bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of Chemically synthesis compared to biosynthesized zinc oxide nanoparticles using extract of Vitex agnus on the expression of MexAB-OprM efflux pump genes of Multi-Drug Resistance Pseudomonas aeruginosa
Authors: Khalis Khames Shams Al-Shomos , Ahmed Shatha Thanoon 
Published: June 2024

كان هدف الدراسة الحالية هو معرفة كيف تؤثر جسيمات اوكسيد الزنك النانوية المصنعة كيميائيا باستخدام طريقة sol-gel مقارنة بالجسيمات المخلقة حيويا باستخدام مستخلص أوراق نبات Vitex agnus-castus على التعبير الجيني لجينات مضخة التدفق (MexAB-OprM) في عزلات بكتريا الزائفة الزنجارية المتعددة المقاومة للادوية MDR .تم استخدام تفاعل البوليميراز المتسلسلPCR للتحري عن جين 16S rRNA في 16 عزلة من P. aeruginosa كان اختبار 16S rRNA ايجابيا لجميع عزلات P. aeruginosa ((100٪ ، وان 7 (43.75٪) من العزلات كانت من نوع MDR. كما أشارت النتائج إلى أن عزلات MDR تحمل جينات OprM و MexA MexB.و تم فحص المواد الكيميائية النباتية في مستخلص نبات V. agnus باستخدام فحص تحليل GC-MS و اظهر الفحص لمستخلص Vitex عن وجود مركبات فعالة. و وكانت احجام جسيمات اوكسيد الزنك النانوية المصنعة تتراوح مابين 22 و 74. بلغت قيم MIC لكل من جسيمات اوكسيد الزنك النانوية المصنعة كيميائيا والمخلقة حيويا وكذلك مستخلص Vitex 1024 و 256 و 512 ميكروغرام / مل على التوالي عند اختبارها ضد عزلات MDR تم دراسة التعبير لجينات MexA و MexB و OprM المعاملة مع ZnO NPs ومستخلص نبات Vitex بواسطة تقنية RT- PCR.اظهرت جسيمات اوكسيد الزنك النانوية المخلقة حيويا تأثير مثبط أكبر على التعبير الجيني لجينات MexA و MexB و OprM من الجسيمات النانوية المصنعة كيميائيا ومستخلص نبات V. agnus في العينات المعاملة بالتركيز المثبط الادنى لكل من جسيمات اوكسيد الزنك النانوية والمستخلص النباتي مقارنة بالعينات غير المعاملة. إحدى الطرق التي تعمل بها الجسيمات النانوية ضد البكتيريا هي قمع التعبير عن جينات مضخة التدفق ، مما يقلل من عدد مضخات التدفق النشطة على سطح الخلية ونتيجة لذلك ، يعتبر ZnO-NPs من قطاع الأدوية خيارا طبيا محتملا.
GC-MS Quantification and Identification of Phytochemical Profiling, Potential Antioxidant Activity by DPPH, and Mineral Elements of Eggplant Powder (Solanum Melongena) in Sulaymaniyah City, Iraq
Authors: Nasraldeen Majeed Srwa 
Published: June 2024

