bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characteristics of longitudinal maternal health studies in sub‐Saharan Africa: A systematic mapping of literature between 2012 and 2022
Authors: Solarin Ijeoma , Dumbura Cherlynn , Lakhoo Darshnika Pemi , Chande Kshama , Maimela Gloria , Luchters Stanley , Chersich Matthew ,   for the HEAT Center
Published: November 2024

AbstractBackgroundHigh maternal mortality rates in sub‐Saharan Africa necessitate the need for aligned research focusing on prevalent causes and neglected conditions in the region.ObjectiveThis mapping review aimed to describe the characteristics of longitudinal maternal health studies between 2012 and 2022 in sub‐Saharan Africa and identify gaps in priority conditions or geographical locations.Search StrategyWe identified references through a Medline (PubMed) search covering September 2012 to June 2022.Selection CriteriaWe included prospective cohort or clinical trials that enrolled at least 1000 pregnant women, with a study site in sub‐Saharan Africa, and published in English or French.Data Collection and AnalysisScreening and data extraction were done in duplicate using EPPI‐reviewer software. Descriptive analysis was used to summarize the results, identifying patterns in studies across time, country, study design, topics, and funders.Main ResultsWe identified 213 eligible studies, which were covered in 534 publications. We identified studies in 33 of the 48 sub‐Saharan African countries, with the majority in east and southern Africa. The predominant study topics were HIV (36.4%), nutrition (20%), and malaria (16.3%), with very few publications on hypertensive disorders of pregnancy (6.4%), ante/postpartum hemorrhage (3.7%), and sexually transmitted infections (3.2%). More studies were cohorts (115/213; 54%) than clinical trials. The National Institutes of Health (31.5%), Bill and Melinda Gates Foundation (22.1%), and USAID (10.8%) were the largest research funders.ConclusionIdentifying research trends and mismatches between research topics and disease burden provides useful information for guiding future research prioritization. In particular, gaps exist for studies on hypertensive disorders of pregnancy and ante/postpartum hemorrhage, among the top causes of maternal mortality in sub‐Saharan Africa.
Machine learning model based on SERPING1, C1QB, and C1QC: A novel diagnostic approach for latent tuberculosis infection
Authors: Li Linsheng , Zhuang Li , Yang Ling , Ye Zhaoyang , Ni Ruizi , An Yajing , Zhao Weiguo , Gong Wenping 
Published: November 2024

AbstractBackgroundLatent tuberculosis infection (LTBI) is a significant source of active tuberculosis (ATB), yet distinguishing between them is challenging because specific biomarkers are lacking.MethodsWe analyzed four microarray datasets (GSE19491, GSE37250, GSE54992, GSE28623) from the gene expression omnibus to identify differentially expressed genes (DEGs). Using protein–protein interaction (PPI) networks and LASSO‐SVM algorithms, we selected three candidate biomarkers and evaluated their diagnostic efficacy. The expression levels of core genes were validated by RNA sequencing of healthy, ATB, and LTBI groups in a real‐world cohort. We conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, predicted shared upstream miRNAs, constructed miRNA–hub and transcription factor (TF)–hub gene networks, and performed immune infiltration analysis.ResultsThree hub genes (SERPING1, C1QC, C1QB) were identified from 45 DEGs by PPI networks and machine learning screening. The diagnostic model based on the three hub genes had an area under the curve (AUC) value of 0.843 in the training set GSE19491 and 0.865 in the validation set GSE28623. Real‐world transcriptome sequencing confirmed the expression trends of the hub genes across healthy, LTBI, and ATB groups. GO analysis showed that the 45 hub genes were primarily associated with immune inflammatory responses and pattern recognition receptors, whereas KEGG analysis indicated enrichment in complement and coagulation cascades. The miRNA–hub and TF–hub gene network analysis identified nine miRNAs and the zinc finger TF GATA2 as potential co‐regulators of SERPING1, C1QC, and C1QB. Immune cell infiltration analysis identified significant differences in the immune microenvironment between LTBI and ATB, with macrophages and natural killer cells showing significant correlations with tuberculosis infection.ConclusionThe diagnostic model with SERPING1, C1QC, and C1QB shows promise in distinguishing LTBI from ATB, indicating its potential as a diagnostic tool.
Predicting the Early Detection of Breast Cancer Using Hybrid Machine Learning Systems and Thermographic Imaging
Authors: Hosseini Mohammad Mehdi , Mosahebeh Zahra , Chakraborty Somenath , Gharahbagh Abdorreza Alavi 
Published: November 2024

