bluuebunny/crossref_metadata_2025_split huggingface dataset:
Emerging methylation-based approaches in microbiome engineering
Authors: Won Changhee , Yim Sung Sun 
Published: July 2024

AbstractBacterial epigenetics, particularly through DNA methylation, exerts significant influence over various biological processes such as DNA replication, uptake, and gene regulation in bacteria. In this review, we explore recent advances in characterizing bacterial epigenomes, accompanied by emerging strategies that harness bacterial epigenetics to elucidate and engineer diverse bacterial species with precision and effectiveness. Furthermore, we delve into the potential of epigenetic modifications to steer microbial functions and influence community dynamics, offering promising opportunities for understanding and modulating microbiomes. Additionally, we investigate the extensive diversity of DNA methyltransferases and emphasize their potential utility in the context of the human microbiome. In summary, this review highlights the potential of DNA methylation as a powerful toolkit for engineering microbiomes.
Antidiabetic potential of Selenium nanoparticles and plasma-rich platelets in diabetic mice
Authors: Karas Rania A. , Alexeree Shaimaa , Elzohery Nora , Abdel-Hafez Shams H. , Attia Yasser A. 
Published: July 2024

AbstractDiabetes mellitus is a widespread endocrine disorder, which is categorized as the fourth leading cause of global mortality. Allopathic medicine has yet to provide a satisfactory cure for this condition. Consequently, there is an urgent demand for innovative antidiabetic treatment approaches with enhanced management and minimum side effects and costs. The study investigated the synergistic antidiabetic potential of combining selenium nanoparticles (Se NPs) and plasma-rich platelets (PRP) in diabetic mice. Antidiabetic activity of the proposed combination (Se NPs and PRP) was evaluated from histopathological and biochemical perspectives. The experiment involved alloxan monohydrate induced diabetic mouse model. In the in vivo study, several biochemical parameters for assessing the antidiabetic effect of the novel combination of (Se NPs and PRP) were performed such as blood glucose levels, body weight, lipid profiles, and liver damage markers (AST and ALT). Scavenging antioxidant activity was assessed by evaluation levels of hepatic and renal GSH, MDA, SOD, and CAT activities. Complete histopathological examinations of vital internal organs were carried out. Results revealed that combining Se NPs and PRP presents a novel approach for better diabetes management and reduced complications associated with the disease. These findings have therapeutic implications for managing diabetes mellitus.
Network analysis of trait aggression among community youths and juvenile offenders
Authors: Chen Chen , Li Chenglong , Fei Shienyu , Chen Wei 
Published: July 2024

Abstract
Objective
The mainstream view in trait aggression research has regarded the structure as representing the latent cause of the cognitions, emotions, and behaviors that supposedly reflect its nature. Under network perspective, trait aggression is not a latent cause of its features but a dynamic system of interacting elements. The current study uses network theory to explain the structure of relationships between trait aggression features in juvenile offenders and their peers.

Methods
Network analysis was applied to investigate the dynamic system of trait aggression operationalized by the Buss-Perry Aggression Questionnaire in a sample of community youths (Mage = 17.46, N = 715) and juvenile offenders (Mage = 18.36, N = 834).

Results
The facet level networks showed that anger is a particularly effective mechanism for activating all other traits. In addition, anger was more strongly associated with physical aggression and the overall network strength was greater in juvenile delinquency networks than in their peers. The item level networks revealed that A4 and A6 exhibited the highest predictability and strength centrality in both samples. Also, the Bayesian network indicated that these two items were positioned at the highest level in the model. There are similarities and differences between juvenile delinquents and community adolescents in trait aggression.

