bluuebunny/crossref_metadata_2025_split huggingface dataset:
Molecular choreography to acute heat exposure in a simulated extreme high temperature environment
Authors: Wen Jirui , Wang Ling , Cheng Juan , Liu Yilin , Ma Tengfei , Wen Qiao , Zhou Jing , Sun Xiaoru , Li Can , Zou Yuhao , Yang Wei , Chen Yineng , He Dehai , You Zhiqiang , Liu Li , Sun Qun , Wan Xuehong , Gao Mingzhong , Xie Heping , Zou Jian , Wu Jiang , Liu Jifeng 
Published: November 2023

Abstract
Occupational exposure to extreme high temperature and increasing global temperatures require enhanced understanding of how heat exposure impacts human health. However, the molecular changes underlying the response to heat exposure in humans remain to be elucidated. This study used longitudinal multi-omics profiling to assess the impact of acute heat exposure (50℃ for 30 min) in 24 subjects from a mine rescue team. Intravenous blood samples were collected before acute heat exposure (baseline) and at 5 min, 30 min, 1 h, and 24 h after acute heat exposure (recovery). In-depth multi-omics profiling was performed on each sample, including plasma proteomics (untargeted), metabolomics (untargeted), and gene expression (transcriptomics) of peripheral blood monocytes and neutrophils. After data curation and annotation, the final dataset contained 5616 analytes, including 478 proteins, 1995 metabolites, and 3143 transcripts. Time-series analysis unveiled an orchestrated molecular choreography of changes involving the immune response, coagulation, acid-base balance, oxidative stress, cytoskeleton and energy metabolism. Further analysis through protein-protein interactions and network analysis revealed potential regulators of acute heat exposure. Moreover, novel blood-based analytes that predicted change in cardiopulmonary function after acute heat exposure were identified. This study provides a comprehensive evaluation of the molecular changes that underlie the complex physiological processes that occur in humans who undergo heat exposure. Findings will help health impact assessment of extreme high temperature and inform future mechanistic and clinical studies.
Traditional plant-based medicines used to treat musculoskeletal disorders in district North Waziristan, Khyber Pakhtunkhwa, Pakistan
Authors: Rehman Sabith , Iqbal Zafar , Qureshi Rahmatullah , Younas Muhammad 
Published: November 2023

Abstract
Background
This paper provides noteworthy information about the use of therapeutic plants for treating musculoskeletal disorders in a district, in North Waziristan, Pakistan. This survey aimed to document information regarding indigenous uses of plants in basic healthcare practices among rural people in regions, which may guide natural drug discovery and development.
Objective
The present study aims to document indigenous knowledge regarding medicinal plants used against musculoskeletal disorders in North Waziristan.
Methods
In total, 130 informants were interviewed using group discussions and semi-structured interviews. Documented ethnomedicinal data was analyzed using quantitative indices of relative frequency of citation (RFC), used value (UV) and fidelity level (FL).
Results
During the present studyatotalof60plant species belonging to39 plant families were reported with their medicinal usage to treat musculoskeletal disorders. The most dominant growth form were herbs (56.67%), decoction was preferred method of utilization (32.31%) and leaves were the most used plant part (21.88%). The highest used category was recorded for rheumatism (26 spp.). The highest use value was reported for Bergenia ciliata (0.87).
Conclusion
This study provides important information about therapeutic plants practice for musculoskeletal disorders among local communities of North Waziristan, Pakistan. The baseline findings of this study might be valuable in compiling information for future pharmacological and phytochemical screening of plant species leading to natural drug discovery and development.
Knowledge and Awareness about Natural Sugar Substitutes: Stevia and Monk fruit, among population in the Syrian Society
Authors: Harsan Loran , Harsan Leen , Hammoud Taghreed 
Published: November 2023