ان مسحوق الباذنجان (Solanum melongen)، هو النبات المستخدم كعلاج غذائي وطبي، له خصائص مضادة للالتهابات ومضادة للأكسدة ومضادة للسرطان. إنها واحدة من الخضروات التي تزرع على نطاق واسع في جميع أنحاء العالم ولديها مجموعة واسعة من أحجام وألوان وأشكال. في هذه الدراسة تم التركيز على اربعة µعشر مركبًا كيميائيًا باستخدام تقنية (GC-MS)، وقد اظهرت النتائج بأن المركب يحتوي على اربعة عشر مكونًا كيميائيًا، مثل:
(2—pentanone-4-hydroxy 4- methyl% 2.30), (1,3-dioxolane-4-methanol %0.17), (formic acid %0.86), (methenamine,N-methoxy %4.528), (1,2,3-propanetriol (glycerin) %40.86), (tetradecane%0.16) ,(hexadecane%0.27), (octadecane %0.16),(myristic acid %3.14), (nonadecane%0.44), (9,12-octadecadienoic acid ( linolenic acid) %0.43), (oleic acid%2.36), and (octacosane %0.62).
ان هذه المركبات التي تم تشخيصها لها خصائص قوية مضادة للجذور الحرة، مع نشاط مضاد للأكسدة في مسحوق الباذنجان. تتميز هذه المركبات كيميائيا بأنها مضادات الأكسدة. باستخدام طريقة EC50، تم تقييم نشاط DPPH-free radical المستخلص، وتزداد مع التركيز، من 4.8828 (µg/ml)) إلى 1250 (µg/ml)). تم التأكد من محتوى العناصر المعدنية في مسحوق الباذنجان باستخدام ICP / OES (مطياف انبعاث البلازما المقترن بالحث). حيث تم العثور على Al وCu وMn و Fe و Ba و Cr و Co و Ni كمعادن رئيسية ذات تراكيز عالية. ومع ذلك، كانت تركيزات (الكادميوم والرصاص) منخفضة بشكل غير متوقع.
New Subclasses of Bi-Univalent Functions Associated with Exponential Functions and Fibonacci Numbers
Authors: Ayash Majd , Baddour Hassan , Ali Mohammad , Wanas Abbas Kareem 
Published: June 2024

تمت مناقشة الفئة للدوال ثنائية أحادية التكافؤ من قبل الباحث لوين وحصل على تقدير للمعامل الثاني فيها، عرَّف ساكار و واناس فئتين فرعيتين جديدتين للدوال ثنائية أحادية التكافؤ وحصلا على الحدود العليا للمعاملات الأولية |a2| و |a3| للتوابع في هذه الفئات الفرعية، دزيوك وآخرون .قدموا الفئة من الدوال الشبيهة بالصدفة المحدبة ، والتي تشير إلى وجود اتصال بين الدالة وأرقام فيبوناتشي. في الآونة الأخيرة تم تعريف العديد من فئات الدوال ثنائية أحادية التكافؤ، استنادًا إلى مؤثرات معروفة مثل مؤثر سيلاجين، ومؤثر تريمبلي، ومؤثر تكامل كوماتو، ومؤثر الالتواء، ومؤثر العبودي التفاضلي وغيرها. يهدف هذا البحث إلى تقديم فئتين فرعيتين جديدتين من الدوال ثنائية أحادية التكافؤ باستخدام التبعية ومؤثر تكامل كوماتو والتي تتضمن الدوال الأسية والمنحنيات الشبيهة بالصدفة مع أرقام فيبوناتشي، وكذلك إيجاد تقدير للمعاملات الأولية لهذه الفئات الفرعية. تم تعريف الفئة الفرعية الأولى باستخدام التبعية لتابع المنحنى الشبيهة بالصدفة المتعلق بأرقام فيبوناتشي وتم تعريف الفئة الفرعية الثانية باستخدام التبعية للدالة الأسية. وتم استخدام مؤثر تكامل كوماتو في كل فئة من هذه الفئات الفرعية. تم الحصول على الحدود للمعاملات الأولية، وتحديداً المعاملين الثاني و الثالث لتوابع هذه الفئات الفرعية.
Halogen-Free Ciamician-Dennstedt Single-Atom Skeletal Editing
Authors: Bi Xihe , Liu Shaopeng , Yang Yong , Song Qingmin , Liu Zhaohong , Sivaguru Paramasivam , Zhang Yifan , Ruiter Graham , Anderson Edward 
Published: June 2024

