bluuebunny/crossref_metadata_2025_split huggingface dataset:
Small Molecules of Lactoferrin Modification by Ionic Liquids Against SARS-CoV-2: Molecular Docking Enhanced the Results
Authors: Senan Ahmed M , Muhammed Muhammed Tilahun , Akkoc Senem , Alhag Sadeq K. , A.Al-Shahari Eman , Al-Shuraym Laila A. , Alsudays Ibtisam M. , AKTAŞ Ahmet Hakan 
Published: February 2024

Ionic liquid MIE-NH2 displays a new role of development of modification of glycoproteins of lactoferrin through a reductive amination mechanism to synthesize versatile pharmaceuticals. This work introduces a new strategy of modification of Lactoferrin by using methylimidazolium N-ethylamine, this ionic liquid MIE-NH2 linked to N-glycans in Lactoferrin derivatives. Using UPLC/ESI-QTOF and MALDI-TOF mass spectrometry to perform and detect the modifying of ionic liquid-linked glycoproteins. Relevantly, modifying the lactoferrin by MIE-H2 as a small molecule of ion liquid lactoferrin (IL-Lf), which could be a potential antiviral drug and it is achieved by inhibiting various targets. The probability of the lactoferrin modified as a small IL-Lf-molecule to inhibit any of these targets was investigated to find out its potency as a SARS-CoV-2 inhibitor. Molecular docking disclosed the activity of modifying glycoproteins - small IL-Lf-molecules containing amino groups and interaction with targeted Mpro, RdRp, TMPRSS2, and PLpro. Clinically, this study shows the a volubility to provide small IL-Lf-molecules as significantly important drugs that target main protease (Mpro), RNA dependent RNA polymerase (RdRp), transmembrane protease serine 2 (TMPRSS2), and Papain-like protease (PLpro).
Risk factors for cognitive impairment in patients with epilepsy: a systematic review and meta-analysis protocol
Authors: Xu Weiwei , Zou Lin , Wang Ying , Wang Sumei , Wang Kaiyue , Li Lili , Zhang Mi , Yang Songyi , Liu Jinmin 
Published: February 2024

Abstract
Introduction Epilepsy is a common neurological disease affecting at least 50 million people globally. Cognitive impairment is a recognised comorbidity in patients with epilepsy (PWE) and is associated with low quality of life. A large number of studies have investigated the relevant risk factors of cognitive impairment in PWE, while these results were not consistent. The main aim of our study is to review the risk factors that increase the likelihood of cognitive impairment in PWE.
Methods and analysis We will search the literature that reported risk factors for cognitive impairment in PWE in the following online databases from inception to October 2022: China National Knowledge Infrastructure, Wan Fang database, VIP, the Chinese clinical trial registry, China Biology Medicine disc, PubMed, the Cochrane Central Register of Controlled Trials, Embase, and Web of science. Two independent authors will screen the eligible literature according to the inclusion and exclusion criteria. Data extraction will also be conducted by two independent authors, and discrepancies will be solved by a third author. We will use the software Revman 5.4 and Stata 15.1 to perform data synthesis, sensitivity analysis, and meta-regression. If data are sufficient, we will also perform the subgroup analysis. The quality of evidence will be evaluated by the Grading of Recommendations Assessment, Development and Evaluation(GRADE).
Discussion Our study can assist clinicians in better understanding and managing this disease through reviewing the risk factors of cognitive impairment in PWE. We also expect this study can provide reliable evidence for this unanswered research question and promote the development of the prevention and treatment of cognitive impairment in PWE.
Registration number CRD 42022365184.
Non-pharmacological interventions to prevent PICS in critically ill adult patients: a protocol for a systematic review and network meta-analysis
Authors: Sun Xiaoying , Tao Qian , Cui Qing , Liu Yaqiong , Cheng Shouzhen 
Published: February 2024