Abstract
Introduction
The stevia plant is used as a natural alternative to sugar and artificial sweeteners, which helps reduce sugar and calorie consumption, thus reducing the risk of chronic diseases such as diabetes and obesity. The stevia plant also provides a natural way to get sweetness without gaining weight, making it a healthy choice for people seeking to lose weight. Therefore, the goal of our research was to measure awareness and knowledge about stevia and monk fruit among a sample of members of Syrian society.
Methods
A cross-sectional study conducted on a sample of Syrian community members, amounting to 729 participants from various Syrian governorates, in the period between 7/23/2023 and 11/3/2023.
Results
Of the 729 participants, males constituted (51.2%) compared to females (48.8%). The age group “between 18–25” was the most participating (38.4%). It was found that the largest percentage of participants had an average economic level (42.4%). Only (12.8%) of the participants had one or both of their parents working in the medical field. Educational level: university (52.7%), pre-university (34.3%). Most of them had not heard of stevia (91.1%), and those who had heard of it (8.9%). Only 4.1% of participants had heard of monk fruit.
Conclusion
Overall, our study participants are not well informed about sugar substitutes, but the controversy surrounding most artificial and natural sweeteners cannot be ignored. Hence, the safety or harm of these sweeteners is of utmost importance to scientists. The benefits of natural sweeteners should be weighed against the harmful effects of using artificial sugar substitutes
The elemental profile of ciders made from different varieties of apples
Authors: Ageyeva Natalia , Khrapov Anton , Shirshova Anastasia , Chemisova Larisa , Ulyanovskaya Elena , Chernutskaya Evgenia 
Published: November 2023

Macro- and microelements are vital components of the nutrient profile of apples and apple juice. Although the mineral composition of apple juices has been well studied, there is a lack of research into the elemental profile of ciders. We aimed to determine the concentrations of macro- and microelements in various samples of ciders.
We studied 25 experimental ciders from apple juice of direct extraction (fresh must) and 4 commercial ciders purchased from a retailer in Krasnodar. Mass concentrations of metal cations were determined by high-performance capillary electrophoresis, atomic absorption spectrometry with electrothermal atomization, and atomic emission spectrometry with inductively coupled plasma.
The concentrations of macroelements in the ciders from fresh must depending on the variety varied significantly in the following ranges (mg/L): 696–1920 for potassium; 6.7–26.8 for sodium; 4.3–35.5 for calcium; and 10.2–36.8 for magnesium. The commercial ciders had significantly lower concentrations of macroelements. The content of iron ranged from 0.86 to 2.26 mg/L. Among microelements, copper cations were detected in the range from 31.0 to 375 μg/L. The concentrations of toxic elements did not exceed the maximum permissible values in any of the samples, including the commercial ones. Finally, ranges of variation were established in the concentrations of macro- and microelements depending on the varietal characteristics of apples.
The pomological varieties of apples used in the study were grown under the same agrotechnical conditions. Therefore, the differences revealed in the elemental profile of the ciders were assumingly due to the genetic characteristics of the respective variety.
Drinking water potability prediction using machine learning approaches: a case study of Indian rivers
Authors: Ainapure Bharati , Baheti Nidhi , Buch Jyot , Appasani Bhargav , Jha Amitkumar V. , Srinivasulu Avireni 
Published: November 2023

Abstract
Drinking water is the most precious resource on Earth. In the past few decades, the quality of drinking water has significantly degraded due to pollution. Water quality assessment is paramount for the well-being of the people since the presence of pollutants can have serious health issues. Particularly, in developing countries such as India, water is not properly assessed for its quality. This work uses machine learning techniques to predict the water quality of Indian rivers. It focuses on finding water potability when provided with the key factors used to calculate the water quality index for the water sample. Important parameters like water temperature, pH value, electrical conductivity, dissolved oxygen, fecal coliform, total coliform counts, and biochemical oxygen demand are used to calculate the water quality index. The approaches that are explored include the use of K-nearest neighbor (KNN), Random Forest, and XGBoost, with and without hyperparameter tuning, and the use of a sequential artificial neural network to see which of the three models helps us to give the most accurate predictions for the potability of water. XGBoost was the most efficient model, with an accuracy of 98.93%.
A water quality prediction model based on signal decomposition and ensemble deep learning techniques
Authors: Dong Jinghan , Wang Zhaocai , Wu Junhao , Huang Jinghan , Zhang Can 
Published: November 2023