Abstract

Single-atom skeletal editing is an increasingly powerful tool for scaffold hopping-based drug discovery. However, the insertion of a single functionalized carbon atom into heteroarenes remains exceedingly rare, especially when performed in complex chemical settings, due to the challenge of overcoming aromaticity without uncontrolled degradation. For example, the Ciamician–Dennstedt rearrangement, in which a carbene is inserted into an indole or pyrrole ring, remains limited to halocarbene precursors despite more than a century of research. Herein, we report a general methodology for the halogen-free Ciamician-Dennstedt reaction, which enables the direct conversion of indoles/pyrroles into structurally diverse quinoline/pyridine scaffolds. The generality and applicability of this methodology were demonstrated by extensive scope investigation and product derivatizations, as well as by concise syntheses and late-stage skeletal editing of complex bioactive compounds. Mechanistic studies reveal a pathway that involves the intermediacy of a 1,4-dihydroquinoline intermediate, which could undergo oxidative aromatization or defluorinative aromatization to form different carbon-atom insertion products.
Effects of solar radiation on the treatment of real domestic wastewater: pollutant removal efficiency, microbial structure, function, and metabolic pathways
Authors: JI JiaAo , Peng XianPai , Zong Yongchen 
Published: June 2024

Abstract

Municipal domestic wastewater often contains a variety of pollutants, such as phosphorus and organic matter. The activated sludge process is often affected by a variety of environmental factors, and it is not clear whether the intense solar radiation at high altitude affects its biodegradation process.In this study, a laboratory-scale A2O reactor was constructed and placed under solar irradiation, and the A2O process was applied to treat domestic wastewater under continuous solar irradiation. The experimental results showed that the average removal rate of COD reached 91.75% at 40 days; the increase in solar irradiation time helped the removal of organic pollutants, but the strong solar radiation would have an inhibiting effect on the removal of TP. 16S rRNA sequencing analysis showed that the dominant phyla that emerged in the system were Bacteroidota and Proteobacteria, and that the above bacteria contributed to organic matter degradation, but the abundance of the major PAOs,DPAOs, and Denitrifiers was suppressed by the accumulation of solar radiation, and the abundance of Nitrobacterias was promoted by the accumulation of solar radiation. The relative abundance of genes involved in central carbon metabolism, such as Glycolysis, Pentose phosphate pathway, and the TCA cycle, was predicted based on PICRUSt2. In addition, two key genes, PPK and PPX, which are involved in encoding hydrolytic and synthetic enzymes of Poly-P, were identified. The above findings may provide new insights into the nutrient removal mechanisms of the A2O process for the treatment of real domestic wastewater in areas of intense solar radiation.
Association of loneliness, social isolation, and neuroticism in Alzheimer’s disease risk: a two-step Mendelian randomization study
Authors: Narayan Vikram , Tanwar Vineeta , Andersen Julie , Kapahi Pankaj 
Published: June 2024

Abstract

Alzheimer's disease (AD) is a significant global health concern, yet psychosocial factors and human behaviours with the potential to influence genetic factors and dementia risk remain largely underexplored. We employed a two-sample Mendelian randomization (MR) approach to investigate the causality of genetically determined loneliness and social isolation in neuropsychiatric symptoms and AD. We uncovered a potentially causal link between loneliness and social isolation with AD, facilitated by a two-step progression involving neuroticism. Our findings also highlight the effects of a healthy and physically engaged lifestyle which may ameliorate the risk of depressive symptoms, but not neuroticism on AD risk. The enduring influence of personality traits, particularly neuroticism, suggests personalized and pharmacological approaches for managing disease outcomes. Finally, we identified two significant gene networks and six genes (PAFAH1B1, GRM5, GNAO1, NFIA, CNTN5, and PROX2) warranting further functional studies in developing therapeutics for AD risk. This paper provides the first genetically informed evidence that reducing loneliness and social isolation may play a causal role in decreasing the risk of AD, and these findings support efforts to reduce loneliness and social isolation to prevent or ameliorate AD, particularly relevant given the long-term effects of the COVID-19 pandemic on mental health.
The impact of Standardized Training Resident on pain management in patients with advanced lung cancer
Authors: Yi Hai-Zhen , Lv Wei , Chen Jin-Jing , Lin Zhan 
Published: June 2024

