bluuebunny/crossref_metadata_2025_split huggingface dataset:
GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier
Authors: Liu Xin , Zhu Bao , Dai Xia-Wei , Xu Zhi-Ao , Li Rui , Qian Yuting , Lu Ya-Ping , Zhang Wenqing , Liu Yong , Zheng Junnian 
Published: December 2023

Abstract
Background
Lysine glutarylation (Kglu) is one of the most important Post-translational modifications (PTMs), which plays significant roles in various cellular functions, including metabolism, mitochondrial processes, and translation. Therefore, accurate identification of the Kglu site is important for elucidating protein molecular function. Due to the time-consuming and expensive limitations of traditional biological experiments, computational-based Kglu site prediction research is gaining more and more attention.

Results
In this paper, we proposed GBDT_KgluSite, a novel Kglu site prediction model based on GBDT and appropriate feature combinations, which achieved satisfactory performance. Specifically, seven features including sequence-based features, physicochemical property-based features, structural-based features, and evolutionary-derived features were used to characterize proteins. NearMiss-3 and Elastic Net were applied to address data imbalance and feature redundancy issues, respectively. The experimental results show that GBDT_KgluSite has good robustness and generalization ability, with accuracy and AUC values of 93.73%, and 98.14% on five-fold cross-validation as well as 90.11%, and 96.75% on the independent test dataset, respectively.

Conclusion
GBDT_KgluSite is an effective computational method for identifying Kglu sites in protein sequences. It has good stability and generalization ability and could be useful for the identification of new Kglu sites in the future. The relevant code and dataset are available at https://github.com/flyinsky6/GBDT_KgluSite.
Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction
Authors: Yan Wu , Tan Li , Mengshan Li , Weihong Zhou , Sheng Sheng , Jun Wang , Fu-an Wu 
Published: December 2023

Abstract
Background
DNA methylation is a form of epigenetic modification that impacts gene expression without modifying the DNA sequence, thereby exerting control over gene function and cellular development. The prediction of DNA methylation is vital for understanding and exploring gene regulatory mechanisms. Currently, machine learning algorithms are primarily used for model construction. However, several challenges remain to be addressed, including limited prediction accuracy, constrained generalization capability, and insufficient learning capacity.

Results
In response to the aforementioned challenges, this paper leverages the similarities between DNA sequences and time series to introduce a time series-based hybrid ensemble learning model, called Multi2-Con-CAPSO-LSTM. The model utilizes multivariate and multidimensional encoding approach, combining three types of time series encodings with three kinds of genetic feature encodings, resulting in a total of nine types of feature encoding matrices. Convolutional Neural Networks are utilized to extract features from DNA sequences, including temporal, positional, physicochemical, and genetic information, thereby creating a comprehensive feature matrix. The Long Short-Term Memory model is then optimized using the Chaotic Accelerated Particle Swarm Optimization algorithm for predicting DNA methylation.

Conclusions
Through cross-validation experiments conducted on 17 species involving three types of DNA methylation (6 mA, 5hmC, and 4mC), the results demonstrate the robust predictive capabilities of the Multi2-Con-CAPSO-LSTM model in DNA methylation prediction across various types and species. Compared with other benchmark models, the Multi2-Con-CAPSO-LSTM model demonstrates significant advantages in sensitivity, specificity, accuracy, and correlation. The model proposed in this paper provides valuable insights and inspiration across various disciplines, including sequence alignment, genetic evolution, time series analysis, and structure–activity relationships.
Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population
Authors: Heo Kyu-Nam , Seok Jeong Yeon , Ah Young-Mi , Kim Kwang-il , Lee Seung-Bo , Lee Ju-Yeun 
Published: December 2023

Abstract
Background
Falls impact over 25% of older adults annually, making fall prevention a critical public health focus. We aimed to develop and validate a machine learning-based prediction model for serious fall-related injuries (FRIs) among community-dwelling older adults, incorporating various medication factors.

Methods
Utilizing annual national patient sample data, we segmented outpatient older adults without FRIs in the preceding three months into development and validation cohorts based on data from 2018 and 2019, respectively. The outcome of interest was serious FRIs, which we defined operationally as incidents necessitating an emergency department visit or hospital admission, identified by the diagnostic codes of injuries that are likely associated with falls. We developed four machine-learning models (light gradient boosting machine, Catboost, eXtreme Gradient Boosting, and Random forest), along with a logistic regression model as a reference.

