviņš Jānis , Kučinskas Vaidutis , Urnikytė Alina
Published: November 2024

The aim of this study was to investigate the relationship among Lithuanian, Latvian, Indian, and some other populations through a genome-wide data analysis of single nucleotide polymorphisms (SNPs). Limited data of Baltic populations were mostly compared with geographically closer modern and ancient populations in the past, but no previous investigation has explored their genetic relationships with distant populations, like the ones of India, in detail. To address this, we collected and merged genome-wide SNP data from diverse publicly available sources to create a comprehensive dataset with a substantial sample size especially from Lithuanians and Latvians. Principal component analysis (PCA) and admixture analysis methods were employed to assess the genetic structure and relationship among the populations under investigation. Additionally, we estimated an effective population size (Ne) and divergence time to shed light on potential past events between the Baltic and Indian populations. To gain a broader perspective, we also incorporated ancient and modern populations from different continents into our analyses. Our findings revealed that the Balts, unsurprisingly, have a closer genetic affinity with individuals from Indian population who speak Indo-European languages, compared to other Indian linguistic groups (such as speakers of Dravidian, Austroasiatic, and Sino-Tibetan languages). However, when compared to other populations from the European continent, which also speak Indo-European and some Uralic languages, the Balts did not exhibit a stronger resemblance to Indo-European-speaking Indians. In conclusion, this study provides an overview of the genetic relationship and structure of the populations investigated, along with insights into their divergence times.
Wilkins: HPC in situ workflows made easy
Authors: Yildiz Orcun , Morozov Dmitriy , Nigmetov Arnur , Nicolae Bogdan , Peterka Tom
Published: November 2024

In situ approaches can accelerate the pace of scientific discoveries by allowing scientists to perform data analysis at simulation time. Current in situ workflow systems, however, face challenges in handling the growing complexity and diverse computational requirements of scientific tasks. In this work, we present Wilkins, an in situ workflow system that is designed for ease-of-use while providing scalable and efficient execution of workflow tasks. Wilkins provides a flexible workflow description interface, employs a high-performance data transport layer based on HDF5, and supports tasks with disparate data rates by providing a flow control mechanism. Wilkins seamlessly couples scientific tasks that already use HDF5, without requiring task code modifications. We demonstrate the above features using both synthetic benchmarks and two science use cases in materials science and cosmology.
Immunome profiling in prostate cancer: a guide for clinicians
Authors: San-Jose Manso Luis , Alfranca Arantzazu , Moreno-Pérez Ignacio , Ruiz-Vico María , Velasco Clara , Toquero Patricia , Pacheco María , Zapatero Almudena , Aldave Diego , Celada Guillermo , Albers Eduardo , Fenor de la Maza María-Dolores , García Jorge , Castro Elena , Olmos David , Colomer Ramón , Romero-Laorden Nuria
Published: November 2024

Tumor immune microenvironment (TIME) plays a key role to understand how tumors respond to prostate cancer (PC) therapies and potential mechanisms of resistance. Previous research has suggested that specific genomic aberrations, such as microsatellite instability (MSI) or CDK12 bi-allelic loss can allow PC patients more likely to respond to immune checkpoint inhibitors (ICI) or other immune therapies. However, responses to these treatments remain highly variable even in selected patients. Thus, it is essential to obtain more information about tumor immune cells that infiltrate these tumors, and on their plasticity and interactions, in order to better understand the underlying biology to allow development of new therapeutic strategies. This review analyzes: 1) How interactions among immune cell populations and other cells infiltrating the tumor stroma can modulate the progression of PC, 2) How the standard therapies to treat PC (such as androgen deprivation therapy, new androgen-directed hormone therapy or chemotherapy) may influence the dynamic changes of the immunome and 3) What are the limitations in characterizing the immune landscape of the host´s response to tumors.