Abstract

Objective
This study aimed to investigate the effects of Standardized Training Resident on pharmacological interventions for pain management in patients with advanced lung cancer.
Methods
A total of 84 patients with advanced lung cancer and associated pain were enrolled in the study from December 2019 to August 2023 and were divided into two groups based on their attending physician: a group managed by physician-ST Training Physicians (Joint group) (n = 42) and physician-only group (Usual group) (n = 42). The Brief Pain Inventory (BPI), oral morphine-equivalent and tlength of hospital stay. Furthermore, the Pain Management Index (PMI) was calculated. Health-related quality of life (HRQoL) was assessed at the 4-week follow-up using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30).
Results
A total of 84 patients with advanced lung cancer and associated pain were enrolled in the study from December 2019 to August 2023 and were divided into two groups based on their attending physician: a group managed by physician-ST Training Physicians (Joint group) (n = 42) and physician-only group (Usual group) (n = 42). The Brief Pain Inventory (BPI) was utilized to evaluate pain intensity, reduced length of hospital stay as well as decreased oral morphine-equivalent(p < 0.05). Furthermore, the Pain Management Index (PMI) was calculated. Health-related quality of life (HRQoL) was assessed at the 4-week follow-up using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30).
Conclusion
Standardized Training for Radiation Oncology Physicians may lead to improved pharmacological interventions, enhanced pain relief. Recognizing the importance of these trainees in the healthcare team is crucial for achieving optimal pain management outcomes.
Discovery of a new type of methylenetetrahydrofolate reductase family that couples with tetrahydrofolate-dependent demethylases
Authors: Senda Toshiya , Yu HongYang , Kamimura Naofumi , Kato Ryo , Senda Miki , Masai Eiji 
Published: June 2024

Abstract

Methylenetetrahydrofolate reductase (MTHFR) is a key enzyme in one-carbon (1C) metabolism, catalyzing the reduction of 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate. Interestingly, Sphingobium lignivorans SYK-6, a model bacterium for the catabolism of lignin-derived aromatic compounds, has a unique MTHFR (S6MTHFR) that has been proposed to catalyze the reverse reaction of typical MTHFRs—namely, the oxidation of 5-methyltetrahydrofolate. However, no direct evidence for this has been obtained. Here, we demonstrated that S6MTHFR catalyzes the oxidation of 5-methyltetrahydrofolate and elucidated the molecular mechanism underlying the unique enzymatic properties of S6MTHFR based on its crystal structure. Furthermore, a database search revealed that a group of bacteria, including S. lignivorans SYK-6, utilize tetrahydrofolate-dependent demethylases to produce 5-methyltetrahydrofolate, which in turn is oxidized by an S6MTHFR-type enzyme. We propose that the combination of a demethylase with an S6MTHFR-type enzyme forms a new type of 1C metabolism that may regulate methionine biosynthesis.
Drone-View Detection of YOLO via Tandem Cross Self-Attention and Contextual-Aware Module
Authors: fang Zhou Li , Xing Wu , feng Wang Zhi , yuan He 
Published: June 2024

Abstract

Unmanned Aerial Vehicle (UAV) target detection involves automatically labeling objects captured during flight based on meaningful features. Compared to tra ditional images, aerial images have a higher viewpoint and often contain small objects and clustered features. Recent studies have demonstrated the advantages of deep learning in recognizing UAV images. However, early methods often had overlapping convolutional layers, which render the original contextual informa tion irrelevant. Besides, the imbalanced distribution of objects in the UAV field of view is prone to introduce the biases in detector attention, which will weaken the model training. This paper proposes an improved YOLOv5 framework for extracting multiple contextual information and enhancing dense region in UAV target detection. First, a context-aware module is introduced into DarkNet so that the multiple contextual information can be integrated skillfully. Addition ally, we propose a novel context semantic fusion approach, which contributes to integrate the varying expansion rates and deformable convolutions. This method effectively expands the perception field and enhances the precision of object boundaries. Finally, a concatenated cross self-attention operation is designed on high-resolution images to focus on dense regions and extract contextual infor mation from each pixel. Experimental results show improvements with average precision (AP50) of 5.9% and 3.89% on the VisDrone and UAVDT datasets, respectively. The findings demonstrate superior performance of the proposed method in UAV detection tasks and its potential application prospects.
Use of Deep Learning for Full-text Data Elements Extraction for Systematic Literature Review Tasks
Authors: Du Jingcheng , Wang Dong , Lin Bin , He Long , Huang Liang-Chin , Wang Jingqi , Manion Frank J. , Li Yeran , Cossrow Nicole , Yao Lixia 
Published: June 2024

