bluuebunny/crossref_metadata_2025_split huggingface dataset:
Challenges and Strategies of Drug Trafficking in the Islamic Republic of Iran: A Qualitative Study Based on Analysis of Online News Media
Authors: Fakhrzad NourolHoda , Yazdi-Feyzabadi Vahid , Fakhrzad Maryam 
Published: April 2023

Abstract
Background: Given the importance of medicine as a strategic commodity in the Iranian health sector, drug trafficking is one of the public health challenges. Therefore, the present study was conducted to identify and explain challenges and proper strategies to control drug trafficking in Iran.
Method: This study was conducted using a qualitative content analysis based on an analysis of news media. The news was retrieved according to keywords related to drugs, medicine, and trafficking from news sources from March 21, 2017 to March 19, 2020. Special and general online news sites and agencies, including Mehr News Agency, Khabar Online, Khane Mellat News Agency, Islamic republic news agency(IRNA), Iranian students news agency(ISNA), Hamshahri, Donya-e-Eqtesad newspapers, Webda, Sepid newspapers, and Irans’ food and drug administration news agency(IFDANA) were selected by purposeful sampling method with a maximum variation. Data were analyzed using conventional content analysis by a data-driven inductive approach. All steps of data coding were done manually in Excel 2016 software.
Results: Totally, 139 news met inclusion criteria. In terms of challenges and strategies, four themes were obtained separately. Challenges and strategies included ten and nine sub-themes, respectively. The four themes were economic, regulatory, structural, infrastructure, and cultural, social, and informational issues. The predominant theme in challenges was regulatory issues, and also the predominant theme was economic strategies.
Conclusion: To reduce trafficking, it is necessary to review policies. Also, there is a need to focus on capacity building and support for the country's pharmaceutical companies. Also, the use of insurance capacity, e-infrastructure, and culturalization are strategies for reducing drug smuggling that must be accompanied by the full participation of stakeholders inside and outside the health system.
First report of molecular identification of Phytophthora infestans causing potato late blight in Yemen
Authors: Harethi Amira Ali Al , Abdullah Qais Y. , Jobory Hala J. Al , Arafa Ramadan A. , Aquil Samar Al 
Published: April 2023

Abstract
Late blight, caused by Phytophthora infestans, is one of the most destructive potato diseases in the world. In Yemen, identification of P. infestans still depends on a visual survey and external examination of late blight symptoms. The objective of this study was to isolate and identify P. infestans by using advanced methods.
We collected 71 disease samples and isolated the pathogen using the tuber slice method. To identify an isolated pathogen, we performed morphological characterization and gene sequence analysis of the coding genes for internal transcribed spacers. We used Koch's hypotheses to confirm the previous results.
In our study. The morphological characters of the mycelium pattern of P. infestans isolates in Yemen were profusely branching, fluffy, and white. The sporangia showed remarkable limoniform papillate sporangial shape. with average length and width of 30.6 and 28.6 µm, respectively. The sequences analysis showed high homology with a degree of identity ranging from 98–100% to the database sequences on GenBank. Pathogenicity tests showed that the P. infestans was the causal agent.
To our knowledge, this is the first study of the isolation and characterization of P. infestans in Yemen.
Effectiveness of m-health-based core strengthening exercise and health education for public safety workers with non-specific chronic low back pain: study protocol for a superiority randomized controlled trial (SAFEBACK)
Authors: Marins Eduardo , Caputo Eduardo , Krüger Victor , Junior Dirceu , Scaglioni Fabricio , Vecchio Fabricio Del , Primo Tiago , Alberton Cristine 
Published: April 2023