Results
In both cohorts, FRIs leading to hospitalization/emergency department visits occurred in approximately 2% of patients. After selecting features from initial set of 187, we retained 26, with 15 of them being medication-related. Catboost emerged as the top model, with area under the receiver operating characteristic of 0.700, along with sensitivity and specificity rates around 65%. The high-risk group showed more than threefold greater risk of FRIs than the low-risk group, and model interpretations aligned with clinical intuition.

Conclusion
We developed and validated an explainable machine-learning model for predicting serious FRIs in community-dwelling older adults. With prospective validation, this model could facilitate targeted fall prevention strategies in primary care or community-pharmacy settings.
Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer’s disease spectrum: a COMPASS-ND study
Authors: Bohn Linzy , Drouin Shannon M. , McFall G. Peggy , Rolfson Darryl B. , Andrew Melissa K. , Dixon Roger A. 
Published: December 2023

Abstract
Background
Frailty indicators can operate in dynamic amalgamations of disease conditions, clinical symptoms, biomarkers, medical signals, cognitive characteristics, and even health beliefs and practices. This study is the first to evaluate which, among these multiple frailty-related indicators, are important and differential predictors of clinical cohorts that represent progression along an Alzheimer’s disease (AD) spectrum. We applied machine-learning technology to such indicators in order to identify the leading predictors of three AD spectrum cohorts; viz., subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and AD. The common benchmark was a cohort of cognitively unimpaired (CU) older adults.

Methods
The four cohorts were from the cross-sectional Comprehensive Assessment of Neurodegeneration and Dementia dataset. We used random forest analysis (Python 3.7) to simultaneously test the relative importance of 83 multi-modal frailty indicators in discriminating the cohorts. We performed an explainable artificial intelligence method (Tree Shapley Additive exPlanation values) for deep interpretation of prediction effects.

Results
We observed strong concurrent prediction results, with clusters varying across cohorts. The SCI model demonstrated excellent prediction accuracy (AUC = 0.89). Three leading predictors were poorer quality of life ([QoL]; memory), abnormal lymphocyte count, and abnormal neutrophil count. The MCI model demonstrated a similarly high AUC (0.88). Five leading predictors were poorer QoL (memory, leisure), male sex, abnormal lymphocyte count, and poorer self-rated eyesight. The AD model demonstrated outstanding prediction accuracy (AUC = 0.98). Ten leading predictors were poorer QoL (memory), reduced olfaction, male sex, increased dependence in activities of daily living (n = 6), and poorer visual contrast.

Conclusions
Both convergent and cohort-specific frailty factors discriminated the AD spectrum cohorts. Convergence was observed as all cohorts were marked by lower quality of life (memory), supporting recent research and clinical attention to subjective experiences of memory aging and their potentially broad ramifications. Diversity was displayed in that, of the 14 leading predictors extracted across models, 11 were selectively sensitive to one cohort. A morbidity intensity trend was indicated by an increasing number and diversity of predictors corresponding to clinical severity, especially in AD. Knowledge of differential deficit predictors across AD clinical cohorts may promote precision interventions.
Correlation between PAPP-A serum levels in the first trimester of pregnancy with the occurrence of gestational diabetes, a multicenter cohort study
Authors: Borna Sedigheh , Ashrafzadeh Masoumeh , Ghaemi Marjan , Eshraghi Nasim , Hivechi Nafiseh , Hantoushzadeh Sedigheh 
Published: December 2023

Abstract
Objective
This study aimed to investigate the association between first-trimester Pregnancy-associated plasma protein A (PAPP-A) levels and subsequent gestational diabetes mellitus (GDM) development.

Method
The study was conducted on 5854 pregnant women who attended routine prenatal care. Maternal biomarkers, including PAPP-A and free beta hCG, were measured for all women in a referral laboratory and converted to MoM values. Pregnant women were divided into two groups, based on the serum concentration of PAPP-A, (PAPP-A > 0.4 (normal) and PAPP-A < 0.4 (low)). Data on the screening test for GDM and pregnancy outcomes were collected and analyzed with appropriate tests.