Abstract
Accurate water quality predictions are critical for water resource protection, and dissolved oxygen (DO) reflects overall river water quality and ecosystem health. This study proposes a hybrid model based on the fusion of signal decomposition and deep learning for predicting river water quality. Initially, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to split the internal series of DO into numerous internal mode functions (IMFs). Subsequently, we employed multi-scale fuzzy entropy (MFE) to compute the entropy values for each IMF component. Time-varying filtered empirical mode decomposition (TVFEMD) is used to further extract features in high-frequency subsequences after linearly aggregating the high-frequency sequences. Finally, support vector machine (SVM) and long short-term memory (LSTM) neural networks are used to predict low- and high-frequency subsequences. Moreover, by comparing it with single models, models based on ‘single layer decomposition-prediction-ensemble’ and combination models using different methods, the feasibility of the proposed model in predicting water quality data for the Xinlian section of Fuhe River and the Chucha section of Ganjiang River was verified. As a result, the combined prediction approach developed in this work has improved generalizability and prediction accuracy, and it may be used to forecast water quality in complicated waters.
Life-cycle analysis of environmental loads from household septic systems in Japan focusing on effluent water discharge
Authors: Mishima Iori , Yoshikawa Naoki , Asakawa Susumu , Noguchi Yuji , Amano Koji 
Published: November 2023

Abstract

Various types of small-scale wastewater treatment systems are widely used in rural areas, and life-cycle assessment (LCA) should be performed to evaluate their environmental performance. In this study, septic systems were first classified into five categories based on their wastewater treatment performance. Effluent samples from actual systems were collected, and their water qualities were determined. A model to evaluate the environmental load from the septic systems using LCA methods was then established. The water-quality values obtained were input to the model, and the life-cycle environmental costs of the classified septic systems were calculated. The mean environmental load of the effluent during the operation stage was 37.6%, confirming that evaluation of an effluent discharge inventory using LCA, inspection, and water-quality monitoring to improve operations is critical for reducing the environmental load. The operation stage accounts for over 99% of the involved eutrophication, biological toxicity, and toxic chemicals, which are strongly related to the quality of the effluent. Evaluation of the effluent discharge inventory using LCA is of great significance, even for small-scale wastewater treatment systems. The set of procedures developed in this study can be used to calculate comprehensive environmental impacts at wastewater treatment plants.
Identification of suitable sites for rainwater and storm water harvesting through spatial analysis and smart sustainable urban water infrastructure in Lahore, Pakistan
Authors: Siddiqui Rumana , Javid Kanwal , Ahamad Muhammad Irfan 
Published: November 2023

Abstract

Sustainable and water-wise cities maintain an eco-friendly urban hydrological cycle (UHC). Regrettably, the UHC of Pakistani cities is not consistently eco-friendly. Pakistan is situated within the influential area of the world's largest monsoon weather system. Cities like Lahore face simultaneous vulnerabilities to urban flooding and water scarcity due to extreme climate change events. Therefore, Pakistan's urban areas necessitate Urban Water Infrastructural Transformation (UWIT), achievable only after identifying suitable Rainwater and Stormwater Harvesting Potential Sites (RSWHPS) in Lahore. Hence, we conducted spatial analysis to pinpoint these RSWHPS within Lahore city for 2020, utilizing the World View Water Index (WV-WI). The results indicate 85.54 km2 of available areas for rain and stormwater harvesting potential during monsoon rains in Lahore. The area with the highest potential in Lahore is Wagha town, featuring 19.96 km2 of stagnant water. Additionally, RSWHPS is classified into four categories based on potential: high, medium, low, and water bodies in Lahore. Urgent transformation is required for the identified storm and rainwater harvesting sites. Consequently, this study will serve as a snapshot for policymakers to systematically address water shortage and urban flooding, making Lahore's hydrological cycle eco-friendly and sustainable.
A Review of Gap Junction Protein and its Potential Role in Nervous
System-Related Disease
Authors: Xiang Qiong , Liu Xiao-Lin , Chen Jia-Jia , Yang Liang , Liu Li-Ni , Deng Jing , Tao Jia-Sheng , Li Xian-Hui 
Published: November 2023