ABSTRACTBreast cancer is a leading cause of mortality among women, emphasizing the critical need for precise early detection and prognosis. However, conventional methods often struggle to differentiate precancerous lesions or tailor treatments effectively. Thermal imaging, capturing subtle temperature variations, presents a promising avenue for non‐invasive cancer detection. While some studies explore thermography for breast cancer detection, integrating it with advanced machine learning for early diagnosis and personalized prediction remains relatively unexplored. This study proposes a novel hybrid machine learning system (HMLS) incorporating deep autoencoder techniques for automated early detection and prognostic stratification of breast cancer patients. By exploiting the temporal dynamics of thermographic data, this approach offers a more comprehensive analysis than static single‐frame approaches. Data processing involves splitting the dataset for training and testing. A predominant infrared image was selected, and matrix factorization was applied to capture temperature changes over time. Integration of convex factor analysis and bell‐curve membership function embedding for dimensionality reduction and feature extraction. The autoencoder deep neural network further reduces dimensionality. HMLS model development included feature selection and optimization of survival prediction algorithms through cross‐validation. Model performance was assessed using accuracy and F‐measure metrics. HMLS, integrating clinical data, achieved 81.6% accuracy, surpassing 77.6% using only convex‐NMF. The best classifier attained 83.2% accuracy on test data. This study demonstrates the effectiveness of thermographic imaging and HMLS for accurate early detection and personalized prediction of breast cancer. The proposed framework holds promise for enhancing patient care and potentially reducing mortality rates.
Impact of nonsurgical periodontal treatment on arterial stiffness outcomes related to endothelial dysfunction: A systematic review and meta‐analysis
Authors: Polizzi Alessandro , Nibali Luigi , Tartaglia Gianluca Martino , Isola Gaetano 
Published: November 2024

AbstractBackgroundTo assess the available evidence on whether nonsurgical periodontal treatment (NSPT) improves arterial stiffness outcomes in patients with periodontitis (PD).MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta‐analyses (PRISMA) guidelines and population, intervention, comparison, outcomes, and study design (PICOS) question, electronic databases were screened for clinical interventional studies addressing the impact of NSPT on pulse wave velocity (PWV), carotid intima‐media thickness (CIMT), and flow‐mediated dilatation (FMD) outcomes in PD patients. Furthermore, the research strategy was implemented using a hand search. Studies were selected, and data were extracted by two independent reviewers. Random effects models were applied to perform a meta‐analysis, and methodological index for nonrandomized studies (MINORS) and Cochrane Rob2 tools were used to assess the risk of bias.ResultsFifteen articles were finally included for qualitative synthesis. Among them, eight single‐arm cohort studies met the final inclusion criteria for meta‐analysis. The Rob2 analysis evidenced that one randomized clinical trial (RCT) had a low risk, three RCTs raised some concerns, and three RCTs had a high risk of bias, while the MINORS scores ranged from 9 to 14. The meta‐analysis showed that NSPT significantly impacted FMD (p < 0.001) and CIMT (p = 0.004), while changes in PWV were not statistically significant. However, there was high heterogeneity among studies (I2 = 78% for FMD andI2 = 62% for CIMT).ConclusionDespite some beneficial effects on FMD and CIMT, due to study limitations, high heterogeneity, and risk of bias, it cannot be concluded that NSPT is effective in improving arterial stiffness. Therefore, further studies are necessary to achieve high‐quality evidence on the effect of NSPT on arterial stiffness outcomes in PD patients.Trial registrationPROSPERO ID CRD42024501399.Plain Language SummaryPeriodontitis (PD) has been associated with alterations in arterial stiffness outcomes related to early endothelial dysfunction. Based on noninterventional studies, this meta‐analysis indicates that nonsurgical periodontal treatment (NSPT) may reduce cardiovascular disease risk in patients with PD. The moderate evidence derived from the studies that were finally included showed that NSPT had beneficial effects on flow‐mediated dilatation and carotid intima‐media thickness, while this trend was not observed for pulse wave velocity. Moreover, the findings of the present meta‐analysis were characterized by high heterogeneity and risk of bias and were derived from uncontrolled clinical trials or randomized clinical trials with limitations. Therefore, more studies with standardized protocols and homogeneous arterial stiffness outcomes are needed to elevate the quality of the present evidence.
Nuciferine inhibits osteoclast formation through suppressing glycolysis metabolic programming and ROS production
Authors: Guo Wen‐Hui , Zhen Guan‐Qi , Wu Feng , Lv Yun‐Peng , Yan Jing‐Long , Zu Jia‐Ning , Song Cheng‐Chao 
Published: November 2024