Result
Of the 5854 pregnant women, 889 (15.19%) developed GDM. The maternal PAPP-A MoM concentrations were significantly lower in GDM cases compared to controls. Indeed, gestational age at delivery and birth weight were significantly lower (p < 0.001) in PAPP-A MoM < 0.4, and the rate of intrauterine growth restriction (IUGR) was significantly higher (p < 0.001). ROC analysis revealed that the sensitivity and specificity of MoM concentration for predicting GDM were 53.3% and 51.9%, respectively.

Conclusion
Lower maternal PAPP-A in early pregnancy can lead to glucose intolerance and increase the risk of subsequent GDM development. In addition, decreased serum concentration of PAPP-A is significantly correlated to lower birth weight and IUGR.
PD-1 inhibitors combined with paclitaxel and cisplatin in first-line treatment of esophageal squamous cell carcinoma (ESCC): a network meta-analysis
Authors: Zhao Jia , Zhang Simeng , Guo Xiaoyu , li Ce , Yang Bowen , Qu Xiujuan , Wang Shuo 
Published: December 2023

Abstract
Background
The combinations of PD-1 inhibitors with paclitaxel/cisplatinum (PD-1 + TP) and fluoropyrimidine/cisplatinum (PD-1 + FP) both have been shown to improve overall survival (OS) and progression-free survival (PFS) in patients with previously untreated, advanced esophageal squamous cell carcinoma (ESCC). However, there is no consensus on which chemotherapy regimen combined with PD-1 has better efficacy. To deal with this important issue in the first-line treatment of patients with ESCC, a network meta-analysis (NMA) was performed.

Methods
Data were collected from eligible studies searched in Medline, Web of Science, PubMed, the Cochrane Library and Embase. The pooled hazard ratio (HR) for the OS, and PFS, odds ratio (OR) for the objective response rate (ORR) and ≥ 3 grade treatment-related adverse events (≥ 3TRAEs) were estimated to evaluate the efficacy of PD-1 inhibitors combined with TP or FP.

Results
Five RCTs and one retrospective study involving 3685 patients and evaluating four treatments were included in this NMA. Compared to other treatments, PD-1 + TP was better. For the PFS, the HRs for PD-1 + TP compared to PD-1 + FP, TP and FP were 0.59 (0.44, 0.80), 0.56 (0.51, 0.61) and 0.45 (0.37, 0.56) respectively. For the OS, PD-1 + TP was also a better treatment compared to other treatments. The HRs were 0.74 (0.56, 0.96), 0.64 (0.57, 0.71), 0.53 (0.43, 0.67) respectively. For the ORR, there was no significant difference between PD-1 + TP and PD-1 + FP, and the ORs were 1.2 (0.69, 2.11). Compare with TP and FP, PD-1 + TP had an obvious advantage, ORs were 2.5 (2.04, 3.04) and 2.95 (1.91, 4.63). For ≥ 3TRAEs, PD-1 + TP compared to other treatments, ORs were 1.34 (0.74, 2.46) and 1.13 (0.92, 1.38) and 2.23 (1.35, 3.69).

Conclusion
PD-1 + TP significantly improved both PFS and OS compared to PD-1 + FP. Taking into account both efficacy and safety, PD-1 + TP may be a superior first-line treatment option for ESCC.
Psychometric properties of an Arabic translation of the Big Three Perfectionism Scale–Short Form (BTPS-SF) in a community sample of adults
Authors: Fekih-Romdhane Feten , Rogoza Radoslaw , Hallit Rabih , Malaeb Diana , Sakr Fouad , Dabbous Mariam , Sawma Toni , Obeid Sahar , Hallit Souheil 
Published: December 2023

Abstract
Background
Despite the high clinical relevance of the perfectionism construct as a transdiagnostic contributor to different mental health symptoms, and the recent burgeoning of research in this area across cultures in the past two decades, the Arab region was one of the cultural settings experiencing the slowest progress in this line of research. This study aimed to make a meaningful contribution to the literature by validating an Arabic-language version of the 16-item Big Three Perfectionism Scale–Short Form (BTPS-SF). In particular, we sought to examine structure and concurrent validity, internal consistency, and measurement invariance across gender groups.

Method
A web-based, convenience sampling method was adopted to collect a sample of Arabic-speaking adults from the general population of Lebanon (N = 515; aged 27.55 ± 10.92 years; 69.9% females). The forward-backward method was applied in translating the Arabic version of the BTPS-SF.

