bluuebunny/crossref_metadata_2025_split huggingface dataset:
Damage evolution law of multi-hole blasting igneous rock and quantitative evaluation model of damage degree based on fractal theory and clustering algorithm
Authors: Zhao Hongbin , Tu Shihao , Miao Kaijun , Tang Long , Ma Jieyang , Guo Benhuan 
Published: July 2024

AbstractThe geological phenomenon of igneous rock invading coal seam is widely distributed, which induces mining risk and affects efficient mining. The pre-splitting blasting method of igneous rock is feasible but difficult to implement accurately, resulting in unnecessary safety and environmental pollution risks. In this paper, the blasting model with penetrating structural plane and the multi-hole blasting model with different hole spacing were established based on the Riedel–Hiermaier–Thoma (RHT) damage constitutive to explore the stress wave propagation law under detonation. The damage cloud diagram and damage degree algorithm were used to quantitatively describe the spatio-temporal evolution of blasting damage. The results show that the explosion stress wave presents a significant reflection stretching effect under the action of the structural plane, which can effectively aggravate the presplitting blasting degree of the rock mass inside the structural plane. The damage range of rock mass is synchronously evolved with the change of blasting hole spacing. The blasting in the igneous rock intrusion area of the 21,914 working face is taken as an application example, and the damage degree of rock mass is reasonably evaluated by the box-counting dimension and K-means clustering method, which proves the effectiveness of the blasting scheme and provides reference value for the implementation of related blasting projects.
Diagnostic accuracy of artificial intelligence in detecting left ventricular hypertrophy by electrocardiograph: a systematic review and meta-analysis
Authors: Siranart Noppachai , Deepan Natee , Techasatian Witina , Phutinart Somkiat , Sowalertrat Walit , Kaewkanha Ponthakorn , Pajareya Patavee , Tokavanich Nithi , Prasitlumkum Narut , Chokesuwattanaskul Ronpichai 
Published: July 2024

AbstractSeveral studies suggested the utility of artificial intelligence (AI) in screening left ventricular hypertrophy (LVH). We hence conducted systematic review and meta-analysis comparing diagnostic accuracy of AI to Sokolow–Lyon’s and Cornell’s criteria. Our aim was to provide a comprehensive overview of the newly developed AI tools for diagnosing LVH. We searched MEDLINE, EMBASE, and Cochrane databases for relevant studies until May 2023. Included were observational studies evaluating AI’s accuracy in LVH detection. The area under the receiver operating characteristic curves (ROC) and pooled sensitivities and specificities assessed AI’s performance against standard criteria. A total of 66,479 participants, with and without LVH, were included. Use of AI was associated with improved diagnostic accuracy with summary ROC (SROC) of 0.87. Sokolow–Lyon’s and Cornell’s criteria had lower accuracy (0.68 and 0.60). AI had sensitivity and specificity of 69% and 87%. In comparison, Sokolow–Lyon’s specificity was 92% with a sensitivity of 25%, while Cornell’s specificity was 94% with a sensitivity of 19%. This indicating its superior diagnostic accuracy of AI based algorithm in LVH detection. Our study demonstrates that AI-based methods for diagnosing LVH exhibit higher diagnostic accuracy compared to conventional criteria, with notable increases in sensitivity. These findings contribute to the validation of AI as a promising tool for LVH detection.
Mitigating drought-induced oxidative stress in wheat (Triticum aestivum L.) through foliar application of sulfhydryl thiourea
Authors: Ishfaq Nazia , Waraich Ejaz Ahmad , Ahmad Muhammad , Hussain Saddam , Zulfiqar Usman , Din Kaleem Ul , Haider Arslan , Yong Jean Wan Hong , Askri Syed Muhammad Hassan , Ali Hayssam M. 
Published: July 2024