Abstract
Background: Low back pain (LBP) is the leading cause of years lived with disability worldwide. Public safety workers are highly exposed to physically demanding activities and inappropriate postures, which can increase the LBP experience in this population. Smartphone app-based self-managed interventions may be an alternative for chronic non-specific LBP (CNSLBP) treatment. This study aims to develop and test the effectiveness of an 8-week smartphone app-based self-managed exercise program plus health education, compared to a health education program alone, on neuromuscular and perceptual outcomes in police officers and firefighters with CNSLBP.
Methods: This is a parallel, two-armed, blinded evaluator randomized clinical trial. Police officers and firefighters (from public safety institutions in the Rio Grande do Sul state, Brazil) will be randomly assigned to m-health self-managed exercise program (twice a week) plus health education or health education alone. Self-management exercise program components are mobility and core resistance exercises, available on the app. Follow-ups will be conducted post-treatment (8 weeks) and 16 weeks after randomization. The primary outcomes will be pain intensity and disability post-treatment (8 weeks). Secondary outcomes will be biopsychosocial factors related to CNSLBP.
Discussion: Our hypothesis is of superiority for the effects of a smartphone app-based self-managed exercise program on primary and secondary outcomes, compared to the effects of only the health education intervention in police officers and firefighters with CNSLBP.
Trial registration: The study was prospectively registered at ClinicalTrials.gov (NCT05481996. Registered on August 01, 2022).
Method development, validation, and application for simultaneous determination of 56 new psychoactive substances in surface water by LC-MS/MS
Authors: Atasoy-Aydin Asli , Gören İsmail Ethem , Yavuz-Guzel Evsen , Daglioglu Nebile 
Published: April 2023

Abstract
Despite preventive legislation, the popularity and consumption of new psychoactive substances (NPS) have been steadily increasing in recent years. This study provides a rapid and sensitive method for the quantitation and the detection of 56 NPS from surface water. Sample clean-up and pre-concentration were performed by solid-phase extraction (SPE) with Oasis HLB (6 cc/500 mg) cartridge. Following the chromatographic separation with Shim-pack FC-ODS column, the all substances were quantified by liquid chromatography-tandem mass spectrometry. The method was optimized and validated for all NPS. Despite the wide variety of physicochemical properties of the analytes, the recoveries for all compounds studied were in the range of 69-117%. The limit of quantitation (LOQ) ranging from 2.5 to 15 ng/L was reached for reliable and accurate quantification of analytes. The analytical method developed was successfully applied to the surface water samples. While synthetic cannabinoids were not detected, mephedrone from the synthetic cathinone group was detected under the LOQ. This novel method was expected to be a part of future environmental routine analyses as a satisfactory method.
Machine Learning to Predict Radiation Enteritis in Patients Undergoing Radical Radiotherapy for Cervical Squamous Cell Carcinoma
Authors: Li Yanqing , Jiang Kaijun , Zhang Lan , Gao Xudong , Ju Yunhe , Ding Xiang , Wang Xiaoli , Xia Qun , Xia Yaoxiong , Ai Yiqin 
Published: April 2023

Abstract
Background
Radiation enteritis (RE) is an adverse event associated with radical radiotherapy (RT) for cervical carcinoma (CC). However, the risk of RE has not been well predicted. We hypothesized that inflammatory markers of pre-/post-treatment complete blood count (CBC)-derived parameters can improve the predictive accuracy for RE using machine learning.
Methods
Patients with cervical squamous cell carcinoma of stage IB2-IIIB receiving radical RT in our hospital from January 1, 2013, to December 31, 2015, were included. Inflammatory markers of pre/post-treatment CBC-derived at the initial diagnosis and after RT were analyzed. A machine learning algorithm was used to develop a generalized linear model (GLM) for predicting RE risk.
Results
A total of 321 patients were eligible, of whom 39.3% (126/321) developed RE2 after RT, whether acute or chronic. The final predictive GLM for RE2 included an inflammatory marker, platelet-to-lymphocyte ratio (PLR1) (P = 0.021); age (P = 0.148); stage (P = 0.017); and RT technique (P = 0.047). A nomogram was constructed based on GLM. Decision curve analysis verified the better predictive power of the model for net clinical benefit.
Conclusions
This is the first study to determine the relationship between hematological inflammatory parameters and RE2 in patients with radical RT CC and to establish a relevant prediction model using machine learning. PLR1 was significantly associated with RE2. This study developed a comprehensive model integrating hematological inflammatory parameters and clinical variables to predict RE before RT, which provides an opportunity to guide clinicians.
Forecasting seed production in perennial plants: identifying challenges and charting a path forward
Authors: Journé Valentin , Hacket-Pain Andrew , Oberklammer Iris , Pesendorfer Mario , Bogdziewicz Michał 
Published: April 2023

