bluuebunny/crossref_metadata_2025_split huggingface dataset:
Self-management of geriatric syndromes - an observational study
Authors: Prell Tino , Wientzek Rebecca , Schönenberg Aline 
Published: November 2023

Abstract
Background
Self-management of health refers to various actions and decisions that impact health outcomes. To improve health, independence, and quality of life (QoL) while reducing healthcare utilization and costs, patients’ self-management abilities can be enhanced. However, disease-specific self-management interventions may not be applicable for older adults with multiple illnesses. Instead, focusing on prevalent geriatric syndromes, such as frailty, cognitive decline, pain, incontinence, or impaired mobility, may be more beneficial. To achieve this, a detailed understanding of the specific needs of the older population is crucial.

Methods
Patients who are 70 years old or older will be chosen from four geriatric hospitals, which include both inpatient and outpatient facilities. At baseline, each participant will undergo a comprehensive geriatric evaluation and answer various questionnaires that focus on their current self-management abilities, self-efficacy, anxiety, aging perception, and QoL. Moreover, extensive data on the presence and impact of geriatric syndromes will be gathered. Three and six months after the initial evaluation, follow-up assessments will be conducted to identify any changes in participants’ health, independence in daily activities, geriatric syndromes, cognition and mood, QoL, and self-management.

Discussion
The present investigation aims to assess the factors that may facilitate or impede self-management in older adults afflicted with geriatric syndromes. Instead of concentrating on particular diseases, this study will analyze the association between self-management and geriatric syndromes. The information obtained will contribute to clinical expertise on the self-management habits of older adults and their effects on their well-being, autonomy, and overall QoL, as well as provide insights into geriatric syndromes. This valuable knowledge will be crucial for developing personalised programs to enhance self-management among older adults.

Trial registration
German Trial Register (Deutsches Register Klinischer Studien) DRKS00031016.
Establishment of a model for predicting preterm birth based on the machine learning algorithm
Authors: Zhang Yao , Du Sisi , Hu Tingting , Xu Shichao , Lu Hongmei , Xu Chunyan , Li Jufang , Zhu Xiaoling 
Published: November 2023

Abstract
Background
The purpose of this study was to construct a preterm birth prediction model based on electronic health records and to provide a reference for preterm birth prediction in the future.

Methods
This was a cross-sectional design. The risk factors for the outcomes of preterm birth were assessed by multifactor logistic regression analysis. In this study, a logical regression model, decision tree, Naive Bayes, support vector machine, and AdaBoost are used to construct the prediction model. Accuracy, recall, precision, F1 value, and receiver operating characteristic curve, were used to evaluate the prediction performance of the model, and the clinical application of the model was verified.

Results
A total of 5411 participants were included and were used for model construction. AdaBoost model has the best prediction ability among the five models. The accuracy of the model for the prediction of “non-preterm birth” was the highest, reaching 100%, and that of “preterm birth” was 72.73%.

Conclusions
By constructing a preterm birth prediction model based on electronic health records, we believe that machine algorithms have great potential for preterm birth identification. However, more relevant studies are needed before its application in the clinic.
Expression of insulin-like growth factor binding protein-3 in HELLP syndrome
Authors: Wei Li , Liping Zhou , Suya Kang 
Published: November 2023

Abstract
Objective
To investigate the expression of insulin-like growth factor binding protein-3(IGFBP-3) in HELLP syndrome and its possible role in the pathogenesis of this disease.

Methods
1) 87 subjects were enrolled, including 29 patients with HELLP syndrome, 29 patients with pre-eclampsia (PE), and 29 healthy gravidae as control. The levels of IGFBP-3, IGF-1, TGF-β1, and VEGF in maternal and umbilical blood of them were detected using ELISA. Correlation analysis was used to observe the correlation between IGFBP-3 and IGF-1/TGF-β1/VEGF in maternal and umbilical blood, as well as that between maternal serum IGFBP-3 and clinical diagnostic indicators of HELLP syndrome. 2) Human hepatic sinusoid endothelial cells (HLSEC) and human umbilical vein endothelial cells (HUVEC) were cultured with different concentrations of IGFBP-3. After 72 h of culture, cell apoptosis and the normal living cells rate were detected and compared.

