bluuebunny/crossref_metadata_2025_split huggingface dataset:
Validation of regression‐based change formulae for mild cognitive impairment and Alzheimer's disease
Authors: Duff Kevin , Sevigny‐Resetco Deborah 
Published: September 2024

AbstractINTRODUCTIONIdentification of cognitive decline is critical in older adults at risk for dementia. In a 2020 study reported in Archives of Clinical Neuropsychology, Kiselica and colleagues developed standardized regression‐based (SRB) change formulae for the Uniform Data Set 3.0 Neuropsychological Battery in cognitively unimpaired older adults. However, validation of their applicability in impaired individuals is needed.METHODSUsing longitudinal data on 5974 participants (cognitively unimpaired, mild cognitive impairment, dementia) from the National Alzheimer's Coordinating Center, SRB change scores were calculated for each individual and compared across groups.RESULTSAcross 6 to 24 months, minimal cognitive change was observed in cognitively unimpaired participants. Modest declines were seen in those with mild cognitive impairment and substantial declines in those with dementia. Change scores were negatively correlated with the Clinical Dementia Rating scale. In impaired individuals, SRB scores indicated more decline in those with positive amyloid scans.DISCUSSIONValidation of SRB scores affords greater confidence in employing them in clinical and research settings.Highlights
Validation of regression‐based cognitive change scores in impaired samples.
Clear differences on change scores across three groups (intact, MCI, dementia).
Largely stable scores in intact participants, but notable decline in MCI and dementia.
Moderate to strong relationship between change scores and the Clinical Dementia Rating scale sum of boxes.
Explainable machine learning and online calculators to predict heart failure mortality in intensive care units
Authors: Chen An‐Tian , Zhang Yuhui , Zhang Jian 
Published: September 2024

AbstractAimsThis study aims to develop explainable machine learning models and clinical tools for predicting mortality in patients in the intensive care unit (ICU) with heart failure (HF).MethodsPatients diagnosed with HF who experienced their first ICU stay lasting between 24 h and 28 days were selected from the Medical Information Mart for Intensive Care IV (MIMIC‐IV) database. The primary outcome was all‐cause mortality within 28 days. Data analysis was performed using Python and R, with feature selection conducted via least absolute shrinkage and selection operator (LASSO) regression. Fifteen models were evaluated, and the most effective model was rendered explainable through the Shapley additive explanations (SHAP) approach. A nomogram was developed based on logistic regression to facilitate interpretation. For external validation, the eICU database was utilized.ResultsAfter selection, the study included 2343 records, with 1808 surviving and 535 deceased patients. The median age of the study population was 70.00, with ~3/5 males (60.31%). The median length of stay in the ICU was 6.00 days. The median age of the survival group was younger than the non‐survival group (69.00 vs. 73.00), and non‐survival patients spent longer time in the ICU. Seventy‐five features were initially selected, including basic information, vital signs, laboratory tests, haemodynamics and oxygen status. LASSO regression determined the shrinkage parameter α = 0.020, and 44 features were chosen for model construction. The linear discriminant analysis (LDA) model showed the best performance, and the accuracy reached 0.8354 in the training cohort and 0.8563 in the testing cohort. It showed satisfying area under the curve (AUC), recall, precision, F1 score, Cohen's kappa score and Matthew's correlation coefficient. The concordance index (c‐index) reached 0.7972 in the training cohort and 0.8125 in the testing cohort. In external validation, the LDA model achieved approximately 0.9 in accuracy, precision, recall and F1 score, with an AUC of 0.79. Univariable analysis was performed in the training cohort. Features that differed significantly between the survival and non‐survival groups were subjected to multiple logistic regression. The nomogram built on multiple logistic regression included 14 features and demonstrated excellent performance. The AUC of the nomogram is 0.852 in the training cohort, 0.855 in the internal validation cohort and 0.770 in the external validation cohort. The calibration curve showed good consistency.ConclusionsThe study developed an LDA and a nomogram model for predicting mortality in HF patients in the ICU. The SHAP approach was employed to elucidate the LDA model, enhancing its utility for clinicians. These models were made accessible online for clinical application.
An energy consumption rectification method based on Bayesian linear regression and heating degree‐days
Authors: Sun Shouchen , Wang Jiandong , Sun Qingdian , Zhao Changsheng 
Published: September 2024