Conclusion
Trait aggression was primarily activated by difficulty controlling one’s temper and feeling like a powder keg.
Examining ALS: reformed PCA and random forest for effective detection of ALS
Authors: Alqahtani Abdullah , Alsubai Shtwai , Sha Mohemmed , Dutta Ashit Kumar 
Published: July 2024

AbstractALS (Amyotrophic Lateral Sclerosis) is a fatal neurodegenerative disease of the human motor system. It is a group of progressive diseases that affects the nerve cells in the brain and spinal cord that control the muscle movement of the body hence, detection and classification of ALS at the right time is considered to be one of the vital aspects that can save the life of humans. Therefore, in various studies, different AI techniques are used for the detection of ALS, however, these methods are considered to be ineffectual in terms of identifying the disease due to the employment of ineffective algorithms. Hence, the proposed model utilizes Modified Principal Component Analysis (MPCA) and Modified Random Forest (MRF) for performing dimensionality reduction of all the potential features considered for effective classification of the ALS presence and absence of ALS causing mutation in the corresponding gene. The MPCA is adapted for capturing all the Low-Importance Data transformation. Furthermore, The MPCA is objected to performing three various approaches: Covariance Matrix Correlation, Eigen Vector- Eigenvalue decomposition, and selecting the desired principal components. This is done in aspects of implying the LI (Lower-Importance) Data Transformation. By choosing these potential components without any loss of features ensures better viability of selecting the attributes for ALS-causing gene classification. This is followed by the classification of the proposed model by using Modified RF by updating the clump detector technique. The clump detector is proceeded by clustering approach using K-means, and the data reduced by their dimension are grouped accordingly. These clustered data are analyzed either for ALS causing or devoid of causing ALS. Finally, the model’s performance is assessed using different evaluation metrics like accuracy, recall, F1 score, and precision, and the proposed model is further compared with the existing models to assess the efficacy of the proposed model.
Emotion AWARE: an artificial intelligence framework for adaptable, robust, explainable, and multi-granular emotion analysis
Authors: Gamage Gihan , De Silva Daswin , Mills Nishan , Alahakoon Damminda , Manic Milos 
Published: July 2024

AbstractEmotions are fundamental to human behaviour. How we feel, individually and collectively, determines how humanity evolves and advances into our shared future. The rapid digitalisation of our personal, social and professional lives means we are frequently using digital media to express, understand and respond to emotions. Although recent developments in Artificial Intelligence (AI) are able to analyse sentiment and detect emotions, they are not effective at comprehending the complexity and ambiguity of digital emotion expressions in knowledge-focused activities of customers, people, and organizations. In this paper, we address this challenge by proposing a novel AI framework for the adaptable, robust, and explainable detection of multi-granular assembles of emotions. This framework consolidates lexicon generation and finetuned Large Language Model (LLM) approaches to formulate multi-granular assembles of two, eight and fourteen emotions. The framework is robust to ambiguous emotion expressions that are implied in conversation, adaptable to domain-specific emotion semantics, and the assembles are explainable using constituent terms and intensity. We conducted nine empirical studies using datasets representing diverse human emotion behaviours. The results of these studies comprehensively demonstrate and evaluate the core capabilities of the framework, and consistently outperforms state-of-the-art approaches in adaptable, robust, and explainable multi-granular emotion detection.
Development of a prognostic risk model of uveal melanoma based on N7-methylguanosine-related regulators
Authors: Wu Pingfan , Zhang Qian , Zhong Peng , Chai Li , Luo Qiong , Jia Chengyou 
Published: July 2024

Abstract
Background
Uveal melanoma (UVM) stands as the predominant type of primary intraocular malignancy among adults. The clinical significance of N7-methylguanosine (m7G), a prevalent RNA modifications, in UVM remains unclear.

Methods
Primary information from 80 UVM patients were analyzed as the training set, incorporating clinical information, mutation annotations and mRNA expression obtained from The Cancer Genome Atlas (TCGA) website. The validation set was carried out using Gene Expression Omnibus (GEO) database GSE22138 and GSE84976. Kaplan–Meier and Cox regression of univariate analyses were subjected to identify m7G-related regulators as prognostic genes.

Result
A prognostic risk model comprising EIF4E2, NUDT16, SNUPN and WDR4 was established through Cox regression of LASSO. Evaluation of the model’s predictability for UVM patients’ prognosis by Receiver Operating Characteristic (ROC) curves in the training set, demonstrated excellent performance Area Under the Curve (AUC) > 0.75. The high-risk prognosis within the TCGA cohort exhibit a notable worse outcome. Additionally, an independent correlation between the risk score and overall survival (OS) among UVM patients were identified. External validation of this model was carried out using the validation sets (GSE22138 and GSE84976). Immune-related analysis revealed that patients with high score of m7G-related risk model exhibited elevated level of immune infiltration and immune checkpoint gene expression.