Results
1) In both maternal and umbilical blood of HELLP group, levels of IGFBP-3 and TGF-β1 were higher than control and PE group, IGF-1was lower than control group, VEGF was lower than control and PE group. IGFBP-3 in maternal blood was correlated with IGF-1/TGF-β1/ VEGF, while IGFBP-3 in umbilical blood was linked to IGF-1/TGF-β1. In maternal blood, there was a negative correlation between PLT and IGFBP-3, and a positive correlation between ALT/AST/LDH and IGFBP-3. 2) After cultured with IGFBP-3, the total apoptosis rate of either HLSEC or HUVEC was considerably elevated, while the normal living rate was decreased.

Conclusion
The expression of IGFBP-3 is elevated in HELLP syndrome, which may subsequently promote cell apoptosis by affecting the expression and function of IGF-1, VEGF, and TGFβ1 in the IGF/PI3K/Akt, TGF-β1/Smad3, and VEGF/eNOS/NO pathways. IGFBP-3 aggravates inflammatory reactions of the vascular endothelium and liver under hypoxia, affects the normal function of cells, and plays a role in the pathogenesis of diseases.
A log-binomial Bayesian geoadditive semiparametric analysis of geographical inequalities in caesarean births in Ghana
Authors: Amoako Johnson Fiifi 
Published: November 2023

Abstract
Background
Caesarean section is a clinical intervention aimed to save the lives of women and their newborns. In Ghana, studies have reported inequalities in use among women of different socioeconomic backgrounds. However, geographical differentials at the district level where health interventions are implemented, have not been systematically studied. This study examined geographical inequalities in caesarean births at the district level in Ghana. The study investigated how pregnancy complications and birth risks, access to health care and affluence correlate with geographical inequalities in caesarean section uptake.

Methods
The data for the analysis was derived from the 2017 Ghana Maternal Health Survey. The log-binomial Bayesian Geoadditive Semiparametric regression technique was used to examine the extent of geographical clustering in caesarean births at the district level and their spatial correlates.

Results
In Ghana, 16.0% (95% CI = 15.3, 16.8) of births were via caesarean section. Geospatial analysis revealed a strong spatial dependence in caesarean births, with a clear north-south divide. Low frequencies of caesarean births were observed among districts in the northern part of the country, while those in the south had high frequencies. The predominant factor associated with the spatial differentials was affluence rather than pregnancy complications and birth risk and access to care.

Conclusions
Strong geographical inequalities in caesarean births exist in Ghana. Targeted and locally relevant interventions including health education and policy support are required at the district level to address the overuse and underuse of caesarean sections, to correspond to the World Health Organisation recommended optimal threshold of 10% to 15%.
Work, travel, or leisure: comparing domain-specific physical activity patterns based on rural–urban location in Canada
Authors: Pelletier Chelsea , White Nicole , Duchesne Annie , Sluggett Larine 
Published: November 2023

Abstract
Background
Physical activity occurs across various domains including leisure/recreation, for transportation, or for work or household reasons. Rural and urban active living environments are characterized by different opportunities for physical activity within each domain which may translate into different patterns of behavior. The aim of this study was to compare rural–urban differences in physical activity across different domains, and explore interactions between sociodemographic factors, physical activity domains, and rurality.

Methods
We used self-reported data collected across three physical activity domains (active transportation, recreation, occupational/household) and relevant sociodemographic variables from the Canadian Community Health Survey. Adjusting for sociodemographic factors, we did two separate cross-sectional analyses: 1) binary logistic regression to determine the odds of reporting any activity in each domain, and 2) ordinary least squares regression using the sub-samples reporting > 0 min per week of activity to compare how much activity was reported in each domain.

Results
Our final survey weighted sample of Canadian adults (mean age 47.4 years) was n = 25,669,018 (unweighted n = 47,266). Rural residents were less likely to report any active transportation (OR = 0.59, 95% CI [0.51, 0.67], p < .0001). For recreational physical activity, rural males had lower odds (OR = 0.75, 95% CI [0.67, 0.83], p < .0001) and rural females had higher odds (OR = 1.19, 95% CI [1.08, 1.30], p = .0002) of reporting any participation compared to urban residents. Rural males (OR = 1.90, 95% CI [1.74, 2.07], p < .0001) and females (OR = 1.33, 95% CI [1.21, 1.46], p < .0001) had higher odds of reporting any occupational or household physical activity.