Results
The examination of the internal structure, using Confirmatory Factor Analysis (CFA), demonstrated that the three-factor model (i.e., rigid, self-critical, and narcissistic perfectionism) fitted well to the data. All three factors of the Arabic 16-item BTPS-SF yielded excellent reliability estimates, with both Cronbach’s alpha and McDonald’s omega ranging from 0.83 to 0.86. Multi-group CFA revealed that fit indices showed no significant difference in model fit at the configural, metric, and scalar levels, thus suggesting that the factor loadings, pattern structure, and item intercepts are invariant across gender groups. Finally, BTPS-SF subscales scores correlated positively with psychological distress (i.e., depression, stress and anxiety), and inversely with subjective well-being, indicating an acceptable concurrent validity.

Conclusion
The present findings allow us to conclude that the Arabic BTPS-SF permits to capture reliably and validly three main factors of perfectionism. We hope that providing this psychometrically sound scale will encourage its large use not only in empirical research, but also in clinical applications, including psychological screening and treatment monitoring.
The usual Interstitial pneumonia pattern in autoimmune rheumatic diseases
Authors: Luppi Fabrizio , Manfredi Andreina , Faverio Paola , Andersen Michael Brun , Bono Francesca , Pagni Fabio , Salvarani Carlo , Bendstrup Elisabeth , Sebastiani Marco 
Published: December 2023

AbstractUsual Interstitial Pneumonia (UIP) is characterized by progression of lung parenchyma that may be observed in various autoimmune rheumatic diseases (ARDs), including rheumatoid arthritis and connective tissue diseases. From a diagnostic point of view, a UIP pattern related to ARDs may display imaging and pathological features able to distinguish it from that related to IPF, such as the “straight-edge” sign at HRCT and lymphoplasmacytic infiltrates at histologic specimens. Multidisciplinary approach (MDD), involving at least pulmonologist, rheumatologist and radiologist, is fundamental in the differential diagnosis process, but MDD is also required in the evaluation of severity, progression and response to treatment, that is based on the combination of changes in symptoms, pulmonary function trends, and, in selected patients, serial CT evaluation. Differently from IPF, in patients with ARDs both functional evaluation and patient-reported outcomes may be affected by systemic involvement and comorbidities, including musculoskeletal manifestations of disease. Finally, in regards to pharmacological treatment, immunosuppressants have been considered the cornerstone of therapy, despite the lack of solid evidence in most cases; recently, antifibrotic drugs were also proposed for the treatment of progressive fibrosing ILDs other than IPF. In ARD-ILD, the therapeutic choice should balance the need for the control of systemic and lung involvements with the risk of adverse events from multi-morbidities and -therapies. Purpose of this review is to summarize the definition, the radiological and morphological features of the UIP pattern in ARDs, together with risk factors, diagnostic criteria, prognostic evaluation, monitoring and management approaches of the UIP-ARDs.
Cost-effectiveness of the anti-vascular endothelial growth factor intravitreal injection and panretinal photocoagulation for patients with proliferative diabetic retinopathy in South Korea
Authors: Lee Hyeon-Jeong , Cho Songhee , Park Jungeun , Jin Yan , Kim Hyung Min , Jee Donghyun 
Published: December 2023

Abstract
Background
We determined the cost-effectiveness of the anti-vascular endothelial growth factor (VEGF) intravitreal injection versus panretinal photocoagulation (PRP) for patients with proliferative diabetic retinopathy (PDR) in South Korea.

Methods
We simulated four treatment strategies using PRP and the anti-VEGF injection by constructing a Markov model for a hypothetical cohort of 50-year-old PDR patients: (1) PRP only; (2) anti-VEGF injection only; (3) PRP first; and (4) anti-VEGF injection first.

Results
In this cost-effectiveness analysis, compared with only-PRP, the incremental cost-effectiveness ratio was $95,456 per quality-adjusted life-year (QALY) for PRP first, $34,375 per QALY for anti-VEGF injection first, and $33,405 per QALY for anti-VEGF injection only from a healthcare perspective. From the societal and payer perspective, strategy (2) was more cost-saving and effective than (1). In the probabilistic sensitivity analysis, only-PRP was cost-effective up to the willingness-to-pay (WTP) of about $42,000, while anti-VEGF injection only was cost-effective from a healthcare perspective. From the societal and payer perspectives, regardless of the value of WTP, anti-VEGF injection only was the most cost-effective strategy.