Abstract

Background:
Systematic literature review (SLR) is an important tool for Health Economics and Outcomes Research (HEOR) evidence synthesis. SLRs involve the identification and selection of pertinent publications and extraction of relevant data elements from full-text articles, which can be a manually intensive procedure. Previously we developed machine learning models to automatically identify relevant publications based on pre-specified inclusion and exclusion criteria.
Objective:
This study investigates the feasibility of applying Natural Language Processing (NLP) approaches to automatically extract data elements from the relevant scientific literature.
Methods:
First, 239 full-text articles were collected and annotated for 12 important variables including study cohort, lab technique, and disease type, for proper SLR summary of Human papillomavirus (HPV) Prevalence, Pneumococcal Epidemiology, and Pneumococcal Economic Burden. The three resulting annotated corpora are shared publicly at [https://github.com/Merck/NLP-SLR-corpora], to provide training data and a benchmark baseline for the NLP community to further research this challenging task. We then compared three start-of-the-art Named Entity Recognition (NER) algorithms, namely Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), and the Bidirectional Encoder Representations from Transformers (BERT) models, to assess performance on the data element extraction task.
Results:
The annotation corpora contain 4,498, 579, and 252 annotated entity mentions for HPV Prevalence, Pneumococcal Epidemiology, and Pneumococcal Economic Burden tasks respectively. Deep learning algorithms achieved superior performance in recognizing the targeted SLR data elements, compared to conventional machine learning algorithms. LSTM models have achieved 0.890, 0.646 and 0.615 micro-averaged F1 scores for three tasks respectively. CRF models could not provide comparable performance on most of the elements of interest. Although BERT-based models are known to generally achieve superior performance on many NLP tasks, we did not observe improvement in our three tasks. Considering the much lower requirements for computation resources, LSTM models will be preferable for deployment in our case.
Conclusions:
Deep learning-based NLP algorithms demonstrated their efficacy in the extraction of SLR data elements, which showed potential in expediting and automating SLR tasks.
CoDi: Contrastive distance cell type annotation for spatially resolved transcriptomics
Authors: Kovacevic Vladimir , Bezulj Marija , Milicevic Nikola , Josic Bojana , Fang Shuangsang , Zhang Yong , Li Junhua 
Published: June 2024

Abstract

In the era of single-cell genomics, deciphering cellular heterogeneity is paramount for understanding complex biological systems.
Providing correct cell type annotations is a crucial task for enabling downstream analysis in Spatial Transcriptomics (ST).
Untargeted ST technologies offer wide insight into the gene landscape, but suffer from gene dropouts, preventing complete
capture of genes present in the cell. Common approaches for cell type annotation employ mapping of the reference high-quality
scRNA gene expressions and cell types, to lower quality ST dataset. However, the accuracy and the performance of cell
type annotation is not objectively evaluated, and methods lack the capacity to perform on large datasets with sparse data
produced by the high-resolution technologies like Slide-seq v2 and Stereo-seq. We present CoDi, an innovative tool designed
for precise cell type annotation leveraging the power of contrastive learning and advanced distance calculation methods
using reference single-cell datasets. CoDi represents a significant advancement by demonstrating superior performance,
and scalability compared to existing solutions on several different evaluation metrics, including highest retention rate of the
marker genes. By harnessing the intrinsic structure of the data, CoDi effectively captures subtle features that characterize
distinct cell types, resulting in enhanced annotation accuracy that can detect rare cell types such as neurons in the heart. In
summary, CoDi represents a valuable tool, contributing to our understanding of cellular heterogeneity and offering insights into
the specificity of various cell types within diverse tissue structures.
Ocean acidification induces changes in circadian alternative splicing profiles in a coral reef fish
Authors: Suresh Sneha , Jarrold Michael , Ravasi Timothy , Munday Philip L. , Schunter Celia 
Published: June 2024