Conclusion
We have developed a risk prediction model based on four m7G-related regulators, facilitating effective estimate UVM patients’ survival by clinicians. Our findings shed novel light on essential role of m7G-related regulators in UVM and suggest potential novel targets for the diagnosis, prognosis and therapy of UVM.
2023 Brazilian Society of Rheumatology guidelines for the treatment of systemic sclerosis
Authors: Kayser Cristiane , de Oliveira Delgado Sandra Maximiano , Zimmermann Adriana Fontes , Horimoto Alex Magno Coelho , Del Rio Ana Paula Toledo , de Souza Müller Carolina , Camargo Cintia Zumstein , Lupo Cristiano Michelini , de Moraes Daniela Aparecida , Do Rosário E Souza Eduardo José , Santos Flávia Patrícia Sena Teixeira , Sekiyama Juliana Yuri , Lonzetti Lilian Scussel , de Oliveira Martins Lucas Victória , Bezerra Mailze Campos , Bredemeier Markus , Oliveira Maria Carolina , da Fonseca Salgado Maria Cecília , Miossi Renata , de Araújo Fontenele Sheila Márcia , Hax Vanessa , Dantas Andrea Tavares , Sampaio-Barros Percival Degrava 
Published: July 2024

Abstract
Background
Systemic sclerosis (SSc) is a rare chronic autoimmune disease with heterogeneous manifestations. In the last decade, several clinical trials have been conducted to evaluate new treatment options for SSc. The purpose of this work is to update the recommendations of the Brazilian Society of Rheumatology in light of the new evidence available for the pharmacological management of SSc.

Methods
A systematic review including randomized clinical trials (RCTs) for predefined questions that were elaborated according to the Patient/Population, Intervention, Comparison, and Outcomes (PICO) strategy was conducted. The rating of the available evidence was performed according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. To become a recommendation, at least 75% agreement of the voting panel was needed.

Results
Six recommendations were elaborated regarding the pharmacological treatment of Raynaud’s phenomenon, the treatment (healing) and prevention of digital ulcers, skin involvement, interstitial lung disease (ILD) and gastrointestinal involvement in SSc patients based on results available from RCTs. New drugs, such as rituximab, were included as therapeutic options for skin involvement, and rituximab, tocilizumab and nintedanib were included as therapeutic options for ILD. Recommendations for the pharmacological treatment of scleroderma renal crisis and musculoskeletal involvement were elaborated based on the expert opinion of the voting panel, as no placebo-controlled RCTs were found.

Conclusion
These guidelines updated and incorporated new treatment options for the management of SSc based on evidence from the literature and expert opinion regarding SSc, providing support for decision-making in clinical practice.
Boll weevils in dry bolls: is there a performance disadvantage?
Authors: Coelho Beatriz S. , Leite Suzany A. , dos Santos Mateus P. , de Mates Eloito C. , de Moraes Beatriz M. L. , Bastos Cristina S. , Guedes Raul N. C. , Moreira Aldenise A. , Castellani Maria A. 
Published: July 2024

Abstract
Background
Anthonomus grandis grandis is the main cotton pest in the Americas, with the potential to become a pest in other continents. The insect uses the plant’s reproductive organs for feeding, oviposition, and survival strategies in the off-season, sheltering in dry bolls. This study aimed to know the reproductive potential, feeding, longevity, and morphometric characteristics of boll weevils from dry bolls, squares, and traps. Adults were obtained from infested squares, dry bolls, and traps installed in cotton fields and the Caatinga area. Thirty weevil pairs were maintained in the laboratory to assess the oviposition, feeding holes, longevity, and morphometric measurements of the parental and F1 generations.

Results
In the parental generation, boll weevils from traps (the Caatinga area and cotton cultivation) showed greater reproductive potential. Boll weevils from squares and the Caatinga area showed greater feeding activity compared with those from cotton cultivation. Greater rostrum length, thorax + abdomen length, and abdomen width were observed in boll weevils from cotton cultivation and dry bolls. The origin of boll weevils parental generation did not affect progeny emergence time. The F1 generation of boll weevils showed significant differences in average longevity depending on the origin of different microhabitats. Furthermore, differences in rostrum lengths were observed, with the boll weevils coming from traps installed in cotton cultivation, the Caatinga area, and dry bolls being the largest. Regarding thorax + abdomen length and abdomen width, the highest values were observed in those from the Caatinga area and cotton cultivation.