AbstractThe time‐varying external environment is one of the main variables influencing heating energy consumptions, so that its influence should be rectified when energy savings of different heating modes are calculated. This paper proposes an energy consumption rectification method based on Bayesian linear regression and heating degree‐days, to obtain heating energy consumptions without the influence of different outdoor temperatures. The proposed method consists of three main steps. First, a physical model of heating houses is used to prove a relationship between energy consumptions and heating degree‐days. Second, Bayesian linear regression is exploited to estimate uncertainty ranges of heating energy consumptions. Finally, heating energy consumptions are rectified, and energy savings with their uncertainty ranges for different heating modes under the same outdoor temperature are obtained. The proposed method does not require the physical parameters of heating houses to facilitate practical implementation. Additionally, it provides uncertainty ranges of heating energy consumptions to measure the estimation accuracy. Numerical and experimental examples show that the proposed method provides more accurate estimates of heating energy consumptions than existing methods.
Potential of natural stimulants and spirulina algae extracts on Cape gooseberry plant: A study on functional properties and enzymatic activity
Authors: Heydarnajad Giglou Rasoul , Torabi Giglou Mousa , Hatami Mehrnaz , Ghorbanpour Mansour 
Published: September 2024

AbstractThe need to discover new strategy to enhance agricultural productivity while minimizing negative effects on cultivation system is the driving force behind much novel research. Little is known about the use of natural growth promoters, in combination with Spirulina algae extract (Sae), on plants' quality and quantity characteristics. This can be an effective strategy to reduce the impact of environmental stress on plants. The aim of this study was to investigate the effects of natural growth promoters in combination with Sae on the functional properties and enzymatic activity of Cape gooseberry plants. For this purpose, natural growth stimulants, Kitoplus® and Azotobacter, were foliar applied to plants in a completely randomized factorial design under greenhouse conditions, with varying concentrations of Sae. Results indicated that the use of Sae in conjunction with natural growth promoters had a significant impact on increasing the yield of Cape gooseberry plants. The study found that using Kitoplus® and Spirulina extract at a concentration of 0.5% increased the yield of Cape gooseberry plants by 35.82% compared to control plants, with a yield of 310.43 g per plant. The leaves of Physalis plants treated with 0.5% Kitoplus®, Azotobacter biofertilizer, and Sae had the highest amount of chlorophyll, which was observed to be 46.2%, 40.1%, and 32.3% higher than that of control, respectively. Exposure to Kitoplus® (0.5%) and Azotobacter biofertilizer (0.5%) resulted in the highest percentage (25.49%) of dry to fresh weight ratio (DF) per fruit in the plant. Additionally, plants treated with Kitoplus® at 0.5% in combination with Azotobacter (0.5%) and Sae (0.5%) showed a 14.83% increase in vitamin C (VITC) levels compared to control plants. The highest levels of total antioxidant content (73.8%) and total carbohydrate content were obtained from plants treated with Spirulina algae extract spray at a concentration of 0.5% in the presence of Kitoplus® and Azotobacter. Results also showed that the highest activity of peroxidase (POD) (by 43.5% and 38.8%) and ascorbate peroxidase (APX) (by 29.1% and 28.4%) enzymes was observed in plants sprayed with natural growth promoters in the presence of Spirulina algae extract at concentrations of 0.25% and 0.5%, respectively. Overall, this research opens the possibility of utilizing foliar spray of effective biostimulants in combination with Sae to improve the quantity and quality of Cape gooseberry plants. Implementing this simple, safe, eco‐friendly, and low‐cost management tool can result in nutritionally rich fruit that benefits the consumer at little or no additional cost.
Kinetic Insights into Methanol Synthesis from CO2 Hydrogenation at Atmospheric Pressure over Intermetallic Pd2Ga Catalyst
Authors: Ahmad Kaisar , Dabbawala Aasif Asharafbhai , Polychronopoulou Kyriaki , Anjum Dalaver , Gacesa Marko , Abi Jaoude Maguy 
Published: September 2024