AbstractDrought stress is a major abiotic stress affecting the performance of wheat (Triticum aestivum L.). The current study evaluated the effects of drought on wheat phenology, physiology, and biochemistry; and assessed the effectiveness of foliar-applied sulfhydryl thiourea to mitigate drought-induced oxidative stress. The treatments were: wheat varieties; V1 = Punjab-2011, V2 = Galaxy-2013, V3 = Ujala-2016, and V4 = Anaaj-2017, drought stress; D1 = control (80% field capacity [FC]) and D2 = drought stress (40% FC), at the reproductive stage, and sulfhydryl thiourea (S) applications; S0 = control-no thiourea and S1 = foliar thiourea application @ 500 mg L−1. Results of this study indicated that growth parameters, including height, dry weight, leaf area index (LAI), leaf area duration (LAD), crop growth rate (CGR), net assimilation rate (NAR) were decreased under drought stress-40% FC, as compared to control-80% FC. Drought stress reduced the photosynthetic efficiency, water potential, transpiration rates, stomatal conductances, and relative water contents by 18, 17, 26, 29, and 55% in wheat varieties as compared to control. In addition, foliar chlorophyll a, and b contents were also lowered under drought stress in all wheat varieties due to an increase in malondialdehyde and electrolyte leakage. Interestingly, thiourea applications restored wheat growth and yield attributes by improving the production and activities of proline, antioxidants, and osmolytes under normal and drought stress as compared to control. Thiourea applications improved the osmolyte defense in wheat varieties as peroxidase, superoxide dismutase, catalase, proline, glycine betaine, and total phenolic were increased by 13, 20, 12, 17, 23, and 52%; while reducing the electrolyte leakage and malondialdehyde content by 49 and 32% as compared to control. Among the wheat varieties, Anaaj-2017 showed better resilience towards drought stress and also gave better response towards thiourea application based on morpho-physiological, biochemical, and yield attributes as compared to Punjab-2011, Galaxy-2013, and Ujala-2016. Eta-square values showed that thiourea applications, drought stress, and wheat varieties were key contributors to most of the parameters measured. In conclusion, the sulfhydryl thiourea applications improved the morpho-physiology, biochemical, and yield attributes of wheat varieties, thereby mitigating the adverse effects of drought. Moving forward, detailed studies pertaining to the molecular and genetic mechanisms under sulfhydryl thiourea-induced drought stress tolerance are warranted.
Better performance of deep learning pulmonary nodule detection using chest radiography with pixel level labels in reference to computed tomography: data quality matters
Authors: Kim Jae Yong , Ryu Wi-Sun , Kim Dongmin , Kim Eun Young 
Published: July 2024

AbstractLabeling errors can significantly impact the performance of deep learning models used for screening chest radiographs. The deep learning model for detecting pulmonary nodules is particularly vulnerable to such errors, mainly because normal chest radiographs and those with nodules obscured by ribs appear similar. Thus, high-quality datasets referred to chest computed tomography (CT) are required to prevent the misclassification of nodular chest radiographs as normal. From this perspective, a deep learning strategy employing chest radiography data with pixel-level annotations referencing chest CT scans may improve nodule detection and localization compared to image-level labels. We trained models using a National Institute of Health chest radiograph-based labeling dataset and an AI-HUB CT-based labeling dataset, employing DenseNet architecture with squeeze-and-excitation blocks. We developed four models to assess whether CT versus chest radiography and pixel-level versus image-level labeling would improve the deep learning model’s performance to detect nodules. The models' performance was evaluated using two external validation datasets. The AI-HUB dataset with image-level labeling outperformed the NIH dataset (AUC 0.88 vs 0.71 and 0.78 vs.
0.73 in two external datasets, respectively; both p < 0.001). However, the AI-HUB data annotated at the pixel level produced the best model (AUC 0.91 and 0.86 in external datasets), and in terms of nodule localization, it significantly outperformed models trained with image-level annotation data, with a Dice coefficient ranging from 0.36 to 0.58. Our findings underscore the importance of accurately labeled data in developing reliable deep learning algorithms for nodule detection in chest radiography.
Seroprevalence of adeno-associated virus types 1, 2, 3, 4, 5, 6, 8, and 9 in a Basque cohort of healthy donors
Authors: Navarro-Oliveros Miguel , Vidaurrazaga Ander , Soares Guerra Gabriel , Castellana Donatello , Embade Nieves , Millet Oscar , Marigorta Urko M. , Abrescia Nicola G. A. 
Published: July 2024