AbstractNuciferine (NCF) is a bioactive compound from lotus leaves and has been proven to prevent osteoclastogenesis and ovariectomy‐induced osteoporosis by our previous research. However, the underlying mechanism is still unclear. In this research, Raw264.7 cells were induced into osteoclast with or without NCF. CCK‐8 and Edu assays were performed to detect the effects of 30 μM NCF on cell viability and proliferation. TRAP staining and bone resorption assays were performed to observe the role of NCF in osteoclastogenesis and bone resorption. RT‐PCR and Western blot were performed to detect the effects of NCF on osteoclast‐related genes, glycolysis‐related genes, and reactive oxygen species (ROS)‐related genes. Seahorse assays, lactate concentration and glucose consumption were performed to observe cell metabolism change. DCFH‐DA fluorescent probe was used to detect ROS level. In this work, 30 μM NCF could not influence cell viability and cell proliferation. Osteoclast differentiation could be inhibited by 30 μM NCF. Bone resorption assay could also observe that bone resorption ability was successfully inhibited by 30 μM NCF. In seahorse assay, we discovered that NCF could decrease extracellular acid rate and increase oxygen consumption. RT‐PCR and Western blot results showed that NCF could decrease the expression of hexokinase2, pyruvate kinase muscle 2, and lactate dehydrogenase A and that NCF could also weaken the concentration of lactate. However, pyruvate kinase muscle 2 activator (GC69716) and lactate addition could promote osteoclastogenesis and bone resorption and promote the expression of c‐Fos and nuclear factor of activated T cells c1. Besides, NCF could also inhibit the production of ROS. In conclusion, NCF might inhibit osteoclast formation through inhibiting glycolysis metabolism and ROS production.
Exploring the Path of Balanced Development: The Decoupling Evaluation of Agricultural Production Efficiency and Land Carrying Capacity
Authors: Hu Yihang , Zhang Junbiao , Liu Qiqi 
Published: November 2024

ABSTRACTHigh‐quality development in agriculture is crucial for maintaining the harmonious balance between human society and the natural environment, and promoting this development model is one of the key measures to alleviate land degradation issues. This study, grounded in the PRED theory (Population, Resources, Environment, and Development theory) framework, establishes an evaluation system for high‐quality agricultural development by selecting 128 cities within the Yangtze River Economic Belt as its samples. It quantifies land carrying capacity, utilizes the Stochastic Frontier Analysis (SFA) model to assess agricultural production efficiency, and applies the Tapio decoupling model to analyze the interplay between these two factors. The results reveal that the land resource carrying index has risen from 1.245 to 1.70, indicating an escalating tension between population and food resources. Furthermore, agricultural production efficiency has seen a 16.56% increase, reflecting positive advancements in agricultural production across the region. Spatial distribution analysis shows that the standard deviation ellipse is concentrated in the mid and lower reaches, centered in Changde, Hunan, and expanding westward, with a broader coverage area and perimeter. Additionally, the decoupling relationship between land carrying capacity and agricultural production efficiency primarily manifests in three forms: strong negative decoupling, weak decoupling, and expansive negative decoupling. This research offers significant insights for effectively mitigating the strain between population growth and resource‐environmental carrying capacity.
Impact of Land Use Conversion on Soil Structure and Hydropedological Functions in an Arid Region
Authors: Han Jingwen , Pan Cunzhen , Sun Yuhang , Chen Zhijun , Xiong Yunwu , Huang Guanhua 
Published: November 2024