AbstractThis study presents a single‐site microkinetic model for methanol synthesis by CO2 hydrogenation over intermetallic Pd2Ga/SiO2. A reaction path analysis (RPA) combining theoretical results and realistic catalyst surface reaction data is established to elucidate the reaction mechanism and kinetic models of CO2 hydrogenation to methanol and CO. The RPA leads to the derivation of rate expressions for both reactions without presumptions about the most abundant reactive intermediate (MARI) and rate‐determining step (rds). The formation of H2COOH* is found to be the rds (step 19) for methanol synthesis via the formate pathway, with CO2 and H‐atoms adsorbed on intermetallic sites as the MARIs. The derived kinetic model is corroborated with experimental data acquired under different reaction conditions, using a lab‐scale fixed‐bed reactor and Pd2Ga/SiO2 nanoparticles prepared by incipient wetness impregnation. The excellent agreement between the experimental data and the kinetic model (R2 = 0.99) substantiates the proposed mechanism with an activation energy of 61.52 kJ mol‐1 for methanol synthesis. The reported catalyst exhibits high selectivity to methanol (96%) at 1 bar, 150 °C, and H2/CO2 ratio of 3:1. These findings provide critical insights to optimize catalysts and processes targeting CO2 hydrogenation at atmospheric pressure and low temperatures for on‐demand energy production.
Microglial morphology aligns with vigilance stage‐specific neuronal oscillations in a brain region‐dependent manner
Authors: Steffens Sarah , Mäkinen Hilla , Stenberg Tarja , Wigren Henna‐Kaisa 
Published: September 2024

AbstractMicroglia, the resident immune cells in the brain, dynamically adapt their morphology based on their functional state. This study explored the relationship between microglial morphology and sleep–wake cycles in mice. Using Iba1 immunostaining to identify microglia, we quantified morphological changes in microglia at different timepoints in multiple brain regions (cortex, hippocampus, basal forebrain, hindbrain, and cerebellum) in B6 male mice using semi‐automated 3D structural analysis. Simultaneously, in a separate group, we monitored wake and sleep stage‐specific brain activity using EEG/EMG recordings. During natural sleep–wake cycles, we observed increased microglial complexity (enlarged volume, territorial coverage, and ramification) during wakefulness, characterized by high‐frequency theta (8–12 Hz) and gamma activity (30–80 Hz). Conversely, during NREM sleep, which is dominated by delta activity (0.5–4 Hz), microglia displayed reduced complexity. Notably, this pattern was absent in brain regions lacking direct functional connections to areas generating vigilance stage‐dependent thalamocortical oscillations. We then extended wakefulness to decouple circadian influence from sleep–wake‐specific neuronal activity. This procedure attenuated the decrease in microglial complexity observed during natural sleep, suggesting a crucial role for neuronal activity. Subsequent recovery sleep restored microglial features, independent of the time of day (zeitgeber time). These findings reveal a dynamic interplay between vigilance stage‐specific thalamocortical activity and microglial morphology across various brain regions. This suggests a potential role for microglia in sleep regulation and warrants further investigation to understand the underlying mechanisms.
Individual‐specific metabolic network based on 18F‐FDG PET revealing multi‐level aberrant metabolisms in Parkinson's disease
Authors: Lu Weizhao , Song Tianbin , Li Jiping , Zhang Yuqing , Lu Jie 
Published: September 2024

AbstractMetabolic network analysis in Parkinson's disease (PD) based on 18F‐FDG PET has revealed PD‐related metabolic patterns. However, alterations at the systemic metabolic network level and at the connection level between different brain regions still remain unknown. This study aimed to explore metabolic network alterations at multiple network levels among PD patients using an individual‐specific metabolic network (ISMN) approach. 18F‐FDG‐PET images of patients with PD (n = 34) and healthy subjects (n = 47) were collected. Healthy subjects were further separated into reference group (n = 28) and control group (n = 19) randomly. Standardized uptake value normalized by lean body mass ratio (SULr) maps was calculated from the PET images. ISMNs were constructed based on SULr maps for PD patients and controls with reference to the reference group. Comparisons of nodal and edge features were performed between PD and control groups. Correlation analysis was conducted between multilevel network properties and clinical scales in PD group. A linear classifier was trained based on nodal or edge features to distinguish PD from controls. The distance from each patient's ISMN to the group‐level difference network showed a negative correlation with Hoehn and Yahr stage (r = −0.390, p = .023). Eight nodes from ISMN were identified which exhibited significantly increased nodal degree in PD patients compared to controls (p < .05). Eleven edges were observed which demonstrated significant distinctions in Z‐score values in comparisons to the control group (p < .05). Furthermore, the nodal and edge features showed comparable performances in PD diagnosis compared to the traditional SULr values, with area under the receiver operating characteristic curve larger than 0.91. The proposed ISMN approach revealed systemic metabolic deviations, as well as nodal and edge distinctions in PD, which might be supplementary to the existing findings on PD‐related metabolic patterns.
Changes in the structure of spontaneous speech predict the disruption of hierarchical brain organization in first‐episode psychosis
Authors: He Rui , Alonso‐Sánchez Maria Francisca , Sepulcre Jorge , Palaniyappan Lena , Hinzen Wolfram 
Published: September 2024