Abstract
Interannual variability of seed production, known as masting, has far-reaching ecological impacts including effects on forest regeneration and the population dynamics of seed consumers. Because the relative timing of management and conservation efforts in ecosystems dominated by masting species often determines their success, there is a need to study masting mechanisms and develop forecasting tools for seed production. Here, we aim to establish seed production forecasting as a new branch of the discipline. We evaluate the predictive capabilities of three models - foreMast, DeltaT, and a sequential model - designed to predict seed production in trees using a pan-European dataset of Fagus sylvatica seed production. The models are moderately successful in recreating seed production dynamics. The availability of high-quality data on prior seed production improved the sequential model's predictive power, suggesting that effective seed production monitoring methods are crucial for creating forecasting tools. In terms of extreme events, the models are better at predicting crop failures than bumper crops, likely because the factors preventing seed production are better understood than the processes leading to large reproductive events. We summarize the current challenges and provide a roadmap to help advance the discipline and encourage the further development of mast forecasting.
Clinical and prognostic profile of SRSF2 and related spliceosome mutations in patients with acute myeloid leukemia
Authors: Jia Wenbo , Guo Xiaodong , Wei Yihong , Liu Jinting , Can Can , Wang Ruiqing , Yang Xinyu , Ji Chunyan , Ma Daoxin 
Published: April 2023

Abstract
Background
Mutations in splicing factor (SF) genes are frequently detected in myelodysplastic syndrome, but rare data about the clinical and prognostic relevance of these mutations in acute myeloid leukemia (AML) have been reported.
Methods
A total of 368 newly diagnosed non-M3 AML patients were included in this study. Next generation sequencing including four SF genes was performed on the genomic DNA. The clinical features and survival were analyzed using statistical analysis. SRSF2P95H function was assessed by CCK8 assay.
Results
We found that 64 of 368 patients harbored SF mutations. The SF mutations were much more frequent in older or male patients compared with SF-wild patients. SRSF2 mutations were shown obviously co-existed with IDH2 mutation. The level of measurable residual disease after the first chemotherapy was higher in SF-mutated patients compared to that in SF-wild patients, while the complete remission rate was significantly decreased. And the overall survival (OS) of SF-mutated patients was shorter than that of SF-wild patients. Moreover, our multivariable analysis suggests that the index of male, Kit mutation or ZRSR2 mutation was the independent risk factor for OS. SRSF2mut was associated with older age, higher proportion of peripheral blasts or abnormal cell proportion by FCM (Flow CytoMetry). Functionally, the mutation of SRSF2P95H significantly promoted the proliferation of AML cells.
Conclusion
Spliceosome mutation is a distinct subgroup of AML frequently associated with clinic-biological features and poor outcome. SRSF2mut could be potential targets for novel treatment in AML.
End-of-century heat and drought stress approaching Europe swiftly
Authors: Suarez-Gutierrez Laura , Müller Wolfgang , Marotzke Jochem 
Published: April 2023

Abstract
Extreme heat and drought levels typical of an end-of-century climate could occur swiftly, and repeatedly. Despite the European climate being potentially prone to multi-year successive extremes due to the influence of the North Atlantic variability, it remains unclear how the likelihood of such successive extremes changes under warming, how early they could reach end-of-century levels, and how this is affected by internal climate variability. Using the MPI Grand Ensemble, we find that even under moderate warming, end-of-century heat and drought levels virtually impossible 20 years ago reach 1-in-10 likelihoods as early as the 2030s. By 2050-2075, two successive years of single or compound end-of-century extremes, unprecedented to date, exceed 1-in-10 likelihoods; while Europe-wide 5-year megadroughts become plausible. Whole decades of end-of-century heat stress could start by 2040, by 2020 for rain-deficit drought, and end-of-century decades starting as early as 2030 become twice as likely under a warm North Atlantic state.
Blueberry Intake Elevates Post-Exercise Anti- inflammatory Oxylipins: A Randomized Trial
Authors: Nieman David C. , Sakaguchi Camila A. , Omar Ashraf M. , Davis Kierstin L. , Shaffner Cameron E. , Strauch Renee C. , Lila Mary Ann , Zhang Qibin 
Published: April 2023