ABSTRACTLand use conversion critically affects soil structure and associated functions. This study investigated variations in soil structure and hydropedological characteristics across different land use types, that is, uncultivated, cultivated, and abandoned land under an arid condition. Water‐stable aggregates in the uncultivated land were 15.4%–37.1% of those in the cultivated and abandoned lands at depths of 0–60 cm. Reclamation of the uncultivated land enhanced soil aggregate stability, while prolonged tillage led to the loss of binding organic matter, breakdown of large aggregates and decrease in aggregate stability. The mean weight diameter of aggregates at 0–40 cm depth in the cultivated land was 39.0% lower than in the abandoned land. The volume fraction of micropores (< 100 μm) in the cultivated soils was 3.7%–39.7% of that in the uncultivated soils, whereas macropores (> 1000 μm) was 1.4–1.8 times greater. Following the abandonment, soil pore diversity recovered and a hierarchical structure developed. In the abandoned land, the volume fraction of micropores (< 100 μm) was 2.4–18.9 times that of the cultivated lands, while macropores (> 1000 μm) constituted 81.4%–93.9% of those in the cultivated lands. The permeability and longitudinal dispersivity of soils in the cultivated land were significantly lower than in the uncultivated and abandoned lands, particularly at deeper soil layers. The increase in large pores in the abandoned land, important for water movement and solute transport, resulted in an order‐of‐magnitude rise in both permeability and longitudinal dispersivity compared with the cultivated lands. Overall, the abandoned land showed potential for rehabilitation from the perspectives of soil aggregates and pore structure. The findings may provide reference for land reclamation and management in arid regions.
In a cohort of 961 clinically suspected Duchenne muscular dystrophy patients, 105 were diagnosed to have other muscular dystrophies (OMDs), with LGMD2E (variant SGCB c.544A>C) being the most common
Authors: Karthikeyan Priya , Kumar Shalini H. , Khanna‐Gupta Arati , Bremadesam Raman Lakshmi 
Published: November 2024

AbstractBackgroundTargeted next generation sequence analyses in a cohort of 961 previously described patients with clinically suspected Duchene muscular dystrophy (DMD) revealed that 145/961 (15%) had variants in genes associated with other muscular dystrophies (OMDs).MethodsNGS was carried out in DMD negative patients after deletion/duplication analysis followed by WES for No variant cases.ResultsThe majority of patients with OMDs had autosomal recessive diseases that included Limb‐Girdle Muscular Dystrophies (LGMDs), Bethlem, Ullrich congenital Myopathies and Emery‐Driefuss muscular dystrophy. 3.5% of patients were identified with other disorders like Charcot‐Marie Tooth and Nemaline myopathy. A small percentage of patients, 0.6% remain undiagnosed. Of a total of 78 genetic variants identified, 44 were found to be novel. Interestingly, a third of patients with OMDs were found to have LGMD2E/R4, a severe form of LGMD that afflicts young children with clinical symptoms similar to DMD. Almost one third of the unrelated LGMD2E/R4 patients had the same point mutation (c.544A>C) in the SGCB gene, suggestive of a founder effect, described here for the first time in India.ConclusionThis study underscores the need for a complete genetic work up to precisely diagnose patients and to initiate appropriate counseling programs, disease management and prevention strategies.
Assessment of Myocardial Fibrosis in Marfan Syndrome Using Cardiac Magnetic Resonance Imaging
Authors: Demolder Anthony , Devos Dan , De Backer Julie , Muiño‐Mosquera Laura 
Published: November 2024

ABSTRACTBackgroundImpaired myocardial function and arrhythmia are important manifestations of Marfan syndrome (MFS). Studies assessing myocardial fibrosis in relation to these manifestations are scarce.MethodsThis cross‐sectional, single‐center study assessed ventricular volumes, ventricular function, and myocardial fibrosis by cardiac magnetic resonance imaging (CMR) in patients with MFS harboring a (likely) pathogenic FBN1 variant. The presence and extent of fibrosis were assessed by late gadolinium enhancement (LGE) and extracellular volume measurement (ECV). Data on 24‐h Holter monitoring and clinical data were extracted from electronic patient records.ResultsThe study included 32 unselected patients with MFS (median age 38 years [range 10–69], 41% women). No focal myocardial fibrosis was detected. Six patients (21%) had diffuse fibrosis (ECV > 29%). No association was found between the presence of diffuse fibrosis and clinically relevant myocardial dysfunction. Five patients (16%) had reduced left ventricular ejection fraction (LVEF < 55%). While all of these exhibited mitral annular disjunction (MAD), only two had ECV > 29%. Patients with MAD had increased indexed LV volumes (median end‐diastolic volume, 92 mL/m2 [IQR, 78–100] vs. 78 mL/m2 [IQR, 71–87]; median end‐systolic volume, 31 mL/m2 [IQR, 23–46] vs. 22 mL/m2 [IQR, 21–28]), also after adjusting for the presence of mitral and aortic valve regurgitation. No differences in ECV were seen between patients with and without MAD.ConclusionsIn this cohort of patients with MFS, focal myocardial fibrosis was not detected using CMR. Although diffuse fibrosis was observed in 21% of patients, no evident connection to clinically relevant myocardial dysfunction was found. Further studies should evaluate the impact of diffuse fibrosis on clinical outcome prediction.
Synergistic Interplay of Diet, Gut Microbiota, and Insulin Resistance: Unraveling the Molecular Nexus
Authors: Gopal Rajesh Kanna , Ganesh Pitchaipillai Sankar , Pathoor Naji Naseef 
Published: November 2024