Abstract
Background
Postintensive care syndrome (PICS) is common in critically ill adults who were treated in the intensive care unit (ICU). Although comparative analyses between types of non-pharmacological measures and usual care to prevent PICS have been performed, it remains unclear which of these potential treatments is the most effective for prevention.
Methods
To obtain the best evidence for non-pharmaceutical interventions in preventing PICS, a systematic review and Bayesian network meta-analyses (NMAs) will be conducted by searching nine electronic databases for randomized controlled trials (RCTs). Two reviewers will carefully screen the titles, abstracts, and full-text papers to identify and extract relevant data. Furthermore, the research team will meticulously check the bibliographic references of the selected studies and related reviews to discover any articles pertinent to this research. The primary focus of the study is to examine the prevalence and severity of PICS among critically ill patients admitted to the ICU. The additional outcomes encompass patient satisfaction and adverse effects related to the preventive intervention. The Cochrane Collaboration’s risk of bias assessment tool will be utilized to evaluate the risk of bias in the included RCTs. To assess the efficacy of various preventative measures, traditional pairwise meta-analysis and Bayesian NMA will be used. To gauge the confidence in the evidence supporting the results, we will utilize the Confidence in NMA tool.
Discussion
There are multiple non-pharmacological interventions available for preventing the occurrence and development of PICS. However, most approaches have only been directly compared to standard care, lacking comprehensive evidence and clinical balance. Although the most effective care methods are still unknown, our research will provide valuable evidence for further non-pharmacological interventions and clinical practices aimed at preventing PICS. The research is expected to offer useful data to help healthcare workers and those creating guidelines decide on the most effective path of action for preventing PICS in adult ICU patients.
Systematic review registration CRD42023439343
Machine Learning Predicts Cerebral Vasospasm in Subarachnoid Hemorrhage Patients
Authors: Zarrin David , Suri Abhinav , McCarthy Karen , Gaonkar Bilwaj , Wilson Bayard , Colby Geoffrey , Freundlich Robert , Macyszyn Luke , Gabel Eilon 
Published: February 2024

Abstract
Background
Cerebral vasospasm (CV) is a feared complication occurring in 20-40% of patients following subarachnoid hemorrhage (SAH) and is known to contribute to delayed cerebral ischemia. It is standard practice to admit SAH patients to intensive care for an extended period of vigilant, resource-intensive, clinical monitoring. We used machine learning to predict CV requiring verapamil (CVRV) in the largest and only multi-center study to date.

Methods
SAH patients admitted to UCLA from 2013-2022 and a validation cohort from VUMC from 2018-2023 were included. For each patient, 172 unique intensive care unit (ICU) variables were extracted through the primary endpoint, namely first verapamil administration or ICU downgrade. At each institution, a light gradient boosting machine (LightGBM) was trained using five-fold cross validation to predict the primary endpoint at various timepoints during hospital admission. Receiver-operator curves (ROC) and precision-recall (PR) curves were generated.

Results
A total of 1,750 patients were included from UCLA, 125 receiving verapamil. LightGBM achieved an area under the ROC (AUC) of 0.88 an average of over one week in advance, and successfully ruled out 8% of non-verapamil patients with zero false negatives. Minimum leukocyte count, maximum platelet count, and maximum intracranial pressure were the variables with highest predictive accuracy. Our models predicted “no CVRV” vs “CVRV within three days” vs “CVRV after three days” with AUCs=0.88, 0.83, and 0.88, respectively. For external validation at VUMC, 1,654 patients were included, 75 receiving verapamil. Predictive models at VUMC performed very similarly to those at UCLA, averaging 0.01 AUC points lower.