AbstractAdeno-associated viruses (AAVs) are promising gene therapy vectors, but challenges arise when treating patients with preexisting neutralizing antibodies. Worldwide seroprevalence studies provide snapshots of existing immunity in diverse populations. Owing to the uniqueness of the Basque socio-geographical landscape, we investigated the seroprevalence of eight AAV serotypes in residents of the Basque Country. We found the highest seroprevalence of AAV3, and the lowest seroprevalence of AAV9. Additionally, less than 50% of the Basque population has neutralizing antibodies against AAV4, AAV6, and AAV9. Our findings provide insight into AAV infections in the Basque region, public health, and the development of AAV-based therapeutics.
Exploring the butyrate metabolism-related shared genes in metabolic associated steatohepatitis and ulcerative colitis
Authors: Deng Beiying , Liu Yinghui , Chen Ying , He Pengzhan , Ma Jingjing , Tan Zongbiao , Zhang Jixiang , Dong Weiguo 
Published: July 2024

AbstractMetabolic-associated steatohepatitis (MASH) and ulcerative colitis (UC) exhibit a complex interconnection with immune dysfunction, dysbiosis of the gut microbiota, and activation of inflammatory pathways. This study aims to identify and validate critical butyrate metabolism-related shared genes between both UC and MASH. Clinical information and gene expression profiles were sourced from the Gene Expression Omnibus (GEO) database. Shared butyrate metabolism-related differentially expressed genes (sBM-DEGs) between UC and MASH were identified via various bioinformatics methods. Functional enrichment analysis was performed, and UC patients were categorized into subtypes using the consensus clustering algorithm based on sBM-DEGs. Key genes within sBM-DEGs were screened through Random Forest, Support Vector Machines-Recursive Feature Elimination, and Light Gradient Boosting. The diagnostic efficacy of these genes was evaluated using receiver operating characteristic (ROC) analysis on independent datasets. Additionally, the expression levels of characteristic genes were validated across multiple independent datasets and human specimens. Forty-nine shared DEGs between UC and MASH were identified, with enrichment analysis highlighting significant involvement in immune, inflammatory, and metabolic pathways. The intersection of butyrate metabolism-related genes with these DEGs produced 10 sBM-DEGs. These genes facilitated the identification of molecular subtypes of UC patients using an unsupervised clustering approach. ANXA5, CD44, and SLC16A1 were pinpointed as hub genes through machine learning algorithms and feature importance rankings. ROC analysis confirmed their diagnostic efficacy in UC and MASH across various datasets. Additionally, the expression levels of these three hub genes showed significant correlations with immune cells. These findings were validated across independent datasets and human specimens, corroborating the bioinformatics analysis results. Integrated bioinformatics identified three significant biomarkers, ANXA5, CD44, and SLC16A1, as DEGs linked to butyrate metabolism. These findings offer new insights into the role of butyrate metabolism in the pathogenesis of UC and MASH, suggesting its potential as a valuable diagnostic biomarker.
Cannabidiol effectively prevents oxidative stress and stabilizes hypoxia-inducible factor-1 alpha (HIF-1α) in an animal model of global hypoxia
Authors: Kletkiewicz Hanna , Wojciechowski Michał S. , Rogalska Justyna 
Published: July 2024

AbstractCannabidiol (CBD) is a non-psychotomimetic phytocannabinoid derived from Cannabis sativa. It has therapeutic effects in different paradigms of brain injury, acting as a neuroprotectant. As oxidative stress is a primary risk factor for brain damage after neonatal hypoxia, we tested the effect of CBD on oxidative status and non-protein-bound iron accumulation in the immature brain after hypoxia. Moreover, we tested whether cannabidiol affects the accumulation of hypoxia-inducible factor-1 alpha (HIF-1α) which plays a key role in the regulation of cellular adaptation to hypoxia and oxidative stress. We used 7-day-old mice randomly assigned to hypoxic or control groups. Immediately after hypoxia or control exposure, pups were randomly assigned to a vehicle or CBD treatment. 24 h later, they were decapitated and the brains were immediately removed and stored for further biochemical analyses. We found that CBD reduced lipid peroxidation and prevented antioxidant depletion. For the first time, we also demonstrated that CBD upregulated HIF-1α protein level. This study indicates that CBD may effective agent in attenuating the detrimental consequences of perinatal asphyxia.
Hyperosmotic sisomicin infusion: a mouse model for hearing loss
Authors: Maraslioglu-Sperber Ayse , Blanc Fabian , Heller Stefan , Benkafadar Nesrine 
Published: July 2024