AbstractThis comprehensive review explores the intricate relationship between gut microbiota, diet, and insulin resistance, emphasizing the novel roles of diet‐induced microbial changes in influencing metabolic health. It highlights how diet significantly influences gut microbiota composition, with different dietary patterns fostering diverse microbial communities. These diet‐induced changes in the microbiome impact human metabolism by affecting inflammation, energy balance, and insulin sensitivity, particularly through microbial metabolites like short‐chain fatty acids (SCFAs). Focusing the key mediators like endotoxemia and systemic inflammation, and introduces personalized microbiome‐based therapeutic strategies, it also investigates the effects of dietary components—fiber, polyphenols, and lipids—on microbiota and insulin sensitivity, along with the roles of protein intake and amino acid metabolism. The study compares the effects of Western and Mediterranean diets on the microbiota‐insulin resistance axis. Therapeutic implications, including probiotics, fecal microbiota transplantation (FMT), and personalized diets, are discussed. Key findings reveal that high‐fat diets, especially those rich in saturated fats, contribute to dysbiosis and increased intestinal permeability, while high‐fiber diets promote beneficial bacteria and SCFAs. The review underscores the future potential of food and microbiota interventions for preventing or managing insulin resistance.
A Miniaturized SISL Bandpass Filter Based on Deep Hole Drilling Capacitor
Authors: Xu Yun‐Yang , Ma Yi‐Hao , Liu Qi Qiang , Hu Yue , Wang Jing , Zhao Wen‐Sheng 
Published: November 2024

ABSTRACTIn this paper, a quasi‐lumped element‐based bandpass filter is implemented by substrate‐integrated suspended line (SISL) technology. To miniaturize the filter size, a deep hole drilling capacitor (DHDC) structure is proposed. The properties of DHDC are derived by parametric analysis and equivalent circuit analysis. Based on derived properties, a quasi‐lumped element bandpass filter using SISL technology is designed and fabricated. Experimental results demonstrate that the proposed bandpass filter achieves the center frequency of 1.48 GHz, with a fractional bandwidth of 50%. The minimum insertion loss within the passband is 0.96 dB, while the return loss exceeds 23.14 dB. The maximum out‐of‐band suppression is over −50 dB, and the filter size is only 0.172 λg × 0.062 λg.
Design of On‐Chip Multi‐Slot Chalcogenide Waveguide for Mid‐Infrared Methane Sensing
Authors: Ma Ningbo , Qin Zhengkun , Pi Mingquan , Wang Xueying , Zheng Chuantao , Min Yuting , Zhao Huan , Song Mingxing 
Published: November 2024

ABSTRACTA chalcogenide (ChG) multi‐slot waveguide gas sensor with ChG as the core layer and silicon dioxide (SiO2) as the under‐cladding layer is proposed. Multi‐slot waveguide can be used in the mid‐infrared gas measurement. The optimized power confinement factor (PCF) of the ChG multi‐slot waveguide can be up to 41.3%, which is ∼20% higher than that of the optimized single‐slot waveguide. At the absorption line located at 3.291 μm for methane (CH4) measurement, the limits of detection (LoD) of single‐slot, double‐slot, triple‐slot, quadruple‐slot waveguide sensors are determined to be 68.9, 57.4, 52, 48.6 parts per million (ppm), respectively. Compared with other waveguide sensors in the mid‐infrared, the PCF of the proposed multi‐slot ChG/SiO2 waveguide sensor is enhanced by five times, which has the potential for highly sensitive gas sensing.