Conclusions
We present an accurate (AUC=0.88) and early (>1 week prior) predictor of CVRV using machine learning over two large cohorts of subarachnoid hemorrhage patients at separate institutions. This represents a significant step towards optimized clinical management and improved resource allocation in the intensive care setting of subarachnoid hemorrhage patients.
The Effect of Chia Seed on Fattening Performance and Meat Shelf Life of Broiler Chickens
Authors: ÇELİKOĞLU Onur , Oguz Fatma Karakas 
Published: February 2024

Abstract
This study aims to determine the effect of chia seeds added to broiler rations at the level of 5% on live weight, live weight gain, feed consumption, feed conversion ratio, breast meat fatty acid profile and meat shelf life. A total of 112 daily-aged Sasso chicks were used in the experiment. Chicks were divided into 2 groups of 56 animals. Each group is divided into 8 subgroups of 7 broilers. The experimental group ration was prepared to contain 5% chia seeds starting from the 15th day. During the experiment, feed and water were given ad libitum. Before the experiment, a 14-day initial ration was applied to all groups. The trial lasted 49 days. In the study, there was no statistical difference between the groups in terms of body weight, live weight gain, feed consumption and feed conversion rates (P>0.05). At the end of the experiment, the breast meat Linolenic acid ratio of the experimental group (2.02%) was significantly higher than the control group (1.07%) (P<0.05). The arachidonic acid ratio was found to be significantly lower in the experimental group than in the control group (P<0.05). While the TBARS value of breast meats kept at +4°C for seven days increased in the control group, it decreased significantly in the experimental group (P<0.05). As a result of this study, it can be said that chia seeds have a positive effect on the shelf life of broiler meat.
Evaluation of the quality of internet content available to patients regarding colorectal involvement of endometriosis and associated surgical treatments
Authors: Chuah Yeong Huei Desmond , Liau Yun Hui , Neary Paul , DeMaio Alison , O'Neill Aoife , Clancy Cillian 
Published: February 2024

Abstract
Background: Endometriosis is a chronic inflammatory condition that can infiltrate the rectum and colon in a significant proportion of those affected. Patients commonly seek information regarding their diagnosis and treatment on the internet. Information available, however, may be incomplete or not applicable to all cases. This study aims to evaluate the quality and content of internet information on colorectal endometriosis treatment.
Methods: The search term ‘colorectal endometriosis’ was used to identify websites on popular search engines including Google, Yahoo, and Bing. Websites were classified into 4 subtypes: Academic Institutions, Private Medical Centres, Non-profit Organisations, and Commercial Websites. Two independent assessors evaluated the quality of the websites using the Journal of the American Medical Association (JAMA) benchmark criteria and the DISCERN scoring system.
Results: Sixteen websites were identified. 25% from Academic Institutions, 25% from Private Medical Centres, 12.5% from Non-profit Organisations, and 37.5% were Commercial Websites. Mean(±SD) JAMA criteria and DISCERN scores for all websites were noted as 1.38±1.29 and 44.75±14.53 respectively. Most websites adequately discussed treatment options, mean(±SD) DISCERN score 3.69±1.40. Risks of surgeries were infrequently discussed, mean(±SD) DISCERN score 2.56±1.50. Only 50% of the websites discussed complications of colorectal surgery including anastomotic leak and stoma requirement.
Conclusion: This study demonstrated variable levels of quality and reliability of internet content regarding colorectal endometriosis treatment and associated complications. These findings can be highlighted to patients using the internet to obtain information on colorectal endometriosis.
A novel Approach in MRI Signal Processing for Unveiling the Intricacies of Brain Axonal Organization
Authors: Puri Ashishi , Kumar Sanjeev 
Published: February 2024

Abstract

This article introduces an innovative methodology to unveil the intricacies of white matter fiber pathways in the brain using diffusion MRI. Relying on the rationale that traditional methods observe a significant decrease in signal intensity values in the direction of higher diffusivity, our novel approach strategically opts for diffusion-sensitizing gradient directions (dSGDs, representing the directions along which signals are generated) aligned with reduced signal intensities. By treating these chosen directions as maximum diffusivity directions, we generate uniformly distributed gradient directions (GDs) around them, which are subsequently employed in the reconstruction process. This approach overcomes drawbacks present in existing methods, such as the uniform gradient directions (UGDs) approach, which exhibits gradient direction redundancy, and the adaptive gradient direction (AGDs) approach, requiring solving the linear system twice per voxel. Our method simultaneously addresses both limitations, offering a more efficient and streamlined process. The effectiveness of our proposed methodology is rigorously evaluated through simulations and experiments involving real data, showcasing its superior performance in uncovering the complex white matter fiber pathways in the brain.
Sodium butyrate improves renal injury in diabetic nephropathy through AMPK/SIRT1/PGC-1α signaling pathway
Authors: Kaili Ye , Yanling Zhao , Wen Huang , Yonglin Zhu 
Published: February 2024