Conclusions
Urban residents tend to participate in more active transportation, while rural residents participate in more occupational or household physical activity. Location-based differences in physical activity are best understood by examining multiple domains and must include appropriate sociodemographic interactions, such as income and sex/gender.
Mechanism underlying the effect of Pulsatilla decoction in hepatocellular carcinoma treatment: a network pharmacology and in vitro analysis
Authors: Liu Kuijie , Cao Zhenyu , Huang Siqi , Kong Fanhua 
Published: November 2023

Abstract
Background
Currently, hepatocellular carcinoma (HCC) is associated with a poor prognosis. Moreover, there exist limited strategies for treating HCC. Pulsatilla decoction (PD), a traditional Chinese medicine formula, has been used to treat inflammatory bowel disease and several cancer types. Accordingly, we explored the mechanism of PD in HCC treatment via network pharmacology and in vitro experiments.

Methods
Online databases were searched for gene data, active components, and potential target genes associated with HCC development. Subsequently, bioinformatics analysis was performed using protein–protein interaction and Network Construction and Kyoto Encyclopedia of Genes and Genomes (KEGG) to screen for potential anticancer components and therapeutic targets of PD. Finally, the effect of PD on HCC was further verified by in vitro experiments.

Results
Network pharmacological analysis revealed that 65 compounds and 180 possible target genes were associated with the effect of PD on HCC. These included PI3K, AKT, NF-κB, FOS, and NFKBIA. KEGG analysis demonstrated that PD exerted its effect on HCC mainly via the PI3K-AKT, IL-17, and TNF signaling pathways. Cell viability and cell cycle experiments revealed that PD could significantly inhibit cancer cell proliferation and kill HCC cells by inducing apoptosis. Furthermore, western blotting confirmed that apoptosis was mediated primarily via the PI3K-AKT, IL-17, and TNF signaling pathways.

Conclusion
To the best of our knowledge, this is the first study to elucidate the molecular mechanism and potential targets of PD in the treatment of HCC using network pharmacology.
Perception, attitude, and practice toward research among medical students in Hadhramout University, Yemen
Authors: Bin-Ghouth Abdullah , Batarfi Suha Ali , Abonemi AbdulRahman Hashim , Maknoon Ahmed Sadeq , Alkhanbshi Ahmad Sa’ad , Khred AlwiAbobaker , Bawazir Amal Abdullah , Ba-Jaber Areej Abdullah , Rezq Aiman Abdullah , Maknoon Isra’aAlwi , Badheeb Khadijah Ahmed , Alkathiri Maha Salah , Ba-Rbaa Majedah Ahmed , Dhaiban Mustafa , Bagumaish Omar Ali , Baslasel Omar Saleh , Ba-rady Roua’a Abdullah , Algadry Souha , Bazanboor Tagwa Omar 
Published: November 2023

Abstract
Background
Research is an important element in the improvement of the quality of health services provided to the public. It is documented that globally; medical students apply research in their school life. In Hadhramaut University, medical students work on research in groups, and it is an important part of the curriculum. There is a formal assessment of the student’s research, but there is still a gap regarding individual viewpoints and challenges faced. This study aimed to assess perception, attitude, and practice toward research among medical students at Hadhramout University.

Methods
This is a cross-sectional descriptive study which was conducted among medical students. This study was undertaken in Hadhramout University in Al-Mukalla district, Yemen, during the academic year 2016–2017. A self-administered pilot-tested questionnaire was used for data collection to assess perception, attitude, and practice toward the research during the educational year 2016–2017.

Results
A total of 265 completed responses were received. The majority had a low Knowledge score (72%). However, the majority had a positive attitude toward research (90.9%). Eighty-three students reported participation in research work. However, (44.4%) expressed research interest. Many barriers were highlighted by students including a lack of time (78.4%) and a lack of training in statistics (75.9%).