Abstract:
Gap junction (GJ) is a special cell membrane structure composed of connexin. Connexin
is widely distributed and expressed in all tissues except differentiated skeletal muscle, red
blood cells, and mature sperm cells, which is related to the occurrence of many genetic diseases
due to its mutation. Its function of regulating immune response, cell proliferation, migration, apoptosis,
and carcinogenesis makes it a therapeutic target for a variety of diseases. In this paper, the
possible mechanism of its action in nervous system-related diseases and treatment are reviewed.
A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data
Authors: Lossio-Ventura Juan Antonio , Weger Rachel , Lee Angela Y , Guinee Emily P , Chung Joyce , Atlas Lauren , Linos Eleni , Pereira Francisco 
Published: November 2023

Background
Health care providers and health-related researchers face significant challenges when applying sentiment analysis tools to health-related free-text survey data. Most state-of-the-art applications were developed in domains such as social media, and their performance in the health care context remains relatively unknown. Moreover, existing studies indicate that these tools often lack accuracy and produce inconsistent results.

Objective
This study aims to address the lack of comparative analysis on sentiment analysis tools applied to health-related free-text survey data in the context of COVID-19. The objective was to automatically predict sentence sentiment for 2 independent COVID-19 survey data sets from the National Institutes of Health and Stanford University.

Methods
Gold standard labels were created for a subset of each data set using a panel of human raters. We compared 8 state-of-the-art sentiment analysis tools on both data sets to evaluate variability and disagreement across tools. In addition, few-shot learning was explored by fine-tuning Open Pre-Trained Transformers (OPT; a large language model [LLM] with publicly available weights) using a small annotated subset and zero-shot learning using ChatGPT (an LLM without available weights).

Results
The comparison of sentiment analysis tools revealed high variability and disagreement across the evaluated tools when applied to health-related survey data. OPT and ChatGPT demonstrated superior performance, outperforming all other sentiment analysis tools. Moreover, ChatGPT outperformed OPT, exhibited higher accuracy by 6% and higher F-measure by 4% to 7%.

Conclusions
This study demonstrates the effectiveness of LLMs, particularly the few-shot learning and zero-shot learning approaches, in the sentiment analysis of health-related survey data. These results have implications for saving human labor and improving efficiency in sentiment analysis tasks, contributing to advancements in the field of automated sentiment analysis.
TRENDING PERSPECTIVE IN EVALUATION OF INSPECTION CHARACTERISTICS OF PHARMACEUTICAL COMPOUND: COMPARATIVE STUDY OF CONTROL CHARTS
Authors: Eissa Mostafa Essam 
Published: November 2023

Background and objectives: The quality and efficacy of the pharmaceutical medicinal product are of paramount importance to the patients. Reproducibility and consistency of the properties of the dosage form start with the constituting ingredients either active or inactive. The present work provides an approach for the trending of one of the crucial inspection characteristics of a well-known excipient that is commonly used as a disintegrant of a water-insoluble synthetic cross-linked Polyvinyl Pyrrolidone (PVP). Establishing manufacturing criteria and quality limits should be assessed from the beginning level of the project using exploratory process-behavior plots.
Materials and Methods: This study involved an assay of the initial 16 batches of the manufactured 1-ethenyl pyrrolidine-2-one expressed as a dried substance using a standard analysis method according to British Pharmacopoeia (BP). Exploratory Shewhart charts were plotted after preliminary distribution identification using Statistical Process Control (SPC) software. Based on the goodness of fit test results, the most appropriate assumed distribution was implemented in drawing the Individual/Moving Range (I/MR) control charts.
Results: The goodness of fit study and probability plot showed that the most close-fitting to the data dispersion pattern was Johnson transformation to the normal spreading, smallest extreme value and Weibull distribution, respectively. While there were no alarms that could be detected from the process mean and the variability charts, further improvement is required from the supplier side to meet the benchmark minimum limit threshold, in addition, the process mean was not centered but shifted towards the Upper Control Limit (UCL).
Conclusion: Implementation of SPC techniques is crucial in the modern competitive healthcare industry. Trending charts play a pivotal role in this perspective. They provide a quantitative estimation for the current situation and the actions needed for the future improvement of the monitored characteristic.