Abstract
This study determined if 18 days of supplementation with blueberries (BL) compared to placebo (PL) could mitigate muscle soreness and damage and improve inflammation resolution in untrained adults (n = 49, ages 30–65 y) after engaging in a 90-minute bout of “weekend warrior” eccentric exercise. The BL freeze dried supplement provided 1 cup of fresh blueberries per day equivalent with 805 mg/d total phenolics and 280 mg/d anthocyanins. Urine levels of eight BL gut-derived phenolics increased after 14- and 18-days supplementation with 83% higher concentrations in BL vs. PL (p < 0.001). The 90-min exercise bout caused significant muscle soreness and damage during 4d of recovery and a decrease in exercise performance with no significant differences between PL and BL. Plasma oxylipins (n = 76) were identified and grouped by fatty acid substrates and enzyme systems. Linoleic acid (LA) oxylipins generated from cytochrome P450 (CYP) (9,10-, 12,13-dihydroxy-9Z-octadecenoic acids) (diHOMEs) were lower in BL vs. PL (treatment effect, p = 0.051). A compositive variable of 9 plasma hydroxydocosahexaenoic acids (HDoHEs) generated from docosahexaenoic acid (DHA, 22:6) and lipoxygenase (LOX) was significantly higher in BL vs. PL (treatment effect, p = 0.008). The composite variable of plasma 14-HDoHE, 17-HDoHE, and the eicosapentaenoic acid (EPA)-derived oxylipin 18-hydroxyeicosapentaenoic acid (18-HEPE) (specialized pro-resolving lipid mediators, SPM, intermediates) was significantly higher in BL vs PL (treatment effect, p = 0.014). Pearson correlations showed positive relationships between post-exercise DHA-LOX HDoHEs and SPM intermediates with urine blueberry gut-derived phenolics (r = 0.324, p = 0.023, and r = 0.349, p = 0.015, respectively). These data indicate that 18d intake of 1 cup/d blueberries compared to PL was linked to a reduction in pro-inflammatory diHOMES and sustained elevations in DHA- and EPA-derived anti-inflammatory oxylipins in response to a 90-min bout of unaccustomed exercise by untrained adults.
Towards inclusive green growth: Does digital economy matter?
Authors: Xin Chunhua , Fan Shuangshuang , Mbanyele William , Shahbaz Muhammad 
Published: April 2023

Abstract
In this decade, China has been pursuing an inclusive green growth strategy. Concurrently, the digital economy, which relies on the Internet of Things, big data and artificial intelligence, has experienced explosive growth in China. The digital economy’s capacity to optimize resource allocation and reduce energy consumption potentially makes it a conducive channel toward sustainability. Using the panel data of 281 cities in China from 2011 to 2020, we theoretically and empirically explore the impact of the digital economy on inclusive green growth. Firstly, we measure the digital economy, and inclusive green growth city levels using Entropy-TOPSIS and DEA approaches, respectively. Then, we apply traditional econometric estimation models and machine learning algorithms in our empirical analysis. The results show that China's high-powered digital economy significantly promotes inclusive green growth. Moreover, we analyze the internal mechanisms behind this impact. We find that innovation and industrial upgrading are two plausible channels that explain this effect. Additionally, we document a nonlinear feature of diminishing marginal effect between the digital economy and inclusive green growth. The heterogeneity analysis shows that the contribution weight of the digital economy to inclusive green growth is more remarkable in eastern region cities, large and medium-sized cities, and cities with high marketization. Overall, these findings shed more light on the digital economy-inclusive green growth nexus and provide new insights into understanding the real effects of the digital economy on sustainable development.
Dynamic functional changes upon thalamotomy in essential tremor depend on baseline brain morphometry
Authors: Bolton Thomas A.W. , De Ville Dimitri Van , Régis Jean , Witjas Tatiana , Girard Nadine , Levivier Marc , Tuleasca Constantin 
Published: April 2023