AbstractPsychosis implicates changes across a broad range of cognitive functions. These functions are cortically organized in the form of a hierarchy ranging from primary sensorimotor (unimodal) to higher‐order association cortices, which involve functions such as language (transmodal). Language has long been documented as undergoing structural changes in psychosis. We hypothesized that these changes as revealed in spontaneous speech patterns may act as readouts of alterations in the configuration of this unimodal‐to‐transmodal axis of cortical organization in psychosis. Results from 29 patients with first‐episodic psychosis (FEP) and 29 controls scanned with 7 T resting‐state fMRI confirmed a compression of the cortical hierarchy in FEP, which affected metrics of the hierarchical distance between the sensorimotor and default mode networks, and of the hierarchical organization within the semantic network. These organizational changes were predicted by graphs representing semantic and syntactic associations between meaningful units in speech produced during picture descriptions. These findings unite psychosis, language, and the cortical hierarchy in a single conceptual scheme, which helps to situate language within the neurocognition of psychosis and opens the clinical prospect for mental dysfunction to become computationally measurable in spontaneous speech.
Evaluating a process‐guided deep learning approach for predicting dissolved oxygen in streams
Authors: Sadler Jeffrey M. , Koenig Lauren E. , Gorski Galen , Carter Alice M. , Hall Robert O. 
Published: September 2024

AbstractDissolved oxygen (DO) is a critical water quality constituent that governs habitat suitability for aquatic biota, biogeochemical reactions and solubility of metals in streams. Recently introduced high‐frequency sensors have increased our ability to measure DO, but we still lack the capacity to understand and predict DO concentrations at high spatial resolutions or in unmonitored locations. Machine learning (ML) has been a commonly used approach for modelling DO, however, conventional ML models have no representation of the limnological processes governing DO dynamics. Here we implement and evaluate two process‐guided deep learning (PGDL) approaches for predicting daily minimum, mean and maximum DO concentrations in rivers from the Delaware River Basin, USA. In both cases, a multi‐task approach was taken in which the PGDL models predicted stream metabolism and gas exchange rates in addition to the DO concentrations themselves. Our results showed that for these sites, the PGDL approaches did not improve upon baseline predictions in temporal and spatially similar holdout experiments. One of the approaches did, however, improve predictions when applied to spatially dissimilar sites. Although this particular PGDL approach did not improve predictive accuracy in most cases, our results suggest that process guidance, perhaps a more constrained approach, could benefit a data‐driven DO model.
Malignant salivary gland tumors of the tongue: A multicenter REFCOR study
Authors: Poissonnet Valentine , Uro‐Coste Emmanuelle , Chabrillac Emilien , Modesto Anouchka , Even Caroline , Fakhry Nicolas , Garrel Renaud , Malard Olivier , Chatelet Florian , Dupin Charles , Jegoux Franck , Schultz Philippe , Culié Dorian , Baudouin Robin , Evrard Diane , Ransy Pierre , Morinière Sylvain , Mirghani Haitham , Mouawad François , Righini Christian , Duflo Suzy , Marie Jean‐Paul , Jourdan‐Soulier Florence , Atallah Sarah , Brenet Esteban , Bertolus Chloé , Ceruse Philippe , Ramin Lionel , Dufour Xavier , Nguyen Duc Trung , Bastit Vianney , Segier Bertille , Vergez Sébastien 
Published: September 2024