Conclusion
The study identified several barriers for undergraduate medical students to undertake research. It is important that these barriers should be addressed in curriculum development, so that students can retain their motivation to engage effectively in research.
Machine learning-based risk models for procedural complications of radiofrequency ablation for atrial fibrillation
Authors: Li Rong , Shen Lan , Ma Wenyan , Li Linfeng , Yan Bo , Wei Yuna , Wang Yao , Pan Changqing , Yuan Junyi 
Published: November 2023

Abstract
Background
Radiofrequency ablation (RFA) for atrial fibrillation (AF) is associated with a risk of complications. This study aimed to develop and validate risk models for predicting complications after radiofrequency ablation of atrial fibrillation patients.

Methods
This retrospective cohort study included 3365 procedures on 3187 patients with atrial fibrillation at a single medical center from 2018 to 2021. The outcome was the occurrence of postoperative procedural complications during hospitalization. Logistic regression, decision tree, random forest, gradient boosting machine, and extreme gradient boosting were used to develop risk models for any postoperative complications, cardiac effusion/tamponade, and hemorrhage, respectively. Patients’ demographic characteristics, medical history, signs, symptoms at presentation, electrocardiographic features, procedural characteristics, laboratory values, and postoperative complications were collected from the medical record. The prediction results were evaluated by performance metrics (i.e., the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F score, and Brier score) with repeated fivefold cross-validation.

Results
Of the 3365 RFA procedures, there were 62 procedural complications with a rate of 1.84% in the entire cohort. The most common complications were cardiac effusion/tamponade (28 cases, 0.83%), and hemorrhage (21 cases, 0.80%). There was no procedure-related mortality. The machine learning algorithms of random forest (RF) outperformed other models for any complication (AUC 0.721 vs 0.627 to 0.707), and hemorrhage (AUC 0.839 vs 0.649 to 0.794). The extreme gradient boosting (XGBoost) model outperformed other models for cardiac effusion/tamponade (AUC 0.696 vs 0.606 to 0.662).

Conclusions
The developed risk models using machine learning algorithms showed good performance in predicting complications after RFA of AF patients. These models help identify patients at high risk of complications and guiding clinical decision-making.
Effect of minocycline, methyl prednisolone, or combination treatment on the colonic bacterial population in a state of colonic inflammation using the murine dextran sulfate sodium model
Authors: Khajah Maitham A. , Hawai Sanaa 
Published: November 2023

Abstract
Background
Several reports demonstrated anti-inflammatory properties of minocycline in various inflammatory disorders including colitis. We have experimental evidence suggesting synergistic anti-inflammatory effect of minocycline with methyl prednisolone in reducing colitis severity in mice, but if this effect is in part related to modulating the composition of colonic microbiota is still unknown.

Methods
the effect of vehicle (V), minocycline (M), methyl prednisolone (MP), or combination (C) regimen on the composition of the microbiota of mice in a state of colon inflammation compared to untreated (UT) healthy mice was determined using 16s metagenomic sequencing, and the taxonomic and functional profiles were summarized.

Results
Overall, the bacterial flora from the phylum Firmicutes followed by Bacteroidota were found to be predominant in all the samples. However, the composition of Firmicutes was decreased relatively in all the treatment groups compared to UT group. A relatively higher percentage of Actinobacteriota was observed in the samples from the C group. At the genus level, Muribaculaceae, Bacteroides, Bifidobacterium, and Lactobacillus were found to be predominant in the samples treated with both drugs (C). Whereas “Lachnospiraceae NK4A136 group” and Helicobacter in the M group, and Helicobacter in the MP group were found to be predominant. But, in the UT group, Weissella and Staphylococcus were found to be predominant. Eubacterium siraeum group, Clostridia vadinBB60 group, Erysipelatoclostridium and Anaeroplasma genera were identified to have a significant (FDR p < 0.05) differential abundance in V compared to C and UT groups. While at the species level, the abundance of Helicobacter mastomyrinus, Massiliomicrobiota timonensis and uncultured Anaeroplasma were identified as significantly low in UT, C, and M compared to V group. Functional categories related to amino acid, carbohydrate, and energy metabolism, cell motility and cell cycle control were dominated overall across all the samples. Methane metabolism was identified as an enriched pathway. For the C group, “Colitis (decrease)” was among the significant (p = 1.81E-6) associations based on the host-intrinsic taxon set.