Abstract

Background
Alternative splicing is a fundamental mechanism of gene expression regulation that increases the mRNA diversity expressed from the genome and can be partially regulated by the circadian clock. The time-dependent production of transcript isoforms from the same gene facilitates coordination of biological processes with the time of day and is a crucial mechanism enabling organisms to cope with environmental changes. In this study, we aim to determine the impact of future ocean acidification conditions on circadian splicing patterns in the brain of fish, while also accounting for diel CO2 fluctuations that naturally occur on coral reefs.
Results
Fish in the control group exhibited a temporal splicing pattern across the 24-hour period, however, these splicing events were largely absent in fish exposed to either stable or fluctuating elevated CO2 conditions. Furthermore, the molecular responses were influenced not only by an overall increase in CO2 concentration but also by its stability, with 6am and 6pm being key timepoints when the majority of the aberrant splicing events were identified. We found that fish in fluctuating CO2 conditions exhibited increased plasticity in transcriptional regulation by varying the proportion of transcript isoforms depending on the time-of-day. This was especially notable for genes associated with neural functioning.
Conclusions
Our findings suggest that fish rely on different molecular mechanisms to respond to elevated CO2 exposure in stable and fluctuating conditions. Periodic variation in CO2 levels in the fluctuating CO2 treatment might enable fish to rely on feed-forward mechanisms to synchronize neural functions with external environmental conditions. Such interconnectedness between external pH changes and transcriptional regulation via alternative splicing may provide fish in fluctuating CO2 environments with greater flexibility in biological responses, which may alleviate sensory and behavioural impairments enabling them to better cope with future ocean acidification conditions.
Leveraging Derma NET for Advanced Skin Cancer Detection in Smart Healthcare Frameworks
Authors: Prasanna Lakshmi , Boda Ravi , R Murali Prasad 
Published: June 2024

Abstract

One of the worst types of cancer is skin cancer, because it spreads to various body parts if it is not treated at early stages. Melanoma accounts for the massive majority of skin cancer related deaths, it is the most well-known forms of skin disease. Initial detection of skin cancer is of interest for medical diagnosis because of visual matching; image classification plays a key role in achieving an appropriate diagnosis of various lesions. In medical assessments, a computer diagnostic system based on deep learning may offer an automatic way to get over this challenging situation. Convolutional Neural Networks are used to increase the skin lesions classification using dermoscopic pictures without the assistance of humans. This research proposes the novel architecture Derma NET, for melanoma classification. In the suggested model, pre-processing techniques like up-sampling is used for augmentation to address the problem of an unequal sample size. From the database the data is divided into Train and Validation. Derma NET extracts various features from the images during training. Relu is a Non activation function is used in the model to extract the complex features. Learning rate is adjusted by utilizing Adam optimizer. Hyperparameter adjustment is done to improve model performance. HAM10000 is publicly available dataset and it is used to train the model. The performance of the suggested model is assessed using ISIC 2017 dataset. To communicate with proposed model an API (Application Programming Interface) is created that runs on Flask and is easily included into the front end. Through this API, users can query the model and get predictions about patient status with reference to skin cancer type. The experiment's findings demonstrate a substantial improvement in classifying skin cancer especially melanoma with 97.9% accuracy, 87% precision, 93% sensitivity, 98% specificity, 90% f1-score and AUC = 1, showcasing its potential for clinical applications in dermatology.