AbstractLosing either type of cochlear sensory hair cells leads to hearing impairment. Inner hair cells act as primary mechanoelectrical transducers, while outer hair cells enhance sound-induced vibrations within the organ of Corti. Established inner ear damage models, such as systemic administration of ototoxic aminoglycosides, yield inconsistent and variable hair cell death in mice. Overcoming this limitation, we developed a method involving surgical delivery of a hyperosmotic sisomicin solution into the posterior semicircular canal of adult mice. This procedure induced rapid and synchronous apoptotic demise of outer hair cells within 14 h, leading to irreversible hearing loss. The combination of sisomicin and hyperosmotic stress caused consistent and synergistic ototoxic damage. Inner hair cells remained until three days post-treatment, after which deterioration in structure and number was observed, culminating in a complete hair cell loss by day seven. This robust animal model provides a valuable tool for otoregenerative research, facilitating single-cell and omics-based studies toward exploring preclinical therapeutic strategies.
Antagonism of propofol anesthesia by alkyl-fluorobenzene derivatives
Authors: Plasencia Diana M. , Rodgers Liam H. , Knighton Alexys R. , Eckenhoff Roderic G. , White E. Railey 
Published: July 2024

AbstractDespite their frequent use across many clinical settings, general anesthetics are medications with lethal side effects and no reversal agents. A fluorinated analogue of propofol has previously been shown to antagonize propofol anesthesia in tadpoles and zebrafish, but little further investigation of this class of molecules as anesthetic antagonists has been conducted. A 13-member library of alkyl-fluorobenzene derivatives was tested in an established behavioral model of anesthesia in zebrafish at 5 days post fertilization. These compounds were examined for their ability to antagonize propofol and two volatile anesthetics, as well as their interaction with the anesthetic-binding model protein apoferritin. Two compounds provided significant antagonism of propofol, and when combined, were synergistic, suggesting more than one antagonist sensitive target site. These compounds did not antagonize the volatile anesthetics, indicating some selectivity amongst general anesthetics. For the compounds with the most antagonistic potency, similarities in structure and binding to apoferritin may be suggestive of competitive antagonism; however, this was not supported by a Schild analysis. This is consistent with multiple targets contributing to general anesthesia, but whether these are physiologic antagonists or are antagonists at only some subset of the many anesthetic potential targets remains unclear, and will require additional investigation.
Study on the reduction of residual stress in laser cladding layers through groove texture
Authors: Zhao Changlong , Ma Chen , Du Weilong , Yu Zice , Zhang Zihao 
Published: July 2024

AbstractIn order to develop a method for the production of crack-free cladding layers, we combined surface texturing technology with laser cladding, establishing a multi-field coupled numerical simulation model. A separate investigation was conducted into the temperature, stress, and fluid fields in laser cladding processes with and without texturing, seeking optimal cladding parameters, and conducted experiments. The results of the numerical simulations indicate that pre-set texturing effectively reduces the temperature gradient during the cladding process, thereby making the thermal cycle curve smoother. The residual stresses in the X, Y, and Z directions are reduced by 34.84%, 3.94%, and 50.22%, respectively. The introduction of texturing reduces the internal flow velocity of the melt pool, preventing the occurrence of a double vortex effect. Experimental results show that the residual stresses in the X, Y, and Z directions of the predefined textured cladding layer are reduced by approximately 41%, 8%, and 47%, respectively, compared to the non-textured cladding layer. This effectively improves the surface roughness and internal grain size of the cladding layer, with no significant defects at the metallurgical bonding positions, providing a reference for future improvements in cladding layer quality.
Factors associated with neonatal sepsis among neonates admitted in Kibungo Referral Hospital, Rwanda
Authors: Niyoyita Jean Claude , Ndayisenga Jerome , Omolo Jared , Niyompano Hosee , Bimenyimana Pierre Celestin , Dzinamarira Tafadzwa , Nsekuye Olivier , Chavez Isabella , Hakizayezu François 
Published: July 2024