AbstractSalivary carcinomas of minor salivary glands are very infrequent tumors. When located in the tongue, the therapeutic strategy may comprise upfront surgery, which may be debilitating, and/or (chemo‐)radiotherapy. The aim of this study was to identify the prognostic factors of salivary carcinomas of the tongue in a population‐based cohort. This retrospective multicentric study, based on the “Réseau d'Expertise Français sur les Cancers ORL Rares” (REFCOR), included all the patients with a salivary carcinoma of the tongue, diagnosed between January 2009 and December 2018. Dubious slides were reviewed by REFCOR expert pathologists to ensure diagnostic accuracy. Treatment was performed in accordance with national REFCOR recommendations. From 28 centers, 103 patients were included in this study. Median age at diagnosis was 63 years, and 60.2% were female. Tumors were adenoid cystic carcinomas (41.7%), mucoepidermoid carcinomas (30.1%), and other adenocarcinomas (28.2%). Primary treatment was surgical for 61.2% of them. Five‐year overall survival (OS) and event‐free survival (EFS) rates were 84.7% and 38.6%, respectively. In multivariable analysis, EFS was significantly worse in case of nonsurgical treatment, alcohol consumption, and glossotonsillar sulcus involvement. N‐positive status was the only significant prognostic factor for OS in multivariable analysis. Salivary carcinomas of the tongue represent a heterogeneous group of rare tumors, with a high risk of recurrence. In this national cohort, surgery was associated with better EFS and N‐status was the main independent prognostic factor for OS.
Network Pharmacology, Molecular Docking, and Molecular Dynamics Study to Explore the Effect of Resveratrol on Type 2 Diabetes
Authors: Martínez‐Esquivias Fernando , Guzmán‐Flores Juan Manuel , Reyes‐Chaparro Andrés , Sánchez‐Enríquez Sergio , Anaya‐Esparza Luis Miguel 
Published: September 2024

ABSTRACTThis network pharmacology study represents a significant step in understanding the potential of Resveratrol as an antidiabetic agent and its molecular targets. Targets for Type 2 diabetes were obtained from the MalaCards and DisGeNET databases, while targets for Resveratrol were sourced from the STP and CTD databases. Subsequently, we performed matching to identify common disease‐compound targets. The identified genes were analyzed using the ShinGO‐0.76.3 database for functional enrichment analysis and KEGG pathway mapping. A protein−protein interaction network was then constructed using Cytoscape software, and hub genes were identified. These hub genes were subjected to molecular docking and dynamic simulations using AutoDock Vina and Gromacs software. According to functional enrichment and KEGG pathway analysis, Resveratrol influences insulin receptors, endoplasmic reticulum functions, and oxidoreductase activity and is involved in the estrogen and HIF‐1 pathways. Ten hub genes were identified, including ESR1, PTGS2, SRC, NOS3, MMP9, IGF1R, CYP19A1, MTOR, MMP2, and PIK3CA. The proteins associated with these genes exhibited high interaction with Resveratrol in the molecular docking analysis, and molecular dynamics showed a stable interaction of Resveratrol with ESR1, MMP9, PIK3CA, and PTGS2. In conclusion, our work enhances the understanding of the antidiabetic activity of Resveratrol, which future studies should experimentally corroborate.
Prenatal and postnatal imaging for early detection of sirenomelia: A case study
Authors: Yao Mengxia , Wang Yin , Liu Yanying 
Published: September 2024

AbstractSirenomelia is a rare congenital caudal abnormality. We applied two‐dimensional, three‐dimensional, and color Doppler ultrasound to diagnose a fetus with sirenomelia at 12 + 6 weeks. The fetus exhibited on ultrasound fused lower limbs, two tibiae in lower legs, no fibulae, knees in retroflexion, pelvic hypoplasia, hypoplasia of the lower lumbar vertebrae and coccyx, bilateral renal agenesis, no bladder, and a single umbilical artery. The postnatal X‐ray revealed a fetus with two femurs, two tibiae, and no fibula. The results of chorionic villus aspiration indicated that the fetus was male with a normal karyotype (46, XY), and the microarray results were normal.
Arylation of 2‐Chloro‐3‐(4,6‐Diaryl‐1,3,5‐Triazin‐2‐yl) Quinolines: Formal Synthesis of 3‐(4,6‐Diaryl‐1,3,5‐Triazin‐2‐yl)‐2‐Substituted Quinolines by Suzuki–Miyaura Reaction
Authors: Jeon Joo‐Hyun , Lee Han‐Joo , Kim Jin‐Hee , Hawsawi Mohammed B. , Jalani Hitesh B. , Jeong Jin‐Hyun 
Published: September 2024

ABSTRACTWe have described herein a simple and formal two‐step synthesis of 3‐(4,6‐diaryl‐1,3,5‐triazin‐2‐yl)‐2‐aryl quinolines from 2‐chloro‐3‐(4,6‐diaryl‐1,3,5‐triazin‐2‐yl) quinolines and boronic acids under the Suzuki–Miyaura conditions. This protocol provides the C‐2 arylation of 2‐chloro‐3‐(4,6‐diaryl‐1,3,5‐triazin‐2‐yl) quinolines under the mild reaction conditions. These newly formed chemo‐types may be useful in drug discovery programs or in material chemistry.