Abstract

Diabetic nephropathy (DN) is a prototypical chronic energy metabolism imbalance disease. The AMPK/Sirt1/PGC-1α signaling pathway plays a pivotal role in regulating energy metabolism throughout the body. Gut microbiota ferment indigestible carbohydrates to produce a variety of metabolites, particularly short-chain fatty acids (SCFAs), which exert positive effects on energy metabolism. However, the potential for SCFAs to ameliorate DN-associated renal injury via the AMPK/Sirt1/PGC-1α pathway remains a matter of debate. In this study, we investigated the effects of sodium butyrate (NaB), a SCFA, on energy metabolism in mice with spontaneous DN at two different doses. Body weight, blood glucose and lipid levels, urinary protein excretion, liver and kidney function, interleukin-6 (IL-6) levels, and the expressions of AMPK, phosphorylated AMPK (p-AMPK), mitofusin 2 (MFN2), optic atrophy 1 (OPA1), and glucagon-like peptide-1 receptor (GLP-1R) were monitored in mice. Additionally, butyrate levels, gut microbiota composition, and diversity in colonic stool were also assessed. Our findings demonstrate that exogenous NaB supplementation can improve hyperglycemia and albuminuria, reduce renal tissue inflammation, inhibit extracellular matrix accumulation and glomerular hypertrophy, and increase intestinal butyrate content while altering the gut microbiota composition in DN. Furthermore, NaB was found to upregulate the expressions of MFN2, OPA1, p-AMPK, and GLP-1R in DN renal tissue. These results suggest that NaB can improve the composition of gut microbiota in DN, activate the AMPK/Sirt1/PGC-1α signaling pathway, and enhance mitochondrial function to regulate energy metabolism throughout the body. Collectively, our findings indicate that NaB may be a novel therapeutic agent for the treatment of DN.
Running ameliorates methamphetamine-associated cognitive impairment by regulating hippocampal neurogenesis through the GSK3β/β-catenin pathway
Authors: Wang Huijun , Lu Jiancong , Chen Chuanxiang , Liu Ce , Zhang Jingyi , Zhang Liang , Zhang Cui , Zhang Yining , Chen Xiaojun , Qiu Feng , Zhao Xu 
Published: February 2024

Abstract

Physical exercise is a non-pharmacological therapy that has been widely used in drug rehabilitation centers for the treatment of methamphetamine (METH). METH caused cognitive impairment and suppresses adult hippocampal neurogenesis (AHN) in experimental animals. Exercise can improve cognitive impairment caused by multiple factors through AHN. However, little is known about the role of AHN and exercise in METH-neurotoxic injury. We aim to investigate whether running could ameliorate METH-related cognitive impairment by promoting AHN and the underlying mechanisms. Behavioral experiments were performed to detect behavioral changes in running exercise-treated mice exposed to METH. Immunofluorescence was used to analyze the hippocampal neurogenic lineage, and western blotting and qRT-PCR were used to analyze the expression levels of GSK3β/β-catenin and downstream transcription factors. AAV-Nestin-Ctnnb1 was used to overexpress β-catenin in neural stem cells (NSCs). We found that low-dose METH induced cognitive impairment and decreased AHN instead of inducing cell death in the hippocampus. Moreover, it diminished the proliferation and differentiation of NSCs in the dentate gyrus. Running ameliorated METH-related cognitive impairment by modulating AHN through the GSK3β/β-catenin pathway. Notably, overexpressing β-catenin in NSCs promoted the expression of its downstream transcription factors, rescued AHN, and exerted effects of ameliorating cognitive impairment. Our findings show that METH could cause cognitive impairment through weaken the AHN, and running could effectively ameliorate METH-related cognitive impairment by enhancing AHN through the GSK3β/β-catenin pathway. In addition, our findings provide insights into how exercise ameliorates METH-related cognitive impairment and theoretical basis for exercise therapy.
Protein affects the digestibility of starch in rice pasta
Authors: Chen Mingfen , Lu Tingjang 
Published: February 2024