AbstractMore than one million neonatal deaths occur every year worldwide, of which 99% take place in low-income countries. In Rwanda, nearly 71% of neonatal deaths are preventable and among these, 10% are due to neonatal sepsis. Nevertheless, limited information exists on neonatal sepsis and its associated factors in Rwanda. The objectives of the study were to find prevalence and factors associated with neonatal sepsis among neonates admitted in Kibungo Referral Hospital, Ngoma District, Rwanda. We used a retrospective cross-sectional study design reviewing a subset of neonatal, maternal and laboratory records from Kibungo Hospital in 2017. Data were reviewed and collected from March to May, 2018. Logistic regression and odds ratios were calculated to identify the factors associated with neonatal sepsis at 95% CI, p < 0.05. Of the 972 total neonates’ medical records from 2017, we randomly selected 422 of which 12.8% (n = 54) had neonatal sepsis. When blood cultures were positive, 62% grew Klebsiella pneumoniae. Among neonates with sepsis, 38 (70%) recovered while 16 (30%) died. Neonatal sepsis was strongly associated with neonatal age less than or equal to three days (aOR: 2.769, 95% CI 1.312–5.843; p = 0.008); and gestational age less than 37 weeks (aOR: 4.149; CI 1.1878–9.167; p ≤ 0.001). Increased use of blood cultures including sensitivity testing, routine surface cultures of the neonatology and maternity wards facilities, and systematic ward cleaning are all important approaches to prevent and treat neonatal infections in additional to regular neonatal sepsis evaluations.
Genome assembly, comparative genomics, and identification of genes/pathways underlying plant growth-promoting traits of an actinobacterial strain, Amycolatopsis sp. (BCA-696)
Authors: Gandham Prasad , Vadla Nandini , Saji Angeo , Srinivas Vadlamudi , Ruperao Pradeep , Selvanayagam Sivasubramani , Saxena Rachit K. , Rathore Abhishek , Gopalakrishnan Subramaniam , Thakur Vivek 
Published: July 2024

AbstractThe draft genome sequence of an agriculturally important actinobacterial species Amycolatopsis sp. BCA-696 was developed and characterized in this study. Amycolatopsis BCA-696 is known for its biocontrol properties against charcoal rot and also for plant growth-promotion (PGP) in several crop species. The next-generation sequencing (NGS)-based draft genome of Amycolatopsis sp. BCA-696 comprised of ~ 9.05 Mb linear chromosome with 68.75% GC content. In total, 8716 protein-coding sequences and 61 RNA-coding sequences were predicted in the genome. This newly developed genome sequence has been also characterized for biosynthetic gene clusters (BGCs) and biosynthetic pathways. Furthermore, we have also reported that the Amycolatopsis sp. BCA-696 produces the glycopeptide antibiotic vancomycin that inhibits the growth of pathogenic gram-positive bacteria. A comparative analysis of the BCA-696 genome with publicly available closely related genomes of 14 strains of Amycolatopsis has also been conducted. The comparative analysis has identified a total of 4733 core and 466 unique orthologous genes present in the BCA-696 genome The unique genes present in BCA-696 was enriched with antibiotic biosynthesis and resistance functions. Genome assembly of the BCA-696 has also provided genes involved in key pathways related to PGP and biocontrol traits such as siderophores, chitinase, and cellulase production.
Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra
Authors: Kok Yong En , Crisford Anna , Parkes Andrew , Venkateswaran Seshasailam , Oreffo Richard , Mahajan Sumeet , Pound Michael 
Published: July 2024

AbstractRaman spectroscopy is a rapid method for analysing the molecular composition of biological material. However, noise contamination in the spectral data necessitates careful pre-processing prior to analysis. Here we propose an end-to-end Convolutional Neural Network to automatically learn an optimal combination of pre-processing strategies, for the classification of Raman spectra of superficial and deep layers of cartilage harvested from 45 Osteoarthritis and 19 Osteoporosis (Healthy controls) patients. Using 6-fold cross-validation, the Multi-Convolutional Neural Network achieves comparable or improved classification accuracy against the best-performing Convolutional Neural Network applied to either the raw or pre-processed spectra. We utilised Integrated Gradients to identify the contributing features (Raman signatures) in the network decision process, showing they are biologically relevant. Using these features, we compared Artificial Neural Networks, Decision Trees and Support Vector Machines for the feature selection task. Results show that training on fewer than 3 and 300 features, respectively, for the disease classification and layer assignment task provide performance comparable to the best-performing CNN-based network applied to the full dataset. Our approach, incorporating multi-channel input and Integrated Gradients, can potentially facilitate the clinical translation of Raman spectroscopy-based diagnosis without the need for laborious manual pre-processing and feature selection.