Conclusion
It was proven that dry bolls did not affect the reproductive, feeding, and longevity performance of boll weevils, enabling better insect fitness (reproduction and size) in relation to those coming from squares. The use of dry bolls as off-season shelter is an important survival strategy for the boll weevil.
Solid self-nanoemulsifying drug delivery systems of nimodipine: development and evaluation
Authors: Kumar Mohit , Chawla Pooja A. , Faruk Abdul , Chawla Viney 
Published: July 2024

Abstract
Background
This study aimed to formulate solid self-nanoemulsifying drug delivery systems (SNEDDS) for nimodipine (NIM). The selection of Cremophor RH 40, Lipoxol 300, and PEG 400 as oil, surfactant, and co-surfactant was based on solubility and self-emulsification assessments. A ternary phase diagram determined the optimal oil to Smix (surfactant/co-surfactant) ratio (40:60). By utilizing liquid SNEDDS (NIM-SNEDDS) as an adsorbate and chitosan EDTA microparticles, developed through spray drying (SD-CHEM) and solvent evaporation (SE-CHEM) as adsorbents, the solid SNEDDS were created (NIM-SD-SSNEDDS and NIM-SE-SSNEDDS, respectively).

Results
Both solid formulations exhibited favourable drug loading (NIM-SD-SSNEDDS = 79.67 ± 2.97%, NIM-SE-SSNEDDS = 77.76 ± 4.29%), excellent flowability, and drug amorphization as per XRD and DSC analysis. Scanning electron microscopy revealed smoothening and filling of adsorbent surfaces by adsorbate (with size range NIM-SD-SSNEDDS = 10–15 μm, NIM-SE-SSNEDDS = 20–25 μm). FTIR confirmed no interaction of drug and excipients. Stability studies demonstrated the physical and thermodynamic stability of reconstituted nanoemulsions with droplet size, PDI, zeta potential, emulsification time, % transmittance and cloud temperature for NIM-SD-SSNEDDS as 247.1 nm, PDI 0.620, 1.353 mV, 38–41 s, 94.64%, 54 °C and for NIM-SE-SSNEDDS as 399.6 nm, PDI 0.821, 1.351 mV, 40–48 s, 92.96%, 49 °C, respectively. FE-SEM images showed globules formed with small sizes, and there was no coalescence evidence, implying the reconstituted nanoemulsions' stability. In vitro dissolution studies revealed a fourfold increase in drug dissolution for NIM-SD-SSNEDDS (84.43%) and NIM-SE-SSNEDDS (76.68%) compared to pure drug (28%). Ex vivo permeation studies indicated almost similar profiles for NIM-SD-SSNEDDS (22.61%) and NIM-SE-SSNEDDS (21.93%) compared to NIM-SNEDDS (25.02%).

Conclusion
NIM-SD-SSNEDDS exhibited superior performance compared to NIM-SE-SSNEDDS, highlighting the efficacy of microparticles developed by the spray drying method (SD-CHEM) as adsorbents for solidification. These results suggest enhanced dissolution and permeation for nimodipine in both the solid SNEDDS.
User-centered design of a mobile app for physical activity advice in cancer care: preliminary study in Morocco at the National Institute of Oncology
Authors: Azemmour Youness , Boutayeb Saber , Elkhalloufi Fahd , Chamlal Hamid , Beddaa Hassan , Bouzekraoui Alaoui Ismail , Daikal Jaouad , Errihani Hassan 
Published: July 2024

Abstract
Context
Utilizing mobile apps to increase physical activity levels is now standard practice in cancer care. The study’s objective was to provide patients with precise physical activity recommendations and/or initiate self-directed exercise programs.

Methods
A multidisciplinary group directed the design procedure. We adhered to the self-determination approach,and accompanying behavior modification strategies, as well as international guidelines in the field of physical activity in cancer care. The design process was user-centered and involved three steps: Understanding the user and the context, conceptualization, and evaluation of the prototype. The testing was carried out by the beneficiaries, healthcare professionals, and two experts. Three criteria were evaluated in the designed solution: usability, quality, and potential for behavior change.