Peer Review History:
Received: 11 August 2023; Revised: 23 September; Accepted: 29 October, Available online: 15 November 2023
Academic Editor: Dr. Amany Mohamed Alboghdadly, Ibn Sina National College for Medical Studies in Jeddah, Saudi Arabia, amanyalboghdadly@gmail.com
Received file: Reviewer's Comments:
Average Peer review marks at initial stage: 6.5/10
Average Peer review marks at publication stage: 7.0/10
Reviewers:
Dr. Taiwo O Elufioye, University of Ibadan, Nigeria, toonitaiwo@yahoo.com
Dr. Bilge Ahsen KARA, Ankara Gazi Mustafa Kemal Hospital, Turkey, ahsndkyc@gmail.com
STRUCTURE-BASED DESIGN OF NOVEL PYRIMIDINE CARBONITRILES ANALOGS TARGETING THE CYSTEINE PROTEASE FALCIPAIN 2 OF PLASMODIUM FALCIPARUM (pfFP2) AT THE TROPHOZOÏTE STAGE WITH FAVORABLE ADME SPECIFICITIES
Authors: Fagnidi Yves Kily Hervé , Ziki Eric , Allangba Koffi N’Guessan Placide Gabin , Toï Beguemsi , Megnassan Eugene 
Published: November 2023

Aim and Objective: Structure-based drug design (SBDD) of new antimalarials at the moment of resistance of the most causative agent, Plasmodium falciparum to the more valuable artemisinin combination therapy (ACT) is even more urgent. Carbonitriles pyrimidine derivatives (CNP) has emerged as potential inhibitors of the cysteine protease falcipain 2 of Plasmodium falciparum (pfFP2), so here we report virtual pharmacophore based screening of the CNP chemical subspace yielding novel CNP analogs with predicted high inhibitory potency against pfFP2.
Methods: A quantitative structure activity relationships (QSAR) complexation model has been developed from a series of fifteen carbonitriles pyrimidine derivatives to establish a linear correlation between the calculated Gibbs free energies (GFE: ΔΔGcom) of pfFP2-CNP complex formation and the experimental half-maximal enzymatic inhibition concentration ( ).The predictive power of the QSAR model was then validated with the generation of a 3D-QSAR-PH4 pharmacophore (PH4) model as CNP chemical subspace (exemplified as a virtual combinatorial library of more than 83.300 CNP analogs) explorer for novel predicted more potent CNP analogs. Finally the best PH4 hits were evaluated with the initial QSAR model for predicted potency ( ) and pharmacokinetic profile.
Results: The QSAR model linear correlation equation: p = -0.1025 x ∆∆Gcom + 7.2867, R2=0.94, the subsequent PH4 model linear correlation between experiment and PH4-estimated IC50: p = 0.9366 x p + 0.2849, R2=0.91 documents the high predictive power of this approach. Finally the screening of the virtual library of CNP analogs yielded 52 orally bioavailable candidates the best reaching a predicted potency ( ) of 14 pM and displaying favorable pharmacokinetic profile.
Conclusion: The combined use of one descriptor complexation QSAR model and 3D-QSAR Pharmacophore model performs well in identifying novel CNP analogs against pfFP2 and the handful of top predicted analogs are worth undergoing synthesis and biological evaluation.

Peer Review History:
Received: 12 August 2023; Revised: 17 September; Accepted: 27 October, Available online: 15 November 2023
Academic Editor: Prof. Dr. Gorkem Dulger, Duzce University, Turkey, gorkemdulger@yandex.com
Received file: Reviewer's Comments:
Average Peer review marks at initial stage: 7.0/10
Average Peer review marks at publication stage: 9.0/10
Reviewers:
Prof. Hassan A.H. Al-Shamahy, Sana'a University, Yemen, shmahe@yemen.net.ye
Prof. Cyprian Ogbonna ONYEJI, Obafemi Awolowo University, Ile-Ife, Nigeria, conyeji@oauife.edu.ng
Dr. U. S. Mahadeva Rao, Universiti Sultan Zainal Abidin, Terengganu Malaysia, raousm@gmail.com
Dr. Hayriye Eda Şatana Kara, Gazi University, Turkey, eda@gazi.edu.tr