Conclusion
In our study, the anti-VEGF injection for PDR was cost-effective from the payer and societal perspectives.
Alpha-defensins inhibit ERK/STAT3 signaling during monocyte-macrophage differentiation and impede macrophage function
Authors: Lee Jungnam , Mohammad Naweed , Lu Yuanqing , Oshins Regina , Aranyos Alek , Brantly Mark 
Published: December 2023

AbstractAlpha-1-antitrypsin deficiency (AATD) is a genetic disorder associated with a 5–tenfold decrease in lung levels of alpha-1-antitrypsin (AAT) and an increased risk for obstructive lung disease. α-defensins are cationic broad-spectrum cytotoxic and pro-inflammatory peptides found in the azurophilic granules of neutrophils. The concentration of α-defensins is less than 30 nM in the bronchoalveolar lavage fluid of healthy controls but is up to 6 μM in AATD individuals with significant lung function impairment. Alveolar macrophages are generally classified into pro-inflammatory (M1) or anti-inflammatory (M2) subsets that play distinct roles in the initiation and resolution of inflammation. Therefore, monocyte-macrophage differentiation should be tightly controlled to maintain lung integrity. In this study, we determined the effect of α-defensins on monocyte-macrophage differentiation and identified the molecular mechanism of this effect. The results of this study demonstrate that 2.5 μM of α-defensins inhibit the phosphorylation of ERK1/2 and STAT3 and suppress the expression of M2 macrophage markers, CD163 and CD206. In addition, a scratch assay shows that the high concentration of α-defensins inhibits cell movement by ~ 50%, and the phagocytosis assay using flow cytometry shows that α-defensins significantly reduce the bacterial phagocytosis rate of monocyte-derived macrophages (MDMs). To examine whether exogenous AAT is able to alleviate the inhibitory effect of α-defensins on macrophage function, we incubated MDMs with AAT prior to α-defensin treatment and demonstrate that AAT improves the migratory ability and phagocytic ability of MDMs compared with MDMs incubated only with α-defensins. Taken together, this study suggests that a high concentration of α-defensins inhibits the activation of ERK/STAT3 signaling, negatively regulates the expression of M2 macrophage markers, and impairs innate immune function of macrophages.
The nutritional composition and cell size of microbial biomass for food applications are defined by the growth conditions
Authors: Sakarika Myrsini , Kerckhof Frederiek-Maarten , Van Peteghem Lotte , Pereira Alexandra , Van Den Bossche Tim , Bouwmeester Robbin , Gabriels Ralf , Van Haver Delphi , Ulčar Barbara , Martens Lennart , Impens Francis , Boon Nico , Ganigué Ramon , Rabaey Korneel 
Published: December 2023

Abstract
Background
It is increasingly recognized that conventional food production systems are not able to meet the globally increasing protein needs, resulting in overexploitation and depletion of resources, and environmental degradation. In this context, microbial biomass has emerged as a promising sustainable protein alternative. Nevertheless, often no consideration is given on the fact that the cultivation conditions affect the composition of microbial cells, and hence their quality and nutritional value. Apart from the properties and nutritional quality of the produced microbial food (ingredient), this can also impact its sustainability. To qualitatively assess these aspects, here, we investigated the link between substrate availability, growth rate, cell composition and size of Cupriavidus necator and Komagataella phaffii.

Results
Biomass with decreased nucleic acid and increased protein content was produced at low growth rates. Conversely, high rates resulted in larger cells, which could enable more efficient biomass harvesting. The proteome allocation varied across the different growth rates, with more ribosomal proteins at higher rates, which could potentially affect the techno-functional properties of the biomass. Considering the distinct amino acid profiles established for the different cellular components, variations in their abundance impacts the product quality leading to higher cysteine and phenylalanine content at low growth rates. Therefore, we hint that costly external amino acid supplementations that are often required to meet the nutritional needs could be avoided by carefully applying conditions that enable targeted growth rates.

Conclusion
In summary, we demonstrate tradeoffs between nutritional quality and production rate, and we discuss the microbial biomass properties that vary according to the growth conditions.