Results
The evaluation results of the designed solution showed good perception by both beneficiaries and healthcare professionals. The average system usability scale score was 90,6 (SD: 7.33) for beneficiaries, and 92.75 (SD: 3.83) for healthcare professionals. The first expert received a mean overall quality score of 4.51 on the mobile app rating scale, while the second received a score of 4.27. The results of the app behavior change scale gave the first expert a score of 17/21 and the second a score of 15/21.

Conclusion
Based on the criteria of usability, quality, and potential for behavior change, the designed app demonstrated positive results. This is a preliminary evaluation, which will be followed by additional, in-depth evaluations with larger sample sizes.
Progeria - the old children
Authors: Dziedziński Dawid , Łoś Dawid , Nowak Aleksandra , Janus Anita , Kaptur Agata 
Published: July 2024

Introduction and Purpose: Progeria, also known as Hutchinson-Gilford progeria syndrome, is a rare genetic disorder characterized by accelerated aging in children. This research article aims to provide a comprehensive overview of progeria, including its etiology, symptoms, diagnosis, and treatment options. By synthesizing existing knowledge, this study seeks to enhance understanding and promote effective management strategies for individuals affected by this condition.

Description of the State of Knowledge: Progeria is an exceedingly rare genetic disorder, with an estimated incidence of approximately one in every 4 to 8 million births worldwide. It manifests early in childhood, leading to premature aging and a spectrum of associated health complications. The condition is primarily caused by a mutation in the LMNA gene, resulting in the production of a defective form of the protein lamin A. This abnormal protein disrupts the structural integrity of the cell nucleus, contributing to the characteristic features of progeria. Despite advances in medical research, there is currently no cure for progeria, and treatment options focus on managing symptoms and improving quality of life.

Summary: This research article provides a comprehensive overview of progeria, highlighting its genetic basis, clinical manifestations, diagnostic criteria, and therapeutic interventions. By elucidating the state of knowledge surrounding progeria, this study aims to facilitate early detection, enhance medical care, and foster ongoing research efforts aimed at advancing treatment modalities for affected individuals.
Application value of machine learning models in predicting intraoperative hypothermia in laparoscopic surgery for polytrauma patients
Authors: Zhu Kun , Zhang Zi-Xuan , Zhang Miao 
Published: July 2024

BACKGROUND
Hypothermia during laparoscopic surgery in patients with multiple trauma is a significant concern owing to its potential complications. Machine learning models offer a promising approach to predict the occurrence of intraoperative hypothermia.
AIM
To investigate the value of machine learning model to predict hypothermia during laparoscopic surgery in patients with multiple trauma.
METHODS
This retrospective study enrolled 220 patients who were admitted with multiple injuries between June 2018 and December 2023. Of these, 154 patients were allocated to a training set and the remaining 66 were allocated to a validation set in a 7:3 ratio. In the training set, 53 cases experienced intraoperative hypothermia and 101 did not. Logistic regression analysis was used to construct a predictive model of intraoperative hypothermia in patients with polytrauma undergoing laparoscopic surgery. The area under the curve (AUC), sensitivity, and specificity were calculated.
RESULTS
Comparison of the hypothermia and non-hypothermia groups found significant differences in sex, age, baseline temperature, intraoperative temperature, duration of anesthesia, duration of surgery, intraoperative fluid infusion, crystalloid infusion, colloid infusion, and pneumoperitoneum volume (P < 0.05). Differences between other characteristics were not significant (P > 0.05). The results of the logistic regression analysis showed that age, baseline temperature, intraoperative temperature, duration of anesthesia, and duration of surgery were independent influencing factors for intraoperative hypothermia during laparoscopic surgery (P < 0.05). Calibration curve analysis showed good consistency between the predicted occurrence of intraoperative hypothermia and the actual occurrence (P > 0.05). The predictive model had AUCs of 0.850 and 0.829 for the training and validation sets, respectively.
CONCLUSION
Machine learning effectively predicted intraoperative hypothermia in polytrauma patients undergoing laparoscopic surgery, which improved surgical safety and patient recovery.