Conclusion
Combination regimen of minocycline plus methyl prednisolone produces a synergistic anti-inflammatory effect which is part related to alternation in the colonic microbiota composition.
Seasonal malaria chemoprevention in a context of high presumed sulfadoxine-pyrimethamine resistance: malaria morbidity and molecular drug resistance profiles in South Sudan
Authors: Molina-de la Fuente Irene , Sagrado Benito María José , Lasry Estrella , Ousley Janet , García Luz , González Vicenta , Pasquale Harriet Akello , Julla Ahmed , Uwiragiye Piex , Abdi Abdirashid M. , Chol Buai Tut , Abubakr Bakri , Benito Agustín , Casademont Cristian , Berzosa Pedro , Nanclares Carolina 
Published: November 2023

Abstract
Background
Seasonal malaria chemoprevention (SMC) using sulfadoxine-pyrimethamine plus amodiaquine (SP-AQ), is a community-based malaria preventive strategy commonly used in the Sahel region of sub-Saharan Africa. However, to date it has not been implemented in East Africa due to high SP resistance levels. This paper is a report on the implementation of SMC outside of the Sahel in an environment with a high level of presumed SP-resistance: five cycles of SMC using SPAQ were administered to children 3–59 months during a period of high malaria transmission (July–December 2019) in 21 villages in South Sudan.

Methods
A population-based SMC coverage survey was combined with a longitudinal time series analysis of health facility and community health data measured after each SMC cycle. SMC campaign effectiveness was assessed by Poisson model. SPAQ molecular resistance markers were additionally analysed from dried blood spots from malaria confirmed patients.

Results
Incidence of uncomplicated malaria was reduced from 6.6 per 100 to an average of 3.2 per 100 after SMC administration (mean reduction: 53%) and incidence of severe malaria showed a reduction from 21 per 10,000 before SMC campaign to a mean of 3.3 per 10,000 after each cycle (mean reduction: 84%) in the target group when compared to before the SMC campaign. The most prevalent molecular haplotype associated with SP resistance was the IRNGE haplotype (quintuple mutant, with 51I/59R/108N mutation in pfdhfr + 437G/540E in pfdhps). In contrast, there was a low frequency of AQ resistance markers and haplotypes resistant to both drugs combined (< 2%).

Conclusions
The SMC campaign was effective and could be used as an additional preventive tool in seasonal malaria settings outside of the Sahel, especially in areas where access to health care is unstable. Malaria case load reduction was observed despite the high level of resistance to SP.
Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis
Authors: Mshani Issa H. , Siria Doreen J. , Mwanga Emmanuel P. , Sow Bazoumana BD. , Sanou Roger , Opiyo Mercy , Sikulu-Lord Maggy T. , Ferguson Heather M. , Diabate Abdoulaye , Wynne Klaas , González-Jiménez Mario , Baldini Francesco , Babayan Simon A. , Okumu Fredros 
Published: November 2023

AbstractStudies on the applications of infrared (IR) spectroscopy and machine learning (ML) in public health have increased greatly in recent years. These technologies show enormous potential for measuring key parameters of malaria, a disease that still causes about 250 million cases and 620,000 deaths, annually. Multiple studies have demonstrated that the combination of IR spectroscopy and machine learning (ML) can yield accurate predictions of epidemiologically relevant parameters of malaria in both laboratory and field surveys. Proven applications now include determining the age, species, and blood-feeding histories of mosquito vectors as well as detecting malaria parasite infections in both humans and mosquitoes. As the World Health Organization encourages malaria-endemic countries to improve their surveillance-response strategies, it is crucial to consider whether IR and ML techniques are likely to meet the relevant feasibility and cost-effectiveness requirements—and how best they can be deployed. This paper reviews current applications of IR spectroscopy and ML approaches for investigating malaria indicators in both field surveys and laboratory settings, and identifies key research gaps relevant to these applications. Additionally, the article suggests initial target product profiles (TPPs) that should be considered when developing or testing these technologies for use in low-income settings.