Abstract
Controlling blood glucose is an important issue and a popular topic of discussion worldwide as it affects a wide range of diseases and demands attention not only from individuals with diabetes, but also from the elderly and even healthy individuals. The digestion of starch is affected by the presence of proteins, which can interact with it. This project aimed to investigate the starch digestibility of rice pasta by replacing the original plant protein with varied plant proteins such as pea protein, soybean protein, and rice protein, and measuring their physicochemical properties, cooking quality, and eating quality after cooking. The results revealed that the addition of plant proteins decreased the peak viscosity, final viscosity, and setback of rice flour, while increasing the cooking loss and decreasing the hardness of rice pasta. Furthermore, the use of pea protein to make rice pasta resulted in a lower estimated glycemic index, which was mainly due to the lower content of rapidly digestible starch.
CoSpred: Machine learning workflow to predict tandem mass spectrum in proteomics
Authors: Stanton Robert , Xue Liang , Tiwary Shivani , Bordyuh Mykola , Beltran Pierre Jean 
Published: February 2024

Abstract
In mass spectrometry-based proteomics, the identification and quantification of peptides and proteins is usually done using database search algorithms or spectral library matching. The use of deep learning algorithms can help improve the identification rates of peptides and proteins through the generation of high-fidelity theoretical spectrum which can be used as the basis of a more complete spectral library than those presently available. Current methods focus on predicting only backbone ions, such as y- and b- ions. However, the inclusion of non-backbone ions is necessary to truly improve spectral library matching. Here we focus on providing a user-friendly machine learning workflow, which we call Complete Spectrum Predictor (CoSpred). Using CoSpred users can create their own machine learning compatible training dataset and then train a Machine Learning model to predict both backbone and non-backbone ions. For the model a transformer encoder architecture is used to predict the complete MS/MS spectrum from a given peptide sequence. This model does not require background knowledge of fragment ion annotations or fragmentation rules. The model outputs the set of pairs (Mi, Ii) where Mi is the m/z (mass-to-charge ratio) of a peak in the spectrum and Ii is the intensity of the peak. The model presented here for validation was trained on the dataset available in the MassIVE data repository and shows superior performance in terms of various metrics (e.g. precision/recall for mass, cosine similarity for peak intensity, etc) between the true and predicted spectra. Furthermore, CoSpred can be used to create custom models that allow for accurate spectrum prediction for different experimental conditions. In addition to the transformer model provided in the package, the code is built modularly to allow for alternate ML models to be easily “plugged in”. The CoSpred workflow (preprocessing->training->inference) provides a path for state-of-art ML capabilities to be more accessible to proteomics scientists.
Traveling waves of a modified Holling-Tanner predator-prey model with degenerate diffusive
Authors: Zhao Zhihong , Cui Huan , Shen Yuwei 
Published: February 2024

Abstract

This paper is concern with the traveling waves for a modified Holling-Tanner predator-prey model with degenerate diffusion. Different from the established approaches of constructing upper-lower solutions, we construct a new and suitable pair of upper-lower solutions by solving three differential equations and establish the existence of traveling waves for any c ≥ c∗ when n ≥ 0. In addition, we obtain the minimal wave speed c∗ = 2√r when n = 0, without reconstructing the upper-lower solutions. Furthermore, the asymptotic behavior of traveling waves at infinity is obtained by the upper-lower solutions and the contracting rectangle method.
Mathematics Subject Classification. 35C07, 35A01, 35K10.