Abstract
Patients with drug-resistant essential tremor (ET) may undergo Gamma Knife stereotactic radiosurgical thalamotomy (SRS-T), where the ventro-intermediate nucleus of the thalamus (Vim) is lesioned by focused beams of gamma radiations to induce clinical improvement. Here, we studied SRS-T impacts on left Vim dynamic functional connectivity (dFC, n = 23 ET patients scanned before and 1 year after intervention), and on surface-based morphometric brain features (n = 34 patients, including those from dFC analysis). In matched healthy controls (HCs), three dFC states were extracted from resting-state functional MRI data. In ET patients, state 1 spatial stability increased upon SRS-T (p = 0.0041). Lower pre-intervention spatial variability in state 2 expression, and more frequent expression of state 3 over state 1, correlated with greater clinical recovery (p = 0.015 and p = 0.008, respectively). ET morphometric profiles showed significantly lower similarity to HCs in 13 regions upon SRS-T (p ≤ 0.02), and a joint analysis revealed that before thalamotomy, morphometric similarity and states 2/3 mean spatial similarity to HCs were anticorrelated, a relationship that disappeared upon SRS-T (p < 0.001). Our results show that left Vim functional dynamics directly relates to upper limb tremor lowering upon intervention, while morphometry instead has a supporting role in reshaping such dynamics.
Equatorial Spread-F Forecasting Model with Local Factors using the Long-Short Term Memory Network
Authors: Thammavongsy Phimmasone , Supnithi Pornchai , Myint Lin Min Min , Hozumi Kornyanat , Lakanchanh Donekeo 
Published: April 2023

Abstract
The predictability of the nighttime equatorial spread-F (ESF) irregularity is essential to the ionospheric disturbance warning system. In this work, we develop the ESF forecasting model using the Long-Short Term Memory (LSTM) network trained with new local input parameters at Chumphon station, Thailand near the magnetic equator, where the ESF onset typically occurs. The LSTM model is trained and tested using the ionogram data from 2008 to 2018 and 2019 during March and September equinoctial months. The input parameters are considered including diurnal variations, seasonal variations, solar activity, magnetic activity, local F-layer height variation, local F-layer drift velocity, and power spectrum of the atmospheric gravity waves. We analyze the ESF forecasting model in terms of monthly probability, daily probability and occurrence, and diurnal predictions. As a result, the proposed LSTM model can achieve 85.4% accuracy when the local F-layer drift velocity and atmospheric grativity waves are utilized. The local gravity wave parameter exhibits an important role on improvements of the LSTM model during post-midnight. When compare to the IRI-2016 model, the proposed LSTM model can provide lower error. Comparison between ANN and LSTM models are exhibited that the LSTM model provides lower error than the ANN in February, March, April, and October.
The integration of quantitative metabolic and proteomic analysis uncovers an augmentation of the sphingolipid biosynthesis pathway during T-cell differentiation
Authors: Kanno Toshio , Konno Ryo , Sato Masaru , Kurabayashi Atsushi , Miyako Keisuke , Nakajima Takahiro , Yokoyama Satoru , Sasamoto Shigemi , Asou Hikari , Ohzeki Junichiro , Hasegawa Yoshinori , Ikeda Kazutaka , Kawashima Yusuke , Ohara Osamu , Endo Yusuke 
Published: April 2023

Abstract

Recent studies have highlighted the significance of cellular metabolism in the initiation of clonal expansion and effector differentiation of T cells. Upon exposure to antigens, naïve CD4+ T cells undergo metabolic reprogramming to meet their metabolic requirements. However, only few studies have simultaneously evaluated the changes in protein and metabolite levels during T cell differentiation. Our research seeks to fill the gap by conducting a comprehensive analysis of changes in levels of metabolites, including sugars, amino acids, intermediates of the TCA cycle, fatty acids, and lipids. By integrating metabolomics and proteomics data, we discovered that the quantity and composition of cellular lipids underwent significant changes in different effector Th cell subsets. Especially, we found that the sphingolipid biosynthesis pathway was commonly activated in Th1, Th2, Th17, and iTreg cells and that inhibition of this pathway led to the suppression of Th17 and iTreg cell differentiation. Additionally, we discovered that Th17 and iTreg cells enhance glycosphingolipid metabolism, and inhibition of this pathway also results in the suppression of Th17 and iTreg cell generation. These findings demonstrate that the utility of our combined metabolomics and proteomics analysis in furthering the understanding of metabolic transition during Th cell differentiation.